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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