0]\n if len(new_df) == 0:\n new_df = df.head(1)\n new_df['reaction'] = np.nan\n new_df['label'] = new_df['gene']\n return new_df","title":""},{"docid":"8f15ae29fe533b404dc846a9c9957e64","score":"0.5465945","text":"def drop_nan(self):\n self.df = self.df.dropna(axis=0, how='any')\n return self.df","title":""},{"docid":"c2d52c882873b9b394ba08aa17a39614","score":"0.54497117","text":"def remove_rows_with_missing_values(self):\n clean_data = []\n for row in self.data:\n hasMissing = False\n for col in row:\n if col == \"NA\" or col == \"\":\n hasMissing = True\n if not hasMissing:\n clean_data.append(row)\n self.data = clean_data","title":""},{"docid":"ae8ead52a7b3c87ce6d0aa2874c477a8","score":"0.54421103","text":"def make_log_no_trips(self):\n self.df['log_trips'] = self.df[(self.df['trips_in_first_30_days'] != 0)].trips_in_first_30_days.apply(np.log)\n self.df['log_trips'] = self.df['log_trips'].apply(lambda x: 0 if np.isnan(x) else x)","title":""},{"docid":"6a2a3655355cff1d9e36930017d6a14d","score":"0.5433729","text":"def remove_empty_values_marks(self, data):\n\n data_new = np.ndarray(shape=data.shape, dtype=data.dtype)\n\n __ACCURACY = 0.00001\n\n sample_number = 0\n for features in data:\n feature_index = 0\n for value in features:\n if abs(self.__EMPTY_VALUE - value) > __ACCURACY:\n data_new[sample_number][feature_index] = value\n else:\n data_new[sample_number][feature_index] = None\n feature_index += 1\n sample_number += 1\n print(\"Remove marks about empty values has done!\")\n return data_new","title":""},{"docid":"2ddec8050279af60f2721fd30cf43fe3","score":"0.54108644","text":"def _remove_nan(values: TypeValList) -> TypeValList:\n values = np.asarray(values, dtype=float)\n return values[~np.isnan(values)]","title":""},{"docid":"85ffd26d43c6c9bd7e9ff00c77464868","score":"0.541016","text":"def check_for_nan(df):\n return df.dropna()","title":""},{"docid":"f070ce783a1e1cc0ab8923b612b26e5e","score":"0.54039186","text":"def filter_all(data):\n #filter individual blocks of continuous data\n datablocks = [list(v) for k,v in groupby(data,np.isfinite) if k]\n filtereddatablocks = [filterfunction(v) for v in datablocks]\n if np.any(np.isnan(data)): #if there are gaps\n #get information about gaps\n gap_indeces = np.where(np.isnan(data))[0]\n gap_locs = []\n gap_lengths = []\n for k, g in groupby(enumerate(gap_indeces), lambda ix:ix[1]-ix[0]):\n temprun = list(map(itemgetter(1), g))\n gap_lengths.append(np.size(temprun))\n gap_locs.append(temprun[0])\n #build filtered data with gaps\n filteredrun = np.array([]);\n if gap_locs[0]==0: #if the first space is a gap, append and remove\n filteredrun = np.append(filteredrun, np.full(gap_lengths[0],np.NaN) )\n gap_locs = np.delete(gap_locs,0)\n gap_lengths = np.delete(gap_lengths,0)\n for i in range(np.size(filtereddatablocks)):\n filteredrun = np.append(filteredrun,filtereddatablocks[i])\n if i<np.size(gap_locs):\n filteredrun = np.append(filteredrun,np.full(gap_lengths[i],np.NaN))\n else: #if no gaps\n filteredrun = filterfunction(data)\n return filteredrun","title":""},{"docid":"8ff79d1cf1232b2cd2bee9677a15f802","score":"0.5403428","text":"def drop_missing_data(df):\n\n return df.dropna()","title":""},{"docid":"58ac718730e57ad72fbd52481e985402","score":"0.5393061","text":"def drop_irrelevant(self):\r\n self.df = self.df.drop(['id2', 'state', 'year', 'asthma_rate'], axis=1)","title":""},{"docid":"0a9a5ca95990bcb1ddfe7419ab9b2409","score":"0.5385711","text":"def drop_lowz(df):\n df = df.dropna(subset=['FIELD'])\n return df","title":""},{"docid":"bfb57879edba1a2b3b4ef96649e53326","score":"0.53795296","text":"def clean_data(df):\n # removes first row time = 0\n df.dropna(how='any', inplace=True)\n df.drop_duplicates(inplace=True)\n\n # remove points where speed is greater than > 100 mph\n df = df[df['speed'] < 100]\n\n # replace unknowns with mean of column\n df = df.replace(-99999, np.nan)\n df = df.fillna(df.mean())\n return df","title":""},{"docid":"0430e6d2ce3529d2c69f6a112c139784","score":"0.5377258","text":"def _clean_missing(data, params):\n attributes = params['attributes']\n cleaning_mode = params['cleaning_mode']\n frag = params['id_frag']\n\n if cleaning_mode == \"REMOVE_COLUMN\":\n\n thresh = params['thresh']\n if thresh:\n subset = []\n cols = params['columns_drop']\n for c in cols.index:\n if cols.loc[c] > thresh:\n subset.append(c)\n else:\n subset = params['columns_drop']\n data = data.drop(subset, axis=1)\n\n elif cleaning_mode == \"MEAN\":\n\n sizes, sums = params['values']\n values = np.divide(sums, sizes)\n\n for v, a in zip(values, attributes):\n data[a] = data[a].fillna(value=v)\n\n elif cleaning_mode == \"MODE\":\n dict_mode = params['dict_mode']\n for att in dict_mode:\n mode = dict_mode[att].idxmax()\n data[att] = data[att].fillna(value=mode)\n\n elif cleaning_mode == 'MEDIAN':\n medians = params['values']\n for att in medians:\n data[att] = data[att].fillna(value=medians[att])\n\n data.reset_index(drop=True, inplace=True)\n info = generate_info(data, frag)\n return data, info","title":""},{"docid":"bc3286b60c67caabbf08b38675cf0de7","score":"0.53675956","text":"def drop_missing(X):\n nonmissing=X[0].copy()\n nonmissing['Nonmissing']=True\n nonmissing=nonmissing['Nonmissing']\n for x in X:\n nonmissing.where(pd.notnull(x).all(axis=1),False,inplace=True)\n\n for i in range(len(X)):\n X[i] = X[i].loc[nonmissing,:]\n\n return tuple(X)","title":""},{"docid":"ed07a105be7b32edd490f39e6279fde8","score":"0.53658754","text":"def flag_na(df,remove_rows=False):\n df = df.copy()\n n_bad = sum(np.ravel(df.isnull().values))\n print('{} bad values in the dataset.')\n if remove_rows == True:\n df.dropna()\n return(df)","title":""},{"docid":"d3ccf0fca21cb7d020e01721e6d73f4f","score":"0.5357542","text":"def _getmissingsensors(self):\n currentcams=[]\n currenttemp=[]\n currentni=[]\n missingcams=[]\n missingtemp=[]\n missingni=[]\n for par in self._paramlist:\n if par[\"type\"]==\"camera\":\n currentcams.append((par[\"vendor\"],par[\"camid\"]))\n if par[\"type\"]==\"temperature\":\n currenttemp.append(par[\"handle\"])\n if par[\"type\"]==\"nianalog\":\n currentni.append(par[\"devstr\"])\n \n for cam in self._connectedcams:\n if not cam in currentcams:\n missingcams.append(cam)\n if not self._connectedtemp is None:\n for temp in self._connectedtemp:\n if not temp in currenttemp:\n missingtemp.append(temp)\n if not self._connectedni is None:\n for ni in self._connectedni:\n if not ni in currentni:\n missingni.append(ni)\n missing={\"camera\":missingcams, \"temperature\":missingtemp,\"nianalog\":missingni}\n return missing","title":""},{"docid":"1c24a661c654de184a93b6d3fcc684cf","score":"0.53526807","text":"def nan_trim(data):\r\n data_diff = np.diff(data)\r\n\r\n for i, diff in reversed(list(enumerate(data_diff))):\r\n if diff == 1:\r\n data.pop(i)\r\n\r\n return data","title":""},{"docid":"d773f973c4e24a2274159b9e08fef1b8","score":"0.53520674","text":"def nans_remove(self):\n msk = np.isfinite(self.y)\n return self.__class__(self.x[msk], self.y[msk])","title":""},{"docid":"11f98e0c9343893178195cbd3f034352","score":"0.5342439","text":"def remove_na_lines(self, max_na_proportion:float=None):\n\n if max_na_proportion:\n drop_lines = list(self.df.loc[self.df.isna().sum(axis=1)>len(self.df.columns)*max_na_proportion].index) \n else:\n drop_lines = self.na_lines\n #print(f'Lines removed:{drop_lines[0:4]}...')\n self.df = self.df.drop(drop_lines)\n return self.df","title":""},{"docid":"075d66d00dfa6a68a225f3dacc5ab7fe","score":"0.5338691","text":"def cleanData(dataToClean):\n for i in range(len(dataToClean)):\n for j in range(len(dataToClean[0])):\n if np.isnan(dataToClean[i][j]):\n dataToClean[i][j]=0","title":""},{"docid":"a12091d6b20c49b7725690d923460001","score":"0.5331909","text":"def clean_df(df):\n df = df.where(pd.notnull(df), None)\n df = df.fillna(value=0)\n return df","title":""},{"docid":"3d6bb36d5febd036ea6d7c4c4267a6a5","score":"0.533119","text":"def _drop_obs(df):\n drop_subj = []\n for sid, row in df.iterrows():\n try:\n if sum(row.dropna()) == 0:\n drop_subj.append(sid)\n elif row.count() == 0:\n drop_subj.append(sid)\n except TypeError: # compatible to ados\n if sum(row.astype(float).dropna()) == 0:\n drop_subj.append(sid)\n elif row.astype(float).count() == 0:\n drop_subj.append(sid)\n return drop_subj","title":""},{"docid":"260be91c0b8510b71ae7a1d740960b03","score":"0.53306866","text":"def NaN_cleaning(df):\n df = df.replace(np.nan, 'unknown')\n return df.reset_index(drop=True)","title":""},{"docid":"5be06db39233a32605860ac8b3689a5c","score":"0.53283554","text":"def _remove_missing_data(\n data_values: NDArray,\n geometry_array: geopandas.GeoSeries,\n) -> tuple[NDArray, geopandas.GeoSeries]:\n not_missing_data = ~pandas.isnull(data_values)\n geometry_array = geometry_array[not_missing_data]\n data_values = data_values[not_missing_data]\n return data_values, geometry_array","title":""},{"docid":"aeb9b42a6eaa6253ee3b8d523139e299","score":"0.5324406","text":"def quick_clean(data):\n rows_missing_data = (data == 0).sum(1)\n return data[rows_missing_data == 0, :]","title":""},{"docid":"82d9e2a4681978a2e091275a72005b3c","score":"0.5316683","text":"def clean_nan(data, data_column):\r\n data.dropna(subset=[data_column, \\\r\n 'Quality'], inplace=True)\r\n return data","title":""},{"docid":"a7f38a2777c41e92eecb61343ef10b22","score":"0.53023773","text":"def filter_zeroes(self):\n self.U[self.U == 0] = np.nan\n self.V[self.V == 0] = np.nan\n self.W[self.W == 0] = np.nan","title":""},{"docid":"985aa5c0aa5f067935e58f73b2100dbc","score":"0.5297176","text":"def remove_nans(array_like):\n data = array_like.copy()\n \n # Astropy table object\n if type(data) is Table:\n data = data.filled(NAN_VALUE)\n # Pandas DataFrame object\n elif type(data) is pd.DataFrame:\n data = data.fillna(NAN_VALUE)\n else:\n print(\"Unrecognized data format:\", type(data))\n return None\n \n # Return final data structure\n return data","title":""},{"docid":"914a197db781cb12a5fe19ab43161b5d","score":"0.5292508","text":"def test_DataCleaner_big_nan_handler_warning(self):\n dc = DataCleaner(max_na_frac=0.01)\n df = pd.DataFrame(\n np.random.randint(0, 100, size=(100, 4)), columns=list(\"ABCD\")\n )\n dc.fit(df, \"D\")\n self.assertEqual(len(dc.warnings), 0)\n\n df[\"A\"].iloc[10:20] = np.nan\n df[\"B\"].iloc[:99] = np.nan\n dc.fit(df, \"D\")\n self.assertEqual(len(dc.warnings), 1)","title":""},{"docid":"dc851de57a11012e4155217bb31adc96","score":"0.52870226","text":"def delete_rows(df):\r\n\r\n df = df.drop(df[df.img.isna()].index)\r\n df = df.drop(df[df.cost.isna()].index)\r\n df = df.drop(df[df.rarity.isna()].index)\r\n return df","title":""},{"docid":"7fbcab9e0a3ed7e49af6f0c03573e528","score":"0.5284864","text":"def test_nans_replaced_none():\n\n #generate test data\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\n dataframe['A'][5] = numpy.nan\n\n #run function on dataframe\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\"none\", removal_time_frame=\"day\",\n fault_placement=\"calendar\")\n\n assert dataframe.size == result_dataframe.size, \"nan has not been dropped\"\n assert result_dataframe['A'][5] != numpy.nan, \"nan was removed but shouldn't have\"","title":""},{"docid":"08006fa93818a2f08ef6539d0e78d6b8","score":"0.5269665","text":"def remove_duplicates_and_full_nulls(data):\n data = data.copy()\n data = data.drop_duplicates()\n data = data.dropna(how ='all')\n return data","title":""},{"docid":"d9cd6b277461a674b517515b1a4294eb","score":"0.52642363","text":"def remove_null(dataframe):\n\n dataframe = dataframe.dropna()\n return dataframe","title":""},{"docid":"52409db8b7b10f8ebaa0732ca7d73c16","score":"0.52546704","text":"def _rmDataWithoutBrightStar(self, neighborStarMap, starMap,\n wavefrontSensors):\n\n # Collect the sensor list without the bright star\n noStarSensorList = []\n for detector, stars in neighborStarMap.items():\n if (len(stars.getId()) == 0):\n noStarSensorList.append(detector)\n\n # Remove the data in map\n for detector in noStarSensorList:\n neighborStarMap.pop(detector)\n starMap.pop(detector)\n wavefrontSensors.pop(detector)","title":""},{"docid":"3392f154927a0b3ea4e7a68b784f6179","score":"0.5254399","text":"def filter_nan(s,o):\n data = np.array([s.flatten(),o.flatten()])\n data = np.transpose(data)\n data = data[~np.isnan(data).any(1)]\n\n return data[:,0],data[:,1]","title":""},{"docid":"bbe7cf61ecf67b86675bc79f599d13c2","score":"0.5251705","text":"def test_filter_by_NaN(self):\n test_scope = scope.Scope(-4.9, -4.70, 48, 49, 0, 1500000000)\n test_preprocessing = preprocessing.Preprocessing(test_scope, self.test_ships)\n\n self.assertEqual(len(self.test_ships), 15)\n test_preprocessing.filter_by_NaN()\n self.assertEqual(len(self.test_ships), 7)\n\n number_states = {'245257000': 29, '228037600': 259, '304091000': 1,\n '227705102': 249, '227443000': 92, '228931000': 21,\n '228131600': 350, '228064900': 4, '37100300': 7}\n\n for ship in self.test_ships:\n self.assertEqual(len(ship.ais_states), number_states[ship.sourcemmsi],\n msg=\"sourcemmsi is \" + ship.sourcemmsi)","title":""},{"docid":"9ee4fe5bb4730d0e6d8c78730fa48d55","score":"0.52495563","text":"def remove_rows_with_missing_values(self):\n row_length = len(self.column_names)\n table_copy = []\n \n for i in range(len(self.data)):\n has_NA = False\n for j in range(row_length):\n if self.data[i][j] == \"NA\" or self.data[i][j] == \"\":\n has_NA = True\n if has_NA == False: \n table_copy.append(self.data[i])\n \n self.data.clear()\n self.data = copy.deepcopy(table_copy) \n pass # TODO: fix this","title":""},{"docid":"16ead744219067e1a2c3a93e51f6bfe2","score":"0.5245453","text":"def _clean_useless_rows(self):\r\n self.temp_stock_data = pandas.DataFrame()\r\n dates = []\r\n for i in xrange(0, self.stock_data[self.company_list.Companies.irow(0)].count()):\r\n #print i\r\n row = self.stock_data.irow(i)\r\n if len(row[numpy.isnan(row)]) == len(row):\r\n dates.append(self.date_list[i])\r\n \r\n for single_date in dates:\r\n self.stock_data = self.stock_data.drop(pandas.Timestamp(single_date))\r\n print \"Data cleaned from useless rows.\"","title":""},{"docid":"6fc475ee4cc92954c4f70406962b6150","score":"0.52325326","text":"def data_cleaning(df: DataFrame) -> DataFrame:\n print(\"Cleaning the data to remove problems\\n \")\n\n nulls = df.isnull().sum()\n nulls = nulls[nulls != 0]\n\n if not nulls.empty:\n print(f\"The columns below some 'NaN' values:\\n{nulls}\")\n\n # for index_with_nan in enumerate(nulls.keys()[:]):\n # print(f\"Column: {index_with_nan} \\n \")\n\n if 'Birth Year' in df.columns.values.tolist():\n df = dob_trim(df)\n\n time.sleep(2)\n\n no_user_type: float = format(df['User Type'].isna().sum()/df.shape[0]*100, '.2g')\n print(f\"A few rows don't have any information for 'User Type' but it is only {no_user_type}% so we'll just drop them\")\n df = df.dropna(axis = 0, subset=['User Type'])\n\n if 'Gender' in df.columns.values.tolist():\n gender_neutral: float = format(df['Gender'].isna().sum()/df.shape[0]*100, '.2g')\n print(f\"{gender_neutral}% of trips don't have any information for 'Gender' so we'll forward-fill.\")\n df.fillna(method=\"ffill\")\n # gender_neutral: float = format(nulls['Gender']/df.shape[0]*100, '.2g')\n # print(f\"A few rows don't have any information for 'Gender' but it is only {gender_neutral}% so we'll just drop them\")\n # df = df.dropna(axis = 0, subset=['Gender'], inplace=True)\n \n return df","title":""},{"docid":"658b7ab760716acff016b703ca146fca","score":"0.5225665","text":"def removeNansSingle(data):\n # set an x array\n x = np.arange(data.size)\n # find locations of nans - this is a bool array with True in locations with nan values\n nanLocs = np.isnan(data)\n # if no nans, do nothing\n if not np.any(nanLocs):\n return data # no nans to remove\n # create mask\n mask = np.ones(data.size, np.bool)\n mask[nanLocs] = 0 # using numpy indexing with bool arrays\n # no need to group, want to remove every nan\n data[nanLocs] = np.interp(x[nanLocs], x[mask], data[mask])\n return data","title":""},{"docid":"5cb6066ce4a1f9dc1a890971b21dec1f","score":"0.5221285","text":"def remove_notes(data):\n return data[pd.to_numeric(data.iloc[:, 0], errors='coerce').notnull()]","title":""},{"docid":"6a937fac4a9481e0cde52d043b27035f","score":"0.52128845","text":"def filter_spikes(df:pd.DataFrame):\n \"\"\"first return argument is non-spike signals, the second one for the spikes\"\"\"\n new_df=pd.DataFrame()\n spike_cluster=pd.DataFrame()\n for i in range(len(df.columns)):\n tmp=df.iloc[:, i] \n uniques=tmp.unique()\n uniques = [x for x in uniques if str(x) != 'nan']\n uniques = [x for x in uniques if x not in [0.0,1.0]]\n if(len(uniques)<=1): #spike like\n spike_cluster.insert(len(spike_cluster.columns),tmp.name,tmp)\n else:\n new_df.insert(len(new_df.columns),tmp.name,tmp)\n return new_df,spike_cluster","title":""},{"docid":"4a372da4cfe63a9a92a7f200bb86f20a","score":"0.5209513","text":"def nan_clean(array):\n for i in range(len(array)):\n if np.isnan(array[i]) == True:\n array[i] = 0.0\n return array","title":""},{"docid":"6a41fc2d468a3aa9657429af2f10331b","score":"0.52041674","text":"def remove_nan_observations(x, y, z):\n x_ = x[~np.isnan(z)]\n y_ = y[~np.isnan(z)]\n z_ = z[~np.isnan(z)]\n return x_, y_, z_","title":""},{"docid":"7b824e07c74f481cca378320af481262","score":"0.5198521","text":"def filterNaN(instr, datacol, outfile, logfile, verbose):\n try:\n nanclean = instr[1].header['NANCLEAN']\n except:\n naxis2 = 0\n for i in range(len(instr[1].columns.names)):\n if 'time' in instr[1].columns.names[i].lower():\n timecol = instr[1].columns.names[i]\n try:\n instr[1].data.field(datacol)\n except:\n msg = (\"ERROR -- KEPIO.FILTERNAN: cannot find column {}\"\n \"in the infile\".format(datacol))\n kepmsg.err(logfile, msg, verbose)\n try:\n for i in range(len(instr[1].data.field(0))):\n if (str(instr[1].data.field(timecol)[i]) != '-inf' and\n str(instr[1].data.field(datacol)[i]) != '-inf'):\n instr[1].data[naxis2] = instr[1].data[i]\n naxis2 += 1\n instr[1].data = instr[1].data[:naxis2]\n comment = 'NaN cadences removed from data'\n kepkey.new('NANCLEAN', True, comment, instr[1], outfile, logfile,\n verbose)\n except:\n errmsg = ('ERROR -- KEPIO.FILTERNAN: Failed to filter NaNs from '\n + outfile)\n kepmsg.err(logfile, errmsg, verbose)\n return instr","title":""},{"docid":"e14120618e0caa9c1a1adf8bf742894f","score":"0.51975524","text":"def fix_na_values(df):\n ignored = [\"SUBJECT_ID\", \"HADM_ID\", \"ADMITTIME\", \"DISCHTIME\"]\n df_core = df.drop(ignored, axis=1)\n\n while df_core.isna().sum().sum():\n # get column with least amount of missing values\n cols_with_na = df_core.isna().sum()\n col = cols_with_na[cols_with_na > 0].idxmin()\n # impute that column\n df_core.loc[df_core[col].isna(), col] = impute_column(df_core, col)\n\n return pd.concat([df_core, df[ignored]], axis=1)","title":""},{"docid":"13995298c15ddffef72188fbf4ae9e9e","score":"0.51944923","text":"def test_nans_dropped():\n\n #generate test data\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\n dataframe['A'][5] = numpy.nan\n\n #run function on dataframe\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\"drop\", removal_time_frame=\"day\", fault_placement=\"calendar\")\n assert dataframe.size != result_dataframe.size, \"nan have not been dropped\"","title":""},{"docid":"9105baa116f0859c24acfe0688351a8b","score":"0.51941323","text":"def drop_missing(self):\n df1 = self.df\n df2 = df1.dropna(axis=0)\n\n self.df = df2","title":""},{"docid":"fe78430afcdbf63973f5fb1eef658c15","score":"0.5192824","text":"def remove_missing_values(x):\n x_clean = x.copy()\n x_clean = x_clean[:, [np.count_nonzero(x.T[i] == -999) == 0 for i in range (x.shape[1])]]\n return x_clean","title":""},{"docid":"26e1f1380c66022fe6c1a3408b34b606","score":"0.51923764","text":"def remove_rcna(df):\n df.dropna(how = 'all', axis = 'columns', inplace = True)\n df.dropna(how = 'all', axis = 'rows', inplace = True)\n return df","title":""},{"docid":"97e3227a5225488f9568a8c415801ea9","score":"0.51922977","text":"def generate_query_ratesnull(self):\n pass","title":""},{"docid":"934058862a1706b0deffe110b8780021","score":"0.518677","text":"def _series_cleaner(self, obs_data: Union[dict, list]) -> pd.DataFrame:\n series = pd.json_normalize(obs_data[\"observations\"])\n series.drop(columns=[\"realtime_start\", \"realtime_end\"], inplace=True)\n series[\"value\"] = series[\"value\"].replace(self.nan_char, np.nan)\n return series","title":""},{"docid":"eabc39526af7b6e52689bfe97d78ffba","score":"0.5184404","text":"def drop_missing(self):\n self.miss = np.rint(len(self.data.columns)*self.miss)\n self.data = self.data[self.data.isnull().sum(axis=1)<=self.miss]","title":""},{"docid":"b6e550833aaa3e203af1bd5115ecd7f9","score":"0.5179664","text":"def clean_data(df):\n data_types = get_missing_columns()\n \n for idx in data_types.index:\n column = idx\n if column in df.columns:\n df[column] = df[column].replace(data_types.loc[column]['nan_vals'], np.nan)\n\n \n # remove selected columns\n df.drop(['AGER_TYP', 'ALTER_HH', 'ALTER_KIND1', 'ALTER_KIND2', 'ALTER_KIND3', 'ALTER_KIND4', 'ALTERSKATEGORIE_FEIN',\\\n 'D19_BANKEN_ONLINE_QUOTE_12', 'D19_GESAMT_ONLINE_QUOTE_12', 'D19_KONSUMTYP', 'D19_LETZTER_KAUF_BRANCHE',\\\n 'D19_LOTTO', 'D19_SOZIALES', 'D19_TELKO_ONLINE_QUOTE_12', 'D19_VERSAND_ONLINE_QUOTE_12',\\\n 'D19_VERSI_ONLINE_QUOTE_12', 'EXTSEL992','GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'TITEL_KZ', 'LNR'],\n axis=1, inplace=True)\n \n print(\"Removal of Columns with over 20% null values complete...\")\n \n # remove selected rows\n df = df[df.isnull().sum(axis=1) < 9].reset_index(drop=True)\n \n # remove selected columns again\n features_not_in_feat_info_42 = ['AKT_DAT_KL', 'ANZ_KINDER', 'ANZ_STATISTISCHE_HAUSHALTE', 'CJT_KATALOGNUTZER','CJT_TYP_1',\\\n 'CJT_TYP_2', 'CJT_TYP_3', 'CJT_TYP_4', 'CJT_TYP_5', 'CJT_TYP_6', 'DSL_FLAG',\\\n 'EINGEZOGENAM_HH_JAHR','FIRMENDICHTE', 'GEMEINDETYP', 'HH_DELTA_FLAG', 'KBA13_ANTG1',\\\n 'KBA13_ANTG2', 'KBA13_ANTG3','KBA13_ANTG4', 'KBA13_BAUMAX', 'KBA13_CCM_1401_2500',\\\n 'KBA13_GBZ', 'KBA13_HHZ', 'KBA13_KMH_210','KONSUMZELLE', 'MOBI_RASTER', 'RT_KEIN_ANREIZ',\\\n 'RT_SCHNAEPPCHEN', 'RT_UEBERGROESSE', 'STRUKTURTYP','UMFELD_ALT', 'UMFELD_JUNG',\\\n 'UNGLEICHENN_FLAG', 'VERDICHTUNGSRAUM', 'VHA', 'VHN', 'VK_DHT4A','VK_DISTANZ',\\\n 'VK_ZG11','D19_KONSUMTYP_MAX', 'KOMBIALTER','EINGEFUEGT_AM']\n \n df.drop(features_not_in_feat_info_42,axis=1, inplace=True)\n \n print(\"Removal of undocumented columns complete...\")\n \n \n # select, re-encode, and engineer column values.\n # # feature engineering of categorical features\n\n df['OST_WEST_KZ'].replace(['W','O'], [1, 0], inplace=True)\n \n multi = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CJT_GESAMTTYP', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',\\\n 'D19_BANKEN_DATUM','D19_BANKEN_OFFLINE_DATUM', 'D19_BANKEN_ONLINE_DATUM', 'D19_GESAMT_ANZ_12',\\\n 'D19_GESAMT_ANZ_24', 'D19_GESAMT_DATUM','D19_GESAMT_OFFLINE_DATUM', 'D19_GESAMT_ONLINE_DATUM',\\\n 'D19_TELKO_DATUM', 'D19_TELKO_OFFLINE_DATUM', 'D19_TELKO_ONLINE_DATUM','D19_VERSAND_DATUM',\\\n 'D19_VERSAND_OFFLINE_DATUM', 'D19_VERSAND_ONLINE_DATUM', 'D19_VERSI_DATUM', 'D19_VERSI_OFFLINE_DATUM',\\\n 'D19_VERSI_ONLINE_DATUM', 'FINANZTYP', 'GEBAEUDETYP', 'GFK_URLAUBERTYP','LP_FAMILIE_FEIN','LP_FAMILIE_GROB',\\\n 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP']\n \n df = pd.get_dummies(df, columns=multi, prefix=multi)\n \n \n # feature engineering of mixed features\n df['PRAEGENDE_JUGENDJAHRE_decade'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_decade)\n df['PRAEGENDE_JUGENDJAHRE_movement'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_movement)\n df.drop('PRAEGENDE_JUGENDJAHRE', axis=1, inplace=True)\n \n print(\"Feature Engineering PRAEGENDE_JUGENDJAHRE complete...\")\n \n \n df['CAMEO_INTL_2015_wealth'] = df['CAMEO_INTL_2015'].apply(make_wealth) \n df['CAMEO_INTL_2015_life_stage'] = df['CAMEO_INTL_2015'].apply(make_life_stage)\n df.drop('CAMEO_INTL_2015', axis=1, inplace=True)\n \n print(\"Feature Engineering CAMEO_INTL_2015 complete...\")\n \n df['WOHNLAGE_rural'] = df['WOHNLAGE'].map({0:0,1:0,2:0,3:0,4:0,5:0,7:1,8:1})\n df['WOHNLAGE_rating_class'] = df['WOHNLAGE'].map({0:0,1:1,2:2,3:3,4:4,5:5,7:0,8:0})\n \n print(\"Feature Engineering WOHNLAGE complete...\")\n \n df = df.drop(['LP_LEBENSPHASE_FEIN','LP_LEBENSPHASE_GROB','WOHNLAGE','PLZ8_BAUMAX'], axis=1)\n \n print(\"Finished!\")\n \n # Return the cleaned dataframe.\n return df","title":""},{"docid":"50b15f9a5bc9f58c3f1fda20bd23a98b","score":"0.5174193","text":"def clean_data(self):\n # Mask files with lat = 0 or lon = 0\n mask = np.where(np.logical_or(self.data['VEH_LAT']==0, \n self.data['VEH_LONG']==0))\n\n for k in self.data.keys():\n if isinstance(self.data[k][0], float):\n self.data[k][mask] = np.nan","title":""},{"docid":"0fb2a849eb9288c2238968e9235b9752","score":"0.51717937","text":"def drop_null_rows(self, x: bool):\n if x:\n total_null_rows = self.df[self.target].isin(\n [' ', 'NULL', np.nan]).sum()\n if total_null_rows > 0:\n print(\"Dropping rows having [' ', 'NULL', numpy.nan] values\")\n self.df.dropna(inplace=True)\n self.df.reset_index(drop=True, inplace=True)\n self.null_values_present = False\n print_in_red(f\"Total Null rows dropped: {total_null_rows}\\n\")\n self._start_analysis()\n else:\n print(colored(\"There is no null rows present.\\n\", \"green\"))","title":""},{"docid":"52449af6e301d2d873cf47553c2b38b8","score":"0.5167622","text":"def filter_nan(s, o):\n data = np.array([s.flatten(), o.flatten()])\n data = np.transpose(data)\n data = data[~np.isnan(data).any(1)]\n return data[:, 0], data[:, 1]","title":""},{"docid":"6512e72d0658c2f1c0378e699cc56170","score":"0.51613533","text":"def clean_timeseries(self):\n if 'tb_incidence' in self.timeseries_log.keys():\n self.timeseries_log['tb_incidence'][0] = 0.","title":""},{"docid":"248a5d7d28357321ce39d2c77fe712a7","score":"0.5160788","text":"def filter_values_2(self, counter):\n inf = float(\"inf\")\n last_mea = list(self.scan_values_2[counter])\n for i in range(len(last_mea)):\n if last_mea[i] == inf:\n for k in range(1, MEASUREMENTS):\n if self.scan_values_2[counter-k] != inf:\n last_mea[i] = self.scan_values_2[k][i]\n break\n scan = LaserScan()\n scan.ranges = last_mea\n self.laser_filtered_pub.publish(scan)","title":""},{"docid":"16cc667bcf39fc41ca499a7580cf48d0","score":"0.51605296","text":"def _get_instr_after_remove(self, remove_size_null, remove_adj_cap_nan):\n if any([remove_size_null, remove_adj_cap_nan]): # will update instr_df\n instr_df = self.concat_instr_df.copy()\n else: # nothing will be changed in self.concat_instr_df so no need to copy\n instr_df = self.concat_instr_df\n if instr_df.empty:\n raise Exception('concat_instr_df is empty. Cannot display metrics')\n if remove_size_null:\n no_instrs = len(instr_df)\n idx_size_null = instr_df['stake'] == 0\n if sum(idx_size_null) > 0:\n self.logger.warning('Dropping {}/{} instructions with stake 0'.format(\n sum(idx_size_null), no_instrs))\n instr_df = instr_df.loc[~idx_size_null]\n if remove_adj_cap_nan:\n if 'adj_cap' not in instr_df.columns:\n raise Exception('concat_instr_df do not have column '\n 'adj_cap and so cannot remove_adj_cap_nan')\n no_instrs = len(instr_df)\n idx_nan = instr_df['adj_cap'].isnull()\n if sum(idx_nan) > 0:\n self.logger.warning('Keeping {}/{} instructions with known capital adjustment'\n .format(no_instrs - sum(idx_nan), no_instrs))\n # remove nan in adjst_capital\n instr_df = instr_df.loc[~idx_nan]\n return instr_df","title":""}],"string":"[\n {\n \"docid\": \"8361c7d6bb8408ccdc93d98e06906cb5\",\n \"score\": \"0.63010675\",\n \"text\": \"def _removerawnans(self):\\n for band in self.data[self.root]:\\n for i, f in enumerate(self.data[self.root][band]['frequency']):\\n if math.isnan(f):\\n for param in self.data[self.root][band]:\\n if len(self.data[self.root][band][param]) == \\\\\\n len(self.data[self.root][band]['frequency']):\\n np.delete(self.data[self.root][band][param],i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96cebc2aed0cbec825c4d54bfa1ccdd9\",\n \"score\": \"0.62637925\",\n \"text\": \"def cleanNaN (raw_df):\\n #first step:cleaning the NaN values when the whole row or whole columns is empty:\\n raw_df.dropna(axis=1, how='all' )\\n raw_df.dropna(axis=0, how='all')\\n \\n\\n while True:\\n\\n #removing indicators (columns)\\n NaN_per_col = raw_df.apply(numMissing, axis=0).to_frame().T #axis=0 defines that function is to be applied on each column\\n max_NaN_per_col = np.amax(NaN_per_col.iloc[0,:].values)\\n min_NaN_per_col=np.amin(NaN_per_col.iloc[0,:].values)\\n if max_NaN_per_col == min_NaN_per_col+2 or max_NaN_per_col == min_NaN_per_col+1 : \\n threshold1=len(raw_df.index)-max_NaN_per_col+1\\n else:\\n threshold1=len(raw_df.index)-max_NaN_per_col+3 #if rate of 2 here, check the condition at +1\\n raw_df=raw_df.dropna(axis=1, thresh=threshold1)\\n \\n \\n # check conditions to stop the while loop\\n if raw_df.isnull().sum().sum() == 0:\\n break\\n \\n \\n #removing countries (rows)\\n NaN_per_row = raw_df.apply(numMissing, axis=1).to_frame().T #axis=1 defines that function is to be applied on each row\\n max_NaN_per_row = np.amax(NaN_per_row.iloc[0,:].values)\\n threshold0=len(raw_df.columns)-max_NaN_per_row +1\\n raw_df=raw_df.dropna(axis=0, thresh=threshold0)\\n \\n\\n # check conditions to stop the while loop\\n if raw_df.isnull().sum().sum() == 0:\\n break\\n \\n return raw_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7414a0fef1fec27eac411ce030507b3\",\n \"score\": \"0.6119339\",\n \"text\": \"def _remove_nan_inf(self):\\n # infinite and nan check\\n self.TB10A[np.isfinite(self.TB10A) is False] = self.bad_data\\n self.TB19A[np.isfinite(self.TB19A) is False] = self.bad_data\\n self.TB37A[np.isfinite(self.TB37A) is False] = self.bad_data\\n self.TB85A[np.isfinite(self.TB85A) is False] = self.bad_data\\n if hasattr(self, 'TB10B'): # Assume if one, all are there\\n self.TB10B[np.isfinite(self.TB10B) is False] = self.bad_data\\n self.TB19B[np.isfinite(self.TB19B) is False] = self.bad_data\\n self.TB37B[np.isfinite(self.TB37B) is False] = self.bad_data\\n self.TB85B[np.isfinite(self.TB85B) is False] = self.bad_data\\n else: # Set all the B channels to bad, these are single-channel data\\n for freq in FREQS:\\n setattr(self, 'TB' + freq + 'B',\\n self.bad_data * np.ones((self.nscans, self.ncross),\\n dtype='float'))\\n # Address geolocations if available\\n if hasattr(self, 'Latitude'):\\n self.Latitude[np.isfinite(self.Latitude) is False] = self.bad_data\\n self.Longitude[np.isfinite(self.Longitude) is False] = \\\\\\n self.bad_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756ddf8bd32157a47110f845a19de702\",\n \"score\": \"0.6115779\",\n \"text\": \"def removeNans(data: Dict):\\n # find nan in the dataset and removes the values\\n for chan in data:\\n data[chan] = removeNansSingle(data[chan])\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77edef415c0e04d3ef1580f96c9c6df4\",\n \"score\": \"0.6110685\",\n \"text\": \"def handle_missing_values(df):\\n print(\\\"just drop all the samples with missing values, consider a better approach\\\")\\n newdf= df.dropna()\\n return newdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3266ab1d07f9f838365bd8fbde7a1f7\",\n \"score\": \"0.604752\",\n \"text\": \"def remove_nans(self):\\n s = \\\"::: removing NaNs from %s :::\\\" % self.name\\n print_text(s, self.color)\\n\\n self.x = self.x[self.good_x]\\n self.y = self.y[self.good_y]\\n self.x_min = self.x.min()\\n self.x_max = self.x.max()\\n self.y_min = self.y.min()\\n self.y_max = self.y.max()\\n self.nx = len(self.x)\\n self.ny = len(self.y)\\n\\n for i in self.data.keys():\\n self.data[i] = self.data[i][self.good_y, : ]\\n self.data[i] = self.data[i][:, self.good_x]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e963843fe13ec0998f958dfce64dfc\",\n \"score\": \"0.5992643\",\n \"text\": \"def dropna(df):\\n df = df[df < math.exp(709)] # big number\\n df = df[df != 0.0]\\n df = df.dropna()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb517438e75d14790c556d6c26530e4\",\n \"score\": \"0.5991817\",\n \"text\": \"def dropna(self):\\n self.transformed_data = list(\\n filter(lambda line_obj: line_obj.has_null is False, self.orig_data))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52259136ad5716d8e4e3adceb77bace3\",\n \"score\": \"0.5938397\",\n \"text\": \"def remove_nans(sig):\\n\\n sig_nans = np.isnan(sig)\\n sig_removed = sig[np.where(~np.isnan(sig))]\\n\\n return sig_removed, sig_nans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59b786a4ada6804cec21d78e75eb0698\",\n \"score\": \"0.59266084\",\n \"text\": \"def dropna(df):\\n df = df[df < math.exp(709)] # big number\\n df = df[df != 0.0]\\n df = df.dropna()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5c0c67abd61e6060f97e87d779570eb\",\n \"score\": \"0.5856776\",\n \"text\": \"def clean_nans(record):\\n floats = [field for field in record if isinstance(record[field], float)]\\n for field in floats:\\n if np.isnan(record[field]):\\n del record[field]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82fb941e9652d6c2263194543d36ee88\",\n \"score\": \"0.57872057\",\n \"text\": \"def prune_records(self,rec):\\n return {k: v for k, v in json.loads(rec).items() if not (v is None or (isinstance(v, list) and sum(pd.isnull(v) > 0)) or\\n (isinstance(v, float) and math.isnan(v))\\n or isinstance(v, pd.tslib.NaTType))}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ccc1c304ac258099fe94a5f4329c384\",\n \"score\": \"0.5760188\",\n \"text\": \"def _strip(session) -> tuple:\\n eeg, *rest = session\\n ind = -next(i for i, value in enumerate(eeg[0, ::-1]) if not np.isnan(value))\\n if ind == 0:\\n ind = None\\n return tuple((eeg[:, :ind], *rest))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc1c465dc06a8c0c07afedbcc6d05af9\",\n \"score\": \"0.57554376\",\n \"text\": \"def remove_null_values(dataframe):\\r\\n dataframe.dropna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968d84511f33f982e53d2dced989779c\",\n \"score\": \"0.57538193\",\n \"text\": \"def filter_values_1(self):\\n inf = float(\\\"inf\\\")\\n last_mea = list(self.scan_values[-1])\\n for i in range(len(last_mea)):\\n if last_mea[i] == inf:\\n for k in reversed(xrange(MEASUREMENTS-1)):\\n if self.scan_values[k][i] != inf:\\n last_mea[i] = self.scan_values[k][i]\\n break\\n scan = LaserScan()\\n scan.ranges = last_mea\\n self.laser_filtered_pub.publish(scan)\\n self.counter = 0\\n self.scan_values = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d79578ca01616242fae7179a5eef34\",\n \"score\": \"0.5741\",\n \"text\": \"def prune (self):\\r\\n #Remove excess zero data points characteristic of a multi-event spectrum acquisition structure that occur inside peaks.\\r\\n #Thermo data sometimes does this depending on acquisition parameters.\\r\\n if self.msevents > 1:\\r\\n zstart = None\\r\\n zend = None\\r\\n for x,intensity in enumerate(self.i):\\r\\n if intensity == 0:\\r\\n if not zstart:\\r\\n zstart = x\\r\\n zend = x\\r\\n else:\\r\\n if zend and zstart:\\r\\n length = zend-zstart+1\\r\\n if length == self.msevents-1:\\r\\n self.i[zstart:zend+1] = list(itertools.repeat(-1,length))\\r\\n zstart = None\\r\\n zend = None\\r\\n while True:\\r\\n try:\\r\\n where = self.i.index(-1)\\r\\n except:\\r\\n break\\r\\n self.i.pop(where)\\r\\n self.t.pop(where)\\r\\n self.time.pop(where)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"413b28d3b07ead5cb86d8f3729fcefd2\",\n \"score\": \"0.57369703\",\n \"text\": \"def nan_filter(self):\\r\\n \\r\\n # filter out nan values in numerical attributes\\r\\n for att in self.catalog_atts:\\r\\n if getattr(self, att).shape[0] == 0:\\r\\n pass\\r\\n elif (type(getattr(self, att)[0]) == str) or (type(getattr(self, att)[0]) == bytes):\\r\\n # FIXME: intent here unclear: \\r\\n # note float('nan') is an IEEE NaN, getattr(.) is a str, and != on NaNs is special\\r\\n i = np.where(getattr(self, att) != float('nan'))[0]\\r\\n self.revise_lists(i)\\r\\n # exclude non-numerical types\\r\\n elif type(getattr(self, att)[0]) not in (np.unicode_, np.string_, np.bool_, bytes):\\r\\n if att == 'coords':\\r\\n i1 = np.where(~np.isnan(self.coords.ra.to('deg').value))[0]\\r\\n i2 = np.where(~np.isnan(self.coords.dec.to('deg').value))[0]\\r\\n i = np.intersect1d(i1,i2)\\r\\n else:\\r\\n i = np.where(~np.isnan(getattr(self, att)))[0]\\r\\n self.revise_lists(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1763b3f28d3ff59fd197556ba56b060a\",\n \"score\": \"0.5727697\",\n \"text\": \"def remove_data_for_training_from_dataframe(data_df,Mask_NA,missing_fraction=0.3):\\n\\n def convert_series_to_diag_df(Series):\\n return pd.DataFrame(columns=Series.index, index=Series.index, data=np.diag(Series.values))\\n \\n #removed_vals = (Mask_NA == 1).any(axis=0).astype(int)\\n NAs = (Mask_NA == 1).any(axis=1).astype(int)\\n NA_diag = convert_series_to_diag_df(NAs)\\n\\n NA_map = PandasFunctions.CompressDataFrame(NA_diag,[1])\\n Val_map = PandasFunctions.CompressDataFrame(Mask_NA,[1])\\n #############################################################\\n np.random.seed(0)\\n remover = np.random.rand(data_df.shape[0], NA_map.shape[0])\\n remover = np.where( missing_fraction>remover , 1, 0)\\n ############################################################# \\n NA_1s = np.matmul(remover, NA_map.values)\\n Values_0s = np.matmul(remover, Val_map.values)\\n ############################################################# \\n data_missing_df = data_df.copy()\\n matrix = data_missing_df.values\\n matrix[NA_1s ==1] = 1\\n matrix[Values_0s==1] = 0\\n data_missing_df[:] = matrix\\n #############################################################\\n return data_missing_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed86b9e8b99ac7b505b79ccb5753c9a8\",\n \"score\": \"0.57137537\",\n \"text\": \"def remove_nan_rows(micro_df: pd.DataFrame):\\n data_cols = micro_df.columns.tolist()[1:]\\n micro_df.dropna(how = 'all', subset = data_cols, inplace=True)\\n micro_df.reset_index(inplace=True, drop=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13cf93e8603b92c72a804674e53a47cc\",\n \"score\": \"0.5667109\",\n \"text\": \"def filter_nan(s,o):\\n '''\\n data = np.array([s.flatten(),o.flatten()])\\n data = np.transpose(data)\\n #data = data[~np.isnan(data).any(1)]\\n data = data[~pd.isnull(data)]\\n #data = data[~np.isnan(data)]\\n return data[:,0],data[:,1]\\n #return data\\n '''\\n s = s.flatten()\\n o = o.flatten()\\n s = np.transpose(s)\\n o = np.transpose(o)\\n s[np.invert(~np.isnan(o))] = np.nan\\n o[np.invert(~np.isnan(s))] = np.nan\\n s = s[~pd.isnull(s)]\\n o = o[~pd.isnull(o)]\\n return s,o\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bbdbe56e3df32572a4fa12f73449eb4\",\n \"score\": \"0.56610817\",\n \"text\": \"def wash_out_all_nan(data):\\n # wash out the data with nan\\n return data[~np.isnan(np.sum(data, 1))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689966188b7db502832026a5b59f8021\",\n \"score\": \"0.5639008\",\n \"text\": \"def clean_data(df):\\n df = df[~df.location.isnull()]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88cb616fb2e42ac45b5a8d4bd9062a4f\",\n \"score\": \"0.56378675\",\n \"text\": \"def process_na_data_in_df(df: 'DataFrame') -> 'DataFrame':\\n\\n # Remove Weekends: Sat & Sun\\n df = df[(df.index.dayofweek != 5)&(df.index.dayofweek != 6)]\\n\\n # Removing all columns with NA values > 10% entries\\n df = df.loc[:, df.isna().sum()/df.shape[0] <= 0.1]\\n\\n # Dropping rows with all NA values\\n df = df.dropna(axis=0,how='all')\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"208cc3adf0e73a81cd66b4d8257283ab\",\n \"score\": \"0.56377816\",\n \"text\": \"def clean_dataset(df):\\n assert isinstance(df, pd.DataFrame), \\\"df needs to be a pd.DataFrame\\\"\\n df.dropna(inplace=True)\\n indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\\n return df[indices_to_keep].astype(np.float64)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d825919fec8aa76095fd7978826676\",\n \"score\": \"0.5636558\",\n \"text\": \"def _filter_na_for_fit(self, X: pd.DataFrame, y):\\n # let's also treat infinite values as NA\\n # scikit-learn's check_estimator might throw those at us\\n with pd.option_context(\\\"mode.use_inf_as_na\\\", True):\\n flt = pd.isna(X).values\\n X_out = X[~flt]\\n if y is not None and len(y) > 0:\\n y_out = y[~flt]\\n else:\\n y_out = y\\n return X_out, y_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8d89a4a2ee9109d29df82591e9344b0\",\n \"score\": \"0.5592618\",\n \"text\": \"def clean_null_values(data_frame):\\n half_count = len(data_frame) / 2\\n # axis = 1 indicates remove of cols, and axis = 0 indicates remove of rows\\n data_frame = data_frame.dropna(thresh=half_count, axis=1) # Drop any column with more than 50% missing values\\n # drop any row with null values\\n data_frame = data_frame.dropna(axis=0, how='any')\\n return data_frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2cf87fea3e49c01afa9f06ca623a419\",\n \"score\": \"0.5588972\",\n \"text\": \"def remove_nan_entries(df):\\n\\tdf_initsize = len(df)\\n\\tprint('Initial dataset size: ', df.shape)\\n\\tdisplay(HTML(df.tail().to_html()))\\n\\tprint('\\\\nRemoving the NaN values...')\\n\\t\\n\\tfor col in df.columns:\\n\\t col_type = df[col].dtype\\n\\t #print('col infos: {} dtype={}'.format(col, col_type))\\n\\t if(col_type != np.dtype('int64')):\\tdf.drop(df[~df[col].notna()].index, inplace=True)\\n\\n\\tdisplay(HTML(df.tail().to_html()))\\n\\tprint('\\\\nDataset size after NaN removing: ', df.shape)\\n\\tred_per = 100*(df_initsize - len(df)) / df_initsize\\n\\tprint(\\\"=> %.0f%% reduction of data.\\\" % red_per)\\n\\n\\treturn df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90230baf2be6f9623288c70f2a631e38\",\n \"score\": \"0.5581642\",\n \"text\": \"def _getnonconnectedsensors(self):\\n currentcams=[]\\n currenttemp=[]\\n currentni=[]\\n overcams=[]\\n overtemp=[]\\n overni=[]\\n for par in self._paramlist:\\n if par[\\\"type\\\"]==\\\"camera\\\":\\n currentcams.append((par[\\\"vendor\\\"],par[\\\"camid\\\"]))\\n if par[\\\"type\\\"]==\\\"temperature\\\":\\n currenttemp.append(par[\\\"handle\\\"])\\n if par[\\\"type\\\"]==\\\"nianalog\\\":\\n currentni.append(par[\\\"devstr\\\"])\\n \\n currentcamsset=set(currentcams)\\n currenttempset=set(currenttemp)\\n currentniset=set(currentni)\\n \\n if not self._connectedcams is None:\\n camintersection=currentcamsset.intersection(self._connectedcams)\\n for cam in list(camintersection):\\n currentcamsset.remove(cam)\\n\\n if not self._connectedtemp is None:\\n tempintersection=currenttempset.intersection(self._connectedtemp)\\n for temp in list(tempintersection):\\n currenttempset.remove(temp)\\n \\n if not self._connectedni is None:\\n niintersection=currentniset.intersection(self._connectedni)\\n for ni in list(niintersection):\\n currentniset.remove(ni)\\n \\n missing={\\\"camera\\\":list(currentcamsset),\\\"temperature\\\":list(currenttempset),\\\"nianalog\\\":list(currentniset)}\\n \\n return missing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1dbb732594bd0adf24b12f262abf13\",\n \"score\": \"0.55662936\",\n \"text\": \"def test_filter_missing_default(self, meas):\\n print(meas.data.columns)\\n meas.set_regression_cols(\\n power='meter_power',\\n poa='met1_poa_refcell',\\n t_amb='met2_amb_temp',\\n w_vel='met1_windspeed',\\n )\\n assert all(meas.rview('all', filtered_data=True).isna().sum() == 0)\\n assert meas.data_filtered.shape[0] == 1440\\n meas.data_filtered.loc['10/9/90 12:00', 'meter_power'] = np.NaN\\n meas.data_filtered.loc['10/9/90 12:30', 'met1_poa_refcell'] = np.NaN\\n meas.data_filtered.loc['10/10/90 12:35', 'met2_amb_temp'] = np.NaN\\n meas.data_filtered.loc['10/10/90 12:50', 'met1_windspeed'] = np.NaN\\n meas.filter_missing()\\n assert meas.data_filtered.shape[0] == 1436\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50765df3afe4062e887fd495f7d20620\",\n \"score\": \"0.5561974\",\n \"text\": \"def eliminate_singles(files):\\n # allocate memory for the output\\n filtered_traces = files.copy()\\n # for all the columns\\n for col in np.arange(files.shape[1]):\\n # get the target trace\\n original_trace = files.iloc[:, col]\\n # find the derivative of the nan trace\\n nan_positions = np.diff(np.isnan(original_trace).astype(np.int32), n=2)\\n # find the coordinates of the singles\\n single_positions = np.argwhere(nan_positions == 2) + 1\\n # single_positions = np.argwhere((nan_positions[:-1] == 1) & (nan_positions[1:] == -1))\\n # nan the singles\\n filtered_traces.iloc[single_positions, col] = np.nan\\n\\n return filtered_traces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d28291e59ab3392cb75585f375154b\",\n \"score\": \"0.5544502\",\n \"text\": \"def clean_df(self, df):\\n df = df.dropna(axis='columns', how='all')\\n df = df.dropna(axis='rows', how='any')\\n df = df.astype('float64')\\n df.index = df.index.astype('int64')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7beeecf20699561c694c5c237529de88\",\n \"score\": \"0.5533206\",\n \"text\": \"def filter_nan(s,o):\\n data = np.array([s.flatten(),o.flatten()])\\n data = np.transpose(data)\\n data = data[~np.isnan(data).any(1)]\\n #data = data[~np.isnan(data)]\\n return data[:,0],data[:,1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6deb83b721aaaea9e4f2f2aac5032b3f\",\n \"score\": \"0.5526761\",\n \"text\": \"def drop_na(self):\\n self.df = self.df.dropna(axis=0, how='any')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"535115677c38ae302efaebcf05b89078\",\n \"score\": \"0.55242294\",\n \"text\": \"def clean_non_targets(self, df):\\n # drop contaminants\\n df = df[df[\\\"Potential contaminant\\\"] != \\\"+\\\"]\\n # drop reverse\\n df = df[df[\\\"Reverse\\\"] != \\\"+\\\"]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302ac812d6a4d6da03d1689fbe1ac5e8\",\n \"score\": \"0.55231535\",\n \"text\": \"def clean_nan_inf(ts):\\n ts = to_np_array(ts)\\n search = (np.isinf(ts) | np.isnan(ts))\\n ts[search] = 0\\n\\n return ts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d584df708be962da48a56ddd6b8d43d2\",\n \"score\": \"0.5511816\",\n \"text\": \"def remove_nulls(self):\\n \\n print('Removing Null Data...')\\n \\n for column in ['Open', 'Close']:\\n for index in self.df[column].index[self.df[column].apply(np.isnan)].tolist():\\n self.df.drop(labels=index,axis=0,inplace=True)\\n print('NULL ELEMENT REMOVED!')\\n\\n print('All Null Data Removed Successfully!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a224e4e86808e5779ba93a7e2c895b22\",\n \"score\": \"0.5505433\",\n \"text\": \"def remove_rows_with_wrong_values(self):\\n print(\\\" removing rows with wrong values...\\\")\\n self.df.dropna(inplace=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0945fb57da5b13eac3a5116d028644a8\",\n \"score\": \"0.54922837\",\n \"text\": \"def drop_row_with_NA(self):\\n\\n # self.main_df = self.main_df[self.main_df[columns] != 0]\\n self.main_df = self.main_df.dropna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"310836c18424bdaa13136de14364ea51\",\n \"score\": \"0.54829514\",\n \"text\": \"def no_modify(data):\\n # wash out the data with nan\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e827c22aa3c1ef2d5d904975bd256876\",\n \"score\": \"0.5474302\",\n \"text\": \"def remove_empty_features(self, tx):\\n tx = np.where(tx == -999, np.NaN, tx)\\n tx = tx[:, ~np.all(np.isnan(tx), axis=0)]\\n return tx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da6d97796efe7051da7f69e82026797c\",\n \"score\": \"0.54705954\",\n \"text\": \"def drop_na_reactions_or_single_row(df):\\n new_df = df.dropna(subset=['reaction'])\\n new_df = new_df[new_df.reaction_count_equiv > 0]\\n if len(new_df) == 0:\\n new_df = df.head(1)\\n new_df['reaction'] = np.nan\\n new_df['label'] = new_df['gene']\\n return new_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f15ae29fe533b404dc846a9c9957e64\",\n \"score\": \"0.5465945\",\n \"text\": \"def drop_nan(self):\\n self.df = self.df.dropna(axis=0, how='any')\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d52c882873b9b394ba08aa17a39614\",\n \"score\": \"0.54497117\",\n \"text\": \"def remove_rows_with_missing_values(self):\\n clean_data = []\\n for row in self.data:\\n hasMissing = False\\n for col in row:\\n if col == \\\"NA\\\" or col == \\\"\\\":\\n hasMissing = True\\n if not hasMissing:\\n clean_data.append(row)\\n self.data = clean_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae8ead52a7b3c87ce6d0aa2874c477a8\",\n \"score\": \"0.54421103\",\n \"text\": \"def make_log_no_trips(self):\\n self.df['log_trips'] = self.df[(self.df['trips_in_first_30_days'] != 0)].trips_in_first_30_days.apply(np.log)\\n self.df['log_trips'] = self.df['log_trips'].apply(lambda x: 0 if np.isnan(x) else x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a2a3655355cff1d9e36930017d6a14d\",\n \"score\": \"0.5433729\",\n \"text\": \"def remove_empty_values_marks(self, data):\\n\\n data_new = np.ndarray(shape=data.shape, dtype=data.dtype)\\n\\n __ACCURACY = 0.00001\\n\\n sample_number = 0\\n for features in data:\\n feature_index = 0\\n for value in features:\\n if abs(self.__EMPTY_VALUE - value) > __ACCURACY:\\n data_new[sample_number][feature_index] = value\\n else:\\n data_new[sample_number][feature_index] = None\\n feature_index += 1\\n sample_number += 1\\n print(\\\"Remove marks about empty values has done!\\\")\\n return data_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ddec8050279af60f2721fd30cf43fe3\",\n \"score\": \"0.54108644\",\n \"text\": \"def _remove_nan(values: TypeValList) -> TypeValList:\\n values = np.asarray(values, dtype=float)\\n return values[~np.isnan(values)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85ffd26d43c6c9bd7e9ff00c77464868\",\n \"score\": \"0.541016\",\n \"text\": \"def check_for_nan(df):\\n return df.dropna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f070ce783a1e1cc0ab8923b612b26e5e\",\n \"score\": \"0.54039186\",\n \"text\": \"def filter_all(data):\\n #filter individual blocks of continuous data\\n datablocks = [list(v) for k,v in groupby(data,np.isfinite) if k]\\n filtereddatablocks = [filterfunction(v) for v in datablocks]\\n if np.any(np.isnan(data)): #if there are gaps\\n #get information about gaps\\n gap_indeces = np.where(np.isnan(data))[0]\\n gap_locs = []\\n gap_lengths = []\\n for k, g in groupby(enumerate(gap_indeces), lambda ix:ix[1]-ix[0]):\\n temprun = list(map(itemgetter(1), g))\\n gap_lengths.append(np.size(temprun))\\n gap_locs.append(temprun[0])\\n #build filtered data with gaps\\n filteredrun = np.array([]);\\n if gap_locs[0]==0: #if the first space is a gap, append and remove\\n filteredrun = np.append(filteredrun, np.full(gap_lengths[0],np.NaN) )\\n gap_locs = np.delete(gap_locs,0)\\n gap_lengths = np.delete(gap_lengths,0)\\n for i in range(np.size(filtereddatablocks)):\\n filteredrun = np.append(filteredrun,filtereddatablocks[i])\\n if i<np.size(gap_locs):\\n filteredrun = np.append(filteredrun,np.full(gap_lengths[i],np.NaN))\\n else: #if no gaps\\n filteredrun = filterfunction(data)\\n return filteredrun\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ff79d1cf1232b2cd2bee9677a15f802\",\n \"score\": \"0.5403428\",\n \"text\": \"def drop_missing_data(df):\\n\\n return df.dropna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58ac718730e57ad72fbd52481e985402\",\n \"score\": \"0.5393061\",\n \"text\": \"def drop_irrelevant(self):\\r\\n self.df = self.df.drop(['id2', 'state', 'year', 'asthma_rate'], axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a9a5ca95990bcb1ddfe7419ab9b2409\",\n \"score\": \"0.5385711\",\n \"text\": \"def drop_lowz(df):\\n df = df.dropna(subset=['FIELD'])\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb57879edba1a2b3b4ef96649e53326\",\n \"score\": \"0.53795296\",\n \"text\": \"def clean_data(df):\\n # removes first row time = 0\\n df.dropna(how='any', inplace=True)\\n df.drop_duplicates(inplace=True)\\n\\n # remove points where speed is greater than > 100 mph\\n df = df[df['speed'] < 100]\\n\\n # replace unknowns with mean of column\\n df = df.replace(-99999, np.nan)\\n df = df.fillna(df.mean())\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0430e6d2ce3529d2c69f6a112c139784\",\n \"score\": \"0.5377258\",\n \"text\": \"def _clean_missing(data, params):\\n attributes = params['attributes']\\n cleaning_mode = params['cleaning_mode']\\n frag = params['id_frag']\\n\\n if cleaning_mode == \\\"REMOVE_COLUMN\\\":\\n\\n thresh = params['thresh']\\n if thresh:\\n subset = []\\n cols = params['columns_drop']\\n for c in cols.index:\\n if cols.loc[c] > thresh:\\n subset.append(c)\\n else:\\n subset = params['columns_drop']\\n data = data.drop(subset, axis=1)\\n\\n elif cleaning_mode == \\\"MEAN\\\":\\n\\n sizes, sums = params['values']\\n values = np.divide(sums, sizes)\\n\\n for v, a in zip(values, attributes):\\n data[a] = data[a].fillna(value=v)\\n\\n elif cleaning_mode == \\\"MODE\\\":\\n dict_mode = params['dict_mode']\\n for att in dict_mode:\\n mode = dict_mode[att].idxmax()\\n data[att] = data[att].fillna(value=mode)\\n\\n elif cleaning_mode == 'MEDIAN':\\n medians = params['values']\\n for att in medians:\\n data[att] = data[att].fillna(value=medians[att])\\n\\n data.reset_index(drop=True, inplace=True)\\n info = generate_info(data, frag)\\n return data, info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc3286b60c67caabbf08b38675cf0de7\",\n \"score\": \"0.53675956\",\n \"text\": \"def drop_missing(X):\\n nonmissing=X[0].copy()\\n nonmissing['Nonmissing']=True\\n nonmissing=nonmissing['Nonmissing']\\n for x in X:\\n nonmissing.where(pd.notnull(x).all(axis=1),False,inplace=True)\\n\\n for i in range(len(X)):\\n X[i] = X[i].loc[nonmissing,:]\\n\\n return tuple(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed07a105be7b32edd490f39e6279fde8\",\n \"score\": \"0.53658754\",\n \"text\": \"def flag_na(df,remove_rows=False):\\n df = df.copy()\\n n_bad = sum(np.ravel(df.isnull().values))\\n print('{} bad values in the dataset.')\\n if remove_rows == True:\\n df.dropna()\\n return(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ccf0fca21cb7d020e01721e6d73f4f\",\n \"score\": \"0.5357542\",\n \"text\": \"def _getmissingsensors(self):\\n currentcams=[]\\n currenttemp=[]\\n currentni=[]\\n missingcams=[]\\n missingtemp=[]\\n missingni=[]\\n for par in self._paramlist:\\n if par[\\\"type\\\"]==\\\"camera\\\":\\n currentcams.append((par[\\\"vendor\\\"],par[\\\"camid\\\"]))\\n if par[\\\"type\\\"]==\\\"temperature\\\":\\n currenttemp.append(par[\\\"handle\\\"])\\n if par[\\\"type\\\"]==\\\"nianalog\\\":\\n currentni.append(par[\\\"devstr\\\"])\\n \\n for cam in self._connectedcams:\\n if not cam in currentcams:\\n missingcams.append(cam)\\n if not self._connectedtemp is None:\\n for temp in self._connectedtemp:\\n if not temp in currenttemp:\\n missingtemp.append(temp)\\n if not self._connectedni is None:\\n for ni in self._connectedni:\\n if not ni in currentni:\\n missingni.append(ni)\\n missing={\\\"camera\\\":missingcams, \\\"temperature\\\":missingtemp,\\\"nianalog\\\":missingni}\\n return missing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c24a661c654de184a93b6d3fcc684cf\",\n \"score\": \"0.53526807\",\n \"text\": \"def nan_trim(data):\\r\\n data_diff = np.diff(data)\\r\\n\\r\\n for i, diff in reversed(list(enumerate(data_diff))):\\r\\n if diff == 1:\\r\\n data.pop(i)\\r\\n\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d773f973c4e24a2274159b9e08fef1b8\",\n \"score\": \"0.53520674\",\n \"text\": \"def nans_remove(self):\\n msk = np.isfinite(self.y)\\n return self.__class__(self.x[msk], self.y[msk])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f98e0c9343893178195cbd3f034352\",\n \"score\": \"0.5342439\",\n \"text\": \"def remove_na_lines(self, max_na_proportion:float=None):\\n\\n if max_na_proportion:\\n drop_lines = list(self.df.loc[self.df.isna().sum(axis=1)>len(self.df.columns)*max_na_proportion].index) \\n else:\\n drop_lines = self.na_lines\\n #print(f'Lines removed:{drop_lines[0:4]}...')\\n self.df = self.df.drop(drop_lines)\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"075d66d00dfa6a68a225f3dacc5ab7fe\",\n \"score\": \"0.5338691\",\n \"text\": \"def cleanData(dataToClean):\\n for i in range(len(dataToClean)):\\n for j in range(len(dataToClean[0])):\\n if np.isnan(dataToClean[i][j]):\\n dataToClean[i][j]=0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a12091d6b20c49b7725690d923460001\",\n \"score\": \"0.5331909\",\n \"text\": \"def clean_df(df):\\n df = df.where(pd.notnull(df), None)\\n df = df.fillna(value=0)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d6bb36d5febd036ea6d7c4c4267a6a5\",\n \"score\": \"0.533119\",\n \"text\": \"def _drop_obs(df):\\n drop_subj = []\\n for sid, row in df.iterrows():\\n try:\\n if sum(row.dropna()) == 0:\\n drop_subj.append(sid)\\n elif row.count() == 0:\\n drop_subj.append(sid)\\n except TypeError: # compatible to ados\\n if sum(row.astype(float).dropna()) == 0:\\n drop_subj.append(sid)\\n elif row.astype(float).count() == 0:\\n drop_subj.append(sid)\\n return drop_subj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260be91c0b8510b71ae7a1d740960b03\",\n \"score\": \"0.53306866\",\n \"text\": \"def NaN_cleaning(df):\\n df = df.replace(np.nan, 'unknown')\\n return df.reset_index(drop=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5be06db39233a32605860ac8b3689a5c\",\n \"score\": \"0.53283554\",\n \"text\": \"def _remove_missing_data(\\n data_values: NDArray,\\n geometry_array: geopandas.GeoSeries,\\n) -> tuple[NDArray, geopandas.GeoSeries]:\\n not_missing_data = ~pandas.isnull(data_values)\\n geometry_array = geometry_array[not_missing_data]\\n data_values = data_values[not_missing_data]\\n return data_values, geometry_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb9b42a6eaa6253ee3b8d523139e299\",\n \"score\": \"0.5324406\",\n \"text\": \"def quick_clean(data):\\n rows_missing_data = (data == 0).sum(1)\\n return data[rows_missing_data == 0, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d9e2a4681978a2e091275a72005b3c\",\n \"score\": \"0.5316683\",\n \"text\": \"def clean_nan(data, data_column):\\r\\n data.dropna(subset=[data_column, \\\\\\r\\n 'Quality'], inplace=True)\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f38a2777c41e92eecb61343ef10b22\",\n \"score\": \"0.53023773\",\n \"text\": \"def filter_zeroes(self):\\n self.U[self.U == 0] = np.nan\\n self.V[self.V == 0] = np.nan\\n self.W[self.W == 0] = np.nan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985aa5c0aa5f067935e58f73b2100dbc\",\n \"score\": \"0.5297176\",\n \"text\": \"def remove_nans(array_like):\\n data = array_like.copy()\\n \\n # Astropy table object\\n if type(data) is Table:\\n data = data.filled(NAN_VALUE)\\n # Pandas DataFrame object\\n elif type(data) is pd.DataFrame:\\n data = data.fillna(NAN_VALUE)\\n else:\\n print(\\\"Unrecognized data format:\\\", type(data))\\n return None\\n \\n # Return final data structure\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"914a197db781cb12a5fe19ab43161b5d\",\n \"score\": \"0.5292508\",\n \"text\": \"def test_DataCleaner_big_nan_handler_warning(self):\\n dc = DataCleaner(max_na_frac=0.01)\\n df = pd.DataFrame(\\n np.random.randint(0, 100, size=(100, 4)), columns=list(\\\"ABCD\\\")\\n )\\n dc.fit(df, \\\"D\\\")\\n self.assertEqual(len(dc.warnings), 0)\\n\\n df[\\\"A\\\"].iloc[10:20] = np.nan\\n df[\\\"B\\\"].iloc[:99] = np.nan\\n dc.fit(df, \\\"D\\\")\\n self.assertEqual(len(dc.warnings), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc851de57a11012e4155217bb31adc96\",\n \"score\": \"0.52870226\",\n \"text\": \"def delete_rows(df):\\r\\n\\r\\n df = df.drop(df[df.img.isna()].index)\\r\\n df = df.drop(df[df.cost.isna()].index)\\r\\n df = df.drop(df[df.rarity.isna()].index)\\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fbcab9e0a3ed7e49af6f0c03573e528\",\n \"score\": \"0.5284864\",\n \"text\": \"def test_nans_replaced_none():\\n\\n #generate test data\\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\\n dataframe['A'][5] = numpy.nan\\n\\n #run function on dataframe\\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\\\"none\\\", removal_time_frame=\\\"day\\\",\\n fault_placement=\\\"calendar\\\")\\n\\n assert dataframe.size == result_dataframe.size, \\\"nan has not been dropped\\\"\\n assert result_dataframe['A'][5] != numpy.nan, \\\"nan was removed but shouldn't have\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08006fa93818a2f08ef6539d0e78d6b8\",\n \"score\": \"0.5269665\",\n \"text\": \"def remove_duplicates_and_full_nulls(data):\\n data = data.copy()\\n data = data.drop_duplicates()\\n data = data.dropna(how ='all')\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9cd6b277461a674b517515b1a4294eb\",\n \"score\": \"0.52642363\",\n \"text\": \"def remove_null(dataframe):\\n\\n dataframe = dataframe.dropna()\\n return dataframe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52409db8b7b10f8ebaa0732ca7d73c16\",\n \"score\": \"0.52546704\",\n \"text\": \"def _rmDataWithoutBrightStar(self, neighborStarMap, starMap,\\n wavefrontSensors):\\n\\n # Collect the sensor list without the bright star\\n noStarSensorList = []\\n for detector, stars in neighborStarMap.items():\\n if (len(stars.getId()) == 0):\\n noStarSensorList.append(detector)\\n\\n # Remove the data in map\\n for detector in noStarSensorList:\\n neighborStarMap.pop(detector)\\n starMap.pop(detector)\\n wavefrontSensors.pop(detector)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3392f154927a0b3ea4e7a68b784f6179\",\n \"score\": \"0.5254399\",\n \"text\": \"def filter_nan(s,o):\\n data = np.array([s.flatten(),o.flatten()])\\n data = np.transpose(data)\\n data = data[~np.isnan(data).any(1)]\\n\\n return data[:,0],data[:,1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe7cf61ecf67b86675bc79f599d13c2\",\n \"score\": \"0.5251705\",\n \"text\": \"def test_filter_by_NaN(self):\\n test_scope = scope.Scope(-4.9, -4.70, 48, 49, 0, 1500000000)\\n test_preprocessing = preprocessing.Preprocessing(test_scope, self.test_ships)\\n\\n self.assertEqual(len(self.test_ships), 15)\\n test_preprocessing.filter_by_NaN()\\n self.assertEqual(len(self.test_ships), 7)\\n\\n number_states = {'245257000': 29, '228037600': 259, '304091000': 1,\\n '227705102': 249, '227443000': 92, '228931000': 21,\\n '228131600': 350, '228064900': 4, '37100300': 7}\\n\\n for ship in self.test_ships:\\n self.assertEqual(len(ship.ais_states), number_states[ship.sourcemmsi],\\n msg=\\\"sourcemmsi is \\\" + ship.sourcemmsi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee4fe5bb4730d0e6d8c78730fa48d55\",\n \"score\": \"0.52495563\",\n \"text\": \"def remove_rows_with_missing_values(self):\\n row_length = len(self.column_names)\\n table_copy = []\\n \\n for i in range(len(self.data)):\\n has_NA = False\\n for j in range(row_length):\\n if self.data[i][j] == \\\"NA\\\" or self.data[i][j] == \\\"\\\":\\n has_NA = True\\n if has_NA == False: \\n table_copy.append(self.data[i])\\n \\n self.data.clear()\\n self.data = copy.deepcopy(table_copy) \\n pass # TODO: fix this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ead744219067e1a2c3a93e51f6bfe2\",\n \"score\": \"0.5245453\",\n \"text\": \"def _clean_useless_rows(self):\\r\\n self.temp_stock_data = pandas.DataFrame()\\r\\n dates = []\\r\\n for i in xrange(0, self.stock_data[self.company_list.Companies.irow(0)].count()):\\r\\n #print i\\r\\n row = self.stock_data.irow(i)\\r\\n if len(row[numpy.isnan(row)]) == len(row):\\r\\n dates.append(self.date_list[i])\\r\\n \\r\\n for single_date in dates:\\r\\n self.stock_data = self.stock_data.drop(pandas.Timestamp(single_date))\\r\\n print \\\"Data cleaned from useless rows.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc475ee4cc92954c4f70406962b6150\",\n \"score\": \"0.52325326\",\n \"text\": \"def data_cleaning(df: DataFrame) -> DataFrame:\\n print(\\\"Cleaning the data to remove problems\\\\n \\\")\\n\\n nulls = df.isnull().sum()\\n nulls = nulls[nulls != 0]\\n\\n if not nulls.empty:\\n print(f\\\"The columns below some 'NaN' values:\\\\n{nulls}\\\")\\n\\n # for index_with_nan in enumerate(nulls.keys()[:]):\\n # print(f\\\"Column: {index_with_nan} \\\\n \\\")\\n\\n if 'Birth Year' in df.columns.values.tolist():\\n df = dob_trim(df)\\n\\n time.sleep(2)\\n\\n no_user_type: float = format(df['User Type'].isna().sum()/df.shape[0]*100, '.2g')\\n print(f\\\"A few rows don't have any information for 'User Type' but it is only {no_user_type}% so we'll just drop them\\\")\\n df = df.dropna(axis = 0, subset=['User Type'])\\n\\n if 'Gender' in df.columns.values.tolist():\\n gender_neutral: float = format(df['Gender'].isna().sum()/df.shape[0]*100, '.2g')\\n print(f\\\"{gender_neutral}% of trips don't have any information for 'Gender' so we'll forward-fill.\\\")\\n df.fillna(method=\\\"ffill\\\")\\n # gender_neutral: float = format(nulls['Gender']/df.shape[0]*100, '.2g')\\n # print(f\\\"A few rows don't have any information for 'Gender' but it is only {gender_neutral}% so we'll just drop them\\\")\\n # df = df.dropna(axis = 0, subset=['Gender'], inplace=True)\\n \\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"658b7ab760716acff016b703ca146fca\",\n \"score\": \"0.5225665\",\n \"text\": \"def removeNansSingle(data):\\n # set an x array\\n x = np.arange(data.size)\\n # find locations of nans - this is a bool array with True in locations with nan values\\n nanLocs = np.isnan(data)\\n # if no nans, do nothing\\n if not np.any(nanLocs):\\n return data # no nans to remove\\n # create mask\\n mask = np.ones(data.size, np.bool)\\n mask[nanLocs] = 0 # using numpy indexing with bool arrays\\n # no need to group, want to remove every nan\\n data[nanLocs] = np.interp(x[nanLocs], x[mask], data[mask])\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb6066ce4a1f9dc1a890971b21dec1f\",\n \"score\": \"0.5221285\",\n \"text\": \"def remove_notes(data):\\n return data[pd.to_numeric(data.iloc[:, 0], errors='coerce').notnull()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a937fac4a9481e0cde52d043b27035f\",\n \"score\": \"0.52128845\",\n \"text\": \"def filter_spikes(df:pd.DataFrame):\\n \\\"\\\"\\\"first return argument is non-spike signals, the second one for the spikes\\\"\\\"\\\"\\n new_df=pd.DataFrame()\\n spike_cluster=pd.DataFrame()\\n for i in range(len(df.columns)):\\n tmp=df.iloc[:, i] \\n uniques=tmp.unique()\\n uniques = [x for x in uniques if str(x) != 'nan']\\n uniques = [x for x in uniques if x not in [0.0,1.0]]\\n if(len(uniques)<=1): #spike like\\n spike_cluster.insert(len(spike_cluster.columns),tmp.name,tmp)\\n else:\\n new_df.insert(len(new_df.columns),tmp.name,tmp)\\n return new_df,spike_cluster\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a372da4cfe63a9a92a7f200bb86f20a\",\n \"score\": \"0.5209513\",\n \"text\": \"def nan_clean(array):\\n for i in range(len(array)):\\n if np.isnan(array[i]) == True:\\n array[i] = 0.0\\n return array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a41fc2d468a3aa9657429af2f10331b\",\n \"score\": \"0.52041674\",\n \"text\": \"def remove_nan_observations(x, y, z):\\n x_ = x[~np.isnan(z)]\\n y_ = y[~np.isnan(z)]\\n z_ = z[~np.isnan(z)]\\n return x_, y_, z_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b824e07c74f481cca378320af481262\",\n \"score\": \"0.5198521\",\n \"text\": \"def filterNaN(instr, datacol, outfile, logfile, verbose):\\n try:\\n nanclean = instr[1].header['NANCLEAN']\\n except:\\n naxis2 = 0\\n for i in range(len(instr[1].columns.names)):\\n if 'time' in instr[1].columns.names[i].lower():\\n timecol = instr[1].columns.names[i]\\n try:\\n instr[1].data.field(datacol)\\n except:\\n msg = (\\\"ERROR -- KEPIO.FILTERNAN: cannot find column {}\\\"\\n \\\"in the infile\\\".format(datacol))\\n kepmsg.err(logfile, msg, verbose)\\n try:\\n for i in range(len(instr[1].data.field(0))):\\n if (str(instr[1].data.field(timecol)[i]) != '-inf' and\\n str(instr[1].data.field(datacol)[i]) != '-inf'):\\n instr[1].data[naxis2] = instr[1].data[i]\\n naxis2 += 1\\n instr[1].data = instr[1].data[:naxis2]\\n comment = 'NaN cadences removed from data'\\n kepkey.new('NANCLEAN', True, comment, instr[1], outfile, logfile,\\n verbose)\\n except:\\n errmsg = ('ERROR -- KEPIO.FILTERNAN: Failed to filter NaNs from '\\n + outfile)\\n kepmsg.err(logfile, errmsg, verbose)\\n return instr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e14120618e0caa9c1a1adf8bf742894f\",\n \"score\": \"0.51975524\",\n \"text\": \"def fix_na_values(df):\\n ignored = [\\\"SUBJECT_ID\\\", \\\"HADM_ID\\\", \\\"ADMITTIME\\\", \\\"DISCHTIME\\\"]\\n df_core = df.drop(ignored, axis=1)\\n\\n while df_core.isna().sum().sum():\\n # get column with least amount of missing values\\n cols_with_na = df_core.isna().sum()\\n col = cols_with_na[cols_with_na > 0].idxmin()\\n # impute that column\\n df_core.loc[df_core[col].isna(), col] = impute_column(df_core, col)\\n\\n return pd.concat([df_core, df[ignored]], axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13995298c15ddffef72188fbf4ae9e9e\",\n \"score\": \"0.51944923\",\n \"text\": \"def test_nans_dropped():\\n\\n #generate test data\\n dataframe = pandas.DataFrame(numpy.random.randint(-100, 100, size=(100, 6)), columns=list('ABCDEF'))\\n dataframe['A'][5] = numpy.nan\\n\\n #run function on dataframe\\n result_dataframe = cleaners.handle_nans(dataframe, data_replacement=\\\"drop\\\", removal_time_frame=\\\"day\\\", fault_placement=\\\"calendar\\\")\\n assert dataframe.size != result_dataframe.size, \\\"nan have not been dropped\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9105baa116f0859c24acfe0688351a8b\",\n \"score\": \"0.51941323\",\n \"text\": \"def drop_missing(self):\\n df1 = self.df\\n df2 = df1.dropna(axis=0)\\n\\n self.df = df2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe78430afcdbf63973f5fb1eef658c15\",\n \"score\": \"0.5192824\",\n \"text\": \"def remove_missing_values(x):\\n x_clean = x.copy()\\n x_clean = x_clean[:, [np.count_nonzero(x.T[i] == -999) == 0 for i in range (x.shape[1])]]\\n return x_clean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26e1f1380c66022fe6c1a3408b34b606\",\n \"score\": \"0.51923764\",\n \"text\": \"def remove_rcna(df):\\n df.dropna(how = 'all', axis = 'columns', inplace = True)\\n df.dropna(how = 'all', axis = 'rows', inplace = True)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97e3227a5225488f9568a8c415801ea9\",\n \"score\": \"0.51922977\",\n \"text\": \"def generate_query_ratesnull(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"934058862a1706b0deffe110b8780021\",\n \"score\": \"0.518677\",\n \"text\": \"def _series_cleaner(self, obs_data: Union[dict, list]) -> pd.DataFrame:\\n series = pd.json_normalize(obs_data[\\\"observations\\\"])\\n series.drop(columns=[\\\"realtime_start\\\", \\\"realtime_end\\\"], inplace=True)\\n series[\\\"value\\\"] = series[\\\"value\\\"].replace(self.nan_char, np.nan)\\n return series\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabc39526af7b6e52689bfe97d78ffba\",\n \"score\": \"0.5184404\",\n \"text\": \"def drop_missing(self):\\n self.miss = np.rint(len(self.data.columns)*self.miss)\\n self.data = self.data[self.data.isnull().sum(axis=1)<=self.miss]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6e550833aaa3e203af1bd5115ecd7f9\",\n \"score\": \"0.5179664\",\n \"text\": \"def clean_data(df):\\n data_types = get_missing_columns()\\n \\n for idx in data_types.index:\\n column = idx\\n if column in df.columns:\\n df[column] = df[column].replace(data_types.loc[column]['nan_vals'], np.nan)\\n\\n \\n # remove selected columns\\n df.drop(['AGER_TYP', 'ALTER_HH', 'ALTER_KIND1', 'ALTER_KIND2', 'ALTER_KIND3', 'ALTER_KIND4', 'ALTERSKATEGORIE_FEIN',\\\\\\n 'D19_BANKEN_ONLINE_QUOTE_12', 'D19_GESAMT_ONLINE_QUOTE_12', 'D19_KONSUMTYP', 'D19_LETZTER_KAUF_BRANCHE',\\\\\\n 'D19_LOTTO', 'D19_SOZIALES', 'D19_TELKO_ONLINE_QUOTE_12', 'D19_VERSAND_ONLINE_QUOTE_12',\\\\\\n 'D19_VERSI_ONLINE_QUOTE_12', 'EXTSEL992','GEBURTSJAHR', 'KBA05_BAUMAX', 'KK_KUNDENTYP', 'TITEL_KZ', 'LNR'],\\n axis=1, inplace=True)\\n \\n print(\\\"Removal of Columns with over 20% null values complete...\\\")\\n \\n # remove selected rows\\n df = df[df.isnull().sum(axis=1) < 9].reset_index(drop=True)\\n \\n # remove selected columns again\\n features_not_in_feat_info_42 = ['AKT_DAT_KL', 'ANZ_KINDER', 'ANZ_STATISTISCHE_HAUSHALTE', 'CJT_KATALOGNUTZER','CJT_TYP_1',\\\\\\n 'CJT_TYP_2', 'CJT_TYP_3', 'CJT_TYP_4', 'CJT_TYP_5', 'CJT_TYP_6', 'DSL_FLAG',\\\\\\n 'EINGEZOGENAM_HH_JAHR','FIRMENDICHTE', 'GEMEINDETYP', 'HH_DELTA_FLAG', 'KBA13_ANTG1',\\\\\\n 'KBA13_ANTG2', 'KBA13_ANTG3','KBA13_ANTG4', 'KBA13_BAUMAX', 'KBA13_CCM_1401_2500',\\\\\\n 'KBA13_GBZ', 'KBA13_HHZ', 'KBA13_KMH_210','KONSUMZELLE', 'MOBI_RASTER', 'RT_KEIN_ANREIZ',\\\\\\n 'RT_SCHNAEPPCHEN', 'RT_UEBERGROESSE', 'STRUKTURTYP','UMFELD_ALT', 'UMFELD_JUNG',\\\\\\n 'UNGLEICHENN_FLAG', 'VERDICHTUNGSRAUM', 'VHA', 'VHN', 'VK_DHT4A','VK_DISTANZ',\\\\\\n 'VK_ZG11','D19_KONSUMTYP_MAX', 'KOMBIALTER','EINGEFUEGT_AM']\\n \\n df.drop(features_not_in_feat_info_42,axis=1, inplace=True)\\n \\n print(\\\"Removal of undocumented columns complete...\\\")\\n \\n \\n # select, re-encode, and engineer column values.\\n # # feature engineering of categorical features\\n\\n df['OST_WEST_KZ'].replace(['W','O'], [1, 0], inplace=True)\\n \\n multi = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CJT_GESAMTTYP', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',\\\\\\n 'D19_BANKEN_DATUM','D19_BANKEN_OFFLINE_DATUM', 'D19_BANKEN_ONLINE_DATUM', 'D19_GESAMT_ANZ_12',\\\\\\n 'D19_GESAMT_ANZ_24', 'D19_GESAMT_DATUM','D19_GESAMT_OFFLINE_DATUM', 'D19_GESAMT_ONLINE_DATUM',\\\\\\n 'D19_TELKO_DATUM', 'D19_TELKO_OFFLINE_DATUM', 'D19_TELKO_ONLINE_DATUM','D19_VERSAND_DATUM',\\\\\\n 'D19_VERSAND_OFFLINE_DATUM', 'D19_VERSAND_ONLINE_DATUM', 'D19_VERSI_DATUM', 'D19_VERSI_OFFLINE_DATUM',\\\\\\n 'D19_VERSI_ONLINE_DATUM', 'FINANZTYP', 'GEBAEUDETYP', 'GFK_URLAUBERTYP','LP_FAMILIE_FEIN','LP_FAMILIE_GROB',\\\\\\n 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP']\\n \\n df = pd.get_dummies(df, columns=multi, prefix=multi)\\n \\n \\n # feature engineering of mixed features\\n df['PRAEGENDE_JUGENDJAHRE_decade'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_decade)\\n df['PRAEGENDE_JUGENDJAHRE_movement'] = df['PRAEGENDE_JUGENDJAHRE'].apply(make_movement)\\n df.drop('PRAEGENDE_JUGENDJAHRE', axis=1, inplace=True)\\n \\n print(\\\"Feature Engineering PRAEGENDE_JUGENDJAHRE complete...\\\")\\n \\n \\n df['CAMEO_INTL_2015_wealth'] = df['CAMEO_INTL_2015'].apply(make_wealth) \\n df['CAMEO_INTL_2015_life_stage'] = df['CAMEO_INTL_2015'].apply(make_life_stage)\\n df.drop('CAMEO_INTL_2015', axis=1, inplace=True)\\n \\n print(\\\"Feature Engineering CAMEO_INTL_2015 complete...\\\")\\n \\n df['WOHNLAGE_rural'] = df['WOHNLAGE'].map({0:0,1:0,2:0,3:0,4:0,5:0,7:1,8:1})\\n df['WOHNLAGE_rating_class'] = df['WOHNLAGE'].map({0:0,1:1,2:2,3:3,4:4,5:5,7:0,8:0})\\n \\n print(\\\"Feature Engineering WOHNLAGE complete...\\\")\\n \\n df = df.drop(['LP_LEBENSPHASE_FEIN','LP_LEBENSPHASE_GROB','WOHNLAGE','PLZ8_BAUMAX'], axis=1)\\n \\n print(\\\"Finished!\\\")\\n \\n # Return the cleaned dataframe.\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b15f9a5bc9f58c3f1fda20bd23a98b\",\n \"score\": \"0.5174193\",\n \"text\": \"def clean_data(self):\\n # Mask files with lat = 0 or lon = 0\\n mask = np.where(np.logical_or(self.data['VEH_LAT']==0, \\n self.data['VEH_LONG']==0))\\n\\n for k in self.data.keys():\\n if isinstance(self.data[k][0], float):\\n self.data[k][mask] = np.nan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fb2a849eb9288c2238968e9235b9752\",\n \"score\": \"0.51717937\",\n \"text\": \"def drop_null_rows(self, x: bool):\\n if x:\\n total_null_rows = self.df[self.target].isin(\\n [' ', 'NULL', np.nan]).sum()\\n if total_null_rows > 0:\\n print(\\\"Dropping rows having [' ', 'NULL', numpy.nan] values\\\")\\n self.df.dropna(inplace=True)\\n self.df.reset_index(drop=True, inplace=True)\\n self.null_values_present = False\\n print_in_red(f\\\"Total Null rows dropped: {total_null_rows}\\\\n\\\")\\n self._start_analysis()\\n else:\\n print(colored(\\\"There is no null rows present.\\\\n\\\", \\\"green\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52449af6e301d2d873cf47553c2b38b8\",\n \"score\": \"0.5167622\",\n \"text\": \"def filter_nan(s, o):\\n data = np.array([s.flatten(), o.flatten()])\\n data = np.transpose(data)\\n data = data[~np.isnan(data).any(1)]\\n return data[:, 0], data[:, 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6512e72d0658c2f1c0378e699cc56170\",\n \"score\": \"0.51613533\",\n \"text\": \"def clean_timeseries(self):\\n if 'tb_incidence' in self.timeseries_log.keys():\\n self.timeseries_log['tb_incidence'][0] = 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"248a5d7d28357321ce39d2c77fe712a7\",\n \"score\": \"0.5160788\",\n \"text\": \"def filter_values_2(self, counter):\\n inf = float(\\\"inf\\\")\\n last_mea = list(self.scan_values_2[counter])\\n for i in range(len(last_mea)):\\n if last_mea[i] == inf:\\n for k in range(1, MEASUREMENTS):\\n if self.scan_values_2[counter-k] != inf:\\n last_mea[i] = self.scan_values_2[k][i]\\n break\\n scan = LaserScan()\\n scan.ranges = last_mea\\n self.laser_filtered_pub.publish(scan)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16cc667bcf39fc41ca499a7580cf48d0\",\n \"score\": \"0.51605296\",\n \"text\": \"def _get_instr_after_remove(self, remove_size_null, remove_adj_cap_nan):\\n if any([remove_size_null, remove_adj_cap_nan]): # will update instr_df\\n instr_df = self.concat_instr_df.copy()\\n else: # nothing will be changed in self.concat_instr_df so no need to copy\\n instr_df = self.concat_instr_df\\n if instr_df.empty:\\n raise Exception('concat_instr_df is empty. Cannot display metrics')\\n if remove_size_null:\\n no_instrs = len(instr_df)\\n idx_size_null = instr_df['stake'] == 0\\n if sum(idx_size_null) > 0:\\n self.logger.warning('Dropping {}/{} instructions with stake 0'.format(\\n sum(idx_size_null), no_instrs))\\n instr_df = instr_df.loc[~idx_size_null]\\n if remove_adj_cap_nan:\\n if 'adj_cap' not in instr_df.columns:\\n raise Exception('concat_instr_df do not have column '\\n 'adj_cap and so cannot remove_adj_cap_nan')\\n no_instrs = len(instr_df)\\n idx_nan = instr_df['adj_cap'].isnull()\\n if sum(idx_nan) > 0:\\n self.logger.warning('Keeping {}/{} instructions with known capital adjustment'\\n .format(no_instrs - sum(idx_nan), no_instrs))\\n # remove nan in adjst_capital\\n instr_df = instr_df.loc[~idx_nan]\\n return instr_df\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":177,"cells":{"query_id":{"kind":"string","value":"014a5481438d0e9b71966a48d4d8bbf6"},"query":{"kind":"string","value":"Generate a NL or LP file from Pyomo, and then do subsequent conversions."},"positive_passages":{"kind":"list like","value":[{"docid":"b50fba180d6eee76d9713189ba7dcb27","score":"0.56642395","text":"def apply(self, *args, **kwds):\n\n import pyomo.scripting.convert\n\n capabilities = kwds.pop(\"capabilities\", None)\n\n # all non-consumed keywords are assumed to be options\n # that should be passed to the writer.\n io_options = {}\n for kwd, value in iteritems(kwds):\n io_options[kwd] = value\n kwds.clear()\n\n # basestring is gone in Python 3.x, merged with str.\n if PY3:\n compare_type = str\n else:\n compare_type = basestring\n\n if isinstance(args[2], compare_type):\n instance = None\n else:\n instance = args[2]\n\n if args[1] == ProblemFormat.cpxlp:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.lp')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.cpxlp,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.cpxlp,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2lp(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2lp conversion \"\n \"returned nonzero error code \"\n \"(%s)\" % ans.errorcode)\n\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n return (problem_filename,),symbol_map\n\n elif args[1] == ProblemFormat.bar:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.bar')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.bar,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.bar,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2bar(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2bar conversion \"\n \"returned nonzero error code \"\n \"(%s)\" % ans.errorcode)\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n return (problem_filename,),symbol_map\n\n elif args[1] in [ProblemFormat.mps, ProblemFormat.nl]:\n if args[1] == ProblemFormat.nl:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.nl')\n if io_options.get(\"symbolic_solver_labels\", False):\n pyutilib.services.TempfileManager.add_tempfile(\n problem_filename[:-3]+\".row\",\n exists=False)\n pyutilib.services.TempfileManager.add_tempfile(\n problem_filename[:-3]+\".col\",\n exists=False)\n else:\n assert args[1] == ProblemFormat.mps\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix = '.pyomo.mps')\n if instance is not None:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=args[1],\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=args[1],\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), symbol_map_id\n else:\n\n #\n # I'm simply exposing a fatal issue with\n # this code path. How would we convert the\n # collected keywords into command-line\n # arguments that can be sent to the writer?\n #\n if len(io_options):\n raise ValueError(\n \"The following io_options will be ignored \"\n \"(please create a bug report):\\n\\t\" +\n \"\\n\\t\".join(\"%s = %s\" % (k,v)\n for k,v in iteritems(io_options)))\n\n ans = pyomo.scripting.convert.\\\n pyomo2nl(['--output',problem_filename,args[2]])\n if ans.errorcode:\n raise RuntimeError(\"pyomo2nl conversion \"\n \"returned nonzero error \"\n \"code (%s)\" % ans.errorcode)\n model = ans.retval\n problem_filename = model.filename\n symbol_map = model.symbol_map\n\n if args[1] == ProblemFormat.nl:\n return (problem_filename,),symbol_map\n #\n # Convert from NL to MPS\n #\n # TBD: We don't support a variable map file when going\n # from NL to MPS within the PICO converter.\n # NOTE: this is a problem with the MPS writer that is\n # provided by COIN-OR\n # NOTE: we should generalize this so it doesn't strictly\n # depend on the PICO converter utility.\n #\n ans = self.pico_converter.apply(ProblemFormat.nl,\n ProblemFormat.mps,\n problem_filename)\n os.remove(problem_filename)\n return ans\n\n elif args[1] == ProblemFormat.osil:\n if False:\n problem_filename = pyutilib.services.TempfileManager.\\\n create_tempfile(suffix='pyomo.osil')\n if instance:\n if isinstance(instance, IBlock):\n symbol_map_id = instance.write(\n problem_filename,\n format=ProblemFormat.osil,\n _solver_capability=capabilities,\n _called_by_solver=True,\n **io_options)\n else:\n (problem_filename, symbol_map_id) = \\\n instance.write(\n filename=problem_filename,\n format=ProblemFormat.osil,\n solver_capability=capabilities,\n io_options=io_options)\n return (problem_filename,), None\n else:\n raise NotImplementedError(\n \"There is currently no \"\n \"script conversion available from \"\n \"Pyomo to OSiL format.\")","title":""}],"string":"[\n {\n \"docid\": \"b50fba180d6eee76d9713189ba7dcb27\",\n \"score\": \"0.56642395\",\n \"text\": \"def apply(self, *args, **kwds):\\n\\n import pyomo.scripting.convert\\n\\n capabilities = kwds.pop(\\\"capabilities\\\", None)\\n\\n # all non-consumed keywords are assumed to be options\\n # that should be passed to the writer.\\n io_options = {}\\n for kwd, value in iteritems(kwds):\\n io_options[kwd] = value\\n kwds.clear()\\n\\n # basestring is gone in Python 3.x, merged with str.\\n if PY3:\\n compare_type = str\\n else:\\n compare_type = basestring\\n\\n if isinstance(args[2], compare_type):\\n instance = None\\n else:\\n instance = args[2]\\n\\n if args[1] == ProblemFormat.cpxlp:\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix = '.pyomo.lp')\\n if instance is not None:\\n if isinstance(instance, IBlock):\\n symbol_map_id = instance.write(\\n problem_filename,\\n format=ProblemFormat.cpxlp,\\n _solver_capability=capabilities,\\n _called_by_solver=True,\\n **io_options)\\n else:\\n (problem_filename, symbol_map_id) = \\\\\\n instance.write(\\n filename=problem_filename,\\n format=ProblemFormat.cpxlp,\\n solver_capability=capabilities,\\n io_options=io_options)\\n return (problem_filename,), symbol_map_id\\n else:\\n\\n #\\n # I'm simply exposing a fatal issue with\\n # this code path. How would we convert the\\n # collected keywords into command-line\\n # arguments that can be sent to the writer?\\n #\\n if len(io_options):\\n raise ValueError(\\n \\\"The following io_options will be ignored \\\"\\n \\\"(please create a bug report):\\\\n\\\\t\\\" +\\n \\\"\\\\n\\\\t\\\".join(\\\"%s = %s\\\" % (k,v)\\n for k,v in iteritems(io_options)))\\n\\n ans = pyomo.scripting.convert.\\\\\\n pyomo2lp(['--output',problem_filename,args[2]])\\n if ans.errorcode:\\n raise RuntimeError(\\\"pyomo2lp conversion \\\"\\n \\\"returned nonzero error code \\\"\\n \\\"(%s)\\\" % ans.errorcode)\\n\\n model = ans.retval\\n problem_filename = model.filename\\n symbol_map = model.symbol_map\\n return (problem_filename,),symbol_map\\n\\n elif args[1] == ProblemFormat.bar:\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix = '.pyomo.bar')\\n if instance is not None:\\n if isinstance(instance, IBlock):\\n symbol_map_id = instance.write(\\n problem_filename,\\n format=ProblemFormat.bar,\\n _solver_capability=capabilities,\\n _called_by_solver=True,\\n **io_options)\\n else:\\n (problem_filename, symbol_map_id) = \\\\\\n instance.write(\\n filename=problem_filename,\\n format=ProblemFormat.bar,\\n solver_capability=capabilities,\\n io_options=io_options)\\n return (problem_filename,), symbol_map_id\\n else:\\n\\n #\\n # I'm simply exposing a fatal issue with\\n # this code path. How would we convert the\\n # collected keywords into command-line\\n # arguments that can be sent to the writer?\\n #\\n if len(io_options):\\n raise ValueError(\\n \\\"The following io_options will be ignored \\\"\\n \\\"(please create a bug report):\\\\n\\\\t\\\" +\\n \\\"\\\\n\\\\t\\\".join(\\\"%s = %s\\\" % (k,v)\\n for k,v in iteritems(io_options)))\\n\\n ans = pyomo.scripting.convert.\\\\\\n pyomo2bar(['--output',problem_filename,args[2]])\\n if ans.errorcode:\\n raise RuntimeError(\\\"pyomo2bar conversion \\\"\\n \\\"returned nonzero error code \\\"\\n \\\"(%s)\\\" % ans.errorcode)\\n model = ans.retval\\n problem_filename = model.filename\\n symbol_map = model.symbol_map\\n return (problem_filename,),symbol_map\\n\\n elif args[1] in [ProblemFormat.mps, ProblemFormat.nl]:\\n if args[1] == ProblemFormat.nl:\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix = '.pyomo.nl')\\n if io_options.get(\\\"symbolic_solver_labels\\\", False):\\n pyutilib.services.TempfileManager.add_tempfile(\\n problem_filename[:-3]+\\\".row\\\",\\n exists=False)\\n pyutilib.services.TempfileManager.add_tempfile(\\n problem_filename[:-3]+\\\".col\\\",\\n exists=False)\\n else:\\n assert args[1] == ProblemFormat.mps\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix = '.pyomo.mps')\\n if instance is not None:\\n if isinstance(instance, IBlock):\\n symbol_map_id = instance.write(\\n problem_filename,\\n format=args[1],\\n _solver_capability=capabilities,\\n _called_by_solver=True,\\n **io_options)\\n else:\\n (problem_filename, symbol_map_id) = \\\\\\n instance.write(\\n filename=problem_filename,\\n format=args[1],\\n solver_capability=capabilities,\\n io_options=io_options)\\n return (problem_filename,), symbol_map_id\\n else:\\n\\n #\\n # I'm simply exposing a fatal issue with\\n # this code path. How would we convert the\\n # collected keywords into command-line\\n # arguments that can be sent to the writer?\\n #\\n if len(io_options):\\n raise ValueError(\\n \\\"The following io_options will be ignored \\\"\\n \\\"(please create a bug report):\\\\n\\\\t\\\" +\\n \\\"\\\\n\\\\t\\\".join(\\\"%s = %s\\\" % (k,v)\\n for k,v in iteritems(io_options)))\\n\\n ans = pyomo.scripting.convert.\\\\\\n pyomo2nl(['--output',problem_filename,args[2]])\\n if ans.errorcode:\\n raise RuntimeError(\\\"pyomo2nl conversion \\\"\\n \\\"returned nonzero error \\\"\\n \\\"code (%s)\\\" % ans.errorcode)\\n model = ans.retval\\n problem_filename = model.filename\\n symbol_map = model.symbol_map\\n\\n if args[1] == ProblemFormat.nl:\\n return (problem_filename,),symbol_map\\n #\\n # Convert from NL to MPS\\n #\\n # TBD: We don't support a variable map file when going\\n # from NL to MPS within the PICO converter.\\n # NOTE: this is a problem with the MPS writer that is\\n # provided by COIN-OR\\n # NOTE: we should generalize this so it doesn't strictly\\n # depend on the PICO converter utility.\\n #\\n ans = self.pico_converter.apply(ProblemFormat.nl,\\n ProblemFormat.mps,\\n problem_filename)\\n os.remove(problem_filename)\\n return ans\\n\\n elif args[1] == ProblemFormat.osil:\\n if False:\\n problem_filename = pyutilib.services.TempfileManager.\\\\\\n create_tempfile(suffix='pyomo.osil')\\n if instance:\\n if isinstance(instance, IBlock):\\n symbol_map_id = instance.write(\\n problem_filename,\\n format=ProblemFormat.osil,\\n _solver_capability=capabilities,\\n _called_by_solver=True,\\n **io_options)\\n else:\\n (problem_filename, symbol_map_id) = \\\\\\n instance.write(\\n filename=problem_filename,\\n format=ProblemFormat.osil,\\n solver_capability=capabilities,\\n io_options=io_options)\\n return (problem_filename,), None\\n else:\\n raise NotImplementedError(\\n \\\"There is currently no \\\"\\n \\\"script conversion available from \\\"\\n \\\"Pyomo to OSiL format.\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"67db92a1a3821a68008dc249af915482","score":"0.5871873","text":"def create_model(self):\r\n\r\n self.try_load_model()\r\n\r\n self.set_correct_problem_instance_name()\r\n\r\n self.add_objective_and_budget_constraints()\r\n self.add_starting_ending_nodes_constraints()\r\n\r\n self.docplex_model.print_information()\r\n\r\n # DEBUG\r\n # for constraint in self.docplex_model.iter_constraints():\r\n # print(constraint)\r","title":""},{"docid":"95e1197a8a29532e1bb1bc651f420051","score":"0.5701343","text":"def formula_generator(known_args, other_args): # pylint: disable=W0613\n poly = Poly(*known_args.format)\n\n if known_args.smt2:\n with open(known_args.smt2, 'w') as smt2_file:\n with redirect_stdout(smt2_file):\n poly.problem(known_args.degree)\n else:\n poly.problem(known_args.degree)","title":""},{"docid":"1717949860fb45cd73863fd5f97acc0e","score":"0.56868994","text":"def __init__(self, model, output_path, wanted_lorentz = [],\n wanted_couplings = [], replace_dict={}):\n\n self.model = model\n self.model_name = export_cpp.ProcessExporterCPP.get_model_name(model['name'])\n self.aloha_model = create_aloha.AbstractALOHAModel(self.model_name)\n\n\n self.dir_path = output_path\n self.default_replace_dict = dict(replace_dict)\n # List of needed ALOHA routines\n self.wanted_lorentz = wanted_lorentz\n self.wanted_couplings = wanted_couplings\n\n # For dependent couplings, only want to update the ones\n # actually used in each process. For other couplings and\n # parameters, just need a list of all.\n self.coups_dep = {} # name -> base_objects.ModelVariable\n self.coups_indep = [] # base_objects.ModelVariable\n self.params_dep = [] # base_objects.ModelVariable\n self.params_indep = [] # base_objects.ModelVariable\n self.p_to_cpp = None \n\n # Prepare parameters and couplings for writeout in C++\n self.prepare_parameters()\n self.prepare_couplings(wanted_couplings)","title":""},{"docid":"c5525bfc1f28e0a667830ec014bdb725","score":"0.56353825","text":"def convertProcessToLcia(O,p):\r\n \r\n # the players\r\n P = O.findUuid(os.path.splitext(p)[0])\r\n print 'Importing: ' + P.El('baseName').text\r\n L = O.createLciaFromTemplate()\r\n \r\n lcia_ns = L.getroot().nsmap\r\n \r\n # the basic data\r\n L.El('name','common').text = P.El('baseName').text\r\n L.El('methodology').text = 'TRACI 2.0'\r\n L.El('impactCategory').text = selectFromList(impactCategories)\r\n L.El('impactIndicator').text = raw_input('Impact Indicator: ')\r\n L.El('referenceYear').text = P.El('referenceYear','common').text\r\n L.El('duration').text = 'indefinite'\r\n L.El('interventionLocation').text = 'US'\r\n L.El('impactLocation').text = 'US'\r\n\r\n L.El('typeOfDataSet').text = selectFromList(indicatorTypes)\r\n L.El('normalisation').text= 'false'\r\n L.El('weighting').text = 'false'\r\n \r\n QR=L.El('quantitativeReference')\r\n CF=L.El('characterisationFactors')\r\n \r\n # the exchanges\r\n Xs = P.El('exchanges').getchildren()\r\n for x in Xs:\r\n flow_el = nonemap(x,'referenceToFlowDataSet')\r\n flow_uuid = flow_el.attrib['refObjectId']\r\n flow_comment = flow_el.getchildren()[0]\r\n if nonemap(x, 'exchangeDirection').text == 'Input':\r\n # Input exchange = reference flow\r\n ref_uuid = getReferenceFlow(O.findUuid(flow_uuid), nsmap = lcia_ns)\r\n RQ = ET.Element('referenceQuantity',\r\n attrib = {'type':'flow property data set',\r\n 'refObjectId':ref_uuid,\r\n 'uri':'../flowproperties/{0}.xml'.format(ref_uuid)},\r\n nsmap = lcia_ns)\r\n RQ.append(flow_comment)\r\n QR.append(RQ)\r\n print 'reference flow: {0} {1}'.format(ref_uuid,flow_comment.text)\r\n \r\n else:\r\n # Output exchange = new factor\r\n mean_value = 1 / float(nonemap(x, 'resultingAmount').text) # note inversion!\r\n F = ET.Element('factor',nsmap = lcia_ns)\r\n FL = ET.Element('referenceToFlowDataSet', \r\n attrib={'type':'flow data set',\r\n 'refObjectId':flow_uuid,\r\n 'uri':'../flows/{0}.xml'.format(flow_uuid)},\r\n nsmap = lcia_ns)\r\n FL.append(flow_comment)\r\n F.append(FL)\r\n ET.SubElement(F, 'exchangeDirection',nsmap = lcia_ns).text='Output'\r\n ET.SubElement(F, 'meanValue', nsmap = lcia_ns).text = str( mean_value ) \r\n CF.append(F)\r\n print 'exchange: {0} {1}'.format(flow_uuid,flow_comment.text)\r\n\r\n return L","title":""},{"docid":"b0f2020c341a65e6214adebd68e7903e","score":"0.56281185","text":"def generate_and_save_all_interpretations_for_model(pci_obj, X, y, mdl, mdl_name):\n \n ## Generate dataset-level interpretation for Logistic Regression model\n print('Dataset-level interpretation generation is in process for ' + mdl_name + '.')\n dataset_level_interpretation = pci_obj.dataset_level_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_dataset_level_interpretation_' + mdl_name + '.txt', dataset_level_interpretation, fmt='%1.8e')\n print('Dataset-level interpretation generation is finished for' + mdl_name + '.')\n \n \n ## Generate whole dataset signed interpretation for Logistic Regression model\n print('Whole dataset signed interpretation generation is in process for ' + mdl_name + '.')\n whole_dataset_signed_interpretation = pci_obj.whole_dataset_signed_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_whole_dataset_signed_interpretation_' + mdl_name + '.txt', whole_dataset_signed_interpretation, fmt='%1.8e')\n print('Whole dataset signed interpretation generation is finished for ' + mdl_name + '.')\n \n ## Generate whole dataset signed interpretation for Logistic Regression model\n print('Whole dataset unsigned interpretation generation is in process for ' + mdl_name + '.')\n whole_dataset_unsigned_interpretation = pci_obj.whole_dataset_unsigned_interpretation(X, y, mdl)\n np.savetxt('./result/' + 'ACES_whole_dataset_unsigned_interpretation_' + mdl_name + '.txt', whole_dataset_unsigned_interpretation, fmt='%1.8e')\n print('Whole dataset unsigned interpretation generation is finished for ' + mdl_name + '.')","title":""},{"docid":"95c5748803c3d19cc5d921a875e0b086","score":"0.5581633","text":"def convert(prototxt_path, caffemodel_path, phase='test', output_model_name='freeze_graph_model.pb'):\n assert_exist_files(prototxt_path, caffemodel_path)\n\n data_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.mat')\n code_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.py')\n standalone_output_path = os.path.join(os.path.dirname(prototxt_path), output_model_name)\n\n try:\n with tf.Session() as sess:\n transformer = TensorFlowTransformer(prototxt_path, caffemodel_path, phase=phase)\n print_stderr('Converting data...')\n if data_output_path is not None:\n data = transformer.transform_data()\n print_stderr('Saving data...')\n with open(data_output_path, 'wb') as data_out:\n np.save(data_out, data)\n if code_output_path is not None:\n print_stderr('Saving source...')\n with open(code_output_path, 'wb') as src_out:\n src_out.write(transformer.transform_source())\n\n if standalone_output_path:\n filename, _ = os.path.splitext(os.path.basename(standalone_output_path))\n temp_folder = os.path.join(os.path.dirname(standalone_output_path), 'tmp')\n\n if not os.path.exists(temp_folder):\n os.makedirs(temp_folder)\n\n if data_output_path is None:\n data = transformer.transform_data()\n print_stderr('Saving data...')\n data_output_path = os.path.join(temp_folder, filename) + '.npy'\n with open(data_output_path, 'wb') as data_out:\n np.save(data_out, data)\n\n if code_output_path is None:\n print_stderr('Saving source...')\n code_output_path = os.path.join(temp_folder, filename) + '.py'\n with open(code_output_path, 'wb') as src_out:\n src_out.write(transformer.transform_source())\n\n checkpoint_path = os.path.join(temp_folder, filename + '.ckpt')\n graph_name = os.path.basename(standalone_output_path)\n graph_folder = os.path.dirname(standalone_output_path)\n input_node = transformer.graph.nodes[0].name\n output_node = transformer.graph.nodes[-1].name\n tensor_shape = transformer.graph.get_node(input_node).output_shape\n tensor_shape_list = [tensor_shape.batch_size, tensor_shape.height, tensor_shape.width, tensor_shape.channels]\n\n sys.path.append(os.path.dirname(code_output_path))\n module = os.path.splitext(os.path.basename(code_output_path))[0]\n class_name = transformer.graph.name\n KaffeNet = getattr(__import__(module), class_name)\n\n data_placeholder = tf.placeholder(tf.float32, tensor_shape_list, name=input_node)\n net = KaffeNet({input_node: data_placeholder})\n\n # load weights stored in numpy format\n net.load(data_output_path, sess)\n\n print_stderr('Saving checkpoint...')\n\n saver = tf.train.Saver()\n saver.save(sess, checkpoint_path)\n\n print_stderr('Saving graph definition as protobuf...')\n tf.train.write_graph(sess.graph.as_graph_def(), graph_folder, graph_name, as_text=False)\n\n input_graph_path = standalone_output_path\n input_saver_def_path = \"\"\n input_binary = True\n input_checkpoint_path = checkpoint_path\n output_node_names = output_node\n restore_op_name = 'save/restore_all'\n filename_tensor_name = 'save/Const:0'\n output_graph_path = standalone_output_path\n clear_devices = True\n\n print_stderr('Saving standalone model...')\n freeze_graph(input_graph_path, input_saver_def_path,\n input_binary, input_checkpoint_path,\n output_node_names, restore_op_name,\n filename_tensor_name, output_graph_path,\n clear_devices, '')\n\n shutil.rmtree(temp_folder)\n\n print_stderr('Done.')\n except KaffeError as err:\n fatal_error('Error encountered: {}'.format(err))","title":""},{"docid":"870fd0325523f72544b469d902b7c2cc","score":"0.5544181","text":"def convert_odml():\n f_in = join(ROOT, \"tmp.xml\")\n f_out = join(ROOT, \"tmpout.xml\")\n\n o_ver_conv = VersionConverter(f_in)\n\n # either write to file\n o_ver_conv.write_to_file(f_out)\n\n # or pass it to the python xml parser\n doc = ODMLReader(parser='XML').from_string(str(o_ver_conv))\n doc.pprint()","title":""},{"docid":"5d270ed9448ab6ff02368268de72a91b","score":"0.5542094","text":"def compare_lp_files(self, filename, ignored=None, my_om=None):\n if my_om is None:\n om = self.get_om()\n else:\n om = my_om\n tmp_filename = filename.replace('.lp', '') + '_tmp.lp'\n new_filename = ospath.join(self.tmppath, tmp_filename)\n om.write(new_filename, io_options={'symbolic_solver_labels': True})\n logging.info(\"Comparing with file: {0}\".format(filename))\n with open(ospath.join(self.tmppath, tmp_filename)) as generated_file:\n with open(ospath.join(ospath.dirname(ospath.realpath(__file__)),\n \"lp_files\",\n filename)) as expected_file:\n\n def chop_trailing_whitespace(lines):\n return [re.sub(r'\\s*$', '', ln) for ln in lines]\n\n def remove(pattern, lines):\n if not pattern:\n return lines\n return re.subn(pattern, \"\",\n \"\\n\".join(lines))[0].split(\"\\n\")\n\n expected = remove(ignored,\n chop_trailing_whitespace(\n expected_file.readlines()))\n generated = remove(ignored,\n chop_trailing_whitespace(\n generated_file.readlines()))\n\n def normalize_to_positive_results(lines):\n negative_result_indices = [\n n for n, line in enumerate(lines)\n if re.match(\"^= -\", line)]\n equation_start_indices = [\n [n for n in reversed(range(0, nri))\n if re.match('.*:$', lines[n])][0]+1\n for nri in negative_result_indices]\n for (start, end) in zip(\n equation_start_indices,\n negative_result_indices):\n for n in range(start, end):\n lines[n] = (\n '-'\n if lines[n] and lines[n][0] == '+'\n else '+'\n if lines[n]\n else lines[n]) + lines[n][1:]\n lines[end] = '= ' + lines[end][3:]\n return lines\n\n expected = normalize_to_positive_results(expected)\n generated = normalize_to_positive_results(generated)\n\n eq_(generated, expected,\n \"Failed matching expected with generated lp file:\\n\" +\n \"\\n\".join(unified_diff(expected, generated,\n fromfile=ospath.relpath(\n expected_file.name),\n tofile=ospath.basename(\n generated_file.name),\n lineterm=\"\")))","title":""},{"docid":"bffa7248d281833024e2e2237090aa94","score":"0.55059963","text":"def model_to_owl(model, fname):\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\n\n try:\n fileOS = autoclass('java.io.FileOutputStream')(fname)\n except JavaException:\n logger.error('Could not open data file %s' % fname)\n return\n l3_factory = autoclass('org.biopax.paxtools.model.BioPAXLevel').L3.getDefaultFactory()\n model_out = l3_factory.createModel()\n for r in model.getObjects().toArray():\n model_out.add(r)\n io.convertToOWL(model_out, fileOS)\n\n fileOS.close()","title":""},{"docid":"016e6167d18373b2c352dac2e0aac10f","score":"0.54671896","text":"def run(in_obo_filename=IN_OBO_FILENAME, in_pkl_filename=IN_PKL_FILENAME, in_tsv_filename=IN_TSV_FILELANE,\n in_monomers_filename=IN_MONOMERS_FILENAME,\n max_num_proteins=None,\n out_pickle_filename=OUT_PICKLE_FILENAME, out_pickle_filename_2=OUT_PICKLE_FILENAME_2,\n out_tsv_filename=OUT_TSV_FILENAME, out_fasta_filename=OUT_FASTA_FILENAME,\n out_fig_filename=OUT_FIG_FILENAME, out_structure_dirname=OUT_STRUCTURE_DIRNAME,\n out_viz_dirname=OUT_VIZ_DIRNAME):\n # get the PRO ontology and extract the modified proteins from the ontology\n # proteins = get_pro_from_obo(obo_filename=in_obo_filename, pkl_filename=in_pkl_filename, max_num_proteins=max_num_proteins)\n proteins = get_pro_from_tsv(in_tsv_filename, max_num_proteins=max_num_proteins)\n\n # parse the modified proteins and retrieve their sequences\n if not os.path.isfile(out_pickle_filename):\n # parse the modified proteins and retrieve their sequences\n parsed_proteins = []\n for i_protein, protein in enumerate(proteins):\n if i_protein % 100 == 0:\n print('Parsing protein {} of {}'.format(i_protein + 1, len(proteins)))\n parsed_proteins.append(parse_protein(protein))\n\n # save the parsed proteins in pickle format\n with open(out_pickle_filename, 'wb') as file:\n pickle.dump(parsed_proteins, file)\n else:\n # load saved parsed proteins in pickle format\n with open(out_pickle_filename, 'rb') as file:\n parsed_proteins = pickle.load(file)\n\n # read list of monomers\n monomers = {}\n with open(in_monomers_filename, 'r') as file:\n reader = csv.DictReader(file, dialect='excel')\n for row in reader:\n monomers[row['PRO id']] = {\n 'mod': bpforms.protein_alphabet.monomers.get(row['BpForms code'], None),\n 'origin': [],\n }\n if row['Base monomer']:\n monomers[row['PRO id']]['origin'] = row['Base monomer'].split(', ')\n\n # generate list of modified monomeric forms\n for protein in parsed_proteins:\n for modification in protein['modifications']:\n if modification['monomer'] not in monomers:\n monomers[modification['monomer']] = {\n 'mod': None,\n 'origin': [],\n }\n\n # print list of unmapped monomers\n unmapped_monomers = []\n for monomer, code in monomers.items():\n if not code['mod']:\n unmapped_monomers.append(monomer)\n unmapped_monomers.sort()\n if unmapped_monomers:\n print('Several PRO monomeric forms have not been mapped to BpForms monomeric forms:\\n {}'.format(\n '\\n '.join(unmapped_monomers)))\n\n # check for inconsistencies between residue and modified monomeric form\n monomer_codes = {}\n for code, monomer in bpforms.protein_alphabet.monomers.items():\n monomer_codes[monomer] = code\n\n for protein in parsed_proteins:\n for modification in protein.get('modifications', []):\n if modification['residue'] and modification['monomer']:\n monomer = monomers.get(modification['monomer'], None)\n if (monomer['mod'] and monomer['mod'].get_canonical_code(monomer_codes) != modification['residue']) \\\n or (monomer['origin'] and modification['residue'] not in monomer['origin']):\n codes = set(monomer['origin'])\n if monomer['mod']:\n codes.add(monomer['mod'].get_canonical_code(monomer_codes))\n msg = 'Modified monomeric form {} potentially inconsistent with residue {} != {}'.format(\n modification['monomer'], modification['residue'],\n ', '.join(codes))\n print(protein['id'] + ': ' + msg)\n\n # generate BpForms for each protein\n if not os.path.isdir(out_structure_dirname):\n os.mkdir(out_structure_dirname)\n\n if not os.path.isdir(out_viz_dirname):\n os.mkdir(out_viz_dirname)\n\n if not os.path.isfile(out_pickle_filename_2):\n for i_protein, protein in enumerate(parsed_proteins):\n if i_protein % 100 == 0:\n print('Generating BpForms {} of {}'.format(i_protein + 1, len(parsed_proteins)))\n\n protein['modified_seq'] = None\n if not protein['uniprot_id']:\n continue\n if not protein['seq']:\n continue\n if protein['pro_errors']:\n continue\n\n processed_form = gen_bpform(protein, monomers, monomer_codes, apply_modifications=False)\n protein['processed_seq'] = str(processed_form)\n if not processed_form.validate():\n processed_formula = processed_form.get_formula()\n protein['processed_formula'] = str(processed_formula)\n protein['processed_mol_wt'] = processed_form.get_mol_wt()\n protein['processed_charge'] = processed_form.get_charge()\n\n if not protein['modifications']:\n continue\n\n modified_form = gen_bpform(protein, monomers, monomer_codes, include_annotations=False)\n protein['modified_seq'] = str(modified_form)\n\n modified_form = gen_bpform(protein, monomers, monomer_codes)\n if not modified_form.validate():\n modified_formula = modified_form.get_formula()\n protein['modified_full_seq'] = str(modified_form)\n protein['modified_formula'] = str(modified_formula)\n protein['modified_mol_wt'] = modified_form.get_mol_wt()\n protein['modified_charge'] = modified_form.get_charge()\n\n protein['modifications_formula'] = str(modified_formula - processed_formula)\n protein['modifications_mol_wt'] = protein['modified_mol_wt'] - protein['processed_mol_wt']\n protein['modifications_charge'] = protein['modified_charge'] - protein['processed_charge']\n\n # with open(os.path.join(out_structure_dirname, protein['id'] + '.cml'), 'w') as file:\n # file.write(modified_form.export('cml'))\n\n form = gen_bpform(protein, monomers, monomer_codes,\n apply_processing=False, include_annotations=True)\n seq_features = []\n if protein['processing']:\n seq_features.append({\n 'label': 'Processed',\n 'color': '#cccccc',\n 'positions': [],\n })\n last = 0\n for p in protein['processing']:\n seq_features[0]['positions'].append([last + 1, p['start'] - 1])\n last = p['end']\n seq_features[0]['positions'].append([\n protein['processing'][-1]['end'] + 1,\n len(form.seq),\n ])\n\n if protein['processing'][0]['start'] == 1:\n seq_features[0]['positions'].pop(0)\n if protein['processing'][-1]['end'] == len(form.seq):\n seq_features[0]['positions'].pop(len(seq_features[0]['positions']) - 1)\n\n with open(os.path.join(out_viz_dirname, protein['id'] + '.svg'), 'w') as file:\n file.write(form.get_genomic_image(seq_features, width=910))\n\n if modified_form.get_canonical_seq(monomer_codes) != protein['processed_seq']:\n protein['pro_errors'].append('Modified sequence for {} not compatible with the processed sequence'.format(\n protein['id']))\n\n # save the parsed proteins in pickle format\n with open(out_pickle_filename_2, 'wb') as file:\n pickle.dump(parsed_proteins, file)\n else:\n with open(out_pickle_filename_2, 'rb') as file:\n parsed_proteins = pickle.load(file)\n\n # save the proteoforms in TSV format\n with open(out_tsv_filename, 'w') as file:\n writer = csv.writer(file, dialect='excel-tab')\n writer.writerow(['PRO id', 'UniProt id', 'Organism',\n 'Unmodified sequence (IUBMB)',\n 'Processing', 'Deletions', 'Processsed sequence (IUBMB)', 'Processsed formula',\n 'Processsed molecular weight', 'Processsed charge',\n 'Modifications', 'Crosslinks', 'Modified sequence (abbreviated BpForms)', 'Modified sequence (BpForms)',\n 'Is modified sequence concrete', 'Modified formula', 'Modified molecular weight', 'Modified charge',\n 'Modifications formula', 'Modifications molecular weight', 'Modifications charge',\n 'PRO issues', 'Monomeric form issues'])\n\n for parsed_protein in parsed_proteins:\n if parsed_protein.get('pro_errors', None):\n pro_errors = '. '.join(parsed_protein['pro_errors']) + '.'\n else:\n pro_errors = None\n\n if parsed_protein.get('modified_errors', None):\n modified_errors = '. '.join(parsed_protein['modified_errors']) + '.'\n else:\n modified_errors = None\n\n writer.writerow([\n parsed_protein['id'],\n parsed_protein.get('uniprot_id', None),\n parsed_protein.get('organism', None),\n parsed_protein.get('seq', None),\n ', '.join('{}-{}'.format(p['start'], p['end']) for p in parsed_protein['processing']),\n ', '.join('{}-{}'.format(deletion[0], deletion[1]) for deletion in parsed_protein.get('deletions', [])),\n parsed_protein.get('processed_seq', None),\n parsed_protein.get('processed_formula', None),\n parsed_protein.get('processed_mol_wt', None),\n parsed_protein.get('processed_charge', None),\n ', '.join('{} --> {} ({})'.format(m['residue'] or '?', m['monomer'], ', '.join(str(p) for p in m['positions']))\n for m in parsed_protein['modifications']),\n ', '.join('{}{}-{}{}'.format(xlink[0][1], xlink[0][0], xlink[1][1], xlink[1][0])\n for xlink in parsed_protein.get('crosslinks', [])),\n parsed_protein.get('modified_seq', None),\n parsed_protein.get('modified_full_seq', None),\n parsed_protein.get('modified_concrete', False),\n parsed_protein.get('modified_formula', None),\n parsed_protein.get('modified_mol_wt', None),\n parsed_protein.get('modified_charge', None),\n parsed_protein.get('modifications_formula', None),\n parsed_protein.get('modifications_mol_wt', None),\n parsed_protein.get('modifications_charge', None),\n pro_errors,\n modified_errors,\n ])\n\n # save the proteoforms in FASTA format\n seqs = (SeqRecord(id='{} | {}'.format(protein['id'], protein['uniprot_id']),\n seq=Seq(protein['modified_seq']),\n description='')\n for protein in parsed_proteins\n if protein['modified_seq'])\n SeqIO.write(seqs, out_fasta_filename, \"fasta\")\n\n # analyze frequency of modifications\n plot_modifications(parsed_proteins, fig_filename=out_fig_filename)\n\n # return proteins\n return proteins, parsed_proteins","title":""},{"docid":"776ae1cf149e78ae0a4114b21438d138","score":"0.54315245","text":"def build_LM(in_file):\r\n print 'building language models...'\r\n # This is an empty method\r\n # Pls implement your code in below\r\n\r\n # Open in_file and split into a list of sentences\r\n input_file = open(in_file, 'r')\r\n train_input_list = input_file.read().split(\"\\r\\n\")\r\n\r\n # Set up dictionaries to store the 4grams generated from input.train\r\n # ngram_count stores the total number of ngrams each language has generated\r\n four_gram_table = {}\r\n ngram_count = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\r\n\r\n '''\r\n NGRAM GENERATION\r\n Multiple for loops through each sentence and then each character for\r\n 4gram generation. Each sentence is padded with 3 'START's and 3 'END's\r\n '''\r\n for sentence in train_input_list:\r\n sentence_parts = sentence.split(None, 1)\r\n # To handle possible empty strings or weird strings in training input\r\n if (len(sentence_parts) != 2):\r\n break\r\n language = sentence_parts[0]\r\n sentence_body = sentence_parts[1]\r\n char_list = list(sentence_body)\r\n for index in range(3):\r\n char_list.insert(0, 'START')\r\n char_list.append('END')\r\n for index in range(len(char_list) - 4):\r\n four_gram = (char_list[index], char_list[index + 1],\r\n char_list[index + 2], char_list[index + 3])\r\n if four_gram in four_gram_table:\r\n four_gram_table[four_gram][language] += 1\r\n ngram_count[language] += 1\r\n else:\r\n # Add-one smoothing for new ngrams\r\n four_gram_table[four_gram] = {'malaysian': 1, 'indonesian': 1,\r\n 'tamil': 1}\r\n for value in ngram_count.itervalues():\r\n value += 1\r\n four_gram_table[four_gram][language] += 1\r\n ngram_count[language] += 1\r\n return [four_gram_table, ngram_count]\r\n\r\n input_file.close()","title":""},{"docid":"8776c3c0636ee64e48b9236afde29d96","score":"0.54051876","text":"def run(args: argparse.Namespace) -> None:\n assert WaymoDataFileReader is not None, (\n \"Please install the requirements in scripts/optional.txt to use\"\n \"Waymo conversion.\"\n )\n if not os.path.exists(args.output):\n os.makedirs(args.output)\n result = from_waymo(\n args.input,\n args.output,\n args.save_images,\n args.use_lidar_labels,\n args.nproc,\n )\n save(os.path.join(args.output, \"scalabel_anns.json\"), result)","title":""},{"docid":"89aa08012b425e9517160341b3b4c2b8","score":"0.5397848","text":"def __init__(self,pedfname = None,title='title',outfname='rgMakeGenModel.phe',\n xtradir=\"/foo/bar\", linkage=True, logf = None, model=additive,\n labels=['FounderNonAff','FounderAff','OffspringNonAff','OffspringAff']):\n ehet = '1'\n ehom1 = '0'\n ehom2 = '2'\n missval = '.'\n missing = ['N','0','.','-']\n if model == additive: # we have ehom2 = 2 major alleles\n modeldict = {ehom1:'2',ehet:'1',ehom2:'0',missval:missval}\n elif model == zadditive:\n modeldict = {ehom1:'1',ehet:'0',ehom2:'-1',missval:missval}\n elif model == dominant:\n modeldict = {ehom1:'1',ehet:'1',ehom2:'0',missval:missval}\n else:\n modeldict = {ehom1:'2',ehet:'0',ehom2:'0',missval:missval}\n # model code translation dicts\n\n swapdict = {ehom1:ehom2,ehom2:ehom1,ehet:ehet,missval:missval} # if initial ref allele was wrong, use these to swap\n mdict = dict(zip(missing,missing))\n pedf = '%s.ped' % pedfname\n f = file(pedf,'r')\n if linkage: # read map file\n map = readMap(pedfname)\n rslist = [x[1] for x in map] # get rs numbers\n else:\n head = f.next().strip()\n rslist = head.split()\n nrs = len(rslist) # number of markers\n elen = 2*nrs + 6 # expected # elements on each line\n logf.write('%s %s: found %d for nrs\\n' % (thisprog,timenow(),nrs))\n gm = {}\n gm['founders'] = [] # array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\n gm['offspring'] = [] # [array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\n adicts = [{} for x in xrange(nrs)] # count of alleles in a dict for each marker\n refallele = [None for x in xrange(nrs)] # list of first observed alleles\n nsubj = 0\n indiv = {'founders':[],'offspring':[]}\n for lnum,l in enumerate(f):\n ll = l.strip().split()\n if (lnum+1) % 200 == 0:\n logf.write('%s %s: Processing line %d\\n' % (thisprog, timenow(),lnum+1))\n if len(ll) < elen: # ? short ?\n logf.write('%s %s: Line %d is %d long, expected %d\\n' % (thisprog, timenow(),\n lnum,len(ll),elen))\n else:\n garray = array.array('c',[missval,]*nrs)\n nsubj += 1\n sid = '%s_%s' % (ll[0],ll[1])\n if sid == '1_1': # eesh\n sid = '%d_%d' % (nsubj,nsubj)\n isFounder = isOff = False\n status = labels[0] # founder unaff\n if ll[2] <> '0' and ll[3] <> '0': # has parent ids\n iclass = 'offspring'\n status = labels[2] # unaffected offspring\n if ll[5] == '2':\n status = labels[3] # affected offspring\n else:\n iclass = 'founders'\n if ll[5] == '2':\n status = labels[1] #change from unaff to aff founder label\n gender = 'M'\n if ll[4] == '2':\n gender = 'F'\n ped = ll[:6]\n indiv[iclass].append(ped) # for row wise output\n for snp in xrange(nrs):\n pos = 2*snp + 6 # first\n g1,g2 = ll[pos],ll[pos+1] # pair of genos\n if mdict.get(g1,None) or mdict.get(g2,None): # one or both missing\n esnp = missval # missing value\n else:\n if not refallele[snp]:\n refallele[snp] = g1 # first one we saw!\n for g in (g1,g2):\n n = adicts[snp].get(g,0)\n n += 1\n adicts[snp][g] = n\n if g1 == g2: # hom\n if g1 == refallele[snp]:\n esnp = ehom2 # 2 copies of current reference allele\n else:\n esnp = ehom1 # no copies\n else:\n esnp = ehet # het - always has one copy of reference allele\n garray[snp] = esnp\n gm[iclass].append(garray) # append genos for this new subject\n for ek in gm.keys():\n lek = len(gm[ek])\n if len(gm[ek]) > 0:\n lek0 = len(gm[ek][0])\n s = 'for %s, have %d subjects with %d markers' % (ek,lek,lek0)\n print s\n logf.write(s)\n for x in range(lek):\n if len(gm[ek][x]) <> lek0:\n s = 'for row %d, len = %d, not %d' % (x, len(gm[ek][x]),lek0)\n print s\n logf.write(s)\n logf.write('%s %s: Now checking major allele assignment and fixing as needed\\n' % (thisprog,timenow()))\n for iclass in gm.keys():\n for subject in xrange(len(gm[iclass])): # for each subject\n for snp in xrange(nrs): # now check to see if reference = major allele\n major = majAllele(adicts[snp])\n if major <> refallele[snp]: # either None or we need to change all the codes\n if major <> None:\n gm[iclass][subject][snp] = swapdict[gm[iclass][subject][snp]]\n for iclass in gm.keys():\n for subject in xrange(len(gm[iclass])): # now convert to genetic model wanted\n gm[iclass][subject] = [modeldict[x] for x in gm[iclass][subject]] # translate to model\n self.gm = gm\n self.model = model\n self.indiv = indiv\n self.nrs = nrs\n self.nsubj = nsubj\n self.logf = logf\n self.basename = title\n self.outfname = outfname\n self.rslist = rslist\n pedhead = 'famid\\tiid\\tfid\\tmid\\tgender\\taffection\\t'\n rs = '\\t'.join(rslist)\n self.outhead = '%s%s\\n' % (pedhead,rs)","title":""},{"docid":"ce63391b0ba53484bcdbfa3c177f5093","score":"0.5392235","text":"def test_LM(in_file, out_file, LM):\n print(\"testing language models...\")\n\n # Parse inputs\n data = read_by_line(in_file)\n model = LM[0]\n languages = LM[1]\n\n # Create an empty probability reference sheet\n basic_probability_ref = dict()\n for l in languages:\n basic_probability_ref[l] = np.float64(1)\n\n # Clear file of any results, to prepare the output file for eval.py\n open(out_file, 'w').close()\n \n with open(out_file, 'a') as f:\n # Process each sentence and make a guess\n for sentence in data:\n p_ref = basic_probability_ref.copy()\n tokens = tokenise(sentence, token_size)\n num_undetected = 0\n\n # Multiple token probabilities for each token\n for t in tokens:\n if t in model:\n # Multiply the respective probability of each language\n counts = model[t]\n for l in languages:\n p_ref[l] += counts[l]\n else:\n num_undetected += 1\n\n # Find the best prediction using the maximum product\n if num_undetected / len(tokens) > other_threshold:\n prediction = other_language\n else:\n prediction = max(p_ref.items(), key=operator.itemgetter(1))[0]\n\n # Write the prediction to the output file\n f.write(prediction + \" \" + sentence + \"\\n\")","title":""},{"docid":"11937e2179eb9e5be7829ede94d45adb","score":"0.5387432","text":"def test_issue4707():\n nlp = English()\n nlp.add_pipe(\"sentencizer\")\n nlp.add_pipe(\"entity_ruler\")\n assert nlp.pipe_names == [\"sentencizer\", \"entity_ruler\"]\n exclude = [\"tokenizer\", \"sentencizer\"]\n with make_tempdir() as tmpdir:\n nlp.to_disk(tmpdir, exclude=exclude)\n new_nlp = load_model_from_path(tmpdir, disable=exclude)\n assert \"sentencizer\" not in new_nlp.pipe_names\n assert \"entity_ruler\" in new_nlp.pipe_names","title":""},{"docid":"fe5676fbca6769a80c88f1d8e1109454","score":"0.5377364","text":"def main(argv):\n parser = argparse.ArgumentParser(\n description='Create all possible determinizations of this domain.')\n parser.add_argument('-d', '--domain', required=True)\n parser.add_argument('-o', '--output', required=True) \n args = parser.parse_args()\n domain_file_name = args.domain\n output_file_name = args.output\n \n # Reading domain file.\n domain_str = ''\n try:\n with open(domain_file_name, 'r') as domain_file:\n for line in domain_file:\n if line.startswith(';;') or line.isspace():\n continue;\n domain_str += line\n except IOError:\n print \"Could not read file:\", domain_file_name\n sys.exit(-1) \n \n # Parsing the domain tree.\n domain_tree = ppddl_util.parse_sexp(domain_str)\n all_prob_effects_list = []\n \n # Getting all possible probabilistic effects.\n ppddl_util.get_all_probabilistic_effects(domain_tree, all_prob_effects_list)\n \n # Getting the most likely outcome determinization.\n mlo_determinization = []\n for probabilistic_effect_info in all_prob_effects_list:\n mlo_determinization.append(\n ppddl_util.get_mlo_determinization_effect(probabilistic_effect_info))\n \n # Writing the MLO determinization to a PPDDL file.\n determinization_ppddl_tree = copy.deepcopy(domain_tree)\n description_text = \"\"\n for effect_info in mlo_determinization:\n description_text += \"%s %d\\n\" % (effect_info[0], effect_info[1][0])\n ppddl_util.determinize_tree(mlo_determinization, determinization_ppddl_tree)\n ppddl_util.clean_up_tree(determinization_ppddl_tree)\n f = open('%s_mlo_det.pddl' % (output_file_name), 'w')\n fd = open('%s_mlo_det.desc' % (output_file_name), 'w')\n f.write(ppddl_util.make_str(determinization_ppddl_tree[0]))\n fd.write(description_text)\n f.close()\n fd.close()\n \n print 'Created MLO determinization for %s' % domain_file_name","title":""},{"docid":"2b6f6722ce9ac5c08f4612e25ef7aec0","score":"0.5376005","text":"def make_model(pbm):\n for stmt in pbm.statements:\n try:\n # Skip statements with no subject\n if stmt.agent_list()[0] is None and \\\n not isinstance(stmt, Conversion):\n continue\n # Assemble statements\n if isinstance(stmt, Modification):\n pbm._assemble_modification(stmt)\n elif isinstance(stmt, RegulateActivity):\n pbm._assemble_regulate_activity(stmt)\n elif isinstance(stmt, RegulateAmount):\n pbm._assemble_regulate_amount(stmt)\n elif isinstance(stmt, Gef):\n pbm._assemble_gef(stmt)\n elif isinstance(stmt, Gap):\n pbm._assemble_gap(stmt)\n elif isinstance(stmt, ActiveForm):\n pbm._assemble_active_form(stmt)\n elif isinstance(stmt, Complex):\n pbm._assemble_complex(stmt)\n elif isinstance(stmt, Conversion):\n pbm._assemble_conversion(stmt)\n elif isinstance(stmt, Autophosphorylation):\n pbm._assemble_autophosphorylation(stmt)\n elif isinstance(stmt, Transphosphorylation):\n pbm._assemble_transphosphorylation(stmt)\n else:\n logger.info('Unhandled statement: %s' % stmt)\n except Exception:\n pass\n return pbm.model","title":""},{"docid":"9045c7c73ca55f814b38834d2149df8b","score":"0.5342216","text":"def try_load_model(self):\r\n filename = 'constraints_problem_size_{0}'.format(self.get_real_n())\r\n path = '../cache_constraints/'\r\n\r\n solution_is_loop = self.sol_is_loop()\r\n\r\n if solution_is_loop:\r\n filename += '_loop'\r\n path_filename = path + filename + '.lp'\r\n\r\n model_reader = ModelReader()\r\n\r\n self.outer_model_interface.check_dir(path)\r\n\r\n try:\r\n # Load the constraints\r\n self.docplex_model = model_reader.read_model(path_filename)\r\n except:\r\n # If they are not cached, create them\r\n print(\"\\nFile {} not found :(\\nI'm gonna create it! :D\".format(path_filename))\r\n self.docplex_model = self.generate_general_constraints(path, filename)","title":""},{"docid":"3c879101600ab140261ce79e742c8479","score":"0.5335524","text":"def generateModel(self):\n\t\tglobal startLoadModel, doneLoadModel\n\t\tprint(\"model type:\" + self.modeltype)\n\t\tstartLoadModel = time.time()\n\t\tif not os.path.exists(self.dictionaryfile):\n\t\t\tprint(\"creating dictionary \" + self.dictionaryfile)\n\t\t\tself.dictionary = self.makeDictionaryMemoryFriendly(self.corpusfile)\n\t\telse:\n\t\t\tprint(\"reading dictionary \" + self.dictionaryfile)\n\t\t\tself.dictionary = self.loadDictionary(self.dictionaryfile)\n\t\tif not os.path.exists(self.corpusmatrixfile):\n\t\t\tprint(\"serializing corpus \" + self.corpusmatrixfile)\n\t\t\tself.serializeCorpusMemoryFriendly(self.corpusfile, self.dictionary)\n\t\tif self.modeltype == \"LDA\":\n\t\t\tprint(\"creating lda model with \" + str(self.topics) + \" topics.\")\n\t\t\tif self.online:\n\t\t\t\tprint(\"Running in online mode with chunksize: \" + str(self.chunksize))\n\t\t\telse:\n\t\t\t\tprint(\"Running in offline mode with \" + str(self.passes) + \" passes\")\n\t\t\tif self.distributed:\n\t\t\t\tprint(\"Running in distributed mode\")\n\t\t\tself.generateLDAModel(self.topics, self.passes, self.distributed)\n\t\telif self.modeltype == \"LSI\":\n\t\t\tprint(\"creating lsi model\")\n\t\t\tself.generateLSIModel(topics)\n\t\telif self.modeltype == \"HDP\":\n\t\t\tprint(\"creating hdp model\")\n\t\t\tself.generateHDPModel()\n\t\tdoneLoadModel = time.time()\n\t\tprint(\"Time to generate model:\" + str(doneLoadModel - startLoadModel))","title":""},{"docid":"91c5431c7473abefa61a82391d7c11c8","score":"0.5323186","text":"def test_LM(in_file, out_file, LM):\r\n print \"testing language models...\"\r\n # This is an empty method\r\n # Pls implement your code in below\r\n\r\n # Setting up data provided from given LM\r\n four_gram_table = LM[0]\r\n ngram_count = LM[1]\r\n\r\n # Open in_file and split into a list of sentences\r\n # Open out_file for writing predictions\r\n input_file = open(in_file, 'r')\r\n test_input_list = input_file.read().split(\"\\r\\n\")\r\n output_file = open(out_file, 'w')\r\n\r\n for sentence in test_input_list:\r\n # To handle possible empty strings or invalid input\r\n if sentence == \"\":\r\n break\r\n # Ngram probability score for current sentence\r\n ngram_score = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\r\n # Counters to keep track of number of unencountered ngrams generated by\r\n # the current sentence.\r\n invalid_ngram_count = 0\r\n total_ngram_count = 0\r\n # Sentence is padded with 3 'START's and 3 'END's\r\n char_list = list(sentence)\r\n for index in range(3):\r\n char_list.insert(0, 'START')\r\n char_list.append('END')\r\n for index in range(len(char_list) - 4):\r\n four_gram = (char_list[index], char_list[index + 1],\r\n char_list[index + 2], char_list[index + 3])\r\n # Log10 is used to prevent underflow of individual ngram_scores\r\n if four_gram in four_gram_table:\r\n for language, count in four_gram_table[four_gram].iteritems():\r\n ngram_score[language] += math.log10(count/ngram_count[language])\r\n else:\r\n invalid_ngram_count += 1\r\n total_ngram_count += 1\r\n # invalid_ngram_ratio represents the proportion of unencountered ngrams\r\n # out of all ngrams generated from current sentence\r\n invalid_ngram_ratio = invalid_ngram_count / total_ngram_count\r\n # The set threshold of 0.6 indicates that the sentence might not be a\r\n # valid language in the given LM\r\n if max(ngram_score.itervalues()) is 0 or invalid_ngram_ratio > 0.6:\r\n predicted_language = 'other'\r\n else:\r\n predicted_language = max(ngram_score.iterkeys(), key=(lambda language: ngram_score[language]))\r\n output_file.write(predicted_language + \" \" + sentence + \"\\n\")\r\n input_file.close\r\n output_file.close","title":""},{"docid":"3c8230e8f56aabea2a2d18b33a22dab9","score":"0.5304721","text":"def generate(args):\n if args.cuda and torch.cuda.is_available():\n device = 0\n use_cuda = True\n elif args.cuda and not torch.cuda.is_available():\n print(\"You do not have CUDA, turning cuda off\")\n device = -1 \n use_cuda = False\n else:\n device = -1\n use_cuda=False\n\n #Load the vocab\n vocab = du.load_vocab(args.vocab)\n eos_id = vocab.stoi[EOS_TOK]\n pad_id = vocab.stoi[PAD_TOK]\n\n if args.ranking: # default is HARD one, the 'Inverse Narrative Cloze' in the paper\n dataset = du.NarrativeClozeDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, LM=False)\n # Batch size during decoding is set to 1\n batches = BatchIter(dataset, 1, sort_key=lambda x:len(x.actual), train=False, device=-1)\n else:\n dataset = du.SentenceDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, add_eos=False) #put in filter pred later\n # Batch size during decoding is set to 1\n batches = BatchIter(dataset, args.batch_size, sort_key=lambda x:len(x.text), train=False, device=-1)\n\n data_len = len(dataset)\n\n #Create the model\n with open(args.load, 'rb') as fi:\n if not use_cuda:\n model = torch.load(fi, map_location=lambda storage, loc : storage)\n else:\n model = torch.load(fi, map_location=torch.device('cuda'))\n\n if not hasattr(model.latent_root, 'nohier'):\n model.latent_root.set_nohier(args.nohier) #for backwards compatibility\n\n model.decoder.eval()\n model.set_use_cuda(use_cuda)\n\n #For reconstruction\n if args.perplexity:\n loss = calc_perplexity(args, model, batches, vocab, data_len)\n print(\"Loss = {}\".format(loss))\n elif args.schema:\n generate_from_seed(args, model, batches, vocab, data_len)\n elif args.ranking:\n do_ranking(args, model, batches, vocab, data_len, use_cuda)\n else:\n# sample_outputs(model, vocab)\n reconstruct(args, model, batches, vocab)","title":""},{"docid":"6b20588231ce0fcb6d4f7434d73e68c3","score":"0.52846843","text":"def generateModel(modelName, pack):\n\n (params, run, cost, optim_date, var_set) = pack\n\n with open('%s_optimized.mo' % modelName, 'w') as file:\n header = \"\"\"model {mn}_optimized \"Generated by PostProcess/postprocess_optim.py {d} with cost {c} lowest at run {r}\"\n extends {mn} (\n \\n\"\"\".format(mn=modelName, d = optim_date, c = '%.6f' % cost, r = '%d' % run)\n footer = ');\\nend %s_optimized;\\n' % modelName\n\n param_lines = (' %s = %.6e' % param for param in params)\n # use join to avoid placing the comma after the last\n lines = ',\\n'.join(param_lines)\n\n\n file.write(header)\n file.writelines(lines)\n file.write(footer)","title":""},{"docid":"4d6897ba3959406b0ee3b87b094e823e","score":"0.5258285","text":"def load_model(length=MAX_UTTERENCE_LENGTH):\n return torch.load(os.path.join(PATH_TO_SAVE, 'generator_{}.pt'.format(length))).to(DEVICE)","title":""},{"docid":"078acbdaf7a42c95ccc2c6c684279376","score":"0.5254087","text":"def compile_po_file(po_path: Path) -> None:\n output_path = po_path.parent\n\n target_json = po_path.with_suffix(\".json\")\n if target_json.exists():\n target_json.unlink()\n convert_catalog_to_json(po_path, output_path, po_path.stem)\n\n target_mo = po_path.with_suffix(\".mo\")\n if target_mo.exists():\n target_mo.unlink()\n compile_to_mo(po_path)","title":""},{"docid":"b3a0443fbd71bfab66c479b31441d52a","score":"0.52386135","text":"def test_LM(in_file, out_file, LM):\r\n print \"testing language models...\"\r\n # This is an empty method\r\n # Pls implement your code in below\r\n malay_LM = LM['malaysian']\r\n indon_LM = LM['indonesian']\r\n tamil_LM = LM['tamil']\r\n\r\n FD_in = open(in_file, 'r')\r\n FD_out = open(out_file, 'w')\r\n\r\n lines = FD_in.read()\r\n lines_array = lines.replace('\\r', ' ').split('\\n')\r\n lines_array.pop(len(lines_array) - 1)\r\n\r\n for line in lines_array:\r\n malay_count = 1\r\n indon_count = 1\r\n tamil_count = 1\r\n\r\n total_count = 0\r\n miss_count = 0\r\n\r\n four_char_array = get_four_char_array(line)\r\n\r\n for four_char in four_char_array:\r\n # key in malayLM = key in all LMs due to add one smoothing\r\n if four_char in malay_LM:\r\n malay_count += math.log10(malay_LM[four_char])\r\n indon_count += math.log10(indon_LM[four_char])\r\n tamil_count += math.log10(tamil_LM[four_char])\r\n else:\r\n miss_count += 1\r\n total_count += 1\r\n\r\n if (miss_count / total_count > 0.7):\r\n FD_out.write(\"other \" + line + \"\\n\")\r\n elif malay_count > indon_count and malay_count > tamil_count:\r\n FD_out.write(\"malaysian \" + line + \"\\n\")\r\n elif indon_count > malay_count and indon_count > tamil_count:\r\n FD_out.write(\"indonesian \" + line + \"\\n\")\r\n else:\r\n FD_out.write(\"tamil \" + line + \"\\n\")\r\n\r\n FD_in.close()\r\n FD_out.close()","title":""},{"docid":"5a76f0d38ddb8ab17ec18597b44bc149","score":"0.5236154","text":"def to_lalinference(self, **kwargs):\n return self.write(file_format=\"lalinference\", package=\"gw\", **kwargs)","title":""},{"docid":"35a86959eb9fc6a9c3356b7be538a93d","score":"0.5229652","text":"def make_input(\n wel_qin,\n wel_qin_1,\n wel_qout,\n wel_qout_1,\n wel_coordsin,\n wel_coordsin_1,\n wel_coordsout,\n wel_coordsout_1,\n model_path,\n name,\n exe_name='mf6',\n verbosity_level=0):\n # created to load, build and save the simulation\n sim = flopy.mf6.MFSimulation(\n sim_name=name,\n sim_ws=model_path, #working folder\n exe_name=exe_name,\n verbosity_level=verbosity_level, #standard output to be written - error messages should be included (=1)\n )\n # time discretization package\n times = (10.0, 120, 1.0)\n tdis_rc = [(1.0, 1, 1.0), times, times, times]\n flopy.mf6.ModflowTdis(\n sim, pname=\"tdis\",\n time_units=\"DAYS\",\n nper=4, # number of stress periods\n perioddata=tdis_rc, # [perlen(len stress period), nstp(time-steps in the stressperiod), tsmult(multiplier of of the lenght of sucessive time-steps]\n )\n #iterative model solution package\n flopy.mf6.ModflowIms(sim) # default solver - nearly linear models - confined or a single unconfined layer that is tick enough to contain the water table within a single layer\n # groundwater flow model\n gwf = flopy.mf6.ModflowGwf(sim, modelname=name, save_flows=True)\n # spatial discretization package\n flopy.mf6.ModflowGwfdis(gwf, nrow=10, ncol=10) # number ir rows and cols since the model has only one layer\n # Initial conditions package\n flopy.mf6.ModflowGwfic(gwf) #models using steady state in the beginning won't be affected\n # node property flow package pane - default is harmonic mean\n flopy.mf6.ModflowGwfnpf(\n gwf,\n save_flows=True,\n save_specific_discharge=True,\n icelltype=[0], # 0 unconfined, -1 confined\n k=[0.5], # initial value of k\n k33=[0.1], # vertical anisotropy\n )\n # storage package specifying sy - specific yield bigger than 0 means the cell is convertible\n sy = flopy.mf6.ModflowGwfsto.sy.empty(\n gwf,\n default_value=0.2 # convertible cells\n )\n #stirage package specifying ss - specific storage\n ss = flopy.mf6.ModflowGwfsto.ss.empty(\n gwf, default_value=0.000001\n )\n # storage package\n flopy.mf6.ModflowGwfsto(\n gwf,\n pname=\"sto\",\n save_flows=True,\n save_specific_discharge=True,\n iconvert=1, # flag for each cell that specifies whether or not a cell is convertible for the storage calculation. 0 indicates confined storage is used. >0 indicates confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top\n ss=ss,\n sy=sy,\n transient={0: True}, # keyword to indicate that stress period IPER is transient. Transient conditions will apply until the STEADY-STATE keyword is specified in a subsequent BEGIN PERIOD block\n )\n #dictionary of boundaries Each well is defined through definition of layer (int), row (int), column (int), flux (float). The simplest form is a dictionary with a lists of boundaries for each stress period, where each list of boundaries itself is a list of boundaries. Indices of the dictionary are the numbers of the stress period\n # if the number of stress periods is larger than the dic than the last speficied will apply until the end\n stress_period_data = {\n 1: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\n 2: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\n 3: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0]\n }\n # defines the well package\n flopy.mf6.ModflowGwfwel(\n gwf,\n stress_period_data=stress_period_data,\n )\n # constant head package\n flopy.mf6.ModflowGwfchd(\n gwf,\n stress_period_data=[\n [(0, 0, 0), 1.], # top left cell [cell_id, head, aux, boundname]\n [(0, 9, 9), 1.]], # bottom right cell\n )\n # variable for the output control\n budget_file = name + '.bud'\n head_file = name + '.hds'\n # output control package\n flopy.mf6.ModflowGwfoc(\n gwf,\n budget_filerecord=budget_file,\n head_filerecord=head_file,\n saverecord=[('HEAD', 'ALL'), ('BUDGET', 'ALL')])\n sim.write_simulation()","title":""},{"docid":"ef942e4bbae34ac5e0e916536c3fd820","score":"0.5222133","text":"def main():\n args = parse_arguments()\n\n if args.output_name:\n output_name = args.output_name\n else:\n output_name = args.xodr_file.rpartition(\".\")[0]\n output_name = f\"{output_name}.xml\" # only name of file\n\n if os.path.isfile(output_name) and not args.force_overwrite:\n print(\n \"Not converting because file exists and option 'force-overwrite' not active\",\n file=sys.stderr,\n )\n sys.exit(-1)\n\n with open(\"{}\".format(args.xodr_file), \"r\") as file_in:\n opendrive = parse_opendrive(etree.parse(file_in).getroot())\n\n scenario = convert_opendrive(opendrive)\n\n if not args.osm:\n writer = ExtendedCommonRoadFileWriter(\n scenario, source=\"OpenDRIVE 2 Lanelet Converter\"\n )\n\n with open(f\"{output_name}\", \"w\") as file_out:\n writer.write_scenario_to_file_io(file_out)\n\n else:\n l2osm = OSMConverter(args.osm)\n osm = l2osm(scenario)\n with open(f\"{output_name}\", \"w\") as file_out:\n file_out.write(etree.tostring(osm, encoding=\"unicode\", pretty_print=True))","title":""},{"docid":"f23cd82b5a7d336e4b8f27a862f78256","score":"0.52209806","text":"def main(model=Path(\"L:\\\\BigData\\\\Env\\\\SavedModels\"), output_dir=Path(\"L:\\\\BigData\\\\Env\\\\SavedModels\")):\n if model is not None:\n nlp = spacy.load(model) # load existing spaCy model\n print(\"Loaded model '%s'\" % model)\n\n ner = nlp.get_pipe(\"ner\")\n\n ner.add_label(\"Email\")# add new entity label to entity recognizer\n ner.add_label(\"Full Name\")\n ner.add_label(\"First Name\")\n ner.add_label(\"Updatedtime\")\n ner.add_label(\"City\")\n ner.add_label(\"Postcode\")\n ner.add_label(\"Date\")\n ner.add_label(\"Phone\")\n ner.add_label(\"State\")\n ner.add_label(\"Country\")\n ner.add_label(\"Street\")\n ner.add_label(\"Id\")\n ner.add_label(\"Last Name\")\n ner.add_label(\"username\")\n ner.add_label(\"Transaction Id\")\n\n move_names = list(ner.move_names)\n\n # test the trained model\n test_text = \"Carlie@marguerite.ca , Glenna Olson , Corbin , 1999-07-05T12:33:47.066Z , Kingshire 77787 1/8/2013 | (695)464-6165 x9467 , Iowa Indonesia 038 Schumm Walks 0 Ortiz Frida 362 \"\n doc = nlp(test_text)\n print(\"Entities in '%s'\" % test_text)\n for ent in doc.ents:\n print(ent.label_, ent.text)\n\n # save model to output directory\n if output_dir is not None:\n output_dir = Path(output_dir)\n if not output_dir.exists():\n output_dir.mkdir() # rename model\n nlp.to_disk(output_dir)\n print(\"Saved model to\", output_dir)\n\n # test the saved model\n print(\"Loading from\", output_dir)\n nlp2 = spacy.load(output_dir)\n # Check the classes have loaded back consistently\n assert nlp2.get_pipe(\"ner\").move_names == move_names\n doc2 = nlp2(test_text)\n for ent in doc2.ents:\n print(ent.label_, ent.text)","title":""},{"docid":"ba506d586a69c49c3403983dbd1f5025","score":"0.52202463","text":"def GenFile(DSLModel, Table, View):","title":""},{"docid":"8666e838db88654723d809adce0d3d19","score":"0.52056324","text":"def generateModel(self,startPoint,start,end,tol,vel):\r\n\t\t\r\n\t\t(code,levels,cantPoints) = Loader().load(self.path,startPoint)\r\n\t\thaveParser = False\r\n\r\n\t\tadded = 0\r\n\t\tself.start = start\r\n\t\tself.end = end\r\n\t\t\r\n\t\tif( tol > 0):\t\t\r\n\t\t\tself.parser = PointsAdder(code,levels,cantPoints,tol)\r\n\t\t\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\r\n\t\t\thaveParser = True\r\n\t\t\t\r\n\t\tif( vel > 0):\t\t\r\n\t\t\tself.parser = VelocityChanger(code,levels,cantPoints,vel)\r\n\t\t\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\r\n\t\t\thaveParser = True\r\n\t\t\t\r\n\t\tif (not haveParser):\r\n\t\t\tself.parser = Parser(code,levels,cantPoints)\r\n\t\t\t(code,levels,cantPoints) = self.parser.parse()\t\r\n\t\r\n\t\tself.modifiedGCode = code\r\n\t\tself.levels = levels\r\n\t\tself.cantPoints = cantPoints","title":""},{"docid":"e9a39b116920aebf857a1c3b136837b0","score":"0.5203123","text":"def compile():\n\n m = dir(modules)\n\n words = []\n for module_name in m:\n try:\n eval(\"words.extend(modules.%s.WORDS)\" % module_name)\n except:\n pass # module probably doesn't have the property\n\n words = list(set(words))\n\n # for spotify module\n words.extend([\"MUSIC\",\"SPOTIFY\"])\n\n # create the dictionary\n pronounced = g2p.translateWords(words)\n zipped = zip(words, pronounced)\n lines = [\"%s %s\" % (x, y) for x, y in zipped]\n\n with open(\"../client/dictionary.dic\", \"w\") as f:\n f.write(\"\\n\".join(lines) + \"\\n\")\n\n # create the language model\n with open(\"../client/sentences.txt\", \"w\") as f:\n f.write(\"\\n\".join(words) + \"\\n\")\n f.write(\"<s> \\n </s> \\n\")\n f.close()\n\n # make language model\n os.system(\n \"text2idngram -vocab ../client/sentences.txt < ../client/sentences.txt -idngram temp.idngram\")\n os.system(\n \"idngram2lm -idngram temp.idngram -vocab ../client/sentences.txt -arpa ../client/languagemodel.lm\")","title":""},{"docid":"49e798a22449fe4ec327a89f2378b7cd","score":"0.51998425","text":"def main() -> None:\n\n args = get_args()\n package = Package()\n ontology = get_ontology(args.ontology_file)\n\n for i, fh in enumerate(args.data_file, start=1):\n print(f'{i:3}: {os.path.basename(fh.name)}')\n fh.close()\n package.infer(fh.name)\n\n for res in package.resources:\n if args.missing_values:\n res.descriptor['schema']['missingValues'].extend(\n args.missing_values)\n\n for field in res.descriptor['schema']['fields']:\n name = field.get('name')\n if not name:\n continue\n\n ont = ontology.get(name)\n if not ont:\n continue\n\n data_type = ont.get('frictionless type', field.get('type'))\n if data_type:\n field['type'] = data_type\n\n data_format = ont.get('frictionless format', field.get('format'))\n if data_format:\n field['format'] = data_format\n\n rdf_type = ont.get('rdf type purl', field.get('rdfType'))\n if rdf_type:\n field['rdfType'] = rdf_type\n\n units = ont.get('units purl', field.get('pm:unitRdfType'))\n if units:\n field['pm:unitRdfType'] = units\n\n source_url = ont.get('pm:source url', field.get('pm:sourceUrl'))\n if source_url:\n field['pm:source url'] = source_url\n\n measure_source = ont.get('measurement source purl',\n field.get('pm:measurementSourceRdfType'))\n if measure_source:\n field['pm:measurementSourceRdfType'] = measure_source\n\n measure_url = ont.get('pm:measurement source protocol',\n field.get('pm:measurement source protocol'))\n if measure_url:\n field['pm:measurementSourceProtocolUrl'] = measure_url\n\n # TODO: Not sure about this\n # is_searchable = ont.get('pm:searchable', field.get('pm:searchable'))\n # if is_searchable:\n # field['pm:searchable'] = is_searchable\n\n package.remove_resource(res.name)\n package.add_resource(res.descriptor)\n\n package.save(args.out_file)\n\n print(f'Done, see \"{args.out_file}\"')","title":""},{"docid":"a3fe6b4a1d97a21eaef6d6f4d02a796d","score":"0.51963997","text":"def actualSolve(self, lp):\n\n if not self.executable(self.path):\n raise PulpSolverError(\"PuLP: cannot execute \" + self.path)\n tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, \"lp\", \"sol\", \"mst\")\n vs = lp.writeLP(tmpLp, writeSOS=1)\n try:\n os.remove(tmpSol)\n except:\n pass\n cmd = self.path\n options = self.options + self.getOptions()\n if self.timeLimit is not None:\n options.append((\"TimeLimit\", self.timeLimit))\n cmd += \" \" + \" \".join([f\"{key}={value}\" for key, value in options])\n cmd += f\" ResultFile={tmpSol}\"\n if self.optionsDict.get(\"warmStart\", False):\n self.writesol(filename=tmpMst, vs=vs)\n cmd += f\" InputFile={tmpMst}\"\n\n if lp.isMIP():\n if not self.mip:\n warnings.warn(\"GUROBI_CMD does not allow a problem to be relaxed\")\n cmd += f\" {tmpLp}\"\n if self.msg:\n pipe = None\n else:\n pipe = open(os.devnull, \"w\")\n\n return_code = subprocess.call(cmd.split(), stdout=pipe, stderr=pipe)\n\n # Close the pipe now if we used it.\n if pipe is not None:\n pipe.close()\n\n if return_code != 0:\n raise PulpSolverError(\"PuLP: Error while trying to execute \" + self.path)\n if not os.path.exists(tmpSol):\n # TODO: the status should be infeasible here, I think\n status = constants.LpStatusNotSolved\n values = reducedCosts = shadowPrices = slacks = None\n else:\n # TODO: the status should be infeasible here, I think\n status, values, reducedCosts, shadowPrices, slacks = self.readsol(tmpSol)\n self.delete_tmp_files(tmpLp, tmpMst, tmpSol, \"gurobi.log\")\n if status != constants.LpStatusInfeasible:\n lp.assignVarsVals(values)\n lp.assignVarsDj(reducedCosts)\n lp.assignConsPi(shadowPrices)\n lp.assignConsSlack(slacks)\n lp.assignStatus(status)\n return status","title":""},{"docid":"148556ac6767aa89118518bfbccc312b","score":"0.5196136","text":"def create_pyomo_model(self, debug_mode=False, debug_cost=2e7):\n\n # work on a local copy of the dataframe\n if not debug_mode and self.df.index.str.contains('DBUG').any():\n # previously ran in debug mode, but now done\n df = self.remove_debug_links()\n df.to_csv(self.linksfile + '-final.csv')\n else:\n df = self.df\n\n self.log.info('Creating Pyomo Model (debug=%s)' % debug_mode)\n\n model = ConcreteModel()\n\n model.N = Set(initialize=self.nodes)\n model.k = Set(initialize=range(15))\n model.A = Set(within=model.N*model.N*model.k, \n initialize=self.links, ordered=True)\n model.source = Param(initialize='SOURCE')\n model.sink = Param(initialize='SINK')\n\n def init_params(p):\n if p == 'cost' and debug_mode:\n return (lambda model,i,j,k: debug_cost \n if ('DBUG' in str(i)+'_'+str(j))\n else 1.0)\n else:\n return lambda model,i,j,k: df.loc[str(i)+'_'+str(j)+'_'+str(k)][p]\n\n model.u = Param(model.A, initialize=init_params('upper_bound'), mutable=True)\n model.l = Param(model.A, initialize=init_params('lower_bound'), mutable=True)\n model.a = Param(model.A, initialize=init_params('amplitude'))\n model.c = Param(model.A, initialize=init_params('cost'))\n\n # The flow over each arc\n model.X = Var(model.A, within=Reals)\n\n # Minimize total cost\n def obj_fxn(model):\n return sum(model.c[i,j,k]*model.X[i,j,k] for (i,j,k) in model.A)\n model.total = Objective(rule=obj_fxn, sense=minimize)\n\n # Enforce an upper bound limit on the flow across each arc\n def limit_rule_upper(model, i, j, k):\n return model.X[i,j,k] <= model.u[i,j,k]\n model.limit_upper = Constraint(model.A, rule=limit_rule_upper)\n\n # Enforce a lower bound limit on the flow across each arc\n def limit_rule_lower(model, i, j, k):\n return model.X[i,j,k] >= model.l[i,j,k]\n model.limit_lower = Constraint(model.A, rule=limit_rule_lower)\n\n # To speed up creating the mass balance constraints, first\n # create dictionaries of arcs_in and arcs_out of every node\n # These are NOT Pyomo data, and Pyomo does not use \"model._\" at all\n arcs_in = {}\n arcs_out = {}\n\n def arc_list_hack(model, i,j,k):\n if j not in arcs_in:\n arcs_in[j] = []\n arcs_in[j].append((i,j,k))\n\n if i not in arcs_out:\n arcs_out[i] = []\n arcs_out[i].append((i,j,k))\n return [0]\n\n model._ = Set(model.A, initialize=arc_list_hack)\n\n # Enforce flow through each node (mass balance)\n def flow_rule(model, node):\n if node in [value(model.source), value(model.sink)]:\n return Constraint.Skip\n outflow = sum(model.X[i,j,k]/model.a[i,j,k] for i,j,k in arcs_out[node])\n inflow = sum(model.X[i,j,k] for i,j,k in arcs_in[node])\n return inflow == outflow\n model.flow = Constraint(model.N, rule=flow_rule)\n\n model.dual = Suffix(direction=Suffix.IMPORT)\n\n self.model = model","title":""},{"docid":"f1d525b72f37243510024f58395907b3","score":"0.5173822","text":"def makeLibSource(self):\n\n # Make vector field (and event) file for compilation\n # This sets the field self._eventNames\n self._prepareEventSpecs()\n\n # Write the model.m file\n allfilestr = self._prepareModelContents()\n modelfile = os.path.join(self._target_dir, self._model_file)\n try:\n file = open(modelfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._model_file+\" for writing\")\n raise IOError(e)\n\n # Write the events.m file\n if len(self._eventNames) > 0:\n allfilestr = self._prepareEventsFileContents() + self._prepareAuxContents()\n eventsfile = os.path.join(self._target_dir, self._events_file)\n try:\n file = open(eventsfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._events_file+\" for writing\")\n raise IOError(e)\n\n\n # Write the initialconditions.m file\n allfilestr = self._prepareICContents()\n icfile = os.path.join(self._target_dir, self._ic_file)\n try:\n file = open(icfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._ic_file+\" for writing\")\n raise IOError(e)\n\n # Write the pars.m file\n allfilestr = self._prepareParamContents()\n paramfile = os.path.join(self._target_dir, self._param_file)\n try:\n file = open(paramfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._param_file+\" for writing\")\n raise IOError(e)\n\n # Write the get.m file\n allfilestr = self._prepareGetFileContents()\n getfile = os.path.join(self._target_dir, self._get_file)\n try:\n file = open(getfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._get_file+\" for writing\")\n raise IOError(e)\n\n # Write the set.m file\n allfilestr = self._prepareSetFileContents()\n setfile = os.path.join(self._target_dir, self._set_file)\n try:\n file = open(setfile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._set_file+\" for writing\")\n raise IOError(e)\n\n # Write the vfield.m file\n# vfdefines = self._prepareVfieldDefines()\n# allfilestr = self._prepareVfieldContents(vfdefines)\n allfilestr = self.funcspec.spec[0] + self._prepareAuxContents()\n vffile = os.path.join(self._target_dir, self._vfield_file)\n try:\n file = open(vffile, 'w')\n file.write(allfilestr)\n file.close()\n except IOError as e:\n print(\"Error opening file \"+self._vfield_file+\" for writing\")\n raise IOError(e)","title":""},{"docid":"870c6fd27ca456de9582a6aa9f045d53","score":"0.51735485","text":"def buildSolverModel(self, lp):\n log.debug(\"create the gurobi model\")\n lp.solverModel = gurobipy.Model(lp.name)\n log.debug(\"set the sense of the problem\")\n if lp.sense == constants.LpMaximize:\n lp.solverModel.setAttr(\"ModelSense\", -1)\n if self.timeLimit:\n lp.solverModel.setParam(\"TimeLimit\", self.timeLimit)\n gapRel = self.optionsDict.get(\"gapRel\")\n logPath = self.optionsDict.get(\"logPath\")\n if gapRel:\n lp.solverModel.setParam(\"MIPGap\", gapRel)\n if logPath:\n lp.solverModel.setParam(\"LogFile\", logPath)\n\n log.debug(\"add the variables to the problem\")\n for var in lp.variables():\n lowBound = var.lowBound\n if lowBound is None:\n lowBound = -gurobipy.GRB.INFINITY\n upBound = var.upBound\n if upBound is None:\n upBound = gurobipy.GRB.INFINITY\n obj = lp.objective.get(var, 0.0)\n varType = gurobipy.GRB.CONTINUOUS\n if var.cat == constants.LpInteger and self.mip:\n varType = gurobipy.GRB.INTEGER\n var.solverVar = lp.solverModel.addVar(\n lowBound, upBound, vtype=varType, obj=obj, name=var.name\n )\n if self.optionsDict.get(\"warmStart\", False):\n # Once lp.variables() has been used at least once in the building of the model.\n # we can use the lp._variables with the cache.\n for var in lp._variables:\n if var.varValue is not None:\n var.solverVar.start = var.varValue\n\n lp.solverModel.update()\n log.debug(\"add the Constraints to the problem\")\n for name, constraint in lp.constraints.items():\n # build the expression\n expr = gurobipy.LinExpr(\n list(constraint.values()), [v.solverVar for v in constraint.keys()]\n )\n if constraint.sense == constants.LpConstraintLE:\n relation = gurobipy.GRB.LESS_EQUAL\n elif constraint.sense == constants.LpConstraintGE:\n relation = gurobipy.GRB.GREATER_EQUAL\n elif constraint.sense == constants.LpConstraintEQ:\n relation = gurobipy.GRB.EQUAL\n else:\n raise PulpSolverError(\"Detected an invalid constraint type\")\n constraint.solverConstraint = lp.solverModel.addConstr(\n expr, relation, -constraint.constant, name\n )\n lp.solverModel.update()","title":""},{"docid":"f807685d80245425900a66f5f81872f5","score":"0.5164706","text":"def import_model(self, file):\n felems = os.path.splitext(file)\n ext = felems[1].lower()\n if ext in (\".gz\", \".zip\",):\n ext = os.path.splitext(felems[0])[1].lower()\n\n if ext == \".cpo\":\n import docplex.cp.cpo.cpo_parser as cpo_parser\n prs = cpo_parser.CpoParser(self)\n prs.parse(file)\n\n elif ext == \".fzn\":\n import docplex.cp.fzn.fzn_parser as fzn_parser\n prs = fzn_parser.FznParser(self)\n prs.parse(file)\n # Get model to force compilation\n prs.get_model()\n\n elif ext == \".lp\":\n import docplex.cp.lp.lp_parser as lp_parser\n prs = lp_parser.LpParser(self)\n prs.parse(file)\n\n else:\n raise CpoException(\"Unknown '{}' file format. Only .cpo, .fzn and .lp are supported.\".format(ext))","title":""},{"docid":"a3c04ce9f517817a518f872f20d83633","score":"0.51574755","text":"def to_automl(data, path='.', name='autodata'):\n # check if folder exists\n dir = os.path.join(path, name+'_automl')\n if not os.path.exists(dir):\n # create folder\n os.mkdir(dir)\n data.descriptors().to_csv(os.path.join(dir, name+'.info'), header=True) # some information\n if data.has_class():\n pd.DataFrame(data.indexes['X']).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\n pd.DataFrame(data.indexes['y']).to_csv(os.path.join(dir, name+'_label.name'), index=False, header=False) # label name\n if 'train' in data.indexes: # train/test and X/y splits\n data.get_data('X_train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\n data.get_data('X_test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\n data.get_data('y_train').to_csv(os.path.join(dir, name+'_train.solution'), sep=' ', index=False, header=False) # train solution\n data.get_data('y_test').to_csv(os.path.join(dir, name+'_test.solution'), sep=' ', index=False, header=False) # test solution\n else: # only X/y split\n data.get_data('X').to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data\n data.get_data('y').to_csv(os.path.join(dir, name+'.solution'), sep=' ', index=False, header=False) # solution\n else:\n pd.DataFrame(data.columns).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\n if 'train' in data.indexes: # only train/test split\n data.get_data('train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\n data.get_data('test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\n else: # no split at all\n data.to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data","title":""},{"docid":"311b06e5d22497504730712619f4da06","score":"0.5155293","text":"def _make_input_file(self, teff_lims, teff_step, logg_lims, logg_step,\n feh_lims, feh_step, vsini_lims, vsini_step, \n vmicro_lims, vmicro_step, resolution):\n output_string = '{:.1f} {:.0f} {:.1f}\\n'.format(teff_lims[0], \n teff_step, \n teff_lims[-1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(logg_lims[0],\n logg_step,\n logg_lims[1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(vmicro_lims[0],\n vmicro_step,\n vmicro_lims[1])\n\n output_string += '{:.1f} {:.1f} {:.1f}\\n'.format(vsini_lims[0],\n vsini_step,\n vsini_lims[1])\n\n output_string += \"skip 0.03 0.02 0.07 !dilution factor\\n\"\n\n output_string += 'skip {:.1f} {:.1f} {:.1f}\\n'.format(feh_lims[0],\n feh_step,\n feh_lims[1])\n \n output_string += 'He 0.04 0.005 0.06 ! Individual abundance\\n'\n\n output_string += '0.0 {:.0f}\\n'.format(resolution)\n\n output_string += '{}\\n{}\\n'.format(self.abundance_table, self.model_dir)\n\n output_string += '2 1 !atmosphere model vmicro and mass\\n'\n output_string += 'ST ! model atmosphere chemical composition flag\\n'\n\n dx = self.data.x[1] - self.data.x[0]\n output_string += '1 {:.5f} fit\\n'.format(dx)\n\n output_string += 'data_sets/{}.txt\\n'.format(self.output_basename)\n\n output_string += '0.5 0.99 0.0 adjust ! RV determination stuff\\n'\n\n xmin, xmax = self.data.x[0]-1, self.data.x[-1]+1\n output_string += '{:.1f} {:.1f}\\n'.format(xmin, xmax)\n\n outfilename = '{}.inp'.format(self.output_basename)\n with open(outfilename, 'w') as outfile:\n outfile.write(output_string)\n\n return outfilename","title":""},{"docid":"8ae6ddb4fe2249629c719de91f2cabee","score":"0.51552427","text":"def write(self, filename='pflotran.in'):\n\n if filename:\n self.filename = filename\n outfile = open(self.filename, 'w')\n\n # Presumes simulation.simulation_type is required\n if self.simulation.simulation_type:\n self._write_simulation(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'simulation is required, it is currently reading as empty')\n\n if self.simulation.subsurface_flow or \\\n self.simulation.subsurface_transport:\n self._write_subsurface_simulation_begin(outfile)\n\n if self.reference_pressure:\n self._write_reference_pressure(outfile)\n\n if self.reference_temperature:\n self._write_reference_temperature(outfile)\n\n if self.co2_database:\n self._write_co2_database(outfile)\n\n if self.regression.cells \\\n or self.regression.cells_per_process \\\n or self.regression.all_cells:\n self._write_regression(outfile)\n\n if self.uniform_velocity.value_list:\n self._write_uniform_velocity(outfile)\n\n if self.specified_velocity:\n self._write_specified_velocity(outfile)\n\n if self.update_flow_permeability:\n self._write_update_flow_permeability(outfile)\n\n if self.reference_liquid_density:\n self._write_reference_liquid_density(outfile)\n\n if self.minimum_hydrostatic_pressure:\n self._write_minimum_hydrostatic_pressure(outfile)\n\n if self.reference_stress_state.value_list:\n self._write_reference_stress_state(outfile)\n\n if self.eoslist:\n self._write_eos(outfile)\n\n if self.klinkenberg_effect:\n self._write_klinkenberg_effect(outfile)\n\n if self.creep_closure_table:\n self._write_creep_closure_table(outfile)\n\n if self.nonuniform_velocity.filename:\n self._write_nonuniform_velocity(outfile)\n\n if self.simulation.mode.upper() == 'MPHASE' and self.co2_database:\n self._write_co2_database(outfile)\n\n if self.multiple_continuum:\n self._write_multiple_continuum(outfile)\n\n if self.overwrite_restart_flow_params:\n self._write_overwrite_restart_flow(outfile)\n\n if self.overwrite_restart_transport:\n self._write_overwrite_restart_transport(outfile)\n\n if self.initialize_flow_from_file is not None:\n self._write_initialize_flow_from_file(outfile)\n\n if self.initialize_transport_from_file is not None:\n self._write_initialize_transport_from_file(outfile)\n\n if self.isothermal:\n self._write_isothermal(outfile)\n\n if self.reference_porosity:\n self._write_reference_porosity(outfile)\n\n if self.datasetlist:\n self._write_dataset(outfile)\n # else: print 'info: dataset name not detected\\n'\n\n if self.chemistry:\n self._write_chemistry(outfile)\n # else: print 'info: chemistry not detected\\n'\n\n if self.grid:\n self._write_grid(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'grid is required, it is currently reading as empty!')\n\n if self.timestepper_flow or self.timestepper_transport:\n self._write_timestepper(outfile)\n # else: print 'info: timestepper not detected\\n'\n\n if self.time:\n self._write_time(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'time is required, it is currently reading as empty!')\n\n if self.proplist:\n self._write_prop(outfile)\n else:\n PyFLOTRAN_WARNING(\n 'material property list is empty! ' +\n ' Using default material property settings')\n self.add(pmaterial())\n self._write_prop(outfile)\n\n if self.lsolverlist:\n self._write_lsolver(outfile)\n # else: print 'info: lsolverlist (linear solver list) not detected\\n'\n\n if self.nsolverlist:\n self._write_nsolver(outfile)\n # else: print 'info: nsolverlist (newton solver list) not detected\\n'\n\n if self.output:\n self._write_output(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'output is required, it is currently reading as empty!')\n\n if self.fluidlist:\n self._write_fluid(outfile)\n\n if self.saturationlist:\n self._write_saturation(outfile)\n elif self.charlist:\n self._write_characteristic_curves(outfile)\n else:\n if self.simulation.subsurface_flow:\n self.add(pcharacteristic_curves())\n self._write_characteristic_curves(outfile)\n PyFLOTRAN_WARNING(\n 'characteristic_curves list or saturation list ' +\n 'is required, it is currently reading as empty! ' +\n ' Using default characteristic_curves settings')\n\n if self.regionlist:\n self._write_region(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'regionlist is required, it is currently reading as empty!')\n\n if self.integral_flux_list:\n self._write_integral_flux(outfile)\n\n if self.observation_list:\n self._write_observation(outfile)\n\n if self.flowlist:\n self._write_flow(outfile)\n\n if self.transportlist:\n self._write_transport(outfile)\n\n if self.initial_condition_list:\n self._write_initial_condition(outfile)\n else:\n raise PyFLOTRAN_ERROR(\n 'initial_condition_list is required,' +\n 'it is currently reading as empty!')\n\n if self.boundary_condition_list:\n self._write_boundary_condition(outfile)\n\n if self.source_sink_list:\n self._write_source_sink(outfile)\n\n if self.source_sink_sandbox_list:\n self._write_source_sink_sandbox(outfile)\n\n if self.reference_saturation:\n self._write_reference_saturation(outfile)\n\n if self.strata_list:\n self._write_strata(outfile)\n else:\n PyFLOTRAN_WARNING(\n 'stratigraphy_coupler is required, ' +\n 'it is currently reading as empty! ' +\n 'Using default settings')\n self.add(pstrata())\n self._write_strata(outfile)\n\n if self.constraint_list:\n self._write_constraint(outfile)\n\n if self.rt_mass_transfer_list:\n self._write_rt_mass_transfer(outfile)\n\n if self.flow_mass_transfer_list:\n self._write_flow_mass_transfer(outfile)\n\n if self.simulation.subsurface_flow or \\\n self.simulation.subsurface_transport:\n self._write_subsurface_simulation_end(outfile)\n\n\n # -----------------------------------------------\n\n if self.simulation.surface_subsurface:\n self._write_surface_subsurface_begin(outfile)\n\n if self.surface_flow:\n self._write_surface_flow(outfile)\n\n self._write_surface_subsurface_end(outfile)\n\n # -----------------------------------------------\n\n if self.simulation.simulation_type == 'hydroquake':\n self._write_hydroquake(outfile)\n\n if self.simulation.simulation_type.lower() == 'geomechanics_subsurface':\n self._write_geomechanics(outfile)\n\n if self.waste_form_general:\n self._write_wasteform_general(outfile)\n\n if self.ufd_decay:\n self._write_ufd_decay(outfile)\n\n if self.ufd_biosphere:\n self._write_ufd_biosphere(outfile)\n\n if self.wipp_source_sink:\n self._write_wipp_source_sink(outfile)\n\n outfile.close()","title":""},{"docid":"6217bff10682c1129e9efb6eb05446d3","score":"0.5149605","text":"def write(solution):\n trimmed_solution = solution\n input_file_name_stripped = trimmed_solution.name\n cwd = os.getcwd()\n output_file_name = os.path.join(cwd,\n 'pym_' +\n input_file_name_stripped +\n '.inp')\n f = open(output_file_name, 'w+')\n\n #Work functions\n\n calories_constant = 4184.0 #number of calories in 1000 Joules of energy\n def eliminate(input_string, char_to_replace, spaces='single'):\n \"\"\"\n Eliminate characters from a string\n\n :param input_string\n string to be modified\n :param char_to_replace\n array of character strings to be removed\n \"\"\"\n for char in char_to_replace:\n input_string = input_string.replace(char, \"\")\n if spaces == 'double':\n input_string = input_string.replace(\" \", \" \")\n return input_string\n\n def section_break(title):\n \"\"\"\n Insert break and new section title into cti file\n\n :param title:\n title string for next section_break\n \"\"\"\n f.write('!'+ \"-\"*75 + '\\n')\n f.write('! ' + title +'\\n')\n f.write('!'+ \"-\"*75 + '\\n')\n\n def replace_multiple(input_string, replace_list):\n \"\"\"\n Replace multiple characters in a string\n\n :param input_string\n string to be modified\n :param replace list\n list containing items to be replaced (value replaces key)\n \"\"\"\n for original_character, new_character in replace_list.items():\n input_string = input_string.replace(original_character,\n new_character)\n return input_string\n\n def build_arrhenius(equation_object, equation_type):\n \"\"\"\n Builds Arrhenius coefficient string\n\n :param equation_objects\n cantera equation object\n :param equation_type:\n string of equation type\n \"\"\"\n coeff_sum = sum(equation_object.reactants.values())\n pre_exponential_factor = equation_object.rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.rate.activation_energy / calories_constant)\n if equation_type == 'ElementaryReaction':\n if coeff_sum == 1:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor))\n if coeff_sum == 2:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**3))\n if coeff_sum == 3:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**6))\n if equation_type == 'ThreeBodyReaction':\n if coeff_sum == 1:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**3))\n if coeff_sum == 2:\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor*10**6))\n if (equation_type != 'ElementaryReaction'\n and equation_type != 'ThreeBodyReaction'):\n pre_exponential_factor = str(\n '{:.3E}'.format(pre_exponential_factor))\n arrhenius = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius\n\n def build_modified_arrhenius(equation_object, t_range):\n \"\"\"\n Builds Arrhenius coefficient strings for high and low temperature ranges\n\n :param equation_objects\n cantera equation object\n :param t_range:\n simple string ('high' or 'low') to designate temperature range\n \"\"\"\n if t_range == 'high':\n pre_exponential_factor = equation_object.high_rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.high_rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.high_rate.activation_energy/calories_constant)\n if len(equation_object.products) == 1:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**3))\n else:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor))\n arrhenius_high = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius_high\n if t_range == 'low':\n\n pre_exponential_factor = equation_object.low_rate.pre_exponential_factor\n temperature_exponent = '{:.3f}'.format(equation_object.low_rate.temperature_exponent)\n activation_energy = '{:.2f}'.format(equation_object.low_rate.activation_energy/calories_constant)\n if len(equation_object.products) == 1:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**6))\n else:\n pre_exponential_factor = str(\n '{:.5E}'.format(pre_exponential_factor*10**3))\n\n arrhenius_low = [pre_exponential_factor,\n temperature_exponent,\n activation_energy]\n return arrhenius_low\n\n\n def build_nasa(nasa_coeffs, row):\n \"\"\"\n Creates string of nasa polynomial coefficients\n\n :param nasa_coeffs\n cantera species thermo coefficients object\n :param row\n which row to write coefficients in\n \"\"\"\n line_coeffs = ''\n lines = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]]\n line_index = lines[row-2]\n for ix, c in enumerate(nasa_coeffs):\n if ix in line_index:\n if c >= 0:\n line_coeffs += ' '\n line_coeffs += str('{:.8e}'.format(c))\n return line_coeffs\n\n def build_species_string():\n \"\"\"\n formats species string for writing\n \"\"\"\n species_list_string = ''\n line = 1\n for sp_index, sp_string in enumerate(trimmed_solution.species_names):\n sp = ' '\n #get length of string next species is added\n length_new = len(sp_string)\n length_string = len(species_list_string)\n total = length_new + length_string + 3\n #if string will go over width, wrap to new line\n if total >= 70*line:\n species_list_string += '\\n'\n line += 1\n species_list_string += sp_string + ((16-len(sp_string))*sp)\n return species_list_string\n\n #Write title block to file\n section_break('Chemkin File converted from Solution Object by pyMARS')\n\n #Write phase definition to file\n element_names = eliminate(str(trimmed_solution.element_names),\n ['[', ']', '\\'', ','])\n element_string = Template(\n 'ELEMENTS\\n' +\n '$element_names\\n' +\n 'END\\n')\n f.write(element_string.substitute(element_names=element_names))\n species_names = build_species_string()\n species_string = Template(\n 'SPECIES\\n' +\n '$species_names\\n'+\n 'END\\n')\n f.write(species_string.substitute(species_names=species_names))\n\n #Write species to file\n section_break('Species data')\n f.write('THERMO ALL' + '\\n' +\n ' 300.000 1000.000 5000.000' +'\\n')\n phase_unknown_list = []\n\n #write data for each species in the Solution object\n for sp_index in xrange(len(trimmed_solution.species_names)):\n d = 3.33564e-30 #1 debye = d coulomb-meters\n species = trimmed_solution.species(sp_index)\n name = str(trimmed_solution.species(sp_index).name)\n nasa_coeffs = trimmed_solution.species(sp_index).thermo.coeffs\n #Species attributes from trimmed solution object\n t_low = '{0:.3f}'.format(species.thermo.min_temp)\n t_max = '{0:.3f}'.format(species.thermo.max_temp)\n t_mid = '{0:.3f}'.format(species.thermo.coeffs[0])\n temp_range = str(t_low) + ' ' + str(t_max) + ' ' + t_mid\n species_comp = ''\n for atom in species.composition:\n species_comp += '{:<4}'.format(atom)\n species_comp += str(int(species.composition[atom]))\n if type(species.transport).__name__ == 'GasTransportData':\n species_phase = 'G'\n else:\n phase_unknown_list.append(name)\n species_phase = 'G'\n line_1 = (\n '{:<18}'.format(name) +\n '{:<6}'.format(' ') +\n '{:<20}'.format(species_comp) +\n '{:<4}'.format(species_phase) +\n '{:<31}'.format(temp_range) +\n '{:<1}'.format('1') +\n '\\n')\n f.write(line_1)\n line_2_coeffs = build_nasa(nasa_coeffs, 2)\n line_2 = line_2_coeffs + ' 2\\n'\n f.write(line_2)\n line_3_coeffs = build_nasa(nasa_coeffs, 3)\n line_3 = line_3_coeffs + ' 3\\n'\n f.write(line_3)\n line_4_coeffs = build_nasa(nasa_coeffs, 4)\n line_4 = line_4_coeffs + ' 4\\n'\n f.write(line_4)\n\n f.write('END\\n')\n\n #Write reactions to file\n section_break('Reaction Data')\n f.write('REACTIONS\\n')\n #write data for each reaction in the Solution Object\n for reac_index in xrange(len(trimmed_solution.reaction_equations())):\n equation_string = str(trimmed_solution.reaction_equation(reac_index))\n equation_string = eliminate(equation_string, ' ', 'single')\n equation_object = trimmed_solution.reaction(reac_index)\n equation_type = type(equation_object).__name__\n m = str(reac_index+1)\n if equation_type == 'ThreeBodyReaction':\n arrhenius = build_arrhenius(equation_object, equation_type)\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arrhenius[0]) +\n '{:>9}'.format(arrhenius[1]) +\n '{:>11}'.format(arrhenius[2]) +\n '\\n')\n f.write(main_line)\n #trimms efficiencies list\n efficiencies = equation_object.efficiencies\n trimmed_efficiencies = equation_object.efficiencies\n for s in efficiencies:\n if s not in trimmed_solution.species_names:\n del trimmed_efficiencies[s]\n replace_list_2 = {\n '{':'',\n '}':'/',\n '\\'':'',\n ':':'/',\n ',':'/'}\n efficiencies_string = replace_multiple(\n str(trimmed_efficiencies),\n replace_list_2)\n secondary_line = str(efficiencies_string) + '\\n'\n if bool(efficiencies) is True:\n f.write(secondary_line)\n if equation_type == 'ElementaryReaction':\n arrhenius = build_arrhenius(equation_object, equation_type)\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arrhenius[0]) +\n '{:>9}'.format(arrhenius[1]) +\n '{:>11}'.format(arrhenius[2]) +\n '\\n')\n f.write(main_line)\n if equation_type == 'FalloffReaction':\n arr_high = build_modified_arrhenius(equation_object, 'high')\n main_line = (\n '{:<51}'.format(equation_string) +\n '{:>9}'.format(arr_high[0]) +\n '{:>9}'.format(arr_high[1]) +\n '{:>11}'.format(arr_high[2]) +\n '\\n')\n f.write(main_line)\n arr_low = build_modified_arrhenius(equation_object, 'low')\n second_line = (\n ' LOW /' +\n ' ' + arr_low[0] +\n ' ' + arr_low[1] +\n ' ' + arr_low[2] + '/\\n')\n f.write(second_line)\n j = equation_object.falloff.parameters\n #If optional Arrhenius data included:\n try:\n third_line = (\n ' TROE/' +\n ' ' + str(j[0]) +\n ' ' + str(j[1]) +\n ' ' + str(j[2]) +\n ' ' + str(j[3]) +' /\\n')\n f.write(third_line)\n except IndexError:\n pass\n #trimms efficiencies list\n efficiencies = equation_object.efficiencies\n trimmed_efficiencies = equation_object.efficiencies\n for s in efficiencies:\n if s not in trimmed_solution.species_names:\n del trimmed_efficiencies[s]\n replace_list_2 = {\n '{':'',\n '}':'/',\n '\\'':'',\n ':':'/',\n ',':'/'}\n efficiencies_string = replace_multiple(\n str(trimmed_efficiencies),\n replace_list_2)\n\n fourth_line = str(efficiencies_string) + '\\n'\n if bool(efficiencies) is True:\n f.write(fourth_line)\n #dupluicate option\n if equation_object.duplicate is True:\n duplicate_line = ' DUPLICATE' +'\\n'\n f.write(duplicate_line)\n f.write('END')\n f.close()\n\n #Test mechanism file\n\n original_solution = solution\n #convert written chemkin file to cti, and get solution\n parser = ck2cti.Parser()\n outName = 'test_file.cti'\n parser.convertMech(output_file_name, outName=outName)\n new_solution = ct.Solution(outName)\n\n #test new solution vs original solutoin\n #test(original_solution, new_solution)\n os.remove(outName)\n return output_file_name\n\n\n \"\"\"\n def build_falloff(j):\n\n Creates falloff reaction Troe parameter string\n\n param j:\n Cantera falloff parameters object\n\n falloff_str = str(',\\n falloff = Troe(' +\n 'A = ' + str(j[0]) +\n ', T3 = ' + str(j[1]) +\n ', T1 = ' + str(j[2]) +\n ', T2 = ' + str(j[3]) +') )\\n\\n')\n return falloff_str\n \"\"\"","title":""},{"docid":"bb9fe475543f8a500b17238e2c01e2a8","score":"0.5131358","text":"def writeDataFile(self):\n model = pe.PEChainBuilder(self.lenPa, self.numPa, self.lenPc, self.numPc,\\\n self.volRatio, self.chargeFraction)\n model.genChains()\n \n \n model.writeFiles(self.directory)\n self.dataFileName = \"data.pe.la{0}.na{1}.lc{2}.nc{3}.rho{4}.r{5}.lammps\".\\\n format(self.lenPa, self.numPa, self.lenPc, self.numPc, \\\n self.volRatio, self.chargeRepeat)","title":""},{"docid":"dc59f59e013fe69fd099020e5707bc62","score":"0.51224095","text":"def createPrismModel(states, transitions, target, mode, nr_actions, reset_transitions, reset_cost):\n out_file = open(TMP_MODEL_PATH, 'w')\n # module\n out_file.write(\"mdp\\n\\nmodule tmp\\n\\n\")\n\n # number of state and initial state\n out_file.write(\"\\ts : [0..\" + str(len(states)) + \"] init \" + str(abs(mode)) + \";\\n\\n\")\n\n # transitions\n for state in range(len(transitions)):\n for action in range(len(transitions[state])):\n if sum(transitions[state][action]) != 0:\n out_file.write(\"\\t[\" + chr(97 + action) + \"] s=\" + str(state) + \"-> \")\n destinations = []\n for dest in range(len(transitions[state][action])):\n if transitions[state][action][dest] != 0:\n destinations.append(str(transitions[state][action][dest]) + \":(s'=\" + str(dest) + \")\")\n out_file.write(\" + \".join(destinations))\n out_file.write(\";\\n\")\n out_file.write(\"\\nendmodule\\n\\n\")\n\n # label target\n out_file.write('label \"target\" = ')\n target = list(set(target))\n target = [\"(s=\" + str(x) + \")\" for x in target]\n if len(target) == 0:\n out_file.write(\"(s=\" + str(len(states) + 1) + \");\\n\")\n else:\n out_file.write(\" | \".join(target))\n out_file.write(\";\\n\")\n\n # label states\n if mode == MODE_MAX:\n out_file.write('label \"sink\" = (s=0);\\n')\n\n for i in range(mode, len(states)):\n out_file.write('label \"s' + str(states[i][0]) + '_obs' + str(states[i][1]) + '\" = (s=' + str(i) + ');\\n')\n\n if mode == MODE_MIN:\n out_file.write(\"\\nrewards\\n\")\n if reset_cost == 1:\n out_file.write(\"\\ttrue:1;\\n\")\n else:\n for i in range(len(states)):\n for j in range(nr_actions):\n if j in reset_transitions[i]:\n out_file.write(\"\\t[\" + chr(97 + j) + \"] (s=\" + str(i) + \") : \" + str(reset_cost) + \";\\n\")\n else:\n out_file.write(\"\\t[\" + chr(97 + j) + \"] (s=\" + str(i) + \") : 1;\\n\")\n\n out_file.write(\"endrewards\\n\")\n\n out_file.close()","title":""},{"docid":"f391c50f49d5b0eae5ebbf6c5f12961a","score":"0.5118555","text":"def build_LM(in_file):\r\n print 'building language models...'\r\n # This is an empty method\r\n # Pls implement your code in below\r\n LMs = {'malaysian': {}, 'indonesian': {}, 'tamil': {}}\r\n malay_LM = LMs['malaysian']\r\n indon_LM = LMs['indonesian']\r\n tamil_LM = LMs['tamil']\r\n\r\n FD = open(in_file, 'r')\r\n\r\n lines = FD.read()\r\n lines_array = lines.replace('\\r', ' ').split('\\n')\r\n lines_array.pop(len(lines_array) - 1)\r\n\r\n for line in lines_array:\r\n line_array = line.split(' ', 1)\r\n lang = line_array[0]\r\n text = line_array[1]\r\n current_LM = LMs[lang]\r\n four_char_array = get_four_char_array(text)\r\n\r\n for four_char in four_char_array:\r\n if four_char not in current_LM:\r\n # performs add one smoothing\r\n malay_LM[four_char] = 1\r\n indon_LM[four_char] = 1\r\n tamil_LM[four_char] = 1\r\n current_LM[four_char] += 1\r\n\r\n # calculates ratio of set frequency over total frequency\r\n # for all sets in each of the three LMs\r\n for LM in LMs.values():\r\n count = 0\r\n for freq_value in LM.values():\r\n count += freq_value\r\n for four_char_key in LM.keys():\r\n LM[four_char_key] /= count\r\n return LMs\r\n\r\n FD.close()","title":""},{"docid":"c862647fcd5ba7f408d748abaf7ab617","score":"0.5115003","text":"def test_ImportUFONoSideEffectNLO(self):\n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('loop_sm'),False)\n original_all_particles = copy.copy(ufo_model.all_particles)\n original_all_vertices = copy.copy(ufo_model.all_vertices)\n original_all_couplings = copy.copy(ufo_model.all_couplings)\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\n original_all_parameters = copy.copy(ufo_model.all_parameters)\n original_all_orders = copy.copy(ufo_model.all_orders)\n original_all_functions = copy.copy(ufo_model.all_functions)\n original_all_CTvertices = copy.copy(ufo_model.all_CTvertices)\n original_all_CTparameters = copy.copy(ufo_model.all_CTparameters)\n\n\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\n model = ufo2mg5_converter.load_model()\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \n\n self.assertEqual(original_all_particles,ufo_model.all_particles)\n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\n# self.assertEqual(original_all_couplings,ufo_model.all_couplings)\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\n self.assertEqual(original_all_orders,ufo_model.all_orders)\n self.assertEqual(original_all_functions,ufo_model.all_functions)\n self.assertEqual(original_all_CTvertices,ufo_model.all_CTvertices)\n self.assertEqual(original_all_CTparameters,ufo_model.all_CTparameters)","title":""},{"docid":"dfe119951be54a560fcefe9057a921e1","score":"0.5112634","text":"def main(models, output_dir):\n\tfor model in models:\n\t\tobj = ParseOmsa(model)\n\t\tobj.bios_version()\n\t\tobj.perc_versions()\n\t\tobj.write_yaml(output_dir)","title":""},{"docid":"00e7eaf7ed485ca9233fa3bbe1a1b96e","score":"0.5110691","text":"def solve_lp(self):\n self._initialize_model()\n self._initialize_node_variables_lp()\n self._initialize_edge_variables_lp()\n self._initialize_objective()\n self._initialize_contraint_nodes()\n self._initialize_constraint_edges()\n self._initialize_constraint_terminals()\n self._run_solver()\n self._calculate_cut_value()\n self._calculate_source_sets()\n self._calculate_possible_terminals_by_node_weak()\n self._calculate_possible_terminals_by_node_strong()","title":""},{"docid":"6586aa3830f43b54dce72f69cbc0963d","score":"0.5104416","text":"def loadLoopToyModel():\n \n mypartlist = base_objects.ParticleList()\n myinterlist = base_objects.InteractionList()\n myloopmodel = loop_base_objects.LoopModel()\n\n # A gluon\n mypartlist.append(base_objects.Particle({'name':'g',\n 'antiname':'g',\n 'spin':3,\n 'color':8,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'G',\n #'antitexname':'G',\n 'line':'curly',\n 'charge':0.,\n 'pdg_code':21,\n #'propagating':True,\n 'propagator':0, \n 'is_part':True,\n 'counterterm':{('QCD', ((6,),)): {0: 'UVWfct_G_1', -1: 'UVWfct_G_1_1eps'}, ('QCD', ((5,),)): {0: 'UVWfct_G_0', -1: 'UVWfct_G_0_1eps'}},\n 'self_antipart':True}))\n \n # A quark U and its antiparticle\n mypartlist.append(base_objects.Particle({'name':'u',\n 'antiname':'u~',\n 'spin':2,\n 'color':3,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'u',\n #'antitexname':'u',\n 'line':'straight',\n 'charge':2. / 3.,\n 'pdg_code':2,\n #'propagating':True,\n 'propagator':'', \n 'is_part':True,\n 'self_antipart':False}))\n antiu = copy.copy(mypartlist[1])\n antiu.set('is_part', False)\n\n # A quark D and its antiparticle\n mypartlist.append(base_objects.Particle({'name':'d',\n 'antiname':'d~',\n 'spin':2,\n 'color':3,\n 'mass':'ZERO',\n 'width':'ZERO',\n #'texname':'d',\n #'antitexname':'d',\n 'line':'straight',\n 'charge':-1. / 3.,\n 'pdg_code':1,\n #'propagating':True,\n 'propagator':'',\n 'is_part':True,\n 'self_antipart':False}))\n antid = copy.copy(mypartlist[2])\n antid.set('is_part', False)\n\n myloopmodel.set('particles', mypartlist)\n myloopmodel.set('couplings', ['QCD']) \n myloopmodel.set('interactions', myinterlist)\n myloopmodel.set('perturbation_couplings', ['QCD'])\n myloopmodel.set('order_hierarchy', {'QCD':1})\n\n return myloopmodel","title":""},{"docid":"695e98b499f902207ba35ccac8891bda","score":"0.5104137","text":"def generate(genconf_model, project_folder):\n\n gendesc = get_generator_desc(genconf_model.gen_name)\n meta = get_language(gendesc.lang)\n\n # Path for templates overrides\n templates_path = os.path.join(project_folder, 'templates',\n genconf_model.gen_name)\n\n output_root = os.path.join(project_folder, genconf_model.output)\n\n def _create_folder(output_file):\n try:\n os.makedirs(os.path.dirname(output_file))\n except FileExistsError:\n pass\n\n # For each model configured in the current genconf\n for model_path in genconf_model.models:\n\n click.echo('Processing model \"{}\"'.format(model_path))\n model = meta.model_from_file(os.path.join(project_folder, 'model',\n model_path))\n\n # Validate model using generator specific validation.\n if gendesc.validate:\n gendesc.validate(model)\n\n params = {}\n # Adding generator params\n for p in genconf_model.params:\n params[p.name] = p.value\n\n # Processing all rules\n for rule in genconf_model.rules:\n\n # Sanity check\n if len(rule.types) != len(rule.var_names):\n raise TextXToolsError('Number of variables don\\'t match'\n ' number of types in rule \"{}\"'\n .format(rule.name))\n\n # Collect all object of each given type\n type_objs = []\n for t in rule.types:\n type_objs.append(children_of_type(model, t))\n\n if rule.all:\n context = {'__builtins__':{}}\n for ind, obj in enumerate(type_objs):\n params[rule.var_names[ind]] = obj\n context.update(params)\n for t in rule.trans:\n target_path = eval(t.python_path_expr, context)\n output_file = os.path.join(output_root, target_path)\n\n click.echo(\"Generating {}\".format(output_file))\n\n _create_folder(output_file)\n with open(output_file, 'w') as f:\n f.write(gendesc.render(t.template_path, params,\n templates_path))\n else:\n if len(rule.types) > 1:\n raise TextXToolsError('Multiple types/variables are not'\n ' possible for \"non-all\" rules.')\n for obj in type_objs[0]:\n params[rule.var_names[0]] = obj\n context = {'__builtins__': {}}\n context.update(params)\n for t in rule.trans:\n target_path = eval(t.python_path_expr, context)\n output_file = os.path.join(output_root, target_path)\n click.echo(\"Generating {}\".format(output_file))\n _create_folder(output_file)\n\n with open(output_file, 'w') as f:\n f.write(gendesc.render(t.template_path, params,\n templates_path))","title":""},{"docid":"8a1835d042cf66486ed099ce14f71906","score":"0.51036656","text":"def generate_pymods(the_parsed_topology_xml, xml_filename, opt):\n if the_parsed_topology_xml.get_namespace():\n if VERBOSE:\n print(\n \"Generating pymods for topology %s::%s\"\n % (\n the_parsed_topology_xml.get_namespace(),\n the_parsed_topology_xml.get_name(),\n )\n )\n else:\n if VERBOSE:\n print(\n \"Generating pymods for topology %s\"\n % (the_parsed_topology_xml.get_name())\n )\n model = TopoFactory.TopoFactory.getInstance()\n topology_model = model.create(the_parsed_topology_xml)\n\n # create list of used parsed component xmls\n parsed_xml_dict = {}\n for comp in the_parsed_topology_xml.get_instances():\n if comp.get_type() in topology_model.get_base_id_dict():\n parsed_xml_dict[comp.get_type()] = comp.get_comp_xml()\n else:\n if VERBOSE:\n print(\n \"Components with type {} aren't in the topology model.\".format(\n comp.get_type()\n )\n )\n\n #\n # Hack to set up deployment path for instanced dictionaries (if one exists remove old one)\n #\n if opt.dict_dir is None:\n os.environ[\"DICT_DIR\"] = os.getcwd()\n else:\n os.environ[\"DICT_DIR\"] = opt.dict_dir\n\n xml_list = []\n for parsed_xml_type in parsed_xml_dict:\n if parsed_xml_dict[parsed_xml_type] is None:\n print(\n \"ERROR: XML of type {} is being used, but has not been parsed correctly. Check if file exists or add xml file with the 'import_component_type' tag to the Topology file.\".format(\n parsed_xml_type\n )\n )\n raise Exception()\n xml_list.append(parsed_xml_dict[parsed_xml_type])\n temp_comp = parsed_xml_dict[parsed_xml_type].get_component()\n if VERBOSE:\n print(\n \"Generating component dicts for %s::%s\"\n % (temp_comp.get_namespace(), temp_comp.get_name())\n )\n write_pymods_from_comp(parsed_xml_dict[parsed_xml_type], opt, topology_model)\n if VERBOSE:\n print(\n \"Generated component dicts for %s::%s\"\n % (temp_comp.get_namespace(), temp_comp.get_name())\n )\n\n topology_model.set_instance_xml_list(xml_list)\n\n if the_parsed_topology_xml.get_namespace():\n if VERBOSE:\n print(\n \"Generated pymods for topology %s::%s\"\n % (\n the_parsed_topology_xml.get_namespace(),\n the_parsed_topology_xml.get_name(),\n )\n )\n else:\n if VERBOSE:\n print(\n \"Generated pymods for topology %s\"\n % (the_parsed_topology_xml.get_name())\n )","title":""},{"docid":"8b7d04b25bee59ebf17f61215a787437","score":"0.51035786","text":"def main(input_dir: str, output_dir: str) -> None:\n input_dir = Path(input_dir)\n output_dir = Path(output_dir)\n output_dir.mkdir(parents=True, exist_ok=True)\n\n spacy_formatted_data = {\"train\": [], \"test\": []}\n\n for partition in spacy_formatted_data:\n note_dir = input_dir / partition / f\"{partition}_note\"\n if partition == \"train\":\n norm_dir = input_dir / partition / f\"{partition}_norm\"\n else:\n # TODO (John): Is this the name of the directory in the official download?\n norm_dir = input_dir / partition / f\"{partition}_norm_cui_replaced_with_unk\"\n\n for note_filepath in note_dir.iterdir():\n if not note_filepath.name.endswith(\".txt\"):\n continue\n\n norm_filepath = norm_dir / f\"{note_filepath.stem}.norm\"\n\n with open(note_filepath, \"r\") as f:\n spacy_formatted_data[partition].append((f.read(), {\"entities\": []}))\n with open(norm_filepath, \"r\") as f:\n for line in f:\n start, end = tuple(map(int, line.strip().split(\"||\")[-2:]))\n spacy_formatted_data[partition][-1][-1][\"entities\"].append((start, end, ENTITY_LABEL))\n\n with open(output_dir / \"spacy_formatted_data.pickle\", \"wb\") as f:\n pickle.dump(spacy_formatted_data, f)","title":""},{"docid":"df99f942c3c7b8f03f573780597898e3","score":"0.5099381","text":"def pmodel(self):\r\n if not hasattr(self, '_pmodel'):\r\n pmodelname = self.get('pmodel-name')\r\n if pmodelname == 'ISO9613':\r\n # create environment\r\n G = tuple([float(x) for x in self.get('pmodel-iso9613-ground-coeffs').strip('()').split(',')])\r\n p = self.getFloat('pmodel-iso9613-atmospheric-pressure')\r\n t = self.getFloat('pmodel-iso9613-atmospheric-temperature')\r\n r = self.getFloat('pmodel-iso9613-atmospheric-humidity')\r\n self._environment = ISO9613Environment(G = G, p = p, t = t, r = r)\r\n # create propagation model\r\n self._pmodel = ISO9613Model(environment = self._environment)\r\n self._pmodel.correction['geometricDivergence'] = self.getBool('pmodel-iso9613-flag-geometric-divergence')\r\n self._pmodel.correction['atmosphericAbsorption'] = self.getBool('pmodel-iso9613-flag-atmospheric-absorption')\r\n self._pmodel.correction['groundEffect'] = self.getBool('pmodel-iso9613-flag-ground-effect')\r\n self._pmodel.correction['sourceDirectivity'] = self.getBool('pmodel-iso9613-flag-source-directivity')\r\n else:\r\n raise Exception('configuration file: Propagation model \"%s\" not known - use \"ISO9613\"' % pmodelname)\r\n return self._pmodel","title":""},{"docid":"7bb2d23548d39b59677cfcfc9082e026","score":"0.50863206","text":"def main():\n\n parser = argparse.ArgumentParser(description='Duolingo shared task baseline model')\n parser.add_argument('--language', default='en_es', help='choose from [es_en, en_es, fr_en]', required=False)\n parser.add_argument('--dataset_path', default='../data/%s/', required=False)\n parser.add_argument('--outputs_path', default='./outputs/', required=False)\n args = parser.parse_args()\n\n dataset_path = args.dataset_path % args.language\n assert os.path.isdir(dataset_path)\n\n train_path = dataset_path + '%s.slam.20190204.train' % args.language\n dev_path = dataset_path + '%s.slam.20190204.dev' % args.language\n test_path = dataset_path + '%s.slam.20190204.test' % args.language\n assert os.path.isfile(train_path)\n assert os.path.isfile(dev_path)\n assert os.path.isfile(test_path)\n\n if not os.path.isdir(args.outputs_path): os.mkdir(args.outputs_path)\n\n # ============================== Hyper Parameter ==============================\n dbg = False\n from_path = None\n # from_path = './saved_model/seq2seq_nomlp_20'\n # from_path = './saved_model/seq2seq_exp_20'\n # from_path = './saved_model/attention_v2_20'\n # from_path = './saved_model/cnn_3'\n # from_path = './saved_model/seq2seq_c_7'\n # from_path = './saved_model/transformer_1'\n epochs = 10 if dbg else 10\n lang = Lang()\n\n # ============================== Data Loading ==============================\n\n print('Begin Data Loading')\n start_time = time.time()\n training_data, training_labels = load_data(train_path, lang, dbg=dbg, use_all_features=True)\n dev_data = load_data(dev_path, lang, use_all_features=True)\n test_data = load_data(test_path, lang, use_all_features=True)\n users = list(get_users(training_data).union(get_users(dev_data)).union(get_users(test_data)))\n lang.addUsers(users)\n for i in range(len(lang.letters)): lang.letter2Index[lang.letters[i]] = i\n end_time = time.time()\n print('Data Loaded\\t Time Taken %0.2fm' % ((end_time - start_time)/60))\n\n model = Model(lang)\n\n # ============================== Training ==============================\n if from_path == None:\n print('Begin Training')\n train_loader = get_dataloader(training_data, lang, training_labels)\n model.train(train_loader, epochs)\n\n # ============================== Inference ==============================\n # print('Begin Inference-Dev', end=' ')\n # start_time = time.time()\n # dev_loader = get_dataloader(dev_data, lang)\n # predictions = model.predict_for_set(dev_loader, from_path)\n # with open(args.outputs_path + '%s_dev_predictions.pred' % args.language, 'wt') as f:\n # for instance_id, prediction in iteritems(predictions):\n # f.write(instance_id + ' ' + str(prediction) + '\\n')\n # end_time = time.time()\n # print('| %0.2fm' % ((end_time-start_time)/60))\n \n print('Begin Inference-Test', end=' ')\n start_time = time.time()\n test_loader = get_dataloader(test_data, lang)\n predictions = model.predict_for_set(test_loader, from_path)\n with open(args.outputs_path + '%s_test_predictions.pred' % args.language, 'wt') as f:\n for instance_id, prediction in predictions.items():\n f.write(instance_id + ' ' + str(prediction) + '\\n')\n end_time = time.time()\n print('| %0.2fm' % ((end_time-start_time)/60))","title":""},{"docid":"aa7982cf4b80468889131323fa94e9d7","score":"0.50660026","text":"def generate_language_model(**kwargs):\n\n lm = Session.query(model.MorphemeLanguageModel).get(kwargs['morpheme_language_model_id'])\n trie_path = lm.get_file_path('trie')\n trie_mod_time = lm.get_modification_time(trie_path)\n lm.generate_succeeded = False\n try:\n lm.write_corpus()\n except Exception, e:\n lm.generate_message = u'Error writing the corpus file. %s' % e\n try:\n lm.write_vocabulary()\n except Exception, e:\n lm.generate_message = u'Error writing the vocabulary file. %s' % e\n try:\n lm.write_arpa(kwargs['timeout'])\n except Exception, e:\n lm.generate_message = u'Error writing the ARPA file. %s' % e\n try:\n lm.generate_trie()\n except Exception, e:\n lm.generate_message = u'Error generating the LMTrie instance. %s' % e\n else:\n if lm.get_modification_time(trie_path) != trie_mod_time:\n lm.generate_succeeded = True\n lm.generate_message = u'Language model successfully generated.'\n else:\n lm.generate_message = u'Error generating the LMTrie instance.'\n lm.generate_attempt = unicode(uuid4())\n lm.modifier_id = kwargs['user_id']\n lm.datetime_modified = h.now()\n Session.commit()","title":""},{"docid":"2cff1d19062392a49928573882a530bd","score":"0.5064492","text":"def load_model(model_path):\r\n nlp = spacy.blank('en')\r\n if 'ner' not in nlp.pipe_names:\r\n ner = nlp.create_pipe('ner')\r\n nlp.add_pipe(ner)\r\n\r\n ner = nlp.from_disk(model_path)\r\n return ner","title":""},{"docid":"fd82b28979eb3edddc68ff36415c4e0c","score":"0.5058911","text":"def _write_imports(\n cls,\n pickle_type: Optional[str] = None,\n mojo_model: Optional[bool] = False,\n binary_h2o_model: Optional[bool] = False,\n tf_model: Optional[bool] = False,\n binary_string: Optional[str] = None,\n ) -> None:\n pickle_type = pickle_type if pickle_type else \"pickle\"\n cls.score_code += (\n f\"import math\\nimport {pickle_type}\\nimport pandas as pd\\n\"\n \"import numpy as np\\nfrom pathlib import Path\\n\\n\"\n )\n \"\"\"\nimport math\nimport pickle\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\n\n \"\"\"\n\n try:\n if current_session().version_info() != 3.5:\n cls.score_code += \"import settings\\n\\n\"\n \"\"\"\nimport settings\n \n \n \"\"\"\n except AttributeError:\n warn(\n \"No current session connection was found to a SAS Viya server. Score \"\n \"code will be written under the assumption that the target server is \"\n \"SAS Viya 4.\"\n )\n\n if mojo_model or binary_h2o_model:\n cls.score_code += \"import h2o\\n\\nh2o.init()\\n\\n\"\n \"\"\"\nimport h2o\n\nh2o.init()\n\n \"\"\"\n elif tf_model:\n cls.score_code += \"import tensorflow as tf\\n\\n\"\n \"\"\"\nimport tensorflow as tf\n\n \"\"\"\n elif binary_string:\n cls.score_code += (\n f'import codecs\\n\\nbinary_string = \"{binary_string}\"'\n f\"\\nmodel = {pickle_type}.loads(codecs.decode(binary_string\"\n '.encode(), \"base64\"))\\n\\n'\n )\n \"\"\"\nimport codecs\n\nbinary_string = \"<binary string>\"\nmodel = pickle.load(codecs.decode(binary_string.encode(), \"base64\"))\n\n \"\"\"","title":""},{"docid":"2c1108513704f5cbba7b473aa2906d50","score":"0.50567424","text":"def write_pymods_from_comp(the_parsed_component_xml, opt, topology_model):\n global BUILD_ROOT\n global DEPLOYMENT\n global VERBOSE\n\n parsed_port_xml_list = []\n parsed_serializable_xml_list = []\n # uses the topology model to process the items\n # checks if the topology model exists\n if topology_model is None:\n PRINT.info(\n \"Topology model was not specified. Please also input a topology model when running this command.\"\n )\n raise OSError\n\n port_type_files_list = the_parsed_component_xml.get_port_type_files()\n\n for port_file in port_type_files_list:\n port_file = search_for_file(\"Port\", port_file)\n xml_parser_obj = XmlPortsParser.XmlPortsParser(port_file)\n parsed_port_xml_list.append(xml_parser_obj)\n del xml_parser_obj\n\n serializable_type_files_list = (\n the_parsed_component_xml.get_serializable_type_files()\n )\n for serializable_file in serializable_type_files_list:\n serializable_file = search_for_file(\"Serializable\", serializable_file)\n xml_parser_obj = XmlSerializeParser.XmlSerializeParser(\n serializable_file\n ) # Telemetry/Params can only use generated serializable types\n # check to make sure that the serializables don't have things that channels and parameters can't have\n # can't have external non-xml members\n if len(xml_parser_obj.get_include_header_files()):\n print(\n \"ERROR: Component include serializables cannot use user-defined types. file: \"\n % serializable_file\n )\n sys.exit(-1)\n\n parsed_serializable_xml_list.append(xml_parser_obj)\n del xml_parser_obj\n\n model = CompFactory.CompFactory.getInstance()\n component_model = model.create(\n the_parsed_component_xml, parsed_port_xml_list, parsed_serializable_xml_list\n )\n\n instChannelWriter = InstChannelWriter.InstChannelWriter()\n instCommandWriter = InstCommandWriter.InstCommandWriter()\n instEventWriter = InstEventWriter.InstEventWriter()\n\n if opt.dict_dir is None:\n if VERBOSE:\n print(\"Dictionary output directory not specified!, defaulting to cwd\")\n opt.dict_dir = os.getcwd()\n os.environ[\"DICT_DIR\"] = opt.dict_dir\n\n # iterate through command instances\n for command_model in component_model.get_commands():\n if VERBOSE:\n print(\"Generating command dict %s\" % command_model.get_mnemonic())\n instCommandWriter.DictStartWrite(command_model, topology_model)\n instCommandWriter.DictHeaderWrite(command_model, topology_model)\n instCommandWriter.DictBodyWrite(command_model, topology_model)\n\n for parameter_model in component_model.get_parameters():\n if VERBOSE:\n print(\"Generating parameter dict %s\" % parameter_model.get_name())\n instCommandWriter.DictStartWrite(parameter_model, topology_model)\n instCommandWriter.DictHeaderWrite(parameter_model, topology_model)\n instCommandWriter.DictBodyWrite(parameter_model, topology_model)\n\n # iterate through command instances\n for event_model in component_model.get_events():\n if VERBOSE:\n print(\"Generating event dict %s\" % event_model.get_name())\n instEventWriter.DictStartWrite(event_model, topology_model)\n instEventWriter.DictHeaderWrite(event_model, topology_model)\n instEventWriter.DictBodyWrite(event_model, topology_model)\n\n # iterate through command instances\n for channel_model in component_model.get_channels():\n if VERBOSE:\n print(\"Generating channel dict %s\" % channel_model.get_name())\n instChannelWriter.DictStartWrite(channel_model, topology_model)\n instChannelWriter.DictHeaderWrite(channel_model, topology_model)\n instChannelWriter.DictBodyWrite(channel_model, topology_model)","title":""},{"docid":"dbac7e9dbefed924c07d9e4bb763e9cb","score":"0.5051691","text":"def convert(self, path, version, target):\n source = self.comparer.get_representation(path)\n lines = [ '# <fortpy version=\"{}\"></fortpy>\\n'.format(version) ]\n\n for line in self.comparer.template.contents[version].preamble:\n lines.append(line.write(source.preamble, source.version, source.stored) + \"\\n\")\n\n for line in self.comparer.template.contents[version].body:\n for valueset in source.body:\n lines.append(line.write(valueset, source.version, source.stored) + \"\\n\")\n\n with open(os.path.expanduser(target), 'w') as f:\n f.writelines(lines)","title":""},{"docid":"34c2bef9339db6de5f9e061ddef3fb47","score":"0.5050992","text":"def convert_to_onnx(pytorch_model, input_shape, output_file, input_names, output_names):\n\n pytorch_model.eval()\n onnx_input_shape = torch.randn(input_shape)\n torch.onnx.export(pytorch_model, onnx_input_shape, output_file,\n verbose=True, input_names=input_names, output_names=output_names)\n\n # Model check after conversion\n import onnx\n model_from_onnx = onnx.load(output_file)\n try:\n onnx.checker.check_model(model_from_onnx)\n print('ONNX check passed successfully.')\n except onnx.onnx_cpp2py_export.checker.ValidationError as exc:\n sys.exit('ONNX check failed with error: ' + str(exc))","title":""},{"docid":"5f4a2a2fd203b35f20cc76a1f1693031","score":"0.5048336","text":"def _ppo(self, m):\n\n m[\"ppo1\"] = m[\"MemoryOp <: po_loc :> Write\"]\n m[\"ppo2\"] = m[\"rdw\"]\n m[\"ppo3\"] = m[\"(AMO + StoreConditional) <: (rf & po)\"]\n m[\"ppo4rr\"] = m[\"(Read <: po :> (FencePR & FenceSR)).(po :> Read)\"]\n m[\"ppo4rw\"] = m[\"(Read <: po :> (FencePR & FenceSW)).(po :> Write)\"]\n m[\"ppo4wr\"] = m[\"(Write <: po :> (FencePW & FenceSR)).(po :> Read)\"]\n m[\"ppo4ww\"] = m[\"(Write <: po :> (FencePW & FenceSW)).(po :> Write)\"]\n m[\"ppo4tso1\"] = m[\"(Read <: po :> (FenceTSO)).(po :> MemoryOp)\"]\n m[\"ppo4tso2\"] = m[\"(MemoryOp <: po :> (FenceTSO)).(po :> Write)\"]\n m[\"ppo4\"] = m[\"ppo4rr + ppo4rw + ppo4wr + ppo4ww + ppo4tso1 + ppo4tso2\"]\n m[\"ppo5\"] = m[\"Aq <: po :> MemoryOp\"]\n m[\"ppo6\"] = m[\"MemoryOp <: po :> Rl\"]\n m[\"ppo7\"] = m[\"RCsc <: po :> RCsc\"]\n m[\"ppo8\"] = m[\"pair\"]\n m[\"ppo9\"] = m[\"addrdep\"]\n m[\"ppo10\"] = m[\"datadep\"]\n m[\"ppo11\"] = m[\"ctrldep.po :> Write\"]\n m[\"ppo12\"] = m[\"(addrdep + datadep).(rf & po)\"]\n m[\"ppo13\"] = m[\"addrdep.po :> Write\"]\n\n return m[\n \"ppo1 + ppo2 + ppo3 + ppo4 + ppo5 + ppo6 + ppo7 + \"\n \"ppo8 + ppo9 + ppo10 + ppo11 + ppo12 + ppo13\"\n ]","title":""},{"docid":"7ac89b0a71c9943d58dd2e15032afde8","score":"0.504813","text":"def makeFile(m, model):\n tempfile = ['#include \"lime.h\"\\n', '#include \"math.h\"\\n',\n '#include \"stdio.h\"\\n', '#include \"stdlib.h\"\\n',\n '#include \"%s\"\\n\\n' % model.header.fn]\n writeModelProperties(tempfile, model)\n writeWeighting(tempfile, model)\n writeImageParameters(tempfile, m, model)\n writeInterpolationFuncs(tempfile, model)\n writeDensity(tempfile, model)\n writeTemperatures(tempfile, model)\n writeAbundance(tempfile, model)\n if model.dust is not None:\n writeGastoDust(tempfile, model)\n writeDoppler(tempfile, model)\n writeVelocityStructure(tempfile, model)\n with open('model_%d.c' % m, 'w') as tosave:\n for line in tempfile:\n tosave.write('%s' % line)\n return","title":""},{"docid":"06ef0a20cae196e2de5c67f6c87df504","score":"0.5047686","text":"def _save_prototxt(self):\n solver_path = osp.dirname(self._solver_prototxt)\n if not osp.exists(solver_path):\n os.makedirs(solver_path)\n with open(self._solver_prototxt, 'w') as f:\n f.write(text_format.MessageToString(self._solver))","title":""},{"docid":"9127df10abf73188e578bcc4dcd4d09c","score":"0.50475544","text":"def owl_to_model(fname):\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\n\n try:\n file_is = autoclass('java.io.FileInputStream')(fname)\n except JavaException:\n logger.error('Could not open data file %s' % fname)\n return\n try:\n biopax_model = io.convertFromOWL(file_is)\n except JavaException as e:\n logger.error('Could not convert data file %s to BioPax model' % fname)\n logger.error(e)\n return\n\n file_is.close()\n\n return biopax_model","title":""},{"docid":"b084295606442608e4e95a1201da837c","score":"0.5046919","text":"def export_model(self, out=None, **kwargs):\n # Remove all code transformations but respect those provided explicitly\n kwargs.setdefault('length_for_alias', None)\n kwargs.setdefault('name_all_constraints', False)\n\n CpoCompiler(self, **kwargs).write(out)","title":""},{"docid":"380e97eb5048bd83b762af621acf5fd7","score":"0.5038669","text":"def main(model=None, output_dir=None, n_iter=100):\n if model is not None:\n nlp = spacy.load(model) # load existing spaCy model\n print(\"Loaded model '%s'\" % model)\n else:\n nlp = spacy.blank(\"en\") # create blank Language class\n print(\"Created blank 'en' model\")\n \n # create the built-in pipeline components and add them to the pipeline\n # nlp.create_pipe works for built-ins that are registered with spaCy\n if \"ner\" not in nlp.pipe_names:\n ner = nlp.create_pipe(\"ner\")\n nlp.add_pipe(ner, last=True)\n # otherwise, get it so we can add labels\n else:\n ner = nlp.get_pipe(\"ner\")\n \n # add labels\n for _, annotations in TRAIN_DATA:\n for ent in annotations.get(\"entities\"):\n ner.add_label(ent[2])\n\n # get names of other pipes to disable them during training\n other_pipes = [pipe for pipe in nlp.pipe_names if pipe != \"ner\"]\n with nlp.disable_pipes(*other_pipes): # only train NER\n # reset and initialize the weights randomly – but only if we're\n # training a new model\n if model is None:\n nlp.begin_training()\n for itn in range(n_iter):\n random.shuffle(TRAIN_DATA)\n losses = {}\n # batch up the examples using spaCy's minibatch\n batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))\n for batch in batches:\n texts, annotations = zip(*batch)\n nlp.update(\n texts, # batch of texts\n annotations, # batch of annotations\n drop=0.5, # dropout - make it harder to memorise data\n losses=losses,\n )\n #print(\"Losses\", losses)\n\n # test the trained model\n for text in TEST_DATA:\n doc = nlp(text)\n print(\"Entities\", [(ent.text, ent.label_) for ent in doc.ents])\n print(\"Tokens\", [(t.text, t.ent_type_, t.ent_iob) for t in doc])\n \n # save model to output directory\n if output_dir is not None:\n output_dir = Path(output_dir)\n if not output_dir.exists():\n output_dir.mkdir()\n nlp.to_disk(output_dir)\n print(\"Saved model to\", output_dir)\n \n # test the saved model\n print(\"Loading from\", output_dir)\n nlp2 = spacy.load(output_dir)\n for text, _ in TRAIN_DATA:\n doc = nlp2(text)\n print(\"Entities\", [(ent.text, ent.label_) for ent in doc.ents])","title":""},{"docid":"4dfa7950183441b0441fb0c52fd47749","score":"0.5037608","text":"def convert2or():\n # parse converter arguments\n args = get_args_convert2or()\n\n # input data parameters\n try:\n workspace_dir, exec_file_name, data_uri, data_path, output_path = get_params()\n entry_filename = os.path.splitext(os.path.basename(exec_file_name))[0]\n except Exception as e:\n print('Invalid arguments, {}'.format(e))\n sys.exit(1)\n\n else:\n # Generate requirements.txt\n try:\n import pipreqs\n except ImportError as e:\n raise RuntimeError('Package installation failed: {}'.format(e))\n\n else:\n time.sleep(1)\n try:\n p = subprocess.Popen([\"pipreqs\", \"--force\", workspace_dir])\n p.wait()\n time.sleep(1)\n\n # fix the bug raising from 'tensorflow', 'tensorflow_gpu'\n filename = os.path.join(workspace_dir, \"requirements.txt\")\n\n rw_file(filename, matplotlib=\"matplotlib\", tensorflow_gpu=\"\", tensorflow=\"tensorflow-gpu\")\n remove_empty_lines(filename)\n print(\"Generated 'requirements.txt' successfully!\")\n\n except Exception as e:\n raise RuntimeError(\"Generating 'requirements.txt' failed: {}\".format(e))\n\n else:\n # Generate params.json\n try:\n exec_file_name_v = os.path.relpath(exec_file_name, start=workspace_dir)\n data_path_v = \"\" if data_path == \"\" else os.path.relpath(data_path, start=workspace_dir)\n output_path_v = \"\" if output_path == \"\" else os.path.relpath(output_path, start=workspace_dir)\n params_json = json.dumps({\"exec_file_name\": exec_file_name_v,\n \"data_uri\": data_uri,\n \"data_path\": data_path_v,\n \"output_path\": output_path_v,\n })\n with open(os.path.join(workspace_dir, \"params.json\"), 'w+') as f:\n f.write(params_json)\n print(\"Generated 'params.json' successfully!\")\n\n except Exception as e:\n raise IOError(\"Generating 'params.json' failed: {}\".format(e))\n\n else:\n time.sleep(2)\n try:\n zip_folder_path = os.path.join(workspace_dir, os.pardir, \"task_files\")\n\n if not os.path.exists(zip_folder_path):\n os.makedirs(zip_folder_path)\n\n output_filename = str(entry_filename) + \"_orion.zip\"\n zip_folder(workspace_dir, os.path.join(zip_folder_path, output_filename))\n print('Zipped files successfully!')\n except Exception as e:\n raise RuntimeError('Zipping files failed: {}'.format(e))\n else:\n try:\n os.remove(os.path.join(workspace_dir, \"params.json\"))\n os.remove(os.path.join(workspace_dir, \"requirements.txt\"))\n except Exception as e:\n raise RuntimeError('Removing files failed: {}'.format(e))","title":""},{"docid":"78aca13d4302fac740b8dddb5e1eb7e6","score":"0.5032639","text":"def descout_to_input( # noqa: C901 - fxn too complex\n outfile,\n infile,\n objective=\"force\",\n optimizer=\"lsq-exact\",\n header=\"#DESC-generated input file\",\n ftol=1e-2,\n xtol=1e-6,\n gtol=1e-6,\n maxiter=100,\n ):\n f = open(outfile, \"w+\")\n\n f.seek(0)\n\n eq = load(infile)\n try:\n eq0 = eq[-1]\n except TypeError:\n eq0 = eq\n\n f.write(header + \"\\n\")\n\n f.write(\"# global parameters\\n\")\n f.write(\"sym = {:1d} \\n\".format(eq0.sym))\n f.write(\"NFP = {:3d} \\n\".format(int(eq0.NFP)))\n f.write(\"Psi = {:.8f} \\n\".format(eq0.Psi))\n\n f.write(\"\\n# spectral resolution\\n\")\n for key, val in {\n \"L_rad\": \"L\",\n \"M_pol\": \"M\",\n \"N_tor\": \"N\",\n \"L_grid\": \"L_grid\",\n \"M_grid\": \"M_grid\",\n \"N_grid\": \"N_grid\",\n }.items():\n f.write(f\"{key} = {getattr(eq0, val)}\\n\")\n\n f.write(\"\\n\\n# solver tolerances\\n\")\n f.write(f\"ftol = {ftol}\\n\")\n f.write(f\"xtol = {xtol}\\n\")\n f.write(f\"gtol = {gtol}\\n\")\n f.write(f\"maxiter = {maxiter}\\n\")\n\n f.write(\"\\n\\n# solver methods\\n\")\n f.write(f\"optimizer = {optimizer}\\n\")\n f.write(f\"objective = {objective}\\n\")\n f.write(\"spectral_indexing = {}\\n\".format(eq0._spectral_indexing))\n f.write(\"node_pattern = {}\\n\".format(eq0._node_pattern))\n\n f.write(\"\\n# pressure and rotational transform/current profiles\\n\")\n\n if eq0.iota:\n assert (\n eq0.pressure.__class__.__name__ == \"PowerSeriesProfile\"\n and eq0.iota.__class__.__name__ == \"PowerSeriesProfile\"\n ), \"Equilibrium must have power series profiles for ascii io\"\n char = \"i\"\n iseven_pres = int(eq0._pressure.basis.sym == \"even\") + 1\n iseven_iota = int(eq0._iota.basis.sym == \"even\") + 1\n pres_profile = np.zeros((eq0.L + 1,))\n iota_profile = np.zeros((eq0.L + 1,))\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\n iota_profile[: eq0.L + 1 : iseven_iota] = eq0._iota.params\n\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\n for l in idxs:\n f.write(\n \"l: {:3d}\\tp = {:16.8E}\\t{} = {:16.8E}\\n\".format(\n int(l), pres_profile[l], char, iota_profile[l]\n )\n )\n else:\n assert (\n eq0.pressure.__class__.__name__ == \"PowerSeriesProfile\"\n and eq0.current.__class__.__name__ == \"PowerSeriesProfile\"\n ), \"Equilibrium must have power series profiles for ascii io\"\n char = \"c\"\n iseven_pres = int(eq0._pressure.basis.sym == \"even\") + 1\n iseven_curr = int(eq0._current.basis.sym == \"even\") + 1\n pres_profile = np.zeros((eq0.L + 1,))\n curr_profile = np.zeros((eq0.L + 1,))\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\n curr_profile[: eq0.L + 1 : iseven_curr] = eq0._current.params\n\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\n for l in idxs:\n f.write(\n \"l: {:3d}\\tp = {:16.8E}\\t{} = {:16.8E}\\n\".format(\n int(l), pres_profile[l], char, curr_profile[l]\n )\n )\n\n f.write(\"\\n\")\n\n f.write(\"\\n# fixed-boundary surface shape\\n\")\n # boundary paramters\n if eq0.sym:\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\n if abs(eq0.Rb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, eq0.Rb_lmn[k], 0\n )\n )\n for k, (l, m, n) in enumerate(eq0.surface.Z_basis.modes):\n if abs(eq0.Zb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, 0, eq0.Zb_lmn[k]\n )\n )\n else:\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\n if abs(eq0.Rb_lmn[k]) > 1e-8 or abs(eq0.Zb_lmn[k]) > 1e-8:\n f.write(\n \"l: {:3d}\\tm: {:3d}\\tn: {:3d}\\tR1 = {:16.8E}\\t\\\n Z1 = {:16.8E}\\n\".format(\n int(0), m, n, eq0.Rb_lmn[k], eq0.Zb_lmn[k]\n )\n )\n\n f.close()","title":""},{"docid":"aac8d9a34c982c21d64ee69aa9b8aada","score":"0.50321543","text":"def models(self):\r\n # make model directory, removing the old one if necessary\r\n rm_rf(self.cfg.model_dir)\r\n mkdir_p(self.cfg.model_dir)\r\n\r\n # run generator script\r\n gen_script = os.path.join(self._prj_root, 'gen.py')\r\n\r\n if 'PYTHON_MSDSL' in os.environ:\r\n python_name = os.environ['PYTHON_MSDSL']\r\n else:\r\n python_name = which('python')\r\n \r\n call([python_name, gen_script, '-o', self.cfg.model_dir, '--dt', str(self.dt)])","title":""},{"docid":"37e59d1ec3cd22e83b1a98ae5bb15330","score":"0.502148","text":"def write_model_to_file(self) -> str:\n\n model_file_content = self.interpreter.get_model_file_content()\n\n db_logger.info(f\"Writing {self.model_file_url}\")\n\n with open(self.model_file_location, 'w') as model:\n model.write(model_file_content)\n\n file_name = os.path.basename(self.model_file_location)\n project_dir = self.model_file_location.replace(file_name, '')\n\n helpers.process_includes(\n self.model_file_url, project_dir, self.interpreter\n )\n\n if not self.no_score:\n self.model_file_location = self.setup_protocol(self.score)\n\n return self.model_file_location","title":""},{"docid":"5a4533cb8662178a596b89ed1d73420b","score":"0.50117064","text":"def test_ImportUFONoSideEffectLO(self): \n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('sm'),False)\n original_all_particles = copy.copy(ufo_model.all_particles)\n original_all_vertices = copy.copy(ufo_model.all_vertices)\n original_all_couplings = copy.copy(ufo_model.all_couplings)\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\n original_all_parameters = copy.copy(ufo_model.all_parameters)\n original_all_orders = copy.copy(ufo_model.all_orders)\n original_all_functions = copy.copy(ufo_model.all_functions)\n\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\n model = ufo2mg5_converter.load_model()\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \n\n self.assertEqual(original_all_particles,ufo_model.all_particles) \n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\n self.assertEqual(original_all_couplings,ufo_model.all_couplings)\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\n self.assertEqual(original_all_orders,ufo_model.all_orders)\n self.assertEqual(original_all_functions,ufo_model.all_functions)","title":""},{"docid":"7e8c2e02b0ee94ddd321ca2db4033cd0","score":"0.50004613","text":"def exe_dyn_mdl(fv, usr):\n\n exe = os.chdir(prg_path + '/dynamic_mdl-0.1') \n exe = os.getcwd() \n pcs = sbp.Popen([exe + '/a.out', str(fv)], stdout=sbp.PIPE, stderr=sbp.PIPE)\n out, error = pcs.communicate()\n if out:\n print('OK', out)\n if error:\n print('Error', error.strip()) \n if not pcs.poll():\n print(\"Dynamic Module execute finish\")\n p_nme = 'Dynamic'\n dest_pth = fles_dir + usr + '/' + p_nme\n dest_pth = dest_pth + '/' + time.get_date_time_hm()\n dummy_sys_file.crt_dir(dest_pth)\n \"\"\"Creating directory for program execution\"\"\"\n\n \"\"\"Names of the generated files\"\"\"\n fle_name1 = 'sk'\n fle_name2 = 'f_self'\n fle_name3 = 'coefficient'\n \"\"\"full path of the outputs of program\"\"\"\n out_fle1 = exe + '/' + 'sk.dat'\n out_fle2 = exe + '/' + 'fself.dat'\n out_fle3 = exe + '/' + 'coeficiente.dat' \n dummy_sys_file.cpy_file_to_pth_dir(out_fle1, dest_pth, fle_name1)\n dummy_sys_file.cpy_file_to_pth_dir(out_fle2, dest_pth, fle_name2)\n dummy_sys_file.cpy_file_to_pth_dir(out_fle3, dest_pth, fle_name3)","title":""},{"docid":"10bf29315de0dab52975584c75ef2dfb","score":"0.49911758","text":"def generate_package(self):\n\n # Create a directory (mymodel)\n cwd = Path(self.dir)\n directory=cwd/'csharp_model'\n if (directory).isdir() :\n self.dir = directory\n else:\n self.dir = directory.mkdir()\n\n files = []\n count = 0\n\n for model in self.models:\n\n\n self.generate_component(model)\n\n ext = '' if count == 0 else str(count)\n filename = self.dir/\"%s.cs\"%signature(model)\n\n with open(filename, \"w\") as python_file:\n python_file.write(self.code.encode('utf-8','ignore'))\n files.append(filename)\n\n model.module_name = str(Path(filename).namebase)\n\n count += 1\n\n return files","title":""},{"docid":"22f27813e63fc901b230aaee14b21dfe","score":"0.49885112","text":"def new_IDP_gen(subj,LUT_txt): #,fix4melviewtxt\n\n\n #remove trailing forward slashes in subject paths\n if subj.endswith(\"/\"):\n subj = subj[:-1]\n\n if not os.path.exists(subj + \"/IDP_files/\"):\n os.makedirs(subj + \"/IDP_files/\")\n\n subjName = subj[subj.rfind(\"/\") + 1 :]\n\n\n # IDP_FC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_FC_dist.txt\")\n # IDP_SC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_SC_dist.txt\")\n # IDP_MELODIC_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_MELODIC_SNR.txt\")\n # IDP_MCFLIRT_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_MCFLIRT_disp.txt\")\n # IDP_homotopic_file = os.path.join(subj + \"/IDP_files/\", \"tvb_IDP_homotopic.txt\")\n # new_IDP_list_file = os.path.join(subj + \"/IDP_files/\", \"tvb_new_IDPs.txt\")\n\n new_IDP_file = os.path.join(subj + \"/IDP_files/\", \"tvb_new_IDP.txt\")\n\n\n # IDP_output_files = [IDP_FC_file,IDP_SC_file,IDP_MELODIC_file,IDP_MCFLIRT_file,IDP_homotopic_file]\n\n # for file in IDP_output_files:\n # with open(file, 'w') as fp:\n # pass\n\n with open(new_IDP_file, 'w') as fp:\n line = '\\t'.join([\"short\",\"num\",\"category\",\"num_in_cat\",\"long\",\"unit\",\"dtype\",\"description\",\"value\"])\n\n fp.write(line)\n\n\n\n fix4melviewtxt=\"\"\n\n FC_distribution(subj)\n SC_distribution(subj)\n MELODIC_SNR(subj,fix4melviewtxt)\n MCFLIRT_displacement(subj) \n\n homotopic(subj,LUT_txt)\n \n\n\n\n\n #TODO TL/distance, or ts.txt","title":""},{"docid":"cde366ecb10bf14e7b028bb5eeb70a26","score":"0.497974","text":"def produce_layout(eoi, graph):\n\n subprocess.run((\"/usr/bin/dot\", \"{}\".format(graph.filename), \"-o\", \n \"{}/layout.dot\".format(eoi)))","title":""},{"docid":"42db4bc248db8077aaa00be0cc702c70","score":"0.4975401","text":"def load_model_output(text):\r\n # uncomment when working in linux and remove subsequent two lines\r\n # nlp = spacy.load(\"../models/quick-spacy/\")\r\n model_path = Path(__file__).parent.absolute() / \"models/quick-spacy/\"\r\n nlp = spacy.load(model_path)\r\n doc = nlp(text)\r\n sentence = [ent.text for ent in doc.ents]\r\n labels = [ent.label_ for ent in doc.ents]\r\n return sentence, labels","title":""},{"docid":"d71b59de5a8a54084e51dfb95444b719","score":"0.49555877","text":"def generate_theory(\n grammar,\n config,\n theory_op_file,\n theorem_prover,\n max_depth,\n min_depth\n):\n # Get Theorem Prover Config and initialize Theorem Prover\n theorem_prover_config = TheoremProverConfig(\n grammar, **config[\"theory\"][\"theorem_prover\"]\n )\n\n num_examples = config[\"theory\"][\"num_examples\"]\n min_num_positive_examples = config[\"theory\"][\"min_num_positive_examples\"]\n max_num_negative_examples = num_examples - min_num_positive_examples\n statement_types = config[\"theory\"][\"statement_types_per_example\"]\n assertion_start_symbol = config[\"assertion\"][\"start_symbol\"]\n example_id_prefix = config.get(\"example_id_prefix\", \"\")\n\n # Generate examples for every required type of statement (Start Symbol type)\n num_true_labels = 0\n num_false_labels = 0\n curr_num_examples = 0\n progress_tracker = tqdm(total=num_examples)\n progress_tracker.set_description(desc=\"Generating Examples...\")\n while (\n curr_num_examples < num_examples and num_true_labels < min_num_positive_examples\n ):\n example = generate_random_example(\n curr_num_examples + 1,\n example_id_prefix,\n grammar,\n theorem_prover_config,\n statement_types,\n assertion_start_symbol,\n theorem_prover,\n max_depth,\n min_depth\n )\n if example is not None:\n if example.theory_assertion_instance.label:\n num_true_labels += 1\n else:\n if num_false_labels == max_num_negative_examples:\n continue\n else:\n num_false_labels += 1\n json.dump(example.to_json(), theory_op_file)\n theory_op_file.write(\"\\n\")\n curr_num_examples += 1\n progress_tracker.update()\n progress_tracker.close()\n print(f\"Generated {curr_num_examples} examples.\")\n print(f\" No. with True label: {num_true_labels}\")\n print(f\" No. with False label: {num_false_labels}\")","title":""},{"docid":"6abcb45148b5a15227491491ca106d5b","score":"0.49505714","text":"def polish_and_save(model_filename,\n save_filename='',\n suffix='.polished',\n *args,\n **kwargs):\n\n save_filename = save_filename or model_filename.replace(\n '.onnx', suffix + '.onnx')\n\n model = onnx.load(model_filename)\n model = polish_model(model, *args, **kwargs)\n onnx.save(model, save_filename)\n logger.info('polished model saved to: %s', save_filename)\n return save_filename","title":""},{"docid":"c70ae932b14d6c0ac6415942b0994419","score":"0.49496683","text":"def output_perplexity(task_letter):\n output_file = open(\"output/group02.perplexity\" + task_letter, mode=\"w\")\n word_dictionary = get_word_dict()\n test_sentences = open(\"data/sentences.eval\", mode=\"r\")\n\n for test_sentence in test_sentences.readlines():\n # predicted_softmax_vecs: 30 X 20.000 X 1 Output Vector with softmax probabilities\n predicted_softmax_vecs = eval_neural_network(test_sentence)\n perp = perplexity(predicted_softmax_vecs, test_sentence, word_dictionary)\n output_file.write(str(perp) + \"\\n\")\n\n output_file.close()","title":""},{"docid":"a208cbaa76b296741b5c20e88440925f","score":"0.49453568","text":"def __init__(self,\n fileName,\n realFileName=None,\n prequelFileName=None,\n preErrorMessages=(), #TODO: check the type\n readFileLater=False,\n fillImportBoxLater=False,\n parseFileLater=False,\n noSymbolChecking=False,\n allowedFeatures=(),\n recognizeUSEOCLNativeModelDefinition=False):\n #type: (Text, Optional[Text], Optional[Text], List[Any], bool, bool, bool, bool, List[Text], bool) -> None\n\n\n # if readFileLater or fillImportBoxLater or parseFileLater:\n # assert finalizeLater\n\n # Create an empty model\n # Not to be moved after super\n # This should be done in all case so that\n # the model attribute always exist even if there\n # are some error in reading the file\n self.model = self.emptyModel() # type: Model\n\n\n\n\n # Call the super class, read the file or not\n try:\n # This can raise an exception for instance if\n # there is a problem reading the file\n super(ModelSourceFile, self).__init__(\n fileName=fileName,\n realFileName=realFileName,\n prequelFileName=prequelFileName,\n preErrorMessages=preErrorMessages,\n doNotReadFiles=readFileLater,\n allowedFeatures=allowedFeatures\n )\n except FatalError:\n pass # an error as already been registered\n\n from modelscripts.megamodels import Megamodel\n Megamodel.registerSource(self)\n Megamodel.registerModel(self.model)\n\n\n # Backward link\n self.model.source=self\n\n # Link issue box\n self.model._issueBox.addParent(self._issueBox)\n\n # Source to ModelElement Mapping\n self._modelMapping=_ModelSourceMapping()\n\n\n # Create first an empty ImportBox.\n self.importBox=ImportBox(self)\n\n # Then fill it by reading megamodel statements,\n # unless specified.\n try:\n if not fillImportBoxLater:\n parseToFillImportBox(\n self,\n noSymbolChecking,\n recognizeUSEOCLNativeModelDefinition)\n\n if not parseFileLater:\n self.parseToFillModel()\n self.finalize()\n\n except FatalError:\n pass # nothing to do, the issue has been registered","title":""},{"docid":"2e5aa18489143959b9fdd129d63b51d0","score":"0.49399826","text":"def preflight(self):\n self.params[\"translations\"][\"mgf_input_file\"] = os.path.join(\n self.params[\"input_dir_path\"], self.params[\"input_file\"]\n )\n self.params[\"translations\"][\"output_file_incl_path\"] = os.path.join(\n self.params[\"output_dir_path\"], self.params[\"output_file\"]\n )\n self.param_file_name = os.path.join(\n self.params[\"translations\"][\"output_file_incl_path\"].strip(\".csv\")\n + \"pipi_params.def\"\n )\n self.created_tmp_files.append(self.param_file_name)\n\n # pprint.pprint(self.params['translations']['_grouped_by_translated_key'])\n # pprint.pprint(self.params)\n # sys.exit(1)\n self.params_to_write = {\n \"version\": self.META_INFO[\"version\"],\n \"output_percolator_input\": 1,\n \"mod10\": \"0.0@X?\",\n \"pepNterm\": 0.0,\n \"pepCterm\": 0.0,\n \"proNterm\": 0.0,\n \"proCterm\": 0.0,\n }\n symbols = [\"~\", \"!\", \"%\", \"^\", \"&\", \"*\", \"+\", \"<\", \">\"]\n for x, element in enumerate(symbols):\n self.params_to_write[\"mod0{0}\".format(x + 1)] = \"0.0@X{0}\".format(element)\n for aa in \"ACDEFGHIKLMNOPQRSTUVWYnc\":\n self.params_to_write[aa] = 0\n\n write_exclusion_list = [\n # 'label',\n \"-Xmx\",\n \"frag_mass_tolerance_unit\",\n \"base_mz\",\n # 'header_translations',\n # 'validation_score_field'\n ]\n\n # additional_15N_modifications = []\n # if self.params['translations']['label'] == '15N':\n # for aminoacid, N15_Diff in ursgal.ukb.DICT_15N_DIFF.items():\n # existing = False\n # for mod_dict in self.params['mods']['fix']:\n # if aminoacid == mod_dict['aa']:\n # mod_dict['mass'] += N15_Diff\n # mod_dict['name'] += '_15N_{0}'.format(aminoacid)\n # existing = True\n # if existing == True:\n # continue\n # else:\n # self.params['mods']['fix'].append(\n # {\n # 'aa': aminoacid,\n # 'mass': N15_Diff,\n # 'name': '_15N_{0}'.format(aminoacid),\n # 'pos': 'any',\n # }\n # )\n\n if self.params[\"translations\"][\"frag_mass_tolerance_unit\"] == \"ppm\":\n self.params[\"translations\"][\"_grouped_by_translated_key\"][\n \"ms2_tolerance\"\n ] = {\n \"frag_mass_tolerance\": ursgal.ucore.convert_ppm_to_dalton(\n self.params[\"translations\"][\"frag_mass_tolerance\"],\n base_mz=self.params[\"base_mz\"],\n )\n }\n\n for pipi_param_name in self.params[\"translations\"][\n \"_grouped_by_translated_key\"\n ].keys():\n for ursgal_param_name, param_value in self.params[\"translations\"][\n \"_grouped_by_translated_key\"\n ][pipi_param_name].items():\n if pipi_param_name in write_exclusion_list:\n continue\n elif pipi_param_name == \"frag_clear_mz_range\":\n min_mz, max_mz = param_value\n self.params_to_write[\"min_clear_mz\"] = min_mz\n self.params_to_write[\"max_clear_mz\"] = max_mz\n elif type(pipi_param_name) is tuple:\n for pn in pipi_param_name:\n self.params_to_write[pn] = param_value\n elif pipi_param_name == \"enzyme\":\n enz, site, aa, inh = param_value.split(\";\")\n self.params_to_write[\"enzyme\"] = \"{0} {1} {2} {3}\".format(\n enz, site, aa, inh\n )\n elif pipi_param_name == \"modifications\":\n \"\"\"\n # specify additional mass and amino acid. DO NOT change the last character.\n # maximum number is 10\n # empty entries must start with 0.0\n mod01 = 79.966331@S~ # Phosphorylation\n mod02 = 79.966331@T! # Phosphorylation\n mod03 = 79.966331@Y% # Phosphorylation\n mod04 = 42.010565@K^ # Acetylation\n mod05 = 15.994915@M& # Oxidation\n mod06 = 14.015650@K* # Methylation\n mod07 = 0.0@X+\n mod08 = 0.0@X<\n mod09 = 0.0@X>\n mod10 = 0.0@X?\n \"\"\"\n n = 0\n for mod_dict in self.params[\"mods\"][\"opt\"]:\n \"\"\"\n {'_id': 0,\n 'aa': '*',\n 'composition': {'C': 2, 'H': 2, 'O': 1},\n 'id': '1',\n 'mass': 42.010565,\n 'name': 'Acetyl',\n 'org': '*,opt,Prot-N-term,Acetyl',\n 'pos': 'Prot-N-term',\n 'unimod': True},\n \"\"\"\n if mod_dict[\"pos\"] == \"Prot-N-term\":\n self.params_to_write[\"proNterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"Prot-C-term\":\n self.params_to_write[\"proCterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"N-term\":\n self.params_to_write[\"pepNterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"C-term\":\n self.params_to_write[\"pepCterm\"] = mod_dict[\"mass\"]\n continue\n elif mod_dict[\"pos\"] == \"any\":\n pass\n else:\n print(\n \"\"\"\n Unknown positional argument for given modification:\n {0}\n PIPI cannot deal with this, please use one of the follwing:\n any, Prot-N-term, Prot-C-term, N-term, C-term\n \"\"\".format(\n mod_dict[\"org\"]\n )\n )\n sys.exit(1)\n n += 1\n assert (\n n <= 10\n ), \"\"\"\n A maximum of 10 optional modifications is allowed in PIPI.\n You specified more than 10.\n \"\"\"\n if n < 10:\n mod_n = \"mod0{0}\".format(n)\n elif n == 10:\n mod_n = \"mod{0}\".format(n)\n else:\n print(\n \"\"\"\n A maximum of 10 optional modifications is allowed in PIPI.\n You specified more than 10.\n \"\"\"\n )\n sys.exit(1)\n self.params_to_write[mod_n] = \"{0}@{1}{2}\".format(\n mod_dict[\"mass\"], mod_dict[\"aa\"], symbols[n - 1]\n )\n\n for mod_dict in self.params[\"mods\"][\"fix\"]:\n if \"N-term\" in mod_dict[\"pos\"]:\n self.params_to_write[\"n\"] = mod_dict[\"mass\"]\n elif \"C-term\" in mod_dict[\"pos\"]:\n self.params_to_write[\"c\"] = mod_dict[\"mass\"]\n else:\n self.params_to_write[mod_dict[\"aa\"]] = mod_dict[\"mass\"]\n else:\n self.params_to_write[pipi_param_name] = param_value\n self.write_params_file()\n\n self.params[\"command_list\"] = [\n \"java\",\n \"-Xmx{0}\".format(\n self.params[\"translations\"][\"_grouped_by_translated_key\"][\"-Xmx\"][\n \"-xmx\"\n ]\n ),\n \"-jar\",\n self.exe,\n self.param_file_name,\n self.params[\"translations\"][\"mgf_input_file\"],\n ]\n\n return self.params","title":""},{"docid":"45b7bcbb2c25384bb8bd0c4ba1275413","score":"0.49382108","text":"def gen_pov(ldraw_path, pov_path):\n model, parts = get_model(ldraw_path)\n\n with open(pov_path, \"w\") as pov_file:\n pov_file.write(POV_HEADER + \"\\n\")\n writer = POVRayWriter(parts, pov_file)\n writer.write(model)\n pov_file.write(POV_TRAILER + \"\\n\")","title":""},{"docid":"b0f8306823fe036ca17dd5e9585dab03","score":"0.49359792","text":"def django_run(target, opt=\"XTAL\"):\r\n # Set up the OpenBaebel conversion modules\r\n sdconv = OBConversion()\r\n ligref = OBMol()\r\n # Define the residues and the proteisn to analyse\r\n if os.path.isfile(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')):\r\n \tres_d = [trans_res(x) for x in ast.literal_eval(open(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')).read())[target.title].split()]\r\n print res_d\r\n# Molecules\r\n\t # Now read in the ligand\r\n plif_method = PlifMethod()\r\n plif_method.text= \"PYPLIF\"\r\n feature_list = [\"POLAR\",\"FACE\",\"EDGE\",\"ACCEPTOR\",\"DONOR\",\"NEGATIVE\",\"POSITIVE\"]\r\n try:\r\n plif_method.validate_unique()\r\n plif_method.save()\r\n except ValidationError:\r\n \t plif_method = PlifMethod.objects.get(text=\"PYPLIF\")\r\n out_d = {}\r\n counter = 0\r\n# Create a file for the protein\r\n t = tempfile.NamedTemporaryFile(suffix=\".pdb\",delete=False)\r\n my_prot = Protein.objects.get(code=target.title+\"TEMP\")\r\n t.write(my_prot.pdb_info.name)\r\n t.close()\r\n protref = read_prot(t.name, res_d)\r\n t = tempfile.NamedTemporaryFile(suffix=\".sdf\",delete=False)\r\n t.close()\r\n sdconv.SetInFormat(\"sdf\")\r\n if opt == \"XTAL\":\r\n mols = Molecule.objects.exclude(prot_id__code__contains=target.title).filter(prot_id__target_id=target)\r\n elif opt == \"LLOOMMPPAA\":\r\n \t mols = []\r\n \t sps = SynthPoint.objects.filter(target_id=target)\r\n \t for s in sps:\r\n \t \t mols.extend([m for m in s.mol_id.all()])\r\n else:\r\n \t print \"UNKNOWN OPTION\"\r\n \t return\r\n for dj_mol in mols:\r\n \t out_sd = Chem.SDWriter(t.name)\r\n \t out_sd.write(Chem.MolFromMolBlock(str(dj_mol.sdf_info)))\r\n \t out_sd.close()\r\n \t sdconv.ReadFile(ligref, t.name)\r\n \t # Now make the new plif\r\n \t new_plif = Plif()\r\n \t new_plif.mol_id = dj_mol\r\n \t new_plif.prot_id = my_prot\r\n \t new_plif.method_id = plif_method\r\n \t try:\r\n \t \t new_plif.validate_unique()\r\n \t \t new_plif.save()\r\n \t except ValidationError:\r\n \t \t new_plif = Plif.objects.get(mol_id=dj_mol,prot_id=my_prot,method_id=plif_method)\r\n \t lig_name = ligref.GetTitle().strip(\",\")\r\n \t prot_name = lig_name.split(\"_\")[0]\r\n \t ligref.AddHydrogens()\r\n \t counter +=1\r\n \t refresdict = pp.getresiduedict(protref, res_d)\r\n \t new_d = get_fp(protref,ligref, res_d)\r\n \t for res in new_d:\r\n \t \t new_res = PlifRes()\r\n \t \t new_res.res_name = res[:3]\r\n \t \t new_res.res_num = int(res[3:])\r\n \t \t new_res.prot_id = my_prot\r\n \t \t try:\r\n \t \t \t new_res.validate_unique()\r\n \t \t \t new_res.save()\r\n \t \t except ValidationError:\r\n \t \t \t new_res = PlifRes.objects.get(res_name=res[:3],res_num=int(res[3:]),prot_id=my_prot)\r\n \t \t new_plif.res_id.add(new_res)\r\n \t \t for bit_num, bit in enumerate(new_d[res]):\r\n \t \t \t new_bit = PlifBit()\r\n \t \t \t new_bit.feature = feature_list[bit_num]\r\n \t \t \t new_bit.method_id = plif_method\r\n \t \t \t new_bit.res_id = new_res\r\n \t \t \t try:\r\n \t \t \t \t new_bit.validate_unique()\r\n \t \t \t \t new_bit.save()\r\n \t \t \t \t my_fun(dj_mol,new_bit,new_plif,bit)\r\n \t \t \t except ValidationError:\r\n\t\t\t\t\t\t\t\t\t\tnew_bit = PlifBit.objects.get(feature=feature_list[bit_num],method_id=plif_method,res_id=new_res)\r\n\t\t\t\t\t\t\t\t\t\tnew_bit.save()\r\n\t\t\t\t\t\t\t\t\t\tnew_plif.bit_id.add(new_bit)\r\n\t\t\t\t\t\t\t\t\t\tmy_fun(dj_mol,new_bit,new_plif,bit)\r\n\t\t\t\t\t\t\t\t\t\t\r\n \t ligref = OBMol()\r\n \t notatend = sdconv.Read(ligref)","title":""},{"docid":"2cea0abf4708ac80030bc376d139c8b2","score":"0.49342123","text":"def writeLP(item_count, capacity, items):\n lpfile = checkpath + '/knapsack.lp'\n f = open(lpfile, 'w')\n f.write(\"Maximize\\n\")\n f.write(\"Objective:\\n\")\n cns = \"\"\n cnt = 0\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n cns = cns + \" + \" + str(items[i].value) + \" \" + xVar\n cnt = cnt + 1\n if cnt % 5 == 4:\n f.write(cns + \"\\n\")\n cns = \"\"\n cnt = 0\n f.write(cns + \"\\n\")\n\n f.write(\"Subject To\\n\")\n cns = \"\"\n cnt = 0\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n cns = cns + \" + \" + str(items[i].weight) + \" \" + xVar\n cnt = cnt + 1\n if cnt % 5 == 4:\n f.write(cns + \"\\n\")\n cns = \"\"\n cnt = 0\n f.write(cns + \" <= \" + str(capacity) + \"\\n\")\n\n f.write(\"Binaries\\n\")\n for i in range(0, item_count):\n xVar = \"x_\"+str(i)\n f.write(xVar + \"\\n\")\n f.write(\"End\")\n f.close()","title":""},{"docid":"aa711305cbfff319951e389f1f16131b","score":"0.49325135","text":"def createModelHandler(self):\n className = self.model.name.upper()\n filename = self.model.name + \".pk\"\n\n return f\"\"\"\n# Copyright 2017 Battelle Energy Alliance, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport sys\nimport pickle\nimport os\nfrom pythonfmu.fmi2slave import Fmi2Type, Fmi2Slave, Fmi2Causality, Fmi2Variability, Integer, Real, Boolean, String\n\nclass {className}(Fmi2Slave):\n #\n # RAVEN (raven.inl.gov) Model-based Python-driven simulator\n #\n author = \"RAVEN Team\"\n description = \"RAVEN Model-based Python-driven simulator\"\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.inputVariables = {self.inputVars}\n self.outputVariables = {self.outVars}\n # set path to raven and the serialized model\n self.raven_path = r\"{self.ravenDir}\"\n # model_path is by default the path to this model that is exported as FMU (serialized). It is stored in the resource folder\n self.model_path = self.resources + \"/\" + \"{filename}\"\n #os.path.sep\n sys.path.append(self.model_path)\n # this flag activates the initialization at the begin of the solve\n self.initialized = False\n # register raven_path variables if needed to be changed\n self.register_variable(String(\"raven_path\", causality=Fmi2Causality.parameter, variability=Fmi2Variability.tunable))\n # register input variables\n for var in self.inputVariables:\n # set var\n self.__dict__[var] = 0.0\n self.register_variable(Real(var, causality=Fmi2Causality.input))\n for var in self.outputVariables:\n # set var\n self.__dict__[var] = 0.0\n self.register_variable(Real(var, causality=Fmi2Causality.output))\n\n def setup_experiment(self, start_time: float):\n self.start_time = start_time\n if not self.initialized:\n sys.path.append(self.raven_path)\n # find the RAVEN framework\n if os.path.isdir(os.path.join(self.raven_path,\"ravenframework\")):\n # we import the Driver to load the RAVEN enviroment for the un-pickling\n try:\n import ravenframework.Driver\n except RuntimeError as ae:\n # we try to add the framework directory\n raise RuntimeError(\"Importing or RAVEN failed with error:\" +str(ae))\n else:\n print(\"framework not found in\",self.raven_path)\n # de-serialize the model\n print(\"model_path\", self.model_path)\n self.model = pickle.load(open(self.model_path, mode='rb'))\n self.initialized = True\n\n def do_step(self, current_time: float, step_size: float) -> bool:\n request = dict()\n for var in self.inputVariables:\n request[var] = self.__dict__[var]\n request['current_time'] = current_time\n request['step_size'] = step_size\n\n return_var = self.model.{self.executeMethod}(request)\n outs = return_var if isinstance(return_var,dict) else return_var[{self.indexReturnDict}]\n\n for var in outs:\n self.__dict__[var] = outs[var]\n return True\n\n\"\"\"","title":""},{"docid":"bfc1b2a35b7e5fd939b8a36de23ec2a2","score":"0.49318784","text":"def buildSolverModel(self, lp):\n log.debug(\"create the glpk model\")\n prob = glpk.glp_create_prob()\n glpk.glp_set_prob_name(prob, lp.name)\n log.debug(\"set the sense of the problem\")\n if lp.sense == constants.LpMaximize:\n glpk.glp_set_obj_dir(prob, glpk.GLP_MAX)\n log.debug(\"add the constraints to the problem\")\n glpk.glp_add_rows(prob, len(list(lp.constraints.keys())))\n for i, v in enumerate(lp.constraints.items(), start=1):\n name, constraint = v\n glpk.glp_set_row_name(prob, i, name)\n if constraint.sense == constants.LpConstraintLE:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_UP, 0.0, -constraint.constant\n )\n elif constraint.sense == constants.LpConstraintGE:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_LO, -constraint.constant, 0.0\n )\n elif constraint.sense == constants.LpConstraintEQ:\n glpk.glp_set_row_bnds(\n prob, i, glpk.GLP_FX, -constraint.constant, -constraint.constant\n )\n else:\n raise PulpSolverError(\"Detected an invalid constraint type\")\n constraint.glpk_index = i\n log.debug(\"add the variables to the problem\")\n glpk.glp_add_cols(prob, len(lp.variables()))\n for j, var in enumerate(lp.variables(), start=1):\n glpk.glp_set_col_name(prob, j, var.name)\n lb = 0.0\n ub = 0.0\n t = glpk.GLP_FR\n if not var.lowBound is None:\n lb = var.lowBound\n t = glpk.GLP_LO\n if not var.upBound is None:\n ub = var.upBound\n t = glpk.GLP_UP\n if not var.upBound is None and not var.lowBound is None:\n if ub == lb:\n t = glpk.GLP_FX\n else:\n t = glpk.GLP_DB\n glpk.glp_set_col_bnds(prob, j, t, lb, ub)\n if var.cat == constants.LpInteger:\n glpk.glp_set_col_kind(prob, j, glpk.GLP_IV)\n assert glpk.glp_get_col_kind(prob, j) == glpk.GLP_IV\n var.glpk_index = j\n log.debug(\"set the objective function\")\n for var in lp.variables():\n value = lp.objective.get(var)\n if value:\n glpk.glp_set_obj_coef(prob, var.glpk_index, value)\n log.debug(\"set the problem matrix\")\n for constraint in lp.constraints.values():\n l = len(list(constraint.items()))\n ind = glpk.intArray(l + 1)\n val = glpk.doubleArray(l + 1)\n for j, v in enumerate(constraint.items(), start=1):\n var, value = v\n ind[j] = var.glpk_index\n val[j] = value\n glpk.glp_set_mat_row(prob, constraint.glpk_index, l, ind, val)\n lp.solverModel = prob\n # glpk.glp_write_lp(prob, None, \"glpk.lp\")","title":""},{"docid":"90dd741a4cce0ae2de8a4b272b8dd9f3","score":"0.4929237","text":"def to_modflow(model, fname):\n try:\n import flopy\n except ImportError as e:\n raise type(e)(str(e) + \", modflow output not possible!\")\n # For modflow we want to create a new folder instead of only a file. The folder name is the base\n # name of the passed filename\n realization_dir = pathlib.Path(fname).parent\n runname = pathlib.Path(fname).name\n mfdir = realization_dir / 'MODFLOW'\n mfdir.mkdir(parents=True, exist_ok=True)\n mfname = str(mfdir / runname)\n\n # Assign name and create modflow model object\n mf = flopy.modflow.Modflow(mfname, exe_name='mf2005')\n\n # Create the discretization object\n ztop = model.grid.z0 + model.grid.lz\n zbot = model.grid.z0\n botm = np.linspace(ztop, zbot, model.grid.nz + 1)\n dis = flopy.modflow.ModflowDis(mf, model.grid.nz, model.grid.nx, model.grid.ny, \n delr=model.grid.dx, delc=model.grid.dy,\n top=ztop, botm=botm[1:])\n\n # Variables for the BAS package\n ibound = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.int32)\n ibound[:, :, 0] = -1\n ibound[:, :, -1] = -1\n\n strt = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.float32)\n strt[:, :, 0] = model.flowtrans['hin'][0]\n strt[:, :, -1] = model.flowtrans['hout'][0]\n\n bas = flopy.modflow.ModflowBas(mf, ibound=ibound, strt=strt)\n\n # Assign hydraulic conductivity\n hyvr_hk = np.transpose(model.data['k_iso'], (2, 0, 1))\n hyvr_layvka = 1 # VKA dataset is ratio of horizontal K\n if 'anirat' in model.data.keys():\n hyvr_vka = np.transpose(model.data['anirat'], (2, 0, 1))\n\n # Add LPF package to the MODFLOW model\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\n hk=hyvr_hk, # Horizontal hydraulic conductivity\n layvka=hyvr_layvka, # Flag for each layer of anisotropic ratio\n vka=hyvr_vka) # Anisotropy ratios.\n else:\n # Add LPF package to the MODFLOW model\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\n hk=hyvr_hk) # Horizontal hydraulic conductivity\n\n oc = flopy.modflow.ModflowOc(mf) # Add OC package to the MODFLOW model\n pcg = flopy.modflow.ModflowPcg(mf) # Add PCG package to the MODFLOW model\n mf.write_input() # Write the MODFLOW model input files","title":""},{"docid":"486cabbe9fb6d10c75ea06f7ee5cf1a7","score":"0.49249116","text":"def write_production_mdp(prefix, rep, temp):\n\n fn = prefix + '_%d.mdp'%rep\n fout = open(fn, 'w')\n fout.write(\"\"\"title = prod_GBSA\ncpp = /lib/cpp\ninclude = -I../top\ndefine = \nintegrator = sd\ndt = 0.002\nnsteps = 25000000 ; 50 ns\nnstxout = 500000 ; every 1 ns \nnstvout = 500000\nnstlog = 5000 ; every 10 ps\nnstxtcout = 5000 ; every 10 ps\nnstenergy = 5000 ; every 10 ps\n\n\nimplicit_solvent = GBSA\ngb_algorithm = OBC\nnstgbradii = 1\ngb_epsilon_solvent = 80.0\nsa_algorithm = Ace-approximation\n\ncomm_mode = ANGULAR\n\nrgbradii = 0.9\ncoulombtype = Cut-off\nrvdw = 0.9\nrlist = 0.9\nrcoulomb = 0.9\n\n; CONSTRAINTS\nconstraints = hbonds\nconstraint_algorithm = LINCS\n\n; other stuff\nbd_fric = 1.0\nnstcomm = 10\ncomm_grps = System\nxtc_grps = System\nenergygrps = System\nnstlist = 10\nns_type = grid\ntc-grps = System \ntau_t = 0.0109\n\nref_t = %d \ncompressibility = 4.5e-5\nref_p = 1.0\ngen_vel = yes\ngen_temp = %d \ngen_seed = 1729\npbc = no\"\"\"%(temp, temp))\n\n fout.close()\n\n return fn","title":""},{"docid":"6bfcf30894e9237e9a45c1cbf4059801","score":"0.49182045","text":"def write_aloha_routines(self):\n \n\n self.aloha_model.add_Lorentz_object(self.model.get('lorentz'))\n self.aloha_model.compute_subset(self.wanted_lorentz)\n # Write out the aloha routines in Python\n aloha_routines = []\n \n # First add the default external wavefunction routines\n wavefunction_routines = open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read() \n open(pjoin(self.dir_path,'wavefunctions.py'),'w').write(\n 'from __future__ import division\\n'+wavefunction_routines)\n\n #aloha_routines.append(open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read())\n\n # Now write the process-depenent Feynman rules ones\n for routine in self.aloha_model.values():\n aloha_routines.append(routine.write(output_dir = None, \n mode='mg5',\n language = 'Python'))\n\n for routine in self.aloha_model.external_routines:\n aloha_routines.append(\n open(self.aloha_model.locate_external(routine, 'Python')).read())\n\n # Now collect imports\n python_imports = []\n new_aloha_routines = []\n for aloha_routine in aloha_routines:\n new_aloha_routine = []\n for line in aloha_routine.split('\\n'):\n if any(line.startswith(token) for token in ['from','import']):\n if line not in python_imports:\n python_imports.append(line)\n else:\n new_aloha_routine.append(line)\n new_aloha_routines.append('\\n'.join(new_aloha_routine))\n aloha_routines = new_aloha_routines\n \n # Veto some imports\n vetoed_imports = ['import aloha.template_files.wavefunctions as wavefunctions']\n python_imports = [pi for pi in python_imports if pi not in vetoed_imports]\n python_imports.insert(0, 'import wavefunctions')\n\n aloha_output = open(pjoin(self.dir_path,'aloha_methods.py'),'w')\n aloha_output.write('from __future__ import division\\n')\n # Write imports\n aloha_output.write('\\n'.join(python_imports))\n aloha_output.write('\\n'*2)\n\n # Write routines\n aloha_output.write('\\n'.join(aloha_routines))\n\n aloha_output.close()","title":""},{"docid":"f73793368d062ed8010c45c9ae955d77","score":"0.49119902","text":"def run(self):\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\n self._openScriptFile()\n self.scriptf.write(\"from phycas import *\\n\\n\")\n\n if self.opts.seed > 0:\n self.scriptf.write('setMasterSeed(%d)\\n\\n' % self.opts.seed)\n\n if self.opts.model == 'jc':\n self.model_jc(0)\n elif self.opts.model == 'jc+i':\n self.model_jc(1)\n elif self.opts.model == 'jc+g':\n self.model_jc(2)\n elif self.opts.model == 'jc+i+g':\n self.model_jc(3)\n elif self.opts.model == 'hky':\n self.model_hky(0)\n elif self.opts.model == 'hky+i':\n self.model_hky(1)\n elif self.opts.model == 'hky+g':\n self.model_hky(2)\n elif self.opts.model == 'hky+i+g':\n self.model_hky(3)\n elif self.opts.model == 'gtr':\n self.model_gtr(0)\n elif self.opts.model == 'gtr+i':\n self.model_gtr(1)\n elif self.opts.model == 'gtr+g':\n self.model_gtr(2)\n elif self.opts.model == 'gtr+i+g':\n self.model_gtr(3)\n elif self.opts.model == 'codon':\n self.model_codon()\n\n self.model_edgelen()\n\n if self.opts.datafile is None:\n self.scriptf.write(\"mcmc.data_source = None\\n\")\n elif self.opts.datafile == 'sample.nex':\n self.saveSampleData()\n self.scriptf.write(\"mcmc.data_source = '%s'\\n\\n\" % self.opts.datafile)\n else:\n self.scriptf.write(\"mcmc.data_source = '%s'\\n\\n\" % self.opts.datafile)\n\n if self.opts.analysis == 'mcmc':\n self.mcmc_analysis()\n self.sump_analysis()\n self.sumt_analysis()\n if self.opts.analysis == 'poly':\n self.polytomy_analysis()\n elif self.opts.analysis == 'cpo':\n self.cpo_analysis()\n #elif self.opts.analysis == 'idr':\n # self.idr_analysis()\n elif self.opts.analysis == 'ss':\n self.steppingstone_analysis()\n\n self._closeScriptFile()","title":""},{"docid":"c3e42d57ee79ba479dae9b3b96b226d2","score":"0.49086118","text":"def build_model(out: ModelOutput = ModelOutput(\"malt/swemalt-1.7.2.mco\"),\n _maltjar: Binary = Binary(\"[malt.jar]\")):\n out.download(\"http://maltparser.org/mco/swedish_parser/swemalt-1.7.2.mco\")","title":""},{"docid":"e9a026d51eff7c21f70c849e56481fa5","score":"0.49045718","text":"def to_alien(self, file_pointer, format='opb', comments=None):\n\n if self.atms and format == 'smt':\n raise NotImplementedError('SMT-LIB2 does not support PB constraints directly; you may want to use Z3\\'s API instead')\n\n cchars = {'lp': '\\\\', 'opb': '*', 'smt': ';'}\n\n # saving formula's internal comments\n for c in self.comments:\n print(cchars[format], c, file=file_pointer)\n\n # saving externally specified comments\n if comments:\n for c in comments:\n print(cchars[format], c, file=file_pointer)\n\n # normalized soft clauses\n soft, hard = [], []\n topv = self.nv + 1\n for cl in self.soft:\n if len(cl) == 1:\n soft.append(cl)\n else:\n hard.append([topv] + cl)\n soft.append([topv])\n topv += 1\n\n if format == 'opb':\n print('* #variable= {0} #constraint= {1}'.format(self.nv, len(self.hard) + len(hard)),\n file=file_pointer)\n print('min:',\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\n ';', file=file_pointer)\n elif format == 'lp':\n print('Minimize', file=file_pointer)\n print('obj:',\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\n file=file_pointer)\n print('Subject To', file=file_pointer)\n elif format == 'smt':\n for v in range(1, self.nv + 1):\n print('(declare-fun x{0} () Bool)'.format(v), file=file_pointer)\n\n for i, cl in enumerate(self.hard + hard, 1):\n line, neg = [], 0\n for l in cl:\n if l > 0:\n if format == 'smt':\n line.append('x{0}'.format(l))\n else:\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', l))\n else:\n if format == 'smt':\n line.append('(not x{0})'.format(-l))\n else:\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', -l))\n neg += 1\n\n if format == 'smt':\n print('(assert (or {0}))'.format(' '.join(line)), file=file_pointer)\n else:\n print('{0}{1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}: '.format(i),\n ' '.join(l for l in line),\n 1 - neg, ';' if format == 'opb' else ''),\n file=file_pointer)\n\n for i, am in enumerate(self.atms, len(self.hard) + len(hard) + 1):\n line, neg = [], 0\n for l in am[0]:\n if l > 0:\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', l))\n neg += 1\n else:\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', -l))\n\n print('{0} {1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}:'.format(i),\n ' '.join(l for l in line),\n len(am[0]) - am[1] - neg, ';' if format == 'opb' else ''),\n file=file_pointer)\n\n if format == 'lp':\n print('Bounds', file=file_pointer)\n for v in range(1, topv):\n print('0 <= x{0} <= 1'.format(v), file=file_pointer)\n print('Binary', file=file_pointer)\n for v in range(1, topv):\n print('x{0}'.format(v), file=file_pointer)\n print('End', file=file_pointer)\n elif format == 'smt':\n for cl, w in zip(soft, self.wght):\n l = 'x{0}'.format(cl[0]) if cl[0] > 0 else '(not x{0})'.format(-cl[0])\n print('(assert-soft {0} :weight {1})'.format(l, w), file=file_pointer)\n\n print('(check-sat)', file=file_pointer)\n print('(get-model)', file=file_pointer)\n print('(get-objectives)', file=file_pointer)\n print('(exit)', file=file_pointer)","title":""},{"docid":"1bed09b7d57987e01641bd6feb172467","score":"0.4902589","text":"def copy_files_export_model(geomodel_dir, geomodeller_file, new_dir, nx, ny, nz, **kwds):\n \n \n \n os.chdir(geomodel_dir)\n \n \n # first step: determine all Geomodeller files in original directory\n geomodel_files = []\n for l in os.listdir(\".\"):\n if os.path.splitext(l)[1] == \".sec\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".s3d\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".md5\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".wsp\":\n geomodel_files.append(l)\n if os.path.splitext(l)[1] == \".xml\":\n # This is the original file - keep for later\n geomodel_xml_ori = l\n \n print geomodel_xml_ori\n os.chdir(new_dir)\n\n for f in geomodel_files:\n shutil.copyfile(os.path.join(geomodel_dir, f), os.path.join(\".\",f))\n\n for l2 in os.listdir(\".\"):\n if l2 == geomodeller_file:\n print l2\n shutil.copyfile(l2,os.path.join(\".\", geomodel_xml_ori))\n if \"original\" in l2:\n new_grid = \"exported_grid_original.txt\"\n new_delxyz = \"delxyz_original.txt\"\n else:\n new_grid = \"exported_grid_\" + os.path.splitext(l2)[0] + \".txt\"\n new_delxyz = \"delxyz_\" + os.path.splitext(l2)[0] + \".txt\"\n \n if kwds.has_key('nz_list'): # cell discretization study\n try:\n i = int(l2[-7:-4])\n except ValueError:\n os.chdir('..') \n continue # original model\n subprocess.call(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, kwds['nz_list'][i]), shell=True)\n else:\n\n while True:\n proc = subprocess.Popen(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\n for i in range(10):\n time.sleep(5)\n if proc.poll() == 0:\n print(\"Process finished successfully\")\n break\n if proc.poll() == None:\n proc.kill()\n print(\"\\n\\n\\tProcess killed, restart!\\n\\n\")\n continue\n elif proc.poll() == 0:\n print(\"Process finished successfully\")\n break\n print(\"\\n\\n\\tProcess not finished correctly!\\n\\n\\n\")\n break\n # subprocess.call(\"/home/flo/bin/export_model %s %d %d %d\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\n shutil.copyfile(\"exported_grid.txt\", os.path.join(\".\", new_grid))\n shutil.copyfile(\"delxyz.txt\", os.path.join(\".\", new_delxyz))","title":""},{"docid":"8dc902536351b3d417ec8d2e8f7e209e","score":"0.48992693","text":"def convert2lite(save_dir,\n lite_valid_places=\"arm\",\n lite_model_type=\"naive_buffer\"):\n\n from paddlelite.lite import Opt\n opt = Opt()\n opt.set_model_dir(save_dir + \"/inference_model\")\n opt.set_valid_places(lite_valid_places)\n opt.set_model_type(lite_model_type)\n opt.set_optimize_out(save_dir + \"/opt\")\n opt.run()","title":""},{"docid":"38860f271edd9ad00978731b25db1a2b","score":"0.48990908","text":"def makefile2dot(**kwargs):\n\n direction = kwargs.get('direction', \"BT\")\n if direction not in [\"LR\", \"RL\", \"BT\", \"TB\"]:\n raise ValueError('direction must be one of \"BT\", \"TB\", \"LR\", RL\"')\n\n output = kwargs.get('output', '')\n view = kwargs.get('view', False)\n\n graph = build_graph(stream_database(), direction=direction)\n if output == \"\":\n if view:\n graph.view()\n else:\n print(graph)\n else:\n with open(output, 'w') as file:\n file.write(str(graph))\n if view:\n graph.view()","title":""},{"docid":"7c4d103572b36b3f5587fd6cccb6325d","score":"0.48969853","text":"def load_model(fp: str):\n pass","title":""},{"docid":"6128a8fd86ce81b50e836d0234155722","score":"0.48924553","text":"def writemodelfile(self, classpy=None):\n if not classpy is None:\n classpy.close()\n pyfile = '%s.py' % os.path.join(self.modelspath, self.thismodule)\n classpy = open(pyfile, 'r')\n lines = classpy.readlines()\n lines.insert(2, '\\n')\n classpy.close()\n froms = list()\n for line in lines:\n prefix = ''\n if 'ForeignKey' in line or 'OneToOneField' in line \\\n or 'ManyToManyField' in line:\n idx = lines.index(line)\n third = ''\n paren = False\n comma = False\n first, second = line.split('(', 1)\n if not 'self' in second: # do nothing if it is\n if ',' in second: # some options exist\n second, third = second.split(',', 1)\n third = ', %s' % third\n if ')' in second: # that's all in the line\n paren = True # so it can be put back\n second = second.replace(')', '').strip()\n if \"'\" in second:\n second = second.replace(\"'\", '').strip()\n if '\"' in second:\n second = second.replace('\"', '').strip()\n relatedmodel = second # totally naked\n assert len(second.split()) == 1\n module = self.checkthisimport(relatedmodel)\n if not module is None:\n if module.startswith('.'):\n prefix = '#'\n line = \"%s('%s'%s\" % (first, second, third)\n else:\n line = \"%s(%s%s\" % (first, second, third)\n if paren:\n line = '%s)\\n' % line.rstrip()\n if not line.endswith('\\n'):\n line = '%s\\n' % line\n if idx:\n lines[idx] = line\n if module:\n impline = '%sfrom %s import %s\\n' % (prefix, module,\n relatedmodel)\n else:\n impline = 'import %s\\n' % model\n if not impline in froms:\n #print(impline)\n froms.insert(0, impline)\n # now find all the other imported items\n chunk = line.replace('=', ' ')\n chunk = chunk.replace('[', ' ')\n chunk = chunk.replace('(', ' ')\n chunk = chunk.replace(')', ' ')\n chunk = chunk.replace('.', ' ')\n chunk = chunk.replace(']', ' ')\n chunk = chunk.replace(',', ' ')\n bits = chunk.split()\n for bit in bits:\n if self.seemsok(bit, line):\n module = self.checkthisimport(bit)\n if not module is None:\n if module == '':\n impline = 'import %s\\n' % bit\n else:\n impline = 'from %s import %s\\n' % (module, bit)\n if not impline in froms:\n froms.insert(0, impline)\n\n clean = list()\n for item in froms:\n if not '__future__' in item:\n if not item in clean:\n clean.insert(0, item)\n cleaner = self.removethismodule(clean)\n del(clean)\n cleaner.sort()\n cleaner.reverse()\n if aggregate:\n cleanest = self.aggregatefroms(cleaner)\n else:\n cleanest = cleaner\n for item in cleanest:\n lines.insert(self.startline, item)\n classpy = open(pyfile, 'w')\n classpy.writelines(lines)\n classpy.close()","title":""},{"docid":"6b24a82db4e8a7271fd5aaa542794f09","score":"0.48900214","text":"def _translate_and_simulate(self, simulate):\n import os\n import shutil\n import jinja2\n import datetime\n\n # Delete output files\n self.deleteOutputFiles()\n\n worDir = self._outputDir_\n\n # Construct the model instance with all parameter values\n # and the package redeclarations\n dec = self._declare_parameters()\n dec.extend(self._modelModifiers_)\n\n if len(dec) == 0:\n model_modifier = \"\"\n else:\n model_modifier = '{dec}'.format(mn=self.modelName, dec=','.join(dec))\n\n file_name = \"{}.py\".format(self.modelName.replace(\".\", \"_\"))\n## self._time_stamp_old_files = datetime.datetime.now()\n with open(os.path.join(worDir, file_name), mode=\"w\", encoding=\"utf-8\") as fil:\n path_to_template = os.path.join(\n os.path.dirname(__file__), os.path.pardir, \"development\")\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(path_to_template))\n\n template = env.get_template(\"optimica_run.template\")\n\n # Note that filter argument must respect glob syntax ([ is escaped with []]) + OPTIMICA mat file\n # stores matrix variables with no space e.g. [1,1].\n txt = template.render(\n model=self.modelName,\n model_modifier=model_modifier,\n ncp=self._simulator_.get('numberOfIntervals'),\n rtol=self._simulator_.get('eps'),\n solver=self._simulator_.get('solver'),\n start_time=self._simulator_.get('t0') if self._simulator_.get(\n 't0') is not None else 'mod.get_default_experiment_start_time()',\n final_time=self._simulator_.get('t1') if self._simulator_.get(\n 't1') is not None else 'mod.get_default_experiment_stop_time()',\n result_file_name=f\"{self._simulator_.get('resultFile')}.mat\",\n simulate=simulate,\n time_out=self._simulator_.get('timeout'),\n filter=self._result_filter,\n generate_html_diagnostics=self._generate_html_diagnostics,\n debug_solver=self._debug_solver,\n debug_solver_interactive_mode=self._debug_solver_interactive_mode)\n\n fil.write(txt)\n shutil.copyfile(\n os.path.join(\n os.path.dirname(__file__),\n \"OutputGrabber.py\"),\n os.path.join(\n worDir,\n \"OutputGrabber.py\"))\n\n try:\n super()._runSimulation([\"jm_ipython.sh\", file_name],\n self._simulator_.get('timeout'),\n worDir)\n\n self._check_simulation_errors(worDir=worDir, simulate=simulate)\n# self._copyNewFiles(worDir)\n# self._deleteTemporaryDirectory(worDir)\n\n except Exception as e: # Catch all possible exceptions\n em = f\"Simulation failed in '{worDir}'\\n Exception: {e}.\\n You need to delete the directory manually.\\n\"\n self._reporter.writeError(em)\n raise e\n os.remove(os.path.join(worDir, \"OutputGrabber.py\"))","title":""},{"docid":"989010fe3938e83cdafa116524121a61","score":"0.4889075","text":"def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):\n from pathlib import Path\n\n from stamp_processing.module.yolov5.models.experimental import attempt_load\n from stamp_processing.module.yolov5.models.yolo import Model\n from stamp_processing.module.yolov5.utils.torch_utils import select_device\n\n file = Path(__file__).absolute()\n\n save_dir = Path(\"\") if str(name).endswith(\".pt\") else file.parent\n path = (save_dir / name).with_suffix(\".pt\") # checkpoint path\n try:\n device = select_device((\"0\" if torch.cuda.is_available() else \"cpu\") if device is None else device)\n\n if pretrained and channels == 3 and classes == 80:\n model = attempt_load(path, map_location=device) # download/load FP32 model\n else:\n cfg = list((Path(__file__).parent / \"models\").rglob(f\"{name}.yaml\"))[0] # model.yaml path\n model = Model(cfg, channels, classes) # create model\n if autoshape:\n model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS\n return model.to(device)\n\n except Exception as e:\n help_url = \"https://github.com/ultralytics/yolov5/issues/36\"\n s = \"Cache may be out of date, try `force_reload=True`. See %s for help.\" % help_url\n raise Exception(s) from e","title":""},{"docid":"1163e643e5b668503b7ff3340a328791","score":"0.48880547","text":"def make_dot(aut_fname, dot_fname):\n coll = collection.Collection()\n aut = load_automaton(coll, aut_fname, False, False)\n\n make_backup_file(dot_fname)\n\n dot_handle = open(dot_fname, 'w')\n dot_handle.write(aut.to_dot())\n dot_handle.close()","title":""}],"string":"[\n {\n \"docid\": \"67db92a1a3821a68008dc249af915482\",\n \"score\": \"0.5871873\",\n \"text\": \"def create_model(self):\\r\\n\\r\\n self.try_load_model()\\r\\n\\r\\n self.set_correct_problem_instance_name()\\r\\n\\r\\n self.add_objective_and_budget_constraints()\\r\\n self.add_starting_ending_nodes_constraints()\\r\\n\\r\\n self.docplex_model.print_information()\\r\\n\\r\\n # DEBUG\\r\\n # for constraint in self.docplex_model.iter_constraints():\\r\\n # print(constraint)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e1197a8a29532e1bb1bc651f420051\",\n \"score\": \"0.5701343\",\n \"text\": \"def formula_generator(known_args, other_args): # pylint: disable=W0613\\n poly = Poly(*known_args.format)\\n\\n if known_args.smt2:\\n with open(known_args.smt2, 'w') as smt2_file:\\n with redirect_stdout(smt2_file):\\n poly.problem(known_args.degree)\\n else:\\n poly.problem(known_args.degree)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1717949860fb45cd73863fd5f97acc0e\",\n \"score\": \"0.56868994\",\n \"text\": \"def __init__(self, model, output_path, wanted_lorentz = [],\\n wanted_couplings = [], replace_dict={}):\\n\\n self.model = model\\n self.model_name = export_cpp.ProcessExporterCPP.get_model_name(model['name'])\\n self.aloha_model = create_aloha.AbstractALOHAModel(self.model_name)\\n\\n\\n self.dir_path = output_path\\n self.default_replace_dict = dict(replace_dict)\\n # List of needed ALOHA routines\\n self.wanted_lorentz = wanted_lorentz\\n self.wanted_couplings = wanted_couplings\\n\\n # For dependent couplings, only want to update the ones\\n # actually used in each process. For other couplings and\\n # parameters, just need a list of all.\\n self.coups_dep = {} # name -> base_objects.ModelVariable\\n self.coups_indep = [] # base_objects.ModelVariable\\n self.params_dep = [] # base_objects.ModelVariable\\n self.params_indep = [] # base_objects.ModelVariable\\n self.p_to_cpp = None \\n\\n # Prepare parameters and couplings for writeout in C++\\n self.prepare_parameters()\\n self.prepare_couplings(wanted_couplings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5525bfc1f28e0a667830ec014bdb725\",\n \"score\": \"0.56353825\",\n \"text\": \"def convertProcessToLcia(O,p):\\r\\n \\r\\n # the players\\r\\n P = O.findUuid(os.path.splitext(p)[0])\\r\\n print 'Importing: ' + P.El('baseName').text\\r\\n L = O.createLciaFromTemplate()\\r\\n \\r\\n lcia_ns = L.getroot().nsmap\\r\\n \\r\\n # the basic data\\r\\n L.El('name','common').text = P.El('baseName').text\\r\\n L.El('methodology').text = 'TRACI 2.0'\\r\\n L.El('impactCategory').text = selectFromList(impactCategories)\\r\\n L.El('impactIndicator').text = raw_input('Impact Indicator: ')\\r\\n L.El('referenceYear').text = P.El('referenceYear','common').text\\r\\n L.El('duration').text = 'indefinite'\\r\\n L.El('interventionLocation').text = 'US'\\r\\n L.El('impactLocation').text = 'US'\\r\\n\\r\\n L.El('typeOfDataSet').text = selectFromList(indicatorTypes)\\r\\n L.El('normalisation').text= 'false'\\r\\n L.El('weighting').text = 'false'\\r\\n \\r\\n QR=L.El('quantitativeReference')\\r\\n CF=L.El('characterisationFactors')\\r\\n \\r\\n # the exchanges\\r\\n Xs = P.El('exchanges').getchildren()\\r\\n for x in Xs:\\r\\n flow_el = nonemap(x,'referenceToFlowDataSet')\\r\\n flow_uuid = flow_el.attrib['refObjectId']\\r\\n flow_comment = flow_el.getchildren()[0]\\r\\n if nonemap(x, 'exchangeDirection').text == 'Input':\\r\\n # Input exchange = reference flow\\r\\n ref_uuid = getReferenceFlow(O.findUuid(flow_uuid), nsmap = lcia_ns)\\r\\n RQ = ET.Element('referenceQuantity',\\r\\n attrib = {'type':'flow property data set',\\r\\n 'refObjectId':ref_uuid,\\r\\n 'uri':'../flowproperties/{0}.xml'.format(ref_uuid)},\\r\\n nsmap = lcia_ns)\\r\\n RQ.append(flow_comment)\\r\\n QR.append(RQ)\\r\\n print 'reference flow: {0} {1}'.format(ref_uuid,flow_comment.text)\\r\\n \\r\\n else:\\r\\n # Output exchange = new factor\\r\\n mean_value = 1 / float(nonemap(x, 'resultingAmount').text) # note inversion!\\r\\n F = ET.Element('factor',nsmap = lcia_ns)\\r\\n FL = ET.Element('referenceToFlowDataSet', \\r\\n attrib={'type':'flow data set',\\r\\n 'refObjectId':flow_uuid,\\r\\n 'uri':'../flows/{0}.xml'.format(flow_uuid)},\\r\\n nsmap = lcia_ns)\\r\\n FL.append(flow_comment)\\r\\n F.append(FL)\\r\\n ET.SubElement(F, 'exchangeDirection',nsmap = lcia_ns).text='Output'\\r\\n ET.SubElement(F, 'meanValue', nsmap = lcia_ns).text = str( mean_value ) \\r\\n CF.append(F)\\r\\n print 'exchange: {0} {1}'.format(flow_uuid,flow_comment.text)\\r\\n\\r\\n return L\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f2020c341a65e6214adebd68e7903e\",\n \"score\": \"0.56281185\",\n \"text\": \"def generate_and_save_all_interpretations_for_model(pci_obj, X, y, mdl, mdl_name):\\n \\n ## Generate dataset-level interpretation for Logistic Regression model\\n print('Dataset-level interpretation generation is in process for ' + mdl_name + '.')\\n dataset_level_interpretation = pci_obj.dataset_level_interpretation(X, y, mdl)\\n np.savetxt('./result/' + 'ACES_dataset_level_interpretation_' + mdl_name + '.txt', dataset_level_interpretation, fmt='%1.8e')\\n print('Dataset-level interpretation generation is finished for' + mdl_name + '.')\\n \\n \\n ## Generate whole dataset signed interpretation for Logistic Regression model\\n print('Whole dataset signed interpretation generation is in process for ' + mdl_name + '.')\\n whole_dataset_signed_interpretation = pci_obj.whole_dataset_signed_interpretation(X, y, mdl)\\n np.savetxt('./result/' + 'ACES_whole_dataset_signed_interpretation_' + mdl_name + '.txt', whole_dataset_signed_interpretation, fmt='%1.8e')\\n print('Whole dataset signed interpretation generation is finished for ' + mdl_name + '.')\\n \\n ## Generate whole dataset signed interpretation for Logistic Regression model\\n print('Whole dataset unsigned interpretation generation is in process for ' + mdl_name + '.')\\n whole_dataset_unsigned_interpretation = pci_obj.whole_dataset_unsigned_interpretation(X, y, mdl)\\n np.savetxt('./result/' + 'ACES_whole_dataset_unsigned_interpretation_' + mdl_name + '.txt', whole_dataset_unsigned_interpretation, fmt='%1.8e')\\n print('Whole dataset unsigned interpretation generation is finished for ' + mdl_name + '.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95c5748803c3d19cc5d921a875e0b086\",\n \"score\": \"0.5581633\",\n \"text\": \"def convert(prototxt_path, caffemodel_path, phase='test', output_model_name='freeze_graph_model.pb'):\\n assert_exist_files(prototxt_path, caffemodel_path)\\n\\n data_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.mat')\\n code_output_path = os.path.join(os.path.dirname(prototxt_path), 'output.py')\\n standalone_output_path = os.path.join(os.path.dirname(prototxt_path), output_model_name)\\n\\n try:\\n with tf.Session() as sess:\\n transformer = TensorFlowTransformer(prototxt_path, caffemodel_path, phase=phase)\\n print_stderr('Converting data...')\\n if data_output_path is not None:\\n data = transformer.transform_data()\\n print_stderr('Saving data...')\\n with open(data_output_path, 'wb') as data_out:\\n np.save(data_out, data)\\n if code_output_path is not None:\\n print_stderr('Saving source...')\\n with open(code_output_path, 'wb') as src_out:\\n src_out.write(transformer.transform_source())\\n\\n if standalone_output_path:\\n filename, _ = os.path.splitext(os.path.basename(standalone_output_path))\\n temp_folder = os.path.join(os.path.dirname(standalone_output_path), 'tmp')\\n\\n if not os.path.exists(temp_folder):\\n os.makedirs(temp_folder)\\n\\n if data_output_path is None:\\n data = transformer.transform_data()\\n print_stderr('Saving data...')\\n data_output_path = os.path.join(temp_folder, filename) + '.npy'\\n with open(data_output_path, 'wb') as data_out:\\n np.save(data_out, data)\\n\\n if code_output_path is None:\\n print_stderr('Saving source...')\\n code_output_path = os.path.join(temp_folder, filename) + '.py'\\n with open(code_output_path, 'wb') as src_out:\\n src_out.write(transformer.transform_source())\\n\\n checkpoint_path = os.path.join(temp_folder, filename + '.ckpt')\\n graph_name = os.path.basename(standalone_output_path)\\n graph_folder = os.path.dirname(standalone_output_path)\\n input_node = transformer.graph.nodes[0].name\\n output_node = transformer.graph.nodes[-1].name\\n tensor_shape = transformer.graph.get_node(input_node).output_shape\\n tensor_shape_list = [tensor_shape.batch_size, tensor_shape.height, tensor_shape.width, tensor_shape.channels]\\n\\n sys.path.append(os.path.dirname(code_output_path))\\n module = os.path.splitext(os.path.basename(code_output_path))[0]\\n class_name = transformer.graph.name\\n KaffeNet = getattr(__import__(module), class_name)\\n\\n data_placeholder = tf.placeholder(tf.float32, tensor_shape_list, name=input_node)\\n net = KaffeNet({input_node: data_placeholder})\\n\\n # load weights stored in numpy format\\n net.load(data_output_path, sess)\\n\\n print_stderr('Saving checkpoint...')\\n\\n saver = tf.train.Saver()\\n saver.save(sess, checkpoint_path)\\n\\n print_stderr('Saving graph definition as protobuf...')\\n tf.train.write_graph(sess.graph.as_graph_def(), graph_folder, graph_name, as_text=False)\\n\\n input_graph_path = standalone_output_path\\n input_saver_def_path = \\\"\\\"\\n input_binary = True\\n input_checkpoint_path = checkpoint_path\\n output_node_names = output_node\\n restore_op_name = 'save/restore_all'\\n filename_tensor_name = 'save/Const:0'\\n output_graph_path = standalone_output_path\\n clear_devices = True\\n\\n print_stderr('Saving standalone model...')\\n freeze_graph(input_graph_path, input_saver_def_path,\\n input_binary, input_checkpoint_path,\\n output_node_names, restore_op_name,\\n filename_tensor_name, output_graph_path,\\n clear_devices, '')\\n\\n shutil.rmtree(temp_folder)\\n\\n print_stderr('Done.')\\n except KaffeError as err:\\n fatal_error('Error encountered: {}'.format(err))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870fd0325523f72544b469d902b7c2cc\",\n \"score\": \"0.5544181\",\n \"text\": \"def convert_odml():\\n f_in = join(ROOT, \\\"tmp.xml\\\")\\n f_out = join(ROOT, \\\"tmpout.xml\\\")\\n\\n o_ver_conv = VersionConverter(f_in)\\n\\n # either write to file\\n o_ver_conv.write_to_file(f_out)\\n\\n # or pass it to the python xml parser\\n doc = ODMLReader(parser='XML').from_string(str(o_ver_conv))\\n doc.pprint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d270ed9448ab6ff02368268de72a91b\",\n \"score\": \"0.5542094\",\n \"text\": \"def compare_lp_files(self, filename, ignored=None, my_om=None):\\n if my_om is None:\\n om = self.get_om()\\n else:\\n om = my_om\\n tmp_filename = filename.replace('.lp', '') + '_tmp.lp'\\n new_filename = ospath.join(self.tmppath, tmp_filename)\\n om.write(new_filename, io_options={'symbolic_solver_labels': True})\\n logging.info(\\\"Comparing with file: {0}\\\".format(filename))\\n with open(ospath.join(self.tmppath, tmp_filename)) as generated_file:\\n with open(ospath.join(ospath.dirname(ospath.realpath(__file__)),\\n \\\"lp_files\\\",\\n filename)) as expected_file:\\n\\n def chop_trailing_whitespace(lines):\\n return [re.sub(r'\\\\s*$', '', ln) for ln in lines]\\n\\n def remove(pattern, lines):\\n if not pattern:\\n return lines\\n return re.subn(pattern, \\\"\\\",\\n \\\"\\\\n\\\".join(lines))[0].split(\\\"\\\\n\\\")\\n\\n expected = remove(ignored,\\n chop_trailing_whitespace(\\n expected_file.readlines()))\\n generated = remove(ignored,\\n chop_trailing_whitespace(\\n generated_file.readlines()))\\n\\n def normalize_to_positive_results(lines):\\n negative_result_indices = [\\n n for n, line in enumerate(lines)\\n if re.match(\\\"^= -\\\", line)]\\n equation_start_indices = [\\n [n for n in reversed(range(0, nri))\\n if re.match('.*:$', lines[n])][0]+1\\n for nri in negative_result_indices]\\n for (start, end) in zip(\\n equation_start_indices,\\n negative_result_indices):\\n for n in range(start, end):\\n lines[n] = (\\n '-'\\n if lines[n] and lines[n][0] == '+'\\n else '+'\\n if lines[n]\\n else lines[n]) + lines[n][1:]\\n lines[end] = '= ' + lines[end][3:]\\n return lines\\n\\n expected = normalize_to_positive_results(expected)\\n generated = normalize_to_positive_results(generated)\\n\\n eq_(generated, expected,\\n \\\"Failed matching expected with generated lp file:\\\\n\\\" +\\n \\\"\\\\n\\\".join(unified_diff(expected, generated,\\n fromfile=ospath.relpath(\\n expected_file.name),\\n tofile=ospath.basename(\\n generated_file.name),\\n lineterm=\\\"\\\")))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bffa7248d281833024e2e2237090aa94\",\n \"score\": \"0.55059963\",\n \"text\": \"def model_to_owl(model, fname):\\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\\n\\n try:\\n fileOS = autoclass('java.io.FileOutputStream')(fname)\\n except JavaException:\\n logger.error('Could not open data file %s' % fname)\\n return\\n l3_factory = autoclass('org.biopax.paxtools.model.BioPAXLevel').L3.getDefaultFactory()\\n model_out = l3_factory.createModel()\\n for r in model.getObjects().toArray():\\n model_out.add(r)\\n io.convertToOWL(model_out, fileOS)\\n\\n fileOS.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"016e6167d18373b2c352dac2e0aac10f\",\n \"score\": \"0.54671896\",\n \"text\": \"def run(in_obo_filename=IN_OBO_FILENAME, in_pkl_filename=IN_PKL_FILENAME, in_tsv_filename=IN_TSV_FILELANE,\\n in_monomers_filename=IN_MONOMERS_FILENAME,\\n max_num_proteins=None,\\n out_pickle_filename=OUT_PICKLE_FILENAME, out_pickle_filename_2=OUT_PICKLE_FILENAME_2,\\n out_tsv_filename=OUT_TSV_FILENAME, out_fasta_filename=OUT_FASTA_FILENAME,\\n out_fig_filename=OUT_FIG_FILENAME, out_structure_dirname=OUT_STRUCTURE_DIRNAME,\\n out_viz_dirname=OUT_VIZ_DIRNAME):\\n # get the PRO ontology and extract the modified proteins from the ontology\\n # proteins = get_pro_from_obo(obo_filename=in_obo_filename, pkl_filename=in_pkl_filename, max_num_proteins=max_num_proteins)\\n proteins = get_pro_from_tsv(in_tsv_filename, max_num_proteins=max_num_proteins)\\n\\n # parse the modified proteins and retrieve their sequences\\n if not os.path.isfile(out_pickle_filename):\\n # parse the modified proteins and retrieve their sequences\\n parsed_proteins = []\\n for i_protein, protein in enumerate(proteins):\\n if i_protein % 100 == 0:\\n print('Parsing protein {} of {}'.format(i_protein + 1, len(proteins)))\\n parsed_proteins.append(parse_protein(protein))\\n\\n # save the parsed proteins in pickle format\\n with open(out_pickle_filename, 'wb') as file:\\n pickle.dump(parsed_proteins, file)\\n else:\\n # load saved parsed proteins in pickle format\\n with open(out_pickle_filename, 'rb') as file:\\n parsed_proteins = pickle.load(file)\\n\\n # read list of monomers\\n monomers = {}\\n with open(in_monomers_filename, 'r') as file:\\n reader = csv.DictReader(file, dialect='excel')\\n for row in reader:\\n monomers[row['PRO id']] = {\\n 'mod': bpforms.protein_alphabet.monomers.get(row['BpForms code'], None),\\n 'origin': [],\\n }\\n if row['Base monomer']:\\n monomers[row['PRO id']]['origin'] = row['Base monomer'].split(', ')\\n\\n # generate list of modified monomeric forms\\n for protein in parsed_proteins:\\n for modification in protein['modifications']:\\n if modification['monomer'] not in monomers:\\n monomers[modification['monomer']] = {\\n 'mod': None,\\n 'origin': [],\\n }\\n\\n # print list of unmapped monomers\\n unmapped_monomers = []\\n for monomer, code in monomers.items():\\n if not code['mod']:\\n unmapped_monomers.append(monomer)\\n unmapped_monomers.sort()\\n if unmapped_monomers:\\n print('Several PRO monomeric forms have not been mapped to BpForms monomeric forms:\\\\n {}'.format(\\n '\\\\n '.join(unmapped_monomers)))\\n\\n # check for inconsistencies between residue and modified monomeric form\\n monomer_codes = {}\\n for code, monomer in bpforms.protein_alphabet.monomers.items():\\n monomer_codes[monomer] = code\\n\\n for protein in parsed_proteins:\\n for modification in protein.get('modifications', []):\\n if modification['residue'] and modification['monomer']:\\n monomer = monomers.get(modification['monomer'], None)\\n if (monomer['mod'] and monomer['mod'].get_canonical_code(monomer_codes) != modification['residue']) \\\\\\n or (monomer['origin'] and modification['residue'] not in monomer['origin']):\\n codes = set(monomer['origin'])\\n if monomer['mod']:\\n codes.add(monomer['mod'].get_canonical_code(monomer_codes))\\n msg = 'Modified monomeric form {} potentially inconsistent with residue {} != {}'.format(\\n modification['monomer'], modification['residue'],\\n ', '.join(codes))\\n print(protein['id'] + ': ' + msg)\\n\\n # generate BpForms for each protein\\n if not os.path.isdir(out_structure_dirname):\\n os.mkdir(out_structure_dirname)\\n\\n if not os.path.isdir(out_viz_dirname):\\n os.mkdir(out_viz_dirname)\\n\\n if not os.path.isfile(out_pickle_filename_2):\\n for i_protein, protein in enumerate(parsed_proteins):\\n if i_protein % 100 == 0:\\n print('Generating BpForms {} of {}'.format(i_protein + 1, len(parsed_proteins)))\\n\\n protein['modified_seq'] = None\\n if not protein['uniprot_id']:\\n continue\\n if not protein['seq']:\\n continue\\n if protein['pro_errors']:\\n continue\\n\\n processed_form = gen_bpform(protein, monomers, monomer_codes, apply_modifications=False)\\n protein['processed_seq'] = str(processed_form)\\n if not processed_form.validate():\\n processed_formula = processed_form.get_formula()\\n protein['processed_formula'] = str(processed_formula)\\n protein['processed_mol_wt'] = processed_form.get_mol_wt()\\n protein['processed_charge'] = processed_form.get_charge()\\n\\n if not protein['modifications']:\\n continue\\n\\n modified_form = gen_bpform(protein, monomers, monomer_codes, include_annotations=False)\\n protein['modified_seq'] = str(modified_form)\\n\\n modified_form = gen_bpform(protein, monomers, monomer_codes)\\n if not modified_form.validate():\\n modified_formula = modified_form.get_formula()\\n protein['modified_full_seq'] = str(modified_form)\\n protein['modified_formula'] = str(modified_formula)\\n protein['modified_mol_wt'] = modified_form.get_mol_wt()\\n protein['modified_charge'] = modified_form.get_charge()\\n\\n protein['modifications_formula'] = str(modified_formula - processed_formula)\\n protein['modifications_mol_wt'] = protein['modified_mol_wt'] - protein['processed_mol_wt']\\n protein['modifications_charge'] = protein['modified_charge'] - protein['processed_charge']\\n\\n # with open(os.path.join(out_structure_dirname, protein['id'] + '.cml'), 'w') as file:\\n # file.write(modified_form.export('cml'))\\n\\n form = gen_bpform(protein, monomers, monomer_codes,\\n apply_processing=False, include_annotations=True)\\n seq_features = []\\n if protein['processing']:\\n seq_features.append({\\n 'label': 'Processed',\\n 'color': '#cccccc',\\n 'positions': [],\\n })\\n last = 0\\n for p in protein['processing']:\\n seq_features[0]['positions'].append([last + 1, p['start'] - 1])\\n last = p['end']\\n seq_features[0]['positions'].append([\\n protein['processing'][-1]['end'] + 1,\\n len(form.seq),\\n ])\\n\\n if protein['processing'][0]['start'] == 1:\\n seq_features[0]['positions'].pop(0)\\n if protein['processing'][-1]['end'] == len(form.seq):\\n seq_features[0]['positions'].pop(len(seq_features[0]['positions']) - 1)\\n\\n with open(os.path.join(out_viz_dirname, protein['id'] + '.svg'), 'w') as file:\\n file.write(form.get_genomic_image(seq_features, width=910))\\n\\n if modified_form.get_canonical_seq(monomer_codes) != protein['processed_seq']:\\n protein['pro_errors'].append('Modified sequence for {} not compatible with the processed sequence'.format(\\n protein['id']))\\n\\n # save the parsed proteins in pickle format\\n with open(out_pickle_filename_2, 'wb') as file:\\n pickle.dump(parsed_proteins, file)\\n else:\\n with open(out_pickle_filename_2, 'rb') as file:\\n parsed_proteins = pickle.load(file)\\n\\n # save the proteoforms in TSV format\\n with open(out_tsv_filename, 'w') as file:\\n writer = csv.writer(file, dialect='excel-tab')\\n writer.writerow(['PRO id', 'UniProt id', 'Organism',\\n 'Unmodified sequence (IUBMB)',\\n 'Processing', 'Deletions', 'Processsed sequence (IUBMB)', 'Processsed formula',\\n 'Processsed molecular weight', 'Processsed charge',\\n 'Modifications', 'Crosslinks', 'Modified sequence (abbreviated BpForms)', 'Modified sequence (BpForms)',\\n 'Is modified sequence concrete', 'Modified formula', 'Modified molecular weight', 'Modified charge',\\n 'Modifications formula', 'Modifications molecular weight', 'Modifications charge',\\n 'PRO issues', 'Monomeric form issues'])\\n\\n for parsed_protein in parsed_proteins:\\n if parsed_protein.get('pro_errors', None):\\n pro_errors = '. '.join(parsed_protein['pro_errors']) + '.'\\n else:\\n pro_errors = None\\n\\n if parsed_protein.get('modified_errors', None):\\n modified_errors = '. '.join(parsed_protein['modified_errors']) + '.'\\n else:\\n modified_errors = None\\n\\n writer.writerow([\\n parsed_protein['id'],\\n parsed_protein.get('uniprot_id', None),\\n parsed_protein.get('organism', None),\\n parsed_protein.get('seq', None),\\n ', '.join('{}-{}'.format(p['start'], p['end']) for p in parsed_protein['processing']),\\n ', '.join('{}-{}'.format(deletion[0], deletion[1]) for deletion in parsed_protein.get('deletions', [])),\\n parsed_protein.get('processed_seq', None),\\n parsed_protein.get('processed_formula', None),\\n parsed_protein.get('processed_mol_wt', None),\\n parsed_protein.get('processed_charge', None),\\n ', '.join('{} --> {} ({})'.format(m['residue'] or '?', m['monomer'], ', '.join(str(p) for p in m['positions']))\\n for m in parsed_protein['modifications']),\\n ', '.join('{}{}-{}{}'.format(xlink[0][1], xlink[0][0], xlink[1][1], xlink[1][0])\\n for xlink in parsed_protein.get('crosslinks', [])),\\n parsed_protein.get('modified_seq', None),\\n parsed_protein.get('modified_full_seq', None),\\n parsed_protein.get('modified_concrete', False),\\n parsed_protein.get('modified_formula', None),\\n parsed_protein.get('modified_mol_wt', None),\\n parsed_protein.get('modified_charge', None),\\n parsed_protein.get('modifications_formula', None),\\n parsed_protein.get('modifications_mol_wt', None),\\n parsed_protein.get('modifications_charge', None),\\n pro_errors,\\n modified_errors,\\n ])\\n\\n # save the proteoforms in FASTA format\\n seqs = (SeqRecord(id='{} | {}'.format(protein['id'], protein['uniprot_id']),\\n seq=Seq(protein['modified_seq']),\\n description='')\\n for protein in parsed_proteins\\n if protein['modified_seq'])\\n SeqIO.write(seqs, out_fasta_filename, \\\"fasta\\\")\\n\\n # analyze frequency of modifications\\n plot_modifications(parsed_proteins, fig_filename=out_fig_filename)\\n\\n # return proteins\\n return proteins, parsed_proteins\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776ae1cf149e78ae0a4114b21438d138\",\n \"score\": \"0.54315245\",\n \"text\": \"def build_LM(in_file):\\r\\n print 'building language models...'\\r\\n # This is an empty method\\r\\n # Pls implement your code in below\\r\\n\\r\\n # Open in_file and split into a list of sentences\\r\\n input_file = open(in_file, 'r')\\r\\n train_input_list = input_file.read().split(\\\"\\\\r\\\\n\\\")\\r\\n\\r\\n # Set up dictionaries to store the 4grams generated from input.train\\r\\n # ngram_count stores the total number of ngrams each language has generated\\r\\n four_gram_table = {}\\r\\n ngram_count = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\\r\\n\\r\\n '''\\r\\n NGRAM GENERATION\\r\\n Multiple for loops through each sentence and then each character for\\r\\n 4gram generation. Each sentence is padded with 3 'START's and 3 'END's\\r\\n '''\\r\\n for sentence in train_input_list:\\r\\n sentence_parts = sentence.split(None, 1)\\r\\n # To handle possible empty strings or weird strings in training input\\r\\n if (len(sentence_parts) != 2):\\r\\n break\\r\\n language = sentence_parts[0]\\r\\n sentence_body = sentence_parts[1]\\r\\n char_list = list(sentence_body)\\r\\n for index in range(3):\\r\\n char_list.insert(0, 'START')\\r\\n char_list.append('END')\\r\\n for index in range(len(char_list) - 4):\\r\\n four_gram = (char_list[index], char_list[index + 1],\\r\\n char_list[index + 2], char_list[index + 3])\\r\\n if four_gram in four_gram_table:\\r\\n four_gram_table[four_gram][language] += 1\\r\\n ngram_count[language] += 1\\r\\n else:\\r\\n # Add-one smoothing for new ngrams\\r\\n four_gram_table[four_gram] = {'malaysian': 1, 'indonesian': 1,\\r\\n 'tamil': 1}\\r\\n for value in ngram_count.itervalues():\\r\\n value += 1\\r\\n four_gram_table[four_gram][language] += 1\\r\\n ngram_count[language] += 1\\r\\n return [four_gram_table, ngram_count]\\r\\n\\r\\n input_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8776c3c0636ee64e48b9236afde29d96\",\n \"score\": \"0.54051876\",\n \"text\": \"def run(args: argparse.Namespace) -> None:\\n assert WaymoDataFileReader is not None, (\\n \\\"Please install the requirements in scripts/optional.txt to use\\\"\\n \\\"Waymo conversion.\\\"\\n )\\n if not os.path.exists(args.output):\\n os.makedirs(args.output)\\n result = from_waymo(\\n args.input,\\n args.output,\\n args.save_images,\\n args.use_lidar_labels,\\n args.nproc,\\n )\\n save(os.path.join(args.output, \\\"scalabel_anns.json\\\"), result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89aa08012b425e9517160341b3b4c2b8\",\n \"score\": \"0.5397848\",\n \"text\": \"def __init__(self,pedfname = None,title='title',outfname='rgMakeGenModel.phe',\\n xtradir=\\\"/foo/bar\\\", linkage=True, logf = None, model=additive,\\n labels=['FounderNonAff','FounderAff','OffspringNonAff','OffspringAff']):\\n ehet = '1'\\n ehom1 = '0'\\n ehom2 = '2'\\n missval = '.'\\n missing = ['N','0','.','-']\\n if model == additive: # we have ehom2 = 2 major alleles\\n modeldict = {ehom1:'2',ehet:'1',ehom2:'0',missval:missval}\\n elif model == zadditive:\\n modeldict = {ehom1:'1',ehet:'0',ehom2:'-1',missval:missval}\\n elif model == dominant:\\n modeldict = {ehom1:'1',ehet:'1',ehom2:'0',missval:missval}\\n else:\\n modeldict = {ehom1:'2',ehet:'0',ehom2:'0',missval:missval}\\n # model code translation dicts\\n\\n swapdict = {ehom1:ehom2,ehom2:ehom1,ehet:ehet,missval:missval} # if initial ref allele was wrong, use these to swap\\n mdict = dict(zip(missing,missing))\\n pedf = '%s.ped' % pedfname\\n f = file(pedf,'r')\\n if linkage: # read map file\\n map = readMap(pedfname)\\n rslist = [x[1] for x in map] # get rs numbers\\n else:\\n head = f.next().strip()\\n rslist = head.split()\\n nrs = len(rslist) # number of markers\\n elen = 2*nrs + 6 # expected # elements on each line\\n logf.write('%s %s: found %d for nrs\\\\n' % (thisprog,timenow(),nrs))\\n gm = {}\\n gm['founders'] = [] # array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\\n gm['offspring'] = [] # [array.array('c',[]) for x in xrange(nrs)] # marker rows, subject cols\\n adicts = [{} for x in xrange(nrs)] # count of alleles in a dict for each marker\\n refallele = [None for x in xrange(nrs)] # list of first observed alleles\\n nsubj = 0\\n indiv = {'founders':[],'offspring':[]}\\n for lnum,l in enumerate(f):\\n ll = l.strip().split()\\n if (lnum+1) % 200 == 0:\\n logf.write('%s %s: Processing line %d\\\\n' % (thisprog, timenow(),lnum+1))\\n if len(ll) < elen: # ? short ?\\n logf.write('%s %s: Line %d is %d long, expected %d\\\\n' % (thisprog, timenow(),\\n lnum,len(ll),elen))\\n else:\\n garray = array.array('c',[missval,]*nrs)\\n nsubj += 1\\n sid = '%s_%s' % (ll[0],ll[1])\\n if sid == '1_1': # eesh\\n sid = '%d_%d' % (nsubj,nsubj)\\n isFounder = isOff = False\\n status = labels[0] # founder unaff\\n if ll[2] <> '0' and ll[3] <> '0': # has parent ids\\n iclass = 'offspring'\\n status = labels[2] # unaffected offspring\\n if ll[5] == '2':\\n status = labels[3] # affected offspring\\n else:\\n iclass = 'founders'\\n if ll[5] == '2':\\n status = labels[1] #change from unaff to aff founder label\\n gender = 'M'\\n if ll[4] == '2':\\n gender = 'F'\\n ped = ll[:6]\\n indiv[iclass].append(ped) # for row wise output\\n for snp in xrange(nrs):\\n pos = 2*snp + 6 # first\\n g1,g2 = ll[pos],ll[pos+1] # pair of genos\\n if mdict.get(g1,None) or mdict.get(g2,None): # one or both missing\\n esnp = missval # missing value\\n else:\\n if not refallele[snp]:\\n refallele[snp] = g1 # first one we saw!\\n for g in (g1,g2):\\n n = adicts[snp].get(g,0)\\n n += 1\\n adicts[snp][g] = n\\n if g1 == g2: # hom\\n if g1 == refallele[snp]:\\n esnp = ehom2 # 2 copies of current reference allele\\n else:\\n esnp = ehom1 # no copies\\n else:\\n esnp = ehet # het - always has one copy of reference allele\\n garray[snp] = esnp\\n gm[iclass].append(garray) # append genos for this new subject\\n for ek in gm.keys():\\n lek = len(gm[ek])\\n if len(gm[ek]) > 0:\\n lek0 = len(gm[ek][0])\\n s = 'for %s, have %d subjects with %d markers' % (ek,lek,lek0)\\n print s\\n logf.write(s)\\n for x in range(lek):\\n if len(gm[ek][x]) <> lek0:\\n s = 'for row %d, len = %d, not %d' % (x, len(gm[ek][x]),lek0)\\n print s\\n logf.write(s)\\n logf.write('%s %s: Now checking major allele assignment and fixing as needed\\\\n' % (thisprog,timenow()))\\n for iclass in gm.keys():\\n for subject in xrange(len(gm[iclass])): # for each subject\\n for snp in xrange(nrs): # now check to see if reference = major allele\\n major = majAllele(adicts[snp])\\n if major <> refallele[snp]: # either None or we need to change all the codes\\n if major <> None:\\n gm[iclass][subject][snp] = swapdict[gm[iclass][subject][snp]]\\n for iclass in gm.keys():\\n for subject in xrange(len(gm[iclass])): # now convert to genetic model wanted\\n gm[iclass][subject] = [modeldict[x] for x in gm[iclass][subject]] # translate to model\\n self.gm = gm\\n self.model = model\\n self.indiv = indiv\\n self.nrs = nrs\\n self.nsubj = nsubj\\n self.logf = logf\\n self.basename = title\\n self.outfname = outfname\\n self.rslist = rslist\\n pedhead = 'famid\\\\tiid\\\\tfid\\\\tmid\\\\tgender\\\\taffection\\\\t'\\n rs = '\\\\t'.join(rslist)\\n self.outhead = '%s%s\\\\n' % (pedhead,rs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce63391b0ba53484bcdbfa3c177f5093\",\n \"score\": \"0.5392235\",\n \"text\": \"def test_LM(in_file, out_file, LM):\\n print(\\\"testing language models...\\\")\\n\\n # Parse inputs\\n data = read_by_line(in_file)\\n model = LM[0]\\n languages = LM[1]\\n\\n # Create an empty probability reference sheet\\n basic_probability_ref = dict()\\n for l in languages:\\n basic_probability_ref[l] = np.float64(1)\\n\\n # Clear file of any results, to prepare the output file for eval.py\\n open(out_file, 'w').close()\\n \\n with open(out_file, 'a') as f:\\n # Process each sentence and make a guess\\n for sentence in data:\\n p_ref = basic_probability_ref.copy()\\n tokens = tokenise(sentence, token_size)\\n num_undetected = 0\\n\\n # Multiple token probabilities for each token\\n for t in tokens:\\n if t in model:\\n # Multiply the respective probability of each language\\n counts = model[t]\\n for l in languages:\\n p_ref[l] += counts[l]\\n else:\\n num_undetected += 1\\n\\n # Find the best prediction using the maximum product\\n if num_undetected / len(tokens) > other_threshold:\\n prediction = other_language\\n else:\\n prediction = max(p_ref.items(), key=operator.itemgetter(1))[0]\\n\\n # Write the prediction to the output file\\n f.write(prediction + \\\" \\\" + sentence + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11937e2179eb9e5be7829ede94d45adb\",\n \"score\": \"0.5387432\",\n \"text\": \"def test_issue4707():\\n nlp = English()\\n nlp.add_pipe(\\\"sentencizer\\\")\\n nlp.add_pipe(\\\"entity_ruler\\\")\\n assert nlp.pipe_names == [\\\"sentencizer\\\", \\\"entity_ruler\\\"]\\n exclude = [\\\"tokenizer\\\", \\\"sentencizer\\\"]\\n with make_tempdir() as tmpdir:\\n nlp.to_disk(tmpdir, exclude=exclude)\\n new_nlp = load_model_from_path(tmpdir, disable=exclude)\\n assert \\\"sentencizer\\\" not in new_nlp.pipe_names\\n assert \\\"entity_ruler\\\" in new_nlp.pipe_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe5676fbca6769a80c88f1d8e1109454\",\n \"score\": \"0.5377364\",\n \"text\": \"def main(argv):\\n parser = argparse.ArgumentParser(\\n description='Create all possible determinizations of this domain.')\\n parser.add_argument('-d', '--domain', required=True)\\n parser.add_argument('-o', '--output', required=True) \\n args = parser.parse_args()\\n domain_file_name = args.domain\\n output_file_name = args.output\\n \\n # Reading domain file.\\n domain_str = ''\\n try:\\n with open(domain_file_name, 'r') as domain_file:\\n for line in domain_file:\\n if line.startswith(';;') or line.isspace():\\n continue;\\n domain_str += line\\n except IOError:\\n print \\\"Could not read file:\\\", domain_file_name\\n sys.exit(-1) \\n \\n # Parsing the domain tree.\\n domain_tree = ppddl_util.parse_sexp(domain_str)\\n all_prob_effects_list = []\\n \\n # Getting all possible probabilistic effects.\\n ppddl_util.get_all_probabilistic_effects(domain_tree, all_prob_effects_list)\\n \\n # Getting the most likely outcome determinization.\\n mlo_determinization = []\\n for probabilistic_effect_info in all_prob_effects_list:\\n mlo_determinization.append(\\n ppddl_util.get_mlo_determinization_effect(probabilistic_effect_info))\\n \\n # Writing the MLO determinization to a PPDDL file.\\n determinization_ppddl_tree = copy.deepcopy(domain_tree)\\n description_text = \\\"\\\"\\n for effect_info in mlo_determinization:\\n description_text += \\\"%s %d\\\\n\\\" % (effect_info[0], effect_info[1][0])\\n ppddl_util.determinize_tree(mlo_determinization, determinization_ppddl_tree)\\n ppddl_util.clean_up_tree(determinization_ppddl_tree)\\n f = open('%s_mlo_det.pddl' % (output_file_name), 'w')\\n fd = open('%s_mlo_det.desc' % (output_file_name), 'w')\\n f.write(ppddl_util.make_str(determinization_ppddl_tree[0]))\\n fd.write(description_text)\\n f.close()\\n fd.close()\\n \\n print 'Created MLO determinization for %s' % domain_file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b6f6722ce9ac5c08f4612e25ef7aec0\",\n \"score\": \"0.5376005\",\n \"text\": \"def make_model(pbm):\\n for stmt in pbm.statements:\\n try:\\n # Skip statements with no subject\\n if stmt.agent_list()[0] is None and \\\\\\n not isinstance(stmt, Conversion):\\n continue\\n # Assemble statements\\n if isinstance(stmt, Modification):\\n pbm._assemble_modification(stmt)\\n elif isinstance(stmt, RegulateActivity):\\n pbm._assemble_regulate_activity(stmt)\\n elif isinstance(stmt, RegulateAmount):\\n pbm._assemble_regulate_amount(stmt)\\n elif isinstance(stmt, Gef):\\n pbm._assemble_gef(stmt)\\n elif isinstance(stmt, Gap):\\n pbm._assemble_gap(stmt)\\n elif isinstance(stmt, ActiveForm):\\n pbm._assemble_active_form(stmt)\\n elif isinstance(stmt, Complex):\\n pbm._assemble_complex(stmt)\\n elif isinstance(stmt, Conversion):\\n pbm._assemble_conversion(stmt)\\n elif isinstance(stmt, Autophosphorylation):\\n pbm._assemble_autophosphorylation(stmt)\\n elif isinstance(stmt, Transphosphorylation):\\n pbm._assemble_transphosphorylation(stmt)\\n else:\\n logger.info('Unhandled statement: %s' % stmt)\\n except Exception:\\n pass\\n return pbm.model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9045c7c73ca55f814b38834d2149df8b\",\n \"score\": \"0.5342216\",\n \"text\": \"def try_load_model(self):\\r\\n filename = 'constraints_problem_size_{0}'.format(self.get_real_n())\\r\\n path = '../cache_constraints/'\\r\\n\\r\\n solution_is_loop = self.sol_is_loop()\\r\\n\\r\\n if solution_is_loop:\\r\\n filename += '_loop'\\r\\n path_filename = path + filename + '.lp'\\r\\n\\r\\n model_reader = ModelReader()\\r\\n\\r\\n self.outer_model_interface.check_dir(path)\\r\\n\\r\\n try:\\r\\n # Load the constraints\\r\\n self.docplex_model = model_reader.read_model(path_filename)\\r\\n except:\\r\\n # If they are not cached, create them\\r\\n print(\\\"\\\\nFile {} not found :(\\\\nI'm gonna create it! :D\\\".format(path_filename))\\r\\n self.docplex_model = self.generate_general_constraints(path, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c879101600ab140261ce79e742c8479\",\n \"score\": \"0.5335524\",\n \"text\": \"def generateModel(self):\\n\\t\\tglobal startLoadModel, doneLoadModel\\n\\t\\tprint(\\\"model type:\\\" + self.modeltype)\\n\\t\\tstartLoadModel = time.time()\\n\\t\\tif not os.path.exists(self.dictionaryfile):\\n\\t\\t\\tprint(\\\"creating dictionary \\\" + self.dictionaryfile)\\n\\t\\t\\tself.dictionary = self.makeDictionaryMemoryFriendly(self.corpusfile)\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"reading dictionary \\\" + self.dictionaryfile)\\n\\t\\t\\tself.dictionary = self.loadDictionary(self.dictionaryfile)\\n\\t\\tif not os.path.exists(self.corpusmatrixfile):\\n\\t\\t\\tprint(\\\"serializing corpus \\\" + self.corpusmatrixfile)\\n\\t\\t\\tself.serializeCorpusMemoryFriendly(self.corpusfile, self.dictionary)\\n\\t\\tif self.modeltype == \\\"LDA\\\":\\n\\t\\t\\tprint(\\\"creating lda model with \\\" + str(self.topics) + \\\" topics.\\\")\\n\\t\\t\\tif self.online:\\n\\t\\t\\t\\tprint(\\\"Running in online mode with chunksize: \\\" + str(self.chunksize))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"Running in offline mode with \\\" + str(self.passes) + \\\" passes\\\")\\n\\t\\t\\tif self.distributed:\\n\\t\\t\\t\\tprint(\\\"Running in distributed mode\\\")\\n\\t\\t\\tself.generateLDAModel(self.topics, self.passes, self.distributed)\\n\\t\\telif self.modeltype == \\\"LSI\\\":\\n\\t\\t\\tprint(\\\"creating lsi model\\\")\\n\\t\\t\\tself.generateLSIModel(topics)\\n\\t\\telif self.modeltype == \\\"HDP\\\":\\n\\t\\t\\tprint(\\\"creating hdp model\\\")\\n\\t\\t\\tself.generateHDPModel()\\n\\t\\tdoneLoadModel = time.time()\\n\\t\\tprint(\\\"Time to generate model:\\\" + str(doneLoadModel - startLoadModel))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91c5431c7473abefa61a82391d7c11c8\",\n \"score\": \"0.5323186\",\n \"text\": \"def test_LM(in_file, out_file, LM):\\r\\n print \\\"testing language models...\\\"\\r\\n # This is an empty method\\r\\n # Pls implement your code in below\\r\\n\\r\\n # Setting up data provided from given LM\\r\\n four_gram_table = LM[0]\\r\\n ngram_count = LM[1]\\r\\n\\r\\n # Open in_file and split into a list of sentences\\r\\n # Open out_file for writing predictions\\r\\n input_file = open(in_file, 'r')\\r\\n test_input_list = input_file.read().split(\\\"\\\\r\\\\n\\\")\\r\\n output_file = open(out_file, 'w')\\r\\n\\r\\n for sentence in test_input_list:\\r\\n # To handle possible empty strings or invalid input\\r\\n if sentence == \\\"\\\":\\r\\n break\\r\\n # Ngram probability score for current sentence\\r\\n ngram_score = {'malaysian': 0, 'indonesian': 0, 'tamil': 0}\\r\\n # Counters to keep track of number of unencountered ngrams generated by\\r\\n # the current sentence.\\r\\n invalid_ngram_count = 0\\r\\n total_ngram_count = 0\\r\\n # Sentence is padded with 3 'START's and 3 'END's\\r\\n char_list = list(sentence)\\r\\n for index in range(3):\\r\\n char_list.insert(0, 'START')\\r\\n char_list.append('END')\\r\\n for index in range(len(char_list) - 4):\\r\\n four_gram = (char_list[index], char_list[index + 1],\\r\\n char_list[index + 2], char_list[index + 3])\\r\\n # Log10 is used to prevent underflow of individual ngram_scores\\r\\n if four_gram in four_gram_table:\\r\\n for language, count in four_gram_table[four_gram].iteritems():\\r\\n ngram_score[language] += math.log10(count/ngram_count[language])\\r\\n else:\\r\\n invalid_ngram_count += 1\\r\\n total_ngram_count += 1\\r\\n # invalid_ngram_ratio represents the proportion of unencountered ngrams\\r\\n # out of all ngrams generated from current sentence\\r\\n invalid_ngram_ratio = invalid_ngram_count / total_ngram_count\\r\\n # The set threshold of 0.6 indicates that the sentence might not be a\\r\\n # valid language in the given LM\\r\\n if max(ngram_score.itervalues()) is 0 or invalid_ngram_ratio > 0.6:\\r\\n predicted_language = 'other'\\r\\n else:\\r\\n predicted_language = max(ngram_score.iterkeys(), key=(lambda language: ngram_score[language]))\\r\\n output_file.write(predicted_language + \\\" \\\" + sentence + \\\"\\\\n\\\")\\r\\n input_file.close\\r\\n output_file.close\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8230e8f56aabea2a2d18b33a22dab9\",\n \"score\": \"0.5304721\",\n \"text\": \"def generate(args):\\n if args.cuda and torch.cuda.is_available():\\n device = 0\\n use_cuda = True\\n elif args.cuda and not torch.cuda.is_available():\\n print(\\\"You do not have CUDA, turning cuda off\\\")\\n device = -1 \\n use_cuda = False\\n else:\\n device = -1\\n use_cuda=False\\n\\n #Load the vocab\\n vocab = du.load_vocab(args.vocab)\\n eos_id = vocab.stoi[EOS_TOK]\\n pad_id = vocab.stoi[PAD_TOK]\\n\\n if args.ranking: # default is HARD one, the 'Inverse Narrative Cloze' in the paper\\n dataset = du.NarrativeClozeDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, LM=False)\\n # Batch size during decoding is set to 1\\n batches = BatchIter(dataset, 1, sort_key=lambda x:len(x.actual), train=False, device=-1)\\n else:\\n dataset = du.SentenceDataset(args.valid_data, vocab, src_seq_length=MAX_EVAL_SEQ_LEN, min_seq_length=MIN_EVAL_SEQ_LEN, add_eos=False) #put in filter pred later\\n # Batch size during decoding is set to 1\\n batches = BatchIter(dataset, args.batch_size, sort_key=lambda x:len(x.text), train=False, device=-1)\\n\\n data_len = len(dataset)\\n\\n #Create the model\\n with open(args.load, 'rb') as fi:\\n if not use_cuda:\\n model = torch.load(fi, map_location=lambda storage, loc : storage)\\n else:\\n model = torch.load(fi, map_location=torch.device('cuda'))\\n\\n if not hasattr(model.latent_root, 'nohier'):\\n model.latent_root.set_nohier(args.nohier) #for backwards compatibility\\n\\n model.decoder.eval()\\n model.set_use_cuda(use_cuda)\\n\\n #For reconstruction\\n if args.perplexity:\\n loss = calc_perplexity(args, model, batches, vocab, data_len)\\n print(\\\"Loss = {}\\\".format(loss))\\n elif args.schema:\\n generate_from_seed(args, model, batches, vocab, data_len)\\n elif args.ranking:\\n do_ranking(args, model, batches, vocab, data_len, use_cuda)\\n else:\\n# sample_outputs(model, vocab)\\n reconstruct(args, model, batches, vocab)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b20588231ce0fcb6d4f7434d73e68c3\",\n \"score\": \"0.52846843\",\n \"text\": \"def generateModel(modelName, pack):\\n\\n (params, run, cost, optim_date, var_set) = pack\\n\\n with open('%s_optimized.mo' % modelName, 'w') as file:\\n header = \\\"\\\"\\\"model {mn}_optimized \\\"Generated by PostProcess/postprocess_optim.py {d} with cost {c} lowest at run {r}\\\"\\n extends {mn} (\\n \\\\n\\\"\\\"\\\".format(mn=modelName, d = optim_date, c = '%.6f' % cost, r = '%d' % run)\\n footer = ');\\\\nend %s_optimized;\\\\n' % modelName\\n\\n param_lines = (' %s = %.6e' % param for param in params)\\n # use join to avoid placing the comma after the last\\n lines = ',\\\\n'.join(param_lines)\\n\\n\\n file.write(header)\\n file.writelines(lines)\\n file.write(footer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d6897ba3959406b0ee3b87b094e823e\",\n \"score\": \"0.5258285\",\n \"text\": \"def load_model(length=MAX_UTTERENCE_LENGTH):\\n return torch.load(os.path.join(PATH_TO_SAVE, 'generator_{}.pt'.format(length))).to(DEVICE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078acbdaf7a42c95ccc2c6c684279376\",\n \"score\": \"0.5254087\",\n \"text\": \"def compile_po_file(po_path: Path) -> None:\\n output_path = po_path.parent\\n\\n target_json = po_path.with_suffix(\\\".json\\\")\\n if target_json.exists():\\n target_json.unlink()\\n convert_catalog_to_json(po_path, output_path, po_path.stem)\\n\\n target_mo = po_path.with_suffix(\\\".mo\\\")\\n if target_mo.exists():\\n target_mo.unlink()\\n compile_to_mo(po_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a0443fbd71bfab66c479b31441d52a\",\n \"score\": \"0.52386135\",\n \"text\": \"def test_LM(in_file, out_file, LM):\\r\\n print \\\"testing language models...\\\"\\r\\n # This is an empty method\\r\\n # Pls implement your code in below\\r\\n malay_LM = LM['malaysian']\\r\\n indon_LM = LM['indonesian']\\r\\n tamil_LM = LM['tamil']\\r\\n\\r\\n FD_in = open(in_file, 'r')\\r\\n FD_out = open(out_file, 'w')\\r\\n\\r\\n lines = FD_in.read()\\r\\n lines_array = lines.replace('\\\\r', ' ').split('\\\\n')\\r\\n lines_array.pop(len(lines_array) - 1)\\r\\n\\r\\n for line in lines_array:\\r\\n malay_count = 1\\r\\n indon_count = 1\\r\\n tamil_count = 1\\r\\n\\r\\n total_count = 0\\r\\n miss_count = 0\\r\\n\\r\\n four_char_array = get_four_char_array(line)\\r\\n\\r\\n for four_char in four_char_array:\\r\\n # key in malayLM = key in all LMs due to add one smoothing\\r\\n if four_char in malay_LM:\\r\\n malay_count += math.log10(malay_LM[four_char])\\r\\n indon_count += math.log10(indon_LM[four_char])\\r\\n tamil_count += math.log10(tamil_LM[four_char])\\r\\n else:\\r\\n miss_count += 1\\r\\n total_count += 1\\r\\n\\r\\n if (miss_count / total_count > 0.7):\\r\\n FD_out.write(\\\"other \\\" + line + \\\"\\\\n\\\")\\r\\n elif malay_count > indon_count and malay_count > tamil_count:\\r\\n FD_out.write(\\\"malaysian \\\" + line + \\\"\\\\n\\\")\\r\\n elif indon_count > malay_count and indon_count > tamil_count:\\r\\n FD_out.write(\\\"indonesian \\\" + line + \\\"\\\\n\\\")\\r\\n else:\\r\\n FD_out.write(\\\"tamil \\\" + line + \\\"\\\\n\\\")\\r\\n\\r\\n FD_in.close()\\r\\n FD_out.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a76f0d38ddb8ab17ec18597b44bc149\",\n \"score\": \"0.5236154\",\n \"text\": \"def to_lalinference(self, **kwargs):\\n return self.write(file_format=\\\"lalinference\\\", package=\\\"gw\\\", **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a86959eb9fc6a9c3356b7be538a93d\",\n \"score\": \"0.5229652\",\n \"text\": \"def make_input(\\n wel_qin,\\n wel_qin_1,\\n wel_qout,\\n wel_qout_1,\\n wel_coordsin,\\n wel_coordsin_1,\\n wel_coordsout,\\n wel_coordsout_1,\\n model_path,\\n name,\\n exe_name='mf6',\\n verbosity_level=0):\\n # created to load, build and save the simulation\\n sim = flopy.mf6.MFSimulation(\\n sim_name=name,\\n sim_ws=model_path, #working folder\\n exe_name=exe_name,\\n verbosity_level=verbosity_level, #standard output to be written - error messages should be included (=1)\\n )\\n # time discretization package\\n times = (10.0, 120, 1.0)\\n tdis_rc = [(1.0, 1, 1.0), times, times, times]\\n flopy.mf6.ModflowTdis(\\n sim, pname=\\\"tdis\\\",\\n time_units=\\\"DAYS\\\",\\n nper=4, # number of stress periods\\n perioddata=tdis_rc, # [perlen(len stress period), nstp(time-steps in the stressperiod), tsmult(multiplier of of the lenght of sucessive time-steps]\\n )\\n #iterative model solution package\\n flopy.mf6.ModflowIms(sim) # default solver - nearly linear models - confined or a single unconfined layer that is tick enough to contain the water table within a single layer\\n # groundwater flow model\\n gwf = flopy.mf6.ModflowGwf(sim, modelname=name, save_flows=True)\\n # spatial discretization package\\n flopy.mf6.ModflowGwfdis(gwf, nrow=10, ncol=10) # number ir rows and cols since the model has only one layer\\n # Initial conditions package\\n flopy.mf6.ModflowGwfic(gwf) #models using steady state in the beginning won't be affected\\n # node property flow package pane - default is harmonic mean\\n flopy.mf6.ModflowGwfnpf(\\n gwf,\\n save_flows=True,\\n save_specific_discharge=True,\\n icelltype=[0], # 0 unconfined, -1 confined\\n k=[0.5], # initial value of k\\n k33=[0.1], # vertical anisotropy\\n )\\n # storage package specifying sy - specific yield bigger than 0 means the cell is convertible\\n sy = flopy.mf6.ModflowGwfsto.sy.empty(\\n gwf,\\n default_value=0.2 # convertible cells\\n )\\n #stirage package specifying ss - specific storage\\n ss = flopy.mf6.ModflowGwfsto.ss.empty(\\n gwf, default_value=0.000001\\n )\\n # storage package\\n flopy.mf6.ModflowGwfsto(\\n gwf,\\n pname=\\\"sto\\\",\\n save_flows=True,\\n save_specific_discharge=True,\\n iconvert=1, # flag for each cell that specifies whether or not a cell is convertible for the storage calculation. 0 indicates confined storage is used. >0 indicates confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top\\n ss=ss,\\n sy=sy,\\n transient={0: True}, # keyword to indicate that stress period IPER is transient. Transient conditions will apply until the STEADY-STATE keyword is specified in a subsequent BEGIN PERIOD block\\n )\\n #dictionary of boundaries Each well is defined through definition of layer (int), row (int), column (int), flux (float). The simplest form is a dictionary with a lists of boundaries for each stress period, where each list of boundaries itself is a list of boundaries. Indices of the dictionary are the numbers of the stress period\\n # if the number of stress periods is larger than the dic than the last speficied will apply until the end\\n stress_period_data = {\\n 1: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\\n 2: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0],\\n 3: _make_wel_stress_period(gwf, wel_qin=wel_qin, wel_qin_1=wel_qin_1, wel_coordsin=wel_coordsin, wel_coordsin_1=wel_coordsin_1,\\n wel_qout=wel_qout, wel_qout_1=wel_qout_1, wel_coordsout=wel_coordsout, wel_coordsout_1=wel_coordsout_1)[0]\\n }\\n # defines the well package\\n flopy.mf6.ModflowGwfwel(\\n gwf,\\n stress_period_data=stress_period_data,\\n )\\n # constant head package\\n flopy.mf6.ModflowGwfchd(\\n gwf,\\n stress_period_data=[\\n [(0, 0, 0), 1.], # top left cell [cell_id, head, aux, boundname]\\n [(0, 9, 9), 1.]], # bottom right cell\\n )\\n # variable for the output control\\n budget_file = name + '.bud'\\n head_file = name + '.hds'\\n # output control package\\n flopy.mf6.ModflowGwfoc(\\n gwf,\\n budget_filerecord=budget_file,\\n head_filerecord=head_file,\\n saverecord=[('HEAD', 'ALL'), ('BUDGET', 'ALL')])\\n sim.write_simulation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef942e4bbae34ac5e0e916536c3fd820\",\n \"score\": \"0.5222133\",\n \"text\": \"def main():\\n args = parse_arguments()\\n\\n if args.output_name:\\n output_name = args.output_name\\n else:\\n output_name = args.xodr_file.rpartition(\\\".\\\")[0]\\n output_name = f\\\"{output_name}.xml\\\" # only name of file\\n\\n if os.path.isfile(output_name) and not args.force_overwrite:\\n print(\\n \\\"Not converting because file exists and option 'force-overwrite' not active\\\",\\n file=sys.stderr,\\n )\\n sys.exit(-1)\\n\\n with open(\\\"{}\\\".format(args.xodr_file), \\\"r\\\") as file_in:\\n opendrive = parse_opendrive(etree.parse(file_in).getroot())\\n\\n scenario = convert_opendrive(opendrive)\\n\\n if not args.osm:\\n writer = ExtendedCommonRoadFileWriter(\\n scenario, source=\\\"OpenDRIVE 2 Lanelet Converter\\\"\\n )\\n\\n with open(f\\\"{output_name}\\\", \\\"w\\\") as file_out:\\n writer.write_scenario_to_file_io(file_out)\\n\\n else:\\n l2osm = OSMConverter(args.osm)\\n osm = l2osm(scenario)\\n with open(f\\\"{output_name}\\\", \\\"w\\\") as file_out:\\n file_out.write(etree.tostring(osm, encoding=\\\"unicode\\\", pretty_print=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23cd82b5a7d336e4b8f27a862f78256\",\n \"score\": \"0.52209806\",\n \"text\": \"def main(model=Path(\\\"L:\\\\\\\\BigData\\\\\\\\Env\\\\\\\\SavedModels\\\"), output_dir=Path(\\\"L:\\\\\\\\BigData\\\\\\\\Env\\\\\\\\SavedModels\\\")):\\n if model is not None:\\n nlp = spacy.load(model) # load existing spaCy model\\n print(\\\"Loaded model '%s'\\\" % model)\\n\\n ner = nlp.get_pipe(\\\"ner\\\")\\n\\n ner.add_label(\\\"Email\\\")# add new entity label to entity recognizer\\n ner.add_label(\\\"Full Name\\\")\\n ner.add_label(\\\"First Name\\\")\\n ner.add_label(\\\"Updatedtime\\\")\\n ner.add_label(\\\"City\\\")\\n ner.add_label(\\\"Postcode\\\")\\n ner.add_label(\\\"Date\\\")\\n ner.add_label(\\\"Phone\\\")\\n ner.add_label(\\\"State\\\")\\n ner.add_label(\\\"Country\\\")\\n ner.add_label(\\\"Street\\\")\\n ner.add_label(\\\"Id\\\")\\n ner.add_label(\\\"Last Name\\\")\\n ner.add_label(\\\"username\\\")\\n ner.add_label(\\\"Transaction Id\\\")\\n\\n move_names = list(ner.move_names)\\n\\n # test the trained model\\n test_text = \\\"Carlie@marguerite.ca , Glenna Olson , Corbin , 1999-07-05T12:33:47.066Z , Kingshire 77787 1/8/2013 | (695)464-6165 x9467 , Iowa Indonesia 038 Schumm Walks 0 Ortiz Frida 362 \\\"\\n doc = nlp(test_text)\\n print(\\\"Entities in '%s'\\\" % test_text)\\n for ent in doc.ents:\\n print(ent.label_, ent.text)\\n\\n # save model to output directory\\n if output_dir is not None:\\n output_dir = Path(output_dir)\\n if not output_dir.exists():\\n output_dir.mkdir() # rename model\\n nlp.to_disk(output_dir)\\n print(\\\"Saved model to\\\", output_dir)\\n\\n # test the saved model\\n print(\\\"Loading from\\\", output_dir)\\n nlp2 = spacy.load(output_dir)\\n # Check the classes have loaded back consistently\\n assert nlp2.get_pipe(\\\"ner\\\").move_names == move_names\\n doc2 = nlp2(test_text)\\n for ent in doc2.ents:\\n print(ent.label_, ent.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba506d586a69c49c3403983dbd1f5025\",\n \"score\": \"0.52202463\",\n \"text\": \"def GenFile(DSLModel, Table, View):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8666e838db88654723d809adce0d3d19\",\n \"score\": \"0.52056324\",\n \"text\": \"def generateModel(self,startPoint,start,end,tol,vel):\\r\\n\\t\\t\\r\\n\\t\\t(code,levels,cantPoints) = Loader().load(self.path,startPoint)\\r\\n\\t\\thaveParser = False\\r\\n\\r\\n\\t\\tadded = 0\\r\\n\\t\\tself.start = start\\r\\n\\t\\tself.end = end\\r\\n\\t\\t\\r\\n\\t\\tif( tol > 0):\\t\\t\\r\\n\\t\\t\\tself.parser = PointsAdder(code,levels,cantPoints,tol)\\r\\n\\t\\t\\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\\r\\n\\t\\t\\thaveParser = True\\r\\n\\t\\t\\t\\r\\n\\t\\tif( vel > 0):\\t\\t\\r\\n\\t\\t\\tself.parser = VelocityChanger(code,levels,cantPoints,vel)\\r\\n\\t\\t\\t((code,levels,cantPoints),(self.start,self.end)) = self.parser.parse(self.start,self.end)\\r\\n\\t\\t\\thaveParser = True\\r\\n\\t\\t\\t\\r\\n\\t\\tif (not haveParser):\\r\\n\\t\\t\\tself.parser = Parser(code,levels,cantPoints)\\r\\n\\t\\t\\t(code,levels,cantPoints) = self.parser.parse()\\t\\r\\n\\t\\r\\n\\t\\tself.modifiedGCode = code\\r\\n\\t\\tself.levels = levels\\r\\n\\t\\tself.cantPoints = cantPoints\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9a39b116920aebf857a1c3b136837b0\",\n \"score\": \"0.5203123\",\n \"text\": \"def compile():\\n\\n m = dir(modules)\\n\\n words = []\\n for module_name in m:\\n try:\\n eval(\\\"words.extend(modules.%s.WORDS)\\\" % module_name)\\n except:\\n pass # module probably doesn't have the property\\n\\n words = list(set(words))\\n\\n # for spotify module\\n words.extend([\\\"MUSIC\\\",\\\"SPOTIFY\\\"])\\n\\n # create the dictionary\\n pronounced = g2p.translateWords(words)\\n zipped = zip(words, pronounced)\\n lines = [\\\"%s %s\\\" % (x, y) for x, y in zipped]\\n\\n with open(\\\"../client/dictionary.dic\\\", \\\"w\\\") as f:\\n f.write(\\\"\\\\n\\\".join(lines) + \\\"\\\\n\\\")\\n\\n # create the language model\\n with open(\\\"../client/sentences.txt\\\", \\\"w\\\") as f:\\n f.write(\\\"\\\\n\\\".join(words) + \\\"\\\\n\\\")\\n f.write(\\\"<s> \\\\n </s> \\\\n\\\")\\n f.close()\\n\\n # make language model\\n os.system(\\n \\\"text2idngram -vocab ../client/sentences.txt < ../client/sentences.txt -idngram temp.idngram\\\")\\n os.system(\\n \\\"idngram2lm -idngram temp.idngram -vocab ../client/sentences.txt -arpa ../client/languagemodel.lm\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e798a22449fe4ec327a89f2378b7cd\",\n \"score\": \"0.51998425\",\n \"text\": \"def main() -> None:\\n\\n args = get_args()\\n package = Package()\\n ontology = get_ontology(args.ontology_file)\\n\\n for i, fh in enumerate(args.data_file, start=1):\\n print(f'{i:3}: {os.path.basename(fh.name)}')\\n fh.close()\\n package.infer(fh.name)\\n\\n for res in package.resources:\\n if args.missing_values:\\n res.descriptor['schema']['missingValues'].extend(\\n args.missing_values)\\n\\n for field in res.descriptor['schema']['fields']:\\n name = field.get('name')\\n if not name:\\n continue\\n\\n ont = ontology.get(name)\\n if not ont:\\n continue\\n\\n data_type = ont.get('frictionless type', field.get('type'))\\n if data_type:\\n field['type'] = data_type\\n\\n data_format = ont.get('frictionless format', field.get('format'))\\n if data_format:\\n field['format'] = data_format\\n\\n rdf_type = ont.get('rdf type purl', field.get('rdfType'))\\n if rdf_type:\\n field['rdfType'] = rdf_type\\n\\n units = ont.get('units purl', field.get('pm:unitRdfType'))\\n if units:\\n field['pm:unitRdfType'] = units\\n\\n source_url = ont.get('pm:source url', field.get('pm:sourceUrl'))\\n if source_url:\\n field['pm:source url'] = source_url\\n\\n measure_source = ont.get('measurement source purl',\\n field.get('pm:measurementSourceRdfType'))\\n if measure_source:\\n field['pm:measurementSourceRdfType'] = measure_source\\n\\n measure_url = ont.get('pm:measurement source protocol',\\n field.get('pm:measurement source protocol'))\\n if measure_url:\\n field['pm:measurementSourceProtocolUrl'] = measure_url\\n\\n # TODO: Not sure about this\\n # is_searchable = ont.get('pm:searchable', field.get('pm:searchable'))\\n # if is_searchable:\\n # field['pm:searchable'] = is_searchable\\n\\n package.remove_resource(res.name)\\n package.add_resource(res.descriptor)\\n\\n package.save(args.out_file)\\n\\n print(f'Done, see \\\"{args.out_file}\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3fe6b4a1d97a21eaef6d6f4d02a796d\",\n \"score\": \"0.51963997\",\n \"text\": \"def actualSolve(self, lp):\\n\\n if not self.executable(self.path):\\n raise PulpSolverError(\\\"PuLP: cannot execute \\\" + self.path)\\n tmpLp, tmpSol, tmpMst = self.create_tmp_files(lp.name, \\\"lp\\\", \\\"sol\\\", \\\"mst\\\")\\n vs = lp.writeLP(tmpLp, writeSOS=1)\\n try:\\n os.remove(tmpSol)\\n except:\\n pass\\n cmd = self.path\\n options = self.options + self.getOptions()\\n if self.timeLimit is not None:\\n options.append((\\\"TimeLimit\\\", self.timeLimit))\\n cmd += \\\" \\\" + \\\" \\\".join([f\\\"{key}={value}\\\" for key, value in options])\\n cmd += f\\\" ResultFile={tmpSol}\\\"\\n if self.optionsDict.get(\\\"warmStart\\\", False):\\n self.writesol(filename=tmpMst, vs=vs)\\n cmd += f\\\" InputFile={tmpMst}\\\"\\n\\n if lp.isMIP():\\n if not self.mip:\\n warnings.warn(\\\"GUROBI_CMD does not allow a problem to be relaxed\\\")\\n cmd += f\\\" {tmpLp}\\\"\\n if self.msg:\\n pipe = None\\n else:\\n pipe = open(os.devnull, \\\"w\\\")\\n\\n return_code = subprocess.call(cmd.split(), stdout=pipe, stderr=pipe)\\n\\n # Close the pipe now if we used it.\\n if pipe is not None:\\n pipe.close()\\n\\n if return_code != 0:\\n raise PulpSolverError(\\\"PuLP: Error while trying to execute \\\" + self.path)\\n if not os.path.exists(tmpSol):\\n # TODO: the status should be infeasible here, I think\\n status = constants.LpStatusNotSolved\\n values = reducedCosts = shadowPrices = slacks = None\\n else:\\n # TODO: the status should be infeasible here, I think\\n status, values, reducedCosts, shadowPrices, slacks = self.readsol(tmpSol)\\n self.delete_tmp_files(tmpLp, tmpMst, tmpSol, \\\"gurobi.log\\\")\\n if status != constants.LpStatusInfeasible:\\n lp.assignVarsVals(values)\\n lp.assignVarsDj(reducedCosts)\\n lp.assignConsPi(shadowPrices)\\n lp.assignConsSlack(slacks)\\n lp.assignStatus(status)\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148556ac6767aa89118518bfbccc312b\",\n \"score\": \"0.5196136\",\n \"text\": \"def create_pyomo_model(self, debug_mode=False, debug_cost=2e7):\\n\\n # work on a local copy of the dataframe\\n if not debug_mode and self.df.index.str.contains('DBUG').any():\\n # previously ran in debug mode, but now done\\n df = self.remove_debug_links()\\n df.to_csv(self.linksfile + '-final.csv')\\n else:\\n df = self.df\\n\\n self.log.info('Creating Pyomo Model (debug=%s)' % debug_mode)\\n\\n model = ConcreteModel()\\n\\n model.N = Set(initialize=self.nodes)\\n model.k = Set(initialize=range(15))\\n model.A = Set(within=model.N*model.N*model.k, \\n initialize=self.links, ordered=True)\\n model.source = Param(initialize='SOURCE')\\n model.sink = Param(initialize='SINK')\\n\\n def init_params(p):\\n if p == 'cost' and debug_mode:\\n return (lambda model,i,j,k: debug_cost \\n if ('DBUG' in str(i)+'_'+str(j))\\n else 1.0)\\n else:\\n return lambda model,i,j,k: df.loc[str(i)+'_'+str(j)+'_'+str(k)][p]\\n\\n model.u = Param(model.A, initialize=init_params('upper_bound'), mutable=True)\\n model.l = Param(model.A, initialize=init_params('lower_bound'), mutable=True)\\n model.a = Param(model.A, initialize=init_params('amplitude'))\\n model.c = Param(model.A, initialize=init_params('cost'))\\n\\n # The flow over each arc\\n model.X = Var(model.A, within=Reals)\\n\\n # Minimize total cost\\n def obj_fxn(model):\\n return sum(model.c[i,j,k]*model.X[i,j,k] for (i,j,k) in model.A)\\n model.total = Objective(rule=obj_fxn, sense=minimize)\\n\\n # Enforce an upper bound limit on the flow across each arc\\n def limit_rule_upper(model, i, j, k):\\n return model.X[i,j,k] <= model.u[i,j,k]\\n model.limit_upper = Constraint(model.A, rule=limit_rule_upper)\\n\\n # Enforce a lower bound limit on the flow across each arc\\n def limit_rule_lower(model, i, j, k):\\n return model.X[i,j,k] >= model.l[i,j,k]\\n model.limit_lower = Constraint(model.A, rule=limit_rule_lower)\\n\\n # To speed up creating the mass balance constraints, first\\n # create dictionaries of arcs_in and arcs_out of every node\\n # These are NOT Pyomo data, and Pyomo does not use \\\"model._\\\" at all\\n arcs_in = {}\\n arcs_out = {}\\n\\n def arc_list_hack(model, i,j,k):\\n if j not in arcs_in:\\n arcs_in[j] = []\\n arcs_in[j].append((i,j,k))\\n\\n if i not in arcs_out:\\n arcs_out[i] = []\\n arcs_out[i].append((i,j,k))\\n return [0]\\n\\n model._ = Set(model.A, initialize=arc_list_hack)\\n\\n # Enforce flow through each node (mass balance)\\n def flow_rule(model, node):\\n if node in [value(model.source), value(model.sink)]:\\n return Constraint.Skip\\n outflow = sum(model.X[i,j,k]/model.a[i,j,k] for i,j,k in arcs_out[node])\\n inflow = sum(model.X[i,j,k] for i,j,k in arcs_in[node])\\n return inflow == outflow\\n model.flow = Constraint(model.N, rule=flow_rule)\\n\\n model.dual = Suffix(direction=Suffix.IMPORT)\\n\\n self.model = model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1d525b72f37243510024f58395907b3\",\n \"score\": \"0.5173822\",\n \"text\": \"def makeLibSource(self):\\n\\n # Make vector field (and event) file for compilation\\n # This sets the field self._eventNames\\n self._prepareEventSpecs()\\n\\n # Write the model.m file\\n allfilestr = self._prepareModelContents()\\n modelfile = os.path.join(self._target_dir, self._model_file)\\n try:\\n file = open(modelfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._model_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the events.m file\\n if len(self._eventNames) > 0:\\n allfilestr = self._prepareEventsFileContents() + self._prepareAuxContents()\\n eventsfile = os.path.join(self._target_dir, self._events_file)\\n try:\\n file = open(eventsfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._events_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n\\n # Write the initialconditions.m file\\n allfilestr = self._prepareICContents()\\n icfile = os.path.join(self._target_dir, self._ic_file)\\n try:\\n file = open(icfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._ic_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the pars.m file\\n allfilestr = self._prepareParamContents()\\n paramfile = os.path.join(self._target_dir, self._param_file)\\n try:\\n file = open(paramfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._param_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the get.m file\\n allfilestr = self._prepareGetFileContents()\\n getfile = os.path.join(self._target_dir, self._get_file)\\n try:\\n file = open(getfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._get_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the set.m file\\n allfilestr = self._prepareSetFileContents()\\n setfile = os.path.join(self._target_dir, self._set_file)\\n try:\\n file = open(setfile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._set_file+\\\" for writing\\\")\\n raise IOError(e)\\n\\n # Write the vfield.m file\\n# vfdefines = self._prepareVfieldDefines()\\n# allfilestr = self._prepareVfieldContents(vfdefines)\\n allfilestr = self.funcspec.spec[0] + self._prepareAuxContents()\\n vffile = os.path.join(self._target_dir, self._vfield_file)\\n try:\\n file = open(vffile, 'w')\\n file.write(allfilestr)\\n file.close()\\n except IOError as e:\\n print(\\\"Error opening file \\\"+self._vfield_file+\\\" for writing\\\")\\n raise IOError(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"870c6fd27ca456de9582a6aa9f045d53\",\n \"score\": \"0.51735485\",\n \"text\": \"def buildSolverModel(self, lp):\\n log.debug(\\\"create the gurobi model\\\")\\n lp.solverModel = gurobipy.Model(lp.name)\\n log.debug(\\\"set the sense of the problem\\\")\\n if lp.sense == constants.LpMaximize:\\n lp.solverModel.setAttr(\\\"ModelSense\\\", -1)\\n if self.timeLimit:\\n lp.solverModel.setParam(\\\"TimeLimit\\\", self.timeLimit)\\n gapRel = self.optionsDict.get(\\\"gapRel\\\")\\n logPath = self.optionsDict.get(\\\"logPath\\\")\\n if gapRel:\\n lp.solverModel.setParam(\\\"MIPGap\\\", gapRel)\\n if logPath:\\n lp.solverModel.setParam(\\\"LogFile\\\", logPath)\\n\\n log.debug(\\\"add the variables to the problem\\\")\\n for var in lp.variables():\\n lowBound = var.lowBound\\n if lowBound is None:\\n lowBound = -gurobipy.GRB.INFINITY\\n upBound = var.upBound\\n if upBound is None:\\n upBound = gurobipy.GRB.INFINITY\\n obj = lp.objective.get(var, 0.0)\\n varType = gurobipy.GRB.CONTINUOUS\\n if var.cat == constants.LpInteger and self.mip:\\n varType = gurobipy.GRB.INTEGER\\n var.solverVar = lp.solverModel.addVar(\\n lowBound, upBound, vtype=varType, obj=obj, name=var.name\\n )\\n if self.optionsDict.get(\\\"warmStart\\\", False):\\n # Once lp.variables() has been used at least once in the building of the model.\\n # we can use the lp._variables with the cache.\\n for var in lp._variables:\\n if var.varValue is not None:\\n var.solverVar.start = var.varValue\\n\\n lp.solverModel.update()\\n log.debug(\\\"add the Constraints to the problem\\\")\\n for name, constraint in lp.constraints.items():\\n # build the expression\\n expr = gurobipy.LinExpr(\\n list(constraint.values()), [v.solverVar for v in constraint.keys()]\\n )\\n if constraint.sense == constants.LpConstraintLE:\\n relation = gurobipy.GRB.LESS_EQUAL\\n elif constraint.sense == constants.LpConstraintGE:\\n relation = gurobipy.GRB.GREATER_EQUAL\\n elif constraint.sense == constants.LpConstraintEQ:\\n relation = gurobipy.GRB.EQUAL\\n else:\\n raise PulpSolverError(\\\"Detected an invalid constraint type\\\")\\n constraint.solverConstraint = lp.solverModel.addConstr(\\n expr, relation, -constraint.constant, name\\n )\\n lp.solverModel.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f807685d80245425900a66f5f81872f5\",\n \"score\": \"0.5164706\",\n \"text\": \"def import_model(self, file):\\n felems = os.path.splitext(file)\\n ext = felems[1].lower()\\n if ext in (\\\".gz\\\", \\\".zip\\\",):\\n ext = os.path.splitext(felems[0])[1].lower()\\n\\n if ext == \\\".cpo\\\":\\n import docplex.cp.cpo.cpo_parser as cpo_parser\\n prs = cpo_parser.CpoParser(self)\\n prs.parse(file)\\n\\n elif ext == \\\".fzn\\\":\\n import docplex.cp.fzn.fzn_parser as fzn_parser\\n prs = fzn_parser.FznParser(self)\\n prs.parse(file)\\n # Get model to force compilation\\n prs.get_model()\\n\\n elif ext == \\\".lp\\\":\\n import docplex.cp.lp.lp_parser as lp_parser\\n prs = lp_parser.LpParser(self)\\n prs.parse(file)\\n\\n else:\\n raise CpoException(\\\"Unknown '{}' file format. Only .cpo, .fzn and .lp are supported.\\\".format(ext))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c04ce9f517817a518f872f20d83633\",\n \"score\": \"0.51574755\",\n \"text\": \"def to_automl(data, path='.', name='autodata'):\\n # check if folder exists\\n dir = os.path.join(path, name+'_automl')\\n if not os.path.exists(dir):\\n # create folder\\n os.mkdir(dir)\\n data.descriptors().to_csv(os.path.join(dir, name+'.info'), header=True) # some information\\n if data.has_class():\\n pd.DataFrame(data.indexes['X']).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\\n pd.DataFrame(data.indexes['y']).to_csv(os.path.join(dir, name+'_label.name'), index=False, header=False) # label name\\n if 'train' in data.indexes: # train/test and X/y splits\\n data.get_data('X_train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\\n data.get_data('X_test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\\n data.get_data('y_train').to_csv(os.path.join(dir, name+'_train.solution'), sep=' ', index=False, header=False) # train solution\\n data.get_data('y_test').to_csv(os.path.join(dir, name+'_test.solution'), sep=' ', index=False, header=False) # test solution\\n else: # only X/y split\\n data.get_data('X').to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data\\n data.get_data('y').to_csv(os.path.join(dir, name+'.solution'), sep=' ', index=False, header=False) # solution\\n else:\\n pd.DataFrame(data.columns).to_csv(os.path.join(dir, name+'_feat.name'), index=False, header=False) # feat name\\n if 'train' in data.indexes: # only train/test split\\n data.get_data('train').to_csv(os.path.join(dir, name+'_train.data'), sep=' ', index=False, header=False) # train data\\n data.get_data('test').to_csv(os.path.join(dir, name+'_test.data'), sep=' ', index=False, header=False) # test data\\n else: # no split at all\\n data.to_csv(os.path.join(dir, name+'.data'), sep=' ', index=False, header=False) # data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311b06e5d22497504730712619f4da06\",\n \"score\": \"0.5155293\",\n \"text\": \"def _make_input_file(self, teff_lims, teff_step, logg_lims, logg_step,\\n feh_lims, feh_step, vsini_lims, vsini_step, \\n vmicro_lims, vmicro_step, resolution):\\n output_string = '{:.1f} {:.0f} {:.1f}\\\\n'.format(teff_lims[0], \\n teff_step, \\n teff_lims[-1])\\n\\n output_string += '{:.1f} {:.1f} {:.1f}\\\\n'.format(logg_lims[0],\\n logg_step,\\n logg_lims[1])\\n\\n output_string += '{:.1f} {:.1f} {:.1f}\\\\n'.format(vmicro_lims[0],\\n vmicro_step,\\n vmicro_lims[1])\\n\\n output_string += '{:.1f} {:.1f} {:.1f}\\\\n'.format(vsini_lims[0],\\n vsini_step,\\n vsini_lims[1])\\n\\n output_string += \\\"skip 0.03 0.02 0.07 !dilution factor\\\\n\\\"\\n\\n output_string += 'skip {:.1f} {:.1f} {:.1f}\\\\n'.format(feh_lims[0],\\n feh_step,\\n feh_lims[1])\\n \\n output_string += 'He 0.04 0.005 0.06 ! Individual abundance\\\\n'\\n\\n output_string += '0.0 {:.0f}\\\\n'.format(resolution)\\n\\n output_string += '{}\\\\n{}\\\\n'.format(self.abundance_table, self.model_dir)\\n\\n output_string += '2 1 !atmosphere model vmicro and mass\\\\n'\\n output_string += 'ST ! model atmosphere chemical composition flag\\\\n'\\n\\n dx = self.data.x[1] - self.data.x[0]\\n output_string += '1 {:.5f} fit\\\\n'.format(dx)\\n\\n output_string += 'data_sets/{}.txt\\\\n'.format(self.output_basename)\\n\\n output_string += '0.5 0.99 0.0 adjust ! RV determination stuff\\\\n'\\n\\n xmin, xmax = self.data.x[0]-1, self.data.x[-1]+1\\n output_string += '{:.1f} {:.1f}\\\\n'.format(xmin, xmax)\\n\\n outfilename = '{}.inp'.format(self.output_basename)\\n with open(outfilename, 'w') as outfile:\\n outfile.write(output_string)\\n\\n return outfilename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae6ddb4fe2249629c719de91f2cabee\",\n \"score\": \"0.51552427\",\n \"text\": \"def write(self, filename='pflotran.in'):\\n\\n if filename:\\n self.filename = filename\\n outfile = open(self.filename, 'w')\\n\\n # Presumes simulation.simulation_type is required\\n if self.simulation.simulation_type:\\n self._write_simulation(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'simulation is required, it is currently reading as empty')\\n\\n if self.simulation.subsurface_flow or \\\\\\n self.simulation.subsurface_transport:\\n self._write_subsurface_simulation_begin(outfile)\\n\\n if self.reference_pressure:\\n self._write_reference_pressure(outfile)\\n\\n if self.reference_temperature:\\n self._write_reference_temperature(outfile)\\n\\n if self.co2_database:\\n self._write_co2_database(outfile)\\n\\n if self.regression.cells \\\\\\n or self.regression.cells_per_process \\\\\\n or self.regression.all_cells:\\n self._write_regression(outfile)\\n\\n if self.uniform_velocity.value_list:\\n self._write_uniform_velocity(outfile)\\n\\n if self.specified_velocity:\\n self._write_specified_velocity(outfile)\\n\\n if self.update_flow_permeability:\\n self._write_update_flow_permeability(outfile)\\n\\n if self.reference_liquid_density:\\n self._write_reference_liquid_density(outfile)\\n\\n if self.minimum_hydrostatic_pressure:\\n self._write_minimum_hydrostatic_pressure(outfile)\\n\\n if self.reference_stress_state.value_list:\\n self._write_reference_stress_state(outfile)\\n\\n if self.eoslist:\\n self._write_eos(outfile)\\n\\n if self.klinkenberg_effect:\\n self._write_klinkenberg_effect(outfile)\\n\\n if self.creep_closure_table:\\n self._write_creep_closure_table(outfile)\\n\\n if self.nonuniform_velocity.filename:\\n self._write_nonuniform_velocity(outfile)\\n\\n if self.simulation.mode.upper() == 'MPHASE' and self.co2_database:\\n self._write_co2_database(outfile)\\n\\n if self.multiple_continuum:\\n self._write_multiple_continuum(outfile)\\n\\n if self.overwrite_restart_flow_params:\\n self._write_overwrite_restart_flow(outfile)\\n\\n if self.overwrite_restart_transport:\\n self._write_overwrite_restart_transport(outfile)\\n\\n if self.initialize_flow_from_file is not None:\\n self._write_initialize_flow_from_file(outfile)\\n\\n if self.initialize_transport_from_file is not None:\\n self._write_initialize_transport_from_file(outfile)\\n\\n if self.isothermal:\\n self._write_isothermal(outfile)\\n\\n if self.reference_porosity:\\n self._write_reference_porosity(outfile)\\n\\n if self.datasetlist:\\n self._write_dataset(outfile)\\n # else: print 'info: dataset name not detected\\\\n'\\n\\n if self.chemistry:\\n self._write_chemistry(outfile)\\n # else: print 'info: chemistry not detected\\\\n'\\n\\n if self.grid:\\n self._write_grid(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'grid is required, it is currently reading as empty!')\\n\\n if self.timestepper_flow or self.timestepper_transport:\\n self._write_timestepper(outfile)\\n # else: print 'info: timestepper not detected\\\\n'\\n\\n if self.time:\\n self._write_time(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'time is required, it is currently reading as empty!')\\n\\n if self.proplist:\\n self._write_prop(outfile)\\n else:\\n PyFLOTRAN_WARNING(\\n 'material property list is empty! ' +\\n ' Using default material property settings')\\n self.add(pmaterial())\\n self._write_prop(outfile)\\n\\n if self.lsolverlist:\\n self._write_lsolver(outfile)\\n # else: print 'info: lsolverlist (linear solver list) not detected\\\\n'\\n\\n if self.nsolverlist:\\n self._write_nsolver(outfile)\\n # else: print 'info: nsolverlist (newton solver list) not detected\\\\n'\\n\\n if self.output:\\n self._write_output(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'output is required, it is currently reading as empty!')\\n\\n if self.fluidlist:\\n self._write_fluid(outfile)\\n\\n if self.saturationlist:\\n self._write_saturation(outfile)\\n elif self.charlist:\\n self._write_characteristic_curves(outfile)\\n else:\\n if self.simulation.subsurface_flow:\\n self.add(pcharacteristic_curves())\\n self._write_characteristic_curves(outfile)\\n PyFLOTRAN_WARNING(\\n 'characteristic_curves list or saturation list ' +\\n 'is required, it is currently reading as empty! ' +\\n ' Using default characteristic_curves settings')\\n\\n if self.regionlist:\\n self._write_region(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'regionlist is required, it is currently reading as empty!')\\n\\n if self.integral_flux_list:\\n self._write_integral_flux(outfile)\\n\\n if self.observation_list:\\n self._write_observation(outfile)\\n\\n if self.flowlist:\\n self._write_flow(outfile)\\n\\n if self.transportlist:\\n self._write_transport(outfile)\\n\\n if self.initial_condition_list:\\n self._write_initial_condition(outfile)\\n else:\\n raise PyFLOTRAN_ERROR(\\n 'initial_condition_list is required,' +\\n 'it is currently reading as empty!')\\n\\n if self.boundary_condition_list:\\n self._write_boundary_condition(outfile)\\n\\n if self.source_sink_list:\\n self._write_source_sink(outfile)\\n\\n if self.source_sink_sandbox_list:\\n self._write_source_sink_sandbox(outfile)\\n\\n if self.reference_saturation:\\n self._write_reference_saturation(outfile)\\n\\n if self.strata_list:\\n self._write_strata(outfile)\\n else:\\n PyFLOTRAN_WARNING(\\n 'stratigraphy_coupler is required, ' +\\n 'it is currently reading as empty! ' +\\n 'Using default settings')\\n self.add(pstrata())\\n self._write_strata(outfile)\\n\\n if self.constraint_list:\\n self._write_constraint(outfile)\\n\\n if self.rt_mass_transfer_list:\\n self._write_rt_mass_transfer(outfile)\\n\\n if self.flow_mass_transfer_list:\\n self._write_flow_mass_transfer(outfile)\\n\\n if self.simulation.subsurface_flow or \\\\\\n self.simulation.subsurface_transport:\\n self._write_subsurface_simulation_end(outfile)\\n\\n\\n # -----------------------------------------------\\n\\n if self.simulation.surface_subsurface:\\n self._write_surface_subsurface_begin(outfile)\\n\\n if self.surface_flow:\\n self._write_surface_flow(outfile)\\n\\n self._write_surface_subsurface_end(outfile)\\n\\n # -----------------------------------------------\\n\\n if self.simulation.simulation_type == 'hydroquake':\\n self._write_hydroquake(outfile)\\n\\n if self.simulation.simulation_type.lower() == 'geomechanics_subsurface':\\n self._write_geomechanics(outfile)\\n\\n if self.waste_form_general:\\n self._write_wasteform_general(outfile)\\n\\n if self.ufd_decay:\\n self._write_ufd_decay(outfile)\\n\\n if self.ufd_biosphere:\\n self._write_ufd_biosphere(outfile)\\n\\n if self.wipp_source_sink:\\n self._write_wipp_source_sink(outfile)\\n\\n outfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6217bff10682c1129e9efb6eb05446d3\",\n \"score\": \"0.5149605\",\n \"text\": \"def write(solution):\\n trimmed_solution = solution\\n input_file_name_stripped = trimmed_solution.name\\n cwd = os.getcwd()\\n output_file_name = os.path.join(cwd,\\n 'pym_' +\\n input_file_name_stripped +\\n '.inp')\\n f = open(output_file_name, 'w+')\\n\\n #Work functions\\n\\n calories_constant = 4184.0 #number of calories in 1000 Joules of energy\\n def eliminate(input_string, char_to_replace, spaces='single'):\\n \\\"\\\"\\\"\\n Eliminate characters from a string\\n\\n :param input_string\\n string to be modified\\n :param char_to_replace\\n array of character strings to be removed\\n \\\"\\\"\\\"\\n for char in char_to_replace:\\n input_string = input_string.replace(char, \\\"\\\")\\n if spaces == 'double':\\n input_string = input_string.replace(\\\" \\\", \\\" \\\")\\n return input_string\\n\\n def section_break(title):\\n \\\"\\\"\\\"\\n Insert break and new section title into cti file\\n\\n :param title:\\n title string for next section_break\\n \\\"\\\"\\\"\\n f.write('!'+ \\\"-\\\"*75 + '\\\\n')\\n f.write('! ' + title +'\\\\n')\\n f.write('!'+ \\\"-\\\"*75 + '\\\\n')\\n\\n def replace_multiple(input_string, replace_list):\\n \\\"\\\"\\\"\\n Replace multiple characters in a string\\n\\n :param input_string\\n string to be modified\\n :param replace list\\n list containing items to be replaced (value replaces key)\\n \\\"\\\"\\\"\\n for original_character, new_character in replace_list.items():\\n input_string = input_string.replace(original_character,\\n new_character)\\n return input_string\\n\\n def build_arrhenius(equation_object, equation_type):\\n \\\"\\\"\\\"\\n Builds Arrhenius coefficient string\\n\\n :param equation_objects\\n cantera equation object\\n :param equation_type:\\n string of equation type\\n \\\"\\\"\\\"\\n coeff_sum = sum(equation_object.reactants.values())\\n pre_exponential_factor = equation_object.rate.pre_exponential_factor\\n temperature_exponent = '{:.3f}'.format(equation_object.rate.temperature_exponent)\\n activation_energy = '{:.2f}'.format(equation_object.rate.activation_energy / calories_constant)\\n if equation_type == 'ElementaryReaction':\\n if coeff_sum == 1:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor))\\n if coeff_sum == 2:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor*10**3))\\n if coeff_sum == 3:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor*10**6))\\n if equation_type == 'ThreeBodyReaction':\\n if coeff_sum == 1:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor*10**3))\\n if coeff_sum == 2:\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor*10**6))\\n if (equation_type != 'ElementaryReaction'\\n and equation_type != 'ThreeBodyReaction'):\\n pre_exponential_factor = str(\\n '{:.3E}'.format(pre_exponential_factor))\\n arrhenius = [pre_exponential_factor,\\n temperature_exponent,\\n activation_energy]\\n return arrhenius\\n\\n def build_modified_arrhenius(equation_object, t_range):\\n \\\"\\\"\\\"\\n Builds Arrhenius coefficient strings for high and low temperature ranges\\n\\n :param equation_objects\\n cantera equation object\\n :param t_range:\\n simple string ('high' or 'low') to designate temperature range\\n \\\"\\\"\\\"\\n if t_range == 'high':\\n pre_exponential_factor = equation_object.high_rate.pre_exponential_factor\\n temperature_exponent = '{:.3f}'.format(equation_object.high_rate.temperature_exponent)\\n activation_energy = '{:.2f}'.format(equation_object.high_rate.activation_energy/calories_constant)\\n if len(equation_object.products) == 1:\\n pre_exponential_factor = str(\\n '{:.5E}'.format(pre_exponential_factor*10**3))\\n else:\\n pre_exponential_factor = str(\\n '{:.5E}'.format(pre_exponential_factor))\\n arrhenius_high = [pre_exponential_factor,\\n temperature_exponent,\\n activation_energy]\\n return arrhenius_high\\n if t_range == 'low':\\n\\n pre_exponential_factor = equation_object.low_rate.pre_exponential_factor\\n temperature_exponent = '{:.3f}'.format(equation_object.low_rate.temperature_exponent)\\n activation_energy = '{:.2f}'.format(equation_object.low_rate.activation_energy/calories_constant)\\n if len(equation_object.products) == 1:\\n pre_exponential_factor = str(\\n '{:.5E}'.format(pre_exponential_factor*10**6))\\n else:\\n pre_exponential_factor = str(\\n '{:.5E}'.format(pre_exponential_factor*10**3))\\n\\n arrhenius_low = [pre_exponential_factor,\\n temperature_exponent,\\n activation_energy]\\n return arrhenius_low\\n\\n\\n def build_nasa(nasa_coeffs, row):\\n \\\"\\\"\\\"\\n Creates string of nasa polynomial coefficients\\n\\n :param nasa_coeffs\\n cantera species thermo coefficients object\\n :param row\\n which row to write coefficients in\\n \\\"\\\"\\\"\\n line_coeffs = ''\\n lines = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]]\\n line_index = lines[row-2]\\n for ix, c in enumerate(nasa_coeffs):\\n if ix in line_index:\\n if c >= 0:\\n line_coeffs += ' '\\n line_coeffs += str('{:.8e}'.format(c))\\n return line_coeffs\\n\\n def build_species_string():\\n \\\"\\\"\\\"\\n formats species string for writing\\n \\\"\\\"\\\"\\n species_list_string = ''\\n line = 1\\n for sp_index, sp_string in enumerate(trimmed_solution.species_names):\\n sp = ' '\\n #get length of string next species is added\\n length_new = len(sp_string)\\n length_string = len(species_list_string)\\n total = length_new + length_string + 3\\n #if string will go over width, wrap to new line\\n if total >= 70*line:\\n species_list_string += '\\\\n'\\n line += 1\\n species_list_string += sp_string + ((16-len(sp_string))*sp)\\n return species_list_string\\n\\n #Write title block to file\\n section_break('Chemkin File converted from Solution Object by pyMARS')\\n\\n #Write phase definition to file\\n element_names = eliminate(str(trimmed_solution.element_names),\\n ['[', ']', '\\\\'', ','])\\n element_string = Template(\\n 'ELEMENTS\\\\n' +\\n '$element_names\\\\n' +\\n 'END\\\\n')\\n f.write(element_string.substitute(element_names=element_names))\\n species_names = build_species_string()\\n species_string = Template(\\n 'SPECIES\\\\n' +\\n '$species_names\\\\n'+\\n 'END\\\\n')\\n f.write(species_string.substitute(species_names=species_names))\\n\\n #Write species to file\\n section_break('Species data')\\n f.write('THERMO ALL' + '\\\\n' +\\n ' 300.000 1000.000 5000.000' +'\\\\n')\\n phase_unknown_list = []\\n\\n #write data for each species in the Solution object\\n for sp_index in xrange(len(trimmed_solution.species_names)):\\n d = 3.33564e-30 #1 debye = d coulomb-meters\\n species = trimmed_solution.species(sp_index)\\n name = str(trimmed_solution.species(sp_index).name)\\n nasa_coeffs = trimmed_solution.species(sp_index).thermo.coeffs\\n #Species attributes from trimmed solution object\\n t_low = '{0:.3f}'.format(species.thermo.min_temp)\\n t_max = '{0:.3f}'.format(species.thermo.max_temp)\\n t_mid = '{0:.3f}'.format(species.thermo.coeffs[0])\\n temp_range = str(t_low) + ' ' + str(t_max) + ' ' + t_mid\\n species_comp = ''\\n for atom in species.composition:\\n species_comp += '{:<4}'.format(atom)\\n species_comp += str(int(species.composition[atom]))\\n if type(species.transport).__name__ == 'GasTransportData':\\n species_phase = 'G'\\n else:\\n phase_unknown_list.append(name)\\n species_phase = 'G'\\n line_1 = (\\n '{:<18}'.format(name) +\\n '{:<6}'.format(' ') +\\n '{:<20}'.format(species_comp) +\\n '{:<4}'.format(species_phase) +\\n '{:<31}'.format(temp_range) +\\n '{:<1}'.format('1') +\\n '\\\\n')\\n f.write(line_1)\\n line_2_coeffs = build_nasa(nasa_coeffs, 2)\\n line_2 = line_2_coeffs + ' 2\\\\n'\\n f.write(line_2)\\n line_3_coeffs = build_nasa(nasa_coeffs, 3)\\n line_3 = line_3_coeffs + ' 3\\\\n'\\n f.write(line_3)\\n line_4_coeffs = build_nasa(nasa_coeffs, 4)\\n line_4 = line_4_coeffs + ' 4\\\\n'\\n f.write(line_4)\\n\\n f.write('END\\\\n')\\n\\n #Write reactions to file\\n section_break('Reaction Data')\\n f.write('REACTIONS\\\\n')\\n #write data for each reaction in the Solution Object\\n for reac_index in xrange(len(trimmed_solution.reaction_equations())):\\n equation_string = str(trimmed_solution.reaction_equation(reac_index))\\n equation_string = eliminate(equation_string, ' ', 'single')\\n equation_object = trimmed_solution.reaction(reac_index)\\n equation_type = type(equation_object).__name__\\n m = str(reac_index+1)\\n if equation_type == 'ThreeBodyReaction':\\n arrhenius = build_arrhenius(equation_object, equation_type)\\n main_line = (\\n '{:<51}'.format(equation_string) +\\n '{:>9}'.format(arrhenius[0]) +\\n '{:>9}'.format(arrhenius[1]) +\\n '{:>11}'.format(arrhenius[2]) +\\n '\\\\n')\\n f.write(main_line)\\n #trimms efficiencies list\\n efficiencies = equation_object.efficiencies\\n trimmed_efficiencies = equation_object.efficiencies\\n for s in efficiencies:\\n if s not in trimmed_solution.species_names:\\n del trimmed_efficiencies[s]\\n replace_list_2 = {\\n '{':'',\\n '}':'/',\\n '\\\\'':'',\\n ':':'/',\\n ',':'/'}\\n efficiencies_string = replace_multiple(\\n str(trimmed_efficiencies),\\n replace_list_2)\\n secondary_line = str(efficiencies_string) + '\\\\n'\\n if bool(efficiencies) is True:\\n f.write(secondary_line)\\n if equation_type == 'ElementaryReaction':\\n arrhenius = build_arrhenius(equation_object, equation_type)\\n main_line = (\\n '{:<51}'.format(equation_string) +\\n '{:>9}'.format(arrhenius[0]) +\\n '{:>9}'.format(arrhenius[1]) +\\n '{:>11}'.format(arrhenius[2]) +\\n '\\\\n')\\n f.write(main_line)\\n if equation_type == 'FalloffReaction':\\n arr_high = build_modified_arrhenius(equation_object, 'high')\\n main_line = (\\n '{:<51}'.format(equation_string) +\\n '{:>9}'.format(arr_high[0]) +\\n '{:>9}'.format(arr_high[1]) +\\n '{:>11}'.format(arr_high[2]) +\\n '\\\\n')\\n f.write(main_line)\\n arr_low = build_modified_arrhenius(equation_object, 'low')\\n second_line = (\\n ' LOW /' +\\n ' ' + arr_low[0] +\\n ' ' + arr_low[1] +\\n ' ' + arr_low[2] + '/\\\\n')\\n f.write(second_line)\\n j = equation_object.falloff.parameters\\n #If optional Arrhenius data included:\\n try:\\n third_line = (\\n ' TROE/' +\\n ' ' + str(j[0]) +\\n ' ' + str(j[1]) +\\n ' ' + str(j[2]) +\\n ' ' + str(j[3]) +' /\\\\n')\\n f.write(third_line)\\n except IndexError:\\n pass\\n #trimms efficiencies list\\n efficiencies = equation_object.efficiencies\\n trimmed_efficiencies = equation_object.efficiencies\\n for s in efficiencies:\\n if s not in trimmed_solution.species_names:\\n del trimmed_efficiencies[s]\\n replace_list_2 = {\\n '{':'',\\n '}':'/',\\n '\\\\'':'',\\n ':':'/',\\n ',':'/'}\\n efficiencies_string = replace_multiple(\\n str(trimmed_efficiencies),\\n replace_list_2)\\n\\n fourth_line = str(efficiencies_string) + '\\\\n'\\n if bool(efficiencies) is True:\\n f.write(fourth_line)\\n #dupluicate option\\n if equation_object.duplicate is True:\\n duplicate_line = ' DUPLICATE' +'\\\\n'\\n f.write(duplicate_line)\\n f.write('END')\\n f.close()\\n\\n #Test mechanism file\\n\\n original_solution = solution\\n #convert written chemkin file to cti, and get solution\\n parser = ck2cti.Parser()\\n outName = 'test_file.cti'\\n parser.convertMech(output_file_name, outName=outName)\\n new_solution = ct.Solution(outName)\\n\\n #test new solution vs original solutoin\\n #test(original_solution, new_solution)\\n os.remove(outName)\\n return output_file_name\\n\\n\\n \\\"\\\"\\\"\\n def build_falloff(j):\\n\\n Creates falloff reaction Troe parameter string\\n\\n param j:\\n Cantera falloff parameters object\\n\\n falloff_str = str(',\\\\n falloff = Troe(' +\\n 'A = ' + str(j[0]) +\\n ', T3 = ' + str(j[1]) +\\n ', T1 = ' + str(j[2]) +\\n ', T2 = ' + str(j[3]) +') )\\\\n\\\\n')\\n return falloff_str\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9fe475543f8a500b17238e2c01e2a8\",\n \"score\": \"0.5131358\",\n \"text\": \"def writeDataFile(self):\\n model = pe.PEChainBuilder(self.lenPa, self.numPa, self.lenPc, self.numPc,\\\\\\n self.volRatio, self.chargeFraction)\\n model.genChains()\\n \\n \\n model.writeFiles(self.directory)\\n self.dataFileName = \\\"data.pe.la{0}.na{1}.lc{2}.nc{3}.rho{4}.r{5}.lammps\\\".\\\\\\n format(self.lenPa, self.numPa, self.lenPc, self.numPc, \\\\\\n self.volRatio, self.chargeRepeat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc59f59e013fe69fd099020e5707bc62\",\n \"score\": \"0.51224095\",\n \"text\": \"def createPrismModel(states, transitions, target, mode, nr_actions, reset_transitions, reset_cost):\\n out_file = open(TMP_MODEL_PATH, 'w')\\n # module\\n out_file.write(\\\"mdp\\\\n\\\\nmodule tmp\\\\n\\\\n\\\")\\n\\n # number of state and initial state\\n out_file.write(\\\"\\\\ts : [0..\\\" + str(len(states)) + \\\"] init \\\" + str(abs(mode)) + \\\";\\\\n\\\\n\\\")\\n\\n # transitions\\n for state in range(len(transitions)):\\n for action in range(len(transitions[state])):\\n if sum(transitions[state][action]) != 0:\\n out_file.write(\\\"\\\\t[\\\" + chr(97 + action) + \\\"] s=\\\" + str(state) + \\\"-> \\\")\\n destinations = []\\n for dest in range(len(transitions[state][action])):\\n if transitions[state][action][dest] != 0:\\n destinations.append(str(transitions[state][action][dest]) + \\\":(s'=\\\" + str(dest) + \\\")\\\")\\n out_file.write(\\\" + \\\".join(destinations))\\n out_file.write(\\\";\\\\n\\\")\\n out_file.write(\\\"\\\\nendmodule\\\\n\\\\n\\\")\\n\\n # label target\\n out_file.write('label \\\"target\\\" = ')\\n target = list(set(target))\\n target = [\\\"(s=\\\" + str(x) + \\\")\\\" for x in target]\\n if len(target) == 0:\\n out_file.write(\\\"(s=\\\" + str(len(states) + 1) + \\\");\\\\n\\\")\\n else:\\n out_file.write(\\\" | \\\".join(target))\\n out_file.write(\\\";\\\\n\\\")\\n\\n # label states\\n if mode == MODE_MAX:\\n out_file.write('label \\\"sink\\\" = (s=0);\\\\n')\\n\\n for i in range(mode, len(states)):\\n out_file.write('label \\\"s' + str(states[i][0]) + '_obs' + str(states[i][1]) + '\\\" = (s=' + str(i) + ');\\\\n')\\n\\n if mode == MODE_MIN:\\n out_file.write(\\\"\\\\nrewards\\\\n\\\")\\n if reset_cost == 1:\\n out_file.write(\\\"\\\\ttrue:1;\\\\n\\\")\\n else:\\n for i in range(len(states)):\\n for j in range(nr_actions):\\n if j in reset_transitions[i]:\\n out_file.write(\\\"\\\\t[\\\" + chr(97 + j) + \\\"] (s=\\\" + str(i) + \\\") : \\\" + str(reset_cost) + \\\";\\\\n\\\")\\n else:\\n out_file.write(\\\"\\\\t[\\\" + chr(97 + j) + \\\"] (s=\\\" + str(i) + \\\") : 1;\\\\n\\\")\\n\\n out_file.write(\\\"endrewards\\\\n\\\")\\n\\n out_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f391c50f49d5b0eae5ebbf6c5f12961a\",\n \"score\": \"0.5118555\",\n \"text\": \"def build_LM(in_file):\\r\\n print 'building language models...'\\r\\n # This is an empty method\\r\\n # Pls implement your code in below\\r\\n LMs = {'malaysian': {}, 'indonesian': {}, 'tamil': {}}\\r\\n malay_LM = LMs['malaysian']\\r\\n indon_LM = LMs['indonesian']\\r\\n tamil_LM = LMs['tamil']\\r\\n\\r\\n FD = open(in_file, 'r')\\r\\n\\r\\n lines = FD.read()\\r\\n lines_array = lines.replace('\\\\r', ' ').split('\\\\n')\\r\\n lines_array.pop(len(lines_array) - 1)\\r\\n\\r\\n for line in lines_array:\\r\\n line_array = line.split(' ', 1)\\r\\n lang = line_array[0]\\r\\n text = line_array[1]\\r\\n current_LM = LMs[lang]\\r\\n four_char_array = get_four_char_array(text)\\r\\n\\r\\n for four_char in four_char_array:\\r\\n if four_char not in current_LM:\\r\\n # performs add one smoothing\\r\\n malay_LM[four_char] = 1\\r\\n indon_LM[four_char] = 1\\r\\n tamil_LM[four_char] = 1\\r\\n current_LM[four_char] += 1\\r\\n\\r\\n # calculates ratio of set frequency over total frequency\\r\\n # for all sets in each of the three LMs\\r\\n for LM in LMs.values():\\r\\n count = 0\\r\\n for freq_value in LM.values():\\r\\n count += freq_value\\r\\n for four_char_key in LM.keys():\\r\\n LM[four_char_key] /= count\\r\\n return LMs\\r\\n\\r\\n FD.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c862647fcd5ba7f408d748abaf7ab617\",\n \"score\": \"0.5115003\",\n \"text\": \"def test_ImportUFONoSideEffectNLO(self):\\n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('loop_sm'),False)\\n original_all_particles = copy.copy(ufo_model.all_particles)\\n original_all_vertices = copy.copy(ufo_model.all_vertices)\\n original_all_couplings = copy.copy(ufo_model.all_couplings)\\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\\n original_all_parameters = copy.copy(ufo_model.all_parameters)\\n original_all_orders = copy.copy(ufo_model.all_orders)\\n original_all_functions = copy.copy(ufo_model.all_functions)\\n original_all_CTvertices = copy.copy(ufo_model.all_CTvertices)\\n original_all_CTparameters = copy.copy(ufo_model.all_CTparameters)\\n\\n\\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\\n model = ufo2mg5_converter.load_model()\\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \\n\\n self.assertEqual(original_all_particles,ufo_model.all_particles)\\n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\\n# self.assertEqual(original_all_couplings,ufo_model.all_couplings)\\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\\n self.assertEqual(original_all_orders,ufo_model.all_orders)\\n self.assertEqual(original_all_functions,ufo_model.all_functions)\\n self.assertEqual(original_all_CTvertices,ufo_model.all_CTvertices)\\n self.assertEqual(original_all_CTparameters,ufo_model.all_CTparameters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfe119951be54a560fcefe9057a921e1\",\n \"score\": \"0.5112634\",\n \"text\": \"def main(models, output_dir):\\n\\tfor model in models:\\n\\t\\tobj = ParseOmsa(model)\\n\\t\\tobj.bios_version()\\n\\t\\tobj.perc_versions()\\n\\t\\tobj.write_yaml(output_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e7eaf7ed485ca9233fa3bbe1a1b96e\",\n \"score\": \"0.5110691\",\n \"text\": \"def solve_lp(self):\\n self._initialize_model()\\n self._initialize_node_variables_lp()\\n self._initialize_edge_variables_lp()\\n self._initialize_objective()\\n self._initialize_contraint_nodes()\\n self._initialize_constraint_edges()\\n self._initialize_constraint_terminals()\\n self._run_solver()\\n self._calculate_cut_value()\\n self._calculate_source_sets()\\n self._calculate_possible_terminals_by_node_weak()\\n self._calculate_possible_terminals_by_node_strong()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6586aa3830f43b54dce72f69cbc0963d\",\n \"score\": \"0.5104416\",\n \"text\": \"def loadLoopToyModel():\\n \\n mypartlist = base_objects.ParticleList()\\n myinterlist = base_objects.InteractionList()\\n myloopmodel = loop_base_objects.LoopModel()\\n\\n # A gluon\\n mypartlist.append(base_objects.Particle({'name':'g',\\n 'antiname':'g',\\n 'spin':3,\\n 'color':8,\\n 'mass':'ZERO',\\n 'width':'ZERO',\\n #'texname':'G',\\n #'antitexname':'G',\\n 'line':'curly',\\n 'charge':0.,\\n 'pdg_code':21,\\n #'propagating':True,\\n 'propagator':0, \\n 'is_part':True,\\n 'counterterm':{('QCD', ((6,),)): {0: 'UVWfct_G_1', -1: 'UVWfct_G_1_1eps'}, ('QCD', ((5,),)): {0: 'UVWfct_G_0', -1: 'UVWfct_G_0_1eps'}},\\n 'self_antipart':True}))\\n \\n # A quark U and its antiparticle\\n mypartlist.append(base_objects.Particle({'name':'u',\\n 'antiname':'u~',\\n 'spin':2,\\n 'color':3,\\n 'mass':'ZERO',\\n 'width':'ZERO',\\n #'texname':'u',\\n #'antitexname':'u',\\n 'line':'straight',\\n 'charge':2. / 3.,\\n 'pdg_code':2,\\n #'propagating':True,\\n 'propagator':'', \\n 'is_part':True,\\n 'self_antipart':False}))\\n antiu = copy.copy(mypartlist[1])\\n antiu.set('is_part', False)\\n\\n # A quark D and its antiparticle\\n mypartlist.append(base_objects.Particle({'name':'d',\\n 'antiname':'d~',\\n 'spin':2,\\n 'color':3,\\n 'mass':'ZERO',\\n 'width':'ZERO',\\n #'texname':'d',\\n #'antitexname':'d',\\n 'line':'straight',\\n 'charge':-1. / 3.,\\n 'pdg_code':1,\\n #'propagating':True,\\n 'propagator':'',\\n 'is_part':True,\\n 'self_antipart':False}))\\n antid = copy.copy(mypartlist[2])\\n antid.set('is_part', False)\\n\\n myloopmodel.set('particles', mypartlist)\\n myloopmodel.set('couplings', ['QCD']) \\n myloopmodel.set('interactions', myinterlist)\\n myloopmodel.set('perturbation_couplings', ['QCD'])\\n myloopmodel.set('order_hierarchy', {'QCD':1})\\n\\n return myloopmodel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695e98b499f902207ba35ccac8891bda\",\n \"score\": \"0.5104137\",\n \"text\": \"def generate(genconf_model, project_folder):\\n\\n gendesc = get_generator_desc(genconf_model.gen_name)\\n meta = get_language(gendesc.lang)\\n\\n # Path for templates overrides\\n templates_path = os.path.join(project_folder, 'templates',\\n genconf_model.gen_name)\\n\\n output_root = os.path.join(project_folder, genconf_model.output)\\n\\n def _create_folder(output_file):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except FileExistsError:\\n pass\\n\\n # For each model configured in the current genconf\\n for model_path in genconf_model.models:\\n\\n click.echo('Processing model \\\"{}\\\"'.format(model_path))\\n model = meta.model_from_file(os.path.join(project_folder, 'model',\\n model_path))\\n\\n # Validate model using generator specific validation.\\n if gendesc.validate:\\n gendesc.validate(model)\\n\\n params = {}\\n # Adding generator params\\n for p in genconf_model.params:\\n params[p.name] = p.value\\n\\n # Processing all rules\\n for rule in genconf_model.rules:\\n\\n # Sanity check\\n if len(rule.types) != len(rule.var_names):\\n raise TextXToolsError('Number of variables don\\\\'t match'\\n ' number of types in rule \\\"{}\\\"'\\n .format(rule.name))\\n\\n # Collect all object of each given type\\n type_objs = []\\n for t in rule.types:\\n type_objs.append(children_of_type(model, t))\\n\\n if rule.all:\\n context = {'__builtins__':{}}\\n for ind, obj in enumerate(type_objs):\\n params[rule.var_names[ind]] = obj\\n context.update(params)\\n for t in rule.trans:\\n target_path = eval(t.python_path_expr, context)\\n output_file = os.path.join(output_root, target_path)\\n\\n click.echo(\\\"Generating {}\\\".format(output_file))\\n\\n _create_folder(output_file)\\n with open(output_file, 'w') as f:\\n f.write(gendesc.render(t.template_path, params,\\n templates_path))\\n else:\\n if len(rule.types) > 1:\\n raise TextXToolsError('Multiple types/variables are not'\\n ' possible for \\\"non-all\\\" rules.')\\n for obj in type_objs[0]:\\n params[rule.var_names[0]] = obj\\n context = {'__builtins__': {}}\\n context.update(params)\\n for t in rule.trans:\\n target_path = eval(t.python_path_expr, context)\\n output_file = os.path.join(output_root, target_path)\\n click.echo(\\\"Generating {}\\\".format(output_file))\\n _create_folder(output_file)\\n\\n with open(output_file, 'w') as f:\\n f.write(gendesc.render(t.template_path, params,\\n templates_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a1835d042cf66486ed099ce14f71906\",\n \"score\": \"0.51036656\",\n \"text\": \"def generate_pymods(the_parsed_topology_xml, xml_filename, opt):\\n if the_parsed_topology_xml.get_namespace():\\n if VERBOSE:\\n print(\\n \\\"Generating pymods for topology %s::%s\\\"\\n % (\\n the_parsed_topology_xml.get_namespace(),\\n the_parsed_topology_xml.get_name(),\\n )\\n )\\n else:\\n if VERBOSE:\\n print(\\n \\\"Generating pymods for topology %s\\\"\\n % (the_parsed_topology_xml.get_name())\\n )\\n model = TopoFactory.TopoFactory.getInstance()\\n topology_model = model.create(the_parsed_topology_xml)\\n\\n # create list of used parsed component xmls\\n parsed_xml_dict = {}\\n for comp in the_parsed_topology_xml.get_instances():\\n if comp.get_type() in topology_model.get_base_id_dict():\\n parsed_xml_dict[comp.get_type()] = comp.get_comp_xml()\\n else:\\n if VERBOSE:\\n print(\\n \\\"Components with type {} aren't in the topology model.\\\".format(\\n comp.get_type()\\n )\\n )\\n\\n #\\n # Hack to set up deployment path for instanced dictionaries (if one exists remove old one)\\n #\\n if opt.dict_dir is None:\\n os.environ[\\\"DICT_DIR\\\"] = os.getcwd()\\n else:\\n os.environ[\\\"DICT_DIR\\\"] = opt.dict_dir\\n\\n xml_list = []\\n for parsed_xml_type in parsed_xml_dict:\\n if parsed_xml_dict[parsed_xml_type] is None:\\n print(\\n \\\"ERROR: XML of type {} is being used, but has not been parsed correctly. Check if file exists or add xml file with the 'import_component_type' tag to the Topology file.\\\".format(\\n parsed_xml_type\\n )\\n )\\n raise Exception()\\n xml_list.append(parsed_xml_dict[parsed_xml_type])\\n temp_comp = parsed_xml_dict[parsed_xml_type].get_component()\\n if VERBOSE:\\n print(\\n \\\"Generating component dicts for %s::%s\\\"\\n % (temp_comp.get_namespace(), temp_comp.get_name())\\n )\\n write_pymods_from_comp(parsed_xml_dict[parsed_xml_type], opt, topology_model)\\n if VERBOSE:\\n print(\\n \\\"Generated component dicts for %s::%s\\\"\\n % (temp_comp.get_namespace(), temp_comp.get_name())\\n )\\n\\n topology_model.set_instance_xml_list(xml_list)\\n\\n if the_parsed_topology_xml.get_namespace():\\n if VERBOSE:\\n print(\\n \\\"Generated pymods for topology %s::%s\\\"\\n % (\\n the_parsed_topology_xml.get_namespace(),\\n the_parsed_topology_xml.get_name(),\\n )\\n )\\n else:\\n if VERBOSE:\\n print(\\n \\\"Generated pymods for topology %s\\\"\\n % (the_parsed_topology_xml.get_name())\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b7d04b25bee59ebf17f61215a787437\",\n \"score\": \"0.51035786\",\n \"text\": \"def main(input_dir: str, output_dir: str) -> None:\\n input_dir = Path(input_dir)\\n output_dir = Path(output_dir)\\n output_dir.mkdir(parents=True, exist_ok=True)\\n\\n spacy_formatted_data = {\\\"train\\\": [], \\\"test\\\": []}\\n\\n for partition in spacy_formatted_data:\\n note_dir = input_dir / partition / f\\\"{partition}_note\\\"\\n if partition == \\\"train\\\":\\n norm_dir = input_dir / partition / f\\\"{partition}_norm\\\"\\n else:\\n # TODO (John): Is this the name of the directory in the official download?\\n norm_dir = input_dir / partition / f\\\"{partition}_norm_cui_replaced_with_unk\\\"\\n\\n for note_filepath in note_dir.iterdir():\\n if not note_filepath.name.endswith(\\\".txt\\\"):\\n continue\\n\\n norm_filepath = norm_dir / f\\\"{note_filepath.stem}.norm\\\"\\n\\n with open(note_filepath, \\\"r\\\") as f:\\n spacy_formatted_data[partition].append((f.read(), {\\\"entities\\\": []}))\\n with open(norm_filepath, \\\"r\\\") as f:\\n for line in f:\\n start, end = tuple(map(int, line.strip().split(\\\"||\\\")[-2:]))\\n spacy_formatted_data[partition][-1][-1][\\\"entities\\\"].append((start, end, ENTITY_LABEL))\\n\\n with open(output_dir / \\\"spacy_formatted_data.pickle\\\", \\\"wb\\\") as f:\\n pickle.dump(spacy_formatted_data, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df99f942c3c7b8f03f573780597898e3\",\n \"score\": \"0.5099381\",\n \"text\": \"def pmodel(self):\\r\\n if not hasattr(self, '_pmodel'):\\r\\n pmodelname = self.get('pmodel-name')\\r\\n if pmodelname == 'ISO9613':\\r\\n # create environment\\r\\n G = tuple([float(x) for x in self.get('pmodel-iso9613-ground-coeffs').strip('()').split(',')])\\r\\n p = self.getFloat('pmodel-iso9613-atmospheric-pressure')\\r\\n t = self.getFloat('pmodel-iso9613-atmospheric-temperature')\\r\\n r = self.getFloat('pmodel-iso9613-atmospheric-humidity')\\r\\n self._environment = ISO9613Environment(G = G, p = p, t = t, r = r)\\r\\n # create propagation model\\r\\n self._pmodel = ISO9613Model(environment = self._environment)\\r\\n self._pmodel.correction['geometricDivergence'] = self.getBool('pmodel-iso9613-flag-geometric-divergence')\\r\\n self._pmodel.correction['atmosphericAbsorption'] = self.getBool('pmodel-iso9613-flag-atmospheric-absorption')\\r\\n self._pmodel.correction['groundEffect'] = self.getBool('pmodel-iso9613-flag-ground-effect')\\r\\n self._pmodel.correction['sourceDirectivity'] = self.getBool('pmodel-iso9613-flag-source-directivity')\\r\\n else:\\r\\n raise Exception('configuration file: Propagation model \\\"%s\\\" not known - use \\\"ISO9613\\\"' % pmodelname)\\r\\n return self._pmodel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb2d23548d39b59677cfcfc9082e026\",\n \"score\": \"0.50863206\",\n \"text\": \"def main():\\n\\n parser = argparse.ArgumentParser(description='Duolingo shared task baseline model')\\n parser.add_argument('--language', default='en_es', help='choose from [es_en, en_es, fr_en]', required=False)\\n parser.add_argument('--dataset_path', default='../data/%s/', required=False)\\n parser.add_argument('--outputs_path', default='./outputs/', required=False)\\n args = parser.parse_args()\\n\\n dataset_path = args.dataset_path % args.language\\n assert os.path.isdir(dataset_path)\\n\\n train_path = dataset_path + '%s.slam.20190204.train' % args.language\\n dev_path = dataset_path + '%s.slam.20190204.dev' % args.language\\n test_path = dataset_path + '%s.slam.20190204.test' % args.language\\n assert os.path.isfile(train_path)\\n assert os.path.isfile(dev_path)\\n assert os.path.isfile(test_path)\\n\\n if not os.path.isdir(args.outputs_path): os.mkdir(args.outputs_path)\\n\\n # ============================== Hyper Parameter ==============================\\n dbg = False\\n from_path = None\\n # from_path = './saved_model/seq2seq_nomlp_20'\\n # from_path = './saved_model/seq2seq_exp_20'\\n # from_path = './saved_model/attention_v2_20'\\n # from_path = './saved_model/cnn_3'\\n # from_path = './saved_model/seq2seq_c_7'\\n # from_path = './saved_model/transformer_1'\\n epochs = 10 if dbg else 10\\n lang = Lang()\\n\\n # ============================== Data Loading ==============================\\n\\n print('Begin Data Loading')\\n start_time = time.time()\\n training_data, training_labels = load_data(train_path, lang, dbg=dbg, use_all_features=True)\\n dev_data = load_data(dev_path, lang, use_all_features=True)\\n test_data = load_data(test_path, lang, use_all_features=True)\\n users = list(get_users(training_data).union(get_users(dev_data)).union(get_users(test_data)))\\n lang.addUsers(users)\\n for i in range(len(lang.letters)): lang.letter2Index[lang.letters[i]] = i\\n end_time = time.time()\\n print('Data Loaded\\\\t Time Taken %0.2fm' % ((end_time - start_time)/60))\\n\\n model = Model(lang)\\n\\n # ============================== Training ==============================\\n if from_path == None:\\n print('Begin Training')\\n train_loader = get_dataloader(training_data, lang, training_labels)\\n model.train(train_loader, epochs)\\n\\n # ============================== Inference ==============================\\n # print('Begin Inference-Dev', end=' ')\\n # start_time = time.time()\\n # dev_loader = get_dataloader(dev_data, lang)\\n # predictions = model.predict_for_set(dev_loader, from_path)\\n # with open(args.outputs_path + '%s_dev_predictions.pred' % args.language, 'wt') as f:\\n # for instance_id, prediction in iteritems(predictions):\\n # f.write(instance_id + ' ' + str(prediction) + '\\\\n')\\n # end_time = time.time()\\n # print('| %0.2fm' % ((end_time-start_time)/60))\\n \\n print('Begin Inference-Test', end=' ')\\n start_time = time.time()\\n test_loader = get_dataloader(test_data, lang)\\n predictions = model.predict_for_set(test_loader, from_path)\\n with open(args.outputs_path + '%s_test_predictions.pred' % args.language, 'wt') as f:\\n for instance_id, prediction in predictions.items():\\n f.write(instance_id + ' ' + str(prediction) + '\\\\n')\\n end_time = time.time()\\n print('| %0.2fm' % ((end_time-start_time)/60))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa7982cf4b80468889131323fa94e9d7\",\n \"score\": \"0.50660026\",\n \"text\": \"def generate_language_model(**kwargs):\\n\\n lm = Session.query(model.MorphemeLanguageModel).get(kwargs['morpheme_language_model_id'])\\n trie_path = lm.get_file_path('trie')\\n trie_mod_time = lm.get_modification_time(trie_path)\\n lm.generate_succeeded = False\\n try:\\n lm.write_corpus()\\n except Exception, e:\\n lm.generate_message = u'Error writing the corpus file. %s' % e\\n try:\\n lm.write_vocabulary()\\n except Exception, e:\\n lm.generate_message = u'Error writing the vocabulary file. %s' % e\\n try:\\n lm.write_arpa(kwargs['timeout'])\\n except Exception, e:\\n lm.generate_message = u'Error writing the ARPA file. %s' % e\\n try:\\n lm.generate_trie()\\n except Exception, e:\\n lm.generate_message = u'Error generating the LMTrie instance. %s' % e\\n else:\\n if lm.get_modification_time(trie_path) != trie_mod_time:\\n lm.generate_succeeded = True\\n lm.generate_message = u'Language model successfully generated.'\\n else:\\n lm.generate_message = u'Error generating the LMTrie instance.'\\n lm.generate_attempt = unicode(uuid4())\\n lm.modifier_id = kwargs['user_id']\\n lm.datetime_modified = h.now()\\n Session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cff1d19062392a49928573882a530bd\",\n \"score\": \"0.5064492\",\n \"text\": \"def load_model(model_path):\\r\\n nlp = spacy.blank('en')\\r\\n if 'ner' not in nlp.pipe_names:\\r\\n ner = nlp.create_pipe('ner')\\r\\n nlp.add_pipe(ner)\\r\\n\\r\\n ner = nlp.from_disk(model_path)\\r\\n return ner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd82b28979eb3edddc68ff36415c4e0c\",\n \"score\": \"0.5058911\",\n \"text\": \"def _write_imports(\\n cls,\\n pickle_type: Optional[str] = None,\\n mojo_model: Optional[bool] = False,\\n binary_h2o_model: Optional[bool] = False,\\n tf_model: Optional[bool] = False,\\n binary_string: Optional[str] = None,\\n ) -> None:\\n pickle_type = pickle_type if pickle_type else \\\"pickle\\\"\\n cls.score_code += (\\n f\\\"import math\\\\nimport {pickle_type}\\\\nimport pandas as pd\\\\n\\\"\\n \\\"import numpy as np\\\\nfrom pathlib import Path\\\\n\\\\n\\\"\\n )\\n \\\"\\\"\\\"\\nimport math\\nimport pickle\\nimport pandas as pd\\nimport numpy as np\\nfrom pathlib import Path\\n\\n\\n \\\"\\\"\\\"\\n\\n try:\\n if current_session().version_info() != 3.5:\\n cls.score_code += \\\"import settings\\\\n\\\\n\\\"\\n \\\"\\\"\\\"\\nimport settings\\n \\n \\n \\\"\\\"\\\"\\n except AttributeError:\\n warn(\\n \\\"No current session connection was found to a SAS Viya server. Score \\\"\\n \\\"code will be written under the assumption that the target server is \\\"\\n \\\"SAS Viya 4.\\\"\\n )\\n\\n if mojo_model or binary_h2o_model:\\n cls.score_code += \\\"import h2o\\\\n\\\\nh2o.init()\\\\n\\\\n\\\"\\n \\\"\\\"\\\"\\nimport h2o\\n\\nh2o.init()\\n\\n \\\"\\\"\\\"\\n elif tf_model:\\n cls.score_code += \\\"import tensorflow as tf\\\\n\\\\n\\\"\\n \\\"\\\"\\\"\\nimport tensorflow as tf\\n\\n \\\"\\\"\\\"\\n elif binary_string:\\n cls.score_code += (\\n f'import codecs\\\\n\\\\nbinary_string = \\\"{binary_string}\\\"'\\n f\\\"\\\\nmodel = {pickle_type}.loads(codecs.decode(binary_string\\\"\\n '.encode(), \\\"base64\\\"))\\\\n\\\\n'\\n )\\n \\\"\\\"\\\"\\nimport codecs\\n\\nbinary_string = \\\"<binary string>\\\"\\nmodel = pickle.load(codecs.decode(binary_string.encode(), \\\"base64\\\"))\\n\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c1108513704f5cbba7b473aa2906d50\",\n \"score\": \"0.50567424\",\n \"text\": \"def write_pymods_from_comp(the_parsed_component_xml, opt, topology_model):\\n global BUILD_ROOT\\n global DEPLOYMENT\\n global VERBOSE\\n\\n parsed_port_xml_list = []\\n parsed_serializable_xml_list = []\\n # uses the topology model to process the items\\n # checks if the topology model exists\\n if topology_model is None:\\n PRINT.info(\\n \\\"Topology model was not specified. Please also input a topology model when running this command.\\\"\\n )\\n raise OSError\\n\\n port_type_files_list = the_parsed_component_xml.get_port_type_files()\\n\\n for port_file in port_type_files_list:\\n port_file = search_for_file(\\\"Port\\\", port_file)\\n xml_parser_obj = XmlPortsParser.XmlPortsParser(port_file)\\n parsed_port_xml_list.append(xml_parser_obj)\\n del xml_parser_obj\\n\\n serializable_type_files_list = (\\n the_parsed_component_xml.get_serializable_type_files()\\n )\\n for serializable_file in serializable_type_files_list:\\n serializable_file = search_for_file(\\\"Serializable\\\", serializable_file)\\n xml_parser_obj = XmlSerializeParser.XmlSerializeParser(\\n serializable_file\\n ) # Telemetry/Params can only use generated serializable types\\n # check to make sure that the serializables don't have things that channels and parameters can't have\\n # can't have external non-xml members\\n if len(xml_parser_obj.get_include_header_files()):\\n print(\\n \\\"ERROR: Component include serializables cannot use user-defined types. file: \\\"\\n % serializable_file\\n )\\n sys.exit(-1)\\n\\n parsed_serializable_xml_list.append(xml_parser_obj)\\n del xml_parser_obj\\n\\n model = CompFactory.CompFactory.getInstance()\\n component_model = model.create(\\n the_parsed_component_xml, parsed_port_xml_list, parsed_serializable_xml_list\\n )\\n\\n instChannelWriter = InstChannelWriter.InstChannelWriter()\\n instCommandWriter = InstCommandWriter.InstCommandWriter()\\n instEventWriter = InstEventWriter.InstEventWriter()\\n\\n if opt.dict_dir is None:\\n if VERBOSE:\\n print(\\\"Dictionary output directory not specified!, defaulting to cwd\\\")\\n opt.dict_dir = os.getcwd()\\n os.environ[\\\"DICT_DIR\\\"] = opt.dict_dir\\n\\n # iterate through command instances\\n for command_model in component_model.get_commands():\\n if VERBOSE:\\n print(\\\"Generating command dict %s\\\" % command_model.get_mnemonic())\\n instCommandWriter.DictStartWrite(command_model, topology_model)\\n instCommandWriter.DictHeaderWrite(command_model, topology_model)\\n instCommandWriter.DictBodyWrite(command_model, topology_model)\\n\\n for parameter_model in component_model.get_parameters():\\n if VERBOSE:\\n print(\\\"Generating parameter dict %s\\\" % parameter_model.get_name())\\n instCommandWriter.DictStartWrite(parameter_model, topology_model)\\n instCommandWriter.DictHeaderWrite(parameter_model, topology_model)\\n instCommandWriter.DictBodyWrite(parameter_model, topology_model)\\n\\n # iterate through command instances\\n for event_model in component_model.get_events():\\n if VERBOSE:\\n print(\\\"Generating event dict %s\\\" % event_model.get_name())\\n instEventWriter.DictStartWrite(event_model, topology_model)\\n instEventWriter.DictHeaderWrite(event_model, topology_model)\\n instEventWriter.DictBodyWrite(event_model, topology_model)\\n\\n # iterate through command instances\\n for channel_model in component_model.get_channels():\\n if VERBOSE:\\n print(\\\"Generating channel dict %s\\\" % channel_model.get_name())\\n instChannelWriter.DictStartWrite(channel_model, topology_model)\\n instChannelWriter.DictHeaderWrite(channel_model, topology_model)\\n instChannelWriter.DictBodyWrite(channel_model, topology_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbac7e9dbefed924c07d9e4bb763e9cb\",\n \"score\": \"0.5051691\",\n \"text\": \"def convert(self, path, version, target):\\n source = self.comparer.get_representation(path)\\n lines = [ '# <fortpy version=\\\"{}\\\"></fortpy>\\\\n'.format(version) ]\\n\\n for line in self.comparer.template.contents[version].preamble:\\n lines.append(line.write(source.preamble, source.version, source.stored) + \\\"\\\\n\\\")\\n\\n for line in self.comparer.template.contents[version].body:\\n for valueset in source.body:\\n lines.append(line.write(valueset, source.version, source.stored) + \\\"\\\\n\\\")\\n\\n with open(os.path.expanduser(target), 'w') as f:\\n f.writelines(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c2bef9339db6de5f9e061ddef3fb47\",\n \"score\": \"0.5050992\",\n \"text\": \"def convert_to_onnx(pytorch_model, input_shape, output_file, input_names, output_names):\\n\\n pytorch_model.eval()\\n onnx_input_shape = torch.randn(input_shape)\\n torch.onnx.export(pytorch_model, onnx_input_shape, output_file,\\n verbose=True, input_names=input_names, output_names=output_names)\\n\\n # Model check after conversion\\n import onnx\\n model_from_onnx = onnx.load(output_file)\\n try:\\n onnx.checker.check_model(model_from_onnx)\\n print('ONNX check passed successfully.')\\n except onnx.onnx_cpp2py_export.checker.ValidationError as exc:\\n sys.exit('ONNX check failed with error: ' + str(exc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4a2a2fd203b35f20cc76a1f1693031\",\n \"score\": \"0.5048336\",\n \"text\": \"def _ppo(self, m):\\n\\n m[\\\"ppo1\\\"] = m[\\\"MemoryOp <: po_loc :> Write\\\"]\\n m[\\\"ppo2\\\"] = m[\\\"rdw\\\"]\\n m[\\\"ppo3\\\"] = m[\\\"(AMO + StoreConditional) <: (rf & po)\\\"]\\n m[\\\"ppo4rr\\\"] = m[\\\"(Read <: po :> (FencePR & FenceSR)).(po :> Read)\\\"]\\n m[\\\"ppo4rw\\\"] = m[\\\"(Read <: po :> (FencePR & FenceSW)).(po :> Write)\\\"]\\n m[\\\"ppo4wr\\\"] = m[\\\"(Write <: po :> (FencePW & FenceSR)).(po :> Read)\\\"]\\n m[\\\"ppo4ww\\\"] = m[\\\"(Write <: po :> (FencePW & FenceSW)).(po :> Write)\\\"]\\n m[\\\"ppo4tso1\\\"] = m[\\\"(Read <: po :> (FenceTSO)).(po :> MemoryOp)\\\"]\\n m[\\\"ppo4tso2\\\"] = m[\\\"(MemoryOp <: po :> (FenceTSO)).(po :> Write)\\\"]\\n m[\\\"ppo4\\\"] = m[\\\"ppo4rr + ppo4rw + ppo4wr + ppo4ww + ppo4tso1 + ppo4tso2\\\"]\\n m[\\\"ppo5\\\"] = m[\\\"Aq <: po :> MemoryOp\\\"]\\n m[\\\"ppo6\\\"] = m[\\\"MemoryOp <: po :> Rl\\\"]\\n m[\\\"ppo7\\\"] = m[\\\"RCsc <: po :> RCsc\\\"]\\n m[\\\"ppo8\\\"] = m[\\\"pair\\\"]\\n m[\\\"ppo9\\\"] = m[\\\"addrdep\\\"]\\n m[\\\"ppo10\\\"] = m[\\\"datadep\\\"]\\n m[\\\"ppo11\\\"] = m[\\\"ctrldep.po :> Write\\\"]\\n m[\\\"ppo12\\\"] = m[\\\"(addrdep + datadep).(rf & po)\\\"]\\n m[\\\"ppo13\\\"] = m[\\\"addrdep.po :> Write\\\"]\\n\\n return m[\\n \\\"ppo1 + ppo2 + ppo3 + ppo4 + ppo5 + ppo6 + ppo7 + \\\"\\n \\\"ppo8 + ppo9 + ppo10 + ppo11 + ppo12 + ppo13\\\"\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac89b0a71c9943d58dd2e15032afde8\",\n \"score\": \"0.504813\",\n \"text\": \"def makeFile(m, model):\\n tempfile = ['#include \\\"lime.h\\\"\\\\n', '#include \\\"math.h\\\"\\\\n',\\n '#include \\\"stdio.h\\\"\\\\n', '#include \\\"stdlib.h\\\"\\\\n',\\n '#include \\\"%s\\\"\\\\n\\\\n' % model.header.fn]\\n writeModelProperties(tempfile, model)\\n writeWeighting(tempfile, model)\\n writeImageParameters(tempfile, m, model)\\n writeInterpolationFuncs(tempfile, model)\\n writeDensity(tempfile, model)\\n writeTemperatures(tempfile, model)\\n writeAbundance(tempfile, model)\\n if model.dust is not None:\\n writeGastoDust(tempfile, model)\\n writeDoppler(tempfile, model)\\n writeVelocityStructure(tempfile, model)\\n with open('model_%d.c' % m, 'w') as tosave:\\n for line in tempfile:\\n tosave.write('%s' % line)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ef0a20cae196e2de5c67f6c87df504\",\n \"score\": \"0.5047686\",\n \"text\": \"def _save_prototxt(self):\\n solver_path = osp.dirname(self._solver_prototxt)\\n if not osp.exists(solver_path):\\n os.makedirs(solver_path)\\n with open(self._solver_prototxt, 'w') as f:\\n f.write(text_format.MessageToString(self._solver))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9127df10abf73188e578bcc4dcd4d09c\",\n \"score\": \"0.50475544\",\n \"text\": \"def owl_to_model(fname):\\n io_class = autoclass('org.biopax.paxtools.io.SimpleIOHandler')\\n io = io_class(autoclass('org.biopax.paxtools.model.BioPAXLevel').L3)\\n\\n try:\\n file_is = autoclass('java.io.FileInputStream')(fname)\\n except JavaException:\\n logger.error('Could not open data file %s' % fname)\\n return\\n try:\\n biopax_model = io.convertFromOWL(file_is)\\n except JavaException as e:\\n logger.error('Could not convert data file %s to BioPax model' % fname)\\n logger.error(e)\\n return\\n\\n file_is.close()\\n\\n return biopax_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b084295606442608e4e95a1201da837c\",\n \"score\": \"0.5046919\",\n \"text\": \"def export_model(self, out=None, **kwargs):\\n # Remove all code transformations but respect those provided explicitly\\n kwargs.setdefault('length_for_alias', None)\\n kwargs.setdefault('name_all_constraints', False)\\n\\n CpoCompiler(self, **kwargs).write(out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380e97eb5048bd83b762af621acf5fd7\",\n \"score\": \"0.5038669\",\n \"text\": \"def main(model=None, output_dir=None, n_iter=100):\\n if model is not None:\\n nlp = spacy.load(model) # load existing spaCy model\\n print(\\\"Loaded model '%s'\\\" % model)\\n else:\\n nlp = spacy.blank(\\\"en\\\") # create blank Language class\\n print(\\\"Created blank 'en' model\\\")\\n \\n # create the built-in pipeline components and add them to the pipeline\\n # nlp.create_pipe works for built-ins that are registered with spaCy\\n if \\\"ner\\\" not in nlp.pipe_names:\\n ner = nlp.create_pipe(\\\"ner\\\")\\n nlp.add_pipe(ner, last=True)\\n # otherwise, get it so we can add labels\\n else:\\n ner = nlp.get_pipe(\\\"ner\\\")\\n \\n # add labels\\n for _, annotations in TRAIN_DATA:\\n for ent in annotations.get(\\\"entities\\\"):\\n ner.add_label(ent[2])\\n\\n # get names of other pipes to disable them during training\\n other_pipes = [pipe for pipe in nlp.pipe_names if pipe != \\\"ner\\\"]\\n with nlp.disable_pipes(*other_pipes): # only train NER\\n # reset and initialize the weights randomly – but only if we're\\n # training a new model\\n if model is None:\\n nlp.begin_training()\\n for itn in range(n_iter):\\n random.shuffle(TRAIN_DATA)\\n losses = {}\\n # batch up the examples using spaCy's minibatch\\n batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))\\n for batch in batches:\\n texts, annotations = zip(*batch)\\n nlp.update(\\n texts, # batch of texts\\n annotations, # batch of annotations\\n drop=0.5, # dropout - make it harder to memorise data\\n losses=losses,\\n )\\n #print(\\\"Losses\\\", losses)\\n\\n # test the trained model\\n for text in TEST_DATA:\\n doc = nlp(text)\\n print(\\\"Entities\\\", [(ent.text, ent.label_) for ent in doc.ents])\\n print(\\\"Tokens\\\", [(t.text, t.ent_type_, t.ent_iob) for t in doc])\\n \\n # save model to output directory\\n if output_dir is not None:\\n output_dir = Path(output_dir)\\n if not output_dir.exists():\\n output_dir.mkdir()\\n nlp.to_disk(output_dir)\\n print(\\\"Saved model to\\\", output_dir)\\n \\n # test the saved model\\n print(\\\"Loading from\\\", output_dir)\\n nlp2 = spacy.load(output_dir)\\n for text, _ in TRAIN_DATA:\\n doc = nlp2(text)\\n print(\\\"Entities\\\", [(ent.text, ent.label_) for ent in doc.ents])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dfa7950183441b0441fb0c52fd47749\",\n \"score\": \"0.5037608\",\n \"text\": \"def convert2or():\\n # parse converter arguments\\n args = get_args_convert2or()\\n\\n # input data parameters\\n try:\\n workspace_dir, exec_file_name, data_uri, data_path, output_path = get_params()\\n entry_filename = os.path.splitext(os.path.basename(exec_file_name))[0]\\n except Exception as e:\\n print('Invalid arguments, {}'.format(e))\\n sys.exit(1)\\n\\n else:\\n # Generate requirements.txt\\n try:\\n import pipreqs\\n except ImportError as e:\\n raise RuntimeError('Package installation failed: {}'.format(e))\\n\\n else:\\n time.sleep(1)\\n try:\\n p = subprocess.Popen([\\\"pipreqs\\\", \\\"--force\\\", workspace_dir])\\n p.wait()\\n time.sleep(1)\\n\\n # fix the bug raising from 'tensorflow', 'tensorflow_gpu'\\n filename = os.path.join(workspace_dir, \\\"requirements.txt\\\")\\n\\n rw_file(filename, matplotlib=\\\"matplotlib\\\", tensorflow_gpu=\\\"\\\", tensorflow=\\\"tensorflow-gpu\\\")\\n remove_empty_lines(filename)\\n print(\\\"Generated 'requirements.txt' successfully!\\\")\\n\\n except Exception as e:\\n raise RuntimeError(\\\"Generating 'requirements.txt' failed: {}\\\".format(e))\\n\\n else:\\n # Generate params.json\\n try:\\n exec_file_name_v = os.path.relpath(exec_file_name, start=workspace_dir)\\n data_path_v = \\\"\\\" if data_path == \\\"\\\" else os.path.relpath(data_path, start=workspace_dir)\\n output_path_v = \\\"\\\" if output_path == \\\"\\\" else os.path.relpath(output_path, start=workspace_dir)\\n params_json = json.dumps({\\\"exec_file_name\\\": exec_file_name_v,\\n \\\"data_uri\\\": data_uri,\\n \\\"data_path\\\": data_path_v,\\n \\\"output_path\\\": output_path_v,\\n })\\n with open(os.path.join(workspace_dir, \\\"params.json\\\"), 'w+') as f:\\n f.write(params_json)\\n print(\\\"Generated 'params.json' successfully!\\\")\\n\\n except Exception as e:\\n raise IOError(\\\"Generating 'params.json' failed: {}\\\".format(e))\\n\\n else:\\n time.sleep(2)\\n try:\\n zip_folder_path = os.path.join(workspace_dir, os.pardir, \\\"task_files\\\")\\n\\n if not os.path.exists(zip_folder_path):\\n os.makedirs(zip_folder_path)\\n\\n output_filename = str(entry_filename) + \\\"_orion.zip\\\"\\n zip_folder(workspace_dir, os.path.join(zip_folder_path, output_filename))\\n print('Zipped files successfully!')\\n except Exception as e:\\n raise RuntimeError('Zipping files failed: {}'.format(e))\\n else:\\n try:\\n os.remove(os.path.join(workspace_dir, \\\"params.json\\\"))\\n os.remove(os.path.join(workspace_dir, \\\"requirements.txt\\\"))\\n except Exception as e:\\n raise RuntimeError('Removing files failed: {}'.format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78aca13d4302fac740b8dddb5e1eb7e6\",\n \"score\": \"0.5032639\",\n \"text\": \"def descout_to_input( # noqa: C901 - fxn too complex\\n outfile,\\n infile,\\n objective=\\\"force\\\",\\n optimizer=\\\"lsq-exact\\\",\\n header=\\\"#DESC-generated input file\\\",\\n ftol=1e-2,\\n xtol=1e-6,\\n gtol=1e-6,\\n maxiter=100,\\n ):\\n f = open(outfile, \\\"w+\\\")\\n\\n f.seek(0)\\n\\n eq = load(infile)\\n try:\\n eq0 = eq[-1]\\n except TypeError:\\n eq0 = eq\\n\\n f.write(header + \\\"\\\\n\\\")\\n\\n f.write(\\\"# global parameters\\\\n\\\")\\n f.write(\\\"sym = {:1d} \\\\n\\\".format(eq0.sym))\\n f.write(\\\"NFP = {:3d} \\\\n\\\".format(int(eq0.NFP)))\\n f.write(\\\"Psi = {:.8f} \\\\n\\\".format(eq0.Psi))\\n\\n f.write(\\\"\\\\n# spectral resolution\\\\n\\\")\\n for key, val in {\\n \\\"L_rad\\\": \\\"L\\\",\\n \\\"M_pol\\\": \\\"M\\\",\\n \\\"N_tor\\\": \\\"N\\\",\\n \\\"L_grid\\\": \\\"L_grid\\\",\\n \\\"M_grid\\\": \\\"M_grid\\\",\\n \\\"N_grid\\\": \\\"N_grid\\\",\\n }.items():\\n f.write(f\\\"{key} = {getattr(eq0, val)}\\\\n\\\")\\n\\n f.write(\\\"\\\\n\\\\n# solver tolerances\\\\n\\\")\\n f.write(f\\\"ftol = {ftol}\\\\n\\\")\\n f.write(f\\\"xtol = {xtol}\\\\n\\\")\\n f.write(f\\\"gtol = {gtol}\\\\n\\\")\\n f.write(f\\\"maxiter = {maxiter}\\\\n\\\")\\n\\n f.write(\\\"\\\\n\\\\n# solver methods\\\\n\\\")\\n f.write(f\\\"optimizer = {optimizer}\\\\n\\\")\\n f.write(f\\\"objective = {objective}\\\\n\\\")\\n f.write(\\\"spectral_indexing = {}\\\\n\\\".format(eq0._spectral_indexing))\\n f.write(\\\"node_pattern = {}\\\\n\\\".format(eq0._node_pattern))\\n\\n f.write(\\\"\\\\n# pressure and rotational transform/current profiles\\\\n\\\")\\n\\n if eq0.iota:\\n assert (\\n eq0.pressure.__class__.__name__ == \\\"PowerSeriesProfile\\\"\\n and eq0.iota.__class__.__name__ == \\\"PowerSeriesProfile\\\"\\n ), \\\"Equilibrium must have power series profiles for ascii io\\\"\\n char = \\\"i\\\"\\n iseven_pres = int(eq0._pressure.basis.sym == \\\"even\\\") + 1\\n iseven_iota = int(eq0._iota.basis.sym == \\\"even\\\") + 1\\n pres_profile = np.zeros((eq0.L + 1,))\\n iota_profile = np.zeros((eq0.L + 1,))\\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\\n iota_profile[: eq0.L + 1 : iseven_iota] = eq0._iota.params\\n\\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\\n for l in idxs:\\n f.write(\\n \\\"l: {:3d}\\\\tp = {:16.8E}\\\\t{} = {:16.8E}\\\\n\\\".format(\\n int(l), pres_profile[l], char, iota_profile[l]\\n )\\n )\\n else:\\n assert (\\n eq0.pressure.__class__.__name__ == \\\"PowerSeriesProfile\\\"\\n and eq0.current.__class__.__name__ == \\\"PowerSeriesProfile\\\"\\n ), \\\"Equilibrium must have power series profiles for ascii io\\\"\\n char = \\\"c\\\"\\n iseven_pres = int(eq0._pressure.basis.sym == \\\"even\\\") + 1\\n iseven_curr = int(eq0._current.basis.sym == \\\"even\\\") + 1\\n pres_profile = np.zeros((eq0.L + 1,))\\n curr_profile = np.zeros((eq0.L + 1,))\\n pres_profile[: eq0.L + 1 : iseven_pres] = eq0._pressure.params\\n curr_profile[: eq0.L + 1 : iseven_curr] = eq0._current.params\\n\\n idxs = np.linspace(0, eq0.L - 1, eq0.L, dtype=int)\\n for l in idxs:\\n f.write(\\n \\\"l: {:3d}\\\\tp = {:16.8E}\\\\t{} = {:16.8E}\\\\n\\\".format(\\n int(l), pres_profile[l], char, curr_profile[l]\\n )\\n )\\n\\n f.write(\\\"\\\\n\\\")\\n\\n f.write(\\\"\\\\n# fixed-boundary surface shape\\\\n\\\")\\n # boundary paramters\\n if eq0.sym:\\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\\n if abs(eq0.Rb_lmn[k]) > 1e-8:\\n f.write(\\n \\\"l: {:3d}\\\\tm: {:3d}\\\\tn: {:3d}\\\\tR1 = {:16.8E}\\\\t\\\\\\n Z1 = {:16.8E}\\\\n\\\".format(\\n int(0), m, n, eq0.Rb_lmn[k], 0\\n )\\n )\\n for k, (l, m, n) in enumerate(eq0.surface.Z_basis.modes):\\n if abs(eq0.Zb_lmn[k]) > 1e-8:\\n f.write(\\n \\\"l: {:3d}\\\\tm: {:3d}\\\\tn: {:3d}\\\\tR1 = {:16.8E}\\\\t\\\\\\n Z1 = {:16.8E}\\\\n\\\".format(\\n int(0), m, n, 0, eq0.Zb_lmn[k]\\n )\\n )\\n else:\\n for k, (l, m, n) in enumerate(eq0.surface.R_basis.modes):\\n if abs(eq0.Rb_lmn[k]) > 1e-8 or abs(eq0.Zb_lmn[k]) > 1e-8:\\n f.write(\\n \\\"l: {:3d}\\\\tm: {:3d}\\\\tn: {:3d}\\\\tR1 = {:16.8E}\\\\t\\\\\\n Z1 = {:16.8E}\\\\n\\\".format(\\n int(0), m, n, eq0.Rb_lmn[k], eq0.Zb_lmn[k]\\n )\\n )\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac8d9a34c982c21d64ee69aa9b8aada\",\n \"score\": \"0.50321543\",\n \"text\": \"def models(self):\\r\\n # make model directory, removing the old one if necessary\\r\\n rm_rf(self.cfg.model_dir)\\r\\n mkdir_p(self.cfg.model_dir)\\r\\n\\r\\n # run generator script\\r\\n gen_script = os.path.join(self._prj_root, 'gen.py')\\r\\n\\r\\n if 'PYTHON_MSDSL' in os.environ:\\r\\n python_name = os.environ['PYTHON_MSDSL']\\r\\n else:\\r\\n python_name = which('python')\\r\\n \\r\\n call([python_name, gen_script, '-o', self.cfg.model_dir, '--dt', str(self.dt)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e59d1ec3cd22e83b1a98ae5bb15330\",\n \"score\": \"0.502148\",\n \"text\": \"def write_model_to_file(self) -> str:\\n\\n model_file_content = self.interpreter.get_model_file_content()\\n\\n db_logger.info(f\\\"Writing {self.model_file_url}\\\")\\n\\n with open(self.model_file_location, 'w') as model:\\n model.write(model_file_content)\\n\\n file_name = os.path.basename(self.model_file_location)\\n project_dir = self.model_file_location.replace(file_name, '')\\n\\n helpers.process_includes(\\n self.model_file_url, project_dir, self.interpreter\\n )\\n\\n if not self.no_score:\\n self.model_file_location = self.setup_protocol(self.score)\\n\\n return self.model_file_location\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a4533cb8662178a596b89ed1d73420b\",\n \"score\": \"0.50117064\",\n \"text\": \"def test_ImportUFONoSideEffectLO(self): \\n ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('sm'),False)\\n original_all_particles = copy.copy(ufo_model.all_particles)\\n original_all_vertices = copy.copy(ufo_model.all_vertices)\\n original_all_couplings = copy.copy(ufo_model.all_couplings)\\n original_all_lorentz = copy.copy(ufo_model.all_lorentz)\\n original_all_parameters = copy.copy(ufo_model.all_parameters)\\n original_all_orders = copy.copy(ufo_model.all_orders)\\n original_all_functions = copy.copy(ufo_model.all_functions)\\n\\n ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)\\n model = ufo2mg5_converter.load_model()\\n parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main() \\n\\n self.assertEqual(original_all_particles,ufo_model.all_particles) \\n self.assertEqual(original_all_vertices,ufo_model.all_vertices)\\n self.assertEqual(original_all_couplings,ufo_model.all_couplings)\\n self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)\\n self.assertEqual(original_all_parameters,ufo_model.all_parameters)\\n self.assertEqual(original_all_orders,ufo_model.all_orders)\\n self.assertEqual(original_all_functions,ufo_model.all_functions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e8c2e02b0ee94ddd321ca2db4033cd0\",\n \"score\": \"0.50004613\",\n \"text\": \"def exe_dyn_mdl(fv, usr):\\n\\n exe = os.chdir(prg_path + '/dynamic_mdl-0.1') \\n exe = os.getcwd() \\n pcs = sbp.Popen([exe + '/a.out', str(fv)], stdout=sbp.PIPE, stderr=sbp.PIPE)\\n out, error = pcs.communicate()\\n if out:\\n print('OK', out)\\n if error:\\n print('Error', error.strip()) \\n if not pcs.poll():\\n print(\\\"Dynamic Module execute finish\\\")\\n p_nme = 'Dynamic'\\n dest_pth = fles_dir + usr + '/' + p_nme\\n dest_pth = dest_pth + '/' + time.get_date_time_hm()\\n dummy_sys_file.crt_dir(dest_pth)\\n \\\"\\\"\\\"Creating directory for program execution\\\"\\\"\\\"\\n\\n \\\"\\\"\\\"Names of the generated files\\\"\\\"\\\"\\n fle_name1 = 'sk'\\n fle_name2 = 'f_self'\\n fle_name3 = 'coefficient'\\n \\\"\\\"\\\"full path of the outputs of program\\\"\\\"\\\"\\n out_fle1 = exe + '/' + 'sk.dat'\\n out_fle2 = exe + '/' + 'fself.dat'\\n out_fle3 = exe + '/' + 'coeficiente.dat' \\n dummy_sys_file.cpy_file_to_pth_dir(out_fle1, dest_pth, fle_name1)\\n dummy_sys_file.cpy_file_to_pth_dir(out_fle2, dest_pth, fle_name2)\\n dummy_sys_file.cpy_file_to_pth_dir(out_fle3, dest_pth, fle_name3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10bf29315de0dab52975584c75ef2dfb\",\n \"score\": \"0.49911758\",\n \"text\": \"def generate_package(self):\\n\\n # Create a directory (mymodel)\\n cwd = Path(self.dir)\\n directory=cwd/'csharp_model'\\n if (directory).isdir() :\\n self.dir = directory\\n else:\\n self.dir = directory.mkdir()\\n\\n files = []\\n count = 0\\n\\n for model in self.models:\\n\\n\\n self.generate_component(model)\\n\\n ext = '' if count == 0 else str(count)\\n filename = self.dir/\\\"%s.cs\\\"%signature(model)\\n\\n with open(filename, \\\"w\\\") as python_file:\\n python_file.write(self.code.encode('utf-8','ignore'))\\n files.append(filename)\\n\\n model.module_name = str(Path(filename).namebase)\\n\\n count += 1\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f27813e63fc901b230aaee14b21dfe\",\n \"score\": \"0.49885112\",\n \"text\": \"def new_IDP_gen(subj,LUT_txt): #,fix4melviewtxt\\n\\n\\n #remove trailing forward slashes in subject paths\\n if subj.endswith(\\\"/\\\"):\\n subj = subj[:-1]\\n\\n if not os.path.exists(subj + \\\"/IDP_files/\\\"):\\n os.makedirs(subj + \\\"/IDP_files/\\\")\\n\\n subjName = subj[subj.rfind(\\\"/\\\") + 1 :]\\n\\n\\n # IDP_FC_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_FC_dist.txt\\\")\\n # IDP_SC_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_SC_dist.txt\\\")\\n # IDP_MELODIC_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_MELODIC_SNR.txt\\\")\\n # IDP_MCFLIRT_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_MCFLIRT_disp.txt\\\")\\n # IDP_homotopic_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_IDP_homotopic.txt\\\")\\n # new_IDP_list_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_new_IDPs.txt\\\")\\n\\n new_IDP_file = os.path.join(subj + \\\"/IDP_files/\\\", \\\"tvb_new_IDP.txt\\\")\\n\\n\\n # IDP_output_files = [IDP_FC_file,IDP_SC_file,IDP_MELODIC_file,IDP_MCFLIRT_file,IDP_homotopic_file]\\n\\n # for file in IDP_output_files:\\n # with open(file, 'w') as fp:\\n # pass\\n\\n with open(new_IDP_file, 'w') as fp:\\n line = '\\\\t'.join([\\\"short\\\",\\\"num\\\",\\\"category\\\",\\\"num_in_cat\\\",\\\"long\\\",\\\"unit\\\",\\\"dtype\\\",\\\"description\\\",\\\"value\\\"])\\n\\n fp.write(line)\\n\\n\\n\\n fix4melviewtxt=\\\"\\\"\\n\\n FC_distribution(subj)\\n SC_distribution(subj)\\n MELODIC_SNR(subj,fix4melviewtxt)\\n MCFLIRT_displacement(subj) \\n\\n homotopic(subj,LUT_txt)\\n \\n\\n\\n\\n\\n #TODO TL/distance, or ts.txt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde366ecb10bf14e7b028bb5eeb70a26\",\n \"score\": \"0.497974\",\n \"text\": \"def produce_layout(eoi, graph):\\n\\n subprocess.run((\\\"/usr/bin/dot\\\", \\\"{}\\\".format(graph.filename), \\\"-o\\\", \\n \\\"{}/layout.dot\\\".format(eoi)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42db4bc248db8077aaa00be0cc702c70\",\n \"score\": \"0.4975401\",\n \"text\": \"def load_model_output(text):\\r\\n # uncomment when working in linux and remove subsequent two lines\\r\\n # nlp = spacy.load(\\\"../models/quick-spacy/\\\")\\r\\n model_path = Path(__file__).parent.absolute() / \\\"models/quick-spacy/\\\"\\r\\n nlp = spacy.load(model_path)\\r\\n doc = nlp(text)\\r\\n sentence = [ent.text for ent in doc.ents]\\r\\n labels = [ent.label_ for ent in doc.ents]\\r\\n return sentence, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d71b59de5a8a54084e51dfb95444b719\",\n \"score\": \"0.49555877\",\n \"text\": \"def generate_theory(\\n grammar,\\n config,\\n theory_op_file,\\n theorem_prover,\\n max_depth,\\n min_depth\\n):\\n # Get Theorem Prover Config and initialize Theorem Prover\\n theorem_prover_config = TheoremProverConfig(\\n grammar, **config[\\\"theory\\\"][\\\"theorem_prover\\\"]\\n )\\n\\n num_examples = config[\\\"theory\\\"][\\\"num_examples\\\"]\\n min_num_positive_examples = config[\\\"theory\\\"][\\\"min_num_positive_examples\\\"]\\n max_num_negative_examples = num_examples - min_num_positive_examples\\n statement_types = config[\\\"theory\\\"][\\\"statement_types_per_example\\\"]\\n assertion_start_symbol = config[\\\"assertion\\\"][\\\"start_symbol\\\"]\\n example_id_prefix = config.get(\\\"example_id_prefix\\\", \\\"\\\")\\n\\n # Generate examples for every required type of statement (Start Symbol type)\\n num_true_labels = 0\\n num_false_labels = 0\\n curr_num_examples = 0\\n progress_tracker = tqdm(total=num_examples)\\n progress_tracker.set_description(desc=\\\"Generating Examples...\\\")\\n while (\\n curr_num_examples < num_examples and num_true_labels < min_num_positive_examples\\n ):\\n example = generate_random_example(\\n curr_num_examples + 1,\\n example_id_prefix,\\n grammar,\\n theorem_prover_config,\\n statement_types,\\n assertion_start_symbol,\\n theorem_prover,\\n max_depth,\\n min_depth\\n )\\n if example is not None:\\n if example.theory_assertion_instance.label:\\n num_true_labels += 1\\n else:\\n if num_false_labels == max_num_negative_examples:\\n continue\\n else:\\n num_false_labels += 1\\n json.dump(example.to_json(), theory_op_file)\\n theory_op_file.write(\\\"\\\\n\\\")\\n curr_num_examples += 1\\n progress_tracker.update()\\n progress_tracker.close()\\n print(f\\\"Generated {curr_num_examples} examples.\\\")\\n print(f\\\" No. with True label: {num_true_labels}\\\")\\n print(f\\\" No. with False label: {num_false_labels}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6abcb45148b5a15227491491ca106d5b\",\n \"score\": \"0.49505714\",\n \"text\": \"def polish_and_save(model_filename,\\n save_filename='',\\n suffix='.polished',\\n *args,\\n **kwargs):\\n\\n save_filename = save_filename or model_filename.replace(\\n '.onnx', suffix + '.onnx')\\n\\n model = onnx.load(model_filename)\\n model = polish_model(model, *args, **kwargs)\\n onnx.save(model, save_filename)\\n logger.info('polished model saved to: %s', save_filename)\\n return save_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70ae932b14d6c0ac6415942b0994419\",\n \"score\": \"0.49496683\",\n \"text\": \"def output_perplexity(task_letter):\\n output_file = open(\\\"output/group02.perplexity\\\" + task_letter, mode=\\\"w\\\")\\n word_dictionary = get_word_dict()\\n test_sentences = open(\\\"data/sentences.eval\\\", mode=\\\"r\\\")\\n\\n for test_sentence in test_sentences.readlines():\\n # predicted_softmax_vecs: 30 X 20.000 X 1 Output Vector with softmax probabilities\\n predicted_softmax_vecs = eval_neural_network(test_sentence)\\n perp = perplexity(predicted_softmax_vecs, test_sentence, word_dictionary)\\n output_file.write(str(perp) + \\\"\\\\n\\\")\\n\\n output_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a208cbaa76b296741b5c20e88440925f\",\n \"score\": \"0.49453568\",\n \"text\": \"def __init__(self,\\n fileName,\\n realFileName=None,\\n prequelFileName=None,\\n preErrorMessages=(), #TODO: check the type\\n readFileLater=False,\\n fillImportBoxLater=False,\\n parseFileLater=False,\\n noSymbolChecking=False,\\n allowedFeatures=(),\\n recognizeUSEOCLNativeModelDefinition=False):\\n #type: (Text, Optional[Text], Optional[Text], List[Any], bool, bool, bool, bool, List[Text], bool) -> None\\n\\n\\n # if readFileLater or fillImportBoxLater or parseFileLater:\\n # assert finalizeLater\\n\\n # Create an empty model\\n # Not to be moved after super\\n # This should be done in all case so that\\n # the model attribute always exist even if there\\n # are some error in reading the file\\n self.model = self.emptyModel() # type: Model\\n\\n\\n\\n\\n # Call the super class, read the file or not\\n try:\\n # This can raise an exception for instance if\\n # there is a problem reading the file\\n super(ModelSourceFile, self).__init__(\\n fileName=fileName,\\n realFileName=realFileName,\\n prequelFileName=prequelFileName,\\n preErrorMessages=preErrorMessages,\\n doNotReadFiles=readFileLater,\\n allowedFeatures=allowedFeatures\\n )\\n except FatalError:\\n pass # an error as already been registered\\n\\n from modelscripts.megamodels import Megamodel\\n Megamodel.registerSource(self)\\n Megamodel.registerModel(self.model)\\n\\n\\n # Backward link\\n self.model.source=self\\n\\n # Link issue box\\n self.model._issueBox.addParent(self._issueBox)\\n\\n # Source to ModelElement Mapping\\n self._modelMapping=_ModelSourceMapping()\\n\\n\\n # Create first an empty ImportBox.\\n self.importBox=ImportBox(self)\\n\\n # Then fill it by reading megamodel statements,\\n # unless specified.\\n try:\\n if not fillImportBoxLater:\\n parseToFillImportBox(\\n self,\\n noSymbolChecking,\\n recognizeUSEOCLNativeModelDefinition)\\n\\n if not parseFileLater:\\n self.parseToFillModel()\\n self.finalize()\\n\\n except FatalError:\\n pass # nothing to do, the issue has been registered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e5aa18489143959b9fdd129d63b51d0\",\n \"score\": \"0.49399826\",\n \"text\": \"def preflight(self):\\n self.params[\\\"translations\\\"][\\\"mgf_input_file\\\"] = os.path.join(\\n self.params[\\\"input_dir_path\\\"], self.params[\\\"input_file\\\"]\\n )\\n self.params[\\\"translations\\\"][\\\"output_file_incl_path\\\"] = os.path.join(\\n self.params[\\\"output_dir_path\\\"], self.params[\\\"output_file\\\"]\\n )\\n self.param_file_name = os.path.join(\\n self.params[\\\"translations\\\"][\\\"output_file_incl_path\\\"].strip(\\\".csv\\\")\\n + \\\"pipi_params.def\\\"\\n )\\n self.created_tmp_files.append(self.param_file_name)\\n\\n # pprint.pprint(self.params['translations']['_grouped_by_translated_key'])\\n # pprint.pprint(self.params)\\n # sys.exit(1)\\n self.params_to_write = {\\n \\\"version\\\": self.META_INFO[\\\"version\\\"],\\n \\\"output_percolator_input\\\": 1,\\n \\\"mod10\\\": \\\"0.0@X?\\\",\\n \\\"pepNterm\\\": 0.0,\\n \\\"pepCterm\\\": 0.0,\\n \\\"proNterm\\\": 0.0,\\n \\\"proCterm\\\": 0.0,\\n }\\n symbols = [\\\"~\\\", \\\"!\\\", \\\"%\\\", \\\"^\\\", \\\"&\\\", \\\"*\\\", \\\"+\\\", \\\"<\\\", \\\">\\\"]\\n for x, element in enumerate(symbols):\\n self.params_to_write[\\\"mod0{0}\\\".format(x + 1)] = \\\"0.0@X{0}\\\".format(element)\\n for aa in \\\"ACDEFGHIKLMNOPQRSTUVWYnc\\\":\\n self.params_to_write[aa] = 0\\n\\n write_exclusion_list = [\\n # 'label',\\n \\\"-Xmx\\\",\\n \\\"frag_mass_tolerance_unit\\\",\\n \\\"base_mz\\\",\\n # 'header_translations',\\n # 'validation_score_field'\\n ]\\n\\n # additional_15N_modifications = []\\n # if self.params['translations']['label'] == '15N':\\n # for aminoacid, N15_Diff in ursgal.ukb.DICT_15N_DIFF.items():\\n # existing = False\\n # for mod_dict in self.params['mods']['fix']:\\n # if aminoacid == mod_dict['aa']:\\n # mod_dict['mass'] += N15_Diff\\n # mod_dict['name'] += '_15N_{0}'.format(aminoacid)\\n # existing = True\\n # if existing == True:\\n # continue\\n # else:\\n # self.params['mods']['fix'].append(\\n # {\\n # 'aa': aminoacid,\\n # 'mass': N15_Diff,\\n # 'name': '_15N_{0}'.format(aminoacid),\\n # 'pos': 'any',\\n # }\\n # )\\n\\n if self.params[\\\"translations\\\"][\\\"frag_mass_tolerance_unit\\\"] == \\\"ppm\\\":\\n self.params[\\\"translations\\\"][\\\"_grouped_by_translated_key\\\"][\\n \\\"ms2_tolerance\\\"\\n ] = {\\n \\\"frag_mass_tolerance\\\": ursgal.ucore.convert_ppm_to_dalton(\\n self.params[\\\"translations\\\"][\\\"frag_mass_tolerance\\\"],\\n base_mz=self.params[\\\"base_mz\\\"],\\n )\\n }\\n\\n for pipi_param_name in self.params[\\\"translations\\\"][\\n \\\"_grouped_by_translated_key\\\"\\n ].keys():\\n for ursgal_param_name, param_value in self.params[\\\"translations\\\"][\\n \\\"_grouped_by_translated_key\\\"\\n ][pipi_param_name].items():\\n if pipi_param_name in write_exclusion_list:\\n continue\\n elif pipi_param_name == \\\"frag_clear_mz_range\\\":\\n min_mz, max_mz = param_value\\n self.params_to_write[\\\"min_clear_mz\\\"] = min_mz\\n self.params_to_write[\\\"max_clear_mz\\\"] = max_mz\\n elif type(pipi_param_name) is tuple:\\n for pn in pipi_param_name:\\n self.params_to_write[pn] = param_value\\n elif pipi_param_name == \\\"enzyme\\\":\\n enz, site, aa, inh = param_value.split(\\\";\\\")\\n self.params_to_write[\\\"enzyme\\\"] = \\\"{0} {1} {2} {3}\\\".format(\\n enz, site, aa, inh\\n )\\n elif pipi_param_name == \\\"modifications\\\":\\n \\\"\\\"\\\"\\n # specify additional mass and amino acid. DO NOT change the last character.\\n # maximum number is 10\\n # empty entries must start with 0.0\\n mod01 = 79.966331@S~ # Phosphorylation\\n mod02 = 79.966331@T! # Phosphorylation\\n mod03 = 79.966331@Y% # Phosphorylation\\n mod04 = 42.010565@K^ # Acetylation\\n mod05 = 15.994915@M& # Oxidation\\n mod06 = 14.015650@K* # Methylation\\n mod07 = 0.0@X+\\n mod08 = 0.0@X<\\n mod09 = 0.0@X>\\n mod10 = 0.0@X?\\n \\\"\\\"\\\"\\n n = 0\\n for mod_dict in self.params[\\\"mods\\\"][\\\"opt\\\"]:\\n \\\"\\\"\\\"\\n {'_id': 0,\\n 'aa': '*',\\n 'composition': {'C': 2, 'H': 2, 'O': 1},\\n 'id': '1',\\n 'mass': 42.010565,\\n 'name': 'Acetyl',\\n 'org': '*,opt,Prot-N-term,Acetyl',\\n 'pos': 'Prot-N-term',\\n 'unimod': True},\\n \\\"\\\"\\\"\\n if mod_dict[\\\"pos\\\"] == \\\"Prot-N-term\\\":\\n self.params_to_write[\\\"proNterm\\\"] = mod_dict[\\\"mass\\\"]\\n continue\\n elif mod_dict[\\\"pos\\\"] == \\\"Prot-C-term\\\":\\n self.params_to_write[\\\"proCterm\\\"] = mod_dict[\\\"mass\\\"]\\n continue\\n elif mod_dict[\\\"pos\\\"] == \\\"N-term\\\":\\n self.params_to_write[\\\"pepNterm\\\"] = mod_dict[\\\"mass\\\"]\\n continue\\n elif mod_dict[\\\"pos\\\"] == \\\"C-term\\\":\\n self.params_to_write[\\\"pepCterm\\\"] = mod_dict[\\\"mass\\\"]\\n continue\\n elif mod_dict[\\\"pos\\\"] == \\\"any\\\":\\n pass\\n else:\\n print(\\n \\\"\\\"\\\"\\n Unknown positional argument for given modification:\\n {0}\\n PIPI cannot deal with this, please use one of the follwing:\\n any, Prot-N-term, Prot-C-term, N-term, C-term\\n \\\"\\\"\\\".format(\\n mod_dict[\\\"org\\\"]\\n )\\n )\\n sys.exit(1)\\n n += 1\\n assert (\\n n <= 10\\n ), \\\"\\\"\\\"\\n A maximum of 10 optional modifications is allowed in PIPI.\\n You specified more than 10.\\n \\\"\\\"\\\"\\n if n < 10:\\n mod_n = \\\"mod0{0}\\\".format(n)\\n elif n == 10:\\n mod_n = \\\"mod{0}\\\".format(n)\\n else:\\n print(\\n \\\"\\\"\\\"\\n A maximum of 10 optional modifications is allowed in PIPI.\\n You specified more than 10.\\n \\\"\\\"\\\"\\n )\\n sys.exit(1)\\n self.params_to_write[mod_n] = \\\"{0}@{1}{2}\\\".format(\\n mod_dict[\\\"mass\\\"], mod_dict[\\\"aa\\\"], symbols[n - 1]\\n )\\n\\n for mod_dict in self.params[\\\"mods\\\"][\\\"fix\\\"]:\\n if \\\"N-term\\\" in mod_dict[\\\"pos\\\"]:\\n self.params_to_write[\\\"n\\\"] = mod_dict[\\\"mass\\\"]\\n elif \\\"C-term\\\" in mod_dict[\\\"pos\\\"]:\\n self.params_to_write[\\\"c\\\"] = mod_dict[\\\"mass\\\"]\\n else:\\n self.params_to_write[mod_dict[\\\"aa\\\"]] = mod_dict[\\\"mass\\\"]\\n else:\\n self.params_to_write[pipi_param_name] = param_value\\n self.write_params_file()\\n\\n self.params[\\\"command_list\\\"] = [\\n \\\"java\\\",\\n \\\"-Xmx{0}\\\".format(\\n self.params[\\\"translations\\\"][\\\"_grouped_by_translated_key\\\"][\\\"-Xmx\\\"][\\n \\\"-xmx\\\"\\n ]\\n ),\\n \\\"-jar\\\",\\n self.exe,\\n self.param_file_name,\\n self.params[\\\"translations\\\"][\\\"mgf_input_file\\\"],\\n ]\\n\\n return self.params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b7bcbb2c25384bb8bd0c4ba1275413\",\n \"score\": \"0.49382108\",\n \"text\": \"def gen_pov(ldraw_path, pov_path):\\n model, parts = get_model(ldraw_path)\\n\\n with open(pov_path, \\\"w\\\") as pov_file:\\n pov_file.write(POV_HEADER + \\\"\\\\n\\\")\\n writer = POVRayWriter(parts, pov_file)\\n writer.write(model)\\n pov_file.write(POV_TRAILER + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f8306823fe036ca17dd5e9585dab03\",\n \"score\": \"0.49359792\",\n \"text\": \"def django_run(target, opt=\\\"XTAL\\\"):\\r\\n # Set up the OpenBaebel conversion modules\\r\\n sdconv = OBConversion()\\r\\n ligref = OBMol()\\r\\n # Define the residues and the proteisn to analyse\\r\\n if os.path.isfile(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')):\\r\\n \\tres_d = [trans_res(x) for x in ast.literal_eval(open(os.path.join(os.path.split(sys.argv[0])[0], 'data/res_def.py')).read())[target.title].split()]\\r\\n print res_d\\r\\n# Molecules\\r\\n\\t # Now read in the ligand\\r\\n plif_method = PlifMethod()\\r\\n plif_method.text= \\\"PYPLIF\\\"\\r\\n feature_list = [\\\"POLAR\\\",\\\"FACE\\\",\\\"EDGE\\\",\\\"ACCEPTOR\\\",\\\"DONOR\\\",\\\"NEGATIVE\\\",\\\"POSITIVE\\\"]\\r\\n try:\\r\\n plif_method.validate_unique()\\r\\n plif_method.save()\\r\\n except ValidationError:\\r\\n \\t plif_method = PlifMethod.objects.get(text=\\\"PYPLIF\\\")\\r\\n out_d = {}\\r\\n counter = 0\\r\\n# Create a file for the protein\\r\\n t = tempfile.NamedTemporaryFile(suffix=\\\".pdb\\\",delete=False)\\r\\n my_prot = Protein.objects.get(code=target.title+\\\"TEMP\\\")\\r\\n t.write(my_prot.pdb_info.name)\\r\\n t.close()\\r\\n protref = read_prot(t.name, res_d)\\r\\n t = tempfile.NamedTemporaryFile(suffix=\\\".sdf\\\",delete=False)\\r\\n t.close()\\r\\n sdconv.SetInFormat(\\\"sdf\\\")\\r\\n if opt == \\\"XTAL\\\":\\r\\n mols = Molecule.objects.exclude(prot_id__code__contains=target.title).filter(prot_id__target_id=target)\\r\\n elif opt == \\\"LLOOMMPPAA\\\":\\r\\n \\t mols = []\\r\\n \\t sps = SynthPoint.objects.filter(target_id=target)\\r\\n \\t for s in sps:\\r\\n \\t \\t mols.extend([m for m in s.mol_id.all()])\\r\\n else:\\r\\n \\t print \\\"UNKNOWN OPTION\\\"\\r\\n \\t return\\r\\n for dj_mol in mols:\\r\\n \\t out_sd = Chem.SDWriter(t.name)\\r\\n \\t out_sd.write(Chem.MolFromMolBlock(str(dj_mol.sdf_info)))\\r\\n \\t out_sd.close()\\r\\n \\t sdconv.ReadFile(ligref, t.name)\\r\\n \\t # Now make the new plif\\r\\n \\t new_plif = Plif()\\r\\n \\t new_plif.mol_id = dj_mol\\r\\n \\t new_plif.prot_id = my_prot\\r\\n \\t new_plif.method_id = plif_method\\r\\n \\t try:\\r\\n \\t \\t new_plif.validate_unique()\\r\\n \\t \\t new_plif.save()\\r\\n \\t except ValidationError:\\r\\n \\t \\t new_plif = Plif.objects.get(mol_id=dj_mol,prot_id=my_prot,method_id=plif_method)\\r\\n \\t lig_name = ligref.GetTitle().strip(\\\",\\\")\\r\\n \\t prot_name = lig_name.split(\\\"_\\\")[0]\\r\\n \\t ligref.AddHydrogens()\\r\\n \\t counter +=1\\r\\n \\t refresdict = pp.getresiduedict(protref, res_d)\\r\\n \\t new_d = get_fp(protref,ligref, res_d)\\r\\n \\t for res in new_d:\\r\\n \\t \\t new_res = PlifRes()\\r\\n \\t \\t new_res.res_name = res[:3]\\r\\n \\t \\t new_res.res_num = int(res[3:])\\r\\n \\t \\t new_res.prot_id = my_prot\\r\\n \\t \\t try:\\r\\n \\t \\t \\t new_res.validate_unique()\\r\\n \\t \\t \\t new_res.save()\\r\\n \\t \\t except ValidationError:\\r\\n \\t \\t \\t new_res = PlifRes.objects.get(res_name=res[:3],res_num=int(res[3:]),prot_id=my_prot)\\r\\n \\t \\t new_plif.res_id.add(new_res)\\r\\n \\t \\t for bit_num, bit in enumerate(new_d[res]):\\r\\n \\t \\t \\t new_bit = PlifBit()\\r\\n \\t \\t \\t new_bit.feature = feature_list[bit_num]\\r\\n \\t \\t \\t new_bit.method_id = plif_method\\r\\n \\t \\t \\t new_bit.res_id = new_res\\r\\n \\t \\t \\t try:\\r\\n \\t \\t \\t \\t new_bit.validate_unique()\\r\\n \\t \\t \\t \\t new_bit.save()\\r\\n \\t \\t \\t \\t my_fun(dj_mol,new_bit,new_plif,bit)\\r\\n \\t \\t \\t except ValidationError:\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tnew_bit = PlifBit.objects.get(feature=feature_list[bit_num],method_id=plif_method,res_id=new_res)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tnew_bit.save()\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tnew_plif.bit_id.add(new_bit)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tmy_fun(dj_mol,new_bit,new_plif,bit)\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\r\\n \\t ligref = OBMol()\\r\\n \\t notatend = sdconv.Read(ligref)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cea0abf4708ac80030bc376d139c8b2\",\n \"score\": \"0.49342123\",\n \"text\": \"def writeLP(item_count, capacity, items):\\n lpfile = checkpath + '/knapsack.lp'\\n f = open(lpfile, 'w')\\n f.write(\\\"Maximize\\\\n\\\")\\n f.write(\\\"Objective:\\\\n\\\")\\n cns = \\\"\\\"\\n cnt = 0\\n for i in range(0, item_count):\\n xVar = \\\"x_\\\"+str(i)\\n cns = cns + \\\" + \\\" + str(items[i].value) + \\\" \\\" + xVar\\n cnt = cnt + 1\\n if cnt % 5 == 4:\\n f.write(cns + \\\"\\\\n\\\")\\n cns = \\\"\\\"\\n cnt = 0\\n f.write(cns + \\\"\\\\n\\\")\\n\\n f.write(\\\"Subject To\\\\n\\\")\\n cns = \\\"\\\"\\n cnt = 0\\n for i in range(0, item_count):\\n xVar = \\\"x_\\\"+str(i)\\n cns = cns + \\\" + \\\" + str(items[i].weight) + \\\" \\\" + xVar\\n cnt = cnt + 1\\n if cnt % 5 == 4:\\n f.write(cns + \\\"\\\\n\\\")\\n cns = \\\"\\\"\\n cnt = 0\\n f.write(cns + \\\" <= \\\" + str(capacity) + \\\"\\\\n\\\")\\n\\n f.write(\\\"Binaries\\\\n\\\")\\n for i in range(0, item_count):\\n xVar = \\\"x_\\\"+str(i)\\n f.write(xVar + \\\"\\\\n\\\")\\n f.write(\\\"End\\\")\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa711305cbfff319951e389f1f16131b\",\n \"score\": \"0.49325135\",\n \"text\": \"def createModelHandler(self):\\n className = self.model.name.upper()\\n filename = self.model.name + \\\".pk\\\"\\n\\n return f\\\"\\\"\\\"\\n# Copyright 2017 Battelle Energy Alliance, LLC\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\nimport sys\\nimport pickle\\nimport os\\nfrom pythonfmu.fmi2slave import Fmi2Type, Fmi2Slave, Fmi2Causality, Fmi2Variability, Integer, Real, Boolean, String\\n\\nclass {className}(Fmi2Slave):\\n #\\n # RAVEN (raven.inl.gov) Model-based Python-driven simulator\\n #\\n author = \\\"RAVEN Team\\\"\\n description = \\\"RAVEN Model-based Python-driven simulator\\\"\\n\\n def __init__(self, **kwargs):\\n super().__init__(**kwargs)\\n self.inputVariables = {self.inputVars}\\n self.outputVariables = {self.outVars}\\n # set path to raven and the serialized model\\n self.raven_path = r\\\"{self.ravenDir}\\\"\\n # model_path is by default the path to this model that is exported as FMU (serialized). It is stored in the resource folder\\n self.model_path = self.resources + \\\"/\\\" + \\\"{filename}\\\"\\n #os.path.sep\\n sys.path.append(self.model_path)\\n # this flag activates the initialization at the begin of the solve\\n self.initialized = False\\n # register raven_path variables if needed to be changed\\n self.register_variable(String(\\\"raven_path\\\", causality=Fmi2Causality.parameter, variability=Fmi2Variability.tunable))\\n # register input variables\\n for var in self.inputVariables:\\n # set var\\n self.__dict__[var] = 0.0\\n self.register_variable(Real(var, causality=Fmi2Causality.input))\\n for var in self.outputVariables:\\n # set var\\n self.__dict__[var] = 0.0\\n self.register_variable(Real(var, causality=Fmi2Causality.output))\\n\\n def setup_experiment(self, start_time: float):\\n self.start_time = start_time\\n if not self.initialized:\\n sys.path.append(self.raven_path)\\n # find the RAVEN framework\\n if os.path.isdir(os.path.join(self.raven_path,\\\"ravenframework\\\")):\\n # we import the Driver to load the RAVEN enviroment for the un-pickling\\n try:\\n import ravenframework.Driver\\n except RuntimeError as ae:\\n # we try to add the framework directory\\n raise RuntimeError(\\\"Importing or RAVEN failed with error:\\\" +str(ae))\\n else:\\n print(\\\"framework not found in\\\",self.raven_path)\\n # de-serialize the model\\n print(\\\"model_path\\\", self.model_path)\\n self.model = pickle.load(open(self.model_path, mode='rb'))\\n self.initialized = True\\n\\n def do_step(self, current_time: float, step_size: float) -> bool:\\n request = dict()\\n for var in self.inputVariables:\\n request[var] = self.__dict__[var]\\n request['current_time'] = current_time\\n request['step_size'] = step_size\\n\\n return_var = self.model.{self.executeMethod}(request)\\n outs = return_var if isinstance(return_var,dict) else return_var[{self.indexReturnDict}]\\n\\n for var in outs:\\n self.__dict__[var] = outs[var]\\n return True\\n\\n\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc1b2a35b7e5fd939b8a36de23ec2a2\",\n \"score\": \"0.49318784\",\n \"text\": \"def buildSolverModel(self, lp):\\n log.debug(\\\"create the glpk model\\\")\\n prob = glpk.glp_create_prob()\\n glpk.glp_set_prob_name(prob, lp.name)\\n log.debug(\\\"set the sense of the problem\\\")\\n if lp.sense == constants.LpMaximize:\\n glpk.glp_set_obj_dir(prob, glpk.GLP_MAX)\\n log.debug(\\\"add the constraints to the problem\\\")\\n glpk.glp_add_rows(prob, len(list(lp.constraints.keys())))\\n for i, v in enumerate(lp.constraints.items(), start=1):\\n name, constraint = v\\n glpk.glp_set_row_name(prob, i, name)\\n if constraint.sense == constants.LpConstraintLE:\\n glpk.glp_set_row_bnds(\\n prob, i, glpk.GLP_UP, 0.0, -constraint.constant\\n )\\n elif constraint.sense == constants.LpConstraintGE:\\n glpk.glp_set_row_bnds(\\n prob, i, glpk.GLP_LO, -constraint.constant, 0.0\\n )\\n elif constraint.sense == constants.LpConstraintEQ:\\n glpk.glp_set_row_bnds(\\n prob, i, glpk.GLP_FX, -constraint.constant, -constraint.constant\\n )\\n else:\\n raise PulpSolverError(\\\"Detected an invalid constraint type\\\")\\n constraint.glpk_index = i\\n log.debug(\\\"add the variables to the problem\\\")\\n glpk.glp_add_cols(prob, len(lp.variables()))\\n for j, var in enumerate(lp.variables(), start=1):\\n glpk.glp_set_col_name(prob, j, var.name)\\n lb = 0.0\\n ub = 0.0\\n t = glpk.GLP_FR\\n if not var.lowBound is None:\\n lb = var.lowBound\\n t = glpk.GLP_LO\\n if not var.upBound is None:\\n ub = var.upBound\\n t = glpk.GLP_UP\\n if not var.upBound is None and not var.lowBound is None:\\n if ub == lb:\\n t = glpk.GLP_FX\\n else:\\n t = glpk.GLP_DB\\n glpk.glp_set_col_bnds(prob, j, t, lb, ub)\\n if var.cat == constants.LpInteger:\\n glpk.glp_set_col_kind(prob, j, glpk.GLP_IV)\\n assert glpk.glp_get_col_kind(prob, j) == glpk.GLP_IV\\n var.glpk_index = j\\n log.debug(\\\"set the objective function\\\")\\n for var in lp.variables():\\n value = lp.objective.get(var)\\n if value:\\n glpk.glp_set_obj_coef(prob, var.glpk_index, value)\\n log.debug(\\\"set the problem matrix\\\")\\n for constraint in lp.constraints.values():\\n l = len(list(constraint.items()))\\n ind = glpk.intArray(l + 1)\\n val = glpk.doubleArray(l + 1)\\n for j, v in enumerate(constraint.items(), start=1):\\n var, value = v\\n ind[j] = var.glpk_index\\n val[j] = value\\n glpk.glp_set_mat_row(prob, constraint.glpk_index, l, ind, val)\\n lp.solverModel = prob\\n # glpk.glp_write_lp(prob, None, \\\"glpk.lp\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90dd741a4cce0ae2de8a4b272b8dd9f3\",\n \"score\": \"0.4929237\",\n \"text\": \"def to_modflow(model, fname):\\n try:\\n import flopy\\n except ImportError as e:\\n raise type(e)(str(e) + \\\", modflow output not possible!\\\")\\n # For modflow we want to create a new folder instead of only a file. The folder name is the base\\n # name of the passed filename\\n realization_dir = pathlib.Path(fname).parent\\n runname = pathlib.Path(fname).name\\n mfdir = realization_dir / 'MODFLOW'\\n mfdir.mkdir(parents=True, exist_ok=True)\\n mfname = str(mfdir / runname)\\n\\n # Assign name and create modflow model object\\n mf = flopy.modflow.Modflow(mfname, exe_name='mf2005')\\n\\n # Create the discretization object\\n ztop = model.grid.z0 + model.grid.lz\\n zbot = model.grid.z0\\n botm = np.linspace(ztop, zbot, model.grid.nz + 1)\\n dis = flopy.modflow.ModflowDis(mf, model.grid.nz, model.grid.nx, model.grid.ny, \\n delr=model.grid.dx, delc=model.grid.dy,\\n top=ztop, botm=botm[1:])\\n\\n # Variables for the BAS package\\n ibound = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.int32)\\n ibound[:, :, 0] = -1\\n ibound[:, :, -1] = -1\\n\\n strt = np.ones((model.grid.nz, model.grid.nx, model.grid.ny), dtype=np.float32)\\n strt[:, :, 0] = model.flowtrans['hin'][0]\\n strt[:, :, -1] = model.flowtrans['hout'][0]\\n\\n bas = flopy.modflow.ModflowBas(mf, ibound=ibound, strt=strt)\\n\\n # Assign hydraulic conductivity\\n hyvr_hk = np.transpose(model.data['k_iso'], (2, 0, 1))\\n hyvr_layvka = 1 # VKA dataset is ratio of horizontal K\\n if 'anirat' in model.data.keys():\\n hyvr_vka = np.transpose(model.data['anirat'], (2, 0, 1))\\n\\n # Add LPF package to the MODFLOW model\\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\\n hk=hyvr_hk, # Horizontal hydraulic conductivity\\n layvka=hyvr_layvka, # Flag for each layer of anisotropic ratio\\n vka=hyvr_vka) # Anisotropy ratios.\\n else:\\n # Add LPF package to the MODFLOW model\\n lpf = flopy.modflow.ModflowLpf(mf, # Modflow object\\n hk=hyvr_hk) # Horizontal hydraulic conductivity\\n\\n oc = flopy.modflow.ModflowOc(mf) # Add OC package to the MODFLOW model\\n pcg = flopy.modflow.ModflowPcg(mf) # Add PCG package to the MODFLOW model\\n mf.write_input() # Write the MODFLOW model input files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486cabbe9fb6d10c75ea06f7ee5cf1a7\",\n \"score\": \"0.49249116\",\n \"text\": \"def write_production_mdp(prefix, rep, temp):\\n\\n fn = prefix + '_%d.mdp'%rep\\n fout = open(fn, 'w')\\n fout.write(\\\"\\\"\\\"title = prod_GBSA\\ncpp = /lib/cpp\\ninclude = -I../top\\ndefine = \\nintegrator = sd\\ndt = 0.002\\nnsteps = 25000000 ; 50 ns\\nnstxout = 500000 ; every 1 ns \\nnstvout = 500000\\nnstlog = 5000 ; every 10 ps\\nnstxtcout = 5000 ; every 10 ps\\nnstenergy = 5000 ; every 10 ps\\n\\n\\nimplicit_solvent = GBSA\\ngb_algorithm = OBC\\nnstgbradii = 1\\ngb_epsilon_solvent = 80.0\\nsa_algorithm = Ace-approximation\\n\\ncomm_mode = ANGULAR\\n\\nrgbradii = 0.9\\ncoulombtype = Cut-off\\nrvdw = 0.9\\nrlist = 0.9\\nrcoulomb = 0.9\\n\\n; CONSTRAINTS\\nconstraints = hbonds\\nconstraint_algorithm = LINCS\\n\\n; other stuff\\nbd_fric = 1.0\\nnstcomm = 10\\ncomm_grps = System\\nxtc_grps = System\\nenergygrps = System\\nnstlist = 10\\nns_type = grid\\ntc-grps = System \\ntau_t = 0.0109\\n\\nref_t = %d \\ncompressibility = 4.5e-5\\nref_p = 1.0\\ngen_vel = yes\\ngen_temp = %d \\ngen_seed = 1729\\npbc = no\\\"\\\"\\\"%(temp, temp))\\n\\n fout.close()\\n\\n return fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bfcf30894e9237e9a45c1cbf4059801\",\n \"score\": \"0.49182045\",\n \"text\": \"def write_aloha_routines(self):\\n \\n\\n self.aloha_model.add_Lorentz_object(self.model.get('lorentz'))\\n self.aloha_model.compute_subset(self.wanted_lorentz)\\n # Write out the aloha routines in Python\\n aloha_routines = []\\n \\n # First add the default external wavefunction routines\\n wavefunction_routines = open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read() \\n open(pjoin(self.dir_path,'wavefunctions.py'),'w').write(\\n 'from __future__ import division\\\\n'+wavefunction_routines)\\n\\n #aloha_routines.append(open(pjoin(MG5DIR,'aloha','template_files','wavefunctions.py'),'r').read())\\n\\n # Now write the process-depenent Feynman rules ones\\n for routine in self.aloha_model.values():\\n aloha_routines.append(routine.write(output_dir = None, \\n mode='mg5',\\n language = 'Python'))\\n\\n for routine in self.aloha_model.external_routines:\\n aloha_routines.append(\\n open(self.aloha_model.locate_external(routine, 'Python')).read())\\n\\n # Now collect imports\\n python_imports = []\\n new_aloha_routines = []\\n for aloha_routine in aloha_routines:\\n new_aloha_routine = []\\n for line in aloha_routine.split('\\\\n'):\\n if any(line.startswith(token) for token in ['from','import']):\\n if line not in python_imports:\\n python_imports.append(line)\\n else:\\n new_aloha_routine.append(line)\\n new_aloha_routines.append('\\\\n'.join(new_aloha_routine))\\n aloha_routines = new_aloha_routines\\n \\n # Veto some imports\\n vetoed_imports = ['import aloha.template_files.wavefunctions as wavefunctions']\\n python_imports = [pi for pi in python_imports if pi not in vetoed_imports]\\n python_imports.insert(0, 'import wavefunctions')\\n\\n aloha_output = open(pjoin(self.dir_path,'aloha_methods.py'),'w')\\n aloha_output.write('from __future__ import division\\\\n')\\n # Write imports\\n aloha_output.write('\\\\n'.join(python_imports))\\n aloha_output.write('\\\\n'*2)\\n\\n # Write routines\\n aloha_output.write('\\\\n'.join(aloha_routines))\\n\\n aloha_output.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73793368d062ed8010c45c9ae955d77\",\n \"score\": \"0.49119902\",\n \"text\": \"def run(self):\\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\\n self._openScriptFile()\\n self.scriptf.write(\\\"from phycas import *\\\\n\\\\n\\\")\\n\\n if self.opts.seed > 0:\\n self.scriptf.write('setMasterSeed(%d)\\\\n\\\\n' % self.opts.seed)\\n\\n if self.opts.model == 'jc':\\n self.model_jc(0)\\n elif self.opts.model == 'jc+i':\\n self.model_jc(1)\\n elif self.opts.model == 'jc+g':\\n self.model_jc(2)\\n elif self.opts.model == 'jc+i+g':\\n self.model_jc(3)\\n elif self.opts.model == 'hky':\\n self.model_hky(0)\\n elif self.opts.model == 'hky+i':\\n self.model_hky(1)\\n elif self.opts.model == 'hky+g':\\n self.model_hky(2)\\n elif self.opts.model == 'hky+i+g':\\n self.model_hky(3)\\n elif self.opts.model == 'gtr':\\n self.model_gtr(0)\\n elif self.opts.model == 'gtr+i':\\n self.model_gtr(1)\\n elif self.opts.model == 'gtr+g':\\n self.model_gtr(2)\\n elif self.opts.model == 'gtr+i+g':\\n self.model_gtr(3)\\n elif self.opts.model == 'codon':\\n self.model_codon()\\n\\n self.model_edgelen()\\n\\n if self.opts.datafile is None:\\n self.scriptf.write(\\\"mcmc.data_source = None\\\\n\\\")\\n elif self.opts.datafile == 'sample.nex':\\n self.saveSampleData()\\n self.scriptf.write(\\\"mcmc.data_source = '%s'\\\\n\\\\n\\\" % self.opts.datafile)\\n else:\\n self.scriptf.write(\\\"mcmc.data_source = '%s'\\\\n\\\\n\\\" % self.opts.datafile)\\n\\n if self.opts.analysis == 'mcmc':\\n self.mcmc_analysis()\\n self.sump_analysis()\\n self.sumt_analysis()\\n if self.opts.analysis == 'poly':\\n self.polytomy_analysis()\\n elif self.opts.analysis == 'cpo':\\n self.cpo_analysis()\\n #elif self.opts.analysis == 'idr':\\n # self.idr_analysis()\\n elif self.opts.analysis == 'ss':\\n self.steppingstone_analysis()\\n\\n self._closeScriptFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e42d57ee79ba479dae9b3b96b226d2\",\n \"score\": \"0.49086118\",\n \"text\": \"def build_model(out: ModelOutput = ModelOutput(\\\"malt/swemalt-1.7.2.mco\\\"),\\n _maltjar: Binary = Binary(\\\"[malt.jar]\\\")):\\n out.download(\\\"http://maltparser.org/mco/swedish_parser/swemalt-1.7.2.mco\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9a026d51eff7c21f70c849e56481fa5\",\n \"score\": \"0.49045718\",\n \"text\": \"def to_alien(self, file_pointer, format='opb', comments=None):\\n\\n if self.atms and format == 'smt':\\n raise NotImplementedError('SMT-LIB2 does not support PB constraints directly; you may want to use Z3\\\\'s API instead')\\n\\n cchars = {'lp': '\\\\\\\\', 'opb': '*', 'smt': ';'}\\n\\n # saving formula's internal comments\\n for c in self.comments:\\n print(cchars[format], c, file=file_pointer)\\n\\n # saving externally specified comments\\n if comments:\\n for c in comments:\\n print(cchars[format], c, file=file_pointer)\\n\\n # normalized soft clauses\\n soft, hard = [], []\\n topv = self.nv + 1\\n for cl in self.soft:\\n if len(cl) == 1:\\n soft.append(cl)\\n else:\\n hard.append([topv] + cl)\\n soft.append([topv])\\n topv += 1\\n\\n if format == 'opb':\\n print('* #variable= {0} #constraint= {1}'.format(self.nv, len(self.hard) + len(hard)),\\n file=file_pointer)\\n print('min:',\\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\\n ';', file=file_pointer)\\n elif format == 'lp':\\n print('Minimize', file=file_pointer)\\n print('obj:',\\n ' '.join(['{0}{1} x{2}'.format('-' if s[0] > 0 else '+', w, abs(s[0])) for s, w in zip(soft, self.wght)]),\\n file=file_pointer)\\n print('Subject To', file=file_pointer)\\n elif format == 'smt':\\n for v in range(1, self.nv + 1):\\n print('(declare-fun x{0} () Bool)'.format(v), file=file_pointer)\\n\\n for i, cl in enumerate(self.hard + hard, 1):\\n line, neg = [], 0\\n for l in cl:\\n if l > 0:\\n if format == 'smt':\\n line.append('x{0}'.format(l))\\n else:\\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', l))\\n else:\\n if format == 'smt':\\n line.append('(not x{0})'.format(-l))\\n else:\\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', -l))\\n neg += 1\\n\\n if format == 'smt':\\n print('(assert (or {0}))'.format(' '.join(line)), file=file_pointer)\\n else:\\n print('{0}{1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}: '.format(i),\\n ' '.join(l for l in line),\\n 1 - neg, ';' if format == 'opb' else ''),\\n file=file_pointer)\\n\\n for i, am in enumerate(self.atms, len(self.hard) + len(hard) + 1):\\n line, neg = [], 0\\n for l in am[0]:\\n if l > 0:\\n line.append('-{0} x{1}'.format('1' if format == 'opb' else '', l))\\n neg += 1\\n else:\\n line.append('+{0} x{1}'.format('1' if format == 'opb' else '', -l))\\n\\n print('{0} {1} >= {2} {3}'.format('' if format == 'opb' else 'c{0}:'.format(i),\\n ' '.join(l for l in line),\\n len(am[0]) - am[1] - neg, ';' if format == 'opb' else ''),\\n file=file_pointer)\\n\\n if format == 'lp':\\n print('Bounds', file=file_pointer)\\n for v in range(1, topv):\\n print('0 <= x{0} <= 1'.format(v), file=file_pointer)\\n print('Binary', file=file_pointer)\\n for v in range(1, topv):\\n print('x{0}'.format(v), file=file_pointer)\\n print('End', file=file_pointer)\\n elif format == 'smt':\\n for cl, w in zip(soft, self.wght):\\n l = 'x{0}'.format(cl[0]) if cl[0] > 0 else '(not x{0})'.format(-cl[0])\\n print('(assert-soft {0} :weight {1})'.format(l, w), file=file_pointer)\\n\\n print('(check-sat)', file=file_pointer)\\n print('(get-model)', file=file_pointer)\\n print('(get-objectives)', file=file_pointer)\\n print('(exit)', file=file_pointer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bed09b7d57987e01641bd6feb172467\",\n \"score\": \"0.4902589\",\n \"text\": \"def copy_files_export_model(geomodel_dir, geomodeller_file, new_dir, nx, ny, nz, **kwds):\\n \\n \\n \\n os.chdir(geomodel_dir)\\n \\n \\n # first step: determine all Geomodeller files in original directory\\n geomodel_files = []\\n for l in os.listdir(\\\".\\\"):\\n if os.path.splitext(l)[1] == \\\".sec\\\":\\n geomodel_files.append(l)\\n if os.path.splitext(l)[1] == \\\".s3d\\\":\\n geomodel_files.append(l)\\n if os.path.splitext(l)[1] == \\\".md5\\\":\\n geomodel_files.append(l)\\n if os.path.splitext(l)[1] == \\\".wsp\\\":\\n geomodel_files.append(l)\\n if os.path.splitext(l)[1] == \\\".xml\\\":\\n # This is the original file - keep for later\\n geomodel_xml_ori = l\\n \\n print geomodel_xml_ori\\n os.chdir(new_dir)\\n\\n for f in geomodel_files:\\n shutil.copyfile(os.path.join(geomodel_dir, f), os.path.join(\\\".\\\",f))\\n\\n for l2 in os.listdir(\\\".\\\"):\\n if l2 == geomodeller_file:\\n print l2\\n shutil.copyfile(l2,os.path.join(\\\".\\\", geomodel_xml_ori))\\n if \\\"original\\\" in l2:\\n new_grid = \\\"exported_grid_original.txt\\\"\\n new_delxyz = \\\"delxyz_original.txt\\\"\\n else:\\n new_grid = \\\"exported_grid_\\\" + os.path.splitext(l2)[0] + \\\".txt\\\"\\n new_delxyz = \\\"delxyz_\\\" + os.path.splitext(l2)[0] + \\\".txt\\\"\\n \\n if kwds.has_key('nz_list'): # cell discretization study\\n try:\\n i = int(l2[-7:-4])\\n except ValueError:\\n os.chdir('..') \\n continue # original model\\n subprocess.call(\\\"/home/flo/bin/export_model %s %d %d %d\\\" % (geomodel_xml_ori, nx, ny, kwds['nz_list'][i]), shell=True)\\n else:\\n\\n while True:\\n proc = subprocess.Popen(\\\"/home/flo/bin/export_model %s %d %d %d\\\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\\n for i in range(10):\\n time.sleep(5)\\n if proc.poll() == 0:\\n print(\\\"Process finished successfully\\\")\\n break\\n if proc.poll() == None:\\n proc.kill()\\n print(\\\"\\\\n\\\\n\\\\tProcess killed, restart!\\\\n\\\\n\\\")\\n continue\\n elif proc.poll() == 0:\\n print(\\\"Process finished successfully\\\")\\n break\\n print(\\\"\\\\n\\\\n\\\\tProcess not finished correctly!\\\\n\\\\n\\\\n\\\")\\n break\\n # subprocess.call(\\\"/home/flo/bin/export_model %s %d %d %d\\\" % (geomodel_xml_ori, nx, ny, nz), shell=True)\\n shutil.copyfile(\\\"exported_grid.txt\\\", os.path.join(\\\".\\\", new_grid))\\n shutil.copyfile(\\\"delxyz.txt\\\", os.path.join(\\\".\\\", new_delxyz))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dc902536351b3d417ec8d2e8f7e209e\",\n \"score\": \"0.48992693\",\n \"text\": \"def convert2lite(save_dir,\\n lite_valid_places=\\\"arm\\\",\\n lite_model_type=\\\"naive_buffer\\\"):\\n\\n from paddlelite.lite import Opt\\n opt = Opt()\\n opt.set_model_dir(save_dir + \\\"/inference_model\\\")\\n opt.set_valid_places(lite_valid_places)\\n opt.set_model_type(lite_model_type)\\n opt.set_optimize_out(save_dir + \\\"/opt\\\")\\n opt.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38860f271edd9ad00978731b25db1a2b\",\n \"score\": \"0.48990908\",\n \"text\": \"def makefile2dot(**kwargs):\\n\\n direction = kwargs.get('direction', \\\"BT\\\")\\n if direction not in [\\\"LR\\\", \\\"RL\\\", \\\"BT\\\", \\\"TB\\\"]:\\n raise ValueError('direction must be one of \\\"BT\\\", \\\"TB\\\", \\\"LR\\\", RL\\\"')\\n\\n output = kwargs.get('output', '')\\n view = kwargs.get('view', False)\\n\\n graph = build_graph(stream_database(), direction=direction)\\n if output == \\\"\\\":\\n if view:\\n graph.view()\\n else:\\n print(graph)\\n else:\\n with open(output, 'w') as file:\\n file.write(str(graph))\\n if view:\\n graph.view()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4d103572b36b3f5587fd6cccb6325d\",\n \"score\": \"0.48969853\",\n \"text\": \"def load_model(fp: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6128a8fd86ce81b50e836d0234155722\",\n \"score\": \"0.48924553\",\n \"text\": \"def writemodelfile(self, classpy=None):\\n if not classpy is None:\\n classpy.close()\\n pyfile = '%s.py' % os.path.join(self.modelspath, self.thismodule)\\n classpy = open(pyfile, 'r')\\n lines = classpy.readlines()\\n lines.insert(2, '\\\\n')\\n classpy.close()\\n froms = list()\\n for line in lines:\\n prefix = ''\\n if 'ForeignKey' in line or 'OneToOneField' in line \\\\\\n or 'ManyToManyField' in line:\\n idx = lines.index(line)\\n third = ''\\n paren = False\\n comma = False\\n first, second = line.split('(', 1)\\n if not 'self' in second: # do nothing if it is\\n if ',' in second: # some options exist\\n second, third = second.split(',', 1)\\n third = ', %s' % third\\n if ')' in second: # that's all in the line\\n paren = True # so it can be put back\\n second = second.replace(')', '').strip()\\n if \\\"'\\\" in second:\\n second = second.replace(\\\"'\\\", '').strip()\\n if '\\\"' in second:\\n second = second.replace('\\\"', '').strip()\\n relatedmodel = second # totally naked\\n assert len(second.split()) == 1\\n module = self.checkthisimport(relatedmodel)\\n if not module is None:\\n if module.startswith('.'):\\n prefix = '#'\\n line = \\\"%s('%s'%s\\\" % (first, second, third)\\n else:\\n line = \\\"%s(%s%s\\\" % (first, second, third)\\n if paren:\\n line = '%s)\\\\n' % line.rstrip()\\n if not line.endswith('\\\\n'):\\n line = '%s\\\\n' % line\\n if idx:\\n lines[idx] = line\\n if module:\\n impline = '%sfrom %s import %s\\\\n' % (prefix, module,\\n relatedmodel)\\n else:\\n impline = 'import %s\\\\n' % model\\n if not impline in froms:\\n #print(impline)\\n froms.insert(0, impline)\\n # now find all the other imported items\\n chunk = line.replace('=', ' ')\\n chunk = chunk.replace('[', ' ')\\n chunk = chunk.replace('(', ' ')\\n chunk = chunk.replace(')', ' ')\\n chunk = chunk.replace('.', ' ')\\n chunk = chunk.replace(']', ' ')\\n chunk = chunk.replace(',', ' ')\\n bits = chunk.split()\\n for bit in bits:\\n if self.seemsok(bit, line):\\n module = self.checkthisimport(bit)\\n if not module is None:\\n if module == '':\\n impline = 'import %s\\\\n' % bit\\n else:\\n impline = 'from %s import %s\\\\n' % (module, bit)\\n if not impline in froms:\\n froms.insert(0, impline)\\n\\n clean = list()\\n for item in froms:\\n if not '__future__' in item:\\n if not item in clean:\\n clean.insert(0, item)\\n cleaner = self.removethismodule(clean)\\n del(clean)\\n cleaner.sort()\\n cleaner.reverse()\\n if aggregate:\\n cleanest = self.aggregatefroms(cleaner)\\n else:\\n cleanest = cleaner\\n for item in cleanest:\\n lines.insert(self.startline, item)\\n classpy = open(pyfile, 'w')\\n classpy.writelines(lines)\\n classpy.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b24a82db4e8a7271fd5aaa542794f09\",\n \"score\": \"0.48900214\",\n \"text\": \"def _translate_and_simulate(self, simulate):\\n import os\\n import shutil\\n import jinja2\\n import datetime\\n\\n # Delete output files\\n self.deleteOutputFiles()\\n\\n worDir = self._outputDir_\\n\\n # Construct the model instance with all parameter values\\n # and the package redeclarations\\n dec = self._declare_parameters()\\n dec.extend(self._modelModifiers_)\\n\\n if len(dec) == 0:\\n model_modifier = \\\"\\\"\\n else:\\n model_modifier = '{dec}'.format(mn=self.modelName, dec=','.join(dec))\\n\\n file_name = \\\"{}.py\\\".format(self.modelName.replace(\\\".\\\", \\\"_\\\"))\\n## self._time_stamp_old_files = datetime.datetime.now()\\n with open(os.path.join(worDir, file_name), mode=\\\"w\\\", encoding=\\\"utf-8\\\") as fil:\\n path_to_template = os.path.join(\\n os.path.dirname(__file__), os.path.pardir, \\\"development\\\")\\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(path_to_template))\\n\\n template = env.get_template(\\\"optimica_run.template\\\")\\n\\n # Note that filter argument must respect glob syntax ([ is escaped with []]) + OPTIMICA mat file\\n # stores matrix variables with no space e.g. [1,1].\\n txt = template.render(\\n model=self.modelName,\\n model_modifier=model_modifier,\\n ncp=self._simulator_.get('numberOfIntervals'),\\n rtol=self._simulator_.get('eps'),\\n solver=self._simulator_.get('solver'),\\n start_time=self._simulator_.get('t0') if self._simulator_.get(\\n 't0') is not None else 'mod.get_default_experiment_start_time()',\\n final_time=self._simulator_.get('t1') if self._simulator_.get(\\n 't1') is not None else 'mod.get_default_experiment_stop_time()',\\n result_file_name=f\\\"{self._simulator_.get('resultFile')}.mat\\\",\\n simulate=simulate,\\n time_out=self._simulator_.get('timeout'),\\n filter=self._result_filter,\\n generate_html_diagnostics=self._generate_html_diagnostics,\\n debug_solver=self._debug_solver,\\n debug_solver_interactive_mode=self._debug_solver_interactive_mode)\\n\\n fil.write(txt)\\n shutil.copyfile(\\n os.path.join(\\n os.path.dirname(__file__),\\n \\\"OutputGrabber.py\\\"),\\n os.path.join(\\n worDir,\\n \\\"OutputGrabber.py\\\"))\\n\\n try:\\n super()._runSimulation([\\\"jm_ipython.sh\\\", file_name],\\n self._simulator_.get('timeout'),\\n worDir)\\n\\n self._check_simulation_errors(worDir=worDir, simulate=simulate)\\n# self._copyNewFiles(worDir)\\n# self._deleteTemporaryDirectory(worDir)\\n\\n except Exception as e: # Catch all possible exceptions\\n em = f\\\"Simulation failed in '{worDir}'\\\\n Exception: {e}.\\\\n You need to delete the directory manually.\\\\n\\\"\\n self._reporter.writeError(em)\\n raise e\\n os.remove(os.path.join(worDir, \\\"OutputGrabber.py\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"989010fe3938e83cdafa116524121a61\",\n \"score\": \"0.4889075\",\n \"text\": \"def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):\\n from pathlib import Path\\n\\n from stamp_processing.module.yolov5.models.experimental import attempt_load\\n from stamp_processing.module.yolov5.models.yolo import Model\\n from stamp_processing.module.yolov5.utils.torch_utils import select_device\\n\\n file = Path(__file__).absolute()\\n\\n save_dir = Path(\\\"\\\") if str(name).endswith(\\\".pt\\\") else file.parent\\n path = (save_dir / name).with_suffix(\\\".pt\\\") # checkpoint path\\n try:\\n device = select_device((\\\"0\\\" if torch.cuda.is_available() else \\\"cpu\\\") if device is None else device)\\n\\n if pretrained and channels == 3 and classes == 80:\\n model = attempt_load(path, map_location=device) # download/load FP32 model\\n else:\\n cfg = list((Path(__file__).parent / \\\"models\\\").rglob(f\\\"{name}.yaml\\\"))[0] # model.yaml path\\n model = Model(cfg, channels, classes) # create model\\n if autoshape:\\n model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS\\n return model.to(device)\\n\\n except Exception as e:\\n help_url = \\\"https://github.com/ultralytics/yolov5/issues/36\\\"\\n s = \\\"Cache may be out of date, try `force_reload=True`. See %s for help.\\\" % help_url\\n raise Exception(s) from e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1163e643e5b668503b7ff3340a328791\",\n \"score\": \"0.48880547\",\n \"text\": \"def make_dot(aut_fname, dot_fname):\\n coll = collection.Collection()\\n aut = load_automaton(coll, aut_fname, False, False)\\n\\n make_backup_file(dot_fname)\\n\\n dot_handle = open(dot_fname, 'w')\\n dot_handle.write(aut.to_dot())\\n dot_handle.close()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":178,"cells":{"query_id":{"kind":"string","value":"129bf3d37fc164404295d1647290238f"},"query":{"kind":"string","value":"Set an instance variable to test that this method gets called"},"positive_passages":{"kind":"list like","value":[{"docid":"abe8787ed3fa3d0a67f544bcbdd156df","score":"0.0","text":"def __parent_setup_2(self):\r\n self.parent_setup_exists += 1","title":""}],"string":"[\n {\n \"docid\": \"abe8787ed3fa3d0a67f544bcbdd156df\",\n \"score\": \"0.0\",\n \"text\": \"def __parent_setup_2(self):\\r\\n self.parent_setup_exists += 1\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"de81fdce13d62ec47641ea1a8c6ee8c7","score":"0.73396957","text":"def before_tester_run(self) -> None:","title":""},{"docid":"f3b2d6e10065ff6b983c1edd3630c152","score":"0.7324083","text":"def classSetUp(self):\r\n assert self.something == True\r\n self.something = False","title":""},{"docid":"51262b0e50f19bfeabbd206ecbc400f8","score":"0.72828907","text":"def _test(self):\n pass","title":""},{"docid":"f755fb8af7ee7ec95e19b5587ea6f098","score":"0.7067964","text":"def testing(self):","title":""},{"docid":"808425a98f90a1aaa5ee11daef2130fd","score":"0.70527375","text":"def _mockme(self):\n pass","title":""},{"docid":"ccc77d904ab17a7c854c8f5789312938","score":"0.69012713","text":"def setup_method(self, method):\n assert method","title":""},{"docid":"f7529441c82c36857491679fc0ad7bba","score":"0.6900921","text":"def test_(self):\n pass","title":""},{"docid":"0714b55a928f10390dd112e4a71d64e7","score":"0.6892391","text":"def test_init(self):\n pass","title":""},{"docid":"6395a0447babd542b1cb1cb13ec3ce90","score":"0.68894523","text":"def before_test_run(self) -> None:","title":""},{"docid":"eaa2604b3e54d4eaa66788fed871de75","score":"0.68563145","text":"def testInit(self):","title":""},{"docid":"39d1e4b8a393b823fec5ed0de63b0ff3","score":"0.6824386","text":"def test(cls):\n pass","title":""},{"docid":"0645a56473c801c578eebf4001ceec3b","score":"0.68234134","text":"def test(self):\n # TODO\n pass","title":""},{"docid":"343d0cce8356ac3e92c0de489f4efcd1","score":"0.67005354","text":"def test(self):\n pass","title":""},{"docid":"343d0cce8356ac3e92c0de489f4efcd1","score":"0.67005354","text":"def test(self):\n pass","title":""},{"docid":"675dfa93100212ef6375295a1a1ce439","score":"0.66937464","text":"def setup_method(self):\n self.out = None\n return","title":""},{"docid":"1b1a5d7ff04d8016d90ae97a5edbdd07","score":"0.6687489","text":"def after_tester_run(self) -> None:","title":""},{"docid":"82815e8f330845c57d8826a78a373780","score":"0.664704","text":"def setUp(self):\n self.obj = SomeClass()","title":""},{"docid":"1996573f8fd08abd8c20cb78f5d337bd","score":"0.6622737","text":"def self_test():","title":""},{"docid":"9f668c50b248a875b1d26bf7f593c018","score":"0.6618984","text":"def __init__(self):\n self.verbose = False\n self.simulate = False\n self.debug = False","title":""},{"docid":"ad8da9c4f89cf541e07ee70f4417550c","score":"0.65945566","text":"def test(self):\n return True","title":""},{"docid":"87ab336d1d39a975629372b0424f8642","score":"0.6570382","text":"def setup_method(self):\n\n reload(pysat.instruments.pysat_testing)\n self.name = 'testing'\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\n return","title":""},{"docid":"e3b18d01113aa19349cad235b54ecf01","score":"0.6537312","text":"def SetUp(self):\n pass","title":""},{"docid":"f9d58dbcc9463628df8a2b5dcadfc8fb","score":"0.6458454","text":"def _on_test_begin(self):\n pass","title":""},{"docid":"12415e51a45655613acc98a6dd303b6a","score":"0.6443678","text":"def setUp(self):\n self.result = ''","title":""},{"docid":"d388939ba349a300a7a20adb599c127d","score":"0.64378744","text":"def __call__(self):\n pass","title":""},{"docid":"d388939ba349a300a7a20adb599c127d","score":"0.64378744","text":"def __call__(self):\n pass","title":""},{"docid":"cb74abd832f67445757a9df7a3220729","score":"0.6430383","text":"def test_set_instance_field(self):\n pass","title":""},{"docid":"06618ec00d1e59a1aa3d17ad542fd572","score":"0.6405326","text":"def setup_method(self):\n\n warnings.simplefilter(\"always\", DeprecationWarning)\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\n self.warn_msgs = []\n self.war = \"\"\n return","title":""},{"docid":"1020f9278cdfaa93e4732c3cd4517798","score":"0.6402218","text":"def test_init(self):\n self.assertTrue(hasattr(self.test, \"mouse_is_on\"))\n self.assertTrue(hasattr(self.test, \"hover\"))\n self.assertTrue(hasattr(self.test, \"request_list\"))\n self.assertTrue(hasattr(self.test, \"command_list\"))","title":""},{"docid":"51f35c96fe367ace0d686a1971bd1021","score":"0.6395603","text":"def test_instrument_init(self):\n\n assert self.testInst.new_thing\n return","title":""},{"docid":"3304793c6d84b318dab1a82509cae13e","score":"0.63714534","text":"def setup_class(self):","title":""},{"docid":"3304793c6d84b318dab1a82509cae13e","score":"0.63714534","text":"def setup_class(self):","title":""},{"docid":"4d89e4e206ecb876b22b77a273dfebbe","score":"0.6368773","text":"def initiate(self):","title":""},{"docid":"0b0e1f4643bfe57e20fa45988b64a3cc","score":"0.63375026","text":"def init(self):\n\t\tpass","title":""},{"docid":"4dcfc871867931eeed4399775081280b","score":"0.63354313","text":"def setUp(self):\n self.options = Options()","title":""},{"docid":"3795754c68868d6a9865aab795995b9d","score":"0.6334769","text":"def setup(self) -> None:","title":""},{"docid":"b269306d60acc935f8f503117a22bcd6","score":"0.6329245","text":"def run(self):\n assert_equal(self, 1, 1, 'test')\n assert_true(self, False, 'test')\n return","title":""},{"docid":"db3d57cbf37ee951907cbac117eeb09b","score":"0.6328691","text":"def test_init(self, hotp):\n hotp.assert_not_called()\n hotp.return_value.verify.assert_not_called()","title":""},{"docid":"f0868c574190f7892a5ed88ea2afca7d","score":"0.63255155","text":"def init(self):\n return True","title":""},{"docid":"cf419d90fd2a0cb0ae308b3de97c90b5","score":"0.63226557","text":"def __init__(self):\r\n self.initialized = False","title":""},{"docid":"cf419d90fd2a0cb0ae308b3de97c90b5","score":"0.63226557","text":"def __init__(self):\r\n self.initialized = False","title":""},{"docid":"19eaeecab013f1002b22a8666ddd1919","score":"0.6319818","text":"def call(self):\n pass","title":""},{"docid":"801be9e54b91396497442b025caecff4","score":"0.6318234","text":"def __init__(self):\n self.initialized = False","title":""},{"docid":"801be9e54b91396497442b025caecff4","score":"0.6318234","text":"def __init__(self):\n self.initialized = False","title":""},{"docid":"801be9e54b91396497442b025caecff4","score":"0.6318234","text":"def __init__(self):\n self.initialized = False","title":""},{"docid":"ab0f4e081e9a97e6d579fb34b09337ac","score":"0.6315708","text":"def test_something(self):\n pass","title":""},{"docid":"ab0f4e081e9a97e6d579fb34b09337ac","score":"0.6315708","text":"def test_something(self):\n pass","title":""},{"docid":"20e315badfcd5ea2fbd2adeb30f65c3d","score":"0.63003427","text":"def init(self, ):\n pass","title":""},{"docid":"fcafb07835deeb463e5bda11509ea028","score":"0.62935686","text":"def test_method(self):","title":""},{"docid":"ff31b1e94630d6d5ec1d74bc327ce48a","score":"0.6287083","text":"def SetUp(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]","title":""},{"docid":"ff31b1e94630d6d5ec1d74bc327ce48a","score":"0.6287083","text":"def SetUp(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]","title":""},{"docid":"4034806bcb3ae3ad4c90b6a3eb9844a0","score":"0.62859535","text":"def setUp(self):\n print(\"setUp\")\n print(\"===========\")","title":""},{"docid":"cb3cfadca177693c1d29f5e79bdd34cd","score":"0.6281448","text":"def setUp(self):\n self.supvisors = MockedSupvisors()","title":""},{"docid":"4acc340f00371ec4a826c5450a6971f1","score":"0.6276333","text":"def test_init(self, hotp):\n hotp.assert_not_called\n hotp.return_value.verify.assert_not_called()","title":""},{"docid":"afa0cd3753bfed9ca350c54f4c15ff2c","score":"0.6259981","text":"def setUpAttr(cls):\n pass","title":""},{"docid":"3c963aade5e955a41445071162555445","score":"0.6255766","text":"def test(self, test):\n\n self._test = test","title":""},{"docid":"3c963aade5e955a41445071162555445","score":"0.6255766","text":"def test(self, test):\n\n self._test = test","title":""},{"docid":"2c82da39e2f0c091b2a1b647b101d2ef","score":"0.6237032","text":"def verify(self):\n raise Exception(\"abstact class called unexpectedly\")","title":""},{"docid":"b931cb4adffb6ce8764de896d8ea98f8","score":"0.6232571","text":"def method(self):\n pass","title":""},{"docid":"19c9f152b244481fe2a33153022c0f29","score":"0.6231853","text":"def setUpClass(cls):\n cls.token = generate_v2_test_token(username='bob')\n addr.const = MagicMock()\n addr.const.VLAB_IPAM_LOG_LEVEL = 'INFO'\n addr.const.VLAB_VERIFY_TOKEN = False","title":""},{"docid":"ac72173d9df6ed7e73a1b9e8adc5e667","score":"0.6227319","text":"def setUp(cls):\n pass","title":""},{"docid":"d7cf04ce463176249d361d722146083a","score":"0.6224186","text":"def setup_method(self):\n\n self.name = 'ndtesting'\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\n return","title":""},{"docid":"40cbf579c77d25fc0bfdb693242b1beb","score":"0.62184924","text":"def __init__ (self):\n\t\tpass","title":""},{"docid":"a5c013bdd1962d473bc29d8ef9f1594c","score":"0.62147206","text":"def test(self):","title":""},{"docid":"6d80dfe989aa65877ae3dc3d77fb57db","score":"0.6212095","text":"def set(self) -> None:\n ...","title":""},{"docid":"0d3a7974a6fa0f3868f469c68d2757c6","score":"0.62030315","text":"def run(self):\n # Subclass and overload\n assert(False)","title":""},{"docid":"403c4dfdd692c82ed5514985279b1191","score":"0.61971235","text":"def setUp(self):\n self.context = Context()","title":""},{"docid":"403c4dfdd692c82ed5514985279b1191","score":"0.61971235","text":"def setUp(self):\n self.context = Context()","title":""},{"docid":"206755d80bf9e86ccc213a1fbc09e89b","score":"0.6192788","text":"def setUp(self):\n\t\tpass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"3e483681071a4e84409738933ef4c0ed","score":"0.6190741","text":"def init(self):\n pass","title":""},{"docid":"82ed2740386a8b33a8212e5597c25781","score":"0.6189197","text":"def setUpClass(cls):\n cls.review = inspect.getmembers(review, inspect.isfunction)","title":""},{"docid":"e1d913a5d348c714c776c3a28899f32b","score":"0.61843926","text":"def test_bad_init(self):","title":""},{"docid":"8a12d13bb0d51fc9b1dc9016725da181","score":"0.6181193","text":"def setUp(self):\n self.amity = Amity()","title":""},{"docid":"fc8f2d6201521d534f054d29a2847b9b","score":"0.6176834","text":"def __post_init__(self) -> None:","title":""},{"docid":"dab9d16dbf4b0efe8d5bf45a9b1de6d4","score":"0.6171858","text":"def test(self):\r\n return False","title":""},{"docid":"7f6ca4697e61e917d06607e64687bb0f","score":"0.61702687","text":"def run_test(self):\n pass","title":""},{"docid":"3aea05982b9de22fb76c4c5f2fdcd5f2","score":"0.6170054","text":"def setup(self) -> bool:\n ...\n\n return True","title":""},{"docid":"8cbc5b40487aa2d2d7445d47ccfdbd26","score":"0.61607534","text":"def pre_test_response(self):","title":""},{"docid":"66342ab51d8d53981de648ca89724675","score":"0.6158772","text":"def test_something(self):\r\n return True","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"2fc2e6931a140e00b1ded6302e3cb897","score":"0.61483204","text":"def __init__(__self__):\n pass","title":""},{"docid":"1e19df646aed902a37f593713904d3a0","score":"0.6147413","text":"def test_run(self):\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6147379","text":"def initialize(self):\r\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6147379","text":"def initialize(self):\r\n pass","title":""},{"docid":"4bcdcb21bc840f71fda45502ffcd7f0c","score":"0.6147379","text":"def initialize(self):\r\n pass","title":""}],"string":"[\n {\n \"docid\": \"de81fdce13d62ec47641ea1a8c6ee8c7\",\n \"score\": \"0.73396957\",\n \"text\": \"def before_tester_run(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b2d6e10065ff6b983c1edd3630c152\",\n \"score\": \"0.7324083\",\n \"text\": \"def classSetUp(self):\\r\\n assert self.something == True\\r\\n self.something = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51262b0e50f19bfeabbd206ecbc400f8\",\n \"score\": \"0.72828907\",\n \"text\": \"def _test(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f755fb8af7ee7ec95e19b5587ea6f098\",\n \"score\": \"0.7067964\",\n \"text\": \"def testing(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808425a98f90a1aaa5ee11daef2130fd\",\n \"score\": \"0.70527375\",\n \"text\": \"def _mockme(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccc77d904ab17a7c854c8f5789312938\",\n \"score\": \"0.69012713\",\n \"text\": \"def setup_method(self, method):\\n assert method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7529441c82c36857491679fc0ad7bba\",\n \"score\": \"0.6900921\",\n \"text\": \"def test_(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0714b55a928f10390dd112e4a71d64e7\",\n \"score\": \"0.6892391\",\n \"text\": \"def test_init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6395a0447babd542b1cb1cb13ec3ce90\",\n \"score\": \"0.68894523\",\n \"text\": \"def before_test_run(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa2604b3e54d4eaa66788fed871de75\",\n \"score\": \"0.68563145\",\n \"text\": \"def testInit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d1e4b8a393b823fec5ed0de63b0ff3\",\n \"score\": \"0.6824386\",\n \"text\": \"def test(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0645a56473c801c578eebf4001ceec3b\",\n \"score\": \"0.68234134\",\n \"text\": \"def test(self):\\n # TODO\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"343d0cce8356ac3e92c0de489f4efcd1\",\n \"score\": \"0.67005354\",\n \"text\": \"def test(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"343d0cce8356ac3e92c0de489f4efcd1\",\n \"score\": \"0.67005354\",\n \"text\": \"def test(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"675dfa93100212ef6375295a1a1ce439\",\n \"score\": \"0.66937464\",\n \"text\": \"def setup_method(self):\\n self.out = None\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b1a5d7ff04d8016d90ae97a5edbdd07\",\n \"score\": \"0.6687489\",\n \"text\": \"def after_tester_run(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82815e8f330845c57d8826a78a373780\",\n \"score\": \"0.664704\",\n \"text\": \"def setUp(self):\\n self.obj = SomeClass()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1996573f8fd08abd8c20cb78f5d337bd\",\n \"score\": \"0.6622737\",\n \"text\": \"def self_test():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f668c50b248a875b1d26bf7f593c018\",\n \"score\": \"0.6618984\",\n \"text\": \"def __init__(self):\\n self.verbose = False\\n self.simulate = False\\n self.debug = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8da9c4f89cf541e07ee70f4417550c\",\n \"score\": \"0.65945566\",\n \"text\": \"def test(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ab336d1d39a975629372b0424f8642\",\n \"score\": \"0.6570382\",\n \"text\": \"def setup_method(self):\\n\\n reload(pysat.instruments.pysat_testing)\\n self.name = 'testing'\\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b18d01113aa19349cad235b54ecf01\",\n \"score\": \"0.6537312\",\n \"text\": \"def SetUp(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9d58dbcc9463628df8a2b5dcadfc8fb\",\n \"score\": \"0.6458454\",\n \"text\": \"def _on_test_begin(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12415e51a45655613acc98a6dd303b6a\",\n \"score\": \"0.6443678\",\n \"text\": \"def setUp(self):\\n self.result = ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d388939ba349a300a7a20adb599c127d\",\n \"score\": \"0.64378744\",\n \"text\": \"def __call__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d388939ba349a300a7a20adb599c127d\",\n \"score\": \"0.64378744\",\n \"text\": \"def __call__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb74abd832f67445757a9df7a3220729\",\n \"score\": \"0.6430383\",\n \"text\": \"def test_set_instance_field(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06618ec00d1e59a1aa3d17ad542fd572\",\n \"score\": \"0.6405326\",\n \"text\": \"def setup_method(self):\\n\\n warnings.simplefilter(\\\"always\\\", DeprecationWarning)\\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\\n self.warn_msgs = []\\n self.war = \\\"\\\"\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1020f9278cdfaa93e4732c3cd4517798\",\n \"score\": \"0.6402218\",\n \"text\": \"def test_init(self):\\n self.assertTrue(hasattr(self.test, \\\"mouse_is_on\\\"))\\n self.assertTrue(hasattr(self.test, \\\"hover\\\"))\\n self.assertTrue(hasattr(self.test, \\\"request_list\\\"))\\n self.assertTrue(hasattr(self.test, \\\"command_list\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f35c96fe367ace0d686a1971bd1021\",\n \"score\": \"0.6395603\",\n \"text\": \"def test_instrument_init(self):\\n\\n assert self.testInst.new_thing\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3304793c6d84b318dab1a82509cae13e\",\n \"score\": \"0.63714534\",\n \"text\": \"def setup_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3304793c6d84b318dab1a82509cae13e\",\n \"score\": \"0.63714534\",\n \"text\": \"def setup_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d89e4e206ecb876b22b77a273dfebbe\",\n \"score\": \"0.6368773\",\n \"text\": \"def initiate(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b0e1f4643bfe57e20fa45988b64a3cc\",\n \"score\": \"0.63375026\",\n \"text\": \"def init(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dcfc871867931eeed4399775081280b\",\n \"score\": \"0.63354313\",\n \"text\": \"def setUp(self):\\n self.options = Options()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3795754c68868d6a9865aab795995b9d\",\n \"score\": \"0.6334769\",\n \"text\": \"def setup(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b269306d60acc935f8f503117a22bcd6\",\n \"score\": \"0.6329245\",\n \"text\": \"def run(self):\\n assert_equal(self, 1, 1, 'test')\\n assert_true(self, False, 'test')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3d57cbf37ee951907cbac117eeb09b\",\n \"score\": \"0.6328691\",\n \"text\": \"def test_init(self, hotp):\\n hotp.assert_not_called()\\n hotp.return_value.verify.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0868c574190f7892a5ed88ea2afca7d\",\n \"score\": \"0.63255155\",\n \"text\": \"def init(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf419d90fd2a0cb0ae308b3de97c90b5\",\n \"score\": \"0.63226557\",\n \"text\": \"def __init__(self):\\r\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf419d90fd2a0cb0ae308b3de97c90b5\",\n \"score\": \"0.63226557\",\n \"text\": \"def __init__(self):\\r\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19eaeecab013f1002b22a8666ddd1919\",\n \"score\": \"0.6319818\",\n \"text\": \"def call(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801be9e54b91396497442b025caecff4\",\n \"score\": \"0.6318234\",\n \"text\": \"def __init__(self):\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801be9e54b91396497442b025caecff4\",\n \"score\": \"0.6318234\",\n \"text\": \"def __init__(self):\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801be9e54b91396497442b025caecff4\",\n \"score\": \"0.6318234\",\n \"text\": \"def __init__(self):\\n self.initialized = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0f4e081e9a97e6d579fb34b09337ac\",\n \"score\": \"0.6315708\",\n \"text\": \"def test_something(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab0f4e081e9a97e6d579fb34b09337ac\",\n \"score\": \"0.6315708\",\n \"text\": \"def test_something(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20e315badfcd5ea2fbd2adeb30f65c3d\",\n \"score\": \"0.63003427\",\n \"text\": \"def init(self, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcafb07835deeb463e5bda11509ea028\",\n \"score\": \"0.62935686\",\n \"text\": \"def test_method(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff31b1e94630d6d5ec1d74bc327ce48a\",\n \"score\": \"0.6287083\",\n \"text\": \"def SetUp(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff31b1e94630d6d5ec1d74bc327ce48a\",\n \"score\": \"0.6287083\",\n \"text\": \"def SetUp(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4034806bcb3ae3ad4c90b6a3eb9844a0\",\n \"score\": \"0.62859535\",\n \"text\": \"def setUp(self):\\n print(\\\"setUp\\\")\\n print(\\\"===========\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3cfadca177693c1d29f5e79bdd34cd\",\n \"score\": \"0.6281448\",\n \"text\": \"def setUp(self):\\n self.supvisors = MockedSupvisors()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acc340f00371ec4a826c5450a6971f1\",\n \"score\": \"0.6276333\",\n \"text\": \"def test_init(self, hotp):\\n hotp.assert_not_called\\n hotp.return_value.verify.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa0cd3753bfed9ca350c54f4c15ff2c\",\n \"score\": \"0.6259981\",\n \"text\": \"def setUpAttr(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c963aade5e955a41445071162555445\",\n \"score\": \"0.6255766\",\n \"text\": \"def test(self, test):\\n\\n self._test = test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c963aade5e955a41445071162555445\",\n \"score\": \"0.6255766\",\n \"text\": \"def test(self, test):\\n\\n self._test = test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c82da39e2f0c091b2a1b647b101d2ef\",\n \"score\": \"0.6237032\",\n \"text\": \"def verify(self):\\n raise Exception(\\\"abstact class called unexpectedly\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b931cb4adffb6ce8764de896d8ea98f8\",\n \"score\": \"0.6232571\",\n \"text\": \"def method(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c9f152b244481fe2a33153022c0f29\",\n \"score\": \"0.6231853\",\n \"text\": \"def setUpClass(cls):\\n cls.token = generate_v2_test_token(username='bob')\\n addr.const = MagicMock()\\n addr.const.VLAB_IPAM_LOG_LEVEL = 'INFO'\\n addr.const.VLAB_VERIFY_TOKEN = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac72173d9df6ed7e73a1b9e8adc5e667\",\n \"score\": \"0.6227319\",\n \"text\": \"def setUp(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7cf04ce463176249d361d722146083a\",\n \"score\": \"0.6224186\",\n \"text\": \"def setup_method(self):\\n\\n self.name = 'ndtesting'\\n self.ref_time = pysat.instruments.pysat_testing._test_dates['']['']\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40cbf579c77d25fc0bfdb693242b1beb\",\n \"score\": \"0.62184924\",\n \"text\": \"def __init__ (self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c013bdd1962d473bc29d8ef9f1594c\",\n \"score\": \"0.62147206\",\n \"text\": \"def test(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d80dfe989aa65877ae3dc3d77fb57db\",\n \"score\": \"0.6212095\",\n \"text\": \"def set(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d3a7974a6fa0f3868f469c68d2757c6\",\n \"score\": \"0.62030315\",\n \"text\": \"def run(self):\\n # Subclass and overload\\n assert(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403c4dfdd692c82ed5514985279b1191\",\n \"score\": \"0.61971235\",\n \"text\": \"def setUp(self):\\n self.context = Context()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403c4dfdd692c82ed5514985279b1191\",\n \"score\": \"0.61971235\",\n \"text\": \"def setUp(self):\\n self.context = Context()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"206755d80bf9e86ccc213a1fbc09e89b\",\n \"score\": \"0.6192788\",\n \"text\": \"def setUp(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e483681071a4e84409738933ef4c0ed\",\n \"score\": \"0.6190741\",\n \"text\": \"def init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ed2740386a8b33a8212e5597c25781\",\n \"score\": \"0.6189197\",\n \"text\": \"def setUpClass(cls):\\n cls.review = inspect.getmembers(review, inspect.isfunction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d913a5d348c714c776c3a28899f32b\",\n \"score\": \"0.61843926\",\n \"text\": \"def test_bad_init(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a12d13bb0d51fc9b1dc9016725da181\",\n \"score\": \"0.6181193\",\n \"text\": \"def setUp(self):\\n self.amity = Amity()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc8f2d6201521d534f054d29a2847b9b\",\n \"score\": \"0.6176834\",\n \"text\": \"def __post_init__(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dab9d16dbf4b0efe8d5bf45a9b1de6d4\",\n \"score\": \"0.6171858\",\n \"text\": \"def test(self):\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f6ca4697e61e917d06607e64687bb0f\",\n \"score\": \"0.61702687\",\n \"text\": \"def run_test(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aea05982b9de22fb76c4c5f2fdcd5f2\",\n \"score\": \"0.6170054\",\n \"text\": \"def setup(self) -> bool:\\n ...\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cbc5b40487aa2d2d7445d47ccfdbd26\",\n \"score\": \"0.61607534\",\n \"text\": \"def pre_test_response(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66342ab51d8d53981de648ca89724675\",\n \"score\": \"0.6158772\",\n \"text\": \"def test_something(self):\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc2e6931a140e00b1ded6302e3cb897\",\n \"score\": \"0.61483204\",\n \"text\": \"def __init__(__self__):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e19df646aed902a37f593713904d3a0\",\n \"score\": \"0.6147413\",\n \"text\": \"def test_run(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6147379\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6147379\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcdcb21bc840f71fda45502ffcd7f0c\",\n \"score\": \"0.6147379\",\n \"text\": \"def initialize(self):\\r\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":179,"cells":{"query_id":{"kind":"string","value":"dde81fd75e8dab25708f015fee2011b9"},"query":{"kind":"string","value":"If the same item appears in the list, sums the quantities and create a unique Food object."},"positive_passages":{"kind":"list like","value":[{"docid":"fc122672b927400040c2b8b6fa8b33a7","score":"0.5547891","text":"def eraseDubloons(foodList):\n resList = []\n foodNames = [f.codsougr_name for f in foodList]\n uniqueFoodNames = list(set(foodNames))\n for name in uniqueFoodNames:\n duplicates = [f for f in foodList if f.codsougr_name == name]\n qte_nette = sum([f.qte_nette for f in duplicates])\n codsougr = [f.codsougr for f in duplicates][0]\n \n newFood = Food(codsougr, name, qte_nette)\n resList.append(newFood)\n return resList","title":""}],"string":"[\n {\n \"docid\": \"fc122672b927400040c2b8b6fa8b33a7\",\n \"score\": \"0.5547891\",\n \"text\": \"def eraseDubloons(foodList):\\n resList = []\\n foodNames = [f.codsougr_name for f in foodList]\\n uniqueFoodNames = list(set(foodNames))\\n for name in uniqueFoodNames:\\n duplicates = [f for f in foodList if f.codsougr_name == name]\\n qte_nette = sum([f.qte_nette for f in duplicates])\\n codsougr = [f.codsougr for f in duplicates][0]\\n \\n newFood = Food(codsougr, name, qte_nette)\\n resList.append(newFood)\\n return resList\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4f021ae31218d83063fc7b7fe95882c6","score":"0.6519353","text":"def __add_muilt(self,food_name,quantity):\n\t\tif (not food_name in self.items):\n\t\t\tself.items[food_name] =0\n\t\tself.items[food_name] = self.items[food_name] + quantity","title":""},{"docid":"232d5321f12a8823e62ae2f05ee7bab2","score":"0.5817523","text":"def addFood(self, num):\n self.camp.data[\"foodQuantity\"] += int(num)","title":""},{"docid":"3f7d71c67883675ad8cf3a3be6f640a0","score":"0.575201","text":"def add(self, name, quantity):\n item = Item(name, quantity)\n if item in self.items:\n # we already have this, so add to it\n self.items[self.items.index(item)] += item\n else:\n self.items.append(item)","title":""},{"docid":"5d5bf92df7dcf5cd7b9da5461a553cbf","score":"0.5631031","text":"def condense_grocery_list(list_of_grocery_items):\n condensed_list=[]\n condensed_dict=dict()\n for grocery_item in list_of_grocery_items:\n dict_key=grocery_item.unit+\"-\"+grocery_item.name\n if dict_key in condensed_dict:\n condensed_dict[dict_key].number = str(float(condensed_dict[dict_key].number) + float(grocery_item.number))\n else:\n condensed_dict[dict_key]=grocery_item\n\n for item in condensed_dict:\n condensed_list.append(condensed_dict[item])\n\n condensed_list.sort(key=lambda x: x.name)\n condensed_dict={}\n for item in condensed_list:\n if item.name in condensed_dict:\n condensed_dict[item.name] = condensed_dict[item.name] + \", \" + item.number.strip() + \" \" + item.unit.strip()\n else:\n condensed_dict[item.name]=item.name + \": \" + item.number.strip() + \" \" + item.unit.strip()\n condensed_list=[]\n for item in condensed_dict:\n new_item=shopping_item()\n new_item.name=item\n new_item.grocery_list_line=condensed_dict[item]\n condensed_list.append(new_item)\n\n return condensed_list","title":""},{"docid":"615ee899602c1129b4b2df5f3f783058","score":"0.5625155","text":"def createMeal(jour, tyrep, itemsName, itemsDict, minItem, maxItem, meanQuantities):\n nItems = random.randint(minItem,maxItem)\n items = random.sample(itemsName, nItems)\n foodItems = [Food(itemsDict[x], x, meanQuantities[x]) for x in items]\n meal = Meal(foodItems, jour=jour, tyrep=tyrep)\n meal.reorder()\n return meal","title":""},{"docid":"ff3c0498b926385879c47b0567a49a81","score":"0.5587279","text":"def consume_food(self, food):\n quantity = self.__items['food'].get(food, 0)\n if quantity > 0:\n self.__items['food'][food] = quantity - 1","title":""},{"docid":"0139cce57cee1ffab79707a72f831baf","score":"0.55362856","text":"def add_to_inventory_v2(inventory, added_items):\n\n for item in added_items:\n if item not in inventory:\n my_inventory.setdefault(item, 1)\n else:\n my_inventory[item] += 1\n return my_inventory","title":""},{"docid":"7d1cce71848b886fcbf253344f3c75a8","score":"0.5494977","text":"def add_to_inventory(inventory, added_items):\n for key in list(inventory):\n for item in added_items:\n if key == item:\n inventory[key] += 1\n else:\n inventory.setdefault(item, 1)\n\n return my_inventory","title":""},{"docid":"21510822cd79307c80383341d84fa004","score":"0.53571904","text":"def add(self, item1, item2):\n for sumField in self.sumFields:\n item1[sumField] = item1[sumField] + item2[sumField]","title":""},{"docid":"ae3d284c0f2807c357fd971d08a6f7f8","score":"0.5330261","text":"def addDish(self,dish):\r\n\t\tself.items[dish.name] = dish\r\n\t\tfor nutrient in TARGET_NUTRIENTS:\r\n\t\t\tself.totals[nutrient]+=dish.nutrition[nutrient]","title":""},{"docid":"f1159106f3c256affb95ba2576b99a40","score":"0.5326817","text":"def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\n pass","title":""},{"docid":"9fa86c34f680edfb331c7e63841be1d7","score":"0.5321849","text":"def update_world_totals(new_item):\n # TODO: Can definitely optimize this search if performance becomes an issue\n for item in world_inv:\n if new_item.equals(item):\n item.set_count(item.count + new_item.count)\n return\n\n if (new_item.damage != 0 and (new_item.full_name in degradeable_tools or new_item.full_name in degradeable_armor)):\n world_inv.append(Item(new_item.full_name, new_item.count, -1, new_item.lore))\n else:\n world_inv.append(Item(new_item.full_name, new_item.count, new_item.damage, new_item.lore))","title":""},{"docid":"90ff6a56e8a808fc9686f492e012e077","score":"0.52610236","text":"def feeding_animal(self):\n for food in self.get_food_set():\n for animal in self.get_animal_set():\n if (abs(animal.position[0] - food.position[0]) < Constants.ANIMAL_WIDTH / 2 + Constants.FOOD_WIDTH / 2 and \\\n abs(animal.position[1] - food.position[1]) < Constants.ANIMAL_HEIGHT / 2 + Constants.FOOD_HEIGHT / 2):\n self.animal_set.remove(animal)\n self.money += 1\n food.used = True\n if food.used:\n self.food_set.remove(food)","title":""},{"docid":"f300c9707bf682e3a6b1d5677eba5aa9","score":"0.52460706","text":"def execute_item_creation(self)->None:\n [ingedient.use_quantity(quantity) for ingedient,quantity in self.ingredient_quantity]","title":""},{"docid":"70b286ea3b1c733d10122c24d15eb782","score":"0.52455187","text":"def digest_fat(self):\n can_move = min(self.population - self.food, self.fat_storage)\n self.fat_storage -= can_move\n self.food += can_move","title":""},{"docid":"ae9736db106bb68027158dacec819627","score":"0.5245462","text":"def add_fruit(inventory, fruit, quantity=0):\n #print fruit, quantity\n inventory[fruit]=quantity\n #return inventory","title":""},{"docid":"bf0a041ec85a5bcb7a4fa271d76f533e","score":"0.5186193","text":"def add_item(self, item_name, item_price):\n if 'none' in self.items_with_price:\n self.items_with_price.clear()\n self.items_with_price.update({str(item_name): float(item_price)})","title":""},{"docid":"379782037ed753eb6be9b48c0ea5b3b7","score":"0.51835597","text":"def add_to_inventory(inventory, added_items):\n for i in added_items:\n if i in inventory.keys():\n inventory[i] += 1\n else:\n inventory[i] = 1\n return inventory","title":""},{"docid":"8fd6eb79281380c997d0ddf13a3b1137","score":"0.51719004","text":"def updateCart(self, replaceItem):\n\t\ttopFoods = self.dashFilter(replaceItem)\n\t\trestOfCart = [item for item in self.foods if item[\"id\"] != replaceItem]\n\t\t#topCombo = self.comboFilter(topFoods, restOfCart)\n\t\ttopCombo = self.randomFilter(topFoods)\n\t\tself.foods = [{\"id\":topCombo[\"id\"],\"name\":topCombo['name'],\"groups\":item['groups'],\"combos\":topCombo['combos']} if item[\"id\"] == replaceItem else item for item in self.foods]\n\t\treturn {'id':topCombo['id'],'name':topCombo['name'].upper(),'img':topCombo['id']+'.jpg'}","title":""},{"docid":"242d58e8f298df51464e9f6224d850aa","score":"0.51659745","text":"def addFood(surface) -> Food:\n\n newFood = Food(\n coord=[randint(0, screen_resolution[0]), randint(0, screen_resolution[1])],\n energy=2,\n surface=surface\n )\n foodHolder.append(newFood)\n\n return newFood","title":""},{"docid":"3c319e9376a5acfcd6692426a6106298","score":"0.51233023","text":"def test_bulk_inventory_quantity_combined(self):\n category = ['Clothing', 'Unisex Adult Clothing', 'Pants']\n\n expected_left_bulk_header = 'Individual Quantity'\n\n new_quantities = ['200', '300', '400', '500']\n\n expected_product_offerings = [\n ['1', '500.00', '550', '200', 't', 'L', '1', 'Black', '1'],\n ['1', '500.00', '550', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['1', '500.00', '550', '400', 't', 'M', '2', 'Black', '1'],\n ['1', '500.00', '550', '500', 't', 'M', '2', 'Pinkish', '2'],\n ['2', '1.00', '111', '200', 't', 'L', '1', 'Black', '1'],\n ['2', '1.00', '111', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['2', '1.00', '111', '400', 't', 'M', '2', 'Black', '1'],\n ['2', '1.00', '111', '500', 't', 'M', '2', 'Pinkish', '2'],\n ['3', '10.00', '222', '200', 't', 'L', '1', 'Black', '1'],\n ['3', '10.00', '222', '300', 't', 'L', '1', 'Pinkish', '2'],\n ['3', '10.00', '222', '400', 't', 'M', '2', 'Black', '1'],\n ['3', '10.00', '222', '500', 't', 'M', '2', 'Pinkish', '2']\n ]\n\n expected_variation_properties = [\n ['1', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['2', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['3', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\n ]\n\n expected_variation_options = [\n ['1', 't', '2119', 'L', '1'],\n ['1', 't', '2116', 'M', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '', 'Pinkish', '2'],\n ['2', 't', '2119', 'L', '1'],\n ['2', 't', '2116', 'M', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '', 'Pinkish', '2'],\n ['3', 't', '2119', 'L', '1'],\n ['3', 't', '2116', 'M', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '', 'Pinkish', '2']\n ]\n\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\n\n # Select category in bulk edit area\n bpiv.select_category(category)\n\n bulk_row = bpiv.bulk_edit_row\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\n\n # Set first variation property\n bpiv.set_property(bulk_row, 0, 'Size', \"Men's\")\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 0, 'L', displayed_name=\"L Men's\")\n bpiv.add_option(bulk_row, 0, 'M', displayed_name=\"M Men's\")\n\n # Set second variation property\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 1, 'Black')\n bpiv.add_custom_option(bulk_row, 1, 'Pinkish')\n\n bpiv.select_inventory_tab(bulk_row, \"Quantity\")\n\n # Switch to individual quantities, check the header is changed\n click(bpiv.bulk_header_checkbox(bulk_row, i=0))\n click(bpiv.bulk_header_checkbox(bulk_row, i=1), delay=0.5)\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\n\n # Enter individual quantities for the property combinations\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=0)\n for i, quantity in enumerate(new_quantities):\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\n\n # Check that only titles are in listing preview as preview is disabled\n check_simplified_preview(bpiv)\n\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\n\n # Apply\n assert bpiv.operation_apply().is_enabled() is True\n click(bpiv.operation_apply())\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\n 'Sync button is not enabled')\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\'t show up'\n\n # Check bulk edit area texts and that Apply button is disabled\n check_bulk_properties_disabled(bpiv)\n\n # Sync updates\n click(bpiv.sync_updates_button())\n\n # Check data in DB\n check_db_state(expected_product_offerings, expected_variation_properties, expected_variation_options)","title":""},{"docid":"8ea81c57ff2db743346ab37434f24574","score":"0.51203","text":"def calculateAmount(self):\n return sum([item.price for item in self.items])","title":""},{"docid":"e5115be39649d3f3029e91969f33dab0","score":"0.5108897","text":"def add_to_inventory(player_inventory, added_items):\n # For each new piece of loot...\n for new_loot_piece in added_items:\n if new_loot_piece in player_inventory:\n # if the loot already existed in inventory, increment it's count\n player_inventory[new_loot_piece] += 1\n else:\n # If the loot was not already in the inventory, create a new piece of loot\n # in the inventory and se it's default value to 1.\n player_inventory.setdefault(new_loot_piece, 1)\n\n return player_inventory","title":""},{"docid":"5e4b677c435c587a9cfddc38115a3c4f","score":"0.5098413","text":"def test_bulk_inventory_single_quantity_individual(self, property_number):\n category = ['Home & Living', 'Furniture']\n\n expected_left_bulk_header = 'Individual Quantity'\n\n new_quantities = ['22', '33']\n\n expected_product_offerings = [\n [\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'White', '2'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'White', '2'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'White', '2'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\n ],\n [\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'wool', '1', 'White', '2'],\n ['1', '500.00', '550', '22', 't', 'cotton', '2', 'Black', '1'],\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'wool', '1', 'White', '2'],\n ['2', '1.00', '111', '22', 't', 'cotton', '2', 'Black', '1'],\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'wool', '1', 'White', '2'],\n ['3', '10.00', '222', '22', 't', 'cotton', '2', 'Black', '1'],\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\n ]\n ]\n\n expected_variation_properties = [\n [\n ['1', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\n ['2', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\n ['3', 't', '502', 'Fabric', '', 'f', 't', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f']\n ],\n [\n ['1', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['2', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\n ['3', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\n ]\n ]\n\n expected_variation_options = [\n [\n ['1', 't', '', 'wool', '1'],\n ['1', 't', '', 'cotton', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '10', 'White', '2'],\n ['2', 't', '', 'wool', '1'],\n ['2', 't', '', 'cotton', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '10', 'White', '2'],\n ['3', 't', '', 'wool', '1'],\n ['3', 't', '', 'cotton', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '10', 'White', '2']\n ],\n [\n ['1', 't', '', 'wool', '1'],\n ['1', 't', '', 'cotton', '2'],\n ['1', 'f', '1', 'Black', '1'],\n ['1', 'f', '10', 'White', '2'],\n ['2', 't', '', 'wool', '1'],\n ['2', 't', '', 'cotton', '2'],\n ['2', 'f', '1', 'Black', '1'],\n ['2', 'f', '10', 'White', '2'],\n ['3', 't', '', 'wool', '1'],\n ['3', 't', '', 'cotton', '2'],\n ['3', 'f', '1', 'Black', '1'],\n ['3', 'f', '10', 'White', '2']\n ]\n ]\n\n\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\n\n # Select category in bulk edit area\n bpiv.select_category(category)\n\n bulk_row = bpiv.bulk_edit_row\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\n\n # Set first variation property\n bpiv.set_property(bulk_row, 0, 'Fabric')\n # Add two options to first property, one by one\n bpiv.add_custom_option(bulk_row, 0, 'wool')\n bpiv.add_custom_option(bulk_row, 0, 'cotton')\n\n # Set second variation property\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\n # Add two options to first property, one by one\n bpiv.add_option(bulk_row, 1, 'Black')\n bpiv.add_option(bulk_row, 1, 'White')\n\n bpiv.select_inventory_tab(bulk_row, \"Quantity\")\n\n # Switch to individual quantities, check the header is changed\n click(bpiv.bulk_header_checkbox(bulk_row, i=property_number), delay=0.5)\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\n\n # Enter individual quantities\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=property_number)\n for i, quantity in enumerate(new_quantities):\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\n\n # Check that only titles are in listing preview as preview is disabled\n check_simplified_preview(bpiv)\n\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\n\n # Apply\n assert bpiv.operation_apply().is_enabled() is True\n click(bpiv.operation_apply())\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\n 'Sync button is not enabled')\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\'t show up'\n\n # Check bulk edit area texts and that Apply button is disabled\n check_bulk_properties_disabled(bpiv)\n\n # Sync updates\n click(bpiv.sync_updates_button())\n\n # Check data in DB\n check_db_state(expected_product_offerings[property_number],\n expected_variation_properties[property_number],\n expected_variation_options[property_number])","title":""},{"docid":"ddde5fc2882d117d910fb6854f8c507f","score":"0.50927","text":"def concatenate_items(self) -> dict:\n test = []\n for text in self.receipt_text:\n if self.text_is_number(text) | self.remove_pound_symbol(text) | self.change_o_to_zero(text):\n text = text[1:]\n else:\n test.append(text)\n\n if self.text_is_number(text):\n if test[-1] in self.dict_to_return:\n self.dict_to_return[test[-1]]['quantity'] += 1\n else:\n self.dict_to_return[test[-1]] = {'name': text, 'quantity': 1}\n test = []\n\n return self.dict_to_return","title":""},{"docid":"28e1c26cb444d691a6d757c89aed7d2b","score":"0.5092597","text":"def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\n self.ingredient_quantity.append((ingredient,quantity))","title":""},{"docid":"0a68a1b31438ff6224ac04a9bb4f05d1","score":"0.50659823","text":"def scan_onto_shelf(self):\n if hasattr(self, \"shelf\"):\n items_added = []\n for item in self.items_stored:\n for product in self.shelf.compartments:\n if product[\"Product Number\"] == item.number:\n if product[\"Items Stored\"] is None:\n product[\"Items Stored\"] = []\n product[\"Items Stored\"].append(item)\n product[\"Quantity\"] += 1\n items_added.append(item)\n Item.sorted_items.append(item)\n Item.shipment.remove(item)\n for item in items_added:\n self.items_stored.remove(item)","title":""},{"docid":"484270c6a6c051655cc77f321d243802","score":"0.50581247","text":"def add_to_inventory(inventory, added_items):\n if added_items in inventory:\n inventory[added_items] += 1\n else:\n inventory[element] = 1","title":""},{"docid":"a4d21353320ad10efa03007bf3481510","score":"0.50539666","text":"def add(self, product, quantity):\n product_id = product.id\n if product_id not in self.basket:\n self.basket[product_id] = {\"price\": float(product.regular_price), \"quantity\": quantity}\n else:\n self.basket[product_id][\"quantity\"] = quantity\n self.save()","title":""},{"docid":"35cf195a68cb0eebf880a0671ed3bb9d","score":"0.50376934","text":"def _add_dict_inventory(self, d, item_pouch): # item_pouch used to find which dictionary to look in\r\n for group in d:\r\n if group in item_pouch:\r\n if item_pouch[group] is None:\r\n item_pouch[group] = d[group]\r\n elif isinstance(item_pouch[group], list):\r\n item_pouch[group].extend(d[group])\r\n elif isinstance(item_pouch[group], dict):\r\n self._add_dict_inventory(d[group], item_pouch[group])\r\n elif isinstance(item_pouch[group], int):\r\n item_pouch[group] = item_pouch.setdefault(group, 0)+d[group]\r\n else:\r\n item_pouch[group] = d[group]","title":""},{"docid":"3ca2e8c63e53f9ab69061677cf963375","score":"0.50190413","text":"def test_adding_item_to_order_model_is_add_one_to_quantity(self):\n cart_item_1 = CartItem.objects.create(order=self.order, item=self.item_1)\n cart_item_1.Add() # quantity = 1 ** payout = self.item_1.price * 1\n cart_item_1.Add() # quantity = 2 ** payout = self.item_1.price * 2\n\n self.assertEqual(CartItem.objects.get(order=self.order, item=self.item_1).quantity, 2)\n self.assertEqual(cart_item_1.total_price, self.item_1.price * 2)\n self.assertEqual(self.order.payout, self.item_1.price * 2)","title":""},{"docid":"f019318a90a39d1d4baa58c66b55a7e8","score":"0.50127226","text":"def uniq_add(my_list=[]):\n\n return sum(set(my_list))","title":""},{"docid":"85016ea7f6bcc15054e712a84e7aaa66","score":"0.5005258","text":"def create_order_item(self, food_item, quantity):\n OrderItem.objects.create(\n user=self.customer,\n food_item=food_item,\n quantity=quantity,\n )","title":""},{"docid":"3f95d8bb1a0a822a0a89287235821bd2","score":"0.50050193","text":"def calculateAmount(self):\n summa = 0\n for item in self.items:\n summa += item.price\n return summa","title":""},{"docid":"0a975cec92256ad6ac4119086b89bcaf","score":"0.50043887","text":"def add(self, product, quantity=None, update_quantity=False):\n if product.price_with_taxes > 0 and quantity != \"0\":\n self.cart[str(product.id)] = {\n 'user_id': self.request.user.id,\n 'article_id': str(product.id),\n 'name': product.name,\n 'quantity': quantity,\n 'price_type': product.price_type,\n 'price_with_taxes': str(product.price_with_taxes),\n 'article_code': product.article_code,\n }\n self.save()","title":""},{"docid":"e946d19a98dd4708d5ea172fdab839a3","score":"0.49940917","text":"def fortune(player):\n random.seed()\n \n # (inventory item, amount received)\n treasures = [\n ('food',random.randint(100,500)),\n ('money',random.randint(200,500)),\n ('bullets',random.randint(30,100)),\n ('kits',random.randint(2,3)),\n ('parts',random.randint(2,3)),\n ]\n \n # Determine random item.\n found = random.choice(treasures)\n \n # Handle special message for food.\n if found[0] == 'food':\n current_food = player.get_from_inventory('food')\n # Handle food inventory limit.\n if current_food + found[1] > 1000:\n added_food = 1000 - current_food\n print(\"You found {} pounds of food in an abandoned wagon\".format(found[1]))\n print(\"You can only add {} pounds to your wagon\".format(added_food))\n player.update_inventory('food',1000)\n else:\n print(\"You found {} pounds of food in an abandoned wagon\".format(found[1]))\n player.add_to_inventory('food',found[1])\n \n # Handle special message for money.\n elif found[0] == 'money':\n print(\"You found ${} in an abandoned wagon\".format(\"%.2f\" %found[1]))\n player.add_to_inventory('money',found[1])\n \n # Handle all other items.\n else:\n print(\"You found {} {} in an abandoned wagon\".format(found[1],found[0]))\n player.add_to_inventory(found[0],found[1])","title":""},{"docid":"1e056e6e443126ef305397e1edf9f00e","score":"0.49848992","text":"def calc_items_total(cls, items):\n shipping_charge = 0\n subtotal = 0\n ad_charge_amt = 0\n ad_charge_breakup = {}\n\n for item in items:\n shipping_charge += item.shipping_charge\n subtotal += item.subtotal\n ad_charge_amt += item.additional_charge\n\n for charge in item.cost_breakup['additional_charge']:\n k = slugify(charge.get('name')).replace('-', '_')\n v = charge.get('amt')\n # Initialize value\n if ad_charge_breakup.get(k, None) is None:\n ad_charge_breakup[k] = 0.0\n ad_charge_breakup[k] += v\n\n return {\n 'shipping_charge': helper.round_off(shipping_charge),\n 'subtotal': helper.round_off(subtotal),\n 'additional_charge': helper.round_off(ad_charge_amt),\n 'cost_breakup': {\n 'additional_charge': ad_charge_breakup\n }\n }","title":""},{"docid":"5ba05c3eea1691cbb2cf7dd27f2811a3","score":"0.49668226","text":"def add(self, comida, quantity=1, update_quantity=False):\n product_id = str(comida.id)\n if product_id not in self.cart:\n self.cart[product_id] = {'quantity': 0,\n 'precio': str(comida.precio)}\n if update_quantity:\n self.cart[product_id]['quantity'] = quantity\n else:\n self.cart[product_id]['quantity'] += quantity\n self.save()","title":""},{"docid":"2a55875101d7602488fcb4f6054d62ad","score":"0.49663317","text":"def initializeFoods(self):\n for i in range(0, self.numFoods):\n self.addFood()","title":""},{"docid":"9da1b12f109a9a6aef2dab62544176d0","score":"0.49556127","text":"def add(self, product, qty):\n product_id = str(product.id)\n\n if product_id in self.basket:\n self.basket[product_id][\"qty\"] = qty\n else:\n self.basket[product_id] = {\"price\": str(product.regular_price), \"qty\": qty}\n\n self.save()","title":""},{"docid":"ffc752d2ca3326fdbd563d1bbeac4d05","score":"0.49489328","text":"def add(self, product, quantity=1, update_quantity=False):\n product_id = str(product.id) # converted to string because JSON use only string data\n if product_id not in self.cart:\n self.cart[product_id] = {'quantity': 0,\n 'price': str(product.price)\n }\n if update_quantity:\n self.cart[product_id]['quantity'] = quantity\n else:\n self.cart[product_id]['quantity'] += quantity # increment the quantity\n self.save() # saving the state of the quantity after update","title":""},{"docid":"3e122449c4307f51ed5cb01667c9a9d1","score":"0.49486324","text":"def eatFood(self):\n # if a prey is in contact with a food, then eat the food by removing the food and increment hunger.\n objIDInContactList = super().getContactObjects()\n # if there are objects in contact with this prey, then determine if they are food and if so eat them!\n foodEaten = 0\n if objIDInContactList:\n for objID in objIDInContactList:\n food = hsm.objIDToObject[objID]\n if food in hsm.foodList:\n hsm.foodList.remove(food)\n hsm.destroy(objID)\n foodEaten += 1\n # incrememnt hunger accordingly\n self.hunger += mv.PREY_INCREMENT_HUNGER \n # throw away excess, prey can only eat so much food at a time.\n if self.hunger > mv.PREY_MAX_HUNGER:\n self.hunger = mv.PREY_MAX_HUNGER\n if foodEaten > 0:\n self.foodTimeStamps.append([hsm.frameCount, foodEaten])","title":""},{"docid":"892adb19f630ba769f49adfe54589f4c","score":"0.4945769","text":"def add_item(self, x):\n self._items_with_price.update(x)","title":""},{"docid":"8b3b75d718473bad062e7134b4e43dd2","score":"0.49438444","text":"def add_item_to_cart(self, product_id, quantity):\n product = Product.objects.filter(product_id=product_id, store=self.customer_id.store_id, deleted_at=None).first()\n if product is None or product.inventory < quantity:\n raise ProductNotFoundException\n\n product.inventory -= quantity\n product.save()\n\n existing_cart_item = CartItem.objects.filter(cart_id=self.cart_id, product_id=product.product_id\n , removed_at=None).first()\n\n self.customer_id.last_cart_activity_at = datetime.now()\n self.customer_id.updated_at = datetime.now()\n self.customer_id.save()\n if existing_cart_item:\n existing_cart_item.quantity += quantity\n existing_cart_item.save()\n return existing_cart_item\n else:\n cart_item = CartItem(\n cart_item_id=str(uuid.uuid4().int),\n product_id=product.product_id,\n cart_id=self.cart_id,\n quantity=quantity\n ).save()\n return cart_item","title":""},{"docid":"106331337e2a2c1d307ba926e7184437","score":"0.49325597","text":"def calculate_total_nutrition_fulldayofeating2(\n specificingredient2_dict_list,\n all_nutrients_and_default_units,\n set_to_zero_if_none,\n):\n\n # Initialize the dictionary which will store the results\n result_total_nutrition_fulldayofeating = {}\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n new_dict = {nutrient_name: 0}\n result_total_nutrition_fulldayofeating.update(\n new_dict\n )\n\n # Go through all the SpecificIngredients related to the FullDayOfEating.\n # For each SpecificIngredient, calculate its Nutrition by\n # multiplying the calculated_amount divided by the reference amount\n # multiplied by each nutrient value in the associated RawIngredient.\n\n # Sum up the total nutrition\n for k in range(len(specificingredient2_dict_list)):\n # For each nutrient, calculate the amount of that nutrient contained\n # in the calculated_amount of the SpecificIngredient\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n result_total_nutrition_fulldayofeating[nutrient_name] = \\\n result_total_nutrition_fulldayofeating[nutrient_name] \\\n + set_to_zero_if_none(specificingredient2_dict_list[k][\n 'raw_ingredient'][nutrient_name]) \\\n * float(specificingredient2_dict_list[k]['calculated_amount'])\\\n / float(specificingredient2_dict_list[k][\n 'raw_ingredient']['reference_amount'])\n\n # Round the values in the result_total_nutrition_fulldayofeating\n # Initialize the dictionary containing the rounded values.\n result_total_nutrition_fulldayofeating_rounded = {}\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n new_dict = {nutrient_name: 0}\n result_total_nutrition_fulldayofeating_rounded.update(\n new_dict\n )\n\n # Round the values and save them to the respective dictionary.\n for dict_k in all_nutrients_and_default_units:\n nutrient_name = dict_k['nutrient_name_measuredfood']\n result_total_nutrition_fulldayofeating_rounded[nutrient_name] = \\\n round(result_total_nutrition_fulldayofeating[nutrient_name], 1)\n\n return result_total_nutrition_fulldayofeating,\\\n result_total_nutrition_fulldayofeating_rounded","title":""},{"docid":"6e950cf94ff01f7be16cc47e54da41ba","score":"0.4931627","text":"def _update_total_nutrition_values(self, unique_id, date, plan, add):\n table = self.dynamodb.Table(c.TABLE_NUTRITIONAL_NEEDS_DAY)\n week = form.get_week_by_date(date)\n\n item_nutrients_for_week = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.WEEK: week\n },\n ProjectionExpression='#toplevel',\n ExpressionAttributeNames={\"#toplevel\": c.NUTRIENTS_FOR_WEEK}\n )\n\n item_nutrients_for_day = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date\n },\n ProjectionExpression='#toplevel',\n ExpressionAttributeNames={\"#toplevel\": c.NUTRIENTS_FOR_DAY}\n )\n\n for container, container_content in plan.iteritems():\n item_nutrients_for_container = table.get_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id, c.DATE: date},\n ProjectionExpression='#toplevel.#container',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_CONTAINER,\n \"#container\": container\n }\n )\n for meal_key, meal_content in container_content.iteritems():\n\n\n\n for n in params.nutrientList:\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\n (1 if add else -1)\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\n (1 if add else -1)\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['VAL'] += \\\n meal_content['nutrients'][n]['VAL'] * (1 if add else -1)\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['UNIT'] = \\\n meal_content['nutrients'][n]['UNIT']\n\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date},\n UpdateExpression='SET #toplevel.#cat = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_CONTAINER,\n \"#cat\": container\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container]\n }\n )\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\n Key={c.UNIQUE_IDENTIFIER: unique_id,\n c.DATE: date},\n UpdateExpression='SET #toplevel = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_DAY\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY]\n }\n )\n\n table.update_item(\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\n Key={\n c.UNIQUE_IDENTIFIER: unique_id,\n c.WEEK: week\n },\n UpdateExpression='SET #toplevel = :value',\n ExpressionAttributeNames={\n \"#toplevel\": c.NUTRIENTS_FOR_WEEK\n },\n ExpressionAttributeValues={\n \":value\": item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK]\n }\n )","title":""},{"docid":"c7dfb948b8301a0430651d0b5bb487bb","score":"0.49241003","text":"def player_eats_food(self, snake, food):\n head = snake[0]\n head_x, head_y = head\n for food_item in food:\n food_x, food_y = food_item\n #Return whether or not the player's received food, adds fat if they have\n if head_x == food_x and head_y == food_y:\n #We've eaten food, update our score\n self.score += int(150 + int(len(snake)/10)*5)\n\n #Add more fat to the user if they already have some or they have a decent score\n self.fat += 8 + int(self.fat/4) + int(self.score/500)\n return True\n return False","title":""},{"docid":"7e5cd209b264fb2dd585ad69bbc06220","score":"0.49199784","text":"def createConsumptionSeq(df):\n mealList = []\n M = {} #Contient les aliments d'un repas \n nomen = df.nomen.iloc[0]\n jour = df.jour.iloc[0]\n tyrep = df.tyrep.iloc[0]\n \n for index, row in df.iterrows(): # Scan tous les aliments de consommation\n #print(row.nomen, row.jour, row.tyrep)\n if (row.nomen == nomen) & (row.jour == jour) & (row.tyrep == tyrep):\n name = str(row.nomen) + '_' + str(index)\n M[name] = Food(row.codsougr, row.codsougr_name, row.qte_nette) # Crée l'objet FoodItem\n else:\n m = list(set(M.values())) #Set of food items in a meal\n uniqueM = eraseDubloons(m)\n meal = Meal(uniqueM, jour = jour, tyrep = tyrep)\n meal.reorder()\n logging.debug('{}'.format(meal.nameTuple))\n if meal:\n mealList.append(meal)\n\n M = {}\n nomen = row.nomen\n jour = row.jour\n tyrep = row.tyrep\n \n meals = [m for m in mealList if m]\n return meals","title":""},{"docid":"c830b9fef7a64c1f072cecdf121412c0","score":"0.4918213","text":"def create_dico(item_list):\n assert type(item_list) is list\n dico = {}\n for items in item_list:\n for item in items:\n if item not in dico:\n dico[item] = 1\n else:\n dico[item] += 1\n return dico","title":""},{"docid":"c830b9fef7a64c1f072cecdf121412c0","score":"0.4918213","text":"def create_dico(item_list):\n assert type(item_list) is list\n dico = {}\n for items in item_list:\n for item in items:\n if item not in dico:\n dico[item] = 1\n else:\n dico[item] += 1\n return dico","title":""},{"docid":"6ddaa4351c2892173251704a2ad04d77","score":"0.49181578","text":"def sum_list(new_list):\n return sum(new_list)\n # return None","title":""},{"docid":"9ee3ad439b54ccc0e9043295f07ea1cd","score":"0.49134657","text":"def saved_meal_item_add(\n self, meal_id, food_id, food_entry_name, serving_id, num_units\n ):\n params = {\n \"method\": \"saved_meal_item.add\",\n \"format\": \"json\",\n \"saved_meal_id\": meal_id,\n \"food_id\": food_id,\n \"food_entry_name\": food_entry_name,\n \"serving_id\": serving_id,\n \"number_of_units\": num_units,\n }\n\n response = self.session.get(self.api_url, params)\n return self.valid_response(response)","title":""},{"docid":"a46b885d595c7bea38ce23fc1d6daa4d","score":"0.4910791","text":"def calculateInventory(self):\n from collections import Counter\n orders = self.model[0]\n shipping = self.model[1]\n inventory = {key:dict() for key in shipping}\n citys = {o.dest for o in orders}\n months = {o.orderTime.month for o in orders}\n products = {o.pro for o in orders}\n #print shipping\n for pro in products:\n for city in citys:\n inventory[pro][city] = Counter()\n for o in orders:\n o.ship = self.chooseBestShipping(o,citys)\n\n inventory[o.pro][o.ship][o.orderTime.month] += o.num\n #self.view.showInventory(inventory)\n self.inventory = inventory\n self.citys = citys\n self.months = months\n self.products = products\n return inventory","title":""},{"docid":"dcbca7ea38a938ec591a33b34ace0ede","score":"0.49077475","text":"def save(self, *args, **kwargs):\n if self.pk is None:\n item = self.item\n item.quantity += self.quantity\n item.save()\n return super(Entry, self).save(*args, **kwargs)","title":""},{"docid":"cbf4880010aa81907064f104b9d60f03","score":"0.49064472","text":"def subtotal(self):\n sub = 0.0\n for i in self.items:\n sub += i.quantity * INVENTORY[i.name]\n return sub","title":""},{"docid":"442dae3041bd85957abe5451209e318b","score":"0.48951203","text":"def _initilizeItemInventory(self):\n for itemName in itemPrice.keys():\n for j in range(5):\n self._itemInventory.add(itemName)","title":""},{"docid":"2bf1960258a00848bdd160a6a903b421","score":"0.48922408","text":"def find_food(self):\n \n if self.check_knife(1):\n self.knife_dur -= 1\n self.food += 1","title":""},{"docid":"4280fe8c4dcd8681a0e6d972b181946f","score":"0.48690817","text":"def createFoodDict(food_item_list):\n food_dict = {}\n for food_item in food_item_list:\n words = food_item.split(' ')\n first_word = words[0]\n food_dict[first_word] = food_item\n return food_dict","title":""},{"docid":"31e19cfc7f9cc7ac082b4629f8df71cb","score":"0.48465908","text":"def scan_onto_cart(self):\n if hasattr(self, \"shelf\"):\n for item in Item.shipment:\n if len(self.items_stored) < self.max_capacity:\n for product in self.shelf.compartments:\n if item.number == product[\"Product Number\"]:\n self.items_stored.append(item)","title":""},{"docid":"bb607bc5bfc19f80144df53d7174c4b2","score":"0.4835511","text":"def returnfrequentitemsets(itemSet, transactionList, minSupport, freqSet):\n\t\t_itemSet = set()\n\t\tlocalSet = defaultdict(int)\n\n\t\tfor item in itemSet:\n\t\t\tfor transaction in transactionList:\n\t\t\t\tif item.issubset(transaction):\n\t\t\t\t\tfreqSet[item] += 1\n\t\t\t\t\tlocalSet[item] += 1\n\n\t\tfor item, count in localSet.items():\n\t\t\t\tsupport = float(count)/len(transactionList)\n\n\t\t\t\tif support >= minSupport:\n\t\t\t\t\t\t_itemSet.add(item)\n\n\t\treturn _itemSet","title":""},{"docid":"f64d95b9e2dbd365c3e3c875f63c4ec1","score":"0.48311788","text":"def add_item(self, item):\n self.items_with_price.update(item)","title":""},{"docid":"b1dea7e98dd8eb0a30e38b7120376a69","score":"0.4829422","text":"def process_item(self, item, spider):\n # looks for a duplicate\n duplicate_check = self.db[self.collection_name].find({'name': item['name'], 'store': item['store'], 'category': item['category'], 'animal': item['animal']}).count()\n if duplicate_check == 0:\n self.db[self.collection_name].insert(dict(item))\n logging.debug('Product added to MongoDB')\n return item\n else:\n # logging.debug('Duplicate found. Skipping product')\n raise DropItem('Duplicate found. Skipping product: %s' % item['name'])\n # return item","title":""},{"docid":"616c81c4094a7fd6dab55793794e5f42","score":"0.48247102","text":"def generate_food(self):\n num_food = ENV_N_FOOD\n\n pos = [(random() * self.W, random() * self.H) for i in range(num_food)]\n\n return [Food(x, y, self.W, self.H) for (x, y) in pos]","title":""},{"docid":"0bdff644a55ea47546734621b9954d60","score":"0.4823412","text":"def budget(data,productsIds, quantity):\r\n \r\n data=data.fillna(0)\r\n budg=0\r\n products=productsIds\r\n quantity=quantity\r\n \r\n dataFilter = data[data['No. Item'].isin(products)]\r\n prices=dataFilter['Precio Unitario'].tolist()\r\n \r\n for i in range(len(quantity)):\r\n budg+=quantity[i]*prices[i]\r\n return budg","title":""},{"docid":"b9b6f3b617d4f2917091b725f1b1b61a","score":"0.48086756","text":"def get_total(self):\n\n base_price = 5\n if self.species == \"Christmas melon\":\n base_price = base_price * 1.5\n\n total = (1 + self.tax) * self.qty * base_price\n \n # if qty is less than ten melons and international, then +$3 to international_order\n if self.qty < 10 and self.order_type == 'international':\n total = total + 3\n\n return total","title":""},{"docid":"a15150d7171093c8a2769cd73df5a8ab","score":"0.4804091","text":"def fruits(fruit_list, fruit):\n\n updated_fruits_list = []\n for item in fruit_list:\n updated_fruits_list.append(item)\n updated_fruits_list.append(fruit)\n\n return updated_fruits_list","title":""},{"docid":"17ff7340eee41c80ad913f6610332c1b","score":"0.47999802","text":"def add_to_cart(self, selection_id, selection_qty):\n # read the data from data storage\n for keys, values in ShoppingCart.__ecommerce_data.items():\n # check if the product contain necessary data before proceed\n if \"products\" and \"sku\" and \"prices\" and \"stock\" in values:\n # check if it is a correct sku code from the data storage\n if selection_id in values[\"sku\"]:\n # add product information to the instance variables\n self.__seller_id.append(keys)\n product_id = values[\"sku\"].index(selection_id)\n self.__seller.append(values[\"first_name\"] + \" \" + values[\"last_name\"])\n self.__sku.append(values[\"sku\"][product_id])\n self.__price.append(values[\"prices\"][product_id])\n self.__product_name.append(values[\"products\"][product_id])\n self.__pos.append(product_id)\n self.__check_out_qty.append(selection_qty)","title":""},{"docid":"c1ac6afebd277ad77e422efa246f2ad9","score":"0.47989768","text":"def addItem(self, itemId, itemQty):\r\n self.items[itemId] += itemQty\r\n return self","title":""},{"docid":"d10ce6669d3c4fce854bab14bf47b1b7","score":"0.47970992","text":"def add_item(self, item):\n LOG.info('Add Product')\n LOG.info(\n \"Add the item: %s , price: %s, quantity: %s\"\n , item.get_name(), item.get_price(), item.get_quantity())\n if not item.get_name() in self.store_stock:\n self.db_manager.insert_element(item)\n else:\n current_item_quantity = \\\n self.store_stock[item.get_name()].get_quantity() \\\n + item.get_quantity()\n self.db_manager.update_quantity_field(quantity=current_item_quantity,\n id=self.store_stock[item.get_name()].get_id())\n LOG.info(\"Complete to add a item successful\")","title":""},{"docid":"1f5be3b43a6445bef2e20fac0d8943eb","score":"0.4792217","text":"def combine_the_anon_cart(self, request):\n cart = request.session.get('cart')\n if cart:\n for item in cart.items():\n product = Product.objects.get(id=item[0])\n if product.user == request.user:\n messages.error(request, 'You can\\'t buy your product')\n else:\n quantity = item[1]['quantity']\n self.add(product, quantity)\n del request.session['cart']","title":""},{"docid":"5f77055a0765db17ee93d22d7bb315d6","score":"0.47913775","text":"def total_items_sold(self, given_date):\n # ensure given_date is a date\n assert isinstance(given_date, date), \"given_date must be datetime.date()\"\n\n # get quantity from order_lines for given_date\n quantities = [\n i[0]\n for i in cur.execute(\"\"\"\n SELECT quantity\n FROM orders\n INNER JOIN order_lines ON orders.id=order_lines.order_id\n WHERE created_at LIKE ?\n \"\"\", (given_date.isoformat()+'%',))\n ]\n\n # sum quantity\n return sum(quantities)","title":""},{"docid":"2a07d888277c16437ea1ed144e02e719","score":"0.47881973","text":"def oh_quantity_by_location(self):\n location_list = self.transactions.all().values(\"location\").distinct().order_by(\"location\")\n my_dict = dict()\n for l in location_list:\n location = Location.objects.get(pk=l[\"location\"])\n my_dict[location] = self.get_oh_quantity(location)\n return my_dict","title":""},{"docid":"b7a2e2d6e3ad23e24d00b97541db3187","score":"0.47833753","text":"def returnItemsWithMinSupport(itemSet, transactionList, minSupport, freqSet):\n _itemSet = set()\n localSet = defaultdict(int)\n\n for item in itemSet:\n for transaction in transactionList:\n if item.issubset(transaction):\n freqSet[item] += 1\n localSet[item] += 1\n\n # print 'localSet',localSet\n for item, count in localSet.items():\n # print item,count\n support = float(count)/len(transactionList)\n\n if support >= minSupport:\n _itemSet.add(item)\n\n return _itemSet","title":""},{"docid":"8e7d505ef1815ef26d24544b025773ca","score":"0.47816607","text":"def main():\r\n group_of_list = []\r\n group = {}\r\n count = 0\r\n dict_input_food_and_price = input()\r\n dict_input_food_and_price = json.loads(dict_input_food_and_price)\r\n while True:\r\n yourname = input()\r\n if yourname == \"End\":\r\n break\r\n group[yourname] = 0\r\n for food, price in dict_input_food_and_price.items():\r\n while True:\r\n yourname = input()\r\n if yourname == \"End\":\r\n break\r\n group_of_list.append(yourname)\r\n count += 1\r\n total = price/count\r\n for food in group_of_list:\r\n group[food] = group.get(food)+total\r\n count = 0\r\n group_of_list = []\r\n print(group)","title":""},{"docid":"57fee8f9f7f940bfc0f162909b7ee2b2","score":"0.47804844","text":"def reducer(L):\n\tdistribution = {}\n\tfor item in L:\n\t\tfor record in item:\n\t\t\tif record in distribution:\n\t\t\t\tdistribution[record] += item[record]\n\t\t\telse:\n\t\t\t\tdistribution[record] = item[record]\n\t\titem.clear()\n\treturn distribution","title":""},{"docid":"7dbbe3574385be484fb2e72fb6016bb1","score":"0.47785863","text":"def add_item(self, new_item):\n self.items_with_price.update(new_item)","title":""},{"docid":"301be53ed8845a4768b33a75ed332e78","score":"0.4775439","text":"def getFood( self, amount ) :\n\t\treturn self.getSupply( amount )","title":""},{"docid":"6e98031becdd3b82c96afc040c16348c","score":"0.47707295","text":"def get_total(self, **kwargs):\n return self.get_price_per_item(**kwargs) * self.get_quantity(**kwargs)","title":""},{"docid":"99bf9dc41926c19f7c1798b9a962799d","score":"0.47650927","text":"def cardlist_sum(cardlist1, cardlist2):\n cards = deepcopy(cardlist1)\n for name, amount in cardlist2.items():\n if name not in cards:\n cards[name] = 0\n cards[name] = cards[name] + amount\n return cards","title":""},{"docid":"af3e40827b1596cd982bb10623718ab9","score":"0.47612268","text":"def food_add_favorite(self, food_id, serving_id=None, number_of_units=None):\n\n params = {\"method\": \"food.add_favorite\", \"format\": \"json\", \"food_id\": food_id}\n\n if serving_id and number_of_units:\n params[\"serving_id\"] = serving_id\n params[\"number_of_units\"] = number_of_units\n\n response = self.session.get(self.api_url, params=params)\n return self.valid_response(response)","title":""},{"docid":"46d8b637023ae9e022ee3a1e96d883cd","score":"0.47490332","text":"def food_insert(self, name, calories, protein, fat, carbs, fiber):\n pass","title":""},{"docid":"7d8c0504c732838f7986103ecf4d9fb0","score":"0.47435322","text":"def add(self, product, quantity=1, update_quantity=False):\n product_vencode = str(product.vencode)\n if product_vencode not in self.cart:\n self.cart[product_vencode] = {'quantity': 0,\n 'price': str(product.price)}\n if update_quantity:\n self.cart[product_vencode]['quantity'] = quantity\n else:\n self.cart[product_vencode]['quantity'] += quantity\n self.save()","title":""},{"docid":"abd12d614dd01fa7d1dc8d540d13a970","score":"0.4740142","text":"def add(self, item):","title":""},{"docid":"96d3c09b955b186c27327828c5c875b7","score":"0.47401217","text":"def test_unique_quantities(self):\n collection = Collection(['air__temperature', 'water__temperature'])\n\n quantities = collection.quantities()\n\n self.assertEqual(len(quantities), 1)\n self.assertTrue('temperature' in quantities)","title":""},{"docid":"3f0904ee6421b4e1fba7c11b177be256","score":"0.47290277","text":"def get_sum(items=[]):\n\t\tresult = 0\n\t\tif items:\n\t\t\tresult = reduce(lambda x, y: x+y, items)\n\t\treturn result","title":""},{"docid":"baa64214b750820c4bb4b85c71e8520d","score":"0.47211656","text":"def _amount_all(self):\n for order in self:\n amount_untaxed = 0.0\n for line in order.depence:\n amount_untaxed += line.montant\n\n order.update({\n\n 'Montant_total': amount_untaxed,\n })","title":""},{"docid":"105d02d685d7278913bf8246ba61d913","score":"0.4696378","text":"def union(a, b):\n c = a.copy()\n for item, quantity in b.items():\n if item in c:\n c[item] += quantity\n else:\n c[item] = quantity\n return c","title":""},{"docid":"43a8f10d46c49f4fc02930e22a6c1f53","score":"0.46955162","text":"def sum_list(input_list: typing.List[float]) -> float:\n return sum(input_list)","title":""},{"docid":"6b6714d61699a90caccf507a1439ba0b","score":"0.46904996","text":"def add_food(self, food: FoodProp) -> None:\n if not self.can_place_food(food):\n raise Exception('Cannot place a food with that color on the colorful plate')\n\n self.foods.append(food)","title":""},{"docid":"68e5cbf07cac2a1423f45e8e5ac6e692","score":"0.46781105","text":"def __len__(self):\n return sum(item[\"qty\"] for item in self.basket.values())","title":""},{"docid":"0b38093e769550aa59eb195f38c23eb6","score":"0.46772686","text":"def add_item(self, product, price):\n if not product in self.items_in_cart:\n self.items_in_cart[product] = price\n print (product + \" added.\")\n else:\n print (product + \" is already in the cart.\")","title":""},{"docid":"1690b8513ce9e630d2fbdaaf6b81ca8d","score":"0.4674813","text":"def sum_mixed_list(input_list: List[Union[int, float]]) -> float:\n return reduce(lambda x, y: x+y, input_list)","title":""},{"docid":"8d97e4fa24429dc883f373bd0ca99355","score":"0.46693996","text":"def _product_association(self):\n # Find orders that bought the target item\n orders = self.data[self.data[self.item_col]==self.target][self.order_col]\n \n # Find all items in those orders\n items = self.data[self.data[self.order_col].isin(orders)]\n \n # Recommend items frequently bought with the target item \n self._find_recs(items.groupby(self.item_col).size())\n self._print_recs(\"popular items other customers bought\")","title":""},{"docid":"e92b39dd58dbb549782499edade31dac","score":"0.46683574","text":"def autoPopulateInventory(self):\n if not self._permanent:\n self.clearInventory()\n\n # Randomly select the itemCount\n itemCount = int(getRandomItemFromList(self._numOfItemsCarried))\n\n if not itemCount: # 0 items carried\n return True\n\n for itemNum in range(1, itemCount + 1): # +1 due to exclusive ranges\n itemId = getRandomItemFromList(self._itemCatalog)\n if not itemId:\n continue\n if \"/\" not in itemId:\n continue\n\n dLog(\"creature.autoPopulateInventory: obj = \" + itemId)\n oType, oNum = itemId.split(\"/\")\n obj1 = ObjectFactory(oType, oNum)\n obj1.load()\n self.addToInventory(obj1)\n return True","title":""},{"docid":"4025849a832f2176dc523dd3048e8ffc","score":"0.46618393","text":"def raw_ingredients_exact(item_set):\n remaining = item_set.copy()\n raw_ingredients = {}\n while any(remaining):\n item, quantity = list(remaining.items())[0]\n item_object = items[item]\n del remaining[item]\n if item_object.raw:\n raw_ingredients = union(raw_ingredients, {item: quantity})\n else:\n item_list = item_object.recipe.ingredients.items()\n for ingredient, ingredient_quantity in item_list:\n remaining = union(\n remaining,\n multiply(\n {ingredient: ingredient_quantity},\n quantity / item_object.recipe.quantity,\n ),\n )\n return raw_ingredients","title":""},{"docid":"5a586b690c5f4ad80786ce24861767f9","score":"0.46590447","text":"def frequentOneItem(self):\n\n candidate = {}\n # global finalPatterns, minSup, Database\n # self.minSup = self.minSup\n for i in range(len(self.Database)):\n for j in range(len(self.Database[i])):\n if self.Database[i][j] not in candidate:\n candidate[self.Database[i][j]] = 1\n else:\n candidate[self.Database[i][j]] += 1\n self.finalPatterns = {keys: value for keys, value in candidate.items() if value >= self.minSup}","title":""},{"docid":"cac81ccf764ad1c0b428a32da2a2677e","score":"0.46445563","text":"def add_item(request):\n\n if request.method == \"GET\":\n return HttpResponseRedirect(reverse(\"index\"))\n \n # Variable definition\n try:\n dish = Dish.objects.get(pk=(request.POST[\"dish\"]))\n except:\n return HttpResponseRedirect(reverse(\"index\"))\n\n extra = request.POST.getlist('extra')\n\n # No toppings Pizza validator\n if (dish.pk == 1 or dish.pk == 2 or dish.pk == 11 or dish.pk == 12) and len(extra) != 0:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 1 topping Pizza validator\n if (dish.pk == 3 or dish.pk == 4 or dish.pk == 13 or dish.pk == 14) and len(extra) != 1:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 2 toppings Pizza validator\n if (dish.pk == 5 or dish.pk == 6 or dish.pk == 15 or dish.pk == 16) and len(extra) != 2:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # 3 toppings Pizza validator\n if (dish.pk == 7 or dish.pk == 8 or dish.pk == 17 or dish.pk == 18) and len(extra) != 3:\n return HttpResponseRedirect(reverse(\"index\"))\n\n # Special Pizza Pizza validator\n if (dish.pk == 9 or dish.pk == 10 or dish.pk == 19 or dish.pk == 20) and len(extra) != 5:\n return HttpResponseRedirect(reverse(\"index\"))\n \n # Item variables\n extras = []\n price = dish.price\n\n for item in extra:\n new_extra = Extra.objects.get(pk=(item))\n extras.append(new_extra)\n price += new_extra.price\n\n new_item = Item(dish=dish, price=price)\n new_item.save()\n for item in extras:\n new_item.extras.add(item)\n\n # Cart Handling\n try:\n cart = Cart.objects.get(user=request.user)\n except:\n cart = Cart(user=request.user)\n cart.save()\n cart.items.add(new_item)\n cart.save()\n\n return HttpResponseRedirect(reverse(\"index\"))","title":""},{"docid":"a923dccac387cb688c107df06e72d941","score":"0.4643559","text":"def sum(items):\n if items is None or len(items) == 0:\n return None\n try:\n return sum(items)\n except:\n msg = \"Error while addition: %s\" %sys.exc_info()[0]\n sys.stderr.write(msg)\n raise","title":""},{"docid":"e5e463dda3a19d276a1fe9f6ee356c72","score":"0.46426684","text":"def test_add_item(self):\n before = len(self.shopping_list_1.items)\n self.shopping_list_1.add_item(self.item_3)\n after = len(self.shopping_list_1.items)\n self.assertEqual(1, after - before,\n msg='The object should add an item to the instance')","title":""},{"docid":"a0d6261296989b18c47c219f2b8c7f83","score":"0.46423283","text":"def get_total(self):\n\n # self.base_price = self.get_base_price()\n\n if self.species == \"Christmas\":\n self.base_price *= 1.5\n total = (1 + self.tax) * self.qty * self.base_price\n return total","title":""}],"string":"[\n {\n \"docid\": \"4f021ae31218d83063fc7b7fe95882c6\",\n \"score\": \"0.6519353\",\n \"text\": \"def __add_muilt(self,food_name,quantity):\\n\\t\\tif (not food_name in self.items):\\n\\t\\t\\tself.items[food_name] =0\\n\\t\\tself.items[food_name] = self.items[food_name] + quantity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232d5321f12a8823e62ae2f05ee7bab2\",\n \"score\": \"0.5817523\",\n \"text\": \"def addFood(self, num):\\n self.camp.data[\\\"foodQuantity\\\"] += int(num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f7d71c67883675ad8cf3a3be6f640a0\",\n \"score\": \"0.575201\",\n \"text\": \"def add(self, name, quantity):\\n item = Item(name, quantity)\\n if item in self.items:\\n # we already have this, so add to it\\n self.items[self.items.index(item)] += item\\n else:\\n self.items.append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d5bf92df7dcf5cd7b9da5461a553cbf\",\n \"score\": \"0.5631031\",\n \"text\": \"def condense_grocery_list(list_of_grocery_items):\\n condensed_list=[]\\n condensed_dict=dict()\\n for grocery_item in list_of_grocery_items:\\n dict_key=grocery_item.unit+\\\"-\\\"+grocery_item.name\\n if dict_key in condensed_dict:\\n condensed_dict[dict_key].number = str(float(condensed_dict[dict_key].number) + float(grocery_item.number))\\n else:\\n condensed_dict[dict_key]=grocery_item\\n\\n for item in condensed_dict:\\n condensed_list.append(condensed_dict[item])\\n\\n condensed_list.sort(key=lambda x: x.name)\\n condensed_dict={}\\n for item in condensed_list:\\n if item.name in condensed_dict:\\n condensed_dict[item.name] = condensed_dict[item.name] + \\\", \\\" + item.number.strip() + \\\" \\\" + item.unit.strip()\\n else:\\n condensed_dict[item.name]=item.name + \\\": \\\" + item.number.strip() + \\\" \\\" + item.unit.strip()\\n condensed_list=[]\\n for item in condensed_dict:\\n new_item=shopping_item()\\n new_item.name=item\\n new_item.grocery_list_line=condensed_dict[item]\\n condensed_list.append(new_item)\\n\\n return condensed_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"615ee899602c1129b4b2df5f3f783058\",\n \"score\": \"0.5625155\",\n \"text\": \"def createMeal(jour, tyrep, itemsName, itemsDict, minItem, maxItem, meanQuantities):\\n nItems = random.randint(minItem,maxItem)\\n items = random.sample(itemsName, nItems)\\n foodItems = [Food(itemsDict[x], x, meanQuantities[x]) for x in items]\\n meal = Meal(foodItems, jour=jour, tyrep=tyrep)\\n meal.reorder()\\n return meal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3c0498b926385879c47b0567a49a81\",\n \"score\": \"0.5587279\",\n \"text\": \"def consume_food(self, food):\\n quantity = self.__items['food'].get(food, 0)\\n if quantity > 0:\\n self.__items['food'][food] = quantity - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0139cce57cee1ffab79707a72f831baf\",\n \"score\": \"0.55362856\",\n \"text\": \"def add_to_inventory_v2(inventory, added_items):\\n\\n for item in added_items:\\n if item not in inventory:\\n my_inventory.setdefault(item, 1)\\n else:\\n my_inventory[item] += 1\\n return my_inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d1cce71848b886fcbf253344f3c75a8\",\n \"score\": \"0.5494977\",\n \"text\": \"def add_to_inventory(inventory, added_items):\\n for key in list(inventory):\\n for item in added_items:\\n if key == item:\\n inventory[key] += 1\\n else:\\n inventory.setdefault(item, 1)\\n\\n return my_inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21510822cd79307c80383341d84fa004\",\n \"score\": \"0.53571904\",\n \"text\": \"def add(self, item1, item2):\\n for sumField in self.sumFields:\\n item1[sumField] = item1[sumField] + item2[sumField]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae3d284c0f2807c357fd971d08a6f7f8\",\n \"score\": \"0.5330261\",\n \"text\": \"def addDish(self,dish):\\r\\n\\t\\tself.items[dish.name] = dish\\r\\n\\t\\tfor nutrient in TARGET_NUTRIENTS:\\r\\n\\t\\t\\tself.totals[nutrient]+=dish.nutrition[nutrient]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1159106f3c256affb95ba2576b99a40\",\n \"score\": \"0.5326817\",\n \"text\": \"def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa86c34f680edfb331c7e63841be1d7\",\n \"score\": \"0.5321849\",\n \"text\": \"def update_world_totals(new_item):\\n # TODO: Can definitely optimize this search if performance becomes an issue\\n for item in world_inv:\\n if new_item.equals(item):\\n item.set_count(item.count + new_item.count)\\n return\\n\\n if (new_item.damage != 0 and (new_item.full_name in degradeable_tools or new_item.full_name in degradeable_armor)):\\n world_inv.append(Item(new_item.full_name, new_item.count, -1, new_item.lore))\\n else:\\n world_inv.append(Item(new_item.full_name, new_item.count, new_item.damage, new_item.lore))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ff6a56e8a808fc9686f492e012e077\",\n \"score\": \"0.52610236\",\n \"text\": \"def feeding_animal(self):\\n for food in self.get_food_set():\\n for animal in self.get_animal_set():\\n if (abs(animal.position[0] - food.position[0]) < Constants.ANIMAL_WIDTH / 2 + Constants.FOOD_WIDTH / 2 and \\\\\\n abs(animal.position[1] - food.position[1]) < Constants.ANIMAL_HEIGHT / 2 + Constants.FOOD_HEIGHT / 2):\\n self.animal_set.remove(animal)\\n self.money += 1\\n food.used = True\\n if food.used:\\n self.food_set.remove(food)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f300c9707bf682e3a6b1d5677eba5aa9\",\n \"score\": \"0.52460706\",\n \"text\": \"def execute_item_creation(self)->None:\\n [ingedient.use_quantity(quantity) for ingedient,quantity in self.ingredient_quantity]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70b286ea3b1c733d10122c24d15eb782\",\n \"score\": \"0.52455187\",\n \"text\": \"def digest_fat(self):\\n can_move = min(self.population - self.food, self.fat_storage)\\n self.fat_storage -= can_move\\n self.food += can_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9736db106bb68027158dacec819627\",\n \"score\": \"0.5245462\",\n \"text\": \"def add_fruit(inventory, fruit, quantity=0):\\n #print fruit, quantity\\n inventory[fruit]=quantity\\n #return inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0a041ec85a5bcb7a4fa271d76f533e\",\n \"score\": \"0.5186193\",\n \"text\": \"def add_item(self, item_name, item_price):\\n if 'none' in self.items_with_price:\\n self.items_with_price.clear()\\n self.items_with_price.update({str(item_name): float(item_price)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379782037ed753eb6be9b48c0ea5b3b7\",\n \"score\": \"0.51835597\",\n \"text\": \"def add_to_inventory(inventory, added_items):\\n for i in added_items:\\n if i in inventory.keys():\\n inventory[i] += 1\\n else:\\n inventory[i] = 1\\n return inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd6eb79281380c997d0ddf13a3b1137\",\n \"score\": \"0.51719004\",\n \"text\": \"def updateCart(self, replaceItem):\\n\\t\\ttopFoods = self.dashFilter(replaceItem)\\n\\t\\trestOfCart = [item for item in self.foods if item[\\\"id\\\"] != replaceItem]\\n\\t\\t#topCombo = self.comboFilter(topFoods, restOfCart)\\n\\t\\ttopCombo = self.randomFilter(topFoods)\\n\\t\\tself.foods = [{\\\"id\\\":topCombo[\\\"id\\\"],\\\"name\\\":topCombo['name'],\\\"groups\\\":item['groups'],\\\"combos\\\":topCombo['combos']} if item[\\\"id\\\"] == replaceItem else item for item in self.foods]\\n\\t\\treturn {'id':topCombo['id'],'name':topCombo['name'].upper(),'img':topCombo['id']+'.jpg'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"242d58e8f298df51464e9f6224d850aa\",\n \"score\": \"0.51659745\",\n \"text\": \"def addFood(surface) -> Food:\\n\\n newFood = Food(\\n coord=[randint(0, screen_resolution[0]), randint(0, screen_resolution[1])],\\n energy=2,\\n surface=surface\\n )\\n foodHolder.append(newFood)\\n\\n return newFood\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c319e9376a5acfcd6692426a6106298\",\n \"score\": \"0.51233023\",\n \"text\": \"def test_bulk_inventory_quantity_combined(self):\\n category = ['Clothing', 'Unisex Adult Clothing', 'Pants']\\n\\n expected_left_bulk_header = 'Individual Quantity'\\n\\n new_quantities = ['200', '300', '400', '500']\\n\\n expected_product_offerings = [\\n ['1', '500.00', '550', '200', 't', 'L', '1', 'Black', '1'],\\n ['1', '500.00', '550', '300', 't', 'L', '1', 'Pinkish', '2'],\\n ['1', '500.00', '550', '400', 't', 'M', '2', 'Black', '1'],\\n ['1', '500.00', '550', '500', 't', 'M', '2', 'Pinkish', '2'],\\n ['2', '1.00', '111', '200', 't', 'L', '1', 'Black', '1'],\\n ['2', '1.00', '111', '300', 't', 'L', '1', 'Pinkish', '2'],\\n ['2', '1.00', '111', '400', 't', 'M', '2', 'Black', '1'],\\n ['2', '1.00', '111', '500', 't', 'M', '2', 'Pinkish', '2'],\\n ['3', '10.00', '222', '200', 't', 'L', '1', 'Black', '1'],\\n ['3', '10.00', '222', '300', 't', 'L', '1', 'Pinkish', '2'],\\n ['3', '10.00', '222', '400', 't', 'M', '2', 'Black', '1'],\\n ['3', '10.00', '222', '500', 't', 'M', '2', 'Pinkish', '2']\\n ]\\n\\n expected_variation_properties = [\\n ['1', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\\n ['2', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\\n ['3', 't', '62809790503', 'Size', '49', 'f', 't', 'f'],\\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\\n ]\\n\\n expected_variation_options = [\\n ['1', 't', '2119', 'L', '1'],\\n ['1', 't', '2116', 'M', '2'],\\n ['1', 'f', '1', 'Black', '1'],\\n ['1', 'f', '', 'Pinkish', '2'],\\n ['2', 't', '2119', 'L', '1'],\\n ['2', 't', '2116', 'M', '2'],\\n ['2', 'f', '1', 'Black', '1'],\\n ['2', 'f', '', 'Pinkish', '2'],\\n ['3', 't', '2119', 'L', '1'],\\n ['3', 't', '2116', 'M', '2'],\\n ['3', 'f', '1', 'Black', '1'],\\n ['3', 'f', '', 'Pinkish', '2']\\n ]\\n\\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\\n\\n # Select category in bulk edit area\\n bpiv.select_category(category)\\n\\n bulk_row = bpiv.bulk_edit_row\\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\\n\\n # Set first variation property\\n bpiv.set_property(bulk_row, 0, 'Size', \\\"Men's\\\")\\n # Add two options to first property, one by one\\n bpiv.add_option(bulk_row, 0, 'L', displayed_name=\\\"L Men's\\\")\\n bpiv.add_option(bulk_row, 0, 'M', displayed_name=\\\"M Men's\\\")\\n\\n # Set second variation property\\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\\n # Add two options to first property, one by one\\n bpiv.add_option(bulk_row, 1, 'Black')\\n bpiv.add_custom_option(bulk_row, 1, 'Pinkish')\\n\\n bpiv.select_inventory_tab(bulk_row, \\\"Quantity\\\")\\n\\n # Switch to individual quantities, check the header is changed\\n click(bpiv.bulk_header_checkbox(bulk_row, i=0))\\n click(bpiv.bulk_header_checkbox(bulk_row, i=1), delay=0.5)\\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\\n\\n # Enter individual quantities for the property combinations\\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=0)\\n for i, quantity in enumerate(new_quantities):\\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\\n\\n # Check that only titles are in listing preview as preview is disabled\\n check_simplified_preview(bpiv)\\n\\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\\n\\n # Apply\\n assert bpiv.operation_apply().is_enabled() is True\\n click(bpiv.operation_apply())\\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\\n 'Sync button is not enabled')\\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\\\'t show up'\\n\\n # Check bulk edit area texts and that Apply button is disabled\\n check_bulk_properties_disabled(bpiv)\\n\\n # Sync updates\\n click(bpiv.sync_updates_button())\\n\\n # Check data in DB\\n check_db_state(expected_product_offerings, expected_variation_properties, expected_variation_options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ea81c57ff2db743346ab37434f24574\",\n \"score\": \"0.51203\",\n \"text\": \"def calculateAmount(self):\\n return sum([item.price for item in self.items])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5115be39649d3f3029e91969f33dab0\",\n \"score\": \"0.5108897\",\n \"text\": \"def add_to_inventory(player_inventory, added_items):\\n # For each new piece of loot...\\n for new_loot_piece in added_items:\\n if new_loot_piece in player_inventory:\\n # if the loot already existed in inventory, increment it's count\\n player_inventory[new_loot_piece] += 1\\n else:\\n # If the loot was not already in the inventory, create a new piece of loot\\n # in the inventory and se it's default value to 1.\\n player_inventory.setdefault(new_loot_piece, 1)\\n\\n return player_inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4b677c435c587a9cfddc38115a3c4f\",\n \"score\": \"0.5098413\",\n \"text\": \"def test_bulk_inventory_single_quantity_individual(self, property_number):\\n category = ['Home & Living', 'Furniture']\\n\\n expected_left_bulk_header = 'Individual Quantity'\\n\\n new_quantities = ['22', '33']\\n\\n expected_product_offerings = [\\n [\\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'White', '2'],\\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'Black', '1'],\\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'White', '2'],\\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'Black', '1'],\\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'White', '2'],\\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'Black', '1'],\\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\\n ],\\n [\\n ['1', '500.00', '550', '22', 't', 'wool', '1', 'Black', '1'],\\n ['1', '500.00', '550', '33', 't', 'wool', '1', 'White', '2'],\\n ['1', '500.00', '550', '22', 't', 'cotton', '2', 'Black', '1'],\\n ['1', '500.00', '550', '33', 't', 'cotton', '2', 'White', '2'],\\n ['2', '1.00', '111', '22', 't', 'wool', '1', 'Black', '1'],\\n ['2', '1.00', '111', '33', 't', 'wool', '1', 'White', '2'],\\n ['2', '1.00', '111', '22', 't', 'cotton', '2', 'Black', '1'],\\n ['2', '1.00', '111', '33', 't', 'cotton', '2', 'White', '2'],\\n ['3', '10.00', '222', '22', 't', 'wool', '1', 'Black', '1'],\\n ['3', '10.00', '222', '33', 't', 'wool', '1', 'White', '2'],\\n ['3', '10.00', '222', '22', 't', 'cotton', '2', 'Black', '1'],\\n ['3', '10.00', '222', '33', 't', 'cotton', '2', 'White', '2']\\n ]\\n ]\\n\\n expected_variation_properties = [\\n [\\n ['1', 't', '502', 'Fabric', '', 'f', 't', 'f'],\\n ['1', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\\n ['2', 't', '502', 'Fabric', '', 'f', 't', 'f'],\\n ['2', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f'],\\n ['3', 't', '502', 'Fabric', '', 'f', 't', 'f'],\\n ['3', 'f', '200', 'Color (primary)', '', 'f', 'f', 'f']\\n ],\\n [\\n ['1', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\\n ['1', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\\n ['2', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\\n ['2', 'f', '200', 'Color (primary)', '', 'f', 't', 'f'],\\n ['3', 't', '502', 'Fabric', '', 'f', 'f', 'f'],\\n ['3', 'f', '200', 'Color (primary)', '', 'f', 't', 'f']\\n ]\\n ]\\n\\n expected_variation_options = [\\n [\\n ['1', 't', '', 'wool', '1'],\\n ['1', 't', '', 'cotton', '2'],\\n ['1', 'f', '1', 'Black', '1'],\\n ['1', 'f', '10', 'White', '2'],\\n ['2', 't', '', 'wool', '1'],\\n ['2', 't', '', 'cotton', '2'],\\n ['2', 'f', '1', 'Black', '1'],\\n ['2', 'f', '10', 'White', '2'],\\n ['3', 't', '', 'wool', '1'],\\n ['3', 't', '', 'cotton', '2'],\\n ['3', 'f', '1', 'Black', '1'],\\n ['3', 'f', '10', 'White', '2']\\n ],\\n [\\n ['1', 't', '', 'wool', '1'],\\n ['1', 't', '', 'cotton', '2'],\\n ['1', 'f', '1', 'Black', '1'],\\n ['1', 'f', '10', 'White', '2'],\\n ['2', 't', '', 'wool', '1'],\\n ['2', 't', '', 'cotton', '2'],\\n ['2', 'f', '1', 'Black', '1'],\\n ['2', 'f', '10', 'White', '2'],\\n ['3', 't', '', 'wool', '1'],\\n ['3', 't', '', 'cotton', '2'],\\n ['3', 'f', '1', 'Black', '1'],\\n ['3', 'f', '10', 'White', '2']\\n ]\\n ]\\n\\n\\n bpiv = BulkPageInventoryVariations(self.driver, self.ts)\\n\\n # Select category in bulk edit area\\n bpiv.select_category(category)\\n\\n bulk_row = bpiv.bulk_edit_row\\n assert bpiv.property_box(bulk_row, 0).text == 'Choose Property'\\n\\n # Set first variation property\\n bpiv.set_property(bulk_row, 0, 'Fabric')\\n # Add two options to first property, one by one\\n bpiv.add_custom_option(bulk_row, 0, 'wool')\\n bpiv.add_custom_option(bulk_row, 0, 'cotton')\\n\\n # Set second variation property\\n bpiv.set_property(bulk_row, 1, 'Color (primary)')\\n # Add two options to first property, one by one\\n bpiv.add_option(bulk_row, 1, 'Black')\\n bpiv.add_option(bulk_row, 1, 'White')\\n\\n bpiv.select_inventory_tab(bulk_row, \\\"Quantity\\\")\\n\\n # Switch to individual quantities, check the header is changed\\n click(bpiv.bulk_header_checkbox(bulk_row, i=property_number), delay=0.5)\\n assert bpiv.bulk_edit_row_header_text(bulk_row, i=0) == expected_left_bulk_header\\n\\n # Enter individual quantities\\n inputs = bpiv.bulk_individual_option_inputs(bulk_row, i=property_number)\\n for i, quantity in enumerate(new_quantities):\\n send_keys(inputs[i], str(quantity) + Keys.RETURN)\\n\\n # Check that only titles are in listing preview as preview is disabled\\n check_simplified_preview(bpiv)\\n\\n # Blue dot is not displayed and sync button is disabled before clicking on Apply\\n assert bpiv.is_part_modified('Variations') is False, 'Blue dot should not be visible yet'\\n assert bpiv.sync_updates_button().is_enabled() is False, 'Sync button should be still disabled'\\n\\n # Apply\\n assert bpiv.operation_apply().is_enabled() is True\\n click(bpiv.operation_apply())\\n # Check that sync button is enabled and blue dot is displayed after clicking on Apply\\n wait_for_web_assert(True, lambda: bpiv.sync_updates_button().is_enabled(),\\n 'Sync button is not enabled')\\n assert bpiv.is_part_modified('Variations') is True, 'Blue dot didn\\\\'t show up'\\n\\n # Check bulk edit area texts and that Apply button is disabled\\n check_bulk_properties_disabled(bpiv)\\n\\n # Sync updates\\n click(bpiv.sync_updates_button())\\n\\n # Check data in DB\\n check_db_state(expected_product_offerings[property_number],\\n expected_variation_properties[property_number],\\n expected_variation_options[property_number])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddde5fc2882d117d910fb6854f8c507f\",\n \"score\": \"0.50927\",\n \"text\": \"def concatenate_items(self) -> dict:\\n test = []\\n for text in self.receipt_text:\\n if self.text_is_number(text) | self.remove_pound_symbol(text) | self.change_o_to_zero(text):\\n text = text[1:]\\n else:\\n test.append(text)\\n\\n if self.text_is_number(text):\\n if test[-1] in self.dict_to_return:\\n self.dict_to_return[test[-1]]['quantity'] += 1\\n else:\\n self.dict_to_return[test[-1]] = {'name': text, 'quantity': 1}\\n test = []\\n\\n return self.dict_to_return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e1c26cb444d691a6d757c89aed7d2b\",\n \"score\": \"0.5092597\",\n \"text\": \"def add_inventory_item_and_quantity(self,ingredient:I_Inventory_Item,quantity:float):\\n self.ingredient_quantity.append((ingredient,quantity))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a68a1b31438ff6224ac04a9bb4f05d1\",\n \"score\": \"0.50659823\",\n \"text\": \"def scan_onto_shelf(self):\\n if hasattr(self, \\\"shelf\\\"):\\n items_added = []\\n for item in self.items_stored:\\n for product in self.shelf.compartments:\\n if product[\\\"Product Number\\\"] == item.number:\\n if product[\\\"Items Stored\\\"] is None:\\n product[\\\"Items Stored\\\"] = []\\n product[\\\"Items Stored\\\"].append(item)\\n product[\\\"Quantity\\\"] += 1\\n items_added.append(item)\\n Item.sorted_items.append(item)\\n Item.shipment.remove(item)\\n for item in items_added:\\n self.items_stored.remove(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"484270c6a6c051655cc77f321d243802\",\n \"score\": \"0.50581247\",\n \"text\": \"def add_to_inventory(inventory, added_items):\\n if added_items in inventory:\\n inventory[added_items] += 1\\n else:\\n inventory[element] = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d21353320ad10efa03007bf3481510\",\n \"score\": \"0.50539666\",\n \"text\": \"def add(self, product, quantity):\\n product_id = product.id\\n if product_id not in self.basket:\\n self.basket[product_id] = {\\\"price\\\": float(product.regular_price), \\\"quantity\\\": quantity}\\n else:\\n self.basket[product_id][\\\"quantity\\\"] = quantity\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35cf195a68cb0eebf880a0671ed3bb9d\",\n \"score\": \"0.50376934\",\n \"text\": \"def _add_dict_inventory(self, d, item_pouch): # item_pouch used to find which dictionary to look in\\r\\n for group in d:\\r\\n if group in item_pouch:\\r\\n if item_pouch[group] is None:\\r\\n item_pouch[group] = d[group]\\r\\n elif isinstance(item_pouch[group], list):\\r\\n item_pouch[group].extend(d[group])\\r\\n elif isinstance(item_pouch[group], dict):\\r\\n self._add_dict_inventory(d[group], item_pouch[group])\\r\\n elif isinstance(item_pouch[group], int):\\r\\n item_pouch[group] = item_pouch.setdefault(group, 0)+d[group]\\r\\n else:\\r\\n item_pouch[group] = d[group]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ca2e8c63e53f9ab69061677cf963375\",\n \"score\": \"0.50190413\",\n \"text\": \"def test_adding_item_to_order_model_is_add_one_to_quantity(self):\\n cart_item_1 = CartItem.objects.create(order=self.order, item=self.item_1)\\n cart_item_1.Add() # quantity = 1 ** payout = self.item_1.price * 1\\n cart_item_1.Add() # quantity = 2 ** payout = self.item_1.price * 2\\n\\n self.assertEqual(CartItem.objects.get(order=self.order, item=self.item_1).quantity, 2)\\n self.assertEqual(cart_item_1.total_price, self.item_1.price * 2)\\n self.assertEqual(self.order.payout, self.item_1.price * 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f019318a90a39d1d4baa58c66b55a7e8\",\n \"score\": \"0.50127226\",\n \"text\": \"def uniq_add(my_list=[]):\\n\\n return sum(set(my_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85016ea7f6bcc15054e712a84e7aaa66\",\n \"score\": \"0.5005258\",\n \"text\": \"def create_order_item(self, food_item, quantity):\\n OrderItem.objects.create(\\n user=self.customer,\\n food_item=food_item,\\n quantity=quantity,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f95d8bb1a0a822a0a89287235821bd2\",\n \"score\": \"0.50050193\",\n \"text\": \"def calculateAmount(self):\\n summa = 0\\n for item in self.items:\\n summa += item.price\\n return summa\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a975cec92256ad6ac4119086b89bcaf\",\n \"score\": \"0.50043887\",\n \"text\": \"def add(self, product, quantity=None, update_quantity=False):\\n if product.price_with_taxes > 0 and quantity != \\\"0\\\":\\n self.cart[str(product.id)] = {\\n 'user_id': self.request.user.id,\\n 'article_id': str(product.id),\\n 'name': product.name,\\n 'quantity': quantity,\\n 'price_type': product.price_type,\\n 'price_with_taxes': str(product.price_with_taxes),\\n 'article_code': product.article_code,\\n }\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e946d19a98dd4708d5ea172fdab839a3\",\n \"score\": \"0.49940917\",\n \"text\": \"def fortune(player):\\n random.seed()\\n \\n # (inventory item, amount received)\\n treasures = [\\n ('food',random.randint(100,500)),\\n ('money',random.randint(200,500)),\\n ('bullets',random.randint(30,100)),\\n ('kits',random.randint(2,3)),\\n ('parts',random.randint(2,3)),\\n ]\\n \\n # Determine random item.\\n found = random.choice(treasures)\\n \\n # Handle special message for food.\\n if found[0] == 'food':\\n current_food = player.get_from_inventory('food')\\n # Handle food inventory limit.\\n if current_food + found[1] > 1000:\\n added_food = 1000 - current_food\\n print(\\\"You found {} pounds of food in an abandoned wagon\\\".format(found[1]))\\n print(\\\"You can only add {} pounds to your wagon\\\".format(added_food))\\n player.update_inventory('food',1000)\\n else:\\n print(\\\"You found {} pounds of food in an abandoned wagon\\\".format(found[1]))\\n player.add_to_inventory('food',found[1])\\n \\n # Handle special message for money.\\n elif found[0] == 'money':\\n print(\\\"You found ${} in an abandoned wagon\\\".format(\\\"%.2f\\\" %found[1]))\\n player.add_to_inventory('money',found[1])\\n \\n # Handle all other items.\\n else:\\n print(\\\"You found {} {} in an abandoned wagon\\\".format(found[1],found[0]))\\n player.add_to_inventory(found[0],found[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e056e6e443126ef305397e1edf9f00e\",\n \"score\": \"0.49848992\",\n \"text\": \"def calc_items_total(cls, items):\\n shipping_charge = 0\\n subtotal = 0\\n ad_charge_amt = 0\\n ad_charge_breakup = {}\\n\\n for item in items:\\n shipping_charge += item.shipping_charge\\n subtotal += item.subtotal\\n ad_charge_amt += item.additional_charge\\n\\n for charge in item.cost_breakup['additional_charge']:\\n k = slugify(charge.get('name')).replace('-', '_')\\n v = charge.get('amt')\\n # Initialize value\\n if ad_charge_breakup.get(k, None) is None:\\n ad_charge_breakup[k] = 0.0\\n ad_charge_breakup[k] += v\\n\\n return {\\n 'shipping_charge': helper.round_off(shipping_charge),\\n 'subtotal': helper.round_off(subtotal),\\n 'additional_charge': helper.round_off(ad_charge_amt),\\n 'cost_breakup': {\\n 'additional_charge': ad_charge_breakup\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba05c3eea1691cbb2cf7dd27f2811a3\",\n \"score\": \"0.49668226\",\n \"text\": \"def add(self, comida, quantity=1, update_quantity=False):\\n product_id = str(comida.id)\\n if product_id not in self.cart:\\n self.cart[product_id] = {'quantity': 0,\\n 'precio': str(comida.precio)}\\n if update_quantity:\\n self.cart[product_id]['quantity'] = quantity\\n else:\\n self.cart[product_id]['quantity'] += quantity\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a55875101d7602488fcb4f6054d62ad\",\n \"score\": \"0.49663317\",\n \"text\": \"def initializeFoods(self):\\n for i in range(0, self.numFoods):\\n self.addFood()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9da1b12f109a9a6aef2dab62544176d0\",\n \"score\": \"0.49556127\",\n \"text\": \"def add(self, product, qty):\\n product_id = str(product.id)\\n\\n if product_id in self.basket:\\n self.basket[product_id][\\\"qty\\\"] = qty\\n else:\\n self.basket[product_id] = {\\\"price\\\": str(product.regular_price), \\\"qty\\\": qty}\\n\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc752d2ca3326fdbd563d1bbeac4d05\",\n \"score\": \"0.49489328\",\n \"text\": \"def add(self, product, quantity=1, update_quantity=False):\\n product_id = str(product.id) # converted to string because JSON use only string data\\n if product_id not in self.cart:\\n self.cart[product_id] = {'quantity': 0,\\n 'price': str(product.price)\\n }\\n if update_quantity:\\n self.cart[product_id]['quantity'] = quantity\\n else:\\n self.cart[product_id]['quantity'] += quantity # increment the quantity\\n self.save() # saving the state of the quantity after update\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e122449c4307f51ed5cb01667c9a9d1\",\n \"score\": \"0.49486324\",\n \"text\": \"def eatFood(self):\\n # if a prey is in contact with a food, then eat the food by removing the food and increment hunger.\\n objIDInContactList = super().getContactObjects()\\n # if there are objects in contact with this prey, then determine if they are food and if so eat them!\\n foodEaten = 0\\n if objIDInContactList:\\n for objID in objIDInContactList:\\n food = hsm.objIDToObject[objID]\\n if food in hsm.foodList:\\n hsm.foodList.remove(food)\\n hsm.destroy(objID)\\n foodEaten += 1\\n # incrememnt hunger accordingly\\n self.hunger += mv.PREY_INCREMENT_HUNGER \\n # throw away excess, prey can only eat so much food at a time.\\n if self.hunger > mv.PREY_MAX_HUNGER:\\n self.hunger = mv.PREY_MAX_HUNGER\\n if foodEaten > 0:\\n self.foodTimeStamps.append([hsm.frameCount, foodEaten])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892adb19f630ba769f49adfe54589f4c\",\n \"score\": \"0.4945769\",\n \"text\": \"def add_item(self, x):\\n self._items_with_price.update(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b3b75d718473bad062e7134b4e43dd2\",\n \"score\": \"0.49438444\",\n \"text\": \"def add_item_to_cart(self, product_id, quantity):\\n product = Product.objects.filter(product_id=product_id, store=self.customer_id.store_id, deleted_at=None).first()\\n if product is None or product.inventory < quantity:\\n raise ProductNotFoundException\\n\\n product.inventory -= quantity\\n product.save()\\n\\n existing_cart_item = CartItem.objects.filter(cart_id=self.cart_id, product_id=product.product_id\\n , removed_at=None).first()\\n\\n self.customer_id.last_cart_activity_at = datetime.now()\\n self.customer_id.updated_at = datetime.now()\\n self.customer_id.save()\\n if existing_cart_item:\\n existing_cart_item.quantity += quantity\\n existing_cart_item.save()\\n return existing_cart_item\\n else:\\n cart_item = CartItem(\\n cart_item_id=str(uuid.uuid4().int),\\n product_id=product.product_id,\\n cart_id=self.cart_id,\\n quantity=quantity\\n ).save()\\n return cart_item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106331337e2a2c1d307ba926e7184437\",\n \"score\": \"0.49325597\",\n \"text\": \"def calculate_total_nutrition_fulldayofeating2(\\n specificingredient2_dict_list,\\n all_nutrients_and_default_units,\\n set_to_zero_if_none,\\n):\\n\\n # Initialize the dictionary which will store the results\\n result_total_nutrition_fulldayofeating = {}\\n for dict_k in all_nutrients_and_default_units:\\n nutrient_name = dict_k['nutrient_name_measuredfood']\\n new_dict = {nutrient_name: 0}\\n result_total_nutrition_fulldayofeating.update(\\n new_dict\\n )\\n\\n # Go through all the SpecificIngredients related to the FullDayOfEating.\\n # For each SpecificIngredient, calculate its Nutrition by\\n # multiplying the calculated_amount divided by the reference amount\\n # multiplied by each nutrient value in the associated RawIngredient.\\n\\n # Sum up the total nutrition\\n for k in range(len(specificingredient2_dict_list)):\\n # For each nutrient, calculate the amount of that nutrient contained\\n # in the calculated_amount of the SpecificIngredient\\n for dict_k in all_nutrients_and_default_units:\\n nutrient_name = dict_k['nutrient_name_measuredfood']\\n result_total_nutrition_fulldayofeating[nutrient_name] = \\\\\\n result_total_nutrition_fulldayofeating[nutrient_name] \\\\\\n + set_to_zero_if_none(specificingredient2_dict_list[k][\\n 'raw_ingredient'][nutrient_name]) \\\\\\n * float(specificingredient2_dict_list[k]['calculated_amount'])\\\\\\n / float(specificingredient2_dict_list[k][\\n 'raw_ingredient']['reference_amount'])\\n\\n # Round the values in the result_total_nutrition_fulldayofeating\\n # Initialize the dictionary containing the rounded values.\\n result_total_nutrition_fulldayofeating_rounded = {}\\n for dict_k in all_nutrients_and_default_units:\\n nutrient_name = dict_k['nutrient_name_measuredfood']\\n new_dict = {nutrient_name: 0}\\n result_total_nutrition_fulldayofeating_rounded.update(\\n new_dict\\n )\\n\\n # Round the values and save them to the respective dictionary.\\n for dict_k in all_nutrients_and_default_units:\\n nutrient_name = dict_k['nutrient_name_measuredfood']\\n result_total_nutrition_fulldayofeating_rounded[nutrient_name] = \\\\\\n round(result_total_nutrition_fulldayofeating[nutrient_name], 1)\\n\\n return result_total_nutrition_fulldayofeating,\\\\\\n result_total_nutrition_fulldayofeating_rounded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e950cf94ff01f7be16cc47e54da41ba\",\n \"score\": \"0.4931627\",\n \"text\": \"def _update_total_nutrition_values(self, unique_id, date, plan, add):\\n table = self.dynamodb.Table(c.TABLE_NUTRITIONAL_NEEDS_DAY)\\n week = form.get_week_by_date(date)\\n\\n item_nutrients_for_week = table.get_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\\n Key={\\n c.UNIQUE_IDENTIFIER: unique_id,\\n c.WEEK: week\\n },\\n ProjectionExpression='#toplevel',\\n ExpressionAttributeNames={\\\"#toplevel\\\": c.NUTRIENTS_FOR_WEEK}\\n )\\n\\n item_nutrients_for_day = table.get_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\\n Key={\\n c.UNIQUE_IDENTIFIER: unique_id,\\n c.DATE: date\\n },\\n ProjectionExpression='#toplevel',\\n ExpressionAttributeNames={\\\"#toplevel\\\": c.NUTRIENTS_FOR_DAY}\\n )\\n\\n for container, container_content in plan.iteritems():\\n item_nutrients_for_container = table.get_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\\n Key={c.UNIQUE_IDENTIFIER: unique_id, c.DATE: date},\\n ProjectionExpression='#toplevel.#container',\\n ExpressionAttributeNames={\\n \\\"#toplevel\\\": c.NUTRIENTS_FOR_CONTAINER,\\n \\\"#container\\\": container\\n }\\n )\\n for meal_key, meal_content in container_content.iteritems():\\n\\n\\n\\n for n in params.nutrientList:\\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\\\\n (1 if add else -1)\\n item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['VAL'] += meal_content['nutrients'][n]['VAL'] * \\\\\\n (1 if add else -1)\\n item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY][n]['UNIT'] = meal_content['nutrients'][n]['UNIT']\\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['VAL'] += \\\\\\n meal_content['nutrients'][n]['VAL'] * (1 if add else -1)\\n item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container][n]['UNIT'] = \\\\\\n meal_content['nutrients'][n]['UNIT']\\n\\n\\n table.update_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\\n Key={c.UNIQUE_IDENTIFIER: unique_id,\\n c.DATE: date},\\n UpdateExpression='SET #toplevel.#cat = :value',\\n ExpressionAttributeNames={\\n \\\"#toplevel\\\": c.NUTRIENTS_FOR_CONTAINER,\\n \\\"#cat\\\": container\\n },\\n ExpressionAttributeValues={\\n \\\":value\\\": item_nutrients_for_container[c.ITEM][c.NUTRIENTS_FOR_CONTAINER][container]\\n }\\n )\\n\\n table.update_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_DAY,\\n Key={c.UNIQUE_IDENTIFIER: unique_id,\\n c.DATE: date},\\n UpdateExpression='SET #toplevel = :value',\\n ExpressionAttributeNames={\\n \\\"#toplevel\\\": c.NUTRIENTS_FOR_DAY\\n },\\n ExpressionAttributeValues={\\n \\\":value\\\": item_nutrients_for_day[c.ITEM][c.NUTRIENTS_FOR_DAY]\\n }\\n )\\n\\n table.update_item(\\n TableName=c.TABLE_NUTRITIONAL_NEEDS_WEEK,\\n Key={\\n c.UNIQUE_IDENTIFIER: unique_id,\\n c.WEEK: week\\n },\\n UpdateExpression='SET #toplevel = :value',\\n ExpressionAttributeNames={\\n \\\"#toplevel\\\": c.NUTRIENTS_FOR_WEEK\\n },\\n ExpressionAttributeValues={\\n \\\":value\\\": item_nutrients_for_week[c.ITEM][c.NUTRIENTS_FOR_WEEK]\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7dfb948b8301a0430651d0b5bb487bb\",\n \"score\": \"0.49241003\",\n \"text\": \"def player_eats_food(self, snake, food):\\n head = snake[0]\\n head_x, head_y = head\\n for food_item in food:\\n food_x, food_y = food_item\\n #Return whether or not the player's received food, adds fat if they have\\n if head_x == food_x and head_y == food_y:\\n #We've eaten food, update our score\\n self.score += int(150 + int(len(snake)/10)*5)\\n\\n #Add more fat to the user if they already have some or they have a decent score\\n self.fat += 8 + int(self.fat/4) + int(self.score/500)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e5cd209b264fb2dd585ad69bbc06220\",\n \"score\": \"0.49199784\",\n \"text\": \"def createConsumptionSeq(df):\\n mealList = []\\n M = {} #Contient les aliments d'un repas \\n nomen = df.nomen.iloc[0]\\n jour = df.jour.iloc[0]\\n tyrep = df.tyrep.iloc[0]\\n \\n for index, row in df.iterrows(): # Scan tous les aliments de consommation\\n #print(row.nomen, row.jour, row.tyrep)\\n if (row.nomen == nomen) & (row.jour == jour) & (row.tyrep == tyrep):\\n name = str(row.nomen) + '_' + str(index)\\n M[name] = Food(row.codsougr, row.codsougr_name, row.qte_nette) # Crée l'objet FoodItem\\n else:\\n m = list(set(M.values())) #Set of food items in a meal\\n uniqueM = eraseDubloons(m)\\n meal = Meal(uniqueM, jour = jour, tyrep = tyrep)\\n meal.reorder()\\n logging.debug('{}'.format(meal.nameTuple))\\n if meal:\\n mealList.append(meal)\\n\\n M = {}\\n nomen = row.nomen\\n jour = row.jour\\n tyrep = row.tyrep\\n \\n meals = [m for m in mealList if m]\\n return meals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c830b9fef7a64c1f072cecdf121412c0\",\n \"score\": \"0.4918213\",\n \"text\": \"def create_dico(item_list):\\n assert type(item_list) is list\\n dico = {}\\n for items in item_list:\\n for item in items:\\n if item not in dico:\\n dico[item] = 1\\n else:\\n dico[item] += 1\\n return dico\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c830b9fef7a64c1f072cecdf121412c0\",\n \"score\": \"0.4918213\",\n \"text\": \"def create_dico(item_list):\\n assert type(item_list) is list\\n dico = {}\\n for items in item_list:\\n for item in items:\\n if item not in dico:\\n dico[item] = 1\\n else:\\n dico[item] += 1\\n return dico\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ddaa4351c2892173251704a2ad04d77\",\n \"score\": \"0.49181578\",\n \"text\": \"def sum_list(new_list):\\n return sum(new_list)\\n # return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ee3ad439b54ccc0e9043295f07ea1cd\",\n \"score\": \"0.49134657\",\n \"text\": \"def saved_meal_item_add(\\n self, meal_id, food_id, food_entry_name, serving_id, num_units\\n ):\\n params = {\\n \\\"method\\\": \\\"saved_meal_item.add\\\",\\n \\\"format\\\": \\\"json\\\",\\n \\\"saved_meal_id\\\": meal_id,\\n \\\"food_id\\\": food_id,\\n \\\"food_entry_name\\\": food_entry_name,\\n \\\"serving_id\\\": serving_id,\\n \\\"number_of_units\\\": num_units,\\n }\\n\\n response = self.session.get(self.api_url, params)\\n return self.valid_response(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a46b885d595c7bea38ce23fc1d6daa4d\",\n \"score\": \"0.4910791\",\n \"text\": \"def calculateInventory(self):\\n from collections import Counter\\n orders = self.model[0]\\n shipping = self.model[1]\\n inventory = {key:dict() for key in shipping}\\n citys = {o.dest for o in orders}\\n months = {o.orderTime.month for o in orders}\\n products = {o.pro for o in orders}\\n #print shipping\\n for pro in products:\\n for city in citys:\\n inventory[pro][city] = Counter()\\n for o in orders:\\n o.ship = self.chooseBestShipping(o,citys)\\n\\n inventory[o.pro][o.ship][o.orderTime.month] += o.num\\n #self.view.showInventory(inventory)\\n self.inventory = inventory\\n self.citys = citys\\n self.months = months\\n self.products = products\\n return inventory\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcbca7ea38a938ec591a33b34ace0ede\",\n \"score\": \"0.49077475\",\n \"text\": \"def save(self, *args, **kwargs):\\n if self.pk is None:\\n item = self.item\\n item.quantity += self.quantity\\n item.save()\\n return super(Entry, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf4880010aa81907064f104b9d60f03\",\n \"score\": \"0.49064472\",\n \"text\": \"def subtotal(self):\\n sub = 0.0\\n for i in self.items:\\n sub += i.quantity * INVENTORY[i.name]\\n return sub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"442dae3041bd85957abe5451209e318b\",\n \"score\": \"0.48951203\",\n \"text\": \"def _initilizeItemInventory(self):\\n for itemName in itemPrice.keys():\\n for j in range(5):\\n self._itemInventory.add(itemName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bf1960258a00848bdd160a6a903b421\",\n \"score\": \"0.48922408\",\n \"text\": \"def find_food(self):\\n \\n if self.check_knife(1):\\n self.knife_dur -= 1\\n self.food += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4280fe8c4dcd8681a0e6d972b181946f\",\n \"score\": \"0.48690817\",\n \"text\": \"def createFoodDict(food_item_list):\\n food_dict = {}\\n for food_item in food_item_list:\\n words = food_item.split(' ')\\n first_word = words[0]\\n food_dict[first_word] = food_item\\n return food_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31e19cfc7f9cc7ac082b4629f8df71cb\",\n \"score\": \"0.48465908\",\n \"text\": \"def scan_onto_cart(self):\\n if hasattr(self, \\\"shelf\\\"):\\n for item in Item.shipment:\\n if len(self.items_stored) < self.max_capacity:\\n for product in self.shelf.compartments:\\n if item.number == product[\\\"Product Number\\\"]:\\n self.items_stored.append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb607bc5bfc19f80144df53d7174c4b2\",\n \"score\": \"0.4835511\",\n \"text\": \"def returnfrequentitemsets(itemSet, transactionList, minSupport, freqSet):\\n\\t\\t_itemSet = set()\\n\\t\\tlocalSet = defaultdict(int)\\n\\n\\t\\tfor item in itemSet:\\n\\t\\t\\tfor transaction in transactionList:\\n\\t\\t\\t\\tif item.issubset(transaction):\\n\\t\\t\\t\\t\\tfreqSet[item] += 1\\n\\t\\t\\t\\t\\tlocalSet[item] += 1\\n\\n\\t\\tfor item, count in localSet.items():\\n\\t\\t\\t\\tsupport = float(count)/len(transactionList)\\n\\n\\t\\t\\t\\tif support >= minSupport:\\n\\t\\t\\t\\t\\t\\t_itemSet.add(item)\\n\\n\\t\\treturn _itemSet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64d95b9e2dbd365c3e3c875f63c4ec1\",\n \"score\": \"0.48311788\",\n \"text\": \"def add_item(self, item):\\n self.items_with_price.update(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1dea7e98dd8eb0a30e38b7120376a69\",\n \"score\": \"0.4829422\",\n \"text\": \"def process_item(self, item, spider):\\n # looks for a duplicate\\n duplicate_check = self.db[self.collection_name].find({'name': item['name'], 'store': item['store'], 'category': item['category'], 'animal': item['animal']}).count()\\n if duplicate_check == 0:\\n self.db[self.collection_name].insert(dict(item))\\n logging.debug('Product added to MongoDB')\\n return item\\n else:\\n # logging.debug('Duplicate found. Skipping product')\\n raise DropItem('Duplicate found. Skipping product: %s' % item['name'])\\n # return item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616c81c4094a7fd6dab55793794e5f42\",\n \"score\": \"0.48247102\",\n \"text\": \"def generate_food(self):\\n num_food = ENV_N_FOOD\\n\\n pos = [(random() * self.W, random() * self.H) for i in range(num_food)]\\n\\n return [Food(x, y, self.W, self.H) for (x, y) in pos]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bdff644a55ea47546734621b9954d60\",\n \"score\": \"0.4823412\",\n \"text\": \"def budget(data,productsIds, quantity):\\r\\n \\r\\n data=data.fillna(0)\\r\\n budg=0\\r\\n products=productsIds\\r\\n quantity=quantity\\r\\n \\r\\n dataFilter = data[data['No. Item'].isin(products)]\\r\\n prices=dataFilter['Precio Unitario'].tolist()\\r\\n \\r\\n for i in range(len(quantity)):\\r\\n budg+=quantity[i]*prices[i]\\r\\n return budg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b6f3b617d4f2917091b725f1b1b61a\",\n \"score\": \"0.48086756\",\n \"text\": \"def get_total(self):\\n\\n base_price = 5\\n if self.species == \\\"Christmas melon\\\":\\n base_price = base_price * 1.5\\n\\n total = (1 + self.tax) * self.qty * base_price\\n \\n # if qty is less than ten melons and international, then +$3 to international_order\\n if self.qty < 10 and self.order_type == 'international':\\n total = total + 3\\n\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a15150d7171093c8a2769cd73df5a8ab\",\n \"score\": \"0.4804091\",\n \"text\": \"def fruits(fruit_list, fruit):\\n\\n updated_fruits_list = []\\n for item in fruit_list:\\n updated_fruits_list.append(item)\\n updated_fruits_list.append(fruit)\\n\\n return updated_fruits_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ff7340eee41c80ad913f6610332c1b\",\n \"score\": \"0.47999802\",\n \"text\": \"def add_to_cart(self, selection_id, selection_qty):\\n # read the data from data storage\\n for keys, values in ShoppingCart.__ecommerce_data.items():\\n # check if the product contain necessary data before proceed\\n if \\\"products\\\" and \\\"sku\\\" and \\\"prices\\\" and \\\"stock\\\" in values:\\n # check if it is a correct sku code from the data storage\\n if selection_id in values[\\\"sku\\\"]:\\n # add product information to the instance variables\\n self.__seller_id.append(keys)\\n product_id = values[\\\"sku\\\"].index(selection_id)\\n self.__seller.append(values[\\\"first_name\\\"] + \\\" \\\" + values[\\\"last_name\\\"])\\n self.__sku.append(values[\\\"sku\\\"][product_id])\\n self.__price.append(values[\\\"prices\\\"][product_id])\\n self.__product_name.append(values[\\\"products\\\"][product_id])\\n self.__pos.append(product_id)\\n self.__check_out_qty.append(selection_qty)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1ac6afebd277ad77e422efa246f2ad9\",\n \"score\": \"0.47989768\",\n \"text\": \"def addItem(self, itemId, itemQty):\\r\\n self.items[itemId] += itemQty\\r\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10ce6669d3c4fce854bab14bf47b1b7\",\n \"score\": \"0.47970992\",\n \"text\": \"def add_item(self, item):\\n LOG.info('Add Product')\\n LOG.info(\\n \\\"Add the item: %s , price: %s, quantity: %s\\\"\\n , item.get_name(), item.get_price(), item.get_quantity())\\n if not item.get_name() in self.store_stock:\\n self.db_manager.insert_element(item)\\n else:\\n current_item_quantity = \\\\\\n self.store_stock[item.get_name()].get_quantity() \\\\\\n + item.get_quantity()\\n self.db_manager.update_quantity_field(quantity=current_item_quantity,\\n id=self.store_stock[item.get_name()].get_id())\\n LOG.info(\\\"Complete to add a item successful\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5be3b43a6445bef2e20fac0d8943eb\",\n \"score\": \"0.4792217\",\n \"text\": \"def combine_the_anon_cart(self, request):\\n cart = request.session.get('cart')\\n if cart:\\n for item in cart.items():\\n product = Product.objects.get(id=item[0])\\n if product.user == request.user:\\n messages.error(request, 'You can\\\\'t buy your product')\\n else:\\n quantity = item[1]['quantity']\\n self.add(product, quantity)\\n del request.session['cart']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f77055a0765db17ee93d22d7bb315d6\",\n \"score\": \"0.47913775\",\n \"text\": \"def total_items_sold(self, given_date):\\n # ensure given_date is a date\\n assert isinstance(given_date, date), \\\"given_date must be datetime.date()\\\"\\n\\n # get quantity from order_lines for given_date\\n quantities = [\\n i[0]\\n for i in cur.execute(\\\"\\\"\\\"\\n SELECT quantity\\n FROM orders\\n INNER JOIN order_lines ON orders.id=order_lines.order_id\\n WHERE created_at LIKE ?\\n \\\"\\\"\\\", (given_date.isoformat()+'%',))\\n ]\\n\\n # sum quantity\\n return sum(quantities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a07d888277c16437ea1ed144e02e719\",\n \"score\": \"0.47881973\",\n \"text\": \"def oh_quantity_by_location(self):\\n location_list = self.transactions.all().values(\\\"location\\\").distinct().order_by(\\\"location\\\")\\n my_dict = dict()\\n for l in location_list:\\n location = Location.objects.get(pk=l[\\\"location\\\"])\\n my_dict[location] = self.get_oh_quantity(location)\\n return my_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7a2e2d6e3ad23e24d00b97541db3187\",\n \"score\": \"0.47833753\",\n \"text\": \"def returnItemsWithMinSupport(itemSet, transactionList, minSupport, freqSet):\\n _itemSet = set()\\n localSet = defaultdict(int)\\n\\n for item in itemSet:\\n for transaction in transactionList:\\n if item.issubset(transaction):\\n freqSet[item] += 1\\n localSet[item] += 1\\n\\n # print 'localSet',localSet\\n for item, count in localSet.items():\\n # print item,count\\n support = float(count)/len(transactionList)\\n\\n if support >= minSupport:\\n _itemSet.add(item)\\n\\n return _itemSet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e7d505ef1815ef26d24544b025773ca\",\n \"score\": \"0.47816607\",\n \"text\": \"def main():\\r\\n group_of_list = []\\r\\n group = {}\\r\\n count = 0\\r\\n dict_input_food_and_price = input()\\r\\n dict_input_food_and_price = json.loads(dict_input_food_and_price)\\r\\n while True:\\r\\n yourname = input()\\r\\n if yourname == \\\"End\\\":\\r\\n break\\r\\n group[yourname] = 0\\r\\n for food, price in dict_input_food_and_price.items():\\r\\n while True:\\r\\n yourname = input()\\r\\n if yourname == \\\"End\\\":\\r\\n break\\r\\n group_of_list.append(yourname)\\r\\n count += 1\\r\\n total = price/count\\r\\n for food in group_of_list:\\r\\n group[food] = group.get(food)+total\\r\\n count = 0\\r\\n group_of_list = []\\r\\n print(group)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57fee8f9f7f940bfc0f162909b7ee2b2\",\n \"score\": \"0.47804844\",\n \"text\": \"def reducer(L):\\n\\tdistribution = {}\\n\\tfor item in L:\\n\\t\\tfor record in item:\\n\\t\\t\\tif record in distribution:\\n\\t\\t\\t\\tdistribution[record] += item[record]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tdistribution[record] = item[record]\\n\\t\\titem.clear()\\n\\treturn distribution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dbbe3574385be484fb2e72fb6016bb1\",\n \"score\": \"0.47785863\",\n \"text\": \"def add_item(self, new_item):\\n self.items_with_price.update(new_item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301be53ed8845a4768b33a75ed332e78\",\n \"score\": \"0.4775439\",\n \"text\": \"def getFood( self, amount ) :\\n\\t\\treturn self.getSupply( amount )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e98031becdd3b82c96afc040c16348c\",\n \"score\": \"0.47707295\",\n \"text\": \"def get_total(self, **kwargs):\\n return self.get_price_per_item(**kwargs) * self.get_quantity(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bf9dc41926c19f7c1798b9a962799d\",\n \"score\": \"0.47650927\",\n \"text\": \"def cardlist_sum(cardlist1, cardlist2):\\n cards = deepcopy(cardlist1)\\n for name, amount in cardlist2.items():\\n if name not in cards:\\n cards[name] = 0\\n cards[name] = cards[name] + amount\\n return cards\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3e40827b1596cd982bb10623718ab9\",\n \"score\": \"0.47612268\",\n \"text\": \"def food_add_favorite(self, food_id, serving_id=None, number_of_units=None):\\n\\n params = {\\\"method\\\": \\\"food.add_favorite\\\", \\\"format\\\": \\\"json\\\", \\\"food_id\\\": food_id}\\n\\n if serving_id and number_of_units:\\n params[\\\"serving_id\\\"] = serving_id\\n params[\\\"number_of_units\\\"] = number_of_units\\n\\n response = self.session.get(self.api_url, params=params)\\n return self.valid_response(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d8b637023ae9e022ee3a1e96d883cd\",\n \"score\": \"0.47490332\",\n \"text\": \"def food_insert(self, name, calories, protein, fat, carbs, fiber):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d8c0504c732838f7986103ecf4d9fb0\",\n \"score\": \"0.47435322\",\n \"text\": \"def add(self, product, quantity=1, update_quantity=False):\\n product_vencode = str(product.vencode)\\n if product_vencode not in self.cart:\\n self.cart[product_vencode] = {'quantity': 0,\\n 'price': str(product.price)}\\n if update_quantity:\\n self.cart[product_vencode]['quantity'] = quantity\\n else:\\n self.cart[product_vencode]['quantity'] += quantity\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd12d614dd01fa7d1dc8d540d13a970\",\n \"score\": \"0.4740142\",\n \"text\": \"def add(self, item):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d3c09b955b186c27327828c5c875b7\",\n \"score\": \"0.47401217\",\n \"text\": \"def test_unique_quantities(self):\\n collection = Collection(['air__temperature', 'water__temperature'])\\n\\n quantities = collection.quantities()\\n\\n self.assertEqual(len(quantities), 1)\\n self.assertTrue('temperature' in quantities)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f0904ee6421b4e1fba7c11b177be256\",\n \"score\": \"0.47290277\",\n \"text\": \"def get_sum(items=[]):\\n\\t\\tresult = 0\\n\\t\\tif items:\\n\\t\\t\\tresult = reduce(lambda x, y: x+y, items)\\n\\t\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baa64214b750820c4bb4b85c71e8520d\",\n \"score\": \"0.47211656\",\n \"text\": \"def _amount_all(self):\\n for order in self:\\n amount_untaxed = 0.0\\n for line in order.depence:\\n amount_untaxed += line.montant\\n\\n order.update({\\n\\n 'Montant_total': amount_untaxed,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"105d02d685d7278913bf8246ba61d913\",\n \"score\": \"0.4696378\",\n \"text\": \"def union(a, b):\\n c = a.copy()\\n for item, quantity in b.items():\\n if item in c:\\n c[item] += quantity\\n else:\\n c[item] = quantity\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43a8f10d46c49f4fc02930e22a6c1f53\",\n \"score\": \"0.46955162\",\n \"text\": \"def sum_list(input_list: typing.List[float]) -> float:\\n return sum(input_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b6714d61699a90caccf507a1439ba0b\",\n \"score\": \"0.46904996\",\n \"text\": \"def add_food(self, food: FoodProp) -> None:\\n if not self.can_place_food(food):\\n raise Exception('Cannot place a food with that color on the colorful plate')\\n\\n self.foods.append(food)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68e5cbf07cac2a1423f45e8e5ac6e692\",\n \"score\": \"0.46781105\",\n \"text\": \"def __len__(self):\\n return sum(item[\\\"qty\\\"] for item in self.basket.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b38093e769550aa59eb195f38c23eb6\",\n \"score\": \"0.46772686\",\n \"text\": \"def add_item(self, product, price):\\n if not product in self.items_in_cart:\\n self.items_in_cart[product] = price\\n print (product + \\\" added.\\\")\\n else:\\n print (product + \\\" is already in the cart.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1690b8513ce9e630d2fbdaaf6b81ca8d\",\n \"score\": \"0.4674813\",\n \"text\": \"def sum_mixed_list(input_list: List[Union[int, float]]) -> float:\\n return reduce(lambda x, y: x+y, input_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d97e4fa24429dc883f373bd0ca99355\",\n \"score\": \"0.46693996\",\n \"text\": \"def _product_association(self):\\n # Find orders that bought the target item\\n orders = self.data[self.data[self.item_col]==self.target][self.order_col]\\n \\n # Find all items in those orders\\n items = self.data[self.data[self.order_col].isin(orders)]\\n \\n # Recommend items frequently bought with the target item \\n self._find_recs(items.groupby(self.item_col).size())\\n self._print_recs(\\\"popular items other customers bought\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92b39dd58dbb549782499edade31dac\",\n \"score\": \"0.46683574\",\n \"text\": \"def autoPopulateInventory(self):\\n if not self._permanent:\\n self.clearInventory()\\n\\n # Randomly select the itemCount\\n itemCount = int(getRandomItemFromList(self._numOfItemsCarried))\\n\\n if not itemCount: # 0 items carried\\n return True\\n\\n for itemNum in range(1, itemCount + 1): # +1 due to exclusive ranges\\n itemId = getRandomItemFromList(self._itemCatalog)\\n if not itemId:\\n continue\\n if \\\"/\\\" not in itemId:\\n continue\\n\\n dLog(\\\"creature.autoPopulateInventory: obj = \\\" + itemId)\\n oType, oNum = itemId.split(\\\"/\\\")\\n obj1 = ObjectFactory(oType, oNum)\\n obj1.load()\\n self.addToInventory(obj1)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4025849a832f2176dc523dd3048e8ffc\",\n \"score\": \"0.46618393\",\n \"text\": \"def raw_ingredients_exact(item_set):\\n remaining = item_set.copy()\\n raw_ingredients = {}\\n while any(remaining):\\n item, quantity = list(remaining.items())[0]\\n item_object = items[item]\\n del remaining[item]\\n if item_object.raw:\\n raw_ingredients = union(raw_ingredients, {item: quantity})\\n else:\\n item_list = item_object.recipe.ingredients.items()\\n for ingredient, ingredient_quantity in item_list:\\n remaining = union(\\n remaining,\\n multiply(\\n {ingredient: ingredient_quantity},\\n quantity / item_object.recipe.quantity,\\n ),\\n )\\n return raw_ingredients\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a586b690c5f4ad80786ce24861767f9\",\n \"score\": \"0.46590447\",\n \"text\": \"def frequentOneItem(self):\\n\\n candidate = {}\\n # global finalPatterns, minSup, Database\\n # self.minSup = self.minSup\\n for i in range(len(self.Database)):\\n for j in range(len(self.Database[i])):\\n if self.Database[i][j] not in candidate:\\n candidate[self.Database[i][j]] = 1\\n else:\\n candidate[self.Database[i][j]] += 1\\n self.finalPatterns = {keys: value for keys, value in candidate.items() if value >= self.minSup}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cac81ccf764ad1c0b428a32da2a2677e\",\n \"score\": \"0.46445563\",\n \"text\": \"def add_item(request):\\n\\n if request.method == \\\"GET\\\":\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n \\n # Variable definition\\n try:\\n dish = Dish.objects.get(pk=(request.POST[\\\"dish\\\"]))\\n except:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n extra = request.POST.getlist('extra')\\n\\n # No toppings Pizza validator\\n if (dish.pk == 1 or dish.pk == 2 or dish.pk == 11 or dish.pk == 12) and len(extra) != 0:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n # 1 topping Pizza validator\\n if (dish.pk == 3 or dish.pk == 4 or dish.pk == 13 or dish.pk == 14) and len(extra) != 1:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n # 2 toppings Pizza validator\\n if (dish.pk == 5 or dish.pk == 6 or dish.pk == 15 or dish.pk == 16) and len(extra) != 2:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n # 3 toppings Pizza validator\\n if (dish.pk == 7 or dish.pk == 8 or dish.pk == 17 or dish.pk == 18) and len(extra) != 3:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n\\n # Special Pizza Pizza validator\\n if (dish.pk == 9 or dish.pk == 10 or dish.pk == 19 or dish.pk == 20) and len(extra) != 5:\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\\n \\n # Item variables\\n extras = []\\n price = dish.price\\n\\n for item in extra:\\n new_extra = Extra.objects.get(pk=(item))\\n extras.append(new_extra)\\n price += new_extra.price\\n\\n new_item = Item(dish=dish, price=price)\\n new_item.save()\\n for item in extras:\\n new_item.extras.add(item)\\n\\n # Cart Handling\\n try:\\n cart = Cart.objects.get(user=request.user)\\n except:\\n cart = Cart(user=request.user)\\n cart.save()\\n cart.items.add(new_item)\\n cart.save()\\n\\n return HttpResponseRedirect(reverse(\\\"index\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a923dccac387cb688c107df06e72d941\",\n \"score\": \"0.4643559\",\n \"text\": \"def sum(items):\\n if items is None or len(items) == 0:\\n return None\\n try:\\n return sum(items)\\n except:\\n msg = \\\"Error while addition: %s\\\" %sys.exc_info()[0]\\n sys.stderr.write(msg)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e463dda3a19d276a1fe9f6ee356c72\",\n \"score\": \"0.46426684\",\n \"text\": \"def test_add_item(self):\\n before = len(self.shopping_list_1.items)\\n self.shopping_list_1.add_item(self.item_3)\\n after = len(self.shopping_list_1.items)\\n self.assertEqual(1, after - before,\\n msg='The object should add an item to the instance')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0d6261296989b18c47c219f2b8c7f83\",\n \"score\": \"0.46423283\",\n \"text\": \"def get_total(self):\\n\\n # self.base_price = self.get_base_price()\\n\\n if self.species == \\\"Christmas\\\":\\n self.base_price *= 1.5\\n total = (1 + self.tax) * self.qty * self.base_price\\n return total\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":180,"cells":{"query_id":{"kind":"string","value":"17ff9d02a0b166bd8a9c253d3fe795a0"},"query":{"kind":"string","value":"Update details of an Experiment. Update details of an Experiment with specific Experiment Id."},"positive_passages":{"kind":"list like","value":[{"docid":"10d33500639a40dacacd744c0f51f89c","score":"0.64625514","text":"async def update(\n self,\n subscription_id: str,\n resource_group_name: str,\n workspace_name: str,\n experiment_id: str,\n body: Optional[\"_models.ModifyExperiment\"] = None,\n **kwargs: Any\n ) -> \"_models.Experiment\":\n cls = kwargs.pop('cls', None) # type: ClsType[\"_models.Experiment\"]\n error_map = {\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\n }\n error_map.update(kwargs.pop('error_map', {}))\n\n content_type = kwargs.pop('content_type', \"application/json\") # type: Optional[str]\n\n if body is not None:\n _json = self._serialize.body(body, 'ModifyExperiment')\n else:\n _json = None\n\n request = build_update_request(\n subscription_id=subscription_id,\n resource_group_name=resource_group_name,\n workspace_name=workspace_name,\n experiment_id=experiment_id,\n content_type=content_type,\n json=_json,\n template_url=self.update.metadata['url'],\n )\n request = _convert_request(request)\n request.url = self._client.format_url(request.url)\n\n pipeline_response = await self._client._pipeline.run( # pylint: disable=protected-access\n request,\n stream=False,\n **kwargs\n )\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\n\n deserialized = self._deserialize('Experiment', pipeline_response)\n\n if cls:\n return cls(pipeline_response, deserialized, {})\n\n return deserialized","title":""}],"string":"[\n {\n \"docid\": \"10d33500639a40dacacd744c0f51f89c\",\n \"score\": \"0.64625514\",\n \"text\": \"async def update(\\n self,\\n subscription_id: str,\\n resource_group_name: str,\\n workspace_name: str,\\n experiment_id: str,\\n body: Optional[\\\"_models.ModifyExperiment\\\"] = None,\\n **kwargs: Any\\n ) -> \\\"_models.Experiment\\\":\\n cls = kwargs.pop('cls', None) # type: ClsType[\\\"_models.Experiment\\\"]\\n error_map = {\\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\\n }\\n error_map.update(kwargs.pop('error_map', {}))\\n\\n content_type = kwargs.pop('content_type', \\\"application/json\\\") # type: Optional[str]\\n\\n if body is not None:\\n _json = self._serialize.body(body, 'ModifyExperiment')\\n else:\\n _json = None\\n\\n request = build_update_request(\\n subscription_id=subscription_id,\\n resource_group_name=resource_group_name,\\n workspace_name=workspace_name,\\n experiment_id=experiment_id,\\n content_type=content_type,\\n json=_json,\\n template_url=self.update.metadata['url'],\\n )\\n request = _convert_request(request)\\n request.url = self._client.format_url(request.url)\\n\\n pipeline_response = await self._client._pipeline.run( # pylint: disable=protected-access\\n request,\\n stream=False,\\n **kwargs\\n )\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200]:\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)\\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\\n\\n deserialized = self._deserialize('Experiment', pipeline_response)\\n\\n if cls:\\n return cls(pipeline_response, deserialized, {})\\n\\n return deserialized\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a90932de5b0dd8cc3450221c5e50b85a","score":"0.7416664","text":"def update_experiment(request, pk):\n\n required_data = request.data.get(\"requiredData\")\n tests_config = request.data.get(\"testsConfig\")\n expiration = request.data.get(\"expiration\")\n allow_multiple_answers = request.data.get(\"allowMultipleAnswers\")\n disabled = request.data.get(\"disabled\")\n name = request.data.get(\"name\")\n\n try:\n print('ENTERED')\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if tests_config is None or len(tests_config) == 0:\n return Response('At least one test must be provided', status=HTTP_400_BAD_REQUEST)\n\n if expiration is None:\n return Response('Valid expiration date must be provided', status=HTTP_400_BAD_REQUEST)\n\n try:\n experiment.name = name\n experiment.requiredDataConfig = required_data\n experiment.testsConfig = tests_config\n experiment.expiration = expiration\n experiment.allowMultipleAnswers = allow_multiple_answers\n experiment.disabled = disabled\n experiment.save()\n except Exception as e:\n return Response(str(e), HTTP_400_BAD_REQUEST)\n\n serialized = ExperimentSerializer(experiment).data\n\n return Response(serialized, HTTP_200_OK)","title":""},{"docid":"0aace5c2053a4192cc2f99b73768ad86","score":"0.67339313","text":"def update_experiment_metadata(\n writer_client, experiment_id, name=None, description=None\n):\n logger.info(\"Modifying experiment %r\", experiment_id)\n request = write_service_pb2.UpdateExperimentRequest()\n request.experiment.experiment_id = experiment_id\n if name is not None:\n logger.info(\"Setting exp %r name to %r\", experiment_id, name)\n request.experiment.name = name\n request.experiment_mask.name = True\n if description is not None:\n logger.info(\n \"Setting exp %r description to %r\", experiment_id, description\n )\n request.experiment.description = description\n request.experiment_mask.description = True\n try:\n grpc_util.call_with_retries(writer_client.UpdateExperiment, request)\n except grpc.RpcError as e:\n if e.code() == grpc.StatusCode.NOT_FOUND:\n raise ExperimentNotFoundError()\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\n raise PermissionDeniedError()\n if e.code() == grpc.StatusCode.INVALID_ARGUMENT:\n raise InvalidArgumentError(e.details())\n raise","title":""},{"docid":"546f21b219d724d0964dc0327c6c25f2","score":"0.66571933","text":"def test_submit_edit_experiment(self):\n experiment = self.create_test_experiment(name=\"old_name\")\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.name, \"new_name\")","title":""},{"docid":"3cfc1af097dea409e1e342c357e44dae","score":"0.61671877","text":"def refresh(self) -> None:\n self.update_from_remote_data(self._api_client.experiment_get(self.uuid))","title":""},{"docid":"9e7efd2d39f68200aa5173115ccf47fd","score":"0.6142266","text":"def test_edit_experiment_view(self):\n experiment = self.create_test_experiment()\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")","title":""},{"docid":"6c08855f31c71f29263100fbc5d63505","score":"0.59430104","text":"def update_emp(db, id, hoursTrained, trainedStatus):\r\n trainingInfo = {\"Hours Trained\" : hoursTrained,\r\n \"Training Completed\" : trainedStatus}\r\n\r\n db.collection(\"Employees\").document(id).set(trainingInfo)","title":""},{"docid":"58e74d8d26fab10630d7b37236175eee","score":"0.5902595","text":"def test_submit_edit_started_experiment_changes_name_and_notes(self):\n experiment = self.create_test_experiment(name=\"old_name\", notes=\"old_notes\",\n tracks_finalized=True)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n old_track = self.create_test_track(experiment=experiment, name=\"old_name_track\")\n experiment_json = {\n \"name\": \"new_name\", \"notes\": \"new_notes\", \"tracks\": [{\"id\": old_track.id,\n \"name\": \"new_track_name\"}],\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment_json)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.name, \"new_name\")\n self.assertEquals(experiment.notes, \"new_notes\")\n self.assertEquals(experiment.tracks.all()[0].name, \"new_track_name\")","title":""},{"docid":"5b3b67f1d59d87115dd9fc81a90aab86","score":"0.58990973","text":"def update(self, collab_id: str, project_id: str, expt_id: str, **updates):\n return self._execute_operation(\n operation=\"put\",\n url=self._generate_single_url(\n collab_id=collab_id,\n project_id=project_id, \n expt_id=expt_id\n ),\n payload=updates\n )","title":""},{"docid":"4272881909e59c29f6e7e94aae595276","score":"0.5827913","text":"def update(id_exp=None):\n\n current_app.logger.info(\"PUT /sync route with id: %s\" % id_exp)\n\n (gfg_id, exp_name, session_id), session, resp = parse_id_exp(id_exp)\n\n # Check JSON validity\n if utils.check_valid_json(request.get_data()):\n valid_json = json.loads(request.get_data())\n # session.datastring = valid_json\n else:\n resp = {\"status\": \"bad request\"}\n current_app.logger.error(\"Invalid JSON\")\n\n current_app.logger.info(\n \"Current trial: %s, unique_id: %s, experiment name: %s, session id: %s \" % (valid_json['currenttrial'],\n valid_json['uniqueid'], valid_json['experimentname'], valid_json['sessionid']))\n\n # For each trial, pass to appropriate parser, if not in db\n for json_trial in valid_json['data']:\n if exp_name == \"category_switch\":\n experiment_class = CategorySwitch\n elif exp_name == \"keep_track\":\n experiment_class = KeepTrack\n else:\n current_app.logger.error(\"%s does not exist\" % (exp_name))\n resp = {\"status\": \"bad request\"}\n\n db_trial, new = db_utils.get_or_create(db.session,\n experiment_class, gfg_id=gfg_id, session_id=session_id,\n trial_num=json_trial['current_trial'])\n\n # If the trial is new, add data\n if new:\n db_trial.add_json_data(json_trial)\n db.session.commit()\n\n # For each event, pass to parser, if not in db\n for json_event in valid_json['eventdata']:\n db_event, new = db_utils.get_or_create(db.session, EventData,\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name, \n timestamp = utils.convert_timestamp(json_event['timestamp']))\n\n if new:\n db_event.add_json_data(json_event)\n db.session.commit()\n\n # For the QuestionData, pass to parser, if not in db\n for json_ques in valid_json['questiondata']:\n db_ques, new = db_utils.get_or_create(db.session, QuestionData,\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name)\n\n if new:\n db_ques.add_json_data(json_ques)\n db.session.commit()\n\n if resp is None:\n resp = {\"status\": \"user data saved\"}\n return jsonify(**resp)","title":""},{"docid":"6b05deea5431803e1eac4738e4cc6a36","score":"0.5811839","text":"def update(self, request, pk=None):\n selected_routine = request.query_params.get('routine', None) \n\n try: \n exercise = Exercise.objects.get(pk=pk)\n\n exercise.routine = Routine.objects.get(pk=selected_routine)\n exercise.description = request.data['description']\n exercise.save()\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)\n \n except ValidationError as ex:\n return Response({\"reason\": ex.message}, status=status.HTTP_400_BAD_REQUEST)","title":""},{"docid":"e1ea28e67ff7a830d61e1851c7da9f33","score":"0.56713605","text":"async def edit_training_examples(\n intent: str,\n id: str,\n request_data: TextData,\n current_user: User = Depends(auth.get_current_user),\n):\n mongo_processor.edit_training_example(\n id, request_data.data, intent.lower(), current_user.get_bot(), current_user.get_user()\n )\n return {\"message\": \"Training Example updated!\"}","title":""},{"docid":"b610548abb62bf8777e59ea744a47f65","score":"0.5661236","text":"def put(self, exploration_id):\n exploration = exp_services.get_exploration_by_id(exploration_id)\n version = self.payload.get('version')\n _require_valid_version(version, exploration.version)\n\n commit_message = self.payload.get('commit_message')\n change_list = self.payload.get('change_list')\n\n try:\n exp_services.update_exploration(\n self.user_id, exploration_id, change_list, commit_message)\n except utils.ValidationError as e:\n raise self.InvalidInputException(e)\n\n self.values.update(self._get_exploration_data(exploration_id))\n self.render_json(self.values)","title":""},{"docid":"4f444f6fc62b19e72dd10f8ea281178b","score":"0.5650311","text":"def update(self, entity_id, details, delete_existing):","title":""},{"docid":"829b7025e64765315044ed5f1182fe55","score":"0.5622866","text":"def causx_edit_an_entity(id):\n project = get_project()\n updated_entry = request.get_json()[\"updated_entry\"]\n entity = causx_set_variable(project, id, updated_entry)\n response=dict(entity=entity)\n calc_params = get_calc_params(project, data_required=False)\n if calc_params:\n response[\"layers\"] = get_qnodes_layer(calc_params)\n return response, 200","title":""},{"docid":"d3a677e8ac2b6e770914054e27e7250c","score":"0.56208825","text":"def test_submit_edit_started_experiment_changes_existing_tracks(self):\n experiment = self.create_test_experiment(name=\"old_name\", tracks_finalized=False,\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n track1 = self.create_test_track(experiment=experiment, name=\"A\")\n track2 = self.create_test_track(experiment=experiment, name=\"B\")\n self.create_test_track_weight(experiment=experiment, track=track1)\n self.create_test_track_weight(experiment=experiment, track=track2)\n track_count = experiment.tracks.count()\n experiment_json = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": False,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": track1.id, \"weighting\": 30, \"name\": \"C\"},\n {\"id\": track2.id, \"weighting\": 70, \"name\": \"D\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment.id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment_json)\n )\n self.assertOkay(response)\n experiment = Experiment.objects.get(id=experiment.id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.tracks.count(), track_count)\n track1 = experiment.tracks.get(id=track1.id)\n track2 = experiment.tracks.get(id=track2.id)\n self.assertEquals(track1.name, \"C\") #Checks name has changed\n self.assertEquals(track2.name, \"D\")\n self.assertEquals(track1.weight.weighting, 30) #Checks weighting has changed\n self.assertEquals(track2.weight.weighting, 70)","title":""},{"docid":"02d99f571b3fa51ed6afda42fff2838c","score":"0.56005096","text":"def put(self, id):\n data = request.get_json()\n return update_a_reporter(id=id, data=data)","title":""},{"docid":"3eca505c37f3bd0881bd1ed010599a0a","score":"0.559187","text":"def test_edit_experiment_view_started_experiment(self):\n experiment = self.create_test_experiment()\n experiment.tracks_finalized = True\n experiment.save()\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")","title":""},{"docid":"e11f93fd1499f93fcb17b45a21891a2d","score":"0.55208725","text":"def test_submit_edit_started_experiment_does_not_change_tracks(self):\n experiment = self.create_test_experiment(name=\"old_name\", tracks_finalized=True,\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_tracks = experiment.tracks.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"},\n {\"id\": None, \"weighting\": None, \"name\": \"B\"},\n {\"id\": None, \"weighting\": None, \"name\": \"C\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.tracks.count(), no_tracks)","title":""},{"docid":"2db940d31dbabec99f670e73eaba4b31","score":"0.5507888","text":"def test_submit_edit_experiment_nonexistent(self):\n experiment_id = NONEXISTENT_EXPERIMENT_ID\n experiment = {\"name\": \"new_name\", \"notes\": \"\"}\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertEquals(response.status_code, 404)","title":""},{"docid":"214e821c70df6edb4046bfd747a7248d","score":"0.5484202","text":"def edit(project):\n if g.user:\n session['exp'] = project\n abstract = s.get_experiment_data(g.user, project, 'abstract')\n gl = s.get_experiment_data(g.user, project, 'gl')\n ec = s.get_experiment_data(g.user, project, 'ec')\n fn = s.get_experiment_data(g.user, project, 'fn')\n return render_template('edit.html', project=project, abstract=abstract, gl=gl, ec=ec, fn=fn)\n return redirect(url_for('login'))","title":""},{"docid":"f70ff7a5bde05f7ffc13d2ef2cf7f6ab","score":"0.5455811","text":"def update(self, entity_id, details, delete_existing=False):\n self._validate_entity_id(entity_id)\n\n if not isinstance(details, dict):\n msg = (_(\"Provided details %s is not valid dict.\")\n % details)\n raise ValueError(msg)\n\n return self._storage.update(\n entity_id, details, delete_existing)","title":""},{"docid":"95f73067644b0b8d431330b8b573696d","score":"0.54527766","text":"def update(self, event_id, event_time=None, **kwargs):\n pass","title":""},{"docid":"95f73067644b0b8d431330b8b573696d","score":"0.54527766","text":"def update(self, event_id, event_time=None, **kwargs):\n pass","title":""},{"docid":"95f73067644b0b8d431330b8b573696d","score":"0.54527766","text":"def update(self, event_id, event_time=None, **kwargs):\n pass","title":""},{"docid":"5ba50b0f354cac4d2eb92581c8b79298","score":"0.5451184","text":"def update(self, id, data):\n return self.make_request('update', '/' + id, data)","title":""},{"docid":"53e08e1e5e63528ecaa3e670768710da","score":"0.54415154","text":"def put(user_id,self):\n form = ResearchInfoUpdateForm(request.form)\n if form.validate():\n \n research_info = ResearchInformation.query.filter((ResearchInformation.id == form.research_id.data) & (ResearchInformation.user_id == user_id))\n if research_info is None:\n return make_response(jsonify({'error':'Please give an appropriate research ID'}),400)\n update_dict = {}\n for key, value in form.data.items():\n if value and key != 'research_id':\n update_dict[key] = value\n try:\n research_info.update(update_dict)\n db.session.commit()\n except:\n return make_response(jsonify({'error' : 'Database Connection Problem'}),500)\n return make_response(jsonify({'msg' : ' Successfully changed'}),201)\n\n else:\n return make_response(jsonify({'error':'Wrong input format'}),400)","title":""},{"docid":"83b67ad4ce2e3e31eb6207122f1a66f3","score":"0.5403792","text":"def put(self):\n return employee_service.edit_employee(get_jwt_identity(), get_uuid(request), **api.payload), 201","title":""},{"docid":"d2ac49e54bcfac440fb624d369239316","score":"0.53886133","text":"def _add_experiment(self, experiment_key, variation_id):\n\n experiment_id = self.config.get_experiment_id(experiment_key)\n self.params[EXPERIMENT_PARAM_FORMAT.format(experiment_prefix=Params.EXPERIMENT_PREFIX,\n experiment_id=experiment_id)] = variation_id","title":""},{"docid":"0fd8bb0e660cd252ad87d2e7d500422a","score":"0.53784376","text":"def put(self, id):\n\t\ttoken_parser.parse_args()\n\t\trecord = Record.query.get(id)\n\t\tif not record:\n\t\t\treturn error_response('RECORD NOT FOUND', 404) \n\t\t# PERMISSION_DENIED because don't want user to know this record id exists\n\t\tif record.runner.id!=int(get_jwt_identity()) and not min_access_level(self, ROLES['admin']):\n\t\t\treturn error_response('RECORD NOT FOUND', 404) \n\n\t\tdata = request.get_json() or {}\n\t\ttry:\n\t\t\trecord.update(data)\n\t\texcept ValueError as error:\n\t\t\treturn error_response('RECORD NOT FOUND', 404)\n\t\texcept Exception as error:\n\t\t\treturn error_response('PERMISSION DENIED', 403)\n\t\tdb.session.commit()\n\t\treturn make_response(jsonify(record.to_dict()), 200)","title":""},{"docid":"7a8c7feb5e4f073a780db8763a8c3a47","score":"0.53570676","text":"def test_edit_report(self):\n report = self.create_report()\n pk = report[0].pk\n my_url = report_url(pk)\n response = self.client.put(\n my_url,\n self.updated_report,\n **self.headers,\n format='json'\n )\n self.assertEqual(response.status_code, status.HTTP_200_OK)","title":""},{"docid":"3c6b8e8628b2258d34781373f3756603","score":"0.5355502","text":"def put(self, id):\n # Get the json object from the request body\n data = request.get_json()\n # Check all fields for availability and get the new data if the field is available\n if 'email' not in data:\n email = None\n else:\n email = data[\"email\"]\n if 'first_name' not in data:\n first_name = None\n else:\n first_name = data[\"first_name\"]\n if 'last_name' not in data:\n last_name = None\n else:\n last_name = data[\"last_name\"]\n if 'availability' not in data:\n availability = None\n else:\n availability = data[\"availability\"]\n\n # Update all requested fields of the requested interviewer, if there is an interviewer with the id, else return\n # an error\n interviewer = interviewer_ctrl.update_interviewer(id, email, first_name, last_name, availability)\n if not interviewer:\n response = \"Interviewer with id %s does not exist or email %s is already taken by another interviewer\" % (\n id, email), 400\n else:\n response = interviewer, 200\n return response","title":""},{"docid":"15c1f7722e5e0c8697db10072db3f404","score":"0.53356797","text":"def put(self, id):\n data = request.json\n args = request.args\n return update_unit(id, data, args)","title":""},{"docid":"f5a0bd39cf438b04b13e0fd2dad2b75f","score":"0.5282175","text":"def update_project(id=None, name=None, description=None):\n pass","title":""},{"docid":"7da3e669c6d3439e143564ac0ff1622b","score":"0.5282065","text":"def test_edit_experiment_view_last_modified_updated(self):\n experiment = self.create_test_experiment()\n experiment.name += \" (updated)\"\n response = self.client.post(reverse(\"ab_testing_tool_submit_edit_experiment\",\n args=(experiment.id,)),\n content_type=\"application/json\",\n data=experiment.to_json())\n self.assertEquals(response.content, \"success\")\n updated_experiment = Experiment.objects.get(id=experiment.id)\n self.assertLess(experiment.updated_on, updated_experiment.updated_on,\n response)","title":""},{"docid":"a63400c45bfcc9bce89953c8ae6b3701","score":"0.5281295","text":"def put(self, id):\n data = request.get_json()\n if 'email' not in data:\n email = None\n else:\n email = data[\"email\"]\n if 'first_name' not in data:\n first_name = None\n else:\n first_name = data[\"first_name\"]\n if 'last_name' not in data:\n last_name = None\n else:\n last_name = data[\"last_name\"]\n if 'availability' not in data:\n availability = None\n else:\n availability = data[\"availability\"]\n interviewee = interviewee_ctrl.update_interviewee(id, email, first_name, last_name, availability)\n if not interviewee:\n response = \"Interviewee with id %s does not exist or email %s is already taken by another interviewee\" % (\n id, email), 400\n else:\n response = interviewee, 200\n return response","title":""},{"docid":"f85bb5f0f4fbddd046d215c53c2b171b","score":"0.52504665","text":"def put(self,request,employee_id):\n try:\n get_data = Employee.objects.get(pk=employee_id)\n employee_data = EmployeeSerializer(get_data,data=request.data)\n if not(employee_data.is_valid()):\n return ApiResponse().error(\"Error while update employee details\",400)\n employee_data.save()\n return ApiResponse().success(employee_data.data,200)\n except Exception as err:\n print(err)\n return ApiResponse().success(\"Error\",500)","title":""},{"docid":"ecba54809858f5347c6dc258ddbd143b","score":"0.5228171","text":"def update_testimonial():\n data = request.get_json()\n description = data.get('description')\n title = data.get('title')\n image = data.get('image')\n rank = data.get('rank')\n testimonial_id = data.get('id')\n\n if (not title) or (not description) or (not rank) or (not testimonial_id):\n return jsonify(result={'failed_msg': \"Unanle to save testimonial with missing fields\"})\n\n return jsonify(result=save_testimonial(title, description, rank, image, _id=testimonial_id))","title":""},{"docid":"997ffa7f45afa19ce3c55b4b84f758f6","score":"0.5227724","text":"async def update(\n self,\n id: int,\n data: EnvironmentUpdateSchema\n ) -> Record:\n\n async with self.database.transaction():\n query = (\n environments_table.update()\n .where(environments_table.c.id == id)\n .values(\n name=data.name,\n description=data.description,\n updated_at=datetime.now()\n )\n .returning(\n environments_table.c.id,\n environments_table.c.name,\n environments_table.c.code,\n environments_table.c.description,\n environments_table.c.created_at,\n environments_table.c.updated_at,\n environments_table.c.deleted_at,\n environments_table.c.is_deleted\n )\n )\n\n return await self.database.fetch_one(query)","title":""},{"docid":"1b7edbfabd11852be3e16d3e6569fbe4","score":"0.52266294","text":"def update(self, model, id, update_with):\n raise NotImplementedError('You have not implemented an update func in your client class')","title":""},{"docid":"15f18479a5de36268b4534b705d906ee","score":"0.5196483","text":"def test_put_experiment_valid_permission(self):\n url = '/' + version + '/collection/unittestcol/experiment/unittestexp/'\n data = {'description': 'A new experiment for unit tests. Updated'}\n\n response = self.client.put(url, data=data)\n self.assertEqual(response.status_code, 200)","title":""},{"docid":"cf3a3156e9bbff73bb66d428cba3cc4a","score":"0.5194518","text":"def test_submit_edit_experiment_changes_assignment_method_to_weighted(self):\n experiment = self.create_test_experiment(name=\"old_name\")\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_track_weights = experiment.track_probabilites.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": False,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": 20, \"name\": \"A\"},\n {\"id\": None, \"weighting\": 80, \"name\": \"B\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\n self.assertEquals(experiment.track_probabilites.count(), no_track_weights + 2)","title":""},{"docid":"3ca66dab826730162433142988260ed8","score":"0.5188264","text":"def test_submit_edit_experiment_changes_assignment_method_to_uniform(self):\n experiment = self.create_test_experiment(\n name=\"old_name\", assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\n experiment_id = experiment.id\n num_experiments = Experiment.objects.count()\n no_tracks = experiment.tracks.count()\n experiment = {\n \"name\": \"new_name\", \"notes\": \"hi\", \"uniformRandom\": True,\n \"csvUpload\": False,\n \"tracks\": [{\"id\": None, \"weighting\": None, \"name\": \"A\"},\n {\"id\": None, \"weighting\": None, \"name\": \"B\"},\n {\"id\": None, \"weighting\": None, \"name\": \"C\"}]\n }\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment_id,)),\n follow=True, content_type=\"application/json\", data=json.dumps(experiment)\n )\n self.assertOkay(response)\n self.assertEquals(num_experiments, Experiment.objects.count())\n experiment = Experiment.objects.get(id=experiment_id)\n self.assertEquals(experiment.assignment_method, Experiment.UNIFORM_RANDOM)\n self.assertEquals(experiment.tracks.count(), no_tracks + 3)","title":""},{"docid":"7565e22ff55c1a4dbfaef25b7830ba89","score":"0.5184008","text":"def put(self, id):\n report = self.mydb.get_by_id(id)\n \n if report:\n data = request.get_json()\n report.createdBy = data['username']\n report.type = data['flag']\n report.location = data['location']\n report.status = data['status']\n report.image = data['image']\n report.video = data['video']\n report.comment = ['comment']\n return make_response(jsonify({\n \"message\":\"Report edited successfully\",\n \"data\": report.serialize()\n }), 201)\n else:\n return make_response(jsonify({\n \"message\": \"ID invalid, no report found\"\n }),400)","title":""},{"docid":"160cb1102a31e22433b0a95d1b19c287","score":"0.5183066","text":"def upload_experiment(self, experiment: Experiment) -> None:\n data = {\n 'device_name': experiment.backend_name,\n 'type': experiment.type,\n 'extra': experiment.extra,\n }\n if experiment.start_datetime:\n data['start_time'] = local_to_utc_str(experiment.start_datetime)\n if experiment.tags:\n data['tags'] = experiment.tags\n if experiment.uuid:\n data['uuid'] = experiment.uuid\n response_data = self._api_client.experiment_upload(data)\n experiment.update_from_remote_data(response_data)","title":""},{"docid":"a0650d7cb60a6083e8c9359cba962c35","score":"0.517982","text":"def update(self, typ, id, **kwargs):\n return self._load(self._request(self._url(typ, id), 'PUT', kwargs))","title":""},{"docid":"8b5e1688a20dad535a152c61484ada45","score":"0.517151","text":"def update(self, step_name, worker_id):\n record = ReportClient().get_record(step_name, worker_id)\n logging.debug(\"Get Record=%s\", str(record))\n self.search_alg.update(record.serialize())\n ParameterSharing().remove()\n logging.info(f\"Update Success. step_name={step_name}, worker_id={worker_id}\")\n logging.info(\"Best values: %s\", ReportServer().print_best(step_name=General.step_name))","title":""},{"docid":"4cc0ccaf21cc95495b47366271ce2579","score":"0.51659554","text":"def post_experiment(self):\n if not self.has_prepared_out:\n self.prepare_experiment()\n\n for exp, prod, desc in self.experiment.overview:\n print exp, prod, desc\n dt = json.dumps(\\\n {'name': exp,\n 'desc': desc})\n resp = requests.post(\n self.server_url + 'experiments/',\n data=dt,\n headers={'Content-type':'application/json'})\n print resp","title":""},{"docid":"3a1a25ea3994e5d6663400ef4dc22a54","score":"0.5152818","text":"def edit_quiz(request_ctx, course_id, id, quiz_notify_of_update, **request_kwargs):\n\n path = '/v1/courses/{course_id}/quizzes/{id}'\n payload = {\n 'quiz[notify_of_update]' : quiz_notify_of_update,\n }\n url = request_ctx.base_api_url + path.format(course_id=course_id, id=id)\n response = client.put(request_ctx, url, payload=payload, **request_kwargs)\n\n return response","title":""},{"docid":"76aca4be1ce01e6f0e21a2963567cc01","score":"0.5147998","text":"def test_edit_experiment_view_nonexistent(self):\n e_id = NONEXISTENT_EXPERIMENT_ID\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(e_id,)))\n self.assertTemplateNotUsed(response, \"ab_tool/edit_experiment.html\")\n self.assertEquals(response.status_code, 404)","title":""},{"docid":"ddb24b14a3442c8472d5c6d09956b062","score":"0.51298386","text":"def update_project(self, id: str, name: str = None, description: str = None) -> Dict:\n pass","title":""},{"docid":"9609d770f7c1fe4197f40d9c80ed4b91","score":"0.5129037","text":"def update_environment(context: click.Context, application_id: str, environment_id: str, name: str, url: str) -> None:\n api = context.obj\n application = api.application_by_id(application_id)\n environment = api.environment_by_id(application, environment_id)\n updated_environment = api.update_environment(application, environment, name, url)\n table = [[\"ID\", \"Name\", \"URL\"], [updated_environment.id, updated_environment.name, updated_environment.url]]\n click.echo(tabulate.tabulate(table, headers=\"firstrow\"))","title":""},{"docid":"da739f61d8dd8506648f0f235b47402d","score":"0.51289636","text":"def edit_experience(request, id):\r\n experience = Experience.objects.get(pk=id)\r\n context = {\r\n 'experience': experience\r\n }\r\n if request.method == 'POST':\r\n experience.user_id = request.POST['user_id' or None]\r\n experience.company_name = request.POST['company_name' or None]\r\n experience.job_title = request.POST['job_title' or None]\r\n experience.job_description = request.POST['job_description' or None]\r\n experience.year_from = request.POST['year_from' or None]\r\n experience.year_to = request.POST['year_to' or None]\r\n\r\n experience.save()\r\n messages.success(request, 'Experience Updated Successfully')\r\n return redirect('dashboard', request.user.pk)\r\n else:\r\n return render(request, 'accounts/edit_experience.html', context)","title":""},{"docid":"1d62bd10f0ddf13b60680d431d1214af","score":"0.5126918","text":"def update_teacher(email=None, zipcode=None, bio=None, \n mobile_number=None, days_of_week=None,teaching_experience_in_hours=None,\n pay_rate_per_hour=None, pod_id=None, img_url=None, covid_risk_profile_id=None):\n\n teacher = db.session.query(Teacher).filter(Teacher.email==email).one()\n teacher.zipcode=zipcode \n teacher.bio=bio \n teacher.mobile_number=mobile_number\n teacher.days_of_week=days_of_week\n teacher.teaching_experience_in_hours=teaching_experience_in_hours\n teacher.pay_rate_per_hour=pay_rate_per_hour\n teacher.pod_id=pod_id\n teacher.img_url=img_url\n teacher.covid_risk_profile_id=covid_risk_profile_id\n\n db.session.commit()\n\n return teacher","title":""},{"docid":"608457875b3bc5272855818c41796e9c","score":"0.5124847","text":"def edit(obj, index, description, duration, code, task_id, project_id):\n with ots_filestore(obj) as timesheet_storage:\n timesheet_edited = timesheet_storage.edit_timesheet(\n index,\n description=description,\n duration=duration,\n task_code=code,\n task_id=task_id,\n project_id=project_id,\n )\n if timesheet_edited:\n click.echo('Timesheet updated.')\n else:\n click.echo('Nothing to update.')","title":""},{"docid":"3cbff9690ae2f28c9373856792e9b190","score":"0.50883085","text":"def update(self, data, id):\n with db.session.begin_nested():\n self.query.filter_by(id=id).update(data)","title":""},{"docid":"f165c0fcebdc404642a446e6277bbe24","score":"0.5067855","text":"def test_submit_edit_experiment_wrong_course(self):\n experiment = self.create_test_experiment(name=\"old_name\",\n course_id=TEST_OTHER_COURSE_ID)\n data = {\"name\": \"new_name\", \"notes\": \"\"}\n response = self.client.post(\n reverse(\"ab_testing_tool_submit_edit_experiment\", args=(experiment.id,)),\n content_type=\"application/json\", data=json.dumps(data)\n )\n self.assertError(response, UNAUTHORIZED_ACCESS)","title":""},{"docid":"a61125735c80419b89b3c2ef8fdb6cc7","score":"0.50544035","text":"def update_suite(suiteId):\n\t res = requests.post('https://api.ghostinspector.com/v1/suites/'+suiteId+'/?apiKey='+apiKey)\n\t return res","title":""},{"docid":"67143faae1b601cdc1166defe338b29d","score":"0.505348","text":"def step_impl(context, post_id):\n context.payload = constants.POST_INFORMATION_TO_UPDATE\n context.response = request.put(context.url + \"/%s\" % post_id, context.payload)","title":""},{"docid":"c923e8f9fc820942ebc8185747323eb5","score":"0.5051238","text":"def get_experiment(experiment_id):\n req = requests.get(\"{}experiments/{}/?format=json\".format(__base_url__, experiment_id))\n metadata = req.json()\n if metadata['status'] != 'error':\n save_metadata(metadata)\n else:\n print 'Error fetching metadata for {}'.format(experiment_id)","title":""},{"docid":"0c7f7bf6fb01612d985fa2b143d8134b","score":"0.5051149","text":"def wxt_update_person(person_id, data, myheaders):\n update_person_url = 'https://api.ciscospark.com/v1/people/{0}'\n response = requests.put(update_person_url.format(person_id),\n data=json.dumps(data),\n headers=myheaders)\n return response","title":""},{"docid":"4a558e28f4fcb1556894539bd864dab7","score":"0.5049372","text":"def experiment_detail(request, exp_name):\n exp = get_object_or_404(Experiment, name=exp_name)\n reports = ExperimentReport.objects.filter(experiment=exp)\n\n return render_to_response(\n \"splango/experiment_detail.html\",\n {\"title\": exp.name, \"exp\": exp, \"reports\": reports},\n RequestContext(request))","title":""},{"docid":"0758bd2dc137efe10c77c05015e4b113","score":"0.50357556","text":"def update_movie_details(db: Session, sl_id: int, details: schema.UpdateMovie):\n db.query(model.Movies).filter(model.Movies.id == sl_id).update(vars(details))\n db.commit()\n return db.query(model.Movies).filter(model.Movies.id == sl_id).first()","title":""},{"docid":"e912a37963fc2345ab045aa5222960ba","score":"0.5034493","text":"def update_by_id(self, id=None, **kwargs):\n if not id:\n raise PardotAPIArgumentError('id is required to update a prospect.')\n response = self._post(path='/do/update/id/{id}'.format(id=id), params=kwargs)\n return response","title":""},{"docid":"bec9d8e407070c9a77a984f35bc91736","score":"0.5032699","text":"def update_test_run(self, run_update_model, project, run_id):\n route_values = {}\n if project is not None:\n route_values['project'] = self._serialize.url('project', project, 'str')\n if run_id is not None:\n route_values['runId'] = self._serialize.url('run_id', run_id, 'int')\n content = self._serialize.body(run_update_model, 'RunUpdateModel')\n response = self._send(http_method='PATCH',\n location_id='cadb3810-d47d-4a3c-a234-fe5f3be50138',\n version='5.0',\n route_values=route_values,\n content=content)\n return self._deserialize('TestRun', response)","title":""},{"docid":"41e64bd368f6696cc8782c9dac2067f6","score":"0.50115055","text":"def update(self, request, pk=None):\n player = Player.objects.get(user=request.auth.user)\n suspect = Suspect.objects.get(pk=pk)\n suspect.player = player\n suspect.name = request.data[\"name\"]\n suspect.description = request.data[\"description\"]\n guilty = Guilty.objects.get(pk=request.data[\"guiltyId\"])\n suspect.guilty = guilty\n movie = Movie.objects.get(pk=request.data[\"movieId\"])\n suspect.suspect_image_url = request.data[\"suspectImageUrl\"]\n suspect.movie = movie\n \n suspect.save()\n\n\n return Response({}, status=status.HTTP_204_NO_CONTENT)","title":""},{"docid":"489dce730c6e0515e0c8902d35851c78","score":"0.49720645","text":"def update_test(testId):\n\t res = requests.post('https://api.ghostinspector.com/v1/tests/'+testId+'/?apiKey='+apiKey)\n\t return res","title":""},{"docid":"6b1c0e0826bdbdb476676fb3b9967ce7","score":"0.49566084","text":"def test_put_experiment_no_permissions(self):\n url = '/' + version + '/collection/col1/experiment/exp1/'\n data = {'description': 'A new experiment for unit tests. Updated'}\n\n response = self.client.put(url, data=data)\n self.assertEqual(response.status_code, 403)","title":""},{"docid":"60ee48539f966381bc6e042c165a54a8","score":"0.49527475","text":"def put(self, id):\n return DAO.update(id, api.payload)","title":""},{"docid":"3c8a67ef451a5b9d3cfbff45c9f08289","score":"0.49498895","text":"def delete_experiment(writer_client, experiment_id):\n logger.info(\"Deleting experiment %r\", experiment_id)\n request = write_service_pb2.DeleteExperimentRequest()\n request.experiment_id = experiment_id\n try:\n grpc_util.call_with_retries(writer_client.DeleteExperiment, request)\n except grpc.RpcError as e:\n if e.code() == grpc.StatusCode.NOT_FOUND:\n raise ExperimentNotFoundError()\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\n raise PermissionDeniedError()\n raise","title":""},{"docid":"60ebeb2bb4b736d0d454c31d04209770","score":"0.49268797","text":"def test_edit_experiment_view_with_tracks_weights(self):\n experiment = self.create_test_experiment()\n experiment.assignment_method = Experiment.WEIGHTED_PROBABILITY_RANDOM\n track1 = self.create_test_track(name=\"track1\", experiment=experiment)\n track2 = self.create_test_track(name=\"track2\", experiment=experiment)\n self.create_test_track_weight(experiment=experiment, track=track1)\n self.create_test_track_weight(experiment=experiment, track=track2)\n response = self.client.get(reverse(\"ab_testing_tool_edit_experiment\", args=(experiment.id,)))\n self.assertTemplateUsed(response, \"ab_tool/edit_experiment.html\")","title":""},{"docid":"1a83bb2f35b27541623f09db41fce462","score":"0.49165675","text":"def update_results(student_id, experiment_id, student_response):\n\n replies = get_experiment_replies(experiment_id)\n replies_dict = json.loads(replies)\n\n replies_dict['replies'].append({\"id\": int(student_id), \"response\": student_response})\n\n new_replies = json.dumps(replies_dict)\n\n con = sql.connect(DB_PATH)\n cur = con.cursor()\n\n cur.execute(\"UPDATE experiments SET replies = ? WHERE id = ?\", (new_replies, experiment_id))\n\n cur.execute(\"DELETE FROM experiments_students \"\n \"WHERE student_id = ? AND experiment_id = ?\", (student_id, experiment_id))\n\n con.commit()\n\n con.close()","title":""},{"docid":"d8b1a5247263b3eaa3ed81d6c8455d38","score":"0.49110433","text":"def update(self, kwargs):\n if self._status in [DEFAULT, DETACHED]:\n if any([k not in self.param_keys() and k in self._specials for k in kwargs]):\n raise ValueError('Key not recognised!')\n else:\n self.__dict__.update(kwargs)\n else:\n raise ValueError('Parameters of a created experiment cannot be updated.')","title":""},{"docid":"b058a3ccc0c492a3a2054e916ba606a3","score":"0.4910217","text":"def update(self, model, rec_id, data):\n\n url = self.base_url + model + '/' + str(rec_id)\n data = format_write_data(data)\n return self.session.put(url, data=data)","title":""},{"docid":"9c3a3f85820ec6698cda066db2a4dae5","score":"0.4900522","text":"def detail_put(self, request, id, *args, **kwargs):\n raise NotAllowed()","title":""},{"docid":"84ef2def248ce0213ba3a195c608662a","score":"0.48950928","text":"def edit_education(request, id):\r\n education = Education.objects.get(pk=id)\r\n context = {\r\n 'education': education\r\n }\r\n if request.method == 'POST':\r\n education.user_id = request.POST['user_id' or None]\r\n education.school = request.POST['school' or None]\r\n education.field_of_study = request.POST['field_of_study' or None]\r\n education.qualification = request.POST['qualification' or None]\r\n education.year = request.POST['year' or None]\r\n\r\n education.save()\r\n messages.success(request, 'Education Updated Successfully, you may add another !')\r\n return redirect('dashboard', request.user.pk)\r\n else:\r\n return render(request, 'accounts/edit_education.html', context)\r\n\r\n # DELETE EDUCATION\r","title":""},{"docid":"1f9b95546a734ec60c96a828783d5784","score":"0.4894346","text":"def test_experiment_specification(self, client, ephemeral_storage):\n\n storage = ephemeral_storage.storage\n\n _add_experiment(storage, name=\"a\", version=1, _id=1)\n _add_trial(storage, experiment=1, id_override=\"ae8\", status=\"completed\")\n\n response = client.simulate_get(\"/experiments/status/a\")\n assert response.status == \"200 OK\"\n assert response.json == {\n \"trials_completed\": 1,\n \"best_trials_id\": \"7bbfdb8c684aa5f4be324a09d7da94af\",\n \"best_evaluation\": 0.05,\n \"start_time\": \"0001-01-01 00:00:00\",\n \"finish_time\": \"0001-01-02 00:00:00\",\n \"max_trials\": 10,\n \"nb_trials\": 1,\n \"progress\": 0.1,\n \"trial_status_count\": {\"completed\": 1},\n \"elapsed_time\": \"23:59:50\",\n \"sum_of_trials_time\": \"23:59:50\",\n \"eta\": \"8 days, 23:58:30\",\n \"eta_milliseconds\": 777510000.0,\n }","title":""},{"docid":"527afa47e52ca91f15e0bf2975749899","score":"0.48926717","text":"def edit(id):\n\n pet = Pet.query.get_or_404(id)\n form = EditPetForm(obj=pet)\n\n if form.validate_on_submit():\n pet.photo_url = form.data[\"photo_url\"]\n pet.notes = form.data[\"notes\"]\n pet.available = form.data['available']\n db.session.commit()\n flash(f\"{pet.name} updated.\", 'success')\n return redirect(url_for('index'))\n\n else:\n return render_template(\"edit_pet_form.html\", form=form, pet=pet)","title":""},{"docid":"f73c9c211624982ebce62505144b3717","score":"0.48906887","text":"def update(self):\n response = self.api(self.id).get()\n self._update_fields(response)","title":""},{"docid":"3aac6ddcfb59a9b9059b7ceec9897878","score":"0.48723197","text":"def put(self, id):\n data = request.json\n if data[\"entityID\"] != id:\n return {\"message\": \"entityID property in the incoming json object and id parameter in the URL path are\"\n \"not matched\"}, 400\n evn = EventService.get_by_id(id)\n if not evn:\n return {\"message\": \"The event does not exist\"}, 404\n else:\n return EventService.update(data, id)","title":""},{"docid":"4ee013b5fc5b6473163ae2bdd9f275cd","score":"0.4865083","text":"def test_experiment_specification(self, client, ephemeral_storage):\n\n storage = ephemeral_storage.storage\n\n _add_experiment(storage, name=\"a\", version=1, _id=1)\n _add_trial(storage, experiment=1, id_override=\"ae8\", status=\"completed\")\n\n response = client.simulate_get(\"/experiments/a\")\n\n assert response.status == \"200 OK\"\n\n assert response.json[\"name\"] == \"a\"\n assert response.json[\"version\"] == 1\n assert response.json[\"status\"] == \"not done\"\n assert response.json[\"trialsCompleted\"] == 1\n assert response.json[\"startTime\"] == \"0001-01-01 00:00:00\" # TODO\n assert response.json[\"endTime\"] == \"0001-01-02 00:00:00\" # TODO\n assert len(response.json[\"user\"])\n assert response.json[\"orionVersion\"] == \"x.y.z\"\n\n _assert_config(response.json[\"config\"])\n _assert_best_trial(response.json[\"bestTrial\"])","title":""},{"docid":"d7d6f7bd49709c1e26e4af92b13dda56","score":"0.48624727","text":"def api_employee_edit(request):\r\n\r\n if request.method.lower() == 'post':\r\n\r\n data = request.POST.dict()\r\n employee_id = int(request.POST['id'])\r\n\r\n try:\r\n employee = Employee.objects.get(id=employee_id)\r\n\r\n form_employee = EmployeeEditForm(data=data, instance=employee)\r\n\r\n if form_employee.is_valid():\r\n form_employee.save()\r\n return ApiResponse.http(status=ApiResponse.ST_SUCCESS, message='Employee successfully updated!', employee_id=employee_id)\r\n else:\r\n errors = dict(form_employee.errors)\r\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Validation errors.', errors=errors)\r\n except Employee.DoesNotExist:\r\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Invalid employee.')\r\n else:\r\n return ApiResponse.http(status=ApiResponse.ST_FORBIDDEN, message='Use Post!')","title":""},{"docid":"9bcea40c6b9b0fdaa251a9fb9aff1687","score":"0.4860615","text":"def delete_experiment(request, pk):\n\n try:\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.deleted is True:\n return Response('Experiment with given ID is already deleted', status=HTTP_400_BAD_REQUEST)\n\n experiment.deleted = True;\n experiment.save()\n\n return Response(HTTP_200_OK)","title":""},{"docid":"c3d2f2168376364b60de974564c9a455","score":"0.48573977","text":"def patch(self, incident_id):\n data = request.get_json()\n\n # script to allow only location request.body\n key_list = [*data]\n if (len(key_list) == 1 and key_list[0] == \"location\"):\n\n new_incident_instance = IncidentsModel()\n res = new_incident_instance.edit_incident(incident_id, data)\n\n if res:\n return {\n \"status\": 200,\n \"data\": [{\n \"id\": res[\"id\"],\n \"message\": \"incident record has been updated\"\n }]\n }, 200\n else:\n return {\n \"status\": 404,\n \"error\": \"Not found for id {}\".format(incident_id)\n }, 404\n else:\n return {\n \"status\": 403,\n \"error\": \"Bad Request only location is allowed\"\n }, 403","title":""},{"docid":"b76af9e574ad062068ef873994d3d48a","score":"0.48331708","text":"def update_teste_cmd(teste_id, **teste_properties):\n return UpdateTesteCommand(teste_id, **teste_properties)","title":""},{"docid":"b77d5e6d015b83efb6dd8d19931b53fe","score":"0.48324943","text":"def get_experiment(request, pk):\n\n try:\n experiment = Experiment.objects.get(pk=pk)\n except Experiment.DoesNotExist:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.deleted is True:\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\n\n if experiment.disabled is True:\n return Response('This experiment is currently unavailable', status=HTTP_423_LOCKED)\n\n if experiment.expiration < timezone.now():\n return Response('Experiment with given ID already expired', status=HTTP_400_BAD_REQUEST)\n\n serialized = ExperimentSerializer(experiment).data\n\n return Response(serialized, status=HTTP_200_OK)","title":""},{"docid":"834ae376dc46243ee0e99aa11693c662","score":"0.48319104","text":"def test_update_resolution(self):\n self.client = APIClient()\n login = self.client.login(username=self.username, password=self.password)\n self.assertEqual(login, True)\n url = reverse('resolution-detail', args=[str(self.upd_pk)])\n\n data = {\n 'description': 'Updated description',\n }\n response = self.client.put(url, data=data, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution update Failed')\n self.assertDictContainsSubset(data, response.data, \"Modified Resolution didn't contain the updated description\")\n\n #lets doublecheck we get updated data back\n response = self.client.get(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution failed to be Retrieved')\n self.assertDictContainsSubset(data, response.data, \"Resolution didn't contain the updated description\")","title":""},{"docid":"4601be0b78971f6a8aa160d438fd449c","score":"0.48254716","text":"def test_modify_report(self):\n print \"(\"+self.test_modify_report.__name__+\")\", self.test_modify_report.__doc__\n resp = self.client.put(self.url,\n data=json.dumps(self.report_1_req),\n headers={\"Content-Type\": JSON})\n self.assertEquals(resp.status_code, 204)\n #Check that the report has been modified\n resp2 = self.client.get(self.url)\n self.assertEquals(resp2.status_code, 200)\n data = json.loads(resp2.data)\n #Check that the report has been modified with the new data\n self.assertEquals(data[\"details\"], self.report_1_req[\"details\"])","title":""},{"docid":"747712a9fe8b9ca2a54838b34b33ff28","score":"0.48183075","text":"def edit(id):\n sensor = Sensor.query.get(id)\n form = SensorForm(request.form, obj=sensor)\n if request.method == 'POST':\n form.populate_obj(sensor)\n if form.validate():\n db.session.commit()\n flash('You have successfully modified the sensor.', 'success')\n return redirect(url_for('sensors.edit', id=sensor.id))\n return render_template('sensors/edit.html', form=form, sensor=sensor)","title":""},{"docid":"a19c62d0f4ff880e9eb69dbb30f04d74","score":"0.48122746","text":"def update(self, obj): \n if isinstance(obj, Entity):\n url_filter = \"v1/ai/skillEntity/name/{}\"\n else:\n url_filter = \"v1/ai/skill/name/{}\"\n url_filter = url_filter.format(obj.name)\n obj._update_json_data()\n response = self.carol.call_api(url_filter, method = 'PUT', data=obj._json_data)\n return response","title":""},{"docid":"6175d3e3097744ca2912d63fc9461e1a","score":"0.48087907","text":"def put(self, id):\n pass","title":""},{"docid":"07dd4375dada7f132f3e9aad7897d557","score":"0.48071754","text":"def update(ctx, name, image, ingredients, id):\n repo = getDrinkRepo()\n\n updateArgs = {}\n if image is not None:\n updateArgs[\"image\"] = image\n\n if ingredients is not None:\n updateArgs[\"ingredients\"] = ingredients\n\n if name is not None and name != \"\":\n updateArgs[\"name\"] = name\n\n success = False if len(updateArgs) <= 0 else repo.updateDrink(id, **updateArgs)\n\n if success:\n ctx.invoke(describe, drink=id)\n else:\n click.echo(\"Unable to update drink. Make sure the ID is right.\")","title":""},{"docid":"568612e39e13d479cbcd2c0e0e449a7d","score":"0.4804053","text":"def update_research_plan(self, id, title, description, assigned_to_id, objective, evaluation_process):\n if (id is None or id == '' or id == 0) or \\\n (title is None or title == '') or \\\n (assigned_to_id is None or assigned_to_id == '' or assigned_to_id == 0):\n raise Exception('id and title, assigned_to_id are required.')\n\n op = Operation(silverpond_schema.Mutation)\n parent = op.update_research_plan(id=id, title=title, description=description, assigned_to_id=assigned_to_id,\n objective=objective, evaluation_process=evaluation_process)\n\n # Field Set\n parent.errors()\n researchplan = parent.research_plan()\n fieldset_research_plan(researchplan)\n fieldset_experiment(researchplan.experiments())\n fieldset_research_plan_metrics(researchplan.research_plan_metrics())\n\n ql_endpoint = HTTPEndpoint(self.GRAPHQL_API_ENDPOINT, self.headers)\n data = ql_endpoint(op)\n obj = op + data\n if len(obj.update_research_plan.errors) > 0:\n raise Exception(obj.update_research_plan.errors[0])\n return obj.update_research_plan.research_plan","title":""},{"docid":"9b0ef8b4263c6629412f32c2845466ca","score":"0.47978824","text":"def edit_recipe(id):\n recipe = mongo.db.recipes.find_one({\"_id\": ObjectId(id)})\n\n if session[\"user\"] != recipe['created_by']:\n return render_template(\"403.html\")\n\n if request.method == \"POST\":\n edit = {\n \"created_by\": session[\"user\"],\n \"image_url\": request.form.get(\"image_url\"),\n \"name\": request.form.get(\"name\"),\n \"desc\": request.form.get(\"desc\"),\n \"instructions\": request.form.getlist(\"step\"),\n \"ingredients\": request.form.getlist(\"ingredient\"),\n \"amount\": request.form.getlist(\"amount\"),\n \"measure\": request.form.getlist(\"measurement\"),\n }\n mongo.db.recipes.update({\"_id\": ObjectId(id)}, edit)\n flash(\"Changes Successfully Saved\")\n\n return render_template(\"edit_recipe.html\", recipe=recipe)","title":""},{"docid":"f62bedbce7972cef7d5388864810ea47","score":"0.47965991","text":"def update(self, request, pk=None):\n raise MethodNotAllowed(method=\"PUT\")","title":""},{"docid":"f62bedbce7972cef7d5388864810ea47","score":"0.47965991","text":"def update(self, request, pk=None):\n raise MethodNotAllowed(method=\"PUT\")","title":""},{"docid":"db18595a922a3022c241d41ec0dd8953","score":"0.47930816","text":"def put(self, id):\n\n data = request.json\n update_service(id, data)\n return None, 204","title":""},{"docid":"0e53e28a07124d9371460d81cf2a1079","score":"0.4790036","text":"def get_experiment_id(self):\n return self.experiment_id","title":""},{"docid":"53e553e91fdc6114550e464616fdb09f","score":"0.47860163","text":"def experiment_id(self) -> str:\n return self._experiment_id","title":""},{"docid":"8f101fff17f778b28c18fdd5714ef7e6","score":"0.47858354","text":"def _update_details(self, details, obj):\n pass","title":""},{"docid":"8a81408420c0f78ee0daaee351debc12","score":"0.4765387","text":"def put(self, id):\n data = request.json\n Team.query.filter(Team.id == id).update({'name': data.get('name')})\n team = Team.query.filter(Team.id == id).one()\n enrollments = data.get('enrollments')\n team = add_enrollments(enrollments, team)\n\n db.session.commit()\n return team, 204","title":""}],"string":"[\n {\n \"docid\": \"a90932de5b0dd8cc3450221c5e50b85a\",\n \"score\": \"0.7416664\",\n \"text\": \"def update_experiment(request, pk):\\n\\n required_data = request.data.get(\\\"requiredData\\\")\\n tests_config = request.data.get(\\\"testsConfig\\\")\\n expiration = request.data.get(\\\"expiration\\\")\\n allow_multiple_answers = request.data.get(\\\"allowMultipleAnswers\\\")\\n disabled = request.data.get(\\\"disabled\\\")\\n name = request.data.get(\\\"name\\\")\\n\\n try:\\n print('ENTERED')\\n experiment = Experiment.objects.get(pk=pk)\\n except Experiment.DoesNotExist:\\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\\n\\n if tests_config is None or len(tests_config) == 0:\\n return Response('At least one test must be provided', status=HTTP_400_BAD_REQUEST)\\n\\n if expiration is None:\\n return Response('Valid expiration date must be provided', status=HTTP_400_BAD_REQUEST)\\n\\n try:\\n experiment.name = name\\n experiment.requiredDataConfig = required_data\\n experiment.testsConfig = tests_config\\n experiment.expiration = expiration\\n experiment.allowMultipleAnswers = allow_multiple_answers\\n experiment.disabled = disabled\\n experiment.save()\\n except Exception as e:\\n return Response(str(e), HTTP_400_BAD_REQUEST)\\n\\n serialized = ExperimentSerializer(experiment).data\\n\\n return Response(serialized, HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aace5c2053a4192cc2f99b73768ad86\",\n \"score\": \"0.67339313\",\n \"text\": \"def update_experiment_metadata(\\n writer_client, experiment_id, name=None, description=None\\n):\\n logger.info(\\\"Modifying experiment %r\\\", experiment_id)\\n request = write_service_pb2.UpdateExperimentRequest()\\n request.experiment.experiment_id = experiment_id\\n if name is not None:\\n logger.info(\\\"Setting exp %r name to %r\\\", experiment_id, name)\\n request.experiment.name = name\\n request.experiment_mask.name = True\\n if description is not None:\\n logger.info(\\n \\\"Setting exp %r description to %r\\\", experiment_id, description\\n )\\n request.experiment.description = description\\n request.experiment_mask.description = True\\n try:\\n grpc_util.call_with_retries(writer_client.UpdateExperiment, request)\\n except grpc.RpcError as e:\\n if e.code() == grpc.StatusCode.NOT_FOUND:\\n raise ExperimentNotFoundError()\\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\\n raise PermissionDeniedError()\\n if e.code() == grpc.StatusCode.INVALID_ARGUMENT:\\n raise InvalidArgumentError(e.details())\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"546f21b219d724d0964dc0327c6c25f2\",\n \"score\": \"0.66571933\",\n \"text\": \"def test_submit_edit_experiment(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\")\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n experiment = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": True,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"A\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.name, \\\"new_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfc1af097dea409e1e342c357e44dae\",\n \"score\": \"0.61671877\",\n \"text\": \"def refresh(self) -> None:\\n self.update_from_remote_data(self._api_client.experiment_get(self.uuid))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7efd2d39f68200aa5173115ccf47fd\",\n \"score\": \"0.6142266\",\n \"text\": \"def test_edit_experiment_view(self):\\n experiment = self.create_test_experiment()\\n response = self.client.get(reverse(\\\"ab_testing_tool_edit_experiment\\\", args=(experiment.id,)))\\n self.assertTemplateUsed(response, \\\"ab_tool/edit_experiment.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c08855f31c71f29263100fbc5d63505\",\n \"score\": \"0.59430104\",\n \"text\": \"def update_emp(db, id, hoursTrained, trainedStatus):\\r\\n trainingInfo = {\\\"Hours Trained\\\" : hoursTrained,\\r\\n \\\"Training Completed\\\" : trainedStatus}\\r\\n\\r\\n db.collection(\\\"Employees\\\").document(id).set(trainingInfo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58e74d8d26fab10630d7b37236175eee\",\n \"score\": \"0.5902595\",\n \"text\": \"def test_submit_edit_started_experiment_changes_name_and_notes(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\", notes=\\\"old_notes\\\",\\n tracks_finalized=True)\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n old_track = self.create_test_track(experiment=experiment, name=\\\"old_name_track\\\")\\n experiment_json = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"new_notes\\\", \\\"tracks\\\": [{\\\"id\\\": old_track.id,\\n \\\"name\\\": \\\"new_track_name\\\"}],\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment_json)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.name, \\\"new_name\\\")\\n self.assertEquals(experiment.notes, \\\"new_notes\\\")\\n self.assertEquals(experiment.tracks.all()[0].name, \\\"new_track_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b3b67f1d59d87115dd9fc81a90aab86\",\n \"score\": \"0.58990973\",\n \"text\": \"def update(self, collab_id: str, project_id: str, expt_id: str, **updates):\\n return self._execute_operation(\\n operation=\\\"put\\\",\\n url=self._generate_single_url(\\n collab_id=collab_id,\\n project_id=project_id, \\n expt_id=expt_id\\n ),\\n payload=updates\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4272881909e59c29f6e7e94aae595276\",\n \"score\": \"0.5827913\",\n \"text\": \"def update(id_exp=None):\\n\\n current_app.logger.info(\\\"PUT /sync route with id: %s\\\" % id_exp)\\n\\n (gfg_id, exp_name, session_id), session, resp = parse_id_exp(id_exp)\\n\\n # Check JSON validity\\n if utils.check_valid_json(request.get_data()):\\n valid_json = json.loads(request.get_data())\\n # session.datastring = valid_json\\n else:\\n resp = {\\\"status\\\": \\\"bad request\\\"}\\n current_app.logger.error(\\\"Invalid JSON\\\")\\n\\n current_app.logger.info(\\n \\\"Current trial: %s, unique_id: %s, experiment name: %s, session id: %s \\\" % (valid_json['currenttrial'],\\n valid_json['uniqueid'], valid_json['experimentname'], valid_json['sessionid']))\\n\\n # For each trial, pass to appropriate parser, if not in db\\n for json_trial in valid_json['data']:\\n if exp_name == \\\"category_switch\\\":\\n experiment_class = CategorySwitch\\n elif exp_name == \\\"keep_track\\\":\\n experiment_class = KeepTrack\\n else:\\n current_app.logger.error(\\\"%s does not exist\\\" % (exp_name))\\n resp = {\\\"status\\\": \\\"bad request\\\"}\\n\\n db_trial, new = db_utils.get_or_create(db.session,\\n experiment_class, gfg_id=gfg_id, session_id=session_id,\\n trial_num=json_trial['current_trial'])\\n\\n # If the trial is new, add data\\n if new:\\n db_trial.add_json_data(json_trial)\\n db.session.commit()\\n\\n # For each event, pass to parser, if not in db\\n for json_event in valid_json['eventdata']:\\n db_event, new = db_utils.get_or_create(db.session, EventData,\\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name, \\n timestamp = utils.convert_timestamp(json_event['timestamp']))\\n\\n if new:\\n db_event.add_json_data(json_event)\\n db.session.commit()\\n\\n # For the QuestionData, pass to parser, if not in db\\n for json_ques in valid_json['questiondata']:\\n db_ques, new = db_utils.get_or_create(db.session, QuestionData,\\n gfg_id=gfg_id, session_id=session_id, exp_name=exp_name)\\n\\n if new:\\n db_ques.add_json_data(json_ques)\\n db.session.commit()\\n\\n if resp is None:\\n resp = {\\\"status\\\": \\\"user data saved\\\"}\\n return jsonify(**resp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b05deea5431803e1eac4738e4cc6a36\",\n \"score\": \"0.5811839\",\n \"text\": \"def update(self, request, pk=None):\\n selected_routine = request.query_params.get('routine', None) \\n\\n try: \\n exercise = Exercise.objects.get(pk=pk)\\n\\n exercise.routine = Routine.objects.get(pk=selected_routine)\\n exercise.description = request.data['description']\\n exercise.save()\\n\\n return Response({}, status=status.HTTP_204_NO_CONTENT)\\n \\n except ValidationError as ex:\\n return Response({\\\"reason\\\": ex.message}, status=status.HTTP_400_BAD_REQUEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ea28e67ff7a830d61e1851c7da9f33\",\n \"score\": \"0.56713605\",\n \"text\": \"async def edit_training_examples(\\n intent: str,\\n id: str,\\n request_data: TextData,\\n current_user: User = Depends(auth.get_current_user),\\n):\\n mongo_processor.edit_training_example(\\n id, request_data.data, intent.lower(), current_user.get_bot(), current_user.get_user()\\n )\\n return {\\\"message\\\": \\\"Training Example updated!\\\"}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b610548abb62bf8777e59ea744a47f65\",\n \"score\": \"0.5661236\",\n \"text\": \"def put(self, exploration_id):\\n exploration = exp_services.get_exploration_by_id(exploration_id)\\n version = self.payload.get('version')\\n _require_valid_version(version, exploration.version)\\n\\n commit_message = self.payload.get('commit_message')\\n change_list = self.payload.get('change_list')\\n\\n try:\\n exp_services.update_exploration(\\n self.user_id, exploration_id, change_list, commit_message)\\n except utils.ValidationError as e:\\n raise self.InvalidInputException(e)\\n\\n self.values.update(self._get_exploration_data(exploration_id))\\n self.render_json(self.values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f444f6fc62b19e72dd10f8ea281178b\",\n \"score\": \"0.5650311\",\n \"text\": \"def update(self, entity_id, details, delete_existing):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"829b7025e64765315044ed5f1182fe55\",\n \"score\": \"0.5622866\",\n \"text\": \"def causx_edit_an_entity(id):\\n project = get_project()\\n updated_entry = request.get_json()[\\\"updated_entry\\\"]\\n entity = causx_set_variable(project, id, updated_entry)\\n response=dict(entity=entity)\\n calc_params = get_calc_params(project, data_required=False)\\n if calc_params:\\n response[\\\"layers\\\"] = get_qnodes_layer(calc_params)\\n return response, 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3a677e8ac2b6e770914054e27e7250c\",\n \"score\": \"0.56208825\",\n \"text\": \"def test_submit_edit_started_experiment_changes_existing_tracks(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\", tracks_finalized=False,\\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n track1 = self.create_test_track(experiment=experiment, name=\\\"A\\\")\\n track2 = self.create_test_track(experiment=experiment, name=\\\"B\\\")\\n self.create_test_track_weight(experiment=experiment, track=track1)\\n self.create_test_track_weight(experiment=experiment, track=track2)\\n track_count = experiment.tracks.count()\\n experiment_json = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": False,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": track1.id, \\\"weighting\\\": 30, \\\"name\\\": \\\"C\\\"},\\n {\\\"id\\\": track2.id, \\\"weighting\\\": 70, \\\"name\\\": \\\"D\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment.id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment_json)\\n )\\n self.assertOkay(response)\\n experiment = Experiment.objects.get(id=experiment.id)\\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n self.assertEquals(experiment.tracks.count(), track_count)\\n track1 = experiment.tracks.get(id=track1.id)\\n track2 = experiment.tracks.get(id=track2.id)\\n self.assertEquals(track1.name, \\\"C\\\") #Checks name has changed\\n self.assertEquals(track2.name, \\\"D\\\")\\n self.assertEquals(track1.weight.weighting, 30) #Checks weighting has changed\\n self.assertEquals(track2.weight.weighting, 70)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d99f571b3fa51ed6afda42fff2838c\",\n \"score\": \"0.56005096\",\n \"text\": \"def put(self, id):\\n data = request.get_json()\\n return update_a_reporter(id=id, data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eca505c37f3bd0881bd1ed010599a0a\",\n \"score\": \"0.559187\",\n \"text\": \"def test_edit_experiment_view_started_experiment(self):\\n experiment = self.create_test_experiment()\\n experiment.tracks_finalized = True\\n experiment.save()\\n response = self.client.get(reverse(\\\"ab_testing_tool_edit_experiment\\\", args=(experiment.id,)))\\n self.assertTemplateUsed(response, \\\"ab_tool/edit_experiment.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e11f93fd1499f93fcb17b45a21891a2d\",\n \"score\": \"0.55208725\",\n \"text\": \"def test_submit_edit_started_experiment_does_not_change_tracks(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\", tracks_finalized=True,\\n assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n no_tracks = experiment.tracks.count()\\n experiment = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": True,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"A\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"B\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"C\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n self.assertEquals(experiment.tracks.count(), no_tracks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2db940d31dbabec99f670e73eaba4b31\",\n \"score\": \"0.5507888\",\n \"text\": \"def test_submit_edit_experiment_nonexistent(self):\\n experiment_id = NONEXISTENT_EXPERIMENT_ID\\n experiment = {\\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"\\\"}\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertEquals(response.status_code, 404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214e821c70df6edb4046bfd747a7248d\",\n \"score\": \"0.5484202\",\n \"text\": \"def edit(project):\\n if g.user:\\n session['exp'] = project\\n abstract = s.get_experiment_data(g.user, project, 'abstract')\\n gl = s.get_experiment_data(g.user, project, 'gl')\\n ec = s.get_experiment_data(g.user, project, 'ec')\\n fn = s.get_experiment_data(g.user, project, 'fn')\\n return render_template('edit.html', project=project, abstract=abstract, gl=gl, ec=ec, fn=fn)\\n return redirect(url_for('login'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70ff7a5bde05f7ffc13d2ef2cf7f6ab\",\n \"score\": \"0.5455811\",\n \"text\": \"def update(self, entity_id, details, delete_existing=False):\\n self._validate_entity_id(entity_id)\\n\\n if not isinstance(details, dict):\\n msg = (_(\\\"Provided details %s is not valid dict.\\\")\\n % details)\\n raise ValueError(msg)\\n\\n return self._storage.update(\\n entity_id, details, delete_existing)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f73067644b0b8d431330b8b573696d\",\n \"score\": \"0.54527766\",\n \"text\": \"def update(self, event_id, event_time=None, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f73067644b0b8d431330b8b573696d\",\n \"score\": \"0.54527766\",\n \"text\": \"def update(self, event_id, event_time=None, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f73067644b0b8d431330b8b573696d\",\n \"score\": \"0.54527766\",\n \"text\": \"def update(self, event_id, event_time=None, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba50b0f354cac4d2eb92581c8b79298\",\n \"score\": \"0.5451184\",\n \"text\": \"def update(self, id, data):\\n return self.make_request('update', '/' + id, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e08e1e5e63528ecaa3e670768710da\",\n \"score\": \"0.54415154\",\n \"text\": \"def put(user_id,self):\\n form = ResearchInfoUpdateForm(request.form)\\n if form.validate():\\n \\n research_info = ResearchInformation.query.filter((ResearchInformation.id == form.research_id.data) & (ResearchInformation.user_id == user_id))\\n if research_info is None:\\n return make_response(jsonify({'error':'Please give an appropriate research ID'}),400)\\n update_dict = {}\\n for key, value in form.data.items():\\n if value and key != 'research_id':\\n update_dict[key] = value\\n try:\\n research_info.update(update_dict)\\n db.session.commit()\\n except:\\n return make_response(jsonify({'error' : 'Database Connection Problem'}),500)\\n return make_response(jsonify({'msg' : ' Successfully changed'}),201)\\n\\n else:\\n return make_response(jsonify({'error':'Wrong input format'}),400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83b67ad4ce2e3e31eb6207122f1a66f3\",\n \"score\": \"0.5403792\",\n \"text\": \"def put(self):\\n return employee_service.edit_employee(get_jwt_identity(), get_uuid(request), **api.payload), 201\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ac49e54bcfac440fb624d369239316\",\n \"score\": \"0.53886133\",\n \"text\": \"def _add_experiment(self, experiment_key, variation_id):\\n\\n experiment_id = self.config.get_experiment_id(experiment_key)\\n self.params[EXPERIMENT_PARAM_FORMAT.format(experiment_prefix=Params.EXPERIMENT_PREFIX,\\n experiment_id=experiment_id)] = variation_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd8bb0e660cd252ad87d2e7d500422a\",\n \"score\": \"0.53784376\",\n \"text\": \"def put(self, id):\\n\\t\\ttoken_parser.parse_args()\\n\\t\\trecord = Record.query.get(id)\\n\\t\\tif not record:\\n\\t\\t\\treturn error_response('RECORD NOT FOUND', 404) \\n\\t\\t# PERMISSION_DENIED because don't want user to know this record id exists\\n\\t\\tif record.runner.id!=int(get_jwt_identity()) and not min_access_level(self, ROLES['admin']):\\n\\t\\t\\treturn error_response('RECORD NOT FOUND', 404) \\n\\n\\t\\tdata = request.get_json() or {}\\n\\t\\ttry:\\n\\t\\t\\trecord.update(data)\\n\\t\\texcept ValueError as error:\\n\\t\\t\\treturn error_response('RECORD NOT FOUND', 404)\\n\\t\\texcept Exception as error:\\n\\t\\t\\treturn error_response('PERMISSION DENIED', 403)\\n\\t\\tdb.session.commit()\\n\\t\\treturn make_response(jsonify(record.to_dict()), 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a8c7feb5e4f073a780db8763a8c3a47\",\n \"score\": \"0.53570676\",\n \"text\": \"def test_edit_report(self):\\n report = self.create_report()\\n pk = report[0].pk\\n my_url = report_url(pk)\\n response = self.client.put(\\n my_url,\\n self.updated_report,\\n **self.headers,\\n format='json'\\n )\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c6b8e8628b2258d34781373f3756603\",\n \"score\": \"0.5355502\",\n \"text\": \"def put(self, id):\\n # Get the json object from the request body\\n data = request.get_json()\\n # Check all fields for availability and get the new data if the field is available\\n if 'email' not in data:\\n email = None\\n else:\\n email = data[\\\"email\\\"]\\n if 'first_name' not in data:\\n first_name = None\\n else:\\n first_name = data[\\\"first_name\\\"]\\n if 'last_name' not in data:\\n last_name = None\\n else:\\n last_name = data[\\\"last_name\\\"]\\n if 'availability' not in data:\\n availability = None\\n else:\\n availability = data[\\\"availability\\\"]\\n\\n # Update all requested fields of the requested interviewer, if there is an interviewer with the id, else return\\n # an error\\n interviewer = interviewer_ctrl.update_interviewer(id, email, first_name, last_name, availability)\\n if not interviewer:\\n response = \\\"Interviewer with id %s does not exist or email %s is already taken by another interviewer\\\" % (\\n id, email), 400\\n else:\\n response = interviewer, 200\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c1f7722e5e0c8697db10072db3f404\",\n \"score\": \"0.53356797\",\n \"text\": \"def put(self, id):\\n data = request.json\\n args = request.args\\n return update_unit(id, data, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a0bd39cf438b04b13e0fd2dad2b75f\",\n \"score\": \"0.5282175\",\n \"text\": \"def update_project(id=None, name=None, description=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7da3e669c6d3439e143564ac0ff1622b\",\n \"score\": \"0.5282065\",\n \"text\": \"def test_edit_experiment_view_last_modified_updated(self):\\n experiment = self.create_test_experiment()\\n experiment.name += \\\" (updated)\\\"\\n response = self.client.post(reverse(\\\"ab_testing_tool_submit_edit_experiment\\\",\\n args=(experiment.id,)),\\n content_type=\\\"application/json\\\",\\n data=experiment.to_json())\\n self.assertEquals(response.content, \\\"success\\\")\\n updated_experiment = Experiment.objects.get(id=experiment.id)\\n self.assertLess(experiment.updated_on, updated_experiment.updated_on,\\n response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63400c45bfcc9bce89953c8ae6b3701\",\n \"score\": \"0.5281295\",\n \"text\": \"def put(self, id):\\n data = request.get_json()\\n if 'email' not in data:\\n email = None\\n else:\\n email = data[\\\"email\\\"]\\n if 'first_name' not in data:\\n first_name = None\\n else:\\n first_name = data[\\\"first_name\\\"]\\n if 'last_name' not in data:\\n last_name = None\\n else:\\n last_name = data[\\\"last_name\\\"]\\n if 'availability' not in data:\\n availability = None\\n else:\\n availability = data[\\\"availability\\\"]\\n interviewee = interviewee_ctrl.update_interviewee(id, email, first_name, last_name, availability)\\n if not interviewee:\\n response = \\\"Interviewee with id %s does not exist or email %s is already taken by another interviewee\\\" % (\\n id, email), 400\\n else:\\n response = interviewee, 200\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85bb5f0f4fbddd046d215c53c2b171b\",\n \"score\": \"0.52504665\",\n \"text\": \"def put(self,request,employee_id):\\n try:\\n get_data = Employee.objects.get(pk=employee_id)\\n employee_data = EmployeeSerializer(get_data,data=request.data)\\n if not(employee_data.is_valid()):\\n return ApiResponse().error(\\\"Error while update employee details\\\",400)\\n employee_data.save()\\n return ApiResponse().success(employee_data.data,200)\\n except Exception as err:\\n print(err)\\n return ApiResponse().success(\\\"Error\\\",500)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecba54809858f5347c6dc258ddbd143b\",\n \"score\": \"0.5228171\",\n \"text\": \"def update_testimonial():\\n data = request.get_json()\\n description = data.get('description')\\n title = data.get('title')\\n image = data.get('image')\\n rank = data.get('rank')\\n testimonial_id = data.get('id')\\n\\n if (not title) or (not description) or (not rank) or (not testimonial_id):\\n return jsonify(result={'failed_msg': \\\"Unanle to save testimonial with missing fields\\\"})\\n\\n return jsonify(result=save_testimonial(title, description, rank, image, _id=testimonial_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"997ffa7f45afa19ce3c55b4b84f758f6\",\n \"score\": \"0.5227724\",\n \"text\": \"async def update(\\n self,\\n id: int,\\n data: EnvironmentUpdateSchema\\n ) -> Record:\\n\\n async with self.database.transaction():\\n query = (\\n environments_table.update()\\n .where(environments_table.c.id == id)\\n .values(\\n name=data.name,\\n description=data.description,\\n updated_at=datetime.now()\\n )\\n .returning(\\n environments_table.c.id,\\n environments_table.c.name,\\n environments_table.c.code,\\n environments_table.c.description,\\n environments_table.c.created_at,\\n environments_table.c.updated_at,\\n environments_table.c.deleted_at,\\n environments_table.c.is_deleted\\n )\\n )\\n\\n return await self.database.fetch_one(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b7edbfabd11852be3e16d3e6569fbe4\",\n \"score\": \"0.52266294\",\n \"text\": \"def update(self, model, id, update_with):\\n raise NotImplementedError('You have not implemented an update func in your client class')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f18479a5de36268b4534b705d906ee\",\n \"score\": \"0.5196483\",\n \"text\": \"def test_put_experiment_valid_permission(self):\\n url = '/' + version + '/collection/unittestcol/experiment/unittestexp/'\\n data = {'description': 'A new experiment for unit tests. Updated'}\\n\\n response = self.client.put(url, data=data)\\n self.assertEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3a3156e9bbff73bb66d428cba3cc4a\",\n \"score\": \"0.5194518\",\n \"text\": \"def test_submit_edit_experiment_changes_assignment_method_to_weighted(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\")\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n no_track_weights = experiment.track_probabilites.count()\\n experiment = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": False,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": None, \\\"weighting\\\": 20, \\\"name\\\": \\\"A\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": 80, \\\"name\\\": \\\"B\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.assignment_method, Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n self.assertEquals(experiment.track_probabilites.count(), no_track_weights + 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ca66dab826730162433142988260ed8\",\n \"score\": \"0.5188264\",\n \"text\": \"def test_submit_edit_experiment_changes_assignment_method_to_uniform(self):\\n experiment = self.create_test_experiment(\\n name=\\\"old_name\\\", assignment_method=Experiment.WEIGHTED_PROBABILITY_RANDOM)\\n experiment_id = experiment.id\\n num_experiments = Experiment.objects.count()\\n no_tracks = experiment.tracks.count()\\n experiment = {\\n \\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"hi\\\", \\\"uniformRandom\\\": True,\\n \\\"csvUpload\\\": False,\\n \\\"tracks\\\": [{\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"A\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"B\\\"},\\n {\\\"id\\\": None, \\\"weighting\\\": None, \\\"name\\\": \\\"C\\\"}]\\n }\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment_id,)),\\n follow=True, content_type=\\\"application/json\\\", data=json.dumps(experiment)\\n )\\n self.assertOkay(response)\\n self.assertEquals(num_experiments, Experiment.objects.count())\\n experiment = Experiment.objects.get(id=experiment_id)\\n self.assertEquals(experiment.assignment_method, Experiment.UNIFORM_RANDOM)\\n self.assertEquals(experiment.tracks.count(), no_tracks + 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7565e22ff55c1a4dbfaef25b7830ba89\",\n \"score\": \"0.5184008\",\n \"text\": \"def put(self, id):\\n report = self.mydb.get_by_id(id)\\n \\n if report:\\n data = request.get_json()\\n report.createdBy = data['username']\\n report.type = data['flag']\\n report.location = data['location']\\n report.status = data['status']\\n report.image = data['image']\\n report.video = data['video']\\n report.comment = ['comment']\\n return make_response(jsonify({\\n \\\"message\\\":\\\"Report edited successfully\\\",\\n \\\"data\\\": report.serialize()\\n }), 201)\\n else:\\n return make_response(jsonify({\\n \\\"message\\\": \\\"ID invalid, no report found\\\"\\n }),400)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"160cb1102a31e22433b0a95d1b19c287\",\n \"score\": \"0.5183066\",\n \"text\": \"def upload_experiment(self, experiment: Experiment) -> None:\\n data = {\\n 'device_name': experiment.backend_name,\\n 'type': experiment.type,\\n 'extra': experiment.extra,\\n }\\n if experiment.start_datetime:\\n data['start_time'] = local_to_utc_str(experiment.start_datetime)\\n if experiment.tags:\\n data['tags'] = experiment.tags\\n if experiment.uuid:\\n data['uuid'] = experiment.uuid\\n response_data = self._api_client.experiment_upload(data)\\n experiment.update_from_remote_data(response_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0650d7cb60a6083e8c9359cba962c35\",\n \"score\": \"0.517982\",\n \"text\": \"def update(self, typ, id, **kwargs):\\n return self._load(self._request(self._url(typ, id), 'PUT', kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b5e1688a20dad535a152c61484ada45\",\n \"score\": \"0.517151\",\n \"text\": \"def update(self, step_name, worker_id):\\n record = ReportClient().get_record(step_name, worker_id)\\n logging.debug(\\\"Get Record=%s\\\", str(record))\\n self.search_alg.update(record.serialize())\\n ParameterSharing().remove()\\n logging.info(f\\\"Update Success. step_name={step_name}, worker_id={worker_id}\\\")\\n logging.info(\\\"Best values: %s\\\", ReportServer().print_best(step_name=General.step_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc0ccaf21cc95495b47366271ce2579\",\n \"score\": \"0.51659554\",\n \"text\": \"def post_experiment(self):\\n if not self.has_prepared_out:\\n self.prepare_experiment()\\n\\n for exp, prod, desc in self.experiment.overview:\\n print exp, prod, desc\\n dt = json.dumps(\\\\\\n {'name': exp,\\n 'desc': desc})\\n resp = requests.post(\\n self.server_url + 'experiments/',\\n data=dt,\\n headers={'Content-type':'application/json'})\\n print resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a1a25ea3994e5d6663400ef4dc22a54\",\n \"score\": \"0.5152818\",\n \"text\": \"def edit_quiz(request_ctx, course_id, id, quiz_notify_of_update, **request_kwargs):\\n\\n path = '/v1/courses/{course_id}/quizzes/{id}'\\n payload = {\\n 'quiz[notify_of_update]' : quiz_notify_of_update,\\n }\\n url = request_ctx.base_api_url + path.format(course_id=course_id, id=id)\\n response = client.put(request_ctx, url, payload=payload, **request_kwargs)\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76aca4be1ce01e6f0e21a2963567cc01\",\n \"score\": \"0.5147998\",\n \"text\": \"def test_edit_experiment_view_nonexistent(self):\\n e_id = NONEXISTENT_EXPERIMENT_ID\\n response = self.client.get(reverse(\\\"ab_testing_tool_edit_experiment\\\", args=(e_id,)))\\n self.assertTemplateNotUsed(response, \\\"ab_tool/edit_experiment.html\\\")\\n self.assertEquals(response.status_code, 404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddb24b14a3442c8472d5c6d09956b062\",\n \"score\": \"0.51298386\",\n \"text\": \"def update_project(self, id: str, name: str = None, description: str = None) -> Dict:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9609d770f7c1fe4197f40d9c80ed4b91\",\n \"score\": \"0.5129037\",\n \"text\": \"def update_environment(context: click.Context, application_id: str, environment_id: str, name: str, url: str) -> None:\\n api = context.obj\\n application = api.application_by_id(application_id)\\n environment = api.environment_by_id(application, environment_id)\\n updated_environment = api.update_environment(application, environment, name, url)\\n table = [[\\\"ID\\\", \\\"Name\\\", \\\"URL\\\"], [updated_environment.id, updated_environment.name, updated_environment.url]]\\n click.echo(tabulate.tabulate(table, headers=\\\"firstrow\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da739f61d8dd8506648f0f235b47402d\",\n \"score\": \"0.51289636\",\n \"text\": \"def edit_experience(request, id):\\r\\n experience = Experience.objects.get(pk=id)\\r\\n context = {\\r\\n 'experience': experience\\r\\n }\\r\\n if request.method == 'POST':\\r\\n experience.user_id = request.POST['user_id' or None]\\r\\n experience.company_name = request.POST['company_name' or None]\\r\\n experience.job_title = request.POST['job_title' or None]\\r\\n experience.job_description = request.POST['job_description' or None]\\r\\n experience.year_from = request.POST['year_from' or None]\\r\\n experience.year_to = request.POST['year_to' or None]\\r\\n\\r\\n experience.save()\\r\\n messages.success(request, 'Experience Updated Successfully')\\r\\n return redirect('dashboard', request.user.pk)\\r\\n else:\\r\\n return render(request, 'accounts/edit_experience.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d62bd10f0ddf13b60680d431d1214af\",\n \"score\": \"0.5126918\",\n \"text\": \"def update_teacher(email=None, zipcode=None, bio=None, \\n mobile_number=None, days_of_week=None,teaching_experience_in_hours=None,\\n pay_rate_per_hour=None, pod_id=None, img_url=None, covid_risk_profile_id=None):\\n\\n teacher = db.session.query(Teacher).filter(Teacher.email==email).one()\\n teacher.zipcode=zipcode \\n teacher.bio=bio \\n teacher.mobile_number=mobile_number\\n teacher.days_of_week=days_of_week\\n teacher.teaching_experience_in_hours=teaching_experience_in_hours\\n teacher.pay_rate_per_hour=pay_rate_per_hour\\n teacher.pod_id=pod_id\\n teacher.img_url=img_url\\n teacher.covid_risk_profile_id=covid_risk_profile_id\\n\\n db.session.commit()\\n\\n return teacher\",\n \"title\": \"\"\n },\n {\n \"docid\": \"608457875b3bc5272855818c41796e9c\",\n \"score\": \"0.5124847\",\n \"text\": \"def edit(obj, index, description, duration, code, task_id, project_id):\\n with ots_filestore(obj) as timesheet_storage:\\n timesheet_edited = timesheet_storage.edit_timesheet(\\n index,\\n description=description,\\n duration=duration,\\n task_code=code,\\n task_id=task_id,\\n project_id=project_id,\\n )\\n if timesheet_edited:\\n click.echo('Timesheet updated.')\\n else:\\n click.echo('Nothing to update.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cbff9690ae2f28c9373856792e9b190\",\n \"score\": \"0.50883085\",\n \"text\": \"def update(self, data, id):\\n with db.session.begin_nested():\\n self.query.filter_by(id=id).update(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f165c0fcebdc404642a446e6277bbe24\",\n \"score\": \"0.5067855\",\n \"text\": \"def test_submit_edit_experiment_wrong_course(self):\\n experiment = self.create_test_experiment(name=\\\"old_name\\\",\\n course_id=TEST_OTHER_COURSE_ID)\\n data = {\\\"name\\\": \\\"new_name\\\", \\\"notes\\\": \\\"\\\"}\\n response = self.client.post(\\n reverse(\\\"ab_testing_tool_submit_edit_experiment\\\", args=(experiment.id,)),\\n content_type=\\\"application/json\\\", data=json.dumps(data)\\n )\\n self.assertError(response, UNAUTHORIZED_ACCESS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61125735c80419b89b3c2ef8fdb6cc7\",\n \"score\": \"0.50544035\",\n \"text\": \"def update_suite(suiteId):\\n\\t res = requests.post('https://api.ghostinspector.com/v1/suites/'+suiteId+'/?apiKey='+apiKey)\\n\\t return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67143faae1b601cdc1166defe338b29d\",\n \"score\": \"0.505348\",\n \"text\": \"def step_impl(context, post_id):\\n context.payload = constants.POST_INFORMATION_TO_UPDATE\\n context.response = request.put(context.url + \\\"/%s\\\" % post_id, context.payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c923e8f9fc820942ebc8185747323eb5\",\n \"score\": \"0.5051238\",\n \"text\": \"def get_experiment(experiment_id):\\n req = requests.get(\\\"{}experiments/{}/?format=json\\\".format(__base_url__, experiment_id))\\n metadata = req.json()\\n if metadata['status'] != 'error':\\n save_metadata(metadata)\\n else:\\n print 'Error fetching metadata for {}'.format(experiment_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c7f7bf6fb01612d985fa2b143d8134b\",\n \"score\": \"0.5051149\",\n \"text\": \"def wxt_update_person(person_id, data, myheaders):\\n update_person_url = 'https://api.ciscospark.com/v1/people/{0}'\\n response = requests.put(update_person_url.format(person_id),\\n data=json.dumps(data),\\n headers=myheaders)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a558e28f4fcb1556894539bd864dab7\",\n \"score\": \"0.5049372\",\n \"text\": \"def experiment_detail(request, exp_name):\\n exp = get_object_or_404(Experiment, name=exp_name)\\n reports = ExperimentReport.objects.filter(experiment=exp)\\n\\n return render_to_response(\\n \\\"splango/experiment_detail.html\\\",\\n {\\\"title\\\": exp.name, \\\"exp\\\": exp, \\\"reports\\\": reports},\\n RequestContext(request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0758bd2dc137efe10c77c05015e4b113\",\n \"score\": \"0.50357556\",\n \"text\": \"def update_movie_details(db: Session, sl_id: int, details: schema.UpdateMovie):\\n db.query(model.Movies).filter(model.Movies.id == sl_id).update(vars(details))\\n db.commit()\\n return db.query(model.Movies).filter(model.Movies.id == sl_id).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e912a37963fc2345ab045aa5222960ba\",\n \"score\": \"0.5034493\",\n \"text\": \"def update_by_id(self, id=None, **kwargs):\\n if not id:\\n raise PardotAPIArgumentError('id is required to update a prospect.')\\n response = self._post(path='/do/update/id/{id}'.format(id=id), params=kwargs)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bec9d8e407070c9a77a984f35bc91736\",\n \"score\": \"0.5032699\",\n \"text\": \"def update_test_run(self, run_update_model, project, run_id):\\n route_values = {}\\n if project is not None:\\n route_values['project'] = self._serialize.url('project', project, 'str')\\n if run_id is not None:\\n route_values['runId'] = self._serialize.url('run_id', run_id, 'int')\\n content = self._serialize.body(run_update_model, 'RunUpdateModel')\\n response = self._send(http_method='PATCH',\\n location_id='cadb3810-d47d-4a3c-a234-fe5f3be50138',\\n version='5.0',\\n route_values=route_values,\\n content=content)\\n return self._deserialize('TestRun', response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e64bd368f6696cc8782c9dac2067f6\",\n \"score\": \"0.50115055\",\n \"text\": \"def update(self, request, pk=None):\\n player = Player.objects.get(user=request.auth.user)\\n suspect = Suspect.objects.get(pk=pk)\\n suspect.player = player\\n suspect.name = request.data[\\\"name\\\"]\\n suspect.description = request.data[\\\"description\\\"]\\n guilty = Guilty.objects.get(pk=request.data[\\\"guiltyId\\\"])\\n suspect.guilty = guilty\\n movie = Movie.objects.get(pk=request.data[\\\"movieId\\\"])\\n suspect.suspect_image_url = request.data[\\\"suspectImageUrl\\\"]\\n suspect.movie = movie\\n \\n suspect.save()\\n\\n\\n return Response({}, status=status.HTTP_204_NO_CONTENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489dce730c6e0515e0c8902d35851c78\",\n \"score\": \"0.49720645\",\n \"text\": \"def update_test(testId):\\n\\t res = requests.post('https://api.ghostinspector.com/v1/tests/'+testId+'/?apiKey='+apiKey)\\n\\t return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b1c0e0826bdbdb476676fb3b9967ce7\",\n \"score\": \"0.49566084\",\n \"text\": \"def test_put_experiment_no_permissions(self):\\n url = '/' + version + '/collection/col1/experiment/exp1/'\\n data = {'description': 'A new experiment for unit tests. Updated'}\\n\\n response = self.client.put(url, data=data)\\n self.assertEqual(response.status_code, 403)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ee48539f966381bc6e042c165a54a8\",\n \"score\": \"0.49527475\",\n \"text\": \"def put(self, id):\\n return DAO.update(id, api.payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8a67ef451a5b9d3cfbff45c9f08289\",\n \"score\": \"0.49498895\",\n \"text\": \"def delete_experiment(writer_client, experiment_id):\\n logger.info(\\\"Deleting experiment %r\\\", experiment_id)\\n request = write_service_pb2.DeleteExperimentRequest()\\n request.experiment_id = experiment_id\\n try:\\n grpc_util.call_with_retries(writer_client.DeleteExperiment, request)\\n except grpc.RpcError as e:\\n if e.code() == grpc.StatusCode.NOT_FOUND:\\n raise ExperimentNotFoundError()\\n if e.code() == grpc.StatusCode.PERMISSION_DENIED:\\n raise PermissionDeniedError()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ebeb2bb4b736d0d454c31d04209770\",\n \"score\": \"0.49268797\",\n \"text\": \"def test_edit_experiment_view_with_tracks_weights(self):\\n experiment = self.create_test_experiment()\\n experiment.assignment_method = Experiment.WEIGHTED_PROBABILITY_RANDOM\\n track1 = self.create_test_track(name=\\\"track1\\\", experiment=experiment)\\n track2 = self.create_test_track(name=\\\"track2\\\", experiment=experiment)\\n self.create_test_track_weight(experiment=experiment, track=track1)\\n self.create_test_track_weight(experiment=experiment, track=track2)\\n response = self.client.get(reverse(\\\"ab_testing_tool_edit_experiment\\\", args=(experiment.id,)))\\n self.assertTemplateUsed(response, \\\"ab_tool/edit_experiment.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a83bb2f35b27541623f09db41fce462\",\n \"score\": \"0.49165675\",\n \"text\": \"def update_results(student_id, experiment_id, student_response):\\n\\n replies = get_experiment_replies(experiment_id)\\n replies_dict = json.loads(replies)\\n\\n replies_dict['replies'].append({\\\"id\\\": int(student_id), \\\"response\\\": student_response})\\n\\n new_replies = json.dumps(replies_dict)\\n\\n con = sql.connect(DB_PATH)\\n cur = con.cursor()\\n\\n cur.execute(\\\"UPDATE experiments SET replies = ? WHERE id = ?\\\", (new_replies, experiment_id))\\n\\n cur.execute(\\\"DELETE FROM experiments_students \\\"\\n \\\"WHERE student_id = ? AND experiment_id = ?\\\", (student_id, experiment_id))\\n\\n con.commit()\\n\\n con.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8b1a5247263b3eaa3ed81d6c8455d38\",\n \"score\": \"0.49110433\",\n \"text\": \"def update(self, kwargs):\\n if self._status in [DEFAULT, DETACHED]:\\n if any([k not in self.param_keys() and k in self._specials for k in kwargs]):\\n raise ValueError('Key not recognised!')\\n else:\\n self.__dict__.update(kwargs)\\n else:\\n raise ValueError('Parameters of a created experiment cannot be updated.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b058a3ccc0c492a3a2054e916ba606a3\",\n \"score\": \"0.4910217\",\n \"text\": \"def update(self, model, rec_id, data):\\n\\n url = self.base_url + model + '/' + str(rec_id)\\n data = format_write_data(data)\\n return self.session.put(url, data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3a3f85820ec6698cda066db2a4dae5\",\n \"score\": \"0.4900522\",\n \"text\": \"def detail_put(self, request, id, *args, **kwargs):\\n raise NotAllowed()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ef2def248ce0213ba3a195c608662a\",\n \"score\": \"0.48950928\",\n \"text\": \"def edit_education(request, id):\\r\\n education = Education.objects.get(pk=id)\\r\\n context = {\\r\\n 'education': education\\r\\n }\\r\\n if request.method == 'POST':\\r\\n education.user_id = request.POST['user_id' or None]\\r\\n education.school = request.POST['school' or None]\\r\\n education.field_of_study = request.POST['field_of_study' or None]\\r\\n education.qualification = request.POST['qualification' or None]\\r\\n education.year = request.POST['year' or None]\\r\\n\\r\\n education.save()\\r\\n messages.success(request, 'Education Updated Successfully, you may add another !')\\r\\n return redirect('dashboard', request.user.pk)\\r\\n else:\\r\\n return render(request, 'accounts/edit_education.html', context)\\r\\n\\r\\n # DELETE EDUCATION\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f9b95546a734ec60c96a828783d5784\",\n \"score\": \"0.4894346\",\n \"text\": \"def test_experiment_specification(self, client, ephemeral_storage):\\n\\n storage = ephemeral_storage.storage\\n\\n _add_experiment(storage, name=\\\"a\\\", version=1, _id=1)\\n _add_trial(storage, experiment=1, id_override=\\\"ae8\\\", status=\\\"completed\\\")\\n\\n response = client.simulate_get(\\\"/experiments/status/a\\\")\\n assert response.status == \\\"200 OK\\\"\\n assert response.json == {\\n \\\"trials_completed\\\": 1,\\n \\\"best_trials_id\\\": \\\"7bbfdb8c684aa5f4be324a09d7da94af\\\",\\n \\\"best_evaluation\\\": 0.05,\\n \\\"start_time\\\": \\\"0001-01-01 00:00:00\\\",\\n \\\"finish_time\\\": \\\"0001-01-02 00:00:00\\\",\\n \\\"max_trials\\\": 10,\\n \\\"nb_trials\\\": 1,\\n \\\"progress\\\": 0.1,\\n \\\"trial_status_count\\\": {\\\"completed\\\": 1},\\n \\\"elapsed_time\\\": \\\"23:59:50\\\",\\n \\\"sum_of_trials_time\\\": \\\"23:59:50\\\",\\n \\\"eta\\\": \\\"8 days, 23:58:30\\\",\\n \\\"eta_milliseconds\\\": 777510000.0,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527afa47e52ca91f15e0bf2975749899\",\n \"score\": \"0.48926717\",\n \"text\": \"def edit(id):\\n\\n pet = Pet.query.get_or_404(id)\\n form = EditPetForm(obj=pet)\\n\\n if form.validate_on_submit():\\n pet.photo_url = form.data[\\\"photo_url\\\"]\\n pet.notes = form.data[\\\"notes\\\"]\\n pet.available = form.data['available']\\n db.session.commit()\\n flash(f\\\"{pet.name} updated.\\\", 'success')\\n return redirect(url_for('index'))\\n\\n else:\\n return render_template(\\\"edit_pet_form.html\\\", form=form, pet=pet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73c9c211624982ebce62505144b3717\",\n \"score\": \"0.48906887\",\n \"text\": \"def update(self):\\n response = self.api(self.id).get()\\n self._update_fields(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aac6ddcfb59a9b9059b7ceec9897878\",\n \"score\": \"0.48723197\",\n \"text\": \"def put(self, id):\\n data = request.json\\n if data[\\\"entityID\\\"] != id:\\n return {\\\"message\\\": \\\"entityID property in the incoming json object and id parameter in the URL path are\\\"\\n \\\"not matched\\\"}, 400\\n evn = EventService.get_by_id(id)\\n if not evn:\\n return {\\\"message\\\": \\\"The event does not exist\\\"}, 404\\n else:\\n return EventService.update(data, id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee013b5fc5b6473163ae2bdd9f275cd\",\n \"score\": \"0.4865083\",\n \"text\": \"def test_experiment_specification(self, client, ephemeral_storage):\\n\\n storage = ephemeral_storage.storage\\n\\n _add_experiment(storage, name=\\\"a\\\", version=1, _id=1)\\n _add_trial(storage, experiment=1, id_override=\\\"ae8\\\", status=\\\"completed\\\")\\n\\n response = client.simulate_get(\\\"/experiments/a\\\")\\n\\n assert response.status == \\\"200 OK\\\"\\n\\n assert response.json[\\\"name\\\"] == \\\"a\\\"\\n assert response.json[\\\"version\\\"] == 1\\n assert response.json[\\\"status\\\"] == \\\"not done\\\"\\n assert response.json[\\\"trialsCompleted\\\"] == 1\\n assert response.json[\\\"startTime\\\"] == \\\"0001-01-01 00:00:00\\\" # TODO\\n assert response.json[\\\"endTime\\\"] == \\\"0001-01-02 00:00:00\\\" # TODO\\n assert len(response.json[\\\"user\\\"])\\n assert response.json[\\\"orionVersion\\\"] == \\\"x.y.z\\\"\\n\\n _assert_config(response.json[\\\"config\\\"])\\n _assert_best_trial(response.json[\\\"bestTrial\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d6f7bd49709c1e26e4af92b13dda56\",\n \"score\": \"0.48624727\",\n \"text\": \"def api_employee_edit(request):\\r\\n\\r\\n if request.method.lower() == 'post':\\r\\n\\r\\n data = request.POST.dict()\\r\\n employee_id = int(request.POST['id'])\\r\\n\\r\\n try:\\r\\n employee = Employee.objects.get(id=employee_id)\\r\\n\\r\\n form_employee = EmployeeEditForm(data=data, instance=employee)\\r\\n\\r\\n if form_employee.is_valid():\\r\\n form_employee.save()\\r\\n return ApiResponse.http(status=ApiResponse.ST_SUCCESS, message='Employee successfully updated!', employee_id=employee_id)\\r\\n else:\\r\\n errors = dict(form_employee.errors)\\r\\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Validation errors.', errors=errors)\\r\\n except Employee.DoesNotExist:\\r\\n return ApiResponse.http(status=ApiResponse.ST_FAILED, message='Invalid employee.')\\r\\n else:\\r\\n return ApiResponse.http(status=ApiResponse.ST_FORBIDDEN, message='Use Post!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcea40c6b9b0fdaa251a9fb9aff1687\",\n \"score\": \"0.4860615\",\n \"text\": \"def delete_experiment(request, pk):\\n\\n try:\\n experiment = Experiment.objects.get(pk=pk)\\n except Experiment.DoesNotExist:\\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\\n\\n if experiment.deleted is True:\\n return Response('Experiment with given ID is already deleted', status=HTTP_400_BAD_REQUEST)\\n\\n experiment.deleted = True;\\n experiment.save()\\n\\n return Response(HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3d2f2168376364b60de974564c9a455\",\n \"score\": \"0.48573977\",\n \"text\": \"def patch(self, incident_id):\\n data = request.get_json()\\n\\n # script to allow only location request.body\\n key_list = [*data]\\n if (len(key_list) == 1 and key_list[0] == \\\"location\\\"):\\n\\n new_incident_instance = IncidentsModel()\\n res = new_incident_instance.edit_incident(incident_id, data)\\n\\n if res:\\n return {\\n \\\"status\\\": 200,\\n \\\"data\\\": [{\\n \\\"id\\\": res[\\\"id\\\"],\\n \\\"message\\\": \\\"incident record has been updated\\\"\\n }]\\n }, 200\\n else:\\n return {\\n \\\"status\\\": 404,\\n \\\"error\\\": \\\"Not found for id {}\\\".format(incident_id)\\n }, 404\\n else:\\n return {\\n \\\"status\\\": 403,\\n \\\"error\\\": \\\"Bad Request only location is allowed\\\"\\n }, 403\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b76af9e574ad062068ef873994d3d48a\",\n \"score\": \"0.48331708\",\n \"text\": \"def update_teste_cmd(teste_id, **teste_properties):\\n return UpdateTesteCommand(teste_id, **teste_properties)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b77d5e6d015b83efb6dd8d19931b53fe\",\n \"score\": \"0.48324943\",\n \"text\": \"def get_experiment(request, pk):\\n\\n try:\\n experiment = Experiment.objects.get(pk=pk)\\n except Experiment.DoesNotExist:\\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\\n\\n if experiment.deleted is True:\\n return Response('Experiment with given ID not found', status=HTTP_404_NOT_FOUND)\\n\\n if experiment.disabled is True:\\n return Response('This experiment is currently unavailable', status=HTTP_423_LOCKED)\\n\\n if experiment.expiration < timezone.now():\\n return Response('Experiment with given ID already expired', status=HTTP_400_BAD_REQUEST)\\n\\n serialized = ExperimentSerializer(experiment).data\\n\\n return Response(serialized, status=HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"834ae376dc46243ee0e99aa11693c662\",\n \"score\": \"0.48319104\",\n \"text\": \"def test_update_resolution(self):\\n self.client = APIClient()\\n login = self.client.login(username=self.username, password=self.password)\\n self.assertEqual(login, True)\\n url = reverse('resolution-detail', args=[str(self.upd_pk)])\\n\\n data = {\\n 'description': 'Updated description',\\n }\\n response = self.client.put(url, data=data, format='json')\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution update Failed')\\n self.assertDictContainsSubset(data, response.data, \\\"Modified Resolution didn't contain the updated description\\\")\\n\\n #lets doublecheck we get updated data back\\n response = self.client.get(url, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK, 'Resolution failed to be Retrieved')\\n self.assertDictContainsSubset(data, response.data, \\\"Resolution didn't contain the updated description\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4601be0b78971f6a8aa160d438fd449c\",\n \"score\": \"0.48254716\",\n \"text\": \"def test_modify_report(self):\\n print \\\"(\\\"+self.test_modify_report.__name__+\\\")\\\", self.test_modify_report.__doc__\\n resp = self.client.put(self.url,\\n data=json.dumps(self.report_1_req),\\n headers={\\\"Content-Type\\\": JSON})\\n self.assertEquals(resp.status_code, 204)\\n #Check that the report has been modified\\n resp2 = self.client.get(self.url)\\n self.assertEquals(resp2.status_code, 200)\\n data = json.loads(resp2.data)\\n #Check that the report has been modified with the new data\\n self.assertEquals(data[\\\"details\\\"], self.report_1_req[\\\"details\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"747712a9fe8b9ca2a54838b34b33ff28\",\n \"score\": \"0.48183075\",\n \"text\": \"def edit(id):\\n sensor = Sensor.query.get(id)\\n form = SensorForm(request.form, obj=sensor)\\n if request.method == 'POST':\\n form.populate_obj(sensor)\\n if form.validate():\\n db.session.commit()\\n flash('You have successfully modified the sensor.', 'success')\\n return redirect(url_for('sensors.edit', id=sensor.id))\\n return render_template('sensors/edit.html', form=form, sensor=sensor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19c62d0f4ff880e9eb69dbb30f04d74\",\n \"score\": \"0.48122746\",\n \"text\": \"def update(self, obj): \\n if isinstance(obj, Entity):\\n url_filter = \\\"v1/ai/skillEntity/name/{}\\\"\\n else:\\n url_filter = \\\"v1/ai/skill/name/{}\\\"\\n url_filter = url_filter.format(obj.name)\\n obj._update_json_data()\\n response = self.carol.call_api(url_filter, method = 'PUT', data=obj._json_data)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6175d3e3097744ca2912d63fc9461e1a\",\n \"score\": \"0.48087907\",\n \"text\": \"def put(self, id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07dd4375dada7f132f3e9aad7897d557\",\n \"score\": \"0.48071754\",\n \"text\": \"def update(ctx, name, image, ingredients, id):\\n repo = getDrinkRepo()\\n\\n updateArgs = {}\\n if image is not None:\\n updateArgs[\\\"image\\\"] = image\\n\\n if ingredients is not None:\\n updateArgs[\\\"ingredients\\\"] = ingredients\\n\\n if name is not None and name != \\\"\\\":\\n updateArgs[\\\"name\\\"] = name\\n\\n success = False if len(updateArgs) <= 0 else repo.updateDrink(id, **updateArgs)\\n\\n if success:\\n ctx.invoke(describe, drink=id)\\n else:\\n click.echo(\\\"Unable to update drink. Make sure the ID is right.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"568612e39e13d479cbcd2c0e0e449a7d\",\n \"score\": \"0.4804053\",\n \"text\": \"def update_research_plan(self, id, title, description, assigned_to_id, objective, evaluation_process):\\n if (id is None or id == '' or id == 0) or \\\\\\n (title is None or title == '') or \\\\\\n (assigned_to_id is None or assigned_to_id == '' or assigned_to_id == 0):\\n raise Exception('id and title, assigned_to_id are required.')\\n\\n op = Operation(silverpond_schema.Mutation)\\n parent = op.update_research_plan(id=id, title=title, description=description, assigned_to_id=assigned_to_id,\\n objective=objective, evaluation_process=evaluation_process)\\n\\n # Field Set\\n parent.errors()\\n researchplan = parent.research_plan()\\n fieldset_research_plan(researchplan)\\n fieldset_experiment(researchplan.experiments())\\n fieldset_research_plan_metrics(researchplan.research_plan_metrics())\\n\\n ql_endpoint = HTTPEndpoint(self.GRAPHQL_API_ENDPOINT, self.headers)\\n data = ql_endpoint(op)\\n obj = op + data\\n if len(obj.update_research_plan.errors) > 0:\\n raise Exception(obj.update_research_plan.errors[0])\\n return obj.update_research_plan.research_plan\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b0ef8b4263c6629412f32c2845466ca\",\n \"score\": \"0.47978824\",\n \"text\": \"def edit_recipe(id):\\n recipe = mongo.db.recipes.find_one({\\\"_id\\\": ObjectId(id)})\\n\\n if session[\\\"user\\\"] != recipe['created_by']:\\n return render_template(\\\"403.html\\\")\\n\\n if request.method == \\\"POST\\\":\\n edit = {\\n \\\"created_by\\\": session[\\\"user\\\"],\\n \\\"image_url\\\": request.form.get(\\\"image_url\\\"),\\n \\\"name\\\": request.form.get(\\\"name\\\"),\\n \\\"desc\\\": request.form.get(\\\"desc\\\"),\\n \\\"instructions\\\": request.form.getlist(\\\"step\\\"),\\n \\\"ingredients\\\": request.form.getlist(\\\"ingredient\\\"),\\n \\\"amount\\\": request.form.getlist(\\\"amount\\\"),\\n \\\"measure\\\": request.form.getlist(\\\"measurement\\\"),\\n }\\n mongo.db.recipes.update({\\\"_id\\\": ObjectId(id)}, edit)\\n flash(\\\"Changes Successfully Saved\\\")\\n\\n return render_template(\\\"edit_recipe.html\\\", recipe=recipe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62bedbce7972cef7d5388864810ea47\",\n \"score\": \"0.47965991\",\n \"text\": \"def update(self, request, pk=None):\\n raise MethodNotAllowed(method=\\\"PUT\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f62bedbce7972cef7d5388864810ea47\",\n \"score\": \"0.47965991\",\n \"text\": \"def update(self, request, pk=None):\\n raise MethodNotAllowed(method=\\\"PUT\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db18595a922a3022c241d41ec0dd8953\",\n \"score\": \"0.47930816\",\n \"text\": \"def put(self, id):\\n\\n data = request.json\\n update_service(id, data)\\n return None, 204\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e53e28a07124d9371460d81cf2a1079\",\n \"score\": \"0.4790036\",\n \"text\": \"def get_experiment_id(self):\\n return self.experiment_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e553e91fdc6114550e464616fdb09f\",\n \"score\": \"0.47860163\",\n \"text\": \"def experiment_id(self) -> str:\\n return self._experiment_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f101fff17f778b28c18fdd5714ef7e6\",\n \"score\": \"0.47858354\",\n \"text\": \"def _update_details(self, details, obj):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a81408420c0f78ee0daaee351debc12\",\n \"score\": \"0.4765387\",\n \"text\": \"def put(self, id):\\n data = request.json\\n Team.query.filter(Team.id == id).update({'name': data.get('name')})\\n team = Team.query.filter(Team.id == id).one()\\n enrollments = data.get('enrollments')\\n team = add_enrollments(enrollments, team)\\n\\n db.session.commit()\\n return team, 204\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":181,"cells":{"query_id":{"kind":"string","value":"97251e022090c1c096acfb522379fe37"},"query":{"kind":"string","value":"Check to see if the tolerance has been met. Returns False if tolerance is met."},"positive_passages":{"kind":"list like","value":[{"docid":"18dae62af5d8fcb325b8c556954aefee","score":"0.0","text":"def check_tol(g, tol, log_scale_param):\n s = np.exp(log_scale_param)\n for tolerance, gradient in zip(tol, g):\n if abs(gradient) > tolerance / s:\n return True\n return False","title":""}],"string":"[\n {\n \"docid\": \"18dae62af5d8fcb325b8c556954aefee\",\n \"score\": \"0.0\",\n \"text\": \"def check_tol(g, tol, log_scale_param):\\n s = np.exp(log_scale_param)\\n for tolerance, gradient in zip(tol, g):\\n if abs(gradient) > tolerance / s:\\n return True\\n return False\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ce055bab6287f8377db1128076fa364f","score":"0.8071129","text":"def _tolerance_check(self, tolerance, value):\n if tolerance != None and np.linalg.norm(value) < tolerance:\n print(\"Variable update tolerance was reached. Terminating Search.\")\n return True\n return False","title":""},{"docid":"352bced7ca72b726560ef38f52549699","score":"0.7769892","text":"def __bool__(self):\n return bool(abs(self) > tolerance)","title":""},{"docid":"a1971746879f68f3648bf7f1b42f064d","score":"0.76606834","text":"def reached_tolerance(self):\n if sum(self.dp) < self.tolerance:\n rospy.logwarn('[%s] Tolerance of %f achieved kp=%f, ki=%f, kd=%f with best error: %f',\n self.name, self.tolerance, self.p[0], self.p[1], self.p[2], self.best_error)\n return True\n return False","title":""},{"docid":"f482c72478d2317b312759080e7312ba","score":"0.7599516","text":"def passed(self):\n return self.value_diff <= self.tolerance","title":""},{"docid":"5e0066709419ead4f4919127aa2b7f3d","score":"0.7546813","text":"def tolerance(computedValue,targetValue,tolerance):\n\n if abs(computedValue-targetValue)<tolerance:\n return True\n return False","title":""},{"docid":"f7a93d71559ef30aa4a60c670707cb2c","score":"0.7538815","text":"def within_tolerance(self, value: float, reference: float) -> bool:","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"0b83b4cce34827842e7ef27ba067fd22","score":"0.69806385","text":"def tolerance(self):\n return self._tolerance","title":""},{"docid":"fdc569d7b54c21a1edbc947696b74106","score":"0.6949747","text":"def CheckTolerance(self, value):\n tolerance = self._unitop.GetTolerance()\n error = self.CalculateError(value)\n if error > tolerance:\n self._unitop.PushConsistencyError(self, value)","title":""},{"docid":"5bb76f3f9c3cbbc265d428d33cda9b7b","score":"0.67973673","text":"def is_satisfied(self, tol=1.0e-6):\n return abs(self()) < tol","title":""},{"docid":"01eed1f9e6d4c494b70631c89a5020eb","score":"0.67568636","text":"def check_tolerance(self, last_obj, new_obj=0.0):\n if last_obj is not None:\n delta_obj = abs(float(new_obj) - float(last_obj))\n self.mention('delta obj ratio: %.2e' % (delta_obj / self.TOLERANCE))\n return delta_obj < self.TOLERANCE\n return False","title":""},{"docid":"1f484cc19e63f22b759133eb18b11f88","score":"0.6700461","text":"def inTolerance(self):\n ...","title":""},{"docid":"78cb818baf81b4d40eeb15a3d8f3427e","score":"0.6625738","text":"def within(a, b, tolerance=1e-5):\n if a==0 and b==0:\n return True\n elif a!=0 and b==0:\n #Avoid divide by zero\n return False\n else:\n return abs((a/b)-1) < tolerance","title":""},{"docid":"df68bc8c4fad2af46f1f98168c37eb99","score":"0.6526349","text":"def check_difference(shift1, shift2, tolerance=0.05):\r\n mean_diff = (shift1[1] - shift2[1]) ** 2\r\n sigma_diff = (shift1[2] + shift2[2]) ** 2\r\n res = mean_diff > sigma_diff\r\n if abs(shift1[1] - shift2[1]) < tolerance:\r\n res = False\r\n return res","title":""},{"docid":"052e05a351af76d3d07aabb0d11920b3","score":"0.6519439","text":"def check_difference(shift1, shift2, tolerance=0.05):\n mean_diff = (shift1[1] - shift2[1]) ** 2\n sigma_diff = (shift1[2] + shift2[2]) ** 2\n res = mean_diff > sigma_diff\n if abs(shift1[1] - shift2[1]) < tolerance:\n res = False\n return res","title":""},{"docid":"54b7b3c683e3287d731d3fc658b0b49e","score":"0.65151095","text":"def approx_eq(x, y, tolerance=1e-5):\n return abs(x - y) < tolerance","title":""},{"docid":"b21b7db657d408a524edb16b0a76a8c5","score":"0.64969355","text":"def relaxed_allclose(x, y, atol=0.01, pct_wrong_allowable=0.1):\n res = torch.isclose(x, y, atol=atol)\n n_wrong = res.numel() - res.sum()\n n_wrong_allowable = pct_wrong_allowable * res.numel()\n return n_wrong <= n_wrong_allowable","title":""},{"docid":"c98b6d633c65381e53c549fda9118dc5","score":"0.64799714","text":"def match(self, other, tolerance=3, amtdev=0.00):\n if (tolerance == ()):\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev)\n else:\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev) and \\\n (abs(self.date-other.date) <= datetime.timedelta(days=tolerance))","title":""},{"docid":"66b0b45511093d926ef07d9c350ee676","score":"0.64740145","text":"def check_convergence(self):\n return False","title":""},{"docid":"f179475d3fa9daa37883a197df0fde02","score":"0.647181","text":"def _check_convergence(self, tol):\n if hasattr(self, 'coeffs_prev'):\n if torch.abs(self.coeffs - self.coeffs_prev).max() < tol:\n return True\n self.coeffs_prev = self.coeffs.detach().clone()\n return False","title":""},{"docid":"cafa9423f1cf921e7a02d68925ddd74a","score":"0.6433718","text":"def check_convergence(new_measure, old_measure, direction, threshold):\n\n sign = 1.0 if direction == 'higher' else -1.0\n\n if sign * (new_measure - old_measure) / old_measure < threshold:\n return True\n else:\n return False","title":""},{"docid":"c897e612f977361f4bbbb71eeb1adec3","score":"0.6422954","text":"def converged(self):\n\n top_lnls = [abs(x['lnL']) for x in self.estimates()][0:2]\n\n # dif = (top_lnls[0] - top_lnls[1]) / (0.5 * (top_lnls[0] + top_lnls[1]))\n dif = top_lnls[0] - top_lnls[1]\n\n if abs(dif) < 0.1:\n return True\n\n else:\n return False","title":""},{"docid":"fae09a0e92a3b5f68c494935dbbc3b46","score":"0.6420245","text":"def tolerance(*args, **kwargs):\n pass","title":""},{"docid":"dfa65081f2e310884cc7d5b8c35c0003","score":"0.63993156","text":"def _check_tolerance(self, content, name, value, mos_file):\n\n if (name + \"=\" == \"tolerance=\" and float(value) > 1e-6):\n self._wrong_parameter(mos_file, name, value)","title":""},{"docid":"97ba0fccb0fd64a1b60f0c57d8f1ae49","score":"0.6392067","text":"def is_not_close_to(self, other, tolerance):\n self._validate_close_to_args(self.val, other, tolerance)\n\n if self.val >= (other-tolerance) and self.val <= (other+tolerance):\n if type(self.val) is datetime.datetime:\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\n h, rem = divmod(tolerance_seconds, 3600)\n m, s = divmod(rem, 60)\n self._err('Expected <%s> to not be close to <%s> within tolerance <%d:%02d:%02d>, but was.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\n else:\n self._err('Expected <%s> to not be close to <%s> within tolerance <%s>, but was.' % (self.val, other, tolerance))\n return self","title":""},{"docid":"598e173b3b57da6b873a58422c1b89d2","score":"0.6380607","text":"def check_below_threshold(distance, epsilon):\n return distance < epsilon","title":""},{"docid":"ad4e1852afe57b0efaf7eb41c4d73868","score":"0.63688385","text":"def test_equal_time(t1, t2, tol=1E-3):\n if abs(t1) <= tol:\n res = abs(t1 - t2) <= tol\n else:\n res = abs(t1 - t2) <= tol * abs(t1)\n return res","title":""},{"docid":"55450f03a699b012ba500303f637fc20","score":"0.6359971","text":"def almost_less(x, y, rtol=1e-14, atol=1e-14):\n return x - y <= atol + np.abs(y) * rtol","title":""},{"docid":"f0ba06fef13eccb4c5e30f8990ea616b","score":"0.6354376","text":"def get_tolerance():\n return parameters.TOLERANCE","title":""},{"docid":"ea156158ab9f5d2aaf9fc4bdbf001e45","score":"0.6347623","text":"def tolerance(self, tol):\n self._tolerance = tol","title":""},{"docid":"ea156158ab9f5d2aaf9fc4bdbf001e45","score":"0.6347623","text":"def tolerance(self, tol):\n self._tolerance = tol","title":""},{"docid":"25409b090e1ef08c7cd44cce91d0d556","score":"0.6339426","text":"def require_accurate_evaluation(self, noisy_val):\n # Check if the point improves over existing points, \n # or if it could be optimal according to tolerances. \n # In this case, perform a double evaluation.\n best_possible = self.fmin\n if ((noisy_val <= best_possible -\n self.l_settings.eps_impr*max(1.0, abs(best_possible))) or\n (noisy_val <= self.l_settings.target_objval +\n self.l_settings.eps_opt*abs(self.l_settings.target_objval))):\n return True\n else:\n return False","title":""},{"docid":"c7e85303ea78fbed7177461921ba142e","score":"0.63144785","text":"def assertNowish(self, timestamp, tolerance=1):\n delta = datetime.now() - timestamp \n self.assertTrue(delta.seconds <= tolerance)","title":""},{"docid":"66fe57c29b7f2dc32be82220469ad628","score":"0.63110167","text":"def is_almost_equal(num_1, num_2):\n threshold = 0.03\n if abs(num_1 - num_2) < threshold:\n return True\n else:\n return False","title":""},{"docid":"e96566cf853f4af55a1845bc40f16ae7","score":"0.6305576","text":"def close_to(self, other, tol=1e-3):\n for diff in self.iter_differences(other):\n if abs(diff) > tol:\n return False\n return True","title":""},{"docid":"97ec08d14fe9c90904c986522080af42","score":"0.62932694","text":"def validate(self, maze, tolerance):\n pos = maze.turtlebot_pos\n\n # all negative positions are invalid\n if any(np.asarray(pos) < 0):\n return False\n elif self.state_pixel is None:\n return True\n # if the position does not change dramatically, then it is valid\n elif np.sum(abs(self.state_pixel - pos)) < tolerance:\n return True\n else:\n return False","title":""},{"docid":"c729962a8c959955b7ce8295b5bcdf55","score":"0.6289345","text":"def tolerance(self):\n return self.settings.hu_tolerance","title":""},{"docid":"c729962a8c959955b7ce8295b5bcdf55","score":"0.6289345","text":"def tolerance(self):\n return self.settings.hu_tolerance","title":""},{"docid":"59c0b8ed0b1ce02b75778415e3dc7bb9","score":"0.628875","text":"def tol(self):\n\n return self._tol","title":""},{"docid":"4e00eb39f18c5d9c9b7cfb437e6f9f4d","score":"0.62810886","text":"def is_goal(self, tolerance):\n \n # if self.goal_pixel is not None:\n # print(\"Distance to goal: \", np.linalg.norm(self.goal_pixel - self.state_pixel))\n return (self.goal_pixel is not None) and np.linalg.norm(self.goal_pixel - self.state_pixel) < tolerance","title":""},{"docid":"e0b5989ad0bf9e75eb987728af518a83","score":"0.6276838","text":"def make_check_values(self, value_calculated:np.array, value_returned:np.array) -> np.ndarray:\n return all(abs(x-y) <= self.eps for x, y in zip(value_calculated, value_returned))","title":""},{"docid":"4e4c8aba4006b0dfda076ed8c32293b3","score":"0.62742877","text":"def converged(self, rtol, atol):\n return self.err < rtol * abs(self.mean) + atol","title":""},{"docid":"81a8fd864474efc6c9860a1953ce10be","score":"0.6266887","text":"def checkAnswer(comment,value,expected,tol=1e-7,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True","title":""},{"docid":"7c499c9f239a76df6adfde8157564e6c","score":"0.6262354","text":"def close_to_exceeding(self) -> bool:\n if self.max_cuts is not None and self.num_cuts >= self.max_cuts:\n return True\n\n thresh = self.longest_seen\n\n if self.max_duration is not None:\n return self.current + thresh >= self.max_duration - 1e-3 # float precision\n return False","title":""},{"docid":"ca048b2f7eed20824b9ec4952b296ff1","score":"0.626226","text":"def testEqualityInvalid(self):\n self.assertNotEqual(mox.IsAlmost(1.899), 1.9)","title":""},{"docid":"3aa76e22b72faa026c4886b1c1042820","score":"0.62560344","text":"def approx_less_eq(number1, number2):\n if number1 < number2 or abs(number1-number2) < 0.0001:\n return True\n else:\n return False","title":""},{"docid":"b20ed3b6e514e81eed5fc1f427ae2ed8","score":"0.62403435","text":"def is_fulfilled(self, tolerance=1e-2) -> bool:\r\n self.satisfied = True\r\n ianionsite = 0\r\n for isite, site in enumerate(self.lse.structure):\r\n if self.lse.valences[isite] < 0:\r\n if (abs(self.anions_bond_strengths[ianionsite]['bond_strengths_sum'] - 2.0)) > tolerance:\r\n self.satisfied = False\r\n\r\n ianionsite = ianionsite + 1\r\n\r\n return self.satisfied","title":""},{"docid":"7ba8649b0078c87521af46929105746a","score":"0.6238806","text":"def should_stop(cls, interval_delta, middle_point, epsilon_rel, epsilon_abs):\n return abs(interval_delta) <= epsilon_rel * abs(middle_point) + epsilon_abs","title":""},{"docid":"16a938f0aa9ae53b0660ef67c3505ce2","score":"0.6237505","text":"def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True","title":""},{"docid":"16a938f0aa9ae53b0660ef67c3505ce2","score":"0.6237505","text":"def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\n if abs(value - expected) > tol:\n print(\"checking answer\",comment,value,\"!=\",expected)\n if updateResults:\n results[\"fail\"] += 1\n return False\n else:\n if updateResults:\n results[\"pass\"] += 1\n return True","title":""},{"docid":"bf8e86ebc3665ab1a22e210725724cc2","score":"0.6235275","text":"def first_failure(self) -> bool:\n if self.error_count == (self._tolerance + 1):\n return True\n return False","title":""},{"docid":"862ceb422d3e20a1aa4297ca1f61debc","score":"0.6231477","text":"def overTolerance(self):\n ...","title":""},{"docid":"761e8922afafb75e52b936db13da0302","score":"0.62229717","text":"def _check_convergence(self, ll_old = None, ll_new = None):\n\t\tif np.isnan(ll_old) or np.isnan(ll_new):\n\t\t\tif np.sum(np.fabs(self.score)) < self.tol:\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\treturn 0\n\t\telse:\n\t\t\tif np.abs(ll_old - ll_new) < self.tol:\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\treturn 0","title":""},{"docid":"42818455aef80a63675e73aa5aa1ce48","score":"0.62129897","text":"def passed(self):\n return self.nominal_length_mm - self.tolerance < self.length_mm < self.nominal_length_mm + self.tolerance","title":""},{"docid":"ff6845c40999f73e9c0068282d2d8d90","score":"0.6206111","text":"def satisfied(self):\n if self.current_t is None:\n return False\n return self.current_t >= self.target_t","title":""},{"docid":"9e43890b5778c1c8e01bd2caef100059","score":"0.62052995","text":"def check_within_epsilon(true_values, estimated_values, epsilon):\n for i in range(len(true_values)):\n if not is_within_epsilon(true_values[i], estimated_values[i], epsilon):\n return False\n return True","title":""},{"docid":"6003c157d1137bfc396a53043442dd09","score":"0.6186162","text":"def _check_termination(self):\n convergence_radius = 0.1\n\n abs_dist = self._get_dist_to_goal()\n if (abs_dist < convergence_radius):\n logging.debug(\"done - success!\")\n return True\n if (self.num_steps > self.MAX_STEPS):\n logging.debug(\"done - max steps reached\")\n logging.debug(\"final delta to goal \\t{}\".format(abs_dist))\n return True\n else:\n return False","title":""},{"docid":"c3968786a87d2361ac9a3411b5ce4ad9","score":"0.6183106","text":"def is_convergence(self, eps=1e-6):\n return 1.0 - self.convergence() < eps","title":""},{"docid":"81a478e3f5c74597d60defb839359e63","score":"0.6168033","text":"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n\treturn abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"6297d904a6c5ecb1de360644656c890f","score":"0.61669457","text":"def check(self, point):\n value = self.calculate_value(point)\n\n if self._equation:\n if math.fabs(value) > EPSILON:\n return False\n return True\n else:\n if value >= 0:\n return True\n return False","title":""},{"docid":"3c466c5bf1a782b9ebeae9e830d5d875","score":"0.61604327","text":"def result_is_close_enough(self, result):\n return (np.linalg.norm(result) / np.linalg.norm(self.b)) < self.precision","title":""},{"docid":"4648393a7a9f827347aea27d99f85684","score":"0.6160404","text":"def converged(self):\n # This insures that the cost has been computed at least twice without checking iterations\n try:\n is_converged = abs(self.cost[-2] - self.cost[-1]) <= self.tol\n except IndexError:\n is_converged = False\n return is_converged","title":""},{"docid":"3ce06aa43bb5ad73239467f4103b9ed0","score":"0.6154088","text":"def withinEpsilon(x, y, epsilon):\r\n return abs(x - y) <= epsilon","title":""},{"docid":"b2fc040f3290c78528ec01bdab21f953","score":"0.6151508","text":"def check_answer(self, x, ftol):\n if (self.lb is not None and np.any(x < self.lb) or\n self.ub is not None and np.any(x > self.ub)):\n return False\n\n f = np.sum(self.fun(x) ** 2)\n return f < (1 + ftol) * self.fopt","title":""},{"docid":"3751932a2738cc6efda700136490a635","score":"0.6150774","text":"def withinToleranceOfGoal(self, possible_node, goal_point, tolerance_m):\n dx, dy = goal_point.x - possible_node.point.x, goal_point.y - possible_node.point.y\n dist = math.sqrt(dx * dx + dy * dy)\n if dist <= tolerance_m:\n return True\n return False","title":""},{"docid":"7877ad34d7fdb9f8246a47f4d0f4651c","score":"0.6150306","text":"def pass_tol(self):\n pass_tol = self.stat < self.tol\n return pass_tol","title":""},{"docid":"9b5665e2b8bf3a5e9a7c07c7467ccc21","score":"0.6146118","text":"def on_curve(self, curve, tol=1e-6):\n return self.distance_to_point(curve.closest_point(self)) < tol","title":""},{"docid":"d7d2de72638ff94b890520db4a3ee527","score":"0.614382","text":"def is_close_to(self, other, tolerance):\n self._validate_close_to_args(self.val, other, tolerance)\n\n if self.val < (other-tolerance) or self.val > (other+tolerance):\n if type(self.val) is datetime.datetime:\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\n h, rem = divmod(tolerance_seconds, 3600)\n m, s = divmod(rem, 60)\n self._err('Expected <%s> to be close to <%s> within tolerance <%d:%02d:%02d>, but was not.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\n else:\n self._err('Expected <%s> to be close to <%s> within tolerance <%s>, but was not.' % (self.val, other, tolerance))\n return self","title":""},{"docid":"1d3859dbee4ccea0f8a16f0893fceca7","score":"0.61364233","text":"def tolerance(focuser):\n return 0","title":""},{"docid":"ea3c1cea00539b49b223f0de89c48038","score":"0.6133284","text":"def win(self):\n return abs(self) < 0.4","title":""},{"docid":"e0075ced987e952c0fe93c9be7232991","score":"0.6107463","text":"def is_satisfied(\n self,\n x: \"CartesianCoordinates\",\n tol: float = 1e-4,\n ) -> bool:\n return abs(self.delta(x)) < tol","title":""},{"docid":"b7d54b5fe2b7b828ebba13bce6f9f414","score":"0.61056906","text":"def approx_equal(x, y, atol=.01):\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol","title":""},{"docid":"b7d54b5fe2b7b828ebba13bce6f9f414","score":"0.61056906","text":"def approx_equal(x, y, atol=.01):\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol","title":""},{"docid":"3a1f4bdc249cb1e97c3ff44c4588e500","score":"0.6102711","text":"def check_convergence(\n prev_loss,\n cur_loss,\n cur_step,\n iter_below_tol,\n abs_loss_chg_tol=0.001,\n min_num_iter=100,\n max_iter_below_tol=50\n):\n has_converged = False\n\n # Check if we have reached the desired loss tolerance.\n loss_diff = abs(prev_loss - cur_loss)\n if loss_diff < abs_loss_chg_tol:\n iter_below_tol += 1\n else:\n iter_below_tol = 0\n\n if iter_below_tol >= max_iter_below_tol:\n has_converged = True\n\n if cur_step < min_num_iter:\n has_converged = False\n\n return has_converged, iter_below_tol","title":""},{"docid":"eff80409fd268c1870d26d9ad330a405","score":"0.6100981","text":"def same(x,y):\n print x,y\n if abs(x-y) < 0.000001:\n return True\n else:\n return False","title":""},{"docid":"666dc76b0995415179aa619056de6e3d","score":"0.6099752","text":"def test_rmsd_convergence(self):\n\n # http://en.wikipedia.org/wiki/\n # Non-linear_least_squares#Convergence_criteria\n try:\n r1 = self.history[\"rmsd\"][-1]\n r2 = self.history[\"rmsd\"][-2]\n except IndexError:\n return False\n\n tests = [\n abs((r2[0] - r1[0]) / r2[0]) < self._rmsd_tolerance if r2[0] > 0 else True\n ]\n\n return all(tests)","title":""},{"docid":"f56d59064290fa3d889a3e8c5b3948dc","score":"0.6090564","text":"def withinEpsilon(x, y, epsilon):\n\t\t return abs(x - y) <= epsilon","title":""},{"docid":"3b136615e4b36f758caaa630be12e493","score":"0.60889125","text":"def is_time_around(expected_time, loop=None, delta=.01):\n now = loop.time() if loop else time.time()\n return (expected_time - delta) <= now <= (expected_time + delta)","title":""},{"docid":"53bc44057f3e8cfc7bf7c71e9074e729","score":"0.608647","text":"def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:\n return abs(value_1 - value_2) <= delta","title":""},{"docid":"5a4e870bbde087be74dc9e5b654a6cda","score":"0.6082421","text":"def check_below_threshold(distance, epsilon):\n\n accepted = False\n for i in range(len(epsilon)):\n if epsilon[i] >= distance[i] >= 0:\n accepted = True\n else:\n accepted = False\n break\n return accepted","title":""},{"docid":"fcd33f8d6dc55e034f7e449659c687d8","score":"0.60819095","text":"def isViolated(self):\n upVar = self._findValue(\"upVar\")\n lowVar = self._findValue(\"lowVar\")\n freeVar = self._findValue(\"freeVar\")\n result = abs(upVar + lowVar) >= const.EPS\n if result:\n log.debug(\n \"isViolated %s, upVar %s, lowVar %s, freeVar %s result %s\"\n % (self.name, upVar, lowVar, freeVar, result)\n )\n log.debug(f\"isViolated value lhs {self.findLHSValue()} constant {self.RHS}\")\n return result","title":""},{"docid":"3e153b1e868caa0e2263a65e3d9fd63d","score":"0.60777473","text":"def check_pass_or_fail(self,\n epsilon_failure_tolerance =np.nan, epsilon_failure_tolerance_default =None,\n non_finite_data_tolerance =np.nan, non_finite_data_tolerance_default =None,\n total_data_failure_tolerance=np.nan, total_data_failure_tolerance_default=None,\n min_acceptable_r_squared =np.nan, min_acceptable_r_squared_default =None\n ) :\n\n passValues = [ ]\n \n # test the epsilon value tolerance\n \n # get the tolerance for failures compared to epsilon\n epsilonTolerance = epsilon_failure_tolerance if epsilon_failure_tolerance is not np.nan else epsilon_failure_tolerance_default\n\n # did we fail based on the epsilon?\n failed_fraction = self.comparison.diff_outside_epsilon_fraction\n passed_epsilon = None if (epsilonTolerance is None) else (failed_fraction <= epsilonTolerance)\n passValues.append(passed_epsilon)\n\n # test the nonfinite tolerance\n \n # get the tolerance for failures in amount of nonfinite data (in spatially valid areas)\n nonfiniteTolerance = non_finite_data_tolerance if non_finite_data_tolerance is not np.nan else non_finite_data_tolerance_default\n \n # did we fail based on nonfinite data\n non_finite_diff_fraction = self.finiteData.finite_in_only_one_fraction\n passed_nonfinite = None if (nonfiniteTolerance is None) else (non_finite_diff_fraction <= nonfiniteTolerance)\n passValues.append(passed_nonfinite)\n\n # test if the total failed percentage is acceptable\n \n # get the total percentage of failed data that is acceptable\n totalFailTolerance = total_data_failure_tolerance if total_data_failure_tolerance is not np.nan else total_data_failure_tolerance_default\n \n # did we fail based on all data failures?\n passed_all_percentage = None if (totalFailTolerance is None) else ((non_finite_diff_fraction + failed_fraction) <= totalFailTolerance)\n passValues.append(passed_all_percentage)\n\n # test the r-squared correlation coefficent\n \n # get the minimum acceptable r-squared correlation coefficient\n min_r_squared = min_acceptable_r_squared if (min_acceptable_r_squared is not np.nan) else min_acceptable_r_squared_default\n\n # did we fail based on the r-squared correlation coefficient?\n r_squared_value = None if (min_r_squared is None) else self.comparison.r_squared_correlation\n passed_r_squared = None if (min_r_squared is None) else (r_squared_value >= min_r_squared)\n passValues.append(passed_r_squared)\n\n # figure out the overall pass/fail result\n didPass = None\n for passValue in passValues :\n # if passValue isn't none, we need to update didPass\n if passValue is not None :\n if didPass is not None :\n didPass = passValue and didPass\n else :\n didPass = passValue\n \n return didPass, failed_fraction, non_finite_diff_fraction, r_squared_value","title":""},{"docid":"dd3456078c758c8906845f50e0b61fcb","score":"0.60761505","text":"def check_distractor_distance_threshold(self, goal, gripper):\n self.current_norm_distance = self.calc_distance(goal, gripper)\n threshold = 0.1\n return self.current_norm_distance < threshold","title":""},{"docid":"3355029114166bf81231fd0d2d801c5d","score":"0.6064035","text":"def isclose(a, b, rel_tol=1e-04, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"32e334610a88894d3aaa5bac1639704b","score":"0.6063143","text":"def testEqualityValid(self):\n self.assertEqual(mox.IsAlmost(1.8999999999), 1.9)","title":""},{"docid":"92458285d0dafe3180d047c506589c65","score":"0.6051033","text":"def assert_approximately_equal(computed_potential, expected_potential, tolerance=ENERGY_TOLERANCE):\n\n # Compute error.\n error = (computed_potential - expected_potential)\n\n # Raise an exception if the error is larger than the tolerance.\n if abs(error) > tolerance:\n raise Exception(\"Computed potential %s, expected %s. Error %s is larger than acceptable tolerance of %s.\" % (computed_potential, expected_potential, error, tolerance))\n \n return","title":""},{"docid":"a1b3eccca005b97c8d678bf85206ff99","score":"0.6048701","text":"def approx_equal(x,y):\n if abs(x-y) < 0.00001:\n return True\n else:\n return False","title":""},{"docid":"fcbdd67840da82380b3179d13588b9e7","score":"0.60423213","text":"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"fcbdd67840da82380b3179d13588b9e7","score":"0.60423213","text":"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"bb6d4c5d6a88c6cdeaabba4604521bb3","score":"0.6019731","text":"def is_almost_equal(x, y, epsilon=1 * 10 ** (-8)):\r\n return abs(x - y) <= epsilon","title":""},{"docid":"c6f0f831144f4f4bc4e2b444dda49c07","score":"0.6016614","text":"def terminate(fitness, tolerance):\n # for i in fitness:\n # if abs((2**(1.0 / 2)) - i) < tolerance:\n # return True\n return False","title":""},{"docid":"4736fe1b1e1ae33fa99ac5411b20e523","score":"0.6011596","text":"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)","title":""},{"docid":"45f49e14c0c463fd5b66be61780bae72","score":"0.60109454","text":"def isViolated(self):\n if abs(value(self.denominator)) >= const.EPS:\n if self.lowTarget is not None:\n if self.lowTarget > self.findLHSValue():\n return True\n if self.upTarget is not None:\n if self.findLHSValue() > self.upTarget:\n return True\n else:\n # if the denominator is zero the constraint is satisfied\n return False","title":""},{"docid":"2f14dead98b6ba6800efd45cdbe674d5","score":"0.6010444","text":"def float_cmp(x, y, rtol=1e-14, atol=1e-14):\n return np.abs(x - y) <= atol + np.abs(y) * rtol","title":""},{"docid":"636a908b2b82f32903e61aae0d60938c","score":"0.5998174","text":"def approx_equal(a, b, tol=1e-9):\n return fabs(a-b) <= max(fabs(a), fabs(b)) * tol","title":""},{"docid":"ced2eec7247f91ef01d988715d9d6ab9","score":"0.5985774","text":"def is_close_enough(x, y):\n\n return abs(x - y) < 0.0001","title":""},{"docid":"4d7d4ae19a201d3f531de04477e5da05","score":"0.5979146","text":"def _check_epsilon(epsilon):\n if not (isinstance(epsilon, float) and 1e-5 <= epsilon <= 0.3):\n print(\"> ERROR: epsilon must be a float between 0.3 and 10^-5\")\n return False\n\n return True","title":""},{"docid":"ffcf0711a0d3f286558a560e3298c417","score":"0.59740263","text":"def getTolerance(self):\n ...","title":""}],"string":"[\n {\n \"docid\": \"ce055bab6287f8377db1128076fa364f\",\n \"score\": \"0.8071129\",\n \"text\": \"def _tolerance_check(self, tolerance, value):\\n if tolerance != None and np.linalg.norm(value) < tolerance:\\n print(\\\"Variable update tolerance was reached. Terminating Search.\\\")\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"352bced7ca72b726560ef38f52549699\",\n \"score\": \"0.7769892\",\n \"text\": \"def __bool__(self):\\n return bool(abs(self) > tolerance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1971746879f68f3648bf7f1b42f064d\",\n \"score\": \"0.76606834\",\n \"text\": \"def reached_tolerance(self):\\n if sum(self.dp) < self.tolerance:\\n rospy.logwarn('[%s] Tolerance of %f achieved kp=%f, ki=%f, kd=%f with best error: %f',\\n self.name, self.tolerance, self.p[0], self.p[1], self.p[2], self.best_error)\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f482c72478d2317b312759080e7312ba\",\n \"score\": \"0.7599516\",\n \"text\": \"def passed(self):\\n return self.value_diff <= self.tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0066709419ead4f4919127aa2b7f3d\",\n \"score\": \"0.7546813\",\n \"text\": \"def tolerance(computedValue,targetValue,tolerance):\\n\\n if abs(computedValue-targetValue)<tolerance:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a93d71559ef30aa4a60c670707cb2c\",\n \"score\": \"0.7538815\",\n \"text\": \"def within_tolerance(self, value: float, reference: float) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b83b4cce34827842e7ef27ba067fd22\",\n \"score\": \"0.69806385\",\n \"text\": \"def tolerance(self):\\n return self._tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc569d7b54c21a1edbc947696b74106\",\n \"score\": \"0.6949747\",\n \"text\": \"def CheckTolerance(self, value):\\n tolerance = self._unitop.GetTolerance()\\n error = self.CalculateError(value)\\n if error > tolerance:\\n self._unitop.PushConsistencyError(self, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb76f3f9c3cbbc265d428d33cda9b7b\",\n \"score\": \"0.67973673\",\n \"text\": \"def is_satisfied(self, tol=1.0e-6):\\n return abs(self()) < tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01eed1f9e6d4c494b70631c89a5020eb\",\n \"score\": \"0.67568636\",\n \"text\": \"def check_tolerance(self, last_obj, new_obj=0.0):\\n if last_obj is not None:\\n delta_obj = abs(float(new_obj) - float(last_obj))\\n self.mention('delta obj ratio: %.2e' % (delta_obj / self.TOLERANCE))\\n return delta_obj < self.TOLERANCE\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f484cc19e63f22b759133eb18b11f88\",\n \"score\": \"0.6700461\",\n \"text\": \"def inTolerance(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78cb818baf81b4d40eeb15a3d8f3427e\",\n \"score\": \"0.6625738\",\n \"text\": \"def within(a, b, tolerance=1e-5):\\n if a==0 and b==0:\\n return True\\n elif a!=0 and b==0:\\n #Avoid divide by zero\\n return False\\n else:\\n return abs((a/b)-1) < tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df68bc8c4fad2af46f1f98168c37eb99\",\n \"score\": \"0.6526349\",\n \"text\": \"def check_difference(shift1, shift2, tolerance=0.05):\\r\\n mean_diff = (shift1[1] - shift2[1]) ** 2\\r\\n sigma_diff = (shift1[2] + shift2[2]) ** 2\\r\\n res = mean_diff > sigma_diff\\r\\n if abs(shift1[1] - shift2[1]) < tolerance:\\r\\n res = False\\r\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"052e05a351af76d3d07aabb0d11920b3\",\n \"score\": \"0.6519439\",\n \"text\": \"def check_difference(shift1, shift2, tolerance=0.05):\\n mean_diff = (shift1[1] - shift2[1]) ** 2\\n sigma_diff = (shift1[2] + shift2[2]) ** 2\\n res = mean_diff > sigma_diff\\n if abs(shift1[1] - shift2[1]) < tolerance:\\n res = False\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b7b3c683e3287d731d3fc658b0b49e\",\n \"score\": \"0.65151095\",\n \"text\": \"def approx_eq(x, y, tolerance=1e-5):\\n return abs(x - y) < tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21b7db657d408a524edb16b0a76a8c5\",\n \"score\": \"0.64969355\",\n \"text\": \"def relaxed_allclose(x, y, atol=0.01, pct_wrong_allowable=0.1):\\n res = torch.isclose(x, y, atol=atol)\\n n_wrong = res.numel() - res.sum()\\n n_wrong_allowable = pct_wrong_allowable * res.numel()\\n return n_wrong <= n_wrong_allowable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98b6d633c65381e53c549fda9118dc5\",\n \"score\": \"0.64799714\",\n \"text\": \"def match(self, other, tolerance=3, amtdev=0.00):\\n if (tolerance == ()):\\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev)\\n else:\\n return (abs(abs(self.amount) - abs(other.amount)) <= amtdev) and \\\\\\n (abs(self.date-other.date) <= datetime.timedelta(days=tolerance))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b0b45511093d926ef07d9c350ee676\",\n \"score\": \"0.64740145\",\n \"text\": \"def check_convergence(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f179475d3fa9daa37883a197df0fde02\",\n \"score\": \"0.647181\",\n \"text\": \"def _check_convergence(self, tol):\\n if hasattr(self, 'coeffs_prev'):\\n if torch.abs(self.coeffs - self.coeffs_prev).max() < tol:\\n return True\\n self.coeffs_prev = self.coeffs.detach().clone()\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cafa9423f1cf921e7a02d68925ddd74a\",\n \"score\": \"0.6433718\",\n \"text\": \"def check_convergence(new_measure, old_measure, direction, threshold):\\n\\n sign = 1.0 if direction == 'higher' else -1.0\\n\\n if sign * (new_measure - old_measure) / old_measure < threshold:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c897e612f977361f4bbbb71eeb1adec3\",\n \"score\": \"0.6422954\",\n \"text\": \"def converged(self):\\n\\n top_lnls = [abs(x['lnL']) for x in self.estimates()][0:2]\\n\\n # dif = (top_lnls[0] - top_lnls[1]) / (0.5 * (top_lnls[0] + top_lnls[1]))\\n dif = top_lnls[0] - top_lnls[1]\\n\\n if abs(dif) < 0.1:\\n return True\\n\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae09a0e92a3b5f68c494935dbbc3b46\",\n \"score\": \"0.6420245\",\n \"text\": \"def tolerance(*args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfa65081f2e310884cc7d5b8c35c0003\",\n \"score\": \"0.63993156\",\n \"text\": \"def _check_tolerance(self, content, name, value, mos_file):\\n\\n if (name + \\\"=\\\" == \\\"tolerance=\\\" and float(value) > 1e-6):\\n self._wrong_parameter(mos_file, name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ba0fccb0fd64a1b60f0c57d8f1ae49\",\n \"score\": \"0.6392067\",\n \"text\": \"def is_not_close_to(self, other, tolerance):\\n self._validate_close_to_args(self.val, other, tolerance)\\n\\n if self.val >= (other-tolerance) and self.val <= (other+tolerance):\\n if type(self.val) is datetime.datetime:\\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\\n h, rem = divmod(tolerance_seconds, 3600)\\n m, s = divmod(rem, 60)\\n self._err('Expected <%s> to not be close to <%s> within tolerance <%d:%02d:%02d>, but was.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\\n else:\\n self._err('Expected <%s> to not be close to <%s> within tolerance <%s>, but was.' % (self.val, other, tolerance))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598e173b3b57da6b873a58422c1b89d2\",\n \"score\": \"0.6380607\",\n \"text\": \"def check_below_threshold(distance, epsilon):\\n return distance < epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad4e1852afe57b0efaf7eb41c4d73868\",\n \"score\": \"0.63688385\",\n \"text\": \"def test_equal_time(t1, t2, tol=1E-3):\\n if abs(t1) <= tol:\\n res = abs(t1 - t2) <= tol\\n else:\\n res = abs(t1 - t2) <= tol * abs(t1)\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55450f03a699b012ba500303f637fc20\",\n \"score\": \"0.6359971\",\n \"text\": \"def almost_less(x, y, rtol=1e-14, atol=1e-14):\\n return x - y <= atol + np.abs(y) * rtol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ba06fef13eccb4c5e30f8990ea616b\",\n \"score\": \"0.6354376\",\n \"text\": \"def get_tolerance():\\n return parameters.TOLERANCE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea156158ab9f5d2aaf9fc4bdbf001e45\",\n \"score\": \"0.6347623\",\n \"text\": \"def tolerance(self, tol):\\n self._tolerance = tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea156158ab9f5d2aaf9fc4bdbf001e45\",\n \"score\": \"0.6347623\",\n \"text\": \"def tolerance(self, tol):\\n self._tolerance = tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25409b090e1ef08c7cd44cce91d0d556\",\n \"score\": \"0.6339426\",\n \"text\": \"def require_accurate_evaluation(self, noisy_val):\\n # Check if the point improves over existing points, \\n # or if it could be optimal according to tolerances. \\n # In this case, perform a double evaluation.\\n best_possible = self.fmin\\n if ((noisy_val <= best_possible -\\n self.l_settings.eps_impr*max(1.0, abs(best_possible))) or\\n (noisy_val <= self.l_settings.target_objval +\\n self.l_settings.eps_opt*abs(self.l_settings.target_objval))):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7e85303ea78fbed7177461921ba142e\",\n \"score\": \"0.63144785\",\n \"text\": \"def assertNowish(self, timestamp, tolerance=1):\\n delta = datetime.now() - timestamp \\n self.assertTrue(delta.seconds <= tolerance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66fe57c29b7f2dc32be82220469ad628\",\n \"score\": \"0.63110167\",\n \"text\": \"def is_almost_equal(num_1, num_2):\\n threshold = 0.03\\n if abs(num_1 - num_2) < threshold:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96566cf853f4af55a1845bc40f16ae7\",\n \"score\": \"0.6305576\",\n \"text\": \"def close_to(self, other, tol=1e-3):\\n for diff in self.iter_differences(other):\\n if abs(diff) > tol:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97ec08d14fe9c90904c986522080af42\",\n \"score\": \"0.62932694\",\n \"text\": \"def validate(self, maze, tolerance):\\n pos = maze.turtlebot_pos\\n\\n # all negative positions are invalid\\n if any(np.asarray(pos) < 0):\\n return False\\n elif self.state_pixel is None:\\n return True\\n # if the position does not change dramatically, then it is valid\\n elif np.sum(abs(self.state_pixel - pos)) < tolerance:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c729962a8c959955b7ce8295b5bcdf55\",\n \"score\": \"0.6289345\",\n \"text\": \"def tolerance(self):\\n return self.settings.hu_tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c729962a8c959955b7ce8295b5bcdf55\",\n \"score\": \"0.6289345\",\n \"text\": \"def tolerance(self):\\n return self.settings.hu_tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59c0b8ed0b1ce02b75778415e3dc7bb9\",\n \"score\": \"0.628875\",\n \"text\": \"def tol(self):\\n\\n return self._tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e00eb39f18c5d9c9b7cfb437e6f9f4d\",\n \"score\": \"0.62810886\",\n \"text\": \"def is_goal(self, tolerance):\\n \\n # if self.goal_pixel is not None:\\n # print(\\\"Distance to goal: \\\", np.linalg.norm(self.goal_pixel - self.state_pixel))\\n return (self.goal_pixel is not None) and np.linalg.norm(self.goal_pixel - self.state_pixel) < tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b5989ad0bf9e75eb987728af518a83\",\n \"score\": \"0.6276838\",\n \"text\": \"def make_check_values(self, value_calculated:np.array, value_returned:np.array) -> np.ndarray:\\n return all(abs(x-y) <= self.eps for x, y in zip(value_calculated, value_returned))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e4c8aba4006b0dfda076ed8c32293b3\",\n \"score\": \"0.62742877\",\n \"text\": \"def converged(self, rtol, atol):\\n return self.err < rtol * abs(self.mean) + atol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a8fd864474efc6c9860a1953ce10be\",\n \"score\": \"0.6266887\",\n \"text\": \"def checkAnswer(comment,value,expected,tol=1e-7,updateResults=True):\\n if abs(value - expected) > tol:\\n print(\\\"checking answer\\\",comment,value,\\\"!=\\\",expected)\\n if updateResults:\\n results[\\\"fail\\\"] += 1\\n return False\\n else:\\n if updateResults:\\n results[\\\"pass\\\"] += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c499c9f239a76df6adfde8157564e6c\",\n \"score\": \"0.6262354\",\n \"text\": \"def close_to_exceeding(self) -> bool:\\n if self.max_cuts is not None and self.num_cuts >= self.max_cuts:\\n return True\\n\\n thresh = self.longest_seen\\n\\n if self.max_duration is not None:\\n return self.current + thresh >= self.max_duration - 1e-3 # float precision\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca048b2f7eed20824b9ec4952b296ff1\",\n \"score\": \"0.626226\",\n \"text\": \"def testEqualityInvalid(self):\\n self.assertNotEqual(mox.IsAlmost(1.899), 1.9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aa76e22b72faa026c4886b1c1042820\",\n \"score\": \"0.62560344\",\n \"text\": \"def approx_less_eq(number1, number2):\\n if number1 < number2 or abs(number1-number2) < 0.0001:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20ed3b6e514e81eed5fc1f427ae2ed8\",\n \"score\": \"0.62403435\",\n \"text\": \"def is_fulfilled(self, tolerance=1e-2) -> bool:\\r\\n self.satisfied = True\\r\\n ianionsite = 0\\r\\n for isite, site in enumerate(self.lse.structure):\\r\\n if self.lse.valences[isite] < 0:\\r\\n if (abs(self.anions_bond_strengths[ianionsite]['bond_strengths_sum'] - 2.0)) > tolerance:\\r\\n self.satisfied = False\\r\\n\\r\\n ianionsite = ianionsite + 1\\r\\n\\r\\n return self.satisfied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba8649b0078c87521af46929105746a\",\n \"score\": \"0.6238806\",\n \"text\": \"def should_stop(cls, interval_delta, middle_point, epsilon_rel, epsilon_abs):\\n return abs(interval_delta) <= epsilon_rel * abs(middle_point) + epsilon_abs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a938f0aa9ae53b0660ef67c3505ce2\",\n \"score\": \"0.6237505\",\n \"text\": \"def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\\n if abs(value - expected) > tol:\\n print(\\\"checking answer\\\",comment,value,\\\"!=\\\",expected)\\n if updateResults:\\n results[\\\"fail\\\"] += 1\\n return False\\n else:\\n if updateResults:\\n results[\\\"pass\\\"] += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a938f0aa9ae53b0660ef67c3505ce2\",\n \"score\": \"0.6237505\",\n \"text\": \"def checkAnswer(comment,value,expected,tol=1e-10,updateResults=True):\\n if abs(value - expected) > tol:\\n print(\\\"checking answer\\\",comment,value,\\\"!=\\\",expected)\\n if updateResults:\\n results[\\\"fail\\\"] += 1\\n return False\\n else:\\n if updateResults:\\n results[\\\"pass\\\"] += 1\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8e86ebc3665ab1a22e210725724cc2\",\n \"score\": \"0.6235275\",\n \"text\": \"def first_failure(self) -> bool:\\n if self.error_count == (self._tolerance + 1):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862ceb422d3e20a1aa4297ca1f61debc\",\n \"score\": \"0.6231477\",\n \"text\": \"def overTolerance(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"761e8922afafb75e52b936db13da0302\",\n \"score\": \"0.62229717\",\n \"text\": \"def _check_convergence(self, ll_old = None, ll_new = None):\\n\\t\\tif np.isnan(ll_old) or np.isnan(ll_new):\\n\\t\\t\\tif np.sum(np.fabs(self.score)) < self.tol:\\n\\t\\t\\t\\treturn 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\tif np.abs(ll_old - ll_new) < self.tol:\\n\\t\\t\\t\\treturn 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42818455aef80a63675e73aa5aa1ce48\",\n \"score\": \"0.62129897\",\n \"text\": \"def passed(self):\\n return self.nominal_length_mm - self.tolerance < self.length_mm < self.nominal_length_mm + self.tolerance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6845c40999f73e9c0068282d2d8d90\",\n \"score\": \"0.6206111\",\n \"text\": \"def satisfied(self):\\n if self.current_t is None:\\n return False\\n return self.current_t >= self.target_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e43890b5778c1c8e01bd2caef100059\",\n \"score\": \"0.62052995\",\n \"text\": \"def check_within_epsilon(true_values, estimated_values, epsilon):\\n for i in range(len(true_values)):\\n if not is_within_epsilon(true_values[i], estimated_values[i], epsilon):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6003c157d1137bfc396a53043442dd09\",\n \"score\": \"0.6186162\",\n \"text\": \"def _check_termination(self):\\n convergence_radius = 0.1\\n\\n abs_dist = self._get_dist_to_goal()\\n if (abs_dist < convergence_radius):\\n logging.debug(\\\"done - success!\\\")\\n return True\\n if (self.num_steps > self.MAX_STEPS):\\n logging.debug(\\\"done - max steps reached\\\")\\n logging.debug(\\\"final delta to goal \\\\t{}\\\".format(abs_dist))\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3968786a87d2361ac9a3411b5ce4ad9\",\n \"score\": \"0.6183106\",\n \"text\": \"def is_convergence(self, eps=1e-6):\\n return 1.0 - self.convergence() < eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a478e3f5c74597d60defb839359e63\",\n \"score\": \"0.6168033\",\n \"text\": \"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\\n\\treturn abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6297d904a6c5ecb1de360644656c890f\",\n \"score\": \"0.61669457\",\n \"text\": \"def check(self, point):\\n value = self.calculate_value(point)\\n\\n if self._equation:\\n if math.fabs(value) > EPSILON:\\n return False\\n return True\\n else:\\n if value >= 0:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c466c5bf1a782b9ebeae9e830d5d875\",\n \"score\": \"0.61604327\",\n \"text\": \"def result_is_close_enough(self, result):\\n return (np.linalg.norm(result) / np.linalg.norm(self.b)) < self.precision\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4648393a7a9f827347aea27d99f85684\",\n \"score\": \"0.6160404\",\n \"text\": \"def converged(self):\\n # This insures that the cost has been computed at least twice without checking iterations\\n try:\\n is_converged = abs(self.cost[-2] - self.cost[-1]) <= self.tol\\n except IndexError:\\n is_converged = False\\n return is_converged\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce06aa43bb5ad73239467f4103b9ed0\",\n \"score\": \"0.6154088\",\n \"text\": \"def withinEpsilon(x, y, epsilon):\\r\\n return abs(x - y) <= epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2fc040f3290c78528ec01bdab21f953\",\n \"score\": \"0.6151508\",\n \"text\": \"def check_answer(self, x, ftol):\\n if (self.lb is not None and np.any(x < self.lb) or\\n self.ub is not None and np.any(x > self.ub)):\\n return False\\n\\n f = np.sum(self.fun(x) ** 2)\\n return f < (1 + ftol) * self.fopt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3751932a2738cc6efda700136490a635\",\n \"score\": \"0.6150774\",\n \"text\": \"def withinToleranceOfGoal(self, possible_node, goal_point, tolerance_m):\\n dx, dy = goal_point.x - possible_node.point.x, goal_point.y - possible_node.point.y\\n dist = math.sqrt(dx * dx + dy * dy)\\n if dist <= tolerance_m:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7877ad34d7fdb9f8246a47f4d0f4651c\",\n \"score\": \"0.6150306\",\n \"text\": \"def pass_tol(self):\\n pass_tol = self.stat < self.tol\\n return pass_tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b5665e2b8bf3a5e9a7c07c7467ccc21\",\n \"score\": \"0.6146118\",\n \"text\": \"def on_curve(self, curve, tol=1e-6):\\n return self.distance_to_point(curve.closest_point(self)) < tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d2de72638ff94b890520db4a3ee527\",\n \"score\": \"0.614382\",\n \"text\": \"def is_close_to(self, other, tolerance):\\n self._validate_close_to_args(self.val, other, tolerance)\\n\\n if self.val < (other-tolerance) or self.val > (other+tolerance):\\n if type(self.val) is datetime.datetime:\\n tolerance_seconds = tolerance.days * 86400 + tolerance.seconds + tolerance.microseconds / 1000000\\n h, rem = divmod(tolerance_seconds, 3600)\\n m, s = divmod(rem, 60)\\n self._err('Expected <%s> to be close to <%s> within tolerance <%d:%02d:%02d>, but was not.' % (self.val.strftime('%Y-%m-%d %H:%M:%S'), other.strftime('%Y-%m-%d %H:%M:%S'), h, m, s))\\n else:\\n self._err('Expected <%s> to be close to <%s> within tolerance <%s>, but was not.' % (self.val, other, tolerance))\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3859dbee4ccea0f8a16f0893fceca7\",\n \"score\": \"0.61364233\",\n \"text\": \"def tolerance(focuser):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3c1cea00539b49b223f0de89c48038\",\n \"score\": \"0.6133284\",\n \"text\": \"def win(self):\\n return abs(self) < 0.4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0075ced987e952c0fe93c9be7232991\",\n \"score\": \"0.6107463\",\n \"text\": \"def is_satisfied(\\n self,\\n x: \\\"CartesianCoordinates\\\",\\n tol: float = 1e-4,\\n ) -> bool:\\n return abs(self.delta(x)) < tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d54b5fe2b7b828ebba13bce6f9f414\",\n \"score\": \"0.61056906\",\n \"text\": \"def approx_equal(x, y, atol=.01):\\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d54b5fe2b7b828ebba13bce6f9f414\",\n \"score\": \"0.61056906\",\n \"text\": \"def approx_equal(x, y, atol=.01):\\n return abs(x - y) <= 1e-2 * abs(x) or abs(x - y) <= 1e-2 * abs(y) or abs(x - y) <= atol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a1f4bdc249cb1e97c3ff44c4588e500\",\n \"score\": \"0.6102711\",\n \"text\": \"def check_convergence(\\n prev_loss,\\n cur_loss,\\n cur_step,\\n iter_below_tol,\\n abs_loss_chg_tol=0.001,\\n min_num_iter=100,\\n max_iter_below_tol=50\\n):\\n has_converged = False\\n\\n # Check if we have reached the desired loss tolerance.\\n loss_diff = abs(prev_loss - cur_loss)\\n if loss_diff < abs_loss_chg_tol:\\n iter_below_tol += 1\\n else:\\n iter_below_tol = 0\\n\\n if iter_below_tol >= max_iter_below_tol:\\n has_converged = True\\n\\n if cur_step < min_num_iter:\\n has_converged = False\\n\\n return has_converged, iter_below_tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff80409fd268c1870d26d9ad330a405\",\n \"score\": \"0.6100981\",\n \"text\": \"def same(x,y):\\n print x,y\\n if abs(x-y) < 0.000001:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"666dc76b0995415179aa619056de6e3d\",\n \"score\": \"0.6099752\",\n \"text\": \"def test_rmsd_convergence(self):\\n\\n # http://en.wikipedia.org/wiki/\\n # Non-linear_least_squares#Convergence_criteria\\n try:\\n r1 = self.history[\\\"rmsd\\\"][-1]\\n r2 = self.history[\\\"rmsd\\\"][-2]\\n except IndexError:\\n return False\\n\\n tests = [\\n abs((r2[0] - r1[0]) / r2[0]) < self._rmsd_tolerance if r2[0] > 0 else True\\n ]\\n\\n return all(tests)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f56d59064290fa3d889a3e8c5b3948dc\",\n \"score\": \"0.6090564\",\n \"text\": \"def withinEpsilon(x, y, epsilon):\\n\\t\\t return abs(x - y) <= epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b136615e4b36f758caaa630be12e493\",\n \"score\": \"0.60889125\",\n \"text\": \"def is_time_around(expected_time, loop=None, delta=.01):\\n now = loop.time() if loop else time.time()\\n return (expected_time - delta) <= now <= (expected_time + delta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53bc44057f3e8cfc7bf7c71e9074e729\",\n \"score\": \"0.608647\",\n \"text\": \"def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:\\n return abs(value_1 - value_2) <= delta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a4e870bbde087be74dc9e5b654a6cda\",\n \"score\": \"0.6082421\",\n \"text\": \"def check_below_threshold(distance, epsilon):\\n\\n accepted = False\\n for i in range(len(epsilon)):\\n if epsilon[i] >= distance[i] >= 0:\\n accepted = True\\n else:\\n accepted = False\\n break\\n return accepted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd33f8d6dc55e034f7e449659c687d8\",\n \"score\": \"0.60819095\",\n \"text\": \"def isViolated(self):\\n upVar = self._findValue(\\\"upVar\\\")\\n lowVar = self._findValue(\\\"lowVar\\\")\\n freeVar = self._findValue(\\\"freeVar\\\")\\n result = abs(upVar + lowVar) >= const.EPS\\n if result:\\n log.debug(\\n \\\"isViolated %s, upVar %s, lowVar %s, freeVar %s result %s\\\"\\n % (self.name, upVar, lowVar, freeVar, result)\\n )\\n log.debug(f\\\"isViolated value lhs {self.findLHSValue()} constant {self.RHS}\\\")\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e153b1e868caa0e2263a65e3d9fd63d\",\n \"score\": \"0.60777473\",\n \"text\": \"def check_pass_or_fail(self,\\n epsilon_failure_tolerance =np.nan, epsilon_failure_tolerance_default =None,\\n non_finite_data_tolerance =np.nan, non_finite_data_tolerance_default =None,\\n total_data_failure_tolerance=np.nan, total_data_failure_tolerance_default=None,\\n min_acceptable_r_squared =np.nan, min_acceptable_r_squared_default =None\\n ) :\\n\\n passValues = [ ]\\n \\n # test the epsilon value tolerance\\n \\n # get the tolerance for failures compared to epsilon\\n epsilonTolerance = epsilon_failure_tolerance if epsilon_failure_tolerance is not np.nan else epsilon_failure_tolerance_default\\n\\n # did we fail based on the epsilon?\\n failed_fraction = self.comparison.diff_outside_epsilon_fraction\\n passed_epsilon = None if (epsilonTolerance is None) else (failed_fraction <= epsilonTolerance)\\n passValues.append(passed_epsilon)\\n\\n # test the nonfinite tolerance\\n \\n # get the tolerance for failures in amount of nonfinite data (in spatially valid areas)\\n nonfiniteTolerance = non_finite_data_tolerance if non_finite_data_tolerance is not np.nan else non_finite_data_tolerance_default\\n \\n # did we fail based on nonfinite data\\n non_finite_diff_fraction = self.finiteData.finite_in_only_one_fraction\\n passed_nonfinite = None if (nonfiniteTolerance is None) else (non_finite_diff_fraction <= nonfiniteTolerance)\\n passValues.append(passed_nonfinite)\\n\\n # test if the total failed percentage is acceptable\\n \\n # get the total percentage of failed data that is acceptable\\n totalFailTolerance = total_data_failure_tolerance if total_data_failure_tolerance is not np.nan else total_data_failure_tolerance_default\\n \\n # did we fail based on all data failures?\\n passed_all_percentage = None if (totalFailTolerance is None) else ((non_finite_diff_fraction + failed_fraction) <= totalFailTolerance)\\n passValues.append(passed_all_percentage)\\n\\n # test the r-squared correlation coefficent\\n \\n # get the minimum acceptable r-squared correlation coefficient\\n min_r_squared = min_acceptable_r_squared if (min_acceptable_r_squared is not np.nan) else min_acceptable_r_squared_default\\n\\n # did we fail based on the r-squared correlation coefficient?\\n r_squared_value = None if (min_r_squared is None) else self.comparison.r_squared_correlation\\n passed_r_squared = None if (min_r_squared is None) else (r_squared_value >= min_r_squared)\\n passValues.append(passed_r_squared)\\n\\n # figure out the overall pass/fail result\\n didPass = None\\n for passValue in passValues :\\n # if passValue isn't none, we need to update didPass\\n if passValue is not None :\\n if didPass is not None :\\n didPass = passValue and didPass\\n else :\\n didPass = passValue\\n \\n return didPass, failed_fraction, non_finite_diff_fraction, r_squared_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd3456078c758c8906845f50e0b61fcb\",\n \"score\": \"0.60761505\",\n \"text\": \"def check_distractor_distance_threshold(self, goal, gripper):\\n self.current_norm_distance = self.calc_distance(goal, gripper)\\n threshold = 0.1\\n return self.current_norm_distance < threshold\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3355029114166bf81231fd0d2d801c5d\",\n \"score\": \"0.6064035\",\n \"text\": \"def isclose(a, b, rel_tol=1e-04, abs_tol=0.0):\\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32e334610a88894d3aaa5bac1639704b\",\n \"score\": \"0.6063143\",\n \"text\": \"def testEqualityValid(self):\\n self.assertEqual(mox.IsAlmost(1.8999999999), 1.9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92458285d0dafe3180d047c506589c65\",\n \"score\": \"0.6051033\",\n \"text\": \"def assert_approximately_equal(computed_potential, expected_potential, tolerance=ENERGY_TOLERANCE):\\n\\n # Compute error.\\n error = (computed_potential - expected_potential)\\n\\n # Raise an exception if the error is larger than the tolerance.\\n if abs(error) > tolerance:\\n raise Exception(\\\"Computed potential %s, expected %s. Error %s is larger than acceptable tolerance of %s.\\\" % (computed_potential, expected_potential, error, tolerance))\\n \\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1b3eccca005b97c8d678bf85206ff99\",\n \"score\": \"0.6048701\",\n \"text\": \"def approx_equal(x,y):\\n if abs(x-y) < 0.00001:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbdd67840da82380b3179d13588b9e7\",\n \"score\": \"0.60423213\",\n \"text\": \"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbdd67840da82380b3179d13588b9e7\",\n \"score\": \"0.60423213\",\n \"text\": \"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb6d4c5d6a88c6cdeaabba4604521bb3\",\n \"score\": \"0.6019731\",\n \"text\": \"def is_almost_equal(x, y, epsilon=1 * 10 ** (-8)):\\r\\n return abs(x - y) <= epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f0f831144f4f4bc4e2b444dda49c07\",\n \"score\": \"0.6016614\",\n \"text\": \"def terminate(fitness, tolerance):\\n # for i in fitness:\\n # if abs((2**(1.0 / 2)) - i) < tolerance:\\n # return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4736fe1b1e1ae33fa99ac5411b20e523\",\n \"score\": \"0.6011596\",\n \"text\": \"def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\\n return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f49e14c0c463fd5b66be61780bae72\",\n \"score\": \"0.60109454\",\n \"text\": \"def isViolated(self):\\n if abs(value(self.denominator)) >= const.EPS:\\n if self.lowTarget is not None:\\n if self.lowTarget > self.findLHSValue():\\n return True\\n if self.upTarget is not None:\\n if self.findLHSValue() > self.upTarget:\\n return True\\n else:\\n # if the denominator is zero the constraint is satisfied\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f14dead98b6ba6800efd45cdbe674d5\",\n \"score\": \"0.6010444\",\n \"text\": \"def float_cmp(x, y, rtol=1e-14, atol=1e-14):\\n return np.abs(x - y) <= atol + np.abs(y) * rtol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"636a908b2b82f32903e61aae0d60938c\",\n \"score\": \"0.5998174\",\n \"text\": \"def approx_equal(a, b, tol=1e-9):\\n return fabs(a-b) <= max(fabs(a), fabs(b)) * tol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ced2eec7247f91ef01d988715d9d6ab9\",\n \"score\": \"0.5985774\",\n \"text\": \"def is_close_enough(x, y):\\n\\n return abs(x - y) < 0.0001\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d7d4ae19a201d3f531de04477e5da05\",\n \"score\": \"0.5979146\",\n \"text\": \"def _check_epsilon(epsilon):\\n if not (isinstance(epsilon, float) and 1e-5 <= epsilon <= 0.3):\\n print(\\\"> ERROR: epsilon must be a float between 0.3 and 10^-5\\\")\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffcf0711a0d3f286558a560e3298c417\",\n \"score\": \"0.59740263\",\n \"text\": \"def getTolerance(self):\\n ...\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":182,"cells":{"query_id":{"kind":"string","value":"3267fcb6ce40a8d0595e6979dd8f0ae1"},"query":{"kind":"string","value":"Return filenames that have a spcified set of extensions."},"positive_passages":{"kind":"list like","value":[{"docid":"841a54f260628de898d042118a6d2804","score":"0.671288","text":"def filenameMatchesAListOfExtensions(filename, extensionList=None):\n if extensionList is not None:\n for currExt in extensionList:\n if filename.lower().endswith(currExt.lower()):\n return True\n return False","title":""}],"string":"[\n {\n \"docid\": \"841a54f260628de898d042118a6d2804\",\n \"score\": \"0.671288\",\n \"text\": \"def filenameMatchesAListOfExtensions(filename, extensionList=None):\\n if extensionList is not None:\\n for currExt in extensionList:\\n if filename.lower().endswith(currExt.lower()):\\n return True\\n return False\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"980d3d0380131c6070595da9574d0e44","score":"0.7548063","text":"def get_files(dirname, extensions=['.png', '.tif', '.jpg']):\n dir_path = Path(dirname)\n\n files = dir_path.glob('**/*')\n\n files = [path.resolve() for path in files]\n\n match = [f for f in files if f.suffix in extensions]\n return match","title":""},{"docid":"756bf2887c3883a51543e44a722ba14b","score":"0.74919146","text":"def ext_filter(directory, exts, ABS_PATHS = True):\n if isinstance(exts,list) or isinstance(exts,tuple):\n exts = list(exts)\n if ABS_PATHS:\n return [opj(directory,file) for file in os.listdir(directory) if \n os.path.splitext(file)[1] in exts]\n else:\n return [file for file in os.listdir(directory) if \n os.path.splitext(file)[1] in exts]","title":""},{"docid":"59861aab08ffbe5d5f4534e9f1ede444","score":"0.73087174","text":"def filter_by_extension(ext):\n def filt(filename):\n return filename.endswith(ext)\n return filt","title":""},{"docid":"1418fdb60218040b473b134833d3322a","score":"0.7305053","text":"def get_files_with_extension(dir, names, ext):\n list = []\n for name in names:\n e = os.path.splitext(name)[1]\n if e == ext:\n list.append(name)\n list.sort()\n return [os.path.join(dir, n) for n in list]","title":""},{"docid":"b4aa8be8863df9601ac1abf7504c66b8","score":"0.726645","text":"def _get_allowed_extensions():\n return [\n ext.lower()\n for ext, file_type in Image.EXTENSION.items()\n if file_type.upper() in MIME_TYPE_TO_PIL_IDENTIFIER.values()\n ]","title":""},{"docid":"aaae5bf503325a9a14f507df67b18904","score":"0.7261796","text":"def filter_by_suffix(files):\n if includesuffix:\n for insuffix in includesuffix:\n extname = '*.' + insuffix\n if fnmatch.fnmatch(files, extname):\n return True\n\n if excludesuffix:\n for exsuffix in excludesuffix:\n extname = '*.' + exsuffix\n if fnmatch.fnmatch(files, extname):\n return False\n\n if includedefault:\n return True\n else:\n return False","title":""},{"docid":"f6ad56e049784f05cd7a715e0f302633","score":"0.7199362","text":"def filter_files(directory, extensions):\n\n return map(lambda x: os.path.join(directory, x),\n filter(lambda x: re.search('\\.({0})$'.format(\n '|'.join(extensions)), x), sorted(os.listdir(directory))))","title":""},{"docid":"069c2ef98f7e6ab26e42a762fee47e2c","score":"0.71652","text":"def allowed_file_exts(files: List, extensions: List[str]) -> bool:\n for f in files:\n ext = get_file_extension(f.filename)\n if ext not in extensions:\n return False\n return True","title":""},{"docid":"1cf1ff4052794423d8f2d4f6e16fb27f","score":"0.71020085","text":"def file_extensions_get(fname_list):\n return [os.path.splitext(fname)[-1] for fname in fname_list]","title":""},{"docid":"df91497740c894b23886e74235c15f45","score":"0.69904","text":"def list_files_with_extensions(directory, ext_list):\n filepath_list = sum(\n [\n glob(\"{0}/*{1}\".format(directory, ext))\n for ext in ext_list\n ],\n [],\n )\n filenames = [os.path.basename(filepath) for filepath in filepath_list]\n return filenames","title":""},{"docid":"8c7239a42c8c11c31617909d28b183ed","score":"0.6975149","text":"def get_file_list(path, ext):\n return [f for f in os.listdir(path) if \n os.path.isfile(os.path.join(path, f)) and f.endswith(ext)]","title":""},{"docid":"65c3dee1bd54f7da56b4079011cad679","score":"0.69076073","text":"def getFileNames():\n included_extenstions = ['json' ]\n file_names = [fn for fn in os.listdir(os.getcwd()) \n if any([fn.endswith(ext) \n for ext in included_extenstions])] \n return file_names","title":""},{"docid":"276a32c3bedac93c824d12a57fa12d6c","score":"0.6899496","text":"def _generate_filename_list(self):\n fq_filename, extension = os.path.splitext(self._fq_infilename)\n\n la_ext_list = [\n 'la{0}'.format(char) for char in string.lowercase]\n da_ext_list = [\n 'da{0}'.format(char) for char in string.lowercase]\n\n extension_list = la_ext_list + da_ext_list\n\n filename_list = [fq_filename + '.{0}'.format(ext)\n for ext in extension_list\n if os.path.exists(fq_filename + '.{0}'.format(ext))]\n\n return filename_list","title":""},{"docid":"f8ec45f3da344c3e14e5a94841f8ca67","score":"0.68790245","text":"def _file_has_extensions(self, filestr, extensions):\r\n for extension in extensions:\r\n if filestr.find(extension) > -1:\r\n return True\r\n return False","title":""},{"docid":"1dd879bf8ef32a15b5c6accde3a3c28d","score":"0.6872849","text":"def get_exts(filepath, all_exts=True):\n \n ext = 42 # initialize and set to non-0 value\n out = [] # the output array of extensions\n \n while ext:\n filepath, ext = path.splitext(filepath)\n\n if ext:\n clean_ext = ext[1:] # strip the \".\" off the front of ext\n out = [clean_ext] + out # append to output array\n if not all_exts: break # if all_exts is False then end the\n # loop after grabbing the first ext\n return out","title":""},{"docid":"b01cc22ff929e71d1968875d4d7497db","score":"0.6780839","text":"def has_file_allowed_extension(filename, extensions):\r\n return filename.lower().endswith(extensions)","title":""},{"docid":"799cf33845c24e316c0f6012022c9d39","score":"0.6763899","text":"def get_files(dir_path, extension):\n files = []\n for file in os.listdir(dir_path):\n if file.endswith(f\".{extension}\"):\n files.append(file)\n return files","title":""},{"docid":"dc9a2178e41285df4b777ad0ee69655b","score":"0.6762181","text":"def test_find_files_returns_only_files_with_correct_extensions(tmpdir):\n folder = tmpdir.mkdir('sub')\n txt = folder.join('file1.txt').write('foo') #noqa\n py = folder.join('file2.py').write('foo') # noqa\n\n exts = ('.txt')\n files = find_files(folder, exts=exts)\n\n # only keep the extensions - makes the assert simpler.\n files = [f[-4:] for f in files]\n\n assert '.txt' in files","title":""},{"docid":"b0dee152af03710c76a66f7b2baa3081","score":"0.67420954","text":"def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)","title":""},{"docid":"b0dee152af03710c76a66f7b2baa3081","score":"0.67420954","text":"def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)","title":""},{"docid":"b0dee152af03710c76a66f7b2baa3081","score":"0.67420954","text":"def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)","title":""},{"docid":"b0dee152af03710c76a66f7b2baa3081","score":"0.67420954","text":"def has_file_allowed_extension(filename, extensions):\n return filename.lower().endswith(extensions)","title":""},{"docid":"2c50aae55f171a1e973c614e2ca4de44","score":"0.67118245","text":"def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\r\n return filename.lower().endswith(extensions)","title":""},{"docid":"5421761bd07d486d4c06a376bbeae8b2","score":"0.67003095","text":"def list_files_with_extension(directory, extension):\n tmp = os.listdir(directory)\n logging.debug(tmp)\n extens = [f for f in tmp if os.path.splitext(f)[1] == \".\"+extension]\n return [os.path.join(directory, f) for f in extens]","title":""},{"docid":"f5eab32a1febb229cc347dceb867e23d","score":"0.667502","text":"def getFiles(self):\n fileList = [os.path.normcase(f) for f in os.listdir(self.directory)]\n fileList = [os.path.join(self.directory, f) for f in fileList if os.path.splitext(f)[1] in self.extList ]\n return [self.getAssertion(f) for f in fileList]","title":""},{"docid":"19726e185e58dfb46ca6cecbfa3fab44","score":"0.66600364","text":"def list():\r\n return [stem for stem, extension in [\r\n os.path.splitext(filename) for filename in os.listdir(_dirname)\r\n ] if extension == '.dat' and stem != 'root']","title":""},{"docid":"81ebd23d3bf8322b15b144d2fafb50b7","score":"0.6659508","text":"def list_of_files_in_dir_for_ext(dir, ext='.xtc') :\n if dir is None : return []\n if not os.path.exists(dir) : return [] \n return sorted([f for f in os.listdir(dir) if os.path.splitext(f)[1] == ext])","title":""},{"docid":"645da5d74ffa9a97b65fa0142e44c844","score":"0.6638439","text":"def file_list(directory, extension='jpg'):\n return [os.path.join(directory,f) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory,f)) and f.endswith(\".{}\".format(extension))]","title":""},{"docid":"7916ced72f4dfb18ecf328bee7073f74","score":"0.6617648","text":"def remove_unwanted_files(files: List[str], extensions: List[str]=['.DS_Store', '.gitignore']):\n for source_file in files:\n for ex in extensions:\n if ex in source_file:\n files.remove(source_file)\n break\n return files","title":""},{"docid":"4318629eeb540df122d4835547b3180e","score":"0.6605068","text":"def has_file_allowed_extension(filename, extensions):\r\n filename_lower = filename.lower()\r\n return any(filename_lower.endswith(ext) for ext in extensions)","title":""},{"docid":"acef0f93bcfae5beca5c606154193b84","score":"0.66009","text":"def filter_srt(files):\n\n out = []\n for filename in files:\n splitted = os.path.splitext(filename)\n srt_identifier = \".srt\"\n if splitted[1] == srt_identifier: \n out.append(filename)\n\n return out","title":""},{"docid":"b0f7c84fe8b6dfd6c72f8c698d1a9204","score":"0.6594698","text":"def search_file(path, ext):\n to_search = []\n for local_files in os.listdir(path):\n if local_files.endswith(ext):\n to_search.append(local_files)\n return to_search","title":""},{"docid":"8bd9f2583a013c4869bd150f748a48cf","score":"0.6586023","text":"def file_extensions_valid(ext_list):\n ext_list_valid = ['.csv','.txt','.xls','.xlsx']\n return ext_list[0] in ext_list_valid","title":""},{"docid":"7658e9c1622a6e670e73581653f33cb5","score":"0.657985","text":"def has_file_allowed_extension(filename: PATH_TYPE, extensions: Tuple[str, ...]) -> bool:\n return str(filename).lower().endswith(extensions)","title":""},{"docid":"e5d429d8ce86144952e4aeb00b3126b7","score":"0.65746367","text":"def get_file_names(path: str, extension: Union[List, str]) -> List:\n if isinstance(extension, List):\n \n images: list = []\n for ext in extension:\n request = os.path.join(path, f'*.{ext}')\n images += glob(request) # to avoid merging opperation after\n\n return images\n\n elif isinstance(extension, str):\n\n request = os.path.join(path, f'*.{extension}')\n return glob(request) #list\n\n else:\n\n raise TypeError('Only list or str are expected')","title":""},{"docid":"300624ac6baeeb201c602b5784d31828","score":"0.6562819","text":"def FileNameList(basepath='.', name='*.*', extension=True, excludename=''):\n files = fnmatch.filter(os.listdir(basepath), name)\n\n if not extension:\n files = [file.split('.')[0] for file in files]\n\n files = ExcludeFromList(files, excludename)\n\n return sorted(files)","title":""},{"docid":"8d86f8d2d9d70ddaf014e1bd0ced7a58","score":"0.65608174","text":"def has_file_allowed_extension(filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)","title":""},{"docid":"8d86f8d2d9d70ddaf014e1bd0ced7a58","score":"0.65608174","text":"def has_file_allowed_extension(filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)","title":""},{"docid":"6ba5686840208032086ef18a90ec8b73","score":"0.6553543","text":"def get_filenames_names_only(*filenames: str) -> List[str]:\n return [os.path.splitext(file)[0] for file in filenames]","title":""},{"docid":"0e5acc64ff532175cfdabccea7afe32a","score":"0.6542695","text":"def _find_files(self, chapter, extension):\n find_pattern = os.path.join(build_config.IMAGES_DIRECTORY,\n chapter, '*%s' % extension)\n found_files = Glob(find_pattern, strings=True)\n # Strip off the extensions.\n length_extension = len(extension)\n found_files = [x[x.index(os.sep) + 1:-length_extension]\n for x in found_files]\n return found_files","title":""},{"docid":"91091c1aac2c6f91219737ba2f01e1a4","score":"0.65398926","text":"def list_files(path, extension=\".cpp\", exclude=\"S.cpp\"):\n return [\"%s/%s\" % (path, f) for f in listdir(path) if f.endswith(extension) and (not f.endswith(exclude))]","title":""},{"docid":"7b35943bad645aa56528ba618476ed27","score":"0.6533559","text":"def _get_files(paths, types):\n files = set()\n for path in paths:\n for dirpath, dirnames, filenames in os.walk(path):\n for filename in filenames:\n try:\n # try-except because [1] and [1:] can fail\n file_ext = os.path.splitext(filename)[1].decode('utf8')[1:]\n if file_ext.lower() in types:\n full_path = os.path.join(dirpath, filename)\n files.add(full_path)\n except:\n pass\n return files","title":""},{"docid":"5077294894066df8af31e946ce04b105","score":"0.6518748","text":"def get_dir_filenames(dir_path, extension='.txt'):\n return [ i for i in listdir(dir_path) if i.find(extension) > 0 ]","title":""},{"docid":"ff5fc702b37c7878ca58605e8048dea1","score":"0.65176266","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.split('.')[-1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"c2ede6286900cf09fee7270c3fa7bda3","score":"0.64979035","text":"def check_extensions(x, elist):\n if not elist: # if no qualifier then we are good\n return True\n\n extension = os.path.splitext(x)[1].lower()\n\n for y in elist: # for each extension we just have to match one\n if '.'+ y == extension:\n return True\n return False","title":""},{"docid":"6abc209052dcc275c09192e3081b8755","score":"0.6495629","text":"def get_filenames(self, prefix, ext='gz'):\n if self.langs:\n return [f'{prefix}.{lang}.{ext}' for lang in self.langs]\n num = len(self.files)\n return [f'{prefix}.{i}.{ext}' for i in range(1, num + 1)]","title":""},{"docid":"651ee5bc19ff5d41c7290c1fb1ddd1c7","score":"0.64897215","text":"def list_of_pathes_in_dir_for_ext(dir, ext='.xtc') :\n return [os.path.join(dir,f) for f in list_of_files_in_dir_for_ext(dir, ext)]","title":""},{"docid":"f3ba78dd868419a763fabf267dbd907d","score":"0.6488085","text":"def get_files(fpath,extensions,recursive=False):\n p = Path(fpath)\n if recursive:\n filelist = [str(x) for x in p.rglob('*') if x.suffix in extensions]\n else:\n filelist = [str(x) for x in p.glob('*') if x.suffix in extensions]\n assert len(filelist) > 0, f\"No files matching extensions {extensions} found in {fpath}\"\n return(filelist)","title":""},{"docid":"1bf7d95d9cbbf7cbbb7487588a5548c1","score":"0.6485401","text":"def filter_lst_files(files):\r\n return [lst_file for lst_file in files if lst_file.endswith(\".lst\")]","title":""},{"docid":"d75973d2539760dbb06cee8f1505f558","score":"0.6484452","text":"def listFiles(dir, ext, ignoreExt=None):\n matches = []\n for root, dirs, files in os.walk(dir):\n for f in files:\n if f.endswith(ext):\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\n matches.append(os.path.join(root, f))\n return matches","title":""},{"docid":"d75973d2539760dbb06cee8f1505f558","score":"0.6484452","text":"def listFiles(dir, ext, ignoreExt=None):\n matches = []\n for root, dirs, files in os.walk(dir):\n for f in files:\n if f.endswith(ext):\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\n matches.append(os.path.join(root, f))\n return matches","title":""},{"docid":"0bd1deb96d18b65d580a0899b7268dec","score":"0.64773446","text":"def mfilter(f):\n return os.path.isfile(f) and f.endswith(ext)","title":""},{"docid":"afbcff433711999ef61b24b30b431e91","score":"0.64706737","text":"def _list_dir(ext):\n return [os.path.join(upload_dir, name) for name in os.listdir(upload_dir) if\n os.path.isfile(os.path.join(upload_dir, name)) and name.endswith(ext)]","title":""},{"docid":"75cee534d7e9514a87d45c139de47294","score":"0.6459723","text":"def get_file_extension_list(self, tpc_type=None):\n file_names = []\n file_extensions = []\n for data_file_name in self.get_data_file_list(tpc_type=tpc_type):\n datum = data_file_name.split(\".\")\n if datum[0] is None or datum[1] is None or datum[0] == \"\" or datum[1] == \"\":\n raise ValueError(\"An attempt was made to parse non eligible files!\")\n file_names.append(datum[0])\n file_extensions.append(datum[1])\n if len(file_names) != len(file_extensions):\n raise ValueError(\"File name list does not match list of file extensions!\")\n return file_names, file_extensions","title":""},{"docid":"954e5050c09733909745fff458aaed44","score":"0.6458051","text":"def lookupExtension(filename):","title":""},{"docid":"d3194b2c6b22e636b9ed62b4210f7497","score":"0.6446683","text":"def _get_fortran_files(srcfiles, extensions=False):\n files_out = []\n for srcfile in srcfiles:\n ext = os.path.splitext(srcfile)[1]\n if ext.lower() in [\".f\", \".for\", \".f90\", \".fpp\"]:\n if extensions:\n # save unique extension\n if ext not in files_out:\n files_out.append(ext)\n else:\n files_out.append(srcfile)\n if len(files_out) < 1:\n files_out = None\n return files_out","title":""},{"docid":"f4291c27f6503ca61ac99590c229b061","score":"0.64121467","text":"def ext_ambiguous_file_list(fn, ext, do_sub_dirs = None) :\n\n ext = ext or \"\"\n if ext and (ext[0] != '.') :\n ext = '.' + ext\n\n if os.path.isdir(fn) :\n fn = os.path.join(fn, '*' + ext)\n else :\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs)\n if not len(fns) : # if there are no ambiguous files to find,\n fn += '*' + ext # then add the extension in case it's not there.\n pass\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs) # find the files\n\n return(fns)","title":""},{"docid":"42aa6b0a35ee1c729ca08ab4246b9dec","score":"0.6408415","text":"def list_all_files(dir_name, exts=[\"jpg\", \"bmp\", \"png\"]):\n result = []\n for dir_, subdirs, file_names in os.walk(dir_name):\n for file_name in file_names:\n if any(file_name.endswith(ext) for ext in exts):\n result.append(os.path.join(dir_, file_name)) \n return result","title":""},{"docid":"c86969a31ae3ab540e6cf0fb67d500bc","score":"0.6398939","text":"def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"c86969a31ae3ab540e6cf0fb67d500bc","score":"0.6398939","text":"def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"c86969a31ae3ab540e6cf0fb67d500bc","score":"0.6398939","text":"def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"4693c015a2f86460acdecbf08217851b","score":"0.63641214","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS","title":""},{"docid":"e45a64d233e1122b068e3d50407b9620","score":"0.63629353","text":"def allowed_file(filename):\n return '.' in filename and filename.rsplit('.', 1)[1] \\\n in ALLOWED_EXTENSIONS","title":""},{"docid":"f107566b36eb4d52c54343511a9eb42f","score":"0.63594925","text":"def test_filter_to_preferred_ext(image_files):\n tiff_files = list(Path(image_files).glob(\"*.tif\"))\n assert len(tiff_files) == 2000\n jpeg_files = list(Path(image_files).glob(\"*.jpg\"))\n assert len(jpeg_files) == 1000\n image_files = list(Path(image_files).iterdir())\n return_files = list(core.filter_to_preferred_ext(image_files, [\".jpg\"]))\n assert len(return_files) == 2000","title":""},{"docid":"d5f2936129dd4078f5c2d4712324b785","score":"0.6341543","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS","title":""},{"docid":"d5f2936129dd4078f5c2d4712324b785","score":"0.6341543","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS","title":""},{"docid":"d5f2936129dd4078f5c2d4712324b785","score":"0.6341543","text":"def allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS","title":""},{"docid":"530c663dd2481cfb9df2f98b2e798f24","score":"0.633947","text":"def has_file_allowed_extension(self, filename, extensions):\n filename_lower = filename.lower()\n return any(filename_lower.endswith(ext) for ext in extensions)","title":""},{"docid":"9b9cc413fd83ab2c29e4e9db0769890a","score":"0.633573","text":"def _kwik_filenames(filename):\n basename, ext = op.splitext(filename)\n return {ext: '{basename}.{ext}'.format(basename=basename, ext=ext)\n for ext in _KWIK_EXTENSIONS}","title":""},{"docid":"e3472c2429f376eda94d88e246754f0d","score":"0.63155866","text":"def allowed_file_extension(filename):\n\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['UPLOAD_EXTENSIONS']","title":""},{"docid":"d3ec7c26f73886c8002746eda9cbb2fd","score":"0.63105655","text":"def allowedFile(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS","title":""},{"docid":"1ec4943a2176c4c5d643e1c0a952924b","score":"0.63059044","text":"def get_files(path, extensions='*'):\n if path[-1] is not '/' and path[-1] is not '\\\\':\n path = path + '/'\n\n if isinstance(extensions, str) or isinstance(extensions, unicode):\n extensions = [extensions]\n\n all_files = [os.path.join(root, filename)\n for root, dirnames, filenames in os.walk(path)\n for filename in filenames]\n\n filtered_files = [\n filename for filename in all_files\n if get_extension(filename) in extensions or '*' in extensions\n ]\n\n return filtered_files","title":""},{"docid":"2a4add0b8a0cf1a8a2dcb24bf22e8f9a","score":"0.6276475","text":"def GetImageExtWildcard():","title":""},{"docid":"b93b2a34639ddd4c3bf1281667a59826","score":"0.62707496","text":"def file_extensions_all_equal(ext_list):\n return len(set(ext_list))==1","title":""},{"docid":"e55ab259acc0438e8e1a7c98a5e1a18c","score":"0.62676054","text":"def find_by_ext(folder, extension):\n found_files = [path.join(dirpath, f)\n for dirpath, dirnames, files in walk(folder)\n for f in files if f.endswith('.' + extension)]\n return found_files","title":""},{"docid":"6a65a86abf2079431459ba72d2a3cf19","score":"0.6267129","text":"def is_valid_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\n\n return any(filename.lower().endswith(ext) for ext in extensions)","title":""},{"docid":"1afcce354f31a092af483f465d296d42","score":"0.62572294","text":"def split_matching_extensions_and_other(self, srcs, exts):\n\n matches = []\n leftovers = []\n\n for src in (srcs or []):\n base, ext = os.path.splitext(src)\n if ext in exts:\n matches.append(src)\n else:\n leftovers.append(src)\n\n return (matches, leftovers)","title":""},{"docid":"8fa9688926f891714b18fb38bf371e1c","score":"0.6253049","text":"def list_files_with_ext(*paths, ext, maxdepth=-1):\n needed_files = []\n for path in paths:\n if ext in path:\n needed_files.append(path)\n continue\n if not os.path.isdir(path):\n print_red(\n qte(path) + \" is not a directory nor does it contain \" +\n qte(ext) + \". Skipping.\"\n )\n continue\n path = os.path.realpath(path)\n start_level = path.count(os.sep)\n # pylint: disable=unused-variable\n # Need dirs to iterate\n for root, dirs, files in os.walk(path):\n depth = root.count(os.sep) - start_level\n if depth > maxdepth and (maxdepth != -1):\n break\n for filename in files:\n if ext in filename:\n needed_files.append(os.path.join(root, filename))\n return needed_files","title":""},{"docid":"b6340c4e87b1f4f711576f1699c15b5c","score":"0.62498426","text":"def get_filtered_files(root_folder, extensions, keywords, strict=False):\n result = []\n unfiltered_files = UtilityBox.get_all_files(root_folder, *extensions)\n for file_path in unfiltered_files:\n if (not strict and any(kw.lower() in file_path.lower() for kw in keywords))\\\n or (strict and all(kw.lower() in file_path.lower() for kw in keywords)):\n result.append(file_path)\n return result","title":""},{"docid":"9073547ed295dc0f959d4a012820c136","score":"0.6249226","text":"def get_paths_by_ext(directory, exts=['JPEG']):\n if not directory.endswith('/'):\n directory += '/'\n\n img_paths = []\n for ext in exts:\n img_paths = img_paths + glob(os.path.join(directory) + '**/*.' + ext, recursive=True)\n return sorted(img_paths)","title":""},{"docid":"e54f73d0236ebe7aad1f4c86f4fca73f","score":"0.624537","text":"def get_file_info(d, extensions):\n for key in extensions:\n for f in os.listdir(d):\n if f.endswith(key):\n pass","title":""},{"docid":"ff03cf7eb8834954b337babf077d85a7","score":"0.622903","text":"def get_related_files(filename):\n related_files = []\n if filename.endswith(\".img\") or filename.endswith(\".hdr\"):\n path, name, ext = split_filename(filename)\n for ext in ['.hdr', '.img', '.mat']:\n related_files.append(os.path.join(path, name + ext))\n elif filename.endswith(\".BRIK\") or filename.endswith(\".HEAD\"):\n path, name, ext = split_filename(filename)\n for ext in ['.BRIK', '.HEAD']:\n related_files.append(os.path.join(path, name + ext))\n if not len(related_files):\n related_files = [filename]\n return related_files","title":""},{"docid":"73a2dceed81f4f2da1fc9fb3e5c52545","score":"0.62266505","text":"def grab_names():\n\n output = []\n original = os.listdir()\n \n for filename in original:\n if \".\" in filename:\n continue\n else:\n output.append(filename)\n\n return output","title":""},{"docid":"d767db9cf7334383a8d7456127fc741b","score":"0.6209032","text":"def get_all_files(dir, ext):\n extension_suffix = f\".{ext}\"\n files = []\n for file in os.listdir(dir):\n if file.endswith(extension_suffix):\n files.append(os.path.join(dir, file))\n return files","title":""},{"docid":"e47ab99363540050623a47b24d81fc9a","score":"0.62019986","text":"def load_images_without_extention(path):\n images = []\n valid_images = [\".jpg\", \".png\", \".jpeg\"]\n for f in os.listdir(path):\n file, ext = os.path.splitext(f)\n if ext.lower() not in valid_images:\n continue\n images.append(file)\n return images","title":""},{"docid":"7eb177ef54ee82e41b2fb88a46dfe5bf","score":"0.6201061","text":"def read_directory(extensions):\n for file in os.listdir('.'):\n filenames = file.split('.')\n if os.path.isdir(file):\n continue\n if filenames[1] not in extensions:\n extensions.append(filenames[1])\n print(extensions)","title":""},{"docid":"8755751c0c0eede4b772f14be8436f93","score":"0.6200504","text":"def get_filenames(format: str = 'json') -> List[str]:\n filenames = []\n for type in get_filetypes(format):\n filenames.extend(glob.glob(f'*.{type}'))\n return filenames","title":""},{"docid":"2cd5d5d20c08c7a82fb7c096ac33b951","score":"0.6196302","text":"def get_files(directory):\n files = []\n for item in os.listdir(directory):\n item = os.path.join(directory, item)\n if not os.path.isfile(item):\n continue\n parts = os.path.splitext(item)\n if parts and len(parts) > 1 and parts[1] == '.py'\\\n and parts[0].lower().find(\"test\") == -1:\n files.append(item)\n return files","title":""},{"docid":"fceba3ab8b472f6765e4facaea1c5bee","score":"0.61914957","text":"def _collect_files(self, path, extensions):\n path_list = []\n for dirpath, _, filenames in os.walk(path):\n path_list.extend(\n [\n os.path.join(dirpath, filename)\n for filename in filenames\n if os.path.splitext(filename)[1].lower() in extensions\n ]\n )\n return path_list","title":""},{"docid":"feecc2c670c46c78eacd50b912e03f26","score":"0.61866087","text":"def filter_files(pattern, file_names):\n p = re.compile(pattern)\n return [p.match(f) for f in file_names if p.match(f) is not None]","title":""},{"docid":"f73372929bb991939b97752d0da98eda","score":"0.6159966","text":"def get_files(dir, suffix):\n files = []\n for file in os.listdir(dir):\n if file.endswith(suffix):\n files.append(file)\n return files","title":""},{"docid":"f73372929bb991939b97752d0da98eda","score":"0.6159966","text":"def get_files(dir, suffix):\n files = []\n for file in os.listdir(dir):\n if file.endswith(suffix):\n files.append(file)\n return files","title":""},{"docid":"ddf7989ea21b31799113ffb7259206cc","score":"0.6147999","text":"def stringsWithImageFileExtensions(listOfStrings):\r\n\r\n #todo: string comparison should ignore case\r\n result = []\r\n for s in listOfStrings:\r\n if (s.find('.tif') != -1) or\\\r\n (s.find('.bmp') != -1) or\\\r\n (s.find('.pgm') != -1) or\\\r\n (s.find('.gif') != -1) or\\\r\n (s.find('.png') != -1):\r\n result.append(s)\r\n return result","title":""},{"docid":"9ea904a64b978053bd910323a78d3c9e","score":"0.6144758","text":"def _get_c_files(srcfiles, extensions=False):\n files_out = []\n for srcfile in srcfiles:\n ext = os.path.splitext(srcfile)[1]\n if ext.lower() in [\".c\", \".cpp\"]:\n if extensions:\n if ext not in files_out:\n files_out.append(ext)\n else:\n files_out.append(srcfile)\n if len(files_out) < 1:\n files_out = None\n return files_out","title":""},{"docid":"00a19e8793e3621beb428a0710c1a5af","score":"0.6140286","text":"def get_file_list(path, extensions):\n if not os.path.isdir(path):\n log_error_exit('Directory invalid.')\n else:\n files_list = []\n # The FITS files are named in numerically ascending order with an optional\n for file in os.listdir(path):\n if file.split('.')[-1] in extensions:\n files_list.append(os.path.join(path, file))\n if len(files_list) == 0:\n log_error_exit('No files with given extensins found !')\n return sorted(files_list)","title":""},{"docid":"c7430a703d144531116f0ef4a00b716f","score":"0.6116649","text":"def get_files_with_suffix(dir_name, suffixes):\n import os\n found_filenames = []\n for localpath, directories, filenames in os.walk(dir_name):\n for filename in filenames:\n for suffix in suffixes:\n if filename.endswith(suffix):\n found_filenames.append(os.path.join(localpath, filename))\n found_filenames.sort()\n return found_filenames","title":""},{"docid":"8227919c4a92055b6e0f0aae097b9067","score":"0.6115884","text":"def _splitext(fname):\n dir, filename = os.path.split(fname)\n for special_ext in ['.nii.gz', '.tar.gz']:\n if filename.endswith(special_ext):\n stem, ext = filename[:-len(special_ext)], special_ext\n return os.path.join(dir, stem), ext\n # If no special case, behaves like the regular splitext\n stem, ext = os.path.splitext(filename)\n return os.path.join(dir, stem), ext","title":""},{"docid":"c98eb8adabba7af1cf0a28c40a232498","score":"0.6114838","text":"def listfiles(mypath, mytype):\n result = []\n # Match Extension.\n for foundfile in Path(mypath).glob(\"**/*.*\"):\n filetype = str(foundfile).split(\".\")[-1]\n if filetype in mytype:\n # Force string type was Posix(path)\n result.append(str(foundfile))\n return result","title":""},{"docid":"73d0dc0b8cca82a9ec101f45f6470ac3","score":"0.61128","text":"def remove_all_file_extensions(filepath: Path, extensions: list = None):\n\n def _remove_next_suffix(f):\n if extensions is not None and len(extensions) > 0:\n return f.suffix in extensions\n return len(f.suffix) > 0\n\n while _remove_next_suffix(filepath):\n filepath = filepath.with_suffix(\"\")\n\n return filepath","title":""},{"docid":"81288495451141c1b16e070d8a7535db","score":"0.61059415","text":"def list_all_extension_files(directory_path, extension='.txt'):\n files_paths = []\n for r, d, f in os.walk(directory_path):\n f = [os.path.join(r,file) for file in f if file.find(extension) != -1]\n files_paths.append(f)\n\n files_paths = list(np.hstack(files_paths))\n files_paths = [tuple((in_file, in_file.replace(\"text_volumes\",\"speeches\").replace(\".txt\",\".csv\"))) for in_file in files_paths]\n\n files_paths = [tuple((in_file,out_file)) for in_file, out_file in files_paths if not os.path.exists(out_file)]\n return files_paths","title":""}],"string":"[\n {\n \"docid\": \"980d3d0380131c6070595da9574d0e44\",\n \"score\": \"0.7548063\",\n \"text\": \"def get_files(dirname, extensions=['.png', '.tif', '.jpg']):\\n dir_path = Path(dirname)\\n\\n files = dir_path.glob('**/*')\\n\\n files = [path.resolve() for path in files]\\n\\n match = [f for f in files if f.suffix in extensions]\\n return match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756bf2887c3883a51543e44a722ba14b\",\n \"score\": \"0.74919146\",\n \"text\": \"def ext_filter(directory, exts, ABS_PATHS = True):\\n if isinstance(exts,list) or isinstance(exts,tuple):\\n exts = list(exts)\\n if ABS_PATHS:\\n return [opj(directory,file) for file in os.listdir(directory) if \\n os.path.splitext(file)[1] in exts]\\n else:\\n return [file for file in os.listdir(directory) if \\n os.path.splitext(file)[1] in exts]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59861aab08ffbe5d5f4534e9f1ede444\",\n \"score\": \"0.73087174\",\n \"text\": \"def filter_by_extension(ext):\\n def filt(filename):\\n return filename.endswith(ext)\\n return filt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1418fdb60218040b473b134833d3322a\",\n \"score\": \"0.7305053\",\n \"text\": \"def get_files_with_extension(dir, names, ext):\\n list = []\\n for name in names:\\n e = os.path.splitext(name)[1]\\n if e == ext:\\n list.append(name)\\n list.sort()\\n return [os.path.join(dir, n) for n in list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4aa8be8863df9601ac1abf7504c66b8\",\n \"score\": \"0.726645\",\n \"text\": \"def _get_allowed_extensions():\\n return [\\n ext.lower()\\n for ext, file_type in Image.EXTENSION.items()\\n if file_type.upper() in MIME_TYPE_TO_PIL_IDENTIFIER.values()\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaae5bf503325a9a14f507df67b18904\",\n \"score\": \"0.7261796\",\n \"text\": \"def filter_by_suffix(files):\\n if includesuffix:\\n for insuffix in includesuffix:\\n extname = '*.' + insuffix\\n if fnmatch.fnmatch(files, extname):\\n return True\\n\\n if excludesuffix:\\n for exsuffix in excludesuffix:\\n extname = '*.' + exsuffix\\n if fnmatch.fnmatch(files, extname):\\n return False\\n\\n if includedefault:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6ad56e049784f05cd7a715e0f302633\",\n \"score\": \"0.7199362\",\n \"text\": \"def filter_files(directory, extensions):\\n\\n return map(lambda x: os.path.join(directory, x),\\n filter(lambda x: re.search('\\\\.({0})$'.format(\\n '|'.join(extensions)), x), sorted(os.listdir(directory))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"069c2ef98f7e6ab26e42a762fee47e2c\",\n \"score\": \"0.71652\",\n \"text\": \"def allowed_file_exts(files: List, extensions: List[str]) -> bool:\\n for f in files:\\n ext = get_file_extension(f.filename)\\n if ext not in extensions:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf1ff4052794423d8f2d4f6e16fb27f\",\n \"score\": \"0.71020085\",\n \"text\": \"def file_extensions_get(fname_list):\\n return [os.path.splitext(fname)[-1] for fname in fname_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df91497740c894b23886e74235c15f45\",\n \"score\": \"0.69904\",\n \"text\": \"def list_files_with_extensions(directory, ext_list):\\n filepath_list = sum(\\n [\\n glob(\\\"{0}/*{1}\\\".format(directory, ext))\\n for ext in ext_list\\n ],\\n [],\\n )\\n filenames = [os.path.basename(filepath) for filepath in filepath_list]\\n return filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c7239a42c8c11c31617909d28b183ed\",\n \"score\": \"0.6975149\",\n \"text\": \"def get_file_list(path, ext):\\n return [f for f in os.listdir(path) if \\n os.path.isfile(os.path.join(path, f)) and f.endswith(ext)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c3dee1bd54f7da56b4079011cad679\",\n \"score\": \"0.69076073\",\n \"text\": \"def getFileNames():\\n included_extenstions = ['json' ]\\n file_names = [fn for fn in os.listdir(os.getcwd()) \\n if any([fn.endswith(ext) \\n for ext in included_extenstions])] \\n return file_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"276a32c3bedac93c824d12a57fa12d6c\",\n \"score\": \"0.6899496\",\n \"text\": \"def _generate_filename_list(self):\\n fq_filename, extension = os.path.splitext(self._fq_infilename)\\n\\n la_ext_list = [\\n 'la{0}'.format(char) for char in string.lowercase]\\n da_ext_list = [\\n 'da{0}'.format(char) for char in string.lowercase]\\n\\n extension_list = la_ext_list + da_ext_list\\n\\n filename_list = [fq_filename + '.{0}'.format(ext)\\n for ext in extension_list\\n if os.path.exists(fq_filename + '.{0}'.format(ext))]\\n\\n return filename_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8ec45f3da344c3e14e5a94841f8ca67\",\n \"score\": \"0.68790245\",\n \"text\": \"def _file_has_extensions(self, filestr, extensions):\\r\\n for extension in extensions:\\r\\n if filestr.find(extension) > -1:\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd879bf8ef32a15b5c6accde3a3c28d\",\n \"score\": \"0.6872849\",\n \"text\": \"def get_exts(filepath, all_exts=True):\\n \\n ext = 42 # initialize and set to non-0 value\\n out = [] # the output array of extensions\\n \\n while ext:\\n filepath, ext = path.splitext(filepath)\\n\\n if ext:\\n clean_ext = ext[1:] # strip the \\\".\\\" off the front of ext\\n out = [clean_ext] + out # append to output array\\n if not all_exts: break # if all_exts is False then end the\\n # loop after grabbing the first ext\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b01cc22ff929e71d1968875d4d7497db\",\n \"score\": \"0.6780839\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\r\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"799cf33845c24e316c0f6012022c9d39\",\n \"score\": \"0.6763899\",\n \"text\": \"def get_files(dir_path, extension):\\n files = []\\n for file in os.listdir(dir_path):\\n if file.endswith(f\\\".{extension}\\\"):\\n files.append(file)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9a2178e41285df4b777ad0ee69655b\",\n \"score\": \"0.6762181\",\n \"text\": \"def test_find_files_returns_only_files_with_correct_extensions(tmpdir):\\n folder = tmpdir.mkdir('sub')\\n txt = folder.join('file1.txt').write('foo') #noqa\\n py = folder.join('file2.py').write('foo') # noqa\\n\\n exts = ('.txt')\\n files = find_files(folder, exts=exts)\\n\\n # only keep the extensions - makes the assert simpler.\\n files = [f[-4:] for f in files]\\n\\n assert '.txt' in files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dee152af03710c76a66f7b2baa3081\",\n \"score\": \"0.67420954\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dee152af03710c76a66f7b2baa3081\",\n \"score\": \"0.67420954\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dee152af03710c76a66f7b2baa3081\",\n \"score\": \"0.67420954\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dee152af03710c76a66f7b2baa3081\",\n \"score\": \"0.67420954\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c50aae55f171a1e973c614e2ca4de44\",\n \"score\": \"0.67118245\",\n \"text\": \"def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\\r\\n return filename.lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5421761bd07d486d4c06a376bbeae8b2\",\n \"score\": \"0.67003095\",\n \"text\": \"def list_files_with_extension(directory, extension):\\n tmp = os.listdir(directory)\\n logging.debug(tmp)\\n extens = [f for f in tmp if os.path.splitext(f)[1] == \\\".\\\"+extension]\\n return [os.path.join(directory, f) for f in extens]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5eab32a1febb229cc347dceb867e23d\",\n \"score\": \"0.667502\",\n \"text\": \"def getFiles(self):\\n fileList = [os.path.normcase(f) for f in os.listdir(self.directory)]\\n fileList = [os.path.join(self.directory, f) for f in fileList if os.path.splitext(f)[1] in self.extList ]\\n return [self.getAssertion(f) for f in fileList]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19726e185e58dfb46ca6cecbfa3fab44\",\n \"score\": \"0.66600364\",\n \"text\": \"def list():\\r\\n return [stem for stem, extension in [\\r\\n os.path.splitext(filename) for filename in os.listdir(_dirname)\\r\\n ] if extension == '.dat' and stem != 'root']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81ebd23d3bf8322b15b144d2fafb50b7\",\n \"score\": \"0.6659508\",\n \"text\": \"def list_of_files_in_dir_for_ext(dir, ext='.xtc') :\\n if dir is None : return []\\n if not os.path.exists(dir) : return [] \\n return sorted([f for f in os.listdir(dir) if os.path.splitext(f)[1] == ext])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"645da5d74ffa9a97b65fa0142e44c844\",\n \"score\": \"0.6638439\",\n \"text\": \"def file_list(directory, extension='jpg'):\\n return [os.path.join(directory,f) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory,f)) and f.endswith(\\\".{}\\\".format(extension))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7916ced72f4dfb18ecf328bee7073f74\",\n \"score\": \"0.6617648\",\n \"text\": \"def remove_unwanted_files(files: List[str], extensions: List[str]=['.DS_Store', '.gitignore']):\\n for source_file in files:\\n for ex in extensions:\\n if ex in source_file:\\n files.remove(source_file)\\n break\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4318629eeb540df122d4835547b3180e\",\n \"score\": \"0.6605068\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\r\\n filename_lower = filename.lower()\\r\\n return any(filename_lower.endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acef0f93bcfae5beca5c606154193b84\",\n \"score\": \"0.66009\",\n \"text\": \"def filter_srt(files):\\n\\n out = []\\n for filename in files:\\n splitted = os.path.splitext(filename)\\n srt_identifier = \\\".srt\\\"\\n if splitted[1] == srt_identifier: \\n out.append(filename)\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0f7c84fe8b6dfd6c72f8c698d1a9204\",\n \"score\": \"0.6594698\",\n \"text\": \"def search_file(path, ext):\\n to_search = []\\n for local_files in os.listdir(path):\\n if local_files.endswith(ext):\\n to_search.append(local_files)\\n return to_search\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bd9f2583a013c4869bd150f748a48cf\",\n \"score\": \"0.6586023\",\n \"text\": \"def file_extensions_valid(ext_list):\\n ext_list_valid = ['.csv','.txt','.xls','.xlsx']\\n return ext_list[0] in ext_list_valid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7658e9c1622a6e670e73581653f33cb5\",\n \"score\": \"0.657985\",\n \"text\": \"def has_file_allowed_extension(filename: PATH_TYPE, extensions: Tuple[str, ...]) -> bool:\\n return str(filename).lower().endswith(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d429d8ce86144952e4aeb00b3126b7\",\n \"score\": \"0.65746367\",\n \"text\": \"def get_file_names(path: str, extension: Union[List, str]) -> List:\\n if isinstance(extension, List):\\n \\n images: list = []\\n for ext in extension:\\n request = os.path.join(path, f'*.{ext}')\\n images += glob(request) # to avoid merging opperation after\\n\\n return images\\n\\n elif isinstance(extension, str):\\n\\n request = os.path.join(path, f'*.{extension}')\\n return glob(request) #list\\n\\n else:\\n\\n raise TypeError('Only list or str are expected')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"300624ac6baeeb201c602b5784d31828\",\n \"score\": \"0.6562819\",\n \"text\": \"def FileNameList(basepath='.', name='*.*', extension=True, excludename=''):\\n files = fnmatch.filter(os.listdir(basepath), name)\\n\\n if not extension:\\n files = [file.split('.')[0] for file in files]\\n\\n files = ExcludeFromList(files, excludename)\\n\\n return sorted(files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d86f8d2d9d70ddaf014e1bd0ced7a58\",\n \"score\": \"0.65608174\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n filename_lower = filename.lower()\\n return any(filename_lower.endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d86f8d2d9d70ddaf014e1bd0ced7a58\",\n \"score\": \"0.65608174\",\n \"text\": \"def has_file_allowed_extension(filename, extensions):\\n filename_lower = filename.lower()\\n return any(filename_lower.endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ba5686840208032086ef18a90ec8b73\",\n \"score\": \"0.6553543\",\n \"text\": \"def get_filenames_names_only(*filenames: str) -> List[str]:\\n return [os.path.splitext(file)[0] for file in filenames]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5acc64ff532175cfdabccea7afe32a\",\n \"score\": \"0.6542695\",\n \"text\": \"def _find_files(self, chapter, extension):\\n find_pattern = os.path.join(build_config.IMAGES_DIRECTORY,\\n chapter, '*%s' % extension)\\n found_files = Glob(find_pattern, strings=True)\\n # Strip off the extensions.\\n length_extension = len(extension)\\n found_files = [x[x.index(os.sep) + 1:-length_extension]\\n for x in found_files]\\n return found_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91091c1aac2c6f91219737ba2f01e1a4\",\n \"score\": \"0.65398926\",\n \"text\": \"def list_files(path, extension=\\\".cpp\\\", exclude=\\\"S.cpp\\\"):\\n return [\\\"%s/%s\\\" % (path, f) for f in listdir(path) if f.endswith(extension) and (not f.endswith(exclude))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b35943bad645aa56528ba618476ed27\",\n \"score\": \"0.6533559\",\n \"text\": \"def _get_files(paths, types):\\n files = set()\\n for path in paths:\\n for dirpath, dirnames, filenames in os.walk(path):\\n for filename in filenames:\\n try:\\n # try-except because [1] and [1:] can fail\\n file_ext = os.path.splitext(filename)[1].decode('utf8')[1:]\\n if file_ext.lower() in types:\\n full_path = os.path.join(dirpath, filename)\\n files.add(full_path)\\n except:\\n pass\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5077294894066df8af31e946ce04b105\",\n \"score\": \"0.6518748\",\n \"text\": \"def get_dir_filenames(dir_path, extension='.txt'):\\n return [ i for i in listdir(dir_path) if i.find(extension) > 0 ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff5fc702b37c7878ca58605e8048dea1\",\n \"score\": \"0.65176266\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.split('.')[-1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2ede6286900cf09fee7270c3fa7bda3\",\n \"score\": \"0.64979035\",\n \"text\": \"def check_extensions(x, elist):\\n if not elist: # if no qualifier then we are good\\n return True\\n\\n extension = os.path.splitext(x)[1].lower()\\n\\n for y in elist: # for each extension we just have to match one\\n if '.'+ y == extension:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6abc209052dcc275c09192e3081b8755\",\n \"score\": \"0.6495629\",\n \"text\": \"def get_filenames(self, prefix, ext='gz'):\\n if self.langs:\\n return [f'{prefix}.{lang}.{ext}' for lang in self.langs]\\n num = len(self.files)\\n return [f'{prefix}.{i}.{ext}' for i in range(1, num + 1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"651ee5bc19ff5d41c7290c1fb1ddd1c7\",\n \"score\": \"0.64897215\",\n \"text\": \"def list_of_pathes_in_dir_for_ext(dir, ext='.xtc') :\\n return [os.path.join(dir,f) for f in list_of_files_in_dir_for_ext(dir, ext)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3ba78dd868419a763fabf267dbd907d\",\n \"score\": \"0.6488085\",\n \"text\": \"def get_files(fpath,extensions,recursive=False):\\n p = Path(fpath)\\n if recursive:\\n filelist = [str(x) for x in p.rglob('*') if x.suffix in extensions]\\n else:\\n filelist = [str(x) for x in p.glob('*') if x.suffix in extensions]\\n assert len(filelist) > 0, f\\\"No files matching extensions {extensions} found in {fpath}\\\"\\n return(filelist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bf7d95d9cbbf7cbbb7487588a5548c1\",\n \"score\": \"0.6485401\",\n \"text\": \"def filter_lst_files(files):\\r\\n return [lst_file for lst_file in files if lst_file.endswith(\\\".lst\\\")]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75973d2539760dbb06cee8f1505f558\",\n \"score\": \"0.6484452\",\n \"text\": \"def listFiles(dir, ext, ignoreExt=None):\\n matches = []\\n for root, dirs, files in os.walk(dir):\\n for f in files:\\n if f.endswith(ext):\\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\\n matches.append(os.path.join(root, f))\\n return matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75973d2539760dbb06cee8f1505f558\",\n \"score\": \"0.6484452\",\n \"text\": \"def listFiles(dir, ext, ignoreExt=None):\\n matches = []\\n for root, dirs, files in os.walk(dir):\\n for f in files:\\n if f.endswith(ext):\\n if not ignoreExt or (ignoreExt and not f.endswith(ignoreExt)):\\n matches.append(os.path.join(root, f))\\n return matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bd1deb96d18b65d580a0899b7268dec\",\n \"score\": \"0.64773446\",\n \"text\": \"def mfilter(f):\\n return os.path.isfile(f) and f.endswith(ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afbcff433711999ef61b24b30b431e91\",\n \"score\": \"0.64706737\",\n \"text\": \"def _list_dir(ext):\\n return [os.path.join(upload_dir, name) for name in os.listdir(upload_dir) if\\n os.path.isfile(os.path.join(upload_dir, name)) and name.endswith(ext)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75cee534d7e9514a87d45c139de47294\",\n \"score\": \"0.6459723\",\n \"text\": \"def get_file_extension_list(self, tpc_type=None):\\n file_names = []\\n file_extensions = []\\n for data_file_name in self.get_data_file_list(tpc_type=tpc_type):\\n datum = data_file_name.split(\\\".\\\")\\n if datum[0] is None or datum[1] is None or datum[0] == \\\"\\\" or datum[1] == \\\"\\\":\\n raise ValueError(\\\"An attempt was made to parse non eligible files!\\\")\\n file_names.append(datum[0])\\n file_extensions.append(datum[1])\\n if len(file_names) != len(file_extensions):\\n raise ValueError(\\\"File name list does not match list of file extensions!\\\")\\n return file_names, file_extensions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954e5050c09733909745fff458aaed44\",\n \"score\": \"0.6458051\",\n \"text\": \"def lookupExtension(filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3194b2c6b22e636b9ed62b4210f7497\",\n \"score\": \"0.6446683\",\n \"text\": \"def _get_fortran_files(srcfiles, extensions=False):\\n files_out = []\\n for srcfile in srcfiles:\\n ext = os.path.splitext(srcfile)[1]\\n if ext.lower() in [\\\".f\\\", \\\".for\\\", \\\".f90\\\", \\\".fpp\\\"]:\\n if extensions:\\n # save unique extension\\n if ext not in files_out:\\n files_out.append(ext)\\n else:\\n files_out.append(srcfile)\\n if len(files_out) < 1:\\n files_out = None\\n return files_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4291c27f6503ca61ac99590c229b061\",\n \"score\": \"0.64121467\",\n \"text\": \"def ext_ambiguous_file_list(fn, ext, do_sub_dirs = None) :\\n\\n ext = ext or \\\"\\\"\\n if ext and (ext[0] != '.') :\\n ext = '.' + ext\\n\\n if os.path.isdir(fn) :\\n fn = os.path.join(fn, '*' + ext)\\n else :\\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs)\\n if not len(fns) : # if there are no ambiguous files to find,\\n fn += '*' + ext # then add the extension in case it's not there.\\n pass\\n fns = ambiguous_file_list(fn, do_sub_dirs = do_sub_dirs) # find the files\\n\\n return(fns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42aa6b0a35ee1c729ca08ab4246b9dec\",\n \"score\": \"0.6408415\",\n \"text\": \"def list_all_files(dir_name, exts=[\\\"jpg\\\", \\\"bmp\\\", \\\"png\\\"]):\\n result = []\\n for dir_, subdirs, file_names in os.walk(dir_name):\\n for file_name in file_names:\\n if any(file_name.endswith(ext) for ext in exts):\\n result.append(os.path.join(dir_, file_name)) \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86969a31ae3ab540e6cf0fb67d500bc\",\n \"score\": \"0.6398939\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86969a31ae3ab540e6cf0fb67d500bc\",\n \"score\": \"0.6398939\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86969a31ae3ab540e6cf0fb67d500bc\",\n \"score\": \"0.6398939\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4693c015a2f86460acdecbf08217851b\",\n \"score\": \"0.63641214\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e45a64d233e1122b068e3d50407b9620\",\n \"score\": \"0.63629353\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and filename.rsplit('.', 1)[1] \\\\\\n in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f107566b36eb4d52c54343511a9eb42f\",\n \"score\": \"0.63594925\",\n \"text\": \"def test_filter_to_preferred_ext(image_files):\\n tiff_files = list(Path(image_files).glob(\\\"*.tif\\\"))\\n assert len(tiff_files) == 2000\\n jpeg_files = list(Path(image_files).glob(\\\"*.jpg\\\"))\\n assert len(jpeg_files) == 1000\\n image_files = list(Path(image_files).iterdir())\\n return_files = list(core.filter_to_preferred_ext(image_files, [\\\".jpg\\\"]))\\n assert len(return_files) == 2000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f2936129dd4078f5c2d4712324b785\",\n \"score\": \"0.6341543\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f2936129dd4078f5c2d4712324b785\",\n \"score\": \"0.6341543\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f2936129dd4078f5c2d4712324b785\",\n \"score\": \"0.6341543\",\n \"text\": \"def allowed_file(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"530c663dd2481cfb9df2f98b2e798f24\",\n \"score\": \"0.633947\",\n \"text\": \"def has_file_allowed_extension(self, filename, extensions):\\n filename_lower = filename.lower()\\n return any(filename_lower.endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b9cc413fd83ab2c29e4e9db0769890a\",\n \"score\": \"0.633573\",\n \"text\": \"def _kwik_filenames(filename):\\n basename, ext = op.splitext(filename)\\n return {ext: '{basename}.{ext}'.format(basename=basename, ext=ext)\\n for ext in _KWIK_EXTENSIONS}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3472c2429f376eda94d88e246754f0d\",\n \"score\": \"0.63155866\",\n \"text\": \"def allowed_file_extension(filename):\\n\\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['UPLOAD_EXTENSIONS']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ec7c26f73886c8002746eda9cbb2fd\",\n \"score\": \"0.63105655\",\n \"text\": \"def allowedFile(filename):\\n return '.' in filename and \\\\\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ec4943a2176c4c5d643e1c0a952924b\",\n \"score\": \"0.63059044\",\n \"text\": \"def get_files(path, extensions='*'):\\n if path[-1] is not '/' and path[-1] is not '\\\\\\\\':\\n path = path + '/'\\n\\n if isinstance(extensions, str) or isinstance(extensions, unicode):\\n extensions = [extensions]\\n\\n all_files = [os.path.join(root, filename)\\n for root, dirnames, filenames in os.walk(path)\\n for filename in filenames]\\n\\n filtered_files = [\\n filename for filename in all_files\\n if get_extension(filename) in extensions or '*' in extensions\\n ]\\n\\n return filtered_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4add0b8a0cf1a8a2dcb24bf22e8f9a\",\n \"score\": \"0.6276475\",\n \"text\": \"def GetImageExtWildcard():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93b2a34639ddd4c3bf1281667a59826\",\n \"score\": \"0.62707496\",\n \"text\": \"def file_extensions_all_equal(ext_list):\\n return len(set(ext_list))==1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55ab259acc0438e8e1a7c98a5e1a18c\",\n \"score\": \"0.62676054\",\n \"text\": \"def find_by_ext(folder, extension):\\n found_files = [path.join(dirpath, f)\\n for dirpath, dirnames, files in walk(folder)\\n for f in files if f.endswith('.' + extension)]\\n return found_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a65a86abf2079431459ba72d2a3cf19\",\n \"score\": \"0.6267129\",\n \"text\": \"def is_valid_extension(filename: str, extensions: Tuple[str, ...]) -> bool:\\n\\n return any(filename.lower().endswith(ext) for ext in extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afcce354f31a092af483f465d296d42\",\n \"score\": \"0.62572294\",\n \"text\": \"def split_matching_extensions_and_other(self, srcs, exts):\\n\\n matches = []\\n leftovers = []\\n\\n for src in (srcs or []):\\n base, ext = os.path.splitext(src)\\n if ext in exts:\\n matches.append(src)\\n else:\\n leftovers.append(src)\\n\\n return (matches, leftovers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa9688926f891714b18fb38bf371e1c\",\n \"score\": \"0.6253049\",\n \"text\": \"def list_files_with_ext(*paths, ext, maxdepth=-1):\\n needed_files = []\\n for path in paths:\\n if ext in path:\\n needed_files.append(path)\\n continue\\n if not os.path.isdir(path):\\n print_red(\\n qte(path) + \\\" is not a directory nor does it contain \\\" +\\n qte(ext) + \\\". Skipping.\\\"\\n )\\n continue\\n path = os.path.realpath(path)\\n start_level = path.count(os.sep)\\n # pylint: disable=unused-variable\\n # Need dirs to iterate\\n for root, dirs, files in os.walk(path):\\n depth = root.count(os.sep) - start_level\\n if depth > maxdepth and (maxdepth != -1):\\n break\\n for filename in files:\\n if ext in filename:\\n needed_files.append(os.path.join(root, filename))\\n return needed_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6340c4e87b1f4f711576f1699c15b5c\",\n \"score\": \"0.62498426\",\n \"text\": \"def get_filtered_files(root_folder, extensions, keywords, strict=False):\\n result = []\\n unfiltered_files = UtilityBox.get_all_files(root_folder, *extensions)\\n for file_path in unfiltered_files:\\n if (not strict and any(kw.lower() in file_path.lower() for kw in keywords))\\\\\\n or (strict and all(kw.lower() in file_path.lower() for kw in keywords)):\\n result.append(file_path)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9073547ed295dc0f959d4a012820c136\",\n \"score\": \"0.6249226\",\n \"text\": \"def get_paths_by_ext(directory, exts=['JPEG']):\\n if not directory.endswith('/'):\\n directory += '/'\\n\\n img_paths = []\\n for ext in exts:\\n img_paths = img_paths + glob(os.path.join(directory) + '**/*.' + ext, recursive=True)\\n return sorted(img_paths)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54f73d0236ebe7aad1f4c86f4fca73f\",\n \"score\": \"0.624537\",\n \"text\": \"def get_file_info(d, extensions):\\n for key in extensions:\\n for f in os.listdir(d):\\n if f.endswith(key):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff03cf7eb8834954b337babf077d85a7\",\n \"score\": \"0.622903\",\n \"text\": \"def get_related_files(filename):\\n related_files = []\\n if filename.endswith(\\\".img\\\") or filename.endswith(\\\".hdr\\\"):\\n path, name, ext = split_filename(filename)\\n for ext in ['.hdr', '.img', '.mat']:\\n related_files.append(os.path.join(path, name + ext))\\n elif filename.endswith(\\\".BRIK\\\") or filename.endswith(\\\".HEAD\\\"):\\n path, name, ext = split_filename(filename)\\n for ext in ['.BRIK', '.HEAD']:\\n related_files.append(os.path.join(path, name + ext))\\n if not len(related_files):\\n related_files = [filename]\\n return related_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a2dceed81f4f2da1fc9fb3e5c52545\",\n \"score\": \"0.62266505\",\n \"text\": \"def grab_names():\\n\\n output = []\\n original = os.listdir()\\n \\n for filename in original:\\n if \\\".\\\" in filename:\\n continue\\n else:\\n output.append(filename)\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d767db9cf7334383a8d7456127fc741b\",\n \"score\": \"0.6209032\",\n \"text\": \"def get_all_files(dir, ext):\\n extension_suffix = f\\\".{ext}\\\"\\n files = []\\n for file in os.listdir(dir):\\n if file.endswith(extension_suffix):\\n files.append(os.path.join(dir, file))\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e47ab99363540050623a47b24d81fc9a\",\n \"score\": \"0.62019986\",\n \"text\": \"def load_images_without_extention(path):\\n images = []\\n valid_images = [\\\".jpg\\\", \\\".png\\\", \\\".jpeg\\\"]\\n for f in os.listdir(path):\\n file, ext = os.path.splitext(f)\\n if ext.lower() not in valid_images:\\n continue\\n images.append(file)\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eb177ef54ee82e41b2fb88a46dfe5bf\",\n \"score\": \"0.6201061\",\n \"text\": \"def read_directory(extensions):\\n for file in os.listdir('.'):\\n filenames = file.split('.')\\n if os.path.isdir(file):\\n continue\\n if filenames[1] not in extensions:\\n extensions.append(filenames[1])\\n print(extensions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8755751c0c0eede4b772f14be8436f93\",\n \"score\": \"0.6200504\",\n \"text\": \"def get_filenames(format: str = 'json') -> List[str]:\\n filenames = []\\n for type in get_filetypes(format):\\n filenames.extend(glob.glob(f'*.{type}'))\\n return filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cd5d5d20c08c7a82fb7c096ac33b951\",\n \"score\": \"0.6196302\",\n \"text\": \"def get_files(directory):\\n files = []\\n for item in os.listdir(directory):\\n item = os.path.join(directory, item)\\n if not os.path.isfile(item):\\n continue\\n parts = os.path.splitext(item)\\n if parts and len(parts) > 1 and parts[1] == '.py'\\\\\\n and parts[0].lower().find(\\\"test\\\") == -1:\\n files.append(item)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fceba3ab8b472f6765e4facaea1c5bee\",\n \"score\": \"0.61914957\",\n \"text\": \"def _collect_files(self, path, extensions):\\n path_list = []\\n for dirpath, _, filenames in os.walk(path):\\n path_list.extend(\\n [\\n os.path.join(dirpath, filename)\\n for filename in filenames\\n if os.path.splitext(filename)[1].lower() in extensions\\n ]\\n )\\n return path_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feecc2c670c46c78eacd50b912e03f26\",\n \"score\": \"0.61866087\",\n \"text\": \"def filter_files(pattern, file_names):\\n p = re.compile(pattern)\\n return [p.match(f) for f in file_names if p.match(f) is not None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73372929bb991939b97752d0da98eda\",\n \"score\": \"0.6159966\",\n \"text\": \"def get_files(dir, suffix):\\n files = []\\n for file in os.listdir(dir):\\n if file.endswith(suffix):\\n files.append(file)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73372929bb991939b97752d0da98eda\",\n \"score\": \"0.6159966\",\n \"text\": \"def get_files(dir, suffix):\\n files = []\\n for file in os.listdir(dir):\\n if file.endswith(suffix):\\n files.append(file)\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf7989ea21b31799113ffb7259206cc\",\n \"score\": \"0.6147999\",\n \"text\": \"def stringsWithImageFileExtensions(listOfStrings):\\r\\n\\r\\n #todo: string comparison should ignore case\\r\\n result = []\\r\\n for s in listOfStrings:\\r\\n if (s.find('.tif') != -1) or\\\\\\r\\n (s.find('.bmp') != -1) or\\\\\\r\\n (s.find('.pgm') != -1) or\\\\\\r\\n (s.find('.gif') != -1) or\\\\\\r\\n (s.find('.png') != -1):\\r\\n result.append(s)\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ea904a64b978053bd910323a78d3c9e\",\n \"score\": \"0.6144758\",\n \"text\": \"def _get_c_files(srcfiles, extensions=False):\\n files_out = []\\n for srcfile in srcfiles:\\n ext = os.path.splitext(srcfile)[1]\\n if ext.lower() in [\\\".c\\\", \\\".cpp\\\"]:\\n if extensions:\\n if ext not in files_out:\\n files_out.append(ext)\\n else:\\n files_out.append(srcfile)\\n if len(files_out) < 1:\\n files_out = None\\n return files_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a19e8793e3621beb428a0710c1a5af\",\n \"score\": \"0.6140286\",\n \"text\": \"def get_file_list(path, extensions):\\n if not os.path.isdir(path):\\n log_error_exit('Directory invalid.')\\n else:\\n files_list = []\\n # The FITS files are named in numerically ascending order with an optional\\n for file in os.listdir(path):\\n if file.split('.')[-1] in extensions:\\n files_list.append(os.path.join(path, file))\\n if len(files_list) == 0:\\n log_error_exit('No files with given extensins found !')\\n return sorted(files_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7430a703d144531116f0ef4a00b716f\",\n \"score\": \"0.6116649\",\n \"text\": \"def get_files_with_suffix(dir_name, suffixes):\\n import os\\n found_filenames = []\\n for localpath, directories, filenames in os.walk(dir_name):\\n for filename in filenames:\\n for suffix in suffixes:\\n if filename.endswith(suffix):\\n found_filenames.append(os.path.join(localpath, filename))\\n found_filenames.sort()\\n return found_filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8227919c4a92055b6e0f0aae097b9067\",\n \"score\": \"0.6115884\",\n \"text\": \"def _splitext(fname):\\n dir, filename = os.path.split(fname)\\n for special_ext in ['.nii.gz', '.tar.gz']:\\n if filename.endswith(special_ext):\\n stem, ext = filename[:-len(special_ext)], special_ext\\n return os.path.join(dir, stem), ext\\n # If no special case, behaves like the regular splitext\\n stem, ext = os.path.splitext(filename)\\n return os.path.join(dir, stem), ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98eb8adabba7af1cf0a28c40a232498\",\n \"score\": \"0.6114838\",\n \"text\": \"def listfiles(mypath, mytype):\\n result = []\\n # Match Extension.\\n for foundfile in Path(mypath).glob(\\\"**/*.*\\\"):\\n filetype = str(foundfile).split(\\\".\\\")[-1]\\n if filetype in mytype:\\n # Force string type was Posix(path)\\n result.append(str(foundfile))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d0dc0b8cca82a9ec101f45f6470ac3\",\n \"score\": \"0.61128\",\n \"text\": \"def remove_all_file_extensions(filepath: Path, extensions: list = None):\\n\\n def _remove_next_suffix(f):\\n if extensions is not None and len(extensions) > 0:\\n return f.suffix in extensions\\n return len(f.suffix) > 0\\n\\n while _remove_next_suffix(filepath):\\n filepath = filepath.with_suffix(\\\"\\\")\\n\\n return filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81288495451141c1b16e070d8a7535db\",\n \"score\": \"0.61059415\",\n \"text\": \"def list_all_extension_files(directory_path, extension='.txt'):\\n files_paths = []\\n for r, d, f in os.walk(directory_path):\\n f = [os.path.join(r,file) for file in f if file.find(extension) != -1]\\n files_paths.append(f)\\n\\n files_paths = list(np.hstack(files_paths))\\n files_paths = [tuple((in_file, in_file.replace(\\\"text_volumes\\\",\\\"speeches\\\").replace(\\\".txt\\\",\\\".csv\\\"))) for in_file in files_paths]\\n\\n files_paths = [tuple((in_file,out_file)) for in_file, out_file in files_paths if not os.path.exists(out_file)]\\n return files_paths\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":183,"cells":{"query_id":{"kind":"string","value":"dfc4c0b6369e884b99877afc55bfd359"},"query":{"kind":"string","value":"Build a Cypher query based on given parameters."},"positive_passages":{"kind":"list like","value":[{"docid":"d75bc1828ce83ede1c9f954653f24192","score":"0.5487266","text":"def _build_dependencies_query(\n cls, team_id, topic, label, node, filter_on_config=False, impacted=False\n ):\n where = \"\"\n order = \"(n)<-[r]-(m)\" if impacted else \"(n)-[r]->(m)\"\n query = (\n \"MATCH(n:{topic}_{label}{{name: '{name}'}}) \"\n \"OPTIONAL MATCH {order} {where}\"\n \"RETURN n,r,m ORDER BY m.name LIMIT 10\"\n )\n\n # Filter the dependencies using the labels declared in the configuration\n if filter_on_config:\n label_config = ConfigController.get_label_config(team_id, label)\n regex = r\"^.*(\\[[A-Za-z]+(, [A-Za-z]+)*?\\])$\"\n\n match = re.search(regex, label_config[\"qos\"])\n if match:\n deps = match.group(1)[1:-1].split(\", \")\n where += \"WHERE '{}_{}' IN LABELS(m) \".format(topic, deps[0])\n\n for dep in deps[1:]:\n where += \"OR '{}_{}' IN LABELS(m) \".format(topic, dep)\n\n return query.format(\n where=where, order=order, topic=topic, label=label, name=node\n )","title":""}],"string":"[\n {\n \"docid\": \"d75bc1828ce83ede1c9f954653f24192\",\n \"score\": \"0.5487266\",\n \"text\": \"def _build_dependencies_query(\\n cls, team_id, topic, label, node, filter_on_config=False, impacted=False\\n ):\\n where = \\\"\\\"\\n order = \\\"(n)<-[r]-(m)\\\" if impacted else \\\"(n)-[r]->(m)\\\"\\n query = (\\n \\\"MATCH(n:{topic}_{label}{{name: '{name}'}}) \\\"\\n \\\"OPTIONAL MATCH {order} {where}\\\"\\n \\\"RETURN n,r,m ORDER BY m.name LIMIT 10\\\"\\n )\\n\\n # Filter the dependencies using the labels declared in the configuration\\n if filter_on_config:\\n label_config = ConfigController.get_label_config(team_id, label)\\n regex = r\\\"^.*(\\\\[[A-Za-z]+(, [A-Za-z]+)*?\\\\])$\\\"\\n\\n match = re.search(regex, label_config[\\\"qos\\\"])\\n if match:\\n deps = match.group(1)[1:-1].split(\\\", \\\")\\n where += \\\"WHERE '{}_{}' IN LABELS(m) \\\".format(topic, deps[0])\\n\\n for dep in deps[1:]:\\n where += \\\"OR '{}_{}' IN LABELS(m) \\\".format(topic, dep)\\n\\n return query.format(\\n where=where, order=order, topic=topic, label=label, name=node\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"72ebf75d0424beb0d9398dd9dcc5f821","score":"0.7439297","text":"def build_query(self, params):\n pass","title":""},{"docid":"a95e410d68282d18b99170eb9c871fae","score":"0.70802385","text":"def _build_query(self, params, boolean='AND'):\n query = []\n for p in params:\n if isinstance(p, str):\n # direct query string\n query.append(p)\n\n elif isinstance(p, tuple) and len(p) == 3:\n # <field> <op> <value> triplet\n (field, op, value) = p\n op_ = '-' if op == '!=' else ''\n\n if isinstance(value, list):\n value_ = '(\"' + '\",\"'.join(value) + '\")'\n elif isinstance(value, tuple):\n value_ = '[%s TO %s]' % value\n else:\n quote = '\"' if not '*' in str(value) else ''\n value_ = quote + str(value) + quote\n\n query.append('%s%s:%s' % (op_, field, value_))\n\n elif isinstance(p, tuple) and len(p) == 2:\n # group/subquery with boolean operator\n (op, subquery) = p\n query.append('(' + self._build_query(subquery, op) + ')')\n\n return (' '+boolean+' ').join(query)","title":""},{"docid":"2c96f74b26acea483825aadcbb969353","score":"0.6558855","text":"def build_query(self, params):\n phrase = params[\"phrase\"]\n slop = params[\"slop\"]\n\n query = {}\n query[\"query\"] = {\"match\":{}}\n\n infl_form = {}\n infl_form[\"type\"] = \"phrase\"\n infl_form[\"query\"] = phrase\n infl_form[\"slop\"] = slop\n query[\"query\"][\"match\"][\"inflected_form\"] = infl_form\n\n query = json.dumps(query)\n return query","title":""},{"docid":"2a8397a2a8e9edc0608094386c96404c","score":"0.653392","text":"def _build_query_string(cls, parameters: dict) -> str:\n\t\tquery_string = '?'\n\n\t\t# Builds a list of tuples for easy iteration like:\n\t\t# [('channel', 'channel-1'), ('channel', 'channel-2'), ('assetId', 'asset-1'), ...]\n\t\tparameter_tuples = [(parameter_name, parameter_value) \n\t\t\tfor parameter_name, parameter_values in parameters.items()\n\t\t\t\tfor parameter_value in parameter_values]\n\n\t\tfor index, parameter_tuple in enumerate(parameter_tuples):\n\t\t\tname, value = parameter_tuple\n\t\t\tvalue = quote(value.encode()) if type(value) == str else value\n\n\t\t\tif name == 'orderBy':\n\t\t\t\tvalue = cls._convert_order_by(value)\n\n\t\t\tquery_string += f'{name}={value}' if index == 0 else f'&{name}={value}'\n\n\t\treturn query_string","title":""},{"docid":"0a18169ab89094b2a6bab17605534b4f","score":"0.6477496","text":"def _build_query(self, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"76313a0e15cad414416780ca9e335fb0","score":"0.6379244","text":"def _build_search(query, params):\n #PLACEHOLDER -- only searches the objects table and does the simplest\n #search possible for now\n query_terms = params.copy() if( params and params != {} ) else _translate_query(query)\n arguments = _args()\n keywords = \"\"\n query_string = \"\"\n\n if 'query_string' in query_terms:\n query_string = query_terms['query_string']\n arguments.update({'query_string':query_string})\n del query_terms['query_string']\n if 'keywords' in query_terms:\n arguments.update({'keywords':query_terms['keywords']})\n del query_terms['keywords']\n # TODO: need to do all the query string processing here \n # but, for now just a simple search assumption will work by default\n\n if query_string != \"\":\n keywords += query_string\n \n return \"ca_objects?q=\"+keywords, arguments","title":""},{"docid":"c157292ed4cc40e351d63d9d0af82576","score":"0.6359702","text":"def generate_query(self):\n return ''.join(['SELECT ',\n ', '.join(self.columns),\n ' ',\n 'FROM molecule ',\n ' '.join(self.joins),\n ' ',\n 'WHERE ',\n '(', ') AND ('.join(self.wheres), ')'])","title":""},{"docid":"cba63cfec2c1ae532de6e05ad75c5ffe","score":"0.6332386","text":"def _set_sql_query(self, query_params):\n query = f'SELECT * FROM {CATS_TABLE}'\n # attributes in query string\n if query_params.get('attribute'):\n query += ' ORDER BY '\n # may be 1>= attrs\n for attr in query_params['attribute']:\n query += f'{attr}, '\n query = query.rstrip(', ')\n\n # order in query string\n if query_params.get('order'):\n order = query_params['order'][0].upper()\n query += f' {order}'\n\n # offset in query string\n if query_params.get('offset'):\n offset = query_params['offset'][0]\n query += f' OFFSET {offset}'\n\n # limit in query string\n if query_params.get('limit'):\n limit = query_params['limit'][0]\n query += f' LIMIT {limit}'\n\n return query","title":""},{"docid":"6aafa03146dc86896e8b4203a577f391","score":"0.6295957","text":"def make_query(self, **kw):\n query = kw.pop(\"query\", {})\n\n query.update(self.get_request_query())\n query.update(self.get_custom_query())\n query.update(self.get_keyword_query(**kw))\n\n sort_on, sort_order = self.get_sort_spec()\n if sort_on and \"sort_on\" not in query:\n query.update({\"sort_on\": sort_on})\n if sort_order and \"sort_order\" not in query:\n query.update({\"sort_order\": sort_order})\n\n logger.info(\"make_query:: query={} | catalog={}\".format(\n query, self.catalog))\n\n return query","title":""},{"docid":"1216cd9552ca67c038d89c1a33795cb6","score":"0.6221052","text":"def build_query(self):\n\n # initalize config\n self.config.update_config()\n\n # get the base query from the config\n query = self.config.get_base_query()\n return query","title":""},{"docid":"d11b2046780debe8f975dcb0277e234c","score":"0.62166256","text":"def buildQuery(self):\n if not self.Valid:\n print(\"Invalid usage: cannot build.\")\n return False\n if not self.needsBuilt:\n return self.queryString\n\n filterDict = dict()\n theFilters = dict()\n if len(self._andFilters) > 0:\n theFilters[\"and\"] = self._andFilters\n if len(self._orFilters) > 0:\n theFilters[\"or\"] = self._orFilters\n filterDict[\"filter\"] = theFilters\n queryString = json.dumps(filterDict)\n\n self.queryString = queryString\n return queryString","title":""},{"docid":"bb15855e6fd47d6e9d3a76d89a8b5ace","score":"0.6071394","text":"def _build_query(symbol: str, start_date: str, end_date: str, order_by: str):\n query = \"select * from stock_price where symbol = '{symbol}'\".format(symbol=symbol)\n if start_date:\n query += \" and time >= '{}'\".format(start_date)\n if end_date:\n query += \" and time <= '{}'\".format(end_date)\n\n if order_by == 'ASC':\n # Default\n pass\n elif order_by == 'DESC':\n query += ' order by time desc'\n\n return query","title":""},{"docid":"d46b3852c7f93eb9f1037b46961e8edb","score":"0.60509795","text":"def query_generator(self, kwargs):\n\n query_addition = \"\"\n\n if \"quarter\" in kwargs:\n query_addition += f\" & qtr == {kwargs['quarter']}\"\n if \"down\" in kwargs:\n query_addition += f\" & down == {kwargs['down']}\"\n if \"half\" in kwargs:\n query_addition += f\" & game_half == 'Half{kwargs['half']}'\"\n if \"close\" in kwargs:\n if kwargs['close'] is True:\n query_addition += f\" & score_differential >= -12 & score_differential <= 12\"\n else:\n query_addition += f\" & (score_differential < -12 | score_differential > 12)\"\n if \"rz\" in kwargs:\n if kwargs['rz'] is True:\n query_addition += f\" & yardline_100 <= 25\"\n else:\n query_addition += f\" & yardline_100 > 25\"\n if \"where\" in kwargs:\n if kwargs['where'] is 'home':\n query_addition += f\" & home_team == '{self.team}'\"\n else:\n query_addition += f\" & away_team == '{self.team}'\"\n if \"play\" in kwargs:\n if kwargs['play'] is 'run':\n query_addition += f\" & rush_attempt == 1\"\n else:\n query_addition += f\" & pass_attempt == 1\"\n if \"since\" in kwargs:\n if kwargs['since'] is 'sep':\n query_addition += f\" & (game_date.str.slice(5, 7) == '09' | game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n elif kwargs['since'] is 'oct':\n query_addition += f\" & (game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n elif kwargs['since'] is 'nov':\n query_addition += f\" & (game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\"\n else:\n query_addition += f\" & game_date.str.slice(5, 7) == '12'\"\n\n return query_addition","title":""},{"docid":"c570052933c609ad72ebabe6e69a8bfd","score":"0.598974","text":"def data_query(self, parents=None):\n filter_q = self.filter_query(parents=parents)\n q = db.session.query()\n stmt, q = self._add_statement(q)\n\n filter_sq = filter_q.subquery()\n q = q.filter(stmt.subject == filter_sq.c.subject)\n q = q.filter(stmt._attribute.in_(self.project()))\n\n q = q.add_column(stmt.subject.label('id'))\n q = q.add_column(stmt._attribute.label('attribute'))\n q = q.add_column(stmt._value.label('value'))\n\n if parents is not None and self.node.attribute:\n parent_stmt, q = self._add_statement(q)\n q = q.filter(stmt.subject == parent_stmt._value)\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\n q = q.add_column(parent_stmt.subject.label('parent_id'))\n\n q = q.order_by(filter_sq.c.subject.desc())\n q = q.order_by(stmt.created_at.asc())\n return q","title":""},{"docid":"dd1bf23e219921f97adc765b4e477518","score":"0.5976437","text":"def make_query(columns, filter):\n if columns is not None:\n # the user only wants to report a subset of the columns\n query = \"SELECT \" + columns + \" FROM variants\"\n# elif cancers != 'none':\n# query = \"SELECT \" + columns + \",civic_gene_abbreviations,cgi_gene_abbreviations FROM variants\"\n else:\n # report the kitchen sink\n query = \"SELECT * FROM variants\"\n if filter is not None:\n # add any non-genotype column limits to the where clause\n query += \" WHERE \" + filter\n return query","title":""},{"docid":"3662e9f0259cba0673eaafff569ccaaa","score":"0.59676003","text":"def make_query(request, **kw):\n\n # build the catalog query\n query = {\n \"sort_limit\": get_sort_limit(request),\n \"sort_on\": get_sort_on(request),\n \"sort_order\": get_sort_order(request),\n \"SearchableText\": get_query(request),\n }\n\n # inject keyword args\n query.update(kw)\n\n # inject the creator if given\n if get_creator(request):\n query[\"Creator\"] = get_creator(request)\n\n logger.info(\"Catalog Query --> %r\", query)\n return query","title":""},{"docid":"a0196bcfee0829e1a676506cb8f483df","score":"0.59151345","text":"def query_from_params(self, params):\n q = {}\n start, rows = 0, 10\n if \"start\" in params:\n start = int(params[\"start\"])\n if \"rows\" in params:\n rows = int(params[\"rows\"])\n\n if \"lat\" in params and \"lon\" in params:\n lat = float(params[\"lat\"])\n lon = float(params[\"lon\"])\n q[\"loc\"] = {\n \"$near\": [lat, lon]\n }\n for atr in [\"city\", \"state\", \"country\"]:\n if atr in params:\n q[atr] = params[atr]\n return self.query(start=start, rows=rows, **q)","title":""},{"docid":"324d683b973caa2910387cddae4780e5","score":"0.5909418","text":"def _create_query_params(self, kwargs):\n if None == kwargs or 0 == len(kwargs):\n return (\"\", [])\n\n if not isinstance(kwargs, basestring):\n pairs = kwargs.items()\n stmt = \" WHERE \" + \\\n \" AND \".join([ x[0] + (None is x[1] and \" IS NULL\" or \" = ?\")\n for x in pairs ])\n\n args = [x[1] for x in filter(lambda x: None is not x[1], pairs)]\n else:\n stmt = \" WHERE \" + kwargs\n args = []\n return (stmt, args)","title":""},{"docid":"49409b5c49174457e063fc4a1f2caf74","score":"0.5909326","text":"def build_query(cols=None, tables=None, group_by=None, order_by=None, **kwargs):\n assert len(tables)\n\n # Create SELECT line\n cols_str = \", \".join(cols) if cols else \"*\"\n query = f'SELECT {cols_str} FROM '\n\n # Create FROM line\n # Automatically add bracelets around strings with space inside them. Useful for nested queries.\n query += ' NATURAL JOIN '.join(f'({table})' if ' ' in table else table for table in tables)\n\n # Apply all filters in the kwargs dict\n constraints = []\n for col_name, value in kwargs.items():\n # Empty filters don't need to be applied\n if value not in (None, ''):\n # Use \"LIKE\" for strings, and \"==\" otherwise\n if isinstance(value, str):\n constraints.append(f'{col_name} LIKE \"{value}\"')\n else:\n constraints.append(f'{col_name} == {value}')\n\n # Create WHERE line if there are constraints\n if constraints:\n query += ' WHERE ' + ' AND '.join(constraints)\n\n # Create GROUP BY line if needed\n if group_by:\n query += ' GROUP BY ' + group_by\n\n # Create ORDER BY line if needed\n if order_by:\n query += ' ORDER BY ' + order_by\n\n return query","title":""},{"docid":"1fb4cc87b5249fb99610ec14d3f62cf8","score":"0.58879614","text":"def GenerateQuery(unused_ref, args, request):\n customer_id = ConvertOrgIdToObfuscatedCustomerId(args.organization)\n labels = FilterLabels(args.labels)\n labels_str = ','.join(labels)\n request.query = 'parent==\\\"customerId/{0}\\\" && \\\"{1}\\\" in labels'.format(\n customer_id, labels_str)\n\n return request","title":""},{"docid":"ff8ba2a0ee0c22e8f3b0dc0581901893","score":"0.5856376","text":"def make_query(self, ns):\n if issubclass(self.model_class, db.Model):\n query = db.Query(self.model_class, namespace=ns)\n for f in self.filters:\n query.filter(\"%s %s\" % (f[0], f[1]), f[2])\n else:\n query = self.model_class.query(namespace=ns)\n for f in self.filters:\n query = query.filter(ndb.FilterNode(*f))\n return query","title":""},{"docid":"a5aeac92141db6643f5dae341690653e","score":"0.5830022","text":"def build_query(\n self,\n regions=None,\n genes=None,\n effect_types=None,\n family_ids=None,\n person_ids=None,\n inheritance=None,\n roles=None,\n sexes=None,\n variant_type=None,\n real_attr_filter=None,\n ultra_rare=None,\n frequency_filter=None,\n return_reference=None,\n return_unknown=None,\n limit=None,\n pedigree_fields=None):\n # pylint: disable=too-many-arguments\n self.query_builder.reset_product()\n\n self.query_builder.build_select()\n\n self.query_builder.build_from()\n\n self.query_builder.build_join()\n\n self.query_builder.build_where(\n regions=regions,\n genes=genes,\n effect_types=effect_types,\n family_ids=family_ids,\n person_ids=person_ids,\n inheritance=inheritance,\n roles=roles,\n sexes=sexes,\n variant_type=variant_type,\n real_attr_filter=real_attr_filter,\n ultra_rare=ultra_rare,\n frequency_filter=frequency_filter,\n return_reference=return_reference,\n return_unknown=return_unknown,\n pedigree_fields=pedigree_fields,\n )\n\n self.query_builder.build_group_by()\n self.query_builder.build_limit(limit)","title":""},{"docid":"9bf3e320668377af9f499baa5b818919","score":"0.5816653","text":"def make_query(config):\n\n query = {}\n\n items = ['object', 'radius', 'max_out', 'source', 'output', 'mime', 'sort']\n\n for item in items\n query[item] = str(config[item])\n\n return query","title":""},{"docid":"2159f3098a64c4db73cdb716ccac6fd2","score":"0.57821774","text":"def make_where_statement(self, table_name, params):\n if not params:\n return (\" 1 \", ())\n column_names = [row[1] for row in self.get_rows(\"PRAGMA table_info(%s)\" % table_name)]\n for column in params.keys():\n if column not in column_names:\n raise KeyError(\"Error: Database column %s not in our database\" % column)\n # Construct our SQL statement\n where_statement = ' AND '.join(['\"%s\" = ?' % column for (column, value) in sorted(params.items())])\n where_values = tuple([value for (column, value) in sorted(params.items())])\n return (where_statement, where_values)","title":""},{"docid":"dd14224ced22e5c08b3414302d16b36f","score":"0.5775194","text":"def build_query(self) -> Query:\n logger.info(\n {\n \"message\": f\"Start building query for resource {self.analysis.definition_id}\",\n \"resource_id\": self.analysis.resource_id,\n },\n )\n\n # We don't need to have columns duplicated in the dataframe\n # so we keep the one we've already seen here.\n # Note that the primary key column is added at the query initialization.\n self._cur_query_columns = {self.analysis.primary_key_column}\n # To avoid duplicated joins, we keep them here\n self._cur_query_join_tables = {}\n\n query = self.session.query(self.get_column(self.analysis.primary_key_column, self._sqlalchemy_pk_table))\n\n # Add attributes to query\n for attribute in self.analysis.attributes:\n query = self.add_attribute_to_query(query, attribute)\n\n # Add filters to query\n query = self.apply_filters(query)\n\n logger.info(\n {\n \"message\": f\"Built query for resource {self.analysis.definition_id}: {query.statement}\",\n \"resource_id\": self.analysis.resource_id,\n },\n )\n return query","title":""},{"docid":"deb662581d7042718da96ca7baef1b0e","score":"0.5773945","text":"def query(self):\n if 'order' in self.config['database']:\n order = 'ORDER BY {order}'.format(**self.config['database'])\n else:\n order = ''\n\n if isinstance(self.cols, tuple):\n cols = self.cols\n else:\n cols = (self.cols,)\n\n where, params = parse_queries(self.selection, self.mapping)\n if where:\n where = 'WHERE {conditions}'.format(conditions=' AND '.join(where))\n else:\n where = ''\n limit = (self.slice[0].stop or sys.maxint) - (self.slice[0].start or 0)\n\n sql = \"\"\"\n SELECT {cols} FROM {table}\n {where}\n {order}\n LIMIT {limit}\n OFFSET {offset}\n \"\"\".format(\n cols=', '.join(self.config[key]['col'] for key in cols),\n table=self.config['database']['table'],\n where=where, order=order, limit=limit,\n offset=self.slice[0].start or 0)\n\n return sql, params","title":""},{"docid":"86f87622ad6a714500a7b6ccb0df8944","score":"0.5773749","text":"def sql_with_params(self):\r\n return self.get_compiler(DEFAULT_DB_ALIAS).as_sql()","title":""},{"docid":"a12abdcbed66b2452ab47d4061b8ce7d","score":"0.5761608","text":"def build_query(self, query_dict):\n\n # Loop over keys in request dictionary, match them up to database fields\n # Build query accounting for the exceptions\n qry_sci_name = ''\n qry_common_name = ''\n qry_group = ''\n qry_native = ''\n qry_pollut_tol = ''\n qry_rarity = ''\n qry_range = ''\n qry_max_age = ''\n qry_mean_weight = ''\n qry_mean_length = ''\n qry_max_length = ''\n qry_habitat = ''\n qry_ubiquity = ''\n qry_extent = ''\n qry_tolerance = ''\n qry_robustness = ''\n\n #Used in a couple of places to remove a trailing 'or '\n trailing_or = ' or'\n trailing_and = ' and'\n\n #This will be used later for habitat related fields that contain 1 for true. E.g. caves=1\n set_keys = set(query_dict.keys())\n\n for req_key, req_val in query_dict.items():\n # Is request param a valid query param\n if req_key in self.attrib:\n\n if (req_key.lower() == 'scientific_name'):\n words = req_val.split('_')\n if len(words) == 1:\n qry_sci_name = (\"( genus LIKE INITCAP('%%{0}%%') or species LIKE LOWER('%%{0}%%'))\")\n qry_sci_name = str.format(qry_sci_name, words[0])\n elif len(words) == 2:\n qry_sci_name = (\" ((genus LIKE INITCAP('%%{0}%%') and species LIKE LOWER('%%{1}%%')) or (species LIKE LOWER('%%{0}%%') and genus LIKE INITCAP('%%{1}%%')))\")\n qry_sci_name = str.format(qry_sci_name, words[0], words[1])\n\n if req_key.lower() == 'common_name':\n words = req_val.split('_')\n if len(words) == 1:\n qry_common_name = (\" commonname LIKE INITCAP('%%{0}%%')\")\n qry_common_name = str.format(qry_common_name, words[0])\n elif len(words) == 2:\n qry_common_name = (\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%'))\")\n qry_common_name = str.format(qry_common_name, words[0], words[1])\n elif len(words) == 3:\n qry_common_name = (\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%') and commonname LIKE INITCAP('%%{2}%%'))\")\n qry_common_name = str.format(qry_common_name, words[0], words[1], words[2])\n\n\n # Can be multiple groups\n #e.g. grp='Black Bass' or grp='Mullet'\n if (req_key.lower() == 'group'):\n words = req_val.lower()\n words = words.replace('_', ' ')\n words = words.split(',')\n for idx, grp in enumerate(words):\n if idx != 0:\n qry_group += \" or \"\n qry_group += str.format(\" lower(grp) = '{0}'\", grp.lower())\n continue\n\n if (req_key.lower() == 'native'):\n qry_native = str.format(\" native='{0}'\",req_val)\n continue\n\n # pollution tolerance can be: \"I\", \"T\", \"M\", or \"U\"\n # if (req_key.lower() == 'pollut_tol'):\n # if (req_key.lower() == 'tolerance'):\n # words = req_val.lower()\n # words = words.split(',')\n # for idx, pollut in enumerate(words):\n # if idx != 0:\n # qry_pollut_tol += \" or \"\n # qry_pollut_tol += str.format(\" lower(pollut_tol) = '{0}'\", pollut)\n # continue\n\n if (req_key.lower() == 'rarity'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_rarity = str.format(\" rarity>={0} and rarity<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'max_age'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_max_age = str.format(\" max_age>={0} and max_age<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'mean_weight'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_mean_weight = str.format(\" mean_weight>={0} and mean_weight<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'mean_length'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_mean_length = str.format(\" mean_length>={0} and mean_length<={1}\", range[0], range[1])\n continue\n\n if (req_key.lower() == 'max_length'):\n range = req_val.split('_')\n if len(range) == 2:\n qry_max_length = str.format(\" max_length>={0} and max_length<={1}\", range[0], range[1])\n continue\n\n ubiquity_range = [0, 75]\n if (req_key.lower() == 'ubiquity'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != ubiquity_range[0] or int(range[1]) != ubiquity_range[1]):\n qry_ubiquity = str.format(\" ubiquity>={0} and ubiquity<={1}\", range[0], range[1])\n continue\n\n extent_range = [0, 77]\n if (req_key.lower() == 'extent'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != extent_range[0] or int(range[1]) != extent_range[1]):\n qry_extent = str.format(\" extent>={0} and extent<={1}\", range[0], range[1])\n continue\n\n tolerance_range = [0, 94]\n if (req_key.lower() == 'tolerance'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != tolerance_range[0] or int(range[1]) != tolerance_range[1]):\n qry_tolerance = str.format(\" tolerance>={0} and tolerance<={1}\", range[0], range[1])\n continue\n\n robustness_range = [0, 47]\n if (req_key.lower() == 'robustness'):\n range = req_val.split('_')\n if len(range) == 2 and (int(range[0]) != robustness_range[0] or int(range[1]) != robustness_range[1]):\n qry_robustness = str.format(\" robustness>={0} and robustness<={1}\", range[0], range[1])\n continue\n\n if qry_habitat.endswith(trailing_and):\n qry_habitat = qry_habitat[:-len(trailing_and)]\n\n # The rest of the query parameters are are used if value == 1\n # e.g. caves=1, springs=1, headwaters=1\n for cat in self.categories:\n intersect = set_keys & self.categories[cat]\n if len(intersect) > 0:\n print(cat)\n print(intersect)\n for param in intersect:\n val = query_dict[param]\n if (val == '1'):\n self.query_categories[cat].append(param)\n\n qry_habitat = ''\n query_cat = ''\n for category in self.query_categories:\n query_cat = ''\n for val in self.query_categories[category]:\n query_cat += str.format(\" {0}='1' or\", val)\n\n if query_cat.endswith(trailing_or):\n query_cat = query_cat[:-len(trailing_or)]\n\n if len(query_cat) > 0:\n qry_habitat += ' (' + query_cat + ') and'\n print(qry_habitat)\n\n if qry_habitat.endswith(trailing_and):\n qry_habitat = qry_habitat[:-len(trailing_and)]\n\n query = \"select * from fishproperties where\"\n first_condition = True\n\n if qry_sci_name != '':\n query += qry_sci_name\n first_condition = False\n\n if qry_common_name != '':\n if not first_condition:\n query += ' and'\n query += qry_common_name\n first_condition = False\n\n if qry_group != '':\n if not first_condition:\n query += ' and'\n query += qry_group\n first_condition = False\n\n if qry_native != '':\n if not first_condition:\n query += ' and'\n query += qry_native\n first_condition = False\n\n if qry_pollut_tol != '':\n if not first_condition:\n query += ' and'\n query += qry_pollut_tol\n first_condition = False\n\n if qry_rarity != '':\n if not first_condition:\n query += ' and'\n query += qry_rarity\n first_condition = False\n\n if qry_range != '':\n if not first_condition:\n query += ' and'\n query += qry_range\n first_condition = False\n\n if qry_max_age != '':\n if not first_condition:\n query += ' and'\n query += qry_max_age\n first_condition = False\n\n if qry_mean_weight != '':\n if not first_condition:\n query += ' and'\n query += qry_mean_weight\n first_condition = False\n\n if qry_mean_length != '':\n if not first_condition:\n query += ' and'\n query += qry_mean_length\n first_condition = False\n\n if qry_max_length != '':\n if not first_condition:\n query += ' and'\n query += qry_max_length\n first_condition = False\n\n if qry_ubiquity != '':\n if not first_condition:\n query += ' and'\n query += qry_ubiquity\n first_condition = False\n\n if qry_extent != '':\n if not first_condition:\n query += ' and'\n query += qry_extent\n first_condition = False\n\n if qry_tolerance != '':\n if not first_condition:\n query += ' and'\n query += qry_tolerance\n first_condition = False\n\n if qry_robustness != '':\n if not first_condition:\n query += ' and'\n query += qry_robustness\n first_condition = False\n\n if qry_habitat != '':\n if not first_condition:\n query += ' and'\n query += qry_habitat\n first_condition = False\n\n print(query)\n\n return query","title":""},{"docid":"c45b3d40d8a26bb7193088e942d5a655","score":"0.5736782","text":"def _construct_query(self, query_chain):\n # here we take the query_chain and convert to a real sql sentence\n res_dict = query_chain.pop(0)\n\n for q in query_chain:\n if q[\"action\"] == \"_db__where\":\n if res_dict[\"action\"] == \"_db__select_all\":\n res_dict.update({\"action\": \"_db__select\"})\n\n condition = res_dict.get(\"condition\", \"\")\n if condition:\n condition = \" AND \".join([condition, q[\"condition\"]])\n else:\n condition = q[\"condition\"]\n\n res_dict.update({\"condition\": condition})\n elif q[\"action\"] == \"_db__select_related\":\n for model_join in q[\"fields\"]:\n join_const = getattr(self, q[\"action\"]).format(**model_join)\n res_dict[\"join\"] += join_const\n\n select = res_dict[\"select\"][:]\n\n if select == \"COUNT(*)\":\n pass\n elif select == \"*\":\n select = select.replace(\n \"*\",\n \"{left_table}.*, {f_formatter}\".format(\n left_table=model_join[\"left_table\"], f_formatter=model_join[\"fields_formatter\"]\n ),\n )\n res_dict[\"select\"] = select\n else:\n res_dict[\"select\"] += \", \" + model_join[\"fields_formatter\"]\n\n # if we are not counting, then we can assign ordering\n operations = [\"COUNT\", \"MAX\", \"MIN\", \"SUM\", \"AVG\", \"STDDEV\"]\n\n # we must get error, if we set only one field and update instance\n if res_dict[\"action\"] == \"_db__update\" and len(res_dict[\"field_values\"]) == 1:\n res_dict.update({\"action\": \"_db__update_single_field\"})\n\n if res_dict.get(\"select\", \"\").split(\"(\")[0] not in operations:\n res_dict[\"ordering\"] = self._ordering_syntax(res_dict.get(\"ordering\", []))\n else:\n res_dict[\"ordering\"] = \"\"\n query = getattr(self, res_dict[\"action\"]).format(**res_dict)\n query = self._query_clean(query)\n\n logger.debug(\"QUERY: {}, VALUES: {}\".format(query, res_dict.get(\"field_values\")))\n return query, res_dict.get(\"field_values\")","title":""},{"docid":"0c13ae92a74d4f3943d1cd2abdd2d58f","score":"0.572707","text":"def _build_query(env, start, end, certname=None):\n query = ExtractOperator()\n query.add_field(FunctionOperator('count'))\n query.add_field('status')\n subquery = AndOperator()\n subquery.add(GreaterEqualOperator('producer_timestamp', start))\n subquery.add(LessOperator('producer_timestamp', end))\n if certname is not None:\n subquery.add(EqualsOperator('certname', certname))\n if env != '*':\n subquery.add(EqualsOperator('environment', env))\n query.add_query(subquery)\n query.add_group_by(\"status\")\n return query","title":""},{"docid":"6a1dd0e6509804a1d36f57e0e6c5750a","score":"0.5720286","text":"def query() -> str:\n return \"\"\"\n MATCH (doc:Publication)<-[:located_in]-(subject)-[association:chemical_to_disease_or_phenotypic_feature_association]->(obj)-[:located_in]->(sec_doc:Publication)\n WHERE association.relation <> 'null' and obj.name <> 'null' and subject.name <> 'null' and doc.pmid in {pmids} and association.relation <> 'null' and type(association) <> 'could_refer_to' and type(association) <> 'located_in' and sec_doc.pmid = doc.pmid\n RETURN DISTINCT subject.name as subject, subject.synonym as subj_syn, association.relation as predicate, obj.name as obj, obj.synonym as disease_synonym, type(association) as rel_type, doc.pmid as pmid, doc.abstract as abstract\n \"\"\"","title":""},{"docid":"6002db82a2bae451e4ed335385a5f2a2","score":"0.57197946","text":"def build_where_clause(args: dict, table_args_dict: dict) -> str:\n where_clause: str = ''\n for key in args.keys():\n if key in table_args_dict.keys():\n if key == 'query':\n # if query arg is supplied than we just need to parse it and only it\n return args[key].strip()\n else:\n values_list: list = argToList(args[key])\n for raw_value in values_list:\n for field in table_args_dict[key]:\n value = raw_value\n if key == 'url':\n value = f'*{raw_value}*'\n if not where_clause:\n # the beginning of the where part should start without OR\n where_clause += f\"{field}'{value}'\"\n else:\n where_clause += f\" OR {field}'{value}'\"\n return where_clause","title":""},{"docid":"fd48cb6a7ac2b019d07866613d70b519","score":"0.5693227","text":"def generate_where_clause(state=None, product=None,\n issue=None, min_complaints=None):\n where_inner = []\n where_outer = []\n\n if state and not state == \"All\":\n where_inner.append(\"ccdb.state = '{}'\".format(state))\n\n if product and not product == \"All\":\n where_inner.append(\"ccdb.product = '{}'\".format(product))\n\n if issue and not issue == \"All\":\n where_inner.append(\"ccdb.issue = '{}'\".format(issue))\n\n if min_complaints:\n where_outer.append(\"complaint_count > {}\".format(min_complaints))\n\n if len(where_inner) > 0:\n where_inner = \"WHERE \" + \" AND \".join(where_inner)\n else:\n where_inner = \"\"\n\n if len(where_outer) > 0:\n where_outer = \"WHERE \" + \" AND \".join(where_outer)\n else:\n where_outer = \"\"\n\n return where_inner, where_outer","title":""},{"docid":"a7e6e63e44f78e69cf88a741ac17d2fb","score":"0.5682573","text":"def build_query(self, params):\n term = params[\"term\"]\n size = params[\"size\"]\n page = params[\"page\"]\n\n query = {}\n query[\"query\"] = {\"match\":{}}\n query[\"query\"][\"match\"][\"inflected_form\"] = term\n\n query[\"sort\"] = []\n query[\"sort\"].append({\"_score\": \"desc\"})\n query[\"sort\"].append({\"word_count\": \"asc\"})\n\n query[\"from\"] = (page - 1) * size\n query[\"size\"] = size\n\n query = json.dumps(query)\n return query","title":""},{"docid":"930b18d9b4e9205bd3e078001ad05e29","score":"0.5674045","text":"def generate(self):\n result = self.query\n for param in self.params:\n result = result.replace(param.raw, param.sql_value())\n\n return result","title":""},{"docid":"2aa19aae92a018fc0f26673cb0f78bfb","score":"0.56575936","text":"def filter_query(self, parents=None):\n q = db.session.query()\n stmt, q = self._add_statement(q)\n q = q.add_column(stmt.subject)\n\n if parents is not None and self.node.attribute:\n parent_stmt, q = self._add_statement(q)\n q = q.filter(stmt.subject == parent_stmt._value)\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\n q = q.filter(parent_stmt.subject.in_(parents))\n\n q = self.filter(q, stmt)\n q = q.group_by(stmt.subject)\n q = q.order_by(stmt.subject.asc())\n\n if self.node.root:\n q = q.limit(self.node.limit)\n q = q.offset(self.node.offset)\n\n return q","title":""},{"docid":"ddd52219e9c4b5adb4afd6a139da0d56","score":"0.56358266","text":"def derived(self, statement, params=(), replace=False):\n return Query(\n db=self.db,\n sql=(statement,) if replace else self.sql + (statement,),\n params=self.params + params,\n )","title":""},{"docid":"2cd8fe36d01097c20060583de2e8bc83","score":"0.562555","text":"def make_query(self):","title":""},{"docid":"9fe5061d0a503021cd5b78f56d96ffed","score":"0.5612804","text":"def build_query(media_type=None, tags=None, sort=None, max_duration=None, published_after=None, categories=None,\n region=None, random=False, limit=constants.DEFAULT_QUERY_LIMIT,\n offset=constants.DEFAULT_QUERY_OFFSET):\n # NOTE: query time skyrockets with added OPTIONAL patterns, see:\n # https://stackoverflow.com/questions/25609691/alternative-for-optional-keyword-in-sparql-queries\n #\n # NOTE: query assumes ordering of group concat operations are same across tag and source - have eyeballed and\n # looks good\n if published_after is not None:\n raise NotImplementedError('The parameter `publishedAfter` is not yet implemented')\n if region is not None:\n raise NotImplementedError('The parameter `region` is not yet implemented')\n if sort is not None and random:\n raise InvalidInputParameterCombination('Cannot specify both `sort` and `random`.')\n\n regular_fields = '?title ?image ?version ?programme ?pid ?media ?duration ?publicationDate ?masterBrand'\n query_string = f\"\"\"\n SELECT\n {regular_fields}\n {build_tags_select_statement()}\n {build_genres_select_statement()}\n\n WHERE {{\n {build_regular_fields_pattern_statement()}\n {build_tags_pattern_statement()}\n {build_genres_pattern_statement()}\n\n {build_sparql_anding_statement(\n object_list=tags,\n predicate_str='datalab:tag/datalab:tagValue',\n object_binding='programme'\n )}\n {build_values_oring_statement('media', media_type)}\n {build_filter_statement('duration', '<', max_duration)}\n {build_filter_statement('published_date', '>', published_after)}\n {build_sparql_anding_statement(\n object_list=categories,\n predicate_str='po:genre/datalab:genreKey',\n object_binding='programme'\n )}\n }}\n GROUP BY {regular_fields}\n {'ORDER BY RAND()' if random else 'ORDER BY ' + build_sort_statement(sort) if sort is not None else ''}\n LIMIT {limit}\n OFFSET {offset}\n \"\"\"\n\n return query_string","title":""},{"docid":"864d460b0beb79116a83d3be73eff240","score":"0.5609568","text":"def compiledQuery(self, columns='', where='', order_by='',\n distinct='', limit='', offset='',\n group_by='', having='', for_update=False,\n relationDict=None,\n bagFields=False,\n count=False, excludeLogicalDeleted=True,excludeDraft=True,\n ignorePartition=False,ignoreTableOrderBy=False,\n addPkeyColumn=True):\n # get the SqlCompiledQuery: an object that mantains all the informations to build the sql text\n self.cpl = SqlCompiledQuery(self.tblobj.sqlfullname,relationDict=relationDict,maintable_as=self.aliasCode(0))\n distinct = distinct or '' # distinct is a text to be inserted in the sql query string\n \n # aggregate: test if the result will aggregate db rows\n aggregate = bool(distinct or group_by)\n \n # group_by == '*': if all columns are aggregate functions, there will be no GROUP BY columns, \n # but SqlQueryCompiler need to know that result will aggregate db rows\n if group_by == '*':\n group_by = None\n\n if not ignoreTableOrderBy and not aggregate:\n order_by = order_by or self.tblobj.attributes.get('order_by')\n self.init()\n if not 'pkey' in self.cpl.relationDict:\n self.cpl.relationDict['pkey'] = self.tblobj.pkey\n \n # normalize the columns string\n columns = columns or ''\n columns = columns.replace(' ', ' ')\n columns = columns.replace('\\n', '')\n columns = columns.replace(' as ', ' AS ')\n columns = columns.replace(' ,', ',')\n if columns and not columns.endswith(','):\n columns = columns + ','\n \n # expand * and *filters: see self.expandMultipleColumns\n if '*' in columns:\n col_list = [col for col in gnrstring.split(columns, ',') if col]\n new_col_list = []\n for col in col_list:\n col = col.strip()\n if col.startswith('*'):\n new_col_list = new_col_list + self.expandMultipleColumns(col[1:], bagFields)\n else:\n new_col_list.append(col)\n columns = ','.join(new_col_list)\n \n # translate @relname.fldname in $_relname_fldname and add them to the relationDict\n if where:\n where = PERIODFINDER.sub(self.expandPeriod, where)\n \n currentEnv = self.db.currentEnv\n env_conditions = dictExtract(currentEnv,'env_%s_condition_' %self.tblobj.fullname.replace('.','_'))\n if env_conditions:\n wherelist = [where] if where else []\n for condition in env_conditions.values():\n wherelist.append('( %s )' %condition)\n where = ' AND '.join(wherelist)\n\n partition_kwargs = dictExtract(self.tblobj.attributes,'partition_')\n\n if not ignorePartition and partition_kwargs:\n wherelist = [where] if where else []\n for k,v in partition_kwargs.items():\n if currentEnv.get('current_%s' %v):\n wherelist.append('( $%s=:env_current_%s )' % (k,v))\n where = ' AND '.join(wherelist)\n columns = self.updateFieldDict(columns)\n where = self.updateFieldDict(where or '')\n order_by = self.updateFieldDict(order_by or '')\n group_by = self.updateFieldDict(group_by or '')\n having = self.updateFieldDict(having or '')\n \n col_list = uniquify([col for col in gnrstring.split(columns, ',') if col])\n new_col_list = []\n for col in col_list:\n col = col.strip()\n if col.startswith('SUM'):\n aggregate = True\n if not ' AS ' in col:\n if col.startswith('$') and col[1:].replace('_', '').isalnum():\n as_ = col[1:]\n else:\n # replace non word char with _ and check for numbers\n as_ = self.db.colToAs(col)\n col = '%s AS \"%s\"' % (col, as_)\n else:\n colbody, as_ = col.split(' AS ', 1)\n # leave the col as is, but save the AS name to recover the db column original name from selection result\n self.cpl.aliasDict[as_.strip()] = colbody.strip()\n new_col_list.append(col)\n \n # build the clean and complete sql string for the columns, but still all fields are expressed as $fieldname\n columns = ',\\n'.join(new_col_list)\n \n # translate all fields and related fields from $fldname to t0.fldname, t1.fldname... and prepare the JOINs\n colPars = {}\n for key, value in self.cpl.relationDict.items():\n # self._currColKey manage exploding columns in recursive getFieldAlias without add too much parameters\n self._currColKey = key\n colPars[key] = self.getFieldAlias(value)\n if count: # if the query is executed in count mode...\n order_by = '' # sort has no meaning\n if group_by: # the number of rows is defined only from GROUP BY cols, so clean aggregate functions from columns.\n columns = group_by # was 't0.%s' % self.tblobj.pkey # ???? \n elif distinct:\n pass # leave columns as is to calculate distinct values\n else:\n columns = 'count(*) AS gnr_row_count' # use the sql count function istead of load all data\n elif addPkeyColumn and self.tblobj.pkey and not aggregate:\n columns = columns + ',\\n' + '%s.%s AS pkey' % (self.aliasCode(0),self.tblobj.pkey) # when possible add pkey to all selections\n columns = columns.lstrip(',') # if columns was '', now it starts with ','\n else:\n columns = columns.strip('\\n').strip(',')\n \n # replace $fldname with tn.fldname: finally the real SQL columns!\n columns = gnrstring.templateReplace(columns, colPars, safeMode=True)\n \n # replace $fldname with tn.fldname: finally the real SQL where!\n \n where = gnrstring.templateReplace(where, colPars)\n #if excludeLogicalDeleted==True we have additional conditions in the where clause\n logicalDeletionField = self.tblobj.logicalDeletionField\n draftField = self.tblobj.draftField\n if logicalDeletionField:\n if excludeLogicalDeleted is True:\n extracnd = '%s.%s IS NULL' % (self.aliasCode(0),logicalDeletionField)\n if where:\n where = '%s AND %s' % (extracnd, where)\n else:\n where = extracnd\n elif excludeLogicalDeleted == 'mark':\n if not (aggregate or count):\n columns = '%s, %s.%s AS _isdeleted' % (columns, self.aliasCode(0),logicalDeletionField) #add logicalDeletionField\n if draftField:\n if excludeDraft is True:\n extracnd = '%s.%s IS NOT TRUE' %(self.aliasCode(0),draftField)\n if where:\n where = '%s AND %s' % (extracnd, where)\n else:\n where = extracnd\n # add a special joinCondition for the main selection, not for JOINs\n if self.joinConditions:\n extracnd, one_one = self.getJoinCondition('*', '*', self.aliasCode(0))\n if extracnd:\n if where:\n where = '(%s) AND (%s)' % (where, extracnd)\n else:\n where = extracnd\n order_by = gnrstring.templateReplace(order_by, colPars)\n having = gnrstring.templateReplace(having, colPars)\n group_by = gnrstring.templateReplace(group_by, colPars)\n \n if distinct:\n distinct = 'DISTINCT '\n elif distinct is None or distinct == '':\n if self._explodingRows:\n if not aggregate: # if there is not yet a group_by\n distinct = 'DISTINCT ' # add a DISTINCT to remove unusefull rows: eg. a JOIN used only for a where, not for columns\n if order_by:\n xorderby=(('%s '%order_by.lower()).replace(' ascending ','').replace(' descending ','').replace(' asc ','').replace(' desc','')).split(',')\n lowercol=columns.lower()\n for xrd in xorderby:\n if not xrd.strip() in lowercol:\n columns = '%s, \\n%s' % (columns, xrd)\n #order_by=None\n if count:\n columns = '%s.%s' % (self.aliasCode(0),self.tblobj.pkey)\n # Count the DISTINCT maintable pkeys, instead of count(*) which will give the number of JOIN rows.\n # That gives the count of rows on the main table: the result is different from the actual number\n # of rows returned by the query, but it is correct in terms of main table records.\n # It is the right behaviour ???? Yes in some cases: see SqlSelection._aggregateRows\n \n self.cpl.distinct = distinct\n self.cpl.columns = columns\n self.cpl.where = where\n self.cpl.group_by = group_by\n self.cpl.having = having\n self.cpl.order_by = order_by\n self.cpl.limit = limit\n self.cpl.offset = offset\n self.cpl.for_update = for_update\n #raise str(self.cpl.get_sqltext(self.db)) # uncomment it for hard debug\n return self.cpl","title":""},{"docid":"3f2a3634474b6bca3382e5a869663d66","score":"0.5586637","text":"def rebuild_query(self, comments=True, pretty=True):\n if not self.tree:\n self._logger.error('El arbol no ha sido generado todavia. Por tanto, todavia no hay ninguna query que '\n 'reconstruir. Por favor ejecuta la funcion parse_query() para generar el arbol')\n raise LookupError\n\n if not self.__queries:\n self._logger.error('El arbol no ha sido procesado todavia. Por favor ejecuta la funcion rename_tree() para '\n 'procesar el arbol.')\n raise LookupError\n\n query = ' '.join(self.tree.leaves())\n query = (self._untokenize(query)\n .replace(' , ', ', ')\n .replace(' ; ', ';')\n .replace(' ( ', ' (')\n .replace(' ) ', ') ')\n #.replace(' = ', '=')\n .replace(' . ', '.')\n )\n\n if self.udfs:\n for point, scape in zip(self.udfs, self._udfs_norm):\n pattern = re.compile(scape, re.IGNORECASE)\n query = pattern.sub(point, query)\n\n if pretty:\n if comments:\n final = '\\n'.join(self.__comments) + '\\n' + sqlparse.format(query, reindent=True, keyword_case='upper')\n self.__comments = []\n return final\n else:\n return sqlparse.format(query, reindent=True, keyword_case='upper')\n else:\n return query","title":""},{"docid":"4df307799efca220bbeb0a0c969810c8","score":"0.55771285","text":"def construct_query(*args, dialect='MSSS', allow_coalesce=True):\n n = len(args)\n assert all([isinstance(o, UserOption) for o in args]), \"Not all args are UserOptions\"\n invalids = [not o.validate() for o in args]\n assert not any(invalids), \"{:d}/{:d} options have not been validated\".format(\n sum(invalids), n)\n assert all([args[0].context == args[i+1].context for i in range(n-1)]), \"Different\" +\\\n \"contexts associated with the User Opts. Ensure these are the same.\"\n stmt = Statement(args[0].context)\n\n # ==== GET ALL TABLES AND FORM BASIC JOIN SUBTREE ============\n context = args[0].context\n nodes = [o.get_table() for o in args]\n unique_nodes = list(set(nodes))\n join_tree = minimum_subtree(unique_nodes)\n\n # The primary table is considered the \"root node\" of the tree -- edges are undirected.\n primary = [o for o in args if not o.is_secondary]\n assert len(primary) == 1, f'Expecting one primary field. Got {len(primary)}.'\n primary = primary[0]\n primary_tbl = primary.get_table()\n\n # topological sort\n edges = [(k, v[0][0]) for (i,(k,v)) in enumerate(join_tree.items()) if i > 0]\n G = graph.Graph(edges, directed=False)\n sorted_nodes = G.topological_sort(leave_until_last=primary_tbl)\n # (Recreate G since the topological_sort mutates G (ikr - should fix this!))\n G = graph.Graph(edges, directed=False)\n\n # ======= CREATE CTEs WHENEVER WE FIND A NESTED AGGREGATION ============\n field_tbl_lkp = defaultdict(list)\n for arg in args:\n field_tbl_lkp[arg.get_table()].append(arg)\n all_fields = dict()\n\n # Traverse the join tree of the selected tables, making CTEs wherever\n # there exists secondary aggregation (i.e. not of the primary variable).\n for i, v in enumerate(sorted_nodes[:-1]):\n v_fields = field_tbl_lkp[v]\n any_v_has_agg = any([arg.has_aggregation for arg in v_fields])\n\n # Get 'child tables' and 'parent tables' (wrt topological sort)\n child_tbls = G._graph[v] - set(sorted_nodes[i:])\n parent_tbl = join_tree[v][0][0]\n parent_fks, v_pks = join_tree[v][0][1], join_tree[v][1][1]\n\n # The current table contains an aggregation, so create a CTE.\n if any_v_has_agg:\n # Get all arguments for table which are not aggregated\n f_non_agg = [arg for arg in v_fields if not arg.has_aggregation]\n f_non_agg += flatten([all_fields[w] for w in child_tbls])\n\n # Add in table PKs if not already included in `f_non_agg`.\n f_non_agg_names = [f.sql_fieldname for f in f_non_agg]\n pk_names_to_add = [f for f in v_pks if f not in f_non_agg_names]\n pks_to_add = [construct_simple_field(f, v, context) for f in pk_names_to_add]\n\n # Make a copy of all non-aggregated AND aggregated fields.\n f_non_agg_cte = [f.copy() for f in f_non_agg]\n f_agg_cte = [arg for arg in v_fields if arg.has_aggregation]\n f_agg = [f.copy() for f in f_agg_cte]\n\n # consolidate CTE fields and add primary designator to (any) field in root\n cte_fields = pks_to_add + f_non_agg_cte + f_agg_cte\n for field in cte_fields:\n if field.sql_fieldname in v_pks:\n field.is_secondary = False\n break\n\n # Defer lookups until the final query\n for field in cte_fields:\n field.perform_lkp = False\n\n # Construct CTE\n cte = CTENode([parent_tbl], # parent\n v_pks, # pk\n cte_fields) # cte_fields\n stmt.ctes.append(cte)\n\n # Point the [references to the aggregations] outside the CTE to the CTE field\n for f in f_agg:\n f.field_alias = f.field_alias # this is *not* a noop! (@property...)\n f.sql_item = '{alias}' + f.field_alias\n f.table = cte\n f.set_aggregation(None, force=True)\n f.set_transform(None, force=True)\n if allow_coalesce:\n f.coalesce = 0 # assumes that aggregation is numeric\n\n for f in f_non_agg:\n f.table = cte\n f.sql_item = '{alias}'+f._field_alias_logic(will_perform_lkp=False)\n\n # Push these pointers to within the CTE up to the parent (although not PKs)\n all_fields[v] = f_non_agg + f_agg\n\n # # overwrite the node `v` in the subtree with `cte`\n # join_cond = tree_final[v]\n # join_cond = (join_cond[0], (cte, join_cond[1][1]))\n # tree_final = replace_in_ordered_dict(tree_final, v, cte, join_cond)\n else:\n all_fields[v] = v_fields + flatten([all_fields[w] for w in child_tbls])\n\n # Set args to the args propagated up to the root node\n primary_children = G._graph[primary_tbl]\n args = field_tbl_lkp[primary_tbl] + flatten([all_fields[c] for c in primary_children])\n # Place the resulting tree into the FROM clause of the Statement object.\n\n nodes = [o.get_table() for o in args]\n unique_nodes = list(set(nodes))\n tree_final = minimum_subtree(unique_nodes)\n\n stmt._from.update(tree_final)\n\n # Add dimension tables (if requested to joins)\n lkp_joins = []\n for o in args:\n if o.perform_lkp:\n dtbl = o.dimension_table\n assert not dtbl.is_cte, \"Currently unable to support CTEs for dimension \" + \\\n \"tables. (But it only requires thinking about how \" + \\\n \"to avoid multiple copies.)\"\n fk = o.sql_fieldname\n lkp_joins.append(((o.table, fk), (dtbl, dtbl.pk[0])))\n\n # Generate statement in order to populate aliases dict\n stmt._from.generate_basic_statement(force_alias=(len(lkp_joins) > 0))\n stmt._from.add_lookups_to_statement(lkp_joins)\n\n # === CONSTRUCT SELECT / WHERE ===========\n has_agg = any([arg.has_aggregation for arg in args])\n\n for o in args:\n # Get table alias for field, depending on whether has lookup table or not\n if not o.perform_lkp:\n alias = stmt.aliases[o.get_table()]\n # coalesce is usually None anyway\n coalesce = o.coalesce if allow_coalesce else None\n else:\n dtbl = o.dimension_table\n fk = rm_alias_placeholder(o.sql_item)\n alias = stmt.lkp_aliases[((o.table, fk), (dtbl, dtbl.pk[0]))]\n coalesce = context.coalesce_default if allow_coalesce else None\n\n sel, where = o.sql_transform(alias=alias, dialect=dialect, coalesce=coalesce)\n # SELECT\n stmt.select.append(sel)\n # WHERE\n if len(where) > 0:\n stmt.where.extend(where)\n # GROUP BY\n if has_agg and not o.has_aggregation:\n gby = re.sub('AS [a-zA-Z0-9_]+$', '', sel).strip()\n stmt.groupby.append(gby)\n\n return stmt.generate_statement(dialect=dialect)","title":""},{"docid":"97b15f5e2a70c1eb7088c3897a2bfef2","score":"0.5570735","text":"def _build_query(self, metric, node, ts_from, ts_to):\n resolution = ts_to - ts_from\n if resolution > PROMETHEUS_TS_LIMIT:\n ts_from = ts_to - PROMETHEUS_TS_LIMIT\n\n query_head = \"http://{}:{}/api/v1/query_range?query=\".format(\n self.tsdb_ip, self.tsdb_port)\n query_times = \"&start={}&end={}&step=1s\".format(ts_from, ts_to)\n\n query_selectors = self._get_query_selectors(metric, node)\n query_selector = '{}{{{}}}'.format(metric, query_selectors)\n\n query = \"{}{}{}\".format(query_head, query_selector, query_times)\n return query","title":""},{"docid":"923405ac27fc863e4ecf89c859843768","score":"0.55545264","text":"def get_clean_query(self, additional_params={}):\n # NOTE: right now this makes us pretty vulnerable\n # to SQL Injection. We should make this safer at some point.\n # Graphs likely have the same problem.\n reg = re.compile('(\\{\\{.*?\\}\\})')\n query = self.query\n matches = reg.findall(query)\n if matches:\n for match in matches:\n attr = match[2:len(match)-2]\n if attr in additional_params:\n query = query.replace(match, additional_params[attr])\n else:\n query = query.replace(match, \"\")\n return query","title":""},{"docid":"374044813c297baa727f456ec5a2a772","score":"0.5546569","text":"def buildQueryFromFilters(date, rainfall, temperature, month, year):\n\t# Main part of the query\n\tquery = sql.SQL(\"SELECT * FROM climate\")\n\n\tconditions = []\n\tvalues = []\n\n\t# Check if each filter is actually applied, \n\t# if so, its respective part of the query is appended to the main part\n\tif date != None:\n\t\tclause = sql.SQL(\"date = to_date(%s, 'DD-MM-YYYY')\")\n\t\tconditions.append(clause)\n\t\tvalues.append(date)\n\tif temperature != None:\n\t\tclause = sql.SQL(\"temperature = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(temperature)\n\tif rainfall != None:\n\t\tclause = sql.SQL(\"rainfall = real '%s'\")\n\t\tconditions.append(clause)\n\t\tvalues.append(rainfall)\n\tif month != None:\n\t\tclause = sql.SQL(\"EXTRACT(MONTH FROM date) = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(month)\n\tif year != None:\n\t\tclause = sql.SQL(\"EXTRACT(YEAR FROM date) = (%s)\")\n\t\tconditions.append(clause)\n\t\tvalues.append(year)\n\n\t# Put all together\n\tif len(conditions) > 0:\n\t\tquery = sql.SQL(\" \").join([query, sql.SQL(\"WHERE\")])\n\n\t\tconj = sql.SQL(\" AND \").join(conditions)\n\n\t\tquery = sql.SQL(\" \").join([query, conj])\n\n\t\n\treturn query, values","title":""},{"docid":"e9b14da9066124004791a46561a7d775","score":"0.55110025","text":"def query(**kw):","title":""},{"docid":"dfaf1e939ed62d271c7e53d6dfd18c69","score":"0.54942393","text":"def create_query(vaccine=None, age_group=\"adult\", without_registration=None, self_payer=False, monday=None,\n tuesday=None, wednesday=None, thursday=None, friday=None, saturday=None, sunday=None):\n\n open_hours = {'monday': monday, 'tuesday': tuesday, 'wednesday': wednesday, 'thursday': thursday, 'friday': friday,\n 'saturday': saturday, 'sunday': sunday}\n\n query = \"\"\"\n SELECT loc.vacc_id, loc.latitude, loc.longitude\n FROM vacc_center_location as loc\n INNER JOIN vacc_center_type as typ USING(vacc_id)\n \"\"\"\n if age_group not in AGE_GROUP_SET: raise Exception('age_group argument can be only: adult, teenage, child')\n if int(self_payer) not in [0, 1]: raise Exception('self_payer argument can be only: True, False')\n query_where = f\"\"\"WHERE typ.{age_group} = 1\n AND typ.self_payers = {int(self_payer)}\n \"\"\"\n if without_registration is not None:\n if int(without_registration) not in [0, 1]: raise Exception('without_registration argument can be only: True, False, None')\n query_where += f\"AND typ.without_registration = {int(without_registration)} \\n\"\n\n if vaccine is not None:\n if vaccine.lower() not in VACCINES_SET:\n raise Exception('vaccine argument can be only: COMIRNATY, SPIKEVAX, JANSSEN, Vaxzevria, None')\n query += \"INNER JOIN vacc_center_vaccines AS vacc USING(vacc_id) \\n\"\n query_where += f\"AND vacc.{vaccine.lower()} = 1 \\n\"\n\n open_hours = {day: hour for (day, hour) in open_hours.items() if hour is not None}\n if len(open_hours) != 0:\n query += \"INNER JOIN vacc_center_hours as hour USING(vacc_id) \\n\"\n list_hours = []\n for (day, hour) in open_hours.items():\n if not ((hour >= 0) and (hour <= 24)): raise Exception('days arguments can be only set to None or number between 0 to 24')\n list_hours.append(f\"(hour.{day}_open <= {hour} AND hour.{day}_closed >= {hour})\")\n\n query_hours = \"AND (\"\n n = len(list_hours)\n for i in range(n):\n query_hours += list_hours[i]\n if (i+1) == n:\n query_hours += \")\"\n else:\n query_hours += \" OR \"\n query_where += query_hours\n return query + query_where","title":""},{"docid":"3f83fb856d12b26ca2684c9d9c9dfb40","score":"0.5437405","text":"def build_goods_query(\n good_ids: List[str],\n currency_id: str,\n is_searching_for_sellers: bool,\n is_search_query: bool,\n) -> Query:\n if is_search_query:\n # the OEF does not accept attribute names consisting of integers only\n good_ids = [PREFIX + good_id for good_id in good_ids]\n\n data_model = _build_goods_datamodel(\n good_ids=good_ids, is_supply=is_searching_for_sellers\n )\n constraints = [Constraint(good_id, ConstraintType(\">=\", 1)) for good_id in good_ids]\n constraints.append(Constraint(\"currency_id\", ConstraintType(\"==\", currency_id)))\n constraint_expr = cast(List[ConstraintExpr], constraints)\n\n if len(good_ids) > 1:\n constraint_expr = [Or(constraint_expr)]\n\n query = Query(constraint_expr, model=data_model)\n return query","title":""},{"docid":"3716fc1f19951155d518f81b705b75c9","score":"0.54263324","text":"def cypher(self, query, params=None):\r\n path = cypher_path\r\n params = dict(query=query,params=params)\r\n resp = self.request.post(path, params)\r\n\r\n # Cypher data hack\r\n resp.total_size = len(resp.results.data)\r\n resp.results = (Neo4jResult(result[0], self.config) for result in resp.results.data)\r\n return resp","title":""},{"docid":"800590f0ff9faf64788181eb91302965","score":"0.5422505","text":"def buildQuery (self, obj_type, ids=None, params=None, attributes=None):\n\n if type(obj_type) is type(''):\n wrapper = KNOWN_WRAPPERS.get(obj_type.lower(), None)\n if wrapper is None:\n raise KeyError(\"obj_type of %s not supported by getOjbects(). E.g. use 'Image' etc\" % obj_type)\n else:\n raise AttributeError(\"getObjects uses a string to define obj_type, E.g. 'Image'\")\n\n q = self.getQueryService()\n if params is None:\n params = omero.sys.Parameters()\n if params.map is None:\n params.map = {}\n\n # get the base query from the instantiated object itself. E.g \"select obj Project as obj\"\n query = wrapper()._getQueryString()\n\n clauses = []\n # getting object by ids\n if ids != None:\n clauses.append(\"obj.id in (:ids)\")\n params.map[\"ids\"] = rlist([rlong(a) for a in ids])\n\n # support filtering by owner (not for some object types)\n if params.theFilter and params.theFilter.ownerId and obj_type.lower() not in [\"experimentergroup\", \"experimenter\"]:\n clauses.append(\"owner.id = (:eid)\")\n params.map[\"eid\"] = params.theFilter.ownerId\n\n # finding by attributes\n if attributes != None:\n for k,v in attributes.items():\n clauses.append('obj.%s=:%s' % (k, k) )\n params.map[k] = omero_type(v)\n\n if clauses:\n query += \" where \" + (\" and \".join(clauses))\n\n return (query, params, wrapper)","title":""},{"docid":"3193232c6e9b86e2cb3be30784d0c07c","score":"0.5413109","text":"def make_query(store, query, prefix=\"\"):\r\n query = common.parse_query(query)\r\n q = Query()\r\n q.prefix = prefix\r\n q.offset = common.safeint(query.pop('offset', None), 0)\r\n q.limit = common.safeint(query.pop('limit', 20), 20)\r\n if q.limit > 1000:\r\n q.limit = 1000\r\n sort = query.pop('sort', None)\r\n \r\n nested = (prefix != \"\")\r\n \r\n for k, v in query.items():\r\n # key foo can also be written as label:foo\r\n k = k.split(':')[-1]\r\n if v is None:\r\n q.requested[k] = v\r\n elif isinstance(v, dict):\r\n # make sure op is ==\r\n v = dict((k + '.' + key, value) for key, value in v.items())\r\n q2 = make_query(store, v, prefix=prefix + k + \".\")\r\n #@@ Anand: Quick-fix\r\n # dbstore.things looks for key to find whether type is required or not. \r\n q2.key = k \r\n if q2.conditions:\r\n q.conditions.append(q2)\r\n else:\r\n q.requested[k] = q2\r\n else:\r\n k, op = parse_key(k)\r\n q.add_condition(k, op, None, v)\r\n \r\n if not nested:\r\n q.assert_type_required()\r\n \r\n type = get_thing(store, q.get_type())\r\n #assert type is not None, 'Not found: ' + q.get_type()\r\n for c in q.conditions:\r\n if not isinstance(c, Query):\r\n c.datatype = find_datatype(type, c.key, c.value)\r\n \r\n if sort:\r\n parse_key(sort) # to validate key\r\n q.sort = web.storage(key=sort, datatype=find_datatype(type, sort, None))\r\n else:\r\n q.sort = None\r\n \r\n return q","title":""},{"docid":"bac716271d29523c3ec034478d633e21","score":"0.54028887","text":"def create_query(neo4j_entities, entity_value):\n\n def get_value(index):\n return SINONIMOS_NEO4J[entity_value[index]] if SINONIMOS_NEO4J.get(entity_value[index])\\\n else entity_value[index]\n\n get_query_part = {\n \"espacio\": lambda x: \"(n:Room {{ type: '{}' }})\".format(get_value(x)),\n \"sensor\": lambda x: (\"(d:Device)\", f\" '{get_value(x)}' in d.type\"),\n \"orientacion\": lambda x: \" n.orientacion CONTAINS '{}'\".format(get_value(x)),\n \"edificio\": lambda x: (\"(b:Building)\", \" b.name CONTAINS '{}'\".format(get_value(x))),\n \"nombre\": lambda x: (\"(b:Building)\", \" b.name CONTAINS '{}'\".format(get_value(x))),\n \"lugares\": lambda x: \" n.name CONTAINS '{}'\".format(get_value(x))\n }\n\n\n dic = {}\n for entity in neo4j_entities:\n key = entity[1]\n if entity[1] == \"nombre\":\n key = \"edificio\"\n dic[key] = get_query_part[entity[1]](entity[0])\n\n espacio = dic[\"espacio\"] if dic.get(\"espacio\") else \"\"\n sensor = dic[\"sensor\"] if dic.get(\"sensor\") else (\"\", \"\")\n orientacion = dic[\"orientacion\"] if dic.get(\"orientacion\") else \"\"\n edificio = dic[\"edificio\"] if dic.get(\"edificio\") else (\"\", \"\")\n lugares = dic[\"lugares\"] if dic.get(\"lugares\") else \"\"\n ret = []\n\n # crear relaciones que son opcionales en la consulta\n if espacio is not \"\" and sensor != (\"\", \"\"):\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n if espacio is not \"\" and edificio != (\"\", \"\"):\n espacio += \"<-[:HAS]-(f:Floor)<-[:HAS]-\"\n\n #Añadir información a la consulta que no se infica pero que es necesaria para poder recorrer las relaciones en Neo4j.\n \n # Si se pregunta por sensor y lugar pero no por espacio.\n if espacio is \"\" and sensor != (\"\", \"\") and lugares is not \"\":\n espaciocio = \"(n:Room)\"\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n # Si se pregunta por nombre u orientación pero no por espacio.\n if espacio is \"\" and (lugares is not \"\" or orientacion is not \"\"):\n espacio = \"(n:Room)\"\n # Si se pregunta por sensores en un edificio sin especificar un espacio.\n if espacio is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n espacio = \"(n:Room)<-[:HAS]-(f:Floor)<-[:HAS]-\"\n sensor = (sensor[0]+\"<-[:HAS]-\", sensor[1])\n # Si hay 4\n # orientacion lugares edificio sensor\n # X X X X\n if orientacion is not \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n edificio = (edificio[0], \"AND \" + edificio[1])\n sensor = (sensor[0], \"AND \" + sensor[1])\n\n # Si hay 3\n # orientacion lugares edificio sensor\n # X X X -\n # X - X X\n # - X X X\n # X - X X\n if orientacion is not \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n edificio = (edificio[0], \"AND \" + edificio[1])\n if orientacion is not \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n sensor = (sensor[0], \"AND \" + sensor[1])\n if orientacion is \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n lugares = \"WHERE \" + lugares\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"AND \" + edificio[1])\n if orientacion is not \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n orientacion = \"WHERE \" + orientacion\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"AND \" + edificio[1])\n\n # Si hay 2\n # orientacion lugares edificio sensor\n # X X - -\n # X - X -\n # X - - X\n # - X X -\n # - X - X\n # - - X X\n\n if orientacion is not \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"AND \" + lugares\n orientacion = \"WHERE \" + orientacion\n if orientacion is not \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"AND \" + edificio[1])\n orientacion = \"WHERE \" + orientacion\n if orientacion is not \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n orientacion = \"WHERE \" + orientacion\n if orientacion is \"\" and lugares is not \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"AND \" + edificio[1])\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"AND \" + sensor[1])\n edificio = (edificio[0], \"WHERE \" + edificio[1])\n # Si hay 1\n # orientacion lugares edificio sensor\n # X - - -\n # - X - -\n # - - X -\n # - - - X\n if orientacion is not \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n orientacion = \"WHERE \" + orientacion\n if orientacion is \"\" and lugares is not \"\" and edificio == (\"\", \"\") and sensor == (\"\", \"\"):\n lugares = \"WHERE \" + lugares\n if orientacion is \"\" and lugares is \"\" and edificio != (\"\", \"\") and sensor == (\"\", \"\"):\n edificio = (edificio[0], \"WHERE \" + edificio[1])\n if orientacion is \"\" and lugares is \"\" and edificio == (\"\", \"\") and sensor != (\"\", \"\"):\n sensor = (sensor[0], \"WHERE \" + sensor[1])\n\n\n\n # Preparar los atributos de salida\n if espacio is not \"\":\n ret.append(\"n.name, n.id\")\n if sensor != (\"\", \"\"):\n ret.append(\"d.id, d.type\")\n if orientacion is not \"\":\n ret.append(\"n.orientacion\")\n\n return TEMPLATE_NEO.format(sensor[0], espacio, edificio[0], orientacion,\\\n lugares, edificio[1], sensor[1], \", \".join(ret))","title":""},{"docid":"4e23579f229cdea216279b1841dfff82","score":"0.53795767","text":"def generate_entrez_query(self):\n ending = ') AND ' + self.gene + '[Gene Name] ' + \\\n 'AND 1000:8000[Sequence Length]'\n types = []\n for hpv_type in itertools.chain(self.harmfull, self.safe):\n types.append('\"Human papillomavirus type %d\"[Organism]' % hpv_type)\n self._query = '(' + ' OR '.join(types) + ending\n return self._query","title":""},{"docid":"68cd306f78e9a696fa45ccc17d112056","score":"0.5368679","text":"def sql_query_str(program):\n node_type, node_children = program[0], program[1:]\n assert node_type in sql_entry_tokens and syntax_valid(program, sql_cfg)\n\n if node_type == \"SELECT_AGG\":\n select_exprs_node, joins_node, group_by_exprs_node = node_children\n where_node = None\n elif node_type == \"SELECT_AGG_WHERE\":\n (select_exprs_node,\n joins_node,\n group_by_exprs_node,\n where_node) = node_children\n else:\n raise RuntimeError(\n \"Missing node-type decoding logic for generating SQL query from AST.\"\n )\n\n return query_str(\n select_exprs=list(sql_select_exprs(select_exprs_node)),\n joins=list(sql_joins(joins_node)),\n where_exprs=list(sql_and_exprs_strs(where_node)) if where_node else [],\n group_by_exprs=list(sql_group_by_exprs(group_by_exprs_node)),\n order_by_exprs=[],\n limit_exprs=[],\n )","title":""},{"docid":"f43c43ecaf245aaab63b127c15a173ed","score":"0.53679913","text":"def _getQueryString(self):\n query = \"select obj from Plate as obj \" \\\n \"join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent \"\\\n \"left outer join fetch obj.screenLinks spl \" \\\n \"left outer join fetch spl.parent sc\"\n return query","title":""},{"docid":"51fd823b07a49d65be55acc10cb16971","score":"0.53457093","text":"def _create_qualifiedname_query(self, type_name, *values):\n query = {\n 'typeName': type_name,\n 'excludeDeletedEntities': True,\n 'limit': 10000\n }\n entity_filter = {\n 'condition': 'AND'\n }\n criterion = []\n n = 0\n for v in values:\n criteria = {\n 'attributeName': 'qualifiedName',\n 'operator': 'STARTSWITH' if n == 0 else 'CONTAINS',\n 'attributeValue': v\n }\n n += 1\n criterion.append(criteria)\n if type_name == 'hive_table':\n # Ignore temporary tables.\n criterion.append({'operator': '=', 'attributeName': 'temporary', 'attributeValue': False})\n entity_filter['criterion'] = criterion\n query['entityFilters'] = entity_filter\n return query","title":""},{"docid":"44970fb2e787db5c9720c86d5e16d3f3","score":"0.53439456","text":"def create_query(\n self, query_text, locale=None, language=None, time_zone=None, timestamp=None\n ):\n if not query_text:\n query_text = \"\"\n if isinstance(query_text, (list, tuple)):\n return self._process_list(\n query_text,\n \"create_query\",\n locale=locale,\n language=language,\n time_zone=time_zone,\n timestamp=timestamp,\n )\n return self.resource_loader.query_factory.create_query(\n query_text,\n language=language,\n locale=locale,\n time_zone=time_zone,\n timestamp=timestamp,\n )","title":""},{"docid":"8b4dabb7c03fa99fdfdd73c857a0b88d","score":"0.5328662","text":"def sql_with_where(self):\n e = get_executor(SqlRule)\n where_clause = \"WHERE \"\n where_time_filter = e.compose_where_time_filter(self)\n where_condition = e.compose_where_condition(self)\n if where_time_filter == \"\" and where_condition == \"\":\n where_clause = \"\"\n elif where_time_filter != \"\" and where_condition != \"\":\n where_clause = f\"{where_clause} {where_time_filter} AND {where_condition}\"\n else:\n where_clause = f\"{where_clause} {where_time_filter} {where_condition}\"\n final_sql = f\"{self.sql} {where_clause}\"\n return self.render_sql(final_sql)","title":""},{"docid":"dae062c702cf1a20a574ddd68d4b28aa","score":"0.532052","text":"def construct_url(self\n , query_params: Dict\n , ) -> str:\n url_const = self.faers_ep\n initialized = False\n for param_q, param_val in query_params.items():\n # Need to add '&' to 2nd,3rd,... query_params\n if initialized:\n url_const += '&'\n else:\n initialized = True\n if type(param_val) == str and len(query_params) > 0 \\\n or type(param_val) == int:\n url_const += f'{param_q}={param_val}'\n elif type(param_val) == dict and len(param_val) > 0:\n url_const += f'{param_q}=' + \"+AND+\".join(\n [f'{sub_p_k}:{sub_p_v}' for sub_p_k, sub_p_v in param_val.items()])\n elif type(param_val) == list and len(param_val) > 0:\n url_const += f'{param_q}=' + \"+AND+\".join(param_val)\n else:\n raise TypeError\n return url_const","title":""},{"docid":"f60d6e0b78286e4e35b88227402bb392","score":"0.5318269","text":"def get_custom_query(self):\n query = {}\n\n # searchable text queries\n q = req.get_query()\n if q:\n query[\"SearchableText\"] = q\n\n # physical path queries\n path = req.get_path()\n if path:\n query[\"path\"] = {'query': path, 'depth': req.get_depth()}\n\n # special handling for recent created/modified\n recent_created = req.get_recent_created()\n if recent_created:\n date = api.calculate_delta_date(recent_created)\n query[\"created\"] = {'query': date, 'range': 'min'}\n\n recent_modified = req.get_recent_modified()\n if recent_modified:\n date = api.calculate_delta_date(recent_modified)\n query[\"modified\"] = {'query': date, 'range': 'min'}\n\n return query","title":""},{"docid":"88503be9fbc27ea6562b4928b4a8d6c0","score":"0.530554","text":"def get_where_clause(columns_to_query_lst):\n where_str = \"where \"\n equals_str =[]\n for row in columns_to_query_lst:\n temp_str = \"c.\" + row + \" = t.\" + row\n equals_str.append(temp_str)\n joined_str = \" AND \".join(equals_str)\n where_str = where_str + joined_str\n return where_str","title":""},{"docid":"746bf5ea5425e576e9ff1f9bce37f702","score":"0.52996916","text":"def create_query(processed_request):\n query = {\n \"variant\": {\n \"referenceBases\": \"\" if not processed_request.get(\"referenceBases\") else processed_request.get(\"referenceBases\"),\n \"alternateBases\": \"\" if not processed_request.get(\"alternateBases\") else processed_request.get(\"alternateBases\"),\n \"referenceName\": \"\" if not processed_request.get(\"referenceName\") else processed_request.get(\"referenceName\"),\n \"start\": None if not processed_request.get(\"start\") else processed_request.get(\"start\"),\n \"end\": None if not processed_request.get(\"end\") else processed_request.get(\"end\"),\n \"assemblyId\": \"\" if not processed_request.get(\"assemblyId\") else processed_request.get(\"assemblyId\")\n },\n \"datasets\": {\n \"datasetIds\": None if not processed_request.get(\"datasetIds\") else processed_request.get(\"datasetIds\"),\n \"includeDatasetResponses\": \"ALL\" if not processed_request.get(\"includeDatasetResponses\") else processed_request.get(\"includeDatasetResponses\")\n },\n \"filters\": None if not processed_request.get(\"filters\") else processed_request.get(\"filters\"),\n \"customFilters\": None if not processed_request.get(\"customFilters\") else processed_request.get(\"customFilters\"),\n }\n\n return query","title":""},{"docid":"e8e6478863c1ea4a7a2b784bc3c49cd9","score":"0.529957","text":"def build_where_stmt(self, ident, filters, q_filters=None, source_class=None):\n if q_filters is not None:\n stmts = self._parse_q_filters(ident, q_filters, source_class)\n if stmts:\n self._ast.where.append(stmts)\n else:\n stmts = []\n for row in filters:\n negate = False\n\n # pre-process NOT cases as they are nested dicts\n if \"__NOT__\" in row and len(row) == 1:\n negate = True\n row = row[\"__NOT__\"]\n\n for prop, operator_and_val in row.items():\n operator, val = operator_and_val\n if operator in _UNARY_OPERATORS:\n # unary operators do not have a parameter\n statement = (\n f\"{'NOT' if negate else ''} {ident}.{prop} {operator}\"\n )\n else:\n place_holder = self._register_place_holder(ident + \"_\" + prop)\n statement = f\"{'NOT' if negate else ''} {ident}.{prop} {operator} ${place_holder}\"\n self._query_params[place_holder] = val\n stmts.append(statement)\n\n self._ast.where.append(\" AND \".join(stmts))","title":""},{"docid":"3c5f867e7aef319a9323809c18719262","score":"0.5287673","text":"def __init__ (self, database, var, conditions = (), echo = False):\n\n self.session = buildSession(\n database=database,\n echo=echo)\n if var == \"ncommits\":\n self.query = self.session.query().select_nscmlog([\"commits\",])\n elif var == \"listcommits\":\n self.query = self.session.query().select_listcommits()\n elif var == \"nauthors\":\n self.query = self.session.query().select_nscmlog([\"authors\",])\n elif var == \"listauthors\":\n self.query = self.session.query().select_listauthors()\n for condition in conditions:\n self.query = condition.filter(self.query)","title":""},{"docid":"d298399d7e7d292b986fa091d5abed50","score":"0.5285017","text":"def full_query_pattern(self, include_conclusion: bool = False) -> str:\n query_pattern = \"\"\n for literal in self.premise:\n query_pattern += literal.sparql_patterns()\n\n if include_conclusion:\n query_pattern += self.conclusion.sparql_patterns()\n\n if \"?b\" not in query_pattern and \"?a\" not in query_pattern:\n query_projection = \"ask \"\n else:\n # insert the selectors for subject and object into the select query if they exist in the query pattern\n query_projection = \"select where \"\n\n # the resulting query would look like \"select ?a ?b ...\" if both cases are true\n if \"?b\" in query_pattern:\n query_projection = query_projection.replace(\"select \", \"select ?b \")\n if \"?a\" in query_pattern:\n query_projection = query_projection.replace(\"select \", \"select ?a \")\n\n # build remaining part of the query and execute it\n query_pattern = \"{\" + query_pattern + \"}\"\n return query_projection + query_pattern","title":""},{"docid":"3f90b7aa24950c089ec7f416c7f71163","score":"0.5283903","text":"def _getQueryString(self):\n return \"select obj from %s obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\" % self.OMERO_CLASS","title":""},{"docid":"edec057b919f8d0df46ebd8515dfc0e2","score":"0.5279631","text":"def _getQueryString(self):\n return \"select obj from BooleanAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\"","title":""},{"docid":"5238770c84b4b535af51b45d8d738378","score":"0.52578133","text":"def query(self):\n\n if not hasattr(self, \"_query\"):\n\n # Set up some convenience aliases.\n Query = db.Query\n Condition = Query.Condition\n Or = Query.Or\n\n # Create a query object.\n columns = \"d.id\", \"d.title\"\n self._query = Query(\"document d\", *columns).order(\"d.title\")\n\n # Add the conditions: one for each field with a value.\n conditions = []\n have_doctype = False\n for field in self.fields:\n\n # See if we got a value for this field.\n value = field.var.strip() if field.var else None\n if not value:\n continue\n\n # Remember the criterion for display to the user.\n self.criteria.append(value)\n\n # If 'selectors' is a string, it's a column in `document`.\n value_op = getQueryOp(value)\n if isinstance(field.selectors, str):\n column = f\"d.{field.selectors}\"\n conditions.append(Condition(column, value, value_op))\n continue\n\n # Build up a subquery for the field.\n have_doctype = True\n subquery = Query(\"query_term\", \"doc_id\").unique()\n\n # These are ORd together.\n selector_conditions = []\n for path in field.selectors:\n path_op = \"LIKE\" if \"%\" in path else \"=\"\n\n # Simple case: test for a string stored in the doc.\n if not path.endswith(\"/@cdr:ref[int_val]\"):\n path_test = Condition(\"path\", path, path_op)\n value_test = Condition(\"value\", value, value_op)\n selector_conditions.append((path_test, value_test))\n\n # Trickier case: find values in linked documents.\n else:\n path = path.replace(\"[int_val]\", \"\")\n title_query = Query(\"document\", \"id\").unique()\n args = \"title\", value, value_op\n title_query.where(Condition(*args))\n args = \"int_val\", title_query, \"IN\"\n title_test = Condition(*args)\n path_test = Condition(\"path\", path, path_op)\n selector_conditions.append((path_test, title_test))\n\n # Add the conditions for this field's selectors to the mix.\n subquery.where(Or(*selector_conditions))\n conditions.append(Condition(\"d.id\", subquery, \"IN\"))\n\n # Sanity check.\n if not conditions:\n raise Exception(\"No search conditions specified\")\n\n # If all the selectors are from the document table (or,\n # view, to be precise), then we still need to narrow\n # the search to this document type.\n if not have_doctype:\n self._query.join(\"doc_type t\", \"t.id = d.doc_type\")\n self._query.where(Condition(\"t.name\", self.doctype))\n # Plug the top-level conditions into the query.\n if self.match_all:\n for condition in conditions:\n self._query.where(condition)\n else:\n self._query.where(Or(*conditions))\n\n # All the fields with values have been folded into the query.\n return self._query","title":""},{"docid":"bc3e93874b247c4bb5a6d828028febe0","score":"0.5255788","text":"def query(self):\n self._check_mandatory_inputs()\n return self._gen_query()","title":""},{"docid":"74e698e0dc148ed3565a45d92b4dec8c","score":"0.5251471","text":"def _get_clause(self):\r\n params = [(primary_key, sql.bindparam(None, type_=primary_key.type))\r\n for primary_key in self.primary_key]\r\n return sql.and_(*[k == v for (k, v) in params]), \\\r\n util.column_dict(params)","title":""},{"docid":"bb58117d3fda3c2db7fe7688c4120b96","score":"0.5243088","text":"def build_query_text_request(\n **kwargs # type: Any\n):\n # type: (...) -> HttpRequest\n\n content_type = kwargs.pop('content_type', None) # type: Optional[str]\n\n api_version = \"2021-05-01-preview\"\n accept = \"application/json\"\n # Construct URL\n url = kwargs.pop(\"template_url\", '/:query-text')\n\n # Construct parameters\n query_parameters = kwargs.pop(\"params\", {}) # type: Dict[str, Any]\n query_parameters['api-version'] = _SERIALIZER.query(\"api_version\", api_version, 'str')\n\n # Construct headers\n header_parameters = kwargs.pop(\"headers\", {}) # type: Dict[str, Any]\n if content_type is not None:\n header_parameters['Content-Type'] = _SERIALIZER.header(\"content_type\", content_type, 'str')\n header_parameters['Accept'] = _SERIALIZER.header(\"accept\", accept, 'str')\n\n return HttpRequest(\n method=\"POST\",\n url=url,\n params=query_parameters,\n headers=header_parameters,\n **kwargs\n )","title":""},{"docid":"5e0b0884588b76f9e715cfc138ae0212","score":"0.5239496","text":"def create_complex_query(self):\n columns = copy(self.columns)\n shuffle(columns)\n query = \"\"\"\\\n SELECT\n {col}\n FROM\n datadog_test.dbo.high_cardinality AS hc1\n JOIN (\n SELECT\n id,\n COUNT(*) col12_float\n FROM\n datadog_test.dbo.high_cardinality AS hc2\n WHERE\n hc2.col1_txt LIKE '%-%'\n AND hc2.col14_int > (\n SELECT\n AVG(hc3.col15_int)\n FROM\n datadog_test.dbo.high_cardinality AS hc3)\n GROUP BY\n hc2.id) AS hc4 ON hc4.id = hc1.id\n JOIN datadog_test.dbo.high_cardinality AS hc5 ON hc5.id = hc1.id\n WHERE\n CAST(hc5.col17_date AS VARCHAR)\n IN('2003-04-23', '2043-09-10', '1996-08-08')\n AND hc5.col13_float IS NOT NULL\n AND hc5.col17_date NOT LIKE '21%';\n \"\"\"\n # Select a range of random columns and prefix to match our alias\n return query.format(col=','.join(['hc1.' + col for col in columns[: randint(1, len(columns) - 1)]]))","title":""},{"docid":"23ac357cf50e246b557978088edbcf6b","score":"0.5237348","text":"def __gencondition(self, querylist):\n conditions = []\n for key in self.PROPERTY_SEARCH_LIST:\n if querylist.get(key):\n conditions.append(self.PROPERTY_SEARCH_LIST[key][1] % (querylist.get(key)))\n return \" AND \".join(conditions)","title":""},{"docid":"b90ad42bba7570be5827a9b27873f0c5","score":"0.52357996","text":"def build_sql(self, table, row, known_fkeys={}):\n # Initialize the builder properties\n self.row = row\n self.known_fkeys = known_fkeys\n return self.build_table_sql(table)","title":""},{"docid":"a69e3debc69789bbd12bb2ee0e29ccfd","score":"0.5234567","text":"def __build_where_clause(where_columns_dictionary):\n return ' AND '.join(key + \" = ? \"\n if value is not None\n else key + \" IS NULL \"\n for key, value\n in where_columns_dictionary.items())","title":""},{"docid":"df322a9786ad715a3d5849f5ed411c2d","score":"0.5219735","text":"def concat_conds(*args):\n stmt = \" WHERE 1\"\n for cond in args:\n stmt = stmt + \" AND \" + cond\n return stmt","title":""},{"docid":"b443f879e890682442787a51400a4a67","score":"0.5218033","text":"def build_query(cls, request, model):\n # print(request.data)\n name = request.data.get(\"search_phrase\")\n criteria = request.data.get(\"search_criteria\")\n frequency = request.data.get(\"frequency\")\n age_distribution = request.data.get(\"age_distribution\")\n letters_range = request.data.get(\"letters_range\")\n\n resp = model.objects.filter()\n\n if frequency is not None and len(frequency) > 0:\n resp = model.objects.filter(frequency__in=frequency).defer(\"double_name\", \"number_of_letters\")\n\n double_name = True in letters_range\n\n # resp = resp.filter(double_name=True)\n\n if letters_range is not None and len(letters_range) > 0:\n if double_name:\n letters_range.remove(True)\n resp = resp.filter(Q(number_of_letters__in=letters_range) | Q(double_name=True))\n else:\n resp = resp.filter(double_name=False)\n resp = resp.filter(number_of_letters__in=letters_range)\n\n if age_distribution is not None and len(age_distribution) > 0:\n mask = \"age_distribution_{}__gt\"\n\n # AND age_distribution way\n # dd = {}\n #\n # for d in age_distribution:\n # key = mask.format(d)\n # dd[key] = 0\n # resp = resp.filter(**dd)\n\n # OR age_distribution way\n or_query_set = Q()\n for d in age_distribution:\n tmp = Q(**{mask.format(d): 0})\n or_query_set |= tmp\n\n resp = resp.filter(or_query_set)\n\n if criteria is not None and len(name) > 0:\n if criteria == \"start\":\n resp = resp.filter(name__lower__istartswith=name.lower())\n elif criteria == \"middle\":\n resp = resp.filter(name__lower__icontains=name.lower())\n elif criteria == \"end\":\n resp = resp.filter(name__lower__iendswith=name.lower())\n else:\n resp = resp.filter(name__lower=name)\n\n return resp","title":""},{"docid":"9a699f49d4c0ba175277a65d66a2562a","score":"0.52174175","text":"def query_to_clojure(obj, params):\n # Serialize the query, sorted keys for deterministic substitution, no pretty printing\n data = json.dumps(obj, sort_keys=True)\n # Split the JSON into parts using 'PARAM' as seperator\n parts = data.split(\"PARAM\")\n # Escape each JSON part by serializing as a JSON list and removing the qruare brackets\n escaped_parts = [json.dumps([part])[1:-1] for part in parts]\n # The amount of parts should be one more than the amount ot parameters\n if len(parts) != len(params) + 1:\n raise RuntimeError(\"Param count mismatch: \" +\n str(len(params)) +\n \" given \" +\n str(len(parts) -1) +\n \" needed ; \" +\n data)\n # Interleave the escaped JSON parts and the parameters and put them in a (str ) clojure expression\n rval = \" \".join(list(itertools.chain(*zip([\"(query (str \"] + params, escaped_parts))) + [\") )\"])\n return rval","title":""},{"docid":"c47f0f449170335a3dd6dfeb971f17fa","score":"0.52149814","text":"def process(self):\n q = \"?q=\"\n lst = []\n for field in self.filter_dict:\n for keyword in self.filter_dict[field]:\n for criteria in self.filter_dict[field][keyword]:\n lst.append(\"{0} {1} '{2}'\".format(field, kw_map[keyword], criteria))\n\n q += \" and \".join(lst)\n\n if self.count:\n q +=\"&count=True\"\n self.query = q\n\n return self","title":""},{"docid":"35ea0ee25067279790679c78c36e66c2","score":"0.52111125","text":"def run_sql_statement(self, request, params=()):\n\n # simple string formatting RISK OF SQL INJECTION, DO NOT USE IN REAL DATABASE\n output = request % tuple([\"'\" + p + \"'\" for p in params])\n\n self.output_sql += output + \"\\n\"","title":""},{"docid":"c29abca87c3ec6ed8e7655886a9e9437","score":"0.52013975","text":"def prepare_sql(self, **kwargs):\n source = kwargs.get(\"source\", self.source)\n if not source:\n raise ValueError(\"No source table or view specified.\")\n\n cutoff = kwargs.get(\"cutoff\", self.cutoff)\n start_time = \"start_time_local\" if self.local else \"start_time\"\n end_time = \"end_time_local\" if self.local else \"end_time\"\n\n predicates = kwargs.get(\"predicates\", [])\n predicates = [predicates] if not isinstance(predicates, list) else predicates\n\n provider_name = kwargs.get(\"provider_name\", self.provider_name)\n\n if provider_name:\n predicates.append(f\"lower(provider_name) = lower('{provider_name}')\")\n\n vts = \"'::vehicle_types,'\"\n vehicle_types = kwargs.get(\"vehicle_types\", self.vehicle_types)\n if vehicle_types:\n if not isinstance(vehicle_types, list):\n vehicle_types = [vehicle_types]\n predicates.append(f\"vehicle_type IN ('{vts.join(vehicle_types)}'::vehicle_types)\")\n\n if len(predicates) > 0:\n predicates = \" AND \".join(predicates) + \" AND \"\n else:\n predicates = \"\"\n\n timeranges = [\n f\"({start_time} <= %(start)s AND {end_time} > %(start)s)\",\n f\"({start_time} < %(end)s AND {end_time} >= %(end)s)\",\n f\"({start_time} >= %(start)s AND {end_time} <= %(end)s)\"\n ]\n\n if cutoff > 0:\n where = f\"\"\"\n {predicates}\n ({end_time} IS NULL AND {start_time} between %(start)s - '{cutoff} days'::interval and %(end)s OR\n (date_part('day', {end_time} - {start_time}) <= {cutoff} AND\n ({\" OR \".join(timeranges)})))\n \"\"\"\n else:\n timeranges.append(f\"({start_time} < %(end)s AND {end_time} IS NULL)\")\n where = f\"\"\"\n {predicates}\n ({\" OR \".join(timeranges)})\n \"\"\"\n\n order_by = kwargs.get(\"order_by\", self.order_by)\n if order_by:\n if not isinstance(order_by, list):\n order_by = [order_by]\n order_by = \",\".join(order_by)\n order_by = f\"ORDER BY {order_by}\"\n\n return f\"\"\"\n SELECT\n *\n FROM\n {source}\n WHERE\n {where}\n {order_by};\n \"\"\"","title":""},{"docid":"37809edf146a1f0665c82f677f3754e7","score":"0.51989144","text":"def generate_select_request(self, conditions=None):\n\n if conditions is None:\n return self._SELECT_REQUEST_STRING\n else:\n request = self._SELECT_REQUEST_STRING + \" WHERE {}={}\"\n request = request.format(*conditions.split(\"=\"))\n return request","title":""},{"docid":"d39ef560bb5a4fecbc1c6cd66834ec85","score":"0.5198576","text":"def _getQueryString(self):\n return \"select obj from CommentAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \"\\\n \"join fetch obj.details.creationEvent\"","title":""},{"docid":"f80c750afc8c0dee265f318d3f44d2f7","score":"0.51965994","text":"def generate_clause(vars, operator):\n return operator(*vars, evaluate=False)","title":""},{"docid":"84e77ba0a35e57f35fb497a8cc5137a0","score":"0.5192215","text":"def subquery(self, withs, filters=None, optional=False):\n input_with = ', '.join(withs)\n new_withs = withs - {self.collection_alias}\n output_with = ', '.join(new_withs) + ', ' + self.with_statement()\n\n where_string = ''\n if filters is not None:\n relevant = []\n for c in filters:\n if c.involves(self):\n relevant.append(c.for_cypher())\n if relevant:\n where_string = 'WHERE ' + '\\nAND '.join(relevant)\n for_match = self.subquery_match_template.format(anchor_alias=self.anchor_node.alias,\n collection_alias=self.collection_alias)\n kwargs = {'for_match': for_match,\n 'optional': '',\n 'collection_alias': self.collection_alias,\n 'output_with_string': output_with}\n if optional:\n kwargs['optional'] = 'OPTIONAL '\n return self.subquery_template.format(**kwargs)","title":""},{"docid":"0c91288d0f92106eb67c80c495661bb2","score":"0.5183175","text":"def build_query(self):\n # initalize config\n query = super(GlobalTaskTableSource, self).build_query()\n query = query.avoid_duplicates()\n return query","title":""},{"docid":"f4c4035fb5ab40bfe3985d56797e9590","score":"0.51793414","text":"def prepareQuery(queryString, initNs={}, base=None):\n ret = translateQuery(parseQuery(queryString), base, initNs)\n ret._original_args = (queryString, initNs, base)\n return ret","title":""},{"docid":"06ac992c3bdb35350862ce9f31ab0d1b","score":"0.51777846","text":"def buildQuery(self, formInfo, user, query, languages):\r\n return query.options(subqueryload('concepts'))\\\r\n .join(concept_list_concepts)\\\r\n .join(Concept)\\\r\n .join(formInfo.formModel, (formInfo.formModel.language_id == languages.foreign.id) & (formInfo.formModel.concept_id == Concept.id))\\\r\n .outerjoin(Mastery, (Mastery.user_id==user.id) & (getattr(Mastery, formInfo.masteryFieldName) == formInfo.formModel.id))\\\r\n .outerjoin(StalenessPeriod)\\\r\n .order_by(formInfo.listModel.averageRatingFor(user, languages.foreign))","title":""},{"docid":"d1c27b14b20da118bc686538eb54c03e","score":"0.51636916","text":"def concat_query_make(query_ends):\n\n\tquery = result_queries.START\n\tstart = True\n\n\tif query_ends:\n\t\tfor end in query_ends:\n\t\t\tif start:\n\t\t\t\tquery += end\n\t\t\t\t#end is the varying return value to an otherwise identical query\n\n\n\t\t\tif not start:\n\t\t\t\tquery += \" || \\\"DELIM\\\" || \"\n\t\t\t\tquery += end\n\t\n\t\t\tstart = False\n\t\t\t\n\telse:\n\t\tquery = \"(no query selected)\"\n\t\n\treturn query","title":""},{"docid":"0ba8011bc739cf5b088eb97c87937d2b","score":"0.51636404","text":"def evaluate_node_sql(node):\n if 'condition' in node:\n # Node is a condition, get the values of the sub-clauses\n sub_pairs = \\\n [evaluate_node_sql(x) for x in node['rules']]\n\n if not sub_pairs:\n # Nothing has been returned, so it is an empty query\n return '', []\n\n # Now combine\n if node['condition'] == 'AND':\n result = '((' + \\\n ') AND ('.join([x for x, __ in sub_pairs]) + '))'\n else:\n result = '((' + \\\n ') OR ('.join([x for x, __ in sub_pairs]) + '))'\n result_fields = \\\n list(itertools.chain.from_iterable([x for __, x in sub_pairs]))\n\n if node.get('not', False):\n result = '(NOT (' + result + '))'\n\n return result, result_fields\n\n # Get the variable name and duplicate the symbol % in case it is part of\n # the variable name (escape needed for SQL processing)\n varname = fix_pctg_in_name(node['field'])\n\n # Get the operator\n operator = node['operator']\n\n # If the operator is between or not_between, there is a special case,\n # the constant cannot be computed because the node['value'] is a pair\n constant = None\n if node['value'] is not None and not isinstance(node['value'], list):\n # Calculate the constant value depending on the type\n if node['type'] == 'integer':\n constant = int(node['value'])\n elif node['type'] == 'double':\n constant = float(node['value'])\n elif node['type'] == 'boolean':\n constant = node['value'] == '1'\n elif node['type'] == 'string':\n constant = node['value']\n elif node['type'] == 'datetime':\n constant = node['value']\n else:\n raise Exception(\n _('No function to translate type {0}').format(node['type'])\n )\n\n # Terminal Node\n result_fields = []\n if operator == 'equal':\n result = '(\"{0}\"'.format(varname) + \\\n ' = %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [str(constant)]\n\n elif operator == 'not_equal':\n result = '(\"{0}\"'.format(varname) + \\\n '!= %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [str(constant)]\n\n elif operator == 'begins_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [node['value'] + \"%\"]\n\n elif operator == 'not_begins_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [node['value'] + \"%\"]\n\n elif operator == 'contains' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [\"%\" + node['value'] + \"%\"]\n\n elif operator == 'not_contains' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [\"%\" + node['value'] + \"%\"]\n\n elif operator == 'ends_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' LIKE %s) AND (\"{0}\" is not null)'.format(varname)\n result_fields = [\"%\" + node['value']]\n\n elif operator == 'not_ends_with' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n ' NOT LIKE %s) OR (\"{0}\" is null)'.format(varname)\n result_fields = [\"%\" + node['value']]\n\n elif operator == 'is_empty' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n \" = '') OR (\\\"{0}\\\" is null)\".format(varname)\n\n elif operator == 'is_empty' and node['type'] != 'string':\n result = \"(\\\"{0}\\\" is null)\".format(varname)\n\n elif operator == 'is_not_empty' and node['type'] == 'string':\n result = '(\"{0}\"'.format(varname) + \\\n \" != '') AND (\\\"{0}\\\" is not null)\".format(varname)\n\n elif operator == 'is_not_empty' and node['type'] != 'string':\n result = \"(\\\"{0}\\\" is not null)\".format(varname)\n\n elif operator == 'less' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' < %s'\n result_fields = [str(constant)]\n\n elif operator == 'less_or_equal' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' <= %s'\n result_fields = [str(constant)]\n\n elif operator == 'greater' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' > %s'\n result_fields = [str(constant)]\n\n elif operator == 'greater_or_equal' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' >= %s'\n result_fields = [str(constant)]\n\n elif operator == 'between' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' BETWEEN %s AND %s'\n result_fields = [str(node['value'][0]), str(node['value'][1])]\n\n elif operator == 'not_between' and \\\n (node['type'] == 'integer' or node['type'] == 'double'\n or node['type'] == 'datetime'):\n result = '\"{0}\"'.format(varname) + ' NOT BETWEEN %s AND %s'\n result_fields = [str(node['value'][0]), str(node['value'][1])]\n\n else:\n raise Exception(\n _('Type, operator, field {0}, {1}, {2} not supported yet').format(\n node['type'], operator, varname\n )\n )\n\n if node.get('not', False):\n raise Exception(_('Negation found in unexpected location'))\n\n return result, result_fields","title":""},{"docid":"0dd8285bd0816627e7f7b40210481df8","score":"0.5158893","text":"def query(\n self, strOrQuery, initBindings={},\n initNs={}, base=None, DEBUG=False):\n\n if not isinstance(strOrQuery, Query):\n parsetree = parseQuery(strOrQuery)\n query = translateQuery(parsetree, base, initNs)\n else:\n query = strOrQuery\n\n return evalQuery(self.graph, query, initBindings, base)","title":""},{"docid":"911fc88c4f06f7a15ccb80b7985bca8c","score":"0.51363057","text":"def execute(self, neo4j_session: Session):\n if self.query == \"\":\n raise ValueError(\n \"The query must be generated before this method is called.\"\n )\n try:\n return neo4j_session.write_transaction(lambda tx: tx.run(self.query))\n except:\n print(\"Query not defined error\")\n return None","title":""},{"docid":"5af7e59578232b5286f33142426d48b4","score":"0.51362425","text":"def buildConnectionString(params):\n return \";\".join([\"%s=%s\" % (k, v) for k, v in params.items()])","title":""},{"docid":"250b21f2993d378d79cd2b0b0096170a","score":"0.5131796","text":"def _row_to_query_statement_string(self, row):\n start_node = 'MERGE (start:{state_lab} {{code:\"{start_state}\"}})'.format(\n state_lab=self.labels.state, start_state=row[self.start_state_col]\n )\n\n end_node = 'MERGE (end:{state_lab} {{code:\"{end_state}\"}})'.format(\n state_lab=self.labels.state, end_state=row[self.end_state_col]\n )\n\n transition = \"MERGE (start)<-[:SOURCE]-(trans:{trans_lab})-[:TARGET]->(end)\".format(\n trans_lab=self.labels.transition\n )\n\n def _conditions_str(row, start_state_col, end_state_col):\n \"\"\"Build the string used to express transition conditions.\n \n This is the part in curly braces specifying the Condition node's\n properties.\n \"\"\"\n row = row.drop([start_state_col, end_state_col])\n count = 0\n s = \"\"\n for i, val in row.iteritems():\n s += i + \":\"\n if isinstance(val, six.string_types):\n s += '\"' + val + '\"'\n elif isinstance(val, bool):\n s += str(val).lower()\n else:\n s += str(val)\n if count < len(row) - 1:\n s += \", \"\n count += 1\n\n return \"{\" + s + \"}\"\n\n condition = \"MERGE (cond:{cond_lab} {cond_str})-[:CAUSES]->(trans)\".format(\n cond_lab=self.labels.condition,\n cond_str=_conditions_str(row, self.start_state_col, self.end_state_col),\n )\n\n query_str = (\n start_node + \" \" + end_node + \" \" + transition + \" \" + condition + \";\"\n )\n\n if self.global_params:\n query_str = self._add_global_params_to_query_string(\n query_str, self.global_params\n )\n\n return query_str","title":""},{"docid":"54c7e0913a92b7dfb4e7cc3e5cb349f6","score":"0.5125673","text":"def build_query(url: str, *args, **kwargs) -> str:\n url = url.strip(\"/\")\n args = map(lambda arg: arg.strip(\"/\"), args)\n kwargs = dict(filter(lambda kv: kv[1] is not None, kwargs.items()))\n url = \"/\".join([url, *args]) + \"?\"\n url += \"&\".join([f\"{key}={value}\" for key, value in kwargs.items()])\n return url","title":""},{"docid":"976a7fa212ec09e76082f81572c24b87","score":"0.51216996","text":"def build_query(field, fields, fuzziness=\"AUTO\"):\n val = fields[field]\n if \"*\" in val:\n qtype = \"wildcard\"\n fval = val\n else:\n qtype = \"match\"\n fval = dict(query=val, fuzziness=fuzziness)\n return Q(qtype, **{field: fval})","title":""},{"docid":"27d86e7c41d5fb86b9a53f995bbc51a9","score":"0.5117474","text":"def q(*args: Any, **kwargs: Any) -> \"Query\":\n return Query(*args, **kwargs)","title":""},{"docid":"f0fd368bbe1ee92e7081900909296ef4","score":"0.51087725","text":"def query_on_params(self,\\\n **kwargs):\n if self.check_empty_query(**kwargs):\n return []\n \n elif 'username' in kwargs and len(kwargs) == 1:\n name = kwargs['username'][0]\n return self.query_on_name(\\\n name,\\\n self.all()) \n\n username_s = kwargs['username'][0]\n fullname_s = kwargs['fullname'][0]\n faculty_s = kwargs['faculty'][0]\n program_s = kwargs['program'][0]\n query_set_l = self.all()\n if username_s != '':\n query_set_l = query_set_l.filter(\\\n username__icontains = username_s)\n if fullname_s != '':\n query_set_l = self.query_on_name(\\\n fullname_s,\\\n query_set_l)\n if faculty_s != '':\n query_set_l = query_set_l.filter(\\\n faculty__exact = faculty_s)\n if program_s != '':\n query_set_l = query_set_l.filter(\\\n program__exact = program_s)\n return query_set_l","title":""},{"docid":"a40795caa1ea72b2cdda71b5e0413200","score":"0.51069057","text":"def sql_condition(\n self,\n conditions=tuple(),\n all_inner=True,\n all_outer=True,\n match_inner=True,\n match_outer=True,\n ):\n args = locals()\n # put the conditions into a dict\n conds = {\n pref: args.get(pref)\n for pref in (\n \"all_inner\",\n \"all_outer\",\n \"match_inner\",\n \"match_outer\",\n )\n }\n conds = { # properly cycle all settings\n pref: itertools.cycle(val if hasattr(val, \"__iter__\") else (val,))\n for pref, val in conds.items()\n }\n conds[\"values\"] = (\n (conditions,) if hasattr(conditions, \"items\") else conditions\n )\n sql_conditions, sql_params = [], []\n for cond in listdict_to_dictlist(conds):\n sanitized_values = {\n self.sanitize(k, brutally=True): self.sanitize(\n v, brutally=False\n )\n if isinstance(v, str)\n else v\n for k, v in cond[\"values\"].items()\n if k in self.tableheader\n }\n inner_sql_cond = (\" AND \" if cond[\"all_inner\"] else \" OR \").join(\n map(\n \"{} {} ?\".format,\n sanitized_values.keys(),\n itertools.cycle(\n (\"IS\" if cond[\"match_inner\"] else \"IS NOT\",)\n ),\n )\n ) or \"1=1\"\n sql_conditions.append(\n \"{}({})\".format(\n \"\" if cond[\"match_outer\"] else \"NOT \", inner_sql_cond\n )\n )\n sql_params.extend(tuple(sanitized_values.values()))\n sql_condition = (\n functools.reduce(\n lambda x, y: (\n \" AND \" if next(conds[\"all_outer\"]) else \" OR \"\n ).join((x, y)),\n sql_conditions,\n )\n if sql_conditions\n else \"1=1\"\n )\n return sql_condition, tuple(sql_params)","title":""},{"docid":"c126e83185b3f095e5bc3f7c32cda876","score":"0.51048845","text":"def post(self, statement, parameters=None):\n payload = {\"query\": statement}\n if parameters:\n payload[\"params\"] = {}\n for key, value in parameters.items():\n if isinstance(value, (Node, Rel, Relationship)):\n value = value._id\n payload[\"params\"][key] = value\n log.info(\"execute %r %r\", payload[\"query\"], payload.get(\"params\", {}))\n return self.resource.post(payload)","title":""}],"string":"[\n {\n \"docid\": \"72ebf75d0424beb0d9398dd9dcc5f821\",\n \"score\": \"0.7439297\",\n \"text\": \"def build_query(self, params):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95e410d68282d18b99170eb9c871fae\",\n \"score\": \"0.70802385\",\n \"text\": \"def _build_query(self, params, boolean='AND'):\\n query = []\\n for p in params:\\n if isinstance(p, str):\\n # direct query string\\n query.append(p)\\n\\n elif isinstance(p, tuple) and len(p) == 3:\\n # <field> <op> <value> triplet\\n (field, op, value) = p\\n op_ = '-' if op == '!=' else ''\\n\\n if isinstance(value, list):\\n value_ = '(\\\"' + '\\\",\\\"'.join(value) + '\\\")'\\n elif isinstance(value, tuple):\\n value_ = '[%s TO %s]' % value\\n else:\\n quote = '\\\"' if not '*' in str(value) else ''\\n value_ = quote + str(value) + quote\\n\\n query.append('%s%s:%s' % (op_, field, value_))\\n\\n elif isinstance(p, tuple) and len(p) == 2:\\n # group/subquery with boolean operator\\n (op, subquery) = p\\n query.append('(' + self._build_query(subquery, op) + ')')\\n\\n return (' '+boolean+' ').join(query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c96f74b26acea483825aadcbb969353\",\n \"score\": \"0.6558855\",\n \"text\": \"def build_query(self, params):\\n phrase = params[\\\"phrase\\\"]\\n slop = params[\\\"slop\\\"]\\n\\n query = {}\\n query[\\\"query\\\"] = {\\\"match\\\":{}}\\n\\n infl_form = {}\\n infl_form[\\\"type\\\"] = \\\"phrase\\\"\\n infl_form[\\\"query\\\"] = phrase\\n infl_form[\\\"slop\\\"] = slop\\n query[\\\"query\\\"][\\\"match\\\"][\\\"inflected_form\\\"] = infl_form\\n\\n query = json.dumps(query)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a8397a2a8e9edc0608094386c96404c\",\n \"score\": \"0.653392\",\n \"text\": \"def _build_query_string(cls, parameters: dict) -> str:\\n\\t\\tquery_string = '?'\\n\\n\\t\\t# Builds a list of tuples for easy iteration like:\\n\\t\\t# [('channel', 'channel-1'), ('channel', 'channel-2'), ('assetId', 'asset-1'), ...]\\n\\t\\tparameter_tuples = [(parameter_name, parameter_value) \\n\\t\\t\\tfor parameter_name, parameter_values in parameters.items()\\n\\t\\t\\t\\tfor parameter_value in parameter_values]\\n\\n\\t\\tfor index, parameter_tuple in enumerate(parameter_tuples):\\n\\t\\t\\tname, value = parameter_tuple\\n\\t\\t\\tvalue = quote(value.encode()) if type(value) == str else value\\n\\n\\t\\t\\tif name == 'orderBy':\\n\\t\\t\\t\\tvalue = cls._convert_order_by(value)\\n\\n\\t\\t\\tquery_string += f'{name}={value}' if index == 0 else f'&{name}={value}'\\n\\n\\t\\treturn query_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a18169ab89094b2a6bab17605534b4f\",\n \"score\": \"0.6477496\",\n \"text\": \"def _build_query(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76313a0e15cad414416780ca9e335fb0\",\n \"score\": \"0.6379244\",\n \"text\": \"def _build_search(query, params):\\n #PLACEHOLDER -- only searches the objects table and does the simplest\\n #search possible for now\\n query_terms = params.copy() if( params and params != {} ) else _translate_query(query)\\n arguments = _args()\\n keywords = \\\"\\\"\\n query_string = \\\"\\\"\\n\\n if 'query_string' in query_terms:\\n query_string = query_terms['query_string']\\n arguments.update({'query_string':query_string})\\n del query_terms['query_string']\\n if 'keywords' in query_terms:\\n arguments.update({'keywords':query_terms['keywords']})\\n del query_terms['keywords']\\n # TODO: need to do all the query string processing here \\n # but, for now just a simple search assumption will work by default\\n\\n if query_string != \\\"\\\":\\n keywords += query_string\\n \\n return \\\"ca_objects?q=\\\"+keywords, arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c157292ed4cc40e351d63d9d0af82576\",\n \"score\": \"0.6359702\",\n \"text\": \"def generate_query(self):\\n return ''.join(['SELECT ',\\n ', '.join(self.columns),\\n ' ',\\n 'FROM molecule ',\\n ' '.join(self.joins),\\n ' ',\\n 'WHERE ',\\n '(', ') AND ('.join(self.wheres), ')'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba63cfec2c1ae532de6e05ad75c5ffe\",\n \"score\": \"0.6332386\",\n \"text\": \"def _set_sql_query(self, query_params):\\n query = f'SELECT * FROM {CATS_TABLE}'\\n # attributes in query string\\n if query_params.get('attribute'):\\n query += ' ORDER BY '\\n # may be 1>= attrs\\n for attr in query_params['attribute']:\\n query += f'{attr}, '\\n query = query.rstrip(', ')\\n\\n # order in query string\\n if query_params.get('order'):\\n order = query_params['order'][0].upper()\\n query += f' {order}'\\n\\n # offset in query string\\n if query_params.get('offset'):\\n offset = query_params['offset'][0]\\n query += f' OFFSET {offset}'\\n\\n # limit in query string\\n if query_params.get('limit'):\\n limit = query_params['limit'][0]\\n query += f' LIMIT {limit}'\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aafa03146dc86896e8b4203a577f391\",\n \"score\": \"0.6295957\",\n \"text\": \"def make_query(self, **kw):\\n query = kw.pop(\\\"query\\\", {})\\n\\n query.update(self.get_request_query())\\n query.update(self.get_custom_query())\\n query.update(self.get_keyword_query(**kw))\\n\\n sort_on, sort_order = self.get_sort_spec()\\n if sort_on and \\\"sort_on\\\" not in query:\\n query.update({\\\"sort_on\\\": sort_on})\\n if sort_order and \\\"sort_order\\\" not in query:\\n query.update({\\\"sort_order\\\": sort_order})\\n\\n logger.info(\\\"make_query:: query={} | catalog={}\\\".format(\\n query, self.catalog))\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1216cd9552ca67c038d89c1a33795cb6\",\n \"score\": \"0.6221052\",\n \"text\": \"def build_query(self):\\n\\n # initalize config\\n self.config.update_config()\\n\\n # get the base query from the config\\n query = self.config.get_base_query()\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11b2046780debe8f975dcb0277e234c\",\n \"score\": \"0.62166256\",\n \"text\": \"def buildQuery(self):\\n if not self.Valid:\\n print(\\\"Invalid usage: cannot build.\\\")\\n return False\\n if not self.needsBuilt:\\n return self.queryString\\n\\n filterDict = dict()\\n theFilters = dict()\\n if len(self._andFilters) > 0:\\n theFilters[\\\"and\\\"] = self._andFilters\\n if len(self._orFilters) > 0:\\n theFilters[\\\"or\\\"] = self._orFilters\\n filterDict[\\\"filter\\\"] = theFilters\\n queryString = json.dumps(filterDict)\\n\\n self.queryString = queryString\\n return queryString\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb15855e6fd47d6e9d3a76d89a8b5ace\",\n \"score\": \"0.6071394\",\n \"text\": \"def _build_query(symbol: str, start_date: str, end_date: str, order_by: str):\\n query = \\\"select * from stock_price where symbol = '{symbol}'\\\".format(symbol=symbol)\\n if start_date:\\n query += \\\" and time >= '{}'\\\".format(start_date)\\n if end_date:\\n query += \\\" and time <= '{}'\\\".format(end_date)\\n\\n if order_by == 'ASC':\\n # Default\\n pass\\n elif order_by == 'DESC':\\n query += ' order by time desc'\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46b3852c7f93eb9f1037b46961e8edb\",\n \"score\": \"0.60509795\",\n \"text\": \"def query_generator(self, kwargs):\\n\\n query_addition = \\\"\\\"\\n\\n if \\\"quarter\\\" in kwargs:\\n query_addition += f\\\" & qtr == {kwargs['quarter']}\\\"\\n if \\\"down\\\" in kwargs:\\n query_addition += f\\\" & down == {kwargs['down']}\\\"\\n if \\\"half\\\" in kwargs:\\n query_addition += f\\\" & game_half == 'Half{kwargs['half']}'\\\"\\n if \\\"close\\\" in kwargs:\\n if kwargs['close'] is True:\\n query_addition += f\\\" & score_differential >= -12 & score_differential <= 12\\\"\\n else:\\n query_addition += f\\\" & (score_differential < -12 | score_differential > 12)\\\"\\n if \\\"rz\\\" in kwargs:\\n if kwargs['rz'] is True:\\n query_addition += f\\\" & yardline_100 <= 25\\\"\\n else:\\n query_addition += f\\\" & yardline_100 > 25\\\"\\n if \\\"where\\\" in kwargs:\\n if kwargs['where'] is 'home':\\n query_addition += f\\\" & home_team == '{self.team}'\\\"\\n else:\\n query_addition += f\\\" & away_team == '{self.team}'\\\"\\n if \\\"play\\\" in kwargs:\\n if kwargs['play'] is 'run':\\n query_addition += f\\\" & rush_attempt == 1\\\"\\n else:\\n query_addition += f\\\" & pass_attempt == 1\\\"\\n if \\\"since\\\" in kwargs:\\n if kwargs['since'] is 'sep':\\n query_addition += f\\\" & (game_date.str.slice(5, 7) == '09' | game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\\\"\\n elif kwargs['since'] is 'oct':\\n query_addition += f\\\" & (game_date.str.slice(5, 7) == '10' | game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\\\"\\n elif kwargs['since'] is 'nov':\\n query_addition += f\\\" & (game_date.str.slice(5, 7) == '11' | game_date.str.slice(5, 7) == '12')\\\"\\n else:\\n query_addition += f\\\" & game_date.str.slice(5, 7) == '12'\\\"\\n\\n return query_addition\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c570052933c609ad72ebabe6e69a8bfd\",\n \"score\": \"0.598974\",\n \"text\": \"def data_query(self, parents=None):\\n filter_q = self.filter_query(parents=parents)\\n q = db.session.query()\\n stmt, q = self._add_statement(q)\\n\\n filter_sq = filter_q.subquery()\\n q = q.filter(stmt.subject == filter_sq.c.subject)\\n q = q.filter(stmt._attribute.in_(self.project()))\\n\\n q = q.add_column(stmt.subject.label('id'))\\n q = q.add_column(stmt._attribute.label('attribute'))\\n q = q.add_column(stmt._value.label('value'))\\n\\n if parents is not None and self.node.attribute:\\n parent_stmt, q = self._add_statement(q)\\n q = q.filter(stmt.subject == parent_stmt._value)\\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\\n q = q.add_column(parent_stmt.subject.label('parent_id'))\\n\\n q = q.order_by(filter_sq.c.subject.desc())\\n q = q.order_by(stmt.created_at.asc())\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd1bf23e219921f97adc765b4e477518\",\n \"score\": \"0.5976437\",\n \"text\": \"def make_query(columns, filter):\\n if columns is not None:\\n # the user only wants to report a subset of the columns\\n query = \\\"SELECT \\\" + columns + \\\" FROM variants\\\"\\n# elif cancers != 'none':\\n# query = \\\"SELECT \\\" + columns + \\\",civic_gene_abbreviations,cgi_gene_abbreviations FROM variants\\\"\\n else:\\n # report the kitchen sink\\n query = \\\"SELECT * FROM variants\\\"\\n if filter is not None:\\n # add any non-genotype column limits to the where clause\\n query += \\\" WHERE \\\" + filter\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3662e9f0259cba0673eaafff569ccaaa\",\n \"score\": \"0.59676003\",\n \"text\": \"def make_query(request, **kw):\\n\\n # build the catalog query\\n query = {\\n \\\"sort_limit\\\": get_sort_limit(request),\\n \\\"sort_on\\\": get_sort_on(request),\\n \\\"sort_order\\\": get_sort_order(request),\\n \\\"SearchableText\\\": get_query(request),\\n }\\n\\n # inject keyword args\\n query.update(kw)\\n\\n # inject the creator if given\\n if get_creator(request):\\n query[\\\"Creator\\\"] = get_creator(request)\\n\\n logger.info(\\\"Catalog Query --> %r\\\", query)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0196bcfee0829e1a676506cb8f483df\",\n \"score\": \"0.59151345\",\n \"text\": \"def query_from_params(self, params):\\n q = {}\\n start, rows = 0, 10\\n if \\\"start\\\" in params:\\n start = int(params[\\\"start\\\"])\\n if \\\"rows\\\" in params:\\n rows = int(params[\\\"rows\\\"])\\n\\n if \\\"lat\\\" in params and \\\"lon\\\" in params:\\n lat = float(params[\\\"lat\\\"])\\n lon = float(params[\\\"lon\\\"])\\n q[\\\"loc\\\"] = {\\n \\\"$near\\\": [lat, lon]\\n }\\n for atr in [\\\"city\\\", \\\"state\\\", \\\"country\\\"]:\\n if atr in params:\\n q[atr] = params[atr]\\n return self.query(start=start, rows=rows, **q)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324d683b973caa2910387cddae4780e5\",\n \"score\": \"0.5909418\",\n \"text\": \"def _create_query_params(self, kwargs):\\n if None == kwargs or 0 == len(kwargs):\\n return (\\\"\\\", [])\\n\\n if not isinstance(kwargs, basestring):\\n pairs = kwargs.items()\\n stmt = \\\" WHERE \\\" + \\\\\\n \\\" AND \\\".join([ x[0] + (None is x[1] and \\\" IS NULL\\\" or \\\" = ?\\\")\\n for x in pairs ])\\n\\n args = [x[1] for x in filter(lambda x: None is not x[1], pairs)]\\n else:\\n stmt = \\\" WHERE \\\" + kwargs\\n args = []\\n return (stmt, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49409b5c49174457e063fc4a1f2caf74\",\n \"score\": \"0.5909326\",\n \"text\": \"def build_query(cols=None, tables=None, group_by=None, order_by=None, **kwargs):\\n assert len(tables)\\n\\n # Create SELECT line\\n cols_str = \\\", \\\".join(cols) if cols else \\\"*\\\"\\n query = f'SELECT {cols_str} FROM '\\n\\n # Create FROM line\\n # Automatically add bracelets around strings with space inside them. Useful for nested queries.\\n query += ' NATURAL JOIN '.join(f'({table})' if ' ' in table else table for table in tables)\\n\\n # Apply all filters in the kwargs dict\\n constraints = []\\n for col_name, value in kwargs.items():\\n # Empty filters don't need to be applied\\n if value not in (None, ''):\\n # Use \\\"LIKE\\\" for strings, and \\\"==\\\" otherwise\\n if isinstance(value, str):\\n constraints.append(f'{col_name} LIKE \\\"{value}\\\"')\\n else:\\n constraints.append(f'{col_name} == {value}')\\n\\n # Create WHERE line if there are constraints\\n if constraints:\\n query += ' WHERE ' + ' AND '.join(constraints)\\n\\n # Create GROUP BY line if needed\\n if group_by:\\n query += ' GROUP BY ' + group_by\\n\\n # Create ORDER BY line if needed\\n if order_by:\\n query += ' ORDER BY ' + order_by\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb4cc87b5249fb99610ec14d3f62cf8\",\n \"score\": \"0.58879614\",\n \"text\": \"def GenerateQuery(unused_ref, args, request):\\n customer_id = ConvertOrgIdToObfuscatedCustomerId(args.organization)\\n labels = FilterLabels(args.labels)\\n labels_str = ','.join(labels)\\n request.query = 'parent==\\\\\\\"customerId/{0}\\\\\\\" && \\\\\\\"{1}\\\\\\\" in labels'.format(\\n customer_id, labels_str)\\n\\n return request\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8ba2a0ee0c22e8f3b0dc0581901893\",\n \"score\": \"0.5856376\",\n \"text\": \"def make_query(self, ns):\\n if issubclass(self.model_class, db.Model):\\n query = db.Query(self.model_class, namespace=ns)\\n for f in self.filters:\\n query.filter(\\\"%s %s\\\" % (f[0], f[1]), f[2])\\n else:\\n query = self.model_class.query(namespace=ns)\\n for f in self.filters:\\n query = query.filter(ndb.FilterNode(*f))\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5aeac92141db6643f5dae341690653e\",\n \"score\": \"0.5830022\",\n \"text\": \"def build_query(\\n self,\\n regions=None,\\n genes=None,\\n effect_types=None,\\n family_ids=None,\\n person_ids=None,\\n inheritance=None,\\n roles=None,\\n sexes=None,\\n variant_type=None,\\n real_attr_filter=None,\\n ultra_rare=None,\\n frequency_filter=None,\\n return_reference=None,\\n return_unknown=None,\\n limit=None,\\n pedigree_fields=None):\\n # pylint: disable=too-many-arguments\\n self.query_builder.reset_product()\\n\\n self.query_builder.build_select()\\n\\n self.query_builder.build_from()\\n\\n self.query_builder.build_join()\\n\\n self.query_builder.build_where(\\n regions=regions,\\n genes=genes,\\n effect_types=effect_types,\\n family_ids=family_ids,\\n person_ids=person_ids,\\n inheritance=inheritance,\\n roles=roles,\\n sexes=sexes,\\n variant_type=variant_type,\\n real_attr_filter=real_attr_filter,\\n ultra_rare=ultra_rare,\\n frequency_filter=frequency_filter,\\n return_reference=return_reference,\\n return_unknown=return_unknown,\\n pedigree_fields=pedigree_fields,\\n )\\n\\n self.query_builder.build_group_by()\\n self.query_builder.build_limit(limit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf3e320668377af9f499baa5b818919\",\n \"score\": \"0.5816653\",\n \"text\": \"def make_query(config):\\n\\n query = {}\\n\\n items = ['object', 'radius', 'max_out', 'source', 'output', 'mime', 'sort']\\n\\n for item in items\\n query[item] = str(config[item])\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2159f3098a64c4db73cdb716ccac6fd2\",\n \"score\": \"0.57821774\",\n \"text\": \"def make_where_statement(self, table_name, params):\\n if not params:\\n return (\\\" 1 \\\", ())\\n column_names = [row[1] for row in self.get_rows(\\\"PRAGMA table_info(%s)\\\" % table_name)]\\n for column in params.keys():\\n if column not in column_names:\\n raise KeyError(\\\"Error: Database column %s not in our database\\\" % column)\\n # Construct our SQL statement\\n where_statement = ' AND '.join(['\\\"%s\\\" = ?' % column for (column, value) in sorted(params.items())])\\n where_values = tuple([value for (column, value) in sorted(params.items())])\\n return (where_statement, where_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd14224ced22e5c08b3414302d16b36f\",\n \"score\": \"0.5775194\",\n \"text\": \"def build_query(self) -> Query:\\n logger.info(\\n {\\n \\\"message\\\": f\\\"Start building query for resource {self.analysis.definition_id}\\\",\\n \\\"resource_id\\\": self.analysis.resource_id,\\n },\\n )\\n\\n # We don't need to have columns duplicated in the dataframe\\n # so we keep the one we've already seen here.\\n # Note that the primary key column is added at the query initialization.\\n self._cur_query_columns = {self.analysis.primary_key_column}\\n # To avoid duplicated joins, we keep them here\\n self._cur_query_join_tables = {}\\n\\n query = self.session.query(self.get_column(self.analysis.primary_key_column, self._sqlalchemy_pk_table))\\n\\n # Add attributes to query\\n for attribute in self.analysis.attributes:\\n query = self.add_attribute_to_query(query, attribute)\\n\\n # Add filters to query\\n query = self.apply_filters(query)\\n\\n logger.info(\\n {\\n \\\"message\\\": f\\\"Built query for resource {self.analysis.definition_id}: {query.statement}\\\",\\n \\\"resource_id\\\": self.analysis.resource_id,\\n },\\n )\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb662581d7042718da96ca7baef1b0e\",\n \"score\": \"0.5773945\",\n \"text\": \"def query(self):\\n if 'order' in self.config['database']:\\n order = 'ORDER BY {order}'.format(**self.config['database'])\\n else:\\n order = ''\\n\\n if isinstance(self.cols, tuple):\\n cols = self.cols\\n else:\\n cols = (self.cols,)\\n\\n where, params = parse_queries(self.selection, self.mapping)\\n if where:\\n where = 'WHERE {conditions}'.format(conditions=' AND '.join(where))\\n else:\\n where = ''\\n limit = (self.slice[0].stop or sys.maxint) - (self.slice[0].start or 0)\\n\\n sql = \\\"\\\"\\\"\\n SELECT {cols} FROM {table}\\n {where}\\n {order}\\n LIMIT {limit}\\n OFFSET {offset}\\n \\\"\\\"\\\".format(\\n cols=', '.join(self.config[key]['col'] for key in cols),\\n table=self.config['database']['table'],\\n where=where, order=order, limit=limit,\\n offset=self.slice[0].start or 0)\\n\\n return sql, params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f87622ad6a714500a7b6ccb0df8944\",\n \"score\": \"0.5773749\",\n \"text\": \"def sql_with_params(self):\\r\\n return self.get_compiler(DEFAULT_DB_ALIAS).as_sql()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a12abdcbed66b2452ab47d4061b8ce7d\",\n \"score\": \"0.5761608\",\n \"text\": \"def build_query(self, query_dict):\\n\\n # Loop over keys in request dictionary, match them up to database fields\\n # Build query accounting for the exceptions\\n qry_sci_name = ''\\n qry_common_name = ''\\n qry_group = ''\\n qry_native = ''\\n qry_pollut_tol = ''\\n qry_rarity = ''\\n qry_range = ''\\n qry_max_age = ''\\n qry_mean_weight = ''\\n qry_mean_length = ''\\n qry_max_length = ''\\n qry_habitat = ''\\n qry_ubiquity = ''\\n qry_extent = ''\\n qry_tolerance = ''\\n qry_robustness = ''\\n\\n #Used in a couple of places to remove a trailing 'or '\\n trailing_or = ' or'\\n trailing_and = ' and'\\n\\n #This will be used later for habitat related fields that contain 1 for true. E.g. caves=1\\n set_keys = set(query_dict.keys())\\n\\n for req_key, req_val in query_dict.items():\\n # Is request param a valid query param\\n if req_key in self.attrib:\\n\\n if (req_key.lower() == 'scientific_name'):\\n words = req_val.split('_')\\n if len(words) == 1:\\n qry_sci_name = (\\\"( genus LIKE INITCAP('%%{0}%%') or species LIKE LOWER('%%{0}%%'))\\\")\\n qry_sci_name = str.format(qry_sci_name, words[0])\\n elif len(words) == 2:\\n qry_sci_name = (\\\" ((genus LIKE INITCAP('%%{0}%%') and species LIKE LOWER('%%{1}%%')) or (species LIKE LOWER('%%{0}%%') and genus LIKE INITCAP('%%{1}%%')))\\\")\\n qry_sci_name = str.format(qry_sci_name, words[0], words[1])\\n\\n if req_key.lower() == 'common_name':\\n words = req_val.split('_')\\n if len(words) == 1:\\n qry_common_name = (\\\" commonname LIKE INITCAP('%%{0}%%')\\\")\\n qry_common_name = str.format(qry_common_name, words[0])\\n elif len(words) == 2:\\n qry_common_name = (\\\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%'))\\\")\\n qry_common_name = str.format(qry_common_name, words[0], words[1])\\n elif len(words) == 3:\\n qry_common_name = (\\\" (commonname LIKE INITCAP('%%{0}%%') and commonname LIKE INITCAP('%%{1}%%') and commonname LIKE INITCAP('%%{2}%%'))\\\")\\n qry_common_name = str.format(qry_common_name, words[0], words[1], words[2])\\n\\n\\n # Can be multiple groups\\n #e.g. grp='Black Bass' or grp='Mullet'\\n if (req_key.lower() == 'group'):\\n words = req_val.lower()\\n words = words.replace('_', ' ')\\n words = words.split(',')\\n for idx, grp in enumerate(words):\\n if idx != 0:\\n qry_group += \\\" or \\\"\\n qry_group += str.format(\\\" lower(grp) = '{0}'\\\", grp.lower())\\n continue\\n\\n if (req_key.lower() == 'native'):\\n qry_native = str.format(\\\" native='{0}'\\\",req_val)\\n continue\\n\\n # pollution tolerance can be: \\\"I\\\", \\\"T\\\", \\\"M\\\", or \\\"U\\\"\\n # if (req_key.lower() == 'pollut_tol'):\\n # if (req_key.lower() == 'tolerance'):\\n # words = req_val.lower()\\n # words = words.split(',')\\n # for idx, pollut in enumerate(words):\\n # if idx != 0:\\n # qry_pollut_tol += \\\" or \\\"\\n # qry_pollut_tol += str.format(\\\" lower(pollut_tol) = '{0}'\\\", pollut)\\n # continue\\n\\n if (req_key.lower() == 'rarity'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_rarity = str.format(\\\" rarity>={0} and rarity<={1}\\\", range[0], range[1])\\n continue\\n\\n if (req_key.lower() == 'max_age'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_max_age = str.format(\\\" max_age>={0} and max_age<={1}\\\", range[0], range[1])\\n continue\\n\\n if (req_key.lower() == 'mean_weight'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_mean_weight = str.format(\\\" mean_weight>={0} and mean_weight<={1}\\\", range[0], range[1])\\n continue\\n\\n if (req_key.lower() == 'mean_length'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_mean_length = str.format(\\\" mean_length>={0} and mean_length<={1}\\\", range[0], range[1])\\n continue\\n\\n if (req_key.lower() == 'max_length'):\\n range = req_val.split('_')\\n if len(range) == 2:\\n qry_max_length = str.format(\\\" max_length>={0} and max_length<={1}\\\", range[0], range[1])\\n continue\\n\\n ubiquity_range = [0, 75]\\n if (req_key.lower() == 'ubiquity'):\\n range = req_val.split('_')\\n if len(range) == 2 and (int(range[0]) != ubiquity_range[0] or int(range[1]) != ubiquity_range[1]):\\n qry_ubiquity = str.format(\\\" ubiquity>={0} and ubiquity<={1}\\\", range[0], range[1])\\n continue\\n\\n extent_range = [0, 77]\\n if (req_key.lower() == 'extent'):\\n range = req_val.split('_')\\n if len(range) == 2 and (int(range[0]) != extent_range[0] or int(range[1]) != extent_range[1]):\\n qry_extent = str.format(\\\" extent>={0} and extent<={1}\\\", range[0], range[1])\\n continue\\n\\n tolerance_range = [0, 94]\\n if (req_key.lower() == 'tolerance'):\\n range = req_val.split('_')\\n if len(range) == 2 and (int(range[0]) != tolerance_range[0] or int(range[1]) != tolerance_range[1]):\\n qry_tolerance = str.format(\\\" tolerance>={0} and tolerance<={1}\\\", range[0], range[1])\\n continue\\n\\n robustness_range = [0, 47]\\n if (req_key.lower() == 'robustness'):\\n range = req_val.split('_')\\n if len(range) == 2 and (int(range[0]) != robustness_range[0] or int(range[1]) != robustness_range[1]):\\n qry_robustness = str.format(\\\" robustness>={0} and robustness<={1}\\\", range[0], range[1])\\n continue\\n\\n if qry_habitat.endswith(trailing_and):\\n qry_habitat = qry_habitat[:-len(trailing_and)]\\n\\n # The rest of the query parameters are are used if value == 1\\n # e.g. caves=1, springs=1, headwaters=1\\n for cat in self.categories:\\n intersect = set_keys & self.categories[cat]\\n if len(intersect) > 0:\\n print(cat)\\n print(intersect)\\n for param in intersect:\\n val = query_dict[param]\\n if (val == '1'):\\n self.query_categories[cat].append(param)\\n\\n qry_habitat = ''\\n query_cat = ''\\n for category in self.query_categories:\\n query_cat = ''\\n for val in self.query_categories[category]:\\n query_cat += str.format(\\\" {0}='1' or\\\", val)\\n\\n if query_cat.endswith(trailing_or):\\n query_cat = query_cat[:-len(trailing_or)]\\n\\n if len(query_cat) > 0:\\n qry_habitat += ' (' + query_cat + ') and'\\n print(qry_habitat)\\n\\n if qry_habitat.endswith(trailing_and):\\n qry_habitat = qry_habitat[:-len(trailing_and)]\\n\\n query = \\\"select * from fishproperties where\\\"\\n first_condition = True\\n\\n if qry_sci_name != '':\\n query += qry_sci_name\\n first_condition = False\\n\\n if qry_common_name != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_common_name\\n first_condition = False\\n\\n if qry_group != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_group\\n first_condition = False\\n\\n if qry_native != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_native\\n first_condition = False\\n\\n if qry_pollut_tol != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_pollut_tol\\n first_condition = False\\n\\n if qry_rarity != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_rarity\\n first_condition = False\\n\\n if qry_range != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_range\\n first_condition = False\\n\\n if qry_max_age != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_max_age\\n first_condition = False\\n\\n if qry_mean_weight != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_mean_weight\\n first_condition = False\\n\\n if qry_mean_length != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_mean_length\\n first_condition = False\\n\\n if qry_max_length != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_max_length\\n first_condition = False\\n\\n if qry_ubiquity != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_ubiquity\\n first_condition = False\\n\\n if qry_extent != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_extent\\n first_condition = False\\n\\n if qry_tolerance != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_tolerance\\n first_condition = False\\n\\n if qry_robustness != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_robustness\\n first_condition = False\\n\\n if qry_habitat != '':\\n if not first_condition:\\n query += ' and'\\n query += qry_habitat\\n first_condition = False\\n\\n print(query)\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45b3d40d8a26bb7193088e942d5a655\",\n \"score\": \"0.5736782\",\n \"text\": \"def _construct_query(self, query_chain):\\n # here we take the query_chain and convert to a real sql sentence\\n res_dict = query_chain.pop(0)\\n\\n for q in query_chain:\\n if q[\\\"action\\\"] == \\\"_db__where\\\":\\n if res_dict[\\\"action\\\"] == \\\"_db__select_all\\\":\\n res_dict.update({\\\"action\\\": \\\"_db__select\\\"})\\n\\n condition = res_dict.get(\\\"condition\\\", \\\"\\\")\\n if condition:\\n condition = \\\" AND \\\".join([condition, q[\\\"condition\\\"]])\\n else:\\n condition = q[\\\"condition\\\"]\\n\\n res_dict.update({\\\"condition\\\": condition})\\n elif q[\\\"action\\\"] == \\\"_db__select_related\\\":\\n for model_join in q[\\\"fields\\\"]:\\n join_const = getattr(self, q[\\\"action\\\"]).format(**model_join)\\n res_dict[\\\"join\\\"] += join_const\\n\\n select = res_dict[\\\"select\\\"][:]\\n\\n if select == \\\"COUNT(*)\\\":\\n pass\\n elif select == \\\"*\\\":\\n select = select.replace(\\n \\\"*\\\",\\n \\\"{left_table}.*, {f_formatter}\\\".format(\\n left_table=model_join[\\\"left_table\\\"], f_formatter=model_join[\\\"fields_formatter\\\"]\\n ),\\n )\\n res_dict[\\\"select\\\"] = select\\n else:\\n res_dict[\\\"select\\\"] += \\\", \\\" + model_join[\\\"fields_formatter\\\"]\\n\\n # if we are not counting, then we can assign ordering\\n operations = [\\\"COUNT\\\", \\\"MAX\\\", \\\"MIN\\\", \\\"SUM\\\", \\\"AVG\\\", \\\"STDDEV\\\"]\\n\\n # we must get error, if we set only one field and update instance\\n if res_dict[\\\"action\\\"] == \\\"_db__update\\\" and len(res_dict[\\\"field_values\\\"]) == 1:\\n res_dict.update({\\\"action\\\": \\\"_db__update_single_field\\\"})\\n\\n if res_dict.get(\\\"select\\\", \\\"\\\").split(\\\"(\\\")[0] not in operations:\\n res_dict[\\\"ordering\\\"] = self._ordering_syntax(res_dict.get(\\\"ordering\\\", []))\\n else:\\n res_dict[\\\"ordering\\\"] = \\\"\\\"\\n query = getattr(self, res_dict[\\\"action\\\"]).format(**res_dict)\\n query = self._query_clean(query)\\n\\n logger.debug(\\\"QUERY: {}, VALUES: {}\\\".format(query, res_dict.get(\\\"field_values\\\")))\\n return query, res_dict.get(\\\"field_values\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c13ae92a74d4f3943d1cd2abdd2d58f\",\n \"score\": \"0.572707\",\n \"text\": \"def _build_query(env, start, end, certname=None):\\n query = ExtractOperator()\\n query.add_field(FunctionOperator('count'))\\n query.add_field('status')\\n subquery = AndOperator()\\n subquery.add(GreaterEqualOperator('producer_timestamp', start))\\n subquery.add(LessOperator('producer_timestamp', end))\\n if certname is not None:\\n subquery.add(EqualsOperator('certname', certname))\\n if env != '*':\\n subquery.add(EqualsOperator('environment', env))\\n query.add_query(subquery)\\n query.add_group_by(\\\"status\\\")\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a1dd0e6509804a1d36f57e0e6c5750a\",\n \"score\": \"0.5720286\",\n \"text\": \"def query() -> str:\\n return \\\"\\\"\\\"\\n MATCH (doc:Publication)<-[:located_in]-(subject)-[association:chemical_to_disease_or_phenotypic_feature_association]->(obj)-[:located_in]->(sec_doc:Publication)\\n WHERE association.relation <> 'null' and obj.name <> 'null' and subject.name <> 'null' and doc.pmid in {pmids} and association.relation <> 'null' and type(association) <> 'could_refer_to' and type(association) <> 'located_in' and sec_doc.pmid = doc.pmid\\n RETURN DISTINCT subject.name as subject, subject.synonym as subj_syn, association.relation as predicate, obj.name as obj, obj.synonym as disease_synonym, type(association) as rel_type, doc.pmid as pmid, doc.abstract as abstract\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6002db82a2bae451e4ed335385a5f2a2\",\n \"score\": \"0.57197946\",\n \"text\": \"def build_where_clause(args: dict, table_args_dict: dict) -> str:\\n where_clause: str = ''\\n for key in args.keys():\\n if key in table_args_dict.keys():\\n if key == 'query':\\n # if query arg is supplied than we just need to parse it and only it\\n return args[key].strip()\\n else:\\n values_list: list = argToList(args[key])\\n for raw_value in values_list:\\n for field in table_args_dict[key]:\\n value = raw_value\\n if key == 'url':\\n value = f'*{raw_value}*'\\n if not where_clause:\\n # the beginning of the where part should start without OR\\n where_clause += f\\\"{field}'{value}'\\\"\\n else:\\n where_clause += f\\\" OR {field}'{value}'\\\"\\n return where_clause\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd48cb6a7ac2b019d07866613d70b519\",\n \"score\": \"0.5693227\",\n \"text\": \"def generate_where_clause(state=None, product=None,\\n issue=None, min_complaints=None):\\n where_inner = []\\n where_outer = []\\n\\n if state and not state == \\\"All\\\":\\n where_inner.append(\\\"ccdb.state = '{}'\\\".format(state))\\n\\n if product and not product == \\\"All\\\":\\n where_inner.append(\\\"ccdb.product = '{}'\\\".format(product))\\n\\n if issue and not issue == \\\"All\\\":\\n where_inner.append(\\\"ccdb.issue = '{}'\\\".format(issue))\\n\\n if min_complaints:\\n where_outer.append(\\\"complaint_count > {}\\\".format(min_complaints))\\n\\n if len(where_inner) > 0:\\n where_inner = \\\"WHERE \\\" + \\\" AND \\\".join(where_inner)\\n else:\\n where_inner = \\\"\\\"\\n\\n if len(where_outer) > 0:\\n where_outer = \\\"WHERE \\\" + \\\" AND \\\".join(where_outer)\\n else:\\n where_outer = \\\"\\\"\\n\\n return where_inner, where_outer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e6e63e44f78e69cf88a741ac17d2fb\",\n \"score\": \"0.5682573\",\n \"text\": \"def build_query(self, params):\\n term = params[\\\"term\\\"]\\n size = params[\\\"size\\\"]\\n page = params[\\\"page\\\"]\\n\\n query = {}\\n query[\\\"query\\\"] = {\\\"match\\\":{}}\\n query[\\\"query\\\"][\\\"match\\\"][\\\"inflected_form\\\"] = term\\n\\n query[\\\"sort\\\"] = []\\n query[\\\"sort\\\"].append({\\\"_score\\\": \\\"desc\\\"})\\n query[\\\"sort\\\"].append({\\\"word_count\\\": \\\"asc\\\"})\\n\\n query[\\\"from\\\"] = (page - 1) * size\\n query[\\\"size\\\"] = size\\n\\n query = json.dumps(query)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930b18d9b4e9205bd3e078001ad05e29\",\n \"score\": \"0.5674045\",\n \"text\": \"def generate(self):\\n result = self.query\\n for param in self.params:\\n result = result.replace(param.raw, param.sql_value())\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aa19aae92a018fc0f26673cb0f78bfb\",\n \"score\": \"0.56575936\",\n \"text\": \"def filter_query(self, parents=None):\\n q = db.session.query()\\n stmt, q = self._add_statement(q)\\n q = q.add_column(stmt.subject)\\n\\n if parents is not None and self.node.attribute:\\n parent_stmt, q = self._add_statement(q)\\n q = q.filter(stmt.subject == parent_stmt._value)\\n q = q.filter(parent_stmt._attribute == self.node.attribute.name)\\n q = q.filter(parent_stmt.subject.in_(parents))\\n\\n q = self.filter(q, stmt)\\n q = q.group_by(stmt.subject)\\n q = q.order_by(stmt.subject.asc())\\n\\n if self.node.root:\\n q = q.limit(self.node.limit)\\n q = q.offset(self.node.offset)\\n\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd52219e9c4b5adb4afd6a139da0d56\",\n \"score\": \"0.56358266\",\n \"text\": \"def derived(self, statement, params=(), replace=False):\\n return Query(\\n db=self.db,\\n sql=(statement,) if replace else self.sql + (statement,),\\n params=self.params + params,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cd8fe36d01097c20060583de2e8bc83\",\n \"score\": \"0.562555\",\n \"text\": \"def make_query(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe5061d0a503021cd5b78f56d96ffed\",\n \"score\": \"0.5612804\",\n \"text\": \"def build_query(media_type=None, tags=None, sort=None, max_duration=None, published_after=None, categories=None,\\n region=None, random=False, limit=constants.DEFAULT_QUERY_LIMIT,\\n offset=constants.DEFAULT_QUERY_OFFSET):\\n # NOTE: query time skyrockets with added OPTIONAL patterns, see:\\n # https://stackoverflow.com/questions/25609691/alternative-for-optional-keyword-in-sparql-queries\\n #\\n # NOTE: query assumes ordering of group concat operations are same across tag and source - have eyeballed and\\n # looks good\\n if published_after is not None:\\n raise NotImplementedError('The parameter `publishedAfter` is not yet implemented')\\n if region is not None:\\n raise NotImplementedError('The parameter `region` is not yet implemented')\\n if sort is not None and random:\\n raise InvalidInputParameterCombination('Cannot specify both `sort` and `random`.')\\n\\n regular_fields = '?title ?image ?version ?programme ?pid ?media ?duration ?publicationDate ?masterBrand'\\n query_string = f\\\"\\\"\\\"\\n SELECT\\n {regular_fields}\\n {build_tags_select_statement()}\\n {build_genres_select_statement()}\\n\\n WHERE {{\\n {build_regular_fields_pattern_statement()}\\n {build_tags_pattern_statement()}\\n {build_genres_pattern_statement()}\\n\\n {build_sparql_anding_statement(\\n object_list=tags,\\n predicate_str='datalab:tag/datalab:tagValue',\\n object_binding='programme'\\n )}\\n {build_values_oring_statement('media', media_type)}\\n {build_filter_statement('duration', '<', max_duration)}\\n {build_filter_statement('published_date', '>', published_after)}\\n {build_sparql_anding_statement(\\n object_list=categories,\\n predicate_str='po:genre/datalab:genreKey',\\n object_binding='programme'\\n )}\\n }}\\n GROUP BY {regular_fields}\\n {'ORDER BY RAND()' if random else 'ORDER BY ' + build_sort_statement(sort) if sort is not None else ''}\\n LIMIT {limit}\\n OFFSET {offset}\\n \\\"\\\"\\\"\\n\\n return query_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"864d460b0beb79116a83d3be73eff240\",\n \"score\": \"0.5609568\",\n \"text\": \"def compiledQuery(self, columns='', where='', order_by='',\\n distinct='', limit='', offset='',\\n group_by='', having='', for_update=False,\\n relationDict=None,\\n bagFields=False,\\n count=False, excludeLogicalDeleted=True,excludeDraft=True,\\n ignorePartition=False,ignoreTableOrderBy=False,\\n addPkeyColumn=True):\\n # get the SqlCompiledQuery: an object that mantains all the informations to build the sql text\\n self.cpl = SqlCompiledQuery(self.tblobj.sqlfullname,relationDict=relationDict,maintable_as=self.aliasCode(0))\\n distinct = distinct or '' # distinct is a text to be inserted in the sql query string\\n \\n # aggregate: test if the result will aggregate db rows\\n aggregate = bool(distinct or group_by)\\n \\n # group_by == '*': if all columns are aggregate functions, there will be no GROUP BY columns, \\n # but SqlQueryCompiler need to know that result will aggregate db rows\\n if group_by == '*':\\n group_by = None\\n\\n if not ignoreTableOrderBy and not aggregate:\\n order_by = order_by or self.tblobj.attributes.get('order_by')\\n self.init()\\n if not 'pkey' in self.cpl.relationDict:\\n self.cpl.relationDict['pkey'] = self.tblobj.pkey\\n \\n # normalize the columns string\\n columns = columns or ''\\n columns = columns.replace(' ', ' ')\\n columns = columns.replace('\\\\n', '')\\n columns = columns.replace(' as ', ' AS ')\\n columns = columns.replace(' ,', ',')\\n if columns and not columns.endswith(','):\\n columns = columns + ','\\n \\n # expand * and *filters: see self.expandMultipleColumns\\n if '*' in columns:\\n col_list = [col for col in gnrstring.split(columns, ',') if col]\\n new_col_list = []\\n for col in col_list:\\n col = col.strip()\\n if col.startswith('*'):\\n new_col_list = new_col_list + self.expandMultipleColumns(col[1:], bagFields)\\n else:\\n new_col_list.append(col)\\n columns = ','.join(new_col_list)\\n \\n # translate @relname.fldname in $_relname_fldname and add them to the relationDict\\n if where:\\n where = PERIODFINDER.sub(self.expandPeriod, where)\\n \\n currentEnv = self.db.currentEnv\\n env_conditions = dictExtract(currentEnv,'env_%s_condition_' %self.tblobj.fullname.replace('.','_'))\\n if env_conditions:\\n wherelist = [where] if where else []\\n for condition in env_conditions.values():\\n wherelist.append('( %s )' %condition)\\n where = ' AND '.join(wherelist)\\n\\n partition_kwargs = dictExtract(self.tblobj.attributes,'partition_')\\n\\n if not ignorePartition and partition_kwargs:\\n wherelist = [where] if where else []\\n for k,v in partition_kwargs.items():\\n if currentEnv.get('current_%s' %v):\\n wherelist.append('( $%s=:env_current_%s )' % (k,v))\\n where = ' AND '.join(wherelist)\\n columns = self.updateFieldDict(columns)\\n where = self.updateFieldDict(where or '')\\n order_by = self.updateFieldDict(order_by or '')\\n group_by = self.updateFieldDict(group_by or '')\\n having = self.updateFieldDict(having or '')\\n \\n col_list = uniquify([col for col in gnrstring.split(columns, ',') if col])\\n new_col_list = []\\n for col in col_list:\\n col = col.strip()\\n if col.startswith('SUM'):\\n aggregate = True\\n if not ' AS ' in col:\\n if col.startswith('$') and col[1:].replace('_', '').isalnum():\\n as_ = col[1:]\\n else:\\n # replace non word char with _ and check for numbers\\n as_ = self.db.colToAs(col)\\n col = '%s AS \\\"%s\\\"' % (col, as_)\\n else:\\n colbody, as_ = col.split(' AS ', 1)\\n # leave the col as is, but save the AS name to recover the db column original name from selection result\\n self.cpl.aliasDict[as_.strip()] = colbody.strip()\\n new_col_list.append(col)\\n \\n # build the clean and complete sql string for the columns, but still all fields are expressed as $fieldname\\n columns = ',\\\\n'.join(new_col_list)\\n \\n # translate all fields and related fields from $fldname to t0.fldname, t1.fldname... and prepare the JOINs\\n colPars = {}\\n for key, value in self.cpl.relationDict.items():\\n # self._currColKey manage exploding columns in recursive getFieldAlias without add too much parameters\\n self._currColKey = key\\n colPars[key] = self.getFieldAlias(value)\\n if count: # if the query is executed in count mode...\\n order_by = '' # sort has no meaning\\n if group_by: # the number of rows is defined only from GROUP BY cols, so clean aggregate functions from columns.\\n columns = group_by # was 't0.%s' % self.tblobj.pkey # ???? \\n elif distinct:\\n pass # leave columns as is to calculate distinct values\\n else:\\n columns = 'count(*) AS gnr_row_count' # use the sql count function istead of load all data\\n elif addPkeyColumn and self.tblobj.pkey and not aggregate:\\n columns = columns + ',\\\\n' + '%s.%s AS pkey' % (self.aliasCode(0),self.tblobj.pkey) # when possible add pkey to all selections\\n columns = columns.lstrip(',') # if columns was '', now it starts with ','\\n else:\\n columns = columns.strip('\\\\n').strip(',')\\n \\n # replace $fldname with tn.fldname: finally the real SQL columns!\\n columns = gnrstring.templateReplace(columns, colPars, safeMode=True)\\n \\n # replace $fldname with tn.fldname: finally the real SQL where!\\n \\n where = gnrstring.templateReplace(where, colPars)\\n #if excludeLogicalDeleted==True we have additional conditions in the where clause\\n logicalDeletionField = self.tblobj.logicalDeletionField\\n draftField = self.tblobj.draftField\\n if logicalDeletionField:\\n if excludeLogicalDeleted is True:\\n extracnd = '%s.%s IS NULL' % (self.aliasCode(0),logicalDeletionField)\\n if where:\\n where = '%s AND %s' % (extracnd, where)\\n else:\\n where = extracnd\\n elif excludeLogicalDeleted == 'mark':\\n if not (aggregate or count):\\n columns = '%s, %s.%s AS _isdeleted' % (columns, self.aliasCode(0),logicalDeletionField) #add logicalDeletionField\\n if draftField:\\n if excludeDraft is True:\\n extracnd = '%s.%s IS NOT TRUE' %(self.aliasCode(0),draftField)\\n if where:\\n where = '%s AND %s' % (extracnd, where)\\n else:\\n where = extracnd\\n # add a special joinCondition for the main selection, not for JOINs\\n if self.joinConditions:\\n extracnd, one_one = self.getJoinCondition('*', '*', self.aliasCode(0))\\n if extracnd:\\n if where:\\n where = '(%s) AND (%s)' % (where, extracnd)\\n else:\\n where = extracnd\\n order_by = gnrstring.templateReplace(order_by, colPars)\\n having = gnrstring.templateReplace(having, colPars)\\n group_by = gnrstring.templateReplace(group_by, colPars)\\n \\n if distinct:\\n distinct = 'DISTINCT '\\n elif distinct is None or distinct == '':\\n if self._explodingRows:\\n if not aggregate: # if there is not yet a group_by\\n distinct = 'DISTINCT ' # add a DISTINCT to remove unusefull rows: eg. a JOIN used only for a where, not for columns\\n if order_by:\\n xorderby=(('%s '%order_by.lower()).replace(' ascending ','').replace(' descending ','').replace(' asc ','').replace(' desc','')).split(',')\\n lowercol=columns.lower()\\n for xrd in xorderby:\\n if not xrd.strip() in lowercol:\\n columns = '%s, \\\\n%s' % (columns, xrd)\\n #order_by=None\\n if count:\\n columns = '%s.%s' % (self.aliasCode(0),self.tblobj.pkey)\\n # Count the DISTINCT maintable pkeys, instead of count(*) which will give the number of JOIN rows.\\n # That gives the count of rows on the main table: the result is different from the actual number\\n # of rows returned by the query, but it is correct in terms of main table records.\\n # It is the right behaviour ???? Yes in some cases: see SqlSelection._aggregateRows\\n \\n self.cpl.distinct = distinct\\n self.cpl.columns = columns\\n self.cpl.where = where\\n self.cpl.group_by = group_by\\n self.cpl.having = having\\n self.cpl.order_by = order_by\\n self.cpl.limit = limit\\n self.cpl.offset = offset\\n self.cpl.for_update = for_update\\n #raise str(self.cpl.get_sqltext(self.db)) # uncomment it for hard debug\\n return self.cpl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2a3634474b6bca3382e5a869663d66\",\n \"score\": \"0.5586637\",\n \"text\": \"def rebuild_query(self, comments=True, pretty=True):\\n if not self.tree:\\n self._logger.error('El arbol no ha sido generado todavia. Por tanto, todavia no hay ninguna query que '\\n 'reconstruir. Por favor ejecuta la funcion parse_query() para generar el arbol')\\n raise LookupError\\n\\n if not self.__queries:\\n self._logger.error('El arbol no ha sido procesado todavia. Por favor ejecuta la funcion rename_tree() para '\\n 'procesar el arbol.')\\n raise LookupError\\n\\n query = ' '.join(self.tree.leaves())\\n query = (self._untokenize(query)\\n .replace(' , ', ', ')\\n .replace(' ; ', ';')\\n .replace(' ( ', ' (')\\n .replace(' ) ', ') ')\\n #.replace(' = ', '=')\\n .replace(' . ', '.')\\n )\\n\\n if self.udfs:\\n for point, scape in zip(self.udfs, self._udfs_norm):\\n pattern = re.compile(scape, re.IGNORECASE)\\n query = pattern.sub(point, query)\\n\\n if pretty:\\n if comments:\\n final = '\\\\n'.join(self.__comments) + '\\\\n' + sqlparse.format(query, reindent=True, keyword_case='upper')\\n self.__comments = []\\n return final\\n else:\\n return sqlparse.format(query, reindent=True, keyword_case='upper')\\n else:\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4df307799efca220bbeb0a0c969810c8\",\n \"score\": \"0.55771285\",\n \"text\": \"def construct_query(*args, dialect='MSSS', allow_coalesce=True):\\n n = len(args)\\n assert all([isinstance(o, UserOption) for o in args]), \\\"Not all args are UserOptions\\\"\\n invalids = [not o.validate() for o in args]\\n assert not any(invalids), \\\"{:d}/{:d} options have not been validated\\\".format(\\n sum(invalids), n)\\n assert all([args[0].context == args[i+1].context for i in range(n-1)]), \\\"Different\\\" +\\\\\\n \\\"contexts associated with the User Opts. Ensure these are the same.\\\"\\n stmt = Statement(args[0].context)\\n\\n # ==== GET ALL TABLES AND FORM BASIC JOIN SUBTREE ============\\n context = args[0].context\\n nodes = [o.get_table() for o in args]\\n unique_nodes = list(set(nodes))\\n join_tree = minimum_subtree(unique_nodes)\\n\\n # The primary table is considered the \\\"root node\\\" of the tree -- edges are undirected.\\n primary = [o for o in args if not o.is_secondary]\\n assert len(primary) == 1, f'Expecting one primary field. Got {len(primary)}.'\\n primary = primary[0]\\n primary_tbl = primary.get_table()\\n\\n # topological sort\\n edges = [(k, v[0][0]) for (i,(k,v)) in enumerate(join_tree.items()) if i > 0]\\n G = graph.Graph(edges, directed=False)\\n sorted_nodes = G.topological_sort(leave_until_last=primary_tbl)\\n # (Recreate G since the topological_sort mutates G (ikr - should fix this!))\\n G = graph.Graph(edges, directed=False)\\n\\n # ======= CREATE CTEs WHENEVER WE FIND A NESTED AGGREGATION ============\\n field_tbl_lkp = defaultdict(list)\\n for arg in args:\\n field_tbl_lkp[arg.get_table()].append(arg)\\n all_fields = dict()\\n\\n # Traverse the join tree of the selected tables, making CTEs wherever\\n # there exists secondary aggregation (i.e. not of the primary variable).\\n for i, v in enumerate(sorted_nodes[:-1]):\\n v_fields = field_tbl_lkp[v]\\n any_v_has_agg = any([arg.has_aggregation for arg in v_fields])\\n\\n # Get 'child tables' and 'parent tables' (wrt topological sort)\\n child_tbls = G._graph[v] - set(sorted_nodes[i:])\\n parent_tbl = join_tree[v][0][0]\\n parent_fks, v_pks = join_tree[v][0][1], join_tree[v][1][1]\\n\\n # The current table contains an aggregation, so create a CTE.\\n if any_v_has_agg:\\n # Get all arguments for table which are not aggregated\\n f_non_agg = [arg for arg in v_fields if not arg.has_aggregation]\\n f_non_agg += flatten([all_fields[w] for w in child_tbls])\\n\\n # Add in table PKs if not already included in `f_non_agg`.\\n f_non_agg_names = [f.sql_fieldname for f in f_non_agg]\\n pk_names_to_add = [f for f in v_pks if f not in f_non_agg_names]\\n pks_to_add = [construct_simple_field(f, v, context) for f in pk_names_to_add]\\n\\n # Make a copy of all non-aggregated AND aggregated fields.\\n f_non_agg_cte = [f.copy() for f in f_non_agg]\\n f_agg_cte = [arg for arg in v_fields if arg.has_aggregation]\\n f_agg = [f.copy() for f in f_agg_cte]\\n\\n # consolidate CTE fields and add primary designator to (any) field in root\\n cte_fields = pks_to_add + f_non_agg_cte + f_agg_cte\\n for field in cte_fields:\\n if field.sql_fieldname in v_pks:\\n field.is_secondary = False\\n break\\n\\n # Defer lookups until the final query\\n for field in cte_fields:\\n field.perform_lkp = False\\n\\n # Construct CTE\\n cte = CTENode([parent_tbl], # parent\\n v_pks, # pk\\n cte_fields) # cte_fields\\n stmt.ctes.append(cte)\\n\\n # Point the [references to the aggregations] outside the CTE to the CTE field\\n for f in f_agg:\\n f.field_alias = f.field_alias # this is *not* a noop! (@property...)\\n f.sql_item = '{alias}' + f.field_alias\\n f.table = cte\\n f.set_aggregation(None, force=True)\\n f.set_transform(None, force=True)\\n if allow_coalesce:\\n f.coalesce = 0 # assumes that aggregation is numeric\\n\\n for f in f_non_agg:\\n f.table = cte\\n f.sql_item = '{alias}'+f._field_alias_logic(will_perform_lkp=False)\\n\\n # Push these pointers to within the CTE up to the parent (although not PKs)\\n all_fields[v] = f_non_agg + f_agg\\n\\n # # overwrite the node `v` in the subtree with `cte`\\n # join_cond = tree_final[v]\\n # join_cond = (join_cond[0], (cte, join_cond[1][1]))\\n # tree_final = replace_in_ordered_dict(tree_final, v, cte, join_cond)\\n else:\\n all_fields[v] = v_fields + flatten([all_fields[w] for w in child_tbls])\\n\\n # Set args to the args propagated up to the root node\\n primary_children = G._graph[primary_tbl]\\n args = field_tbl_lkp[primary_tbl] + flatten([all_fields[c] for c in primary_children])\\n # Place the resulting tree into the FROM clause of the Statement object.\\n\\n nodes = [o.get_table() for o in args]\\n unique_nodes = list(set(nodes))\\n tree_final = minimum_subtree(unique_nodes)\\n\\n stmt._from.update(tree_final)\\n\\n # Add dimension tables (if requested to joins)\\n lkp_joins = []\\n for o in args:\\n if o.perform_lkp:\\n dtbl = o.dimension_table\\n assert not dtbl.is_cte, \\\"Currently unable to support CTEs for dimension \\\" + \\\\\\n \\\"tables. (But it only requires thinking about how \\\" + \\\\\\n \\\"to avoid multiple copies.)\\\"\\n fk = o.sql_fieldname\\n lkp_joins.append(((o.table, fk), (dtbl, dtbl.pk[0])))\\n\\n # Generate statement in order to populate aliases dict\\n stmt._from.generate_basic_statement(force_alias=(len(lkp_joins) > 0))\\n stmt._from.add_lookups_to_statement(lkp_joins)\\n\\n # === CONSTRUCT SELECT / WHERE ===========\\n has_agg = any([arg.has_aggregation for arg in args])\\n\\n for o in args:\\n # Get table alias for field, depending on whether has lookup table or not\\n if not o.perform_lkp:\\n alias = stmt.aliases[o.get_table()]\\n # coalesce is usually None anyway\\n coalesce = o.coalesce if allow_coalesce else None\\n else:\\n dtbl = o.dimension_table\\n fk = rm_alias_placeholder(o.sql_item)\\n alias = stmt.lkp_aliases[((o.table, fk), (dtbl, dtbl.pk[0]))]\\n coalesce = context.coalesce_default if allow_coalesce else None\\n\\n sel, where = o.sql_transform(alias=alias, dialect=dialect, coalesce=coalesce)\\n # SELECT\\n stmt.select.append(sel)\\n # WHERE\\n if len(where) > 0:\\n stmt.where.extend(where)\\n # GROUP BY\\n if has_agg and not o.has_aggregation:\\n gby = re.sub('AS [a-zA-Z0-9_]+$', '', sel).strip()\\n stmt.groupby.append(gby)\\n\\n return stmt.generate_statement(dialect=dialect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b15f5e2a70c1eb7088c3897a2bfef2\",\n \"score\": \"0.5570735\",\n \"text\": \"def _build_query(self, metric, node, ts_from, ts_to):\\n resolution = ts_to - ts_from\\n if resolution > PROMETHEUS_TS_LIMIT:\\n ts_from = ts_to - PROMETHEUS_TS_LIMIT\\n\\n query_head = \\\"http://{}:{}/api/v1/query_range?query=\\\".format(\\n self.tsdb_ip, self.tsdb_port)\\n query_times = \\\"&start={}&end={}&step=1s\\\".format(ts_from, ts_to)\\n\\n query_selectors = self._get_query_selectors(metric, node)\\n query_selector = '{}{{{}}}'.format(metric, query_selectors)\\n\\n query = \\\"{}{}{}\\\".format(query_head, query_selector, query_times)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923405ac27fc863e4ecf89c859843768\",\n \"score\": \"0.55545264\",\n \"text\": \"def get_clean_query(self, additional_params={}):\\n # NOTE: right now this makes us pretty vulnerable\\n # to SQL Injection. We should make this safer at some point.\\n # Graphs likely have the same problem.\\n reg = re.compile('(\\\\{\\\\{.*?\\\\}\\\\})')\\n query = self.query\\n matches = reg.findall(query)\\n if matches:\\n for match in matches:\\n attr = match[2:len(match)-2]\\n if attr in additional_params:\\n query = query.replace(match, additional_params[attr])\\n else:\\n query = query.replace(match, \\\"\\\")\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"374044813c297baa727f456ec5a2a772\",\n \"score\": \"0.5546569\",\n \"text\": \"def buildQueryFromFilters(date, rainfall, temperature, month, year):\\n\\t# Main part of the query\\n\\tquery = sql.SQL(\\\"SELECT * FROM climate\\\")\\n\\n\\tconditions = []\\n\\tvalues = []\\n\\n\\t# Check if each filter is actually applied, \\n\\t# if so, its respective part of the query is appended to the main part\\n\\tif date != None:\\n\\t\\tclause = sql.SQL(\\\"date = to_date(%s, 'DD-MM-YYYY')\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(date)\\n\\tif temperature != None:\\n\\t\\tclause = sql.SQL(\\\"temperature = (%s)\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(temperature)\\n\\tif rainfall != None:\\n\\t\\tclause = sql.SQL(\\\"rainfall = real '%s'\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(rainfall)\\n\\tif month != None:\\n\\t\\tclause = sql.SQL(\\\"EXTRACT(MONTH FROM date) = (%s)\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(month)\\n\\tif year != None:\\n\\t\\tclause = sql.SQL(\\\"EXTRACT(YEAR FROM date) = (%s)\\\")\\n\\t\\tconditions.append(clause)\\n\\t\\tvalues.append(year)\\n\\n\\t# Put all together\\n\\tif len(conditions) > 0:\\n\\t\\tquery = sql.SQL(\\\" \\\").join([query, sql.SQL(\\\"WHERE\\\")])\\n\\n\\t\\tconj = sql.SQL(\\\" AND \\\").join(conditions)\\n\\n\\t\\tquery = sql.SQL(\\\" \\\").join([query, conj])\\n\\n\\t\\n\\treturn query, values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b14da9066124004791a46561a7d775\",\n \"score\": \"0.55110025\",\n \"text\": \"def query(**kw):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfaf1e939ed62d271c7e53d6dfd18c69\",\n \"score\": \"0.54942393\",\n \"text\": \"def create_query(vaccine=None, age_group=\\\"adult\\\", without_registration=None, self_payer=False, monday=None,\\n tuesday=None, wednesday=None, thursday=None, friday=None, saturday=None, sunday=None):\\n\\n open_hours = {'monday': monday, 'tuesday': tuesday, 'wednesday': wednesday, 'thursday': thursday, 'friday': friday,\\n 'saturday': saturday, 'sunday': sunday}\\n\\n query = \\\"\\\"\\\"\\n SELECT loc.vacc_id, loc.latitude, loc.longitude\\n FROM vacc_center_location as loc\\n INNER JOIN vacc_center_type as typ USING(vacc_id)\\n \\\"\\\"\\\"\\n if age_group not in AGE_GROUP_SET: raise Exception('age_group argument can be only: adult, teenage, child')\\n if int(self_payer) not in [0, 1]: raise Exception('self_payer argument can be only: True, False')\\n query_where = f\\\"\\\"\\\"WHERE typ.{age_group} = 1\\n AND typ.self_payers = {int(self_payer)}\\n \\\"\\\"\\\"\\n if without_registration is not None:\\n if int(without_registration) not in [0, 1]: raise Exception('without_registration argument can be only: True, False, None')\\n query_where += f\\\"AND typ.without_registration = {int(without_registration)} \\\\n\\\"\\n\\n if vaccine is not None:\\n if vaccine.lower() not in VACCINES_SET:\\n raise Exception('vaccine argument can be only: COMIRNATY, SPIKEVAX, JANSSEN, Vaxzevria, None')\\n query += \\\"INNER JOIN vacc_center_vaccines AS vacc USING(vacc_id) \\\\n\\\"\\n query_where += f\\\"AND vacc.{vaccine.lower()} = 1 \\\\n\\\"\\n\\n open_hours = {day: hour for (day, hour) in open_hours.items() if hour is not None}\\n if len(open_hours) != 0:\\n query += \\\"INNER JOIN vacc_center_hours as hour USING(vacc_id) \\\\n\\\"\\n list_hours = []\\n for (day, hour) in open_hours.items():\\n if not ((hour >= 0) and (hour <= 24)): raise Exception('days arguments can be only set to None or number between 0 to 24')\\n list_hours.append(f\\\"(hour.{day}_open <= {hour} AND hour.{day}_closed >= {hour})\\\")\\n\\n query_hours = \\\"AND (\\\"\\n n = len(list_hours)\\n for i in range(n):\\n query_hours += list_hours[i]\\n if (i+1) == n:\\n query_hours += \\\")\\\"\\n else:\\n query_hours += \\\" OR \\\"\\n query_where += query_hours\\n return query + query_where\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f83fb856d12b26ca2684c9d9c9dfb40\",\n \"score\": \"0.5437405\",\n \"text\": \"def build_goods_query(\\n good_ids: List[str],\\n currency_id: str,\\n is_searching_for_sellers: bool,\\n is_search_query: bool,\\n) -> Query:\\n if is_search_query:\\n # the OEF does not accept attribute names consisting of integers only\\n good_ids = [PREFIX + good_id for good_id in good_ids]\\n\\n data_model = _build_goods_datamodel(\\n good_ids=good_ids, is_supply=is_searching_for_sellers\\n )\\n constraints = [Constraint(good_id, ConstraintType(\\\">=\\\", 1)) for good_id in good_ids]\\n constraints.append(Constraint(\\\"currency_id\\\", ConstraintType(\\\"==\\\", currency_id)))\\n constraint_expr = cast(List[ConstraintExpr], constraints)\\n\\n if len(good_ids) > 1:\\n constraint_expr = [Or(constraint_expr)]\\n\\n query = Query(constraint_expr, model=data_model)\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3716fc1f19951155d518f81b705b75c9\",\n \"score\": \"0.54263324\",\n \"text\": \"def cypher(self, query, params=None):\\r\\n path = cypher_path\\r\\n params = dict(query=query,params=params)\\r\\n resp = self.request.post(path, params)\\r\\n\\r\\n # Cypher data hack\\r\\n resp.total_size = len(resp.results.data)\\r\\n resp.results = (Neo4jResult(result[0], self.config) for result in resp.results.data)\\r\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800590f0ff9faf64788181eb91302965\",\n \"score\": \"0.5422505\",\n \"text\": \"def buildQuery (self, obj_type, ids=None, params=None, attributes=None):\\n\\n if type(obj_type) is type(''):\\n wrapper = KNOWN_WRAPPERS.get(obj_type.lower(), None)\\n if wrapper is None:\\n raise KeyError(\\\"obj_type of %s not supported by getOjbects(). E.g. use 'Image' etc\\\" % obj_type)\\n else:\\n raise AttributeError(\\\"getObjects uses a string to define obj_type, E.g. 'Image'\\\")\\n\\n q = self.getQueryService()\\n if params is None:\\n params = omero.sys.Parameters()\\n if params.map is None:\\n params.map = {}\\n\\n # get the base query from the instantiated object itself. E.g \\\"select obj Project as obj\\\"\\n query = wrapper()._getQueryString()\\n\\n clauses = []\\n # getting object by ids\\n if ids != None:\\n clauses.append(\\\"obj.id in (:ids)\\\")\\n params.map[\\\"ids\\\"] = rlist([rlong(a) for a in ids])\\n\\n # support filtering by owner (not for some object types)\\n if params.theFilter and params.theFilter.ownerId and obj_type.lower() not in [\\\"experimentergroup\\\", \\\"experimenter\\\"]:\\n clauses.append(\\\"owner.id = (:eid)\\\")\\n params.map[\\\"eid\\\"] = params.theFilter.ownerId\\n\\n # finding by attributes\\n if attributes != None:\\n for k,v in attributes.items():\\n clauses.append('obj.%s=:%s' % (k, k) )\\n params.map[k] = omero_type(v)\\n\\n if clauses:\\n query += \\\" where \\\" + (\\\" and \\\".join(clauses))\\n\\n return (query, params, wrapper)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3193232c6e9b86e2cb3be30784d0c07c\",\n \"score\": \"0.5413109\",\n \"text\": \"def make_query(store, query, prefix=\\\"\\\"):\\r\\n query = common.parse_query(query)\\r\\n q = Query()\\r\\n q.prefix = prefix\\r\\n q.offset = common.safeint(query.pop('offset', None), 0)\\r\\n q.limit = common.safeint(query.pop('limit', 20), 20)\\r\\n if q.limit > 1000:\\r\\n q.limit = 1000\\r\\n sort = query.pop('sort', None)\\r\\n \\r\\n nested = (prefix != \\\"\\\")\\r\\n \\r\\n for k, v in query.items():\\r\\n # key foo can also be written as label:foo\\r\\n k = k.split(':')[-1]\\r\\n if v is None:\\r\\n q.requested[k] = v\\r\\n elif isinstance(v, dict):\\r\\n # make sure op is ==\\r\\n v = dict((k + '.' + key, value) for key, value in v.items())\\r\\n q2 = make_query(store, v, prefix=prefix + k + \\\".\\\")\\r\\n #@@ Anand: Quick-fix\\r\\n # dbstore.things looks for key to find whether type is required or not. \\r\\n q2.key = k \\r\\n if q2.conditions:\\r\\n q.conditions.append(q2)\\r\\n else:\\r\\n q.requested[k] = q2\\r\\n else:\\r\\n k, op = parse_key(k)\\r\\n q.add_condition(k, op, None, v)\\r\\n \\r\\n if not nested:\\r\\n q.assert_type_required()\\r\\n \\r\\n type = get_thing(store, q.get_type())\\r\\n #assert type is not None, 'Not found: ' + q.get_type()\\r\\n for c in q.conditions:\\r\\n if not isinstance(c, Query):\\r\\n c.datatype = find_datatype(type, c.key, c.value)\\r\\n \\r\\n if sort:\\r\\n parse_key(sort) # to validate key\\r\\n q.sort = web.storage(key=sort, datatype=find_datatype(type, sort, None))\\r\\n else:\\r\\n q.sort = None\\r\\n \\r\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac716271d29523c3ec034478d633e21\",\n \"score\": \"0.54028887\",\n \"text\": \"def create_query(neo4j_entities, entity_value):\\n\\n def get_value(index):\\n return SINONIMOS_NEO4J[entity_value[index]] if SINONIMOS_NEO4J.get(entity_value[index])\\\\\\n else entity_value[index]\\n\\n get_query_part = {\\n \\\"espacio\\\": lambda x: \\\"(n:Room {{ type: '{}' }})\\\".format(get_value(x)),\\n \\\"sensor\\\": lambda x: (\\\"(d:Device)\\\", f\\\" '{get_value(x)}' in d.type\\\"),\\n \\\"orientacion\\\": lambda x: \\\" n.orientacion CONTAINS '{}'\\\".format(get_value(x)),\\n \\\"edificio\\\": lambda x: (\\\"(b:Building)\\\", \\\" b.name CONTAINS '{}'\\\".format(get_value(x))),\\n \\\"nombre\\\": lambda x: (\\\"(b:Building)\\\", \\\" b.name CONTAINS '{}'\\\".format(get_value(x))),\\n \\\"lugares\\\": lambda x: \\\" n.name CONTAINS '{}'\\\".format(get_value(x))\\n }\\n\\n\\n dic = {}\\n for entity in neo4j_entities:\\n key = entity[1]\\n if entity[1] == \\\"nombre\\\":\\n key = \\\"edificio\\\"\\n dic[key] = get_query_part[entity[1]](entity[0])\\n\\n espacio = dic[\\\"espacio\\\"] if dic.get(\\\"espacio\\\") else \\\"\\\"\\n sensor = dic[\\\"sensor\\\"] if dic.get(\\\"sensor\\\") else (\\\"\\\", \\\"\\\")\\n orientacion = dic[\\\"orientacion\\\"] if dic.get(\\\"orientacion\\\") else \\\"\\\"\\n edificio = dic[\\\"edificio\\\"] if dic.get(\\\"edificio\\\") else (\\\"\\\", \\\"\\\")\\n lugares = dic[\\\"lugares\\\"] if dic.get(\\\"lugares\\\") else \\\"\\\"\\n ret = []\\n\\n # crear relaciones que son opcionales en la consulta\\n if espacio is not \\\"\\\" and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0]+\\\"<-[:HAS]-\\\", sensor[1])\\n if espacio is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\"):\\n espacio += \\\"<-[:HAS]-(f:Floor)<-[:HAS]-\\\"\\n\\n #Añadir información a la consulta que no se infica pero que es necesaria para poder recorrer las relaciones en Neo4j.\\n \\n # Si se pregunta por sensor y lugar pero no por espacio.\\n if espacio is \\\"\\\" and sensor != (\\\"\\\", \\\"\\\") and lugares is not \\\"\\\":\\n espaciocio = \\\"(n:Room)\\\"\\n sensor = (sensor[0]+\\\"<-[:HAS]-\\\", sensor[1])\\n # Si se pregunta por nombre u orientación pero no por espacio.\\n if espacio is \\\"\\\" and (lugares is not \\\"\\\" or orientacion is not \\\"\\\"):\\n espacio = \\\"(n:Room)\\\"\\n # Si se pregunta por sensores en un edificio sin especificar un espacio.\\n if espacio is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n espacio = \\\"(n:Room)<-[:HAS]-(f:Floor)<-[:HAS]-\\\"\\n sensor = (sensor[0]+\\\"<-[:HAS]-\\\", sensor[1])\\n # Si hay 4\\n # orientacion lugares edificio sensor\\n # X X X X\\n if orientacion is not \\\"\\\" and lugares is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n lugares = \\\"AND \\\" + lugares\\n orientacion = \\\"WHERE \\\" + orientacion\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n\\n # Si hay 3\\n # orientacion lugares edificio sensor\\n # X X X -\\n # X - X X\\n # - X X X\\n # X - X X\\n if orientacion is not \\\"\\\" and lugares is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n lugares = \\\"AND \\\" + lugares\\n orientacion = \\\"WHERE \\\" + orientacion\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n if orientacion is not \\\"\\\" and lugares is not \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n lugares = \\\"AND \\\" + lugares\\n orientacion = \\\"WHERE \\\" + orientacion\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n if orientacion is \\\"\\\" and lugares is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n lugares = \\\"WHERE \\\" + lugares\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n if orientacion is not \\\"\\\" and lugares is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n orientacion = \\\"WHERE \\\" + orientacion\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n\\n # Si hay 2\\n # orientacion lugares edificio sensor\\n # X X - -\\n # X - X -\\n # X - - X\\n # - X X -\\n # - X - X\\n # - - X X\\n\\n if orientacion is not \\\"\\\" and lugares is not \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n lugares = \\\"AND \\\" + lugares\\n orientacion = \\\"WHERE \\\" + orientacion\\n if orientacion is not \\\"\\\" and lugares is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n orientacion = \\\"WHERE \\\" + orientacion\\n if orientacion is not \\\"\\\" and lugares is \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n orientacion = \\\"WHERE \\\" + orientacion\\n if orientacion is \\\"\\\" and lugares is not \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n edificio = (edificio[0], \\\"AND \\\" + edificio[1])\\n lugares = \\\"WHERE \\\" + lugares\\n if orientacion is \\\"\\\" and lugares is not \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n lugares = \\\"WHERE \\\" + lugares\\n if orientacion is \\\"\\\" and lugares is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0], \\\"AND \\\" + sensor[1])\\n edificio = (edificio[0], \\\"WHERE \\\" + edificio[1])\\n # Si hay 1\\n # orientacion lugares edificio sensor\\n # X - - -\\n # - X - -\\n # - - X -\\n # - - - X\\n if orientacion is not \\\"\\\" and lugares is \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n orientacion = \\\"WHERE \\\" + orientacion\\n if orientacion is \\\"\\\" and lugares is not \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n lugares = \\\"WHERE \\\" + lugares\\n if orientacion is \\\"\\\" and lugares is \\\"\\\" and edificio != (\\\"\\\", \\\"\\\") and sensor == (\\\"\\\", \\\"\\\"):\\n edificio = (edificio[0], \\\"WHERE \\\" + edificio[1])\\n if orientacion is \\\"\\\" and lugares is \\\"\\\" and edificio == (\\\"\\\", \\\"\\\") and sensor != (\\\"\\\", \\\"\\\"):\\n sensor = (sensor[0], \\\"WHERE \\\" + sensor[1])\\n\\n\\n\\n # Preparar los atributos de salida\\n if espacio is not \\\"\\\":\\n ret.append(\\\"n.name, n.id\\\")\\n if sensor != (\\\"\\\", \\\"\\\"):\\n ret.append(\\\"d.id, d.type\\\")\\n if orientacion is not \\\"\\\":\\n ret.append(\\\"n.orientacion\\\")\\n\\n return TEMPLATE_NEO.format(sensor[0], espacio, edificio[0], orientacion,\\\\\\n lugares, edificio[1], sensor[1], \\\", \\\".join(ret))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e23579f229cdea216279b1841dfff82\",\n \"score\": \"0.53795767\",\n \"text\": \"def generate_entrez_query(self):\\n ending = ') AND ' + self.gene + '[Gene Name] ' + \\\\\\n 'AND 1000:8000[Sequence Length]'\\n types = []\\n for hpv_type in itertools.chain(self.harmfull, self.safe):\\n types.append('\\\"Human papillomavirus type %d\\\"[Organism]' % hpv_type)\\n self._query = '(' + ' OR '.join(types) + ending\\n return self._query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68cd306f78e9a696fa45ccc17d112056\",\n \"score\": \"0.5368679\",\n \"text\": \"def sql_query_str(program):\\n node_type, node_children = program[0], program[1:]\\n assert node_type in sql_entry_tokens and syntax_valid(program, sql_cfg)\\n\\n if node_type == \\\"SELECT_AGG\\\":\\n select_exprs_node, joins_node, group_by_exprs_node = node_children\\n where_node = None\\n elif node_type == \\\"SELECT_AGG_WHERE\\\":\\n (select_exprs_node,\\n joins_node,\\n group_by_exprs_node,\\n where_node) = node_children\\n else:\\n raise RuntimeError(\\n \\\"Missing node-type decoding logic for generating SQL query from AST.\\\"\\n )\\n\\n return query_str(\\n select_exprs=list(sql_select_exprs(select_exprs_node)),\\n joins=list(sql_joins(joins_node)),\\n where_exprs=list(sql_and_exprs_strs(where_node)) if where_node else [],\\n group_by_exprs=list(sql_group_by_exprs(group_by_exprs_node)),\\n order_by_exprs=[],\\n limit_exprs=[],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f43c43ecaf245aaab63b127c15a173ed\",\n \"score\": \"0.53679913\",\n \"text\": \"def _getQueryString(self):\\n query = \\\"select obj from Plate as obj \\\" \\\\\\n \\\"join fetch obj.details.owner as owner join fetch obj.details.group \\\"\\\\\\n \\\"join fetch obj.details.creationEvent \\\"\\\\\\n \\\"left outer join fetch obj.screenLinks spl \\\" \\\\\\n \\\"left outer join fetch spl.parent sc\\\"\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51fd823b07a49d65be55acc10cb16971\",\n \"score\": \"0.53457093\",\n \"text\": \"def _create_qualifiedname_query(self, type_name, *values):\\n query = {\\n 'typeName': type_name,\\n 'excludeDeletedEntities': True,\\n 'limit': 10000\\n }\\n entity_filter = {\\n 'condition': 'AND'\\n }\\n criterion = []\\n n = 0\\n for v in values:\\n criteria = {\\n 'attributeName': 'qualifiedName',\\n 'operator': 'STARTSWITH' if n == 0 else 'CONTAINS',\\n 'attributeValue': v\\n }\\n n += 1\\n criterion.append(criteria)\\n if type_name == 'hive_table':\\n # Ignore temporary tables.\\n criterion.append({'operator': '=', 'attributeName': 'temporary', 'attributeValue': False})\\n entity_filter['criterion'] = criterion\\n query['entityFilters'] = entity_filter\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44970fb2e787db5c9720c86d5e16d3f3\",\n \"score\": \"0.53439456\",\n \"text\": \"def create_query(\\n self, query_text, locale=None, language=None, time_zone=None, timestamp=None\\n ):\\n if not query_text:\\n query_text = \\\"\\\"\\n if isinstance(query_text, (list, tuple)):\\n return self._process_list(\\n query_text,\\n \\\"create_query\\\",\\n locale=locale,\\n language=language,\\n time_zone=time_zone,\\n timestamp=timestamp,\\n )\\n return self.resource_loader.query_factory.create_query(\\n query_text,\\n language=language,\\n locale=locale,\\n time_zone=time_zone,\\n timestamp=timestamp,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b4dabb7c03fa99fdfdd73c857a0b88d\",\n \"score\": \"0.5328662\",\n \"text\": \"def sql_with_where(self):\\n e = get_executor(SqlRule)\\n where_clause = \\\"WHERE \\\"\\n where_time_filter = e.compose_where_time_filter(self)\\n where_condition = e.compose_where_condition(self)\\n if where_time_filter == \\\"\\\" and where_condition == \\\"\\\":\\n where_clause = \\\"\\\"\\n elif where_time_filter != \\\"\\\" and where_condition != \\\"\\\":\\n where_clause = f\\\"{where_clause} {where_time_filter} AND {where_condition}\\\"\\n else:\\n where_clause = f\\\"{where_clause} {where_time_filter} {where_condition}\\\"\\n final_sql = f\\\"{self.sql} {where_clause}\\\"\\n return self.render_sql(final_sql)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dae062c702cf1a20a574ddd68d4b28aa\",\n \"score\": \"0.532052\",\n \"text\": \"def construct_url(self\\n , query_params: Dict\\n , ) -> str:\\n url_const = self.faers_ep\\n initialized = False\\n for param_q, param_val in query_params.items():\\n # Need to add '&' to 2nd,3rd,... query_params\\n if initialized:\\n url_const += '&'\\n else:\\n initialized = True\\n if type(param_val) == str and len(query_params) > 0 \\\\\\n or type(param_val) == int:\\n url_const += f'{param_q}={param_val}'\\n elif type(param_val) == dict and len(param_val) > 0:\\n url_const += f'{param_q}=' + \\\"+AND+\\\".join(\\n [f'{sub_p_k}:{sub_p_v}' for sub_p_k, sub_p_v in param_val.items()])\\n elif type(param_val) == list and len(param_val) > 0:\\n url_const += f'{param_q}=' + \\\"+AND+\\\".join(param_val)\\n else:\\n raise TypeError\\n return url_const\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60d6e0b78286e4e35b88227402bb392\",\n \"score\": \"0.5318269\",\n \"text\": \"def get_custom_query(self):\\n query = {}\\n\\n # searchable text queries\\n q = req.get_query()\\n if q:\\n query[\\\"SearchableText\\\"] = q\\n\\n # physical path queries\\n path = req.get_path()\\n if path:\\n query[\\\"path\\\"] = {'query': path, 'depth': req.get_depth()}\\n\\n # special handling for recent created/modified\\n recent_created = req.get_recent_created()\\n if recent_created:\\n date = api.calculate_delta_date(recent_created)\\n query[\\\"created\\\"] = {'query': date, 'range': 'min'}\\n\\n recent_modified = req.get_recent_modified()\\n if recent_modified:\\n date = api.calculate_delta_date(recent_modified)\\n query[\\\"modified\\\"] = {'query': date, 'range': 'min'}\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88503be9fbc27ea6562b4928b4a8d6c0\",\n \"score\": \"0.530554\",\n \"text\": \"def get_where_clause(columns_to_query_lst):\\n where_str = \\\"where \\\"\\n equals_str =[]\\n for row in columns_to_query_lst:\\n temp_str = \\\"c.\\\" + row + \\\" = t.\\\" + row\\n equals_str.append(temp_str)\\n joined_str = \\\" AND \\\".join(equals_str)\\n where_str = where_str + joined_str\\n return where_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746bf5ea5425e576e9ff1f9bce37f702\",\n \"score\": \"0.52996916\",\n \"text\": \"def create_query(processed_request):\\n query = {\\n \\\"variant\\\": {\\n \\\"referenceBases\\\": \\\"\\\" if not processed_request.get(\\\"referenceBases\\\") else processed_request.get(\\\"referenceBases\\\"),\\n \\\"alternateBases\\\": \\\"\\\" if not processed_request.get(\\\"alternateBases\\\") else processed_request.get(\\\"alternateBases\\\"),\\n \\\"referenceName\\\": \\\"\\\" if not processed_request.get(\\\"referenceName\\\") else processed_request.get(\\\"referenceName\\\"),\\n \\\"start\\\": None if not processed_request.get(\\\"start\\\") else processed_request.get(\\\"start\\\"),\\n \\\"end\\\": None if not processed_request.get(\\\"end\\\") else processed_request.get(\\\"end\\\"),\\n \\\"assemblyId\\\": \\\"\\\" if not processed_request.get(\\\"assemblyId\\\") else processed_request.get(\\\"assemblyId\\\")\\n },\\n \\\"datasets\\\": {\\n \\\"datasetIds\\\": None if not processed_request.get(\\\"datasetIds\\\") else processed_request.get(\\\"datasetIds\\\"),\\n \\\"includeDatasetResponses\\\": \\\"ALL\\\" if not processed_request.get(\\\"includeDatasetResponses\\\") else processed_request.get(\\\"includeDatasetResponses\\\")\\n },\\n \\\"filters\\\": None if not processed_request.get(\\\"filters\\\") else processed_request.get(\\\"filters\\\"),\\n \\\"customFilters\\\": None if not processed_request.get(\\\"customFilters\\\") else processed_request.get(\\\"customFilters\\\"),\\n }\\n\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e6478863c1ea4a7a2b784bc3c49cd9\",\n \"score\": \"0.529957\",\n \"text\": \"def build_where_stmt(self, ident, filters, q_filters=None, source_class=None):\\n if q_filters is not None:\\n stmts = self._parse_q_filters(ident, q_filters, source_class)\\n if stmts:\\n self._ast.where.append(stmts)\\n else:\\n stmts = []\\n for row in filters:\\n negate = False\\n\\n # pre-process NOT cases as they are nested dicts\\n if \\\"__NOT__\\\" in row and len(row) == 1:\\n negate = True\\n row = row[\\\"__NOT__\\\"]\\n\\n for prop, operator_and_val in row.items():\\n operator, val = operator_and_val\\n if operator in _UNARY_OPERATORS:\\n # unary operators do not have a parameter\\n statement = (\\n f\\\"{'NOT' if negate else ''} {ident}.{prop} {operator}\\\"\\n )\\n else:\\n place_holder = self._register_place_holder(ident + \\\"_\\\" + prop)\\n statement = f\\\"{'NOT' if negate else ''} {ident}.{prop} {operator} ${place_holder}\\\"\\n self._query_params[place_holder] = val\\n stmts.append(statement)\\n\\n self._ast.where.append(\\\" AND \\\".join(stmts))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c5f867e7aef319a9323809c18719262\",\n \"score\": \"0.5287673\",\n \"text\": \"def __init__ (self, database, var, conditions = (), echo = False):\\n\\n self.session = buildSession(\\n database=database,\\n echo=echo)\\n if var == \\\"ncommits\\\":\\n self.query = self.session.query().select_nscmlog([\\\"commits\\\",])\\n elif var == \\\"listcommits\\\":\\n self.query = self.session.query().select_listcommits()\\n elif var == \\\"nauthors\\\":\\n self.query = self.session.query().select_nscmlog([\\\"authors\\\",])\\n elif var == \\\"listauthors\\\":\\n self.query = self.session.query().select_listauthors()\\n for condition in conditions:\\n self.query = condition.filter(self.query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d298399d7e7d292b986fa091d5abed50\",\n \"score\": \"0.5285017\",\n \"text\": \"def full_query_pattern(self, include_conclusion: bool = False) -> str:\\n query_pattern = \\\"\\\"\\n for literal in self.premise:\\n query_pattern += literal.sparql_patterns()\\n\\n if include_conclusion:\\n query_pattern += self.conclusion.sparql_patterns()\\n\\n if \\\"?b\\\" not in query_pattern and \\\"?a\\\" not in query_pattern:\\n query_projection = \\\"ask \\\"\\n else:\\n # insert the selectors for subject and object into the select query if they exist in the query pattern\\n query_projection = \\\"select where \\\"\\n\\n # the resulting query would look like \\\"select ?a ?b ...\\\" if both cases are true\\n if \\\"?b\\\" in query_pattern:\\n query_projection = query_projection.replace(\\\"select \\\", \\\"select ?b \\\")\\n if \\\"?a\\\" in query_pattern:\\n query_projection = query_projection.replace(\\\"select \\\", \\\"select ?a \\\")\\n\\n # build remaining part of the query and execute it\\n query_pattern = \\\"{\\\" + query_pattern + \\\"}\\\"\\n return query_projection + query_pattern\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f90b7aa24950c089ec7f416c7f71163\",\n \"score\": \"0.5283903\",\n \"text\": \"def _getQueryString(self):\\n return \\\"select obj from %s obj join fetch obj.details.owner as owner join fetch obj.details.group \\\"\\\\\\n \\\"join fetch obj.details.creationEvent\\\" % self.OMERO_CLASS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edec057b919f8d0df46ebd8515dfc0e2\",\n \"score\": \"0.5279631\",\n \"text\": \"def _getQueryString(self):\\n return \\\"select obj from BooleanAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \\\"\\\\\\n \\\"join fetch obj.details.creationEvent\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5238770c84b4b535af51b45d8d738378\",\n \"score\": \"0.52578133\",\n \"text\": \"def query(self):\\n\\n if not hasattr(self, \\\"_query\\\"):\\n\\n # Set up some convenience aliases.\\n Query = db.Query\\n Condition = Query.Condition\\n Or = Query.Or\\n\\n # Create a query object.\\n columns = \\\"d.id\\\", \\\"d.title\\\"\\n self._query = Query(\\\"document d\\\", *columns).order(\\\"d.title\\\")\\n\\n # Add the conditions: one for each field with a value.\\n conditions = []\\n have_doctype = False\\n for field in self.fields:\\n\\n # See if we got a value for this field.\\n value = field.var.strip() if field.var else None\\n if not value:\\n continue\\n\\n # Remember the criterion for display to the user.\\n self.criteria.append(value)\\n\\n # If 'selectors' is a string, it's a column in `document`.\\n value_op = getQueryOp(value)\\n if isinstance(field.selectors, str):\\n column = f\\\"d.{field.selectors}\\\"\\n conditions.append(Condition(column, value, value_op))\\n continue\\n\\n # Build up a subquery for the field.\\n have_doctype = True\\n subquery = Query(\\\"query_term\\\", \\\"doc_id\\\").unique()\\n\\n # These are ORd together.\\n selector_conditions = []\\n for path in field.selectors:\\n path_op = \\\"LIKE\\\" if \\\"%\\\" in path else \\\"=\\\"\\n\\n # Simple case: test for a string stored in the doc.\\n if not path.endswith(\\\"/@cdr:ref[int_val]\\\"):\\n path_test = Condition(\\\"path\\\", path, path_op)\\n value_test = Condition(\\\"value\\\", value, value_op)\\n selector_conditions.append((path_test, value_test))\\n\\n # Trickier case: find values in linked documents.\\n else:\\n path = path.replace(\\\"[int_val]\\\", \\\"\\\")\\n title_query = Query(\\\"document\\\", \\\"id\\\").unique()\\n args = \\\"title\\\", value, value_op\\n title_query.where(Condition(*args))\\n args = \\\"int_val\\\", title_query, \\\"IN\\\"\\n title_test = Condition(*args)\\n path_test = Condition(\\\"path\\\", path, path_op)\\n selector_conditions.append((path_test, title_test))\\n\\n # Add the conditions for this field's selectors to the mix.\\n subquery.where(Or(*selector_conditions))\\n conditions.append(Condition(\\\"d.id\\\", subquery, \\\"IN\\\"))\\n\\n # Sanity check.\\n if not conditions:\\n raise Exception(\\\"No search conditions specified\\\")\\n\\n # If all the selectors are from the document table (or,\\n # view, to be precise), then we still need to narrow\\n # the search to this document type.\\n if not have_doctype:\\n self._query.join(\\\"doc_type t\\\", \\\"t.id = d.doc_type\\\")\\n self._query.where(Condition(\\\"t.name\\\", self.doctype))\\n # Plug the top-level conditions into the query.\\n if self.match_all:\\n for condition in conditions:\\n self._query.where(condition)\\n else:\\n self._query.where(Or(*conditions))\\n\\n # All the fields with values have been folded into the query.\\n return self._query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc3e93874b247c4bb5a6d828028febe0\",\n \"score\": \"0.5255788\",\n \"text\": \"def query(self):\\n self._check_mandatory_inputs()\\n return self._gen_query()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74e698e0dc148ed3565a45d92b4dec8c\",\n \"score\": \"0.5251471\",\n \"text\": \"def _get_clause(self):\\r\\n params = [(primary_key, sql.bindparam(None, type_=primary_key.type))\\r\\n for primary_key in self.primary_key]\\r\\n return sql.and_(*[k == v for (k, v) in params]), \\\\\\r\\n util.column_dict(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb58117d3fda3c2db7fe7688c4120b96\",\n \"score\": \"0.5243088\",\n \"text\": \"def build_query_text_request(\\n **kwargs # type: Any\\n):\\n # type: (...) -> HttpRequest\\n\\n content_type = kwargs.pop('content_type', None) # type: Optional[str]\\n\\n api_version = \\\"2021-05-01-preview\\\"\\n accept = \\\"application/json\\\"\\n # Construct URL\\n url = kwargs.pop(\\\"template_url\\\", '/:query-text')\\n\\n # Construct parameters\\n query_parameters = kwargs.pop(\\\"params\\\", {}) # type: Dict[str, Any]\\n query_parameters['api-version'] = _SERIALIZER.query(\\\"api_version\\\", api_version, 'str')\\n\\n # Construct headers\\n header_parameters = kwargs.pop(\\\"headers\\\", {}) # type: Dict[str, Any]\\n if content_type is not None:\\n header_parameters['Content-Type'] = _SERIALIZER.header(\\\"content_type\\\", content_type, 'str')\\n header_parameters['Accept'] = _SERIALIZER.header(\\\"accept\\\", accept, 'str')\\n\\n return HttpRequest(\\n method=\\\"POST\\\",\\n url=url,\\n params=query_parameters,\\n headers=header_parameters,\\n **kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0b0884588b76f9e715cfc138ae0212\",\n \"score\": \"0.5239496\",\n \"text\": \"def create_complex_query(self):\\n columns = copy(self.columns)\\n shuffle(columns)\\n query = \\\"\\\"\\\"\\\\\\n SELECT\\n {col}\\n FROM\\n datadog_test.dbo.high_cardinality AS hc1\\n JOIN (\\n SELECT\\n id,\\n COUNT(*) col12_float\\n FROM\\n datadog_test.dbo.high_cardinality AS hc2\\n WHERE\\n hc2.col1_txt LIKE '%-%'\\n AND hc2.col14_int > (\\n SELECT\\n AVG(hc3.col15_int)\\n FROM\\n datadog_test.dbo.high_cardinality AS hc3)\\n GROUP BY\\n hc2.id) AS hc4 ON hc4.id = hc1.id\\n JOIN datadog_test.dbo.high_cardinality AS hc5 ON hc5.id = hc1.id\\n WHERE\\n CAST(hc5.col17_date AS VARCHAR)\\n IN('2003-04-23', '2043-09-10', '1996-08-08')\\n AND hc5.col13_float IS NOT NULL\\n AND hc5.col17_date NOT LIKE '21%';\\n \\\"\\\"\\\"\\n # Select a range of random columns and prefix to match our alias\\n return query.format(col=','.join(['hc1.' + col for col in columns[: randint(1, len(columns) - 1)]]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23ac357cf50e246b557978088edbcf6b\",\n \"score\": \"0.5237348\",\n \"text\": \"def __gencondition(self, querylist):\\n conditions = []\\n for key in self.PROPERTY_SEARCH_LIST:\\n if querylist.get(key):\\n conditions.append(self.PROPERTY_SEARCH_LIST[key][1] % (querylist.get(key)))\\n return \\\" AND \\\".join(conditions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b90ad42bba7570be5827a9b27873f0c5\",\n \"score\": \"0.52357996\",\n \"text\": \"def build_sql(self, table, row, known_fkeys={}):\\n # Initialize the builder properties\\n self.row = row\\n self.known_fkeys = known_fkeys\\n return self.build_table_sql(table)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a69e3debc69789bbd12bb2ee0e29ccfd\",\n \"score\": \"0.5234567\",\n \"text\": \"def __build_where_clause(where_columns_dictionary):\\n return ' AND '.join(key + \\\" = ? \\\"\\n if value is not None\\n else key + \\\" IS NULL \\\"\\n for key, value\\n in where_columns_dictionary.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df322a9786ad715a3d5849f5ed411c2d\",\n \"score\": \"0.5219735\",\n \"text\": \"def concat_conds(*args):\\n stmt = \\\" WHERE 1\\\"\\n for cond in args:\\n stmt = stmt + \\\" AND \\\" + cond\\n return stmt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b443f879e890682442787a51400a4a67\",\n \"score\": \"0.5218033\",\n \"text\": \"def build_query(cls, request, model):\\n # print(request.data)\\n name = request.data.get(\\\"search_phrase\\\")\\n criteria = request.data.get(\\\"search_criteria\\\")\\n frequency = request.data.get(\\\"frequency\\\")\\n age_distribution = request.data.get(\\\"age_distribution\\\")\\n letters_range = request.data.get(\\\"letters_range\\\")\\n\\n resp = model.objects.filter()\\n\\n if frequency is not None and len(frequency) > 0:\\n resp = model.objects.filter(frequency__in=frequency).defer(\\\"double_name\\\", \\\"number_of_letters\\\")\\n\\n double_name = True in letters_range\\n\\n # resp = resp.filter(double_name=True)\\n\\n if letters_range is not None and len(letters_range) > 0:\\n if double_name:\\n letters_range.remove(True)\\n resp = resp.filter(Q(number_of_letters__in=letters_range) | Q(double_name=True))\\n else:\\n resp = resp.filter(double_name=False)\\n resp = resp.filter(number_of_letters__in=letters_range)\\n\\n if age_distribution is not None and len(age_distribution) > 0:\\n mask = \\\"age_distribution_{}__gt\\\"\\n\\n # AND age_distribution way\\n # dd = {}\\n #\\n # for d in age_distribution:\\n # key = mask.format(d)\\n # dd[key] = 0\\n # resp = resp.filter(**dd)\\n\\n # OR age_distribution way\\n or_query_set = Q()\\n for d in age_distribution:\\n tmp = Q(**{mask.format(d): 0})\\n or_query_set |= tmp\\n\\n resp = resp.filter(or_query_set)\\n\\n if criteria is not None and len(name) > 0:\\n if criteria == \\\"start\\\":\\n resp = resp.filter(name__lower__istartswith=name.lower())\\n elif criteria == \\\"middle\\\":\\n resp = resp.filter(name__lower__icontains=name.lower())\\n elif criteria == \\\"end\\\":\\n resp = resp.filter(name__lower__iendswith=name.lower())\\n else:\\n resp = resp.filter(name__lower=name)\\n\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a699f49d4c0ba175277a65d66a2562a\",\n \"score\": \"0.52174175\",\n \"text\": \"def query_to_clojure(obj, params):\\n # Serialize the query, sorted keys for deterministic substitution, no pretty printing\\n data = json.dumps(obj, sort_keys=True)\\n # Split the JSON into parts using 'PARAM' as seperator\\n parts = data.split(\\\"PARAM\\\")\\n # Escape each JSON part by serializing as a JSON list and removing the qruare brackets\\n escaped_parts = [json.dumps([part])[1:-1] for part in parts]\\n # The amount of parts should be one more than the amount ot parameters\\n if len(parts) != len(params) + 1:\\n raise RuntimeError(\\\"Param count mismatch: \\\" +\\n str(len(params)) +\\n \\\" given \\\" +\\n str(len(parts) -1) +\\n \\\" needed ; \\\" +\\n data)\\n # Interleave the escaped JSON parts and the parameters and put them in a (str ) clojure expression\\n rval = \\\" \\\".join(list(itertools.chain(*zip([\\\"(query (str \\\"] + params, escaped_parts))) + [\\\") )\\\"])\\n return rval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c47f0f449170335a3dd6dfeb971f17fa\",\n \"score\": \"0.52149814\",\n \"text\": \"def process(self):\\n q = \\\"?q=\\\"\\n lst = []\\n for field in self.filter_dict:\\n for keyword in self.filter_dict[field]:\\n for criteria in self.filter_dict[field][keyword]:\\n lst.append(\\\"{0} {1} '{2}'\\\".format(field, kw_map[keyword], criteria))\\n\\n q += \\\" and \\\".join(lst)\\n\\n if self.count:\\n q +=\\\"&count=True\\\"\\n self.query = q\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ea0ee25067279790679c78c36e66c2\",\n \"score\": \"0.52111125\",\n \"text\": \"def run_sql_statement(self, request, params=()):\\n\\n # simple string formatting RISK OF SQL INJECTION, DO NOT USE IN REAL DATABASE\\n output = request % tuple([\\\"'\\\" + p + \\\"'\\\" for p in params])\\n\\n self.output_sql += output + \\\"\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29abca87c3ec6ed8e7655886a9e9437\",\n \"score\": \"0.52013975\",\n \"text\": \"def prepare_sql(self, **kwargs):\\n source = kwargs.get(\\\"source\\\", self.source)\\n if not source:\\n raise ValueError(\\\"No source table or view specified.\\\")\\n\\n cutoff = kwargs.get(\\\"cutoff\\\", self.cutoff)\\n start_time = \\\"start_time_local\\\" if self.local else \\\"start_time\\\"\\n end_time = \\\"end_time_local\\\" if self.local else \\\"end_time\\\"\\n\\n predicates = kwargs.get(\\\"predicates\\\", [])\\n predicates = [predicates] if not isinstance(predicates, list) else predicates\\n\\n provider_name = kwargs.get(\\\"provider_name\\\", self.provider_name)\\n\\n if provider_name:\\n predicates.append(f\\\"lower(provider_name) = lower('{provider_name}')\\\")\\n\\n vts = \\\"'::vehicle_types,'\\\"\\n vehicle_types = kwargs.get(\\\"vehicle_types\\\", self.vehicle_types)\\n if vehicle_types:\\n if not isinstance(vehicle_types, list):\\n vehicle_types = [vehicle_types]\\n predicates.append(f\\\"vehicle_type IN ('{vts.join(vehicle_types)}'::vehicle_types)\\\")\\n\\n if len(predicates) > 0:\\n predicates = \\\" AND \\\".join(predicates) + \\\" AND \\\"\\n else:\\n predicates = \\\"\\\"\\n\\n timeranges = [\\n f\\\"({start_time} <= %(start)s AND {end_time} > %(start)s)\\\",\\n f\\\"({start_time} < %(end)s AND {end_time} >= %(end)s)\\\",\\n f\\\"({start_time} >= %(start)s AND {end_time} <= %(end)s)\\\"\\n ]\\n\\n if cutoff > 0:\\n where = f\\\"\\\"\\\"\\n {predicates}\\n ({end_time} IS NULL AND {start_time} between %(start)s - '{cutoff} days'::interval and %(end)s OR\\n (date_part('day', {end_time} - {start_time}) <= {cutoff} AND\\n ({\\\" OR \\\".join(timeranges)})))\\n \\\"\\\"\\\"\\n else:\\n timeranges.append(f\\\"({start_time} < %(end)s AND {end_time} IS NULL)\\\")\\n where = f\\\"\\\"\\\"\\n {predicates}\\n ({\\\" OR \\\".join(timeranges)})\\n \\\"\\\"\\\"\\n\\n order_by = kwargs.get(\\\"order_by\\\", self.order_by)\\n if order_by:\\n if not isinstance(order_by, list):\\n order_by = [order_by]\\n order_by = \\\",\\\".join(order_by)\\n order_by = f\\\"ORDER BY {order_by}\\\"\\n\\n return f\\\"\\\"\\\"\\n SELECT\\n *\\n FROM\\n {source}\\n WHERE\\n {where}\\n {order_by};\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37809edf146a1f0665c82f677f3754e7\",\n \"score\": \"0.51989144\",\n \"text\": \"def generate_select_request(self, conditions=None):\\n\\n if conditions is None:\\n return self._SELECT_REQUEST_STRING\\n else:\\n request = self._SELECT_REQUEST_STRING + \\\" WHERE {}={}\\\"\\n request = request.format(*conditions.split(\\\"=\\\"))\\n return request\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d39ef560bb5a4fecbc1c6cd66834ec85\",\n \"score\": \"0.5198576\",\n \"text\": \"def _getQueryString(self):\\n return \\\"select obj from CommentAnnotation obj join fetch obj.details.owner as owner join fetch obj.details.group \\\"\\\\\\n \\\"join fetch obj.details.creationEvent\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f80c750afc8c0dee265f318d3f44d2f7\",\n \"score\": \"0.51965994\",\n \"text\": \"def generate_clause(vars, operator):\\n return operator(*vars, evaluate=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e77ba0a35e57f35fb497a8cc5137a0\",\n \"score\": \"0.5192215\",\n \"text\": \"def subquery(self, withs, filters=None, optional=False):\\n input_with = ', '.join(withs)\\n new_withs = withs - {self.collection_alias}\\n output_with = ', '.join(new_withs) + ', ' + self.with_statement()\\n\\n where_string = ''\\n if filters is not None:\\n relevant = []\\n for c in filters:\\n if c.involves(self):\\n relevant.append(c.for_cypher())\\n if relevant:\\n where_string = 'WHERE ' + '\\\\nAND '.join(relevant)\\n for_match = self.subquery_match_template.format(anchor_alias=self.anchor_node.alias,\\n collection_alias=self.collection_alias)\\n kwargs = {'for_match': for_match,\\n 'optional': '',\\n 'collection_alias': self.collection_alias,\\n 'output_with_string': output_with}\\n if optional:\\n kwargs['optional'] = 'OPTIONAL '\\n return self.subquery_template.format(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c91288d0f92106eb67c80c495661bb2\",\n \"score\": \"0.5183175\",\n \"text\": \"def build_query(self):\\n # initalize config\\n query = super(GlobalTaskTableSource, self).build_query()\\n query = query.avoid_duplicates()\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c4035fb5ab40bfe3985d56797e9590\",\n \"score\": \"0.51793414\",\n \"text\": \"def prepareQuery(queryString, initNs={}, base=None):\\n ret = translateQuery(parseQuery(queryString), base, initNs)\\n ret._original_args = (queryString, initNs, base)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ac992c3bdb35350862ce9f31ab0d1b\",\n \"score\": \"0.51777846\",\n \"text\": \"def buildQuery(self, formInfo, user, query, languages):\\r\\n return query.options(subqueryload('concepts'))\\\\\\r\\n .join(concept_list_concepts)\\\\\\r\\n .join(Concept)\\\\\\r\\n .join(formInfo.formModel, (formInfo.formModel.language_id == languages.foreign.id) & (formInfo.formModel.concept_id == Concept.id))\\\\\\r\\n .outerjoin(Mastery, (Mastery.user_id==user.id) & (getattr(Mastery, formInfo.masteryFieldName) == formInfo.formModel.id))\\\\\\r\\n .outerjoin(StalenessPeriod)\\\\\\r\\n .order_by(formInfo.listModel.averageRatingFor(user, languages.foreign))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1c27b14b20da118bc686538eb54c03e\",\n \"score\": \"0.51636916\",\n \"text\": \"def concat_query_make(query_ends):\\n\\n\\tquery = result_queries.START\\n\\tstart = True\\n\\n\\tif query_ends:\\n\\t\\tfor end in query_ends:\\n\\t\\t\\tif start:\\n\\t\\t\\t\\tquery += end\\n\\t\\t\\t\\t#end is the varying return value to an otherwise identical query\\n\\n\\n\\t\\t\\tif not start:\\n\\t\\t\\t\\tquery += \\\" || \\\\\\\"DELIM\\\\\\\" || \\\"\\n\\t\\t\\t\\tquery += end\\n\\t\\n\\t\\t\\tstart = False\\n\\t\\t\\t\\n\\telse:\\n\\t\\tquery = \\\"(no query selected)\\\"\\n\\t\\n\\treturn query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba8011bc739cf5b088eb97c87937d2b\",\n \"score\": \"0.51636404\",\n \"text\": \"def evaluate_node_sql(node):\\n if 'condition' in node:\\n # Node is a condition, get the values of the sub-clauses\\n sub_pairs = \\\\\\n [evaluate_node_sql(x) for x in node['rules']]\\n\\n if not sub_pairs:\\n # Nothing has been returned, so it is an empty query\\n return '', []\\n\\n # Now combine\\n if node['condition'] == 'AND':\\n result = '((' + \\\\\\n ') AND ('.join([x for x, __ in sub_pairs]) + '))'\\n else:\\n result = '((' + \\\\\\n ') OR ('.join([x for x, __ in sub_pairs]) + '))'\\n result_fields = \\\\\\n list(itertools.chain.from_iterable([x for __, x in sub_pairs]))\\n\\n if node.get('not', False):\\n result = '(NOT (' + result + '))'\\n\\n return result, result_fields\\n\\n # Get the variable name and duplicate the symbol % in case it is part of\\n # the variable name (escape needed for SQL processing)\\n varname = fix_pctg_in_name(node['field'])\\n\\n # Get the operator\\n operator = node['operator']\\n\\n # If the operator is between or not_between, there is a special case,\\n # the constant cannot be computed because the node['value'] is a pair\\n constant = None\\n if node['value'] is not None and not isinstance(node['value'], list):\\n # Calculate the constant value depending on the type\\n if node['type'] == 'integer':\\n constant = int(node['value'])\\n elif node['type'] == 'double':\\n constant = float(node['value'])\\n elif node['type'] == 'boolean':\\n constant = node['value'] == '1'\\n elif node['type'] == 'string':\\n constant = node['value']\\n elif node['type'] == 'datetime':\\n constant = node['value']\\n else:\\n raise Exception(\\n _('No function to translate type {0}').format(node['type'])\\n )\\n\\n # Terminal Node\\n result_fields = []\\n if operator == 'equal':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' = %s) AND (\\\"{0}\\\" is not null)'.format(varname)\\n result_fields = [str(constant)]\\n\\n elif operator == 'not_equal':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n '!= %s) OR (\\\"{0}\\\" is null)'.format(varname)\\n result_fields = [str(constant)]\\n\\n elif operator == 'begins_with' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' LIKE %s) AND (\\\"{0}\\\" is not null)'.format(varname)\\n result_fields = [node['value'] + \\\"%\\\"]\\n\\n elif operator == 'not_begins_with' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' NOT LIKE %s) OR (\\\"{0}\\\" is null)'.format(varname)\\n result_fields = [node['value'] + \\\"%\\\"]\\n\\n elif operator == 'contains' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' LIKE %s) AND (\\\"{0}\\\" is not null)'.format(varname)\\n result_fields = [\\\"%\\\" + node['value'] + \\\"%\\\"]\\n\\n elif operator == 'not_contains' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' NOT LIKE %s) OR (\\\"{0}\\\" is null)'.format(varname)\\n result_fields = [\\\"%\\\" + node['value'] + \\\"%\\\"]\\n\\n elif operator == 'ends_with' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' LIKE %s) AND (\\\"{0}\\\" is not null)'.format(varname)\\n result_fields = [\\\"%\\\" + node['value']]\\n\\n elif operator == 'not_ends_with' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n ' NOT LIKE %s) OR (\\\"{0}\\\" is null)'.format(varname)\\n result_fields = [\\\"%\\\" + node['value']]\\n\\n elif operator == 'is_empty' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n \\\" = '') OR (\\\\\\\"{0}\\\\\\\" is null)\\\".format(varname)\\n\\n elif operator == 'is_empty' and node['type'] != 'string':\\n result = \\\"(\\\\\\\"{0}\\\\\\\" is null)\\\".format(varname)\\n\\n elif operator == 'is_not_empty' and node['type'] == 'string':\\n result = '(\\\"{0}\\\"'.format(varname) + \\\\\\n \\\" != '') AND (\\\\\\\"{0}\\\\\\\" is not null)\\\".format(varname)\\n\\n elif operator == 'is_not_empty' and node['type'] != 'string':\\n result = \\\"(\\\\\\\"{0}\\\\\\\" is not null)\\\".format(varname)\\n\\n elif operator == 'less' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' < %s'\\n result_fields = [str(constant)]\\n\\n elif operator == 'less_or_equal' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' <= %s'\\n result_fields = [str(constant)]\\n\\n elif operator == 'greater' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' > %s'\\n result_fields = [str(constant)]\\n\\n elif operator == 'greater_or_equal' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' >= %s'\\n result_fields = [str(constant)]\\n\\n elif operator == 'between' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' BETWEEN %s AND %s'\\n result_fields = [str(node['value'][0]), str(node['value'][1])]\\n\\n elif operator == 'not_between' and \\\\\\n (node['type'] == 'integer' or node['type'] == 'double'\\n or node['type'] == 'datetime'):\\n result = '\\\"{0}\\\"'.format(varname) + ' NOT BETWEEN %s AND %s'\\n result_fields = [str(node['value'][0]), str(node['value'][1])]\\n\\n else:\\n raise Exception(\\n _('Type, operator, field {0}, {1}, {2} not supported yet').format(\\n node['type'], operator, varname\\n )\\n )\\n\\n if node.get('not', False):\\n raise Exception(_('Negation found in unexpected location'))\\n\\n return result, result_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd8285bd0816627e7f7b40210481df8\",\n \"score\": \"0.5158893\",\n \"text\": \"def query(\\n self, strOrQuery, initBindings={},\\n initNs={}, base=None, DEBUG=False):\\n\\n if not isinstance(strOrQuery, Query):\\n parsetree = parseQuery(strOrQuery)\\n query = translateQuery(parsetree, base, initNs)\\n else:\\n query = strOrQuery\\n\\n return evalQuery(self.graph, query, initBindings, base)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"911fc88c4f06f7a15ccb80b7985bca8c\",\n \"score\": \"0.51363057\",\n \"text\": \"def execute(self, neo4j_session: Session):\\n if self.query == \\\"\\\":\\n raise ValueError(\\n \\\"The query must be generated before this method is called.\\\"\\n )\\n try:\\n return neo4j_session.write_transaction(lambda tx: tx.run(self.query))\\n except:\\n print(\\\"Query not defined error\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af7e59578232b5286f33142426d48b4\",\n \"score\": \"0.51362425\",\n \"text\": \"def buildConnectionString(params):\\n return \\\";\\\".join([\\\"%s=%s\\\" % (k, v) for k, v in params.items()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"250b21f2993d378d79cd2b0b0096170a\",\n \"score\": \"0.5131796\",\n \"text\": \"def _row_to_query_statement_string(self, row):\\n start_node = 'MERGE (start:{state_lab} {{code:\\\"{start_state}\\\"}})'.format(\\n state_lab=self.labels.state, start_state=row[self.start_state_col]\\n )\\n\\n end_node = 'MERGE (end:{state_lab} {{code:\\\"{end_state}\\\"}})'.format(\\n state_lab=self.labels.state, end_state=row[self.end_state_col]\\n )\\n\\n transition = \\\"MERGE (start)<-[:SOURCE]-(trans:{trans_lab})-[:TARGET]->(end)\\\".format(\\n trans_lab=self.labels.transition\\n )\\n\\n def _conditions_str(row, start_state_col, end_state_col):\\n \\\"\\\"\\\"Build the string used to express transition conditions.\\n \\n This is the part in curly braces specifying the Condition node's\\n properties.\\n \\\"\\\"\\\"\\n row = row.drop([start_state_col, end_state_col])\\n count = 0\\n s = \\\"\\\"\\n for i, val in row.iteritems():\\n s += i + \\\":\\\"\\n if isinstance(val, six.string_types):\\n s += '\\\"' + val + '\\\"'\\n elif isinstance(val, bool):\\n s += str(val).lower()\\n else:\\n s += str(val)\\n if count < len(row) - 1:\\n s += \\\", \\\"\\n count += 1\\n\\n return \\\"{\\\" + s + \\\"}\\\"\\n\\n condition = \\\"MERGE (cond:{cond_lab} {cond_str})-[:CAUSES]->(trans)\\\".format(\\n cond_lab=self.labels.condition,\\n cond_str=_conditions_str(row, self.start_state_col, self.end_state_col),\\n )\\n\\n query_str = (\\n start_node + \\\" \\\" + end_node + \\\" \\\" + transition + \\\" \\\" + condition + \\\";\\\"\\n )\\n\\n if self.global_params:\\n query_str = self._add_global_params_to_query_string(\\n query_str, self.global_params\\n )\\n\\n return query_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54c7e0913a92b7dfb4e7cc3e5cb349f6\",\n \"score\": \"0.5125673\",\n \"text\": \"def build_query(url: str, *args, **kwargs) -> str:\\n url = url.strip(\\\"/\\\")\\n args = map(lambda arg: arg.strip(\\\"/\\\"), args)\\n kwargs = dict(filter(lambda kv: kv[1] is not None, kwargs.items()))\\n url = \\\"/\\\".join([url, *args]) + \\\"?\\\"\\n url += \\\"&\\\".join([f\\\"{key}={value}\\\" for key, value in kwargs.items()])\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976a7fa212ec09e76082f81572c24b87\",\n \"score\": \"0.51216996\",\n \"text\": \"def build_query(field, fields, fuzziness=\\\"AUTO\\\"):\\n val = fields[field]\\n if \\\"*\\\" in val:\\n qtype = \\\"wildcard\\\"\\n fval = val\\n else:\\n qtype = \\\"match\\\"\\n fval = dict(query=val, fuzziness=fuzziness)\\n return Q(qtype, **{field: fval})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27d86e7c41d5fb86b9a53f995bbc51a9\",\n \"score\": \"0.5117474\",\n \"text\": \"def q(*args: Any, **kwargs: Any) -> \\\"Query\\\":\\n return Query(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0fd368bbe1ee92e7081900909296ef4\",\n \"score\": \"0.51087725\",\n \"text\": \"def query_on_params(self,\\\\\\n **kwargs):\\n if self.check_empty_query(**kwargs):\\n return []\\n \\n elif 'username' in kwargs and len(kwargs) == 1:\\n name = kwargs['username'][0]\\n return self.query_on_name(\\\\\\n name,\\\\\\n self.all()) \\n\\n username_s = kwargs['username'][0]\\n fullname_s = kwargs['fullname'][0]\\n faculty_s = kwargs['faculty'][0]\\n program_s = kwargs['program'][0]\\n query_set_l = self.all()\\n if username_s != '':\\n query_set_l = query_set_l.filter(\\\\\\n username__icontains = username_s)\\n if fullname_s != '':\\n query_set_l = self.query_on_name(\\\\\\n fullname_s,\\\\\\n query_set_l)\\n if faculty_s != '':\\n query_set_l = query_set_l.filter(\\\\\\n faculty__exact = faculty_s)\\n if program_s != '':\\n query_set_l = query_set_l.filter(\\\\\\n program__exact = program_s)\\n return query_set_l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a40795caa1ea72b2cdda71b5e0413200\",\n \"score\": \"0.51069057\",\n \"text\": \"def sql_condition(\\n self,\\n conditions=tuple(),\\n all_inner=True,\\n all_outer=True,\\n match_inner=True,\\n match_outer=True,\\n ):\\n args = locals()\\n # put the conditions into a dict\\n conds = {\\n pref: args.get(pref)\\n for pref in (\\n \\\"all_inner\\\",\\n \\\"all_outer\\\",\\n \\\"match_inner\\\",\\n \\\"match_outer\\\",\\n )\\n }\\n conds = { # properly cycle all settings\\n pref: itertools.cycle(val if hasattr(val, \\\"__iter__\\\") else (val,))\\n for pref, val in conds.items()\\n }\\n conds[\\\"values\\\"] = (\\n (conditions,) if hasattr(conditions, \\\"items\\\") else conditions\\n )\\n sql_conditions, sql_params = [], []\\n for cond in listdict_to_dictlist(conds):\\n sanitized_values = {\\n self.sanitize(k, brutally=True): self.sanitize(\\n v, brutally=False\\n )\\n if isinstance(v, str)\\n else v\\n for k, v in cond[\\\"values\\\"].items()\\n if k in self.tableheader\\n }\\n inner_sql_cond = (\\\" AND \\\" if cond[\\\"all_inner\\\"] else \\\" OR \\\").join(\\n map(\\n \\\"{} {} ?\\\".format,\\n sanitized_values.keys(),\\n itertools.cycle(\\n (\\\"IS\\\" if cond[\\\"match_inner\\\"] else \\\"IS NOT\\\",)\\n ),\\n )\\n ) or \\\"1=1\\\"\\n sql_conditions.append(\\n \\\"{}({})\\\".format(\\n \\\"\\\" if cond[\\\"match_outer\\\"] else \\\"NOT \\\", inner_sql_cond\\n )\\n )\\n sql_params.extend(tuple(sanitized_values.values()))\\n sql_condition = (\\n functools.reduce(\\n lambda x, y: (\\n \\\" AND \\\" if next(conds[\\\"all_outer\\\"]) else \\\" OR \\\"\\n ).join((x, y)),\\n sql_conditions,\\n )\\n if sql_conditions\\n else \\\"1=1\\\"\\n )\\n return sql_condition, tuple(sql_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c126e83185b3f095e5bc3f7c32cda876\",\n \"score\": \"0.51048845\",\n \"text\": \"def post(self, statement, parameters=None):\\n payload = {\\\"query\\\": statement}\\n if parameters:\\n payload[\\\"params\\\"] = {}\\n for key, value in parameters.items():\\n if isinstance(value, (Node, Rel, Relationship)):\\n value = value._id\\n payload[\\\"params\\\"][key] = value\\n log.info(\\\"execute %r %r\\\", payload[\\\"query\\\"], payload.get(\\\"params\\\", {}))\\n return self.resource.post(payload)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":184,"cells":{"query_id":{"kind":"string","value":"152c3edbb3aa0b05d956c5dcb6acb205"},"query":{"kind":"string","value":"Initialize the Decomposable OperatorValued Kernel."},"positive_passages":{"kind":"list like","value":[{"docid":"f09118c547cd865e208584e4cfd55bdd","score":"0.0","text":"def __init__(self, X, gamma):\n super(RBFDivFreeKernelMap, self).__init__(gamma)\n self.n = X.shape[0]\n self.d = X.shape[1]\n self.p = X.shape[1]\n self.X = X\n self.Gs_train = None","title":""}],"string":"[\n {\n \"docid\": \"f09118c547cd865e208584e4cfd55bdd\",\n \"score\": \"0.0\",\n \"text\": \"def __init__(self, X, gamma):\\n super(RBFDivFreeKernelMap, self).__init__(gamma)\\n self.n = X.shape[0]\\n self.d = X.shape[1]\\n self.p = X.shape[1]\\n self.X = X\\n self.Gs_train = None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"31c1f825722e935a9f2040878ee62b3c","score":"0.6014059","text":"def init_op(self):\n return self._init_op","title":""},{"docid":"933dc4e128744f5c26c4a36acf306da6","score":"0.59667915","text":"def __init__(self,kernel_fn,*args,order=2,**kwargs):\n super().__init__(*args,**kwargs)\n self.dkernel_fn = DiscretizedKernelFN(kernel_fn,order)","title":""},{"docid":"6d9845f2e29e914f28540bd4faadf50b","score":"0.5955899","text":"def __init__(self, X, A, scalar_kernel, scalar_kernel_params):\n super(DecomposableKernelMap, self).__init__(A, scalar_kernel,\n scalar_kernel_params)\n self.n = X.shape[0]\n self.d = X.shape[1]\n self.X = X\n self.Gs_train = None","title":""},{"docid":"4fca6d74bf6efc98b6afae92b5d813d2","score":"0.594024","text":"def __init__(self):\n # set the kernel. One can play with the parameters.\n self.set_kernel(1, 2.0, 0.98)","title":""},{"docid":"0ab1359409ab9f3bea19235d57510e45","score":"0.5931874","text":"def __init__(self):\n self.d = dd(lambda : 0)","title":""},{"docid":"679da9cba2b0e04380ef1933feec9920","score":"0.579953","text":"def _setup_primitive_kernel(request) -> SDEKernel:\n return _create_markovflow_primitive_kernel(request.param)","title":""},{"docid":"32cd9d7e5acbca27580e7960d902ce69","score":"0.56664544","text":"def __init__(self, kernel, adopt_thresh, state, target, maxsize, adaptive=True, forget_rate=0.0):\n\t\tself.dp = KernelDict(kernel, adopt_thresh, state, target, maxsize, adaptive, forget_rate)\n\t\tself.P = [[1]]\n\t\tself.Alpha = np.dot(self.dp.Kinv, target)","title":""},{"docid":"31d3655e599882c02607bb11bff69504","score":"0.56652945","text":"def __init__(self, kernel_size):\n super().__init__()\n self.kernel_size = kernel_size","title":""},{"docid":"31d3655e599882c02607bb11bff69504","score":"0.56652945","text":"def __init__(self, kernel_size):\n super().__init__()\n self.kernel_size = kernel_size","title":""},{"docid":"31d3655e599882c02607bb11bff69504","score":"0.56652945","text":"def __init__(self, kernel_size):\n super().__init__()\n self.kernel_size = kernel_size","title":""},{"docid":"31d3655e599882c02607bb11bff69504","score":"0.56652945","text":"def __init__(self, kernel_size):\n super().__init__()\n self.kernel_size = kernel_size","title":""},{"docid":"5acf0ce085e1d9c8d75204b2a828c8ad","score":"0.56514794","text":"def _init_op(new_op):\n\n def _impl(inputs, attrs):\n assert len(inputs) == 0\n shape = attrs.get_int_tuple(\"shape\")\n dtype = attrs.get_str(\"dtype\", \"float32\")\n return new_op(shape=shape, dtype=dtype)\n\n return _impl","title":""},{"docid":"d18640e6b96ae414bc449aa49d579501","score":"0.5644447","text":"def __init__(self, kernel=None, noise_var=None, exact_feval=False, optimizer='bfgs',\n max_iters=1000, optimize_restarts=5, sparse=None, num_inducing=10,\n verbose=False, ARD=False, seed=42, normalize_Y=True, n_subspaces=12, update_freq=5):\n self.kernel = kernel\n self.noise_var = noise_var\n self.exact_feval = exact_feval\n self.optimize_restarts = optimize_restarts\n self.optimizer = optimizer\n self.max_iters = max_iters\n self.verbose = verbose\n self.sparse = sparse\n self.sparse_surrogate = False\n self.num_inducing = num_inducing\n self.model = None\n self.ARD = ARD\n self.seed = seed\n self.update_interval = update_freq\n self.normalize_Y = normalize_Y\n self.n_sub = n_subspaces\n self.n_decomp = 20\n np.random.rand(self.seed)","title":""},{"docid":"abb773c00e94f7cd4d00189b8b26809d","score":"0.5515068","text":"def __call__(self):\r\n new_node = Op.__call__(self)\r\n new_node.inputs = []\r\n new_node.name = \"Global_Variables_Initializer\"\r\n return new_node","title":""},{"docid":"c8983388747a5dd2343aaa072cff2509","score":"0.5513527","text":"def kernel(self, value):\n\n if value is not None:\n assert hasattr(\n value,\n '__call__'), ('\"{0}\" attribute: \"{1}\" is not callable!'.format(\n 'kernel', value))\n\n self._kernel = value","title":""},{"docid":"a05329e78028c09e4d065fb146db76c7","score":"0.5498108","text":"def __init__(\n self,\n params: grid_parametrization.GridParametrization,\n kernel_op: get_kernel_fn.ApplyKernelOp,\n solver_option: poisson_solver_pb2.PoissonSolver,\n ):\n super().__init__(params, kernel_op, solver_option)\n\n self._kernel_op.add_kernel({'weighted_sum_121': ([1.0, 2.0, 1.0], 1)})\n self._omega = solver_option.jacobi.omega\n self._num_iters = solver_option.jacobi.max_iterations\n self._halo_width = solver_option.jacobi.halo_width\n\n self._factor_b = 0.5 / (params.dx**-2 + params.dy**-2 + params.dz**-2)\n self._factor_x = params.dx**2 / self._factor_b\n self._factor_y = params.dy**2 / self._factor_b\n self._factor_z = params.dz**2 / self._factor_b","title":""},{"docid":"1dfd0e455ad0824814bf1628e531aa99","score":"0.5469124","text":"def __init__(self, initial_value, *args, dtype=None, **kwargs):\n # Variables by default use the current device scope for placement. This\n # wrapper has them follow the initial value's placement instead (which will\n # be the DTensor device if the initial value has a layout).\n\n # Pop layout from kwargs since keras make_variable may pass a 'layout'\n # keyword argument. We need to pop it because we are passing kwargs to\n # super class constructor.\n layout = kwargs.pop('layout', None)\n shape = kwargs.get('shape', None)\n\n if callable(initial_value):\n unwrapped = initial_value\n if issubclass(type(initial_value), functools.partial):\n unwrapped = initial_value.func\n\n # If wrapped is a CheckpointInitialValueCallable, this means that\n # we are creating a Variable during a checkpoint restore.\n # Thus the restore will happen now through this callable\n # and we will create the DVariable with the restored dtensor.\n if issubclass(type(unwrapped), trackable.CheckpointInitialValueCallable):\n if not shape or not layout:\n raise ValueError('Expected shape and layout to be not None.')\n\n # CheckpointInitialValueCallable will call an eager tf.RestoreV2,\n # which does not have any shape information or layout information\n # attached. Thus we will do two things to have them correctly specified:\n #\n # The default layout scope allows us to correctly specify the output\n # layout of the tf.RestoreV2 that will be called\n #\n # Passing shard_info with the correct shape allows the tf.RestoreV2\n # ShapeInference to extract the shape.\n initial_value = api.call_with_layout(\n initial_value,\n layout,\n shard_info=trackable.ShardInfo(\n shape=shape, offset=[0] * len(shape)))\n else:\n initial_value = initial_value()\n\n # When the initial value came from a Checkpoint restoration, fetch tensor.\n if isinstance(initial_value, trackable.CheckpointInitialValue):\n initial_value = initial_value.wrapped_value\n\n initial_value = ops.convert_to_tensor(initial_value, dtype=dtype)\n variable_device = initial_value.device\n self._save_as_bf16 = False\n # TODO(b/159035705): The following code enables variable creation inside\n # a tf.function. However, it requires a global dtensor device.\n # if not variable_device and not tf.executing_eagerly():\n # try:\n # initial_value.op.get_attr(\"_layout\")\n # except ValueError:\n # pass\n # else:\n # # The initial value is a DTensor, but because the DTensor device is\n # # only active during eager execution at the moment we need to\n # # translate that into a placement for the eager VarHandleOp.\n # variable_device = _dtensor_device().name\n with ops.device(variable_device):\n # If initial tensor assigned to DVariable is DTensor, record the layout of\n # the resource so that this can be queried.\n if context.executing_eagerly():\n if api.is_dtensor(initial_value):\n value_layout = api.fetch_layout(initial_value)\n if layout is not None and layout != value_layout:\n raise errors_impl.InvalidArgumentError(\n None,\n None,\n 'Conflicting layout are provided for initial '\n f'value layout ({value_layout}) and variable ({layout}).',\n )\n layout = value_layout\n elif layout is not None:\n initial_value = api.relayout(initial_value, layout)\n else:\n raise errors_impl.InvalidArgumentError(\n None,\n None,\n 'Neither layout nor DTensor initial value are provided.',\n )\n self.layout = layout\n with api.default_mesh(layout.mesh):\n super(DVariable, self).__init__(\n initial_value, *args, dtype=dtype, **kwargs\n )\n else:\n # FIXME(175928457): Record value layout in graph mode.\n if layout is not None:\n initial_value = api.relayout(initial_value, layout)\n super(DVariable, self).__init__(\n initial_value, *args, dtype=dtype, **kwargs)","title":""},{"docid":"c3de15c5a768f66fe4fbaed3a7d68cc4","score":"0.54562724","text":"def __init__(self, kernel=None, hp=None):\n\n self.kernel = kernel\n self.hp = hp\n self.name = kernel.__name__","title":""},{"docid":"95f099c9ceb72599c3eb191e27acebee","score":"0.5444765","text":"def __init__(\n self,\n kernel='linear', # kernel type\n max_iter=180, # maximum iteration times\n conv=1e-3, # convergence criterion\n beta=1e3, # initial beta\n bias_used=True # bias\n ):\n self.kernel = kernel\n self.n_iter = max_iter\n self.conv = conv\n self.beta = beta\n self.bias_used = bias_used\n self.alpha, self.alpha_, self.alpha_old, self.beta_, self.relevance, self.phi, self.phi_,\\\n self.y, self.mean_, self.sigma_, self.bias = [None]*11","title":""},{"docid":"2fa80d6f5fdd7a6d6ca7a2bed06d3ca9","score":"0.5426685","text":"def __init__(self, model):\n self.model = model\n self.opcache = {} # a cache of operators, which should get initialized by from_vector calls","title":""},{"docid":"4b823c3cb49da97065fb9a3afeeb8a89","score":"0.53927","text":"def __call__(self, initial_value, dtype = None, shape = None, name = \"Variable\"):\r\n new_node = Op.__call__(self)\r\n if shape is not None:\r\n assert shape == initial_value.shape\r\n if dtype is not None:\r\n if isinstance(initial_value, np.ndarray):\r\n global_variables[new_node] = initial_value.astype(dtype)\r\n else:\r\n global_variables[new_node] = np.array(initial_value).astype(dtype)\r\n else:\r\n global_variables[new_node] = initial_value\r\n new_node.name = name\r\n return new_node","title":""},{"docid":"303057902312f28c3b3789db58ed8b76","score":"0.5379558","text":"def __init__(self, kernel_size):\n\n self.stride = kernel_size\n self.kernel_size = kernel_size","title":""},{"docid":"75b72de45026680e384ba08990058900","score":"0.5367593","text":"def __init__(self, **kwargs):\n\n LOG.info(\"%s: initing...\" % str(self))\n\n super(Generic2dOscillator, self).__init__(**kwargs)\n\n #self._state_variables = [\"V\", \"W\"]\n self._nvar = 2\n self.cvar = numpy.array([0], dtype=numpy.int32)\n\n LOG.debug(\"%s: inited.\" % repr(self))","title":""},{"docid":"b7aab43ddc245a9aeebd561fb21486f2","score":"0.53663516","text":"def _setup_impl(self):\n\n self.bpm_program = OCLProgram(absPath(\"kernels/bpm_3d_kernels.cl\"))\n\n Nx, Ny, Nz = self.size\n\n self._plan = fft_plan((Ny,Nx))\n\n\n self._H_g = OCLArray.from_array(self._H.astype(np.complex64))\n\n if not self.dn is None and self.n_volumes==1:\n self.dn_g = OCLArray.from_array(self.dn)\n\n\n self.scatter_weights_g = OCLArray.from_array(self.scatter_weights.astype(np.float32))\n self.gfactor_weights_g = OCLArray.from_array(self.gfactor_weights.astype(np.float32))\n\n self.scatter_cross_sec_g = OCLArray.zeros(Nz,\"float32\")\n self.gfactor_g = OCLArray.zeros(Nz,\"float32\")\n\n\n\n # self.reduce_kernel = OCLReductionKernel(\n # np.float32, neutral=\"0\",\n # reduce_expr=\"a+b\",\n # map_expr=\"weights[i]*cfloat_abs(field[i]-(i==0)*plain)*cfloat_abs(field[i]-(i==0)*plain)\",\n # arguments=\"__global cfloat_t *field, __global float * weights,cfloat_t plain\")","title":""},{"docid":"5fdf5263c27b7114fd643c548c5c86cc","score":"0.5359017","text":"def __init__(self, value):\n\t\tsuper(Functor, self).__init__(value)","title":""},{"docid":"6bb8bab55f68310770b6b4a2897fe8af","score":"0.53568095","text":"def __init__(self, kernel_type=None):\r\n self.kernel_type = kernel_type\r\n self.gamma = 0\r\n self.coef = 0\r\n self.degree = 0\r\n self.num_classes = 0\r\n self.labels = None\r\n self.total_sv = 0\r\n self.rho = 0\r\n self.sv = []","title":""},{"docid":"0967a0baddc13a0d6db87af07c84175b","score":"0.5346729","text":"def initialize_operator(self, operator=None, matrix=False, eval_at_once=False):\n # TODO: Make this more efficient, only compute values needed at each (r,c) step.\n # For this, 'operator' must support the 'component=(r,c)' option.\n # Operator is None is interpreted as identity transformation\n if operator is None:\n self._operator = lambda nodes, dummy, entry=None: ones((1, nodes.shape[1])) if entry[0] == entry[1] else zeros((1, nodes.shape[1]))\n else:\n # Wrap the operator inside a lambda ignoring the dummy parameter\n # This allows to use the same nsd code for quadrature and matrix construction.\n if matrix is False:\n self._operator = lambda nodes, dummy, entry=None: operator(nodes, entry=entry)\n else:\n self._operator = operator\n self._eval_at_once = eval_at_once","title":""},{"docid":"b68ca023fe7092aeaef64e1da8254009","score":"0.5345871","text":"def _init_routine(self):\n if self.is_initialized is False:\n # decide the routine\n if self.sn_def['op'] in {'d', 'project'}:\n # for d kernel_shape [num_in, num_out]; for project, kernel shape [num_class, num_in]\n assert len(self.kernel_shape) == 2, \\\n '{}: kernel shape {} does not have length 2'.format(self.name_in_err, self.kernel_shape)\n num_in, num_out = self.kernel_shape\n # self.use_u = True\n self.use_u = True if num_in <= num_out else False\n x_shape = [1, num_in] if self.use_u else [1, num_out]\n self.forward = self._dense_ if self.use_u else self._dense_t_\n self.backward = self._dense_t_ if self.use_u else self._dense_\n elif self.sn_def['op'] in {'cd'}: # kernel_shape [num_class, num_in, num_out]\n assert len(self.kernel_shape) == 3, \\\n '{}: kernel shape {} does not have length 3'.format(self.name_in_err, self.kernel_shape)\n num_class, num_in, num_out = self.kernel_shape\n self.use_u = True if num_in <= num_out else False\n x_shape = [num_class, 1, num_in] if self.use_u else [num_class, 1, num_out]\n self.forward = self._dense_ if self.use_u else self._dense_t_\n self.backward = self._dense_t_ if self.use_u else self._dense_\n elif self.sn_def['op'] in {'dck'}: # convolution * conditional scale\n assert isinstance(self.kernel_shape, (list, tuple)) and len(self.kernel_shape) == 2, \\\n '{}: kernel shape must be a list of length 2. Got {}'.format(self.name_in_err, self.kernel_shape)\n assert len(self.kernel_shape[0]) == 2 and len(self.kernel_shape[1]) == 2, \\\n '{}: kernel shape {} does not have length 2'.format(self.name_in_err, self.kernel_shape)\n num_in, num_out = self.kernel_shape[0]\n num_class = self.kernel_shape[1][0]\n self.use_u = True if num_in <= num_out else False\n x_shape = [num_class, num_in] if self.use_u else [num_class, num_out]\n self.forward = (lambda x: self._scalar_(self._dense_(x, index=0), index=1, offset=1.0)) \\\n if self.use_u else (lambda y: self._dense_t_(self._scalar_(y, index=1, offset=1.0), index=0))\n self.backward = (lambda y: self._dense_t_(self._scalar_(y, index=1, offset=1.0), index=0)) \\\n if self.use_u else (lambda x: self._scalar_(self._dense_(x, index=0), index=1, offset=1.0))\n elif self.sn_def['op'] in {'c', 'tc'}:\n assert len(self.kernel_shape) == 4, \\\n '{}: kernel shape {} does not have length 4'.format(self.name_in_err, self.kernel_shape)\n # self.use_u = True\n self.use_u = True \\\n if np.prod(self.sn_def['input_shape'][1:]) <= np.prod(self.sn_def['output_shape'][1:]) \\\n else False\n if self.sn_def['op'] in {'c'}: # input / output shape NCHW or NHWC\n x_shape = self.sn_def['input_shape'].copy() if self.use_u else self.sn_def['output_shape'].copy()\n x_shape[0] = 1\n y_shape = self.sn_def['input_shape'].copy()\n y_shape[0] = 1\n elif self.sn_def['op'] in {'tc'}: # tc\n x_shape = self.sn_def['output_shape'].copy() if self.use_u else self.sn_def['input_shape'].copy()\n x_shape[0] = 1\n y_shape = self.sn_def['output_shape'].copy()\n y_shape[0] = 1\n else:\n raise NotImplementedError('{}: {} not implemented.'.format(self.name_in_err, self.sn_def['op']))\n self.forward = self._conv_ if self.use_u else (lambda y: self._conv_t_(y, x_shape=y_shape))\n self.backward = (lambda y: self._conv_t_(y, x_shape=y_shape)) if self.use_u else self._conv_\n elif self.sn_def['op'] in {'cck', 'tcck'}: # convolution * conditional scale\n assert isinstance(self.kernel_shape, (list, tuple)) and len(self.kernel_shape) == 2, \\\n '{}: kernel shape must be a list of length 2. Got {}'.format(self.name_in_err, self.kernel_shape)\n assert len(self.kernel_shape[0]) == 4 and len(self.kernel_shape[1]) == 4, \\\n '{}: kernel shape {} does not have length 4'.format(self.name_in_err, self.kernel_shape)\n self.use_u = True \\\n if np.prod(self.sn_def['input_shape'][1:]) <= np.prod(self.sn_def['output_shape'][1:]) \\\n else False\n num_class = self.kernel_shape[1][0]\n if self.sn_def['op'] in {'cck'}: # input / output shape NCHW or NHWC\n x_shape = self.sn_def['input_shape'].copy() if self.use_u else self.sn_def['output_shape'].copy()\n x_shape[0] = num_class\n y_shape = self.sn_def['input_shape'].copy()\n y_shape[0] = num_class\n self.forward = (lambda x: self._scalar_(self._conv_(x, index=0), index=1, offset=1.0)) \\\n if self.use_u \\\n else (lambda y: self._conv_t_(self._scalar_(y, index=1, offset=1.0), x_shape=y_shape, index=0))\n self.backward = (lambda y: self._conv_t_(\n self._scalar_(y, index=1, offset=1.0), x_shape=y_shape, index=0)) \\\n if self.use_u else (lambda x: self._scalar_(self._conv_(x, index=0), index=1, offset=1.0))\n elif self.sn_def['op'] in {'tcck'}: # tcck\n x_shape = self.sn_def['output_shape'].copy() if self.use_u else self.sn_def['input_shape'].copy()\n x_shape[0] = num_class\n y_shape = self.sn_def['output_shape'].copy()\n y_shape[0] = num_class\n self.forward = (lambda x: self._conv_(self._scalar_(x, index=1, offset=1.0), index=0)) \\\n if self.use_u \\\n else (lambda y: self._scalar_(self._conv_t_(y, x_shape=y_shape, index=0), index=1, offset=1.0))\n self.backward = (lambda y: self._scalar_(\n self._conv_t_(y, x_shape=y_shape, index=0), index=1, offset=1.0)) \\\n if self.use_u else (lambda x: self._conv_(self._scalar_(x, index=1, offset=1.0), index=0))\n else:\n raise NotImplementedError('{}: {} not implemented.'.format(self.name_in_err, self.sn_def['op']))\n else:\n raise NotImplementedError('{}: {} is not implemented.'.format(self.name_in_err, self.sn_def['op']))\n\n self.x = tf.compat.v1.get_variable(\n 'in_rand', shape=x_shape, dtype=tf.float32,\n initializer=tf.truncated_normal_initializer(), trainable=False)\n\n self.is_initialized = True","title":""},{"docid":"f2a6967944c915daed81a3fe3422a498","score":"0.5284371","text":"def __init__(self, shape, k, filters, kernels):\n self.dims = shape\n self.k = k\n self.filters = filters\n self.kernels = kernels","title":""},{"docid":"3d489be3b563e75c12cba4f31c651635","score":"0.52758646","text":"def __init__(self, var, accum, linear, grad, indices,\n lr, lr_power, l1, l2, l2_shrinkage, kernel_name):\n super().__init__(var, grad, indices, kernel_name)\n self.lr = lr\n self.lr_power = lr_power\n self.l1 = l1\n self.l2 = l2\n self.l2_shrinkage = l2_shrinkage\n self.lr_vrec = 1.0 / self.lr\n self.var_shape = var.get(\"shape\")\n self.var_dtype = var.get(\"dtype\").lower()\n self.accum_shape = accum.get(\"shape\")\n self.accum_dtype = accum.get(\"dtype\").lower()\n self.linear_shape = linear.get(\"shape\")\n self.linear_dtype = linear.get(\"dtype\").lower()\n self.check_param()","title":""},{"docid":"a0686a0db70ea3e597fcc88adaa8c3f5","score":"0.52641636","text":"def __init__(self):\n self.da_val = DynamicArray()\n self.da_max = DynamicArray()","title":""},{"docid":"b24468922248ebc17217ff925e070f54","score":"0.526289","text":"def __init__(self, D, kernel_gamma, m, step_size, gamma2=0.1, schedule=None, acc_star=0.234,\n update_kernel_gamma=None, update_kernel_gamma_schedule=None, update_kernel_gamma_tol=0.1):\n self.kernel_gamma = kernel_gamma\n self.m = m\n self.D = D\n self.gamma2 = gamma2\n self.step_size = step_size\n self.schedule = schedule\n self.acc_star = acc_star\n self.update_kernel_gamma = update_kernel_gamma\n self.update_kernel_gamma_schedule = update_kernel_gamma_schedule\n self.update_kernel_gamma_tol = update_kernel_gamma_tol\n \n # scaling parameter evolution might be collected to assess convergence\n self.nu2s = [step_size]\n \n # some sanity checks\n if acc_star is not None:\n assert acc_star > 0 and acc_star < 1\n \n if schedule is not None:\n lmbdas = np.array([schedule(t) for t in np.arange(100)])\n assert np.all(lmbdas > 0)\n assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)\n \n if self.update_kernel_gamma:\n self.past_samples = collections.deque()\n \n if self.update_kernel_gamma_schedule is not None:\n lmbdas = np.array([update_kernel_gamma_schedule(t) for t in np.arange(100)])\n assert np.all(lmbdas > 0)\n assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)\n \n self._initialise()","title":""},{"docid":"260cd30aa6404a0db73c4f2663e7a1d5","score":"0.5251513","text":"def __init__(self, value):\n self.np_value = value","title":""},{"docid":"1b98432c1827a929281f2a7f25c397f1","score":"0.52501065","text":"def __init__(self, initializer: ArrayOp, index_op: IndexOp):\n self.initializer = initializer\n self.index_op = index_op","title":""},{"docid":"a6498103bfa75e7a4a24ec77f577d87d","score":"0.52426285","text":"def __init__(self, value):\n self._v = value","title":""},{"docid":"601f3074c3929a4c542d73770542d2a7","score":"0.52349836","text":"def __init__(self, *args, **kwargs):\n super(DPTuner, self).__init__(*args, **kwargs)\n self._num_states = self._max_num_states = None\n self._stage_dict = {}\n self._dep_dict = {}\n self._counted_nodes_set = set()\n\n self._global_data_dict = {\n \"dtype\": self._dtype,\n \"counted_nodes_set\": self._counted_nodes_set,\n \"stage_dict\": self._stage_dict,\n \"in_nodes_dict\": self._in_nodes_dict,\n \"out_nodes_dict\": self._out_nodes_dict,\n \"dep_dict\": self._dep_dict,\n \"node_list\": self._node_list,\n \"input_shapes\": self._input_shapes,\n \"layout_transform_interlayer_cost\": self._layout_transform_interlayer_cost,\n }","title":""},{"docid":"fe05bf701bd73f4f90dc69bea2b7c5e7","score":"0.5229092","text":"def __init__(self, pars=[], kernel=\"\"):\n\n self._pars = pars\n self._kernel = kernel","title":""},{"docid":"86c32e4f4c1420ad4bad9df9f953f1cf","score":"0.52148443","text":"def init_vector(ocp):\n v_init = np.ndarray((0, 1))\n\n # For states\n for i_phase in range(len(ocp.nlp)):\n current_nlp = ocp.nlp[i_phase]\n\n repeat = 1\n if current_nlp.ode_solver.is_direct_collocation:\n repeat += current_nlp.ode_solver.polynomial_degree\n\n nlp = ocp.nlp[current_nlp.use_states_from_phase_idx]\n OptimizationVectorHelper._set_node_index(nlp, 0)\n for key in nlp.states:\n if key in nlp.x_init.keys():\n n_points = OptimizationVectorHelper._nb_points(nlp, nlp.x_init[key].type)\n nlp.x_init[key].check_and_adjust_dimensions(nlp.states[key].cx.shape[0], n_points)\n\n for k in range(nlp.ns + 1):\n OptimizationVectorHelper._set_node_index(nlp, k)\n for p in range(repeat if k != nlp.ns else 1):\n point = k if k != 0 else 0 if p == 0 else 1\n\n collapsed_values = np.ndarray((nlp.states.shape, 1))\n for key in nlp.states:\n if key in nlp.x_init.keys():\n point_to_eval = point\n if nlp.x_init[key].type == InterpolationType.ALL_POINTS:\n point_to_eval = k * repeat + p\n value = (\n nlp.x_init[key].init.evaluate_at(shooting_point=point_to_eval, repeat=repeat)\n / nlp.x_scaling[key].scaling\n )[:, np.newaxis]\n else:\n value = 0\n # Organize the controls according to the correct indices\n collapsed_values[nlp.states[key].index, :] = value\n\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\n\n # For controls\n for i_phase in range(len(ocp.nlp)):\n current_nlp = ocp.nlp[i_phase]\n\n nlp = ocp.nlp[current_nlp.use_controls_from_phase_idx]\n OptimizationVectorHelper._set_node_index(nlp, 0)\n if nlp.control_type in (ControlType.CONSTANT, ControlType.NONE):\n ns = nlp.ns\n elif nlp.control_type in (ControlType.LINEAR_CONTINUOUS, ControlType.CONSTANT_WITH_LAST_NODE):\n ns = nlp.ns + 1\n else:\n raise NotImplementedError(f\"Multiple shooting problem not implemented yet for {nlp.control_type}\")\n\n for key in nlp.controls.keys():\n if key in nlp.u_init.keys():\n nlp.u_init[key].check_and_adjust_dimensions(nlp.controls[key].cx.shape[0], ns - 1)\n\n for k in range(ns):\n OptimizationVectorHelper._set_node_index(nlp, k)\n collapsed_values = np.ndarray((nlp.controls.shape, 1))\n for key in nlp.controls:\n if key in nlp.u_init.keys():\n value = nlp.u_init[key].init.evaluate_at(shooting_point=k) / nlp.u_scaling[key].scaling\n else:\n value = 0\n\n # Organize the controls according to the correct indices\n collapsed_values[nlp.controls[key].index, 0] = value\n\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\n\n # For parameters\n collapsed_values = np.zeros((ocp.parameters.shape, 1))\n for key in ocp.parameters.keys():\n if key not in ocp.parameter_init.keys():\n v_init = np.concatenate((v_init, np.zeros((ocp.parameters[key].size, 1))))\n continue\n\n scaled_init = ocp.parameter_init[key].scale(ocp.parameters[key].scaling)\n collapsed_values[ocp.parameters[key].index, :] = scaled_init.init\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\n\n # For stochastic variables\n for i_phase in range(len(ocp.nlp)):\n nlp = ocp.nlp[i_phase]\n OptimizationVectorHelper._set_node_index(nlp, 0)\n\n for key in nlp.stochastic_variables.keys():\n if key in nlp.s_init.keys():\n nlp.s_init[key].check_and_adjust_dimensions(nlp.stochastic_variables[key].cx.shape[0], nlp.ns)\n\n for k in range(nlp.ns + 1):\n OptimizationVectorHelper._set_node_index(nlp, k)\n collapsed_values = np.ndarray((nlp.stochastic_variables.shape, 1))\n for key in nlp.stochastic_variables:\n if key in nlp.s_init.keys():\n value = nlp.s_init[key].init.evaluate_at(shooting_point=k) / nlp.s_scaling[key].scaling\n else:\n value = 0\n\n # Organize the stochastic variables according to the correct indices\n collapsed_values[nlp.stochastic_variables[key].index, 0] = value\n\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\n\n for i_phase in range(len(ocp.nlp)):\n nlp = ocp.nlp[i_phase]\n if nlp.motor_noise is not None:\n n_motor_noise = nlp.motor_noise.shape[0]\n n_sensory_noise = nlp.sensory_noise.shape[0]\n v_init = np.concatenate((v_init, np.zeros((n_motor_noise, 1))))\n v_init = np.concatenate((v_init, np.zeros((n_sensory_noise, 1))))\n\n return v_init","title":""},{"docid":"f21f06197faa3e88badb2b42da306fe1","score":"0.52086794","text":"def __init__(self):\r\n self.inputs = []\r\n self.op = None\r\n self.const_attr = None\r\n self.name = \"\"","title":""},{"docid":"7d475a0425781c6d7fa1542d8463f437","score":"0.5186119","text":"def __init__(self, name: tp.Optional[str] = None, dtype: np.dtype = jnp.float32):\n self.name = name if name else utils.lower_snake_case(self.__class__.__name__)\n self.dtype = dtype\n self._params = {}\n self._states = []\n self._submodules = []\n self._dynamic_submodules = []\n self._initialized = False\n self._trainable = True\n\n _init = self.init\n\n def init(*args, **kwargs):\n return _init(*args, **kwargs)\n\n self.init = init\n\n utils.wraps(self.call)(self.init)\n utils.wraps(self.call)(self)\n\n self._jit_functions()","title":""},{"docid":"ede1473867441e7e3658e63982b4fb4e","score":"0.5181392","text":"def Do_kernel(self):\n return self.Kernel","title":""},{"docid":"ec7c2366c48398d16ed6ca58d56cb137","score":"0.5176585","text":"def __init__(self, x=None):\n self.v = x\n self.cl = None\n self.cr = None\n return None","title":""},{"docid":"89a718b8ed355be4f8f776d69cf7d2da","score":"0.51614505","text":"def __init__(self, pyo, t=None):\n if type(pyo) is Numeric.ArrayType:\n k = _arraytok(pyo) \n elif type(pyo) is _K:\n k = pyo\n if t and t != k.t:\n raise NotImplemented\n else:\n if not t:\n t = _guesst(pyo)\n else:\n if t > 0:\n pyt = _pyt[t-1]\n pyo = pyt(pyo)\n elif t < 0:\n pyt = _pyt[-t-1]\n pyo = map(pyt, pyo)\n kgen = _kgen(t)\n k = kgen(pyo)\n _K.__init__(self, k)","title":""},{"docid":"b870f3d7f5b6706d9c9597f891c73f95","score":"0.5157406","text":"def _v__init__(self):","title":""},{"docid":"0ba1f95b84ca10b7b07a5b2ccf6e80a2","score":"0.514581","text":"def __init__(self, shape, dtype):\n self._dtype = dtype\n self._sum = tf.Variable(lambda: tf.zeros(shape, dtype), False)\n self._count = tf.Variable(lambda: 0, trainable=False)","title":""},{"docid":"ff5d8627423d0ced3926413340e0e896","score":"0.51323915","text":"def __init__(self, master=None, value=None, name=None):\n _TK.Variable.__init__(self, master, value, name)","title":""},{"docid":"0bb7d41df8f841d7b93509b616075367","score":"0.5131436","text":"def __init__(self,kernel,args,invocations=None,type='VERTICAL'):\n\t\tself.args = []\n\t\tself.kernel = \"\"\n\t\tif(invocations == None):\n\t\t\ttemp = kernel.getSub(\"clEnqueueNDRangeKernel\")\n\t\t\tif(temp is not None):\n\t\t\t\tself.kernel = temp\n\t\t\telse:\n\t\t\t\tprint \"Invalid kernel statement\"\n\t\t\tfor arg in args:\n\t\t\t\ttemp = arg.getSub(\"clSetKernelArg\")\n\t\t\t\tif(temp is not None):\n\t\t\t\t\tif(temp.children[0] == self.kernel.children[1]):\n\t\t\t\t\t\tself.args.append((temp.children[2],temp.children[3]))\n\t\t\t\telse:\n\t\t\t\t\tprint \"invalid kernel argument\"\n\t\telse:\n\t\t\tself.kernel = copy.deepcopy(invocations[0].kernel)\n\t\t\tself.kernel.children[1].tokens[0].value = \"\"\n\t\t\tfor invocation in invocations:\n\t\t\t\tself.kernel.children[1].tokens[0].value += invocation.kernel.children[1].tokens[0].value.split('_')[0] + \"_\"\n\t\t\t\tself.args += invocation.args\n\t\t\tself.kernel.children[1].tokens[0].value += \"kernel\"\n\t\t\tif(type == 'HORIZONTAL'):\n\t\t\t\t\"\"\"Append an h\"\"\"\n\t\t\t\tself.kernel.children[1].tokens[0].value +='h'\t\n\t\t\tself.args = removeDuplicates2(self.args)","title":""},{"docid":"429e749ed7cc10f710ceb9a638caf71f","score":"0.5110538","text":"def __init__(self, device_params = DEFAULT_DEVICE_PARAMS):\n self.device_names = list(device_params.keys())\n self.dirichlet_params = list(device_params.values())\n self.p_vector = dirichlet(self.dirichlet_params).rvs().reshape(-1,)","title":""},{"docid":"e245d650938297578a09e6890a46c981","score":"0.51071066","text":"def __init__(self, potential, flow_operator_mask_v, flow_operator_mask_k):\n self._potential = potential\n self._v = potential.without_weights()\n self._k = potential.kinetic_energy()\n self._lam = potential.lam\n self._flow_op_mask_v = flow_operator_mask_v\n self._flow_op_mask_k = flow_operator_mask_k\n self._flow = 'lambda'","title":""},{"docid":"07d03dfd8f3467b96acfbfa3c3a0d282","score":"0.5101611","text":"def __init__(\n self,\n kernel_op: get_kernel_fn.ApplyKernelOp,\n params: parameters_lib.SwirlLMParameters,\n scalar_name: str,\n thermodynamics: thermodynamics_manager.ThermodynamicsManager,\n ):\n self._kernel_op = kernel_op\n self._params = params\n self._bc_types = copy.deepcopy(params.bc_type)\n self._scalar_name = scalar_name\n self._thermodynamics = thermodynamics\n\n self._h = (self._params.dx, self._params.dy, self._params.dz)\n\n self._scalar_params = None\n for scalar in self._params.scalars:\n if scalar.name == self._scalar_name:\n self._scalar_params = scalar\n break\n assert (\n self._scalar_params is not None\n ), f'{self._scalar_name} is not configured.'\n\n for override_bc in self._scalar_params.override_bc_type:\n dim = override_bc.dim\n face = override_bc.face\n logging.info('BC type for scalar: %s '\n 'will be reset to %s (originally %s) for dimension %d at '\n 'face %d', self._scalar_name,\n str(boundary_condition_utils.BoundaryType.UNKNOWN),\n str(self._bc_types[dim][face]), dim, face)\n self._bc_types[dim][face] = (\n boundary_condition_utils.BoundaryType.UNKNOWN)\n\n # Find the direction of gravity. Only vector along a particular dimension is\n # considered currently.\n self._g_vec = (\n self._params.gravity_direction if self._params.gravity_direction else [\n 0.0,\n ] * 3)\n self._g_dim = None\n for i in range(3):\n if np.abs(np.abs(self._g_vec[i]) - 1.0) < _G_THRESHOLD:\n self._g_dim = i\n break\n\n # Prepare diffusion related models.\n self._diffusion_fn = diffusion.diffusion_scalar(self._params)\n self._use_sgs = self._params.use_sgs\n filter_widths = (self._params.dx, self._params.dy, self._params.dz)\n if self._use_sgs:\n self._sgs_model = sgs_model.SgsModel(self._kernel_op, filter_widths,\n params.sgs_model)","title":""},{"docid":"c6c3eaa187c603279b9eacd627bd1e8a","score":"0.5097064","text":"def __init__(self, K, delta, c, np = None):\n\n self.K = float(K)\n self.K_int = int(K)\n self.delta = delta\n self.c = c\n\n S = self.calc_S()\n cdf, Z = gen_rsd_cdf(K, S, delta)\n self.cdf = cdf\n self.Z = Z\n\n #self.inter = inter.interp1d(np.concatenate(([0], cdf)), range(0,K+1))\n self.np_rand = RandomState(1)\n self.np = np\n\n self.state = 1","title":""},{"docid":"b025b34a1936ef033d369b14517a6320","score":"0.5088485","text":"def __init__(self, ntok, ndim, init=df.init.ortho_svd()):\n df.Module.__init__(self)\n\n self.ndim = ndim\n self.W = self._addparam((ntok, ndim), init, name='Wemb_{}x{}'.format(ntok, ndim))","title":""},{"docid":"670b1ee85ca5f25840cd22b783692716","score":"0.5082001","text":"def __init__(self, k: float = 1.0, x_shift: float = 0.0, y_shift: float = 0.0):\n\n self.constants = {self.k: k, self.x_shift: x_shift, self.y_shift: y_shift}\n self.V = self.V_functional.subs(self.constants)\n self.dVdpos = sp.diff(self.V, self.position)\n\n super().__init__()","title":""},{"docid":"5ed8bb627d644964e64e4280b08c7088","score":"0.50803864","text":"def __init__(self, target: \"Expr\", operator: UnOp) -> None:\n self.target = target\n self.operator = operator","title":""},{"docid":"1d3bce030f98c517a2d9c05a1062aa7d","score":"0.5075545","text":"def _kernel_init(scale=1.0, seed=None):\n scale = 2. * scale\n return tf.keras.initializers.VarianceScaling(\n scale=scale, mode='fan_in', distribution='truncated_normal', seed=seed)","title":""},{"docid":"2e3b771b5507abee7a05e1de11e5d55b","score":"0.50715685","text":"def __init__(self, batch_size, embedding_size, k=None, device=None):\n super(SimpleStruct, self).__init__(batch_size, embedding_size)\n operations = [Operation.push, Operation.pop]\n self._reg_trackers = [None for _ in operations]\n self._read_strength = k\n self._values = []\n self._strengths = []\n self._device = device","title":""},{"docid":"14767bc57bcafb626a78c7673062cca6","score":"0.50543296","text":"def __init__(self, tsr):\n super(DenseLinearOperator, self).__init__(tsr)\n self.tensor = tsr","title":""},{"docid":"b638c603a0064f8e19d723f0d6a1a7fb","score":"0.5053925","text":"def _initialise_state(self) -> None:\n super()._initialise_state()\n self.test_window = tf.gather(self.x_ref_eff, self.init_test_inds)\n self.k_xtc = self.kernel(self.test_window, self.kernel_centers)","title":""},{"docid":"1f8713d2cff480f49e5a16bb5caa601b","score":"0.5046923","text":"def __init__(self,\n kernel,\n batch_size,\n max_num_context,\n x_size=1,\n y_size=1,\n testing=False,\n device = torch.device(\"cpu\")):\n self.kernel = kernel\n self._batch_size = batch_size\n self._max_num_context = max_num_context\n self._x_size = x_size\n self._y_size = y_size\n self._testing = testing\n self.device = device","title":""},{"docid":"827ed719975ed277602a055df1fe7bc9","score":"0.50449395","text":"def __init__(self, initializer_params={}, verbose=True, seed=0):\n\n super(Constant, self).__init__(initializer_params, verbose, seed)","title":""},{"docid":"5277911cc013a07ef6cceb5bcfc2fc05","score":"0.5040688","text":"def __init__(self, vec, evotype=\"auto\"):\n vec = SPAMVec.convert_to_vector(vec)\n if evotype == \"auto\":\n evotype = \"statevec\" if _np.iscomplexobj(vec) else \"densitymx\"\n assert(evotype in (\"statevec\", \"densitymx\")), \\\n \"Invalid evolution type '%s' for %s\" % (evotype, self.__class__.__name__)\n DenseSPAMVec.__init__(self, vec, evotype)","title":""},{"docid":"8f2a5100d4a03294b66cfeb709965644","score":"0.5024744","text":"def kernel(self, *args):\n raise NotImplementedError()","title":""},{"docid":"f077d60cbbc17c88aa5fb673dd71f0c3","score":"0.5020952","text":"def __init__(self, vec, evotype=\"auto\"):\n vec = SPAMVec.convert_to_vector(vec)\n if evotype == \"auto\":\n evotype = \"statevec\" if _np.iscomplexobj(vec) else \"densitymx\"\n elif evotype == \"statevec\":\n vec = _np.asarray(vec, complex) # ensure all statevec vecs are complex (densitymx could be either?)\n\n assert(evotype in (\"statevec\", \"densitymx\")), \\\n \"Invalid evolution type '%s' for %s\" % (evotype, self.__class__.__name__)\n DenseSPAMVec.__init__(self, vec, evotype)","title":""},{"docid":"5b73f58df49cdc4fb04ea592cd5a8341","score":"0.5013608","text":"def _define_kernel(self):\n def k(x,y,\n dist=self.dist,\n commonKernel=self.commonKernel,\n blockKernel=self.blockKernel):\n # Geodesic distance. \n d=dist(x[1:],y[1:])\n\n # Calculate the underlying kernel for all subjects.\n cov=commonKernel(d)\n\n # Add individual specific covariance.\n if x[0]==y[0]:\n cov+=blockKernel[int(x[0])](d)\n return cov\n \n self.kernel=k\n \n # Update GPR under-the-hood that relies on this kernel.\n self._update_gp()","title":""},{"docid":"c357b6703e4e6f0eb142d7347a729a71","score":"0.50009483","text":"def _init_variable(self, name, val):\n # return tf.get_variable(name, (), dtype=tf.float32, trainable=False,\n # initializer=tf.constant_initializer(val))\n return tf.constant(name=name, shape=(), dtype=tf.float32, value=val)","title":""},{"docid":"b119ae4642062f2847a8127122d8ef4f","score":"0.50004977","text":"def __init__(self, n_features, kernel):\n self.n_features = n_features\n self.kernel = kernel","title":""},{"docid":"77a92b259d5be88049f3e71e4212ebd7","score":"0.49999592","text":"def __init__(self, kernel, itspace_extents, *args, **kwargs):\n if self._initialized:\n return\n self._parloop = kwargs.get('parloop')\n self.comm = itspace_extents.comm\n self._kernel = self._parloop._kernel\n self._config = kwargs.get('config')\n self._initialized = True","title":""},{"docid":"5ad8752674ae08b5ffe6f6e6d4d2aa61","score":"0.49982068","text":"def __init__(self\n , **kwargs\n ):\n self.t_functorArgs = {}\n self.t_cstArgs = {}\n \n # The data to which the functor is applied\n self.o_vectorDataSet = None\n \n # The functor returned data\n self.x_fData = None\n \n # First get T_FUNCTOR_ARGS\n for t_funcArgTuple in self.__class__.getFunctorArgs():\n \n s_funcArgKey = t_funcArgTuple[ self.U_FUNC_ARG_KEY_INDEX ]\n b_required = t_funcArgTuple[ self.U_FUNC_ARG_REQUIRED_INDEX ]\n \n if not kwargs.has_key( s_funcArgKey ):\n if b_required:\n raise QArkFunctorMissingRequiredFunctorArgError( self.__class__.__name__, s_funcArgKey )\n else:\n self.t_functorArgs[ s_funcArgKey ] = None \n else:\n # Get the QArkFunctorArg\n o_functorArg = kwargs[ s_funcArgKey ]\n self.t_functorArgs[ s_funcArgKey ] = o_functorArg.getValue()\n \n # Get the constant value parameters\n for t_cstArgTuple in self.__class__.getCstArgs():\n \n s_cstArgKey = t_cstArgTuple[ self.U_FUNC_ARG_KEY_INDEX ]\n b_required = t_cstArgTuple[ self.U_FUNC_ARG_REQUIRED_INDEX ]\n \n if not kwargs.has_key( s_cstArgKey ):\n if b_required:\n raise QArkFunctorMissingRequiredFunctorArgError( self.__class__.__name__, s_cstArgKey )\n else:\n self.t_cstArgs[ s_cstArgKey ] = None \n else:\n # Get the QArkFunctorArg\n o_cstArg = kwargs[ s_cstArgKey ]\n self.t_cstArgs[ s_cstArgKey ] = o_cstArg.getValue()","title":""},{"docid":"2dea2f57a1f90dc4b827a67ba680564f","score":"0.4976893","text":"def __init__(self, env, k):\n gym.Wrapper.__init__(self, env)\n self.k = k\n self.states = deque([], maxlen=k)\n shp = self.observation_space.n\n self.observation_space = (k, shp)","title":""},{"docid":"3b767c6f0501614ef6a73b62c8e8eaa9","score":"0.49731472","text":"def init_op_pattern():\n fusion_manager.init_current_op_pattern()","title":""},{"docid":"1e53c3d8b3fa5a4ff22e11b3435270ca","score":"0.49591565","text":"def init(self, net_dim, state_dim, action_dim):","title":""},{"docid":"1e53c3d8b3fa5a4ff22e11b3435270ca","score":"0.49591565","text":"def init(self, net_dim, state_dim, action_dim):","title":""},{"docid":"49a81ef952dca4e2e241406f697edc54","score":"0.49527246","text":"def __init__(self, X, mu, p):\n super(DotProductKernelMap, self).__init__(mu, p)\n self.n = X.shape[0]\n self.d = X.shape[1]\n self.X = X\n self.Gs_train = None","title":""},{"docid":"79466e46f7a122d0032ad903f386dd53","score":"0.4948092","text":"def init_accumulators(self):\n torch.nn.init.constant_(self.mu_numerator, self.eps)\n torch.nn.init.constant_(self.mu_denominator, self.eps * self.C)\n torch.nn.init.constant_(self.var_numerator, self.eps)\n torch.nn.init.constant_(self.var_denominator, self.eps * self.C)","title":""},{"docid":"251831a91ce68da00733b1975980b5f5","score":"0.49430963","text":"def __init__(self, **kwargs):\n LOG.info(\"%s: initing...\" % str(self))\n super(JansenRitDavid, self).__init__(**kwargs)\n\n #self._state_variables = [\"y0\", \"y1\", \"y2\", \"y3\", \"y4\", \"y5\"]\n self._nvar = 8\n\n self.cvar = numpy.array([1,2], dtype=numpy.int32)\n\n #TODO: adding an update_derived_parameters method to remove some of the\n # redundant parameter multiplication in dfun should gain about 7%\n # maybe not worth it... The three exp() kill us at ~90 times *\n #self.nu_max2 = None #2.0 * self.nu_max\n #self.Aa = None # self.A * self.a\n #self.Bb = None # self.B * self.b\n #self.aa = None # self.a**2\n #self.a2 = None # 2.0 * self.a\n #self.b2 = None # 2.0 * self.b\n #self.a_1J = None # self.a_1 * self.J\n #self.a_2J = None # self.a_2 * self.J\n #self.a_3J = None # self.a_3 * self.J\n #self.a_4J = None # self.a_4 * self.J\n\n LOG.debug('%s: inited.' % repr(self))","title":""},{"docid":"8b8907041e456b0c34a14714a13a8534","score":"0.4932093","text":"def init(self):\n self._model = {}\n self._value_function = {}\n self._terminal_state_set = set()\n self._state_action = None","title":""},{"docid":"5399a96be429116cc61fcfd03f89ba41","score":"0.49313113","text":"def _do_data_dependent_init():\n w_fc_normalized = tf.nn.l2_normalize(w_fc.read_value(), [0])\n output_init = tf.matmul(embeddings, w_fc_normalized)\n mean_init, var_init = tf.nn.moments(output_init, [0])\n # Data-dependent init values.\n g_init_value = 1. / tf.sqrt(var_init + 1e-10)\n ops = [tf.assign(g, g_init_value)]\n if not cosine_classifier:\n # Also initialize a bias in a data-dependent way.\n b_fc_init_value = -mean_init * g_init_value\n ops.append(tf.assign(b_fc, b_fc_init_value))\n # Mark that the data-dependent initialization is done to prevent it from\n # happening again in the future.\n ops.append(tf.assign(data_dependent_init_done, 1))\n return tf.group(*ops)","title":""},{"docid":"44c5613c40d97e6454db8adf43bf569c","score":"0.49255443","text":"def init_variable(v, init, name=\"init\"):\n with ops.name_scope(None, v.op.name + \"/\", [v, init]):\n with ops.name_scope(name) as scope:\n with ops.colocate_with(v):\n if callable(init):\n assert v.get_shape().is_fully_defined(), \"Variable shape unknown.\"\n # TODO(mrry): Convert to v.shape when the property and\n # accessor are reconciled (and all initializers support\n # tf.TensorShape objects).\n value = init(v.get_shape().as_list(), v.dtype.base_dtype)\n value = ops.convert_to_tensor(value, name=\"value\")\n return gen_state_ops.assign(v, value, name=scope)\n else:\n init = ops.convert_to_tensor(init, name=\"init\")\n return gen_state_ops.assign(v, init, name=scope)","title":""},{"docid":"7ad5823f394b26d1fc23bd233430ddc7","score":"0.4921869","text":"def __init__(self, component, name, shape, dtype):\n self._name = name\n self._shape = shape\n self._component = component\n beta = tf.get_variable(\n 'beta_%s' % name,\n shape=shape,\n dtype=dtype,\n initializer=tf.zeros_initializer())\n gamma = tf.get_variable(\n 'gamma_%s' % name,\n shape=shape,\n dtype=dtype,\n initializer=tf.ones_initializer())\n self._params = [beta, gamma]","title":""},{"docid":"0729f28466be1f8662a2650d69888e6c","score":"0.49182227","text":"def __init__(self, name, **okwargs):\n assert name in AVAILABLE_KERNELS, \"Unknown kernel %s\" % name\n super(SparseKernel, self).__init__(name, **okwargs)\n self.use_rbf = okwargs.get('use_rbf', name in RBF_KERNELS)\n if name not in RBF_KERNELS and self.use_rbf:\n raise ValueError('Cannot use RBF with non-RBF kernel {}'.format(name))\n # get the module used to compute the sparse distances (FIXME ugly)\n spdist = None\n exec('import sparse_distances.sparse_%s as spdist' % name)\n # define \"raw\" distance functions between sparse vectors (from C library)\n self._v2v_f = spdist.v2v # raw dist. between vectors\n self._v2m_f = spdist.v2m # raw dist. between a vector and a matrix\n self._m2m_f = spdist.m2m # raw dist. between a (test) matrix and a (train) matrix\n self._gram_f = spdist.gram # raw gram matrix of a list of vectors\n if name in ('linear', 'intersection'):\n self.is_additive = True","title":""},{"docid":"696f0b83531e773d2f9e7679ffa66996","score":"0.49122977","text":"def __init__(self, dataset, kernel, model=Exact(), mean=None, name=None, rescale_x=False):\n \n if not isinstance(dataset, DataSet):\n dataset = DataSet(dataset)\n if dataset.get_output_dims() == 0:\n raise ValueError(\"dataset must have at least one channel\")\n names = [name for name in dataset.get_names() if name is not None]\n if len(set(names)) != len(names):\n raise ValueError(\"all data channels must have unique names\")\n\n if rescale_x:\n dataset.rescale_x()\n else:\n for channel in dataset:\n for dim in range(channel.get_input_dims()):\n xran = np.max(channel.X[dim].transformed) - np.min(channel.X[dim].transformed)\n if xran < 1e-3:\n logger.warning(\"Very small X range may give problems, it is suggested to scale up your X axis\")\n elif 1e4 < xran:\n logger.warning(\"Very large X range may give problems, it is suggested to scale down your X axis\")\n\n self.name = name\n self.dataset = dataset\n self.kernel = kernel\n\n X = [[x[channel.mask] for x in channel.X] for channel in self.dataset]\n Y = [np.array(channel.Y[channel.mask]) for channel in self.dataset]\n x, y = self._to_kernel_format(X, Y)\n\n self.model = model.build(kernel, x, y, mean, name)\n if issubclass(type(kernel), MultiOutputKernel) and issubclass(type(model), Exact):\n self.model.variance.assign(0.0, lower=0.0, trainable=False) # handled by MultiOutputKernel","title":""},{"docid":"8cf3117e76c5fabe7b753fb2c1e95073","score":"0.49072382","text":"def __init__(self,oscillators,comb_op):\n self.oscillators=oscillators\n self.comb_op=comb_op","title":""},{"docid":"6a80a836cd045a2779e2c83a489f40bd","score":"0.4900156","text":"def __init__(self, **kwargs):\n LOG.info('%s: initing...' % str(self))\n super(LileySteynRoss, self).__init__(**kwargs)\n #self._state_variables = [\"E\", \"I\"]\n self._nvar = 2\n self.cvar = numpy.array([0, 1], dtype=numpy.int32)\n LOG.debug('%s: inited.' % repr(self))","title":""},{"docid":"a26bdf4c22d69c8784e808227fb4785a","score":"0.4893577","text":"def __init__(self, name, copy=0, val=0, size=1, **kwargs):\r\n self.value = val\r\n if isinstance(self.value, numpy.ndarray):\r\n self.size = self.value.shape[0]\r\n elif isinstance(self.value, list):\r\n self.size = len(self.value)\r\n else:\r\n self.size = size\r\n\r\n super(IndVar, self).__init__(name, copy, **kwargs)\r\n \r\n if 'u_scal' in self.kwargs:\r\n self.u_scal = self.kwargs['u_scal']\r\n else:\r\n self.u_scal = 1.0\r\n if 'f_scal' in self.kwargs:\r\n self.f_scal = self.kwargs['f_scal']\r\n else:\r\n self.f_scal = 1.0","title":""},{"docid":"7f13d984fb9004cdb7e465aa197e71b8","score":"0.4890825","text":"def __init__(self, pool_size, stride, padding=\"VALID\"):\n super().__init__()\n self.kernel_shape = pool_size\n self.stride = stride\n\n self.padding_mode = padding\n self.padding = None\n\n self.input = None\n self.X = None","title":""},{"docid":"20d37517a9cbdc62dadf4417a61d5731","score":"0.48837817","text":"def __init__(self):\n self.da = DynamicArray()","title":""},{"docid":"b0d23029791c70659c983e74c8c7aa81","score":"0.48798135","text":"def setKernel(self, k):\n self.__kernel = k","title":""},{"docid":"2b365597f362f5204053091ab359913b","score":"0.48740718","text":"def __init__(\n self,\n dtensor_components: Tuple[tensor.Tensor],\n global_element_spec: tensor_spec.TensorSpec,\n layouts: Any):\n # dtensor_components is expected to be a single-element tuple.\n [self._iterator_resource_dtensor] = dtensor_components\n self._global_element_spec = global_element_spec\n self._layouts = layouts\n self._layouts_str = nest.map_structure(\n lambda layout: layout.to_string(), layouts)\n\n super().__init__(\n components=dtensor_components, element_spec=global_element_spec)","title":""},{"docid":"0a7fe2b29bb7123c470f655eedadb589","score":"0.48736146","text":"def __init__(self, stride_shape, convolution_shape, num_kernels):\n\n # Inherit parent attributes\n super().__init__()\n\n self.stride_shape = stride_shape\n self.stride_y, self.stride_x = 0, 0\n\n self.convolution_shape = convolution_shape\n self.input_tensor_shape = None\n self.input_tensor = None\n\n self.num_kernels = num_kernels\n\n # Initialize the filter uniformly random in the range [0, 1) as tensor of size: [Nr_kernels x C x M (x N)].\n self.weights = np.random.uniform(size=(self.num_kernels, *self.convolution_shape))\n\n # Initialize the bias uniformly random in the range [0, 1) for each kernel as a single value.\n self.bias = np.random.uniform(size=self.num_kernels)\n\n self.gradient_weights = None # Gradient with respect to the weights.\n self.gradient_bias = None # Gradient with respect to the bias.\n\n self.optimizer = None # Optimizer of this layer.","title":""},{"docid":"c1c2bb027440f1d4c616441288b1acd3","score":"0.48729536","text":"def __init__(self, *args):\n this = _openmm.new_VariableVerletIntegrator(*args)\n try:\n self.this.append(this)\n except:\n self.this = this","title":""},{"docid":"a66eeb8f71b64a13fbd03d9940288a98","score":"0.48687768","text":"def _scalar_op(self, op, o):\n new_parts = []\n new_terms = {term: getattr(term, op)(o) for term in self.expgate_sets.keys()}\n new_parts = ({targets: new_terms[term]\n for targets, term in part.items()}\n for part in self.parts)\n new = self.__class__(*new_parts)\n if self._dense is not None:\n new.dense = getattr(self.dense, op)(o)\n if self._circuit is not None:\n new._circuit = self._circuit\n new._circuit.dt = None\n new.expgate_sets = {new_terms[term]: gate_set\n for term, gate_set in self.expgate_sets.items()}\n return new","title":""},{"docid":"03d9055aa5ebd994aa9b36d5ecc6417d","score":"0.4867485","text":"def __init__(self):\n self.node: Node[K, V] = EmptyNode()","title":""},{"docid":"00babe8f5fa91fe8a9f10ef7c506e6ef","score":"0.4864531","text":"def __init__(self, rho: float, batch_multiplier: float, lambda_vae: float, lambda_cos: float, lambda_l1: float):\n self.rho = rho\n self.lambda_vae = lambda_vae\n self.lambda_cos = lambda_cos\n self.lambda_l1 = lambda_l1\n self.batch_multiplier = batch_multiplier","title":""},{"docid":"665007793f12574d1c320285c82d3a00","score":"0.48640746","text":"def __init__(self, target, key, values=0, active=0):\n \n #Initialized variables\n self.target = target\n self.key = key\n self.values = values\n self.active = active","title":""},{"docid":"6f42e931ee21cb79a96e0b09c1f99d14","score":"0.48613185","text":"def __init__(self, dim): #, length_scale, length_scale_bounds=()):\n# assert isinstance(column, (list, tuple, int)), \"must be int or list of ints\"\n# self.column = [column] if isinstance(column, int) else column\n# assert all(isinstance(i, int) for i in self.column), \"must be integers\"\n self.dim = dim\n \n kernels = [Projection(RBF(), [c]) for c in range(dim)]\n\n # combine the kernels into a single product kernel\n self.kernel = reduce(lambda k0, k1 : k0 * k1, kernels)","title":""},{"docid":"91dee435523b3f227ca893f9e528764f","score":"0.48593938","text":"def __init__(self, operator_id):\n\n _base.Base.__init__(self, _types.COMPONENT_TYPE_OPERATOR)\n self.__op_id = operator_id","title":""},{"docid":"54500e01c5648df0d8f588ea9a0ff3b6","score":"0.48528546","text":"def init(self) -> None:\n self.layout = self.attrs['data_layout']\n\n logger.info(\"Init {} layer: {}\".format(\n 'Conv2D' if self.kernel_groups == 1 else 'DepthWiseConv2D',\n self.layout))\n logger.info(self.input_shapes)\n\n input_shapes, kernel, biases = \\\n self.input_shapes, self.kernel, self.biases\n if kernel is not None:\n kernel = \\\n tf.compat.v1.placeholder_with_default(kernel, kernel.shape)\n else:\n kernel = \\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\n shape=input_shapes[1])\n\n if biases is not None:\n biases = \\\n tf.compat.v1.placeholder_with_default(biases, biases.shape)\n else:\n biases = \\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\n shape=input_shapes[2])\n\n inpt = \\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\n shape=input_shapes[0])\n\n self.inpts = [inpt, kernel, biases]\n\n self.quant_output = self._get_conv_tensor(self.inpts)\n self.res = self.get_output_tensors(self.inpts)[0]\n logger.info(\"Res shape: {}\".format(self.res.shape))","title":""},{"docid":"f3c96da5ae9c96c7d103042f8d26c505","score":"0.4852539","text":"def glv_init(self):\n pass","title":""},{"docid":"453f264a9edca02a883467f14ebb4701","score":"0.4848578","text":"def __init__(self, sg_values): \n self.__a = {} \n self.__init_coefs(sg_values)","title":""},{"docid":"ca7dd23f379b010e3624e42573da451f","score":"0.48449957","text":"def __init__(self):\n self.U, self.s, self.V = (None,None,None)\n self.user_encoder = None\n self.item_encoder = None\n self.mat = None\n self.decomp = False","title":""},{"docid":"159c7de3ad234f5ac44629ce829f08c0","score":"0.48449928","text":"def __init__(self, *args):\n _gp.gp_Vec2d_swiginit(self,_gp.new_gp_Vec2d(*args))","title":""}],"string":"[\n {\n \"docid\": \"31c1f825722e935a9f2040878ee62b3c\",\n \"score\": \"0.6014059\",\n \"text\": \"def init_op(self):\\n return self._init_op\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933dc4e128744f5c26c4a36acf306da6\",\n \"score\": \"0.59667915\",\n \"text\": \"def __init__(self,kernel_fn,*args,order=2,**kwargs):\\n super().__init__(*args,**kwargs)\\n self.dkernel_fn = DiscretizedKernelFN(kernel_fn,order)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d9845f2e29e914f28540bd4faadf50b\",\n \"score\": \"0.5955899\",\n \"text\": \"def __init__(self, X, A, scalar_kernel, scalar_kernel_params):\\n super(DecomposableKernelMap, self).__init__(A, scalar_kernel,\\n scalar_kernel_params)\\n self.n = X.shape[0]\\n self.d = X.shape[1]\\n self.X = X\\n self.Gs_train = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fca6d74bf6efc98b6afae92b5d813d2\",\n \"score\": \"0.594024\",\n \"text\": \"def __init__(self):\\n # set the kernel. One can play with the parameters.\\n self.set_kernel(1, 2.0, 0.98)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab1359409ab9f3bea19235d57510e45\",\n \"score\": \"0.5931874\",\n \"text\": \"def __init__(self):\\n self.d = dd(lambda : 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679da9cba2b0e04380ef1933feec9920\",\n \"score\": \"0.579953\",\n \"text\": \"def _setup_primitive_kernel(request) -> SDEKernel:\\n return _create_markovflow_primitive_kernel(request.param)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32cd9d7e5acbca27580e7960d902ce69\",\n \"score\": \"0.56664544\",\n \"text\": \"def __init__(self, kernel, adopt_thresh, state, target, maxsize, adaptive=True, forget_rate=0.0):\\n\\t\\tself.dp = KernelDict(kernel, adopt_thresh, state, target, maxsize, adaptive, forget_rate)\\n\\t\\tself.P = [[1]]\\n\\t\\tself.Alpha = np.dot(self.dp.Kinv, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d3655e599882c02607bb11bff69504\",\n \"score\": \"0.56652945\",\n \"text\": \"def __init__(self, kernel_size):\\n super().__init__()\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d3655e599882c02607bb11bff69504\",\n \"score\": \"0.56652945\",\n \"text\": \"def __init__(self, kernel_size):\\n super().__init__()\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d3655e599882c02607bb11bff69504\",\n \"score\": \"0.56652945\",\n \"text\": \"def __init__(self, kernel_size):\\n super().__init__()\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31d3655e599882c02607bb11bff69504\",\n \"score\": \"0.56652945\",\n \"text\": \"def __init__(self, kernel_size):\\n super().__init__()\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5acf0ce085e1d9c8d75204b2a828c8ad\",\n \"score\": \"0.56514794\",\n \"text\": \"def _init_op(new_op):\\n\\n def _impl(inputs, attrs):\\n assert len(inputs) == 0\\n shape = attrs.get_int_tuple(\\\"shape\\\")\\n dtype = attrs.get_str(\\\"dtype\\\", \\\"float32\\\")\\n return new_op(shape=shape, dtype=dtype)\\n\\n return _impl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d18640e6b96ae414bc449aa49d579501\",\n \"score\": \"0.5644447\",\n \"text\": \"def __init__(self, kernel=None, noise_var=None, exact_feval=False, optimizer='bfgs',\\n max_iters=1000, optimize_restarts=5, sparse=None, num_inducing=10,\\n verbose=False, ARD=False, seed=42, normalize_Y=True, n_subspaces=12, update_freq=5):\\n self.kernel = kernel\\n self.noise_var = noise_var\\n self.exact_feval = exact_feval\\n self.optimize_restarts = optimize_restarts\\n self.optimizer = optimizer\\n self.max_iters = max_iters\\n self.verbose = verbose\\n self.sparse = sparse\\n self.sparse_surrogate = False\\n self.num_inducing = num_inducing\\n self.model = None\\n self.ARD = ARD\\n self.seed = seed\\n self.update_interval = update_freq\\n self.normalize_Y = normalize_Y\\n self.n_sub = n_subspaces\\n self.n_decomp = 20\\n np.random.rand(self.seed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abb773c00e94f7cd4d00189b8b26809d\",\n \"score\": \"0.5515068\",\n \"text\": \"def __call__(self):\\r\\n new_node = Op.__call__(self)\\r\\n new_node.inputs = []\\r\\n new_node.name = \\\"Global_Variables_Initializer\\\"\\r\\n return new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8983388747a5dd2343aaa072cff2509\",\n \"score\": \"0.5513527\",\n \"text\": \"def kernel(self, value):\\n\\n if value is not None:\\n assert hasattr(\\n value,\\n '__call__'), ('\\\"{0}\\\" attribute: \\\"{1}\\\" is not callable!'.format(\\n 'kernel', value))\\n\\n self._kernel = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a05329e78028c09e4d065fb146db76c7\",\n \"score\": \"0.5498108\",\n \"text\": \"def __init__(\\n self,\\n params: grid_parametrization.GridParametrization,\\n kernel_op: get_kernel_fn.ApplyKernelOp,\\n solver_option: poisson_solver_pb2.PoissonSolver,\\n ):\\n super().__init__(params, kernel_op, solver_option)\\n\\n self._kernel_op.add_kernel({'weighted_sum_121': ([1.0, 2.0, 1.0], 1)})\\n self._omega = solver_option.jacobi.omega\\n self._num_iters = solver_option.jacobi.max_iterations\\n self._halo_width = solver_option.jacobi.halo_width\\n\\n self._factor_b = 0.5 / (params.dx**-2 + params.dy**-2 + params.dz**-2)\\n self._factor_x = params.dx**2 / self._factor_b\\n self._factor_y = params.dy**2 / self._factor_b\\n self._factor_z = params.dz**2 / self._factor_b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dfd0e455ad0824814bf1628e531aa99\",\n \"score\": \"0.5469124\",\n \"text\": \"def __init__(self, initial_value, *args, dtype=None, **kwargs):\\n # Variables by default use the current device scope for placement. This\\n # wrapper has them follow the initial value's placement instead (which will\\n # be the DTensor device if the initial value has a layout).\\n\\n # Pop layout from kwargs since keras make_variable may pass a 'layout'\\n # keyword argument. We need to pop it because we are passing kwargs to\\n # super class constructor.\\n layout = kwargs.pop('layout', None)\\n shape = kwargs.get('shape', None)\\n\\n if callable(initial_value):\\n unwrapped = initial_value\\n if issubclass(type(initial_value), functools.partial):\\n unwrapped = initial_value.func\\n\\n # If wrapped is a CheckpointInitialValueCallable, this means that\\n # we are creating a Variable during a checkpoint restore.\\n # Thus the restore will happen now through this callable\\n # and we will create the DVariable with the restored dtensor.\\n if issubclass(type(unwrapped), trackable.CheckpointInitialValueCallable):\\n if not shape or not layout:\\n raise ValueError('Expected shape and layout to be not None.')\\n\\n # CheckpointInitialValueCallable will call an eager tf.RestoreV2,\\n # which does not have any shape information or layout information\\n # attached. Thus we will do two things to have them correctly specified:\\n #\\n # The default layout scope allows us to correctly specify the output\\n # layout of the tf.RestoreV2 that will be called\\n #\\n # Passing shard_info with the correct shape allows the tf.RestoreV2\\n # ShapeInference to extract the shape.\\n initial_value = api.call_with_layout(\\n initial_value,\\n layout,\\n shard_info=trackable.ShardInfo(\\n shape=shape, offset=[0] * len(shape)))\\n else:\\n initial_value = initial_value()\\n\\n # When the initial value came from a Checkpoint restoration, fetch tensor.\\n if isinstance(initial_value, trackable.CheckpointInitialValue):\\n initial_value = initial_value.wrapped_value\\n\\n initial_value = ops.convert_to_tensor(initial_value, dtype=dtype)\\n variable_device = initial_value.device\\n self._save_as_bf16 = False\\n # TODO(b/159035705): The following code enables variable creation inside\\n # a tf.function. However, it requires a global dtensor device.\\n # if not variable_device and not tf.executing_eagerly():\\n # try:\\n # initial_value.op.get_attr(\\\"_layout\\\")\\n # except ValueError:\\n # pass\\n # else:\\n # # The initial value is a DTensor, but because the DTensor device is\\n # # only active during eager execution at the moment we need to\\n # # translate that into a placement for the eager VarHandleOp.\\n # variable_device = _dtensor_device().name\\n with ops.device(variable_device):\\n # If initial tensor assigned to DVariable is DTensor, record the layout of\\n # the resource so that this can be queried.\\n if context.executing_eagerly():\\n if api.is_dtensor(initial_value):\\n value_layout = api.fetch_layout(initial_value)\\n if layout is not None and layout != value_layout:\\n raise errors_impl.InvalidArgumentError(\\n None,\\n None,\\n 'Conflicting layout are provided for initial '\\n f'value layout ({value_layout}) and variable ({layout}).',\\n )\\n layout = value_layout\\n elif layout is not None:\\n initial_value = api.relayout(initial_value, layout)\\n else:\\n raise errors_impl.InvalidArgumentError(\\n None,\\n None,\\n 'Neither layout nor DTensor initial value are provided.',\\n )\\n self.layout = layout\\n with api.default_mesh(layout.mesh):\\n super(DVariable, self).__init__(\\n initial_value, *args, dtype=dtype, **kwargs\\n )\\n else:\\n # FIXME(175928457): Record value layout in graph mode.\\n if layout is not None:\\n initial_value = api.relayout(initial_value, layout)\\n super(DVariable, self).__init__(\\n initial_value, *args, dtype=dtype, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3de15c5a768f66fe4fbaed3a7d68cc4\",\n \"score\": \"0.54562724\",\n \"text\": \"def __init__(self, kernel=None, hp=None):\\n\\n self.kernel = kernel\\n self.hp = hp\\n self.name = kernel.__name__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f099c9ceb72599c3eb191e27acebee\",\n \"score\": \"0.5444765\",\n \"text\": \"def __init__(\\n self,\\n kernel='linear', # kernel type\\n max_iter=180, # maximum iteration times\\n conv=1e-3, # convergence criterion\\n beta=1e3, # initial beta\\n bias_used=True # bias\\n ):\\n self.kernel = kernel\\n self.n_iter = max_iter\\n self.conv = conv\\n self.beta = beta\\n self.bias_used = bias_used\\n self.alpha, self.alpha_, self.alpha_old, self.beta_, self.relevance, self.phi, self.phi_,\\\\\\n self.y, self.mean_, self.sigma_, self.bias = [None]*11\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa80d6f5fdd7a6d6ca7a2bed06d3ca9\",\n \"score\": \"0.5426685\",\n \"text\": \"def __init__(self, model):\\n self.model = model\\n self.opcache = {} # a cache of operators, which should get initialized by from_vector calls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b823c3cb49da97065fb9a3afeeb8a89\",\n \"score\": \"0.53927\",\n \"text\": \"def __call__(self, initial_value, dtype = None, shape = None, name = \\\"Variable\\\"):\\r\\n new_node = Op.__call__(self)\\r\\n if shape is not None:\\r\\n assert shape == initial_value.shape\\r\\n if dtype is not None:\\r\\n if isinstance(initial_value, np.ndarray):\\r\\n global_variables[new_node] = initial_value.astype(dtype)\\r\\n else:\\r\\n global_variables[new_node] = np.array(initial_value).astype(dtype)\\r\\n else:\\r\\n global_variables[new_node] = initial_value\\r\\n new_node.name = name\\r\\n return new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"303057902312f28c3b3789db58ed8b76\",\n \"score\": \"0.5379558\",\n \"text\": \"def __init__(self, kernel_size):\\n\\n self.stride = kernel_size\\n self.kernel_size = kernel_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b72de45026680e384ba08990058900\",\n \"score\": \"0.5367593\",\n \"text\": \"def __init__(self, **kwargs):\\n\\n LOG.info(\\\"%s: initing...\\\" % str(self))\\n\\n super(Generic2dOscillator, self).__init__(**kwargs)\\n\\n #self._state_variables = [\\\"V\\\", \\\"W\\\"]\\n self._nvar = 2\\n self.cvar = numpy.array([0], dtype=numpy.int32)\\n\\n LOG.debug(\\\"%s: inited.\\\" % repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7aab43ddc245a9aeebd561fb21486f2\",\n \"score\": \"0.53663516\",\n \"text\": \"def _setup_impl(self):\\n\\n self.bpm_program = OCLProgram(absPath(\\\"kernels/bpm_3d_kernels.cl\\\"))\\n\\n Nx, Ny, Nz = self.size\\n\\n self._plan = fft_plan((Ny,Nx))\\n\\n\\n self._H_g = OCLArray.from_array(self._H.astype(np.complex64))\\n\\n if not self.dn is None and self.n_volumes==1:\\n self.dn_g = OCLArray.from_array(self.dn)\\n\\n\\n self.scatter_weights_g = OCLArray.from_array(self.scatter_weights.astype(np.float32))\\n self.gfactor_weights_g = OCLArray.from_array(self.gfactor_weights.astype(np.float32))\\n\\n self.scatter_cross_sec_g = OCLArray.zeros(Nz,\\\"float32\\\")\\n self.gfactor_g = OCLArray.zeros(Nz,\\\"float32\\\")\\n\\n\\n\\n # self.reduce_kernel = OCLReductionKernel(\\n # np.float32, neutral=\\\"0\\\",\\n # reduce_expr=\\\"a+b\\\",\\n # map_expr=\\\"weights[i]*cfloat_abs(field[i]-(i==0)*plain)*cfloat_abs(field[i]-(i==0)*plain)\\\",\\n # arguments=\\\"__global cfloat_t *field, __global float * weights,cfloat_t plain\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fdf5263c27b7114fd643c548c5c86cc\",\n \"score\": \"0.5359017\",\n \"text\": \"def __init__(self, value):\\n\\t\\tsuper(Functor, self).__init__(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb8bab55f68310770b6b4a2897fe8af\",\n \"score\": \"0.53568095\",\n \"text\": \"def __init__(self, kernel_type=None):\\r\\n self.kernel_type = kernel_type\\r\\n self.gamma = 0\\r\\n self.coef = 0\\r\\n self.degree = 0\\r\\n self.num_classes = 0\\r\\n self.labels = None\\r\\n self.total_sv = 0\\r\\n self.rho = 0\\r\\n self.sv = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0967a0baddc13a0d6db87af07c84175b\",\n \"score\": \"0.5346729\",\n \"text\": \"def initialize_operator(self, operator=None, matrix=False, eval_at_once=False):\\n # TODO: Make this more efficient, only compute values needed at each (r,c) step.\\n # For this, 'operator' must support the 'component=(r,c)' option.\\n # Operator is None is interpreted as identity transformation\\n if operator is None:\\n self._operator = lambda nodes, dummy, entry=None: ones((1, nodes.shape[1])) if entry[0] == entry[1] else zeros((1, nodes.shape[1]))\\n else:\\n # Wrap the operator inside a lambda ignoring the dummy parameter\\n # This allows to use the same nsd code for quadrature and matrix construction.\\n if matrix is False:\\n self._operator = lambda nodes, dummy, entry=None: operator(nodes, entry=entry)\\n else:\\n self._operator = operator\\n self._eval_at_once = eval_at_once\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b68ca023fe7092aeaef64e1da8254009\",\n \"score\": \"0.5345871\",\n \"text\": \"def _init_routine(self):\\n if self.is_initialized is False:\\n # decide the routine\\n if self.sn_def['op'] in {'d', 'project'}:\\n # for d kernel_shape [num_in, num_out]; for project, kernel shape [num_class, num_in]\\n assert len(self.kernel_shape) == 2, \\\\\\n '{}: kernel shape {} does not have length 2'.format(self.name_in_err, self.kernel_shape)\\n num_in, num_out = self.kernel_shape\\n # self.use_u = True\\n self.use_u = True if num_in <= num_out else False\\n x_shape = [1, num_in] if self.use_u else [1, num_out]\\n self.forward = self._dense_ if self.use_u else self._dense_t_\\n self.backward = self._dense_t_ if self.use_u else self._dense_\\n elif self.sn_def['op'] in {'cd'}: # kernel_shape [num_class, num_in, num_out]\\n assert len(self.kernel_shape) == 3, \\\\\\n '{}: kernel shape {} does not have length 3'.format(self.name_in_err, self.kernel_shape)\\n num_class, num_in, num_out = self.kernel_shape\\n self.use_u = True if num_in <= num_out else False\\n x_shape = [num_class, 1, num_in] if self.use_u else [num_class, 1, num_out]\\n self.forward = self._dense_ if self.use_u else self._dense_t_\\n self.backward = self._dense_t_ if self.use_u else self._dense_\\n elif self.sn_def['op'] in {'dck'}: # convolution * conditional scale\\n assert isinstance(self.kernel_shape, (list, tuple)) and len(self.kernel_shape) == 2, \\\\\\n '{}: kernel shape must be a list of length 2. Got {}'.format(self.name_in_err, self.kernel_shape)\\n assert len(self.kernel_shape[0]) == 2 and len(self.kernel_shape[1]) == 2, \\\\\\n '{}: kernel shape {} does not have length 2'.format(self.name_in_err, self.kernel_shape)\\n num_in, num_out = self.kernel_shape[0]\\n num_class = self.kernel_shape[1][0]\\n self.use_u = True if num_in <= num_out else False\\n x_shape = [num_class, num_in] if self.use_u else [num_class, num_out]\\n self.forward = (lambda x: self._scalar_(self._dense_(x, index=0), index=1, offset=1.0)) \\\\\\n if self.use_u else (lambda y: self._dense_t_(self._scalar_(y, index=1, offset=1.0), index=0))\\n self.backward = (lambda y: self._dense_t_(self._scalar_(y, index=1, offset=1.0), index=0)) \\\\\\n if self.use_u else (lambda x: self._scalar_(self._dense_(x, index=0), index=1, offset=1.0))\\n elif self.sn_def['op'] in {'c', 'tc'}:\\n assert len(self.kernel_shape) == 4, \\\\\\n '{}: kernel shape {} does not have length 4'.format(self.name_in_err, self.kernel_shape)\\n # self.use_u = True\\n self.use_u = True \\\\\\n if np.prod(self.sn_def['input_shape'][1:]) <= np.prod(self.sn_def['output_shape'][1:]) \\\\\\n else False\\n if self.sn_def['op'] in {'c'}: # input / output shape NCHW or NHWC\\n x_shape = self.sn_def['input_shape'].copy() if self.use_u else self.sn_def['output_shape'].copy()\\n x_shape[0] = 1\\n y_shape = self.sn_def['input_shape'].copy()\\n y_shape[0] = 1\\n elif self.sn_def['op'] in {'tc'}: # tc\\n x_shape = self.sn_def['output_shape'].copy() if self.use_u else self.sn_def['input_shape'].copy()\\n x_shape[0] = 1\\n y_shape = self.sn_def['output_shape'].copy()\\n y_shape[0] = 1\\n else:\\n raise NotImplementedError('{}: {} not implemented.'.format(self.name_in_err, self.sn_def['op']))\\n self.forward = self._conv_ if self.use_u else (lambda y: self._conv_t_(y, x_shape=y_shape))\\n self.backward = (lambda y: self._conv_t_(y, x_shape=y_shape)) if self.use_u else self._conv_\\n elif self.sn_def['op'] in {'cck', 'tcck'}: # convolution * conditional scale\\n assert isinstance(self.kernel_shape, (list, tuple)) and len(self.kernel_shape) == 2, \\\\\\n '{}: kernel shape must be a list of length 2. Got {}'.format(self.name_in_err, self.kernel_shape)\\n assert len(self.kernel_shape[0]) == 4 and len(self.kernel_shape[1]) == 4, \\\\\\n '{}: kernel shape {} does not have length 4'.format(self.name_in_err, self.kernel_shape)\\n self.use_u = True \\\\\\n if np.prod(self.sn_def['input_shape'][1:]) <= np.prod(self.sn_def['output_shape'][1:]) \\\\\\n else False\\n num_class = self.kernel_shape[1][0]\\n if self.sn_def['op'] in {'cck'}: # input / output shape NCHW or NHWC\\n x_shape = self.sn_def['input_shape'].copy() if self.use_u else self.sn_def['output_shape'].copy()\\n x_shape[0] = num_class\\n y_shape = self.sn_def['input_shape'].copy()\\n y_shape[0] = num_class\\n self.forward = (lambda x: self._scalar_(self._conv_(x, index=0), index=1, offset=1.0)) \\\\\\n if self.use_u \\\\\\n else (lambda y: self._conv_t_(self._scalar_(y, index=1, offset=1.0), x_shape=y_shape, index=0))\\n self.backward = (lambda y: self._conv_t_(\\n self._scalar_(y, index=1, offset=1.0), x_shape=y_shape, index=0)) \\\\\\n if self.use_u else (lambda x: self._scalar_(self._conv_(x, index=0), index=1, offset=1.0))\\n elif self.sn_def['op'] in {'tcck'}: # tcck\\n x_shape = self.sn_def['output_shape'].copy() if self.use_u else self.sn_def['input_shape'].copy()\\n x_shape[0] = num_class\\n y_shape = self.sn_def['output_shape'].copy()\\n y_shape[0] = num_class\\n self.forward = (lambda x: self._conv_(self._scalar_(x, index=1, offset=1.0), index=0)) \\\\\\n if self.use_u \\\\\\n else (lambda y: self._scalar_(self._conv_t_(y, x_shape=y_shape, index=0), index=1, offset=1.0))\\n self.backward = (lambda y: self._scalar_(\\n self._conv_t_(y, x_shape=y_shape, index=0), index=1, offset=1.0)) \\\\\\n if self.use_u else (lambda x: self._conv_(self._scalar_(x, index=1, offset=1.0), index=0))\\n else:\\n raise NotImplementedError('{}: {} not implemented.'.format(self.name_in_err, self.sn_def['op']))\\n else:\\n raise NotImplementedError('{}: {} is not implemented.'.format(self.name_in_err, self.sn_def['op']))\\n\\n self.x = tf.compat.v1.get_variable(\\n 'in_rand', shape=x_shape, dtype=tf.float32,\\n initializer=tf.truncated_normal_initializer(), trainable=False)\\n\\n self.is_initialized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2a6967944c915daed81a3fe3422a498\",\n \"score\": \"0.5284371\",\n \"text\": \"def __init__(self, shape, k, filters, kernels):\\n self.dims = shape\\n self.k = k\\n self.filters = filters\\n self.kernels = kernels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d489be3b563e75c12cba4f31c651635\",\n \"score\": \"0.52758646\",\n \"text\": \"def __init__(self, var, accum, linear, grad, indices,\\n lr, lr_power, l1, l2, l2_shrinkage, kernel_name):\\n super().__init__(var, grad, indices, kernel_name)\\n self.lr = lr\\n self.lr_power = lr_power\\n self.l1 = l1\\n self.l2 = l2\\n self.l2_shrinkage = l2_shrinkage\\n self.lr_vrec = 1.0 / self.lr\\n self.var_shape = var.get(\\\"shape\\\")\\n self.var_dtype = var.get(\\\"dtype\\\").lower()\\n self.accum_shape = accum.get(\\\"shape\\\")\\n self.accum_dtype = accum.get(\\\"dtype\\\").lower()\\n self.linear_shape = linear.get(\\\"shape\\\")\\n self.linear_dtype = linear.get(\\\"dtype\\\").lower()\\n self.check_param()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0686a0db70ea3e597fcc88adaa8c3f5\",\n \"score\": \"0.52641636\",\n \"text\": \"def __init__(self):\\n self.da_val = DynamicArray()\\n self.da_max = DynamicArray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24468922248ebc17217ff925e070f54\",\n \"score\": \"0.526289\",\n \"text\": \"def __init__(self, D, kernel_gamma, m, step_size, gamma2=0.1, schedule=None, acc_star=0.234,\\n update_kernel_gamma=None, update_kernel_gamma_schedule=None, update_kernel_gamma_tol=0.1):\\n self.kernel_gamma = kernel_gamma\\n self.m = m\\n self.D = D\\n self.gamma2 = gamma2\\n self.step_size = step_size\\n self.schedule = schedule\\n self.acc_star = acc_star\\n self.update_kernel_gamma = update_kernel_gamma\\n self.update_kernel_gamma_schedule = update_kernel_gamma_schedule\\n self.update_kernel_gamma_tol = update_kernel_gamma_tol\\n \\n # scaling parameter evolution might be collected to assess convergence\\n self.nu2s = [step_size]\\n \\n # some sanity checks\\n if acc_star is not None:\\n assert acc_star > 0 and acc_star < 1\\n \\n if schedule is not None:\\n lmbdas = np.array([schedule(t) for t in np.arange(100)])\\n assert np.all(lmbdas > 0)\\n assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)\\n \\n if self.update_kernel_gamma:\\n self.past_samples = collections.deque()\\n \\n if self.update_kernel_gamma_schedule is not None:\\n lmbdas = np.array([update_kernel_gamma_schedule(t) for t in np.arange(100)])\\n assert np.all(lmbdas > 0)\\n assert np.allclose(np.sort(lmbdas)[::-1], lmbdas)\\n \\n self._initialise()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"260cd30aa6404a0db73c4f2663e7a1d5\",\n \"score\": \"0.5251513\",\n \"text\": \"def __init__(self, value):\\n self.np_value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b98432c1827a929281f2a7f25c397f1\",\n \"score\": \"0.52501065\",\n \"text\": \"def __init__(self, initializer: ArrayOp, index_op: IndexOp):\\n self.initializer = initializer\\n self.index_op = index_op\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6498103bfa75e7a4a24ec77f577d87d\",\n \"score\": \"0.52426285\",\n \"text\": \"def __init__(self, value):\\n self._v = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"601f3074c3929a4c542d73770542d2a7\",\n \"score\": \"0.52349836\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super(DPTuner, self).__init__(*args, **kwargs)\\n self._num_states = self._max_num_states = None\\n self._stage_dict = {}\\n self._dep_dict = {}\\n self._counted_nodes_set = set()\\n\\n self._global_data_dict = {\\n \\\"dtype\\\": self._dtype,\\n \\\"counted_nodes_set\\\": self._counted_nodes_set,\\n \\\"stage_dict\\\": self._stage_dict,\\n \\\"in_nodes_dict\\\": self._in_nodes_dict,\\n \\\"out_nodes_dict\\\": self._out_nodes_dict,\\n \\\"dep_dict\\\": self._dep_dict,\\n \\\"node_list\\\": self._node_list,\\n \\\"input_shapes\\\": self._input_shapes,\\n \\\"layout_transform_interlayer_cost\\\": self._layout_transform_interlayer_cost,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe05bf701bd73f4f90dc69bea2b7c5e7\",\n \"score\": \"0.5229092\",\n \"text\": \"def __init__(self, pars=[], kernel=\\\"\\\"):\\n\\n self._pars = pars\\n self._kernel = kernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c32e4f4c1420ad4bad9df9f953f1cf\",\n \"score\": \"0.52148443\",\n \"text\": \"def init_vector(ocp):\\n v_init = np.ndarray((0, 1))\\n\\n # For states\\n for i_phase in range(len(ocp.nlp)):\\n current_nlp = ocp.nlp[i_phase]\\n\\n repeat = 1\\n if current_nlp.ode_solver.is_direct_collocation:\\n repeat += current_nlp.ode_solver.polynomial_degree\\n\\n nlp = ocp.nlp[current_nlp.use_states_from_phase_idx]\\n OptimizationVectorHelper._set_node_index(nlp, 0)\\n for key in nlp.states:\\n if key in nlp.x_init.keys():\\n n_points = OptimizationVectorHelper._nb_points(nlp, nlp.x_init[key].type)\\n nlp.x_init[key].check_and_adjust_dimensions(nlp.states[key].cx.shape[0], n_points)\\n\\n for k in range(nlp.ns + 1):\\n OptimizationVectorHelper._set_node_index(nlp, k)\\n for p in range(repeat if k != nlp.ns else 1):\\n point = k if k != 0 else 0 if p == 0 else 1\\n\\n collapsed_values = np.ndarray((nlp.states.shape, 1))\\n for key in nlp.states:\\n if key in nlp.x_init.keys():\\n point_to_eval = point\\n if nlp.x_init[key].type == InterpolationType.ALL_POINTS:\\n point_to_eval = k * repeat + p\\n value = (\\n nlp.x_init[key].init.evaluate_at(shooting_point=point_to_eval, repeat=repeat)\\n / nlp.x_scaling[key].scaling\\n )[:, np.newaxis]\\n else:\\n value = 0\\n # Organize the controls according to the correct indices\\n collapsed_values[nlp.states[key].index, :] = value\\n\\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\\n\\n # For controls\\n for i_phase in range(len(ocp.nlp)):\\n current_nlp = ocp.nlp[i_phase]\\n\\n nlp = ocp.nlp[current_nlp.use_controls_from_phase_idx]\\n OptimizationVectorHelper._set_node_index(nlp, 0)\\n if nlp.control_type in (ControlType.CONSTANT, ControlType.NONE):\\n ns = nlp.ns\\n elif nlp.control_type in (ControlType.LINEAR_CONTINUOUS, ControlType.CONSTANT_WITH_LAST_NODE):\\n ns = nlp.ns + 1\\n else:\\n raise NotImplementedError(f\\\"Multiple shooting problem not implemented yet for {nlp.control_type}\\\")\\n\\n for key in nlp.controls.keys():\\n if key in nlp.u_init.keys():\\n nlp.u_init[key].check_and_adjust_dimensions(nlp.controls[key].cx.shape[0], ns - 1)\\n\\n for k in range(ns):\\n OptimizationVectorHelper._set_node_index(nlp, k)\\n collapsed_values = np.ndarray((nlp.controls.shape, 1))\\n for key in nlp.controls:\\n if key in nlp.u_init.keys():\\n value = nlp.u_init[key].init.evaluate_at(shooting_point=k) / nlp.u_scaling[key].scaling\\n else:\\n value = 0\\n\\n # Organize the controls according to the correct indices\\n collapsed_values[nlp.controls[key].index, 0] = value\\n\\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\\n\\n # For parameters\\n collapsed_values = np.zeros((ocp.parameters.shape, 1))\\n for key in ocp.parameters.keys():\\n if key not in ocp.parameter_init.keys():\\n v_init = np.concatenate((v_init, np.zeros((ocp.parameters[key].size, 1))))\\n continue\\n\\n scaled_init = ocp.parameter_init[key].scale(ocp.parameters[key].scaling)\\n collapsed_values[ocp.parameters[key].index, :] = scaled_init.init\\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\\n\\n # For stochastic variables\\n for i_phase in range(len(ocp.nlp)):\\n nlp = ocp.nlp[i_phase]\\n OptimizationVectorHelper._set_node_index(nlp, 0)\\n\\n for key in nlp.stochastic_variables.keys():\\n if key in nlp.s_init.keys():\\n nlp.s_init[key].check_and_adjust_dimensions(nlp.stochastic_variables[key].cx.shape[0], nlp.ns)\\n\\n for k in range(nlp.ns + 1):\\n OptimizationVectorHelper._set_node_index(nlp, k)\\n collapsed_values = np.ndarray((nlp.stochastic_variables.shape, 1))\\n for key in nlp.stochastic_variables:\\n if key in nlp.s_init.keys():\\n value = nlp.s_init[key].init.evaluate_at(shooting_point=k) / nlp.s_scaling[key].scaling\\n else:\\n value = 0\\n\\n # Organize the stochastic variables according to the correct indices\\n collapsed_values[nlp.stochastic_variables[key].index, 0] = value\\n\\n v_init = np.concatenate((v_init, np.reshape(collapsed_values.T, (-1, 1))))\\n\\n for i_phase in range(len(ocp.nlp)):\\n nlp = ocp.nlp[i_phase]\\n if nlp.motor_noise is not None:\\n n_motor_noise = nlp.motor_noise.shape[0]\\n n_sensory_noise = nlp.sensory_noise.shape[0]\\n v_init = np.concatenate((v_init, np.zeros((n_motor_noise, 1))))\\n v_init = np.concatenate((v_init, np.zeros((n_sensory_noise, 1))))\\n\\n return v_init\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f21f06197faa3e88badb2b42da306fe1\",\n \"score\": \"0.52086794\",\n \"text\": \"def __init__(self):\\r\\n self.inputs = []\\r\\n self.op = None\\r\\n self.const_attr = None\\r\\n self.name = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d475a0425781c6d7fa1542d8463f437\",\n \"score\": \"0.5186119\",\n \"text\": \"def __init__(self, name: tp.Optional[str] = None, dtype: np.dtype = jnp.float32):\\n self.name = name if name else utils.lower_snake_case(self.__class__.__name__)\\n self.dtype = dtype\\n self._params = {}\\n self._states = []\\n self._submodules = []\\n self._dynamic_submodules = []\\n self._initialized = False\\n self._trainable = True\\n\\n _init = self.init\\n\\n def init(*args, **kwargs):\\n return _init(*args, **kwargs)\\n\\n self.init = init\\n\\n utils.wraps(self.call)(self.init)\\n utils.wraps(self.call)(self)\\n\\n self._jit_functions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede1473867441e7e3658e63982b4fb4e\",\n \"score\": \"0.5181392\",\n \"text\": \"def Do_kernel(self):\\n return self.Kernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7c2366c48398d16ed6ca58d56cb137\",\n \"score\": \"0.5176585\",\n \"text\": \"def __init__(self, x=None):\\n self.v = x\\n self.cl = None\\n self.cr = None\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a718b8ed355be4f8f776d69cf7d2da\",\n \"score\": \"0.51614505\",\n \"text\": \"def __init__(self, pyo, t=None):\\n if type(pyo) is Numeric.ArrayType:\\n k = _arraytok(pyo) \\n elif type(pyo) is _K:\\n k = pyo\\n if t and t != k.t:\\n raise NotImplemented\\n else:\\n if not t:\\n t = _guesst(pyo)\\n else:\\n if t > 0:\\n pyt = _pyt[t-1]\\n pyo = pyt(pyo)\\n elif t < 0:\\n pyt = _pyt[-t-1]\\n pyo = map(pyt, pyo)\\n kgen = _kgen(t)\\n k = kgen(pyo)\\n _K.__init__(self, k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b870f3d7f5b6706d9c9597f891c73f95\",\n \"score\": \"0.5157406\",\n \"text\": \"def _v__init__(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba1f95b84ca10b7b07a5b2ccf6e80a2\",\n \"score\": \"0.514581\",\n \"text\": \"def __init__(self, shape, dtype):\\n self._dtype = dtype\\n self._sum = tf.Variable(lambda: tf.zeros(shape, dtype), False)\\n self._count = tf.Variable(lambda: 0, trainable=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff5d8627423d0ced3926413340e0e896\",\n \"score\": \"0.51323915\",\n \"text\": \"def __init__(self, master=None, value=None, name=None):\\n _TK.Variable.__init__(self, master, value, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bb7d41df8f841d7b93509b616075367\",\n \"score\": \"0.5131436\",\n \"text\": \"def __init__(self,kernel,args,invocations=None,type='VERTICAL'):\\n\\t\\tself.args = []\\n\\t\\tself.kernel = \\\"\\\"\\n\\t\\tif(invocations == None):\\n\\t\\t\\ttemp = kernel.getSub(\\\"clEnqueueNDRangeKernel\\\")\\n\\t\\t\\tif(temp is not None):\\n\\t\\t\\t\\tself.kernel = temp\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint \\\"Invalid kernel statement\\\"\\n\\t\\t\\tfor arg in args:\\n\\t\\t\\t\\ttemp = arg.getSub(\\\"clSetKernelArg\\\")\\n\\t\\t\\t\\tif(temp is not None):\\n\\t\\t\\t\\t\\tif(temp.children[0] == self.kernel.children[1]):\\n\\t\\t\\t\\t\\t\\tself.args.append((temp.children[2],temp.children[3]))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint \\\"invalid kernel argument\\\"\\n\\t\\telse:\\n\\t\\t\\tself.kernel = copy.deepcopy(invocations[0].kernel)\\n\\t\\t\\tself.kernel.children[1].tokens[0].value = \\\"\\\"\\n\\t\\t\\tfor invocation in invocations:\\n\\t\\t\\t\\tself.kernel.children[1].tokens[0].value += invocation.kernel.children[1].tokens[0].value.split('_')[0] + \\\"_\\\"\\n\\t\\t\\t\\tself.args += invocation.args\\n\\t\\t\\tself.kernel.children[1].tokens[0].value += \\\"kernel\\\"\\n\\t\\t\\tif(type == 'HORIZONTAL'):\\n\\t\\t\\t\\t\\\"\\\"\\\"Append an h\\\"\\\"\\\"\\n\\t\\t\\t\\tself.kernel.children[1].tokens[0].value +='h'\\t\\n\\t\\t\\tself.args = removeDuplicates2(self.args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429e749ed7cc10f710ceb9a638caf71f\",\n \"score\": \"0.5110538\",\n \"text\": \"def __init__(self, device_params = DEFAULT_DEVICE_PARAMS):\\n self.device_names = list(device_params.keys())\\n self.dirichlet_params = list(device_params.values())\\n self.p_vector = dirichlet(self.dirichlet_params).rvs().reshape(-1,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e245d650938297578a09e6890a46c981\",\n \"score\": \"0.51071066\",\n \"text\": \"def __init__(self, potential, flow_operator_mask_v, flow_operator_mask_k):\\n self._potential = potential\\n self._v = potential.without_weights()\\n self._k = potential.kinetic_energy()\\n self._lam = potential.lam\\n self._flow_op_mask_v = flow_operator_mask_v\\n self._flow_op_mask_k = flow_operator_mask_k\\n self._flow = 'lambda'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d03dfd8f3467b96acfbfa3c3a0d282\",\n \"score\": \"0.5101611\",\n \"text\": \"def __init__(\\n self,\\n kernel_op: get_kernel_fn.ApplyKernelOp,\\n params: parameters_lib.SwirlLMParameters,\\n scalar_name: str,\\n thermodynamics: thermodynamics_manager.ThermodynamicsManager,\\n ):\\n self._kernel_op = kernel_op\\n self._params = params\\n self._bc_types = copy.deepcopy(params.bc_type)\\n self._scalar_name = scalar_name\\n self._thermodynamics = thermodynamics\\n\\n self._h = (self._params.dx, self._params.dy, self._params.dz)\\n\\n self._scalar_params = None\\n for scalar in self._params.scalars:\\n if scalar.name == self._scalar_name:\\n self._scalar_params = scalar\\n break\\n assert (\\n self._scalar_params is not None\\n ), f'{self._scalar_name} is not configured.'\\n\\n for override_bc in self._scalar_params.override_bc_type:\\n dim = override_bc.dim\\n face = override_bc.face\\n logging.info('BC type for scalar: %s '\\n 'will be reset to %s (originally %s) for dimension %d at '\\n 'face %d', self._scalar_name,\\n str(boundary_condition_utils.BoundaryType.UNKNOWN),\\n str(self._bc_types[dim][face]), dim, face)\\n self._bc_types[dim][face] = (\\n boundary_condition_utils.BoundaryType.UNKNOWN)\\n\\n # Find the direction of gravity. Only vector along a particular dimension is\\n # considered currently.\\n self._g_vec = (\\n self._params.gravity_direction if self._params.gravity_direction else [\\n 0.0,\\n ] * 3)\\n self._g_dim = None\\n for i in range(3):\\n if np.abs(np.abs(self._g_vec[i]) - 1.0) < _G_THRESHOLD:\\n self._g_dim = i\\n break\\n\\n # Prepare diffusion related models.\\n self._diffusion_fn = diffusion.diffusion_scalar(self._params)\\n self._use_sgs = self._params.use_sgs\\n filter_widths = (self._params.dx, self._params.dy, self._params.dz)\\n if self._use_sgs:\\n self._sgs_model = sgs_model.SgsModel(self._kernel_op, filter_widths,\\n params.sgs_model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6c3eaa187c603279b9eacd627bd1e8a\",\n \"score\": \"0.5097064\",\n \"text\": \"def __init__(self, K, delta, c, np = None):\\n\\n self.K = float(K)\\n self.K_int = int(K)\\n self.delta = delta\\n self.c = c\\n\\n S = self.calc_S()\\n cdf, Z = gen_rsd_cdf(K, S, delta)\\n self.cdf = cdf\\n self.Z = Z\\n\\n #self.inter = inter.interp1d(np.concatenate(([0], cdf)), range(0,K+1))\\n self.np_rand = RandomState(1)\\n self.np = np\\n\\n self.state = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b025b34a1936ef033d369b14517a6320\",\n \"score\": \"0.5088485\",\n \"text\": \"def __init__(self, ntok, ndim, init=df.init.ortho_svd()):\\n df.Module.__init__(self)\\n\\n self.ndim = ndim\\n self.W = self._addparam((ntok, ndim), init, name='Wemb_{}x{}'.format(ntok, ndim))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"670b1ee85ca5f25840cd22b783692716\",\n \"score\": \"0.5082001\",\n \"text\": \"def __init__(self, k: float = 1.0, x_shift: float = 0.0, y_shift: float = 0.0):\\n\\n self.constants = {self.k: k, self.x_shift: x_shift, self.y_shift: y_shift}\\n self.V = self.V_functional.subs(self.constants)\\n self.dVdpos = sp.diff(self.V, self.position)\\n\\n super().__init__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed8bb627d644964e64e4280b08c7088\",\n \"score\": \"0.50803864\",\n \"text\": \"def __init__(self, target: \\\"Expr\\\", operator: UnOp) -> None:\\n self.target = target\\n self.operator = operator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3bce030f98c517a2d9c05a1062aa7d\",\n \"score\": \"0.5075545\",\n \"text\": \"def _kernel_init(scale=1.0, seed=None):\\n scale = 2. * scale\\n return tf.keras.initializers.VarianceScaling(\\n scale=scale, mode='fan_in', distribution='truncated_normal', seed=seed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3b771b5507abee7a05e1de11e5d55b\",\n \"score\": \"0.50715685\",\n \"text\": \"def __init__(self, batch_size, embedding_size, k=None, device=None):\\n super(SimpleStruct, self).__init__(batch_size, embedding_size)\\n operations = [Operation.push, Operation.pop]\\n self._reg_trackers = [None for _ in operations]\\n self._read_strength = k\\n self._values = []\\n self._strengths = []\\n self._device = device\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14767bc57bcafb626a78c7673062cca6\",\n \"score\": \"0.50543296\",\n \"text\": \"def __init__(self, tsr):\\n super(DenseLinearOperator, self).__init__(tsr)\\n self.tensor = tsr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b638c603a0064f8e19d723f0d6a1a7fb\",\n \"score\": \"0.5053925\",\n \"text\": \"def _initialise_state(self) -> None:\\n super()._initialise_state()\\n self.test_window = tf.gather(self.x_ref_eff, self.init_test_inds)\\n self.k_xtc = self.kernel(self.test_window, self.kernel_centers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f8713d2cff480f49e5a16bb5caa601b\",\n \"score\": \"0.5046923\",\n \"text\": \"def __init__(self,\\n kernel,\\n batch_size,\\n max_num_context,\\n x_size=1,\\n y_size=1,\\n testing=False,\\n device = torch.device(\\\"cpu\\\")):\\n self.kernel = kernel\\n self._batch_size = batch_size\\n self._max_num_context = max_num_context\\n self._x_size = x_size\\n self._y_size = y_size\\n self._testing = testing\\n self.device = device\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827ed719975ed277602a055df1fe7bc9\",\n \"score\": \"0.50449395\",\n \"text\": \"def __init__(self, initializer_params={}, verbose=True, seed=0):\\n\\n super(Constant, self).__init__(initializer_params, verbose, seed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5277911cc013a07ef6cceb5bcfc2fc05\",\n \"score\": \"0.5040688\",\n \"text\": \"def __init__(self, vec, evotype=\\\"auto\\\"):\\n vec = SPAMVec.convert_to_vector(vec)\\n if evotype == \\\"auto\\\":\\n evotype = \\\"statevec\\\" if _np.iscomplexobj(vec) else \\\"densitymx\\\"\\n assert(evotype in (\\\"statevec\\\", \\\"densitymx\\\")), \\\\\\n \\\"Invalid evolution type '%s' for %s\\\" % (evotype, self.__class__.__name__)\\n DenseSPAMVec.__init__(self, vec, evotype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2a5100d4a03294b66cfeb709965644\",\n \"score\": \"0.5024744\",\n \"text\": \"def kernel(self, *args):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f077d60cbbc17c88aa5fb673dd71f0c3\",\n \"score\": \"0.5020952\",\n \"text\": \"def __init__(self, vec, evotype=\\\"auto\\\"):\\n vec = SPAMVec.convert_to_vector(vec)\\n if evotype == \\\"auto\\\":\\n evotype = \\\"statevec\\\" if _np.iscomplexobj(vec) else \\\"densitymx\\\"\\n elif evotype == \\\"statevec\\\":\\n vec = _np.asarray(vec, complex) # ensure all statevec vecs are complex (densitymx could be either?)\\n\\n assert(evotype in (\\\"statevec\\\", \\\"densitymx\\\")), \\\\\\n \\\"Invalid evolution type '%s' for %s\\\" % (evotype, self.__class__.__name__)\\n DenseSPAMVec.__init__(self, vec, evotype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b73f58df49cdc4fb04ea592cd5a8341\",\n \"score\": \"0.5013608\",\n \"text\": \"def _define_kernel(self):\\n def k(x,y,\\n dist=self.dist,\\n commonKernel=self.commonKernel,\\n blockKernel=self.blockKernel):\\n # Geodesic distance. \\n d=dist(x[1:],y[1:])\\n\\n # Calculate the underlying kernel for all subjects.\\n cov=commonKernel(d)\\n\\n # Add individual specific covariance.\\n if x[0]==y[0]:\\n cov+=blockKernel[int(x[0])](d)\\n return cov\\n \\n self.kernel=k\\n \\n # Update GPR under-the-hood that relies on this kernel.\\n self._update_gp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c357b6703e4e6f0eb142d7347a729a71\",\n \"score\": \"0.50009483\",\n \"text\": \"def _init_variable(self, name, val):\\n # return tf.get_variable(name, (), dtype=tf.float32, trainable=False,\\n # initializer=tf.constant_initializer(val))\\n return tf.constant(name=name, shape=(), dtype=tf.float32, value=val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b119ae4642062f2847a8127122d8ef4f\",\n \"score\": \"0.50004977\",\n \"text\": \"def __init__(self, n_features, kernel):\\n self.n_features = n_features\\n self.kernel = kernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77a92b259d5be88049f3e71e4212ebd7\",\n \"score\": \"0.49999592\",\n \"text\": \"def __init__(self, kernel, itspace_extents, *args, **kwargs):\\n if self._initialized:\\n return\\n self._parloop = kwargs.get('parloop')\\n self.comm = itspace_extents.comm\\n self._kernel = self._parloop._kernel\\n self._config = kwargs.get('config')\\n self._initialized = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad8752674ae08b5ffe6f6e6d4d2aa61\",\n \"score\": \"0.49982068\",\n \"text\": \"def __init__(self\\n , **kwargs\\n ):\\n self.t_functorArgs = {}\\n self.t_cstArgs = {}\\n \\n # The data to which the functor is applied\\n self.o_vectorDataSet = None\\n \\n # The functor returned data\\n self.x_fData = None\\n \\n # First get T_FUNCTOR_ARGS\\n for t_funcArgTuple in self.__class__.getFunctorArgs():\\n \\n s_funcArgKey = t_funcArgTuple[ self.U_FUNC_ARG_KEY_INDEX ]\\n b_required = t_funcArgTuple[ self.U_FUNC_ARG_REQUIRED_INDEX ]\\n \\n if not kwargs.has_key( s_funcArgKey ):\\n if b_required:\\n raise QArkFunctorMissingRequiredFunctorArgError( self.__class__.__name__, s_funcArgKey )\\n else:\\n self.t_functorArgs[ s_funcArgKey ] = None \\n else:\\n # Get the QArkFunctorArg\\n o_functorArg = kwargs[ s_funcArgKey ]\\n self.t_functorArgs[ s_funcArgKey ] = o_functorArg.getValue()\\n \\n # Get the constant value parameters\\n for t_cstArgTuple in self.__class__.getCstArgs():\\n \\n s_cstArgKey = t_cstArgTuple[ self.U_FUNC_ARG_KEY_INDEX ]\\n b_required = t_cstArgTuple[ self.U_FUNC_ARG_REQUIRED_INDEX ]\\n \\n if not kwargs.has_key( s_cstArgKey ):\\n if b_required:\\n raise QArkFunctorMissingRequiredFunctorArgError( self.__class__.__name__, s_cstArgKey )\\n else:\\n self.t_cstArgs[ s_cstArgKey ] = None \\n else:\\n # Get the QArkFunctorArg\\n o_cstArg = kwargs[ s_cstArgKey ]\\n self.t_cstArgs[ s_cstArgKey ] = o_cstArg.getValue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dea2f57a1f90dc4b827a67ba680564f\",\n \"score\": \"0.4976893\",\n \"text\": \"def __init__(self, env, k):\\n gym.Wrapper.__init__(self, env)\\n self.k = k\\n self.states = deque([], maxlen=k)\\n shp = self.observation_space.n\\n self.observation_space = (k, shp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b767c6f0501614ef6a73b62c8e8eaa9\",\n \"score\": \"0.49731472\",\n \"text\": \"def init_op_pattern():\\n fusion_manager.init_current_op_pattern()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e53c3d8b3fa5a4ff22e11b3435270ca\",\n \"score\": \"0.49591565\",\n \"text\": \"def init(self, net_dim, state_dim, action_dim):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e53c3d8b3fa5a4ff22e11b3435270ca\",\n \"score\": \"0.49591565\",\n \"text\": \"def init(self, net_dim, state_dim, action_dim):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a81ef952dca4e2e241406f697edc54\",\n \"score\": \"0.49527246\",\n \"text\": \"def __init__(self, X, mu, p):\\n super(DotProductKernelMap, self).__init__(mu, p)\\n self.n = X.shape[0]\\n self.d = X.shape[1]\\n self.X = X\\n self.Gs_train = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79466e46f7a122d0032ad903f386dd53\",\n \"score\": \"0.4948092\",\n \"text\": \"def init_accumulators(self):\\n torch.nn.init.constant_(self.mu_numerator, self.eps)\\n torch.nn.init.constant_(self.mu_denominator, self.eps * self.C)\\n torch.nn.init.constant_(self.var_numerator, self.eps)\\n torch.nn.init.constant_(self.var_denominator, self.eps * self.C)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251831a91ce68da00733b1975980b5f5\",\n \"score\": \"0.49430963\",\n \"text\": \"def __init__(self, **kwargs):\\n LOG.info(\\\"%s: initing...\\\" % str(self))\\n super(JansenRitDavid, self).__init__(**kwargs)\\n\\n #self._state_variables = [\\\"y0\\\", \\\"y1\\\", \\\"y2\\\", \\\"y3\\\", \\\"y4\\\", \\\"y5\\\"]\\n self._nvar = 8\\n\\n self.cvar = numpy.array([1,2], dtype=numpy.int32)\\n\\n #TODO: adding an update_derived_parameters method to remove some of the\\n # redundant parameter multiplication in dfun should gain about 7%\\n # maybe not worth it... The three exp() kill us at ~90 times *\\n #self.nu_max2 = None #2.0 * self.nu_max\\n #self.Aa = None # self.A * self.a\\n #self.Bb = None # self.B * self.b\\n #self.aa = None # self.a**2\\n #self.a2 = None # 2.0 * self.a\\n #self.b2 = None # 2.0 * self.b\\n #self.a_1J = None # self.a_1 * self.J\\n #self.a_2J = None # self.a_2 * self.J\\n #self.a_3J = None # self.a_3 * self.J\\n #self.a_4J = None # self.a_4 * self.J\\n\\n LOG.debug('%s: inited.' % repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b8907041e456b0c34a14714a13a8534\",\n \"score\": \"0.4932093\",\n \"text\": \"def init(self):\\n self._model = {}\\n self._value_function = {}\\n self._terminal_state_set = set()\\n self._state_action = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5399a96be429116cc61fcfd03f89ba41\",\n \"score\": \"0.49313113\",\n \"text\": \"def _do_data_dependent_init():\\n w_fc_normalized = tf.nn.l2_normalize(w_fc.read_value(), [0])\\n output_init = tf.matmul(embeddings, w_fc_normalized)\\n mean_init, var_init = tf.nn.moments(output_init, [0])\\n # Data-dependent init values.\\n g_init_value = 1. / tf.sqrt(var_init + 1e-10)\\n ops = [tf.assign(g, g_init_value)]\\n if not cosine_classifier:\\n # Also initialize a bias in a data-dependent way.\\n b_fc_init_value = -mean_init * g_init_value\\n ops.append(tf.assign(b_fc, b_fc_init_value))\\n # Mark that the data-dependent initialization is done to prevent it from\\n # happening again in the future.\\n ops.append(tf.assign(data_dependent_init_done, 1))\\n return tf.group(*ops)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c5613c40d97e6454db8adf43bf569c\",\n \"score\": \"0.49255443\",\n \"text\": \"def init_variable(v, init, name=\\\"init\\\"):\\n with ops.name_scope(None, v.op.name + \\\"/\\\", [v, init]):\\n with ops.name_scope(name) as scope:\\n with ops.colocate_with(v):\\n if callable(init):\\n assert v.get_shape().is_fully_defined(), \\\"Variable shape unknown.\\\"\\n # TODO(mrry): Convert to v.shape when the property and\\n # accessor are reconciled (and all initializers support\\n # tf.TensorShape objects).\\n value = init(v.get_shape().as_list(), v.dtype.base_dtype)\\n value = ops.convert_to_tensor(value, name=\\\"value\\\")\\n return gen_state_ops.assign(v, value, name=scope)\\n else:\\n init = ops.convert_to_tensor(init, name=\\\"init\\\")\\n return gen_state_ops.assign(v, init, name=scope)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad5823f394b26d1fc23bd233430ddc7\",\n \"score\": \"0.4921869\",\n \"text\": \"def __init__(self, component, name, shape, dtype):\\n self._name = name\\n self._shape = shape\\n self._component = component\\n beta = tf.get_variable(\\n 'beta_%s' % name,\\n shape=shape,\\n dtype=dtype,\\n initializer=tf.zeros_initializer())\\n gamma = tf.get_variable(\\n 'gamma_%s' % name,\\n shape=shape,\\n dtype=dtype,\\n initializer=tf.ones_initializer())\\n self._params = [beta, gamma]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0729f28466be1f8662a2650d69888e6c\",\n \"score\": \"0.49182227\",\n \"text\": \"def __init__(self, name, **okwargs):\\n assert name in AVAILABLE_KERNELS, \\\"Unknown kernel %s\\\" % name\\n super(SparseKernel, self).__init__(name, **okwargs)\\n self.use_rbf = okwargs.get('use_rbf', name in RBF_KERNELS)\\n if name not in RBF_KERNELS and self.use_rbf:\\n raise ValueError('Cannot use RBF with non-RBF kernel {}'.format(name))\\n # get the module used to compute the sparse distances (FIXME ugly)\\n spdist = None\\n exec('import sparse_distances.sparse_%s as spdist' % name)\\n # define \\\"raw\\\" distance functions between sparse vectors (from C library)\\n self._v2v_f = spdist.v2v # raw dist. between vectors\\n self._v2m_f = spdist.v2m # raw dist. between a vector and a matrix\\n self._m2m_f = spdist.m2m # raw dist. between a (test) matrix and a (train) matrix\\n self._gram_f = spdist.gram # raw gram matrix of a list of vectors\\n if name in ('linear', 'intersection'):\\n self.is_additive = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"696f0b83531e773d2f9e7679ffa66996\",\n \"score\": \"0.49122977\",\n \"text\": \"def __init__(self, dataset, kernel, model=Exact(), mean=None, name=None, rescale_x=False):\\n \\n if not isinstance(dataset, DataSet):\\n dataset = DataSet(dataset)\\n if dataset.get_output_dims() == 0:\\n raise ValueError(\\\"dataset must have at least one channel\\\")\\n names = [name for name in dataset.get_names() if name is not None]\\n if len(set(names)) != len(names):\\n raise ValueError(\\\"all data channels must have unique names\\\")\\n\\n if rescale_x:\\n dataset.rescale_x()\\n else:\\n for channel in dataset:\\n for dim in range(channel.get_input_dims()):\\n xran = np.max(channel.X[dim].transformed) - np.min(channel.X[dim].transformed)\\n if xran < 1e-3:\\n logger.warning(\\\"Very small X range may give problems, it is suggested to scale up your X axis\\\")\\n elif 1e4 < xran:\\n logger.warning(\\\"Very large X range may give problems, it is suggested to scale down your X axis\\\")\\n\\n self.name = name\\n self.dataset = dataset\\n self.kernel = kernel\\n\\n X = [[x[channel.mask] for x in channel.X] for channel in self.dataset]\\n Y = [np.array(channel.Y[channel.mask]) for channel in self.dataset]\\n x, y = self._to_kernel_format(X, Y)\\n\\n self.model = model.build(kernel, x, y, mean, name)\\n if issubclass(type(kernel), MultiOutputKernel) and issubclass(type(model), Exact):\\n self.model.variance.assign(0.0, lower=0.0, trainable=False) # handled by MultiOutputKernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf3117e76c5fabe7b753fb2c1e95073\",\n \"score\": \"0.49072382\",\n \"text\": \"def __init__(self,oscillators,comb_op):\\n self.oscillators=oscillators\\n self.comb_op=comb_op\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a80a836cd045a2779e2c83a489f40bd\",\n \"score\": \"0.4900156\",\n \"text\": \"def __init__(self, **kwargs):\\n LOG.info('%s: initing...' % str(self))\\n super(LileySteynRoss, self).__init__(**kwargs)\\n #self._state_variables = [\\\"E\\\", \\\"I\\\"]\\n self._nvar = 2\\n self.cvar = numpy.array([0, 1], dtype=numpy.int32)\\n LOG.debug('%s: inited.' % repr(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a26bdf4c22d69c8784e808227fb4785a\",\n \"score\": \"0.4893577\",\n \"text\": \"def __init__(self, name, copy=0, val=0, size=1, **kwargs):\\r\\n self.value = val\\r\\n if isinstance(self.value, numpy.ndarray):\\r\\n self.size = self.value.shape[0]\\r\\n elif isinstance(self.value, list):\\r\\n self.size = len(self.value)\\r\\n else:\\r\\n self.size = size\\r\\n\\r\\n super(IndVar, self).__init__(name, copy, **kwargs)\\r\\n \\r\\n if 'u_scal' in self.kwargs:\\r\\n self.u_scal = self.kwargs['u_scal']\\r\\n else:\\r\\n self.u_scal = 1.0\\r\\n if 'f_scal' in self.kwargs:\\r\\n self.f_scal = self.kwargs['f_scal']\\r\\n else:\\r\\n self.f_scal = 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f13d984fb9004cdb7e465aa197e71b8\",\n \"score\": \"0.4890825\",\n \"text\": \"def __init__(self, pool_size, stride, padding=\\\"VALID\\\"):\\n super().__init__()\\n self.kernel_shape = pool_size\\n self.stride = stride\\n\\n self.padding_mode = padding\\n self.padding = None\\n\\n self.input = None\\n self.X = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d37517a9cbdc62dadf4417a61d5731\",\n \"score\": \"0.48837817\",\n \"text\": \"def __init__(self):\\n self.da = DynamicArray()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0d23029791c70659c983e74c8c7aa81\",\n \"score\": \"0.48798135\",\n \"text\": \"def setKernel(self, k):\\n self.__kernel = k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b365597f362f5204053091ab359913b\",\n \"score\": \"0.48740718\",\n \"text\": \"def __init__(\\n self,\\n dtensor_components: Tuple[tensor.Tensor],\\n global_element_spec: tensor_spec.TensorSpec,\\n layouts: Any):\\n # dtensor_components is expected to be a single-element tuple.\\n [self._iterator_resource_dtensor] = dtensor_components\\n self._global_element_spec = global_element_spec\\n self._layouts = layouts\\n self._layouts_str = nest.map_structure(\\n lambda layout: layout.to_string(), layouts)\\n\\n super().__init__(\\n components=dtensor_components, element_spec=global_element_spec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7fe2b29bb7123c470f655eedadb589\",\n \"score\": \"0.48736146\",\n \"text\": \"def __init__(self, stride_shape, convolution_shape, num_kernels):\\n\\n # Inherit parent attributes\\n super().__init__()\\n\\n self.stride_shape = stride_shape\\n self.stride_y, self.stride_x = 0, 0\\n\\n self.convolution_shape = convolution_shape\\n self.input_tensor_shape = None\\n self.input_tensor = None\\n\\n self.num_kernels = num_kernels\\n\\n # Initialize the filter uniformly random in the range [0, 1) as tensor of size: [Nr_kernels x C x M (x N)].\\n self.weights = np.random.uniform(size=(self.num_kernels, *self.convolution_shape))\\n\\n # Initialize the bias uniformly random in the range [0, 1) for each kernel as a single value.\\n self.bias = np.random.uniform(size=self.num_kernels)\\n\\n self.gradient_weights = None # Gradient with respect to the weights.\\n self.gradient_bias = None # Gradient with respect to the bias.\\n\\n self.optimizer = None # Optimizer of this layer.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c2bb027440f1d4c616441288b1acd3\",\n \"score\": \"0.48729536\",\n \"text\": \"def __init__(self, *args):\\n this = _openmm.new_VariableVerletIntegrator(*args)\\n try:\\n self.this.append(this)\\n except:\\n self.this = this\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a66eeb8f71b64a13fbd03d9940288a98\",\n \"score\": \"0.48687768\",\n \"text\": \"def _scalar_op(self, op, o):\\n new_parts = []\\n new_terms = {term: getattr(term, op)(o) for term in self.expgate_sets.keys()}\\n new_parts = ({targets: new_terms[term]\\n for targets, term in part.items()}\\n for part in self.parts)\\n new = self.__class__(*new_parts)\\n if self._dense is not None:\\n new.dense = getattr(self.dense, op)(o)\\n if self._circuit is not None:\\n new._circuit = self._circuit\\n new._circuit.dt = None\\n new.expgate_sets = {new_terms[term]: gate_set\\n for term, gate_set in self.expgate_sets.items()}\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03d9055aa5ebd994aa9b36d5ecc6417d\",\n \"score\": \"0.4867485\",\n \"text\": \"def __init__(self):\\n self.node: Node[K, V] = EmptyNode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00babe8f5fa91fe8a9f10ef7c506e6ef\",\n \"score\": \"0.4864531\",\n \"text\": \"def __init__(self, rho: float, batch_multiplier: float, lambda_vae: float, lambda_cos: float, lambda_l1: float):\\n self.rho = rho\\n self.lambda_vae = lambda_vae\\n self.lambda_cos = lambda_cos\\n self.lambda_l1 = lambda_l1\\n self.batch_multiplier = batch_multiplier\",\n \"title\": \"\"\n },\n {\n \"docid\": \"665007793f12574d1c320285c82d3a00\",\n \"score\": \"0.48640746\",\n \"text\": \"def __init__(self, target, key, values=0, active=0):\\n \\n #Initialized variables\\n self.target = target\\n self.key = key\\n self.values = values\\n self.active = active\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f42e931ee21cb79a96e0b09c1f99d14\",\n \"score\": \"0.48613185\",\n \"text\": \"def __init__(self, dim): #, length_scale, length_scale_bounds=()):\\n# assert isinstance(column, (list, tuple, int)), \\\"must be int or list of ints\\\"\\n# self.column = [column] if isinstance(column, int) else column\\n# assert all(isinstance(i, int) for i in self.column), \\\"must be integers\\\"\\n self.dim = dim\\n \\n kernels = [Projection(RBF(), [c]) for c in range(dim)]\\n\\n # combine the kernels into a single product kernel\\n self.kernel = reduce(lambda k0, k1 : k0 * k1, kernels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91dee435523b3f227ca893f9e528764f\",\n \"score\": \"0.48593938\",\n \"text\": \"def __init__(self, operator_id):\\n\\n _base.Base.__init__(self, _types.COMPONENT_TYPE_OPERATOR)\\n self.__op_id = operator_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54500e01c5648df0d8f588ea9a0ff3b6\",\n \"score\": \"0.48528546\",\n \"text\": \"def init(self) -> None:\\n self.layout = self.attrs['data_layout']\\n\\n logger.info(\\\"Init {} layer: {}\\\".format(\\n 'Conv2D' if self.kernel_groups == 1 else 'DepthWiseConv2D',\\n self.layout))\\n logger.info(self.input_shapes)\\n\\n input_shapes, kernel, biases = \\\\\\n self.input_shapes, self.kernel, self.biases\\n if kernel is not None:\\n kernel = \\\\\\n tf.compat.v1.placeholder_with_default(kernel, kernel.shape)\\n else:\\n kernel = \\\\\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\\n shape=input_shapes[1])\\n\\n if biases is not None:\\n biases = \\\\\\n tf.compat.v1.placeholder_with_default(biases, biases.shape)\\n else:\\n biases = \\\\\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\\n shape=input_shapes[2])\\n\\n inpt = \\\\\\n tf.compat.v1.placeholder(RtLayerTF.dtype_to_tf[self.dtype],\\n shape=input_shapes[0])\\n\\n self.inpts = [inpt, kernel, biases]\\n\\n self.quant_output = self._get_conv_tensor(self.inpts)\\n self.res = self.get_output_tensors(self.inpts)[0]\\n logger.info(\\\"Res shape: {}\\\".format(self.res.shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c96da5ae9c96c7d103042f8d26c505\",\n \"score\": \"0.4852539\",\n \"text\": \"def glv_init(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"453f264a9edca02a883467f14ebb4701\",\n \"score\": \"0.4848578\",\n \"text\": \"def __init__(self, sg_values): \\n self.__a = {} \\n self.__init_coefs(sg_values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca7dd23f379b010e3624e42573da451f\",\n \"score\": \"0.48449957\",\n \"text\": \"def __init__(self):\\n self.U, self.s, self.V = (None,None,None)\\n self.user_encoder = None\\n self.item_encoder = None\\n self.mat = None\\n self.decomp = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"159c7de3ad234f5ac44629ce829f08c0\",\n \"score\": \"0.48449928\",\n \"text\": \"def __init__(self, *args):\\n _gp.gp_Vec2d_swiginit(self,_gp.new_gp_Vec2d(*args))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":185,"cells":{"query_id":{"kind":"string","value":"b385b0580c451d27151a0b7f7ebf8293"},"query":{"kind":"string","value":"Return a restricted zscore."},"positive_passages":{"kind":"list like","value":[{"docid":"d9b8dbd9271cb251514b6863c4b30b2c","score":"0.70605326","text":"def zscoreOtherRestricted(measure, power, median, variationCoefficient, computeFinalZScore):\n zscoreNorm = zscore(measure, power, median, variationCoefficient)\n if math.fabs(zscoreNorm) > 3 and computeFinalZScore:\n if zscoreNorm > 3:\n std3Pos = cutoff(3, power, median, variationCoefficient)\n std23Pos = std3Pos - cutoff(2, power, median, variationCoefficient)\n zscoreNorm = 3 + ((measure - std3Pos) / std23Pos)\n elif zscoreNorm < 3:\n std3Neg = cutoff(-3, power, median, variationCoefficient)\n std23Neg = cutoff(-2, power, median, variationCoefficient) - std3Neg\n zscoreNorm = -3 + ((measure - std3Neg) / std23Neg)\n return zscoreNorm","title":""}],"string":"[\n {\n \"docid\": \"d9b8dbd9271cb251514b6863c4b30b2c\",\n \"score\": \"0.70605326\",\n \"text\": \"def zscoreOtherRestricted(measure, power, median, variationCoefficient, computeFinalZScore):\\n zscoreNorm = zscore(measure, power, median, variationCoefficient)\\n if math.fabs(zscoreNorm) > 3 and computeFinalZScore:\\n if zscoreNorm > 3:\\n std3Pos = cutoff(3, power, median, variationCoefficient)\\n std23Pos = std3Pos - cutoff(2, power, median, variationCoefficient)\\n zscoreNorm = 3 + ((measure - std3Pos) / std23Pos)\\n elif zscoreNorm < 3:\\n std3Neg = cutoff(-3, power, median, variationCoefficient)\\n std23Neg = cutoff(-2, power, median, variationCoefficient) - std3Neg\\n zscoreNorm = -3 + ((measure - std3Neg) / std23Neg)\\n return zscoreNorm\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9ef0a40399c631949df3b99157924f0d","score":"0.72016364","text":"def get_zscore(self, zscore_value):\n\n s = self.get_score(self.prov_types['zscore'], zscore_value)\n return s","title":""},{"docid":"953a1114c01dd842fe0a8694202b17b3","score":"0.7117339","text":"def zscore(x):\n return (x - x.mean()) / x.std()","title":""},{"docid":"9394d33a1d8cdcc699a9e78d16db20d9","score":"0.6993641","text":"def zscore(rate, mean, std):\n zscore = (rate - mean) / std\n return zscore","title":""},{"docid":"5460d67193924f289c030ec4183feda1","score":"0.6947195","text":"def compute_z_score(self):\n\t\tx_scaled = (self.x - self.x.mean())/self.x.std()\n\t\treturn x_scaled","title":""},{"docid":"af3719b97714cf1fbb5f7b1abf8762cc","score":"0.6893497","text":"def zscore(raw, mean, stddev):\n\n zscore = (raw - mean) / stddev\n\n return zscore","title":""},{"docid":"23dac8796a09bd083c06ea7a94804d96","score":"0.6865544","text":"def zscore(x, mu, std):\n\n return (x-mu)/std","title":""},{"docid":"d3d988f044779167aeff2c0b2dd226e9","score":"0.6859767","text":"def z_score(self, x):\n return (x - self.mean) / self.stddev","title":""},{"docid":"d3d988f044779167aeff2c0b2dd226e9","score":"0.6859767","text":"def z_score(self, x):\n return (x - self.mean) / self.stddev","title":""},{"docid":"d3d988f044779167aeff2c0b2dd226e9","score":"0.6859767","text":"def z_score(self, x):\n return (x - self.mean) / self.stddev","title":""},{"docid":"d0b7c03dfe37befe5b0e7529dbc69e0f","score":"0.67672366","text":"def zscore(self, *args):\n if self._cluster:\n return self.execute(u'ZSCORE', *args, shard_key=args[0])\n return self.execute(u'ZSCORE', *args)","title":""},{"docid":"963aafe67d75b527d31b4529d57ce7a3","score":"0.66174215","text":"def get_worst_zscore(self):\n worst_zscore = None\n for indicator in VisitStatistics.INDICATORS:\n if hasattr(self, indicator):\n zandp = getattr(self, indicator)\n if zandp and not healthdb.util.isNaN(zandp.zscore):\n if worst_zscore is None or worst_zscore > zandp.zscore:\n# logging.info(\"new worst_zscore = %s\" % zandp.zscore)\n worst_zscore = zandp.zscore\n# logging.info(\"worst_zscore = %s\" % worst_zscore)\n return worst_zscore","title":""},{"docid":"dd34f7cb38892869ec81637ad9eb5f5b","score":"0.64894617","text":"def _max_score(self):\n try:\n return self.zscore(self.__getitem__(-1))\n except IndexError:\n return None","title":""},{"docid":"79af348f22e96b9985b97269f8c1892b","score":"0.6463926","text":"def test_getZScores(self):\n values = [12,13,9,18,7,9,14,16,10,12,7,13,14,19,10,16,12,16,19,11]\n arr = numpy.array(values,dtype=numpy.float64)\n\n zscore = Stats.getZscore(arr)\n self.assertAlmostEqual(1.63977, zscore[4], places = 4)\n self.assertAlmostEqual(0.32235, zscore[6], places = 4)\n\n modZ = Stats.getModifiedZscore(arr)\n self.assertAlmostEqual(1.23658, modZ[4], places = 4)\n self.assertAlmostEqual(0.33725, modZ[6], places = 4)\n\n # Test the sorted argument still works. Remove this when the function is removed\n # sorted=True only ever affected the order\n zscore = Stats.getZscore(arr, sorted=True)\n self.assertAlmostEqual(1.63977, zscore[4], places = 4)\n self.assertAlmostEqual(0.32235, zscore[6], places = 4)","title":""},{"docid":"0940662b45e0cde7a986b1275d9724c5","score":"0.6352521","text":"def _min_score(self):\n try:\n return self.zscore(self.__getitem__(0))\n except IndexError:\n return None","title":""},{"docid":"20f5abc792709c0f3acd896f348248a3","score":"0.6240699","text":"def zscore(self, elem):\n return self.redis.zscore(self.key, self.serialize(elem))","title":""},{"docid":"80a138434df927a55d202d6a4c6817ea","score":"0.62341267","text":"def zscore(data):\n datalength = len(data)\n try:\n data = (data - np.mean(data)) / np.std(data)\n except:\n data = np.zeros(datalen)\n\n return data","title":""},{"docid":"a3d418bed63651d05af4a8c8dd922b7b","score":"0.62231916","text":"def _impaired_or_not(z_score, cutoff):\n if z_score <= cutoff:\n return 1\n else:\n return 0","title":""},{"docid":"ac30978fc86ba5d8a99918673b987d7b","score":"0.6182298","text":"def compute_zscore(self, data, slope, intercept):\n spread = (data['PEP'].price - (slope * data['KO'].price + intercept))\n self.spreads.append(spread)\n spread_wind = self.spreads[-self.window_length:]\n zscore = (spread - np.mean(spread_wind)) / np.std(spread_wind)\n return zscore","title":""},{"docid":"d5f1571d2b2bce9a8648d59f5f8a68e9","score":"0.6143231","text":"def get_highest_z(self) -> float:\n assert False, \"get_highest_z only supported on engine core\"","title":""},{"docid":"24308779304fdd0ed05600ca107a58aa","score":"0.6113914","text":"def z_score_xr(dr):\n other_dims = [d for d in dr.dims if d != 'neuron']\n return (dr - dr.mean(other_dims)) / dr.std(other_dims)","title":""},{"docid":"3df5e6e4b48aeafa746a58814735697b","score":"0.61133456","text":"def z_score(x, lst):\n if not lst or not len(lst):\n return nan\n mu = mean(lst)\n sdv = sd(lst)\n return (x - mu) / sdv","title":""},{"docid":"1e236c46b9707f1430564c47d6d8750d","score":"0.61009943","text":"def score(self, member):\n return self.zscore(member)","title":""},{"docid":"9dbe17dd42a08fcd259adccf4b4b02c6","score":"0.609521","text":"def scale_zscore(zscore, mean, std):\n zscaled = zscore * std + mean\n return zscaled","title":""},{"docid":"2523ad7970c4346a9b39754236d4f142","score":"0.6089465","text":"def z(inlist, score):\n z = (score - mean(inlist)) / samplestdev(inlist)\n return z","title":""},{"docid":"022687a206d6cdb454293405fc51f052","score":"0.6046075","text":"def _to_z_score(scaled_score, expected_score, test):\n denominator_dict = {'sdmt': 2.790,\n 'bvmt': 2.793,\n 'cvlt': 2.801}\n\n denominator = denominator_dict.get(test)\n\n z_score = (scaled_score - expected_score)/denominator\n\n return z_score","title":""},{"docid":"db42d74e4ba1ed69cf6c45ab5c7544ee","score":"0.6023797","text":"def get_score(self) -> float:\n pass","title":""},{"docid":"7a2947259caa75efb1d8d35827aa97bb","score":"0.60219604","text":"def score(self, member):\r\n return self.client.zscore(self.name, member)","title":""},{"docid":"c6f3a0bf0ba08e71441d9017bef9d20b","score":"0.6010244","text":"def z_score(self):\n control = VariantStat(self.experiment.control, self.experiment)\n\n alternative = self\n\n if control.name == alternative.name:\n return 'N/A'\n\n conv_c = control.conversion_rate\n conv_a = alternative.conversion_rate\n\n num_c = control.participant_count\n num_a = alternative.participant_count\n\n if conv_c == 0 or conv_a == 0:\n return 0\n\n numerator = conv_a - conv_c\n\n frac_c = (conv_c * (1 - conv_c)) / float(num_c)\n frac_a = (conv_a * (1 - conv_a)) / float(num_a)\n\n if frac_c + frac_a == 0:\n # square root of 0 is 0, so no need to calculate\n return 0\n elif frac_c + frac_a < 0:\n # can't take a square root of a negative number,\n # so return 'Invalid'\n return 'Invalid'\n\n return numerator / math.sqrt((frac_c + frac_a))","title":""},{"docid":"db9623e3168adac427129f947467fe01","score":"0.59958607","text":"def SquaredZScoreInWorldSpace(self, point: 'itkPointD3') -> \"double\":\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject3_SquaredZScoreInWorldSpace(self, point)","title":""},{"docid":"d414d8a0b9dac5c06073d50832ad5685","score":"0.59949404","text":"def SquaredZScoreInObjectSpace(self, point: 'itkPointD3') -> \"double\":\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject3_SquaredZScoreInObjectSpace(self, point)","title":""},{"docid":"cbeb0aa9fca4b72da8f4275f00e9ab99","score":"0.59595954","text":"def Zscore(self, kmer):\n mean = self.eValue(kmer)\n N = self.N\n p = mean/N #finds p from the mean.\n stdDev = (N*p*(1 - p))**0.5 #finds stdDeviation\n\n Ztop = self.kmerCounts[kmer][0] - mean\n \n return Ztop/stdDev","title":""},{"docid":"35d13d515807810bbea8099b2ee5a663","score":"0.59485126","text":"def z_score(dist, num):\n mu = float(sum((key * val for (key, val)\n in zip(dist.keys(),\n dist.values())))) / 1000\n\n # print show_hist(dist)\n print \"mean is: \" + str(mu)\n sigma = math.sqrt(float(sum(((key - mu) ** 2) * val for (key, val)\n in zip(dist.keys(),\n dist.values()))) / 1000)\n\n print \"standart is: \" + str(sigma)\n print \"tripple st dev:\" + str(mu + 3 * sigma)\n return (num - mu) / sigma","title":""},{"docid":"9e774de9fc5eaa8a9a96da62a24700f2","score":"0.5948267","text":"def set_zscores(self):\n pass","title":""},{"docid":"4f76f124efa1c1489560ceaaa1b1057f","score":"0.5924716","text":"def SquaredZScoreInObjectSpace(self, point: 'itkPointD2') -> \"double\":\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject2_SquaredZScoreInObjectSpace(self, point)","title":""},{"docid":"ecd7b02f7a5dde6074639eceee4fb1a3","score":"0.59235656","text":"def sorted_score(self, k: str, m: str) -> Union[float, None]:\n conn = self.get_conn()\n return conn.zscore(k, m)","title":""},{"docid":"94d7ebb287e80fdfa23f90a6c66fe585","score":"0.59227055","text":"def SquaredZScoreInWorldSpace(self, point: 'itkPointD2') -> \"double\":\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject2_SquaredZScoreInWorldSpace(self, point)","title":""},{"docid":"db854da7e2241d1c9618ba95aa0b6a8b","score":"0.5864021","text":"def z_eval(self):\n if not hasattr(self, '_zeval'):\n\n if self._args['evaluate_mc_at_zlens']:\n self._zeval = self.z\n else:\n self._zeval = self.z_infall\n\n return self._zeval","title":""},{"docid":"81c40100f9e55858b0ab51e81aa4ce62","score":"0.5846473","text":"def z(self) -> float:\n return self._z","title":""},{"docid":"8ac6c28e2e253ff235afab6da251fe3a","score":"0.58452505","text":"def threshold_by_zscore(zscored_data, time, minimum_duration=0.015, zscore_threshold=2):\n is_above_mean = zscored_data >= 0\n is_above_threshold = zscored_data >= zscore_threshold\n\n return extend_threshold_to_mean(\n is_above_mean, is_above_threshold, time, minimum_duration=minimum_duration\n )","title":""},{"docid":"5f68c7214412be2bd4790b674ebe0ad7","score":"0.5815507","text":"def get_score(self):\n pass","title":""},{"docid":"e18da72954e262bc2bc0eb509ab11a46","score":"0.5778263","text":"def wZ(self, z):\r\n return self.w0 * np.sqrt(1 + (z / self.zR) ** 2)","title":""},{"docid":"1b3ce039d4fce67e51f63cdff71b27f1","score":"0.5752515","text":"def z_critical_value(cls, alpha, dir):\n if dir == cls.TWO_TAILED_TEST:\n alpha /= 2.0\n\n z = norm.ppf(1 - alpha)\n if dir == StatTool.ONE_TAILED_NEGATIVE_TEST:\n z = 0 - z\n return z","title":""},{"docid":"a84a09df14c8964e5f8753ec4498895f","score":"0.57146937","text":"def normalize_zscore(data, z=2, offset=0.5, clip=False):\n mean = np.mean(data)\n std = np.std(data)\n img = ((data - mean) / (2 * std * z) + offset) \n if clip:\n # print ('Before')\n # print (np.min(img), np.max(img))\n img = np.clip(img, -0.0, 1.0)\n # print ('After clip')\n # print (np.min(img), np.max(img))\n return img","title":""},{"docid":"0ac26db3b124e3947506297470d4ed5f","score":"0.571298","text":"def z_get(self, proxy, key):\n return self._client.zscore(key, proxy)","title":""},{"docid":"3c8f389df059e4999a4a9b0369104525","score":"0.57076114","text":"def probability_for_z(cls, z_score, dir):\n assert dir in cls.valid_dirs\n\n # cdv is the probability that any sample will have LESS than z_score\n cdv = norm.cdf(z_score)\n\n if dir == cls.TWO_TAILED_TEST:\n if z_score < 0:\n dir = cls.ONE_TAILED_NEGATIVE_TEST\n else:\n dir = cls.ONE_TAILED_POSITIVE_TEST\n\n if dir == cls.ONE_TAILED_POSITIVE_TEST:\n return 1 - cdv\n else:\n return cdv","title":""},{"docid":"ddff10cdc4fcf7c3ae8aa9d0dea252a6","score":"0.5698196","text":"def var_modified(r, level = 5):\n #compute the Z score assuming it was Gausian\n z= norm.ppf(level/100)\n #if modified:\n # modify the Z score based on observed skewness and kurtosis\n s=skewness(r)\n k=kurtosis(r)\n z=(z +\n (z**2-1)*s/6 + \n (z**3 -3*z)*(k-3)/24 -\n (2*z**3 - 5*z)*(s**2)/36\n )\n return -(r.mean() + z*r.std(ddof = 0))","title":""},{"docid":"2f433132958d74f2171254fe150c2033","score":"0.56827646","text":"def zmax(self):\n if self._zmax is None:\n self._zmax = self.rmax\n return self._zmax","title":""},{"docid":"bbb52be9a9b0ab7ada2fffe4dc821349","score":"0.56279254","text":"def get_score(self):\n return self._score","title":""},{"docid":"86513526f07539ac8651bf91d21c69e8","score":"0.56194705","text":"def z_score(tableau, ur):\n zScore = 0\n for j in tableau[ur]:\n zScore += tableau[ur][j][2]\n return zScore","title":""},{"docid":"05717db3354e3172f361963721c1d58a","score":"0.5546481","text":"def z_value(x: float, mean: float, std: float) -> float:\n return (x-mean) / std","title":""},{"docid":"4479528e768431bd5e61a0d9eb3d352d","score":"0.55305403","text":"def R2(self, z, zpred):\n\n\n zmean = np.average(z)\n\n return 1 - np.sum((z - zpred)**2)/np.sum((z - zmean)**2)","title":""},{"docid":"12e94247201b5761983384f234e6755d","score":"0.5527806","text":"def zscore(A, axis=None):\n return (A - np.mean(A,axis=axis)) / np.std(A,axis=axis,ddof=1)","title":""},{"docid":"66559194e54019dbd45ca832f8553605","score":"0.55251133","text":"def outliers_modified_z_score(self, ys, y):\n threshold = 3.5\n\n median_y = np.median(ys)\n median_absolute_deviation_y = np.median([np.abs(y - median_y) for y in ys])\n modified_z_scores = 0.6745 * (y - median_y) / median_absolute_deviation_y\n return np.where(np.abs(modified_z_scores) > threshold)","title":""},{"docid":"437de78c57999e8bc2d3967e81698b4d","score":"0.5522258","text":"def z_eval(self):\n return self.z","title":""},{"docid":"d2258ec09fbf7d74eb4dc3878cee20a5","score":"0.55171925","text":"def get_a_from_z(self, z):\n z = np.array(z)\n if np.any(z < 0.0):\n raise ValueError(\n \"Cannot convert negative redshift to scale factor\")\n return 1.0/(1.0+z)","title":""},{"docid":"ef7e30711e78b06da46136ccf1bc31d2","score":"0.54868895","text":"def get_clinical_Z_range(): \n return (-105.0, 100000000.0) # in mm","title":""},{"docid":"bf3accf9bcd511a1b5a157bdbb04cfa6","score":"0.54471064","text":"def raw_score(self):\n return self._raw_score","title":""},{"docid":"58c3cbe7285b3c0cba21e9786e7c813b","score":"0.5436805","text":"def get_score(self):\n raise NotImplementedError(\"This method must be overridden\")","title":""},{"docid":"aafa86dc8337437eff19f347d4e7db14","score":"0.542814","text":"def zscoreToPercentile(zscore):\n retVal = healthdb.util.NaN\n # WHO technical specs chapter 7: \"However, a restriction was imposed on\n # all indicators to enable the derivation of percentiles only within\n # the interval corresponding to z-scores between -3 and 3. The\n # underlying reasoning is that percentiles beyond +-3 SD are invariant\n # to changes in equivalent z-scores. The loss accruing to this\n # restriction is small since the inclusion range corresponds to the\n # 0.135th to 99.865th percentiles.\"\n if math.fabs(zscore) <= 3:\n absVal = math.fabs(zscore)\n P1 = (1 - 1 / math.sqrt(2 * math.pi) * math.exp(-math.pow(absVal, 2) / 2)\n * (\n 0.31938 * (1 / (1 + 0.2316419 * absVal))\n - 0.356563782 * math.pow((1 / (1 + 0.2316419 * absVal)), 2)\n + 1.781477937 * math.pow((1 / (1 + 0.2316419 * absVal)), 3)\n - 1.82125 * math.pow((1 / (1 + 0.2316419 * absVal)), 4)\n + 1.330274429 * math.pow((1 / (1 + 0.2316419 * absVal)), 5)\n ))\n \n if zscore > 0:\n P1 = P1 * 100\n else:\n P1 = 100 - P1 * 100\n \n if 0 <= P1 and P1 <= 100:\n retVal = P1\n return retVal","title":""},{"docid":"a19b11413fb191d6925413b7ebf74b01","score":"0.53977555","text":"def getZScore(self, index):\n # Calculate mean and standard deviation of the list of values\n mu = np.mean(self.matrix[:, index], axis=0)\n sd = np.std(self.matrix[:, index], axis=0)\n # Calculate Z-score for the given column for each gene\n zscore = (self.matrix[:, index] - mu) / sd\n mygenes = {}\n for (name, ndx) in self.genes.items():\n try:\n mygenes[name] = zscore[ndx, :]\n except IndexError:\n mygenes[name] = zscore[ndx]\n # Return the dictionary of Z-scores\n return mygenes","title":""},{"docid":"e326cfd1ca90e434102efeb46af2d1f6","score":"0.53955096","text":"def get_score(self, value):\n try:\n encodedvalue = PickleEncoder().encode(value)\n except Exception, e:\n raise RedisError(\"encode error:%s\" % e)\n\n try:\n score = self._db.zscore(self.namespace, encodedvalue)\n return score\n except Exception, e:\n raise RedisError(\"redis error:%s\" % e)","title":""},{"docid":"ed457a87f10952f48019978b8e1e1d36","score":"0.5376343","text":"def zscoreFromAttribute(anthroConfig):\n for row in csv.DictReader(open(anthroConfig.fileName)):\n if 'age' in row:\n dataAgeHeightOrLength = row['age']\n elif 'length' in row:\n dataAgeHeightOrLength = row['length']\n elif 'height' in row:\n dataAgeHeightOrLength = row['height']\n if int(row['sex']) == anthroConfig.sex and float(dataAgeHeightOrLength) == anthroConfig.ageHeightOrLength:\n return zscoreOtherRestricted(anthroConfig.measureKey, float(row['l']), float(row['m']), float(row['s']), True) \n return healthdb.util.NaN","title":""},{"docid":"fad2d8b9197199d7edc5fda8187214db","score":"0.5350002","text":"def score(self):\n pos = self.scores.filter(value=1).count()\n n = self.scores.count()\n return wilson_ci_lower_bound(n, pos)","title":""},{"docid":"bfed7dbefd669d898835324cabe153d1","score":"0.53445476","text":"def NO_SCORE() -> int:\n return -1","title":""},{"docid":"8538005b30ce8cfd3ed977e86d84b93a","score":"0.5340626","text":"def Z(self) -> int:\r\n return self._Z","title":""},{"docid":"ea26fb8ca8fcf09b92a47eeba81c975a","score":"0.5333083","text":"def get_best_score(self):\n return self.best_score","title":""},{"docid":"1716b29e9d7a608b2938d8cc1634e9c6","score":"0.5331177","text":"def compute(cls, observation, prediction):\n assert type(observation) is dict\n assert type(prediction) is dict\n try:\n p_value = prediction['mean'] # Use the prediction's mean. \n except (TypeError,KeyError): # If there isn't one...\n try:\n p_value = prediction['value'] # Use the prediction's value. \n except TypeError: # If there isn't one...\n p_value = prediction # Use the prediction (assume it is numeric).\n o_mean = observation['mean']\n o_std = observation['std']\n value = (p_value - o_mean)/o_std\n value = utils.assert_dimensionless(value)\n return ZScore(value)","title":""},{"docid":"100ea5e8e6e0474083b076a4a68719d7","score":"0.53240174","text":"def compute_best_risk(self):\n has_chi2 = (self.chi2_fit != 0).sum(axis=1) > 0 \n mask = (has_chi2) & (self.zbest > self.zgrid[0])\n \n zbest_grid = np.dot(self.zbest[mask, None],\n np.ones_like(self.zgrid)[None,:])\n L = self._loss((zbest_grid-self.zgrid)/(1+self.zgrid))\n #dz = np.gradient(self.zgrid)\n \n zbest_risk = np.zeros(self.NOBJ, dtype=self.ARRAY_DTYPE)-1\n zbest_risk[mask] = np.dot(np.exp(self.lnp[mask,:])*L, self.trdz)\n \n del(has_chi2)\n del(mask)\n del(zbest_grid)\n del(L)\n \n return zbest_risk","title":""},{"docid":"c70832f4b5008dc1a9bab31f1fb77d2c","score":"0.5320918","text":"def score_one(self, x: dict) -> float:","title":""},{"docid":"c70832f4b5008dc1a9bab31f1fb77d2c","score":"0.5320918","text":"def score_one(self, x: dict) -> float:","title":""},{"docid":"00b65cb58654be9d87f5140f9f692e8d","score":"0.5320872","text":"def wZ(w0, z, zR):\r\n return w0 * np.sqrt(1 + (z / zR) ** 2)","title":""},{"docid":"57bd528109934590edcc37520b8aecc6","score":"0.53161615","text":"def get_score(self):\n return self._points","title":""},{"docid":"d145a7dc3c8c2db905526386567322af","score":"0.5306321","text":"def norm_score(self):\n return self.score","title":""},{"docid":"10ba4834d38dd73afa4faf78179c59de","score":"0.53024745","text":"def compute_full_risk(self):\n zsq = np.dot(self.zgrid[:,None], np.ones_like(self.zgrid)[None,:])\n L = self._loss((zsq-self.zgrid)/(1+self.zgrid))\n \n Rz = self.lnp*0.\n \n has_chi2 = (self.chi2_fit != 0).sum(axis=1) > 0 \n hasz = self.zbest > self.zgrid[0]\n idx = self.idx[hasz & (has_chi2)]\n \n for i in idx:\n Rz[i,:] = np.dot(np.exp(self.lnp[i,:])*L, self.trdz)\n \n del(zsq)\n del(L)\n del(has_chi2)\n del(hasz)\n del(idx) \n return Rz\n \n #self.full_risk = Rz\n #self.min_risk = self.zgrid[np.argmin(Rz, axis=1)]","title":""},{"docid":"35b8f1cc00b5b07453048380c9183176","score":"0.5287981","text":"def getScore(self):\n return self._score","title":""},{"docid":"35b8f1cc00b5b07453048380c9183176","score":"0.5287981","text":"def getScore(self):\n return self._score","title":""},{"docid":"dcb862fa989f902036a49cc1625a4f90","score":"0.528489","text":"def max_score(self):\n return self.weight if self.has_score else None","title":""},{"docid":"3440f70adf1c7fd88bdefc2f668b642d","score":"0.5280114","text":"def constant_score(self):\n return self._constant_score","title":""},{"docid":"8dc96d30401cecf5b2207a98a0df1a10","score":"0.5262474","text":"def get_score(self):\n score = 1\n if self.answer:\n score = 3\n if not self.correct:\n score *= -1\n return score","title":""},{"docid":"7c7e62a3d3390f0de2732aa78ed1d28e","score":"0.5253121","text":"def __call__(self, Z):\n\n return tf.math.maximum(Z, 0)","title":""},{"docid":"1836f7e1518a3851013eb49b808aa889","score":"0.52435356","text":"def score(self):\n return self.__score","title":""},{"docid":"3de3634c605416248a0eb6a0e5726904","score":"0.52232313","text":"def score(self):\n check_is_fitted(self, [\"lower_bound_\"])\n return self.lower_bound_","title":""},{"docid":"0a79fbce580c66f934eacea64394c344","score":"0.52211654","text":"def z(self):\n return self._vct.z","title":""},{"docid":"58de9a1a700da120b9ef3b0822a54958","score":"0.5218734","text":"def saclhitsrate(self) :\n try :\n return self._saclhitsrate\n except Exception as e:\n raise e","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"789844b69cf207afb25c9696fe51dbb2","score":"0.5213474","text":"def score(self):\n return self._score","title":""},{"docid":"953ef2864b7c9b9b26aa003dab0961b4","score":"0.5205887","text":"def fowlkes_mallows_index(y_true: np.array, y_score: np.array) -> float:\n ppv = positive_predictive_value(y_true, y_score)\n tpr = true_positive_rate(y_true, y_score)\n fm = (ppv * tpr) ** 0.5\n return fm","title":""},{"docid":"a542313a7482d565193ff9df0cef3138","score":"0.5202119","text":"def detect_outlier_zscore(df):\n\tthreshold = 3\n\tz = np.abs(stats.zscore(df))\n\toutliers = np.where(z > threshold)\n\treturn outliers","title":""},{"docid":"6f99c4afcdafcff521b56c5a458723e4","score":"0.5199307","text":"def getZonePercentageFeatureValue(self):\n Nz = self.coefficients['Nz']\n Np = self.coefficients['Np']\n\n zp = Nz / Np\n return zp","title":""},{"docid":"81da7f92b66bb19c61c2617346cad4fe","score":"0.5194981","text":"def zprob(z):\n Z_MAX = 6.0 # maximum meaningful z-value\n if z == 0.0:\n x = 0.0\n else:\n y = 0.5 * math.fabs(z)\n if y >= (Z_MAX * 0.5):\n x = 1.0\n elif (y < 1.0):\n w = y * y\n x = ((((((((0.000124818987 * w\n - 0.001075204047) * w + 0.005198775019) * w\n - 0.019198292004) * w + 0.059054035642) * w\n - 0.151968751364) * w + 0.319152932694) * w\n - 0.531923007300) * w + 0.797884560593) * y * 2.0\n else:\n y = y - 2.0\n x = (((((((((((((-0.000045255659 * y\n + 0.000152529290) * y - 0.000019538132) * y\n - 0.000676904986) * y + 0.001390604284) * y\n - 0.000794620820) * y - 0.002034254874) * y\n + 0.006549791214) * y - 0.010557625006) * y\n + 0.011630447319) * y - 0.009279453341) * y\n + 0.005353579108) * y - 0.002141268741) * y\n + 0.000535310849) * y + 0.999936657524\n if z > 0.0:\n prob = ((x + 1.0) * 0.5)\n else:\n prob = ((1.0 - x) * 0.5)\n return prob","title":""},{"docid":"23444449548c751d57eb8a7312433623","score":"0.5192021","text":"def credit_score(self):\n return self._credit_score","title":""},{"docid":"1706217b6bb470a64b395196f3a35f76","score":"0.5191078","text":"def res_function(X, Z):\n X_col = X.reshape((-1, 1))\n Z_row = Z.reshape((1, -1))\n return np.maximum(Z_row - X_col + margin, 0).mean()","title":""},{"docid":"5b4d0a2f8fbc43bd9a9ce40b6891f4a6","score":"0.5191033","text":"def lcz(self, zbest=None):\n if zbest is None:\n zbest = self.zbest\n \n _lcz = np.dot(1/(1+zbest[:, np.newaxis]), self.pivot[np.newaxis,:])\n return _lcz","title":""},{"docid":"350f7ffdbf74204002d650b60f84004b","score":"0.5189782","text":"def Score(cls):\n\n return ScoreMask()","title":""},{"docid":"595a292a563ab3746659d808dd8b2ba1","score":"0.51834446","text":"def custom_score(game, player):\n # TODO: finish this function!\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n def calculate_score(moves):\n if len(moves) == 0:\n return 0\n centered_locs = np.array(moves) - (game.height/2.0, game.width/2.0)\n #print(centered_locs)\n return np.sum(1/LA.norm(centered_locs,ord=2,axis=1))\n\n own_moves = game.get_legal_moves(player)\n opp_moves = game.get_legal_moves(game.get_opponent(player))\n\n return float(calculate_score(own_moves)-calculate_score(opp_moves))","title":""}],"string":"[\n {\n \"docid\": \"9ef0a40399c631949df3b99157924f0d\",\n \"score\": \"0.72016364\",\n \"text\": \"def get_zscore(self, zscore_value):\\n\\n s = self.get_score(self.prov_types['zscore'], zscore_value)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"953a1114c01dd842fe0a8694202b17b3\",\n \"score\": \"0.7117339\",\n \"text\": \"def zscore(x):\\n return (x - x.mean()) / x.std()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9394d33a1d8cdcc699a9e78d16db20d9\",\n \"score\": \"0.6993641\",\n \"text\": \"def zscore(rate, mean, std):\\n zscore = (rate - mean) / std\\n return zscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5460d67193924f289c030ec4183feda1\",\n \"score\": \"0.6947195\",\n \"text\": \"def compute_z_score(self):\\n\\t\\tx_scaled = (self.x - self.x.mean())/self.x.std()\\n\\t\\treturn x_scaled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3719b97714cf1fbb5f7b1abf8762cc\",\n \"score\": \"0.6893497\",\n \"text\": \"def zscore(raw, mean, stddev):\\n\\n zscore = (raw - mean) / stddev\\n\\n return zscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23dac8796a09bd083c06ea7a94804d96\",\n \"score\": \"0.6865544\",\n \"text\": \"def zscore(x, mu, std):\\n\\n return (x-mu)/std\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d988f044779167aeff2c0b2dd226e9\",\n \"score\": \"0.6859767\",\n \"text\": \"def z_score(self, x):\\n return (x - self.mean) / self.stddev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d988f044779167aeff2c0b2dd226e9\",\n \"score\": \"0.6859767\",\n \"text\": \"def z_score(self, x):\\n return (x - self.mean) / self.stddev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d988f044779167aeff2c0b2dd226e9\",\n \"score\": \"0.6859767\",\n \"text\": \"def z_score(self, x):\\n return (x - self.mean) / self.stddev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0b7c03dfe37befe5b0e7529dbc69e0f\",\n \"score\": \"0.67672366\",\n \"text\": \"def zscore(self, *args):\\n if self._cluster:\\n return self.execute(u'ZSCORE', *args, shard_key=args[0])\\n return self.execute(u'ZSCORE', *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963aafe67d75b527d31b4529d57ce7a3\",\n \"score\": \"0.66174215\",\n \"text\": \"def get_worst_zscore(self):\\n worst_zscore = None\\n for indicator in VisitStatistics.INDICATORS:\\n if hasattr(self, indicator):\\n zandp = getattr(self, indicator)\\n if zandp and not healthdb.util.isNaN(zandp.zscore):\\n if worst_zscore is None or worst_zscore > zandp.zscore:\\n# logging.info(\\\"new worst_zscore = %s\\\" % zandp.zscore)\\n worst_zscore = zandp.zscore\\n# logging.info(\\\"worst_zscore = %s\\\" % worst_zscore)\\n return worst_zscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd34f7cb38892869ec81637ad9eb5f5b\",\n \"score\": \"0.64894617\",\n \"text\": \"def _max_score(self):\\n try:\\n return self.zscore(self.__getitem__(-1))\\n except IndexError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79af348f22e96b9985b97269f8c1892b\",\n \"score\": \"0.6463926\",\n \"text\": \"def test_getZScores(self):\\n values = [12,13,9,18,7,9,14,16,10,12,7,13,14,19,10,16,12,16,19,11]\\n arr = numpy.array(values,dtype=numpy.float64)\\n\\n zscore = Stats.getZscore(arr)\\n self.assertAlmostEqual(1.63977, zscore[4], places = 4)\\n self.assertAlmostEqual(0.32235, zscore[6], places = 4)\\n\\n modZ = Stats.getModifiedZscore(arr)\\n self.assertAlmostEqual(1.23658, modZ[4], places = 4)\\n self.assertAlmostEqual(0.33725, modZ[6], places = 4)\\n\\n # Test the sorted argument still works. Remove this when the function is removed\\n # sorted=True only ever affected the order\\n zscore = Stats.getZscore(arr, sorted=True)\\n self.assertAlmostEqual(1.63977, zscore[4], places = 4)\\n self.assertAlmostEqual(0.32235, zscore[6], places = 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0940662b45e0cde7a986b1275d9724c5\",\n \"score\": \"0.6352521\",\n \"text\": \"def _min_score(self):\\n try:\\n return self.zscore(self.__getitem__(0))\\n except IndexError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f5abc792709c0f3acd896f348248a3\",\n \"score\": \"0.6240699\",\n \"text\": \"def zscore(self, elem):\\n return self.redis.zscore(self.key, self.serialize(elem))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80a138434df927a55d202d6a4c6817ea\",\n \"score\": \"0.62341267\",\n \"text\": \"def zscore(data):\\n datalength = len(data)\\n try:\\n data = (data - np.mean(data)) / np.std(data)\\n except:\\n data = np.zeros(datalen)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d418bed63651d05af4a8c8dd922b7b\",\n \"score\": \"0.62231916\",\n \"text\": \"def _impaired_or_not(z_score, cutoff):\\n if z_score <= cutoff:\\n return 1\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac30978fc86ba5d8a99918673b987d7b\",\n \"score\": \"0.6182298\",\n \"text\": \"def compute_zscore(self, data, slope, intercept):\\n spread = (data['PEP'].price - (slope * data['KO'].price + intercept))\\n self.spreads.append(spread)\\n spread_wind = self.spreads[-self.window_length:]\\n zscore = (spread - np.mean(spread_wind)) / np.std(spread_wind)\\n return zscore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f1571d2b2bce9a8648d59f5f8a68e9\",\n \"score\": \"0.6143231\",\n \"text\": \"def get_highest_z(self) -> float:\\n assert False, \\\"get_highest_z only supported on engine core\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24308779304fdd0ed05600ca107a58aa\",\n \"score\": \"0.6113914\",\n \"text\": \"def z_score_xr(dr):\\n other_dims = [d for d in dr.dims if d != 'neuron']\\n return (dr - dr.mean(other_dims)) / dr.std(other_dims)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3df5e6e4b48aeafa746a58814735697b\",\n \"score\": \"0.61133456\",\n \"text\": \"def z_score(x, lst):\\n if not lst or not len(lst):\\n return nan\\n mu = mean(lst)\\n sdv = sd(lst)\\n return (x - mu) / sdv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e236c46b9707f1430564c47d6d8750d\",\n \"score\": \"0.61009943\",\n \"text\": \"def score(self, member):\\n return self.zscore(member)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dbe17dd42a08fcd259adccf4b4b02c6\",\n \"score\": \"0.609521\",\n \"text\": \"def scale_zscore(zscore, mean, std):\\n zscaled = zscore * std + mean\\n return zscaled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2523ad7970c4346a9b39754236d4f142\",\n \"score\": \"0.6089465\",\n \"text\": \"def z(inlist, score):\\n z = (score - mean(inlist)) / samplestdev(inlist)\\n return z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"022687a206d6cdb454293405fc51f052\",\n \"score\": \"0.6046075\",\n \"text\": \"def _to_z_score(scaled_score, expected_score, test):\\n denominator_dict = {'sdmt': 2.790,\\n 'bvmt': 2.793,\\n 'cvlt': 2.801}\\n\\n denominator = denominator_dict.get(test)\\n\\n z_score = (scaled_score - expected_score)/denominator\\n\\n return z_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db42d74e4ba1ed69cf6c45ab5c7544ee\",\n \"score\": \"0.6023797\",\n \"text\": \"def get_score(self) -> float:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a2947259caa75efb1d8d35827aa97bb\",\n \"score\": \"0.60219604\",\n \"text\": \"def score(self, member):\\r\\n return self.client.zscore(self.name, member)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f3a0bf0ba08e71441d9017bef9d20b\",\n \"score\": \"0.6010244\",\n \"text\": \"def z_score(self):\\n control = VariantStat(self.experiment.control, self.experiment)\\n\\n alternative = self\\n\\n if control.name == alternative.name:\\n return 'N/A'\\n\\n conv_c = control.conversion_rate\\n conv_a = alternative.conversion_rate\\n\\n num_c = control.participant_count\\n num_a = alternative.participant_count\\n\\n if conv_c == 0 or conv_a == 0:\\n return 0\\n\\n numerator = conv_a - conv_c\\n\\n frac_c = (conv_c * (1 - conv_c)) / float(num_c)\\n frac_a = (conv_a * (1 - conv_a)) / float(num_a)\\n\\n if frac_c + frac_a == 0:\\n # square root of 0 is 0, so no need to calculate\\n return 0\\n elif frac_c + frac_a < 0:\\n # can't take a square root of a negative number,\\n # so return 'Invalid'\\n return 'Invalid'\\n\\n return numerator / math.sqrt((frac_c + frac_a))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db9623e3168adac427129f947467fe01\",\n \"score\": \"0.59958607\",\n \"text\": \"def SquaredZScoreInWorldSpace(self, point: 'itkPointD3') -> \\\"double\\\":\\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject3_SquaredZScoreInWorldSpace(self, point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d414d8a0b9dac5c06073d50832ad5685\",\n \"score\": \"0.59949404\",\n \"text\": \"def SquaredZScoreInObjectSpace(self, point: 'itkPointD3') -> \\\"double\\\":\\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject3_SquaredZScoreInObjectSpace(self, point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbeb0aa9fca4b72da8f4275f00e9ab99\",\n \"score\": \"0.59595954\",\n \"text\": \"def Zscore(self, kmer):\\n mean = self.eValue(kmer)\\n N = self.N\\n p = mean/N #finds p from the mean.\\n stdDev = (N*p*(1 - p))**0.5 #finds stdDeviation\\n\\n Ztop = self.kmerCounts[kmer][0] - mean\\n \\n return Ztop/stdDev\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d13d515807810bbea8099b2ee5a663\",\n \"score\": \"0.59485126\",\n \"text\": \"def z_score(dist, num):\\n mu = float(sum((key * val for (key, val)\\n in zip(dist.keys(),\\n dist.values())))) / 1000\\n\\n # print show_hist(dist)\\n print \\\"mean is: \\\" + str(mu)\\n sigma = math.sqrt(float(sum(((key - mu) ** 2) * val for (key, val)\\n in zip(dist.keys(),\\n dist.values()))) / 1000)\\n\\n print \\\"standart is: \\\" + str(sigma)\\n print \\\"tripple st dev:\\\" + str(mu + 3 * sigma)\\n return (num - mu) / sigma\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e774de9fc5eaa8a9a96da62a24700f2\",\n \"score\": \"0.5948267\",\n \"text\": \"def set_zscores(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f76f124efa1c1489560ceaaa1b1057f\",\n \"score\": \"0.5924716\",\n \"text\": \"def SquaredZScoreInObjectSpace(self, point: 'itkPointD2') -> \\\"double\\\":\\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject2_SquaredZScoreInObjectSpace(self, point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd7b02f7a5dde6074639eceee4fb1a3\",\n \"score\": \"0.59235656\",\n \"text\": \"def sorted_score(self, k: str, m: str) -> Union[float, None]:\\n conn = self.get_conn()\\n return conn.zscore(k, m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d7ebb287e80fdfa23f90a6c66fe585\",\n \"score\": \"0.59227055\",\n \"text\": \"def SquaredZScoreInWorldSpace(self, point: 'itkPointD2') -> \\\"double\\\":\\n return _itkGaussianSpatialObjectPython.itkGaussianSpatialObject2_SquaredZScoreInWorldSpace(self, point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db854da7e2241d1c9618ba95aa0b6a8b\",\n \"score\": \"0.5864021\",\n \"text\": \"def z_eval(self):\\n if not hasattr(self, '_zeval'):\\n\\n if self._args['evaluate_mc_at_zlens']:\\n self._zeval = self.z\\n else:\\n self._zeval = self.z_infall\\n\\n return self._zeval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c40100f9e55858b0ab51e81aa4ce62\",\n \"score\": \"0.5846473\",\n \"text\": \"def z(self) -> float:\\n return self._z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ac6c28e2e253ff235afab6da251fe3a\",\n \"score\": \"0.58452505\",\n \"text\": \"def threshold_by_zscore(zscored_data, time, minimum_duration=0.015, zscore_threshold=2):\\n is_above_mean = zscored_data >= 0\\n is_above_threshold = zscored_data >= zscore_threshold\\n\\n return extend_threshold_to_mean(\\n is_above_mean, is_above_threshold, time, minimum_duration=minimum_duration\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f68c7214412be2bd4790b674ebe0ad7\",\n \"score\": \"0.5815507\",\n \"text\": \"def get_score(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18da72954e262bc2bc0eb509ab11a46\",\n \"score\": \"0.5778263\",\n \"text\": \"def wZ(self, z):\\r\\n return self.w0 * np.sqrt(1 + (z / self.zR) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3ce039d4fce67e51f63cdff71b27f1\",\n \"score\": \"0.5752515\",\n \"text\": \"def z_critical_value(cls, alpha, dir):\\n if dir == cls.TWO_TAILED_TEST:\\n alpha /= 2.0\\n\\n z = norm.ppf(1 - alpha)\\n if dir == StatTool.ONE_TAILED_NEGATIVE_TEST:\\n z = 0 - z\\n return z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a84a09df14c8964e5f8753ec4498895f\",\n \"score\": \"0.57146937\",\n \"text\": \"def normalize_zscore(data, z=2, offset=0.5, clip=False):\\n mean = np.mean(data)\\n std = np.std(data)\\n img = ((data - mean) / (2 * std * z) + offset) \\n if clip:\\n # print ('Before')\\n # print (np.min(img), np.max(img))\\n img = np.clip(img, -0.0, 1.0)\\n # print ('After clip')\\n # print (np.min(img), np.max(img))\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac26db3b124e3947506297470d4ed5f\",\n \"score\": \"0.571298\",\n \"text\": \"def z_get(self, proxy, key):\\n return self._client.zscore(key, proxy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c8f389df059e4999a4a9b0369104525\",\n \"score\": \"0.57076114\",\n \"text\": \"def probability_for_z(cls, z_score, dir):\\n assert dir in cls.valid_dirs\\n\\n # cdv is the probability that any sample will have LESS than z_score\\n cdv = norm.cdf(z_score)\\n\\n if dir == cls.TWO_TAILED_TEST:\\n if z_score < 0:\\n dir = cls.ONE_TAILED_NEGATIVE_TEST\\n else:\\n dir = cls.ONE_TAILED_POSITIVE_TEST\\n\\n if dir == cls.ONE_TAILED_POSITIVE_TEST:\\n return 1 - cdv\\n else:\\n return cdv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddff10cdc4fcf7c3ae8aa9d0dea252a6\",\n \"score\": \"0.5698196\",\n \"text\": \"def var_modified(r, level = 5):\\n #compute the Z score assuming it was Gausian\\n z= norm.ppf(level/100)\\n #if modified:\\n # modify the Z score based on observed skewness and kurtosis\\n s=skewness(r)\\n k=kurtosis(r)\\n z=(z +\\n (z**2-1)*s/6 + \\n (z**3 -3*z)*(k-3)/24 -\\n (2*z**3 - 5*z)*(s**2)/36\\n )\\n return -(r.mean() + z*r.std(ddof = 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f433132958d74f2171254fe150c2033\",\n \"score\": \"0.56827646\",\n \"text\": \"def zmax(self):\\n if self._zmax is None:\\n self._zmax = self.rmax\\n return self._zmax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbb52be9a9b0ab7ada2fffe4dc821349\",\n \"score\": \"0.56279254\",\n \"text\": \"def get_score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86513526f07539ac8651bf91d21c69e8\",\n \"score\": \"0.56194705\",\n \"text\": \"def z_score(tableau, ur):\\n zScore = 0\\n for j in tableau[ur]:\\n zScore += tableau[ur][j][2]\\n return zScore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05717db3354e3172f361963721c1d58a\",\n \"score\": \"0.5546481\",\n \"text\": \"def z_value(x: float, mean: float, std: float) -> float:\\n return (x-mean) / std\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4479528e768431bd5e61a0d9eb3d352d\",\n \"score\": \"0.55305403\",\n \"text\": \"def R2(self, z, zpred):\\n\\n\\n zmean = np.average(z)\\n\\n return 1 - np.sum((z - zpred)**2)/np.sum((z - zmean)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e94247201b5761983384f234e6755d\",\n \"score\": \"0.5527806\",\n \"text\": \"def zscore(A, axis=None):\\n return (A - np.mean(A,axis=axis)) / np.std(A,axis=axis,ddof=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66559194e54019dbd45ca832f8553605\",\n \"score\": \"0.55251133\",\n \"text\": \"def outliers_modified_z_score(self, ys, y):\\n threshold = 3.5\\n\\n median_y = np.median(ys)\\n median_absolute_deviation_y = np.median([np.abs(y - median_y) for y in ys])\\n modified_z_scores = 0.6745 * (y - median_y) / median_absolute_deviation_y\\n return np.where(np.abs(modified_z_scores) > threshold)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437de78c57999e8bc2d3967e81698b4d\",\n \"score\": \"0.5522258\",\n \"text\": \"def z_eval(self):\\n return self.z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2258ec09fbf7d74eb4dc3878cee20a5\",\n \"score\": \"0.55171925\",\n \"text\": \"def get_a_from_z(self, z):\\n z = np.array(z)\\n if np.any(z < 0.0):\\n raise ValueError(\\n \\\"Cannot convert negative redshift to scale factor\\\")\\n return 1.0/(1.0+z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef7e30711e78b06da46136ccf1bc31d2\",\n \"score\": \"0.54868895\",\n \"text\": \"def get_clinical_Z_range(): \\n return (-105.0, 100000000.0) # in mm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf3accf9bcd511a1b5a157bdbb04cfa6\",\n \"score\": \"0.54471064\",\n \"text\": \"def raw_score(self):\\n return self._raw_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c3cbe7285b3c0cba21e9786e7c813b\",\n \"score\": \"0.5436805\",\n \"text\": \"def get_score(self):\\n raise NotImplementedError(\\\"This method must be overridden\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aafa86dc8337437eff19f347d4e7db14\",\n \"score\": \"0.542814\",\n \"text\": \"def zscoreToPercentile(zscore):\\n retVal = healthdb.util.NaN\\n # WHO technical specs chapter 7: \\\"However, a restriction was imposed on\\n # all indicators to enable the derivation of percentiles only within\\n # the interval corresponding to z-scores between -3 and 3. The\\n # underlying reasoning is that percentiles beyond +-3 SD are invariant\\n # to changes in equivalent z-scores. The loss accruing to this\\n # restriction is small since the inclusion range corresponds to the\\n # 0.135th to 99.865th percentiles.\\\"\\n if math.fabs(zscore) <= 3:\\n absVal = math.fabs(zscore)\\n P1 = (1 - 1 / math.sqrt(2 * math.pi) * math.exp(-math.pow(absVal, 2) / 2)\\n * (\\n 0.31938 * (1 / (1 + 0.2316419 * absVal))\\n - 0.356563782 * math.pow((1 / (1 + 0.2316419 * absVal)), 2)\\n + 1.781477937 * math.pow((1 / (1 + 0.2316419 * absVal)), 3)\\n - 1.82125 * math.pow((1 / (1 + 0.2316419 * absVal)), 4)\\n + 1.330274429 * math.pow((1 / (1 + 0.2316419 * absVal)), 5)\\n ))\\n \\n if zscore > 0:\\n P1 = P1 * 100\\n else:\\n P1 = 100 - P1 * 100\\n \\n if 0 <= P1 and P1 <= 100:\\n retVal = P1\\n return retVal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19b11413fb191d6925413b7ebf74b01\",\n \"score\": \"0.53977555\",\n \"text\": \"def getZScore(self, index):\\n # Calculate mean and standard deviation of the list of values\\n mu = np.mean(self.matrix[:, index], axis=0)\\n sd = np.std(self.matrix[:, index], axis=0)\\n # Calculate Z-score for the given column for each gene\\n zscore = (self.matrix[:, index] - mu) / sd\\n mygenes = {}\\n for (name, ndx) in self.genes.items():\\n try:\\n mygenes[name] = zscore[ndx, :]\\n except IndexError:\\n mygenes[name] = zscore[ndx]\\n # Return the dictionary of Z-scores\\n return mygenes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e326cfd1ca90e434102efeb46af2d1f6\",\n \"score\": \"0.53955096\",\n \"text\": \"def get_score(self, value):\\n try:\\n encodedvalue = PickleEncoder().encode(value)\\n except Exception, e:\\n raise RedisError(\\\"encode error:%s\\\" % e)\\n\\n try:\\n score = self._db.zscore(self.namespace, encodedvalue)\\n return score\\n except Exception, e:\\n raise RedisError(\\\"redis error:%s\\\" % e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed457a87f10952f48019978b8e1e1d36\",\n \"score\": \"0.5376343\",\n \"text\": \"def zscoreFromAttribute(anthroConfig):\\n for row in csv.DictReader(open(anthroConfig.fileName)):\\n if 'age' in row:\\n dataAgeHeightOrLength = row['age']\\n elif 'length' in row:\\n dataAgeHeightOrLength = row['length']\\n elif 'height' in row:\\n dataAgeHeightOrLength = row['height']\\n if int(row['sex']) == anthroConfig.sex and float(dataAgeHeightOrLength) == anthroConfig.ageHeightOrLength:\\n return zscoreOtherRestricted(anthroConfig.measureKey, float(row['l']), float(row['m']), float(row['s']), True) \\n return healthdb.util.NaN\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fad2d8b9197199d7edc5fda8187214db\",\n \"score\": \"0.5350002\",\n \"text\": \"def score(self):\\n pos = self.scores.filter(value=1).count()\\n n = self.scores.count()\\n return wilson_ci_lower_bound(n, pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfed7dbefd669d898835324cabe153d1\",\n \"score\": \"0.53445476\",\n \"text\": \"def NO_SCORE() -> int:\\n return -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8538005b30ce8cfd3ed977e86d84b93a\",\n \"score\": \"0.5340626\",\n \"text\": \"def Z(self) -> int:\\r\\n return self._Z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea26fb8ca8fcf09b92a47eeba81c975a\",\n \"score\": \"0.5333083\",\n \"text\": \"def get_best_score(self):\\n return self.best_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1716b29e9d7a608b2938d8cc1634e9c6\",\n \"score\": \"0.5331177\",\n \"text\": \"def compute(cls, observation, prediction):\\n assert type(observation) is dict\\n assert type(prediction) is dict\\n try:\\n p_value = prediction['mean'] # Use the prediction's mean. \\n except (TypeError,KeyError): # If there isn't one...\\n try:\\n p_value = prediction['value'] # Use the prediction's value. \\n except TypeError: # If there isn't one...\\n p_value = prediction # Use the prediction (assume it is numeric).\\n o_mean = observation['mean']\\n o_std = observation['std']\\n value = (p_value - o_mean)/o_std\\n value = utils.assert_dimensionless(value)\\n return ZScore(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"100ea5e8e6e0474083b076a4a68719d7\",\n \"score\": \"0.53240174\",\n \"text\": \"def compute_best_risk(self):\\n has_chi2 = (self.chi2_fit != 0).sum(axis=1) > 0 \\n mask = (has_chi2) & (self.zbest > self.zgrid[0])\\n \\n zbest_grid = np.dot(self.zbest[mask, None],\\n np.ones_like(self.zgrid)[None,:])\\n L = self._loss((zbest_grid-self.zgrid)/(1+self.zgrid))\\n #dz = np.gradient(self.zgrid)\\n \\n zbest_risk = np.zeros(self.NOBJ, dtype=self.ARRAY_DTYPE)-1\\n zbest_risk[mask] = np.dot(np.exp(self.lnp[mask,:])*L, self.trdz)\\n \\n del(has_chi2)\\n del(mask)\\n del(zbest_grid)\\n del(L)\\n \\n return zbest_risk\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70832f4b5008dc1a9bab31f1fb77d2c\",\n \"score\": \"0.5320918\",\n \"text\": \"def score_one(self, x: dict) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70832f4b5008dc1a9bab31f1fb77d2c\",\n \"score\": \"0.5320918\",\n \"text\": \"def score_one(self, x: dict) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b65cb58654be9d87f5140f9f692e8d\",\n \"score\": \"0.5320872\",\n \"text\": \"def wZ(w0, z, zR):\\r\\n return w0 * np.sqrt(1 + (z / zR) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57bd528109934590edcc37520b8aecc6\",\n \"score\": \"0.53161615\",\n \"text\": \"def get_score(self):\\n return self._points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d145a7dc3c8c2db905526386567322af\",\n \"score\": \"0.5306321\",\n \"text\": \"def norm_score(self):\\n return self.score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ba4834d38dd73afa4faf78179c59de\",\n \"score\": \"0.53024745\",\n \"text\": \"def compute_full_risk(self):\\n zsq = np.dot(self.zgrid[:,None], np.ones_like(self.zgrid)[None,:])\\n L = self._loss((zsq-self.zgrid)/(1+self.zgrid))\\n \\n Rz = self.lnp*0.\\n \\n has_chi2 = (self.chi2_fit != 0).sum(axis=1) > 0 \\n hasz = self.zbest > self.zgrid[0]\\n idx = self.idx[hasz & (has_chi2)]\\n \\n for i in idx:\\n Rz[i,:] = np.dot(np.exp(self.lnp[i,:])*L, self.trdz)\\n \\n del(zsq)\\n del(L)\\n del(has_chi2)\\n del(hasz)\\n del(idx) \\n return Rz\\n \\n #self.full_risk = Rz\\n #self.min_risk = self.zgrid[np.argmin(Rz, axis=1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b8f1cc00b5b07453048380c9183176\",\n \"score\": \"0.5287981\",\n \"text\": \"def getScore(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b8f1cc00b5b07453048380c9183176\",\n \"score\": \"0.5287981\",\n \"text\": \"def getScore(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb862fa989f902036a49cc1625a4f90\",\n \"score\": \"0.528489\",\n \"text\": \"def max_score(self):\\n return self.weight if self.has_score else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3440f70adf1c7fd88bdefc2f668b642d\",\n \"score\": \"0.5280114\",\n \"text\": \"def constant_score(self):\\n return self._constant_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dc96d30401cecf5b2207a98a0df1a10\",\n \"score\": \"0.5262474\",\n \"text\": \"def get_score(self):\\n score = 1\\n if self.answer:\\n score = 3\\n if not self.correct:\\n score *= -1\\n return score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7e62a3d3390f0de2732aa78ed1d28e\",\n \"score\": \"0.5253121\",\n \"text\": \"def __call__(self, Z):\\n\\n return tf.math.maximum(Z, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1836f7e1518a3851013eb49b808aa889\",\n \"score\": \"0.52435356\",\n \"text\": \"def score(self):\\n return self.__score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3de3634c605416248a0eb6a0e5726904\",\n \"score\": \"0.52232313\",\n \"text\": \"def score(self):\\n check_is_fitted(self, [\\\"lower_bound_\\\"])\\n return self.lower_bound_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a79fbce580c66f934eacea64394c344\",\n \"score\": \"0.52211654\",\n \"text\": \"def z(self):\\n return self._vct.z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58de9a1a700da120b9ef3b0822a54958\",\n \"score\": \"0.5218734\",\n \"text\": \"def saclhitsrate(self) :\\n try :\\n return self._saclhitsrate\\n except Exception as e:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789844b69cf207afb25c9696fe51dbb2\",\n \"score\": \"0.5213474\",\n \"text\": \"def score(self):\\n return self._score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"953ef2864b7c9b9b26aa003dab0961b4\",\n \"score\": \"0.5205887\",\n \"text\": \"def fowlkes_mallows_index(y_true: np.array, y_score: np.array) -> float:\\n ppv = positive_predictive_value(y_true, y_score)\\n tpr = true_positive_rate(y_true, y_score)\\n fm = (ppv * tpr) ** 0.5\\n return fm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a542313a7482d565193ff9df0cef3138\",\n \"score\": \"0.5202119\",\n \"text\": \"def detect_outlier_zscore(df):\\n\\tthreshold = 3\\n\\tz = np.abs(stats.zscore(df))\\n\\toutliers = np.where(z > threshold)\\n\\treturn outliers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f99c4afcdafcff521b56c5a458723e4\",\n \"score\": \"0.5199307\",\n \"text\": \"def getZonePercentageFeatureValue(self):\\n Nz = self.coefficients['Nz']\\n Np = self.coefficients['Np']\\n\\n zp = Nz / Np\\n return zp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81da7f92b66bb19c61c2617346cad4fe\",\n \"score\": \"0.5194981\",\n \"text\": \"def zprob(z):\\n Z_MAX = 6.0 # maximum meaningful z-value\\n if z == 0.0:\\n x = 0.0\\n else:\\n y = 0.5 * math.fabs(z)\\n if y >= (Z_MAX * 0.5):\\n x = 1.0\\n elif (y < 1.0):\\n w = y * y\\n x = ((((((((0.000124818987 * w\\n - 0.001075204047) * w + 0.005198775019) * w\\n - 0.019198292004) * w + 0.059054035642) * w\\n - 0.151968751364) * w + 0.319152932694) * w\\n - 0.531923007300) * w + 0.797884560593) * y * 2.0\\n else:\\n y = y - 2.0\\n x = (((((((((((((-0.000045255659 * y\\n + 0.000152529290) * y - 0.000019538132) * y\\n - 0.000676904986) * y + 0.001390604284) * y\\n - 0.000794620820) * y - 0.002034254874) * y\\n + 0.006549791214) * y - 0.010557625006) * y\\n + 0.011630447319) * y - 0.009279453341) * y\\n + 0.005353579108) * y - 0.002141268741) * y\\n + 0.000535310849) * y + 0.999936657524\\n if z > 0.0:\\n prob = ((x + 1.0) * 0.5)\\n else:\\n prob = ((1.0 - x) * 0.5)\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23444449548c751d57eb8a7312433623\",\n \"score\": \"0.5192021\",\n \"text\": \"def credit_score(self):\\n return self._credit_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1706217b6bb470a64b395196f3a35f76\",\n \"score\": \"0.5191078\",\n \"text\": \"def res_function(X, Z):\\n X_col = X.reshape((-1, 1))\\n Z_row = Z.reshape((1, -1))\\n return np.maximum(Z_row - X_col + margin, 0).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b4d0a2f8fbc43bd9a9ce40b6891f4a6\",\n \"score\": \"0.5191033\",\n \"text\": \"def lcz(self, zbest=None):\\n if zbest is None:\\n zbest = self.zbest\\n \\n _lcz = np.dot(1/(1+zbest[:, np.newaxis]), self.pivot[np.newaxis,:])\\n return _lcz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350f7ffdbf74204002d650b60f84004b\",\n \"score\": \"0.5189782\",\n \"text\": \"def Score(cls):\\n\\n return ScoreMask()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595a292a563ab3746659d808dd8b2ba1\",\n \"score\": \"0.51834446\",\n \"text\": \"def custom_score(game, player):\\n # TODO: finish this function!\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n def calculate_score(moves):\\n if len(moves) == 0:\\n return 0\\n centered_locs = np.array(moves) - (game.height/2.0, game.width/2.0)\\n #print(centered_locs)\\n return np.sum(1/LA.norm(centered_locs,ord=2,axis=1))\\n\\n own_moves = game.get_legal_moves(player)\\n opp_moves = game.get_legal_moves(game.get_opponent(player))\\n\\n return float(calculate_score(own_moves)-calculate_score(opp_moves))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":186,"cells":{"query_id":{"kind":"string","value":"bdeff234398c45a1b8d7776f2ff0c2a2"},"query":{"kind":"string","value":"Class method, which allows the user to populate the Database"},"positive_passages":{"kind":"list like","value":[{"docid":"5e4f589475625e693b789b714ddcc790","score":"0.0","text":"def populate(self):\n data = api_research_off()\n for product in data['products']:\n product_name = self.get_product_name(product)\n nutriscore = self.get_nutriscore(product)\n if nutriscore is None:\n continue\n nutritional_list = self.get_nutritional_mark_for_100g(product)\n if nutritional_list is None:\n continue\n url_page = self.get_url_page(product)\n url_image = self.get_url_image(product)\n if(not url_image):\n continue\n id_product = self.get_id_product(product)\n if(not id_product):\n continue\n last_modified_date = self.get_last_modified(product)\n food = Food(name=product_name,\n nutriscore=nutriscore,\n url=url_page,\n url_picture=url_image,\n fat_100g=nutritional_list[0],\n saturated_fat_100g=nutritional_list[2],\n sugars_100g=nutritional_list[4],\n salt_100g=nutritional_list[6],\n fat_level=nutritional_list[1],\n salt_level=nutritional_list[7],\n saturated_fat_level=nutritional_list[3],\n sugars_level=nutritional_list[5],\n last_modified=last_modified_date,\n openff_id=id_product)\n food.save()\n self.get_and_insert_categories_in_db(product, food)","title":""}],"string":"[\n {\n \"docid\": \"5e4f589475625e693b789b714ddcc790\",\n \"score\": \"0.0\",\n \"text\": \"def populate(self):\\n data = api_research_off()\\n for product in data['products']:\\n product_name = self.get_product_name(product)\\n nutriscore = self.get_nutriscore(product)\\n if nutriscore is None:\\n continue\\n nutritional_list = self.get_nutritional_mark_for_100g(product)\\n if nutritional_list is None:\\n continue\\n url_page = self.get_url_page(product)\\n url_image = self.get_url_image(product)\\n if(not url_image):\\n continue\\n id_product = self.get_id_product(product)\\n if(not id_product):\\n continue\\n last_modified_date = self.get_last_modified(product)\\n food = Food(name=product_name,\\n nutriscore=nutriscore,\\n url=url_page,\\n url_picture=url_image,\\n fat_100g=nutritional_list[0],\\n saturated_fat_100g=nutritional_list[2],\\n sugars_100g=nutritional_list[4],\\n salt_100g=nutritional_list[6],\\n fat_level=nutritional_list[1],\\n salt_level=nutritional_list[7],\\n saturated_fat_level=nutritional_list[3],\\n sugars_level=nutritional_list[5],\\n last_modified=last_modified_date,\\n openff_id=id_product)\\n food.save()\\n self.get_and_insert_categories_in_db(product, food)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"11c5c10dcb7519f823633e95d691706e","score":"0.7409577","text":"def _populateTables(cls):\n for method in dir(Populate):\n if not method.startswith('__'):\n populateTable = getattr(Populate, method)\n print 'Inserting data for %s ...' % method\n populateTable()\n\n db.session.commit()","title":""},{"docid":"dda5e8e7e0ff2e9f2e1e78366231c75b","score":"0.7352905","text":"def __call__(self):\n self.create_database()","title":""},{"docid":"e04dc2fb4584516abc29cd5b12401ab9","score":"0.7279164","text":"def populate():","title":""},{"docid":"3738361c8de7ca311743c12b407f463c","score":"0.7264388","text":"def populate_db_command():\n populate_db()\n click.echo(\"Created dummy data.\")","title":""},{"docid":"aeb3e562c42462ac59c49c99c1f6c53e","score":"0.72629017","text":"def handle(self, *args, **options):\n self._populate_db()","title":""},{"docid":"e755fbb017c46d5659f9eac88663f994","score":"0.72245425","text":"def populate_db():\n database.create_tables([Donor, Donation])\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n #database = SqliteDatabase('donor.db')\n\n logger.info('Working with Person class')\n logger.info('Note how I use constants and a list of tuples as a simple schema')\n logger.info('Normally you probably will have prompted for this from a user')\n\n# NAME = 0\n# DONATION = 1\n# \n donors = [('Karl', [100, 200, 300.55]),\n ('Luis', [400, 500, 600]),\n ('Woody', [700, 800, 900]),\n ('Mary', [455.99])]\n \n logger.info('Creating Donor records: iterate through the list of tuples')\n logger.info('Prepare to explain any errors with exceptions')\n logger.info('and the transaction tells the database to fail on error')\n \n for users, donations in donors:\n user = Donor.create(username=users)\n for donation in donations:\n Donation.create(user=user, donation=float(donation))","title":""},{"docid":"13029ab8865339e29a5c71c47127fda5","score":"0.71562296","text":"def data_into_database():\n pass","title":""},{"docid":"d20c034a8c3cbc0832dc74bb4da1b0f3","score":"0.71474314","text":"def fromDB(self):\n #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n pass","title":""},{"docid":"fdc0072cd1eefb95b2048c8061190efe","score":"0.7144774","text":"def initiate(cls):\n cls._purgeTables()\n cls._populateTables()\n print 'DB initialization finished!!'","title":""},{"docid":"8f9911dbbe0284a285ab8f29ced0957c","score":"0.7093239","text":"def populate(self, connection, system):","title":""},{"docid":"7efc43d24ebff7e863c3941c631b63d4","score":"0.704847","text":"def init_db():\n\n with app.app_context():\n data = json.loads(urllib2.urlopen(DATA_URL).read())\n ds = DataSource(data['data'], data['meta']['view']['columns'])\n headers = ds.get_headers(\n set([\n 'Applicant', \n 'Address', \n 'FoodItems', \n 'Latitude', \n 'Longitude'\n ]),\n 'name'\n )\n db = get_db()\n db.init_database()\n for item in ds.gen_items(headers):\n try:\n db.add_row(\n item['Applicant'], \n item['Address'], \n item['FoodItems'], \n item['Latitude'], \n item['Longitude']\n )\n except psycopg2.IntegrityError:\n pass","title":""},{"docid":"8a8560696f49cd0ff48c227dff9a1f49","score":"0.69914603","text":"def populate_db2(self):\n db.create_all()\n\n informantRelationships = self.create_informant_relationships()\n users = self.create_users()\n finalCodes = self.create_final_codes()\n states = self.create_states()\n abstractStatuses = self.create_abstract_statuses()\n sexes = self.create_sexes()\n races = self.create_races()\n ethnicities = self.create_ethnicities()\n vitals = self.create_vital_statuses()\n contacts = self.create_contacts()\n inactives = self.create_inactives()\n ucrReportTypes = self.create_ucr_report_types()\n physicianStatuses = self.create_physician_statuses()\n physFacilityStatuses = self.create_physician_facility_statuses()\n phoneTypes = self.create_phone_types()\n irbHolders = self.create_irb_holders()\n projectTypes = self.create_project_types()\n contactStatuses = self.create_contact_statuses()\n contactSources = self.create_contact_sources()\n grantStatuses = self.create_grant_statuses()\n fundingSources = self.create_funding_sources()\n reviewCommitteeStatuses = self.create_review_committee_statuses()\n projectStatuses = self.create_project_statuses()\n logTypes = self.create_log_subjects()\n reviewCommittees = self.create_review_committees()\n staffRoles = self.create_staff_roles()\n projectPhases = self.create_project_phases()\n hsts = self.create_human_subject_trainings()\n tracingSources = self.create_tracing_sources()\n contactTypes = self.create_contact_types()\n ucrRoles = self.create_ucr_roles()\n giftCards = self.create_gift_cards()\n\n project1 = models.Project(\n projectTypeID=1,\n irbHolderID=1,\n projectTitle=\"Test Project\",\n shortTitle=\"Test Project\",\n projectSummary=\"Summary\",\n sop=\"sop\",\n ucrProposal=\"ucr_proposal\",\n budgetDoc=\"budget_doc\",\n ucrFee=\"no\",\n ucrNoFee=\"yes\",\n previousShortTitle=\"t short\",\n dateAdded=datetime(2016, 2, 2),\n finalRecruitmentReport=\"report\",\n ongoingContact=True,\n activityStartDate=datetime(2016, 2, 2),\n activityEndDate=datetime(2016, 2, 2))\n\n project2 = models.Project(\n projectTypeID=1,\n irbHolderID=1,\n projectTitle=\"Test Project\",\n shortTitle=\"Test Project\",\n projectSummary=\"Summary\",\n sop=\"sop\",\n ucrProposal=\"ucr_proposal\",\n budgetDoc=\"budget_doc\",\n ucrFee=\"no\",\n ucrNoFee=\"yes\",\n previousShortTitle=\"t short\",\n dateAdded=datetime(2016, 2, 2),\n finalRecruitmentReport=\"report\",\n ongoingContact=True,\n activityStartDate=datetime(2016, 2, 2),\n activityEndDate=datetime(2016, 2, 2))\n\n budget1 = models.Budget(\n projectID=1,\n numPeriods=1,\n periodStart=datetime(2016, 2, 2),\n periodEnd=datetime(2016, 2, 2),\n periodTotal=1.23,\n periodComment=\"comment\")\n\n rc = models.ReviewCommittee(\n projectID=1,\n reviewCommitteeStatusID=1,\n reviewCommitteeLUTID=1,\n reviewCommitteeNumber=\"1\",\n dateInitialReview=datetime(2016, 2, 2),\n dateExpires=datetime(2016, 2, 2),\n rcNote=\"rc_note\",\n rcProtocol=\"rc_proto\",\n rcApproval=\"rc_approval\")\n\n ucr = models.UCRReport(\n projectID=1,\n reportTypeID=1,\n reportSubmitted=datetime(2016, 2, 2),\n reportDue=datetime(2016, 2, 2),\n reportDoc=\"doc\"\n )\n arcReview = models.ArcReview(\n projectID=1,\n reviewType=1,\n dateSentToReviewer=datetime(2016, 2, 2),\n reviewer1=1,\n reviewer1Rec=1,\n reviewer1SigDate=datetime(2016, 2, 2),\n reviewer1Comments=\"test comment\",\n reviewer2=2,\n reviewer2Rec=2,\n reviewer2SigDate=datetime(2016, 2, 2),\n reviewer2Comments=\"test comment\",\n research=1,\n linkage=False,\n contact=True,\n engaged=True,\n nonPublicData=True)\n\n funding = models.Funding(\n grantStatusID=1,\n projectID=1,\n fundingSourceID=1,\n primaryFundingSource=\"pfs\",\n secondaryFundingSource=\"sfs\",\n fundingNumber=\"number\",\n grantTitle=\"title\",\n dateStatus=datetime(2016, 2, 2),\n grantPi=1,\n primaryChartfield=\"pcf\",\n secondaryChartfield=\"scf\"\n )\n\n staff = models.Staff(\n userID=1,\n firstName=\"Aaron\",\n lastName=\"Thomas\",\n middleName=\"Pulver\",\n email=\"aaron.pulver@utah.edu\",\n phoneNumber=\"phone\",\n phoneComment=\"phoneComment\",\n institution=\"institution\",\n department=\"department\",\n position=\"position\",\n credentials=\"credentials\",\n street=\"street\",\n city=\"city\",\n stateID=1,\n ucrRoleID=1\n )\n staff2 = models.Staff(\n userID=2,\n firstName=\"Phoebe\",\n lastName=\"\",\n middleName=\"McNeally\",\n email=\"email\",\n phoneNumber=\"phone\",\n phoneComment=\"phoneComment\",\n institution=\"institution\",\n department=\"department\",\n position=\"position\",\n credentials=\"credentials\",\n street=\"street\",\n city=\"city\",\n stateID=2,\n ucrRoleID=1\n )\n\n projStatus = models.ProjectStatus(\n projectStatusTypeID=1,\n projectID=1,\n staffID=1,\n statusDate=datetime(2016, 2, 2),\n statusNotes=\"notes\"\n )\n\n preApp = models.PreApplication(\n projectID=1,\n piFirstName=\"pi_fname\",\n piLastName=\"pi_lname\",\n piEmail=\"pi_email\",\n piPhone=\"pi_phone\",\n contactFirstName=\"contact_fname\",\n contactLastName=\"contact_lname\",\n contactPhone=\"contact_phone\",\n contactEmail=\"contact_email\",\n institution=\"institution\",\n institution2=\"institution2\",\n uid=\"uid\",\n udoh=1,\n projectTitle=\"project_title\",\n purpose=\"purpose\",\n irb0=True,\n irb1=True,\n irb2=True,\n irb3=True,\n irb4=True,\n otherIrb=\"other_irb\",\n updb=True,\n ptContact=True,\n startDate=datetime(2016, 2, 2),\n link=True,\n deliveryDate=datetime(2016, 2, 2),\n description=\"description\"\n )\n log = models.Log(\n logSubjectID=1,\n projectID=1,\n staffID=1,\n phaseStatusID=1,\n note=\"note\",\n date=datetime(2016, 2, 2)\n )\n projectStaff = models.ProjectStaff(\n staffRoleID=1,\n projectID=1,\n staffID=1,\n datePledge=datetime(2016, 2, 2),\n dateRevoked=datetime(2016, 2, 2),\n contactID=1,\n inactiveID=1\n )\n staffTraining = models.StaffTraining(\n staffID=1,\n humanSubjectTrainingID=1,\n dateTaken=datetime(2016, 2, 2),\n dateExpires=datetime(2016, 2, 2)\n )\n patient = models.Patient(\n patID=\"1\",\n ucrDistID=1,\n UPDBID=1,\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"mname\",\n maidenName=\"maiden_name\",\n aliasFirstName=\"alias_fname\",\n aliasLastName=\"alias_lname\",\n aliasMiddleName=\"alias_middle\",\n dobDay=15,\n dobMonth=2,\n dobYear=1990,\n SSN=\"999999999\",\n sexID=2,\n raceID=1,\n ethnicityID=1,\n vitalStatusID=1\n )\n patient2 = models.Patient(\n patID=\"1\",\n ucrDistID=1,\n UPDBID=1,\n firstName=\"fname2\",\n lastName=\"lname2\",\n middleName=\"mname2\",\n maidenName=\"maiden_name\",\n aliasFirstName=\"alias_fname\",\n aliasLastName=\"alias_lname\",\n aliasMiddleName=\"alias_middle\",\n dobDay=26,\n dobMonth=4,\n dobYear=1970,\n SSN=\"999999999\",\n sexID=1,\n raceID=2,\n ethnicityID=1,\n vitalStatusID=2\n )\n\n patientAddress = models.PatientAddress(\n contactInfoSourceID=1,\n participantID=1,\n contactInfoStatusID=1,\n street=\"street\",\n street2=\"street2\",\n city=\"city\",\n stateID=1,\n zip=\"12345\",\n addressStatusDate=datetime(2016, 2, 2),\n )\n\n patientEmail = models.PatientEmail(\n contactInfoSourceID=1,\n participantID=1,\n contactInfoStatusID=1,\n email=\"email\",\n emailStatusDate=datetime(2016, 2, 2)\n )\n patientPhone = models.PatientPhone(\n contactInfoSourceID=1,\n participantID=1,\n contactInfoStatusID=1,\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n patientPhone2 = models.PatientPhone(\n contactInfoSourceID=1,\n participantID=1,\n contactInfoStatusID=1,\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n informant1 = models.Informant(\n participantID=1,\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"middle_name\",\n informantPrimary=True,\n informantRelationshipID=1,\n notes=\"notes\"\n )\n informant2 = models.Informant(\n participantID=1,\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"middle_name\",\n informantPrimary=True,\n informantRelationshipID=1,\n notes=\"notes\"\n )\n informantAddress = models.InformantAddress(\n contactInfoSourceID=1,\n informantID=1,\n contactInfoStatusID=1,\n street=\"street\",\n street2=\"street2\",\n city=\"city\",\n stateID=2,\n zip=\"12345\",\n addressStatusDate=datetime(2016, 2, 2),\n )\n informantPhone = models.InformantPhone(\n contactInfoSourceID=1,\n informantID=1,\n contactInfoStatusID=1,\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n informantPhone2 = models.InformantPhone(\n contactInfoSourceID=1,\n informantID=1,\n contactInfoStatusID=1,\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n ctc1 = models.CTC(\n participantID=1,\n dxDateDay=2,\n dxDateMonth=7,\n dxDateYear=1988,\n site=\"Site 2\",\n histology=\"histology\",\n behavior=\"behavior\",\n ctcSequence=\"sequence\",\n stage=\"stage\",\n dxAge=1,\n dxStreet1=\"street1\",\n dxStreet2=\"street2\",\n dxCity=\"city\",\n dxStateID=1,\n dxZip=99999,\n dxCounty=\"county\",\n dnc=\"dnc\",\n dncReason=\"dnc_reason\",\n recordID=\"abc321\"\n )\n ctc2 = models.CTC(\n participantID=1,\n dxDateDay=3,\n dxDateMonth=10,\n dxDateYear=1958,\n site=\"Site 1\",\n histology=\"histology\",\n behavior=\"behavior\",\n ctcSequence=\"sequence\",\n stage=\"stage\",\n dxAge=1,\n dxStreet1=\"street1\",\n dxStreet2=\"street2\",\n dxCity=\"city\",\n dxStateID=2,\n dxZip=99999,\n dxCounty=\"county\",\n dnc=\"dnc\",\n dncReason=\"dnc_reason\",\n recordID=\"abc123\"\n )\n projectPatient = models.ProjectPatient(\n projectID=1,\n staffID=1,\n ctcID=1,\n currentAge=1,\n batch=1,\n siteGrp=1,\n finalCodeID=1,\n finalCodeDate=datetime(2016, 2, 2),\n enrollmentDate=datetime(2016, 2, 2),\n dateCoordSigned=datetime(2016, 2, 2),\n importDate=datetime(2016, 2, 2),\n finalCodeStaffID=1,\n enrollmentStaffID=1,\n dateCoordSignedStaffID=1,\n abstractStatusID=1,\n abstractStatusDate=datetime(2016, 2, 2),\n abstractStatusStaffID=1,\n sentToAbstractorDate=datetime(2016, 2, 2),\n sentToAbstractorStaffID=1,\n abstractedDate=datetime(2016, 2, 2),\n abstractorStaffID=1,\n researcherDate=datetime(2016, 2, 2),\n researcherStaffID=1,\n consentLink=\"link\",\n medRecordReleaseSigned=True,\n medRecordReleaseLink=\"link\",\n medRecordReleaseStaffID=1,\n medRecordReleaseDate=datetime(2016, 2, 2),\n surveyToResearcher=datetime(2016, 2, 2),\n surveyToResearcherStaffID=1,\n qualityControl=True,\n )\n\n projectPatient2 = models.ProjectPatient(\n projectID=1,\n staffID=1,\n ctcID=1,\n currentAge=1,\n batch=1,\n siteGrp=1,\n finalCodeID=1,\n finalCodeDate=datetime(2016, 2, 2),\n enrollmentDate=datetime(2016, 2, 2),\n dateCoordSigned=datetime(2016, 2, 2),\n importDate=datetime(2016, 2, 2),\n finalCodeStaffID=1,\n enrollmentStaffID=1,\n dateCoordSignedStaffID=1,\n abstractStatusID=1,\n abstractStatusDate=datetime(2016, 2, 2),\n abstractStatusStaffID=1,\n sentToAbstractorDate=datetime(2016, 2, 2),\n sentToAbstractorStaffID=1,\n abstractedDate=datetime(2016, 2, 2),\n abstractorStaffID=1,\n researcherDate=datetime(2016, 2, 2),\n researcherStaffID=1,\n consentLink=\"link\",\n medRecordReleaseSigned=True,\n medRecordReleaseLink=\"link\",\n medRecordReleaseStaffID=1,\n medRecordReleaseDate=datetime(2016, 2, 2),\n surveyToResearcher=datetime(2016, 2, 2),\n surveyToResearcherStaffID=1,\n qualityControl=False\n )\n\n tracing = models.Tracing(\n tracingSourceID=1,\n participantID=1,\n date=datetime(2016, 2, 2),\n staffID=1,\n notes=\"notes\"\n )\n physician = models.Physician(\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"middle_name\",\n credentials=\"credentials\",\n specialty=\"specialty\",\n aliasFirstName=\"alias_fname\",\n aliasLastName=\"alias_lname\",\n aliasMiddleName=\"alias_middle_name\",\n physicianStatusID=1,\n physicianStatusDate=datetime(2016, 2, 2),\n )\n\n physician2 = models.Physician(\n firstName=\"fname\",\n lastName=\"lname\",\n middleName=\"middle_name\",\n credentials=\"credentials\",\n specialty=\"specialty\",\n aliasFirstName=\"alias_fname\",\n aliasLastName=\"alias_lname\",\n aliasMiddleName=\"alias_middle_name\",\n physicianStatusID=1,\n physicianStatusDate=datetime(2016, 2, 2),\n )\n physicianAddress = models.PhysicianAddress(\n contactInfoSourceID=1,\n physicianID=1,\n contactInfoStatusID=1,\n street=\"street\",\n street2=\"street2\",\n city=\"city\",\n stateID=1,\n zip=\"12345\",\n addressStatusDate=datetime(2016, 2, 2),\n )\n\n physicianEmail = models.PhysicianEmail(\n contactInfoSourceID=1,\n physicianID=1,\n contactInfoStatusID=1,\n email=\"email\",\n emailStatusDate=datetime(2016, 2, 2)\n )\n\n physicianPhone = models.PhysicianPhone(\n contactInfoSourceID=1,\n physicianID=1,\n contactInfoStatusID=1,\n phoneNumber=\"phone\",\n phoneTypeID=1,\n phoneStatusDate=datetime(2016, 2, 2)\n )\n physicianPhone2 = models.PhysicianPhone(\n contactInfoSourceID=1,\n physicianID=1,\n contactInfoStatusID=1,\n phoneNumber=\"phone\",\n phoneTypeID=1,\n phoneStatusDate=datetime(2016, 2, 2)\n )\n physicianToCTC = models.PhysicianToCTC(\n physicianID=1,\n ctcID=1\n )\n facility1 = models.Facility(\n facilityName=\"name\",\n contactFirstName=\"fname\",\n contactLastName=\"lname\",\n facilityStatus=1,\n facilityStatusDate=datetime(2016, 2, 2),\n contact2FirstName=\"fname\",\n contact2LastName=\"lname\"\n )\n facility2 = models.Facility(\n facilityName=\"name\",\n contactFirstName=\"fname\",\n contactLastName=\"lname\",\n facilityStatus=1,\n facilityStatusDate=datetime(2016, 2, 2),\n contact2FirstName=\"fname\",\n contact2LastName=\"lname\"\n )\n facilityAddress = models.FacilityAddress(\n contactInfoSourceID=1,\n facilityID=1,\n contactInfoStatusID=1,\n street=\"street\",\n street2=\"street2\",\n city=\"city\",\n stateID=1,\n zip=\"12345\",\n addressStatusDate=datetime(2016, 2, 2),\n )\n\n facilityPhone = models.FacilityPhone(\n contactInfoSourceID=1,\n facilityID=1,\n contactInfoStatusID=1,\n clinicName=\"clinic\",\n phoneTypeID=1,\n phoneNumber=\"phone\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n facilityPhone2 = models.FacilityPhone(\n contactInfoSourceID=1,\n facilityID=1,\n contactInfoStatusID=1,\n clinicName=\"clinic\",\n phoneTypeID=1,\n phoneNumber=\"phone2\",\n phoneStatusDate=datetime(2016, 2, 2)\n )\n patientProjectStatusType1 = models.PatientProjectStatusLUT(\n statusDescription=\"desc\"\n )\n patientProjectStatusType2 = models.PatientProjectStatusLUT(\n statusDescription=\"desc\"\n )\n patientProjectStatus = models.PatientProjectStatus(\n patientProjectStatusTypeID=1,\n participantID=1,\n statusDate=datetime(2016,2,2)\n )\n physicianFacility = models.PhysicianFacility(\n facilityID=1,\n physicianID=1,\n physFacilityStatusID=1,\n physFacilityStatusDate=datetime(2016, 2, 2)\n )\n contact = models.Contact(\n contactTypeLUTID=1,\n participantID=1,\n staffID=1,\n informantID=1,\n informantPhoneID=1,\n description=\"desc\",\n contactDate=datetime(2016, 2, 2),\n initials=\"atp\",\n notes=\"notes\"\n )\n contact2 = models.Contact(\n contactTypeLUTID=1,\n participantID=1,\n staffID=1,\n facilityID=1,\n facilityPhoneID=1,\n description=\"desc\",\n contactDate=datetime(2016, 2, 2),\n initials=\"atp\",\n notes=\"notes\"\n )\n contact3 = models.Contact(\n contactTypeLUTID=1,\n participantID=1,\n staffID=1,\n physicianID=1,\n physicianPhoneID=1,\n description=\"desc\",\n contactDate=datetime(2016, 2, 2),\n initials=\"atp\",\n notes=\"notes\"\n )\n contact4 = models.Contact(\n contactTypeLUTID=1,\n participantID=1,\n staffID=1,\n patientPhoneID=1,\n description=\"desc\",\n contactDate=datetime(2016, 2, 2),\n initials=\"atp\",\n notes=\"notes\"\n )\n ctcFacility = models.CTCFacility(\n ctcID=1,\n facilityID=1,\n coc=123\n )\n incentive = models.Incentive(\n participantID=1,\n incentiveDescription=\"desc\",\n barcode=\"123456789\",\n dateGiven=datetime(2016, 4, 3)\n )\n db.session.add_all(ucrRoles)\n db.session.add_all(informantRelationships)\n db.session.add_all(users)\n db.session.add_all(states)\n db.session.add_all(finalCodes)\n db.session.add_all(sexes)\n db.session.add_all(abstractStatuses)\n db.session.add_all(races)\n db.session.add_all(ethnicities)\n db.session.add_all(vitals)\n db.session.add_all(contacts)\n db.session.add_all(inactives)\n db.session.add_all(ucrReportTypes)\n db.session.add_all(physicianStatuses)\n db.session.add_all(physFacilityStatuses)\n db.session.add_all(phoneTypes)\n db.session.add_all(irbHolders)\n db.session.add_all(projectTypes)\n db.session.add_all(contactStatuses)\n db.session.add_all(contactSources)\n db.session.add_all(grantStatuses)\n db.session.add_all(fundingSources)\n db.session.add_all(reviewCommitteeStatuses)\n db.session.add_all(projectStatuses)\n db.session.add_all(logTypes)\n db.session.add_all(reviewCommittees)\n db.session.add_all(staffRoles)\n db.session.add_all(projectPhases)\n db.session.add_all(hsts)\n db.session.add_all(tracingSources)\n db.session.add_all(contactTypes)\n db.session.add_all(giftCards)\n db.session.add(staff)\n db.session.add(staff2)\n db.session.add(project1)\n db.session.add(project2)\n db.session.add(funding)\n db.session.add(budget1)\n db.session.add(rc)\n db.session.add(ucr)\n db.session.add(arcReview)\n db.session.add(preApp)\n db.session.add(log)\n db.session.add(projectStaff)\n db.session.add(staffTraining)\n db.session.add(patient)\n db.session.add(patient2)\n db.session.add(patientAddress)\n db.session.add(patientEmail)\n db.session.add(patientPhone)\n db.session.add(patientPhone2)\n db.session.add(informant1)\n db.session.add(informant2)\n db.session.add(informantAddress)\n db.session.add(informantPhone)\n db.session.add(informantPhone2)\n db.session.add(ctc1)\n db.session.add(ctc2)\n db.session.add(projectPatient)\n db.session.add(projectPatient2)\n db.session.add(projStatus)\n db.session.add(tracing)\n db.session.add(physician)\n db.session.add(physician2)\n db.session.add(physicianAddress)\n db.session.add(physicianEmail)\n db.session.add(physicianPhone)\n db.session.add(physicianPhone2)\n db.session.add(physicianToCTC)\n db.session.add(facility1)\n db.session.add(facility2)\n db.session.add(facilityAddress)\n db.session.add(facilityPhone)\n db.session.add(facilityPhone2)\n db.session.add(patientProjectStatusType1)\n db.session.add(patientProjectStatusType2)\n db.session.add(patientProjectStatus)\n db.session.add(physicianFacility)\n db.session.add(contact)\n db.session.add(contact2)\n db.session.add(contact3)\n db.session.add(contact4)\n db.session.add(ctcFacility)\n db.session.add(incentive)\n db.session.commit()","title":""},{"docid":"f1f4704030f5c33ae04a6f6ed3fc8c9d","score":"0.699089","text":"def populate(self):\n pass","title":""},{"docid":"77c4cbc11305536344a963ae28dd0f92","score":"0.69689226","text":"def populate_db():\n values = db.load_config()\n logging.info(values)\n logging.info(\"Started import \")\n logging.info(f'Importing into DB: {values[\"database\"]}')\n # host, db, user, password\n conn = db.get_connection_local_pg( values)\n logging.info(\"Finished import \")\n if ( not os.path.isdir(\"data/2014-i2b2-nlp-evaluation-data-txt\") ):\n logging.exception(\"Data not downloaded, run the main.py get_data first \")\n os.sys.exit(1)\n db.import_data(conn, \"data/2014-i2b2-nlp-evaluation-data-txt\", \"data/testing-PHI-Gold-fixed\")","title":""},{"docid":"eab65cea50449a2f2f4271702528c59f","score":"0.69370925","text":"def __init__(self):\n self.db = db\n self.db.create_all()","title":""},{"docid":"53db6e0a06eccebdfecb42251ceff176","score":"0.6923225","text":"def do_populate(self, arg):\n if arg.strip():\n self.print('Bad syntax!')\n return\n\n self.client.populate_db(numElem=50, numVp=5)\n self.print('OK!')","title":""},{"docid":"b0597bdfe794be623b11017593a932e0","score":"0.69071597","text":"def _init_db_structure(self):\n #with self._db as conn: conn.execute('')\n pass","title":""},{"docid":"db9b4b3df1fe7585313948ae354a3a87","score":"0.6852799","text":"def _populate_db():\n data_1 = json.dumps(DATA_1)\n data_2 = json.dumps({\n \"organization\": \"Skynet Papercorp\", \"reported_at\": \"2015-04-22\",\n \"created_at\": \"2015-04-23\",\n \"inventory\": [{\"name\": \"paper\", \"price\": \"4.00\"}]\n })\n report_1 = Report(data=data_1)\n report_2 = Report(data=data_2)\n db.session.add(report_1)\n db.session.add(report_2)\n db.session.commit()","title":""},{"docid":"6a0ccdb9a18f76b2e8cd24c1104a62ad","score":"0.6847385","text":"def update_database(self):","title":""},{"docid":"c47700b5a4ddf0cb55f3bcad8e6ef127","score":"0.6742828","text":"def create_db_samples():\n init_users()\n init_categories()\n init_items()","title":""},{"docid":"f19de7f49a4903a12523b88a31e718e7","score":"0.6733228","text":"def run(self, args):\n super(PopulateCommand, self).run(args)\n try:\n init_conf(\"populate.log\")\n# cfg.CONF.log_file = \"populate.log\"\n# cfg.CONF.use_stderr = True\n LOG = api.LOG = get_logger(\"populate\")\n LOG.info(_(\"Loading environment\"))\n self.load_app()\n LOG.info(_(\"Building schema\"))\n LOG.info(_(\"Starting a transaction...\"))\n models.start()\n\n # FIXME: There's no create_all equivalent for Music.\n\n # Valet\n Group.create_table()\n Placement.create_table()\n Plan.create_table()\n\n # Ostro\n Event.create_table()\n PlacementRequest.create_table()\n PlacementResult.create_table()\n except Exception as ex:\n models.rollback()\n LOG.error(\"Rolling back... %s\" % ex)\n raise\n else:\n LOG.info(_(\"Committing.\"))\n models.commit()","title":""},{"docid":"dda6cc12fabb7bd264b4028d965e6e4f","score":"0.66867036","text":"def insert_data(self):\n # Make a connexion with a mock database\n self.generate_data_collection()","title":""},{"docid":"f9d68f7ddaf994169e9ef7ea55297614","score":"0.664742","text":"def init_db():\n global app\n Supplier.init_db(app)","title":""},{"docid":"1a0f57d7958b9a592b22a30b870e439a","score":"0.66439116","text":"def toDB(self):\n #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n pass","title":""},{"docid":"9c7a639687c0a13a0d5426588108c1e4","score":"0.66213745","text":"def populate_db():\n from datetime import datetime\n from models import Post, Tag, post_to_tag\n Post.query.delete()\n Tag.query.delete()\n db.session.execute(post_to_tag.delete())\n\n personalwebapp = Tag(name='personalwebapp')\n db.session.add(personalwebapp)\n\n post = Post(title='Hello PersonalWebApp!', content=FIRST_POST, author_id=1, timezone=-5)\n post.tags.append(personalwebapp)\n post.state_id = 2\n post.published_at = datetime.utcnow()\n post.url = 'hello_personal_webapp'\n db.session.add(post)\n\n post = Post(title='Example Draft Post', content='This is the first draft. I\\'ll finish it later.',\n author_id=1, timezone=-5)\n post.url = 'example_draft_post'\n db.session.add(post)\n\n db.session.commit()\n\n print('\"personalwebapp\" tag'\n 'and \"Hello PersonalWebApp!\" published post and \"Example Draft Post\" draft post have been generated.')","title":""},{"docid":"d910fe18fbef4319354befcc35d03628","score":"0.65966296","text":"def create_db(self):\n return None","title":""},{"docid":"ca9c7e87e76ef17c1834e51d3ec5b2fe","score":"0.65857136","text":"def populate_db():\n\n api_url = BOOK_URL\n\n api_response = requests.get(api_url)\n print(api_response.text)\n json_api_response = api_response.json()\n if api_response.status_code == 200:\n for book in json_api_response:\n name = book['name']\n isbn = book[\"isbn\"]\n authors = json.dumps(book[\"authors\"])\n country = book['country']\n number_of_pages = int(book[\"numberOfPages\"])\n publisher = book[\"publisher\"]\n release_date = datetime.strptime(book[\"released\"], \"%Y-%m-%dT%H:%M:%S\").date()\n\n book_insert = Books(None, name, isbn, authors, country, number_of_pages, publisher, release_date, datetime.now(),\n datetime.now())\n db.session.add(book_insert)\n db.session.commit()","title":""},{"docid":"02692e1ea91042a4b1bb734b743fdd70","score":"0.65577847","text":"def populate_database(query, write):\n db = ingest.load_database()\n db = ingest.populate_database(db, query=query, write=write)","title":""},{"docid":"019188738626cd084af97385709e6cb4","score":"0.6507313","text":"def initialize_db():\n # Drop the database and create tables\n setup_database = TestDatabase()\n setup_database.drop()\n setup_database.create()\n setup_database.populate()","title":""},{"docid":"738e02b7d0516b0517e04b9cd2313a1b","score":"0.64941686","text":"def run(self):\n self.db.table('users').insert({\n 'name': 'sjahn',\n 'email': 'asj214@naver.com',\n 'password': generate_password_hash('1234')\n })","title":""},{"docid":"c96b463c4afba4b8874e42597aee6b3b","score":"0.64413","text":"def seed_data():\n typer.echo(\"Creating initial data\")\n db = SessionLocal()\n init_db(db)\n typer.echo(\"Initial data created\")","title":""},{"docid":"a2bc2d10880289fafb45d98b7d9b9758","score":"0.6428364","text":"def dbcreation():\n db.create_all() \n return \"Hi\"","title":""},{"docid":"d23d384becf2768d3e555256fd4cf92f","score":"0.6423991","text":"def populate(self):\n raise NotImplementedError","title":""},{"docid":"ef264dd3df50c3020dbd0dd2de6672e7","score":"0.6420109","text":"def __populate(self):\n self._populate_cli()\n self._populate_model()","title":""},{"docid":"c52a7b7a7c599071e0c42c0dc4163f5e","score":"0.6419514","text":"def setUp(self):\n\t\tdb.create_all()","title":""},{"docid":"d0e326115fecf07a75034ac464aff76c","score":"0.6418617","text":"def initialize():\n db.connect()\n db.create_tables([Product], safe=True)","title":""},{"docid":"22618966909c2b541155c9cccc016691","score":"0.64135474","text":"def initialize():\n db.connect()\n db.create_tables([Entry], safe=True)","title":""},{"docid":"253b0f1c720f5fda12e6a428c5ab086f","score":"0.6398332","text":"def initialisation(self):\n self.cursor.execute('create database if not exists open_food_facts;')\n\n with open(\"creation_bdd.sql\", 'r') as bdd:\n file = bdd.read()\n requetes = file.split(';')\n for line in requetes:\n self.cursor.execute(line)\n\n self.connexion.commit()","title":""},{"docid":"58caf7c1e15f11c58efc8b15d2e8bd2f","score":"0.6390033","text":"def get_database_data(self):\r\n pass","title":""},{"docid":"aa52c5d6fcf41a0a51cbbf90cf49e5f4","score":"0.6380734","text":"def create_db():\n db.create_all()\n db_fill_data()","title":""},{"docid":"739094ba7ea9b2a730020bba9cfc3cab","score":"0.6374668","text":"def init_db_command():\n db = init_db()\n import_parsed_data(db)","title":""},{"docid":"220077a23ec1b23d82b202cd9f0c39c2","score":"0.6373129","text":"def init_db():\n\n create_model_tables([\n Text,\n Citation,\n ], fail_silently=True)","title":""},{"docid":"c5957b336c45f673120ecf164d9b315a","score":"0.63699794","text":"def build_database():\n\tfrom fake_data import setup_initial_database, more_data\n\n\tprint (\"Updating Local Development Database...\")\n\n\tsetup_initial_database()\n\n\tprint (\"Finished!\")\n\n\treturn","title":""},{"docid":"5734b372ff7c325ebedb7ccee68dd464","score":"0.63501185","text":"def initialize_db(self):\r\n click.echo('[OrientModel_initialize_db_%s] Starting process...' % (get_datetime()))\r\n if self.checks['created'] == False:\r\n self.create_db()\r\n if self.checks['open_db'] == False:\r\n self.open_db()\r\n sql = \"\"\r\n for m in self.models:\r\n sql = sql+\"create class %s extends %s;\\n\" % (m, self.models[m]['class'])\r\n for k in self.models[m].keys():\r\n if k != 'class':\r\n sql = sql+\"create property %s.%s %s;\\n\" % (m, k, self.models[m][k])\r\n if 'id' in str(k):\r\n sql = sql + \"create index %s_%s on %s (%s) UNIQUE ;\\n\" % (m, k, m, k)\r\n\r\n sql = sql + \"create sequence idseq type ordered;\"\r\n click.echo('[OrientModel_initialize_db_%s]'\r\n ' Initializing db with following batch statement'\r\n '\\n*************** SQL ***************\\n'\r\n '%s\\n*************** SQL ***************\\n' % (get_datetime(), sql))\r\n self.checks['initialized'] = True\r\n\r\n return self.client.batch(sql)","title":""},{"docid":"b45da97885b18b7a1e9b14a83ba0820f","score":"0.6349823","text":"def creation_database_loading_started(self):\n heading.main_page_loading()\n print('\\t\\tCreation of the Database ... 0%\\n\\n'\n '\\t\\t- - - - - - - - - - - - - - - - - - - - -\\n\\n')","title":""},{"docid":"3f2a1691c8b3e0c77cb7b7cacc2bbcf3","score":"0.63198614","text":"def init_db(self):\n # Connecting to the database file\n conn = sqlite3.connect(DatabaseAccessor.DATABASE_PATH)\n c = conn.cursor()\n\n # Create new tables\n for sql in self.SQL_CREATES:\n c.execute(sql)\n\n # Committing changes and closing the connection to the database file\n conn.commit()\n conn.close()","title":""},{"docid":"06a678067aaeca2b31e147bf8b83af57","score":"0.6313009","text":"def _init_database(self):\r\n\r\n for schema in self._object_schemas:\r\n type_specs = [self._create_sql_for_column(name, schema_item)\r\n for (name, schema_item) in schema.fields]\r\n self.cursor.execute(\"CREATE TABLE %s (%s)\" %\r\n (schema.table_name, ', '.join(type_specs)))\r\n for name, columns in schema.indexes:\r\n self.cursor.execute(\"CREATE INDEX %s ON %s (%s)\" %\r\n (name, schema.table_name, ', '.join(columns)))\r\n for name, columns in schema.unique_indexes:\r\n self.cursor.execute(\"CREATE UNIQUE INDEX %s ON %s (%s)\" %\r\n (name, schema.table_name, ', '.join(columns)))\r\n self._create_variables_table()\r\n self.set_version()\r\n self.setup_fulltext_search()","title":""},{"docid":"bf0c15cd3fe6ac70b7ba41350f4bb2c8","score":"0.63018775","text":"def initialize_database(self):\n self.database = self.loader.request_library(\"common_libs\", \"database\")\n self.database.create_connection(\"production\")\n self.database.load_mappings()\n\n self.migrator = self.loader.request_library(\"database_tools\", \"migrator\")\n self.migrator.migrate()","title":""},{"docid":"46b5beb195eb88b0cf9a0d1aa2d98dc4","score":"0.6297803","text":"def initialize_db():\n print(\"Do init db\")\n db.create_all()\n print(\"init db done\")","title":""},{"docid":"b3e440fe43530ddba4ca0b4a847d00a9","score":"0.62872404","text":"def _create_tables(self):","title":""},{"docid":"1631bc9ae71fad0be54bbace58f0a6ce","score":"0.62842786","text":"def create_database():\n print('Creating database...')\n init_db()","title":""},{"docid":"aca2caa00fa9fc1fbc76530b0e78a022","score":"0.6271357","text":"def database():\n pass","title":""},{"docid":"4c4b447b309d4ac61d1d972e740693d4","score":"0.6265521","text":"def populate_db():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('personjob.db')\n\n logger.info('Working with Person class')\n\n PERSON_NAME = 0\n LIVES_IN_TOWN = 1\n NICKNAME = 2\n\n people = [\n ('Andrew', 'Sumner', 'Andy'),\n ('Peter', 'Seattle', None),\n ('Susan', 'Boston', 'Beannie'),\n ('Pam', 'Coventry', 'PJ'),\n ('Steven', 'Colchester', None),\n ]\n\n JOB_NAME = 0\n START_DATE = 1\n END_DATE = 2\n SALARY = 3\n PERSON_EMPLOYED = 4\n\n jobs = [\n ('Analyst', '2001-09-22', '2003-01-30', 65500, 'Andrew'),\n ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew'),\n ('Senior business analyst', '2006-10-23', '2016-12-24', 80000,\n 'Andrew'),\n ('Admin supervisor', '2012-10-01', '2014-11-10', 45900, 'Peter'),\n ('Admin manager', '2014-11-14', '2018-01-05', 45900, 'Peter')\n ]\n\n DEPARTMENT_NUMBER = 0\n DEPARTMENT_NAME = 1\n DEPARTMENT_MANAGER = 2\n NAME_OF_JOB = 3\n\n departments = [\n ('H153', 'Business Intelligence', 'John Phillips', 'Analyst'),\n ('H567', 'Product Development', 'Rose Adams', 'Senior analyst'),\n ('H195', 'Enterprise Strategy', 'Jonathan Range', 'Senior business '\n 'analyst'),\n ('K976', 'Production Engineering', 'Kathryn Allen', 'Admin '\n 'supervisor'),\n ('L763', 'Reliability Engineering', 'Adam Stunt', 'Admin manager')\n ]\n\n logger.info('Creating Person and Job records: iterate through the list of '\n 'tuples')\n logger.info('Prepare to explain any errors with exceptions')\n logger.info('and the transaction tells the database to fail on error')\n\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n for person in people:\n with database.transaction():\n new_person = Person.create(\n person_name=person[PERSON_NAME],\n lives_in_town=person[LIVES_IN_TOWN],\n nickname=person[NICKNAME])\n new_person.save()\n logger.info('Database Person add successful')\n\n logger.info('Print the Person records we saved...')\n for saved_person in Person:\n logger.info(f'{saved_person.person_name} lives in '\n f'{saved_person.lives_in_town} ' +\\\n f'and likes to be known as {saved_person.nickname}')\n\n for job in jobs:\n date_end = datetime.datetime.strptime(job[END_DATE],\"%Y-%m-%d\")\n date_start = datetime.datetime.strptime(job[START_DATE],\"%Y-%m-%d\")\n duration = date_end - date_start\n with database.transaction():\n new_job = Job.create(\n job_name=job[JOB_NAME],\n start_date=job[START_DATE],\n end_date=job[END_DATE],\n salary=job[SALARY],\n employment_duration=duration.days,\n person_employed=job[PERSON_EMPLOYED])\n new_job.save()\n logger.info('Database job add successful')\n\n logger.info('Print the Job records we saved...')\n for saved_job in Job:\n logger.info(f'{saved_job.person_employed} held the job of '\n f'{saved_job.job_name} from '\n f'{saved_job.start_date} to '\n f'{saved_job.end_date} with a salary of '\n f'{saved_job.salary}')\n\n for department in departments:\n with database.transaction():\n new_department = Department.create(\n department_number=department[DEPARTMENT_NUMBER],\n department_name=department[DEPARTMENT_NAME],\n department_manager=department[DEPARTMENT_MANAGER],\n name_of_job=department[NAME_OF_JOB])\n new_department.save()\n logger.info('Database department add successful')\n\n logger.info('Print the department records we saved...')\n for saved_department in Department:\n logger.info('Department ' f'{saved_department.department_number} '\n 'is named ' f'{saved_department.department_name}'\n ', managed by ' \n f'{saved_department.department_manager}, and has a '\n f'job of ' f'{saved_department.name_of_job}')\n\n except Exception as e:\n logger.info(f'Error creating = {person[PERSON_NAME]}')\n logger.info(e)\n logger.info('See how the database protects our data')\n\n finally:\n logger.info('database closes')\n database.close()","title":""},{"docid":"74f9e654494a3765f3c2805f2b73e971","score":"0.6265285","text":"def db(self):\n raise NotImplementedError","title":""},{"docid":"86203a90ca15a10a52cec642d40e5c70","score":"0.6262716","text":"def init_db():\n global app\n Recommendation.init_db(app)","title":""},{"docid":"faa54e515457a672171567e91ed392ac","score":"0.62594074","text":"def fill_db():\n dbname = get_database()\n collection_name = dbname[\"templates\"]\n try:\n collection_name.delete_many({})\n except Exception as err:\n logger.debug(f\"ERR: {err}\")\n\n try:\n collection_name.insert_many([*templates_1, *templates_2])\n except Exception as err:\n logger.debug(f\"ERR: {err}\")\n yield\n\n\n try:\n collection_name.delete_many({})\n except Exception as err:\n logger.debug(f\"ERR: {err}\")","title":""},{"docid":"36f62cdc6ff20f32e10aba355418de0a","score":"0.62396115","text":"async def db_create(self, ctx):\n\t\tawait self.rebuild_database()","title":""},{"docid":"75f6c4419807df47b1696e2cf4aaf341","score":"0.62381905","text":"def initializing(self):\n self.cursor.executescript(INIT_USERS_DATABASE)\n self.cursor.executescript(INIT_INVENTORY_DATABASE)","title":""},{"docid":"807dead6cba37df60dcfdc6251d057fe","score":"0.6236994","text":"def populate_database():\n dom = DomainFactory(name=\"test.com\", quota=50)\n admin = UserFactory(\n username=\"admin@test.com\", groups=(\"DomainAdmins\", ),\n password=\"{PLAIN}toto\"\n )\n MailboxFactory(address=\"admin\", domain=dom, user=admin)\n account = UserFactory.create(\n username=\"user@test.com\", groups=(\"SimpleUsers\",),\n )\n MailboxFactory.create(address=\"user\", domain=dom, user=account)\n\n al = AliasFactory.create(\n address=\"forward@test.com\", domain=dom\n )\n AliasRecipientFactory.create(\n address=\"user@external.com\", alias=al)\n\n al = AliasFactory.create(\n address=\"alias@test.com\", domain=dom\n )\n mb = account.mailbox\n AliasRecipientFactory.create(\n address=mb.full_address, alias=al, r_mailbox=mb)\n\n al = AliasFactory.create(\n address=\"postmaster@test.com\", domain=dom\n )\n for address in [\"toto@titi.com\", \"test@truc.fr\"]:\n AliasRecipientFactory.create(address=address, alias=al)\n\n dom.add_admin(admin)\n\n dom2 = DomainFactory.create(name=\"test2.com\", default_mailbox_quota=0)\n admin = UserFactory.create(\n username=\"admin@test2.com\", groups=(\"DomainAdmins\",),\n password=\"{PLAIN}toto\"\n )\n MailboxFactory.create(address=\"admin\", domain=dom2, user=admin)\n u = UserFactory.create(\n username=\"user@test2.com\", groups=(\"SimpleUsers\",)\n )\n MailboxFactory.create(address=\"user\", domain=dom2, user=u)\n dom2.add_admin(admin)","title":""},{"docid":"2c04d5ec3dffc2dcda802bf5284869d1","score":"0.6226869","text":"def example_data():\n\n user_1 = User(email=\"hellohellohello@gmail.com\", password=\"1234\")\n search_1 = Search(search_term=\"testestest\")\n outlet_1 = Outlet(outlet_name='Best News', outlet_popularity=10, outlet_bias='Right-Center')\n user_search_1 = User_Search(user_id=1, search_id=1)\n\n db.session.add_all([user_1, search_1, outlet_1])\n db.session.commit()","title":""},{"docid":"0c41314e33f88a438daf047f9b64236a","score":"0.6223169","text":"def __init__(self, database):\n self.database = database","title":""},{"docid":"7d412440f18ffb4fe6a755293360a1a1","score":"0.62127787","text":"def init_db():\n global app\n Inventory.init_db(app)","title":""},{"docid":"f08630fdffbb4548a9384d670878c7c8","score":"0.6210465","text":"def _populate_data(self):\n\n pass","title":""},{"docid":"b4e83ec014e7a72cfb35bcc9baaa4f77","score":"0.6210141","text":"def setUp(self):\n self.database = Database()","title":""},{"docid":"8d99aa9a5b920aa730a6517506b70c69","score":"0.61997527","text":"def load_data(self, db_name):\r\n create_schema = CreateSchema(db_name)\r\n offices = create_schema.load_offices()\r\n livingspaces = create_schema.load_living_space()\r\n staffs = create_schema.load_staff()\r\n fellows = create_schema.load_fellow()\r\n office_staff_alloc = create_schema.load_office_staff_allocations()\r\n office_fellow_alloc = create_schema.load_office_fellow_allocations()\r\n livingspaces_alloc = create_schema.load_living_space_allocations()\r\n #Adding offices\r\n for office in offices:\r\n self.create_room(\"OFFICE\", office[0])\r\n #Adding living spaces\r\n for livingspace in livingspaces:\r\n self.create_room(\"LIVINGSPACE\", livingspace[0])\r\n #Adding staff\r\n for staff in staffs:\r\n self.add_person(\"STAFF\", staff[0])\r\n #Adding fellows\r\n for fellow in fellows:\r\n self.add_person(\"FELLOW\", fellow[0])\r\n #Creating office staff allocations'\r\n for alloc_office in office_staff_alloc:\r\n value = self.staff_dict[str(alloc_office[1])]\r\n self.allocate_rooms(value, \"OFFICE\")\r\n #Creating office fellow allocations\r\n for alloc_office in office_fellow_alloc:\r\n value = self.fellow_dict[str(alloc_office[2])]\r\n self.allocate_rooms(value, \"OFFICE\")\r\n # Allocating Living space\r\n for alloc_living_space in livingspaces_alloc:\r\n value = self.fellow_dict[str(alloc_living_space[1])]\r\n self.allocate_rooms(value, \"LIVINGSPACE\")","title":""},{"docid":"083997557b55c3c6c5815ab710f0e1e7","score":"0.61968344","text":"def create_database(self):\n self._create_tables()\n self._create_functions()\n self._create_triggers()","title":""},{"docid":"04afa680de176ecafcefb3de5841ad9d","score":"0.6194173","text":"def init():\n try:\n DataBase.create_table()\n DataBase.create_price_table()\n except Exception:\n print(MainMessage.db_critical_error)\n sys.exit(0)\n print(MainMessage.db_init_success)\n if DataBase.read_record_from_pricedb():\n (Stakes.stake, Stakes.term, Stakes.substake, Stakes.subterm) = DataBase.read_record_from_pricedb()\n else:\n DataBase.insert_record_to_pricedb(Stakes.stake, Stakes.term, Stakes.substake, Stakes.subterm)\n Stakes.change_stakes()","title":""},{"docid":"9355d57b29c60ed9ecb2f2147062f036","score":"0.61881137","text":"def init_database(self):\n self._drop_tables()\n self._create_tables()","title":""},{"docid":"51e9772beb911f40e0222101163fe454","score":"0.6180436","text":"def update_database():\n pass","title":""},{"docid":"c791e0bc0d4578a3d0bbc0f415eed76f","score":"0.6174021","text":"def init_table(self):\n self.create_table()","title":""},{"docid":"b1dcbe9a9c1b48448873415bf2213b59","score":"0.61739","text":"def create_db():\n\n # TODO:\n pass","title":""},{"docid":"959a78bb1df3bc568390da2ab88111ec","score":"0.6173813","text":"def initialize():\r\n db.connect()\r\n db.create_tables([BaseModel], safe=True)\r\n db.create_tables([Records], safe=True)","title":""},{"docid":"1ff935099c2772641552fbf8e0f244b2","score":"0.61727023","text":"def setup_tables(self):","title":""},{"docid":"ff634b9defb5c8b9f625cc35fcecb901","score":"0.61722714","text":"def dbcreate(self):\r\n self.db.createtable()","title":""},{"docid":"79c1b2e3b68a6b4c34d605a6188648f3","score":"0.61717933","text":"def __init__(self, database):\n self.db = database","title":""},{"docid":"92ab14e391589055ac911fad939895a5","score":"0.6170633","text":"def init_db_command():\n from .data.database import init_db\n init_db()\n click.echo('Initialized the database.')","title":""},{"docid":"d492e45f540121a62e6f784eafc01ada","score":"0.61700296","text":"def init_db():\r\n\r\n db = Scraper_DB()\r\n try:\r\n db.create_tables()\r\n except Exception as e:\r\n print(\"{0}\".format(e))","title":""},{"docid":"3286660208fee39a3d20c9ecd4a39791","score":"0.616117","text":"def initialize():\n DATABASE.connect(reuse_if_open=True)\n DATABASE.create_tables([User, ToDo], safe=True)\n DATABASE.close()","title":""},{"docid":"2e163e477fb3afc401b0661de598cf24","score":"0.61580926","text":"def populateInitialDataSet(self):","title":""},{"docid":"96d33114588b0147f80274744c9144f3","score":"0.6151307","text":"def generate_db(self):\n print('Generating LANL Earthquake Prediction Database...')\n # Unzip the database\n self._unzip_db()\n\n # Get test dataset\n self._get_test_db()\n\n # Get train dataset\n self._get_train_db()\n print('Complete!\\n')","title":""},{"docid":"5c47cf554648cdddb92ff0a42d78e2ce","score":"0.6147908","text":"def dbSetUp(self):\n pass","title":""},{"docid":"753de69c4c3126921e551c944d3b7777","score":"0.6147715","text":"def creation_database(self):\n heading.main_page_loading()\n print('\\t\\tPlease, write the name of the database\\n')\n self.choice_creation_database = input('[Database Menu] : #dm\\n'\n '[Quit] : #quit\\n\\n\\t=> ')\n\n return self.choice_creation_database","title":""},{"docid":"0f12a34445caf9eb081b7c0332a02764","score":"0.6136958","text":"def initdb():\n db.create_all()\n print \":creo las tablas de la base de datos:\"\n poblardb()","title":""},{"docid":"641745680596c09f4ccc1753c64609b6","score":"0.6131554","text":"def transfertodb():","title":""},{"docid":"4f32db0d5a6f2dedaeedfadb7aba8090","score":"0.61295503","text":"def setUp(self):\n db.create_all()","title":""},{"docid":"c1041a33217543f2273e244a6771a296","score":"0.61277354","text":"def build_data_base(self):\n\n self.db.create_table(\"STUDENTS\", [[\"Firstname\", \"TEXT\"], [\"LastName\", \"TEXT\"], [\"Id\", \"TEXT\"],\n [\"Gender\", \"TEXT\"], [\"Country\", \"TEXT\"], [\"Age\", \"TEXT\"],\n [\"Username\", \"TEXT\"], [\"Password\", \"TEXT\"]])\n\n self.db.create_table(\"OnlineLessons\", [[\"GeneralSubject\", \"TEXT\"], [\"MainSubject\", \"TEXT\"],\n [\"TargetAudience\", \"TEXT\"], [\"AuthorName\", \"TEXT\"],\n [\"AuthorAge\", \"TEXT\"], [\"LastName\", \"TEXT\"],\n [\"ExceptedLength\", \"TEXT\"]])","title":""},{"docid":"309e67e55289e682909ea0827a4471ad","score":"0.6122299","text":"def init_db():\n db = get_db()\n with app.open_resource('schema.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n with app.open_resource('knowledge.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n with app.open_resource('users.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n \n db.commit()","title":""},{"docid":"38e132347272880dac991ab250774e67","score":"0.6111997","text":"def create_database():\n init_db()\n click.echo(\"Initialized the database.\")","title":""},{"docid":"aa5275a31cd425c5d356faeb8958f47a","score":"0.6109491","text":"def initial_setup():\n db = _get_db()\n db.execute(SCHEMA)\n db.commit()","title":""},{"docid":"d24efa6d151640a258ecc9cece982fdd","score":"0.6107269","text":"def __init__(self):\n # try:\n\n self.filename = os.path.join(CONFIG_DATABASE_PATH,\n CONFIG_DATABASE_NAME)\n self.connection = db.connect(self.filename)\n\n # if not db.Connection.\n # print \"error\"\n \"\"\"creating a cursor\"\"\"\n\n #cursor = self.connection.cursor()\n # cursor.execute(\n # '''INSERT INTO excavation (projectID, projectTitle, projectType, projectSubType, site, studyArea)\n # VALUES (4, 'test', 'field', 'xx', 'ga', 'ta')''')\n #self.connection.commit()\n #\n # cursor = connection.execute('select * from excavation')\n # print cursor.fetchall()\n\n #self.connection.close()","title":""},{"docid":"f4d3fbe5edf0d1582f8979019e09d465","score":"0.6106553","text":"def __init__(self, databaseName):\r\n\r\n print(\"\\n------------| DATABASE CREATION |------------\\n\")\r\n self.database_name = databaseName\r\n\r\n # Create DB\r\n self.conn = sqlite3.connect(self.database_name)\r\n\r\n # Variable to access DB\r\n self.c = self.conn.cursor()\r\n\r\n print(\"> Database\", self.database_name, \"was created successfully\")\r\n self.createTables()\r\n self.closeDB()","title":""},{"docid":"9132cf670cc0bc8535fada9e5d53fa33","score":"0.6106523","text":"def _initializeDatabase(self):\n self.bot = GridBotModel(\n name = self.name,\n symbol = self.symbol,\n exchange = self.exchange.exchange_id,\n starting_balance = self.total_amount,\n current_balance = self.total_amount,\n trade_amount = self.trade_amount,\n trade_step = self.trade_step,\n test_run = self.test_mode)\n self.session.add(self.bot)\n self.session.commit()","title":""},{"docid":"251dc8d2a87a8f12d31b19a5a9953e1f","score":"0.6104041","text":"def init_db():\n print 'Initialize DB'\n from yamp.app import db, engine\n from yamp.models import Base, __all__ as model_files\n from importlib import import_module\n for model_file in model_files:\n import_module('yamp.models.' + model_file, __name__)\n print '* Model \"%s\" loaded.' % model_file\n Base.metadata.create_all(bind=engine)\n db.commit()\n print '... All tables created!'\n raise SystemExit","title":""},{"docid":"e3514015410b72f25ec9f2ccb21c09d3","score":"0.6102784","text":"def init_users():\n UserModel('thinh', '1234').save_to_db()\n UserModel('long', '1234').save_to_db()","title":""},{"docid":"25bd78f2cf3726b2f9fd8f86e7b7d4e7","score":"0.60980797","text":"def setUp(self):\n schema(DBPATH)\n seed(DBPATH)","title":""},{"docid":"6d6902cae012870febfcc1c047d0450c","score":"0.6095673","text":"def _instantiate(self):\n print(\"creating database with table(s) at \" + DB_PATH)\n\n with self._connect() as db_cur:\n\n # create disasters table\n db_cur.execute(\"\"\"CREATE TABLE IF NOT EXISTS slack_calls (\n id integer PRIMARY KEY ,\n team_id text UNIQUE NOT NULL ,\n team_name text ,\n channel_id text UNIQUE NOT NULL ,\n access_token text UNIQUE NOT NULL ,\n bot_access_token text UNIQUE NOT NULL ,\n );\n \"\"\")\n\n # indexing columns for faster access\n db_cur.execute(\"\"\"CREATE UNIQUE INDEX IF NOT EXISTS indexed_team_id ON slack_calls (\n team_id\n );\n \"\"\")","title":""},{"docid":"a1deabb1a8aa6863203ab301bd1ab2db","score":"0.60897547","text":"def manual_enter(self):\n self._dbconnect = sqlite3.connect(self._db_file)\n\n # Set row_factory to access columns by name\n self._dbconnect.row_factory = sqlite3.Row\n\n # Create a cursor to work with the db\n self._cursor = self._dbconnect.cursor()","title":""},{"docid":"ea3633fce200e85f45e90f30ef071775","score":"0.60858333","text":"def initialize():\n db.connect()\n db.create_tables([Task], safe=True)","title":""},{"docid":"a20d295df90cd4681dabefe43ef7696d","score":"0.60857296","text":"def createData(self):\n self.data = self.dbroot['data'] = OOBTree()\n self.meta = self.dbroot['meta'] = Meta('PEAT-DB database')\n self.meta.staticfields = { 'name': 'text', 'Mutations':'text',\n 'Structure':'PDB'}\n self.blobs = self.dbroot['blobs'] = OOBTree() \n #use meta.info for any misc settings specific to the DB\n self.meta.info = PersistentMapping(self.defaultinfo)\n self.commit()\n return","title":""},{"docid":"e776e6da8c21cf8b3eb86d0ad5f64480","score":"0.60816956","text":"def initalize_user_tables(db):\n \n from shotglass2.users.models import init_db as users_init_db \n users_init_db(db)","title":""},{"docid":"dcc46ccf775d7e2ce6ddb240a0fd1e10","score":"0.6078222","text":"def database_access(db):","title":""}],"string":"[\n {\n \"docid\": \"11c5c10dcb7519f823633e95d691706e\",\n \"score\": \"0.7409577\",\n \"text\": \"def _populateTables(cls):\\n for method in dir(Populate):\\n if not method.startswith('__'):\\n populateTable = getattr(Populate, method)\\n print 'Inserting data for %s ...' % method\\n populateTable()\\n\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda5e8e7e0ff2e9f2e1e78366231c75b\",\n \"score\": \"0.7352905\",\n \"text\": \"def __call__(self):\\n self.create_database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e04dc2fb4584516abc29cd5b12401ab9\",\n \"score\": \"0.7279164\",\n \"text\": \"def populate():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3738361c8de7ca311743c12b407f463c\",\n \"score\": \"0.7264388\",\n \"text\": \"def populate_db_command():\\n populate_db()\\n click.echo(\\\"Created dummy data.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb3e562c42462ac59c49c99c1f6c53e\",\n \"score\": \"0.72629017\",\n \"text\": \"def handle(self, *args, **options):\\n self._populate_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e755fbb017c46d5659f9eac88663f994\",\n \"score\": \"0.72245425\",\n \"text\": \"def populate_db():\\n database.create_tables([Donor, Donation])\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n #database = SqliteDatabase('donor.db')\\n\\n logger.info('Working with Person class')\\n logger.info('Note how I use constants and a list of tuples as a simple schema')\\n logger.info('Normally you probably will have prompted for this from a user')\\n\\n# NAME = 0\\n# DONATION = 1\\n# \\n donors = [('Karl', [100, 200, 300.55]),\\n ('Luis', [400, 500, 600]),\\n ('Woody', [700, 800, 900]),\\n ('Mary', [455.99])]\\n \\n logger.info('Creating Donor records: iterate through the list of tuples')\\n logger.info('Prepare to explain any errors with exceptions')\\n logger.info('and the transaction tells the database to fail on error')\\n \\n for users, donations in donors:\\n user = Donor.create(username=users)\\n for donation in donations:\\n Donation.create(user=user, donation=float(donation))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13029ab8865339e29a5c71c47127fda5\",\n \"score\": \"0.71562296\",\n \"text\": \"def data_into_database():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d20c034a8c3cbc0832dc74bb4da1b0f3\",\n \"score\": \"0.71474314\",\n \"text\": \"def fromDB(self):\\n #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc0072cd1eefb95b2048c8061190efe\",\n \"score\": \"0.7144774\",\n \"text\": \"def initiate(cls):\\n cls._purgeTables()\\n cls._populateTables()\\n print 'DB initialization finished!!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f9911dbbe0284a285ab8f29ced0957c\",\n \"score\": \"0.7093239\",\n \"text\": \"def populate(self, connection, system):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7efc43d24ebff7e863c3941c631b63d4\",\n \"score\": \"0.704847\",\n \"text\": \"def init_db():\\n\\n with app.app_context():\\n data = json.loads(urllib2.urlopen(DATA_URL).read())\\n ds = DataSource(data['data'], data['meta']['view']['columns'])\\n headers = ds.get_headers(\\n set([\\n 'Applicant', \\n 'Address', \\n 'FoodItems', \\n 'Latitude', \\n 'Longitude'\\n ]),\\n 'name'\\n )\\n db = get_db()\\n db.init_database()\\n for item in ds.gen_items(headers):\\n try:\\n db.add_row(\\n item['Applicant'], \\n item['Address'], \\n item['FoodItems'], \\n item['Latitude'], \\n item['Longitude']\\n )\\n except psycopg2.IntegrityError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a8560696f49cd0ff48c227dff9a1f49\",\n \"score\": \"0.69914603\",\n \"text\": \"def populate_db2(self):\\n db.create_all()\\n\\n informantRelationships = self.create_informant_relationships()\\n users = self.create_users()\\n finalCodes = self.create_final_codes()\\n states = self.create_states()\\n abstractStatuses = self.create_abstract_statuses()\\n sexes = self.create_sexes()\\n races = self.create_races()\\n ethnicities = self.create_ethnicities()\\n vitals = self.create_vital_statuses()\\n contacts = self.create_contacts()\\n inactives = self.create_inactives()\\n ucrReportTypes = self.create_ucr_report_types()\\n physicianStatuses = self.create_physician_statuses()\\n physFacilityStatuses = self.create_physician_facility_statuses()\\n phoneTypes = self.create_phone_types()\\n irbHolders = self.create_irb_holders()\\n projectTypes = self.create_project_types()\\n contactStatuses = self.create_contact_statuses()\\n contactSources = self.create_contact_sources()\\n grantStatuses = self.create_grant_statuses()\\n fundingSources = self.create_funding_sources()\\n reviewCommitteeStatuses = self.create_review_committee_statuses()\\n projectStatuses = self.create_project_statuses()\\n logTypes = self.create_log_subjects()\\n reviewCommittees = self.create_review_committees()\\n staffRoles = self.create_staff_roles()\\n projectPhases = self.create_project_phases()\\n hsts = self.create_human_subject_trainings()\\n tracingSources = self.create_tracing_sources()\\n contactTypes = self.create_contact_types()\\n ucrRoles = self.create_ucr_roles()\\n giftCards = self.create_gift_cards()\\n\\n project1 = models.Project(\\n projectTypeID=1,\\n irbHolderID=1,\\n projectTitle=\\\"Test Project\\\",\\n shortTitle=\\\"Test Project\\\",\\n projectSummary=\\\"Summary\\\",\\n sop=\\\"sop\\\",\\n ucrProposal=\\\"ucr_proposal\\\",\\n budgetDoc=\\\"budget_doc\\\",\\n ucrFee=\\\"no\\\",\\n ucrNoFee=\\\"yes\\\",\\n previousShortTitle=\\\"t short\\\",\\n dateAdded=datetime(2016, 2, 2),\\n finalRecruitmentReport=\\\"report\\\",\\n ongoingContact=True,\\n activityStartDate=datetime(2016, 2, 2),\\n activityEndDate=datetime(2016, 2, 2))\\n\\n project2 = models.Project(\\n projectTypeID=1,\\n irbHolderID=1,\\n projectTitle=\\\"Test Project\\\",\\n shortTitle=\\\"Test Project\\\",\\n projectSummary=\\\"Summary\\\",\\n sop=\\\"sop\\\",\\n ucrProposal=\\\"ucr_proposal\\\",\\n budgetDoc=\\\"budget_doc\\\",\\n ucrFee=\\\"no\\\",\\n ucrNoFee=\\\"yes\\\",\\n previousShortTitle=\\\"t short\\\",\\n dateAdded=datetime(2016, 2, 2),\\n finalRecruitmentReport=\\\"report\\\",\\n ongoingContact=True,\\n activityStartDate=datetime(2016, 2, 2),\\n activityEndDate=datetime(2016, 2, 2))\\n\\n budget1 = models.Budget(\\n projectID=1,\\n numPeriods=1,\\n periodStart=datetime(2016, 2, 2),\\n periodEnd=datetime(2016, 2, 2),\\n periodTotal=1.23,\\n periodComment=\\\"comment\\\")\\n\\n rc = models.ReviewCommittee(\\n projectID=1,\\n reviewCommitteeStatusID=1,\\n reviewCommitteeLUTID=1,\\n reviewCommitteeNumber=\\\"1\\\",\\n dateInitialReview=datetime(2016, 2, 2),\\n dateExpires=datetime(2016, 2, 2),\\n rcNote=\\\"rc_note\\\",\\n rcProtocol=\\\"rc_proto\\\",\\n rcApproval=\\\"rc_approval\\\")\\n\\n ucr = models.UCRReport(\\n projectID=1,\\n reportTypeID=1,\\n reportSubmitted=datetime(2016, 2, 2),\\n reportDue=datetime(2016, 2, 2),\\n reportDoc=\\\"doc\\\"\\n )\\n arcReview = models.ArcReview(\\n projectID=1,\\n reviewType=1,\\n dateSentToReviewer=datetime(2016, 2, 2),\\n reviewer1=1,\\n reviewer1Rec=1,\\n reviewer1SigDate=datetime(2016, 2, 2),\\n reviewer1Comments=\\\"test comment\\\",\\n reviewer2=2,\\n reviewer2Rec=2,\\n reviewer2SigDate=datetime(2016, 2, 2),\\n reviewer2Comments=\\\"test comment\\\",\\n research=1,\\n linkage=False,\\n contact=True,\\n engaged=True,\\n nonPublicData=True)\\n\\n funding = models.Funding(\\n grantStatusID=1,\\n projectID=1,\\n fundingSourceID=1,\\n primaryFundingSource=\\\"pfs\\\",\\n secondaryFundingSource=\\\"sfs\\\",\\n fundingNumber=\\\"number\\\",\\n grantTitle=\\\"title\\\",\\n dateStatus=datetime(2016, 2, 2),\\n grantPi=1,\\n primaryChartfield=\\\"pcf\\\",\\n secondaryChartfield=\\\"scf\\\"\\n )\\n\\n staff = models.Staff(\\n userID=1,\\n firstName=\\\"Aaron\\\",\\n lastName=\\\"Thomas\\\",\\n middleName=\\\"Pulver\\\",\\n email=\\\"aaron.pulver@utah.edu\\\",\\n phoneNumber=\\\"phone\\\",\\n phoneComment=\\\"phoneComment\\\",\\n institution=\\\"institution\\\",\\n department=\\\"department\\\",\\n position=\\\"position\\\",\\n credentials=\\\"credentials\\\",\\n street=\\\"street\\\",\\n city=\\\"city\\\",\\n stateID=1,\\n ucrRoleID=1\\n )\\n staff2 = models.Staff(\\n userID=2,\\n firstName=\\\"Phoebe\\\",\\n lastName=\\\"\\\",\\n middleName=\\\"McNeally\\\",\\n email=\\\"email\\\",\\n phoneNumber=\\\"phone\\\",\\n phoneComment=\\\"phoneComment\\\",\\n institution=\\\"institution\\\",\\n department=\\\"department\\\",\\n position=\\\"position\\\",\\n credentials=\\\"credentials\\\",\\n street=\\\"street\\\",\\n city=\\\"city\\\",\\n stateID=2,\\n ucrRoleID=1\\n )\\n\\n projStatus = models.ProjectStatus(\\n projectStatusTypeID=1,\\n projectID=1,\\n staffID=1,\\n statusDate=datetime(2016, 2, 2),\\n statusNotes=\\\"notes\\\"\\n )\\n\\n preApp = models.PreApplication(\\n projectID=1,\\n piFirstName=\\\"pi_fname\\\",\\n piLastName=\\\"pi_lname\\\",\\n piEmail=\\\"pi_email\\\",\\n piPhone=\\\"pi_phone\\\",\\n contactFirstName=\\\"contact_fname\\\",\\n contactLastName=\\\"contact_lname\\\",\\n contactPhone=\\\"contact_phone\\\",\\n contactEmail=\\\"contact_email\\\",\\n institution=\\\"institution\\\",\\n institution2=\\\"institution2\\\",\\n uid=\\\"uid\\\",\\n udoh=1,\\n projectTitle=\\\"project_title\\\",\\n purpose=\\\"purpose\\\",\\n irb0=True,\\n irb1=True,\\n irb2=True,\\n irb3=True,\\n irb4=True,\\n otherIrb=\\\"other_irb\\\",\\n updb=True,\\n ptContact=True,\\n startDate=datetime(2016, 2, 2),\\n link=True,\\n deliveryDate=datetime(2016, 2, 2),\\n description=\\\"description\\\"\\n )\\n log = models.Log(\\n logSubjectID=1,\\n projectID=1,\\n staffID=1,\\n phaseStatusID=1,\\n note=\\\"note\\\",\\n date=datetime(2016, 2, 2)\\n )\\n projectStaff = models.ProjectStaff(\\n staffRoleID=1,\\n projectID=1,\\n staffID=1,\\n datePledge=datetime(2016, 2, 2),\\n dateRevoked=datetime(2016, 2, 2),\\n contactID=1,\\n inactiveID=1\\n )\\n staffTraining = models.StaffTraining(\\n staffID=1,\\n humanSubjectTrainingID=1,\\n dateTaken=datetime(2016, 2, 2),\\n dateExpires=datetime(2016, 2, 2)\\n )\\n patient = models.Patient(\\n patID=\\\"1\\\",\\n ucrDistID=1,\\n UPDBID=1,\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"mname\\\",\\n maidenName=\\\"maiden_name\\\",\\n aliasFirstName=\\\"alias_fname\\\",\\n aliasLastName=\\\"alias_lname\\\",\\n aliasMiddleName=\\\"alias_middle\\\",\\n dobDay=15,\\n dobMonth=2,\\n dobYear=1990,\\n SSN=\\\"999999999\\\",\\n sexID=2,\\n raceID=1,\\n ethnicityID=1,\\n vitalStatusID=1\\n )\\n patient2 = models.Patient(\\n patID=\\\"1\\\",\\n ucrDistID=1,\\n UPDBID=1,\\n firstName=\\\"fname2\\\",\\n lastName=\\\"lname2\\\",\\n middleName=\\\"mname2\\\",\\n maidenName=\\\"maiden_name\\\",\\n aliasFirstName=\\\"alias_fname\\\",\\n aliasLastName=\\\"alias_lname\\\",\\n aliasMiddleName=\\\"alias_middle\\\",\\n dobDay=26,\\n dobMonth=4,\\n dobYear=1970,\\n SSN=\\\"999999999\\\",\\n sexID=1,\\n raceID=2,\\n ethnicityID=1,\\n vitalStatusID=2\\n )\\n\\n patientAddress = models.PatientAddress(\\n contactInfoSourceID=1,\\n participantID=1,\\n contactInfoStatusID=1,\\n street=\\\"street\\\",\\n street2=\\\"street2\\\",\\n city=\\\"city\\\",\\n stateID=1,\\n zip=\\\"12345\\\",\\n addressStatusDate=datetime(2016, 2, 2),\\n )\\n\\n patientEmail = models.PatientEmail(\\n contactInfoSourceID=1,\\n participantID=1,\\n contactInfoStatusID=1,\\n email=\\\"email\\\",\\n emailStatusDate=datetime(2016, 2, 2)\\n )\\n patientPhone = models.PatientPhone(\\n contactInfoSourceID=1,\\n participantID=1,\\n contactInfoStatusID=1,\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n patientPhone2 = models.PatientPhone(\\n contactInfoSourceID=1,\\n participantID=1,\\n contactInfoStatusID=1,\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n informant1 = models.Informant(\\n participantID=1,\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"middle_name\\\",\\n informantPrimary=True,\\n informantRelationshipID=1,\\n notes=\\\"notes\\\"\\n )\\n informant2 = models.Informant(\\n participantID=1,\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"middle_name\\\",\\n informantPrimary=True,\\n informantRelationshipID=1,\\n notes=\\\"notes\\\"\\n )\\n informantAddress = models.InformantAddress(\\n contactInfoSourceID=1,\\n informantID=1,\\n contactInfoStatusID=1,\\n street=\\\"street\\\",\\n street2=\\\"street2\\\",\\n city=\\\"city\\\",\\n stateID=2,\\n zip=\\\"12345\\\",\\n addressStatusDate=datetime(2016, 2, 2),\\n )\\n informantPhone = models.InformantPhone(\\n contactInfoSourceID=1,\\n informantID=1,\\n contactInfoStatusID=1,\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n informantPhone2 = models.InformantPhone(\\n contactInfoSourceID=1,\\n informantID=1,\\n contactInfoStatusID=1,\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n ctc1 = models.CTC(\\n participantID=1,\\n dxDateDay=2,\\n dxDateMonth=7,\\n dxDateYear=1988,\\n site=\\\"Site 2\\\",\\n histology=\\\"histology\\\",\\n behavior=\\\"behavior\\\",\\n ctcSequence=\\\"sequence\\\",\\n stage=\\\"stage\\\",\\n dxAge=1,\\n dxStreet1=\\\"street1\\\",\\n dxStreet2=\\\"street2\\\",\\n dxCity=\\\"city\\\",\\n dxStateID=1,\\n dxZip=99999,\\n dxCounty=\\\"county\\\",\\n dnc=\\\"dnc\\\",\\n dncReason=\\\"dnc_reason\\\",\\n recordID=\\\"abc321\\\"\\n )\\n ctc2 = models.CTC(\\n participantID=1,\\n dxDateDay=3,\\n dxDateMonth=10,\\n dxDateYear=1958,\\n site=\\\"Site 1\\\",\\n histology=\\\"histology\\\",\\n behavior=\\\"behavior\\\",\\n ctcSequence=\\\"sequence\\\",\\n stage=\\\"stage\\\",\\n dxAge=1,\\n dxStreet1=\\\"street1\\\",\\n dxStreet2=\\\"street2\\\",\\n dxCity=\\\"city\\\",\\n dxStateID=2,\\n dxZip=99999,\\n dxCounty=\\\"county\\\",\\n dnc=\\\"dnc\\\",\\n dncReason=\\\"dnc_reason\\\",\\n recordID=\\\"abc123\\\"\\n )\\n projectPatient = models.ProjectPatient(\\n projectID=1,\\n staffID=1,\\n ctcID=1,\\n currentAge=1,\\n batch=1,\\n siteGrp=1,\\n finalCodeID=1,\\n finalCodeDate=datetime(2016, 2, 2),\\n enrollmentDate=datetime(2016, 2, 2),\\n dateCoordSigned=datetime(2016, 2, 2),\\n importDate=datetime(2016, 2, 2),\\n finalCodeStaffID=1,\\n enrollmentStaffID=1,\\n dateCoordSignedStaffID=1,\\n abstractStatusID=1,\\n abstractStatusDate=datetime(2016, 2, 2),\\n abstractStatusStaffID=1,\\n sentToAbstractorDate=datetime(2016, 2, 2),\\n sentToAbstractorStaffID=1,\\n abstractedDate=datetime(2016, 2, 2),\\n abstractorStaffID=1,\\n researcherDate=datetime(2016, 2, 2),\\n researcherStaffID=1,\\n consentLink=\\\"link\\\",\\n medRecordReleaseSigned=True,\\n medRecordReleaseLink=\\\"link\\\",\\n medRecordReleaseStaffID=1,\\n medRecordReleaseDate=datetime(2016, 2, 2),\\n surveyToResearcher=datetime(2016, 2, 2),\\n surveyToResearcherStaffID=1,\\n qualityControl=True,\\n )\\n\\n projectPatient2 = models.ProjectPatient(\\n projectID=1,\\n staffID=1,\\n ctcID=1,\\n currentAge=1,\\n batch=1,\\n siteGrp=1,\\n finalCodeID=1,\\n finalCodeDate=datetime(2016, 2, 2),\\n enrollmentDate=datetime(2016, 2, 2),\\n dateCoordSigned=datetime(2016, 2, 2),\\n importDate=datetime(2016, 2, 2),\\n finalCodeStaffID=1,\\n enrollmentStaffID=1,\\n dateCoordSignedStaffID=1,\\n abstractStatusID=1,\\n abstractStatusDate=datetime(2016, 2, 2),\\n abstractStatusStaffID=1,\\n sentToAbstractorDate=datetime(2016, 2, 2),\\n sentToAbstractorStaffID=1,\\n abstractedDate=datetime(2016, 2, 2),\\n abstractorStaffID=1,\\n researcherDate=datetime(2016, 2, 2),\\n researcherStaffID=1,\\n consentLink=\\\"link\\\",\\n medRecordReleaseSigned=True,\\n medRecordReleaseLink=\\\"link\\\",\\n medRecordReleaseStaffID=1,\\n medRecordReleaseDate=datetime(2016, 2, 2),\\n surveyToResearcher=datetime(2016, 2, 2),\\n surveyToResearcherStaffID=1,\\n qualityControl=False\\n )\\n\\n tracing = models.Tracing(\\n tracingSourceID=1,\\n participantID=1,\\n date=datetime(2016, 2, 2),\\n staffID=1,\\n notes=\\\"notes\\\"\\n )\\n physician = models.Physician(\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"middle_name\\\",\\n credentials=\\\"credentials\\\",\\n specialty=\\\"specialty\\\",\\n aliasFirstName=\\\"alias_fname\\\",\\n aliasLastName=\\\"alias_lname\\\",\\n aliasMiddleName=\\\"alias_middle_name\\\",\\n physicianStatusID=1,\\n physicianStatusDate=datetime(2016, 2, 2),\\n )\\n\\n physician2 = models.Physician(\\n firstName=\\\"fname\\\",\\n lastName=\\\"lname\\\",\\n middleName=\\\"middle_name\\\",\\n credentials=\\\"credentials\\\",\\n specialty=\\\"specialty\\\",\\n aliasFirstName=\\\"alias_fname\\\",\\n aliasLastName=\\\"alias_lname\\\",\\n aliasMiddleName=\\\"alias_middle_name\\\",\\n physicianStatusID=1,\\n physicianStatusDate=datetime(2016, 2, 2),\\n )\\n physicianAddress = models.PhysicianAddress(\\n contactInfoSourceID=1,\\n physicianID=1,\\n contactInfoStatusID=1,\\n street=\\\"street\\\",\\n street2=\\\"street2\\\",\\n city=\\\"city\\\",\\n stateID=1,\\n zip=\\\"12345\\\",\\n addressStatusDate=datetime(2016, 2, 2),\\n )\\n\\n physicianEmail = models.PhysicianEmail(\\n contactInfoSourceID=1,\\n physicianID=1,\\n contactInfoStatusID=1,\\n email=\\\"email\\\",\\n emailStatusDate=datetime(2016, 2, 2)\\n )\\n\\n physicianPhone = models.PhysicianPhone(\\n contactInfoSourceID=1,\\n physicianID=1,\\n contactInfoStatusID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneTypeID=1,\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n physicianPhone2 = models.PhysicianPhone(\\n contactInfoSourceID=1,\\n physicianID=1,\\n contactInfoStatusID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneTypeID=1,\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n physicianToCTC = models.PhysicianToCTC(\\n physicianID=1,\\n ctcID=1\\n )\\n facility1 = models.Facility(\\n facilityName=\\\"name\\\",\\n contactFirstName=\\\"fname\\\",\\n contactLastName=\\\"lname\\\",\\n facilityStatus=1,\\n facilityStatusDate=datetime(2016, 2, 2),\\n contact2FirstName=\\\"fname\\\",\\n contact2LastName=\\\"lname\\\"\\n )\\n facility2 = models.Facility(\\n facilityName=\\\"name\\\",\\n contactFirstName=\\\"fname\\\",\\n contactLastName=\\\"lname\\\",\\n facilityStatus=1,\\n facilityStatusDate=datetime(2016, 2, 2),\\n contact2FirstName=\\\"fname\\\",\\n contact2LastName=\\\"lname\\\"\\n )\\n facilityAddress = models.FacilityAddress(\\n contactInfoSourceID=1,\\n facilityID=1,\\n contactInfoStatusID=1,\\n street=\\\"street\\\",\\n street2=\\\"street2\\\",\\n city=\\\"city\\\",\\n stateID=1,\\n zip=\\\"12345\\\",\\n addressStatusDate=datetime(2016, 2, 2),\\n )\\n\\n facilityPhone = models.FacilityPhone(\\n contactInfoSourceID=1,\\n facilityID=1,\\n contactInfoStatusID=1,\\n clinicName=\\\"clinic\\\",\\n phoneTypeID=1,\\n phoneNumber=\\\"phone\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n facilityPhone2 = models.FacilityPhone(\\n contactInfoSourceID=1,\\n facilityID=1,\\n contactInfoStatusID=1,\\n clinicName=\\\"clinic\\\",\\n phoneTypeID=1,\\n phoneNumber=\\\"phone2\\\",\\n phoneStatusDate=datetime(2016, 2, 2)\\n )\\n patientProjectStatusType1 = models.PatientProjectStatusLUT(\\n statusDescription=\\\"desc\\\"\\n )\\n patientProjectStatusType2 = models.PatientProjectStatusLUT(\\n statusDescription=\\\"desc\\\"\\n )\\n patientProjectStatus = models.PatientProjectStatus(\\n patientProjectStatusTypeID=1,\\n participantID=1,\\n statusDate=datetime(2016,2,2)\\n )\\n physicianFacility = models.PhysicianFacility(\\n facilityID=1,\\n physicianID=1,\\n physFacilityStatusID=1,\\n physFacilityStatusDate=datetime(2016, 2, 2)\\n )\\n contact = models.Contact(\\n contactTypeLUTID=1,\\n participantID=1,\\n staffID=1,\\n informantID=1,\\n informantPhoneID=1,\\n description=\\\"desc\\\",\\n contactDate=datetime(2016, 2, 2),\\n initials=\\\"atp\\\",\\n notes=\\\"notes\\\"\\n )\\n contact2 = models.Contact(\\n contactTypeLUTID=1,\\n participantID=1,\\n staffID=1,\\n facilityID=1,\\n facilityPhoneID=1,\\n description=\\\"desc\\\",\\n contactDate=datetime(2016, 2, 2),\\n initials=\\\"atp\\\",\\n notes=\\\"notes\\\"\\n )\\n contact3 = models.Contact(\\n contactTypeLUTID=1,\\n participantID=1,\\n staffID=1,\\n physicianID=1,\\n physicianPhoneID=1,\\n description=\\\"desc\\\",\\n contactDate=datetime(2016, 2, 2),\\n initials=\\\"atp\\\",\\n notes=\\\"notes\\\"\\n )\\n contact4 = models.Contact(\\n contactTypeLUTID=1,\\n participantID=1,\\n staffID=1,\\n patientPhoneID=1,\\n description=\\\"desc\\\",\\n contactDate=datetime(2016, 2, 2),\\n initials=\\\"atp\\\",\\n notes=\\\"notes\\\"\\n )\\n ctcFacility = models.CTCFacility(\\n ctcID=1,\\n facilityID=1,\\n coc=123\\n )\\n incentive = models.Incentive(\\n participantID=1,\\n incentiveDescription=\\\"desc\\\",\\n barcode=\\\"123456789\\\",\\n dateGiven=datetime(2016, 4, 3)\\n )\\n db.session.add_all(ucrRoles)\\n db.session.add_all(informantRelationships)\\n db.session.add_all(users)\\n db.session.add_all(states)\\n db.session.add_all(finalCodes)\\n db.session.add_all(sexes)\\n db.session.add_all(abstractStatuses)\\n db.session.add_all(races)\\n db.session.add_all(ethnicities)\\n db.session.add_all(vitals)\\n db.session.add_all(contacts)\\n db.session.add_all(inactives)\\n db.session.add_all(ucrReportTypes)\\n db.session.add_all(physicianStatuses)\\n db.session.add_all(physFacilityStatuses)\\n db.session.add_all(phoneTypes)\\n db.session.add_all(irbHolders)\\n db.session.add_all(projectTypes)\\n db.session.add_all(contactStatuses)\\n db.session.add_all(contactSources)\\n db.session.add_all(grantStatuses)\\n db.session.add_all(fundingSources)\\n db.session.add_all(reviewCommitteeStatuses)\\n db.session.add_all(projectStatuses)\\n db.session.add_all(logTypes)\\n db.session.add_all(reviewCommittees)\\n db.session.add_all(staffRoles)\\n db.session.add_all(projectPhases)\\n db.session.add_all(hsts)\\n db.session.add_all(tracingSources)\\n db.session.add_all(contactTypes)\\n db.session.add_all(giftCards)\\n db.session.add(staff)\\n db.session.add(staff2)\\n db.session.add(project1)\\n db.session.add(project2)\\n db.session.add(funding)\\n db.session.add(budget1)\\n db.session.add(rc)\\n db.session.add(ucr)\\n db.session.add(arcReview)\\n db.session.add(preApp)\\n db.session.add(log)\\n db.session.add(projectStaff)\\n db.session.add(staffTraining)\\n db.session.add(patient)\\n db.session.add(patient2)\\n db.session.add(patientAddress)\\n db.session.add(patientEmail)\\n db.session.add(patientPhone)\\n db.session.add(patientPhone2)\\n db.session.add(informant1)\\n db.session.add(informant2)\\n db.session.add(informantAddress)\\n db.session.add(informantPhone)\\n db.session.add(informantPhone2)\\n db.session.add(ctc1)\\n db.session.add(ctc2)\\n db.session.add(projectPatient)\\n db.session.add(projectPatient2)\\n db.session.add(projStatus)\\n db.session.add(tracing)\\n db.session.add(physician)\\n db.session.add(physician2)\\n db.session.add(physicianAddress)\\n db.session.add(physicianEmail)\\n db.session.add(physicianPhone)\\n db.session.add(physicianPhone2)\\n db.session.add(physicianToCTC)\\n db.session.add(facility1)\\n db.session.add(facility2)\\n db.session.add(facilityAddress)\\n db.session.add(facilityPhone)\\n db.session.add(facilityPhone2)\\n db.session.add(patientProjectStatusType1)\\n db.session.add(patientProjectStatusType2)\\n db.session.add(patientProjectStatus)\\n db.session.add(physicianFacility)\\n db.session.add(contact)\\n db.session.add(contact2)\\n db.session.add(contact3)\\n db.session.add(contact4)\\n db.session.add(ctcFacility)\\n db.session.add(incentive)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f4704030f5c33ae04a6f6ed3fc8c9d\",\n \"score\": \"0.699089\",\n \"text\": \"def populate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c4cbc11305536344a963ae28dd0f92\",\n \"score\": \"0.69689226\",\n \"text\": \"def populate_db():\\n values = db.load_config()\\n logging.info(values)\\n logging.info(\\\"Started import \\\")\\n logging.info(f'Importing into DB: {values[\\\"database\\\"]}')\\n # host, db, user, password\\n conn = db.get_connection_local_pg( values)\\n logging.info(\\\"Finished import \\\")\\n if ( not os.path.isdir(\\\"data/2014-i2b2-nlp-evaluation-data-txt\\\") ):\\n logging.exception(\\\"Data not downloaded, run the main.py get_data first \\\")\\n os.sys.exit(1)\\n db.import_data(conn, \\\"data/2014-i2b2-nlp-evaluation-data-txt\\\", \\\"data/testing-PHI-Gold-fixed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab65cea50449a2f2f4271702528c59f\",\n \"score\": \"0.69370925\",\n \"text\": \"def __init__(self):\\n self.db = db\\n self.db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53db6e0a06eccebdfecb42251ceff176\",\n \"score\": \"0.6923225\",\n \"text\": \"def do_populate(self, arg):\\n if arg.strip():\\n self.print('Bad syntax!')\\n return\\n\\n self.client.populate_db(numElem=50, numVp=5)\\n self.print('OK!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0597bdfe794be623b11017593a932e0\",\n \"score\": \"0.69071597\",\n \"text\": \"def _init_db_structure(self):\\n #with self._db as conn: conn.execute('')\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db9b4b3df1fe7585313948ae354a3a87\",\n \"score\": \"0.6852799\",\n \"text\": \"def _populate_db():\\n data_1 = json.dumps(DATA_1)\\n data_2 = json.dumps({\\n \\\"organization\\\": \\\"Skynet Papercorp\\\", \\\"reported_at\\\": \\\"2015-04-22\\\",\\n \\\"created_at\\\": \\\"2015-04-23\\\",\\n \\\"inventory\\\": [{\\\"name\\\": \\\"paper\\\", \\\"price\\\": \\\"4.00\\\"}]\\n })\\n report_1 = Report(data=data_1)\\n report_2 = Report(data=data_2)\\n db.session.add(report_1)\\n db.session.add(report_2)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a0ccdb9a18f76b2e8cd24c1104a62ad\",\n \"score\": \"0.6847385\",\n \"text\": \"def update_database(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c47700b5a4ddf0cb55f3bcad8e6ef127\",\n \"score\": \"0.6742828\",\n \"text\": \"def create_db_samples():\\n init_users()\\n init_categories()\\n init_items()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f19de7f49a4903a12523b88a31e718e7\",\n \"score\": \"0.6733228\",\n \"text\": \"def run(self, args):\\n super(PopulateCommand, self).run(args)\\n try:\\n init_conf(\\\"populate.log\\\")\\n# cfg.CONF.log_file = \\\"populate.log\\\"\\n# cfg.CONF.use_stderr = True\\n LOG = api.LOG = get_logger(\\\"populate\\\")\\n LOG.info(_(\\\"Loading environment\\\"))\\n self.load_app()\\n LOG.info(_(\\\"Building schema\\\"))\\n LOG.info(_(\\\"Starting a transaction...\\\"))\\n models.start()\\n\\n # FIXME: There's no create_all equivalent for Music.\\n\\n # Valet\\n Group.create_table()\\n Placement.create_table()\\n Plan.create_table()\\n\\n # Ostro\\n Event.create_table()\\n PlacementRequest.create_table()\\n PlacementResult.create_table()\\n except Exception as ex:\\n models.rollback()\\n LOG.error(\\\"Rolling back... %s\\\" % ex)\\n raise\\n else:\\n LOG.info(_(\\\"Committing.\\\"))\\n models.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda6cc12fabb7bd264b4028d965e6e4f\",\n \"score\": \"0.66867036\",\n \"text\": \"def insert_data(self):\\n # Make a connexion with a mock database\\n self.generate_data_collection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9d68f7ddaf994169e9ef7ea55297614\",\n \"score\": \"0.664742\",\n \"text\": \"def init_db():\\n global app\\n Supplier.init_db(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a0f57d7958b9a592b22a30b870e439a\",\n \"score\": \"0.66439116\",\n \"text\": \"def toDB(self):\\n #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c7a639687c0a13a0d5426588108c1e4\",\n \"score\": \"0.66213745\",\n \"text\": \"def populate_db():\\n from datetime import datetime\\n from models import Post, Tag, post_to_tag\\n Post.query.delete()\\n Tag.query.delete()\\n db.session.execute(post_to_tag.delete())\\n\\n personalwebapp = Tag(name='personalwebapp')\\n db.session.add(personalwebapp)\\n\\n post = Post(title='Hello PersonalWebApp!', content=FIRST_POST, author_id=1, timezone=-5)\\n post.tags.append(personalwebapp)\\n post.state_id = 2\\n post.published_at = datetime.utcnow()\\n post.url = 'hello_personal_webapp'\\n db.session.add(post)\\n\\n post = Post(title='Example Draft Post', content='This is the first draft. I\\\\'ll finish it later.',\\n author_id=1, timezone=-5)\\n post.url = 'example_draft_post'\\n db.session.add(post)\\n\\n db.session.commit()\\n\\n print('\\\"personalwebapp\\\" tag'\\n 'and \\\"Hello PersonalWebApp!\\\" published post and \\\"Example Draft Post\\\" draft post have been generated.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d910fe18fbef4319354befcc35d03628\",\n \"score\": \"0.65966296\",\n \"text\": \"def create_db(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca9c7e87e76ef17c1834e51d3ec5b2fe\",\n \"score\": \"0.65857136\",\n \"text\": \"def populate_db():\\n\\n api_url = BOOK_URL\\n\\n api_response = requests.get(api_url)\\n print(api_response.text)\\n json_api_response = api_response.json()\\n if api_response.status_code == 200:\\n for book in json_api_response:\\n name = book['name']\\n isbn = book[\\\"isbn\\\"]\\n authors = json.dumps(book[\\\"authors\\\"])\\n country = book['country']\\n number_of_pages = int(book[\\\"numberOfPages\\\"])\\n publisher = book[\\\"publisher\\\"]\\n release_date = datetime.strptime(book[\\\"released\\\"], \\\"%Y-%m-%dT%H:%M:%S\\\").date()\\n\\n book_insert = Books(None, name, isbn, authors, country, number_of_pages, publisher, release_date, datetime.now(),\\n datetime.now())\\n db.session.add(book_insert)\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02692e1ea91042a4b1bb734b743fdd70\",\n \"score\": \"0.65577847\",\n \"text\": \"def populate_database(query, write):\\n db = ingest.load_database()\\n db = ingest.populate_database(db, query=query, write=write)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019188738626cd084af97385709e6cb4\",\n \"score\": \"0.6507313\",\n \"text\": \"def initialize_db():\\n # Drop the database and create tables\\n setup_database = TestDatabase()\\n setup_database.drop()\\n setup_database.create()\\n setup_database.populate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"738e02b7d0516b0517e04b9cd2313a1b\",\n \"score\": \"0.64941686\",\n \"text\": \"def run(self):\\n self.db.table('users').insert({\\n 'name': 'sjahn',\\n 'email': 'asj214@naver.com',\\n 'password': generate_password_hash('1234')\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96b463c4afba4b8874e42597aee6b3b\",\n \"score\": \"0.64413\",\n \"text\": \"def seed_data():\\n typer.echo(\\\"Creating initial data\\\")\\n db = SessionLocal()\\n init_db(db)\\n typer.echo(\\\"Initial data created\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2bc2d10880289fafb45d98b7d9b9758\",\n \"score\": \"0.6428364\",\n \"text\": \"def dbcreation():\\n db.create_all() \\n return \\\"Hi\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d23d384becf2768d3e555256fd4cf92f\",\n \"score\": \"0.6423991\",\n \"text\": \"def populate(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef264dd3df50c3020dbd0dd2de6672e7\",\n \"score\": \"0.6420109\",\n \"text\": \"def __populate(self):\\n self._populate_cli()\\n self._populate_model()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c52a7b7a7c599071e0c42c0dc4163f5e\",\n \"score\": \"0.6419514\",\n \"text\": \"def setUp(self):\\n\\t\\tdb.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e326115fecf07a75034ac464aff76c\",\n \"score\": \"0.6418617\",\n \"text\": \"def initialize():\\n db.connect()\\n db.create_tables([Product], safe=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22618966909c2b541155c9cccc016691\",\n \"score\": \"0.64135474\",\n \"text\": \"def initialize():\\n db.connect()\\n db.create_tables([Entry], safe=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"253b0f1c720f5fda12e6a428c5ab086f\",\n \"score\": \"0.6398332\",\n \"text\": \"def initialisation(self):\\n self.cursor.execute('create database if not exists open_food_facts;')\\n\\n with open(\\\"creation_bdd.sql\\\", 'r') as bdd:\\n file = bdd.read()\\n requetes = file.split(';')\\n for line in requetes:\\n self.cursor.execute(line)\\n\\n self.connexion.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58caf7c1e15f11c58efc8b15d2e8bd2f\",\n \"score\": \"0.6390033\",\n \"text\": \"def get_database_data(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa52c5d6fcf41a0a51cbbf90cf49e5f4\",\n \"score\": \"0.6380734\",\n \"text\": \"def create_db():\\n db.create_all()\\n db_fill_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739094ba7ea9b2a730020bba9cfc3cab\",\n \"score\": \"0.6374668\",\n \"text\": \"def init_db_command():\\n db = init_db()\\n import_parsed_data(db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"220077a23ec1b23d82b202cd9f0c39c2\",\n \"score\": \"0.6373129\",\n \"text\": \"def init_db():\\n\\n create_model_tables([\\n Text,\\n Citation,\\n ], fail_silently=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5957b336c45f673120ecf164d9b315a\",\n \"score\": \"0.63699794\",\n \"text\": \"def build_database():\\n\\tfrom fake_data import setup_initial_database, more_data\\n\\n\\tprint (\\\"Updating Local Development Database...\\\")\\n\\n\\tsetup_initial_database()\\n\\n\\tprint (\\\"Finished!\\\")\\n\\n\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5734b372ff7c325ebedb7ccee68dd464\",\n \"score\": \"0.63501185\",\n \"text\": \"def initialize_db(self):\\r\\n click.echo('[OrientModel_initialize_db_%s] Starting process...' % (get_datetime()))\\r\\n if self.checks['created'] == False:\\r\\n self.create_db()\\r\\n if self.checks['open_db'] == False:\\r\\n self.open_db()\\r\\n sql = \\\"\\\"\\r\\n for m in self.models:\\r\\n sql = sql+\\\"create class %s extends %s;\\\\n\\\" % (m, self.models[m]['class'])\\r\\n for k in self.models[m].keys():\\r\\n if k != 'class':\\r\\n sql = sql+\\\"create property %s.%s %s;\\\\n\\\" % (m, k, self.models[m][k])\\r\\n if 'id' in str(k):\\r\\n sql = sql + \\\"create index %s_%s on %s (%s) UNIQUE ;\\\\n\\\" % (m, k, m, k)\\r\\n\\r\\n sql = sql + \\\"create sequence idseq type ordered;\\\"\\r\\n click.echo('[OrientModel_initialize_db_%s]'\\r\\n ' Initializing db with following batch statement'\\r\\n '\\\\n*************** SQL ***************\\\\n'\\r\\n '%s\\\\n*************** SQL ***************\\\\n' % (get_datetime(), sql))\\r\\n self.checks['initialized'] = True\\r\\n\\r\\n return self.client.batch(sql)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b45da97885b18b7a1e9b14a83ba0820f\",\n \"score\": \"0.6349823\",\n \"text\": \"def creation_database_loading_started(self):\\n heading.main_page_loading()\\n print('\\\\t\\\\tCreation of the Database ... 0%\\\\n\\\\n'\\n '\\\\t\\\\t- - - - - - - - - - - - - - - - - - - - -\\\\n\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2a1691c8b3e0c77cb7b7cacc2bbcf3\",\n \"score\": \"0.63198614\",\n \"text\": \"def init_db(self):\\n # Connecting to the database file\\n conn = sqlite3.connect(DatabaseAccessor.DATABASE_PATH)\\n c = conn.cursor()\\n\\n # Create new tables\\n for sql in self.SQL_CREATES:\\n c.execute(sql)\\n\\n # Committing changes and closing the connection to the database file\\n conn.commit()\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06a678067aaeca2b31e147bf8b83af57\",\n \"score\": \"0.6313009\",\n \"text\": \"def _init_database(self):\\r\\n\\r\\n for schema in self._object_schemas:\\r\\n type_specs = [self._create_sql_for_column(name, schema_item)\\r\\n for (name, schema_item) in schema.fields]\\r\\n self.cursor.execute(\\\"CREATE TABLE %s (%s)\\\" %\\r\\n (schema.table_name, ', '.join(type_specs)))\\r\\n for name, columns in schema.indexes:\\r\\n self.cursor.execute(\\\"CREATE INDEX %s ON %s (%s)\\\" %\\r\\n (name, schema.table_name, ', '.join(columns)))\\r\\n for name, columns in schema.unique_indexes:\\r\\n self.cursor.execute(\\\"CREATE UNIQUE INDEX %s ON %s (%s)\\\" %\\r\\n (name, schema.table_name, ', '.join(columns)))\\r\\n self._create_variables_table()\\r\\n self.set_version()\\r\\n self.setup_fulltext_search()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0c15cd3fe6ac70b7ba41350f4bb2c8\",\n \"score\": \"0.63018775\",\n \"text\": \"def initialize_database(self):\\n self.database = self.loader.request_library(\\\"common_libs\\\", \\\"database\\\")\\n self.database.create_connection(\\\"production\\\")\\n self.database.load_mappings()\\n\\n self.migrator = self.loader.request_library(\\\"database_tools\\\", \\\"migrator\\\")\\n self.migrator.migrate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b5beb195eb88b0cf9a0d1aa2d98dc4\",\n \"score\": \"0.6297803\",\n \"text\": \"def initialize_db():\\n print(\\\"Do init db\\\")\\n db.create_all()\\n print(\\\"init db done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3e440fe43530ddba4ca0b4a847d00a9\",\n \"score\": \"0.62872404\",\n \"text\": \"def _create_tables(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1631bc9ae71fad0be54bbace58f0a6ce\",\n \"score\": \"0.62842786\",\n \"text\": \"def create_database():\\n print('Creating database...')\\n init_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aca2caa00fa9fc1fbc76530b0e78a022\",\n \"score\": \"0.6271357\",\n \"text\": \"def database():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4b447b309d4ac61d1d972e740693d4\",\n \"score\": \"0.6265521\",\n \"text\": \"def populate_db():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('personjob.db')\\n\\n logger.info('Working with Person class')\\n\\n PERSON_NAME = 0\\n LIVES_IN_TOWN = 1\\n NICKNAME = 2\\n\\n people = [\\n ('Andrew', 'Sumner', 'Andy'),\\n ('Peter', 'Seattle', None),\\n ('Susan', 'Boston', 'Beannie'),\\n ('Pam', 'Coventry', 'PJ'),\\n ('Steven', 'Colchester', None),\\n ]\\n\\n JOB_NAME = 0\\n START_DATE = 1\\n END_DATE = 2\\n SALARY = 3\\n PERSON_EMPLOYED = 4\\n\\n jobs = [\\n ('Analyst', '2001-09-22', '2003-01-30', 65500, 'Andrew'),\\n ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew'),\\n ('Senior business analyst', '2006-10-23', '2016-12-24', 80000,\\n 'Andrew'),\\n ('Admin supervisor', '2012-10-01', '2014-11-10', 45900, 'Peter'),\\n ('Admin manager', '2014-11-14', '2018-01-05', 45900, 'Peter')\\n ]\\n\\n DEPARTMENT_NUMBER = 0\\n DEPARTMENT_NAME = 1\\n DEPARTMENT_MANAGER = 2\\n NAME_OF_JOB = 3\\n\\n departments = [\\n ('H153', 'Business Intelligence', 'John Phillips', 'Analyst'),\\n ('H567', 'Product Development', 'Rose Adams', 'Senior analyst'),\\n ('H195', 'Enterprise Strategy', 'Jonathan Range', 'Senior business '\\n 'analyst'),\\n ('K976', 'Production Engineering', 'Kathryn Allen', 'Admin '\\n 'supervisor'),\\n ('L763', 'Reliability Engineering', 'Adam Stunt', 'Admin manager')\\n ]\\n\\n logger.info('Creating Person and Job records: iterate through the list of '\\n 'tuples')\\n logger.info('Prepare to explain any errors with exceptions')\\n logger.info('and the transaction tells the database to fail on error')\\n\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n for person in people:\\n with database.transaction():\\n new_person = Person.create(\\n person_name=person[PERSON_NAME],\\n lives_in_town=person[LIVES_IN_TOWN],\\n nickname=person[NICKNAME])\\n new_person.save()\\n logger.info('Database Person add successful')\\n\\n logger.info('Print the Person records we saved...')\\n for saved_person in Person:\\n logger.info(f'{saved_person.person_name} lives in '\\n f'{saved_person.lives_in_town} ' +\\\\\\n f'and likes to be known as {saved_person.nickname}')\\n\\n for job in jobs:\\n date_end = datetime.datetime.strptime(job[END_DATE],\\\"%Y-%m-%d\\\")\\n date_start = datetime.datetime.strptime(job[START_DATE],\\\"%Y-%m-%d\\\")\\n duration = date_end - date_start\\n with database.transaction():\\n new_job = Job.create(\\n job_name=job[JOB_NAME],\\n start_date=job[START_DATE],\\n end_date=job[END_DATE],\\n salary=job[SALARY],\\n employment_duration=duration.days,\\n person_employed=job[PERSON_EMPLOYED])\\n new_job.save()\\n logger.info('Database job add successful')\\n\\n logger.info('Print the Job records we saved...')\\n for saved_job in Job:\\n logger.info(f'{saved_job.person_employed} held the job of '\\n f'{saved_job.job_name} from '\\n f'{saved_job.start_date} to '\\n f'{saved_job.end_date} with a salary of '\\n f'{saved_job.salary}')\\n\\n for department in departments:\\n with database.transaction():\\n new_department = Department.create(\\n department_number=department[DEPARTMENT_NUMBER],\\n department_name=department[DEPARTMENT_NAME],\\n department_manager=department[DEPARTMENT_MANAGER],\\n name_of_job=department[NAME_OF_JOB])\\n new_department.save()\\n logger.info('Database department add successful')\\n\\n logger.info('Print the department records we saved...')\\n for saved_department in Department:\\n logger.info('Department ' f'{saved_department.department_number} '\\n 'is named ' f'{saved_department.department_name}'\\n ', managed by ' \\n f'{saved_department.department_manager}, and has a '\\n f'job of ' f'{saved_department.name_of_job}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {person[PERSON_NAME]}')\\n logger.info(e)\\n logger.info('See how the database protects our data')\\n\\n finally:\\n logger.info('database closes')\\n database.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74f9e654494a3765f3c2805f2b73e971\",\n \"score\": \"0.6265285\",\n \"text\": \"def db(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86203a90ca15a10a52cec642d40e5c70\",\n \"score\": \"0.6262716\",\n \"text\": \"def init_db():\\n global app\\n Recommendation.init_db(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa54e515457a672171567e91ed392ac\",\n \"score\": \"0.62594074\",\n \"text\": \"def fill_db():\\n dbname = get_database()\\n collection_name = dbname[\\\"templates\\\"]\\n try:\\n collection_name.delete_many({})\\n except Exception as err:\\n logger.debug(f\\\"ERR: {err}\\\")\\n\\n try:\\n collection_name.insert_many([*templates_1, *templates_2])\\n except Exception as err:\\n logger.debug(f\\\"ERR: {err}\\\")\\n yield\\n\\n\\n try:\\n collection_name.delete_many({})\\n except Exception as err:\\n logger.debug(f\\\"ERR: {err}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36f62cdc6ff20f32e10aba355418de0a\",\n \"score\": \"0.62396115\",\n \"text\": \"async def db_create(self, ctx):\\n\\t\\tawait self.rebuild_database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75f6c4419807df47b1696e2cf4aaf341\",\n \"score\": \"0.62381905\",\n \"text\": \"def initializing(self):\\n self.cursor.executescript(INIT_USERS_DATABASE)\\n self.cursor.executescript(INIT_INVENTORY_DATABASE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"807dead6cba37df60dcfdc6251d057fe\",\n \"score\": \"0.6236994\",\n \"text\": \"def populate_database():\\n dom = DomainFactory(name=\\\"test.com\\\", quota=50)\\n admin = UserFactory(\\n username=\\\"admin@test.com\\\", groups=(\\\"DomainAdmins\\\", ),\\n password=\\\"{PLAIN}toto\\\"\\n )\\n MailboxFactory(address=\\\"admin\\\", domain=dom, user=admin)\\n account = UserFactory.create(\\n username=\\\"user@test.com\\\", groups=(\\\"SimpleUsers\\\",),\\n )\\n MailboxFactory.create(address=\\\"user\\\", domain=dom, user=account)\\n\\n al = AliasFactory.create(\\n address=\\\"forward@test.com\\\", domain=dom\\n )\\n AliasRecipientFactory.create(\\n address=\\\"user@external.com\\\", alias=al)\\n\\n al = AliasFactory.create(\\n address=\\\"alias@test.com\\\", domain=dom\\n )\\n mb = account.mailbox\\n AliasRecipientFactory.create(\\n address=mb.full_address, alias=al, r_mailbox=mb)\\n\\n al = AliasFactory.create(\\n address=\\\"postmaster@test.com\\\", domain=dom\\n )\\n for address in [\\\"toto@titi.com\\\", \\\"test@truc.fr\\\"]:\\n AliasRecipientFactory.create(address=address, alias=al)\\n\\n dom.add_admin(admin)\\n\\n dom2 = DomainFactory.create(name=\\\"test2.com\\\", default_mailbox_quota=0)\\n admin = UserFactory.create(\\n username=\\\"admin@test2.com\\\", groups=(\\\"DomainAdmins\\\",),\\n password=\\\"{PLAIN}toto\\\"\\n )\\n MailboxFactory.create(address=\\\"admin\\\", domain=dom2, user=admin)\\n u = UserFactory.create(\\n username=\\\"user@test2.com\\\", groups=(\\\"SimpleUsers\\\",)\\n )\\n MailboxFactory.create(address=\\\"user\\\", domain=dom2, user=u)\\n dom2.add_admin(admin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c04d5ec3dffc2dcda802bf5284869d1\",\n \"score\": \"0.6226869\",\n \"text\": \"def example_data():\\n\\n user_1 = User(email=\\\"hellohellohello@gmail.com\\\", password=\\\"1234\\\")\\n search_1 = Search(search_term=\\\"testestest\\\")\\n outlet_1 = Outlet(outlet_name='Best News', outlet_popularity=10, outlet_bias='Right-Center')\\n user_search_1 = User_Search(user_id=1, search_id=1)\\n\\n db.session.add_all([user_1, search_1, outlet_1])\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c41314e33f88a438daf047f9b64236a\",\n \"score\": \"0.6223169\",\n \"text\": \"def __init__(self, database):\\n self.database = database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d412440f18ffb4fe6a755293360a1a1\",\n \"score\": \"0.62127787\",\n \"text\": \"def init_db():\\n global app\\n Inventory.init_db(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f08630fdffbb4548a9384d670878c7c8\",\n \"score\": \"0.6210465\",\n \"text\": \"def _populate_data(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4e83ec014e7a72cfb35bcc9baaa4f77\",\n \"score\": \"0.6210141\",\n \"text\": \"def setUp(self):\\n self.database = Database()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d99aa9a5b920aa730a6517506b70c69\",\n \"score\": \"0.61997527\",\n \"text\": \"def load_data(self, db_name):\\r\\n create_schema = CreateSchema(db_name)\\r\\n offices = create_schema.load_offices()\\r\\n livingspaces = create_schema.load_living_space()\\r\\n staffs = create_schema.load_staff()\\r\\n fellows = create_schema.load_fellow()\\r\\n office_staff_alloc = create_schema.load_office_staff_allocations()\\r\\n office_fellow_alloc = create_schema.load_office_fellow_allocations()\\r\\n livingspaces_alloc = create_schema.load_living_space_allocations()\\r\\n #Adding offices\\r\\n for office in offices:\\r\\n self.create_room(\\\"OFFICE\\\", office[0])\\r\\n #Adding living spaces\\r\\n for livingspace in livingspaces:\\r\\n self.create_room(\\\"LIVINGSPACE\\\", livingspace[0])\\r\\n #Adding staff\\r\\n for staff in staffs:\\r\\n self.add_person(\\\"STAFF\\\", staff[0])\\r\\n #Adding fellows\\r\\n for fellow in fellows:\\r\\n self.add_person(\\\"FELLOW\\\", fellow[0])\\r\\n #Creating office staff allocations'\\r\\n for alloc_office in office_staff_alloc:\\r\\n value = self.staff_dict[str(alloc_office[1])]\\r\\n self.allocate_rooms(value, \\\"OFFICE\\\")\\r\\n #Creating office fellow allocations\\r\\n for alloc_office in office_fellow_alloc:\\r\\n value = self.fellow_dict[str(alloc_office[2])]\\r\\n self.allocate_rooms(value, \\\"OFFICE\\\")\\r\\n # Allocating Living space\\r\\n for alloc_living_space in livingspaces_alloc:\\r\\n value = self.fellow_dict[str(alloc_living_space[1])]\\r\\n self.allocate_rooms(value, \\\"LIVINGSPACE\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"083997557b55c3c6c5815ab710f0e1e7\",\n \"score\": \"0.61968344\",\n \"text\": \"def create_database(self):\\n self._create_tables()\\n self._create_functions()\\n self._create_triggers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04afa680de176ecafcefb3de5841ad9d\",\n \"score\": \"0.6194173\",\n \"text\": \"def init():\\n try:\\n DataBase.create_table()\\n DataBase.create_price_table()\\n except Exception:\\n print(MainMessage.db_critical_error)\\n sys.exit(0)\\n print(MainMessage.db_init_success)\\n if DataBase.read_record_from_pricedb():\\n (Stakes.stake, Stakes.term, Stakes.substake, Stakes.subterm) = DataBase.read_record_from_pricedb()\\n else:\\n DataBase.insert_record_to_pricedb(Stakes.stake, Stakes.term, Stakes.substake, Stakes.subterm)\\n Stakes.change_stakes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9355d57b29c60ed9ecb2f2147062f036\",\n \"score\": \"0.61881137\",\n \"text\": \"def init_database(self):\\n self._drop_tables()\\n self._create_tables()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e9772beb911f40e0222101163fe454\",\n \"score\": \"0.6180436\",\n \"text\": \"def update_database():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c791e0bc0d4578a3d0bbc0f415eed76f\",\n \"score\": \"0.6174021\",\n \"text\": \"def init_table(self):\\n self.create_table()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1dcbe9a9c1b48448873415bf2213b59\",\n \"score\": \"0.61739\",\n \"text\": \"def create_db():\\n\\n # TODO:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"959a78bb1df3bc568390da2ab88111ec\",\n \"score\": \"0.6173813\",\n \"text\": \"def initialize():\\r\\n db.connect()\\r\\n db.create_tables([BaseModel], safe=True)\\r\\n db.create_tables([Records], safe=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff935099c2772641552fbf8e0f244b2\",\n \"score\": \"0.61727023\",\n \"text\": \"def setup_tables(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff634b9defb5c8b9f625cc35fcecb901\",\n \"score\": \"0.61722714\",\n \"text\": \"def dbcreate(self):\\r\\n self.db.createtable()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c1b2e3b68a6b4c34d605a6188648f3\",\n \"score\": \"0.61717933\",\n \"text\": \"def __init__(self, database):\\n self.db = database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92ab14e391589055ac911fad939895a5\",\n \"score\": \"0.6170633\",\n \"text\": \"def init_db_command():\\n from .data.database import init_db\\n init_db()\\n click.echo('Initialized the database.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d492e45f540121a62e6f784eafc01ada\",\n \"score\": \"0.61700296\",\n \"text\": \"def init_db():\\r\\n\\r\\n db = Scraper_DB()\\r\\n try:\\r\\n db.create_tables()\\r\\n except Exception as e:\\r\\n print(\\\"{0}\\\".format(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3286660208fee39a3d20c9ecd4a39791\",\n \"score\": \"0.616117\",\n \"text\": \"def initialize():\\n DATABASE.connect(reuse_if_open=True)\\n DATABASE.create_tables([User, ToDo], safe=True)\\n DATABASE.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e163e477fb3afc401b0661de598cf24\",\n \"score\": \"0.61580926\",\n \"text\": \"def populateInitialDataSet(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d33114588b0147f80274744c9144f3\",\n \"score\": \"0.6151307\",\n \"text\": \"def generate_db(self):\\n print('Generating LANL Earthquake Prediction Database...')\\n # Unzip the database\\n self._unzip_db()\\n\\n # Get test dataset\\n self._get_test_db()\\n\\n # Get train dataset\\n self._get_train_db()\\n print('Complete!\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c47cf554648cdddb92ff0a42d78e2ce\",\n \"score\": \"0.6147908\",\n \"text\": \"def dbSetUp(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"753de69c4c3126921e551c944d3b7777\",\n \"score\": \"0.6147715\",\n \"text\": \"def creation_database(self):\\n heading.main_page_loading()\\n print('\\\\t\\\\tPlease, write the name of the database\\\\n')\\n self.choice_creation_database = input('[Database Menu] : #dm\\\\n'\\n '[Quit] : #quit\\\\n\\\\n\\\\t=> ')\\n\\n return self.choice_creation_database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f12a34445caf9eb081b7c0332a02764\",\n \"score\": \"0.6136958\",\n \"text\": \"def initdb():\\n db.create_all()\\n print \\\":creo las tablas de la base de datos:\\\"\\n poblardb()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"641745680596c09f4ccc1753c64609b6\",\n \"score\": \"0.6131554\",\n \"text\": \"def transfertodb():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f32db0d5a6f2dedaeedfadb7aba8090\",\n \"score\": \"0.61295503\",\n \"text\": \"def setUp(self):\\n db.create_all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1041a33217543f2273e244a6771a296\",\n \"score\": \"0.61277354\",\n \"text\": \"def build_data_base(self):\\n\\n self.db.create_table(\\\"STUDENTS\\\", [[\\\"Firstname\\\", \\\"TEXT\\\"], [\\\"LastName\\\", \\\"TEXT\\\"], [\\\"Id\\\", \\\"TEXT\\\"],\\n [\\\"Gender\\\", \\\"TEXT\\\"], [\\\"Country\\\", \\\"TEXT\\\"], [\\\"Age\\\", \\\"TEXT\\\"],\\n [\\\"Username\\\", \\\"TEXT\\\"], [\\\"Password\\\", \\\"TEXT\\\"]])\\n\\n self.db.create_table(\\\"OnlineLessons\\\", [[\\\"GeneralSubject\\\", \\\"TEXT\\\"], [\\\"MainSubject\\\", \\\"TEXT\\\"],\\n [\\\"TargetAudience\\\", \\\"TEXT\\\"], [\\\"AuthorName\\\", \\\"TEXT\\\"],\\n [\\\"AuthorAge\\\", \\\"TEXT\\\"], [\\\"LastName\\\", \\\"TEXT\\\"],\\n [\\\"ExceptedLength\\\", \\\"TEXT\\\"]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"309e67e55289e682909ea0827a4471ad\",\n \"score\": \"0.6122299\",\n \"text\": \"def init_db():\\n db = get_db()\\n with app.open_resource('schema.sql', mode='r') as f:\\n db.cursor().executescript(f.read())\\n with app.open_resource('knowledge.sql', mode='r') as f:\\n db.cursor().executescript(f.read())\\n with app.open_resource('users.sql', mode='r') as f:\\n db.cursor().executescript(f.read())\\n \\n db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38e132347272880dac991ab250774e67\",\n \"score\": \"0.6111997\",\n \"text\": \"def create_database():\\n init_db()\\n click.echo(\\\"Initialized the database.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5275a31cd425c5d356faeb8958f47a\",\n \"score\": \"0.6109491\",\n \"text\": \"def initial_setup():\\n db = _get_db()\\n db.execute(SCHEMA)\\n db.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d24efa6d151640a258ecc9cece982fdd\",\n \"score\": \"0.6107269\",\n \"text\": \"def __init__(self):\\n # try:\\n\\n self.filename = os.path.join(CONFIG_DATABASE_PATH,\\n CONFIG_DATABASE_NAME)\\n self.connection = db.connect(self.filename)\\n\\n # if not db.Connection.\\n # print \\\"error\\\"\\n \\\"\\\"\\\"creating a cursor\\\"\\\"\\\"\\n\\n #cursor = self.connection.cursor()\\n # cursor.execute(\\n # '''INSERT INTO excavation (projectID, projectTitle, projectType, projectSubType, site, studyArea)\\n # VALUES (4, 'test', 'field', 'xx', 'ga', 'ta')''')\\n #self.connection.commit()\\n #\\n # cursor = connection.execute('select * from excavation')\\n # print cursor.fetchall()\\n\\n #self.connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d3fbe5edf0d1582f8979019e09d465\",\n \"score\": \"0.6106553\",\n \"text\": \"def __init__(self, databaseName):\\r\\n\\r\\n print(\\\"\\\\n------------| DATABASE CREATION |------------\\\\n\\\")\\r\\n self.database_name = databaseName\\r\\n\\r\\n # Create DB\\r\\n self.conn = sqlite3.connect(self.database_name)\\r\\n\\r\\n # Variable to access DB\\r\\n self.c = self.conn.cursor()\\r\\n\\r\\n print(\\\"> Database\\\", self.database_name, \\\"was created successfully\\\")\\r\\n self.createTables()\\r\\n self.closeDB()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9132cf670cc0bc8535fada9e5d53fa33\",\n \"score\": \"0.6106523\",\n \"text\": \"def _initializeDatabase(self):\\n self.bot = GridBotModel(\\n name = self.name,\\n symbol = self.symbol,\\n exchange = self.exchange.exchange_id,\\n starting_balance = self.total_amount,\\n current_balance = self.total_amount,\\n trade_amount = self.trade_amount,\\n trade_step = self.trade_step,\\n test_run = self.test_mode)\\n self.session.add(self.bot)\\n self.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251dc8d2a87a8f12d31b19a5a9953e1f\",\n \"score\": \"0.6104041\",\n \"text\": \"def init_db():\\n print 'Initialize DB'\\n from yamp.app import db, engine\\n from yamp.models import Base, __all__ as model_files\\n from importlib import import_module\\n for model_file in model_files:\\n import_module('yamp.models.' + model_file, __name__)\\n print '* Model \\\"%s\\\" loaded.' % model_file\\n Base.metadata.create_all(bind=engine)\\n db.commit()\\n print '... All tables created!'\\n raise SystemExit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3514015410b72f25ec9f2ccb21c09d3\",\n \"score\": \"0.6102784\",\n \"text\": \"def init_users():\\n UserModel('thinh', '1234').save_to_db()\\n UserModel('long', '1234').save_to_db()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bd78f2cf3726b2f9fd8f86e7b7d4e7\",\n \"score\": \"0.60980797\",\n \"text\": \"def setUp(self):\\n schema(DBPATH)\\n seed(DBPATH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d6902cae012870febfcc1c047d0450c\",\n \"score\": \"0.6095673\",\n \"text\": \"def _instantiate(self):\\n print(\\\"creating database with table(s) at \\\" + DB_PATH)\\n\\n with self._connect() as db_cur:\\n\\n # create disasters table\\n db_cur.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS slack_calls (\\n id integer PRIMARY KEY ,\\n team_id text UNIQUE NOT NULL ,\\n team_name text ,\\n channel_id text UNIQUE NOT NULL ,\\n access_token text UNIQUE NOT NULL ,\\n bot_access_token text UNIQUE NOT NULL ,\\n );\\n \\\"\\\"\\\")\\n\\n # indexing columns for faster access\\n db_cur.execute(\\\"\\\"\\\"CREATE UNIQUE INDEX IF NOT EXISTS indexed_team_id ON slack_calls (\\n team_id\\n );\\n \\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1deabb1a8aa6863203ab301bd1ab2db\",\n \"score\": \"0.60897547\",\n \"text\": \"def manual_enter(self):\\n self._dbconnect = sqlite3.connect(self._db_file)\\n\\n # Set row_factory to access columns by name\\n self._dbconnect.row_factory = sqlite3.Row\\n\\n # Create a cursor to work with the db\\n self._cursor = self._dbconnect.cursor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3633fce200e85f45e90f30ef071775\",\n \"score\": \"0.60858333\",\n \"text\": \"def initialize():\\n db.connect()\\n db.create_tables([Task], safe=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a20d295df90cd4681dabefe43ef7696d\",\n \"score\": \"0.60857296\",\n \"text\": \"def createData(self):\\n self.data = self.dbroot['data'] = OOBTree()\\n self.meta = self.dbroot['meta'] = Meta('PEAT-DB database')\\n self.meta.staticfields = { 'name': 'text', 'Mutations':'text',\\n 'Structure':'PDB'}\\n self.blobs = self.dbroot['blobs'] = OOBTree() \\n #use meta.info for any misc settings specific to the DB\\n self.meta.info = PersistentMapping(self.defaultinfo)\\n self.commit()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e776e6da8c21cf8b3eb86d0ad5f64480\",\n \"score\": \"0.60816956\",\n \"text\": \"def initalize_user_tables(db):\\n \\n from shotglass2.users.models import init_db as users_init_db \\n users_init_db(db)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc46ccf775d7e2ce6ddb240a0fd1e10\",\n \"score\": \"0.6078222\",\n \"text\": \"def database_access(db):\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":187,"cells":{"query_id":{"kind":"string","value":"28ecbd2ee477b01b0414a979344fe7a1"},"query":{"kind":"string","value":"Function that Cleans the text and handles the superscript and subscript tags in the text"},"positive_passages":{"kind":"list like","value":[{"docid":"bada213dfa474c405bf524914fe10ab8","score":"0.815164","text":"def cleaning(text):\n \n ind = 0\n text = text.replace(\":\",\"\")\n n = len(text)\n\n \"\"\"Replacing sup tag with supercript\"\"\"\n while (ind < len(text)):\n if(text[ind]==\"<\" and ind < n-4 and text[ind+1:ind+4]==\"sup\"):\n curr = ind\n curr += 5\n temp = \"\"\n while(True):\n if(text[curr]==\"<\" and text[curr+1:curr+5]==\"/sup\"):\n break\n temp += text[curr]\n curr += 1\n text = text[:ind] + convert_to_superscript(temp)+text[curr + 6:]\n ind = curr + 6\n else:\n ind += 1\n\n\n \"\"\"Replacing sub tag with subcript\"\"\"\n ind = 0\n while (ind < len(text)):\n if(text[ind]==\"<\" and ind < n-4 and text[ind+1:ind+4]==\"sub\"):\n curr = ind\n curr += 5\n temp = \"\"\n while(True):\n if(text[curr]==\"<\" and text[curr+1:curr+5]==\"/sub\"):\n break\n temp += text[curr]\n curr += 1\n text = text[:ind] + convert_to_superscript(temp)+text[curr + 6:]\n ind = curr + 6\n else:\n ind += 1\n\n x = text\n y = re.sub('\\\\n', ' ',x)\n y = re.sub('\\\\r', ' ',y)\n y = re.sub('\\\\t', ' ',y)\n y = re.sub('<[^>]*>', ' ',y)\n y = y.strip()\n return y","title":""}],"string":"[\n {\n \"docid\": \"bada213dfa474c405bf524914fe10ab8\",\n \"score\": \"0.815164\",\n \"text\": \"def cleaning(text):\\n \\n ind = 0\\n text = text.replace(\\\":\\\",\\\"\\\")\\n n = len(text)\\n\\n \\\"\\\"\\\"Replacing sup tag with supercript\\\"\\\"\\\"\\n while (ind < len(text)):\\n if(text[ind]==\\\"<\\\" and ind < n-4 and text[ind+1:ind+4]==\\\"sup\\\"):\\n curr = ind\\n curr += 5\\n temp = \\\"\\\"\\n while(True):\\n if(text[curr]==\\\"<\\\" and text[curr+1:curr+5]==\\\"/sup\\\"):\\n break\\n temp += text[curr]\\n curr += 1\\n text = text[:ind] + convert_to_superscript(temp)+text[curr + 6:]\\n ind = curr + 6\\n else:\\n ind += 1\\n\\n\\n \\\"\\\"\\\"Replacing sub tag with subcript\\\"\\\"\\\"\\n ind = 0\\n while (ind < len(text)):\\n if(text[ind]==\\\"<\\\" and ind < n-4 and text[ind+1:ind+4]==\\\"sub\\\"):\\n curr = ind\\n curr += 5\\n temp = \\\"\\\"\\n while(True):\\n if(text[curr]==\\\"<\\\" and text[curr+1:curr+5]==\\\"/sub\\\"):\\n break\\n temp += text[curr]\\n curr += 1\\n text = text[:ind] + convert_to_superscript(temp)+text[curr + 6:]\\n ind = curr + 6\\n else:\\n ind += 1\\n\\n x = text\\n y = re.sub('\\\\\\\\n', ' ',x)\\n y = re.sub('\\\\\\\\r', ' ',y)\\n y = re.sub('\\\\\\\\t', ' ',y)\\n y = re.sub('<[^>]*>', ' ',y)\\n y = y.strip()\\n return y\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"bcff0b3207f4a1f287474fa3ebeea447","score":"0.70005953","text":"def clean_text(text):\n # Replace newlines by space. We want only one doc vector.\n text = text.replace('\\n', ' ').lower()\n # Remove URLs\n #text = re.sub(r\"http\\S+\", \"\", text)\n # Expand contractions: you're to you are and so on.\n text = contractions.fix(text)\n # Remove stop words\n #text = preprocessing.remove_stopwords(text)\n \n #text = preprocessing.strip_tags(text)\n # Remove punctuation -- all special characters\n text = preprocessing.strip_multiple_whitespaces(preprocessing.strip_punctuation(text))\n return text","title":""},{"docid":"2da14da586b935e9a7da3e19d0a684ac","score":"0.6929299","text":"def clean_text(text):\n # Replace newlines by space. We want only one doc vector.\n text = text.replace('\\n', ' ').lower()\n # Remove URLs\n text = re.sub(r\"http\\S+\", \"\", text)\n # Expand contractions: you're to you are and so on.\n text = contractions.fix(text)\n # Remove stop words\n text = preprocessing.remove_stopwords(text)\n # Remove punctuation -- all special characters\n text = preprocessing.strip_multiple_whitespaces(preprocessing.strip_non_alphanum(text))\n return text","title":""},{"docid":"3dc1580629da1f55178692a2c74494d2","score":"0.6823169","text":"def clean_text(text): #cleantext\n replace_char = [\n \"[\",\n \"]\",\n \"u'\",\n \"None\",\n \"Thank you\",\n \"-\",\n \"(\",\n \")\",\n \"#\",\n \"Done\",\n \">\",\n \"<\",\n \"-\",\n \"|\",\n \"/\",\n \"\\\"\",\n \"Hint\",\n \"\\n\",\n \"'\"]\n for l in replace_char:\n text = text.replace(l, \"\")\n text = re.sub(' +', ' ', text)\n return text","title":""},{"docid":"a73ad21d266a49d35abc6fe7f189fdf3","score":"0.6713591","text":"def _clean_text(self, text):\n text = re.sub('[-=+,#/\\?:^$.@*\\\"※~&%ㆍ!』\\\\‘|\\(\\)\\[\\]\\<\\>`…》]', '', text)\n return text","title":""},{"docid":"33cad90681ef72942de02b7463b0bde4","score":"0.6710349","text":"def clean_text(text):\n # try:\n # text = text.encode(\"ascii\", errors=\"ignore\").decode()\n # except:\n # print(\"error in parsing text - \"+ text)\n # pass\n\n text = re.sub(r'\\([^)]*\\)', '', text)\n text = re.sub(r' +', ' ', text)\n\n text = remove_titles(text)\n return text","title":""},{"docid":"439d9bbdc1cdff15c6b24bd045f96de1","score":"0.67070097","text":"def clean_text(text):\n\n text = re.sub('<[^>]+>', '', text)\n text = re.sub(r'[^A-Za-z0-9\\s]+', '', text)\n return text","title":""},{"docid":"8cd7847dced4e22c557ff2b18816f239","score":"0.66308665","text":"def clean_text(self):\n cleanedtext = re.sub(r'\\n\\s*\\n', '', self.text) # extra lines\n cleanedtext = re.sub(r'[^\\w\\s]', '', cleanedtext) # punctuation\n # for numbers\n # cleanedtext = re.sub(r'[^A-Za-z0-9(),!?@\\'\\`\\\"\\_\\n]', ' ', cleanedtext)\n # cleanedtext = re.sub(r'\\w*\\d\\w*','',cleanedtext) # words with number plust letters\n # remove_digits = str.maketrans('', '', digits)\n # cleanedtext = cleanedtext.translate(remove_digits)\n cleanedtext = re.sub(' +', ' ', cleanedtext) # extra white spaces\n cleanedtext = cleanedtext.lower()\n self.cleanedtext = cleanedtext","title":""},{"docid":"be79e585f11ff26f695f9bd4c24a03cc","score":"0.6597903","text":"def clean_text(text: str) -> str:\n fixed_text = re.sub('<[^>]+>', ' ', text) # removing everything inside <>\n fixed_text = re.sub('\\n', ' ', fixed_text)\n fixed_text = re.sub(' ', ' ', fixed_text)\n fixed_text = re.sub('nbsp', ' ', fixed_text)\n fixed_text = re.sub('—', ' ', fixed_text)\n fixed_text = re.sub('\\t', ' ', fixed_text)\n fixed_text = re.sub('\\r', ' ', fixed_text)\n fixed_text = re.sub('\"…', ' ', fixed_text)\n fixed_text = re.sub('>', ' ', fixed_text)\n fixed_text = re.sub(r'[^\\w\\s]', ' ', fixed_text) # removing the punctuation\n fixed_text = re.sub(' +', ' ', fixed_text) # fix multiple spaces\n fixed_text = fixed_text.strip(' ')\n return fixed_text","title":""},{"docid":"de370f2a8e6ba8efac5bacfa16e14e20","score":"0.6591571","text":"def textCleaning(self,text):\n newText = text.replace('\\n',' ').replace('\\r',' ').replace('\\t',' ').replace('<p>',' ').replace('<br>',' ').replace('</p>',' ').replace('</br>',' ').strip()\n return newText","title":""},{"docid":"46dc40ef3429faddc40feea26c90edce","score":"0.6588975","text":"def cleanText(text):\n # clean: xml tags and mathtype -> spaces\n cleanText = replaceWithSpaces(xmlTagsRe, text)\n cleanText = replaceWithSpaces(mathTypeRe, text)\n # clean: non-letters -> spaces\n cleanText = nonLetterRe.sub(\" \", cleanText)\n cleanText = digitRe.sub(\" \", cleanText)\n return cleanText","title":""},{"docid":"cf7970fb79abc26376642f89ef03a92b","score":"0.65831715","text":"def cleanup_text(text):\n # Remove empty paragraphs\n text = re.sub(r'<p[^>]*?>(\\s|<br>|<br/>|<br />|\\xc2\\xa0)*?</p>', '', text,\n flags=re.UNICODE)\n return text","title":""},{"docid":"6bee9bd22cb054c1e24b2fb6ca4026d5","score":"0.65464205","text":"def clean_text(text):\r\n text = text.lower().strip() # lowercase and remove trailing whitespaces\r\n text = re.sub(r\"i'm\", \"i am\", text)\r\n text = re.sub(r\"he's\", \"he is\", text)\r\n text = re.sub(r\"she's\", \"she is\", text)\r\n text = re.sub(r\"it's\", \"it is\", text)\r\n text = re.sub(r\"that's\", \"that is\", text)\r\n text = re.sub(r\"what's\", \"what is\", text)\r\n text = re.sub(r\"where's\", \"where is\", text)\r\n text = re.sub(r\"there's\", \"there is\", text)\r\n text = re.sub(r\"how's\", \"how is\", text)\r\n text = re.sub(r\"\\'ll\", \" will\", text)\r\n text = re.sub(r\"\\'ve\", \" have\", text)\r\n text = re.sub(r\"\\'re\", \" are\", text)\r\n text = re.sub(r\"\\'d\", \" would\", text)\r\n text = re.sub(r\"\\'re\", \" are\", text)\r\n text = re.sub(r\"won't\", \"will not\", text)\r\n text = re.sub(r\"can't\", \"cannot\", text)\r\n text = re.sub(r\"n't\", \" not\", text)\r\n text = re.sub(r\"n'\", \"ng\", text)\r\n text = re.sub(r\"'bout\", \"about\", text)\r\n text = re.sub(r\"'til\", \"until\", text)\r\n text = re.sub(r'[\" \"]+', \" \", text) # remove extra spaces in between\r\n text = re.sub(r\"[-()\\\"#/@;:<>{}`+=~|.!?,]\", \"\", text)\r\n text = '<start> ' + text + ' <end>'\r\n return text","title":""},{"docid":"04edfda390fef2b3f014fc3061a5fceb","score":"0.6516315","text":"def _remove_any_numbers_and_non_english_characters_from_text(pos_tagged_text):\n for i in xrange(len(pos_tagged_text)):\n word = pos_tagged_text[i][0]\n # Remove numbers\n for digit in string.digits:\n word = word.replace(digit, '')\n # Remove words with non-Latin characters\n try:\n word.decode('ascii')\n except UnicodeDecodeError:\n word = ''\n # Write the preprocessed word back\n pos_tagged_text[i][0] = word\n return pos_tagged_text","title":""},{"docid":"67bf39279b32d413e779e3dad888c4c9","score":"0.65065104","text":"def clean_article_text(text):\r\n text = remove_piece_of_text(text, '<a', '>')\r\n text = text.replace('</a>', '')\r\n return text","title":""},{"docid":"616fcc6dd08b5608264b3d94abfe134c","score":"0.6477654","text":"def clean_up_text(text):\n # TODO add special characters\n text = re.sub('\\( \\(([^\\)]+)\\)\\)', ' ', text)\n text = re.sub('([a-z])([.!?])([A-Z])', r'\\1\\2 \\3', text)\n text = re.sub('[\\s]+', ' ', text)\n\n return text","title":""},{"docid":"283fc4c63a23b691da69908c290cb32e","score":"0.6468758","text":"def cleantext(text):\n # Convert any non-ASCII characters to XML like 'ꀀabcd޴'\n text = text.encode(\"ascii\", \"xmlcharrefreplace\").decode(\"ascii\")\n lines = [x.strip() for x in text.splitlines()]\n text = \" \".join(lines)\n for match, out in REPLACES:\n text = re.sub(match, out, text)\n text = text.strip() # some spaces may have been generated\n\n return text","title":""},{"docid":"6e0e7484b5ba593baa7ca9e6f233b94e","score":"0.64462006","text":"def cleanText(self):\n text = self.text().strip()\n if self.__prefix and text.startswith(self.__prefix):\n text = text[len(self.__prefix):]\n if self.__suffix and text.endswith(self.__suffix):\n text = text[:-len(self.__suffix)]\n return text.strip()","title":""},{"docid":"6e0e7484b5ba593baa7ca9e6f233b94e","score":"0.64462006","text":"def cleanText(self):\n text = self.text().strip()\n if self.__prefix and text.startswith(self.__prefix):\n text = text[len(self.__prefix):]\n if self.__suffix and text.endswith(self.__suffix):\n text = text[:-len(self.__suffix)]\n return text.strip()","title":""},{"docid":"6476d255266d39d7491fea86957199fc","score":"0.6420874","text":"def clean_text(text):\n \n # remove Unicode characters\n text = re.sub(r'[^\\x00-\\x7F]+', '', text)\n\n # taking sequences of characters with alphanumeric characters separated by other characters\n text = re.sub(r\"[-?!&]\",' ',text)\n text = re.sub(r'''[\"#$%()*+,./:;<=>@[\\]^_`{|}~]''','',text)\n \n # remove all numeric charracters\n text = re.sub(r'[0-9]','',text)\n\n # Remove new line characters\n text = re.sub(r'\\s+', ' ', text)\n\n # remove all numeric charracters\n text = re.sub(r'^https?:\\/\\/.*[\\r\\n]*', '',text)\n\n Apos_dict={\"'s\":\" is\",\"n't\":\" not\",\"'m\":\" am\",\"'ll\":\" will\",\n \"'d\":\" would\",\"'ve\":\" have\",\"'re\":\" are\"}\n \n #replace the contractions\n for key,value in Apos_dict.items():\n if key in text:\n text=text.replace(key,value)\n\n # convert to lowercase to maintain consistency\n text = text.lower()\n \n return text","title":""},{"docid":"0f49d845d0d6f6ff334cfea8b6766cbf","score":"0.63663334","text":"def clean_text(text: str):\n position = text.lower().rfind(\"references\")\n text = text[:position] # removing bibliography\n text = text.replace(\"\\n\", \" \") # removing newlines\n text = re.sub(r\"\\[[^\\[^\\]]*\\]\", \"\", text) # removing references\n return text","title":""},{"docid":"9e29448fa18d208e71d63ed06e30ce1b","score":"0.63571817","text":"def _remove_html_tags(self, text):\r\n return re.compile(r'<[^>]+>').sub('', text)","title":""},{"docid":"bc6604b9eac2108048a41f4543e4766b","score":"0.6338481","text":"def strip_markup(wikitext):\n if not wikitext:\n return ''\n\n # remove templates\n text = remove_templates(wikitext)\n\n # remove irrelevant spans\n text = re_comments.sub('', text)\n text = re_ignored_html_tags.sub(r'\\2', text)\n text = re_self_closing_html_tags.sub('', text)\n text = re_dropped_elements.sub('', text)\n text = re_categories.sub('', text)\n text = re_files_images.sub('', text) # TODO: keep file/image captions?\n\n # replace external links with just labels or just URLs\n text = replace_external_links(text)\n\n # drop magic words behavioral switches\n text = re_magic_words.sub('', text)\n\n # replace internal links with just their labels\n text = replace_internal_links(text)\n # text = replace_internal_links(text) # TODO: is this needed?\n\n # remove table markup\n text = text.replace('||', '\\n|').replace('!!', '\\n!') # put each cell on a separate line\n text = re_table_formatting.sub('\\n', text) # remove formatting lines\n text = re_table_cell_formatting.sub('\\n\\\\3', text) # leave only cell content\n\n # strip out text formatting\n text = re_italic_quote.sub(r'\"\\1\"', text)\n text = re_bold_italic.sub(r'\\1', text)\n text = re_quote_quote.sub(r'\"\\1\"', text)\n\n # unescape html entities\n text = ftfy.fixes.unescape_html(text)\n\n # final cleanup\n text = re_headings.sub(r'\\n\\n\\2\\n\\n', text)\n text = re_dots.sub('...', text)\n text = re_brackets.sub(r'', text)\n text = text.replace('[[', '').replace(']]', '')\n text = text.replace('<<', '«').replace('>>', '»')\n text = re_random_cruft.sub(r'\\1', text)\n text = re.sub(r'\\n\\W+?\\n', r'\\n', text, flags=re.UNICODE)\n text = text.replace(',,', ',').replace(',.', '.')\n text = re_spaces.sub(' ', text)\n text = re_linebreaks.sub(r'\\n\\n', text)\n\n return text.strip()","title":""},{"docid":"dd990b81f16b5ea23281fcb150630477","score":"0.63193804","text":"def cleanse_sentence(text):\n \n text = re.sub(hash_regex, hash_repl, text)\n text = re.sub(hndl_regex, hndl_repl, text)\n text = re.sub(url_regex, ' __URL ', text)\n \n for (repl, regx) in emoticons_regex :\n text = re.sub(regx, ' '+repl+' ', text)\n\n text = text.replace('\\'','')\n text = re.sub( word_bound_regex , punctuations_repl, text )\n text = re.sub( rpt_regex, rpt_repl, text )\n \n return text","title":""},{"docid":"43f85bc2347365b9c12ca2580d44a348","score":"0.6308067","text":"def clean_text(text):\n text = re.sub('[-=+,#/\\?:^$.@*\\\"※~&%ㆍ!』\\\\‘|\\(\\)\\[\\]\\<\\>`…》]', '', text)\n return text","title":""},{"docid":"5c7d913e073cac757804fda010b6c80c","score":"0.6296954","text":"def clean_text_1(text, stpwds, valid_tags, stemming=False):\n\tcleaned_text = clean_basic(text)\n\ttokens = cleaned_text.split()\n\ttokens = postagging(tokens, valid_tags)\n\ttokens = remove_stopwords(tokens, stpwds)\n\ttokens = remove_short_tokens(tokens)\n\tif stemming:\n\t\ttokens = stem(tokens)\n\tcleaned_text = remove_hyphen(' '.join(tokens))\n\treturn cleaned_text","title":""},{"docid":"9247a1dbe906adbe476252f075860812","score":"0.6290065","text":"def clean_text(text):\n # remove html tags, lowercase, remove nonsense, remove non-letter\n aff = BeautifulSoup(text, \"lxml\").text \n aff = aff.lower()\n aff = re.sub(r'xa0|#n#‡#n#|#tab#|#r#|\\[|\\]', \"\", aff)\n aff = re.sub(r'[^a-z]+', ' ', aff)\n return aff","title":""},{"docid":"4549a815eef93b30dc8e49e520ea9d4c","score":"0.6278033","text":"def demarkup_text(text):\n\n # keep the opening tag name of certain tags that contains these strings\n # note: <s> are from debian copyright files\n kept_tags = (\n 'lic', 'copy', 'www', 'http', 'auth', 'contr', 'leg', 'inc', '@', \n '<s>', '</s>', '169', 'a9'\n )\n\n # find start and closing tags or the first white space whichever comes first\n # or entities\n # this regex is such that ' '.join(tags.split(a))==a\n\n tags_ents = re.compile(r'(</?[^\\s></]+(?:>|\\s)?|&[^\\s&]+;|href|[\\'\"]?\\/\\>)', re.IGNORECASE).split\n\n cleaned = []\n for token in tags_ents(text):\n if token.lower().startswith(('<', '&', 'href')) and not any(k in token.lower() for k in kept_tags):\n continue\n else:\n cleaned.append(token)\n return u' '.join(cleaned)","title":""},{"docid":"47cfd092920700f1c2f23d0fba4d528b","score":"0.6265034","text":"def remove_tashkeel(self, input_text):\n\n p_tashkeel = re.compile(r'[\\u0617-\\u061A\\u064B-\\u0652]')\n return re.sub(p_tashkeel,\"\", input_text)","title":""},{"docid":"3afa4198b471da03ad24ac8551e73fa3","score":"0.62647754","text":"def clean(self):\n # Replace double \\n's in case this ever happens on windows for some reason.\n if self.text.__contains__('\\n\\n'):\n self.text = self.text.replace('\\n\\n', '\\n')","title":""},{"docid":"b33f5fa7f977b65ed4d9a97b616ecbba","score":"0.6247174","text":"def preprocess(self):\n self.text = remove_control_characters(self.text)","title":""},{"docid":"3503132af39ea4b05ddf42ac5ed12ffb","score":"0.6229983","text":"def strip_markup(text):\n return (text\n .replace(\"<strong class=\\\"label\\\">\", \"\")\n .replace(\"</strong>\", \"\")\n .replace(\"<span class=\\\"hotel-type\\\">\", \"\")\n .replace(\"</span>\", \"\")\n .replace(\"<strong>\", \"\")\n )","title":""},{"docid":"f27009c1809229c97e68cb97aa67e615","score":"0.62280345","text":"def _del_text(self):\r\n # check whether element already has text node\r\n if self._root.HasChildNodes and \\\r\n self._root.FirstChild and \\\r\n isinstance(self._root.FirstChild, XmlText):\r\n self._root.RemoveChild(self._root.FirstChild)","title":""},{"docid":"16f35e7f296801a447055b16f013b54b","score":"0.6215954","text":"def clean_text(text):\n # lowercase text\n text = text.lower()\n\n # substitute contractions\n # text = re.sub(r\"i'm\", \"i am\", text)\n # text = re.sub(r\"he's\", \"he is\", text)\n # text = re.sub(r\"she's\", \"she is\", text)\n # text = re.sub(r\"it's\", \"it is\", text)\n # text = re.sub(r\"that's\", \"that is\", text)\n # text = re.sub(r\"what's\", \"what is\", text)\n # text = re.sub(r\"where's\", \"where is\", text)\n # text = re.sub(r\"how's\", \"how is\", text)\n # text = re.sub(r\"\\'ll\", \" will\", text)\n # text = re.sub(r\"\\'ve\", \" have\", text)\n # text = re.sub(r\"\\'re\", \" are\", text)\n # text = re.sub(r\"\\'d\", \" would\", text)\n # text = re.sub(r\"\\'re\", \" are\", text)\n # text = re.sub(r\"won't\", \"will not\", text)\n # text = re.sub(r\"can't\", \"cannot\", text)\n # text = re.sub(r\"n't\", \" not\", text)\n # text = re.sub(r\"n'\", \"ng\", text)\n # text = re.sub(r\"'bout\", \"about\", text)\n # text = re.sub(r\"'til\", \"until\", text)\n\n # get rid of username handles\n text = re.sub(r\"(?:@)(\\S+|$)|\", \"\", text)\n\n # get rid of special characters\n text = re.sub(r\"[-()\\\"#/@;:<>{}`+=~|.!?,]\", \"\", text)\n\n return text","title":""},{"docid":"88c982c1d7c70e2702c8b9fafed7d4d5","score":"0.6204147","text":"def strip_tags(text):\n\n return re.sub('<[^<]+?>', '', text)","title":""},{"docid":"0fd6daf8f52ba6aeedce909badebd364","score":"0.6194485","text":"def clean_text(raw_text):\n # Must strip HTML tags out first!\n text = re.sub('<[^<]+?>', ' ', raw_text)\n text = handle_unicode(text)\n text = handle_html_unquote(text)\n text = handle_mac_quotes(text)\n text = handle_text_break_dash(text)\n text = text.lower()\n\n regex_subs = ['\\t', '\\n', '\\r', '\\s+', '&']\n for regex_sub in regex_subs:\n text = re.sub(regex_sub, ' ', text)\n return text","title":""},{"docid":"14b2ba621e89303b47c25fd967a686e4","score":"0.6182298","text":"def _clean_text(source):\n source = str(source)\n source = source.upper()\n source = re.sub('[？|?|！|!|，|,|.|。|#|《|》|<|>|（|）|(|)|〉|、|／|…|-]', '', source)\n source = re.sub('[哈｜嘿｜呵]', '', source)\n source = re.sub('NAN', '', source)\n source = re.sub('\\xa0', '', source)\n return source","title":""},{"docid":"30006085bad7c937637e584674a6cc02","score":"0.6176192","text":"def _clean_bad_chars(self, text):\n return text.replace(\"\\u200b\", \"\")","title":""},{"docid":"1c3428df1fe332453e101d924bcc714b","score":"0.6171018","text":"def text2cleanish(text):\n logger.info(\"Cleaning text ...\")\n text = CLEANER1.sub(r\"\\1 \\2 \\3\", text)\n text = CLEANER2.sub(r\"\\1 ... \\3\", text)\n text = CLEANER3.sub(r\"\\1 ... \", text)\n # text = CLEANER4.sub(r\"\\1 X... \\3\", text)\n return text","title":""},{"docid":"928336714d22728e7f69363c3345b4c8","score":"0.6169784","text":"def clean(self, text = None) -> str:\n return self.normalize(\n self.clean_double_whitespaces(\n self.clean_edge_punctuation(\n self.clean_numeric(\n self.clean_web_tags(\n self.clean_html_tags(\n self.clean_non_ascii(\n text\n )\n )\n )\n )\n )\n )\n )","title":""},{"docid":"cff7fa0a0cef7e0a4aa8555fc2fa3982","score":"0.61514693","text":"def clean_text_remove_puncs(cls, text):\n if isinstance(text, str):\n try:\n text = cls.clean_text(text)\n text = cls._remove_punctuations(text)\n\n except (TypeError, AttributeError):\n logger.warning(\"Error occured in preprocessing text: {}\".format(text))\n sys.exit(0)\n\n return text.strip()\n\n return \"\"","title":""},{"docid":"6400609a86643ee6fd2cb125e5b5aae5","score":"0.61486775","text":"def safe_clean_text(text):\n # strip\n text = text.strip()\n\n # weird words\n text = delete_words(delete_list, text)\n\n # handle \\\\\\'t \\\\\\'ve\n text = text.replace(r\"\\\\\", \"\")\n\n # remove extra spaces\n text = re.sub(r'\\s+', ' ', text)\n\n # fix puncutations\n for p in punctuations:\n text = text.replace(p, p[1:])\n text = re.sub(\" (\\u2018|\\u2019|') \", r\"\\1\", text)\n\n # replace to EN dash\n text = re.sub(u\"\\u2014\", \"-\", text)\n text = re.sub(u\"\\u2013\", \"-\", text)\n\n return text","title":""},{"docid":"6857e424290706ed34fd0100320e8694","score":"0.61465544","text":"def remove_tags(text):\n clean = re.compile('<.*?>')\n return re.sub(clean, '', text)","title":""},{"docid":"5086afb5e199b8a13a21c4f58efbca2f","score":"0.61259377","text":"def remove_smart_quotes(self, text):\r\n return text.replace(u\"\\u201c\",\"\").replace(u\"\\u201d\", \"\")","title":""},{"docid":"49b44d02ed0cc749e2cc7f1d32af1451","score":"0.6104445","text":"def clean_txt(self, text):\n\n text = re.sub(\"'\", \"\", text)\n text = re.sub(\"(\\\\W)+\", \" \", text)\n return text","title":""},{"docid":"faaffdfe0444eca15482157446d68b10","score":"0.6088906","text":"def clean_text(text):\n translation_dict = {\n '’':\"'\",\n '‘':\"'\",\n # \"œ\":'oe',\n }\n # remove control characters not supported by XML.\n text = strip_chars(text)\n text = unicodedata.normalize('NFC', text.strip())\n text = re.sub(r'(\\n\\n )+', ' ', text)\n text = re.sub(r'([a-zA-Zàâçéèêëîïôûùüÿñæœ,;-])(\\n)+([^\\s])', r'\\1 \\3', text)\n return text.translate(str.maketrans(translation_dict))","title":""},{"docid":"ab72be9d87c9f54053505d1b1eaf6b2f","score":"0.6088736","text":"def clearMyText(self):\n self.clearText(self.textName)\n self.clearText(self.textCR)\n self.clearText(self.textAL)\n self.clearText(self.textEC)\n self.clearText(self.textIA)","title":""},{"docid":"b81ac2e011b854386493cc068a9e46dd","score":"0.60839474","text":"def clear_texts(self):\n\n for t in self.texts[:]:\n if not t[4]:\n self.texts.remove(t)","title":""},{"docid":"b3eb01e6647165fa5f3f6cc66a802663","score":"0.6076601","text":"def cleanText(self, s):\n\n\t\tdef _sent_to_lines(sent):\n\t\t\t\"\"\"Converts a string of text to one sentence each line.\"\"\"\n\t\t\tsent = re.sub(r\"\\n\", \" \", sent)\n\t\t\treturn re.sub(r\"(\\.|\\!|\\?|--)+\\s*\", \"\\n\", sent)\n\n\t\tdef _strip_punct(sent):\n\t\t\t\"\"\"Strips away all punctuations from the text.\"\"\"\n\t\t\treturn sent.translate(string.maketrans(\"\",\"\"), string.punctuation)\n\n\t\treturn _strip_punct(_sent_to_lines(s)).lower()","title":""},{"docid":"e1727f2673de7c749d5ef2a767231eba","score":"0.60653853","text":"def clean_message(self, text):\n text = strip_tags(text)\n return text","title":""},{"docid":"a8d8abba63fc621d1b31a7dad4026d4e","score":"0.60498005","text":"def __remove_tags(self, html_text):\n tags_regex = re.compile(\n '<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});')\n tag_free_text = re.sub(tags_regex, ' ', html_text)\n\n return tag_free_text","title":""},{"docid":"9ba3be36a740be33aa131f168e63ba4e","score":"0.6047604","text":"def clean_text(text):\n if text:\n text = html2text.html2text(clean_markdown(text))\n return re.sub(r'\\s+', ' ', text).strip()","title":""},{"docid":"9a1fbdfc06773a8ebff8e45493c51bd1","score":"0.603565","text":"def remove_html_tags(text):\r\n clean = re.compile('\\{<.*?>\\}')\r\n return re.sub(clean, '', text)","title":""},{"docid":"4c1f72d624a2444e1814a702ea0cf7e8","score":"0.6031818","text":"def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","title":""},{"docid":"4c1f72d624a2444e1814a702ea0cf7e8","score":"0.6031818","text":"def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","title":""},{"docid":"e4682d3a218fa9f846931042e24d0607","score":"0.60222965","text":"def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or self._is_control(char):\n continue\n if self._is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","title":""},{"docid":"e4f3d2d2e47513b04b3d7b686b294cdd","score":"0.6021114","text":"def _clean_text(self, listing_text):\n if len(listing_text)>0:\n listing_text_clean = ' '.join(listing_text[0].findAll(text=True))\n listing_text_clean = re.sub('\\n|\\t', ' ', listing_text_clean)\n listing_text_clean = re.sub('\\s+', ' ', listing_text_clean)\n listing_text_clean = re.sub(\"'\", '', listing_text_clean)\n listing_text_clean = re.sub('\"', '', listing_text_clean)\n else:\n listing_text_clean = ' ' \n return listing_text_clean","title":""},{"docid":"df88aa6338c651f311f8313abd30f022","score":"0.6015763","text":"def clean(text, tokenizer, stemmer):\n doc = ''.join(text).lower()\n doc = re.sub(r'[<>\\{}/;|\\[\\]-]', ' ', doc)\n doc = re.sub(r'[0-9]', ' ', doc)\n doc = re.sub(r'\\'', ' ', doc)\n doc = re.sub(r'=', ' ', doc)\n doc = re.sub(r':', ' ', doc)\n doc = re.sub(r'\"', ' ', doc)\n doc = re.sub(r'\\s+', ' ', doc)\n doc = re.sub(r'\\(', ' ', doc)\n doc = re.sub(r'\\)', ' ', doc)\n doc = re.sub(r'\\s{2,}', ' ', doc)\n doc = re.sub(r'\\.', '', doc)\n doc = re.sub(r',', '', doc)\n\n return doc","title":""},{"docid":"6d531dc571d409ba0f71d86b949d095b","score":"0.6010883","text":"def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","title":""},{"docid":"2c801ea1e26a1508d732d0d546871740","score":"0.60044676","text":"def ocr_cleaner(text):\n\n text = text.lower().replace('\\n', ' ').replace('\\r', ' ').replace('\\t', ' ').strip() # replace all whitespace, lower\n text = ' '.join(text.split()) # remove consecutive spaces\n text = re.sub(r'(?<=[a-z]{3})[.]', '.\\n', text)\n text = text.encode('ascii', errors='ignore').decode(encoding='utf8').replace(r'\\\\', '').replace('\"', '')\n text = re.sub(r'(?<=nae)', '\\n', text)\n text = re.sub(r'(?<=dae)', '\\n', text)\n text = text.replace('life histories of cascadia butterflies', '').replace('\\\\', '').replace('|', '')\n text = text.replace('adult biology', '').replace('immature stage biology', '')\n text = text.replace('description of immature stages', '').replace('discussion', '')\n text = re.sub(r'([0-9]){3}', '', text)\n text = re.sub(r'([i])\\b', '', text)\n text = re.sub(r'(family|subfamily).*', '', text)\n text = text.replace('-', ' ').replace('tlie', 'the').replace('ihis', 'this').replace('ditilicult', 'difficult')\n text = re.sub(r'^\\s*$', '', text)\n text = ' '.join(text.split()) # remove consecutive spaces\n text = re.sub(r'(?<=[a-z]{4})[.]', '.\\n', text)\n text = text.strip()\n text = re.sub(r'[.,();\\']', '', text)\n\n return text","title":""},{"docid":"9bbe6034a0e46963cb33decf1d35dfa4","score":"0.59949124","text":"def remove_punc(data):\n \n #All text to lower case\n data = data.lower()\n #Punctuation subbed out for ''\n data = punc.sub( '', data)\n \n return data","title":""},{"docid":"cc5773a680ca43dad9ee13f3609035f2","score":"0.5977697","text":"def _remove_all_other_punctuation(pos_tagged_text):\n for j in xrange(len(pos_tagged_text)):\n word = pos_tagged_text[j][0]\n for symbol in (',', '.', ';', '-'):\n word = word.replace(symbol, ' ')\n # Write the preprocessed word back\n pos_tagged_text[j][0] = word\n return pos_tagged_text","title":""},{"docid":"64bca5555bdd1a051e9978c44b240c42","score":"0.597209","text":"def remove_html(text):\n return html.unescape(re.sub(doubleSpacingRe, ' ', re.sub(nbspRe, ' ', text)))","title":""},{"docid":"f8f082dba8399241d49d19b16ec80984","score":"0.5968257","text":"def get_cleaned_text(self, text):\n cleaned_text = text.replace('\\\"','').replace('\\'','').replace('-',' ')\n cleaned_text = self.remove_non_ascii_chars(cleaned_text)\n \n # retweet\n if re.match(r'RT @[_A-Za-z0-9]+:',cleaned_text): # retweet\n if self.remove_retweets: return ''\n retweet_info = cleaned_text[:cleaned_text.index(':')+2] # 'RT @name: ' will be again added in the text after cleaning\n cleaned_text = cleaned_text[cleaned_text.index(':')+2:]\n else:\n retweet_info = ''\n cleaned_text = self.remove_hyperlinks(cleaned_text)\n cleaned_text = cleaned_text.replace('#','HASHTAGSYMBOL').replace('@','ATSYMBOL') # to avoid being removed while removing punctuations\n \n tokens = [w.translate(self.punc_table) for w in word_tokenize(cleaned_text)] # remove punctuations and tokenize\n tokens = [w for w in tokens if not w.lower() in self.stop_words and len(w)>1] # remove stopwords and single length words\n cleaned_text = ' '.join(tokens)\n \n cleaned_text = cleaned_text.replace('HASHTAGSYMBOL','#').replace('ATSYMBOL','@')\n cleaned_text = retweet_info + cleaned_text\n \n return cleaned_text","title":""},{"docid":"5e0c7d2d36d3f9e03b06bb850b936efa","score":"0.5966438","text":"def remove_char(stringedText):\n\ttext = stringedText\n\t#cleanedText = '' # need a place to store the chars that actually belong\n\t\n\t# new method that skips all the if statement branches\n\t# 1. Put all the special chars in a single list\n\t# 2. Iterate through the list when analyzing each char\n\t# 3. recurs as before\n\n\t# if it's Alice.txt, then use the full list. otherwise use shorter list\n\tif sys.argv[2] == '-2':\n\t\tlistOfSpecialChars = [',', '.', '?', '!', '`', '<del>', '<esc>', '<cntrl>', '<tab>', '<cmd>', '<right>', '<left>', '<down>', '<up>', '<shift>']\n\telse:\n\t\tlistOfSpecialChars = ['<del>', '<esc>', '<cntrl>', '<tab>', '<cmd>', '<right>', '<left>', '<down>', '<up>', '<shift>']\n\n\n\tfor item in listOfSpecialChars:\n\t\tif item in text:\n\t\t\ttext = text.replace(item, '')\n\t\t\tremove_char(text) # recursively call until item no longer in text, then fail condition\n\treturn text","title":""},{"docid":"6ce6cdf443d7400675c4686b8fff22d3","score":"0.59407234","text":"def remove_html(self, text):\n soup = BeautifulSoup(text, 'html.parser')\n return soup.get_text(separator=\" \")","title":""},{"docid":"6fadf8ad9b6688d84f136701aeff7897","score":"0.59321564","text":"def clean_text(doc):\n \n words = re.sub(\"< ?/?[a-z]+ ?>|\\n\", \"\", doc)\n words = tokenize_text(words)\n words = lemmatize_text(words)\n words = remove_stopwords(words)\n doc = [w for w in words if w.isalnum()]\n doc = ' '.join(doc)\n \n return doc","title":""},{"docid":"83730e0c1709a337afd4043992bf3a87","score":"0.5919678","text":"def preprocess(self, text):\r\n return text","title":""},{"docid":"895767f9567130d8209cec47ed740cd6","score":"0.5917767","text":"def basic_cleaners(self, text):\n text = self.lowercase(text)\n text = self.collapse_whitespace(text)\n return text","title":""},{"docid":"acefa458000e3f328bffb60228142df1","score":"0.5913148","text":"def clear_text(self):\n self.ax.texts.clear()","title":""},{"docid":"15b60b67b4c52b195697d002d8f248cc","score":"0.59017366","text":"def _do_smart_punctuation(self, text):\r\n if \"'\" in text: # guard for perf\r\n text = self._do_smart_contractions(text)\r\n text = self._opening_single_quote_re.sub(\"‘\", text)\r\n text = self._closing_single_quote_re.sub(\"’\", text)\r\n\r\n if '\"' in text: # guard for perf\r\n text = self._opening_double_quote_re.sub(\"“\", text)\r\n text = self._closing_double_quote_re.sub(\"”\", text)\r\n\r\n text = text.replace(\"---\", \"—\")\r\n text = text.replace(\"--\", \"–\")\r\n text = text.replace(\"...\", \"…\")\r\n text = text.replace(\" . . . \", \"…\")\r\n text = text.replace(\". . .\", \"…\")\r\n return text","title":""},{"docid":"d69ee53fb8109b8eac653e744ce6cf16","score":"0.5900395","text":"def preprocess_text(data):\n if 'Body' in data:\n data['Body'] = sanitise_xml(data['Body'])\n \n if 'Tags' in data:\n data['Tags'] = re.findall('\\<([^>]+)', data['Tags'])\n \n return data","title":""},{"docid":"ee91042b95d6239ece6d61938a4c2c0c","score":"0.589461","text":"def cleanupText(path):\n \n text_cleaned = ''\n try:\n f = open(path)\n raw = f.read().lower()\n text = raw \n text_cleaned = re.sub(r'[-.?!,\":;()/|0-9]','', text)\n \n# splitword = text_cleaned.split(\" \")\n# \n# for word in splitword:\n# if word in stop_word:\n# splitword.remove(word)\n# text_cleaned = \" \".join(splitword)\n# # print \"\\n Word count after:\" + str(len(text_translated.split())) + \"\\n\"\n# text_cleaned = ' '.join([word for word in text_cleaned.split(' ') if (word and len(word) > 1)])\n finally:\n f.close()\n return text_cleaned","title":""},{"docid":"56e9121c9e15b11560fa8798e6959444","score":"0.58939534","text":"def normalize_text(self):\n self.normalized_text = normalize_text(remove_nonwords(self.raw_text))\n return True","title":""},{"docid":"ae4d9363dc24767294beb89a6c113215","score":"0.5891619","text":"def delete_text(self):\n self.in_title.delete(0, \"end\")\n self.in_author.delete(0, \"end\")\n self.answer.configure(text=\"\")\n self.answer.configure(text=\"\")","title":""},{"docid":"9777cfd2650717c6e9d64f74f792a8dc","score":"0.58881503","text":"def clean_text(s: str) -> str:\n s = unicodedata.normalize(\"NFKD\", s) # simplify unicode of string\n s = html.unescape(s) # convert html entities to unicode characters\n s = re.sub(r\"(</p>|</li>)\", \"\\n\", s) # replace p or li close with newline \n s = re.sub(r\"<li>\", \"• \", s) # replace list item start with bullets\n s = re.sub(r\"<[^<]+?>\", \"\", s) # otherwise strip HTML tags\n return s","title":""},{"docid":"c27e37a99ab23a9aadfc8937ecf3c815","score":"0.5877997","text":"def remove_html_tags(text):\n return HTMLTagsStripper.strip_tags_(text)","title":""},{"docid":"d22683fdf5d6c00468b6eac0349e1afe","score":"0.587424","text":"def remove_meta(text):\n text = re.sub(r'(\\[.*?\\]+)', '', text) # e.g. [one] or [[ two ]]\n text = re.sub(r'(\\{.*?\\}+)', '', text) # e.g. {one} or {{ two }}\n text = re.sub(r'(<.*?>+)', '', text) # e.g. <one> or <<two>>\n return text","title":""},{"docid":"39cb9610b3068c092810921d8c94fb7b","score":"0.5866584","text":"def clean_latex(text):\n # edge case\n text = re.sub(r'\\[math\\]', ' LaTex math ', text)\n text = re.sub(r'\\[\\/math\\]', ' LaTex math ', text)\n text = re.sub(r'\\\\', ' LaTex ', text)\n\n pattern_to_sub = {\n r'\\\\mathrm': ' LaTex math mode ',\n r'\\\\mathbb': ' LaTex math mode ',\n r'\\\\boxed': ' LaTex equation ',\n r'\\\\begin': ' LaTex equation ',\n r'\\\\end': ' LaTex equation ',\n r'\\\\left': ' LaTex equation ',\n r'\\\\right': ' LaTex equation ',\n r'\\\\(over|under)brace': ' LaTex equation ',\n r'\\\\text': ' LaTex equation ',\n r'\\\\vec': ' vector ',\n r'\\\\var': ' variable ',\n r'\\\\theta': ' theta ',\n r'\\\\mu': ' average ',\n r'\\\\min': ' minimum ',\n r'\\\\max': ' maximum ',\n r'\\\\sum': ' + ',\n r'\\\\times': ' * ',\n r'\\\\cdot': ' * ',\n r'\\\\hat': ' ^ ',\n r'\\\\frac': ' / ',\n r'\\\\div': ' / ',\n r'\\\\sin': ' Sine ',\n r'\\\\cos': ' Cosine ',\n r'\\\\tan': ' Tangent ',\n r'\\\\infty': ' infinity ',\n r'\\\\int': ' integer ',\n r'\\\\in': ' in ',\n }\n # post process for look up\n pattern_dict = {k.strip('\\\\'): v for k, v in pattern_to_sub.items()}\n # init re\n patterns = pattern_to_sub.keys()\n pattern_re = re.compile('(%s)' % '|'.join(patterns))\n\n def _replace(match):\n \"\"\"\n reference: https://www.kaggle.com/hengzheng/attention-capsule-why-not-both-lb-0-694 # noqa\n \"\"\"\n try:\n word = pattern_dict.get(match.group(0).strip('\\\\'))\n except KeyError:\n word = match.group(0)\n print('!!Error: Could Not Find Key: {}'.format(word))\n return word\n return pattern_re.sub(_replace, text)","title":""},{"docid":"39cb9610b3068c092810921d8c94fb7b","score":"0.5866584","text":"def clean_latex(text):\n # edge case\n text = re.sub(r'\\[math\\]', ' LaTex math ', text)\n text = re.sub(r'\\[\\/math\\]', ' LaTex math ', text)\n text = re.sub(r'\\\\', ' LaTex ', text)\n\n pattern_to_sub = {\n r'\\\\mathrm': ' LaTex math mode ',\n r'\\\\mathbb': ' LaTex math mode ',\n r'\\\\boxed': ' LaTex equation ',\n r'\\\\begin': ' LaTex equation ',\n r'\\\\end': ' LaTex equation ',\n r'\\\\left': ' LaTex equation ',\n r'\\\\right': ' LaTex equation ',\n r'\\\\(over|under)brace': ' LaTex equation ',\n r'\\\\text': ' LaTex equation ',\n r'\\\\vec': ' vector ',\n r'\\\\var': ' variable ',\n r'\\\\theta': ' theta ',\n r'\\\\mu': ' average ',\n r'\\\\min': ' minimum ',\n r'\\\\max': ' maximum ',\n r'\\\\sum': ' + ',\n r'\\\\times': ' * ',\n r'\\\\cdot': ' * ',\n r'\\\\hat': ' ^ ',\n r'\\\\frac': ' / ',\n r'\\\\div': ' / ',\n r'\\\\sin': ' Sine ',\n r'\\\\cos': ' Cosine ',\n r'\\\\tan': ' Tangent ',\n r'\\\\infty': ' infinity ',\n r'\\\\int': ' integer ',\n r'\\\\in': ' in ',\n }\n # post process for look up\n pattern_dict = {k.strip('\\\\'): v for k, v in pattern_to_sub.items()}\n # init re\n patterns = pattern_to_sub.keys()\n pattern_re = re.compile('(%s)' % '|'.join(patterns))\n\n def _replace(match):\n \"\"\"\n reference: https://www.kaggle.com/hengzheng/attention-capsule-why-not-both-lb-0-694 # noqa\n \"\"\"\n try:\n word = pattern_dict.get(match.group(0).strip('\\\\'))\n except KeyError:\n word = match.group(0)\n print('!!Error: Could Not Find Key: {}'.format(word))\n return word\n return pattern_re.sub(_replace, text)","title":""},{"docid":"8c3df43473fa1625aa47e63ad2cfc900","score":"0.58606964","text":"def _clear_tag_string(self, tag_string):\n #clean = re.compile(\".?>\")\n clean = re.compile('.?>|\\\"|.?/>|</.+')\n text = re.sub(clean, \"\", tag_string)\n\n return text","title":""},{"docid":"0f836543dc5442e2680791c89d334a8b","score":"0.58566207","text":"def remove_html_tags(text):\n tag_free = re.sub(r\"<.*?>\", \" \", text)\n return re.sub(r\" +\", \" \", tag_free).strip() # make it nice","title":""},{"docid":"bb4c313f87e01e348ee6179acdfb5eaa","score":"0.58543164","text":"def remove_unicode(text):\n regex = r\"(\\\\u....)\"\n text = re.sub(regex, ' ', text)\n return text","title":""},{"docid":"1d664d3566821921a5d68e2c86a08931","score":"0.5848827","text":"def remove_html_tags(text):\r\n import re\r\n clean = re.compile('<.*?>')\r\n return re.sub(clean, '', text)","title":""},{"docid":"ddd0c2368ecf797bcb9dfcd574fc6ff7","score":"0.584826","text":"def clean_text(self):\n return self.sanitize_html(self.cleaned_data['text'])","title":""},{"docid":"e482c773dc5fd21e1ebd09d9eb3ef6ad","score":"0.58404464","text":"def _process_text(self,\n index: int,\n lower=True,\n delete_whitespace=True,\n delete_urls=True) -> str:\n text = self._texts[index]\n if not text:\n return None\n\n if not isinstance(text, str):\n raise TypeError(\"Text value is not string\")\n\n if lower:\n text = text.lower()\n if delete_whitespace:\n text = re.sub(r'[\\s]+', r' ', text).strip()\n if delete_urls:\n text = URLS.sub('', text)\n return text","title":""},{"docid":"7e4f38466182c733dd2f112000f2c369","score":"0.5832301","text":"def clean(self, text):\n\n return text.replace(\"$=\", \"<=\")","title":""},{"docid":"7db598df2c8505b0d3bd9bcdd77e327c","score":"0.5831346","text":"def textilize(s):\n s = s.replace(\"<p>\", \" \").replace(' ', ' ')\n return _re_html.sub(\"\", s)","title":""},{"docid":"595bc2579550e46f323aeeb024cfdaea","score":"0.5830553","text":"def character_cleanup(self, char):\n if self.tagcategory:\n char.tags.remove(category=self.tagcategory)\n char.attributes.remove(category=self.tagcategory)","title":""},{"docid":"523d6b90dc8d3f1d665155c824d38e3d","score":"0.5829617","text":"def epub_to_clean_text(ebook):\n out = epub2text(ebook)\n text=\"\"\n for chapter in out : \n text += filter_nonprintable(chapter) \n return text","title":""},{"docid":"15535fe57d25f9aa6661008b09fe0ecc","score":"0.5823075","text":"def remove_html_tags(text):\n\t#print(text)\n\t#First replace linebreaks to something NON-HTML so that it wont be cleared in the next step.\n\ttext = text.replace(\"<br>\", \"BRBRBR\")\n\t#print(text)\n\n\timport re\n\tclean = re.compile('<.*?>')\n\tclean = re.sub(clean, '', text)\n\t#print(clean)\n\t#Again replace back our linebreaks to HTML format\n\tclean = clean.replace(\"BRBRBR\", \"<br>\")\n\t#print(clean)\n\t#print(\"---end---\")\n\treturn Markup(clean)","title":""},{"docid":"40d8dc5dfdcf10201dd1eb69952be726","score":"0.5818737","text":"def text_standardize(text):\n text = text.replace('—', '-')\n text = text.replace('–', '-')\n text = text.replace('―', '-')\n text = text.replace('…', '...')\n text = text.replace('´', \"'\")\n text = re.sub(r'''(-+|~+|!+|\"+|;+|\\?+|\\++|,+|\\)+|\\(+|\\\\+|\\/+|\\*+|\\[+|\\]+|}+|{+|\\|+|_+)''', r' \\1 ', text)\n text = re.sub(r'\\s*\\n\\s*', ' \\n ', text)\n text = re.sub(r'[^\\S\\n]+', ' ', text)\n return text.strip()","title":""},{"docid":"5e09095356905e64ce0cbd9278becd9a","score":"0.581436","text":"def clean_text(text):\n digi_punct = \"[^a-zA-Z.1234567890'# ]\" # used by clean_text(text_corpus)\n text = text.replace(\"´\", \"'\")\n text = re.sub(digi_punct, \" \", text)\n\n # digi_punct = \"[^a-zA-Z.1234567890' ]\"\n text = \" \".join(text.split())\n text = text.lower()\n return text","title":""},{"docid":"6dc88ebeb02bbc3589d10ad07550933f","score":"0.58119494","text":"def remove_speech_meta(text):\n text = re.sub(r'^(\\s*\\([^)]*\\)\\s*)', '', text) # e.g. (to Ray) Hello\n text = re.sub(r'(\\s*\\([^)]*\\)\\s*)$', '', text) # e.g. Hello (motions at Ray)\n return text","title":""},{"docid":"95693e0352cd0d901a0f5a44a8f4cfe8","score":"0.58030546","text":"def clean_tweet_text(tweet):\n tweet = tweet.strip(\"\\n\")\n tweet = tweet.strip(\"\\t\")\n tweet = remove_emojis(tweet)\n tweet = remove_hashtag_symbol(tweet)\n return tweet","title":""},{"docid":"09587b5540da31a7dfd6b6bbd3d7785d","score":"0.5796727","text":"def clean(text: str) -> str:\n\n clean_str = ''\n for char in text:\n if char.isalnum() or char == UNDERSCORE:\n clean_str = clean_str + char\n elif char == HASHTAG_SYMBOL or char == MENTION_SYMBOL:\n clean_str = clean_str + SPACE + char\n else:\n clean_str = clean_str + SPACE\n return clean_str","title":""},{"docid":"4136548aa14d7139072f51ccbad881f9","score":"0.57962835","text":"def _remove_tags(html):\n html = _replace_ampersands(html)\n html = re.sub('\\<sup\\>|\\</sup\\>', '', html)\n html = re.sub('\\<sub\\>|\\</sub\\>', '', html)\n tagrx = re.compile('\\<.+?\\>', flags=re.DOTALL)\n html = tagrx.sub(' ', html)\n return html","title":""},{"docid":"cdd98a8e4b041ee8fc317d9868467d75","score":"0.57946014","text":"def clean_paragraph(p):\n\n\tif p.find_all(\"span\") == None:\n\t\tp_text = p\n\telse:\n\t\tfor span_tag in p.find_all(\"span\"):\n\t\t\tspan_tag.replace_with(\"\")\n\t\tp_text = str(p)\n\tcorrected_p = re.sub(r' \\(.*?\\)', \"\", p_text)\n\treturn corrected_p","title":""},{"docid":"f60bbf591aad17c3da73f1146cab065d","score":"0.5793579","text":"def compact_text(data):\n interim_codes = compact_text_interim(data)\n return (_compact_chunk(chunk) for chunk in chunks(interim_codes, 2))","title":""},{"docid":"7546c60a27ce53548e27a01df5be84cc","score":"0.57929564","text":"def normalize_text(text):\r\n text = text.replace('\\r\\n', '\\n').replace('\\r', '\\n').expandtabs()\r\n if not text.endswith('\\n'):\r\n text += '\\n'\r\n\r\n # ignore BOM chars at the begining of template\r\n BOM = '\\xef\\xbb\\xbf'\r\n if isinstance(text, str) and text.startswith(BOM):\r\n text = text[len(BOM):]\r\n \r\n # support fort \\$ for backward-compatibility \r\n text = text.replace(r'\\$', '$$')\r\n return text","title":""},{"docid":"b56abfb553c3d5584260c5b0cf4c18d3","score":"0.57876676","text":"def delete_chars(text, unwanted_chars):\n for char in unwanted_chars:\n text = text.replace(char, '')\n return text","title":""}],"string":"[\n {\n \"docid\": \"bcff0b3207f4a1f287474fa3ebeea447\",\n \"score\": \"0.70005953\",\n \"text\": \"def clean_text(text):\\n # Replace newlines by space. We want only one doc vector.\\n text = text.replace('\\\\n', ' ').lower()\\n # Remove URLs\\n #text = re.sub(r\\\"http\\\\S+\\\", \\\"\\\", text)\\n # Expand contractions: you're to you are and so on.\\n text = contractions.fix(text)\\n # Remove stop words\\n #text = preprocessing.remove_stopwords(text)\\n \\n #text = preprocessing.strip_tags(text)\\n # Remove punctuation -- all special characters\\n text = preprocessing.strip_multiple_whitespaces(preprocessing.strip_punctuation(text))\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2da14da586b935e9a7da3e19d0a684ac\",\n \"score\": \"0.6929299\",\n \"text\": \"def clean_text(text):\\n # Replace newlines by space. We want only one doc vector.\\n text = text.replace('\\\\n', ' ').lower()\\n # Remove URLs\\n text = re.sub(r\\\"http\\\\S+\\\", \\\"\\\", text)\\n # Expand contractions: you're to you are and so on.\\n text = contractions.fix(text)\\n # Remove stop words\\n text = preprocessing.remove_stopwords(text)\\n # Remove punctuation -- all special characters\\n text = preprocessing.strip_multiple_whitespaces(preprocessing.strip_non_alphanum(text))\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc1580629da1f55178692a2c74494d2\",\n \"score\": \"0.6823169\",\n \"text\": \"def clean_text(text): #cleantext\\n replace_char = [\\n \\\"[\\\",\\n \\\"]\\\",\\n \\\"u'\\\",\\n \\\"None\\\",\\n \\\"Thank you\\\",\\n \\\"-\\\",\\n \\\"(\\\",\\n \\\")\\\",\\n \\\"#\\\",\\n \\\"Done\\\",\\n \\\">\\\",\\n \\\"<\\\",\\n \\\"-\\\",\\n \\\"|\\\",\\n \\\"/\\\",\\n \\\"\\\\\\\"\\\",\\n \\\"Hint\\\",\\n \\\"\\\\n\\\",\\n \\\"'\\\"]\\n for l in replace_char:\\n text = text.replace(l, \\\"\\\")\\n text = re.sub(' +', ' ', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73ad21d266a49d35abc6fe7f189fdf3\",\n \"score\": \"0.6713591\",\n \"text\": \"def _clean_text(self, text):\\n text = re.sub('[-=+,#/\\\\?:^$.@*\\\\\\\"※~&%ㆍ!』\\\\\\\\‘|\\\\(\\\\)\\\\[\\\\]\\\\<\\\\>`…》]', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33cad90681ef72942de02b7463b0bde4\",\n \"score\": \"0.6710349\",\n \"text\": \"def clean_text(text):\\n # try:\\n # text = text.encode(\\\"ascii\\\", errors=\\\"ignore\\\").decode()\\n # except:\\n # print(\\\"error in parsing text - \\\"+ text)\\n # pass\\n\\n text = re.sub(r'\\\\([^)]*\\\\)', '', text)\\n text = re.sub(r' +', ' ', text)\\n\\n text = remove_titles(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"439d9bbdc1cdff15c6b24bd045f96de1\",\n \"score\": \"0.67070097\",\n \"text\": \"def clean_text(text):\\n\\n text = re.sub('<[^>]+>', '', text)\\n text = re.sub(r'[^A-Za-z0-9\\\\s]+', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd7847dced4e22c557ff2b18816f239\",\n \"score\": \"0.66308665\",\n \"text\": \"def clean_text(self):\\n cleanedtext = re.sub(r'\\\\n\\\\s*\\\\n', '', self.text) # extra lines\\n cleanedtext = re.sub(r'[^\\\\w\\\\s]', '', cleanedtext) # punctuation\\n # for numbers\\n # cleanedtext = re.sub(r'[^A-Za-z0-9(),!?@\\\\'\\\\`\\\\\\\"\\\\_\\\\n]', ' ', cleanedtext)\\n # cleanedtext = re.sub(r'\\\\w*\\\\d\\\\w*','',cleanedtext) # words with number plust letters\\n # remove_digits = str.maketrans('', '', digits)\\n # cleanedtext = cleanedtext.translate(remove_digits)\\n cleanedtext = re.sub(' +', ' ', cleanedtext) # extra white spaces\\n cleanedtext = cleanedtext.lower()\\n self.cleanedtext = cleanedtext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be79e585f11ff26f695f9bd4c24a03cc\",\n \"score\": \"0.6597903\",\n \"text\": \"def clean_text(text: str) -> str:\\n fixed_text = re.sub('<[^>]+>', ' ', text) # removing everything inside <>\\n fixed_text = re.sub('\\\\n', ' ', fixed_text)\\n fixed_text = re.sub(' ', ' ', fixed_text)\\n fixed_text = re.sub('nbsp', ' ', fixed_text)\\n fixed_text = re.sub('—', ' ', fixed_text)\\n fixed_text = re.sub('\\\\t', ' ', fixed_text)\\n fixed_text = re.sub('\\\\r', ' ', fixed_text)\\n fixed_text = re.sub('\\\"…', ' ', fixed_text)\\n fixed_text = re.sub('>', ' ', fixed_text)\\n fixed_text = re.sub(r'[^\\\\w\\\\s]', ' ', fixed_text) # removing the punctuation\\n fixed_text = re.sub(' +', ' ', fixed_text) # fix multiple spaces\\n fixed_text = fixed_text.strip(' ')\\n return fixed_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de370f2a8e6ba8efac5bacfa16e14e20\",\n \"score\": \"0.6591571\",\n \"text\": \"def textCleaning(self,text):\\n newText = text.replace('\\\\n',' ').replace('\\\\r',' ').replace('\\\\t',' ').replace('<p>',' ').replace('<br>',' ').replace('</p>',' ').replace('</br>',' ').strip()\\n return newText\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46dc40ef3429faddc40feea26c90edce\",\n \"score\": \"0.6588975\",\n \"text\": \"def cleanText(text):\\n # clean: xml tags and mathtype -> spaces\\n cleanText = replaceWithSpaces(xmlTagsRe, text)\\n cleanText = replaceWithSpaces(mathTypeRe, text)\\n # clean: non-letters -> spaces\\n cleanText = nonLetterRe.sub(\\\" \\\", cleanText)\\n cleanText = digitRe.sub(\\\" \\\", cleanText)\\n return cleanText\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf7970fb79abc26376642f89ef03a92b\",\n \"score\": \"0.65831715\",\n \"text\": \"def cleanup_text(text):\\n # Remove empty paragraphs\\n text = re.sub(r'<p[^>]*?>(\\\\s|<br>|<br/>|<br />|\\\\xc2\\\\xa0)*?</p>', '', text,\\n flags=re.UNICODE)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bee9bd22cb054c1e24b2fb6ca4026d5\",\n \"score\": \"0.65464205\",\n \"text\": \"def clean_text(text):\\r\\n text = text.lower().strip() # lowercase and remove trailing whitespaces\\r\\n text = re.sub(r\\\"i'm\\\", \\\"i am\\\", text)\\r\\n text = re.sub(r\\\"he's\\\", \\\"he is\\\", text)\\r\\n text = re.sub(r\\\"she's\\\", \\\"she is\\\", text)\\r\\n text = re.sub(r\\\"it's\\\", \\\"it is\\\", text)\\r\\n text = re.sub(r\\\"that's\\\", \\\"that is\\\", text)\\r\\n text = re.sub(r\\\"what's\\\", \\\"what is\\\", text)\\r\\n text = re.sub(r\\\"where's\\\", \\\"where is\\\", text)\\r\\n text = re.sub(r\\\"there's\\\", \\\"there is\\\", text)\\r\\n text = re.sub(r\\\"how's\\\", \\\"how is\\\", text)\\r\\n text = re.sub(r\\\"\\\\'ll\\\", \\\" will\\\", text)\\r\\n text = re.sub(r\\\"\\\\'ve\\\", \\\" have\\\", text)\\r\\n text = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", text)\\r\\n text = re.sub(r\\\"\\\\'d\\\", \\\" would\\\", text)\\r\\n text = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", text)\\r\\n text = re.sub(r\\\"won't\\\", \\\"will not\\\", text)\\r\\n text = re.sub(r\\\"can't\\\", \\\"cannot\\\", text)\\r\\n text = re.sub(r\\\"n't\\\", \\\" not\\\", text)\\r\\n text = re.sub(r\\\"n'\\\", \\\"ng\\\", text)\\r\\n text = re.sub(r\\\"'bout\\\", \\\"about\\\", text)\\r\\n text = re.sub(r\\\"'til\\\", \\\"until\\\", text)\\r\\n text = re.sub(r'[\\\" \\\"]+', \\\" \\\", text) # remove extra spaces in between\\r\\n text = re.sub(r\\\"[-()\\\\\\\"#/@;:<>{}`+=~|.!?,]\\\", \\\"\\\", text)\\r\\n text = '<start> ' + text + ' <end>'\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04edfda390fef2b3f014fc3061a5fceb\",\n \"score\": \"0.6516315\",\n \"text\": \"def _remove_any_numbers_and_non_english_characters_from_text(pos_tagged_text):\\n for i in xrange(len(pos_tagged_text)):\\n word = pos_tagged_text[i][0]\\n # Remove numbers\\n for digit in string.digits:\\n word = word.replace(digit, '')\\n # Remove words with non-Latin characters\\n try:\\n word.decode('ascii')\\n except UnicodeDecodeError:\\n word = ''\\n # Write the preprocessed word back\\n pos_tagged_text[i][0] = word\\n return pos_tagged_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67bf39279b32d413e779e3dad888c4c9\",\n \"score\": \"0.65065104\",\n \"text\": \"def clean_article_text(text):\\r\\n text = remove_piece_of_text(text, '<a', '>')\\r\\n text = text.replace('</a>', '')\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"616fcc6dd08b5608264b3d94abfe134c\",\n \"score\": \"0.6477654\",\n \"text\": \"def clean_up_text(text):\\n # TODO add special characters\\n text = re.sub('\\\\( \\\\(([^\\\\)]+)\\\\)\\\\)', ' ', text)\\n text = re.sub('([a-z])([.!?])([A-Z])', r'\\\\1\\\\2 \\\\3', text)\\n text = re.sub('[\\\\s]+', ' ', text)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283fc4c63a23b691da69908c290cb32e\",\n \"score\": \"0.6468758\",\n \"text\": \"def cleantext(text):\\n # Convert any non-ASCII characters to XML like 'ꀀabcd޴'\\n text = text.encode(\\\"ascii\\\", \\\"xmlcharrefreplace\\\").decode(\\\"ascii\\\")\\n lines = [x.strip() for x in text.splitlines()]\\n text = \\\" \\\".join(lines)\\n for match, out in REPLACES:\\n text = re.sub(match, out, text)\\n text = text.strip() # some spaces may have been generated\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0e7484b5ba593baa7ca9e6f233b94e\",\n \"score\": \"0.64462006\",\n \"text\": \"def cleanText(self):\\n text = self.text().strip()\\n if self.__prefix and text.startswith(self.__prefix):\\n text = text[len(self.__prefix):]\\n if self.__suffix and text.endswith(self.__suffix):\\n text = text[:-len(self.__suffix)]\\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0e7484b5ba593baa7ca9e6f233b94e\",\n \"score\": \"0.64462006\",\n \"text\": \"def cleanText(self):\\n text = self.text().strip()\\n if self.__prefix and text.startswith(self.__prefix):\\n text = text[len(self.__prefix):]\\n if self.__suffix and text.endswith(self.__suffix):\\n text = text[:-len(self.__suffix)]\\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6476d255266d39d7491fea86957199fc\",\n \"score\": \"0.6420874\",\n \"text\": \"def clean_text(text):\\n \\n # remove Unicode characters\\n text = re.sub(r'[^\\\\x00-\\\\x7F]+', '', text)\\n\\n # taking sequences of characters with alphanumeric characters separated by other characters\\n text = re.sub(r\\\"[-?!&]\\\",' ',text)\\n text = re.sub(r'''[\\\"#$%()*+,./:;<=>@[\\\\]^_`{|}~]''','',text)\\n \\n # remove all numeric charracters\\n text = re.sub(r'[0-9]','',text)\\n\\n # Remove new line characters\\n text = re.sub(r'\\\\s+', ' ', text)\\n\\n # remove all numeric charracters\\n text = re.sub(r'^https?:\\\\/\\\\/.*[\\\\r\\\\n]*', '',text)\\n\\n Apos_dict={\\\"'s\\\":\\\" is\\\",\\\"n't\\\":\\\" not\\\",\\\"'m\\\":\\\" am\\\",\\\"'ll\\\":\\\" will\\\",\\n \\\"'d\\\":\\\" would\\\",\\\"'ve\\\":\\\" have\\\",\\\"'re\\\":\\\" are\\\"}\\n \\n #replace the contractions\\n for key,value in Apos_dict.items():\\n if key in text:\\n text=text.replace(key,value)\\n\\n # convert to lowercase to maintain consistency\\n text = text.lower()\\n \\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f49d845d0d6f6ff334cfea8b6766cbf\",\n \"score\": \"0.63663334\",\n \"text\": \"def clean_text(text: str):\\n position = text.lower().rfind(\\\"references\\\")\\n text = text[:position] # removing bibliography\\n text = text.replace(\\\"\\\\n\\\", \\\" \\\") # removing newlines\\n text = re.sub(r\\\"\\\\[[^\\\\[^\\\\]]*\\\\]\\\", \\\"\\\", text) # removing references\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e29448fa18d208e71d63ed06e30ce1b\",\n \"score\": \"0.63571817\",\n \"text\": \"def _remove_html_tags(self, text):\\r\\n return re.compile(r'<[^>]+>').sub('', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc6604b9eac2108048a41f4543e4766b\",\n \"score\": \"0.6338481\",\n \"text\": \"def strip_markup(wikitext):\\n if not wikitext:\\n return ''\\n\\n # remove templates\\n text = remove_templates(wikitext)\\n\\n # remove irrelevant spans\\n text = re_comments.sub('', text)\\n text = re_ignored_html_tags.sub(r'\\\\2', text)\\n text = re_self_closing_html_tags.sub('', text)\\n text = re_dropped_elements.sub('', text)\\n text = re_categories.sub('', text)\\n text = re_files_images.sub('', text) # TODO: keep file/image captions?\\n\\n # replace external links with just labels or just URLs\\n text = replace_external_links(text)\\n\\n # drop magic words behavioral switches\\n text = re_magic_words.sub('', text)\\n\\n # replace internal links with just their labels\\n text = replace_internal_links(text)\\n # text = replace_internal_links(text) # TODO: is this needed?\\n\\n # remove table markup\\n text = text.replace('||', '\\\\n|').replace('!!', '\\\\n!') # put each cell on a separate line\\n text = re_table_formatting.sub('\\\\n', text) # remove formatting lines\\n text = re_table_cell_formatting.sub('\\\\n\\\\\\\\3', text) # leave only cell content\\n\\n # strip out text formatting\\n text = re_italic_quote.sub(r'\\\"\\\\1\\\"', text)\\n text = re_bold_italic.sub(r'\\\\1', text)\\n text = re_quote_quote.sub(r'\\\"\\\\1\\\"', text)\\n\\n # unescape html entities\\n text = ftfy.fixes.unescape_html(text)\\n\\n # final cleanup\\n text = re_headings.sub(r'\\\\n\\\\n\\\\2\\\\n\\\\n', text)\\n text = re_dots.sub('...', text)\\n text = re_brackets.sub(r'', text)\\n text = text.replace('[[', '').replace(']]', '')\\n text = text.replace('<<', '«').replace('>>', '»')\\n text = re_random_cruft.sub(r'\\\\1', text)\\n text = re.sub(r'\\\\n\\\\W+?\\\\n', r'\\\\n', text, flags=re.UNICODE)\\n text = text.replace(',,', ',').replace(',.', '.')\\n text = re_spaces.sub(' ', text)\\n text = re_linebreaks.sub(r'\\\\n\\\\n', text)\\n\\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd990b81f16b5ea23281fcb150630477\",\n \"score\": \"0.63193804\",\n \"text\": \"def cleanse_sentence(text):\\n \\n text = re.sub(hash_regex, hash_repl, text)\\n text = re.sub(hndl_regex, hndl_repl, text)\\n text = re.sub(url_regex, ' __URL ', text)\\n \\n for (repl, regx) in emoticons_regex :\\n text = re.sub(regx, ' '+repl+' ', text)\\n\\n text = text.replace('\\\\'','')\\n text = re.sub( word_bound_regex , punctuations_repl, text )\\n text = re.sub( rpt_regex, rpt_repl, text )\\n \\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f85bc2347365b9c12ca2580d44a348\",\n \"score\": \"0.6308067\",\n \"text\": \"def clean_text(text):\\n text = re.sub('[-=+,#/\\\\?:^$.@*\\\\\\\"※~&%ㆍ!』\\\\\\\\‘|\\\\(\\\\)\\\\[\\\\]\\\\<\\\\>`…》]', '', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c7d913e073cac757804fda010b6c80c\",\n \"score\": \"0.6296954\",\n \"text\": \"def clean_text_1(text, stpwds, valid_tags, stemming=False):\\n\\tcleaned_text = clean_basic(text)\\n\\ttokens = cleaned_text.split()\\n\\ttokens = postagging(tokens, valid_tags)\\n\\ttokens = remove_stopwords(tokens, stpwds)\\n\\ttokens = remove_short_tokens(tokens)\\n\\tif stemming:\\n\\t\\ttokens = stem(tokens)\\n\\tcleaned_text = remove_hyphen(' '.join(tokens))\\n\\treturn cleaned_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9247a1dbe906adbe476252f075860812\",\n \"score\": \"0.6290065\",\n \"text\": \"def clean_text(text):\\n # remove html tags, lowercase, remove nonsense, remove non-letter\\n aff = BeautifulSoup(text, \\\"lxml\\\").text \\n aff = aff.lower()\\n aff = re.sub(r'xa0|#n#‡#n#|#tab#|#r#|\\\\[|\\\\]', \\\"\\\", aff)\\n aff = re.sub(r'[^a-z]+', ' ', aff)\\n return aff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4549a815eef93b30dc8e49e520ea9d4c\",\n \"score\": \"0.6278033\",\n \"text\": \"def demarkup_text(text):\\n\\n # keep the opening tag name of certain tags that contains these strings\\n # note: <s> are from debian copyright files\\n kept_tags = (\\n 'lic', 'copy', 'www', 'http', 'auth', 'contr', 'leg', 'inc', '@', \\n '<s>', '</s>', '169', 'a9'\\n )\\n\\n # find start and closing tags or the first white space whichever comes first\\n # or entities\\n # this regex is such that ' '.join(tags.split(a))==a\\n\\n tags_ents = re.compile(r'(</?[^\\\\s></]+(?:>|\\\\s)?|&[^\\\\s&]+;|href|[\\\\'\\\"]?\\\\/\\\\>)', re.IGNORECASE).split\\n\\n cleaned = []\\n for token in tags_ents(text):\\n if token.lower().startswith(('<', '&', 'href')) and not any(k in token.lower() for k in kept_tags):\\n continue\\n else:\\n cleaned.append(token)\\n return u' '.join(cleaned)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47cfd092920700f1c2f23d0fba4d528b\",\n \"score\": \"0.6265034\",\n \"text\": \"def remove_tashkeel(self, input_text):\\n\\n p_tashkeel = re.compile(r'[\\\\u0617-\\\\u061A\\\\u064B-\\\\u0652]')\\n return re.sub(p_tashkeel,\\\"\\\", input_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3afa4198b471da03ad24ac8551e73fa3\",\n \"score\": \"0.62647754\",\n \"text\": \"def clean(self):\\n # Replace double \\\\n's in case this ever happens on windows for some reason.\\n if self.text.__contains__('\\\\n\\\\n'):\\n self.text = self.text.replace('\\\\n\\\\n', '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b33f5fa7f977b65ed4d9a97b616ecbba\",\n \"score\": \"0.6247174\",\n \"text\": \"def preprocess(self):\\n self.text = remove_control_characters(self.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3503132af39ea4b05ddf42ac5ed12ffb\",\n \"score\": \"0.6229983\",\n \"text\": \"def strip_markup(text):\\n return (text\\n .replace(\\\"<strong class=\\\\\\\"label\\\\\\\">\\\", \\\"\\\")\\n .replace(\\\"</strong>\\\", \\\"\\\")\\n .replace(\\\"<span class=\\\\\\\"hotel-type\\\\\\\">\\\", \\\"\\\")\\n .replace(\\\"</span>\\\", \\\"\\\")\\n .replace(\\\"<strong>\\\", \\\"\\\")\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f27009c1809229c97e68cb97aa67e615\",\n \"score\": \"0.62280345\",\n \"text\": \"def _del_text(self):\\r\\n # check whether element already has text node\\r\\n if self._root.HasChildNodes and \\\\\\r\\n self._root.FirstChild and \\\\\\r\\n isinstance(self._root.FirstChild, XmlText):\\r\\n self._root.RemoveChild(self._root.FirstChild)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f35e7f296801a447055b16f013b54b\",\n \"score\": \"0.6215954\",\n \"text\": \"def clean_text(text):\\n # lowercase text\\n text = text.lower()\\n\\n # substitute contractions\\n # text = re.sub(r\\\"i'm\\\", \\\"i am\\\", text)\\n # text = re.sub(r\\\"he's\\\", \\\"he is\\\", text)\\n # text = re.sub(r\\\"she's\\\", \\\"she is\\\", text)\\n # text = re.sub(r\\\"it's\\\", \\\"it is\\\", text)\\n # text = re.sub(r\\\"that's\\\", \\\"that is\\\", text)\\n # text = re.sub(r\\\"what's\\\", \\\"what is\\\", text)\\n # text = re.sub(r\\\"where's\\\", \\\"where is\\\", text)\\n # text = re.sub(r\\\"how's\\\", \\\"how is\\\", text)\\n # text = re.sub(r\\\"\\\\'ll\\\", \\\" will\\\", text)\\n # text = re.sub(r\\\"\\\\'ve\\\", \\\" have\\\", text)\\n # text = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", text)\\n # text = re.sub(r\\\"\\\\'d\\\", \\\" would\\\", text)\\n # text = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", text)\\n # text = re.sub(r\\\"won't\\\", \\\"will not\\\", text)\\n # text = re.sub(r\\\"can't\\\", \\\"cannot\\\", text)\\n # text = re.sub(r\\\"n't\\\", \\\" not\\\", text)\\n # text = re.sub(r\\\"n'\\\", \\\"ng\\\", text)\\n # text = re.sub(r\\\"'bout\\\", \\\"about\\\", text)\\n # text = re.sub(r\\\"'til\\\", \\\"until\\\", text)\\n\\n # get rid of username handles\\n text = re.sub(r\\\"(?:@)(\\\\S+|$)|\\\", \\\"\\\", text)\\n\\n # get rid of special characters\\n text = re.sub(r\\\"[-()\\\\\\\"#/@;:<>{}`+=~|.!?,]\\\", \\\"\\\", text)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88c982c1d7c70e2702c8b9fafed7d4d5\",\n \"score\": \"0.6204147\",\n \"text\": \"def strip_tags(text):\\n\\n return re.sub('<[^<]+?>', '', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd6daf8f52ba6aeedce909badebd364\",\n \"score\": \"0.6194485\",\n \"text\": \"def clean_text(raw_text):\\n # Must strip HTML tags out first!\\n text = re.sub('<[^<]+?>', ' ', raw_text)\\n text = handle_unicode(text)\\n text = handle_html_unquote(text)\\n text = handle_mac_quotes(text)\\n text = handle_text_break_dash(text)\\n text = text.lower()\\n\\n regex_subs = ['\\\\t', '\\\\n', '\\\\r', '\\\\s+', '&']\\n for regex_sub in regex_subs:\\n text = re.sub(regex_sub, ' ', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14b2ba621e89303b47c25fd967a686e4\",\n \"score\": \"0.6182298\",\n \"text\": \"def _clean_text(source):\\n source = str(source)\\n source = source.upper()\\n source = re.sub('[？|?|！|!|，|,|.|。|#|《|》|<|>|（|）|(|)|〉|、|／|…|-]', '', source)\\n source = re.sub('[哈｜嘿｜呵]', '', source)\\n source = re.sub('NAN', '', source)\\n source = re.sub('\\\\xa0', '', source)\\n return source\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30006085bad7c937637e584674a6cc02\",\n \"score\": \"0.6176192\",\n \"text\": \"def _clean_bad_chars(self, text):\\n return text.replace(\\\"\\\\u200b\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c3428df1fe332453e101d924bcc714b\",\n \"score\": \"0.6171018\",\n \"text\": \"def text2cleanish(text):\\n logger.info(\\\"Cleaning text ...\\\")\\n text = CLEANER1.sub(r\\\"\\\\1 \\\\2 \\\\3\\\", text)\\n text = CLEANER2.sub(r\\\"\\\\1 ... \\\\3\\\", text)\\n text = CLEANER3.sub(r\\\"\\\\1 ... \\\", text)\\n # text = CLEANER4.sub(r\\\"\\\\1 X... \\\\3\\\", text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"928336714d22728e7f69363c3345b4c8\",\n \"score\": \"0.6169784\",\n \"text\": \"def clean(self, text = None) -> str:\\n return self.normalize(\\n self.clean_double_whitespaces(\\n self.clean_edge_punctuation(\\n self.clean_numeric(\\n self.clean_web_tags(\\n self.clean_html_tags(\\n self.clean_non_ascii(\\n text\\n )\\n )\\n )\\n )\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cff7fa0a0cef7e0a4aa8555fc2fa3982\",\n \"score\": \"0.61514693\",\n \"text\": \"def clean_text_remove_puncs(cls, text):\\n if isinstance(text, str):\\n try:\\n text = cls.clean_text(text)\\n text = cls._remove_punctuations(text)\\n\\n except (TypeError, AttributeError):\\n logger.warning(\\\"Error occured in preprocessing text: {}\\\".format(text))\\n sys.exit(0)\\n\\n return text.strip()\\n\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6400609a86643ee6fd2cb125e5b5aae5\",\n \"score\": \"0.61486775\",\n \"text\": \"def safe_clean_text(text):\\n # strip\\n text = text.strip()\\n\\n # weird words\\n text = delete_words(delete_list, text)\\n\\n # handle \\\\\\\\\\\\'t \\\\\\\\\\\\'ve\\n text = text.replace(r\\\"\\\\\\\\\\\", \\\"\\\")\\n\\n # remove extra spaces\\n text = re.sub(r'\\\\s+', ' ', text)\\n\\n # fix puncutations\\n for p in punctuations:\\n text = text.replace(p, p[1:])\\n text = re.sub(\\\" (\\\\u2018|\\\\u2019|') \\\", r\\\"\\\\1\\\", text)\\n\\n # replace to EN dash\\n text = re.sub(u\\\"\\\\u2014\\\", \\\"-\\\", text)\\n text = re.sub(u\\\"\\\\u2013\\\", \\\"-\\\", text)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6857e424290706ed34fd0100320e8694\",\n \"score\": \"0.61465544\",\n \"text\": \"def remove_tags(text):\\n clean = re.compile('<.*?>')\\n return re.sub(clean, '', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5086afb5e199b8a13a21c4f58efbca2f\",\n \"score\": \"0.61259377\",\n \"text\": \"def remove_smart_quotes(self, text):\\r\\n return text.replace(u\\\"\\\\u201c\\\",\\\"\\\").replace(u\\\"\\\\u201d\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b44d02ed0cc749e2cc7f1d32af1451\",\n \"score\": \"0.6104445\",\n \"text\": \"def clean_txt(self, text):\\n\\n text = re.sub(\\\"'\\\", \\\"\\\", text)\\n text = re.sub(\\\"(\\\\\\\\W)+\\\", \\\" \\\", text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faaffdfe0444eca15482157446d68b10\",\n \"score\": \"0.6088906\",\n \"text\": \"def clean_text(text):\\n translation_dict = {\\n '’':\\\"'\\\",\\n '‘':\\\"'\\\",\\n # \\\"œ\\\":'oe',\\n }\\n # remove control characters not supported by XML.\\n text = strip_chars(text)\\n text = unicodedata.normalize('NFC', text.strip())\\n text = re.sub(r'(\\\\n\\\\n )+', ' ', text)\\n text = re.sub(r'([a-zA-Zàâçéèêëîïôûùüÿñæœ,;-])(\\\\n)+([^\\\\s])', r'\\\\1 \\\\3', text)\\n return text.translate(str.maketrans(translation_dict))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab72be9d87c9f54053505d1b1eaf6b2f\",\n \"score\": \"0.6088736\",\n \"text\": \"def clearMyText(self):\\n self.clearText(self.textName)\\n self.clearText(self.textCR)\\n self.clearText(self.textAL)\\n self.clearText(self.textEC)\\n self.clearText(self.textIA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b81ac2e011b854386493cc068a9e46dd\",\n \"score\": \"0.60839474\",\n \"text\": \"def clear_texts(self):\\n\\n for t in self.texts[:]:\\n if not t[4]:\\n self.texts.remove(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3eb01e6647165fa5f3f6cc66a802663\",\n \"score\": \"0.6076601\",\n \"text\": \"def cleanText(self, s):\\n\\n\\t\\tdef _sent_to_lines(sent):\\n\\t\\t\\t\\\"\\\"\\\"Converts a string of text to one sentence each line.\\\"\\\"\\\"\\n\\t\\t\\tsent = re.sub(r\\\"\\\\n\\\", \\\" \\\", sent)\\n\\t\\t\\treturn re.sub(r\\\"(\\\\.|\\\\!|\\\\?|--)+\\\\s*\\\", \\\"\\\\n\\\", sent)\\n\\n\\t\\tdef _strip_punct(sent):\\n\\t\\t\\t\\\"\\\"\\\"Strips away all punctuations from the text.\\\"\\\"\\\"\\n\\t\\t\\treturn sent.translate(string.maketrans(\\\"\\\",\\\"\\\"), string.punctuation)\\n\\n\\t\\treturn _strip_punct(_sent_to_lines(s)).lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1727f2673de7c749d5ef2a767231eba\",\n \"score\": \"0.60653853\",\n \"text\": \"def clean_message(self, text):\\n text = strip_tags(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d8abba63fc621d1b31a7dad4026d4e\",\n \"score\": \"0.60498005\",\n \"text\": \"def __remove_tags(self, html_text):\\n tags_regex = re.compile(\\n '<.*?>|&([a-z0-9]+|#[0-9]{1,6}|#x[0-9a-f]{1,6});')\\n tag_free_text = re.sub(tags_regex, ' ', html_text)\\n\\n return tag_free_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ba3be36a740be33aa131f168e63ba4e\",\n \"score\": \"0.6047604\",\n \"text\": \"def clean_text(text):\\n if text:\\n text = html2text.html2text(clean_markdown(text))\\n return re.sub(r'\\\\s+', ' ', text).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a1fbdfc06773a8ebff8e45493c51bd1\",\n \"score\": \"0.603565\",\n \"text\": \"def remove_html_tags(text):\\r\\n clean = re.compile('\\\\{<.*?>\\\\}')\\r\\n return re.sub(clean, '', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1f72d624a2444e1814a702ea0cf7e8\",\n \"score\": \"0.6031818\",\n \"text\": \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1f72d624a2444e1814a702ea0cf7e8\",\n \"score\": \"0.6031818\",\n \"text\": \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4682d3a218fa9f846931042e24d0607\",\n \"score\": \"0.60222965\",\n \"text\": \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or self._is_control(char):\\n continue\\n if self._is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4f3d2d2e47513b04b3d7b686b294cdd\",\n \"score\": \"0.6021114\",\n \"text\": \"def _clean_text(self, listing_text):\\n if len(listing_text)>0:\\n listing_text_clean = ' '.join(listing_text[0].findAll(text=True))\\n listing_text_clean = re.sub('\\\\n|\\\\t', ' ', listing_text_clean)\\n listing_text_clean = re.sub('\\\\s+', ' ', listing_text_clean)\\n listing_text_clean = re.sub(\\\"'\\\", '', listing_text_clean)\\n listing_text_clean = re.sub('\\\"', '', listing_text_clean)\\n else:\\n listing_text_clean = ' ' \\n return listing_text_clean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df88aa6338c651f311f8313abd30f022\",\n \"score\": \"0.6015763\",\n \"text\": \"def clean(text, tokenizer, stemmer):\\n doc = ''.join(text).lower()\\n doc = re.sub(r'[<>\\\\{}/;|\\\\[\\\\]-]', ' ', doc)\\n doc = re.sub(r'[0-9]', ' ', doc)\\n doc = re.sub(r'\\\\'', ' ', doc)\\n doc = re.sub(r'=', ' ', doc)\\n doc = re.sub(r':', ' ', doc)\\n doc = re.sub(r'\\\"', ' ', doc)\\n doc = re.sub(r'\\\\s+', ' ', doc)\\n doc = re.sub(r'\\\\(', ' ', doc)\\n doc = re.sub(r'\\\\)', ' ', doc)\\n doc = re.sub(r'\\\\s{2,}', ' ', doc)\\n doc = re.sub(r'\\\\.', '', doc)\\n doc = re.sub(r',', '', doc)\\n\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d531dc571d409ba0f71d86b949d095b\",\n \"score\": \"0.6010883\",\n \"text\": \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c801ea1e26a1508d732d0d546871740\",\n \"score\": \"0.60044676\",\n \"text\": \"def ocr_cleaner(text):\\n\\n text = text.lower().replace('\\\\n', ' ').replace('\\\\r', ' ').replace('\\\\t', ' ').strip() # replace all whitespace, lower\\n text = ' '.join(text.split()) # remove consecutive spaces\\n text = re.sub(r'(?<=[a-z]{3})[.]', '.\\\\n', text)\\n text = text.encode('ascii', errors='ignore').decode(encoding='utf8').replace(r'\\\\\\\\', '').replace('\\\"', '')\\n text = re.sub(r'(?<=nae)', '\\\\n', text)\\n text = re.sub(r'(?<=dae)', '\\\\n', text)\\n text = text.replace('life histories of cascadia butterflies', '').replace('\\\\\\\\', '').replace('|', '')\\n text = text.replace('adult biology', '').replace('immature stage biology', '')\\n text = text.replace('description of immature stages', '').replace('discussion', '')\\n text = re.sub(r'([0-9]){3}', '', text)\\n text = re.sub(r'([i])\\\\b', '', text)\\n text = re.sub(r'(family|subfamily).*', '', text)\\n text = text.replace('-', ' ').replace('tlie', 'the').replace('ihis', 'this').replace('ditilicult', 'difficult')\\n text = re.sub(r'^\\\\s*$', '', text)\\n text = ' '.join(text.split()) # remove consecutive spaces\\n text = re.sub(r'(?<=[a-z]{4})[.]', '.\\\\n', text)\\n text = text.strip()\\n text = re.sub(r'[.,();\\\\']', '', text)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bbe6034a0e46963cb33decf1d35dfa4\",\n \"score\": \"0.59949124\",\n \"text\": \"def remove_punc(data):\\n \\n #All text to lower case\\n data = data.lower()\\n #Punctuation subbed out for ''\\n data = punc.sub( '', data)\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc5773a680ca43dad9ee13f3609035f2\",\n \"score\": \"0.5977697\",\n \"text\": \"def _remove_all_other_punctuation(pos_tagged_text):\\n for j in xrange(len(pos_tagged_text)):\\n word = pos_tagged_text[j][0]\\n for symbol in (',', '.', ';', '-'):\\n word = word.replace(symbol, ' ')\\n # Write the preprocessed word back\\n pos_tagged_text[j][0] = word\\n return pos_tagged_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64bca5555bdd1a051e9978c44b240c42\",\n \"score\": \"0.597209\",\n \"text\": \"def remove_html(text):\\n return html.unescape(re.sub(doubleSpacingRe, ' ', re.sub(nbspRe, ' ', text)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f082dba8399241d49d19b16ec80984\",\n \"score\": \"0.5968257\",\n \"text\": \"def get_cleaned_text(self, text):\\n cleaned_text = text.replace('\\\\\\\"','').replace('\\\\'','').replace('-',' ')\\n cleaned_text = self.remove_non_ascii_chars(cleaned_text)\\n \\n # retweet\\n if re.match(r'RT @[_A-Za-z0-9]+:',cleaned_text): # retweet\\n if self.remove_retweets: return ''\\n retweet_info = cleaned_text[:cleaned_text.index(':')+2] # 'RT @name: ' will be again added in the text after cleaning\\n cleaned_text = cleaned_text[cleaned_text.index(':')+2:]\\n else:\\n retweet_info = ''\\n cleaned_text = self.remove_hyperlinks(cleaned_text)\\n cleaned_text = cleaned_text.replace('#','HASHTAGSYMBOL').replace('@','ATSYMBOL') # to avoid being removed while removing punctuations\\n \\n tokens = [w.translate(self.punc_table) for w in word_tokenize(cleaned_text)] # remove punctuations and tokenize\\n tokens = [w for w in tokens if not w.lower() in self.stop_words and len(w)>1] # remove stopwords and single length words\\n cleaned_text = ' '.join(tokens)\\n \\n cleaned_text = cleaned_text.replace('HASHTAGSYMBOL','#').replace('ATSYMBOL','@')\\n cleaned_text = retweet_info + cleaned_text\\n \\n return cleaned_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0c7d2d36d3f9e03b06bb850b936efa\",\n \"score\": \"0.5966438\",\n \"text\": \"def remove_char(stringedText):\\n\\ttext = stringedText\\n\\t#cleanedText = '' # need a place to store the chars that actually belong\\n\\t\\n\\t# new method that skips all the if statement branches\\n\\t# 1. Put all the special chars in a single list\\n\\t# 2. Iterate through the list when analyzing each char\\n\\t# 3. recurs as before\\n\\n\\t# if it's Alice.txt, then use the full list. otherwise use shorter list\\n\\tif sys.argv[2] == '-2':\\n\\t\\tlistOfSpecialChars = [',', '.', '?', '!', '`', '<del>', '<esc>', '<cntrl>', '<tab>', '<cmd>', '<right>', '<left>', '<down>', '<up>', '<shift>']\\n\\telse:\\n\\t\\tlistOfSpecialChars = ['<del>', '<esc>', '<cntrl>', '<tab>', '<cmd>', '<right>', '<left>', '<down>', '<up>', '<shift>']\\n\\n\\n\\tfor item in listOfSpecialChars:\\n\\t\\tif item in text:\\n\\t\\t\\ttext = text.replace(item, '')\\n\\t\\t\\tremove_char(text) # recursively call until item no longer in text, then fail condition\\n\\treturn text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce6cdf443d7400675c4686b8fff22d3\",\n \"score\": \"0.59407234\",\n \"text\": \"def remove_html(self, text):\\n soup = BeautifulSoup(text, 'html.parser')\\n return soup.get_text(separator=\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fadf8ad9b6688d84f136701aeff7897\",\n \"score\": \"0.59321564\",\n \"text\": \"def clean_text(doc):\\n \\n words = re.sub(\\\"< ?/?[a-z]+ ?>|\\\\n\\\", \\\"\\\", doc)\\n words = tokenize_text(words)\\n words = lemmatize_text(words)\\n words = remove_stopwords(words)\\n doc = [w for w in words if w.isalnum()]\\n doc = ' '.join(doc)\\n \\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83730e0c1709a337afd4043992bf3a87\",\n \"score\": \"0.5919678\",\n \"text\": \"def preprocess(self, text):\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"895767f9567130d8209cec47ed740cd6\",\n \"score\": \"0.5917767\",\n \"text\": \"def basic_cleaners(self, text):\\n text = self.lowercase(text)\\n text = self.collapse_whitespace(text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acefa458000e3f328bffb60228142df1\",\n \"score\": \"0.5913148\",\n \"text\": \"def clear_text(self):\\n self.ax.texts.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b60b67b4c52b195697d002d8f248cc\",\n \"score\": \"0.59017366\",\n \"text\": \"def _do_smart_punctuation(self, text):\\r\\n if \\\"'\\\" in text: # guard for perf\\r\\n text = self._do_smart_contractions(text)\\r\\n text = self._opening_single_quote_re.sub(\\\"‘\\\", text)\\r\\n text = self._closing_single_quote_re.sub(\\\"’\\\", text)\\r\\n\\r\\n if '\\\"' in text: # guard for perf\\r\\n text = self._opening_double_quote_re.sub(\\\"“\\\", text)\\r\\n text = self._closing_double_quote_re.sub(\\\"”\\\", text)\\r\\n\\r\\n text = text.replace(\\\"---\\\", \\\"—\\\")\\r\\n text = text.replace(\\\"--\\\", \\\"–\\\")\\r\\n text = text.replace(\\\"...\\\", \\\"…\\\")\\r\\n text = text.replace(\\\" . . . \\\", \\\"…\\\")\\r\\n text = text.replace(\\\". . .\\\", \\\"…\\\")\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d69ee53fb8109b8eac653e744ce6cf16\",\n \"score\": \"0.5900395\",\n \"text\": \"def preprocess_text(data):\\n if 'Body' in data:\\n data['Body'] = sanitise_xml(data['Body'])\\n \\n if 'Tags' in data:\\n data['Tags'] = re.findall('\\\\<([^>]+)', data['Tags'])\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee91042b95d6239ece6d61938a4c2c0c\",\n \"score\": \"0.589461\",\n \"text\": \"def cleanupText(path):\\n \\n text_cleaned = ''\\n try:\\n f = open(path)\\n raw = f.read().lower()\\n text = raw \\n text_cleaned = re.sub(r'[-.?!,\\\":;()/|0-9]','', text)\\n \\n# splitword = text_cleaned.split(\\\" \\\")\\n# \\n# for word in splitword:\\n# if word in stop_word:\\n# splitword.remove(word)\\n# text_cleaned = \\\" \\\".join(splitword)\\n# # print \\\"\\\\n Word count after:\\\" + str(len(text_translated.split())) + \\\"\\\\n\\\"\\n# text_cleaned = ' '.join([word for word in text_cleaned.split(' ') if (word and len(word) > 1)])\\n finally:\\n f.close()\\n return text_cleaned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e9121c9e15b11560fa8798e6959444\",\n \"score\": \"0.58939534\",\n \"text\": \"def normalize_text(self):\\n self.normalized_text = normalize_text(remove_nonwords(self.raw_text))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4d9363dc24767294beb89a6c113215\",\n \"score\": \"0.5891619\",\n \"text\": \"def delete_text(self):\\n self.in_title.delete(0, \\\"end\\\")\\n self.in_author.delete(0, \\\"end\\\")\\n self.answer.configure(text=\\\"\\\")\\n self.answer.configure(text=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9777cfd2650717c6e9d64f74f792a8dc\",\n \"score\": \"0.58881503\",\n \"text\": \"def clean_text(s: str) -> str:\\n s = unicodedata.normalize(\\\"NFKD\\\", s) # simplify unicode of string\\n s = html.unescape(s) # convert html entities to unicode characters\\n s = re.sub(r\\\"(</p>|</li>)\\\", \\\"\\\\n\\\", s) # replace p or li close with newline \\n s = re.sub(r\\\"<li>\\\", \\\"• \\\", s) # replace list item start with bullets\\n s = re.sub(r\\\"<[^<]+?>\\\", \\\"\\\", s) # otherwise strip HTML tags\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27e37a99ab23a9aadfc8937ecf3c815\",\n \"score\": \"0.5877997\",\n \"text\": \"def remove_html_tags(text):\\n return HTMLTagsStripper.strip_tags_(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22683fdf5d6c00468b6eac0349e1afe\",\n \"score\": \"0.587424\",\n \"text\": \"def remove_meta(text):\\n text = re.sub(r'(\\\\[.*?\\\\]+)', '', text) # e.g. [one] or [[ two ]]\\n text = re.sub(r'(\\\\{.*?\\\\}+)', '', text) # e.g. {one} or {{ two }}\\n text = re.sub(r'(<.*?>+)', '', text) # e.g. <one> or <<two>>\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cb9610b3068c092810921d8c94fb7b\",\n \"score\": \"0.5866584\",\n \"text\": \"def clean_latex(text):\\n # edge case\\n text = re.sub(r'\\\\[math\\\\]', ' LaTex math ', text)\\n text = re.sub(r'\\\\[\\\\/math\\\\]', ' LaTex math ', text)\\n text = re.sub(r'\\\\\\\\', ' LaTex ', text)\\n\\n pattern_to_sub = {\\n r'\\\\\\\\mathrm': ' LaTex math mode ',\\n r'\\\\\\\\mathbb': ' LaTex math mode ',\\n r'\\\\\\\\boxed': ' LaTex equation ',\\n r'\\\\\\\\begin': ' LaTex equation ',\\n r'\\\\\\\\end': ' LaTex equation ',\\n r'\\\\\\\\left': ' LaTex equation ',\\n r'\\\\\\\\right': ' LaTex equation ',\\n r'\\\\\\\\(over|under)brace': ' LaTex equation ',\\n r'\\\\\\\\text': ' LaTex equation ',\\n r'\\\\\\\\vec': ' vector ',\\n r'\\\\\\\\var': ' variable ',\\n r'\\\\\\\\theta': ' theta ',\\n r'\\\\\\\\mu': ' average ',\\n r'\\\\\\\\min': ' minimum ',\\n r'\\\\\\\\max': ' maximum ',\\n r'\\\\\\\\sum': ' + ',\\n r'\\\\\\\\times': ' * ',\\n r'\\\\\\\\cdot': ' * ',\\n r'\\\\\\\\hat': ' ^ ',\\n r'\\\\\\\\frac': ' / ',\\n r'\\\\\\\\div': ' / ',\\n r'\\\\\\\\sin': ' Sine ',\\n r'\\\\\\\\cos': ' Cosine ',\\n r'\\\\\\\\tan': ' Tangent ',\\n r'\\\\\\\\infty': ' infinity ',\\n r'\\\\\\\\int': ' integer ',\\n r'\\\\\\\\in': ' in ',\\n }\\n # post process for look up\\n pattern_dict = {k.strip('\\\\\\\\'): v for k, v in pattern_to_sub.items()}\\n # init re\\n patterns = pattern_to_sub.keys()\\n pattern_re = re.compile('(%s)' % '|'.join(patterns))\\n\\n def _replace(match):\\n \\\"\\\"\\\"\\n reference: https://www.kaggle.com/hengzheng/attention-capsule-why-not-both-lb-0-694 # noqa\\n \\\"\\\"\\\"\\n try:\\n word = pattern_dict.get(match.group(0).strip('\\\\\\\\'))\\n except KeyError:\\n word = match.group(0)\\n print('!!Error: Could Not Find Key: {}'.format(word))\\n return word\\n return pattern_re.sub(_replace, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39cb9610b3068c092810921d8c94fb7b\",\n \"score\": \"0.5866584\",\n \"text\": \"def clean_latex(text):\\n # edge case\\n text = re.sub(r'\\\\[math\\\\]', ' LaTex math ', text)\\n text = re.sub(r'\\\\[\\\\/math\\\\]', ' LaTex math ', text)\\n text = re.sub(r'\\\\\\\\', ' LaTex ', text)\\n\\n pattern_to_sub = {\\n r'\\\\\\\\mathrm': ' LaTex math mode ',\\n r'\\\\\\\\mathbb': ' LaTex math mode ',\\n r'\\\\\\\\boxed': ' LaTex equation ',\\n r'\\\\\\\\begin': ' LaTex equation ',\\n r'\\\\\\\\end': ' LaTex equation ',\\n r'\\\\\\\\left': ' LaTex equation ',\\n r'\\\\\\\\right': ' LaTex equation ',\\n r'\\\\\\\\(over|under)brace': ' LaTex equation ',\\n r'\\\\\\\\text': ' LaTex equation ',\\n r'\\\\\\\\vec': ' vector ',\\n r'\\\\\\\\var': ' variable ',\\n r'\\\\\\\\theta': ' theta ',\\n r'\\\\\\\\mu': ' average ',\\n r'\\\\\\\\min': ' minimum ',\\n r'\\\\\\\\max': ' maximum ',\\n r'\\\\\\\\sum': ' + ',\\n r'\\\\\\\\times': ' * ',\\n r'\\\\\\\\cdot': ' * ',\\n r'\\\\\\\\hat': ' ^ ',\\n r'\\\\\\\\frac': ' / ',\\n r'\\\\\\\\div': ' / ',\\n r'\\\\\\\\sin': ' Sine ',\\n r'\\\\\\\\cos': ' Cosine ',\\n r'\\\\\\\\tan': ' Tangent ',\\n r'\\\\\\\\infty': ' infinity ',\\n r'\\\\\\\\int': ' integer ',\\n r'\\\\\\\\in': ' in ',\\n }\\n # post process for look up\\n pattern_dict = {k.strip('\\\\\\\\'): v for k, v in pattern_to_sub.items()}\\n # init re\\n patterns = pattern_to_sub.keys()\\n pattern_re = re.compile('(%s)' % '|'.join(patterns))\\n\\n def _replace(match):\\n \\\"\\\"\\\"\\n reference: https://www.kaggle.com/hengzheng/attention-capsule-why-not-both-lb-0-694 # noqa\\n \\\"\\\"\\\"\\n try:\\n word = pattern_dict.get(match.group(0).strip('\\\\\\\\'))\\n except KeyError:\\n word = match.group(0)\\n print('!!Error: Could Not Find Key: {}'.format(word))\\n return word\\n return pattern_re.sub(_replace, text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3df43473fa1625aa47e63ad2cfc900\",\n \"score\": \"0.58606964\",\n \"text\": \"def _clear_tag_string(self, tag_string):\\n #clean = re.compile(\\\".?>\\\")\\n clean = re.compile('.?>|\\\\\\\"|.?/>|</.+')\\n text = re.sub(clean, \\\"\\\", tag_string)\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f836543dc5442e2680791c89d334a8b\",\n \"score\": \"0.58566207\",\n \"text\": \"def remove_html_tags(text):\\n tag_free = re.sub(r\\\"<.*?>\\\", \\\" \\\", text)\\n return re.sub(r\\\" +\\\", \\\" \\\", tag_free).strip() # make it nice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4c313f87e01e348ee6179acdfb5eaa\",\n \"score\": \"0.58543164\",\n \"text\": \"def remove_unicode(text):\\n regex = r\\\"(\\\\\\\\u....)\\\"\\n text = re.sub(regex, ' ', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d664d3566821921a5d68e2c86a08931\",\n \"score\": \"0.5848827\",\n \"text\": \"def remove_html_tags(text):\\r\\n import re\\r\\n clean = re.compile('<.*?>')\\r\\n return re.sub(clean, '', text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd0c2368ecf797bcb9dfcd574fc6ff7\",\n \"score\": \"0.584826\",\n \"text\": \"def clean_text(self):\\n return self.sanitize_html(self.cleaned_data['text'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e482c773dc5fd21e1ebd09d9eb3ef6ad\",\n \"score\": \"0.58404464\",\n \"text\": \"def _process_text(self,\\n index: int,\\n lower=True,\\n delete_whitespace=True,\\n delete_urls=True) -> str:\\n text = self._texts[index]\\n if not text:\\n return None\\n\\n if not isinstance(text, str):\\n raise TypeError(\\\"Text value is not string\\\")\\n\\n if lower:\\n text = text.lower()\\n if delete_whitespace:\\n text = re.sub(r'[\\\\s]+', r' ', text).strip()\\n if delete_urls:\\n text = URLS.sub('', text)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4f38466182c733dd2f112000f2c369\",\n \"score\": \"0.5832301\",\n \"text\": \"def clean(self, text):\\n\\n return text.replace(\\\"$=\\\", \\\"<=\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db598df2c8505b0d3bd9bcdd77e327c\",\n \"score\": \"0.5831346\",\n \"text\": \"def textilize(s):\\n s = s.replace(\\\"<p>\\\", \\\" \\\").replace(' ', ' ')\\n return _re_html.sub(\\\"\\\", s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595bc2579550e46f323aeeb024cfdaea\",\n \"score\": \"0.5830553\",\n \"text\": \"def character_cleanup(self, char):\\n if self.tagcategory:\\n char.tags.remove(category=self.tagcategory)\\n char.attributes.remove(category=self.tagcategory)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"523d6b90dc8d3f1d665155c824d38e3d\",\n \"score\": \"0.5829617\",\n \"text\": \"def epub_to_clean_text(ebook):\\n out = epub2text(ebook)\\n text=\\\"\\\"\\n for chapter in out : \\n text += filter_nonprintable(chapter) \\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15535fe57d25f9aa6661008b09fe0ecc\",\n \"score\": \"0.5823075\",\n \"text\": \"def remove_html_tags(text):\\n\\t#print(text)\\n\\t#First replace linebreaks to something NON-HTML so that it wont be cleared in the next step.\\n\\ttext = text.replace(\\\"<br>\\\", \\\"BRBRBR\\\")\\n\\t#print(text)\\n\\n\\timport re\\n\\tclean = re.compile('<.*?>')\\n\\tclean = re.sub(clean, '', text)\\n\\t#print(clean)\\n\\t#Again replace back our linebreaks to HTML format\\n\\tclean = clean.replace(\\\"BRBRBR\\\", \\\"<br>\\\")\\n\\t#print(clean)\\n\\t#print(\\\"---end---\\\")\\n\\treturn Markup(clean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40d8dc5dfdcf10201dd1eb69952be726\",\n \"score\": \"0.5818737\",\n \"text\": \"def text_standardize(text):\\n text = text.replace('—', '-')\\n text = text.replace('–', '-')\\n text = text.replace('―', '-')\\n text = text.replace('…', '...')\\n text = text.replace('´', \\\"'\\\")\\n text = re.sub(r'''(-+|~+|!+|\\\"+|;+|\\\\?+|\\\\++|,+|\\\\)+|\\\\(+|\\\\\\\\+|\\\\/+|\\\\*+|\\\\[+|\\\\]+|}+|{+|\\\\|+|_+)''', r' \\\\1 ', text)\\n text = re.sub(r'\\\\s*\\\\n\\\\s*', ' \\\\n ', text)\\n text = re.sub(r'[^\\\\S\\\\n]+', ' ', text)\\n return text.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e09095356905e64ce0cbd9278becd9a\",\n \"score\": \"0.581436\",\n \"text\": \"def clean_text(text):\\n digi_punct = \\\"[^a-zA-Z.1234567890'# ]\\\" # used by clean_text(text_corpus)\\n text = text.replace(\\\"´\\\", \\\"'\\\")\\n text = re.sub(digi_punct, \\\" \\\", text)\\n\\n # digi_punct = \\\"[^a-zA-Z.1234567890' ]\\\"\\n text = \\\" \\\".join(text.split())\\n text = text.lower()\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc88ebeb02bbc3589d10ad07550933f\",\n \"score\": \"0.58119494\",\n \"text\": \"def remove_speech_meta(text):\\n text = re.sub(r'^(\\\\s*\\\\([^)]*\\\\)\\\\s*)', '', text) # e.g. (to Ray) Hello\\n text = re.sub(r'(\\\\s*\\\\([^)]*\\\\)\\\\s*)$', '', text) # e.g. Hello (motions at Ray)\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95693e0352cd0d901a0f5a44a8f4cfe8\",\n \"score\": \"0.58030546\",\n \"text\": \"def clean_tweet_text(tweet):\\n tweet = tweet.strip(\\\"\\\\n\\\")\\n tweet = tweet.strip(\\\"\\\\t\\\")\\n tweet = remove_emojis(tweet)\\n tweet = remove_hashtag_symbol(tweet)\\n return tweet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09587b5540da31a7dfd6b6bbd3d7785d\",\n \"score\": \"0.5796727\",\n \"text\": \"def clean(text: str) -> str:\\n\\n clean_str = ''\\n for char in text:\\n if char.isalnum() or char == UNDERSCORE:\\n clean_str = clean_str + char\\n elif char == HASHTAG_SYMBOL or char == MENTION_SYMBOL:\\n clean_str = clean_str + SPACE + char\\n else:\\n clean_str = clean_str + SPACE\\n return clean_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4136548aa14d7139072f51ccbad881f9\",\n \"score\": \"0.57962835\",\n \"text\": \"def _remove_tags(html):\\n html = _replace_ampersands(html)\\n html = re.sub('\\\\<sup\\\\>|\\\\</sup\\\\>', '', html)\\n html = re.sub('\\\\<sub\\\\>|\\\\</sub\\\\>', '', html)\\n tagrx = re.compile('\\\\<.+?\\\\>', flags=re.DOTALL)\\n html = tagrx.sub(' ', html)\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdd98a8e4b041ee8fc317d9868467d75\",\n \"score\": \"0.57946014\",\n \"text\": \"def clean_paragraph(p):\\n\\n\\tif p.find_all(\\\"span\\\") == None:\\n\\t\\tp_text = p\\n\\telse:\\n\\t\\tfor span_tag in p.find_all(\\\"span\\\"):\\n\\t\\t\\tspan_tag.replace_with(\\\"\\\")\\n\\t\\tp_text = str(p)\\n\\tcorrected_p = re.sub(r' \\\\(.*?\\\\)', \\\"\\\", p_text)\\n\\treturn corrected_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60bbf591aad17c3da73f1146cab065d\",\n \"score\": \"0.5793579\",\n \"text\": \"def compact_text(data):\\n interim_codes = compact_text_interim(data)\\n return (_compact_chunk(chunk) for chunk in chunks(interim_codes, 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7546c60a27ce53548e27a01df5be84cc\",\n \"score\": \"0.57929564\",\n \"text\": \"def normalize_text(text):\\r\\n text = text.replace('\\\\r\\\\n', '\\\\n').replace('\\\\r', '\\\\n').expandtabs()\\r\\n if not text.endswith('\\\\n'):\\r\\n text += '\\\\n'\\r\\n\\r\\n # ignore BOM chars at the begining of template\\r\\n BOM = '\\\\xef\\\\xbb\\\\xbf'\\r\\n if isinstance(text, str) and text.startswith(BOM):\\r\\n text = text[len(BOM):]\\r\\n \\r\\n # support fort \\\\$ for backward-compatibility \\r\\n text = text.replace(r'\\\\$', '$$')\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b56abfb553c3d5584260c5b0cf4c18d3\",\n \"score\": \"0.57876676\",\n \"text\": \"def delete_chars(text, unwanted_chars):\\n for char in unwanted_chars:\\n text = text.replace(char, '')\\n return text\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":188,"cells":{"query_id":{"kind":"string","value":"ffb57bc6ba104f32dc98f65948a63fa8"},"query":{"kind":"string","value":"Returns best solution and energy after all runs"},"positive_passages":{"kind":"list like","value":[{"docid":"4e583b5900abb5f15a91f16db7969b24","score":"0.0","text":"def mws(model):\n \n def max_score_local(x_val, k = 0):\n \"\"\"\n Generates best neighbor upon mutating in one random direction\n Input: x_val to be mutated\n Output: neighbor with best score along index k in x_val\n \"\"\"\n steps = 10\n best_neigh = x_val[:]\n mutated_neigh = x_val\n if isinstance(model.max_x, list):\n step_max = model.max_x[k]\n step_min = model.min_x[k]\n else:\n step_max = model.max_x\n step_min = model.min_x\n increment = (step_max - step_min)/steps\n for i in xrange(steps):\n mutated_neigh[k] = step_min + increment*i\n mutated_neigh = model.ok(mutated_neigh)\n mutant_e = model.aggregator(mutated_neigh)\n best_e = model.aggregator(best_neigh)\n if mutant_e > best_e:\n best_neigh = mutated_neigh\n return best_neigh\n \n \n max_tries = 100\n max_changes = 50\n threshold = 100\n p = 0.5\n k = 0\n output = \" \"\n better_count = 0\n confused_count = 0\n best_count = 0\n init_solution, init_score = model.solution()\n #print init_solution, init_score\n print \"#\"*120\n print \"Running MWS for \", type(model).__name__\n print \"#\"*120\n print \"Constraints: \"\n print \"Lower Bound for x= \", model.min_x\n print \"Upper Bound for x= \", model.max_x\n print \"Max trials = %d, Max changes = %d, p = %0.2f, threshold = %d\" % (max_tries, max_changes, p, threshold)\n print \"-\"*120\n for i in range(max_tries):\n \n x_vec, x_score = model.solution()\n for j in range(max_changes):\n c = randrange(0, len(init_solution))\n if x_score > threshold:\n return x_vec\n\n if p < random():\n x_vec, x_score = model.mutate_solution(x_vec, c)\n output += \"?\"\n confused_count += 1\n \n else:\n out_x = max_score_local(x_vec, c)\n if out_x == x_vec:\n output += \".\"\n better_count += 1\n else:\n output += \"+\"\n x_vec = out_x\n best_count += 1\n \n if x_score > init_score:\n init_solution = x_vec\n init_score = x_score\n\n if k % 50 == 0: \n print \"eb = %6f | ? = %d | + = %d | . = %d | %s\" % (init_score, confused_count, best_count, better_count, output)\n output = \" \"\n better_count = 0\n confused_count = 0\n best_count = 0\n k = k + 1\n print \"-\"*120\n print \"Best solution: \", init_solution\n print \"Best energy: \", init_score","title":""}],"string":"[\n {\n \"docid\": \"4e583b5900abb5f15a91f16db7969b24\",\n \"score\": \"0.0\",\n \"text\": \"def mws(model):\\n \\n def max_score_local(x_val, k = 0):\\n \\\"\\\"\\\"\\n Generates best neighbor upon mutating in one random direction\\n Input: x_val to be mutated\\n Output: neighbor with best score along index k in x_val\\n \\\"\\\"\\\"\\n steps = 10\\n best_neigh = x_val[:]\\n mutated_neigh = x_val\\n if isinstance(model.max_x, list):\\n step_max = model.max_x[k]\\n step_min = model.min_x[k]\\n else:\\n step_max = model.max_x\\n step_min = model.min_x\\n increment = (step_max - step_min)/steps\\n for i in xrange(steps):\\n mutated_neigh[k] = step_min + increment*i\\n mutated_neigh = model.ok(mutated_neigh)\\n mutant_e = model.aggregator(mutated_neigh)\\n best_e = model.aggregator(best_neigh)\\n if mutant_e > best_e:\\n best_neigh = mutated_neigh\\n return best_neigh\\n \\n \\n max_tries = 100\\n max_changes = 50\\n threshold = 100\\n p = 0.5\\n k = 0\\n output = \\\" \\\"\\n better_count = 0\\n confused_count = 0\\n best_count = 0\\n init_solution, init_score = model.solution()\\n #print init_solution, init_score\\n print \\\"#\\\"*120\\n print \\\"Running MWS for \\\", type(model).__name__\\n print \\\"#\\\"*120\\n print \\\"Constraints: \\\"\\n print \\\"Lower Bound for x= \\\", model.min_x\\n print \\\"Upper Bound for x= \\\", model.max_x\\n print \\\"Max trials = %d, Max changes = %d, p = %0.2f, threshold = %d\\\" % (max_tries, max_changes, p, threshold)\\n print \\\"-\\\"*120\\n for i in range(max_tries):\\n \\n x_vec, x_score = model.solution()\\n for j in range(max_changes):\\n c = randrange(0, len(init_solution))\\n if x_score > threshold:\\n return x_vec\\n\\n if p < random():\\n x_vec, x_score = model.mutate_solution(x_vec, c)\\n output += \\\"?\\\"\\n confused_count += 1\\n \\n else:\\n out_x = max_score_local(x_vec, c)\\n if out_x == x_vec:\\n output += \\\".\\\"\\n better_count += 1\\n else:\\n output += \\\"+\\\"\\n x_vec = out_x\\n best_count += 1\\n \\n if x_score > init_score:\\n init_solution = x_vec\\n init_score = x_score\\n\\n if k % 50 == 0: \\n print \\\"eb = %6f | ? = %d | + = %d | . = %d | %s\\\" % (init_score, confused_count, best_count, better_count, output)\\n output = \\\" \\\"\\n better_count = 0\\n confused_count = 0\\n best_count = 0\\n k = k + 1\\n print \\\"-\\\"*120\\n print \\\"Best solution: \\\", init_solution\\n print \\\"Best energy: \\\", init_score\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"dc23f9d302e3c5b94328c54bbe84fee4","score":"0.7375325","text":"def solution(self):\n return self.Mbest","title":""},{"docid":"a7a458447c66af157f126dfa185c1878","score":"0.6866638","text":"def optimize(self):\n\n # Initiate particles\n self.__initPart()\n self.listOfPos = []\n\n NFC = 0\n while(NFC < self.maxGen):\n #print \"Run: \" + str(NFC) + \" Best: \" + str(self.globBestFit)\n\n # Perform search\n self.update()\n\n # Acceptably close to solution\n #if self.globBestFit < self.epsError:\n #return self.globBestPos, self.globBestFit\n\n # next gen\n NFC += 1\n self.listOfPos.append(self.globBestFit)\n # Search finished\n return self.globBestPos, self.globBestFit, self.listOfPos","title":""},{"docid":"8f7c273cb7bfaf99e55d77e2de198e94","score":"0.68366396","text":"def solve(self):\r\n\r\n nfev, nit, warning_flag = 0, 0, False\r\n status_message = _status_message['success']\r\n\r\n # calculate energies to start with\r\n for index, candidate in enumerate(self.population):\r\n parameters = self._scale_parameters(candidate)\r\n self.population_energies[index] = self.func(parameters,\r\n *self.args)\r\n nfev += 1\r\n\r\n if nfev > self.maxfun:\r\n warning_flag = True\r\n status_message = _status_message['maxfev']\r\n break\r\n\r\n minval = np.argmin(self.population_energies)\r\n\r\n # put the lowest energy into the best solution position.\r\n lowest_energy = self.population_energies[minval]\r\n self.population_energies[minval] = self.population_energies[0]\r\n self.population_energies[0] = lowest_energy\r\n\r\n self.population[[0, minval], :] = self.population[[minval, 0], :]\r\n\r\n if warning_flag:\r\n return OptimizeResult(\r\n x=self.x,\r\n fun=self.population_energies[0],\r\n nfev=nfev,\r\n nit=nit,\r\n message=status_message,\r\n success=(warning_flag != True))\r\n\r\n # do the optimisation.\r\n for nit in range(1, self.maxiter + 1):\r\n if self.dither is not None:\r\n self.scale = self.random_number_generator.rand(\r\n ) * (self.dither[1] - self.dither[0]) + self.dither[0]\r\n for candidate in range(np.size(self.population, 0)):\r\n if nfev > self.maxfun:\r\n warning_flag = True\r\n status_message = _status_message['maxfev']\r\n break\r\n\r\n trial = self._mutate(candidate)\r\n self._ensure_constraint(trial)\r\n parameters = self._scale_parameters(trial)\r\n\r\n energy = self.func(parameters, *self.args)\r\n nfev += 1\r\n\r\n if energy < self.population_energies[candidate]:\r\n self.population[candidate] = trial\r\n self.population_energies[candidate] = energy\r\n\r\n if energy < self.population_energies[0]:\r\n self.population_energies[0] = energy\r\n self.population[0] = trial\r\n\r\n # stop when the fractional s.d. of the population is less than tol\r\n # of the mean energy\r\n convergence = (np.std(self.population_energies) /\r\n np.abs(np.mean(self.population_energies) +\r\n _MACHEPS))\r\n\r\n if self.disp:\r\n print(\"differential_evolution step %d: f(x)= %g\"\r\n % (nit,\r\n self.population_energies[0]))\r\n\r\n if (self.callback and\r\n self.callback(self._scale_parameters(self.population[0]),\r\n convergence=self.tol / convergence) is True):\r\n\r\n warning_flag = True\r\n status_message = ('callback function requested stop early '\r\n 'by returning True')\r\n break\r\n\r\n if convergence < self.tol or warning_flag:\r\n break\r\n\r\n else:\r\n status_message = _status_message['maxiter']\r\n warning_flag = True\r\n\r\n DE_result = OptimizeResult(\r\n x=self.x,\r\n fun=self.population_energies[0],\r\n nfev=nfev,\r\n nit=nit,\r\n message=status_message,\r\n success=(warning_flag != True))\r\n\r\n if self.polish:\r\n result = minimize(self.func,\r\n np.copy(DE_result.x),\r\n method='L-BFGS-B',\r\n bounds=self.limits.T,\r\n args=self.args)\r\n\r\n nfev += result.nfev\r\n DE_result.nfev = nfev\r\n\r\n if result.fun < DE_result.fun:\r\n DE_result.fun = result.fun\r\n DE_result.x = result.x\r\n DE_result.jac = result.jac\r\n # to keep internal state consistent\r\n self.population_energies[0] = result.fun\r\n self.population[0] = self._unscale_parameters(result.x)\r\n\r\n return DE_result","title":""},{"docid":"a0117546e96810312ac163daf208b031","score":"0.6755187","text":"def search_optimal(self):\n\n for i in range(self.get_nsource()):\n ith_bee_fitness = self.bee[i].get_fitness()\n best_fitness = self.optimal.get_fitness()\n if best_fitness < ith_bee_fitness:\n self.suboptimal.greedy_choice(self.optimal)\n self.optimal.greedy_choice(self.bee[i])\n elif best_fitness == ith_bee_fitness:\n pass # protect updating suboptimal in this case\n elif self.suboptimal.get_fitness() < ith_bee_fitness:\n self.suboptimal.greedy_choice(self.bee[i])","title":""},{"docid":"f474c1ee4cea7c9306c18d7443490471","score":"0.6726404","text":"def _getBestSolution(obj):\n filename = _getOutputFilename(obj.folder_output + BEST_SOLUTION_FILENAME)\n obj.bestSolution = _loadCSV(filename)","title":""},{"docid":"6112d959c74a77caaeb6a8ef2f3e1704","score":"0.6724941","text":"def getstart_temp(self):\n\n assert(not self.dims is None)\n lrange = self.lower\n urange = self.upper\n fmax = _double_min\n fmin = _double_max\n for _ in range(self.Ninit):\n x0 = random.uniform(size=self.dims)*(urange-lrange) + lrange\n fval = self.func(x0, *self.args)\n self.feval += 1\n if fval > fmax:\n fmax = fval\n if fval < fmin:\n fmin = fval\n bestEnergy = fval\n bestSolution = array(x0)\n\n self.T0 = (fmax-fmin)*1.5\n return bestSolution, bestEnergy","title":""},{"docid":"57f818ca9f877589cbc25e77d1157ae1","score":"0.6616518","text":"def get_best():\n\n best_h = 3\n best_not = 3\n runs = 7\n\n np.save(\"H_START_S_BEST.npy\",0)\n np.save(\"NOT_START_S_BEST.npy\",0)\n np.save(\"H_START_S.npy\",0)\n np.save(\"NOT_START_S.npy\",0)\n\n for i in range(runs):\n \n # Run Sim-H\n sim_h.run_main_h()\n # Run Sim-NOT\n sim_not.run_main_not()\n\n hloss = run_waves(\"H\")\n print(\"Loss of H Gate\")\n print(hloss)\n nloss = run_waves(\"N\")\n print(\"Loss of NOT Gate\")\n print(nloss)\n\n # If this is a better H value, update\n if (hloss < best_h):\n best_h = hloss\n overwriteH()\n\n # If this is a better NOT value, update\n if (nloss < best_not):\n best_not = nloss\n overwriteNOT()\n\n\n h_waves_list = np.load(\"H_START_S_BEST.npy\")\n not_waves_list = np.load(\"NOT_START_S_BEST.npy\")\n\n return (h_waves_list, not_waves_list)","title":""},{"docid":"90862e6480a983a07b72ffac8f4a63f1","score":"0.6562416","text":"def solve(self):\n self.runtime = Timer() # Start the timer to measure the runtime\n self.model.min_to_max() # Convert minimization problems to maximization problems\n self.model.construct_payoff() # Construct a payoff table from the objective function values\n self.model.find_obj_range() # Find the range of each objective function\n self.model.convert_prob() # Convert the payoff table\n\n self._find_solutions() # Find all solutions to the optimization problem\n self._process_solutions() # Identify the unique solutions\n self._get_hv_indicator() # Compute the HV indicator\n\n # Save the results to an Excel file if requested\n if self.opts.output_excel:\n self._output_excel()\n\n # Compute the total runtime and print a summary of the results\n self.runtime = round(self.runtime.get(), 2)\n Helper.clear_line()\n print(\n f\"Solved {self.model.models_solved.value()} models for \"\n f\"{self.num_unique_pareto_sols} unique Pareto solutions in \"\n f\"{self.runtime} seconds\"\n )\n\n # Log a summary of the results\n self.logger.info(Helper.separator())\n self.logger.info(f\"Runtime: {self.runtime} seconds\")\n self.logger.info(f\"Models solved: {self.model.models_solved.value()}\")\n self.logger.info(f\"Infeasibilities: {self.model.infeasibilities.value()}\")\n self.logger.info(f\"Solutions: {self.num_sols}\")\n self.logger.info(f\"Unique solutions: {self.num_unique_sols}\")\n self.logger.info(f\"Unique Pareto solutions: {self.num_unique_pareto_sols}\")\n self.logger.info(f\"Hypervolume indicator: {self.hv_indicator}\")","title":""},{"docid":"7f57fe14d76194d4919d36fb73dcce81","score":"0.6554548","text":"def get_best_result(self):\n # self.result_data\n # raise NotImplementedError\n param_names = ['r', 'z', 'phi', 'scale', 'align', 'cloud']\n \n\n for chan in self.chan_list:\n for i in self.index:\n self.best_params[chan][i] = []\n #loops over all the fit waveforms\n # for model_num,single_wf_model in enumerate(self.wf_model):\n model_num = i\n single_wf_model = self.wf_model[i]\n print(\"Calculating best values for {}\".format(model_num))\n\n for j in range(single_wf_model.num_params):\n # loops over all the parameters\n param_values = self.result_data[i][j]\n\n param_avg = np.mean(param_values)\n param_std = np.std(param_values)\n\n self.best_params[model_num].append(param_avg)\n\n print(\"{name}: {avg:4.4f} +/- {std:4.4f}\".format(\n name=param_names[j], \n avg=param_avg, \n std=param_std)\n )","title":""},{"docid":"c22ab22848e4af7f6852a38c863bd374","score":"0.6523312","text":"def solve(self):\n fit_partial = self.fit_partial\n n_jobs = self.n_jobs\n gen = self.gen\n\n # get results in parallel\n all_res = Parallel(n_jobs=n_jobs)(\n delayed(fit_partial)(\n order=order,\n seasonal_order=seasonal_order,\n )\n for order, seasonal_order in gen\n )\n\n sorted_fits = _sort_and_filter_fits(all_res)\n if self.trace and sorted_fits:\n print(f\"\\nBest model: {str(sorted_fits[0])}\")\n\n return sorted_fits","title":""},{"docid":"390db28702298605d2d7b3a6236568e8","score":"0.6472718","text":"def run(self):\n self.time_start = time.time()\n self.step = 0\n self.step_idle = 0\n\n E = self.energy()\n\n prev_state = self.copy_state(self.state)\n prev_energy = E\n\n self.best_state = self.copy_state(self.state)\n self.best_energy = E\n self.best_step = 0\n self.energy_history = [E] * self.history_length\n Ehmean = E\n Ehvar = 0.\n Nvar = float(max(self.history_length - 1, 1))\n\n if self.updates_every > 0:\n self.update(self.step, self.step_idle, E, Ehmean, Ehvar)\n steps_since_update = 0\n\n while not self.terminate_search() and not self.user_exit:\n self.move()\n E = self.energy()\n\n if E >= prev_energy:\n self.step_idle += 1\n else:\n self.step_idle = 0\n\n v = self.step % self.history_length\n Ev = self.energy_history[v]\n if E < Ev or E <= prev_energy:\n # accept candidate state\n prev_state = self.copy_state(self.state)\n prev_energy = E\n\n if E < self.best_energy:\n self.best_state = self.copy_state(self.state)\n self.best_energy = E\n self.best_step = self.step\n else:\n # restore previous state\n self.state = self.copy_state(prev_state)\n E = prev_energy\n\n if E < Ev:\n # Update energy history\n self.energy_history[v] = E\n\n # and its mean and variance\n dE = E - Ev\n Ehmean_old = Ehmean\n\n Ehmean += dE / self.history_length\n Ehvar += dE * (E-Ehmean+Ev-Ehmean_old) / Nvar\n\n self.step += 1\n steps_since_update += 1\n\n if steps_since_update == self.updates_every:\n self.update(self.step, self.step_idle, E, Ehmean, Ehvar)\n steps_since_update = 0\n\n self.state = self.copy_state(self.best_state)\n if self.save_state_on_exit:\n self.save_state()\n\n self.time_end = time.time()\n return self.best_state, self.best_energy","title":""},{"docid":"7ec8c15419ad8bc00193cb7e262615f0","score":"0.646268","text":"def optimal(self):\n optimal_check = self.checker(True)\n return optimal_check","title":""},{"docid":"e8707ac8ab5a432499cd686262931a91","score":"0.6461675","text":"def get_best_solution(self):\n print \"getting best solution\"\n greatest_number_of_words = 0\n prelim_best_solutions = []\n for psol in self.potential_solutions:\n l = len(\" \".join(map(lambda x:x['title'],psol)))\n if l == greatest_number_of_words:\n prelim_best_solutions.append(psol)\n elif l > greatest_number_of_words:\n greatest_number_of_words = l\n prelim_best_solutions = [psol]\n best_solution = None\n least_number_of_tracks = 999999\n for bsol in prelim_best_solutions:\n if len(bsol) < least_number_of_tracks:\n least_number_of_tracks = len(bsol)\n best_solution = bsol\n return best_solution","title":""},{"docid":"dbbafe1f015464da4a494c735c33ee07","score":"0.6442411","text":"def getSolution(self):\n self.var_constr_dict = self.mapVarToConstraints()\n\n start = time.time()\n solution = self.solver.getSolution(self)\n self.runtime = time.time() - start\n # solution is an assignment of problem, where the assignments for all variables are filled in. \n return solution, self.getStatistics()","title":""},{"docid":"ede2c84a0104dd19a0e4737190f698ad","score":"0.63996756","text":"def save_best_sol(self):\n\n self.save(result=self.best_solutions_list)","title":""},{"docid":"b4ba86d89bccfe98a869ed480491d257","score":"0.6390985","text":"def optimize(self):\n rankedSol = self._rankSolutions()\n bestSol = rankedSol[0]\n # include best solution in next generation solutions\n newSols = [bestSol]\n\n for s in rankedSol[1:]:\n if np.random.uniform(0.0, 1.0) > 0.5:\n a = self._computeA()\n normA = np.linalg.norm(a)\n if normA < 1.0:\n newS = self._encircle(s, bestSol, a)\n else:\n # ##select random sol\n randomSol = self._sols[np.random.randint(self._sols.shape[0])]\n newS = self._search(s, randomSol, a)\n else:\n newS = self._attack(s, bestSol)\n newSols.append(self._constrain_solution(newS))\n\n self._sols = np.stack(newSols)\n self._a -= self._aStep","title":""},{"docid":"75c1eca33dce422738cb450fdee65c6e","score":"0.63894033","text":"def solver_optimize(self):\n parts = self.feats_obj.feat_partitions\n self.N_s = [None for i in parts]\n self.opt_sol = [None for i in parts]\n self.norm_z = [None for i in parts]\n self.compute_zero_stats()\n\n for i, partition in enumerate(parts):\n self.compute_data_stats(partition, i)\n self.compute_zc(partition, i)\n self.compute_theta(partition, i)\n\n # print('N_s is: ', self.N_s)\n # print('Z_c is: ', self.norm_z)\n # print('Theta is: ', self.opt_sol)","title":""},{"docid":"8d4b156c2e290526811e8a8e8d75a345","score":"0.6357096","text":"def _solve(self):\n #algorithm = self.config['algorithm'](self.config)\n \n self.output = self.algorithm.run(self.config['u_0'],self.config['TOL'],self.config['MAXIT'])\n print('Iterations:' + str(self.output['iter']))","title":""},{"docid":"98e45c5f527db22d70313ad80ec8673d","score":"0.6339497","text":"def solve(self):\n\n while True:\n self.iteration += 1\n\n logger.debug('iteration: n=%s', self.iteration)\n\n self._iterate()\n\n if self._converged():\n break\n\n return self._find_strongest_solution()","title":""},{"docid":"149015545ba83e85aff88252b4aa13f7","score":"0.63234216","text":"def run(self, iterations, run_from_start=True):\n if run_from_start:\n self.run_init()\n for _ in range(iterations):\n self._build_ant_solutions()\n self._update_iteration_best()\n self._update_global_best()\n self.global_pheromone_update(self.global_best)\n self.iteration += 1\n return self.global_best","title":""},{"docid":"7e2f056ef0ae1d598ecbc81e870cab69","score":"0.62895966","text":"def best_fitness(self):\n return self.fitness[self.order[-1]]","title":""},{"docid":"c0f291c79ddcfe9833db29d6e0a3b0f6","score":"0.62888795","text":"def _solve_impl(self):\n\n self.start_time = clock() # Used to show the total time took the process\n bellman_updates = 0 # used to track the performance improvement.\n converged = False\n iteration = 0\n\n num_states = self.representation.num_states_total\n\n while self.has_time() and not converged:\n iteration += 1\n\n # Store the weight vector for comparison\n prev_weight = self.representation.weight.copy()\n\n # Sweep The State Space\n for i in range(num_states):\n\n s = self.representation.stateID2state(i)\n # Sweep through possible actions\n if self.domain.is_terminal(s):\n continue\n for a in self.domain.possible_actions(s):\n\n self.bellman_backup(s, a, ns_samples=self.ns_samples)\n bellman_updates += 1\n\n # Create Log\n if bellman_updates % self.log_interval == 0:\n performance_return, _, _, _ = self.performance_run()\n self._log_updates(performance_return, bellman_updates)\n\n # check for convergence\n weight_diff = l_norm(prev_weight - self.representation.weight)\n converged = weight_diff < self.convergence_threshold\n\n # log the stats\n (\n perf_return,\n perf_steps,\n perf_term,\n perf_disc_return,\n ) = self.performance_run()\n self.logger.info(\n \"PI #%d [%s]: BellmanUpdates=%d, ||delta-weight_vec||=%0.4f, \"\n \"Return=%0.4f, Steps=%d\"\n % (\n iteration,\n hhmmss(deltaT(self.start_time)),\n bellman_updates,\n weight_diff,\n perf_return,\n perf_steps,\n )\n )\n\n # Show the domain and value function\n if self._visualize_mode:\n self.domain.show_learning(self.representation)\n\n # store stats\n self.result[\"bellman_updates\"].append(bellman_updates)\n self.result[\"return\"].append(perf_return)\n self.result[\"planning_time\"].append(deltaT(self.start_time))\n self.result[\"num_features\"].append(self.representation.features_num)\n self.result[\"steps\"].append(perf_steps)\n self.result[\"terminated\"].append(perf_term)\n self.result[\"discounted_return\"].append(perf_disc_return)\n self.result[\"iteration\"].append(iteration)\n\n if converged:\n self.logger.info(\"Converged!\")\n self.log_value()","title":""},{"docid":"d2b1c534c039f242d2a7c458ea6fb951","score":"0.6226838","text":"def calculateBest():\r\n\r\n if var.score>var.best:\r\n setNewBest()\r\n return var.score\r\n else: return var.best\r\n #if the actual score is bigger than the best score\r\n #set the best score to the actual score\r","title":""},{"docid":"c7fe40017c2980b624d976cb8e5d1632","score":"0.6216328","text":"def search(self, config):\n self.iteration = 0\n while self.iteration < self.maxIterations:\n self.GAStep(config)\n self.iteration += 1\n\n print (\"Total iterations: \",self.iteration)\n print (\"Best Solution: \", self.best.getFitness())","title":""},{"docid":"b0ba52f8f0040d777dcae8c696c03ef8","score":"0.6213454","text":"def _solution(self):\n return self.__solution","title":""},{"docid":"07d78b5b7da1a48b218ae692e26ffa46","score":"0.62130654","text":"def solve(self):\n \n # Do the actual run to find the fit and trendline\n self.fitModel = WarmingFitModel(self.dates, self.temps)\n self.constants = self.fitModel.solve()\n self.devs = self.fitModel.deviations()\n \n # Verify the optimality of solutions\n xvariabilities = { \"min\" : [], \"max\" : [] }\n if not self.DISABLE_VARIABILITY_CHECK:\n for op in xvariabilities.keys():\n for xsub in [0, 1]:\n variabilityModel = WarmingVariabilityModel(self.dates, self.temps, xsub, op, self.constants, sum(list(map(abs, self.devs))))\n xvariabilities[op].append(variabilityModel.solve())\n pprint.pprint(xvariabilities)\n \n # Verify the chosen value for solar cycle length\n cycleLengths = [9.7 + 0.2 * i for i in range(11)]\n cycleDevs = []\n if not self.DISABLE_CYCLE_LENGTH_CHECK:\n for cycleLength in cycleLengths:\n solarFitModel = WarmingFitModel(self.dates, self.temps, solarCycle=cycleLength)\n cycleDevs.append(sum([abs(dev) for dev in solarFitModel.deviations()]))\n pprint.pprint(cycleDevs)\n \n stdevs = [0 for x in self.constants]\n if not self.DISABLE_CONFIDENCE_INTERVALS:\n # Do a bunch more iterations to find a confidence interval for the original fit\n ITER_COUNT = 50\n random.seed()\n xstar = []\n for fuzzIter in range(ITER_COUNT):\n fuzzedTemps = [self.temps[i] + random.choice(self.devs) for i in list(range(len(self.dates)))]\n fuzzedModel = WarmingFitModel(self.dates, fuzzedTemps)\n fuzzedConstants = fuzzedModel.solve()\n print(\"Fuzzy constants: \" + str(fuzzedConstants))\n xstar.append(fuzzedConstants)\n \n # Actually compute the confidence interval\n if ITER_COUNT > 0:\n cMeans = [sum(fuzzedX[xpos] for fuzzedX in xstar) / float(ITER_COUNT) for xpos in range(len(self.constants))]\n for pos in range(len(self.constants)):\n cDevs = [(xstar[i][pos] - cMeans[pos]) ** 2 for i in range(ITER_COUNT)]\n cStdDev = math.sqrt(sum(cDevs) / float(len(cDevs)))\n stdevs[pos] = cStdDev\n \n return {\"fit\" : [(self.constants[i], 2 * stdevs[i]) for i in range(len(self.constants))], \\\n \"xvariabilities\" : xvariabilities, \\\n \"cycle\" : (cycleLengths, cycleDevs) }","title":""},{"docid":"21b1caf17582b8c1975b073d829c4e34","score":"0.62079585","text":"def find_solution(self):\n start_time = timer.time()\n result = []\n constraints = []\n \n ##############################\n # Task 2.4 : Addressing Failure\n #reordering agent priority using heuristic costs\n agent_max_cost = []\n for agent in range(self.num_of_agents):\n max_cost = -1\n for loc, cost in self.heuristics[agent].items():\n print(loc, cost)\n if cost > max_cost:\n max_cost = cost\n agent_max_cost.append([cost, agent])\n agent_max_cost.sort()\n agent_max_cost.reverse()\n \n for _, i in agent_max_cost:\n ##############################\n\n # original\n # for i in range(self.num_of_agents): # Find path for each agent\n \n path = a_star(self.my_map, self.starts[i], self.goals[i], self.heuristics[i],\n i, constraints)\n if path is None:\n raise BaseException('No solutions')\n result.append(path)\n \n print('Agent[{}]'.format(i), 'path = ', path)\n \n ##############################\n # Task 2: Add constraints here\n # Useful variables:\n # * path contains the solution path of the current (i'th) agent, e.g., [(1,1),(1,2),(1,3)]\n # * self.num_of_agents has the number of total agents\n # * constraints: array of constraints to consider for future A* searches\n\n # Task 2.1 : adding vertex constraints\n # for node in path:\n # constraint = dict()\n # constraint['agent'] = i\n # constraint['loc'] = [node[0], None]\n # constraint['timestep'] = node[1]\n # constraints.append(constraint)\n \n # build constraint from a path\n for p in range(len(path)):\n curr_loc = path[p]\n next_loc = None\n curr_timestep = p\n \n # vertex constraint\n constraint = dict()\n constraint['agent'] = i\n constraint['loc'] = [curr_loc, next_loc]\n constraint['timestep'] = curr_timestep\n constraints.append(constraint)\n \n # edge constraint except start cell\n if p > 0:\n prev_loc = path[p - 1]\n next_timestep = p\n \n constraint = dict()\n constraint['agent'] = i\n constraint['loc'] = [curr_loc, prev_loc]\n constraint['timestep'] = next_timestep\n constraints.append(constraint)\n \n ##############################\n \n # Task 2.3: additional constraints\n # agent_0 reached its goal cell at timestep 3,\n # but, agent_1 will reach to the goal cell at timestep 7\n # then, agent_0 will be wait \n \n # but, we don't know the value 7\n # so, just estimate this value with the prior knowledge\n # for example, # of a valid cell\n \n ##############################\n \n last_loc = path[-1]\n last_timestep = len(path) -1\n \n max_cells = 0\n \n for x in range(len(self.my_map)):\n for y in range(len(self.my_map[0])):\n if not self.my_map[x][y]:\n max_cells += 1\n \n for timestep in range(last_timestep + 1, max_cells):\n constraint = dict()\n constraint['agent'] = i\n constraint['loc'] = [last_loc, None]\n constraint['timestep'] = timestep\n constraints.append(constraint) \n\n self.CPU_time = timer.time() - start_time\n\n print(\"\\n Found a solution! \\n\")\n print(\"CPU time (s): {:.2f}\".format(self.CPU_time))\n print(\"Sum of costs: {}\".format(get_sum_of_cost(result)))\n print(result)\n return result","title":""},{"docid":"b65a27ab5b4a47ccc18024490760dc46","score":"0.61903894","text":"def result(self):\n return self.best.x","title":""},{"docid":"2e2e8b4ec0309484e14adeec64980705","score":"0.6185776","text":"def getBestSol(sqe, Res, c_ms):\n #---docstring------------------------------------------------------------------\n #---end-docstring--------------------------------------------------------------\n res = dc(Res)\n C_ms = dc(c_ms)\n lsqSc = numpy.zeros(len(res),'d') + numpy.inf \n lsqMu = numpy.zeros(len(res),'d') + numpy.inf \n lsqSl = numpy.zeros(len(res),'d') + numpy.inf\n for i in range(len(res)):\n try:\n zeroCut = sqe.zeroInd + 1\n sqeCut = sqe.zeroInd+res[i][1].cutoffInd\n slopeCut = sqe.slopeCut()\n sqeLin = sqe.se[sqeCut:slopeCut]\n mulSqeLin = nar(nar(res[i][0][1:]))[sqeCut:slopeCut]\n eLin = sqe.e[sqeCut:slopeCut]\n sqeSlope,inter = mfit.poly(eLin,sqeLin ,1)\n mulSlope,inter = mfit.poly(eLin,mulSqeLin,1)\n line = mfit.poly_of_x(eLin,mfit.poly(eLin,sqeLin ,1) )\n lsqSl[i] = (sqeSlope-mulSlope)**2.0\n lsqMu[i] = nar( (mulSqeLin - line )**2.0 /float(len(line)) )\n lsqSc[i] = nar( nar( (sqe.sqe - nar(res[i][0]) )**2.0 ) )\n except:\n pass\n \n start = 0\n while start<len(lsqSc) and \\\n (numpy.isnan(lsqSc[start]) or numpy.isinf(lsqSc[start]) \\\n or numpy.isnan(lsqMu[start]) or numpy.isinf(lsqMu[start]) \\\n or numpy.isnan(lsqSl[start]) or numpy.isinf(lsqSl[start])):\n start += 1\n \n C_ms = C_ms[start:]\n res = res[start:]\n lsqSc = lsqSc[start:]\n lsqMu = lsqMu[start:]\n lsqSl = lsqSl[start:]\n lsqSc_copy = numpy.array(lsqSc)\n lsqMu_copy = numpy.array(lsqMu)\n lsqSl_copy = numpy.array(lsqSl)\n numBetter = lambda x,c : len(filter(lambda y : y < 0.98*x,c) )\n for i in range(len(C_ms)):\n lsqSc_copy[i] = numBetter(lsqSc[i],lsqSc)\n lsqMu_copy[i] = numBetter(lsqMu[i],lsqMu)\n lsqSl_copy[i] = numBetter(lsqSl[i],lsqSl)\n lsqSc= numpy.array(lsqSc_copy )\n lsqMu= numpy.array(lsqMu_copy )\n lsqSl= numpy.array(lsqSl_copy )\n \n LSQ = (lsqSc + lsqMu + lsqSl)/3.0\n \n best = LSQ.argmin()\n sqeCalc = res[best][0]\n dosCalc = res[best][1]\n cmsCalc = res[best][2]\n return sqeCalc,dosCalc,cmsCalc,res,C_ms,lsqSc,lsqMu,lsqSl,LSQ","title":""},{"docid":"144adfb5462ee5a69aaeb4fed0faad88","score":"0.61844385","text":"def optimize(self):\n\n if self.scheme == \"linear\":\n iterations = floor(self.temp/self.cooling_rate)\n\n elif self.scheme == \"exp\":\n iterations = floor(log(1/self.temp)/log(1 - self.cooling_rate))\n\n for i in range(iterations):\n progress = round((i/iterations)*100, 2)\n if i % 20 == 0:\n print(f\"Progress: {progress}%\")\n if i == 0:\n self.get_costs()\n best_costs = copy.deepcopy(self.all_costs[0])\n best_option = copy.deepcopy(self.batteries)\n\n self.anneal()\n self.get_costs()\n\n if i > 0 and self.all_costs[-1] < best_costs:\n best_option = copy.deepcopy(self.batteries)\n best_costs = self.all_costs[-1]\n\n if self.scheme == \"linear\":\n self.temp = self.temp - self.cooling_rate\n elif self.scheme == \"exp\":\n self.temp *= 1 - self.cooling_rate\n\n self.batteries = best_option","title":""},{"docid":"8e51584fbf426cc78699e1fa846fb810","score":"0.61729866","text":"def post_optimization(pdb):\n print('Optimizing...')\n energies = optimize(pdb, os.path.abspath(os.getcwd()))\n print('Energy before optimizing: %s' % str(energies[0][0]))\n print('Energy after optimizing: %s' % str(energies[1][0]))\n print('Model completed')","title":""},{"docid":"5cbbdf5b657c88ce44e3b8c8d148fa37","score":"0.6171333","text":"def guessBest(self):\n #Get the true(past tries excluded) possible values for each value\n search_field = {i:(self.possibleValues[i] - self.impossibleValues[i]) for i in self.possibleValues}\n #Get the counts of times a number is a possible value @deprecated\n #counts = {i:sum(i in search_field[coord_values] for coord_values in search_field if len(search_field[coord_values])) for i in range(10)} \n #Get the coord with the least number of possible values\n bestCoord = min(search_field, key=lambda k: len(search_field[k]) if len(search_field[k]) > 1 else 10)\n #Apply the additional heuristic, sort the chosen coordinates possible values by how often those values could be other solutions @ deprecated\n #vals = sorted(search_field[bestCoord], key = lambda k: counts[k], reverse=True)\n vals = list(search_field[bestCoord])\n #print \"Guessing\", bestCoord, vals[0]\n #self.printData()\n self.setValue(bestCoord, guess=True, guessVal = vals[0])\n self.root = (bestCoord, self.getCoordValue(bestCoord))\n self.possibleValues[bestCoord] = {self.getCoordValue(bestCoord)}","title":""},{"docid":"379f6fcd7411622d26dc90b601fbb81e","score":"0.61595905","text":"def extra_convergence_criteria(self, results):\n # type: (List[Result]) -> bool\n self.iter_count += len(results)\n\n # Skip the setup period\n if not results:\n return False\n\n # Update the best local\n crtl_elapsed_time = 0\n self.best_unchange += len(results)\n valid_result = 0\n iter_best_result = None\n for result in results:\n latency = result.time\n resource = result.size\n crtl_elapsed_time = max(crtl_elapsed_time, result.elapsed)\n if latency == float('inf'):\n continue\n elif resource >= 1.0:\n continue\n\n valid_result += 1\n if (not iter_best_result) or latency < iter_best_result.time:\n iter_best_result = result\n\n self.elapsed_time += crtl_elapsed_time\n\n # Terminate this partition if we cannot get a valid\n # result from the seed design point.\n #if (not self.best_result and self.iter_count >= len(self.seeds)\n # and valid_result == 0):\n # return True\n\n # Update the history\n if iter_best_result:\n if ((not self.best_result) or\n iter_best_result.time < self.best_result.time):\n self.best_result = iter_best_result\n self.best_unchange = -1\n if self.best_result is not None:\n self.log_best(self.iter_count, self.elapsed_time,\n self.best_result.time)\n\n # Keep the best result of current iteration for computing\n # the entropy at the next iteration.\n self.prev_result = iter_best_result\n\n # Update the best global\n count = 1\n new_ranking = float('inf')\n if os.path.exists('.who_is_the_best'):\n with open('.who_is_the_best') as filep:\n for line in filep:\n latency = float(line.replace('\\n', ''))\n if self.best_result:\n if latency == float(self.best_result.time):\n # Found itself\n new_ranking = count\n count += 1\n new_ranking /= count\n\n # Add one thread as the manager requested\n if os.path.exists('.add_threads'):\n with open('.add_threads') as filep:\n line = filep.readline()\n addition_thread = int(line.replace('\\n', ''))\n self.more_parallelism(addition_thread)\n run_command('rm -f .add_threads')\n\n # Skip the warm up period\n if new_ranking == float('inf') or count < 4:\n return False\n\n # Skip the rest criteria to let partitions run as long as possible\n return False","title":""},{"docid":"32bff97f4800c45fadf86ac73a30a350","score":"0.61526996","text":"def get_best_values(self):\n return self.g_best_score, self.g_best_params","title":""},{"docid":"d035d1802dc286f7a043d398d9b8e29d","score":"0.6148896","text":"def optimize():\n stopwatch = StopWatch()\n\n ANNEAL_AREA = False\n logger = LoggerUtils.configure_log(name='Optimization script', use_console=True, use_file=True)\n\n click.clear()\n\n metrics = GeneticAlgorithmConstants();\n\n # Generate initial population\n population = Evolution.populate(initial=True)\n\n # Calculate fitness for each individual and sort them\n ranked_pop = Evolution.adaptability(population)\n \n generation = 0\n same_solution_count = 0\n optimal = {\"genome\": None, \"fitness\": None, \"links\": None}\n\n logger.info('Starting first optimization loop.')\n # The first optimization loop defines the number of nodes for each of the line instalation areas, \n # while keeping squared areas number of nodes constant\n\n # Main loop for the first optimization\n while(generation < metrics.MAX_GENERATIONS and same_solution_count != metrics.MAX_STAGNATED_OPTIMAL):\n generation += 1\n \n # Perform the crossover \n ranked_pop = Evolution.crossover(ranked_pop, metrics.CROSSOVER_PROBABILITY)\n\n # Mutate population\n ranked_pop = Evolution.mutate(ranked_pop, metrics.MUTATION_PROB)\n\n # Next generation\n population = [individual.get('genome') for individual in ranked_pop]\n\n # Calculate fitness for each individual and sort them\n ranked_pop = Evolution.adaptability(population)\n\n # Update the optimal value and count improvements\n if optimal.get('fitness') is None:\n optimal = copy.deepcopy(ranked_pop[0])\n elif ranked_pop[0].get('fitness') < optimal.get('fitness'):\n optimal = copy.deepcopy(ranked_pop[0])\n same_solution_count = 0\n else:\n same_solution_count += 1\n\n # Perform environment pressure\n ranked_pop = Evolution.environment_pressure(ranked_pop)\n \n logger.info(''.join(['[Gen {}] '.format(generation), 'Optimal:{solution: ', str(optimal.get('genome')), ', fitness: ', str(optimal.get('fitness')), \n ', min_links: ', str(optimal.get('links')), '}']))\n \n logger.info('Finished first optimization with %s generations. Total time: %s.' % (generation, stopwatch.read()))\n logger.info('OPTIMAL FOR FIRST OPTIMIZATION: N1: {}, N2: {}, N3: {}, N4: {} '.format(optimal['genome'][0], \n optimal['genome'][1], optimal['genome'][2], optimal['genome'][3]))\n \n # SECOND OPTIMIZATION ROUTINE:\n # Voronoi : generate optimized points distribution in an area.\n # L-BFGS-B : \n # POSSIBILITY: Model the second optimization problem as max f(x) = min SUM distances(i,j) \n\n # GETTING READY FOR SECOND OPTIMIZATION\n\n n1, n2, n3, n4 = optimal['genome']\n # network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4)\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, n4)\n\n area_nodes = n4\n generation = 0\n\n logger.info('Starting second optimization.')\n # The second optimization cares to find the min number of nodes within squared areas. At this part of the code\n # it's assumed that the min number of nodes for the line areas are known due the first optimization loop.\n\n logger.info('Initial genome [N1 = {}, N2 = {}, N3 = {}, N4 = {}].'. format(n1, n2, n3, n4))\n stopwatch_loop_2 = StopWatch()\n\n\n network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4) \n lines = ['N1', 'N2', 'N3', 'N4']\n nodeArray = [network.get('SINK')]\n for line in lines:\n nodeArray += list(network.get(line))\n # Plotter.plot_node_list(nodeArray, title='First Optimization Result', xLabel='Grid Coordinate (m)', yLabel='Grid Coordinate (m)')\n\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'first.geojson')\n\n # Main loop for the second optimization\n while generation < metrics.MAX_GENERATIONS and fitness.minValidLinks >= 3:\n # First find the min number n of nodes of node at each side of the nxn grid\n \n generation += 1\n area_nodes -= 1\n\n if area_nodes == 0:\n break\n\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, area_nodes)\n\n logger.info('[Gen {}] Fitness for N4 = {}: {}'.format(generation, area_nodes, fitness))\n\n # When the loop ends, area_nodes have a number with invalid fitness for n4.\n # So the correct min number is the result area_nodes + 1.\n n4 = area_nodes + 1\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, n4)\n\n logger.info('Finished second optimization with %s generations. Total time: %s.' % (generation, stopwatch_loop_2.read()))\n logger.info('Optimal for second optimization: N1: {}, N2: {}, N3: {}, N4: {} '.format(n1, n2, n3, n4))\n logger.info('Fitness for second optimization: {}'.format(fitness))\n\n # Retrieve network \n network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4)\n\n metrics = SelectiveAneelingConstants()\n\n networkArea = list(network.get('N4'))\n current_optimal_network_area = networkArea \n\n if len(networkArea) >= 2 :\n fitness = NetworkModel.getFitnessForNetwork(networkArea)\n generation = 1\n\n # Define area boundaries\n boundaries = dict()\n boundaries['MIN_X'] = GlobalParameters.N4_DIM.top_left[0]\n boundaries['MAX_X'] = GlobalParameters.N4_DIM.botom_right[0]\n boundaries['MIN_Y'] = GlobalParameters.N4_DIM.botom_right[1]\n boundaries['MAX_Y'] = GlobalParameters.N4_DIM.top_left[1]\n\n if ANNEAL_AREA: logger.info('Starting third optimization. Selective aneeling')\n\n # Finds the optimal number of nodes within area maximizing the distance between them\n while generation < metrics.MAX_GENERATIONS and fitness.minValidLinks >= 2 and len(networkArea) >= 2 and ANNEAL_AREA:\n\n current_optimal_network_area = networkArea\n networkArea.pop()\n\n # Configuring Annealer\n annealer = DistanceAnnealing(networkArea, boundaries)\n annealer.copy_strategy = metrics.COPY_STRATEGY\n\n # Calibrating Annealer\n click.secho('Calibrating annealer', fg='yellow')\n auto_schedule = annealer.auto(minutes=1)\n annealer.set_schedule(auto_schedule)\n click.secho('Annealer calibrated! Parameters: {}'.format(auto_schedule), fg='green')\n\n networkArea, energy = annealer.anneal()\n \n fitness = NetworkModel.getFitnessForNetwork(networkArea)\n\n logger.info(\"[GEN {}] Number of Links: {} Energy: {}, fitness: {}\".format(len(networkArea), generation, energy, fitness))\n\n generation += 1\n \n \n logger.info('Finished second optimization. Nodes positions inside area defined. {} nodes installed.'.format(len(current_optimal_network_area)))\n\n # Saving the new area node disposition\n network['N4'] = np.array(current_optimal_network_area)\n\n # Adding all nodes of the network in one array\n\n lines = ['N1', 'N2', 'N3', 'N4']\n nodeArray = [network.get('SINK')]\n\n for line in lines:\n nodeArray += list(network.get(line))\n \n Plotter.plot_node_list(nodeArray, add=True, annotate=False)\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'second.geojson')\n \n logger.info('Annealing nodes. Current configuration have %s nodes.' %(len(nodeArray)))\n annealer = PositionAnnealing(nodeArray)\n annealer.copy_strategy = metrics.COPY_STRATEGY\n\n # click.secho('Calibrating annealer.', fg='yellow')\n\n # auto_schedule = annealer.auto(minutes=1)\n # annealer.set_schedule(auto_schedule)\n\n # click.secho('Annealer calibrated!', fg='green')\n # logger.info('Annealer parameters: {}'.format(auto_schedule))\n\n annealer.Tmax=1.5\n annealer.Tmin=1e-10\n\n nodeArray, energy = annealer.anneal()\n\n network[line] = nodeArray\n \n click.secho('Finished Optimization', fg='green')\n logger.info('Finished network optimization. Total elapsed time: {}'.format(stopwatch.read()))\n logger.info('Writing result geoJSON at {}.'.format(OUTPUT_PATH))\n\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'result.geojson')\n\n Plotter.plot_node_list(nodeArray, title='Representação gráfica da Rede', xLabel='Longitude', yLabel='Latitude',\n color='red', annotate=False)","title":""},{"docid":"8a6b2d1b0b101462372338089711edf7","score":"0.61201286","text":"def calc_energy(self, force=True):\n if self.atoms_moved:\n self.timer.switch_on(\"energy\")\n if self.qeq:\n self.timer.switch_to(\"qeq\")\n self.qeq.preopt_q()\n self.timer.switch_to(\"energy\")\n if self.virtual_atoms:\n self.timer.switch_to(\"virt atoms set pos\")\n self.virt_atom_set_pos()\n self.timer.switch_to(\"energy\")\n if self.enforce_nlist_rebuild:\n # if some operation (move atoms/molecules around) could break the neigborlist enforce its rebuild\n self.dlp_nlst.nlst_notest = True\n self.dlp_nlst.nlst_rebuild = True\n ### nullify stress tensor\n self.dlp_conf.stress[:]=0.0\n ### MAIN ROUTINE TO COMPUTE ENERGY AND FORCE IN DL_POLY ###\n self.dlp.calc_energy_force()\n ### ################################################### ###\n ### MAIN ROUTINE TO COMPUTE ENERGY AND FORCE IN DL_POLY ###\n if self.QMMM:\n self.timer.switch_to(\"qmmm\")\n self.QMMM_eng = self.QMMM_interface(force)\n self.timer.switch_to(\"energy\")\n # for a QMMM run we report the energies every step\n self.report_energies()\n if self.virtual_atoms:\n self.timer.switch_to(\"virt atoms dist force\")\n self.virt_atom_distribute_force()\n self.timer.switch_to(\"energy\")\n self.atoms_moved = False\n if self.enforce_nlist_rebuild:\n self.dlp_nlst.nlst_notest = False\n self.dlp_nlst.nlst_rebuild = False\n self.enforce_nlist_rebuild = False\n# if self.QMMM:\n# self.timer.switch_to(\"qmmm\")\n# self.QMMM_eng = self.QMMM_interface(force)\n# self.timer.switch_to(\"energy\")\n# # for a QMMM run we report the energies every step\n# self.report_energies()\n if self.extra_term:\n self.timer.switch_to(\"extra term\")\n self.extra_eng = self.extra_term()\n self.timer.switch_to(\"energy\")\n if self.plumed:\n self.timer.switch_to(\"metamd plumed\")\n self.meta_eng = self.plumed_energy()\n self.timer.switch_to(\"energy\")\n if self.constraint:\n self.multiply_force(self.constraint_mask)\n for s in self.extra_systems:\n self.timer.switch_to(s.get_name())\n s.calc_energy()\n self.timer.switch_to(\"energy\")\n self.timer.switch_off()\n # return configuration energy in current units\n energy = self.dlp.engcfg/self.dlp.engunit\n if self.QMMM: energy += self.QMMM_eng\n if self.extra_term: energy += self.extra_eng\n if self.plumed: energy += self.meta_eng\n return energy","title":""},{"docid":"8abf4c4ebfe4d352c7ad6e941203b1e7","score":"0.61065435","text":"def get_solutions_fitness(self, solution):\n value = self.get_solutions_value(solution)\n return self.get_fitness(value)","title":""},{"docid":"9d65270fe6d8d9e8590e16fe04479aff","score":"0.6098882","text":"def compute_solution(self,dataset):\n\t\tpass","title":""},{"docid":"50508a445861c4588a2e65c595bf6459","score":"0.60829246","text":"def getOwnBestFitness(self):\r\n\t\t\r\n\t\treturn self.ownBestFitness","title":""},{"docid":"af4add5b38f8e282631cc6b0b016fc55","score":"0.6068796","text":"def optimal(self):\n return np.max(self.expected_rewards())","title":""},{"docid":"330e6fdf9357f731486615c89f7e2c32","score":"0.6067864","text":"def getBestFit(self):\n self.envLock.acquire()\n best = self.environment.getMostAdapted()\n self.envLock.release()\n return best","title":""},{"docid":"380cba45db1ed8aaf173abe49ca24a3a","score":"0.6064149","text":"def get_solution(self):\n index = -1\n max_f = -math.inf\n\n for i in range(self.N):\n if self.train_v[i] > 1.2 and self.train_f[i] > max_f:\n max_f = self.train_f[i]\n index = i\n return self.train_x[index]","title":""},{"docid":"4596652f65f223725709173384ec358d","score":"0.6045643","text":"def greedy():\n\tglobal table, weights, values, capacity, max_value, best_solution, unit_values\n\tunit_values = [((values[i]*1.0)/weights[i],i,weights[i],values[i]) for i in xrange(len(weights))]\n\tunit_values.sort()\n\tinv_sort_order = [(unit_values[i][1],i) for i in xrange(len(unit_values))]\n\tinv_sort_order.sort()\n\tsort_order = [x[1] for x in inv_sort_order]\n\t\n\tweight = 0\n\tmax_value = 0\n\tbest_solution = [0]*len(weights)\n\ti = len(weights) - 1\n\twhile(weight < capacity and i >= 0):\n\t\tif weight + unit_values[i][2] <= capacity:\n\t\t\tbest_solution[i] = 1\n\t\t\tmax_value += unit_values[i][3]\n\t\t\tweight += unit_values[i][2]\n\t\telse:\n\t\t\tbest_solution[i]=0\n\t\ti -= 1\n\n\tbest_solution = [best_solution[i] for i in sort_order]\n\t\n\treturn {'value':max_value, 'taken':best_solution, 'optimality':0}","title":""},{"docid":"af394f586b631d9fba6859afb2e6cc75","score":"0.60455203","text":"def solve(self):\n convergence = False\n val = 0\n for i in range(self.maxiter):\n grad = self.dR()\n self.x = self.x - self.optStep()*grad\n if np.sum(np.abs(grad)) < self.eps*(np.log(self.x.shape[0])):\n convergence = True\n break\n\n if not convergence:\n print('WARNING: Did not converge. Try increasing maxiter or a smaller eps.') \n\n return(self.eig,self.x)","title":""},{"docid":"dc50367fe11ae1b2a1e4fd8e6b9c5b37","score":"0.60270655","text":"def betterEvaluationFunction(currentGameState):\n \"*** YOUR CODE HERE ***\"\n #more food = worse score\n #closer food = good score\n #closer than 3 ghost = bad score\n \n foodGrid = currentGameState.getFood()\n food = foodGrid.asList()\n pos = currentGameState.getPacmanPosition()\n ghosts = []\n gdists = []\n for i in range(1, currentGameState.getNumAgents()):\n g = currentGameState.getGhostPosition(i)\n ghosts.append(g)\n gdists.append(manhattanDistance(pos, g))\n \n #fterm = len(food)\n fterm = foodGrid.width*foodGrid.height-len(food)\n #gterm = closest(pos, ghosts)[0]\n gterm = 0\n fdist, floc = closest(pos, food)\n \n # breadth first search\n #problem = PositionSearchProblem(currentGameState, goal=floc, start=pos, warn=False)\n #realDist = len(breadthFirstSearch(problem))\n \n dterm = 1/float(fdist+1)\n \n #coefficients\n a = 10\n b = 1\n c = 15\n\n for d in gdists:\n if d<3:\n gterm-=50\n if d<2:\n gterm-=50\n \n total = a*fterm + b*gterm + c*dterm\n \n # break ties\n #if right:\n # total+=2\n #if up:\n # total+=1\n return total","title":""},{"docid":"287e49f8017272b659afe2b7a22ade44","score":"0.60227233","text":"def solve(self):","title":""},{"docid":"80d1d5c90fceaa679fdec499b70a5175","score":"0.60214704","text":"def evaluate_best_agent(self):\n\n eval_eps = Config.EVALUATION_EPISODES\n reward_sum = 0\n log_list = []\n\n print(\"Evaluate The Best Network with Test data: episode \", self.best_episode, \" reward \", self.best_reward)\n self.restore_best_network_to_train_network()\n\n for i in range(eval_eps):\n [reward, dist, trainsize, pred] = self.evaluate(isValidation=False)\n reward_sum = reward_sum + reward\n log = {\n \"episode\": None,\n \"top_reward\": reward,\n \"exp_rate\": None,\n \"trainsize\": trainsize,\n \"top_dist\": dist,\n \"top_pred\": pred\n }\n log_list.append(log)\n\n AgentLogger.print_trianing_results(log)\n\n mean_reward = reward_sum/eval_eps\n print(\"Mean: \", mean_reward)\n AgentLogger.log_evaluation_results(log_list, self.logger, self.best_episode, self.best_reward, mean_reward)","title":""},{"docid":"2147cc31fc145610dbcba7b9c5f6d2ea","score":"0.6020431","text":"def find_best_object_and_behavior(self):\n potential_event_deflection = np.inf\n\n if not (self.model.network_structure is None):\n # check if network structure was passed and only consider\n # candidate objects to which actor has link\n possible_ids = [i for i,link\n in enumerate(self.model.network_structure[self.unique_id])\n if link == 1]\n potential_objects = [ag for ag in self.model.schedule.agents\n if ag.unique_id in possible_ids]\n if self.model.action_on_group:\n potential_objects += [self.model.group]\n else:\n potential_objects = [ag for ag in self.model.schedule.agents\n if ag.unique_id != self.unique_id]\n if self.model.action_on_group:\n potential_objects += [self.model.group]\n\n if self.model.object_choice == \"random\":\n potential_object = np.random.choice(potential_objects)\n potential_action = compute_opt_behavior(self, potential_object)\n potential_transients = compute_transients(\n self.current_transients,\n potential_action,\n potential_object.current_transients,\n self.model.abo_coefficients)\n else:\n np.random.shuffle(potential_objects)\n\n for obj in potential_objects:\n opt_beh = compute_opt_behavior(self, obj)\n transients = compute_transients(self.current_transients,\n opt_beh, obj.current_transients,\n self.model.abo_coefficients)\n\n if self.model.object_choice == \"min event-tension\":\n fundamentals_stack = np.hstack((self.fundamentals, opt_beh,\n obj.fundamentals))\n event_deflection = np.linalg.norm(fundamentals_stack\n - transients)**2\n\n elif self.model.object_choice == \"max deflection-reduction\":\n actor_deflection_diff = (\n np.linalg.norm(self.fundamentals - transients[:3])**2\n - self.personal_deflection\n )\n beh_deflection = np.linalg.norm(opt_beh - transients[3:6])**2\n # we include the actual deflection reduction of all agents\n # in the object choice, not just the group object\n if obj.unique_id == -1:\n object_deflection_diff = (\n np.linalg.norm(obj.fundamentals - transients[6:])**2\n - obj.personal_deflection\n )\n for ag in [agent for agent in self.model.schedule.agents\n if agent.unique_id != self.unique_id]:\n transients_g = compute_transients(\n self.current_transients, opt_beh,\n ag.current_transients,\n self.model.abo_coefficients\n )\n object_deflection_diff+= (\n np.linalg.norm(ag.fundamentals - transients_g[6:])**2\n - ag.personal_deflection\n )\n else:\n object_deflection_diff = (\n np.linalg.norm(obj.fundamentals - transients[6:])**2\n - obj.personal_deflection\n )\n\n event_deflection = ( actor_deflection_diff\n + beh_deflection\n + object_deflection_diff )\n else:\n print(\"unknown or no object selection criterion specified\")\n\n # check if deflection for this behavior is smaller\n # than for other behaviors\n if event_deflection < potential_event_deflection:\n potential_event_deflection = event_deflection\n potential_transients = transients\n potential_object = obj\n potential_action = opt_beh\n\n return (potential_object, potential_action, potential_transients)","title":""},{"docid":"45ccb88b554cf570c6a44f750a0c3ef0","score":"0.60071445","text":"def run(self):\n self.logger.info(\"\\n\\n************************* Genetic Algorithm *************************\")\n self.logger.info(f\"Objective: {self.objective}\")\n self.logger.info(f\"============================ Execution with seed: {self._seed} ============================\")\n self.logger.info(f\"{time.strftime('%d/%m/%Y %H:%M:%S')}\\n\\n\")\n\n random.seed(self._seed)\n np.random.seed(self._seed)\n\n self._generate_initial_population()\n self._population_statistic()\n self._print_population_info(self.new_pop, 0)\n\n best_fitness = self.new_pop.bestFitness\n count_patience = 0\n\n compare_op_obj = partial(ge) if self.objective == 'maximization' else partial(le)\n compare_op_best = partial(gt) if self.objective == 'maximization' else partial(lt)\n\n for gen in range(1, self.nr_max_gen + 1):\n self._generating_new_population()\n self._population_statistic()\n self._print_population_info(self.new_pop, gen)\n\n if compare_op_best(self.new_pop.bestFitness, best_fitness):\n best_fitness = self.new_pop.bestFitness\n count_patience = 0\n else:\n count_patience += 1\n\n if self.objective_fitness is not None:\n if compare_op_obj(self.new_pop.bestFitness, self.objective_fitness):\n self.logger.info(f\"Stop because found the objective fitness {self.objective_fitness}\")\n break\n\n if self.patience > 0:\n if count_patience >= self.patience:\n self.logger.info(f\"Stop because patience limit\")\n break\n\n self.logger.info(f\"Best solution found: {self._statistic[-1]['best_indiv']}\")\n save_statistic(self.output_dir, self._statistic)","title":""},{"docid":"ef7acdb6e0620fe4b000accc940aa89b","score":"0.5998345","text":"def get_energy(self):\n\n D, self.e, self.C = self.build_density(self.H)\n\n E_old = 0.0\n F_old = None\n\n for iteration in range(self.max_iter):\n\n J = np.einsum(\"pqrs, rs -> pq\", self.g, D)\n K = np.einsum(\"prqs, rs -> pq\", self.g, D)\n\n # F = H_pq + 2 * G_pqrs D_rs - G_prqs D_rs\n F = self.H + 2.0 * J - K\n\n if self.damping == 'on' and iteration >= self.damp_start:\n F = self.damp(F_old, F)\n\n F_old = F\n\n if self.diis == 'on' and iteration >= self.diis_start:\n F = diis_helper.diis(F, D, self.diis_start,self.diis_vector)\n\n E_electric = np.sum((F + self.H) * D)\n self.E = E_electric + self.mol.nuclear_repulsion_energy()\n\n E_diff = self.E - E_old\n E_old = self.E\n\n # Build the AO gradient\n grad = F @ D @ self.S - self.S @ D @ F\n grad_rms = np.mean(grad ** 2) ** 0.5\n\n D, self.e, self.C = self.build_density(F)\n\n print(\"Iteration: %3d Energy: % 16.12f Energy difference: % 8.4f Gradient difference: % 8.4f\" % (iteration, self.E, E_diff,grad_rms))\n\n if E_diff < self.e_conv and grad_rms < self.d_conv:\n break\n\n print(\"SCF has finished! \\n\")","title":""},{"docid":"7b24c4d70a946f3b9b496e46c1a8767e","score":"0.5992288","text":"def initPopulation(self):\n for i in range(0, self.popSize):\n individual = Individual(self.genSize, self.data)\n individual.computeFitness()\n self.population.append(individual)\n\n self.best = self.population[0].copy()\n for ind_i in self.population:\n if self.best.getFitness() > ind_i.getFitness():\n self.best = ind_i.copy()\n print (\"Best initial sol: \",self.best.getFitness())","title":""},{"docid":"65ed1251824de1d9a976769dd87cfabf","score":"0.59906185","text":"def _find_solutions(self):\n # Set progress message\n self.model.progress.set_message(\"finding solutions\")\n\n # Generate grid of indices to search for solutions\n grid_range = range(self.opts.gp)\n indices = [tuple([n for n in grid_range]) for _ in self.model.iter_obj2]\n self.cp = list(itertools.product(*indices))\n self.cp = [i[::-1] for i in self.cp]\n\n # Pickle the model and initialize queue and process handlers\n self.model.pickle()\n self.queues = QueueHandler(self.cp, self.opts)\n self.queues.split_work()\n self.procs = ProcessHandler(self.opts, self.model, self.queues)\n\n # Start processes and wait for results\n self.procs.start()\n self.unprocessed_sols = []\n while not self.procs.join():\n self.unprocessed_sols.extend(self.queues.get_result())\n # One last sweep to make sure all results have been collected\n self.unprocessed_sols.extend(self.queues.get_result())\n\n # In case all worker processes were killed, not all work may have been done.\n if self.procs.any_killed:\n raise Exception(\"At least one worker exited prematurely, not all computations may have been done.\")\n\n # Clean the pickled model\n self.model.clean()","title":""},{"docid":"7fb69b984732c6d732aacba92def1e51","score":"0.5982387","text":"def solution_most_iterations_done(self) -> int:\n return int(self.dss_obj.SolutionI(ctypes.c_int32(41), ctypes.c_int32(0)))","title":""},{"docid":"17959bc5f94063bea50630dbccfdc9af","score":"0.59580135","text":"def solve(self, problem):\r\n recommended_solns = []\r\n intermediate_budgets = []\r\n expended_budget = 0\r\n delta_max = self.factors[\"delta_max\"]\r\n gamma_0 = self.factors[\"gamma_0\"]\r\n delta_candidate = [gamma_0 * delta_max, delta_max, delta_max / gamma_0]\r\n #print(delta_candidate)\r\n\r\n # default values\r\n eta_1 = self.factors[\"eta_1\"]\r\n eta_2 = self.factors[\"eta_2\"]\r\n gamma_1 = self.factors[\"gamma_1\"]\r\n gamma_2 = self.factors[\"gamma_2\"]\r\n k = 0 # iteration number\r\n\r\n # Start with the initial solution\r\n new_x = problem.factors[\"initial_solution\"]\r\n new_solution = self.create_new_solution(new_x, problem)\r\n recommended_solns.append(new_solution)\r\n intermediate_budgets.append(expended_budget)\r\n\r\n # Parameter tuning run\r\n tp_final_ob_pt, k, delta, recommended_solns, intermediate_budgets, expended_budget, new_x = self.parameter_tuning(\r\n delta_candidate[0], problem)\r\n for i in range(1, 3):\r\n final_ob_pt, k_pt, delta_pt, recommended_solns_pt, intermediate_budgets_pt, expended_budget_pt, new_x_pt = self.parameter_tuning(\r\n delta_candidate[i], problem)\r\n expended_budget += expended_budget_pt\r\n if -1 * problem.minmax[0] * final_ob_pt < -1 * problem.minmax[0] * tp_final_ob_pt:\r\n k = k_pt\r\n delta = delta_pt\r\n recommended_solns = recommended_solns_pt\r\n intermediate_budgets = intermediate_budgets_pt\r\n new_x = new_x_pt\r\n\r\n intermediate_budgets = (\r\n intermediate_budgets + 2 * np.ones(len(intermediate_budgets)) * problem.factors[\"budget\"] * 0.01).tolist()\r\n intermediate_budgets[0] = 0\r\n\r\n while expended_budget < problem.factors[\"budget\"]:\r\n k += 1\r\n fval, Y, q, grad, Hessian, delta_k, expended_budget = self.model_construction(new_x, delta, k, problem,\r\n expended_budget)\r\n\r\n # Cauchy reduction\r\n if np.matmul(np.matmul(grad, Hessian), grad) <= 0:\r\n tau = 1\r\n else:\r\n tau = min(1, norm(grad) ** 3 / (delta * np.matmul(np.matmul(grad, Hessian), grad)))\r\n\r\n grad = np.reshape(grad, (1, problem.dim))[0]\r\n candidate_x = new_x - tau * delta * grad / norm(grad)\r\n\r\n for i in range(problem.dim):\r\n if candidate_x[i] < problem.lower_bounds[i]:\r\n candidate_x[i] = problem.lower_bounds[i] + 0.01\r\n elif candidate_x[i] > problem.upper_bounds[i]:\r\n candidate_x[i] = problem.upper_bounds[i] - 0.01\r\n\r\n candidate_solution = self.create_new_solution(tuple(candidate_x), problem)\r\n\r\n # adaptive sampling needed\r\n problem.simulate(candidate_solution, 1)\r\n expended_budget += 1\r\n sample_size = 1\r\n\r\n # Adaptive sampling\r\n while True:\r\n problem.simulate(candidate_solution, 1)\r\n expended_budget += 1\r\n sample_size += 1\r\n sig2 = candidate_solution.objectives_var\r\n if sample_size >= self.samplesize(k, sig2, delta_k):\r\n break\r\n\r\n # calculate success ratio\r\n fval_tilde = -1 * problem.minmax[0] * candidate_solution.objectives_mean\r\n\r\n # replace the candidate x if the interpolation set has lower objective function value\r\n if min(fval) < fval_tilde:\r\n minpos = fval.index(min(fval))\r\n fval_tilde = min(fval)\r\n candidate_x = Y[minpos][0]\r\n\r\n if (self.local_model_evaluate(np.zeros(problem.dim), q) - self.local_model_evaluate(\r\n np.array(candidate_x) - np.array(new_x), q)) == 0:\r\n rho = 0\r\n else:\r\n rho = (fval[0] - fval_tilde) / (\r\n self.local_model_evaluate(np.zeros(problem.dim), q) - self.local_model_evaluate(\r\n candidate_x - new_x, q));\r\n\r\n if rho >= eta_2: # very successful\r\n new_x = candidate_x\r\n delta_k = min(gamma_1 * delta_k, delta_max)\r\n recommended_solns.append(candidate_solution)\r\n intermediate_budgets.append(expended_budget)\r\n elif rho >= eta_1: # successful\r\n new_x = candidate_x\r\n delta_k = min(delta_k, delta_max)\r\n recommended_solns.append(candidate_solution)\r\n intermediate_budgets.append(expended_budget)\r\n else:\r\n delta_k = min(gamma_2 * delta_k, delta_max)\r\n\r\n return recommended_solns, intermediate_budgets","title":""},{"docid":"2df6e82235fe60278712b06fb8d042ae","score":"0.5955973","text":"def calculate(self):\n # initialize transformation from canonic to energy\n # optimal states (if necessary)\n if self.W_mn is None:\n self.initialize_orbitals(rattle=self.rattle)\n\n # optimize the non-unitary invariant part of the\n # functional\n self.unitary_optimization()\n\n return self.esic, self.ekin","title":""},{"docid":"ad29ddb80d7682684f4cf035f8b96f23","score":"0.59508735","text":"def overall_fitness(self):\n # Use a decyaing function to generate fitness\n # Use two step decaying function\n # First block gives importance to exploration and when as soon\n # food has been found, the next block will focus on dropping\n # the food on hub\n self.individual[0].fitness = (1 - self.beta) * self.delayed_reward \\\n + self.exploration_fitness() + self.carrying_fitness() \\\n + self.food_collected","title":""},{"docid":"faa0eaf976005e56d162f1b3f95a98d0","score":"0.5949581","text":"def solveAll(self):\r\n return None","title":""},{"docid":"b4f00dadad3e1e7502978585f5eeb041","score":"0.5946176","text":"def optimzeGeneralParameters():\n\t#initialize qm energies dictionaries for each molecule\n\tglobal qm_energies\n\tqm_energies = {mol : {} for mol in molecules}\n\n\t#set parameters for minimize function\n\tmaxiter = 5000\n\tpgtol = -1e-17\n\tftol = 1e-17\n\n\t#get parameter bounds for minimization\n\tall_bnds = make_bounds_list(initialValuesList, True)\n\ta_bnds = make_bounds_list(initialValuesList, False)\n\n\t#optimize A parameters for each energy component, store in final_params list\n\tparameterList = initialValuesList\n\tmetaPOInter_initial_list = remove_C_params(parameterList)\n\tglobal fitting_component\n\twhile fitting_component < 4:\n\n\t\t#make list of bounds for component to pass down to POInter. Only include B bound if fitting exchange\n\t\tif fitting_component == 0:\n\t\t\tbnds = all_bnds\n\t\telse:\n\t\t\tbnds = a_bnds\n\n\t\t#make list of parameters for component to pass down to POInter\n\t\t#get only parameters for the component we are fitting\n\n\t\t#TODO: make so user can choose whether or not to fit B params\n\t\t#TODO: make sure input B params are the same for every molecule\n\t\t\t#TODO: use same .exp file ()\n\t\tmetaPOInter_input = get_component_parameters(metaPOInter_initial_list)\n\n\t\tres = minimize(metaPOInter,metaPOInter_input,method='L-BFGS-B',\\\n\t\t\t\t\t\t\tjac=True,\\\n\t\t\t\t\t\t\toptions={'disp':True,'gtol':pgtol,'ftol':ftol,'maxiter':maxiter},\\\n\t\t\t\t\t\t\tbounds = bnds)\n\n\t\tpopt = res.x\n\t\tprint \"Optimized parameters for component \" + str(fitting_component) + \" : \" + str(popt)\n\n\t\tif not res.success:\n\t\t\tprint 'Warning! Optimizer did not terminate successfully, and quit with the following error message:'\n\t\t\tprint\n\t\t\tprint res.message\n\t\t\tprint\n\t\t\treturn\n\n\t\tglobal optimized_params\n\t\toptimized_params[energyComponents[fitting_component]] = popt\n\n\t\t#reset new B params in POInter objects after fitting exchange component\n\t\tif fitting_component == 0:\n\t\t\tupdated_B_params = get_updated_B_from_exchange(popt)\n\t\t\tfor mol in molecules.keys():\n\t\t\t\ti = 0\n\t\t\t\tfor atomtype in allAtomtypes:\n\t\t\t\t\tpointerModel = molecule_POInter_objects[mol]\n\t\t\t\t\tpointerModel.params[atomtype][0]['B'] = updated_B_params[i]\n\t\t\t\t\ti += 1\n\t\t\n\t\tfitting_component += 1\n\n\t#write output JSON file with optimized parameters\n\toptimized_params[\"Dispersion\"] = [1.0 for atomtype in allAtomtypes]\n\tfinal_params_list = write_output_params_to_list(optimized_params)\n\tmap_params(final_params_list, \"optimized_params\")\n\n\t#calc dispersion lsq_error and store\n\tdisp_lsq_error = calc_disp_lsq_error()\n\tcomponent_lsq_error[energyComponents[fitting_component]] = disp_lsq_error\n\n\t#write lsq_error output file\n\twith open(\"component_lsq_error.txt\", 'w') as f:\n\t\tfor component in energyComponents:\n\t\t\tf.write(component + \":\" + '\\t')\n\t\t\tf.write(str(component_lsq_error[component]) + '\\n')\n\n\tprint \"===========================================================================\"\n\tprint \" Optimized parameters successfully written to optimized_params.constraints \"\n\tprint \"===========================================================================\"\n\n\treturn res","title":""},{"docid":"036faa1e19c7270a93947e53c76cf9c0","score":"0.5943793","text":"def get_solution(self):\n\n return self._funcs[\"get_solution\"](self.problem, self.variable_dict)","title":""},{"docid":"469e51f3864b44e39addeb0ee1556f22","score":"0.59420794","text":"def get_analysis(self):\n return self.best_fitness, self.avg_fitness, self.worst_fitness","title":""},{"docid":"88e417ac9a10cd6b79cb7903657a121a","score":"0.593701","text":"def solutions(self):\r\n if self.results is None:\r\n warning('results')\r\n return \r\n if self.is_isoschizomer():\r\n iso = True\r\n else:\r\n iso = False\r\n if type(self.best) == list:\r\n print('Results obtained using the following group of enzymes:')\r\n for e in self.best:\r\n print(e, sep=',')\r\n print('Other possible solutions:')\r\n for el in self.results:\r\n if el != self.best:\r\n print(el)\r\n else:\r\n if iso:\r\n warning('enzyme3')\r\n print('Best enzyme:\\n', self.best)\r\n print('Other possible solutions:')\r\n b = self.results.index(self.best)\r\n print(self.results[0:b]+ self.results[b+1:])\r\n\r\n return","title":""},{"docid":"8f362cc586c58c587ff2150bc663a74b","score":"0.5930106","text":"def optimize(self):\n\n\t\tstill_needed = []\n\t\t# Verify the required information\n\t\tif self.preference_input is None:\n\t\t\tstill_needed.append(\"Preferences\")\n\t\tif self.LP_input is None:\n\t\t\tstill_needed.append(\"LP_input\")\n\t\tif self.teacher_file is None:\n\t\t\tstill_needed.append(\"Teacher File (secondary)\")\n\n\t\ts = \"\"\n\t\tif len(still_needed) > 0:\n\t\t\ts = str(still_needed)\n\t\t\tmessagebox.showerror(\"Error\", \"You are missing the following\\n\\n\" + s)\n\t\t\treturn \n\n\t\tGAP = self.slider.get()\n\t\tprint(GAP)\n\t\t\n\t\tprint(\"All good\")\n\n\t\t# optimize_schedule(self.preference_input, self.LP_input, None,\n\t\t# \tself.teacher_file, GAP, self.requirements, self.save_location)\n\t\t# The none is for Grades, as I just hard coded that in the opt code for now\n\n\t\t# Create optimization instance\n\t\tO = Optimizer(self.preference_input, self.LP_input, None,\n\t\t\t\tself.teacher_file, GAP, self.requirements, self.save_location)\n\t\tO.optimize()\n\n\n\n\n\n\t\t# Things we need to think abou thow to deal with:\n\t\t# \tGrades\n\t\t#\tProximity\n\t\t#\t","title":""},{"docid":"b2320927b5675caba0e0b584d30231a5","score":"0.59246856","text":"def solve(self):\n self.DSSSolution.Solve()","title":""},{"docid":"79aa869a66d8a075a889aaa4a27ba494","score":"0.5923873","text":"def APSP_1(self):\n\n\t\tt_start = time.time()\n\t\tres = float('inf')\n\t\tfor s in self.G.keys():\n\t\t\tprint('Working on node {} of total {} nodes... time elapsed: {:5.2f} minutes.'\\\n\t\t\t\t.format(s, self.n, (time.time() - t_start)/60))\n\t\t\tdp = self.Bellman_Ford(s, self.G)\n\t\t\t\n\t\t\tif dp is None:\n\t\t\t\tprint('\\nThere is a negative cost cycle. Exit the algorithm.')\n\t\t\t\treturn None\n\t\t\tres = min(res, min(dp.values()))\n\n\t\treturn res","title":""},{"docid":"de4dcf4702ef09949cd1b9ec034b0e13","score":"0.59213895","text":"def run(self, max_iterations=100) -> Tuple[List[str], List[float]]:\n\n\n for i in range(max_iterations):\n # Generate Moveset\n moves = self.generate_moveset(self.state)\n\n if len(moves) == 0:\n return (self.state, self.costs)\n\n # Choose S i randomly from Moveset(S)\n m = random.choice(moves)\n\n # Define dV=V(S i )-V(S)\n v_s = self.graph.solution_cost(self.state)\n v_m = self.graph.solution_cost(m)\n dV = v_s - v_m\n\n # If dV>0 then S←S i else with probability p, S←S i\n if dV > 0 or (random.random() <= self.generate_p(dV)):\n self.state = m\n self.costs.append(v_m)\n self.decrease_temperature( i/max_iterations)\n else:\n self.costs.append(v_s)\n # If downhill descent is minimal, terminate\n if abs(dV/v_s) < SimulatedAnnealing.STOPPAGE_VALUE:\n return (self.state, self.costs)\n\n return (self.state, self.costs)","title":""},{"docid":"5913a3f71cbfc93d162a9dbdedf2bd1c","score":"0.5912698","text":"def startSearch(self):\n current = self.initNode\n bestScore = float(\"inf\")\n for i in range(0,100):\n current = self.initNode\n while True:\n neighbors = current.expand()\n if not neighbors:\n break\n current = min(neighbors, key=attrgetter(\"h\"))\n \n # Random minimum\n listOfMins = [x for x in neighbors if current.h == x.h]\n index = random.randint(0, len(listOfMins)-1)\n current = listOfMins[index]\n \n if self.successTest(current.state):\n if current.g < bestScore:\n bestScore = current.g\n bestOne = current\n break\n return self._extractPlan(bestOne)","title":""},{"docid":"a96eb04d01c5e9d4aff74f3f921f6ca9","score":"0.5908016","text":"def best(self):\n if self._best is None:\n raise BestNotFound()\n return self._best","title":""},{"docid":"c72e6d6de196d127dfef8df3d116ae88","score":"0.590735","text":"def employed_bees_phase(self):\n for i in range(self.get_nsource()):\n # nominate neighbour solution except\n k = self.random_nominee_except(i)\n # randomly select soln vector element xi to change\n xi = rand.randint(0,self.get_dimension()-1)\n phi = self.a * rand.uniform(-1,1)\n # get current food source\n eith_src = self.bee[i].get_source()\n # generate a new solution with all vector element in current\n # vector and change the randomly selected parameter only\n enew_src = np.copy(eith_src)\n ekth_src = self.bee[k].get_source()\n # generate new candidate food source\n enew_src[xi] = eith_src[xi] + phi * (eith_src[xi] - ekth_src[xi])\n # update new soln in range of solution lower_limit\n enew_src = self.band_filter(enew_src)\n enew_cost = self.cost_of(enew_src)\n # generate new bee with new_src\n\n enew_bee = food.Nectar(enew_src, enew_cost)\n # greedy selection by current employed bee\n self.bee[i].greedy_choice(enew_bee)","title":""},{"docid":"cd0932f9ee1bdccd14136fe6af53b0f2","score":"0.58952796","text":"def report_best(self):\n lowest_Err = min(self.Err)\n index = self.Err.index(lowest_Err)\n best_x = self.x[index]\n formatted_x = '[' + ', '.join(['%.2f' % xi for xi in best_x]) + ']'\n print 'best x: %s' % formatted_x\n print 'lowest Err: %.3E' % lowest_Err\n return best_x","title":""},{"docid":"3cce4fa387bf2542bc60e2516c287211","score":"0.5893321","text":"def computeOptimalPlays(self):\n\n # Base Cases\n for i in range(self.size):\n # If there is only one pot of gold left, take that pot\n self.cache[(i, i)] = {'score': self.gold[i], 'choice': 'left'}\n for i in range(self.size - 1):\n # If there are two pots of gold left, take the larger one\n self.cache[(i, i+1)] = {\n 'score': max(self.gold[i], self.gold[i+1]),\n 'choice': 'left' if self.gold[i] >= self.gold[i+1] else 'right'\n }\n\n # Inductive Step\n for length in range(2,self.size):\n for j in range(self.size - length):\n k = j + length\n # Here, we decide whether to take from the left (the j side)\n # or the right (the k side). If we take from the left, then\n # (if) our opponent makes the optimal move, we'll be left with\n # the minimum of two pots taken from the left or one pot taken\n # from either side (because it is a zero-sum game).\n # Similarly, if we take from the right, then our worst-case\n # score is the minimum of the subgame situation where either\n # two pots are taken from the right or one from either side.\n # The subgame structure is what makes this a DP problem.\n leftMove = self.gold[j]\n rightMove = self.gold[k]\n afterLeftMove = min(self.score(j+2,k), self.score(j+1,k-1))\n afterRightMove = min(self.score(j+1,k-1), self.score(j,k-2))\n\n if leftMove + afterLeftMove >= rightMove + afterRightMove:\n score = leftMove + afterLeftMove\n choice = 'left'\n else:\n score = rightMove + afterRightMove\n choice = 'right'\n\n self.cache[(j,k)] = {\n 'score': score,\n 'choice': choice\n }","title":""},{"docid":"e515c8f7954ca9a5e4c489dc52a14b0d","score":"0.5886591","text":"def best_ind(self):\n random.seed(64)\n NGEN = 10\n MU = 10\n pop = self.toolbox.population(n=MU)\n hof = tools.ParetoFront()\n stats = tools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"avg\", numpy.mean, axis=0)\n stats.register(\"std\", numpy.std, axis=0)\n stats.register(\"min\", numpy.min, axis=0)\n stats.register(\"max\", numpy.max, axis=0)\n # algorithms.eaMuPlusLambda(pop, self.toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats, halloffame=hof)\n algorithms.eaSimple(pop, self.toolbox, cxpb=0.6, mutpb=0.05, ngen=NGEN, halloffame=hof)\n print(\"The best individual is :\", hof[-1])\n # print(\"The best fitness is :\", eval_ind(self, hof[-1]))\n return hof[-1]","title":""},{"docid":"a3d1dea5cd1d94568fde8f01cc0d0532","score":"0.5884986","text":"def optimise(self, verbose=False, guess=None, \n elitism=5, method='GA',cooling_rate=0.1, maxtime=60, maxstallgen=100,\n nswap=3, popsize=50, mut=0.02, maxgens=50, dimension_bits=1):\n # TODO: check length of init guess\n from scipy.optimize import minimize\n from scipy.optimize import basinhopping\n self.models = []\n def eval_func_in(x):\n return self.eval_func(x, prior=[])\n\n N = self.N_reactions\n\n import inspyred\n from random import Random\n rand = Random()\n if method == 'GA':\n ea = inspyred.ec.GA(rand)\n else:\n ea = inspyred.ec.SA(rand)\n self.scores = []\n self.lower = 0\n self.upper = 1\n self.best_score = 1\n\n self.scores_all = []\n def evaluator(candidates, args):\n dimensions = args['dimensions']\n dimension_bits = args['dimension_bits']\n fitness = []\n for cs in enumerate(candidates):\n #if len(params) != dimensions:\n #print(cs, dimensions, dimension_bits)\n if method == 'GA':\n params = self._binary_to_real(cs[1], dimensions, dimension_bits)\n else:\n params = cs[1]\n score = eval_func_in(params)\n #score = eval_func_in(cs)\n fitness.append(score)\n # TODO selection pressure could be added here \n self.scores_all.extend(fitness)\n self.scores.append(min(np.array(fitness)))\n if self.scores[-1] < self.best_score:\n self.best_score = self.scores[-1]\n i = np.argmin(np.array(fitness))\n self.best_individual = candidates[i]\n start = False\n print(self.scores[-1], self.best_score)\n\n return fitness\n dimensions = N\n #dimension_bits = 4 # preceision is 1/(2**n-1)\n def generator(random, args):\n # For the first generation only.\n #print('HERE')\n dimension_bits = args['dimension_bits']\n #guess = args['guess'][:]\n pop = [random.choice([0, 1]) \n for _ in xrange(self.N_reactions * dimension_bits)]\n #if guess is not None:\n # pop[0] = guess\n self.pop = pop\n if hasattr(self, 'best_individual'):\n pop = self.best_individual\n return pop\n\n import inspyred.ec\n import inspyred.ec.ec\n ea.terminator = inspyred.ec.terminators.evaluation_termination\n bounder = inspyred.ec.ec.Bounder(0,1)\n\n self.storage = []\n\n def my_observer(population, num_generations, num_evaluations, args):\n #best = max(population)\n #print('{0:6} -- {1} : '.format(num_generations, best.fitness))\n\n #print(population)\n self.storage.append(population[0].candidate[:])\n for pop in population:\n self.models.append(pop.candidate[:])\n ea.observer = my_observer\n\n\n ea.selector = inspyred.ec.selectors.uniform_selection\n #ea.selector = inspyred.ec.selectors.rank_selection\n #ea.selector = inspyred.ec.selectors.fitness_proportionate_selection\n #ea.selector = inspyred.ec.selectors.tournament_selection\n\n ea.replacer = inspyred.ec.replacers.truncation_replacement\n #ea.replacer = inspyred.ec.replacers.plus_replacement\n self.ea = ea\n\n final = ea.evolve(generator=generator, evaluator=evaluator, \n maximize=False, bounder=bounder, temperature=100, cooling_rate=cooling_rate,\n max_evaluations=popsize*maxgens,dimensions=len(self.model.reactions),\n dimension_bits = dimension_bits, guess=guess, gaussian_mean=0.5, gaussian_std=0.5,\n pop_size=popsize, mutation_rate=mut, num_elites=elitism)\n\n self.ea = ea\n self.final = final\n res = None\n self.best_bitstring = max(self.ea.population).candidate\n print self.best_bitstring\n try:\n self.best_bitstring = self._binary_to_real(self.best_bitstring,\n len(self.model.reactions), dimension_bits)\n except:\n pass","title":""},{"docid":"7a446de06c2fc8fde29effb2ef29f743","score":"0.5871639","text":"def potential_energy_distribution(self):\n\n if len(self.traces) is None:\n raise ValueError(\"Can't predict final energy with just one trace!\")\n\n self.run_all_iterations()\n return self.energy_model.predict_from_trace(self.observed_energies)[0]","title":""},{"docid":"2080004ed77bb58b93101bbcdb5a9740","score":"0.5868522","text":"def result(self):\n return (self.best_mu, self.best_reward,\n self.curr_best_reward, self.sigma)","title":""},{"docid":"99d29e673ca70278dd187ed8c0e891f2","score":"0.5866907","text":"def get_optimal():\n LOG.info(\"Retrieving Optimal with url : %s\", request.url)\n recipe = request.get_json()\n LOG.info(recipe)\n LOG.info(str(recipe['name']))\n current_time = int(time.time())\n workload_name = 'optimal_'+str(current_time)\n # if 'ts_from' in recipe:\n # LOG.debug(recipe['ts_from'])\n # LOG.debug(recipe['ts_to'])\n # workload = Workload(str(recipe['name']), ts_from= recipe['ts_from'], ts_to= recipe['ts_to'])\n # eng = Engine()\n # eng.run('optimal', recipe['name'], recipe['ts_from'], recipe['ts_to'])\n # else:\n # workload = Workload(str(recipe['name']))\n config = {}\n if recipe.get('device_id'):\n config['device_id'] = recipe['device_id'].strip(\n ).lower().replace('-', '_')\n if recipe.get('project'):\n config['project'] = recipe['project']\n if recipe.get('sort_order'):\n config['sort_order'] = recipe['sort_order']\n if recipe.get('telemetry_filter', None) is not None:\n config['telemetry_filter'] = recipe['telemetry_filter']\n else:\n config['telemetry_filter'] = False\n workload = Workload(workload_name, workload_config=config)\n # storing initial recipe\n # TODO: validate recipe format\n recipe_bean = Recipe()\n recipe_bean.from_json(recipe)\n workload.add_recipe(\n int(\"{}{}\".format(int(round(time.time())), '000000000')), recipe_bean)\n pipe_exec = OptimalPipe()\n node_type = 'machine'\n try:\n workload = pipe_exec.run(workload, node_type)\n except KeyError:\n return Response('Service not ready yet, please wait or restart landscape', status=202)\n if workload.get_latest_graph() is None and config.get('device_id') is not None:\n return Response('Device not found', status=404)\n if workload.get_latest_graph() is None:\n return Response('Landscape not ready yet?', status=202)\n results = workload.get_metadata(OptimalFilter.__filter_name__)\n # return Response(results.to_json(), mimetype=MIME)\n # return Response(results.to_dict('results'), mimetype=MIME)\n json_results = json.dumps(results.to_dict('results'))\n return Response(json_results, mimetype=MIME)","title":""},{"docid":"6f76478ca755f6f62b9a4feabf72ea7b","score":"0.58448356","text":"def solve(self):\n start_time = time.time()\n maxdepth = 0\n break_cond = False\n\n while(not break_cond):\n if self.algo == \"bfs\":\n # Pop the leftmost element if doing a bfs (Queue)\n current_state = self.frontier.popleft()\n elif self.algo == \"dfs\":\n # Pop the rightmost element if doing a dfs (Stack)\n current_state = self.frontier.pop()\n else:\n # Get element with highest priority if doing A* (Heap)\n current_state = self.frontier.get()\n self.frontier_set.remove(current_state)\n if (self.isFinalState(current_state)):\n soln = self.get_solution_moves(current_state)\n end_time = time.time()\n stats = {}\n stats[\"nodes_expanded\"] = len(self.explored)\n stats[\"search_depth\"] = current_state.depth\n stats[\"max_search_depth\"] = maxdepth\n stats[\"cost_of_path\"] = len(soln)\n stats[\"time\"] = end_time - start_time\n stats[\"path\"] = soln\n return stats\n neighbors = current_state.generate_possible_states()\n if self.algo == \"dfs\":\n neighbors.reverse()\n\n for neighbor in neighbors:\n if neighbor not in self.explored and neighbor not in self.frontier_set:\n if self.algo == \"bfs\" or self.algo == \"dfs\":\n self.frontier.append(neighbor)\n else:\n self.frontier.put(neighbor)\n self.frontier_set.add(neighbor)\n if neighbor.depth > maxdepth:\n maxdepth = neighbor.depth\n self.explored.add(current_state)\n if self.algo == \"bfs\" or self.algo == \"dfs\":\n frontier_sz = len(self.frontier)\n else:\n frontier_sz = self.frontier.qsize()\n logging.debug(\"Frontier size = \" +\n str(frontier_sz) +\n \"; Explored size = \" +\n str(len(self.explored)))\n if self.algo == \"bfs\" or self.algo == \"dfs\":\n break_cond = len(self.frontier) == 0\n else:\n break_cond = self.frontier.empty()\n logging.error(\"This is an unsolvable board!\")\n return None","title":""},{"docid":"468d623199cf946ade4500c72622bb3f","score":"0.58404225","text":"def doit(self):\n self.reduceSolutions()\n keyz = self.solutions.keys()\n keyz.sort()\n prodarr = self.mergeIdent(keyz)\n keyz += self.chkForMore(keyz,prodarr)\n localans = self.solvePell()\n setx = set(keyz+localans)\n ansvec = list(setx)\n answer = 1+sum(ansvec)\n return answer","title":""},{"docid":"370c27f8417ed8239b7d1a695857c183","score":"0.5814705","text":"def first_state(self):\n new_sol = Solution(\n self.all_ts[0][:1],\n self.all_ys[0][:, :1],\n self.all_models[:1],\n self.all_inputs[:1],\n None,\n None,\n \"final time\",\n )\n new_sol._all_inputs_casadi = self.all_inputs_casadi[:1]\n new_sol._sub_solutions = self.sub_solutions[:1]\n\n new_sol.solve_time = 0\n new_sol.integration_time = 0\n new_sol.set_up_time = 0\n\n return new_sol","title":""},{"docid":"3cc528770f4cb159b4a99b6fd7ff1d8f","score":"0.5813268","text":"def run(self):\n problem = self.testproblem.problem\n results = TestResults(self.testproblem, self.config)\n # results = Test\n try:\n start = time.time() # Time the solve\n print(\"starting\",self.testproblem.id,\"with config\",self.config.id,\"at\",start)\n problem.solve(solver = self.config.solver, verbose = self.config.verbose, **self.config.kwargs)\n print(\"finished solve for\", self.testproblem.id, \"with config\", self.config.id)\n if problem.solver_stats.solve_time is not None:\n results.solve_time = problem.solver_stats.solve_time\n else:\n results.solve_time = time.time() - start\n if problem.solver_stats.setup_time is not None:\n results.setup_time = problem.solver_stats.setup_time\n if problem.solver_stats.num_iters is not None:\n results.num_iters = problem.solver_stats.num_iters\n\n results.status = problem.status\n results.opt_val = problem.value\n except Exception as e:\n print(e)\n # Configuration could not solve the given problem\n results = TestResults(self.testproblem, self.config)\n results.size_metrics = problem.size_metrics\n print(\"failure solving\",self.testproblem.id,\"with config\",self.config.id)\n return results\n\n # Record residual gross stats:\n results.avg_abs_resid, results.max_resid = TestInstance.compute_residual_stats(problem)\n print(\"computed stats for\", self.testproblem.id, \"with config\", self.config.id)\n\n # Record problem metrics:\n results.size_metrics = problem.size_metrics\n\n print(\"finished\",self.testproblem.id,\"with config\",self.config.id,\"at\",time.time()-start)\n return results","title":""},{"docid":"1a4ddcf70a8389e9ebf5384e8f000f5b","score":"0.5812091","text":"def optimize(self):\n self._add_constraints()\n self.Model.update()\n self.Model.optimize()\n if (\n self.Model.Status == 2\n ): # Status = 2 means optimal solution found and model is solved\n self._solved = True\n self._head_results = self._get_head_results()\n print(\"OPTIMIZATION COMPLETE\")\n else:\n self._head_results = None\n print(\"OPTIMIZATION NOT COMPLETE\")","title":""},{"docid":"9d1185bafadfc3331c9f21166a9d22f2","score":"0.5804594","text":"def baseSearch(self, gameState):\n\t\tactions = gameState.getLegalActions(self.index)\n\n\t\t# You can profile your evaluation time by uncommenting these lines\n\t\t# start = time.time()\n\t\tv = []\n\t\tfor action in actions:\n\t\t\tif action == 'Stop':\n\t\t\t\tcontinue\n\t\t\tsuccessor = gameState.generateSuccessor(self.index, action)\n\t\t\tv.append((self.evaluate(successor), action))\n\n\t\t# print 'eval time for agent %d: %.4f' % (self.index, time.time() - start)\n\n\t\t# print \"Values in baseSearch \", v\n\t\tmaxValues = [i for i in v if i == max(v)]\n\t\tmaxValue = random.choice(maxValues)\n\n\t\t# print maxValue\n\t\tbestAction = maxValue[1]\n\t\t# print bestAction\n\t\tfoodLeft = len(self.getFood(gameState).asList())\n\t\t# print foodLeft\n\t\tif foodLeft <= 2:\n\t\t\t# print \"foodl <= 2\"\n\t\t\tbestDist = 9999\n\t\t\tfor action in actions:\n\t\t\t\tif action == 'Stop':\n\t\t\t\t\tcontinue\n\t\t\t\tsuccessor = gameState.generateSuccessor(self.index, action)\n\t\t\t\tpos2 = successor.getAgentPosition(self.index)\n\t\t\t\tdist = self.getMazeDistance(gameState.getAgentPosition(self.index), pos2)\n\t\t\t\t# print \"Comparing {0} < {1}\".format(dist, bestDist)\n\t\t\t\tif dist < bestDist:\n\t\t\t\t\t# print \"Change action to \",action\n\t\t\t\t\tbestAction = action\n\t\t\t\t\tbestDist = dist\n\t\t\t# print \"Base search return with \", bestAction\n\t\t\treturn bestAction\n\t\t# print \"Base search return with \",bestAction\n\t\treturn bestAction","title":""},{"docid":"2e9eb1c81491450c937f6743bbc7fd60","score":"0.5800815","text":"def judge(self):\r\n temp = -1\r\n best_one = None\r\n for individual in self.individuals:\r\n distance = self.get_disctance(individual)\r\n if temp == -1 or distance < temp:\r\n temp = distance\r\n best_one = individual\r\n\r\n self.best_ones.append(best_one)","title":""},{"docid":"9f58e89ad63958879fc3bcc91bf5057c","score":"0.5797411","text":"def optimal_eps(self):\n Network = self.Network\n W = self.get_W()\n B = self._get_B(W)\n N = Network.size()\n eps = np.zeros([N,N])\n for j in range(N):\n eps += self._get_eps_j(j,W[j],B[j])\n return eps","title":""},{"docid":"35d5185a0acaa718fea4eb3c5221e4da","score":"0.5795139","text":"def test_griewangk():\n\n print \"Testing Griewangk:\"\n print \"Expected: x=[0.]*10 and f=0\"\n from mystic.models import griewangk as costfunc\n ndim = 10\n lb = [-400.]*ndim\n ub = [400.]*ndim\n maxiter = 10000\n seed = 123 # Re-seed for each solver to have them all start at same x0\n \n # DifferentialEvolutionSolver\n print \"\\nUsing DifferentialEvolutionSolver:\"\n npop = 50\n random_seed(seed)\n from mystic.solvers import DifferentialEvolutionSolver\n from mystic.termination import ChangeOverGeneration as COG\n from mystic.termination import CandidateRelativeTolerance as CRT\n from mystic.termination import VTR\n from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp\n esow = Monitor()\n ssow = Monitor() \n solver = DifferentialEvolutionSolver(ndim, npop)\n solver.SetRandomInitialPoints(lb, ub)\n solver.SetStrictRanges(lb, ub)\n solver.SetEvaluationLimits(generations=maxiter)\n solver.SetEvaluationMonitor(esow)\n solver.SetGenerationMonitor(ssow)\n solver.enable_signal_handler()\n #term = COG(1e-10)\n #term = CRT()\n term = VTR(0.)\n time1 = time.time() # Is this an ok way of timing?\n solver.Solve(costfunc, term, strategy=Rand1Exp, \\\n CrossProbability=0.3, ScalingFactor=1.0)\n sol = solver.Solution()\n time_elapsed = time.time() - time1\n fx = solver.bestEnergy\n print \"Solution: \", sol\n print \"f value: \", fx\n print \"Iterations: \", solver.generations\n print \"Function evaluations: \", len(esow.x)\n print \"Time elapsed: \", time_elapsed, \" seconds\"\n assert almostEqual(fx, 0.0, tol=3e-3)\n\n # DifferentialEvolutionSolver2\n print \"\\nUsing DifferentialEvolutionSolver2:\"\n npop = 50\n random_seed(seed)\n from mystic.solvers import DifferentialEvolutionSolver2\n from mystic.termination import ChangeOverGeneration as COG\n from mystic.termination import CandidateRelativeTolerance as CRT\n from mystic.termination import VTR\n from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp\n esow = Monitor()\n ssow = Monitor() \n solver = DifferentialEvolutionSolver2(ndim, npop)\n solver.SetRandomInitialPoints(lb, ub)\n solver.SetStrictRanges(lb, ub)\n solver.SetEvaluationLimits(generations=maxiter)\n solver.SetEvaluationMonitor(esow)\n solver.SetGenerationMonitor(ssow)\n #term = COG(1e-10)\n #term = CRT()\n term = VTR(0.)\n time1 = time.time() # Is this an ok way of timing?\n solver.Solve(costfunc, term, strategy=Rand1Exp, \\\n CrossProbability=0.3, ScalingFactor=1.0)\n sol = solver.Solution()\n time_elapsed = time.time() - time1\n fx = solver.bestEnergy\n print \"Solution: \", sol\n print \"f value: \", fx\n print \"Iterations: \", solver.generations\n print \"Function evaluations: \", len(esow.x)\n print \"Time elapsed: \", time_elapsed, \" seconds\"\n assert almostEqual(fx, 0.0, tol=3e-3)","title":""},{"docid":"cc6b76e94e2da8452d3f6eb07296ad32","score":"0.57891214","text":"def evolutionary_algorithm(self):\n # Creating the Individual Using DEAP\n creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n creator.create(\"Individual\", list, fitness=creator.FitnessMax)\n\n # Creating ToolBox For The DEAP Framework\n toolbox = base.Toolbox()\n toolbox.register(\"attr_bool\", random.randint, 0, 1)\n toolbox.register(\"individual\", tools.initRepeat, creator.Individual, toolbox.attr_bool, self.n_features)\n toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n toolbox.register(\"evaluate\", self.fitness_test)\n toolbox.register(\"mate\", tools.cxOnePoint)\n toolbox.register(\"mutate\", tools.mutFlipBit, indpb=0.05)\n toolbox.register(\"select\", tools.selTournament, tournsize=3)\n\n # Initialize Parameters\n pop = toolbox.population(n=self.n_pop)\n hof = tools.HallOfFame(self.n_pop * self.n_gen)\n stats = tools.Statistics(lambda ind: ind.fitness.values)\n stats.register(\"avg\", np.mean)\n stats.register(\"min\", np.min)\n stats.register(\"max\", np.max)\n\n # Genetic Algorithm\n pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=self.n_gen, stats=stats, halloffame=hof,verbose=True)\n\n # Return Fall Of Home\n return hof","title":""},{"docid":"763350b629661a962875e4097a558371","score":"0.5786748","text":"def best_model() :\n file_path = os.path.join(os.path.os.getcwd(), cf.PERF_FILE)\n with open(file_path, 'r') as perf_file:\n prec = perf_file.readlines()[0].split(';')\n\n precisions = [t for t in prec if len(t)>0]\n\n models = [t.split(':')[0] for t in precisions]\n models = sorted(models)\n precisions = np.array([float(t.split(':')[1]) for t in precisions])\n index_best_model = np.argmax(precisions)\n #best_model = models[index_best_model]\n\n output_files, model_dict = get_output_files()\n\n chosen_model = model_dict[int(index_best_model)+1]\n chosen_output_file = output_files[int(index_best_model)+1]\n\n print('Best model is ' + chosen_model + ' with precision ' + str(max(precisions)) + '%')\n\n return chosen_model, chosen_output_file","title":""},{"docid":"152015f9a8d342528e38fde1197c42a4","score":"0.57838523","text":"def get_solution(self):\n if self.is_solution_available():\n pass\n raise errors.IllegalState()","title":""},{"docid":"1af224aec567cf6f051e98f8188c816f","score":"0.5780064","text":"def run(self):\n if self.current_control:\n target_value = self.current_density\n else:\n target_value = self.average_cell_voltage * self.stack.n_cells\n if not isinstance(target_value, (list, tuple, np.ndarray)):\n target_value = [target_value]\n cell_voltages = []\n current_densities = []\n local_data_list = []\n global_data_list = []\n for i, tar_value in enumerate(target_value):\n current_errors = []\n temp_errors = []\n convergence_flags = []\n self.timing['simulation'] = 0.0\n self.timing['output'] = 0.0\n simulation_start_time = timeit.default_timer()\n counter = 0\n while True:\n if counter == 0:\n if self.current_control:\n self.stack.update(current_density=tar_value)\n else:\n self.stack.update(voltage=tar_value)\n else:\n self.stack.update()\n if self.stack.break_program:\n break\n current_error, temp_error = self.calc_convergence_criteria()\n current_errors.append(current_error)\n temp_errors.append(temp_error)\n if len(target_value) < 1:\n print(counter)\n counter += 1\n if ((current_error < self.it_crit and temp_error < self.it_crit)\n and counter > self.min_it) or counter > self.max_it:\n break\n if counter > self.max_it:\n convergence_flag = False\n else:\n convergence_flag = True\n convergence_flags.append(convergence_flag)\n simulation_stop_time = timeit.default_timer()\n simulation_time = simulation_stop_time - simulation_start_time\n self.timing['simulation'] += simulation_time\n output_start_time = timeit.default_timer()\n\n if not self.stack.break_program:\n # voltage_loss = self.get_voltage_losses(self.stack)\n cell_voltages.append(np.average([cell.v for cell in\n self.stack.cells]))\n current_densities.append(self.stack.i_cd_avg)\n\n case_name = 'Case'+str(i)\n self.output.save(case_name, self.stack)\n local_data_dict = self.output.get_data(self.stack)\n local_data_dict['Iterations'] = \\\n {'value': [list(range(counter))], 'units': '-'}\n local_data_dict['Current Density Error'] = \\\n {'value': [current_errors], 'units': '-',\n 'xkey': 'Iterations'}\n local_data_dict['Temperature Error'] = \\\n {'value': [current_errors], 'units': '-',\n 'xkey': 'Iterations'}\n\n local_data_list.append(local_data_dict)\n if self.output.save_plot:\n path = os.path.join(self.output.output_dir, case_name,\n 'plots', 'Convergence.png')\n self.output.create_figure(path, list(range(counter)),\n [current_errors, temp_errors],\n xlabels='Iteration',\n ylabels='Error',\n yscale='log',\n legend=['Current Density',\n 'Temperature'])\n else:\n target_value = target_value[0:-i]\n break\n\n average_current_density = \\\n np.average([np.average(cell.i_cd, weights=cell.active_area_dx)\n for cell in self.stack.cells])\n if self.stack.coolant_circuit is None:\n cool_mass_flow = 0.0\n else:\n cool_mass_flow = self.stack.coolant_circuit.mass_flow_in\n global_data = \\\n {\n 'Convergence':\n {'value': convergence_flag, 'units': ' '},\n 'Stack Voltage': {'value': self.stack.v_stack, 'units': 'V'},\n 'Average Cell Voltage':\n {'value': self.stack.v_stack / self.stack.n_cells,\n 'units': 'V'},\n 'Minimum Cell Voltage':\n {'value': np.min(self.stack.v), 'units': 'V'},\n 'Maximum Cell Voltage':\n {'value': np.max(self.stack.v), 'units': 'V'},\n 'Average Current Density':\n {'value': average_current_density, 'units': 'A/m²'},\n 'Stack Power Density':\n {'value': self.stack.v_stack * average_current_density,\n 'units': 'W/m²'},\n 'Stack Power':\n {'value': self.stack.v_stack * average_current_density\n * self.stack.cells[0].active_area,\n 'units': 'W'},\n 'Cooling Mass Flow Rate:':\n {'value': cool_mass_flow,\n 'units': 'kg/s', 'format': '.4E'},\n 'Cathode Mass Flow Rate:':\n {'value': self.stack.fuel_circuits[0].mass_flow_in,\n 'units': 'kg/s', 'format': '.4E'},\n 'Anode Mass Flow Rate:':\n {'value': self.stack.fuel_circuits[1].mass_flow_in,\n 'units': 'kg/s', 'format': '.4E'},\n }\n global_data_list.append(global_data)\n output_stop_time = timeit.default_timer()\n self.timing['output'] += output_stop_time - output_start_time\n self.output.print_global_data(self, global_data)\n self.output.save_global_results(global_data)\n\n output_start_time = timeit.default_timer()\n\n # print polarization curve, if more than one current_density value\n # was provided\n if len(target_value) > 1:\n self.output.write_data(current_densities, cell_voltages,\n 'Current Density [A/m²]', 'Cell Voltage',\n units='V', directory=self.output.output_dir,\n # save_csv=True, save_plot=True,\n write_mode='w')\n\n output_stop_time = timeit.default_timer()\n self.timing['output'] += output_stop_time - output_start_time\n\n return global_data_list, local_data_list","title":""},{"docid":"182e8144cc9cf7df0a32def1d95bd1d8","score":"0.5779991","text":"def _eval_energy(self):\n S = self.S\n M = S.mera\n SIlayer = self.SIlayer # = 2\n SIlayer_p1 = SIlayer + 1\n\n for i in range(self.num_of_SIlayer):\n #这里是反复地从 SIlayer ascending 到 SIlayer_p1, WITH tau changing\n if i>0:\n for o in self.ham_op_names:\n S.__getattribute__(o)[SIlayer][0].data[:] = S.__getattribute__(o)[SIlayer_p1][0].data[:]\n self.ascending_ham(M, S, SIlayer, tau=SIlayer + i)\n #above only update H, no need update rho, as the later is scale-invar not changed in each layer\n \n if 1:\n S.rho_2[SIlayer_p1][0].data[:] = S.rho_2[SIlayer][0].data[:]\n if not S.only_NN:\n S.rho_3[SIlayer_p1][0].data[:] = S.rho_3[SIlayer][0].data[:]\n #in the end eval eng at the layer above the first SIlayer\n S.eng_ham(SIlayer_p1)","title":""},{"docid":"682efc983522090afcf58edc4c64d068","score":"0.5779628","text":"def optimize(self):\n self.open_output_files()\n while (self.n_iter < self.n_maxiter and not self.is_converged):\n self.n_iter += 1\n self.choose_step_direction(self.opt_type)\n self.line_search(-1.0 * self.step_dir)\n self.update_energy()\n self.update_gradient()\n self.traj.append_step(self.mol)\n self.update_criteria()\n self.check_convergence()\n self.print_status()\n self.close_output_files()","title":""},{"docid":"3ec4666d8a6ab245f0cab4066f997667","score":"0.57755536","text":"def optimize_ga(self):\n\n converged = False\n ngens = 1\n generation = 1\n difference = self.tol * 10000.0\n self.solution_history = np.zeros(self.max_generation + 1)\n self.solution_history[0] = np.min(self.parent_fitness)\n\n run_time = 0.0\n start_time = time.time()\n while converged is False and ngens < self.max_generation and \\\n run_time < self.max_time:\n self.crossover()\n self.mutate()\n # determine fitness of offspring\n for i in range(self.population_size):\n self.chromosome_2_variables(self.offspring_population[i])\n self.offspring_fitness[i] = \\\n self.objective_function(self.design_variables)\n\n # rank the total population from best to worst\n total_fitness = np.append(self.parent_fitness,\n self.offspring_fitness)\n ranked_fitness = \\\n np.argsort(total_fitness)[0:int(self.population_size)]\n\n total_population = \\\n np.vstack([self.parent_population, self.offspring_population])\n self.parent_population[:, :] = total_population[ranked_fitness, :]\n self.parent_fitness[:] = total_fitness[ranked_fitness]\n\n # store solution history and wrap up generation\n self.solution_history[generation] = np.min(self.parent_fitness)\n\n if generation > self.convergence_iters:\n difference = \\\n self.solution_history[generation - self.convergence_iters]\\\n - self.solution_history[generation]\n else:\n difference = 1000\n if abs(difference) <= self.tol:\n converged = True\n\n # shuffle up the order of the population\n shuffle_order = np.arange(1, self.population_size)\n np.random.shuffle(shuffle_order)\n shuffle_order = np.append([0], shuffle_order)\n self.parent_population = self.parent_population[shuffle_order]\n self.parent_fitness = self.parent_fitness[shuffle_order]\n\n generation += 1\n ngens += 1\n\n run_time = time.time() - start_time\n\n # Assign final outputs\n self.solution_history = self.solution_history[0:ngens]\n self.optimized_function_value = np.min(self.parent_fitness)\n self.chromosome_2_variables(\n self.parent_population[np.argmin(self.parent_fitness)])\n self.optimized_design_variables = self.design_variables\n\n logger.debug('The GA ran for this many generations: {}'\n .format(ngens))\n logger.debug('The GA ran for this many seconds: {:.3f}'\n .format(run_time))\n logger.debug('The optimized function value was: {:.3e}'\n .format(self.optimized_function_value))\n logger.debug('The optimal design variables were: {}'\n .format(self.optimized_design_variables))","title":""},{"docid":"22f62b19d875ff1dfa2e3d4bfd9a5172","score":"0.57655716","text":"def return_fitness(self, e):\r\n\t\treturn e.fitness","title":""},{"docid":"83fe6af1079e34a1c9740f124d26c109","score":"0.576445","text":"def solve():\n return round(7 * (1 - choose(60, 20) / choose(70, 20)), 10)","title":""},{"docid":"595ecfd9facfe91a02000cddd7224112","score":"0.57595515","text":"def fittest(self):\n return self.best_fit, self.best_fit_fitness","title":""},{"docid":"b1c1e9395f4e30e4b41f6aa74720d615","score":"0.57595515","text":"def test_best_improvement():\n process_list = [1, 2, 4, 1, 3]\n expected_solution = [3, 1, 4, 2, 1]\n current_neighborhood = 0\n\n best_solution = best_improvement(process_list, current_neighborhood, 60)\n assert best_solution == expected_solution","title":""},{"docid":"1774df4c382a9a263ca22e025ab62161","score":"0.5756853","text":"def save_result(self, output_dir):\n self.best_solver.save_tree(output_dir)\n self.best_solver.save_best_solution(output_dir)\n for i in range(self.best_solver.tube_num):\n self.best_solver.visualize_full_search(output_dir, tube_num=i, with_solution=True)\n\n filename = output_dir / \"optimize_result.txt\"\n # noinspection PyTypeChecker\n optimize_result = open(filename, \"w\")\n\n optimize_result.write(f\"Optimizer Success: {self.success}\\n\")\n optimize_result.write(f\"Goal Reached: {self.best_solver.found_solution}\\n\")\n optimize_result.write(f\"Completion Time: {self.completion_time}\\n\")\n optimize_result.write(f\"Number of Iterations: {self.num_iterations}\\n\")\n optimize_result.write(f\"Number of Function Evals: {self.num_function_eval}\\n\")\n q = \", \".join(str(i) for i in self.best_q)\n optimize_result.write(f\"Optimized Q: {q}\\n\")\n\n optimize_result.close()\n\n filename = output_dir / \"optimize_process.txt\"\n # noinspection PyTypeChecker\n optimize_process = open(filename, \"w\")\n\n for i in range(len(self.optimize_process)):\n q = \", \".join(str(j) for j in self.optimize_process[i].get('q'))\n cost = self.optimize_process[i].get('cost')\n optimize_process.write(f\"{q} | {cost}\\n\")\n\n optimize_process.close()","title":""},{"docid":"8d155e5d49b180fe2d888a7e23955477","score":"0.5756708","text":"def betterEvaluationFunction(currentGameState):\n \"*** YOUR CODE HERE ***\"\n foods = currentGameState.getFood()\n newGhostStates=currentGameState.getGhostStates()\n newPos = currentGameState.getPacmanPosition()\n print 'New position:', newPos\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\n\n\n\n optimal_food = []\n optimal_ghost = []\n food = currentGameState.getFood().asList()\n\n for f in food:\n optimal_food.append(manhattanDistance(newPos, f))\n if len(optimal_food) == 0:\n return 0\n a = min(optimal_food)\n\n if a == 0:\n a = currentGameState.getScore()\n cap=[]\n b = max(optimal_food)\n f=currentGameState.getNumFood()\n\n # for g in currentGameState.getGhostPositions():\n # optimal_ghost.append(manhattanDistance(newPos, g))\n # if manhattanDistance(newPos, g) == 0:\n # return -100000.0\n # elif manhattanDistance(newPos, g) > 0.0 and manhattanDistance(newPos, g) <= 1.0:\n # return -100000.0/6\n\n ghost_PM_dist = []\n ghost_dist_nearest = 0\n for ghost in newGhostStates:\n Ghost_Pos = ghost.getPosition()\n GP_dist = manhattanDistance(Ghost_Pos, newPos)\n ghost_PM_dist.append(GP_dist)\n\n if len(ghost_PM_dist) > 0:\n ghost_dist_nearest = min(ghost_PM_dist)\n else:\n ghost_dist_nearest = currentGameState.getScore()\n # print ghost_dist_nearest\n ghost_feature = 1 / (ghost_dist_nearest + 1)\n score = 0\n # c=currentGameState.getCapsules()\n # print 'cap',c\n # for i in c:\n # cap.append(manhattanDistance(newPos,i))\n # if len(optimal_ghost) > 0:\n # ghost_dist_nearest = min(optimal_ghost)\n # else:\n # ghost_dist_nearest = currentGameState.getScore()\n # # print ghost_dist_nearest\n # ghost_feature = 1 / (ghost_dist_nearest + 1)\n # score = 0\n # score=0\n # if currentGameState.isWin():\n # score = 1000000\n return currentGameState.getScore() + 1.0/(a) +100*score+ 2*(ghost_feature)\n\n util.raiseNotDefined()","title":""},{"docid":"14c0ae96cd69e709a3affb6aa640c97d","score":"0.5754821","text":"def best_result(self):\n if self.result is not None:\n return self.result[0]\n return None","title":""},{"docid":"7c226930b9f22f2d5ed885f9483f4c19","score":"0.57431376","text":"def evolve(self):\n #Configure properly each island\n self.__parametrize__()\n\n # Get evolution generator of each island\n self._rc[:].execute(\"gen = gp_engine.sequentially_evolve()\", block=True)\n\n # Loop over all the sessions\n i = 0\n while True:\n logging.info('Launch generation evolution')\n\n #Do generation computing\n self._rc[:].execute(\"chosen = gen.next()\")\n\n #Print best individual\n logging.info('Get best individuals')\n self._rc[:].execute(\"best = gp_engine.get_best_individual()\")\n bests = self._rc[:]['best']\n\n print \"Generation %d\" % i\n print \"=\"*15\n print \"Island\\t| fit\\t\"\n print \"\\n\".join(\\\n [ \"%d\\t%f\" % (j, bests[j][1]) \\\n for j in range(len(bests))])\n\n print \"AVG\\t%f\" %np.mean( [bests[j][1] for j in range(len(bests))])\n i = i + 1\n\n logging.info('Test if end of generation')\n #Test if the process is finished\n self._rc[:].execute('end=gp_engine.__evolver__.is_evolution_ended()')\n\n if np.any( self._rc[:]['end']):\n break\n\n #Operate the migration\n logging.info('Launch migration process')\n self.__migration_operator__.manage_migration()\n\n #loop again\n\n print 'Evolution terminated'\n return bests","title":""},{"docid":"d73f2751a1cb356fcb986044d948ed77","score":"0.5730213","text":"def is_better_then_global_best(self, solution):\n return solution.personal_best.fitness_value < self.best_solution.fitness_value","title":""},{"docid":"6b31563d2c606a0c2b5ebe046d544277","score":"0.5728114","text":"def random_search():\t\t\n\tsim = simulator()\t\t\t\t\t\t\t# simulator object, to be used during this algorithm\n\tnum_search = 20\t\t\t\t\t\t\t\t# number of robots evaluated\n\tnum_tetr_added = 14\t\t\t\t\t\t\t# this is the size of our robots ( 15 tetrahedron total )\n\n\trs_fit = np.zeros(num_search)\t\t\t\t# learning curve array\n\tbest_fit = 0\t\t\t\t\t\t\t\t# best fitness place holder\n\tbest_array = np.empty(8)\t\t\t\t\t# top performing genome place holder\n\t\n\tfor i in range(num_search):\t\t\t\t\t# do the random search\t\n\t\tprint('counter: ', i)\n\n\t\trobot = tetrahedron()\t\t\t\t\t\t\t\t\t\t\t\t\t# make a random robot\n\t\trobot.set_tetr( np.array((0 , 0 , 0)) , np.array((0 , 0 , 0)))\t\t\t# make the seed\n\t\trobot.set_genome(num_tetr_added)\t\t\t\t\t\t\t\t\t\t# generate a random genome\n\t\trobot.build_robot()\t\t\t\t\t\t\t\t\t\t\t\t\t\t# builds the robot \n\t\t\n\t\tmass_list = robot.mass_list\t\t\t\t# center the robot\n\t\tcom = robot.get_com(mass_list)\t\t\t# \t\t....\n\t\trobot.center_object(com)\t\t\t\t# \t\t....\n\n\t\tfitness = calc_fitness( robot , sim)\t# evaluate the robot's fitness\n\n\t\tif fitness > best_fit:\t\t\t\t\t# if the fitness is better than current best\n\t\t\tbest_fit = fitness \t\t\t\t\t# update placeholders\n\t\t\tbest_array = np.copy(robot.genome)\t# \t\t....\n\n\t\trs_fit[i] = best_fit\t\t\t\t\t# add the best fit to the learning curve\n\n\tnp.savetxt(\"rs_robot.csv\", best_array, delimiter=\",\")\n\tnp.savetxt(\"rs_learning.csv\", rs_fit, delimiter=\",\")","title":""}],"string":"[\n {\n \"docid\": \"dc23f9d302e3c5b94328c54bbe84fee4\",\n \"score\": \"0.7375325\",\n \"text\": \"def solution(self):\\n return self.Mbest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a458447c66af157f126dfa185c1878\",\n \"score\": \"0.6866638\",\n \"text\": \"def optimize(self):\\n\\n # Initiate particles\\n self.__initPart()\\n self.listOfPos = []\\n\\n NFC = 0\\n while(NFC < self.maxGen):\\n #print \\\"Run: \\\" + str(NFC) + \\\" Best: \\\" + str(self.globBestFit)\\n\\n # Perform search\\n self.update()\\n\\n # Acceptably close to solution\\n #if self.globBestFit < self.epsError:\\n #return self.globBestPos, self.globBestFit\\n\\n # next gen\\n NFC += 1\\n self.listOfPos.append(self.globBestFit)\\n # Search finished\\n return self.globBestPos, self.globBestFit, self.listOfPos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f7c273cb7bfaf99e55d77e2de198e94\",\n \"score\": \"0.68366396\",\n \"text\": \"def solve(self):\\r\\n\\r\\n nfev, nit, warning_flag = 0, 0, False\\r\\n status_message = _status_message['success']\\r\\n\\r\\n # calculate energies to start with\\r\\n for index, candidate in enumerate(self.population):\\r\\n parameters = self._scale_parameters(candidate)\\r\\n self.population_energies[index] = self.func(parameters,\\r\\n *self.args)\\r\\n nfev += 1\\r\\n\\r\\n if nfev > self.maxfun:\\r\\n warning_flag = True\\r\\n status_message = _status_message['maxfev']\\r\\n break\\r\\n\\r\\n minval = np.argmin(self.population_energies)\\r\\n\\r\\n # put the lowest energy into the best solution position.\\r\\n lowest_energy = self.population_energies[minval]\\r\\n self.population_energies[minval] = self.population_energies[0]\\r\\n self.population_energies[0] = lowest_energy\\r\\n\\r\\n self.population[[0, minval], :] = self.population[[minval, 0], :]\\r\\n\\r\\n if warning_flag:\\r\\n return OptimizeResult(\\r\\n x=self.x,\\r\\n fun=self.population_energies[0],\\r\\n nfev=nfev,\\r\\n nit=nit,\\r\\n message=status_message,\\r\\n success=(warning_flag != True))\\r\\n\\r\\n # do the optimisation.\\r\\n for nit in range(1, self.maxiter + 1):\\r\\n if self.dither is not None:\\r\\n self.scale = self.random_number_generator.rand(\\r\\n ) * (self.dither[1] - self.dither[0]) + self.dither[0]\\r\\n for candidate in range(np.size(self.population, 0)):\\r\\n if nfev > self.maxfun:\\r\\n warning_flag = True\\r\\n status_message = _status_message['maxfev']\\r\\n break\\r\\n\\r\\n trial = self._mutate(candidate)\\r\\n self._ensure_constraint(trial)\\r\\n parameters = self._scale_parameters(trial)\\r\\n\\r\\n energy = self.func(parameters, *self.args)\\r\\n nfev += 1\\r\\n\\r\\n if energy < self.population_energies[candidate]:\\r\\n self.population[candidate] = trial\\r\\n self.population_energies[candidate] = energy\\r\\n\\r\\n if energy < self.population_energies[0]:\\r\\n self.population_energies[0] = energy\\r\\n self.population[0] = trial\\r\\n\\r\\n # stop when the fractional s.d. of the population is less than tol\\r\\n # of the mean energy\\r\\n convergence = (np.std(self.population_energies) /\\r\\n np.abs(np.mean(self.population_energies) +\\r\\n _MACHEPS))\\r\\n\\r\\n if self.disp:\\r\\n print(\\\"differential_evolution step %d: f(x)= %g\\\"\\r\\n % (nit,\\r\\n self.population_energies[0]))\\r\\n\\r\\n if (self.callback and\\r\\n self.callback(self._scale_parameters(self.population[0]),\\r\\n convergence=self.tol / convergence) is True):\\r\\n\\r\\n warning_flag = True\\r\\n status_message = ('callback function requested stop early '\\r\\n 'by returning True')\\r\\n break\\r\\n\\r\\n if convergence < self.tol or warning_flag:\\r\\n break\\r\\n\\r\\n else:\\r\\n status_message = _status_message['maxiter']\\r\\n warning_flag = True\\r\\n\\r\\n DE_result = OptimizeResult(\\r\\n x=self.x,\\r\\n fun=self.population_energies[0],\\r\\n nfev=nfev,\\r\\n nit=nit,\\r\\n message=status_message,\\r\\n success=(warning_flag != True))\\r\\n\\r\\n if self.polish:\\r\\n result = minimize(self.func,\\r\\n np.copy(DE_result.x),\\r\\n method='L-BFGS-B',\\r\\n bounds=self.limits.T,\\r\\n args=self.args)\\r\\n\\r\\n nfev += result.nfev\\r\\n DE_result.nfev = nfev\\r\\n\\r\\n if result.fun < DE_result.fun:\\r\\n DE_result.fun = result.fun\\r\\n DE_result.x = result.x\\r\\n DE_result.jac = result.jac\\r\\n # to keep internal state consistent\\r\\n self.population_energies[0] = result.fun\\r\\n self.population[0] = self._unscale_parameters(result.x)\\r\\n\\r\\n return DE_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0117546e96810312ac163daf208b031\",\n \"score\": \"0.6755187\",\n \"text\": \"def search_optimal(self):\\n\\n for i in range(self.get_nsource()):\\n ith_bee_fitness = self.bee[i].get_fitness()\\n best_fitness = self.optimal.get_fitness()\\n if best_fitness < ith_bee_fitness:\\n self.suboptimal.greedy_choice(self.optimal)\\n self.optimal.greedy_choice(self.bee[i])\\n elif best_fitness == ith_bee_fitness:\\n pass # protect updating suboptimal in this case\\n elif self.suboptimal.get_fitness() < ith_bee_fitness:\\n self.suboptimal.greedy_choice(self.bee[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f474c1ee4cea7c9306c18d7443490471\",\n \"score\": \"0.6726404\",\n \"text\": \"def _getBestSolution(obj):\\n filename = _getOutputFilename(obj.folder_output + BEST_SOLUTION_FILENAME)\\n obj.bestSolution = _loadCSV(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6112d959c74a77caaeb6a8ef2f3e1704\",\n \"score\": \"0.6724941\",\n \"text\": \"def getstart_temp(self):\\n\\n assert(not self.dims is None)\\n lrange = self.lower\\n urange = self.upper\\n fmax = _double_min\\n fmin = _double_max\\n for _ in range(self.Ninit):\\n x0 = random.uniform(size=self.dims)*(urange-lrange) + lrange\\n fval = self.func(x0, *self.args)\\n self.feval += 1\\n if fval > fmax:\\n fmax = fval\\n if fval < fmin:\\n fmin = fval\\n bestEnergy = fval\\n bestSolution = array(x0)\\n\\n self.T0 = (fmax-fmin)*1.5\\n return bestSolution, bestEnergy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f818ca9f877589cbc25e77d1157ae1\",\n \"score\": \"0.6616518\",\n \"text\": \"def get_best():\\n\\n best_h = 3\\n best_not = 3\\n runs = 7\\n\\n np.save(\\\"H_START_S_BEST.npy\\\",0)\\n np.save(\\\"NOT_START_S_BEST.npy\\\",0)\\n np.save(\\\"H_START_S.npy\\\",0)\\n np.save(\\\"NOT_START_S.npy\\\",0)\\n\\n for i in range(runs):\\n \\n # Run Sim-H\\n sim_h.run_main_h()\\n # Run Sim-NOT\\n sim_not.run_main_not()\\n\\n hloss = run_waves(\\\"H\\\")\\n print(\\\"Loss of H Gate\\\")\\n print(hloss)\\n nloss = run_waves(\\\"N\\\")\\n print(\\\"Loss of NOT Gate\\\")\\n print(nloss)\\n\\n # If this is a better H value, update\\n if (hloss < best_h):\\n best_h = hloss\\n overwriteH()\\n\\n # If this is a better NOT value, update\\n if (nloss < best_not):\\n best_not = nloss\\n overwriteNOT()\\n\\n\\n h_waves_list = np.load(\\\"H_START_S_BEST.npy\\\")\\n not_waves_list = np.load(\\\"NOT_START_S_BEST.npy\\\")\\n\\n return (h_waves_list, not_waves_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90862e6480a983a07b72ffac8f4a63f1\",\n \"score\": \"0.6562416\",\n \"text\": \"def solve(self):\\n self.runtime = Timer() # Start the timer to measure the runtime\\n self.model.min_to_max() # Convert minimization problems to maximization problems\\n self.model.construct_payoff() # Construct a payoff table from the objective function values\\n self.model.find_obj_range() # Find the range of each objective function\\n self.model.convert_prob() # Convert the payoff table\\n\\n self._find_solutions() # Find all solutions to the optimization problem\\n self._process_solutions() # Identify the unique solutions\\n self._get_hv_indicator() # Compute the HV indicator\\n\\n # Save the results to an Excel file if requested\\n if self.opts.output_excel:\\n self._output_excel()\\n\\n # Compute the total runtime and print a summary of the results\\n self.runtime = round(self.runtime.get(), 2)\\n Helper.clear_line()\\n print(\\n f\\\"Solved {self.model.models_solved.value()} models for \\\"\\n f\\\"{self.num_unique_pareto_sols} unique Pareto solutions in \\\"\\n f\\\"{self.runtime} seconds\\\"\\n )\\n\\n # Log a summary of the results\\n self.logger.info(Helper.separator())\\n self.logger.info(f\\\"Runtime: {self.runtime} seconds\\\")\\n self.logger.info(f\\\"Models solved: {self.model.models_solved.value()}\\\")\\n self.logger.info(f\\\"Infeasibilities: {self.model.infeasibilities.value()}\\\")\\n self.logger.info(f\\\"Solutions: {self.num_sols}\\\")\\n self.logger.info(f\\\"Unique solutions: {self.num_unique_sols}\\\")\\n self.logger.info(f\\\"Unique Pareto solutions: {self.num_unique_pareto_sols}\\\")\\n self.logger.info(f\\\"Hypervolume indicator: {self.hv_indicator}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f57fe14d76194d4919d36fb73dcce81\",\n \"score\": \"0.6554548\",\n \"text\": \"def get_best_result(self):\\n # self.result_data\\n # raise NotImplementedError\\n param_names = ['r', 'z', 'phi', 'scale', 'align', 'cloud']\\n \\n\\n for chan in self.chan_list:\\n for i in self.index:\\n self.best_params[chan][i] = []\\n #loops over all the fit waveforms\\n # for model_num,single_wf_model in enumerate(self.wf_model):\\n model_num = i\\n single_wf_model = self.wf_model[i]\\n print(\\\"Calculating best values for {}\\\".format(model_num))\\n\\n for j in range(single_wf_model.num_params):\\n # loops over all the parameters\\n param_values = self.result_data[i][j]\\n\\n param_avg = np.mean(param_values)\\n param_std = np.std(param_values)\\n\\n self.best_params[model_num].append(param_avg)\\n\\n print(\\\"{name}: {avg:4.4f} +/- {std:4.4f}\\\".format(\\n name=param_names[j], \\n avg=param_avg, \\n std=param_std)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c22ab22848e4af7f6852a38c863bd374\",\n \"score\": \"0.6523312\",\n \"text\": \"def solve(self):\\n fit_partial = self.fit_partial\\n n_jobs = self.n_jobs\\n gen = self.gen\\n\\n # get results in parallel\\n all_res = Parallel(n_jobs=n_jobs)(\\n delayed(fit_partial)(\\n order=order,\\n seasonal_order=seasonal_order,\\n )\\n for order, seasonal_order in gen\\n )\\n\\n sorted_fits = _sort_and_filter_fits(all_res)\\n if self.trace and sorted_fits:\\n print(f\\\"\\\\nBest model: {str(sorted_fits[0])}\\\")\\n\\n return sorted_fits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"390db28702298605d2d7b3a6236568e8\",\n \"score\": \"0.6472718\",\n \"text\": \"def run(self):\\n self.time_start = time.time()\\n self.step = 0\\n self.step_idle = 0\\n\\n E = self.energy()\\n\\n prev_state = self.copy_state(self.state)\\n prev_energy = E\\n\\n self.best_state = self.copy_state(self.state)\\n self.best_energy = E\\n self.best_step = 0\\n self.energy_history = [E] * self.history_length\\n Ehmean = E\\n Ehvar = 0.\\n Nvar = float(max(self.history_length - 1, 1))\\n\\n if self.updates_every > 0:\\n self.update(self.step, self.step_idle, E, Ehmean, Ehvar)\\n steps_since_update = 0\\n\\n while not self.terminate_search() and not self.user_exit:\\n self.move()\\n E = self.energy()\\n\\n if E >= prev_energy:\\n self.step_idle += 1\\n else:\\n self.step_idle = 0\\n\\n v = self.step % self.history_length\\n Ev = self.energy_history[v]\\n if E < Ev or E <= prev_energy:\\n # accept candidate state\\n prev_state = self.copy_state(self.state)\\n prev_energy = E\\n\\n if E < self.best_energy:\\n self.best_state = self.copy_state(self.state)\\n self.best_energy = E\\n self.best_step = self.step\\n else:\\n # restore previous state\\n self.state = self.copy_state(prev_state)\\n E = prev_energy\\n\\n if E < Ev:\\n # Update energy history\\n self.energy_history[v] = E\\n\\n # and its mean and variance\\n dE = E - Ev\\n Ehmean_old = Ehmean\\n\\n Ehmean += dE / self.history_length\\n Ehvar += dE * (E-Ehmean+Ev-Ehmean_old) / Nvar\\n\\n self.step += 1\\n steps_since_update += 1\\n\\n if steps_since_update == self.updates_every:\\n self.update(self.step, self.step_idle, E, Ehmean, Ehvar)\\n steps_since_update = 0\\n\\n self.state = self.copy_state(self.best_state)\\n if self.save_state_on_exit:\\n self.save_state()\\n\\n self.time_end = time.time()\\n return self.best_state, self.best_energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ec8c15419ad8bc00193cb7e262615f0\",\n \"score\": \"0.646268\",\n \"text\": \"def optimal(self):\\n optimal_check = self.checker(True)\\n return optimal_check\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8707ac8ab5a432499cd686262931a91\",\n \"score\": \"0.6461675\",\n \"text\": \"def get_best_solution(self):\\n print \\\"getting best solution\\\"\\n greatest_number_of_words = 0\\n prelim_best_solutions = []\\n for psol in self.potential_solutions:\\n l = len(\\\" \\\".join(map(lambda x:x['title'],psol)))\\n if l == greatest_number_of_words:\\n prelim_best_solutions.append(psol)\\n elif l > greatest_number_of_words:\\n greatest_number_of_words = l\\n prelim_best_solutions = [psol]\\n best_solution = None\\n least_number_of_tracks = 999999\\n for bsol in prelim_best_solutions:\\n if len(bsol) < least_number_of_tracks:\\n least_number_of_tracks = len(bsol)\\n best_solution = bsol\\n return best_solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbafe1f015464da4a494c735c33ee07\",\n \"score\": \"0.6442411\",\n \"text\": \"def getSolution(self):\\n self.var_constr_dict = self.mapVarToConstraints()\\n\\n start = time.time()\\n solution = self.solver.getSolution(self)\\n self.runtime = time.time() - start\\n # solution is an assignment of problem, where the assignments for all variables are filled in. \\n return solution, self.getStatistics()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede2c84a0104dd19a0e4737190f698ad\",\n \"score\": \"0.63996756\",\n \"text\": \"def save_best_sol(self):\\n\\n self.save(result=self.best_solutions_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ba86d89bccfe98a869ed480491d257\",\n \"score\": \"0.6390985\",\n \"text\": \"def optimize(self):\\n rankedSol = self._rankSolutions()\\n bestSol = rankedSol[0]\\n # include best solution in next generation solutions\\n newSols = [bestSol]\\n\\n for s in rankedSol[1:]:\\n if np.random.uniform(0.0, 1.0) > 0.5:\\n a = self._computeA()\\n normA = np.linalg.norm(a)\\n if normA < 1.0:\\n newS = self._encircle(s, bestSol, a)\\n else:\\n # ##select random sol\\n randomSol = self._sols[np.random.randint(self._sols.shape[0])]\\n newS = self._search(s, randomSol, a)\\n else:\\n newS = self._attack(s, bestSol)\\n newSols.append(self._constrain_solution(newS))\\n\\n self._sols = np.stack(newSols)\\n self._a -= self._aStep\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c1eca33dce422738cb450fdee65c6e\",\n \"score\": \"0.63894033\",\n \"text\": \"def solver_optimize(self):\\n parts = self.feats_obj.feat_partitions\\n self.N_s = [None for i in parts]\\n self.opt_sol = [None for i in parts]\\n self.norm_z = [None for i in parts]\\n self.compute_zero_stats()\\n\\n for i, partition in enumerate(parts):\\n self.compute_data_stats(partition, i)\\n self.compute_zc(partition, i)\\n self.compute_theta(partition, i)\\n\\n # print('N_s is: ', self.N_s)\\n # print('Z_c is: ', self.norm_z)\\n # print('Theta is: ', self.opt_sol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d4b156c2e290526811e8a8e8d75a345\",\n \"score\": \"0.6357096\",\n \"text\": \"def _solve(self):\\n #algorithm = self.config['algorithm'](self.config)\\n \\n self.output = self.algorithm.run(self.config['u_0'],self.config['TOL'],self.config['MAXIT'])\\n print('Iterations:' + str(self.output['iter']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e45c5f527db22d70313ad80ec8673d\",\n \"score\": \"0.6339497\",\n \"text\": \"def solve(self):\\n\\n while True:\\n self.iteration += 1\\n\\n logger.debug('iteration: n=%s', self.iteration)\\n\\n self._iterate()\\n\\n if self._converged():\\n break\\n\\n return self._find_strongest_solution()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149015545ba83e85aff88252b4aa13f7\",\n \"score\": \"0.63234216\",\n \"text\": \"def run(self, iterations, run_from_start=True):\\n if run_from_start:\\n self.run_init()\\n for _ in range(iterations):\\n self._build_ant_solutions()\\n self._update_iteration_best()\\n self._update_global_best()\\n self.global_pheromone_update(self.global_best)\\n self.iteration += 1\\n return self.global_best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e2f056ef0ae1d598ecbc81e870cab69\",\n \"score\": \"0.62895966\",\n \"text\": \"def best_fitness(self):\\n return self.fitness[self.order[-1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0f291c79ddcfe9833db29d6e0a3b0f6\",\n \"score\": \"0.62888795\",\n \"text\": \"def _solve_impl(self):\\n\\n self.start_time = clock() # Used to show the total time took the process\\n bellman_updates = 0 # used to track the performance improvement.\\n converged = False\\n iteration = 0\\n\\n num_states = self.representation.num_states_total\\n\\n while self.has_time() and not converged:\\n iteration += 1\\n\\n # Store the weight vector for comparison\\n prev_weight = self.representation.weight.copy()\\n\\n # Sweep The State Space\\n for i in range(num_states):\\n\\n s = self.representation.stateID2state(i)\\n # Sweep through possible actions\\n if self.domain.is_terminal(s):\\n continue\\n for a in self.domain.possible_actions(s):\\n\\n self.bellman_backup(s, a, ns_samples=self.ns_samples)\\n bellman_updates += 1\\n\\n # Create Log\\n if bellman_updates % self.log_interval == 0:\\n performance_return, _, _, _ = self.performance_run()\\n self._log_updates(performance_return, bellman_updates)\\n\\n # check for convergence\\n weight_diff = l_norm(prev_weight - self.representation.weight)\\n converged = weight_diff < self.convergence_threshold\\n\\n # log the stats\\n (\\n perf_return,\\n perf_steps,\\n perf_term,\\n perf_disc_return,\\n ) = self.performance_run()\\n self.logger.info(\\n \\\"PI #%d [%s]: BellmanUpdates=%d, ||delta-weight_vec||=%0.4f, \\\"\\n \\\"Return=%0.4f, Steps=%d\\\"\\n % (\\n iteration,\\n hhmmss(deltaT(self.start_time)),\\n bellman_updates,\\n weight_diff,\\n perf_return,\\n perf_steps,\\n )\\n )\\n\\n # Show the domain and value function\\n if self._visualize_mode:\\n self.domain.show_learning(self.representation)\\n\\n # store stats\\n self.result[\\\"bellman_updates\\\"].append(bellman_updates)\\n self.result[\\\"return\\\"].append(perf_return)\\n self.result[\\\"planning_time\\\"].append(deltaT(self.start_time))\\n self.result[\\\"num_features\\\"].append(self.representation.features_num)\\n self.result[\\\"steps\\\"].append(perf_steps)\\n self.result[\\\"terminated\\\"].append(perf_term)\\n self.result[\\\"discounted_return\\\"].append(perf_disc_return)\\n self.result[\\\"iteration\\\"].append(iteration)\\n\\n if converged:\\n self.logger.info(\\\"Converged!\\\")\\n self.log_value()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2b1c534c039f242d2a7c458ea6fb951\",\n \"score\": \"0.6226838\",\n \"text\": \"def calculateBest():\\r\\n\\r\\n if var.score>var.best:\\r\\n setNewBest()\\r\\n return var.score\\r\\n else: return var.best\\r\\n #if the actual score is bigger than the best score\\r\\n #set the best score to the actual score\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7fe40017c2980b624d976cb8e5d1632\",\n \"score\": \"0.6216328\",\n \"text\": \"def search(self, config):\\n self.iteration = 0\\n while self.iteration < self.maxIterations:\\n self.GAStep(config)\\n self.iteration += 1\\n\\n print (\\\"Total iterations: \\\",self.iteration)\\n print (\\\"Best Solution: \\\", self.best.getFitness())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ba52f8f0040d777dcae8c696c03ef8\",\n \"score\": \"0.6213454\",\n \"text\": \"def _solution(self):\\n return self.__solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07d78b5b7da1a48b218ae692e26ffa46\",\n \"score\": \"0.62130654\",\n \"text\": \"def solve(self):\\n \\n # Do the actual run to find the fit and trendline\\n self.fitModel = WarmingFitModel(self.dates, self.temps)\\n self.constants = self.fitModel.solve()\\n self.devs = self.fitModel.deviations()\\n \\n # Verify the optimality of solutions\\n xvariabilities = { \\\"min\\\" : [], \\\"max\\\" : [] }\\n if not self.DISABLE_VARIABILITY_CHECK:\\n for op in xvariabilities.keys():\\n for xsub in [0, 1]:\\n variabilityModel = WarmingVariabilityModel(self.dates, self.temps, xsub, op, self.constants, sum(list(map(abs, self.devs))))\\n xvariabilities[op].append(variabilityModel.solve())\\n pprint.pprint(xvariabilities)\\n \\n # Verify the chosen value for solar cycle length\\n cycleLengths = [9.7 + 0.2 * i for i in range(11)]\\n cycleDevs = []\\n if not self.DISABLE_CYCLE_LENGTH_CHECK:\\n for cycleLength in cycleLengths:\\n solarFitModel = WarmingFitModel(self.dates, self.temps, solarCycle=cycleLength)\\n cycleDevs.append(sum([abs(dev) for dev in solarFitModel.deviations()]))\\n pprint.pprint(cycleDevs)\\n \\n stdevs = [0 for x in self.constants]\\n if not self.DISABLE_CONFIDENCE_INTERVALS:\\n # Do a bunch more iterations to find a confidence interval for the original fit\\n ITER_COUNT = 50\\n random.seed()\\n xstar = []\\n for fuzzIter in range(ITER_COUNT):\\n fuzzedTemps = [self.temps[i] + random.choice(self.devs) for i in list(range(len(self.dates)))]\\n fuzzedModel = WarmingFitModel(self.dates, fuzzedTemps)\\n fuzzedConstants = fuzzedModel.solve()\\n print(\\\"Fuzzy constants: \\\" + str(fuzzedConstants))\\n xstar.append(fuzzedConstants)\\n \\n # Actually compute the confidence interval\\n if ITER_COUNT > 0:\\n cMeans = [sum(fuzzedX[xpos] for fuzzedX in xstar) / float(ITER_COUNT) for xpos in range(len(self.constants))]\\n for pos in range(len(self.constants)):\\n cDevs = [(xstar[i][pos] - cMeans[pos]) ** 2 for i in range(ITER_COUNT)]\\n cStdDev = math.sqrt(sum(cDevs) / float(len(cDevs)))\\n stdevs[pos] = cStdDev\\n \\n return {\\\"fit\\\" : [(self.constants[i], 2 * stdevs[i]) for i in range(len(self.constants))], \\\\\\n \\\"xvariabilities\\\" : xvariabilities, \\\\\\n \\\"cycle\\\" : (cycleLengths, cycleDevs) }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b1caf17582b8c1975b073d829c4e34\",\n \"score\": \"0.62079585\",\n \"text\": \"def find_solution(self):\\n start_time = timer.time()\\n result = []\\n constraints = []\\n \\n ##############################\\n # Task 2.4 : Addressing Failure\\n #reordering agent priority using heuristic costs\\n agent_max_cost = []\\n for agent in range(self.num_of_agents):\\n max_cost = -1\\n for loc, cost in self.heuristics[agent].items():\\n print(loc, cost)\\n if cost > max_cost:\\n max_cost = cost\\n agent_max_cost.append([cost, agent])\\n agent_max_cost.sort()\\n agent_max_cost.reverse()\\n \\n for _, i in agent_max_cost:\\n ##############################\\n\\n # original\\n # for i in range(self.num_of_agents): # Find path for each agent\\n \\n path = a_star(self.my_map, self.starts[i], self.goals[i], self.heuristics[i],\\n i, constraints)\\n if path is None:\\n raise BaseException('No solutions')\\n result.append(path)\\n \\n print('Agent[{}]'.format(i), 'path = ', path)\\n \\n ##############################\\n # Task 2: Add constraints here\\n # Useful variables:\\n # * path contains the solution path of the current (i'th) agent, e.g., [(1,1),(1,2),(1,3)]\\n # * self.num_of_agents has the number of total agents\\n # * constraints: array of constraints to consider for future A* searches\\n\\n # Task 2.1 : adding vertex constraints\\n # for node in path:\\n # constraint = dict()\\n # constraint['agent'] = i\\n # constraint['loc'] = [node[0], None]\\n # constraint['timestep'] = node[1]\\n # constraints.append(constraint)\\n \\n # build constraint from a path\\n for p in range(len(path)):\\n curr_loc = path[p]\\n next_loc = None\\n curr_timestep = p\\n \\n # vertex constraint\\n constraint = dict()\\n constraint['agent'] = i\\n constraint['loc'] = [curr_loc, next_loc]\\n constraint['timestep'] = curr_timestep\\n constraints.append(constraint)\\n \\n # edge constraint except start cell\\n if p > 0:\\n prev_loc = path[p - 1]\\n next_timestep = p\\n \\n constraint = dict()\\n constraint['agent'] = i\\n constraint['loc'] = [curr_loc, prev_loc]\\n constraint['timestep'] = next_timestep\\n constraints.append(constraint)\\n \\n ##############################\\n \\n # Task 2.3: additional constraints\\n # agent_0 reached its goal cell at timestep 3,\\n # but, agent_1 will reach to the goal cell at timestep 7\\n # then, agent_0 will be wait \\n \\n # but, we don't know the value 7\\n # so, just estimate this value with the prior knowledge\\n # for example, # of a valid cell\\n \\n ##############################\\n \\n last_loc = path[-1]\\n last_timestep = len(path) -1\\n \\n max_cells = 0\\n \\n for x in range(len(self.my_map)):\\n for y in range(len(self.my_map[0])):\\n if not self.my_map[x][y]:\\n max_cells += 1\\n \\n for timestep in range(last_timestep + 1, max_cells):\\n constraint = dict()\\n constraint['agent'] = i\\n constraint['loc'] = [last_loc, None]\\n constraint['timestep'] = timestep\\n constraints.append(constraint) \\n\\n self.CPU_time = timer.time() - start_time\\n\\n print(\\\"\\\\n Found a solution! \\\\n\\\")\\n print(\\\"CPU time (s): {:.2f}\\\".format(self.CPU_time))\\n print(\\\"Sum of costs: {}\\\".format(get_sum_of_cost(result)))\\n print(result)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b65a27ab5b4a47ccc18024490760dc46\",\n \"score\": \"0.61903894\",\n \"text\": \"def result(self):\\n return self.best.x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2e8b4ec0309484e14adeec64980705\",\n \"score\": \"0.6185776\",\n \"text\": \"def getBestSol(sqe, Res, c_ms):\\n #---docstring------------------------------------------------------------------\\n #---end-docstring--------------------------------------------------------------\\n res = dc(Res)\\n C_ms = dc(c_ms)\\n lsqSc = numpy.zeros(len(res),'d') + numpy.inf \\n lsqMu = numpy.zeros(len(res),'d') + numpy.inf \\n lsqSl = numpy.zeros(len(res),'d') + numpy.inf\\n for i in range(len(res)):\\n try:\\n zeroCut = sqe.zeroInd + 1\\n sqeCut = sqe.zeroInd+res[i][1].cutoffInd\\n slopeCut = sqe.slopeCut()\\n sqeLin = sqe.se[sqeCut:slopeCut]\\n mulSqeLin = nar(nar(res[i][0][1:]))[sqeCut:slopeCut]\\n eLin = sqe.e[sqeCut:slopeCut]\\n sqeSlope,inter = mfit.poly(eLin,sqeLin ,1)\\n mulSlope,inter = mfit.poly(eLin,mulSqeLin,1)\\n line = mfit.poly_of_x(eLin,mfit.poly(eLin,sqeLin ,1) )\\n lsqSl[i] = (sqeSlope-mulSlope)**2.0\\n lsqMu[i] = nar( (mulSqeLin - line )**2.0 /float(len(line)) )\\n lsqSc[i] = nar( nar( (sqe.sqe - nar(res[i][0]) )**2.0 ) )\\n except:\\n pass\\n \\n start = 0\\n while start<len(lsqSc) and \\\\\\n (numpy.isnan(lsqSc[start]) or numpy.isinf(lsqSc[start]) \\\\\\n or numpy.isnan(lsqMu[start]) or numpy.isinf(lsqMu[start]) \\\\\\n or numpy.isnan(lsqSl[start]) or numpy.isinf(lsqSl[start])):\\n start += 1\\n \\n C_ms = C_ms[start:]\\n res = res[start:]\\n lsqSc = lsqSc[start:]\\n lsqMu = lsqMu[start:]\\n lsqSl = lsqSl[start:]\\n lsqSc_copy = numpy.array(lsqSc)\\n lsqMu_copy = numpy.array(lsqMu)\\n lsqSl_copy = numpy.array(lsqSl)\\n numBetter = lambda x,c : len(filter(lambda y : y < 0.98*x,c) )\\n for i in range(len(C_ms)):\\n lsqSc_copy[i] = numBetter(lsqSc[i],lsqSc)\\n lsqMu_copy[i] = numBetter(lsqMu[i],lsqMu)\\n lsqSl_copy[i] = numBetter(lsqSl[i],lsqSl)\\n lsqSc= numpy.array(lsqSc_copy )\\n lsqMu= numpy.array(lsqMu_copy )\\n lsqSl= numpy.array(lsqSl_copy )\\n \\n LSQ = (lsqSc + lsqMu + lsqSl)/3.0\\n \\n best = LSQ.argmin()\\n sqeCalc = res[best][0]\\n dosCalc = res[best][1]\\n cmsCalc = res[best][2]\\n return sqeCalc,dosCalc,cmsCalc,res,C_ms,lsqSc,lsqMu,lsqSl,LSQ\",\n \"title\": \"\"\n },\n {\n \"docid\": \"144adfb5462ee5a69aaeb4fed0faad88\",\n \"score\": \"0.61844385\",\n \"text\": \"def optimize(self):\\n\\n if self.scheme == \\\"linear\\\":\\n iterations = floor(self.temp/self.cooling_rate)\\n\\n elif self.scheme == \\\"exp\\\":\\n iterations = floor(log(1/self.temp)/log(1 - self.cooling_rate))\\n\\n for i in range(iterations):\\n progress = round((i/iterations)*100, 2)\\n if i % 20 == 0:\\n print(f\\\"Progress: {progress}%\\\")\\n if i == 0:\\n self.get_costs()\\n best_costs = copy.deepcopy(self.all_costs[0])\\n best_option = copy.deepcopy(self.batteries)\\n\\n self.anneal()\\n self.get_costs()\\n\\n if i > 0 and self.all_costs[-1] < best_costs:\\n best_option = copy.deepcopy(self.batteries)\\n best_costs = self.all_costs[-1]\\n\\n if self.scheme == \\\"linear\\\":\\n self.temp = self.temp - self.cooling_rate\\n elif self.scheme == \\\"exp\\\":\\n self.temp *= 1 - self.cooling_rate\\n\\n self.batteries = best_option\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e51584fbf426cc78699e1fa846fb810\",\n \"score\": \"0.61729866\",\n \"text\": \"def post_optimization(pdb):\\n print('Optimizing...')\\n energies = optimize(pdb, os.path.abspath(os.getcwd()))\\n print('Energy before optimizing: %s' % str(energies[0][0]))\\n print('Energy after optimizing: %s' % str(energies[1][0]))\\n print('Model completed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cbbdf5b657c88ce44e3b8c8d148fa37\",\n \"score\": \"0.6171333\",\n \"text\": \"def guessBest(self):\\n #Get the true(past tries excluded) possible values for each value\\n search_field = {i:(self.possibleValues[i] - self.impossibleValues[i]) for i in self.possibleValues}\\n #Get the counts of times a number is a possible value @deprecated\\n #counts = {i:sum(i in search_field[coord_values] for coord_values in search_field if len(search_field[coord_values])) for i in range(10)} \\n #Get the coord with the least number of possible values\\n bestCoord = min(search_field, key=lambda k: len(search_field[k]) if len(search_field[k]) > 1 else 10)\\n #Apply the additional heuristic, sort the chosen coordinates possible values by how often those values could be other solutions @ deprecated\\n #vals = sorted(search_field[bestCoord], key = lambda k: counts[k], reverse=True)\\n vals = list(search_field[bestCoord])\\n #print \\\"Guessing\\\", bestCoord, vals[0]\\n #self.printData()\\n self.setValue(bestCoord, guess=True, guessVal = vals[0])\\n self.root = (bestCoord, self.getCoordValue(bestCoord))\\n self.possibleValues[bestCoord] = {self.getCoordValue(bestCoord)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379f6fcd7411622d26dc90b601fbb81e\",\n \"score\": \"0.61595905\",\n \"text\": \"def extra_convergence_criteria(self, results):\\n # type: (List[Result]) -> bool\\n self.iter_count += len(results)\\n\\n # Skip the setup period\\n if not results:\\n return False\\n\\n # Update the best local\\n crtl_elapsed_time = 0\\n self.best_unchange += len(results)\\n valid_result = 0\\n iter_best_result = None\\n for result in results:\\n latency = result.time\\n resource = result.size\\n crtl_elapsed_time = max(crtl_elapsed_time, result.elapsed)\\n if latency == float('inf'):\\n continue\\n elif resource >= 1.0:\\n continue\\n\\n valid_result += 1\\n if (not iter_best_result) or latency < iter_best_result.time:\\n iter_best_result = result\\n\\n self.elapsed_time += crtl_elapsed_time\\n\\n # Terminate this partition if we cannot get a valid\\n # result from the seed design point.\\n #if (not self.best_result and self.iter_count >= len(self.seeds)\\n # and valid_result == 0):\\n # return True\\n\\n # Update the history\\n if iter_best_result:\\n if ((not self.best_result) or\\n iter_best_result.time < self.best_result.time):\\n self.best_result = iter_best_result\\n self.best_unchange = -1\\n if self.best_result is not None:\\n self.log_best(self.iter_count, self.elapsed_time,\\n self.best_result.time)\\n\\n # Keep the best result of current iteration for computing\\n # the entropy at the next iteration.\\n self.prev_result = iter_best_result\\n\\n # Update the best global\\n count = 1\\n new_ranking = float('inf')\\n if os.path.exists('.who_is_the_best'):\\n with open('.who_is_the_best') as filep:\\n for line in filep:\\n latency = float(line.replace('\\\\n', ''))\\n if self.best_result:\\n if latency == float(self.best_result.time):\\n # Found itself\\n new_ranking = count\\n count += 1\\n new_ranking /= count\\n\\n # Add one thread as the manager requested\\n if os.path.exists('.add_threads'):\\n with open('.add_threads') as filep:\\n line = filep.readline()\\n addition_thread = int(line.replace('\\\\n', ''))\\n self.more_parallelism(addition_thread)\\n run_command('rm -f .add_threads')\\n\\n # Skip the warm up period\\n if new_ranking == float('inf') or count < 4:\\n return False\\n\\n # Skip the rest criteria to let partitions run as long as possible\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32bff97f4800c45fadf86ac73a30a350\",\n \"score\": \"0.61526996\",\n \"text\": \"def get_best_values(self):\\n return self.g_best_score, self.g_best_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d035d1802dc286f7a043d398d9b8e29d\",\n \"score\": \"0.6148896\",\n \"text\": \"def optimize():\\n stopwatch = StopWatch()\\n\\n ANNEAL_AREA = False\\n logger = LoggerUtils.configure_log(name='Optimization script', use_console=True, use_file=True)\\n\\n click.clear()\\n\\n metrics = GeneticAlgorithmConstants();\\n\\n # Generate initial population\\n population = Evolution.populate(initial=True)\\n\\n # Calculate fitness for each individual and sort them\\n ranked_pop = Evolution.adaptability(population)\\n \\n generation = 0\\n same_solution_count = 0\\n optimal = {\\\"genome\\\": None, \\\"fitness\\\": None, \\\"links\\\": None}\\n\\n logger.info('Starting first optimization loop.')\\n # The first optimization loop defines the number of nodes for each of the line instalation areas, \\n # while keeping squared areas number of nodes constant\\n\\n # Main loop for the first optimization\\n while(generation < metrics.MAX_GENERATIONS and same_solution_count != metrics.MAX_STAGNATED_OPTIMAL):\\n generation += 1\\n \\n # Perform the crossover \\n ranked_pop = Evolution.crossover(ranked_pop, metrics.CROSSOVER_PROBABILITY)\\n\\n # Mutate population\\n ranked_pop = Evolution.mutate(ranked_pop, metrics.MUTATION_PROB)\\n\\n # Next generation\\n population = [individual.get('genome') for individual in ranked_pop]\\n\\n # Calculate fitness for each individual and sort them\\n ranked_pop = Evolution.adaptability(population)\\n\\n # Update the optimal value and count improvements\\n if optimal.get('fitness') is None:\\n optimal = copy.deepcopy(ranked_pop[0])\\n elif ranked_pop[0].get('fitness') < optimal.get('fitness'):\\n optimal = copy.deepcopy(ranked_pop[0])\\n same_solution_count = 0\\n else:\\n same_solution_count += 1\\n\\n # Perform environment pressure\\n ranked_pop = Evolution.environment_pressure(ranked_pop)\\n \\n logger.info(''.join(['[Gen {}] '.format(generation), 'Optimal:{solution: ', str(optimal.get('genome')), ', fitness: ', str(optimal.get('fitness')), \\n ', min_links: ', str(optimal.get('links')), '}']))\\n \\n logger.info('Finished first optimization with %s generations. Total time: %s.' % (generation, stopwatch.read()))\\n logger.info('OPTIMAL FOR FIRST OPTIMIZATION: N1: {}, N2: {}, N3: {}, N4: {} '.format(optimal['genome'][0], \\n optimal['genome'][1], optimal['genome'][2], optimal['genome'][3]))\\n \\n # SECOND OPTIMIZATION ROUTINE:\\n # Voronoi : generate optimized points distribution in an area.\\n # L-BFGS-B : \\n # POSSIBILITY: Model the second optimization problem as max f(x) = min SUM distances(i,j) \\n\\n # GETTING READY FOR SECOND OPTIMIZATION\\n\\n n1, n2, n3, n4 = optimal['genome']\\n # network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4)\\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, n4)\\n\\n area_nodes = n4\\n generation = 0\\n\\n logger.info('Starting second optimization.')\\n # The second optimization cares to find the min number of nodes within squared areas. At this part of the code\\n # it's assumed that the min number of nodes for the line areas are known due the first optimization loop.\\n\\n logger.info('Initial genome [N1 = {}, N2 = {}, N3 = {}, N4 = {}].'. format(n1, n2, n3, n4))\\n stopwatch_loop_2 = StopWatch()\\n\\n\\n network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4) \\n lines = ['N1', 'N2', 'N3', 'N4']\\n nodeArray = [network.get('SINK')]\\n for line in lines:\\n nodeArray += list(network.get(line))\\n # Plotter.plot_node_list(nodeArray, title='First Optimization Result', xLabel='Grid Coordinate (m)', yLabel='Grid Coordinate (m)')\\n\\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'first.geojson')\\n\\n # Main loop for the second optimization\\n while generation < metrics.MAX_GENERATIONS and fitness.minValidLinks >= 3:\\n # First find the min number n of nodes of node at each side of the nxn grid\\n \\n generation += 1\\n area_nodes -= 1\\n\\n if area_nodes == 0:\\n break\\n\\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, area_nodes)\\n\\n logger.info('[Gen {}] Fitness for N4 = {}: {}'.format(generation, area_nodes, fitness))\\n\\n # When the loop ends, area_nodes have a number with invalid fitness for n4.\\n # So the correct min number is the result area_nodes + 1.\\n n4 = area_nodes + 1\\n fitness = NetworkModel.getFitnessForVariables(n1, n2, n3, n4)\\n\\n logger.info('Finished second optimization with %s generations. Total time: %s.' % (generation, stopwatch_loop_2.read()))\\n logger.info('Optimal for second optimization: N1: {}, N2: {}, N3: {}, N4: {} '.format(n1, n2, n3, n4))\\n logger.info('Fitness for second optimization: {}'.format(fitness))\\n\\n # Retrieve network \\n network = PreProcess.generateNetworkForConstants(n1, n2, n3, n4)\\n\\n metrics = SelectiveAneelingConstants()\\n\\n networkArea = list(network.get('N4'))\\n current_optimal_network_area = networkArea \\n\\n if len(networkArea) >= 2 :\\n fitness = NetworkModel.getFitnessForNetwork(networkArea)\\n generation = 1\\n\\n # Define area boundaries\\n boundaries = dict()\\n boundaries['MIN_X'] = GlobalParameters.N4_DIM.top_left[0]\\n boundaries['MAX_X'] = GlobalParameters.N4_DIM.botom_right[0]\\n boundaries['MIN_Y'] = GlobalParameters.N4_DIM.botom_right[1]\\n boundaries['MAX_Y'] = GlobalParameters.N4_DIM.top_left[1]\\n\\n if ANNEAL_AREA: logger.info('Starting third optimization. Selective aneeling')\\n\\n # Finds the optimal number of nodes within area maximizing the distance between them\\n while generation < metrics.MAX_GENERATIONS and fitness.minValidLinks >= 2 and len(networkArea) >= 2 and ANNEAL_AREA:\\n\\n current_optimal_network_area = networkArea\\n networkArea.pop()\\n\\n # Configuring Annealer\\n annealer = DistanceAnnealing(networkArea, boundaries)\\n annealer.copy_strategy = metrics.COPY_STRATEGY\\n\\n # Calibrating Annealer\\n click.secho('Calibrating annealer', fg='yellow')\\n auto_schedule = annealer.auto(minutes=1)\\n annealer.set_schedule(auto_schedule)\\n click.secho('Annealer calibrated! Parameters: {}'.format(auto_schedule), fg='green')\\n\\n networkArea, energy = annealer.anneal()\\n \\n fitness = NetworkModel.getFitnessForNetwork(networkArea)\\n\\n logger.info(\\\"[GEN {}] Number of Links: {} Energy: {}, fitness: {}\\\".format(len(networkArea), generation, energy, fitness))\\n\\n generation += 1\\n \\n \\n logger.info('Finished second optimization. Nodes positions inside area defined. {} nodes installed.'.format(len(current_optimal_network_area)))\\n\\n # Saving the new area node disposition\\n network['N4'] = np.array(current_optimal_network_area)\\n\\n # Adding all nodes of the network in one array\\n\\n lines = ['N1', 'N2', 'N3', 'N4']\\n nodeArray = [network.get('SINK')]\\n\\n for line in lines:\\n nodeArray += list(network.get(line))\\n \\n Plotter.plot_node_list(nodeArray, add=True, annotate=False)\\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'second.geojson')\\n \\n logger.info('Annealing nodes. Current configuration have %s nodes.' %(len(nodeArray)))\\n annealer = PositionAnnealing(nodeArray)\\n annealer.copy_strategy = metrics.COPY_STRATEGY\\n\\n # click.secho('Calibrating annealer.', fg='yellow')\\n\\n # auto_schedule = annealer.auto(minutes=1)\\n # annealer.set_schedule(auto_schedule)\\n\\n # click.secho('Annealer calibrated!', fg='green')\\n # logger.info('Annealer parameters: {}'.format(auto_schedule))\\n\\n annealer.Tmax=1.5\\n annealer.Tmin=1e-10\\n\\n nodeArray, energy = annealer.anneal()\\n\\n network[line] = nodeArray\\n \\n click.secho('Finished Optimization', fg='green')\\n logger.info('Finished network optimization. Total elapsed time: {}'.format(stopwatch.read()))\\n logger.info('Writing result geoJSON at {}.'.format(OUTPUT_PATH))\\n\\n nodesCoordinates = [node.getCoordinates() for node in nodeArray]\\n GeoUtils.writeGeoJSON(nodesCoordinates, OUTPUT_PATH + 'result.geojson')\\n\\n Plotter.plot_node_list(nodeArray, title='Representação gráfica da Rede', xLabel='Longitude', yLabel='Latitude',\\n color='red', annotate=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6b2d1b0b101462372338089711edf7\",\n \"score\": \"0.61201286\",\n \"text\": \"def calc_energy(self, force=True):\\n if self.atoms_moved:\\n self.timer.switch_on(\\\"energy\\\")\\n if self.qeq:\\n self.timer.switch_to(\\\"qeq\\\")\\n self.qeq.preopt_q()\\n self.timer.switch_to(\\\"energy\\\")\\n if self.virtual_atoms:\\n self.timer.switch_to(\\\"virt atoms set pos\\\")\\n self.virt_atom_set_pos()\\n self.timer.switch_to(\\\"energy\\\")\\n if self.enforce_nlist_rebuild:\\n # if some operation (move atoms/molecules around) could break the neigborlist enforce its rebuild\\n self.dlp_nlst.nlst_notest = True\\n self.dlp_nlst.nlst_rebuild = True\\n ### nullify stress tensor\\n self.dlp_conf.stress[:]=0.0\\n ### MAIN ROUTINE TO COMPUTE ENERGY AND FORCE IN DL_POLY ###\\n self.dlp.calc_energy_force()\\n ### ################################################### ###\\n ### MAIN ROUTINE TO COMPUTE ENERGY AND FORCE IN DL_POLY ###\\n if self.QMMM:\\n self.timer.switch_to(\\\"qmmm\\\")\\n self.QMMM_eng = self.QMMM_interface(force)\\n self.timer.switch_to(\\\"energy\\\")\\n # for a QMMM run we report the energies every step\\n self.report_energies()\\n if self.virtual_atoms:\\n self.timer.switch_to(\\\"virt atoms dist force\\\")\\n self.virt_atom_distribute_force()\\n self.timer.switch_to(\\\"energy\\\")\\n self.atoms_moved = False\\n if self.enforce_nlist_rebuild:\\n self.dlp_nlst.nlst_notest = False\\n self.dlp_nlst.nlst_rebuild = False\\n self.enforce_nlist_rebuild = False\\n# if self.QMMM:\\n# self.timer.switch_to(\\\"qmmm\\\")\\n# self.QMMM_eng = self.QMMM_interface(force)\\n# self.timer.switch_to(\\\"energy\\\")\\n# # for a QMMM run we report the energies every step\\n# self.report_energies()\\n if self.extra_term:\\n self.timer.switch_to(\\\"extra term\\\")\\n self.extra_eng = self.extra_term()\\n self.timer.switch_to(\\\"energy\\\")\\n if self.plumed:\\n self.timer.switch_to(\\\"metamd plumed\\\")\\n self.meta_eng = self.plumed_energy()\\n self.timer.switch_to(\\\"energy\\\")\\n if self.constraint:\\n self.multiply_force(self.constraint_mask)\\n for s in self.extra_systems:\\n self.timer.switch_to(s.get_name())\\n s.calc_energy()\\n self.timer.switch_to(\\\"energy\\\")\\n self.timer.switch_off()\\n # return configuration energy in current units\\n energy = self.dlp.engcfg/self.dlp.engunit\\n if self.QMMM: energy += self.QMMM_eng\\n if self.extra_term: energy += self.extra_eng\\n if self.plumed: energy += self.meta_eng\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8abf4c4ebfe4d352c7ad6e941203b1e7\",\n \"score\": \"0.61065435\",\n \"text\": \"def get_solutions_fitness(self, solution):\\n value = self.get_solutions_value(solution)\\n return self.get_fitness(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d65270fe6d8d9e8590e16fe04479aff\",\n \"score\": \"0.6098882\",\n \"text\": \"def compute_solution(self,dataset):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50508a445861c4588a2e65c595bf6459\",\n \"score\": \"0.60829246\",\n \"text\": \"def getOwnBestFitness(self):\\r\\n\\t\\t\\r\\n\\t\\treturn self.ownBestFitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4add5b38f8e282631cc6b0b016fc55\",\n \"score\": \"0.6068796\",\n \"text\": \"def optimal(self):\\n return np.max(self.expected_rewards())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330e6fdf9357f731486615c89f7e2c32\",\n \"score\": \"0.6067864\",\n \"text\": \"def getBestFit(self):\\n self.envLock.acquire()\\n best = self.environment.getMostAdapted()\\n self.envLock.release()\\n return best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380cba45db1ed8aaf173abe49ca24a3a\",\n \"score\": \"0.6064149\",\n \"text\": \"def get_solution(self):\\n index = -1\\n max_f = -math.inf\\n\\n for i in range(self.N):\\n if self.train_v[i] > 1.2 and self.train_f[i] > max_f:\\n max_f = self.train_f[i]\\n index = i\\n return self.train_x[index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4596652f65f223725709173384ec358d\",\n \"score\": \"0.6045643\",\n \"text\": \"def greedy():\\n\\tglobal table, weights, values, capacity, max_value, best_solution, unit_values\\n\\tunit_values = [((values[i]*1.0)/weights[i],i,weights[i],values[i]) for i in xrange(len(weights))]\\n\\tunit_values.sort()\\n\\tinv_sort_order = [(unit_values[i][1],i) for i in xrange(len(unit_values))]\\n\\tinv_sort_order.sort()\\n\\tsort_order = [x[1] for x in inv_sort_order]\\n\\t\\n\\tweight = 0\\n\\tmax_value = 0\\n\\tbest_solution = [0]*len(weights)\\n\\ti = len(weights) - 1\\n\\twhile(weight < capacity and i >= 0):\\n\\t\\tif weight + unit_values[i][2] <= capacity:\\n\\t\\t\\tbest_solution[i] = 1\\n\\t\\t\\tmax_value += unit_values[i][3]\\n\\t\\t\\tweight += unit_values[i][2]\\n\\t\\telse:\\n\\t\\t\\tbest_solution[i]=0\\n\\t\\ti -= 1\\n\\n\\tbest_solution = [best_solution[i] for i in sort_order]\\n\\t\\n\\treturn {'value':max_value, 'taken':best_solution, 'optimality':0}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af394f586b631d9fba6859afb2e6cc75\",\n \"score\": \"0.60455203\",\n \"text\": \"def solve(self):\\n convergence = False\\n val = 0\\n for i in range(self.maxiter):\\n grad = self.dR()\\n self.x = self.x - self.optStep()*grad\\n if np.sum(np.abs(grad)) < self.eps*(np.log(self.x.shape[0])):\\n convergence = True\\n break\\n\\n if not convergence:\\n print('WARNING: Did not converge. Try increasing maxiter or a smaller eps.') \\n\\n return(self.eig,self.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc50367fe11ae1b2a1e4fd8e6b9c5b37\",\n \"score\": \"0.60270655\",\n \"text\": \"def betterEvaluationFunction(currentGameState):\\n \\\"*** YOUR CODE HERE ***\\\"\\n #more food = worse score\\n #closer food = good score\\n #closer than 3 ghost = bad score\\n \\n foodGrid = currentGameState.getFood()\\n food = foodGrid.asList()\\n pos = currentGameState.getPacmanPosition()\\n ghosts = []\\n gdists = []\\n for i in range(1, currentGameState.getNumAgents()):\\n g = currentGameState.getGhostPosition(i)\\n ghosts.append(g)\\n gdists.append(manhattanDistance(pos, g))\\n \\n #fterm = len(food)\\n fterm = foodGrid.width*foodGrid.height-len(food)\\n #gterm = closest(pos, ghosts)[0]\\n gterm = 0\\n fdist, floc = closest(pos, food)\\n \\n # breadth first search\\n #problem = PositionSearchProblem(currentGameState, goal=floc, start=pos, warn=False)\\n #realDist = len(breadthFirstSearch(problem))\\n \\n dterm = 1/float(fdist+1)\\n \\n #coefficients\\n a = 10\\n b = 1\\n c = 15\\n\\n for d in gdists:\\n if d<3:\\n gterm-=50\\n if d<2:\\n gterm-=50\\n \\n total = a*fterm + b*gterm + c*dterm\\n \\n # break ties\\n #if right:\\n # total+=2\\n #if up:\\n # total+=1\\n return total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287e49f8017272b659afe2b7a22ade44\",\n \"score\": \"0.60227233\",\n \"text\": \"def solve(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80d1d5c90fceaa679fdec499b70a5175\",\n \"score\": \"0.60214704\",\n \"text\": \"def evaluate_best_agent(self):\\n\\n eval_eps = Config.EVALUATION_EPISODES\\n reward_sum = 0\\n log_list = []\\n\\n print(\\\"Evaluate The Best Network with Test data: episode \\\", self.best_episode, \\\" reward \\\", self.best_reward)\\n self.restore_best_network_to_train_network()\\n\\n for i in range(eval_eps):\\n [reward, dist, trainsize, pred] = self.evaluate(isValidation=False)\\n reward_sum = reward_sum + reward\\n log = {\\n \\\"episode\\\": None,\\n \\\"top_reward\\\": reward,\\n \\\"exp_rate\\\": None,\\n \\\"trainsize\\\": trainsize,\\n \\\"top_dist\\\": dist,\\n \\\"top_pred\\\": pred\\n }\\n log_list.append(log)\\n\\n AgentLogger.print_trianing_results(log)\\n\\n mean_reward = reward_sum/eval_eps\\n print(\\\"Mean: \\\", mean_reward)\\n AgentLogger.log_evaluation_results(log_list, self.logger, self.best_episode, self.best_reward, mean_reward)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2147cc31fc145610dbcba7b9c5f6d2ea\",\n \"score\": \"0.6020431\",\n \"text\": \"def find_best_object_and_behavior(self):\\n potential_event_deflection = np.inf\\n\\n if not (self.model.network_structure is None):\\n # check if network structure was passed and only consider\\n # candidate objects to which actor has link\\n possible_ids = [i for i,link\\n in enumerate(self.model.network_structure[self.unique_id])\\n if link == 1]\\n potential_objects = [ag for ag in self.model.schedule.agents\\n if ag.unique_id in possible_ids]\\n if self.model.action_on_group:\\n potential_objects += [self.model.group]\\n else:\\n potential_objects = [ag for ag in self.model.schedule.agents\\n if ag.unique_id != self.unique_id]\\n if self.model.action_on_group:\\n potential_objects += [self.model.group]\\n\\n if self.model.object_choice == \\\"random\\\":\\n potential_object = np.random.choice(potential_objects)\\n potential_action = compute_opt_behavior(self, potential_object)\\n potential_transients = compute_transients(\\n self.current_transients,\\n potential_action,\\n potential_object.current_transients,\\n self.model.abo_coefficients)\\n else:\\n np.random.shuffle(potential_objects)\\n\\n for obj in potential_objects:\\n opt_beh = compute_opt_behavior(self, obj)\\n transients = compute_transients(self.current_transients,\\n opt_beh, obj.current_transients,\\n self.model.abo_coefficients)\\n\\n if self.model.object_choice == \\\"min event-tension\\\":\\n fundamentals_stack = np.hstack((self.fundamentals, opt_beh,\\n obj.fundamentals))\\n event_deflection = np.linalg.norm(fundamentals_stack\\n - transients)**2\\n\\n elif self.model.object_choice == \\\"max deflection-reduction\\\":\\n actor_deflection_diff = (\\n np.linalg.norm(self.fundamentals - transients[:3])**2\\n - self.personal_deflection\\n )\\n beh_deflection = np.linalg.norm(opt_beh - transients[3:6])**2\\n # we include the actual deflection reduction of all agents\\n # in the object choice, not just the group object\\n if obj.unique_id == -1:\\n object_deflection_diff = (\\n np.linalg.norm(obj.fundamentals - transients[6:])**2\\n - obj.personal_deflection\\n )\\n for ag in [agent for agent in self.model.schedule.agents\\n if agent.unique_id != self.unique_id]:\\n transients_g = compute_transients(\\n self.current_transients, opt_beh,\\n ag.current_transients,\\n self.model.abo_coefficients\\n )\\n object_deflection_diff+= (\\n np.linalg.norm(ag.fundamentals - transients_g[6:])**2\\n - ag.personal_deflection\\n )\\n else:\\n object_deflection_diff = (\\n np.linalg.norm(obj.fundamentals - transients[6:])**2\\n - obj.personal_deflection\\n )\\n\\n event_deflection = ( actor_deflection_diff\\n + beh_deflection\\n + object_deflection_diff )\\n else:\\n print(\\\"unknown or no object selection criterion specified\\\")\\n\\n # check if deflection for this behavior is smaller\\n # than for other behaviors\\n if event_deflection < potential_event_deflection:\\n potential_event_deflection = event_deflection\\n potential_transients = transients\\n potential_object = obj\\n potential_action = opt_beh\\n\\n return (potential_object, potential_action, potential_transients)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ccb88b554cf570c6a44f750a0c3ef0\",\n \"score\": \"0.60071445\",\n \"text\": \"def run(self):\\n self.logger.info(\\\"\\\\n\\\\n************************* Genetic Algorithm *************************\\\")\\n self.logger.info(f\\\"Objective: {self.objective}\\\")\\n self.logger.info(f\\\"============================ Execution with seed: {self._seed} ============================\\\")\\n self.logger.info(f\\\"{time.strftime('%d/%m/%Y %H:%M:%S')}\\\\n\\\\n\\\")\\n\\n random.seed(self._seed)\\n np.random.seed(self._seed)\\n\\n self._generate_initial_population()\\n self._population_statistic()\\n self._print_population_info(self.new_pop, 0)\\n\\n best_fitness = self.new_pop.bestFitness\\n count_patience = 0\\n\\n compare_op_obj = partial(ge) if self.objective == 'maximization' else partial(le)\\n compare_op_best = partial(gt) if self.objective == 'maximization' else partial(lt)\\n\\n for gen in range(1, self.nr_max_gen + 1):\\n self._generating_new_population()\\n self._population_statistic()\\n self._print_population_info(self.new_pop, gen)\\n\\n if compare_op_best(self.new_pop.bestFitness, best_fitness):\\n best_fitness = self.new_pop.bestFitness\\n count_patience = 0\\n else:\\n count_patience += 1\\n\\n if self.objective_fitness is not None:\\n if compare_op_obj(self.new_pop.bestFitness, self.objective_fitness):\\n self.logger.info(f\\\"Stop because found the objective fitness {self.objective_fitness}\\\")\\n break\\n\\n if self.patience > 0:\\n if count_patience >= self.patience:\\n self.logger.info(f\\\"Stop because patience limit\\\")\\n break\\n\\n self.logger.info(f\\\"Best solution found: {self._statistic[-1]['best_indiv']}\\\")\\n save_statistic(self.output_dir, self._statistic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef7acdb6e0620fe4b000accc940aa89b\",\n \"score\": \"0.5998345\",\n \"text\": \"def get_energy(self):\\n\\n D, self.e, self.C = self.build_density(self.H)\\n\\n E_old = 0.0\\n F_old = None\\n\\n for iteration in range(self.max_iter):\\n\\n J = np.einsum(\\\"pqrs, rs -> pq\\\", self.g, D)\\n K = np.einsum(\\\"prqs, rs -> pq\\\", self.g, D)\\n\\n # F = H_pq + 2 * G_pqrs D_rs - G_prqs D_rs\\n F = self.H + 2.0 * J - K\\n\\n if self.damping == 'on' and iteration >= self.damp_start:\\n F = self.damp(F_old, F)\\n\\n F_old = F\\n\\n if self.diis == 'on' and iteration >= self.diis_start:\\n F = diis_helper.diis(F, D, self.diis_start,self.diis_vector)\\n\\n E_electric = np.sum((F + self.H) * D)\\n self.E = E_electric + self.mol.nuclear_repulsion_energy()\\n\\n E_diff = self.E - E_old\\n E_old = self.E\\n\\n # Build the AO gradient\\n grad = F @ D @ self.S - self.S @ D @ F\\n grad_rms = np.mean(grad ** 2) ** 0.5\\n\\n D, self.e, self.C = self.build_density(F)\\n\\n print(\\\"Iteration: %3d Energy: % 16.12f Energy difference: % 8.4f Gradient difference: % 8.4f\\\" % (iteration, self.E, E_diff,grad_rms))\\n\\n if E_diff < self.e_conv and grad_rms < self.d_conv:\\n break\\n\\n print(\\\"SCF has finished! \\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b24c4d70a946f3b9b496e46c1a8767e\",\n \"score\": \"0.5992288\",\n \"text\": \"def initPopulation(self):\\n for i in range(0, self.popSize):\\n individual = Individual(self.genSize, self.data)\\n individual.computeFitness()\\n self.population.append(individual)\\n\\n self.best = self.population[0].copy()\\n for ind_i in self.population:\\n if self.best.getFitness() > ind_i.getFitness():\\n self.best = ind_i.copy()\\n print (\\\"Best initial sol: \\\",self.best.getFitness())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ed1251824de1d9a976769dd87cfabf\",\n \"score\": \"0.59906185\",\n \"text\": \"def _find_solutions(self):\\n # Set progress message\\n self.model.progress.set_message(\\\"finding solutions\\\")\\n\\n # Generate grid of indices to search for solutions\\n grid_range = range(self.opts.gp)\\n indices = [tuple([n for n in grid_range]) for _ in self.model.iter_obj2]\\n self.cp = list(itertools.product(*indices))\\n self.cp = [i[::-1] for i in self.cp]\\n\\n # Pickle the model and initialize queue and process handlers\\n self.model.pickle()\\n self.queues = QueueHandler(self.cp, self.opts)\\n self.queues.split_work()\\n self.procs = ProcessHandler(self.opts, self.model, self.queues)\\n\\n # Start processes and wait for results\\n self.procs.start()\\n self.unprocessed_sols = []\\n while not self.procs.join():\\n self.unprocessed_sols.extend(self.queues.get_result())\\n # One last sweep to make sure all results have been collected\\n self.unprocessed_sols.extend(self.queues.get_result())\\n\\n # In case all worker processes were killed, not all work may have been done.\\n if self.procs.any_killed:\\n raise Exception(\\\"At least one worker exited prematurely, not all computations may have been done.\\\")\\n\\n # Clean the pickled model\\n self.model.clean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb69b984732c6d732aacba92def1e51\",\n \"score\": \"0.5982387\",\n \"text\": \"def solution_most_iterations_done(self) -> int:\\n return int(self.dss_obj.SolutionI(ctypes.c_int32(41), ctypes.c_int32(0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17959bc5f94063bea50630dbccfdc9af\",\n \"score\": \"0.59580135\",\n \"text\": \"def solve(self, problem):\\r\\n recommended_solns = []\\r\\n intermediate_budgets = []\\r\\n expended_budget = 0\\r\\n delta_max = self.factors[\\\"delta_max\\\"]\\r\\n gamma_0 = self.factors[\\\"gamma_0\\\"]\\r\\n delta_candidate = [gamma_0 * delta_max, delta_max, delta_max / gamma_0]\\r\\n #print(delta_candidate)\\r\\n\\r\\n # default values\\r\\n eta_1 = self.factors[\\\"eta_1\\\"]\\r\\n eta_2 = self.factors[\\\"eta_2\\\"]\\r\\n gamma_1 = self.factors[\\\"gamma_1\\\"]\\r\\n gamma_2 = self.factors[\\\"gamma_2\\\"]\\r\\n k = 0 # iteration number\\r\\n\\r\\n # Start with the initial solution\\r\\n new_x = problem.factors[\\\"initial_solution\\\"]\\r\\n new_solution = self.create_new_solution(new_x, problem)\\r\\n recommended_solns.append(new_solution)\\r\\n intermediate_budgets.append(expended_budget)\\r\\n\\r\\n # Parameter tuning run\\r\\n tp_final_ob_pt, k, delta, recommended_solns, intermediate_budgets, expended_budget, new_x = self.parameter_tuning(\\r\\n delta_candidate[0], problem)\\r\\n for i in range(1, 3):\\r\\n final_ob_pt, k_pt, delta_pt, recommended_solns_pt, intermediate_budgets_pt, expended_budget_pt, new_x_pt = self.parameter_tuning(\\r\\n delta_candidate[i], problem)\\r\\n expended_budget += expended_budget_pt\\r\\n if -1 * problem.minmax[0] * final_ob_pt < -1 * problem.minmax[0] * tp_final_ob_pt:\\r\\n k = k_pt\\r\\n delta = delta_pt\\r\\n recommended_solns = recommended_solns_pt\\r\\n intermediate_budgets = intermediate_budgets_pt\\r\\n new_x = new_x_pt\\r\\n\\r\\n intermediate_budgets = (\\r\\n intermediate_budgets + 2 * np.ones(len(intermediate_budgets)) * problem.factors[\\\"budget\\\"] * 0.01).tolist()\\r\\n intermediate_budgets[0] = 0\\r\\n\\r\\n while expended_budget < problem.factors[\\\"budget\\\"]:\\r\\n k += 1\\r\\n fval, Y, q, grad, Hessian, delta_k, expended_budget = self.model_construction(new_x, delta, k, problem,\\r\\n expended_budget)\\r\\n\\r\\n # Cauchy reduction\\r\\n if np.matmul(np.matmul(grad, Hessian), grad) <= 0:\\r\\n tau = 1\\r\\n else:\\r\\n tau = min(1, norm(grad) ** 3 / (delta * np.matmul(np.matmul(grad, Hessian), grad)))\\r\\n\\r\\n grad = np.reshape(grad, (1, problem.dim))[0]\\r\\n candidate_x = new_x - tau * delta * grad / norm(grad)\\r\\n\\r\\n for i in range(problem.dim):\\r\\n if candidate_x[i] < problem.lower_bounds[i]:\\r\\n candidate_x[i] = problem.lower_bounds[i] + 0.01\\r\\n elif candidate_x[i] > problem.upper_bounds[i]:\\r\\n candidate_x[i] = problem.upper_bounds[i] - 0.01\\r\\n\\r\\n candidate_solution = self.create_new_solution(tuple(candidate_x), problem)\\r\\n\\r\\n # adaptive sampling needed\\r\\n problem.simulate(candidate_solution, 1)\\r\\n expended_budget += 1\\r\\n sample_size = 1\\r\\n\\r\\n # Adaptive sampling\\r\\n while True:\\r\\n problem.simulate(candidate_solution, 1)\\r\\n expended_budget += 1\\r\\n sample_size += 1\\r\\n sig2 = candidate_solution.objectives_var\\r\\n if sample_size >= self.samplesize(k, sig2, delta_k):\\r\\n break\\r\\n\\r\\n # calculate success ratio\\r\\n fval_tilde = -1 * problem.minmax[0] * candidate_solution.objectives_mean\\r\\n\\r\\n # replace the candidate x if the interpolation set has lower objective function value\\r\\n if min(fval) < fval_tilde:\\r\\n minpos = fval.index(min(fval))\\r\\n fval_tilde = min(fval)\\r\\n candidate_x = Y[minpos][0]\\r\\n\\r\\n if (self.local_model_evaluate(np.zeros(problem.dim), q) - self.local_model_evaluate(\\r\\n np.array(candidate_x) - np.array(new_x), q)) == 0:\\r\\n rho = 0\\r\\n else:\\r\\n rho = (fval[0] - fval_tilde) / (\\r\\n self.local_model_evaluate(np.zeros(problem.dim), q) - self.local_model_evaluate(\\r\\n candidate_x - new_x, q));\\r\\n\\r\\n if rho >= eta_2: # very successful\\r\\n new_x = candidate_x\\r\\n delta_k = min(gamma_1 * delta_k, delta_max)\\r\\n recommended_solns.append(candidate_solution)\\r\\n intermediate_budgets.append(expended_budget)\\r\\n elif rho >= eta_1: # successful\\r\\n new_x = candidate_x\\r\\n delta_k = min(delta_k, delta_max)\\r\\n recommended_solns.append(candidate_solution)\\r\\n intermediate_budgets.append(expended_budget)\\r\\n else:\\r\\n delta_k = min(gamma_2 * delta_k, delta_max)\\r\\n\\r\\n return recommended_solns, intermediate_budgets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2df6e82235fe60278712b06fb8d042ae\",\n \"score\": \"0.5955973\",\n \"text\": \"def calculate(self):\\n # initialize transformation from canonic to energy\\n # optimal states (if necessary)\\n if self.W_mn is None:\\n self.initialize_orbitals(rattle=self.rattle)\\n\\n # optimize the non-unitary invariant part of the\\n # functional\\n self.unitary_optimization()\\n\\n return self.esic, self.ekin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad29ddb80d7682684f4cf035f8b96f23\",\n \"score\": \"0.59508735\",\n \"text\": \"def overall_fitness(self):\\n # Use a decyaing function to generate fitness\\n # Use two step decaying function\\n # First block gives importance to exploration and when as soon\\n # food has been found, the next block will focus on dropping\\n # the food on hub\\n self.individual[0].fitness = (1 - self.beta) * self.delayed_reward \\\\\\n + self.exploration_fitness() + self.carrying_fitness() \\\\\\n + self.food_collected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa0eaf976005e56d162f1b3f95a98d0\",\n \"score\": \"0.5949581\",\n \"text\": \"def solveAll(self):\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4f00dadad3e1e7502978585f5eeb041\",\n \"score\": \"0.5946176\",\n \"text\": \"def optimzeGeneralParameters():\\n\\t#initialize qm energies dictionaries for each molecule\\n\\tglobal qm_energies\\n\\tqm_energies = {mol : {} for mol in molecules}\\n\\n\\t#set parameters for minimize function\\n\\tmaxiter = 5000\\n\\tpgtol = -1e-17\\n\\tftol = 1e-17\\n\\n\\t#get parameter bounds for minimization\\n\\tall_bnds = make_bounds_list(initialValuesList, True)\\n\\ta_bnds = make_bounds_list(initialValuesList, False)\\n\\n\\t#optimize A parameters for each energy component, store in final_params list\\n\\tparameterList = initialValuesList\\n\\tmetaPOInter_initial_list = remove_C_params(parameterList)\\n\\tglobal fitting_component\\n\\twhile fitting_component < 4:\\n\\n\\t\\t#make list of bounds for component to pass down to POInter. Only include B bound if fitting exchange\\n\\t\\tif fitting_component == 0:\\n\\t\\t\\tbnds = all_bnds\\n\\t\\telse:\\n\\t\\t\\tbnds = a_bnds\\n\\n\\t\\t#make list of parameters for component to pass down to POInter\\n\\t\\t#get only parameters for the component we are fitting\\n\\n\\t\\t#TODO: make so user can choose whether or not to fit B params\\n\\t\\t#TODO: make sure input B params are the same for every molecule\\n\\t\\t\\t#TODO: use same .exp file ()\\n\\t\\tmetaPOInter_input = get_component_parameters(metaPOInter_initial_list)\\n\\n\\t\\tres = minimize(metaPOInter,metaPOInter_input,method='L-BFGS-B',\\\\\\n\\t\\t\\t\\t\\t\\t\\tjac=True,\\\\\\n\\t\\t\\t\\t\\t\\t\\toptions={'disp':True,'gtol':pgtol,'ftol':ftol,'maxiter':maxiter},\\\\\\n\\t\\t\\t\\t\\t\\t\\tbounds = bnds)\\n\\n\\t\\tpopt = res.x\\n\\t\\tprint \\\"Optimized parameters for component \\\" + str(fitting_component) + \\\" : \\\" + str(popt)\\n\\n\\t\\tif not res.success:\\n\\t\\t\\tprint 'Warning! Optimizer did not terminate successfully, and quit with the following error message:'\\n\\t\\t\\tprint\\n\\t\\t\\tprint res.message\\n\\t\\t\\tprint\\n\\t\\t\\treturn\\n\\n\\t\\tglobal optimized_params\\n\\t\\toptimized_params[energyComponents[fitting_component]] = popt\\n\\n\\t\\t#reset new B params in POInter objects after fitting exchange component\\n\\t\\tif fitting_component == 0:\\n\\t\\t\\tupdated_B_params = get_updated_B_from_exchange(popt)\\n\\t\\t\\tfor mol in molecules.keys():\\n\\t\\t\\t\\ti = 0\\n\\t\\t\\t\\tfor atomtype in allAtomtypes:\\n\\t\\t\\t\\t\\tpointerModel = molecule_POInter_objects[mol]\\n\\t\\t\\t\\t\\tpointerModel.params[atomtype][0]['B'] = updated_B_params[i]\\n\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\n\\t\\tfitting_component += 1\\n\\n\\t#write output JSON file with optimized parameters\\n\\toptimized_params[\\\"Dispersion\\\"] = [1.0 for atomtype in allAtomtypes]\\n\\tfinal_params_list = write_output_params_to_list(optimized_params)\\n\\tmap_params(final_params_list, \\\"optimized_params\\\")\\n\\n\\t#calc dispersion lsq_error and store\\n\\tdisp_lsq_error = calc_disp_lsq_error()\\n\\tcomponent_lsq_error[energyComponents[fitting_component]] = disp_lsq_error\\n\\n\\t#write lsq_error output file\\n\\twith open(\\\"component_lsq_error.txt\\\", 'w') as f:\\n\\t\\tfor component in energyComponents:\\n\\t\\t\\tf.write(component + \\\":\\\" + '\\\\t')\\n\\t\\t\\tf.write(str(component_lsq_error[component]) + '\\\\n')\\n\\n\\tprint \\\"===========================================================================\\\"\\n\\tprint \\\" Optimized parameters successfully written to optimized_params.constraints \\\"\\n\\tprint \\\"===========================================================================\\\"\\n\\n\\treturn res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036faa1e19c7270a93947e53c76cf9c0\",\n \"score\": \"0.5943793\",\n \"text\": \"def get_solution(self):\\n\\n return self._funcs[\\\"get_solution\\\"](self.problem, self.variable_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469e51f3864b44e39addeb0ee1556f22\",\n \"score\": \"0.59420794\",\n \"text\": \"def get_analysis(self):\\n return self.best_fitness, self.avg_fitness, self.worst_fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e417ac9a10cd6b79cb7903657a121a\",\n \"score\": \"0.593701\",\n \"text\": \"def solutions(self):\\r\\n if self.results is None:\\r\\n warning('results')\\r\\n return \\r\\n if self.is_isoschizomer():\\r\\n iso = True\\r\\n else:\\r\\n iso = False\\r\\n if type(self.best) == list:\\r\\n print('Results obtained using the following group of enzymes:')\\r\\n for e in self.best:\\r\\n print(e, sep=',')\\r\\n print('Other possible solutions:')\\r\\n for el in self.results:\\r\\n if el != self.best:\\r\\n print(el)\\r\\n else:\\r\\n if iso:\\r\\n warning('enzyme3')\\r\\n print('Best enzyme:\\\\n', self.best)\\r\\n print('Other possible solutions:')\\r\\n b = self.results.index(self.best)\\r\\n print(self.results[0:b]+ self.results[b+1:])\\r\\n\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f362cc586c58c587ff2150bc663a74b\",\n \"score\": \"0.5930106\",\n \"text\": \"def optimize(self):\\n\\n\\t\\tstill_needed = []\\n\\t\\t# Verify the required information\\n\\t\\tif self.preference_input is None:\\n\\t\\t\\tstill_needed.append(\\\"Preferences\\\")\\n\\t\\tif self.LP_input is None:\\n\\t\\t\\tstill_needed.append(\\\"LP_input\\\")\\n\\t\\tif self.teacher_file is None:\\n\\t\\t\\tstill_needed.append(\\\"Teacher File (secondary)\\\")\\n\\n\\t\\ts = \\\"\\\"\\n\\t\\tif len(still_needed) > 0:\\n\\t\\t\\ts = str(still_needed)\\n\\t\\t\\tmessagebox.showerror(\\\"Error\\\", \\\"You are missing the following\\\\n\\\\n\\\" + s)\\n\\t\\t\\treturn \\n\\n\\t\\tGAP = self.slider.get()\\n\\t\\tprint(GAP)\\n\\t\\t\\n\\t\\tprint(\\\"All good\\\")\\n\\n\\t\\t# optimize_schedule(self.preference_input, self.LP_input, None,\\n\\t\\t# \\tself.teacher_file, GAP, self.requirements, self.save_location)\\n\\t\\t# The none is for Grades, as I just hard coded that in the opt code for now\\n\\n\\t\\t# Create optimization instance\\n\\t\\tO = Optimizer(self.preference_input, self.LP_input, None,\\n\\t\\t\\t\\tself.teacher_file, GAP, self.requirements, self.save_location)\\n\\t\\tO.optimize()\\n\\n\\n\\n\\n\\n\\t\\t# Things we need to think abou thow to deal with:\\n\\t\\t# \\tGrades\\n\\t\\t#\\tProximity\\n\\t\\t#\\t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2320927b5675caba0e0b584d30231a5\",\n \"score\": \"0.59246856\",\n \"text\": \"def solve(self):\\n self.DSSSolution.Solve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79aa869a66d8a075a889aaa4a27ba494\",\n \"score\": \"0.5923873\",\n \"text\": \"def APSP_1(self):\\n\\n\\t\\tt_start = time.time()\\n\\t\\tres = float('inf')\\n\\t\\tfor s in self.G.keys():\\n\\t\\t\\tprint('Working on node {} of total {} nodes... time elapsed: {:5.2f} minutes.'\\\\\\n\\t\\t\\t\\t.format(s, self.n, (time.time() - t_start)/60))\\n\\t\\t\\tdp = self.Bellman_Ford(s, self.G)\\n\\t\\t\\t\\n\\t\\t\\tif dp is None:\\n\\t\\t\\t\\tprint('\\\\nThere is a negative cost cycle. Exit the algorithm.')\\n\\t\\t\\t\\treturn None\\n\\t\\t\\tres = min(res, min(dp.values()))\\n\\n\\t\\treturn res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de4dcf4702ef09949cd1b9ec034b0e13\",\n \"score\": \"0.59213895\",\n \"text\": \"def run(self, max_iterations=100) -> Tuple[List[str], List[float]]:\\n\\n\\n for i in range(max_iterations):\\n # Generate Moveset\\n moves = self.generate_moveset(self.state)\\n\\n if len(moves) == 0:\\n return (self.state, self.costs)\\n\\n # Choose S i randomly from Moveset(S)\\n m = random.choice(moves)\\n\\n # Define dV=V(S i )-V(S)\\n v_s = self.graph.solution_cost(self.state)\\n v_m = self.graph.solution_cost(m)\\n dV = v_s - v_m\\n\\n # If dV>0 then S←S i else with probability p, S←S i\\n if dV > 0 or (random.random() <= self.generate_p(dV)):\\n self.state = m\\n self.costs.append(v_m)\\n self.decrease_temperature( i/max_iterations)\\n else:\\n self.costs.append(v_s)\\n # If downhill descent is minimal, terminate\\n if abs(dV/v_s) < SimulatedAnnealing.STOPPAGE_VALUE:\\n return (self.state, self.costs)\\n\\n return (self.state, self.costs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5913a3f71cbfc93d162a9dbdedf2bd1c\",\n \"score\": \"0.5912698\",\n \"text\": \"def startSearch(self):\\n current = self.initNode\\n bestScore = float(\\\"inf\\\")\\n for i in range(0,100):\\n current = self.initNode\\n while True:\\n neighbors = current.expand()\\n if not neighbors:\\n break\\n current = min(neighbors, key=attrgetter(\\\"h\\\"))\\n \\n # Random minimum\\n listOfMins = [x for x in neighbors if current.h == x.h]\\n index = random.randint(0, len(listOfMins)-1)\\n current = listOfMins[index]\\n \\n if self.successTest(current.state):\\n if current.g < bestScore:\\n bestScore = current.g\\n bestOne = current\\n break\\n return self._extractPlan(bestOne)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96eb04d01c5e9d4aff74f3f921f6ca9\",\n \"score\": \"0.5908016\",\n \"text\": \"def best(self):\\n if self._best is None:\\n raise BestNotFound()\\n return self._best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72e6d6de196d127dfef8df3d116ae88\",\n \"score\": \"0.590735\",\n \"text\": \"def employed_bees_phase(self):\\n for i in range(self.get_nsource()):\\n # nominate neighbour solution except\\n k = self.random_nominee_except(i)\\n # randomly select soln vector element xi to change\\n xi = rand.randint(0,self.get_dimension()-1)\\n phi = self.a * rand.uniform(-1,1)\\n # get current food source\\n eith_src = self.bee[i].get_source()\\n # generate a new solution with all vector element in current\\n # vector and change the randomly selected parameter only\\n enew_src = np.copy(eith_src)\\n ekth_src = self.bee[k].get_source()\\n # generate new candidate food source\\n enew_src[xi] = eith_src[xi] + phi * (eith_src[xi] - ekth_src[xi])\\n # update new soln in range of solution lower_limit\\n enew_src = self.band_filter(enew_src)\\n enew_cost = self.cost_of(enew_src)\\n # generate new bee with new_src\\n\\n enew_bee = food.Nectar(enew_src, enew_cost)\\n # greedy selection by current employed bee\\n self.bee[i].greedy_choice(enew_bee)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd0932f9ee1bdccd14136fe6af53b0f2\",\n \"score\": \"0.58952796\",\n \"text\": \"def report_best(self):\\n lowest_Err = min(self.Err)\\n index = self.Err.index(lowest_Err)\\n best_x = self.x[index]\\n formatted_x = '[' + ', '.join(['%.2f' % xi for xi in best_x]) + ']'\\n print 'best x: %s' % formatted_x\\n print 'lowest Err: %.3E' % lowest_Err\\n return best_x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cce4fa387bf2542bc60e2516c287211\",\n \"score\": \"0.5893321\",\n \"text\": \"def computeOptimalPlays(self):\\n\\n # Base Cases\\n for i in range(self.size):\\n # If there is only one pot of gold left, take that pot\\n self.cache[(i, i)] = {'score': self.gold[i], 'choice': 'left'}\\n for i in range(self.size - 1):\\n # If there are two pots of gold left, take the larger one\\n self.cache[(i, i+1)] = {\\n 'score': max(self.gold[i], self.gold[i+1]),\\n 'choice': 'left' if self.gold[i] >= self.gold[i+1] else 'right'\\n }\\n\\n # Inductive Step\\n for length in range(2,self.size):\\n for j in range(self.size - length):\\n k = j + length\\n # Here, we decide whether to take from the left (the j side)\\n # or the right (the k side). If we take from the left, then\\n # (if) our opponent makes the optimal move, we'll be left with\\n # the minimum of two pots taken from the left or one pot taken\\n # from either side (because it is a zero-sum game).\\n # Similarly, if we take from the right, then our worst-case\\n # score is the minimum of the subgame situation where either\\n # two pots are taken from the right or one from either side.\\n # The subgame structure is what makes this a DP problem.\\n leftMove = self.gold[j]\\n rightMove = self.gold[k]\\n afterLeftMove = min(self.score(j+2,k), self.score(j+1,k-1))\\n afterRightMove = min(self.score(j+1,k-1), self.score(j,k-2))\\n\\n if leftMove + afterLeftMove >= rightMove + afterRightMove:\\n score = leftMove + afterLeftMove\\n choice = 'left'\\n else:\\n score = rightMove + afterRightMove\\n choice = 'right'\\n\\n self.cache[(j,k)] = {\\n 'score': score,\\n 'choice': choice\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e515c8f7954ca9a5e4c489dc52a14b0d\",\n \"score\": \"0.5886591\",\n \"text\": \"def best_ind(self):\\n random.seed(64)\\n NGEN = 10\\n MU = 10\\n pop = self.toolbox.population(n=MU)\\n hof = tools.ParetoFront()\\n stats = tools.Statistics(lambda ind: ind.fitness.values)\\n stats.register(\\\"avg\\\", numpy.mean, axis=0)\\n stats.register(\\\"std\\\", numpy.std, axis=0)\\n stats.register(\\\"min\\\", numpy.min, axis=0)\\n stats.register(\\\"max\\\", numpy.max, axis=0)\\n # algorithms.eaMuPlusLambda(pop, self.toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats, halloffame=hof)\\n algorithms.eaSimple(pop, self.toolbox, cxpb=0.6, mutpb=0.05, ngen=NGEN, halloffame=hof)\\n print(\\\"The best individual is :\\\", hof[-1])\\n # print(\\\"The best fitness is :\\\", eval_ind(self, hof[-1]))\\n return hof[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d1dea5cd1d94568fde8f01cc0d0532\",\n \"score\": \"0.5884986\",\n \"text\": \"def optimise(self, verbose=False, guess=None, \\n elitism=5, method='GA',cooling_rate=0.1, maxtime=60, maxstallgen=100,\\n nswap=3, popsize=50, mut=0.02, maxgens=50, dimension_bits=1):\\n # TODO: check length of init guess\\n from scipy.optimize import minimize\\n from scipy.optimize import basinhopping\\n self.models = []\\n def eval_func_in(x):\\n return self.eval_func(x, prior=[])\\n\\n N = self.N_reactions\\n\\n import inspyred\\n from random import Random\\n rand = Random()\\n if method == 'GA':\\n ea = inspyred.ec.GA(rand)\\n else:\\n ea = inspyred.ec.SA(rand)\\n self.scores = []\\n self.lower = 0\\n self.upper = 1\\n self.best_score = 1\\n\\n self.scores_all = []\\n def evaluator(candidates, args):\\n dimensions = args['dimensions']\\n dimension_bits = args['dimension_bits']\\n fitness = []\\n for cs in enumerate(candidates):\\n #if len(params) != dimensions:\\n #print(cs, dimensions, dimension_bits)\\n if method == 'GA':\\n params = self._binary_to_real(cs[1], dimensions, dimension_bits)\\n else:\\n params = cs[1]\\n score = eval_func_in(params)\\n #score = eval_func_in(cs)\\n fitness.append(score)\\n # TODO selection pressure could be added here \\n self.scores_all.extend(fitness)\\n self.scores.append(min(np.array(fitness)))\\n if self.scores[-1] < self.best_score:\\n self.best_score = self.scores[-1]\\n i = np.argmin(np.array(fitness))\\n self.best_individual = candidates[i]\\n start = False\\n print(self.scores[-1], self.best_score)\\n\\n return fitness\\n dimensions = N\\n #dimension_bits = 4 # preceision is 1/(2**n-1)\\n def generator(random, args):\\n # For the first generation only.\\n #print('HERE')\\n dimension_bits = args['dimension_bits']\\n #guess = args['guess'][:]\\n pop = [random.choice([0, 1]) \\n for _ in xrange(self.N_reactions * dimension_bits)]\\n #if guess is not None:\\n # pop[0] = guess\\n self.pop = pop\\n if hasattr(self, 'best_individual'):\\n pop = self.best_individual\\n return pop\\n\\n import inspyred.ec\\n import inspyred.ec.ec\\n ea.terminator = inspyred.ec.terminators.evaluation_termination\\n bounder = inspyred.ec.ec.Bounder(0,1)\\n\\n self.storage = []\\n\\n def my_observer(population, num_generations, num_evaluations, args):\\n #best = max(population)\\n #print('{0:6} -- {1} : '.format(num_generations, best.fitness))\\n\\n #print(population)\\n self.storage.append(population[0].candidate[:])\\n for pop in population:\\n self.models.append(pop.candidate[:])\\n ea.observer = my_observer\\n\\n\\n ea.selector = inspyred.ec.selectors.uniform_selection\\n #ea.selector = inspyred.ec.selectors.rank_selection\\n #ea.selector = inspyred.ec.selectors.fitness_proportionate_selection\\n #ea.selector = inspyred.ec.selectors.tournament_selection\\n\\n ea.replacer = inspyred.ec.replacers.truncation_replacement\\n #ea.replacer = inspyred.ec.replacers.plus_replacement\\n self.ea = ea\\n\\n final = ea.evolve(generator=generator, evaluator=evaluator, \\n maximize=False, bounder=bounder, temperature=100, cooling_rate=cooling_rate,\\n max_evaluations=popsize*maxgens,dimensions=len(self.model.reactions),\\n dimension_bits = dimension_bits, guess=guess, gaussian_mean=0.5, gaussian_std=0.5,\\n pop_size=popsize, mutation_rate=mut, num_elites=elitism)\\n\\n self.ea = ea\\n self.final = final\\n res = None\\n self.best_bitstring = max(self.ea.population).candidate\\n print self.best_bitstring\\n try:\\n self.best_bitstring = self._binary_to_real(self.best_bitstring,\\n len(self.model.reactions), dimension_bits)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a446de06c2fc8fde29effb2ef29f743\",\n \"score\": \"0.5871639\",\n \"text\": \"def potential_energy_distribution(self):\\n\\n if len(self.traces) is None:\\n raise ValueError(\\\"Can't predict final energy with just one trace!\\\")\\n\\n self.run_all_iterations()\\n return self.energy_model.predict_from_trace(self.observed_energies)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2080004ed77bb58b93101bbcdb5a9740\",\n \"score\": \"0.5868522\",\n \"text\": \"def result(self):\\n return (self.best_mu, self.best_reward,\\n self.curr_best_reward, self.sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d29e673ca70278dd187ed8c0e891f2\",\n \"score\": \"0.5866907\",\n \"text\": \"def get_optimal():\\n LOG.info(\\\"Retrieving Optimal with url : %s\\\", request.url)\\n recipe = request.get_json()\\n LOG.info(recipe)\\n LOG.info(str(recipe['name']))\\n current_time = int(time.time())\\n workload_name = 'optimal_'+str(current_time)\\n # if 'ts_from' in recipe:\\n # LOG.debug(recipe['ts_from'])\\n # LOG.debug(recipe['ts_to'])\\n # workload = Workload(str(recipe['name']), ts_from= recipe['ts_from'], ts_to= recipe['ts_to'])\\n # eng = Engine()\\n # eng.run('optimal', recipe['name'], recipe['ts_from'], recipe['ts_to'])\\n # else:\\n # workload = Workload(str(recipe['name']))\\n config = {}\\n if recipe.get('device_id'):\\n config['device_id'] = recipe['device_id'].strip(\\n ).lower().replace('-', '_')\\n if recipe.get('project'):\\n config['project'] = recipe['project']\\n if recipe.get('sort_order'):\\n config['sort_order'] = recipe['sort_order']\\n if recipe.get('telemetry_filter', None) is not None:\\n config['telemetry_filter'] = recipe['telemetry_filter']\\n else:\\n config['telemetry_filter'] = False\\n workload = Workload(workload_name, workload_config=config)\\n # storing initial recipe\\n # TODO: validate recipe format\\n recipe_bean = Recipe()\\n recipe_bean.from_json(recipe)\\n workload.add_recipe(\\n int(\\\"{}{}\\\".format(int(round(time.time())), '000000000')), recipe_bean)\\n pipe_exec = OptimalPipe()\\n node_type = 'machine'\\n try:\\n workload = pipe_exec.run(workload, node_type)\\n except KeyError:\\n return Response('Service not ready yet, please wait or restart landscape', status=202)\\n if workload.get_latest_graph() is None and config.get('device_id') is not None:\\n return Response('Device not found', status=404)\\n if workload.get_latest_graph() is None:\\n return Response('Landscape not ready yet?', status=202)\\n results = workload.get_metadata(OptimalFilter.__filter_name__)\\n # return Response(results.to_json(), mimetype=MIME)\\n # return Response(results.to_dict('results'), mimetype=MIME)\\n json_results = json.dumps(results.to_dict('results'))\\n return Response(json_results, mimetype=MIME)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f76478ca755f6f62b9a4feabf72ea7b\",\n \"score\": \"0.58448356\",\n \"text\": \"def solve(self):\\n start_time = time.time()\\n maxdepth = 0\\n break_cond = False\\n\\n while(not break_cond):\\n if self.algo == \\\"bfs\\\":\\n # Pop the leftmost element if doing a bfs (Queue)\\n current_state = self.frontier.popleft()\\n elif self.algo == \\\"dfs\\\":\\n # Pop the rightmost element if doing a dfs (Stack)\\n current_state = self.frontier.pop()\\n else:\\n # Get element with highest priority if doing A* (Heap)\\n current_state = self.frontier.get()\\n self.frontier_set.remove(current_state)\\n if (self.isFinalState(current_state)):\\n soln = self.get_solution_moves(current_state)\\n end_time = time.time()\\n stats = {}\\n stats[\\\"nodes_expanded\\\"] = len(self.explored)\\n stats[\\\"search_depth\\\"] = current_state.depth\\n stats[\\\"max_search_depth\\\"] = maxdepth\\n stats[\\\"cost_of_path\\\"] = len(soln)\\n stats[\\\"time\\\"] = end_time - start_time\\n stats[\\\"path\\\"] = soln\\n return stats\\n neighbors = current_state.generate_possible_states()\\n if self.algo == \\\"dfs\\\":\\n neighbors.reverse()\\n\\n for neighbor in neighbors:\\n if neighbor not in self.explored and neighbor not in self.frontier_set:\\n if self.algo == \\\"bfs\\\" or self.algo == \\\"dfs\\\":\\n self.frontier.append(neighbor)\\n else:\\n self.frontier.put(neighbor)\\n self.frontier_set.add(neighbor)\\n if neighbor.depth > maxdepth:\\n maxdepth = neighbor.depth\\n self.explored.add(current_state)\\n if self.algo == \\\"bfs\\\" or self.algo == \\\"dfs\\\":\\n frontier_sz = len(self.frontier)\\n else:\\n frontier_sz = self.frontier.qsize()\\n logging.debug(\\\"Frontier size = \\\" +\\n str(frontier_sz) +\\n \\\"; Explored size = \\\" +\\n str(len(self.explored)))\\n if self.algo == \\\"bfs\\\" or self.algo == \\\"dfs\\\":\\n break_cond = len(self.frontier) == 0\\n else:\\n break_cond = self.frontier.empty()\\n logging.error(\\\"This is an unsolvable board!\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"468d623199cf946ade4500c72622bb3f\",\n \"score\": \"0.58404225\",\n \"text\": \"def doit(self):\\n self.reduceSolutions()\\n keyz = self.solutions.keys()\\n keyz.sort()\\n prodarr = self.mergeIdent(keyz)\\n keyz += self.chkForMore(keyz,prodarr)\\n localans = self.solvePell()\\n setx = set(keyz+localans)\\n ansvec = list(setx)\\n answer = 1+sum(ansvec)\\n return answer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"370c27f8417ed8239b7d1a695857c183\",\n \"score\": \"0.5814705\",\n \"text\": \"def first_state(self):\\n new_sol = Solution(\\n self.all_ts[0][:1],\\n self.all_ys[0][:, :1],\\n self.all_models[:1],\\n self.all_inputs[:1],\\n None,\\n None,\\n \\\"final time\\\",\\n )\\n new_sol._all_inputs_casadi = self.all_inputs_casadi[:1]\\n new_sol._sub_solutions = self.sub_solutions[:1]\\n\\n new_sol.solve_time = 0\\n new_sol.integration_time = 0\\n new_sol.set_up_time = 0\\n\\n return new_sol\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc528770f4cb159b4a99b6fd7ff1d8f\",\n \"score\": \"0.5813268\",\n \"text\": \"def run(self):\\n problem = self.testproblem.problem\\n results = TestResults(self.testproblem, self.config)\\n # results = Test\\n try:\\n start = time.time() # Time the solve\\n print(\\\"starting\\\",self.testproblem.id,\\\"with config\\\",self.config.id,\\\"at\\\",start)\\n problem.solve(solver = self.config.solver, verbose = self.config.verbose, **self.config.kwargs)\\n print(\\\"finished solve for\\\", self.testproblem.id, \\\"with config\\\", self.config.id)\\n if problem.solver_stats.solve_time is not None:\\n results.solve_time = problem.solver_stats.solve_time\\n else:\\n results.solve_time = time.time() - start\\n if problem.solver_stats.setup_time is not None:\\n results.setup_time = problem.solver_stats.setup_time\\n if problem.solver_stats.num_iters is not None:\\n results.num_iters = problem.solver_stats.num_iters\\n\\n results.status = problem.status\\n results.opt_val = problem.value\\n except Exception as e:\\n print(e)\\n # Configuration could not solve the given problem\\n results = TestResults(self.testproblem, self.config)\\n results.size_metrics = problem.size_metrics\\n print(\\\"failure solving\\\",self.testproblem.id,\\\"with config\\\",self.config.id)\\n return results\\n\\n # Record residual gross stats:\\n results.avg_abs_resid, results.max_resid = TestInstance.compute_residual_stats(problem)\\n print(\\\"computed stats for\\\", self.testproblem.id, \\\"with config\\\", self.config.id)\\n\\n # Record problem metrics:\\n results.size_metrics = problem.size_metrics\\n\\n print(\\\"finished\\\",self.testproblem.id,\\\"with config\\\",self.config.id,\\\"at\\\",time.time()-start)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a4ddcf70a8389e9ebf5384e8f000f5b\",\n \"score\": \"0.5812091\",\n \"text\": \"def optimize(self):\\n self._add_constraints()\\n self.Model.update()\\n self.Model.optimize()\\n if (\\n self.Model.Status == 2\\n ): # Status = 2 means optimal solution found and model is solved\\n self._solved = True\\n self._head_results = self._get_head_results()\\n print(\\\"OPTIMIZATION COMPLETE\\\")\\n else:\\n self._head_results = None\\n print(\\\"OPTIMIZATION NOT COMPLETE\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d1185bafadfc3331c9f21166a9d22f2\",\n \"score\": \"0.5804594\",\n \"text\": \"def baseSearch(self, gameState):\\n\\t\\tactions = gameState.getLegalActions(self.index)\\n\\n\\t\\t# You can profile your evaluation time by uncommenting these lines\\n\\t\\t# start = time.time()\\n\\t\\tv = []\\n\\t\\tfor action in actions:\\n\\t\\t\\tif action == 'Stop':\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tsuccessor = gameState.generateSuccessor(self.index, action)\\n\\t\\t\\tv.append((self.evaluate(successor), action))\\n\\n\\t\\t# print 'eval time for agent %d: %.4f' % (self.index, time.time() - start)\\n\\n\\t\\t# print \\\"Values in baseSearch \\\", v\\n\\t\\tmaxValues = [i for i in v if i == max(v)]\\n\\t\\tmaxValue = random.choice(maxValues)\\n\\n\\t\\t# print maxValue\\n\\t\\tbestAction = maxValue[1]\\n\\t\\t# print bestAction\\n\\t\\tfoodLeft = len(self.getFood(gameState).asList())\\n\\t\\t# print foodLeft\\n\\t\\tif foodLeft <= 2:\\n\\t\\t\\t# print \\\"foodl <= 2\\\"\\n\\t\\t\\tbestDist = 9999\\n\\t\\t\\tfor action in actions:\\n\\t\\t\\t\\tif action == 'Stop':\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tsuccessor = gameState.generateSuccessor(self.index, action)\\n\\t\\t\\t\\tpos2 = successor.getAgentPosition(self.index)\\n\\t\\t\\t\\tdist = self.getMazeDistance(gameState.getAgentPosition(self.index), pos2)\\n\\t\\t\\t\\t# print \\\"Comparing {0} < {1}\\\".format(dist, bestDist)\\n\\t\\t\\t\\tif dist < bestDist:\\n\\t\\t\\t\\t\\t# print \\\"Change action to \\\",action\\n\\t\\t\\t\\t\\tbestAction = action\\n\\t\\t\\t\\t\\tbestDist = dist\\n\\t\\t\\t# print \\\"Base search return with \\\", bestAction\\n\\t\\t\\treturn bestAction\\n\\t\\t# print \\\"Base search return with \\\",bestAction\\n\\t\\treturn bestAction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e9eb1c81491450c937f6743bbc7fd60\",\n \"score\": \"0.5800815\",\n \"text\": \"def judge(self):\\r\\n temp = -1\\r\\n best_one = None\\r\\n for individual in self.individuals:\\r\\n distance = self.get_disctance(individual)\\r\\n if temp == -1 or distance < temp:\\r\\n temp = distance\\r\\n best_one = individual\\r\\n\\r\\n self.best_ones.append(best_one)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f58e89ad63958879fc3bcc91bf5057c\",\n \"score\": \"0.5797411\",\n \"text\": \"def optimal_eps(self):\\n Network = self.Network\\n W = self.get_W()\\n B = self._get_B(W)\\n N = Network.size()\\n eps = np.zeros([N,N])\\n for j in range(N):\\n eps += self._get_eps_j(j,W[j],B[j])\\n return eps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d5185a0acaa718fea4eb3c5221e4da\",\n \"score\": \"0.5795139\",\n \"text\": \"def test_griewangk():\\n\\n print \\\"Testing Griewangk:\\\"\\n print \\\"Expected: x=[0.]*10 and f=0\\\"\\n from mystic.models import griewangk as costfunc\\n ndim = 10\\n lb = [-400.]*ndim\\n ub = [400.]*ndim\\n maxiter = 10000\\n seed = 123 # Re-seed for each solver to have them all start at same x0\\n \\n # DifferentialEvolutionSolver\\n print \\\"\\\\nUsing DifferentialEvolutionSolver:\\\"\\n npop = 50\\n random_seed(seed)\\n from mystic.solvers import DifferentialEvolutionSolver\\n from mystic.termination import ChangeOverGeneration as COG\\n from mystic.termination import CandidateRelativeTolerance as CRT\\n from mystic.termination import VTR\\n from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp\\n esow = Monitor()\\n ssow = Monitor() \\n solver = DifferentialEvolutionSolver(ndim, npop)\\n solver.SetRandomInitialPoints(lb, ub)\\n solver.SetStrictRanges(lb, ub)\\n solver.SetEvaluationLimits(generations=maxiter)\\n solver.SetEvaluationMonitor(esow)\\n solver.SetGenerationMonitor(ssow)\\n solver.enable_signal_handler()\\n #term = COG(1e-10)\\n #term = CRT()\\n term = VTR(0.)\\n time1 = time.time() # Is this an ok way of timing?\\n solver.Solve(costfunc, term, strategy=Rand1Exp, \\\\\\n CrossProbability=0.3, ScalingFactor=1.0)\\n sol = solver.Solution()\\n time_elapsed = time.time() - time1\\n fx = solver.bestEnergy\\n print \\\"Solution: \\\", sol\\n print \\\"f value: \\\", fx\\n print \\\"Iterations: \\\", solver.generations\\n print \\\"Function evaluations: \\\", len(esow.x)\\n print \\\"Time elapsed: \\\", time_elapsed, \\\" seconds\\\"\\n assert almostEqual(fx, 0.0, tol=3e-3)\\n\\n # DifferentialEvolutionSolver2\\n print \\\"\\\\nUsing DifferentialEvolutionSolver2:\\\"\\n npop = 50\\n random_seed(seed)\\n from mystic.solvers import DifferentialEvolutionSolver2\\n from mystic.termination import ChangeOverGeneration as COG\\n from mystic.termination import CandidateRelativeTolerance as CRT\\n from mystic.termination import VTR\\n from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp\\n esow = Monitor()\\n ssow = Monitor() \\n solver = DifferentialEvolutionSolver2(ndim, npop)\\n solver.SetRandomInitialPoints(lb, ub)\\n solver.SetStrictRanges(lb, ub)\\n solver.SetEvaluationLimits(generations=maxiter)\\n solver.SetEvaluationMonitor(esow)\\n solver.SetGenerationMonitor(ssow)\\n #term = COG(1e-10)\\n #term = CRT()\\n term = VTR(0.)\\n time1 = time.time() # Is this an ok way of timing?\\n solver.Solve(costfunc, term, strategy=Rand1Exp, \\\\\\n CrossProbability=0.3, ScalingFactor=1.0)\\n sol = solver.Solution()\\n time_elapsed = time.time() - time1\\n fx = solver.bestEnergy\\n print \\\"Solution: \\\", sol\\n print \\\"f value: \\\", fx\\n print \\\"Iterations: \\\", solver.generations\\n print \\\"Function evaluations: \\\", len(esow.x)\\n print \\\"Time elapsed: \\\", time_elapsed, \\\" seconds\\\"\\n assert almostEqual(fx, 0.0, tol=3e-3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6b76e94e2da8452d3f6eb07296ad32\",\n \"score\": \"0.57891214\",\n \"text\": \"def evolutionary_algorithm(self):\\n # Creating the Individual Using DEAP\\n creator.create(\\\"FitnessMax\\\", base.Fitness, weights=(1.0,))\\n creator.create(\\\"Individual\\\", list, fitness=creator.FitnessMax)\\n\\n # Creating ToolBox For The DEAP Framework\\n toolbox = base.Toolbox()\\n toolbox.register(\\\"attr_bool\\\", random.randint, 0, 1)\\n toolbox.register(\\\"individual\\\", tools.initRepeat, creator.Individual, toolbox.attr_bool, self.n_features)\\n toolbox.register(\\\"population\\\", tools.initRepeat, list, toolbox.individual)\\n toolbox.register(\\\"evaluate\\\", self.fitness_test)\\n toolbox.register(\\\"mate\\\", tools.cxOnePoint)\\n toolbox.register(\\\"mutate\\\", tools.mutFlipBit, indpb=0.05)\\n toolbox.register(\\\"select\\\", tools.selTournament, tournsize=3)\\n\\n # Initialize Parameters\\n pop = toolbox.population(n=self.n_pop)\\n hof = tools.HallOfFame(self.n_pop * self.n_gen)\\n stats = tools.Statistics(lambda ind: ind.fitness.values)\\n stats.register(\\\"avg\\\", np.mean)\\n stats.register(\\\"min\\\", np.min)\\n stats.register(\\\"max\\\", np.max)\\n\\n # Genetic Algorithm\\n pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=self.n_gen, stats=stats, halloffame=hof,verbose=True)\\n\\n # Return Fall Of Home\\n return hof\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763350b629661a962875e4097a558371\",\n \"score\": \"0.5786748\",\n \"text\": \"def best_model() :\\n file_path = os.path.join(os.path.os.getcwd(), cf.PERF_FILE)\\n with open(file_path, 'r') as perf_file:\\n prec = perf_file.readlines()[0].split(';')\\n\\n precisions = [t for t in prec if len(t)>0]\\n\\n models = [t.split(':')[0] for t in precisions]\\n models = sorted(models)\\n precisions = np.array([float(t.split(':')[1]) for t in precisions])\\n index_best_model = np.argmax(precisions)\\n #best_model = models[index_best_model]\\n\\n output_files, model_dict = get_output_files()\\n\\n chosen_model = model_dict[int(index_best_model)+1]\\n chosen_output_file = output_files[int(index_best_model)+1]\\n\\n print('Best model is ' + chosen_model + ' with precision ' + str(max(precisions)) + '%')\\n\\n return chosen_model, chosen_output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152015f9a8d342528e38fde1197c42a4\",\n \"score\": \"0.57838523\",\n \"text\": \"def get_solution(self):\\n if self.is_solution_available():\\n pass\\n raise errors.IllegalState()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af224aec567cf6f051e98f8188c816f\",\n \"score\": \"0.5780064\",\n \"text\": \"def run(self):\\n if self.current_control:\\n target_value = self.current_density\\n else:\\n target_value = self.average_cell_voltage * self.stack.n_cells\\n if not isinstance(target_value, (list, tuple, np.ndarray)):\\n target_value = [target_value]\\n cell_voltages = []\\n current_densities = []\\n local_data_list = []\\n global_data_list = []\\n for i, tar_value in enumerate(target_value):\\n current_errors = []\\n temp_errors = []\\n convergence_flags = []\\n self.timing['simulation'] = 0.0\\n self.timing['output'] = 0.0\\n simulation_start_time = timeit.default_timer()\\n counter = 0\\n while True:\\n if counter == 0:\\n if self.current_control:\\n self.stack.update(current_density=tar_value)\\n else:\\n self.stack.update(voltage=tar_value)\\n else:\\n self.stack.update()\\n if self.stack.break_program:\\n break\\n current_error, temp_error = self.calc_convergence_criteria()\\n current_errors.append(current_error)\\n temp_errors.append(temp_error)\\n if len(target_value) < 1:\\n print(counter)\\n counter += 1\\n if ((current_error < self.it_crit and temp_error < self.it_crit)\\n and counter > self.min_it) or counter > self.max_it:\\n break\\n if counter > self.max_it:\\n convergence_flag = False\\n else:\\n convergence_flag = True\\n convergence_flags.append(convergence_flag)\\n simulation_stop_time = timeit.default_timer()\\n simulation_time = simulation_stop_time - simulation_start_time\\n self.timing['simulation'] += simulation_time\\n output_start_time = timeit.default_timer()\\n\\n if not self.stack.break_program:\\n # voltage_loss = self.get_voltage_losses(self.stack)\\n cell_voltages.append(np.average([cell.v for cell in\\n self.stack.cells]))\\n current_densities.append(self.stack.i_cd_avg)\\n\\n case_name = 'Case'+str(i)\\n self.output.save(case_name, self.stack)\\n local_data_dict = self.output.get_data(self.stack)\\n local_data_dict['Iterations'] = \\\\\\n {'value': [list(range(counter))], 'units': '-'}\\n local_data_dict['Current Density Error'] = \\\\\\n {'value': [current_errors], 'units': '-',\\n 'xkey': 'Iterations'}\\n local_data_dict['Temperature Error'] = \\\\\\n {'value': [current_errors], 'units': '-',\\n 'xkey': 'Iterations'}\\n\\n local_data_list.append(local_data_dict)\\n if self.output.save_plot:\\n path = os.path.join(self.output.output_dir, case_name,\\n 'plots', 'Convergence.png')\\n self.output.create_figure(path, list(range(counter)),\\n [current_errors, temp_errors],\\n xlabels='Iteration',\\n ylabels='Error',\\n yscale='log',\\n legend=['Current Density',\\n 'Temperature'])\\n else:\\n target_value = target_value[0:-i]\\n break\\n\\n average_current_density = \\\\\\n np.average([np.average(cell.i_cd, weights=cell.active_area_dx)\\n for cell in self.stack.cells])\\n if self.stack.coolant_circuit is None:\\n cool_mass_flow = 0.0\\n else:\\n cool_mass_flow = self.stack.coolant_circuit.mass_flow_in\\n global_data = \\\\\\n {\\n 'Convergence':\\n {'value': convergence_flag, 'units': ' '},\\n 'Stack Voltage': {'value': self.stack.v_stack, 'units': 'V'},\\n 'Average Cell Voltage':\\n {'value': self.stack.v_stack / self.stack.n_cells,\\n 'units': 'V'},\\n 'Minimum Cell Voltage':\\n {'value': np.min(self.stack.v), 'units': 'V'},\\n 'Maximum Cell Voltage':\\n {'value': np.max(self.stack.v), 'units': 'V'},\\n 'Average Current Density':\\n {'value': average_current_density, 'units': 'A/m²'},\\n 'Stack Power Density':\\n {'value': self.stack.v_stack * average_current_density,\\n 'units': 'W/m²'},\\n 'Stack Power':\\n {'value': self.stack.v_stack * average_current_density\\n * self.stack.cells[0].active_area,\\n 'units': 'W'},\\n 'Cooling Mass Flow Rate:':\\n {'value': cool_mass_flow,\\n 'units': 'kg/s', 'format': '.4E'},\\n 'Cathode Mass Flow Rate:':\\n {'value': self.stack.fuel_circuits[0].mass_flow_in,\\n 'units': 'kg/s', 'format': '.4E'},\\n 'Anode Mass Flow Rate:':\\n {'value': self.stack.fuel_circuits[1].mass_flow_in,\\n 'units': 'kg/s', 'format': '.4E'},\\n }\\n global_data_list.append(global_data)\\n output_stop_time = timeit.default_timer()\\n self.timing['output'] += output_stop_time - output_start_time\\n self.output.print_global_data(self, global_data)\\n self.output.save_global_results(global_data)\\n\\n output_start_time = timeit.default_timer()\\n\\n # print polarization curve, if more than one current_density value\\n # was provided\\n if len(target_value) > 1:\\n self.output.write_data(current_densities, cell_voltages,\\n 'Current Density [A/m²]', 'Cell Voltage',\\n units='V', directory=self.output.output_dir,\\n # save_csv=True, save_plot=True,\\n write_mode='w')\\n\\n output_stop_time = timeit.default_timer()\\n self.timing['output'] += output_stop_time - output_start_time\\n\\n return global_data_list, local_data_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"182e8144cc9cf7df0a32def1d95bd1d8\",\n \"score\": \"0.5779991\",\n \"text\": \"def _eval_energy(self):\\n S = self.S\\n M = S.mera\\n SIlayer = self.SIlayer # = 2\\n SIlayer_p1 = SIlayer + 1\\n\\n for i in range(self.num_of_SIlayer):\\n #这里是反复地从 SIlayer ascending 到 SIlayer_p1, WITH tau changing\\n if i>0:\\n for o in self.ham_op_names:\\n S.__getattribute__(o)[SIlayer][0].data[:] = S.__getattribute__(o)[SIlayer_p1][0].data[:]\\n self.ascending_ham(M, S, SIlayer, tau=SIlayer + i)\\n #above only update H, no need update rho, as the later is scale-invar not changed in each layer\\n \\n if 1:\\n S.rho_2[SIlayer_p1][0].data[:] = S.rho_2[SIlayer][0].data[:]\\n if not S.only_NN:\\n S.rho_3[SIlayer_p1][0].data[:] = S.rho_3[SIlayer][0].data[:]\\n #in the end eval eng at the layer above the first SIlayer\\n S.eng_ham(SIlayer_p1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"682efc983522090afcf58edc4c64d068\",\n \"score\": \"0.5779628\",\n \"text\": \"def optimize(self):\\n self.open_output_files()\\n while (self.n_iter < self.n_maxiter and not self.is_converged):\\n self.n_iter += 1\\n self.choose_step_direction(self.opt_type)\\n self.line_search(-1.0 * self.step_dir)\\n self.update_energy()\\n self.update_gradient()\\n self.traj.append_step(self.mol)\\n self.update_criteria()\\n self.check_convergence()\\n self.print_status()\\n self.close_output_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ec4666d8a6ab245f0cab4066f997667\",\n \"score\": \"0.57755536\",\n \"text\": \"def optimize_ga(self):\\n\\n converged = False\\n ngens = 1\\n generation = 1\\n difference = self.tol * 10000.0\\n self.solution_history = np.zeros(self.max_generation + 1)\\n self.solution_history[0] = np.min(self.parent_fitness)\\n\\n run_time = 0.0\\n start_time = time.time()\\n while converged is False and ngens < self.max_generation and \\\\\\n run_time < self.max_time:\\n self.crossover()\\n self.mutate()\\n # determine fitness of offspring\\n for i in range(self.population_size):\\n self.chromosome_2_variables(self.offspring_population[i])\\n self.offspring_fitness[i] = \\\\\\n self.objective_function(self.design_variables)\\n\\n # rank the total population from best to worst\\n total_fitness = np.append(self.parent_fitness,\\n self.offspring_fitness)\\n ranked_fitness = \\\\\\n np.argsort(total_fitness)[0:int(self.population_size)]\\n\\n total_population = \\\\\\n np.vstack([self.parent_population, self.offspring_population])\\n self.parent_population[:, :] = total_population[ranked_fitness, :]\\n self.parent_fitness[:] = total_fitness[ranked_fitness]\\n\\n # store solution history and wrap up generation\\n self.solution_history[generation] = np.min(self.parent_fitness)\\n\\n if generation > self.convergence_iters:\\n difference = \\\\\\n self.solution_history[generation - self.convergence_iters]\\\\\\n - self.solution_history[generation]\\n else:\\n difference = 1000\\n if abs(difference) <= self.tol:\\n converged = True\\n\\n # shuffle up the order of the population\\n shuffle_order = np.arange(1, self.population_size)\\n np.random.shuffle(shuffle_order)\\n shuffle_order = np.append([0], shuffle_order)\\n self.parent_population = self.parent_population[shuffle_order]\\n self.parent_fitness = self.parent_fitness[shuffle_order]\\n\\n generation += 1\\n ngens += 1\\n\\n run_time = time.time() - start_time\\n\\n # Assign final outputs\\n self.solution_history = self.solution_history[0:ngens]\\n self.optimized_function_value = np.min(self.parent_fitness)\\n self.chromosome_2_variables(\\n self.parent_population[np.argmin(self.parent_fitness)])\\n self.optimized_design_variables = self.design_variables\\n\\n logger.debug('The GA ran for this many generations: {}'\\n .format(ngens))\\n logger.debug('The GA ran for this many seconds: {:.3f}'\\n .format(run_time))\\n logger.debug('The optimized function value was: {:.3e}'\\n .format(self.optimized_function_value))\\n logger.debug('The optimal design variables were: {}'\\n .format(self.optimized_design_variables))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f62b19d875ff1dfa2e3d4bfd9a5172\",\n \"score\": \"0.57655716\",\n \"text\": \"def return_fitness(self, e):\\r\\n\\t\\treturn e.fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83fe6af1079e34a1c9740f124d26c109\",\n \"score\": \"0.576445\",\n \"text\": \"def solve():\\n return round(7 * (1 - choose(60, 20) / choose(70, 20)), 10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595ecfd9facfe91a02000cddd7224112\",\n \"score\": \"0.57595515\",\n \"text\": \"def fittest(self):\\n return self.best_fit, self.best_fit_fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c1e9395f4e30e4b41f6aa74720d615\",\n \"score\": \"0.57595515\",\n \"text\": \"def test_best_improvement():\\n process_list = [1, 2, 4, 1, 3]\\n expected_solution = [3, 1, 4, 2, 1]\\n current_neighborhood = 0\\n\\n best_solution = best_improvement(process_list, current_neighborhood, 60)\\n assert best_solution == expected_solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1774df4c382a9a263ca22e025ab62161\",\n \"score\": \"0.5756853\",\n \"text\": \"def save_result(self, output_dir):\\n self.best_solver.save_tree(output_dir)\\n self.best_solver.save_best_solution(output_dir)\\n for i in range(self.best_solver.tube_num):\\n self.best_solver.visualize_full_search(output_dir, tube_num=i, with_solution=True)\\n\\n filename = output_dir / \\\"optimize_result.txt\\\"\\n # noinspection PyTypeChecker\\n optimize_result = open(filename, \\\"w\\\")\\n\\n optimize_result.write(f\\\"Optimizer Success: {self.success}\\\\n\\\")\\n optimize_result.write(f\\\"Goal Reached: {self.best_solver.found_solution}\\\\n\\\")\\n optimize_result.write(f\\\"Completion Time: {self.completion_time}\\\\n\\\")\\n optimize_result.write(f\\\"Number of Iterations: {self.num_iterations}\\\\n\\\")\\n optimize_result.write(f\\\"Number of Function Evals: {self.num_function_eval}\\\\n\\\")\\n q = \\\", \\\".join(str(i) for i in self.best_q)\\n optimize_result.write(f\\\"Optimized Q: {q}\\\\n\\\")\\n\\n optimize_result.close()\\n\\n filename = output_dir / \\\"optimize_process.txt\\\"\\n # noinspection PyTypeChecker\\n optimize_process = open(filename, \\\"w\\\")\\n\\n for i in range(len(self.optimize_process)):\\n q = \\\", \\\".join(str(j) for j in self.optimize_process[i].get('q'))\\n cost = self.optimize_process[i].get('cost')\\n optimize_process.write(f\\\"{q} | {cost}\\\\n\\\")\\n\\n optimize_process.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d155e5d49b180fe2d888a7e23955477\",\n \"score\": \"0.5756708\",\n \"text\": \"def betterEvaluationFunction(currentGameState):\\n \\\"*** YOUR CODE HERE ***\\\"\\n foods = currentGameState.getFood()\\n newGhostStates=currentGameState.getGhostStates()\\n newPos = currentGameState.getPacmanPosition()\\n print 'New position:', newPos\\n newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\\n\\n\\n\\n optimal_food = []\\n optimal_ghost = []\\n food = currentGameState.getFood().asList()\\n\\n for f in food:\\n optimal_food.append(manhattanDistance(newPos, f))\\n if len(optimal_food) == 0:\\n return 0\\n a = min(optimal_food)\\n\\n if a == 0:\\n a = currentGameState.getScore()\\n cap=[]\\n b = max(optimal_food)\\n f=currentGameState.getNumFood()\\n\\n # for g in currentGameState.getGhostPositions():\\n # optimal_ghost.append(manhattanDistance(newPos, g))\\n # if manhattanDistance(newPos, g) == 0:\\n # return -100000.0\\n # elif manhattanDistance(newPos, g) > 0.0 and manhattanDistance(newPos, g) <= 1.0:\\n # return -100000.0/6\\n\\n ghost_PM_dist = []\\n ghost_dist_nearest = 0\\n for ghost in newGhostStates:\\n Ghost_Pos = ghost.getPosition()\\n GP_dist = manhattanDistance(Ghost_Pos, newPos)\\n ghost_PM_dist.append(GP_dist)\\n\\n if len(ghost_PM_dist) > 0:\\n ghost_dist_nearest = min(ghost_PM_dist)\\n else:\\n ghost_dist_nearest = currentGameState.getScore()\\n # print ghost_dist_nearest\\n ghost_feature = 1 / (ghost_dist_nearest + 1)\\n score = 0\\n # c=currentGameState.getCapsules()\\n # print 'cap',c\\n # for i in c:\\n # cap.append(manhattanDistance(newPos,i))\\n # if len(optimal_ghost) > 0:\\n # ghost_dist_nearest = min(optimal_ghost)\\n # else:\\n # ghost_dist_nearest = currentGameState.getScore()\\n # # print ghost_dist_nearest\\n # ghost_feature = 1 / (ghost_dist_nearest + 1)\\n # score = 0\\n # score=0\\n # if currentGameState.isWin():\\n # score = 1000000\\n return currentGameState.getScore() + 1.0/(a) +100*score+ 2*(ghost_feature)\\n\\n util.raiseNotDefined()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c0ae96cd69e709a3affb6aa640c97d\",\n \"score\": \"0.5754821\",\n \"text\": \"def best_result(self):\\n if self.result is not None:\\n return self.result[0]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c226930b9f22f2d5ed885f9483f4c19\",\n \"score\": \"0.57431376\",\n \"text\": \"def evolve(self):\\n #Configure properly each island\\n self.__parametrize__()\\n\\n # Get evolution generator of each island\\n self._rc[:].execute(\\\"gen = gp_engine.sequentially_evolve()\\\", block=True)\\n\\n # Loop over all the sessions\\n i = 0\\n while True:\\n logging.info('Launch generation evolution')\\n\\n #Do generation computing\\n self._rc[:].execute(\\\"chosen = gen.next()\\\")\\n\\n #Print best individual\\n logging.info('Get best individuals')\\n self._rc[:].execute(\\\"best = gp_engine.get_best_individual()\\\")\\n bests = self._rc[:]['best']\\n\\n print \\\"Generation %d\\\" % i\\n print \\\"=\\\"*15\\n print \\\"Island\\\\t| fit\\\\t\\\"\\n print \\\"\\\\n\\\".join(\\\\\\n [ \\\"%d\\\\t%f\\\" % (j, bests[j][1]) \\\\\\n for j in range(len(bests))])\\n\\n print \\\"AVG\\\\t%f\\\" %np.mean( [bests[j][1] for j in range(len(bests))])\\n i = i + 1\\n\\n logging.info('Test if end of generation')\\n #Test if the process is finished\\n self._rc[:].execute('end=gp_engine.__evolver__.is_evolution_ended()')\\n\\n if np.any( self._rc[:]['end']):\\n break\\n\\n #Operate the migration\\n logging.info('Launch migration process')\\n self.__migration_operator__.manage_migration()\\n\\n #loop again\\n\\n print 'Evolution terminated'\\n return bests\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73f2751a1cb356fcb986044d948ed77\",\n \"score\": \"0.5730213\",\n \"text\": \"def is_better_then_global_best(self, solution):\\n return solution.personal_best.fitness_value < self.best_solution.fitness_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b31563d2c606a0c2b5ebe046d544277\",\n \"score\": \"0.5728114\",\n \"text\": \"def random_search():\\t\\t\\n\\tsim = simulator()\\t\\t\\t\\t\\t\\t\\t# simulator object, to be used during this algorithm\\n\\tnum_search = 20\\t\\t\\t\\t\\t\\t\\t\\t# number of robots evaluated\\n\\tnum_tetr_added = 14\\t\\t\\t\\t\\t\\t\\t# this is the size of our robots ( 15 tetrahedron total )\\n\\n\\trs_fit = np.zeros(num_search)\\t\\t\\t\\t# learning curve array\\n\\tbest_fit = 0\\t\\t\\t\\t\\t\\t\\t\\t# best fitness place holder\\n\\tbest_array = np.empty(8)\\t\\t\\t\\t\\t# top performing genome place holder\\n\\t\\n\\tfor i in range(num_search):\\t\\t\\t\\t\\t# do the random search\\t\\n\\t\\tprint('counter: ', i)\\n\\n\\t\\trobot = tetrahedron()\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# make a random robot\\n\\t\\trobot.set_tetr( np.array((0 , 0 , 0)) , np.array((0 , 0 , 0)))\\t\\t\\t# make the seed\\n\\t\\trobot.set_genome(num_tetr_added)\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# generate a random genome\\n\\t\\trobot.build_robot()\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# builds the robot \\n\\t\\t\\n\\t\\tmass_list = robot.mass_list\\t\\t\\t\\t# center the robot\\n\\t\\tcom = robot.get_com(mass_list)\\t\\t\\t# \\t\\t....\\n\\t\\trobot.center_object(com)\\t\\t\\t\\t# \\t\\t....\\n\\n\\t\\tfitness = calc_fitness( robot , sim)\\t# evaluate the robot's fitness\\n\\n\\t\\tif fitness > best_fit:\\t\\t\\t\\t\\t# if the fitness is better than current best\\n\\t\\t\\tbest_fit = fitness \\t\\t\\t\\t\\t# update placeholders\\n\\t\\t\\tbest_array = np.copy(robot.genome)\\t# \\t\\t....\\n\\n\\t\\trs_fit[i] = best_fit\\t\\t\\t\\t\\t# add the best fit to the learning curve\\n\\n\\tnp.savetxt(\\\"rs_robot.csv\\\", best_array, delimiter=\\\",\\\")\\n\\tnp.savetxt(\\\"rs_learning.csv\\\", rs_fit, delimiter=\\\",\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":189,"cells":{"query_id":{"kind":"string","value":"a232b5ed960453ab1d2673894b52333d"},"query":{"kind":"string","value":"submit a dos calculation and interpolate result if returns complete"},"positive_passages":{"kind":"list like","value":[{"docid":"5e0224e23e34529b3523d980d025abb2","score":"0.0","text":"def get_dos(self):\n\n label = 'KKR DOS calc.'\n dosdict = self.ctx.dos_params_dict\n description = 'dos calc: emin= {}, emax= {}, nepts= {}, tempr={}, kmesh={}'.format(dosdict['emin'], dosdict['emax'], dosdict['nepts'], dosdict['tempr'], dosdict['kmesh'])\n code = self.inputs.kkr\n remote = self.inputs.remote_data\n params = self.ctx.dos_kkrparams\n options = {\"max_wallclock_seconds\": self.ctx.max_wallclock_seconds,\n \"resources\": self.ctx.resources,\n \"queue_name\" : self.ctx.queue}#,\n if self.ctx.custom_scheduler_commands:\n options[\"custom_scheduler_commands\"] = self.ctx.custom_scheduler_commands\n inputs = get_inputs_kkr(code, remote, options, label, description, parameters=params, serial=(not self.ctx.use_mpi))\n\n # run the DOS calculation\n self.report('INFO: doing calculation')\n dosrun = self.submit(KkrCalculation, **inputs)\n\n # for restart workflow:\n self.ctx.last_calc = dosrun\n\n return ToContext(dosrun=dosrun)","title":""}],"string":"[\n {\n \"docid\": \"5e0224e23e34529b3523d980d025abb2\",\n \"score\": \"0.0\",\n \"text\": \"def get_dos(self):\\n\\n label = 'KKR DOS calc.'\\n dosdict = self.ctx.dos_params_dict\\n description = 'dos calc: emin= {}, emax= {}, nepts= {}, tempr={}, kmesh={}'.format(dosdict['emin'], dosdict['emax'], dosdict['nepts'], dosdict['tempr'], dosdict['kmesh'])\\n code = self.inputs.kkr\\n remote = self.inputs.remote_data\\n params = self.ctx.dos_kkrparams\\n options = {\\\"max_wallclock_seconds\\\": self.ctx.max_wallclock_seconds,\\n \\\"resources\\\": self.ctx.resources,\\n \\\"queue_name\\\" : self.ctx.queue}#,\\n if self.ctx.custom_scheduler_commands:\\n options[\\\"custom_scheduler_commands\\\"] = self.ctx.custom_scheduler_commands\\n inputs = get_inputs_kkr(code, remote, options, label, description, parameters=params, serial=(not self.ctx.use_mpi))\\n\\n # run the DOS calculation\\n self.report('INFO: doing calculation')\\n dosrun = self.submit(KkrCalculation, **inputs)\\n\\n # for restart workflow:\\n self.ctx.last_calc = dosrun\\n\\n return ToContext(dosrun=dosrun)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"215366153e758085c8bc79a022ff8b6c","score":"0.6252689","text":"def calculate(self):\n item_number = self.fields.curselection()\n polynomial = self.json_data[\"functions\"][int(item_number[0])]\n result_floating = None\n result_interval = None\n if self.json_data[\"method\"] == \"regula_falsi\":\n try:\n result_floating = regula_falsi(polynomial[\"a\"],\n polynomial[\"b\"],\n function_from_list,\n polynomial[\"coeff\"]\n )\n except MyError as error:\n result_floating = error.value\n except ZeroDivisionError:\n result_floating = \"Unfortunately, division by 0 happened\"\n try:\n result_interval = regula_falsi_interval(polynomial[\"a\"],\n polynomial[\"b\"],\n function_from_list,\n polynomial[\"coeff\"]\n )\n result_interval = result_interval.format('%.20E')[9:]\n result_interval = result_interval[:-1]\n except MyError as error:\n result_interval = error.value\n elif self.json_data[\"method\"] == \"newton\":\n try:\n result_floating = newton(polynomial[\"x\"],\n polynomial[\"epsilon\"],\n polynomial[\"max_iterations\"],\n function_from_list,\n polynomial[\"coeff\"],\n polynomial[\"derivative_coeff\"]\n )\n except ZeroDivisionError:\n result_floating = \"Unfortunately, division by zero happened\"\n except MyError as error:\n result_floating = error.value\n try:\n result_interval = newton_interval(polynomial[\"x\"],\n polynomial[\"epsilon\"],\n polynomial[\"max_iterations\"],\n function_from_list,\n polynomial[\"coeff\"],\n polynomial[\"derivative_coeff\"]\n )\n result_interval = result_interval.format('%.20E')[9:]\n result_interval = result_interval[:-1]\n except MyError as error:\n result_interval = error.value\n else:\n result_interval = \"You propably\"\n result_floating = \"gave me wrong JSON\"\n self.show_result_floating.config(text=result_interval)\n self.show_result_interval.config(text=result_floating)","title":""},{"docid":"e0830761115955abace478bf63277abd","score":"0.6215401","text":"def calculate_results():\n\n print('Calculating results..')","title":""},{"docid":"d07116c7437a3ffc65ec7ced8caca88f","score":"0.6053697","text":"def _calculate(self):\n ...","title":""},{"docid":"977d47a74d67da7d19a1b22a8e36d483","score":"0.59952414","text":"def do_calculation():\n dT = config.getfloat(\"thermodynamics\", \"Tmin\")\n try:\n nmax = config.getint(\"thermodynamics\", \"nmax\")\n except ValueError:\n nmax = config.get(\"thermodynamics\", \"nmax\")\n if not nmax == \"automatic\":\n print(\"nmax has to be given as integer or \\\"automatic\\\"\") \n try:\n analytic_n0 = config.get(\"geometry\", \"analytic_n0\")\n except ValueError:\n print(\"analytic_n0 is not given correctly: \", analytic_n0)\n analytic_n0 = True\n if type(geom.r1) == list or type(geom.r2) == list:\n analytic_n0 = False\n Tmax = config.getfloat(\"thermodynamics\", \"Tmax\")\n f = thermodynamics.finiteT(dT, nmax, Tmax, geom, analytic_n0)\n quantity = config.get(\"thermodynamics\", \"quantity\")\n result = eval(\"f.%s\" %quantity)\n return result, f","title":""},{"docid":"f05dd3eeb2320b01c30e115c6928fb5f","score":"0.5965643","text":"def error_calculation():","title":""},{"docid":"69c8ad689e17b9cd1efc84e5ba7563bd","score":"0.59115","text":"def calc(self, *args):\n pass","title":""},{"docid":"387f4fedb43cb0287db06ecf37b13dc3","score":"0.5901406","text":"def calc():\r\n nonlocal func\r\n func = e.get()\r\n func = func.replace(\" \", \"\")\r\n func = toReversePolish(check(fixmult(findNeg(tokenizer(func)))))\r\n result.set(calculate(func, float(e2.get())))","title":""},{"docid":"b3ba20e0c2fecb5806fb08b0ca839ef9","score":"0.58904105","text":"def calculate(**kwargs):\n return None","title":""},{"docid":"29a6f09fde14e1f16e33b66e2b65226c","score":"0.58634573","text":"def _postCalculate(self,retList):\n\t\tpass","title":""},{"docid":"3eccf3447683a2dc735f5799fbff98c3","score":"0.58459675","text":"def calculate(self, *args, **kwargs) -> Any:","title":""},{"docid":"f37e62a56b03972b4b59b80389e4d31d","score":"0.5809612","text":"def compute(self):\n\n self.setInitialValue()\n self.partialResult = self.initialValue\n self.elementResult = None\n\n self.updateFunctionPort()\n\n self.set_output('Result', self.partialResult)","title":""},{"docid":"81d0a1c04bf6c68b3a921b158c6c03fb","score":"0.57289326","text":"def OnCalcDone(self, event):\n if event.data is None:\n self.log(\"No result from calculation!\")\n self.PSF_HDUlist = None\n else:\n self.PSF_HDUlist = event.data\n for w in ['Display PSF', 'Display profiles', 'Save PSF As...']:\n self.widgets[w].Enable(True)\n\n self._refresh_window()\n self.log(\"PSF calculation complete\")\n # In either event, the worker is done\n self.calcthread = None","title":""},{"docid":"3ae7ea9f7af562970e90b832e81e037a","score":"0.5720635","text":"def calculate(self):\n pass","title":""},{"docid":"fd18a5ecb864880f5b28ca9d88e14303","score":"0.56965977","text":"def on_evaluate(self, interp):\n return 0.000000","title":""},{"docid":"b8afeea6242d6a4492b1855f0c649af5","score":"0.5681769","text":"def Calculate():\r\n values = GetValues()\r\n values = SolveEqn(values)\r\n DisplayValues(values)","title":""},{"docid":"b38fe131032ab41802c0adfc8618129c","score":"0.5660559","text":"def _run_calculation(self):\n # collect input\n input_vs = OrderedDict()\n for intf in self.input_intfs:\n if intf.sibling_intf_allowed:\n input_vs.setdefault(intf.family_name, []).append(intf.get_calculated_value())\n else:\n input_vs[intf] = intf.get_calculated_value()\n\n # run actual function defined by user\n try:\n results = self.calculate(*input_vs.values())\n except Exception as e:\n # set all outputs NULL\n results = [NullValue(f\"calculation fail of {self}\")] * len(self.output_intfs)\n # record and print error status\n self._calculation_status = e\n self.print_status()\n raise\n else:\n # wrap singular result and match length\n results = [results] if not isinstance(results, (list, tuple)) else list(results)\n results += [NullValue(\"calculation didn't return enough values\")] * (len(self.output_intfs) - len(results))\n self._calculation_status = ''\n finally:\n # push calculation result down to outputs\n for result, intf in zip(results, self.output_intfs):\n intf.set_provoke_value(result)","title":""},{"docid":"e3918df179bdebeaa8326fa3d94277d7","score":"0.5617621","text":"def compute_value(self):\n result = 0.00000000\n if self.value1 and self.tasa:\n result = (self.value1 * 1) / self.tasa \n self.result = result\n if not self.flag1:\n self.id_record = self.env.context.get('active_id')\n self.act_model = self.env.context.get('active_model')\n self.flag1 = True\n return {\n 'view_mode': 'form',\n 'res_model': 'compute.crypto.value',\n 'res_id': self.id,\n 'view_id': False,\n 'type': 'ir.actions.act_window',\n 'target': 'new',\n 'context': {\"default_act_model\":self.act_model, \"default_result\": result, \"default_id_record\": self.id_record,},\n }\n else:\n raise UserError(\"You need set all values first.\")","title":""},{"docid":"e43cd1561f63a67897cd240b00229f59","score":"0.55982625","text":"def process_final(self, *args):\n return self._sum / self._count","title":""},{"docid":"aad11d6d48fea29f5ab6cfd496c2674f","score":"0.5591634","text":"def doCalculation()->JSON_RESPONSE: \n\n # Get JSON data from client\n first:int = int(request.json['first'])\n second:int = int(request.json['second'])\n oper:str = request.json['operation']\n\n rst:int = performCalc(first, second, oper)\n\n # Prepare the return structure\n result:dict[str,int] = {\"result\":rst}\n\n # Send JSON back to the client\n return json.dumps(result)","title":""},{"docid":"37453e9332d7d6b5fbee48eb2fe7abf2","score":"0.55907995","text":"def handle_calculate(self, value):\n #try:\n\n result = self.valid_number_check() * 1.6093\n self.root.ids.output_label.text = str(result)\n print(result)\n #except: ValueError","title":""},{"docid":"11725e0d4aa23fdfc0173c1231d04274","score":"0.5529947","text":"def _compute(self):","title":""},{"docid":"85e680e952960f28dbfe0efb0c4bc700","score":"0.5507777","text":"def calc(self):\n raise Exception(\"Workflow calculation not implemented yet\")","title":""},{"docid":"43ddd32b2c49f7c13ef977e81a00da9d","score":"0.54745024","text":"def calc_results():\n global num_solutions\n subt = 100*get_subt(0) + 10*get_subt(1) + get_subt(2)\n diff = 100*get_diff(0) + 10*get_diff(1) + get_diff(2)\n minu = 1000*get_minu(0) + 100*get_minu(1) + 10*get_minu(2) + get_minu(3)\n if (subt + diff == minu) and (minu >= 1000):\n print minu, \"-\", subt, \"==\", diff\n num_solutions += 1","title":""},{"docid":"4fb90820e644680bc6d5e502aead6fde","score":"0.5453361","text":"def calculate(self, structure, **kwargs):","title":""},{"docid":"70fffa82abf3ab29e63d6e09fafe4f39","score":"0.54355496","text":"def _postCalculate(self,retList,time):\n\t\tif len(retList) > 0: \n\t\t\tret = pandas.concat(retList,ignore_index=True)\n\t\t\tret = self.calculateThresholdPolygon(ret,time)\n\t\t\tret['percentEffected'] = 1.\n\t\telse: \n\t\t\tret = None\n\t\treturn ret","title":""},{"docid":"a94e16cfebaa89484f51c5df781ba26c","score":"0.5427222","text":"def __check_calc(self):\n # nothing done\n return","title":""},{"docid":"166673f4de6c078d348a3759ee698ae2","score":"0.54081076","text":"def calculate(self, var_list):\n # Run dummy calculation.\n index = var_list[1].split(' ')[0]\n submit = call('cp files/pw-scf_{}.out pw-scf.out'.format(index),\n shell=True)\n if submit != 0:\n raise AssertionError('Could not submit job.')\n energy = self.scrape_output(self.output_file)\n\n print('iteration {0} energy {1}'.format(index, energy))\n\n return energy","title":""},{"docid":"b8ed09a292242295d9bd0a9dc162cc28","score":"0.54075795","text":"def calificacion_final(par1, par2, par3):\n calfinal = par1 + par2 + par3\n promedio = calfinal / 3\n return promedio","title":""},{"docid":"d8124e8fa74cc5964d34246b27cb46bf","score":"0.5406774","text":"def run_interp():\n func = str(input('Enter string or trigonometric function: '))\n print('Value l')\n l_a = float_input(positive=True)\n print('Value u')\n u_b = float_input(positive=True)\n print('Value n')\n n_c = integer_input()\n print('Your intermediate results: {}'.format(interp(func, l_a, u_b, n_c)))","title":""},{"docid":"36a407754c15e9a2256c75e9d606681f","score":"0.53978","text":"def __calc(self):\n timezone = self.timezone # in hours, east is positive\n longitude= self.long # in decimal degrees, east is positive\n latitude = self.lat # in decimal degrees, north is positive\n\n time = self.time # percentage past midnight, i.e. noon is 0.5\n day = self.day # daynumber 1=1/1/1900\n \n Jday =day+2415018.5+time-timezone/24 # Julian day\n Jcent =(Jday-2451545)/36525 # Julian century\n\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\n Mobliq = 23+(26+((21.448-Jcent*(46.815+Jcent*(0.00059-Jcent*0.001813))))/60)/60\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent))+sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+sin(rad(3*Manom))*0.000289\n Struelong= Mlong+Seqcent\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\n \n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))-0.5*vary*vary*sin(4*rad(Mlong))-1.25*Eccent*Eccent*sin(2*rad(Manom)))\n\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))*cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\n\n self.solarnoon_t=(720-4*longitude-eqtime+timezone*60)/1440\n self.sunrise_t =self.solarnoon_t-hourangle*4/1440\n self.sunset_t =self.solarnoon_t+hourangle*4/1440","title":""},{"docid":"9393ef12dcb8b1ff273f5733e0539ddb","score":"0.53911626","text":"def compute(self) -> float:\n raise NotImplementedError","title":""},{"docid":"b254264ee7ae0c135148c1ce91717f2d","score":"0.5388854","text":"def Exemples_calculs():\n while True :\n (xA,yA)=input_coord(\"A\")\n while True :\n (xB,yB)=input_coord(\"B\")\n if xB!=xA or yB!=yA :\n break\n else :\n display(\"Il faut deux points distincts pour faire une droite ! Recommencez !\")\n\n intro=Latex(\"\"\"Nous allons calculer le coefficient directeur de la droite (AB) avec\n A( {} ; {} ) et B( {} ; {} )\"\"\".format(xA,yA,xB,yB))\n if xA==xB :\n\n calcul=Latex(\"\"\"Les droites parallèles à l'axe des ordonnées n'ont pas de coefficient directeur !\n Il faut donc que vos points n'aient pas la même abscisse :\"\"\")\n else :\n frac=fractions.Fraction((yB-yA),(xB-xA))\n if frac==int(frac):\n frac=str(int(frac))\n else :\n frac=\"\\dfrac{{{numerator}}}{{{denominator}}}\".format(numerator=frac.numerator,denominator=frac.denominator)\n calcul=Math(\"m= \\dfrac{{y_B-y_A}}{{x_B-x_A}}= \\dfrac{{{yB}-{yA}}}{{{xB}-{xA}}}= \\dfrac{{{yByA}}}{{{xBxA}}}={frac}\"\n .format(xA=xA,yA=yA,xB=xB,yB=yB,yByA=yB-yA,xBxA=xB-xA,frac=frac))\n Trace_droite(xA,yA,xB,yB)\n display(intro,calcul)\n s=input(\"Voulez-vous recommencer ?(o/n)\")\n if s.lower() in ['n','non','no'] :\n break\n else :\n clear_output()","title":""},{"docid":"5dd7780b29a79b6fd8d3b696267d256c","score":"0.53845376","text":"def processAlgorithm(self, parameters, context, feedback):\n\n # Load present raster\n present_raster = self.parameterAsRasterLayer(parameters, self.INPUT1, context)\n ds_present = gdal.Open(present_raster.dataProvider().dataSourceUri())\n arr_present = ds_present.GetRasterBand(1).ReadAsArray()\n # Clean negative values\n arr_present[arr_present<0] = 0\n\n # Load future raster\n future_raster = self.parameterAsRasterLayer(parameters, self.INPUT2, context)\n ds_future = gdal.Open(future_raster.dataProvider().dataSourceUri())\n arr_future = ds_future.GetRasterBand(1).ReadAsArray()\n # Clean negative values\n arr_future[arr_future<0] = 0\n\n print(arr_future)\n print(np.unique(arr_future))\n # Parameters\n V = self.parameterAsDouble(parameters, self.INPUT5, context)\n\n r = 0\n\n c = 3\n\n area_pixel = self.parameterAsInt(parameters, self.PIXEL_RES, context) * self.parameterAsInt(\n parameters, self.PIXEL_RES, context)\n # Years\n present = self.parameterAsInt(parameters, self.INPUT3, context)\n future = self.parameterAsInt(parameters, self.INPUT4, context)\n\n # Calculate coeff sequestration\n arr_diff = arr_future - arr_present\n arr_diff_norm = arr_diff / float((future - present))\n arr_years = np.array(range(0, future - present))\n coeff = sum(1 / ((1 + r / 100) ** arr_years * ((1 + c / 100) ** arr_years)))\n carbon_sequestration_value = V * arr_diff_norm * coeff\n carbon_sequestration_difference = arr_future - arr_present\n # Initialize and write on output raster\n path_output = self.parameterAsString(parameters, self.OUTPUT, context)\n file_output = path_output + '/SE_01_carbon_sequestration_delta_euro.tiff'\n driver = gdal.GetDriverByName(\"GTiff\")\n [cols, rows] = carbon_sequestration_value.shape\n carbon_sequestration_difference_area = np.sum(carbon_sequestration_difference) / (cols * rows * area_pixel)\n outdata = driver.Create(file_output, rows, cols, 1, gdal.GDT_Float64)\n outdata.SetGeoTransform(ds_present.GetGeoTransform())##sets same geotransform as input\n outdata.SetProjection(ds_present.GetProjection())##sets same projection as input\n outdata.GetRasterBand(1).WriteArray(carbon_sequestration_value)\n print(np.max(outdata.GetRasterBand(1).ReadAsArray()))\n outdata.FlushCache() ##saves to disk!!\n outdata = None\n band = None\n ds = None\n print(np.sum(arr_present))\n report_output = path_output + '/SE_sequestro_carbonio.txt'\n f = open(report_output, \"w+\")\n today = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\n f.write(\"Sommario dell'analisi del sequestro di carbonio\\n\")\n f.write(\"Data: \" + today +\"\\n\\n\\n\")\n f.write(\"Analisi stato di fatto\\n\\n\")\n f.write(\"Anno corrente: %i \\n\" % (present))\n f.write(\"Sequestro carbonio Stato attuale (ton Corg): %f \\n\" % (np.sum(arr_present)))\n f.write(\"Valore medio del carbonio sequestrato per unità di superficie - Stato attuale (ton Corg/ha): : %f \\n\" % (\n np.sum(arr_present) / (cols * rows * area_pixel) * 10000))\n f.write(\"Valore totale del sequestro di carbonio (€): %f \\n\\n\\n\" % ((np.sum(arr_present)*V*coeff)/ float((future - present))))\n f.write(\"Analisi stato di progetto\\n\\n\")\n f.write(\"Anno progetto: %i \\n\" % (future))\n f.write(\"Sequestro carbonio Stato di progetto (ton Corg): %f \\n\" % (np.sum(arr_future)))\n f.write(\"Valore medio del carbonio sequestrato per unità di superficie - Stato di progetto (ton Corg/ha): %f \\n\" % (\n np.sum(arr_future) / (cols * rows * area_pixel) * 10000))\n f.write(\"Valore totale del sequestro di carbonio (€): %f \\n\\n\\n\" % ((np.sum(arr_future)*V*coeff) / float((future - present))))\n f.write(\"Differenze tra stato di progetto e stato attuale\\n\\n\")\n f.write(\"Periodo di analisi: %i - %i\\n\" % (present, future))\n f.write(\"Differenza di sequestro carbonio (ton Corg): %f \\n\" % (np.sum(arr_diff)))\n f.write(\"Differenza carbonio sequestrato per unità di superficie (ton Corg/ha): %f \\n\" % (\n carbon_sequestration_difference_area * 10000))\n f.write(\"Differenza in termini economici del SE di sequestro di carbonio (stato di progetto – stato attuale) (€):%d \\n\" % (\n np.sum(carbon_sequestration_value)))\n return {self.OUTPUT: carbon_sequestration_value}\n\n \n # ----------------------------------------------------------------------------------- \n # Copyright (c) 2021 Città di Torino.\n # \n # This material is free software: you can redistribute it and/or modify\n # it under the terms of the GNU General Public License as published by\n # the Free Software Foundation, either version 2 of the License, or\n # (at your option) any later version.\n # \n # This program is distributed in the hope that it will be useful,\n # but WITHOUT ANY WARRANTY; without even the implied warranty of\n # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n # GNU General Public License for more details.\n # \n # You should have received a copy of the GNU General Public License\n # along with this program. If not, see http://www.gnu.org/licenses.\n # ----------------------------------------------------------------------------------- ","title":""},{"docid":"ddba8492bd02da00237d8e1464e5a126","score":"0.5371956","text":"def _run(self):\n self._solve()\n return self._ret","title":""},{"docid":"11080b93adcf2859b7f8662c9f7e6fff","score":"0.53654003","text":"def _compute(y, t, N, b, k):\n S, I, R = y\n #print(t)\n '''The Susceptible Equation'''\n dSdt = -b[int(t)] * S * I / N\n \n '''The Infected Equation = -The Susceptible Equation - The Recovered Equation'''\n dIdt = b[int(t)] * S * I / N - k * I\n \n '''The Recovered Equation'''\n dRdt = k * I\n return dSdt, dIdt, dRdt","title":""},{"docid":"fd122062e4d6256eaf6d4aa2bbf05994","score":"0.53594244","text":"def integral(self, time1, time2):","title":""},{"docid":"1fb010d6f5ea79ce4b09974b0bc0d8c9","score":"0.5352363","text":"def bulk_calc(self):\n self.calc_csa()\n self.calc_CCC()\n self.calc_impedence()\n self.calc_runVd()","title":""},{"docid":"7ca2393dd1b3cb44fb5ee38d774cc7de","score":"0.5340337","text":"def compute_values(self):\n pass","title":""},{"docid":"7ca2393dd1b3cb44fb5ee38d774cc7de","score":"0.5340337","text":"def compute_values(self):\n pass","title":""},{"docid":"c8e6b33eeb34f158ca8b2ddbd6257563","score":"0.5340099","text":"def run_calculation(self):\n\n if w.ignore_history_false.isChecked():\n\n fp = w.input_file_line_edit.text()\n\n w.df, w.cache = read_from_file(fp, raw_mt=True)\n # TODO: Check if the filter index is still necessary when matching string indices in match_target_mt below.\n filter_index = w.df.query('status == \"mt\"').index\n w.df = w.df.loc[filter_index]\n\n # Create boolean filter with all repetitions except for first occurrences set to True\n filter_index = w.df.query('stype == \"source\"').duplicated('text')\n w.df = w.df[-filter_index]\n\n else:\n w.df = new_translation(w.df, w.cache, w.sample_object)\n\n df_mt = match_target_mt(w.df)\n\n w.cache = pe_density(df_mt, w.cache)\n w.actionSave_as.setEnabled(True)\n w.textOutput.setText(str('Your Post-Edit Density score is {:.3f}\\n'.format(w.cache['ped'])))\n w.statistics()\n\n w.autosave()","title":""},{"docid":"db551929b95c9e61285499b44233dd40","score":"0.5333397","text":"def calc(self, x):\n pass","title":""},{"docid":"9ab45ce819ad5f27ffcf041865253bdf","score":"0.53273827","text":"def post(self, start_value, end_value):\n start_value, end_value = int(start_value), int(end_value)\n engine_name = get_engine_name()\n if os.path.exists(engine_name):\n self.__clean_hashes()\n\n if not os.path.exists(json_filename):\n create_minion_json()\n engine_db_name = 'sqlite:///' + engine_name\n try:\n p = Process(target=calculate_range, args=(start_value, end_value, engine_db_name, m_id))\n p.start()\n processes.append(p)\n except:\n print('Error: failed to run calculation on minion ' + str(m_id) + '.')\n return {'status': 'error', 'message': 'calculation failed'}\n update_status('busy')\n return {'status': 'busy', 'message': 'calculation started'}","title":""},{"docid":"81f4e7059b0fd4d4a86a03694951504c","score":"0.5327031","text":"def compute():\n message1[\"text\"] = \"\" #reset messages\n message2[\"text\"] = \"\"\n message4[\"text\"] = \"\"\n message5[\"text\"] = \"\" \n message6[\"text\"] = \"\" \n message7[\"text\"] = \"\" \n \n smux=str(smux_entry.get()) #return the smux value in the tkinter entry \n try:\n volts_min=float(volt_min_entry.get()) # min voltage \n volts_max=float(volt_max_entry.get()) # max voltage \n R=float(resistance_entry.get()) # protection resistance \n if abs(volts_max)>20 or abs(volts_min)>20:\n texte5=\"abs(volt) <=10\"\n message5[\"text\"] = texte5 \n print(texte5) \n else: \n nb=int(point_number_entry.get()) #measures nb\n if nb<1:\n texte4=\"nb>0\"\n message4[\"text\"] = texte4 \n print(texte4) \n else:\n # delay=float(delay_entry.get()) #not needed\n # print(smux,volts_min,volts_max,nb,delay) #used to debug\n try:\n complete_measure(volts_min,volts_max,nb,smux=smux,R=R,compliance=compliance)\n texte7=\"Measures done\"\n message7[\"text\"] = texte7 \n print(texte7) \n except Exception as ex:\n texte6=\"Error from measurement detected: %s\" % str(ex) #display the error name\n message6[\"text\"] = texte6 \n print(texte6) \n reset() \n except:\n texte2=\"floats\"\n message2[\"text\"] = texte2 \n print(texte2)","title":""},{"docid":"1594a7502a7cae5142f313f0066993f3","score":"0.53201216","text":"def func(progress_remaining: float) -> float:\n return initial_value","title":""},{"docid":"c176dcfdf4a68d52eaa145558ca732f5","score":"0.53149915","text":"def calc(self, pvalues, lamb):\n\t\traise NotImplementedError","title":""},{"docid":"2c01c7f7208d17f565623b9efa9f2829","score":"0.5313737","text":"def _evaluate_function(self, inputs, aliases):\n # TODO how to handle aliases????\n method = self._sub_name\n request = inputs.pop('request', None) # sometimes just the inputs will be used without the request, like in elasticity curves\n result = self._obj.evaluate(method, request, inputs)\n balance = result[0]\n meta = result[1]\n return balance, meta","title":""},{"docid":"0b7bf3ab90c18dae7b8553fba4cb5d3e","score":"0.53023535","text":"def calculate(self, mol: Molecule) -> float:","title":""},{"docid":"034d41aa8d8f3385ce7f1536f5f8b7b5","score":"0.52879363","text":"def __calc(self):\n timezone = self.timezone # in hours, east is positive\n longitude= self.long # in decimal degrees, east is positive\n latitude = self.lat # in decimal degrees, north is positive\n\n time = self.time # percentage past midnight, i.e. noon is 0.5\n day = self.day # daynumber 1=1/1/1900\n \n # NOAA Spreadsheet\n Jday = day+2415018.5+time-timezone/24. # Julian day\n Jcent = (Jday-2451545)/36525 # Julian century\n\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\n Mobliq = 23+(26+((21.448-Jcent*(46.815+ \\\n Jcent*(0.00059-Jcent*0.001813))))/60)/60\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent)) \\\n +sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+ \\\n sin(rad(3*Manom))*0.000289\n Struelong= Mlong+Seqcent\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\n \n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+ \\\n 4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))- \\\n 0.5*vary*vary*sin(4*rad(Mlong))- \\\n 1.25*Eccent*Eccent*sin(2*rad(Manom)))\n\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))* \\\n cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\n\n self.solarnoon_t = (720-4*longitude-eqtime+timezone*60)/1440.\n self.sunrise_t = self.solarnoon_t-hourangle*4/1440.\n self.sunset_t = self.solarnoon_t+hourangle*4/1440.","title":""},{"docid":"ee1d7a33c192af97967f2c8a2e3c713b","score":"0.5286321","text":"def execute_strategy(self):\n data = self.results.copy().dropna()\n data['position'] = np.sign(data['returns'].rolling(self.mom).mean())\n data['strategy'] = data['position'].shift(1) * data['returns']\n # determine when trades take place\n # trades = data['position'].diff().fillna(0) != 0\n # subtract transaction costs from return where trades take place\n # data['strategy'][trades] -= self.tc\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)","title":""},{"docid":"7b38b01232a3f553a7ee77b5f648b7ec","score":"0.5280257","text":"def calc(self):\n sorted_pop = self.sortpop()\n initial_route = self.population[sorted_pop[0][0]]\n distance = 1 / sorted_pop[0][1]\n progress = [ distance ]\n if callable(self.animate):\n self.plot = True\n individual = Individual(initial_route)\n stop_animation = Event()\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\n else:\n self.plot = False\n if self.prn:\n print(f\"Initial distance: {distance}\")\n try:\n if self.plot:\n for i in range(self.generations):\n population = self.next_gen()\n sorted_pop = self.sortpop()\n distance = 1 / sorted_pop[0][1]\n progress.append(distance)\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n route = population[sorted_pop[0][0]]\n individual.update(route)\n else:\n for i in range(self.generations):\n population = self.next_gen()\n distance = 1 / self.sortpop()[0][1]\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n \n \n except KeyboardInterrupt:\n pass\n try:\n stop_animation.set()\n except NameError:\n pass\n final_route_index = self.sortpop()[0][0]\n final_route = population[final_route_index]\n if self.prn:\n print(\"Final route:\", final_route)\n\n return initial_route, final_route, distance","title":""},{"docid":"7b38b01232a3f553a7ee77b5f648b7ec","score":"0.5280257","text":"def calc(self):\n sorted_pop = self.sortpop()\n initial_route = self.population[sorted_pop[0][0]]\n distance = 1 / sorted_pop[0][1]\n progress = [ distance ]\n if callable(self.animate):\n self.plot = True\n individual = Individual(initial_route)\n stop_animation = Event()\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\n else:\n self.plot = False\n if self.prn:\n print(f\"Initial distance: {distance}\")\n try:\n if self.plot:\n for i in range(self.generations):\n population = self.next_gen()\n sorted_pop = self.sortpop()\n distance = 1 / sorted_pop[0][1]\n progress.append(distance)\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n route = population[sorted_pop[0][0]]\n individual.update(route)\n else:\n for i in range(self.generations):\n population = self.next_gen()\n distance = 1 / self.sortpop()[0][1]\n if self.prn:\n print(f\"[Generation:{i}] Current distance: {distance}\")\n \n \n except KeyboardInterrupt:\n pass\n try:\n stop_animation.set()\n except NameError:\n pass\n final_route_index = self.sortpop()[0][0]\n final_route = population[final_route_index]\n if self.prn:\n print(\"Final route:\", final_route)\n\n return initial_route, final_route, distance","title":""},{"docid":"75cc49ea82b117f6b2d47af646f819d0","score":"0.527877","text":"def _evaluate(data, context):\n # print \"Received : \", data, \" Sequence Number : \", context[\"data_sequence_no\"]\n score = 0\n secondary_score = 0\n\n for k in range(100):\n time.sleep(random.randint(0,100)/1000.0)\n percent_complete = k*1.0/100 * 100\n update_progress(context, percent_complete, \"\")\n # print \"Context Response Channel ::: \", context['response_channel']\n # if k%20==0:\n # # print \"Update : \", percent_complete\n score += random.randint(1,100)*1.0/0.7 / 100\n secondary_score += random.randint(1,100)*1.0/0.7 / 100\n\n _result_object = {\n \"score\" : score,\n \"secondary_score\" : secondary_score,\n }\n return _result_object","title":""},{"docid":"e0178d47f659aae4e12fbcd69db2468c","score":"0.52750695","text":"def on_pushButton_calc_clicked(self):\r\n # TODO: not implemented yet\r\n if self.str1 == '':\r\n a = 0\r\n else:\r\n a = float(self.str1)\r\n if self.str2 == '':\r\n b = 0\r\n else:\r\n b = float(self.str2)\r\n res = 0\r\n if self.op == '+':\r\n res = a + b\r\n elif self.op == '-':\r\n res = a - b\r\n elif self.op == '*':\r\n res = a * b\r\n elif self.op == '/':\r\n if b == 0:\r\n res = 0\r\n else:\r\n res = a / b\r\n\r\n self.lineEdit_result.setText(str(res))\r\n self.flag = 2\r\n\r\n # raise NotImplementedError\r","title":""},{"docid":"320ea0ff97999dcee761fcc123dd114c","score":"0.52461845","text":"def calculate(self):\n self.correct_extinction()\n self.rdistance = calculate_radial_distance(self.position, self.center,\n self.distance)\n self.r23 = calculate_r23(self.fluxes)\n self.calculate_OH()\n self.SFR = calculate_sfr(self.distance, self.fluxes['halpha'])","title":""},{"docid":"23dd86b596bed8c6aac3dcd00301e5c7","score":"0.5244022","text":"def compute(self, today, assets, out, opens, closes):\n out[:] = (opens[-1] - closes[0]) / closes[0]","title":""},{"docid":"23dd86b596bed8c6aac3dcd00301e5c7","score":"0.5244022","text":"def compute(self, today, assets, out, opens, closes):\n out[:] = (opens[-1] - closes[0]) / closes[0]","title":""},{"docid":"a33836659fccc659301fd2466ce5d572","score":"0.5242563","text":"def run(self):\n self.evaluate()\n self.accumulate()\n self.summarize()","title":""},{"docid":"675188c454c830f80a91cf94327c7e3e","score":"0.52329695","text":"def process_final(self, *args):\n mean = self._sum / self._count\n mean = mean ** 2\n variance = (self._sum_sq / self._count) - mean\n if variance < 0:\n return 0\n else: \n return variance ** 0.5","title":""},{"docid":"598dc322c12f3f2638ec51cc2bcf894c","score":"0.52291757","text":"def func(progress_remaining: float):\n return progress_remaining * initial_value","title":""},{"docid":"14aa6575bdb29a5d01bfab980c63bd12","score":"0.5228993","text":"def linear_schedule(progress):\n return 1 - progress","title":""},{"docid":"c27142e62aa568661d8bbdf43d69f8b6","score":"0.5228111","text":"def computation(self, results, *parameters):\n\n return self.add_computation(computation(results, *parameters))","title":""},{"docid":"7768d4b0f72db48a4418dd627fdd9c73","score":"0.52274144","text":"def _compute(self):\n pass","title":""},{"docid":"6abf202e5afb80f54132416bc47cb3f1","score":"0.52269894","text":"def inspect_calculation(self):\n converged_mc = True\n self.ctx.restart_calc = self.ctx.calculation['retrieved']\n if converged_mc:\n self.report(\"Calculation converged, terminating the workflow\")\n self.ctx.done = True","title":""},{"docid":"28e2904de8eda761267f0b480736bd0d","score":"0.52208334","text":"def thread_start_calculating(self, distance_value):\n print(\"Breakpoint Distance value:{}\".format(distance_value))\n logger.info('Slot:thread_start_calculating :', \n extra={'sim_index': self._data_store.get_simulation_index()})\n\n if distance_value == 0:\n logger.info('Slot:thread_start_calculating SINGLE STEP NOT IMPLEMENTED:', \n extra={'sim_index': self._data_store.get_simulation_index()})\n #TODO - finish this breakpoint case\n self.simulationComputing = False\n\n elif distance_value == -1:\n logger.info('Slot:thread_start_calculating RUN TO COMPLETION :', \n extra={'sim_index': self._data_store.get_simulation_index()})\n # set the breakpoint to be a very large number to indicate run to completion\n self.breakpointDistance = 9999999\n self.simulationComputing = True\n else:\n # run to the distance value point in the track\n sim_index = self._data_store.get_simulation_index()\n if distance_value > self._data_store.get_distance_at_index(sim_index) :\n logger.info('Slot:thread_start_calculating RUN TO DISTANCE :', \n extra={'sim_index': sim_index})\n # requested breakpoint is further down the track\n self.breakpointDistance = distance_value\n # Start computing and acknowledge to MainWindow by sending a signal back \n self.simulationThreadSignal.emit(\"Calculating...\")\n # \"state\" variable indicating thread should be calculating\n self.simulationComputing = True\n else:\n logger.info('Slot:thread_start_calculating PAST REQUESTED DISTANCE :', \n extra={'sim_index': sim_index})\n # simulation has already past this point in the track, don't proceed\n self.simulationComputing = False","title":""},{"docid":"4213b442ff6408444f7a99b113d82637","score":"0.52148896","text":"def _calculate_avg():\n while (True):\n _avg_num(_q_uwb_a, q_to_a, \"to_a\", 8)\n _avg_num(_q_uwb_b, q_to_b, \"to_b\", 8)\n time.sleep(0.015)","title":""},{"docid":"6a06c78d7eb16247bf403e1389083f14","score":"0.52093","text":"def integrate(self, t):","title":""},{"docid":"4ee388bb9a884aa4cea1bef1a52b88fa","score":"0.5208004","text":"def __call__(self, *args, **kwargs) -> Any:\n return self.calculate(*args, **kwargs)","title":""},{"docid":"a7a19b61964963525e9d4ad7f5b8669f","score":"0.5207855","text":"def func(progress_remaining: float) -> float:\n return progress_remaining * initial_value","title":""},{"docid":"a7a19b61964963525e9d4ad7f5b8669f","score":"0.5207855","text":"def func(progress_remaining: float) -> float:\n return progress_remaining * initial_value","title":""},{"docid":"54047f7afc5fafe5f39587ee486749fd","score":"0.5203203","text":"def __cost_function(self, sense='single'):\n\n self.SnappyHexMeshrun()\n self.Optimisationrun()\n if sense==\"single\":\n t = self.RunUtilities()\n return p\n elif sense==\"multi\":\n t, p = self.RunUtilities(sense=sense)\n return t, p\n else:\n print(\"Invalid input for sense: \", sense)\n print(\"Available options are: 'single' or 'multi'\")\n return None","title":""},{"docid":"30447d2a14ea9b45e16c20607615711e","score":"0.5196032","text":"def calculate_derivative(param):","title":""},{"docid":"e1106def928c6e8ab53fb563e3bff385","score":"0.51902646","text":"def evaluate(self) -> None:\n\n self._generate_paths()\n self._compute_payoff_fn()\n self._is_evaluated = True","title":""},{"docid":"f1b28047d6734cbcd08da68de38b381b","score":"0.51892775","text":"def done(self, result):\n output_file = result['output']\n GLF = result['GLF']\n Qt.QApplication.restoreOverrideCursor()\n self.statusBar().showMessage('GLF = {:0.1f}%'.format(GLF*100))\n # Show a MessageBox.\n QtWidgets.QMessageBox.information(self, 'Ausgabe erzeugt',\n str(output_file),\n QtWidgets.QMessageBox.Ok)\n self.start_button.setEnabled(True)\n\n if self.plot_timeseries:\n try: # Create a line plot of the time series in a new window\n self.plot_window = PlotWindow()\n self.plot_window.update_lineplot(result['df_sum'])\n self.plot_window.show()\n except Exception as e:\n logger.exception(e)\n QtWidgets.QMessageBox.critical(self, 'Fehler', str(e),\n QtWidgets.QMessageBox.Ok)","title":""},{"docid":"daceaadc42197c7d5f2b3753e3192aee","score":"0.5189256","text":"def when_computation_done(self, out, param):\r\n tkMessageBox.showinfo('Computation is done', out)\r\n self.set_state('normal')\r\n\r\n if self.plugin_type == \"Chimera\":\r\n self.profile_wd = os.path.realpath(\r\n os.path.normcase(self.main_parameters[1] + \"/chimera/\"))\r\n\r\n tunnels_script = os.path.join(self.profile_wd, 'complex.py')\r\n paths_script = os.path.join(self.wd, 'paths.py')\r\n pores_script = os.path.join(self.wd, 'pores.py')\r\n autopores_script = os.path.join(self.wd, 'autopores.py')\r\n dom = parse(os.path.join(self.xml_wd, 'tunnels.xml'))\r\n\r\n for e in dom.getElementsByTagName('Exception'):\r\n tkMessageBox.showinfo('Exception', e.getAttribute('Text'))\r\n\r\n if self.plugin_type == \"PyMOL\":\r\n\r\n if param == \"pores\":\r\n self.group_objects(tunnels_script, 'Pores', \"MOLE_tunnels\")\r\n else:\r\n self.group_objects(tunnels_script, 'Tunnels', \"MOLE_pores\")\r\n\r\n self.group_objects(paths_script, 'Paths', 'MOLE_Paths')\r\n self.group_objects(pores_script, 'Pores', 'MOLE_Pores')\r\n self.group_objects(autopores_script, 'AutoPores', 'MOLE_AutoPores')\r\n plugin().show(\"cgo\", \"Interior*\")\r\n plugin().set_view(self.original_view)\r\n\r\n else:\r\n plugin.run(tunnels_script)\r\n\r\n self.set_state('normal')","title":""},{"docid":"8bc4859850d902bbbabda8a6f64c2238","score":"0.51880544","text":"def render(self, request):\n self._calculatePi(request).addCallback(self._gotResult, request)\n return server.NOT_DONE_YET","title":""},{"docid":"7174f03ecc0b1631771bcd650b043bf5","score":"0.5187847","text":"def _postCalculate(self,retList,time):\n\t\tmodList = []\n\t\tfor vals in retList: \n\t\t\tvals[\"percentEffected\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\n\t\t\tvals = self.calculateThresholdPolygon(vals,time)\n\t\t\tmodList.append(vals)\n\n\t\treturn pandas.concat(modList,ignore_index=True)","title":""},{"docid":"7174f03ecc0b1631771bcd650b043bf5","score":"0.5187847","text":"def _postCalculate(self,retList,time):\n\t\tmodList = []\n\t\tfor vals in retList: \n\t\t\tvals[\"percentEffected\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\n\t\t\tvals = self.calculateThresholdPolygon(vals,time)\n\t\t\tmodList.append(vals)\n\n\t\treturn pandas.concat(modList,ignore_index=True)","title":""},{"docid":"36b0c8b9bdb177f85aa5d45a556a0e22","score":"0.5185649","text":"def Calculate(self):\n pass","title":""},{"docid":"b214cdc4438aebfa3d63f75c8e78c910","score":"0.5171598","text":"def compute_pi_main(workers, accuracy, batchsize):\n n_total = 0\n s_total = 0\n tasks_queue = mp.Queue() # each task (or batch) is represented by the batchsize\n done_queue = mp.Queue() # result of each task is the number of successes in that batch\n processes = mp.Pool(workers, initializer=compute_pi_worker, initargs=(tasks_queue, done_queue))\n\n for _ in range(workers):\n tasks_queue.put(batchsize)\n\n while True:\n s = done_queue.get(block=True)\n s_total += s\n n_total += batchsize\n pi_est = (4.0 * s_total) / n_total\n error = abs(math.pi - pi_est)\n\n if error > accuracy:\n tasks_queue.put(batchsize)\n else:\n processes.terminate()\n processes.join()\n break\n\n print(\"Estimate:\", pi_est)\n print(\"Error:\", error)\n print(\"Steps:\", n_total)\n return pi_est, error, n_total","title":""},{"docid":"67da759295be1801c868c36af3dd3052","score":"0.5168196","text":"def calculate_float(self, default: int):\n self.total_float = self.latest_finish - self.earliest_finish\n\n if not self.successors:\n self.free_float = default - self.earliest_finish\n else:\n self.free_float = min(s.earliest_start for s in self.successors) - self.earliest_finish","title":""},{"docid":"ce99a8d17f1502a9ede8378469d864af","score":"0.5166104","text":"def calculate(s):","title":""},{"docid":"fff35d5e25b8885f6ecc9ff6d4200e5b","score":"0.5155409","text":"def handler(event, context):\n try:\n parsed_event = CalculatorEvent(**event)\n except ValidationError as exp:\n raise Exception(json.dumps(f\"[BadRequest] {exp}\"))\n\n if parsed_event.year not in crypto_prices[parsed_event.query]['old_prices']:\n raise Exception(json.dumps(f\"[BadRequest] Data for requested year not available\"))\n\n\n current_value = (parsed_event.amount/crypto_prices[parsed_event.query]['old_prices'][parsed_event.year]) * crypto_prices[parsed_event.query]['current_price']\n \n return round(current_value,2)","title":""},{"docid":"a28c312c92b4217aed7e3c0932a1bfe0","score":"0.5150363","text":"def execute_strategy(self):\n data = self.results.copy().dropna()\n data['position'] = np.where(data['SMA1'] > data['SMA2'], 1, 0)\n data['strategy'] = data['position'].shift(1) * data['returns']\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)","title":""},{"docid":"da3563d9154880d9234b4a9a1fead992","score":"0.5143086","text":"def evaluate_metric(self, assignments):\n sleep_seconds = 10\n print('{0} - Sleeping for {1} seconds to simulate expensive computation...'.format(threading.current_thread(), sleep_seconds))\n time.sleep(sleep_seconds)\n return sigopt.examples.franke_function(assignments['x1'], assignments['x2'])","title":""},{"docid":"0647838977b755e73ac53acb14e7ed14","score":"0.5141489","text":"def execute_strategy(self):\n data = self.results.copy().dropna()\n data['sma'] = data['returns'].rolling(self.sma).mean()\n data['distance'] = data['close'] - data['sma']\n # sell signals\n data['position'] = np.where(data['distance'] > self.threshold, -1, np.nan)\n # buy signals\n data['position'] = np.where(data['distance'] < -self.threshold, 1, data['position'])\n # cross of current price and SMA (zero distance)\n data['position'] = np.where(data['distance'] * data['distance'].shift(1) < 0, 0, data['position'])\n data['position'] = data['position'].ffill().fillna(0)\n data['strategy'] = data['position'].shift(1) * data['returns']\n # determine when trades take place\n # trades = data['position'].diff().fillna(0) != 0\n # subtract transaction costs from return where trades take place\n # data['strategy'][trades] -= self.tc\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\n self.results = data\n # absolute performance of indicator\n aperf = data['cstrategy'].ix[-1]\n # out/under performance of indicator\n operf = aperf - data['creturns'].ix[-1]\n return round(aperf, 2), round(operf, 2)","title":""},{"docid":"ab977317ec5b5501427a8c5d5683e375","score":"0.51393765","text":"def calculate(self):\n super().calculate()\n\n\n tr_key = Constants.get_key(\"TR\")\n trn_key = Constants.get_key(\"TRn\")\n\n dm_plus_key = Constants.get_key(\"DMplus\")\n dm_plus_n_key = Constants.get_key(\"DMplusN\")\n\n dm_minus_key = Constants.get_key(\"DMminus\")\n dm_minus_n_key = Constants.get_key(\"DMminusN\")\n di_plus_n_key = Constants.get_key(\"DIplusN\")\n di_minus_n_key = Constants.get_key(\"DIminusN\")\n di_diff_key = Constants.get_key(\"DIdiff\")\n di_sum_key = Constants.get_key(\"DIsum\")\n dx_key = Constants.get_key(\"DX\")\n\n df_result = []\n\n for ticker in self.tickers:\n\n df_data_atr = self.df[[ticker]].copy()\n atr_ind = ATR(df=df_data_atr, n=14)\n\n df_data = self.df[ticker].copy()\n # the period parameter of ATR function does not matter because period does not influence TR calculation\n df_data[tr_key] = atr_ind.calculate()[ticker][[tr_key]]\n\n df_data[dm_plus_key] = \\\n np.where(\n (df_data[self.high_key] - df_data[self.high_key].shift(1)) >\n (df_data[self.low_key].shift(1) - df_data[self.low_key]),\n df_data[self.high_key] - df_data[self.high_key].shift(1),\n 0)\n\n df_data[dm_plus_key] = \\\n np.where(\n df_data[dm_plus_key] < 0,\n 0,\n df_data[dm_plus_key])\n\n df_data[dm_minus_key] = \\\n np.where((df_data[self.low_key].shift(1) - df_data[self.low_key]) >\n (df_data[self.high_key] - df_data[self.high_key].shift(1)),\n df_data[self.low_key].shift(1) - df_data[self.low_key],\n 0)\n\n df_data[dm_minus_key] = np.where(df_data[dm_minus_key] < 0, 0, df_data[dm_minus_key])\n\n TRn = []\n DMplusN = []\n DMminusN = []\n TR = df_data[tr_key].tolist()\n DMplus = df_data[dm_plus_key].tolist()\n DMminus = df_data[dm_minus_key].tolist()\n\n for i in range(len(df_data)):\n if i < self.n:\n TRn.append(np.NaN)\n DMplusN.append(np.NaN)\n DMminusN.append(np.NaN)\n elif i == self.n:\n TRn.append(df_data[tr_key].rolling(self.n).sum().tolist()[self.n])\n DMplusN.append(df_data[dm_plus_key].rolling(self.n).sum().tolist()[self.n])\n DMminusN.append(df_data[dm_minus_key].rolling(self.n).sum().tolist()[self.n])\n elif i > self.n:\n TRn.append(TRn[i - 1] - (TRn[i - 1] / 14) + TR[i])\n DMplusN.append(DMplusN[i - 1] - (DMplusN[i - 1] / 14) + DMplus[i])\n DMminusN.append(DMminusN[i - 1] - (DMminusN[i - 1] / 14) + DMminus[i])\n\n df_data[trn_key] = np.array(TRn)\n df_data[dm_plus_n_key] = np.array(DMplusN)\n df_data[dm_minus_n_key] = np.array(DMminusN)\n df_data[di_plus_n_key] = 100 * (df_data[dm_plus_n_key] / df_data[trn_key])\n df_data[di_minus_n_key] = 100 * (df_data[dm_minus_n_key] / df_data[trn_key])\n df_data[di_diff_key] = abs(df_data[di_plus_n_key] - df_data[di_minus_n_key])\n df_data[di_sum_key] = df_data[di_plus_n_key] + df_data[di_minus_n_key]\n df_data[dx_key] = 100 * (df_data[di_diff_key] / df_data[di_sum_key])\n ADX = []\n DX = df_data[dx_key].tolist()\n for j in range(len(df_data)):\n if j < 2 * self.n - 1:\n ADX.append(np.NaN)\n elif j == 2 * self.n - 1:\n ADX.append(df_data[dx_key][j - self.n + 1:j + 1].mean())\n elif j > 2 * self.n - 1:\n ADX.append(((self.n - 1) * ADX[j - 1] + DX[j]) / self.n)\n df_data[self.indicator_key] = np.array(ADX)\n\n df_result.append(df_data.loc[:, [self.indicator_key]])\n\n self.df = pd.concat(df_result, axis=1, keys=self.tickers)\n\n return self.df","title":""},{"docid":"eafd971da2d043a48e336831a0a68557","score":"0.5138379","text":"def process_queue_fringes(self):\n\n try:\n result = self.queue.get(0)\n\n if result == \"ABORT\":\n self.addlog(\"Mission aborted.\", self.warningcolor2)\n self.abortmission.set(0)\n self.totaltime = 0\n self.removeprogressbar()\n return\n\n # Show result of the task if needed\n self.entry_23.delete(0, END)\n self.entry_23.insert(0, '{0:.2f}'.format(result[0]))\n self.entry_24.delete(0, END)\n self.entry_24.insert(0, '{0:.2f}'.format(result[1]))\n\n self.setoffsets()\n\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list = [], [], [], []\n\n for i in range(1, self.layernumber + 1):\n self.layertype_list.append(getattr(self, \"layertypevar{}\".format(i)).get())\n self.entry_x_list.append(float(getattr(self, \"entry_x_{}\".format(i)).get()))\n self.entry_d_list.append(float(getattr(self, \"entry_d_{}\".format(i)).get()))\n self.checklayer_list.append(int(getattr(self, \"checklayer{}\".format(i)).get()))\n\n fitobject = FIT_FTIR(self.Temp, self.wavenumbers_cut, self.trans_cut, self.entry_d_0.get(),\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list,\n float(self.entry_21.get()), self.angle,\n float(self.entry_23.get()), float(self.entry_24.get()), self.subtype, 2,\n self.listbox, self.progress_var, self.wn_beingcalculated,\n self.FTIRplot, self.absorptionplot, self.canvas, self.blindcal, self.abortmission)\n self.peakvalues_fit = fitobject.returnpeakvalues()\n\n self.addlog('Fitting fringes complete. Total time: {:.1f}s. MSE={}'.format(self.totaltime, result[2]))\n\n self.totaltime = 0\n\n self.try_remove_fitline(self.fitline_trans)\n self.fitline_trans = self.plot_and_show(self.FTIRplot, self.fitline_trans, 0, self.wavenumbers_cut,\n self.peakvalues_fit, 'r', '', 'Transmission (%)', 0, 'upper right')\n\n self.removeprogressbar()\n\n except queue.Empty:\n self.totaltime += 0.1\n self.after(100, self.process_queue_fringes)","title":""},{"docid":"5820bfa6b2e06e705d2fad4ec1847994","score":"0.5132884","text":"def compute(\n self,\n blade_cost,\n blade_mass,\n hub_system_cost,\n hub_system_mass,\n nacelle_mass,\n nacelle_cost,\n tower_cost,\n tower_mass,\n blade_number=3,\n offshore=True,\n ):\n\n # Variables\n self.blade_cost = (\n blade_cost # Float(0.0, units='USD', iotype='in', desc='cost for a single wind turbine blade')\n )\n self.blade_mass = blade_mass # Float(0.0, units='kg', iotype='in', desc='mass for a single wind turbine blade')\n self.hub_system_cost = hub_system_cost # Float(0.0, units='USD', iotype='in', desc='hub system cost')\n self.hub_system_mass = hub_system_mass # Float(0.0, units='kg', iotype='in', desc='hub system mass')\n self.nacelle_mass = nacelle_mass # Float(0.0, units='kg', iotype='in', desc='nacelle mass')\n self.nacelle_cost = nacelle_cost # Float(0.0, units='USD', iotype='in', desc='nacelle cost')\n self.tower_cost = tower_cost # Float(0.0, units='USD', iotype='in', desc='cost for a tower')\n self.tower_mass = tower_mass # Float(0.0, units='kg', iotype='in', desc='mass for a turbine tower')\n\n # Parameters (and ignored inputs)\n self.blade_number = blade_number # Int(3, iotype='in', desc = 'number of rotor blades')\n self.offshore = offshore # Bool(False, iotype='in', desc= 'boolean for offshore')\n\n # high level output assignment\n self.rotor_mass = self.blade_mass * self.blade_number + self.hub_system_mass\n self.rotor_cost = self.blade_cost * self.blade_number + self.hub_system_cost\n self.turbine_mass = self.rotor_mass + self.nacelle_mass + self.tower_mass\n self.turbine_cost = self.rotor_cost + self.nacelle_cost + self.tower_cost\n\n if self.offshore:\n self.turbine_cost *= 1.1\n\n # derivatives\n self.d_mass_d_blade_mass = self.blade_number\n self.d_mass_d_hub_mass = 1.0\n self.d_mass_d_nacelle_mass = 1.0\n self.d_mass_d_tower_mass = 1.0\n\n if self.offshore:\n self.d_cost_d_blade_cost = 1.1 * self.blade_number\n self.d_cost_d_hub_cost = 1.1\n self.d_cost_d_nacelle_cost = 1.1\n self.d_cost_d_tower_cost = 1.1\n else:\n self.d_cost_d_blade_cost = self.blade_number\n self.d_cost_d_hub_cost = 1.0\n self.d_cost_d_nacelle_cost = 1.0\n self.d_cost_d_tower_cost = 1.0","title":""},{"docid":"a2818a75b1740c04b7c7c9442d420e04","score":"0.5128805","text":"def getval(self):\n \n if self.averagingAttemps > 1:\n result = 0.0\n readings = 0.0\n readingsc = 0.0\n\t readings_array = {}\n for i in range(0, self.averagingAttemps):\n reading = self.getReading()\n if (reading != 0 and reading != 1023):\n if self.averagingMethod == \"max\":\n if reading > readings:\n\t\t\t \"\"\"print(str(reading) + \" higher as \" + str(readings))\"\"\"\n readings = reading\n else:\n readings = readings + reading\n readingsc = readingsc + 1\n\t\t if reading in readings_array:\n\t\t readings_array[reading] = readings_array[reading] + 1\n\t\t else:\n\t\t\treadings_array[reading] = 1\n time.sleep(self.averagingTimeout)\n if readingsc == 0:\n result = self.getReading()\n else:\n\t\tif self.averagingMethod == \"most\":\n\t\t sorted_x = sorted(readings_array.items(), key=operator.itemgetter(1), reverse=True)\n\t\t result = sorted_x[0][0]\n elif self.averagingMethod == \"max\":\n result = readings\n else:\n result = readings / readingsc\n else:\n result = self.getReading()\n \n if result == 0:\n msg = \"Error: Check wiring for the \" + self.sensorname\n msg += \" measurement, no voltage detected on ADC input \"\n msg += str(self.adcpin)\n print(msg)\n return None\n if result == 1023:\n if self.sensorname == \"LDR\":\n # Carrying on with 1023 gives divide by zero error below\n result = 1022\n else:\n msg = \"Error: Check wiring for the \" + self.sensorname\n msg += \" measurement, full voltage detected on ADC input \"\n msg += str(self.adcpin)\n print(msg)\n return None\n\n\tif self.sensorname == \"WindDirection\":\n\t return result\n\n\t\"\"\"print(self.sensorname + \" \" + str(self.sensorvoltage))\"\"\"\n\n vout = float(result)/1023 * self.sensorvoltage\n if self.pulldown != None:\n resout = (self.pulldown * self.sensorvoltage) / vout - self.pulldown\n elif self.pullup != None:\n resout = self.pullup / ((self.sensorvoltage / vout) - 1)\n else:\n resout = vout * 1000\n return resout","title":""},{"docid":"621c70cbb28fa1e4a9c190a799b7b1e6","score":"0.51253504","text":"def compute(self): # pylint: disable=E0202\n pass","title":""},{"docid":"621c70cbb28fa1e4a9c190a799b7b1e6","score":"0.51253504","text":"def compute(self): # pylint: disable=E0202\n pass","title":""},{"docid":"01998f20124e1512ef226959005fc520","score":"0.512176","text":"def calculate_callback(self):\n\n # Create the interpreter\n from asteval import Interpreter\n aeval = Interpreter()\n\n # Grab the calculation from the text box which the user wants to do\n calculation = str(self.calculation_text.text())\n\n lhs = calculation.split('=')[0].strip()\n\n # Use the package asteval to do the calculation, we are going to\n # assume here that the lhs of the equals sign is going to be the output named variable\n\n try:\n if lhs in self.data_components:\n raise KeyError('{} is already in the data components, use a different variable on the left hand side.'.format(lhs))\n\n # Pull in the required data and run the calculation\n for dc in self.data_components:\n if dc in calculation:\n aeval.symtable[dc] = self.data[dc]\n aeval(calculation)\n\n # Pull out the output data and add to the proper drop-downs\n out_data = aeval.symtable[lhs]\n self.data.add_component(out_data, lhs)\n\n # Add the new data to the list of available data for arithemitic operations\n self.data_components.append(lhs)\n\n self.close()\n\n except KeyError as e:\n self.calculation_text.setStyleSheet(\"background-color: rgba(255, 0, 0, 128);\")\n\n # Display the error in the Qt popup\n if aeval.error_msg:\n self.error_label_text.setText('{}'.format(aeval.error_msg))\n else:\n self.error_label_text.setText('{}'.format(e))\n\n self.error_label_text.setStyleSheet(\"color: rgba(255, 0, 0, 128)\")","title":""},{"docid":"9ad61c57a35376b206230052921c1336","score":"0.512145","text":"def update(self,current_value):\n\n self.error = self.set_point - current_value\n\n self.P_value = self.Kp * self.error\n if (self.last_value >= current_value):\n change = self.error - self.last_error\n else:\n change = 0.0\n\n if self.error > 0.0:\n self.I_value = self.Integrator * self.Ki\n else:\n self.I_value = (self.Integrator * self.Ki)\n\n\n #self.D_value = self.Kd * ( self.error - self.Derivator)\n self.D_value = self.Kd * change\n self.Derivator = self.error\n\n self.Integrator = self.Integrator + self.error\n\n if self.Integrator > self.Integrator_max:\n self.Integrator = self.Integrator_max\n elif self.Integrator < self.Integrator_min:\n self.Integrator = self.Integrator_min\n\n self.last_error = self.error\n self.last_value = current_value\n\n PID = self.P_value + self.I_value + self.D_value\n\n self._z_data[\"data\"][\"P\"] = self.P_value\n self._z_data[\"data\"][\"I\"] = self.I_value\n self._z_data[\"data\"][\"D\"] = self.D_value\n self._z_data[\"data\"][\"E\"] = self.error\n self._z_data[\"data\"][\"SP\"] = self.set_point\n self._z_data[\"data\"][\"OUT\"] = PID\n\n if self._zmq:\n try:\n self._zmq.send_json(self._z_data, zmq.NOBLOCK)\n except zmq.error.Again:\n pass\n\n return PID","title":""},{"docid":"472ea9d79a812c81d9dbbb43bbe45dc9","score":"0.50997347","text":"def _calc_result(self):\n import mcmc\n\n g=zeros(2)\n gcov=zeros((2,2))\n gsens = zeros(2)\n\n if self.eta:\n eta1vals,eta2vals=self.trials[:,2], self.trials[:,3]\n g1vals,g2vals=self._get_gvals_from_eta(eta1vals,eta2vals)\n else:\n g1vals=self.trials[:,2]\n g2vals=self.trials[:,3]\n\n prior = self.gprior(g1vals,g2vals)\n dpri_by_g1 = self.gprior.dbyg1(g1vals,g2vals)\n dpri_by_g2 = self.gprior.dbyg2(g1vals,g2vals)\n\n psum = prior.sum()\n\n g0=None\n gcov0=None\n if self.when_prior=='after':\n # this will be eta not g if using that parametrization\n g0,gcov0 = mcmc.extract_stats(self.trials[:,2:4])\n\n pars, pcov = mcmc.extract_stats(self.trials,weights=prior)\n # we need to multiply each by the prior\n \"\"\"\n g[0] = (g1vals*prior).sum()/psum\n g[1] = (g2vals*prior).sum()/psum\n\n\n gcov[0,0] = (g1diff**2*prior).sum()/psum\n gcov[0,1] = (g1diff*g2diff*prior).sum()/psum\n gcov[1,0] = gcov[0,1]\n gcov[1,1] = (g2diff**2*prior).sum()/psum\n \"\"\"\n\n g[:] = pars[2:4]\n gcov[:,:] = pcov[2:4, 2:4]\n\n # now the sensitivity is \n # sum( (<g>-g) L*dP/dg )\n # ----------------------\n # sum(L*P)\n #\n # the likelihood is already in the points\n\n g1diff = g[0]-g1vals\n g2diff = g[1]-g2vals\n gsens[0] = 1. - (g1diff*dpri_by_g1).sum()/psum\n gsens[1] = 1. - (g2diff*dpri_by_g2).sum()/psum\n else:\n # prior is already in the distribution of\n # points. This is simpler for most things but\n # for sensitivity we need a factor of (1/P)dP/de\n\n pars,pcov = mcmc.extract_stats(self.trials)\n #g, gcov = mcmc.extract_stats(self.trials[:,2:2+2])\n\n g[:] = pars[2:4]\n gcov[:,:] = pcov[2:4, 2:4]\n\n g1diff = g[0]-g1vals\n g2diff = g[1]-g2vals\n\n w,=where(prior > 0)\n if w.size == 0:\n raise ValueError(\"no prior values > 0!\")\n\n gsens[0]= 1.-(g1diff[w]*dpri_by_g1[w]/prior[w]).mean()\n gsens[1]= 1.-(g2diff[w]*dpri_by_g2[w]/prior[w]).mean()\n\n \n arates = self._emcee_sampler.acceptance_fraction\n arate = arates.mean()\n #print 'acceptance rate:',w.size/float(self.trials.size)\n\n # weighted s/n based on the most likely point\n s2n,loglike,chi2per,dof,prob=self._calculate_maxlike_stats()\n if self.eta:\n g0name='eta'\n else:\n g0name='g'\n\n Tmean=pars[4]\n Terr=sqrt(pcov[4,4])\n Ts2n=pars[4]/sqrt(pcov[4,4])\n #print 'T s/n:',Ts2n\n self._result={'model':self.model,\n 'g':g,\n 'gcov':gcov,\n 'gsens':gsens,\n g0name+'0':g0,\n g0name+'cov0':gcov0,\n 'pars':pars,\n 'pcov':pcov,\n 'Tmean':Tmean,\n 'Terr':Terr,\n 'Ts2n':Ts2n,\n 'arate':arate,\n 's2n_w':s2n,\n 'loglike':loglike,\n 'chi2per':chi2per,\n 'dof':dof,\n 'fit_prob':prob}\n #wlog(\"arate:\",self._result['arate'])","title":""},{"docid":"81e1d43e967bd2ad66fe32eeb4f512d8","score":"0.5096479","text":"def interpolate(self, double: float) -> float:\n ...","title":""},{"docid":"f9ccb6ccc97f671fb95181dc45fb44f8","score":"0.5088823","text":"def postsolve(self, lbfgs_result):\n raise NotImplementedError","title":""},{"docid":"7a7d46d8d9ffadc367fe21aad2f8c9bb","score":"0.50876844","text":"def result_relative(self, result1, result2):\n value1 = self.objective.get_mean(result1)\n value2 = self.objective.get_mean(result2)\n if value2 == 0:\n return float('inf') * value1\n return value1 / value2","title":""},{"docid":"0bc1f58636b2ddf43f909825f55e8d1e","score":"0.50867045","text":"def calcul_throughput(self, spectrumrangeairmass):\n \"\"\"\n data_mag = spectrumrangeairmass.data_mag.T\n data_mag -= np.log(self.data_calspec)\n spectrumrangeairmass.data_mag = data_mag.T\n\n data_order2 = spectrumrangeairmass.order2.T\n data_order2 /= self.data_calspec\n spectrumrangeairmass.order2 = data_order2.T\n #print(self.data_calspec)\n #print(self.data_calspec.T)\n #print(self.data_calspec.T @ self.data_calspec)\n MULT = np.zeros((len(self.data_calspec),len(self.data_calspec)))\n for i in range(len(self.data_calspec)):\n for j in range(len(self.data_calspec)):\n MULT[i][j], MULT[j][i] = self.data_calspec[i] * self.data_calspec[j], self.data_calspec[i] * self.data_calspec[j]\n\n spectrumrangeairmass.INVCOV *= MULT\n spectrumrangeairmass.cov /= MULT\n \"\"\"\n\n self.slope, self.ord, self.err_slope, self.err_ord = spectrumrangeairmass.bouguer_line()\n disp = np.loadtxt(self.rep_disp_name)\n Data_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1]/100, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n tel = np.loadtxt(self.rep_tel_name)\n Data_tel = sp.interpolate.interp1d(tel.T[0], tel.T[1], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err_tel = sp.interpolate.interp1d(tel.T[0], tel.T[2], kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n \"\"\"\n data_disp_new_lambda = Data_disp(self.new_lambda)\n err_disp_new_lambda = Err_disp(self.new_lambda)\n data_tel_new_lambda = Data_tel(self.new_lambda)\n err_tel_new_lambda = Err_tel(self.new_lambda)\n \"\"\"\n self.data_bouguer = np.exp(self.ord)\n err_bouguer = self.err_ord * self.data_bouguer\n self.data = self.data_bouguer / self.data_calspec\n self.lambdas = self.new_lambda\n self.err = err_bouguer / self.data_calspec\n\n # self.data = filter_detect_lines(self.lambdas, self.data, self.plot_filt, self.save_filter)\n\n Data = sp.interpolate.interp1d(self.lambdas, self.data, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err = sp.interpolate.interp1d(self.lambdas, self.err, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n self.lambdas = self.new_lambda\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\n\n self.data_tel = Data_tel(self.lambdas)\n self.data_tel_err = Err_tel(self.lambdas)\n self.data_disp = Data_disp(self.lambdas)\n self.data_disp_err = Err_disp(self.lambdas)\n self.data = Data(self.lambdas)\n self.err = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n # self.data = sp.signal.savgol_filter(self.data, 111, 2)\n\n #spectrumrangeairmass.archi_mega_fit()\n if self.order2 and not self.mega_fit:\n self.slope2, self.ord2, self.err_slope2, self.err_ord2, self.A2, self.A2_err = spectrumrangeairmass.bouguer_line_order2()\n self.data_bouguer = np.exp(self.ord2)\n err_bouguer = self.err_ord2 * self.data_bouguer\n self.data_order2 = self.data_bouguer / self.data_calspec\n self.lambdas = self.new_lambda\n self.err_order2 = err_bouguer / self.data_calspec\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n self.lambdas = self.new_lambda\n self.data_order2 = Data(self.lambdas)\n self.err_order2 = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n # self.data_order2 = sp.signal.savgol_filter(self.data_order2, 111, 2)\n\n if self.mega_fit:\n self.ord2, self.err_order2 = spectrumrangeairmass.megafit_emcee()\n print(self.ord2)\n print(len(self.ord2))\n self.data_order2 = self.ord2 / self.data_calspec\n self.err_order2 = self.err_order2 / self.data_calspec\n self.lambdas = self.new_lambda\n\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.ord2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\"linear\", bounds_error=False,\n fill_value=\"extrapolate\")\n\n self.lambdas = self.new_lambda\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\n self.data_order2 = Data(self.lambdas)\n self.err_order2 = Err(self.lambdas)\n self.data_bouguer = Data_bouguer(self.lambdas)\n self.data_order2[21:-21] = sp.signal.savgol_filter(self.data_order2[21:-21], 11, 2)","title":""},{"docid":"83954746e4599a297a386e46edd8832a","score":"0.5083051","text":"def solve(self, show_progress=True):\n dt = self.dt\n old_dt = self.dt\n\n if self.in_parallel:\n show = False\n else:\n show = show_progress\n bar = FloatPBar(self.t, self.tf, show=show)\n\n # Initial solution\n self.dump_output()\n self.barrier() # everybody waits for this to complete\n\n # initial solution damping time\n tdamp = self.tdamp\n\n # Compute the accelerations once for the predictor corrector\n # integrator to work correctly at the first time step.\n self.acceleration_eval.compute(self.t, dt)\n\n # solution output times\n output_at_times = numpy.array( self.output_at_times )\n\n while self.t < self.tf:\n\n # perform any pre step functions\n for func in self.pre_step_functions:\n func.eval(self)\n\n if self.rank == 0:\n logger.debug(\n \"Iteration=%d, time=%f, timestep=%f\" % \\\n (self.count, self.t, dt)\n )\n # perform the integration and update the time.\n #print 'Solver Iteration', self.count, dt, self.t, tdamp\n self.integrator.step(self.t, dt)\n\n # perform any post step functions\n for func in self.post_step_functions:\n func.eval(self)\n\n # update time and iteration counters if successfully\n # integrated\n self.t += dt\n self.count += 1\n\n # dump output if the iteration number is a multiple of the\n # printing frequency\n if self.count % self.pfreq == 0:\n self.dump_output()\n self.barrier()\n\n # dump output if forced\n if self.force_output:\n self.dump_output()\n self.barrier()\n\n self.force_output = False\n\n # re-set the time-step to the old time step before it\n # was adjusted\n dt = old_dt\n\n # update progress bar\n bar.update(self.t)\n\n # update the time for all arrays\n self.update_particle_time()\n\n # compute the new time step across all processors\n if self.adaptive_timestep:\n\n # locally stable time step\n dt = self.integrator.compute_time_step(\n self.dt, self.cfl)\n\n # damp the initial solution\n if self.t < tdamp:\n dt *= 0.5 * (numpy.sin(numpy.pi*(-0.5+self.t/tdamp)) + 1.0)\n\n # set the globally stable time step\n if self.in_parallel:\n dt = self.pm.update_time_steps(dt)\n\n # adjust dt to land on final time\n if self.t + dt > self.tf:\n dt = self.tf - self.t\n\n # adjust dt to land on specific output times\n if output_at_times.size > 0:\n tdiff = output_at_times - self.t\n condition = (tdiff > 0) & (tdiff < dt)\n\n if numpy.any( condition ):\n output_time = output_at_times[ numpy.where(condition) ]\n\n # save the old time-step and compute the new\n # time-step to fall on the specified output time\n # instant\n old_dt = dt\n dt = float( output_time - self.t )\n\n self.force_output = True\n\n # set the adjusted time-step for the solver\n self.dt = dt\n\n if self.execute_commands is not None:\n if self.count % self.command_interval == 0:\n self.execute_commands(self)\n\n # close the progress bar\n bar.finish()\n\n # final output save\n self.dump_output()","title":""},{"docid":"a0a145553849371012e1201d8c01f8d3","score":"0.50810856","text":"def evaluate(solutions, results, rollouts=100): # pipaek : rollout 100 -> 10 , originally 100\n index_min = np.argmin(results)\n best_guess = solutions[index_min]\n restimates = []\n\n for s_id in range(rollouts):\n print('p_queue.put(), s_id=%d' % s_id)\n p_queue.put((s_id, best_guess))\n print('>>>rollout_routine!!')\n rollout_routine() # pipaek : 여기서도 p_queue.put 하자마자 바로 처리..\n\n print(\">>>Evaluating...\")\n for _ in tqdm(range(rollouts)):\n #while r_queue.empty():\n # sleep(.1) # pipaek : multi-process가 아니므로\n if not r_queue.empty(): # pipaek : 20180718 r_queue.get()에서 stuck되어 있는 것을 방지하기 위해 체크!!\n #print('r_queue.get()')\n #restimates.append(r_queue.get()[1])\n r_s_id, r = r_queue.get()\n print('in evaluate r_queue.get() r_s_id=%d, r_queue remain=%d' % (r_s_id, r_queue.qsize()))\n restimates.append(r)\n else:\n print('r_queue.empty() -> break!!')\n break\n\n return best_guess, np.mean(restimates), np.std(restimates)","title":""},{"docid":"5ce81706280fd507142c3a9781b2eaa7","score":"0.5079482","text":"def test_calc_final_slope():\n calc_final_slope(gridv1.data)\n assert True","title":""}],"string":"[\n {\n \"docid\": \"215366153e758085c8bc79a022ff8b6c\",\n \"score\": \"0.6252689\",\n \"text\": \"def calculate(self):\\n item_number = self.fields.curselection()\\n polynomial = self.json_data[\\\"functions\\\"][int(item_number[0])]\\n result_floating = None\\n result_interval = None\\n if self.json_data[\\\"method\\\"] == \\\"regula_falsi\\\":\\n try:\\n result_floating = regula_falsi(polynomial[\\\"a\\\"],\\n polynomial[\\\"b\\\"],\\n function_from_list,\\n polynomial[\\\"coeff\\\"]\\n )\\n except MyError as error:\\n result_floating = error.value\\n except ZeroDivisionError:\\n result_floating = \\\"Unfortunately, division by 0 happened\\\"\\n try:\\n result_interval = regula_falsi_interval(polynomial[\\\"a\\\"],\\n polynomial[\\\"b\\\"],\\n function_from_list,\\n polynomial[\\\"coeff\\\"]\\n )\\n result_interval = result_interval.format('%.20E')[9:]\\n result_interval = result_interval[:-1]\\n except MyError as error:\\n result_interval = error.value\\n elif self.json_data[\\\"method\\\"] == \\\"newton\\\":\\n try:\\n result_floating = newton(polynomial[\\\"x\\\"],\\n polynomial[\\\"epsilon\\\"],\\n polynomial[\\\"max_iterations\\\"],\\n function_from_list,\\n polynomial[\\\"coeff\\\"],\\n polynomial[\\\"derivative_coeff\\\"]\\n )\\n except ZeroDivisionError:\\n result_floating = \\\"Unfortunately, division by zero happened\\\"\\n except MyError as error:\\n result_floating = error.value\\n try:\\n result_interval = newton_interval(polynomial[\\\"x\\\"],\\n polynomial[\\\"epsilon\\\"],\\n polynomial[\\\"max_iterations\\\"],\\n function_from_list,\\n polynomial[\\\"coeff\\\"],\\n polynomial[\\\"derivative_coeff\\\"]\\n )\\n result_interval = result_interval.format('%.20E')[9:]\\n result_interval = result_interval[:-1]\\n except MyError as error:\\n result_interval = error.value\\n else:\\n result_interval = \\\"You propably\\\"\\n result_floating = \\\"gave me wrong JSON\\\"\\n self.show_result_floating.config(text=result_interval)\\n self.show_result_interval.config(text=result_floating)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0830761115955abace478bf63277abd\",\n \"score\": \"0.6215401\",\n \"text\": \"def calculate_results():\\n\\n print('Calculating results..')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07116c7437a3ffc65ec7ced8caca88f\",\n \"score\": \"0.6053697\",\n \"text\": \"def _calculate(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977d47a74d67da7d19a1b22a8e36d483\",\n \"score\": \"0.59952414\",\n \"text\": \"def do_calculation():\\n dT = config.getfloat(\\\"thermodynamics\\\", \\\"Tmin\\\")\\n try:\\n nmax = config.getint(\\\"thermodynamics\\\", \\\"nmax\\\")\\n except ValueError:\\n nmax = config.get(\\\"thermodynamics\\\", \\\"nmax\\\")\\n if not nmax == \\\"automatic\\\":\\n print(\\\"nmax has to be given as integer or \\\\\\\"automatic\\\\\\\"\\\") \\n try:\\n analytic_n0 = config.get(\\\"geometry\\\", \\\"analytic_n0\\\")\\n except ValueError:\\n print(\\\"analytic_n0 is not given correctly: \\\", analytic_n0)\\n analytic_n0 = True\\n if type(geom.r1) == list or type(geom.r2) == list:\\n analytic_n0 = False\\n Tmax = config.getfloat(\\\"thermodynamics\\\", \\\"Tmax\\\")\\n f = thermodynamics.finiteT(dT, nmax, Tmax, geom, analytic_n0)\\n quantity = config.get(\\\"thermodynamics\\\", \\\"quantity\\\")\\n result = eval(\\\"f.%s\\\" %quantity)\\n return result, f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f05dd3eeb2320b01c30e115c6928fb5f\",\n \"score\": \"0.5965643\",\n \"text\": \"def error_calculation():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c8ad689e17b9cd1efc84e5ba7563bd\",\n \"score\": \"0.59115\",\n \"text\": \"def calc(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"387f4fedb43cb0287db06ecf37b13dc3\",\n \"score\": \"0.5901406\",\n \"text\": \"def calc():\\r\\n nonlocal func\\r\\n func = e.get()\\r\\n func = func.replace(\\\" \\\", \\\"\\\")\\r\\n func = toReversePolish(check(fixmult(findNeg(tokenizer(func)))))\\r\\n result.set(calculate(func, float(e2.get())))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ba20e0c2fecb5806fb08b0ca839ef9\",\n \"score\": \"0.58904105\",\n \"text\": \"def calculate(**kwargs):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29a6f09fde14e1f16e33b66e2b65226c\",\n \"score\": \"0.58634573\",\n \"text\": \"def _postCalculate(self,retList):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eccf3447683a2dc735f5799fbff98c3\",\n \"score\": \"0.58459675\",\n \"text\": \"def calculate(self, *args, **kwargs) -> Any:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37e62a56b03972b4b59b80389e4d31d\",\n \"score\": \"0.5809612\",\n \"text\": \"def compute(self):\\n\\n self.setInitialValue()\\n self.partialResult = self.initialValue\\n self.elementResult = None\\n\\n self.updateFunctionPort()\\n\\n self.set_output('Result', self.partialResult)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d0a1c04bf6c68b3a921b158c6c03fb\",\n \"score\": \"0.57289326\",\n \"text\": \"def OnCalcDone(self, event):\\n if event.data is None:\\n self.log(\\\"No result from calculation!\\\")\\n self.PSF_HDUlist = None\\n else:\\n self.PSF_HDUlist = event.data\\n for w in ['Display PSF', 'Display profiles', 'Save PSF As...']:\\n self.widgets[w].Enable(True)\\n\\n self._refresh_window()\\n self.log(\\\"PSF calculation complete\\\")\\n # In either event, the worker is done\\n self.calcthread = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae7ea9f7af562970e90b832e81e037a\",\n \"score\": \"0.5720635\",\n \"text\": \"def calculate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd18a5ecb864880f5b28ca9d88e14303\",\n \"score\": \"0.56965977\",\n \"text\": \"def on_evaluate(self, interp):\\n return 0.000000\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8afeea6242d6a4492b1855f0c649af5\",\n \"score\": \"0.5681769\",\n \"text\": \"def Calculate():\\r\\n values = GetValues()\\r\\n values = SolveEqn(values)\\r\\n DisplayValues(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38fe131032ab41802c0adfc8618129c\",\n \"score\": \"0.5660559\",\n \"text\": \"def _run_calculation(self):\\n # collect input\\n input_vs = OrderedDict()\\n for intf in self.input_intfs:\\n if intf.sibling_intf_allowed:\\n input_vs.setdefault(intf.family_name, []).append(intf.get_calculated_value())\\n else:\\n input_vs[intf] = intf.get_calculated_value()\\n\\n # run actual function defined by user\\n try:\\n results = self.calculate(*input_vs.values())\\n except Exception as e:\\n # set all outputs NULL\\n results = [NullValue(f\\\"calculation fail of {self}\\\")] * len(self.output_intfs)\\n # record and print error status\\n self._calculation_status = e\\n self.print_status()\\n raise\\n else:\\n # wrap singular result and match length\\n results = [results] if not isinstance(results, (list, tuple)) else list(results)\\n results += [NullValue(\\\"calculation didn't return enough values\\\")] * (len(self.output_intfs) - len(results))\\n self._calculation_status = ''\\n finally:\\n # push calculation result down to outputs\\n for result, intf in zip(results, self.output_intfs):\\n intf.set_provoke_value(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3918df179bdebeaa8326fa3d94277d7\",\n \"score\": \"0.5617621\",\n \"text\": \"def compute_value(self):\\n result = 0.00000000\\n if self.value1 and self.tasa:\\n result = (self.value1 * 1) / self.tasa \\n self.result = result\\n if not self.flag1:\\n self.id_record = self.env.context.get('active_id')\\n self.act_model = self.env.context.get('active_model')\\n self.flag1 = True\\n return {\\n 'view_mode': 'form',\\n 'res_model': 'compute.crypto.value',\\n 'res_id': self.id,\\n 'view_id': False,\\n 'type': 'ir.actions.act_window',\\n 'target': 'new',\\n 'context': {\\\"default_act_model\\\":self.act_model, \\\"default_result\\\": result, \\\"default_id_record\\\": self.id_record,},\\n }\\n else:\\n raise UserError(\\\"You need set all values first.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43cd1561f63a67897cd240b00229f59\",\n \"score\": \"0.55982625\",\n \"text\": \"def process_final(self, *args):\\n return self._sum / self._count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aad11d6d48fea29f5ab6cfd496c2674f\",\n \"score\": \"0.5591634\",\n \"text\": \"def doCalculation()->JSON_RESPONSE: \\n\\n # Get JSON data from client\\n first:int = int(request.json['first'])\\n second:int = int(request.json['second'])\\n oper:str = request.json['operation']\\n\\n rst:int = performCalc(first, second, oper)\\n\\n # Prepare the return structure\\n result:dict[str,int] = {\\\"result\\\":rst}\\n\\n # Send JSON back to the client\\n return json.dumps(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37453e9332d7d6b5fbee48eb2fe7abf2\",\n \"score\": \"0.55907995\",\n \"text\": \"def handle_calculate(self, value):\\n #try:\\n\\n result = self.valid_number_check() * 1.6093\\n self.root.ids.output_label.text = str(result)\\n print(result)\\n #except: ValueError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11725e0d4aa23fdfc0173c1231d04274\",\n \"score\": \"0.5529947\",\n \"text\": \"def _compute(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85e680e952960f28dbfe0efb0c4bc700\",\n \"score\": \"0.5507777\",\n \"text\": \"def calc(self):\\n raise Exception(\\\"Workflow calculation not implemented yet\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ddd32b2c49f7c13ef977e81a00da9d\",\n \"score\": \"0.54745024\",\n \"text\": \"def calc_results():\\n global num_solutions\\n subt = 100*get_subt(0) + 10*get_subt(1) + get_subt(2)\\n diff = 100*get_diff(0) + 10*get_diff(1) + get_diff(2)\\n minu = 1000*get_minu(0) + 100*get_minu(1) + 10*get_minu(2) + get_minu(3)\\n if (subt + diff == minu) and (minu >= 1000):\\n print minu, \\\"-\\\", subt, \\\"==\\\", diff\\n num_solutions += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb90820e644680bc6d5e502aead6fde\",\n \"score\": \"0.5453361\",\n \"text\": \"def calculate(self, structure, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70fffa82abf3ab29e63d6e09fafe4f39\",\n \"score\": \"0.54355496\",\n \"text\": \"def _postCalculate(self,retList,time):\\n\\t\\tif len(retList) > 0: \\n\\t\\t\\tret = pandas.concat(retList,ignore_index=True)\\n\\t\\t\\tret = self.calculateThresholdPolygon(ret,time)\\n\\t\\t\\tret['percentEffected'] = 1.\\n\\t\\telse: \\n\\t\\t\\tret = None\\n\\t\\treturn ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94e16cfebaa89484f51c5df781ba26c\",\n \"score\": \"0.5427222\",\n \"text\": \"def __check_calc(self):\\n # nothing done\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166673f4de6c078d348a3759ee698ae2\",\n \"score\": \"0.54081076\",\n \"text\": \"def calculate(self, var_list):\\n # Run dummy calculation.\\n index = var_list[1].split(' ')[0]\\n submit = call('cp files/pw-scf_{}.out pw-scf.out'.format(index),\\n shell=True)\\n if submit != 0:\\n raise AssertionError('Could not submit job.')\\n energy = self.scrape_output(self.output_file)\\n\\n print('iteration {0} energy {1}'.format(index, energy))\\n\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ed09a292242295d9bd0a9dc162cc28\",\n \"score\": \"0.54075795\",\n \"text\": \"def calificacion_final(par1, par2, par3):\\n calfinal = par1 + par2 + par3\\n promedio = calfinal / 3\\n return promedio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8124e8fa74cc5964d34246b27cb46bf\",\n \"score\": \"0.5406774\",\n \"text\": \"def run_interp():\\n func = str(input('Enter string or trigonometric function: '))\\n print('Value l')\\n l_a = float_input(positive=True)\\n print('Value u')\\n u_b = float_input(positive=True)\\n print('Value n')\\n n_c = integer_input()\\n print('Your intermediate results: {}'.format(interp(func, l_a, u_b, n_c)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36a407754c15e9a2256c75e9d606681f\",\n \"score\": \"0.53978\",\n \"text\": \"def __calc(self):\\n timezone = self.timezone # in hours, east is positive\\n longitude= self.long # in decimal degrees, east is positive\\n latitude = self.lat # in decimal degrees, north is positive\\n\\n time = self.time # percentage past midnight, i.e. noon is 0.5\\n day = self.day # daynumber 1=1/1/1900\\n \\n Jday =day+2415018.5+time-timezone/24 # Julian day\\n Jcent =(Jday-2451545)/36525 # Julian century\\n\\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\\n Mobliq = 23+(26+((21.448-Jcent*(46.815+Jcent*(0.00059-Jcent*0.001813))))/60)/60\\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent))+sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+sin(rad(3*Manom))*0.000289\\n Struelong= Mlong+Seqcent\\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\\n \\n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))-0.5*vary*vary*sin(4*rad(Mlong))-1.25*Eccent*Eccent*sin(2*rad(Manom)))\\n\\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))*cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\\n\\n self.solarnoon_t=(720-4*longitude-eqtime+timezone*60)/1440\\n self.sunrise_t =self.solarnoon_t-hourangle*4/1440\\n self.sunset_t =self.solarnoon_t+hourangle*4/1440\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9393ef12dcb8b1ff273f5733e0539ddb\",\n \"score\": \"0.53911626\",\n \"text\": \"def compute(self) -> float:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b254264ee7ae0c135148c1ce91717f2d\",\n \"score\": \"0.5388854\",\n \"text\": \"def Exemples_calculs():\\n while True :\\n (xA,yA)=input_coord(\\\"A\\\")\\n while True :\\n (xB,yB)=input_coord(\\\"B\\\")\\n if xB!=xA or yB!=yA :\\n break\\n else :\\n display(\\\"Il faut deux points distincts pour faire une droite ! Recommencez !\\\")\\n\\n intro=Latex(\\\"\\\"\\\"Nous allons calculer le coefficient directeur de la droite (AB) avec\\n A( {} ; {} ) et B( {} ; {} )\\\"\\\"\\\".format(xA,yA,xB,yB))\\n if xA==xB :\\n\\n calcul=Latex(\\\"\\\"\\\"Les droites parallèles à l'axe des ordonnées n'ont pas de coefficient directeur !\\n Il faut donc que vos points n'aient pas la même abscisse :\\\"\\\"\\\")\\n else :\\n frac=fractions.Fraction((yB-yA),(xB-xA))\\n if frac==int(frac):\\n frac=str(int(frac))\\n else :\\n frac=\\\"\\\\dfrac{{{numerator}}}{{{denominator}}}\\\".format(numerator=frac.numerator,denominator=frac.denominator)\\n calcul=Math(\\\"m= \\\\dfrac{{y_B-y_A}}{{x_B-x_A}}= \\\\dfrac{{{yB}-{yA}}}{{{xB}-{xA}}}= \\\\dfrac{{{yByA}}}{{{xBxA}}}={frac}\\\"\\n .format(xA=xA,yA=yA,xB=xB,yB=yB,yByA=yB-yA,xBxA=xB-xA,frac=frac))\\n Trace_droite(xA,yA,xB,yB)\\n display(intro,calcul)\\n s=input(\\\"Voulez-vous recommencer ?(o/n)\\\")\\n if s.lower() in ['n','non','no'] :\\n break\\n else :\\n clear_output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dd7780b29a79b6fd8d3b696267d256c\",\n \"score\": \"0.53845376\",\n \"text\": \"def processAlgorithm(self, parameters, context, feedback):\\n\\n # Load present raster\\n present_raster = self.parameterAsRasterLayer(parameters, self.INPUT1, context)\\n ds_present = gdal.Open(present_raster.dataProvider().dataSourceUri())\\n arr_present = ds_present.GetRasterBand(1).ReadAsArray()\\n # Clean negative values\\n arr_present[arr_present<0] = 0\\n\\n # Load future raster\\n future_raster = self.parameterAsRasterLayer(parameters, self.INPUT2, context)\\n ds_future = gdal.Open(future_raster.dataProvider().dataSourceUri())\\n arr_future = ds_future.GetRasterBand(1).ReadAsArray()\\n # Clean negative values\\n arr_future[arr_future<0] = 0\\n\\n print(arr_future)\\n print(np.unique(arr_future))\\n # Parameters\\n V = self.parameterAsDouble(parameters, self.INPUT5, context)\\n\\n r = 0\\n\\n c = 3\\n\\n area_pixel = self.parameterAsInt(parameters, self.PIXEL_RES, context) * self.parameterAsInt(\\n parameters, self.PIXEL_RES, context)\\n # Years\\n present = self.parameterAsInt(parameters, self.INPUT3, context)\\n future = self.parameterAsInt(parameters, self.INPUT4, context)\\n\\n # Calculate coeff sequestration\\n arr_diff = arr_future - arr_present\\n arr_diff_norm = arr_diff / float((future - present))\\n arr_years = np.array(range(0, future - present))\\n coeff = sum(1 / ((1 + r / 100) ** arr_years * ((1 + c / 100) ** arr_years)))\\n carbon_sequestration_value = V * arr_diff_norm * coeff\\n carbon_sequestration_difference = arr_future - arr_present\\n # Initialize and write on output raster\\n path_output = self.parameterAsString(parameters, self.OUTPUT, context)\\n file_output = path_output + '/SE_01_carbon_sequestration_delta_euro.tiff'\\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n [cols, rows] = carbon_sequestration_value.shape\\n carbon_sequestration_difference_area = np.sum(carbon_sequestration_difference) / (cols * rows * area_pixel)\\n outdata = driver.Create(file_output, rows, cols, 1, gdal.GDT_Float64)\\n outdata.SetGeoTransform(ds_present.GetGeoTransform())##sets same geotransform as input\\n outdata.SetProjection(ds_present.GetProjection())##sets same projection as input\\n outdata.GetRasterBand(1).WriteArray(carbon_sequestration_value)\\n print(np.max(outdata.GetRasterBand(1).ReadAsArray()))\\n outdata.FlushCache() ##saves to disk!!\\n outdata = None\\n band = None\\n ds = None\\n print(np.sum(arr_present))\\n report_output = path_output + '/SE_sequestro_carbonio.txt'\\n f = open(report_output, \\\"w+\\\")\\n today = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\\n f.write(\\\"Sommario dell'analisi del sequestro di carbonio\\\\n\\\")\\n f.write(\\\"Data: \\\" + today +\\\"\\\\n\\\\n\\\\n\\\")\\n f.write(\\\"Analisi stato di fatto\\\\n\\\\n\\\")\\n f.write(\\\"Anno corrente: %i \\\\n\\\" % (present))\\n f.write(\\\"Sequestro carbonio Stato attuale (ton Corg): %f \\\\n\\\" % (np.sum(arr_present)))\\n f.write(\\\"Valore medio del carbonio sequestrato per unità di superficie - Stato attuale (ton Corg/ha): : %f \\\\n\\\" % (\\n np.sum(arr_present) / (cols * rows * area_pixel) * 10000))\\n f.write(\\\"Valore totale del sequestro di carbonio (€): %f \\\\n\\\\n\\\\n\\\" % ((np.sum(arr_present)*V*coeff)/ float((future - present))))\\n f.write(\\\"Analisi stato di progetto\\\\n\\\\n\\\")\\n f.write(\\\"Anno progetto: %i \\\\n\\\" % (future))\\n f.write(\\\"Sequestro carbonio Stato di progetto (ton Corg): %f \\\\n\\\" % (np.sum(arr_future)))\\n f.write(\\\"Valore medio del carbonio sequestrato per unità di superficie - Stato di progetto (ton Corg/ha): %f \\\\n\\\" % (\\n np.sum(arr_future) / (cols * rows * area_pixel) * 10000))\\n f.write(\\\"Valore totale del sequestro di carbonio (€): %f \\\\n\\\\n\\\\n\\\" % ((np.sum(arr_future)*V*coeff) / float((future - present))))\\n f.write(\\\"Differenze tra stato di progetto e stato attuale\\\\n\\\\n\\\")\\n f.write(\\\"Periodo di analisi: %i - %i\\\\n\\\" % (present, future))\\n f.write(\\\"Differenza di sequestro carbonio (ton Corg): %f \\\\n\\\" % (np.sum(arr_diff)))\\n f.write(\\\"Differenza carbonio sequestrato per unità di superficie (ton Corg/ha): %f \\\\n\\\" % (\\n carbon_sequestration_difference_area * 10000))\\n f.write(\\\"Differenza in termini economici del SE di sequestro di carbonio (stato di progetto – stato attuale) (€):%d \\\\n\\\" % (\\n np.sum(carbon_sequestration_value)))\\n return {self.OUTPUT: carbon_sequestration_value}\\n\\n \\n # ----------------------------------------------------------------------------------- \\n # Copyright (c) 2021 Città di Torino.\\n # \\n # This material is free software: you can redistribute it and/or modify\\n # it under the terms of the GNU General Public License as published by\\n # the Free Software Foundation, either version 2 of the License, or\\n # (at your option) any later version.\\n # \\n # This program is distributed in the hope that it will be useful,\\n # but WITHOUT ANY WARRANTY; without even the implied warranty of\\n # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\\n # GNU General Public License for more details.\\n # \\n # You should have received a copy of the GNU General Public License\\n # along with this program. If not, see http://www.gnu.org/licenses.\\n # ----------------------------------------------------------------------------------- \",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddba8492bd02da00237d8e1464e5a126\",\n \"score\": \"0.5371956\",\n \"text\": \"def _run(self):\\n self._solve()\\n return self._ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11080b93adcf2859b7f8662c9f7e6fff\",\n \"score\": \"0.53654003\",\n \"text\": \"def _compute(y, t, N, b, k):\\n S, I, R = y\\n #print(t)\\n '''The Susceptible Equation'''\\n dSdt = -b[int(t)] * S * I / N\\n \\n '''The Infected Equation = -The Susceptible Equation - The Recovered Equation'''\\n dIdt = b[int(t)] * S * I / N - k * I\\n \\n '''The Recovered Equation'''\\n dRdt = k * I\\n return dSdt, dIdt, dRdt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd122062e4d6256eaf6d4aa2bbf05994\",\n \"score\": \"0.53594244\",\n \"text\": \"def integral(self, time1, time2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb010d6f5ea79ce4b09974b0bc0d8c9\",\n \"score\": \"0.5352363\",\n \"text\": \"def bulk_calc(self):\\n self.calc_csa()\\n self.calc_CCC()\\n self.calc_impedence()\\n self.calc_runVd()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca2393dd1b3cb44fb5ee38d774cc7de\",\n \"score\": \"0.5340337\",\n \"text\": \"def compute_values(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca2393dd1b3cb44fb5ee38d774cc7de\",\n \"score\": \"0.5340337\",\n \"text\": \"def compute_values(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e6b33eeb34f158ca8b2ddbd6257563\",\n \"score\": \"0.5340099\",\n \"text\": \"def run_calculation(self):\\n\\n if w.ignore_history_false.isChecked():\\n\\n fp = w.input_file_line_edit.text()\\n\\n w.df, w.cache = read_from_file(fp, raw_mt=True)\\n # TODO: Check if the filter index is still necessary when matching string indices in match_target_mt below.\\n filter_index = w.df.query('status == \\\"mt\\\"').index\\n w.df = w.df.loc[filter_index]\\n\\n # Create boolean filter with all repetitions except for first occurrences set to True\\n filter_index = w.df.query('stype == \\\"source\\\"').duplicated('text')\\n w.df = w.df[-filter_index]\\n\\n else:\\n w.df = new_translation(w.df, w.cache, w.sample_object)\\n\\n df_mt = match_target_mt(w.df)\\n\\n w.cache = pe_density(df_mt, w.cache)\\n w.actionSave_as.setEnabled(True)\\n w.textOutput.setText(str('Your Post-Edit Density score is {:.3f}\\\\n'.format(w.cache['ped'])))\\n w.statistics()\\n\\n w.autosave()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db551929b95c9e61285499b44233dd40\",\n \"score\": \"0.5333397\",\n \"text\": \"def calc(self, x):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ab45ce819ad5f27ffcf041865253bdf\",\n \"score\": \"0.53273827\",\n \"text\": \"def post(self, start_value, end_value):\\n start_value, end_value = int(start_value), int(end_value)\\n engine_name = get_engine_name()\\n if os.path.exists(engine_name):\\n self.__clean_hashes()\\n\\n if not os.path.exists(json_filename):\\n create_minion_json()\\n engine_db_name = 'sqlite:///' + engine_name\\n try:\\n p = Process(target=calculate_range, args=(start_value, end_value, engine_db_name, m_id))\\n p.start()\\n processes.append(p)\\n except:\\n print('Error: failed to run calculation on minion ' + str(m_id) + '.')\\n return {'status': 'error', 'message': 'calculation failed'}\\n update_status('busy')\\n return {'status': 'busy', 'message': 'calculation started'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f4e7059b0fd4d4a86a03694951504c\",\n \"score\": \"0.5327031\",\n \"text\": \"def compute():\\n message1[\\\"text\\\"] = \\\"\\\" #reset messages\\n message2[\\\"text\\\"] = \\\"\\\"\\n message4[\\\"text\\\"] = \\\"\\\"\\n message5[\\\"text\\\"] = \\\"\\\" \\n message6[\\\"text\\\"] = \\\"\\\" \\n message7[\\\"text\\\"] = \\\"\\\" \\n \\n smux=str(smux_entry.get()) #return the smux value in the tkinter entry \\n try:\\n volts_min=float(volt_min_entry.get()) # min voltage \\n volts_max=float(volt_max_entry.get()) # max voltage \\n R=float(resistance_entry.get()) # protection resistance \\n if abs(volts_max)>20 or abs(volts_min)>20:\\n texte5=\\\"abs(volt) <=10\\\"\\n message5[\\\"text\\\"] = texte5 \\n print(texte5) \\n else: \\n nb=int(point_number_entry.get()) #measures nb\\n if nb<1:\\n texte4=\\\"nb>0\\\"\\n message4[\\\"text\\\"] = texte4 \\n print(texte4) \\n else:\\n # delay=float(delay_entry.get()) #not needed\\n # print(smux,volts_min,volts_max,nb,delay) #used to debug\\n try:\\n complete_measure(volts_min,volts_max,nb,smux=smux,R=R,compliance=compliance)\\n texte7=\\\"Measures done\\\"\\n message7[\\\"text\\\"] = texte7 \\n print(texte7) \\n except Exception as ex:\\n texte6=\\\"Error from measurement detected: %s\\\" % str(ex) #display the error name\\n message6[\\\"text\\\"] = texte6 \\n print(texte6) \\n reset() \\n except:\\n texte2=\\\"floats\\\"\\n message2[\\\"text\\\"] = texte2 \\n print(texte2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1594a7502a7cae5142f313f0066993f3\",\n \"score\": \"0.53201216\",\n \"text\": \"def func(progress_remaining: float) -> float:\\n return initial_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c176dcfdf4a68d52eaa145558ca732f5\",\n \"score\": \"0.53149915\",\n \"text\": \"def calc(self, pvalues, lamb):\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c01c7f7208d17f565623b9efa9f2829\",\n \"score\": \"0.5313737\",\n \"text\": \"def _evaluate_function(self, inputs, aliases):\\n # TODO how to handle aliases????\\n method = self._sub_name\\n request = inputs.pop('request', None) # sometimes just the inputs will be used without the request, like in elasticity curves\\n result = self._obj.evaluate(method, request, inputs)\\n balance = result[0]\\n meta = result[1]\\n return balance, meta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7bf3ab90c18dae7b8553fba4cb5d3e\",\n \"score\": \"0.53023535\",\n \"text\": \"def calculate(self, mol: Molecule) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034d41aa8d8f3385ce7f1536f5f8b7b5\",\n \"score\": \"0.52879363\",\n \"text\": \"def __calc(self):\\n timezone = self.timezone # in hours, east is positive\\n longitude= self.long # in decimal degrees, east is positive\\n latitude = self.lat # in decimal degrees, north is positive\\n\\n time = self.time # percentage past midnight, i.e. noon is 0.5\\n day = self.day # daynumber 1=1/1/1900\\n \\n # NOAA Spreadsheet\\n Jday = day+2415018.5+time-timezone/24. # Julian day\\n Jcent = (Jday-2451545)/36525 # Julian century\\n\\n Manom = 357.52911+Jcent*(35999.05029-0.0001537*Jcent)\\n Mlong = 280.46646+Jcent*(36000.76983+Jcent*0.0003032)%360\\n Eccent = 0.016708634-Jcent*(0.000042037+0.0001537*Jcent)\\n Mobliq = 23+(26+((21.448-Jcent*(46.815+ \\\\\\n Jcent*(0.00059-Jcent*0.001813))))/60)/60\\n obliq = Mobliq+0.00256*cos(rad(125.04-1934.136*Jcent))\\n vary = tan(rad(obliq/2))*tan(rad(obliq/2))\\n Seqcent = sin(rad(Manom))*(1.914602-Jcent*(0.004817+0.000014*Jcent)) \\\\\\n +sin(rad(2*Manom))*(0.019993-0.000101*Jcent)+ \\\\\\n sin(rad(3*Manom))*0.000289\\n Struelong= Mlong+Seqcent\\n Sapplong = Struelong-0.00569-0.00478*sin(rad(125.04-1934.136*Jcent))\\n declination = deg(asin(sin(rad(obliq))*sin(rad(Sapplong))))\\n \\n eqtime = 4*deg(vary*sin(2*rad(Mlong))-2*Eccent*sin(rad(Manom))+ \\\\\\n 4*Eccent*vary*sin(rad(Manom))*cos(2*rad(Mlong))- \\\\\\n 0.5*vary*vary*sin(4*rad(Mlong))- \\\\\\n 1.25*Eccent*Eccent*sin(2*rad(Manom)))\\n\\n hourangle= deg(acos(cos(rad(90.833))/(cos(rad(latitude))* \\\\\\n cos(rad(declination)))-tan(rad(latitude))*tan(rad(declination))))\\n\\n self.solarnoon_t = (720-4*longitude-eqtime+timezone*60)/1440.\\n self.sunrise_t = self.solarnoon_t-hourangle*4/1440.\\n self.sunset_t = self.solarnoon_t+hourangle*4/1440.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee1d7a33c192af97967f2c8a2e3c713b\",\n \"score\": \"0.5286321\",\n \"text\": \"def execute_strategy(self):\\n data = self.results.copy().dropna()\\n data['position'] = np.sign(data['returns'].rolling(self.mom).mean())\\n data['strategy'] = data['position'].shift(1) * data['returns']\\n # determine when trades take place\\n # trades = data['position'].diff().fillna(0) != 0\\n # subtract transaction costs from return where trades take place\\n # data['strategy'][trades] -= self.tc\\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\\n self.results = data\\n # absolute performance of indicator\\n aperf = data['cstrategy'].ix[-1]\\n # out/under performance of indicator\\n operf = aperf - data['creturns'].ix[-1]\\n return round(aperf, 2), round(operf, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b38b01232a3f553a7ee77b5f648b7ec\",\n \"score\": \"0.5280257\",\n \"text\": \"def calc(self):\\n sorted_pop = self.sortpop()\\n initial_route = self.population[sorted_pop[0][0]]\\n distance = 1 / sorted_pop[0][1]\\n progress = [ distance ]\\n if callable(self.animate):\\n self.plot = True\\n individual = Individual(initial_route)\\n stop_animation = Event()\\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\\n else:\\n self.plot = False\\n if self.prn:\\n print(f\\\"Initial distance: {distance}\\\")\\n try:\\n if self.plot:\\n for i in range(self.generations):\\n population = self.next_gen()\\n sorted_pop = self.sortpop()\\n distance = 1 / sorted_pop[0][1]\\n progress.append(distance)\\n if self.prn:\\n print(f\\\"[Generation:{i}] Current distance: {distance}\\\")\\n route = population[sorted_pop[0][0]]\\n individual.update(route)\\n else:\\n for i in range(self.generations):\\n population = self.next_gen()\\n distance = 1 / self.sortpop()[0][1]\\n if self.prn:\\n print(f\\\"[Generation:{i}] Current distance: {distance}\\\")\\n \\n \\n except KeyboardInterrupt:\\n pass\\n try:\\n stop_animation.set()\\n except NameError:\\n pass\\n final_route_index = self.sortpop()[0][0]\\n final_route = population[final_route_index]\\n if self.prn:\\n print(\\\"Final route:\\\", final_route)\\n\\n return initial_route, final_route, distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b38b01232a3f553a7ee77b5f648b7ec\",\n \"score\": \"0.5280257\",\n \"text\": \"def calc(self):\\n sorted_pop = self.sortpop()\\n initial_route = self.population[sorted_pop[0][0]]\\n distance = 1 / sorted_pop[0][1]\\n progress = [ distance ]\\n if callable(self.animate):\\n self.plot = True\\n individual = Individual(initial_route)\\n stop_animation = Event()\\n self.animate(individual, progress, stop_animation, plot_conclusion=initial_route)\\n else:\\n self.plot = False\\n if self.prn:\\n print(f\\\"Initial distance: {distance}\\\")\\n try:\\n if self.plot:\\n for i in range(self.generations):\\n population = self.next_gen()\\n sorted_pop = self.sortpop()\\n distance = 1 / sorted_pop[0][1]\\n progress.append(distance)\\n if self.prn:\\n print(f\\\"[Generation:{i}] Current distance: {distance}\\\")\\n route = population[sorted_pop[0][0]]\\n individual.update(route)\\n else:\\n for i in range(self.generations):\\n population = self.next_gen()\\n distance = 1 / self.sortpop()[0][1]\\n if self.prn:\\n print(f\\\"[Generation:{i}] Current distance: {distance}\\\")\\n \\n \\n except KeyboardInterrupt:\\n pass\\n try:\\n stop_animation.set()\\n except NameError:\\n pass\\n final_route_index = self.sortpop()[0][0]\\n final_route = population[final_route_index]\\n if self.prn:\\n print(\\\"Final route:\\\", final_route)\\n\\n return initial_route, final_route, distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75cc49ea82b117f6b2d47af646f819d0\",\n \"score\": \"0.527877\",\n \"text\": \"def _evaluate(data, context):\\n # print \\\"Received : \\\", data, \\\" Sequence Number : \\\", context[\\\"data_sequence_no\\\"]\\n score = 0\\n secondary_score = 0\\n\\n for k in range(100):\\n time.sleep(random.randint(0,100)/1000.0)\\n percent_complete = k*1.0/100 * 100\\n update_progress(context, percent_complete, \\\"\\\")\\n # print \\\"Context Response Channel ::: \\\", context['response_channel']\\n # if k%20==0:\\n # # print \\\"Update : \\\", percent_complete\\n score += random.randint(1,100)*1.0/0.7 / 100\\n secondary_score += random.randint(1,100)*1.0/0.7 / 100\\n\\n _result_object = {\\n \\\"score\\\" : score,\\n \\\"secondary_score\\\" : secondary_score,\\n }\\n return _result_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0178d47f659aae4e12fbcd69db2468c\",\n \"score\": \"0.52750695\",\n \"text\": \"def on_pushButton_calc_clicked(self):\\r\\n # TODO: not implemented yet\\r\\n if self.str1 == '':\\r\\n a = 0\\r\\n else:\\r\\n a = float(self.str1)\\r\\n if self.str2 == '':\\r\\n b = 0\\r\\n else:\\r\\n b = float(self.str2)\\r\\n res = 0\\r\\n if self.op == '+':\\r\\n res = a + b\\r\\n elif self.op == '-':\\r\\n res = a - b\\r\\n elif self.op == '*':\\r\\n res = a * b\\r\\n elif self.op == '/':\\r\\n if b == 0:\\r\\n res = 0\\r\\n else:\\r\\n res = a / b\\r\\n\\r\\n self.lineEdit_result.setText(str(res))\\r\\n self.flag = 2\\r\\n\\r\\n # raise NotImplementedError\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"320ea0ff97999dcee761fcc123dd114c\",\n \"score\": \"0.52461845\",\n \"text\": \"def calculate(self):\\n self.correct_extinction()\\n self.rdistance = calculate_radial_distance(self.position, self.center,\\n self.distance)\\n self.r23 = calculate_r23(self.fluxes)\\n self.calculate_OH()\\n self.SFR = calculate_sfr(self.distance, self.fluxes['halpha'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23dd86b596bed8c6aac3dcd00301e5c7\",\n \"score\": \"0.5244022\",\n \"text\": \"def compute(self, today, assets, out, opens, closes):\\n out[:] = (opens[-1] - closes[0]) / closes[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23dd86b596bed8c6aac3dcd00301e5c7\",\n \"score\": \"0.5244022\",\n \"text\": \"def compute(self, today, assets, out, opens, closes):\\n out[:] = (opens[-1] - closes[0]) / closes[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33836659fccc659301fd2466ce5d572\",\n \"score\": \"0.5242563\",\n \"text\": \"def run(self):\\n self.evaluate()\\n self.accumulate()\\n self.summarize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"675188c454c830f80a91cf94327c7e3e\",\n \"score\": \"0.52329695\",\n \"text\": \"def process_final(self, *args):\\n mean = self._sum / self._count\\n mean = mean ** 2\\n variance = (self._sum_sq / self._count) - mean\\n if variance < 0:\\n return 0\\n else: \\n return variance ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598dc322c12f3f2638ec51cc2bcf894c\",\n \"score\": \"0.52291757\",\n \"text\": \"def func(progress_remaining: float):\\n return progress_remaining * initial_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14aa6575bdb29a5d01bfab980c63bd12\",\n \"score\": \"0.5228993\",\n \"text\": \"def linear_schedule(progress):\\n return 1 - progress\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c27142e62aa568661d8bbdf43d69f8b6\",\n \"score\": \"0.5228111\",\n \"text\": \"def computation(self, results, *parameters):\\n\\n return self.add_computation(computation(results, *parameters))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7768d4b0f72db48a4418dd627fdd9c73\",\n \"score\": \"0.52274144\",\n \"text\": \"def _compute(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6abf202e5afb80f54132416bc47cb3f1\",\n \"score\": \"0.52269894\",\n \"text\": \"def inspect_calculation(self):\\n converged_mc = True\\n self.ctx.restart_calc = self.ctx.calculation['retrieved']\\n if converged_mc:\\n self.report(\\\"Calculation converged, terminating the workflow\\\")\\n self.ctx.done = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e2904de8eda761267f0b480736bd0d\",\n \"score\": \"0.52208334\",\n \"text\": \"def thread_start_calculating(self, distance_value):\\n print(\\\"Breakpoint Distance value:{}\\\".format(distance_value))\\n logger.info('Slot:thread_start_calculating :', \\n extra={'sim_index': self._data_store.get_simulation_index()})\\n\\n if distance_value == 0:\\n logger.info('Slot:thread_start_calculating SINGLE STEP NOT IMPLEMENTED:', \\n extra={'sim_index': self._data_store.get_simulation_index()})\\n #TODO - finish this breakpoint case\\n self.simulationComputing = False\\n\\n elif distance_value == -1:\\n logger.info('Slot:thread_start_calculating RUN TO COMPLETION :', \\n extra={'sim_index': self._data_store.get_simulation_index()})\\n # set the breakpoint to be a very large number to indicate run to completion\\n self.breakpointDistance = 9999999\\n self.simulationComputing = True\\n else:\\n # run to the distance value point in the track\\n sim_index = self._data_store.get_simulation_index()\\n if distance_value > self._data_store.get_distance_at_index(sim_index) :\\n logger.info('Slot:thread_start_calculating RUN TO DISTANCE :', \\n extra={'sim_index': sim_index})\\n # requested breakpoint is further down the track\\n self.breakpointDistance = distance_value\\n # Start computing and acknowledge to MainWindow by sending a signal back \\n self.simulationThreadSignal.emit(\\\"Calculating...\\\")\\n # \\\"state\\\" variable indicating thread should be calculating\\n self.simulationComputing = True\\n else:\\n logger.info('Slot:thread_start_calculating PAST REQUESTED DISTANCE :', \\n extra={'sim_index': sim_index})\\n # simulation has already past this point in the track, don't proceed\\n self.simulationComputing = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4213b442ff6408444f7a99b113d82637\",\n \"score\": \"0.52148896\",\n \"text\": \"def _calculate_avg():\\n while (True):\\n _avg_num(_q_uwb_a, q_to_a, \\\"to_a\\\", 8)\\n _avg_num(_q_uwb_b, q_to_b, \\\"to_b\\\", 8)\\n time.sleep(0.015)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a06c78d7eb16247bf403e1389083f14\",\n \"score\": \"0.52093\",\n \"text\": \"def integrate(self, t):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ee388bb9a884aa4cea1bef1a52b88fa\",\n \"score\": \"0.5208004\",\n \"text\": \"def __call__(self, *args, **kwargs) -> Any:\\n return self.calculate(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a19b61964963525e9d4ad7f5b8669f\",\n \"score\": \"0.5207855\",\n \"text\": \"def func(progress_remaining: float) -> float:\\n return progress_remaining * initial_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a19b61964963525e9d4ad7f5b8669f\",\n \"score\": \"0.5207855\",\n \"text\": \"def func(progress_remaining: float) -> float:\\n return progress_remaining * initial_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54047f7afc5fafe5f39587ee486749fd\",\n \"score\": \"0.5203203\",\n \"text\": \"def __cost_function(self, sense='single'):\\n\\n self.SnappyHexMeshrun()\\n self.Optimisationrun()\\n if sense==\\\"single\\\":\\n t = self.RunUtilities()\\n return p\\n elif sense==\\\"multi\\\":\\n t, p = self.RunUtilities(sense=sense)\\n return t, p\\n else:\\n print(\\\"Invalid input for sense: \\\", sense)\\n print(\\\"Available options are: 'single' or 'multi'\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30447d2a14ea9b45e16c20607615711e\",\n \"score\": \"0.5196032\",\n \"text\": \"def calculate_derivative(param):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1106def928c6e8ab53fb563e3bff385\",\n \"score\": \"0.51902646\",\n \"text\": \"def evaluate(self) -> None:\\n\\n self._generate_paths()\\n self._compute_payoff_fn()\\n self._is_evaluated = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1b28047d6734cbcd08da68de38b381b\",\n \"score\": \"0.51892775\",\n \"text\": \"def done(self, result):\\n output_file = result['output']\\n GLF = result['GLF']\\n Qt.QApplication.restoreOverrideCursor()\\n self.statusBar().showMessage('GLF = {:0.1f}%'.format(GLF*100))\\n # Show a MessageBox.\\n QtWidgets.QMessageBox.information(self, 'Ausgabe erzeugt',\\n str(output_file),\\n QtWidgets.QMessageBox.Ok)\\n self.start_button.setEnabled(True)\\n\\n if self.plot_timeseries:\\n try: # Create a line plot of the time series in a new window\\n self.plot_window = PlotWindow()\\n self.plot_window.update_lineplot(result['df_sum'])\\n self.plot_window.show()\\n except Exception as e:\\n logger.exception(e)\\n QtWidgets.QMessageBox.critical(self, 'Fehler', str(e),\\n QtWidgets.QMessageBox.Ok)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daceaadc42197c7d5f2b3753e3192aee\",\n \"score\": \"0.5189256\",\n \"text\": \"def when_computation_done(self, out, param):\\r\\n tkMessageBox.showinfo('Computation is done', out)\\r\\n self.set_state('normal')\\r\\n\\r\\n if self.plugin_type == \\\"Chimera\\\":\\r\\n self.profile_wd = os.path.realpath(\\r\\n os.path.normcase(self.main_parameters[1] + \\\"/chimera/\\\"))\\r\\n\\r\\n tunnels_script = os.path.join(self.profile_wd, 'complex.py')\\r\\n paths_script = os.path.join(self.wd, 'paths.py')\\r\\n pores_script = os.path.join(self.wd, 'pores.py')\\r\\n autopores_script = os.path.join(self.wd, 'autopores.py')\\r\\n dom = parse(os.path.join(self.xml_wd, 'tunnels.xml'))\\r\\n\\r\\n for e in dom.getElementsByTagName('Exception'):\\r\\n tkMessageBox.showinfo('Exception', e.getAttribute('Text'))\\r\\n\\r\\n if self.plugin_type == \\\"PyMOL\\\":\\r\\n\\r\\n if param == \\\"pores\\\":\\r\\n self.group_objects(tunnels_script, 'Pores', \\\"MOLE_tunnels\\\")\\r\\n else:\\r\\n self.group_objects(tunnels_script, 'Tunnels', \\\"MOLE_pores\\\")\\r\\n\\r\\n self.group_objects(paths_script, 'Paths', 'MOLE_Paths')\\r\\n self.group_objects(pores_script, 'Pores', 'MOLE_Pores')\\r\\n self.group_objects(autopores_script, 'AutoPores', 'MOLE_AutoPores')\\r\\n plugin().show(\\\"cgo\\\", \\\"Interior*\\\")\\r\\n plugin().set_view(self.original_view)\\r\\n\\r\\n else:\\r\\n plugin.run(tunnels_script)\\r\\n\\r\\n self.set_state('normal')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bc4859850d902bbbabda8a6f64c2238\",\n \"score\": \"0.51880544\",\n \"text\": \"def render(self, request):\\n self._calculatePi(request).addCallback(self._gotResult, request)\\n return server.NOT_DONE_YET\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7174f03ecc0b1631771bcd650b043bf5\",\n \"score\": \"0.5187847\",\n \"text\": \"def _postCalculate(self,retList,time):\\n\\t\\tmodList = []\\n\\t\\tfor vals in retList: \\n\\t\\t\\tvals[\\\"percentEffected\\\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\\n\\t\\t\\tvals = self.calculateThresholdPolygon(vals,time)\\n\\t\\t\\tmodList.append(vals)\\n\\n\\t\\treturn pandas.concat(modList,ignore_index=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7174f03ecc0b1631771bcd650b043bf5\",\n \"score\": \"0.5187847\",\n \"text\": \"def _postCalculate(self,retList,time):\\n\\t\\tmodList = []\\n\\t\\tfor vals in retList: \\n\\t\\t\\tvals[\\\"percentEffected\\\"] = vals.apply(lambda x: self.getPercent(x['severity'],x['ToxicLoad']),axis=1)\\n\\t\\t\\tvals = self.calculateThresholdPolygon(vals,time)\\n\\t\\t\\tmodList.append(vals)\\n\\n\\t\\treturn pandas.concat(modList,ignore_index=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b0c8b9bdb177f85aa5d45a556a0e22\",\n \"score\": \"0.5185649\",\n \"text\": \"def Calculate(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b214cdc4438aebfa3d63f75c8e78c910\",\n \"score\": \"0.5171598\",\n \"text\": \"def compute_pi_main(workers, accuracy, batchsize):\\n n_total = 0\\n s_total = 0\\n tasks_queue = mp.Queue() # each task (or batch) is represented by the batchsize\\n done_queue = mp.Queue() # result of each task is the number of successes in that batch\\n processes = mp.Pool(workers, initializer=compute_pi_worker, initargs=(tasks_queue, done_queue))\\n\\n for _ in range(workers):\\n tasks_queue.put(batchsize)\\n\\n while True:\\n s = done_queue.get(block=True)\\n s_total += s\\n n_total += batchsize\\n pi_est = (4.0 * s_total) / n_total\\n error = abs(math.pi - pi_est)\\n\\n if error > accuracy:\\n tasks_queue.put(batchsize)\\n else:\\n processes.terminate()\\n processes.join()\\n break\\n\\n print(\\\"Estimate:\\\", pi_est)\\n print(\\\"Error:\\\", error)\\n print(\\\"Steps:\\\", n_total)\\n return pi_est, error, n_total\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67da759295be1801c868c36af3dd3052\",\n \"score\": \"0.5168196\",\n \"text\": \"def calculate_float(self, default: int):\\n self.total_float = self.latest_finish - self.earliest_finish\\n\\n if not self.successors:\\n self.free_float = default - self.earliest_finish\\n else:\\n self.free_float = min(s.earliest_start for s in self.successors) - self.earliest_finish\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce99a8d17f1502a9ede8378469d864af\",\n \"score\": \"0.5166104\",\n \"text\": \"def calculate(s):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff35d5e25b8885f6ecc9ff6d4200e5b\",\n \"score\": \"0.5155409\",\n \"text\": \"def handler(event, context):\\n try:\\n parsed_event = CalculatorEvent(**event)\\n except ValidationError as exp:\\n raise Exception(json.dumps(f\\\"[BadRequest] {exp}\\\"))\\n\\n if parsed_event.year not in crypto_prices[parsed_event.query]['old_prices']:\\n raise Exception(json.dumps(f\\\"[BadRequest] Data for requested year not available\\\"))\\n\\n\\n current_value = (parsed_event.amount/crypto_prices[parsed_event.query]['old_prices'][parsed_event.year]) * crypto_prices[parsed_event.query]['current_price']\\n \\n return round(current_value,2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a28c312c92b4217aed7e3c0932a1bfe0\",\n \"score\": \"0.5150363\",\n \"text\": \"def execute_strategy(self):\\n data = self.results.copy().dropna()\\n data['position'] = np.where(data['SMA1'] > data['SMA2'], 1, 0)\\n data['strategy'] = data['position'].shift(1) * data['returns']\\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\\n self.results = data\\n # absolute performance of indicator\\n aperf = data['cstrategy'].ix[-1]\\n # out/under performance of indicator\\n operf = aperf - data['creturns'].ix[-1]\\n return round(aperf, 2), round(operf, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da3563d9154880d9234b4a9a1fead992\",\n \"score\": \"0.5143086\",\n \"text\": \"def evaluate_metric(self, assignments):\\n sleep_seconds = 10\\n print('{0} - Sleeping for {1} seconds to simulate expensive computation...'.format(threading.current_thread(), sleep_seconds))\\n time.sleep(sleep_seconds)\\n return sigopt.examples.franke_function(assignments['x1'], assignments['x2'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0647838977b755e73ac53acb14e7ed14\",\n \"score\": \"0.5141489\",\n \"text\": \"def execute_strategy(self):\\n data = self.results.copy().dropna()\\n data['sma'] = data['returns'].rolling(self.sma).mean()\\n data['distance'] = data['close'] - data['sma']\\n # sell signals\\n data['position'] = np.where(data['distance'] > self.threshold, -1, np.nan)\\n # buy signals\\n data['position'] = np.where(data['distance'] < -self.threshold, 1, data['position'])\\n # cross of current price and SMA (zero distance)\\n data['position'] = np.where(data['distance'] * data['distance'].shift(1) < 0, 0, data['position'])\\n data['position'] = data['position'].ffill().fillna(0)\\n data['strategy'] = data['position'].shift(1) * data['returns']\\n # determine when trades take place\\n # trades = data['position'].diff().fillna(0) != 0\\n # subtract transaction costs from return where trades take place\\n # data['strategy'][trades] -= self.tc\\n data['creturns'] = data['returns'].cumsum().apply(np.exp)\\n data['cstrategy'] = data['strategy'].cumsum().apply(np.exp)\\n self.results = data\\n # absolute performance of indicator\\n aperf = data['cstrategy'].ix[-1]\\n # out/under performance of indicator\\n operf = aperf - data['creturns'].ix[-1]\\n return round(aperf, 2), round(operf, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab977317ec5b5501427a8c5d5683e375\",\n \"score\": \"0.51393765\",\n \"text\": \"def calculate(self):\\n super().calculate()\\n\\n\\n tr_key = Constants.get_key(\\\"TR\\\")\\n trn_key = Constants.get_key(\\\"TRn\\\")\\n\\n dm_plus_key = Constants.get_key(\\\"DMplus\\\")\\n dm_plus_n_key = Constants.get_key(\\\"DMplusN\\\")\\n\\n dm_minus_key = Constants.get_key(\\\"DMminus\\\")\\n dm_minus_n_key = Constants.get_key(\\\"DMminusN\\\")\\n di_plus_n_key = Constants.get_key(\\\"DIplusN\\\")\\n di_minus_n_key = Constants.get_key(\\\"DIminusN\\\")\\n di_diff_key = Constants.get_key(\\\"DIdiff\\\")\\n di_sum_key = Constants.get_key(\\\"DIsum\\\")\\n dx_key = Constants.get_key(\\\"DX\\\")\\n\\n df_result = []\\n\\n for ticker in self.tickers:\\n\\n df_data_atr = self.df[[ticker]].copy()\\n atr_ind = ATR(df=df_data_atr, n=14)\\n\\n df_data = self.df[ticker].copy()\\n # the period parameter of ATR function does not matter because period does not influence TR calculation\\n df_data[tr_key] = atr_ind.calculate()[ticker][[tr_key]]\\n\\n df_data[dm_plus_key] = \\\\\\n np.where(\\n (df_data[self.high_key] - df_data[self.high_key].shift(1)) >\\n (df_data[self.low_key].shift(1) - df_data[self.low_key]),\\n df_data[self.high_key] - df_data[self.high_key].shift(1),\\n 0)\\n\\n df_data[dm_plus_key] = \\\\\\n np.where(\\n df_data[dm_plus_key] < 0,\\n 0,\\n df_data[dm_plus_key])\\n\\n df_data[dm_minus_key] = \\\\\\n np.where((df_data[self.low_key].shift(1) - df_data[self.low_key]) >\\n (df_data[self.high_key] - df_data[self.high_key].shift(1)),\\n df_data[self.low_key].shift(1) - df_data[self.low_key],\\n 0)\\n\\n df_data[dm_minus_key] = np.where(df_data[dm_minus_key] < 0, 0, df_data[dm_minus_key])\\n\\n TRn = []\\n DMplusN = []\\n DMminusN = []\\n TR = df_data[tr_key].tolist()\\n DMplus = df_data[dm_plus_key].tolist()\\n DMminus = df_data[dm_minus_key].tolist()\\n\\n for i in range(len(df_data)):\\n if i < self.n:\\n TRn.append(np.NaN)\\n DMplusN.append(np.NaN)\\n DMminusN.append(np.NaN)\\n elif i == self.n:\\n TRn.append(df_data[tr_key].rolling(self.n).sum().tolist()[self.n])\\n DMplusN.append(df_data[dm_plus_key].rolling(self.n).sum().tolist()[self.n])\\n DMminusN.append(df_data[dm_minus_key].rolling(self.n).sum().tolist()[self.n])\\n elif i > self.n:\\n TRn.append(TRn[i - 1] - (TRn[i - 1] / 14) + TR[i])\\n DMplusN.append(DMplusN[i - 1] - (DMplusN[i - 1] / 14) + DMplus[i])\\n DMminusN.append(DMminusN[i - 1] - (DMminusN[i - 1] / 14) + DMminus[i])\\n\\n df_data[trn_key] = np.array(TRn)\\n df_data[dm_plus_n_key] = np.array(DMplusN)\\n df_data[dm_minus_n_key] = np.array(DMminusN)\\n df_data[di_plus_n_key] = 100 * (df_data[dm_plus_n_key] / df_data[trn_key])\\n df_data[di_minus_n_key] = 100 * (df_data[dm_minus_n_key] / df_data[trn_key])\\n df_data[di_diff_key] = abs(df_data[di_plus_n_key] - df_data[di_minus_n_key])\\n df_data[di_sum_key] = df_data[di_plus_n_key] + df_data[di_minus_n_key]\\n df_data[dx_key] = 100 * (df_data[di_diff_key] / df_data[di_sum_key])\\n ADX = []\\n DX = df_data[dx_key].tolist()\\n for j in range(len(df_data)):\\n if j < 2 * self.n - 1:\\n ADX.append(np.NaN)\\n elif j == 2 * self.n - 1:\\n ADX.append(df_data[dx_key][j - self.n + 1:j + 1].mean())\\n elif j > 2 * self.n - 1:\\n ADX.append(((self.n - 1) * ADX[j - 1] + DX[j]) / self.n)\\n df_data[self.indicator_key] = np.array(ADX)\\n\\n df_result.append(df_data.loc[:, [self.indicator_key]])\\n\\n self.df = pd.concat(df_result, axis=1, keys=self.tickers)\\n\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eafd971da2d043a48e336831a0a68557\",\n \"score\": \"0.5138379\",\n \"text\": \"def process_queue_fringes(self):\\n\\n try:\\n result = self.queue.get(0)\\n\\n if result == \\\"ABORT\\\":\\n self.addlog(\\\"Mission aborted.\\\", self.warningcolor2)\\n self.abortmission.set(0)\\n self.totaltime = 0\\n self.removeprogressbar()\\n return\\n\\n # Show result of the task if needed\\n self.entry_23.delete(0, END)\\n self.entry_23.insert(0, '{0:.2f}'.format(result[0]))\\n self.entry_24.delete(0, END)\\n self.entry_24.insert(0, '{0:.2f}'.format(result[1]))\\n\\n self.setoffsets()\\n\\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list = [], [], [], []\\n\\n for i in range(1, self.layernumber + 1):\\n self.layertype_list.append(getattr(self, \\\"layertypevar{}\\\".format(i)).get())\\n self.entry_x_list.append(float(getattr(self, \\\"entry_x_{}\\\".format(i)).get()))\\n self.entry_d_list.append(float(getattr(self, \\\"entry_d_{}\\\".format(i)).get()))\\n self.checklayer_list.append(int(getattr(self, \\\"checklayer{}\\\".format(i)).get()))\\n\\n fitobject = FIT_FTIR(self.Temp, self.wavenumbers_cut, self.trans_cut, self.entry_d_0.get(),\\n self.layertype_list, self.entry_x_list, self.entry_d_list, self.checklayer_list,\\n float(self.entry_21.get()), self.angle,\\n float(self.entry_23.get()), float(self.entry_24.get()), self.subtype, 2,\\n self.listbox, self.progress_var, self.wn_beingcalculated,\\n self.FTIRplot, self.absorptionplot, self.canvas, self.blindcal, self.abortmission)\\n self.peakvalues_fit = fitobject.returnpeakvalues()\\n\\n self.addlog('Fitting fringes complete. Total time: {:.1f}s. MSE={}'.format(self.totaltime, result[2]))\\n\\n self.totaltime = 0\\n\\n self.try_remove_fitline(self.fitline_trans)\\n self.fitline_trans = self.plot_and_show(self.FTIRplot, self.fitline_trans, 0, self.wavenumbers_cut,\\n self.peakvalues_fit, 'r', '', 'Transmission (%)', 0, 'upper right')\\n\\n self.removeprogressbar()\\n\\n except queue.Empty:\\n self.totaltime += 0.1\\n self.after(100, self.process_queue_fringes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5820bfa6b2e06e705d2fad4ec1847994\",\n \"score\": \"0.5132884\",\n \"text\": \"def compute(\\n self,\\n blade_cost,\\n blade_mass,\\n hub_system_cost,\\n hub_system_mass,\\n nacelle_mass,\\n nacelle_cost,\\n tower_cost,\\n tower_mass,\\n blade_number=3,\\n offshore=True,\\n ):\\n\\n # Variables\\n self.blade_cost = (\\n blade_cost # Float(0.0, units='USD', iotype='in', desc='cost for a single wind turbine blade')\\n )\\n self.blade_mass = blade_mass # Float(0.0, units='kg', iotype='in', desc='mass for a single wind turbine blade')\\n self.hub_system_cost = hub_system_cost # Float(0.0, units='USD', iotype='in', desc='hub system cost')\\n self.hub_system_mass = hub_system_mass # Float(0.0, units='kg', iotype='in', desc='hub system mass')\\n self.nacelle_mass = nacelle_mass # Float(0.0, units='kg', iotype='in', desc='nacelle mass')\\n self.nacelle_cost = nacelle_cost # Float(0.0, units='USD', iotype='in', desc='nacelle cost')\\n self.tower_cost = tower_cost # Float(0.0, units='USD', iotype='in', desc='cost for a tower')\\n self.tower_mass = tower_mass # Float(0.0, units='kg', iotype='in', desc='mass for a turbine tower')\\n\\n # Parameters (and ignored inputs)\\n self.blade_number = blade_number # Int(3, iotype='in', desc = 'number of rotor blades')\\n self.offshore = offshore # Bool(False, iotype='in', desc= 'boolean for offshore')\\n\\n # high level output assignment\\n self.rotor_mass = self.blade_mass * self.blade_number + self.hub_system_mass\\n self.rotor_cost = self.blade_cost * self.blade_number + self.hub_system_cost\\n self.turbine_mass = self.rotor_mass + self.nacelle_mass + self.tower_mass\\n self.turbine_cost = self.rotor_cost + self.nacelle_cost + self.tower_cost\\n\\n if self.offshore:\\n self.turbine_cost *= 1.1\\n\\n # derivatives\\n self.d_mass_d_blade_mass = self.blade_number\\n self.d_mass_d_hub_mass = 1.0\\n self.d_mass_d_nacelle_mass = 1.0\\n self.d_mass_d_tower_mass = 1.0\\n\\n if self.offshore:\\n self.d_cost_d_blade_cost = 1.1 * self.blade_number\\n self.d_cost_d_hub_cost = 1.1\\n self.d_cost_d_nacelle_cost = 1.1\\n self.d_cost_d_tower_cost = 1.1\\n else:\\n self.d_cost_d_blade_cost = self.blade_number\\n self.d_cost_d_hub_cost = 1.0\\n self.d_cost_d_nacelle_cost = 1.0\\n self.d_cost_d_tower_cost = 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2818a75b1740c04b7c7c9442d420e04\",\n \"score\": \"0.5128805\",\n \"text\": \"def getval(self):\\n \\n if self.averagingAttemps > 1:\\n result = 0.0\\n readings = 0.0\\n readingsc = 0.0\\n\\t readings_array = {}\\n for i in range(0, self.averagingAttemps):\\n reading = self.getReading()\\n if (reading != 0 and reading != 1023):\\n if self.averagingMethod == \\\"max\\\":\\n if reading > readings:\\n\\t\\t\\t \\\"\\\"\\\"print(str(reading) + \\\" higher as \\\" + str(readings))\\\"\\\"\\\"\\n readings = reading\\n else:\\n readings = readings + reading\\n readingsc = readingsc + 1\\n\\t\\t if reading in readings_array:\\n\\t\\t readings_array[reading] = readings_array[reading] + 1\\n\\t\\t else:\\n\\t\\t\\treadings_array[reading] = 1\\n time.sleep(self.averagingTimeout)\\n if readingsc == 0:\\n result = self.getReading()\\n else:\\n\\t\\tif self.averagingMethod == \\\"most\\\":\\n\\t\\t sorted_x = sorted(readings_array.items(), key=operator.itemgetter(1), reverse=True)\\n\\t\\t result = sorted_x[0][0]\\n elif self.averagingMethod == \\\"max\\\":\\n result = readings\\n else:\\n result = readings / readingsc\\n else:\\n result = self.getReading()\\n \\n if result == 0:\\n msg = \\\"Error: Check wiring for the \\\" + self.sensorname\\n msg += \\\" measurement, no voltage detected on ADC input \\\"\\n msg += str(self.adcpin)\\n print(msg)\\n return None\\n if result == 1023:\\n if self.sensorname == \\\"LDR\\\":\\n # Carrying on with 1023 gives divide by zero error below\\n result = 1022\\n else:\\n msg = \\\"Error: Check wiring for the \\\" + self.sensorname\\n msg += \\\" measurement, full voltage detected on ADC input \\\"\\n msg += str(self.adcpin)\\n print(msg)\\n return None\\n\\n\\tif self.sensorname == \\\"WindDirection\\\":\\n\\t return result\\n\\n\\t\\\"\\\"\\\"print(self.sensorname + \\\" \\\" + str(self.sensorvoltage))\\\"\\\"\\\"\\n\\n vout = float(result)/1023 * self.sensorvoltage\\n if self.pulldown != None:\\n resout = (self.pulldown * self.sensorvoltage) / vout - self.pulldown\\n elif self.pullup != None:\\n resout = self.pullup / ((self.sensorvoltage / vout) - 1)\\n else:\\n resout = vout * 1000\\n return resout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"621c70cbb28fa1e4a9c190a799b7b1e6\",\n \"score\": \"0.51253504\",\n \"text\": \"def compute(self): # pylint: disable=E0202\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"621c70cbb28fa1e4a9c190a799b7b1e6\",\n \"score\": \"0.51253504\",\n \"text\": \"def compute(self): # pylint: disable=E0202\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01998f20124e1512ef226959005fc520\",\n \"score\": \"0.512176\",\n \"text\": \"def calculate_callback(self):\\n\\n # Create the interpreter\\n from asteval import Interpreter\\n aeval = Interpreter()\\n\\n # Grab the calculation from the text box which the user wants to do\\n calculation = str(self.calculation_text.text())\\n\\n lhs = calculation.split('=')[0].strip()\\n\\n # Use the package asteval to do the calculation, we are going to\\n # assume here that the lhs of the equals sign is going to be the output named variable\\n\\n try:\\n if lhs in self.data_components:\\n raise KeyError('{} is already in the data components, use a different variable on the left hand side.'.format(lhs))\\n\\n # Pull in the required data and run the calculation\\n for dc in self.data_components:\\n if dc in calculation:\\n aeval.symtable[dc] = self.data[dc]\\n aeval(calculation)\\n\\n # Pull out the output data and add to the proper drop-downs\\n out_data = aeval.symtable[lhs]\\n self.data.add_component(out_data, lhs)\\n\\n # Add the new data to the list of available data for arithemitic operations\\n self.data_components.append(lhs)\\n\\n self.close()\\n\\n except KeyError as e:\\n self.calculation_text.setStyleSheet(\\\"background-color: rgba(255, 0, 0, 128);\\\")\\n\\n # Display the error in the Qt popup\\n if aeval.error_msg:\\n self.error_label_text.setText('{}'.format(aeval.error_msg))\\n else:\\n self.error_label_text.setText('{}'.format(e))\\n\\n self.error_label_text.setStyleSheet(\\\"color: rgba(255, 0, 0, 128)\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad61c57a35376b206230052921c1336\",\n \"score\": \"0.512145\",\n \"text\": \"def update(self,current_value):\\n\\n self.error = self.set_point - current_value\\n\\n self.P_value = self.Kp * self.error\\n if (self.last_value >= current_value):\\n change = self.error - self.last_error\\n else:\\n change = 0.0\\n\\n if self.error > 0.0:\\n self.I_value = self.Integrator * self.Ki\\n else:\\n self.I_value = (self.Integrator * self.Ki)\\n\\n\\n #self.D_value = self.Kd * ( self.error - self.Derivator)\\n self.D_value = self.Kd * change\\n self.Derivator = self.error\\n\\n self.Integrator = self.Integrator + self.error\\n\\n if self.Integrator > self.Integrator_max:\\n self.Integrator = self.Integrator_max\\n elif self.Integrator < self.Integrator_min:\\n self.Integrator = self.Integrator_min\\n\\n self.last_error = self.error\\n self.last_value = current_value\\n\\n PID = self.P_value + self.I_value + self.D_value\\n\\n self._z_data[\\\"data\\\"][\\\"P\\\"] = self.P_value\\n self._z_data[\\\"data\\\"][\\\"I\\\"] = self.I_value\\n self._z_data[\\\"data\\\"][\\\"D\\\"] = self.D_value\\n self._z_data[\\\"data\\\"][\\\"E\\\"] = self.error\\n self._z_data[\\\"data\\\"][\\\"SP\\\"] = self.set_point\\n self._z_data[\\\"data\\\"][\\\"OUT\\\"] = PID\\n\\n if self._zmq:\\n try:\\n self._zmq.send_json(self._z_data, zmq.NOBLOCK)\\n except zmq.error.Again:\\n pass\\n\\n return PID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"472ea9d79a812c81d9dbbb43bbe45dc9\",\n \"score\": \"0.50997347\",\n \"text\": \"def _calc_result(self):\\n import mcmc\\n\\n g=zeros(2)\\n gcov=zeros((2,2))\\n gsens = zeros(2)\\n\\n if self.eta:\\n eta1vals,eta2vals=self.trials[:,2], self.trials[:,3]\\n g1vals,g2vals=self._get_gvals_from_eta(eta1vals,eta2vals)\\n else:\\n g1vals=self.trials[:,2]\\n g2vals=self.trials[:,3]\\n\\n prior = self.gprior(g1vals,g2vals)\\n dpri_by_g1 = self.gprior.dbyg1(g1vals,g2vals)\\n dpri_by_g2 = self.gprior.dbyg2(g1vals,g2vals)\\n\\n psum = prior.sum()\\n\\n g0=None\\n gcov0=None\\n if self.when_prior=='after':\\n # this will be eta not g if using that parametrization\\n g0,gcov0 = mcmc.extract_stats(self.trials[:,2:4])\\n\\n pars, pcov = mcmc.extract_stats(self.trials,weights=prior)\\n # we need to multiply each by the prior\\n \\\"\\\"\\\"\\n g[0] = (g1vals*prior).sum()/psum\\n g[1] = (g2vals*prior).sum()/psum\\n\\n\\n gcov[0,0] = (g1diff**2*prior).sum()/psum\\n gcov[0,1] = (g1diff*g2diff*prior).sum()/psum\\n gcov[1,0] = gcov[0,1]\\n gcov[1,1] = (g2diff**2*prior).sum()/psum\\n \\\"\\\"\\\"\\n\\n g[:] = pars[2:4]\\n gcov[:,:] = pcov[2:4, 2:4]\\n\\n # now the sensitivity is \\n # sum( (<g>-g) L*dP/dg )\\n # ----------------------\\n # sum(L*P)\\n #\\n # the likelihood is already in the points\\n\\n g1diff = g[0]-g1vals\\n g2diff = g[1]-g2vals\\n gsens[0] = 1. - (g1diff*dpri_by_g1).sum()/psum\\n gsens[1] = 1. - (g2diff*dpri_by_g2).sum()/psum\\n else:\\n # prior is already in the distribution of\\n # points. This is simpler for most things but\\n # for sensitivity we need a factor of (1/P)dP/de\\n\\n pars,pcov = mcmc.extract_stats(self.trials)\\n #g, gcov = mcmc.extract_stats(self.trials[:,2:2+2])\\n\\n g[:] = pars[2:4]\\n gcov[:,:] = pcov[2:4, 2:4]\\n\\n g1diff = g[0]-g1vals\\n g2diff = g[1]-g2vals\\n\\n w,=where(prior > 0)\\n if w.size == 0:\\n raise ValueError(\\\"no prior values > 0!\\\")\\n\\n gsens[0]= 1.-(g1diff[w]*dpri_by_g1[w]/prior[w]).mean()\\n gsens[1]= 1.-(g2diff[w]*dpri_by_g2[w]/prior[w]).mean()\\n\\n \\n arates = self._emcee_sampler.acceptance_fraction\\n arate = arates.mean()\\n #print 'acceptance rate:',w.size/float(self.trials.size)\\n\\n # weighted s/n based on the most likely point\\n s2n,loglike,chi2per,dof,prob=self._calculate_maxlike_stats()\\n if self.eta:\\n g0name='eta'\\n else:\\n g0name='g'\\n\\n Tmean=pars[4]\\n Terr=sqrt(pcov[4,4])\\n Ts2n=pars[4]/sqrt(pcov[4,4])\\n #print 'T s/n:',Ts2n\\n self._result={'model':self.model,\\n 'g':g,\\n 'gcov':gcov,\\n 'gsens':gsens,\\n g0name+'0':g0,\\n g0name+'cov0':gcov0,\\n 'pars':pars,\\n 'pcov':pcov,\\n 'Tmean':Tmean,\\n 'Terr':Terr,\\n 'Ts2n':Ts2n,\\n 'arate':arate,\\n 's2n_w':s2n,\\n 'loglike':loglike,\\n 'chi2per':chi2per,\\n 'dof':dof,\\n 'fit_prob':prob}\\n #wlog(\\\"arate:\\\",self._result['arate'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81e1d43e967bd2ad66fe32eeb4f512d8\",\n \"score\": \"0.5096479\",\n \"text\": \"def interpolate(self, double: float) -> float:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ccb6ccc97f671fb95181dc45fb44f8\",\n \"score\": \"0.5088823\",\n \"text\": \"def postsolve(self, lbfgs_result):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a7d46d8d9ffadc367fe21aad2f8c9bb\",\n \"score\": \"0.50876844\",\n \"text\": \"def result_relative(self, result1, result2):\\n value1 = self.objective.get_mean(result1)\\n value2 = self.objective.get_mean(result2)\\n if value2 == 0:\\n return float('inf') * value1\\n return value1 / value2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bc1f58636b2ddf43f909825f55e8d1e\",\n \"score\": \"0.50867045\",\n \"text\": \"def calcul_throughput(self, spectrumrangeairmass):\\n \\\"\\\"\\\"\\n data_mag = spectrumrangeairmass.data_mag.T\\n data_mag -= np.log(self.data_calspec)\\n spectrumrangeairmass.data_mag = data_mag.T\\n\\n data_order2 = spectrumrangeairmass.order2.T\\n data_order2 /= self.data_calspec\\n spectrumrangeairmass.order2 = data_order2.T\\n #print(self.data_calspec)\\n #print(self.data_calspec.T)\\n #print(self.data_calspec.T @ self.data_calspec)\\n MULT = np.zeros((len(self.data_calspec),len(self.data_calspec)))\\n for i in range(len(self.data_calspec)):\\n for j in range(len(self.data_calspec)):\\n MULT[i][j], MULT[j][i] = self.data_calspec[i] * self.data_calspec[j], self.data_calspec[i] * self.data_calspec[j]\\n\\n spectrumrangeairmass.INVCOV *= MULT\\n spectrumrangeairmass.cov /= MULT\\n \\\"\\\"\\\"\\n\\n self.slope, self.ord, self.err_slope, self.err_ord = spectrumrangeairmass.bouguer_line()\\n disp = np.loadtxt(self.rep_disp_name)\\n Data_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1], kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Err_disp = sp.interpolate.interp1d(disp.T[0], disp.T[1]/100, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n tel = np.loadtxt(self.rep_tel_name)\\n Data_tel = sp.interpolate.interp1d(tel.T[0], tel.T[1], kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Err_tel = sp.interpolate.interp1d(tel.T[0], tel.T[2], kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n \\\"\\\"\\\"\\n data_disp_new_lambda = Data_disp(self.new_lambda)\\n err_disp_new_lambda = Err_disp(self.new_lambda)\\n data_tel_new_lambda = Data_tel(self.new_lambda)\\n err_tel_new_lambda = Err_tel(self.new_lambda)\\n \\\"\\\"\\\"\\n self.data_bouguer = np.exp(self.ord)\\n err_bouguer = self.err_ord * self.data_bouguer\\n self.data = self.data_bouguer / self.data_calspec\\n self.lambdas = self.new_lambda\\n self.err = err_bouguer / self.data_calspec\\n\\n # self.data = filter_detect_lines(self.lambdas, self.data, self.plot_filt, self.save_filter)\\n\\n Data = sp.interpolate.interp1d(self.lambdas, self.data, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Err = sp.interpolate.interp1d(self.lambdas, self.err, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n self.lambdas = self.new_lambda\\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\\n\\n self.data_tel = Data_tel(self.lambdas)\\n self.data_tel_err = Err_tel(self.lambdas)\\n self.data_disp = Data_disp(self.lambdas)\\n self.data_disp_err = Err_disp(self.lambdas)\\n self.data = Data(self.lambdas)\\n self.err = Err(self.lambdas)\\n self.data_bouguer = Data_bouguer(self.lambdas)\\n # self.data = sp.signal.savgol_filter(self.data, 111, 2)\\n\\n #spectrumrangeairmass.archi_mega_fit()\\n if self.order2 and not self.mega_fit:\\n self.slope2, self.ord2, self.err_slope2, self.err_ord2, self.A2, self.A2_err = spectrumrangeairmass.bouguer_line_order2()\\n self.data_bouguer = np.exp(self.ord2)\\n err_bouguer = self.err_ord2 * self.data_bouguer\\n self.data_order2 = self.data_bouguer / self.data_calspec\\n self.lambdas = self.new_lambda\\n self.err_order2 = err_bouguer / self.data_calspec\\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.data_bouguer, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n self.lambdas = self.new_lambda\\n self.data_order2 = Data(self.lambdas)\\n self.err_order2 = Err(self.lambdas)\\n self.data_bouguer = Data_bouguer(self.lambdas)\\n # self.data_order2 = sp.signal.savgol_filter(self.data_order2, 111, 2)\\n\\n if self.mega_fit:\\n self.ord2, self.err_order2 = spectrumrangeairmass.megafit_emcee()\\n print(self.ord2)\\n print(len(self.ord2))\\n self.data_order2 = self.ord2 / self.data_calspec\\n self.err_order2 = self.err_order2 / self.data_calspec\\n self.lambdas = self.new_lambda\\n\\n # self.data_order2 = filter_detect_lines(self.lambdas, self.data_order2, self.plot_filt, self.save_filter)\\n Data = sp.interpolate.interp1d(self.lambdas, self.data_order2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n\\n Data_bouguer = sp.interpolate.interp1d(self.lambdas, self.ord2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n\\n Err = sp.interpolate.interp1d(self.lambdas, self.err_order2, kind=\\\"linear\\\", bounds_error=False,\\n fill_value=\\\"extrapolate\\\")\\n\\n self.lambdas = self.new_lambda\\n #self.lambdas = np.arange(self.lambda_min, self.lambda_max, 1)\\n self.data_order2 = Data(self.lambdas)\\n self.err_order2 = Err(self.lambdas)\\n self.data_bouguer = Data_bouguer(self.lambdas)\\n self.data_order2[21:-21] = sp.signal.savgol_filter(self.data_order2[21:-21], 11, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83954746e4599a297a386e46edd8832a\",\n \"score\": \"0.5083051\",\n \"text\": \"def solve(self, show_progress=True):\\n dt = self.dt\\n old_dt = self.dt\\n\\n if self.in_parallel:\\n show = False\\n else:\\n show = show_progress\\n bar = FloatPBar(self.t, self.tf, show=show)\\n\\n # Initial solution\\n self.dump_output()\\n self.barrier() # everybody waits for this to complete\\n\\n # initial solution damping time\\n tdamp = self.tdamp\\n\\n # Compute the accelerations once for the predictor corrector\\n # integrator to work correctly at the first time step.\\n self.acceleration_eval.compute(self.t, dt)\\n\\n # solution output times\\n output_at_times = numpy.array( self.output_at_times )\\n\\n while self.t < self.tf:\\n\\n # perform any pre step functions\\n for func in self.pre_step_functions:\\n func.eval(self)\\n\\n if self.rank == 0:\\n logger.debug(\\n \\\"Iteration=%d, time=%f, timestep=%f\\\" % \\\\\\n (self.count, self.t, dt)\\n )\\n # perform the integration and update the time.\\n #print 'Solver Iteration', self.count, dt, self.t, tdamp\\n self.integrator.step(self.t, dt)\\n\\n # perform any post step functions\\n for func in self.post_step_functions:\\n func.eval(self)\\n\\n # update time and iteration counters if successfully\\n # integrated\\n self.t += dt\\n self.count += 1\\n\\n # dump output if the iteration number is a multiple of the\\n # printing frequency\\n if self.count % self.pfreq == 0:\\n self.dump_output()\\n self.barrier()\\n\\n # dump output if forced\\n if self.force_output:\\n self.dump_output()\\n self.barrier()\\n\\n self.force_output = False\\n\\n # re-set the time-step to the old time step before it\\n # was adjusted\\n dt = old_dt\\n\\n # update progress bar\\n bar.update(self.t)\\n\\n # update the time for all arrays\\n self.update_particle_time()\\n\\n # compute the new time step across all processors\\n if self.adaptive_timestep:\\n\\n # locally stable time step\\n dt = self.integrator.compute_time_step(\\n self.dt, self.cfl)\\n\\n # damp the initial solution\\n if self.t < tdamp:\\n dt *= 0.5 * (numpy.sin(numpy.pi*(-0.5+self.t/tdamp)) + 1.0)\\n\\n # set the globally stable time step\\n if self.in_parallel:\\n dt = self.pm.update_time_steps(dt)\\n\\n # adjust dt to land on final time\\n if self.t + dt > self.tf:\\n dt = self.tf - self.t\\n\\n # adjust dt to land on specific output times\\n if output_at_times.size > 0:\\n tdiff = output_at_times - self.t\\n condition = (tdiff > 0) & (tdiff < dt)\\n\\n if numpy.any( condition ):\\n output_time = output_at_times[ numpy.where(condition) ]\\n\\n # save the old time-step and compute the new\\n # time-step to fall on the specified output time\\n # instant\\n old_dt = dt\\n dt = float( output_time - self.t )\\n\\n self.force_output = True\\n\\n # set the adjusted time-step for the solver\\n self.dt = dt\\n\\n if self.execute_commands is not None:\\n if self.count % self.command_interval == 0:\\n self.execute_commands(self)\\n\\n # close the progress bar\\n bar.finish()\\n\\n # final output save\\n self.dump_output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a145553849371012e1201d8c01f8d3\",\n \"score\": \"0.50810856\",\n \"text\": \"def evaluate(solutions, results, rollouts=100): # pipaek : rollout 100 -> 10 , originally 100\\n index_min = np.argmin(results)\\n best_guess = solutions[index_min]\\n restimates = []\\n\\n for s_id in range(rollouts):\\n print('p_queue.put(), s_id=%d' % s_id)\\n p_queue.put((s_id, best_guess))\\n print('>>>rollout_routine!!')\\n rollout_routine() # pipaek : 여기서도 p_queue.put 하자마자 바로 처리..\\n\\n print(\\\">>>Evaluating...\\\")\\n for _ in tqdm(range(rollouts)):\\n #while r_queue.empty():\\n # sleep(.1) # pipaek : multi-process가 아니므로\\n if not r_queue.empty(): # pipaek : 20180718 r_queue.get()에서 stuck되어 있는 것을 방지하기 위해 체크!!\\n #print('r_queue.get()')\\n #restimates.append(r_queue.get()[1])\\n r_s_id, r = r_queue.get()\\n print('in evaluate r_queue.get() r_s_id=%d, r_queue remain=%d' % (r_s_id, r_queue.qsize()))\\n restimates.append(r)\\n else:\\n print('r_queue.empty() -> break!!')\\n break\\n\\n return best_guess, np.mean(restimates), np.std(restimates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce81706280fd507142c3a9781b2eaa7\",\n \"score\": \"0.5079482\",\n \"text\": \"def test_calc_final_slope():\\n calc_final_slope(gridv1.data)\\n assert True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":190,"cells":{"query_id":{"kind":"string","value":"3510ac42e4ef7ae99ff81d34dfc62eaa"},"query":{"kind":"string","value":"Get List of field names when a container name is given. Container names if repeated in the snapshot metadata, will always contain the same internal metadata, so the parent of the container doesn't matter. container_field_name The container of whose List of field names are required List of field names"},"positive_passages":{"kind":"list like","value":[{"docid":"19a19c2a810e3f9fd8aa2036cfc9c0ba","score":"0.8049855","text":"def data_field_container_field_name_list_get(self, container_field_name):\n def dfs_helper(dict_to_find_in, container_field_name, ret_list):\n if dict_to_find_in is None:\n return\n if container_field_name in dict_to_find_in:\n for field_name, field in list(dict_to_find_in[container_field_name].container_dict.items()):\n ret_list.append(field.name)\n return\n else:\n for field_name, field in list(dict_to_find_in.items()):\n return dfs_helper(field.container_dict, container_field_name, ret_list)\n ret_list = []\n dfs_helper(self.data_dict, container_field_name, ret_list)\n return ret_list","title":""}],"string":"[\n {\n \"docid\": \"19a19c2a810e3f9fd8aa2036cfc9c0ba\",\n \"score\": \"0.8049855\",\n \"text\": \"def data_field_container_field_name_list_get(self, container_field_name):\\n def dfs_helper(dict_to_find_in, container_field_name, ret_list):\\n if dict_to_find_in is None:\\n return\\n if container_field_name in dict_to_find_in:\\n for field_name, field in list(dict_to_find_in[container_field_name].container_dict.items()):\\n ret_list.append(field.name)\\n return\\n else:\\n for field_name, field in list(dict_to_find_in.items()):\\n return dfs_helper(field.container_dict, container_field_name, ret_list)\\n ret_list = []\\n dfs_helper(self.data_dict, container_field_name, ret_list)\\n return ret_list\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"30c975d7087e5406555f5a29ddfc5d2f","score":"0.61451197","text":"def _get_list(container, name):\n\n try:\n return container[name]\n except KeyError:\n return []","title":""},{"docid":"f10c4c60092e3146611d5e905294f06d","score":"0.5811185","text":"def _field_names_(self):\n ret = []\n\n def _merge(f, rename):\n if not f:\n return\n\n if not rename:\n ret.extend([n for n, _ in f])\n else:\n for n, _ in f:\n new_n = rename.get(n, None)\n ret.append(new_n) if new_n else ret.append(n)\n\n for b in self.__class__.__mro__:\n _merge(\n getattr(b, '__swagger_fields__', None),\n getattr(b, '__swagger_rename__', None)\n )\n\n return ret","title":""},{"docid":"437d5d3800f0ce0776ef979ed27f1d87","score":"0.5712169","text":"def fields_container(self):\n return self._fields_container","title":""},{"docid":"437d5d3800f0ce0776ef979ed27f1d87","score":"0.5712169","text":"def fields_container(self):\n return self._fields_container","title":""},{"docid":"437d5d3800f0ce0776ef979ed27f1d87","score":"0.5712169","text":"def fields_container(self):\n return self._fields_container","title":""},{"docid":"b1441683baa86cccd7332f66af646866","score":"0.5601177","text":"def get_container_entries(self, container, prefix):\n try:\n headers = self._headers()\n swift_objects = self.conn.get_container(container,\n prefix=prefix,\n full_listing=True,\n headers=headers)[1]\n except socket.error as err:\n raise exception.SwiftConnectionFailed(reason=err)\n swift_object_names = [swift_obj['name'] for swift_obj in swift_objects]\n return swift_object_names","title":""},{"docid":"4d8c066c5cc911b046911ca8ae57b598","score":"0.5588793","text":"def GetContainerFields(request, layer):\n icon = request.get('icon')\n icon = util.GetInstance(model.Resource, icon, layer, required=False)\n if icon and icon.type != 'icon':\n raise util.BadRequest('Invalid (non-icon) resource specified.')\n\n folder = request.get('folder')\n folder = util.GetInstance(model.Folder, folder, layer, required=False)\n folder_index = request.get('folder_index', None)\n if folder_index is not None: folder_index = int(folder_index)\n\n region = request.get('region')\n region = util.GetInstance(model.Region, region, layer, required=False)\n\n return {\n 'icon': icon,\n 'folder': folder,\n 'folder_index': folder_index,\n 'region': region,\n 'name': request.get('name', None),\n 'description': request.get('description', None),\n 'item_type': request.get('item_type', None),\n 'custom_kml': request.get('custom_kml', None)\n }","title":""},{"docid":"9876c3a1dd2807b45e9acca543a94186","score":"0.551232","text":"def list_containers(self):\r\n if self._container_names is None:\r\n visited = set([id(self)])\r\n if hasattr(self, '_parent'): # fix for weird unpickling bug\r\n visited.add(id(self.parent))\r\n names = []\r\n for n, v in self.__dict__.items():\r\n if is_instance(v, Container) and id(v) not in visited:\r\n visited.add(id(v))\r\n names.append(n)\r\n self._container_names = names\r\n return self._container_names","title":""},{"docid":"b619bd1f3f17cba660356f07ab0102bd","score":"0.55013055","text":"def field_names(self) -> List[str]:\n return [x.name for x in self.fields.all()]","title":""},{"docid":"49731fc3f52b6436791ae50e39483390","score":"0.5474507","text":"def get_list_of_fields_for_node(node_name):\n return _value_to_node_fields[node_name]","title":""},{"docid":"1f462a5031e0d1a0da6991690b93c2ef","score":"0.5405363","text":"def flatten_container(self, container):\n for names in ARG_MAP.values():\n if names[TransformationTypes.CHRONOS.value]['name'] and \\\n '.' in names[TransformationTypes.CHRONOS.value]['name']:\n chronos_dotted_name = names[TransformationTypes.CHRONOS.value]['name']\n parts = chronos_dotted_name.split('.')\n\n if parts[-2] == 'parameters':\n # Special lookup for docker parameters\n common_type = names[TransformationTypes.CHRONOS.value].get('type')\n result = self._lookup_parameter(container, parts[-1], common_type)\n if result:\n container[chronos_dotted_name] = result\n else:\n result = lookup_nested_dict(container, *parts)\n if result:\n container[chronos_dotted_name] = result\n return container","title":""},{"docid":"8d7120d4d10f2d84545c761e0ae1cb6e","score":"0.53995526","text":"def data_field_name_list_get(self, action_name=None):\n ret_field_name_list = []\n if action_name is not None:\n ret_field_name_list=[field_name_ for field_name_, data_ in \n list(self.action_dict[self.action_dict_allname[action_name]].data_dict.items())]\n for data_name_, data_ in list(self.data_dict.items()):\n ret_field_name_list.append(data_name_)\n return ret_field_name_list","title":""},{"docid":"613b688dbcd887d59bea4ed78d7433a4","score":"0.5380753","text":"def get_field_names(self):\n\n field_names = set()\n\n # Add field names from each component.\n for component in self.components:\n field_names.update(component.get_field_names())\n\n return list(field_names)","title":""},{"docid":"5390ba244ba96352a1d6c9f1db4879ab","score":"0.53681356","text":"def get_field_names(self, format_string):\n return self.name_pattern.findall(format_string)","title":""},{"docid":"d862b976f871cbcee5188ed6f6cb4e95","score":"0.536005","text":"def get_container_list(self, source):\n storage_client = None\n container = None\n\n if 'fileGroup' in source:\n # Input data stored in auto-storage\n storage_client = self.resolve_storage_account()\n container = _get_container_name(source['fileGroup'])\n elif 'containerUrl' in source:\n uri = urlsplit(source['containerUrl'])\n if not uri.query:\n raise ValueError('Invalid container url.')\n storage_account_name = uri.netloc.split('.')[0]\n sas_token = uri.query\n storage_client = BlockBlobService(account_name=storage_account_name,\n sas_token=sas_token)\n container = uri.pathname.split('/')[1]\n else:\n raise ValueError('Unknown source.')\n\n return self.list_container_contents(source, container, storage_client)","title":""},{"docid":"a0dfb7fb0ea38f44ad645e2780ee0cca","score":"0.535109","text":"def getAllFields(layer):\n fields = layer.fields()\n fieldLists = []\n for field in fields:\n fieldLists.append(field.name())\n return fieldLists","title":""},{"docid":"7d0822d0c9a73ae8026a352e77623b96","score":"0.5347515","text":"def get_field_names(self):\n raise NotImplementedError","title":""},{"docid":"cb33f81c7565c3cc3e972a6b30431442","score":"0.52762234","text":"def get_field_names(self):\n\n field_names = set(sch_field_id_to_name.values())\n for component in self.components:\n field_names.update(component.get_field_names())\n return list(field_names)","title":""},{"docid":"70dc8549c75e78f1846fe87ad0c2d70a","score":"0.5217515","text":"def getFieldNames(self):\n return self.fieldNames","title":""},{"docid":"5ee8470c4b9687c8fe862ec39f66c2a4","score":"0.5198622","text":"def get_field_names(\n project_type,\n table_name,\n):\n # sql = \"SELECT * FROM [{}]\".format(table_name)\n sql = \"PRAGMA table_info( [{}] );\".format(table_name)\n\n data = run_query(sql, project_type)\n if data:\n return [x.get(\"name\") for x in data]\n else:\n return []","title":""},{"docid":"b674a72881c343ca3e65f83343c954a6","score":"0.51946247","text":"def data_field_name_list_get(self, *unused):\n return [data_name_ for data_name_, data_ in list(self.data_dict.items())]","title":""},{"docid":"b2434dc23b4245c69caa615e0fd07d5d","score":"0.5171875","text":"def get_containers_names(client, prefix=DOCK_CONTAINER_NAME_PREFIX_BTC):\n return [container.name for container in client.containers.list(\"all\") if prefix in container.name]","title":""},{"docid":"7b77ae64070e8055affd29d40113c872","score":"0.5169079","text":"def _get_list_field_names(self):\n\n all_fields = ()\n if not self.list_fields:\n all_fields = self._get_default_list_fields()\n else:\n all_fields = list(self.list_fields)\n if self._is_list_pk_required():\n self._inject_primary_key(all_fields)\n\n all_fields = self._extract_field_names(all_fields, allow_string=True)\n if self.list_indexed is True:\n index_name = self.list_index_name\n if index_name in (None, ''):\n index_name = config_services.get('api', 'schema', 'index_name')\n\n all_fields = list(all_fields)\n all_fields.insert(0, index_name)\n all_fields = tuple(all_fields)\n\n duplicates = misc_utils.get_duplicates(all_fields)\n if duplicates:\n raise DuplicateListFieldNamesError('There are some duplicate field names '\n 'in \"list_fields\" of [{admin}] class. '\n 'this will make the find result incorrect. '\n 'please remove duplicate fields or set '\n 'unique labels for them: {duplicates}'\n .format(admin=self, duplicates=duplicates))\n\n return all_fields","title":""},{"docid":"c48e94f111a0111011a30f30aff4c88a","score":"0.5166085","text":"def _field_names(self):\n names = []\n self._read_token('{')\n if self._is_field_name():\n names.append(self._read_token().value)\n while self._next_token().kind == ',':\n self._read_token(',')\n if self._is_field_name():\n name == self._read_token().value\n if name in names:\n raise Exception(\"Repeated field name: %s\" % name)\n names.append(name)\n else:\n raise Exception(\"field name expected, found %s\" %\n self._next_token().value)\n self._read_token('}')\n return names","title":""},{"docid":"55843e0a9f4a7fe56c2087de43caa3c4","score":"0.5160062","text":"def field_names(self):\r\n return self._names","title":""},{"docid":"25d221ff14221b5be396cb2fd14b695f","score":"0.51470417","text":"def get_field_names(self):\n\n field_names = set()\n for f in self.fields:\n try:\n field_names.add(unquote(f[\"name\"]))\n except KeyError:\n pass\n field_names.discard(\"\")\n return field_names","title":""},{"docid":"2c14e57dbd84db2ec879f1e1adf15d5f","score":"0.5139172","text":"def list(self) -> list:\n labels = []\n for k, v in self._labels.items():\n labels.append(\"{}={}\".format(k, v))\n\n #TODO: return names\n containers = self.client.containers.list(filters={\"label\": labels})\n return list(map(lambda s: s.name, containers))","title":""},{"docid":"b6c2df00f269215d05e90947cd79bb3f","score":"0.51312125","text":"def export_field_names(self) -> List[str]:\n return [f.name for f in self.fields.all()]","title":""},{"docid":"b7aae64056ae801052747746a5423abd","score":"0.5128673","text":"def fields(self):\r\n try:\r\n self._field_name_cache\r\n except AttributeError:\r\n self._fill_fields_cache()\r\n return self._field_name_cache","title":""},{"docid":"e7d6dc8c87f29a148679a18694ce2684","score":"0.51274216","text":"def get_field_names(self):\n\n return {f[\"name\"] for f in self.fields}","title":""},{"docid":"3a797639a31c6fabc6d35cc0ef6e6484","score":"0.5123486","text":"def get_versioned_field_names(self, version_number):\n return []","title":""},{"docid":"7b77852ac9831875ec06d5fe55dde409","score":"0.5094264","text":"def get_field_names(self):\n field_names = []\n for field in self.data_fields.all():\n field_names.append(field.name)\n return field_names","title":""},{"docid":"aa3d8bbaa16f7920fd4f9fcc1da8b28c","score":"0.50611734","text":"def get_column_names_list(record_info_in: sdk.RecordInfo) -> List[str]:\n return [field.name for field in record_info_in]","title":""},{"docid":"704297f96eac44cd01a88ca465d5b805","score":"0.5060997","text":"def field_names(self):\n return frozenset(self._children.keys())","title":""},{"docid":"f64563f859bbb1a7450773db6425e8bd","score":"0.5054078","text":"def list(self, container_name: str) -> None:\n try:\n res = requests.get(self.url + 'list', json={'container_name': container_name})\n if res.status_code == 200:\n self.logger.info(f\"Listing files of container {container_name}\")\n return json.loads(res.content)\n else:\n return None\n except ConnectionError as ce:\n raise ce","title":""},{"docid":"d3dfb9b4a47eed06de1d9963222288d2","score":"0.5046736","text":"def get_field_names(self):\n return self.fields.values()","title":""},{"docid":"f23b83640bca611ba5973965e550f962","score":"0.50379735","text":"def get_container_name(self, container_id):\n if not os.path.isdir(self.containersdir):\n return []\n link_list = []\n for fname in os.listdir(self.containersdir):\n container = self.containersdir + \"/\" + fname\n if os.path.islink(container):\n real_container = os.readlink(container)\n if os.path.basename(real_container) == container_id:\n link_list.append(fname)\n return link_list","title":""},{"docid":"4636b4e0dcd5bfaf02de8b0ee0c445e3","score":"0.5035241","text":"def fieldsNames_get(self,recordset):\r\n\t\tif recordset.upper().startswith('SELECT'):\r\n\t\t\trs=self.__open_recordset(self.conn, recordset, maxrecords=1)\r\n\t\telse:\r\n\t\t\trs=self.__open_recordset(self.conn, '['+recordset+']', maxrecords=1)\r\n\r\n\t\tif rs.Fields.Count>0:\r\n\t\t\tfields=[rs.Fields.Item(item).Name for item in range(rs.Fields.Count)]\r\n\t\telse:\r\n\t\t\tfields=[]\r\n\t\treturn fields","title":""},{"docid":"50f25ecf9257d7170423361e610a667f","score":"0.50169486","text":"def get_fields(self, field_name):\r\n if field_name not in self.fields:\r\n raise OGRException('invalid field name: %s' % field_name)\r\n return [feat.get(field_name) for feat in self]","title":""},{"docid":"7d34c0f7d6e4e777f87562f000824e1f","score":"0.4993697","text":"def get_fieldnames_from_cursor(cursor: Cursor) -> List[str]:\n return [i[0] for i in cursor.description]","title":""},{"docid":"4d3d15cdef20f82411536b5fcbf3cacc","score":"0.49833426","text":"def fields(self):\r\n return [force_text(capi.get_field_name(capi.get_field_defn(self._ldefn, i)),\r\n self._ds.encoding, strings_only=True)\r\n for i in xrange(self.num_fields)]","title":""},{"docid":"cc2836b21ba3be6d217c40e9a0be9a63","score":"0.49688077","text":"def _extract_field_names(self, fields, allow_string=True):\n\n names = []\n for item in fields:\n if self._is_valid_field(item):\n names.append(item.key)\n elif allow_string is True and isinstance(item, str) \\\n and self._is_valid_method(item):\n names.append(item)\n else:\n message = 'Provided field [{field}] is not a valid value. ' \\\n 'it must be a column attribute{sign} ' \\\n 'expression level hybrid property{end}'\n\n if allow_string is True:\n message = message.format(\n field=str(item), sign=',',\n end=' or a string representing a method name of [{admin}] class.')\n else:\n message = message.format(field=str(item), sign=' or',\n end='.')\n\n raise InvalidListFieldError(message.format(admin=self))\n\n return tuple(names)","title":""},{"docid":"f24c7585e363e44d0434d30c329dbd1e","score":"0.49683803","text":"def field_list(cls) -> List[str]:\n return list(attr.fields_dict(cls).keys())","title":""},{"docid":"4f6299e85d77d37267f451d0f8648195","score":"0.495515","text":"def _to_fields(d: DictAny) -> List[str]:\n cs: List[DictAny] = d[\"meta\"][\"view\"][\"columns\"]\n\n # written this way to get type annotations to work\n def f(x: DictAny) -> str:\n sx: str = x['name']\n return sx\n\n return list(map(f, cs))","title":""},{"docid":"574c75a4ad74382c72ae9e5f91447525","score":"0.49546686","text":"def container_image_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"container_image_names\")","title":""},{"docid":"8a079a0ce17f9ad49ef4125b8aff75ab","score":"0.49508816","text":"def stored_field_names(self):\r\n \r\n bn = self._by_name\r\n return [name for name in self._names if bn[name].stored]","title":""},{"docid":"f72b6899c69e686da1f2003d0a10b4cc","score":"0.49477538","text":"def key_field_name_list_get(self):\n return list(self.key_dict.keys())","title":""},{"docid":"e9f44857f2954aef14200b01f3bdce23","score":"0.49375585","text":"def get_names(collection):\n return [element.name for element in collection]","title":""},{"docid":"f1ea66213ccf40c7c8ec94541b1078fb","score":"0.49365887","text":"def fields(self):\n return [self._fields[name] for name in self.fieldNames]","title":""},{"docid":"8638226e6ca75fe8bb4d4eb091341832","score":"0.49363112","text":"def field_names(self, record_type=\"\"):\n return [f[\"name\"] for f in self.record_types[record_type][\"fields\"]]","title":""},{"docid":"06107a2cdf60711c2ff205daad75e3a5","score":"0.49352884","text":"def fieldnames(self):\n return self._fieldnames","title":""},{"docid":"0fc69c4893af6b3663f692a42e03452b","score":"0.49313736","text":"def get_canonical_container_name(container):\n return sorted(container['Names'], key=lambda name: len(name))[0][1:]","title":""},{"docid":"5ca31ef9e8cbb6928657eed4cbea6060","score":"0.48984218","text":"def containers(self, module_name):\n\t\tbase, name = split_module_name(module_name)\n\n\t\t# the printer modules does not have the same name scheme\n\t\tif module_name == 'shares/print':\n\t\t\tbase = 'printers'\n\n\t\tself._read_directories()\n\n\t\tif self.directory is None:\n\t\t\treturn []\n\t\treturn map(lambda x: {'id': x, 'label': ldap_dn2path(x)}, self.directory.info.get(base, []))","title":""},{"docid":"bec166b06411e731bed78a486883042f","score":"0.48848704","text":"def get_field_list(self):\n return list(self.table_format_dict)","title":""},{"docid":"1918484db22ccaff5f158a9c670452d1","score":"0.48847437","text":"def fields(self):\r\n try:\r\n return self._fields[self.tname]\r\n except KeyError:\r\n # introspect the field names from the database\r\n # by relying on the DB API 2.0\r\n # NB: we cannot trust the ordering in the Django model\r\n curs = connections[self.alias].cursor()\r\n curs.execute('select * from %s where 1=0' % self.tname)\r\n names = self._fields[self.tname] = [\r\n r[0] for r in curs.description if r[0] != 'id']\r\n return names","title":""},{"docid":"4e066b142abbdaa5a587ebabadc21915","score":"0.48470128","text":"def get_field_names(self):\n key_fields = [ d[0] for d in self.fpkey.fields ]\n data_fields = [ d[0] for d in self.fpdata.fields ]\n return key_fields + data_fields","title":""},{"docid":"c44ff81bc789d7aee7edef6b560353f0","score":"0.48232073","text":"def get_field_names(table):\n return [field['name'] for field in get_db().execute(f'SELECT name FROM {FIELDS + get_table_id(table)}').fetchall()]","title":""},{"docid":"899305a9f751ae70d7a33b5600f4746a","score":"0.47798315","text":"def list_fields(self):\n _require_metadata(gnats.MINIMAL_METADATA)\n return list(self.ordered_fields)","title":""},{"docid":"da861e81945237457eaed0a3ff7571d6","score":"0.47780028","text":"def get_field_names(self):\n return sorted(self.column_name_describer.keys())","title":""},{"docid":"92c4c389c1b8081ba8e278620cc39858","score":"0.47687584","text":"def fields(cls) -> List[str]:\n fields = attr.fields(cls)\n field_types = [\n (f.name, innermost_type(f.type)) for f in fields if f.name != \"gid\"\n ]\n field_names = [\n [f\"{name}.{f}\" for f in typ.fields()]\n if issubclass(typ, _HasFields)\n else [name]\n for (name, typ) in field_types\n ]\n return [name for names in field_names for name in names]","title":""},{"docid":"72667bc449108aded84cbcafc14597fe","score":"0.47650254","text":"def _list_field_names(self, field_type):\n fields = self._properties.get('indexedField', {})\n return fields.get(field_type)","title":""},{"docid":"b09a9db0023271ff3cce904fceae0e09","score":"0.47543573","text":"def fields(self):\n return self.field_list","title":""},{"docid":"f3a8bf83e5d8353fb269facfffe1d9f9","score":"0.4749665","text":"def fields(self) -> t.List[str]:\n return []","title":""},{"docid":"06b4eb3cb3fd1ecb3f1e60574510dad1","score":"0.47387376","text":"def list_collection_fields(self, environment_id, collection_id, **kwargs):\n\n if environment_id is None:\n raise ValueError('environment_id must be provided')\n if collection_id is None:\n raise ValueError('collection_id must be provided')\n\n headers = {}\n if 'headers' in kwargs:\n headers.update(kwargs.get('headers'))\n sdk_headers = get_sdk_headers('discovery', 'V1',\n 'list_collection_fields')\n headers.update(sdk_headers)\n\n params = {'version': self.version}\n\n url = '/v1/environments/{0}/collections/{1}/fields'.format(\n *self._encode_path_vars(environment_id, collection_id))\n response = self.request(\n method='GET',\n url=url,\n headers=headers,\n params=params,\n accept_json=True)\n return response","title":""},{"docid":"c323dd4c958bbb31f175a4ea32a98404","score":"0.4738311","text":"def fields(self):\n\n if hasattr(self, '__parameters__'):\n return self.__parameters__\n else:\n return [f'f{i}' for i in self.keys()]","title":""},{"docid":"3975d8dac6769204bb26cdba3ace7719","score":"0.47211117","text":"def get_network_container_hostnames(name):\n for network in get_networks():\n if network[\"Name\"] == name:\n return [get_container_hostname(container) for container in network[\"Containers\"]]","title":""},{"docid":"499acedfa5db0e6d0724a7a7620e9479","score":"0.47182256","text":"def get_parameter_names(diagnostic_fields: List[List[str]]) -> List[List[str]]:\n parameter_names = []\n for condition in diagnostic_fields:\n if isinstance(condition, list):\n parameter_names.append(get_parameter_names(condition))\n elif is_variable(condition):\n parameter_names.append(condition)\n return parameter_names","title":""},{"docid":"e40fba1fc77f5cea111df8942e072ae9","score":"0.47160146","text":"def _string_helper(self, field_name): # pylint: disable=g-ambiguous-str-annotation\n result = [\"{} {}\".format(str(self.node), str(field_name))]\n for k, v in self._children.items():\n recursive = v._string_helper(k) # pylint: disable=protected-access\n result.extend([\" {}\".format(x) for x in recursive])\n return result","title":""},{"docid":"2d12959f5bd0d6a8af770d946d3c9bcb","score":"0.47127327","text":"def _get_dates_from_fields(self):\n\n if not self._fields_container:\n return []\n\n dates = []\n # Widgets are arranged in the reverse order of their addition.\n for field in reversed(self._fields_container.children):\n try:\n date = datetime.datetime.strptime(field.text, \"%d/%m/%Y\").date()\n except ValueError:\n date = None\n dates.append(date)\n\n return dates","title":""},{"docid":"1eca8a0bdc4cc6aa80b7a640dd671a0c","score":"0.47007495","text":"def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:\n from .planner_container_type import PlannerContainerType\n\n from .planner_container_type import PlannerContainerType\n\n fields: Dict[str, Callable[[Any], None]] = {\n \"containerId\": lambda n : setattr(self, 'container_id', n.get_str_value()),\n \"@odata.type\": lambda n : setattr(self, 'odata_type', n.get_str_value()),\n \"type\": lambda n : setattr(self, 'type', n.get_enum_value(PlannerContainerType)),\n \"url\": lambda n : setattr(self, 'url', n.get_str_value()),\n }\n return fields","title":""},{"docid":"340c5011c1971fe81abd7d8ab3651ba7","score":"0.4667802","text":"def fieldnames(self):\n fieldnames = {} # sets are unordered\n for row in self.rows:\n for field in row:\n fieldnames[field] = True\n return list(fieldnames.keys())","title":""},{"docid":"6ca6b7bfdfdbc0d80d69b191611a1481","score":"0.46639836","text":"def tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:","title":""},{"docid":"13a3989ca8336b4ac5811f4c12b9894c","score":"0.46563387","text":"def get_containers_list(self, dir_only=True):\n containers_list = []\n if not os.path.isdir(self.containersdir):\n return []\n for fname in os.listdir(self.containersdir):\n container_dir = self.containersdir + '/' + fname\n if os.path.isdir(container_dir):\n try:\n filep = open(container_dir + \"/imagerepo.name\", 'r')\n except (IOError, OSError):\n reponame = \"\"\n else:\n reponame = filep.read()\n filep.close()\n if dir_only:\n containers_list.append(container_dir)\n elif not os.path.islink(container_dir):\n names = self.get_container_name(fname)\n if not names:\n names = \"\"\n containers_list.append((fname, reponame, str(names)))\n return containers_list","title":""},{"docid":"f5f79f4914c26655b3a3e72b6b5129e7","score":"0.46369","text":"def get_split_names(self):\n try:\n return [split.name for split in self.get_all()]\n except Exception:\n _LOGGER.error('Error getting split names from storage')\n _LOGGER.debug('Error: ', exc_info=True)\n return None","title":""},{"docid":"da11509e1da3a03ab4800f720bc7a9de","score":"0.4634459","text":"def name_get(self):\n result = []\n for document in self:\n if document.version:\n name = '[%s] %s'%(document.version, document.name)\n else: \n name = document.name\n \n result.append((document.id, name))\n \n return result","title":""},{"docid":"49c2e57520127645e0075b6b0029a0b4","score":"0.46329495","text":"def _get_field_names(reader: IO) -> Sequence[str]:\n csv_reader = csv.DictReader(reader)\n next(csv_reader)\n reader.seek(0)\n return csv_reader.fieldnames or []","title":""},{"docid":"3660cc3d64c393055d4ac42e3b877e8f","score":"0.4630384","text":"def get_fields(ms_name):\n logger.debug(\"Getting antenna names\")\n\n subname = \"::\".join((ms_name, \"FIELD\"))\n field_subtab = list(xm.xds_from_table(subname))\n field_subtab = field_subtab[0]\n field_names = field_subtab.NAME\n\n logger.debug(f\"Fields found: {', '.join(field_names.values)}\")\n return field_names","title":""},{"docid":"7e2bf9854325734e655c6fe8dad3c739","score":"0.46236715","text":"def dependencies(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:","title":""},{"docid":"b8ca2acf5377bdb047a8b663c874b75c","score":"0.46212012","text":"def _get_user_field_names(self):\n field_names = []\n internal_fields = (\"left\", \"right\", \"tree_id\", \"level\")\n for field in self._meta.fields:\n if (field.name not in internal_fields) and (not isinstance(field, AutoField)) and (not field.primary_key):\n field_names.append(field.name)\n return field_names","title":""},{"docid":"0ab96239137f7598c22ea2ed46b559b2","score":"0.46098047","text":"def list_fields(table):\r\n if not table:\r\n return []\r\n cols = table.__table__.columns if hasattr(table, '__table__') else table.columns\r\n return [f.name for f in cols]","title":""},{"docid":"5cb28afef9eb9d2da7ce3083fc7e4c48","score":"0.46097603","text":"def field_names(cls):\n return [field.name for field in cls()]","title":""},{"docid":"0e676c72e977e1d12726b12347010650","score":"0.46061674","text":"def _GetToListFields(self, get_fields=None):\n if get_fields:\n list_fields = set()\n for field in get_fields:\n list_fields.add('items/' + field)\n return list_fields","title":""},{"docid":"adf73010cff289c4920cac47f74bb3a2","score":"0.4604988","text":"def container_name(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"containerName\"),\n )","title":""},{"docid":"e1fe3030e55145e0e75c85e0b470c3a5","score":"0.46005386","text":"def __init__(self, field_list, field_container, *args, **kwargs):\r\n super(DynamicForm, self).__init__(*args, **kwargs)\r\n\r\n self.field_structure = []\r\n\r\n for field_name in field_list:\r\n field_data = getattr(field_container, field_name)[1]\r\n\r\n field_object = getattr(forms.fields, field_data['type'])(**field_data['kwargs'])\r\n self.fields[field_name] = field_object\r\n\r\n field_data['field_name'] = field_name\r\n self.field_structure.append(field_data)","title":""},{"docid":"dc90e0b264c2830b135d06667b3634bc","score":"0.45973927","text":"def fld_lst(type):\n lst = []\n if isinstance(type, dict):\n return list(type.keys())\n elif isinstance(type, list):\n for i in range(1, len(type) + 1):\n lst.append(\"element \" + str(i))\n return lst","title":""},{"docid":"1d47eaac5c428fd666c6b79881ef1b66","score":"0.45942882","text":"def get_signature_fields(interview_metadata_dict):\r\n signature_fields = []\r\n for field_dict in (interview_metadata_dict.get('built_in_fields_used',[]) + interview_metadata_dict.get('fields',[])):\r\n field = map_names(field_dict.get('variable',''))\r\n if field.endswith('.signature'):\r\n signature_fields.append(field)\r\n return signature_fields","title":""},{"docid":"6cd0478bf7c2cea9e76455ff40a8f67f","score":"0.4587273","text":"def flatten_container_info(self, inner_ip):\n def iter_container(tg):\n for g in tg:\n for d in g['data']['containerStatuses']:\n c = {'name': d['name'],\n 'image': d['image'],\n 'status': d['status'].lower(),\n 'container_id': d['containerID']}\n yield c\n taskgroups = self.get_unit_info(inner_ip, fields='data,namespace').get('data') or []\n containers = sorted([i for i in iter_container(taskgroups)],\n key=lambda x: constants.DockerStatusOrdering.get(\n x['status'], constants.DockerStatusDefaultOrder))\n return containers","title":""},{"docid":"e1dee9b7b5498c9877ec8ba5dbdf9fc9","score":"0.45822173","text":"def get_field_name_order(field_option_list):\n\n field_name_list = [None] * len(field_option_list)\n while not all(field_name_list):\n for idx, field_option in enumerate(field_option_list):\n if len(field_option) == 1:\n found_name = field_option.pop()\n field_name_list[idx] = found_name\n for field_set in field_option_list:\n field_set.discard(found_name)\n break\n\n return field_name_list","title":""},{"docid":"335a0e40733d20c568f2211893f67242","score":"0.4575407","text":"def get_all_field_names(self):\r\n try:\r\n cache = self._name_map\r\n except AttributeError:\r\n cache = self.init_name_map()\r\n names = sorted(cache.keys())\r\n # Internal-only names end with \"+\" (symmetrical m2m related names being\r\n # the main example). Trim them.\r\n return [val for val in names if not val.endswith('+')]","title":""},{"docid":"e70dd224a19862e060ab3b2ef1410db9","score":"0.4568496","text":"def test_schema_sql_field_list(self):\n sql_fields = BQTestSchema.get_sql_field_names()\n self.assertEqual('descr, timestamp, nested', sql_fields)\n\n sql_fields = BQTestSchema.get_sql_field_names(exclude_fields=['timestamp'])\n self.assertEqual('descr, nested', sql_fields)","title":""},{"docid":"212711c27e5a6607162d499c7949a51a","score":"0.45502537","text":"def get_fields(self, field_name: str) -> Optional[PropertyValues]:\n for p in self.properties:\n if p.name == field_name:\n return p.values\n return None","title":""},{"docid":"95b9ae9153f14cf90dae683977ade3e8","score":"0.45465776","text":"def list_containers():","title":""},{"docid":"6d778c5d87a68079760c520f0f65fa8c","score":"0.45192477","text":"def list_names(self):\n return self.child_names","title":""},{"docid":"9ff30b92abf478bba51988c5f5f550f4","score":"0.45170188","text":"def get_list_fields(self):\n return self.fields","title":""},{"docid":"7386c2c08d3ec1ca914444f07634040d","score":"0.45169434","text":"def get_field_ids_by_name(self, name_regex):\n d = {k: v for k, v in self._get_id_to_name_dict().items() if name_regex.match(v) is not None}\n return d","title":""},{"docid":"417fcc89d4af8adfbb5d16facc1a5543","score":"0.45124117","text":"def getContainerName(ctx, containerFlavor):\n return getattr(ctx.cf.ocp.containers, containerFlavor).name","title":""},{"docid":"ce030cf1a66b95ea5ba9759c1b2a34f5","score":"0.45090052","text":"def _string_helper(self, field_name: path.Step) -> Sequence[str]:\n result = [\"{} {}\".format(str(self.node), str(field_name))]\n for k, v in self._children.items():\n recursive = v._string_helper(k) # pylint: disable=protected-access\n result.extend([\" {}\".format(x) for x in recursive])\n return result","title":""},{"docid":"0762fbc266f3c0da9136fea60d69961c","score":"0.44986236","text":"def _list_containers(self):\n response = self._session.get(\"http+unix://%2Fvar%2Frun%2Fdocker.sock/containers/json?all=1\")\n if not response.ok:\n raise Exception(\"Couldn't get Containers list. Check if your docker daemon alive or not\")\n\n strio = self.stringIO(response.text)\n json_response = self.json.load(strio)\n lst_containers = [str(container[\"Names\"])[3:-2] for container in json_response]\n return lst_containers","title":""},{"docid":"85b097ce3af3f1fcd437792f757f3e58","score":"0.44954124","text":"def get_list(self):\n\n if self.mode == 'history':\n container_list = self.dopq.history\n elif self.mode == 'enqueued':\n container_list = self.dopq.container_list\n else:\n raise ValueError('invalid mode of operation: {}'.format(self.mode))\n\n if len(container_list) > self.max_containers:\n container_list = container_list[:self.max_containers]\n\n return container_list","title":""},{"docid":"9a2e65a55f6d8a915330cf30b44d59e3","score":"0.44941473","text":"def getFields(context, field_names=None):\n pairs = []\n if context.Schema(): # is archetype\n schema = context.Schema()\n fields = schema.fields()\n if field_names:\n for field_name in field_names:\n val = None\n pairs.append(schema[field_name].getName())\n val = schema[field_name].get(context)\n if isinstance(val, tuple) or isinstance(val, list):\n val = iterToTags(val)\n elif str(val).find('\\n') != -1:\n val = newlinesToTags(val)\n pairs.append(val)\n else:\n for field in fields:\n val = None\n pairs.append(field.getName())\n val = str(field.get(context))\n if val.find('\\n') != -1:\n val = newlinesToTags(val)\n pairs.append(val)\n return pairs","title":""}],"string":"[\n {\n \"docid\": \"30c975d7087e5406555f5a29ddfc5d2f\",\n \"score\": \"0.61451197\",\n \"text\": \"def _get_list(container, name):\\n\\n try:\\n return container[name]\\n except KeyError:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f10c4c60092e3146611d5e905294f06d\",\n \"score\": \"0.5811185\",\n \"text\": \"def _field_names_(self):\\n ret = []\\n\\n def _merge(f, rename):\\n if not f:\\n return\\n\\n if not rename:\\n ret.extend([n for n, _ in f])\\n else:\\n for n, _ in f:\\n new_n = rename.get(n, None)\\n ret.append(new_n) if new_n else ret.append(n)\\n\\n for b in self.__class__.__mro__:\\n _merge(\\n getattr(b, '__swagger_fields__', None),\\n getattr(b, '__swagger_rename__', None)\\n )\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d5d3800f0ce0776ef979ed27f1d87\",\n \"score\": \"0.5712169\",\n \"text\": \"def fields_container(self):\\n return self._fields_container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d5d3800f0ce0776ef979ed27f1d87\",\n \"score\": \"0.5712169\",\n \"text\": \"def fields_container(self):\\n return self._fields_container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437d5d3800f0ce0776ef979ed27f1d87\",\n \"score\": \"0.5712169\",\n \"text\": \"def fields_container(self):\\n return self._fields_container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1441683baa86cccd7332f66af646866\",\n \"score\": \"0.5601177\",\n \"text\": \"def get_container_entries(self, container, prefix):\\n try:\\n headers = self._headers()\\n swift_objects = self.conn.get_container(container,\\n prefix=prefix,\\n full_listing=True,\\n headers=headers)[1]\\n except socket.error as err:\\n raise exception.SwiftConnectionFailed(reason=err)\\n swift_object_names = [swift_obj['name'] for swift_obj in swift_objects]\\n return swift_object_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d8c066c5cc911b046911ca8ae57b598\",\n \"score\": \"0.5588793\",\n \"text\": \"def GetContainerFields(request, layer):\\n icon = request.get('icon')\\n icon = util.GetInstance(model.Resource, icon, layer, required=False)\\n if icon and icon.type != 'icon':\\n raise util.BadRequest('Invalid (non-icon) resource specified.')\\n\\n folder = request.get('folder')\\n folder = util.GetInstance(model.Folder, folder, layer, required=False)\\n folder_index = request.get('folder_index', None)\\n if folder_index is not None: folder_index = int(folder_index)\\n\\n region = request.get('region')\\n region = util.GetInstance(model.Region, region, layer, required=False)\\n\\n return {\\n 'icon': icon,\\n 'folder': folder,\\n 'folder_index': folder_index,\\n 'region': region,\\n 'name': request.get('name', None),\\n 'description': request.get('description', None),\\n 'item_type': request.get('item_type', None),\\n 'custom_kml': request.get('custom_kml', None)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9876c3a1dd2807b45e9acca543a94186\",\n \"score\": \"0.551232\",\n \"text\": \"def list_containers(self):\\r\\n if self._container_names is None:\\r\\n visited = set([id(self)])\\r\\n if hasattr(self, '_parent'): # fix for weird unpickling bug\\r\\n visited.add(id(self.parent))\\r\\n names = []\\r\\n for n, v in self.__dict__.items():\\r\\n if is_instance(v, Container) and id(v) not in visited:\\r\\n visited.add(id(v))\\r\\n names.append(n)\\r\\n self._container_names = names\\r\\n return self._container_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b619bd1f3f17cba660356f07ab0102bd\",\n \"score\": \"0.55013055\",\n \"text\": \"def field_names(self) -> List[str]:\\n return [x.name for x in self.fields.all()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49731fc3f52b6436791ae50e39483390\",\n \"score\": \"0.5474507\",\n \"text\": \"def get_list_of_fields_for_node(node_name):\\n return _value_to_node_fields[node_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f462a5031e0d1a0da6991690b93c2ef\",\n \"score\": \"0.5405363\",\n \"text\": \"def flatten_container(self, container):\\n for names in ARG_MAP.values():\\n if names[TransformationTypes.CHRONOS.value]['name'] and \\\\\\n '.' in names[TransformationTypes.CHRONOS.value]['name']:\\n chronos_dotted_name = names[TransformationTypes.CHRONOS.value]['name']\\n parts = chronos_dotted_name.split('.')\\n\\n if parts[-2] == 'parameters':\\n # Special lookup for docker parameters\\n common_type = names[TransformationTypes.CHRONOS.value].get('type')\\n result = self._lookup_parameter(container, parts[-1], common_type)\\n if result:\\n container[chronos_dotted_name] = result\\n else:\\n result = lookup_nested_dict(container, *parts)\\n if result:\\n container[chronos_dotted_name] = result\\n return container\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7120d4d10f2d84545c761e0ae1cb6e\",\n \"score\": \"0.53995526\",\n \"text\": \"def data_field_name_list_get(self, action_name=None):\\n ret_field_name_list = []\\n if action_name is not None:\\n ret_field_name_list=[field_name_ for field_name_, data_ in \\n list(self.action_dict[self.action_dict_allname[action_name]].data_dict.items())]\\n for data_name_, data_ in list(self.data_dict.items()):\\n ret_field_name_list.append(data_name_)\\n return ret_field_name_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"613b688dbcd887d59bea4ed78d7433a4\",\n \"score\": \"0.5380753\",\n \"text\": \"def get_field_names(self):\\n\\n field_names = set()\\n\\n # Add field names from each component.\\n for component in self.components:\\n field_names.update(component.get_field_names())\\n\\n return list(field_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5390ba244ba96352a1d6c9f1db4879ab\",\n \"score\": \"0.53681356\",\n \"text\": \"def get_field_names(self, format_string):\\n return self.name_pattern.findall(format_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d862b976f871cbcee5188ed6f6cb4e95\",\n \"score\": \"0.536005\",\n \"text\": \"def get_container_list(self, source):\\n storage_client = None\\n container = None\\n\\n if 'fileGroup' in source:\\n # Input data stored in auto-storage\\n storage_client = self.resolve_storage_account()\\n container = _get_container_name(source['fileGroup'])\\n elif 'containerUrl' in source:\\n uri = urlsplit(source['containerUrl'])\\n if not uri.query:\\n raise ValueError('Invalid container url.')\\n storage_account_name = uri.netloc.split('.')[0]\\n sas_token = uri.query\\n storage_client = BlockBlobService(account_name=storage_account_name,\\n sas_token=sas_token)\\n container = uri.pathname.split('/')[1]\\n else:\\n raise ValueError('Unknown source.')\\n\\n return self.list_container_contents(source, container, storage_client)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0dfb7fb0ea38f44ad645e2780ee0cca\",\n \"score\": \"0.535109\",\n \"text\": \"def getAllFields(layer):\\n fields = layer.fields()\\n fieldLists = []\\n for field in fields:\\n fieldLists.append(field.name())\\n return fieldLists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d0822d0c9a73ae8026a352e77623b96\",\n \"score\": \"0.5347515\",\n \"text\": \"def get_field_names(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb33f81c7565c3cc3e972a6b30431442\",\n \"score\": \"0.52762234\",\n \"text\": \"def get_field_names(self):\\n\\n field_names = set(sch_field_id_to_name.values())\\n for component in self.components:\\n field_names.update(component.get_field_names())\\n return list(field_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70dc8549c75e78f1846fe87ad0c2d70a\",\n \"score\": \"0.5217515\",\n \"text\": \"def getFieldNames(self):\\n return self.fieldNames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ee8470c4b9687c8fe862ec39f66c2a4\",\n \"score\": \"0.5198622\",\n \"text\": \"def get_field_names(\\n project_type,\\n table_name,\\n):\\n # sql = \\\"SELECT * FROM [{}]\\\".format(table_name)\\n sql = \\\"PRAGMA table_info( [{}] );\\\".format(table_name)\\n\\n data = run_query(sql, project_type)\\n if data:\\n return [x.get(\\\"name\\\") for x in data]\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b674a72881c343ca3e65f83343c954a6\",\n \"score\": \"0.51946247\",\n \"text\": \"def data_field_name_list_get(self, *unused):\\n return [data_name_ for data_name_, data_ in list(self.data_dict.items())]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2434dc23b4245c69caa615e0fd07d5d\",\n \"score\": \"0.5171875\",\n \"text\": \"def get_containers_names(client, prefix=DOCK_CONTAINER_NAME_PREFIX_BTC):\\n return [container.name for container in client.containers.list(\\\"all\\\") if prefix in container.name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b77ae64070e8055affd29d40113c872\",\n \"score\": \"0.5169079\",\n \"text\": \"def _get_list_field_names(self):\\n\\n all_fields = ()\\n if not self.list_fields:\\n all_fields = self._get_default_list_fields()\\n else:\\n all_fields = list(self.list_fields)\\n if self._is_list_pk_required():\\n self._inject_primary_key(all_fields)\\n\\n all_fields = self._extract_field_names(all_fields, allow_string=True)\\n if self.list_indexed is True:\\n index_name = self.list_index_name\\n if index_name in (None, ''):\\n index_name = config_services.get('api', 'schema', 'index_name')\\n\\n all_fields = list(all_fields)\\n all_fields.insert(0, index_name)\\n all_fields = tuple(all_fields)\\n\\n duplicates = misc_utils.get_duplicates(all_fields)\\n if duplicates:\\n raise DuplicateListFieldNamesError('There are some duplicate field names '\\n 'in \\\"list_fields\\\" of [{admin}] class. '\\n 'this will make the find result incorrect. '\\n 'please remove duplicate fields or set '\\n 'unique labels for them: {duplicates}'\\n .format(admin=self, duplicates=duplicates))\\n\\n return all_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48e94f111a0111011a30f30aff4c88a\",\n \"score\": \"0.5166085\",\n \"text\": \"def _field_names(self):\\n names = []\\n self._read_token('{')\\n if self._is_field_name():\\n names.append(self._read_token().value)\\n while self._next_token().kind == ',':\\n self._read_token(',')\\n if self._is_field_name():\\n name == self._read_token().value\\n if name in names:\\n raise Exception(\\\"Repeated field name: %s\\\" % name)\\n names.append(name)\\n else:\\n raise Exception(\\\"field name expected, found %s\\\" %\\n self._next_token().value)\\n self._read_token('}')\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55843e0a9f4a7fe56c2087de43caa3c4\",\n \"score\": \"0.5160062\",\n \"text\": \"def field_names(self):\\r\\n return self._names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25d221ff14221b5be396cb2fd14b695f\",\n \"score\": \"0.51470417\",\n \"text\": \"def get_field_names(self):\\n\\n field_names = set()\\n for f in self.fields:\\n try:\\n field_names.add(unquote(f[\\\"name\\\"]))\\n except KeyError:\\n pass\\n field_names.discard(\\\"\\\")\\n return field_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c14e57dbd84db2ec879f1e1adf15d5f\",\n \"score\": \"0.5139172\",\n \"text\": \"def list(self) -> list:\\n labels = []\\n for k, v in self._labels.items():\\n labels.append(\\\"{}={}\\\".format(k, v))\\n\\n #TODO: return names\\n containers = self.client.containers.list(filters={\\\"label\\\": labels})\\n return list(map(lambda s: s.name, containers))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c2df00f269215d05e90947cd79bb3f\",\n \"score\": \"0.51312125\",\n \"text\": \"def export_field_names(self) -> List[str]:\\n return [f.name for f in self.fields.all()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7aae64056ae801052747746a5423abd\",\n \"score\": \"0.5128673\",\n \"text\": \"def fields(self):\\r\\n try:\\r\\n self._field_name_cache\\r\\n except AttributeError:\\r\\n self._fill_fields_cache()\\r\\n return self._field_name_cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7d6dc8c87f29a148679a18694ce2684\",\n \"score\": \"0.51274216\",\n \"text\": \"def get_field_names(self):\\n\\n return {f[\\\"name\\\"] for f in self.fields}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a797639a31c6fabc6d35cc0ef6e6484\",\n \"score\": \"0.5123486\",\n \"text\": \"def get_versioned_field_names(self, version_number):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b77852ac9831875ec06d5fe55dde409\",\n \"score\": \"0.5094264\",\n \"text\": \"def get_field_names(self):\\n field_names = []\\n for field in self.data_fields.all():\\n field_names.append(field.name)\\n return field_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa3d8bbaa16f7920fd4f9fcc1da8b28c\",\n \"score\": \"0.50611734\",\n \"text\": \"def get_column_names_list(record_info_in: sdk.RecordInfo) -> List[str]:\\n return [field.name for field in record_info_in]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"704297f96eac44cd01a88ca465d5b805\",\n \"score\": \"0.5060997\",\n \"text\": \"def field_names(self):\\n return frozenset(self._children.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64563f859bbb1a7450773db6425e8bd\",\n \"score\": \"0.5054078\",\n \"text\": \"def list(self, container_name: str) -> None:\\n try:\\n res = requests.get(self.url + 'list', json={'container_name': container_name})\\n if res.status_code == 200:\\n self.logger.info(f\\\"Listing files of container {container_name}\\\")\\n return json.loads(res.content)\\n else:\\n return None\\n except ConnectionError as ce:\\n raise ce\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3dfb9b4a47eed06de1d9963222288d2\",\n \"score\": \"0.5046736\",\n \"text\": \"def get_field_names(self):\\n return self.fields.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23b83640bca611ba5973965e550f962\",\n \"score\": \"0.50379735\",\n \"text\": \"def get_container_name(self, container_id):\\n if not os.path.isdir(self.containersdir):\\n return []\\n link_list = []\\n for fname in os.listdir(self.containersdir):\\n container = self.containersdir + \\\"/\\\" + fname\\n if os.path.islink(container):\\n real_container = os.readlink(container)\\n if os.path.basename(real_container) == container_id:\\n link_list.append(fname)\\n return link_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4636b4e0dcd5bfaf02de8b0ee0c445e3\",\n \"score\": \"0.5035241\",\n \"text\": \"def fieldsNames_get(self,recordset):\\r\\n\\t\\tif recordset.upper().startswith('SELECT'):\\r\\n\\t\\t\\trs=self.__open_recordset(self.conn, recordset, maxrecords=1)\\r\\n\\t\\telse:\\r\\n\\t\\t\\trs=self.__open_recordset(self.conn, '['+recordset+']', maxrecords=1)\\r\\n\\r\\n\\t\\tif rs.Fields.Count>0:\\r\\n\\t\\t\\tfields=[rs.Fields.Item(item).Name for item in range(rs.Fields.Count)]\\r\\n\\t\\telse:\\r\\n\\t\\t\\tfields=[]\\r\\n\\t\\treturn fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f25ecf9257d7170423361e610a667f\",\n \"score\": \"0.50169486\",\n \"text\": \"def get_fields(self, field_name):\\r\\n if field_name not in self.fields:\\r\\n raise OGRException('invalid field name: %s' % field_name)\\r\\n return [feat.get(field_name) for feat in self]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d34c0f7d6e4e777f87562f000824e1f\",\n \"score\": \"0.4993697\",\n \"text\": \"def get_fieldnames_from_cursor(cursor: Cursor) -> List[str]:\\n return [i[0] for i in cursor.description]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3d15cdef20f82411536b5fcbf3cacc\",\n \"score\": \"0.49833426\",\n \"text\": \"def fields(self):\\r\\n return [force_text(capi.get_field_name(capi.get_field_defn(self._ldefn, i)),\\r\\n self._ds.encoding, strings_only=True)\\r\\n for i in xrange(self.num_fields)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2836b21ba3be6d217c40e9a0be9a63\",\n \"score\": \"0.49688077\",\n \"text\": \"def _extract_field_names(self, fields, allow_string=True):\\n\\n names = []\\n for item in fields:\\n if self._is_valid_field(item):\\n names.append(item.key)\\n elif allow_string is True and isinstance(item, str) \\\\\\n and self._is_valid_method(item):\\n names.append(item)\\n else:\\n message = 'Provided field [{field}] is not a valid value. ' \\\\\\n 'it must be a column attribute{sign} ' \\\\\\n 'expression level hybrid property{end}'\\n\\n if allow_string is True:\\n message = message.format(\\n field=str(item), sign=',',\\n end=' or a string representing a method name of [{admin}] class.')\\n else:\\n message = message.format(field=str(item), sign=' or',\\n end='.')\\n\\n raise InvalidListFieldError(message.format(admin=self))\\n\\n return tuple(names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24c7585e363e44d0434d30c329dbd1e\",\n \"score\": \"0.49683803\",\n \"text\": \"def field_list(cls) -> List[str]:\\n return list(attr.fields_dict(cls).keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f6299e85d77d37267f451d0f8648195\",\n \"score\": \"0.495515\",\n \"text\": \"def _to_fields(d: DictAny) -> List[str]:\\n cs: List[DictAny] = d[\\\"meta\\\"][\\\"view\\\"][\\\"columns\\\"]\\n\\n # written this way to get type annotations to work\\n def f(x: DictAny) -> str:\\n sx: str = x['name']\\n return sx\\n\\n return list(map(f, cs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"574c75a4ad74382c72ae9e5f91447525\",\n \"score\": \"0.49546686\",\n \"text\": \"def container_image_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"container_image_names\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a079a0ce17f9ad49ef4125b8aff75ab\",\n \"score\": \"0.49508816\",\n \"text\": \"def stored_field_names(self):\\r\\n \\r\\n bn = self._by_name\\r\\n return [name for name in self._names if bn[name].stored]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f72b6899c69e686da1f2003d0a10b4cc\",\n \"score\": \"0.49477538\",\n \"text\": \"def key_field_name_list_get(self):\\n return list(self.key_dict.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f44857f2954aef14200b01f3bdce23\",\n \"score\": \"0.49375585\",\n \"text\": \"def get_names(collection):\\n return [element.name for element in collection]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1ea66213ccf40c7c8ec94541b1078fb\",\n \"score\": \"0.49365887\",\n \"text\": \"def fields(self):\\n return [self._fields[name] for name in self.fieldNames]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8638226e6ca75fe8bb4d4eb091341832\",\n \"score\": \"0.49363112\",\n \"text\": \"def field_names(self, record_type=\\\"\\\"):\\n return [f[\\\"name\\\"] for f in self.record_types[record_type][\\\"fields\\\"]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06107a2cdf60711c2ff205daad75e3a5\",\n \"score\": \"0.49352884\",\n \"text\": \"def fieldnames(self):\\n return self._fieldnames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fc69c4893af6b3663f692a42e03452b\",\n \"score\": \"0.49313736\",\n \"text\": \"def get_canonical_container_name(container):\\n return sorted(container['Names'], key=lambda name: len(name))[0][1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ca31ef9e8cbb6928657eed4cbea6060\",\n \"score\": \"0.48984218\",\n \"text\": \"def containers(self, module_name):\\n\\t\\tbase, name = split_module_name(module_name)\\n\\n\\t\\t# the printer modules does not have the same name scheme\\n\\t\\tif module_name == 'shares/print':\\n\\t\\t\\tbase = 'printers'\\n\\n\\t\\tself._read_directories()\\n\\n\\t\\tif self.directory is None:\\n\\t\\t\\treturn []\\n\\t\\treturn map(lambda x: {'id': x, 'label': ldap_dn2path(x)}, self.directory.info.get(base, []))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bec166b06411e731bed78a486883042f\",\n \"score\": \"0.48848704\",\n \"text\": \"def get_field_list(self):\\n return list(self.table_format_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1918484db22ccaff5f158a9c670452d1\",\n \"score\": \"0.48847437\",\n \"text\": \"def fields(self):\\r\\n try:\\r\\n return self._fields[self.tname]\\r\\n except KeyError:\\r\\n # introspect the field names from the database\\r\\n # by relying on the DB API 2.0\\r\\n # NB: we cannot trust the ordering in the Django model\\r\\n curs = connections[self.alias].cursor()\\r\\n curs.execute('select * from %s where 1=0' % self.tname)\\r\\n names = self._fields[self.tname] = [\\r\\n r[0] for r in curs.description if r[0] != 'id']\\r\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e066b142abbdaa5a587ebabadc21915\",\n \"score\": \"0.48470128\",\n \"text\": \"def get_field_names(self):\\n key_fields = [ d[0] for d in self.fpkey.fields ]\\n data_fields = [ d[0] for d in self.fpdata.fields ]\\n return key_fields + data_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44ff81bc789d7aee7edef6b560353f0\",\n \"score\": \"0.48232073\",\n \"text\": \"def get_field_names(table):\\n return [field['name'] for field in get_db().execute(f'SELECT name FROM {FIELDS + get_table_id(table)}').fetchall()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"899305a9f751ae70d7a33b5600f4746a\",\n \"score\": \"0.47798315\",\n \"text\": \"def list_fields(self):\\n _require_metadata(gnats.MINIMAL_METADATA)\\n return list(self.ordered_fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da861e81945237457eaed0a3ff7571d6\",\n \"score\": \"0.47780028\",\n \"text\": \"def get_field_names(self):\\n return sorted(self.column_name_describer.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c4c389c1b8081ba8e278620cc39858\",\n \"score\": \"0.47687584\",\n \"text\": \"def fields(cls) -> List[str]:\\n fields = attr.fields(cls)\\n field_types = [\\n (f.name, innermost_type(f.type)) for f in fields if f.name != \\\"gid\\\"\\n ]\\n field_names = [\\n [f\\\"{name}.{f}\\\" for f in typ.fields()]\\n if issubclass(typ, _HasFields)\\n else [name]\\n for (name, typ) in field_types\\n ]\\n return [name for names in field_names for name in names]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72667bc449108aded84cbcafc14597fe\",\n \"score\": \"0.47650254\",\n \"text\": \"def _list_field_names(self, field_type):\\n fields = self._properties.get('indexedField', {})\\n return fields.get(field_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b09a9db0023271ff3cce904fceae0e09\",\n \"score\": \"0.47543573\",\n \"text\": \"def fields(self):\\n return self.field_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a8bf83e5d8353fb269facfffe1d9f9\",\n \"score\": \"0.4749665\",\n \"text\": \"def fields(self) -> t.List[str]:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06b4eb3cb3fd1ecb3f1e60574510dad1\",\n \"score\": \"0.47387376\",\n \"text\": \"def list_collection_fields(self, environment_id, collection_id, **kwargs):\\n\\n if environment_id is None:\\n raise ValueError('environment_id must be provided')\\n if collection_id is None:\\n raise ValueError('collection_id must be provided')\\n\\n headers = {}\\n if 'headers' in kwargs:\\n headers.update(kwargs.get('headers'))\\n sdk_headers = get_sdk_headers('discovery', 'V1',\\n 'list_collection_fields')\\n headers.update(sdk_headers)\\n\\n params = {'version': self.version}\\n\\n url = '/v1/environments/{0}/collections/{1}/fields'.format(\\n *self._encode_path_vars(environment_id, collection_id))\\n response = self.request(\\n method='GET',\\n url=url,\\n headers=headers,\\n params=params,\\n accept_json=True)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c323dd4c958bbb31f175a4ea32a98404\",\n \"score\": \"0.4738311\",\n \"text\": \"def fields(self):\\n\\n if hasattr(self, '__parameters__'):\\n return self.__parameters__\\n else:\\n return [f'f{i}' for i in self.keys()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3975d8dac6769204bb26cdba3ace7719\",\n \"score\": \"0.47211117\",\n \"text\": \"def get_network_container_hostnames(name):\\n for network in get_networks():\\n if network[\\\"Name\\\"] == name:\\n return [get_container_hostname(container) for container in network[\\\"Containers\\\"]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"499acedfa5db0e6d0724a7a7620e9479\",\n \"score\": \"0.47182256\",\n \"text\": \"def get_parameter_names(diagnostic_fields: List[List[str]]) -> List[List[str]]:\\n parameter_names = []\\n for condition in diagnostic_fields:\\n if isinstance(condition, list):\\n parameter_names.append(get_parameter_names(condition))\\n elif is_variable(condition):\\n parameter_names.append(condition)\\n return parameter_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40fba1fc77f5cea111df8942e072ae9\",\n \"score\": \"0.47160146\",\n \"text\": \"def _string_helper(self, field_name): # pylint: disable=g-ambiguous-str-annotation\\n result = [\\\"{} {}\\\".format(str(self.node), str(field_name))]\\n for k, v in self._children.items():\\n recursive = v._string_helper(k) # pylint: disable=protected-access\\n result.extend([\\\" {}\\\".format(x) for x in recursive])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d12959f5bd0d6a8af770d946d3c9bcb\",\n \"score\": \"0.47127327\",\n \"text\": \"def _get_dates_from_fields(self):\\n\\n if not self._fields_container:\\n return []\\n\\n dates = []\\n # Widgets are arranged in the reverse order of their addition.\\n for field in reversed(self._fields_container.children):\\n try:\\n date = datetime.datetime.strptime(field.text, \\\"%d/%m/%Y\\\").date()\\n except ValueError:\\n date = None\\n dates.append(date)\\n\\n return dates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eca8a0bdc4cc6aa80b7a640dd671a0c\",\n \"score\": \"0.47007495\",\n \"text\": \"def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:\\n from .planner_container_type import PlannerContainerType\\n\\n from .planner_container_type import PlannerContainerType\\n\\n fields: Dict[str, Callable[[Any], None]] = {\\n \\\"containerId\\\": lambda n : setattr(self, 'container_id', n.get_str_value()),\\n \\\"@odata.type\\\": lambda n : setattr(self, 'odata_type', n.get_str_value()),\\n \\\"type\\\": lambda n : setattr(self, 'type', n.get_enum_value(PlannerContainerType)),\\n \\\"url\\\": lambda n : setattr(self, 'url', n.get_str_value()),\\n }\\n return fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340c5011c1971fe81abd7d8ab3651ba7\",\n \"score\": \"0.4667802\",\n \"text\": \"def fieldnames(self):\\n fieldnames = {} # sets are unordered\\n for row in self.rows:\\n for field in row:\\n fieldnames[field] = True\\n return list(fieldnames.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ca6b7bfdfdbc0d80d69b191611a1481\",\n \"score\": \"0.46639836\",\n \"text\": \"def tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a3989ca8336b4ac5811f4c12b9894c\",\n \"score\": \"0.46563387\",\n \"text\": \"def get_containers_list(self, dir_only=True):\\n containers_list = []\\n if not os.path.isdir(self.containersdir):\\n return []\\n for fname in os.listdir(self.containersdir):\\n container_dir = self.containersdir + '/' + fname\\n if os.path.isdir(container_dir):\\n try:\\n filep = open(container_dir + \\\"/imagerepo.name\\\", 'r')\\n except (IOError, OSError):\\n reponame = \\\"\\\"\\n else:\\n reponame = filep.read()\\n filep.close()\\n if dir_only:\\n containers_list.append(container_dir)\\n elif not os.path.islink(container_dir):\\n names = self.get_container_name(fname)\\n if not names:\\n names = \\\"\\\"\\n containers_list.append((fname, reponame, str(names)))\\n return containers_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f79f4914c26655b3a3e72b6b5129e7\",\n \"score\": \"0.46369\",\n \"text\": \"def get_split_names(self):\\n try:\\n return [split.name for split in self.get_all()]\\n except Exception:\\n _LOGGER.error('Error getting split names from storage')\\n _LOGGER.debug('Error: ', exc_info=True)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da11509e1da3a03ab4800f720bc7a9de\",\n \"score\": \"0.4634459\",\n \"text\": \"def name_get(self):\\n result = []\\n for document in self:\\n if document.version:\\n name = '[%s] %s'%(document.version, document.name)\\n else: \\n name = document.name\\n \\n result.append((document.id, name))\\n \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49c2e57520127645e0075b6b0029a0b4\",\n \"score\": \"0.46329495\",\n \"text\": \"def _get_field_names(reader: IO) -> Sequence[str]:\\n csv_reader = csv.DictReader(reader)\\n next(csv_reader)\\n reader.seek(0)\\n return csv_reader.fieldnames or []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3660cc3d64c393055d4ac42e3b877e8f\",\n \"score\": \"0.4630384\",\n \"text\": \"def get_fields(ms_name):\\n logger.debug(\\\"Getting antenna names\\\")\\n\\n subname = \\\"::\\\".join((ms_name, \\\"FIELD\\\"))\\n field_subtab = list(xm.xds_from_table(subname))\\n field_subtab = field_subtab[0]\\n field_names = field_subtab.NAME\\n\\n logger.debug(f\\\"Fields found: {', '.join(field_names.values)}\\\")\\n return field_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e2bf9854325734e655c6fe8dad3c739\",\n \"score\": \"0.46236715\",\n \"text\": \"def dependencies(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ca2acf5377bdb047a8b663c874b75c\",\n \"score\": \"0.46212012\",\n \"text\": \"def _get_user_field_names(self):\\n field_names = []\\n internal_fields = (\\\"left\\\", \\\"right\\\", \\\"tree_id\\\", \\\"level\\\")\\n for field in self._meta.fields:\\n if (field.name not in internal_fields) and (not isinstance(field, AutoField)) and (not field.primary_key):\\n field_names.append(field.name)\\n return field_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab96239137f7598c22ea2ed46b559b2\",\n \"score\": \"0.46098047\",\n \"text\": \"def list_fields(table):\\r\\n if not table:\\r\\n return []\\r\\n cols = table.__table__.columns if hasattr(table, '__table__') else table.columns\\r\\n return [f.name for f in cols]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cb28afef9eb9d2da7ce3083fc7e4c48\",\n \"score\": \"0.46097603\",\n \"text\": \"def field_names(cls):\\n return [field.name for field in cls()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e676c72e977e1d12726b12347010650\",\n \"score\": \"0.46061674\",\n \"text\": \"def _GetToListFields(self, get_fields=None):\\n if get_fields:\\n list_fields = set()\\n for field in get_fields:\\n list_fields.add('items/' + field)\\n return list_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf73010cff289c4920cac47f74bb3a2\",\n \"score\": \"0.4604988\",\n \"text\": \"def container_name(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"containerName\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1fe3030e55145e0e75c85e0b470c3a5\",\n \"score\": \"0.46005386\",\n \"text\": \"def __init__(self, field_list, field_container, *args, **kwargs):\\r\\n super(DynamicForm, self).__init__(*args, **kwargs)\\r\\n\\r\\n self.field_structure = []\\r\\n\\r\\n for field_name in field_list:\\r\\n field_data = getattr(field_container, field_name)[1]\\r\\n\\r\\n field_object = getattr(forms.fields, field_data['type'])(**field_data['kwargs'])\\r\\n self.fields[field_name] = field_object\\r\\n\\r\\n field_data['field_name'] = field_name\\r\\n self.field_structure.append(field_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc90e0b264c2830b135d06667b3634bc\",\n \"score\": \"0.45973927\",\n \"text\": \"def fld_lst(type):\\n lst = []\\n if isinstance(type, dict):\\n return list(type.keys())\\n elif isinstance(type, list):\\n for i in range(1, len(type) + 1):\\n lst.append(\\\"element \\\" + str(i))\\n return lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d47eaac5c428fd666c6b79881ef1b66\",\n \"score\": \"0.45942882\",\n \"text\": \"def get_signature_fields(interview_metadata_dict):\\r\\n signature_fields = []\\r\\n for field_dict in (interview_metadata_dict.get('built_in_fields_used',[]) + interview_metadata_dict.get('fields',[])):\\r\\n field = map_names(field_dict.get('variable',''))\\r\\n if field.endswith('.signature'):\\r\\n signature_fields.append(field)\\r\\n return signature_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd0478bf7c2cea9e76455ff40a8f67f\",\n \"score\": \"0.4587273\",\n \"text\": \"def flatten_container_info(self, inner_ip):\\n def iter_container(tg):\\n for g in tg:\\n for d in g['data']['containerStatuses']:\\n c = {'name': d['name'],\\n 'image': d['image'],\\n 'status': d['status'].lower(),\\n 'container_id': d['containerID']}\\n yield c\\n taskgroups = self.get_unit_info(inner_ip, fields='data,namespace').get('data') or []\\n containers = sorted([i for i in iter_container(taskgroups)],\\n key=lambda x: constants.DockerStatusOrdering.get(\\n x['status'], constants.DockerStatusDefaultOrder))\\n return containers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1dee9b7b5498c9877ec8ba5dbdf9fc9\",\n \"score\": \"0.45822173\",\n \"text\": \"def get_field_name_order(field_option_list):\\n\\n field_name_list = [None] * len(field_option_list)\\n while not all(field_name_list):\\n for idx, field_option in enumerate(field_option_list):\\n if len(field_option) == 1:\\n found_name = field_option.pop()\\n field_name_list[idx] = found_name\\n for field_set in field_option_list:\\n field_set.discard(found_name)\\n break\\n\\n return field_name_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"335a0e40733d20c568f2211893f67242\",\n \"score\": \"0.4575407\",\n \"text\": \"def get_all_field_names(self):\\r\\n try:\\r\\n cache = self._name_map\\r\\n except AttributeError:\\r\\n cache = self.init_name_map()\\r\\n names = sorted(cache.keys())\\r\\n # Internal-only names end with \\\"+\\\" (symmetrical m2m related names being\\r\\n # the main example). Trim them.\\r\\n return [val for val in names if not val.endswith('+')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e70dd224a19862e060ab3b2ef1410db9\",\n \"score\": \"0.4568496\",\n \"text\": \"def test_schema_sql_field_list(self):\\n sql_fields = BQTestSchema.get_sql_field_names()\\n self.assertEqual('descr, timestamp, nested', sql_fields)\\n\\n sql_fields = BQTestSchema.get_sql_field_names(exclude_fields=['timestamp'])\\n self.assertEqual('descr, nested', sql_fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"212711c27e5a6607162d499c7949a51a\",\n \"score\": \"0.45502537\",\n \"text\": \"def get_fields(self, field_name: str) -> Optional[PropertyValues]:\\n for p in self.properties:\\n if p.name == field_name:\\n return p.values\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95b9ae9153f14cf90dae683977ade3e8\",\n \"score\": \"0.45465776\",\n \"text\": \"def list_containers():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d778c5d87a68079760c520f0f65fa8c\",\n \"score\": \"0.45192477\",\n \"text\": \"def list_names(self):\\n return self.child_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ff30b92abf478bba51988c5f5f550f4\",\n \"score\": \"0.45170188\",\n \"text\": \"def get_list_fields(self):\\n return self.fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7386c2c08d3ec1ca914444f07634040d\",\n \"score\": \"0.45169434\",\n \"text\": \"def get_field_ids_by_name(self, name_regex):\\n d = {k: v for k, v in self._get_id_to_name_dict().items() if name_regex.match(v) is not None}\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"417fcc89d4af8adfbb5d16facc1a5543\",\n \"score\": \"0.45124117\",\n \"text\": \"def getContainerName(ctx, containerFlavor):\\n return getattr(ctx.cf.ocp.containers, containerFlavor).name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce030cf1a66b95ea5ba9759c1b2a34f5\",\n \"score\": \"0.45090052\",\n \"text\": \"def _string_helper(self, field_name: path.Step) -> Sequence[str]:\\n result = [\\\"{} {}\\\".format(str(self.node), str(field_name))]\\n for k, v in self._children.items():\\n recursive = v._string_helper(k) # pylint: disable=protected-access\\n result.extend([\\\" {}\\\".format(x) for x in recursive])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0762fbc266f3c0da9136fea60d69961c\",\n \"score\": \"0.44986236\",\n \"text\": \"def _list_containers(self):\\n response = self._session.get(\\\"http+unix://%2Fvar%2Frun%2Fdocker.sock/containers/json?all=1\\\")\\n if not response.ok:\\n raise Exception(\\\"Couldn't get Containers list. Check if your docker daemon alive or not\\\")\\n\\n strio = self.stringIO(response.text)\\n json_response = self.json.load(strio)\\n lst_containers = [str(container[\\\"Names\\\"])[3:-2] for container in json_response]\\n return lst_containers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85b097ce3af3f1fcd437792f757f3e58\",\n \"score\": \"0.44954124\",\n \"text\": \"def get_list(self):\\n\\n if self.mode == 'history':\\n container_list = self.dopq.history\\n elif self.mode == 'enqueued':\\n container_list = self.dopq.container_list\\n else:\\n raise ValueError('invalid mode of operation: {}'.format(self.mode))\\n\\n if len(container_list) > self.max_containers:\\n container_list = container_list[:self.max_containers]\\n\\n return container_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a2e65a55f6d8a915330cf30b44d59e3\",\n \"score\": \"0.44941473\",\n \"text\": \"def getFields(context, field_names=None):\\n pairs = []\\n if context.Schema(): # is archetype\\n schema = context.Schema()\\n fields = schema.fields()\\n if field_names:\\n for field_name in field_names:\\n val = None\\n pairs.append(schema[field_name].getName())\\n val = schema[field_name].get(context)\\n if isinstance(val, tuple) or isinstance(val, list):\\n val = iterToTags(val)\\n elif str(val).find('\\\\n') != -1:\\n val = newlinesToTags(val)\\n pairs.append(val)\\n else:\\n for field in fields:\\n val = None\\n pairs.append(field.getName())\\n val = str(field.get(context))\\n if val.find('\\\\n') != -1:\\n val = newlinesToTags(val)\\n pairs.append(val)\\n return pairs\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":191,"cells":{"query_id":{"kind":"string","value":"3d86c1af870937aabf2ff3595bf01c6e"},"query":{"kind":"string","value":"Return package metadata information."},"positive_passages":{"kind":"list like","value":[{"docid":"5c54becc64e16a019dde1941f01c5892","score":"0.0","text":"def get_classifiers():\n return [\n 'Private :: Do Not Upload',\n 'Development Status :: 4 - Beta',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3.7',\n 'Intended Audience :: System Administrators',\n 'License :: OSI Approved :: The MIT License (MIT)',\n ]","title":""}],"string":"[\n {\n \"docid\": \"5c54becc64e16a019dde1941f01c5892\",\n \"score\": \"0.0\",\n \"text\": \"def get_classifiers():\\n return [\\n 'Private :: Do Not Upload',\\n 'Development Status :: 4 - Beta',\\n 'Operating System :: POSIX :: Linux',\\n 'Programming Language :: Python',\\n 'Programming Language :: Python :: 3.7',\\n 'Intended Audience :: System Administrators',\\n 'License :: OSI Approved :: The MIT License (MIT)',\\n ]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ba35cfa5e926725340a889cc98973d81","score":"0.8002718","text":"def get_package_metadata(self, id=None, name=None, version=None, architecture=None):\n url = self._package_url(id, name, version, architecture)\n return self._get_request(url)","title":""},{"docid":"dfc47df194b466847bdda6c4beb22510","score":"0.76922953","text":"def get_metadata(distribution_name='imgclas'):\n\n pkg = pkg_resources.get_distribution(distribution_name)\n meta = {\n 'Name': None,\n 'Version': None,\n 'Summary': None,\n 'Home-page': None,\n 'Author': None,\n 'Author-email': None,\n 'License': None,\n }\n\n for line in pkg.get_metadata_lines(\"PKG-INFO\"):\n for par in meta:\n if line.startswith(par):\n _, value = line.split(\": \", 1)\n meta[par] = value\n\n # Update information with Docker info (provided as 'CONTAINER_*' env variables)\n r = re.compile(\"^CONTAINER_(.*?)$\")\n container_vars = list(filter(r.match, list(os.environ)))\n for var in container_vars:\n meta[var.capitalize()] = os.getenv(var)\n\n return meta","title":""},{"docid":"0ac9aa610a51c65a9debacae3f4fb6f0","score":"0.76084256","text":"def getPackageInfo( self ):\n pkgData = {}\n\n pkgInfo = get_distribution('keggimporter').get_metadata('PKG-INFO')\n\n rawData = pkgInfo.split(\"\\n\")\n rawData.pop()\n\n reData = re.compile('^(.*?):\\s(.*)')\n for data in rawData:\n records = reData.search( data ) \n\n key = records.group(1)\n value = records.group(2)\n\n pkgData.update( { key: value } ) \n\n return pkgData","title":""},{"docid":"68cf248a7e87a034c3b46db7dce258b3","score":"0.7495511","text":"def package_info(self) -> None:\n self.package_info_cmake()\n self.package_info_env()","title":""},{"docid":"1007f0110f55beb346aa74b6bcd4880a","score":"0.7289923","text":"def package_info(self, pref):\n return self.for_package_file(pref, CONANINFO)","title":""},{"docid":"6a7e3aa84b21d2936df412f0ad5b7aac","score":"0.7280466","text":"def get_metadata(self):\n pass","title":""},{"docid":"ff36622adedfe06276943c2a74d2017f","score":"0.72115207","text":"def sdist_metadata(self):\n if self.is_wheel:\n raise TypeError(\"Requirement is not a source distribution!\")\n return self.pip_requirement.pkg_info()","title":""},{"docid":"c1254a764867fcda711b4061940be3a8","score":"0.71683055","text":"def get_metadata(self):\n return self.metadata","title":""},{"docid":"dd9932c4b48b7eba32c0650cbbd0c014","score":"0.7132883","text":"def metadata(self):\n return self.metadata_at_version(self.max_version)","title":""},{"docid":"fe57db91318a21e3e84b585ebaeea23f","score":"0.71302515","text":"def getMetadata(self):\n return self._metadata","title":""},{"docid":"a0102f653530de87a0e764b126b64a1b","score":"0.7083335","text":"def get_metadata(self):\n return self.metadata","title":""},{"docid":"313d484fd58e8569b09855fa18f677d7","score":"0.7056691","text":"def metadata(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"0b7711f833b4a1fc8e95e6f563e74c1c","score":"0.7032857","text":"def metadata(self) -> Dict[str, Any]:\n pass","title":""},{"docid":"0b7711f833b4a1fc8e95e6f563e74c1c","score":"0.7032857","text":"def metadata(self) -> Dict[str, Any]:\n pass","title":""},{"docid":"1eaf5eed246b8d49b257ac5a678b0695","score":"0.7015979","text":"def get_metadata(session, name):\n resp = session.get(\"https://pypi.python.org/pypi/{}/json\".format(name))\n if resp.status_code == 200:\n return resp.json()\n return {}","title":""},{"docid":"d938306b829603f77ccce0d944cb1c1c","score":"0.6989091","text":"def get_metadata(self):\n return self._metadata","title":""},{"docid":"6bab4d88f7bf555ff963f51d1281f055","score":"0.6965754","text":"def get_metadata(self):\n return self.config.metadata","title":""},{"docid":"58434e76703ecfd4c6a3b9449382b19c","score":"0.6961348","text":"def get_metadata(self):\n return self.client._perform_json(\"GET\", \"/projects/%s/metadata\" % self.project_key)","title":""},{"docid":"7b3059f61caf561f1c7c9e54676c6e2f","score":"0.6959908","text":"def _get_package_metadata(\n self, submitted_metadata, file_name, file_size, hashes, urls, contents\n ):\n\n def _get_filename_from_urls(submitted_metadata, urls):\n file_name = \"\"\n if not urls:\n logging.warning(f\"No URLs provided for: {submitted_metadata}\")\n for url in urls:\n _file_name = os.path.basename(url)\n if not file_name:\n file_name = _file_name\n else:\n if file_name != _file_name:\n logging.warning(\n f\"Received multiple URLs with different file names; will use the first URL (file name '{file_name}'): {submitted_metadata}\"\n )\n return file_name\n\n file_name_from_url = _get_filename_from_urls(submitted_metadata, urls)\n if not file_name:\n file_name = file_name_from_url\n\n now = str(datetime.utcnow())\n metadata = {\n \"type\": \"package\",\n \"package\": {\n \"version\": \"0.1\",\n \"file_name\": file_name,\n \"created_time\": now,\n \"updated_time\": now,\n \"size\": file_size,\n \"hashes\": hashes,\n \"contents\": contents or None,\n },\n \"_upload_status\": \"uploaded\",\n }\n return metadata","title":""},{"docid":"3b265be06c1a51c782d13c4a662992f0","score":"0.6948777","text":"def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"3b265be06c1a51c782d13c4a662992f0","score":"0.6948777","text":"def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"93bf09352497e0598c30511875afee00","score":"0.69407064","text":"def get_package_metadata(\n self,\n package_name: str,\n package_version: str,\n repository_name: Optional[str] = None,\n ) -> PythonPackagePayload:\n if repository_name:\n repo_config = self._config.get_repository_configuration(repository_name)\n else:\n repo_config = self._config.default_repository_configuration\n\n repo = self._repo_factory.get_repository(repo_config)\n\n metadata = repo.show(package_name, package_version)\n\n return PythonPackagePayload(\n repository_base_url=repo_config.base_url,\n package_name=package_name,\n package_version=package_version,\n package_metadata=metadata,\n )","title":""},{"docid":"b06810d8c09ac0aa1fc53ae4bea9e99f","score":"0.6907203","text":"def metadata(self) -> Optional[Any]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"b06810d8c09ac0aa1fc53ae4bea9e99f","score":"0.6907203","text":"def metadata(self) -> Optional[Any]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"4f55338024553b3bff3d71189a7c55bb","score":"0.6883791","text":"def get_metadata():\n message = {\n 'dasher_version': __version__,\n 'repo': 'https://github.com/lsst-sqre/ltd-dasher'\n }\n return jsonify(message), 200","title":""},{"docid":"8a5ded6e33a74f60e3b56eff81dbdf82","score":"0.6876241","text":"def metadata(self):\n pass","title":""},{"docid":"8a5ded6e33a74f60e3b56eff81dbdf82","score":"0.6876241","text":"def metadata(self):\n pass","title":""},{"docid":"123ac441393e251d1eb9282dc67a2e23","score":"0.68461096","text":"def getPackageInfo(self, pid):\n info = self.core.files.getPackageInfo(pid)\n if not info:\n raise PackageDoesNotExists(pid)\n return info","title":""},{"docid":"fb584ab32b0d36795f375e3605fd35fe","score":"0.6830067","text":"def metadata():\n return MetadataConfig","title":""},{"docid":"6eed88201e21210965c066fb1d60dfb2","score":"0.6826499","text":"def get_meta_information():\n raise NotImplementedError()","title":""},{"docid":"1b4dabdee30365ee52fd7bf2830f6ef5","score":"0.6817423","text":"def global_metadata():\n from ctapipe import __version__ as ctapipe_version\n from ctapipe_io_lst import __version__ as ctapipe_io_lst_version\n from .. import __version__ as lstchain_version\n\n metadata = MetaData()\n metadata.LSTCHAIN_VERSION = lstchain_version\n metadata.CTAPIPE_VERSION = ctapipe_version\n metadata.CTAPIPE_IO_LST_VERSION = ctapipe_io_lst_version\n metadata.CONTACT = \"LST Consortium\"\n\n return metadata","title":""},{"docid":"6916c01dffbfac6104f7b478a6ac5b10","score":"0.68164563","text":"def get_metadata(name):","title":""},{"docid":"6916c01dffbfac6104f7b478a6ac5b10","score":"0.68164563","text":"def get_metadata(name):","title":""},{"docid":"781fe4aa2c8086a4b8b0685f5fb5801c","score":"0.6803053","text":"def metadata(self):\n raise NotImplementedError()","title":""},{"docid":"801021b776b04da66dda1cbc70f648f1","score":"0.67808557","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"801021b776b04da66dda1cbc70f648f1","score":"0.67808557","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"801021b776b04da66dda1cbc70f648f1","score":"0.67808557","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"801021b776b04da66dda1cbc70f648f1","score":"0.67808557","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"9d0e426fd621e83779148306521a2154","score":"0.6772988","text":"def info(self):\n return self._driver.GetMetadata()","title":""},{"docid":"036fa730fe1c1a9fc4e36edc04999122","score":"0.6763207","text":"def metadata_dictionary(self) -> Dict[str, MetadataValue]:\n meta = self.metadata\n data: Dict[str, MetadataValue] = {\n # identify release\n \"name\": self.safe_name,\n \"version\": meta.version,\n # file content\n \"filetype\": self.filetype,\n \"pyversion\": self.python_version,\n # additional meta-data\n \"metadata_version\": meta.metadata_version,\n \"summary\": meta.summary,\n \"home_page\": meta.home_page,\n \"author\": meta.author,\n \"author_email\": meta.author_email,\n \"maintainer\": meta.maintainer,\n \"maintainer_email\": meta.maintainer_email,\n \"license\": meta.license,\n \"description\": meta.description,\n \"keywords\": meta.keywords,\n \"platform\": meta.platforms,\n \"classifiers\": meta.classifiers,\n \"download_url\": meta.download_url,\n \"supported_platform\": meta.supported_platforms,\n \"comment\": self.comment,\n \"sha256_digest\": self.sha2_digest,\n # PEP 314\n \"provides\": meta.provides,\n \"requires\": meta.requires,\n \"obsoletes\": meta.obsoletes,\n # Metadata 1.2\n \"project_urls\": meta.project_urls,\n \"provides_dist\": meta.provides_dist,\n \"obsoletes_dist\": meta.obsoletes_dist,\n \"requires_dist\": meta.requires_dist,\n \"requires_external\": meta.requires_external,\n \"requires_python\": meta.requires_python,\n # Metadata 2.1\n \"provides_extras\": meta.provides_extras,\n \"description_content_type\": meta.description_content_type,\n # Metadata 2.2\n \"dynamic\": meta.dynamic,\n }\n\n if self.gpg_signature is not None:\n data[\"gpg_signature\"] = self.gpg_signature\n\n # FIPS disables MD5 and Blake2, making the digest values None. Some package\n # repositories don't allow null values, so this only sends non-null values.\n # See also: https://github.com/pypa/twine/issues/775\n if self.md5_digest:\n data[\"md5_digest\"] = self.md5_digest\n\n if self.blake2_256_digest:\n data[\"blake2_256_digest\"] = self.blake2_256_digest\n\n return data","title":""},{"docid":"832edcf06b78d2aa52937b3a0b67659f","score":"0.6761121","text":"def get_package_metadata(f: typing.BinaryIO,\n workspace_pkg_meta: dict = {},\n ) -> PackageMetadata:\n cargo_toml = tomllib.load(f)\n\n if \"package\" not in cargo_toml and \"workspace\" in cargo_toml:\n raise WorkspaceCargoTomlError(\n \"Specified directory seems to be a workspace root, please run \"\n \"pycargoebuild on one of its members instead: \"\n f\"{' '.join(cargo_toml['workspace']['members'])}\")\n\n pkg_meta = cargo_toml[\"package\"]\n _get_meta_key = functools.partial(get_meta_key,\n pkg_meta=pkg_meta,\n workspace_pkg_meta=workspace_pkg_meta)\n\n pkg_license = _get_meta_key(\"license\")\n if pkg_license is not None:\n pkg_license = cargo_to_spdx(pkg_license)\n\n pkg_version = _get_meta_key(\"version\")\n if pkg_version is None:\n raise ValueError(f\"No version found in {f.name}\")\n\n return PackageMetadata(\n name=pkg_meta[\"name\"],\n version=pkg_version,\n license=pkg_license,\n license_file=_get_meta_key(\"license-file\"),\n description=_get_meta_key(\"description\"),\n homepage=_get_meta_key(\"homepage\"))","title":""},{"docid":"43cce22e0e8fd0700f5089403ce7ec38","score":"0.6746312","text":"def get_metadata(package_name, cache_dir=None, max_age=ONE_DAY):\n url = '{base_url}/{package_name}/json'.format(\n base_url=PYPI_SERVER, package_name=package_name)\n if cache_dir:\n metadata = get_cached_metadata(package_name, cache_dir, max_age)\n if metadata is not None:\n if metadata == {}:\n headers = email.message_from_string('\\n\\n')\n raise urllib.error.HTTPError(url, 404, 'Not Found (cached)',\n headers, StringIO())\n return metadata\n try:\n metadata = get_json(url)\n except urllib.error.HTTPError as e:\n if e.code == 404 and cache_dir:\n put_cached_metadata(package_name, cache_dir, {})\n raise\n if cache_dir:\n put_cached_metadata(package_name, cache_dir, metadata)\n return metadata","title":""},{"docid":"2e27056fe7bc95a31b5f9354a91d3f28","score":"0.67442584","text":"def rknn_package_info():\n import pkg_resources\n toolkit = pkg_resources.working_set.by_key.get('rknn-toolkit', None)\n toolkit = pkg_resources.working_set.by_key.get('rknn-toolkit2', toolkit)\n if toolkit is None:\n return dict(name=None, version=None)\n else:\n return dict(name=toolkit.project_name, version=toolkit.version)","title":""},{"docid":"d6fdacbbdaef58841093f2e9cc230265","score":"0.6739671","text":"def extract_interesting_information(metadata):\n info = metadata['info']\n return dict(version=info['version'],\n supports=extract_py_versions(info['classifiers']),\n sdist_url=extract_sdist_url(metadata))","title":""},{"docid":"35c8411e074b5b92479bf09665f7f714","score":"0.6736538","text":"def get_metadata(self):\n api_call = self.api + \"/api/catalog/\" + self.catalog_id\n\n response = get_response(api_call)\n return json.load(response)","title":""},{"docid":"b2df54058c20ded92130188b46ab1d32","score":"0.6729516","text":"def get_metadata(self):\n raise NotImplementedError(self.get_metadata)","title":""},{"docid":"12e0cddb41202eac9708968055360b8c","score":"0.67191714","text":"def getMetadata(self):\n return self.jonesmeta","title":""},{"docid":"722f168872a75b677f9d83722ddfc9c5","score":"0.6719154","text":"def Metadata(self):\n return self.get('Metadata')","title":""},{"docid":"30f9c03c99fda600dde75c1608981b66","score":"0.6705416","text":"def metadata(self) -> Optional[str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"72c543ce036eabbc7b3f9792c5adb0a0","score":"0.6690269","text":"def getMetaDataDict(self):\n return self._meta_data","title":""},{"docid":"dd768878be679a81d51219a42f09179b","score":"0.6674757","text":"def info(self):\n file = apt.apt_pkg.Config.FindFile(\"Dir::Cache::pkgcache\")\n return dict(\n timestamp = os.path.getmtime(file),\n values = [\n dict(name = \"installedsize\", desc = \"installed size\"),\n dict(name = \"packagesize\", desc = \"package size\")\n ])","title":""},{"docid":"59e0d35b76afcb6e4168e5e731d9a749","score":"0.6673888","text":"def metadata(self):\n return self.file_source.metadata()","title":""},{"docid":"c1d7e03021f66cb913849eec069dc2dd","score":"0.6672617","text":"def get_metadata(config):\n version = 'python-%s' % __version__\n ip_address, hostname = _get_hostname_and_ip(config)\n return SdkMetadata(version, hostname, ip_address)","title":""},{"docid":"0a7e2f3469eaa3b2a0e39f631b778e93","score":"0.6670984","text":"def generate_metadata(self):\n if len(self.files) > 0:\n this_netcdf = self.files[0].file_location\n ds, df_summ = self.files[0].process_netcdf(netcdf=this_netcdf)\n self.license = ds.attrs['license']\n self.title = ds.attrs['title']\n self.creator = ds.attrs['creator_name']\n self.creator_email = ds.attrs['creator_email']\n self.institution = ds.attrs['institution']\n self.aknowledgements = ds.attrs['acknowledgement']\n self.feature_type = ds.attrs['featureType']\n self.summary = ds.attrs['summary']\n self.conventions = ds.attrs['Conventions']\n self.naming_authority = ds.attrs['naming_authority']\n if self.date is None:\n from datetime import datetime\n self.date = datetime.fromordinal(\n datetime.toordinal(datetime(\n year=self.year, month=1, day=1)\n ) + self.doy - 1\n )\n self.month = self.date.month\n self.day = self.date.day\n self.parse_files()\n self.save()\n slack.chat.post_message(\n '#mpalatower',\n self.slack(),\n username='Mpala Tower',\n icon_emoji=':package:')\n return self\n else:\n return \"No files found for %d, day of year %d\" \\\n % (self.year, self.doy)","title":""},{"docid":"3836962bcb2d08f66f30d5971b5b5b98","score":"0.66429526","text":"def metadata(self):\r\n if self._metadata is None:\r\n from ._metadata import MetadataManager\r\n self._metadata = MetadataManager(gis=self._gis)\r\n return self._metadata","title":""},{"docid":"e07437fa75e2f2fde49582ffd07d79dd","score":"0.6639232","text":"def metadata(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"e07437fa75e2f2fde49582ffd07d79dd","score":"0.6639232","text":"def metadata(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"5532678da1f9f0b68eae561323b14148","score":"0.6638328","text":"def cpp_info(self):\n return self.cpp.package","title":""},{"docid":"10e8e8ac44bc646a901bda3f07df0fe8","score":"0.6635488","text":"def metadata(self):\n return self.properties.get(\"metadata\", None)","title":""},{"docid":"110972e40e3fea17a4d2bbe71077718d","score":"0.6622566","text":"def get_metadata(self):\n return {\"file_url\": self.file_name, \"file_date\": self.file_date}","title":""},{"docid":"83c70623b8a045c06ec71d9adc6c883c","score":"0.6593214","text":"def metadata(self) -> typing.Optional[OrderedDict]:\n return self._metadata","title":""},{"docid":"d3860fd5b4d8c15e8482c5b4b6ddb3d0","score":"0.6559086","text":"def metadata(self) -> \"Metadata\":\n return self._metadata","title":""},{"docid":"f4776f7ac12a82ee05fdc51eff7d0c73","score":"0.65374756","text":"def metadata(self):\n if self._metadata is None:\n from ._metadata import MetadataManager\n self._metadata = MetadataManager(gis=self._gis)\n return self._metadata","title":""},{"docid":"a1000d0703702469bdef8f3b250c6913","score":"0.6504106","text":"def metadata(self):\n\n json_response = self.my_http_request('GET', '/metadata')\n diction = json.loads(json_response)\n return diction[\"name\"], diction[\"thumbnail\"], diction[\"vector\"], diction[\"video\"], diction[\"size\"]","title":""},{"docid":"44c567e74857cc487f0122e205712e91","score":"0.64919186","text":"def metadata(self, name):\n return self.get_member(str(name)).metadata","title":""},{"docid":"1a26f464e9a2d88110da87941ec84150","score":"0.6489349","text":"def info(self):\n return (self._title, self._version, self._descr)","title":""},{"docid":"98be9a01b954a98ad4ef20a3c5d45cfc","score":"0.64881223","text":"def metadata(self):\n return self.db.metadata","title":""},{"docid":"abe75cd8bae6a9e4cf34f9a16c5012e2","score":"0.64858735","text":"def get_package_info(self, pref, remote):\n pref = self._resolve_latest_pref(pref, remote)\n return self._call_remote(remote, \"get_package_info\", pref), pref","title":""},{"docid":"66cd491c59f84623372a3ae377715738","score":"0.64818406","text":"def get_metadata_for(name):","title":""},{"docid":"27a90178be0933965bdf6417d2077104","score":"0.6446554","text":"def get_metadata(root: str) -> Metadata:\n name = dataset_name(root)\n dataset = load_dataset(name)\n return Metadata(coordinates=dataset.coordinates, labels=dataset.labels)","title":""},{"docid":"b974e3130720f51fd4a3dbafb54ea2d4","score":"0.64415383","text":"def metadata(self):\n return {}","title":""},{"docid":"f895baa107fe0e546f9c66feb46f420b","score":"0.6436223","text":"def metadata(self) -> Metadata:\n return self._metadata","title":""},{"docid":"f2c709a4f61c7e97e9b2ce9798ce4d92","score":"0.6422315","text":"def get_metadata_from_pack(pack_name):\n\n with open(find_pack_path(pack_name)[0], \"r\") as pack_metadata:\n pack_meta_file_content = json.loads(pack_metadata.read())\n\n return pack_meta_file_content","title":""},{"docid":"e709a77b8134766aa38b74043bdf665a","score":"0.6422189","text":"def dist_metainfo_dict(dist):\n\n distmetadata = dist.get_metadata(\"PKG-INFO\")\n ret = Parser(policy=default).parsestr(distmetadata)\n return ret","title":""},{"docid":"93f34a7e0c0001967fe8ecc29f3bb44e","score":"0.64209324","text":"def get_metadata():\n bd = config_drive()\n if bd:\n return bd.get_metadata()\n return {}","title":""},{"docid":"1f333089fc8b35d099aa62ca67751ad1","score":"0.6407901","text":"def metadata(self) -> Optional[ArbitraryMetadataMapping]:\n return self._metadata","title":""},{"docid":"77c29f8b92f2acfa2ef5cd264ae92d55","score":"0.6402572","text":"def about_dict(repo_root, package):\n package_init = os.path.join(repo_root, package, \"__init__.py\")\n spec = iutil.spec_from_file_location(\"package\", package_init)\n package = iutil.module_from_spec(spec)\n spec.loader.exec_module(package)\n\n return package.__dict__","title":""},{"docid":"831de987a235ee68af5a2e06d32fa661","score":"0.638527","text":"def get_metadata(self):\n return self.variable_metadata","title":""},{"docid":"4ea2558bad54c5de2e4ccfca695fe8d8","score":"0.6373588","text":"def metadata(zclient, module):\n url_path = \"settings/modules/\" + module\n return zclient._get(url_path, {})","title":""},{"docid":"f23fe4b1d36ac241574346230763ca8a","score":"0.6357783","text":"def package(self):\n\t\treturn self.get('package')","title":""},{"docid":"4c178dca1d0a34176f11fc3658cb8e5e","score":"0.6349725","text":"def get_metadata(self):\n raw_metadata = self.redis.get(self.key)\n\n if raw_metadata is None:\n self.logger.debug(\"No metadata found for: {} {}\".format(self.name, self.version))\n return None\n\n metadata = loads(raw_metadata)\n\n if Version.FILENAME not in metadata:\n self.logger.debug(\"Incomplete metadata for: {} {}\".format(self.name, self.version))\n return None\n\n return metadata","title":""},{"docid":"88a26f1bdf7d478cbc3a27b55f7fc19e","score":"0.6346251","text":"def metadata(self):\r\n meta = {k: getattr(self, k) for k in self.valid_metadata}\r\n meta[\"filename\"] = self.filename\r\n meta[\"time_points\"] = tuple(self.time_points)\r\n meta.update(self.compression_params)\r\n\r\n # Ordered dictionary by keys is easiest to inspect\r\n return OrderedDict(sorted(meta.items(), key=lambda t: t[0]))","title":""},{"docid":"b2ae1ae01f0267fab58f9391c9a5572f","score":"0.63295174","text":"def assert_basic_meta_data(self, package_path):\n _LOGGER.debug(\"Begin asserting metadata for %s\", package_path)\n\n raw_found_metadata = self._pkg_inspector.get_meta_data(package_path)\n _LOGGER.debug(\"Found raw metadata: %s\", str(raw_found_metadata))\n\n try:\n self._pkg_inspector.validate_meta_data(raw_found_metadata)\n except AssertionError as error:\n pytest.fail(\"Package at {} failed metadata validation: {}\".format(package_path, error))\n else:\n expected_metadata = {\n key: self._expected_values[key] for key in ['project_name', 'package_version']\n }\n found_metadata = {\n 'project_name': raw_found_metadata.project_name,\n 'package_version': raw_found_metadata.version,\n }\n testfixtures.compare(expected_metadata, found_metadata)\n\n _LOGGER.debug(\"Finished asserting metadata for %s\", package_path)","title":""},{"docid":"fc7a534f45634c86dcdda48cafe3c5d8","score":"0.63201094","text":"def fetch_metadata():\n\tproducts_releases = fetch_products_and_releases()\n\t# print(pretty(products_releases))\n\tproducts_releases_versions, projects_tarballs = fetch_projects_versions_and_tarballs(products_releases)\n\t# print(pretty(products_releases_versions))\n\tprojects_tarballs = fetch_unreferenced_projects(projects_tarballs)\n\tprojects_tarballs = fetch_unreferenced_versions(projects_tarballs)\n\n\tprint(pretty(projects_tarballs))\n\tprint(pretty(products_releases_versions))\n\n\twith open('projects_tarballs.json', 'w') as outfile:\n\t json.dump(projects_tarballs, outfile, sort_keys=True,indent=2)\n\n\twith open('products_releases_versions.json', 'w') as outfile:\n\t json.dump(products_releases_versions, outfile,sort_keys=True,indent=2)","title":""},{"docid":"0accc00d15254fd933a8c397c21bc547","score":"0.63126266","text":"def default_metadata(pkg_info):\n if pkg_info.is_dependency:\n return dict(_default_dep_metadata)\n\n if pkg_info.name in _shipped_metadata:\n metadata = dict(_default_pkg_metadata)\n metadata.update(_shipped_metadata.get(pkg_info.name))\n\n if not pkg_info.has_possible_overrides(simple=False):\n metadata[\"version\"] = \"Sublime %s\" % sublime.version()\n\n if pkg_info.name == \"User\":\n metadata[\"version\"] = \"Unversioned\"\n\n if pkg_info.name not in _closed_default_packages:\n metadata[\"url\"] = \"https://github.com/sublimehq/Packages\"\n\n return metadata\n\n return dict(_default_pkg_metadata)","title":""},{"docid":"b9e11f4ceb1002b1dcddfab323ddc71e","score":"0.63126","text":"def metadata():\n\treturn {\n\t\t\"name\": \"Data Type\",\n\t\t\"description\": \"Defines a type of plug-in that defines a data type, so that different components are certain they can interact with each other via this data type.\",\n\t\t\"version\": 1,\n\t\t\"dependencies\": {},\n\n\t\t\"type\": { #This is a \"plug-in type\" plug-in.\n\t\t\t\"type_name\": \"data\",\n\t\t\t\"api\": datatype.data,\n\t\t\t\"validate_metadata\": validate_metadata\n\t\t}\n\t}","title":""},{"docid":"dfa3f03046a1025b0aed0a8109d953a2","score":"0.6308763","text":"def parse_package_meta(package_path, package, require=True):\n # TODO: Allow passing in full model path and only require one argument\n # instead of path and package name. This lets us avoid passing in an awkward\n # empty string in spacy.load() if user supplies full model path.\n location = package_path / package / 'meta.json'\n if location.is_file():\n return read_json(location)\n elif require:\n raise IOError(\"Could not read meta.json from %s\" % location)\n else:\n return None","title":""},{"docid":"1f3fb9e0a972db552788b8b81692123d","score":"0.6305958","text":"def get_metadata(data):\n return _read_spss(data, metadata_only = True)[1]","title":""},{"docid":"1b3e3b4f39add93e562073fc235b1d56","score":"0.6303926","text":"def _read_metadata_element(self):\n if self.manifest is not None:\n metadata = self.manifest.xpath('/pkg:package/pkg:metadata', namespaces=ns)\n\n if metadata is not None:\n return metadata[0]","title":""},{"docid":"e4cd6c4d38b31a60b45e5d4378a61fe3","score":"0.6296391","text":"def info(repo=False):\n if repo:\n return False\n return (\"pkg_info\", \"-aoQ\")","title":""},{"docid":"7bafe305358beaf2759e470f28f97a32","score":"0.62835026","text":"def export_getCompatibleMetadata(self,metaDict):\n return fcDB.dmeta.getCompatibleMetadata(metaDict, self.getRemoteCredentials())","title":""},{"docid":"ada907d6b390f9e7b4abcd8cf9b26dbc","score":"0.6273457","text":"def metadata(self) -> Optional['outputs.ServiceTemplateMetadata']:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"28e2471c6dedce5d119dd1e3efba9cba","score":"0.62575465","text":"def get_info(self):\n return self.common.get_info()","title":""},{"docid":"6805d9231865395d1d2830bd4261f43a","score":"0.6234828","text":"def metadata(self):\n information = {'competition': self.competition,\n 'wordnet_version': self.info['wn_version'],\n 'answers_downloaded_from': self.info[\n 'answers_downloaded_from'],\n 'date_of_downloading': self.info[\n 'answers_downloaded_at'],\n 'paper': self.info['paper'],\n 'bibtex': self.info['bibtex']}\n\n # df = pd.DataFrame.from_dict({'categories': list(information.keys()),\n # 'values': list(information.values())})\n\n # display(df)\n wordnet_utils.print_dict(information)","title":""},{"docid":"6db141588facaab15029599d2fca2f1d","score":"0.62202054","text":"def metadata(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"6db141588facaab15029599d2fca2f1d","score":"0.62202054","text":"def metadata(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"50621d403d46ad868502580266c8cb08","score":"0.6218983","text":"def GetMetadata(self, rpc, request, response):\n raise NotImplementedError","title":""},{"docid":"50b9ac5cfee9a834121015d353b2ce21","score":"0.62061596","text":"def meta(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\n return pulumi.get(self, \"meta\")","title":""},{"docid":"9f3f733cf94e951aecf44bec44a9f109","score":"0.6199301","text":"def GetMetadata(self):\n basic_data = {\n 'spark_service': self.SERVICE_NAME,\n 'spark_svc_cloud': self.CLOUD,\n 'spark_cluster_id': self.cluster_id,\n 'spark_cluster_zone': getattr(self, 'zone', None) or 'unknown'\n }\n # TODO grab this information for user_managed clusters.\n if not self.user_managed:\n basic_data.update({'num_workers': str(self.spec.worker_group.vm_count),\n 'worker_machine_type':\n str(self.spec.worker_group.vm_spec.machine_type)})\n return basic_data","title":""},{"docid":"531b6d11b6df849a0e3b204584ffbed0","score":"0.6188202","text":"def getInternalMetadata(self, **kwargs):\n return {'omeinfo': self._omeinfo}","title":""},{"docid":"980b08b8218793f4694888b53954bdb7","score":"0.61798024","text":"def getInfo():","title":""}],"string":"[\n {\n \"docid\": \"ba35cfa5e926725340a889cc98973d81\",\n \"score\": \"0.8002718\",\n \"text\": \"def get_package_metadata(self, id=None, name=None, version=None, architecture=None):\\n url = self._package_url(id, name, version, architecture)\\n return self._get_request(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc47df194b466847bdda6c4beb22510\",\n \"score\": \"0.76922953\",\n \"text\": \"def get_metadata(distribution_name='imgclas'):\\n\\n pkg = pkg_resources.get_distribution(distribution_name)\\n meta = {\\n 'Name': None,\\n 'Version': None,\\n 'Summary': None,\\n 'Home-page': None,\\n 'Author': None,\\n 'Author-email': None,\\n 'License': None,\\n }\\n\\n for line in pkg.get_metadata_lines(\\\"PKG-INFO\\\"):\\n for par in meta:\\n if line.startswith(par):\\n _, value = line.split(\\\": \\\", 1)\\n meta[par] = value\\n\\n # Update information with Docker info (provided as 'CONTAINER_*' env variables)\\n r = re.compile(\\\"^CONTAINER_(.*?)$\\\")\\n container_vars = list(filter(r.match, list(os.environ)))\\n for var in container_vars:\\n meta[var.capitalize()] = os.getenv(var)\\n\\n return meta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ac9aa610a51c65a9debacae3f4fb6f0\",\n \"score\": \"0.76084256\",\n \"text\": \"def getPackageInfo( self ):\\n pkgData = {}\\n\\n pkgInfo = get_distribution('keggimporter').get_metadata('PKG-INFO')\\n\\n rawData = pkgInfo.split(\\\"\\\\n\\\")\\n rawData.pop()\\n\\n reData = re.compile('^(.*?):\\\\s(.*)')\\n for data in rawData:\\n records = reData.search( data ) \\n\\n key = records.group(1)\\n value = records.group(2)\\n\\n pkgData.update( { key: value } ) \\n\\n return pkgData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68cf248a7e87a034c3b46db7dce258b3\",\n \"score\": \"0.7495511\",\n \"text\": \"def package_info(self) -> None:\\n self.package_info_cmake()\\n self.package_info_env()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1007f0110f55beb346aa74b6bcd4880a\",\n \"score\": \"0.7289923\",\n \"text\": \"def package_info(self, pref):\\n return self.for_package_file(pref, CONANINFO)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a7e3aa84b21d2936df412f0ad5b7aac\",\n \"score\": \"0.7280466\",\n \"text\": \"def get_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff36622adedfe06276943c2a74d2017f\",\n \"score\": \"0.72115207\",\n \"text\": \"def sdist_metadata(self):\\n if self.is_wheel:\\n raise TypeError(\\\"Requirement is not a source distribution!\\\")\\n return self.pip_requirement.pkg_info()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1254a764867fcda711b4061940be3a8\",\n \"score\": \"0.71683055\",\n \"text\": \"def get_metadata(self):\\n return self.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9932c4b48b7eba32c0650cbbd0c014\",\n \"score\": \"0.7132883\",\n \"text\": \"def metadata(self):\\n return self.metadata_at_version(self.max_version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe57db91318a21e3e84b585ebaeea23f\",\n \"score\": \"0.71302515\",\n \"text\": \"def getMetadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0102f653530de87a0e764b126b64a1b\",\n \"score\": \"0.7083335\",\n \"text\": \"def get_metadata(self):\\n return self.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"313d484fd58e8569b09855fa18f677d7\",\n \"score\": \"0.7056691\",\n \"text\": \"def metadata(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7711f833b4a1fc8e95e6f563e74c1c\",\n \"score\": \"0.7032857\",\n \"text\": \"def metadata(self) -> Dict[str, Any]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7711f833b4a1fc8e95e6f563e74c1c\",\n \"score\": \"0.7032857\",\n \"text\": \"def metadata(self) -> Dict[str, Any]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eaf5eed246b8d49b257ac5a678b0695\",\n \"score\": \"0.7015979\",\n \"text\": \"def get_metadata(session, name):\\n resp = session.get(\\\"https://pypi.python.org/pypi/{}/json\\\".format(name))\\n if resp.status_code == 200:\\n return resp.json()\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d938306b829603f77ccce0d944cb1c1c\",\n \"score\": \"0.6989091\",\n \"text\": \"def get_metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bab4d88f7bf555ff963f51d1281f055\",\n \"score\": \"0.6965754\",\n \"text\": \"def get_metadata(self):\\n return self.config.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58434e76703ecfd4c6a3b9449382b19c\",\n \"score\": \"0.6961348\",\n \"text\": \"def get_metadata(self):\\n return self.client._perform_json(\\\"GET\\\", \\\"/projects/%s/metadata\\\" % self.project_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3059f61caf561f1c7c9e54676c6e2f\",\n \"score\": \"0.6959908\",\n \"text\": \"def _get_package_metadata(\\n self, submitted_metadata, file_name, file_size, hashes, urls, contents\\n ):\\n\\n def _get_filename_from_urls(submitted_metadata, urls):\\n file_name = \\\"\\\"\\n if not urls:\\n logging.warning(f\\\"No URLs provided for: {submitted_metadata}\\\")\\n for url in urls:\\n _file_name = os.path.basename(url)\\n if not file_name:\\n file_name = _file_name\\n else:\\n if file_name != _file_name:\\n logging.warning(\\n f\\\"Received multiple URLs with different file names; will use the first URL (file name '{file_name}'): {submitted_metadata}\\\"\\n )\\n return file_name\\n\\n file_name_from_url = _get_filename_from_urls(submitted_metadata, urls)\\n if not file_name:\\n file_name = file_name_from_url\\n\\n now = str(datetime.utcnow())\\n metadata = {\\n \\\"type\\\": \\\"package\\\",\\n \\\"package\\\": {\\n \\\"version\\\": \\\"0.1\\\",\\n \\\"file_name\\\": file_name,\\n \\\"created_time\\\": now,\\n \\\"updated_time\\\": now,\\n \\\"size\\\": file_size,\\n \\\"hashes\\\": hashes,\\n \\\"contents\\\": contents or None,\\n },\\n \\\"_upload_status\\\": \\\"uploaded\\\",\\n }\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b265be06c1a51c782d13c4a662992f0\",\n \"score\": \"0.6948777\",\n \"text\": \"def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b265be06c1a51c782d13c4a662992f0\",\n \"score\": \"0.6948777\",\n \"text\": \"def metadata(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93bf09352497e0598c30511875afee00\",\n \"score\": \"0.69407064\",\n \"text\": \"def get_package_metadata(\\n self,\\n package_name: str,\\n package_version: str,\\n repository_name: Optional[str] = None,\\n ) -> PythonPackagePayload:\\n if repository_name:\\n repo_config = self._config.get_repository_configuration(repository_name)\\n else:\\n repo_config = self._config.default_repository_configuration\\n\\n repo = self._repo_factory.get_repository(repo_config)\\n\\n metadata = repo.show(package_name, package_version)\\n\\n return PythonPackagePayload(\\n repository_base_url=repo_config.base_url,\\n package_name=package_name,\\n package_version=package_version,\\n package_metadata=metadata,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06810d8c09ac0aa1fc53ae4bea9e99f\",\n \"score\": \"0.6907203\",\n \"text\": \"def metadata(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06810d8c09ac0aa1fc53ae4bea9e99f\",\n \"score\": \"0.6907203\",\n \"text\": \"def metadata(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f55338024553b3bff3d71189a7c55bb\",\n \"score\": \"0.6883791\",\n \"text\": \"def get_metadata():\\n message = {\\n 'dasher_version': __version__,\\n 'repo': 'https://github.com/lsst-sqre/ltd-dasher'\\n }\\n return jsonify(message), 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a5ded6e33a74f60e3b56eff81dbdf82\",\n \"score\": \"0.6876241\",\n \"text\": \"def metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a5ded6e33a74f60e3b56eff81dbdf82\",\n \"score\": \"0.6876241\",\n \"text\": \"def metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123ac441393e251d1eb9282dc67a2e23\",\n \"score\": \"0.68461096\",\n \"text\": \"def getPackageInfo(self, pid):\\n info = self.core.files.getPackageInfo(pid)\\n if not info:\\n raise PackageDoesNotExists(pid)\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb584ab32b0d36795f375e3605fd35fe\",\n \"score\": \"0.6830067\",\n \"text\": \"def metadata():\\n return MetadataConfig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eed88201e21210965c066fb1d60dfb2\",\n \"score\": \"0.6826499\",\n \"text\": \"def get_meta_information():\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b4dabdee30365ee52fd7bf2830f6ef5\",\n \"score\": \"0.6817423\",\n \"text\": \"def global_metadata():\\n from ctapipe import __version__ as ctapipe_version\\n from ctapipe_io_lst import __version__ as ctapipe_io_lst_version\\n from .. import __version__ as lstchain_version\\n\\n metadata = MetaData()\\n metadata.LSTCHAIN_VERSION = lstchain_version\\n metadata.CTAPIPE_VERSION = ctapipe_version\\n metadata.CTAPIPE_IO_LST_VERSION = ctapipe_io_lst_version\\n metadata.CONTACT = \\\"LST Consortium\\\"\\n\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6916c01dffbfac6104f7b478a6ac5b10\",\n \"score\": \"0.68164563\",\n \"text\": \"def get_metadata(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6916c01dffbfac6104f7b478a6ac5b10\",\n \"score\": \"0.68164563\",\n \"text\": \"def get_metadata(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"781fe4aa2c8086a4b8b0685f5fb5801c\",\n \"score\": \"0.6803053\",\n \"text\": \"def metadata(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801021b776b04da66dda1cbc70f648f1\",\n \"score\": \"0.67808557\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801021b776b04da66dda1cbc70f648f1\",\n \"score\": \"0.67808557\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801021b776b04da66dda1cbc70f648f1\",\n \"score\": \"0.67808557\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"801021b776b04da66dda1cbc70f648f1\",\n \"score\": \"0.67808557\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0e426fd621e83779148306521a2154\",\n \"score\": \"0.6772988\",\n \"text\": \"def info(self):\\n return self._driver.GetMetadata()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036fa730fe1c1a9fc4e36edc04999122\",\n \"score\": \"0.6763207\",\n \"text\": \"def metadata_dictionary(self) -> Dict[str, MetadataValue]:\\n meta = self.metadata\\n data: Dict[str, MetadataValue] = {\\n # identify release\\n \\\"name\\\": self.safe_name,\\n \\\"version\\\": meta.version,\\n # file content\\n \\\"filetype\\\": self.filetype,\\n \\\"pyversion\\\": self.python_version,\\n # additional meta-data\\n \\\"metadata_version\\\": meta.metadata_version,\\n \\\"summary\\\": meta.summary,\\n \\\"home_page\\\": meta.home_page,\\n \\\"author\\\": meta.author,\\n \\\"author_email\\\": meta.author_email,\\n \\\"maintainer\\\": meta.maintainer,\\n \\\"maintainer_email\\\": meta.maintainer_email,\\n \\\"license\\\": meta.license,\\n \\\"description\\\": meta.description,\\n \\\"keywords\\\": meta.keywords,\\n \\\"platform\\\": meta.platforms,\\n \\\"classifiers\\\": meta.classifiers,\\n \\\"download_url\\\": meta.download_url,\\n \\\"supported_platform\\\": meta.supported_platforms,\\n \\\"comment\\\": self.comment,\\n \\\"sha256_digest\\\": self.sha2_digest,\\n # PEP 314\\n \\\"provides\\\": meta.provides,\\n \\\"requires\\\": meta.requires,\\n \\\"obsoletes\\\": meta.obsoletes,\\n # Metadata 1.2\\n \\\"project_urls\\\": meta.project_urls,\\n \\\"provides_dist\\\": meta.provides_dist,\\n \\\"obsoletes_dist\\\": meta.obsoletes_dist,\\n \\\"requires_dist\\\": meta.requires_dist,\\n \\\"requires_external\\\": meta.requires_external,\\n \\\"requires_python\\\": meta.requires_python,\\n # Metadata 2.1\\n \\\"provides_extras\\\": meta.provides_extras,\\n \\\"description_content_type\\\": meta.description_content_type,\\n # Metadata 2.2\\n \\\"dynamic\\\": meta.dynamic,\\n }\\n\\n if self.gpg_signature is not None:\\n data[\\\"gpg_signature\\\"] = self.gpg_signature\\n\\n # FIPS disables MD5 and Blake2, making the digest values None. Some package\\n # repositories don't allow null values, so this only sends non-null values.\\n # See also: https://github.com/pypa/twine/issues/775\\n if self.md5_digest:\\n data[\\\"md5_digest\\\"] = self.md5_digest\\n\\n if self.blake2_256_digest:\\n data[\\\"blake2_256_digest\\\"] = self.blake2_256_digest\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"832edcf06b78d2aa52937b3a0b67659f\",\n \"score\": \"0.6761121\",\n \"text\": \"def get_package_metadata(f: typing.BinaryIO,\\n workspace_pkg_meta: dict = {},\\n ) -> PackageMetadata:\\n cargo_toml = tomllib.load(f)\\n\\n if \\\"package\\\" not in cargo_toml and \\\"workspace\\\" in cargo_toml:\\n raise WorkspaceCargoTomlError(\\n \\\"Specified directory seems to be a workspace root, please run \\\"\\n \\\"pycargoebuild on one of its members instead: \\\"\\n f\\\"{' '.join(cargo_toml['workspace']['members'])}\\\")\\n\\n pkg_meta = cargo_toml[\\\"package\\\"]\\n _get_meta_key = functools.partial(get_meta_key,\\n pkg_meta=pkg_meta,\\n workspace_pkg_meta=workspace_pkg_meta)\\n\\n pkg_license = _get_meta_key(\\\"license\\\")\\n if pkg_license is not None:\\n pkg_license = cargo_to_spdx(pkg_license)\\n\\n pkg_version = _get_meta_key(\\\"version\\\")\\n if pkg_version is None:\\n raise ValueError(f\\\"No version found in {f.name}\\\")\\n\\n return PackageMetadata(\\n name=pkg_meta[\\\"name\\\"],\\n version=pkg_version,\\n license=pkg_license,\\n license_file=_get_meta_key(\\\"license-file\\\"),\\n description=_get_meta_key(\\\"description\\\"),\\n homepage=_get_meta_key(\\\"homepage\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43cce22e0e8fd0700f5089403ce7ec38\",\n \"score\": \"0.6746312\",\n \"text\": \"def get_metadata(package_name, cache_dir=None, max_age=ONE_DAY):\\n url = '{base_url}/{package_name}/json'.format(\\n base_url=PYPI_SERVER, package_name=package_name)\\n if cache_dir:\\n metadata = get_cached_metadata(package_name, cache_dir, max_age)\\n if metadata is not None:\\n if metadata == {}:\\n headers = email.message_from_string('\\\\n\\\\n')\\n raise urllib.error.HTTPError(url, 404, 'Not Found (cached)',\\n headers, StringIO())\\n return metadata\\n try:\\n metadata = get_json(url)\\n except urllib.error.HTTPError as e:\\n if e.code == 404 and cache_dir:\\n put_cached_metadata(package_name, cache_dir, {})\\n raise\\n if cache_dir:\\n put_cached_metadata(package_name, cache_dir, metadata)\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e27056fe7bc95a31b5f9354a91d3f28\",\n \"score\": \"0.67442584\",\n \"text\": \"def rknn_package_info():\\n import pkg_resources\\n toolkit = pkg_resources.working_set.by_key.get('rknn-toolkit', None)\\n toolkit = pkg_resources.working_set.by_key.get('rknn-toolkit2', toolkit)\\n if toolkit is None:\\n return dict(name=None, version=None)\\n else:\\n return dict(name=toolkit.project_name, version=toolkit.version)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6fdacbbdaef58841093f2e9cc230265\",\n \"score\": \"0.6739671\",\n \"text\": \"def extract_interesting_information(metadata):\\n info = metadata['info']\\n return dict(version=info['version'],\\n supports=extract_py_versions(info['classifiers']),\\n sdist_url=extract_sdist_url(metadata))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c8411e074b5b92479bf09665f7f714\",\n \"score\": \"0.6736538\",\n \"text\": \"def get_metadata(self):\\n api_call = self.api + \\\"/api/catalog/\\\" + self.catalog_id\\n\\n response = get_response(api_call)\\n return json.load(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2df54058c20ded92130188b46ab1d32\",\n \"score\": \"0.6729516\",\n \"text\": \"def get_metadata(self):\\n raise NotImplementedError(self.get_metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e0cddb41202eac9708968055360b8c\",\n \"score\": \"0.67191714\",\n \"text\": \"def getMetadata(self):\\n return self.jonesmeta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722f168872a75b677f9d83722ddfc9c5\",\n \"score\": \"0.6719154\",\n \"text\": \"def Metadata(self):\\n return self.get('Metadata')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f9c03c99fda600dde75c1608981b66\",\n \"score\": \"0.6705416\",\n \"text\": \"def metadata(self) -> Optional[str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c543ce036eabbc7b3f9792c5adb0a0\",\n \"score\": \"0.6690269\",\n \"text\": \"def getMetaDataDict(self):\\n return self._meta_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd768878be679a81d51219a42f09179b\",\n \"score\": \"0.6674757\",\n \"text\": \"def info(self):\\n file = apt.apt_pkg.Config.FindFile(\\\"Dir::Cache::pkgcache\\\")\\n return dict(\\n timestamp = os.path.getmtime(file),\\n values = [\\n dict(name = \\\"installedsize\\\", desc = \\\"installed size\\\"),\\n dict(name = \\\"packagesize\\\", desc = \\\"package size\\\")\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e0d35b76afcb6e4168e5e731d9a749\",\n \"score\": \"0.6673888\",\n \"text\": \"def metadata(self):\\n return self.file_source.metadata()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d7e03021f66cb913849eec069dc2dd\",\n \"score\": \"0.6672617\",\n \"text\": \"def get_metadata(config):\\n version = 'python-%s' % __version__\\n ip_address, hostname = _get_hostname_and_ip(config)\\n return SdkMetadata(version, hostname, ip_address)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7e2f3469eaa3b2a0e39f631b778e93\",\n \"score\": \"0.6670984\",\n \"text\": \"def generate_metadata(self):\\n if len(self.files) > 0:\\n this_netcdf = self.files[0].file_location\\n ds, df_summ = self.files[0].process_netcdf(netcdf=this_netcdf)\\n self.license = ds.attrs['license']\\n self.title = ds.attrs['title']\\n self.creator = ds.attrs['creator_name']\\n self.creator_email = ds.attrs['creator_email']\\n self.institution = ds.attrs['institution']\\n self.aknowledgements = ds.attrs['acknowledgement']\\n self.feature_type = ds.attrs['featureType']\\n self.summary = ds.attrs['summary']\\n self.conventions = ds.attrs['Conventions']\\n self.naming_authority = ds.attrs['naming_authority']\\n if self.date is None:\\n from datetime import datetime\\n self.date = datetime.fromordinal(\\n datetime.toordinal(datetime(\\n year=self.year, month=1, day=1)\\n ) + self.doy - 1\\n )\\n self.month = self.date.month\\n self.day = self.date.day\\n self.parse_files()\\n self.save()\\n slack.chat.post_message(\\n '#mpalatower',\\n self.slack(),\\n username='Mpala Tower',\\n icon_emoji=':package:')\\n return self\\n else:\\n return \\\"No files found for %d, day of year %d\\\" \\\\\\n % (self.year, self.doy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3836962bcb2d08f66f30d5971b5b5b98\",\n \"score\": \"0.66429526\",\n \"text\": \"def metadata(self):\\r\\n if self._metadata is None:\\r\\n from ._metadata import MetadataManager\\r\\n self._metadata = MetadataManager(gis=self._gis)\\r\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07437fa75e2f2fde49582ffd07d79dd\",\n \"score\": \"0.6639232\",\n \"text\": \"def metadata(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e07437fa75e2f2fde49582ffd07d79dd\",\n \"score\": \"0.6639232\",\n \"text\": \"def metadata(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5532678da1f9f0b68eae561323b14148\",\n \"score\": \"0.6638328\",\n \"text\": \"def cpp_info(self):\\n return self.cpp.package\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e8e8ac44bc646a901bda3f07df0fe8\",\n \"score\": \"0.6635488\",\n \"text\": \"def metadata(self):\\n return self.properties.get(\\\"metadata\\\", None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110972e40e3fea17a4d2bbe71077718d\",\n \"score\": \"0.6622566\",\n \"text\": \"def get_metadata(self):\\n return {\\\"file_url\\\": self.file_name, \\\"file_date\\\": self.file_date}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c70623b8a045c06ec71d9adc6c883c\",\n \"score\": \"0.6593214\",\n \"text\": \"def metadata(self) -> typing.Optional[OrderedDict]:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3860fd5b4d8c15e8482c5b4b6ddb3d0\",\n \"score\": \"0.6559086\",\n \"text\": \"def metadata(self) -> \\\"Metadata\\\":\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4776f7ac12a82ee05fdc51eff7d0c73\",\n \"score\": \"0.65374756\",\n \"text\": \"def metadata(self):\\n if self._metadata is None:\\n from ._metadata import MetadataManager\\n self._metadata = MetadataManager(gis=self._gis)\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1000d0703702469bdef8f3b250c6913\",\n \"score\": \"0.6504106\",\n \"text\": \"def metadata(self):\\n\\n json_response = self.my_http_request('GET', '/metadata')\\n diction = json.loads(json_response)\\n return diction[\\\"name\\\"], diction[\\\"thumbnail\\\"], diction[\\\"vector\\\"], diction[\\\"video\\\"], diction[\\\"size\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c567e74857cc487f0122e205712e91\",\n \"score\": \"0.64919186\",\n \"text\": \"def metadata(self, name):\\n return self.get_member(str(name)).metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a26f464e9a2d88110da87941ec84150\",\n \"score\": \"0.6489349\",\n \"text\": \"def info(self):\\n return (self._title, self._version, self._descr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98be9a01b954a98ad4ef20a3c5d45cfc\",\n \"score\": \"0.64881223\",\n \"text\": \"def metadata(self):\\n return self.db.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe75cd8bae6a9e4cf34f9a16c5012e2\",\n \"score\": \"0.64858735\",\n \"text\": \"def get_package_info(self, pref, remote):\\n pref = self._resolve_latest_pref(pref, remote)\\n return self._call_remote(remote, \\\"get_package_info\\\", pref), pref\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66cd491c59f84623372a3ae377715738\",\n \"score\": \"0.64818406\",\n \"text\": \"def get_metadata_for(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27a90178be0933965bdf6417d2077104\",\n \"score\": \"0.6446554\",\n \"text\": \"def get_metadata(root: str) -> Metadata:\\n name = dataset_name(root)\\n dataset = load_dataset(name)\\n return Metadata(coordinates=dataset.coordinates, labels=dataset.labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b974e3130720f51fd4a3dbafb54ea2d4\",\n \"score\": \"0.64415383\",\n \"text\": \"def metadata(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f895baa107fe0e546f9c66feb46f420b\",\n \"score\": \"0.6436223\",\n \"text\": \"def metadata(self) -> Metadata:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c709a4f61c7e97e9b2ce9798ce4d92\",\n \"score\": \"0.6422315\",\n \"text\": \"def get_metadata_from_pack(pack_name):\\n\\n with open(find_pack_path(pack_name)[0], \\\"r\\\") as pack_metadata:\\n pack_meta_file_content = json.loads(pack_metadata.read())\\n\\n return pack_meta_file_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e709a77b8134766aa38b74043bdf665a\",\n \"score\": \"0.6422189\",\n \"text\": \"def dist_metainfo_dict(dist):\\n\\n distmetadata = dist.get_metadata(\\\"PKG-INFO\\\")\\n ret = Parser(policy=default).parsestr(distmetadata)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f34a7e0c0001967fe8ecc29f3bb44e\",\n \"score\": \"0.64209324\",\n \"text\": \"def get_metadata():\\n bd = config_drive()\\n if bd:\\n return bd.get_metadata()\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f333089fc8b35d099aa62ca67751ad1\",\n \"score\": \"0.6407901\",\n \"text\": \"def metadata(self) -> Optional[ArbitraryMetadataMapping]:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c29f8b92f2acfa2ef5cd264ae92d55\",\n \"score\": \"0.6402572\",\n \"text\": \"def about_dict(repo_root, package):\\n package_init = os.path.join(repo_root, package, \\\"__init__.py\\\")\\n spec = iutil.spec_from_file_location(\\\"package\\\", package_init)\\n package = iutil.module_from_spec(spec)\\n spec.loader.exec_module(package)\\n\\n return package.__dict__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831de987a235ee68af5a2e06d32fa661\",\n \"score\": \"0.638527\",\n \"text\": \"def get_metadata(self):\\n return self.variable_metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ea2558bad54c5de2e4ccfca695fe8d8\",\n \"score\": \"0.6373588\",\n \"text\": \"def metadata(zclient, module):\\n url_path = \\\"settings/modules/\\\" + module\\n return zclient._get(url_path, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23fe4b1d36ac241574346230763ca8a\",\n \"score\": \"0.6357783\",\n \"text\": \"def package(self):\\n\\t\\treturn self.get('package')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c178dca1d0a34176f11fc3658cb8e5e\",\n \"score\": \"0.6349725\",\n \"text\": \"def get_metadata(self):\\n raw_metadata = self.redis.get(self.key)\\n\\n if raw_metadata is None:\\n self.logger.debug(\\\"No metadata found for: {} {}\\\".format(self.name, self.version))\\n return None\\n\\n metadata = loads(raw_metadata)\\n\\n if Version.FILENAME not in metadata:\\n self.logger.debug(\\\"Incomplete metadata for: {} {}\\\".format(self.name, self.version))\\n return None\\n\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88a26f1bdf7d478cbc3a27b55f7fc19e\",\n \"score\": \"0.6346251\",\n \"text\": \"def metadata(self):\\r\\n meta = {k: getattr(self, k) for k in self.valid_metadata}\\r\\n meta[\\\"filename\\\"] = self.filename\\r\\n meta[\\\"time_points\\\"] = tuple(self.time_points)\\r\\n meta.update(self.compression_params)\\r\\n\\r\\n # Ordered dictionary by keys is easiest to inspect\\r\\n return OrderedDict(sorted(meta.items(), key=lambda t: t[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ae1ae01f0267fab58f9391c9a5572f\",\n \"score\": \"0.63295174\",\n \"text\": \"def assert_basic_meta_data(self, package_path):\\n _LOGGER.debug(\\\"Begin asserting metadata for %s\\\", package_path)\\n\\n raw_found_metadata = self._pkg_inspector.get_meta_data(package_path)\\n _LOGGER.debug(\\\"Found raw metadata: %s\\\", str(raw_found_metadata))\\n\\n try:\\n self._pkg_inspector.validate_meta_data(raw_found_metadata)\\n except AssertionError as error:\\n pytest.fail(\\\"Package at {} failed metadata validation: {}\\\".format(package_path, error))\\n else:\\n expected_metadata = {\\n key: self._expected_values[key] for key in ['project_name', 'package_version']\\n }\\n found_metadata = {\\n 'project_name': raw_found_metadata.project_name,\\n 'package_version': raw_found_metadata.version,\\n }\\n testfixtures.compare(expected_metadata, found_metadata)\\n\\n _LOGGER.debug(\\\"Finished asserting metadata for %s\\\", package_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7a534f45634c86dcdda48cafe3c5d8\",\n \"score\": \"0.63201094\",\n \"text\": \"def fetch_metadata():\\n\\tproducts_releases = fetch_products_and_releases()\\n\\t# print(pretty(products_releases))\\n\\tproducts_releases_versions, projects_tarballs = fetch_projects_versions_and_tarballs(products_releases)\\n\\t# print(pretty(products_releases_versions))\\n\\tprojects_tarballs = fetch_unreferenced_projects(projects_tarballs)\\n\\tprojects_tarballs = fetch_unreferenced_versions(projects_tarballs)\\n\\n\\tprint(pretty(projects_tarballs))\\n\\tprint(pretty(products_releases_versions))\\n\\n\\twith open('projects_tarballs.json', 'w') as outfile:\\n\\t json.dump(projects_tarballs, outfile, sort_keys=True,indent=2)\\n\\n\\twith open('products_releases_versions.json', 'w') as outfile:\\n\\t json.dump(products_releases_versions, outfile,sort_keys=True,indent=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0accc00d15254fd933a8c397c21bc547\",\n \"score\": \"0.63126266\",\n \"text\": \"def default_metadata(pkg_info):\\n if pkg_info.is_dependency:\\n return dict(_default_dep_metadata)\\n\\n if pkg_info.name in _shipped_metadata:\\n metadata = dict(_default_pkg_metadata)\\n metadata.update(_shipped_metadata.get(pkg_info.name))\\n\\n if not pkg_info.has_possible_overrides(simple=False):\\n metadata[\\\"version\\\"] = \\\"Sublime %s\\\" % sublime.version()\\n\\n if pkg_info.name == \\\"User\\\":\\n metadata[\\\"version\\\"] = \\\"Unversioned\\\"\\n\\n if pkg_info.name not in _closed_default_packages:\\n metadata[\\\"url\\\"] = \\\"https://github.com/sublimehq/Packages\\\"\\n\\n return metadata\\n\\n return dict(_default_pkg_metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e11f4ceb1002b1dcddfab323ddc71e\",\n \"score\": \"0.63126\",\n \"text\": \"def metadata():\\n\\treturn {\\n\\t\\t\\\"name\\\": \\\"Data Type\\\",\\n\\t\\t\\\"description\\\": \\\"Defines a type of plug-in that defines a data type, so that different components are certain they can interact with each other via this data type.\\\",\\n\\t\\t\\\"version\\\": 1,\\n\\t\\t\\\"dependencies\\\": {},\\n\\n\\t\\t\\\"type\\\": { #This is a \\\"plug-in type\\\" plug-in.\\n\\t\\t\\t\\\"type_name\\\": \\\"data\\\",\\n\\t\\t\\t\\\"api\\\": datatype.data,\\n\\t\\t\\t\\\"validate_metadata\\\": validate_metadata\\n\\t\\t}\\n\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfa3f03046a1025b0aed0a8109d953a2\",\n \"score\": \"0.6308763\",\n \"text\": \"def parse_package_meta(package_path, package, require=True):\\n # TODO: Allow passing in full model path and only require one argument\\n # instead of path and package name. This lets us avoid passing in an awkward\\n # empty string in spacy.load() if user supplies full model path.\\n location = package_path / package / 'meta.json'\\n if location.is_file():\\n return read_json(location)\\n elif require:\\n raise IOError(\\\"Could not read meta.json from %s\\\" % location)\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3fb9e0a972db552788b8b81692123d\",\n \"score\": \"0.6305958\",\n \"text\": \"def get_metadata(data):\\n return _read_spss(data, metadata_only = True)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3e3b4f39add93e562073fc235b1d56\",\n \"score\": \"0.6303926\",\n \"text\": \"def _read_metadata_element(self):\\n if self.manifest is not None:\\n metadata = self.manifest.xpath('/pkg:package/pkg:metadata', namespaces=ns)\\n\\n if metadata is not None:\\n return metadata[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4cd6c4d38b31a60b45e5d4378a61fe3\",\n \"score\": \"0.6296391\",\n \"text\": \"def info(repo=False):\\n if repo:\\n return False\\n return (\\\"pkg_info\\\", \\\"-aoQ\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bafe305358beaf2759e470f28f97a32\",\n \"score\": \"0.62835026\",\n \"text\": \"def export_getCompatibleMetadata(self,metaDict):\\n return fcDB.dmeta.getCompatibleMetadata(metaDict, self.getRemoteCredentials())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ada907d6b390f9e7b4abcd8cf9b26dbc\",\n \"score\": \"0.6273457\",\n \"text\": \"def metadata(self) -> Optional['outputs.ServiceTemplateMetadata']:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e2471c6dedce5d119dd1e3efba9cba\",\n \"score\": \"0.62575465\",\n \"text\": \"def get_info(self):\\n return self.common.get_info()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6805d9231865395d1d2830bd4261f43a\",\n \"score\": \"0.6234828\",\n \"text\": \"def metadata(self):\\n information = {'competition': self.competition,\\n 'wordnet_version': self.info['wn_version'],\\n 'answers_downloaded_from': self.info[\\n 'answers_downloaded_from'],\\n 'date_of_downloading': self.info[\\n 'answers_downloaded_at'],\\n 'paper': self.info['paper'],\\n 'bibtex': self.info['bibtex']}\\n\\n # df = pd.DataFrame.from_dict({'categories': list(information.keys()),\\n # 'values': list(information.values())})\\n\\n # display(df)\\n wordnet_utils.print_dict(information)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db141588facaab15029599d2fca2f1d\",\n \"score\": \"0.62202054\",\n \"text\": \"def metadata(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db141588facaab15029599d2fca2f1d\",\n \"score\": \"0.62202054\",\n \"text\": \"def metadata(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50621d403d46ad868502580266c8cb08\",\n \"score\": \"0.6218983\",\n \"text\": \"def GetMetadata(self, rpc, request, response):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b9ac5cfee9a834121015d353b2ce21\",\n \"score\": \"0.62061596\",\n \"text\": \"def meta(self) -> pulumi.Output[Optional[Mapping[str, str]]]:\\n return pulumi.get(self, \\\"meta\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f3f733cf94e951aecf44bec44a9f109\",\n \"score\": \"0.6199301\",\n \"text\": \"def GetMetadata(self):\\n basic_data = {\\n 'spark_service': self.SERVICE_NAME,\\n 'spark_svc_cloud': self.CLOUD,\\n 'spark_cluster_id': self.cluster_id,\\n 'spark_cluster_zone': getattr(self, 'zone', None) or 'unknown'\\n }\\n # TODO grab this information for user_managed clusters.\\n if not self.user_managed:\\n basic_data.update({'num_workers': str(self.spec.worker_group.vm_count),\\n 'worker_machine_type':\\n str(self.spec.worker_group.vm_spec.machine_type)})\\n return basic_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"531b6d11b6df849a0e3b204584ffbed0\",\n \"score\": \"0.6188202\",\n \"text\": \"def getInternalMetadata(self, **kwargs):\\n return {'omeinfo': self._omeinfo}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980b08b8218793f4694888b53954bdb7\",\n \"score\": \"0.61798024\",\n \"text\": \"def getInfo():\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":192,"cells":{"query_id":{"kind":"string","value":"4370a2db89aa376572fa763677d8f568"},"query":{"kind":"string","value":"Add the hooks to the bot."},"positive_passages":{"kind":"list like","value":[{"docid":"f6a523a74b100be16e1871ae1e1ba3f3","score":"0.579434","text":"def setup(bot: commands.Bot):\n bot.before_invoke(before_invoke)\n bot.after_invoke(after_invoke)","title":""}],"string":"[\n {\n \"docid\": \"f6a523a74b100be16e1871ae1e1ba3f3\",\n \"score\": \"0.579434\",\n \"text\": \"def setup(bot: commands.Bot):\\n bot.before_invoke(before_invoke)\\n bot.after_invoke(after_invoke)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"42d07c0d20fbaab7a3ce210e4325d89b","score":"0.7362344","text":"def hook_events(self):\n for cmd in self.commands:\n self.core.add_command(cmd, self)\n for event in self.hooks:\n self.core.add_callback(event, self)\n for cmd in self.rawhooks:\n self.core.add_raw(cmd, self)","title":""},{"docid":"5450bb7786ed53fe294cc35583a4b091","score":"0.7053781","text":"def set_game_hooks(hooks: GameHooks) -> None:\n global _game_hooks\n _game_hooks = hooks","title":""},{"docid":"1d717911c38f804986b4721041fd10b5","score":"0.69996136","text":"def register_hooks(self, hooks):\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)","title":""},{"docid":"1d717911c38f804986b4721041fd10b5","score":"0.69996136","text":"def register_hooks(self, hooks):\n hooks = [h for h in hooks if h is not None]\n for h in hooks:\n assert isinstance(h, HookBase)\n # To avoid circular reference, hooks and trainer cannot own each other.\n # This normally does not matter, but will cause memory leak if the\n # involved objects contain __del__:\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n h.trainer = weakref.proxy(self)\n self._hooks.extend(hooks)","title":""},{"docid":"8a249d756642ea01dd9ad4457bff3712","score":"0.6859483","text":"def enable_hooks(self) -> None:\n self.hooks_enabled = True","title":""},{"docid":"11a1126a6ab2f0b85fc66e57b16e028e","score":"0.6821246","text":"def putInHooks ():\n\n # Need to modify Leo's Kernel first\n # Overwrite existing Leo methods.\n g.funcToMethod(replaceTargetFileIfDifferent,leoAtFile.atFile)\n g.funcToMethod(massageComment,leoImport.leoImportCommands)\n\n # Add new methods used by this plugin to various classes.\n g.funcToMethod(openForRead,leoAtFile.atFile)\n g.funcToMethod(openForWrite,leoAtFile.atFile)\n g.funcToMethod(gotoLineNumberOpen,leoCommands.Commands)\n g.funcToMethod(applyLineNumberMappingIfAny, leoCommands.Commands)","title":""},{"docid":"88d6493b3885f6278f8212ed6423e014","score":"0.67838776","text":"def prepare_hooks(self, hooks):\r\n for event in hooks:\r\n self.register_hook(event, hooks[event])","title":""},{"docid":"88d6493b3885f6278f8212ed6423e014","score":"0.67838776","text":"def prepare_hooks(self, hooks):\r\n for event in hooks:\r\n self.register_hook(event, hooks[event])","title":""},{"docid":"cc16bf846e0425e5913d8b44001ce900","score":"0.6763753","text":"def configure_hooks_feature(self):\n hooks_folder = getattr(config, \"HOOKS_FOLDER\", \"./hooks\")\n if not os.path.exists(hooks_folder):\n self.logger.info(\"Hooks folder '%s' doesn't exist, creating it\" % hooks_folder)\n os.makedirs(hooks_folder)\n self.hook_files = glob.glob(os.path.join(hooks_folder, \"*.py\"))","title":""},{"docid":"8e754d61588c30b277f496a6f5fe68f2","score":"0.6674685","text":"def _init_hooks(self):\n self._hooks = Hooks()\n self._hooks.ready_to_run = self._init_hexrays_hooks\n self._hooks.hook()","title":""},{"docid":"8e754d61588c30b277f496a6f5fe68f2","score":"0.6674685","text":"def _init_hooks(self):\n self._hooks = Hooks()\n self._hooks.ready_to_run = self._init_hexrays_hooks\n self._hooks.hook()","title":""},{"docid":"0489a542c08e02331af5086e2981ff82","score":"0.66713774","text":"def hook(self):\n\n\t\tself.db.begin()\n\n\t\tret = self.db.get_hook()\n\t\texisting = [dict(_)['name'] for _ in ret]\n\n\t\t# Add hook modules\n\t\tif len(self.args.hook):\n\t\t\tfor h in self.args.hook:\n\t\t\t\th = h.strip()\n\t\t\t\tif h in existing:\n\t\t\t\t\tprint(\"Hook module '%s' already in hook list\" % h)\n\t\t\t\telse:\n\t\t\t\t\tself.db.add_hook(h)\n\t\t\t\t\tself.db.commit()\n\t\t\t\t\tprint(\"Added: %s\" % h)\n\n\t\t# List all hooks\n\t\telse:\n\t\t\tif len(existing):\n\t\t\t\tprint(\"Existing hooks:\")\n\t\t\t\texisting = sorted(existing)\n\n\t\t\t\tfor h in existing:\n\t\t\t\t\tprint(\"\\t%s\" % h)\n\n\t\t\telse:\n\t\t\t\tprint(\"No existing hooks\")","title":""},{"docid":"b303d72b01b79b91713af6bf520ec161","score":"0.66632324","text":"def go_hook_all():\n global hooks\n priority = ''\n version = weechat.info_get('version_number', '') or 0\n # use high priority for hook to prevent conflict with other plugins/scripts\n # (WeeChat >= 0.3.4 only)\n if int(version) >= 0x00030400:\n priority = '2000|'\n for hook, value in HOOK_COMMAND_RUN.items():\n if hook not in hooks:\n hooks[hook] = weechat.hook_command_run(\n '%s%s' % (priority, value[0]),\n value[1], '')\n if 'modifier' not in hooks:\n hooks['modifier'] = weechat.hook_modifier(\n 'input_text_display_with_cursor', 'go_input_modifier', '')","title":""},{"docid":"b14f7b51831e081ee793e34d442312a4","score":"0.6640559","text":"def register_hooks(self, hooks: List[Hook]):\n if not hasattr(self, '_hooks'):\n self._hooks: List[Hook] = []\n for hook in hooks:\n hook.register()\n self._hooks.append(hook)","title":""},{"docid":"c29c6b427d21004ac609bfb2d25391cc","score":"0.6605009","text":"def hook_all():\n global hook_command_run, hooks\n priority = \"\"\n version = weechat.info_get(\"version_number\", \"\") or 0\n # use high priority for hook to prevent conflict with other plugins/scripts\n # (WeeChat >= 0.3.4 only)\n if int(version) >= 0x00030400:\n priority = \"2000|\"\n for hook, value in hook_command_run.items():\n if hook not in hooks:\n hooks[hook] = weechat.hook_command_run(\"%s%s\" % (priority, value[0]),\n value[1], \"\")\n if \"modifier\" not in hooks:\n hooks[\"modifier\"] = weechat.hook_modifier(\n \"input_text_display_with_cursor\", \"input_modifier\", \"\")","title":""},{"docid":"28d97868f2cac67cd0e8cc6006ca1d2c","score":"0.65615565","text":"def add_hook(self, hook):\n if hook not in [type(me) for me in self.hooks]:\n self.__hooks += [hook()]","title":""},{"docid":"0cb8b3979e2bf906d4dc8356ad14ebb7","score":"0.6503424","text":"def _setup_hooks(self): # pragma no cover\n pass","title":""},{"docid":"fa0b1cadc33681909a5166075eac3202","score":"0.6501873","text":"def addhook(self, name, uname, function):\n self.server.addhook(name, \"%s:%s\" % (self.index, uname), function)\n self.hooks.append((name, \"%s:%s\" % (self.index, uname)))","title":""},{"docid":"c5c7e9824dc521cf94e916cb68430537","score":"0.64930224","text":"def hooks_initializer(initializer):\n # Register default build hooks\n initializer.register_hook(hooks.add_versions_chooser)\n initializer.register_hook(hooks.add_warning)","title":""},{"docid":"4344c7329eb0f2cf2e62da3a45bb6d00","score":"0.6403244","text":"def _register_required_hooks(self):\n\n packaging_services.register_hook(PackagingHook())","title":""},{"docid":"1f736d2001be5931c63ce218fa4d8bb9","score":"0.6390347","text":"def register_hooks(self):\n\n self.on_pre_GET += on_list\n self.on_delete_item += on_delete\n self.on_insert += init_document_with_acl\n self.on_update += on_update","title":""},{"docid":"fb89b8f5d17bf4cb987b08cf0ec8efdc","score":"0.63517725","text":"def pytest_addhooks(pluginmanager) -> None:\n from . import newhooks\n\n pluginmanager.add_hookspecs(newhooks)","title":""},{"docid":"08edef69582cf0ea9d879b20c97d29c6","score":"0.63485664","text":"def init_hooks():\n raise NotImplementedError()","title":""},{"docid":"988c259581ed00239a5e4b0bb9dfd80f","score":"0.6284471","text":"def run_hooks(self, config):\n for hook in self.setup_hooks:\n hook(config)","title":""},{"docid":"ddfac105d4a5917c847228d02b558d0c","score":"0.6270114","text":"def register_hook(self, hook):\n self._hooks.append(hook)","title":""},{"docid":"63d9dee33b36016b6d95e7ff7079755f","score":"0.6270062","text":"def pytest_addhooks(pluginmanager):\n from seekret.apitest.pytest_plugin import newhooks\n pluginmanager.add_hookspecs(newhooks)","title":""},{"docid":"8b98798e5110a7ae6e8ac8286d29d7c3","score":"0.6247726","text":"def add_events(self):\n self.bot.event(self.on_ready)","title":""},{"docid":"fed26d9ec1952a689b322a0a269bff07","score":"0.6242092","text":"def _addAEHooks():\n # Realflow uses the AEshapeHooks global variable as a convention for sharing AEshapeTemplate overrides,\n # so we will too, unless a more popular convention is found.\n pm.melGlobals.initVar('string[]', 'AEshapeHooks')\n hooks = list(pm.melGlobals['AEshapeHooks'])\n import mtoa.ui.ae.templates\n procName = utils.pyToMelProc(mtoa.ui.ae.templates.loadArnoldTemplate, [('string', 'nodeName')], useName=True)\n hooks.append(procName)\n pm.melGlobals['AEshapeHooks'] = hooks","title":""},{"docid":"afbcf0e45ef1bcc1330e7d298d50d166","score":"0.62211084","text":"def add_hook(self, command, method):\n self.hooks[command] = method","title":""},{"docid":"b24452a621b7bae3da58941d871f16db","score":"0.6214256","text":"def install_hooks(self, pipe):\n assert len(self.hooks) == 0\n text_encoder_targets = self._get_target_modules(pipe.text_encoder, \"lora_te\", [\"CLIPAttention\", \"CLIPMLP\"])\n unet_targets = self._get_target_modules(pipe.unet, \"lora_unet\", [\"Transformer2DModel\", \"Attention\"])\n for name, target_module in text_encoder_targets + unet_targets:\n hook = LoRAHook()\n hook.install(target_module)\n self.hooks[name] = hook\n # print(name)\n\n self.device = pipe.device\n self.dtype = pipe.unet.dtype","title":""},{"docid":"f5be4d905626ef49be5a636ab0ea8ee5","score":"0.6170191","text":"def game_hooks() -> GameHooks:\n return _game_hooks","title":""},{"docid":"1484d616169295d164f73b32366494ad","score":"0.6154442","text":"def run(self, hooks):\n if settings['disable_hooks']:\n return\n\n queue = []\n for value in hooks.values():\n value = seedlib.apply_template(value, self.values)\n queue.append(value)\n \n for command in queue:\n print ('info: running hook \"%s\"' % command)\n seedlib.run_pipe(command)","title":""},{"docid":"482eb32d67492d3848f3b2001ca25120","score":"0.61518055","text":"def setup(bot):\n bot.add_cog(Events(bot))","title":""},{"docid":"e40ea95a597c036314c0ac0b5a4bb00d","score":"0.6124972","text":"def startHook(self):\n # Empty ","title":""},{"docid":"e5b43f0224a2a54a6a30b1fdb6d5b8f1","score":"0.6113805","text":"def set_bottle_hooks(self, app, shutdown, rollback):\n if hasattr(app, 'hook'):\n app.hook('after_request')(shutdown)","title":""},{"docid":"925a2a709e21bed37d6e591bc6029891","score":"0.611031","text":"def install_hooks(config):\n config.on_create.extend([copy_environ, hide_cookie])\n config.on_create.insert(0, update_report)","title":""},{"docid":"f6c60de55b36b553993bc477dfb188a7","score":"0.607913","text":"def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n parse_source=parse_source,\n )\n return hook","title":""},{"docid":"b44b466b97d7a6660a00d78c539037c5","score":"0.6075598","text":"def add_hook(self, hook):\n hook_addr = hook.identifier\n \n if self.hooks.has_key(hook_addr):\n self.hooks[hook_addr].append(hook)\n logging.debug(\"Adding another hook for addr 0x%08x (name: %s)\" % \\\n (hook_addr,\n hook.name))\n else:\n self.hooks[hook_addr] = [hook]","title":""},{"docid":"a8255d4c578bc301629d6436a109124a","score":"0.60621554","text":"async def initialize_code_hook_functions(self):\n for ability in await self.data_svc.locate('abilities'):\n for executor in ability.executors:\n if executor.code and executor.language:\n executor.HOOKS[executor.language] = self.generate_ability_execution_method","title":""},{"docid":"8b80ce9f5a3f87e9bb9c1642bdcfb4a3","score":"0.60540694","text":"def setup(bot):\n bot.add_cog(EventLoops(bot))","title":""},{"docid":"0c45dce46acba5ba5710436ac8ac26b6","score":"0.6000275","text":"def setup(bot):\n bot.add_cog(Logs(bot))","title":""},{"docid":"36d5b30cf031b019fc2c3b8fed1a6dee","score":"0.5998982","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Tags(bot))","title":""},{"docid":"1944791d46eca2a6bbb32d2297613235","score":"0.59902436","text":"def assign_standard_hooks(obj, get, conf):\n hook_names = ['step_hook', 'post_process_hook',\n 'post_process_hook_final', 'pre_process_hook']\n for hook_name in hook_names:\n setattr(obj, hook_name, conf.get_function(get(hook_name, None)))","title":""},{"docid":"c773fd5a7366f71b8bc48cf16391dd3a","score":"0.59853834","text":"def _register_hook(self, hook_name, func):\r\n self.hooks.setdefault(hook_name, []).append(func)","title":""},{"docid":"a5a3262430aa75a2dc4230898591f488","score":"0.59784955","text":"def add_hook(self, level, location, hook, position=-1):\n\n if location not in self.levels[level].hooks:\n self.levels[level].hooks[location] = []\n\n if position == -1:\n position = len(self.levels[level].hooks[location])\n\n self.levels[level].hooks[location].insert(position, hook)","title":""},{"docid":"cc99595a1169c0fa58e110dd5336a50f","score":"0.59615684","text":"def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n source_init=source_init,\n transform_source=transform_source,\n )\n return hook","title":""},{"docid":"cc99595a1169c0fa58e110dd5336a50f","score":"0.59615684","text":"def add_hook(**_kwargs):\n hook = import_hook.create_hook(\n hook_name=__name__,\n source_init=source_init,\n transform_source=transform_source,\n )\n return hook","title":""},{"docid":"42af3093cb3fc5de769600b2d9bcde98","score":"0.5953135","text":"def run_hooks(hooks, data):\n for hook_key, hook_class in hooks.items():\n hook = hook_class(data)\n log.info('Running {} hook'.format(hook_key))\n hook.run()","title":""},{"docid":"b0a8b923b37eba8f732792eb53cb6b71","score":"0.5936176","text":"async def run_init_hooks(self) -> None:\n self.logger.info(\"Running init hooks\")\n for hook in self.init_hooks:\n self.logger.info(f\"Running init hook for plugin {hook.plugin}\")\n await hook.func() if inspect.iscoroutinefunction(hook.func) else hook.func()","title":""},{"docid":"d65c171195ea46e096e3c1c55ae1c291","score":"0.5925443","text":"def on(self, hook):\n\n def decorator(handler):\n self.registry[hook].append(handler)\n\n return handler\n\n return decorator","title":""},{"docid":"792e20dbb3796793cf5123be2589cedc","score":"0.59254354","text":"def register_hook(self, func):\r\n\r\n if func not in self.hooks:\r\n self.hooks.append(func)","title":""},{"docid":"992e660201db15b027d78bbf73909ecd","score":"0.5917185","text":"def _before_application_run(self):\n\n for hook in self._get_hooks():\n hook.before_application_run()","title":""},{"docid":"4098a25602a66288543c0be8a1720c0a","score":"0.59140986","text":"def setup(bot):\n bot.add_cog(Dictionary())","title":""},{"docid":"128feab0e9e690fd164055f3dc6224ce","score":"0.5912825","text":"def _after_application_loaded(self):\n\n for hook in self._get_hooks():\n hook.after_application_loaded()","title":""},{"docid":"7a3b2b4a6f0402b41ae4eff4cf4fc845","score":"0.5884704","text":"def on_start(self):\n self.add_bot(ScoutManager(bot_player=self))\n self.add_bot(BuildManager(bot_player=self))\n self.add_bot(GatherManager(bot_player=self))\n self.add_bot(DefenseManager(bot_player=self))\n self.init_request_board()","title":""},{"docid":"2a582bbb4bf0a67821652519bd94f5ec","score":"0.588175","text":"def add_hook(cls, hook_type, hook_func):\r\n if hook_type not in cls._hooks_map:\r\n cls._hooks_map[hook_type] = []\r\n\r\n cls._hooks_map[hook_type].append(hook_func)","title":""},{"docid":"200dd6ea332f6486cfb958496dd7eafe","score":"0.5878965","text":"async def setup(bot) -> None:\n await bot.add_cog(HotReload(bot))","title":""},{"docid":"61cbfc3f4a2d622fbd0030e22de81d12","score":"0.58777094","text":"def setup(bot: Bot):\n bot.add_cog(EventoryCog(bot))\n log.info(\"loaded Eventory extension!\")","title":""},{"docid":"269b00d45f4e69768306379771ce086d","score":"0.5848767","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Utilities(bot))","title":""},{"docid":"69a5ab4cf1d8372d05b8687ae7d4fdc2","score":"0.58468145","text":"def setup(bot):\n bot.add_cog(Tickets(bot))","title":""},{"docid":"bae9f0da4d2e05b9bdea26aa011ab59a","score":"0.58353835","text":"def setup(bot: Bot) -> None:\n bot.add_cog(LemonStuff(bot))\n log.info(\"Cog loaded: LemonStuff\")","title":""},{"docid":"9ca07bcea77b5998babc1f8f475dda35","score":"0.5832757","text":"def hooks(self):\n return self.__hooks","title":""},{"docid":"b6d22ce1884a7faa025d158e62aa578e","score":"0.5821258","text":"def hook_dispatch(self) -> None:\n if sys.argv[2].strip().lower() == \"add\":\n self.add_hook(\n hook_state = sys.argv[3],\n cmd = \" \".join(sys.argv[4:]),\n )\n\n elif sys.argv[2].strip().lower() == \"del\":\n self.del_hook(\n hook_state = sys.argv[3],\n )","title":""},{"docid":"e666c8c599a3a0a56b13e57e58b45c50","score":"0.58076733","text":"def _add_trachook(self, req, hookfile, current):\n base_url = self.config.get('trac', 'base_url')\n # try to append hook\n try:\n fp = open(hookfile, 'a')\n fp.writelines(os.linesep)\n if 'REPOS=\"$1\"' not in current:\n fp.writelines('REPOS=\"$1\"' + os.linesep)\n if 'REV=\"$2\"' not in current:\n fp.writelines('REV=\"$2\"' + os.linesep)\n if 'LOG=' not in current:\n fp.writelines('LOG=`/usr/bin/svnlook log -r $REV $REPOS`' \\\n + os.linesep)\n if 'AUTHOR=' not in current:\n fp.writelines('AUTHOR=`/usr/bin/svnlook author -r $REV ' + \\\n '$REPOS`' + os.linesep)\n if 'TRAC_ENV=' not in current:\n fp.writelines('TRAC_ENV=\"'+self.env.path+'\"' + os.linesep)\n if 'TRAC_URL=' not in current:\n fp.writelines('TRAC_URL=\"'+base_url+'\"' + os.linesep)\n fp.writelines('/usr/bin/python /usr/share/trac/contrib/' + \\\n 'trac-post-commit-hook \\\\' + os.linesep)\n fp.writelines('-p \"$TRAC_ENV\" \\\\' + os.linesep)\n fp.writelines('-r \"$REV\" \\\\' + os.linesep)\n fp.writelines('-u \"$AUTHOR\" \\\\' + os.linesep)\n fp.writelines('-m \"$LOG\" \\\\' + os.linesep)\n fp.writelines('-s \"$TRAC_URL\"' + os.linesep)\n fp.writelines(os.linesep)\n fp.close()\n except Exception:\n raise TracError(\"Can't write repository hook %s\" % hookfile)","title":""},{"docid":"b1a651ece69c23f871ae87944408d516","score":"0.5799101","text":"def run_hooks(cls, hook_type, *args, **kwargs):\r\n hook_funcs = cls._hooks_map.get(hook_type) or []\r\n for hook_func in hook_funcs:\r\n hook_func(*args, **kwargs)","title":""},{"docid":"4fcf31c68e866cf847c0a5373217c2b3","score":"0.5785056","text":"def register_default_hooks(self, *hooks: Hook, as_group: bool = True):\n self.hooks.extend(hooks)\n\n if as_group and self.group is not None:\n for g in get_group_by_name(self.group):\n if g is not self:\n g.register_hooks(*hooks, as_group=False)","title":""},{"docid":"396772b6a60c6762461d747ec1f4b8dc","score":"0.5783806","text":"async def setup(bot) -> None:\n await bot.add_cog(Gambling(bot))","title":""},{"docid":"f50c868844e933e05acf6e0224d81642","score":"0.57818186","text":"def install_custom_hook(self, hook_name):\n self._install_custom_hook(hook_name)","title":""},{"docid":"e677a9f8d0ec6ba94293c47993d275df","score":"0.5769153","text":"def add_hook(self,\n hook_state: str,\n cmd: str,\n ) -> None:\n state = hook_state.strip().lower()\n\n if state not in self.valid_hook_states:\n print(f\"Invalid hook state: {hook_state}. Valid states are: {self.valid_hook_states}\")\n return\n\n # At this point we simply overwrite any existing command\n # and store it to the data file\n data = self.load()\n data[\"hooks\"][state] = cmd\n self.save(data)\n\n # If cmd is None this function as been called from within del_hook()\n if cmd is None:\n print(f\"Deleted hook command for state {state}\")\n else:\n print(f\"Added hook command for state {state}: {cmd}\")","title":""},{"docid":"b8c18e6f43da52ca2d550b82ddba2845","score":"0.5764374","text":"def install_api_hooks(dbg, dll_api_hooks):\n # _pt_log(\"remain system dlls to install api hook: %d\" % (len(dll_api_hooks)))\n\n new_sys_dll = dbg.system_dlls[-1]\n dll_name = new_sys_dll.name.lower()\n\n for (dll_name, api_ctrl_list) in dll_api_hooks.items():\n if dbg.check_has_system_dll(dll_name):\n for api_ctrl in api_ctrl_list:\n dbg_set_api_bp(dbg, api_ctrl)\n del dll_api_hooks[dll_name]","title":""},{"docid":"e63584e75c449d989e5334871a61d146","score":"0.574739","text":"def setup(bot):\n bot.add_cog(Messages(bot))","title":""},{"docid":"ba6688fa7d65bc9590cd3596cc6eead6","score":"0.5747233","text":"def InstallHook(self):\r\n pass","title":""},{"docid":"e17e625fb36c2298dbac9f5647e71522","score":"0.5714983","text":"def setup(bot: Bot) -> None:\n bot.add_cog(MessageLogCog(bot))","title":""},{"docid":"efb0f516bd6a1e7fe91485da5245ead6","score":"0.5709663","text":"def registerhooks(entry=[], exit=[], *args, **kwargs):\n def wrap(f):\n _kwargs = dict(kwargs)\n _kwargs.update({'entry': entry, 'exit': exit})\n def wrapped_f(*args, **kwargs):\n return f(*args, **kwargs)\n wrapped_f.__hooks = _kwargs\n return wrapped_f\n return wrap","title":""},{"docid":"044a2ad7b8e51eec0d32c615365ec6bc","score":"0.570686","text":"def setup(bot):\n bt.INFO('Loading Misc.py')\n bot.add_cog(Misc(bot))","title":""},{"docid":"71fa5b3abe2be9c9c55ea45b9c82dd43","score":"0.56982493","text":"async def setup(self) -> None:\n if self.playing_title is not None:\n await self.change_presence(\n activity=discord.Game(name=self.playing_title),\n )\n\n self.tree.clear_commands(guild=None)\n for guild in self.guilds:\n self.tree.clear_commands(guild=guild)\n\n commands = registered_app_commands()\n for command in commands:\n self.tree.add_command(command)\n logger.info(f'registered {len(commands)} app commands')\n\n listeners = registered_listeners()\n for listener in listeners:\n self.add_listener(listener.func, listener.event)\n logger.info(f'registered {len(listeners)} listeners')\n\n if self._cmd_group_exts is not None:\n for ext in self._cmd_group_exts:\n self.tree.add_command(ext)\n await ext.post_init(self)\n logger.info(f'registered {ext.name} command group')\n\n await self.tree.sync()\n for guild in self.guilds:\n await self.tree.sync(guild=guild)","title":""},{"docid":"fccb4dede5aa12489b6365d7dac5f30b","score":"0.5684656","text":"def setup(bot):\n bot.add_cog(LoadHelp(bot))","title":""},{"docid":"59ace20514f392a89f07428e9134c5bc","score":"0.5670448","text":"def list_hooks(self) -> None:\n data = self.load()\n hooks = data.get(\"hooks\", {})\n\n print(\"Current hook command configuration:\")\n for state in self.valid_hook_states:\n cmd = hooks.get(state, None)\n print(f\" {state:6s} {cmd}\")","title":""},{"docid":"c13e1b3cbfa89944d2a510fb35f6ed58","score":"0.5668042","text":"def on_bot_startup(bot):\n pass","title":""},{"docid":"5a8ba5a4c77b793ba734b50ced6e7aaf","score":"0.56633395","text":"def register(name):\r\n global SUPPORTED_HOOKS\r\n if not name in SUPPORTED_HOOKS:\r\n SUPPORTED_HOOKS.append(name)","title":""},{"docid":"5ad29e41de5a20f7deb3ed694f518194","score":"0.5659891","text":"def add_permissions_hook(self, hook: commands.CheckPredicate) -> None:\n self._permissions_hooks.append(hook)","title":""},{"docid":"85d8761c3de9dfeea1cd5f99b7ec825c","score":"0.56558925","text":"def register_hook(self, event, hook):\r\n\r\n if isinstance(hook, collections.Callable):\r\n self.hooks[event].append(hook)\r\n elif hasattr(hook, '__iter__'):\r\n self.hooks[event].extend(h for h in hook if isinstance(h, collections.Callable))","title":""},{"docid":"3258ecf3f6e2c20cf1fd7f7cecca9cc6","score":"0.5652265","text":"def get_hooks():\n tensor_to_log = {'loss': loss}\n # tensor_to_log = {}\n logging_tensor_hook = tf.train.LoggingTensorHook(\n tensors=tensor_to_log,\n every_n_iter=2\n )\n return [logging_tensor_hook]","title":""},{"docid":"55c7449a2267ac9fd64c1310ad51e382","score":"0.56491655","text":"def apply_hooks():\n for line in fileinput.input():\n std_input = line.split(\" \")\n oldrev, newrev, refname = [item.strip() for item in std_input]\n # Check for zero commit, check branch deletions\n # also, avoid new package additions\n if ZERO_COMMIT in (oldrev, newrev):\n continue\n # insert new hooks here\n package_start_build(oldrev, newrev, refname)\n # Pass in 'length' as an optional argument\n # default 'length' of feed is 499\n write_rss_feed(oldrev, newrev, refname, length=499)\n return","title":""},{"docid":"f70c1c6821cc9de1cb8cab705ac6ee85","score":"0.5649146","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Internal(bot))","title":""},{"docid":"0f6a737d4f0384ad0b93c99010ebf635","score":"0.5637375","text":"def hook(self, server=None):\n\n if server is not None:\n logging.debug('Adding hook route to web server')\n server.add_route(\n Wrapper(callable=self.get_hook(),\n route=self.context.get('server.hook', '/hook')))\n\n if (self.context.get('server.binding') is not None\n and self.context.get('server.url') is not None):\n\n self.space.register(\n hook_url=self.context.get('server.url')\n + self.context.get('server.hook', '/hook'))","title":""},{"docid":"1b50dc58fd7d07d03898d7d1205095a7","score":"0.56359833","text":"def setup(bot):\n\n bot.add_cog(Eval(bot))","title":""},{"docid":"b683c47d5bdae345391754b51c5bac6f","score":"0.5632748","text":"def add_callback(self, hook, function, pre=False):\n if hook not in self._hooks:\n raise Exception('This program has no hook named \"%s\"' % hook)\n hook_def = self._hook_defs.get(hook, None)\n\n if (hook_def is None or not isinstance(hook_def, FunctionChain)):\n raise TypeError(\"Cannot add callback to hook '%s'; not a \"\n \"FunctionChain. (%s)\" % (hook, type(hook_def)))\n\n if pre:\n hook_def.insert(0, function)\n else:\n hook_def.append(function)\n\n # TODO: remove or resurrect\n #self._install_dep_callbacks(function)\n self._need_build = True","title":""},{"docid":"d4499e1842dac2feaba1f51e52a73f48","score":"0.56314707","text":"def run_hooks():\n\n hookdir = '/usr/lib/oem-config/post-install'\n\n if os.path.isdir(hookdir):\n # Exclude hooks containing '.', so that *.dpkg-* et al are avoided.\n hooks = filter(lambda entry: '.' not in entry, os.listdir(hookdir))\n child_env = dict(os.environ)\n child_env['DEBIAN_FRONTEND'] = 'noninteractive'\n if 'DEBIAN_HAS_FRONTEND' in child_env:\n del child_env['DEBIAN_HAS_FRONTEND']\n for hookentry in hooks:\n hook = os.path.join(hookdir, hookentry)\n if os.access(hook, os.X_OK):\n # Errors are ignored at present, although this may change.\n subprocess.call([hook], env=child_env)","title":""},{"docid":"1b864916beac1e7a3522c10bae5c0f74","score":"0.56313306","text":"def run_hook(db, hook_name, **kwargs):\n\n\t# Get all hooks in the DB\n\thooks = db.get_hook()\n\thooks = [dict(_)['name'] for _ in hooks]\n\n\t# Iteratue through the hooks\n\tfor hook in hooks:\n\t\t# Try importing the hook module and finding any functions that have used the ydl.hook decorate\n\t\t# which means it has a _hooks attribute indicating which hooks it accepts\n\t\ttry:\n\t\t\tz = importlib.import_module(hook)\n\t\t\tfor x in dir(z):\n\t\t\t\to = getattr(z, x)\n\t\t\t\tif hasattr(o, '_hooks') and hook_name in o._hooks:\n\t\t\t\t\to(hook_name, db, **kwargs)\n\t\t\t\tdel o\n\t\t\tdel z\n\t\texcept Exception as e:\n\t\t\ttraceback.print_exc()\n\t\t\t# Move along","title":""},{"docid":"2cfa6dc8d943c488df97857dd75522ee","score":"0.5624743","text":"def setup(bot: Bot) -> None:\n bot.add_cog(Reminder(bot))\n log.info(\"Cog loaded: reminder\")","title":""},{"docid":"e7df076bc40f988cd86f24cb3e5db89e","score":"0.56241655","text":"def setup(bot: Bot) -> None:\n bot.add_cog(SpookyEightBall())","title":""},{"docid":"53fa260f6f97a677c52fabbf44b86b38","score":"0.5622936","text":"def add_commands(self, *args):\n for command in args:\n self._script += command + '\\n'","title":""},{"docid":"7496c40c7d9e4b36c3df9cb62d2c5ad4","score":"0.55818033","text":"def run(self, hook, *args, **kwargs):\n\n assert hook in self._callbacks, f\"hook '{hook}' not found in callbacks {self._callbacks}\"\n\n for logger in self._callbacks[hook]:\n logger['callback'](*args, **kwargs)","title":""},{"docid":"9499c2213ce0272e0a32ffd4439f8e08","score":"0.5580782","text":"def _init_airflow_core_hooks(self):\n core_dummy_hooks = {\n \"generic\": \"Generic\",\n \"email\": \"Email\",\n \"mesos_framework-id\": \"Mesos Framework ID\",\n }\n for key, display in core_dummy_hooks.items():\n self._hooks_lazy_dict[key] = HookInfo(\n hook_class_name=None,\n connection_id_attribute_name=None,\n package_name=None,\n hook_name=display,\n connection_type=None,\n connection_testable=False,\n )\n for cls in [FSHook, PackageIndexHook]:\n package_name = cls.__module__\n hook_class_name = f\"{cls.__module__}.{cls.__name__}\"\n hook_info = self._import_hook(\n connection_type=None,\n provider_info=None,\n hook_class_name=hook_class_name,\n package_name=package_name,\n )\n self._hook_provider_dict[hook_info.connection_type] = HookClassProvider(\n hook_class_name=hook_class_name, package_name=package_name\n )\n self._hooks_lazy_dict[hook_info.connection_type] = hook_info","title":""},{"docid":"4fd2b1eb01258336636e0bd8adc0178b","score":"0.5579576","text":"def _trigger_hooks(self, hook_dict, hook_name, **kwargs):\n for attached_callback in hook_dict[hook_name].attached_callbacks:\n try:\n if attached_callback.callback(**kwargs):\n if attached_callback.action == MMHookAct.LOAD_MODULE:\n # load the module!\n self.logger.debug('some hook returned true, '\n 'loading module {}'\n .format(attached_callback.argument))\n # module must accept same kwargs, this is mandatory\n # with this discovery event\n try:\n cb_arg = attached_callback.argument\n module_name = cb_arg.get_module_desc().arg_name\n self.load_module(module_name,\n **kwargs)\n except Exception as ex:\n self.logger.error('loading of module of class '\n '\"{}\" failed with: {}'\n .format(cb_arg.__name__,\n str(ex)))\n elif attached_callback.action == MMHookAct.UNLOAD_MODULE:\n # unload the attached module\n self.logger.debug('a hook required module '\n '{} to be unloaded'\n .format(attached_callback.argument))\n self.unload_module(attached_callback.argument)\n except Exception as ex:\n self.logger.error('failed to call function '\n '{} attached to \"{}\" with: {}'\n .format(attached_callback,\n hook_name,\n str(ex)))","title":""},{"docid":"b19e4deca1d85266e102d958ffa41ff0","score":"0.55784094","text":"def setup(bot):\n bot.add_cog(ReminderCog(bot))","title":""},{"docid":"8e2d79bb76fc224f12b869daf1739030","score":"0.55750185","text":"def runHook(self):\n self.run()","title":""},{"docid":"a78408bd292126b7640c238bfb0c8ec8","score":"0.55676615","text":"async def setup(bot: Bot) -> None:\n await bot.add_cog(Patreon(bot))","title":""},{"docid":"05f45412830a1b3a2b2c9b7bdb0d1cbb","score":"0.556712","text":"def hooks(self) -> MutableMapping[str, HookInfo | None]:\n self.initialize_providers_hooks()\n # When we return hooks here it will only be used to retrieve hook information\n return self._hooks_lazy_dict","title":""}],"string":"[\n {\n \"docid\": \"42d07c0d20fbaab7a3ce210e4325d89b\",\n \"score\": \"0.7362344\",\n \"text\": \"def hook_events(self):\\n for cmd in self.commands:\\n self.core.add_command(cmd, self)\\n for event in self.hooks:\\n self.core.add_callback(event, self)\\n for cmd in self.rawhooks:\\n self.core.add_raw(cmd, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5450bb7786ed53fe294cc35583a4b091\",\n \"score\": \"0.7053781\",\n \"text\": \"def set_game_hooks(hooks: GameHooks) -> None:\\n global _game_hooks\\n _game_hooks = hooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d717911c38f804986b4721041fd10b5\",\n \"score\": \"0.69996136\",\n \"text\": \"def register_hooks(self, hooks):\\n hooks = [h for h in hooks if h is not None]\\n for h in hooks:\\n assert isinstance(h, HookBase)\\n # To avoid circular reference, hooks and trainer cannot own each other.\\n # This normally does not matter, but will cause memory leak if the\\n # involved objects contain __del__:\\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\\n h.trainer = weakref.proxy(self)\\n self._hooks.extend(hooks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d717911c38f804986b4721041fd10b5\",\n \"score\": \"0.69996136\",\n \"text\": \"def register_hooks(self, hooks):\\n hooks = [h for h in hooks if h is not None]\\n for h in hooks:\\n assert isinstance(h, HookBase)\\n # To avoid circular reference, hooks and trainer cannot own each other.\\n # This normally does not matter, but will cause memory leak if the\\n # involved objects contain __del__:\\n # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\\n h.trainer = weakref.proxy(self)\\n self._hooks.extend(hooks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a249d756642ea01dd9ad4457bff3712\",\n \"score\": \"0.6859483\",\n \"text\": \"def enable_hooks(self) -> None:\\n self.hooks_enabled = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a1126a6ab2f0b85fc66e57b16e028e\",\n \"score\": \"0.6821246\",\n \"text\": \"def putInHooks ():\\n\\n # Need to modify Leo's Kernel first\\n # Overwrite existing Leo methods.\\n g.funcToMethod(replaceTargetFileIfDifferent,leoAtFile.atFile)\\n g.funcToMethod(massageComment,leoImport.leoImportCommands)\\n\\n # Add new methods used by this plugin to various classes.\\n g.funcToMethod(openForRead,leoAtFile.atFile)\\n g.funcToMethod(openForWrite,leoAtFile.atFile)\\n g.funcToMethod(gotoLineNumberOpen,leoCommands.Commands)\\n g.funcToMethod(applyLineNumberMappingIfAny, leoCommands.Commands)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d6493b3885f6278f8212ed6423e014\",\n \"score\": \"0.67838776\",\n \"text\": \"def prepare_hooks(self, hooks):\\r\\n for event in hooks:\\r\\n self.register_hook(event, hooks[event])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d6493b3885f6278f8212ed6423e014\",\n \"score\": \"0.67838776\",\n \"text\": \"def prepare_hooks(self, hooks):\\r\\n for event in hooks:\\r\\n self.register_hook(event, hooks[event])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc16bf846e0425e5913d8b44001ce900\",\n \"score\": \"0.6763753\",\n \"text\": \"def configure_hooks_feature(self):\\n hooks_folder = getattr(config, \\\"HOOKS_FOLDER\\\", \\\"./hooks\\\")\\n if not os.path.exists(hooks_folder):\\n self.logger.info(\\\"Hooks folder '%s' doesn't exist, creating it\\\" % hooks_folder)\\n os.makedirs(hooks_folder)\\n self.hook_files = glob.glob(os.path.join(hooks_folder, \\\"*.py\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e754d61588c30b277f496a6f5fe68f2\",\n \"score\": \"0.6674685\",\n \"text\": \"def _init_hooks(self):\\n self._hooks = Hooks()\\n self._hooks.ready_to_run = self._init_hexrays_hooks\\n self._hooks.hook()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e754d61588c30b277f496a6f5fe68f2\",\n \"score\": \"0.6674685\",\n \"text\": \"def _init_hooks(self):\\n self._hooks = Hooks()\\n self._hooks.ready_to_run = self._init_hexrays_hooks\\n self._hooks.hook()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0489a542c08e02331af5086e2981ff82\",\n \"score\": \"0.66713774\",\n \"text\": \"def hook(self):\\n\\n\\t\\tself.db.begin()\\n\\n\\t\\tret = self.db.get_hook()\\n\\t\\texisting = [dict(_)['name'] for _ in ret]\\n\\n\\t\\t# Add hook modules\\n\\t\\tif len(self.args.hook):\\n\\t\\t\\tfor h in self.args.hook:\\n\\t\\t\\t\\th = h.strip()\\n\\t\\t\\t\\tif h in existing:\\n\\t\\t\\t\\t\\tprint(\\\"Hook module '%s' already in hook list\\\" % h)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.db.add_hook(h)\\n\\t\\t\\t\\t\\tself.db.commit()\\n\\t\\t\\t\\t\\tprint(\\\"Added: %s\\\" % h)\\n\\n\\t\\t# List all hooks\\n\\t\\telse:\\n\\t\\t\\tif len(existing):\\n\\t\\t\\t\\tprint(\\\"Existing hooks:\\\")\\n\\t\\t\\t\\texisting = sorted(existing)\\n\\n\\t\\t\\t\\tfor h in existing:\\n\\t\\t\\t\\t\\tprint(\\\"\\\\t%s\\\" % h)\\n\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"No existing hooks\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b303d72b01b79b91713af6bf520ec161\",\n \"score\": \"0.66632324\",\n \"text\": \"def go_hook_all():\\n global hooks\\n priority = ''\\n version = weechat.info_get('version_number', '') or 0\\n # use high priority for hook to prevent conflict with other plugins/scripts\\n # (WeeChat >= 0.3.4 only)\\n if int(version) >= 0x00030400:\\n priority = '2000|'\\n for hook, value in HOOK_COMMAND_RUN.items():\\n if hook not in hooks:\\n hooks[hook] = weechat.hook_command_run(\\n '%s%s' % (priority, value[0]),\\n value[1], '')\\n if 'modifier' not in hooks:\\n hooks['modifier'] = weechat.hook_modifier(\\n 'input_text_display_with_cursor', 'go_input_modifier', '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14f7b51831e081ee793e34d442312a4\",\n \"score\": \"0.6640559\",\n \"text\": \"def register_hooks(self, hooks: List[Hook]):\\n if not hasattr(self, '_hooks'):\\n self._hooks: List[Hook] = []\\n for hook in hooks:\\n hook.register()\\n self._hooks.append(hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29c6b427d21004ac609bfb2d25391cc\",\n \"score\": \"0.6605009\",\n \"text\": \"def hook_all():\\n global hook_command_run, hooks\\n priority = \\\"\\\"\\n version = weechat.info_get(\\\"version_number\\\", \\\"\\\") or 0\\n # use high priority for hook to prevent conflict with other plugins/scripts\\n # (WeeChat >= 0.3.4 only)\\n if int(version) >= 0x00030400:\\n priority = \\\"2000|\\\"\\n for hook, value in hook_command_run.items():\\n if hook not in hooks:\\n hooks[hook] = weechat.hook_command_run(\\\"%s%s\\\" % (priority, value[0]),\\n value[1], \\\"\\\")\\n if \\\"modifier\\\" not in hooks:\\n hooks[\\\"modifier\\\"] = weechat.hook_modifier(\\n \\\"input_text_display_with_cursor\\\", \\\"input_modifier\\\", \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28d97868f2cac67cd0e8cc6006ca1d2c\",\n \"score\": \"0.65615565\",\n \"text\": \"def add_hook(self, hook):\\n if hook not in [type(me) for me in self.hooks]:\\n self.__hooks += [hook()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb8b3979e2bf906d4dc8356ad14ebb7\",\n \"score\": \"0.6503424\",\n \"text\": \"def _setup_hooks(self): # pragma no cover\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0b1cadc33681909a5166075eac3202\",\n \"score\": \"0.6501873\",\n \"text\": \"def addhook(self, name, uname, function):\\n self.server.addhook(name, \\\"%s:%s\\\" % (self.index, uname), function)\\n self.hooks.append((name, \\\"%s:%s\\\" % (self.index, uname)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5c7e9824dc521cf94e916cb68430537\",\n \"score\": \"0.64930224\",\n \"text\": \"def hooks_initializer(initializer):\\n # Register default build hooks\\n initializer.register_hook(hooks.add_versions_chooser)\\n initializer.register_hook(hooks.add_warning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4344c7329eb0f2cf2e62da3a45bb6d00\",\n \"score\": \"0.6403244\",\n \"text\": \"def _register_required_hooks(self):\\n\\n packaging_services.register_hook(PackagingHook())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f736d2001be5931c63ce218fa4d8bb9\",\n \"score\": \"0.6390347\",\n \"text\": \"def register_hooks(self):\\n\\n self.on_pre_GET += on_list\\n self.on_delete_item += on_delete\\n self.on_insert += init_document_with_acl\\n self.on_update += on_update\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb89b8f5d17bf4cb987b08cf0ec8efdc\",\n \"score\": \"0.63517725\",\n \"text\": \"def pytest_addhooks(pluginmanager) -> None:\\n from . import newhooks\\n\\n pluginmanager.add_hookspecs(newhooks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08edef69582cf0ea9d879b20c97d29c6\",\n \"score\": \"0.63485664\",\n \"text\": \"def init_hooks():\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"988c259581ed00239a5e4b0bb9dfd80f\",\n \"score\": \"0.6284471\",\n \"text\": \"def run_hooks(self, config):\\n for hook in self.setup_hooks:\\n hook(config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddfac105d4a5917c847228d02b558d0c\",\n \"score\": \"0.6270114\",\n \"text\": \"def register_hook(self, hook):\\n self._hooks.append(hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63d9dee33b36016b6d95e7ff7079755f\",\n \"score\": \"0.6270062\",\n \"text\": \"def pytest_addhooks(pluginmanager):\\n from seekret.apitest.pytest_plugin import newhooks\\n pluginmanager.add_hookspecs(newhooks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b98798e5110a7ae6e8ac8286d29d7c3\",\n \"score\": \"0.6247726\",\n \"text\": \"def add_events(self):\\n self.bot.event(self.on_ready)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fed26d9ec1952a689b322a0a269bff07\",\n \"score\": \"0.6242092\",\n \"text\": \"def _addAEHooks():\\n # Realflow uses the AEshapeHooks global variable as a convention for sharing AEshapeTemplate overrides,\\n # so we will too, unless a more popular convention is found.\\n pm.melGlobals.initVar('string[]', 'AEshapeHooks')\\n hooks = list(pm.melGlobals['AEshapeHooks'])\\n import mtoa.ui.ae.templates\\n procName = utils.pyToMelProc(mtoa.ui.ae.templates.loadArnoldTemplate, [('string', 'nodeName')], useName=True)\\n hooks.append(procName)\\n pm.melGlobals['AEshapeHooks'] = hooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afbcf0e45ef1bcc1330e7d298d50d166\",\n \"score\": \"0.62211084\",\n \"text\": \"def add_hook(self, command, method):\\n self.hooks[command] = method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24452a621b7bae3da58941d871f16db\",\n \"score\": \"0.6214256\",\n \"text\": \"def install_hooks(self, pipe):\\n assert len(self.hooks) == 0\\n text_encoder_targets = self._get_target_modules(pipe.text_encoder, \\\"lora_te\\\", [\\\"CLIPAttention\\\", \\\"CLIPMLP\\\"])\\n unet_targets = self._get_target_modules(pipe.unet, \\\"lora_unet\\\", [\\\"Transformer2DModel\\\", \\\"Attention\\\"])\\n for name, target_module in text_encoder_targets + unet_targets:\\n hook = LoRAHook()\\n hook.install(target_module)\\n self.hooks[name] = hook\\n # print(name)\\n\\n self.device = pipe.device\\n self.dtype = pipe.unet.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5be4d905626ef49be5a636ab0ea8ee5\",\n \"score\": \"0.6170191\",\n \"text\": \"def game_hooks() -> GameHooks:\\n return _game_hooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1484d616169295d164f73b32366494ad\",\n \"score\": \"0.6154442\",\n \"text\": \"def run(self, hooks):\\n if settings['disable_hooks']:\\n return\\n\\n queue = []\\n for value in hooks.values():\\n value = seedlib.apply_template(value, self.values)\\n queue.append(value)\\n \\n for command in queue:\\n print ('info: running hook \\\"%s\\\"' % command)\\n seedlib.run_pipe(command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"482eb32d67492d3848f3b2001ca25120\",\n \"score\": \"0.61518055\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Events(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40ea95a597c036314c0ac0b5a4bb00d\",\n \"score\": \"0.6124972\",\n \"text\": \"def startHook(self):\\n # Empty \",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5b43f0224a2a54a6a30b1fdb6d5b8f1\",\n \"score\": \"0.6113805\",\n \"text\": \"def set_bottle_hooks(self, app, shutdown, rollback):\\n if hasattr(app, 'hook'):\\n app.hook('after_request')(shutdown)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"925a2a709e21bed37d6e591bc6029891\",\n \"score\": \"0.611031\",\n \"text\": \"def install_hooks(config):\\n config.on_create.extend([copy_environ, hide_cookie])\\n config.on_create.insert(0, update_report)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c60de55b36b553993bc477dfb188a7\",\n \"score\": \"0.607913\",\n \"text\": \"def add_hook(**_kwargs):\\n hook = import_hook.create_hook(\\n hook_name=__name__,\\n parse_source=parse_source,\\n )\\n return hook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44b466b97d7a6660a00d78c539037c5\",\n \"score\": \"0.6075598\",\n \"text\": \"def add_hook(self, hook):\\n hook_addr = hook.identifier\\n \\n if self.hooks.has_key(hook_addr):\\n self.hooks[hook_addr].append(hook)\\n logging.debug(\\\"Adding another hook for addr 0x%08x (name: %s)\\\" % \\\\\\n (hook_addr,\\n hook.name))\\n else:\\n self.hooks[hook_addr] = [hook]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8255d4c578bc301629d6436a109124a\",\n \"score\": \"0.60621554\",\n \"text\": \"async def initialize_code_hook_functions(self):\\n for ability in await self.data_svc.locate('abilities'):\\n for executor in ability.executors:\\n if executor.code and executor.language:\\n executor.HOOKS[executor.language] = self.generate_ability_execution_method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b80ce9f5a3f87e9bb9c1642bdcfb4a3\",\n \"score\": \"0.60540694\",\n \"text\": \"def setup(bot):\\n bot.add_cog(EventLoops(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c45dce46acba5ba5710436ac8ac26b6\",\n \"score\": \"0.6000275\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Logs(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d5b30cf031b019fc2c3b8fed1a6dee\",\n \"score\": \"0.5998982\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Tags(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1944791d46eca2a6bbb32d2297613235\",\n \"score\": \"0.59902436\",\n \"text\": \"def assign_standard_hooks(obj, get, conf):\\n hook_names = ['step_hook', 'post_process_hook',\\n 'post_process_hook_final', 'pre_process_hook']\\n for hook_name in hook_names:\\n setattr(obj, hook_name, conf.get_function(get(hook_name, None)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c773fd5a7366f71b8bc48cf16391dd3a\",\n \"score\": \"0.59853834\",\n \"text\": \"def _register_hook(self, hook_name, func):\\r\\n self.hooks.setdefault(hook_name, []).append(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5a3262430aa75a2dc4230898591f488\",\n \"score\": \"0.59784955\",\n \"text\": \"def add_hook(self, level, location, hook, position=-1):\\n\\n if location not in self.levels[level].hooks:\\n self.levels[level].hooks[location] = []\\n\\n if position == -1:\\n position = len(self.levels[level].hooks[location])\\n\\n self.levels[level].hooks[location].insert(position, hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc99595a1169c0fa58e110dd5336a50f\",\n \"score\": \"0.59615684\",\n \"text\": \"def add_hook(**_kwargs):\\n hook = import_hook.create_hook(\\n hook_name=__name__,\\n source_init=source_init,\\n transform_source=transform_source,\\n )\\n return hook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc99595a1169c0fa58e110dd5336a50f\",\n \"score\": \"0.59615684\",\n \"text\": \"def add_hook(**_kwargs):\\n hook = import_hook.create_hook(\\n hook_name=__name__,\\n source_init=source_init,\\n transform_source=transform_source,\\n )\\n return hook\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42af3093cb3fc5de769600b2d9bcde98\",\n \"score\": \"0.5953135\",\n \"text\": \"def run_hooks(hooks, data):\\n for hook_key, hook_class in hooks.items():\\n hook = hook_class(data)\\n log.info('Running {} hook'.format(hook_key))\\n hook.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a8b923b37eba8f732792eb53cb6b71\",\n \"score\": \"0.5936176\",\n \"text\": \"async def run_init_hooks(self) -> None:\\n self.logger.info(\\\"Running init hooks\\\")\\n for hook in self.init_hooks:\\n self.logger.info(f\\\"Running init hook for plugin {hook.plugin}\\\")\\n await hook.func() if inspect.iscoroutinefunction(hook.func) else hook.func()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65c171195ea46e096e3c1c55ae1c291\",\n \"score\": \"0.5925443\",\n \"text\": \"def on(self, hook):\\n\\n def decorator(handler):\\n self.registry[hook].append(handler)\\n\\n return handler\\n\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"792e20dbb3796793cf5123be2589cedc\",\n \"score\": \"0.59254354\",\n \"text\": \"def register_hook(self, func):\\r\\n\\r\\n if func not in self.hooks:\\r\\n self.hooks.append(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"992e660201db15b027d78bbf73909ecd\",\n \"score\": \"0.5917185\",\n \"text\": \"def _before_application_run(self):\\n\\n for hook in self._get_hooks():\\n hook.before_application_run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4098a25602a66288543c0be8a1720c0a\",\n \"score\": \"0.59140986\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Dictionary())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128feab0e9e690fd164055f3dc6224ce\",\n \"score\": \"0.5912825\",\n \"text\": \"def _after_application_loaded(self):\\n\\n for hook in self._get_hooks():\\n hook.after_application_loaded()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3b2b4a6f0402b41ae4eff4cf4fc845\",\n \"score\": \"0.5884704\",\n \"text\": \"def on_start(self):\\n self.add_bot(ScoutManager(bot_player=self))\\n self.add_bot(BuildManager(bot_player=self))\\n self.add_bot(GatherManager(bot_player=self))\\n self.add_bot(DefenseManager(bot_player=self))\\n self.init_request_board()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a582bbb4bf0a67821652519bd94f5ec\",\n \"score\": \"0.588175\",\n \"text\": \"def add_hook(cls, hook_type, hook_func):\\r\\n if hook_type not in cls._hooks_map:\\r\\n cls._hooks_map[hook_type] = []\\r\\n\\r\\n cls._hooks_map[hook_type].append(hook_func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"200dd6ea332f6486cfb958496dd7eafe\",\n \"score\": \"0.5878965\",\n \"text\": \"async def setup(bot) -> None:\\n await bot.add_cog(HotReload(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cbfc3f4a2d622fbd0030e22de81d12\",\n \"score\": \"0.58777094\",\n \"text\": \"def setup(bot: Bot):\\n bot.add_cog(EventoryCog(bot))\\n log.info(\\\"loaded Eventory extension!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"269b00d45f4e69768306379771ce086d\",\n \"score\": \"0.5848767\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Utilities(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a5ab4cf1d8372d05b8687ae7d4fdc2\",\n \"score\": \"0.58468145\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Tickets(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bae9f0da4d2e05b9bdea26aa011ab59a\",\n \"score\": \"0.58353835\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(LemonStuff(bot))\\n log.info(\\\"Cog loaded: LemonStuff\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ca07bcea77b5998babc1f8f475dda35\",\n \"score\": \"0.5832757\",\n \"text\": \"def hooks(self):\\n return self.__hooks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6d22ce1884a7faa025d158e62aa578e\",\n \"score\": \"0.5821258\",\n \"text\": \"def hook_dispatch(self) -> None:\\n if sys.argv[2].strip().lower() == \\\"add\\\":\\n self.add_hook(\\n hook_state = sys.argv[3],\\n cmd = \\\" \\\".join(sys.argv[4:]),\\n )\\n\\n elif sys.argv[2].strip().lower() == \\\"del\\\":\\n self.del_hook(\\n hook_state = sys.argv[3],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e666c8c599a3a0a56b13e57e58b45c50\",\n \"score\": \"0.58076733\",\n \"text\": \"def _add_trachook(self, req, hookfile, current):\\n base_url = self.config.get('trac', 'base_url')\\n # try to append hook\\n try:\\n fp = open(hookfile, 'a')\\n fp.writelines(os.linesep)\\n if 'REPOS=\\\"$1\\\"' not in current:\\n fp.writelines('REPOS=\\\"$1\\\"' + os.linesep)\\n if 'REV=\\\"$2\\\"' not in current:\\n fp.writelines('REV=\\\"$2\\\"' + os.linesep)\\n if 'LOG=' not in current:\\n fp.writelines('LOG=`/usr/bin/svnlook log -r $REV $REPOS`' \\\\\\n + os.linesep)\\n if 'AUTHOR=' not in current:\\n fp.writelines('AUTHOR=`/usr/bin/svnlook author -r $REV ' + \\\\\\n '$REPOS`' + os.linesep)\\n if 'TRAC_ENV=' not in current:\\n fp.writelines('TRAC_ENV=\\\"'+self.env.path+'\\\"' + os.linesep)\\n if 'TRAC_URL=' not in current:\\n fp.writelines('TRAC_URL=\\\"'+base_url+'\\\"' + os.linesep)\\n fp.writelines('/usr/bin/python /usr/share/trac/contrib/' + \\\\\\n 'trac-post-commit-hook \\\\\\\\' + os.linesep)\\n fp.writelines('-p \\\"$TRAC_ENV\\\" \\\\\\\\' + os.linesep)\\n fp.writelines('-r \\\"$REV\\\" \\\\\\\\' + os.linesep)\\n fp.writelines('-u \\\"$AUTHOR\\\" \\\\\\\\' + os.linesep)\\n fp.writelines('-m \\\"$LOG\\\" \\\\\\\\' + os.linesep)\\n fp.writelines('-s \\\"$TRAC_URL\\\"' + os.linesep)\\n fp.writelines(os.linesep)\\n fp.close()\\n except Exception:\\n raise TracError(\\\"Can't write repository hook %s\\\" % hookfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a651ece69c23f871ae87944408d516\",\n \"score\": \"0.5799101\",\n \"text\": \"def run_hooks(cls, hook_type, *args, **kwargs):\\r\\n hook_funcs = cls._hooks_map.get(hook_type) or []\\r\\n for hook_func in hook_funcs:\\r\\n hook_func(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fcf31c68e866cf847c0a5373217c2b3\",\n \"score\": \"0.5785056\",\n \"text\": \"def register_default_hooks(self, *hooks: Hook, as_group: bool = True):\\n self.hooks.extend(hooks)\\n\\n if as_group and self.group is not None:\\n for g in get_group_by_name(self.group):\\n if g is not self:\\n g.register_hooks(*hooks, as_group=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"396772b6a60c6762461d747ec1f4b8dc\",\n \"score\": \"0.5783806\",\n \"text\": \"async def setup(bot) -> None:\\n await bot.add_cog(Gambling(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f50c868844e933e05acf6e0224d81642\",\n \"score\": \"0.57818186\",\n \"text\": \"def install_custom_hook(self, hook_name):\\n self._install_custom_hook(hook_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e677a9f8d0ec6ba94293c47993d275df\",\n \"score\": \"0.5769153\",\n \"text\": \"def add_hook(self,\\n hook_state: str,\\n cmd: str,\\n ) -> None:\\n state = hook_state.strip().lower()\\n\\n if state not in self.valid_hook_states:\\n print(f\\\"Invalid hook state: {hook_state}. Valid states are: {self.valid_hook_states}\\\")\\n return\\n\\n # At this point we simply overwrite any existing command\\n # and store it to the data file\\n data = self.load()\\n data[\\\"hooks\\\"][state] = cmd\\n self.save(data)\\n\\n # If cmd is None this function as been called from within del_hook()\\n if cmd is None:\\n print(f\\\"Deleted hook command for state {state}\\\")\\n else:\\n print(f\\\"Added hook command for state {state}: {cmd}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c18e6f43da52ca2d550b82ddba2845\",\n \"score\": \"0.5764374\",\n \"text\": \"def install_api_hooks(dbg, dll_api_hooks):\\n # _pt_log(\\\"remain system dlls to install api hook: %d\\\" % (len(dll_api_hooks)))\\n\\n new_sys_dll = dbg.system_dlls[-1]\\n dll_name = new_sys_dll.name.lower()\\n\\n for (dll_name, api_ctrl_list) in dll_api_hooks.items():\\n if dbg.check_has_system_dll(dll_name):\\n for api_ctrl in api_ctrl_list:\\n dbg_set_api_bp(dbg, api_ctrl)\\n del dll_api_hooks[dll_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e63584e75c449d989e5334871a61d146\",\n \"score\": \"0.574739\",\n \"text\": \"def setup(bot):\\n bot.add_cog(Messages(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba6688fa7d65bc9590cd3596cc6eead6\",\n \"score\": \"0.5747233\",\n \"text\": \"def InstallHook(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17e625fb36c2298dbac9f5647e71522\",\n \"score\": \"0.5714983\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(MessageLogCog(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efb0f516bd6a1e7fe91485da5245ead6\",\n \"score\": \"0.5709663\",\n \"text\": \"def registerhooks(entry=[], exit=[], *args, **kwargs):\\n def wrap(f):\\n _kwargs = dict(kwargs)\\n _kwargs.update({'entry': entry, 'exit': exit})\\n def wrapped_f(*args, **kwargs):\\n return f(*args, **kwargs)\\n wrapped_f.__hooks = _kwargs\\n return wrapped_f\\n return wrap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044a2ad7b8e51eec0d32c615365ec6bc\",\n \"score\": \"0.570686\",\n \"text\": \"def setup(bot):\\n bt.INFO('Loading Misc.py')\\n bot.add_cog(Misc(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71fa5b3abe2be9c9c55ea45b9c82dd43\",\n \"score\": \"0.56982493\",\n \"text\": \"async def setup(self) -> None:\\n if self.playing_title is not None:\\n await self.change_presence(\\n activity=discord.Game(name=self.playing_title),\\n )\\n\\n self.tree.clear_commands(guild=None)\\n for guild in self.guilds:\\n self.tree.clear_commands(guild=guild)\\n\\n commands = registered_app_commands()\\n for command in commands:\\n self.tree.add_command(command)\\n logger.info(f'registered {len(commands)} app commands')\\n\\n listeners = registered_listeners()\\n for listener in listeners:\\n self.add_listener(listener.func, listener.event)\\n logger.info(f'registered {len(listeners)} listeners')\\n\\n if self._cmd_group_exts is not None:\\n for ext in self._cmd_group_exts:\\n self.tree.add_command(ext)\\n await ext.post_init(self)\\n logger.info(f'registered {ext.name} command group')\\n\\n await self.tree.sync()\\n for guild in self.guilds:\\n await self.tree.sync(guild=guild)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fccb4dede5aa12489b6365d7dac5f30b\",\n \"score\": \"0.5684656\",\n \"text\": \"def setup(bot):\\n bot.add_cog(LoadHelp(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ace20514f392a89f07428e9134c5bc\",\n \"score\": \"0.5670448\",\n \"text\": \"def list_hooks(self) -> None:\\n data = self.load()\\n hooks = data.get(\\\"hooks\\\", {})\\n\\n print(\\\"Current hook command configuration:\\\")\\n for state in self.valid_hook_states:\\n cmd = hooks.get(state, None)\\n print(f\\\" {state:6s} {cmd}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c13e1b3cbfa89944d2a510fb35f6ed58\",\n \"score\": \"0.5668042\",\n \"text\": \"def on_bot_startup(bot):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8ba5a4c77b793ba734b50ced6e7aaf\",\n \"score\": \"0.56633395\",\n \"text\": \"def register(name):\\r\\n global SUPPORTED_HOOKS\\r\\n if not name in SUPPORTED_HOOKS:\\r\\n SUPPORTED_HOOKS.append(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad29e41de5a20f7deb3ed694f518194\",\n \"score\": \"0.5659891\",\n \"text\": \"def add_permissions_hook(self, hook: commands.CheckPredicate) -> None:\\n self._permissions_hooks.append(hook)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d8761c3de9dfeea1cd5f99b7ec825c\",\n \"score\": \"0.56558925\",\n \"text\": \"def register_hook(self, event, hook):\\r\\n\\r\\n if isinstance(hook, collections.Callable):\\r\\n self.hooks[event].append(hook)\\r\\n elif hasattr(hook, '__iter__'):\\r\\n self.hooks[event].extend(h for h in hook if isinstance(h, collections.Callable))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3258ecf3f6e2c20cf1fd7f7cecca9cc6\",\n \"score\": \"0.5652265\",\n \"text\": \"def get_hooks():\\n tensor_to_log = {'loss': loss}\\n # tensor_to_log = {}\\n logging_tensor_hook = tf.train.LoggingTensorHook(\\n tensors=tensor_to_log,\\n every_n_iter=2\\n )\\n return [logging_tensor_hook]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c7449a2267ac9fd64c1310ad51e382\",\n \"score\": \"0.56491655\",\n \"text\": \"def apply_hooks():\\n for line in fileinput.input():\\n std_input = line.split(\\\" \\\")\\n oldrev, newrev, refname = [item.strip() for item in std_input]\\n # Check for zero commit, check branch deletions\\n # also, avoid new package additions\\n if ZERO_COMMIT in (oldrev, newrev):\\n continue\\n # insert new hooks here\\n package_start_build(oldrev, newrev, refname)\\n # Pass in 'length' as an optional argument\\n # default 'length' of feed is 499\\n write_rss_feed(oldrev, newrev, refname, length=499)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70c1c6821cc9de1cb8cab705ac6ee85\",\n \"score\": \"0.5649146\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Internal(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6a737d4f0384ad0b93c99010ebf635\",\n \"score\": \"0.5637375\",\n \"text\": \"def hook(self, server=None):\\n\\n if server is not None:\\n logging.debug('Adding hook route to web server')\\n server.add_route(\\n Wrapper(callable=self.get_hook(),\\n route=self.context.get('server.hook', '/hook')))\\n\\n if (self.context.get('server.binding') is not None\\n and self.context.get('server.url') is not None):\\n\\n self.space.register(\\n hook_url=self.context.get('server.url')\\n + self.context.get('server.hook', '/hook'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b50dc58fd7d07d03898d7d1205095a7\",\n \"score\": \"0.56359833\",\n \"text\": \"def setup(bot):\\n\\n bot.add_cog(Eval(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b683c47d5bdae345391754b51c5bac6f\",\n \"score\": \"0.5632748\",\n \"text\": \"def add_callback(self, hook, function, pre=False):\\n if hook not in self._hooks:\\n raise Exception('This program has no hook named \\\"%s\\\"' % hook)\\n hook_def = self._hook_defs.get(hook, None)\\n\\n if (hook_def is None or not isinstance(hook_def, FunctionChain)):\\n raise TypeError(\\\"Cannot add callback to hook '%s'; not a \\\"\\n \\\"FunctionChain. (%s)\\\" % (hook, type(hook_def)))\\n\\n if pre:\\n hook_def.insert(0, function)\\n else:\\n hook_def.append(function)\\n\\n # TODO: remove or resurrect\\n #self._install_dep_callbacks(function)\\n self._need_build = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4499e1842dac2feaba1f51e52a73f48\",\n \"score\": \"0.56314707\",\n \"text\": \"def run_hooks():\\n\\n hookdir = '/usr/lib/oem-config/post-install'\\n\\n if os.path.isdir(hookdir):\\n # Exclude hooks containing '.', so that *.dpkg-* et al are avoided.\\n hooks = filter(lambda entry: '.' not in entry, os.listdir(hookdir))\\n child_env = dict(os.environ)\\n child_env['DEBIAN_FRONTEND'] = 'noninteractive'\\n if 'DEBIAN_HAS_FRONTEND' in child_env:\\n del child_env['DEBIAN_HAS_FRONTEND']\\n for hookentry in hooks:\\n hook = os.path.join(hookdir, hookentry)\\n if os.access(hook, os.X_OK):\\n # Errors are ignored at present, although this may change.\\n subprocess.call([hook], env=child_env)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b864916beac1e7a3522c10bae5c0f74\",\n \"score\": \"0.56313306\",\n \"text\": \"def run_hook(db, hook_name, **kwargs):\\n\\n\\t# Get all hooks in the DB\\n\\thooks = db.get_hook()\\n\\thooks = [dict(_)['name'] for _ in hooks]\\n\\n\\t# Iteratue through the hooks\\n\\tfor hook in hooks:\\n\\t\\t# Try importing the hook module and finding any functions that have used the ydl.hook decorate\\n\\t\\t# which means it has a _hooks attribute indicating which hooks it accepts\\n\\t\\ttry:\\n\\t\\t\\tz = importlib.import_module(hook)\\n\\t\\t\\tfor x in dir(z):\\n\\t\\t\\t\\to = getattr(z, x)\\n\\t\\t\\t\\tif hasattr(o, '_hooks') and hook_name in o._hooks:\\n\\t\\t\\t\\t\\to(hook_name, db, **kwargs)\\n\\t\\t\\t\\tdel o\\n\\t\\t\\tdel z\\n\\t\\texcept Exception as e:\\n\\t\\t\\ttraceback.print_exc()\\n\\t\\t\\t# Move along\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cfa6dc8d943c488df97857dd75522ee\",\n \"score\": \"0.5624743\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(Reminder(bot))\\n log.info(\\\"Cog loaded: reminder\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7df076bc40f988cd86f24cb3e5db89e\",\n \"score\": \"0.56241655\",\n \"text\": \"def setup(bot: Bot) -> None:\\n bot.add_cog(SpookyEightBall())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53fa260f6f97a677c52fabbf44b86b38\",\n \"score\": \"0.5622936\",\n \"text\": \"def add_commands(self, *args):\\n for command in args:\\n self._script += command + '\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7496c40c7d9e4b36c3df9cb62d2c5ad4\",\n \"score\": \"0.55818033\",\n \"text\": \"def run(self, hook, *args, **kwargs):\\n\\n assert hook in self._callbacks, f\\\"hook '{hook}' not found in callbacks {self._callbacks}\\\"\\n\\n for logger in self._callbacks[hook]:\\n logger['callback'](*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9499c2213ce0272e0a32ffd4439f8e08\",\n \"score\": \"0.5580782\",\n \"text\": \"def _init_airflow_core_hooks(self):\\n core_dummy_hooks = {\\n \\\"generic\\\": \\\"Generic\\\",\\n \\\"email\\\": \\\"Email\\\",\\n \\\"mesos_framework-id\\\": \\\"Mesos Framework ID\\\",\\n }\\n for key, display in core_dummy_hooks.items():\\n self._hooks_lazy_dict[key] = HookInfo(\\n hook_class_name=None,\\n connection_id_attribute_name=None,\\n package_name=None,\\n hook_name=display,\\n connection_type=None,\\n connection_testable=False,\\n )\\n for cls in [FSHook, PackageIndexHook]:\\n package_name = cls.__module__\\n hook_class_name = f\\\"{cls.__module__}.{cls.__name__}\\\"\\n hook_info = self._import_hook(\\n connection_type=None,\\n provider_info=None,\\n hook_class_name=hook_class_name,\\n package_name=package_name,\\n )\\n self._hook_provider_dict[hook_info.connection_type] = HookClassProvider(\\n hook_class_name=hook_class_name, package_name=package_name\\n )\\n self._hooks_lazy_dict[hook_info.connection_type] = hook_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fd2b1eb01258336636e0bd8adc0178b\",\n \"score\": \"0.5579576\",\n \"text\": \"def _trigger_hooks(self, hook_dict, hook_name, **kwargs):\\n for attached_callback in hook_dict[hook_name].attached_callbacks:\\n try:\\n if attached_callback.callback(**kwargs):\\n if attached_callback.action == MMHookAct.LOAD_MODULE:\\n # load the module!\\n self.logger.debug('some hook returned true, '\\n 'loading module {}'\\n .format(attached_callback.argument))\\n # module must accept same kwargs, this is mandatory\\n # with this discovery event\\n try:\\n cb_arg = attached_callback.argument\\n module_name = cb_arg.get_module_desc().arg_name\\n self.load_module(module_name,\\n **kwargs)\\n except Exception as ex:\\n self.logger.error('loading of module of class '\\n '\\\"{}\\\" failed with: {}'\\n .format(cb_arg.__name__,\\n str(ex)))\\n elif attached_callback.action == MMHookAct.UNLOAD_MODULE:\\n # unload the attached module\\n self.logger.debug('a hook required module '\\n '{} to be unloaded'\\n .format(attached_callback.argument))\\n self.unload_module(attached_callback.argument)\\n except Exception as ex:\\n self.logger.error('failed to call function '\\n '{} attached to \\\"{}\\\" with: {}'\\n .format(attached_callback,\\n hook_name,\\n str(ex)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19e4deca1d85266e102d958ffa41ff0\",\n \"score\": \"0.55784094\",\n \"text\": \"def setup(bot):\\n bot.add_cog(ReminderCog(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e2d79bb76fc224f12b869daf1739030\",\n \"score\": \"0.55750185\",\n \"text\": \"def runHook(self):\\n self.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a78408bd292126b7640c238bfb0c8ec8\",\n \"score\": \"0.55676615\",\n \"text\": \"async def setup(bot: Bot) -> None:\\n await bot.add_cog(Patreon(bot))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05f45412830a1b3a2b2c9b7bdb0d1cbb\",\n \"score\": \"0.556712\",\n \"text\": \"def hooks(self) -> MutableMapping[str, HookInfo | None]:\\n self.initialize_providers_hooks()\\n # When we return hooks here it will only be used to retrieve hook information\\n return self._hooks_lazy_dict\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":193,"cells":{"query_id":{"kind":"string","value":"0678ed29cb4ed04785fd661416546558"},"query":{"kind":"string","value":"Check is a path is a broken symlink."},"positive_passages":{"kind":"list like","value":[{"docid":"202729acc69cd2b9313649493f56ef9f","score":"0.870235","text":"def is_symlink_broken(path):\n if os.path.islink(path):\n return not os.path.exists(os.readlink(path))\n else:\n raise TypeError('path={!r} is not a symbolic link'.format(path))","title":""}],"string":"[\n {\n \"docid\": \"202729acc69cd2b9313649493f56ef9f\",\n \"score\": \"0.870235\",\n \"text\": \"def is_symlink_broken(path):\\n if os.path.islink(path):\\n return not os.path.exists(os.readlink(path))\\n else:\\n raise TypeError('path={!r} is not a symbolic link'.format(path))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"71564a56fc163c8e7d254c575b623e30","score":"0.7800918","text":"def checklink(path):\n # mktemp is not racy because symlink creation will fail if the\n # file already exists\n name = tempfile.mktemp(dir=path)\n try:\n os.symlink(\".\", name)\n os.unlink(name)\n return True\n except (OSError, AttributeError):\n return False","title":""},{"docid":"1301b1cb5da838eaea975a866ca6bb1f","score":"0.7800738","text":"def test_is_symlink(path: Path) -> None:\n assert (path / \"link\").is_symlink()","title":""},{"docid":"b1c304a94d85d6653cded13e445a0b19","score":"0.7560013","text":"def is_symlink(self):\n try:\n return S_ISLNK(self.lstat().st_mode)\n except OSError as e:\n if e.errno not in (ENOENT, ENOTDIR):\n raise\n # Path doesn't exist\n return False","title":""},{"docid":"45e1ac62ad49004dab7dc483330692cb","score":"0.75018257","text":"def is_symlink(self):\n return False","title":""},{"docid":"896891d9194d8ad8e3c7b93d9f4735d6","score":"0.7391739","text":"def test_symlink_on_unknown_file(self):\n root = URI(self.baseurl)\n notexisting_path = root / 'ma' / 'moo'\n tee_path = root / 'helloworld'\n notexisting_path.symlink(tee_path)\n\n self.assertTrue(tee_path.islink())\n self.assertEqual(tee_path.readlink(), notexisting_path)\n self.assertTrue(not notexisting_path.exists())","title":""},{"docid":"9543b5f7076123e979ef23a52736cca5","score":"0.7297981","text":"def test_symlink_missing_src(file, source):\n target = source.parent / \"symlink.lnk\"\n missing_source = source.parent / \"missing.txt\"\n try:\n file.symlink(str(missing_source), str(target))\n assert salt.utils.path.islink(str(target))\n finally:\n target.unlink()","title":""},{"docid":"faaf23de79bf29e8868d7acf350b1324","score":"0.72364473","text":"def islink(path):\r\n return False","title":""},{"docid":"117fd290d3a937d8daa06de391ab888b","score":"0.7161739","text":"def islink(self, path):\n path = ftputil.tool.as_unicode(path)\n try:\n lstat_result = self._host.lstat(\n path, _exception_for_missing_path=False)\n except ftputil.error.RootDirError:\n return False\n else:\n if lstat_result is None:\n # Non-existent path\n return False\n else:\n return stat.S_ISLNK(lstat_result.st_mode)","title":""},{"docid":"7e466325a1eb6f6c43507803db038e87","score":"0.7131687","text":"def islink(path):\r\n try:\r\n st = os.lstat(path)\r\n except (os.error, AttributeError):\r\n return False\r\n return stat.S_ISLNK(st.st_mode)","title":""},{"docid":"e1f68b0566a7c14c2badd11901266889","score":"0.71089053","text":"def is_symlink(self):\n if os.name == \"posix\": return os.path.islink(self.path)\n if os.name == \"nt\":\n import win32api\n import win32con\n num = win32con.FILE_ATTRIBUTE_REPARSE_POINT\n return bool(win32api.GetFileAttributes(self.path) & num)","title":""},{"docid":"dac7484c1ede678b11d426cfd0dff2ef","score":"0.71058583","text":"def islink(path):\n try:\n st = os.lstat(path)\n except (OSError, AttributeError):\n return False\n return stat.S_ISLNK(st.st_mode)","title":""},{"docid":"dac7484c1ede678b11d426cfd0dff2ef","score":"0.71058583","text":"def islink(path):\n try:\n st = os.lstat(path)\n except (OSError, AttributeError):\n return False\n return stat.S_ISLNK(st.st_mode)","title":""},{"docid":"4c1aec9e1a0da95eb624cbc8cdd68e79","score":"0.70764434","text":"def test_symlink_exists_different(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n with pytest.raises(CommandExecutionError) as exc:\n file.symlink(str(source), str(target))\n assert \"Found existing symlink:\" in exc.value.message\n finally:\n target.unlink()","title":""},{"docid":"8816e12f05b8fce70a4dc0a21726d931","score":"0.70211464","text":"def test_symlink_exists_different_force(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n file.symlink(str(source), str(target), force=True)\n assert salt.utils.path.readlink(str(target)) == str(source)\n finally:\n target.unlink()","title":""},{"docid":"bb95c61abb8f5354e7e9d4d715e4ef7a","score":"0.6982841","text":"def test_readlink(path: Path) -> None:\n assert PurePath(\"file\") == (path / \"link\").readlink()","title":""},{"docid":"f6c1e77dc34b5c401947222e0bbf911a","score":"0.6982368","text":"def _is_link(self, path):\n return os.path.islink(os.path.expanduser(path))","title":""},{"docid":"8627bffa0d7e759f5d4710ef759afa77","score":"0.69228035","text":"def is_symlink(self):\n if self._is_symlink is None:\n self._is_symlink = os.path.islink(self.storage_url.object_name)\n return self._is_symlink","title":""},{"docid":"fe21b7cecc2c9973a253737963706133","score":"0.68756634","text":"def test_constrain_symlinks_to_filesystem() -> None:\n path = root(\n {\n \"file\": \"text\",\n \"dir\": {\"link\": PurePath(\"..\", \"file\")},\n },\n innerpath=PurePath(\"dir\"),\n )\n # XXX Should we rewrite the error message to hide the 'dir/' prefix?\n with pytest.raises(FileNotFoundError, match=\"file not found: dir/file\"):\n assert (path / \"link\").read_text()","title":""},{"docid":"923a01647ae572518a7cb698b01d2789","score":"0.6867287","text":"def test_symlink_exists_file(file, source):\n with pytest.helpers.temp_file(\"symlink.txt\", contents=\"Source content\") as target:\n with pytest.raises(CommandExecutionError) as exc:\n file.symlink(str(source), str(target))\n assert \"Existing path is not a symlink:\" in exc.value.message","title":""},{"docid":"41bff0c324b7c71a826535c2a5479b5c","score":"0.6856679","text":"def test_symlink(file, source):\n target = source.parent / \"symlink.lnk\"\n try:\n file.symlink(str(source), str(target))\n assert salt.utils.path.islink(str(target))\n finally:\n target.unlink()","title":""},{"docid":"1d7a46dd208c245d602979b6f8c24590","score":"0.68321455","text":"def test_symlink_exists_same(file, source):\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(source)\n try:\n before_time = os.stat(str(target)).st_mtime\n ret = file.symlink(str(source), str(target))\n after_time = os.stat(str(target)).st_mtime\n assert before_time == after_time\n assert ret is True\n finally:\n target.unlink()","title":""},{"docid":"73c3309bca6d764f2d429a94c01e2867","score":"0.68014234","text":"def is_symlink(\n linkfile: T.Union[str, pathlib.Path],\n src: T.Union[None, str, pathlib.Path] = None,\n) -> bool:\n linkfile = pathlib.Path(linkfile)\n if not linkfile.is_symlink():\n return False\n\n if src is None:\n return True\n\n return linkfile.resolve() == pathlib.Path(src).resolve()","title":""},{"docid":"6a5673d6915d6847708663b71cf2eab7","score":"0.67836535","text":"def check_if_relative_link(link):\n\n if not link.startswith(\"http\"):\n if not link.startswith(\"/\"):\n return True\n return False","title":""},{"docid":"1067f2b5ebb747c4d55bca1975463a6a","score":"0.6782696","text":"def _valid_link(self, link, fail_on_disk_check=True):\n\n if not link in self._links:\n raise Exception('link [{0}] not found in timeline!'.format(link))\n\n if not os.path.islink(self._links[link]['path']):\n msg = 'link [{0}] not found!'.format(self._links[link]['path'])\n if fail_on_disk_check:\n raise Exception(msg)\n self.logger.warning(msg)\n return False\n\n if not os.path.exists(self._links[link]['path']):\n msg = 'link [{0}] is broken!'.format(self._links[link]['path'])\n if fail_on_disk_check:\n raise Exception(msg)\n self.logger.warning(msg)\n return False\n\n return True","title":""},{"docid":"968ba0654b4a586ad577e6ad741ee0ed","score":"0.6718646","text":"def is_symlink(self):\n return self.type == self.Type.SYMLINK","title":""},{"docid":"f7331b2df6b96881496979b7a913c461","score":"0.6693001","text":"def is_symlink(self):\n if (\n not self.custom_fields\n or resource_util.SYMLINK_METADATA_KEY not in self.custom_fields\n ):\n return False\n return (\n self.custom_fields[resource_util.SYMLINK_METADATA_KEY].lower() == 'true'\n )","title":""},{"docid":"81d65ef687eac4cb308d24311371b04b","score":"0.66920704","text":"def islink(self, path: AnyStr) -> bool:\n return self.filesystem.islink(path)","title":""},{"docid":"0a1435142fcb2dce5b3f51f29d763b77","score":"0.66706246","text":"def islink (self) :\n try :\n return stat.S_ISLNK (self.mode)\n except OSError :\n return 0","title":""},{"docid":"318e97be28a1d07940b9a69de3c8b1f6","score":"0.6630757","text":"def copy_if_symlink_fails(\n src: Union[str, Path],\n dst: Union[str, Path],\n backend_args: Optional[dict] = None,\n) -> bool:\n backend = get_file_backend(\n src, backend_args=backend_args, enable_singleton=True)\n return backend.copy_if_symlink_fails(src, dst)","title":""},{"docid":"8c3050fd54816a14c87c867d2e9a3748","score":"0.6584976","text":"def test_through_symlink(self) -> None:\n link1 = os.path.join(self.tmp_dir, \"link1\")\n os.symlink(self.mount, link1)\n\n info1 = json.loads(self.eden.run_cmd(\"info\", link1))\n self.assertEqual(self.mount, info1[\"mount\"])\n\n # Create a non-normalized symlink pointing to the parent directory\n # of the mount\n link2 = os.path.join(self.tmp_dir, \"mounts_link\")\n os.symlink(self.mount + \"//..\", link2)\n mount_through_link2 = os.path.join(link2, self.repo_name)\n\n info2 = json.loads(self.eden.run_cmd(\"info\", mount_through_link2))\n self.assertEqual(self.mount, info2[\"mount\"])","title":""},{"docid":"fc10782d2de274eef803bde25fbaf972","score":"0.65710294","text":"def test_symlink_exists_different_atomic(file, source):\n dif_source = source.parent / \"dif_source.txt\"\n target = source.parent / \"symlink.lnk\"\n target.symlink_to(dif_source)\n try:\n file.symlink(str(source), str(target), atomic=True)\n assert salt.utils.path.readlink(str(target)) == str(source)\n finally:\n target.unlink()","title":""},{"docid":"741e573a30028ce18a81c363e3d5a5e9","score":"0.6550941","text":"def islink(s):\n\n try:\n import Carbon.File\n return Carbon.File.ResolveAliasFile(s, 0)[2]\n except:\n return False","title":""},{"docid":"3b04b3104b1dd15775e4fb15a939ca49","score":"0.6517931","text":"def islink(pathname):\n from os import listdir\n from os.path import isdir, isfile\n import os.path\n pathname = pathname.rstrip(\"/\")\n if os.path.islink(pathname): return True\n elif isfile(pathname) and ishidden(pathname): return True\n elif isdir(pathname):\n entries = listdir(pathname)\n for entry in entries:\n if not islink(pathname+\"/\"+entry): return False\n return True\n else: return False","title":""},{"docid":"2c17a63c3f070a9ace82b381e0997881","score":"0.6510741","text":"def check_file(filename,path):\n chksumX = chksum_file(\"%s/%s\" % (path,filename))\n (pathY,chksumY) = find_file(filename,path,chksumX)\n if chksumX == chksumY:\n\thardlink(filename,path,pathY)","title":""},{"docid":"6924dff27d54d6cab5d242f412cc20a7","score":"0.6508725","text":"def silent_remove_symlink(path): # TODO: test\n result = {'error': ''}\n if symlink_exists(path):\n try:\n os.unlink(path)\n except OSError as e:\n result['error'] = str(e)\n return result","title":""},{"docid":"595ab9271ea617ec1ff274f308897593","score":"0.64662755","text":"def has_valid_link(dotfile, dotfiles_dir):\n homefolder_path = get_homefolder_path(dotfile, dotfiles_dir)\n if os.path.islink(homefolder_path) and os.readlink(homefolder_path) == dotfile:\n return True\n\n return False","title":""},{"docid":"b8b6fb9618f82b8429612697328e30ae","score":"0.6455614","text":"def is_valid_file(abs_path):\n if os.path.exists(abs_path) and os.path.isfile(abs_path) \\\n and not os.path.islink(abs_path):\n return True\n\n else: return False","title":""},{"docid":"8a6353f544288001a6c15f9a0558223e","score":"0.6414611","text":"def ismount_raw(path):\r\n try:\r\n s1 = os.lstat(path)\r\n except os.error as err:\r\n if err.errno == errno.ENOENT:\r\n # It doesn't exist -- so not a mount point :-)\r\n return False\r\n raise\r\n\r\n if stat.S_ISLNK(s1.st_mode):\r\n # A symlink can never be a mount point\r\n return False\r\n\r\n s2 = os.lstat(os.path.join(path, '..'))\r\n dev1 = s1.st_dev\r\n dev2 = s2.st_dev\r\n if dev1 != dev2:\r\n # path/.. on a different device as path\r\n return True\r\n\r\n ino1 = s1.st_ino\r\n ino2 = s2.st_ino\r\n if ino1 == ino2:\r\n # path/.. is the same i-node as path\r\n return True\r\n\r\n return False","title":""},{"docid":"8d851ba210920164362039bc52ea42fd","score":"0.6394149","text":"def is_linked(self) -> bool:\n return self.parent_path.is_symlink() \\\n and (not self.child_path.is_symlink()) \\\n and self.child_path.samefile(self.parent_path)","title":""},{"docid":"6b635c87ed41938fbf0245d78d827c98","score":"0.63874704","text":"def symlinks_supported():\n with tempfile.TemporaryDirectory() as temp_dir:\n original_path = os.path.join(temp_dir, 'original')\n symlink_path = os.path.join(temp_dir, 'symlink')\n os.makedirs(original_path)\n try:\n os.symlink(original_path, symlink_path)\n supported = True\n except (OSError, NotImplementedError):\n supported = False\n return supported","title":""},{"docid":"89bd6c13a93b82e506573bc794a24693","score":"0.6330474","text":"def check_path(data_pointer, log, msg):\n if not os.path.exists(data_pointer):\n log.debug(msg)\n return False\n else:\n return data_pointer","title":""},{"docid":"13fb5713db8da8497f3a87fe901a9745","score":"0.6315982","text":"def test_symlink_target_relative_path(file, source):\n target = \"..{}symlink.lnk\".format(os.path.sep)\n with pytest.raises(SaltInvocationError) as exc:\n file.symlink(str(source), str(target))\n assert \"Link path must be absolute\" in exc.value.message","title":""},{"docid":"0f87c41104cb5341d0b2c9ad58a4970e","score":"0.6286066","text":"def _resolve_symlinks_and_relative_paths(path):\n # type: pathlib.Path -> pathlib.Path\n try:\n path = path.resolve()\n except WindowsError: # TODO: check for more specific error\n pass\n except OSError as e:\n if e.errno == 2:\n pass\n else:\n raise\n\n return path","title":""},{"docid":"d75a5874ba22708116a98d08093a24ae","score":"0.6252006","text":"def check_path(path):\n if(os.path.exists(path) == False):\n print_error_and_exit(\"Cannot find %s\" % path)","title":""},{"docid":"3d007389a31ebd7fb16f28910d95422f","score":"0.62242776","text":"def symlink(self, target, source):\n if self.debug == True:\n logging.debug(\"in symlink\")\n raise FuseOSError(ENOENT)","title":""},{"docid":"90387b542107e8c28fba24a7fd822dad","score":"0.62149453","text":"def path_ok(match_tuple: MatchTuple) -> bool:\n relative_path = match_tuple.link.split(\"#\")[0]\n full_path = os.path.join(os.path.dirname(str(match_tuple.source)), relative_path)\n return os.path.exists(full_path)","title":""},{"docid":"c41d676430289b18beaa7f4c28d60dc6","score":"0.6214474","text":"def test_clear_broken_symlink(self):\n nonexistent_file_path = os.path.join(settings.STATIC_ROOT, 'nonexistent.txt')\n broken_symlink_path = os.path.join(settings.STATIC_ROOT, 'symlink.txt')\n os.symlink(nonexistent_file_path, broken_symlink_path)\n self.run_collectstatic(clear=True)\n self.assertFalse(os.path.lexists(broken_symlink_path))","title":""},{"docid":"7db9930863d4dcb3dd9b4a03dbd9b6d5","score":"0.6211496","text":"def test_symlinks():\n\n config_file = \"study/profit_symlink.yaml\"\n config = BaseConfig.from_file(config_file)\n base_file = \"./study/run_000/mockup.in\"\n link_file = \"./study/run_000/some_subdir/symlink_link.txt\"\n try:\n run(f\"profit run {config_file}\", shell=True, timeout=TIMEOUT)\n with open(link_file, \"r\") as link:\n with open(base_file, \"r\") as base:\n link_data = link.read()\n base_data = base.read()\n assert link_data == base_data and not link_data.startswith(\"{\")\n finally:\n clean(config)\n run(f\"profit clean --all {config_file}\", shell=True, timeout=CLEAN_TIMEOUT)","title":""},{"docid":"de9e9a27b97a6b79b9f1382fe5c6addc","score":"0.6205026","text":"def do_ismount(path):\n try:\n s1 = os.lstat(path)\n except os.error as err:\n if err.errno == errno.ENOENT:\n # It doesn't exist -- so not a mount point :-)\n return False\n else:\n raise GlusterFileSystemOSError(\n err.errno, '%s, os.lstat(\"%s\")' % (err.strerror, path))\n\n if stat.S_ISLNK(s1.st_mode):\n # A symlink can never be a mount point\n return False\n\n try:\n s2 = os.lstat(os.path.join(path, '..'))\n except os.error as err:\n raise GlusterFileSystemOSError(\n err.errno, '%s, os.lstat(\"%s\")' % (err.strerror,\n os.path.join(path, '..')))\n\n dev1 = s1.st_dev\n dev2 = s2.st_dev\n if dev1 != dev2:\n # path/.. on a different device as path\n return True\n\n ino1 = s1.st_ino\n ino2 = s2.st_ino\n if ino1 == ino2:\n # path/.. is the same i-node as path\n return True\n\n return False","title":""},{"docid":"d97181c311338ad47abd8855991b9b9a","score":"0.6189933","text":"def test_broken_symlink(self):\n path = os.path.join(settings.STATIC_ROOT, 'test.txt')\n os.unlink(path)\n self.run_collectstatic()\n self.assertTrue(os.path.islink(path))","title":""},{"docid":"a4525da4084481db3ff2dc0510d16515","score":"0.6175588","text":"def lexists(self, path: AnyStr) -> bool:\n return self.filesystem.exists(path, check_link=True)","title":""},{"docid":"e869be7867b9731154d811f96ce03792","score":"0.6169737","text":"def check_if_link_in_use(filepath, link):\n\n if not \"previous\" in link:\n if not in_use_links.get(link):\n new_file_name = remove_extra_from_path(filepath)\n\n if new_file_name.startswith(\"\\\\\"):\n new_file_name = new_file_name.replace(\"\\\\\", \"/\")\n\n if new_file_name != link:\n in_use_links[link] = True","title":""},{"docid":"e3a6e146797833743267c59619036c22","score":"0.61650556","text":"def ismount(path):\n try:\n s1 = os.lstat(path)\n except OSError:\n # It doesn't exist -- so not a mount point. :-)\n return False\n else:\n # A symlink can never be a mount point\n if stat.S_ISLNK(s1.st_mode):\n return False\n\n if isinstance(path, bytes):\n parent = join(path, b'..')\n else:\n parent = join(path, '..')\n try:\n s2 = os.lstat(parent)\n except OSError:\n return False\n\n dev1 = s1.st_dev\n dev2 = s2.st_dev\n if dev1 != dev2:\n return True # path/.. on a different device as path\n ino1 = s1.st_ino\n ino2 = s2.st_ino\n if ino1 == ino2:\n return True # path/.. is the same i-node as path\n return False","title":""},{"docid":"2e371c24edcbab6afb4eab8ea6a95def","score":"0.61448544","text":"def check_path(path):\r\n if os.path.exists(path):\r\n return True\r\n return False","title":""},{"docid":"6bfbb920868b15f86a5bd492a0366588","score":"0.6144068","text":"def test_check_permissions_and_broken_symlinks(self, filesystem_world_writable_whitelist, filesystem_ignore_patterns):\n stack = [] # Used for storing directories to parse\n starting_dir = \"/\"\n failed = False\n\n # Helper functions\n is_dir = lambda x: stat.S_ISDIR( x.st_mode)\n is_lnk = lambda x: stat.S_ISLNK(x.st_mode)\n is_sock = lambda x: stat.S_ISSOCK(x.st_mode)\n is_chr = lambda x: stat.S_ISCHR(x.st_mode)\n is_blk = lambda x: stat.S_ISBLK(x.st_mode)\n is_reg = lambda x: stat.S_ISREG(x.st_mode)\n is_world_writable = lambda x: stat.S_IWOTH & x.st_mode\n\n # Start it up\n stack.append(starting_dir)\n while len(stack) > 0:\n # Get actual directory\n directory = stack.pop()\n # Parse through it\n for entry in os.listdir(directory):\n # Stat it\n path = \"%s/%s\" % (directory, entry)\n if path.startswith(\"//\"): # Strip beginning // because of root dir\n path = path[1:]\n\n # Skip this file/dir, has the wrong pattern\n if any([fnmatch.fnmatch(path, p) for p in filesystem_ignore_patterns]):\n sys.stdout.write(\"[ignoring] %s\\n\" % (path,))\n continue\n\n info = os.lstat(path)\n if is_lnk(info):\n # Check if it's broken or not\n if not Test.Shell.exists_in_path(os.readlink(path), os.path.abspath(directory)):\n sys.stderr.write(\"[broken-symlink] %s -> %s\\n\" % (path, os.readlink(path)))\n failed = True\n else:\n if is_dir(info):\n stack.append(path)\n\n # FIXME - determine whether file is truly writable (parent dir\n # may not be readable)\n # Check its permissions, if wrong, append it\n if is_world_writable(info):\n # If the path isn't on the whitelist ... we found a failure\n if not any([fnmatch.fnmatch(path, p) for p in filesystem_world_writable_whitelist]):\n sys.stderr.write(\"[world-writable] %s %s\\n\" % (self.fmt_mode_str(info.st_mode), path))\n failed = True\n if failed:\n Test.Fail(msg=\"Test failed\")","title":""},{"docid":"8ffe407236011d5ddfea62710e51f2d8","score":"0.6135178","text":"def is_device_link_fail(self):\n return self._tag == 'device_link_fail'","title":""},{"docid":"3d9a697a78f12fdb87286c0b5c3558e3","score":"0.6132719","text":"def test_symlink_file5(self):\n src = os.path.join(self.rootdir,\"non-existing-file\") \n target = os.path.join(self.targetdir,os.path.basename(src))\n with self.assertRaises(transfer.TransferError):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"771b570903a84e5ab0839ea57aff428e","score":"0.6085972","text":"def silent_symlink(src, dst):\n try:\n os.symlink(src, dst)\n except OSError:\n pass","title":""},{"docid":"b346d5a2d9646b6ff0a8844a820c0b6e","score":"0.60828763","text":"def readlink(path):\n if not path:\n return path\n\n if path[0] == \"~\":\n path = os.path.expanduser(path)\n\n return run_bash_cmd([\"readlink\", \"-f\", path]) or path","title":""},{"docid":"bdc8a241fd5bbbd50d166cdb6622d9a4","score":"0.6063484","text":"def _is_valid_path(path):\r\n try:\r\n urlresolvers.resolve(path)\r\n return True\r\n except urlresolvers.Resolver404:\r\n return False","title":""},{"docid":"a8bae97837e4ff75dd6ca0ac4d853a0f","score":"0.6037345","text":"def exists(path, followlinks=True):\n if path is None:\n raise Tools.exceptions.Value(\"Path is not passed in system.fs.exists\")\n found = False\n try:\n st = os.lstat(path)\n found = True\n except (OSError, AttributeError):\n pass\n if found and followlinks and stat.S_ISLNK(st.st_mode):\n if MyEnv.debug:\n Tools.log(\"path %s exists\" % str(path.encode(\"utf-8\")))\n linkpath = os.readlink(path)\n if linkpath[0] != \"/\":\n linkpath = os.path.join(Tools.path_parent(path), linkpath)\n return Tools.exists(linkpath)\n if found:\n return True\n # Tools.log('path %s does not exist' % str(path.encode(\"utf-8\")))\n return False","title":""},{"docid":"dd3c3cb6566686327f09cc836e98684d","score":"0.60363925","text":"def check_path(p):\n if not os.path.exists(p):\n msg = 'Error: Path \"{}\" does not exist.'.format(p)\n print_help(msg)","title":""},{"docid":"b6931c31cb4ea78fdd2dcb5957104c1b","score":"0.6035318","text":"def check_path(path):\n if (not os.path.isfile(path)):\n raise Exception('The path specified does not point to a file')","title":""},{"docid":"bfdbda55742febd9b0ba06e3c8969c13","score":"0.6016378","text":"def symlinks_test(dirname):\n\n links = []\n\n for (path, dirs, files) in os.walk(dirname):\n if path.find('/.git') > -1:\n continue\n\n # search for directory links\n for d in dirs:\n dname = os.path.join(path, d)\n if os.path.islink(dname):\n links.append(os.path.relpath(dname, dirname)+'/')\n\n # search for file links\n for f in files:\n fname = os.path.join(path, f)\n if os.path.islink(fname):\n links.append(os.path.relpath(fname, dirname))\n\n if links:\n return (FAIL, '\\n'.join(links))\n else:\n return (PASS, \"None\")","title":""},{"docid":"a97ceeb3e2a1ef2d4f8add3a4d41507d","score":"0.60025454","text":"def test_force_symlink_overwrite(tmpdir):\n src = join(str(tmpdir), \"src\")\n dst = join(str(tmpdir), \"dst\")\n\n with open(src, \"w\") as f:\n f.write(\"Correct.\")\n\n with open(dst, \"w\") as f:\n f.write(\"Wrong.\")\n\n utils.force_symlink(src, dst)\n assert os.path.islink(dst)\n\n with open(dst, \"r\") as f:\n assert \"Correct\" in f.read()","title":""},{"docid":"5fbf806aa189307c157cd564dbcd1a8d","score":"0.59952956","text":"def fix_all_git_symlinked(topdir):\n # Determine whether or not symlinks need fixing (they don’t if installing\n # from a .tar.gz file)\n with io.open(topdir + r'\\nikola\\data\\symlink-test-link.txt', 'r', encoding='utf-8-sig') as f:\n text = f.read()\n if text.startswith(\"NIKOLA_SYMLINKS=OK\"):\n return -1\n with io.open(topdir + r'\\nikola\\data\\symlinked.txt', 'r', encoding='utf-8-sig') as f:\n text = f.read()\n # expect each line a relpath from git or zip root,\n # smoke test relpaths are relative to git root\n if text.startswith('.'):\n raise Exception(r'Bad data in \\nikola\\data\\symlinked.txt')\n relnames = text.split('\\n')\n relnames = [name.strip().replace('/', '\\\\') for name in relnames]\n relnames = [name for name in relnames if name]\n\n failures = 0\n for name in relnames:\n # build dst path and do some basic validation\n dst = os.path.join(topdir, name)\n # don't access files outside topdir\n if not is_file_into_dir(dst, topdir):\n continue\n if os.path.isdir(dst):\n # assume the file was de-symlinked\n continue\n\n # build src path and do some basic validation\n with io.open(os.path.join(topdir, dst), 'r', encoding='utf-8-sig') as f:\n text = f.read()\n dst_dir = os.path.dirname(dst)\n try:\n src = os.path.normpath(os.path.join(dst_dir, text))\n if not os.path.exists(src):\n # assume the file was de-symlinked before\n continue\n # don't access files outside topdir\n if not is_file_into_dir(src, topdir):\n continue\n except Exception:\n # assume the file was de-symlinked before\n continue\n\n # copy src to dst\n try:\n if os.path.isdir(src):\n os.unlink(dst)\n shutil.copytree(src, dst)\n else:\n shutil.copy2(src, dst)\n except Exception:\n failures += 1\n print(\"*** copy failed for\")\n print(\"\\t src:\", src)\n print(\"\\t dst:\", dst)\n\n return failures","title":""},{"docid":"727d2ea9be0ae6be316b42e299d3ad3b","score":"0.599236","text":"def test_hardlink(self):\n # Prevent errors from being caught\n self.tar.errorlevel = 1\n\n self.tar.extract(\"0-REGTYPE\", dirname())\n try:\n # Extract 1-LNKTYPE which is a hardlink to 0-REGTYPE\n self.tar.extract(\"1-LNKTYPE\", dirname())\n except EnvironmentError, e:\n import errno\n if e.errno == errno.ENOENT:\n self.fail(\"hardlink not extracted properly\")","title":""},{"docid":"e9745941b98c710c4c4ec6d21f02e9e1","score":"0.59648645","text":"def test_symlink_folder7(self):\n src = self.rootdir\n target = os.path.join(self.targetdir,os.path.basename(src))\n with mock.patch.object(\n transfer.os,\n 'symlink',\n side_effect=OSError(\"Mocked error\")):\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"01c8991464103ff5c306a31023d7fce3","score":"0.5945459","text":"def can_symlink_to(self, outfile_name, symlink_candidate):\n if symlink_candidate == outfile_name:\n # Symlinking to ourself is *not* ok.\n return False\n\n if symlink_candidate in self._db.transaction_keys():\n # Don't try to symlink if our target file is being updated\n # itself; we can't tell whether we're 'equivalent' to it\n # or not if it's currently being modified!\n return False\n\n symlink_mtime_map = self._db.get(symlink_candidate)\n if symlink_mtime_map is None:\n # We don't have up-to-date-ness information on the candidate.\n return False\n\n # outfile_name is currently in a transaction (changed_files()\n # returned false), so we have to get its mtime info that way.\n outfile_mtime_map = self._db.get_transaction(outfile_name)\n\n # Get a copy of mtime_maps that do not include the output file.\n pruned_outfile_mtime_map = outfile_mtime_map.copy()\n pruned_outfile_mtime_map.pop(outfile_name)\n pruned_symlink_mtime_map = symlink_mtime_map.copy()\n pruned_symlink_mtime_map.pop(symlink_candidate)\n\n if (frozenset(pruned_outfile_mtime_map.keys()) !=\n frozenset(pruned_symlink_mtime_map.keys())):\n # This means symlink_candidate has different deps that we\n # do (common case).\n return False\n\n for (k, v) in pruned_outfile_mtime_map.iteritems():\n # This means symlink_candidate has the same deps as us,\n # but those deps aren't up to date.\n # This holds because infile_map has the *current* mtimes of\n # the input files, and if the (db-based) values of\n # symlink_candidate don't match, that means it's out of date.\n symlink_v = pruned_symlink_mtime_map.get(k, (None, None, None))\n if not file_info_equal(v, symlink_v):\n return False\n\n if not file_info_equal(get_file_info(symlink_candidate),\n symlink_mtime_map[symlink_candidate]):\n # This finishes off the 'other_outfile is up-to-date' check.\n # We tested all the input files are up-to-date above, now we\n # need to test the output file too. Note we never bother to\n # do the crc check here; we assume that as a generated file,\n # other_outfile will not suffer from the\n # 'git-changed-my-mtime' problem.\n return False\n\n return True","title":""},{"docid":"1e9b2fc97f39ea65d0aff639dfc352fb","score":"0.5944636","text":"def verify_path(self, path):\n if Path(path).exists():\n return True\n return False","title":""},{"docid":"28f897359724e00998034cf686012bf2","score":"0.59438217","text":"def check_path(path):\n assert isinstance(path, basestring), \"Expected string or unicode, found %s.\" % type(path)\n try:\n open(path, \"r\")\n except IOError:\n raise IOError('File does not exist: %s' % path)\n return path","title":""},{"docid":"1d2dcd5fddfe2a01bb5a15519baa67b1","score":"0.59393996","text":"def test_symlink_folder8(self):\n src = self.rootdir\n target = self.targetdir\n with mock.patch.object(\n transfer.os.path,\n 'exists',\n side_effect=Exception(\"Mocked error\")):\n with self.assertRaises(Exception):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"a04cd212041a2c05af0b7a22f90a585d","score":"0.5926641","text":"def is_wsl2_path(path: p.PurePath) -> bool:\n return re.match(r\"^/mnt/[a-z]/\", path.as_posix()) is not None","title":""},{"docid":"72eaf06e2133216050eaec901507e2aa","score":"0.59235525","text":"def S_ISLNK(mode):\n return S_IFMT(mode) == S_IFLNK","title":""},{"docid":"2b55905af587ce30f9e7fd3e568d64b0","score":"0.59146184","text":"def is_malformed_path(self):\n return self._tag == 'malformed_path'","title":""},{"docid":"b32a7f3b0b52be5499eccabc3f8c8143","score":"0.59071994","text":"def is_link_ok(link: str) -> bool:\n return not any([re.search(exc, link) for exc in excludes])","title":""},{"docid":"e1dfb6e5ef886289956a028ef3bd3d34","score":"0.5889858","text":"def is_safe_path(basedir, path, follow_symlinks=True):\n if basedir is None:\n return True\n if follow_symlinks:\n return os.path.realpath(path).startswith(basedir.encode('utf-8'))\n return os.path.abspath(path).startswith(basedir.encode('utf-8'))","title":""},{"docid":"d5ffdb8f73ffd102df61f9778afce65d","score":"0.5889097","text":"def valid_permlink(permlink, allow_empty=False):\n if not permlink:\n assert allow_empty, 'permlink cannot be blank'\n return \"\"\n assert isinstance(permlink, str), 'permlink must be string'\n assert len(permlink) <= 256, \"invalid permlink length\"\n return permlink","title":""},{"docid":"d5ffdb8f73ffd102df61f9778afce65d","score":"0.5889097","text":"def valid_permlink(permlink, allow_empty=False):\n if not permlink:\n assert allow_empty, 'permlink cannot be blank'\n return \"\"\n assert isinstance(permlink, str), 'permlink must be string'\n assert len(permlink) <= 256, \"invalid permlink length\"\n return permlink","title":""},{"docid":"7413b59212368171f2e2be09dfcd62d8","score":"0.58867145","text":"def check_links(params):\n\n\tfor s in _file_links:\n\t\t#print \"check:\", s, cfg.out_dir\n\t\tout_file = os.path.join(cfg.out_dir, s)\n\t\tif not os.path.exists(out_file):\n\t \t\twarning(\"%s: invalid link to '%s'\" % (_file_links[s], s))","title":""},{"docid":"e62c55844b430153e153df0db262b290","score":"0.588417","text":"def _resolve_link(path):\r\n paths_seen = []\r\n while islink(path):\r\n if path in paths_seen:\r\n # Already seen this path, so we must have a symlink loop\r\n return None\r\n paths_seen.append(path)\r\n # Resolve where the link points to\r\n resolved = os.readlink(path)\r\n if not isabs(resolved):\r\n dir = dirname(path)\r\n path = normpath(join(dir, resolved))\r\n else:\r\n path = normpath(resolved)\r\n return path","title":""},{"docid":"c8b85174b6188a7a19df4eff61790b94","score":"0.58817095","text":"def hardlink(source, link_name):\n try:\n os.link(source, link_name)\n except FileExistsError:\n # It's possible that the user created a different file with the same name as the\n # one we're trying to download. Thus we need to check the if the inode is different\n # and raise an error in this case.\n source_stat = os.stat(source)\n dest_stat = os.stat(link_name)\n # Check device first because different drives can have the same inode number\n if source_stat.st_dev != dest_stat.st_dev or source_stat.st_ino != dest_stat.st_ino:\n raise","title":""},{"docid":"962858e7a2194d210b9dfedd9b981e87","score":"0.5866979","text":"def _force_symlink(\n root: Path, target: Union[str, PurePath], link_to: Union[str, Path]\n) -> None:\n current_symlink = root.joinpath(target)\n try:\n current_symlink.unlink()\n except OSError:\n pass\n try:\n current_symlink.symlink_to(link_to)\n except Exception:\n pass","title":""},{"docid":"adf4744cb862d5219816f94cf0ef9f77","score":"0.58613175","text":"def _process_symlink(self, target, path): #aka target_path, dotfile_path\n\n if not os.path.lexists(target):\n self._error = True\n print('\\tmissing target: {}'.format(target))\n return ('DOTFILE NOT FOUND', None)\n\n if not os.path.lexists(path):\n self._changed = True\n if self.args.test:\n return ('missing', None)\n else:\n create_dotfile_link(target, path)\n return ('missing', 'synced')\n\n existing_realpath = os.path.realpath(path)\n if existing_realpath != os.path.realpath(target):\n if self.args.debug:\n print('\\tCurrent target path: {}'.format(existing_realpath))\n status = 'unmanaged' if os.path.lexists(existing_realpath) else 'broken'\n\n if self.args.verbose:\n pass # XXX: TODO: ...\n\n if status != 'broken' and not self.args.force:\n return (status, 'skipping')\n self._changed = True\n if self.args.test:\n return (status, None)\n\n #import pdb; pdb.set_trace()\n remove_any(path)\n create_dotfile_link(target, path)\n return (status, 'removed; synced')\n\n return ('ok', None)","title":""},{"docid":"0f124a12bcf057e1e114461e076db3da","score":"0.58529246","text":"def _checkLink(link):\n try:\n link = link.attrs['href']\n except KeyError:\n return -1\n if link[0] == '/':\n link = '://'.join(list(urllib.parse.urlparse(self.address)[0:2])) + link\n elif re.match(r'^mailto|^#', link):\n return None\n good = ['200', '206', '301', '302']\n try:\n status, response = self.http.request(link)\n if status['status'] not in good:\n self.badlinks.append((status['status'], link))\n else:\n self.goodlinks.append(link)\n except httplib2.ServerNotFoundError:\n self.other.append((\"httplib2.ServerNotFoundError\", link))\n except httplib2.ssl.SSLError:\n self.other.append((\"httplib2.ssl.SSLError\", link))\n except ConnectionResetError:\n self.other.append((\"ConnectionResetError\", link))\n except UnicodeError:\n self.other.append((\"UnicodeError\", link))","title":""},{"docid":"af98442fe78a61c211571253d3a076e5","score":"0.58304495","text":"def internal_link_checker(filepath, html_str):\n\n # Flag to determine if internal link is broken\n internal_link_error = False\n\n # \n\n problems = []\n relative_links = []\n\n # find all links\n for prefix in [\"href\", \"src\"]:\n\n links = re.findall(\n f\"{prefix}\\s?=\\s?[\\\"']([{allowed_in_link}]+)[\\\"']\", html_str)\n\n # check if link has a dest\n for link in links:\n\n # Check if link is relative path\n is_relative = check_if_relative_link(link)\n\n # Add to relative links list if relative\n if is_relative:\n if not link in relative_links:\n relative_links.append(link)\n\n # Get correct path\n link = get_correct_link(link)\n\n # Check if link is in use\n check_if_link_in_use(filepath, link)\n\n if link in links_list:\n if links_list[link]:\n if link not in problems:\n problems.append(link) \n else:\n if internal_link_test(link):\n problems.append(link)\n links_list[link] = True\n\n if not internal_link_error:\n internal_link_error = True\n else:\n links_list[link] = False\n\n return problems, relative_links, internal_link_error","title":""},{"docid":"8a8c415ec1777ae102d937832bd39339","score":"0.5820183","text":"def symlink (self, src, dst):\n if self.system ('/bin/ln -sf %s %s'%(src, dst)):\n raise OSError, 'can\\'t symlink [%s] as [%s]' % (src, dst)","title":""},{"docid":"bf5255caff8fa0c01af5e9a0e3b0a7b8","score":"0.58178663","text":"def test_symlink_file6(self):\n src = self.rootdir\n target = os.path.join(self.targetdir,\"target-file\")\n open(target,'w').close()\n with mock.patch.object(\n transfer.os,\n 'unlink',\n side_effect=OSError(\"Mocked error\")):\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"9b019bad4cd351d9787fe08c2bc3f228","score":"0.57966596","text":"def symlink(self, req, link, parent, name):\n self.reply_err(req, EROFS)","title":""},{"docid":"b9684816cb8583b1d44b66d7cd04ca40","score":"0.5782404","text":"def test_force_link(tmpdir):\n src = join(str(tmpdir), \"src\")\n dst = join(str(tmpdir), \"dst\")\n\n with open(src, \"w\") as f:\n f.write(\"Not empty.\")\n\n utils.force_link(src, dst)\n assert os.path.isfile(dst)\n assert not os.path.islink(dst)","title":""},{"docid":"6419b2fcb183ad206a852387f3bc8d86","score":"0.57806474","text":"def test_scan_files(self):\n self.assertFalse(fdf.valid_path('some thing wrong'))\n result = fdf.scan_files(join(getcwd(), 'test'))\n self.assertNotIn(join(getcwd(), 'test/symlink/symlink'), result)","title":""},{"docid":"e2fcb55acc8c1275ebfe5f75e72876a0","score":"0.57789207","text":"def test_non_html_links_are_path_sensitive(self):\n validator = self._create_validator(\n \"\"\"Use [this CSV](data.csv) for the exercise.\"\"\")\n self.assertFalse(validator._validate_links())","title":""},{"docid":"169daa1a343f03131a9d00db91a8b596","score":"0.5773865","text":"def test_symlink_folder4(self):\n src = os.path.join(self.rootdir) \n target = os.path.join(self.targetdir)\n with mock.patch('taca.utils.transfer.os.path') as mockobj:\n mockobj.ismount.return_value = False\n mockobj.isfile.return_value = False\n mockobj.islink.return_value = False\n mockobj.isdir.return_value = False\n with self.assertRaises(transfer.SymlinkError):\n transfer.SymlinkAgent(src,target).transfer()","title":""},{"docid":"7a3cd10319fcfb6990ceba408de225a7","score":"0.5773567","text":"def is_file_link(self, link):\n return link.startswith(\"###file\") or link.startswith('/repository') or \\\n (link.startswith('../') and '/files/' in link)","title":""},{"docid":"78c2fa09a69a0c70db6567fbff768f2d","score":"0.57712406","text":"def provide_link(dest, src):\n if not os.path.exists(dest):\n # strip trailing /'s just in case it exists\n os.symlink(src, dest.rstrip(\"/\"))","title":""},{"docid":"f5141a7e126b929ce6f8259580dbeb5c","score":"0.5764898","text":"def filepath_validator(path):\n if isinstance(path, str):\n path = Path(path)\n return path.exists() and path.isfile()","title":""},{"docid":"308f791745e59acf2880a0ec1831b152","score":"0.5761583","text":"def test_symlink_lock(self):\n\n # Get an image object and tests its manual lock mechanism.\n api_obj = self.get_img_api_obj()\n img = api_obj.img\n\n # Verify a lock file is created.\n img.lock()\n lfpath = os.path.join(img.imgdir, \"lock\")\n self.assertTrue(os.path.exists(lfpath))\n img.unlock()\n os.remove(lfpath)\n\n # Make lock file a symlink by pointing it to a random file .\n tmp_file = os.path.join(img.imgdir, \"test_symlink\")\n fo = open(tmp_file, 'wb+')\n fo.close()\n os.symlink(tmp_file, lfpath)\n\n # Verify that both pkg install and refresh generate an error\n # if the lock file is a symlink.\n self.pkg(\"install foo\", su_wrap=True, exit=1)\n self.assertTrue(\"contains a symlink\" in self.errout)\n\n self.pkg(\"refresh --full\", su_wrap=True, exit=1)\n self.assertTrue(\"contains a symlink\" in self.errout)","title":""},{"docid":"18658f8e04412069e3a03d80b360b5b3","score":"0.57601094","text":"def check_path_existence(fpath: str) -> bool:\n return os.path.exists(fpath)","title":""},{"docid":"31411b5398f85370a62fa13f46f581a5","score":"0.5740772","text":"def validate_path(self, path):\n if not os.path.exists(path):\n raise IOError('Path {} does not exist'.format(path))\n return True","title":""},{"docid":"042985e4ccc6c626626e50eb20ef3c8a","score":"0.5739684","text":"def ismount(path):\r\n try:\r\n s1 = os.lstat(path)\r\n s2 = os.lstat(join(path, '..'))\r\n except os.error:\r\n return False # It doesn't exist -- so not a mount point :-)\r\n dev1 = s1.st_dev\r\n dev2 = s2.st_dev\r\n if dev1 != dev2:\r\n return True # path/.. on a different device as path\r\n ino1 = s1.st_ino\r\n ino2 = s2.st_ino\r\n if ino1 == ino2:\r\n return True # path/.. is the same i-node as path\r\n return False","title":""}],"string":"[\n {\n \"docid\": \"71564a56fc163c8e7d254c575b623e30\",\n \"score\": \"0.7800918\",\n \"text\": \"def checklink(path):\\n # mktemp is not racy because symlink creation will fail if the\\n # file already exists\\n name = tempfile.mktemp(dir=path)\\n try:\\n os.symlink(\\\".\\\", name)\\n os.unlink(name)\\n return True\\n except (OSError, AttributeError):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1301b1cb5da838eaea975a866ca6bb1f\",\n \"score\": \"0.7800738\",\n \"text\": \"def test_is_symlink(path: Path) -> None:\\n assert (path / \\\"link\\\").is_symlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c304a94d85d6653cded13e445a0b19\",\n \"score\": \"0.7560013\",\n \"text\": \"def is_symlink(self):\\n try:\\n return S_ISLNK(self.lstat().st_mode)\\n except OSError as e:\\n if e.errno not in (ENOENT, ENOTDIR):\\n raise\\n # Path doesn't exist\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e1ac62ad49004dab7dc483330692cb\",\n \"score\": \"0.75018257\",\n \"text\": \"def is_symlink(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"896891d9194d8ad8e3c7b93d9f4735d6\",\n \"score\": \"0.7391739\",\n \"text\": \"def test_symlink_on_unknown_file(self):\\n root = URI(self.baseurl)\\n notexisting_path = root / 'ma' / 'moo'\\n tee_path = root / 'helloworld'\\n notexisting_path.symlink(tee_path)\\n\\n self.assertTrue(tee_path.islink())\\n self.assertEqual(tee_path.readlink(), notexisting_path)\\n self.assertTrue(not notexisting_path.exists())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9543b5f7076123e979ef23a52736cca5\",\n \"score\": \"0.7297981\",\n \"text\": \"def test_symlink_missing_src(file, source):\\n target = source.parent / \\\"symlink.lnk\\\"\\n missing_source = source.parent / \\\"missing.txt\\\"\\n try:\\n file.symlink(str(missing_source), str(target))\\n assert salt.utils.path.islink(str(target))\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faaf23de79bf29e8868d7acf350b1324\",\n \"score\": \"0.72364473\",\n \"text\": \"def islink(path):\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"117fd290d3a937d8daa06de391ab888b\",\n \"score\": \"0.7161739\",\n \"text\": \"def islink(self, path):\\n path = ftputil.tool.as_unicode(path)\\n try:\\n lstat_result = self._host.lstat(\\n path, _exception_for_missing_path=False)\\n except ftputil.error.RootDirError:\\n return False\\n else:\\n if lstat_result is None:\\n # Non-existent path\\n return False\\n else:\\n return stat.S_ISLNK(lstat_result.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e466325a1eb6f6c43507803db038e87\",\n \"score\": \"0.7131687\",\n \"text\": \"def islink(path):\\r\\n try:\\r\\n st = os.lstat(path)\\r\\n except (os.error, AttributeError):\\r\\n return False\\r\\n return stat.S_ISLNK(st.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f68b0566a7c14c2badd11901266889\",\n \"score\": \"0.71089053\",\n \"text\": \"def is_symlink(self):\\n if os.name == \\\"posix\\\": return os.path.islink(self.path)\\n if os.name == \\\"nt\\\":\\n import win32api\\n import win32con\\n num = win32con.FILE_ATTRIBUTE_REPARSE_POINT\\n return bool(win32api.GetFileAttributes(self.path) & num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac7484c1ede678b11d426cfd0dff2ef\",\n \"score\": \"0.71058583\",\n \"text\": \"def islink(path):\\n try:\\n st = os.lstat(path)\\n except (OSError, AttributeError):\\n return False\\n return stat.S_ISLNK(st.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac7484c1ede678b11d426cfd0dff2ef\",\n \"score\": \"0.71058583\",\n \"text\": \"def islink(path):\\n try:\\n st = os.lstat(path)\\n except (OSError, AttributeError):\\n return False\\n return stat.S_ISLNK(st.st_mode)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1aec9e1a0da95eb624cbc8cdd68e79\",\n \"score\": \"0.70764434\",\n \"text\": \"def test_symlink_exists_different(file, source):\\n dif_source = source.parent / \\\"dif_source.txt\\\"\\n target = source.parent / \\\"symlink.lnk\\\"\\n target.symlink_to(dif_source)\\n try:\\n with pytest.raises(CommandExecutionError) as exc:\\n file.symlink(str(source), str(target))\\n assert \\\"Found existing symlink:\\\" in exc.value.message\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8816e12f05b8fce70a4dc0a21726d931\",\n \"score\": \"0.70211464\",\n \"text\": \"def test_symlink_exists_different_force(file, source):\\n dif_source = source.parent / \\\"dif_source.txt\\\"\\n target = source.parent / \\\"symlink.lnk\\\"\\n target.symlink_to(dif_source)\\n try:\\n file.symlink(str(source), str(target), force=True)\\n assert salt.utils.path.readlink(str(target)) == str(source)\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb95c61abb8f5354e7e9d4d715e4ef7a\",\n \"score\": \"0.6982841\",\n \"text\": \"def test_readlink(path: Path) -> None:\\n assert PurePath(\\\"file\\\") == (path / \\\"link\\\").readlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c1e77dc34b5c401947222e0bbf911a\",\n \"score\": \"0.6982368\",\n \"text\": \"def _is_link(self, path):\\n return os.path.islink(os.path.expanduser(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8627bffa0d7e759f5d4710ef759afa77\",\n \"score\": \"0.69228035\",\n \"text\": \"def is_symlink(self):\\n if self._is_symlink is None:\\n self._is_symlink = os.path.islink(self.storage_url.object_name)\\n return self._is_symlink\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe21b7cecc2c9973a253737963706133\",\n \"score\": \"0.68756634\",\n \"text\": \"def test_constrain_symlinks_to_filesystem() -> None:\\n path = root(\\n {\\n \\\"file\\\": \\\"text\\\",\\n \\\"dir\\\": {\\\"link\\\": PurePath(\\\"..\\\", \\\"file\\\")},\\n },\\n innerpath=PurePath(\\\"dir\\\"),\\n )\\n # XXX Should we rewrite the error message to hide the 'dir/' prefix?\\n with pytest.raises(FileNotFoundError, match=\\\"file not found: dir/file\\\"):\\n assert (path / \\\"link\\\").read_text()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923a01647ae572518a7cb698b01d2789\",\n \"score\": \"0.6867287\",\n \"text\": \"def test_symlink_exists_file(file, source):\\n with pytest.helpers.temp_file(\\\"symlink.txt\\\", contents=\\\"Source content\\\") as target:\\n with pytest.raises(CommandExecutionError) as exc:\\n file.symlink(str(source), str(target))\\n assert \\\"Existing path is not a symlink:\\\" in exc.value.message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41bff0c324b7c71a826535c2a5479b5c\",\n \"score\": \"0.6856679\",\n \"text\": \"def test_symlink(file, source):\\n target = source.parent / \\\"symlink.lnk\\\"\\n try:\\n file.symlink(str(source), str(target))\\n assert salt.utils.path.islink(str(target))\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d7a46dd208c245d602979b6f8c24590\",\n \"score\": \"0.68321455\",\n \"text\": \"def test_symlink_exists_same(file, source):\\n target = source.parent / \\\"symlink.lnk\\\"\\n target.symlink_to(source)\\n try:\\n before_time = os.stat(str(target)).st_mtime\\n ret = file.symlink(str(source), str(target))\\n after_time = os.stat(str(target)).st_mtime\\n assert before_time == after_time\\n assert ret is True\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c3309bca6d764f2d429a94c01e2867\",\n \"score\": \"0.68014234\",\n \"text\": \"def is_symlink(\\n linkfile: T.Union[str, pathlib.Path],\\n src: T.Union[None, str, pathlib.Path] = None,\\n) -> bool:\\n linkfile = pathlib.Path(linkfile)\\n if not linkfile.is_symlink():\\n return False\\n\\n if src is None:\\n return True\\n\\n return linkfile.resolve() == pathlib.Path(src).resolve()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5673d6915d6847708663b71cf2eab7\",\n \"score\": \"0.67836535\",\n \"text\": \"def check_if_relative_link(link):\\n\\n if not link.startswith(\\\"http\\\"):\\n if not link.startswith(\\\"/\\\"):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1067f2b5ebb747c4d55bca1975463a6a\",\n \"score\": \"0.6782696\",\n \"text\": \"def _valid_link(self, link, fail_on_disk_check=True):\\n\\n if not link in self._links:\\n raise Exception('link [{0}] not found in timeline!'.format(link))\\n\\n if not os.path.islink(self._links[link]['path']):\\n msg = 'link [{0}] not found!'.format(self._links[link]['path'])\\n if fail_on_disk_check:\\n raise Exception(msg)\\n self.logger.warning(msg)\\n return False\\n\\n if not os.path.exists(self._links[link]['path']):\\n msg = 'link [{0}] is broken!'.format(self._links[link]['path'])\\n if fail_on_disk_check:\\n raise Exception(msg)\\n self.logger.warning(msg)\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"968ba0654b4a586ad577e6ad741ee0ed\",\n \"score\": \"0.6718646\",\n \"text\": \"def is_symlink(self):\\n return self.type == self.Type.SYMLINK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7331b2df6b96881496979b7a913c461\",\n \"score\": \"0.6693001\",\n \"text\": \"def is_symlink(self):\\n if (\\n not self.custom_fields\\n or resource_util.SYMLINK_METADATA_KEY not in self.custom_fields\\n ):\\n return False\\n return (\\n self.custom_fields[resource_util.SYMLINK_METADATA_KEY].lower() == 'true'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d65ef687eac4cb308d24311371b04b\",\n \"score\": \"0.66920704\",\n \"text\": \"def islink(self, path: AnyStr) -> bool:\\n return self.filesystem.islink(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1435142fcb2dce5b3f51f29d763b77\",\n \"score\": \"0.66706246\",\n \"text\": \"def islink (self) :\\n try :\\n return stat.S_ISLNK (self.mode)\\n except OSError :\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318e97be28a1d07940b9a69de3c8b1f6\",\n \"score\": \"0.6630757\",\n \"text\": \"def copy_if_symlink_fails(\\n src: Union[str, Path],\\n dst: Union[str, Path],\\n backend_args: Optional[dict] = None,\\n) -> bool:\\n backend = get_file_backend(\\n src, backend_args=backend_args, enable_singleton=True)\\n return backend.copy_if_symlink_fails(src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c3050fd54816a14c87c867d2e9a3748\",\n \"score\": \"0.6584976\",\n \"text\": \"def test_through_symlink(self) -> None:\\n link1 = os.path.join(self.tmp_dir, \\\"link1\\\")\\n os.symlink(self.mount, link1)\\n\\n info1 = json.loads(self.eden.run_cmd(\\\"info\\\", link1))\\n self.assertEqual(self.mount, info1[\\\"mount\\\"])\\n\\n # Create a non-normalized symlink pointing to the parent directory\\n # of the mount\\n link2 = os.path.join(self.tmp_dir, \\\"mounts_link\\\")\\n os.symlink(self.mount + \\\"//..\\\", link2)\\n mount_through_link2 = os.path.join(link2, self.repo_name)\\n\\n info2 = json.loads(self.eden.run_cmd(\\\"info\\\", mount_through_link2))\\n self.assertEqual(self.mount, info2[\\\"mount\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc10782d2de274eef803bde25fbaf972\",\n \"score\": \"0.65710294\",\n \"text\": \"def test_symlink_exists_different_atomic(file, source):\\n dif_source = source.parent / \\\"dif_source.txt\\\"\\n target = source.parent / \\\"symlink.lnk\\\"\\n target.symlink_to(dif_source)\\n try:\\n file.symlink(str(source), str(target), atomic=True)\\n assert salt.utils.path.readlink(str(target)) == str(source)\\n finally:\\n target.unlink()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"741e573a30028ce18a81c363e3d5a5e9\",\n \"score\": \"0.6550941\",\n \"text\": \"def islink(s):\\n\\n try:\\n import Carbon.File\\n return Carbon.File.ResolveAliasFile(s, 0)[2]\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b04b3104b1dd15775e4fb15a939ca49\",\n \"score\": \"0.6517931\",\n \"text\": \"def islink(pathname):\\n from os import listdir\\n from os.path import isdir, isfile\\n import os.path\\n pathname = pathname.rstrip(\\\"/\\\")\\n if os.path.islink(pathname): return True\\n elif isfile(pathname) and ishidden(pathname): return True\\n elif isdir(pathname):\\n entries = listdir(pathname)\\n for entry in entries:\\n if not islink(pathname+\\\"/\\\"+entry): return False\\n return True\\n else: return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c17a63c3f070a9ace82b381e0997881\",\n \"score\": \"0.6510741\",\n \"text\": \"def check_file(filename,path):\\n chksumX = chksum_file(\\\"%s/%s\\\" % (path,filename))\\n (pathY,chksumY) = find_file(filename,path,chksumX)\\n if chksumX == chksumY:\\n\\thardlink(filename,path,pathY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6924dff27d54d6cab5d242f412cc20a7\",\n \"score\": \"0.6508725\",\n \"text\": \"def silent_remove_symlink(path): # TODO: test\\n result = {'error': ''}\\n if symlink_exists(path):\\n try:\\n os.unlink(path)\\n except OSError as e:\\n result['error'] = str(e)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595ab9271ea617ec1ff274f308897593\",\n \"score\": \"0.64662755\",\n \"text\": \"def has_valid_link(dotfile, dotfiles_dir):\\n homefolder_path = get_homefolder_path(dotfile, dotfiles_dir)\\n if os.path.islink(homefolder_path) and os.readlink(homefolder_path) == dotfile:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b6fb9618f82b8429612697328e30ae\",\n \"score\": \"0.6455614\",\n \"text\": \"def is_valid_file(abs_path):\\n if os.path.exists(abs_path) and os.path.isfile(abs_path) \\\\\\n and not os.path.islink(abs_path):\\n return True\\n\\n else: return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a6353f544288001a6c15f9a0558223e\",\n \"score\": \"0.6414611\",\n \"text\": \"def ismount_raw(path):\\r\\n try:\\r\\n s1 = os.lstat(path)\\r\\n except os.error as err:\\r\\n if err.errno == errno.ENOENT:\\r\\n # It doesn't exist -- so not a mount point :-)\\r\\n return False\\r\\n raise\\r\\n\\r\\n if stat.S_ISLNK(s1.st_mode):\\r\\n # A symlink can never be a mount point\\r\\n return False\\r\\n\\r\\n s2 = os.lstat(os.path.join(path, '..'))\\r\\n dev1 = s1.st_dev\\r\\n dev2 = s2.st_dev\\r\\n if dev1 != dev2:\\r\\n # path/.. on a different device as path\\r\\n return True\\r\\n\\r\\n ino1 = s1.st_ino\\r\\n ino2 = s2.st_ino\\r\\n if ino1 == ino2:\\r\\n # path/.. is the same i-node as path\\r\\n return True\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d851ba210920164362039bc52ea42fd\",\n \"score\": \"0.6394149\",\n \"text\": \"def is_linked(self) -> bool:\\n return self.parent_path.is_symlink() \\\\\\n and (not self.child_path.is_symlink()) \\\\\\n and self.child_path.samefile(self.parent_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b635c87ed41938fbf0245d78d827c98\",\n \"score\": \"0.63874704\",\n \"text\": \"def symlinks_supported():\\n with tempfile.TemporaryDirectory() as temp_dir:\\n original_path = os.path.join(temp_dir, 'original')\\n symlink_path = os.path.join(temp_dir, 'symlink')\\n os.makedirs(original_path)\\n try:\\n os.symlink(original_path, symlink_path)\\n supported = True\\n except (OSError, NotImplementedError):\\n supported = False\\n return supported\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89bd6c13a93b82e506573bc794a24693\",\n \"score\": \"0.6330474\",\n \"text\": \"def check_path(data_pointer, log, msg):\\n if not os.path.exists(data_pointer):\\n log.debug(msg)\\n return False\\n else:\\n return data_pointer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13fb5713db8da8497f3a87fe901a9745\",\n \"score\": \"0.6315982\",\n \"text\": \"def test_symlink_target_relative_path(file, source):\\n target = \\\"..{}symlink.lnk\\\".format(os.path.sep)\\n with pytest.raises(SaltInvocationError) as exc:\\n file.symlink(str(source), str(target))\\n assert \\\"Link path must be absolute\\\" in exc.value.message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f87c41104cb5341d0b2c9ad58a4970e\",\n \"score\": \"0.6286066\",\n \"text\": \"def _resolve_symlinks_and_relative_paths(path):\\n # type: pathlib.Path -> pathlib.Path\\n try:\\n path = path.resolve()\\n except WindowsError: # TODO: check for more specific error\\n pass\\n except OSError as e:\\n if e.errno == 2:\\n pass\\n else:\\n raise\\n\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75a5874ba22708116a98d08093a24ae\",\n \"score\": \"0.6252006\",\n \"text\": \"def check_path(path):\\n if(os.path.exists(path) == False):\\n print_error_and_exit(\\\"Cannot find %s\\\" % path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d007389a31ebd7fb16f28910d95422f\",\n \"score\": \"0.62242776\",\n \"text\": \"def symlink(self, target, source):\\n if self.debug == True:\\n logging.debug(\\\"in symlink\\\")\\n raise FuseOSError(ENOENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90387b542107e8c28fba24a7fd822dad\",\n \"score\": \"0.62149453\",\n \"text\": \"def path_ok(match_tuple: MatchTuple) -> bool:\\n relative_path = match_tuple.link.split(\\\"#\\\")[0]\\n full_path = os.path.join(os.path.dirname(str(match_tuple.source)), relative_path)\\n return os.path.exists(full_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c41d676430289b18beaa7f4c28d60dc6\",\n \"score\": \"0.6214474\",\n \"text\": \"def test_clear_broken_symlink(self):\\n nonexistent_file_path = os.path.join(settings.STATIC_ROOT, 'nonexistent.txt')\\n broken_symlink_path = os.path.join(settings.STATIC_ROOT, 'symlink.txt')\\n os.symlink(nonexistent_file_path, broken_symlink_path)\\n self.run_collectstatic(clear=True)\\n self.assertFalse(os.path.lexists(broken_symlink_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db9930863d4dcb3dd9b4a03dbd9b6d5\",\n \"score\": \"0.6211496\",\n \"text\": \"def test_symlinks():\\n\\n config_file = \\\"study/profit_symlink.yaml\\\"\\n config = BaseConfig.from_file(config_file)\\n base_file = \\\"./study/run_000/mockup.in\\\"\\n link_file = \\\"./study/run_000/some_subdir/symlink_link.txt\\\"\\n try:\\n run(f\\\"profit run {config_file}\\\", shell=True, timeout=TIMEOUT)\\n with open(link_file, \\\"r\\\") as link:\\n with open(base_file, \\\"r\\\") as base:\\n link_data = link.read()\\n base_data = base.read()\\n assert link_data == base_data and not link_data.startswith(\\\"{\\\")\\n finally:\\n clean(config)\\n run(f\\\"profit clean --all {config_file}\\\", shell=True, timeout=CLEAN_TIMEOUT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9e9a27b97a6b79b9f1382fe5c6addc\",\n \"score\": \"0.6205026\",\n \"text\": \"def do_ismount(path):\\n try:\\n s1 = os.lstat(path)\\n except os.error as err:\\n if err.errno == errno.ENOENT:\\n # It doesn't exist -- so not a mount point :-)\\n return False\\n else:\\n raise GlusterFileSystemOSError(\\n err.errno, '%s, os.lstat(\\\"%s\\\")' % (err.strerror, path))\\n\\n if stat.S_ISLNK(s1.st_mode):\\n # A symlink can never be a mount point\\n return False\\n\\n try:\\n s2 = os.lstat(os.path.join(path, '..'))\\n except os.error as err:\\n raise GlusterFileSystemOSError(\\n err.errno, '%s, os.lstat(\\\"%s\\\")' % (err.strerror,\\n os.path.join(path, '..')))\\n\\n dev1 = s1.st_dev\\n dev2 = s2.st_dev\\n if dev1 != dev2:\\n # path/.. on a different device as path\\n return True\\n\\n ino1 = s1.st_ino\\n ino2 = s2.st_ino\\n if ino1 == ino2:\\n # path/.. is the same i-node as path\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97181c311338ad47abd8855991b9b9a\",\n \"score\": \"0.6189933\",\n \"text\": \"def test_broken_symlink(self):\\n path = os.path.join(settings.STATIC_ROOT, 'test.txt')\\n os.unlink(path)\\n self.run_collectstatic()\\n self.assertTrue(os.path.islink(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4525da4084481db3ff2dc0510d16515\",\n \"score\": \"0.6175588\",\n \"text\": \"def lexists(self, path: AnyStr) -> bool:\\n return self.filesystem.exists(path, check_link=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e869be7867b9731154d811f96ce03792\",\n \"score\": \"0.6169737\",\n \"text\": \"def check_if_link_in_use(filepath, link):\\n\\n if not \\\"previous\\\" in link:\\n if not in_use_links.get(link):\\n new_file_name = remove_extra_from_path(filepath)\\n\\n if new_file_name.startswith(\\\"\\\\\\\\\\\"):\\n new_file_name = new_file_name.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n\\n if new_file_name != link:\\n in_use_links[link] = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a6e146797833743267c59619036c22\",\n \"score\": \"0.61650556\",\n \"text\": \"def ismount(path):\\n try:\\n s1 = os.lstat(path)\\n except OSError:\\n # It doesn't exist -- so not a mount point. :-)\\n return False\\n else:\\n # A symlink can never be a mount point\\n if stat.S_ISLNK(s1.st_mode):\\n return False\\n\\n if isinstance(path, bytes):\\n parent = join(path, b'..')\\n else:\\n parent = join(path, '..')\\n try:\\n s2 = os.lstat(parent)\\n except OSError:\\n return False\\n\\n dev1 = s1.st_dev\\n dev2 = s2.st_dev\\n if dev1 != dev2:\\n return True # path/.. on a different device as path\\n ino1 = s1.st_ino\\n ino2 = s2.st_ino\\n if ino1 == ino2:\\n return True # path/.. is the same i-node as path\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e371c24edcbab6afb4eab8ea6a95def\",\n \"score\": \"0.61448544\",\n \"text\": \"def check_path(path):\\r\\n if os.path.exists(path):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bfbb920868b15f86a5bd492a0366588\",\n \"score\": \"0.6144068\",\n \"text\": \"def test_check_permissions_and_broken_symlinks(self, filesystem_world_writable_whitelist, filesystem_ignore_patterns):\\n stack = [] # Used for storing directories to parse\\n starting_dir = \\\"/\\\"\\n failed = False\\n\\n # Helper functions\\n is_dir = lambda x: stat.S_ISDIR( x.st_mode)\\n is_lnk = lambda x: stat.S_ISLNK(x.st_mode)\\n is_sock = lambda x: stat.S_ISSOCK(x.st_mode)\\n is_chr = lambda x: stat.S_ISCHR(x.st_mode)\\n is_blk = lambda x: stat.S_ISBLK(x.st_mode)\\n is_reg = lambda x: stat.S_ISREG(x.st_mode)\\n is_world_writable = lambda x: stat.S_IWOTH & x.st_mode\\n\\n # Start it up\\n stack.append(starting_dir)\\n while len(stack) > 0:\\n # Get actual directory\\n directory = stack.pop()\\n # Parse through it\\n for entry in os.listdir(directory):\\n # Stat it\\n path = \\\"%s/%s\\\" % (directory, entry)\\n if path.startswith(\\\"//\\\"): # Strip beginning // because of root dir\\n path = path[1:]\\n\\n # Skip this file/dir, has the wrong pattern\\n if any([fnmatch.fnmatch(path, p) for p in filesystem_ignore_patterns]):\\n sys.stdout.write(\\\"[ignoring] %s\\\\n\\\" % (path,))\\n continue\\n\\n info = os.lstat(path)\\n if is_lnk(info):\\n # Check if it's broken or not\\n if not Test.Shell.exists_in_path(os.readlink(path), os.path.abspath(directory)):\\n sys.stderr.write(\\\"[broken-symlink] %s -> %s\\\\n\\\" % (path, os.readlink(path)))\\n failed = True\\n else:\\n if is_dir(info):\\n stack.append(path)\\n\\n # FIXME - determine whether file is truly writable (parent dir\\n # may not be readable)\\n # Check its permissions, if wrong, append it\\n if is_world_writable(info):\\n # If the path isn't on the whitelist ... we found a failure\\n if not any([fnmatch.fnmatch(path, p) for p in filesystem_world_writable_whitelist]):\\n sys.stderr.write(\\\"[world-writable] %s %s\\\\n\\\" % (self.fmt_mode_str(info.st_mode), path))\\n failed = True\\n if failed:\\n Test.Fail(msg=\\\"Test failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ffe407236011d5ddfea62710e51f2d8\",\n \"score\": \"0.6135178\",\n \"text\": \"def is_device_link_fail(self):\\n return self._tag == 'device_link_fail'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d9a697a78f12fdb87286c0b5c3558e3\",\n \"score\": \"0.6132719\",\n \"text\": \"def test_symlink_file5(self):\\n src = os.path.join(self.rootdir,\\\"non-existing-file\\\") \\n target = os.path.join(self.targetdir,os.path.basename(src))\\n with self.assertRaises(transfer.TransferError):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"771b570903a84e5ab0839ea57aff428e\",\n \"score\": \"0.6085972\",\n \"text\": \"def silent_symlink(src, dst):\\n try:\\n os.symlink(src, dst)\\n except OSError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b346d5a2d9646b6ff0a8844a820c0b6e\",\n \"score\": \"0.60828763\",\n \"text\": \"def readlink(path):\\n if not path:\\n return path\\n\\n if path[0] == \\\"~\\\":\\n path = os.path.expanduser(path)\\n\\n return run_bash_cmd([\\\"readlink\\\", \\\"-f\\\", path]) or path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc8a241fd5bbbd50d166cdb6622d9a4\",\n \"score\": \"0.6063484\",\n \"text\": \"def _is_valid_path(path):\\r\\n try:\\r\\n urlresolvers.resolve(path)\\r\\n return True\\r\\n except urlresolvers.Resolver404:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8bae97837e4ff75dd6ca0ac4d853a0f\",\n \"score\": \"0.6037345\",\n \"text\": \"def exists(path, followlinks=True):\\n if path is None:\\n raise Tools.exceptions.Value(\\\"Path is not passed in system.fs.exists\\\")\\n found = False\\n try:\\n st = os.lstat(path)\\n found = True\\n except (OSError, AttributeError):\\n pass\\n if found and followlinks and stat.S_ISLNK(st.st_mode):\\n if MyEnv.debug:\\n Tools.log(\\\"path %s exists\\\" % str(path.encode(\\\"utf-8\\\")))\\n linkpath = os.readlink(path)\\n if linkpath[0] != \\\"/\\\":\\n linkpath = os.path.join(Tools.path_parent(path), linkpath)\\n return Tools.exists(linkpath)\\n if found:\\n return True\\n # Tools.log('path %s does not exist' % str(path.encode(\\\"utf-8\\\")))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd3c3cb6566686327f09cc836e98684d\",\n \"score\": \"0.60363925\",\n \"text\": \"def check_path(p):\\n if not os.path.exists(p):\\n msg = 'Error: Path \\\"{}\\\" does not exist.'.format(p)\\n print_help(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6931c31cb4ea78fdd2dcb5957104c1b\",\n \"score\": \"0.6035318\",\n \"text\": \"def check_path(path):\\n if (not os.path.isfile(path)):\\n raise Exception('The path specified does not point to a file')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfdbda55742febd9b0ba06e3c8969c13\",\n \"score\": \"0.6016378\",\n \"text\": \"def symlinks_test(dirname):\\n\\n links = []\\n\\n for (path, dirs, files) in os.walk(dirname):\\n if path.find('/.git') > -1:\\n continue\\n\\n # search for directory links\\n for d in dirs:\\n dname = os.path.join(path, d)\\n if os.path.islink(dname):\\n links.append(os.path.relpath(dname, dirname)+'/')\\n\\n # search for file links\\n for f in files:\\n fname = os.path.join(path, f)\\n if os.path.islink(fname):\\n links.append(os.path.relpath(fname, dirname))\\n\\n if links:\\n return (FAIL, '\\\\n'.join(links))\\n else:\\n return (PASS, \\\"None\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a97ceeb3e2a1ef2d4f8add3a4d41507d\",\n \"score\": \"0.60025454\",\n \"text\": \"def test_force_symlink_overwrite(tmpdir):\\n src = join(str(tmpdir), \\\"src\\\")\\n dst = join(str(tmpdir), \\\"dst\\\")\\n\\n with open(src, \\\"w\\\") as f:\\n f.write(\\\"Correct.\\\")\\n\\n with open(dst, \\\"w\\\") as f:\\n f.write(\\\"Wrong.\\\")\\n\\n utils.force_symlink(src, dst)\\n assert os.path.islink(dst)\\n\\n with open(dst, \\\"r\\\") as f:\\n assert \\\"Correct\\\" in f.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fbf806aa189307c157cd564dbcd1a8d\",\n \"score\": \"0.59952956\",\n \"text\": \"def fix_all_git_symlinked(topdir):\\n # Determine whether or not symlinks need fixing (they don’t if installing\\n # from a .tar.gz file)\\n with io.open(topdir + r'\\\\nikola\\\\data\\\\symlink-test-link.txt', 'r', encoding='utf-8-sig') as f:\\n text = f.read()\\n if text.startswith(\\\"NIKOLA_SYMLINKS=OK\\\"):\\n return -1\\n with io.open(topdir + r'\\\\nikola\\\\data\\\\symlinked.txt', 'r', encoding='utf-8-sig') as f:\\n text = f.read()\\n # expect each line a relpath from git or zip root,\\n # smoke test relpaths are relative to git root\\n if text.startswith('.'):\\n raise Exception(r'Bad data in \\\\nikola\\\\data\\\\symlinked.txt')\\n relnames = text.split('\\\\n')\\n relnames = [name.strip().replace('/', '\\\\\\\\') for name in relnames]\\n relnames = [name for name in relnames if name]\\n\\n failures = 0\\n for name in relnames:\\n # build dst path and do some basic validation\\n dst = os.path.join(topdir, name)\\n # don't access files outside topdir\\n if not is_file_into_dir(dst, topdir):\\n continue\\n if os.path.isdir(dst):\\n # assume the file was de-symlinked\\n continue\\n\\n # build src path and do some basic validation\\n with io.open(os.path.join(topdir, dst), 'r', encoding='utf-8-sig') as f:\\n text = f.read()\\n dst_dir = os.path.dirname(dst)\\n try:\\n src = os.path.normpath(os.path.join(dst_dir, text))\\n if not os.path.exists(src):\\n # assume the file was de-symlinked before\\n continue\\n # don't access files outside topdir\\n if not is_file_into_dir(src, topdir):\\n continue\\n except Exception:\\n # assume the file was de-symlinked before\\n continue\\n\\n # copy src to dst\\n try:\\n if os.path.isdir(src):\\n os.unlink(dst)\\n shutil.copytree(src, dst)\\n else:\\n shutil.copy2(src, dst)\\n except Exception:\\n failures += 1\\n print(\\\"*** copy failed for\\\")\\n print(\\\"\\\\t src:\\\", src)\\n print(\\\"\\\\t dst:\\\", dst)\\n\\n return failures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"727d2ea9be0ae6be316b42e299d3ad3b\",\n \"score\": \"0.599236\",\n \"text\": \"def test_hardlink(self):\\n # Prevent errors from being caught\\n self.tar.errorlevel = 1\\n\\n self.tar.extract(\\\"0-REGTYPE\\\", dirname())\\n try:\\n # Extract 1-LNKTYPE which is a hardlink to 0-REGTYPE\\n self.tar.extract(\\\"1-LNKTYPE\\\", dirname())\\n except EnvironmentError, e:\\n import errno\\n if e.errno == errno.ENOENT:\\n self.fail(\\\"hardlink not extracted properly\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9745941b98c710c4c4ec6d21f02e9e1\",\n \"score\": \"0.59648645\",\n \"text\": \"def test_symlink_folder7(self):\\n src = self.rootdir\\n target = os.path.join(self.targetdir,os.path.basename(src))\\n with mock.patch.object(\\n transfer.os,\\n 'symlink',\\n side_effect=OSError(\\\"Mocked error\\\")):\\n with self.assertRaises(transfer.SymlinkError):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c8991464103ff5c306a31023d7fce3\",\n \"score\": \"0.5945459\",\n \"text\": \"def can_symlink_to(self, outfile_name, symlink_candidate):\\n if symlink_candidate == outfile_name:\\n # Symlinking to ourself is *not* ok.\\n return False\\n\\n if symlink_candidate in self._db.transaction_keys():\\n # Don't try to symlink if our target file is being updated\\n # itself; we can't tell whether we're 'equivalent' to it\\n # or not if it's currently being modified!\\n return False\\n\\n symlink_mtime_map = self._db.get(symlink_candidate)\\n if symlink_mtime_map is None:\\n # We don't have up-to-date-ness information on the candidate.\\n return False\\n\\n # outfile_name is currently in a transaction (changed_files()\\n # returned false), so we have to get its mtime info that way.\\n outfile_mtime_map = self._db.get_transaction(outfile_name)\\n\\n # Get a copy of mtime_maps that do not include the output file.\\n pruned_outfile_mtime_map = outfile_mtime_map.copy()\\n pruned_outfile_mtime_map.pop(outfile_name)\\n pruned_symlink_mtime_map = symlink_mtime_map.copy()\\n pruned_symlink_mtime_map.pop(symlink_candidate)\\n\\n if (frozenset(pruned_outfile_mtime_map.keys()) !=\\n frozenset(pruned_symlink_mtime_map.keys())):\\n # This means symlink_candidate has different deps that we\\n # do (common case).\\n return False\\n\\n for (k, v) in pruned_outfile_mtime_map.iteritems():\\n # This means symlink_candidate has the same deps as us,\\n # but those deps aren't up to date.\\n # This holds because infile_map has the *current* mtimes of\\n # the input files, and if the (db-based) values of\\n # symlink_candidate don't match, that means it's out of date.\\n symlink_v = pruned_symlink_mtime_map.get(k, (None, None, None))\\n if not file_info_equal(v, symlink_v):\\n return False\\n\\n if not file_info_equal(get_file_info(symlink_candidate),\\n symlink_mtime_map[symlink_candidate]):\\n # This finishes off the 'other_outfile is up-to-date' check.\\n # We tested all the input files are up-to-date above, now we\\n # need to test the output file too. Note we never bother to\\n # do the crc check here; we assume that as a generated file,\\n # other_outfile will not suffer from the\\n # 'git-changed-my-mtime' problem.\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9b2fc97f39ea65d0aff639dfc352fb\",\n \"score\": \"0.5944636\",\n \"text\": \"def verify_path(self, path):\\n if Path(path).exists():\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28f897359724e00998034cf686012bf2\",\n \"score\": \"0.59438217\",\n \"text\": \"def check_path(path):\\n assert isinstance(path, basestring), \\\"Expected string or unicode, found %s.\\\" % type(path)\\n try:\\n open(path, \\\"r\\\")\\n except IOError:\\n raise IOError('File does not exist: %s' % path)\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2dcd5fddfe2a01bb5a15519baa67b1\",\n \"score\": \"0.59393996\",\n \"text\": \"def test_symlink_folder8(self):\\n src = self.rootdir\\n target = self.targetdir\\n with mock.patch.object(\\n transfer.os.path,\\n 'exists',\\n side_effect=Exception(\\\"Mocked error\\\")):\\n with self.assertRaises(Exception):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a04cd212041a2c05af0b7a22f90a585d\",\n \"score\": \"0.5926641\",\n \"text\": \"def is_wsl2_path(path: p.PurePath) -> bool:\\n return re.match(r\\\"^/mnt/[a-z]/\\\", path.as_posix()) is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72eaf06e2133216050eaec901507e2aa\",\n \"score\": \"0.59235525\",\n \"text\": \"def S_ISLNK(mode):\\n return S_IFMT(mode) == S_IFLNK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b55905af587ce30f9e7fd3e568d64b0\",\n \"score\": \"0.59146184\",\n \"text\": \"def is_malformed_path(self):\\n return self._tag == 'malformed_path'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b32a7f3b0b52be5499eccabc3f8c8143\",\n \"score\": \"0.59071994\",\n \"text\": \"def is_link_ok(link: str) -> bool:\\n return not any([re.search(exc, link) for exc in excludes])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1dfb6e5ef886289956a028ef3bd3d34\",\n \"score\": \"0.5889858\",\n \"text\": \"def is_safe_path(basedir, path, follow_symlinks=True):\\n if basedir is None:\\n return True\\n if follow_symlinks:\\n return os.path.realpath(path).startswith(basedir.encode('utf-8'))\\n return os.path.abspath(path).startswith(basedir.encode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ffdb8f73ffd102df61f9778afce65d\",\n \"score\": \"0.5889097\",\n \"text\": \"def valid_permlink(permlink, allow_empty=False):\\n if not permlink:\\n assert allow_empty, 'permlink cannot be blank'\\n return \\\"\\\"\\n assert isinstance(permlink, str), 'permlink must be string'\\n assert len(permlink) <= 256, \\\"invalid permlink length\\\"\\n return permlink\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ffdb8f73ffd102df61f9778afce65d\",\n \"score\": \"0.5889097\",\n \"text\": \"def valid_permlink(permlink, allow_empty=False):\\n if not permlink:\\n assert allow_empty, 'permlink cannot be blank'\\n return \\\"\\\"\\n assert isinstance(permlink, str), 'permlink must be string'\\n assert len(permlink) <= 256, \\\"invalid permlink length\\\"\\n return permlink\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7413b59212368171f2e2be09dfcd62d8\",\n \"score\": \"0.58867145\",\n \"text\": \"def check_links(params):\\n\\n\\tfor s in _file_links:\\n\\t\\t#print \\\"check:\\\", s, cfg.out_dir\\n\\t\\tout_file = os.path.join(cfg.out_dir, s)\\n\\t\\tif not os.path.exists(out_file):\\n\\t \\t\\twarning(\\\"%s: invalid link to '%s'\\\" % (_file_links[s], s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e62c55844b430153e153df0db262b290\",\n \"score\": \"0.588417\",\n \"text\": \"def _resolve_link(path):\\r\\n paths_seen = []\\r\\n while islink(path):\\r\\n if path in paths_seen:\\r\\n # Already seen this path, so we must have a symlink loop\\r\\n return None\\r\\n paths_seen.append(path)\\r\\n # Resolve where the link points to\\r\\n resolved = os.readlink(path)\\r\\n if not isabs(resolved):\\r\\n dir = dirname(path)\\r\\n path = normpath(join(dir, resolved))\\r\\n else:\\r\\n path = normpath(resolved)\\r\\n return path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b85174b6188a7a19df4eff61790b94\",\n \"score\": \"0.58817095\",\n \"text\": \"def hardlink(source, link_name):\\n try:\\n os.link(source, link_name)\\n except FileExistsError:\\n # It's possible that the user created a different file with the same name as the\\n # one we're trying to download. Thus we need to check the if the inode is different\\n # and raise an error in this case.\\n source_stat = os.stat(source)\\n dest_stat = os.stat(link_name)\\n # Check device first because different drives can have the same inode number\\n if source_stat.st_dev != dest_stat.st_dev or source_stat.st_ino != dest_stat.st_ino:\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962858e7a2194d210b9dfedd9b981e87\",\n \"score\": \"0.5866979\",\n \"text\": \"def _force_symlink(\\n root: Path, target: Union[str, PurePath], link_to: Union[str, Path]\\n) -> None:\\n current_symlink = root.joinpath(target)\\n try:\\n current_symlink.unlink()\\n except OSError:\\n pass\\n try:\\n current_symlink.symlink_to(link_to)\\n except Exception:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adf4744cb862d5219816f94cf0ef9f77\",\n \"score\": \"0.58613175\",\n \"text\": \"def _process_symlink(self, target, path): #aka target_path, dotfile_path\\n\\n if not os.path.lexists(target):\\n self._error = True\\n print('\\\\tmissing target: {}'.format(target))\\n return ('DOTFILE NOT FOUND', None)\\n\\n if not os.path.lexists(path):\\n self._changed = True\\n if self.args.test:\\n return ('missing', None)\\n else:\\n create_dotfile_link(target, path)\\n return ('missing', 'synced')\\n\\n existing_realpath = os.path.realpath(path)\\n if existing_realpath != os.path.realpath(target):\\n if self.args.debug:\\n print('\\\\tCurrent target path: {}'.format(existing_realpath))\\n status = 'unmanaged' if os.path.lexists(existing_realpath) else 'broken'\\n\\n if self.args.verbose:\\n pass # XXX: TODO: ...\\n\\n if status != 'broken' and not self.args.force:\\n return (status, 'skipping')\\n self._changed = True\\n if self.args.test:\\n return (status, None)\\n\\n #import pdb; pdb.set_trace()\\n remove_any(path)\\n create_dotfile_link(target, path)\\n return (status, 'removed; synced')\\n\\n return ('ok', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f124a12bcf057e1e114461e076db3da\",\n \"score\": \"0.58529246\",\n \"text\": \"def _checkLink(link):\\n try:\\n link = link.attrs['href']\\n except KeyError:\\n return -1\\n if link[0] == '/':\\n link = '://'.join(list(urllib.parse.urlparse(self.address)[0:2])) + link\\n elif re.match(r'^mailto|^#', link):\\n return None\\n good = ['200', '206', '301', '302']\\n try:\\n status, response = self.http.request(link)\\n if status['status'] not in good:\\n self.badlinks.append((status['status'], link))\\n else:\\n self.goodlinks.append(link)\\n except httplib2.ServerNotFoundError:\\n self.other.append((\\\"httplib2.ServerNotFoundError\\\", link))\\n except httplib2.ssl.SSLError:\\n self.other.append((\\\"httplib2.ssl.SSLError\\\", link))\\n except ConnectionResetError:\\n self.other.append((\\\"ConnectionResetError\\\", link))\\n except UnicodeError:\\n self.other.append((\\\"UnicodeError\\\", link))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af98442fe78a61c211571253d3a076e5\",\n \"score\": \"0.58304495\",\n \"text\": \"def internal_link_checker(filepath, html_str):\\n\\n # Flag to determine if internal link is broken\\n internal_link_error = False\\n\\n # \\n\\n problems = []\\n relative_links = []\\n\\n # find all links\\n for prefix in [\\\"href\\\", \\\"src\\\"]:\\n\\n links = re.findall(\\n f\\\"{prefix}\\\\s?=\\\\s?[\\\\\\\"']([{allowed_in_link}]+)[\\\\\\\"']\\\", html_str)\\n\\n # check if link has a dest\\n for link in links:\\n\\n # Check if link is relative path\\n is_relative = check_if_relative_link(link)\\n\\n # Add to relative links list if relative\\n if is_relative:\\n if not link in relative_links:\\n relative_links.append(link)\\n\\n # Get correct path\\n link = get_correct_link(link)\\n\\n # Check if link is in use\\n check_if_link_in_use(filepath, link)\\n\\n if link in links_list:\\n if links_list[link]:\\n if link not in problems:\\n problems.append(link) \\n else:\\n if internal_link_test(link):\\n problems.append(link)\\n links_list[link] = True\\n\\n if not internal_link_error:\\n internal_link_error = True\\n else:\\n links_list[link] = False\\n\\n return problems, relative_links, internal_link_error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a8c415ec1777ae102d937832bd39339\",\n \"score\": \"0.5820183\",\n \"text\": \"def symlink (self, src, dst):\\n if self.system ('/bin/ln -sf %s %s'%(src, dst)):\\n raise OSError, 'can\\\\'t symlink [%s] as [%s]' % (src, dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf5255caff8fa0c01af5e9a0e3b0a7b8\",\n \"score\": \"0.58178663\",\n \"text\": \"def test_symlink_file6(self):\\n src = self.rootdir\\n target = os.path.join(self.targetdir,\\\"target-file\\\")\\n open(target,'w').close()\\n with mock.patch.object(\\n transfer.os,\\n 'unlink',\\n side_effect=OSError(\\\"Mocked error\\\")):\\n with self.assertRaises(transfer.SymlinkError):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b019bad4cd351d9787fe08c2bc3f228\",\n \"score\": \"0.57966596\",\n \"text\": \"def symlink(self, req, link, parent, name):\\n self.reply_err(req, EROFS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9684816cb8583b1d44b66d7cd04ca40\",\n \"score\": \"0.5782404\",\n \"text\": \"def test_force_link(tmpdir):\\n src = join(str(tmpdir), \\\"src\\\")\\n dst = join(str(tmpdir), \\\"dst\\\")\\n\\n with open(src, \\\"w\\\") as f:\\n f.write(\\\"Not empty.\\\")\\n\\n utils.force_link(src, dst)\\n assert os.path.isfile(dst)\\n assert not os.path.islink(dst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6419b2fcb183ad206a852387f3bc8d86\",\n \"score\": \"0.57806474\",\n \"text\": \"def test_scan_files(self):\\n self.assertFalse(fdf.valid_path('some thing wrong'))\\n result = fdf.scan_files(join(getcwd(), 'test'))\\n self.assertNotIn(join(getcwd(), 'test/symlink/symlink'), result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2fcb55acc8c1275ebfe5f75e72876a0\",\n \"score\": \"0.57789207\",\n \"text\": \"def test_non_html_links_are_path_sensitive(self):\\n validator = self._create_validator(\\n \\\"\\\"\\\"Use [this CSV](data.csv) for the exercise.\\\"\\\"\\\")\\n self.assertFalse(validator._validate_links())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"169daa1a343f03131a9d00db91a8b596\",\n \"score\": \"0.5773865\",\n \"text\": \"def test_symlink_folder4(self):\\n src = os.path.join(self.rootdir) \\n target = os.path.join(self.targetdir)\\n with mock.patch('taca.utils.transfer.os.path') as mockobj:\\n mockobj.ismount.return_value = False\\n mockobj.isfile.return_value = False\\n mockobj.islink.return_value = False\\n mockobj.isdir.return_value = False\\n with self.assertRaises(transfer.SymlinkError):\\n transfer.SymlinkAgent(src,target).transfer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3cd10319fcfb6990ceba408de225a7\",\n \"score\": \"0.5773567\",\n \"text\": \"def is_file_link(self, link):\\n return link.startswith(\\\"###file\\\") or link.startswith('/repository') or \\\\\\n (link.startswith('../') and '/files/' in link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c2fa09a69a0c70db6567fbff768f2d\",\n \"score\": \"0.57712406\",\n \"text\": \"def provide_link(dest, src):\\n if not os.path.exists(dest):\\n # strip trailing /'s just in case it exists\\n os.symlink(src, dest.rstrip(\\\"/\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5141a7e126b929ce6f8259580dbeb5c\",\n \"score\": \"0.5764898\",\n \"text\": \"def filepath_validator(path):\\n if isinstance(path, str):\\n path = Path(path)\\n return path.exists() and path.isfile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"308f791745e59acf2880a0ec1831b152\",\n \"score\": \"0.5761583\",\n \"text\": \"def test_symlink_lock(self):\\n\\n # Get an image object and tests its manual lock mechanism.\\n api_obj = self.get_img_api_obj()\\n img = api_obj.img\\n\\n # Verify a lock file is created.\\n img.lock()\\n lfpath = os.path.join(img.imgdir, \\\"lock\\\")\\n self.assertTrue(os.path.exists(lfpath))\\n img.unlock()\\n os.remove(lfpath)\\n\\n # Make lock file a symlink by pointing it to a random file .\\n tmp_file = os.path.join(img.imgdir, \\\"test_symlink\\\")\\n fo = open(tmp_file, 'wb+')\\n fo.close()\\n os.symlink(tmp_file, lfpath)\\n\\n # Verify that both pkg install and refresh generate an error\\n # if the lock file is a symlink.\\n self.pkg(\\\"install foo\\\", su_wrap=True, exit=1)\\n self.assertTrue(\\\"contains a symlink\\\" in self.errout)\\n\\n self.pkg(\\\"refresh --full\\\", su_wrap=True, exit=1)\\n self.assertTrue(\\\"contains a symlink\\\" in self.errout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18658f8e04412069e3a03d80b360b5b3\",\n \"score\": \"0.57601094\",\n \"text\": \"def check_path_existence(fpath: str) -> bool:\\n return os.path.exists(fpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31411b5398f85370a62fa13f46f581a5\",\n \"score\": \"0.5740772\",\n \"text\": \"def validate_path(self, path):\\n if not os.path.exists(path):\\n raise IOError('Path {} does not exist'.format(path))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"042985e4ccc6c626626e50eb20ef3c8a\",\n \"score\": \"0.5739684\",\n \"text\": \"def ismount(path):\\r\\n try:\\r\\n s1 = os.lstat(path)\\r\\n s2 = os.lstat(join(path, '..'))\\r\\n except os.error:\\r\\n return False # It doesn't exist -- so not a mount point :-)\\r\\n dev1 = s1.st_dev\\r\\n dev2 = s2.st_dev\\r\\n if dev1 != dev2:\\r\\n return True # path/.. on a different device as path\\r\\n ino1 = s1.st_ino\\r\\n ino2 = s2.st_ino\\r\\n if ino1 == ino2:\\r\\n return True # path/.. is the same i-node as path\\r\\n return False\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":194,"cells":{"query_id":{"kind":"string","value":"54c87c218016afc006e423f7687dcaee"},"query":{"kind":"string","value":"Returns a boolean indicating whether this type is an 'interface' type that is implemented in Gecko. At the moment, this returns true for all interface types that are not types from the TypedArray spec."},"positive_passages":{"kind":"list like","value":[{"docid":"50186e06c1db50f28ee3b3f7d75a25e9","score":"0.700691","text":"def isGeckoInterface(self):\n return self.isInterface() and not self.isSpiderMonkeyInterface()","title":""}],"string":"[\n {\n \"docid\": \"50186e06c1db50f28ee3b3f7d75a25e9\",\n \"score\": \"0.700691\",\n \"text\": \"def isGeckoInterface(self):\\n return self.isInterface() and not self.isSpiderMonkeyInterface()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3fd0af121674f7ce21b6fec67acc48c9","score":"0.75389546","text":"def isSpiderMonkeyInterface(self):\n return self.isInterface() and (self.isArrayBuffer() or \\\n self.isArrayBufferView() or \\\n self.isTypedArray())","title":""},{"docid":"f22ccea57db2c6f27555c3c7019e94e2","score":"0.74693924","text":"def is_interface(self):\n\n return True","title":""},{"docid":"f22ccea57db2c6f27555c3c7019e94e2","score":"0.74693924","text":"def is_interface(self):\n\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"8df4e30ccecf6df007a1799056144bd7","score":"0.7385049","text":"def is_interface(self):\n return True","title":""},{"docid":"b10dfb26a4192cdf8ba42575e9069538","score":"0.6441149","text":"def has_interface(self, name):\n if not self.all_interfaces:\n self.find_interfaces()\n return name in self.all_interfaces","title":""},{"docid":"45b6ef24706b35f4b5afbd0e1d5f38d2","score":"0.5977207","text":"def obj_has_interface(obj, *ifaces):\r\n for iface in ifaces:\r\n if iface.providedBy(obj):\r\n return True\r\n return False","title":""},{"docid":"533ee492d61d1c5bc1503a56ef9018b8","score":"0.5907879","text":"def is_implemented(self):\n try:\n self.coxeter_diagram()\n return True\n except Exception:\n return False","title":""},{"docid":"a118f27e95bb4e2efdd0e2efeaa97b85","score":"0.5775072","text":"def InterfaceType(self):\n return self._get_attribute('interfaceType')","title":""},{"docid":"c6611878a3ca087c3654d231659f4be6","score":"0.5677758","text":"def hasImplementation(self):\n return False","title":""},{"docid":"56bb5520d6d5bbc20c414ff8f77589ec","score":"0.5661154","text":"def is_interface_available(interface_name):\n\n try:\n interface = NXCLI('show run interface %s' %interface_name)\n return True\n except:\n return False","title":""},{"docid":"de489f3501d8d96a1747af4e3fa8365a","score":"0.56517386","text":"def is_available(self, hub, interface_name):\n \n result = False\n\n names_dict = self._names[hub.interface_type]\n if interface_name in names_dict: result = True\n\n return result","title":""},{"docid":"6e398ccc621d69c564f8f847747c78c4","score":"0.5499881","text":"def is_ipython() -> bool:\n\n try:\n __IPYTHON__ # type: ignore[name-defined] # pylint: disable=pointless-statement\n return True\n except NameError:\n return False","title":""},{"docid":"2b1f1e38910f5021d929657b36f4107b","score":"0.5479125","text":"def interface_type(self) -> Optional[str]:\n return pulumi.get(self, \"interface_type\")","title":""},{"docid":"76782b80ec61eb9faf8d19425d74ec52","score":"0.5415978","text":"def has_web_interface(self) -> bool:\n for port in self._open_ports:\n if port in WEB_PORTS:\n return True\n return False","title":""},{"docid":"68a8c3def7d14b06861b675d1393a489","score":"0.5412653","text":"def _is_ipython() -> bool:\n try:\n import IPython # pylint: disable=g-import-not-at-top\n ipy = IPython.get_ipython()\n if ipy is None:\n return False\n except ImportError:\n return False\n\n return True","title":""},{"docid":"9b8e18a44978fd4eb1e209c2c287ec51","score":"0.54086965","text":"def Interfaces(self):\n return self._get_attribute('interfaces')","title":""},{"docid":"a3fea24ba206c82a91dda73264705530","score":"0.5395206","text":"def interface(self) -> \"RegisterArray.Interface\":\n\n return self._interface","title":""},{"docid":"3e9d2e76a21ccc26f741935f1d57e547","score":"0.538823","text":"def _fallback_hasinterface(self, conn, ident, obj, typename):\r\n # It's very difficult to get an arbitrary `typename` here.\r\n dot = typename.rfind('.')\r\n module_name = typename[:dot]\r\n class_name = typename[dot+1:]\r\n try:\r\n module = __import__(module_name)\r\n except ImportError: #pragma no cover\r\n return False\r\n module = sys.modules[module_name]\r\n try:\r\n cls = getattr(module, class_name)\r\n except AttributeError: #pragma no cover\r\n return False\r\n return obj_has_interface(obj, cls)","title":""},{"docid":"cac6848b7d97d0e550c1e19e3a5b2931","score":"0.53677785","text":"def has_name(self, hub, interface_name):\n \n result = False\n \n if not self.is_available(hub, interface_name): return result\n\n interface_cls_name = self.get_cls_name(hub, interface_name) \n if interface_cls_name in hub.get_interface_map(): result = True\n \n return result","title":""},{"docid":"14c87943a24ff4af02ded3128e5f9cfb","score":"0.53670704","text":"def validate_interface(self, interface):\n url = 'http://0.0.0.0:8181/api/kytos/topology/v3/interfaces'\n headers = {'Content-Type': 'application/json'}\n current_interfaces = requests.get(url, headers=headers).json()\n\n return interface in [current_interfaces['interfaces'][key]['id'] for key in current_interfaces['interfaces']]","title":""},{"docid":"2b8c14d1778a807ad3477e673e5f52ae","score":"0.5335405","text":"def is_using_ipython():\n try:\n __IPYTHON__ # pylint: disable=undefined-variable, pointless-statement\n return True\n except NameError:\n return False","title":""},{"docid":"877ebdf13c62ad5cc4e9d351404c0b5a","score":"0.53146756","text":"def connected(self):\n return self._interface is not None","title":""},{"docid":"3b65645c6a7e2b0dde1897ad823f21a9","score":"0.53040075","text":"def interface(self) -> \"Register.Interface\":\n\n return self._interface","title":""},{"docid":"f26c30c251276dcf6c03b41f175a6d71","score":"0.52766734","text":"def is_enumerated_type(self):\n return self.enumerated_values is not None","title":""},{"docid":"8b0df933c507721b9942727b22ad436a","score":"0.5256926","text":"def implements(self, calculation_type: \"CalculationType\") -> bool:","title":""},{"docid":"f2518bc585dd4a9de207fd4517c48cbc","score":"0.5226839","text":"def _is_intf_deconfigured(self, interface):\n _logger.debug('%s\\n%s', where_am_i(), pformat(interface, indent=4))\n for deconf in self.vnic_info.get('deconfig', ()):\n if deconf in (interface['IFACE'], interface['VNIC'], interface['ADDR']):\n return True\n return False","title":""},{"docid":"f5f354b72d13a9226af14d6c81f81035","score":"0.5220707","text":"def interface_profile_has_metrics(profile):\n for m in profile.metrics:\n if m.is_active and m.metric_type.name in (\n \"Interface | Load | In\",\n \"Interface | Load | Out\"\n ):\n return True\n return False","title":""},{"docid":"fda1a9bf8244ba8f27d6fdf5af8308a1","score":"0.5209826","text":"def has_alibi_witness (self):\n if not self.alibi:\n return False\n\n if self.alibi.witness == -1:\n return False\n\n return True","title":""},{"docid":"6517bec04735935d4ee0ff5369e567b2","score":"0.51612246","text":"def interfaces(self):\n return self._view_by_kind(Database._Kind.INTERFACE)","title":""},{"docid":"9361244c8e0054f3dfbc19e65431e661","score":"0.515429","text":"def is_notebook():\n try:\n from IPython import get_ipython\n\n if \"IPKernelApp\" not in get_ipython().config: # pragma: no cover\n return False\n except ImportError:\n return False\n except AttributeError:\n return False\n return True","title":""},{"docid":"59bb25ccb55103b904b985f4bc55107c","score":"0.5145271","text":"def isinstance(self, obj: Any) -> bool:\n raise NotImplementedError()","title":""},{"docid":"003fb219083b8ab7fd5ffbadc5bf29be","score":"0.5139457","text":"def using_ipython() -> bool:\n return hasattr(builtins, '__IPYTHON__')","title":""},{"docid":"538031a7ad259ac07679a53e8f09cde4","score":"0.51392484","text":"def interface_exists(interface):\n return os.path.exists(\"/sys/class/net/%s\" % interface)","title":""},{"docid":"aee1f9d0829c76e4f029811214eaaff8","score":"0.51390105","text":"def test_implemented_by_subinterface(self):\n self.assertTrue(IFoo.implemented_by(IFooBar))","title":""},{"docid":"8c36c1320b613f243935dd850a8b2ded","score":"0.513061","text":"def implements_attribute_manager(obj):\n return all([hasattr(obj, n) for n in\n ['keys', '__getitem__', '__contains__', '__iter__']])","title":""},{"docid":"b62457039b5b99728ebd277fe83408b8","score":"0.5127941","text":"def interfaces(self):\n return self._interfaces","title":""},{"docid":"b62457039b5b99728ebd277fe83408b8","score":"0.5127941","text":"def interfaces(self):\n return self._interfaces","title":""},{"docid":"7ab9332932fc0556505ed054e4534e36","score":"0.5107286","text":"def implements_dataset(obj):\n return all([hasattr(obj, n) for n in\n ['attrs', 'keys', 'items',\n '__getitem__', '__contains__', '__iter__']])","title":""},{"docid":"08bc9a476b5488ef4062c42d2385795a","score":"0.50944895","text":"def is_type(self):\n return True","title":""},{"docid":"ab35b1572f9c98cfe710abd8db4719b5","score":"0.50910896","text":"def interface_exists(interface):\n return os.path.exists(\"/sys/class/net/\" + interface)","title":""},{"docid":"3abfdcdaa155732f67dec65ff27eb22a","score":"0.5085412","text":"def _currently_in_ipython():\n try:\n __IPYTHON__ # pylint: disable=undefined-variable\n return True\n except NameError:\n return False","title":""},{"docid":"2c118e3dfce2fd85243a6f79eb006fb1","score":"0.50795716","text":"def _was_registered(m, interface):\n \n for item in m.call_args_list:\n if item[0][-1] == interface:\n return True\n \n return False","title":""},{"docid":"d88ac3ffa427897baf9ab47042c839d8","score":"0.5077435","text":"def interface(self):\n return self.__interface","title":""},{"docid":"4ec85e70ccdb9bd315392ad5fd59f147","score":"0.50658995","text":"def in_ipython():\n try:\n shell = get_ipython().__class__.__name__\n if shell == \"ZMQInteractiveShell\":\n return True # Jupyter notebook or qtconsole\n elif shell == \"TerminalInteractiveShell\":\n return False # Terminal running IPython\n else:\n return False # Other type (?)\n except NameError:\n return False # Probably standard Python interpreter","title":""},{"docid":"cefc0b8f212634a96a065179fee635a8","score":"0.50610983","text":"def is_abstract(obj: typing.Type) -> bool:\n try:\n return ABC in obj.__bases__\n except AttributeError:\n return False","title":""},{"docid":"d9eca26713378f7517471cf61b8928f5","score":"0.50561816","text":"def GetInterfaces(self):\n return self._interfaces","title":""},{"docid":"289f245efa808a9a32ad5962d51e5c8d","score":"0.5047994","text":"def get_interface_class() -> Interface:\n interface = Office.get_interface() # gets str name of interface class\n if not interface:\n raise ValueError('Should be run as macro using XLWings or PyUno.'\n 'Neither could be detected.')\n return getattr(Office, interface)","title":""},{"docid":"52ebfab792e76b181e3ad009385342d2","score":"0.5029029","text":"def is_this_run_in_ipython():\n try:\n __IPYTHON__\n return True\n except NameError:\n return False","title":""},{"docid":"e5e8dbc9fc4e755ad5723910adda3862","score":"0.50271434","text":"def interface(self):\n return self._interface","title":""},{"docid":"e5e8dbc9fc4e755ad5723910adda3862","score":"0.50271434","text":"def interface(self):\n return self._interface","title":""},{"docid":"565ddff6ab951dd08895c4b3ac15d578","score":"0.501947","text":"def is_mpsse_interface(self, interface: int) -> bool:\n if not self.has_mpsse:\n return False\n if self.device_version == 0x0800 and interface > 2:\n return False\n if self.device_version == 0x3600 and interface > 2:\n return False\n return True","title":""},{"docid":"018e794216fecc1b79414a808c496be8","score":"0.49981865","text":"def _is_intf_excluded(self, interface):\n _logger.debug('%s\\n%s', where_am_i(), pformat(interface, indent=4))\n for excl in self.vnic_info.get('exclude', ()):\n if excl in (interface['IFACE'], interface['VNIC'], interface['ADDR']):\n return True\n return False","title":""},{"docid":"dc45a0530a6ce88744047b14cb31fae3","score":"0.4976998","text":"def get_interface() -> str or None:\n # test for Python Uno\n try:\n XSCRIPTCONTEXT # if this variable exists, PyUno is being used.\n except NameError:\n pass\n else:\n return 'Uno'\n\n if xw is not None:\n return 'XW'\n\n # otherwise, return None / False","title":""},{"docid":"5f5dd9da15c84c05dd10c6e4f2bf8554","score":"0.4964717","text":"def _in_ipython():\n\n try:\n # See https://stackoverflow.com/questions/15411967\n shell = get_ipython().__class__.__name__\n return bool(shell == \"TerminalInteractiveShell\")\n except NameError:\n return False","title":""},{"docid":"4ce46daaa6dd2bbf0905ee3bc8dc17a1","score":"0.49643916","text":"def interface_mode(self):\n return self._interface_mode.value if self._interface_mode else None","title":""},{"docid":"7dd29c00eb8a74ef6f6863fa100b1b37","score":"0.49566942","text":"def isnotebook(self):\n try:\n shell = get_ipython().__class__.__name__\n if (shell == 'ZMQInteractiveShell' or shell == 'TerminalInteractiveShell'):\n return True # Jupyter notebook or IPython\n else:\n return False # Other type (?)\n except NameError:\n return False","title":""},{"docid":"0494fee01a6c84336625999ec71e9e88","score":"0.49517235","text":"def exposed_interfaces(self):\n raise exceptions.NotImplementedError()","title":""},{"docid":"9370580bbbb45adf1a90aee162216377","score":"0.49513036","text":"def __eq__(self, other):\n if not isinstance(other, Interface):\n return False\n\n return self.to_dict() == other.to_dict()","title":""},{"docid":"3e95ddf88ba69823cfcf93bffd8f262b","score":"0.49503726","text":"def interface(self):\n return self._if.interface()","title":""},{"docid":"2642419c8ab4cb9c915374e4d5b11405","score":"0.49493083","text":"def has_dax_datatypes(intf):\n xnat_datatypes = intf.inspect.datatypes()\n for dax_datatype in DAX_SETTINGS.get_xsitype_include():\n if dax_datatype not in xnat_datatypes:\n return False\n return True","title":""},{"docid":"c2485db54dc47373ad9a3a24581a01ce","score":"0.49441043","text":"def is_integer(self) -> bool:\n return isinstance(self, IntegerTyp)","title":""},{"docid":"ce814aee006c85f85177fbe1b80a91d5","score":"0.49327436","text":"def getInterface(self):\n return self.__interface","title":""},{"docid":"eb9bafa5ebb16cf0c63b3a9320dde068","score":"0.49253488","text":"def initial_interface_checks(iface_name):\n if not interface.is_valid_interface(iface_name):\n print 'Error: %s is not a valid interface.' % iface_name\n return False\n\n return True","title":""},{"docid":"e54278cd2bd316da7523d00f43e4cbd5","score":"0.4919012","text":"def test_interface_glue(self):\r\n iface = self.create(items.InterfaceItem, UML.Interface)\r\n impl = self.create(items.ImplementationItem)\r\n\r\n glued = self.allow(impl, impl.head, iface)\r\n self.assertTrue(glued)","title":""},{"docid":"56aeeefcd366178ea0ba238a08926672","score":"0.49182153","text":"def verify_ospf_interface_type(device, interface, interface_type, \r\n max_time=60, check_interval=10):\r\n timeout = Timeout(max_time, check_interval)\r\n while timeout.iterate():\r\n out = None\r\n try:\r\n out = device.parse('show ospf interface extensive')\r\n except SchemaEmptyParserError:\r\n timeout.sleep()\r\n continue\r\n for ospf_interface in out.q.get_values('ospf-interface'):\r\n intf = ospf_interface.get('interface-name', None)\r\n intf_type = ospf_interface.get('interface-type', None)\r\n if intf == interface and intf_type == interface_type:\r\n return True\r\n timeout.sleep()\r\n return False","title":""},{"docid":"d4d5ffb882554298c0be64514283fa9f","score":"0.48915172","text":"def isframe(object):\n return isinstance(object, types.FrameType)","title":""},{"docid":"d72ec5d1dcac59b3959fbe6302505c3c","score":"0.48835492","text":"def is_image(self):\n if self.axes is None:\n return True\n return len(self.axes) == 0","title":""},{"docid":"607beb0c4acf51ab0e4a475909c1bf4f","score":"0.48734248","text":"def enterInterfaceDeclaration(self, ctx:JavaParserLabeled.InterfaceDeclarationContext):\n \n self.__current_class_or_interface = ctx.IDENTIFIER().getText()\n self.__classes.append(ctx.IDENTIFIER().getText())\n self.__methods_of_classes[self.__current_class_or_interface] = []\n self.__class_interface[ctx.IDENTIFIER().getText()] = True\n\n if ctx.EXTENDS() is not None and ctx.typeList().getText() in self.__classes:\n self.__parent_and_child_classes[ctx.IDENTIFIER().getText()] = ctx.typeList().getText()","title":""},{"docid":"d32a13afe4db6cc3251dcfeb92b8725f","score":"0.48706833","text":"def ProtocolInterface(self):\n return self._get_attribute('protocolInterface')","title":""},{"docid":"fbe526509239c5808b11866ccc137425","score":"0.48549366","text":"def is_known_type(cls, ioc_type: str) -> bool:\n return ioc_type in IoCType.__members__ and ioc_type != \"unknown\"","title":""},{"docid":"b3c71b64bcf1f098b63928f0c2fbc4bc","score":"0.48411354","text":"def is_simulator(self) -> bool:\n return isinstance(self._backend, Simulator)","title":""},{"docid":"e18c61d197fa747c4617a6582d110ba0","score":"0.48401403","text":"def is_elemental(self):\n return self._is_elemental","title":""},{"docid":"f61697810a3675c5564cf631af3d2d91","score":"0.48228928","text":"def interfaces():\n pass","title":""},{"docid":"913c32a9fad13671240a3accbca77d37","score":"0.48065004","text":"def SupportsPlugins(cls):\n return cls._plugin_classes is not None","title":""},{"docid":"a1ed8f865cc32f16b7caa6a7baaebbcb","score":"0.4798193","text":"def __ne__(self, other):\n if not isinstance(other, Interface):\n return True\n\n return self.to_dict() != other.to_dict()","title":""},{"docid":"8f3f206bf554e28eac92333360608cdf","score":"0.47953272","text":"def in_ipynb():\n try:\n if \"terminal\" in str(type(get_ipython())):\n return False\n else:\n return True\n except NameError:\n return False","title":""},{"docid":"89148e8b9fea47ebc8679ffdb438721b","score":"0.47926775","text":"def get_interfaces(self):\n return self._interfaces","title":""},{"docid":"e5c5decf9179fb87c752b8e5fdac1663","score":"0.47859254","text":"def is_supported(self) -> bool:\n pass","title":""},{"docid":"67585469ad50e8769ef5e4346dcb255d","score":"0.47641656","text":"def isnotebook():\r\n try:\r\n shell = get_ipython().__class__.__name__\r\n if shell == 'ZMQInteractiveShell':\r\n return True # Jupyter notebook or qtconsole\r\n elif shell == 'TerminalInteractiveShell':\r\n return False # Terminal running IPython\r\n else:\r\n return False # Other type (?)\r\n except NameError:\r\n return False # Probably standard Python interpreter\r","title":""},{"docid":"25a503865f67cf1555d23e56fe98cf76","score":"0.47600353","text":"def is_simulator(self) -> bool:\n return isinstance(self._backend, OT3Simulator)","title":""},{"docid":"8a5c5cd0af7c197859e68fa667250759","score":"0.47472557","text":"def test_interface(self):\n flags = self.config_flags.NAT_IS_INSIDE\n self.vapi.nat64_add_del_interface(\n is_add=1, flags=flags, sw_if_index=self.pg0.sw_if_index\n )\n self.vapi.nat64_add_del_interface(\n is_add=1, flags=0, sw_if_index=self.pg1.sw_if_index\n )\n\n interfaces = self.vapi.nat64_interface_dump()\n self.assertEqual(len(interfaces), 2)\n pg0_found = False\n pg1_found = False\n for intf in interfaces:\n if intf.sw_if_index == self.pg0.sw_if_index:\n self.assertEqual(intf.flags, self.config_flags.NAT_IS_INSIDE)\n pg0_found = True\n elif intf.sw_if_index == self.pg1.sw_if_index:\n self.assertEqual(intf.flags, self.config_flags.NAT_IS_OUTSIDE)\n pg1_found = True\n self.assertTrue(pg0_found)\n self.assertTrue(pg1_found)\n\n features = self.vapi.cli(\"show interface features pg0\")\n self.assertIn(\"nat64-in2out\", features)\n features = self.vapi.cli(\"show interface features pg1\")\n self.assertIn(\"nat64-out2in\", features)\n\n self.vapi.nat64_add_del_interface(\n is_add=0, flags=flags, sw_if_index=self.pg0.sw_if_index\n )\n self.vapi.nat64_add_del_interface(\n is_add=0, flags=flags, sw_if_index=self.pg1.sw_if_index\n )\n\n interfaces = self.vapi.nat64_interface_dump()\n self.assertEqual(len(interfaces), 0)","title":""},{"docid":"28b4220da7617062876632280a83bcb1","score":"0.47413203","text":"def has_nat(self):\n return self.typeof in self","title":""},{"docid":"843d7ba37f9c30d53aeab8fdf39117e2","score":"0.47389","text":"def check_if_interface_exists(device_name, interface_type, interface_number):\n with ncs.maapi.single_write_trans('admin', 'python', groups=['ncsadmin']) as t:\n root = ncs.maagic.get_root(t)\n device = root.devices.device[device_name]\n print type(device.interface[interface_type])\n if interface_number in device.interface[interface_type]:\n print(\"Interface is on the device!\")\n else:\n print(\"Interface is not on the device!\")","title":""},{"docid":"252c601cfbd7392ad20a08385be7058d","score":"0.47366798","text":"def has_donor_cbginterface(self,cbg):\n if hasattr(cbg,\"_CBGinterface3p\") and cbg._CBGinterface3p:\n return True\n else:\n return False","title":""},{"docid":"b79bc03e384ef302826e112ba6ed98c1","score":"0.47240645","text":"def test_subclass_provides_interface(self):\n class IFooBarSubclass(self.FooBarHasFooBarBaz):\n\n def provides_interface(self, interface):\n return interface.implemented_by(IFooBaz)\n\n obj = IFooBarSubclass()\n with self.assertRaises(TypeError):\n IFooBar(obj)\n foobaz = IFooBaz(obj)\n foobaz.baz()\n self.assertEqual(obj.foo, 3)","title":""},{"docid":"d6eef8e9daccf5af22fc3e60aa0db7da","score":"0.4722961","text":"def any_extension_types(self) -> bool:\n return False # any(block.is_extension for block in self.blocks)","title":""},{"docid":"5c6e122479958a19399bf44fd93c3b6c","score":"0.47229505","text":"def is_interface_connected_to_source(self):\n source_ip = self.get_tree_id()[0]\n for network in self.get_all_interface_networks():\n if ipaddress.ip_address(source_ip) in ipaddress.ip_network(network):\n return True\n return False","title":""},{"docid":"bd25262cd7cec55d00e342d756b3109e","score":"0.47206524","text":"def isa(self, other):\r\n\r\n m = self\r\n while m and m is not other:\r\n m = m.inherits\r\n return bool(m)","title":""},{"docid":"f3547b097146dd27e26dae98c79f66fe","score":"0.47147197","text":"def declare_interface(ea):\n kind = (get_struct_val(ea, 'go_type0.kind') & 0x1f)\n if kind != TYPEKIND_VALS['kindInterface'][0]:\n msg = 'type at 0x%s not an interface' % ea\n LOG.error(msg)\n raise Exception(msg)\n\n module_idx = resolve_moduledata_idx(ea)\n type_size = get_ida_struc_size('go_type0')\n tflag = (get_struct_val(ea, 'go_type0.tflag') & 0x7)\n\n iface_name = 'anonymous'\n if tflag & TYPE_TFLAGS['named']:\n iface_name = normalize_name(get_type_name(ea), ea,\n prepend_type_info=False)\n if tflag & TYPE_TFLAGS['extraStar']:\n iface_name = iface_name[1:]\n LOG.info('declare_interface: declaring iface %s at 0x%x',\n iface_name, ea)\n\n iface_ea = ea + type_size\n iface_mhdr_ea = iface_ea + PTRSIZE\n mhdr_ea = get_struct_val(iface_mhdr_ea, 'go_slice.array')\n mhdr_len = get_struct_val(iface_mhdr_ea, 'go_slice.len')\n iface_methods = []\n for i in range(0, mhdr_len):\n mhdr_entry_ea = mhdr_ea + (i * PTRSIZE)\n imethod_struc_name = 'go_type%d_metadata_imethod' % module_idx\n imethod_struc_size = get_ida_struc_size(imethod_struc_name)\n name_offset = nameoff(\n get_struct_val(mhdr_entry_ea,\n '%s.name' % imethod_struc_name),\n module_idx)\n imethod_name = get_raw_name_str(name_offset)\n LOG.info('found interface method %s.%s()',\n iface_name, imethod_name)\n ida_bytes.del_items(mhdr_entry_ea, imethod_struc_size)\n ida_bytes.create_struct(mhdr_entry_ea,\n imethod_struc_size,\n ida_struct.get_struc_id(imethod_struc_name))\n iface_methods.append((\n normalize_name(imethod_name, None,\n prepend_type_info=False),\n PTRSIZE,\n FF_PTR|FF_DATA|off_flag(),\n OFF_CURRENT_SEGMENT, None, None\n ))\n\n itab_fields = [\n ('inter', PTRSIZE, FF_PTR|FF_DATA|off_flag(),\n OFF_CURRENT_SEGMENT, None, None),\n ('_type', PTRSIZE, FF_PTR|FF_DATA|off_flag(),\n OFF_CURRENT_SEGMENT, None, None),\n ('hash', 4, FF_DWORD|FF_DATA, None, None, None),\n ('_unused', 4, FF_DWORD|FF_DATA, None, None, None)\n ] + iface_methods\n LOG.debug('Creating new itab for %s', iface_name)\n create_and_populate_struct('go_itab__%s' % iface_name,\n itab_fields)\n\n LOG.debug('Creating new iface for %s', iface_name)\n interface_tinfo = ida_typeinf.tinfo_t()\n ida_typeinf.parse_decl(interface_tinfo,\n idaapi.cvar.idati,\n 'go_itab__%s *;' % iface_name,\n ida_typeinf.PT_TYP)\n iface_fields = [\n ('tab', PTRSIZE, FF_PTR|FF_DATA|off_flag(), OFF_CURRENT_SEGMENT,\n None, None),\n ('data', PTRSIZE, FF_PTR|FF_DATA|off_flag(), OFF_CURRENT_SEGMENT,\n None, None)\n ]\n iface_tinfos = [interface_tinfo, None]\n iface_name = 'go_iface__%s' % iface_name\n create_and_populate_struct(iface_name,\n iface_fields, None,\n iface_tinfos)\n return iface_name","title":""},{"docid":"c6ee064dbb5293432396b13c5e23c308","score":"0.47141168","text":"def inherited_interface(self):\n raise exceptions.NotImplementedError()","title":""},{"docid":"0d6f031d8e91e7018033fd8f3d7e0118","score":"0.47140905","text":"def is_array(obj):\n return hasattr(obj, 'dtype')","title":""},{"docid":"d25fcdb091462da09938c70988e017d7","score":"0.47132087","text":"def isnotebook():\n try:\n shell = get_ipython().__class__.__name__\n if shell == 'ZMQInteractiveShell':\n return True # Jupyter notebook or qtconsole\n elif shell == 'TerminalInteractiveShell':\n return False # Terminal running IPython\n else:\n return False # Other type (?)\n except NameError:\n return False","title":""},{"docid":"9b3021caa56b8e96d786105815561d54","score":"0.47128734","text":"def are_supported(config: configuration.NamespaceConfig,\n installer: Optional[interfaces.Installer]) -> bool:\n for enh in enabled_enhancements(config):\n if not isinstance(installer, enh[\"class\"]):\n return False\n return True","title":""}],"string":"[\n {\n \"docid\": \"3fd0af121674f7ce21b6fec67acc48c9\",\n \"score\": \"0.75389546\",\n \"text\": \"def isSpiderMonkeyInterface(self):\\n return self.isInterface() and (self.isArrayBuffer() or \\\\\\n self.isArrayBufferView() or \\\\\\n self.isTypedArray())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22ccea57db2c6f27555c3c7019e94e2\",\n \"score\": \"0.74693924\",\n \"text\": \"def is_interface(self):\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22ccea57db2c6f27555c3c7019e94e2\",\n \"score\": \"0.74693924\",\n \"text\": \"def is_interface(self):\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df4e30ccecf6df007a1799056144bd7\",\n \"score\": \"0.7385049\",\n \"text\": \"def is_interface(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b10dfb26a4192cdf8ba42575e9069538\",\n \"score\": \"0.6441149\",\n \"text\": \"def has_interface(self, name):\\n if not self.all_interfaces:\\n self.find_interfaces()\\n return name in self.all_interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b6ef24706b35f4b5afbd0e1d5f38d2\",\n \"score\": \"0.5977207\",\n \"text\": \"def obj_has_interface(obj, *ifaces):\\r\\n for iface in ifaces:\\r\\n if iface.providedBy(obj):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533ee492d61d1c5bc1503a56ef9018b8\",\n \"score\": \"0.5907879\",\n \"text\": \"def is_implemented(self):\\n try:\\n self.coxeter_diagram()\\n return True\\n except Exception:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a118f27e95bb4e2efdd0e2efeaa97b85\",\n \"score\": \"0.5775072\",\n \"text\": \"def InterfaceType(self):\\n return self._get_attribute('interfaceType')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6611878a3ca087c3654d231659f4be6\",\n \"score\": \"0.5677758\",\n \"text\": \"def hasImplementation(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56bb5520d6d5bbc20c414ff8f77589ec\",\n \"score\": \"0.5661154\",\n \"text\": \"def is_interface_available(interface_name):\\n\\n try:\\n interface = NXCLI('show run interface %s' %interface_name)\\n return True\\n except:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de489f3501d8d96a1747af4e3fa8365a\",\n \"score\": \"0.56517386\",\n \"text\": \"def is_available(self, hub, interface_name):\\n \\n result = False\\n\\n names_dict = self._names[hub.interface_type]\\n if interface_name in names_dict: result = True\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e398ccc621d69c564f8f847747c78c4\",\n \"score\": \"0.5499881\",\n \"text\": \"def is_ipython() -> bool:\\n\\n try:\\n __IPYTHON__ # type: ignore[name-defined] # pylint: disable=pointless-statement\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b1f1e38910f5021d929657b36f4107b\",\n \"score\": \"0.5479125\",\n \"text\": \"def interface_type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"interface_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76782b80ec61eb9faf8d19425d74ec52\",\n \"score\": \"0.5415978\",\n \"text\": \"def has_web_interface(self) -> bool:\\n for port in self._open_ports:\\n if port in WEB_PORTS:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68a8c3def7d14b06861b675d1393a489\",\n \"score\": \"0.5412653\",\n \"text\": \"def _is_ipython() -> bool:\\n try:\\n import IPython # pylint: disable=g-import-not-at-top\\n ipy = IPython.get_ipython()\\n if ipy is None:\\n return False\\n except ImportError:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b8e18a44978fd4eb1e209c2c287ec51\",\n \"score\": \"0.54086965\",\n \"text\": \"def Interfaces(self):\\n return self._get_attribute('interfaces')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3fea24ba206c82a91dda73264705530\",\n \"score\": \"0.5395206\",\n \"text\": \"def interface(self) -> \\\"RegisterArray.Interface\\\":\\n\\n return self._interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e9d2e76a21ccc26f741935f1d57e547\",\n \"score\": \"0.538823\",\n \"text\": \"def _fallback_hasinterface(self, conn, ident, obj, typename):\\r\\n # It's very difficult to get an arbitrary `typename` here.\\r\\n dot = typename.rfind('.')\\r\\n module_name = typename[:dot]\\r\\n class_name = typename[dot+1:]\\r\\n try:\\r\\n module = __import__(module_name)\\r\\n except ImportError: #pragma no cover\\r\\n return False\\r\\n module = sys.modules[module_name]\\r\\n try:\\r\\n cls = getattr(module, class_name)\\r\\n except AttributeError: #pragma no cover\\r\\n return False\\r\\n return obj_has_interface(obj, cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cac6848b7d97d0e550c1e19e3a5b2931\",\n \"score\": \"0.53677785\",\n \"text\": \"def has_name(self, hub, interface_name):\\n \\n result = False\\n \\n if not self.is_available(hub, interface_name): return result\\n\\n interface_cls_name = self.get_cls_name(hub, interface_name) \\n if interface_cls_name in hub.get_interface_map(): result = True\\n \\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14c87943a24ff4af02ded3128e5f9cfb\",\n \"score\": \"0.53670704\",\n \"text\": \"def validate_interface(self, interface):\\n url = 'http://0.0.0.0:8181/api/kytos/topology/v3/interfaces'\\n headers = {'Content-Type': 'application/json'}\\n current_interfaces = requests.get(url, headers=headers).json()\\n\\n return interface in [current_interfaces['interfaces'][key]['id'] for key in current_interfaces['interfaces']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b8c14d1778a807ad3477e673e5f52ae\",\n \"score\": \"0.5335405\",\n \"text\": \"def is_using_ipython():\\n try:\\n __IPYTHON__ # pylint: disable=undefined-variable, pointless-statement\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877ebdf13c62ad5cc4e9d351404c0b5a\",\n \"score\": \"0.53146756\",\n \"text\": \"def connected(self):\\n return self._interface is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b65645c6a7e2b0dde1897ad823f21a9\",\n \"score\": \"0.53040075\",\n \"text\": \"def interface(self) -> \\\"Register.Interface\\\":\\n\\n return self._interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f26c30c251276dcf6c03b41f175a6d71\",\n \"score\": \"0.52766734\",\n \"text\": \"def is_enumerated_type(self):\\n return self.enumerated_values is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b0df933c507721b9942727b22ad436a\",\n \"score\": \"0.5256926\",\n \"text\": \"def implements(self, calculation_type: \\\"CalculationType\\\") -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2518bc585dd4a9de207fd4517c48cbc\",\n \"score\": \"0.5226839\",\n \"text\": \"def _is_intf_deconfigured(self, interface):\\n _logger.debug('%s\\\\n%s', where_am_i(), pformat(interface, indent=4))\\n for deconf in self.vnic_info.get('deconfig', ()):\\n if deconf in (interface['IFACE'], interface['VNIC'], interface['ADDR']):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5f354b72d13a9226af14d6c81f81035\",\n \"score\": \"0.5220707\",\n \"text\": \"def interface_profile_has_metrics(profile):\\n for m in profile.metrics:\\n if m.is_active and m.metric_type.name in (\\n \\\"Interface | Load | In\\\",\\n \\\"Interface | Load | Out\\\"\\n ):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fda1a9bf8244ba8f27d6fdf5af8308a1\",\n \"score\": \"0.5209826\",\n \"text\": \"def has_alibi_witness (self):\\n if not self.alibi:\\n return False\\n\\n if self.alibi.witness == -1:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6517bec04735935d4ee0ff5369e567b2\",\n \"score\": \"0.51612246\",\n \"text\": \"def interfaces(self):\\n return self._view_by_kind(Database._Kind.INTERFACE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9361244c8e0054f3dfbc19e65431e661\",\n \"score\": \"0.515429\",\n \"text\": \"def is_notebook():\\n try:\\n from IPython import get_ipython\\n\\n if \\\"IPKernelApp\\\" not in get_ipython().config: # pragma: no cover\\n return False\\n except ImportError:\\n return False\\n except AttributeError:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59bb25ccb55103b904b985f4bc55107c\",\n \"score\": \"0.5145271\",\n \"text\": \"def isinstance(self, obj: Any) -> bool:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"003fb219083b8ab7fd5ffbadc5bf29be\",\n \"score\": \"0.5139457\",\n \"text\": \"def using_ipython() -> bool:\\n return hasattr(builtins, '__IPYTHON__')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538031a7ad259ac07679a53e8f09cde4\",\n \"score\": \"0.51392484\",\n \"text\": \"def interface_exists(interface):\\n return os.path.exists(\\\"/sys/class/net/%s\\\" % interface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aee1f9d0829c76e4f029811214eaaff8\",\n \"score\": \"0.51390105\",\n \"text\": \"def test_implemented_by_subinterface(self):\\n self.assertTrue(IFoo.implemented_by(IFooBar))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c36c1320b613f243935dd850a8b2ded\",\n \"score\": \"0.513061\",\n \"text\": \"def implements_attribute_manager(obj):\\n return all([hasattr(obj, n) for n in\\n ['keys', '__getitem__', '__contains__', '__iter__']])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62457039b5b99728ebd277fe83408b8\",\n \"score\": \"0.5127941\",\n \"text\": \"def interfaces(self):\\n return self._interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62457039b5b99728ebd277fe83408b8\",\n \"score\": \"0.5127941\",\n \"text\": \"def interfaces(self):\\n return self._interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ab9332932fc0556505ed054e4534e36\",\n \"score\": \"0.5107286\",\n \"text\": \"def implements_dataset(obj):\\n return all([hasattr(obj, n) for n in\\n ['attrs', 'keys', 'items',\\n '__getitem__', '__contains__', '__iter__']])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bc9a476b5488ef4062c42d2385795a\",\n \"score\": \"0.50944895\",\n \"text\": \"def is_type(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab35b1572f9c98cfe710abd8db4719b5\",\n \"score\": \"0.50910896\",\n \"text\": \"def interface_exists(interface):\\n return os.path.exists(\\\"/sys/class/net/\\\" + interface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3abfdcdaa155732f67dec65ff27eb22a\",\n \"score\": \"0.5085412\",\n \"text\": \"def _currently_in_ipython():\\n try:\\n __IPYTHON__ # pylint: disable=undefined-variable\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c118e3dfce2fd85243a6f79eb006fb1\",\n \"score\": \"0.50795716\",\n \"text\": \"def _was_registered(m, interface):\\n \\n for item in m.call_args_list:\\n if item[0][-1] == interface:\\n return True\\n \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d88ac3ffa427897baf9ab47042c839d8\",\n \"score\": \"0.5077435\",\n \"text\": \"def interface(self):\\n return self.__interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec85e70ccdb9bd315392ad5fd59f147\",\n \"score\": \"0.50658995\",\n \"text\": \"def in_ipython():\\n try:\\n shell = get_ipython().__class__.__name__\\n if shell == \\\"ZMQInteractiveShell\\\":\\n return True # Jupyter notebook or qtconsole\\n elif shell == \\\"TerminalInteractiveShell\\\":\\n return False # Terminal running IPython\\n else:\\n return False # Other type (?)\\n except NameError:\\n return False # Probably standard Python interpreter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cefc0b8f212634a96a065179fee635a8\",\n \"score\": \"0.50610983\",\n \"text\": \"def is_abstract(obj: typing.Type) -> bool:\\n try:\\n return ABC in obj.__bases__\\n except AttributeError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9eca26713378f7517471cf61b8928f5\",\n \"score\": \"0.50561816\",\n \"text\": \"def GetInterfaces(self):\\n return self._interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289f245efa808a9a32ad5962d51e5c8d\",\n \"score\": \"0.5047994\",\n \"text\": \"def get_interface_class() -> Interface:\\n interface = Office.get_interface() # gets str name of interface class\\n if not interface:\\n raise ValueError('Should be run as macro using XLWings or PyUno.'\\n 'Neither could be detected.')\\n return getattr(Office, interface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52ebfab792e76b181e3ad009385342d2\",\n \"score\": \"0.5029029\",\n \"text\": \"def is_this_run_in_ipython():\\n try:\\n __IPYTHON__\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e8dbc9fc4e755ad5723910adda3862\",\n \"score\": \"0.50271434\",\n \"text\": \"def interface(self):\\n return self._interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e8dbc9fc4e755ad5723910adda3862\",\n \"score\": \"0.50271434\",\n \"text\": \"def interface(self):\\n return self._interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"565ddff6ab951dd08895c4b3ac15d578\",\n \"score\": \"0.501947\",\n \"text\": \"def is_mpsse_interface(self, interface: int) -> bool:\\n if not self.has_mpsse:\\n return False\\n if self.device_version == 0x0800 and interface > 2:\\n return False\\n if self.device_version == 0x3600 and interface > 2:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"018e794216fecc1b79414a808c496be8\",\n \"score\": \"0.49981865\",\n \"text\": \"def _is_intf_excluded(self, interface):\\n _logger.debug('%s\\\\n%s', where_am_i(), pformat(interface, indent=4))\\n for excl in self.vnic_info.get('exclude', ()):\\n if excl in (interface['IFACE'], interface['VNIC'], interface['ADDR']):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc45a0530a6ce88744047b14cb31fae3\",\n \"score\": \"0.4976998\",\n \"text\": \"def get_interface() -> str or None:\\n # test for Python Uno\\n try:\\n XSCRIPTCONTEXT # if this variable exists, PyUno is being used.\\n except NameError:\\n pass\\n else:\\n return 'Uno'\\n\\n if xw is not None:\\n return 'XW'\\n\\n # otherwise, return None / False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f5dd9da15c84c05dd10c6e4f2bf8554\",\n \"score\": \"0.4964717\",\n \"text\": \"def _in_ipython():\\n\\n try:\\n # See https://stackoverflow.com/questions/15411967\\n shell = get_ipython().__class__.__name__\\n return bool(shell == \\\"TerminalInteractiveShell\\\")\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce46daaa6dd2bbf0905ee3bc8dc17a1\",\n \"score\": \"0.49643916\",\n \"text\": \"def interface_mode(self):\\n return self._interface_mode.value if self._interface_mode else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd29c00eb8a74ef6f6863fa100b1b37\",\n \"score\": \"0.49566942\",\n \"text\": \"def isnotebook(self):\\n try:\\n shell = get_ipython().__class__.__name__\\n if (shell == 'ZMQInteractiveShell' or shell == 'TerminalInteractiveShell'):\\n return True # Jupyter notebook or IPython\\n else:\\n return False # Other type (?)\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0494fee01a6c84336625999ec71e9e88\",\n \"score\": \"0.49517235\",\n \"text\": \"def exposed_interfaces(self):\\n raise exceptions.NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9370580bbbb45adf1a90aee162216377\",\n \"score\": \"0.49513036\",\n \"text\": \"def __eq__(self, other):\\n if not isinstance(other, Interface):\\n return False\\n\\n return self.to_dict() == other.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e95ddf88ba69823cfcf93bffd8f262b\",\n \"score\": \"0.49503726\",\n \"text\": \"def interface(self):\\n return self._if.interface()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2642419c8ab4cb9c915374e4d5b11405\",\n \"score\": \"0.49493083\",\n \"text\": \"def has_dax_datatypes(intf):\\n xnat_datatypes = intf.inspect.datatypes()\\n for dax_datatype in DAX_SETTINGS.get_xsitype_include():\\n if dax_datatype not in xnat_datatypes:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2485db54dc47373ad9a3a24581a01ce\",\n \"score\": \"0.49441043\",\n \"text\": \"def is_integer(self) -> bool:\\n return isinstance(self, IntegerTyp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce814aee006c85f85177fbe1b80a91d5\",\n \"score\": \"0.49327436\",\n \"text\": \"def getInterface(self):\\n return self.__interface\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb9bafa5ebb16cf0c63b3a9320dde068\",\n \"score\": \"0.49253488\",\n \"text\": \"def initial_interface_checks(iface_name):\\n if not interface.is_valid_interface(iface_name):\\n print 'Error: %s is not a valid interface.' % iface_name\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54278cd2bd316da7523d00f43e4cbd5\",\n \"score\": \"0.4919012\",\n \"text\": \"def test_interface_glue(self):\\r\\n iface = self.create(items.InterfaceItem, UML.Interface)\\r\\n impl = self.create(items.ImplementationItem)\\r\\n\\r\\n glued = self.allow(impl, impl.head, iface)\\r\\n self.assertTrue(glued)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56aeeefcd366178ea0ba238a08926672\",\n \"score\": \"0.49182153\",\n \"text\": \"def verify_ospf_interface_type(device, interface, interface_type, \\r\\n max_time=60, check_interval=10):\\r\\n timeout = Timeout(max_time, check_interval)\\r\\n while timeout.iterate():\\r\\n out = None\\r\\n try:\\r\\n out = device.parse('show ospf interface extensive')\\r\\n except SchemaEmptyParserError:\\r\\n timeout.sleep()\\r\\n continue\\r\\n for ospf_interface in out.q.get_values('ospf-interface'):\\r\\n intf = ospf_interface.get('interface-name', None)\\r\\n intf_type = ospf_interface.get('interface-type', None)\\r\\n if intf == interface and intf_type == interface_type:\\r\\n return True\\r\\n timeout.sleep()\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d5ffb882554298c0be64514283fa9f\",\n \"score\": \"0.48915172\",\n \"text\": \"def isframe(object):\\n return isinstance(object, types.FrameType)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72ec5d1dcac59b3959fbe6302505c3c\",\n \"score\": \"0.48835492\",\n \"text\": \"def is_image(self):\\n if self.axes is None:\\n return True\\n return len(self.axes) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"607beb0c4acf51ab0e4a475909c1bf4f\",\n \"score\": \"0.48734248\",\n \"text\": \"def enterInterfaceDeclaration(self, ctx:JavaParserLabeled.InterfaceDeclarationContext):\\n \\n self.__current_class_or_interface = ctx.IDENTIFIER().getText()\\n self.__classes.append(ctx.IDENTIFIER().getText())\\n self.__methods_of_classes[self.__current_class_or_interface] = []\\n self.__class_interface[ctx.IDENTIFIER().getText()] = True\\n\\n if ctx.EXTENDS() is not None and ctx.typeList().getText() in self.__classes:\\n self.__parent_and_child_classes[ctx.IDENTIFIER().getText()] = ctx.typeList().getText()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32a13afe4db6cc3251dcfeb92b8725f\",\n \"score\": \"0.48706833\",\n \"text\": \"def ProtocolInterface(self):\\n return self._get_attribute('protocolInterface')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbe526509239c5808b11866ccc137425\",\n \"score\": \"0.48549366\",\n \"text\": \"def is_known_type(cls, ioc_type: str) -> bool:\\n return ioc_type in IoCType.__members__ and ioc_type != \\\"unknown\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c71b64bcf1f098b63928f0c2fbc4bc\",\n \"score\": \"0.48411354\",\n \"text\": \"def is_simulator(self) -> bool:\\n return isinstance(self._backend, Simulator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18c61d197fa747c4617a6582d110ba0\",\n \"score\": \"0.48401403\",\n \"text\": \"def is_elemental(self):\\n return self._is_elemental\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61697810a3675c5564cf631af3d2d91\",\n \"score\": \"0.48228928\",\n \"text\": \"def interfaces():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913c32a9fad13671240a3accbca77d37\",\n \"score\": \"0.48065004\",\n \"text\": \"def SupportsPlugins(cls):\\n return cls._plugin_classes is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1ed8f865cc32f16b7caa6a7baaebbcb\",\n \"score\": \"0.4798193\",\n \"text\": \"def __ne__(self, other):\\n if not isinstance(other, Interface):\\n return True\\n\\n return self.to_dict() != other.to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3f206bf554e28eac92333360608cdf\",\n \"score\": \"0.47953272\",\n \"text\": \"def in_ipynb():\\n try:\\n if \\\"terminal\\\" in str(type(get_ipython())):\\n return False\\n else:\\n return True\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89148e8b9fea47ebc8679ffdb438721b\",\n \"score\": \"0.47926775\",\n \"text\": \"def get_interfaces(self):\\n return self._interfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c5decf9179fb87c752b8e5fdac1663\",\n \"score\": \"0.47859254\",\n \"text\": \"def is_supported(self) -> bool:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67585469ad50e8769ef5e4346dcb255d\",\n \"score\": \"0.47641656\",\n \"text\": \"def isnotebook():\\r\\n try:\\r\\n shell = get_ipython().__class__.__name__\\r\\n if shell == 'ZMQInteractiveShell':\\r\\n return True # Jupyter notebook or qtconsole\\r\\n elif shell == 'TerminalInteractiveShell':\\r\\n return False # Terminal running IPython\\r\\n else:\\r\\n return False # Other type (?)\\r\\n except NameError:\\r\\n return False # Probably standard Python interpreter\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a503865f67cf1555d23e56fe98cf76\",\n \"score\": \"0.47600353\",\n \"text\": \"def is_simulator(self) -> bool:\\n return isinstance(self._backend, OT3Simulator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a5c5cd0af7c197859e68fa667250759\",\n \"score\": \"0.47472557\",\n \"text\": \"def test_interface(self):\\n flags = self.config_flags.NAT_IS_INSIDE\\n self.vapi.nat64_add_del_interface(\\n is_add=1, flags=flags, sw_if_index=self.pg0.sw_if_index\\n )\\n self.vapi.nat64_add_del_interface(\\n is_add=1, flags=0, sw_if_index=self.pg1.sw_if_index\\n )\\n\\n interfaces = self.vapi.nat64_interface_dump()\\n self.assertEqual(len(interfaces), 2)\\n pg0_found = False\\n pg1_found = False\\n for intf in interfaces:\\n if intf.sw_if_index == self.pg0.sw_if_index:\\n self.assertEqual(intf.flags, self.config_flags.NAT_IS_INSIDE)\\n pg0_found = True\\n elif intf.sw_if_index == self.pg1.sw_if_index:\\n self.assertEqual(intf.flags, self.config_flags.NAT_IS_OUTSIDE)\\n pg1_found = True\\n self.assertTrue(pg0_found)\\n self.assertTrue(pg1_found)\\n\\n features = self.vapi.cli(\\\"show interface features pg0\\\")\\n self.assertIn(\\\"nat64-in2out\\\", features)\\n features = self.vapi.cli(\\\"show interface features pg1\\\")\\n self.assertIn(\\\"nat64-out2in\\\", features)\\n\\n self.vapi.nat64_add_del_interface(\\n is_add=0, flags=flags, sw_if_index=self.pg0.sw_if_index\\n )\\n self.vapi.nat64_add_del_interface(\\n is_add=0, flags=flags, sw_if_index=self.pg1.sw_if_index\\n )\\n\\n interfaces = self.vapi.nat64_interface_dump()\\n self.assertEqual(len(interfaces), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b4220da7617062876632280a83bcb1\",\n \"score\": \"0.47413203\",\n \"text\": \"def has_nat(self):\\n return self.typeof in self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"843d7ba37f9c30d53aeab8fdf39117e2\",\n \"score\": \"0.47389\",\n \"text\": \"def check_if_interface_exists(device_name, interface_type, interface_number):\\n with ncs.maapi.single_write_trans('admin', 'python', groups=['ncsadmin']) as t:\\n root = ncs.maagic.get_root(t)\\n device = root.devices.device[device_name]\\n print type(device.interface[interface_type])\\n if interface_number in device.interface[interface_type]:\\n print(\\\"Interface is on the device!\\\")\\n else:\\n print(\\\"Interface is not on the device!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252c601cfbd7392ad20a08385be7058d\",\n \"score\": \"0.47366798\",\n \"text\": \"def has_donor_cbginterface(self,cbg):\\n if hasattr(cbg,\\\"_CBGinterface3p\\\") and cbg._CBGinterface3p:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79bc03e384ef302826e112ba6ed98c1\",\n \"score\": \"0.47240645\",\n \"text\": \"def test_subclass_provides_interface(self):\\n class IFooBarSubclass(self.FooBarHasFooBarBaz):\\n\\n def provides_interface(self, interface):\\n return interface.implemented_by(IFooBaz)\\n\\n obj = IFooBarSubclass()\\n with self.assertRaises(TypeError):\\n IFooBar(obj)\\n foobaz = IFooBaz(obj)\\n foobaz.baz()\\n self.assertEqual(obj.foo, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6eef8e9daccf5af22fc3e60aa0db7da\",\n \"score\": \"0.4722961\",\n \"text\": \"def any_extension_types(self) -> bool:\\n return False # any(block.is_extension for block in self.blocks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6e122479958a19399bf44fd93c3b6c\",\n \"score\": \"0.47229505\",\n \"text\": \"def is_interface_connected_to_source(self):\\n source_ip = self.get_tree_id()[0]\\n for network in self.get_all_interface_networks():\\n if ipaddress.ip_address(source_ip) in ipaddress.ip_network(network):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd25262cd7cec55d00e342d756b3109e\",\n \"score\": \"0.47206524\",\n \"text\": \"def isa(self, other):\\r\\n\\r\\n m = self\\r\\n while m and m is not other:\\r\\n m = m.inherits\\r\\n return bool(m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3547b097146dd27e26dae98c79f66fe\",\n \"score\": \"0.47147197\",\n \"text\": \"def declare_interface(ea):\\n kind = (get_struct_val(ea, 'go_type0.kind') & 0x1f)\\n if kind != TYPEKIND_VALS['kindInterface'][0]:\\n msg = 'type at 0x%s not an interface' % ea\\n LOG.error(msg)\\n raise Exception(msg)\\n\\n module_idx = resolve_moduledata_idx(ea)\\n type_size = get_ida_struc_size('go_type0')\\n tflag = (get_struct_val(ea, 'go_type0.tflag') & 0x7)\\n\\n iface_name = 'anonymous'\\n if tflag & TYPE_TFLAGS['named']:\\n iface_name = normalize_name(get_type_name(ea), ea,\\n prepend_type_info=False)\\n if tflag & TYPE_TFLAGS['extraStar']:\\n iface_name = iface_name[1:]\\n LOG.info('declare_interface: declaring iface %s at 0x%x',\\n iface_name, ea)\\n\\n iface_ea = ea + type_size\\n iface_mhdr_ea = iface_ea + PTRSIZE\\n mhdr_ea = get_struct_val(iface_mhdr_ea, 'go_slice.array')\\n mhdr_len = get_struct_val(iface_mhdr_ea, 'go_slice.len')\\n iface_methods = []\\n for i in range(0, mhdr_len):\\n mhdr_entry_ea = mhdr_ea + (i * PTRSIZE)\\n imethod_struc_name = 'go_type%d_metadata_imethod' % module_idx\\n imethod_struc_size = get_ida_struc_size(imethod_struc_name)\\n name_offset = nameoff(\\n get_struct_val(mhdr_entry_ea,\\n '%s.name' % imethod_struc_name),\\n module_idx)\\n imethod_name = get_raw_name_str(name_offset)\\n LOG.info('found interface method %s.%s()',\\n iface_name, imethod_name)\\n ida_bytes.del_items(mhdr_entry_ea, imethod_struc_size)\\n ida_bytes.create_struct(mhdr_entry_ea,\\n imethod_struc_size,\\n ida_struct.get_struc_id(imethod_struc_name))\\n iface_methods.append((\\n normalize_name(imethod_name, None,\\n prepend_type_info=False),\\n PTRSIZE,\\n FF_PTR|FF_DATA|off_flag(),\\n OFF_CURRENT_SEGMENT, None, None\\n ))\\n\\n itab_fields = [\\n ('inter', PTRSIZE, FF_PTR|FF_DATA|off_flag(),\\n OFF_CURRENT_SEGMENT, None, None),\\n ('_type', PTRSIZE, FF_PTR|FF_DATA|off_flag(),\\n OFF_CURRENT_SEGMENT, None, None),\\n ('hash', 4, FF_DWORD|FF_DATA, None, None, None),\\n ('_unused', 4, FF_DWORD|FF_DATA, None, None, None)\\n ] + iface_methods\\n LOG.debug('Creating new itab for %s', iface_name)\\n create_and_populate_struct('go_itab__%s' % iface_name,\\n itab_fields)\\n\\n LOG.debug('Creating new iface for %s', iface_name)\\n interface_tinfo = ida_typeinf.tinfo_t()\\n ida_typeinf.parse_decl(interface_tinfo,\\n idaapi.cvar.idati,\\n 'go_itab__%s *;' % iface_name,\\n ida_typeinf.PT_TYP)\\n iface_fields = [\\n ('tab', PTRSIZE, FF_PTR|FF_DATA|off_flag(), OFF_CURRENT_SEGMENT,\\n None, None),\\n ('data', PTRSIZE, FF_PTR|FF_DATA|off_flag(), OFF_CURRENT_SEGMENT,\\n None, None)\\n ]\\n iface_tinfos = [interface_tinfo, None]\\n iface_name = 'go_iface__%s' % iface_name\\n create_and_populate_struct(iface_name,\\n iface_fields, None,\\n iface_tinfos)\\n return iface_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6ee064dbb5293432396b13c5e23c308\",\n \"score\": \"0.47141168\",\n \"text\": \"def inherited_interface(self):\\n raise exceptions.NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d6f031d8e91e7018033fd8f3d7e0118\",\n \"score\": \"0.47140905\",\n \"text\": \"def is_array(obj):\\n return hasattr(obj, 'dtype')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d25fcdb091462da09938c70988e017d7\",\n \"score\": \"0.47132087\",\n \"text\": \"def isnotebook():\\n try:\\n shell = get_ipython().__class__.__name__\\n if shell == 'ZMQInteractiveShell':\\n return True # Jupyter notebook or qtconsole\\n elif shell == 'TerminalInteractiveShell':\\n return False # Terminal running IPython\\n else:\\n return False # Other type (?)\\n except NameError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b3021caa56b8e96d786105815561d54\",\n \"score\": \"0.47128734\",\n \"text\": \"def are_supported(config: configuration.NamespaceConfig,\\n installer: Optional[interfaces.Installer]) -> bool:\\n for enh in enabled_enhancements(config):\\n if not isinstance(installer, enh[\\\"class\\\"]):\\n return False\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":195,"cells":{"query_id":{"kind":"string","value":"260a4b8f6a111255d751b34ee4804b99"},"query":{"kind":"string","value":"Return an annual probability given a return period $$AEP = 1 \\exp{1/ARI}$$"},"positive_passages":{"kind":"list like","value":[{"docid":"76ed3955f395457f23e1a92d5ee923b0","score":"0.78453267","text":"def probability(ari):\n aep = 1.0 - np.exp(-1.0/ari)\n return aep","title":""}],"string":"[\n {\n \"docid\": \"76ed3955f395457f23e1a92d5ee923b0\",\n \"score\": \"0.78453267\",\n \"text\": \"def probability(ari):\\n aep = 1.0 - np.exp(-1.0/ari)\\n return aep\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a969b34f946ea4cab04180a858e705ce","score":"0.66628295","text":"def probability(e, en, t):\n if t == 0:\n return 0\n else:\n return exp((e-en)/t)","title":""},{"docid":"6d171e23a9d819ff00ddad221521a2ea","score":"0.64231294","text":"def annuity(tot_sum: float, required_return: float, period: int) -> float:\n v = 1 / (1 + required_return)\n a_t = (1 - v ** period) / required_return\n return tot_sum / a_t","title":""},{"docid":"e54c4f992753aed19aae3a0fbf9e69b6","score":"0.63084","text":"def _prob(self, rate):\n return 1 - m.exp(-rate * self.timestep_len)","title":""},{"docid":"07232e7572226eacc7c8ad4d764030eb","score":"0.6257997","text":"def annualized_return(df,periodicity=252):\n # convert return series to numpy array (in case Pandas series is provided)\n df = np.asarray(df)\n # how many years of returns data is provided in df\n difference_in_years = len(df)/periodicity\n # starting net asset value / NAV (assumed to be 1) and cumulative returns (r) over time period provided in returns data\n start_NAV=1.0\n r = np.nancumprod(df+start_NAV)\n # end NAV based on final cumulative return\n end_NAV=r[-1]\n # determine annualized return\n AnnualReturn = end_NAV**(1 / difference_in_years) - 1\n return AnnualReturn","title":""},{"docid":"154922c23546a6280747801269452348","score":"0.60345423","text":"def annualized_return(returns, start_date, end_date):\n total_return = ((1 + returns).cumprod())[-1]\n t = (end_date - start_date).days / 365.25\n ar = (total_return ** (1 / t)) - 1\n return ar","title":""},{"docid":"66b4443c3b33f1455b678df725351107","score":"0.60135186","text":"def probability(self, v, a):\n return self.effective_energy(v, a).exp()","title":""},{"docid":"d93567a5d1ddf14025996e3afb62cbb8","score":"0.59987706","text":"def ev_log_p(eta):\n return ev_log_h(eta) + ev_t(eta) @ eta - a(eta)","title":""},{"docid":"cc13a260398e24edc6261513dec9992a","score":"0.5968521","text":"def annualized_returns(ser):\n return ser.mean() * DAYS_IN_YEAR","title":""},{"docid":"41c09486a9eefe1f4ecd2286d6e97b57","score":"0.5949281","text":"def get_annualized_percent_change_per_year(diff, years):\n return math.exp(math.log(diff) / years) - 1","title":""},{"docid":"eccfd5ead284dde79de143510d846be1","score":"0.59371513","text":"def probability(self):\n product=self.features.dot(self.w)\n #print(product)\n expr=np.exp(-product)\n \n return 1/(1+expr)","title":""},{"docid":"17ad259a90ab4b35af7af1020d840805","score":"0.5879564","text":"def avg_rate(p):\n return (p[-1][0]-p[0][0])/(p[-1][1]-p[0][1])","title":""},{"docid":"e88279a38ba012acd7789ced66e89ea0","score":"0.58563554","text":"def getfreq(pianonum):\n \n exponential = 2 ** ((pianonum - 49) / 12)\n return exponential * 440","title":""},{"docid":"d08118845d5b36ce5bd753f63e3d3b10","score":"0.58353317","text":"def annualise(num):\n anld = float(num) * 12 * 100\n return anld","title":""},{"docid":"017db4fe9f5b64e04d26169fa1596b1a","score":"0.5829257","text":"def Probability(o):\n return o / (o + 1)","title":""},{"docid":"8d6e15058f1871d5a04c9c8ec9906aed","score":"0.5813514","text":"def ioniz_prob(ergs):\n prob = np.exp(0.05*ergs - 8.0)\n return prob","title":""},{"docid":"f28da29699709c641d11b78e12aa222a","score":"0.576968","text":"def dbl_logistic_model ( p, x ):\n return p[0] + p[1]* ( 1./(1+np.exp(p[2]*(365.*x-p[3]))) + \\\n 1./(1+np.exp(p[4]*(365.*x-p[5]))) - 1 )","title":""},{"docid":"8983ec782f85690e786fb0777161d298","score":"0.57667994","text":"def probability(self, observation, next_state, action):\n if self.sample(next_state, action) == observation:\n return 1.0 - self.epsilon\n else:\n return self.epsilon","title":""},{"docid":"cf2ed7994e322867aa727db1a84dc57f","score":"0.5764005","text":"def _annualizationFactor(lifetime, r=4.0):\n if r==0: return lifetime\n return (1-(1+(r/100.0))**-lifetime)/(r/100.0)","title":""},{"docid":"7998a972bd9d384b08964c1ef376da06","score":"0.576322","text":"def _eta(data):\r\n # if len(data) <= 1: return 0\r\n ldata = len(list(data))\r\n if ldata <= 1: return 0\r\n _exp = exp(1)\r\n counts = Counter()\r\n for d in data:\r\n counts[d] += 1\r\n # probs = [float(c) / len(data) for c in counts.values()]\r\n probs = [float(c) / ldata for c in counts.values()]\r\n probs = [p for p in probs if p > 0.]\r\n ent = 0\r\n for p in probs:\r\n if p > 0.:\r\n ent -= p * log(p, _exp)\r\n return ent","title":""},{"docid":"1bb053fe634110591f254c113c674cd4","score":"0.5735856","text":"def payback_period(self, proforma):\r\n capex = abs(proforma.loc['CAPEX Year', :].sum())\r\n\r\n first_opt_year = min(self.opt_years)\r\n yearlynetbenefit = proforma.loc[pd.Period(year=first_opt_year, freq='y'), :].sum()\r\n\r\n return capex/yearlynetbenefit","title":""},{"docid":"ba1bd10268b1aa5df2ab5497a287b4f8","score":"0.5728151","text":"def prob(self, Pn):\n if self.norm==\"Scargle\": return exp(-Pn)\n if self.norm==\"HorneBaliunas\": return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\n if self.norm==\"Cumming\": return (1.+2.*Pn/(self.N-3.))**(-(self.N-3.)/2.)","title":""},{"docid":"ba1bd10268b1aa5df2ab5497a287b4f8","score":"0.5728151","text":"def prob(self, Pn):\n if self.norm==\"Scargle\": return exp(-Pn)\n if self.norm==\"HorneBaliunas\": return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\n if self.norm==\"Cumming\": return (1.+2.*Pn/(self.N-3.))**(-(self.N-3.)/2.)","title":""},{"docid":"36452380e66393425596e4644ba5f147","score":"0.57024795","text":"def airdensity(p,t):\n Re = 287.04 \n rho = p / ( Re * t ) \n return rho","title":""},{"docid":"cede57d7caad42c6c13cfe71a4d66b9f","score":"0.56989205","text":"def amplitude_calc(x, p, beta=1, alpha=0):\n return (1+alpha**2)/beta/2 * x**2 + alpha*x*p + beta/2*p**2","title":""},{"docid":"baea0c4c34222f3ca14644480b1365ab","score":"0.5695328","text":"def find_aps(r,kappain):\n return -2.*np.log(r) - kappain**2./(np.exp(1.)*r**2.)","title":""},{"docid":"2e175a0a8d31194ee97285b667f9f17c","score":"0.5669334","text":"def P(self):\n\t\tP = 2*pi * sqrt( (self.a()*AU)**3. / (G*(self.mCent+self.mass)*mSun) )/day\n\t\treturn P","title":""},{"docid":"f3b5dd9bb01f02b96740daacd3610301","score":"0.5666913","text":"def P(self):\n\t\tP = 2*pi * sqrt( (self.a*AU)**3. / (G*(self.M+self.m)*mSun) )/(24*3600.)\n\t\treturn P","title":""},{"docid":"2031ab84851620cb2d48a6d0123baee0","score":"0.5654314","text":"def _calculate_aprior_probabilities(aposterior_probabilities: np.array) -> np.array:\n return aposterior_probabilities.mean(axis=0)","title":""},{"docid":"6c64cde34a2700c76d76fc56c57511d9","score":"0.5640836","text":"def get_a(x):\n\n return np.exp(x)","title":""},{"docid":"b5526b4851786a92173c9ec8c0b34e99","score":"0.56370974","text":"def p(self, x: np.array) -> float:\n p = - x[0]**2 / 200.0 - 0.5 * (x[1] - self.b * x[0]**2 +\n 100.0 * self.b)**2\n p_sum = - 0.5 * np.sum(x[2:-1]**2)\n p = p + p_sum\n return np.exp(p)","title":""},{"docid":"7512fe5873ab477ff6210d31de27da47","score":"0.5598786","text":"def aire(R):\n return pi * (R ** 2)","title":""},{"docid":"e4a3541d9cae87df9cc65e10c53d9c51","score":"0.5589556","text":"def eta_ideal(self):\n return 1. - (self.engine_in.pr_ovr ** ((1 - self.engine_in.ambient.kappa_air ) /\n self.engine_in.ambient.kappa_air))","title":""},{"docid":"e1d890d991df68637d2ed572b546f57f","score":"0.5578697","text":"def adiabatic_lapse_rate_from_t(SA, t, p):\n\n return -gibbs(n0, n1, n1, SA, t, p) / (gibbs(n0, n2, n0, SA, t, p))","title":""},{"docid":"441f4390671151a658ae641b017047d6","score":"0.556664","text":"def ppmv2pa(x, p):\n return x * p / (1e6 + x)","title":""},{"docid":"d27ca1e003b53ece5ab9f1f0e8f2ce93","score":"0.55485916","text":"def f_pseudo_periodique(beta, omega, A, B, t):\n return math.exp(-beta*t)*(A*math.cos(omega*t)+B*math.sin(omega*t))","title":""},{"docid":"06cd4e2088c2c1d43960776b6e1a579e","score":"0.5546343","text":"def annualized_return(self) -> float:\n return self.__annualized_return","title":""},{"docid":"fe98470dccf2e8ced11da6af1252fce1","score":"0.5527254","text":"def getProbability(self):\n return self.p","title":""},{"docid":"daeb75883fd9d23198eaf7361e39c953","score":"0.5521143","text":"def p_alpha(p,p0,th,s):\n a11,a12,a13,a14,a15,a16,a21,a22,a23,a24,a25,a26,b11,b12,b13,b21,b22,b23=eosben07_const()\n #\n b1i=1/(b11+b12*th+b13*s)\n a1=(a11+th*(a12+a14*th+a15*s)+s*(a13+a16*s))*b1i\n a2=(a21+th*(a22+a24*th+a25*s)+s*(a23+a26*s))*b1i\n b2=(b21+b22*th+b23*s)*b1i\n #\n r=b2*(p-p0)+(a2-a1*b2)*ma.log((a1+p)/(a1+p0))\n #\n return r","title":""},{"docid":"5e3d6309e5ae8993e438d8b810b110c3","score":"0.55092055","text":"def proton_distribution_probability(product, substrate, precursor):\n global BASIC_AA\n p = substrate[1]\n q = substrate[2]\n p1 = product[1]\n p2 = p - p1\n q1 = product[2]\n q2 = q - q1\n p0 = precursor[1]\n nu = len(substrate[0])\n nu1 = len(product[0])\n #nu = sum(aa in BASIC_AA for aa in substrate[0])\n #nu1 = sum(aa in BASIC_AA for aa in product[0])\n nu2 = nu - nu1\n # Distribution over initial charging on product sequence:\n # Hypergeometric with population nu, successes nu1, trials p0\n XA1 = hypergeom_pmf_iterator(N=nu, k=nu1, n=p0)\n XA1_0 = max(0, p0-nu2)\n PTR = iterate_over_ptr_probabilities(p1=p1, q1=q1, p=p, q=q, p0=p0)\n PTR_0 = p1+q1+1\n kmin = max(XA1_0, PTR_0)\n kmax = min(nu1, p0-p2-q2+1)\n # Moving past the \"null\" summands\n for i in range(max(0, kmin - PTR_0 - 1)):\n PTR.next()\n for i in range(max(0, kmin - XA1_0 - 1)):\n XA1.next()\n pr = 0.\n for i in range(kmin, kmax+1):\n A, B = PTR.next()\n C = XA1.next()\n pr += (float(A)*float(nu2)/nu + float(nu1)/nu)*B*C\n #pr = sum((float(A)*float(nu2)/nu + float(nu1)/nu)*B*C for i, (A, B), C in enumerate(zip(PTR, XA1)) if i + kmin < kmax)\n return pr","title":""},{"docid":"d1a724016faa87efe811cac38a976021","score":"0.55043864","text":"def avg_amp(self, params):\n amps = params[:self.no_ants*2:2]\n if self.logamp:\n amps = np.exp(amps)\n return np.mean(amps) - 1","title":""},{"docid":"66fe6cf3be98ef6d29b947bf9083ca27","score":"0.54913193","text":"def compute_period(a, M, m):\n return np.sqrt(4 * np.pi **2 / (takahe.constants.G * (M+m) * takahe.constants.SOLAR_MASS) * (a * takahe.constants.SOLAR_RADIUS)**3) / (60 * 60 * 24)","title":""},{"docid":"e5a5607d44f9bb2f5301283876b8f61d","score":"0.5488452","text":"def prob(self, at, log = False):","title":""},{"docid":"7ca10d9bd1a5946e2d0dff1c986e69c1","score":"0.54698384","text":"def exp_decay(p,x):\n a, b, c = p\n\n return a + b * np.exp(-x / c)","title":""},{"docid":"8f906cdb8b7cc43327c26bd266d0974a","score":"0.5467352","text":"def get_annual_eps_value(eps_data: dict):\n years = list(set([int(k.split('-')[0]) for (k, v) in eps_data.items()]))\n max_year = max(years)\n three_years_ago = max_year - 3\n filter_years = sorted([str(x) for x in years if max_year >= x >= three_years_ago], reverse=True)\n values = list()\n for year in filter_years:\n values.append(round(sum([v for (k, v) in eps_data.items() if year in k]), 2))\n return dict(zip(filter_years, values))","title":""},{"docid":"71d8b77d14e51ab8e133db3f50a7fe87","score":"0.5463908","text":"def prob(self, Pn):\n return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)","title":""},{"docid":"5878d8693907bf81216b28f335fdc419","score":"0.5459113","text":"def rate(e):\n d = np.abs(e - e[-1])\n p = []\n for i in range(len(d)-1):\n p.append(np.log(d[i+1])/np.log(d[i]))\n return p","title":""},{"docid":"7be049e111a0f8923aebf37b90cef533","score":"0.5458327","text":"def paretovariate_impl(alpha):\n # Jain, pg. 495\n u = 1.0 - _random()\n return 1.0 / u ** (1.0/alpha)","title":""},{"docid":"6aa974bfaae301fa79ac0fbeabb3bb21","score":"0.54536045","text":"def EP(d):\n return 4*d*(5/6)**d + 1 - (5/6)**d","title":""},{"docid":"46e76d91ba872e7607db1518181831cf","score":"0.54457945","text":"def calc_alpha(epsilon):\n\n if (epsilon == 0):\n return np.inf\n\n\n return 0.5 * np.log( (1. - epsilon)/epsilon )","title":""},{"docid":"0c4c2f4d23858b4b4c3cc0ad0d940f8d","score":"0.5436166","text":"def beamfrac(FWHM, length, angle):\n height_of_sample = length * np.sin(np.radians(angle))\n beam_sd = FWHM / 2 / np.sqrt(2 * np.log(2))\n probability = 2.0 * (\n stats.norm.cdf(height_of_sample / 2.0 / beam_sd) - 0.5\n )\n return probability","title":""},{"docid":"d0f6ccb38864d16aea5f119c73c0b0fb","score":"0.54260474","text":"def theta(self, *args, eps=0.001):\n # Fix the random generator seed for MonteCarlo pricing\n st = np.random.get_state()\n price1 = self.price(*args)\n np.random.set_state(st)\n self.term -= eps\n price2 = self.price(*args)\n self.term += eps\n return (price2 - price1) / (365 * eps)","title":""},{"docid":"bad795ccd1af697171146833dd55aaa9","score":"0.5425807","text":"def _calculate_PAYE(period_income, period):\r\n predicted_annual_income = period * period_income\r\n \r\n income_tax = _calculate_income_tax(predicted_annual_income)\r\n acc_levy = _calculate_ACC_levy(predicted_annual_income)\r\n \r\n total_tax_paid = income_tax + acc_levy\r\n \r\n return round(total_tax_paid / period, 2)","title":""},{"docid":"e00a47c7e5892e10021e3d495160bb4d","score":"0.5419779","text":"def e_var(partition):\n P = np.log(partition)\n S = len(partition)\n X = 0\n for x in P:\n X += (x - np.mean(P))**2/S\n evar = 1 - 2/math.pi*np.arctan(X) \n return(evar)","title":""},{"docid":"55297a2ff4086c2bf1acf53482780e23","score":"0.5414374","text":"def expected_earnings(mao):\r\n return (.5 - (mao - ((mao ** 2) / 2)))","title":""},{"docid":"244d6a5bac71aaacecf03ad1089d25dc","score":"0.5397943","text":"def calculate_probability(x, mean, stdev):\n exponent = np.exp(-1.0 * ((x-mean)**2 / (2*(stdev**2))))\n return (1/(np.sqrt(2.0 * np.pi) * stdev)) * exponent","title":""},{"docid":"2dac13767a930f12ba0d31b9db65a986","score":"0.5397827","text":"def empirical_prob(self, *items, delta = 0):\n assert (len(items) in [1, 2])\n if (len(items) == 1):\n denominator = self.total_counts + delta * self.num_vocab ** 2\n else:\n denominator = self.total_edges + delta * self.num_possible_pairs\n return self.empirical_freq(*items, delta = delta) / denominator","title":""},{"docid":"647fe4d57e4a1de86bbc9cd34a971c9c","score":"0.5396929","text":"def soilmoisture( self, evaporative_fraction ):\n\t\t\tresult = (exp((evaporative_fraction-1.2836)/0.4213))/0.511\n\t\t\treturn result","title":""},{"docid":"0a486069b20a599edb89c810c19a2d7b","score":"0.53961486","text":"def gaussian_moment(p):\n if p % 2 == 0:\n return factorial2(p-1)\n else:\n return 0","title":""},{"docid":"49ab846f51909ca0b71e08b8f97013e3","score":"0.53892523","text":"def logit2probability(log_odds):\n # convert log odds to odds\n odds = math.exp(log_odds)\n # return probability\n return odds / float(1 + odds)","title":""},{"docid":"8170543ac5ff1d2bba1fd16b24f8d220","score":"0.5387339","text":"def decay(t, p):\n return p[0] * np.exp(-t / p[1]) + p[2]","title":""},{"docid":"40958abdc2ac4eefc3a72a6573351082","score":"0.53814656","text":"def _approx_ap(airf,pres):\n pvap = pres * (1-airf)/(airf*_EPSW + 1-airf)\n v = numpy.log(pvap/_PTPE)/_BVI\n x = maths4.lamb2(v,_AVI/_BVI)\n temp = _TTP/x\n dhum = (pres-pvap + _EPSW*pvap) / (_RDRY*temp)\n return temp, dhum","title":""},{"docid":"cba8b19e85284fbfe2a59ea77809b5ed","score":"0.53794813","text":"def startprob(self):\n return np.exp(self._log_startprob)","title":""},{"docid":"008acf3c978605d22a56d6110134eaba","score":"0.5377723","text":"def get_exp_probabilities(self, fluxes, inst_params):\n prob = 1 - np.exp(-fluxes / inst_params[\"scale\"])\n\n return prob","title":""},{"docid":"3c14c31873659ea1950017763c7e4e2b","score":"0.5364507","text":"def get_ev(a: np.ndarray) -> Param:\n\n return a / np.sum(a),","title":""},{"docid":"54881653f7ce52de6c69bcc2f8abd6f2","score":"0.53623134","text":"def topp(v):\n return np.exp(v) / np.sum(np.exp(v))","title":""},{"docid":"eb044c957e5b8785a3ac8b0bf516416f","score":"0.5361167","text":"def probability(self, next_state, state, action):\n if self.sample(state, action) == next_state:\n return 1.0 - self.epsilon\n else:\n return self.epsilon","title":""},{"docid":"d9c6ca6447b8cce60ebe738b1842c361","score":"0.5350514","text":"def test_getAngularDistribution_PWave(self):\n egrid = self.RRR1PRes.generateEnergyGrid()\n eStep = 1\n J = 5.5 # for this test data\n S = 4.5 # for this test data\n from numericalFunctions import angularMomentumCoupling as nf_amc\n self.assertAlmostEqual(\n nf_amc.z_coefficient(2, int(2 * J), 2, int(2 * J), int(2 * S), 4) / nf_amc.z_coefficient(2, int(2 * J), 2,\n int(2 * J),\n int(2 * S), 0),\n math.sqrt(0.33090909)) # nf_amc functions use the 2J trick\n for E in egrid[::eStep]:\n B = self.RRR1PRes.getAngularDistribution(E)\n self.assertAllWithinXPercent([x / B[0] for x in B], [1.0, 0.0, 0.33090909])","title":""},{"docid":"54012fa6ca93fc00bc7a9e27ee1fe249","score":"0.5335799","text":"def offer_prob(year, month, sex, race, referral, precomputed_emp_dist=None):\n if precomputed_emp_dist is not None:\n emp = precomputed_emp_dist[year][month - 1][race.name][sex.name]\n else:\n emp = employment_dist(year, month, sex, race)\n\n # quick-and-dirty mapping to race keys\n if not isinstance(race, str):\n if race.name == 'aian':\n race = 'nativeamerican'\n elif race.name == 'white':\n # b/c white and hispanic are merged together\n # TODO find dataset that separates these?\n race = random.choice(['hispanic', 'white'])\n elif race.name in ['chinese', 'japanese', 'api']:\n race = 'asian'\n elif race.name == 'black':\n race = 'black'\n\n # mean of two random races. this is a sloppy guess,\n # since the actual evidence may not work this way\n elif race.name in ['other', 'two', 'three_plus']:\n races = list(p_offer['offered']['race'].keys())\n op1 = _offer_prob(sex, random.choice(races), referral, emp)\n op2 = _offer_prob(sex, random.choice(races), referral, emp)\n return (op1 + op2)/2\n\n return _offer_prob(sex, race, referral, emp)","title":""},{"docid":"f7550b5825aea3cfb202d2a09abfeabe","score":"0.53341776","text":"def ampsAtPeriod(period):\n checkPeriod(period, \"ampsAtPeriod::period\")\n return startingSequence[period-1][1]","title":""},{"docid":"8680f547542f4aa8920e1df44de4b6b9","score":"0.5333341","text":"def AIC(self):\n RSS = self.getRMSE()\n k = len(self.variables)\n n = len(self.exp_data)\n AIC = 2 * k + n * (log(2 * pi * RSS/n) + 1)\n return AIC","title":""},{"docid":"879fc398a4991981587ecdfa58f50ded","score":"0.5328824","text":"def prediction_interval(self, xp=None, yp=None, alpha=0.05):\n if not (xp is None) ^ (yp is None):\n raise ValueError('Only one of [xp, yp] must be specified.')\n\n if xp is not None:\n return self._y_pi(xp, alpha=alpha)\n if yp is not None:\n return self._x_pi(yp, alpha=alpha)","title":""},{"docid":"243d4ce62d5695a4a1cde42e12efba40","score":"0.5327332","text":"def alpha_wrt_t_exact(SA, t, p):\n\n return gibbs(n0, n1, n1, SA, t, p) / gibbs(n0, n0, n1, SA, t, p)","title":""},{"docid":"dbaeb65c4918cd305cc1d2de1739b203","score":"0.53269297","text":"def probability():\n f1 = AffineTransform(0, 0, 0, 0.16, 0, 0)\n f2 = AffineTransform(0.85, 0.04, -0.04, 0.85, 0, 1.60)\n f3 = AffineTransform(0.20, -0.26, 0.23, 0.22, 0, 1.60)\n f4 = AffineTransform(-0.15, 0.28, 0.26, 0.24, 0, 0.44)\n functions = [f1, f2, f3, f4]\n p1 = 0.01\n p2 = 0.85\n p3 = 0.07\n p4 = 0.07\n p_cumulative = np.array([[0, p1],\n [1, p1+p2],\n [2, p1+p2+p3],\n [3, p1+p2+p3+p4]])\n r = np.random.random()\n for j, p in p_cumulative:\n if r < p:\n return functions[int(j)]","title":""},{"docid":"7a47e37d6559b69bf1b357b7e8a0d017","score":"0.5326326","text":"def exponential_var(p,x):\n \n \n plt.subplot(311)\n plt.semilogy(x, exp_decay(np.array([p[0[0]],p[1[3]],p[2[3]]])), x)\n plt.savefig('KEG_exponential_var.pdf', bbox_inches='tight', dpi=300)","title":""},{"docid":"90e6a4c6840c0695d1f98535c676f6f3","score":"0.5323668","text":"def date_of_annual_after(title, eff_date):\n publication_date = date(eff_date.year, publication_month(title), 1)\n if eff_date <= publication_date:\n return publication_date\n else:\n return publication_date.replace(year=eff_date.year + 1)","title":""},{"docid":"2b026ba3820417246be18a6149607298","score":"0.53191286","text":"def cronbach_alpha(data):\n n_items = data.shape[0]\n\n valid_mask = data != INVALID_RESPONSE\n \n item_variance = np.var(data, axis=1, ddof=1, where=valid_mask).sum()\n people_variance = (n_items * n_items\n * np.mean(data, axis=0, where=valid_mask).var(ddof=1))\n \n return (n_items / (n_items - 1) \n * (1 - item_variance / people_variance))","title":""},{"docid":"7e73ca74ce81005f48c4c98ed9cd9001","score":"0.53163207","text":"def calc_extreme_value(params,distribution,nyear,datafraction=1.0):\n probability=1.0/(nyear*365.0*datafraction)\n \n x=np.arange(0,10000,10)\n pdf=distribution.pdf(x,*params)\n pos=np.where((pdf>=probability) & (pdf>0))[0]\n \n if len(pos)==0:return 0\n pos=pos[-1]\n\n x=np.arange(x[pos]-10,x[pos]+10,0.1)\n pdf=distribution.pdf(x,*params)\n pos=np.where((pdf<=probability) &(pdf>0))[0]\n\n if x[pos[0]]<0:return 0\n return x[pos[0]]","title":""},{"docid":"a87e4e5986c8905599dc2a722d7a32cd","score":"0.5314459","text":"def period(r):\n m = 0\n d = 1\n a = a0 = floor(r)\n p = []\n\n while a != 2*a0:\n m = round(d*a - m)\n d = round((r*r - m*m)/d)\n a = round(floor((a0 + m)/d))\n p.append(a)\n return (a0, p)","title":""},{"docid":"2330a22f92cb5bc3db086c0c7218a8cb","score":"0.5306773","text":"def calculateAnom(var):\n ### Import modules\n import numpy as np\n \n mean = np.nanmean(var,axis=0)\n anom = var - mean\n \n print('Completed: Calculate anomalies for polar cap!')\n return anom","title":""},{"docid":"c6a555f2566874f2dfe5d796711f788f","score":"0.53059053","text":"def exponential_annealing(init_value, min_value, decay_ratio):\n result = init_value\n\n while True:\n result = max(min_value, result * decay_ratio)\n yield result","title":""},{"docid":"ccaab3a5cf7e0e80c93134e6ef44d7e9","score":"0.53049403","text":"def per(soup):\r\n\r\n period = soup.find(id=\"planet_period_0\").text\r\n\r\n period = float(period.split(\" \")[0])\r\n \r\n return period","title":""},{"docid":"80192f78f06ba42dced0be35570c30c2","score":"0.5300492","text":"def calculate_probability(self, value, mean, std_dev):\n # Calculates the exponent.\n exponent = math.exp(-(math.pow(value-mean,2)/(2*math.pow(std_dev,2))))\n # Returns the probability.\n return (1 / (math.sqrt(2*math.pi) * std_dev)) * exponent","title":""},{"docid":"d683d54fb547bb0d5ffb95227395954c","score":"0.52974075","text":"def getFrequency(self):\n if self.period: return 1.0/self.period\n return 0","title":""},{"docid":"8af254d526217ba25a3ddee9e94c8a83","score":"0.52971166","text":"def annual_eval(out_dir, pts):\n dgp_value = np.genfromtxt(f\"{out_dir}dgp_{pts}.out\", delimiter=\" \", usecols=3)\n fdgpe = np.zeros(3) # DGP > 0.35, 0.4, 0.45\n for i in range(len(dgp_value)):\n for j in range(3):\n if dgp_value[i] > (0.35 + j * 0.05):\n fdgpe[j] = fdgpe[j] + 1\n for j in range(3):\n fdgpe[j] = fdgpe[j] * 5.43 / 1304\n np.savetxt(f\"{out_dir}fDGPe_{pts}.out\", fdgpe, delimiter=\" \", fmt=\"%1.3f\")","title":""},{"docid":"db93ccc3207dc8aa7a31f25624b10f80","score":"0.52957386","text":"def zolotarev_pdf_exponentiated(self, x, alpha):\n denominator = pow(sinc(alpha * x), alpha) \\\n * pow(sinc((1. - alpha) * x), (1. - alpha))\n numerator = sinc(x)\n return numerator / denominator","title":""},{"docid":"5133c303c323d063c9da40af348b07b2","score":"0.5292389","text":"def bar2psia(a):\n return (a + 1.013) * 14.50377","title":""},{"docid":"433b64525c26c03fe98d5aacf62f0afd","score":"0.5291426","text":"def p_from_am(sma, ms):\n a = sma * u.au.to(u.m)\n ms = ms * u.Msun.to(u.kg)\n G = c.G.value\n p = np.sqrt((4 * np.pi ** 2 * a ** 3) / (G * ms))\n return p * u.second.to(u.day)","title":""},{"docid":"b7ea2e4a7c9d0289f55632f817962b64","score":"0.52898544","text":"def damping_ratio(self):\n return self._eta","title":""},{"docid":"60f281fb55f9ec9866f8938ebd39e617","score":"0.5287896","text":"def mean(p):\n\n v = 0.0\n for i, pi in zip(_I, p):\n v += i*pi\n\n return v","title":""},{"docid":"8d5dacdd4a7189d99c9572027701b844","score":"0.52864736","text":"def get_probsrsa_vectorized(number, exp_rate):\r\n return (np.power(exp_rate, number))*(np.exp(-exp_rate))/(scipy.special.factorial(number))","title":""},{"docid":"5b25b7c54d9e8dc6830baa497956688d","score":"0.52847904","text":"def arc_length(Eps, alpha):\n return Eps * alpha","title":""},{"docid":"b5fc16f5816f57878f83370c9daf880f","score":"0.5284486","text":"def int_eta0(self,p,T,muB,muQ,muS,**kwargs):\n Ep = np.sqrt(p**2.+self.M(T,muB,muQ,muS,**kwargs)**2.) # pole energy\n ffq = self.fq(Ep,T,muB,muQ,muS) # distribution function\n return 1./(15.*T)*self.dg*(4.*pi*p**2.)/((2.*pi)**3)*(p**4.)/(Ep**2.)*ffq*(1-self.BEFD*ffq)/(2*self.w(T,muB,muQ,muS,**kwargs))","title":""},{"docid":"5f95cf88f90172d89ed05f46bff261c0","score":"0.52806705","text":"def period(x, y, ofac=4, hifac):\n n = len(x)\n (wi, wpi, wpr, wr, px, py) = ([0]*n, [0]*n, [0]*n, [0]*n, [0]*n)\n nout = int(.5*ofac*hifac*n)\n if np < nout:\n px.np.ndarray.resize(nout)\n py.np.ndarray.resize(nout)\n ave = np.avearge(y)\n var = np.var(y)\n if var == 0:\n print(\"Zero variacne in period\")\n return\n xmax = xmin = x[0]\n for j in range(0, n):\n if x[j] > xmax:\n xmax = x[j]\n if x[j] < xmin:\n xmin = x[j]\n xdif = xmax - xmin\n xave = .5*(xmax+xmin)\n pymax =0\n pnow = 1/(xdif*ofac) # starting frequency\n for j in range(0, n): # initialize values for the trigonometric recurrences at each data point\n arg = 2*np.pi*(x[j]-ave)*pnow\n wpr[j] = -2*np.sqrt(np.sin(.5*arg))\n wpi[j] = np.sin(arg)\n wr[j] = np.cos(arg)\n wi[j] = wpi[j]\n for i in range(0, nout):\n px[i] = pnow\n sumsh = sumc = 0\n for j in range(0,n):\n c = wr[j]\n s = wi[j]\n sumsh += s*c\n sumc += (c-s)*(c+s)\n wtau = .5*np.arctan(p2*sumsh, sumc])\n swtau = np.sin(wtau)\n cwtau = np.cos(wtau)\n sums = sumc= sumsy = sumcy = 0\n for j in range(0, n): # loop over the data again to get the periiodogram value\n s = wi[j]\n c = wr[j]\n ss = s * cwtau - c*swtau\n cc = c*cwtau + s*swtau\n sums += ss*ss\n sumc += cc*cc\n yy = y[j] - ave\n sumsy += yy*ss\n sumcy += yy*cc\n wtemp = wr[j]\n wr[j] = (wtemp*wpr[j] - wr[j] * wpi[j]) + wr[j] # trigonometric recurrences\n wi[j] = (wi[j] * wpr[j] + wtemp * wpi[j]) + wi[j]\n py[i] = .5*(sumcy * sumcy/sumc + sumsy * sumsy/sums)/var\n if py[i] >= pymax:\n jmax = i\n pymax = py[jmax]\n pnow += 1/(ofac*xdif)\n expy = np.exp(-pymax)\n effm = 2*nout/ofac\n prob = effm * expy\n if prob > .01:\n prob = 1 - np.power(1 - expy, effm)\n return px, py, prob","title":""},{"docid":"73fa5608009670c1ebc32a92411e499a","score":"0.5279629","text":"def _probability_of_title_with_length(self, length):\n sigma = 6\n return np.exp(-1 * ((length - self.avg_length) ** 2) / (2 * (sigma ** 2))) / (math.sqrt(2 * np.pi) * sigma)","title":""},{"docid":"f4d435eac6074cd3164079f5ca5870ba","score":"0.5273136","text":"def frac_position(eta,eta_distribution):\n pass","title":""},{"docid":"a337c988c1ebbf9f39e6dda243a101f1","score":"0.52646464","text":"def apo(price, period_slow = 26, period_fast = 12, ma = \"sma\"):\n if ma.lower() not in [\"sma\", \"ema\"]:\n raise ValueError(\"param 'ma' needs to be 'ema' or 'sma'\")\n\n return apo_calc(price, period_slow, period_fast, ma.lower())","title":""},{"docid":"7dea0661caea45ea6b8f392fa9985659","score":"0.5260097","text":"def rand_exp(rate):\n \n return -log(random()) / rate","title":""},{"docid":"c0621abd8797937864d627e54d54a7c8","score":"0.5259038","text":"def discounted_payback_period(self, proforma):\r\n payback_period = self.payback_period(proforma) # This is simply (capex/yearlynetbenefit)\r\n\r\n if not self.npv_discount_rate:\r\n dr = self.npv_discount_rate*.01\r\n discounted_pp = np.log(1/(1-(dr*payback_period)))/np.log(1+dr)\r\n else:\r\n # if discount_rate = 0, then the equation simplifies to np.log(1)/np.log(1)-- which is undefined, so we return nan\r\n discounted_pp = 'nan'\r\n\r\n return discounted_pp","title":""},{"docid":"6c08eac2e3e543e690b820889ca1868d","score":"0.5257021","text":"def ann_return1_year(self) -> float:\n return self.__ann_return1_year","title":""}],"string":"[\n {\n \"docid\": \"a969b34f946ea4cab04180a858e705ce\",\n \"score\": \"0.66628295\",\n \"text\": \"def probability(e, en, t):\\n if t == 0:\\n return 0\\n else:\\n return exp((e-en)/t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d171e23a9d819ff00ddad221521a2ea\",\n \"score\": \"0.64231294\",\n \"text\": \"def annuity(tot_sum: float, required_return: float, period: int) -> float:\\n v = 1 / (1 + required_return)\\n a_t = (1 - v ** period) / required_return\\n return tot_sum / a_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54c4f992753aed19aae3a0fbf9e69b6\",\n \"score\": \"0.63084\",\n \"text\": \"def _prob(self, rate):\\n return 1 - m.exp(-rate * self.timestep_len)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07232e7572226eacc7c8ad4d764030eb\",\n \"score\": \"0.6257997\",\n \"text\": \"def annualized_return(df,periodicity=252):\\n # convert return series to numpy array (in case Pandas series is provided)\\n df = np.asarray(df)\\n # how many years of returns data is provided in df\\n difference_in_years = len(df)/periodicity\\n # starting net asset value / NAV (assumed to be 1) and cumulative returns (r) over time period provided in returns data\\n start_NAV=1.0\\n r = np.nancumprod(df+start_NAV)\\n # end NAV based on final cumulative return\\n end_NAV=r[-1]\\n # determine annualized return\\n AnnualReturn = end_NAV**(1 / difference_in_years) - 1\\n return AnnualReturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154922c23546a6280747801269452348\",\n \"score\": \"0.60345423\",\n \"text\": \"def annualized_return(returns, start_date, end_date):\\n total_return = ((1 + returns).cumprod())[-1]\\n t = (end_date - start_date).days / 365.25\\n ar = (total_return ** (1 / t)) - 1\\n return ar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66b4443c3b33f1455b678df725351107\",\n \"score\": \"0.60135186\",\n \"text\": \"def probability(self, v, a):\\n return self.effective_energy(v, a).exp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d93567a5d1ddf14025996e3afb62cbb8\",\n \"score\": \"0.59987706\",\n \"text\": \"def ev_log_p(eta):\\n return ev_log_h(eta) + ev_t(eta) @ eta - a(eta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc13a260398e24edc6261513dec9992a\",\n \"score\": \"0.5968521\",\n \"text\": \"def annualized_returns(ser):\\n return ser.mean() * DAYS_IN_YEAR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c09486a9eefe1f4ecd2286d6e97b57\",\n \"score\": \"0.5949281\",\n \"text\": \"def get_annualized_percent_change_per_year(diff, years):\\n return math.exp(math.log(diff) / years) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eccfd5ead284dde79de143510d846be1\",\n \"score\": \"0.59371513\",\n \"text\": \"def probability(self):\\n product=self.features.dot(self.w)\\n #print(product)\\n expr=np.exp(-product)\\n \\n return 1/(1+expr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ad259a90ab4b35af7af1020d840805\",\n \"score\": \"0.5879564\",\n \"text\": \"def avg_rate(p):\\n return (p[-1][0]-p[0][0])/(p[-1][1]-p[0][1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e88279a38ba012acd7789ced66e89ea0\",\n \"score\": \"0.58563554\",\n \"text\": \"def getfreq(pianonum):\\n \\n exponential = 2 ** ((pianonum - 49) / 12)\\n return exponential * 440\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08118845d5b36ce5bd753f63e3d3b10\",\n \"score\": \"0.58353317\",\n \"text\": \"def annualise(num):\\n anld = float(num) * 12 * 100\\n return anld\",\n \"title\": \"\"\n },\n {\n \"docid\": \"017db4fe9f5b64e04d26169fa1596b1a\",\n \"score\": \"0.5829257\",\n \"text\": \"def Probability(o):\\n return o / (o + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6e15058f1871d5a04c9c8ec9906aed\",\n \"score\": \"0.5813514\",\n \"text\": \"def ioniz_prob(ergs):\\n prob = np.exp(0.05*ergs - 8.0)\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28da29699709c641d11b78e12aa222a\",\n \"score\": \"0.576968\",\n \"text\": \"def dbl_logistic_model ( p, x ):\\n return p[0] + p[1]* ( 1./(1+np.exp(p[2]*(365.*x-p[3]))) + \\\\\\n 1./(1+np.exp(p[4]*(365.*x-p[5]))) - 1 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8983ec782f85690e786fb0777161d298\",\n \"score\": \"0.57667994\",\n \"text\": \"def probability(self, observation, next_state, action):\\n if self.sample(next_state, action) == observation:\\n return 1.0 - self.epsilon\\n else:\\n return self.epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2ed7994e322867aa727db1a84dc57f\",\n \"score\": \"0.5764005\",\n \"text\": \"def _annualizationFactor(lifetime, r=4.0):\\n if r==0: return lifetime\\n return (1-(1+(r/100.0))**-lifetime)/(r/100.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7998a972bd9d384b08964c1ef376da06\",\n \"score\": \"0.576322\",\n \"text\": \"def _eta(data):\\r\\n # if len(data) <= 1: return 0\\r\\n ldata = len(list(data))\\r\\n if ldata <= 1: return 0\\r\\n _exp = exp(1)\\r\\n counts = Counter()\\r\\n for d in data:\\r\\n counts[d] += 1\\r\\n # probs = [float(c) / len(data) for c in counts.values()]\\r\\n probs = [float(c) / ldata for c in counts.values()]\\r\\n probs = [p for p in probs if p > 0.]\\r\\n ent = 0\\r\\n for p in probs:\\r\\n if p > 0.:\\r\\n ent -= p * log(p, _exp)\\r\\n return ent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bb053fe634110591f254c113c674cd4\",\n \"score\": \"0.5735856\",\n \"text\": \"def payback_period(self, proforma):\\r\\n capex = abs(proforma.loc['CAPEX Year', :].sum())\\r\\n\\r\\n first_opt_year = min(self.opt_years)\\r\\n yearlynetbenefit = proforma.loc[pd.Period(year=first_opt_year, freq='y'), :].sum()\\r\\n\\r\\n return capex/yearlynetbenefit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1bd10268b1aa5df2ab5497a287b4f8\",\n \"score\": \"0.5728151\",\n \"text\": \"def prob(self, Pn):\\n if self.norm==\\\"Scargle\\\": return exp(-Pn)\\n if self.norm==\\\"HorneBaliunas\\\": return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\\n if self.norm==\\\"Cumming\\\": return (1.+2.*Pn/(self.N-3.))**(-(self.N-3.)/2.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1bd10268b1aa5df2ab5497a287b4f8\",\n \"score\": \"0.5728151\",\n \"text\": \"def prob(self, Pn):\\n if self.norm==\\\"Scargle\\\": return exp(-Pn)\\n if self.norm==\\\"HorneBaliunas\\\": return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\\n if self.norm==\\\"Cumming\\\": return (1.+2.*Pn/(self.N-3.))**(-(self.N-3.)/2.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36452380e66393425596e4644ba5f147\",\n \"score\": \"0.57024795\",\n \"text\": \"def airdensity(p,t):\\n Re = 287.04 \\n rho = p / ( Re * t ) \\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cede57d7caad42c6c13cfe71a4d66b9f\",\n \"score\": \"0.56989205\",\n \"text\": \"def amplitude_calc(x, p, beta=1, alpha=0):\\n return (1+alpha**2)/beta/2 * x**2 + alpha*x*p + beta/2*p**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baea0c4c34222f3ca14644480b1365ab\",\n \"score\": \"0.5695328\",\n \"text\": \"def find_aps(r,kappain):\\n return -2.*np.log(r) - kappain**2./(np.exp(1.)*r**2.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e175a0a8d31194ee97285b667f9f17c\",\n \"score\": \"0.5669334\",\n \"text\": \"def P(self):\\n\\t\\tP = 2*pi * sqrt( (self.a()*AU)**3. / (G*(self.mCent+self.mass)*mSun) )/day\\n\\t\\treturn P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b5dd9bb01f02b96740daacd3610301\",\n \"score\": \"0.5666913\",\n \"text\": \"def P(self):\\n\\t\\tP = 2*pi * sqrt( (self.a*AU)**3. / (G*(self.M+self.m)*mSun) )/(24*3600.)\\n\\t\\treturn P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2031ab84851620cb2d48a6d0123baee0\",\n \"score\": \"0.5654314\",\n \"text\": \"def _calculate_aprior_probabilities(aposterior_probabilities: np.array) -> np.array:\\n return aposterior_probabilities.mean(axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c64cde34a2700c76d76fc56c57511d9\",\n \"score\": \"0.5640836\",\n \"text\": \"def get_a(x):\\n\\n return np.exp(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5526b4851786a92173c9ec8c0b34e99\",\n \"score\": \"0.56370974\",\n \"text\": \"def p(self, x: np.array) -> float:\\n p = - x[0]**2 / 200.0 - 0.5 * (x[1] - self.b * x[0]**2 +\\n 100.0 * self.b)**2\\n p_sum = - 0.5 * np.sum(x[2:-1]**2)\\n p = p + p_sum\\n return np.exp(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7512fe5873ab477ff6210d31de27da47\",\n \"score\": \"0.5598786\",\n \"text\": \"def aire(R):\\n return pi * (R ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a3541d9cae87df9cc65e10c53d9c51\",\n \"score\": \"0.5589556\",\n \"text\": \"def eta_ideal(self):\\n return 1. - (self.engine_in.pr_ovr ** ((1 - self.engine_in.ambient.kappa_air ) /\\n self.engine_in.ambient.kappa_air))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d890d991df68637d2ed572b546f57f\",\n \"score\": \"0.5578697\",\n \"text\": \"def adiabatic_lapse_rate_from_t(SA, t, p):\\n\\n return -gibbs(n0, n1, n1, SA, t, p) / (gibbs(n0, n2, n0, SA, t, p))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"441f4390671151a658ae641b017047d6\",\n \"score\": \"0.556664\",\n \"text\": \"def ppmv2pa(x, p):\\n return x * p / (1e6 + x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27ca1e003b53ece5ab9f1f0e8f2ce93\",\n \"score\": \"0.55485916\",\n \"text\": \"def f_pseudo_periodique(beta, omega, A, B, t):\\n return math.exp(-beta*t)*(A*math.cos(omega*t)+B*math.sin(omega*t))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06cd4e2088c2c1d43960776b6e1a579e\",\n \"score\": \"0.5546343\",\n \"text\": \"def annualized_return(self) -> float:\\n return self.__annualized_return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe98470dccf2e8ced11da6af1252fce1\",\n \"score\": \"0.5527254\",\n \"text\": \"def getProbability(self):\\n return self.p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daeb75883fd9d23198eaf7361e39c953\",\n \"score\": \"0.5521143\",\n \"text\": \"def p_alpha(p,p0,th,s):\\n a11,a12,a13,a14,a15,a16,a21,a22,a23,a24,a25,a26,b11,b12,b13,b21,b22,b23=eosben07_const()\\n #\\n b1i=1/(b11+b12*th+b13*s)\\n a1=(a11+th*(a12+a14*th+a15*s)+s*(a13+a16*s))*b1i\\n a2=(a21+th*(a22+a24*th+a25*s)+s*(a23+a26*s))*b1i\\n b2=(b21+b22*th+b23*s)*b1i\\n #\\n r=b2*(p-p0)+(a2-a1*b2)*ma.log((a1+p)/(a1+p0))\\n #\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e3d6309e5ae8993e438d8b810b110c3\",\n \"score\": \"0.55092055\",\n \"text\": \"def proton_distribution_probability(product, substrate, precursor):\\n global BASIC_AA\\n p = substrate[1]\\n q = substrate[2]\\n p1 = product[1]\\n p2 = p - p1\\n q1 = product[2]\\n q2 = q - q1\\n p0 = precursor[1]\\n nu = len(substrate[0])\\n nu1 = len(product[0])\\n #nu = sum(aa in BASIC_AA for aa in substrate[0])\\n #nu1 = sum(aa in BASIC_AA for aa in product[0])\\n nu2 = nu - nu1\\n # Distribution over initial charging on product sequence:\\n # Hypergeometric with population nu, successes nu1, trials p0\\n XA1 = hypergeom_pmf_iterator(N=nu, k=nu1, n=p0)\\n XA1_0 = max(0, p0-nu2)\\n PTR = iterate_over_ptr_probabilities(p1=p1, q1=q1, p=p, q=q, p0=p0)\\n PTR_0 = p1+q1+1\\n kmin = max(XA1_0, PTR_0)\\n kmax = min(nu1, p0-p2-q2+1)\\n # Moving past the \\\"null\\\" summands\\n for i in range(max(0, kmin - PTR_0 - 1)):\\n PTR.next()\\n for i in range(max(0, kmin - XA1_0 - 1)):\\n XA1.next()\\n pr = 0.\\n for i in range(kmin, kmax+1):\\n A, B = PTR.next()\\n C = XA1.next()\\n pr += (float(A)*float(nu2)/nu + float(nu1)/nu)*B*C\\n #pr = sum((float(A)*float(nu2)/nu + float(nu1)/nu)*B*C for i, (A, B), C in enumerate(zip(PTR, XA1)) if i + kmin < kmax)\\n return pr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a724016faa87efe811cac38a976021\",\n \"score\": \"0.55043864\",\n \"text\": \"def avg_amp(self, params):\\n amps = params[:self.no_ants*2:2]\\n if self.logamp:\\n amps = np.exp(amps)\\n return np.mean(amps) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66fe6cf3be98ef6d29b947bf9083ca27\",\n \"score\": \"0.54913193\",\n \"text\": \"def compute_period(a, M, m):\\n return np.sqrt(4 * np.pi **2 / (takahe.constants.G * (M+m) * takahe.constants.SOLAR_MASS) * (a * takahe.constants.SOLAR_RADIUS)**3) / (60 * 60 * 24)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a5607d44f9bb2f5301283876b8f61d\",\n \"score\": \"0.5488452\",\n \"text\": \"def prob(self, at, log = False):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ca10d9bd1a5946e2d0dff1c986e69c1\",\n \"score\": \"0.54698384\",\n \"text\": \"def exp_decay(p,x):\\n a, b, c = p\\n\\n return a + b * np.exp(-x / c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f906cdb8b7cc43327c26bd266d0974a\",\n \"score\": \"0.5467352\",\n \"text\": \"def get_annual_eps_value(eps_data: dict):\\n years = list(set([int(k.split('-')[0]) for (k, v) in eps_data.items()]))\\n max_year = max(years)\\n three_years_ago = max_year - 3\\n filter_years = sorted([str(x) for x in years if max_year >= x >= three_years_ago], reverse=True)\\n values = list()\\n for year in filter_years:\\n values.append(round(sum([v for (k, v) in eps_data.items() if year in k]), 2))\\n return dict(zip(filter_years, values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d8b77d14e51ab8e133db3f50a7fe87\",\n \"score\": \"0.5463908\",\n \"text\": \"def prob(self, Pn):\\n return (1.-2.*Pn/(self.N-1.))**((self.N-3.)/2.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5878d8693907bf81216b28f335fdc419\",\n \"score\": \"0.5459113\",\n \"text\": \"def rate(e):\\n d = np.abs(e - e[-1])\\n p = []\\n for i in range(len(d)-1):\\n p.append(np.log(d[i+1])/np.log(d[i]))\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be049e111a0f8923aebf37b90cef533\",\n \"score\": \"0.5458327\",\n \"text\": \"def paretovariate_impl(alpha):\\n # Jain, pg. 495\\n u = 1.0 - _random()\\n return 1.0 / u ** (1.0/alpha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aa974bfaae301fa79ac0fbeabb3bb21\",\n \"score\": \"0.54536045\",\n \"text\": \"def EP(d):\\n return 4*d*(5/6)**d + 1 - (5/6)**d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e76d91ba872e7607db1518181831cf\",\n \"score\": \"0.54457945\",\n \"text\": \"def calc_alpha(epsilon):\\n\\n if (epsilon == 0):\\n return np.inf\\n\\n\\n return 0.5 * np.log( (1. - epsilon)/epsilon )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4c2f4d23858b4b4c3cc0ad0d940f8d\",\n \"score\": \"0.5436166\",\n \"text\": \"def beamfrac(FWHM, length, angle):\\n height_of_sample = length * np.sin(np.radians(angle))\\n beam_sd = FWHM / 2 / np.sqrt(2 * np.log(2))\\n probability = 2.0 * (\\n stats.norm.cdf(height_of_sample / 2.0 / beam_sd) - 0.5\\n )\\n return probability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f6ccb38864d16aea5f119c73c0b0fb\",\n \"score\": \"0.54260474\",\n \"text\": \"def theta(self, *args, eps=0.001):\\n # Fix the random generator seed for MonteCarlo pricing\\n st = np.random.get_state()\\n price1 = self.price(*args)\\n np.random.set_state(st)\\n self.term -= eps\\n price2 = self.price(*args)\\n self.term += eps\\n return (price2 - price1) / (365 * eps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad795ccd1af697171146833dd55aaa9\",\n \"score\": \"0.5425807\",\n \"text\": \"def _calculate_PAYE(period_income, period):\\r\\n predicted_annual_income = period * period_income\\r\\n \\r\\n income_tax = _calculate_income_tax(predicted_annual_income)\\r\\n acc_levy = _calculate_ACC_levy(predicted_annual_income)\\r\\n \\r\\n total_tax_paid = income_tax + acc_levy\\r\\n \\r\\n return round(total_tax_paid / period, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e00a47c7e5892e10021e3d495160bb4d\",\n \"score\": \"0.5419779\",\n \"text\": \"def e_var(partition):\\n P = np.log(partition)\\n S = len(partition)\\n X = 0\\n for x in P:\\n X += (x - np.mean(P))**2/S\\n evar = 1 - 2/math.pi*np.arctan(X) \\n return(evar)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55297a2ff4086c2bf1acf53482780e23\",\n \"score\": \"0.5414374\",\n \"text\": \"def expected_earnings(mao):\\r\\n return (.5 - (mao - ((mao ** 2) / 2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244d6a5bac71aaacecf03ad1089d25dc\",\n \"score\": \"0.5397943\",\n \"text\": \"def calculate_probability(x, mean, stdev):\\n exponent = np.exp(-1.0 * ((x-mean)**2 / (2*(stdev**2))))\\n return (1/(np.sqrt(2.0 * np.pi) * stdev)) * exponent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dac13767a930f12ba0d31b9db65a986\",\n \"score\": \"0.5397827\",\n \"text\": \"def empirical_prob(self, *items, delta = 0):\\n assert (len(items) in [1, 2])\\n if (len(items) == 1):\\n denominator = self.total_counts + delta * self.num_vocab ** 2\\n else:\\n denominator = self.total_edges + delta * self.num_possible_pairs\\n return self.empirical_freq(*items, delta = delta) / denominator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"647fe4d57e4a1de86bbc9cd34a971c9c\",\n \"score\": \"0.5396929\",\n \"text\": \"def soilmoisture( self, evaporative_fraction ):\\n\\t\\t\\tresult = (exp((evaporative_fraction-1.2836)/0.4213))/0.511\\n\\t\\t\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a486069b20a599edb89c810c19a2d7b\",\n \"score\": \"0.53961486\",\n \"text\": \"def gaussian_moment(p):\\n if p % 2 == 0:\\n return factorial2(p-1)\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49ab846f51909ca0b71e08b8f97013e3\",\n \"score\": \"0.53892523\",\n \"text\": \"def logit2probability(log_odds):\\n # convert log odds to odds\\n odds = math.exp(log_odds)\\n # return probability\\n return odds / float(1 + odds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8170543ac5ff1d2bba1fd16b24f8d220\",\n \"score\": \"0.5387339\",\n \"text\": \"def decay(t, p):\\n return p[0] * np.exp(-t / p[1]) + p[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40958abdc2ac4eefc3a72a6573351082\",\n \"score\": \"0.53814656\",\n \"text\": \"def _approx_ap(airf,pres):\\n pvap = pres * (1-airf)/(airf*_EPSW + 1-airf)\\n v = numpy.log(pvap/_PTPE)/_BVI\\n x = maths4.lamb2(v,_AVI/_BVI)\\n temp = _TTP/x\\n dhum = (pres-pvap + _EPSW*pvap) / (_RDRY*temp)\\n return temp, dhum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba8b19e85284fbfe2a59ea77809b5ed\",\n \"score\": \"0.53794813\",\n \"text\": \"def startprob(self):\\n return np.exp(self._log_startprob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"008acf3c978605d22a56d6110134eaba\",\n \"score\": \"0.5377723\",\n \"text\": \"def get_exp_probabilities(self, fluxes, inst_params):\\n prob = 1 - np.exp(-fluxes / inst_params[\\\"scale\\\"])\\n\\n return prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c14c31873659ea1950017763c7e4e2b\",\n \"score\": \"0.5364507\",\n \"text\": \"def get_ev(a: np.ndarray) -> Param:\\n\\n return a / np.sum(a),\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54881653f7ce52de6c69bcc2f8abd6f2\",\n \"score\": \"0.53623134\",\n \"text\": \"def topp(v):\\n return np.exp(v) / np.sum(np.exp(v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb044c957e5b8785a3ac8b0bf516416f\",\n \"score\": \"0.5361167\",\n \"text\": \"def probability(self, next_state, state, action):\\n if self.sample(state, action) == next_state:\\n return 1.0 - self.epsilon\\n else:\\n return self.epsilon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c6ca6447b8cce60ebe738b1842c361\",\n \"score\": \"0.5350514\",\n \"text\": \"def test_getAngularDistribution_PWave(self):\\n egrid = self.RRR1PRes.generateEnergyGrid()\\n eStep = 1\\n J = 5.5 # for this test data\\n S = 4.5 # for this test data\\n from numericalFunctions import angularMomentumCoupling as nf_amc\\n self.assertAlmostEqual(\\n nf_amc.z_coefficient(2, int(2 * J), 2, int(2 * J), int(2 * S), 4) / nf_amc.z_coefficient(2, int(2 * J), 2,\\n int(2 * J),\\n int(2 * S), 0),\\n math.sqrt(0.33090909)) # nf_amc functions use the 2J trick\\n for E in egrid[::eStep]:\\n B = self.RRR1PRes.getAngularDistribution(E)\\n self.assertAllWithinXPercent([x / B[0] for x in B], [1.0, 0.0, 0.33090909])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54012fa6ca93fc00bc7a9e27ee1fe249\",\n \"score\": \"0.5335799\",\n \"text\": \"def offer_prob(year, month, sex, race, referral, precomputed_emp_dist=None):\\n if precomputed_emp_dist is not None:\\n emp = precomputed_emp_dist[year][month - 1][race.name][sex.name]\\n else:\\n emp = employment_dist(year, month, sex, race)\\n\\n # quick-and-dirty mapping to race keys\\n if not isinstance(race, str):\\n if race.name == 'aian':\\n race = 'nativeamerican'\\n elif race.name == 'white':\\n # b/c white and hispanic are merged together\\n # TODO find dataset that separates these?\\n race = random.choice(['hispanic', 'white'])\\n elif race.name in ['chinese', 'japanese', 'api']:\\n race = 'asian'\\n elif race.name == 'black':\\n race = 'black'\\n\\n # mean of two random races. this is a sloppy guess,\\n # since the actual evidence may not work this way\\n elif race.name in ['other', 'two', 'three_plus']:\\n races = list(p_offer['offered']['race'].keys())\\n op1 = _offer_prob(sex, random.choice(races), referral, emp)\\n op2 = _offer_prob(sex, random.choice(races), referral, emp)\\n return (op1 + op2)/2\\n\\n return _offer_prob(sex, race, referral, emp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7550b5825aea3cfb202d2a09abfeabe\",\n \"score\": \"0.53341776\",\n \"text\": \"def ampsAtPeriod(period):\\n checkPeriod(period, \\\"ampsAtPeriod::period\\\")\\n return startingSequence[period-1][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8680f547542f4aa8920e1df44de4b6b9\",\n \"score\": \"0.5333341\",\n \"text\": \"def AIC(self):\\n RSS = self.getRMSE()\\n k = len(self.variables)\\n n = len(self.exp_data)\\n AIC = 2 * k + n * (log(2 * pi * RSS/n) + 1)\\n return AIC\",\n \"title\": \"\"\n },\n {\n \"docid\": \"879fc398a4991981587ecdfa58f50ded\",\n \"score\": \"0.5328824\",\n \"text\": \"def prediction_interval(self, xp=None, yp=None, alpha=0.05):\\n if not (xp is None) ^ (yp is None):\\n raise ValueError('Only one of [xp, yp] must be specified.')\\n\\n if xp is not None:\\n return self._y_pi(xp, alpha=alpha)\\n if yp is not None:\\n return self._x_pi(yp, alpha=alpha)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"243d4ce62d5695a4a1cde42e12efba40\",\n \"score\": \"0.5327332\",\n \"text\": \"def alpha_wrt_t_exact(SA, t, p):\\n\\n return gibbs(n0, n1, n1, SA, t, p) / gibbs(n0, n0, n1, SA, t, p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbaeb65c4918cd305cc1d2de1739b203\",\n \"score\": \"0.53269297\",\n \"text\": \"def probability():\\n f1 = AffineTransform(0, 0, 0, 0.16, 0, 0)\\n f2 = AffineTransform(0.85, 0.04, -0.04, 0.85, 0, 1.60)\\n f3 = AffineTransform(0.20, -0.26, 0.23, 0.22, 0, 1.60)\\n f4 = AffineTransform(-0.15, 0.28, 0.26, 0.24, 0, 0.44)\\n functions = [f1, f2, f3, f4]\\n p1 = 0.01\\n p2 = 0.85\\n p3 = 0.07\\n p4 = 0.07\\n p_cumulative = np.array([[0, p1],\\n [1, p1+p2],\\n [2, p1+p2+p3],\\n [3, p1+p2+p3+p4]])\\n r = np.random.random()\\n for j, p in p_cumulative:\\n if r < p:\\n return functions[int(j)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a47e37d6559b69bf1b357b7e8a0d017\",\n \"score\": \"0.5326326\",\n \"text\": \"def exponential_var(p,x):\\n \\n \\n plt.subplot(311)\\n plt.semilogy(x, exp_decay(np.array([p[0[0]],p[1[3]],p[2[3]]])), x)\\n plt.savefig('KEG_exponential_var.pdf', bbox_inches='tight', dpi=300)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e6a4c6840c0695d1f98535c676f6f3\",\n \"score\": \"0.5323668\",\n \"text\": \"def date_of_annual_after(title, eff_date):\\n publication_date = date(eff_date.year, publication_month(title), 1)\\n if eff_date <= publication_date:\\n return publication_date\\n else:\\n return publication_date.replace(year=eff_date.year + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b026ba3820417246be18a6149607298\",\n \"score\": \"0.53191286\",\n \"text\": \"def cronbach_alpha(data):\\n n_items = data.shape[0]\\n\\n valid_mask = data != INVALID_RESPONSE\\n \\n item_variance = np.var(data, axis=1, ddof=1, where=valid_mask).sum()\\n people_variance = (n_items * n_items\\n * np.mean(data, axis=0, where=valid_mask).var(ddof=1))\\n \\n return (n_items / (n_items - 1) \\n * (1 - item_variance / people_variance))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e73ca74ce81005f48c4c98ed9cd9001\",\n \"score\": \"0.53163207\",\n \"text\": \"def calc_extreme_value(params,distribution,nyear,datafraction=1.0):\\n probability=1.0/(nyear*365.0*datafraction)\\n \\n x=np.arange(0,10000,10)\\n pdf=distribution.pdf(x,*params)\\n pos=np.where((pdf>=probability) & (pdf>0))[0]\\n \\n if len(pos)==0:return 0\\n pos=pos[-1]\\n\\n x=np.arange(x[pos]-10,x[pos]+10,0.1)\\n pdf=distribution.pdf(x,*params)\\n pos=np.where((pdf<=probability) &(pdf>0))[0]\\n\\n if x[pos[0]]<0:return 0\\n return x[pos[0]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a87e4e5986c8905599dc2a722d7a32cd\",\n \"score\": \"0.5314459\",\n \"text\": \"def period(r):\\n m = 0\\n d = 1\\n a = a0 = floor(r)\\n p = []\\n\\n while a != 2*a0:\\n m = round(d*a - m)\\n d = round((r*r - m*m)/d)\\n a = round(floor((a0 + m)/d))\\n p.append(a)\\n return (a0, p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2330a22f92cb5bc3db086c0c7218a8cb\",\n \"score\": \"0.5306773\",\n \"text\": \"def calculateAnom(var):\\n ### Import modules\\n import numpy as np\\n \\n mean = np.nanmean(var,axis=0)\\n anom = var - mean\\n \\n print('Completed: Calculate anomalies for polar cap!')\\n return anom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6a555f2566874f2dfe5d796711f788f\",\n \"score\": \"0.53059053\",\n \"text\": \"def exponential_annealing(init_value, min_value, decay_ratio):\\n result = init_value\\n\\n while True:\\n result = max(min_value, result * decay_ratio)\\n yield result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccaab3a5cf7e0e80c93134e6ef44d7e9\",\n \"score\": \"0.53049403\",\n \"text\": \"def per(soup):\\r\\n\\r\\n period = soup.find(id=\\\"planet_period_0\\\").text\\r\\n\\r\\n period = float(period.split(\\\" \\\")[0])\\r\\n \\r\\n return period\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80192f78f06ba42dced0be35570c30c2\",\n \"score\": \"0.5300492\",\n \"text\": \"def calculate_probability(self, value, mean, std_dev):\\n # Calculates the exponent.\\n exponent = math.exp(-(math.pow(value-mean,2)/(2*math.pow(std_dev,2))))\\n # Returns the probability.\\n return (1 / (math.sqrt(2*math.pi) * std_dev)) * exponent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d683d54fb547bb0d5ffb95227395954c\",\n \"score\": \"0.52974075\",\n \"text\": \"def getFrequency(self):\\n if self.period: return 1.0/self.period\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8af254d526217ba25a3ddee9e94c8a83\",\n \"score\": \"0.52971166\",\n \"text\": \"def annual_eval(out_dir, pts):\\n dgp_value = np.genfromtxt(f\\\"{out_dir}dgp_{pts}.out\\\", delimiter=\\\" \\\", usecols=3)\\n fdgpe = np.zeros(3) # DGP > 0.35, 0.4, 0.45\\n for i in range(len(dgp_value)):\\n for j in range(3):\\n if dgp_value[i] > (0.35 + j * 0.05):\\n fdgpe[j] = fdgpe[j] + 1\\n for j in range(3):\\n fdgpe[j] = fdgpe[j] * 5.43 / 1304\\n np.savetxt(f\\\"{out_dir}fDGPe_{pts}.out\\\", fdgpe, delimiter=\\\" \\\", fmt=\\\"%1.3f\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db93ccc3207dc8aa7a31f25624b10f80\",\n \"score\": \"0.52957386\",\n \"text\": \"def zolotarev_pdf_exponentiated(self, x, alpha):\\n denominator = pow(sinc(alpha * x), alpha) \\\\\\n * pow(sinc((1. - alpha) * x), (1. - alpha))\\n numerator = sinc(x)\\n return numerator / denominator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5133c303c323d063c9da40af348b07b2\",\n \"score\": \"0.5292389\",\n \"text\": \"def bar2psia(a):\\n return (a + 1.013) * 14.50377\",\n \"title\": \"\"\n },\n {\n \"docid\": \"433b64525c26c03fe98d5aacf62f0afd\",\n \"score\": \"0.5291426\",\n \"text\": \"def p_from_am(sma, ms):\\n a = sma * u.au.to(u.m)\\n ms = ms * u.Msun.to(u.kg)\\n G = c.G.value\\n p = np.sqrt((4 * np.pi ** 2 * a ** 3) / (G * ms))\\n return p * u.second.to(u.day)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ea2e4a7c9d0289f55632f817962b64\",\n \"score\": \"0.52898544\",\n \"text\": \"def damping_ratio(self):\\n return self._eta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60f281fb55f9ec9866f8938ebd39e617\",\n \"score\": \"0.5287896\",\n \"text\": \"def mean(p):\\n\\n v = 0.0\\n for i, pi in zip(_I, p):\\n v += i*pi\\n\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d5dacdd4a7189d99c9572027701b844\",\n \"score\": \"0.52864736\",\n \"text\": \"def get_probsrsa_vectorized(number, exp_rate):\\r\\n return (np.power(exp_rate, number))*(np.exp(-exp_rate))/(scipy.special.factorial(number))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b25b7c54d9e8dc6830baa497956688d\",\n \"score\": \"0.52847904\",\n \"text\": \"def arc_length(Eps, alpha):\\n return Eps * alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5fc16f5816f57878f83370c9daf880f\",\n \"score\": \"0.5284486\",\n \"text\": \"def int_eta0(self,p,T,muB,muQ,muS,**kwargs):\\n Ep = np.sqrt(p**2.+self.M(T,muB,muQ,muS,**kwargs)**2.) # pole energy\\n ffq = self.fq(Ep,T,muB,muQ,muS) # distribution function\\n return 1./(15.*T)*self.dg*(4.*pi*p**2.)/((2.*pi)**3)*(p**4.)/(Ep**2.)*ffq*(1-self.BEFD*ffq)/(2*self.w(T,muB,muQ,muS,**kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f95cf88f90172d89ed05f46bff261c0\",\n \"score\": \"0.52806705\",\n \"text\": \"def period(x, y, ofac=4, hifac):\\n n = len(x)\\n (wi, wpi, wpr, wr, px, py) = ([0]*n, [0]*n, [0]*n, [0]*n, [0]*n)\\n nout = int(.5*ofac*hifac*n)\\n if np < nout:\\n px.np.ndarray.resize(nout)\\n py.np.ndarray.resize(nout)\\n ave = np.avearge(y)\\n var = np.var(y)\\n if var == 0:\\n print(\\\"Zero variacne in period\\\")\\n return\\n xmax = xmin = x[0]\\n for j in range(0, n):\\n if x[j] > xmax:\\n xmax = x[j]\\n if x[j] < xmin:\\n xmin = x[j]\\n xdif = xmax - xmin\\n xave = .5*(xmax+xmin)\\n pymax =0\\n pnow = 1/(xdif*ofac) # starting frequency\\n for j in range(0, n): # initialize values for the trigonometric recurrences at each data point\\n arg = 2*np.pi*(x[j]-ave)*pnow\\n wpr[j] = -2*np.sqrt(np.sin(.5*arg))\\n wpi[j] = np.sin(arg)\\n wr[j] = np.cos(arg)\\n wi[j] = wpi[j]\\n for i in range(0, nout):\\n px[i] = pnow\\n sumsh = sumc = 0\\n for j in range(0,n):\\n c = wr[j]\\n s = wi[j]\\n sumsh += s*c\\n sumc += (c-s)*(c+s)\\n wtau = .5*np.arctan(p2*sumsh, sumc])\\n swtau = np.sin(wtau)\\n cwtau = np.cos(wtau)\\n sums = sumc= sumsy = sumcy = 0\\n for j in range(0, n): # loop over the data again to get the periiodogram value\\n s = wi[j]\\n c = wr[j]\\n ss = s * cwtau - c*swtau\\n cc = c*cwtau + s*swtau\\n sums += ss*ss\\n sumc += cc*cc\\n yy = y[j] - ave\\n sumsy += yy*ss\\n sumcy += yy*cc\\n wtemp = wr[j]\\n wr[j] = (wtemp*wpr[j] - wr[j] * wpi[j]) + wr[j] # trigonometric recurrences\\n wi[j] = (wi[j] * wpr[j] + wtemp * wpi[j]) + wi[j]\\n py[i] = .5*(sumcy * sumcy/sumc + sumsy * sumsy/sums)/var\\n if py[i] >= pymax:\\n jmax = i\\n pymax = py[jmax]\\n pnow += 1/(ofac*xdif)\\n expy = np.exp(-pymax)\\n effm = 2*nout/ofac\\n prob = effm * expy\\n if prob > .01:\\n prob = 1 - np.power(1 - expy, effm)\\n return px, py, prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73fa5608009670c1ebc32a92411e499a\",\n \"score\": \"0.5279629\",\n \"text\": \"def _probability_of_title_with_length(self, length):\\n sigma = 6\\n return np.exp(-1 * ((length - self.avg_length) ** 2) / (2 * (sigma ** 2))) / (math.sqrt(2 * np.pi) * sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d435eac6074cd3164079f5ca5870ba\",\n \"score\": \"0.5273136\",\n \"text\": \"def frac_position(eta,eta_distribution):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a337c988c1ebbf9f39e6dda243a101f1\",\n \"score\": \"0.52646464\",\n \"text\": \"def apo(price, period_slow = 26, period_fast = 12, ma = \\\"sma\\\"):\\n if ma.lower() not in [\\\"sma\\\", \\\"ema\\\"]:\\n raise ValueError(\\\"param 'ma' needs to be 'ema' or 'sma'\\\")\\n\\n return apo_calc(price, period_slow, period_fast, ma.lower())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dea0661caea45ea6b8f392fa9985659\",\n \"score\": \"0.5260097\",\n \"text\": \"def rand_exp(rate):\\n \\n return -log(random()) / rate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0621abd8797937864d627e54d54a7c8\",\n \"score\": \"0.5259038\",\n \"text\": \"def discounted_payback_period(self, proforma):\\r\\n payback_period = self.payback_period(proforma) # This is simply (capex/yearlynetbenefit)\\r\\n\\r\\n if not self.npv_discount_rate:\\r\\n dr = self.npv_discount_rate*.01\\r\\n discounted_pp = np.log(1/(1-(dr*payback_period)))/np.log(1+dr)\\r\\n else:\\r\\n # if discount_rate = 0, then the equation simplifies to np.log(1)/np.log(1)-- which is undefined, so we return nan\\r\\n discounted_pp = 'nan'\\r\\n\\r\\n return discounted_pp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c08eac2e3e543e690b820889ca1868d\",\n \"score\": \"0.5257021\",\n \"text\": \"def ann_return1_year(self) -> float:\\n return self.__ann_return1_year\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":196,"cells":{"query_id":{"kind":"string","value":"a2553f674f9dde5c403aedc77dda2d72"},"query":{"kind":"string","value":"Gets the metadata for the copyright."},"positive_passages":{"kind":"list like","value":[{"docid":"add6e0375766c1ae82f03796afa040ba","score":"0.8633044","text":"def get_copyright_metadata(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"add6e0375766c1ae82f03796afa040ba\",\n \"score\": \"0.8633044\",\n \"text\": \"def get_copyright_metadata(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"24f84680cf596560ecdac39cce9e64bc","score":"0.8069426","text":"def get_copyright_registration_metadata(self):\n pass","title":""},{"docid":"9b439f8061f824cb732d55672fc3eaff","score":"0.7513539","text":"def copyright():\n # pull and print the meta-data\n return print(meta.header)","title":""},{"docid":"9b439f8061f824cb732d55672fc3eaff","score":"0.7513539","text":"def copyright():\n # pull and print the meta-data\n return print(meta.header)","title":""},{"docid":"9ce9dfaa695a5f317033c21935fab989","score":"0.72402924","text":"def copyright(self):\n return self._copyright","title":""},{"docid":"69c61a3fb10cf9c01b25ed094a4ef720","score":"0.7208793","text":"def copyright(self) -> str:\n return pulumi.get(self, \"copyright\")","title":""},{"docid":"69c61a3fb10cf9c01b25ed094a4ef720","score":"0.7208793","text":"def copyright(self) -> str:\n return pulumi.get(self, \"copyright\")","title":""},{"docid":"69acb209ae4f2b9afe7528869611d6af","score":"0.702879","text":"def get_copyright(self):\n return self._payload.get_copyright()","title":""},{"docid":"f60a01cb4b03c7e41fdce6f74baa2074","score":"0.7024526","text":"def copyright():\n return print(meta.header)","title":""},{"docid":"dc89c32813693811b9f0d3f971f18ea0","score":"0.698063","text":"def _copyright(self):\n return self.copyright","title":""},{"docid":"2160cef48267207c30a1b6b4184ac02f","score":"0.69552654","text":"def getCopyright(self):\n return self.copyright","title":""},{"docid":"6d8b1679f609c6d0124c424dcbf59974","score":"0.69459635","text":"def get_metadata(self, citation):\n pass","title":""},{"docid":"bad2ebb93b92f073ae4061987b3d7f07","score":"0.6943443","text":"def GetCopyright(self):","title":""},{"docid":"ec88284d34c75931c56892c679c7a4bb","score":"0.6834667","text":"def feed_copyright(self):\n return _siteinfo().copyright","title":""},{"docid":"671c3475d5dbaafeb2cd46f5c7684610","score":"0.6762198","text":"def meta_copyright(cls, text, time=0.0):\n return cls._textmeta(mm.kCopyRight, text, time)","title":""},{"docid":"97a01f19b6c844cfe4b5f92954152630","score":"0.6670829","text":"def item_copyright(self):\n return _siteinfo().copyright","title":""},{"docid":"9c6a1486503376ff18f1a8044bc275f8","score":"0.66507226","text":"def get_metadata(self, soup):\n metadata = {}\n meta = soup.find_all('meta')\n for m in meta:\n if m.has_attr('name') and m.get('name').startswith('dcterms'):\n metadata[m.get('name')] = m.get('content')\n return metadata","title":""},{"docid":"83dbd6adadea9e223f97c4bc546b48d0","score":"0.6647738","text":"def copyright():\n # generate the message\n return print(meta.copyright)","title":""},{"docid":"d59e15d78633fd7875488fb65753797d","score":"0.6624673","text":"def metadata(self):\n # identifiers\n dump = self.download()\n if dump['totalItems'] == 0:\n return 0\n\n google_books_data = dump['items'][0]['volumeInfo']\n authors = \"\"\n for author in google_books_data['authors']:\n authors = author + \",\" + authors\n authors = authors[0:-1]\n # i=0\n # for val in meta_data.keys():\n # t = google_books_data[kickstr[i]]\n # if val == 'cover':\n # t = google_books_data['imageLinks']['thumbnail']\n # elif val == 'published_date':\n # t=\n # meta_data[val] = t\n # i+=1\n meta_data = {\n 'google_url' : \"\",\n 'description' : \"\",\n 'tags' : \"\",\n 'cover' : \"\",\n 'published_date': \"\",\n 'publisher' : \"\",\n 'authors' : \"\",\n 'page_count' : \"\",\n 'title' : \"\"\n }\n try:\n meta_data['google_url'] = google_books_data['canonicalVolumeLink']\n except:\n meta_data['google_url'] = None\n try:\n meta_data['description'] = google_books_data['description']\n except:\n meta_data['description'] = None\n try:\n meta_data['tags'] = google_books_data['categories']\n except:\n meta_data['tags'] = None\n try:\n meta_data['cover'] = google_books_data['imageLinks']['thumbnail']\n except:\n meta_data['cover'] = None\n try:\n meta_data['published_date'] = parser.parse(google_books_data['publishedDate'])\n except:\n meta_data['published_date'] = None\n try:\n meta_data['publisher'] = google_books_data['publisher']\n except:\n meta_data['publisher'] = None\n try:\n meta_data['authors'] = authors\n except:\n meta_data['authors'] = None\n try:\n meta_data['page_count'] = google_books_data['pageCount']\n except:\n meta_data['page_count'] = None\n try:\n meta_data['title'] = google_books_data['title']\n except:\n meta_data['title'] = \"Title\"\n print(meta_data)\n print(google_books_data)\n return meta_data","title":""},{"docid":"a4f7eb2f8c6a2c093a771176339fc58d","score":"0.6615866","text":"def get_metadata(self):\n data = {\n \"license_code\": self.license_code,\n \"version\": self.version,\n \"title_english\": self.title_english,\n }\n if self.jurisdiction_code:\n data[\"jurisdiction\"] = self.jurisdiction_code\n\n data[\"permits_derivative_works\"] = self.permits_derivative_works\n data[\"permits_reproduction\"] = self.permits_reproduction\n data[\"permits_distribution\"] = self.permits_distribution\n data[\"permits_sharing\"] = self.permits_sharing\n data[\"requires_share_alike\"] = self.requires_share_alike\n data[\"requires_notice\"] = self.requires_notice\n data[\"requires_attribution\"] = self.requires_attribution\n data[\"requires_source_code\"] = self.requires_source_code\n data[\"prohibits_commercial_use\"] = self.prohibits_commercial_use\n data[\n \"prohibits_high_income_nation_use\"\n ] = self.prohibits_high_income_nation_use\n\n data[\"translations\"] = {}\n for lc in self.legal_codes.order_by(\"language_code\"):\n language_code = lc.language_code\n with active_translation(lc.get_translation_object()):\n data[\"translations\"][language_code] = {\n \"license\": lc.license_url,\n \"deed\": lc.deed_url,\n \"title\": gettext(self.title_english),\n }\n\n return data","title":""},{"docid":"930ea34840c9e82253cc2b16322d01cf","score":"0.65950376","text":"def copyright(self):\n\n return self.tagsToFind[self.COPYRIGHT_TAG].strip('\\x20\\x00')","title":""},{"docid":"7be0226f9ced814809bed0f04b879888","score":"0.6556654","text":"def copyright(self, format=\"json\"):\n\n endpoint = \"copyright\"\n\n return self._make_request(endpoint, {\"format\": format})","title":""},{"docid":"6d0fe9427617aca6967e17a2b63681dc","score":"0.6530905","text":"def copyright_str(self) -> str:\n with self.path.open() as f:\n match = re.search(self.copyright_re, f.read(1000))\n return match.group() if match is not None else ''","title":""},{"docid":"f11dac91b5ae360193af2698e24cbfa6","score":"0.64764947","text":"def get_metadata(self):\n return self.content[\"metadata\"]","title":""},{"docid":"5a02524fe6ba0d458c650a2c566f23b7","score":"0.63898975","text":"def getMetadata():","title":""},{"docid":"ddf409025c90dbd123a7ac7132e8c2ce","score":"0.633487","text":"def get_copyright_registration(self):\n return self._payload.get_copyright_registration()","title":""},{"docid":"5cc7229c6196b75042c80dd2fb36e5ab","score":"0.6308308","text":"def copyrights(self) -> List[CopyrightObject]:\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]","title":""},{"docid":"5cc7229c6196b75042c80dd2fb36e5ab","score":"0.6308308","text":"def copyrights(self) -> List[CopyrightObject]:\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]","title":""},{"docid":"5cc7229c6196b75042c80dd2fb36e5ab","score":"0.6308308","text":"def copyrights(self) -> List[CopyrightObject]:\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]","title":""},{"docid":"bb0d25b59c21e2cbc2ce757c420006cc","score":"0.6302299","text":"def get_metadata():\n here = os.path.abspath(os.path.dirname(__file__))\n init_path = os.path.join(here, 'src', 'serde_ext', '__init__.py')\n readme_path = os.path.join(here, 'README.rst')\n\n with io.open(init_path, encoding='utf-8') as f:\n about_text = f.read()\n\n metadata = {\n key: re.search(r'__' + key + r'__ = [\"\\'](.*?)[\"\\']', about_text).group(1)\n for key in ('title', 'version', 'url', 'author', 'author_email', 'license', 'description')\n }\n metadata['name'] = metadata.pop('title')\n\n with io.open(readme_path, encoding='utf-8') as f:\n metadata['long_description'] = f.read()\n\n return metadata","title":""},{"docid":"78d4d24772433b15772260ee08b53245","score":"0.62911433","text":"def get_metadata(html, text, filename):\n blog, post = filename.split(\"_\")\n url = \"http://\" + blog + \".hypotheses.org/\" + str(post)\n title = get_title(html, filename)\n author = get_author(html)\n date = get_date(html)\n licence = get_licence(html)\n language = check_language(text)\n category = get_category(text, title)\n numwords = get_numwords(text)\n metadata = [filename, language, author, numwords, category, date, licence, blog, post, url, title]\n return metadata","title":""},{"docid":"70d5607b649ee929aa19bf87e13a4a9d","score":"0.6261161","text":"def get_metadata(self, **kwargs):\n return self.get_entry(\n METADATA_URI, desired_class=gdata.docs.data.Metadata, **kwargs)","title":""},{"docid":"e92c69e3e660e418673713ba774eaf56","score":"0.62316877","text":"def get_license():\n data_license = {}\n data_license[\"name\"] = \"cc-by-4.0\"\n data_license[\"url\"] = \"https://creativecommons.org/licenses/by/4.0/\"\n data_license[\n \"description\"] = \"The content can be shared and adapted but you must\\\n give appropriate credit and cannot restrict access to others.\"\n return data_license","title":""},{"docid":"db49b6089ece2e54a555d92c706d8067","score":"0.6197543","text":"def get_metadata(self):\n api_call = self._get_url(\"catalog\")\n\n response = get_response(api_call)\n return json.load(response)","title":""},{"docid":"6916c01dffbfac6104f7b478a6ac5b10","score":"0.619042","text":"def get_metadata(name):","title":""},{"docid":"661b5544077ce492c145dac1c9cebc98","score":"0.6173343","text":"def getMetadata(content):","title":""},{"docid":"b4dea74a7d52e3b49307d8736c3d2022","score":"0.616945","text":"def meta(data):\n return data[1]['metadata']","title":""},{"docid":"2a09a6ed670672426b8d110a4b474f1b","score":"0.6166778","text":"def _GetMetadata(self, version):\n raw_json = None\n version_base = os.path.join(self.gs_base, version)\n with self.misc_cache.Lookup(\n (self.board, version, constants.METADATA_JSON)) as ref:\n if ref.Exists(lock=True):\n raw_json = osutils.ReadFile(ref.path)\n else:\n raw_json = self.gs_ctx.Cat(\n os.path.join(version_base, constants.METADATA_JSON)).output\n ref.AssignText(raw_json)\n\n return json.loads(raw_json)","title":""},{"docid":"f3aab84491c21d1127d87bfaa6edee3d","score":"0.6166168","text":"def metadata(self):\r\n url = \"{url}/info/metadata\".format(url=self._url)\r\n params = {\"f\": \"json\"}\r\n return self._con.get(url, params, token=self._token)","title":""},{"docid":"75b4e254ee8240f4ac8da787b98023aa","score":"0.61653906","text":"def get_metadata(self, identifier):","title":""},{"docid":"ecd4d1e1ec210a4543fda79dfb14e941","score":"0.61573976","text":"def get_dataObject_modac_meta(data_object_path):\n #data_object_path = encode_path(data_object_path)\n auth = authenticate_modac()\n\n get_response = requests.get(data_object_path, auth = auth)\n if get_response.status_code != 200:\n print(\"Error downloading from modac.cancer.gov\", data_object_path)\n raise Exception(\"Response code: {0}, Response message: {1}\".format(get_response.status_code, get_response.text))\n\n metadata_dic = json.loads(get_response.text)\n self_metadata = metadata_dic['metadataEntries']['selfMetadataEntries']['systemMetadataEntries']\n self_dic = {}\n for pair in self_metadata:\n self_dic[pair['attribute']] = pair['value'] \n\n return self_dic","title":""},{"docid":"3f5dd041ea3c0bdcc8389558c52fb93f","score":"0.6153275","text":"def get_metadata(self):\n\t\treturn self._metadata","title":""},{"docid":"3f5dd041ea3c0bdcc8389558c52fb93f","score":"0.6153275","text":"def get_metadata(self):\n\t\treturn self._metadata","title":""},{"docid":"3f5dd041ea3c0bdcc8389558c52fb93f","score":"0.6153275","text":"def get_metadata(self):\n\t\treturn self._metadata","title":""},{"docid":"3f5dd041ea3c0bdcc8389558c52fb93f","score":"0.6153275","text":"def get_metadata(self):\n\t\treturn self._metadata","title":""},{"docid":"4c1a5d4d20aca43089c913959ba5b359","score":"0.6152495","text":"def get_metadata(self) -> Mapping[int, Any]:\n return self._call(CredentialManagement.CMD.GET_CREDS_METADATA)","title":""},{"docid":"2791defc294d703d6056962a5e568286","score":"0.61457705","text":"def extract_metadata(self) -> DocumentMetadata:\n raise NotImplementedError","title":""},{"docid":"b6717d0b3afb11db4705a85098f55754","score":"0.6125887","text":"def get_metadata():\n try:\n metadata = cache.getDataset(os.environ.get('GCS_BUCKET'),\n os.environ.get('METADATA_FILENAME'))\n except Exception as err:\n logging.error(err)\n return 'Internal server error: {}'.format(err), 500\n\n def generate_response(data: bytes):\n next_row = b'['\n for row in data.splitlines():\n yield next_row\n next_row = row + b','\n yield next_row.rstrip(b',') + b']'\n headers = Headers()\n headers.add('Content-Disposition', 'attachment',\n filename=os.environ.get('METADATA_FILENAME'))\n headers.add('Vary', 'Accept-Encoding')\n return Response(generate_response(metadata), mimetype='application/json',\n headers=headers)","title":""},{"docid":"6d19e00e8e2c4b71131d2c0a0c3d7852","score":"0.61157686","text":"def __md(self):\n p_l = self.__basepl('metadata')\n p_l['content'] = 'metadata'\n return RCRequest(self.url, p_l, 'metadata').execute()[0]","title":""},{"docid":"6677f88ea288b5f1cd71ffa7b6c7309d","score":"0.61138695","text":"def metadata(self):\n self._parse_metadata_dates()\n if self._single_result:\n return self._metadata[0]\n else:\n return self._metadata","title":""},{"docid":"f7f1b36e9ad5f893583292d517e8d016","score":"0.6051656","text":"def copyright(self, plexus, **kwds):\n # show the copyright note\n plexus.info.log(hello.meta.copyright)\n # all done\n return","title":""},{"docid":"d73e78de7590fd2b5f905a784502060e","score":"0.60199094","text":"def metadata(self):\n return self._metadata","title":""},{"docid":"fe079f4baf7615ee412f97917cef5c70","score":"0.6006893","text":"def get_published_metadata(self):\n pass","title":""},{"docid":"a0102f653530de87a0e764b126b64a1b","score":"0.6002597","text":"def get_metadata(self):\n return self.metadata","title":""},{"docid":"538f7cb4a8353bd46997b84091fa8913","score":"0.6001879","text":"def _get_metadata(cfg, relpath, doc_type):\n return {\n 'document_id': _get_document_id(cfg, relpath, doc_type),\n 'compilation_date': cfg['timestamp']['date'],\n 'timebox_id': cfg['timestamp']['timebox_id'],\n 'configuration_id': cfg['defined_baseline']['short_hexsha']}","title":""},{"docid":"9c321faf1c3d2465ee5a469c968dda67","score":"0.598438","text":"def get_metadata(self, params=None):\n metadata_url = \"{CDN_URL}/{handle}/metadata\".format(\n CDN_URL=CDN_URL, handle=self.handle\n )\n response = utils.make_call(metadata_url, 'get',\n params=params,\n security=self.security)\n return response.json()","title":""},{"docid":"2c4f2a4feca8651f337f99957dac17b2","score":"0.59841037","text":"def get_metadata(self, region, locale):\n resource = \"/hearthstone/metadata\"\n query_params = {\"locale\": locale}\n return super().get_resource(resource, region, query_params)","title":""},{"docid":"db16e2f10afadc62d177dad977a75d96","score":"0.5975165","text":"def get_title_metadata(self):\n pass","title":""},{"docid":"6a5061a1edd34767f69b736b9d627df0","score":"0.59744596","text":"def metadata(self) -> str:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"6a5061a1edd34767f69b736b9d627df0","score":"0.59744596","text":"def metadata(self) -> str:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"6a5061a1edd34767f69b736b9d627df0","score":"0.59744596","text":"def metadata(self) -> str:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"c04a536b7e84c43786e374e02689c96b","score":"0.59680593","text":"def metadata(self):\n return metadata(self.data)","title":""},{"docid":"bd6cfe5148d4e25ae2c6fd4ea9638c39","score":"0.59559137","text":"def metadata(self) -> 'outputs.MetadataResponse':\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"bd6cfe5148d4e25ae2c6fd4ea9638c39","score":"0.59559137","text":"def metadata(self) -> 'outputs.MetadataResponse':\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"8cbc65d3d9618dbbba2ce5834dbce34a","score":"0.59523225","text":"def get_assets_metadata(self):\n return # osid.Metadata","title":""},{"docid":"2489839cae2ffcb513a9cb6d44d5a3ca","score":"0.59516597","text":"def get_metadata(self, key, version=None):\n rsp = urllib2.urlopen(self.commit_uri(key, version) + \"/meta\")\n return json.loads(rsp.read())","title":""},{"docid":"be9138810d5a6abda32fd82152f8f6bd","score":"0.59148645","text":"def get_metadata_fields(corpus_name):\n if corpus_name == 'sample_novels':\n return ['author', 'date', 'title', 'country_publication', 'author_gender', 'filename', 'notes']\n else:\n return common.METADATA_LIST","title":""},{"docid":"59e0d35b76afcb6e4168e5e731d9a749","score":"0.591214","text":"def metadata(self):\n return self.file_source.metadata()","title":""},{"docid":"d938306b829603f77ccce0d944cb1c1c","score":"0.59070027","text":"def get_metadata(self):\n return self._metadata","title":""},{"docid":"d938306b829603f77ccce0d944cb1c1c","score":"0.59070027","text":"def get_metadata(self):\n return self._metadata","title":""},{"docid":"05a395d61ee79ebb591a6d627cd4eb5d","score":"0.590246","text":"def metadata(self):\r\n return self._md","title":""},{"docid":"33e6f7385b46d30f36840fa3e286b657","score":"0.5901217","text":"def metadata(self):\n\n if self._metadata is None:\n # metadata_url = \"https://raw.githubusercontent.com/{owner}/{repo_name}/master/metadata.yaml\".format(owner=self.repo_owner,\n # repo_name=self.repo_name)\n\n # self._metadata = metadata.pandata.Pandata(metadata_url)\n\n self._metadata = metadata.pandata.Pandata(datafile=None)\n md_hash = self.gh_repo.contents(\"metadata.yaml\")\n # if the file exists, load the content\n if md_hash is not None:\n self._metadata.metadata = yaml.safe_load(md_hash.decoded)\n return self._metadata\n else:\n return self._metadata","title":""},{"docid":"f7a66a1b2c1db8d2055399b9710bf827","score":"0.5895827","text":"def get_metadata(self):\n metadata_path = \"{}/{}/{}\".format(self.url,\n self.metadata_dir,\n self.metadata_file)\n metadata_sig_path = \"{}/{}/{}.sig\".format(self.url.rstrip('/'),\n self.metadata_dir,\n self.metadata_file)\n # load metadata\n req = requests.get(metadata_path)\n if req.status_code is 200:\n raw_metadata = req.content\n else:\n raise RepositoryError(metadata_path, (\"status code not 200: \"\n \"{}\".format(req.status_code)))\n\n if self.gpg_verify:\n self.verify_data_signature(metadata_sig_path, metadata_path,\n raw_metadata)\n\n return self.parse_metadata(raw_metadata)","title":""},{"docid":"a13eec55499ba96229f465afe282ce4d","score":"0.5892936","text":"def process_license(license):\n\n ind = license.find('Attribution')\n ind = license.find('creativecommons.org/licenses/')\n if ind == -1:\n zlicense = { \"description\": {\"en\": license},\n \"title\": {\"en\": \"Custom license\"} }\n else:\n zlicense = {\"id\": \"-\".join([\"cc\", license[ind:].split(\"/\")[2],\"4.0\"])}\n return zlicense","title":""},{"docid":"93943df688f99276f31135ff8816b065","score":"0.58883893","text":"def get_share_metadata(self, share, microversion=None):\n metadata_raw = self.manila(\n 'metadata-show %s' % share, microversion=microversion)\n metadata = output_parser.details(metadata_raw)\n return metadata","title":""},{"docid":"d10ca39cdcff87db3ff6582c6a897a61","score":"0.5878597","text":"def license():\n # print it\n return print(meta.license)","title":""},{"docid":"471733d816771050fa7bfb8e10b643fc","score":"0.58772045","text":"def get_socrata_metadata(credentials, nadac_parameters):\n client = setup_socrata_client(credentials, nadac_parameters)\n metadata = client.get_metadata(nadac_parameters['DATA_LOCATION'],\n content_type='json')\n\n check_build_filepath('raw_data')\n save_to_disk('raw_data/price_metadata.json', metadata)\n client.close()","title":""},{"docid":"db72656e8108d61bfdd93f1a3161f692","score":"0.58748287","text":"def get_infos(self):\n infos = super().get_infos()\n infos[\"licenses\"].append(chime5_license)\n return infos","title":""},{"docid":"c7f3c442696494bee65b316832b76d3d","score":"0.58630574","text":"def get_metadata(self,mp3):\n metadata = None\n if self.mp3items.has_key(mp3):\n metadata = self.mp3items[mp3]['metadata']\n \n return metadata","title":""},{"docid":"e1ae2264504cb454f98acedbc9a9e486","score":"0.5856848","text":"def _extractMetadata(self):\n\n try:\n dcMetadata = dicom.read_file(self.path, stop_before_pixels=True)\n if dcMetadata:\n self.metadata = dict()\n for item in dcMetadata:\n # pass forces rawDataElement to DataElement conversion\n pass\n if not item.tag.is_private:\n name = str(item.name).replace('.', '')\n value = str(item.value)\n self.metadata[name] = value\n self.metadata['Info Extracted'] = True\n else:\n self.metadata = None\n\n except dicom.filereader.InvalidDicomError:\n self.metadata = None","title":""},{"docid":"36e33dd772743148f1dae807f728d1ef","score":"0.5852637","text":"def extract_metadata(self):\n self.contacts_list = self.metadata['chanlabels']\n try:\n # comment out\n self.modality = self.metadata['modality']\n except Exception as e:\n self.metadata['modality'] = 'ieeg'\n self.modality = self.metadata['modality']\n print(\"Loading result object. Error in extracting metadata: \", e)\n\n # convert channel labels into a Contacts data struct\n if self.reference == 'bipolar' or self.modality == 'scalp':\n self.contacts = Contacts(self.contacts_list, require_matching=False)\n else:\n self.contacts = Contacts(self.contacts_list)","title":""},{"docid":"72a2c0e421539a33a7afbeec62a64b64","score":"0.5843592","text":"def set_img_copyright(self, uri): \n \n tags = [\"MWG:Copyright\"]\n key = \"copyright\"\n self.set_meta_prop(tags, uri, key)","title":""},{"docid":"7dc2755324d92788991852a56f33beb0","score":"0.58300406","text":"def get_metadata():\n res = requests.get(URL).json()\n return res[\"response\"][\"docs\"]","title":""},{"docid":"87ba37b3dbaa8401e45f44686382e2fd","score":"0.5820466","text":"def get_metadata(publisher, package):\n try:\n metadata = MetaDataDB.query.filter_by(name=package, publisher=publisher).first().descriptor\n return jsonify({\"data\": metadata, \"status\": \"OK\"}), 200\n except Exception as e:\n return jsonify({'status': 'KO', 'message': e.message}), 500","title":""},{"docid":"7fb812cf1b4c681f3e913eb98d7cfed2","score":"0.58103585","text":"def metadata(self):\n if self.__last_get_response is None:\n if self.__initial_metadata is not None:\n return self.__initial_metadata\n self.get()\n return self.__last_get_response.content","title":""},{"docid":"3abfbd4d70ab9fab15ccb068927f8b4c","score":"0.579478","text":"def getMetadata(self, private_key, client_id):\n return self.proxy.samp.hub.getMetadata(private_key, client_id)","title":""},{"docid":"b39a28ca457275565c55db1f7331e0be","score":"0.57906055","text":"def get_courses_metadata(self):\n return # osid.Metadata","title":""},{"docid":"e1cd31ac6dcb6d8e9adf6c1218e1daf7","score":"0.578709","text":"def create_metadata(title, comment='', author=None):\n author = 'https://github.com/narimiran' if author is None else author\n return dict(artist=author, title=title, comment=comment)","title":""},{"docid":"80f03cdcdbcf6f0f4de1c9d9204f4851","score":"0.5785481","text":"def metadata(self):\n metadata_service = self.find_service_by_type(ServiceTypes.METADATA)\n return metadata_service.values['metadata']","title":""},{"docid":"ae30f97bc9dda8b92cc34dc15d1adf6c","score":"0.57851726","text":"def get_license_data(self):\n return licenses.get_license_by_url(self.license or \"\")","title":""},{"docid":"afeddd9616b57cd176036fd0071ee294","score":"0.5769014","text":"def metadata(forge):\n forge.metadata()","title":""},{"docid":"9a751d8085f977491b2730caf4593e12","score":"0.5764288","text":"def get_source_metadata(self):\n pass","title":""},{"docid":"e936e6fd083d940e003160caa3de0391","score":"0.576013","text":"def files_license_info(self) -> Sequence[str]:\n return pulumi.get(self, \"files_license_info\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"1dc3a89bbab7c806e9d09f629e01a7ad","score":"0.5758056","text":"def metadata(self) -> Mapping[str, str]:\n return pulumi.get(self, \"metadata\")","title":""},{"docid":"4ac9d5037085069356d9b5b257996854","score":"0.5740702","text":"def getMetadata(self):\n return self._metadata","title":""},{"docid":"b06810d8c09ac0aa1fc53ae4bea9e99f","score":"0.57405835","text":"def metadata(self) -> Optional[Any]:\n return pulumi.get(self, \"metadata\")","title":""}],"string":"[\n {\n \"docid\": \"24f84680cf596560ecdac39cce9e64bc\",\n \"score\": \"0.8069426\",\n \"text\": \"def get_copyright_registration_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b439f8061f824cb732d55672fc3eaff\",\n \"score\": \"0.7513539\",\n \"text\": \"def copyright():\\n # pull and print the meta-data\\n return print(meta.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b439f8061f824cb732d55672fc3eaff\",\n \"score\": \"0.7513539\",\n \"text\": \"def copyright():\\n # pull and print the meta-data\\n return print(meta.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce9dfaa695a5f317033c21935fab989\",\n \"score\": \"0.72402924\",\n \"text\": \"def copyright(self):\\n return self._copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c61a3fb10cf9c01b25ed094a4ef720\",\n \"score\": \"0.7208793\",\n \"text\": \"def copyright(self) -> str:\\n return pulumi.get(self, \\\"copyright\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c61a3fb10cf9c01b25ed094a4ef720\",\n \"score\": \"0.7208793\",\n \"text\": \"def copyright(self) -> str:\\n return pulumi.get(self, \\\"copyright\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69acb209ae4f2b9afe7528869611d6af\",\n \"score\": \"0.702879\",\n \"text\": \"def get_copyright(self):\\n return self._payload.get_copyright()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f60a01cb4b03c7e41fdce6f74baa2074\",\n \"score\": \"0.7024526\",\n \"text\": \"def copyright():\\n return print(meta.header)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc89c32813693811b9f0d3f971f18ea0\",\n \"score\": \"0.698063\",\n \"text\": \"def _copyright(self):\\n return self.copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2160cef48267207c30a1b6b4184ac02f\",\n \"score\": \"0.69552654\",\n \"text\": \"def getCopyright(self):\\n return self.copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8b1679f609c6d0124c424dcbf59974\",\n \"score\": \"0.69459635\",\n \"text\": \"def get_metadata(self, citation):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad2ebb93b92f073ae4061987b3d7f07\",\n \"score\": \"0.6943443\",\n \"text\": \"def GetCopyright(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec88284d34c75931c56892c679c7a4bb\",\n \"score\": \"0.6834667\",\n \"text\": \"def feed_copyright(self):\\n return _siteinfo().copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671c3475d5dbaafeb2cd46f5c7684610\",\n \"score\": \"0.6762198\",\n \"text\": \"def meta_copyright(cls, text, time=0.0):\\n return cls._textmeta(mm.kCopyRight, text, time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97a01f19b6c844cfe4b5f92954152630\",\n \"score\": \"0.6670829\",\n \"text\": \"def item_copyright(self):\\n return _siteinfo().copyright\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c6a1486503376ff18f1a8044bc275f8\",\n \"score\": \"0.66507226\",\n \"text\": \"def get_metadata(self, soup):\\n metadata = {}\\n meta = soup.find_all('meta')\\n for m in meta:\\n if m.has_attr('name') and m.get('name').startswith('dcterms'):\\n metadata[m.get('name')] = m.get('content')\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83dbd6adadea9e223f97c4bc546b48d0\",\n \"score\": \"0.6647738\",\n \"text\": \"def copyright():\\n # generate the message\\n return print(meta.copyright)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59e15d78633fd7875488fb65753797d\",\n \"score\": \"0.6624673\",\n \"text\": \"def metadata(self):\\n # identifiers\\n dump = self.download()\\n if dump['totalItems'] == 0:\\n return 0\\n\\n google_books_data = dump['items'][0]['volumeInfo']\\n authors = \\\"\\\"\\n for author in google_books_data['authors']:\\n authors = author + \\\",\\\" + authors\\n authors = authors[0:-1]\\n # i=0\\n # for val in meta_data.keys():\\n # t = google_books_data[kickstr[i]]\\n # if val == 'cover':\\n # t = google_books_data['imageLinks']['thumbnail']\\n # elif val == 'published_date':\\n # t=\\n # meta_data[val] = t\\n # i+=1\\n meta_data = {\\n 'google_url' : \\\"\\\",\\n 'description' : \\\"\\\",\\n 'tags' : \\\"\\\",\\n 'cover' : \\\"\\\",\\n 'published_date': \\\"\\\",\\n 'publisher' : \\\"\\\",\\n 'authors' : \\\"\\\",\\n 'page_count' : \\\"\\\",\\n 'title' : \\\"\\\"\\n }\\n try:\\n meta_data['google_url'] = google_books_data['canonicalVolumeLink']\\n except:\\n meta_data['google_url'] = None\\n try:\\n meta_data['description'] = google_books_data['description']\\n except:\\n meta_data['description'] = None\\n try:\\n meta_data['tags'] = google_books_data['categories']\\n except:\\n meta_data['tags'] = None\\n try:\\n meta_data['cover'] = google_books_data['imageLinks']['thumbnail']\\n except:\\n meta_data['cover'] = None\\n try:\\n meta_data['published_date'] = parser.parse(google_books_data['publishedDate'])\\n except:\\n meta_data['published_date'] = None\\n try:\\n meta_data['publisher'] = google_books_data['publisher']\\n except:\\n meta_data['publisher'] = None\\n try:\\n meta_data['authors'] = authors\\n except:\\n meta_data['authors'] = None\\n try:\\n meta_data['page_count'] = google_books_data['pageCount']\\n except:\\n meta_data['page_count'] = None\\n try:\\n meta_data['title'] = google_books_data['title']\\n except:\\n meta_data['title'] = \\\"Title\\\"\\n print(meta_data)\\n print(google_books_data)\\n return meta_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f7eb2f8c6a2c093a771176339fc58d\",\n \"score\": \"0.6615866\",\n \"text\": \"def get_metadata(self):\\n data = {\\n \\\"license_code\\\": self.license_code,\\n \\\"version\\\": self.version,\\n \\\"title_english\\\": self.title_english,\\n }\\n if self.jurisdiction_code:\\n data[\\\"jurisdiction\\\"] = self.jurisdiction_code\\n\\n data[\\\"permits_derivative_works\\\"] = self.permits_derivative_works\\n data[\\\"permits_reproduction\\\"] = self.permits_reproduction\\n data[\\\"permits_distribution\\\"] = self.permits_distribution\\n data[\\\"permits_sharing\\\"] = self.permits_sharing\\n data[\\\"requires_share_alike\\\"] = self.requires_share_alike\\n data[\\\"requires_notice\\\"] = self.requires_notice\\n data[\\\"requires_attribution\\\"] = self.requires_attribution\\n data[\\\"requires_source_code\\\"] = self.requires_source_code\\n data[\\\"prohibits_commercial_use\\\"] = self.prohibits_commercial_use\\n data[\\n \\\"prohibits_high_income_nation_use\\\"\\n ] = self.prohibits_high_income_nation_use\\n\\n data[\\\"translations\\\"] = {}\\n for lc in self.legal_codes.order_by(\\\"language_code\\\"):\\n language_code = lc.language_code\\n with active_translation(lc.get_translation_object()):\\n data[\\\"translations\\\"][language_code] = {\\n \\\"license\\\": lc.license_url,\\n \\\"deed\\\": lc.deed_url,\\n \\\"title\\\": gettext(self.title_english),\\n }\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930ea34840c9e82253cc2b16322d01cf\",\n \"score\": \"0.65950376\",\n \"text\": \"def copyright(self):\\n\\n return self.tagsToFind[self.COPYRIGHT_TAG].strip('\\\\x20\\\\x00')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be0226f9ced814809bed0f04b879888\",\n \"score\": \"0.6556654\",\n \"text\": \"def copyright(self, format=\\\"json\\\"):\\n\\n endpoint = \\\"copyright\\\"\\n\\n return self._make_request(endpoint, {\\\"format\\\": format})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d0fe9427617aca6967e17a2b63681dc\",\n \"score\": \"0.6530905\",\n \"text\": \"def copyright_str(self) -> str:\\n with self.path.open() as f:\\n match = re.search(self.copyright_re, f.read(1000))\\n return match.group() if match is not None else ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f11dac91b5ae360193af2698e24cbfa6\",\n \"score\": \"0.64764947\",\n \"text\": \"def get_metadata(self):\\n return self.content[\\\"metadata\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a02524fe6ba0d458c650a2c566f23b7\",\n \"score\": \"0.63898975\",\n \"text\": \"def getMetadata():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf409025c90dbd123a7ac7132e8c2ce\",\n \"score\": \"0.633487\",\n \"text\": \"def get_copyright_registration(self):\\n return self._payload.get_copyright_registration()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc7229c6196b75042c80dd2fb36e5ab\",\n \"score\": \"0.6308308\",\n \"text\": \"def copyrights(self) -> List[CopyrightObject]:\\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc7229c6196b75042c80dd2fb36e5ab\",\n \"score\": \"0.6308308\",\n \"text\": \"def copyrights(self) -> List[CopyrightObject]:\\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc7229c6196b75042c80dd2fb36e5ab\",\n \"score\": \"0.6308308\",\n \"text\": \"def copyrights(self) -> List[CopyrightObject]:\\n return [CopyrightObject(item) for item in self._json_dict['copyrights']]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb0d25b59c21e2cbc2ce757c420006cc\",\n \"score\": \"0.6302299\",\n \"text\": \"def get_metadata():\\n here = os.path.abspath(os.path.dirname(__file__))\\n init_path = os.path.join(here, 'src', 'serde_ext', '__init__.py')\\n readme_path = os.path.join(here, 'README.rst')\\n\\n with io.open(init_path, encoding='utf-8') as f:\\n about_text = f.read()\\n\\n metadata = {\\n key: re.search(r'__' + key + r'__ = [\\\"\\\\'](.*?)[\\\"\\\\']', about_text).group(1)\\n for key in ('title', 'version', 'url', 'author', 'author_email', 'license', 'description')\\n }\\n metadata['name'] = metadata.pop('title')\\n\\n with io.open(readme_path, encoding='utf-8') as f:\\n metadata['long_description'] = f.read()\\n\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d4d24772433b15772260ee08b53245\",\n \"score\": \"0.62911433\",\n \"text\": \"def get_metadata(html, text, filename):\\n blog, post = filename.split(\\\"_\\\")\\n url = \\\"http://\\\" + blog + \\\".hypotheses.org/\\\" + str(post)\\n title = get_title(html, filename)\\n author = get_author(html)\\n date = get_date(html)\\n licence = get_licence(html)\\n language = check_language(text)\\n category = get_category(text, title)\\n numwords = get_numwords(text)\\n metadata = [filename, language, author, numwords, category, date, licence, blog, post, url, title]\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d5607b649ee929aa19bf87e13a4a9d\",\n \"score\": \"0.6261161\",\n \"text\": \"def get_metadata(self, **kwargs):\\n return self.get_entry(\\n METADATA_URI, desired_class=gdata.docs.data.Metadata, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92c69e3e660e418673713ba774eaf56\",\n \"score\": \"0.62316877\",\n \"text\": \"def get_license():\\n data_license = {}\\n data_license[\\\"name\\\"] = \\\"cc-by-4.0\\\"\\n data_license[\\\"url\\\"] = \\\"https://creativecommons.org/licenses/by/4.0/\\\"\\n data_license[\\n \\\"description\\\"] = \\\"The content can be shared and adapted but you must\\\\\\n give appropriate credit and cannot restrict access to others.\\\"\\n return data_license\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db49b6089ece2e54a555d92c706d8067\",\n \"score\": \"0.6197543\",\n \"text\": \"def get_metadata(self):\\n api_call = self._get_url(\\\"catalog\\\")\\n\\n response = get_response(api_call)\\n return json.load(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6916c01dffbfac6104f7b478a6ac5b10\",\n \"score\": \"0.619042\",\n \"text\": \"def get_metadata(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"661b5544077ce492c145dac1c9cebc98\",\n \"score\": \"0.6173343\",\n \"text\": \"def getMetadata(content):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4dea74a7d52e3b49307d8736c3d2022\",\n \"score\": \"0.616945\",\n \"text\": \"def meta(data):\\n return data[1]['metadata']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a09a6ed670672426b8d110a4b474f1b\",\n \"score\": \"0.6166778\",\n \"text\": \"def _GetMetadata(self, version):\\n raw_json = None\\n version_base = os.path.join(self.gs_base, version)\\n with self.misc_cache.Lookup(\\n (self.board, version, constants.METADATA_JSON)) as ref:\\n if ref.Exists(lock=True):\\n raw_json = osutils.ReadFile(ref.path)\\n else:\\n raw_json = self.gs_ctx.Cat(\\n os.path.join(version_base, constants.METADATA_JSON)).output\\n ref.AssignText(raw_json)\\n\\n return json.loads(raw_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3aab84491c21d1127d87bfaa6edee3d\",\n \"score\": \"0.6166168\",\n \"text\": \"def metadata(self):\\r\\n url = \\\"{url}/info/metadata\\\".format(url=self._url)\\r\\n params = {\\\"f\\\": \\\"json\\\"}\\r\\n return self._con.get(url, params, token=self._token)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b4e254ee8240f4ac8da787b98023aa\",\n \"score\": \"0.61653906\",\n \"text\": \"def get_metadata(self, identifier):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd4d1e1ec210a4543fda79dfb14e941\",\n \"score\": \"0.61573976\",\n \"text\": \"def get_dataObject_modac_meta(data_object_path):\\n #data_object_path = encode_path(data_object_path)\\n auth = authenticate_modac()\\n\\n get_response = requests.get(data_object_path, auth = auth)\\n if get_response.status_code != 200:\\n print(\\\"Error downloading from modac.cancer.gov\\\", data_object_path)\\n raise Exception(\\\"Response code: {0}, Response message: {1}\\\".format(get_response.status_code, get_response.text))\\n\\n metadata_dic = json.loads(get_response.text)\\n self_metadata = metadata_dic['metadataEntries']['selfMetadataEntries']['systemMetadataEntries']\\n self_dic = {}\\n for pair in self_metadata:\\n self_dic[pair['attribute']] = pair['value'] \\n\\n return self_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5dd041ea3c0bdcc8389558c52fb93f\",\n \"score\": \"0.6153275\",\n \"text\": \"def get_metadata(self):\\n\\t\\treturn self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5dd041ea3c0bdcc8389558c52fb93f\",\n \"score\": \"0.6153275\",\n \"text\": \"def get_metadata(self):\\n\\t\\treturn self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5dd041ea3c0bdcc8389558c52fb93f\",\n \"score\": \"0.6153275\",\n \"text\": \"def get_metadata(self):\\n\\t\\treturn self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f5dd041ea3c0bdcc8389558c52fb93f\",\n \"score\": \"0.6153275\",\n \"text\": \"def get_metadata(self):\\n\\t\\treturn self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1a5d4d20aca43089c913959ba5b359\",\n \"score\": \"0.6152495\",\n \"text\": \"def get_metadata(self) -> Mapping[int, Any]:\\n return self._call(CredentialManagement.CMD.GET_CREDS_METADATA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2791defc294d703d6056962a5e568286\",\n \"score\": \"0.61457705\",\n \"text\": \"def extract_metadata(self) -> DocumentMetadata:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6717d0b3afb11db4705a85098f55754\",\n \"score\": \"0.6125887\",\n \"text\": \"def get_metadata():\\n try:\\n metadata = cache.getDataset(os.environ.get('GCS_BUCKET'),\\n os.environ.get('METADATA_FILENAME'))\\n except Exception as err:\\n logging.error(err)\\n return 'Internal server error: {}'.format(err), 500\\n\\n def generate_response(data: bytes):\\n next_row = b'['\\n for row in data.splitlines():\\n yield next_row\\n next_row = row + b','\\n yield next_row.rstrip(b',') + b']'\\n headers = Headers()\\n headers.add('Content-Disposition', 'attachment',\\n filename=os.environ.get('METADATA_FILENAME'))\\n headers.add('Vary', 'Accept-Encoding')\\n return Response(generate_response(metadata), mimetype='application/json',\\n headers=headers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d19e00e8e2c4b71131d2c0a0c3d7852\",\n \"score\": \"0.61157686\",\n \"text\": \"def __md(self):\\n p_l = self.__basepl('metadata')\\n p_l['content'] = 'metadata'\\n return RCRequest(self.url, p_l, 'metadata').execute()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6677f88ea288b5f1cd71ffa7b6c7309d\",\n \"score\": \"0.61138695\",\n \"text\": \"def metadata(self):\\n self._parse_metadata_dates()\\n if self._single_result:\\n return self._metadata[0]\\n else:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7f1b36e9ad5f893583292d517e8d016\",\n \"score\": \"0.6051656\",\n \"text\": \"def copyright(self, plexus, **kwds):\\n # show the copyright note\\n plexus.info.log(hello.meta.copyright)\\n # all done\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73e78de7590fd2b5f905a784502060e\",\n \"score\": \"0.60199094\",\n \"text\": \"def metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe079f4baf7615ee412f97917cef5c70\",\n \"score\": \"0.6006893\",\n \"text\": \"def get_published_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0102f653530de87a0e764b126b64a1b\",\n \"score\": \"0.6002597\",\n \"text\": \"def get_metadata(self):\\n return self.metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"538f7cb4a8353bd46997b84091fa8913\",\n \"score\": \"0.6001879\",\n \"text\": \"def _get_metadata(cfg, relpath, doc_type):\\n return {\\n 'document_id': _get_document_id(cfg, relpath, doc_type),\\n 'compilation_date': cfg['timestamp']['date'],\\n 'timebox_id': cfg['timestamp']['timebox_id'],\\n 'configuration_id': cfg['defined_baseline']['short_hexsha']}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c321faf1c3d2465ee5a469c968dda67\",\n \"score\": \"0.598438\",\n \"text\": \"def get_metadata(self, params=None):\\n metadata_url = \\\"{CDN_URL}/{handle}/metadata\\\".format(\\n CDN_URL=CDN_URL, handle=self.handle\\n )\\n response = utils.make_call(metadata_url, 'get',\\n params=params,\\n security=self.security)\\n return response.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c4f2a4feca8651f337f99957dac17b2\",\n \"score\": \"0.59841037\",\n \"text\": \"def get_metadata(self, region, locale):\\n resource = \\\"/hearthstone/metadata\\\"\\n query_params = {\\\"locale\\\": locale}\\n return super().get_resource(resource, region, query_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db16e2f10afadc62d177dad977a75d96\",\n \"score\": \"0.5975165\",\n \"text\": \"def get_title_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5061a1edd34767f69b736b9d627df0\",\n \"score\": \"0.59744596\",\n \"text\": \"def metadata(self) -> str:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5061a1edd34767f69b736b9d627df0\",\n \"score\": \"0.59744596\",\n \"text\": \"def metadata(self) -> str:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a5061a1edd34767f69b736b9d627df0\",\n \"score\": \"0.59744596\",\n \"text\": \"def metadata(self) -> str:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c04a536b7e84c43786e374e02689c96b\",\n \"score\": \"0.59680593\",\n \"text\": \"def metadata(self):\\n return metadata(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6cfe5148d4e25ae2c6fd4ea9638c39\",\n \"score\": \"0.59559137\",\n \"text\": \"def metadata(self) -> 'outputs.MetadataResponse':\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6cfe5148d4e25ae2c6fd4ea9638c39\",\n \"score\": \"0.59559137\",\n \"text\": \"def metadata(self) -> 'outputs.MetadataResponse':\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cbc65d3d9618dbbba2ce5834dbce34a\",\n \"score\": \"0.59523225\",\n \"text\": \"def get_assets_metadata(self):\\n return # osid.Metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2489839cae2ffcb513a9cb6d44d5a3ca\",\n \"score\": \"0.59516597\",\n \"text\": \"def get_metadata(self, key, version=None):\\n rsp = urllib2.urlopen(self.commit_uri(key, version) + \\\"/meta\\\")\\n return json.loads(rsp.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9138810d5a6abda32fd82152f8f6bd\",\n \"score\": \"0.59148645\",\n \"text\": \"def get_metadata_fields(corpus_name):\\n if corpus_name == 'sample_novels':\\n return ['author', 'date', 'title', 'country_publication', 'author_gender', 'filename', 'notes']\\n else:\\n return common.METADATA_LIST\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e0d35b76afcb6e4168e5e731d9a749\",\n \"score\": \"0.591214\",\n \"text\": \"def metadata(self):\\n return self.file_source.metadata()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d938306b829603f77ccce0d944cb1c1c\",\n \"score\": \"0.59070027\",\n \"text\": \"def get_metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d938306b829603f77ccce0d944cb1c1c\",\n \"score\": \"0.59070027\",\n \"text\": \"def get_metadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05a395d61ee79ebb591a6d627cd4eb5d\",\n \"score\": \"0.590246\",\n \"text\": \"def metadata(self):\\r\\n return self._md\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e6f7385b46d30f36840fa3e286b657\",\n \"score\": \"0.5901217\",\n \"text\": \"def metadata(self):\\n\\n if self._metadata is None:\\n # metadata_url = \\\"https://raw.githubusercontent.com/{owner}/{repo_name}/master/metadata.yaml\\\".format(owner=self.repo_owner,\\n # repo_name=self.repo_name)\\n\\n # self._metadata = metadata.pandata.Pandata(metadata_url)\\n\\n self._metadata = metadata.pandata.Pandata(datafile=None)\\n md_hash = self.gh_repo.contents(\\\"metadata.yaml\\\")\\n # if the file exists, load the content\\n if md_hash is not None:\\n self._metadata.metadata = yaml.safe_load(md_hash.decoded)\\n return self._metadata\\n else:\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a66a1b2c1db8d2055399b9710bf827\",\n \"score\": \"0.5895827\",\n \"text\": \"def get_metadata(self):\\n metadata_path = \\\"{}/{}/{}\\\".format(self.url,\\n self.metadata_dir,\\n self.metadata_file)\\n metadata_sig_path = \\\"{}/{}/{}.sig\\\".format(self.url.rstrip('/'),\\n self.metadata_dir,\\n self.metadata_file)\\n # load metadata\\n req = requests.get(metadata_path)\\n if req.status_code is 200:\\n raw_metadata = req.content\\n else:\\n raise RepositoryError(metadata_path, (\\\"status code not 200: \\\"\\n \\\"{}\\\".format(req.status_code)))\\n\\n if self.gpg_verify:\\n self.verify_data_signature(metadata_sig_path, metadata_path,\\n raw_metadata)\\n\\n return self.parse_metadata(raw_metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a13eec55499ba96229f465afe282ce4d\",\n \"score\": \"0.5892936\",\n \"text\": \"def process_license(license):\\n\\n ind = license.find('Attribution')\\n ind = license.find('creativecommons.org/licenses/')\\n if ind == -1:\\n zlicense = { \\\"description\\\": {\\\"en\\\": license},\\n \\\"title\\\": {\\\"en\\\": \\\"Custom license\\\"} }\\n else:\\n zlicense = {\\\"id\\\": \\\"-\\\".join([\\\"cc\\\", license[ind:].split(\\\"/\\\")[2],\\\"4.0\\\"])}\\n return zlicense\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93943df688f99276f31135ff8816b065\",\n \"score\": \"0.58883893\",\n \"text\": \"def get_share_metadata(self, share, microversion=None):\\n metadata_raw = self.manila(\\n 'metadata-show %s' % share, microversion=microversion)\\n metadata = output_parser.details(metadata_raw)\\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10ca39cdcff87db3ff6582c6a897a61\",\n \"score\": \"0.5878597\",\n \"text\": \"def license():\\n # print it\\n return print(meta.license)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"471733d816771050fa7bfb8e10b643fc\",\n \"score\": \"0.58772045\",\n \"text\": \"def get_socrata_metadata(credentials, nadac_parameters):\\n client = setup_socrata_client(credentials, nadac_parameters)\\n metadata = client.get_metadata(nadac_parameters['DATA_LOCATION'],\\n content_type='json')\\n\\n check_build_filepath('raw_data')\\n save_to_disk('raw_data/price_metadata.json', metadata)\\n client.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db72656e8108d61bfdd93f1a3161f692\",\n \"score\": \"0.58748287\",\n \"text\": \"def get_infos(self):\\n infos = super().get_infos()\\n infos[\\\"licenses\\\"].append(chime5_license)\\n return infos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7f3c442696494bee65b316832b76d3d\",\n \"score\": \"0.58630574\",\n \"text\": \"def get_metadata(self,mp3):\\n metadata = None\\n if self.mp3items.has_key(mp3):\\n metadata = self.mp3items[mp3]['metadata']\\n \\n return metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ae2264504cb454f98acedbc9a9e486\",\n \"score\": \"0.5856848\",\n \"text\": \"def _extractMetadata(self):\\n\\n try:\\n dcMetadata = dicom.read_file(self.path, stop_before_pixels=True)\\n if dcMetadata:\\n self.metadata = dict()\\n for item in dcMetadata:\\n # pass forces rawDataElement to DataElement conversion\\n pass\\n if not item.tag.is_private:\\n name = str(item.name).replace('.', '')\\n value = str(item.value)\\n self.metadata[name] = value\\n self.metadata['Info Extracted'] = True\\n else:\\n self.metadata = None\\n\\n except dicom.filereader.InvalidDicomError:\\n self.metadata = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e33dd772743148f1dae807f728d1ef\",\n \"score\": \"0.5852637\",\n \"text\": \"def extract_metadata(self):\\n self.contacts_list = self.metadata['chanlabels']\\n try:\\n # comment out\\n self.modality = self.metadata['modality']\\n except Exception as e:\\n self.metadata['modality'] = 'ieeg'\\n self.modality = self.metadata['modality']\\n print(\\\"Loading result object. Error in extracting metadata: \\\", e)\\n\\n # convert channel labels into a Contacts data struct\\n if self.reference == 'bipolar' or self.modality == 'scalp':\\n self.contacts = Contacts(self.contacts_list, require_matching=False)\\n else:\\n self.contacts = Contacts(self.contacts_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a2c0e421539a33a7afbeec62a64b64\",\n \"score\": \"0.5843592\",\n \"text\": \"def set_img_copyright(self, uri): \\n \\n tags = [\\\"MWG:Copyright\\\"]\\n key = \\\"copyright\\\"\\n self.set_meta_prop(tags, uri, key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dc2755324d92788991852a56f33beb0\",\n \"score\": \"0.58300406\",\n \"text\": \"def get_metadata():\\n res = requests.get(URL).json()\\n return res[\\\"response\\\"][\\\"docs\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87ba37b3dbaa8401e45f44686382e2fd\",\n \"score\": \"0.5820466\",\n \"text\": \"def get_metadata(publisher, package):\\n try:\\n metadata = MetaDataDB.query.filter_by(name=package, publisher=publisher).first().descriptor\\n return jsonify({\\\"data\\\": metadata, \\\"status\\\": \\\"OK\\\"}), 200\\n except Exception as e:\\n return jsonify({'status': 'KO', 'message': e.message}), 500\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb812cf1b4c681f3e913eb98d7cfed2\",\n \"score\": \"0.58103585\",\n \"text\": \"def metadata(self):\\n if self.__last_get_response is None:\\n if self.__initial_metadata is not None:\\n return self.__initial_metadata\\n self.get()\\n return self.__last_get_response.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3abfbd4d70ab9fab15ccb068927f8b4c\",\n \"score\": \"0.579478\",\n \"text\": \"def getMetadata(self, private_key, client_id):\\n return self.proxy.samp.hub.getMetadata(private_key, client_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b39a28ca457275565c55db1f7331e0be\",\n \"score\": \"0.57906055\",\n \"text\": \"def get_courses_metadata(self):\\n return # osid.Metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1cd31ac6dcb6d8e9adf6c1218e1daf7\",\n \"score\": \"0.578709\",\n \"text\": \"def create_metadata(title, comment='', author=None):\\n author = 'https://github.com/narimiran' if author is None else author\\n return dict(artist=author, title=title, comment=comment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80f03cdcdbcf6f0f4de1c9d9204f4851\",\n \"score\": \"0.5785481\",\n \"text\": \"def metadata(self):\\n metadata_service = self.find_service_by_type(ServiceTypes.METADATA)\\n return metadata_service.values['metadata']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae30f97bc9dda8b92cc34dc15d1adf6c\",\n \"score\": \"0.57851726\",\n \"text\": \"def get_license_data(self):\\n return licenses.get_license_by_url(self.license or \\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afeddd9616b57cd176036fd0071ee294\",\n \"score\": \"0.5769014\",\n \"text\": \"def metadata(forge):\\n forge.metadata()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a751d8085f977491b2730caf4593e12\",\n \"score\": \"0.5764288\",\n \"text\": \"def get_source_metadata(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e936e6fd083d940e003160caa3de0391\",\n \"score\": \"0.576013\",\n \"text\": \"def files_license_info(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"files_license_info\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc3a89bbab7c806e9d09f629e01a7ad\",\n \"score\": \"0.5758056\",\n \"text\": \"def metadata(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ac9d5037085069356d9b5b257996854\",\n \"score\": \"0.5740702\",\n \"text\": \"def getMetadata(self):\\n return self._metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06810d8c09ac0aa1fc53ae4bea9e99f\",\n \"score\": \"0.57405835\",\n \"text\": \"def metadata(self) -> Optional[Any]:\\n return pulumi.get(self, \\\"metadata\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":197,"cells":{"query_id":{"kind":"string","value":"f5f36a0534f4ea94adb8aad46271138a"},"query":{"kind":"string","value":"Can this RecurringCost be enacted"},"positive_passages":{"kind":"list like","value":[{"docid":"757ffa3fd3a6bd9841075b459360f26d","score":"0.49190748","text":"def is_enactable(self, as_of):\n return \\\n not self.disabled and \\\n not self.archived and \\\n not self._is_finished(as_of) and \\\n self._is_ready(as_of) and \\\n not self._is_billing_complete()","title":""}],"string":"[\n {\n \"docid\": \"757ffa3fd3a6bd9841075b459360f26d\",\n \"score\": \"0.49190748\",\n \"text\": \"def is_enactable(self, as_of):\\n return \\\\\\n not self.disabled and \\\\\\n not self.archived and \\\\\\n not self._is_finished(as_of) and \\\\\\n self._is_ready(as_of) and \\\\\\n not self._is_billing_complete()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4c04ec54a498200fddebe61bfad145c7","score":"0.6974328","text":"def enact(self, billing_cycle, disable_if_done=True):\n as_of = billing_cycle.date_range.lower\n if not self.is_enactable(as_of):\n raise CannotEnactUnenactableRecurringCostError(\n \"RecurringCost {} is unenactable.\".format(self.uuid)\n )\n\n if self.has_enacted(billing_cycle):\n raise RecurringCostAlreadyEnactedForBillingCycle(\n 'RecurringCost cost {} already enacted for {}'.format(self, billing_cycle)\n )\n\n with db_transaction.atomic():\n recurred_cost = RecurredCost(\n recurring_cost=self,\n billing_cycle=billing_cycle,\n )\n recurred_cost.make_transaction()\n recurred_cost.save()\n\n if disable_if_done:\n self.disable_if_done(billing_cycle)","title":""},{"docid":"b7445a2491181d4ec5c6d4f004d143bf","score":"0.6717582","text":"def has_enacted(self, billing_cycle):\n return RecurredCost.objects.filter(\n recurring_cost=self,\n billing_cycle=billing_cycle,\n ).exists()","title":""},{"docid":"a365ef9fd5b0a002ccf6bc7bdc0e6573","score":"0.62935936","text":"def make_eligible(self):\n pass","title":""},{"docid":"d8dff91131c125b350fd2012d9790602","score":"0.5993728","text":"def charge_recurring(self, grace_period=None):\n pass","title":""},{"docid":"9039e5f6aa07f9bcc62aac117fff3c64","score":"0.5735424","text":"def confirmed(self, cr, uid, ids, context=None):\n\tallow_archive_line_obj = self.pool.get('services.contracts.allowances.lines')\n for record in self.browse(cr, uid, ids, context=context):\n\t\tif not record.allowances_lines_before :\n \traise osv.except_osv(_('Partner Lines !'), _('Sorry no partner Lines!'))\n\n\t \tlines_ids = [line.id for line in record.allowances_lines_after]\n \tallow_archive_line_obj.unlink(cr,uid,lines_ids,context=context)\n\n\t\tfor lines in record.allowances_lines_before:\n\t\t\tif lines.percentage_rating < 0 or lines.percentage_rating > 100 :\n \t\traise osv.except_osv(_('Rate Error !'), _('Sorry you insert wrong rate ... rate is between (0,100)!'))\n \t\tamount_after_rate_id = allow_archive_line_obj.create(cr, uid, {\n \t\t\t\t'cost_of_rent':lines.cost_of_rent,\n \t\t\t\t'amount_untaxed':round (lines.amount_untaxed*lines.percentage_rating/100,2),\n \t\t\t\t'amount_tax':round(lines.amount_tax*lines.percentage_rating/100,2),\n \t\t\t\t'amount_total':round(lines.amount_total*lines.percentage_rating/100,2),\n \t\t\t\t'deduct_days':lines.deduct_days,\n \t\t\t\t'deduct_amount':lines.deduct_amount,\n \t\t\t\t'contract_id':lines.contract_id.id,\n\t\t\t\t\t'env_allow_id_after_rate':record.id,\n\t\t\t\t\t'type': 'after',\n 'category_id':lines.category_id.id,\n\t\t\t\t\t'percentage_rating':lines.percentage_rating,\n\n })\n\t\t\n \n self.write(cr, uid, ids, {'state':'confirmed'})\n return True","title":""},{"docid":"e06878f1f73ebe95cde824d74b51178a","score":"0.57182837","text":"def credit_deliverer():\n return True","title":""},{"docid":"072e3adb9a2b8b733955c2d0470f5cfd","score":"0.57024777","text":"def disable_costs():\n RecurringCost.objects.all().disable_if_done()","title":""},{"docid":"729f7ea408343845dae0e20a444735a6","score":"0.5669726","text":"def awaiting_payment(self):","title":""},{"docid":"46b89439cb1fb4ae0622c0559693f5b3","score":"0.56223905","text":"def test_add_recurring_schedule(self):\n pass","title":""},{"docid":"43c2e807bcff1aea5874de05585dc1b9","score":"0.55890375","text":"def test_renew_user_pending_cancel(self):\n self.braintree_customer.active = True\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.subscription_id = \"ValidSubscriptionID\"\n\n result = SubscriptionManager.renew(self.braintree_customer)\n self.assertEqual(\"ValidSubscriptionID\",result)\n self.assertFalse(self.braintree_customer.pending_cancel)","title":""},{"docid":"c4473da0917973c1c85d7b653ba0f05d","score":"0.5505976","text":"def _check_cost(self, cr, uid, ids, context=None):\n for enrich in self.browse(cr, uid, ids, context=context):\n if enrich.amount <= 0:\n raise osv.except_osv(_('ValidateError'), _('The Cost Must Be Greater Than Zero!'))\n return True","title":""},{"docid":"2e2212377ef5be3f290b7c7bf4d129b1","score":"0.5493613","text":"def test_subscribe_already_subscribed(self):\n self.braintree_customer.active = True\n self.braintree_customer.save()\n with self.assertRaises(BraintreeError):\n SubscriptionManager.subscribe(self.braintree_customer)\n\n # Check state not altered\n self.assertTrue(self.braintree_customer.active)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertIsNone(self.braintree_customer.expiry_date)","title":""},{"docid":"0eff043c23370b9890345712648bb3b8","score":"0.54642403","text":"def test_renew_attempt_on_active_subscription(self):\n self.braintree_customer.pending_cancel = False\n self.braintree_customer.active = True\n\n with self.assertRaises(BraintreeError):\n SubscriptionManager.renew(self.braintree_customer)","title":""},{"docid":"2878f031423ce10692b6087cd1179530","score":"0.5447805","text":"def make_transaction(self):\n if self.pk:\n raise CannotRecreateTransactionOnRecurredCost(\n 'The transaction for this recurred cost has already been created. You cannot create it again.'\n )\n\n amount = self.recurring_cost.get_amount(self.billing_cycle)\n\n # It is quite possible that there will be nothing to bill, in which\n # case we cannot create a transaction with no legs, nor can we create\n # legs with zero values. Therefore we don't create any transaction.\n if not amount:\n return None\n\n self.transaction = Transaction.objects.create(\n description='Created by recurring cost',\n date=self.billing_cycle.date_range.lower\n )\n\n # Use the SplitManager's custom queryset's split() method to get the\n # amount to be billed for each split\n splits = self.recurring_cost.splits.all().split(amount)\n\n # Create the transaction leg for the outbound funds\n # (normally to an expense account)\n self.transaction.legs.add(Leg.objects.create(\n transaction=self.transaction,\n amount=Money(amount, self.recurring_cost.currency),\n account=self.recurring_cost.to_account,\n ))\n\n for split, split_amount in splits:\n # Create the transaction legs for the inbound funds\n # (from housemate accounts)\n if split_amount:\n self.transaction.legs.add(Leg.objects.create(\n transaction=self.transaction,\n amount=Money(split_amount * -1, self.recurring_cost.currency),\n account=split.from_account,\n ))\n\n return self.transaction","title":""},{"docid":"ba47d041e96bc2b902f7e951534eb141","score":"0.5434562","text":"def _cost(self, action):\n raise NotImplementedError","title":""},{"docid":"0e3085b8493d861cc1b7d398c776e29a","score":"0.54062164","text":"def is_recurring(self):\n return self.__is_recurring","title":""},{"docid":"c624f7dcb4ff52e7b73c58f67c9e6318","score":"0.5402047","text":"def test_request_cancel_already_pending_cancel(self):\n self.braintree_customer.subscription_id = \"1234\"\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.save()\n\n self.assertFalse(SubscriptionManager.request_cancel(self.braintree_customer))\n self.assertTrue(self.braintree_customer.pending_cancel)","title":""},{"docid":"33fbd06b632f15c7255f0e18c5897445","score":"0.53812355","text":"def is_invited_pending_activation(self):\n if self.registration_method == self.INVITED \\\n and self.is_pending_activation():\n return True\n else:\n return False","title":""},{"docid":"ceb71fff2f7605e3d2c71500830dcbfa","score":"0.53572863","text":"def give_raise(self):\r\n self.hourly_pay = 12.00","title":""},{"docid":"a1633c21b982abbaba97996b7c87877d","score":"0.5347127","text":"def charge(self, other):\n if self.flag:\n self.credit += other\n return \"{} Tomans has been added to your card credit and now the credit of your card is {} Tomans\".format(\n other, self.credit)\n else:\n return \"Sorry, your card has expired.\"","title":""},{"docid":"8a0bca7e754388a34640f20c91afa68b","score":"0.534086","text":"def payment_approval(self, house_cost: (int, float)):\n if self.money_available >= house_cost: # Person has enough available money to make a deal with Realtor\n self.money_available -= house_cost\n print(f'Payment from {self.name} was approved')\n return True\n print(f'{self.name} doesn\\'t have enough money to buy this house')\n return False","title":""},{"docid":"a34fc2ccd08cf094015a4a7a71287468","score":"0.53131855","text":"def test_request_cancel_active_subscription(self):\n self.braintree_customer.subscription_id = \"1234\"\n self.braintree_customer.pending_cancel = False\n self.braintree_customer.save()\n self.assertTrue(SubscriptionManager.request_cancel(self.braintree_customer))\n self.assertTrue(self.braintree_customer.pending_cancel)","title":""},{"docid":"3e8c9b966e3614e15ccd1ab4a2563ef9","score":"0.5306632","text":"def test_overlap(self):\r\n t = Expense(name = \"fake lunch\",\r\n amount = 1.,\r\n on = (WeeklyRecurring(FR,\r\n fromdt = self.fromdt,\r\n todt = self.todt),\r\n DailyRecurring(fromdt = self.fromdt, \r\n todt = self.todt)))\r\n\r\n self.m.addTransaction(t)\r\n self.assertEqual(self.m.totalSaved(self.fromdt, self.todt), -365.)","title":""},{"docid":"fa3965aee40434d2d49b4ec667e75657","score":"0.5294787","text":"def reports_editable(self):\n end_plus_time = self.datetime_end + datetime.timedelta(days=CCR_DELTA)\n return timezone.now() < end_plus_time","title":""},{"docid":"bc118ce9f8f76044d0d8341c976cc671","score":"0.5265665","text":"def make_payment(self, cost):\n self.process_coins()\n if self.money_received >= cost:\n change = round(self.money_received - cost, 2)\n print(f\"Here is {self.CURRENCY}{change} in change.\")\n self.profit += cost\n self.money_received = 0\n return True\n else:\n print(\"Sorry that's not enough money. Money refunded.\")\n self.money_received = 0\n return False","title":""},{"docid":"58a11d04b563ecf30411d522d7a9987d","score":"0.5261785","text":"def test_create_sugar_cpn_exp_task(self):\n offset_days = 2\n coupon = Coupon.objects.get(id=416)\n coupon.expiration_date = (datetime.date.today() + \n datetime.timedelta(days=offset_days))\n coupon.save()\n self.assertEqual(create_sugar_coupon_expire_task(MockSugar(), \n offset_days=offset_days), None)","title":""},{"docid":"27b0305fb2f568ecd0568b90978b95e3","score":"0.52602494","text":"async def resign(self, ctx):\n currency = await bank.get_currency_name(ctx.guild)\n await self.config.user(ctx.author).gameRole.set(\"User\")\n await ctx.send(\n f\"{ctx.author} has spent 10,000 {currency}- to resign from their current job.\"\n )","title":""},{"docid":"8491f0b61b51e158a8d7c69100f4d319","score":"0.5213568","text":"def test_subscription_transaction_declined_suspend(self):\n\n # We'll create a plan that starts here\n start_date = dt.date(2019, 1, 1)\n\n # And the trial date ends here too\n trial_end_date = dt.date(2019, 1, 1)\n\n # The customer will use some metered features here\n metered_usage_on = dt.date(2019, 1, 10)\n\n # Docs will be generated to bill here.\n prev_billing_date = dt.date(2019, 1, 3)\n\n # So, the customer grace period ends here.\n # First billing interval: dt.date(2019, 2, 1)\n # \n billing_grace_exp = dt.date(2019, 2, 3)\n\n # The next billing check should discover that the subscription\n # is unpaid.\n # Billing due date is: dt.date(2019, 2, 6)\n # With the grace period: dt.date(2019, 2, 9)\n # \n billing_post_grace_check = dt.date(2019, 2, 10)\n\n # Create a customer\n #\n customer = CustomerFactory.create(sales_tax_percent=Decimal('0.00'),\n payment_due_days=3)\n PaymentMethodFactory.create(\n payment_processor=triggered_processor, customer=customer,\n canceled=False,\n verified=True,\n )\n\n # Create a metered feature\n #\n mf_price = Decimal('2.5')\n metered_feature = MeteredFeatureFactory(\n included_units_during_trial=Decimal('0.00'),\n price_per_unit=mf_price)\n currency = 'USD'\n\n # Crate a plan with metered features. Generate the invoice after\n # the 5 day trial period, the plan will be billed every 30 days.\n # \n generate_after = 5\n plan = PlanFactory.create(interval=Plan.INTERVALS.DAY,\n interval_count=30,\n generate_after=generate_after,\n enabled=True,\n amount=Decimal('20.00'),\n trial_period_days=1,\n metered_features=[metered_feature],\n currency=currency)\n\n # Subscribe the customer\n #\n subscription = SubscriptionFactory.create(\n plan=plan, start_date=start_date, customer=customer)\n subscription.activate()\n subscription.save()\n\n # Log some metered usage\n consumed_1 = Decimal('5.00')\n consumed_2 = Decimal('5.00')\n mf_log = MeteredFeatureUnitsLogFactory.create(\n subscription=subscription,\n metered_feature=metered_feature,\n start_date=metered_usage_on,\n end_date=subscription.trial_end,\n consumed_units=consumed_1)\n\n # Generate the docs\n call_command('generate_docs',\n billing_date=prev_billing_date,\n stdout=self.output)\n\n proforma = Proforma.objects.first()\n\n assert proforma.proforma_entries.count() != 0\n assert Subscription.objects.all().count() == 1\n assert Invoice.objects.all().count() == 0\n assert Proforma.objects.all()[0].total > Decimal('0.00')\n\n # Consume more units\n mf_log.consumed_units += consumed_2\n mf_log.save()\n\n call_command('generate_docs',\n billing_date=billing_grace_exp,\n stdout=self.output)\n\n assert Proforma.objects.all().count() != 0\n assert Invoice.objects.all().count() == 0\n\n for pf in Proforma.objects.all():\n # # Issue the proforma to generate transactions\n # proforma = Proforma.objects.all()[1]\n pf.issue()\n pf.save()\n\n self.assertEqual(pf.state, Proforma.STATES.ISSUED)\n # Fail the transaction\n for tx in pf.transactions:\n # tx = proforma.transactions[0]\n tx.fail()\n tx.save()\n self.assertEqual(tx.state, Transaction.States.Failed)\n\n assert Transaction.objects.all().count() != 0\n\n call_command('check_subscriptions',\n billing_date=billing_post_grace_check,\n stdout=self.output)\n\n subscr = Subscription.objects.first()\n\n # Scan for subscriptions with unpaid documents\n logging.debug(\"subscr %s\" % subscr)\n self.assertEqual(subscr.state, Subscription.STATES.CANCELED)","title":""},{"docid":"5417e376b07167245209d7488eceae5c","score":"0.5208941","text":"def test_get_active_coupon(self):\n coupon = COUPON_FACTORY.create_coupon()\n slot = SLOT_FACTORY.create_slot(coupon=coupon)\n future_date = datetime.date.today() + datetime.timedelta(weeks=1)\n coupon.expiration_date = future_date\n coupon.save()\n self.assertEqual(slot.get_active_coupon(), coupon)","title":""},{"docid":"257ba26e9929795ad5f5632d4cddacf1","score":"0.52071846","text":"def test_create_sugar_cc_exp_task(self):\n current_date = datetime.datetime.now()\n order = Order.objects.create(billing_record_id=114)\n credit_card = CreditCard.objects.get(id=500)\n credit_card.exp_month = int(current_date.strftime(\"%m\"))\n credit_card.exp_year = int(current_date.strftime(\"%y\"))\n credit_card.encrypt_cc('4111111111111111')\n credit_card.clean()\n credit_card.save()\n Payment.objects.create(order=order, credit_card=credit_card,\n amount='50.00', method='C', status='p')\n self.assertEqual(create_sugar_cc_expire_task(MockSugar(), \n test_mode=True), None)","title":""},{"docid":"eb78960a18fbea5f420521a73cc9d91e","score":"0.5201274","text":"def save(self, *args, **kwargs):\n if not self.pk:\n self.start_time_rent = datetime.date.today()\n self.end_time_rent = self.start_time_rent + datetime.timedelta(days=7)\n self.reservation.isrented = True\n self.reservation.save()\n return super(Rental, self).save(*args, **kwargs)","title":""},{"docid":"f499e3c7140c536233b59aa5018b00e0","score":"0.5198995","text":"def life_insurance_to_recive_total(self):\n pass","title":""},{"docid":"93e3ba91aae2b3005b29c825e9df6617","score":"0.51950544","text":"def pay_costs(self):\r\n cost = self.cost\r\n if cost:\r\n self.owner.player.char_ob.pay_money(cost)\r\n self.owner.player.msg(\"You pay %s coins for the event.\" % cost)","title":""},{"docid":"4a554eae4772018c64970255353249ec","score":"0.5190392","text":"def accepting(self):\n quote = self.get_quotes('labor')[0]\n quantity = max(0, quote.price - 14)\n self.create('labor', quantity)\n self.accept_quote_partial(quote, quantity)","title":""},{"docid":"da7b38cc190f388ab6d0549317e33158","score":"0.51902777","text":"def recap(self, request, pk=None):\n retreat = self.get_object()\n # This is a hard-coded limitation to allow anonymous users to call\n # the function.\n time_limit = retreat.end_time - timedelta(days=1)\n if timezone.now() < time_limit:\n response_data = {\n 'detail': \"Retreat ends in more than 1 day.\"\n }\n return Response(response_data, status=status.HTTP_200_OK)\n\n # Notify a user for every reserved seat\n emails = []\n for reservation in retreat.reservations.filter(\n is_active=True,\n post_event_send=False):\n send_post_retreat_email(reservation.user, retreat)\n reservation.post_event_send = True\n reservation.save()\n emails.append(reservation.user.email)\n\n response_data = {\n 'stop': True,\n 'emails': emails\n }\n return Response(response_data, status=status.HTTP_200_OK)","title":""},{"docid":"40e1634797064f5c392aeeff54879a26","score":"0.5186644","text":"def check_costs(self):\r\n if self.cost > self.owner.player.char_ob.currency:\r\n self.add_error(\r\n \"celebration_tier\",\r\n \"You cannot afford to pay the cost of %s.\" % self.cost,\r\n )","title":""},{"docid":"d10a00c4c4117a81dd34a039b5016bec","score":"0.51843166","text":"def test_no_credit_change_for_same_subscription(self):\n credits = 20\n\n current_plan = Subscription.get_plan_by_id('pro')\n new_plan = Subscription.get_plan_by_id('pro')\n\n may_29_2015 = datetime.datetime(2015, 5, 29, 0, 0, 0)\n may_29_2015 = pytz.utc.localize(may_29_2015)\n\n credits = add_subscription_credits(credits, current_plan, new_plan,\n may_29_2015)\n\n assert credits == 20","title":""},{"docid":"f6cfe667dbc270d1f0777d11ac37720c","score":"0.51762897","text":"def test_remove_recurring_schedule(self):\n pass","title":""},{"docid":"ffd0405d5d1dcce4d6e9af01483ebde3","score":"0.5167032","text":"def confirmed(self):","title":""},{"docid":"eb1bb3838c74ae2e2fc33261a8bcad5d","score":"0.51638985","text":"def test_retire_rate_plan(self):\n pass","title":""},{"docid":"acd5fc643f60a16857bf0dbbe97c12f5","score":"0.5128787","text":"def add_to_excess(self) -> None:\n if self.msg.value <= 0:\n revert(\"No amount added to excess\")\n self._treasury_balance.set(self.icx.get_balance(self.address))\n self.FundReceived(self.msg.sender, self.msg.value, f\"{self.msg.value} added to excess\")","title":""},{"docid":"e170852adf1c091d578eada0184631b9","score":"0.51178837","text":"async def admin_credit(self, ctx, target: discord.Member, sum: int = 100):\n if is_registered(target.id):\n \n inventories = get_file(\"inventories\")\n inventories[str(target.id)][\"balance\"] += sum\n update_file(\"inventories\", inventories)\n\n embed = discord.Embed(color=admin_color)\n embed.set_author(name=\"🛠️ Admin\")\n embed.add_field(name=\"💰 Credit\",\n value=f\"{ctx.author.mention}, {target.mention} a été crédité de `{sum}` PO (pièces d'or)\")\n embed = set_footer(embed, ctx)\n await ctx.send(embed=embed)","title":""},{"docid":"41e253144f1d44f70f72725a3cdfd381","score":"0.5104723","text":"def test_plan_autorenew_at_expire(self):\n up = baker.make(\"UserPlan\", expire=date(2020, 1, 1))\n self.assertEqual(up.plan_autorenew_at(), date(2020, 1, 1))","title":""},{"docid":"6ba629d8534d92877f8ce279d15aafa1","score":"0.5101618","text":"def trigger_expiration_notices(at_time=None, nb_days=15, dry_run=False):\n\n def _handle_organization_notices(organization):\n if organization.processor_card_key:\n card = organization.retrieve_card()\n try:\n exp_month, exp_year = card['exp_date'].split('/')\n exp_date = datetime(year=int(exp_year),\n month=int(exp_month), day=1, tzinfo=at_time.tzinfo)\n if lower >= exp_date:\n LOGGER.info(\"payment method expires soon for %s\",\n organization)\n if not dry_run:\n signals.card_expires_soon.send(\n sender=__name__, organization=organization,\n nb_days=nb_days)\n except (KeyError, ValueError):\n # exp info is missing or the format is incorrect\n pass\n else:\n LOGGER.info(\"%s doesn't have a payment method attached\",\n organization)\n if not dry_run:\n signals.payment_method_absent.send(sender=__name__,\n organization=organization)\n\n at_time = datetime_or_now(at_time)\n lower = at_time + relativedelta(days=nb_days)\n upper = at_time + relativedelta(days=nb_days + 1)\n LOGGER.info(\n \"trigger notifications for subscription expiring within [%s,%s[ ...\",\n lower, upper)\n prev_organization = None\n subscription = None\n for subscription in Subscription.objects.valid_for(ends_at__gte=lower,\n ends_at__lt=upper).order_by('organization'):\n org = subscription.organization\n plan = subscription.plan\n\n try:\n if subscription.auto_renew:\n if plan.renewal_type == plan.AUTO_RENEW:\n if org.id != prev_organization:\n _handle_organization_notices(org)\n\n prev_organization = org.id\n else:\n if plan.renewal_type == plan.ONE_TIME:\n LOGGER.info(\"trigger upgrade soon for %s\", subscription)\n if not dry_run:\n signals.subscription_upgrade.send(sender=__name__,\n subscription=subscription, nb_days=nb_days)\n\n elif plan.renewal_type == plan.REPEAT:\n LOGGER.info(\"trigger expires soon for %s\", subscription)\n if not dry_run:\n signals.expires_soon.send(sender=__name__,\n subscription=subscription, nb_days=nb_days)\n\n except Exception as err: #pylint:disable=broad-except\n # We use `Exception` because the email server might be\n # unavailable but ConnectionRefusedError is not a subclass\n # of RuntimeError.\n LOGGER.exception(\"error: %s\", err)\n\n # flushing the last organization\n if subscription and subscription.organization.id != prev_organization:\n if subscription.auto_renew:\n if subscription.plan.renewal_type == subscription.plan.AUTO_RENEW:\n _handle_organization_notices(subscription.organization)","title":""},{"docid":"0c66ea0657cc0bf9e28af5aaeb6647c4","score":"0.5100977","text":"def redeem(self, instance, customer, save=True):\n start = timezone.now().date()\n end = start + relativedelta(months=self.duration)\n discount = Discount(instance=instance,\n coupon=self,\n start=start,\n end=end,\n customer=customer)\n discount.full_clean()\n if save:\n discount.save()\n return discount","title":""},{"docid":"be729754fa4102aa1c8b61477d0cdb82","score":"0.50970876","text":"def customer_wants_condiments(self):\n return True","title":""},{"docid":"58052ca7885a95388e8a943ad79fc9ee","score":"0.50891936","text":"def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 11, 10, tzinfo=dt_timezone.utc\n )\n self.ticketed_event.ticket_cost = 0\n self.ticketed_event.save()\n self.assertFalse(self.unpaid.cancelled)\n self.assertFalse(self.paid.cancelled)\n\n management.call_command('cancel_unpaid_ticket_bookings')\n # emails are sent to user per cancelled booking and studio once for all\n # cancelled bookings\n self.unpaid.refresh_from_db()\n self.paid.refresh_from_db()\n self.assertEqual(len(mail.outbox), 0)\n self.assertFalse(self.unpaid.cancelled)\n self.assertFalse(self.paid.cancelled)","title":""},{"docid":"c02baa7b4f7d3c8e78826cc17010a89a","score":"0.5078882","text":"def register_withdraw(self, withdraw_intent): \n if withdraw_intent > 0:\n self.teo.register_withdraw(self, withdraw_intent)","title":""},{"docid":"87a75db34bf124af171ddcacdf0705d9","score":"0.5062819","text":"def reckon(self):","title":""},{"docid":"3ef1115a68c9a4958c538af0517b7e7f","score":"0.5059867","text":"def reimport_eligible(self) -> bool:\n return pulumi.get(self, \"reimport_eligible\")","title":""},{"docid":"cabd4c0d5435e6616abe559534230b62","score":"0.5054001","text":"def test_add_taxation_strategy_to_rate_plan(self):\n pass","title":""},{"docid":"e3d7067817879f0fa2eb4466e3f35086","score":"0.50444573","text":"def can_act(self) -> bool:\n return self.cooldown < 1","title":""},{"docid":"e3d7067817879f0fa2eb4466e3f35086","score":"0.50444573","text":"def can_act(self) -> bool:\n return self.cooldown < 1","title":""},{"docid":"57a314d8c47829fe7aea4b854dab54cb","score":"0.5037591","text":"def restock(self):\n self.money = 9999","title":""},{"docid":"5b99cdf09d4409ff6cacc373e96ffd86","score":"0.5028068","text":"def may_send_escalation(self, source_type: str, escalation_reminder_cadence: timedelta) -> bool:\n with self.session.begin() as session:\n last_escalated = (\n session.query(EventRecord.escalated_at)\n .filter(EventRecord.source_type == source_type)\n .order_by(EventRecord.escalated_at.desc())\n .limit(1)\n .one_or_none()\n )\n\n if not last_escalated[0]:\n return True\n\n return last_escalated[0] <= datetime.utcnow() - escalation_reminder_cadence","title":""},{"docid":"846e2e0abaea6b50e2948f1d6060a39c","score":"0.50160515","text":"def filter(self, event, *args, **kwargs):\n return event[\"expiration_datetime\"] <= datetime.now()","title":""},{"docid":"201aa010ced8cf71f548ecf6d393575b","score":"0.5011571","text":"def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 10, 10, tzinfo=dt_timezone.utc\n )\n self.event.cost = 0\n self.event.save()\n self.assertEqual(\n self.unpaid.status, 'OPEN', self.unpaid.status\n )\n self.assertEqual(\n self.paid.status, 'OPEN', self.paid.status\n )\n management.call_command('cancel_unpaid_bookings')\n # emails are sent to user per cancelled booking and studio once for all\n # cancelled bookings\n unpaid_booking = Booking.objects.get(id=self.unpaid.id)\n paid_booking = Booking.objects.get(id=self.paid.id)\n self.assertEqual(len(mail.outbox), 0)\n self.assertEqual(\n unpaid_booking.status, 'OPEN', unpaid_booking.status\n )\n self.assertEqual(\n paid_booking.status, 'OPEN', paid_booking.status\n )\n\n # auto_cancelled set to True only on cancelled bookings\n self.assertFalse(unpaid_booking.auto_cancelled)\n self.assertFalse(paid_booking.auto_cancelled)","title":""},{"docid":"820d1f3307e066ab24821548eb04bf4c","score":"0.50115657","text":"def isOnSaleSoon(ticket):\n return ticket.start_time > timezone.now()","title":""},{"docid":"99f1843e58d485cff565bd73b3ba806c","score":"0.500794","text":"def _claim_iscore(self) -> None:\n iscore_details_dict = self._system.queryIScore(self.address)\n if iscore_details_dict['estimatedICX'] != 0:\n amount = iscore_details_dict[\"estimatedICX\"]\n self._system.claimIScore()\n self._daily_reward.set(amount)\n self._rate.set(self.getRate())\n self._total_lifetime_reward.set(self.getLifetimeReward() + amount)\n self._distributing.set(True)","title":""},{"docid":"800b7a21c89f88bfe6327f7bb4361f22","score":"0.5006686","text":"def should_pay_attention(self):\n return random.randint(1,100) > self.wander","title":""},{"docid":"d3c1c2ca303c52e34683b990d4edc7da","score":"0.50048196","text":"def test_authorize_pending_payment(self):\n pass","title":""},{"docid":"e9eeb30737a0056e7591f58ee9369f45","score":"0.5003403","text":"def acknowledge_visitor(visitor):\n visitor.acknowledged = True\n visitor.acknowledged_time = datetime.now()\n visitor.save()","title":""},{"docid":"a4fcf332d5161239a6433b289ae158ed","score":"0.50032485","text":"def resent(self):\n return 'Resent-Date' in self","title":""},{"docid":"42ef7fe9adf9b416d5c880d173273e84","score":"0.49881572","text":"def pre_approve(self, cr, uid, ids, context={}):\n \tfor voucher in self.browse(cr, uid, ids, context=context):\n \t if not voucher.department_id.analytic_account_id:\n \t raise osv.except_osv(_('Configration Check!'), _(\"Please add cost center for your department!\"))\n \t periods = self.pool.get('account.period').search(cr, uid, [('date_start','<=',voucher.date),('date_stop','>=',voucher.date),('company_id','=',voucher.company_id.id)], context=context)\n\n\n res=0.0\n if voucher.purpose:\n if not voucher.purpose.account_id: raise osv.except_osv(_('Warning!'), _('Please configure account for this purpose!')) \n voucher_line = {\n \t\t'voucher_id': voucher.id,\n \t\t'partner_id': voucher.partner_id.id,\n \t\t'untax_amount': voucher.amount,\n \t\t'amount': voucher.amount,\n 'name': voucher.narration,\n \t\t'type': 'dr',\n \t\t'account_analytic_id': voucher.department_id.analytic_account_id and voucher.department_id.analytic_account_id.id,\n 'account_id': voucher.purpose.account_id.id,\n \t }\n new_amount = res and res or voucher.amount \n voucher_line.update({'amount':new_amount,'untax_amount':new_amount})\n \t if voucher.line_ids :\n for line in voucher.line_ids:\n \t\t self.pool.get('account.voucher.line').write(cr, uid, line.id, {\n \t\t'voucher_id': voucher.id,\n \t\t'partner_id': voucher.partner_id.id,\n \t\t'untax_amount': res or line.amount,\n \t\t'amount': line.amount,\n 'name': voucher.narration,\n \t\t'type': 'dr',\n \t\t'account_analytic_id': line.account_analytic_id and line.account_analytic_id.id or voucher.department_id.analytic_account_id.id,\n 'account_id': voucher.purpose.account_id.id or line.account_id.id,\n \t }, context=context)\n \t else:\n\n \t\t new_voucher_line = self.pool.get('account.voucher.line').create(cr, uid, voucher_line, context=context)\n context.update({'purchase':True})\n self.create_budget_confirmation(cr, uid, [voucher.id], context)\n \tself.write(cr, uid, ids,{'state': 'preapprove','type':'purchase','ratification':True}, context=context)\n #cxt = context.copy()\n #cxt.update({'type':'ratification'})\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\n self.write(cr, uid, ids, {'state': 'approved'}, context=context)\n\n \t'''self.write(cr, uid, ids, {'state': 'preapprove'})\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\n self.write(cr, uid, ids, {'state': 'approve','type':'purchase','ratification':True}, context=context)'''\n return True","title":""},{"docid":"9bcdcd6a69d116b1f4b370a2e026f1a4","score":"0.49817756","text":"def test_update_rate_plan(self):\n pass","title":""},{"docid":"4acf948659222da3835bbf26fc3a9017","score":"0.49740785","text":"def test_carryover(self):\n carryover = Money(100)\n available = {Decimal(0.5): carryover}\n self.forecast(available)\n self.assertEqual(\n self.forecast.carryover,\n carryover)","title":""},{"docid":"3c2504305e30cfc1efa4189640cffd33","score":"0.4970885","text":"def recurring(self):\n return self.filter(total_billing_cycles__isnull=True)","title":""},{"docid":"29d0e95adc90beb142a910e5cbf8e54c","score":"0.4959831","text":"def activate_card(**kwargs):\n for card in ClientClubCard.objects.filter(\n date_begin__isnull=True, is_paid_activate=False):\n # period for activate\n days = card.club_card.period_activation\n if card.date_start + timedelta(days - 1) < date.today():\n card.activate()","title":""},{"docid":"12d51add250d35f7917a5213649c725c","score":"0.49592566","text":"def aspiration_criteria_satisfied(self,index):\n\t\tnewEnergy = self.currentEnergy + self._diffEi[index]#self.count_energy(state)\n\t\t# newTax = self._taxes[index]#self.count_tax(state=state)\n\t\ttmpState = copy(self.currentState)\n\t\ttmpState[index] = 1 - tmpState[index]\n\t\tnewTax = self.count_tax(state=tmpState)\n\t\toutput(\"Checking aspiration criteria: oldEnergy={}, current tax = {}; new energy = {}, new tax = {}\"\\\n\t\t\t\t\t \t\t\t\t.format(self.currentEnergy,self.currentTax,newEnergy,newTax),isDebug=True,tabsNum=1)\n\t\tif newEnergy < self.localMinimumEnergy or newTax < self.localMinimumTax:\n\t\t\treturn True\n\t\treturn False","title":""},{"docid":"b5cc8f4771dd58297a6e3b452182daef","score":"0.4951388","text":"def agree_to_tos(self, regr):\n return self.update_registration(\n regr.update(\n body=regr.body.update(\n agreement=regr.terms_of_service)))","title":""},{"docid":"75ab2bff6b751ae042892e7659f71902","score":"0.49507055","text":"def test_deny_pending_payment(self):\n pass","title":""},{"docid":"3edd57c4e43f7b72baf285f6459805fb","score":"0.49433905","text":"def test_4(self):\n c1 = Store.Customer(\"harold\", \"qcf\", False)\n self.assertFalse(c1.is_premium_member(), \"IS premium member\")","title":""},{"docid":"e9b6b7553ae5fcebc14a9b407f801a55","score":"0.4942659","text":"def test_rate_cost_type_valid(self):\n self.ocp_data[\"rates\"][0][\"tiered_rates\"] = [\n {\n \"unit\": \"USD\",\n \"value\": 0.22,\n \"usage\": {\"usage_start\": None, \"usage_end\": None},\n \"cost_type\": \"Infrastructure\",\n }\n ]\n\n with tenant_context(self.tenant):\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\n if serializer.is_valid(raise_exception=True):\n serializer.save()\n\n self.ocp_data[\"rates\"][0][\"tiered_rates\"] = [\n {\n \"unit\": \"USD\",\n \"value\": 0.22,\n \"usage\": {\"usage_start\": None, \"usage_end\": None},\n \"cost_type\": \"Supplementary\",\n }\n ]\n\n with tenant_context(self.tenant):\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\n if serializer.is_valid(raise_exception=True):\n serializer.save()","title":""},{"docid":"89995bce1c766439b816fae14b3a212a","score":"0.49375176","text":"def test_cancel_on_braintree_success(self):\n self.braintree_customer.pending_cancel = True\n self.braintree_customer.active = True\n self.braintree_customer.subscription_id = self.cancellable_id\n\n SubscriptionManager.cancel_on_braintree(self.braintree_customer)\n\n self.assertEqual(\"\",self.braintree_customer.subscription_id)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertFalse(self.braintree_customer.active)","title":""},{"docid":"ceb541e8c03f9188a6ad804fc06af67f","score":"0.49262586","text":"def test_create_new_order_VIES_fault(self):\n rup = baker.make(\n \"RecurringUserPlan\",\n user_plan__user__billinginfo__country=\"CZ\",\n user_plan__user__billinginfo__tax_number=\"CZ0123\",\n amount=10,\n tax=11,\n )\n with no_connection():\n order = rup.create_renew_order()\n self.assertEqual(order.tax, 11)","title":""},{"docid":"b1d65a3322432bdf78bb8926583c4261","score":"0.49252683","text":"def test_charge_correct_for_regular_after_close_1_day(self):\n rental = create_test_rental(\n book=self.book3,\n customer=self.user1,\n date_borrowed=\"2019-05-24 00:00:00.400952+00:00\",\n )\n close_rental_url = reverse(\"close_rental\", kwargs={\"pk\": rental.pk})\n\n data = {\"date_returned\": \"2019-05-25 13:46:57.249145+03:00\"}\n response = self.client.put(close_rental_url, data=data, format=\"json\")\n\n self.assertEqual(response.data[\"amount_charged\"], \"2.00\")\n self.assertEqual(response.data[\"rental_status\"], \"Closed\")\n self.assertEqual(response.data[\"currency\"], CURRENCY)","title":""},{"docid":"01d505b0decf38a647955a28599ddb50","score":"0.4904093","text":"def modify_expiration(self, new_expiration):\n if self.is_cancellable:\n if self.expiration!=new_expiration:\n log.info(\"bo#%s: modify expiration of pending order\" % self.ticket)\n dt = get_datetime()\n new_expiration = dt_td_to_dt(new_expiration, dt)\n not_implemented_error(\"Can't modify modify expiration of pending order\")\n else:\n return","title":""},{"docid":"8dbfc818a7ffbd6d8f09400f973bae44","score":"0.49022606","text":"def contribute(k):\n global amount\n global _alarm\n amount = amount + k\n # remove the alarm\n if amount >= initial_amount * 0.3:\n _alarm = False","title":""},{"docid":"d2a8259def53576441e2c3cbf731cff5","score":"0.48961106","text":"def test_3(self):\n c1 = Store.Customer(\"harold\", \"qcf\", True)\n self.assertTrue(c1.is_premium_member(), \"not premium member\")","title":""},{"docid":"ed609928d752e18a160ececd58722048","score":"0.48943877","text":"def done(self,cr,uid,ids,context=None):\n for record in self.browse(cr, uid, ids, context=context):\n search_result = self.pool.get('payment.enrich').browse(cr, uid,record.enrich_id.id)\n if record.cost < 1 :\n raise osv.except_osv(_('Invalid Action Error'), _('The Entered Cost Is Wrong!'))\n if record.cost > search_result.residual_amount :\n raise osv.except_osv(_('Invalid Action Error'), _('Your Residual Balance Is Less Than Your Cost!'))\n if context:\n if 'owner' in context and 'model_id' in context:\n owner = context['owner']\n owner = int(owner)\n model_id = context['model_id']\n if str(model_id) == 'fleet.vehicle.log.contract':\n model_obj = self.pool.get(model_id)\n model = model_obj.browse(cr, uid, owner, context=context)\n model.write({'state':'open'})\n return self.write(cr, uid, ids, {'state':'done'},context=context)","title":""},{"docid":"69a162a79de7a75785f9e6b2d9f07588","score":"0.48916712","text":"def use(self):\n if self.flag:\n if self.credit < self.price_of_trip:\n return \"Your credit is not enough, please increase your credit\"\n else:\n self.credit -= self.price_of_trip\n return \"Done\"\n else:\n return \"Sorry, your card has expired.\"","title":""},{"docid":"8f42aed0cc21bdb3278ce55b720546c9","score":"0.48887736","text":"def save(self, *args, **kwargs): #pylint: disable=W0221\n created = not self.uuid\n if created:\n existing_balance = self.account.calculated_balance\n else:\n all_other_active_transactions = [x for x in self.account.transactions.all() if x != self and x.active]\n existing_balance = sum(x.amount for x in all_other_active_transactions)\n\n if not self.active:\n pass\n elif (existing_balance + self.amount) < 0:\n raise AccountBalanceError(\n 'Balance of account {} would be brought below 0'.format(self.account)\n )\n\n instance = super().save(*args, **kwargs)\n self.account.update_balance()\n return instance","title":""},{"docid":"b363d7bccb6a832fb10e43ce7a25224e","score":"0.48865578","text":"def test_check_cost():","title":""},{"docid":"5414a8c5839816743bf0cbd8ac9e56b2","score":"0.4879177","text":"def test_subscribe_no_previous_subscription(self):\n self.braintree_customer.payment_method_token = 'valid_payment_token'\n SubscriptionManager.subscribe(self.braintree_customer)\n\n # fetch customer again\n self.braintree_customer = BraintreeUser.objects.get(user=self.user)\n self.assertTrue(self.braintree_customer.active)\n self.assertFalse(self.braintree_customer.pending_cancel)\n self.assertEqual(self.braintree_customer.expiry_date, timezone.make_aware(datetime.datetime(2017,7,25,0,0),pytz.utc))","title":""},{"docid":"8ca9f8a29d701d9d48f8e45102180e20","score":"0.48787516","text":"def use(self):\n if self.credit < self.price_of_trip:\n print(\"Your credit is not enough, please increase your credit\")\n else:\n self.credit -= self.price_of_trip\n print(\"Done\")","title":""},{"docid":"9cd45ad080c1c56c8cbfc9207b5b5102","score":"0.48765975","text":"def user_added_credit(self):\n return (self.user.Credit > 0)","title":""},{"docid":"062c77ab3749342bb1564bcd2adac432","score":"0.48746642","text":"def can_accept_credit(self, value):\n return value >= 0","title":""},{"docid":"8122483a63fd15da82f48566f50c3229","score":"0.48686194","text":"def can_reschedule(self) -> bool:\n return pulumi.get(self, \"can_reschedule\")","title":""},{"docid":"16f6380e73fd49f8b86458e7fc9e0b33","score":"0.48665464","text":"def evaluate_cost(self, msg):\n raise NotImplementedError()","title":""},{"docid":"6f89aace960b6791c1f3e1ff7d144a06","score":"0.4865596","text":"def event_charge(order_type):\r\n if order_type == \"Promo event\":\r\n return 400\r\n\r\n else:\r\n return 0\r\n # promo event adds $400\r","title":""},{"docid":"47d5396dbeb716ea766bf4f1d486f78b","score":"0.48641136","text":"def accept_venue_transferring(venue_transferring_id, notice_id):\n result = False\n try:\n venue_transferring = VenueAccountTransferring.objects.get(pk=venue_transferring_id)\n except VenueAccountTransferring.DoesNotExist:\n venue_transferring = None\n\n if venue_transferring and venue_transferring.target:\n venue_account = venue_transferring.venue_account\n from_user = venue_transferring.venue_account.account.user\n target = venue_transferring.target.user\n\n venue_account.account = venue_transferring.target\n venue_account.save()\n\n venue_account.venue.user = venue_transferring.target.user\n venue_account.venue.save()\n\n Event.events.filter(venue_account_owner_id=venue_account.id).update(owner=venue_transferring.target.user.id)\n\n venue_transferring.delete()\n try:\n notice = Notice.objects.get(pk=notice_id)\n except Notice.DoesNotExist:\n notice = None\n\n if notice:\n notice_data = json.loads(notice.log)\n notice_data['state'] = 'Accepted'\n notice.log = json.dumps(notice_data)\n notice.read = True\n notice.save()\n\n target_name = target.username\n target_link = reverse('userena_profile_detail', kwargs={'username': target.username})\n notice_service.create_notice('venue_transferring_accepting', from_user, {\n 'subject': 'Cityfusion: transferring of your venue has been accepted.',\n 'user': from_user,\n 'venue_account': venue_account,\n 'target_name': target_name,\n 'target_link': target_link,\n }, {\n 'venue_name': venue_account.venue.name,\n 'venue_link': reverse('public_venue_account', kwargs={'slug': venue_account.slug}),\n 'target_name': target_name,\n 'target_link': target_link,\n 'date': datetime.datetime.now().strftime('%A, %b. %d, %I:%M %p'),\n })\n\n result = True\n return result","title":""},{"docid":"49498e9ce2c5bce44185577c91b23796","score":"0.48574036","text":"def test_calendar_query_expanded_recurring(self):\n raise SkipTest(\"test unimplemented\")","title":""},{"docid":"c6324c28d862c00ffdc2ae0dc5e70e6b","score":"0.48518384","text":"def canAct(self) -> bool:\n return self.cooldown < 1","title":""},{"docid":"f45779ffbce56bfee3e2be30e2f8f871","score":"0.48489207","text":"def can_act(self, **kwargs):\n return True","title":""},{"docid":"93a9c89b27742aba82795030c9f364b2","score":"0.48466724","text":"def allowed(self, user, amount):\n return True","title":""},{"docid":"03ed1b16924abba4e92036f3818dbe89","score":"0.48446918","text":"def accept_transfer(self):\n self.is_accepted = True\n self.date_time_accepted = models.DateTimeField(auto_now=True)","title":""},{"docid":"1840fd1a32ea102c2e1a3f9b81b39e3c","score":"0.4842639","text":"def reservation_mark_entrance(user: User, reservation: Reservation):\n owns_restaurant = reservation.restaurant.operator == user\n if owns_restaurant and reservation.status is ReservationState.ACCEPTED and reservation.reservation_time <= datetime.datetime.now():\n #Might want to add user notification\n reservation.entrance_time = datetime.datetime.now()\n reservation.status = ReservationState.SEATED\n db.session.commit()\n return True\n\n return False","title":""},{"docid":"feb54b24ff87cfae41c9d6f70d27ac2f","score":"0.48392415","text":"def is_pending_approval(self):\n if self.registration_method == self.REQUESTED \\\n and self.is_pending_activation():\n return True\n else:\n return False","title":""},{"docid":"19cfca091b1463122f1d72df8cd26df0","score":"0.48392242","text":"def save(self, *args, **kwargs):\n self.expire_date = self.sent_date + timedelta(days=self.period)\n super(Referral, self).save(*args, **kwargs)","title":""},{"docid":"a0cc51ab46ff66425dd3db7cba10015c","score":"0.48387","text":"def test_no_credit_change_for_subscription_downgrade(self):\n credits = 20\n\n current_plan = Subscription.get_plan_by_id('pro')\n new_plan = Subscription.get_plan_by_id('standard')\n\n credits = add_subscription_credits(credits, current_plan, new_plan, None)\n\n assert credits == 20","title":""}],"string":"[\n {\n \"docid\": \"4c04ec54a498200fddebe61bfad145c7\",\n \"score\": \"0.6974328\",\n \"text\": \"def enact(self, billing_cycle, disable_if_done=True):\\n as_of = billing_cycle.date_range.lower\\n if not self.is_enactable(as_of):\\n raise CannotEnactUnenactableRecurringCostError(\\n \\\"RecurringCost {} is unenactable.\\\".format(self.uuid)\\n )\\n\\n if self.has_enacted(billing_cycle):\\n raise RecurringCostAlreadyEnactedForBillingCycle(\\n 'RecurringCost cost {} already enacted for {}'.format(self, billing_cycle)\\n )\\n\\n with db_transaction.atomic():\\n recurred_cost = RecurredCost(\\n recurring_cost=self,\\n billing_cycle=billing_cycle,\\n )\\n recurred_cost.make_transaction()\\n recurred_cost.save()\\n\\n if disable_if_done:\\n self.disable_if_done(billing_cycle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7445a2491181d4ec5c6d4f004d143bf\",\n \"score\": \"0.6717582\",\n \"text\": \"def has_enacted(self, billing_cycle):\\n return RecurredCost.objects.filter(\\n recurring_cost=self,\\n billing_cycle=billing_cycle,\\n ).exists()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a365ef9fd5b0a002ccf6bc7bdc0e6573\",\n \"score\": \"0.62935936\",\n \"text\": \"def make_eligible(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8dff91131c125b350fd2012d9790602\",\n \"score\": \"0.5993728\",\n \"text\": \"def charge_recurring(self, grace_period=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9039e5f6aa07f9bcc62aac117fff3c64\",\n \"score\": \"0.5735424\",\n \"text\": \"def confirmed(self, cr, uid, ids, context=None):\\n\\tallow_archive_line_obj = self.pool.get('services.contracts.allowances.lines')\\n for record in self.browse(cr, uid, ids, context=context):\\n\\t\\tif not record.allowances_lines_before :\\n \\traise osv.except_osv(_('Partner Lines !'), _('Sorry no partner Lines!'))\\n\\n\\t \\tlines_ids = [line.id for line in record.allowances_lines_after]\\n \\tallow_archive_line_obj.unlink(cr,uid,lines_ids,context=context)\\n\\n\\t\\tfor lines in record.allowances_lines_before:\\n\\t\\t\\tif lines.percentage_rating < 0 or lines.percentage_rating > 100 :\\n \\t\\traise osv.except_osv(_('Rate Error !'), _('Sorry you insert wrong rate ... rate is between (0,100)!'))\\n \\t\\tamount_after_rate_id = allow_archive_line_obj.create(cr, uid, {\\n \\t\\t\\t\\t'cost_of_rent':lines.cost_of_rent,\\n \\t\\t\\t\\t'amount_untaxed':round (lines.amount_untaxed*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'amount_tax':round(lines.amount_tax*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'amount_total':round(lines.amount_total*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'deduct_days':lines.deduct_days,\\n \\t\\t\\t\\t'deduct_amount':lines.deduct_amount,\\n \\t\\t\\t\\t'contract_id':lines.contract_id.id,\\n\\t\\t\\t\\t\\t'env_allow_id_after_rate':record.id,\\n\\t\\t\\t\\t\\t'type': 'after',\\n 'category_id':lines.category_id.id,\\n\\t\\t\\t\\t\\t'percentage_rating':lines.percentage_rating,\\n\\n })\\n\\t\\t\\n \\n self.write(cr, uid, ids, {'state':'confirmed'})\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e06878f1f73ebe95cde824d74b51178a\",\n \"score\": \"0.57182837\",\n \"text\": \"def credit_deliverer():\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"072e3adb9a2b8b733955c2d0470f5cfd\",\n \"score\": \"0.57024777\",\n \"text\": \"def disable_costs():\\n RecurringCost.objects.all().disable_if_done()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729f7ea408343845dae0e20a444735a6\",\n \"score\": \"0.5669726\",\n \"text\": \"def awaiting_payment(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b89439cb1fb4ae0622c0559693f5b3\",\n \"score\": \"0.56223905\",\n \"text\": \"def test_add_recurring_schedule(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c2e807bcff1aea5874de05585dc1b9\",\n \"score\": \"0.55890375\",\n \"text\": \"def test_renew_user_pending_cancel(self):\\n self.braintree_customer.active = True\\n self.braintree_customer.pending_cancel = True\\n self.braintree_customer.subscription_id = \\\"ValidSubscriptionID\\\"\\n\\n result = SubscriptionManager.renew(self.braintree_customer)\\n self.assertEqual(\\\"ValidSubscriptionID\\\",result)\\n self.assertFalse(self.braintree_customer.pending_cancel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4473da0917973c1c85d7b653ba0f05d\",\n \"score\": \"0.5505976\",\n \"text\": \"def _check_cost(self, cr, uid, ids, context=None):\\n for enrich in self.browse(cr, uid, ids, context=context):\\n if enrich.amount <= 0:\\n raise osv.except_osv(_('ValidateError'), _('The Cost Must Be Greater Than Zero!'))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2212377ef5be3f290b7c7bf4d129b1\",\n \"score\": \"0.5493613\",\n \"text\": \"def test_subscribe_already_subscribed(self):\\n self.braintree_customer.active = True\\n self.braintree_customer.save()\\n with self.assertRaises(BraintreeError):\\n SubscriptionManager.subscribe(self.braintree_customer)\\n\\n # Check state not altered\\n self.assertTrue(self.braintree_customer.active)\\n self.assertFalse(self.braintree_customer.pending_cancel)\\n self.assertIsNone(self.braintree_customer.expiry_date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eff043c23370b9890345712648bb3b8\",\n \"score\": \"0.54642403\",\n \"text\": \"def test_renew_attempt_on_active_subscription(self):\\n self.braintree_customer.pending_cancel = False\\n self.braintree_customer.active = True\\n\\n with self.assertRaises(BraintreeError):\\n SubscriptionManager.renew(self.braintree_customer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2878f031423ce10692b6087cd1179530\",\n \"score\": \"0.5447805\",\n \"text\": \"def make_transaction(self):\\n if self.pk:\\n raise CannotRecreateTransactionOnRecurredCost(\\n 'The transaction for this recurred cost has already been created. You cannot create it again.'\\n )\\n\\n amount = self.recurring_cost.get_amount(self.billing_cycle)\\n\\n # It is quite possible that there will be nothing to bill, in which\\n # case we cannot create a transaction with no legs, nor can we create\\n # legs with zero values. Therefore we don't create any transaction.\\n if not amount:\\n return None\\n\\n self.transaction = Transaction.objects.create(\\n description='Created by recurring cost',\\n date=self.billing_cycle.date_range.lower\\n )\\n\\n # Use the SplitManager's custom queryset's split() method to get the\\n # amount to be billed for each split\\n splits = self.recurring_cost.splits.all().split(amount)\\n\\n # Create the transaction leg for the outbound funds\\n # (normally to an expense account)\\n self.transaction.legs.add(Leg.objects.create(\\n transaction=self.transaction,\\n amount=Money(amount, self.recurring_cost.currency),\\n account=self.recurring_cost.to_account,\\n ))\\n\\n for split, split_amount in splits:\\n # Create the transaction legs for the inbound funds\\n # (from housemate accounts)\\n if split_amount:\\n self.transaction.legs.add(Leg.objects.create(\\n transaction=self.transaction,\\n amount=Money(split_amount * -1, self.recurring_cost.currency),\\n account=split.from_account,\\n ))\\n\\n return self.transaction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba47d041e96bc2b902f7e951534eb141\",\n \"score\": \"0.5434562\",\n \"text\": \"def _cost(self, action):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e3085b8493d861cc1b7d398c776e29a\",\n \"score\": \"0.54062164\",\n \"text\": \"def is_recurring(self):\\n return self.__is_recurring\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c624f7dcb4ff52e7b73c58f67c9e6318\",\n \"score\": \"0.5402047\",\n \"text\": \"def test_request_cancel_already_pending_cancel(self):\\n self.braintree_customer.subscription_id = \\\"1234\\\"\\n self.braintree_customer.pending_cancel = True\\n self.braintree_customer.save()\\n\\n self.assertFalse(SubscriptionManager.request_cancel(self.braintree_customer))\\n self.assertTrue(self.braintree_customer.pending_cancel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33fbd06b632f15c7255f0e18c5897445\",\n \"score\": \"0.53812355\",\n \"text\": \"def is_invited_pending_activation(self):\\n if self.registration_method == self.INVITED \\\\\\n and self.is_pending_activation():\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb71fff2f7605e3d2c71500830dcbfa\",\n \"score\": \"0.53572863\",\n \"text\": \"def give_raise(self):\\r\\n self.hourly_pay = 12.00\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1633c21b982abbaba97996b7c87877d\",\n \"score\": \"0.5347127\",\n \"text\": \"def charge(self, other):\\n if self.flag:\\n self.credit += other\\n return \\\"{} Tomans has been added to your card credit and now the credit of your card is {} Tomans\\\".format(\\n other, self.credit)\\n else:\\n return \\\"Sorry, your card has expired.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a0bca7e754388a34640f20c91afa68b\",\n \"score\": \"0.534086\",\n \"text\": \"def payment_approval(self, house_cost: (int, float)):\\n if self.money_available >= house_cost: # Person has enough available money to make a deal with Realtor\\n self.money_available -= house_cost\\n print(f'Payment from {self.name} was approved')\\n return True\\n print(f'{self.name} doesn\\\\'t have enough money to buy this house')\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34fc2ccd08cf094015a4a7a71287468\",\n \"score\": \"0.53131855\",\n \"text\": \"def test_request_cancel_active_subscription(self):\\n self.braintree_customer.subscription_id = \\\"1234\\\"\\n self.braintree_customer.pending_cancel = False\\n self.braintree_customer.save()\\n self.assertTrue(SubscriptionManager.request_cancel(self.braintree_customer))\\n self.assertTrue(self.braintree_customer.pending_cancel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e8c9b966e3614e15ccd1ab4a2563ef9\",\n \"score\": \"0.5306632\",\n \"text\": \"def test_overlap(self):\\r\\n t = Expense(name = \\\"fake lunch\\\",\\r\\n amount = 1.,\\r\\n on = (WeeklyRecurring(FR,\\r\\n fromdt = self.fromdt,\\r\\n todt = self.todt),\\r\\n DailyRecurring(fromdt = self.fromdt, \\r\\n todt = self.todt)))\\r\\n\\r\\n self.m.addTransaction(t)\\r\\n self.assertEqual(self.m.totalSaved(self.fromdt, self.todt), -365.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3965aee40434d2d49b4ec667e75657\",\n \"score\": \"0.5294787\",\n \"text\": \"def reports_editable(self):\\n end_plus_time = self.datetime_end + datetime.timedelta(days=CCR_DELTA)\\n return timezone.now() < end_plus_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc118ce9f8f76044d0d8341c976cc671\",\n \"score\": \"0.5265665\",\n \"text\": \"def make_payment(self, cost):\\n self.process_coins()\\n if self.money_received >= cost:\\n change = round(self.money_received - cost, 2)\\n print(f\\\"Here is {self.CURRENCY}{change} in change.\\\")\\n self.profit += cost\\n self.money_received = 0\\n return True\\n else:\\n print(\\\"Sorry that's not enough money. Money refunded.\\\")\\n self.money_received = 0\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a11d04b563ecf30411d522d7a9987d\",\n \"score\": \"0.5261785\",\n \"text\": \"def test_create_sugar_cpn_exp_task(self):\\n offset_days = 2\\n coupon = Coupon.objects.get(id=416)\\n coupon.expiration_date = (datetime.date.today() + \\n datetime.timedelta(days=offset_days))\\n coupon.save()\\n self.assertEqual(create_sugar_coupon_expire_task(MockSugar(), \\n offset_days=offset_days), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27b0305fb2f568ecd0568b90978b95e3\",\n \"score\": \"0.52602494\",\n \"text\": \"async def resign(self, ctx):\\n currency = await bank.get_currency_name(ctx.guild)\\n await self.config.user(ctx.author).gameRole.set(\\\"User\\\")\\n await ctx.send(\\n f\\\"{ctx.author} has spent 10,000 {currency}- to resign from their current job.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8491f0b61b51e158a8d7c69100f4d319\",\n \"score\": \"0.5213568\",\n \"text\": \"def test_subscription_transaction_declined_suspend(self):\\n\\n # We'll create a plan that starts here\\n start_date = dt.date(2019, 1, 1)\\n\\n # And the trial date ends here too\\n trial_end_date = dt.date(2019, 1, 1)\\n\\n # The customer will use some metered features here\\n metered_usage_on = dt.date(2019, 1, 10)\\n\\n # Docs will be generated to bill here.\\n prev_billing_date = dt.date(2019, 1, 3)\\n\\n # So, the customer grace period ends here.\\n # First billing interval: dt.date(2019, 2, 1)\\n # \\n billing_grace_exp = dt.date(2019, 2, 3)\\n\\n # The next billing check should discover that the subscription\\n # is unpaid.\\n # Billing due date is: dt.date(2019, 2, 6)\\n # With the grace period: dt.date(2019, 2, 9)\\n # \\n billing_post_grace_check = dt.date(2019, 2, 10)\\n\\n # Create a customer\\n #\\n customer = CustomerFactory.create(sales_tax_percent=Decimal('0.00'),\\n payment_due_days=3)\\n PaymentMethodFactory.create(\\n payment_processor=triggered_processor, customer=customer,\\n canceled=False,\\n verified=True,\\n )\\n\\n # Create a metered feature\\n #\\n mf_price = Decimal('2.5')\\n metered_feature = MeteredFeatureFactory(\\n included_units_during_trial=Decimal('0.00'),\\n price_per_unit=mf_price)\\n currency = 'USD'\\n\\n # Crate a plan with metered features. Generate the invoice after\\n # the 5 day trial period, the plan will be billed every 30 days.\\n # \\n generate_after = 5\\n plan = PlanFactory.create(interval=Plan.INTERVALS.DAY,\\n interval_count=30,\\n generate_after=generate_after,\\n enabled=True,\\n amount=Decimal('20.00'),\\n trial_period_days=1,\\n metered_features=[metered_feature],\\n currency=currency)\\n\\n # Subscribe the customer\\n #\\n subscription = SubscriptionFactory.create(\\n plan=plan, start_date=start_date, customer=customer)\\n subscription.activate()\\n subscription.save()\\n\\n # Log some metered usage\\n consumed_1 = Decimal('5.00')\\n consumed_2 = Decimal('5.00')\\n mf_log = MeteredFeatureUnitsLogFactory.create(\\n subscription=subscription,\\n metered_feature=metered_feature,\\n start_date=metered_usage_on,\\n end_date=subscription.trial_end,\\n consumed_units=consumed_1)\\n\\n # Generate the docs\\n call_command('generate_docs',\\n billing_date=prev_billing_date,\\n stdout=self.output)\\n\\n proforma = Proforma.objects.first()\\n\\n assert proforma.proforma_entries.count() != 0\\n assert Subscription.objects.all().count() == 1\\n assert Invoice.objects.all().count() == 0\\n assert Proforma.objects.all()[0].total > Decimal('0.00')\\n\\n # Consume more units\\n mf_log.consumed_units += consumed_2\\n mf_log.save()\\n\\n call_command('generate_docs',\\n billing_date=billing_grace_exp,\\n stdout=self.output)\\n\\n assert Proforma.objects.all().count() != 0\\n assert Invoice.objects.all().count() == 0\\n\\n for pf in Proforma.objects.all():\\n # # Issue the proforma to generate transactions\\n # proforma = Proforma.objects.all()[1]\\n pf.issue()\\n pf.save()\\n\\n self.assertEqual(pf.state, Proforma.STATES.ISSUED)\\n # Fail the transaction\\n for tx in pf.transactions:\\n # tx = proforma.transactions[0]\\n tx.fail()\\n tx.save()\\n self.assertEqual(tx.state, Transaction.States.Failed)\\n\\n assert Transaction.objects.all().count() != 0\\n\\n call_command('check_subscriptions',\\n billing_date=billing_post_grace_check,\\n stdout=self.output)\\n\\n subscr = Subscription.objects.first()\\n\\n # Scan for subscriptions with unpaid documents\\n logging.debug(\\\"subscr %s\\\" % subscr)\\n self.assertEqual(subscr.state, Subscription.STATES.CANCELED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5417e376b07167245209d7488eceae5c\",\n \"score\": \"0.5208941\",\n \"text\": \"def test_get_active_coupon(self):\\n coupon = COUPON_FACTORY.create_coupon()\\n slot = SLOT_FACTORY.create_slot(coupon=coupon)\\n future_date = datetime.date.today() + datetime.timedelta(weeks=1)\\n coupon.expiration_date = future_date\\n coupon.save()\\n self.assertEqual(slot.get_active_coupon(), coupon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"257ba26e9929795ad5f5632d4cddacf1\",\n \"score\": \"0.52071846\",\n \"text\": \"def test_create_sugar_cc_exp_task(self):\\n current_date = datetime.datetime.now()\\n order = Order.objects.create(billing_record_id=114)\\n credit_card = CreditCard.objects.get(id=500)\\n credit_card.exp_month = int(current_date.strftime(\\\"%m\\\"))\\n credit_card.exp_year = int(current_date.strftime(\\\"%y\\\"))\\n credit_card.encrypt_cc('4111111111111111')\\n credit_card.clean()\\n credit_card.save()\\n Payment.objects.create(order=order, credit_card=credit_card,\\n amount='50.00', method='C', status='p')\\n self.assertEqual(create_sugar_cc_expire_task(MockSugar(), \\n test_mode=True), None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb78960a18fbea5f420521a73cc9d91e\",\n \"score\": \"0.5201274\",\n \"text\": \"def save(self, *args, **kwargs):\\n if not self.pk:\\n self.start_time_rent = datetime.date.today()\\n self.end_time_rent = self.start_time_rent + datetime.timedelta(days=7)\\n self.reservation.isrented = True\\n self.reservation.save()\\n return super(Rental, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f499e3c7140c536233b59aa5018b00e0\",\n \"score\": \"0.5198995\",\n \"text\": \"def life_insurance_to_recive_total(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e3ba91aae2b3005b29c825e9df6617\",\n \"score\": \"0.51950544\",\n \"text\": \"def pay_costs(self):\\r\\n cost = self.cost\\r\\n if cost:\\r\\n self.owner.player.char_ob.pay_money(cost)\\r\\n self.owner.player.msg(\\\"You pay %s coins for the event.\\\" % cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a554eae4772018c64970255353249ec\",\n \"score\": \"0.5190392\",\n \"text\": \"def accepting(self):\\n quote = self.get_quotes('labor')[0]\\n quantity = max(0, quote.price - 14)\\n self.create('labor', quantity)\\n self.accept_quote_partial(quote, quantity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da7b38cc190f388ab6d0549317e33158\",\n \"score\": \"0.51902777\",\n \"text\": \"def recap(self, request, pk=None):\\n retreat = self.get_object()\\n # This is a hard-coded limitation to allow anonymous users to call\\n # the function.\\n time_limit = retreat.end_time - timedelta(days=1)\\n if timezone.now() < time_limit:\\n response_data = {\\n 'detail': \\\"Retreat ends in more than 1 day.\\\"\\n }\\n return Response(response_data, status=status.HTTP_200_OK)\\n\\n # Notify a user for every reserved seat\\n emails = []\\n for reservation in retreat.reservations.filter(\\n is_active=True,\\n post_event_send=False):\\n send_post_retreat_email(reservation.user, retreat)\\n reservation.post_event_send = True\\n reservation.save()\\n emails.append(reservation.user.email)\\n\\n response_data = {\\n 'stop': True,\\n 'emails': emails\\n }\\n return Response(response_data, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e1634797064f5c392aeeff54879a26\",\n \"score\": \"0.5186644\",\n \"text\": \"def check_costs(self):\\r\\n if self.cost > self.owner.player.char_ob.currency:\\r\\n self.add_error(\\r\\n \\\"celebration_tier\\\",\\r\\n \\\"You cannot afford to pay the cost of %s.\\\" % self.cost,\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10a00c4c4117a81dd34a039b5016bec\",\n \"score\": \"0.51843166\",\n \"text\": \"def test_no_credit_change_for_same_subscription(self):\\n credits = 20\\n\\n current_plan = Subscription.get_plan_by_id('pro')\\n new_plan = Subscription.get_plan_by_id('pro')\\n\\n may_29_2015 = datetime.datetime(2015, 5, 29, 0, 0, 0)\\n may_29_2015 = pytz.utc.localize(may_29_2015)\\n\\n credits = add_subscription_credits(credits, current_plan, new_plan,\\n may_29_2015)\\n\\n assert credits == 20\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cfe667dbc270d1f0777d11ac37720c\",\n \"score\": \"0.51762897\",\n \"text\": \"def test_remove_recurring_schedule(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd0405d5d1dcce4d6e9af01483ebde3\",\n \"score\": \"0.5167032\",\n \"text\": \"def confirmed(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1bb3838c74ae2e2fc33261a8bcad5d\",\n \"score\": \"0.51638985\",\n \"text\": \"def test_retire_rate_plan(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd5fc643f60a16857bf0dbbe97c12f5\",\n \"score\": \"0.5128787\",\n \"text\": \"def add_to_excess(self) -> None:\\n if self.msg.value <= 0:\\n revert(\\\"No amount added to excess\\\")\\n self._treasury_balance.set(self.icx.get_balance(self.address))\\n self.FundReceived(self.msg.sender, self.msg.value, f\\\"{self.msg.value} added to excess\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e170852adf1c091d578eada0184631b9\",\n \"score\": \"0.51178837\",\n \"text\": \"async def admin_credit(self, ctx, target: discord.Member, sum: int = 100):\\n if is_registered(target.id):\\n \\n inventories = get_file(\\\"inventories\\\")\\n inventories[str(target.id)][\\\"balance\\\"] += sum\\n update_file(\\\"inventories\\\", inventories)\\n\\n embed = discord.Embed(color=admin_color)\\n embed.set_author(name=\\\"🛠️ Admin\\\")\\n embed.add_field(name=\\\"💰 Credit\\\",\\n value=f\\\"{ctx.author.mention}, {target.mention} a été crédité de `{sum}` PO (pièces d'or)\\\")\\n embed = set_footer(embed, ctx)\\n await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41e253144f1d44f70f72725a3cdfd381\",\n \"score\": \"0.5104723\",\n \"text\": \"def test_plan_autorenew_at_expire(self):\\n up = baker.make(\\\"UserPlan\\\", expire=date(2020, 1, 1))\\n self.assertEqual(up.plan_autorenew_at(), date(2020, 1, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ba629d8534d92877f8ce279d15aafa1\",\n \"score\": \"0.5101618\",\n \"text\": \"def trigger_expiration_notices(at_time=None, nb_days=15, dry_run=False):\\n\\n def _handle_organization_notices(organization):\\n if organization.processor_card_key:\\n card = organization.retrieve_card()\\n try:\\n exp_month, exp_year = card['exp_date'].split('/')\\n exp_date = datetime(year=int(exp_year),\\n month=int(exp_month), day=1, tzinfo=at_time.tzinfo)\\n if lower >= exp_date:\\n LOGGER.info(\\\"payment method expires soon for %s\\\",\\n organization)\\n if not dry_run:\\n signals.card_expires_soon.send(\\n sender=__name__, organization=organization,\\n nb_days=nb_days)\\n except (KeyError, ValueError):\\n # exp info is missing or the format is incorrect\\n pass\\n else:\\n LOGGER.info(\\\"%s doesn't have a payment method attached\\\",\\n organization)\\n if not dry_run:\\n signals.payment_method_absent.send(sender=__name__,\\n organization=organization)\\n\\n at_time = datetime_or_now(at_time)\\n lower = at_time + relativedelta(days=nb_days)\\n upper = at_time + relativedelta(days=nb_days + 1)\\n LOGGER.info(\\n \\\"trigger notifications for subscription expiring within [%s,%s[ ...\\\",\\n lower, upper)\\n prev_organization = None\\n subscription = None\\n for subscription in Subscription.objects.valid_for(ends_at__gte=lower,\\n ends_at__lt=upper).order_by('organization'):\\n org = subscription.organization\\n plan = subscription.plan\\n\\n try:\\n if subscription.auto_renew:\\n if plan.renewal_type == plan.AUTO_RENEW:\\n if org.id != prev_organization:\\n _handle_organization_notices(org)\\n\\n prev_organization = org.id\\n else:\\n if plan.renewal_type == plan.ONE_TIME:\\n LOGGER.info(\\\"trigger upgrade soon for %s\\\", subscription)\\n if not dry_run:\\n signals.subscription_upgrade.send(sender=__name__,\\n subscription=subscription, nb_days=nb_days)\\n\\n elif plan.renewal_type == plan.REPEAT:\\n LOGGER.info(\\\"trigger expires soon for %s\\\", subscription)\\n if not dry_run:\\n signals.expires_soon.send(sender=__name__,\\n subscription=subscription, nb_days=nb_days)\\n\\n except Exception as err: #pylint:disable=broad-except\\n # We use `Exception` because the email server might be\\n # unavailable but ConnectionRefusedError is not a subclass\\n # of RuntimeError.\\n LOGGER.exception(\\\"error: %s\\\", err)\\n\\n # flushing the last organization\\n if subscription and subscription.organization.id != prev_organization:\\n if subscription.auto_renew:\\n if subscription.plan.renewal_type == subscription.plan.AUTO_RENEW:\\n _handle_organization_notices(subscription.organization)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c66ea0657cc0bf9e28af5aaeb6647c4\",\n \"score\": \"0.5100977\",\n \"text\": \"def redeem(self, instance, customer, save=True):\\n start = timezone.now().date()\\n end = start + relativedelta(months=self.duration)\\n discount = Discount(instance=instance,\\n coupon=self,\\n start=start,\\n end=end,\\n customer=customer)\\n discount.full_clean()\\n if save:\\n discount.save()\\n return discount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be729754fa4102aa1c8b61477d0cdb82\",\n \"score\": \"0.50970876\",\n \"text\": \"def customer_wants_condiments(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58052ca7885a95388e8a943ad79fc9ee\",\n \"score\": \"0.50891936\",\n \"text\": \"def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\\n mock_tz.now.return_value = datetime(\\n 2015, 2, 11, 10, tzinfo=dt_timezone.utc\\n )\\n self.ticketed_event.ticket_cost = 0\\n self.ticketed_event.save()\\n self.assertFalse(self.unpaid.cancelled)\\n self.assertFalse(self.paid.cancelled)\\n\\n management.call_command('cancel_unpaid_ticket_bookings')\\n # emails are sent to user per cancelled booking and studio once for all\\n # cancelled bookings\\n self.unpaid.refresh_from_db()\\n self.paid.refresh_from_db()\\n self.assertEqual(len(mail.outbox), 0)\\n self.assertFalse(self.unpaid.cancelled)\\n self.assertFalse(self.paid.cancelled)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c02baa7b4f7d3c8e78826cc17010a89a\",\n \"score\": \"0.5078882\",\n \"text\": \"def register_withdraw(self, withdraw_intent): \\n if withdraw_intent > 0:\\n self.teo.register_withdraw(self, withdraw_intent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87a75db34bf124af171ddcacdf0705d9\",\n \"score\": \"0.5062819\",\n \"text\": \"def reckon(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef1115a68c9a4958c538af0517b7e7f\",\n \"score\": \"0.5059867\",\n \"text\": \"def reimport_eligible(self) -> bool:\\n return pulumi.get(self, \\\"reimport_eligible\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabd4c0d5435e6616abe559534230b62\",\n \"score\": \"0.5054001\",\n \"text\": \"def test_add_taxation_strategy_to_rate_plan(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d7067817879f0fa2eb4466e3f35086\",\n \"score\": \"0.50444573\",\n \"text\": \"def can_act(self) -> bool:\\n return self.cooldown < 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d7067817879f0fa2eb4466e3f35086\",\n \"score\": \"0.50444573\",\n \"text\": \"def can_act(self) -> bool:\\n return self.cooldown < 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57a314d8c47829fe7aea4b854dab54cb\",\n \"score\": \"0.5037591\",\n \"text\": \"def restock(self):\\n self.money = 9999\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b99cdf09d4409ff6cacc373e96ffd86\",\n \"score\": \"0.5028068\",\n \"text\": \"def may_send_escalation(self, source_type: str, escalation_reminder_cadence: timedelta) -> bool:\\n with self.session.begin() as session:\\n last_escalated = (\\n session.query(EventRecord.escalated_at)\\n .filter(EventRecord.source_type == source_type)\\n .order_by(EventRecord.escalated_at.desc())\\n .limit(1)\\n .one_or_none()\\n )\\n\\n if not last_escalated[0]:\\n return True\\n\\n return last_escalated[0] <= datetime.utcnow() - escalation_reminder_cadence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"846e2e0abaea6b50e2948f1d6060a39c\",\n \"score\": \"0.50160515\",\n \"text\": \"def filter(self, event, *args, **kwargs):\\n return event[\\\"expiration_datetime\\\"] <= datetime.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"201aa010ced8cf71f548ecf6d393575b\",\n \"score\": \"0.5011571\",\n \"text\": \"def test_dont_cancel_for_events_with_no_cost(self, mock_tz):\\n mock_tz.now.return_value = datetime(\\n 2015, 2, 10, 10, tzinfo=dt_timezone.utc\\n )\\n self.event.cost = 0\\n self.event.save()\\n self.assertEqual(\\n self.unpaid.status, 'OPEN', self.unpaid.status\\n )\\n self.assertEqual(\\n self.paid.status, 'OPEN', self.paid.status\\n )\\n management.call_command('cancel_unpaid_bookings')\\n # emails are sent to user per cancelled booking and studio once for all\\n # cancelled bookings\\n unpaid_booking = Booking.objects.get(id=self.unpaid.id)\\n paid_booking = Booking.objects.get(id=self.paid.id)\\n self.assertEqual(len(mail.outbox), 0)\\n self.assertEqual(\\n unpaid_booking.status, 'OPEN', unpaid_booking.status\\n )\\n self.assertEqual(\\n paid_booking.status, 'OPEN', paid_booking.status\\n )\\n\\n # auto_cancelled set to True only on cancelled bookings\\n self.assertFalse(unpaid_booking.auto_cancelled)\\n self.assertFalse(paid_booking.auto_cancelled)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"820d1f3307e066ab24821548eb04bf4c\",\n \"score\": \"0.50115657\",\n \"text\": \"def isOnSaleSoon(ticket):\\n return ticket.start_time > timezone.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f1843e58d485cff565bd73b3ba806c\",\n \"score\": \"0.500794\",\n \"text\": \"def _claim_iscore(self) -> None:\\n iscore_details_dict = self._system.queryIScore(self.address)\\n if iscore_details_dict['estimatedICX'] != 0:\\n amount = iscore_details_dict[\\\"estimatedICX\\\"]\\n self._system.claimIScore()\\n self._daily_reward.set(amount)\\n self._rate.set(self.getRate())\\n self._total_lifetime_reward.set(self.getLifetimeReward() + amount)\\n self._distributing.set(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"800b7a21c89f88bfe6327f7bb4361f22\",\n \"score\": \"0.5006686\",\n \"text\": \"def should_pay_attention(self):\\n return random.randint(1,100) > self.wander\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3c1c2ca303c52e34683b990d4edc7da\",\n \"score\": \"0.50048196\",\n \"text\": \"def test_authorize_pending_payment(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9eeb30737a0056e7591f58ee9369f45\",\n \"score\": \"0.5003403\",\n \"text\": \"def acknowledge_visitor(visitor):\\n visitor.acknowledged = True\\n visitor.acknowledged_time = datetime.now()\\n visitor.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4fcf332d5161239a6433b289ae158ed\",\n \"score\": \"0.50032485\",\n \"text\": \"def resent(self):\\n return 'Resent-Date' in self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ef7fe9adf9b416d5c880d173273e84\",\n \"score\": \"0.49881572\",\n \"text\": \"def pre_approve(self, cr, uid, ids, context={}):\\n \\tfor voucher in self.browse(cr, uid, ids, context=context):\\n \\t if not voucher.department_id.analytic_account_id:\\n \\t raise osv.except_osv(_('Configration Check!'), _(\\\"Please add cost center for your department!\\\"))\\n \\t periods = self.pool.get('account.period').search(cr, uid, [('date_start','<=',voucher.date),('date_stop','>=',voucher.date),('company_id','=',voucher.company_id.id)], context=context)\\n\\n\\n res=0.0\\n if voucher.purpose:\\n if not voucher.purpose.account_id: raise osv.except_osv(_('Warning!'), _('Please configure account for this purpose!')) \\n voucher_line = {\\n \\t\\t'voucher_id': voucher.id,\\n \\t\\t'partner_id': voucher.partner_id.id,\\n \\t\\t'untax_amount': voucher.amount,\\n \\t\\t'amount': voucher.amount,\\n 'name': voucher.narration,\\n \\t\\t'type': 'dr',\\n \\t\\t'account_analytic_id': voucher.department_id.analytic_account_id and voucher.department_id.analytic_account_id.id,\\n 'account_id': voucher.purpose.account_id.id,\\n \\t }\\n new_amount = res and res or voucher.amount \\n voucher_line.update({'amount':new_amount,'untax_amount':new_amount})\\n \\t if voucher.line_ids :\\n for line in voucher.line_ids:\\n \\t\\t self.pool.get('account.voucher.line').write(cr, uid, line.id, {\\n \\t\\t'voucher_id': voucher.id,\\n \\t\\t'partner_id': voucher.partner_id.id,\\n \\t\\t'untax_amount': res or line.amount,\\n \\t\\t'amount': line.amount,\\n 'name': voucher.narration,\\n \\t\\t'type': 'dr',\\n \\t\\t'account_analytic_id': line.account_analytic_id and line.account_analytic_id.id or voucher.department_id.analytic_account_id.id,\\n 'account_id': voucher.purpose.account_id.id or line.account_id.id,\\n \\t }, context=context)\\n \\t else:\\n\\n \\t\\t new_voucher_line = self.pool.get('account.voucher.line').create(cr, uid, voucher_line, context=context)\\n context.update({'purchase':True})\\n self.create_budget_confirmation(cr, uid, [voucher.id], context)\\n \\tself.write(cr, uid, ids,{'state': 'preapprove','type':'purchase','ratification':True}, context=context)\\n #cxt = context.copy()\\n #cxt.update({'type':'ratification'})\\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\\n self.write(cr, uid, ids, {'state': 'approved'}, context=context)\\n\\n \\t'''self.write(cr, uid, ids, {'state': 'preapprove'})\\n if not super(account_voucher, self).create_budget_confirmation(cr, uid, ids, context=context):\\n self.write(cr, uid, ids, {'state': 'approve','type':'purchase','ratification':True}, context=context)'''\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcdcd6a69d116b1f4b370a2e026f1a4\",\n \"score\": \"0.49817756\",\n \"text\": \"def test_update_rate_plan(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acf948659222da3835bbf26fc3a9017\",\n \"score\": \"0.49740785\",\n \"text\": \"def test_carryover(self):\\n carryover = Money(100)\\n available = {Decimal(0.5): carryover}\\n self.forecast(available)\\n self.assertEqual(\\n self.forecast.carryover,\\n carryover)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2504305e30cfc1efa4189640cffd33\",\n \"score\": \"0.4970885\",\n \"text\": \"def recurring(self):\\n return self.filter(total_billing_cycles__isnull=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d0e95adc90beb142a910e5cbf8e54c\",\n \"score\": \"0.4959831\",\n \"text\": \"def activate_card(**kwargs):\\n for card in ClientClubCard.objects.filter(\\n date_begin__isnull=True, is_paid_activate=False):\\n # period for activate\\n days = card.club_card.period_activation\\n if card.date_start + timedelta(days - 1) < date.today():\\n card.activate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12d51add250d35f7917a5213649c725c\",\n \"score\": \"0.49592566\",\n \"text\": \"def aspiration_criteria_satisfied(self,index):\\n\\t\\tnewEnergy = self.currentEnergy + self._diffEi[index]#self.count_energy(state)\\n\\t\\t# newTax = self._taxes[index]#self.count_tax(state=state)\\n\\t\\ttmpState = copy(self.currentState)\\n\\t\\ttmpState[index] = 1 - tmpState[index]\\n\\t\\tnewTax = self.count_tax(state=tmpState)\\n\\t\\toutput(\\\"Checking aspiration criteria: oldEnergy={}, current tax = {}; new energy = {}, new tax = {}\\\"\\\\\\n\\t\\t\\t\\t\\t \\t\\t\\t\\t.format(self.currentEnergy,self.currentTax,newEnergy,newTax),isDebug=True,tabsNum=1)\\n\\t\\tif newEnergy < self.localMinimumEnergy or newTax < self.localMinimumTax:\\n\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5cc8f4771dd58297a6e3b452182daef\",\n \"score\": \"0.4951388\",\n \"text\": \"def agree_to_tos(self, regr):\\n return self.update_registration(\\n regr.update(\\n body=regr.body.update(\\n agreement=regr.terms_of_service)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ab2bff6b751ae042892e7659f71902\",\n \"score\": \"0.49507055\",\n \"text\": \"def test_deny_pending_payment(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3edd57c4e43f7b72baf285f6459805fb\",\n \"score\": \"0.49433905\",\n \"text\": \"def test_4(self):\\n c1 = Store.Customer(\\\"harold\\\", \\\"qcf\\\", False)\\n self.assertFalse(c1.is_premium_member(), \\\"IS premium member\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b6b7553ae5fcebc14a9b407f801a55\",\n \"score\": \"0.4942659\",\n \"text\": \"def test_rate_cost_type_valid(self):\\n self.ocp_data[\\\"rates\\\"][0][\\\"tiered_rates\\\"] = [\\n {\\n \\\"unit\\\": \\\"USD\\\",\\n \\\"value\\\": 0.22,\\n \\\"usage\\\": {\\\"usage_start\\\": None, \\\"usage_end\\\": None},\\n \\\"cost_type\\\": \\\"Infrastructure\\\",\\n }\\n ]\\n\\n with tenant_context(self.tenant):\\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\\n if serializer.is_valid(raise_exception=True):\\n serializer.save()\\n\\n self.ocp_data[\\\"rates\\\"][0][\\\"tiered_rates\\\"] = [\\n {\\n \\\"unit\\\": \\\"USD\\\",\\n \\\"value\\\": 0.22,\\n \\\"usage\\\": {\\\"usage_start\\\": None, \\\"usage_end\\\": None},\\n \\\"cost_type\\\": \\\"Supplementary\\\",\\n }\\n ]\\n\\n with tenant_context(self.tenant):\\n serializer = CostModelSerializer(data=self.ocp_data, context=self.request_context)\\n if serializer.is_valid(raise_exception=True):\\n serializer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89995bce1c766439b816fae14b3a212a\",\n \"score\": \"0.49375176\",\n \"text\": \"def test_cancel_on_braintree_success(self):\\n self.braintree_customer.pending_cancel = True\\n self.braintree_customer.active = True\\n self.braintree_customer.subscription_id = self.cancellable_id\\n\\n SubscriptionManager.cancel_on_braintree(self.braintree_customer)\\n\\n self.assertEqual(\\\"\\\",self.braintree_customer.subscription_id)\\n self.assertFalse(self.braintree_customer.pending_cancel)\\n self.assertFalse(self.braintree_customer.active)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb541e8c03f9188a6ad804fc06af67f\",\n \"score\": \"0.49262586\",\n \"text\": \"def test_create_new_order_VIES_fault(self):\\n rup = baker.make(\\n \\\"RecurringUserPlan\\\",\\n user_plan__user__billinginfo__country=\\\"CZ\\\",\\n user_plan__user__billinginfo__tax_number=\\\"CZ0123\\\",\\n amount=10,\\n tax=11,\\n )\\n with no_connection():\\n order = rup.create_renew_order()\\n self.assertEqual(order.tax, 11)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d65a3322432bdf78bb8926583c4261\",\n \"score\": \"0.49252683\",\n \"text\": \"def test_charge_correct_for_regular_after_close_1_day(self):\\n rental = create_test_rental(\\n book=self.book3,\\n customer=self.user1,\\n date_borrowed=\\\"2019-05-24 00:00:00.400952+00:00\\\",\\n )\\n close_rental_url = reverse(\\\"close_rental\\\", kwargs={\\\"pk\\\": rental.pk})\\n\\n data = {\\\"date_returned\\\": \\\"2019-05-25 13:46:57.249145+03:00\\\"}\\n response = self.client.put(close_rental_url, data=data, format=\\\"json\\\")\\n\\n self.assertEqual(response.data[\\\"amount_charged\\\"], \\\"2.00\\\")\\n self.assertEqual(response.data[\\\"rental_status\\\"], \\\"Closed\\\")\\n self.assertEqual(response.data[\\\"currency\\\"], CURRENCY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01d505b0decf38a647955a28599ddb50\",\n \"score\": \"0.4904093\",\n \"text\": \"def modify_expiration(self, new_expiration):\\n if self.is_cancellable:\\n if self.expiration!=new_expiration:\\n log.info(\\\"bo#%s: modify expiration of pending order\\\" % self.ticket)\\n dt = get_datetime()\\n new_expiration = dt_td_to_dt(new_expiration, dt)\\n not_implemented_error(\\\"Can't modify modify expiration of pending order\\\")\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dbfc818a7ffbd6d8f09400f973bae44\",\n \"score\": \"0.49022606\",\n \"text\": \"def contribute(k):\\n global amount\\n global _alarm\\n amount = amount + k\\n # remove the alarm\\n if amount >= initial_amount * 0.3:\\n _alarm = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2a8259def53576441e2c3cbf731cff5\",\n \"score\": \"0.48961106\",\n \"text\": \"def test_3(self):\\n c1 = Store.Customer(\\\"harold\\\", \\\"qcf\\\", True)\\n self.assertTrue(c1.is_premium_member(), \\\"not premium member\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed609928d752e18a160ececd58722048\",\n \"score\": \"0.48943877\",\n \"text\": \"def done(self,cr,uid,ids,context=None):\\n for record in self.browse(cr, uid, ids, context=context):\\n search_result = self.pool.get('payment.enrich').browse(cr, uid,record.enrich_id.id)\\n if record.cost < 1 :\\n raise osv.except_osv(_('Invalid Action Error'), _('The Entered Cost Is Wrong!'))\\n if record.cost > search_result.residual_amount :\\n raise osv.except_osv(_('Invalid Action Error'), _('Your Residual Balance Is Less Than Your Cost!'))\\n if context:\\n if 'owner' in context and 'model_id' in context:\\n owner = context['owner']\\n owner = int(owner)\\n model_id = context['model_id']\\n if str(model_id) == 'fleet.vehicle.log.contract':\\n model_obj = self.pool.get(model_id)\\n model = model_obj.browse(cr, uid, owner, context=context)\\n model.write({'state':'open'})\\n return self.write(cr, uid, ids, {'state':'done'},context=context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a162a79de7a75785f9e6b2d9f07588\",\n \"score\": \"0.48916712\",\n \"text\": \"def use(self):\\n if self.flag:\\n if self.credit < self.price_of_trip:\\n return \\\"Your credit is not enough, please increase your credit\\\"\\n else:\\n self.credit -= self.price_of_trip\\n return \\\"Done\\\"\\n else:\\n return \\\"Sorry, your card has expired.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f42aed0cc21bdb3278ce55b720546c9\",\n \"score\": \"0.48887736\",\n \"text\": \"def save(self, *args, **kwargs): #pylint: disable=W0221\\n created = not self.uuid\\n if created:\\n existing_balance = self.account.calculated_balance\\n else:\\n all_other_active_transactions = [x for x in self.account.transactions.all() if x != self and x.active]\\n existing_balance = sum(x.amount for x in all_other_active_transactions)\\n\\n if not self.active:\\n pass\\n elif (existing_balance + self.amount) < 0:\\n raise AccountBalanceError(\\n 'Balance of account {} would be brought below 0'.format(self.account)\\n )\\n\\n instance = super().save(*args, **kwargs)\\n self.account.update_balance()\\n return instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b363d7bccb6a832fb10e43ce7a25224e\",\n \"score\": \"0.48865578\",\n \"text\": \"def test_check_cost():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5414a8c5839816743bf0cbd8ac9e56b2\",\n \"score\": \"0.4879177\",\n \"text\": \"def test_subscribe_no_previous_subscription(self):\\n self.braintree_customer.payment_method_token = 'valid_payment_token'\\n SubscriptionManager.subscribe(self.braintree_customer)\\n\\n # fetch customer again\\n self.braintree_customer = BraintreeUser.objects.get(user=self.user)\\n self.assertTrue(self.braintree_customer.active)\\n self.assertFalse(self.braintree_customer.pending_cancel)\\n self.assertEqual(self.braintree_customer.expiry_date, timezone.make_aware(datetime.datetime(2017,7,25,0,0),pytz.utc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca9f8a29d701d9d48f8e45102180e20\",\n \"score\": \"0.48787516\",\n \"text\": \"def use(self):\\n if self.credit < self.price_of_trip:\\n print(\\\"Your credit is not enough, please increase your credit\\\")\\n else:\\n self.credit -= self.price_of_trip\\n print(\\\"Done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd45ad080c1c56c8cbfc9207b5b5102\",\n \"score\": \"0.48765975\",\n \"text\": \"def user_added_credit(self):\\n return (self.user.Credit > 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"062c77ab3749342bb1564bcd2adac432\",\n \"score\": \"0.48746642\",\n \"text\": \"def can_accept_credit(self, value):\\n return value >= 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8122483a63fd15da82f48566f50c3229\",\n \"score\": \"0.48686194\",\n \"text\": \"def can_reschedule(self) -> bool:\\n return pulumi.get(self, \\\"can_reschedule\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f6380e73fd49f8b86458e7fc9e0b33\",\n \"score\": \"0.48665464\",\n \"text\": \"def evaluate_cost(self, msg):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f89aace960b6791c1f3e1ff7d144a06\",\n \"score\": \"0.4865596\",\n \"text\": \"def event_charge(order_type):\\r\\n if order_type == \\\"Promo event\\\":\\r\\n return 400\\r\\n\\r\\n else:\\r\\n return 0\\r\\n # promo event adds $400\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d5396dbeb716ea766bf4f1d486f78b\",\n \"score\": \"0.48641136\",\n \"text\": \"def accept_venue_transferring(venue_transferring_id, notice_id):\\n result = False\\n try:\\n venue_transferring = VenueAccountTransferring.objects.get(pk=venue_transferring_id)\\n except VenueAccountTransferring.DoesNotExist:\\n venue_transferring = None\\n\\n if venue_transferring and venue_transferring.target:\\n venue_account = venue_transferring.venue_account\\n from_user = venue_transferring.venue_account.account.user\\n target = venue_transferring.target.user\\n\\n venue_account.account = venue_transferring.target\\n venue_account.save()\\n\\n venue_account.venue.user = venue_transferring.target.user\\n venue_account.venue.save()\\n\\n Event.events.filter(venue_account_owner_id=venue_account.id).update(owner=venue_transferring.target.user.id)\\n\\n venue_transferring.delete()\\n try:\\n notice = Notice.objects.get(pk=notice_id)\\n except Notice.DoesNotExist:\\n notice = None\\n\\n if notice:\\n notice_data = json.loads(notice.log)\\n notice_data['state'] = 'Accepted'\\n notice.log = json.dumps(notice_data)\\n notice.read = True\\n notice.save()\\n\\n target_name = target.username\\n target_link = reverse('userena_profile_detail', kwargs={'username': target.username})\\n notice_service.create_notice('venue_transferring_accepting', from_user, {\\n 'subject': 'Cityfusion: transferring of your venue has been accepted.',\\n 'user': from_user,\\n 'venue_account': venue_account,\\n 'target_name': target_name,\\n 'target_link': target_link,\\n }, {\\n 'venue_name': venue_account.venue.name,\\n 'venue_link': reverse('public_venue_account', kwargs={'slug': venue_account.slug}),\\n 'target_name': target_name,\\n 'target_link': target_link,\\n 'date': datetime.datetime.now().strftime('%A, %b. %d, %I:%M %p'),\\n })\\n\\n result = True\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49498e9ce2c5bce44185577c91b23796\",\n \"score\": \"0.48574036\",\n \"text\": \"def test_calendar_query_expanded_recurring(self):\\n raise SkipTest(\\\"test unimplemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6324c28d862c00ffdc2ae0dc5e70e6b\",\n \"score\": \"0.48518384\",\n \"text\": \"def canAct(self) -> bool:\\n return self.cooldown < 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f45779ffbce56bfee3e2be30e2f8f871\",\n \"score\": \"0.48489207\",\n \"text\": \"def can_act(self, **kwargs):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a9c89b27742aba82795030c9f364b2\",\n \"score\": \"0.48466724\",\n \"text\": \"def allowed(self, user, amount):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ed1b16924abba4e92036f3818dbe89\",\n \"score\": \"0.48446918\",\n \"text\": \"def accept_transfer(self):\\n self.is_accepted = True\\n self.date_time_accepted = models.DateTimeField(auto_now=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1840fd1a32ea102c2e1a3f9b81b39e3c\",\n \"score\": \"0.4842639\",\n \"text\": \"def reservation_mark_entrance(user: User, reservation: Reservation):\\n owns_restaurant = reservation.restaurant.operator == user\\n if owns_restaurant and reservation.status is ReservationState.ACCEPTED and reservation.reservation_time <= datetime.datetime.now():\\n #Might want to add user notification\\n reservation.entrance_time = datetime.datetime.now()\\n reservation.status = ReservationState.SEATED\\n db.session.commit()\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb54b24ff87cfae41c9d6f70d27ac2f\",\n \"score\": \"0.48392415\",\n \"text\": \"def is_pending_approval(self):\\n if self.registration_method == self.REQUESTED \\\\\\n and self.is_pending_activation():\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19cfca091b1463122f1d72df8cd26df0\",\n \"score\": \"0.48392242\",\n \"text\": \"def save(self, *args, **kwargs):\\n self.expire_date = self.sent_date + timedelta(days=self.period)\\n super(Referral, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0cc51ab46ff66425dd3db7cba10015c\",\n \"score\": \"0.48387\",\n \"text\": \"def test_no_credit_change_for_subscription_downgrade(self):\\n credits = 20\\n\\n current_plan = Subscription.get_plan_by_id('pro')\\n new_plan = Subscription.get_plan_by_id('standard')\\n\\n credits = add_subscription_credits(credits, current_plan, new_plan, None)\\n\\n assert credits == 20\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":198,"cells":{"query_id":{"kind":"string","value":"ff858a38afa1ebd09c9cc6d02fb8e698"},"query":{"kind":"string","value":"r\"\"\" Computes the reduced density matrix representation of the state. May be numerical or symbolic."},"positive_passages":{"kind":"list like","value":[{"docid":"5571e02f616db6f7dcabfb10903524cd","score":"0.6066919","text":"def reduced_dm(self, modes, **kwargs):\n if modes == list(range(self.num_modes)):\n # reduced state is full state\n return self.dm(**kwargs)\n\n if isinstance(modes, int):\n modes = [modes]\n if modes != sorted(modes):\n raise ValueError(\"The specified modes cannot be duplicated.\")\n if len(modes) > self.num_modes:\n raise ValueError(\n \"The number of specified modes cannot \" \"be larger than the number of subsystems.\"\n )\n\n reduced = self.dm(**kwargs)\n reduced = reduced_density_matrix(reduced, modes, False, batched=self.batched)\n\n s = tf.identity(reduced, name=\"reduced_density_matrix\")\n return s","title":""}],"string":"[\n {\n \"docid\": \"5571e02f616db6f7dcabfb10903524cd\",\n \"score\": \"0.6066919\",\n \"text\": \"def reduced_dm(self, modes, **kwargs):\\n if modes == list(range(self.num_modes)):\\n # reduced state is full state\\n return self.dm(**kwargs)\\n\\n if isinstance(modes, int):\\n modes = [modes]\\n if modes != sorted(modes):\\n raise ValueError(\\\"The specified modes cannot be duplicated.\\\")\\n if len(modes) > self.num_modes:\\n raise ValueError(\\n \\\"The number of specified modes cannot \\\" \\\"be larger than the number of subsystems.\\\"\\n )\\n\\n reduced = self.dm(**kwargs)\\n reduced = reduced_density_matrix(reduced, modes, False, batched=self.batched)\\n\\n s = tf.identity(reduced, name=\\\"reduced_density_matrix\\\")\\n return s\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"364f54a74c07f748ea0080ffc36d9b1d","score":"0.6417854","text":"def subsystem_reduced_density_matrix(self, bit_labels):\n bit_labels_inds = [i-1 for i in bit_labels]\n other_labels_inds = list(set(range(self.system_size)) - set(bit_labels_inds))\n state_reshape = np.reshape(self.state, tuple([2]*self.system_size))\n state_reshape = np.transpose(state_reshape, bit_labels_inds + other_labels_inds)\n state_reshape = np.reshape(state_reshape, (2**len(bit_labels_inds), 2**len(other_labels_inds)))\n state_reshape = np.asmatrix(state_reshape)\n rho = state_reshape*state_reshape.H\n return rho","title":""},{"docid":"f06c3a022b5422d6e7db9221dbeeb3a6","score":"0.6406758","text":"def purity(state):\n mat = np.reshape(state, (2, 2))\n reduced_rho = np.dot(mat, np.conj(mat.T))\n return np.trace(np.dot(reduced_rho, reduced_rho))","title":""},{"docid":"06ee8f9e55a76e8517b4a14e76094cf4","score":"0.6384309","text":"def reduced_density_matrix(self, left_bits, right_bits, which='left'):\n reshaped_state = np.reshape(self.state, (2**left_bits,\n 2**right_bits)) \n reshaped_state = np.asmatrix(reshaped_state)\n if which == 'left':\n rho = reshaped_state*reshaped_state.H\n return rho","title":""},{"docid":"85162c067d7fa099823f5501dad6eedc","score":"0.57764155","text":"def _derivatives(self, state):\n ad_state = ad.adnumber(state)\n state_deriv = self.force_model(0, ad_state)\n a_matrix = jacobian(state_deriv, ad_state)\n # remove ad number from all state values before returning\n state_deriv = [state.real for state in state_deriv]\n\n return state_deriv, a_matrix","title":""},{"docid":"79cf5a3c466bbd9f31a3da7ef43a8265","score":"0.5732339","text":"def get_state_realspace_density(self, b, k, s, dim=None):\n\n\t\tself.check_c_projectors()\n\t\tif dim is not None:\n\t\t\tself.update_dim(np.array(dim)//2)\n\t\treturn self._get_realspace_state_density(b, k, s)","title":""},{"docid":"e4bb90809375f1c7d4c2865bd2ccec94","score":"0.558464","text":"def get_density(self):\n return nx.density(self.G)","title":""},{"docid":"05d106b220b1ee6e4f25244506b6018c","score":"0.55698353","text":"def quantum_density(self):\n d12 = self._D12.sum()\n\n rho = np.array(\n [[self._D11.sum(), d12], [d12.conjugate(), self._D22.sum()]]\n )\n\n rho *= self.dqdp\n\n # For the following consistency checks, get eigenvalues\n p = np.linalg.eigvalsh(rho)\n\n assert np.allclose(p[p < 0], 0), \"Quantum density matrix must be non-negative\"\n\n assert np.allclose(p.sum(), 1), \"Trace of quantum density matrix must be one\"\n\n return rho","title":""},{"docid":"1c698a987e20025f57dbbd45c3e3d824","score":"0.5514214","text":"def NE2001_dens():\n pass","title":""},{"docid":"954fe278d25fb5a651c45cd128595f60","score":"0.5507463","text":"def _density(self):\n prof_copy = self.profile.copy()\n ptype = prof_copy.pop('type', \"NFW\")\n return density_factory(ptype, **prof_copy)","title":""},{"docid":"1dc786523a52221b43a05731b29580a9","score":"0.5466165","text":"def stateDensity(self, Eps):\n return self._stateDensity(Eps)\n '''\n # Alternative calculaltion without interpolation\n if not isinstance(Eps, (collections.Sequence, np.ndarray)):\n return 16.*np.pi**2 * quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0]\n return 16.*np.pi**2 *np.array([quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0] for Eps in Eps])\n '''","title":""},{"docid":"1e7e001fdb35f9e2b0b2b37b56ddebaa","score":"0.54642904","text":"def __call__(self, state: tf.Tensor, is_density_matrix: bool = False\n ) -> tf.Tensor:\n if self._nqubits is None:\n if is_density_matrix:\n self.nqubits = len(tuple(state.shape)) // 2\n else:\n self.nqubits = len(tuple(state.shape))\n\n if self.is_controlled_by:\n return self._controlled_by_call(state, is_density_matrix)\n\n if is_density_matrix:\n state = self.einsum(self.calculation_cache.right, state,\n tf.math.conj(self.matrix))\n state = self.einsum(self.calculation_cache.left, state, self.matrix)\n return state\n\n return self.einsum(self.calculation_cache.vector, state, self.matrix)","title":""},{"docid":"73ac4e6dcdd8dfcf436fc9926b6cf18a","score":"0.54595035","text":"def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)\n self.mode.fourier = None; self.mode.energies = None\n densStates = self.mode.getDensityOfStates(Elist)","title":""},{"docid":"f6cec97371d2389325ad11ed52f664f0","score":"0.54263574","text":"def density(self, state: c_void_p, temperature: float) -> float:\n return self.api.glycolDensity(state, self.instance, temperature)","title":""},{"docid":"b5a522d307701bceef7662e3d7225273","score":"0.53983563","text":"def mass_density(self):\n return self._get_data('density') * pq.g / pq.cm**3","title":""},{"docid":"efcf24d459728bfefcbcfc4a9558562f","score":"0.53568625","text":"def calcStatePopulationDensity(self) -> None:\n\t\t# selecting rows that correspond with the state\n\t\tsumPopArea = self.stateCensusTractGDF['DP0010001'].sum(axis=0)\n\t\tsumLandArea = self.stateCensusTractGDF['ALAND10'].sum(axis=0)\n\t\t# sums_population_area= self.state.sum(axis=0)\n\t\t# pop_density_calclulated= sums_population_area.DP001001/sums_population_area.LAND_AREA\n\t\tpopDensityCalculated = sumPopArea / sumLandArea\n\t\t# adding new col\n\t\t# self.nationalCensusTractsGDF['Population Density']=None\n\t\tself.nationalCensusTractsGDF['statePopDensity'] = popDensityCalculated","title":""},{"docid":"fc4ed56fbc646f9391728c57ddf0c7cb","score":"0.53507805","text":"def W_red(W):\n assert issparse(W), \"Matrix needs to be sparse\"\n D_inv = degree_matrix(W, squared=False, indegree=False) # Assumes undireced graph, thus in-degree = out-degree.\n D_inv.data[:] = np.power(D_inv.data, -0.5) # D^(-1/2)\n return D_inv.dot(W).dot(D_inv)","title":""},{"docid":"24ad33e776fd43339cbc1a24518cc4b1","score":"0.5348118","text":"def saturated_density(self, state: c_void_p, temperature: float, quality: float) -> float:\n return self.api.refrigerantSaturatedDensity(state, self.instance, temperature, quality)","title":""},{"docid":"166bdca3b0bc2ee9a22ccd5d071e23ae","score":"0.5335536","text":"def cc_dense(self):\n cc_dense = self.iweights[[\"pid\", \"STATE\", \"iweight_state\"]].copy()\n # Use PID to match initial weights with weighted targets\n cc_dense = cc_dense.merge(self.puf_2017_filter, on=\"pid\")\n for var in self.targ_list:\n state_var = var + \"_targ\"\n cc_dense[state_var] = cc_dense[var] * cc_dense[\"iweight_state\"]\n cc_dense = cc_dense.drop(labels=self.var_list, axis=1)\n cc_dense = cc_dense.drop(labels=[\"s006\", \"AGI_STUB\"], axis=1)\n # j is an index for x, the variable we will solve for\n cc_dense[\"j\"] = np.arange(len(cc_dense)) + 1\n return cc_dense","title":""},{"docid":"89e3b690833477628c973f2500d5ee5c","score":"0.5307281","text":"def density(self):\n return DensityModel(a=14.1 * units.gram /\n units.cm**3, model='constant')","title":""},{"docid":"614c386f70be9342154c0d4d615222e9","score":"0.530172","text":"def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)","title":""},{"docid":"614c386f70be9342154c0d4d615222e9","score":"0.530172","text":"def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)","title":""},{"docid":"614c386f70be9342154c0d4d615222e9","score":"0.530172","text":"def testDensityOfStates(self):\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\n densStates = self.mode.getDensityOfStates(Elist)","title":""},{"docid":"e30eb831acf5f41270a6137d9ea4fed5","score":"0.5297523","text":"def reduced_state(self, wires):\n if len(wires) == self.num_wires:\n # reduced state is full state\n return self._state\n\n # translate to wire labels used by device\n device_wires = self.map_wires(wires)\n\n # reduce rho down to specified subsystems\n ind = np.concatenate([device_wires.toarray(), device_wires.toarray() + self.num_wires])\n rows = ind.reshape(-1, 1)\n cols = ind.reshape(1, -1)\n\n return self._state[0][rows, cols], self._state[1][ind]","title":""},{"docid":"956672841395e0ef4f499aa106f010f1","score":"0.52795523","text":"def density_matrix(psi):\n return np.dot(psi[:, np.newaxis], psi[np.newaxis, :])","title":""},{"docid":"825591400c59d579f1da6146edd1c41e","score":"0.52540666","text":"def compute_density(self):\n return density(self.coef_)","title":""},{"docid":"825591400c59d579f1da6146edd1c41e","score":"0.52540666","text":"def compute_density(self):\n return density(self.coef_)","title":""},{"docid":"84dfdc84274e9b5827a102a50f6fb572","score":"0.52432853","text":"def _discrete_state(self):\n return np.array(int(round(self.S * self.N)))","title":""},{"docid":"72b2749f4888f4104463f042150bf700","score":"0.5217364","text":"def DO(self):\n # statevector.py and operators.py have circular import\n from . import operators\n return operators.DensityOperator(self ^ self.conj(), self.basis)","title":""},{"docid":"fe31db5ab488dc0f4a5f72a53c34cf52","score":"0.520201","text":"def deq_mm(ssa):\n return 1e3 * 6 / (917 * np.asarray(ssa))","title":""},{"docid":"03b2c137509c41dff51bf722abe37482","score":"0.5191413","text":"def reactive_density(\n assignments, sinks, source=None, all_reactive=False, verbose=False):\n if not all_reactive:\n pathways = reactive_pathways(assignments, sinks, verbose=verbose)\n else:\n pathways = assignments\n\n state_counts = np.array(\n np.bincount(np.concatenate(pathways)))\n densities = state_counts / np.sum(state_counts)\n\n return densities","title":""},{"docid":"c10bea322cdeeb893df7b384935f3487","score":"0.5177796","text":"def __getDressedMatrixElements(self,UNmat):\n UNmatdimension = len(UNmat.toarray())\n #print(UNmat.toarray())\n n0 = self.n\n j0 = self.j\n l0 = self.l\n m0 = self.m1\n state_main = [self.nn,self.ll,self.jj,self.m2]\n d0 = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\n Omega_array = []\n Omg0 = self.Omega0 \n for i in range(UNmatdimension):\n d = 0 \n if (self.basisStates[i][:4] == state_main) or (self.basisStates[i][4:] == state_main):\n d = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\n Omega_array = np.append(Omega_array,0.5*Omg0*d/d0)\n row = np.zeros(UNmatdimension) + 1\n col = np.arange(UNmatdimension)\n mat = csr_matrix((Omega_array, (row, col)), shape=(2, UNmatdimension))\n UNmat = vstack([mat,UNmat])\n row = np.arange(UNmatdimension+2)\n row = np.concatenate((np.array([1]), row))\n col = np.zeros(UNmatdimension+2) + 1\n col = np.concatenate((np.array([0]), col))\n Omega_array = np.concatenate((np.array([Omg0*0.5,Omg0*0.5,self.Delta0]), Omega_array))\n mat = csr_matrix((Omega_array, (row, col)), shape=(UNmatdimension+2, 2))\n UNmat = hstack([mat,UNmat])\n UNmat = csr_matrix(UNmat)\n #print(UNmat.toarray())\n return UNmat","title":""},{"docid":"c93bea98cc7212c6e4a8d5a3c7305310","score":"0.51725036","text":"def dm(self, **kwargs):\n if self.is_pure:\n if self.batched:\n batch_index = indices[0]\n free_indices = indices[1:]\n else:\n batch_index = \"\"\n free_indices = indices\n ket = self.data\n left_str = [batch_index] + [free_indices[i] for i in range(0, 2 * self.num_modes, 2)]\n right_str = [batch_index] + [free_indices[i] for i in range(1, 2 * self.num_modes, 2)]\n out_str = [batch_index] + [free_indices[: 2 * self.num_modes]]\n einstr = \"\".join(left_str + [\",\"] + right_str + [\"->\"] + out_str)\n s = tf.einsum(einstr, ket, tf.math.conj(ket))\n else:\n s = tf.identity(self.data, name=\"density_matrix\")\n\n return s","title":""},{"docid":"9c48980a00fc054b50cebc53e52fd805","score":"0.515427","text":"def to_valid_density_matrix(\n density_matrix_rep: Union[int, np.ndarray],\n num_qubits: Optional[int] = None,\n *, # Force keyword arguments\n qid_shape: Optional[Tuple[int, ...]] = None,\n dtype: Type[np.number] = np.complex64,\n atol: float = 1e-7) -> np.ndarray:\n qid_shape = _qid_shape_from_args(num_qubits, qid_shape)\n if (isinstance(density_matrix_rep, np.ndarray) and\n density_matrix_rep.ndim == 2):\n if density_matrix_rep.shape != (np.prod(qid_shape, dtype=int),) * 2:\n raise ValueError(\n 'Density matrix was not square and of size 2 ** num_qubit, '\n 'instead was {}'.format(density_matrix_rep.shape))\n if not np.allclose(density_matrix_rep,\n np.transpose(np.conj(density_matrix_rep)),\n atol=atol):\n raise ValueError('The density matrix is not hermitian.')\n if not np.isclose(np.trace(density_matrix_rep), 1.0, atol=atol):\n raise ValueError(\n 'Density matrix did not have trace 1 but instead {}'.format(\n np.trace(density_matrix_rep)))\n if density_matrix_rep.dtype != dtype:\n raise ValueError(\n 'Density matrix had dtype {} but expected {}'.format(\n density_matrix_rep.dtype, dtype))\n if not np.all(np.linalg.eigvalsh(density_matrix_rep) > -atol):\n raise ValueError('The density matrix is not positive semidefinite.')\n return density_matrix_rep\n\n state_vector = to_valid_state_vector(density_matrix_rep,\n len(qid_shape),\n qid_shape=qid_shape,\n dtype=dtype)\n return np.outer(state_vector, np.conj(state_vector))","title":""},{"docid":"63f5bd7522ce30ae82d10f2d485eea53","score":"0.5153912","text":"def density(self):\n return(self.mass() / ((4.0/3.0)*scipy.pi*(self.radius()**3)))","title":""},{"docid":"9ce4558b5432a7aad8b52addf8a14f14","score":"0.51530224","text":"def density_matrix_from_state_vector(\n state_vector: Sequence,\n indices: Optional[Iterable[int]] = None,\n qid_shape: Optional[Tuple[int, ...]] = None,\n) -> np.ndarray:\n shape = validate_qid_shape(state_vector, qid_shape)\n n_qubits = len(shape)\n\n if indices is None:\n return np.outer(state_vector, np.conj(state_vector))\n\n indices = list(indices)\n validate_indices(n_qubits, indices)\n\n state_vector = np.asarray(state_vector).reshape(shape)\n\n sum_inds = np.array(range(n_qubits))\n sum_inds[indices] += n_qubits\n\n rho = np.einsum(state_vector, list(range(n_qubits)), np.conj(state_vector),\n sum_inds.tolist(), indices + sum_inds[indices].tolist())\n new_shape = np.prod([shape[i] for i in indices], dtype=int)\n\n return rho.reshape((new_shape, new_shape))","title":""},{"docid":"6a3b6f070d782e552643309a0e086854","score":"0.51415163","text":"def density_model(self) -> BaseModel1D:\n return self._dens_model","title":""},{"docid":"134a3fd7dc38484a5d225c4c8a41acc5","score":"0.5096904","text":"def spd_decomposition(S):\n\tP = force_symmetric_to_symmetric_positive_definite(S)\n\tM = symmetric_positive_definite_decomposition(P)\n\treturn M","title":""},{"docid":"0771801c8c7fff1b3ad82e4b2a1da3ff","score":"0.5084259","text":"def diagonalize(reduced_density_matrix):\n try:\n eigenvals, eigenvecs = np.linalg.eigh(reduced_density_matrix)\t\n except LinAlgError:\n\traise DMRGException(\"Error diagonalizing the reduced DM\")\n\n return (eigenvals, eigenvecs)","title":""},{"docid":"7ef40d865d68440d621ba2b4df6efb3b","score":"0.50785637","text":"def deco_symmatrix ( m ) :\n\n if m in LinAlg.decorated_matrices : return m \n\n LinAlg.deco_matrix ( m )\n \n R = m.rep_type\n \n m.__str__ = LinAlg.MS_STR\n m.__repr__ = LinAlg.MS_STR\n m.table = LinAlg.MS_STR\n\n m.correlations = LinAlg.MS_CORR\n \n m.Sim = LinAlg.SIM\n m.sim = LinAlg.SIM\n m.SimT = LinAlg.SIMT\n m.simT = LinAlg.SIMT\n \n m.eigenValues = LinAlg.MS_EIGENVALUES \n m.eigenVectors = LinAlg.MS_EIGENVECTORS\n m.eigen_values = LinAlg.MS_EIGENVALUES \n m.eigen_vectors = LinAlg.MS_EIGENVECTORS\n\n m.__reduce__ = LinAlg.MS_REDUCE \n m.rep_size = classgetter ( lambda cls : cls.rep_type.kSize ) \n \n return m","title":""},{"docid":"d02996822e121457dbadc79e77833684","score":"0.50629157","text":"def normalized(self):\n # swap cols and rows and row reduce\n A = sp.Matrix(self.mat).transpose().rref()[0]\n # ensure all entries are integral by multiplying by the LCD\n A *= reduce(lcm, map(lambda x: x.as_numer_denom()[1], # get denominators\n sp.utilities.iterables.flatten(A)))\n # see https://groups.google.com/forum/#!topic/sympy/e8hcF4QAldc\n A = np.array(sp.lambdify((), A, 'numpy')()) # convert to numpy array\n A = A[np.any(A,1),:].transpose() # remove 0 rows, swap rows and columns\n return HashableMatrix(np.matrix(A))","title":""},{"docid":"e9d2948fdd0278c0960eac13394ff777","score":"0.50607157","text":"def test_dephasing(self):\n\n # Check for identity on |11> state\n test_density_matrix = (dephasing_channel(self.density_matrix, 1, 1))\n self.assertAlmostEqual(norm(self.density_matrix - test_density_matrix),\n 0.0)\n\n test_density_matrix = (dephasing_channel(self.density_matrix,\n 1,\n 1,\n transpose=True))\n\n correct_matrix = np.array([[0., 0., 0., 0.], [0., 0., 0., 0.],\n [0., 0., 0.5, -0.5], [0., 0., -0.5, 1.]])\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)\n\n # Check for correct action on cat state\n # With probability = 0\n test_density_matrix = (dephasing_channel(self.cat_matrix, 0, 1))\n self.assertAlmostEqual(norm(self.cat_matrix - test_density_matrix), 0.0)\n # With probability = 1\n\n correct_matrix = np.array([[0.50, 0.25, 0.00, 0.00],\n [0.25, 0.25, 0.00, -0.25],\n [0.00, 0.00, 0.00, 0.00],\n [0.00, -0.25, 0.00, 0.50]])\n test_density_matrix = (dephasing_channel(self.cat_matrix, 1, 1))\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)","title":""},{"docid":"3fc538d60bed88cd11599c3c6959f4b5","score":"0.5043117","text":"def density(self):\r\n graph = self.__graph_dict.copy()\r\n vertexes = len(graph.keys())\r\n edges = len(self.edges())\r\n\r\n return 2.0 * edges / (vertexes *(vertexes - 1))","title":""},{"docid":"e5f9f9e4020c8f8330863b342a455e0c","score":"0.50127727","text":"def density(compound, state, value_index=0):\n table = density_table(compound)\n state_idx = state_index(state)\n try:\n return list(table.loc[(table['state'] == state_idx), 'value'])[value_index]\n except IndexError:\n print('Invalid state or compound')","title":""},{"docid":"3f9417401b3ef118e225d0562a146075","score":"0.50119233","text":"def dd(self, mu: int, nu: int):\n\t\tif self.evaluated[mu,nu]: return self.Ric[mu,nu]\n\t\tRic = 0\n\t\tfor rho in range(self.dim):\n\t\t\tRic += self.R.uddd(rho,mu,rho,nu)\n\t\tself.Ric[mu,nu] = Ric.simplify()\n\t\tself.evaluated[mu,nu] = True\n\t\treturn self.Ric[mu,nu]","title":""},{"docid":"ef79b4384b1a00538764f5c98445ce4d","score":"0.50119007","text":"def density(self):\n return sum(np.array(mass(self.symbols))) / self.volume","title":""},{"docid":"b87cc4a601fab0d64ed28e35a4405a96","score":"0.50103897","text":"def density(self, state: c_void_p, barometric_pressure: float, dry_bulb_temp: float, humidity_ratio: float) -> float:\n return self.api.psyRhoFnPbTdbW(state, barometric_pressure, dry_bulb_temp, humidity_ratio)","title":""},{"docid":"fe0da1191fb5e7d644d2e8a32e1b3aed","score":"0.5010093","text":"def classical_density(self):\n rho = self._D11 + self._D22\n\n # Check for the normalization condition\n assert np.allclose(rho.sum() * self.dqdp, 1), \"Classical density must be normalized.\"\n\n # Check for the positivity of the density\n assert np.allclose(rho[rho < 0], 0), \"Classical density mus be nonnegative\"\n\n assert not np.isnan(rho).any(), \"Classical density contains NaNs.\"\n\n # Make sure all values are real\n assert isinstance(rho[0, 0], np.float), \"Classical density must be real\"\n\n return rho","title":""},{"docid":"2fe6a799ba948be43589b718b1442f2a","score":"0.5002014","text":"def get_reversible_rate_matrix(nstates):\n M = np.exp(np.random.randn(nstates, nstates))\n pre_Q = 0.5 * (M + M.T)\n v = sample_distn(nstates)\n v_sqrt = np.sqrt(v)\n pre_Q = (pre_Q.T / v_sqrt).T * v_sqrt\n Q = pre_Q - np.diag(np.sum(pre_Q, axis=1))\n assert_reversibility(Q, v)\n return Q, v","title":""},{"docid":"080916809178ad11c99fcca08377dc79","score":"0.49931666","text":"def density(self):\n return self._density","title":""},{"docid":"ea7bff0064c282b34ff4c47214f0f4ed","score":"0.49819955","text":"def evaluate_density_vec(self, x):\r\n return self._evaluate_scalar_quantity(x[:,0], x[:,1], x[:,2], MNnModel.mn_density)","title":""},{"docid":"8fe41ebade879197650c9af2761a6dfc","score":"0.4976917","text":"def get_density(self,wavefunction): \n if wavefunction is None:\n return self.density\n \n density=np.zeros(self.shape,dtype=np.float)\n for k in range(self.NK):\n for n in range(self.nbands):\n for m in range(self.nbands):\n density+=2*self.wk[k]*self.f[k,n]*np.abs(wavefunction[k,m,n]*self.ukn[k,m])**2\n return density","title":""},{"docid":"5d65c01ac6a9758392a6b3882f17e447","score":"0.49748877","text":"def get_density(self, x):\n \n return self.get_logdensity(x).exp()","title":""},{"docid":"c272faaf51fe765b66950a3fcb109cce","score":"0.4974089","text":"def generate_reduced_sim_mat(self):\n self.reduced_sim_mat = np.zeros((self.k, self.k), dtype='float32')\n\n for r in range(2, self.k + 1):\n for s in (range(1, r) if not self.drop_edges_between_irregular_pairs else self.regularity_list[r - 2]):\n if self.is_weighted:\n cl_pair = WeightedClassesPair(self.sim_mat, self.adj_mat, self.classes, r, s, self.epsilon)\n else:\n cl_pair = ClassesPair(self.adj_mat, self.classes, r, s, self.epsilon)\n self.reduced_sim_mat[r - 1, s - 1] = self.reduced_sim_mat[s - 1, r - 1] = cl_pair.bip_density","title":""},{"docid":"30bb90332888e55a02603a36f21e30ef","score":"0.4962091","text":"def initialize_density(x,dx,normalization=1):\n rho=exp(-x**2)\n A=simps(rho,x)\n return normalization/A*rho","title":""},{"docid":"c9ef96029d241edd7a4d21334f51ce44","score":"0.4958901","text":"def density(self, r):\n pass","title":""},{"docid":"1ebd7cc640cd6a72cce91981a2d5ea69","score":"0.49578607","text":"def mass_matrix(self):\r\n if not self._fr or not self._frstar:\r\n raise ValueError('Need to compute Fr, Fr* first.')\r\n return Matrix([self._k_d, self._k_dnh])","title":""},{"docid":"5af34f7f073abed1d0aba606c4e3d4c5","score":"0.49560684","text":"def _matrix(self):\n n, d = self._x.shape\n if n < d:\n return np.dot(self._x, self._x.T) / float(n - 1)\n return np.dot(self._x.T, self._x) / float(n - 1)","title":""},{"docid":"b72faf3dd76737395397e71108fe09c0","score":"0.49559936","text":"def depurify(self, eps):\n probs = np.random.random(self.num_rows)\n probs = probs/np.sum(probs)\n arr = (self.arr + eps*np.diag(probs)) / (self.trace() + eps)\n return DenMat(self.num_rows, self.row_shape, arr)","title":""},{"docid":"a5db345c5c728b77651f7354563b650c","score":"0.49555916","text":"def irreducible(self):\r\n\t\treturn farq((self.num/MCD(self.den,self.num)),(self.den/MCD(self.den,self.num)))","title":""},{"docid":"0acd9a2c48d8dba88077ac1ef6e038df","score":"0.4938685","text":"def compute_diagonalizing_gates(\n basis_state, wires\n ): # pylint: disable=arguments-differ,unused-argument\n return []","title":""},{"docid":"22cbc58a6fc66c8903026332ba3d82ec","score":"0.49319252","text":"def distribution_matrix(mat):\n return np.diag(stationary(mat))","title":""},{"docid":"b9ae00ca62821f82771a40d1b404a03f","score":"0.49295992","text":"def normalize_adj(adj):\n # adj: dense adj\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1)) # D\n d_inv_sqrt = np.power(rowsum, -0.5).flatten() # D^-0.5\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) # D^-0.5\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() # D^-0.5AD^0.5","title":""},{"docid":"a94ce23a0f9d4ffeea5706804d3a5d4d","score":"0.49190745","text":"def densityFactorDry(weather):\n temperature = extractTemperature(weather, useKelvin=True)\n waterVaporPressure = humidityToPressure(weather)\n airPressure = weather.getAirPressure()*units.pascal\n dryPressure = airPressure - waterVaporPressure\n eqn = dryPressure.to_value(cds.mbar)*(57.90E-8 - 9.3250E-4/temperature.to_value(units.Kelvin)\n + 0.25844/temperature.to_value(units.Kelvin)**2.)\n densityFactor = (1. + eqn)*dryPressure.to_value(cds.mbar)/temperature.to_value(units.Kelvin)\n return densityFactor","title":""},{"docid":"8fd36f9555dc547e0a2e2a0b67384f63","score":"0.49190095","text":"def strain_displacement_matrix(self):\n L = np.zeros((3,4)) # matrix used for mapping shape function derivatives into strains\n L[0,0] = 1; L[1,3] = 1; L[2,1:3] = 1;\n xi = 0; eta = 0; # evaluation point\n _,_,dNmat = self.mesh.eval_shape_func(xi,eta) # differentiated shape functions evaluated in (xi,eta)\n Jac = np.array([[0.5,0],[0,0.5]]) # mapping back to global coordinates\n detJ = np.linalg.det(Jac)\n invJ = 1/detJ*np.array([[Jac[1,1], -Jac[0,1]], [-Jac[1,0], Jac[0,0]]])\n G = np.zeros((4,4)) # mapping matrix between rectangular element space coordinates and global coordinates\n G[:2,:2] = invJ; G[-2:,-2:] = invJ\n B0 = np.matmul(np.matmul(L,G),dNmat) # strain displacement matrix\n\n return B0","title":""},{"docid":"7acb1f9b47bb5ec27070f96bdb361bc2","score":"0.491127","text":"def evaluate_density(self, x, y, z):\r\n return self._evaluate_scalar_quantity(x, y, z, MNnModel.mn_density)","title":""},{"docid":"9e7e01cd867d1edacc06d2d11684496c","score":"0.48791188","text":"def _densityfunction(\n r: float,\n core_wavenumber: floatcomplex,\n shell_wavenumber: floatcomplex,\n core_width: float,\n shell_width: float,\n) -> float:\n return (\n abs(\n _wavefunction(r, core_wavenumber, shell_wavenumber, core_width, shell_width)\n )\n ** 2\n )","title":""},{"docid":"7ef32ca4bb9d73ed6bf0304dda863126","score":"0.4866826","text":"def cost_down(states):\n return np.linalg.norm(states)","title":""},{"docid":"7873db806f156abc30c0f468865452ee","score":"0.48629946","text":"def getDensity(self):\n return self.__density","title":""},{"docid":"9936c5f50045750f35b6449fe5ae7d59","score":"0.4831887","text":"def _d_sigma(self):\n try:\n dd = self.d_derivs\n except ValueError:\n return None\n den = self.density\n dv = np.matrix(np.zeros((len(den.deriv_params))))\n for i, pname in enumerate(den.deriv_params):\n dv[0, i] = dd[pname].angularIntegral(self.psi_max)[0]\n return np.sqrt((dv * self.density.covar * dv.T)[0, 0])","title":""},{"docid":"0743541340bf045e3787e5333449e532","score":"0.48263004","text":"def renormalise(self):\n\t\trn = self.compute_partition_function_fast()\n\t\tfor prod in self.productions:\n\t\t\tif rn[prod[0]] == 0:\n\t\t\t\tself.parameters[prod] = 0\n\t\t\telse:\n\t\t\t\tif len(prod) == 3:\n\t\t\t\t\ta,b,c = prod\n\t\t\t\t\tself.parameters[prod] *= (rn[b] * rn[c])/rn[a] \n\t\t\t\telse:\n\t\t\t\t\tself.parameters[prod] *= 1.0/rn[prod[0]]\n\t\tself.trim_zeros()\n\t\tself.set_log_parameters()\n\t\treturn self","title":""},{"docid":"0e562d84de910866b7f2e9ce09c62f04","score":"0.48175946","text":"def densFunc(z):\t\n\t\treturn rho00 + 0.5 * (rho01-rho00) * (1 + np.tanh((z-ze)/we))","title":""},{"docid":"12b2038a3047cfa21d8adad453bad6a9","score":"0.4816128","text":"def build_distribution_normalization_matrix(self):\n vector = np.array((self.distribution_matrix * self.xtable_matrix).sum(axis=1)).T\n mask = vector > 0\n vector[mask] = 1 / vector[mask]\n return diags(vector, [0], format=\"csr\", dtype=np.float32)","title":""},{"docid":"a495785d1aad265edf5f3d89225b4319","score":"0.4811534","text":"def d2matrix(dx):\n N, Nngb, dim = dx.shape\n N_derivs = 2*dim + comb(dim, 2, exact=True)\n A = np.empty((N, Nngb, N_derivs), dtype=np.float64) \n for k in range(N):\n for i in range(Nngb):\n for j in range(N_derivs):\n if j < dim:\n A[k,i,j] = dx[k,i,j] \n elif j < 2*dim:\n A[k,i,j] = dx[k,i,j-dim] * dx[k,i,j-dim] / 2\n else:\n A[k,i,j] = dx[k,i,(j+1)%dim] * dx[k,i,(j+2)%dim] # this does the cross-terms, e.g. xy, xz, yz\n return A","title":""},{"docid":"170bba8658ede3ab821a07eb00ffc8e7","score":"0.48088142","text":"def d(self):\n\n # Calculate and return the local displacement vector\n return matmul(self.T, self.D())","title":""},{"docid":"b836ed4e795f661348350e710cb2bb65","score":"0.48069096","text":"def density(self, q, t=0.0):\n q = self._remove_units_prepare_shape(q)\n orig_shape, q = self._get_c_valid_arr(q)\n t = self._validate_prepare_time(t, q)\n ret_unit = self.units[\"mass\"] / self.units[\"length\"] ** 3\n return (self._density(q, t=t).T * ret_unit).to(self.units[\"mass density\"])","title":""},{"docid":"8cc5445f9fee2382cf934dd683ea59bf","score":"0.48019257","text":"def calculate_density(self):\n wavelet_density = len(self.phi_matrices[0].nonzero()[0])/(self.graph.number_of_nodes()**2)\n wavelet_density = str(round(100*wavelet_density, 2))\n inverse_wavelet_density = len(self.phi_matrices[1].nonzero()[0])/(self.graph.number_of_nodes()**2)\n inverse_wavelet_density = str(round(100*inverse_wavelet_density, 2))\n print(\"Density of wavelets: \"+wavelet_density+\"%.\")\n print(\"Density of inverse wavelets: \"+inverse_wavelet_density+\"%.\\n\")","title":""},{"docid":"a22dc3d06834558bc2cdfe95c4af1565","score":"0.48018953","text":"def DesignMatrix(states):\n N = np.size(states, 0)\n size3 = (N, L ** 2)\n X = np.zeros(size3)\n\n for i in range(0, N):\n X[i] = np.outer(states[i, :], states[i, :]).reshape(1, -1) # .ravel()\n return X","title":""},{"docid":"92a80ae3e4b8a7df27f667e195552e8e","score":"0.4795498","text":"def _normalize_adj_diag_enhance_dense(adj, diag_lambda:float, improved=False):\n fill_value = 1 if not improved else 2\n \n adj = add_self_loop_dense(adj, fill_value=fill_value)\n degree = torch.sum(adj, dim=-1)\n degree_inv = 1 / degree\n degree_inv[degree_inv == float(\"inf\")] = 0\n adj = adj * degree_inv.reshape(-1, 1)\n\n adj += (diag_lambda * adj.diag()).diag()\n\n return adj","title":""},{"docid":"b0377a23d2b9b5ab5ed62edfd4e05488","score":"0.4789015","text":"def _dcorss_dm(self):\n\n dcdm = np.empty((self.num_mog_comp, self.num_latent_proc, self.num_inducing))\n for j in range(self.num_latent_proc):\n dcdm[:, j, :] = -cho_solve((self.chol[j, :, :], True), self.MoG.m[:, j, :].T).T * self.MoG.pi[:, np.newaxis]\n return dcdm","title":""},{"docid":"eb66346d8ffa513bf4e6829ee29cc39d","score":"0.478672","text":"def FssInvDiag(self, recompute = True):\r\n raise NotImplementedError","title":""},{"docid":"bc864306e26f90c471f48a40ac16a0ec","score":"0.4782814","text":"def rh_density(self, init_dens, gamma, mach):\n\n dens_ratio = ((gamma+1)*mach**2)/(2+(gamma-1)*mach**2)\n\n final_dens = dens_ratio*init_dens\n\n return final_dens","title":""},{"docid":"039502c9e7c7e8d4ad0fd52010a922ed","score":"0.47798538","text":"def evalulate_function(self, disk_density: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]) -> torch.Tensor:\n rmat, zmat = torch.meshgrid(self.rmid, self.zmid)\n return disk_density(rmat, zmat)","title":""},{"docid":"9564f033e5d7d266c571edf867268ed7","score":"0.4779669","text":"def _dcross_ds(self):\n\n dc_ds = np.empty((self.num_mog_comp, self.num_latent_proc, self.MoG.get_sjk_size()))\n for j in range(self.num_latent_proc):\n dc_ds[:, j] = -1. / 2 * np.array(\n [self.MoG.dAinvS_dS(self.chol[j, :, :], k, j) * self.MoG.pi[k] for k in range(self.num_mog_comp)])\n return dc_ds","title":""},{"docid":"43d44e17c8cab7dfd74d9dfe4bbc03ce","score":"0.47766522","text":"def compute_dci_d(importances):\n\n # normalize across the state_variable dimension, so we get the importance of\n # each state variable for a slot\n slot_normalized_importances = importances / importances.sum(axis=0)\n sni = slot_normalized_importances\n\n # each entropy computed is the entropy for a slot over all the state variables\n slot_entropy = entropy(sni, base=importances.shape[0])\n dci_ds = 1 - slot_entropy\n dci_d = dci_ds.mean()\n\n return dci_d","title":""},{"docid":"63108ffb70e437a436de9e0fe5091598","score":"0.47744197","text":"def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)","title":""},{"docid":"63108ffb70e437a436de9e0fe5091598","score":"0.47744197","text":"def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)","title":""},{"docid":"63108ffb70e437a436de9e0fe5091598","score":"0.47744197","text":"def current_density(self, xyz):\n return self.sigma * self.electric_field(xyz)","title":""},{"docid":"a8697247f45a6c2c24dd1e3ea7b3ffc0","score":"0.47690842","text":"def stiffness_matrix_assembly(self,rho,sparse=True):\n rho = rho.flatten(order='F')\n if sparse is False:\n self.stiffness_matrix = np.zeros((self.mesh.ndof,self.mesh.ndof))\n mat_idx = np.indices((8,8))\n I = mat_idx[0].flatten()\n J = mat_idx[1].flatten()\n # assemble stiffness matrix\n for ele in range(len(self.mesh.edofMat)):\n edof = self.mesh.edofMat[ele]\n self.stiffness_matrix[edof[I],edof[J]] += (self.Emin+(rho[ele])**self.penal*(self.Emax-self.Emin))*self.KE[I,J]\n elif sparse is True:\n iK = np.kron(self.mesh.edofMat,np.ones((8,1))).flatten()\n jK = np.kron(self.mesh.edofMat,np.ones((1,8))).flatten()\n sK=((self.KE.flatten()[np.newaxis]).T*(self.Emin+(rho)**self.penal*(self.Emax-self.Emin))).flatten(order='F')\n self.stiffness_matrix = sp.coo_matrix((sK,(iK,jK)),shape=(self.mesh.ndof,self.mesh.ndof)).tocsc()\n\n return self.stiffness_matrix","title":""},{"docid":"af13d60edf934fd69ff211074ce62cb7","score":"0.47683695","text":"def simplify(self):\r\n fact = greatest_common_divisor(self.__numerator, self.__denominator)\r\n self.__numerator = self.__numerator//fact\r\n self.__denominator = self.__denominator//fact","title":""},{"docid":"2e023b8c609745368bc2f84375d6a06c","score":"0.47601324","text":"def simplify_state(state: SDFGState, remove_views: bool = False) -> MultiDiGraph:\n\n sdfg = state.parent\n\n # Copy the whole state\n G = MultiDiGraph()\n for n in state.nodes():\n G.add_node(n)\n for n in state.nodes():\n for e in state.all_edges(n):\n G.add_edge(e.src, e.dst)\n # Collapse all mappings and their scopes into one node\n scope_children = state.scope_children()\n for n in scope_children[None]:\n if isinstance(n, nodes.EntryNode):\n G.add_edges_from([(n, x) for (y, x) in G.out_edges(state.exit_node(n))])\n G.remove_nodes_from(scope_children[n])\n # Remove all nodes that are not AccessNodes or have incoming\n # wcr edges and connect their predecessors and successors\n for n in state.nodes():\n if n in G.nodes():\n if (not isinstance(n, nodes.AccessNode) or (remove_views and isinstance(sdfg.arrays[n.data], data.View))):\n for p in G.predecessors(n):\n for c in G.successors(n):\n G.add_edge(p, c)\n G.remove_node(n)\n else:\n for e in state.all_edges(n):\n if e.data.wcr is not None:\n for p in G.predecessors(n):\n for s in G.successors(n):\n G.add_edge(p, s)\n G.remove_node(n)\n break\n\n return G","title":""},{"docid":"5852be77521a54263f39fb8e44ef4dce","score":"0.47589034","text":"def ddx(n):\n return sp.spdiags((np.ones((n + 1, 1)) * [-1, 1]).T, [0, 1], n, n + 1, format=\"csr\")","title":""},{"docid":"1305563ab5d730662b91cba0c765426e","score":"0.4755412","text":"def compute(self):\n m, n = self.D.shape\n\n # Allocate memory.\n # We need +2 because we use indices starting from 1\n # and to deal with edge cases in the backward recursion.\n self.R_ = np.zeros((m+2, n+2), dtype=np.float64)\n\n _soft_dtw(self.D, self.R_, gamma=self.gamma)\n\n return self.R_[m, n]","title":""},{"docid":"2ff928f269c5ac8d7b3d2ae000cd42a5","score":"0.47552603","text":"def density(self, r):\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**3, 0.)","title":""},{"docid":"779305611eb50458e49f536acc482e7f","score":"0.47546223","text":"def to_dense(self):\n if self.size is None:\n size = max(self.d.keys()) + 1\n else:\n size = self.size\n dense = np.zeros(size)\n for k,v in self.d.items():\n dense[k] = v\n return dense","title":""},{"docid":"1db22c46cf4d3e4a22ca0797871bab30","score":"0.4754565","text":"def density(self, r):\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**2, 0.)","title":""},{"docid":"be8eb30b81846b14a296dec1b1e7d2ea","score":"0.47464868","text":"def reduced_formula(self):\n if self.structure is not None:\n return self.composition.reduced_formula\n return \"Unkonwn\"","title":""},{"docid":"efbd2abf113aed04471eebea96b78baf","score":"0.47410783","text":"def s_d(self) -> float:\n return self.s0 + 1 / (self.w * self.k_x) * self.v_t","title":""},{"docid":"f15bc534a6a908e854b492720e967cf5","score":"0.47379398","text":"def diagonalize_del_dm_in_sbasis_in_degenerate_spaces(self):\n classes = ut.get_equiv_classes(list(self.dm0_eigen_sys[0]))\n umat = np.eye(self.del_dm.num_rows, dtype=complex)\n # eq = equivalence\n for eq_class in classes:\n dim = len(eq_class)\n if dim > 1:\n if self.verbose:\n print(\"non-trivial eq class\", eq_class)\n eq_class = sorted(eq_class)\n arr = np.empty((dim, dim), dtype=complex)\n for k1, kk1 in enumerate(eq_class):\n for k2, kk2 in enumerate(eq_class):\n arr[k1, k2] = self.del_dm_in_sbasis[kk1, kk2]\n norm_arr = np.linalg.norm(arr)\n # print('norm arr', norm_arr)\n if norm_arr > 1e-4:\n _, evec_cols = np.linalg.eigh(arr)\n assert ut.is_unitary_arr(evec_cols)\n # print('bbbbbbbb', np.around(np.dot(np.dot(\n # evec_cols.conj().T,\n # arr), evec_cols).real,4))\n for k1, kk1 in enumerate(eq_class):\n for k2, kk2 in enumerate(eq_class):\n umat[kk1, kk2] = evec_cols[k1, k2]\n assert ut.is_unitary_arr(umat)\n # print('ccccccccnorm of rotated_del_dm', np.linalg.norm(\n # self.del_dm_in_sbasis.arr))\n rotated_del_dm = np.dot(\n np.dot(umat.conj().T, self.del_dm_in_sbasis.arr), umat)\n # print('norm of rotated_del_dm', np.linalg.norm(rotated_del_dm))\n self.del_dm_in_sbasis.arr = rotated_del_dm","title":""},{"docid":"b666a78a3c4f8253b314a53a530136dd","score":"0.4735516","text":"def pgradient(self):\n d = {}\n\n # Det coeff\n curr_val = self.value()\n nonzero = curr_val[0] != 0.0\n\n # dets[spin][ (phase,log), configuration, determinant]\n dets = (\n self._dets[0][:, :, self._det_map[0]],\n self._dets[1][:, :, self._det_map[1]],\n )\n\n d[\"det_coeff\"] = gpu.cp.zeros(dets[0].shape[1:], dtype=dets[0].dtype)\n d[\"det_coeff\"][nonzero, :] = (\n dets[0][0, nonzero, :]\n * dets[1][0, nonzero, :]\n * gpu.cp.exp(\n dets[0][1, nonzero, :]\n + dets[1][1, nonzero, :]\n - gpu.cp.array(curr_val[1][nonzero, np.newaxis])\n )\n / gpu.cp.array(curr_val[0][nonzero, np.newaxis])\n )\n\n for s, parm in zip([0, 1], [\"mo_coeff_alpha\", \"mo_coeff_beta\"]):\n ao = self._aovals[\n :, :, s * self._nelec[0] : self._nelec[s] + s * self._nelec[0], :\n ]\n\n split, aos = self.orbitals.pgradient(ao, s)\n mos = gpu.cp.split(gpu.cp.arange(split[-1]), gpu.asnumpy(split).astype(int))\n # Compute dj Diu/Diu\n nao = aos[0].shape[-1]\n nconf = aos[0].shape[0]\n nmo = int(split[-1])\n deriv = gpu.cp.zeros(\n (len(self._det_occup[s]), nconf, nao, nmo), dtype=curr_val[0].dtype\n )\n for det, occ in enumerate(self._det_occup[s]):\n for ao, mo in zip(aos, mos):\n for i in mo:\n if i in occ:\n col = occ.index(i)\n deriv[det, :, :, i] = self._testcol(det, col, s, ao)\n\n # now we reduce over determinants\n d[parm] = gpu.cp.zeros(deriv.shape[1:], dtype=curr_val[0].dtype)\n for di, coeff in enumerate(self.parameters[\"det_coeff\"]):\n whichdet = self._det_map[s][di]\n d[parm] += (\n deriv[whichdet]\n * coeff\n * d[\"det_coeff\"][:, di, np.newaxis, np.newaxis]\n )\n\n for k, v in d.items():\n d[k] = gpu.asnumpy(v)\n\n for k in list(d.keys()):\n if np.prod(d[k].shape) == 0:\n del d[k]\n return d","title":""},{"docid":"5b62944f2f5f14daf76514400d4af117","score":"0.4733377","text":"def __repr__(self):\n stats = self.population.getStats(self.truncFrac)\n outStr = (\"\"\"DeMin state:\n Generation: %s\n best trial solution's cost value: %s\n best trial solution: %s\\n\n worst trial solution's cost value: %s\n worst trial solution: %s\\n\n mean cost value: %s\n standard deviation of cost values: %s \n fractional tolerance: %s\n chi-square: %s\n ndf: %s\n np_trunc: %s\\n\n Parameter mean values:\\n%s\\n\n Covariance Matrix for np_trunc members:\\n%s\n \"\"\" % (self.population.generation,\n stats[0],\n self.population[self.population.ibest].x,\n stats[1],\n self.population[self.population.iworst].x,\n stats[2],\n stats[3],\n stats[4],\n stats[5],\n stats[6],\n self.population.getNpTrunc(self.truncFrac),\n stats[7],\n self.population.getCovMatRepr(stats[8])))\n return outStr","title":""}],"string":"[\n {\n \"docid\": \"364f54a74c07f748ea0080ffc36d9b1d\",\n \"score\": \"0.6417854\",\n \"text\": \"def subsystem_reduced_density_matrix(self, bit_labels):\\n bit_labels_inds = [i-1 for i in bit_labels]\\n other_labels_inds = list(set(range(self.system_size)) - set(bit_labels_inds))\\n state_reshape = np.reshape(self.state, tuple([2]*self.system_size))\\n state_reshape = np.transpose(state_reshape, bit_labels_inds + other_labels_inds)\\n state_reshape = np.reshape(state_reshape, (2**len(bit_labels_inds), 2**len(other_labels_inds)))\\n state_reshape = np.asmatrix(state_reshape)\\n rho = state_reshape*state_reshape.H\\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f06c3a022b5422d6e7db9221dbeeb3a6\",\n \"score\": \"0.6406758\",\n \"text\": \"def purity(state):\\n mat = np.reshape(state, (2, 2))\\n reduced_rho = np.dot(mat, np.conj(mat.T))\\n return np.trace(np.dot(reduced_rho, reduced_rho))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ee8f9e55a76e8517b4a14e76094cf4\",\n \"score\": \"0.6384309\",\n \"text\": \"def reduced_density_matrix(self, left_bits, right_bits, which='left'):\\n reshaped_state = np.reshape(self.state, (2**left_bits,\\n 2**right_bits)) \\n reshaped_state = np.asmatrix(reshaped_state)\\n if which == 'left':\\n rho = reshaped_state*reshaped_state.H\\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85162c067d7fa099823f5501dad6eedc\",\n \"score\": \"0.57764155\",\n \"text\": \"def _derivatives(self, state):\\n ad_state = ad.adnumber(state)\\n state_deriv = self.force_model(0, ad_state)\\n a_matrix = jacobian(state_deriv, ad_state)\\n # remove ad number from all state values before returning\\n state_deriv = [state.real for state in state_deriv]\\n\\n return state_deriv, a_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79cf5a3c466bbd9f31a3da7ef43a8265\",\n \"score\": \"0.5732339\",\n \"text\": \"def get_state_realspace_density(self, b, k, s, dim=None):\\n\\n\\t\\tself.check_c_projectors()\\n\\t\\tif dim is not None:\\n\\t\\t\\tself.update_dim(np.array(dim)//2)\\n\\t\\treturn self._get_realspace_state_density(b, k, s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bb90809375f1c7d4c2865bd2ccec94\",\n \"score\": \"0.558464\",\n \"text\": \"def get_density(self):\\n return nx.density(self.G)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05d106b220b1ee6e4f25244506b6018c\",\n \"score\": \"0.55698353\",\n \"text\": \"def quantum_density(self):\\n d12 = self._D12.sum()\\n\\n rho = np.array(\\n [[self._D11.sum(), d12], [d12.conjugate(), self._D22.sum()]]\\n )\\n\\n rho *= self.dqdp\\n\\n # For the following consistency checks, get eigenvalues\\n p = np.linalg.eigvalsh(rho)\\n\\n assert np.allclose(p[p < 0], 0), \\\"Quantum density matrix must be non-negative\\\"\\n\\n assert np.allclose(p.sum(), 1), \\\"Trace of quantum density matrix must be one\\\"\\n\\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c698a987e20025f57dbbd45c3e3d824\",\n \"score\": \"0.5514214\",\n \"text\": \"def NE2001_dens():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954fe278d25fb5a651c45cd128595f60\",\n \"score\": \"0.5507463\",\n \"text\": \"def _density(self):\\n prof_copy = self.profile.copy()\\n ptype = prof_copy.pop('type', \\\"NFW\\\")\\n return density_factory(ptype, **prof_copy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc786523a52221b43a05731b29580a9\",\n \"score\": \"0.5466165\",\n \"text\": \"def stateDensity(self, Eps):\\n return self._stateDensity(Eps)\\n '''\\n # Alternative calculaltion without interpolation\\n if not isinstance(Eps, (collections.Sequence, np.ndarray)):\\n return 16.*np.pi**2 * quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0]\\n return 16.*np.pi**2 *np.array([quad(lambda r: r**2 * np.sqrt(2.*self.potential(r) - 2.*Eps), 0., self.r_of_Eps(Eps))[0] for Eps in Eps])\\n '''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e7e001fdb35f9e2b0b2b37b56ddebaa\",\n \"score\": \"0.54642904\",\n \"text\": \"def __call__(self, state: tf.Tensor, is_density_matrix: bool = False\\n ) -> tf.Tensor:\\n if self._nqubits is None:\\n if is_density_matrix:\\n self.nqubits = len(tuple(state.shape)) // 2\\n else:\\n self.nqubits = len(tuple(state.shape))\\n\\n if self.is_controlled_by:\\n return self._controlled_by_call(state, is_density_matrix)\\n\\n if is_density_matrix:\\n state = self.einsum(self.calculation_cache.right, state,\\n tf.math.conj(self.matrix))\\n state = self.einsum(self.calculation_cache.left, state, self.matrix)\\n return state\\n\\n return self.einsum(self.calculation_cache.vector, state, self.matrix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73ac4e6dcdd8dfcf436fc9926b6cf18a\",\n \"score\": \"0.54595035\",\n \"text\": \"def testDensityOfStates(self):\\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\\n densStates = self.mode.getDensityOfStates(Elist)\\n self.mode.fourier = None; self.mode.energies = None\\n densStates = self.mode.getDensityOfStates(Elist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cec97371d2389325ad11ed52f664f0\",\n \"score\": \"0.54263574\",\n \"text\": \"def density(self, state: c_void_p, temperature: float) -> float:\\n return self.api.glycolDensity(state, self.instance, temperature)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a522d307701bceef7662e3d7225273\",\n \"score\": \"0.53983563\",\n \"text\": \"def mass_density(self):\\n return self._get_data('density') * pq.g / pq.cm**3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efcf24d459728bfefcbcfc4a9558562f\",\n \"score\": \"0.53568625\",\n \"text\": \"def calcStatePopulationDensity(self) -> None:\\n\\t\\t# selecting rows that correspond with the state\\n\\t\\tsumPopArea = self.stateCensusTractGDF['DP0010001'].sum(axis=0)\\n\\t\\tsumLandArea = self.stateCensusTractGDF['ALAND10'].sum(axis=0)\\n\\t\\t# sums_population_area= self.state.sum(axis=0)\\n\\t\\t# pop_density_calclulated= sums_population_area.DP001001/sums_population_area.LAND_AREA\\n\\t\\tpopDensityCalculated = sumPopArea / sumLandArea\\n\\t\\t# adding new col\\n\\t\\t# self.nationalCensusTractsGDF['Population Density']=None\\n\\t\\tself.nationalCensusTractsGDF['statePopDensity'] = popDensityCalculated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc4ed56fbc646f9391728c57ddf0c7cb\",\n \"score\": \"0.53507805\",\n \"text\": \"def W_red(W):\\n assert issparse(W), \\\"Matrix needs to be sparse\\\"\\n D_inv = degree_matrix(W, squared=False, indegree=False) # Assumes undireced graph, thus in-degree = out-degree.\\n D_inv.data[:] = np.power(D_inv.data, -0.5) # D^(-1/2)\\n return D_inv.dot(W).dot(D_inv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ad33e776fd43339cbc1a24518cc4b1\",\n \"score\": \"0.5348118\",\n \"text\": \"def saturated_density(self, state: c_void_p, temperature: float, quality: float) -> float:\\n return self.api.refrigerantSaturatedDensity(state, self.instance, temperature, quality)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166bdca3b0bc2ee9a22ccd5d071e23ae\",\n \"score\": \"0.5335536\",\n \"text\": \"def cc_dense(self):\\n cc_dense = self.iweights[[\\\"pid\\\", \\\"STATE\\\", \\\"iweight_state\\\"]].copy()\\n # Use PID to match initial weights with weighted targets\\n cc_dense = cc_dense.merge(self.puf_2017_filter, on=\\\"pid\\\")\\n for var in self.targ_list:\\n state_var = var + \\\"_targ\\\"\\n cc_dense[state_var] = cc_dense[var] * cc_dense[\\\"iweight_state\\\"]\\n cc_dense = cc_dense.drop(labels=self.var_list, axis=1)\\n cc_dense = cc_dense.drop(labels=[\\\"s006\\\", \\\"AGI_STUB\\\"], axis=1)\\n # j is an index for x, the variable we will solve for\\n cc_dense[\\\"j\\\"] = np.arange(len(cc_dense)) + 1\\n return cc_dense\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e3b690833477628c973f2500d5ee5c\",\n \"score\": \"0.5307281\",\n \"text\": \"def density(self):\\n return DensityModel(a=14.1 * units.gram /\\n units.cm**3, model='constant')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614c386f70be9342154c0d4d615222e9\",\n \"score\": \"0.530172\",\n \"text\": \"def testDensityOfStates(self):\\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\\n densStates = self.mode.getDensityOfStates(Elist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614c386f70be9342154c0d4d615222e9\",\n \"score\": \"0.530172\",\n \"text\": \"def testDensityOfStates(self):\\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\\n densStates = self.mode.getDensityOfStates(Elist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614c386f70be9342154c0d4d615222e9\",\n \"score\": \"0.530172\",\n \"text\": \"def testDensityOfStates(self):\\n Elist = numpy.arange(0, 100000, 2000, numpy.float)\\n densStates = self.mode.getDensityOfStates(Elist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e30eb831acf5f41270a6137d9ea4fed5\",\n \"score\": \"0.5297523\",\n \"text\": \"def reduced_state(self, wires):\\n if len(wires) == self.num_wires:\\n # reduced state is full state\\n return self._state\\n\\n # translate to wire labels used by device\\n device_wires = self.map_wires(wires)\\n\\n # reduce rho down to specified subsystems\\n ind = np.concatenate([device_wires.toarray(), device_wires.toarray() + self.num_wires])\\n rows = ind.reshape(-1, 1)\\n cols = ind.reshape(1, -1)\\n\\n return self._state[0][rows, cols], self._state[1][ind]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"956672841395e0ef4f499aa106f010f1\",\n \"score\": \"0.52795523\",\n \"text\": \"def density_matrix(psi):\\n return np.dot(psi[:, np.newaxis], psi[np.newaxis, :])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825591400c59d579f1da6146edd1c41e\",\n \"score\": \"0.52540666\",\n \"text\": \"def compute_density(self):\\n return density(self.coef_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825591400c59d579f1da6146edd1c41e\",\n \"score\": \"0.52540666\",\n \"text\": \"def compute_density(self):\\n return density(self.coef_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84dfdc84274e9b5827a102a50f6fb572\",\n \"score\": \"0.52432853\",\n \"text\": \"def _discrete_state(self):\\n return np.array(int(round(self.S * self.N)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72b2749f4888f4104463f042150bf700\",\n \"score\": \"0.5217364\",\n \"text\": \"def DO(self):\\n # statevector.py and operators.py have circular import\\n from . import operators\\n return operators.DensityOperator(self ^ self.conj(), self.basis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe31db5ab488dc0f4a5f72a53c34cf52\",\n \"score\": \"0.520201\",\n \"text\": \"def deq_mm(ssa):\\n return 1e3 * 6 / (917 * np.asarray(ssa))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b2c137509c41dff51bf722abe37482\",\n \"score\": \"0.5191413\",\n \"text\": \"def reactive_density(\\n assignments, sinks, source=None, all_reactive=False, verbose=False):\\n if not all_reactive:\\n pathways = reactive_pathways(assignments, sinks, verbose=verbose)\\n else:\\n pathways = assignments\\n\\n state_counts = np.array(\\n np.bincount(np.concatenate(pathways)))\\n densities = state_counts / np.sum(state_counts)\\n\\n return densities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10bea322cdeeb893df7b384935f3487\",\n \"score\": \"0.5177796\",\n \"text\": \"def __getDressedMatrixElements(self,UNmat):\\n UNmatdimension = len(UNmat.toarray())\\n #print(UNmat.toarray())\\n n0 = self.n\\n j0 = self.j\\n l0 = self.l\\n m0 = self.m1\\n state_main = [self.nn,self.ll,self.jj,self.m2]\\n d0 = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\\n Omega_array = []\\n Omg0 = self.Omega0 \\n for i in range(UNmatdimension):\\n d = 0 \\n if (self.basisStates[i][:4] == state_main) or (self.basisStates[i][4:] == state_main):\\n d = self.atom1.getDipoleMatrixElement(n0,l0,j0,m0,self.nn,self.ll,self.jj,self.m2,0)\\n Omega_array = np.append(Omega_array,0.5*Omg0*d/d0)\\n row = np.zeros(UNmatdimension) + 1\\n col = np.arange(UNmatdimension)\\n mat = csr_matrix((Omega_array, (row, col)), shape=(2, UNmatdimension))\\n UNmat = vstack([mat,UNmat])\\n row = np.arange(UNmatdimension+2)\\n row = np.concatenate((np.array([1]), row))\\n col = np.zeros(UNmatdimension+2) + 1\\n col = np.concatenate((np.array([0]), col))\\n Omega_array = np.concatenate((np.array([Omg0*0.5,Omg0*0.5,self.Delta0]), Omega_array))\\n mat = csr_matrix((Omega_array, (row, col)), shape=(UNmatdimension+2, 2))\\n UNmat = hstack([mat,UNmat])\\n UNmat = csr_matrix(UNmat)\\n #print(UNmat.toarray())\\n return UNmat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93bea98cc7212c6e4a8d5a3c7305310\",\n \"score\": \"0.51725036\",\n \"text\": \"def dm(self, **kwargs):\\n if self.is_pure:\\n if self.batched:\\n batch_index = indices[0]\\n free_indices = indices[1:]\\n else:\\n batch_index = \\\"\\\"\\n free_indices = indices\\n ket = self.data\\n left_str = [batch_index] + [free_indices[i] for i in range(0, 2 * self.num_modes, 2)]\\n right_str = [batch_index] + [free_indices[i] for i in range(1, 2 * self.num_modes, 2)]\\n out_str = [batch_index] + [free_indices[: 2 * self.num_modes]]\\n einstr = \\\"\\\".join(left_str + [\\\",\\\"] + right_str + [\\\"->\\\"] + out_str)\\n s = tf.einsum(einstr, ket, tf.math.conj(ket))\\n else:\\n s = tf.identity(self.data, name=\\\"density_matrix\\\")\\n\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c48980a00fc054b50cebc53e52fd805\",\n \"score\": \"0.515427\",\n \"text\": \"def to_valid_density_matrix(\\n density_matrix_rep: Union[int, np.ndarray],\\n num_qubits: Optional[int] = None,\\n *, # Force keyword arguments\\n qid_shape: Optional[Tuple[int, ...]] = None,\\n dtype: Type[np.number] = np.complex64,\\n atol: float = 1e-7) -> np.ndarray:\\n qid_shape = _qid_shape_from_args(num_qubits, qid_shape)\\n if (isinstance(density_matrix_rep, np.ndarray) and\\n density_matrix_rep.ndim == 2):\\n if density_matrix_rep.shape != (np.prod(qid_shape, dtype=int),) * 2:\\n raise ValueError(\\n 'Density matrix was not square and of size 2 ** num_qubit, '\\n 'instead was {}'.format(density_matrix_rep.shape))\\n if not np.allclose(density_matrix_rep,\\n np.transpose(np.conj(density_matrix_rep)),\\n atol=atol):\\n raise ValueError('The density matrix is not hermitian.')\\n if not np.isclose(np.trace(density_matrix_rep), 1.0, atol=atol):\\n raise ValueError(\\n 'Density matrix did not have trace 1 but instead {}'.format(\\n np.trace(density_matrix_rep)))\\n if density_matrix_rep.dtype != dtype:\\n raise ValueError(\\n 'Density matrix had dtype {} but expected {}'.format(\\n density_matrix_rep.dtype, dtype))\\n if not np.all(np.linalg.eigvalsh(density_matrix_rep) > -atol):\\n raise ValueError('The density matrix is not positive semidefinite.')\\n return density_matrix_rep\\n\\n state_vector = to_valid_state_vector(density_matrix_rep,\\n len(qid_shape),\\n qid_shape=qid_shape,\\n dtype=dtype)\\n return np.outer(state_vector, np.conj(state_vector))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f5bd7522ce30ae82d10f2d485eea53\",\n \"score\": \"0.5153912\",\n \"text\": \"def density(self):\\n return(self.mass() / ((4.0/3.0)*scipy.pi*(self.radius()**3)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce4558b5432a7aad8b52addf8a14f14\",\n \"score\": \"0.51530224\",\n \"text\": \"def density_matrix_from_state_vector(\\n state_vector: Sequence,\\n indices: Optional[Iterable[int]] = None,\\n qid_shape: Optional[Tuple[int, ...]] = None,\\n) -> np.ndarray:\\n shape = validate_qid_shape(state_vector, qid_shape)\\n n_qubits = len(shape)\\n\\n if indices is None:\\n return np.outer(state_vector, np.conj(state_vector))\\n\\n indices = list(indices)\\n validate_indices(n_qubits, indices)\\n\\n state_vector = np.asarray(state_vector).reshape(shape)\\n\\n sum_inds = np.array(range(n_qubits))\\n sum_inds[indices] += n_qubits\\n\\n rho = np.einsum(state_vector, list(range(n_qubits)), np.conj(state_vector),\\n sum_inds.tolist(), indices + sum_inds[indices].tolist())\\n new_shape = np.prod([shape[i] for i in indices], dtype=int)\\n\\n return rho.reshape((new_shape, new_shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a3b6f070d782e552643309a0e086854\",\n \"score\": \"0.51415163\",\n \"text\": \"def density_model(self) -> BaseModel1D:\\n return self._dens_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134a3fd7dc38484a5d225c4c8a41acc5\",\n \"score\": \"0.5096904\",\n \"text\": \"def spd_decomposition(S):\\n\\tP = force_symmetric_to_symmetric_positive_definite(S)\\n\\tM = symmetric_positive_definite_decomposition(P)\\n\\treturn M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0771801c8c7fff1b3ad82e4b2a1da3ff\",\n \"score\": \"0.5084259\",\n \"text\": \"def diagonalize(reduced_density_matrix):\\n try:\\n eigenvals, eigenvecs = np.linalg.eigh(reduced_density_matrix)\\t\\n except LinAlgError:\\n\\traise DMRGException(\\\"Error diagonalizing the reduced DM\\\")\\n\\n return (eigenvals, eigenvecs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef40d865d68440d621ba2b4df6efb3b\",\n \"score\": \"0.50785637\",\n \"text\": \"def deco_symmatrix ( m ) :\\n\\n if m in LinAlg.decorated_matrices : return m \\n\\n LinAlg.deco_matrix ( m )\\n \\n R = m.rep_type\\n \\n m.__str__ = LinAlg.MS_STR\\n m.__repr__ = LinAlg.MS_STR\\n m.table = LinAlg.MS_STR\\n\\n m.correlations = LinAlg.MS_CORR\\n \\n m.Sim = LinAlg.SIM\\n m.sim = LinAlg.SIM\\n m.SimT = LinAlg.SIMT\\n m.simT = LinAlg.SIMT\\n \\n m.eigenValues = LinAlg.MS_EIGENVALUES \\n m.eigenVectors = LinAlg.MS_EIGENVECTORS\\n m.eigen_values = LinAlg.MS_EIGENVALUES \\n m.eigen_vectors = LinAlg.MS_EIGENVECTORS\\n\\n m.__reduce__ = LinAlg.MS_REDUCE \\n m.rep_size = classgetter ( lambda cls : cls.rep_type.kSize ) \\n \\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02996822e121457dbadc79e77833684\",\n \"score\": \"0.50629157\",\n \"text\": \"def normalized(self):\\n # swap cols and rows and row reduce\\n A = sp.Matrix(self.mat).transpose().rref()[0]\\n # ensure all entries are integral by multiplying by the LCD\\n A *= reduce(lcm, map(lambda x: x.as_numer_denom()[1], # get denominators\\n sp.utilities.iterables.flatten(A)))\\n # see https://groups.google.com/forum/#!topic/sympy/e8hcF4QAldc\\n A = np.array(sp.lambdify((), A, 'numpy')()) # convert to numpy array\\n A = A[np.any(A,1),:].transpose() # remove 0 rows, swap rows and columns\\n return HashableMatrix(np.matrix(A))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d2948fdd0278c0960eac13394ff777\",\n \"score\": \"0.50607157\",\n \"text\": \"def test_dephasing(self):\\n\\n # Check for identity on |11> state\\n test_density_matrix = (dephasing_channel(self.density_matrix, 1, 1))\\n self.assertAlmostEqual(norm(self.density_matrix - test_density_matrix),\\n 0.0)\\n\\n test_density_matrix = (dephasing_channel(self.density_matrix,\\n 1,\\n 1,\\n transpose=True))\\n\\n correct_matrix = np.array([[0., 0., 0., 0.], [0., 0., 0., 0.],\\n [0., 0., 0.5, -0.5], [0., 0., -0.5, 1.]])\\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)\\n\\n # Check for correct action on cat state\\n # With probability = 0\\n test_density_matrix = (dephasing_channel(self.cat_matrix, 0, 1))\\n self.assertAlmostEqual(norm(self.cat_matrix - test_density_matrix), 0.0)\\n # With probability = 1\\n\\n correct_matrix = np.array([[0.50, 0.25, 0.00, 0.00],\\n [0.25, 0.25, 0.00, -0.25],\\n [0.00, 0.00, 0.00, 0.00],\\n [0.00, -0.25, 0.00, 0.50]])\\n test_density_matrix = (dephasing_channel(self.cat_matrix, 1, 1))\\n self.assertAlmostEqual(norm(correct_matrix - test_density_matrix), 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc538d60bed88cd11599c3c6959f4b5\",\n \"score\": \"0.5043117\",\n \"text\": \"def density(self):\\r\\n graph = self.__graph_dict.copy()\\r\\n vertexes = len(graph.keys())\\r\\n edges = len(self.edges())\\r\\n\\r\\n return 2.0 * edges / (vertexes *(vertexes - 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f9f9e4020c8f8330863b342a455e0c\",\n \"score\": \"0.50127727\",\n \"text\": \"def density(compound, state, value_index=0):\\n table = density_table(compound)\\n state_idx = state_index(state)\\n try:\\n return list(table.loc[(table['state'] == state_idx), 'value'])[value_index]\\n except IndexError:\\n print('Invalid state or compound')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9417401b3ef118e225d0562a146075\",\n \"score\": \"0.50119233\",\n \"text\": \"def dd(self, mu: int, nu: int):\\n\\t\\tif self.evaluated[mu,nu]: return self.Ric[mu,nu]\\n\\t\\tRic = 0\\n\\t\\tfor rho in range(self.dim):\\n\\t\\t\\tRic += self.R.uddd(rho,mu,rho,nu)\\n\\t\\tself.Ric[mu,nu] = Ric.simplify()\\n\\t\\tself.evaluated[mu,nu] = True\\n\\t\\treturn self.Ric[mu,nu]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef79b4384b1a00538764f5c98445ce4d\",\n \"score\": \"0.50119007\",\n \"text\": \"def density(self):\\n return sum(np.array(mass(self.symbols))) / self.volume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b87cc4a601fab0d64ed28e35a4405a96\",\n \"score\": \"0.50103897\",\n \"text\": \"def density(self, state: c_void_p, barometric_pressure: float, dry_bulb_temp: float, humidity_ratio: float) -> float:\\n return self.api.psyRhoFnPbTdbW(state, barometric_pressure, dry_bulb_temp, humidity_ratio)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe0da1191fb5e7d644d2e8a32e1b3aed\",\n \"score\": \"0.5010093\",\n \"text\": \"def classical_density(self):\\n rho = self._D11 + self._D22\\n\\n # Check for the normalization condition\\n assert np.allclose(rho.sum() * self.dqdp, 1), \\\"Classical density must be normalized.\\\"\\n\\n # Check for the positivity of the density\\n assert np.allclose(rho[rho < 0], 0), \\\"Classical density mus be nonnegative\\\"\\n\\n assert not np.isnan(rho).any(), \\\"Classical density contains NaNs.\\\"\\n\\n # Make sure all values are real\\n assert isinstance(rho[0, 0], np.float), \\\"Classical density must be real\\\"\\n\\n return rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fe6a799ba948be43589b718b1442f2a\",\n \"score\": \"0.5002014\",\n \"text\": \"def get_reversible_rate_matrix(nstates):\\n M = np.exp(np.random.randn(nstates, nstates))\\n pre_Q = 0.5 * (M + M.T)\\n v = sample_distn(nstates)\\n v_sqrt = np.sqrt(v)\\n pre_Q = (pre_Q.T / v_sqrt).T * v_sqrt\\n Q = pre_Q - np.diag(np.sum(pre_Q, axis=1))\\n assert_reversibility(Q, v)\\n return Q, v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080916809178ad11c99fcca08377dc79\",\n \"score\": \"0.49931666\",\n \"text\": \"def density(self):\\n return self._density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea7bff0064c282b34ff4c47214f0f4ed\",\n \"score\": \"0.49819955\",\n \"text\": \"def evaluate_density_vec(self, x):\\r\\n return self._evaluate_scalar_quantity(x[:,0], x[:,1], x[:,2], MNnModel.mn_density)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe41ebade879197650c9af2761a6dfc\",\n \"score\": \"0.4976917\",\n \"text\": \"def get_density(self,wavefunction): \\n if wavefunction is None:\\n return self.density\\n \\n density=np.zeros(self.shape,dtype=np.float)\\n for k in range(self.NK):\\n for n in range(self.nbands):\\n for m in range(self.nbands):\\n density+=2*self.wk[k]*self.f[k,n]*np.abs(wavefunction[k,m,n]*self.ukn[k,m])**2\\n return density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d65c01ac6a9758392a6b3882f17e447\",\n \"score\": \"0.49748877\",\n \"text\": \"def get_density(self, x):\\n \\n return self.get_logdensity(x).exp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c272faaf51fe765b66950a3fcb109cce\",\n \"score\": \"0.4974089\",\n \"text\": \"def generate_reduced_sim_mat(self):\\n self.reduced_sim_mat = np.zeros((self.k, self.k), dtype='float32')\\n\\n for r in range(2, self.k + 1):\\n for s in (range(1, r) if not self.drop_edges_between_irregular_pairs else self.regularity_list[r - 2]):\\n if self.is_weighted:\\n cl_pair = WeightedClassesPair(self.sim_mat, self.adj_mat, self.classes, r, s, self.epsilon)\\n else:\\n cl_pair = ClassesPair(self.adj_mat, self.classes, r, s, self.epsilon)\\n self.reduced_sim_mat[r - 1, s - 1] = self.reduced_sim_mat[s - 1, r - 1] = cl_pair.bip_density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30bb90332888e55a02603a36f21e30ef\",\n \"score\": \"0.4962091\",\n \"text\": \"def initialize_density(x,dx,normalization=1):\\n rho=exp(-x**2)\\n A=simps(rho,x)\\n return normalization/A*rho\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ef96029d241edd7a4d21334f51ce44\",\n \"score\": \"0.4958901\",\n \"text\": \"def density(self, r):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ebd7cc640cd6a72cce91981a2d5ea69\",\n \"score\": \"0.49578607\",\n \"text\": \"def mass_matrix(self):\\r\\n if not self._fr or not self._frstar:\\r\\n raise ValueError('Need to compute Fr, Fr* first.')\\r\\n return Matrix([self._k_d, self._k_dnh])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5af34f7f073abed1d0aba606c4e3d4c5\",\n \"score\": \"0.49560684\",\n \"text\": \"def _matrix(self):\\n n, d = self._x.shape\\n if n < d:\\n return np.dot(self._x, self._x.T) / float(n - 1)\\n return np.dot(self._x.T, self._x) / float(n - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b72faf3dd76737395397e71108fe09c0\",\n \"score\": \"0.49559936\",\n \"text\": \"def depurify(self, eps):\\n probs = np.random.random(self.num_rows)\\n probs = probs/np.sum(probs)\\n arr = (self.arr + eps*np.diag(probs)) / (self.trace() + eps)\\n return DenMat(self.num_rows, self.row_shape, arr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5db345c5c728b77651f7354563b650c\",\n \"score\": \"0.49555916\",\n \"text\": \"def irreducible(self):\\r\\n\\t\\treturn farq((self.num/MCD(self.den,self.num)),(self.den/MCD(self.den,self.num)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0acd9a2c48d8dba88077ac1ef6e038df\",\n \"score\": \"0.4938685\",\n \"text\": \"def compute_diagonalizing_gates(\\n basis_state, wires\\n ): # pylint: disable=arguments-differ,unused-argument\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22cbc58a6fc66c8903026332ba3d82ec\",\n \"score\": \"0.49319252\",\n \"text\": \"def distribution_matrix(mat):\\n return np.diag(stationary(mat))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ae00ca62821f82771a40d1b404a03f\",\n \"score\": \"0.49295992\",\n \"text\": \"def normalize_adj(adj):\\n # adj: dense adj\\n adj = sp.coo_matrix(adj)\\n rowsum = np.array(adj.sum(1)) # D\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten() # D^-0.5\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) # D^-0.5\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() # D^-0.5AD^0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a94ce23a0f9d4ffeea5706804d3a5d4d\",\n \"score\": \"0.49190745\",\n \"text\": \"def densityFactorDry(weather):\\n temperature = extractTemperature(weather, useKelvin=True)\\n waterVaporPressure = humidityToPressure(weather)\\n airPressure = weather.getAirPressure()*units.pascal\\n dryPressure = airPressure - waterVaporPressure\\n eqn = dryPressure.to_value(cds.mbar)*(57.90E-8 - 9.3250E-4/temperature.to_value(units.Kelvin)\\n + 0.25844/temperature.to_value(units.Kelvin)**2.)\\n densityFactor = (1. + eqn)*dryPressure.to_value(cds.mbar)/temperature.to_value(units.Kelvin)\\n return densityFactor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd36f9555dc547e0a2e2a0b67384f63\",\n \"score\": \"0.49190095\",\n \"text\": \"def strain_displacement_matrix(self):\\n L = np.zeros((3,4)) # matrix used for mapping shape function derivatives into strains\\n L[0,0] = 1; L[1,3] = 1; L[2,1:3] = 1;\\n xi = 0; eta = 0; # evaluation point\\n _,_,dNmat = self.mesh.eval_shape_func(xi,eta) # differentiated shape functions evaluated in (xi,eta)\\n Jac = np.array([[0.5,0],[0,0.5]]) # mapping back to global coordinates\\n detJ = np.linalg.det(Jac)\\n invJ = 1/detJ*np.array([[Jac[1,1], -Jac[0,1]], [-Jac[1,0], Jac[0,0]]])\\n G = np.zeros((4,4)) # mapping matrix between rectangular element space coordinates and global coordinates\\n G[:2,:2] = invJ; G[-2:,-2:] = invJ\\n B0 = np.matmul(np.matmul(L,G),dNmat) # strain displacement matrix\\n\\n return B0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7acb1f9b47bb5ec27070f96bdb361bc2\",\n \"score\": \"0.491127\",\n \"text\": \"def evaluate_density(self, x, y, z):\\r\\n return self._evaluate_scalar_quantity(x, y, z, MNnModel.mn_density)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7e01cd867d1edacc06d2d11684496c\",\n \"score\": \"0.48791188\",\n \"text\": \"def _densityfunction(\\n r: float,\\n core_wavenumber: floatcomplex,\\n shell_wavenumber: floatcomplex,\\n core_width: float,\\n shell_width: float,\\n) -> float:\\n return (\\n abs(\\n _wavefunction(r, core_wavenumber, shell_wavenumber, core_width, shell_width)\\n )\\n ** 2\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef32ca4bb9d73ed6bf0304dda863126\",\n \"score\": \"0.4866826\",\n \"text\": \"def cost_down(states):\\n return np.linalg.norm(states)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7873db806f156abc30c0f468865452ee\",\n \"score\": \"0.48629946\",\n \"text\": \"def getDensity(self):\\n return self.__density\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9936c5f50045750f35b6449fe5ae7d59\",\n \"score\": \"0.4831887\",\n \"text\": \"def _d_sigma(self):\\n try:\\n dd = self.d_derivs\\n except ValueError:\\n return None\\n den = self.density\\n dv = np.matrix(np.zeros((len(den.deriv_params))))\\n for i, pname in enumerate(den.deriv_params):\\n dv[0, i] = dd[pname].angularIntegral(self.psi_max)[0]\\n return np.sqrt((dv * self.density.covar * dv.T)[0, 0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0743541340bf045e3787e5333449e532\",\n \"score\": \"0.48263004\",\n \"text\": \"def renormalise(self):\\n\\t\\trn = self.compute_partition_function_fast()\\n\\t\\tfor prod in self.productions:\\n\\t\\t\\tif rn[prod[0]] == 0:\\n\\t\\t\\t\\tself.parameters[prod] = 0\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif len(prod) == 3:\\n\\t\\t\\t\\t\\ta,b,c = prod\\n\\t\\t\\t\\t\\tself.parameters[prod] *= (rn[b] * rn[c])/rn[a] \\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.parameters[prod] *= 1.0/rn[prod[0]]\\n\\t\\tself.trim_zeros()\\n\\t\\tself.set_log_parameters()\\n\\t\\treturn self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e562d84de910866b7f2e9ce09c62f04\",\n \"score\": \"0.48175946\",\n \"text\": \"def densFunc(z):\\t\\n\\t\\treturn rho00 + 0.5 * (rho01-rho00) * (1 + np.tanh((z-ze)/we))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12b2038a3047cfa21d8adad453bad6a9\",\n \"score\": \"0.4816128\",\n \"text\": \"def build_distribution_normalization_matrix(self):\\n vector = np.array((self.distribution_matrix * self.xtable_matrix).sum(axis=1)).T\\n mask = vector > 0\\n vector[mask] = 1 / vector[mask]\\n return diags(vector, [0], format=\\\"csr\\\", dtype=np.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a495785d1aad265edf5f3d89225b4319\",\n \"score\": \"0.4811534\",\n \"text\": \"def d2matrix(dx):\\n N, Nngb, dim = dx.shape\\n N_derivs = 2*dim + comb(dim, 2, exact=True)\\n A = np.empty((N, Nngb, N_derivs), dtype=np.float64) \\n for k in range(N):\\n for i in range(Nngb):\\n for j in range(N_derivs):\\n if j < dim:\\n A[k,i,j] = dx[k,i,j] \\n elif j < 2*dim:\\n A[k,i,j] = dx[k,i,j-dim] * dx[k,i,j-dim] / 2\\n else:\\n A[k,i,j] = dx[k,i,(j+1)%dim] * dx[k,i,(j+2)%dim] # this does the cross-terms, e.g. xy, xz, yz\\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170bba8658ede3ab821a07eb00ffc8e7\",\n \"score\": \"0.48088142\",\n \"text\": \"def d(self):\\n\\n # Calculate and return the local displacement vector\\n return matmul(self.T, self.D())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b836ed4e795f661348350e710cb2bb65\",\n \"score\": \"0.48069096\",\n \"text\": \"def density(self, q, t=0.0):\\n q = self._remove_units_prepare_shape(q)\\n orig_shape, q = self._get_c_valid_arr(q)\\n t = self._validate_prepare_time(t, q)\\n ret_unit = self.units[\\\"mass\\\"] / self.units[\\\"length\\\"] ** 3\\n return (self._density(q, t=t).T * ret_unit).to(self.units[\\\"mass density\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cc5445f9fee2382cf934dd683ea59bf\",\n \"score\": \"0.48019257\",\n \"text\": \"def calculate_density(self):\\n wavelet_density = len(self.phi_matrices[0].nonzero()[0])/(self.graph.number_of_nodes()**2)\\n wavelet_density = str(round(100*wavelet_density, 2))\\n inverse_wavelet_density = len(self.phi_matrices[1].nonzero()[0])/(self.graph.number_of_nodes()**2)\\n inverse_wavelet_density = str(round(100*inverse_wavelet_density, 2))\\n print(\\\"Density of wavelets: \\\"+wavelet_density+\\\"%.\\\")\\n print(\\\"Density of inverse wavelets: \\\"+inverse_wavelet_density+\\\"%.\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22dc3d06834558bc2cdfe95c4af1565\",\n \"score\": \"0.48018953\",\n \"text\": \"def DesignMatrix(states):\\n N = np.size(states, 0)\\n size3 = (N, L ** 2)\\n X = np.zeros(size3)\\n\\n for i in range(0, N):\\n X[i] = np.outer(states[i, :], states[i, :]).reshape(1, -1) # .ravel()\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a80ae3e4b8a7df27f667e195552e8e\",\n \"score\": \"0.4795498\",\n \"text\": \"def _normalize_adj_diag_enhance_dense(adj, diag_lambda:float, improved=False):\\n fill_value = 1 if not improved else 2\\n \\n adj = add_self_loop_dense(adj, fill_value=fill_value)\\n degree = torch.sum(adj, dim=-1)\\n degree_inv = 1 / degree\\n degree_inv[degree_inv == float(\\\"inf\\\")] = 0\\n adj = adj * degree_inv.reshape(-1, 1)\\n\\n adj += (diag_lambda * adj.diag()).diag()\\n\\n return adj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0377a23d2b9b5ab5ed62edfd4e05488\",\n \"score\": \"0.4789015\",\n \"text\": \"def _dcorss_dm(self):\\n\\n dcdm = np.empty((self.num_mog_comp, self.num_latent_proc, self.num_inducing))\\n for j in range(self.num_latent_proc):\\n dcdm[:, j, :] = -cho_solve((self.chol[j, :, :], True), self.MoG.m[:, j, :].T).T * self.MoG.pi[:, np.newaxis]\\n return dcdm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb66346d8ffa513bf4e6829ee29cc39d\",\n \"score\": \"0.478672\",\n \"text\": \"def FssInvDiag(self, recompute = True):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc864306e26f90c471f48a40ac16a0ec\",\n \"score\": \"0.4782814\",\n \"text\": \"def rh_density(self, init_dens, gamma, mach):\\n\\n dens_ratio = ((gamma+1)*mach**2)/(2+(gamma-1)*mach**2)\\n\\n final_dens = dens_ratio*init_dens\\n\\n return final_dens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039502c9e7c7e8d4ad0fd52010a922ed\",\n \"score\": \"0.47798538\",\n \"text\": \"def evalulate_function(self, disk_density: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]) -> torch.Tensor:\\n rmat, zmat = torch.meshgrid(self.rmid, self.zmid)\\n return disk_density(rmat, zmat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9564f033e5d7d266c571edf867268ed7\",\n \"score\": \"0.4779669\",\n \"text\": \"def _dcross_ds(self):\\n\\n dc_ds = np.empty((self.num_mog_comp, self.num_latent_proc, self.MoG.get_sjk_size()))\\n for j in range(self.num_latent_proc):\\n dc_ds[:, j] = -1. / 2 * np.array(\\n [self.MoG.dAinvS_dS(self.chol[j, :, :], k, j) * self.MoG.pi[k] for k in range(self.num_mog_comp)])\\n return dc_ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d44e17c8cab7dfd74d9dfe4bbc03ce\",\n \"score\": \"0.47766522\",\n \"text\": \"def compute_dci_d(importances):\\n\\n # normalize across the state_variable dimension, so we get the importance of\\n # each state variable for a slot\\n slot_normalized_importances = importances / importances.sum(axis=0)\\n sni = slot_normalized_importances\\n\\n # each entropy computed is the entropy for a slot over all the state variables\\n slot_entropy = entropy(sni, base=importances.shape[0])\\n dci_ds = 1 - slot_entropy\\n dci_d = dci_ds.mean()\\n\\n return dci_d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63108ffb70e437a436de9e0fe5091598\",\n \"score\": \"0.47744197\",\n \"text\": \"def current_density(self, xyz):\\n return self.sigma * self.electric_field(xyz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63108ffb70e437a436de9e0fe5091598\",\n \"score\": \"0.47744197\",\n \"text\": \"def current_density(self, xyz):\\n return self.sigma * self.electric_field(xyz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63108ffb70e437a436de9e0fe5091598\",\n \"score\": \"0.47744197\",\n \"text\": \"def current_density(self, xyz):\\n return self.sigma * self.electric_field(xyz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8697247f45a6c2c24dd1e3ea7b3ffc0\",\n \"score\": \"0.47690842\",\n \"text\": \"def stiffness_matrix_assembly(self,rho,sparse=True):\\n rho = rho.flatten(order='F')\\n if sparse is False:\\n self.stiffness_matrix = np.zeros((self.mesh.ndof,self.mesh.ndof))\\n mat_idx = np.indices((8,8))\\n I = mat_idx[0].flatten()\\n J = mat_idx[1].flatten()\\n # assemble stiffness matrix\\n for ele in range(len(self.mesh.edofMat)):\\n edof = self.mesh.edofMat[ele]\\n self.stiffness_matrix[edof[I],edof[J]] += (self.Emin+(rho[ele])**self.penal*(self.Emax-self.Emin))*self.KE[I,J]\\n elif sparse is True:\\n iK = np.kron(self.mesh.edofMat,np.ones((8,1))).flatten()\\n jK = np.kron(self.mesh.edofMat,np.ones((1,8))).flatten()\\n sK=((self.KE.flatten()[np.newaxis]).T*(self.Emin+(rho)**self.penal*(self.Emax-self.Emin))).flatten(order='F')\\n self.stiffness_matrix = sp.coo_matrix((sK,(iK,jK)),shape=(self.mesh.ndof,self.mesh.ndof)).tocsc()\\n\\n return self.stiffness_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af13d60edf934fd69ff211074ce62cb7\",\n \"score\": \"0.47683695\",\n \"text\": \"def simplify(self):\\r\\n fact = greatest_common_divisor(self.__numerator, self.__denominator)\\r\\n self.__numerator = self.__numerator//fact\\r\\n self.__denominator = self.__denominator//fact\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e023b8c609745368bc2f84375d6a06c\",\n \"score\": \"0.47601324\",\n \"text\": \"def simplify_state(state: SDFGState, remove_views: bool = False) -> MultiDiGraph:\\n\\n sdfg = state.parent\\n\\n # Copy the whole state\\n G = MultiDiGraph()\\n for n in state.nodes():\\n G.add_node(n)\\n for n in state.nodes():\\n for e in state.all_edges(n):\\n G.add_edge(e.src, e.dst)\\n # Collapse all mappings and their scopes into one node\\n scope_children = state.scope_children()\\n for n in scope_children[None]:\\n if isinstance(n, nodes.EntryNode):\\n G.add_edges_from([(n, x) for (y, x) in G.out_edges(state.exit_node(n))])\\n G.remove_nodes_from(scope_children[n])\\n # Remove all nodes that are not AccessNodes or have incoming\\n # wcr edges and connect their predecessors and successors\\n for n in state.nodes():\\n if n in G.nodes():\\n if (not isinstance(n, nodes.AccessNode) or (remove_views and isinstance(sdfg.arrays[n.data], data.View))):\\n for p in G.predecessors(n):\\n for c in G.successors(n):\\n G.add_edge(p, c)\\n G.remove_node(n)\\n else:\\n for e in state.all_edges(n):\\n if e.data.wcr is not None:\\n for p in G.predecessors(n):\\n for s in G.successors(n):\\n G.add_edge(p, s)\\n G.remove_node(n)\\n break\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5852be77521a54263f39fb8e44ef4dce\",\n \"score\": \"0.47589034\",\n \"text\": \"def ddx(n):\\n return sp.spdiags((np.ones((n + 1, 1)) * [-1, 1]).T, [0, 1], n, n + 1, format=\\\"csr\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1305563ab5d730662b91cba0c765426e\",\n \"score\": \"0.4755412\",\n \"text\": \"def compute(self):\\n m, n = self.D.shape\\n\\n # Allocate memory.\\n # We need +2 because we use indices starting from 1\\n # and to deal with edge cases in the backward recursion.\\n self.R_ = np.zeros((m+2, n+2), dtype=np.float64)\\n\\n _soft_dtw(self.D, self.R_, gamma=self.gamma)\\n\\n return self.R_[m, n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ff928f269c5ac8d7b3d2ae000cd42a5\",\n \"score\": \"0.47552603\",\n \"text\": \"def density(self, r):\\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**3, 0.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"779305611eb50458e49f536acc482e7f\",\n \"score\": \"0.47546223\",\n \"text\": \"def to_dense(self):\\n if self.size is None:\\n size = max(self.d.keys()) + 1\\n else:\\n size = self.size\\n dense = np.zeros(size)\\n for k,v in self.d.items():\\n dense[k] = v\\n return dense\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1db22c46cf4d3e4a22ca0797871bab30\",\n \"score\": \"0.4754565\",\n \"text\": \"def density(self, r):\\n return np.where(r > self.r_min, self.rho_s / (r/self.r_s) / (1. + r/self.r_s)**2, 0.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8eb30b81846b14a296dec1b1e7d2ea\",\n \"score\": \"0.47464868\",\n \"text\": \"def reduced_formula(self):\\n if self.structure is not None:\\n return self.composition.reduced_formula\\n return \\\"Unkonwn\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efbd2abf113aed04471eebea96b78baf\",\n \"score\": \"0.47410783\",\n \"text\": \"def s_d(self) -> float:\\n return self.s0 + 1 / (self.w * self.k_x) * self.v_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15bc534a6a908e854b492720e967cf5\",\n \"score\": \"0.47379398\",\n \"text\": \"def diagonalize_del_dm_in_sbasis_in_degenerate_spaces(self):\\n classes = ut.get_equiv_classes(list(self.dm0_eigen_sys[0]))\\n umat = np.eye(self.del_dm.num_rows, dtype=complex)\\n # eq = equivalence\\n for eq_class in classes:\\n dim = len(eq_class)\\n if dim > 1:\\n if self.verbose:\\n print(\\\"non-trivial eq class\\\", eq_class)\\n eq_class = sorted(eq_class)\\n arr = np.empty((dim, dim), dtype=complex)\\n for k1, kk1 in enumerate(eq_class):\\n for k2, kk2 in enumerate(eq_class):\\n arr[k1, k2] = self.del_dm_in_sbasis[kk1, kk2]\\n norm_arr = np.linalg.norm(arr)\\n # print('norm arr', norm_arr)\\n if norm_arr > 1e-4:\\n _, evec_cols = np.linalg.eigh(arr)\\n assert ut.is_unitary_arr(evec_cols)\\n # print('bbbbbbbb', np.around(np.dot(np.dot(\\n # evec_cols.conj().T,\\n # arr), evec_cols).real,4))\\n for k1, kk1 in enumerate(eq_class):\\n for k2, kk2 in enumerate(eq_class):\\n umat[kk1, kk2] = evec_cols[k1, k2]\\n assert ut.is_unitary_arr(umat)\\n # print('ccccccccnorm of rotated_del_dm', np.linalg.norm(\\n # self.del_dm_in_sbasis.arr))\\n rotated_del_dm = np.dot(\\n np.dot(umat.conj().T, self.del_dm_in_sbasis.arr), umat)\\n # print('norm of rotated_del_dm', np.linalg.norm(rotated_del_dm))\\n self.del_dm_in_sbasis.arr = rotated_del_dm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b666a78a3c4f8253b314a53a530136dd\",\n \"score\": \"0.4735516\",\n \"text\": \"def pgradient(self):\\n d = {}\\n\\n # Det coeff\\n curr_val = self.value()\\n nonzero = curr_val[0] != 0.0\\n\\n # dets[spin][ (phase,log), configuration, determinant]\\n dets = (\\n self._dets[0][:, :, self._det_map[0]],\\n self._dets[1][:, :, self._det_map[1]],\\n )\\n\\n d[\\\"det_coeff\\\"] = gpu.cp.zeros(dets[0].shape[1:], dtype=dets[0].dtype)\\n d[\\\"det_coeff\\\"][nonzero, :] = (\\n dets[0][0, nonzero, :]\\n * dets[1][0, nonzero, :]\\n * gpu.cp.exp(\\n dets[0][1, nonzero, :]\\n + dets[1][1, nonzero, :]\\n - gpu.cp.array(curr_val[1][nonzero, np.newaxis])\\n )\\n / gpu.cp.array(curr_val[0][nonzero, np.newaxis])\\n )\\n\\n for s, parm in zip([0, 1], [\\\"mo_coeff_alpha\\\", \\\"mo_coeff_beta\\\"]):\\n ao = self._aovals[\\n :, :, s * self._nelec[0] : self._nelec[s] + s * self._nelec[0], :\\n ]\\n\\n split, aos = self.orbitals.pgradient(ao, s)\\n mos = gpu.cp.split(gpu.cp.arange(split[-1]), gpu.asnumpy(split).astype(int))\\n # Compute dj Diu/Diu\\n nao = aos[0].shape[-1]\\n nconf = aos[0].shape[0]\\n nmo = int(split[-1])\\n deriv = gpu.cp.zeros(\\n (len(self._det_occup[s]), nconf, nao, nmo), dtype=curr_val[0].dtype\\n )\\n for det, occ in enumerate(self._det_occup[s]):\\n for ao, mo in zip(aos, mos):\\n for i in mo:\\n if i in occ:\\n col = occ.index(i)\\n deriv[det, :, :, i] = self._testcol(det, col, s, ao)\\n\\n # now we reduce over determinants\\n d[parm] = gpu.cp.zeros(deriv.shape[1:], dtype=curr_val[0].dtype)\\n for di, coeff in enumerate(self.parameters[\\\"det_coeff\\\"]):\\n whichdet = self._det_map[s][di]\\n d[parm] += (\\n deriv[whichdet]\\n * coeff\\n * d[\\\"det_coeff\\\"][:, di, np.newaxis, np.newaxis]\\n )\\n\\n for k, v in d.items():\\n d[k] = gpu.asnumpy(v)\\n\\n for k in list(d.keys()):\\n if np.prod(d[k].shape) == 0:\\n del d[k]\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b62944f2f5f14daf76514400d4af117\",\n \"score\": \"0.4733377\",\n \"text\": \"def __repr__(self):\\n stats = self.population.getStats(self.truncFrac)\\n outStr = (\\\"\\\"\\\"DeMin state:\\n Generation: %s\\n best trial solution's cost value: %s\\n best trial solution: %s\\\\n\\n worst trial solution's cost value: %s\\n worst trial solution: %s\\\\n\\n mean cost value: %s\\n standard deviation of cost values: %s \\n fractional tolerance: %s\\n chi-square: %s\\n ndf: %s\\n np_trunc: %s\\\\n\\n Parameter mean values:\\\\n%s\\\\n\\n Covariance Matrix for np_trunc members:\\\\n%s\\n \\\"\\\"\\\" % (self.population.generation,\\n stats[0],\\n self.population[self.population.ibest].x,\\n stats[1],\\n self.population[self.population.iworst].x,\\n stats[2],\\n stats[3],\\n stats[4],\\n stats[5],\\n stats[6],\\n self.population.getNpTrunc(self.truncFrac),\\n stats[7],\\n self.population.getCovMatRepr(stats[8])))\\n return outStr\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":199,"cells":{"query_id":{"kind":"string","value":"8c117fa933aa0261b2f095be44a1acc1"},"query":{"kind":"string","value":"Datatype of data in the Column."},"positive_passages":{"kind":"list like","value":[{"docid":"05b88be13604b1f61d82f087e7106449","score":"0.0","text":"def type(self) -> str:\n return pulumi.get(self, \"type\")","title":""}],"string":"[\n {\n \"docid\": \"05b88be13604b1f61d82f087e7106449\",\n \"score\": \"0.0\",\n \"text\": \"def type(self) -> str:\\n return pulumi.get(self, \\\"type\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5b8088fbe0ac252d28bc703bdde1e7c3","score":"0.8037336","text":"def datatype(self):\n return self._datatype","title":""},{"docid":"48de7eb74ceee9fe56fd65453b09294b","score":"0.7955723","text":"def dtype(self):\n return self._datatype","title":""},{"docid":"eb12f10ae87cbd514e47d2c2795b615f","score":"0.78110397","text":"def data_type(self):\n return self.__data_type","title":""},{"docid":"6d4ed81bb5320d6c2423bb3d69df574e","score":"0.7808144","text":"def data_type(self):\n return self._data_type","title":""},{"docid":"40c7e089bbf793d39ec281cc356feabe","score":"0.778775","text":"def data_type(self):\n\n return self._data_type","title":""},{"docid":"52e16ba8c58e3994b0c6693904280082","score":"0.75562376","text":"def getDataType(self):\n return self.data_type","title":""},{"docid":"fe38f5416da9523ff5363ef844ee1eee","score":"0.7527811","text":"def dtype(self):\n return self.data.dtype","title":""},{"docid":"fe38f5416da9523ff5363ef844ee1eee","score":"0.7527811","text":"def dtype(self):\n return self.data.dtype","title":""},{"docid":"fe38f5416da9523ff5363ef844ee1eee","score":"0.7527811","text":"def dtype(self):\n return self.data.dtype","title":""},{"docid":"3e025b354e1a7c9e829d89648c7c1087","score":"0.7440406","text":"def dtype(self):\n return self._data.dtype","title":""},{"docid":"3d7efb8bff7b892e5d43fe3f26645ce4","score":"0.74388254","text":"def get_data_type(self):\n return self._data_type","title":""},{"docid":"35c0a3df1a58449164ab6d9e7873efef","score":"0.7437267","text":"def GetColumnType(self, col):","title":""},{"docid":"35c0a3df1a58449164ab6d9e7873efef","score":"0.7437267","text":"def GetColumnType(self, col):","title":""},{"docid":"b0e1c4ffe8d888a052cd055327d933a5","score":"0.74280405","text":"def type(self):\n return self.data_type","title":""},{"docid":"66e145ddf40c8ad79cf7f5ccd449da7c","score":"0.74093187","text":"def _derive_data_type(column: Dict[str, Any]) -> str:\n data_type = column['data_type']\n\n if data_type in ['bigint',\n 'int',\n 'smallint',\n 'tinyint',\n 'bit',\n 'money',\n 'smallmoney',\n 'float',\n 'real',\n 'date',\n 'datetime',\n 'datetime2',\n 'datetimeoffset',\n 'smalldatetime',\n 'time',\n 'text',\n 'ntext',\n 'binary',\n 'image',\n 'xml',\n 'geography',\n 'geometry',\n 'sysname'\n ]:\n return data_type\n\n if data_type in ['decimal', 'numeric']:\n return 'decimal({0:d},{1:d})'.format(column['precision'], column['scale'])\n\n if data_type == 'char':\n return 'char({0:d})'.format(column['max_length'])\n\n if data_type == 'varchar':\n if column['max_length'] == -1:\n return 'varchar(max)'\n\n return 'varchar({0:d})'.format(column['max_length'])\n\n if data_type == 'nchar':\n return 'nchar({0:d})'.format(int(column['max_length'] / 2))\n\n if data_type == 'nvarchar':\n if column['max_length'] == -1:\n return 'nvarchar(max)'\n\n return 'nvarchar({0:d})'.format(int(column['max_length'] / 2))\n\n if data_type == 'varbinary':\n return 'varbinary({0:d})'.format(column['max_length'])\n\n raise Exception(\"Unexpected data type '{0}'\".format(data_type))","title":""},{"docid":"a879494c135a9817e6cda2d6c14a3a40","score":"0.7405552","text":"def type(self):\n return self.dtype().value","title":""},{"docid":"a1c3a4c4cfae33d72a8a32d47fd78116","score":"0.7386811","text":"def dtype(self):\r\n return self._dtype","title":""},{"docid":"73d5c050d0850073e083a35754827e37","score":"0.73845667","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"e01c3da06324ecb23284e4c7ccb2cf30","score":"0.7310465","text":"def get_data_type(self) -> np.dtype:\n return self.type","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"c5f63fcfec5353a3291c309f11fa05fd","score":"0.7302992","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"2908046b45c31bc75a4a2a8806286407","score":"0.72685397","text":"def get_dtype(self):\n pass","title":""},{"docid":"d574b68629284c0f77e3c5f614641524","score":"0.7255222","text":"def getDataType(self):\n\n return 'Scalar'","title":""},{"docid":"8759282617828147bf72a15054f23221","score":"0.72273684","text":"def dtype(self):\n return self.__dtype","title":""},{"docid":"af54a1addb8847db50a38cb5f6a5ec52","score":"0.7204325","text":"def dtype(self):\n if self.view_attr is None and self.schema.nattr > 1:\n raise NotImplementedError(\"Multi-attribute does not have single dtype!\")\n return self.schema.attr(0).dtype","title":""},{"docid":"ffd9c01758dc76f6ddf927924726b4c1","score":"0.71553457","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"ffd9c01758dc76f6ddf927924726b4c1","score":"0.71553457","text":"def dtype(self):\n return self._dtype","title":""},{"docid":"a846b510367f7c1ed56f736981e17466","score":"0.7122086","text":"def getDataType(self) -> ghidra.program.model.data.DataType:\n ...","title":""},{"docid":"8fb6045337f96701627acbda5c3ae2ae","score":"0.71182996","text":"def dtype(self):\n return self._impl.dtype","title":""},{"docid":"115f152d1e6edb1fa8e49d3d0c8a01b5","score":"0.71133626","text":"def dtype(self):\n\n return self.description._v_dtype","title":""},{"docid":"95ea02a66e8954707b382f68edbb70e0","score":"0.7089385","text":"def get_dtype(self):\n return self.dtype","title":""},{"docid":"2d9d34d1426fa35423718cbbb253ae45","score":"0.7053333","text":"def export_datatype(self):\n raise NotImplementedError","title":""},{"docid":"64f92513c029247743fde4da8da4c601","score":"0.70321596","text":"def data_type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"cdce282b1c2cb466e62f311a8e837196","score":"0.69717824","text":"def data_type(self):\n # datetime and date to int\n date_cols = ['visit_date', 'prev_visit_date', 'prev_item_move_date',\n 'last_edit_date', 'creation_date']\n for col in date_cols:\n # convert multiple time formats into single string format\n self.df[col] = pd.to_datetime(self.df[col]).dt.strftime('%Y-%m-%d %H:%M:%S')\n # make time features specific data type in order to distinguish from other numberic values\n self.df['{}_int'.format(col)] = self.df.apply(self._date_to_int, col=col, axis=1).astype(np.float32)\n # convert string format back into datetime\n self.df[col] = pd.to_datetime(self.df[col])\n # objects\n obj_cols = ['ship_id', 'address1', 'customer_id', 'sales_rep_id', 'item_id', 'old_item_id',\n 'item_UPC', 'old_item_UPC', 'ship_list_pk', 'sales_rep_id_2', 'list_header_id']\n for col in obj_cols:\n self.df[col] = self.df[col].astype(object)","title":""},{"docid":"970d2776f6fcf0c468381762f0026068","score":"0.69492733","text":"def dtype(x):\n\t return x.dtype.name","title":""},{"docid":"a4d35de6d937f89f506dd8465fa7e55f","score":"0.69273543","text":"def dtype(self):\n return self._dats[0].dtype","title":""},{"docid":"cc13474be25c43ba2a4f847adfad3c1e","score":"0.69220364","text":"def data_type(self) -> Optional[str]:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"cc13474be25c43ba2a4f847adfad3c1e","score":"0.69220364","text":"def data_type(self) -> Optional[str]:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"2e581d5c45d038177e9e16ff2d3f0f65","score":"0.69217163","text":"def getDtype(self):\n return self.dtype_str","title":""},{"docid":"4b20ec86c0865de94c347bbe146ffe34","score":"0.6917254","text":"def get_data_type(self, df, col):\n if col not in df.columns:\n raise KeyError(f'Column \"{col:s}\" not in input dataframe.')\n dt = dict(df.dtypes)[col]\n # spark conversions to numpy or python equivalent\n if dt == \"string\":\n dt = \"str\"\n elif dt in [\"timestamp\", \"date\"]:\n dt = np.datetime64\n elif dt == \"boolean\":\n dt = bool\n elif dt == \"bigint\":\n dt = np.int64\n elif dt.startswith(\"decimal(\"):\n return np.dtype(float, metadata={\"decimal\": True})\n\n return np.dtype(dt)","title":""},{"docid":"6e6a4dbea08f1e018c1f3e584fd04e63","score":"0.6893777","text":"def dtype(self) -> torch.dtype:\n pass # pragma: no cover","title":""},{"docid":"6e11f987f48a548c782a79d62674f408","score":"0.6882582","text":"def ctype(self):\n return self.data.ctype","title":""},{"docid":"5d70b338904a1e355f32e2c3c8c9575d","score":"0.68728685","text":"def data_type(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"f17bd447341f0fed24483c839c5da49f","score":"0.6854025","text":"def as_type(self):\n\t\ttypes = {\n\t\t\t'float': self.numeric_f,\n\t\t\t'bool': self.binary_class_f,\n\t\t\t'str': self.multi_class_f + self.id_f,\n\t\t\t'datetime64[ns]': self.datetime_f\n\t\t}\n\n\t\tfor i in types.keys():\n\t\t\tif len(types[i]) > 0:\n\t\t\t\tif i == 'bool':\n\t\t\t\t\tself.dataset.loc[:, types[i]] = self.dataset.loc[:, types[i]].astype(i).astype('float')\n\t\t\t\telse:\n\t\t\t\t\tself.dataset.loc[:, types[i]] = self.dataset.loc[:, types[i]].astype(i)","title":""},{"docid":"2240a99a83a8ce6962bb7e2d8dfc4adb","score":"0.68359935","text":"def transform_datatype(self, datatype):\n return datatype","title":""},{"docid":"698bd5a61e5e91af828f4d065d7a5ca1","score":"0.68257904","text":"def dtype(self):\n return self._snode._dtype","title":""},{"docid":"8e59b8ca93a41098a598cdf8ae49cfdb","score":"0.6806045","text":"def _get_column_type(self) -> None:\n rows = self._dl.get_all_table_columns()\n for row in rows:\n key = '@{0}.{1}.{2}%type@'.format(row['schema_name'], row['table_name'], row['column_name'])\n key = key.lower()\n\n value = self._derive_data_type(row)\n\n self._replace_pairs[key] = value\n\n self._io.text('Selected {0} column types for substitution'.format(len(rows)))","title":""},{"docid":"34c9b7ff2489d05df3d53ede2b72a675","score":"0.6774951","text":"def table_column_type(self, i):\n with self.free_cstring(_lib.TableColumnType(self.table, i)) as typ:\n return typ","title":""},{"docid":"4efb21d1ed016e4286e0138e960e1427","score":"0.6770946","text":"def dtype(self):\n return self.tensor.dtype","title":""},{"docid":"c44f8e10a39e055c04ffe566290dff5b","score":"0.67646646","text":"def ctype(self):\n return as_cstr(self.dtype)","title":""},{"docid":"3de070e4c310a5a07ad0e41eb0e5af3a","score":"0.6740433","text":"def form_datatype(self):\n dt = self.dataype_name()\n nD = str(len(self.nda.shape))\n et = get_lh5_element_type(self)\n return dt + '<' + nD + '>{' + et + '}'","title":""},{"docid":"7ab6124b611861e8f7261b35fba0b7ba","score":"0.67115027","text":"def get_type(self):\n return self.header.data_type","title":""},{"docid":"30eb89c3b7c6e89211db4d8a2626c2ae","score":"0.6709729","text":"def dtype(self):\n return self._get_component(\"array\").dtype","title":""},{"docid":"fbc6a7de0d3fa23da85fef18a59483ac","score":"0.66972804","text":"def _get_predefined_datatype(self):\n return None","title":""},{"docid":"d72ee64ed3cb0350c1e14d545c185955","score":"0.669686","text":"def convert_datatype(coldatatype):\n\n datatype = SqlType.text()\n def_value = ''\n \n if 'datetime' in coldatatype.lower():\n datatype = SqlType.timestamp()\n elif 'str' in coldatatype.lower():\n datatype = SqlType.text()\n elif 'boolean' in coldatatype.lower():\n datatype = SqlType.bool()\n elif 'int' in coldatatype.lower():\n datatype = SqlType.int()\n def_value = 0\n elif 'float' in coldatatype.lower():\n datatype = SqlType.double()\n def_value = 0\n elif 'period' in coldatatype.lower():\n datatype = SqlType.interval()\n elif 'object' in coldatatype.lower():\n datatype = SqlType.text()\n else:\n datatype = SqlType.text()\n \n return (datatype,def_value)","title":""},{"docid":"7d21875c687169cd72cd948c52e38935","score":"0.66671395","text":"def dtypes_of(data):\n return data.dtype","title":""},{"docid":"1e6961728ac2e377ee3f8d845ee1a929","score":"0.6664429","text":"def data_type(self) -> pulumi.Input['InstructionDataType']:\n return pulumi.get(self, \"data_type\")","title":""},{"docid":"2eb54b93b32955b17f8f324a6815ba54","score":"0.66327494","text":"def dtype(self):\n\n hdr = self.header()\n\n # Get numpy data type\n dtype = {'char': np.byte,\n 'short': np.short,\n 'int': np.intc,\n 'long': np.int_,\n 'ushort': np.ushort,\n 'uint': np.uintc,\n 'float': np.single,\n 'vec3f': np.single,\n 'vec9f': np.single,\n 'double': np.double}[hdr['type']]\n dtype = np.dtype(dtype)\n\n # Get python byte order string\n endian = {'little_endian': '<',\n 'big_endian': '>'}[hdr['systeme']]\n\n # Return data type with correct order\n return dtype.newbyteorder(endian)","title":""},{"docid":"d3ff223c19791fe116cb325d66c1bf29","score":"0.6632438","text":"def dtype(self) -> 'np.dtype':\n pass","title":""},{"docid":"81f1542bddac468ee8c73316b63c956b","score":"0.66217273","text":"def value_type(self):\n return self._value_type","title":""},{"docid":"ca8e1547158b09092fcee6c52435901b","score":"0.6602762","text":"def get_data_type(cls) -> str:\n raise NotImplementedError","title":""},{"docid":"afccd0b6a180a8c7c07bab235818ef65","score":"0.65588313","text":"def dtype(self) -> np.dtype:\n return self.__dtype","title":""},{"docid":"54fd9c1f535c68e9c4072ec355200553","score":"0.65281224","text":"def detect_datatype(data):\n if type(data) == type(np.empty(0)):\n return DataType.NUMPY\n elif type(data) == type(pd.DataFrame({'A': []})):\n return DataType.DATAFRAME","title":""},{"docid":"abbb449013006308c392dbf38126154f","score":"0.6517055","text":"def dtype(self):\n\n return self.descr._v_dtypes[self.name].base # Get rid of shape info","title":""},{"docid":"ce26c164a3ccce72b91a16f49eb2d172","score":"0.65145123","text":"def dtypes(self) -> List[str]:\n pass","title":""},{"docid":"ad950edaae613c0833cc9ef9121bb241","score":"0.65138894","text":"def dtype(self):\n return type(self._get_sequence_iloc_(0)) if len(self) > 0 else None","title":""},{"docid":"4dabbabfbf6955d71241653343428b38","score":"0.6507724","text":"def getSizeDataType(self) -> ghidra.program.model.data.DataType:\n ...","title":""},{"docid":"cb0db954e693796b7be6c46f5f716fce","score":"0.65049326","text":"def _determine_column_type(data_types):\n # Allow None which will be converted to NaN.\n if all(\n issubclass(t, (_numbers.Number, type(None))) and not issubclass(t, bool)\n for t in data_types\n ):\n return 'number'\n return 'string'","title":""},{"docid":"491f6c839537d99f47524e1b16fea22e","score":"0.6501615","text":"def type_value(ctype: Any, value: Any):\n\n # Boolean\n if ctype == platform.xlrd.XL_CELL_BOOLEAN:\n return bool(value)\n\n # Excel numbers are only float\n # Float with no decimals can be cast into int\n if ctype == platform.xlrd.XL_CELL_NUMBER and value == value // 1:\n return int(value)\n\n # Datetime\n if ctype == platform.xlrd.XL_CELL_DATE:\n return platform.xlrd.xldate.xldate_as_datetime(value, book.datemode)\n\n return value","title":""},{"docid":"eff8705d409d3f0d737ed36858dddd70","score":"0.6487331","text":"def get_field_type(self, data_type, row):\n return DatabaseIntrospection.data_types_reverse[row[7]]","title":""},{"docid":"d6e1e39e63e4d422f5e3267c1fd821fd","score":"0.64852417","text":"def _get_dtype(self) -> np.dtype:\n return self._dtype","title":""},{"docid":"06216a5ce712b85ec41a8045033e184f","score":"0.64840126","text":"def get_data_type(self, column, table):\n\t\ttry:\n\t\t\ttype_override = self.type_override[column[\"column_type\"]]\n\t\t\toverride_to = type_override[\"override_to\"]\n\t\t\toverride_tables = type_override[\"override_tables\"]\n\t\t\t\n\t\t\tif override_tables[0] == '*' or table in override_tables:\n\t\t\t\tcolumn_type = override_to\n\t\t\telse:\n\t\t\t\tcolumn_type = self.type_dictionary[column[\"data_type\"]]\n\t\texcept:\n\t\t\tcolumn_type = self.type_dictionary[column[\"data_type\"]]\n\t\treturn column_type","title":""},{"docid":"e43c2ffb38d4ac01066a916166a7693b","score":"0.64505047","text":"def data_types(self) -> dict:\n return self._data_types","title":""},{"docid":"963156aaa9eba32adec44835ab8da0ba","score":"0.6433906","text":"def evaluateColumnTypeV2(valColumn):\r\n res = \"numerical\"\r\n\r\n # get a column without missing val\r\n l = [val for val in valColumn if val != \"\"]\r\n\r\n if (len(l) == 0):\r\n res = \"empty\"\r\n else:\r\n try:\r\n for val in l:\r\n float(val)\r\n\r\n res = \"numerical\"\r\n pass\r\n except ValueError:\r\n res = \"categorical\"\r\n pass\r\n \r\n return res","title":""},{"docid":"6463851be879cbcc32965429551efc01","score":"0.64146054","text":"def test_datatype(self):\n self.assertTrue(lib.INTEGER.kind_of(lib.NUMBER))\n self.assertTrue(lib.FLOAT.kind_of(lib.NUMBER))\n self.assertTrue(lib.INTEGER.kind_of(env.ANY))","title":""},{"docid":"af0d2ae1fbf4adc2260cf4b13988c771","score":"0.64124054","text":"def data_type(a):\n\ta_type =type(a)\n\tif a_type == str:\n\t\tif len(a) == 0:\n\t\t\treturn 'no value'\n\t\telse:\n\t\t\treturn len(a)\n\t\t\n\telif a_type ==bool:\n\t\treturn a\n\telif a_type ==int:\n\n\t\tif a == 100:\n\t\t\treturn 'equal to 100'\n\t\telif a < 100:\n\t\t\treturn 'less than 100'\n\n\t\telse:\n\t\t\treturn 'more than 100'\n\n\telif a_type ==list:\n\t\ttry:\n\t\t\tif a[2]:\n\t\t\t\treturn a[2]\n\t\texcept(IndexError):\n\t\t\treturn None\n\t\telse:\n\t\t\treturn 'no value'","title":""},{"docid":"49436abf6ea1757f0b1750bdd28bcbc4","score":"0.63909143","text":"def column_data(self, column_name):\n for col in self.raw_frame.columns:\n if col.name == column_name:\n if col.dtype == fpb.INTEGER:\n return col.ints\n elif col.dtype == fpb.FLOAT:\n return col.floats\n elif col.dtype == fpb.STRING:\n return col.strings\n elif col.dtype == fpb.TIME:\n return col.times\n elif col.dtype == fpb.BOOLEAN:\n return col.bools\n else:\n raise ReadError('{} - unsupported type - {}'.format(column_name, col.dtype))\n\n raise ReadError('No column named \"{}\"'.format(column_name))","title":""},{"docid":"6d8ac16c1589d50950dd97a4040f2144","score":"0.63876945","text":"def column_types(self):\n return self._column_types","title":""},{"docid":"822ed2a73282d41b745f1ac6b8c604d7","score":"0.6382394","text":"def dtype(self) -> StringDtype: # type: ignore[override]\n return self._dtype","title":""},{"docid":"aba956cd8d21abd0d782256aba8e033a","score":"0.63798404","text":"def cell_type(self, row, col):\n if col == 0: return 'heading'\n else: return 'data'","title":""},{"docid":"ccedb8e1a1ae08ee787daf348c6814b6","score":"0.6374998","text":"def dtype(self) -> torch.dtype:\n return self.mvn.loc.dtype","title":""},{"docid":"94113e48de1e1265adaed5b57226040f","score":"0.6372429","text":"def generate_col_data(self, col: dict) -> Any:\n if self.null_column_value(col):\n return None\n\n elif col.get(\"enum\"):\n return self.fake_enum(col.get(\"enum\"))\n\n elif col[\"type\"] in [\"int\", \"long\"]:\n return self.fake_int(col)\n\n elif col[\"type\"] in [\"double\", \"float\"]:\n return self.fake_double(col)\n\n elif col[\"type\"] in [\"date\", \"datetime\"]:\n return self.fake_datetime(col)\n\n elif col[\"type\"] == \"boolean\":\n return self.fake_bool()\n\n elif col[\"type\"] == \"character\":\n return self.fake_character(special_type=self.special_cols.get(col[\"name\"]))\n else:\n n = col[\"name\"]\n t = col[\"type\"]\n raise TypeError(f\"Column: {n} has unsupported type: {t}\")","title":""},{"docid":"20b0117e6714bcff7701ddac17787f5e","score":"0.6360994","text":"def return_type(self) -> pyspark.sql.connect.proto.types_pb2.DataType:","title":""},{"docid":"0618311691836a44481f571981a2ebb3","score":"0.63605905","text":"def getDtype(self):\n return self.params.get(\"dtype\", str)","title":""},{"docid":"1bd90187a38fff80a194a06ed3175960","score":"0.63502675","text":"def dtype(self):\n return self._sparseMap.dtype","title":""},{"docid":"c3e8c8137c68f40a9b1a3039362769d5","score":"0.6344556","text":"def validate_datatype(self, datatype):\n pass","title":""},{"docid":"0a5f42bc17246b9f9362049fe4a978ec","score":"0.63274246","text":"def dtype(self, cid):\n\n # grab a small piece of data\n ind = tuple([slice(0, 1)] * self.ndim)\n arr = self.get_data(cid, view=ind)\n return arr.dtype","title":""},{"docid":"658ed7647a1702558e7c7d63c4b3b069","score":"0.63121635","text":"def dtype(self) -> torch.dtype:\n return self.test_mean.dtype","title":""},{"docid":"dff00028133ea3dbd1628442aca30d65","score":"0.63052446","text":"def convert_column_type(self,column,type=type(1)):\n self.Data_Frame[column]=self.Data_Frame[column].astype(type)","title":""},{"docid":"d49e169d9add8732b4a23006a99fb576","score":"0.63043797","text":"def GetTypeOfCol(self, *args):\n return _gdal.RasterAttributeTable_GetTypeOfCol(self, *args)","title":""},{"docid":"4c97ff9a9f5608d6e124c9fe865478e4","score":"0.63002914","text":"def get_data_type(self):\n if self.dtype == 0:\n return(\"Pumping rate\")\n elif self.dtype == 1:\n return(\"Drawdown vs time\")\n elif self.dtype == 2:\n return(\"First drawdown derivative\")\n elif self.dtype == 3:\n return(\"First drawdown derivative\")\n elif self.dtype == 4:\n return(\"Drawdown vs distance\")","title":""},{"docid":"5c4d3ad159d19f5bc6e660839c39499f","score":"0.62953544","text":"def get_datatype(self):\n return \"SET\"","title":""},{"docid":"1647d03568832267144127b0bb767492","score":"0.62765014","text":"def dtype(self):\n bitpix = self._obj._orig_bitpix\n if self._obj._orig_bscale == 1 and self._obj._orig_bzero == 0:\n dtype = fits.BITPIX2DTYPE[bitpix]\n else:\n # this method from astropy will return the dtype if the data\n # needs to be converted to unsigned int or scaled to float\n dtype = self._obj._dtype_for_bitpix()\n\n if dtype is None:\n if bitpix < 0:\n dtype = np.dtype('float{}'.format(abs(bitpix)))\n if (self._obj.header['EXTNAME'] == 'DQ' or self._obj._uint and\n self._obj._orig_bscale == 1 and bitpix == 8):\n dtype = np.uint16\n return dtype","title":""},{"docid":"921a4592765aa88fca4a8f065c3ef7ed","score":"0.62666196","text":"def _get_column_type(data, column_index):\n data_types = set()\n for row in data:\n cell = row[column_index]\n t = type(_get_value(cell))\n is_known_type = (\n cell is None or issubclass(t, _numbers.Number) or issubclass(t, str)\n )\n if not is_known_type:\n t = str\n data_types.add(t)\n return _determine_column_type(data_types)","title":""},{"docid":"487a3388fa1a65d48de03b5828ea56b3","score":"0.62574834","text":"def db_type(self):\n return 'varchar(100)'","title":""},{"docid":"cece28ed05ef7b4c4b3b2971a00c4bb5","score":"0.6256657","text":"def dtype(self):\n if hasattr(self, \"_dtype\"):\n return self._dtype\n\n self._dtype = self.get_frame(0).dtype\n return self._dtype","title":""},{"docid":"b5097c8f62ad072974a1356fb844be87","score":"0.6234462","text":"def current_datatype(self):\n app.notify(subtitle=f\"Current C lang datatype: {self.datatype}\")","title":""},{"docid":"5f57a9754a423eaa3d67e11234e443c1","score":"0.62313956","text":"def get_column_type(self,column_name):\n #import pdb;pdb.set_trace()\n out = None\n cols = self._get_table_info()\n \n for col in cols:\n if col[1] == column_name:\n out = col['type'].upper()\n break\n \n if out == None:\n raise KeyError(\"That column name is not in this table\")\n \n return out","title":""}],"string":"[\n {\n \"docid\": \"5b8088fbe0ac252d28bc703bdde1e7c3\",\n \"score\": \"0.8037336\",\n \"text\": \"def datatype(self):\\n return self._datatype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48de7eb74ceee9fe56fd65453b09294b\",\n \"score\": \"0.7955723\",\n \"text\": \"def dtype(self):\\n return self._datatype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb12f10ae87cbd514e47d2c2795b615f\",\n \"score\": \"0.78110397\",\n \"text\": \"def data_type(self):\\n return self.__data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4ed81bb5320d6c2423bb3d69df574e\",\n \"score\": \"0.7808144\",\n \"text\": \"def data_type(self):\\n return self._data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c7e089bbf793d39ec281cc356feabe\",\n \"score\": \"0.778775\",\n \"text\": \"def data_type(self):\\n\\n return self._data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52e16ba8c58e3994b0c6693904280082\",\n \"score\": \"0.75562376\",\n \"text\": \"def getDataType(self):\\n return self.data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe38f5416da9523ff5363ef844ee1eee\",\n \"score\": \"0.7527811\",\n \"text\": \"def dtype(self):\\n return self.data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe38f5416da9523ff5363ef844ee1eee\",\n \"score\": \"0.7527811\",\n \"text\": \"def dtype(self):\\n return self.data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe38f5416da9523ff5363ef844ee1eee\",\n \"score\": \"0.7527811\",\n \"text\": \"def dtype(self):\\n return self.data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e025b354e1a7c9e829d89648c7c1087\",\n \"score\": \"0.7440406\",\n \"text\": \"def dtype(self):\\n return self._data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d7efb8bff7b892e5d43fe3f26645ce4\",\n \"score\": \"0.74388254\",\n \"text\": \"def get_data_type(self):\\n return self._data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c0a3df1a58449164ab6d9e7873efef\",\n \"score\": \"0.7437267\",\n \"text\": \"def GetColumnType(self, col):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c0a3df1a58449164ab6d9e7873efef\",\n \"score\": \"0.7437267\",\n \"text\": \"def GetColumnType(self, col):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0e1c4ffe8d888a052cd055327d933a5\",\n \"score\": \"0.74280405\",\n \"text\": \"def type(self):\\n return self.data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e145ddf40c8ad79cf7f5ccd449da7c\",\n \"score\": \"0.74093187\",\n \"text\": \"def _derive_data_type(column: Dict[str, Any]) -> str:\\n data_type = column['data_type']\\n\\n if data_type in ['bigint',\\n 'int',\\n 'smallint',\\n 'tinyint',\\n 'bit',\\n 'money',\\n 'smallmoney',\\n 'float',\\n 'real',\\n 'date',\\n 'datetime',\\n 'datetime2',\\n 'datetimeoffset',\\n 'smalldatetime',\\n 'time',\\n 'text',\\n 'ntext',\\n 'binary',\\n 'image',\\n 'xml',\\n 'geography',\\n 'geometry',\\n 'sysname'\\n ]:\\n return data_type\\n\\n if data_type in ['decimal', 'numeric']:\\n return 'decimal({0:d},{1:d})'.format(column['precision'], column['scale'])\\n\\n if data_type == 'char':\\n return 'char({0:d})'.format(column['max_length'])\\n\\n if data_type == 'varchar':\\n if column['max_length'] == -1:\\n return 'varchar(max)'\\n\\n return 'varchar({0:d})'.format(column['max_length'])\\n\\n if data_type == 'nchar':\\n return 'nchar({0:d})'.format(int(column['max_length'] / 2))\\n\\n if data_type == 'nvarchar':\\n if column['max_length'] == -1:\\n return 'nvarchar(max)'\\n\\n return 'nvarchar({0:d})'.format(int(column['max_length'] / 2))\\n\\n if data_type == 'varbinary':\\n return 'varbinary({0:d})'.format(column['max_length'])\\n\\n raise Exception(\\\"Unexpected data type '{0}'\\\".format(data_type))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a879494c135a9817e6cda2d6c14a3a40\",\n \"score\": \"0.7405552\",\n \"text\": \"def type(self):\\n return self.dtype().value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1c3a4c4cfae33d72a8a32d47fd78116\",\n \"score\": \"0.7386811\",\n \"text\": \"def dtype(self):\\r\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d5c050d0850073e083a35754827e37\",\n \"score\": \"0.73845667\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e01c3da06324ecb23284e4c7ccb2cf30\",\n \"score\": \"0.7310465\",\n \"text\": \"def get_data_type(self) -> np.dtype:\\n return self.type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f63fcfec5353a3291c309f11fa05fd\",\n \"score\": \"0.7302992\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2908046b45c31bc75a4a2a8806286407\",\n \"score\": \"0.72685397\",\n \"text\": \"def get_dtype(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d574b68629284c0f77e3c5f614641524\",\n \"score\": \"0.7255222\",\n \"text\": \"def getDataType(self):\\n\\n return 'Scalar'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8759282617828147bf72a15054f23221\",\n \"score\": \"0.72273684\",\n \"text\": \"def dtype(self):\\n return self.__dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af54a1addb8847db50a38cb5f6a5ec52\",\n \"score\": \"0.7204325\",\n \"text\": \"def dtype(self):\\n if self.view_attr is None and self.schema.nattr > 1:\\n raise NotImplementedError(\\\"Multi-attribute does not have single dtype!\\\")\\n return self.schema.attr(0).dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd9c01758dc76f6ddf927924726b4c1\",\n \"score\": \"0.71553457\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd9c01758dc76f6ddf927924726b4c1\",\n \"score\": \"0.71553457\",\n \"text\": \"def dtype(self):\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a846b510367f7c1ed56f736981e17466\",\n \"score\": \"0.7122086\",\n \"text\": \"def getDataType(self) -> ghidra.program.model.data.DataType:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb6045337f96701627acbda5c3ae2ae\",\n \"score\": \"0.71182996\",\n \"text\": \"def dtype(self):\\n return self._impl.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"115f152d1e6edb1fa8e49d3d0c8a01b5\",\n \"score\": \"0.71133626\",\n \"text\": \"def dtype(self):\\n\\n return self.description._v_dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ea02a66e8954707b382f68edbb70e0\",\n \"score\": \"0.7089385\",\n \"text\": \"def get_dtype(self):\\n return self.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9d34d1426fa35423718cbbb253ae45\",\n \"score\": \"0.7053333\",\n \"text\": \"def export_datatype(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f92513c029247743fde4da8da4c601\",\n \"score\": \"0.70321596\",\n \"text\": \"def data_type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdce282b1c2cb466e62f311a8e837196\",\n \"score\": \"0.69717824\",\n \"text\": \"def data_type(self):\\n # datetime and date to int\\n date_cols = ['visit_date', 'prev_visit_date', 'prev_item_move_date',\\n 'last_edit_date', 'creation_date']\\n for col in date_cols:\\n # convert multiple time formats into single string format\\n self.df[col] = pd.to_datetime(self.df[col]).dt.strftime('%Y-%m-%d %H:%M:%S')\\n # make time features specific data type in order to distinguish from other numberic values\\n self.df['{}_int'.format(col)] = self.df.apply(self._date_to_int, col=col, axis=1).astype(np.float32)\\n # convert string format back into datetime\\n self.df[col] = pd.to_datetime(self.df[col])\\n # objects\\n obj_cols = ['ship_id', 'address1', 'customer_id', 'sales_rep_id', 'item_id', 'old_item_id',\\n 'item_UPC', 'old_item_UPC', 'ship_list_pk', 'sales_rep_id_2', 'list_header_id']\\n for col in obj_cols:\\n self.df[col] = self.df[col].astype(object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"970d2776f6fcf0c468381762f0026068\",\n \"score\": \"0.69492733\",\n \"text\": \"def dtype(x):\\n\\t return x.dtype.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d35de6d937f89f506dd8465fa7e55f\",\n \"score\": \"0.69273543\",\n \"text\": \"def dtype(self):\\n return self._dats[0].dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc13474be25c43ba2a4f847adfad3c1e\",\n \"score\": \"0.69220364\",\n \"text\": \"def data_type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc13474be25c43ba2a4f847adfad3c1e\",\n \"score\": \"0.69220364\",\n \"text\": \"def data_type(self) -> Optional[str]:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e581d5c45d038177e9e16ff2d3f0f65\",\n \"score\": \"0.69217163\",\n \"text\": \"def getDtype(self):\\n return self.dtype_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b20ec86c0865de94c347bbe146ffe34\",\n \"score\": \"0.6917254\",\n \"text\": \"def get_data_type(self, df, col):\\n if col not in df.columns:\\n raise KeyError(f'Column \\\"{col:s}\\\" not in input dataframe.')\\n dt = dict(df.dtypes)[col]\\n # spark conversions to numpy or python equivalent\\n if dt == \\\"string\\\":\\n dt = \\\"str\\\"\\n elif dt in [\\\"timestamp\\\", \\\"date\\\"]:\\n dt = np.datetime64\\n elif dt == \\\"boolean\\\":\\n dt = bool\\n elif dt == \\\"bigint\\\":\\n dt = np.int64\\n elif dt.startswith(\\\"decimal(\\\"):\\n return np.dtype(float, metadata={\\\"decimal\\\": True})\\n\\n return np.dtype(dt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6a4dbea08f1e018c1f3e584fd04e63\",\n \"score\": \"0.6893777\",\n \"text\": \"def dtype(self) -> torch.dtype:\\n pass # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e11f987f48a548c782a79d62674f408\",\n \"score\": \"0.6882582\",\n \"text\": \"def ctype(self):\\n return self.data.ctype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d70b338904a1e355f32e2c3c8c9575d\",\n \"score\": \"0.68728685\",\n \"text\": \"def data_type(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17bd447341f0fed24483c839c5da49f\",\n \"score\": \"0.6854025\",\n \"text\": \"def as_type(self):\\n\\t\\ttypes = {\\n\\t\\t\\t'float': self.numeric_f,\\n\\t\\t\\t'bool': self.binary_class_f,\\n\\t\\t\\t'str': self.multi_class_f + self.id_f,\\n\\t\\t\\t'datetime64[ns]': self.datetime_f\\n\\t\\t}\\n\\n\\t\\tfor i in types.keys():\\n\\t\\t\\tif len(types[i]) > 0:\\n\\t\\t\\t\\tif i == 'bool':\\n\\t\\t\\t\\t\\tself.dataset.loc[:, types[i]] = self.dataset.loc[:, types[i]].astype(i).astype('float')\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.dataset.loc[:, types[i]] = self.dataset.loc[:, types[i]].astype(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2240a99a83a8ce6962bb7e2d8dfc4adb\",\n \"score\": \"0.68359935\",\n \"text\": \"def transform_datatype(self, datatype):\\n return datatype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"698bd5a61e5e91af828f4d065d7a5ca1\",\n \"score\": \"0.68257904\",\n \"text\": \"def dtype(self):\\n return self._snode._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e59b8ca93a41098a598cdf8ae49cfdb\",\n \"score\": \"0.6806045\",\n \"text\": \"def _get_column_type(self) -> None:\\n rows = self._dl.get_all_table_columns()\\n for row in rows:\\n key = '@{0}.{1}.{2}%type@'.format(row['schema_name'], row['table_name'], row['column_name'])\\n key = key.lower()\\n\\n value = self._derive_data_type(row)\\n\\n self._replace_pairs[key] = value\\n\\n self._io.text('Selected {0} column types for substitution'.format(len(rows)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c9b7ff2489d05df3d53ede2b72a675\",\n \"score\": \"0.6774951\",\n \"text\": \"def table_column_type(self, i):\\n with self.free_cstring(_lib.TableColumnType(self.table, i)) as typ:\\n return typ\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4efb21d1ed016e4286e0138e960e1427\",\n \"score\": \"0.6770946\",\n \"text\": \"def dtype(self):\\n return self.tensor.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c44f8e10a39e055c04ffe566290dff5b\",\n \"score\": \"0.67646646\",\n \"text\": \"def ctype(self):\\n return as_cstr(self.dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3de070e4c310a5a07ad0e41eb0e5af3a\",\n \"score\": \"0.6740433\",\n \"text\": \"def form_datatype(self):\\n dt = self.dataype_name()\\n nD = str(len(self.nda.shape))\\n et = get_lh5_element_type(self)\\n return dt + '<' + nD + '>{' + et + '}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ab6124b611861e8f7261b35fba0b7ba\",\n \"score\": \"0.67115027\",\n \"text\": \"def get_type(self):\\n return self.header.data_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30eb89c3b7c6e89211db4d8a2626c2ae\",\n \"score\": \"0.6709729\",\n \"text\": \"def dtype(self):\\n return self._get_component(\\\"array\\\").dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc6a7de0d3fa23da85fef18a59483ac\",\n \"score\": \"0.66972804\",\n \"text\": \"def _get_predefined_datatype(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72ee64ed3cb0350c1e14d545c185955\",\n \"score\": \"0.669686\",\n \"text\": \"def convert_datatype(coldatatype):\\n\\n datatype = SqlType.text()\\n def_value = ''\\n \\n if 'datetime' in coldatatype.lower():\\n datatype = SqlType.timestamp()\\n elif 'str' in coldatatype.lower():\\n datatype = SqlType.text()\\n elif 'boolean' in coldatatype.lower():\\n datatype = SqlType.bool()\\n elif 'int' in coldatatype.lower():\\n datatype = SqlType.int()\\n def_value = 0\\n elif 'float' in coldatatype.lower():\\n datatype = SqlType.double()\\n def_value = 0\\n elif 'period' in coldatatype.lower():\\n datatype = SqlType.interval()\\n elif 'object' in coldatatype.lower():\\n datatype = SqlType.text()\\n else:\\n datatype = SqlType.text()\\n \\n return (datatype,def_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d21875c687169cd72cd948c52e38935\",\n \"score\": \"0.66671395\",\n \"text\": \"def dtypes_of(data):\\n return data.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6961728ac2e377ee3f8d845ee1a929\",\n \"score\": \"0.6664429\",\n \"text\": \"def data_type(self) -> pulumi.Input['InstructionDataType']:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eb54b93b32955b17f8f324a6815ba54\",\n \"score\": \"0.66327494\",\n \"text\": \"def dtype(self):\\n\\n hdr = self.header()\\n\\n # Get numpy data type\\n dtype = {'char': np.byte,\\n 'short': np.short,\\n 'int': np.intc,\\n 'long': np.int_,\\n 'ushort': np.ushort,\\n 'uint': np.uintc,\\n 'float': np.single,\\n 'vec3f': np.single,\\n 'vec9f': np.single,\\n 'double': np.double}[hdr['type']]\\n dtype = np.dtype(dtype)\\n\\n # Get python byte order string\\n endian = {'little_endian': '<',\\n 'big_endian': '>'}[hdr['systeme']]\\n\\n # Return data type with correct order\\n return dtype.newbyteorder(endian)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ff223c19791fe116cb325d66c1bf29\",\n \"score\": \"0.6632438\",\n \"text\": \"def dtype(self) -> 'np.dtype':\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81f1542bddac468ee8c73316b63c956b\",\n \"score\": \"0.66217273\",\n \"text\": \"def value_type(self):\\n return self._value_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca8e1547158b09092fcee6c52435901b\",\n \"score\": \"0.6602762\",\n \"text\": \"def get_data_type(cls) -> str:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afccd0b6a180a8c7c07bab235818ef65\",\n \"score\": \"0.65588313\",\n \"text\": \"def dtype(self) -> np.dtype:\\n return self.__dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54fd9c1f535c68e9c4072ec355200553\",\n \"score\": \"0.65281224\",\n \"text\": \"def detect_datatype(data):\\n if type(data) == type(np.empty(0)):\\n return DataType.NUMPY\\n elif type(data) == type(pd.DataFrame({'A': []})):\\n return DataType.DATAFRAME\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abbb449013006308c392dbf38126154f\",\n \"score\": \"0.6517055\",\n \"text\": \"def dtype(self):\\n\\n return self.descr._v_dtypes[self.name].base # Get rid of shape info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce26c164a3ccce72b91a16f49eb2d172\",\n \"score\": \"0.65145123\",\n \"text\": \"def dtypes(self) -> List[str]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad950edaae613c0833cc9ef9121bb241\",\n \"score\": \"0.65138894\",\n \"text\": \"def dtype(self):\\n return type(self._get_sequence_iloc_(0)) if len(self) > 0 else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dabbabfbf6955d71241653343428b38\",\n \"score\": \"0.6507724\",\n \"text\": \"def getSizeDataType(self) -> ghidra.program.model.data.DataType:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb0db954e693796b7be6c46f5f716fce\",\n \"score\": \"0.65049326\",\n \"text\": \"def _determine_column_type(data_types):\\n # Allow None which will be converted to NaN.\\n if all(\\n issubclass(t, (_numbers.Number, type(None))) and not issubclass(t, bool)\\n for t in data_types\\n ):\\n return 'number'\\n return 'string'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"491f6c839537d99f47524e1b16fea22e\",\n \"score\": \"0.6501615\",\n \"text\": \"def type_value(ctype: Any, value: Any):\\n\\n # Boolean\\n if ctype == platform.xlrd.XL_CELL_BOOLEAN:\\n return bool(value)\\n\\n # Excel numbers are only float\\n # Float with no decimals can be cast into int\\n if ctype == platform.xlrd.XL_CELL_NUMBER and value == value // 1:\\n return int(value)\\n\\n # Datetime\\n if ctype == platform.xlrd.XL_CELL_DATE:\\n return platform.xlrd.xldate.xldate_as_datetime(value, book.datemode)\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff8705d409d3f0d737ed36858dddd70\",\n \"score\": \"0.6487331\",\n \"text\": \"def get_field_type(self, data_type, row):\\n return DatabaseIntrospection.data_types_reverse[row[7]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e1e39e63e4d422f5e3267c1fd821fd\",\n \"score\": \"0.64852417\",\n \"text\": \"def _get_dtype(self) -> np.dtype:\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06216a5ce712b85ec41a8045033e184f\",\n \"score\": \"0.64840126\",\n \"text\": \"def get_data_type(self, column, table):\\n\\t\\ttry:\\n\\t\\t\\ttype_override = self.type_override[column[\\\"column_type\\\"]]\\n\\t\\t\\toverride_to = type_override[\\\"override_to\\\"]\\n\\t\\t\\toverride_tables = type_override[\\\"override_tables\\\"]\\n\\t\\t\\t\\n\\t\\t\\tif override_tables[0] == '*' or table in override_tables:\\n\\t\\t\\t\\tcolumn_type = override_to\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcolumn_type = self.type_dictionary[column[\\\"data_type\\\"]]\\n\\t\\texcept:\\n\\t\\t\\tcolumn_type = self.type_dictionary[column[\\\"data_type\\\"]]\\n\\t\\treturn column_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43c2ffb38d4ac01066a916166a7693b\",\n \"score\": \"0.64505047\",\n \"text\": \"def data_types(self) -> dict:\\n return self._data_types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"963156aaa9eba32adec44835ab8da0ba\",\n \"score\": \"0.6433906\",\n \"text\": \"def evaluateColumnTypeV2(valColumn):\\r\\n res = \\\"numerical\\\"\\r\\n\\r\\n # get a column without missing val\\r\\n l = [val for val in valColumn if val != \\\"\\\"]\\r\\n\\r\\n if (len(l) == 0):\\r\\n res = \\\"empty\\\"\\r\\n else:\\r\\n try:\\r\\n for val in l:\\r\\n float(val)\\r\\n\\r\\n res = \\\"numerical\\\"\\r\\n pass\\r\\n except ValueError:\\r\\n res = \\\"categorical\\\"\\r\\n pass\\r\\n \\r\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6463851be879cbcc32965429551efc01\",\n \"score\": \"0.64146054\",\n \"text\": \"def test_datatype(self):\\n self.assertTrue(lib.INTEGER.kind_of(lib.NUMBER))\\n self.assertTrue(lib.FLOAT.kind_of(lib.NUMBER))\\n self.assertTrue(lib.INTEGER.kind_of(env.ANY))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af0d2ae1fbf4adc2260cf4b13988c771\",\n \"score\": \"0.64124054\",\n \"text\": \"def data_type(a):\\n\\ta_type =type(a)\\n\\tif a_type == str:\\n\\t\\tif len(a) == 0:\\n\\t\\t\\treturn 'no value'\\n\\t\\telse:\\n\\t\\t\\treturn len(a)\\n\\t\\t\\n\\telif a_type ==bool:\\n\\t\\treturn a\\n\\telif a_type ==int:\\n\\n\\t\\tif a == 100:\\n\\t\\t\\treturn 'equal to 100'\\n\\t\\telif a < 100:\\n\\t\\t\\treturn 'less than 100'\\n\\n\\t\\telse:\\n\\t\\t\\treturn 'more than 100'\\n\\n\\telif a_type ==list:\\n\\t\\ttry:\\n\\t\\t\\tif a[2]:\\n\\t\\t\\t\\treturn a[2]\\n\\t\\texcept(IndexError):\\n\\t\\t\\treturn None\\n\\t\\telse:\\n\\t\\t\\treturn 'no value'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49436abf6ea1757f0b1750bdd28bcbc4\",\n \"score\": \"0.63909143\",\n \"text\": \"def column_data(self, column_name):\\n for col in self.raw_frame.columns:\\n if col.name == column_name:\\n if col.dtype == fpb.INTEGER:\\n return col.ints\\n elif col.dtype == fpb.FLOAT:\\n return col.floats\\n elif col.dtype == fpb.STRING:\\n return col.strings\\n elif col.dtype == fpb.TIME:\\n return col.times\\n elif col.dtype == fpb.BOOLEAN:\\n return col.bools\\n else:\\n raise ReadError('{} - unsupported type - {}'.format(column_name, col.dtype))\\n\\n raise ReadError('No column named \\\"{}\\\"'.format(column_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d8ac16c1589d50950dd97a4040f2144\",\n \"score\": \"0.63876945\",\n \"text\": \"def column_types(self):\\n return self._column_types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"822ed2a73282d41b745f1ac6b8c604d7\",\n \"score\": \"0.6382394\",\n \"text\": \"def dtype(self) -> StringDtype: # type: ignore[override]\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba956cd8d21abd0d782256aba8e033a\",\n \"score\": \"0.63798404\",\n \"text\": \"def cell_type(self, row, col):\\n if col == 0: return 'heading'\\n else: return 'data'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccedb8e1a1ae08ee787daf348c6814b6\",\n \"score\": \"0.6374998\",\n \"text\": \"def dtype(self) -> torch.dtype:\\n return self.mvn.loc.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94113e48de1e1265adaed5b57226040f\",\n \"score\": \"0.6372429\",\n \"text\": \"def generate_col_data(self, col: dict) -> Any:\\n if self.null_column_value(col):\\n return None\\n\\n elif col.get(\\\"enum\\\"):\\n return self.fake_enum(col.get(\\\"enum\\\"))\\n\\n elif col[\\\"type\\\"] in [\\\"int\\\", \\\"long\\\"]:\\n return self.fake_int(col)\\n\\n elif col[\\\"type\\\"] in [\\\"double\\\", \\\"float\\\"]:\\n return self.fake_double(col)\\n\\n elif col[\\\"type\\\"] in [\\\"date\\\", \\\"datetime\\\"]:\\n return self.fake_datetime(col)\\n\\n elif col[\\\"type\\\"] == \\\"boolean\\\":\\n return self.fake_bool()\\n\\n elif col[\\\"type\\\"] == \\\"character\\\":\\n return self.fake_character(special_type=self.special_cols.get(col[\\\"name\\\"]))\\n else:\\n n = col[\\\"name\\\"]\\n t = col[\\\"type\\\"]\\n raise TypeError(f\\\"Column: {n} has unsupported type: {t}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b0117e6714bcff7701ddac17787f5e\",\n \"score\": \"0.6360994\",\n \"text\": \"def return_type(self) -> pyspark.sql.connect.proto.types_pb2.DataType:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0618311691836a44481f571981a2ebb3\",\n \"score\": \"0.63605905\",\n \"text\": \"def getDtype(self):\\n return self.params.get(\\\"dtype\\\", str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd90187a38fff80a194a06ed3175960\",\n \"score\": \"0.63502675\",\n \"text\": \"def dtype(self):\\n return self._sparseMap.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e8c8137c68f40a9b1a3039362769d5\",\n \"score\": \"0.6344556\",\n \"text\": \"def validate_datatype(self, datatype):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a5f42bc17246b9f9362049fe4a978ec\",\n \"score\": \"0.63274246\",\n \"text\": \"def dtype(self, cid):\\n\\n # grab a small piece of data\\n ind = tuple([slice(0, 1)] * self.ndim)\\n arr = self.get_data(cid, view=ind)\\n return arr.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"658ed7647a1702558e7c7d63c4b3b069\",\n \"score\": \"0.63121635\",\n \"text\": \"def dtype(self) -> torch.dtype:\\n return self.test_mean.dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dff00028133ea3dbd1628442aca30d65\",\n \"score\": \"0.63052446\",\n \"text\": \"def convert_column_type(self,column,type=type(1)):\\n self.Data_Frame[column]=self.Data_Frame[column].astype(type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d49e169d9add8732b4a23006a99fb576\",\n \"score\": \"0.63043797\",\n \"text\": \"def GetTypeOfCol(self, *args):\\n return _gdal.RasterAttributeTable_GetTypeOfCol(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c97ff9a9f5608d6e124c9fe865478e4\",\n \"score\": \"0.63002914\",\n \"text\": \"def get_data_type(self):\\n if self.dtype == 0:\\n return(\\\"Pumping rate\\\")\\n elif self.dtype == 1:\\n return(\\\"Drawdown vs time\\\")\\n elif self.dtype == 2:\\n return(\\\"First drawdown derivative\\\")\\n elif self.dtype == 3:\\n return(\\\"First drawdown derivative\\\")\\n elif self.dtype == 4:\\n return(\\\"Drawdown vs distance\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c4d3ad159d19f5bc6e660839c39499f\",\n \"score\": \"0.62953544\",\n \"text\": \"def get_datatype(self):\\n return \\\"SET\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1647d03568832267144127b0bb767492\",\n \"score\": \"0.62765014\",\n \"text\": \"def dtype(self):\\n bitpix = self._obj._orig_bitpix\\n if self._obj._orig_bscale == 1 and self._obj._orig_bzero == 0:\\n dtype = fits.BITPIX2DTYPE[bitpix]\\n else:\\n # this method from astropy will return the dtype if the data\\n # needs to be converted to unsigned int or scaled to float\\n dtype = self._obj._dtype_for_bitpix()\\n\\n if dtype is None:\\n if bitpix < 0:\\n dtype = np.dtype('float{}'.format(abs(bitpix)))\\n if (self._obj.header['EXTNAME'] == 'DQ' or self._obj._uint and\\n self._obj._orig_bscale == 1 and bitpix == 8):\\n dtype = np.uint16\\n return dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921a4592765aa88fca4a8f065c3ef7ed\",\n \"score\": \"0.62666196\",\n \"text\": \"def _get_column_type(data, column_index):\\n data_types = set()\\n for row in data:\\n cell = row[column_index]\\n t = type(_get_value(cell))\\n is_known_type = (\\n cell is None or issubclass(t, _numbers.Number) or issubclass(t, str)\\n )\\n if not is_known_type:\\n t = str\\n data_types.add(t)\\n return _determine_column_type(data_types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487a3388fa1a65d48de03b5828ea56b3\",\n \"score\": \"0.62574834\",\n \"text\": \"def db_type(self):\\n return 'varchar(100)'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cece28ed05ef7b4c4b3b2971a00c4bb5\",\n \"score\": \"0.6256657\",\n \"text\": \"def dtype(self):\\n if hasattr(self, \\\"_dtype\\\"):\\n return self._dtype\\n\\n self._dtype = self.get_frame(0).dtype\\n return self._dtype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5097c8f62ad072974a1356fb844be87\",\n \"score\": \"0.6234462\",\n \"text\": \"def current_datatype(self):\\n app.notify(subtitle=f\\\"Current C lang datatype: {self.datatype}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f57a9754a423eaa3d67e11234e443c1\",\n \"score\": \"0.62313956\",\n \"text\": \"def get_column_type(self,column_name):\\n #import pdb;pdb.set_trace()\\n out = None\\n cols = self._get_table_info()\\n \\n for col in cols:\\n if col[1] == column_name:\\n out = col['type'].upper()\\n break\\n \\n if out == None:\\n raise KeyError(\\\"That column name is not in this table\\\")\\n \\n return out\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
625027057adfb81e74205b67f5098fb6 | Initialises an bootstrap regression object. | [
{
"docid": "15bf4fe75cae3b8a7bb5d8e482c58e9f",
"score": "0.6192862",
"text": "def __init__(self, X_data, y_data, reg):\n\n assert X_data.shape[0] == len(y_data), (\"x and y data not of equal\"\n \" lengths\")\n\n assert hasattr(reg, \"fit\"), ... | [
{
"docid": "ee09708f26f2200f9859604ff3d61d46",
"score": "0.67858565",
"text": "def initialize(self):\n self.logreg = linear_model.LogisticRegression(C=1e5)",
"title": ""
},
{
"docid": "f589575f2419ec69db992f331c348cab",
"score": "0.64760876",
"text": "def __test_bootstrap_fit(... |
329cc8ceae202def4653bbdb4e20f1e9 | Toplevel ``on_close`` logic that provides cleanup logic for realtime communication sessions. | [
{
"docid": "c495918fcefa2583801aa3d0e3203d5d",
"score": "0.0",
"text": "def on_close(self, handler, socket): # pragma: no cover\n\n if not socket.state is socket.State.ERROR:\n socket.set_state(socket.State.CLOSED)\n handler.on_close(socket.state is not socket.State.ERROR)\n return self",... | [
{
"docid": "a4cd00493476fdb45cd6d87da097eb94",
"score": "0.7646872",
"text": "def on_close(self, *args):",
"title": ""
},
{
"docid": "14fa41a093a45f741fdc1c28af7d6ed6",
"score": "0.73240906",
"text": "def on_close(_):\n logging.info('Websocket connection closed.')",
"title... |
c55d4eeccc5a2094e0571823aa305396 | Test aall for gt Array code i. General test odd length array without SIMD. | [
{
"docid": "3f2746b88d212b25fc12f6bb1dabfa96",
"score": "0.0",
"text": "def test_aall_basic_gt_b3(self):\n\t\t# One test value near the end of the array is equal to the test value.\n\t\ttestval = 842150449\n\t\tarrayval = 1052688062\n\t\ttestdata = array.array('i', [arrayval] * self.arraylength)\n\t\tte... | [
{
"docid": "2f27d5e3b57c09c8c86baa613f177d15",
"score": "0.69521815",
"text": "def test_aall_basic_gt_b2(self):\n\t\t# All data values are equal to the test value.\n\t\ttestval = 1633771875\n\t\tarrayval = testval\n\t\ttestdata = array.array('I', [arrayval] * self.arraylength)\n\n\t\t# Verify test compa... |
66319ebc81fc61a9b8194f91f5fbe55b | Port format from javascript to python version of Overcooked | [
{
"docid": "9327d0b064b33d66aafa8ae053ee0aa1",
"score": "0.0",
"text": "def json_joint_action_to_python_action(json_joint_action):\n if type(json_joint_action) is str:\n json_joint_action = eval(json_joint_action)\n return tuple(json_action_to_python_action(a) for a in json_joint_action)",
... | [
{
"docid": "d1fce47ddabd33bf2fffe6e3e7c29fe9",
"score": "0.5453539",
"text": "def washJSONinput(jsontext):\n #jsontext = re.sub('\\n', '', jsontext)\n jsontext = re.sub(r\"(?<!\\\\)(\\n\\r|\\n|\\r)\", \" \", jsontext)\n jsontext = re.sub(',\\s*]', ']', jsontext)\n jsontext = re.sub(',\\s*}',... |
0a9782ee81bb26fb84e039a1ecbdf41a | Get the identifying parameters. | [
{
"docid": "e543393755b1051c6b633dcab2fa8d74",
"score": "0.6742015",
"text": "def _identifying_params(self) -> Mapping[str, Any]:\n return {\n \"model_id\": self.model_id,\n \"model_kwargs\": self.model_kwargs,\n }",
"title": ""
}
] | [
{
"docid": "d0eb583f6810b34496922a0da6388052",
"score": "0.723974",
"text": "def getParams(self):\n pass",
"title": ""
},
{
"docid": "73464deb87e88bdec05ef72afc5f7f3b",
"score": "0.7190109",
"text": "def get_params(self):\n raise NotImplementedError",
"title": ""
... |
a85ff5c8323343bb03dfbcf523a3880f | Actual Sending of the sms | [
{
"docid": "68efa6b52c41765c3886efd016195294",
"score": "0.7036426",
"text": "def send_message(self, sms_gateway_id, from_number, to_number, sms_content, my_model_name='', my_record_id=0, media=None, queued_sms_message=None, media_filename=False):\n sms_account = self.env['sms.account'].browse(sm... | [
{
"docid": "99a9ead6ee65b8d6a10b557b8b247a88",
"score": "0.7578458",
"text": "def _send(self, message):\r\n charset='UTF-8'\r\n params = {\r\n 'action' : 'sendsms',\r\n 'user' : self.get_username(),\r\n 'password' : self.get_password(),\r\n 'from' : mess... |
4af6ee9402e059ed8fad043e8af712a8 | Safely JSONencode an object. To protect against XSS attacks, HTML special characters (, &) and unicode newlines are replaced by escaped unicode characters. Django does not escape these characters by default. Output of this method is not marked as HTML safe. If you use it inside an HTML | [
{
"docid": "9e7a27980d8bc0da5b37ef78e5665b10",
"score": "0.7651096",
"text": "def safe_json(data):\n unsafe_chars = {\n \"&\": \"\\\\u0026\",\n \"<\": \"\\\\u003c\",\n \">\": \"\\\\u003e\",\n \"\\u2028\": \"\\\\u2028\",\n \"\\u2029\": \"\\\\u2029\",\n }\n json... | [
{
"docid": "d1642861764950e40025644edb7a72f9",
"score": "0.75244474",
"text": "def json_encode(obj):\r\n return to_unicode(_json_encode(obj))",
"title": ""
},
{
"docid": "9e1e4e8590df6c0ca5534c6946317323",
"score": "0.71535826",
"text": "def _safe_str(obj):\n try:\n retu... |
504cd070164a98c21131c2e0fc61571e | Exclude certain well types from aggregate or individual metrics. | [
{
"docid": "f528baa413011893daa3efb05e640201",
"score": "0.0",
"text": "def build_exclude_query(query, exclusions, joined_entities):\n if EXCLUDE_LOW_EVENTS in exclusions:\n if WellMetric not in joined_entities:\n query = query.join(WellMetric).filter(WellMetric.accepted_event_count... | [
{
"docid": "47d34d37d13fe7831c767a526b208400",
"score": "0.59837747",
"text": "def remove_measurements(self):\n def ismeasurement(gate):\n return isinstance(gate, qiskit.circuit.measure.Measure)\n self.data = [data for data in self.data if not ismeasurement(data[0])]\n re... |
bd8e878260d0ab711a53aff554de6151 | Selects a piece when it is clicked. | [
{
"docid": "2075c1dd5a97e285e31d5632515370a7",
"score": "0.61129594",
"text": "def handleMouseRelease(self, event):\n \n # if the piece is already selected when it is clicked, it deselects it\n # and changes its border color back\n if self._selected:\n self.changeC... | [
{
"docid": "219190222d15d19ce3881bf63f04aa43",
"score": "0.7438641",
"text": "def click(self, event) -> None:\n colsize = rowsize = 64\n\n # The canvas is 512 by 512.\n # But our board is 8 by 8\n # This converts our clicked position to what it is on the board.\n # (in... |
6ecb231ff5096ce78fbd6defbb166c28 | r"""__bool__(DoubleVector self) > bool | [
{
"docid": "4692d5c6054462722656174c599970a4",
"score": "0.8644724",
"text": "def __bool__(self) -> \"bool\":\n return _model.DoubleVector___bool__(self)",
"title": ""
}
] | [
{
"docid": "1df04d9ce4808370831cdb9d7edd5e08",
"score": "0.8609209",
"text": "def __bool__(self) -> \"bool\":\n return _model.DoubleVectorVector___bool__(self)",
"title": ""
},
{
"docid": "1df04d9ce4808370831cdb9d7edd5e08",
"score": "0.8609209",
"text": "def __bool__(self) -> ... |
2293d781810fba97ee398b65fb1842c4 | Constructs a ResNet18 model. | [
{
"docid": "3bb9cb5b6202c6c99c26eae0170eee3a",
"score": "0.0",
"text": "def se_resnet18(num_classes):\n model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes)\n model.avgpool = nn.AdaptiveAvgPool2d(1)\n return model",
"title": ""
}
] | [
{
"docid": "2c8d7eb1332a08e0aab78e517cc4e588",
"score": "0.7498665",
"text": "def resnet18(pretrained=True, **kwargs):\n model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)\n # model.class_classifier: (512, cls)\n if pretrained:\n model.load_state_dict(model_zoo.load_url(\n mod... |
c180c31a7a2b1a63b65435bd5ca34831 | makes a colon delimited list from List | [
{
"docid": "fa75ac79feca99b06e944e975eadf784",
"score": "0.7750035",
"text": "def makelist(List):\n clist=\"\"\n for element in List:\n clist=clist+element+\":\"\n return clist[:-1]",
"title": ""
}
] | [
{
"docid": "11d1cb31209e79b9f40e262fba2d7268",
"score": "0.651048",
"text": "def splitingFunction(splitList):\n newlist = []\n for item in splitList:\n split = item.split(':')[0]\n newlist.append(split)\n return newlist",
"title": ""
},
{
"docid": "8669db494fbd1b91d743... |
988e2a7752f8000ebc216e3b5fe90fff | This is the creator. It can be passed the the min/max X and Y values for the plane, and a transformation function (f). There are default values if the parameters are not passed to the creator. The creator then generates a 2D plane filled with the X & Y complex number coordinates of the specified plane transformed by th... | [
{
"docid": "ad9e077f02647ddb17277f388d0ccd4c",
"score": "0.64841443",
"text": "def __init__(self, newXmin=-5., newXmax=5., newXlen=1001, newYmin=-5., newYmax=5., newYlen=1001, f=lambda x: x, maxLoop=100):\n self.xmin = newXmin\n self.xmax = newXmax\n self.ymin = newYmin\n sel... | [
{
"docid": "5e86faa6a75ba40509e0990332270853",
"score": "0.6098736",
"text": "def make_plane():\n\n source = vtk.vtkPlaneSource()\n source.SetOrigin(-10.0, -10.0, 0.0)\n source.SetPoint2(-10.0, 10.0, 0.0)\n source.SetPoint1(10.0, -10.0, 0.0)\n source.SetXResolution(20)\n source.SetYRes... |
0432d19c960ac28feb72115c29bf9549 | This generator outputs unique lines from a file line by line. | [
{
"docid": "8af6f1e81a26b94d3e539cfb7442eae7",
"score": "0.7510291",
"text": "def read_file_line_by_line(file_name):\n with open(file_name) as f:\n unique_strings = []\n while True:\n line = f.readline()\n if line not in unique_strings:\n unique_stri... | [
{
"docid": "8d23355987653c742f5f54eaca177c26",
"score": "0.6710934",
"text": "def gen_lines(files):\n for file in files:\n with open(file, 'r') as fin:\n for line in fin.readlines():\n yield line",
"title": ""
},
{
"docid": "7ace98676562ef4bdfae958f481201d... |
217c6404ce15f75ab8975641f863146e | Discretization of scattering energies. | [
{
"docid": "0d2a37ec01b25e7aa9ff57dfa9c9c6a7",
"score": "0.53659004",
"text": "def discrete_energies(E, dE, N=1024, WE=8.):\n # Discrete energies that contains the \"elastic\" points:\n # nu0=nu2 and nu0=nu3.\n NdE = np.ceil(N / 2 / (dE / 2 + WE) * dE / 2)\n Lq = N / 2 / NdE * dE / 2 if np.a... | [
{
"docid": "336d994e96b34208653edd65317db622",
"score": "0.5948378",
"text": "def _spatial_dispersal(self, iteration: int, n_iterations: int) -> None:\n\n coef = ((n_iterations - iteration) ** self.e) / (\n (n_iterations + c.EPSILON) ** self.e\n )\n\n self.sigma = coef * ... |
4f6e8955cbc1ecda2a3c874da38e334e | Convert the dict of probabilities into a single numpy array. Used to pass the probabilities to the C numpy extension. | [
{
"docid": "7357f9b100e6c8991a9109526506d64c",
"score": "0.63100165",
"text": "def _prob_dict_to_np(self, nframes, naive_bayes=False):\n\n # Get a list of classes for results\n clses = self._classnames\n k = clses[0]\n\n # Allocate and fill each array\n sprobs = np.zer... | [
{
"docid": "00e96822a67220e93768623e53908805",
"score": "0.68825066",
"text": "def dict_to_array(dicti):\n vals = [i for i in dicti.keys()]\n counts = [i for _, i in dicti.items()]\n return np.repeat(vals, counts)",
"title": ""
},
{
"docid": "00e96822a67220e93768623e53908805",
"... |
67185509529d539e7c34a5b449ea8449 | Stores the given value via the specified ``overlay`` and ``key``. | [
{
"docid": "e3998d155d60a5f22ecf7f83b4401d29",
"score": "0.80998534",
"text": "def setData(self, overlay, key, value):\n ovlDict = self.__overlayData.get(overlay, None)\n\n if ovlDict is not None:\n ovlDict[key] = value\n else:\n self.__overlayData[overlay] = {... | [
{
"docid": "b6bf62ad11dfb2ed2bf62b7d4870f25d",
"score": "0.60467625",
"text": "def put(key, value):",
"title": ""
},
{
"docid": "65cad391471ae0eb47c07344470a771c",
"score": "0.59677273",
"text": "def put(self, key, value):",
"title": ""
},
{
"docid": "e578787b9ae54aa9ef9e... |
a406a3ccb0968cd4c5d2af4f392707c4 | Get the first element returned by parse_baseline. | [
{
"docid": "576c1609710a86c974ee0caefe1fb4c0",
"score": "0.7085118",
"text": "def get_baseline_0(row: element.Tag) -> Optional[str]:\n try:\n return parse_baseline(row)[0].strip()\n except Exception as e:\n logger.debug(\"Function get_baseline_0 for row %s : %s\", row, e)\n re... | [
{
"docid": "710e2239fe3c5649e4a0b2809f187014",
"score": "0.7128509",
"text": "def get_baseline_1(row: element.Tag) -> Optional[str]:\n try:\n return parse_baseline(row)[1].strip()\n except Exception as e:\n logger.debug(\"Function get_baseline_1 for row %s : %s\", row, e)\n re... |
958dc260041318e94a7d2992e82f4966 | Either pass name or enum itself, e.g `"local"` or `ChannelPicker.local`, or a list of one or more of those, e.g. `["national", "local"]`. If `remove_variants` is True, only keep the canonical form (the last) where there are multiple entries with the same title. Exclude the children's channel "CBeebies" (does not parse ... | [
{
"docid": "66f31fb67bbbdca81de26329cd0c585a",
"score": "0.4037361",
"text": "def keys_by_category(cls, category, remove_variants=False, remove_cbeebies=True):\n keys = []\n if isinstance(category, list):\n for c in category:\n keys.extend(cls.keys_by_category(c, ... | [
{
"docid": "7e995dec9689c3c0d5f15c1a0d2c4a80",
"score": "0.49973446",
"text": "def filter_releases_by_channel(releases: List[dict], channel: str):\n if channel not in [\"stable\", \"beta\", \"alpha\"]:\n raise ValueError(\"Channel must be one of 'stable', 'beta', 'alpha'\")\n\n if channel =... |
855be140d8423db09b16cf922a2d0314 | Try to get event from event queue | [
{
"docid": "66dff9d1e0011204d646601720f70914",
"score": "0.72469306",
"text": "def getEvent(self):\n try:\n ev = self.eventq.get(False)\n except:\n return None\n return ev",
"title": ""
}
] | [
{
"docid": "f433116c31a9524023c0c3aef5ba3e43",
"score": "0.7179411",
"text": "def pick_event(self):\n logger.debug(\"checking event queue\")\n event = snap7.snap7types.SrvEvent()\n ready = ctypes.c_int32()\n code = self.library.Srv_PickEvent(self.pointer, ctypes.byref(event),... |
b0cdacba661f704d7960a2fbf39e3eab | Override reset_target_position from hectorquad_env to also delete trees | [
{
"docid": "7e8ba560a58f499be4c3538e731e91bf",
"score": "0.60241246",
"text": "def reset_target_position(self):\n self.goal = self._sample_goal()\n target_cmd = \"gz model -m \" + self.target_name + \" -x {} -y {} -z {}\".format(*self.goal)\n subprocess.call(target_cmd,shell=True)\n... | [
{
"docid": "dafeb299af4a0d6607b9c620b7636deb",
"score": "0.58593154",
"text": "def untarget_location():\n global __GRAPH__\n __GRAPH__ = None",
"title": ""
},
{
"docid": "19a9661811e6e471c109de440e7e6888",
"score": "0.58495784",
"text": "def destroy_tree(self, tree):\n p... |
d2d4c69573a6987380877809bb98ee11 | Numeric value confirming this is the zone's DNSKEY | [
{
"docid": "3e58e1dc66f48d867c5c3c91c0f42095",
"score": "0.0",
"text": "def flags(self):\n self._pull()\n return self._flags",
"title": ""
}
] | [
{
"docid": "9233a0eda3bf8db5065f4038cc0725eb",
"score": "0.5917154",
"text": "def keytag(dnskey):\n if dnskey.algorithm == 1:\n a = ord(dnskey.key[-3]) << 8\n b = ord(dnskey.key[-2])\n return a + b\n else:\n header = struct.pack(\"!HBB\", dnskey.flags, dnskey.protocol,\... |
dde64f202a48bd01e8833eb1d28427af | Creates or updates a remediation at resource group scope. | [
{
"docid": "b11475b6785c9d970d4dfdbbabb455e9",
"score": "0.7138369",
"text": "def create_or_update_at_resource_group(\n self,\n resource_group_name, # type: str\n remediation_name, # type: str\n parameters, # type: \"_models.Remediation\"\n **kwargs # type: Any\n ... | [
{
"docid": "af2222181999b70c89a1243fa3ad1d4d",
"score": "0.6849127",
"text": "def create_or_update_at_management_group(\n self,\n management_group_id, # type: str\n remediation_name, # type: str\n parameters, # type: \"_models.Remediation\"\n **kwargs # type: Any\n... |
3c04d8af50b3919f9e97e929008fb5ff | Returns the noise covariance for the motion model. Assumes a diagonal structure for now. | [
{
"docid": "b2de8502e03abc6778161866151c328b",
"score": "0.55802447",
"text": "def get_motion_covar(self, dt):\n return self.motion_covar",
"title": ""
}
] | [
{
"docid": "52217a99a1ba06b728716177e5ef9ae1",
"score": "0.72257763",
"text": "def getNoiseCov2D(self):\n noise_cov_mat = np.array([\n [self.forward_velocity_noise_std ** 2., 0., 0.],\n [0., self.forward_velocity_noise_std ** 2., 0.],\n [0., 0., self.yaw_rate_nois... |
5b24a3d925dee9eea90f6bb8e80ea452 | Create an empty file with the specified path. | [
{
"docid": "e6b3a69da0e27387cd0b89ed40baa79d",
"score": "0.6326646",
"text": "def create_file(self, path: str, filename: str):\n self._change_dir(path)\n self.ftp.storbinary(f'STOR {filename}', BytesIO())",
"title": ""
}
] | [
{
"docid": "0105c4a95500b0b8ebc9f23dc501ec83",
"score": "0.8275744",
"text": "def make_empty_file(file_path: str):\r\n open(file_path, 'a').close()",
"title": ""
},
{
"docid": "3f6c989939edb8c91e3aca2899ded9e8",
"score": "0.7651341",
"text": "def create_file(path):\n with open(... |
5c16a482f7a757991ddf0cdaf4bb15a4 | Sets the specified host's ability to accept new instances. | [
{
"docid": "6b19995ff5d8a1e937b499be3b6fa6b8",
"score": "0.57665277",
"text": "def set_host_enabled(self, host, enabled):\n raise NotImplementedError()",
"title": ""
}
] | [
{
"docid": "4beedd366207c71c155936dc1228c681",
"score": "0.5856326",
"text": "def addBan(self, hostmask, expiration=0):\r\n assert not conf.supybot.protocols.irc.strictRfc() or \\\r\n ircutils.isUserHostmask(hostmask), 'got %s' % hostmask\r\n self.bans[hostmask] = int(expira... |
429f77e50426e92b6376973ec62b2161 | Returns true if both objects are not equal | [
{
"docid": "6b6c9d0a192510f895b0a5b6b6744b9a",
"score": "0.0",
"text": "def __ne__(self, other):\n if not isinstance(other, ConfigurationList):\n return True\n\n return self.to_dict() != other.to_dict()",
"title": ""
}
] | [
{
"docid": "c15c693dce4313646a829d4ac4001f79",
"score": "0.84119445",
"text": "def __ne__(self, other: object) -> bool:\n return not self == other",
"title": ""
},
{
"docid": "08a3a7ddcafaa068642f3c6cdfde6129",
"score": "0.8392633",
"text": "def __ne__(self, other: object) -> ... |
d459f0d3f8b05ab0775fd17971c9d7c6 | Test creating a policy with the default values | [
{
"docid": "b7da8e756309a176023a1cd47ca2de83",
"score": "0.71639955",
"text": "def test_defaults(self):\n p = RequestPolicy.from_dict({})\n self.assertTrue(p.validate_signatures)",
"title": ""
}
] | [
{
"docid": "16990dc1123a821d58bc52b849f9d3dd",
"score": "0.71139294",
"text": "def test_create_alert_policy(self):\n pass",
"title": ""
},
{
"docid": "53e5896b50d3184c74a003708c5aafec",
"score": "0.68160045",
"text": "def test_execute_policy(self):\n pass",
"title":... |
ea53c144dfd2ef432f2ae9576ba9d3b4 | Run testing command. Raises Exception If there are any failed tests. | [
{
"docid": "9d3392e632071b2ee08ddeb10b5d9578",
"score": "0.7361913",
"text": "def _run_tests() -> None:\r\n logger.info('testing command started.')\r\n stdout: str = _run_command(\r\n command=(\r\n 'pytest --cov=./apysc tests/ -v -s --workers auto '\r\n '--cov-report t... | [
{
"docid": "d92ce6264f9f77f50e3bc91a4cb02d68",
"score": "0.78962797",
"text": "def run_tests(self):\n # import here, cause outside the eggs aren't loaded\n import tox\n errcode = tox.cmdline(self.test_args)\n sys.exit(errcode)",
"title": ""
},
{
"docid": "d92ce626... |
023824f8b4f2c854626422b86deda60a | The name of an already existing project | [
{
"docid": "9ba58577ef24966af47b261a7f6d5f52",
"score": "0.738892",
"text": "def project_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"project_name\")",
"title": ""
}
] | [
{
"docid": "9830d1238f46a7354a4b7bb521948ce9",
"score": "0.81454676",
"text": "def project_name(self) -> str:\n ...",
"title": ""
},
{
"docid": "0cbb6fa22a254b64ffb7d9c2d600262d",
"score": "0.8130705",
"text": "def project_name() -> str:\n return PROJECT_NAME",
"title":... |
023be0204b65a30b3822649b328a09be | r"""Writes a checkpoint to the file system. | [
{
"docid": "3e4f7a53e6fef623357078b309a28868",
"score": "0.67102903",
"text": "def __call__(\n self,\n checkpoint: Union[Checkpoint, Summary],\n ) -> Union[Checkpoint, Summary]:\n return self.write(checkpoint)",
"title": ""
}
] | [
{
"docid": "cfa0f7d273333324ccf8a8aed844a768",
"score": "0.7748798",
"text": "def write_checkpoint(self):\n\n print('checkpointing at',self.tm_minutes,'minutes')\n success=write_fits(self.checkpoint_file, self.data)",
"title": ""
},
{
"docid": "00f05d164a5452b257b48bd4fe59e01e"... |
4ac2e54590f993f25d4c93be27d37de1 | An object to perform Mean Pooling that ignores PADtoken representations | [
{
"docid": "fce1104ec07a2850aa4a0b1b96958266",
"score": "0.6255539",
"text": "def __init__(self):\n super(MeanMaskedPooling, self).__init__()",
"title": ""
}
] | [
{
"docid": "ddf59ab9f023d2a3c12019cd0472996b",
"score": "0.5281446",
"text": "def __init__(\n self,\n pools: list[LiquidityPair],\n token_in: Token,\n token_out: Token,\n ):\n self.pools: LiquidityPair\n\n tokens = [token_in]\n for pair in pools:\n ... |
c85978c1d384804b672ed6f512e49e4a | Creates a CROD card | [
{
"docid": "4d20a4bfeacf4d99e100688379ffb27a",
"score": "0.0",
"text": "def __init__(self, eid, pid, nids, comment=''):\n RodElement.__init__(self)\n if comment:\n self.comment = comment\n self.eid = eid\n self.pid = pid\n self.nodes = self.prepare_node_ids(... | [
{
"docid": "befd41ff71f86542ff2345867d5668d9",
"score": "0.74394673",
"text": "def create_card(self, data: dict) -> Any:\n\n return self._post('/create/card', data)",
"title": ""
},
{
"docid": "8cb4c0b4b6afd4be477ea060678d534c",
"score": "0.74331784",
"text": "def test_create_... |
33d0e9c7beacc6d5cd3bf11770cd25e4 | Loads all data from file as a list of lines | [
{
"docid": "6e588041fdd09e5400429f132a83d461",
"score": "0.7420914",
"text": "def loadLines(callingFile):\n\n filePath = File.getRealPath(callingFile) + \"/data.txt\"\n\n with open(filePath, 'r') as file:\n data = file.readlines()\n\n return data",
"title": ""
}
] | [
{
"docid": "a1ad9c21c9802b1e0e18c648191b0541",
"score": "0.780131",
"text": "def load_lines(filename):\n try:\n in_handler = open(filename)\n lines = [l.strip() for l in in_handler.readlines()]\n in_handler.close()\n return lines \n except Exception, e:\n sys.exi... |
3463fa2a7195aa259691ae9fe130ef14 | Decorates a function with args so it can be used within an arg_scope. | [
{
"docid": "0c53418651a9027814b4fb43bc4eca4c",
"score": "0.7400191",
"text": "def add_arg_scope(func):\n @functools.wraps(func)\n def func_with_args(*args, **kwargs):\n current_scope = _current_arg_scope()\n current_args = kwargs\n key_func = _key_op(func)\n if key_func in current_scope:\n... | [
{
"docid": "2ff98bbb53e97fe0c10e1aae00c485aa",
"score": "0.77008224",
"text": "def _decorator(func):\n add_arg(func, *args, **kwargs)\n return func",
"title": ""
},
{
"docid": "8ca7fcfaf5b9f97666178435f80a7e76",
"score": "0.73563796",
"text": "def expanded_args(func):\n... |
984de8f76634970f9ee3ff2bd069c0de | returns dates to buy on | [
{
"docid": "5d944fced81ddaaed27b318bf33a2d15",
"score": "0.0",
"text": "def buy_orders(self):\n \traise NotImplementedError",
"title": ""
}
] | [
{
"docid": "3de819151e518daf959dce419ae9ac67",
"score": "0.65969247",
"text": "def daily():",
"title": ""
},
{
"docid": "962b7089a27f4c95efe8c39661c98eb8",
"score": "0.64363086",
"text": "def get_buy_day(self):\n # Write your code here\n profit = 0\n buy_day = 0\... |
3f1bd3557a40150f6f3867fe96f4f28d | display all the assignments as per subjects | [
{
"docid": "18ffe5b655118cc61e341ba7eda2744a",
"score": "0.5649913",
"text": "def single(sub_id):\n db = con()\n \n assignments = db.execute(\"SELECT * FROM assignments WHERE subject_id =?\",(sub_id,)).fetchall()\n subject = db.execute(\"SELECT * FROM subjects WHERE subject_id =?\",(sub_id,))... | [
{
"docid": "a8c318c38ea786352f82bfc466e06831",
"score": "0.7014018",
"text": "def printSubjects(subjects):\n totalVal, totalWork = 0,0\n if len(subjects) == 0:\n return 'Empty SubjectList'\n res = 'Course\\tValue\\tWork\\n======\\t====\\t=====\\n'\n subNames = subjects.keys()\n sub... |
14dc3b3ec7b206da8a0aac6584868cdb | Marks the list of barcodes as ready in the databse and sends email | [
{
"docid": "7e79f1f9bffed340ce4ad21cb22ac6c3",
"score": "0.7422296",
"text": "def mark_results_ready(self, barcodes, debug=False):\n debug = {}\n ready_sql = \"\"\"UPDATE ag.ag_kit_barcodes\n SET results_ready = 'Y'\n WHERE barcode IN %s\n ... | [
{
"docid": "68b6afc64486c9532780ec1bc33d5e9d",
"score": "0.64812684",
"text": "def mark_barcodes_sent_to_qiita(self, barcodes):\n if barcodes:\n sql = \"\"\"UPDATE project_qiita_buffer\n SET pushed_to_qiita = 'Y'\n WHERE barcode IN %s\"\"\"\n ... |
882625070fb2d7b72cc9a9a1c266da2d | Display prints information about what just happened to stdout. | [
{
"docid": "5a2075a745cfad3dfe8fc7d1e44e7ac6",
"score": "0.0",
"text": "def Display(self, unused_args, result):\n printer = util.PrettyPrinter(0)\n printer.Print('Result of the restore-backup operation:')\n printer.PrintOperation(result)",
"title": ""
}
] | [
{
"docid": "de71a50deacff66ac51efe1d380bfad3",
"score": "0.7455874",
"text": "def display():\n\n # Check the pipe setup.\n check_pipe_setup(sequence=True, j=True)\n\n # Call the write method with sys.stdout as the file.\n write(file=sys.stdout)",
"title": ""
},
{
"docid": "1c3ced... |
7b4e00c839b2b884cd8f2d137e5deb74 | Maps each S. cervisiae ORF to ortholog ORFs in 8 closely related yeast species | [
{
"docid": "06afd725cd38a46eb969ad288ccea146",
"score": "0.48885584",
"text": "def blast_closely_related_species(ortho_species_dir,scer_DB_dir,outDir,summaryFile,scer_closely_related): \n\t\n\tprint (\"----------- Find orthologues in closely related species -----------\")\n\t\n\tif not os.path.exists(ou... | [
{
"docid": "5a11091e3c63cbba32f7d82ce5c6a064",
"score": "0.6164072",
"text": "def ortho_final_coord(ortho_dict):#rna_ortho_dict,\n\tfinal_coord_dict = dict()\n\tfor k, v in ortho_dict.iteritems():\n\t\tupstream = v[0]\n\t\tdownstream = v[1]\n\t\tuscafs = set()\n\t\tdscafs = set()\n\t\tfor gene in upstre... |
098f625be7728afddfd2b2c1387e0124 | Test the system removes files. First create a temporary empty file in the temporary path then remove it. | [
{
"docid": "6627f05b2bc7c4b188734c79718fa220",
"score": "0.7850327",
"text": "def test_remove(self):\n test_file = os.path.join(self._system.get_temporary_path(), \"nusoft.test\")\n with open(test_file, 'a'):\n os.utime(test_file, None)\n self.assertTrue(os.path.exists(te... | [
{
"docid": "2d968d9edc44d54ee6dad643d82abd51",
"score": "0.81269306",
"text": "def test_removed(self):\n path = None\n with TemporaryDirectory() as tmp:\n path = tmp\n self.assertTrue(os.path.isdir(tmp))\n tmpfile = os.path.join(tmp, \"a_temp_file\")\n ... |
92ae2600eb34202e158228d1cda285c0 | Send an IObjectCopiedEvent for object. `original` is the object the copy was created from. | [
{
"docid": "0d5bd68503e07959a932e521c8752d40",
"score": "0.6960044",
"text": "def copied(object, original):",
"title": ""
}
] | [
{
"docid": "d97ab3e40ca5419cf0fc9f77c17777cc",
"score": "0.56083983",
"text": "def copy_view(self, request, object_id, extra_context=None):\n opts = self.model._meta\n app_label = opts.app_label\n\n if not self.draft_copy_allowed:\n return HttpResponseBadRequest(\"Draft c... |
b4aed3b4a87ea4aef402346694d0ff72 | Plot the distribution of each variable in the joint probability space using matplotlib. | [
{
"docid": "1fa41e1ea2a607c9d83a6b231e539917",
"score": "0.6270921",
"text": "def Plot(self):\n inf = 10**30\n plotDict = {}\n minX = inf\n maxX = -inf\n numPts = 200\n pdfs = []\n for v in self.fieldList:\n d = self.distr(v)\n minva... | [
{
"docid": "e51c6607e2bead15b80003b660fb3059",
"score": "0.7280935",
"text": "def plot(self):\r\n\t\t#should be as simple as calling the hist method of the particle list then the plot method of \r\n\t\t#the distributions datamember\r",
"title": ""
},
{
"docid": "f15678733146d91a320fcd41ae9f2... |
8aeb8a3700f1ce37cf4f91212577812e | setter for the maximum attribute | [
{
"docid": "f4d8de4754d871aa86f66900eb55dae8",
"score": "0.8005382",
"text": "def maximum(self, maximum):\n self._maximum = maximum",
"title": ""
}
] | [
{
"docid": "4a092107baaf3de27d2f66d9ed3abd1d",
"score": "0.8707602",
"text": "def setMaximum(self, maxValue):\n self.attrs[\"Maximum\"] = maxValue",
"title": ""
},
{
"docid": "baa8ac2a2dcea71742c037e43c142c63",
"score": "0.84917045",
"text": "def set_max(self, max):\n i... |
135b9aa187dcc0e58bf633f36c64c2ca | Gets the url of this DashboardMetadata. | [
{
"docid": "3cab8eb5451712f7c55e14a0a0cea1ab",
"score": "0.7339102",
"text": "def url(self):\n return self._url",
"title": ""
}
] | [
{
"docid": "2fbe73001cbd34a95b62e79f502d7012",
"score": "0.7927961",
"text": "def dashboard_url(self) -> str:\n return self._dashboard_url",
"title": ""
},
{
"docid": "c703c957fbe09cf7c45ebb07023d3e91",
"score": "0.7727256",
"text": "def url(self):\n return self.data[\"... |
8a55eab1ea93d2f7b415a1a505232965 | Add the processor to the available processors that __next__ returns. | [
{
"docid": "747bf753849a90c40ac9e19dc462340f",
"score": "0.7581475",
"text": "def add_processor(self, processor):\n raise NotImplementedError()",
"title": ""
}
] | [
{
"docid": "fb6f43761ac94070a64eefdf1c7b95a1",
"score": "0.76851845",
"text": "def __next__(self):\n for processor in self._processors:\n\n processor()",
"title": ""
},
{
"docid": "d91c4595fb0c24cff850e46a1bf542dc",
"score": "0.7611795",
"text": "def add_processor(s... |
a5b7cd94ad797db3374c86c3d84dd89e | Loads the dbt manifest. | [
{
"docid": "218f3c12ef21f4e5270383042bd1e288",
"score": "0.6806921",
"text": "def dbt_manifest(self):\n # Identity function used for macro hooks\n def identity(x):\n return x\n\n # Set dbt not to run tracking. We don't load\n # a dull project and so some tracking r... | [
{
"docid": "f7d9e68a6289002b2b30bb5e1dce5a7e",
"score": "0.6945915",
"text": "def read_manifest(self): # -> None:\n ...",
"title": ""
},
{
"docid": "99a973b3f287f2f28ff302065825d6f6",
"score": "0.66541666",
"text": "def load_manifest(path: Path):\n with open(path, \"rt... |
2c4f9f20da031ff4db9ea3dc8043a8b7 | Perform forward pass of encoder. Returns mean with shape [batch_size, 784]. | [
{
"docid": "528fe2d56683e6b95bdf138bf0067021",
"score": "0.57269955",
"text": "def forward(self, input):\n hidden = self.z_hidden_vector(input)\n self.transformed_input = self.relu(hidden) \n self.transformed_input = self.hidden_linear_mean(self.transformed_input)\n\n mean = ... | [
{
"docid": "a65da797b7c1b5d048f0d5f35949b5c1",
"score": "0.67233974",
"text": "def forward(self, input):\n mean = self.decoder(input)\n\n return mean",
"title": ""
},
{
"docid": "b5e10e0285e3058556788eb624c3f824",
"score": "0.6601578",
"text": "def forward(self, inputs)... |
0e08c2053cefb4745ed8ee41b1a1530f | Gets the content of a resource | [
{
"docid": "8dccfbc4eec6b6eb13db2655e677441e",
"score": "0.0",
"text": "def load_resource(resource_path):\n resource_content = pkg_resources.resource_string(__name__, resource_path)\n return unicode(resource_content)",
"title": ""
}
] | [
{
"docid": "4734bb5b047d6b7b31a78cee1d6808e6",
"score": "0.8737112",
"text": "def _get_content(self, resource):",
"title": ""
},
{
"docid": "0f26c74e63b2210f70c0d0c161313c0e",
"score": "0.80320984",
"text": "def get_resource(self):\n try:\n print('fetching:', self.p... |
4a9881689bc78af4cfb52834600bea05 | Gets a set of slides (.PDL1.mrxs) and divides each one of them into patches. The final patches are stored in a folder with the same name as the slide. | [
{
"docid": "64780dffa2afde7f412f5be70ad82c01",
"score": "0.6368157",
"text": "def patch_division(slides, outpath, level, tile_size=224, tissue_ratio=0.50, jobs=1):\n\n # Creates directory outpath if doesn't exist yet\n try:\n os.mkdir(outpath)\n print(\"Directory\", outpath, \"create... | [
{
"docid": "119b53bb19bd42ac90c7ad21c6d8612e",
"score": "0.60560036",
"text": "def get_patches(slidepath, outpath, level=10, tissue_ratio=0.25, size=256):\n\n # Opens the slide with OpenSlide\n slide = OpenSlide(slidepath)\n\n # Gets deepzoom tile division\n slide_dz = deepzoom.DeepZoomGener... |
a04d0abb2c5939e86faec2939ceb53d6 | Turn off the WLED nightlight switch. | [
{
"docid": "5c9433e7d86174c8309e7d0bba78df87",
"score": "0.8082955",
"text": "async def async_turn_off(self, **kwargs: Any) -> None:\n await self.coordinator.wled.nightlight(on=False)",
"title": ""
}
] | [
{
"docid": "dc32254499822b5c7b3c605da071785c",
"score": "0.7715766",
"text": "def switch_off(self):\n self.SwitchOff()",
"title": ""
},
{
"docid": "dc32254499822b5c7b3c605da071785c",
"score": "0.7715766",
"text": "def switch_off(self):\n self.SwitchOff()",
"title": ... |
e6b1fb77624f011eedc523ee9a96c89f | Inline query handler for the bot. Returns the appropriate shows as a result. | [
{
"docid": "4389659bef73bc81b8f1323de6e6796e",
"score": "0.7259084",
"text": "def inline_query_handler(update, context):\n query_text = update.inline_query.query\n query_id = update.inline_query.id\n\n if not query_text:\n query_text = \"all\"\n\n logging.info(\"Query %s\", query_text... | [
{
"docid": "828ae1b9f8c8e39f5f7957021b31a780",
"score": "0.7084049",
"text": "async def on_inline_query(self, msg):\n\n async def compute_answer():\n \"\"\"\n Function generating the answer for the handler.\n :return: Lyrics as articles\n \"\"\"\n ... |
e5d0c52e17e4e5e5e0f1ed744b6195bc | Compute the probability of the different services contained in the data using the given classifier. | [
{
"docid": "31822774ceb7b6860b32167010e772e7",
"score": "0.7801408",
"text": "def services(self, data, classifier):\n values = self.__generate_values(data, classifier)\n max_value = 0\n # Search max index in probabilities (number of categories)\n for val in values:\n ... | [
{
"docid": "c13a61aa62c3aa58c2f8926ba1620388",
"score": "0.6992532",
"text": "def probability(self, data, classifier):\n values = self.__generate_values(data, classifier)\n max_value = 0\n # Search max index in probabilities (number of categories)\n for val in values:\n ... |
42f2bfcea28db02ee805f37e98d23681 | String que representa al objeto Libro | [
{
"docid": "b7ae2c10f2a195a56f527606368b4323",
"score": "0.0",
"text": "def __str__(self):\n\t\treturn self.titulo",
"title": ""
}
] | [
{
"docid": "ee0aec15a332c3d07dd070f692de3b44",
"score": "0.7063576",
"text": "def __str__(self):\n\t\treturn '%s (%s)' % (self.id, self.libro.titulo)\n\t\t# return '{0} ({1})'.format(self.id,self.libro.titulo) \n\t\t# return f'{self.id} ({self.libro.titulo})'",
"title": ""
},
{
"docid": "804... |
7a6c02e370059d1e75397d934e382c1b | Compatability wrapper for advancing an iterator. | [
{
"docid": "b1c96ea935867c12aba7d0143c07edb3",
"score": "0.6273643",
"text": "def next(iter):\r\n return iter.next()",
"title": ""
}
] | [
{
"docid": "4054233d5049ece5e6898be151e34502",
"score": "0.73166263",
"text": "def __add__(self, other: Iterator[T]):\n try:\n return self.chain(other)\n except Exception:\n pass\n return NotImplemented",
"title": ""
},
{
"docid": "2edfa18bd364406c3... |
b8904ef7b4372200ba43f32d2075f657 | Evalutes the the top curve of a circle using xcoordinate | [
{
"docid": "7bba9db2092483da7011fc322b984abe",
"score": "0.6089204",
"text": "def circle(xpos) : \r\n \r\n yPos = np.array(np.sqrt(1 - xpos**2))\r\n \r\n return yPos",
"title": ""
}
] | [
{
"docid": "b097246dc5c0474d62f8675c80afe6c9",
"score": "0.59327",
"text": "def _x(self, curve):\n return self._c(curve)[:, 0]",
"title": ""
},
{
"docid": "d9671ef8ff6e3f4984a09931e59f9a1b",
"score": "0.5818806",
"text": "def least_squares_ellipse(\r\n x,y):\r\n \r\n... |
e8beb57de21a98204f851d42285263eb | Delete a push rule. Args specify the row to be deleted and can be any of the columns in the push_rule table, but below are the standard ones | [
{
"docid": "ed5a6eee00e3991a616774f9628d1ffd",
"score": "0.7285327",
"text": "def delete_push_rule(self, user_name, rule_id):\n yield self._simple_delete_one(\n PushRuleTable.table_name,\n {'user_name': user_name, 'rule_id': rule_id},\n desc=\"delete_push_rule\",\... | [
{
"docid": "d6f4919bf5fa7ac48d2a7c4d6719df1c",
"score": "0.74705184",
"text": "def deletePgPushRule(self, pgPushRuleUri):\r\n return self.proto.dbpool.runInteraction(self._deletePgPushRule, pgPushRuleUri)",
"title": ""
},
{
"docid": "c237d86d544a2804f4bb3bd3e16bb690",
"score": "0.71... |
7e0ac660f95bc7e4dacced0fbe04d5bf | Detects the ball using color thresholding | [
{
"docid": "b32ec2d27abc563b3e381ef5a3c38254",
"score": "0.70945865",
"text": "def detect_ball(self):\n\n #read webcam image\n ret, img_in = self.capture.read()\n\n #convert to HSV colorspace\n img_hsv = cv2.cvtColor(img_in, cv2.COLOR_BGR2HSV)\n\n #color thresholding\n... | [
{
"docid": "d9a6b39ff0cc5578f9266d595f94fbd6",
"score": "0.71530026",
"text": "def detect_color(image):\n # Resize the frame, blur it, and convert it to the HSV color space\n frame = imutils.resize(image, width=FRAME_SIZE)\n\n blurred = cv2.GaussianBlur(frame, (11, 11), 0)\n hsv = cv2.cvtCol... |
b3e43d797607acdf8b5629062592b977 | Construct a new Colorscale object | [
{
"docid": "f8dd8b3727da32f08a1779c47d6a6a6b",
"score": "0.7413053",
"text": "def __init__(\n self,\n arg=None,\n diverging=None,\n sequential=None,\n sequentialminus=None,\n **kwargs\n ):\n super(Colorscale, self).__init__('colorscale')\n\n # V... | [
{
"docid": "d2823f7261d8f63a796bdd0389487e34",
"score": "0.63869935",
"text": "def __init__(self, z_scale=1., y_scale=1., x_scale=1.):\n \n self.z_scale = z_scale\n self.y_scale = y_scale\n self.x_scale = x_scale",
"title": ""
},
{
"docid": "56efac36e43d5f479cb583... |
a096da259efff9e160ccbea446040e40 | Private function to get the absolute path to the downloaded file. | [
{
"docid": "515b8459a0fbd85ad7dab27c0aa5f909",
"score": "0.0",
"text": "def _get_data(path, scraper_name, project_id):\n cwd = os.path.abspath(os.path.dirname(__file__))\n return os.path.join(cwd, 'pdf', scraper_name, project_id, path)",
"title": ""
}
] | [
{
"docid": "83ad940bd914fa9b3f8790f35f6843a2",
"score": "0.81307155",
"text": "def download_path(self):\n download_path = self.TMP_DIR.joinpath(self.DOWNLOAD_NAME).resolve()\n return download_path",
"title": ""
},
{
"docid": "a4a1c4e8c019224e8a5dcad9c716aeae",
"score": "0.7... |
bc6a04eba5b3f41ca429d548419f5b6c | Changes the text of the label to the summary of this scenario. | [
{
"docid": "acbf0f299549d2a525eb8b693beb5897",
"score": "0.888844",
"text": "def summary_scenario(self, label):\n label.setText(self.summary)",
"title": ""
}
] | [
{
"docid": "eddd18e56ca8206d5c49cb5e6baf457a",
"score": "0.6996269",
"text": "def _update_label(label, new_text):\n label.setText(\"Score: {}\".format(new_text))",
"title": ""
},
{
"docid": "17aacdff760629a5ac94ef60b2a5cf2b",
"score": "0.658206",
"text": "def summary(self, sum... |
b4b1a94bf941996732d66220a294dc7a | Sets the email of this Consultation. | [
{
"docid": "edf73bdd43acf1b4fa8bf69a59e0f338",
"score": "0.77411693",
"text": "def email(self, email):\n\n self._email = email",
"title": ""
}
] | [
{
"docid": "3edda176cb95d00051c3d2ccb6af96ba",
"score": "0.782061",
"text": "def email(self, email):\n self._email = email",
"title": ""
},
{
"docid": "3edda176cb95d00051c3d2ccb6af96ba",
"score": "0.782061",
"text": "def email(self, email):\n self._email = email",
"... |
2222136541f6285ea02f40b676a113e6 | Returns the model properties as a dict | [
{
"docid": "12d68e7daf0075b9d7336e08d677b525",
"score": "0.0",
"text": "def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n ... | [
{
"docid": "d5fac65c7140c97c1960355ec6927973",
"score": "0.7789736",
"text": "def get_properties(self):\n return {}",
"title": ""
},
{
"docid": "d5fac65c7140c97c1960355ec6927973",
"score": "0.7789736",
"text": "def get_properties(self):\n return {}",
"title": ""
}... |
e2fd56eff8036e5cd205722f5a691e1f | pillar['master']['file_roots'] is overwritten by the master in order to use the fileclient interface to read the pillar files. We should restore the actual file_roots when we send the pillar back to the minion. | [
{
"docid": "1bd53991876d9a27761e854c675850d0",
"score": "0.6599424",
"text": "def test_issue_5449_report_actual_file_roots_in_pillar(\n salt_call_cli, pillar_tree, base_env_state_tree_root_dir\n):\n ret = salt_call_cli.run(\"pillar.data\")\n assert ret.returncode == 0\n assert ret.data\n ... | [
{
"docid": "ad44959dc2368d29845f68104be29acc",
"score": "0.5513161",
"text": "def changeRoot(self, root):\n self.path_holder.root = root\n for s in self.subfiles:\n s.path_holder.root = root",
"title": ""
},
{
"docid": "fd02d487b6e859a684615b8454404306",
"score":... |
fe8646ab98b360f3a828c884c49582f2 | Creates a new instance of data page and assigns its values. | [
{
"docid": "18e8b588f7434cdc93fc9c7d71063882",
"score": "0.5780925",
"text": "def __init__(self, data: List[Any], token: str = None, total: int = None):\n # The total amount of items in a request.\n self.total: int = total\n # The starting point for the next search.\n self.to... | [
{
"docid": "eb6cc19f2a08202ef11920f75b300e40",
"score": "0.6493468",
"text": "def data(self):\n if self.options[\"type\"].value == MAP_TYPE:\n pages = self.create_map_page()\n else:\n pages = self.create_island_posters()\n return pages",
"title": ""
},
... |
3d4382612aa7770d02e39bdc03b6f970 | Subtract Array or scalar from Array and return an Array. | [
{
"docid": "59464f622b7797b7b2a44aebf4a383d3",
"score": "0.5544128",
"text": "def __rsub__(self,other):\n return self._data.__rsub__(other)",
"title": ""
}
] | [
{
"docid": "544f638cf0221de411c0dd23bb37b663",
"score": "0.67351913",
"text": "async def np_subtract(self, a, b):\n stype = type(b) if isinstance(b, self.SecureArray) else type(a)\n a_shape = getattr(a, 'shape', (1,))\n b_shape = getattr(b, 'shape', (1,))\n shape = np.broadca... |
34735739c4aec822adaeec95f1a32577 | Test resend stored flows. | [
{
"docid": "85345835261afa6f632b05828456798e",
"score": "0.71355134",
"text": "def test_resend_stored_flows(self, mock_install_flows):\n dpid = \"00:00:00:00:00:00:00:01\"\n switch = get_switch_mock(dpid, 0x04)\n mock_event = MagicMock()\n flow = {\"command\": \"add\", \"flow... | [
{
"docid": "78c89020740b93fa4642991d3e31423f",
"score": "0.58568835",
"text": "def test_store_changed_flows(self, mock_save_flow, _):\n dpid = \"00:00:00:00:00:00:00:01\"\n switch = get_switch_mock(dpid, 0x04)\n switch.id = dpid\n flow = {\n \"priority\": 17,\n ... |
b809052736a786be5ebbddb33b1bc9ce | Creates a Path object representing the full path of an output feature class in the KML/KMZ format. | [
{
"docid": "0a4454bcc59bea43f01939fe42e7cff8",
"score": "0.6005049",
"text": "def _feature_class_default_name(self, desc, output_workspace, **kwargs):\n return output_workspace.joinpath(desc.name + \".kmz\")",
"title": ""
}
] | [
{
"docid": "0f5b0e3516d0e736cb18463ee0e55493",
"score": "0.6318198",
"text": "def export_kml():\n folder = 'cad'\n name = get_dataset_filename()\n \n # Create a cad folder in the temp directory if it does not exist\n working_folder = catalog_publish_folder + name + \"//\" + temp_folder + ... |
d15c16502af6ce62d6e0a6aa48a02c38 | First implentation of the two precedent algorithm | [
{
"docid": "b1f85f94a850720f0d292914e99787b4",
"score": "0.0",
"text": "def master_algorithme(set_point, heta):\n #find the perfect hash table\n hash_table = epsilon_variation_algo(set_point, len(set_point))\n \n #define the minimal number of rectangle\n min_nb_rectangle = sqrt(len(set_po... | [
{
"docid": "a8dcf31285714d06d3927c1343a0f07a",
"score": "0.70782053",
"text": "def algorithm(self):",
"title": ""
},
{
"docid": "75f9f33adb7fc5ee9bca5067ef34a714",
"score": "0.58826745",
"text": "def next_trick(self):",
"title": ""
},
{
"docid": "f5cd32a98def5a9aeaf4aa06e... |
8ffc57df12cae62a251b68661b73e7b5 | Construct an object from a parsed response. | [
{
"docid": "0e9dde3a2dac743da0d5c1f024147fc1",
"score": "0.0",
"text": "def from_json(cls, attributes):\n return cls(**{to_snake_case(k): v for k, v in attributes.items()})",
"title": ""
}
] | [
{
"docid": "38fb22532f23588d56b0cb3828560113",
"score": "0.71406657",
"text": "def __init__(self, res):\n self.fromResponseObj(res)",
"title": ""
},
{
"docid": "38fb22532f23588d56b0cb3828560113",
"score": "0.71406657",
"text": "def __init__(self, res):\n self.fromRespon... |
20c21940fc9dd704dacf8ea88aa5ee69 | Check the type of address (v4, v6, mac) and split out the address, prefix, and port. Values are None if they don't exist. | [
{
"docid": "0ab101390fc832573508d0eda37e7ee3",
"score": "0.692085",
"text": "def _split_addr(addr_str: str) -> Tuple:\n address = possible_addr = prefix = port = possible_port = None\n\n try:\n address, prefix = addr_str.rsplit('/', maxsplit=1)\n except Exception:\n address = addr... | [
{
"docid": "4f797d46fe6ac632ffd5a9a1ab83eeeb",
"score": "0.64934886",
"text": "def parse_address(address):\n ipv6 = 0\n for i in range(12):\n if address[i] != 0:\n ipv6 = 1\n break\n if ipv6 == 1:\n return socket.inet_ntop(\n ... |
e7f79e2f79009c5fe91885bf8e3476a1 | Get the route location and customer location of a customer. | [
{
"docid": "826d0525eebbe19e2e0d8df2fd11fe00",
"score": "0.5834146",
"text": "def cust_loc(self, sol, cust):\n cust_ind = [] # [route_loc, cust_loc]\n for i, rt in enumerate(sol):\n if cust in rt:\n cust_ind.append(i)\n cust_ind.append(rt.index(cus... | [
{
"docid": "a5ec4d134d11f24bc1d5418d50483a79",
"score": "0.6389137",
"text": "def GetLocation():\n\t\tif not clc.LOCATION: Account.GetAccounts()\n\t\treturn(clc.LOCATION)",
"title": ""
},
{
"docid": "08bd0dd35f42aa342ea747da89233e48",
"score": "0.62559897",
"text": "def customer_inf... |
e1765654b9adf837458da050bd051eaf | Wrapper Function for telnet interface. This uses underlying python telnet libraries Read until one from a list of a regular expressions matches. | [
{
"docid": "4489f58e627d6910de71e6b7564d008d",
"score": "0.0",
"text": "def loc_expect(self, output: any) -> object:\n print(\"Telnet expect with {}\".format(output))\n (i, obj, res) = self.telnet_con.expect([output.encode('ascii')], 5)\n print(\"i inside is {} \".format(i))\n ... | [
{
"docid": "1e83f6c81f96a3a34cfe616ec39ddc25",
"score": "0.639549",
"text": "def _telnet_read(self):\n return self.tn.read_until(\"\\n\", self.timeout).rstrip('\\n') # Telnet reply, with termination chars removed",
"title": ""
},
{
"docid": "4d5010c53f8ddd74b9249bb012d1d113",
"sc... |
0216413026baa5891f5309f4e1972205 | Formats event values using the helper. | [
{
"docid": "43c9d59e8073cde2d1018052190f9cf9",
"score": "0.62962395",
"text": "def FormatEventValues(self, output_mediator, event_values):\n if not self._winevt_resources_helper:\n self._winevt_resources_helper = output_mediator.GetWinevtResourcesHelper()\n\n message_string = None\n provid... | [
{
"docid": "d1973e8892563aa058b554c01322cbf0",
"score": "0.61856985",
"text": "def format_events(self, event):\n date = dateparser.parse(event['start']).strftime('%m/%d')\n title = event['title']\n venue = event['venue']\n id = event['id']\n\n formatted = u\"\"\"\n ... |
a49cad5154db3f4173ed1f408c04a4ca | Convert XML derived dict to tf.Example proto. Notice that this function normalizes the bounding box coordinates provided by the raw data. | [
{
"docid": "f89a97e7a31b3db1b5ae28192b332a36",
"score": "0.0",
"text": "def selective_search(data,\n dataset_directory,\n label_map_dict,\n ignore_difficult_instances=False,\n image_subdirectory='JPEGImages'):\n img_path = ... | [
{
"docid": "5cedbb9d92ce614e221c52c0c553315a",
"score": "0.69029593",
"text": "def parse_example_proto(example_serialized):\n # Dense features in Example proto.\n feature_map = {\n \"image/encoded\": tf.io.FixedLenFeature([], dtype=tf.string, default_value=\"\"),\n \"image/class/labe... |
e034371cf2f71c0b059b61452b9388f5 | stops the running process | [
{
"docid": "d7d36dd564e8ddaceec19438abbc6509",
"score": "0.73107016",
"text": "def stop(self):\n self.network.deregister(self.pid)\n self.reader_task.cancel()\n self.writer_task.cancel()\n self.subproc.terminate()",
"title": ""
}
] | [
{
"docid": "f8d24493529d9cb44476dfeb0b9f4e86",
"score": "0.8454015",
"text": "def stop(self):\n # Terminate the process\n self.proc.terminate() # sends a SIGTERM",
"title": ""
},
{
"docid": "e97a3af72b6deb2246430e5a2e9ea23f",
"score": "0.8393155",
"text": "def stop_pro... |
c075a0b9bee4f53b1a1973941890ba65 | since gateways don't always start at the same time, some secods don't have corresponding rssi and hence have NaN instead. Currenty machine learning lagorithms don't know how to handle NaN. As a temp fix, we're removing all records that have NaN in them. | [
{
"docid": "f517c54d00537dd43ad203abd3dec444",
"score": "0.71056527",
"text": "def remove_nan(self):\n\t\tdense_data = copy.deepcopy(self.data_frame)\n\t\ttimestamps_to_remove = []\n\t\tfor index, row in self.data_frame.iterrows():\n\t\t\tfor gateway in self.gateway_list:\n\n\t\t\t\tif np.isnan(row[gate... | [
{
"docid": "8361c7d6bb8408ccdc93d98e06906cb5",
"score": "0.63010675",
"text": "def _removerawnans(self):\n for band in self.data[self.root]:\n for i, f in enumerate(self.data[self.root][band]['frequency']):\n if math.isnan(f):\n for param in self.data[... |
014a5481438d0e9b71966a48d4d8bbf6 | Generate a NL or LP file from Pyomo, and then do subsequent conversions. | [
{
"docid": "b50fba180d6eee76d9713189ba7dcb27",
"score": "0.56642395",
"text": "def apply(self, *args, **kwds):\n\n import pyomo.scripting.convert\n\n capabilities = kwds.pop(\"capabilities\", None)\n\n # all non-consumed keywords are assumed to be options\n # that should be p... | [
{
"docid": "67db92a1a3821a68008dc249af915482",
"score": "0.5871873",
"text": "def create_model(self):\r\n\r\n self.try_load_model()\r\n\r\n self.set_correct_problem_instance_name()\r\n\r\n self.add_objective_and_budget_constraints()\r\n self.add_starting_ending_nodes_constrai... |
129bf3d37fc164404295d1647290238f | Set an instance variable to test that this method gets called | [
{
"docid": "abe8787ed3fa3d0a67f544bcbdd156df",
"score": "0.0",
"text": "def __parent_setup_2(self):\r\n self.parent_setup_exists += 1",
"title": ""
}
] | [
{
"docid": "de81fdce13d62ec47641ea1a8c6ee8c7",
"score": "0.73396957",
"text": "def before_tester_run(self) -> None:",
"title": ""
},
{
"docid": "f3b2d6e10065ff6b983c1edd3630c152",
"score": "0.7324083",
"text": "def classSetUp(self):\r\n assert self.something == True\r\n ... |
dde81fd75e8dab25708f015fee2011b9 | If the same item appears in the list, sums the quantities and create a unique Food object. | [
{
"docid": "fc122672b927400040c2b8b6fa8b33a7",
"score": "0.5547891",
"text": "def eraseDubloons(foodList):\n resList = []\n foodNames = [f.codsougr_name for f in foodList]\n uniqueFoodNames = list(set(foodNames))\n for name in uniqueFoodNames:\n duplicates = [f for f in foodList if f... | [
{
"docid": "4f021ae31218d83063fc7b7fe95882c6",
"score": "0.6519353",
"text": "def __add_muilt(self,food_name,quantity):\n\t\tif (not food_name in self.items):\n\t\t\tself.items[food_name] =0\n\t\tself.items[food_name] = self.items[food_name] + quantity",
"title": ""
},
{
"docid": "232d5321f1... |
17ff9d02a0b166bd8a9c253d3fe795a0 | Update details of an Experiment. Update details of an Experiment with specific Experiment Id. | [
{
"docid": "10d33500639a40dacacd744c0f51f89c",
"score": "0.64625514",
"text": "async def update(\n self,\n subscription_id: str,\n resource_group_name: str,\n workspace_name: str,\n experiment_id: str,\n body: Optional[\"_models.ModifyExperiment\"] = None,\n ... | [
{
"docid": "a90932de5b0dd8cc3450221c5e50b85a",
"score": "0.7416664",
"text": "def update_experiment(request, pk):\n\n required_data = request.data.get(\"requiredData\")\n tests_config = request.data.get(\"testsConfig\")\n expiration = request.data.get(\"expiration\")\n allow_multiple_answers... |
97251e022090c1c096acfb522379fe37 | Check to see if the tolerance has been met. Returns False if tolerance is met. | [
{
"docid": "18dae62af5d8fcb325b8c556954aefee",
"score": "0.0",
"text": "def check_tol(g, tol, log_scale_param):\n s = np.exp(log_scale_param)\n for tolerance, gradient in zip(tol, g):\n if abs(gradient) > tolerance / s:\n return True\n return False",
"title": ""
}
] | [
{
"docid": "ce055bab6287f8377db1128076fa364f",
"score": "0.8071129",
"text": "def _tolerance_check(self, tolerance, value):\n if tolerance != None and np.linalg.norm(value) < tolerance:\n print(\"Variable update tolerance was reached. Terminating Search.\")\n return True\n ... |
3267fcb6ce40a8d0595e6979dd8f0ae1 | Return filenames that have a spcified set of extensions. | [
{
"docid": "841a54f260628de898d042118a6d2804",
"score": "0.671288",
"text": "def filenameMatchesAListOfExtensions(filename, extensionList=None):\n if extensionList is not None:\n for currExt in extensionList:\n if filename.lower().endswith(currExt.lower()):\n return T... | [
{
"docid": "980d3d0380131c6070595da9574d0e44",
"score": "0.7548063",
"text": "def get_files(dirname, extensions=['.png', '.tif', '.jpg']):\n dir_path = Path(dirname)\n\n files = dir_path.glob('**/*')\n\n files = [path.resolve() for path in files]\n\n match = [f for f in files if f.suffix in ... |
dfc4c0b6369e884b99877afc55bfd359 | Build a Cypher query based on given parameters. | [
{
"docid": "d75bc1828ce83ede1c9f954653f24192",
"score": "0.5487266",
"text": "def _build_dependencies_query(\n cls, team_id, topic, label, node, filter_on_config=False, impacted=False\n ):\n where = \"\"\n order = \"(n)<-[r]-(m)\" if impacted else \"(n)-[r]->(m)\"\n query ... | [
{
"docid": "72ebf75d0424beb0d9398dd9dcc5f821",
"score": "0.7439297",
"text": "def build_query(self, params):\n pass",
"title": ""
},
{
"docid": "a95e410d68282d18b99170eb9c871fae",
"score": "0.70802385",
"text": "def _build_query(self, params, boolean='AND'):\n query = [... |
152c3edbb3aa0b05d956c5dcb6acb205 | Initialize the Decomposable OperatorValued Kernel. | [
{
"docid": "f09118c547cd865e208584e4cfd55bdd",
"score": "0.0",
"text": "def __init__(self, X, gamma):\n super(RBFDivFreeKernelMap, self).__init__(gamma)\n self.n = X.shape[0]\n self.d = X.shape[1]\n self.p = X.shape[1]\n self.X = X\n self.Gs_train = None",
"... | [
{
"docid": "31c1f825722e935a9f2040878ee62b3c",
"score": "0.6014059",
"text": "def init_op(self):\n return self._init_op",
"title": ""
},
{
"docid": "933dc4e128744f5c26c4a36acf306da6",
"score": "0.59667915",
"text": "def __init__(self,kernel_fn,*args,order=2,**kwargs):\n sup... |
b385b0580c451d27151a0b7f7ebf8293 | Return a restricted zscore. | [
{
"docid": "d9b8dbd9271cb251514b6863c4b30b2c",
"score": "0.70605326",
"text": "def zscoreOtherRestricted(measure, power, median, variationCoefficient, computeFinalZScore):\n zscoreNorm = zscore(measure, power, median, variationCoefficient)\n if math.fabs(zscoreNorm) > 3 and computeFinalZScore:\n if... | [
{
"docid": "9ef0a40399c631949df3b99157924f0d",
"score": "0.72016364",
"text": "def get_zscore(self, zscore_value):\n\n s = self.get_score(self.prov_types['zscore'], zscore_value)\n return s",
"title": ""
},
{
"docid": "953a1114c01dd842fe0a8694202b17b3",
"score": "0.7117339"... |
bdeff234398c45a1b8d7776f2ff0c2a2 | Class method, which allows the user to populate the Database | [
{
"docid": "5e4f589475625e693b789b714ddcc790",
"score": "0.0",
"text": "def populate(self):\n data = api_research_off()\n for product in data['products']:\n product_name = self.get_product_name(product)\n nutriscore = self.get_nutriscore(product)\n if nutri... | [
{
"docid": "11c5c10dcb7519f823633e95d691706e",
"score": "0.7409577",
"text": "def _populateTables(cls):\n for method in dir(Populate):\n if not method.startswith('__'):\n populateTable = getattr(Populate, method)\n print 'Inserting data for %s ...' % metho... |
28ecbd2ee477b01b0414a979344fe7a1 | Function that Cleans the text and handles the superscript and subscript tags in the text | [
{
"docid": "bada213dfa474c405bf524914fe10ab8",
"score": "0.815164",
"text": "def cleaning(text):\n \n ind = 0\n text = text.replace(\":\",\"\")\n n = len(text)\n\n \"\"\"Replacing sup tag with supercript\"\"\"\n while (ind < len(text)):\n if(text[ind]==\"<\" and ind < n-4 and text[ind+1:ind+4... | [
{
"docid": "bcff0b3207f4a1f287474fa3ebeea447",
"score": "0.70005953",
"text": "def clean_text(text):\n # Replace newlines by space. We want only one doc vector.\n text = text.replace('\\n', ' ').lower()\n # Remove URLs\n #text = re.sub(r\"http\\S+\", \"\", text)\n # Expand contractions: y... |
ffb57bc6ba104f32dc98f65948a63fa8 | Returns best solution and energy after all runs | [
{
"docid": "4e583b5900abb5f15a91f16db7969b24",
"score": "0.0",
"text": "def mws(model):\n \n def max_score_local(x_val, k = 0):\n \"\"\"\n Generates best neighbor upon mutating in one random direction\n Input: x_val to be mutated\n Output: neighbor with best score along... | [
{
"docid": "dc23f9d302e3c5b94328c54bbe84fee4",
"score": "0.7375325",
"text": "def solution(self):\n return self.Mbest",
"title": ""
},
{
"docid": "a7a458447c66af157f126dfa185c1878",
"score": "0.6866638",
"text": "def optimize(self):\n\n # Initiate particles\n sel... |
a232b5ed960453ab1d2673894b52333d | submit a dos calculation and interpolate result if returns complete | [
{
"docid": "5e0224e23e34529b3523d980d025abb2",
"score": "0.0",
"text": "def get_dos(self):\n\n label = 'KKR DOS calc.'\n dosdict = self.ctx.dos_params_dict\n description = 'dos calc: emin= {}, emax= {}, nepts= {}, tempr={}, kmesh={}'.format(dosdict['emin'], dosdict['emax'], dosdict[... | [
{
"docid": "215366153e758085c8bc79a022ff8b6c",
"score": "0.6252689",
"text": "def calculate(self):\n item_number = self.fields.curselection()\n polynomial = self.json_data[\"functions\"][int(item_number[0])]\n result_floating = None\n result_interval = None\n if self.j... |
3510ac42e4ef7ae99ff81d34dfc62eaa | Get List of field names when a container name is given. Container names if repeated in the snapshot metadata, will always contain the same internal metadata, so the parent of the container doesn't matter. container_field_name The container of whose List of field names are required List of field names | [
{
"docid": "19a19c2a810e3f9fd8aa2036cfc9c0ba",
"score": "0.8049855",
"text": "def data_field_container_field_name_list_get(self, container_field_name):\n def dfs_helper(dict_to_find_in, container_field_name, ret_list):\n if dict_to_find_in is None:\n return\n ... | [
{
"docid": "30c975d7087e5406555f5a29ddfc5d2f",
"score": "0.61451197",
"text": "def _get_list(container, name):\n\n try:\n return container[name]\n except KeyError:\n return []",
"title": ""
},
{
"docid": "f10c4c60092e3146611d5e905294f06d",
"score": "0.... |
3d86c1af870937aabf2ff3595bf01c6e | Return package metadata information. | [
{
"docid": "5c54becc64e16a019dde1941f01c5892",
"score": "0.0",
"text": "def get_classifiers():\n return [\n 'Private :: Do Not Upload',\n 'Development Status :: 4 - Beta',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python',\n 'Programming Lan... | [
{
"docid": "ba35cfa5e926725340a889cc98973d81",
"score": "0.8002718",
"text": "def get_package_metadata(self, id=None, name=None, version=None, architecture=None):\n url = self._package_url(id, name, version, architecture)\n return self._get_request(url)",
"title": ""
},
{
"doci... |
4370a2db89aa376572fa763677d8f568 | Add the hooks to the bot. | [
{
"docid": "f6a523a74b100be16e1871ae1e1ba3f3",
"score": "0.579434",
"text": "def setup(bot: commands.Bot):\n bot.before_invoke(before_invoke)\n bot.after_invoke(after_invoke)",
"title": ""
}
] | [
{
"docid": "42d07c0d20fbaab7a3ce210e4325d89b",
"score": "0.7362344",
"text": "def hook_events(self):\n for cmd in self.commands:\n self.core.add_command(cmd, self)\n for event in self.hooks:\n self.core.add_callback(event, self)\n for cmd in self.rawhooks:\n ... |
0678ed29cb4ed04785fd661416546558 | Check is a path is a broken symlink. | [
{
"docid": "202729acc69cd2b9313649493f56ef9f",
"score": "0.870235",
"text": "def is_symlink_broken(path):\n if os.path.islink(path):\n return not os.path.exists(os.readlink(path))\n else:\n raise TypeError('path={!r} is not a symbolic link'.format(path))",
"title": ""
}
] | [
{
"docid": "71564a56fc163c8e7d254c575b623e30",
"score": "0.7800918",
"text": "def checklink(path):\n # mktemp is not racy because symlink creation will fail if the\n # file already exists\n name = tempfile.mktemp(dir=path)\n try:\n os.symlink(\".\", name)\n os.unlink(name)\n ... |
54c87c218016afc006e423f7687dcaee | Returns a boolean indicating whether this type is an 'interface' type that is implemented in Gecko. At the moment, this returns true for all interface types that are not types from the TypedArray spec. | [
{
"docid": "50186e06c1db50f28ee3b3f7d75a25e9",
"score": "0.700691",
"text": "def isGeckoInterface(self):\n return self.isInterface() and not self.isSpiderMonkeyInterface()",
"title": ""
}
] | [
{
"docid": "3fd0af121674f7ce21b6fec67acc48c9",
"score": "0.75389546",
"text": "def isSpiderMonkeyInterface(self):\n return self.isInterface() and (self.isArrayBuffer() or \\\n self.isArrayBufferView() or \\\n self.isTyped... |
260a4b8f6a111255d751b34ee4804b99 | Return an annual probability given a return period $$AEP = 1 \exp{1/ARI}$$ | [
{
"docid": "76ed3955f395457f23e1a92d5ee923b0",
"score": "0.78453267",
"text": "def probability(ari):\n aep = 1.0 - np.exp(-1.0/ari)\n return aep",
"title": ""
}
] | [
{
"docid": "a969b34f946ea4cab04180a858e705ce",
"score": "0.66628295",
"text": "def probability(e, en, t):\n if t == 0:\n return 0\n else:\n return exp((e-en)/t)",
"title": ""
},
{
"docid": "6d171e23a9d819ff00ddad221521a2ea",
"score": "0.64231294",
"text": "def ann... |
a2553f674f9dde5c403aedc77dda2d72 | Gets the metadata for the copyright. | [
{
"docid": "add6e0375766c1ae82f03796afa040ba",
"score": "0.8633044",
"text": "def get_copyright_metadata(self):\n pass",
"title": ""
}
] | [
{
"docid": "24f84680cf596560ecdac39cce9e64bc",
"score": "0.8069426",
"text": "def get_copyright_registration_metadata(self):\n pass",
"title": ""
},
{
"docid": "9b439f8061f824cb732d55672fc3eaff",
"score": "0.7513539",
"text": "def copyright():\n # pull and print the meta-da... |
f5f36a0534f4ea94adb8aad46271138a | Can this RecurringCost be enacted | [
{
"docid": "757ffa3fd3a6bd9841075b459360f26d",
"score": "0.49190748",
"text": "def is_enactable(self, as_of):\n return \\\n not self.disabled and \\\n not self.archived and \\\n not self._is_finished(as_of) and \\\n self._is_ready(as_of) and \\\n ... | [
{
"docid": "4c04ec54a498200fddebe61bfad145c7",
"score": "0.6974328",
"text": "def enact(self, billing_cycle, disable_if_done=True):\n as_of = billing_cycle.date_range.lower\n if not self.is_enactable(as_of):\n raise CannotEnactUnenactableRecurringCostError(\n \"Re... |
ff858a38afa1ebd09c9cc6d02fb8e698 | r""" Computes the reduced density matrix representation of the state. May be numerical or symbolic. | [
{
"docid": "5571e02f616db6f7dcabfb10903524cd",
"score": "0.6066919",
"text": "def reduced_dm(self, modes, **kwargs):\n if modes == list(range(self.num_modes)):\n # reduced state is full state\n return self.dm(**kwargs)\n\n if isinstance(modes, int):\n modes... | [
{
"docid": "364f54a74c07f748ea0080ffc36d9b1d",
"score": "0.6417854",
"text": "def subsystem_reduced_density_matrix(self, bit_labels):\n bit_labels_inds = [i-1 for i in bit_labels]\n other_labels_inds = list(set(range(self.system_size)) - set(bit_labels_inds))\n state_reshape = np.re... |
8c117fa933aa0261b2f095be44a1acc1 | Datatype of data in the Column. | [
{
"docid": "05b88be13604b1f61d82f087e7106449",
"score": "0.0",
"text": "def type(self) -> str:\n return pulumi.get(self, \"type\")",
"title": ""
}
] | [
{
"docid": "5b8088fbe0ac252d28bc703bdde1e7c3",
"score": "0.8037336",
"text": "def datatype(self):\n return self._datatype",
"title": ""
},
{
"docid": "48de7eb74ceee9fe56fd65453b09294b",
"score": "0.7955723",
"text": "def dtype(self):\n return self._datatype",
"title... |