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How to use tag_policy method in localstack
Best Python code snippet using localstack_python
error_analysis.py
Source:error_analysis.py 
1from typing import Dict, List2import pandas as pd3import seaborn as sns4from matplotlib import pyplot as plt5from pandas import DataFrame6from sklearn.metrics import confusion_matrix7import numpy as np8from nlu.error_structure import MentionTypeError, MergeSplitError, FalseError, SimpleSpanError, NERCorrect, NERErrorComposite, \9 ComplicatedError10from nlu.ext_utils.confusion_matrix_pretty_print import pretty_plot_confusion_matrix11from nlu.parser import ConllParser12from nlu.utils import groupby, two_level_groupby, sort_two_level_group, add_total_column_row, fprint, make_autopct13cmap = sns.cubehelix_palette(as_cmap=True, light=.9)14def is_error(ems_pair):15 return isinstance(ems_pair.result, NERErrorComposite)16def is_correct(ems_pair):17 return isinstance(ems_pair.result, NERCorrect)18def is_span_error(ems_pair):19 return is_error(ems_pair) and isinstance(ems_pair.result.span_error, SimpleSpanError)20def is_only_span_error(ems_pair):21 return is_span_error(ems_pair) and not is_type_error(ems_pair) and not is_complicate_error(ems_pair)22def is_false_error(ems_pair):23 return is_error(ems_pair) and isinstance(ems_pair.result.false_error, FalseError)24def is_only_type_error(ems_pair):25 return is_type_error(ems_pair) and not is_span_error(ems_pair) and not is_complicate_error(ems_pair)26def is_fn(ems_pair):27 return is_false_error(ems_pair) and ems_pair.result.false_error.false_type == 'False Negative'28def is_fp(ems_pair):29 return is_false_error(ems_pair) and ems_pair.result.false_error.false_type == 'False Positive'30def is_ex(ems_pair):31 return is_span_error(ems_pair) and ems_pair.result.span_error.span_type == 'Expansion'32def is_re(ems_pair):33 return is_ex(ems_pair) and ems_pair.result.span_error.direction == 'Right'34def is_le(ems_pair):35 return is_ex(ems_pair) and ems_pair.result.span_error.direction == 'Left'36def is_rle(ems_pair):37 return is_ex(ems_pair) and ems_pair.result.span_error.direction == 'Right Left'38def is_dim(ems_pair):39 return is_span_error(ems_pair) and ems_pair.result.span_error.span_type == 'Diminished'40def is_rd(ems_pair):41 return is_dim(ems_pair) and ems_pair.result.span_error.direction == 'Right'42def is_ld(ems_pair):43 return is_dim(ems_pair) and ems_pair.result.span_error.direction == 'Left'44def is_rld(ems_pair):45 return is_dim(ems_pair) and ems_pair.result.span_error.direction == 'Right Left'46def is_cross(ems_pair):47 return is_span_error(ems_pair) and 'Crossed' in ems_pair.result.span_error.type48def is_rc(ems_pair):49 return is_cross(ems_pair) and ems_pair.result.span_error.direction == 'Right'50def is_lc(ems_pair):51 return is_cross(ems_pair) and ems_pair.result.span_error.direction == 'Left'52def is_merge_split(ems_pair):53 return is_error(ems_pair) and isinstance(ems_pair.result.span_error, MergeSplitError)54def is_merge(ems_pair):55 return is_merge_split(ems_pair) and ems_pair.result.span_error.type == 'Spans Merged'56def is_split(ems_pair):57 return is_merge_split(ems_pair) and ems_pair.result.span_error.type == 'Span Split'58def is_type_error(ems_pair):59 return is_error(ems_pair) and isinstance(ems_pair.result.type_error, MentionTypeError)60def is_complicate_error(ems_pair):61 return is_error(ems_pair) and isinstance(ems_pair.result.span_error, ComplicatedError)62def filtered_results(parser, is_funcs, boolean=all) -> List[NERErrorComposite]:63 """64 :param parser: `ConllParser` class65 :param is_funcs: filter functions that return boolean value66 :param boolean: `any` or `all`67 :return: List of `NERErrorComposite`68 """69 errors = []70 for doc in parser.docs:71 for sent in doc:72 if sent.ems_pairs:73 for ems_pair in sent.ems_pairs:74 # overloading75 try: # funcs as a list76 if boolean([is_func(ems_pair) for is_func in is_funcs]):77 errors.append(ems_pair.result)78 except TypeError: # funcs as only a function79 if is_funcs(ems_pair):80 errors.append(ems_pair.result)81 except NameError:82 raise NameError('is_funcs is not one of the predefined result types')83 return errors84def get_false_errors_category(parser):85 _is_types = {'False Positive': is_fp, 'False Negative': is_fn, 'All False Errors': is_false_error}86 return {name: filtered_results(parser, is_func) for name, is_func in _is_types.items()}87def get_all_false_errors(parser):88 return filtered_results(parser, is_false_error)89def get_false_positives(parser):90 return filtered_results(parser, is_fp)91def get_false_negatives(parser):92 return filtered_results(parser, is_fn)93def get_span_errors_category(parser):94 _is_types = {'Right Expansion': is_re, 'Left Expansion': is_le, 'Right Left Expansion': is_rle,95 'Right Diminished': is_rd, 'Left Diminished': is_ld, 'Right Left Diminished': is_rld,96 'Right Crossed': is_rc, 'Left Crossed': is_lc,97 'Spans Merged': is_merge, 'Span Split': is_split,98 'Complicate': is_complicate_error,99 'All Span Errors': is_span_error}100 return {name: filtered_results(parser, is_func) for name, is_func in _is_types.items()}101def get_all_span_errors(parser):102 return filtered_results(parser, is_span_error)103def get_only_span_errors(parser):104 return filtered_results(parser, is_only_span_error)105def get_all_type_errors(parser):106 return filtered_results(parser, is_type_error)107def get_only_type_errors(parser):108 return filtered_results(parser, is_only_type_error)109def get_span_and_type_composite_errors(parser):110 return filtered_results(parser, [is_type_error, is_span_error])111def get_only_span_and_type_composite_errors(parser):112 return filtered_results(parser, [is_type_error, is_span_error, lambda e: not is_complicate_error(e)])113def get_complicate_errors(parser):114 return filtered_results(parser, is_complicate_error)115def print_type_errors(parser):116 all_type_errors = get_all_type_errors(parser)117 error_table = {}118 for error in all_type_errors:119 type = str(error.type_error)120 if type not in error_table.keys():121 error_table[type] = 1122 else:123 error_table[type] += 1124 for key in sorted(error_table.keys()):125 print(key + ": " + str(error_table[key]))126def print_list_len_in_dict(dict_: Dict):127 for name, list_ in dict_.items():128 print('{}: {}'.format(name, len(list_)))129class NERErrorAnalyzer:130 """131 input: DocumentsWithErrorAnn132 """133 @classmethod134 def print_report(cls):135 pass136 @classmethod137 def save_report(cls, parser, tag_policy='conll'):138 fprint('Error Analysis')139 # print140 fnames = ['error_pie.png', 'false_error_heatmap.png', 'span_error_heatmap.png', 'confusion_matrix.png']141 cls.print_error_pie(parser, fnames[0])142 cls.print_false_error_heatmap(parser, tag_policy=tag_policy, save_file=fnames[1])143 cls.print_span_error_heatmap(parser, tag_policy=tag_policy, save_file=fnames[2])144 cls.pprint_ner_confusion_matrix(parser, tag_policy=tag_policy, save_file=fnames[3])145 @staticmethod146 def get_span_error_category_df(parser, transpose=True, tag_policy='conll'):147 errs = get_only_span_errors(parser)148 key1 = lambda err: err.span_error.type149 key2 = lambda err: err.ptypes[0]150 g = two_level_groupby(errs, key1, key2, count=True)151 df = DataFrame(g)152 if transpose:153 df = df.T154 span_etype_order = ['Right Expansion', 'Left Expansion', 'Right Left Expansion',155 'Right Diminished', 'Left Diminished', 'Right Left Diminished',156 'Right Crossed', 'Left Crossed',157 'Spans Merged', 'Span Split',158 'Complicate']159 if tag_policy == 'wnut':160 etypes = ['person', 'location', 'corporation', 'group', 'creative-work', 'product']161 elif tag_policy == 'conll':162 etypes = ['PER', 'LOC', 'ORG', 'MISC']163 df = sort_two_level_group(df, span_etype_order, etypes)164 add_total_column_row(df)165 return df166 @staticmethod167 def get_false_error_category_df(parser, transpose=True, tag_policy='conll'):168 errs = filtered_results(parser, is_false_error)169 key1 = lambda e: e.false_error.false_type170 key2 = lambda err: err.false_error.em_type171 g = two_level_groupby(errs, key1, key2, count=True)172 df = DataFrame(g)173 if transpose:174 df = df.T175 if tag_policy == 'wnut':176 etypes = ['person', 'location', 'corporation', 'group', 'creative-work', 'product']177 elif tag_policy == 'conll':178 etypes = ['PER', 'LOC', 'ORG', 'MISC']179 df = sort_two_level_group(df, ['False Positive', 'False Negative'], etypes)180 add_total_column_row(df)181 return df182 @staticmethod183 def print_error_pie(parser, save_file=None):184 only_span_errors = get_only_span_errors(parser)185 only_type_errors = get_only_type_errors(parser)186 only_span_and_type_composite_errors = get_only_span_and_type_composite_errors(parser)187 false_negatives = get_false_negatives(parser)188 false_positives = get_false_positives(parser)189 complicate_erorrs = get_complicate_errors(parser)190 # Pie chart191 labels = ['Only Span', 'Only Type', 'Span and Type', 'Complicate Errors', 'False Positive', 'False Negative']192 sizes = [len(only_span_errors), len(only_type_errors), len(only_span_and_type_composite_errors),193 len(complicate_erorrs),194 len(false_positives), len(false_negatives)]195 # colors196 # colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']197 fig1, ax1 = plt.subplots()198 ax1.pie(sizes, labels=labels, autopct=make_autopct(sizes), startangle=90)199 # draw circle200 centre_circle = plt.Circle((0, 0), 0.70, fc='white')201 fig = plt.gcf()202 fig.gca().add_artist(centre_circle)203 # Equal aspect ratio ensures that pie is drawn as a circle204 ax1.axis('equal')205 plt.tight_layout()206 if save_file is not None:207 plt.savefig(save_file)208 else:209 plt.show()210 @staticmethod211 def print_span_error_pie(): #TODO212 pass213 @staticmethod214 def print_composite_errors(parser):215 # get complicated error216 com_err_array = get_complicate_errors(parser)217 print(com_err_array)218 @staticmethod219 def print_false_errors(parser, tag_policy='conll'): #TODO: duplicated?220 if tag_policy == 'wnut':221 etypes = ['person', 'location', 'corporation', 'group', 'creative-work', 'product']222 elif tag_policy == 'conll':223 etypes = ['PER', 'LOC', 'ORG', 'MISC']224 # group fp/fn by entity type225 fns = get_false_negatives(parser)226 fps = get_false_positives(parser)227 key = lambda err: err.false_error.em_type228 fn_groups = groupby(fns, key)229 fp_groups = groupby(fps, key)230 order = {key: i for i, key in enumerate(etypes)}231 for fp_type, fps in sorted(fp_groups.items(), key=lambda x: order[x[0]]):232 print('{} - {}'.format(fp_type, len(fps)))233 for fn_type, fns in sorted(fn_groups.items(), key=lambda x: order[x[0]]):234 print('{} - {}'.format(fn_type, len(fns)))235 @staticmethod236 def print_span_error_heatmap(parser, tag_policy='conll', save_file=None, **kwargs):237 err_df = NERErrorAnalyzer.get_span_error_category_df(parser, tag_policy=tag_policy)238 plt.figure()239 hm = sns.heatmap(err_df, annot=True, linewidth=1, fmt='.0f', cmap=cmap, **kwargs)240 hm.set_facecolor(np.append(cmap.colors[0][:-1], 0.5))241 hm.set_xticklabels(hm.get_xticklabels(), rotation=45)242 if save_file is not None:243 plt.savefig(save_file, bbox_inches='tight')244 else:245 plt.show()246 @staticmethod247 def print_false_error_heatmap(parser, save_file=None, tag_policy='conll', **kwargs):248 err_df = NERErrorAnalyzer.get_false_error_category_df(parser, tag_policy=tag_policy)249 plt.figure()250 hm = sns.heatmap(err_df, annot=True, linewidth=1, fmt='.0f', cmap=cmap, **kwargs)251 hm.set_facecolor(np.append(cmap.colors[0][:-1], 0.5))252 hm.set_xticklabels(hm.get_xticklabels(), rotation=45)253 hm.set_yticklabels(hm.get_yticklabels(), rotation=0)254 if save_file is not None:255 plt.savefig(save_file, bbox_inches='tight')256 else:257 plt.show()258 @classmethod259 def get_confusion_matrix_df(cls, parser: ConllParser, tag_policy='conll') -> DataFrame:260 wnut_types = ["person", "location", "corporation", "group", "creative-work", "product"]261 conll_types = ["PER", "LOC", "ORG", "MISC"]262 labels = wnut_types if tag_policy == 'wnut' else conll_types263 cm = cls.get_confusion_matrix(parser, tag_policy)264 return pd.DataFrame(cm, index=labels, columns=labels)265 @staticmethod266 def get_confusion_matrix(parser: ConllParser, tag_policy='conll') -> List[List[int]]:267 y_true = []268 y_pred = []269 for doc in parser.docs:270 for sentence in doc:271 if sentence.ems_pairs:272 for ems_pair in sentence.ems_pairs:273 if len(ems_pair.result.gtypes) == 1 and len(ems_pair.result.ptypes) == 1:274 y_true.append(ems_pair.result.gtypes[0])275 y_pred.append(ems_pair.result.ptypes[0])276 wnut_types = ["person", "location", "corporation", "group", "creative-work", "product"]277 conll_types = ["PER", "LOC", "ORG", "MISC"]278 labels = wnut_types if tag_policy == 'wnut' else conll_types279 return confusion_matrix(y_true, y_pred, labels=labels)280 @classmethod281 def print_ner_confusion_matrix(cls, parser, tag_policy='conll', **kwargs):282 cm = cls.get_confusion_matrix(parser, tag_policy=tag_policy)283 cls.print_confusion_matrix(cm, tag_policy=tag_policy, **kwargs)284 @classmethod285 def pprint_ner_confusion_matrix(cls, parser, tag_policy='conll', **kwargs):286 cm = cls.get_confusion_matrix(parser, tag_policy=tag_policy)287 cls.pprint_confusion_matrix(cm, tag_policy=tag_policy, **kwargs)288 @staticmethod289 def print_confusion_matrix(cm: List[List[int]], tag_policy='conll'):290 fig = plt.figure()291 ax = fig.add_subplot(111)292 cax = ax.matshow(cm)293 wnut_types = ["person", "location", "corporation", "group", "creative-work", "product"]294 conll_types = ["PER", "LOC", "ORG", "MISC"]295 labels = wnut_types if tag_policy == 'wnut' else conll_types296 plt.title('Confusion matrix of the classifier')297 fig.colorbar(cax)298 ax.set_xticklabels([''] + labels)299 ax.set_yticklabels([''] + labels)300 ax.xaxis.tick_bottom()301 plt.xticks(rotation=45, ha='right', rotation_mode='anchor')302 plt.xlabel('Predicted')303 plt.ylabel('True')304 # Loop over data dimensions and create text annotations.305 for i in range(len(labels)):306 for j in range(len(labels)):307 ax.text(j, i, cm[i, j], ha="center", va="center", color="w")308 plt.show()309 @staticmethod310 def pprint_confusion_matrix(cm: List[List[int]], tag_policy='conll', **kwargs):311 wnut_types = ["person", "location", "corporation", "group", "creative-work", "product"]312 conll_types = ["PER", "LOC", "ORG", "MISC"]313 labels = wnut_types if tag_policy == 'wnut' else conll_types314 df_cm = pd.DataFrame(cm, index=labels, columns=labels)315 pretty_plot_confusion_matrix(df_cm, pred_val_axis='x', show_null_values=0, **kwargs)316if __name__ == '__main__':...
avalens.py
Source:avalens.py
1import os2import numpy as np3import pandas as pd4from datetime import datetime5from avatarpy import Avatar6class AvaLens:7 def __init__(self, id_policy='filepath', tag_policy='provide'):8 r"""Lens for group analysis of avatars9 :param id_policy: {'filepath'(default))|'incremental'|'provide'|'basename'}10 :param tag_policy: {'provide'(default))|'dirname'}11 """12 assert id_policy in ['filepath','incremental','provide','basename'], 'wrong argument for id_policy'13 assert tag_policy in ['provide','dirname'], 'wrong argument for id_policy'14 self._avatars = []15 self.id_policy = id_policy16 self.tag_policy = tag_policy17 def __repr__(self):18 return f'AvaLens instance containing avatars: {self.avatars}'19 @property20 def avatars(self):21 """User Added avatars"""22 return self._avatars23 def add_file(self, csv_path, ID=None, tags={}, verbose=1):24 if self.id_policy == 'filepath':25 ID = csv_path26 elif self.id_policy == 'incremental':27 ID = len(self.avatars)28 elif self.id_policy == 'provide':29 assert ID is not None, 'User should provide ID or select id_policy among ["filpath", "incremental", "basname"]'30 ID = ID31 elif self.id_policy == 'basename':32 ID = os.path.splitext(os.path.basename(csv_path))[0]33 if self.tag_policy == 'dirname':34 tags = dict(tag=os.path.basename(os.path.dirname(csv_path)))35 elif self.tag_policy == 'provide':36 tags = tags37 avatar = Avatar(csv_path=csv_path, ID=ID, tags=tags)38 self.avatars.append(avatar)39 if verbose==1:40 print(f'[{datetime.now()}] Added new Avatar(csv_path={csv_path}, ID={ID}, tags={tags})', end='\r')41 if verbose==2:42 print(f'[{datetime.now()}] Added new Avatar(csv_path={csv_path}, ID={ID}, tags={tags})')43 return self44 def add_folder(self, root, ID=None, tags={}, verbose=1):45 for path, subdirs, files in os.walk(root):46 for name in files:47 if name.lower().endswith('.csv'):48 csv_path = os.path.join(path, name)49 self.add_file(csv_path, ID, tags, verbose=verbose)50 return self51 def describe(self, include=['corr', 'stat'], func_kws={}, indices=None, assign_ID=True, assign_tags=True):52 describes = []53 for avatar in self.avatars:54 if func_kws:55 for name, func in func_kws.items():56 avatar.annotation.add(by=func, name=name)57 indices = avatar.annotation.get_indices(name)58 desc = avatar.describe(indices=indices, include=include, assign_ID=assign_ID, assign_tags=assign_tags).assign(event=name)59 describes.append(desc)60 else:61 desc = avatar.describe(indices=indices, include=include, assign_ID=assign_ID, assign_tags=assign_tags)62 describes.append(desc)63 return pd.concat(describes).reset_index(drop=True)64 @property65 def search_event(self):66 return SearchEvent(parent=self)67class SearchEvent:68 def __init__(self, parent=None):69 self.__parent = parent70 self.__events = []71 self.__event_name = ''72 def __call__(self, func, name, length=20, verbos=1):73 """Search event by given function.74 """75 assert callable(func), 'func should be callable'76 self.__events = []77 self.__event_name = name78 for avatar in self.__parent.avatars:79 boolean_series = func(avatar)80 assert isinstance(boolean_series, pd.Series), 'func should return pd.Series of boolean with index'81 assert boolean_series.dtype == bool, 'dtype of boolean_series should be bool'82 events = self.split_boolean_series(boolean_series)83 filtered_events = []84 for arr in events:85 if len(arr)<length:86 continue87 else:88 while len(arr)>=length:89 filtered_events.append(arr[:length])90 arr = arr[length:]91 if verbos==1:92 print(f'Total {len(filtered_events)} event was detected', end='\r')93 if verbos==2:94 print(f'Total {len(filtered_events)} event was detected')95 self.__events.append(filtered_events)96 return self97 @staticmethod98 def split_boolean_series(boolean_series):99 indices, boolean = boolean_series.index, boolean_series.values100 pos = np.nonzero(boolean[1:] != boolean[:-1])[0] + 1101 arrs = np.split(indices, pos)102 arrs = arrs[0::2] if boolean[0] else arrs[1::2]103 return arrs104 def describe(self, include=['corr', 'stat'], assign_ID=True, assign_tags=True, assign_event_name=True):105 describes = []106 for avatar, events in zip(self.__parent.avatars, self.__events) :107 for indices in events:108 desc = avatar.describe(indices=indices, include=include, assign_ID=assign_ID, assign_tags=assign_tags)109 describes.append(desc)110 df = pd.concat(describes).reset_index(drop=True)111 if assign_event_name:112 df = df.assign(event=self.__event_name)113 return df114 #TODO...
index.py
Source:index.py
1import json2import os3TAG_POLICY_DIR = "tag-policies"4SCP_DIR = "service-control-policies"5RESOURCE_TO_ACTION_MAP = "resource-syntax-map.json"6def valid_statement(statement):7 if "Condition" not in statement:8 return False9 elif "Action" not in statement or len(statement["Action"]) == 0:10 return False11 elif "Resource" not in statement or len(statement["Resource"]) == 0:12 return False13 return True14def validate_and_optimize_statement(s):15 if not valid_statement(s):16 return False17 for attr in ["Action", "Resource"]:18 if isinstance(s[attr], list) and len(s[attr]) == 1:19 s[attr] = s[attr][0]20 elif isinstance(s[attr], list):21 s[attr] = s[attr] = list(dict.fromkeys(s[attr]))22 return s23def inject_tag_to_condition_template(tag, condition):24 new_condition = {}25 for operator in condition:26 new_condition[operator] = {}27 for key, val in condition[operator].items():28 new_key = key.replace("<tag>", tag)29 new_condition[operator][new_key] = val.replace("<tag>", tag)30 return new_condition31def tag_and_resource_to_statement(sid, tag_name, resource_name, resource):32 print(f"--- Generating SCP statement for {tag_name} / {resource_name}")33 statement = {34 "Sid": sid,35 "Effect": "Deny",36 }37 if "Condition" in resource and len(resource["Condition"].keys()) > 0:38 statement["Condition"] = inject_tag_to_condition_template(tag_name, resource["Condition"])39 else:40 statement["Condition"] = {41 "StringNotLike": {42 f"aws:RequestTag/{tag_name}": "?*",43 }44 }45 for attr in ["Action", "Resource"]:46 if attr in resource:47 statement[attr] = resource[attr]48 print(49 f"||| Optimizing generated statement for {tag_name} / {resource_name}")50 return validate_and_optimize_statement(statement)51def convert_tag_policy_to_scp_statements(tag_policy):52 print(f"--- Reading IAM / Tag Policy Resource Map")53 try:54 with open(os.path.join(".", RESOURCE_TO_ACTION_MAP)) as json_file:55 resource_map = json.load(json_file)56 except Exception as e:57 print(58 f"!!! Error reading IAM Action to Resources map: {RESOURCE_TO_ACTION_MAP}")59 print(e)60 quit()61 print(f"--- Reading enforced resources")62 statements = []63 inheritance_operators = ["@@assign", "@@append"]64 itr = 065 for tag_name in tag_policy["tags"]:66 if "enforced_for" in tag_policy["tags"][tag_name]:67 print(f"||| Enforced resources for {tag_name}:")68 for io in inheritance_operators:69 if io in tag_policy["tags"][tag_name]["enforced_for"]:70 for resource_name in tag_policy["tags"][tag_name]["enforced_for"][io]:71 # Tag / Resource match for enforcement72 if resource_name in resource_map.keys():73 itr += 174 s = tag_and_resource_to_statement(75 f"tag{itr}", tag_name, resource_name, resource_map[resource_name])76 if s:77 statements.append(s)78 # Handle wildcard resources in resource map79 elif resource_name.endswith(':*'):80 wildcard_resources = [r for r in resource_map.keys(81 ) if r.startswith(resource_name.split(':')[0])]82 for wildcard_resource_name in wildcard_resources:83 s = tag_and_resource_to_statement(84 f"tag{itr}", tag_name, wildcard_resource_name, resource_map[wildcard_resource_name])85 if s:86 statements.append(s)87 return statements88def create_statements_for_tag(tag_name, tag_statement, resource_map):89 statements = []90 return statements91def convert_tag_policy_to_scp(filename, tag_policy):92 statements = convert_tag_policy_to_scp_statements(tag_policy)93 print(f"--- Converted {filename} to SCP")94 return {"Version": "2012-10-17", "Statement": statements}95def write_scp_to_disk(filename, scp):96 print(f"--- Writing SCP: {filename}")97 with open(os.path.join(SCP_DIR, filename), "w") as scp_file:98 json.dump(scp, scp_file, indent=2)99def main():100 print("--- Reading tag policies")101 tag_policy_files = [f for f in os.listdir(102 TAG_POLICY_DIR) if f.endswith(".json")]103 for filename in tag_policy_files:104 print(f"||| Reading tag policy: {filename}")105 with open(os.path.join(TAG_POLICY_DIR, filename)) as json_file:106 tag_policy = json.load(json_file)107 scp = convert_tag_policy_to_scp(filename, tag_policy)108 write_scp_to_disk(filename, scp)109if __name__ == "__main__":...
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