How to use iteration method in Slash

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GameOfSocialDistancing.py

Source:GameOfSocialDistancing.py Github

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1def convert_to_list(data):2 '''3 Converts a string to a list of strings.4 '''5 entirity = []6 for i in range(len(data)):7 output = []8 row = data[i]9 for j in range(len(row)):10 output.append(row[j])11 entirity.append(output)12 return entirity13data = open("Day 11\input11", "r+")14data = data.read()15data = data.split("\n")16data = convert_to_list(data)17def fill_seats(data):18 '''19 Plays minesweeper and leaves the seat if 4 occupied seats are adjacent.20 '''21 still_simulating = True22 while(still_simulating == True):23 still_simulating = False24 #print("Iteration start")25 for i in range(len(data)):26 row = data[i]27 occupied = 028 for j in range(len(row)):29 if(row[j] == "L"):30 if(j == 0):31 if(row[j+1] != "#" and row[j+1] != "1"):32 if(i == len(data) - 1):33 above = data[i-1]34 if(above[j] != "#" and above[j+1] != "#"35 and above[j] != "1" and above[j+1] != "1"):36 row[j] = "0"37 still_simulating = True38 elif(i == 0):39 below = data[i+1]40 if(below[j] != "#" and below[j+1] != "#"41 and below[j] != "1" and below[j+1] != "1"):42 row[j] = "0"43 still_simulating = True44 else:45 above = data[i-1]46 below = data[i+1]47 if(below[j] != "#" and below[j+1] != "#"48 and below[j] != "1" and below[j+1] != "1"49 and above[j] != "#" and above[j+1] != "#"50 and above[j] != "1" and above[j+1] != "1"):51 row[j] = "0"52 still_simulating = True53 elif(j == len(row) - 1):54 if(row[j-1] != "#" and row[j-1] != "1"):55 if(i == len(data) - 1):56 above = data[i-1]57 if(above[j] != "#" and above[j-1] != "#"58 and above[j] != "1" and above[j-1] != "1"):59 row[j] = "0"60 still_simulating = True61 elif(i == 0):62 below = data[i+1]63 if(below[j] != "#" and below[j-1] != "#"64 and below[j] != "1" and below[j-1] != "1"):65 row[j] = "0"66 still_simulating = True67 else:68 above = data[i-1]69 below = data[i+1]70 if(below[j] != "#" and below[j-1] != "#"71 and below[j] != "1" and below[j-1] != "1"72 and above[j] != "#" and above[j-1] != "#"73 and above[j] != "1" and above[j-1] != "1"):74 row[j] = "0"75 still_simulating = True76 else:77 if(row[j-1] != "#" and row[j+1] != "#"78 and row[j-1] != "1" and row[j+1] != "1"):79 if(i == len(data) - 1):80 above = data[i-1]81 if(above[j] != "#" and above[j-1] != "#" and above[j+1] != "#"82 and above[j] != "1" and above[j-1] != "1" and above[j+1] != "1"):83 row[j] = "0"84 still_simulating = True85 elif(i == 0):86 below = data[i+1]87 if(below[j] != "#" and below[j-1] != "#" and below[j+1] != "#"88 and below[j] != "1" and below[j-1] != "1" and below[j+1] != "1"):89 row[j] = "0"90 still_simulating = True91 else:92 above = data[i-1]93 below = data[i+1]94 if(below[j] != "#" and below[j-1] != "#" and below[j+1] != "#"95 and below[j] != "1" and below[j-1] != "1" and below[j+1] != "1"96 and above[j] != "#" and above[j-1] != "#" and above[j+1] != "#"97 and above[j] != "1" and above[j-1] != "1" and above[j+1] != "1"):98 row[j] = "0"99 still_simulating = True100 elif(row[j] == "#"):101 occupied = 0102 if(j == 0):103 if(row[j+1] == "#" or row[j+1] == "1"):104 occupied += 1105 if(i == len(data) - 1):106 above = data[i-1]107 if(above[j] == "#" or above[j] == "1"):108 occupied +=1109 if(above[j+1] == "#" or above[j+1] == "1"):110 occupied +=1111 elif(i == 0):112 below = data[i+1]113 if(below[j] == "#" or below[j] == "1"):114 occupied +=1115 if(below[j+1] == "#" or below[j+1] == "1"):116 occupied +=1117 else:118 above = data[i-1]119 below = data[i+1]120 if(above[j] == "#" or above[j] == "1"):121 occupied +=1122 if(above[j+1] == "#" or above[j+1] == "1"):123 occupied +=1124 if(below[j] == "#" or below[j] == "1"):125 occupied +=1126 if(below[j+1] == "#" or below[j+1] == "1"):127 occupied +=1128 elif(j == len(row) - 1):129 if(row[j-1] == "#" or row[j-1] == "1"):130 occupied += 1131 if(i == len(data) - 1):132 above = data[i-1]133 if(above[j] == "#" or above[j] == "1"):134 occupied +=1135 if(above[j-1] == "#" or above[j-1] == "1"):136 occupied +=1137 elif(i == 0):138 below = data[i+1]139 if(below[j] == "#" or below[j] == "1"):140 occupied +=1141 if(below[j-1] == "#" or below[j-1] == "1"):142 occupied +=1143 else:144 above = data[i-1]145 below = data[i+1]146 if(above[j] == "#" or above[j] == "1"):147 occupied +=1148 if(above[j-1] == "#" or above[j-1] == "1"):149 occupied +=1150 if(below[j] == "#" or below[j] == "1"):151 occupied +=1152 if(below[j-1] == "#" or below[j-1] == "1"):153 occupied +=1154 else:155 if(row[j+1] == "#" or row[j+1] == "1"):156 occupied += 1157 if(row[j-1] == "#" or row[j-1] == "1"):158 occupied += 1159 if(i == len(data) - 1):160 above = data[i-1]161 if(above[j] == "#" or above[j] == "1"):162 occupied +=1163 if(above[j-1] == "#" or above[j-1] == "1"):164 occupied +=1165 if(above[j+1] == "#" or above[j+1] == "1"):166 occupied +=1167 elif(i == 0):168 below = data[i+1]169 if(below[j] == "#" or below[j] == "1"):170 occupied +=1171 if(below[j-1] == "#" or below[j-1] == "1"):172 occupied +=1173 if(below[j+1] == "#" or below[j+1] == "1"):174 occupied +=1175 else:176 above = data[i-1]177 below = data[i+1]178 if(above[j] == "#" or above[j] == "1"):179 occupied +=1180 if(above[j-1] == "#" or above[j-1] == "1"):181 occupied +=1182 if(below[j] == "#" or below[j] == "1"):183 occupied +=1184 if(below[j-1] == "#" or below[j-1] == "1"):185 occupied +=1186 if(below[j+1] == "#" or below[j+1] == "1"):187 occupied +=1188 if(above[j+1] == "#" or above[j+1] == "1"):189 occupied +=1190 if(occupied >= 4):191 row[j] = "1"192 still_simulating = True193 #print(i, row)194 data[i] = row195 for x in range(len(data)):196 row = data[x]197 for y in range(len(row)):198 if(row[y] == "0"):199 row[y] = "#"200 elif(row[y] == "1"):201 row[y] = "L"202 data[x] = row203 return data204def social_distancing(data):205 '''206 Plays minesweeper^2 leaves the seat if at least 5 of the first seats of each direction (diagonals included) are occupied seats.207 '''208 still_simulating = True209 while(still_simulating == True):210 still_simulating = False211 for i in range(len(data)):212 row = data[i]213 occupied = 0214 for j in range(len(row)):215 if(row[j] == "L"):216 occupied = 0217 iteration = j - 1218 while(iteration >= 0):219 if(row[iteration] == "#" or row[iteration] == "1"):220 occupied +=1221 break222 if(row[iteration] == "L" or row[iteration] == "0"):223 break224 iteration -=1225 iteration = j + 1226 while(iteration < len(row)):227 if(row[iteration] == "#" or row[iteration] == "1"):228 occupied +=1229 break230 if(row[iteration] == "L" or row[iteration] == "0"):231 break232 iteration +=1233 iterationI = i - 1234 iterationJ = j - 1235 while(iterationJ >= 0 and iterationI >= 0):236 if(data[iterationI][iterationJ] == "#"237 or data[iterationI][iterationJ] == "1"):238 occupied +=1239 break240 if(data[iterationI][iterationJ] == "L"241 or data[iterationI][iterationJ] == "0"):242 break243 iterationI -=1244 iterationJ -=1245 iterationI = i - 1246 while(iterationI >= 0):247 if(data[iterationI][j] == "#"248 or data[iterationI][j] == "1"):249 occupied +=1250 break251 if(data[iterationI][j] == "L"252 or data[iterationI][j] == "0"):253 break254 iterationI -=1255 iterationI = i - 1256 iterationJ = j + 1257 while(iterationJ < len(row) and iterationI >= 0):258 if(data[iterationI][iterationJ] == "#"259 or data[iterationI][iterationJ] == "1"):260 occupied +=1261 break262 if(data[iterationI][iterationJ] == "L"263 or data[iterationI][iterationJ] == "0"):264 break265 iterationI -=1266 iterationJ +=1267 iterationI = i + 1268 iterationJ = j - 1269 while(iterationJ >= 0 and iterationI < len(data)):270 if(data[iterationI][iterationJ] == "#"271 or data[iterationI][iterationJ] == "1"):272 occupied +=1273 break274 if(data[iterationI][iterationJ] == "L"275 or data[iterationI][iterationJ] == "0"):276 break277 iterationI +=1278 iterationJ -=1279 iterationI = i + 1280 while(iterationI < len(data)):281 if(data[iterationI][j] == "#"282 or data[iterationI][j] == "1"):283 occupied +=1284 break285 if(data[iterationI][j] == "L"286 or data[iterationI][j] == "0"):287 break288 iterationI +=1289 iterationI = i + 1290 iterationJ = j + 1291 while(iterationJ < len(row) and iterationI < len(data)):292 if(data[iterationI][iterationJ] == "#"293 or data[iterationI][iterationJ] == "1"):294 occupied +=1295 break296 if(data[iterationI][iterationJ] == "L"297 or data[iterationI][iterationJ] == "0"):298 break299 iterationI +=1300 iterationJ +=1301 if(occupied == 0):302 row[j] = "0"303 still_simulating = True304 elif(row[j] == "#"):305 occupied = 0306 iteration = j - 1307 while(iteration >= 0):308 if(row[iteration] == "#" or row[iteration] == "1"):309 occupied +=1310 break311 if(row[iteration] == "L" or row[iteration] == "0"):312 break313 iteration -=1314 iteration = j + 1315 while(iteration < len(row)):316 if(row[iteration] == "#" or row[iteration] == "1"):317 occupied +=1318 break319 if(row[iteration] == "L" or row[iteration] == "0"):320 break321 iteration +=1322 iterationI = i - 1323 iterationJ = j - 1324 while(iterationJ >= 0 and iterationI >= 0):325 if(data[iterationI][iterationJ] == "#"326 or data[iterationI][iterationJ] == "1"):327 occupied +=1328 break329 if(data[iterationI][iterationJ] == "L"330 or data[iterationI][iterationJ] == "0"):331 break332 iterationI -=1333 iterationJ -=1334 iterationI = i - 1335 while(iterationI >= 0):336 if(data[iterationI][j] == "#"337 or data[iterationI][j] == "1"):338 occupied +=1339 break340 if(data[iterationI][j] == "L"341 or data[iterationI][j] == "0"):342 break343 iterationI -=1344 iterationI = i - 1345 iterationJ = j + 1346 while(iterationJ < len(row) and iterationI >= 0):347 if(data[iterationI][iterationJ] == "#"348 or data[iterationI][iterationJ] == "1"):349 occupied +=1350 break351 if(data[iterationI][iterationJ] == "L"352 or data[iterationI][iterationJ] == "0"):353 break354 iterationI -=1355 iterationJ +=1356 iterationI = i + 1357 iterationJ = j - 1358 while(iterationJ >= 0 and iterationI < len(data)):359 if(data[iterationI][iterationJ] == "#"360 or data[iterationI][iterationJ] == "1"):361 occupied +=1362 break363 if(data[iterationI][iterationJ] == "L"364 or data[iterationI][iterationJ] == "0"):365 break366 iterationI +=1367 iterationJ -=1368 iterationI = i + 1369 while(iterationI < len(data)):370 if(data[iterationI][j] == "#"371 or data[iterationI][j] == "1"):372 occupied +=1373 break374 if(data[iterationI][j] == "L"375 or data[iterationI][j] == "0"):376 break377 iterationI +=1378 iterationI = i + 1379 iterationJ = j + 1380 while(iterationJ < len(row) and iterationI < len(data)):381 if(data[iterationI][iterationJ] == "#"382 or data[iterationI][iterationJ] == "1"):383 occupied +=1384 break385 if(data[iterationI][iterationJ] == "L"386 or data[iterationI][iterationJ] == "0"):387 break388 iterationI +=1389 iterationJ +=1390 if(occupied >= 5):391 row[j] = "1"392 still_simulating = True393 #print(i, row)394 data[i] = row395 for x in range(len(data)):396 row = data[x]397 for y in range(len(row)):398 if(row[y] == "0"):399 row[y] = "#"400 elif(row[y] == "1"):401 row[y] = "L"402 data[x] = row403 return data404def count_occupied_seats(data):405 '''406 counts all the occupied seats407 '''408 count = 0409 for i in range(len(data)):410 row = data[i]411 for j in range(len(row)):412 if (row[j] == "#"):413 count += 1414 return count415seats = fill_seats(data)416puzzle1 = count_occupied_seats(seats)417data = open("Day 11\input11", "r+")418data = data.read()419data = data.split("\n")420data = convert_to_list(data)421seats = social_distancing(data)422puzzle2 = count_occupied_seats(seats)...

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query_optimizer_v2.py

Source:query_optimizer_v2.py Github

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1import json2from collections import Counter3from collections import defaultdict4from django.db.models.query import QuerySet5from django.core.exceptions import FieldError6from django.db.models.fields.related import ForeignKey7from django.db.models.fields.related import ManyToManyField8from django.db.models.fields.reverse_related import ManyToManyRel9from django.db.models.fields.reverse_related import ManyToOneRel10from django.db.models.fields.reverse_related import OneToOneRel11"""12query AllCities {13 allCities {14 id15 name16 state {17 name18 }19 # mayor {20 # firstName21 # city {22 # id23 # name24 # }25 # }26 district {27 name28 city {29 name30 mayor {31 lastName32 city {33 name34 district {35 name36 }37 }38 }39 }40 }41 }42}43"""44# FIXME: Uncomment mayor query and run the code. Fix this case!45class GQOptimizer():46 """47 Class optimizes QuerySet base on information extracted from graphql query.48 """49 def __init__(self, info):50 self.info = info51 self.gql_query = info.field_nodes[0].loc.source.body52 self.select_related = set()53 self.prefetch_related = set()54 print(self.gql_query)55 def optimize(self, queryset: QuerySet, stop_fields=[]) -> QuerySet:56 self.stop_fields = stop_fields57 paths = self.__extract_paths()58 print('before normalization', paths)59 if self.stop_fields:60 paths = self.__normalize_paths(paths)61 print('normalized paths', paths)62 select_related = []63 prefetch_related = []64 for path in paths:65 # Get first model fro the model relations path66 first_model = path67 if '__' in path:68 first_model = path.split('__')[0]69 # Extract filed from first model70 field = queryset.model._meta.get_field(first_model)71 # Select related models72 if isinstance(field, (ForeignKey, ManyToManyField, OneToOneRel)):73 select_related.append(path)74 # Prefetch related models75 if isinstance(field, (ManyToOneRel, ManyToManyRel)):76 if path not in select_related:77 prefetch_related.append(path)78 print(f'Select related: {select_related}')79 if select_related:80 queryset = queryset.select_related(*select_related)81 print(f'Prefetch_related: {prefetch_related}')82 if prefetch_related:83 queryset = queryset.prefetch_related(*prefetch_related)84 return queryset85 def print_types(self):86 """Print all graphql types"""87 for k, v in self.info.schema.type_map.items():88 print('Types', k, v)89 def __normalize_paths(self, paths: list):90 normalized_paths = []91 for stop_field in self.stop_fields:92 for path in paths:93 path = path.replace(stop_field, '')94 path = path.strip('_')95 models = path.split('__')96 if len(models) == 1:97 normalized_paths.append(path)98 else:99 models_count = dict(Counter(models))100 for model, count in models_count.items():101 if count >= 2:102 path = model.join(path.split(model, 2)[:2])103 path = path.strip('_')104 normalized_paths.append(path)105 return normalized_paths106 def __extract_paths(self):107 root = self.info.field_name108 paths = defaultdict(list)109 iteration = 0110 has_leaves = True111 while has_leaves:112 has_leaves = False113 # 1st level on nesting114 if iteration == 0:115 print('Iteration', iteration)116 leaves = []117 # Extract fields from root of the query118 selection_set = self.info.field_nodes[0].selection_set119 for idx, selection in enumerate(selection_set.selections):120 # Check if extracted field is a Type121 if self.__filed_type(selection.name.value):122 # Check for nested leaves inside selection123 selection_set = self.info.field_nodes[0] \124 .selection_set.selections[idx] \125 .selection_set126 # has_leaves = self.__selection_has_leaves(selections)127 has_leaves = self.__selection_has_leaves(selection_set)128 # Create metadata filed129 paths[iteration].append({130 'has_leaves': has_leaves,131 'index': idx,132 'root': root,133 'parent': root,134 'field_name': selection.name.value,135 'selection': root + '__' + selection.name.value136 })137 leaves.append(True)138 else:139 leaves.append(False)140 # Check that leaves exist141 has_leaves, iteration = self.__increment(iteration, leaves)142 # 2nd level of nesting143 if iteration == 1:144 print('Iteration', iteration)145 leaves = []146 first_iteration = iteration - 1147 for first_leaf_meta_field in paths[first_iteration]:148 first_leaf_idx = first_leaf_meta_field['index']149 selection_set = self.info.field_nodes[0] \150 .selection_set.selections[first_leaf_idx] \151 .selection_set152 for idx, selection in enumerate(selection_set.selections):153 if self.__filed_type(selection.name.value):154 # Check for nested leaves inside selection155 selection_set = self.info.field_nodes[0] \156 .selection_set.selections[first_leaf_idx] \157 .selection_set.selections[idx] \158 .selection_set159 has_leaves = self.__selection_has_leaves(selection_set)160 # Create metadata filed and add it to current iteration161 paths[iteration].append({162 'has_leaves': has_leaves,163 'index': idx,164 'root': root,165 'parent': first_leaf_meta_field['field_name'],166 'field_name': selection.name.value,167 'selection': first_leaf_meta_field['selection'] + '__' + selection.name.value168 })169 leaves.append(True)170 else:171 leaves.append(False)172 # Check that leaves exist173 has_leaves, iteration = self.__increment(iteration, leaves)174 # 3rd level of nesting175 if iteration == 2:176 print('Iteration', iteration)177 leaves = []178 first_iteration = iteration - 2179 second_iteration = iteration - 1180 for first_leaf_meta_field in paths[first_iteration]:181 first_leaf_idx = first_leaf_meta_field['index']182 if first_leaf_meta_field['has_leaves']:183 for second_leaf_meta_field in paths[second_iteration]:184 second_leaf_idx = second_leaf_meta_field['index']185 if second_leaf_meta_field['has_leaves']:186 selection_set = self.info.field_nodes[0] \187 .selection_set.selections[first_leaf_idx] \188 .selection_set.selections[second_leaf_idx] \189 .selection_set190 for idx, selection in enumerate(selection_set.selections):191 # Create metadata filed and add it to current iteration192 if self.__filed_type(selection.name.value):193 selection_set = self.info.field_nodes[0] \194 .selection_set.selections[first_leaf_idx] \195 .selection_set.selections[second_leaf_idx] \196 .selection_set.selections[idx] \197 .selection_set198 # Check for nested leaves inside selection199 has_leaves = self.__selection_has_leaves(selection_set)200 paths[iteration].append({201 'has_leaves': has_leaves,202 'index': idx,203 'root': root,204 'parent': second_leaf_meta_field['field_name'],205 'field_name': selection.name.value,206 'selection': second_leaf_meta_field['selection'] + '__' + selection.name.value207 })208 leaves.append(True)209 else:210 leaves.append(False)211 # Check that leaves exist212 has_leaves, iteration = self.__increment(iteration, leaves)213 # 4th level of nesting214 if iteration == 3:215 print('Iteration', iteration)216 leaves = []217 first_iteration = iteration - 3218 second_iteration = iteration - 2219 third_iteration = iteration - 1220 for first_leaf_meta_field in paths[first_iteration]:221 first_leaf_idx = first_leaf_meta_field['index']222 if first_leaf_meta_field['has_leaves']:223 for second_leaf_meta_field in paths[second_iteration]:224 second_leaf_idx = second_leaf_meta_field['index']225 for third_leaf_meta_field in paths[third_iteration]:226 third_leaf_idx = third_leaf_meta_field['index']227 if third_leaf_meta_field['has_leaves']:228 selection_set = self.info.field_nodes[0] \229 .selection_set.selections[first_leaf_idx] \230 .selection_set.selections[second_leaf_idx] \231 .selection_set.selections[third_leaf_idx] \232 .selection_set233 for idx, selection in enumerate(selection_set.selections):234 # Create metadata filed and add it to current iteration235 if self.__filed_type(selection.name.value):236 selection_set = self.info.field_nodes[0] \237 .selection_set.selections[first_leaf_idx] \238 .selection_set.selections[second_leaf_idx] \239 .selection_set.selections[third_leaf_idx] \240 .selection_set.selections[idx] \241 .selection_set242 # Check for nested leaves inside selection243 has_leaves = self.__selection_has_leaves(selection_set)244 paths[iteration].append({245 'has_leaves': has_leaves,246 'index': idx,247 'root': root,248 'parent': third_leaf_meta_field['field_name'],249 'field_name': selection.name.value,250 'selection': third_leaf_meta_field['selection'] + '__' + selection.name.value251 })252 leaves.append(True)253 else:254 leaves.append(False)255 # Check that leaves exist256 has_leaves, iteration = self.__increment(iteration, leaves)257 # 5th level of nesting258 if iteration == 4:259 print('Iteration', iteration)260 leaves = []261 first_iteration = iteration - 4262 second_iteration = iteration - 3263 third_iteration = iteration - 2264 fourth_iteration = iteration - 1265 for first_leaf_meta_field in paths[first_iteration]:266 first_leaf_idx = first_leaf_meta_field['index']267 if first_leaf_meta_field['has_leaves']:268 for second_leaf_meta_field in paths[second_iteration]:269 second_leaf_idx = second_leaf_meta_field['index']270 for third_leaf_meta_field in paths[third_iteration]:271 third_leaf_idx = third_leaf_meta_field['index']272 for fourth_leaf_meta_field in paths[fourth_iteration]:273 fourth_leaf_idx = fourth_leaf_meta_field['index']274 if third_leaf_meta_field['has_leaves']:275 selection_set = self.info.field_nodes[0] \276 .selection_set.selections[first_leaf_idx] \277 .selection_set.selections[second_leaf_idx] \278 .selection_set.selections[third_leaf_idx] \279 .selection_set.selections[fourth_leaf_idx] \280 .selection_set281 for idx, selection in enumerate(selection_set.selections):282 # Create metadata filed and add it to current iteration283 if self.__filed_type(selection.name.value):284 selection_set = self.info.field_nodes[0] \285 .selection_set.selections[first_leaf_idx] \286 .selection_set.selections[second_leaf_idx] \287 .selection_set.selections[third_leaf_idx] \288 .selection_set.selections[fourth_leaf_idx] \289 .selection_set.selections[idx] \290 .selection_set291 # Check for nested leaves inside selection292 has_leaves = self.__selection_has_leaves(selection_set)293 paths[iteration].append({294 'has_leaves': has_leaves,295 'index': idx,296 'root': root,297 'parent': fourth_leaf_meta_field['field_name'],298 'field_name': selection.name.value,299 'selection': fourth_leaf_meta_field['selection'] + '__' + selection.name.value300 })301 leaves.append(True)302 else:303 leaves.append(False)304 # Check that leaves exist305 has_leaves, iteration = self.__increment(iteration, leaves)306 print(json.dumps(paths, indent=2, sort_keys=False))307 filtered_paths = set()308 for paths in paths.values():309 for field in paths:310 if not field['has_leaves']:311 filtered_paths.add(field['selection'])312 return filtered_paths313 def __increment(self, iteration: int, leaves: list) -> tuple:314 """Check that section_has leaves"""315 has_leaves = any(leaves)316 if has_leaves:317 iteration += 1318 return (has_leaves, iteration)319 def __selection_has_leaves(self, selection_set) -> bool:320 """Check if selection has leaves"""321 selection = self.gql_query[selection_set.loc.start:selection_set.loc.end]322 return selection.count('{') >= 2323 def __filed_type(self, field_name) -> bool:324 """Check if field name has its Type"""325 field_name = field_name[0].upper() + field_name[1:] + 'Type'...

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SleepyCustoms.py

Source:SleepyCustoms.py Github

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1data = open("Day 6\input6", "r+")2data = data.read()3data = data.split("\n")4def split_entries_up(data=None):5 '''6 Splits all answers into groups as a two dimensional list!7 '''8 answers = [[]]9 entry = 010 group = 011 while(entry < len(data)):12 if(data[entry]==""):13 group += 114 entry += 115 answers.append([])16 answers[group].append(data[entry])17 else:18 answers[group].append(data[entry])19 entry+=120 return answers21def count_group_or(group=None):22 '''23 How not to do it:24 Count up all the occurances one-by-one (because someone underestimated the task)25 Returns the sum for each letter that has occured within in a group26 (not the total numbers of letters).27 '''28 a = 029 b = 030 c = 031 d = 032 e = 033 f = 034 g = 035 h = 036 i = 037 j = 038 k = 039 l = 040 m = 041 n = 042 o = 043 p = 044 q = 045 r = 046 s = 047 t = 048 u = 049 v = 050 w = 051 x = 052 y = 053 z = 054 for firstIteration in range(len(group)):55 answers = group[firstIteration]56 for secondIteration in range(len(answers)):57 if(answers[secondIteration]=="a"):58 a = 159 elif(answers[secondIteration]=="b"):60 b = 161 elif(answers[secondIteration]=="c"):62 c = 163 elif(answers[secondIteration]=="d"):64 d = 165 elif(answers[secondIteration]=="e"):66 e = 167 elif(answers[secondIteration]=="f"):68 f = 169 elif(answers[secondIteration]=="g"):70 g = 171 elif(answers[secondIteration]=="h"):72 h = 173 elif(answers[secondIteration]=="i"):74 i = 175 elif(answers[secondIteration]=="j"):76 j = 177 elif(answers[secondIteration]=="k"):78 k = 179 elif(answers[secondIteration]=="l"):80 l = 181 elif(answers[secondIteration]=="m"):82 m = 183 elif(answers[secondIteration]=="n"):84 n = 185 elif(answers[secondIteration]=="o"):86 o = 187 elif(answers[secondIteration]=="p"):88 p = 189 elif(answers[secondIteration]=="q"):90 q = 191 elif(answers[secondIteration]=="r"):92 r = 193 elif(answers[secondIteration]=="s"):94 s = 195 elif(answers[secondIteration]=="t"):96 t = 197 elif(answers[secondIteration]=="u"):98 u = 199 elif(answers[secondIteration]=="v"):100 v = 1101 elif(answers[secondIteration]=="w"):102 w = 1103 elif(answers[secondIteration]=="x"):104 x = 1105 elif(answers[secondIteration]=="y"):106 y = 1107 elif(answers[secondIteration]=="z"):108 z = 1109 return(a+b+c+d+e+f+g+h+i+j+k+l+m+n+o+p+q+r+s+t+u+v+w+x+y+z)110def count_group_and(group=None):111 '''112 How not to do it:113 Count up all the occurances one-by-one (because someone underestimated the task)114 Returns the sum for each letter that has occured for all members in a group115 (not the total numbers of letters).116 '''117 a = 0118 b = 0119 c = 0120 d = 0121 e = 0122 f = 0123 g = 0124 h = 0125 i = 0126 j = 0127 k = 0128 l = 0129 m = 0130 n = 0131 o = 0132 p = 0133 q = 0134 r = 0135 s = 0136 t = 0137 u = 0138 v = 0139 w = 0140 x = 0141 y = 0142 z = 0143 for firstIteration in range(len(group)):144 answers = group[firstIteration]145 hasA = False146 hasB = False147 hasC = False148 hasD = False149 hasE = False150 hasF = False151 hasG = False152 hasH = False153 hasI = False154 hasJ = False155 hasK = False156 hasL = False157 hasM = False158 hasN = False159 hasO = False160 hasP = False161 hasQ = False162 hasR = False163 hasS = False164 hasT = False165 hasU = False166 hasV = False167 hasW = False168 hasX = False169 hasY = False170 hasZ = False171 172 for secondIteration in range(len(answers)):173 if(answers[secondIteration]=="a" and hasA == False):174 a += 1175 elif(answers[secondIteration]=="b" and hasB == False):176 b += 1177 elif(answers[secondIteration]=="c" and hasC == False):178 c += 1179 elif(answers[secondIteration]=="d" and hasD == False):180 d += 1181 elif(answers[secondIteration]=="e" and hasE == False):182 e += 1183 elif(answers[secondIteration]=="f" and hasF == False):184 f += 1185 elif(answers[secondIteration]=="g" and hasG == False):186 g += 1187 elif(answers[secondIteration]=="h" and hasH == False):188 h += 1189 elif(answers[secondIteration]=="i" and hasI == False):190 i += 1191 elif(answers[secondIteration]=="j" and hasJ == False):192 j += 1193 elif(answers[secondIteration]=="k" and hasK == False):194 k += 1195 elif(answers[secondIteration]=="l" and hasL == False):196 l += 1197 elif(answers[secondIteration]=="m" and hasM == False):198 m += 1199 elif(answers[secondIteration]=="n" and hasN == False):200 n += 1201 elif(answers[secondIteration]=="o" and hasO == False):202 o += 1203 elif(answers[secondIteration]=="p" and hasP == False):204 p += 1205 elif(answers[secondIteration]=="q" and hasQ == False):206 q += 1207 elif(answers[secondIteration]=="r" and hasR == False):208 r += 1209 elif(answers[secondIteration]=="s" and hasS == False):210 s += 1211 elif(answers[secondIteration]=="t" and hasT == False):212 t += 1213 elif(answers[secondIteration]=="u" and hasU == False):214 u += 1215 elif(answers[secondIteration]=="v" and hasV == False):216 v += 1217 elif(answers[secondIteration]=="w" and hasW == False):218 w += 1219 elif(answers[secondIteration]=="x" and hasX == False):220 x += 1221 elif(answers[secondIteration]=="y" and hasY == False):222 y += 1223 elif(answers[secondIteration]=="z" and hasZ == False):224 z += 1225 if(a == len(group)):226 a = 1227 else:228 a = 0229 if(b == len(group)):230 b = 1231 else:232 b = 0233 if(c == len(group)):234 c = 1235 else:236 c = 0237 if(d == len(group)):238 d = 1239 else:240 d = 0241 if(e == len(group)):242 e = 1243 else:244 e = 0245 if(f == len(group)):246 f = 1247 else:248 f = 0249 if(g == len(group)):250 g = 1251 else:252 g = 0253 if(h == len(group)):254 h = 1255 else:256 h = 0257 if(i == len(group)):258 i = 1259 else:260 i = 0261 if(j == len(group)):262 j = 1263 else:264 j = 0265 if(k == len(group)):266 k = 1267 else:268 k = 0269 if(l == len(group)):270 l = 1271 else:272 l = 0273 if(m == len(group)):274 m = 1275 else:276 m = 0277 if(n == len(group)):278 n = 1279 else:280 n = 0281 if(o == len(group)):282 o = 1283 else:284 o = 0285 if(p == len(group)):286 p = 1287 else:288 p = 0289 if(q == len(group)):290 q = 1291 else:292 q = 0293 if(r == len(group)):294 r = 1295 else:296 r = 0297 if(s == len(group)):298 s = 1299 else:300 s = 0301 if(t == len(group)):302 t = 1303 else:304 t = 0305 if(u == len(group)):306 u = 1307 else:308 u = 0309 if(v == len(group)):310 v = 1311 else:312 v = 0313 if(w == len(group)):314 w = 1315 else:316 w = 0317 if(x == len(group)):318 x = 1319 else:320 x = 0321 if(y == len(group)):322 y = 1323 else:324 y = 0325 if(z == len(group)):326 z = 1327 else:328 z = 0329 330 return(a+b+c+d+e+f+g+h+i+j+k+l+m+n+o+p+q+r+s+t+u+v+w+x+y+z)331def check_everyone(data=None):332 '''333 Checks the two functions above on all groups.334 '''335 sumOr = 0336 sumAnd = 0337 for i in range(len(data)):338 sumOr += count_group_or(data[i])339 sumAnd += count_group_and(data[i])340 return (sumOr, sumAnd)341groups_data = split_entries_up(data)342puzzle1, puzzle2 = check_everyone(groups_data)...

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lr_finder.py

Source:lr_finder.py Github

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1import matplotlib.pyplot as plt2import math3class LRFinder:4 def __init__(5 self, optimizer, 6 min_lr=1e-4, max_lr=2e-2, 7 steps_per_epoch=None, epochs=None8 ):9 self.optimizer = optimizer10 self.min_lr = min_lr11 self.max_lr = max_lr12 self.total_iterations = steps_per_epoch * epochs13 self.iteration = 014 self.history = {}15 self.batch_step(self.iteration)16 def get_lr(self):17 '''Calculate the learning rate.'''18 x = (self.iteration % self.total_iterations) / self.total_iterations19 lr = self.min_lr + (self.max_lr - self.min_lr) * x20 lrs = list()21 for param_group in self.optimizer.param_groups:22 lrs.append(lr)23 return lrs24 def batch_step(self, batch_iteration=None, logs=None):25 self.iteration = batch_iteration or self.iteration + 126 for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):27 param_group['lr'] = lr28 if logs is not None:29 self.history.setdefault('lr', []).append(lr)30 self.history.setdefault('iterations', []).append(self.iteration)31 for k, v in logs.items():32 self.history.setdefault(k, []).append(v)33 34 def plot_lr(self):35 '''Helper function to quickly inspect the learning rate schedule.'''36 plt.plot(self.history['iterations'], self.history['lr'])37 plt.yscale('log')38 plt.xlabel('Iteration')39 plt.ylabel('Learning rate')40 41 def plot_loss(self):42 '''Helper function to quickly observe the learning rate experiment results.'''43 plt.plot(self.history['lr'], self.history['loss'])44 plt.xscale('log')45 plt.xlabel('Learning rate')46 plt.ylabel('Loss')47def find_lr(model, datagen_params, min_lr=1e-5, max_lr=1e-2, epochs=3):48 train_datagen, val_datagen = get_datagens(max_negatives=2000, **datagen_params)49 opt = torch.optim.SGD(model.parameters(), lr=min_lr, momentum=.9, weight_decay=1e-4)50 learner = RetinaLearner(model=model, opt=opt, loss=None, clf_loss=None, metrics=[], clf_reg_alpha=.5, ignored_keys=['clf_out'])51 print('steps per epoch: {}'.format(len(train_datagen)))52 lr_scheduler = LRFinder(learner.opt, min_lr=min_lr, max_lr=max_lr, steps_per_epoch=len(train_datagen), epochs=epochs)53 learner, history = orchestrate(54 learner=learner, train_datagen=train_datagen, val_datagen=val_datagen, epochs=epochs,55 lr_scheduler=lr_scheduler, checkpoints_pth=None, nb_freezed_epchs=-1, df=datagen_params['df'],56 )57 return learner, lr_scheduler58class Pilo:59 def __init__(60 self, optimizer, 61 min_lr=1e-4, max_lr=2e-2, 62 coeff = 1.,63 steps_per_epoch=None64 ):65 self.optimizer = optimizer66 self.min_lr = min_lr67 self.max_lr = max_lr68 self.total_iterations = steps_per_epoch69 self.iteration = 070 self.history = {}71 self.coeff = coeff72 self.batch_step(self.iteration)73 def get_lr(self):74 '''Calculate the learning rate.'''75 x = float(self.iteration % self.total_iterations) / self.total_iterations76 lr = self.max_lr - (self.max_lr - self.min_lr) * x77 lrs = list()78 for param_group in self.optimizer.param_groups:79 lrs.append(lr)80 return lrs81 def batch_step(self, batch_iteration=None, logs=None):82 self.iteration = batch_iteration or self.iteration + 183 for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):84 param_group['lr'] = lr85 if logs is not None:86 self.history.setdefault('lr', []).append(lr)87 self.history.setdefault('iterations', []).append(self.iteration)88 def step(self, batch_iteration=None, logs=None):89 self.max_lr *= self.coeff90 self.min_lr *= self.coeff91 92 def plot_lr(self):93 '''Helper function to quickly inspect the learning rate schedule.'''94 plt.plot(self.history['lr'])95# plt.yscale('log')96 plt.xlabel('Iteration')97 plt.ylabel('Learning rate')98class PiloExt:99 def __init__(100 self, optimizer, 101 multiplier=.1,102 coeff=1.,103 steps_per_epoch=None104 ):105 self.optimizer = optimizer106 self.multiplier = multiplier107 self.total_iterations = steps_per_epoch108 self.param_groups_old = list()109 for param_group in self.optimizer.param_groups:110 self.param_groups_old.append(float(param_group['lr']))111 self.iteration = 0112 self.history = {}113 self.coeff = coeff114 self.batch_step(self.iteration)115 116 def get_lr(self):117 '''Calculate the learning rate.'''118 x = float(self.iteration % self.total_iterations) / self.total_iterations119 lrs = list()120 for i, param_group in enumerate(self.optimizer.param_groups):121 lrs.append(self.param_groups_old[i] * (1 - x) + self.param_groups_old[i] * self.multiplier * x)122 return lrs123 def batch_step(self, batch_iteration=None, logs=None):124 self.iteration = batch_iteration or self.iteration + 1125 for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):126 param_group['lr'] = lr127 if logs is not None:128 self.history.setdefault('lr', []).append(lr)129 self.history.setdefault('iterations', []).append(self.iteration)130 def step(self, batch_iteration=None, logs=None):131 for i, param_group in enumerate(self.param_groups_old):132 self.param_groups_old[i] *= self.coeff133 134 def plot_lr(self):135 '''Helper function to quickly inspect the learning rate schedule.'''136 plt.plot(self.history['lr'])137# plt.yscale('log')138 plt.xlabel('Iteration')139 plt.ylabel('Learning rate')140class CosinePiloExt(PiloExt):141 def get_lr(self):142 '''Calculate the learning rate.'''143 x = float(self.iteration % self.total_iterations) / self.total_iterations144 lrs = list()145 for i, param_group in enumerate(self.optimizer.param_groups):146 lrs.append(147 self.param_groups_old[i] * self.multiplier 148 + (self.param_groups_old[i] - self.param_groups_old[i] * self.multiplier) 149 * (1 + math.cos(math.pi * x)) / 2150 )...

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