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How to use filter_options method in molecule
Best Python code snippet using molecule_python
test_relative_tt_residuals.py
Source:test_relative_tt_residuals.py 
1#!/usr/bin/env python2from __future__ import absolute_import3# pylint: disable=invalid-name, missing-docstring4import numpy as np5import pytest6from seismic.gps_corrections import relative_tt_residuals_plotter as rttr7def test_global_filtering(df_picks):8 ## Test filter_limit_channels9 channels_broad = rttr.filter_limit_channels(df_picks, rttr.CHANNEL_PREF_NO_SHZ)10 assert len(channels_broad) == 4011 channels_broad_shz = rttr.filter_limit_channels(df_picks, rttr.CHANNEL_PREF_BALANCED)12 assert len(channels_broad_shz) == 40 + 613 channels_all = rttr.filter_limit_channels(df_picks, rttr.CHANNEL_PREF_GREEDY)14 assert len(channels_all) == 40 + 6 + 515 ## Test filter_to_teleseismic16 df_ts = rttr.filter_to_teleseismic(df_picks, 0, 10)17 assert df_ts.empty18 df_ts = rttr.filter_to_teleseismic(df_picks, 0, 180)19 assert len(df_ts) == 5120 assert df_ts['distance'].min() >= 021 assert df_ts['distance'].max() <= 18022 df_ts = rttr.filter_to_teleseismic(df_picks, 0, 20)23 assert len(df_ts) == 1024 assert df_ts['distance'].min() >= 025 assert df_ts['distance'].max() <= 2026 df_ts = rttr.filter_to_teleseismic(df_picks, 20, 35)27 assert len(df_ts) == 3428 assert df_ts['distance'].min() >= 2029 assert df_ts['distance'].max() <= 3530 df_ts = rttr.filter_to_teleseismic(df_picks, 35, 90)31 assert len(df_ts) == 732 assert df_ts['distance'].min() >= 3533 assert df_ts['distance'].max() <= 9034 df_ts = rttr.filter_to_teleseismic(df_picks, 50, 90)35 assert df_ts.empty36def test_get_iris_station_codes(iris_stntxt_file):37 df_au = rttr.get_iris_station_codes(iris_stntxt_file, 'AU')38 assert list(df_au.index) == ['ARMA', 'EIDS', 'KMBL', 'MILA', 'NRFK', 'RABL', 'XMI']39 assert np.allclose(df_au['lat'], [-30.4198, -25.369101, -31.366899, -37.054699, -29.040001, -4.127967, -10.4498],40 rtol=1.0e-6)41 assert np.allclose(df_au['lon'], [151.628006, 151.081696, 121.882103, 149.154999, 167.962997, 152.108765,42 105.688950], rtol=1.0e-6)43 df_ge = rttr.get_iris_station_codes(iris_stntxt_file, 'GE')44 assert list(df_ge.index) == ['BKNI', 'DAG', 'GSI', 'KAAM', 'KWP', 'MARJ', 'MORC', 'TRTE']45 assert np.allclose(df_ge['lat'], [0.3262, 76.771301, 1.3039, 0.49264, 49.630501, 32.522598, 49.83105, 58.378601],46 rtol=1.0e-6)47 assert np.allclose(df_ge['lon'], [101.039597, -18.655001, 97.5755, 72.994858, 22.7078, 20.8776, 17.577573,48 26.720501], rtol=1.0e-6)49def test_apply_event_quality_filtering(df_picks):50 '''51 self.strict_filtering = DEFAULT_STRICT_FILTERING52 self.min_event_snr = DEFAULT_MIN_EVENT_SNR53 self.cwt_cutoff = DEFAULT_CWT_CUTOFF54 self.slope_cutoff = DEFAULT_SLOPE_CUTOFF55 self.nsigma_cutoff = DEFAULT_NSIGMA_CUTOFF56 self.min_event_mag = DEFAULT_MIN_EVENT_MAG57 self.channel_preference = CHANNEL_PREF58 '''59 filter_options = rttr.FilterOptions()60 # Set filter to loosest settings first, then test adjusting one filter variable at a time.61 filter_options.strict_filtering = False62 filter_options.min_event_mag = 5.063 filter_options.min_event_snr = 0.064 filter_options.cwt_cutoff = 0.065 filter_options.slope_cutoff = 0.066 filter_options.nsigma_cutoff = 067 net_sta = {'net': ['AU'], 'sta': ['MEEK']}68 # Test magnitude filter69 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)70 assert len(df_filt) == 5171 filter_options.min_event_mag = 6.072 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)73 assert len(df_filt) == 4974 filter_options.min_event_mag = 7.075 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)76 assert len(df_filt) == 4877 filter_options.min_event_mag = 8.078 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)79 assert len(df_filt) == 4680 filter_options.min_event_mag = 9.081 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)82 assert len(df_filt) == 4483 # Test SNR filter84 filter_options.min_event_mag = 5.085 filter_options.min_event_snr = 5.086 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)87 assert len(df_filt) == 4788 filter_options.min_event_snr = 10.089 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)90 assert len(df_filt) == 3491 filter_options.min_event_snr = 15.092 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)93 assert len(df_filt) == 2394 # This encompasses in AU.MEEK, which gets ignored since filtering is non-strict.95 filter_options.min_event_snr = 20.096 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)97 assert len(df_filt) == 1298 # Turn on strict filtering and check filter criteria is now applied to AU.MEEK.99 filter_options.strict_filtering = True100 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)101 assert len(df_filt) == 11102 filter_options.min_event_snr = 25.0103 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)104 assert len(df_filt) == 7105 filter_options.strict_filtering = False106 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)107 assert len(df_filt) == 8108 # Test CWT cutoff109 filter_options.min_event_snr = 0.0110 filter_options.cwt_cutoff = 5111 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)112 assert len(df_filt) == 39113 # This encompasses in AU.MEEK, which gets ignored since filtering is non-strict.114 filter_options.cwt_cutoff = 10115 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)116 assert len(df_filt) == 30117 # Turn on strict filtering and check filter criteria is now applied to AU.MEEK.118 filter_options.strict_filtering = True119 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)120 assert len(df_filt) == 29121 filter_options.cwt_cutoff = 20122 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)123 assert len(df_filt) == 22124 filter_options.cwt_cutoff = 50125 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)126 assert len(df_filt) == 12127 filter_options.strict_filtering = False128 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)129 assert len(df_filt) == 13130 # Test slope cutoff131 filter_options.cwt_cutoff = 0.0132 filter_options.slope_cutoff = 1.0133 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)134 assert len(df_filt) == 43135 filter_options.slope_cutoff = 2.0136 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)137 assert len(df_filt) == 28138 # This encompasses in AU.MEEK, which gets ignored since filtering is non-strict.139 filter_options.slope_cutoff = 5.0140 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)141 assert len(df_filt) == 16142 # Turn on strict filtering and check filter criteria is now applied to AU.MEEK.143 filter_options.strict_filtering = True144 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)145 assert len(df_filt) == 15146 # Test nSigma cutoff147 filter_options.strict_filtering = False148 filter_options.slope_cutoff = 0.0149 filter_options.nsigma_cutoff = 5150 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)151 assert len(df_filt) == 45152 filter_options.nsigma_cutoff = 6153 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)154 assert len(df_filt) == 43155 filter_options.nsigma_cutoff = 7156 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)157 assert len(df_filt) == 41158 ## Test combined filtering159 filter_options.strict_filtering = True160 filter_options.min_event_mag = 6.0161 filter_options.min_event_snr = 8.0162 filter_options.cwt_cutoff = 2.0163 filter_options.slope_cutoff = 1.2164 filter_options.nsigma_cutoff = 5165 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)166 assert len(df_filt) == 5 + 24 # 5 from the zero quality metrics group + 24 from the rest167 mask_zero_metrics = (df_filt[['snr', 'qualityMeasureCWT', 'qualityMeasureSlope', 'nSigma']] == 0).all(axis=1)168 df_zero_mets = df_filt[mask_zero_metrics]169 df_other = df_filt[~mask_zero_metrics]170 assert np.all(df_zero_mets['mag'] >= filter_options.min_event_mag)171 assert np.all((df_other['snr'] >= filter_options.min_event_snr) &172 (df_other['qualityMeasureCWT'] >= filter_options.cwt_cutoff) &173 (df_other['qualityMeasureSlope'] >= filter_options.slope_cutoff) &174 (df_other['nSigma'] >= filter_options.nsigma_cutoff))175 filter_options.slope_cutoff = 3.0176 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)177 assert len(df_filt) == 18 # 5 from the zero quality metrics group + 13 from the rest178 # Not applying filtering to AU.MEEK179 filter_options.strict_filtering = False180 df_filt = rttr.apply_event_quality_filtering(df_picks, net_sta, filter_options)181 assert len(df_filt) == 19182 # Test passing more than one target station183 net_sta['net'].append('GE')184 net_sta['sta'].append('WSI')185 pytest.raises(ValueError, rttr.apply_event_quality_filtering, df_picks, net_sta, filter_options)186def test_broadcast_ref_residual_per_event(df_picks):187 filter_options = rttr.FilterOptions()188 df_ref = rttr.broadcast_ref_residual_per_event(df_picks, 'AU', 'MEEK', filter_options)189 # AU.MEEK is only there in event4, so only that event will have a non-Nan reference residual.190 expected_residual = 1.029852191 assert np.all(df_ref.loc[df_ref['#eventID'] == 'event4', 'ttResidualRef'] == expected_residual)192 assert np.all(df_ref.loc[df_ref['#eventID'] != 'event4', 'ttResidualRef'].isnull())193 # Repeat with a station that appears in multiple events194 df_ref = rttr.broadcast_ref_residual_per_event(df_picks, 'AU', 'KNA', filter_options)195 expected_event0_residual = -0.134955196 expected_event4_residual = 1.192448197 assert np.allclose(df_ref.loc[df_ref['#eventID'] == 'event0', 'ttResidualRef'], expected_event0_residual)198 assert np.allclose(df_ref.loc[df_ref['#eventID'] == 'event4', 'ttResidualRef'], expected_event4_residual)199 assert np.all(df_ref.loc[(df_ref['#eventID'] != 'event0') & (df_ref['#eventID'] != 'event4'),200 'ttResidualRef'].isnull())201 # Repeat with NWAO which is common to event1 and event4202 df_ref = rttr.broadcast_ref_residual_per_event(df_picks, 'IR', 'NWAO', filter_options)203 expected_event1_residual = -1.786222204 expected_event4_residual = -0.692545205 assert np.allclose(df_ref.loc[df_ref['#eventID'] == 'event1', 'ttResidualRef'], expected_event1_residual)206 assert np.allclose(df_ref.loc[df_ref['#eventID'] == 'event4', 'ttResidualRef'], expected_event4_residual)207 assert np.all(df_ref.loc[(df_ref['#eventID'] != 'event1') & (df_ref['#eventID'] != 'event4'),208 'ttResidualRef'].isnull())209def test_analyze_target_relative_to_ref(df_picks):210 filter_options = rttr.FilterOptions()211 # Set filter params to include the events on AU.KNA212 filter_options.min_event_snr = 5.0213 filter_options.cwt_cutoff = 5.0214 filter_options.slope_cutoff = 2.0215 target_net_sta = {'net': ['AU'], 'sta': ['KNA']}216 # Analyze AU.MEEK relative to all the GE stations217 ref_net_sta = {'net': ['GE']*15,218 'sta': ['WSI', 'ARMA', 'MEEK', 'CNB', 'CHN', 'FUQ', 'NZJ', 'NGO1', 'NAH1', 'SSE',219 'NJ2', 'TIA', 'KAPI', 'KMBL', 'MEEK']220 }221 df_plottable = rttr.analyze_target_relative_to_ref(df_picks, target_net_sta, ref_net_sta, filter_options)222 assert 'ttResidualRef' in df_plottable223 assert list(df_plottable['#eventID'].unique()) == ['event0', 'event4']224 assert list(df_plottable['ttResidualRef'].unique()) == [-0.134955, 1.192448]225 # For event0, only GE.WSI and GE.ARMA are present, and both pass the filtering criteria.226 assert np.all(np.all(df_plottable.loc[df_plottable['#eventID'] == 'event0', ['net', 'sta']].values ==227 np.array([['AU', 'KNA'], ['GE', 'WSI'], ['GE', 'ARMA']]), axis=1))228 # For event4, GE.KAPI, GE.KMBL and GE.MEEK are present, but GE.KMBL does not pass the filtering criteria229 # due to slope metric.230 assert np.all(np.all(df_plottable.loc[df_plottable['#eventID'] == 'event4', ['net', 'sta']].values ==231 np.array([['AU', 'KNA'], ['GE', 'KAPI'], ['GE', 'MEEK']]), axis=1))232 # Exercise the code path for plotting to detect some regressions.233 batch_options = rttr.BatchOptions()234 batch_options.batch_label = '_strict'235 display_options = rttr.DisplayOptions()236 display_options.events = rttr.generate_large_events_catalog(df_picks, 8.0)237 display_options.deployments = rttr._get_known_temporary_deployments() # pylint: disable=protected-access238 rttr._plot_network_relative_to_ref_station(df_plottable, target_net_sta, ref_net_sta, # pylint: disable=protected-access239 batch_options, filter_options, display_options)240if __name__ == "__main__":241 # Select explicit test to run.242 # import conftest243 # picks = conftest._read_picks()244 # test_analyze_target_relative_to_ref(picks)...
storage.py
Source:storage.py
1import json2from abc import abstractmethod3from argparse import ArgumentTypeError4from functools import reduce5import pymongo6class Collection(object):7 pass8class Storage(object):9 def __init__(self, config):10 self.config = config11 @abstractmethod12 def get_field(self, collection_name, key, **filter_options):13 pass14 @abstractmethod15 def set_field(self, collection_name, key, new_value, multi=False, **filter_options):16 pass17 # work with entire objects from collection18 # TODO: add deprecations19 @abstractmethod20 def get_object(self, collection_name, filter_options, multi=False):21 pass22 @abstractmethod23 def set_object(self, collection_name, new_object, filter_options, multi=False):24 pass25 @abstractmethod26 def remove_object(self, collection_name, filter_options, multi=False):27 pass28 def collection(self, collection_name):29 coll = Collection()30 def decorated_get_field(key, **filter_options):31 return self.get_field(collection_name, key, **filter_options)32 def decorated_set_field(key, new_value, multi=False, **filter_options):33 return self.set_field(collection_name, key, new_value, multi=multi, **filter_options)34 def decorated_get_object(filter_options, multi=False):35 return self.get_object(collection_name, filter_options, multi)36 def decorated_set_object(new_object, filter_options, multi=False):37 return self.set_object(collection_name, new_object, filter_options, multi)38 def decorated_remove_object(filter_options, multi=False):39 return self.remove_object(collection_name, filter_options, multi)40 decorated_get_field.__name__ = 'get_field'41 decorated_set_field.__name__ = 'set_field'42 decorated_get_object.__name__ = 'get_object'43 decorated_set_object.__name__ = 'set_object'44 decorated_remove_object.__name__ = 'remove_object'45 setattr(coll, decorated_get_field.__name__, decorated_get_field)46 setattr(coll, decorated_set_field.__name__, decorated_set_field)47 setattr(coll, decorated_get_object.__name__, decorated_get_object)48 setattr(coll, decorated_set_object.__name__, decorated_set_object)49 setattr(coll, decorated_remove_object.__name__, decorated_remove_object)50 return coll51class InMemoryStorage(Storage):52 def __init__(self, config):53 super().__init__(config)54 self.store = {}55 @staticmethod56 def _find_conforming_objects(collection, filter_options):57 conforming_objects = []58 for obj in collection:59 conforms = True60 for filter_key in filter_options.keys():61 if obj.get(filter_key) != filter_options[filter_key]:62 conforms = False63 break64 if conforms:65 # object fully conforms66 conforming_objects.append(obj)67 return conforming_objects68 def get_field(self, collection_name, key, **filter_options):69 arr = self.store.get(collection_name, [])70 if len(arr) < 1:71 return []72 reduce_filter_func = lambda dict_key, elem, res: \73 res and (elem.get(dict_key) == filter_options.get(dict_key))74 arr_elem_meets_filter = lambda elem: reduce(lambda res, dict_key: reduce_filter_func(dict_key, elem, res), filter_options.keys(), True)75 filtered_arr = tuple(filter(lambda elem: arr_elem_meets_filter(elem), arr))76 if len(filtered_arr) < 1:77 return []78 resulting_arr = list(filter(lambda elem: elem is not None, map(lambda elem: elem.get(key, None), filtered_arr)))79 return resulting_arr80 def set_field(self, collection_name, key, new_value, multi=False, **filter_options):81 collection = self.store.get(collection_name, [])82 def create_new_obj(filter_options, key, new_value):83 obj = dict(filter_options)84 obj[key] = new_value85 return obj86 if len(collection) < 1:87 self.store[collection_name] = collection88 collection.append(create_new_obj(filter_options, key, new_value))89 return True90 def find_conforming_obj(collection, filter_options):91 for obj in collection:92 conforms = True93 for filter_key in filter_options.keys():94 if obj.get(filter_key) != filter_options[filter_key]:95 conforms = False96 break97 if conforms:98 return obj99 return None100 found_obj = find_conforming_obj(collection, filter_options)101 if found_obj is None:102 collection.append(create_new_obj(filter_options, key, new_value))103 else:104 found_obj[key] = new_value105 return True106 def get_object(self, collection_name, filter_options, multi=False):107 if filter_options is None or not isinstance(filter_options, dict) or len(filter_options.keys()) < 1:108 raise ArgumentTypeError('filter_options must be a non-empty dict!')109 arr = self.store.get(collection_name, [])110 if len(arr) < 1:111 if multi:112 return []113 return None114 reduce_filter_func = lambda dict_key, elem, res: \115 res and (elem.get(dict_key) == filter_options.get(dict_key))116 arr_elem_meets_filter = lambda elem: reduce(lambda res, dict_key: reduce_filter_func(dict_key, elem, res),117 filter_options.keys(), True)118 filtered_arr = list(filter(lambda elem: arr_elem_meets_filter(elem), arr))119 if len(filtered_arr) < 1:120 if multi:121 return []122 else:123 return None124 if multi:125 return filtered_arr126 else:127 return filtered_arr[0]128 def set_object(self, collection_name, new_object, filter_options, multi=False):129 if filter_options is None or not isinstance(filter_options, dict) or len(filter_options.keys()) < 1:130 raise ArgumentTypeError('filter_options must be a non-empty dict!')131 collection = self.store.get(collection_name, [])132 if len(collection) < 1:133 self.store[collection_name] = collection134 collection.append(new_object)135 return True136 found_objects = self._find_conforming_objects(collection, filter_options)137 if len(found_objects) > 0:138 for found_object in found_objects:139 collection.remove(found_object)140 if not multi:141 # we've already removed one142 break143 collection.append(new_object)144 return True145 def remove_object(self, collection_name, filter_options, multi=False):146 if filter_options is None or not isinstance(filter_options, dict) or len(filter_options.keys()) < 1:147 raise ArgumentTypeError('filter_options must be a non-empty dict!')148 collection = self.store.get(collection_name, [])149 if len(collection) < 1:150 return False151 found_objects = self._find_conforming_objects(collection, filter_options)152 if len(found_objects) > 0:153 for found_object in found_objects:154 collection.remove(found_object)155 if not multi:156 # we've already removed one157 break158 else:159 return False160 return True161class DiskStorage(InMemoryStorage):162 def __init__(self, config):163 super().__init__(config)164 self.storage_file_path = config['file_path']165 try:166 self.store = json.load(open(self.storage_file_path, 'r'))167 except (json.JSONDecodeError, FileNotFoundError) as e:168 self.store = {}169 def set_field(self, collection_name, key, new_value, multi=False, **filter_options):170 result = super().set_field(collection_name, key, new_value, multi, **filter_options)171 json.dump(self.store, open(self.storage_file_path, 'w'))172 return result173 def get_field(self, collection_name, key, **filter_options):174 result = super().get_field(collection_name, key, **filter_options)175 return result176 def set_object(self, collection_name, new_object, filter_options, multi=False):177 result = super().set_object(collection_name, new_object, filter_options, multi)178 json.dump(self.store, open(self.storage_file_path, 'w'))179 return result180 def get_object(self, collection_name, filter_options, multi=False):181 result = super().get_object(collection_name, filter_options, multi)182 return result183 def remove_object(self, collection_name, filter_options, multi=False):184 result = super().remove_object(collection_name, filter_options, multi)185 json.dump(self.store, open(self.storage_file_path, 'w'))186 return result187class MongoStorage(Storage):188 def __init__(self, config):189 super().__init__(config)190 mongo_client = pymongo.MongoClient(config['host'], config['port'])191 self.db = mongo_client[config['database']]192 def set_field(self, collection_name, key, new_value, multi=False, **filter_options):193 return self.db[collection_name].update(filter_options, {'$set': {key: new_value}}, upsert=True, multi=multi)194 def get_field(self, collection_name, key, **filter_options):195 return self.db[collection_name].distinct(key, filter_options)196 def get_object(self, collection_name, filter_options, multi=False):197 if multi:198 return self.db[collection_name].find(filter_options)199 else:200 return self.db[collection_name].find_one(filter_options)201 def set_object(self, collection_name, new_object, filter_options, multi=False):202 return self.db[collection_name].update(filter_options, new_object, upsert=True, multi=multi)203 def remove_object(self, collection_name, filter_options, multi=False):204 if multi:205 return self.db[collection_name].delete_many(filter_options)206 else:...
_filter.py
Source:_filter.py
1from _print import print_list2from _options_values import filter_values, filter_options3def massive_filter(wk_list, enable=False):4 """5 According to the selected filter options returns a filtered list of image files.6 :param list wk_list: List from _build_list().build_new_list7 :param enable: Enable/disable function8 :return: New filtered list or exit9 """10 if not enable:11 return 'filter not enabled'12 if not filter_options['size_on'] and \13 not filter_options['resolution_on'] and \14 not filter_options['len_on'] and \15 not filter_options['orient_on'] and \16 not filter_options['side_difference_on'] and \17 not filter_options['format_on'] and \18 not filter_options['mpx_on'] and \19 not filter_options['path_on'] and \20 not filter_options['search_word']:21 print_list(wk_list, 'All filters are OFF')22 return wk_list23 chk_sum = 0 # variable for sum of TURN ON filtres24 chk_count = 0 # variable for matching filters25 new_list = []26 for item in wk_list:27 if filter_options['size_on']:28 # if on, chk_sum are 1, and if filter matching, then chk_count 129 chk_sum += 130 if filter_values['size'][0] <= item['im_size'] <= filter_values['size'][1]:31 chk_count += 132 if filter_options['resolution_on']:33 chk_sum += 134 if (35 filter_values['width'][0] <= item['im_width'] <= filter_values['width'][1]36 ) and (37 filter_values['height'][0] <= item['im_height'] <= filter_values['height'][1]38 ):39 chk_count += 140 if filter_options['len_on']:41 chk_sum += 142 if item['im_name_len'] >= filter_values['name_min_len']:43 chk_count += 144 if filter_options['orient_on']:45 chk_sum += 146 if item['im_orient'] == filter_values['orientation']:47 chk_count += 148 if filter_options['side_difference_on']:49 chk_sum += 150 if filter_values['side_difference'][0] <= item['im_side_diff'] <= filter_values['side_difference'][1]:51 chk_count += 152 if filter_options['format_on']:53 chk_sum += 154 if item['im_format'].upper() == filter_values['format'].upper():55 chk_count += 156 if filter_options['mpx_on']:57 chk_sum += 158 if filter_values['mpx'][0] <= item['im_mpx'] <= filter_values['mpx'][1]:59 chk_count += 160 if filter_options['path_on']:61 chk_sum += 162 if filter_values['srch_path'] == item['im_path']:63 chk_count += 164 if filter_options['search_word']:65 chk_sum += 166 if filter_values['srch_word'].lower() in item['im_name'].lower():67 chk_count += 168 if chk_sum == chk_count:69 # if all enabled filtres == matching filtres, then add into list70 new_list.append(item)71 chk_sum, chk_count = 0, 0 # to null72 # str contains what enabled in filter for output73 what_enabled = "{:}{:}{:}{:}{:}{:}{:}{:}{:}".format(74 'Size,' * filter_options['size_on'],75 'Resolution,' * filter_options['resolution_on'],76 'Lenght,' * filter_options['len_on'],77 'Orientation,' * filter_options['orient_on'],78 'Side Difference,' * filter_options['side_difference_on'],79 'Format,' * filter_options['format_on'],80 'Mpx,' * filter_options['mpx_on'],81 'Path,' * filter_options['path_on'],82 'Word,' * filter_options['search_word']83 )84 print_list(new_list, 'Filtered List', 'FILTERS: ' + what_enabled[:-1])...
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