fumiyau/python_comments_free_500000 · Datasets at Hugging Face

1c3822b069ee64df66a75ee6dfa970295d128aba

23,556

py

Python

bl3save/cli_prof_edit.py

DataCluster0/wonderlands-cli-saveedit

91c7cf30289d62b5d51963da39f80d85c8cb97c5

[ "Zlib" ]

78

2020-04-05T19:49:31.000Z

2022-03-09T10:01:40.000Z

bl3save/cli_prof_edit.py

DataCluster0/wonderlands-cli-saveedit

91c7cf30289d62b5d51963da39f80d85c8cb97c5

[ "Zlib" ]

24

2020-04-10T16:22:09.000Z

2022-03-26T11:46:55.000Z

bl3save/cli_prof_edit.py

DataCluster0/wonderlands-cli-saveedit

91c7cf30289d62b5d51963da39f80d85c8cb97c5

[ "Zlib" ]

19

2020-04-10T17:32:56.000Z

2022-03-27T22:23:05.000Z

#!/usr/bin/env python3 # vim: set expandtab tabstop=4 shiftwidth=4: # Copyright (c) 2020-2021 CJ Kucera (cj@apocalyptech.com) # # This software is provided 'as-is', without any express or implied warranty. # In no event will the authors be held liable for any damages arising from # the use of this software. # # Permission is granted to anyone to use this software for any purpose, # including commercial applications, and to alter it and redistribute it # freely, subject to the following restrictions: # # 1. The origin of this software must not be misrepresented; you must not # claim that you wrote the original software. If you use this software in a # product, an acknowledgment in the product documentation would be # appreciated but is not required. # # 2. Altered source versions must be plainly marked as such, and must not be # misrepresented as being the original software. # # 3. This notice may not be removed or altered from any source distribution. import os import sys import bl3save import argparse from . import cli_common from bl3save.bl3profile import BL3Profile def main(): # Set up args parser = argparse.ArgumentParser( description='Borderlands 3 CLI Profile Editor v{} (PC Only)'.format(bl3save.__version__), formatter_class=argparse.ArgumentDefaultsHelpFormatter, epilog=""" The default output type of "profile" will output theoretically-valid profile which can be loaded into BL3. The output type "protobuf" will save out the extracted, decrypted protobufs. The output type "json" will output a JSON-encoded version of the protobufs in question. The output type "items" will output a text file containing base64-encoded representations of items in the user's bank. These can be read back in using the -i/--import-items option. Note that these are NOT the same as the item strings used by the BL3 Memory Editor. """ ) parser.add_argument('-V', '--version', action='version', version='BL3 CLI SaveEdit v{}'.format(bl3save.__version__), ) parser.add_argument('-o', '--output', choices=['profile', 'protobuf', 'json', 'items'], default='profile', help='Output file format', ) parser.add_argument('--csv', action='store_true', help='When importing or exporting items, use CSV files', ) parser.add_argument('-f', '--force', action='store_true', help='Force output file to overwrite', ) parser.add_argument('-q', '--quiet', action='store_true', help='Supress all non-essential output') # Now the actual arguments parser.add_argument('--golden-keys', dest='golden_keys', type=int, help='Number of Golden Keys in the profile', ) parser.add_argument('--diamond-keys', dest='diamond_keys', type=int, help='Number of Diamond Keys in the profile', ) parser.add_argument('--vaultcard1-keys', dest='vaultcard1_keys', type=int, help='Number of Vault Card 1 Keys in the profile', ) parser.add_argument('--vaultcard1-chests', dest='vaultcard1_chests', type=int, help='Number of Vault Card 1 Chests available in the profile', ) parser.add_argument('--vaultcard2-keys', dest='vaultcard2_keys', type=int, help='Number of Vault Card 2 Keys in the profile', ) parser.add_argument('--vaultcard2-chests', dest='vaultcard2_chests', type=int, help='Number of Vault Card 2 Chests available in the profile', ) parser.add_argument('--vaultcard3-keys', dest='vaultcard3_keys', type=int, help='Number of Vault Card 3 Keys in the profile', ) parser.add_argument('--vaultcard3-chests', dest='vaultcard3_chests', type=int, help='Number of Vault Card 3 Chests available in the profile', ) # Arguably we could be using a mutually-exclusive group for many of these # GR options, but I can see some potential value in specifying more than # one, so I'm not bothering. parser.add_argument('--zero-guardian-rank', dest='zero_guardian_rank', action='store_true', help='Zero out profile Guardian Rank', ) parser.add_argument('--min-guardian-rank', dest='min_guardian_rank', action='store_true', help='Set Guardian Rank to minimum required to prevent overwriting by saves', ) parser.add_argument('--guardian-rank-rewards', dest='guardian_rank_rewards', type=int, help='Set Guardian Rank rewards to the specified number of tokens each', ) parser.add_argument('--guardian-rank-tokens', dest='guardian_rank_tokens', type=int, help="Number of available Guardian Rank tokens", ) parser.add_argument('--reset-borderlands-science', dest='reset_borderlands_science', action='store_true', help='Reset Borderlands Science progression', ) parser.add_argument('--max-borderlands-science', dest='max_borderlands_science', action='store_true', help='Maximize Borderlands Science progression, unlocking True Tannis', ) parser.add_argument('--remove-borderlands-science-boosts', dest='remove_borderlands_science_boosts', action='store_true', help='Remove the currently active borderlands science boost', ) parser.add_argument('--borderlands-science-tokens', dest='borderlands_science_tokens', type=int, help="Number of available Borderlands Science tokens", ) itemlevelgroup = parser.add_mutually_exclusive_group() itemlevelgroup.add_argument('--item-levels-max', dest='item_levels_max', action='store_true', help='Set all bank items to max level') itemlevelgroup.add_argument('--item-levels', dest='item_levels', type=int, help='Set all bank items to the specified level') itemmayhemgroup = parser.add_mutually_exclusive_group() itemmayhemgroup.add_argument('--item-mayhem-max', dest='item_mayhem_max', action='store_true', help='Set all bank items to the maximum Mayhem level ({})'.format(bl3save.mayhem_max)) itemmayhemgroup.add_argument('--item-mayhem-levels', dest='item_mayhem_levels', type=int, choices=range(bl3save.mayhem_max+1), help='Set all bank items to the specified Mayhem level (0 to remove)') parser.add_argument('-i', '--import-items', dest='import_items', type=str, help='Import items from file', ) parser.add_argument('--allow-fabricator', dest='allow_fabricator', action='store_true', help='Allow importing Fabricator when importing items from file', ) parser.add_argument('--clear-customizations', dest='clear_customizations', action='store_true', help='Remove all unlocked customizations', ) parser.add_argument('--alpha', dest='alpha', action='store_true', help='Alphabetize unlocked room decorations, trinkets, and weapon skins', ) unlock_choices = [ 'lostloot', 'bank', 'skins', 'heads', 'echothemes', 'emotes', 'decos', 'weaponskins', 'trinkets', 'customizations', ] parser.add_argument('--unlock', action=cli_common.DictAction, choices=unlock_choices + ['all'], default={}, help='Game features to unlock', ) # Positional args parser.add_argument('input_filename', help='Input filename', ) parser.add_argument('output_filename', help='Output filename', ) # Parse args args = parser.parse_args() # Expand any of our "all" unlock actions if 'all' in args.unlock: args.unlock = {k: True for k in unlock_choices} elif 'customizations' in args.unlock: args.unlock['skins'] = True args.unlock['heads'] = True args.unlock['echothemes'] = True args.unlock['emotes'] = True args.unlock['decos'] = True args.unlock['weaponskins'] = True args.unlock['trinkets'] = True # Set max item level arg if args.item_levels_max: args.item_levels = bl3save.max_level # Set max mayhem arg if args.item_mayhem_max: args.item_mayhem_levels = bl3save.mayhem_max # Check key counts; don't let them be below zero if args.golden_keys is not None and args.golden_keys < 0: raise argparse.ArgumentTypeError('Golden keys cannot be negative') if args.diamond_keys is not None and args.diamond_keys < 0: raise argparse.ArgumentTypeError('Diamond keys cannot be negative') if args.vaultcard1_keys is not None and args.vaultcard1_keys < 0: raise argparse.ArgumentTypeError('Vault Card 1 keys cannot be negative') if args.vaultcard1_chests is not None and args.vaultcard1_chests < 0: raise argparse.ArgumentTypeError('Vault Card 1 chests cannot be negative') if args.vaultcard2_keys is not None and args.vaultcard2_keys < 0: raise argparse.ArgumentTypeError('Vault Card 2 keys cannot be negative') if args.vaultcard2_chests is not None and args.vaultcard2_chests < 0: raise argparse.ArgumentTypeError('Vault Card 2 chests cannot be negative') if args.vaultcard3_keys is not None and args.vaultcard3_keys < 0: raise argparse.ArgumentTypeError('Vault Card 3 keys cannot be negative') if args.vaultcard3_chests is not None and args.vaultcard3_chests < 0: raise argparse.ArgumentTypeError('Vault Card 3 chests cannot be negative') # Check item level. The max storeable in the serial number is 127, but the # effective limit in-game is 100, thanks to MaxGameStage attributes. We # could use `bl3save.max_level` here, too, of course, but in the event that # I don't get this updated in a timely fashion, having it higher would let # this util potentially continue to be able to level up gear. if args.item_levels: if args.item_levels < 1 or args.item_levels > 100: raise argparse.ArgumentTypeError('Valid item level range is 1 through 100') if args.item_levels > bl3save.max_level: print('WARNING: Setting item levels to {}, when {} is the currently-known max'.format( args.item_levels, bl3save.max_level, )) # Check for overwrite warnings if os.path.exists(args.output_filename) and not args.force: if args.output_filename == args.input_filename: confirm_msg = 'Really overwrite {} with specified changes (no backup will be made)'.format(args.output_filename) else: confirm_msg = '{} already exists. Overwrite'.format(args.output_filename) sys.stdout.write('WARNING: {} [y/N]? '.format(confirm_msg)) sys.stdout.flush() response = sys.stdin.readline().strip().lower() if len(response) == 0 or response[0] != 'y': print('Aborting!') sys.exit(1) print('') # Now load the profile if not args.quiet: print('Loading {}'.format(args.input_filename)) profile = BL3Profile(args.input_filename) if not args.quiet: print('') # Check to see if we have any changes to make have_changes = any([ args.golden_keys is not None, args.diamond_keys is not None, args.vaultcard1_keys is not None, args.vaultcard1_chests is not None, args.vaultcard2_keys is not None, args.vaultcard2_chests is not None, args.vaultcard3_keys is not None, args.vaultcard3_chests is not None, args.zero_guardian_rank, args.min_guardian_rank, args.guardian_rank_rewards is not None, args.guardian_rank_tokens is not None, args.reset_borderlands_science, args.max_borderlands_science, args.remove_borderlands_science_boosts, args.borderlands_science_tokens is not None, len(args.unlock) > 0, args.import_items, args.item_levels, args.clear_customizations, args.alpha, args.item_mayhem_levels is not None, ]) # Alert about Guardian Rank stuff guardian_rank_alert = False # Make changes if have_changes: if not args.quiet: print('Making requested changes...') print('') # Golden Keys if args.golden_keys is not None: if not args.quiet: print(' - Setting Golden Key count to {}'.format(args.golden_keys)) profile.set_golden_keys(args.golden_keys) # Diamond Keys if args.diamond_keys is not None: if not args.quiet: print(' - Setting Diamond Key count to {}'.format(args.diamond_keys)) profile.set_diamond_keys(args.diamond_keys) # Vault Card 1 Keys if args.vaultcard1_keys is not None: if not args.quiet: print(' - Setting Vault Card 1 Key count to {}'.format(args.vaultcard1_keys)) profile.set_vaultcard1_keys(args.vaultcard1_keys) # Vault Card 1 Chests if args.vaultcard1_chests is not None: if not args.quiet: print(' - Setting Vault Card 1 Chest count to {}'.format(args.vaultcard1_chests)) profile.set_vaultcard1_chests(args.vaultcard1_chests) # Vault Card 2 Keys if args.vaultcard2_keys is not None: if not args.quiet: print(' - Setting Vault Card 2 Key count to {}'.format(args.vaultcard2_keys)) profile.set_vaultcard2_keys(args.vaultcard2_keys) # Vault Card 2 Chests if args.vaultcard2_chests is not None: if not args.quiet: print(' - Setting Vault Card 2 Chest count to {}'.format(args.vaultcard2_chests)) profile.set_vaultcard2_chests(args.vaultcard2_chests) # Vault Card 3 Keys if args.vaultcard3_keys is not None: if not args.quiet: print(' - Setting Vault Card 3 Key count to {}'.format(args.vaultcard3_keys)) profile.set_vaultcard3_keys(args.vaultcard3_keys) # Vault Card 3 Chests if args.vaultcard3_chests is not None: if not args.quiet: print(' - Setting Vault Card 3 Chest count to {}'.format(args.vaultcard3_chests)) profile.set_vaultcard3_chests(args.vaultcard3_chests) # Zeroing Guardian Rank if args.zero_guardian_rank: if not args.quiet: print(' - Zeroing Guardian Rank') if not args.min_guardian_rank \ and args.guardian_rank_rewards is None \ and args.guardian_rank_tokens is None: print(' NOTE: A profile with a zeroed Guardian Rank will probably have its') print(' Guardian Rank info populated from the first savegame loaded by the game') profile.zero_guardian_rank() # Setting Guardian rank to Minimum if args.min_guardian_rank: if not args.quiet: print(' - Setting Guardian Rank to minimum (to prevent overwriting by savefiles)') new_gr = profile.min_guardian_rank() if new_gr is not None and not args.quiet: print(' - Guardian Rank set to {}'.format(new_gr)) guardian_rank_alert = True # Setting arbitrary Guardian Rank rewards if args.guardian_rank_rewards is not None: if not args.quiet: if args.guardian_rank_rewards == 1: plural = '' else: plural = 's' print(' - Setting Guardian Rank rewards to {} point{}'.format(args.guardian_rank_rewards, plural)) new_gr = profile.set_guardian_rank_reward_levels(args.guardian_rank_rewards, force=True) if new_gr is not None and not args.quiet: print(' - Also set Guardian Rank level to {}'.format(new_gr)) guardian_rank_alert = True # Setting Guardian Rank tokens if args.guardian_rank_tokens is not None: if not args.quiet: print(' - Setting available Guardian Rank tokens to {}'.format(args.guardian_rank_tokens)) new_gr = profile.set_guardian_rank_tokens(args.guardian_rank_tokens) if new_gr is not None and not args.quiet: print(' - Also set Guardian Rank level to {}'.format(new_gr)) guardian_rank_alert = True # Reset Borderlands Science progression if args.reset_borderlands_science: if not args.quiet: print(" - Resetting Borderlands Science progression") profile.reset_borderlands_science() if args.max_borderlands_science: if not args.quiet: print(" - Maximizing Borderlands Science progression") profile.max_borderlands_science() # Removing active Borderlands Science boosts if args.remove_borderlands_science_boosts: if not args.quiet: print(" - Removing active Borderlands Science boosts") profile.remove_borderlands_science_boosts() # Setting Borderlands Science tokens if args.borderlands_science_tokens is not None: if not args.quiet: print(" - Setting available Borderlands Science tokens to {}".format(args.borderlands_science_tokens)) profile.set_borderlands_science_tokens(args.borderlands_science_tokens) # Clear Customizations (do this *before* explicit customization unlocks) if args.clear_customizations: if not args.quiet: print(' - Clearing all customizations') profile.clear_all_customizations() # Unlocks if len(args.unlock) > 0: if not args.quiet: print(' - Processing Unlocks:') # Lost Loot if 'lostloot' in args.unlock: if not args.quiet: print(' - Lost Loot SDUs') profile.set_max_sdus([bl3save.PSDU_LOSTLOOT]) # Bank if 'bank' in args.unlock: if not args.quiet: print(' - Bank SDUs') profile.set_max_sdus([bl3save.PSDU_BANK]) # Skins if 'skins' in args.unlock: if not args.quiet: print(' - Character Skins') profile.unlock_char_skins() # Heads if 'heads' in args.unlock: if not args.quiet: print(' - Character Heads') profile.unlock_char_heads() # ECHO Themes if 'echothemes' in args.unlock: if not args.quiet: print(' - ECHO Themes') profile.unlock_echo_themes() # Emotes if 'emotes' in args.unlock: if not args.quiet: print(' - Emotes') profile.unlock_emotes() # Room Decorations if 'decos' in args.unlock: if not args.quiet: print(' - Room Decorations') profile.unlock_room_decos() # Weapon Skins if 'weaponskins' in args.unlock: if not args.quiet: print(' - Weapon Skins') profile.unlock_weapon_skins() # Weapon Trinkets if 'trinkets' in args.unlock: if not args.quiet: print(' - Weapon Trinkets') profile.unlock_weapon_trinkets() # Customization Alphabetization if args.alpha: if not args.quiet: print(' - Alphabetizing Room Decorations, Trinkets, and Weapon Skins') profile.alphabetize_cosmetics() # Import Items if args.import_items: cli_common.import_items(args.import_items, profile.create_new_item_encoded, profile.add_bank_item, file_csv=args.csv, allow_fabricator=args.allow_fabricator, quiet=args.quiet, ) # Setting item levels. Keep in mind that we'll want to do this *after* # various of the actions above. If we've been asked to change the level # of items, we'll want to do it after the item import. if args.item_levels: cli_common.update_item_levels(profile.get_bank_items(), args.item_levels, quiet=args.quiet, ) # Item Mayhem level if args.item_mayhem_levels is not None: cli_common.update_item_mayhem_levels(profile.get_bank_items(), args.item_mayhem_levels, quiet=args.quiet, ) # Guardian Rank Alert if not args.quiet and guardian_rank_alert: print(' - NOTE: Make sure to zero out your savegame Guardian Ranks, if making') print(' changes to Guardian Rank in your profile, otherwise the changes might') print(' not take effect properly.') # Newline at the end of all this. if not args.quiet: print('') # Write out if args.output == 'profile': profile.save_to(args.output_filename) if not args.quiet: print('Wrote profile to {}'.format(args.output_filename)) elif args.output == 'protobuf': profile.save_protobuf_to(args.output_filename) if not args.quiet: print('Wrote protobuf to {}'.format(args.output_filename)) elif args.output == 'json': profile.save_json_to(args.output_filename) if not args.quiet: print('Wrote JSON to {}'.format(args.output_filename)) elif args.output == 'items': if args.csv: cli_common.export_items_csv( profile.get_bank_items(), args.output_filename, quiet=args.quiet, ) else: cli_common.export_items( profile.get_bank_items(), args.output_filename, quiet=args.quiet, ) else: # Not sure how we'd ever get here raise Exception('Invalid output format specified: {}'.format(args.output)) if __name__ == '__main__': main()

38.616393

124

0.598022

import os import sys import bl3save import argparse from . import cli_common from bl3save.bl3profile import BL3Profile def main(): parser = argparse.ArgumentParser( description='Borderlands 3 CLI Profile Editor v{} (PC Only)'.format(bl3save.__version__), formatter_class=argparse.ArgumentDefaultsHelpFormatter, epilog=""" The default output type of "profile" will output theoretically-valid profile which can be loaded into BL3. The output type "protobuf" will save out the extracted, decrypted protobufs. The output type "json" will output a JSON-encoded version of the protobufs in question. The output type "items" will output a text file containing base64-encoded representations of items in the user's bank. These can be read back in using the -i/--import-items option. Note that these are NOT the same as the item strings used by the BL3 Memory Editor. """ ) parser.add_argument('-V', '--version', action='version', version='BL3 CLI SaveEdit v{}'.format(bl3save.__version__), ) parser.add_argument('-o', '--output', choices=['profile', 'protobuf', 'json', 'items'], default='profile', help='Output file format', ) parser.add_argument('--csv', action='store_true', help='When importing or exporting items, use CSV files', ) parser.add_argument('-f', '--force', action='store_true', help='Force output file to overwrite', ) parser.add_argument('-q', '--quiet', action='store_true', help='Supress all non-essential output') # Now the actual arguments parser.add_argument('--golden-keys', dest='golden_keys', type=int, help='Number of Golden Keys in the profile', ) parser.add_argument('--diamond-keys', dest='diamond_keys', type=int, help='Number of Diamond Keys in the profile', ) parser.add_argument('--vaultcard1-keys', dest='vaultcard1_keys', type=int, help='Number of Vault Card 1 Keys in the profile', ) parser.add_argument('--vaultcard1-chests', dest='vaultcard1_chests', type=int, help='Number of Vault Card 1 Chests available in the profile', ) parser.add_argument('--vaultcard2-keys', dest='vaultcard2_keys', type=int, help='Number of Vault Card 2 Keys in the profile', ) parser.add_argument('--vaultcard2-chests', dest='vaultcard2_chests', type=int, help='Number of Vault Card 2 Chests available in the profile', ) parser.add_argument('--vaultcard3-keys', dest='vaultcard3_keys', type=int, help='Number of Vault Card 3 Keys in the profile', ) parser.add_argument('--vaultcard3-chests', dest='vaultcard3_chests', type=int, help='Number of Vault Card 3 Chests available in the profile', ) # Arguably we could be using a mutually-exclusive group for many of these # GR options, but I can see some potential value in specifying more than # one, so I'm not bothering. parser.add_argument('--zero-guardian-rank', dest='zero_guardian_rank', action='store_true', help='Zero out profile Guardian Rank', ) parser.add_argument('--min-guardian-rank', dest='min_guardian_rank', action='store_true', help='Set Guardian Rank to minimum required to prevent overwriting by saves', ) parser.add_argument('--guardian-rank-rewards', dest='guardian_rank_rewards', type=int, help='Set Guardian Rank rewards to the specified number of tokens each', ) parser.add_argument('--guardian-rank-tokens', dest='guardian_rank_tokens', type=int, help="Number of available Guardian Rank tokens", ) parser.add_argument('--reset-borderlands-science', dest='reset_borderlands_science', action='store_true', help='Reset Borderlands Science progression', ) parser.add_argument('--max-borderlands-science', dest='max_borderlands_science', action='store_true', help='Maximize Borderlands Science progression, unlocking True Tannis', ) parser.add_argument('--remove-borderlands-science-boosts', dest='remove_borderlands_science_boosts', action='store_true', help='Remove the currently active borderlands science boost', ) parser.add_argument('--borderlands-science-tokens', dest='borderlands_science_tokens', type=int, help="Number of available Borderlands Science tokens", ) itemlevelgroup = parser.add_mutually_exclusive_group() itemlevelgroup.add_argument('--item-levels-max', dest='item_levels_max', action='store_true', help='Set all bank items to max level') itemlevelgroup.add_argument('--item-levels', dest='item_levels', type=int, help='Set all bank items to the specified level') itemmayhemgroup = parser.add_mutually_exclusive_group() itemmayhemgroup.add_argument('--item-mayhem-max', dest='item_mayhem_max', action='store_true', help='Set all bank items to the maximum Mayhem level ({})'.format(bl3save.mayhem_max)) itemmayhemgroup.add_argument('--item-mayhem-levels', dest='item_mayhem_levels', type=int, choices=range(bl3save.mayhem_max+1), help='Set all bank items to the specified Mayhem level (0 to remove)') parser.add_argument('-i', '--import-items', dest='import_items', type=str, help='Import items from file', ) parser.add_argument('--allow-fabricator', dest='allow_fabricator', action='store_true', help='Allow importing Fabricator when importing items from file', ) parser.add_argument('--clear-customizations', dest='clear_customizations', action='store_true', help='Remove all unlocked customizations', ) parser.add_argument('--alpha', dest='alpha', action='store_true', help='Alphabetize unlocked room decorations, trinkets, and weapon skins', ) unlock_choices = [ 'lostloot', 'bank', 'skins', 'heads', 'echothemes', 'emotes', 'decos', 'weaponskins', 'trinkets', 'customizations', ] parser.add_argument('--unlock', action=cli_common.DictAction, choices=unlock_choices + ['all'], default={}, help='Game features to unlock', ) parser.add_argument('input_filename', help='Input filename', ) parser.add_argument('output_filename', help='Output filename', ) args = parser.parse_args() if 'all' in args.unlock: args.unlock = {k: True for k in unlock_choices} elif 'customizations' in args.unlock: args.unlock['skins'] = True args.unlock['heads'] = True args.unlock['echothemes'] = True args.unlock['emotes'] = True args.unlock['decos'] = True args.unlock['weaponskins'] = True args.unlock['trinkets'] = True if args.item_levels_max: args.item_levels = bl3save.max_level if args.item_mayhem_max: args.item_mayhem_levels = bl3save.mayhem_max if args.golden_keys is not None and args.golden_keys < 0: raise argparse.ArgumentTypeError('Golden keys cannot be negative') if args.diamond_keys is not None and args.diamond_keys < 0: raise argparse.ArgumentTypeError('Diamond keys cannot be negative') if args.vaultcard1_keys is not None and args.vaultcard1_keys < 0: raise argparse.ArgumentTypeError('Vault Card 1 keys cannot be negative') if args.vaultcard1_chests is not None and args.vaultcard1_chests < 0: raise argparse.ArgumentTypeError('Vault Card 1 chests cannot be negative') if args.vaultcard2_keys is not None and args.vaultcard2_keys < 0: raise argparse.ArgumentTypeError('Vault Card 2 keys cannot be negative') if args.vaultcard2_chests is not None and args.vaultcard2_chests < 0: raise argparse.ArgumentTypeError('Vault Card 2 chests cannot be negative') if args.vaultcard3_keys is not None and args.vaultcard3_keys < 0: raise argparse.ArgumentTypeError('Vault Card 3 keys cannot be negative') if args.vaultcard3_chests is not None and args.vaultcard3_chests < 0: raise argparse.ArgumentTypeError('Vault Card 3 chests cannot be negative') # Check item level. The max storeable in the serial number is 127, but the # effective limit in-game is 100, thanks to MaxGameStage attributes. We # could use `bl3save.max_level` here, too, of course, but in the event that # I don't get this updated in a timely fashion, having it higher would let if args.item_levels: if args.item_levels < 1 or args.item_levels > 100: raise argparse.ArgumentTypeError('Valid item level range is 1 through 100') if args.item_levels > bl3save.max_level: print('WARNING: Setting item levels to {}, when {} is the currently-known max'.format( args.item_levels, bl3save.max_level, )) if os.path.exists(args.output_filename) and not args.force: if args.output_filename == args.input_filename: confirm_msg = 'Really overwrite {} with specified changes (no backup will be made)'.format(args.output_filename) else: confirm_msg = '{} already exists. Overwrite'.format(args.output_filename) sys.stdout.write('WARNING: {} [y/N]? '.format(confirm_msg)) sys.stdout.flush() response = sys.stdin.readline().strip().lower() if len(response) == 0 or response[0] != 'y': print('Aborting!') sys.exit(1) print('') if not args.quiet: print('Loading {}'.format(args.input_filename)) profile = BL3Profile(args.input_filename) if not args.quiet: print('') have_changes = any([ args.golden_keys is not None, args.diamond_keys is not None, args.vaultcard1_keys is not None, args.vaultcard1_chests is not None, args.vaultcard2_keys is not None, args.vaultcard2_chests is not None, args.vaultcard3_keys is not None, args.vaultcard3_chests is not None, args.zero_guardian_rank, args.min_guardian_rank, args.guardian_rank_rewards is not None, args.guardian_rank_tokens is not None, args.reset_borderlands_science, args.max_borderlands_science, args.remove_borderlands_science_boosts, args.borderlands_science_tokens is not None, len(args.unlock) > 0, args.import_items, args.item_levels, args.clear_customizations, args.alpha, args.item_mayhem_levels is not None, ]) guardian_rank_alert = False if have_changes: if not args.quiet: print('Making requested changes...') print('') if args.golden_keys is not None: if not args.quiet: print(' - Setting Golden Key count to {}'.format(args.golden_keys)) profile.set_golden_keys(args.golden_keys) if args.diamond_keys is not None: if not args.quiet: print(' - Setting Diamond Key count to {}'.format(args.diamond_keys)) profile.set_diamond_keys(args.diamond_keys) if args.vaultcard1_keys is not None: if not args.quiet: print(' - Setting Vault Card 1 Key count to {}'.format(args.vaultcard1_keys)) profile.set_vaultcard1_keys(args.vaultcard1_keys) if args.vaultcard1_chests is not None: if not args.quiet: print(' - Setting Vault Card 1 Chest count to {}'.format(args.vaultcard1_chests)) profile.set_vaultcard1_chests(args.vaultcard1_chests) if args.vaultcard2_keys is not None: if not args.quiet: print(' - Setting Vault Card 2 Key count to {}'.format(args.vaultcard2_keys)) profile.set_vaultcard2_keys(args.vaultcard2_keys) if args.vaultcard2_chests is not None: if not args.quiet: print(' - Setting Vault Card 2 Chest count to {}'.format(args.vaultcard2_chests)) profile.set_vaultcard2_chests(args.vaultcard2_chests) if args.vaultcard3_keys is not None: if not args.quiet: print(' - Setting Vault Card 3 Key count to {}'.format(args.vaultcard3_keys)) profile.set_vaultcard3_keys(args.vaultcard3_keys) if args.vaultcard3_chests is not None: if not args.quiet: print(' - Setting Vault Card 3 Chest count to {}'.format(args.vaultcard3_chests)) profile.set_vaultcard3_chests(args.vaultcard3_chests) if args.zero_guardian_rank: if not args.quiet: print(' - Zeroing Guardian Rank') if not args.min_guardian_rank \ and args.guardian_rank_rewards is None \ and args.guardian_rank_tokens is None: print(' NOTE: A profile with a zeroed Guardian Rank will probably have its') print(' Guardian Rank info populated from the first savegame loaded by the game') profile.zero_guardian_rank() if args.min_guardian_rank: if not args.quiet: print(' - Setting Guardian Rank to minimum (to prevent overwriting by savefiles)') new_gr = profile.min_guardian_rank() if new_gr is not None and not args.quiet: print(' - Guardian Rank set to {}'.format(new_gr)) guardian_rank_alert = True if args.guardian_rank_rewards is not None: if not args.quiet: if args.guardian_rank_rewards == 1: plural = '' else: plural = 's' print(' - Setting Guardian Rank rewards to {} point{}'.format(args.guardian_rank_rewards, plural)) new_gr = profile.set_guardian_rank_reward_levels(args.guardian_rank_rewards, force=True) if new_gr is not None and not args.quiet: print(' - Also set Guardian Rank level to {}'.format(new_gr)) guardian_rank_alert = True if args.guardian_rank_tokens is not None: if not args.quiet: print(' - Setting available Guardian Rank tokens to {}'.format(args.guardian_rank_tokens)) new_gr = profile.set_guardian_rank_tokens(args.guardian_rank_tokens) if new_gr is not None and not args.quiet: print(' - Also set Guardian Rank level to {}'.format(new_gr)) guardian_rank_alert = True if args.reset_borderlands_science: if not args.quiet: print(" - Resetting Borderlands Science progression") profile.reset_borderlands_science() if args.max_borderlands_science: if not args.quiet: print(" - Maximizing Borderlands Science progression") profile.max_borderlands_science() if args.remove_borderlands_science_boosts: if not args.quiet: print(" - Removing active Borderlands Science boosts") profile.remove_borderlands_science_boosts() if args.borderlands_science_tokens is not None: if not args.quiet: print(" - Setting available Borderlands Science tokens to {}".format(args.borderlands_science_tokens)) profile.set_borderlands_science_tokens(args.borderlands_science_tokens) if args.clear_customizations: if not args.quiet: print(' - Clearing all customizations') profile.clear_all_customizations() if len(args.unlock) > 0: if not args.quiet: print(' - Processing Unlocks:') if 'lostloot' in args.unlock: if not args.quiet: print(' - Lost Loot SDUs') profile.set_max_sdus([bl3save.PSDU_LOSTLOOT]) if 'bank' in args.unlock: if not args.quiet: print(' - Bank SDUs') profile.set_max_sdus([bl3save.PSDU_BANK]) if 'skins' in args.unlock: if not args.quiet: print(' - Character Skins') profile.unlock_char_skins() if 'heads' in args.unlock: if not args.quiet: print(' - Character Heads') profile.unlock_char_heads() if 'echothemes' in args.unlock: if not args.quiet: print(' - ECHO Themes') profile.unlock_echo_themes() if 'emotes' in args.unlock: if not args.quiet: print(' - Emotes') profile.unlock_emotes() if 'decos' in args.unlock: if not args.quiet: print(' - Room Decorations') profile.unlock_room_decos() if 'weaponskins' in args.unlock: if not args.quiet: print(' - Weapon Skins') profile.unlock_weapon_skins() if 'trinkets' in args.unlock: if not args.quiet: print(' - Weapon Trinkets') profile.unlock_weapon_trinkets() if args.alpha: if not args.quiet: print(' - Alphabetizing Room Decorations, Trinkets, and Weapon Skins') profile.alphabetize_cosmetics() if args.import_items: cli_common.import_items(args.import_items, profile.create_new_item_encoded, profile.add_bank_item, file_csv=args.csv, allow_fabricator=args.allow_fabricator, quiet=args.quiet, ) # various of the actions above. If we've been asked to change the level if args.item_levels: cli_common.update_item_levels(profile.get_bank_items(), args.item_levels, quiet=args.quiet, ) # Item Mayhem level if args.item_mayhem_levels is not None: cli_common.update_item_mayhem_levels(profile.get_bank_items(), args.item_mayhem_levels, quiet=args.quiet, ) # Guardian Rank Alert if not args.quiet and guardian_rank_alert: print(' - NOTE: Make sure to zero out your savegame Guardian Ranks, if making') print(' changes to Guardian Rank in your profile, otherwise the changes might') print(' not take effect properly.') # Newline at the end of all this. if not args.quiet: print('') # Write out if args.output == 'profile': profile.save_to(args.output_filename) if not args.quiet: print('Wrote profile to {}'.format(args.output_filename)) elif args.output == 'protobuf': profile.save_protobuf_to(args.output_filename) if not args.quiet: print('Wrote protobuf to {}'.format(args.output_filename)) elif args.output == 'json': profile.save_json_to(args.output_filename) if not args.quiet: print('Wrote JSON to {}'.format(args.output_filename)) elif args.output == 'items': if args.csv: cli_common.export_items_csv( profile.get_bank_items(), args.output_filename, quiet=args.quiet, ) else: cli_common.export_items( profile.get_bank_items(), args.output_filename, quiet=args.quiet, ) else: # Not sure how we'd ever get here raise Exception('Invalid output format specified: {}'.format(args.output)) if __name__ == '__main__': main()

true

1c3822d343c152c3c84298afdba5a208cab24a5a

62,879

py

Python

src/pretix/api/serializers/order.py

fakegit/pretix

b6e9e64ff967f7b4f91fe88694f4157d8a0787b4

[ "Apache-2.0" ]

null

src/pretix/api/serializers/order.py

fakegit/pretix

b6e9e64ff967f7b4f91fe88694f4157d8a0787b4

[ "Apache-2.0" ]

56

2020-05-07T07:54:17.000Z

2021-04-19T12:14:14.000Z

src/pretix/api/serializers/order.py

fakegit/pretix

b6e9e64ff967f7b4f91fe88694f4157d8a0787b4

[ "Apache-2.0" ]

null

# # This file is part of pretix (Community Edition). # # Copyright (C) 2014-2020 Raphael Michel and contributors # Copyright (C) 2020-2021 rami.io GmbH and contributors # # This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General # Public License as published by the Free Software Foundation in version 3 of the License. # # ADDITIONAL TERMS APPLY: Pursuant to Section 7 of the GNU Affero General Public License, additional terms are # applicable granting you additional permissions and placing additional restrictions on your usage of this software. # Please refer to the pretix LICENSE file to obtain the full terms applicable to this work. If you did not receive # this file, see <https://pretix.eu/about/en/license>. # # This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied # warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more # details. # # You should have received a copy of the GNU Affero General Public License along with this program. If not, see # <https://www.gnu.org/licenses/>. # import json from collections import Counter, defaultdict from decimal import Decimal import pycountry from django.core.files import File from django.db.models import F, Q from django.utils.timezone import now from django.utils.translation import gettext_lazy from django_countries.fields import Country from rest_framework import serializers from rest_framework.exceptions import ValidationError from rest_framework.relations import SlugRelatedField from rest_framework.reverse import reverse from pretix.api.serializers.event import SubEventSerializer from pretix.api.serializers.i18n import I18nAwareModelSerializer from pretix.api.serializers.item import ( InlineItemVariationSerializer, ItemSerializer, ) from pretix.base.channels import get_all_sales_channels from pretix.base.decimal import round_decimal from pretix.base.i18n import language from pretix.base.models import ( CachedFile, Checkin, Invoice, InvoiceAddress, InvoiceLine, Item, ItemVariation, Order, OrderPosition, Question, QuestionAnswer, Seat, SubEvent, TaxRule, Voucher, ) from pretix.base.models.orders import ( CartPosition, OrderFee, OrderPayment, OrderRefund, RevokedTicketSecret, ) from pretix.base.pdf import get_images, get_variables from pretix.base.services.cart import error_messages from pretix.base.services.locking import NoLockManager from pretix.base.services.pricing import get_price from pretix.base.settings import COUNTRIES_WITH_STATE_IN_ADDRESS from pretix.base.signals import register_ticket_outputs from pretix.multidomain.urlreverse import build_absolute_uri class CompatibleCountryField(serializers.Field): def to_internal_value(self, data): return {self.field_name: Country(data)} def to_representation(self, instance: InvoiceAddress): if instance.country: return str(instance.country) elif hasattr(instance, 'country_old'): return instance.country_old class CountryField(serializers.Field): def to_internal_value(self, data): return {self.field_name: Country(data)} def to_representation(self, src): return str(src) if src else None class InvoiceAddressSerializer(I18nAwareModelSerializer): country = CompatibleCountryField(source='*') name = serializers.CharField(required=False) class Meta: model = InvoiceAddress fields = ('last_modified', 'is_business', 'company', 'name', 'name_parts', 'street', 'zipcode', 'city', 'country', 'state', 'vat_id', 'vat_id_validated', 'internal_reference') read_only_fields = ('last_modified',) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) for v in self.fields.values(): v.required = False v.allow_blank = True def validate(self, data): if data.get('name') and data.get('name_parts'): raise ValidationError( {'name': ['Do not specify name if you specified name_parts.']} ) if data.get('name_parts') and '_scheme' not in data.get('name_parts'): data['name_parts']['_scheme'] = self.context['request'].event.settings.name_scheme if data.get('country'): if not pycountry.countries.get(alpha_2=data.get('country').code): raise ValidationError( {'country': ['Invalid country code.']} ) if data.get('state'): cc = str(data.get('country') or self.instance.country or '') if cc not in COUNTRIES_WITH_STATE_IN_ADDRESS: raise ValidationError( {'state': ['States are not supported in country "{}".'.format(cc)]} ) if not pycountry.subdivisions.get(code=cc + '-' + data.get('state')): raise ValidationError( {'state': ['"{}" is not a known subdivision of the country "{}".'.format(data.get('state'), cc)]} ) return data class AnswerQuestionIdentifierField(serializers.Field): def to_representation(self, instance: QuestionAnswer): return instance.question.identifier class AnswerQuestionOptionsIdentifierField(serializers.Field): def to_representation(self, instance: QuestionAnswer): if isinstance(instance, WrappedModel) or instance.pk: return [o.identifier for o in instance.options.all()] return [] class InlineSeatSerializer(I18nAwareModelSerializer): class Meta: model = Seat fields = ('id', 'name', 'seat_guid') class AnswerSerializer(I18nAwareModelSerializer): question_identifier = AnswerQuestionIdentifierField(source='*', read_only=True) option_identifiers = AnswerQuestionOptionsIdentifierField(source='*', read_only=True) def to_representation(self, instance): r = super().to_representation(instance) if r['answer'].startswith('file://') and instance.orderposition: r['answer'] = reverse('api-v1:orderposition-answer', kwargs={ 'organizer': instance.orderposition.order.event.organizer.slug, 'event': instance.orderposition.order.event.slug, 'pk': instance.orderposition.pk, 'question': instance.question_id, }, request=self.context['request']) return r class Meta: model = QuestionAnswer fields = ('question', 'answer', 'question_identifier', 'options', 'option_identifiers') def validate_question(self, q): if q.event != self.context['event']: raise ValidationError( 'The specified question does not belong to this event.' ) return q def _handle_file_upload(self, data): if data['answer'] == 'file:keep': return data try: ao = self.context["request"].user or self.context["request"].auth cf = CachedFile.objects.get( session_key=f'api-upload-{str(type(ao))}-{ao.pk}', file__isnull=False, pk=data['answer'][len("file:"):], ) except (ValidationError, IndexError): # invalid uuid raise ValidationError('The submitted file ID "{fid}" was not found.'.format(fid=data)) except CachedFile.DoesNotExist: raise ValidationError('The submitted file ID "{fid}" was not found.'.format(fid=data)) allowed_types = ( 'image/png', 'image/jpeg', 'image/gif', 'application/pdf' ) if cf.type not in allowed_types: raise ValidationError('The submitted file "{fid}" has a file type that is not allowed in this field.'.format(fid=data)) if cf.file.size > 10 * 1024 * 1024: raise ValidationError('The submitted file "{fid}" is too large to be used in this field.'.format(fid=data)) data['options'] = [] data['answer'] = cf.file return data def validate(self, data): if data.get('question').type == Question.TYPE_FILE: return self._handle_file_upload(data) elif data.get('question').type in (Question.TYPE_CHOICE, Question.TYPE_CHOICE_MULTIPLE): if not data.get('options'): raise ValidationError( 'You need to specify options if the question is of a choice type.' ) if data.get('question').type == Question.TYPE_CHOICE and len(data.get('options')) > 1: raise ValidationError( 'You can specify at most one option for this question.' ) for o in data.get('options'): if o.question_id != data.get('question').pk: raise ValidationError( 'The specified option does not belong to this question.' ) data['answer'] = ", ".join([str(o) for o in data.get('options')]) else: if data.get('options'): raise ValidationError( 'You should not specify options if the question is not of a choice type.' ) if data.get('question').type == Question.TYPE_BOOLEAN: if data.get('answer') in ['true', 'True', '1', 'TRUE']: data['answer'] = 'True' elif data.get('answer') in ['false', 'False', '0', 'FALSE']: data['answer'] = 'False' else: raise ValidationError( 'Please specify "true" or "false" for boolean questions.' ) elif data.get('question').type == Question.TYPE_NUMBER: serializers.DecimalField( max_digits=50, decimal_places=25 ).to_internal_value(data.get('answer')) elif data.get('question').type == Question.TYPE_DATE: data['answer'] = serializers.DateField().to_internal_value(data.get('answer')) elif data.get('question').type == Question.TYPE_TIME: data['answer'] = serializers.TimeField().to_internal_value(data.get('answer')) elif data.get('question').type == Question.TYPE_DATETIME: data['answer'] = serializers.DateTimeField().to_internal_value(data.get('answer')) return data class CheckinSerializer(I18nAwareModelSerializer): class Meta: model = Checkin fields = ('id', 'datetime', 'list', 'auto_checked_in', 'type') class OrderDownloadsField(serializers.Field): def to_representation(self, instance: Order): if instance.status != Order.STATUS_PAID: if instance.status != Order.STATUS_PENDING or instance.require_approval or not instance.event.settings.ticket_download_pending: return [] request = self.context['request'] res = [] responses = register_ticket_outputs.send(instance.event) for receiver, response in responses: provider = response(instance.event) if provider.is_enabled: res.append({ 'output': provider.identifier, 'url': reverse('api-v1:order-download', kwargs={ 'organizer': instance.event.organizer.slug, 'event': instance.event.slug, 'code': instance.code, 'output': provider.identifier, }, request=request) }) return res class PositionDownloadsField(serializers.Field): def to_representation(self, instance: OrderPosition): if instance.order.status != Order.STATUS_PAID: if instance.order.status != Order.STATUS_PENDING or instance.order.require_approval or not instance.order.event.settings.ticket_download_pending: return [] if not instance.generate_ticket: return [] request = self.context['request'] res = [] responses = register_ticket_outputs.send(instance.order.event) for receiver, response in responses: provider = response(instance.order.event) if provider.is_enabled: res.append({ 'output': provider.identifier, 'url': reverse('api-v1:orderposition-download', kwargs={ 'organizer': instance.order.event.organizer.slug, 'event': instance.order.event.slug, 'pk': instance.pk, 'output': provider.identifier, }, request=request) }) return res class PdfDataSerializer(serializers.Field): def to_representation(self, instance: OrderPosition): res = {} ev = instance.subevent or instance.order.event with language(instance.order.locale, instance.order.event.settings.region): # This needs to have some extra performance improvements to avoid creating hundreds of queries when # we serialize a list. if 'vars' not in self.context: self.context['vars'] = get_variables(self.context['request'].event) if 'vars_images' not in self.context: self.context['vars_images'] = get_images(self.context['request'].event) for k, f in self.context['vars'].items(): res[k] = f['evaluate'](instance, instance.order, ev) if not hasattr(ev, '_cached_meta_data'): ev._cached_meta_data = ev.meta_data for k, v in ev._cached_meta_data.items(): res['meta:' + k] = v if not hasattr(instance.item, '_cached_meta_data'): instance.item._cached_meta_data = instance.item.meta_data for k, v in instance.item._cached_meta_data.items(): res['itemmeta:' + k] = v res['images'] = {} for k, f in self.context['vars_images'].items(): if 'etag' in f: has_image = etag = f['etag'](instance, instance.order, ev) else: has_image = f['etag'](instance, instance.order, ev) etag = None if has_image: url = reverse('api-v1:orderposition-pdf_image', kwargs={ 'organizer': instance.order.event.organizer.slug, 'event': instance.order.event.slug, 'pk': instance.pk, 'key': k, }, request=self.context['request']) if etag: url += f'#etag={etag}' res['images'][k] = url else: res['images'][k] = None return res class OrderPositionSerializer(I18nAwareModelSerializer): checkins = CheckinSerializer(many=True, read_only=True) answers = AnswerSerializer(many=True) downloads = PositionDownloadsField(source='*', read_only=True) order = serializers.SlugRelatedField(slug_field='code', read_only=True) pdf_data = PdfDataSerializer(source='*', read_only=True) seat = InlineSeatSerializer(read_only=True) country = CompatibleCountryField(source='*') attendee_name = serializers.CharField(required=False) class Meta: model = OrderPosition fields = ('id', 'order', 'positionid', 'item', 'variation', 'price', 'attendee_name', 'attendee_name_parts', 'company', 'street', 'zipcode', 'city', 'country', 'state', 'attendee_email', 'voucher', 'tax_rate', 'tax_value', 'secret', 'addon_to', 'subevent', 'checkins', 'downloads', 'answers', 'tax_rule', 'pseudonymization_id', 'pdf_data', 'seat', 'canceled') read_only_fields = ( 'id', 'order', 'positionid', 'item', 'variation', 'price', 'voucher', 'tax_rate', 'tax_value', 'secret', 'addon_to', 'subevent', 'checkins', 'downloads', 'answers', 'tax_rule', 'pseudonymization_id', 'pdf_data', 'seat', 'canceled' ) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) request = self.context.get('request') if request and (not request.query_params.get('pdf_data', 'false') == 'true' or 'can_view_orders' not in request.eventpermset): self.fields.pop('pdf_data', None) def validate(self, data): if data.get('attendee_name') and data.get('attendee_name_parts'): raise ValidationError( {'attendee_name': ['Do not specify attendee_name if you specified attendee_name_parts.']} ) if data.get('attendee_name_parts') and '_scheme' not in data.get('attendee_name_parts'): data['attendee_name_parts']['_scheme'] = self.context['request'].event.settings.name_scheme if data.get('country'): if not pycountry.countries.get(alpha_2=data.get('country').code): raise ValidationError( {'country': ['Invalid country code.']} ) if data.get('state'): cc = str(data.get('country') or self.instance.country or '') if cc not in COUNTRIES_WITH_STATE_IN_ADDRESS: raise ValidationError( {'state': ['States are not supported in country "{}".'.format(cc)]} ) if not pycountry.subdivisions.get(code=cc + '-' + data.get('state')): raise ValidationError( {'state': ['"{}" is not a known subdivision of the country "{}".'.format(data.get('state'), cc)]} ) return data def update(self, instance, validated_data): # Even though all fields that shouldn't be edited are marked as read_only in the serializer # (hopefully), we'll be extra careful here and be explicit about the model fields we update. update_fields = [ 'attendee_name_parts', 'company', 'street', 'zipcode', 'city', 'country', 'state', 'attendee_email', ] answers_data = validated_data.pop('answers', None) name = validated_data.pop('attendee_name', '') if name and not validated_data.get('attendee_name_parts'): validated_data['attendee_name_parts'] = { '_legacy': name } for attr, value in validated_data.items(): if attr in update_fields: setattr(instance, attr, value) instance.save(update_fields=update_fields) if answers_data is not None: qs_seen = set() answercache = { a.question_id: a for a in instance.answers.all() } for answ_data in answers_data: options = answ_data.pop('options', []) if answ_data['question'].pk in qs_seen: raise ValidationError(f'Question {answ_data["question"]} was sent twice.') if answ_data['question'].pk in answercache: a = answercache[answ_data['question'].pk] if isinstance(answ_data['answer'], File): a.file.save(answ_data['answer'].name, answ_data['answer'], save=False) a.answer = 'file://' + a.file.name elif a.answer.startswith('file://') and answ_data['answer'] == "file:keep": pass # keep current file else: for attr, value in answ_data.items(): setattr(a, attr, value) a.save() else: if isinstance(answ_data['answer'], File): an = answ_data.pop('answer') a = instance.answers.create(**answ_data, answer='') a.file.save(an.name, an, save=False) a.answer = 'file://' + a.file.name a.save() else: a = instance.answers.create(**answ_data) a.options.set(options) qs_seen.add(a.question_id) for qid, a in answercache.items(): if qid not in qs_seen: a.delete() return instance class RequireAttentionField(serializers.Field): def to_representation(self, instance: OrderPosition): return instance.order.checkin_attention or instance.item.checkin_attention class AttendeeNameField(serializers.Field): def to_representation(self, instance: OrderPosition): an = instance.attendee_name if not an: if instance.addon_to_id: an = instance.addon_to.attendee_name if not an: try: an = instance.order.invoice_address.name except InvoiceAddress.DoesNotExist: pass return an class AttendeeNamePartsField(serializers.Field): def to_representation(self, instance: OrderPosition): an = instance.attendee_name p = instance.attendee_name_parts if not an: if instance.addon_to_id: an = instance.addon_to.attendee_name p = instance.addon_to.attendee_name_parts if not an: try: p = instance.order.invoice_address.name_parts except InvoiceAddress.DoesNotExist: pass return p class CheckinListOrderPositionSerializer(OrderPositionSerializer): require_attention = RequireAttentionField(source='*') attendee_name = AttendeeNameField(source='*') attendee_name_parts = AttendeeNamePartsField(source='*') order__status = serializers.SlugRelatedField(read_only=True, slug_field='status', source='order') class Meta: model = OrderPosition fields = ('id', 'order', 'positionid', 'item', 'variation', 'price', 'attendee_name', 'attendee_name_parts', 'company', 'street', 'zipcode', 'city', 'country', 'state', 'attendee_email', 'voucher', 'tax_rate', 'tax_value', 'secret', 'addon_to', 'subevent', 'checkins', 'downloads', 'answers', 'tax_rule', 'pseudonymization_id', 'pdf_data', 'seat', 'require_attention', 'order__status') def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if 'subevent' in self.context['request'].query_params.getlist('expand'): self.fields['subevent'] = SubEventSerializer(read_only=True) if 'item' in self.context['request'].query_params.getlist('expand'): self.fields['item'] = ItemSerializer(read_only=True) if 'variation' in self.context['request'].query_params.getlist('expand'): self.fields['variation'] = InlineItemVariationSerializer(read_only=True) class OrderPaymentTypeField(serializers.Field): # TODO: Remove after pretix 2.2 def to_representation(self, instance: Order): t = None for p in instance.payments.all(): t = p.provider return t class OrderPaymentDateField(serializers.DateField): # TODO: Remove after pretix 2.2 def to_representation(self, instance: Order): t = None for p in instance.payments.all(): t = p.payment_date or t if t: return super().to_representation(t.date()) class OrderFeeSerializer(I18nAwareModelSerializer): class Meta: model = OrderFee fields = ('fee_type', 'value', 'description', 'internal_type', 'tax_rate', 'tax_value', 'tax_rule', 'canceled') class PaymentURLField(serializers.URLField): def to_representation(self, instance: OrderPayment): if instance.state != OrderPayment.PAYMENT_STATE_CREATED: return None return build_absolute_uri(self.context['event'], 'presale:event.order.pay', kwargs={ 'order': instance.order.code, 'secret': instance.order.secret, 'payment': instance.pk, }) class PaymentDetailsField(serializers.Field): def to_representation(self, value: OrderPayment): pp = value.payment_provider if not pp: return {} return pp.api_payment_details(value) class OrderPaymentSerializer(I18nAwareModelSerializer): payment_url = PaymentURLField(source='*', allow_null=True, read_only=True) details = PaymentDetailsField(source='*', allow_null=True, read_only=True) class Meta: model = OrderPayment fields = ('local_id', 'state', 'amount', 'created', 'payment_date', 'provider', 'payment_url', 'details') class OrderRefundSerializer(I18nAwareModelSerializer): payment = SlugRelatedField(slug_field='local_id', read_only=True) class Meta: model = OrderRefund fields = ('local_id', 'state', 'source', 'amount', 'payment', 'created', 'execution_date', 'comment', 'provider') class OrderURLField(serializers.URLField): def to_representation(self, instance: Order): return build_absolute_uri(self.context['event'], 'presale:event.order', kwargs={ 'order': instance.code, 'secret': instance.secret, }) class OrderSerializer(I18nAwareModelSerializer): invoice_address = InvoiceAddressSerializer(allow_null=True) positions = OrderPositionSerializer(many=True, read_only=True) fees = OrderFeeSerializer(many=True, read_only=True) downloads = OrderDownloadsField(source='*', read_only=True) payments = OrderPaymentSerializer(many=True, read_only=True) refunds = OrderRefundSerializer(many=True, read_only=True) payment_date = OrderPaymentDateField(source='*', read_only=True) payment_provider = OrderPaymentTypeField(source='*', read_only=True) url = OrderURLField(source='*', read_only=True) class Meta: model = Order fields = ( 'code', 'status', 'testmode', 'secret', 'email', 'phone', 'locale', 'datetime', 'expires', 'payment_date', 'payment_provider', 'fees', 'total', 'comment', 'invoice_address', 'positions', 'downloads', 'checkin_attention', 'last_modified', 'payments', 'refunds', 'require_approval', 'sales_channel', 'url' ) read_only_fields = ( 'code', 'status', 'testmode', 'secret', 'datetime', 'expires', 'payment_date', 'payment_provider', 'fees', 'total', 'positions', 'downloads', 'last_modified', 'payments', 'refunds', 'require_approval', 'sales_channel' ) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if not self.context['request'].query_params.get('pdf_data', 'false') == 'true': self.fields['positions'].child.fields.pop('pdf_data', None) for exclude_field in self.context['request'].query_params.getlist('exclude'): p = exclude_field.split('.') if p[0] in self.fields: if len(p) == 1: del self.fields[p[0]] elif len(p) == 2: self.fields[p[0]].child.fields.pop(p[1]) def validate_locale(self, l): if l not in set(k for k in self.instance.event.settings.locales): raise ValidationError('"{}" is not a supported locale for this event.'.format(l)) return l def update(self, instance, validated_data): # Even though all fields that shouldn't be edited are marked as read_only in the serializer # (hopefully), we'll be extra careful here and be explicit about the model fields we update. update_fields = ['comment', 'checkin_attention', 'email', 'locale', 'phone'] if 'invoice_address' in validated_data: iadata = validated_data.pop('invoice_address') if not iadata: try: instance.invoice_address.delete() except InvoiceAddress.DoesNotExist: pass else: name = iadata.pop('name', '') if name and not iadata.get('name_parts'): iadata['name_parts'] = { '_legacy': name } try: ia = instance.invoice_address if iadata.get('vat_id') != ia.vat_id and 'vat_id_validated' not in iadata: ia.vat_id_validated = False self.fields['invoice_address'].update(ia, iadata) except InvoiceAddress.DoesNotExist: InvoiceAddress.objects.create(order=instance, **iadata) for attr, value in validated_data.items(): if attr in update_fields: setattr(instance, attr, value) instance.save(update_fields=update_fields) return instance class AnswerQuestionOptionsField(serializers.Field): def to_representation(self, instance: QuestionAnswer): return [o.pk for o in instance.options.all()] class SimulatedAnswerSerializer(AnswerSerializer): options = AnswerQuestionOptionsField(read_only=True, source='*') class SimulatedOrderPositionSerializer(OrderPositionSerializer): answers = SimulatedAnswerSerializer(many=True) addon_to = serializers.SlugRelatedField(read_only=True, slug_field='positionid') class SimulatedOrderSerializer(OrderSerializer): positions = SimulatedOrderPositionSerializer(many=True, read_only=True) class PriceCalcSerializer(serializers.Serializer): item = serializers.PrimaryKeyRelatedField(queryset=Item.objects.none(), required=False, allow_null=True) variation = serializers.PrimaryKeyRelatedField(queryset=ItemVariation.objects.none(), required=False, allow_null=True) subevent = serializers.PrimaryKeyRelatedField(queryset=SubEvent.objects.none(), required=False, allow_null=True) tax_rule = serializers.PrimaryKeyRelatedField(queryset=TaxRule.objects.none(), required=False, allow_null=True) locale = serializers.CharField(allow_null=True, required=False) def __init__(self, *args, **kwargs): event = kwargs.pop('event') super().__init__(*args, **kwargs) self.fields['item'].queryset = event.items.all() self.fields['tax_rule'].queryset = event.tax_rules.all() self.fields['variation'].queryset = ItemVariation.objects.filter(item__event=event) if event.has_subevents: self.fields['subevent'].queryset = event.subevents.all() else: del self.fields['subevent'] class AnswerCreateSerializer(AnswerSerializer): pass class OrderFeeCreateSerializer(I18nAwareModelSerializer): _treat_value_as_percentage = serializers.BooleanField(default=False, required=False) _split_taxes_like_products = serializers.BooleanField(default=False, required=False) class Meta: model = OrderFee fields = ('fee_type', 'value', 'description', 'internal_type', 'tax_rule', '_treat_value_as_percentage', '_split_taxes_like_products') def validate_tax_rule(self, tr): if tr and tr.event != self.context['event']: raise ValidationError( 'The specified tax rate does not belong to this event.' ) return tr class OrderPositionCreateSerializer(I18nAwareModelSerializer): answers = AnswerCreateSerializer(many=True, required=False) addon_to = serializers.IntegerField(required=False, allow_null=True) secret = serializers.CharField(required=False) attendee_name = serializers.CharField(required=False, allow_null=True) seat = serializers.CharField(required=False, allow_null=True) price = serializers.DecimalField(required=False, allow_null=True, decimal_places=2, max_digits=10) voucher = serializers.SlugRelatedField(slug_field='code', queryset=Voucher.objects.none(), required=False, allow_null=True) country = CompatibleCountryField(source='*') class Meta: model = OrderPosition fields = ('positionid', 'item', 'variation', 'price', 'attendee_name', 'attendee_name_parts', 'attendee_email', 'company', 'street', 'zipcode', 'city', 'country', 'state', 'secret', 'addon_to', 'subevent', 'answers', 'seat', 'voucher') def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) for k, v in self.fields.items(): if k in ('company', 'street', 'zipcode', 'city', 'country', 'state'): v.required = False v.allow_blank = True v.allow_null = True def validate_secret(self, secret): if secret and OrderPosition.all.filter(order__event=self.context['event'], secret=secret).exists(): raise ValidationError( 'You cannot assign a position secret that already exists.' ) return secret def validate_item(self, item): if item.event != self.context['event']: raise ValidationError( 'The specified item does not belong to this event.' ) if not item.active: raise ValidationError( 'The specified item is not active.' ) return item def validate_subevent(self, subevent): if self.context['event'].has_subevents: if not subevent: raise ValidationError( 'You need to set a subevent.' ) if subevent.event != self.context['event']: raise ValidationError( 'The specified subevent does not belong to this event.' ) elif subevent: raise ValidationError( 'You cannot set a subevent for this event.' ) return subevent def validate(self, data): if data.get('item'): if data.get('item').has_variations: if not data.get('variation'): raise ValidationError({'variation': ['You should specify a variation for this item.']}) else: if data.get('variation').item != data.get('item'): raise ValidationError( {'variation': ['The specified variation does not belong to the specified item.']} ) elif data.get('variation'): raise ValidationError( {'variation': ['You cannot specify a variation for this item.']} ) if data.get('attendee_name') and data.get('attendee_name_parts'): raise ValidationError( {'attendee_name': ['Do not specify attendee_name if you specified attendee_name_parts.']} ) if data.get('attendee_name_parts') and '_scheme' not in data.get('attendee_name_parts'): data['attendee_name_parts']['_scheme'] = self.context['request'].event.settings.name_scheme if data.get('country'): if not pycountry.countries.get(alpha_2=data.get('country').code): raise ValidationError( {'country': ['Invalid country code.']} ) if data.get('state'): cc = str(data.get('country') or self.instance.country or '') if cc not in COUNTRIES_WITH_STATE_IN_ADDRESS: raise ValidationError( {'state': ['States are not supported in country "{}".'.format(cc)]} ) if not pycountry.subdivisions.get(code=cc + '-' + data.get('state')): raise ValidationError( {'state': ['"{}" is not a known subdivision of the country "{}".'.format(data.get('state'), cc)]} ) return data class CompatibleJSONField(serializers.JSONField): def to_internal_value(self, data): try: return json.dumps(data) except (TypeError, ValueError): self.fail('invalid') def to_representation(self, value): if value: return json.loads(value) return value class WrappedList: def __init__(self, data): self._data = data def all(self): return self._data class WrappedModel: def __init__(self, model): self._wrapped = model def __getattr__(self, item): return getattr(self._wrapped, item) def save(self, *args, **kwargs): raise NotImplementedError def delete(self, *args, **kwargs): raise NotImplementedError class OrderCreateSerializer(I18nAwareModelSerializer): invoice_address = InvoiceAddressSerializer(required=False) positions = OrderPositionCreateSerializer(many=True, required=True) fees = OrderFeeCreateSerializer(many=True, required=False) status = serializers.ChoiceField(choices=( ('n', Order.STATUS_PENDING), ('p', Order.STATUS_PAID), ), default='n', required=False) code = serializers.CharField( required=False, max_length=16, min_length=5 ) comment = serializers.CharField(required=False, allow_blank=True) payment_provider = serializers.CharField(required=False, allow_null=True) payment_info = CompatibleJSONField(required=False) consume_carts = serializers.ListField(child=serializers.CharField(), required=False) force = serializers.BooleanField(default=False, required=False) payment_date = serializers.DateTimeField(required=False, allow_null=True) send_email = serializers.BooleanField(default=False, required=False) simulate = serializers.BooleanField(default=False, required=False) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fields['positions'].child.fields['voucher'].queryset = self.context['event'].vouchers.all() class Meta: model = Order fields = ('code', 'status', 'testmode', 'email', 'phone', 'locale', 'payment_provider', 'fees', 'comment', 'sales_channel', 'invoice_address', 'positions', 'checkin_attention', 'payment_info', 'payment_date', 'consume_carts', 'force', 'send_email', 'simulate') def validate_payment_provider(self, pp): if pp is None: return None if pp not in self.context['event'].get_payment_providers(): raise ValidationError('The given payment provider is not known.') return pp def validate_sales_channel(self, channel): if channel not in get_all_sales_channels(): raise ValidationError('Unknown sales channel.') return channel def validate_code(self, code): if code and Order.objects.filter(event__organizer=self.context['event'].organizer, code=code).exists(): raise ValidationError( 'This order code is already in use.' ) if any(c not in 'ABCDEFGHJKLMNPQRSTUVWXYZ1234567890' for c in code): raise ValidationError( 'This order code contains invalid characters.' ) return code def validate_positions(self, data): if not data: raise ValidationError( 'An order cannot be empty.' ) errs = [{} for p in data] if any([p.get('positionid') for p in data]): if not all([p.get('positionid') for p in data]): for i, p in enumerate(data): if not p.get('positionid'): errs[i]['positionid'] = [ 'If you set position IDs manually, you need to do so for all positions.' ] raise ValidationError(errs) last_non_add_on = None last_posid = 0 for i, p in enumerate(data): if p['positionid'] != last_posid + 1: errs[i]['positionid'] = [ 'Position IDs need to be consecutive.' ] if p.get('addon_to') and p['addon_to'] != last_non_add_on: errs[i]['addon_to'] = [ "If you set addon_to, you need to make sure that the referenced " "position ID exists and is transmitted directly before its add-ons." ] if not p.get('addon_to'): last_non_add_on = p['positionid'] last_posid = p['positionid'] elif any([p.get('addon_to') for p in data]): errs = [ {'positionid': ["If you set addon_to on any position, you need to specify position IDs manually."]} for p in data ] else: for i, p in enumerate(data): p['positionid'] = i + 1 if any(errs): raise ValidationError(errs) return data def validate_testmode(self, testmode): if 'sales_channel' in self.initial_data: try: sales_channel = get_all_sales_channels()[self.initial_data['sales_channel']] if testmode and not sales_channel.testmode_supported: raise ValidationError('This sales channel does not provide support for test mode.') except KeyError: # We do not need to raise a ValidationError here, since there is another check to validate the # sales_channel pass return testmode def create(self, validated_data): fees_data = validated_data.pop('fees') if 'fees' in validated_data else [] positions_data = validated_data.pop('positions') if 'positions' in validated_data else [] payment_provider = validated_data.pop('payment_provider', None) payment_info = validated_data.pop('payment_info', '{}') payment_date = validated_data.pop('payment_date', now()) force = validated_data.pop('force', False) simulate = validated_data.pop('simulate', False) self._send_mail = validated_data.pop('send_email', False) if 'invoice_address' in validated_data: iadata = validated_data.pop('invoice_address') name = iadata.pop('name', '') if name and not iadata.get('name_parts'): iadata['name_parts'] = { '_legacy': name } ia = InvoiceAddress(**iadata) else: ia = None lockfn = self.context['event'].lock if simulate: lockfn = NoLockManager with lockfn() as now_dt: free_seats = set() seats_seen = set() consume_carts = validated_data.pop('consume_carts', []) delete_cps = [] quota_avail_cache = {} v_budget = {} voucher_usage = Counter() if consume_carts: for cp in CartPosition.objects.filter( event=self.context['event'], cart_id__in=consume_carts, expires__gt=now() ): quotas = (cp.variation.quotas.filter(subevent=cp.subevent) if cp.variation else cp.item.quotas.filter(subevent=cp.subevent)) for quota in quotas: if quota not in quota_avail_cache: quota_avail_cache[quota] = list(quota.availability()) if quota_avail_cache[quota][1] is not None: quota_avail_cache[quota][1] += 1 if cp.voucher: voucher_usage[cp.voucher] -= 1 if cp.expires > now_dt: if cp.seat: free_seats.add(cp.seat) delete_cps.append(cp) errs = [{} for p in positions_data] for i, pos_data in enumerate(positions_data): if pos_data.get('voucher'): v = pos_data['voucher'] if pos_data.get('addon_to'): errs[i]['voucher'] = ['Vouchers are currently not supported for add-on products.'] continue if not v.applies_to(pos_data['item'], pos_data.get('variation')): errs[i]['voucher'] = [error_messages['voucher_invalid_item']] continue if v.subevent_id and pos_data.get('subevent').pk != v.subevent_id: errs[i]['voucher'] = [error_messages['voucher_invalid_subevent']] continue if v.valid_until is not None and v.valid_until < now_dt: errs[i]['voucher'] = [error_messages['voucher_expired']] continue voucher_usage[v] += 1 if voucher_usage[v] > 0: redeemed_in_carts = CartPosition.objects.filter( Q(voucher=pos_data['voucher']) & Q(event=self.context['event']) & Q(expires__gte=now_dt) ).exclude(pk__in=[cp.pk for cp in delete_cps]) v_avail = v.max_usages - v.redeemed - redeemed_in_carts.count() if v_avail < voucher_usage[v]: errs[i]['voucher'] = [ 'The voucher has already been used the maximum number of times.' ] if v.budget is not None: price = pos_data.get('price') if price is None: price = get_price( item=pos_data.get('item'), variation=pos_data.get('variation'), voucher=v, custom_price=None, subevent=pos_data.get('subevent'), addon_to=pos_data.get('addon_to'), invoice_address=ia, ).gross pbv = get_price( item=pos_data['item'], variation=pos_data.get('variation'), voucher=None, custom_price=None, subevent=pos_data.get('subevent'), addon_to=pos_data.get('addon_to'), invoice_address=ia, ) if v not in v_budget: v_budget[v] = v.budget - v.budget_used() disc = pbv.gross - price if disc > v_budget[v]: new_disc = v_budget[v] v_budget[v] -= new_disc if new_disc == Decimal('0.00') or pos_data.get('price') is not None: errs[i]['voucher'] = [ 'The voucher has a remaining budget of {}, therefore a discount of {} can not be ' 'given.'.format(v_budget[v] + new_disc, disc) ] continue pos_data['price'] = price + (disc - new_disc) else: v_budget[v] -= disc seated = pos_data.get('item').seat_category_mappings.filter(subevent=pos_data.get('subevent')).exists() if pos_data.get('seat'): if not seated: errs[i]['seat'] = ['The specified product does not allow to choose a seat.'] try: seat = self.context['event'].seats.get(seat_guid=pos_data['seat'], subevent=pos_data.get('subevent')) except Seat.DoesNotExist: errs[i]['seat'] = ['The specified seat does not exist.'] else: pos_data['seat'] = seat if (seat not in free_seats and not seat.is_available(sales_channel=validated_data.get('sales_channel', 'web'))) or seat in seats_seen: errs[i]['seat'] = [gettext_lazy('The selected seat "{seat}" is not available.').format(seat=seat.name)] seats_seen.add(seat) elif seated: errs[i]['seat'] = ['The specified product requires to choose a seat.'] if not force: for i, pos_data in enumerate(positions_data): if pos_data.get('voucher'): if pos_data['voucher'].allow_ignore_quota or pos_data['voucher'].block_quota: continue if pos_data.get('subevent'): if pos_data.get('item').pk in pos_data['subevent'].item_overrides and pos_data['subevent'].item_overrides[pos_data['item'].pk].disabled: errs[i]['item'] = [gettext_lazy('The product "{}" is not available on this date.').format( str(pos_data.get('item')) )] if ( pos_data.get('variation') and pos_data['variation'].pk in pos_data['subevent'].var_overrides and pos_data['subevent'].var_overrides[pos_data['variation'].pk].disabled ): errs[i]['item'] = [gettext_lazy('The product "{}" is not available on this date.').format( str(pos_data.get('item')) )] new_quotas = (pos_data.get('variation').quotas.filter(subevent=pos_data.get('subevent')) if pos_data.get('variation') else pos_data.get('item').quotas.filter(subevent=pos_data.get('subevent'))) if len(new_quotas) == 0: errs[i]['item'] = [gettext_lazy('The product "{}" is not assigned to a quota.').format( str(pos_data.get('item')) )] else: for quota in new_quotas: if quota not in quota_avail_cache: quota_avail_cache[quota] = list(quota.availability()) if quota_avail_cache[quota][1] is not None: quota_avail_cache[quota][1] -= 1 if quota_avail_cache[quota][1] < 0: errs[i]['item'] = [ gettext_lazy('There is not enough quota available on quota "{}" to perform the operation.').format( quota.name ) ] if any(errs): raise ValidationError({'positions': errs}) if validated_data.get('locale', None) is None: validated_data['locale'] = self.context['event'].settings.locale order = Order(event=self.context['event'], **validated_data) order.set_expires(subevents=[p.get('subevent') for p in positions_data]) order.meta_info = "{}" order.total = Decimal('0.00') if simulate: order = WrappedModel(order) order.last_modified = now() order.code = 'PREVIEW' else: order.save() if ia: if not simulate: ia.order = order ia.save() else: order.invoice_address = ia ia.last_modified = now() pos_map = {} for pos_data in positions_data: answers_data = pos_data.pop('answers', []) addon_to = pos_data.pop('addon_to', None) attendee_name = pos_data.pop('attendee_name', '') if attendee_name and not pos_data.get('attendee_name_parts'): pos_data['attendee_name_parts'] = { '_legacy': attendee_name } pos = OrderPosition(**pos_data) if simulate: pos.order = order._wrapped else: pos.order = order if addon_to: if simulate: pos.addon_to = pos_map[addon_to]._wrapped else: pos.addon_to = pos_map[addon_to] if pos.price is None: price = get_price( item=pos.item, variation=pos.variation, voucher=pos.voucher, custom_price=None, subevent=pos.subevent, addon_to=pos.addon_to, invoice_address=ia, ) pos.price = price.gross pos.tax_rate = price.rate pos.tax_value = price.tax pos.tax_rule = pos.item.tax_rule else: pos._calculate_tax() pos.price_before_voucher = get_price( item=pos.item, variation=pos.variation, voucher=None, custom_price=None, subevent=pos.subevent, addon_to=pos.addon_to, invoice_address=ia, ).gross if simulate: pos = WrappedModel(pos) pos.id = 0 answers = [] for answ_data in answers_data: options = answ_data.pop('options', []) answ = WrappedModel(QuestionAnswer(**answ_data)) answ.options = WrappedList(options) answers.append(answ) pos.answers = answers pos.pseudonymization_id = "PREVIEW" else: if pos.voucher: Voucher.objects.filter(pk=pos.voucher.pk).update(redeemed=F('redeemed') + 1) pos.save() for answ_data in answers_data: options = answ_data.pop('options', []) if isinstance(answ_data['answer'], File): an = answ_data.pop('answer') answ = pos.answers.create(**answ_data, answer='') answ.file.save(an.name, an, save=False) answ.answer = 'file://' + answ.file.name answ.save() else: answ = pos.answers.create(**answ_data) answ.options.add(*options) pos_map[pos.positionid] = pos if not simulate: for cp in delete_cps: cp.delete() order.total = sum([p.price for p in pos_map.values()]) fees = [] for fee_data in fees_data: is_percentage = fee_data.pop('_treat_value_as_percentage', False) if is_percentage: fee_data['value'] = round_decimal(order.total * (fee_data['value'] / Decimal('100.00')), self.context['event'].currency) is_split_taxes = fee_data.pop('_split_taxes_like_products', False) if is_split_taxes: d = defaultdict(lambda: Decimal('0.00')) trz = TaxRule.zero() for p in pos_map.values(): tr = p.tax_rule d[tr] += p.price - p.tax_value base_values = sorted([tuple(t) for t in d.items()], key=lambda t: (t[0] or trz).rate) sum_base = sum(t[1] for t in base_values) fee_values = [(t[0], round_decimal(fee_data['value'] * t[1] / sum_base, self.context['event'].currency)) for t in base_values] sum_fee = sum(t[1] for t in fee_values) # If there are rounding differences, we fix them up, but always leaning to the benefit of the tax # authorities if sum_fee > fee_data['value']: fee_values[0] = (fee_values[0][0], fee_values[0][1] + (fee_data['value'] - sum_fee)) elif sum_fee < fee_data['value']: fee_values[-1] = (fee_values[-1][0], fee_values[-1][1] + (fee_data['value'] - sum_fee)) for tr, val in fee_values: fee_data['tax_rule'] = tr fee_data['value'] = val f = OrderFee(**fee_data) f.order = order._wrapped if simulate else order f._calculate_tax() fees.append(f) if not simulate: f.save() else: f = OrderFee(**fee_data) f.order = order._wrapped if simulate else order f._calculate_tax() fees.append(f) if not simulate: f.save() order.total += sum([f.value for f in fees]) if simulate: order.fees = fees order.positions = pos_map.values() return order # ignore payments else: order.save(update_fields=['total']) if order.total == Decimal('0.00') and validated_data.get('status') == Order.STATUS_PAID and not payment_provider: payment_provider = 'free' if order.total == Decimal('0.00') and validated_data.get('status') != Order.STATUS_PAID: order.status = Order.STATUS_PAID order.save() order.payments.create( amount=order.total, provider='free', state=OrderPayment.PAYMENT_STATE_CONFIRMED, payment_date=now() ) elif payment_provider == "free" and order.total != Decimal('0.00'): raise ValidationError('You cannot use the "free" payment provider for non-free orders.') elif validated_data.get('status') == Order.STATUS_PAID: if not payment_provider: raise ValidationError('You cannot create a paid order without a payment provider.') order.payments.create( amount=order.total, provider=payment_provider, info=payment_info, payment_date=payment_date, state=OrderPayment.PAYMENT_STATE_CONFIRMED ) elif payment_provider: order.payments.create( amount=order.total, provider=payment_provider, info=payment_info, state=OrderPayment.PAYMENT_STATE_CREATED ) return order class LinePositionField(serializers.IntegerField): """ Internally, the position field is stored starting at 0, but for the API, starting at 1 makes it more consistent with other models """ def to_representation(self, value): return super().to_representation(value) + 1 def to_internal_value(self, data): return super().to_internal_value(data) - 1 class InlineInvoiceLineSerializer(I18nAwareModelSerializer): position = LinePositionField(read_only=True) class Meta: model = InvoiceLine fields = ('position', 'description', 'item', 'variation', 'attendee_name', 'event_date_from', 'event_date_to', 'gross_value', 'tax_value', 'tax_rate', 'tax_name') class InvoiceSerializer(I18nAwareModelSerializer): order = serializers.SlugRelatedField(slug_field='code', read_only=True) refers = serializers.SlugRelatedField(slug_field='full_invoice_no', read_only=True) lines = InlineInvoiceLineSerializer(many=True) invoice_to_country = CountryField() invoice_from_country = CountryField() class Meta: model = Invoice fields = ('order', 'number', 'is_cancellation', 'invoice_from', 'invoice_from_name', 'invoice_from_zipcode', 'invoice_from_city', 'invoice_from_country', 'invoice_from_tax_id', 'invoice_from_vat_id', 'invoice_to', 'invoice_to_company', 'invoice_to_name', 'invoice_to_street', 'invoice_to_zipcode', 'invoice_to_city', 'invoice_to_state', 'invoice_to_country', 'invoice_to_vat_id', 'invoice_to_beneficiary', 'custom_field', 'date', 'refers', 'locale', 'introductory_text', 'additional_text', 'payment_provider_text', 'footer_text', 'lines', 'foreign_currency_display', 'foreign_currency_rate', 'foreign_currency_rate_date', 'internal_reference') class OrderPaymentCreateSerializer(I18nAwareModelSerializer): provider = serializers.CharField(required=True, allow_null=False, allow_blank=False) info = CompatibleJSONField(required=False) class Meta: model = OrderPayment fields = ('state', 'amount', 'payment_date', 'provider', 'info') def create(self, validated_data): order = OrderPayment(order=self.context['order'], **validated_data) order.save() return order class OrderRefundCreateSerializer(I18nAwareModelSerializer): payment = serializers.IntegerField(required=False, allow_null=True) provider = serializers.CharField(required=True, allow_null=False, allow_blank=False) info = CompatibleJSONField(required=False) class Meta: model = OrderRefund fields = ('state', 'source', 'amount', 'payment', 'execution_date', 'provider', 'info', 'comment') def create(self, validated_data): pid = validated_data.pop('payment', None) if pid: try: p = self.context['order'].payments.get(local_id=pid) except OrderPayment.DoesNotExist: raise ValidationError('Unknown payment ID.') else: p = None order = OrderRefund(order=self.context['order'], payment=p, **validated_data) order.save() return order class RevokedTicketSecretSerializer(I18nAwareModelSerializer): class Meta: model = RevokedTicketSecret fields = ('id', 'secret', 'created')

43.63567

160

0.571908

import json from collections import Counter, defaultdict from decimal import Decimal import pycountry from django.core.files import File from django.db.models import F, Q from django.utils.timezone import now from django.utils.translation import gettext_lazy from django_countries.fields import Country from rest_framework import serializers from rest_framework.exceptions import ValidationError from rest_framework.relations import SlugRelatedField from rest_framework.reverse import reverse from pretix.api.serializers.event import SubEventSerializer from pretix.api.serializers.i18n import I18nAwareModelSerializer from pretix.api.serializers.item import ( InlineItemVariationSerializer, ItemSerializer, ) from pretix.base.channels import get_all_sales_channels from pretix.base.decimal import round_decimal from pretix.base.i18n import language from pretix.base.models import ( CachedFile, Checkin, Invoice, InvoiceAddress, InvoiceLine, Item, ItemVariation, Order, OrderPosition, Question, QuestionAnswer, Seat, SubEvent, TaxRule, Voucher, ) from pretix.base.models.orders import ( CartPosition, OrderFee, OrderPayment, OrderRefund, RevokedTicketSecret, ) from pretix.base.pdf import get_images, get_variables from pretix.base.services.cart import error_messages from pretix.base.services.locking import NoLockManager from pretix.base.services.pricing import get_price from pretix.base.settings import COUNTRIES_WITH_STATE_IN_ADDRESS from pretix.base.signals import register_ticket_outputs from pretix.multidomain.urlreverse import build_absolute_uri class CompatibleCountryField(serializers.Field): def to_internal_value(self, data): return {self.field_name: Country(data)} def to_representation(self, instance: InvoiceAddress): if instance.country: return str(instance.country) elif hasattr(instance, 'country_old'): return instance.country_old class CountryField(serializers.Field): def to_internal_value(self, data): return {self.field_name: Country(data)} def to_representation(self, src): return str(src) if src else None class InvoiceAddressSerializer(I18nAwareModelSerializer): country = CompatibleCountryField(source='*') name = serializers.CharField(required=False) class Meta: model = InvoiceAddress fields = ('last_modified', 'is_business', 'company', 'name', 'name_parts', 'street', 'zipcode', 'city', 'country', 'state', 'vat_id', 'vat_id_validated', 'internal_reference') read_only_fields = ('last_modified',) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) for v in self.fields.values(): v.required = False v.allow_blank = True def validate(self, data): if data.get('name') and data.get('name_parts'): raise ValidationError( {'name': ['Do not specify name if you specified name_parts.']} ) if data.get('name_parts') and '_scheme' not in data.get('name_parts'): data['name_parts']['_scheme'] = self.context['request'].event.settings.name_scheme if data.get('country'): if not pycountry.countries.get(alpha_2=data.get('country').code): raise ValidationError( {'country': ['Invalid country code.']} ) if data.get('state'): cc = str(data.get('country') or self.instance.country or '') if cc not in COUNTRIES_WITH_STATE_IN_ADDRESS: raise ValidationError( {'state': ['States are not supported in country "{}".'.format(cc)]} ) if not pycountry.subdivisions.get(code=cc + '-' + data.get('state')): raise ValidationError( {'state': ['"{}" is not a known subdivision of the country "{}".'.format(data.get('state'), cc)]} ) return data class AnswerQuestionIdentifierField(serializers.Field): def to_representation(self, instance: QuestionAnswer): return instance.question.identifier class AnswerQuestionOptionsIdentifierField(serializers.Field): def to_representation(self, instance: QuestionAnswer): if isinstance(instance, WrappedModel) or instance.pk: return [o.identifier for o in instance.options.all()] return [] class InlineSeatSerializer(I18nAwareModelSerializer): class Meta: model = Seat fields = ('id', 'name', 'seat_guid') class AnswerSerializer(I18nAwareModelSerializer): question_identifier = AnswerQuestionIdentifierField(source='*', read_only=True) option_identifiers = AnswerQuestionOptionsIdentifierField(source='*', read_only=True) def to_representation(self, instance): r = super().to_representation(instance) if r['answer'].startswith('file://') and instance.orderposition: r['answer'] = reverse('api-v1:orderposition-answer', kwargs={ 'organizer': instance.orderposition.order.event.organizer.slug, 'event': instance.orderposition.order.event.slug, 'pk': instance.orderposition.pk, 'question': instance.question_id, }, request=self.context['request']) return r class Meta: model = QuestionAnswer fields = ('question', 'answer', 'question_identifier', 'options', 'option_identifiers') def validate_question(self, q): if q.event != self.context['event']: raise ValidationError( 'The specified question does not belong to this event.' ) return q def _handle_file_upload(self, data): if data['answer'] == 'file:keep': return data try: ao = self.context["request"].user or self.context["request"].auth cf = CachedFile.objects.get( session_key=f'api-upload-{str(type(ao))}-{ao.pk}', file__isnull=False, pk=data['answer'][len("file:"):], ) except (ValidationError, IndexError): raise ValidationError('The submitted file ID "{fid}" was not found.'.format(fid=data)) except CachedFile.DoesNotExist: raise ValidationError('The submitted file ID "{fid}" was not found.'.format(fid=data)) allowed_types = ( 'image/png', 'image/jpeg', 'image/gif', 'application/pdf' ) if cf.type not in allowed_types: raise ValidationError('The submitted file "{fid}" has a file type that is not allowed in this field.'.format(fid=data)) if cf.file.size > 10 * 1024 * 1024: raise ValidationError('The submitted file "{fid}" is too large to be used in this field.'.format(fid=data)) data['options'] = [] data['answer'] = cf.file return data def validate(self, data): if data.get('question').type == Question.TYPE_FILE: return self._handle_file_upload(data) elif data.get('question').type in (Question.TYPE_CHOICE, Question.TYPE_CHOICE_MULTIPLE): if not data.get('options'): raise ValidationError( 'You need to specify options if the question is of a choice type.' ) if data.get('question').type == Question.TYPE_CHOICE and len(data.get('options')) > 1: raise ValidationError( 'You can specify at most one option for this question.' ) for o in data.get('options'): if o.question_id != data.get('question').pk: raise ValidationError( 'The specified option does not belong to this question.' ) data['answer'] = ", ".join([str(o) for o in data.get('options')]) else: if data.get('options'): raise ValidationError( 'You should not specify options if the question is not of a choice type.' ) if data.get('question').type == Question.TYPE_BOOLEAN: if data.get('answer') in ['true', 'True', '1', 'TRUE']: data['answer'] = 'True' elif data.get('answer') in ['false', 'False', '0', 'FALSE']: data['answer'] = 'False' else: raise ValidationError( 'Please specify "true" or "false" for boolean questions.' ) elif data.get('question').type == Question.TYPE_NUMBER: serializers.DecimalField( max_digits=50, decimal_places=25 ).to_internal_value(data.get('answer')) elif data.get('question').type == Question.TYPE_DATE: data['answer'] = serializers.DateField().to_internal_value(data.get('answer')) elif data.get('question').type == Question.TYPE_TIME: data['answer'] = serializers.TimeField().to_internal_value(data.get('answer')) elif data.get('question').type == Question.TYPE_DATETIME: data['answer'] = serializers.DateTimeField().to_internal_value(data.get('answer')) return data class CheckinSerializer(I18nAwareModelSerializer): class Meta: model = Checkin fields = ('id', 'datetime', 'list', 'auto_checked_in', 'type') class OrderDownloadsField(serializers.Field): def to_representation(self, instance: Order): if instance.status != Order.STATUS_PAID: if instance.status != Order.STATUS_PENDING or instance.require_approval or not instance.event.settings.ticket_download_pending: return [] request = self.context['request'] res = [] responses = register_ticket_outputs.send(instance.event) for receiver, response in responses: provider = response(instance.event) if provider.is_enabled: res.append({ 'output': provider.identifier, 'url': reverse('api-v1:order-download', kwargs={ 'organizer': instance.event.organizer.slug, 'event': instance.event.slug, 'code': instance.code, 'output': provider.identifier, }, request=request) }) return res class PositionDownloadsField(serializers.Field): def to_representation(self, instance: OrderPosition): if instance.order.status != Order.STATUS_PAID: if instance.order.status != Order.STATUS_PENDING or instance.order.require_approval or not instance.order.event.settings.ticket_download_pending: return [] if not instance.generate_ticket: return [] request = self.context['request'] res = [] responses = register_ticket_outputs.send(instance.order.event) for receiver, response in responses: provider = response(instance.order.event) if provider.is_enabled: res.append({ 'output': provider.identifier, 'url': reverse('api-v1:orderposition-download', kwargs={ 'organizer': instance.order.event.organizer.slug, 'event': instance.order.event.slug, 'pk': instance.pk, 'output': provider.identifier, }, request=request) }) return res class PdfDataSerializer(serializers.Field): def to_representation(self, instance: OrderPosition): res = {} ev = instance.subevent or instance.order.event with language(instance.order.locale, instance.order.event.settings.region): if 'vars' not in self.context: self.context['vars'] = get_variables(self.context['request'].event) if 'vars_images' not in self.context: self.context['vars_images'] = get_images(self.context['request'].event) for k, f in self.context['vars'].items(): res[k] = f['evaluate'](instance, instance.order, ev) if not hasattr(ev, '_cached_meta_data'): ev._cached_meta_data = ev.meta_data for k, v in ev._cached_meta_data.items(): res['meta:' + k] = v if not hasattr(instance.item, '_cached_meta_data'): instance.item._cached_meta_data = instance.item.meta_data for k, v in instance.item._cached_meta_data.items(): res['itemmeta:' + k] = v res['images'] = {} for k, f in self.context['vars_images'].items(): if 'etag' in f: has_image = etag = f['etag'](instance, instance.order, ev) else: has_image = f['etag'](instance, instance.order, ev) etag = None if has_image: url = reverse('api-v1:orderposition-pdf_image', kwargs={ 'organizer': instance.order.event.organizer.slug, 'event': instance.order.event.slug, 'pk': instance.pk, 'key': k, }, request=self.context['request']) if etag: url += f'#etag={etag}' res['images'][k] = url else: res['images'][k] = None return res class OrderPositionSerializer(I18nAwareModelSerializer): checkins = CheckinSerializer(many=True, read_only=True) answers = AnswerSerializer(many=True) downloads = PositionDownloadsField(source='*', read_only=True) order = serializers.SlugRelatedField(slug_field='code', read_only=True) pdf_data = PdfDataSerializer(source='*', read_only=True) seat = InlineSeatSerializer(read_only=True) country = CompatibleCountryField(source='*') attendee_name = serializers.CharField(required=False) class Meta: model = OrderPosition fields = ('id', 'order', 'positionid', 'item', 'variation', 'price', 'attendee_name', 'attendee_name_parts', 'company', 'street', 'zipcode', 'city', 'country', 'state', 'attendee_email', 'voucher', 'tax_rate', 'tax_value', 'secret', 'addon_to', 'subevent', 'checkins', 'downloads', 'answers', 'tax_rule', 'pseudonymization_id', 'pdf_data', 'seat', 'canceled') read_only_fields = ( 'id', 'order', 'positionid', 'item', 'variation', 'price', 'voucher', 'tax_rate', 'tax_value', 'secret', 'addon_to', 'subevent', 'checkins', 'downloads', 'answers', 'tax_rule', 'pseudonymization_id', 'pdf_data', 'seat', 'canceled' ) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) request = self.context.get('request') if request and (not request.query_params.get('pdf_data', 'false') == 'true' or 'can_view_orders' not in request.eventpermset): self.fields.pop('pdf_data', None) def validate(self, data): if data.get('attendee_name') and data.get('attendee_name_parts'): raise ValidationError( {'attendee_name': ['Do not specify attendee_name if you specified attendee_name_parts.']} ) if data.get('attendee_name_parts') and '_scheme' not in data.get('attendee_name_parts'): data['attendee_name_parts']['_scheme'] = self.context['request'].event.settings.name_scheme if data.get('country'): if not pycountry.countries.get(alpha_2=data.get('country').code): raise ValidationError( {'country': ['Invalid country code.']} ) if data.get('state'): cc = str(data.get('country') or self.instance.country or '') if cc not in COUNTRIES_WITH_STATE_IN_ADDRESS: raise ValidationError( {'state': ['States are not supported in country "{}".'.format(cc)]} ) if not pycountry.subdivisions.get(code=cc + '-' + data.get('state')): raise ValidationError( {'state': ['"{}" is not a known subdivision of the country "{}".'.format(data.get('state'), cc)]} ) return data def update(self, instance, validated_data): # (hopefully), we'll be extra careful here and be explicit about the model fields we update. update_fields = [ 'attendee_name_parts', 'company', 'street', 'zipcode', 'city', 'country', 'state', 'attendee_email', ] answers_data = validated_data.pop('answers', None) name = validated_data.pop('attendee_name', '') if name and not validated_data.get('attendee_name_parts'): validated_data['attendee_name_parts'] = { '_legacy': name } for attr, value in validated_data.items(): if attr in update_fields: setattr(instance, attr, value) instance.save(update_fields=update_fields) if answers_data is not None: qs_seen = set() answercache = { a.question_id: a for a in instance.answers.all() } for answ_data in answers_data: options = answ_data.pop('options', []) if answ_data['question'].pk in qs_seen: raise ValidationError(f'Question {answ_data["question"]} was sent twice.') if answ_data['question'].pk in answercache: a = answercache[answ_data['question'].pk] if isinstance(answ_data['answer'], File): a.file.save(answ_data['answer'].name, answ_data['answer'], save=False) a.answer = 'file://' + a.file.name elif a.answer.startswith('file://') and answ_data['answer'] == "file:keep": pass else: for attr, value in answ_data.items(): setattr(a, attr, value) a.save() else: if isinstance(answ_data['answer'], File): an = answ_data.pop('answer') a = instance.answers.create(**answ_data, answer='') a.file.save(an.name, an, save=False) a.answer = 'file://' + a.file.name a.save() else: a = instance.answers.create(**answ_data) a.options.set(options) qs_seen.add(a.question_id) for qid, a in answercache.items(): if qid not in qs_seen: a.delete() return instance class RequireAttentionField(serializers.Field): def to_representation(self, instance: OrderPosition): return instance.order.checkin_attention or instance.item.checkin_attention class AttendeeNameField(serializers.Field): def to_representation(self, instance: OrderPosition): an = instance.attendee_name if not an: if instance.addon_to_id: an = instance.addon_to.attendee_name if not an: try: an = instance.order.invoice_address.name except InvoiceAddress.DoesNotExist: pass return an class AttendeeNamePartsField(serializers.Field): def to_representation(self, instance: OrderPosition): an = instance.attendee_name p = instance.attendee_name_parts if not an: if instance.addon_to_id: an = instance.addon_to.attendee_name p = instance.addon_to.attendee_name_parts if not an: try: p = instance.order.invoice_address.name_parts except InvoiceAddress.DoesNotExist: pass return p class CheckinListOrderPositionSerializer(OrderPositionSerializer): require_attention = RequireAttentionField(source='*') attendee_name = AttendeeNameField(source='*') attendee_name_parts = AttendeeNamePartsField(source='*') order__status = serializers.SlugRelatedField(read_only=True, slug_field='status', source='order') class Meta: model = OrderPosition fields = ('id', 'order', 'positionid', 'item', 'variation', 'price', 'attendee_name', 'attendee_name_parts', 'company', 'street', 'zipcode', 'city', 'country', 'state', 'attendee_email', 'voucher', 'tax_rate', 'tax_value', 'secret', 'addon_to', 'subevent', 'checkins', 'downloads', 'answers', 'tax_rule', 'pseudonymization_id', 'pdf_data', 'seat', 'require_attention', 'order__status') def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if 'subevent' in self.context['request'].query_params.getlist('expand'): self.fields['subevent'] = SubEventSerializer(read_only=True) if 'item' in self.context['request'].query_params.getlist('expand'): self.fields['item'] = ItemSerializer(read_only=True) if 'variation' in self.context['request'].query_params.getlist('expand'): self.fields['variation'] = InlineItemVariationSerializer(read_only=True) class OrderPaymentTypeField(serializers.Field): def to_representation(self, instance: Order): t = None for p in instance.payments.all(): t = p.provider return t class OrderPaymentDateField(serializers.DateField): def to_representation(self, instance: Order): t = None for p in instance.payments.all(): t = p.payment_date or t if t: return super().to_representation(t.date()) class OrderFeeSerializer(I18nAwareModelSerializer): class Meta: model = OrderFee fields = ('fee_type', 'value', 'description', 'internal_type', 'tax_rate', 'tax_value', 'tax_rule', 'canceled') class PaymentURLField(serializers.URLField): def to_representation(self, instance: OrderPayment): if instance.state != OrderPayment.PAYMENT_STATE_CREATED: return None return build_absolute_uri(self.context['event'], 'presale:event.order.pay', kwargs={ 'order': instance.order.code, 'secret': instance.order.secret, 'payment': instance.pk, }) class PaymentDetailsField(serializers.Field): def to_representation(self, value: OrderPayment): pp = value.payment_provider if not pp: return {} return pp.api_payment_details(value) class OrderPaymentSerializer(I18nAwareModelSerializer): payment_url = PaymentURLField(source='*', allow_null=True, read_only=True) details = PaymentDetailsField(source='*', allow_null=True, read_only=True) class Meta: model = OrderPayment fields = ('local_id', 'state', 'amount', 'created', 'payment_date', 'provider', 'payment_url', 'details') class OrderRefundSerializer(I18nAwareModelSerializer): payment = SlugRelatedField(slug_field='local_id', read_only=True) class Meta: model = OrderRefund fields = ('local_id', 'state', 'source', 'amount', 'payment', 'created', 'execution_date', 'comment', 'provider') class OrderURLField(serializers.URLField): def to_representation(self, instance: Order): return build_absolute_uri(self.context['event'], 'presale:event.order', kwargs={ 'order': instance.code, 'secret': instance.secret, }) class OrderSerializer(I18nAwareModelSerializer): invoice_address = InvoiceAddressSerializer(allow_null=True) positions = OrderPositionSerializer(many=True, read_only=True) fees = OrderFeeSerializer(many=True, read_only=True) downloads = OrderDownloadsField(source='*', read_only=True) payments = OrderPaymentSerializer(many=True, read_only=True) refunds = OrderRefundSerializer(many=True, read_only=True) payment_date = OrderPaymentDateField(source='*', read_only=True) payment_provider = OrderPaymentTypeField(source='*', read_only=True) url = OrderURLField(source='*', read_only=True) class Meta: model = Order fields = ( 'code', 'status', 'testmode', 'secret', 'email', 'phone', 'locale', 'datetime', 'expires', 'payment_date', 'payment_provider', 'fees', 'total', 'comment', 'invoice_address', 'positions', 'downloads', 'checkin_attention', 'last_modified', 'payments', 'refunds', 'require_approval', 'sales_channel', 'url' ) read_only_fields = ( 'code', 'status', 'testmode', 'secret', 'datetime', 'expires', 'payment_date', 'payment_provider', 'fees', 'total', 'positions', 'downloads', 'last_modified', 'payments', 'refunds', 'require_approval', 'sales_channel' ) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if not self.context['request'].query_params.get('pdf_data', 'false') == 'true': self.fields['positions'].child.fields.pop('pdf_data', None) for exclude_field in self.context['request'].query_params.getlist('exclude'): p = exclude_field.split('.') if p[0] in self.fields: if len(p) == 1: del self.fields[p[0]] elif len(p) == 2: self.fields[p[0]].child.fields.pop(p[1]) def validate_locale(self, l): if l not in set(k for k in self.instance.event.settings.locales): raise ValidationError('"{}" is not a supported locale for this event.'.format(l)) return l def update(self, instance, validated_data): # (hopefully), we'll be extra careful here and be explicit about the model fields we update. update_fields = ['comment', 'checkin_attention', 'email', 'locale', 'phone'] if 'invoice_address' in validated_data: iadata = validated_data.pop('invoice_address') if not iadata: try: instance.invoice_address.delete() except InvoiceAddress.DoesNotExist: pass else: name = iadata.pop('name', '') if name and not iadata.get('name_parts'): iadata['name_parts'] = { '_legacy': name } try: ia = instance.invoice_address if iadata.get('vat_id') != ia.vat_id and 'vat_id_validated' not in iadata: ia.vat_id_validated = False self.fields['invoice_address'].update(ia, iadata) except InvoiceAddress.DoesNotExist: InvoiceAddress.objects.create(order=instance, **iadata) for attr, value in validated_data.items(): if attr in update_fields: setattr(instance, attr, value) instance.save(update_fields=update_fields) return instance class AnswerQuestionOptionsField(serializers.Field): def to_representation(self, instance: QuestionAnswer): return [o.pk for o in instance.options.all()] class SimulatedAnswerSerializer(AnswerSerializer): options = AnswerQuestionOptionsField(read_only=True, source='*') class SimulatedOrderPositionSerializer(OrderPositionSerializer): answers = SimulatedAnswerSerializer(many=True) addon_to = serializers.SlugRelatedField(read_only=True, slug_field='positionid') class SimulatedOrderSerializer(OrderSerializer): positions = SimulatedOrderPositionSerializer(many=True, read_only=True) class PriceCalcSerializer(serializers.Serializer): item = serializers.PrimaryKeyRelatedField(queryset=Item.objects.none(), required=False, allow_null=True) variation = serializers.PrimaryKeyRelatedField(queryset=ItemVariation.objects.none(), required=False, allow_null=True) subevent = serializers.PrimaryKeyRelatedField(queryset=SubEvent.objects.none(), required=False, allow_null=True) tax_rule = serializers.PrimaryKeyRelatedField(queryset=TaxRule.objects.none(), required=False, allow_null=True) locale = serializers.CharField(allow_null=True, required=False) def __init__(self, *args, **kwargs): event = kwargs.pop('event') super().__init__(*args, **kwargs) self.fields['item'].queryset = event.items.all() self.fields['tax_rule'].queryset = event.tax_rules.all() self.fields['variation'].queryset = ItemVariation.objects.filter(item__event=event) if event.has_subevents: self.fields['subevent'].queryset = event.subevents.all() else: del self.fields['subevent'] class AnswerCreateSerializer(AnswerSerializer): pass class OrderFeeCreateSerializer(I18nAwareModelSerializer): _treat_value_as_percentage = serializers.BooleanField(default=False, required=False) _split_taxes_like_products = serializers.BooleanField(default=False, required=False) class Meta: model = OrderFee fields = ('fee_type', 'value', 'description', 'internal_type', 'tax_rule', '_treat_value_as_percentage', '_split_taxes_like_products') def validate_tax_rule(self, tr): if tr and tr.event != self.context['event']: raise ValidationError( 'The specified tax rate does not belong to this event.' ) return tr class OrderPositionCreateSerializer(I18nAwareModelSerializer): answers = AnswerCreateSerializer(many=True, required=False) addon_to = serializers.IntegerField(required=False, allow_null=True) secret = serializers.CharField(required=False) attendee_name = serializers.CharField(required=False, allow_null=True) seat = serializers.CharField(required=False, allow_null=True) price = serializers.DecimalField(required=False, allow_null=True, decimal_places=2, max_digits=10) voucher = serializers.SlugRelatedField(slug_field='code', queryset=Voucher.objects.none(), required=False, allow_null=True) country = CompatibleCountryField(source='*') class Meta: model = OrderPosition fields = ('positionid', 'item', 'variation', 'price', 'attendee_name', 'attendee_name_parts', 'attendee_email', 'company', 'street', 'zipcode', 'city', 'country', 'state', 'secret', 'addon_to', 'subevent', 'answers', 'seat', 'voucher') def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) for k, v in self.fields.items(): if k in ('company', 'street', 'zipcode', 'city', 'country', 'state'): v.required = False v.allow_blank = True v.allow_null = True def validate_secret(self, secret): if secret and OrderPosition.all.filter(order__event=self.context['event'], secret=secret).exists(): raise ValidationError( 'You cannot assign a position secret that already exists.' ) return secret def validate_item(self, item): if item.event != self.context['event']: raise ValidationError( 'The specified item does not belong to this event.' ) if not item.active: raise ValidationError( 'The specified item is not active.' ) return item def validate_subevent(self, subevent): if self.context['event'].has_subevents: if not subevent: raise ValidationError( 'You need to set a subevent.' ) if subevent.event != self.context['event']: raise ValidationError( 'The specified subevent does not belong to this event.' ) elif subevent: raise ValidationError( 'You cannot set a subevent for this event.' ) return subevent def validate(self, data): if data.get('item'): if data.get('item').has_variations: if not data.get('variation'): raise ValidationError({'variation': ['You should specify a variation for this item.']}) else: if data.get('variation').item != data.get('item'): raise ValidationError( {'variation': ['The specified variation does not belong to the specified item.']} ) elif data.get('variation'): raise ValidationError( {'variation': ['You cannot specify a variation for this item.']} ) if data.get('attendee_name') and data.get('attendee_name_parts'): raise ValidationError( {'attendee_name': ['Do not specify attendee_name if you specified attendee_name_parts.']} ) if data.get('attendee_name_parts') and '_scheme' not in data.get('attendee_name_parts'): data['attendee_name_parts']['_scheme'] = self.context['request'].event.settings.name_scheme if data.get('country'): if not pycountry.countries.get(alpha_2=data.get('country').code): raise ValidationError( {'country': ['Invalid country code.']} ) if data.get('state'): cc = str(data.get('country') or self.instance.country or '') if cc not in COUNTRIES_WITH_STATE_IN_ADDRESS: raise ValidationError( {'state': ['States are not supported in country "{}".'.format(cc)]} ) if not pycountry.subdivisions.get(code=cc + '-' + data.get('state')): raise ValidationError( {'state': ['"{}" is not a known subdivision of the country "{}".'.format(data.get('state'), cc)]} ) return data class CompatibleJSONField(serializers.JSONField): def to_internal_value(self, data): try: return json.dumps(data) except (TypeError, ValueError): self.fail('invalid') def to_representation(self, value): if value: return json.loads(value) return value class WrappedList: def __init__(self, data): self._data = data def all(self): return self._data class WrappedModel: def __init__(self, model): self._wrapped = model def __getattr__(self, item): return getattr(self._wrapped, item) def save(self, *args, **kwargs): raise NotImplementedError def delete(self, *args, **kwargs): raise NotImplementedError class OrderCreateSerializer(I18nAwareModelSerializer): invoice_address = InvoiceAddressSerializer(required=False) positions = OrderPositionCreateSerializer(many=True, required=True) fees = OrderFeeCreateSerializer(many=True, required=False) status = serializers.ChoiceField(choices=( ('n', Order.STATUS_PENDING), ('p', Order.STATUS_PAID), ), default='n', required=False) code = serializers.CharField( required=False, max_length=16, min_length=5 ) comment = serializers.CharField(required=False, allow_blank=True) payment_provider = serializers.CharField(required=False, allow_null=True) payment_info = CompatibleJSONField(required=False) consume_carts = serializers.ListField(child=serializers.CharField(), required=False) force = serializers.BooleanField(default=False, required=False) payment_date = serializers.DateTimeField(required=False, allow_null=True) send_email = serializers.BooleanField(default=False, required=False) simulate = serializers.BooleanField(default=False, required=False) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fields['positions'].child.fields['voucher'].queryset = self.context['event'].vouchers.all() class Meta: model = Order fields = ('code', 'status', 'testmode', 'email', 'phone', 'locale', 'payment_provider', 'fees', 'comment', 'sales_channel', 'invoice_address', 'positions', 'checkin_attention', 'payment_info', 'payment_date', 'consume_carts', 'force', 'send_email', 'simulate') def validate_payment_provider(self, pp): if pp is None: return None if pp not in self.context['event'].get_payment_providers(): raise ValidationError('The given payment provider is not known.') return pp def validate_sales_channel(self, channel): if channel not in get_all_sales_channels(): raise ValidationError('Unknown sales channel.') return channel def validate_code(self, code): if code and Order.objects.filter(event__organizer=self.context['event'].organizer, code=code).exists(): raise ValidationError( 'This order code is already in use.' ) if any(c not in 'ABCDEFGHJKLMNPQRSTUVWXYZ1234567890' for c in code): raise ValidationError( 'This order code contains invalid characters.' ) return code def validate_positions(self, data): if not data: raise ValidationError( 'An order cannot be empty.' ) errs = [{} for p in data] if any([p.get('positionid') for p in data]): if not all([p.get('positionid') for p in data]): for i, p in enumerate(data): if not p.get('positionid'): errs[i]['positionid'] = [ 'If you set position IDs manually, you need to do so for all positions.' ] raise ValidationError(errs) last_non_add_on = None last_posid = 0 for i, p in enumerate(data): if p['positionid'] != last_posid + 1: errs[i]['positionid'] = [ 'Position IDs need to be consecutive.' ] if p.get('addon_to') and p['addon_to'] != last_non_add_on: errs[i]['addon_to'] = [ "If you set addon_to, you need to make sure that the referenced " "position ID exists and is transmitted directly before its add-ons." ] if not p.get('addon_to'): last_non_add_on = p['positionid'] last_posid = p['positionid'] elif any([p.get('addon_to') for p in data]): errs = [ {'positionid': ["If you set addon_to on any position, you need to specify position IDs manually."]} for p in data ] else: for i, p in enumerate(data): p['positionid'] = i + 1 if any(errs): raise ValidationError(errs) return data def validate_testmode(self, testmode): if 'sales_channel' in self.initial_data: try: sales_channel = get_all_sales_channels()[self.initial_data['sales_channel']] if testmode and not sales_channel.testmode_supported: raise ValidationError('This sales channel does not provide support for test mode.') except KeyError: pass return testmode def create(self, validated_data): fees_data = validated_data.pop('fees') if 'fees' in validated_data else [] positions_data = validated_data.pop('positions') if 'positions' in validated_data else [] payment_provider = validated_data.pop('payment_provider', None) payment_info = validated_data.pop('payment_info', '{}') payment_date = validated_data.pop('payment_date', now()) force = validated_data.pop('force', False) simulate = validated_data.pop('simulate', False) self._send_mail = validated_data.pop('send_email', False) if 'invoice_address' in validated_data: iadata = validated_data.pop('invoice_address') name = iadata.pop('name', '') if name and not iadata.get('name_parts'): iadata['name_parts'] = { '_legacy': name } ia = InvoiceAddress(**iadata) else: ia = None lockfn = self.context['event'].lock if simulate: lockfn = NoLockManager with lockfn() as now_dt: free_seats = set() seats_seen = set() consume_carts = validated_data.pop('consume_carts', []) delete_cps = [] quota_avail_cache = {} v_budget = {} voucher_usage = Counter() if consume_carts: for cp in CartPosition.objects.filter( event=self.context['event'], cart_id__in=consume_carts, expires__gt=now() ): quotas = (cp.variation.quotas.filter(subevent=cp.subevent) if cp.variation else cp.item.quotas.filter(subevent=cp.subevent)) for quota in quotas: if quota not in quota_avail_cache: quota_avail_cache[quota] = list(quota.availability()) if quota_avail_cache[quota][1] is not None: quota_avail_cache[quota][1] += 1 if cp.voucher: voucher_usage[cp.voucher] -= 1 if cp.expires > now_dt: if cp.seat: free_seats.add(cp.seat) delete_cps.append(cp) errs = [{} for p in positions_data] for i, pos_data in enumerate(positions_data): if pos_data.get('voucher'): v = pos_data['voucher'] if pos_data.get('addon_to'): errs[i]['voucher'] = ['Vouchers are currently not supported for add-on products.'] continue if not v.applies_to(pos_data['item'], pos_data.get('variation')): errs[i]['voucher'] = [error_messages['voucher_invalid_item']] continue if v.subevent_id and pos_data.get('subevent').pk != v.subevent_id: errs[i]['voucher'] = [error_messages['voucher_invalid_subevent']] continue if v.valid_until is not None and v.valid_until < now_dt: errs[i]['voucher'] = [error_messages['voucher_expired']] continue voucher_usage[v] += 1 if voucher_usage[v] > 0: redeemed_in_carts = CartPosition.objects.filter( Q(voucher=pos_data['voucher']) & Q(event=self.context['event']) & Q(expires__gte=now_dt) ).exclude(pk__in=[cp.pk for cp in delete_cps]) v_avail = v.max_usages - v.redeemed - redeemed_in_carts.count() if v_avail < voucher_usage[v]: errs[i]['voucher'] = [ 'The voucher has already been used the maximum number of times.' ] if v.budget is not None: price = pos_data.get('price') if price is None: price = get_price( item=pos_data.get('item'), variation=pos_data.get('variation'), voucher=v, custom_price=None, subevent=pos_data.get('subevent'), addon_to=pos_data.get('addon_to'), invoice_address=ia, ).gross pbv = get_price( item=pos_data['item'], variation=pos_data.get('variation'), voucher=None, custom_price=None, subevent=pos_data.get('subevent'), addon_to=pos_data.get('addon_to'), invoice_address=ia, ) if v not in v_budget: v_budget[v] = v.budget - v.budget_used() disc = pbv.gross - price if disc > v_budget[v]: new_disc = v_budget[v] v_budget[v] -= new_disc if new_disc == Decimal('0.00') or pos_data.get('price') is not None: errs[i]['voucher'] = [ 'The voucher has a remaining budget of {}, therefore a discount of {} can not be ' 'given.'.format(v_budget[v] + new_disc, disc) ] continue pos_data['price'] = price + (disc - new_disc) else: v_budget[v] -= disc seated = pos_data.get('item').seat_category_mappings.filter(subevent=pos_data.get('subevent')).exists() if pos_data.get('seat'): if not seated: errs[i]['seat'] = ['The specified product does not allow to choose a seat.'] try: seat = self.context['event'].seats.get(seat_guid=pos_data['seat'], subevent=pos_data.get('subevent')) except Seat.DoesNotExist: errs[i]['seat'] = ['The specified seat does not exist.'] else: pos_data['seat'] = seat if (seat not in free_seats and not seat.is_available(sales_channel=validated_data.get('sales_channel', 'web'))) or seat in seats_seen: errs[i]['seat'] = [gettext_lazy('The selected seat "{seat}" is not available.').format(seat=seat.name)] seats_seen.add(seat) elif seated: errs[i]['seat'] = ['The specified product requires to choose a seat.'] if not force: for i, pos_data in enumerate(positions_data): if pos_data.get('voucher'): if pos_data['voucher'].allow_ignore_quota or pos_data['voucher'].block_quota: continue if pos_data.get('subevent'): if pos_data.get('item').pk in pos_data['subevent'].item_overrides and pos_data['subevent'].item_overrides[pos_data['item'].pk].disabled: errs[i]['item'] = [gettext_lazy('The product "{}" is not available on this date.').format( str(pos_data.get('item')) )] if ( pos_data.get('variation') and pos_data['variation'].pk in pos_data['subevent'].var_overrides and pos_data['subevent'].var_overrides[pos_data['variation'].pk].disabled ): errs[i]['item'] = [gettext_lazy('The product "{}" is not available on this date.').format( str(pos_data.get('item')) )] new_quotas = (pos_data.get('variation').quotas.filter(subevent=pos_data.get('subevent')) if pos_data.get('variation') else pos_data.get('item').quotas.filter(subevent=pos_data.get('subevent'))) if len(new_quotas) == 0: errs[i]['item'] = [gettext_lazy('The product "{}" is not assigned to a quota.').format( str(pos_data.get('item')) )] else: for quota in new_quotas: if quota not in quota_avail_cache: quota_avail_cache[quota] = list(quota.availability()) if quota_avail_cache[quota][1] is not None: quota_avail_cache[quota][1] -= 1 if quota_avail_cache[quota][1] < 0: errs[i]['item'] = [ gettext_lazy('There is not enough quota available on quota "{}" to perform the operation.').format( quota.name ) ] if any(errs): raise ValidationError({'positions': errs}) if validated_data.get('locale', None) is None: validated_data['locale'] = self.context['event'].settings.locale order = Order(event=self.context['event'], **validated_data) order.set_expires(subevents=[p.get('subevent') for p in positions_data]) order.meta_info = "{}" order.total = Decimal('0.00') if simulate: order = WrappedModel(order) order.last_modified = now() order.code = 'PREVIEW' else: order.save() if ia: if not simulate: ia.order = order ia.save() else: order.invoice_address = ia ia.last_modified = now() pos_map = {} for pos_data in positions_data: answers_data = pos_data.pop('answers', []) addon_to = pos_data.pop('addon_to', None) attendee_name = pos_data.pop('attendee_name', '') if attendee_name and not pos_data.get('attendee_name_parts'): pos_data['attendee_name_parts'] = { '_legacy': attendee_name } pos = OrderPosition(**pos_data) if simulate: pos.order = order._wrapped else: pos.order = order if addon_to: if simulate: pos.addon_to = pos_map[addon_to]._wrapped else: pos.addon_to = pos_map[addon_to] if pos.price is None: price = get_price( item=pos.item, variation=pos.variation, voucher=pos.voucher, custom_price=None, subevent=pos.subevent, addon_to=pos.addon_to, invoice_address=ia, ) pos.price = price.gross pos.tax_rate = price.rate pos.tax_value = price.tax pos.tax_rule = pos.item.tax_rule else: pos._calculate_tax() pos.price_before_voucher = get_price( item=pos.item, variation=pos.variation, voucher=None, custom_price=None, subevent=pos.subevent, addon_to=pos.addon_to, invoice_address=ia, ).gross if simulate: pos = WrappedModel(pos) pos.id = 0 answers = [] for answ_data in answers_data: options = answ_data.pop('options', []) answ = WrappedModel(QuestionAnswer(**answ_data)) answ.options = WrappedList(options) answers.append(answ) pos.answers = answers pos.pseudonymization_id = "PREVIEW" else: if pos.voucher: Voucher.objects.filter(pk=pos.voucher.pk).update(redeemed=F('redeemed') + 1) pos.save() for answ_data in answers_data: options = answ_data.pop('options', []) if isinstance(answ_data['answer'], File): an = answ_data.pop('answer') answ = pos.answers.create(**answ_data, answer='') answ.file.save(an.name, an, save=False) answ.answer = 'file://' + answ.file.name answ.save() else: answ = pos.answers.create(**answ_data) answ.options.add(*options) pos_map[pos.positionid] = pos if not simulate: for cp in delete_cps: cp.delete() order.total = sum([p.price for p in pos_map.values()]) fees = [] for fee_data in fees_data: is_percentage = fee_data.pop('_treat_value_as_percentage', False) if is_percentage: fee_data['value'] = round_decimal(order.total * (fee_data['value'] / Decimal('100.00')), self.context['event'].currency) is_split_taxes = fee_data.pop('_split_taxes_like_products', False) if is_split_taxes: d = defaultdict(lambda: Decimal('0.00')) trz = TaxRule.zero() for p in pos_map.values(): tr = p.tax_rule d[tr] += p.price - p.tax_value base_values = sorted([tuple(t) for t in d.items()], key=lambda t: (t[0] or trz).rate) sum_base = sum(t[1] for t in base_values) fee_values = [(t[0], round_decimal(fee_data['value'] * t[1] / sum_base, self.context['event'].currency)) for t in base_values] sum_fee = sum(t[1] for t in fee_values) if sum_fee > fee_data['value']: fee_values[0] = (fee_values[0][0], fee_values[0][1] + (fee_data['value'] - sum_fee)) elif sum_fee < fee_data['value']: fee_values[-1] = (fee_values[-1][0], fee_values[-1][1] + (fee_data['value'] - sum_fee)) for tr, val in fee_values: fee_data['tax_rule'] = tr fee_data['value'] = val f = OrderFee(**fee_data) f.order = order._wrapped if simulate else order f._calculate_tax() fees.append(f) if not simulate: f.save() else: f = OrderFee(**fee_data) f.order = order._wrapped if simulate else order f._calculate_tax() fees.append(f) if not simulate: f.save() order.total += sum([f.value for f in fees]) if simulate: order.fees = fees order.positions = pos_map.values() return order else: order.save(update_fields=['total']) if order.total == Decimal('0.00') and validated_data.get('status') == Order.STATUS_PAID and not payment_provider: payment_provider = 'free' if order.total == Decimal('0.00') and validated_data.get('status') != Order.STATUS_PAID: order.status = Order.STATUS_PAID order.save() order.payments.create( amount=order.total, provider='free', state=OrderPayment.PAYMENT_STATE_CONFIRMED, payment_date=now() ) elif payment_provider == "free" and order.total != Decimal('0.00'): raise ValidationError('You cannot use the "free" payment provider for non-free orders.') elif validated_data.get('status') == Order.STATUS_PAID: if not payment_provider: raise ValidationError('You cannot create a paid order without a payment provider.') order.payments.create( amount=order.total, provider=payment_provider, info=payment_info, payment_date=payment_date, state=OrderPayment.PAYMENT_STATE_CONFIRMED ) elif payment_provider: order.payments.create( amount=order.total, provider=payment_provider, info=payment_info, state=OrderPayment.PAYMENT_STATE_CREATED ) return order class LinePositionField(serializers.IntegerField): def to_representation(self, value): return super().to_representation(value) + 1 def to_internal_value(self, data): return super().to_internal_value(data) - 1 class InlineInvoiceLineSerializer(I18nAwareModelSerializer): position = LinePositionField(read_only=True) class Meta: model = InvoiceLine fields = ('position', 'description', 'item', 'variation', 'attendee_name', 'event_date_from', 'event_date_to', 'gross_value', 'tax_value', 'tax_rate', 'tax_name') class InvoiceSerializer(I18nAwareModelSerializer): order = serializers.SlugRelatedField(slug_field='code', read_only=True) refers = serializers.SlugRelatedField(slug_field='full_invoice_no', read_only=True) lines = InlineInvoiceLineSerializer(many=True) invoice_to_country = CountryField() invoice_from_country = CountryField() class Meta: model = Invoice fields = ('order', 'number', 'is_cancellation', 'invoice_from', 'invoice_from_name', 'invoice_from_zipcode', 'invoice_from_city', 'invoice_from_country', 'invoice_from_tax_id', 'invoice_from_vat_id', 'invoice_to', 'invoice_to_company', 'invoice_to_name', 'invoice_to_street', 'invoice_to_zipcode', 'invoice_to_city', 'invoice_to_state', 'invoice_to_country', 'invoice_to_vat_id', 'invoice_to_beneficiary', 'custom_field', 'date', 'refers', 'locale', 'introductory_text', 'additional_text', 'payment_provider_text', 'footer_text', 'lines', 'foreign_currency_display', 'foreign_currency_rate', 'foreign_currency_rate_date', 'internal_reference') class OrderPaymentCreateSerializer(I18nAwareModelSerializer): provider = serializers.CharField(required=True, allow_null=False, allow_blank=False) info = CompatibleJSONField(required=False) class Meta: model = OrderPayment fields = ('state', 'amount', 'payment_date', 'provider', 'info') def create(self, validated_data): order = OrderPayment(order=self.context['order'], **validated_data) order.save() return order class OrderRefundCreateSerializer(I18nAwareModelSerializer): payment = serializers.IntegerField(required=False, allow_null=True) provider = serializers.CharField(required=True, allow_null=False, allow_blank=False) info = CompatibleJSONField(required=False) class Meta: model = OrderRefund fields = ('state', 'source', 'amount', 'payment', 'execution_date', 'provider', 'info', 'comment') def create(self, validated_data): pid = validated_data.pop('payment', None) if pid: try: p = self.context['order'].payments.get(local_id=pid) except OrderPayment.DoesNotExist: raise ValidationError('Unknown payment ID.') else: p = None order = OrderRefund(order=self.context['order'], payment=p, **validated_data) order.save() return order class RevokedTicketSecretSerializer(I18nAwareModelSerializer): class Meta: model = RevokedTicketSecret fields = ('id', 'secret', 'created')

true

1c3822e9820d11b876c263fd47b55ca874718491

876

py

Python

tinypipe/utils/general.py

dave-msk/tinypipe

0be1c3cafb2a1cb2f672e4688e84889fa52dad30

[ "Apache-2.0" ]

null

tinypipe/utils/general.py

dave-msk/tinypipe

0be1c3cafb2a1cb2f672e4688e84889fa52dad30

[ "Apache-2.0" ]

null

tinypipe/utils/general.py

dave-msk/tinypipe

0be1c3cafb2a1cb2f672e4688e84889fa52dad30

[ "Apache-2.0" ]

null

# Copyright 2019 Siu-Kei Muk (David). All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function def val_or_default(val, default): return val if val is not None else default

39.818182

80

0.705479

from __future__ import absolute_import from __future__ import division from __future__ import print_function def val_or_default(val, default): return val if val is not None else default

true

1c3823c76dbf00f9fce4c4498a7bed366e31e3ef

90

py

Python

cms/__init__.py

fp4code/django-cms

e382fc7fe039733fee187658066a7b4af656c49e

[ "BSD-3-Clause" ]

1

2019-09-14T09:45:12.000Z

cms/__init__.py

zahedbri/django-cms

70c11c90d14459ef63472f3d0e6c90086c6098c5

[ "BSD-3-Clause" ]

null

cms/__init__.py

zahedbri/django-cms

70c11c90d14459ef63472f3d0e6c90086c6098c5

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- __version__ = '3.4.5' default_app_config = 'cms.apps.CMSConfig'

15

41

0.644444

__version__ = '3.4.5' default_app_config = 'cms.apps.CMSConfig'

true

1c3823cc1f57943e9f990aa7d2ac42167d0e0e23

4,779

py

Python

composition_root.py

plievana/python-ddd

44b1932344260b33ee0cce538c0be0b56b21327d

[ "MIT" ]

null

composition_root.py

plievana/python-ddd

44b1932344260b33ee0cce538c0be0b56b21327d

[ "MIT" ]

null

composition_root.py

plievana/python-ddd

44b1932344260b33ee0cce538c0be0b56b21327d

[ "MIT" ]

null

import dependency_injector.containers as containers import dependency_injector.providers as providers from application.command_bus import CommandBus from application.command_handlers import AddItemCommandHandler from application.query_bus import QueryBus from application.query_handlers import GetItemsQueryHandler from application.services import IdentityHashingService from infrastructure.framework.falcon.controllers import (InfoController, ItemsController) from infrastructure.framework.flask.controllers import (InfoController as FlaskInfoController, ItemsController as FlaskItemsController) from infrastructure.repositories.auction_items_repository import AuctionItemsRepository from infrastructure.repositories.users_repository import InMemoryUsersRepository from infrastructure.framework.falcon.authentication import BasicAuthenticationService from infrastructure.framework.flask.authentication import BasicAuthenticationService as FlaskBasicAuthenticationService class ObjectiveCommandHandler(): def __init__(self, logger): self.logger = logger def handle(self, command): print('objective handler is handling', command, self.logger) def functional_handler(logger): def handle(command): print('functional handler is handling', command, logger) return handle class BaseContainer(containers.DeclarativeContainer): hashing_service_factory = providers.Singleton(IdentityHashingService) falcon_authentication_service_factory = providers.Factory(BasicAuthenticationService, users_repository=providers.Factory(InMemoryUsersRepository, hashing_service=hashing_service_factory) ) flask_authentication_service_factory = providers.Factory(FlaskBasicAuthenticationService, users_repository=providers.Factory( InMemoryUsersRepository, hashing_service=hashing_service_factory) ) class CommandBusContainer(containers.DeclarativeContainer): items_repository = providers.Singleton(AuctionItemsRepository) command_handler_factory = providers.FactoryAggregate( AddItemCommand=providers.Factory(AddItemCommandHandler, items_repository=items_repository ) ) command_bus_factory = providers.Factory( CommandBus, command_handler_factory=providers.DelegatedFactory( command_handler_factory) ) class QueryBusContainer(containers.DeclarativeContainer): items_repository = providers.Singleton(AuctionItemsRepository) query_handler_factory = providers.FactoryAggregate( GetItemsQuery=providers.Factory( GetItemsQueryHandler, items_repository=items_repository) ) query_bus_factory = providers.Factory( QueryBus, query_handler_factory=providers.DelegatedFactory(query_handler_factory)) class FalconContainer(containers.DeclarativeContainer): items_controller_factory = providers.Factory(ItemsController, command_bus=CommandBusContainer.command_bus_factory, query_bus=QueryBusContainer.query_bus_factory, authentication_service=BaseContainer.falcon_authentication_service_factory, ) info_controller_factory = providers.Factory(InfoController, authentication_service=BaseContainer.falcon_authentication_service_factory ) class FlaskContainer(containers.DeclarativeContainer): info_controller_factory = providers.Factory(FlaskInfoController, authentication_service=BaseContainer.flask_authentication_service_factory ) items_controller_factory = providers.Factory(FlaskItemsController, command_bus=CommandBusContainer.command_bus_factory, query_bus=QueryBusContainer.query_bus_factory, authentication_service=BaseContainer.flask_authentication_service_factory, )

51.387097

162

0.642394

import dependency_injector.containers as containers import dependency_injector.providers as providers from application.command_bus import CommandBus from application.command_handlers import AddItemCommandHandler from application.query_bus import QueryBus from application.query_handlers import GetItemsQueryHandler from application.services import IdentityHashingService from infrastructure.framework.falcon.controllers import (InfoController, ItemsController) from infrastructure.framework.flask.controllers import (InfoController as FlaskInfoController, ItemsController as FlaskItemsController) from infrastructure.repositories.auction_items_repository import AuctionItemsRepository from infrastructure.repositories.users_repository import InMemoryUsersRepository from infrastructure.framework.falcon.authentication import BasicAuthenticationService from infrastructure.framework.flask.authentication import BasicAuthenticationService as FlaskBasicAuthenticationService class ObjectiveCommandHandler(): def __init__(self, logger): self.logger = logger def handle(self, command): print('objective handler is handling', command, self.logger) def functional_handler(logger): def handle(command): print('functional handler is handling', command, logger) return handle class BaseContainer(containers.DeclarativeContainer): hashing_service_factory = providers.Singleton(IdentityHashingService) falcon_authentication_service_factory = providers.Factory(BasicAuthenticationService, users_repository=providers.Factory(InMemoryUsersRepository, hashing_service=hashing_service_factory) ) flask_authentication_service_factory = providers.Factory(FlaskBasicAuthenticationService, users_repository=providers.Factory( InMemoryUsersRepository, hashing_service=hashing_service_factory) ) class CommandBusContainer(containers.DeclarativeContainer): items_repository = providers.Singleton(AuctionItemsRepository) command_handler_factory = providers.FactoryAggregate( AddItemCommand=providers.Factory(AddItemCommandHandler, items_repository=items_repository ) ) command_bus_factory = providers.Factory( CommandBus, command_handler_factory=providers.DelegatedFactory( command_handler_factory) ) class QueryBusContainer(containers.DeclarativeContainer): items_repository = providers.Singleton(AuctionItemsRepository) query_handler_factory = providers.FactoryAggregate( GetItemsQuery=providers.Factory( GetItemsQueryHandler, items_repository=items_repository) ) query_bus_factory = providers.Factory( QueryBus, query_handler_factory=providers.DelegatedFactory(query_handler_factory)) class FalconContainer(containers.DeclarativeContainer): items_controller_factory = providers.Factory(ItemsController, command_bus=CommandBusContainer.command_bus_factory, query_bus=QueryBusContainer.query_bus_factory, authentication_service=BaseContainer.falcon_authentication_service_factory, ) info_controller_factory = providers.Factory(InfoController, authentication_service=BaseContainer.falcon_authentication_service_factory ) class FlaskContainer(containers.DeclarativeContainer): info_controller_factory = providers.Factory(FlaskInfoController, authentication_service=BaseContainer.flask_authentication_service_factory ) items_controller_factory = providers.Factory(FlaskItemsController, command_bus=CommandBusContainer.command_bus_factory, query_bus=QueryBusContainer.query_bus_factory, authentication_service=BaseContainer.flask_authentication_service_factory, )

true

1c3823cef3b0698023be0f415936f0db8a9d85de

2,198

py

Python

qpricesim/model_code/economic_environment.py

ToFeWe/qpricesim

2d4312ed1d1356449f0c168835a0662b238a27bb

[ "MIT" ]

2

2022-03-22T12:16:37.000Z

2022-03-22T12:48:46.000Z

qpricesim/model_code/economic_environment.py

ToFeWe/qpricesim

2d4312ed1d1356449f0c168835a0662b238a27bb

[ "MIT" ]

null

qpricesim/model_code/economic_environment.py

ToFeWe/qpricesim

2d4312ed1d1356449f0c168835a0662b238a27bb

[ "MIT" ]

null

""" A module that defines the economic environment the agents are interacting in. """ import numpy as np from numba import njit @njit def calc_winning_price(all_prices): """ Helper function that takes in the array of all prices in the market and returns the winning price and the number of firms that picked this winning price. Args: all_prices (array): Array of all prices in the given round in the market Returns: tuple: winning_price, n_winning_price winning_price (integer): Current market price n_winning_price (integer): Number of firms that played the market price """ # Get winning price # Lowest price wins the market winning_price = np.min(all_prices) # Get the number of players that played the winning price n_winning_price = np.sum(np.where(all_prices == winning_price, 1, 0)) return winning_price, n_winning_price @njit def calc_reward(p_i, winning_price, n_winning_price, reservation_price, m_consumer): """ A function that calculates the reward given a simple Bertrand environment with homogenous goods. Use calc_winning_price() to retrieve winning_price, n_winning_price for the given market prices first. Args: p_i (integer): Price the agent picked in the given round (Non-index reprenstation of the action). winning_price (integer): Market price n_winning_price (integer): Number of firms that played the market price reservation_price (integer): Maximal price the consumers are willing to pay m_consumer (integer): Number of consumers in the market Returns: float: Economics profit/reward for the agent in the given period """ # If the agents charges a price above reservation price, he comes home with zero. # If he plays the winning price, he shares the market with the others who played # the winning price. # If his price is above the winning price, he also goes home with zero. if p_i > reservation_price: return 0 elif p_i == winning_price: return (1 / n_winning_price) * p_i * m_consumer else: return 0

32.80597

86

0.696087

import numpy as np from numba import njit @njit def calc_winning_price(all_prices): winning_price = np.min(all_prices) n_winning_price = np.sum(np.where(all_prices == winning_price, 1, 0)) return winning_price, n_winning_price @njit def calc_reward(p_i, winning_price, n_winning_price, reservation_price, m_consumer): if p_i > reservation_price: return 0 elif p_i == winning_price: return (1 / n_winning_price) * p_i * m_consumer else: return 0

true

1c3824dda1e45856c1c591405392e5f8ca1dd8b1

8,686

py

Python

dosna/backends/s3.py

ijjorama/DosNa

8e5322a0e1b93a377a9a443d442253b45957dac2

[ "Apache-2.0" ]

null

dosna/backends/s3.py

ijjorama/DosNa

8e5322a0e1b93a377a9a443d442253b45957dac2

[ "Apache-2.0" ]

null

dosna/backends/s3.py

ijjorama/DosNa

8e5322a0e1b93a377a9a443d442253b45957dac2

[ "Apache-2.0" ]

null

#!/usr/bin/env python """Backend s3 uses a S3 interface to store the dataset and chunks data""" import logging import numpy as np import boto3 from botocore.exceptions import ClientError from dosna.backends import Backend from dosna.backends.base import (BackendConnection, BackendDataChunk, BackendDataset, ConnectionError, DatasetNotFoundError) from dosna.util import dtype2str, shape2str, str2shape from dosna.util.data import slices2shape _DATASET_ROOT = 'dataset_root' _SIGNATURE = "DosNa Dataset" _SHAPE = 'shape' _DTYPE = 'dtype' _FILLVALUE = 'fillvalue' _CHUNK_GRID = 'chunk-grid' _CHUNK_SIZE = 'chunk-size' log = logging.getLogger(__name__) # Sanitise bucket name to conform to AWS conventions def bucketName(name): return name.replace('_', '-').lower() class S3Connection(BackendConnection): """ A S3 Connection that wraps boto3 S3 client """ def __init__(self, name, endpoint_url=None, verify=True, profile_name='default', *args, **kwargs): super(S3Connection, self).__init__(name, *args, **kwargs) self._endpoint_url = endpoint_url self._verify = verify self._client = None self._profile_name = profile_name super(S3Connection, self).__init__(name, *args, **kwargs) def connect(self): if self.connected: raise ConnectionError( 'Connection {} is already open'.format(self.name)) session = boto3.session.Session(profile_name=self._profile_name) # Use access key and secret_key in call to client? self._client = session.client( service_name='s3', endpoint_url=self._endpoint_url, verify=self._verify ) # Check bucket exists and is writable super(S3Connection, self).connect() def disconnect(self): if self.connected: super(S3Connection, self).disconnect() @property def client(self): return self._client def create_dataset(self, name, shape=None, dtype=np.float32, fillvalue=0, data=None, chunk_size=None): if not ((shape is not None and dtype is not None) or data is not None): raise Exception('Provide `shape` and `dtype` or `data`') if self.has_dataset(name): raise Exception('Dataset `%s` already exists' % name) if data is not None: shape = data.shape dtype = data.dtype if chunk_size is None: chunk_size = shape chunk_grid = (np.ceil(np.asarray(shape, float) / chunk_size))\ .astype(int) name = bucketName(name) log.debug('creating dataset %s with shape:%s chunk_size:%s ' 'chunk_grid:%s', name, shape, chunk_size, chunk_grid) try: self._client.create_bucket(Bucket=name, ACL='private') except ClientError as e: code = e.response['Error']['Code'] if code is not None: log.error('connect: create_bucket returns %s', code) return None metadata = { _SHAPE: shape2str(shape), _DTYPE: dtype2str(dtype), _FILLVALUE: repr(fillvalue), _CHUNK_GRID: shape2str(chunk_grid), _CHUNK_SIZE: shape2str(chunk_size) } self._client.put_object( Bucket=name, Key=_DATASET_ROOT, Body=bytes(_SIGNATURE), Metadata=metadata ) dataset = S3Dataset( self, name, shape, dtype, fillvalue, chunk_grid, chunk_size ) return dataset def get_dataset(self, name): if not self.has_dataset(name): raise DatasetNotFoundError('Dataset `%s` does not exist' % name) metadata = self._dataset_root['Metadata'] if metadata is None: raise DatasetNotFoundError( 'Dataset `%s` does not have required DosNa metadata' % name ) shape = str2shape(metadata[_SHAPE]) dtype = metadata[_DTYPE] fillvalue = int(metadata[_FILLVALUE]) chunk_grid = str2shape(metadata[_CHUNK_GRID]) chunk_size = str2shape(metadata[_CHUNK_SIZE]) dataset = S3Dataset( self, name, shape, dtype, fillvalue, chunk_grid, chunk_size ) return dataset def get_dataset_root(self, name): name = bucketName(name) dataset_root = None try: dataset_root = self._client.get_object( Bucket=name, Key=_DATASET_ROOT ) content = dataset_root['Body'].read() if not content == _SIGNATURE: dataset_root = None except Exception: pass # Don't need to report errors here return dataset_root def has_dataset(self, name): self._dataset_root = self.get_dataset_root(name) if self._dataset_root is None: log.info("has_dataset: dataset %s does not exist", name) return self._dataset_root is not None def del_dataset(self, name): if self.has_dataset(name): name = bucketName(name) try: self._client.delete_object(Bucket=name, Key=_DATASET_ROOT) self._client.delete_bucket(Bucket=name) except ClientError as e: log.error('del_dataset: cannot delete %s: %s', name, e.response['Error']) else: raise DatasetNotFoundError( 'Dataset `{}` does not exist'.format(name)) class S3Dataset(BackendDataset): """ S3Dataset """ @property def client(self): return self.connection.client def _idx2name(self, idx): return '.'.join(map(str, idx)) def create_chunk(self, idx, data=None, slices=None): if self.has_chunk(idx): raise Exception('DataChunk `{}{}` already exists'. format(self.name, idx)) name = self._idx2name(idx) # print "Name = %s" % (name) dtype = self.dtype shape = self.chunk_size fillvalue = self.fillvalue datachunk = S3DataChunk(self, idx, name, shape, dtype, fillvalue) if data is None: data = np.full(shape, fillvalue, dtype) datachunk.set_data(data, slices, fill_others=True) return datachunk def get_chunk(self, idx): if self.has_chunk(idx): name = self._idx2name(idx) dtype = self.dtype shape = self.chunk_size fillvalue = self.fillvalue return S3DataChunk(self, idx, name, shape, dtype, fillvalue) return self.create_chunk(idx) def has_chunk(self, idx): has_chunk = False name = self._idx2name(idx) try: self.client.head_object(Bucket=bucketName(self._name), Key=name) has_chunk = True except ClientError as e: log.debug("ClientError: %s", e.response['Error']['Code']) return has_chunk def del_chunk(self, idx): if self.has_chunk(idx): self.client.delete_object( Bucket=bucketName(self._name), Key=self._idx2name(idx) ) class S3DataChunk(BackendDataChunk): @property def client(self): return self.dataset.client def get_data(self, slices=None): if slices is None: slices = slice(None) data = np.fromstring(self.read(), dtype=self.dtype, count=self.size) data.shape = self.shape return data[slices] def set_data(self, values, slices=None, fill_others=False): if slices is None or slices2shape(slices) == self.shape: self.write_full(values.tobytes()) else: if fill_others: cdata = np.full(self.shape, self.fillvalue, self.dtype) else: cdata = self.get_data() cdata[slices] = values self.write_full(cdata.tobytes()) def write_full(self, data): self.client.put_object( Bucket=bucketName(self.dataset.name), Key=self.name, Body=data ) def read(self, length=None, offset=0): if length is None: length = self.byte_count byteRange = 'bytes={}-{}'.format(offset, offset+length-1) return self.client.get_object( Bucket=bucketName(self.dataset.name), Key=self.name, Range=byteRange )['Body'].read() _backend = Backend('s3', S3Connection, S3Dataset, S3DataChunk)

29.544218

79

0.590145

import logging import numpy as np import boto3 from botocore.exceptions import ClientError from dosna.backends import Backend from dosna.backends.base import (BackendConnection, BackendDataChunk, BackendDataset, ConnectionError, DatasetNotFoundError) from dosna.util import dtype2str, shape2str, str2shape from dosna.util.data import slices2shape _DATASET_ROOT = 'dataset_root' _SIGNATURE = "DosNa Dataset" _SHAPE = 'shape' _DTYPE = 'dtype' _FILLVALUE = 'fillvalue' _CHUNK_GRID = 'chunk-grid' _CHUNK_SIZE = 'chunk-size' log = logging.getLogger(__name__) def bucketName(name): return name.replace('_', '-').lower() class S3Connection(BackendConnection): def __init__(self, name, endpoint_url=None, verify=True, profile_name='default', *args, **kwargs): super(S3Connection, self).__init__(name, *args, **kwargs) self._endpoint_url = endpoint_url self._verify = verify self._client = None self._profile_name = profile_name super(S3Connection, self).__init__(name, *args, **kwargs) def connect(self): if self.connected: raise ConnectionError( 'Connection {} is already open'.format(self.name)) session = boto3.session.Session(profile_name=self._profile_name) self._client = session.client( service_name='s3', endpoint_url=self._endpoint_url, verify=self._verify ) super(S3Connection, self).connect() def disconnect(self): if self.connected: super(S3Connection, self).disconnect() @property def client(self): return self._client def create_dataset(self, name, shape=None, dtype=np.float32, fillvalue=0, data=None, chunk_size=None): if not ((shape is not None and dtype is not None) or data is not None): raise Exception('Provide `shape` and `dtype` or `data`') if self.has_dataset(name): raise Exception('Dataset `%s` already exists' % name) if data is not None: shape = data.shape dtype = data.dtype if chunk_size is None: chunk_size = shape chunk_grid = (np.ceil(np.asarray(shape, float) / chunk_size))\ .astype(int) name = bucketName(name) log.debug('creating dataset %s with shape:%s chunk_size:%s ' 'chunk_grid:%s', name, shape, chunk_size, chunk_grid) try: self._client.create_bucket(Bucket=name, ACL='private') except ClientError as e: code = e.response['Error']['Code'] if code is not None: log.error('connect: create_bucket returns %s', code) return None metadata = { _SHAPE: shape2str(shape), _DTYPE: dtype2str(dtype), _FILLVALUE: repr(fillvalue), _CHUNK_GRID: shape2str(chunk_grid), _CHUNK_SIZE: shape2str(chunk_size) } self._client.put_object( Bucket=name, Key=_DATASET_ROOT, Body=bytes(_SIGNATURE), Metadata=metadata ) dataset = S3Dataset( self, name, shape, dtype, fillvalue, chunk_grid, chunk_size ) return dataset def get_dataset(self, name): if not self.has_dataset(name): raise DatasetNotFoundError('Dataset `%s` does not exist' % name) metadata = self._dataset_root['Metadata'] if metadata is None: raise DatasetNotFoundError( 'Dataset `%s` does not have required DosNa metadata' % name ) shape = str2shape(metadata[_SHAPE]) dtype = metadata[_DTYPE] fillvalue = int(metadata[_FILLVALUE]) chunk_grid = str2shape(metadata[_CHUNK_GRID]) chunk_size = str2shape(metadata[_CHUNK_SIZE]) dataset = S3Dataset( self, name, shape, dtype, fillvalue, chunk_grid, chunk_size ) return dataset def get_dataset_root(self, name): name = bucketName(name) dataset_root = None try: dataset_root = self._client.get_object( Bucket=name, Key=_DATASET_ROOT ) content = dataset_root['Body'].read() if not content == _SIGNATURE: dataset_root = None except Exception: pass return dataset_root def has_dataset(self, name): self._dataset_root = self.get_dataset_root(name) if self._dataset_root is None: log.info("has_dataset: dataset %s does not exist", name) return self._dataset_root is not None def del_dataset(self, name): if self.has_dataset(name): name = bucketName(name) try: self._client.delete_object(Bucket=name, Key=_DATASET_ROOT) self._client.delete_bucket(Bucket=name) except ClientError as e: log.error('del_dataset: cannot delete %s: %s', name, e.response['Error']) else: raise DatasetNotFoundError( 'Dataset `{}` does not exist'.format(name)) class S3Dataset(BackendDataset): @property def client(self): return self.connection.client def _idx2name(self, idx): return '.'.join(map(str, idx)) def create_chunk(self, idx, data=None, slices=None): if self.has_chunk(idx): raise Exception('DataChunk `{}{}` already exists'. format(self.name, idx)) name = self._idx2name(idx) # print "Name = %s" % (name) dtype = self.dtype shape = self.chunk_size fillvalue = self.fillvalue datachunk = S3DataChunk(self, idx, name, shape, dtype, fillvalue) if data is None: data = np.full(shape, fillvalue, dtype) datachunk.set_data(data, slices, fill_others=True) return datachunk def get_chunk(self, idx): if self.has_chunk(idx): name = self._idx2name(idx) dtype = self.dtype shape = self.chunk_size fillvalue = self.fillvalue return S3DataChunk(self, idx, name, shape, dtype, fillvalue) return self.create_chunk(idx) def has_chunk(self, idx): has_chunk = False name = self._idx2name(idx) try: self.client.head_object(Bucket=bucketName(self._name), Key=name) has_chunk = True except ClientError as e: log.debug("ClientError: %s", e.response['Error']['Code']) return has_chunk def del_chunk(self, idx): if self.has_chunk(idx): self.client.delete_object( Bucket=bucketName(self._name), Key=self._idx2name(idx) ) class S3DataChunk(BackendDataChunk): @property def client(self): return self.dataset.client def get_data(self, slices=None): if slices is None: slices = slice(None) data = np.fromstring(self.read(), dtype=self.dtype, count=self.size) data.shape = self.shape return data[slices] def set_data(self, values, slices=None, fill_others=False): if slices is None or slices2shape(slices) == self.shape: self.write_full(values.tobytes()) else: if fill_others: cdata = np.full(self.shape, self.fillvalue, self.dtype) else: cdata = self.get_data() cdata[slices] = values self.write_full(cdata.tobytes()) def write_full(self, data): self.client.put_object( Bucket=bucketName(self.dataset.name), Key=self.name, Body=data ) def read(self, length=None, offset=0): if length is None: length = self.byte_count byteRange = 'bytes={}-{}'.format(offset, offset+length-1) return self.client.get_object( Bucket=bucketName(self.dataset.name), Key=self.name, Range=byteRange )['Body'].read() _backend = Backend('s3', S3Connection, S3Dataset, S3DataChunk)

true

1c3825a7c22eda2611ef396149cf33214f4ab6ae

8,509

py

Python

benchmarks/f3_wrong_hints_permutations/scaling_software_termination/18-2Nested_false-termination_5.py

EnricoMagnago/F3

c863215c318d7d5f258eb9be38c6962cf6863b52

[ "MIT" ]

3

2021-04-23T23:29:26.000Z

2022-03-23T10:00:30.000Z

benchmarks/f3_wrong_hints_permutations/scaling_software_termination/18-2Nested_false-termination_5.py

EnricoMagnago/F3

c863215c318d7d5f258eb9be38c6962cf6863b52

[ "MIT" ]

null

benchmarks/f3_wrong_hints_permutations/scaling_software_termination/18-2Nested_false-termination_5.py

EnricoMagnago/F3

c863215c318d7d5f258eb9be38c6962cf6863b52

[ "MIT" ]

1

2021-11-17T22:02:56.000Z

from typing import Tuple, FrozenSet from pysmt.environment import Environment as PysmtEnv from pysmt.fnode import FNode import pysmt.typing as types from utils import symb_to_next from hint import Hint, Location def transition_system(env: PysmtEnv) -> Tuple[FrozenSet[FNode], FNode, FNode, FNode]: assert isinstance(env, PysmtEnv) mgr = env.formula_manager pc = mgr.Symbol("pc", types.INT) x = mgr.Symbol("x", types.INT) y = mgr.Symbol("y", types.INT) x_pc = symb_to_next(mgr, pc) x_x = symb_to_next(mgr, x) x_y = symb_to_next(mgr, y) symbols = frozenset([pc, x, y]) m_1 = mgr.Int(-1) n_locs = 3 max_int = n_locs ints = [] pcs = [] x_pcs = [] for idx in range(n_locs): num = mgr.Int(idx) ints.append(num) pcs.append(mgr.Equals(pc, num)) x_pcs.append(mgr.Equals(x_pc, num)) for idx in range(n_locs, max_int): num = mgr.Int(idx) ints.append(num) pcend = mgr.Equals(pc, m_1) x_pcend = mgr.Equals(x_pc, m_1) init = pcs[0] cfg = [] # pc = 0 & (x >= 0) -> pc' = 1 cond = mgr.GE(x, ints[0]) cfg.append(mgr.Implies(mgr.And(pcs[0], cond), x_pcs[1])) # pc = 0 & !(x >= 0) -> pc' = -1 cfg.append(mgr.Implies(mgr.And(pcs[0], mgr.Not(cond)), x_pcend)) # pc = 1 -> pc' = 2 cfg.append(mgr.Implies(pcs[1], x_pcs[2])) # pc = 2 -> pc' = 0 cfg.append(mgr.Implies(pcs[2], x_pcs[0])) # pc = -1 -> pc' = -1 cfg.append(mgr.Implies(pcend, x_pcend)) trans = [] same_x = mgr.Equals(x_x, x) same_y = mgr.Equals(x_y, y) same = mgr.And(same_x, same_y) # pc = 0 -> same trans.append(mgr.Implies(pcs[0], same)) # pc = 1 -> x' = x + y & same_y trans.append(mgr.Implies(pcs[1], mgr.And(mgr.Equals(x_x, mgr.Plus(x, y)), same_y))) # pc = 2 -> same_x & y' = y + 1 trans.append(mgr.Implies(pcs[2], mgr.And(same_x, mgr.Equals(x_y, mgr.Plus(y, ints[1]))))) # pc = end -> same trans.append(mgr.Implies(pcend, same)) trans = mgr.And(*cfg, *trans) fairness = mgr.Not(mgr.Equals(pc, m_1)) return symbols, init, trans, fairness def hints(env: PysmtEnv) -> FrozenSet[Hint]: assert isinstance(env, PysmtEnv) mgr = env.formula_manager pc = mgr.Symbol("pc", types.INT) x = mgr.Symbol("x", types.INT) y = mgr.Symbol("y", types.INT) symbs = frozenset([pc, x, y]) m_100 = mgr.Int(-100) m_1 = mgr.Int(-1) i_0 = mgr.Int(0) i_1 = mgr.Int(1) i_2 = mgr.Int(2) i_4 = mgr.Int(4) i_20 = mgr.Int(20) x_pc = symb_to_next(mgr, pc) x_x = symb_to_next(mgr, x) x_y = symb_to_next(mgr, y) res = [] loc0 = Location(env, mgr.Equals(pc, i_1)) loc0.set_progress(1, mgr.GT(x_pc, pc)) loc1 = Location(env, mgr.GE(pc, i_2)) loc1.set_progress(0, mgr.Equals(x_pc, mgr.Div(pc, pc))) h_pc = Hint("h_pc2", env, frozenset([pc]), symbs) h_pc.set_locs([loc0, loc1]) res.append(h_pc) stutter = mgr.Equals(x_y, y) loc = Location(env, mgr.TRUE(), mgr.LE(x, i_20), stutterT=stutter) loc.set_progress(0, mgr.Equals(x_y, mgr.Plus(x, y))) h_y = Hint("h_y1", env, frozenset([y]), symbs) h_y.set_locs([loc]) res.append(h_y) loc0 = Location(env, mgr.LE(x, i_0)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Times(x, x))) loc1 = Location(env, mgr.GE(x, i_0)) loc1.set_progress(0, mgr.LT(x_x, mgr.Times(m_1, x, x))) h_x = Hint("h_x5", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1]) res.append(h_x) loc0 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Times(x, y))) loc1 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc1.set_progress(2, mgr.GT(x_x, y)) loc2 = Location(env, mgr.GE(x, i_2)) loc2.set_progress(0, mgr.GE(x_x, i_20)) h_x = Hint("h_x4", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1, loc2]) res.append(h_x) loc0 = Location(env, mgr.GE(y, m_100), mgr.LE(x, i_20)) loc0.set_progress(1, mgr.Equals(x_y, mgr.Times(x, y))) loc1 = Location(env, mgr.TRUE(), mgr.GE(x, m_100)) loc1.set_progress(2, mgr.GE(x_y, i_20)) loc2 = Location(env, mgr.TRUE()) loc2.set_progress(0, mgr.And(mgr.GE(x_y, m_100), mgr.LE(x_y, i_0))) h_y = Hint("h_y4", env, frozenset([y]), symbs) h_y.set_locs([loc0, loc1, loc2]) res.append(h_y) loc0 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Times(x, y))) loc1 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc1.set_progress(0, mgr.Equals(x_x, y)) h_x = Hint("h_x3", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1]) res.append(h_x) stutter = mgr.Equals(x_y, y) loc = Location(env, mgr.TRUE(), mgr.LE(x, i_20), stutterT=stutter) loc.set_progress(0, mgr.Equals(x_y, mgr.Plus(i_1, y))) h_y = Hint("h_y0", env, frozenset([y]), symbs) h_y.set_locs([loc]) res.append(h_y) loc0 = Location(env, mgr.Equals(pc, i_1)) loc0.set_progress(1, mgr.GT(x_pc, mgr.Plus(pc, i_1))) loc1 = Location(env, mgr.GT(pc, i_2)) loc1.set_progress(0, mgr.Equals(x_pc, i_1)) h_pc = Hint("h_pc0", env, frozenset([pc]), symbs) h_pc.set_locs([loc0, loc1]) res.append(h_pc) loc0 = Location(env, mgr.LE(x, i_20)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Plus(mgr.Times(x, x), i_1))) loc1 = Location(env, mgr.GE(x, i_20)) loc1.set_progress(0, mgr.LT(x_x, mgr.Times(m_1, x, x))) h_x = Hint("h_x6", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1]) res.append(h_x) loc0 = Location(env, mgr.GE(y, m_100), mgr.LE(x, i_20)) loc0.set_progress(1, mgr.Equals(x_y, mgr.Times(x, y))) loc1 = Location(env, mgr.TRUE(), mgr.GE(x, m_100)) loc1.set_progress(0, mgr.Equals(x_y, m_100)) h_y = Hint("h_y3", env, frozenset([y]), symbs) h_y.set_locs([loc0, loc1]) res.append(h_y) loc0 = Location(env, mgr.LE(pc, i_1)) loc0.set_progress(1, mgr.GT(x_pc, pc)) loc1 = Location(env, mgr.LE(pc, i_2)) loc1.set_progress(0, mgr.Equals(x_pc, mgr.Div(pc, pc))) h_pc = Hint("h_pc3", env, frozenset([pc]), symbs) h_pc.set_locs([loc0, loc1]) res.append(h_pc) loc0 = Location(env, mgr.GE(y, m_100)) loc0.set_progress(0, mgr.Equals(x_y, mgr.Times(y, y))) h_y = Hint("h_y5", env, frozenset([y]), symbs) h_y.set_locs([loc0]) res.append(h_y) loc0 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Plus(x, y))) loc1 = Location(env, mgr.GE(x, i_2), mgr.GE(y, i_1)) loc1.set_progress(0, mgr.Equals(x_x, y)) h_x = Hint("h_x2", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1]) res.append(h_x) loc0 = Location(env, mgr.GE(y, m_100), mgr.LE(x, i_20)) loc0.set_progress(1, mgr.Equals(x_y, mgr.Plus(x, y))) loc1 = Location(env, mgr.TRUE(), mgr.GE(x, m_100)) loc1.set_progress(0, mgr.Equals(x_y, m_100)) h_y = Hint("h_y2", env, frozenset([y]), symbs) h_y.set_locs([loc0, loc1]) res.append(h_y) loc0 = Location(env, mgr.GE(y, m_100)) loc0.set_progress(1, mgr.Equals(x_y, mgr.Times(y, y))) loc1 = Location(env, mgr.GE(y, i_0)) loc1.set_progress(0, mgr.GE(x_y, mgr.Plus(y, i_1))) h_y = Hint("h_y6", env, frozenset([y]), symbs) h_y.set_locs([loc0, loc1]) res.append(h_y) stutter = mgr.Equals(x_x, x) loc = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1), stutterT=stutter) loc.set_progress(0, mgr.Equals(x_x, mgr.Plus(x, y))) h_x = Hint("h_x1", env, frozenset([x]), symbs) h_x.set_locs([loc]) res.append(h_x) loc0 = Location(env, mgr.GE(x, i_1), mgr.GT(y, i_1)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Plus(mgr.Times(x, y), i_1))) loc1 = Location(env, mgr.GE(x, i_2)) loc1.set_progress(2, mgr.LT(x_x, mgr.Times(m_1, x, x))) loc2 = Location(env, mgr.LE(x, i_4)) loc2.set_progress(0, mgr.GE(x_x, mgr.Div(x, x))) h_x = Hint("h_x7", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1, loc2]) res.append(h_x) stutter = mgr.Equals(x_x, x) loc = Location(env, mgr.GE(x, i_20), mgr.GE(y, i_1), stutterT=stutter) loc.set_progress(0, mgr.Equals(x_x, mgr.Plus(x, y))) h_x = Hint("h_x0", env, frozenset([x]), symbs) h_x.set_locs([loc]) res.append(h_x) return frozenset(res)

31.75

77

0.590786

from typing import Tuple, FrozenSet from pysmt.environment import Environment as PysmtEnv from pysmt.fnode import FNode import pysmt.typing as types from utils import symb_to_next from hint import Hint, Location def transition_system(env: PysmtEnv) -> Tuple[FrozenSet[FNode], FNode, FNode, FNode]: assert isinstance(env, PysmtEnv) mgr = env.formula_manager pc = mgr.Symbol("pc", types.INT) x = mgr.Symbol("x", types.INT) y = mgr.Symbol("y", types.INT) x_pc = symb_to_next(mgr, pc) x_x = symb_to_next(mgr, x) x_y = symb_to_next(mgr, y) symbols = frozenset([pc, x, y]) m_1 = mgr.Int(-1) n_locs = 3 max_int = n_locs ints = [] pcs = [] x_pcs = [] for idx in range(n_locs): num = mgr.Int(idx) ints.append(num) pcs.append(mgr.Equals(pc, num)) x_pcs.append(mgr.Equals(x_pc, num)) for idx in range(n_locs, max_int): num = mgr.Int(idx) ints.append(num) pcend = mgr.Equals(pc, m_1) x_pcend = mgr.Equals(x_pc, m_1) init = pcs[0] cfg = [] cond = mgr.GE(x, ints[0]) cfg.append(mgr.Implies(mgr.And(pcs[0], cond), x_pcs[1])) # pc = 0 & !(x >= 0) -> pc' = -1 cfg.append(mgr.Implies(mgr.And(pcs[0], mgr.Not(cond)), x_pcend)) cfg.append(mgr.Implies(pcs[1], x_pcs[2])) # pc = 2 -> pc' = 0 cfg.append(mgr.Implies(pcs[2], x_pcs[0])) cfg.append(mgr.Implies(pcend, x_pcend)) trans = [] same_x = mgr.Equals(x_x, x) same_y = mgr.Equals(x_y, y) same = mgr.And(same_x, same_y) # pc = 0 -> same trans.append(mgr.Implies(pcs[0], same)) # pc = 1 -> x' = x + y & same_y trans.append(mgr.Implies(pcs[1], mgr.And(mgr.Equals(x_x, mgr.Plus(x, y)), same_y))) trans.append(mgr.Implies(pcs[2], mgr.And(same_x, mgr.Equals(x_y, mgr.Plus(y, ints[1]))))) # pc = end -> same trans.append(mgr.Implies(pcend, same)) trans = mgr.And(*cfg, *trans) fairness = mgr.Not(mgr.Equals(pc, m_1)) return symbols, init, trans, fairness def hints(env: PysmtEnv) -> FrozenSet[Hint]: assert isinstance(env, PysmtEnv) mgr = env.formula_manager pc = mgr.Symbol("pc", types.INT) x = mgr.Symbol("x", types.INT) y = mgr.Symbol("y", types.INT) symbs = frozenset([pc, x, y]) m_100 = mgr.Int(-100) m_1 = mgr.Int(-1) i_0 = mgr.Int(0) i_1 = mgr.Int(1) i_2 = mgr.Int(2) i_4 = mgr.Int(4) i_20 = mgr.Int(20) x_pc = symb_to_next(mgr, pc) x_x = symb_to_next(mgr, x) x_y = symb_to_next(mgr, y) res = [] loc0 = Location(env, mgr.Equals(pc, i_1)) loc0.set_progress(1, mgr.GT(x_pc, pc)) loc1 = Location(env, mgr.GE(pc, i_2)) loc1.set_progress(0, mgr.Equals(x_pc, mgr.Div(pc, pc))) h_pc = Hint("h_pc2", env, frozenset([pc]), symbs) h_pc.set_locs([loc0, loc1]) res.append(h_pc) stutter = mgr.Equals(x_y, y) loc = Location(env, mgr.TRUE(), mgr.LE(x, i_20), stutterT=stutter) loc.set_progress(0, mgr.Equals(x_y, mgr.Plus(x, y))) h_y = Hint("h_y1", env, frozenset([y]), symbs) h_y.set_locs([loc]) res.append(h_y) loc0 = Location(env, mgr.LE(x, i_0)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Times(x, x))) loc1 = Location(env, mgr.GE(x, i_0)) loc1.set_progress(0, mgr.LT(x_x, mgr.Times(m_1, x, x))) h_x = Hint("h_x5", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1]) res.append(h_x) loc0 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Times(x, y))) loc1 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc1.set_progress(2, mgr.GT(x_x, y)) loc2 = Location(env, mgr.GE(x, i_2)) loc2.set_progress(0, mgr.GE(x_x, i_20)) h_x = Hint("h_x4", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1, loc2]) res.append(h_x) loc0 = Location(env, mgr.GE(y, m_100), mgr.LE(x, i_20)) loc0.set_progress(1, mgr.Equals(x_y, mgr.Times(x, y))) loc1 = Location(env, mgr.TRUE(), mgr.GE(x, m_100)) loc1.set_progress(2, mgr.GE(x_y, i_20)) loc2 = Location(env, mgr.TRUE()) loc2.set_progress(0, mgr.And(mgr.GE(x_y, m_100), mgr.LE(x_y, i_0))) h_y = Hint("h_y4", env, frozenset([y]), symbs) h_y.set_locs([loc0, loc1, loc2]) res.append(h_y) loc0 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Times(x, y))) loc1 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc1.set_progress(0, mgr.Equals(x_x, y)) h_x = Hint("h_x3", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1]) res.append(h_x) stutter = mgr.Equals(x_y, y) loc = Location(env, mgr.TRUE(), mgr.LE(x, i_20), stutterT=stutter) loc.set_progress(0, mgr.Equals(x_y, mgr.Plus(i_1, y))) h_y = Hint("h_y0", env, frozenset([y]), symbs) h_y.set_locs([loc]) res.append(h_y) loc0 = Location(env, mgr.Equals(pc, i_1)) loc0.set_progress(1, mgr.GT(x_pc, mgr.Plus(pc, i_1))) loc1 = Location(env, mgr.GT(pc, i_2)) loc1.set_progress(0, mgr.Equals(x_pc, i_1)) h_pc = Hint("h_pc0", env, frozenset([pc]), symbs) h_pc.set_locs([loc0, loc1]) res.append(h_pc) loc0 = Location(env, mgr.LE(x, i_20)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Plus(mgr.Times(x, x), i_1))) loc1 = Location(env, mgr.GE(x, i_20)) loc1.set_progress(0, mgr.LT(x_x, mgr.Times(m_1, x, x))) h_x = Hint("h_x6", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1]) res.append(h_x) loc0 = Location(env, mgr.GE(y, m_100), mgr.LE(x, i_20)) loc0.set_progress(1, mgr.Equals(x_y, mgr.Times(x, y))) loc1 = Location(env, mgr.TRUE(), mgr.GE(x, m_100)) loc1.set_progress(0, mgr.Equals(x_y, m_100)) h_y = Hint("h_y3", env, frozenset([y]), symbs) h_y.set_locs([loc0, loc1]) res.append(h_y) loc0 = Location(env, mgr.LE(pc, i_1)) loc0.set_progress(1, mgr.GT(x_pc, pc)) loc1 = Location(env, mgr.LE(pc, i_2)) loc1.set_progress(0, mgr.Equals(x_pc, mgr.Div(pc, pc))) h_pc = Hint("h_pc3", env, frozenset([pc]), symbs) h_pc.set_locs([loc0, loc1]) res.append(h_pc) loc0 = Location(env, mgr.GE(y, m_100)) loc0.set_progress(0, mgr.Equals(x_y, mgr.Times(y, y))) h_y = Hint("h_y5", env, frozenset([y]), symbs) h_y.set_locs([loc0]) res.append(h_y) loc0 = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Plus(x, y))) loc1 = Location(env, mgr.GE(x, i_2), mgr.GE(y, i_1)) loc1.set_progress(0, mgr.Equals(x_x, y)) h_x = Hint("h_x2", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1]) res.append(h_x) loc0 = Location(env, mgr.GE(y, m_100), mgr.LE(x, i_20)) loc0.set_progress(1, mgr.Equals(x_y, mgr.Plus(x, y))) loc1 = Location(env, mgr.TRUE(), mgr.GE(x, m_100)) loc1.set_progress(0, mgr.Equals(x_y, m_100)) h_y = Hint("h_y2", env, frozenset([y]), symbs) h_y.set_locs([loc0, loc1]) res.append(h_y) loc0 = Location(env, mgr.GE(y, m_100)) loc0.set_progress(1, mgr.Equals(x_y, mgr.Times(y, y))) loc1 = Location(env, mgr.GE(y, i_0)) loc1.set_progress(0, mgr.GE(x_y, mgr.Plus(y, i_1))) h_y = Hint("h_y6", env, frozenset([y]), symbs) h_y.set_locs([loc0, loc1]) res.append(h_y) stutter = mgr.Equals(x_x, x) loc = Location(env, mgr.GE(x, i_1), mgr.GE(y, i_1), stutterT=stutter) loc.set_progress(0, mgr.Equals(x_x, mgr.Plus(x, y))) h_x = Hint("h_x1", env, frozenset([x]), symbs) h_x.set_locs([loc]) res.append(h_x) loc0 = Location(env, mgr.GE(x, i_1), mgr.GT(y, i_1)) loc0.set_progress(1, mgr.Equals(x_x, mgr.Plus(mgr.Times(x, y), i_1))) loc1 = Location(env, mgr.GE(x, i_2)) loc1.set_progress(2, mgr.LT(x_x, mgr.Times(m_1, x, x))) loc2 = Location(env, mgr.LE(x, i_4)) loc2.set_progress(0, mgr.GE(x_x, mgr.Div(x, x))) h_x = Hint("h_x7", env, frozenset([x]), symbs) h_x.set_locs([loc0, loc1, loc2]) res.append(h_x) stutter = mgr.Equals(x_x, x) loc = Location(env, mgr.GE(x, i_20), mgr.GE(y, i_1), stutterT=stutter) loc.set_progress(0, mgr.Equals(x_x, mgr.Plus(x, y))) h_x = Hint("h_x0", env, frozenset([x]), symbs) h_x.set_locs([loc]) res.append(h_x) return frozenset(res)

true

1c38271b2f5390e82710ce248c2a449c33d2bc3e

1,659

py

Python

src/genie/libs/parser/iosxe/tests/ShowLispInstanceIdIpv4MapCache/cli/equal/golden_output6_metric_expected.py

nielsvanhooy/genieparser

9a1955749697a6777ca614f0af4d5f3a2c254ccd

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowLispInstanceIdIpv4MapCache/cli/equal/golden_output6_metric_expected.py

nielsvanhooy/genieparser

9a1955749697a6777ca614f0af4d5f3a2c254ccd

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowLispInstanceIdIpv4MapCache/cli/equal/golden_output6_metric_expected.py

nielsvanhooy/genieparser

9a1955749697a6777ca614f0af4d5f3a2c254ccd

[ "Apache-2.0" ]

null

expected_output = { 'lisp_id': { 0: { 'instance_id': { 111: { 'eid_prefix': { '0.0.0.0/0': { 'action': 'send-map-request + Encapsulating to proxy ETR', 'expiry_time': '00:14:59', 'locators': { '21.21.21.21': { 'encap_iid': '-', 'metric': None, 'priority': 255, 'rloc_state': 'admin-down', 'uptime': '00:00:23', 'weight': 50, }, '24.24.24.24': { 'encap_iid': '-', 'metric': 0, 'priority': 50, 'rloc_state': 'up', 'uptime': '00:00:23', 'weight': 50, }, }, 'map_reply_state': 'unknown-eid-forward', 'negative_cache_entry': False, 'uptime': '00:00:01', 'via': 'map-reply', }, }, 'eid_table': 'vrf prov ', 'entries': 1, }, }, }, }, }

41.475

87

0.221218

expected_output = { 'lisp_id': { 0: { 'instance_id': { 111: { 'eid_prefix': { '0.0.0.0/0': { 'action': 'send-map-request + Encapsulating to proxy ETR', 'expiry_time': '00:14:59', 'locators': { '21.21.21.21': { 'encap_iid': '-', 'metric': None, 'priority': 255, 'rloc_state': 'admin-down', 'uptime': '00:00:23', 'weight': 50, }, '24.24.24.24': { 'encap_iid': '-', 'metric': 0, 'priority': 50, 'rloc_state': 'up', 'uptime': '00:00:23', 'weight': 50, }, }, 'map_reply_state': 'unknown-eid-forward', 'negative_cache_entry': False, 'uptime': '00:00:01', 'via': 'map-reply', }, }, 'eid_table': 'vrf prov ', 'entries': 1, }, }, }, }, }

true

1c382759860b98ee6972f4f10fd270f3d5268410

665

py

Python

nnabla_nas/optimizer/__init__.py

sony/nnabla-nas

269deb8229fda0f0901c47d21ac5ce244f403f63

[ "Apache-2.0" ]

16

2020-07-10T08:31:18.000Z

2022-03-24T13:28:15.000Z

nnabla_nas/optimizer/__init__.py

sony/nnabla-nas

269deb8229fda0f0901c47d21ac5ce244f403f63

[ "Apache-2.0" ]

1

2020-10-21T12:46:30.000Z

2021-02-03T00:18:29.000Z

nnabla_nas/optimizer/__init__.py

sony/nnabla-nas

269deb8229fda0f0901c47d21ac5ce244f403f63

[ "Apache-2.0" ]

3

2020-07-15T11:42:11.000Z

2022-03-25T16:54:49.000Z

# Copyright (c) 2020 Sony Corporation. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .optimizer import Optimizer __all__ = ['Optimizer']

36.944444

74

0.760902

from .optimizer import Optimizer __all__ = ['Optimizer']

true

1c382788882dfc7e712f0027de33754dbb491241

3,234

py

Python

src/DanceCV.py

stbnps/DanceCV

ac94f5f715c246a50f67f99abbe926a7461fba11

[ "BSD-3-Clause" ]

33

2015-01-02T08:14:39.000Z

2021-12-08T16:13:12.000Z

src/DanceCV.py

stbnps/DanceCV

ac94f5f715c246a50f67f99abbe926a7461fba11

[ "BSD-3-Clause" ]

2

2018-03-03T09:18:58.000Z

2020-06-29T08:25:20.000Z

src/DanceCV.py

stbnps/DanceCV

ac94f5f715c246a50f67f99abbe926a7461fba11

[ "BSD-3-Clause" ]

17

2015-11-22T03:42:59.000Z

2021-11-18T13:05:05.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Copyright (c) 2014, Esteban Pardo Sánchez All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' from Resource import Resource from Audio import Audio from Scene import Scene from Song import Song, Note, loadSong from Input import Input import pygame from pygame.locals import * import sys import getopt import Constants #import cProfile as profile class DanceCV(): def __init__(self, song, speed): self.input = Input() self.resource = Resource() self.audio = Audio() self.audio.pre_open() pygame.init() self.audio.open() if song != None: self.song = loadSong(self.resource, song) else: self.song = loadSong(self.resource, "gangnam") self.clock = pygame.time.Clock() pygame.display.set_mode((Constants.SCREEN_WIDTH, Constants.SCREEN_HEIGHT)) pygame.display.set_caption("DanceCV") screen = pygame.display.get_surface() if speed != None: self.scene = Scene(self.resource, self.song, screen, self.input, speed) else: self.scene = Scene(self.resource, self.song, screen, self.input, 2) def run(self): while True: for events in pygame.event.get(): if events.type == QUIT: sys.exit(0) self.input.run() self.scene.run() pygame.display.update() self.clock.tick(30) if __name__ == "__main__": song = None speed = None options, remainder = getopt.getopt(sys.argv[1:], 's:x:') for opt, arg in options: if opt in ('-s'): song = arg elif opt in ('-x'): speed = float(arg) game = DanceCV(song, speed) game.run()

34.404255

83

0.683055

from Resource import Resource from Audio import Audio from Scene import Scene from Song import Song, Note, loadSong from Input import Input import pygame from pygame.locals import * import sys import getopt import Constants class DanceCV(): def __init__(self, song, speed): self.input = Input() self.resource = Resource() self.audio = Audio() self.audio.pre_open() pygame.init() self.audio.open() if song != None: self.song = loadSong(self.resource, song) else: self.song = loadSong(self.resource, "gangnam") self.clock = pygame.time.Clock() pygame.display.set_mode((Constants.SCREEN_WIDTH, Constants.SCREEN_HEIGHT)) pygame.display.set_caption("DanceCV") screen = pygame.display.get_surface() if speed != None: self.scene = Scene(self.resource, self.song, screen, self.input, speed) else: self.scene = Scene(self.resource, self.song, screen, self.input, 2) def run(self): while True: for events in pygame.event.get(): if events.type == QUIT: sys.exit(0) self.input.run() self.scene.run() pygame.display.update() self.clock.tick(30) if __name__ == "__main__": song = None speed = None options, remainder = getopt.getopt(sys.argv[1:], 's:x:') for opt, arg in options: if opt in ('-s'): song = arg elif opt in ('-x'): speed = float(arg) game = DanceCV(song, speed) game.run()

true

1c3828893b4eae531bd9c2811241ec3653b28a34

4,349

tac

Python

mailmynet/Maildir/proxy_postfix/Twisted-11.0.0/doc/core/howto/tutorial/listings/finger/finger18.tac

SPIN-UMass/SWEET

1b0f39222e7064f70812e3293ca023619295741d

[ "MIT" ]

3

2020-04-02T06:23:44.000Z

2020-08-13T20:32:31.000Z

mailmynet/Maildir/proxy_postfix/Twisted-11.0.0/doc/core/howto/tutorial/listings/finger/finger18.tac

SPIN-UMass/SWEET

1b0f39222e7064f70812e3293ca023619295741d

[ "MIT" ]

null

mailmynet/Maildir/proxy_postfix/Twisted-11.0.0/doc/core/howto/tutorial/listings/finger/finger18.tac

SPIN-UMass/SWEET

1b0f39222e7064f70812e3293ca023619295741d

[ "MIT" ]

1

2020-04-02T06:26:10.000Z

# Do everything properly from twisted.application import internet, service from twisted.internet import protocol, reactor, defer from twisted.words.protocols import irc from twisted.protocols import basic from twisted.web import resource, server, static, xmlrpc import cgi def catchError(err): return "Internal error in server" class FingerProtocol(basic.LineReceiver): def lineReceived(self, user): d = self.factory.getUser(user) d.addErrback(catchError) def writeValue(value): self.transport.write(value+'\r\n') self.transport.loseConnection() d.addCallback(writeValue) class IRCReplyBot(irc.IRCClient): def connectionMade(self): self.nickname = self.factory.nickname irc.IRCClient.connectionMade(self) def privmsg(self, user, channel, msg): user = user.split('!')[0] if self.nickname.lower() == channel.lower(): d = self.factory.getUser(msg) d.addErrback(catchError) d.addCallback(lambda m: "Status of %s: %s" % (msg, m)) d.addCallback(lambda m: self.msg(user, m)) class UserStatusTree(resource.Resource): def __init__(self, service): resource.Resource.__init__(self) self.service = service def render_GET(self, request): d = self.service.getUsers() def formatUsers(users): l = ['<li><a href="%s">%s</a></li>' % (user, user) for user in users] return '<ul>'+''.join(l)+'</ul>' d.addCallback(formatUsers) d.addCallback(request.write) d.addCallback(lambda _: request.finish()) return server.NOT_DONE_YET def getChild(self, path, request): if path=="": return UserStatusTree(self.service) else: return UserStatus(path, self.service) class UserStatus(resource.Resource): def __init__(self, user, service): resource.Resource.__init__(self) self.user = user self.service = service def render_GET(self, request): d = self.service.getUser(self.user) d.addCallback(cgi.escape) d.addCallback(lambda m: '<h1>%s</h1>'%self.user+'<p>%s</p>'%m) d.addCallback(request.write) d.addCallback(lambda _: request.finish()) return server.NOT_DONE_YET class UserStatusXR(xmlrpc.XMLRPC): def __init__(self, service): xmlrpc.XMLRPC.__init__(self) self.service = service def xmlrpc_getUser(self, user): return self.service.getUser(user) class FingerService(service.Service): def __init__(self, filename): self.filename = filename self.users = {} def _read(self): self.users.clear() for line in file(self.filename): user, status = line.split(':', 1) user = user.strip() status = status.strip() self.users[user] = status self.call = reactor.callLater(30, self._read) def getUser(self, user): return defer.succeed(self.users.get(user, "No such user")) def getUsers(self): return defer.succeed(self.users.keys()) def getFingerFactory(self): f = protocol.ServerFactory() f.protocol = FingerProtocol f.getUser = self.getUser return f def getResource(self): r = UserStatusTree(self) x = UserStatusXR(self) r.putChild('RPC2', x) return r def getIRCBot(self, nickname): f = protocol.ReconnectingClientFactory() f.protocol = IRCReplyBot f.nickname = nickname f.getUser = self.getUser return f def startService(self): self._read() service.Service.startService(self) def stopService(self): service.Service.stopService(self) self.call.cancel() application = service.Application('finger', uid=1, gid=1) f = FingerService('/etc/users') serviceCollection = service.IServiceCollection(application) internet.TCPServer(79, f.getFingerFactory() ).setServiceParent(serviceCollection) internet.TCPServer(8000, server.Site(f.getResource()) ).setServiceParent(serviceCollection) internet.TCPClient('irc.freenode.org', 6667, f.getIRCBot('fingerbot') ).setServiceParent(serviceCollection)

29.585034

69

0.627271

from twisted.application import internet, service from twisted.internet import protocol, reactor, defer from twisted.words.protocols import irc from twisted.protocols import basic from twisted.web import resource, server, static, xmlrpc import cgi def catchError(err): return "Internal error in server" class FingerProtocol(basic.LineReceiver): def lineReceived(self, user): d = self.factory.getUser(user) d.addErrback(catchError) def writeValue(value): self.transport.write(value+'\r\n') self.transport.loseConnection() d.addCallback(writeValue) class IRCReplyBot(irc.IRCClient): def connectionMade(self): self.nickname = self.factory.nickname irc.IRCClient.connectionMade(self) def privmsg(self, user, channel, msg): user = user.split('!')[0] if self.nickname.lower() == channel.lower(): d = self.factory.getUser(msg) d.addErrback(catchError) d.addCallback(lambda m: "Status of %s: %s" % (msg, m)) d.addCallback(lambda m: self.msg(user, m)) class UserStatusTree(resource.Resource): def __init__(self, service): resource.Resource.__init__(self) self.service = service def render_GET(self, request): d = self.service.getUsers() def formatUsers(users): l = ['<li><a href="%s">%s</a></li>' % (user, user) for user in users] return '<ul>'+''.join(l)+'</ul>' d.addCallback(formatUsers) d.addCallback(request.write) d.addCallback(lambda _: request.finish()) return server.NOT_DONE_YET def getChild(self, path, request): if path=="": return UserStatusTree(self.service) else: return UserStatus(path, self.service) class UserStatus(resource.Resource): def __init__(self, user, service): resource.Resource.__init__(self) self.user = user self.service = service def render_GET(self, request): d = self.service.getUser(self.user) d.addCallback(cgi.escape) d.addCallback(lambda m: '<h1>%s</h1>'%self.user+'<p>%s</p>'%m) d.addCallback(request.write) d.addCallback(lambda _: request.finish()) return server.NOT_DONE_YET class UserStatusXR(xmlrpc.XMLRPC): def __init__(self, service): xmlrpc.XMLRPC.__init__(self) self.service = service def xmlrpc_getUser(self, user): return self.service.getUser(user) class FingerService(service.Service): def __init__(self, filename): self.filename = filename self.users = {} def _read(self): self.users.clear() for line in file(self.filename): user, status = line.split(':', 1) user = user.strip() status = status.strip() self.users[user] = status self.call = reactor.callLater(30, self._read) def getUser(self, user): return defer.succeed(self.users.get(user, "No such user")) def getUsers(self): return defer.succeed(self.users.keys()) def getFingerFactory(self): f = protocol.ServerFactory() f.protocol = FingerProtocol f.getUser = self.getUser return f def getResource(self): r = UserStatusTree(self) x = UserStatusXR(self) r.putChild('RPC2', x) return r def getIRCBot(self, nickname): f = protocol.ReconnectingClientFactory() f.protocol = IRCReplyBot f.nickname = nickname f.getUser = self.getUser return f def startService(self): self._read() service.Service.startService(self) def stopService(self): service.Service.stopService(self) self.call.cancel() application = service.Application('finger', uid=1, gid=1) f = FingerService('/etc/users') serviceCollection = service.IServiceCollection(application) internet.TCPServer(79, f.getFingerFactory() ).setServiceParent(serviceCollection) internet.TCPServer(8000, server.Site(f.getResource()) ).setServiceParent(serviceCollection) internet.TCPClient('irc.freenode.org', 6667, f.getIRCBot('fingerbot') ).setServiceParent(serviceCollection)

true

1c3828ea6b5762a786575e6a8677fcca9f43fee1

3,220

py

Python

app/db/__init__.py

MoeZilla/BlueMoonVampireBot

af074f846b56e7a4f262740d7b65b32ab11a4e42

[ "MIT" ]

1

2022-01-25T06:28:51.000Z

app/db/__init__.py

MoeZilla/BlueMoonVampireBot

af074f846b56e7a4f262740d7b65b32ab11a4e42

[ "MIT" ]

null

app/db/__init__.py

MoeZilla/BlueMoonVampireBot

af074f846b56e7a4f262740d7b65b32ab11a4e42

[ "MIT" ]

null

""" MIT License Copyright (C) 2021-2022, NkSama Copyright (C) 2021-2022 Moezilla Copyright (c) 2021, Sylviorus, <https://github.com/Sylviorus/BlueMoonVampireBot> This file is part of @BlueMoonVampireBot (Antispam Telegram Bot) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import json from operator import index from re import S from pymongo import MongoClient import os class DATABASE: def __init__(self, db_url) -> None: self.db_url = db_url self.role_db = MongoClient(self.db_url)['Sylviorus']['CUSTOM_ROLES'] def already_exists(self, user_id): x = self.db.find_one({"user_id": user_id}) if x: return True else: return False def add_role(self, user_id, role): if self.role_db.find_one({"user_id": user_id}): self.role_db.update_one({"user_id": user_id}, {"$set": { "role": role }}) else: self.role_db.insert_one({"user_id": user_id, "role": role}) def get_role(self, user_id): if self.role_db.find_one({"user_id": user_id}): role = self.role_db.find_one({"user_id": user_id}) final = { "user_id": role['user_id'], "role": role['role'], "status": True, } return final else: return {"status": False} class LocalDb: def __init__(self, db_name) -> None: self.db_name = f"{db_name}.json" def create_db(self): x = open(self.db_name, "w+") x.write("""{\"hello" : "world\"}""") x.close() def add_reason(self, key, value): if os.path.exists(self.db_name): with open(self.db_name) as f: db = json.load(f) db.update({key: value}) with open(self.db_name, "w") as f: json.dump(db, f) else: return "Create db first" def get_reason(self, key): if os.path.exists(self.db_name): with open(self.db_name, "r") as f: db = json.load(f) return db[key] else: return "No Data"

33.195876

80

0.614907

import json from operator import index from re import S from pymongo import MongoClient import os class DATABASE: def __init__(self, db_url) -> None: self.db_url = db_url self.role_db = MongoClient(self.db_url)['Sylviorus']['CUSTOM_ROLES'] def already_exists(self, user_id): x = self.db.find_one({"user_id": user_id}) if x: return True else: return False def add_role(self, user_id, role): if self.role_db.find_one({"user_id": user_id}): self.role_db.update_one({"user_id": user_id}, {"$set": { "role": role }}) else: self.role_db.insert_one({"user_id": user_id, "role": role}) def get_role(self, user_id): if self.role_db.find_one({"user_id": user_id}): role = self.role_db.find_one({"user_id": user_id}) final = { "user_id": role['user_id'], "role": role['role'], "status": True, } return final else: return {"status": False} class LocalDb: def __init__(self, db_name) -> None: self.db_name = f"{db_name}.json" def create_db(self): x = open(self.db_name, "w+") x.write("""{\"hello" : "world\"}""") x.close() def add_reason(self, key, value): if os.path.exists(self.db_name): with open(self.db_name) as f: db = json.load(f) db.update({key: value}) with open(self.db_name, "w") as f: json.dump(db, f) else: return "Create db first" def get_reason(self, key): if os.path.exists(self.db_name): with open(self.db_name, "r") as f: db = json.load(f) return db[key] else: return "No Data"

true

1c38290c557a6708c9109fea74a5db7debcf6324

3,521

py

Python

school/models.py

ntwaliandy/school-system

93b81f62a21ae477ac524a99d2fbb6f16ef2efae

[ "MIT" ]

null

school/models.py

ntwaliandy/school-system

93b81f62a21ae477ac524a99d2fbb6f16ef2efae

[ "MIT" ]

null

school/models.py

ntwaliandy/school-system

93b81f62a21ae477ac524a99d2fbb6f16ef2efae

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import User # Create your models here. class TeacherExtra(models.Model): user=models.OneToOneField(User,on_delete=models.CASCADE) salary = models.PositiveIntegerField(null=False) joindate=models.DateField(auto_now_add=True) mobile = models.CharField(max_length=40) status=models.BooleanField(default=False) def __str__(self): return self.user.first_name @property def get_id(self): return self.user.id @property def get_name(self): return self.user.first_name+" "+self.user.last_name classes=[('one','one'),('two','two'),('three','three'), ('four','four'),('five','five'),('six','six'),('seven','seven'),('eight','eight'),('nine','nine'),('ten','ten')] class StudentExtra(models.Model): user=models.OneToOneField(User,on_delete=models.CASCADE) roll = models.CharField(max_length=10) mobile = models.CharField(max_length=40,null=True) fee=models.PositiveIntegerField(null=True) cl= models.CharField(max_length=10,choices=classes,default='one') status=models.BooleanField(default=False) @property def get_name(self): return self.user.first_name+" "+self.user.last_name @property def get_id(self): return self.user.id def __str__(self): return self.user.first_name + ' ' + self.user.last_name + ' ==>> ' + self.cl + ' ' + 'level' class Attendance(models.Model): roll=models.CharField(max_length=10,null=True) date=models.DateField() cl=models.CharField(max_length=10) present_status = models.CharField(max_length=10) def __str__(self): return str(self.date) + ' ' + self.present_status class Notice(models.Model): date=models.DateField(auto_now=True) by=models.CharField(max_length=20,null=True,default='school') message=models.CharField(max_length=500) file=models.FileField() def __str__(self): return self.message + ' by ' + self.by class Notes(models.Model): date=models.DateField(auto_now=True) by=models.CharField(max_length=30,null=True, default='Your Name') year=models.CharField(max_length=10, choices=classes, default='one') roll=models.CharField(max_length=10) message=models.CharField(max_length=500) file=models.FileField() def __str__(self): return self.message + ' by ' + self.by class Grading(models.Model): date=models.DateField(auto_now=True) by=models.CharField(max_length=30, null=True, default='Your Name') student_first_name=models.CharField(max_length=30, null=True) student_last_name=models.CharField(max_length=30, null=True) roll=models.CharField(max_length=50) course_unit=models.CharField(max_length=100) assignments=models.DecimalField(max_digits=3, decimal_places=1) Tests=models.DecimalField(max_digits=6, decimal_places=1) exam=models.DecimalField(max_digits=6, decimal_places=1) total=models.DecimalField(max_digits=6, decimal_places=1, default='0.0') def save(self): self.total = self.assignments + self.Tests + self.exam return super(Grading, self).save() def __str__(self): return self.student_first_name + ' ==>> ' + self.course_unit class Help(models.Model): date=models.DateField(auto_now=True) message=models.TextField(blank=True, max_length=1000) student=models.CharField(max_length=30, null=True, default='student name') def __str__(self): return self.student + ' ' + 'complaint/s'

36.298969

112

0.703777

from django.db import models from django.contrib.auth.models import User class TeacherExtra(models.Model): user=models.OneToOneField(User,on_delete=models.CASCADE) salary = models.PositiveIntegerField(null=False) joindate=models.DateField(auto_now_add=True) mobile = models.CharField(max_length=40) status=models.BooleanField(default=False) def __str__(self): return self.user.first_name @property def get_id(self): return self.user.id @property def get_name(self): return self.user.first_name+" "+self.user.last_name classes=[('one','one'),('two','two'),('three','three'), ('four','four'),('five','five'),('six','six'),('seven','seven'),('eight','eight'),('nine','nine'),('ten','ten')] class StudentExtra(models.Model): user=models.OneToOneField(User,on_delete=models.CASCADE) roll = models.CharField(max_length=10) mobile = models.CharField(max_length=40,null=True) fee=models.PositiveIntegerField(null=True) cl= models.CharField(max_length=10,choices=classes,default='one') status=models.BooleanField(default=False) @property def get_name(self): return self.user.first_name+" "+self.user.last_name @property def get_id(self): return self.user.id def __str__(self): return self.user.first_name + ' ' + self.user.last_name + ' ==>> ' + self.cl + ' ' + 'level' class Attendance(models.Model): roll=models.CharField(max_length=10,null=True) date=models.DateField() cl=models.CharField(max_length=10) present_status = models.CharField(max_length=10) def __str__(self): return str(self.date) + ' ' + self.present_status class Notice(models.Model): date=models.DateField(auto_now=True) by=models.CharField(max_length=20,null=True,default='school') message=models.CharField(max_length=500) file=models.FileField() def __str__(self): return self.message + ' by ' + self.by class Notes(models.Model): date=models.DateField(auto_now=True) by=models.CharField(max_length=30,null=True, default='Your Name') year=models.CharField(max_length=10, choices=classes, default='one') roll=models.CharField(max_length=10) message=models.CharField(max_length=500) file=models.FileField() def __str__(self): return self.message + ' by ' + self.by class Grading(models.Model): date=models.DateField(auto_now=True) by=models.CharField(max_length=30, null=True, default='Your Name') student_first_name=models.CharField(max_length=30, null=True) student_last_name=models.CharField(max_length=30, null=True) roll=models.CharField(max_length=50) course_unit=models.CharField(max_length=100) assignments=models.DecimalField(max_digits=3, decimal_places=1) Tests=models.DecimalField(max_digits=6, decimal_places=1) exam=models.DecimalField(max_digits=6, decimal_places=1) total=models.DecimalField(max_digits=6, decimal_places=1, default='0.0') def save(self): self.total = self.assignments + self.Tests + self.exam return super(Grading, self).save() def __str__(self): return self.student_first_name + ' ==>> ' + self.course_unit class Help(models.Model): date=models.DateField(auto_now=True) message=models.TextField(blank=True, max_length=1000) student=models.CharField(max_length=30, null=True, default='student name') def __str__(self): return self.student + ' ' + 'complaint/s'

true

1c38298251177b9e862ba52d259770df7599f35e

11,130

py

Python

tests/func/test_scm.py

lucasalavapena/dvc

230eb7087df7f063ded7422af7ae45bd04eb794a

[ "Apache-2.0" ]

null

tests/func/test_scm.py

lucasalavapena/dvc

230eb7087df7f063ded7422af7ae45bd04eb794a

[ "Apache-2.0" ]

87

2021-04-27T08:17:31.000Z

2022-03-30T12:12:40.000Z

tests/func/test_scm.py

lucasalavapena/dvc

230eb7087df7f063ded7422af7ae45bd04eb794a

[ "Apache-2.0" ]

null

import os import sys import pytest from git import Repo from dvc.scm import SCM, Git, NoSCM from dvc.scm.base import SCMError from dvc.system import System from tests.basic_env import TestGitSubmodule from tests.utils import get_gitignore_content def test_init_none(tmp_dir): assert isinstance(SCM(os.fspath(tmp_dir), no_scm=True), NoSCM) def test_init_git(tmp_dir): Repo.init(os.fspath(tmp_dir)) assert isinstance(SCM(os.fspath(tmp_dir)), Git) def test_init_no_git(tmp_dir): with pytest.raises(SCMError): SCM(os.fspath(tmp_dir)) def test_init_sub_dir(tmp_dir): Repo.init(os.fspath(tmp_dir)) subdir = tmp_dir / "dir" subdir.mkdir() scm = SCM(os.fspath(subdir)) assert scm.root_dir == os.fspath(tmp_dir) def test_commit(tmp_dir, scm): tmp_dir.gen({"foo": "foo"}) scm.add(["foo"]) scm.commit("add") assert "foo" in scm.gitpython.git.ls_files() def test_is_tracked(tmp_dir, scm): tmp_dir.gen({"foo": "foo", "тест": "проверка"}) scm.add(["foo", "тест"]) abs_foo = os.path.abspath("foo") assert scm.is_tracked(abs_foo) assert scm.is_tracked("foo") assert scm.is_tracked("тест") scm.commit("add") assert scm.is_tracked(abs_foo) assert scm.is_tracked("foo") scm.gitpython.repo.index.remove(["foo"], working_tree=True) assert not scm.is_tracked(abs_foo) assert not scm.is_tracked("foo") assert not scm.is_tracked("not-existing-file") class TestSCMGitSubmodule(TestGitSubmodule): def test_git_submodule(self): self.assertIsInstance(SCM(os.curdir), Git) def test_commit_in_submodule(self): G = Git(self._root_dir) G.add(["foo"]) G.commit("add") self.assertTrue("foo" in self.git.git.ls_files()) def _count_gitignore_entries(line): lines = get_gitignore_content() return lines.count(line) def test_ignore(tmp_dir, scm): foo = os.fspath(tmp_dir / "foo") target = "/foo" scm.ignore(foo) assert (tmp_dir / ".gitignore").is_file() scm._reset() assert _count_gitignore_entries(target) == 1 scm.ignore(foo) assert (tmp_dir / ".gitignore").is_file() scm._reset() assert _count_gitignore_entries(target) == 1 scm.ignore_remove(foo) assert not (tmp_dir / ".gitignore").exists() def test_ignored(tmp_dir, scm): tmp_dir.gen({"dir1": {"file1.jpg": "cont", "file2.txt": "cont"}}) tmp_dir.gen({".gitignore": "dir1/*.jpg"}) assert scm.is_ignored(tmp_dir / "dir1" / "file1.jpg") assert not scm.is_ignored(tmp_dir / "dir1" / "file2.txt") def test_ignored_dir_unignored_subdirs(tmp_dir, scm): tmp_dir.gen({".gitignore": "data/**\n!data/**/\n!data/**/*.csv"}) scm.add([".gitignore"]) tmp_dir.gen( { os.path.join("data", "raw", "tracked.csv"): "cont", os.path.join("data", "raw", "not_tracked.json"): "cont", } ) assert not scm.is_ignored(tmp_dir / "data" / "raw" / "tracked.csv") assert scm.is_ignored(tmp_dir / "data" / "raw" / "not_tracked.json") assert not scm.is_ignored(tmp_dir / "data" / "raw" / "non_existent.csv") assert scm.is_ignored(tmp_dir / "data" / "raw" / "non_existent.json") assert not scm.is_ignored(tmp_dir / "data" / "non_existent.csv") assert scm.is_ignored(tmp_dir / "data" / "non_existent.json") assert not scm.is_ignored(f"data{os.sep}") # git check-ignore would now mark "data/raw" as ignored # after detecting it's a directory in the file system; # instead, we rely on the trailing separator to determine if handling a # a directory - for consistency between existent and non-existent paths assert scm.is_ignored(os.path.join("data", "raw")) assert not scm.is_ignored(os.path.join("data", f"raw{os.sep}")) assert scm.is_ignored(os.path.join("data", "non_existent")) assert not scm.is_ignored(os.path.join("data", f"non_existent{os.sep}")) def test_get_gitignore(tmp_dir, scm): tmp_dir.gen({"file1": "contents", "dir": {}}) data_dir = os.fspath(tmp_dir / "file1") entry, gitignore = scm._get_gitignore(data_dir) assert entry == "/file1" assert gitignore == os.fspath(tmp_dir / ".gitignore") data_dir = os.fspath(tmp_dir / "dir") entry, gitignore = scm._get_gitignore(data_dir) assert entry == "/dir" assert gitignore == os.fspath(tmp_dir / ".gitignore") def test_get_gitignore_symlink(tmp_dir, scm): tmp_dir.gen({"dir": {"subdir": {"data": "contents"}}}) link = os.fspath(tmp_dir / "link") target = os.fspath(tmp_dir / "dir" / "subdir" / "data") System.symlink(target, link) entry, gitignore = scm._get_gitignore(link) assert entry == "/link" assert gitignore == os.fspath(tmp_dir / ".gitignore") def test_get_gitignore_subdir(tmp_dir, scm): tmp_dir.gen({"dir1": {"file1": "cont", "dir2": {}}}) data_dir = os.fspath(tmp_dir / "dir1" / "file1") entry, gitignore = scm._get_gitignore(data_dir) assert entry == "/file1" assert gitignore == os.fspath(tmp_dir / "dir1" / ".gitignore") data_dir = os.fspath(tmp_dir / "dir1" / "dir2") entry, gitignore = scm._get_gitignore(data_dir) assert entry == "/dir2" assert gitignore == os.fspath(tmp_dir / "dir1" / ".gitignore") def test_gitignore_should_end_with_newline(tmp_dir, scm): tmp_dir.gen({"foo": "foo", "bar": "bar"}) foo = os.fspath(tmp_dir / "foo") bar = os.fspath(tmp_dir / "bar") gitignore = tmp_dir / ".gitignore" scm.ignore(foo) assert gitignore.read_text().endswith("\n") scm.ignore(bar) assert gitignore.read_text().endswith("\n") def test_gitignore_should_append_newline_to_gitignore(tmp_dir, scm): tmp_dir.gen({"foo": "foo", "bar": "bar"}) bar_path = os.fspath(tmp_dir / "bar") gitignore = tmp_dir / ".gitignore" gitignore.write_text("/foo") assert not gitignore.read_text().endswith("\n") scm.ignore(bar_path) contents = gitignore.read_text() assert gitignore.read_text().endswith("\n") assert contents.splitlines() == ["/foo", "/bar"] def test_git_detach_head(tmp_dir, scm): tmp_dir.scm_gen({"file": "0"}, commit="init") init_rev = scm.get_rev() with scm.detach_head() as rev: assert init_rev == rev assert init_rev == (tmp_dir / ".git" / "HEAD").read_text().strip() assert ( "ref: refs/heads/master" == (tmp_dir / ".git" / "HEAD").read_text().strip() ) def test_git_stash_workspace(tmp_dir, scm): tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen("file", "1") with scm.stash_workspace(): assert not scm.is_dirty() assert "0" == (tmp_dir / "file").read_text() assert scm.is_dirty() assert "1" == (tmp_dir / "file").read_text() @pytest.mark.parametrize( "ref, include_untracked", [ (None, True), (None, False), ("refs/foo/stash", True), ("refs/foo/stash", False), ], ) def test_git_stash_push(tmp_dir, scm, ref, include_untracked): from dvc.scm.git import Stash tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen({"file": "1", "untracked": "0"}) stash = Stash(scm, ref=ref) rev = stash.push(include_untracked=include_untracked) assert rev == scm.get_ref(stash.ref) assert "0" == (tmp_dir / "file").read_text() assert include_untracked != (tmp_dir / "untracked").exists() assert len(stash) == 1 stash.apply(rev) assert "1" == (tmp_dir / "file").read_text() assert "0" == (tmp_dir / "untracked").read_text() parts = list(stash.ref.split("/")) assert os.path.exists(os.path.join(os.fspath(tmp_dir), ".git", *parts)) assert os.path.exists( os.path.join(os.fspath(tmp_dir), ".git", "logs", *parts) ) @pytest.mark.parametrize("ref", [None, "refs/foo/stash"]) def test_git_stash_drop(tmp_dir, scm, ref): from dvc.scm.git import Stash tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen("file", "1") stash = Stash(scm, ref=ref) stash.push() tmp_dir.gen("file", "2") expected = stash.push() stash.drop(1) assert expected == scm.get_ref(stash.ref) assert len(stash) == 1 reason = """libgit2 stash_save() is flaky on linux when run inside pytest https://github.com/iterative/dvc/pull/5286#issuecomment-792574294""" @pytest.mark.parametrize( "ref", [ pytest.param( None, marks=pytest.mark.xfail( sys.platform == "linux", raises=AssertionError, reason=reason ), ), "refs/foo/stash", ], ) def test_git_stash_pop(tmp_dir, scm, ref): from dvc.scm.git import Stash tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen("file", "1") stash = Stash(scm, ref=ref) first = stash.push() tmp_dir.gen("file", "2") second = stash.push() assert second == stash.pop() assert len(stash) == 1 assert first == scm.get_ref(stash.ref) assert "2" == (tmp_dir / "file").read_text() @pytest.mark.parametrize("ref", [None, "refs/foo/stash"]) def test_git_stash_clear(tmp_dir, scm, ref): from dvc.scm.git import Stash tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen("file", "1") stash = Stash(scm, ref=ref) stash.push() tmp_dir.gen("file", "2") stash.push() stash.clear() assert len(stash) == 0 parts = list(stash.ref.split("/")) assert not os.path.exists(os.path.join(os.fspath(tmp_dir), ".git", *parts)) # NOTE: some backends will completely remove reflog file on clear, some # will only truncate it, either case means an empty stash log_path = os.path.join(os.fspath(tmp_dir), ".git", "logs", *parts) assert ( not os.path.exists(log_path) or not open(log_path, encoding="utf-8").read() ) @pytest.mark.needs_internet @pytest.mark.parametrize("server", [pytest.lazy_fixture("git_ssh")]) def test_git_ssh(tmp_dir, scm, server): from dulwich.repo import Repo as DulwichRepo from sshfs import SSHFileSystem from dvc.utils.fs import remove from tests.remotes.ssh import TEST_SSH_KEY_PATH, TEST_SSH_USER fs = SSHFileSystem( host=server.host, port=server.port, username=TEST_SSH_USER, client_keys=[TEST_SSH_KEY_PATH], ) server._ssh.execute("git init --bare test-repo.git") url = f"ssh://{TEST_SSH_USER}@{server.host}:{server.port}/~/test-repo.git" tmp_dir.scm_gen("foo", "foo", commit="init") rev = scm.get_rev() scm.push_refspec( url, "refs/heads/master", "refs/heads/master", force=True, key_filename=TEST_SSH_KEY_PATH, ) assert ( rev.encode("ascii") == fs.open("test-repo.git/refs/heads/master").read().strip() ) remove(tmp_dir / ".git") remove(tmp_dir / "foo") DulwichRepo.init(str(tmp_dir)) scm.fetch_refspecs( url, ["refs/heads/master"], force=True, key_filename=TEST_SSH_KEY_PATH, ) assert rev == scm.get_ref("refs/heads/master") scm.checkout("master") assert "foo" == (tmp_dir / "foo").read_text()

29.060052

79

0.637556

import os import sys import pytest from git import Repo from dvc.scm import SCM, Git, NoSCM from dvc.scm.base import SCMError from dvc.system import System from tests.basic_env import TestGitSubmodule from tests.utils import get_gitignore_content def test_init_none(tmp_dir): assert isinstance(SCM(os.fspath(tmp_dir), no_scm=True), NoSCM) def test_init_git(tmp_dir): Repo.init(os.fspath(tmp_dir)) assert isinstance(SCM(os.fspath(tmp_dir)), Git) def test_init_no_git(tmp_dir): with pytest.raises(SCMError): SCM(os.fspath(tmp_dir)) def test_init_sub_dir(tmp_dir): Repo.init(os.fspath(tmp_dir)) subdir = tmp_dir / "dir" subdir.mkdir() scm = SCM(os.fspath(subdir)) assert scm.root_dir == os.fspath(tmp_dir) def test_commit(tmp_dir, scm): tmp_dir.gen({"foo": "foo"}) scm.add(["foo"]) scm.commit("add") assert "foo" in scm.gitpython.git.ls_files() def test_is_tracked(tmp_dir, scm): tmp_dir.gen({"foo": "foo", "тест": "проверка"}) scm.add(["foo", "тест"]) abs_foo = os.path.abspath("foo") assert scm.is_tracked(abs_foo) assert scm.is_tracked("foo") assert scm.is_tracked("тест") scm.commit("add") assert scm.is_tracked(abs_foo) assert scm.is_tracked("foo") scm.gitpython.repo.index.remove(["foo"], working_tree=True) assert not scm.is_tracked(abs_foo) assert not scm.is_tracked("foo") assert not scm.is_tracked("not-existing-file") class TestSCMGitSubmodule(TestGitSubmodule): def test_git_submodule(self): self.assertIsInstance(SCM(os.curdir), Git) def test_commit_in_submodule(self): G = Git(self._root_dir) G.add(["foo"]) G.commit("add") self.assertTrue("foo" in self.git.git.ls_files()) def _count_gitignore_entries(line): lines = get_gitignore_content() return lines.count(line) def test_ignore(tmp_dir, scm): foo = os.fspath(tmp_dir / "foo") target = "/foo" scm.ignore(foo) assert (tmp_dir / ".gitignore").is_file() scm._reset() assert _count_gitignore_entries(target) == 1 scm.ignore(foo) assert (tmp_dir / ".gitignore").is_file() scm._reset() assert _count_gitignore_entries(target) == 1 scm.ignore_remove(foo) assert not (tmp_dir / ".gitignore").exists() def test_ignored(tmp_dir, scm): tmp_dir.gen({"dir1": {"file1.jpg": "cont", "file2.txt": "cont"}}) tmp_dir.gen({".gitignore": "dir1/*.jpg"}) assert scm.is_ignored(tmp_dir / "dir1" / "file1.jpg") assert not scm.is_ignored(tmp_dir / "dir1" / "file2.txt") def test_ignored_dir_unignored_subdirs(tmp_dir, scm): tmp_dir.gen({".gitignore": "data/**\n!data/**/\n!data/**/*.csv"}) scm.add([".gitignore"]) tmp_dir.gen( { os.path.join("data", "raw", "tracked.csv"): "cont", os.path.join("data", "raw", "not_tracked.json"): "cont", } ) assert not scm.is_ignored(tmp_dir / "data" / "raw" / "tracked.csv") assert scm.is_ignored(tmp_dir / "data" / "raw" / "not_tracked.json") assert not scm.is_ignored(tmp_dir / "data" / "raw" / "non_existent.csv") assert scm.is_ignored(tmp_dir / "data" / "raw" / "non_existent.json") assert not scm.is_ignored(tmp_dir / "data" / "non_existent.csv") assert scm.is_ignored(tmp_dir / "data" / "non_existent.json") assert not scm.is_ignored(f"data{os.sep}") # instead, we rely on the trailing separator to determine if handling a # a directory - for consistency between existent and non-existent paths assert scm.is_ignored(os.path.join("data", "raw")) assert not scm.is_ignored(os.path.join("data", f"raw{os.sep}")) assert scm.is_ignored(os.path.join("data", "non_existent")) assert not scm.is_ignored(os.path.join("data", f"non_existent{os.sep}")) def test_get_gitignore(tmp_dir, scm): tmp_dir.gen({"file1": "contents", "dir": {}}) data_dir = os.fspath(tmp_dir / "file1") entry, gitignore = scm._get_gitignore(data_dir) assert entry == "/file1" assert gitignore == os.fspath(tmp_dir / ".gitignore") data_dir = os.fspath(tmp_dir / "dir") entry, gitignore = scm._get_gitignore(data_dir) assert entry == "/dir" assert gitignore == os.fspath(tmp_dir / ".gitignore") def test_get_gitignore_symlink(tmp_dir, scm): tmp_dir.gen({"dir": {"subdir": {"data": "contents"}}}) link = os.fspath(tmp_dir / "link") target = os.fspath(tmp_dir / "dir" / "subdir" / "data") System.symlink(target, link) entry, gitignore = scm._get_gitignore(link) assert entry == "/link" assert gitignore == os.fspath(tmp_dir / ".gitignore") def test_get_gitignore_subdir(tmp_dir, scm): tmp_dir.gen({"dir1": {"file1": "cont", "dir2": {}}}) data_dir = os.fspath(tmp_dir / "dir1" / "file1") entry, gitignore = scm._get_gitignore(data_dir) assert entry == "/file1" assert gitignore == os.fspath(tmp_dir / "dir1" / ".gitignore") data_dir = os.fspath(tmp_dir / "dir1" / "dir2") entry, gitignore = scm._get_gitignore(data_dir) assert entry == "/dir2" assert gitignore == os.fspath(tmp_dir / "dir1" / ".gitignore") def test_gitignore_should_end_with_newline(tmp_dir, scm): tmp_dir.gen({"foo": "foo", "bar": "bar"}) foo = os.fspath(tmp_dir / "foo") bar = os.fspath(tmp_dir / "bar") gitignore = tmp_dir / ".gitignore" scm.ignore(foo) assert gitignore.read_text().endswith("\n") scm.ignore(bar) assert gitignore.read_text().endswith("\n") def test_gitignore_should_append_newline_to_gitignore(tmp_dir, scm): tmp_dir.gen({"foo": "foo", "bar": "bar"}) bar_path = os.fspath(tmp_dir / "bar") gitignore = tmp_dir / ".gitignore" gitignore.write_text("/foo") assert not gitignore.read_text().endswith("\n") scm.ignore(bar_path) contents = gitignore.read_text() assert gitignore.read_text().endswith("\n") assert contents.splitlines() == ["/foo", "/bar"] def test_git_detach_head(tmp_dir, scm): tmp_dir.scm_gen({"file": "0"}, commit="init") init_rev = scm.get_rev() with scm.detach_head() as rev: assert init_rev == rev assert init_rev == (tmp_dir / ".git" / "HEAD").read_text().strip() assert ( "ref: refs/heads/master" == (tmp_dir / ".git" / "HEAD").read_text().strip() ) def test_git_stash_workspace(tmp_dir, scm): tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen("file", "1") with scm.stash_workspace(): assert not scm.is_dirty() assert "0" == (tmp_dir / "file").read_text() assert scm.is_dirty() assert "1" == (tmp_dir / "file").read_text() @pytest.mark.parametrize( "ref, include_untracked", [ (None, True), (None, False), ("refs/foo/stash", True), ("refs/foo/stash", False), ], ) def test_git_stash_push(tmp_dir, scm, ref, include_untracked): from dvc.scm.git import Stash tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen({"file": "1", "untracked": "0"}) stash = Stash(scm, ref=ref) rev = stash.push(include_untracked=include_untracked) assert rev == scm.get_ref(stash.ref) assert "0" == (tmp_dir / "file").read_text() assert include_untracked != (tmp_dir / "untracked").exists() assert len(stash) == 1 stash.apply(rev) assert "1" == (tmp_dir / "file").read_text() assert "0" == (tmp_dir / "untracked").read_text() parts = list(stash.ref.split("/")) assert os.path.exists(os.path.join(os.fspath(tmp_dir), ".git", *parts)) assert os.path.exists( os.path.join(os.fspath(tmp_dir), ".git", "logs", *parts) ) @pytest.mark.parametrize("ref", [None, "refs/foo/stash"]) def test_git_stash_drop(tmp_dir, scm, ref): from dvc.scm.git import Stash tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen("file", "1") stash = Stash(scm, ref=ref) stash.push() tmp_dir.gen("file", "2") expected = stash.push() stash.drop(1) assert expected == scm.get_ref(stash.ref) assert len(stash) == 1 reason = """libgit2 stash_save() is flaky on linux when run inside pytest https://github.com/iterative/dvc/pull/5286#issuecomment-792574294""" @pytest.mark.parametrize( "ref", [ pytest.param( None, marks=pytest.mark.xfail( sys.platform == "linux", raises=AssertionError, reason=reason ), ), "refs/foo/stash", ], ) def test_git_stash_pop(tmp_dir, scm, ref): from dvc.scm.git import Stash tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen("file", "1") stash = Stash(scm, ref=ref) first = stash.push() tmp_dir.gen("file", "2") second = stash.push() assert second == stash.pop() assert len(stash) == 1 assert first == scm.get_ref(stash.ref) assert "2" == (tmp_dir / "file").read_text() @pytest.mark.parametrize("ref", [None, "refs/foo/stash"]) def test_git_stash_clear(tmp_dir, scm, ref): from dvc.scm.git import Stash tmp_dir.scm_gen({"file": "0"}, commit="init") tmp_dir.gen("file", "1") stash = Stash(scm, ref=ref) stash.push() tmp_dir.gen("file", "2") stash.push() stash.clear() assert len(stash) == 0 parts = list(stash.ref.split("/")) assert not os.path.exists(os.path.join(os.fspath(tmp_dir), ".git", *parts)) # NOTE: some backends will completely remove reflog file on clear, some # will only truncate it, either case means an empty stash log_path = os.path.join(os.fspath(tmp_dir), ".git", "logs", *parts) assert ( not os.path.exists(log_path) or not open(log_path, encoding="utf-8").read() ) @pytest.mark.needs_internet @pytest.mark.parametrize("server", [pytest.lazy_fixture("git_ssh")]) def test_git_ssh(tmp_dir, scm, server): from dulwich.repo import Repo as DulwichRepo from sshfs import SSHFileSystem from dvc.utils.fs import remove from tests.remotes.ssh import TEST_SSH_KEY_PATH, TEST_SSH_USER fs = SSHFileSystem( host=server.host, port=server.port, username=TEST_SSH_USER, client_keys=[TEST_SSH_KEY_PATH], ) server._ssh.execute("git init --bare test-repo.git") url = f"ssh://{TEST_SSH_USER}@{server.host}:{server.port}/~/test-repo.git" tmp_dir.scm_gen("foo", "foo", commit="init") rev = scm.get_rev() scm.push_refspec( url, "refs/heads/master", "refs/heads/master", force=True, key_filename=TEST_SSH_KEY_PATH, ) assert ( rev.encode("ascii") == fs.open("test-repo.git/refs/heads/master").read().strip() ) remove(tmp_dir / ".git") remove(tmp_dir / "foo") DulwichRepo.init(str(tmp_dir)) scm.fetch_refspecs( url, ["refs/heads/master"], force=True, key_filename=TEST_SSH_KEY_PATH, ) assert rev == scm.get_ref("refs/heads/master") scm.checkout("master") assert "foo" == (tmp_dir / "foo").read_text()

true

1c382bdec7b3a619d5432493cd7bf74eed54be9e

1,236

py

Python

src/ikea_api/endpoints/search.py

RCRalph/ikea-api-client

51c71bfc423d0b995f60add11837d7860d14f754

[ "MIT" ]

null

src/ikea_api/endpoints/search.py

RCRalph/ikea-api-client

51c71bfc423d0b995f60add11837d7860d14f754

[ "MIT" ]

null

src/ikea_api/endpoints/search.py

RCRalph/ikea-api-client

51c71bfc423d0b995f60add11837d7860d14f754

[ "MIT" ]

null

from __future__ import annotations from typing import Any, Literal from ikea_api.abc import Endpoint, SessionInfo, endpoint from ikea_api.base_ikea_api import BaseIkeaAPI from ikea_api.error_handlers import handle_json_decode_error SearchType = Literal["PRODUCT", "CONTENT", "PLANNER", "REFINED_SEARCHES", "ANSWER"] class Search(BaseIkeaAPI): def _get_session_info(self) -> SessionInfo: url = f"https://sik.search.blue.cdtapps.com/{self._const.country}/{self._const.language}/search-result-page" return SessionInfo(base_url=url, headers=self._extend_default_headers({})) @endpoint(handlers=[handle_json_decode_error]) def search( self, query: str, *, limit: int = 24, types: list[SearchType] = ["PRODUCT"], ) -> Endpoint[dict[str, Any]]: params = { "autocorrect": "true", "subcategories-style": "tree-navigation", "types": ",".join(types), "q": query, "size": limit, "c": "sr", # API client: sr - search results, sb - search bar "v": "20210322", # API version } response = yield self._RequestInfo("GET", params=params) return response.json

34.333333

116

0.631877

from __future__ import annotations from typing import Any, Literal from ikea_api.abc import Endpoint, SessionInfo, endpoint from ikea_api.base_ikea_api import BaseIkeaAPI from ikea_api.error_handlers import handle_json_decode_error SearchType = Literal["PRODUCT", "CONTENT", "PLANNER", "REFINED_SEARCHES", "ANSWER"] class Search(BaseIkeaAPI): def _get_session_info(self) -> SessionInfo: url = f"https://sik.search.blue.cdtapps.com/{self._const.country}/{self._const.language}/search-result-page" return SessionInfo(base_url=url, headers=self._extend_default_headers({})) @endpoint(handlers=[handle_json_decode_error]) def search( self, query: str, *, limit: int = 24, types: list[SearchType] = ["PRODUCT"], ) -> Endpoint[dict[str, Any]]: params = { "autocorrect": "true", "subcategories-style": "tree-navigation", "types": ",".join(types), "q": query, "size": limit, "c": "sr", "v": "20210322", } response = yield self._RequestInfo("GET", params=params) return response.json

true

1c382c4356470aefb4f62003845ae4e4c6777f19

1,608

py

Python

CFM_main/reader.py

michaelleerilee/CommunityFirnModel

312fc30b62b7e36a609660e5b10e3269eb090bae

[ "MIT" ]

null

CFM_main/reader.py

michaelleerilee/CommunityFirnModel

312fc30b62b7e36a609660e5b10e3269eb090bae

[ "MIT" ]

null

CFM_main/reader.py

michaelleerilee/CommunityFirnModel

312fc30b62b7e36a609660e5b10e3269eb090bae

[ "MIT" ]

null

#!usr/bin/env python ''' Functions to read model inputs. ''' import os import numpy as np # from string import join from constants import * import h5py def read_input(filename,StartDate=None): ''' Read in data from csv input files :param filename: name of the file which holds the accumulation rate data :return input_data: vector of field of interest (e.g. temperature, accumulation rate from a specified csv file :return input_year: corresponding time vector (in years) ''' spot = os.getcwd() FID = os.path.join(spot, filename) data = np.loadtxt(FID, delimiter=',') #changed 3/6/17 to loadtxt from genfromtxt; much faster xx,yy = np.shape(data) if xx>yy: input_year = data[:, 0] input_data = data[:, 1] else: input_year = data[0, :] input_data = data[1, :] input_year_full = input_year.copy() input_data_full = input_data.copy() if StartDate==None: pass else: StartInd = np.where(input_year>=StartDate)[0] input_year = input_year[StartInd] input_data = input_data[StartInd] return input_data, input_year, input_data_full, input_year_full def read_init(folder, resultsFileName, varname): ''' Read in data for initial depth, age, density, and temperature to run the model without spinup :param folder: the folder containing the files holding depth, age, density, and temperature ''' f5 = h5py.File(os.path.join(folder, resultsFileName),'r') init_value = f5[varname][:] f5.close() return init_value

26.8

114

0.659204

import os import numpy as np from constants import * import h5py def read_input(filename,StartDate=None): spot = os.getcwd() FID = os.path.join(spot, filename) data = np.loadtxt(FID, delimiter=',') xx,yy = np.shape(data) if xx>yy: input_year = data[:, 0] input_data = data[:, 1] else: input_year = data[0, :] input_data = data[1, :] input_year_full = input_year.copy() input_data_full = input_data.copy() if StartDate==None: pass else: StartInd = np.where(input_year>=StartDate)[0] input_year = input_year[StartInd] input_data = input_data[StartInd] return input_data, input_year, input_data_full, input_year_full def read_init(folder, resultsFileName, varname): f5 = h5py.File(os.path.join(folder, resultsFileName),'r') init_value = f5[varname][:] f5.close() return init_value

true

1c382cadea0a62ded77480b47a1fb27b85d96974

12,384

py

Python

third_party/html5lib/tests/test_treewalkers.py

dewitt/appengine-unshorten

1bb9e277eb67cefbf176b104d3297da5402e67b7

[ "Apache-2.0" ]

1

2020-09-03T23:55:06.000Z

third_party/html5lib/tests/test_treewalkers.py

dewitt/appengine-unshorten

1bb9e277eb67cefbf176b104d3297da5402e67b7

[ "Apache-2.0" ]

null

third_party/html5lib/tests/test_treewalkers.py

dewitt/appengine-unshorten

1bb9e277eb67cefbf176b104d3297da5402e67b7

[ "Apache-2.0" ]

null

import os import sys import StringIO import unittest import warnings warnings.simplefilter("error") from support import html5lib_test_files, TestData, convertExpected from html5lib import html5parser, treewalkers, treebuilders, constants from html5lib.filters.lint import Filter as LintFilter, LintError def PullDOMAdapter(node): from xml.dom import Node from xml.dom.pulldom import START_ELEMENT, END_ELEMENT, COMMENT, CHARACTERS if node.nodeType in (Node.DOCUMENT_NODE, Node.DOCUMENT_FRAGMENT_NODE): for childNode in node.childNodes: for event in PullDOMAdapter(childNode): yield event elif node.nodeType == Node.DOCUMENT_TYPE_NODE: raise NotImplementedError("DOCTYPE nodes are not supported by PullDOM") elif node.nodeType == Node.COMMENT_NODE: yield COMMENT, node elif node.nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE): yield CHARACTERS, node elif node.nodeType == Node.ELEMENT_NODE: yield START_ELEMENT, node for childNode in node.childNodes: for event in PullDOMAdapter(childNode): yield event yield END_ELEMENT, node else: raise NotImplementedError("Node type not supported: " + str(node.nodeType)) treeTypes = { "simpletree": {"builder": treebuilders.getTreeBuilder("simpletree"), "walker": treewalkers.getTreeWalker("simpletree")}, "DOM": {"builder": treebuilders.getTreeBuilder("dom"), "walker": treewalkers.getTreeWalker("dom")}, "PullDOM": {"builder": treebuilders.getTreeBuilder("dom"), "adapter": PullDOMAdapter, "walker": treewalkers.getTreeWalker("pulldom")}, } #Try whatever etree implementations are available from a list that are #"supposed" to work try: import xml.etree.ElementTree as ElementTree treeTypes['ElementTree'] = \ {"builder": treebuilders.getTreeBuilder("etree", ElementTree), "walker": treewalkers.getTreeWalker("etree", ElementTree)} except ImportError: try: import elementtree.ElementTree as ElementTree treeTypes['ElementTree'] = \ {"builder": treebuilders.getTreeBuilder("etree", ElementTree), "walker": treewalkers.getTreeWalker("etree", ElementTree)} except ImportError: pass try: import xml.etree.cElementTree as ElementTree treeTypes['cElementTree'] = \ {"builder": treebuilders.getTreeBuilder("etree", ElementTree), "walker": treewalkers.getTreeWalker("etree", ElementTree)} except ImportError: try: import cElementTree as ElementTree treeTypes['cElementTree'] = \ {"builder": treebuilders.getTreeBuilder("etree", ElementTree), "walker": treewalkers.getTreeWalker("etree", ElementTree)} except ImportError: pass try: import lxml.etree as ElementTree # treeTypes['lxml_as_etree'] = \ # {"builder": treebuilders.getTreeBuilder("etree", ElementTree), # "walker": treewalkers.getTreeWalker("etree", ElementTree)} treeTypes['lxml_native'] = \ {"builder": treebuilders.getTreeBuilder("lxml"), "walker": treewalkers.getTreeWalker("lxml")} except ImportError: pass try: import BeautifulSoup treeTypes["beautifulsoup"] = \ {"builder": treebuilders.getTreeBuilder("beautifulsoup"), "walker": treewalkers.getTreeWalker("beautifulsoup")} except ImportError: pass #Try whatever etree implementations are available from a list that are #"supposed" to work try: import pxdom treeTypes['pxdom'] = \ {"builder": treebuilders.getTreeBuilder("dom", pxdom), "walker": treewalkers.getTreeWalker("dom")} except ImportError: pass try: from genshi.core import QName, Attrs from genshi.core import START, END, TEXT, COMMENT, DOCTYPE def GenshiAdapter(tree): text = None for token in treewalkers.getTreeWalker("simpletree")(tree): type = token["type"] if type in ("Characters", "SpaceCharacters"): if text is None: text = token["data"] else: text += token["data"] elif text is not None: yield TEXT, text, (None, -1, -1) text = None if type in ("StartTag", "EmptyTag"): if token["namespace"]: name = u"{%s}%s" % (token["namespace"], token["name"]) else: name = token["name"] yield (START, (QName(name), Attrs([(QName(attr),value) for attr,value in token["data"]])), (None, -1, -1)) if type == "EmptyTag": type = "EndTag" if type == "EndTag": yield END, QName(token["name"]), (None, -1, -1) elif type == "Comment": yield COMMENT, token["data"], (None, -1, -1) elif type == "Doctype": yield DOCTYPE, (token["name"], token["publicId"], token["systemId"]), (None, -1, -1) else: pass # FIXME: What to do? if text is not None: yield TEXT, text, (None, -1, -1) #treeTypes["genshi"] = \ # {"builder": treebuilders.getTreeBuilder("simpletree"), # "adapter": GenshiAdapter, # "walker": treewalkers.getTreeWalker("genshi")} except ImportError: pass def concatenateCharacterTokens(tokens): charactersToken = None for token in tokens: type = token["type"] if type in ("Characters", "SpaceCharacters"): if charactersToken is None: charactersToken = {"type": "Characters", "data": token["data"]} else: charactersToken["data"] += token["data"] else: if charactersToken is not None: yield charactersToken charactersToken = None yield token if charactersToken is not None: yield charactersToken def convertTokens(tokens): output = [] indent = 0 for token in concatenateCharacterTokens(tokens): type = token["type"] if type in ("StartTag", "EmptyTag"): if (token["namespace"] and token["namespace"] != constants.namespaces["html"]): if token["namespace"] in constants.prefixes: name = constants.prefixes[token["namespace"]] else: name = token["namespace"] name += u" " + token["name"] else: name = token["name"] output.append(u"%s<%s>" % (" "*indent, name)) indent += 2 attrs = token["data"] if attrs: attrs.sort(lambda a,b: cmp(a["name"], b["name"])) for attr in attrs: if attr["namespace"]: if attr["namespace"] in constants.prefixes: name = constants.prefixes[attr["namespace"]] else: name = attr["namespace"] name += u" " + attr["name"] else: name = attr["name"] output.append(u"%s%s=\"%s\"" % (" "*indent, name, attr["value"])) if type == "EmptyTag": indent -= 2 elif type == "EndTag": indent -= 2 elif type == "Comment": output.append("%s" % (" "*indent, token["data"])) elif type == "Doctype": if token["name"]: if token["publicId"]: output.append("""%s<!DOCTYPE %s "%s" "%s">"""% (" "*indent, token["name"], token["publicId"], token["systemId"] and token["systemId"] or "")) elif token["systemId"]: output.append("""%s<!DOCTYPE %s "" "%s">"""% (" "*indent, token["name"], token["systemId"])) else: output.append("%s<!DOCTYPE %s>"%(" "*indent, token["name"])) else: output.append("%s<!DOCTYPE >" % (" "*indent,)) elif type in ("Characters", "SpaceCharacters"): output.append("%s\"%s\"" % (" "*indent, token["data"])) else: pass # TODO: what to do with errors? return u"\n".join(output) import re attrlist = re.compile(r"^(\s+)\w+=.*(\n\1\w+=.*)+",re.M) def sortattrs(x): lines = x.group(0).split("\n") lines.sort() return "\n".join(lines) class TestCase(unittest.TestCase): def runTest(self, innerHTML, input, expected, errors, treeClass): try: p = html5parser.HTMLParser(tree = treeClass["builder"]) if innerHTML: document = p.parseFragment(StringIO.StringIO(input), innerHTML) else: document = p.parse(StringIO.StringIO(input)) except constants.DataLossWarning: #Ignore testcases we know we don't pass return document = treeClass.get("adapter", lambda x: x)(document) try: output = convertTokens(treeClass["walker"](document)) output = attrlist.sub(sortattrs, output) expected = attrlist.sub(sortattrs, convertExpected(expected)) self.assertEquals(expected, output, "\n".join([ "", "Input:", input, "", "Expected:", expected, "", "Received:", output ])) except NotImplementedError: pass # Amnesty for those that confess... class TokenTestCase(unittest.TestCase): def test_all_tokens(self): expected = [ {'data': [], 'type': 'StartTag', 'name': u'html'}, {'data': [], 'type': 'StartTag', 'name': u'head'}, {'data': [], 'type': 'EndTag', 'name': u'head'}, {'data': [], 'type': 'StartTag', 'name': u'body'}, {'data': u'a', 'type': 'Characters'}, {'data': [], 'type': 'StartTag', 'name': u'div'}, {'data': u'b', 'type': 'Characters'}, {'data': [], 'type': 'EndTag', 'name': u'div'}, {'data': u'c', 'type': 'Characters'}, {'data': [], 'type': 'EndTag', 'name': u'body'}, {'data': [], 'type': 'EndTag', 'name': u'html'} ] for treeName, treeCls in treeTypes.iteritems(): p = html5parser.HTMLParser(tree = treeCls["builder"]) document = p.parse("<html><head></head><body>a<div>b</div>c</body></html>") document = treeCls.get("adapter", lambda x: x)(document) output = treeCls["walker"](document) for expectedToken, outputToken in zip(expected, output): self.assertEquals(expectedToken, outputToken) def buildTestSuite(): sys.stdout.write('Testing tree walkers '+ " ".join(treeTypes.keys()) + "\n") for treeName, treeCls in treeTypes.iteritems(): files = html5lib_test_files('tree-construction') for filename in files: testName = os.path.basename(filename).replace(".dat","") tests = TestData(filename, "data") for index, test in enumerate(tests): (input, errors, innerHTML, expected) = [test[key] for key in ("data", "errors", "document-fragment", "document")] errors = errors.split("\n") def testFunc(self, innerHTML=innerHTML, input=input, expected=expected, errors=errors, treeCls=treeCls): self.runTest(innerHTML, input, expected, errors, treeCls) setattr(TestCase, "test_%s_%d_%s" % (testName,index+1,treeName), testFunc) return unittest.TestLoader().loadTestsFromTestCase(TestCase) def main(): buildTestSuite() unittest.main() if __name__ == "__main__": main()

38.222222

87

0.537791

import os import sys import StringIO import unittest import warnings warnings.simplefilter("error") from support import html5lib_test_files, TestData, convertExpected from html5lib import html5parser, treewalkers, treebuilders, constants from html5lib.filters.lint import Filter as LintFilter, LintError def PullDOMAdapter(node): from xml.dom import Node from xml.dom.pulldom import START_ELEMENT, END_ELEMENT, COMMENT, CHARACTERS if node.nodeType in (Node.DOCUMENT_NODE, Node.DOCUMENT_FRAGMENT_NODE): for childNode in node.childNodes: for event in PullDOMAdapter(childNode): yield event elif node.nodeType == Node.DOCUMENT_TYPE_NODE: raise NotImplementedError("DOCTYPE nodes are not supported by PullDOM") elif node.nodeType == Node.COMMENT_NODE: yield COMMENT, node elif node.nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE): yield CHARACTERS, node elif node.nodeType == Node.ELEMENT_NODE: yield START_ELEMENT, node for childNode in node.childNodes: for event in PullDOMAdapter(childNode): yield event yield END_ELEMENT, node else: raise NotImplementedError("Node type not supported: " + str(node.nodeType)) treeTypes = { "simpletree": {"builder": treebuilders.getTreeBuilder("simpletree"), "walker": treewalkers.getTreeWalker("simpletree")}, "DOM": {"builder": treebuilders.getTreeBuilder("dom"), "walker": treewalkers.getTreeWalker("dom")}, "PullDOM": {"builder": treebuilders.getTreeBuilder("dom"), "adapter": PullDOMAdapter, "walker": treewalkers.getTreeWalker("pulldom")}, } try: import xml.etree.ElementTree as ElementTree treeTypes['ElementTree'] = \ {"builder": treebuilders.getTreeBuilder("etree", ElementTree), "walker": treewalkers.getTreeWalker("etree", ElementTree)} except ImportError: try: import elementtree.ElementTree as ElementTree treeTypes['ElementTree'] = \ {"builder": treebuilders.getTreeBuilder("etree", ElementTree), "walker": treewalkers.getTreeWalker("etree", ElementTree)} except ImportError: pass try: import xml.etree.cElementTree as ElementTree treeTypes['cElementTree'] = \ {"builder": treebuilders.getTreeBuilder("etree", ElementTree), "walker": treewalkers.getTreeWalker("etree", ElementTree)} except ImportError: try: import cElementTree as ElementTree treeTypes['cElementTree'] = \ {"builder": treebuilders.getTreeBuilder("etree", ElementTree), "walker": treewalkers.getTreeWalker("etree", ElementTree)} except ImportError: pass try: import lxml.etree as ElementTree treeTypes['lxml_native'] = \ {"builder": treebuilders.getTreeBuilder("lxml"), "walker": treewalkers.getTreeWalker("lxml")} except ImportError: pass try: import BeautifulSoup treeTypes["beautifulsoup"] = \ {"builder": treebuilders.getTreeBuilder("beautifulsoup"), "walker": treewalkers.getTreeWalker("beautifulsoup")} except ImportError: pass try: import pxdom treeTypes['pxdom'] = \ {"builder": treebuilders.getTreeBuilder("dom", pxdom), "walker": treewalkers.getTreeWalker("dom")} except ImportError: pass try: from genshi.core import QName, Attrs from genshi.core import START, END, TEXT, COMMENT, DOCTYPE def GenshiAdapter(tree): text = None for token in treewalkers.getTreeWalker("simpletree")(tree): type = token["type"] if type in ("Characters", "SpaceCharacters"): if text is None: text = token["data"] else: text += token["data"] elif text is not None: yield TEXT, text, (None, -1, -1) text = None if type in ("StartTag", "EmptyTag"): if token["namespace"]: name = u"{%s}%s" % (token["namespace"], token["name"]) else: name = token["name"] yield (START, (QName(name), Attrs([(QName(attr),value) for attr,value in token["data"]])), (None, -1, -1)) if type == "EmptyTag": type = "EndTag" if type == "EndTag": yield END, QName(token["name"]), (None, -1, -1) elif type == "Comment": yield COMMENT, token["data"], (None, -1, -1) elif type == "Doctype": yield DOCTYPE, (token["name"], token["publicId"], token["systemId"]), (None, -1, -1) else: pass if text is not None: yield TEXT, text, (None, -1, -1) except ImportError: pass def concatenateCharacterTokens(tokens): charactersToken = None for token in tokens: type = token["type"] if type in ("Characters", "SpaceCharacters"): if charactersToken is None: charactersToken = {"type": "Characters", "data": token["data"]} else: charactersToken["data"] += token["data"] else: if charactersToken is not None: yield charactersToken charactersToken = None yield token if charactersToken is not None: yield charactersToken def convertTokens(tokens): output = [] indent = 0 for token in concatenateCharacterTokens(tokens): type = token["type"] if type in ("StartTag", "EmptyTag"): if (token["namespace"] and token["namespace"] != constants.namespaces["html"]): if token["namespace"] in constants.prefixes: name = constants.prefixes[token["namespace"]] else: name = token["namespace"] name += u" " + token["name"] else: name = token["name"] output.append(u"%s<%s>" % (" "*indent, name)) indent += 2 attrs = token["data"] if attrs: attrs.sort(lambda a,b: cmp(a["name"], b["name"])) for attr in attrs: if attr["namespace"]: if attr["namespace"] in constants.prefixes: name = constants.prefixes[attr["namespace"]] else: name = attr["namespace"] name += u" " + attr["name"] else: name = attr["name"] output.append(u"%s%s=\"%s\"" % (" "*indent, name, attr["value"])) if type == "EmptyTag": indent -= 2 elif type == "EndTag": indent -= 2 elif type == "Comment": output.append("%s" % (" "*indent, token["data"])) elif type == "Doctype": if token["name"]: if token["publicId"]: output.append("""%s<!DOCTYPE %s "%s" "%s">"""% (" "*indent, token["name"], token["publicId"], token["systemId"] and token["systemId"] or "")) elif token["systemId"]: output.append("""%s<!DOCTYPE %s "" "%s">"""% (" "*indent, token["name"], token["systemId"])) else: output.append("%s<!DOCTYPE %s>"%(" "*indent, token["name"])) else: output.append("%s<!DOCTYPE >" % (" "*indent,)) elif type in ("Characters", "SpaceCharacters"): output.append("%s\"%s\"" % (" "*indent, token["data"])) else: pass return u"\n".join(output) import re attrlist = re.compile(r"^(\s+)\w+=.*(\n\1\w+=.*)+",re.M) def sortattrs(x): lines = x.group(0).split("\n") lines.sort() return "\n".join(lines) class TestCase(unittest.TestCase): def runTest(self, innerHTML, input, expected, errors, treeClass): try: p = html5parser.HTMLParser(tree = treeClass["builder"]) if innerHTML: document = p.parseFragment(StringIO.StringIO(input), innerHTML) else: document = p.parse(StringIO.StringIO(input)) except constants.DataLossWarning: return document = treeClass.get("adapter", lambda x: x)(document) try: output = convertTokens(treeClass["walker"](document)) output = attrlist.sub(sortattrs, output) expected = attrlist.sub(sortattrs, convertExpected(expected)) self.assertEquals(expected, output, "\n".join([ "", "Input:", input, "", "Expected:", expected, "", "Received:", output ])) except NotImplementedError: pass # Amnesty for those that confess... class TokenTestCase(unittest.TestCase): def test_all_tokens(self): expected = [ {'data': [], 'type': 'StartTag', 'name': u'html'}, {'data': [], 'type': 'StartTag', 'name': u'head'}, {'data': [], 'type': 'EndTag', 'name': u'head'}, {'data': [], 'type': 'StartTag', 'name': u'body'}, {'data': u'a', 'type': 'Characters'}, {'data': [], 'type': 'StartTag', 'name': u'div'}, {'data': u'b', 'type': 'Characters'}, {'data': [], 'type': 'EndTag', 'name': u'div'}, {'data': u'c', 'type': 'Characters'}, {'data': [], 'type': 'EndTag', 'name': u'body'}, {'data': [], 'type': 'EndTag', 'name': u'html'} ] for treeName, treeCls in treeTypes.iteritems(): p = html5parser.HTMLParser(tree = treeCls["builder"]) document = p.parse("<html><head></head><body>a<div>b</div>c</body></html>") document = treeCls.get("adapter", lambda x: x)(document) output = treeCls["walker"](document) for expectedToken, outputToken in zip(expected, output): self.assertEquals(expectedToken, outputToken) def buildTestSuite(): sys.stdout.write('Testing tree walkers '+ " ".join(treeTypes.keys()) + "\n") for treeName, treeCls in treeTypes.iteritems(): files = html5lib_test_files('tree-construction') for filename in files: testName = os.path.basename(filename).replace(".dat","") tests = TestData(filename, "data") for index, test in enumerate(tests): (input, errors, innerHTML, expected) = [test[key] for key in ("data", "errors", "document-fragment", "document")] errors = errors.split("\n") def testFunc(self, innerHTML=innerHTML, input=input, expected=expected, errors=errors, treeCls=treeCls): self.runTest(innerHTML, input, expected, errors, treeCls) setattr(TestCase, "test_%s_%d_%s" % (testName,index+1,treeName), testFunc) return unittest.TestLoader().loadTestsFromTestCase(TestCase) def main(): buildTestSuite() unittest.main() if __name__ == "__main__": main()

true

1c382cbe929885479c5aacb20d6f2814d53176b1

10,360

py

Python

rnn-gru-random/train.py

manashpratim/Classification-of-Song-Attributes-using-Lyrics

801b7d45bd98f3edbb4219d7e947a1bd20ec206c

[ "MIT" ]

1

2020-01-17T03:05:48.000Z

rnn-gru-random/train.py

manashpratim/Classification-of-Song-Attributes-using-Lyrics

801b7d45bd98f3edbb4219d7e947a1bd20ec206c

[ "MIT" ]

null

rnn-gru-random/train.py

manashpratim/Classification-of-Song-Attributes-using-Lyrics

801b7d45bd98f3edbb4219d7e947a1bd20ec206c

[ "MIT" ]

null

import tensorflow as tf import numpy as np import os,sys import datetime import time from rnn import RNN import data_helpers # Parameters # ================================================== did = sys.argv[1] f1 = open("log_{}".format(did),"w") # Data loading params tf.flags.DEFINE_string("pos_dir", "data/rt-polaritydata/rt-polarity.pos", "Path of positive data") tf.flags.DEFINE_string("neg_dir", "data/rt-polaritydata/rt-polarity.neg", "Path of negative data") tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation") #tf.flags.DEFINE_integer("max_sentence_length", 100, "Max sentence length in train/test data (Default: 100)") # Model Hyperparameters tf.flags.DEFINE_string("cell_type", "lstm", "Type of rnn cell. Choose 'vanilla' or 'lstm' or 'gru' (Default: vanilla)") tf.flags.DEFINE_string("word2vec", None, "Word2vec file with pre-trained embeddings") tf.flags.DEFINE_integer("embedding_dim", 300, "Dimensionality of character embedding (Default: 300)") tf.flags.DEFINE_integer("hidden_size", 64, "Dimensionality of character embedding (Default: 128)") tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (Default: 0.5)") tf.flags.DEFINE_float("l2_reg_lambda", 3.0, "L2 regularization lambda (Default: 3.0)") # Training parameters tf.flags.DEFINE_integer("batch_size", 60, "Batch Size (Default: 64)") tf.flags.DEFINE_integer("num_epochs", 30, "Number of training epochs (Default: 100)") tf.flags.DEFINE_integer("display_every", 50, "Number of iterations to display training info.") tf.flags.DEFINE_integer("evaluate_every", 50, "Evaluate model on dev set after this many steps") tf.flags.DEFINE_integer("checkpoint_every", 1000, "Save model after this many steps") tf.flags.DEFINE_integer("num_checkpoints", 11, "Number of checkpoints to store") tf.flags.DEFINE_float("learning_rate", 1e-3, "Which learning rate to start with. (Default: 1e-3)") # Misc Parameters tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") FLAGS = tf.flags.FLAGS FLAGS._parse_flags() print("\nParameters:") f1.write("\nParameters:\n") for attr, value in sorted(FLAGS.__flags.items()): print("{} = {}".format(attr.upper(), value)) f1.write("{} = {}\n".format(attr.upper(), value)) print("") def train(x_train, y_train, vocabulary, x_dev, y_dev,cell): #with tf.device('/cpu:0'): # x_text, y = data_helpers.load_data_and_labels(FLAGS.pos_dir, FLAGS.neg_dir) #text_vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(FLAGS.max_sentence_length) #x = np.array(list(text_vocab_processor.fit_transform(x_text))) #print("Text Vocabulary Size: {:d}".format(len(text_vocab_processor.vocabulary_))) #print("x = {0}".format(x.shape)) #print("y = {0}".format(y.shape)) #print("") # Randomly shuffle data #np.random.seed(10) #shuffle_indices = np.random.permutation(np.arange(len(y))) #x_shuffled = x[shuffle_indices] #y_shuffled = y[shuffle_indices] # Split train/test set # TODO: This is very crude, should use cross-validation #dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y))) #x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:] #y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:] #print("Train/Dev split: {:d}/{:d}\n".format(len(y_train), len(y_dev))) print("*************************************************"+cell+"****************************************************") with tf.Graph().as_default(): gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.40) session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement, gpu_options=gpu_options) sess = tf.Session(config=session_conf) with sess.as_default(): rnn = RNN( sequence_length=x_train.shape[1], num_classes=y_train.shape[1], vocab_size=len(vocabulary), embedding_size=FLAGS.embedding_dim, cell_type=cell, hidden_size=FLAGS.hidden_size, l2_reg_lambda=FLAGS.l2_reg_lambda ) # Define Training procedure global_step = tf.Variable(0, name="global_step", trainable=False) train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(rnn.loss, global_step=global_step) # Output directory for models and summaries #timestamp = str(int(time.time())) #out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp)) #print("Writing to {}\n".format(out_dir)) # Summaries for loss and accuracy loss_summary = tf.summary.scalar("loss", rnn.loss) acc_summary = tf.summary.scalar("accuracy", rnn.accuracy) # Train Summaries #train_summary_op = tf.summary.merge([loss_summary, acc_summary]) #train_summary_dir = os.path.join(out_dir, "summaries", "train") #train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph) # Dev summaries #dev_summary_op = tf.summary.merge([loss_summary, acc_summary]) #dev_summary_dir = os.path.join(out_dir, "summaries", "dev") #dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph) # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it checkpoint_dir = "models/" checkpoint_prefix = os.path.join(checkpoint_dir, "model_{}_{}".format(cell,did)) if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints) # Write vocabulary #text_vocab_processor.save(os.path.join(out_dir, "text_vocab")) # Initialize all variables sess.run(tf.global_variables_initializer()) # Pre-trained word2vec if FLAGS.word2vec: # initial matrix with random uniform initW = np.random.uniform(-0.25, 0.25, (len(text_vocab_processor.vocabulary_), FLAGS.embedding_dim)) # load any vectors from the word2vec print("Load word2vec file {0}".format(FLAGS.word2vec)) with open(FLAGS.word2vec, "rb") as f: header = f.readline() vocab_size, layer1_size = map(int, header.split()) binary_len = np.dtype('float32').itemsize * layer1_size for line in range(vocab_size): word = [] while True: ch = f.read(1).decode('latin-1') if ch == ' ': word = ''.join(word) break if ch != '\n': word.append(ch) idx = text_vocab_processor.vocabulary_.get(word) if idx != 0: initW[idx] = np.fromstring(f.read(binary_len), dtype='float32') else: f.read(binary_len) sess.run(rnn.W_text.assign(initW)) print("Success to load pre-trained word2vec model!\n") # Generate batches batches = data_helpers.batch_iter( list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) # Training loop. For each batch... for batch in batches: x_batch, y_batch = zip(*batch) # Train feed_dict = { rnn.input_text: x_batch, rnn.input_y: y_batch, rnn.dropout_keep_prob: FLAGS.dropout_keep_prob } _, step, loss, accuracy = sess.run( [train_op, global_step, rnn.loss, rnn.accuracy], feed_dict) #train_summary_writer.add_summary(summaries, step) # Training log display if step % FLAGS.display_every == 0: time_str = datetime.datetime.now().isoformat() print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy)) f1.write("{}: step {}, loss {:g}, acc {:g}\n".format(time_str, step, loss, accuracy)) # Evaluation if step % FLAGS.evaluate_every == 0: f1.write("\nEvaluation:\n") print("\nEvaluation:") feed_dict_dev = { rnn.input_text: x_dev, rnn.input_y: y_dev, rnn.dropout_keep_prob: 1.0 } step, loss, accuracy = sess.run( [global_step, rnn.loss, rnn.accuracy], feed_dict_dev) #dev_summary_writer.add_summary(summaries_dev, step) time_str = datetime.datetime.now().isoformat() print("{}: step {}, loss {:g}, acc {:g}\n".format(time_str, step, loss, accuracy)) f1.write("{}: step {}, loss {:g}, acc {:g}\n".format(time_str, step, loss, accuracy)) # Model checkpoint if step % FLAGS.checkpoint_every == 0: path = saver.save(sess, checkpoint_prefix, global_step=step) print("Saved model checkpoint to {}\n".format(path)) f1.write("Saved model checkpoint to {}\n".format(path)) path = saver.save(sess, "models/model-final_{}_{}".format(cell,did)) print("Saved model checkpoint to {}\n".format(path)) f1.write("Saved model checkpoint to {}\n".format(path)) def main(_): x_train, y_train, vocabulary, x_dev, y_dev = data_helpers.preprocess(did) cell_types =["gru"] for cell_type in cell_types: train(x_train, y_train, vocabulary, x_dev, y_dev,cell_type) if __name__ == "__main__": tf.app.run()

48.867925

122

0.597587

import tensorflow as tf import numpy as np import os,sys import datetime import time from rnn import RNN import data_helpers did = sys.argv[1] f1 = open("log_{}".format(did),"w") tf.flags.DEFINE_string("pos_dir", "data/rt-polaritydata/rt-polarity.pos", "Path of positive data") tf.flags.DEFINE_string("neg_dir", "data/rt-polaritydata/rt-polarity.neg", "Path of negative data") tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation") tf.flags.DEFINE_string("cell_type", "lstm", "Type of rnn cell. Choose 'vanilla' or 'lstm' or 'gru' (Default: vanilla)") tf.flags.DEFINE_string("word2vec", None, "Word2vec file with pre-trained embeddings") tf.flags.DEFINE_integer("embedding_dim", 300, "Dimensionality of character embedding (Default: 300)") tf.flags.DEFINE_integer("hidden_size", 64, "Dimensionality of character embedding (Default: 128)") tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (Default: 0.5)") tf.flags.DEFINE_float("l2_reg_lambda", 3.0, "L2 regularization lambda (Default: 3.0)") tf.flags.DEFINE_integer("batch_size", 60, "Batch Size (Default: 64)") tf.flags.DEFINE_integer("num_epochs", 30, "Number of training epochs (Default: 100)") tf.flags.DEFINE_integer("display_every", 50, "Number of iterations to display training info.") tf.flags.DEFINE_integer("evaluate_every", 50, "Evaluate model on dev set after this many steps") tf.flags.DEFINE_integer("checkpoint_every", 1000, "Save model after this many steps") tf.flags.DEFINE_integer("num_checkpoints", 11, "Number of checkpoints to store") tf.flags.DEFINE_float("learning_rate", 1e-3, "Which learning rate to start with. (Default: 1e-3)") tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") FLAGS = tf.flags.FLAGS FLAGS._parse_flags() print("\nParameters:") f1.write("\nParameters:\n") for attr, value in sorted(FLAGS.__flags.items()): print("{} = {}".format(attr.upper(), value)) f1.write("{} = {}\n".format(attr.upper(), value)) print("") def train(x_train, y_train, vocabulary, x_dev, y_dev,cell): print("*************************************************"+cell+"****************************************************") with tf.Graph().as_default(): gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.40) session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement, gpu_options=gpu_options) sess = tf.Session(config=session_conf) with sess.as_default(): rnn = RNN( sequence_length=x_train.shape[1], num_classes=y_train.shape[1], vocab_size=len(vocabulary), embedding_size=FLAGS.embedding_dim, cell_type=cell, hidden_size=FLAGS.hidden_size, l2_reg_lambda=FLAGS.l2_reg_lambda ) global_step = tf.Variable(0, name="global_step", trainable=False) train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(rnn.loss, global_step=global_step) loss_summary = tf.summary.scalar("loss", rnn.loss) acc_summary = tf.summary.scalar("accuracy", rnn.accuracy) checkpoint_dir = "models/" checkpoint_prefix = os.path.join(checkpoint_dir, "model_{}_{}".format(cell,did)) if not os.path.exists(checkpoint_dir): os.makedirs(checkpoint_dir) saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints) sess.run(tf.global_variables_initializer()) if FLAGS.word2vec: initW = np.random.uniform(-0.25, 0.25, (len(text_vocab_processor.vocabulary_), FLAGS.embedding_dim)) print("Load word2vec file {0}".format(FLAGS.word2vec)) with open(FLAGS.word2vec, "rb") as f: header = f.readline() vocab_size, layer1_size = map(int, header.split()) binary_len = np.dtype('float32').itemsize * layer1_size for line in range(vocab_size): word = [] while True: ch = f.read(1).decode('latin-1') if ch == ' ': word = ''.join(word) break if ch != '\n': word.append(ch) idx = text_vocab_processor.vocabulary_.get(word) if idx != 0: initW[idx] = np.fromstring(f.read(binary_len), dtype='float32') else: f.read(binary_len) sess.run(rnn.W_text.assign(initW)) print("Success to load pre-trained word2vec model!\n") batches = data_helpers.batch_iter( list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs) for batch in batches: x_batch, y_batch = zip(*batch) feed_dict = { rnn.input_text: x_batch, rnn.input_y: y_batch, rnn.dropout_keep_prob: FLAGS.dropout_keep_prob } _, step, loss, accuracy = sess.run( [train_op, global_step, rnn.loss, rnn.accuracy], feed_dict) if step % FLAGS.display_every == 0: time_str = datetime.datetime.now().isoformat() print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy)) f1.write("{}: step {}, loss {:g}, acc {:g}\n".format(time_str, step, loss, accuracy)) if step % FLAGS.evaluate_every == 0: f1.write("\nEvaluation:\n") print("\nEvaluation:") feed_dict_dev = { rnn.input_text: x_dev, rnn.input_y: y_dev, rnn.dropout_keep_prob: 1.0 } step, loss, accuracy = sess.run( [global_step, rnn.loss, rnn.accuracy], feed_dict_dev) time_str = datetime.datetime.now().isoformat() print("{}: step {}, loss {:g}, acc {:g}\n".format(time_str, step, loss, accuracy)) f1.write("{}: step {}, loss {:g}, acc {:g}\n".format(time_str, step, loss, accuracy)) if step % FLAGS.checkpoint_every == 0: path = saver.save(sess, checkpoint_prefix, global_step=step) print("Saved model checkpoint to {}\n".format(path)) f1.write("Saved model checkpoint to {}\n".format(path)) path = saver.save(sess, "models/model-final_{}_{}".format(cell,did)) print("Saved model checkpoint to {}\n".format(path)) f1.write("Saved model checkpoint to {}\n".format(path)) def main(_): x_train, y_train, vocabulary, x_dev, y_dev = data_helpers.preprocess(did) cell_types =["gru"] for cell_type in cell_types: train(x_train, y_train, vocabulary, x_dev, y_dev,cell_type) if __name__ == "__main__": tf.app.run()

true

1c382d2b8800ba69f78fa9e0859b7d4c992c4f1e

4,842

py

Python

earth_enterprise/src/server/wsgi/serve/constants.py

jsuberza/earthenterprise

e661a41f7cc218c60b269b0c9911df928cd9ce12

[ "Apache-2.0" ]

1

2020-06-17T12:20:52.000Z

earth_enterprise/src/server/wsgi/serve/constants.py

jsuberza/earthenterprise

e661a41f7cc218c60b269b0c9911df928cd9ce12

[ "Apache-2.0" ]

null

earth_enterprise/src/server/wsgi/serve/constants.py

jsuberza/earthenterprise

e661a41f7cc218c60b269b0c9911df928cd9ce12

[ "Apache-2.0" ]

1

2020-12-16T09:26:10.000Z

#!/usr/bin/python # # Copyright 2017 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Publisher constants: request commands and parameters, response header names. """ # Delimiter for multi-part parameters (array-parameters). MULTI_PART_PARAMETER_DELIMITER = "," POI_SEARCH_SERVICE_NAME = "POISearch" DEFAULT_SEARCH_SERVICE_NAME = "GeocodingFederated" HOST_NAME = "Host" # Request commands. CMD = "Cmd" CMD_PING = "Ping" CMD_RESET = "Reset" CMD_QUERY = "Query" CMD_ADD_DB = "AddDb" CMD_DELETE_DB = "DeleteDb" CMD_CLEANUP_DB = "CleanupDb" CMD_PUBLISH_DB = "PublishDb" CMD_REPUBLISH_DB = "RepublishDb" CMD_SWAP_TARGETS = "SwapTargets" CMD_UNPUBLISH_DB = "UnPublishDb" CMD_SYNC_DB = "SyncDb" CMD_ADD_VS = "AddVs" CMD_DELETE_VS = "DeleteVs" CMD_DISABLE_VS = "DisableVs" CMD_ADD_SEARCH_DEF = "AddSearchDef" CMD_DELETE_SEARCH_DEF = "DeleteSearchDef" CMD_ADD_PLUGIN = "AddPlugin" CMD_DELETE_PLUGIN = "DeletePlugin" CMD_DECREMENT_COUNT = "DecrementCount" CMD_LOCAL_TRANSFER = "LocalTransfer" CMD_GARBAGE_COLLECT = "GarbageCollect" CMD_CLEANUP = "Cleanup" CMD_ADD_SNIPPET_SET = "AddSnippetSet" CMD_DELETE_SNIPPET_SET = "DeleteSnippetSet" # Request Params. QUERY_CMD = "QueryCmd" QUERY_CMD_LIST_DBS = "ListDbs" QUERY_CMD_LIST_ASSETS = "ListAllAssets" QUERY_CMD_DB_DETAILS = "DbDetails" QUERY_CMD_LIST_VSS = "ListVss" QUERY_CMD_VS_DETAILS = "VsDetails" QUERY_CMD_LIST_TGS = "ListTgs" QUERY_CMD_TARGET_DETAILS = "TargetDetails" QUERY_CMD_LIST_SEARCH_DEFS = "ListSearchDefs" QUERY_CMD_SEARCH_DEF_DETAILS = "SearchDefDetails" QUERY_CMD_LIST_SNIPPET_SETS = "ListSnippetSets" QUERY_CMD_SNIPPET_SET_DETAILS = "SnippetSetDetails" QUERY_CMD_LIST_META_FIELD_PATHS = "ListMetaFieldPaths" QUERY_CMD_META_FIELDS_SET = "MetaFieldsSet" QUERY_CMD_PUBLISHED_DB_DETAILS = "PublishedDbDetails" QUERY_CMD_PUBLISHED_DBS = "PublishedDbs" QUERY_CMD_SERVER_PREFIX = "ServerPrefix" QUERY_CMD_HOST_ROOT = "HostRoot" QUERY_CMD_SERVER_HOST = "ServerHost" QUERY_CMD_ALLOW_SYM_LINKS = "AllowSymLinks" QUERY_CMD_LIST_PLUGIND = "ListPlugins" QUERY_CMD_GEDB_PATH = "GeDbPath" DB_ID = "DbId" DB_NAME = "DbName" DB_PRETTY_NAME = "DbPrettyName" DB_TYPE = "DbType" DB_TIMESTAMP = "DbTimestamp" DB_SIZE = "DbSize" DB_USE_GOOGLE_BASEMAP = "DbUseGoogleBasemap" FILE_PATH = "FilePath" FILE_SIZE = "FileSize" VS_NAME = "VsName" VS_TYPE = "VsType" VS_URL = "VsUrl" VS_SSL = "VsSsl" VS_CACHE_LEVEL = "VsCacheLevel" PLUGIN_NAME = "PluginName" CLASS_NAME = "ClassName" SEARCH_URL = "SearchUrl" SEARCH_VS_NAME = "SearchVsName" DEST_FILE_PATH = "DestFilePath" FORCE_COPY = "ForceCopy" PREFER_COPY = "PreferCopy" TARGET_PATH = "TargetPath" TARGET_PATH_A = "TargetPathA" TARGET_PATH_B = "TargetPathB" VIRTUAL_HOST_NAME = "VirtualHostName" SEARCH_DEF_NAME = "SearchDefName" SUPPLEMENTAL_SEARCH_DEF_NAME = "SupSearchDefName" SEARCH_DEF = "SearchDef" POI_FEDERATED = "PoiFederated" POI_SUGGESTION = "PoiSuggestion" NEED_SEARCH_TAB_ID = "NeedSearchTabId" SUPPLEMENTAL_UI_LABEL = "SupUiLabel" SNIPPET_SET_NAME = "SnippetSetName" SNIPPET_SET = "SnippetSet" SERVE_WMS = "ServeWms" EC_DEFAULT_DB = "EcDefaultDb" ORIGIN_REQUEST_HOST = "OriginRequestHost" # Response header names. HDR_STATUS_CODE = "Gepublish-StatusCode" HDR_STATUS_MESSAGE = "Gepublish-StatusMessage" HDR_FILE_NAME = "Gepublish-FileName" HDR_PLUGIN_DETAILS = "Gepublish-PluginDetails" HDR_HOST_NAME = "Gepublish-HostName" HDR_DB_NAME = "Gepublish-DbName" HDR_DB_PRETTY_NAME = "Gepublish-DbPrettyName" HDR_TARGET_PATH = "Gepublish-TargetPath" HDR_VS_TYPE = "Gepublish-VsType" HDR_VS_NAME = "Gepublish-VsName" HDR_SERVER_PREFIX = "Gepublish-ServerPrefix" HDR_SERVER_HOST = "Gepublish-ServerHost" HDR_SERVER_HOST_FULL = "Gepublish-ServerHostFull" HDR_ALLOW_SYM_LINKS = "Gepublish-AllowSymLinks" HDR_VS_URL = "Gepublish-VsUrl" HDR_DB_ID = "Gepublish-DbId" HDR_HOST_ROOT = "Gepublish-HostRoot" HDR_DELETE_COUNT = "Gepublish-DeleteCount" HDR_DELETE_SIZE = "Gepublish-DeleteSize" HDR_SEARCH_URL = "Gepublish-SearchUrl" HDR_PLUGIN_NAME = "Gepublish-PluginName" HDR_PLUGIN_CLASS_NAME = "Gepublish-PluginClassName" HDR_DATA = "Gepublish-Data" HDR_JSON_RESULTS = "results" HDR_JSON_STATUS_CODE = "status_code" HDR_JSON_STATUS_MESSAGE = "status_message" # TODO: Get from mod_fdb!? CUTTER_GLOBES_PATH = "/opt/google/gehttpd/htdocs/cutter/globes" # Response status codes. STATUS_FAILURE = -1 STATUS_SUCCESS = 0 STATUS_UPLOAD_NEEDED = 1

31.855263

79

0.803594

MULTI_PART_PARAMETER_DELIMITER = "," POI_SEARCH_SERVICE_NAME = "POISearch" DEFAULT_SEARCH_SERVICE_NAME = "GeocodingFederated" HOST_NAME = "Host" CMD = "Cmd" CMD_PING = "Ping" CMD_RESET = "Reset" CMD_QUERY = "Query" CMD_ADD_DB = "AddDb" CMD_DELETE_DB = "DeleteDb" CMD_CLEANUP_DB = "CleanupDb" CMD_PUBLISH_DB = "PublishDb" CMD_REPUBLISH_DB = "RepublishDb" CMD_SWAP_TARGETS = "SwapTargets" CMD_UNPUBLISH_DB = "UnPublishDb" CMD_SYNC_DB = "SyncDb" CMD_ADD_VS = "AddVs" CMD_DELETE_VS = "DeleteVs" CMD_DISABLE_VS = "DisableVs" CMD_ADD_SEARCH_DEF = "AddSearchDef" CMD_DELETE_SEARCH_DEF = "DeleteSearchDef" CMD_ADD_PLUGIN = "AddPlugin" CMD_DELETE_PLUGIN = "DeletePlugin" CMD_DECREMENT_COUNT = "DecrementCount" CMD_LOCAL_TRANSFER = "LocalTransfer" CMD_GARBAGE_COLLECT = "GarbageCollect" CMD_CLEANUP = "Cleanup" CMD_ADD_SNIPPET_SET = "AddSnippetSet" CMD_DELETE_SNIPPET_SET = "DeleteSnippetSet" QUERY_CMD = "QueryCmd" QUERY_CMD_LIST_DBS = "ListDbs" QUERY_CMD_LIST_ASSETS = "ListAllAssets" QUERY_CMD_DB_DETAILS = "DbDetails" QUERY_CMD_LIST_VSS = "ListVss" QUERY_CMD_VS_DETAILS = "VsDetails" QUERY_CMD_LIST_TGS = "ListTgs" QUERY_CMD_TARGET_DETAILS = "TargetDetails" QUERY_CMD_LIST_SEARCH_DEFS = "ListSearchDefs" QUERY_CMD_SEARCH_DEF_DETAILS = "SearchDefDetails" QUERY_CMD_LIST_SNIPPET_SETS = "ListSnippetSets" QUERY_CMD_SNIPPET_SET_DETAILS = "SnippetSetDetails" QUERY_CMD_LIST_META_FIELD_PATHS = "ListMetaFieldPaths" QUERY_CMD_META_FIELDS_SET = "MetaFieldsSet" QUERY_CMD_PUBLISHED_DB_DETAILS = "PublishedDbDetails" QUERY_CMD_PUBLISHED_DBS = "PublishedDbs" QUERY_CMD_SERVER_PREFIX = "ServerPrefix" QUERY_CMD_HOST_ROOT = "HostRoot" QUERY_CMD_SERVER_HOST = "ServerHost" QUERY_CMD_ALLOW_SYM_LINKS = "AllowSymLinks" QUERY_CMD_LIST_PLUGIND = "ListPlugins" QUERY_CMD_GEDB_PATH = "GeDbPath" DB_ID = "DbId" DB_NAME = "DbName" DB_PRETTY_NAME = "DbPrettyName" DB_TYPE = "DbType" DB_TIMESTAMP = "DbTimestamp" DB_SIZE = "DbSize" DB_USE_GOOGLE_BASEMAP = "DbUseGoogleBasemap" FILE_PATH = "FilePath" FILE_SIZE = "FileSize" VS_NAME = "VsName" VS_TYPE = "VsType" VS_URL = "VsUrl" VS_SSL = "VsSsl" VS_CACHE_LEVEL = "VsCacheLevel" PLUGIN_NAME = "PluginName" CLASS_NAME = "ClassName" SEARCH_URL = "SearchUrl" SEARCH_VS_NAME = "SearchVsName" DEST_FILE_PATH = "DestFilePath" FORCE_COPY = "ForceCopy" PREFER_COPY = "PreferCopy" TARGET_PATH = "TargetPath" TARGET_PATH_A = "TargetPathA" TARGET_PATH_B = "TargetPathB" VIRTUAL_HOST_NAME = "VirtualHostName" SEARCH_DEF_NAME = "SearchDefName" SUPPLEMENTAL_SEARCH_DEF_NAME = "SupSearchDefName" SEARCH_DEF = "SearchDef" POI_FEDERATED = "PoiFederated" POI_SUGGESTION = "PoiSuggestion" NEED_SEARCH_TAB_ID = "NeedSearchTabId" SUPPLEMENTAL_UI_LABEL = "SupUiLabel" SNIPPET_SET_NAME = "SnippetSetName" SNIPPET_SET = "SnippetSet" SERVE_WMS = "ServeWms" EC_DEFAULT_DB = "EcDefaultDb" ORIGIN_REQUEST_HOST = "OriginRequestHost" HDR_STATUS_CODE = "Gepublish-StatusCode" HDR_STATUS_MESSAGE = "Gepublish-StatusMessage" HDR_FILE_NAME = "Gepublish-FileName" HDR_PLUGIN_DETAILS = "Gepublish-PluginDetails" HDR_HOST_NAME = "Gepublish-HostName" HDR_DB_NAME = "Gepublish-DbName" HDR_DB_PRETTY_NAME = "Gepublish-DbPrettyName" HDR_TARGET_PATH = "Gepublish-TargetPath" HDR_VS_TYPE = "Gepublish-VsType" HDR_VS_NAME = "Gepublish-VsName" HDR_SERVER_PREFIX = "Gepublish-ServerPrefix" HDR_SERVER_HOST = "Gepublish-ServerHost" HDR_SERVER_HOST_FULL = "Gepublish-ServerHostFull" HDR_ALLOW_SYM_LINKS = "Gepublish-AllowSymLinks" HDR_VS_URL = "Gepublish-VsUrl" HDR_DB_ID = "Gepublish-DbId" HDR_HOST_ROOT = "Gepublish-HostRoot" HDR_DELETE_COUNT = "Gepublish-DeleteCount" HDR_DELETE_SIZE = "Gepublish-DeleteSize" HDR_SEARCH_URL = "Gepublish-SearchUrl" HDR_PLUGIN_NAME = "Gepublish-PluginName" HDR_PLUGIN_CLASS_NAME = "Gepublish-PluginClassName" HDR_DATA = "Gepublish-Data" HDR_JSON_RESULTS = "results" HDR_JSON_STATUS_CODE = "status_code" HDR_JSON_STATUS_MESSAGE = "status_message" CUTTER_GLOBES_PATH = "/opt/google/gehttpd/htdocs/cutter/globes" STATUS_FAILURE = -1 STATUS_SUCCESS = 0 STATUS_UPLOAD_NEEDED = 1

true

1c382f14053fc6ec2c7e73bebc1a8bdd0037777f

333

py

Python

scripts/5.2.plot_stability.py

nmningmei/agent_models

8380f1203e6d5a18f18f9adeb6bd36b23b2ae61b

[ "MIT" ]

null

scripts/5.2.plot_stability.py

nmningmei/agent_models

8380f1203e6d5a18f18f9adeb6bd36b23b2ae61b

[ "MIT" ]

null

scripts/5.2.plot_stability.py

nmningmei/agent_models

8380f1203e6d5a18f18f9adeb6bd36b23b2ae61b

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jun 29 08:37:19 2020 @author: nmei """ import os import gc from glob import glob from tqdm import tqdm import numpy as np import pandas as pd import seaborn as sns working_dir = '../stability' working_data = glob(os.path.join(working_dir,'*','stability*.npy'))

15.136364

67

0.6997

import os import gc from glob import glob from tqdm import tqdm import numpy as np import pandas as pd import seaborn as sns working_dir = '../stability' working_data = glob(os.path.join(working_dir,'*','stability*.npy'))

true

1c382f9eb7b88a9524ab2bb8a7334e5fa289e7f2

3,001

py

Python

vim/choices/letter.py

GoNZooo/dragonfly-grammars

22d639d8f86f4f5a7c44caa73e75c4938c0ce199

[ "MIT" ]

3

2020-09-06T10:40:19.000Z

2020-09-29T20:39:52.000Z

vim/choices/letter.py

GoNZooo/dragonfly-grammars

22d639d8f86f4f5a7c44caa73e75c4938c0ce199

[ "MIT" ]

null

vim/choices/letter.py

GoNZooo/dragonfly-grammars

22d639d8f86f4f5a7c44caa73e75c4938c0ce199

[ "MIT" ]

null

from dragonfly import (Choice) def letter_choice(name="letter"): return Choice(name, { 'air': 'a', 'bat': 'b', 'cap': 'c', 'drum': 'd', 'each': 'e', 'fine': 'f', 'gust': 'g', 'harp': 'h', 'sit': 'i', 'jim': 'j', 'kick': 'k', 'look': 'l', 'made': 'm', 'near': 'n', 'on': 'o', 'pig': 'p', 'quench': 'q', 'red': 'r', 'sun': 's', 'trap': 't', 'urge': 'u', 'vest': 'v', 'whale': 'w', 'plex': 'x', 'yep': 'y', 'zip': 'z', 'ship air': 'A', 'ship bat': 'B', 'ship cap': 'C', 'ship drum': 'D', 'ship each': 'E', 'ship fine': 'F', 'ship gust': 'G', 'ship harp': 'H', 'ship sit': 'I', 'ship jim': 'J', 'ship kick': 'K', 'ship look': 'L', 'ship made': 'M', 'ship near': 'N', 'ship on': 'O', 'ship pig': 'P', 'ship quench': 'Q', 'ship red': 'R', 'ship sun': 'S', 'ship trap': 'T', 'ship urge': 'U', 'ship vest': 'V', 'ship whale': 'W', 'ship plex': 'X', 'ship yep': 'Y', 'ship zip': 'Z', 'zero': '0', 'one': '1', 'two': '2', 'three': '3', 'four': '4', 'five': '5', 'six': '6', 'seven': '7', 'eight': '8', 'nine': '9', 'space': 'space', 'tabby': 'tab', 'backtick': 'backtick', 'pipe': 'bar', # vim functionality symbols 'colon': 'colon', 'slash': 'slash', 'hat': 'caret', 'dollar': 'dollar', # more or less important symbols for code 'spamma': 'comma', 'assign': 'equal', 'bang': 'exclamation', # quotes '(double|dub)': 'dquote', '(single|sing)': 'squote', 'score': 'underscore', 'quest': 'question', 'ampersand': 'ampersand', '(apostrophe | post)': 'apostrophe', 'starling': 'asterisk', 'at sign': 'at', 'backslash': 'backslash', 'prick': 'dot', 'hashtag': 'hash', 'hyphen': 'hyphen', 'minus': 'minus', 'purse': 'percent', 'plus': 'plus', 'semi': 'semicolon', 'tilde': 'tilde', # parens 'lice': 'lparen', 'rice': 'rparen', # angle brackets 'langle': 'langle', 'rangle': 'rangle', # curly braces 'lace': 'lbrace', 'race': 'rbrace', # square brackets 'lack': 'lbracket', 'rack': 'rbracket', # simple composite comparison operators 'seek': 'slash,equal', 'eek': 'equal,equal', 'beak': 'exclamation,equal', # editing 'backspace': 'backspace', '(escape|okay)': 'escape', 'slap': 'enter' })

22.22963

49

0.377541

from dragonfly import (Choice) def letter_choice(name="letter"): return Choice(name, { 'air': 'a', 'bat': 'b', 'cap': 'c', 'drum': 'd', 'each': 'e', 'fine': 'f', 'gust': 'g', 'harp': 'h', 'sit': 'i', 'jim': 'j', 'kick': 'k', 'look': 'l', 'made': 'm', 'near': 'n', 'on': 'o', 'pig': 'p', 'quench': 'q', 'red': 'r', 'sun': 's', 'trap': 't', 'urge': 'u', 'vest': 'v', 'whale': 'w', 'plex': 'x', 'yep': 'y', 'zip': 'z', 'ship air': 'A', 'ship bat': 'B', 'ship cap': 'C', 'ship drum': 'D', 'ship each': 'E', 'ship fine': 'F', 'ship gust': 'G', 'ship harp': 'H', 'ship sit': 'I', 'ship jim': 'J', 'ship kick': 'K', 'ship look': 'L', 'ship made': 'M', 'ship near': 'N', 'ship on': 'O', 'ship pig': 'P', 'ship quench': 'Q', 'ship red': 'R', 'ship sun': 'S', 'ship trap': 'T', 'ship urge': 'U', 'ship vest': 'V', 'ship whale': 'W', 'ship plex': 'X', 'ship yep': 'Y', 'ship zip': 'Z', 'zero': '0', 'one': '1', 'two': '2', 'three': '3', 'four': '4', 'five': '5', 'six': '6', 'seven': '7', 'eight': '8', 'nine': '9', 'space': 'space', 'tabby': 'tab', 'backtick': 'backtick', 'pipe': 'bar', 'colon': 'colon', 'slash': 'slash', 'hat': 'caret', 'dollar': 'dollar', 'spamma': 'comma', 'assign': 'equal', 'bang': 'exclamation', '(double|dub)': 'dquote', '(single|sing)': 'squote', 'score': 'underscore', 'quest': 'question', 'ampersand': 'ampersand', '(apostrophe | post)': 'apostrophe', 'starling': 'asterisk', 'at sign': 'at', 'backslash': 'backslash', 'prick': 'dot', 'hashtag': 'hash', 'hyphen': 'hyphen', 'minus': 'minus', 'purse': 'percent', 'plus': 'plus', 'semi': 'semicolon', 'tilde': 'tilde', 'lice': 'lparen', 'rice': 'rparen', 'langle': 'langle', 'rangle': 'rangle', 'lace': 'lbrace', 'race': 'rbrace', 'lack': 'lbracket', 'rack': 'rbracket', 'seek': 'slash,equal', 'eek': 'equal,equal', 'beak': 'exclamation,equal', 'backspace': 'backspace', '(escape|okay)': 'escape', 'slap': 'enter' })

true

1c38306c2d00deac55934d2428e42f226f59dead

1,096

py

Python

app/app/urls.py

gbr-mendes/ead-courses-api

fd0fcc8aef996c4b671f34d51a8a74aafdf78480

[ "MIT" ]

1

2022-01-31T18:12:42.000Z

app/app/urls.py

gbr-mendes/ead-courses-api

fd0fcc8aef996c4b671f34d51a8a74aafdf78480

[ "MIT" ]

null

app/app/urls.py

gbr-mendes/ead-courses-api

fd0fcc8aef996c4b671f34d51a8a74aafdf78480

[ "MIT" ]

null

"""app URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include from university import views urlpatterns = [ # Django urls path('admin/', admin.site.urls), # Third party urls path('api-auth/', include('rest_framework.urls')), # API URL's path('', views.ExibitionView.as_view(), name='home_message'),# apresatation of project path('api/accounts/', include('accounts.urls')), path('api/university/', include('university.urls')), ]

36.533333

90

0.69708

from django.contrib import admin from django.urls import path, include from university import views urlpatterns = [ path('admin/', admin.site.urls), path('api-auth/', include('rest_framework.urls')), path('', views.ExibitionView.as_view(), name='home_message'),# apresatation of project path('api/accounts/', include('accounts.urls')), path('api/university/', include('university.urls')), ]

true

1c3830931ecef6f54d56e54465037878caf81142

8,102

py

Python

spatial_reasoning/models/uvfa_text.py

MarcCote/spatial-reasoning

06c57cfafbd1c24b68d6ab634d19806964d867f3

[ "MIT" ]

null

spatial_reasoning/models/uvfa_text.py

MarcCote/spatial-reasoning

06c57cfafbd1c24b68d6ab634d19806964d867f3

[ "MIT" ]

null

spatial_reasoning/models/uvfa_text.py

MarcCote/spatial-reasoning

06c57cfafbd1c24b68d6ab634d19806964d867f3

[ "MIT" ]

null

## predicts entire value map ## rather than a single value import torch import math, torch.nn as nn, pdb import torch.nn.functional as F from torch.autograd import Variable import spatial_reasoning.models as models import spatial_reasoning.utils as utils class UVFA_text(nn.Module): def __init__(self, lstm, state_vocab, object_vocab, args, map_dim = 10, batch_size = 32): super(UVFA_text, self).__init__() self.state_vocab = state_vocab self.object_vocab = object_vocab self.lstm = lstm self.rank = args.rank self.map_dim = map_dim self.batch_size = batch_size self.positions = self.__agent_pos() ## add one for agent position self.state_dim = (self.state_vocab+1) * (map_dim**2) # self.state_dim = self.state_vocab * map_dim**2 self.state_layers = [self.state_dim, 128, 128, args.rank] self.state_mlp = models.MLP(self.state_layers) self.object_dim = self.object_vocab * (map_dim**2) self.object_layers = [self.object_dim, 128, 128, args.rank] self.object_mlp = models.MLP(self.object_layers) ''' returns tensor with one-hot vector encoding [1, 2, 3, ..., map_dim] repeated batch_size times < batch_size * map_dim, state_vocab > ''' def __agent_pos(self): size = self.map_dim**2 positions = torch.zeros(self.batch_size*size, 100, 1) # print(positions.size()) for ind in range(size): # print(ind, ind*self.batch_size, (ind+1)*self.batch_size, ind, positions.size()) # positions[ind*self.batch_size:(ind+1)*self.batch_size, ind] = 1 positions[ind:self.batch_size*size:size, ind] = 1 # pdb.set_trace() return Variable( positions.cuda() ) def __repeat_position(self, x): if x.size() == 2: return x.unsqueeze(1).repeat(1,self.map_dim**2,1) else: return x.unsqueeze(1).repeat(1,self.map_dim**2,1,1) ''' < batch_size x N > < batch_size*100 x N > ''' def __construct_inp(self, state, obj, text): state = self.__repeat_position(state) state = state.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.state_vocab) ## add agent position state = torch.cat( (state, self.positions), -1) ## reshape to (batched) vector for input to MLPs state = state.view(-1, self.state_dim) obj = self.__repeat_position(obj) obj = obj.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.object_vocab) obj = obj.view(-1, self.object_dim) instr_length = text.size(1) ## < batch x length > ## < batch x 100 x length > text = self.__repeat_position(text) ## < batch*100 x length > text = text.view(self.batch_size*self.map_dim**2,instr_length) ## < length x batch*100 > text = text.transpose(0,1) ## < batch*100 x rank > return state, obj, text def forward(self, inp): (state, obj, text) = inp batch_size = state.size(0) # text = text.transpose(0,1) hidden = self.lstm.init_hidden(batch_size * self.map_dim**2) if batch_size != self.batch_size: self.batch_size = batch_size self.positions = self.__agent_pos() ## reshape to (batched) vectors ## can't scatter Variables state = state.data.view(-1, self.map_dim**2, 1) obj = obj.data.view(-1, self.map_dim**2, 1) ## make state / object indices into one-hot vectors state_binary = torch.zeros(batch_size, self.map_dim**2, self.state_vocab).cuda() object_binary = torch.zeros(batch_size, self.map_dim**2, self.object_vocab).cuda() state_binary.scatter_(2, state, 1) object_binary.scatter_(2, obj, 1) state_binary = Variable( state_binary ) object_binary = Variable( object_binary ) state_binary, object_binary, text = self.__construct_inp(state_binary, object_binary, text) # print(state_binary.size(), object_binary.size(), text.size()) # object_binary = self.__repeat_position(object_binary) # object_binary = object_binary.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.object_vocab) ## add in agent position ## < batch x 100 x 2 > ## < batch x 100 x 100 x 2 > # state_binary = self.__repeat_position(state_binary) ## < batch*100 x 100 x 2 > # state_binary = state_binary.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.state_vocab) ## add agent position # state_binary = torch.cat( (state_binary, self.positions), -1) # pdb.set_trace() # print(state_binary.size(), object_binary.size()) ## reshape to (batched) vectors for input to MLPs ## turn back into Variables for backprop # state_binary = state_binary.view(-1, self.state_dim) # object_binary = object_binary.view(-1, self.object_dim) ## < batch*100 x rank > state_out = self.state_mlp(state_binary) object_out = self.object_mlp(object_binary) lstm_out = self.lstm.forward(text, hidden) # print(lstm_out.size()) values = state_out * object_out * lstm_out map_pred = values.sum(1, keepdim=True).view(self.batch_size, self.map_dim, self.map_dim) return map_pred # def forward(self, inp): # (state, obj, text) = inp # batch_size = state.size(0) # text = text.transpose(0,1) # hidden = self.lstm.init_hidden(batch_size) # if batch_size != self.batch_size: # self.batch_size = batch_size # # self.positions = self.__agent_pos() # ## reshape to (batched) vectors # ## can't scatter Variables # state = state.data.view(-1, self.map_dim**2, 1) # obj = obj.data.view(-1, self.map_dim**2, 1) # ## make state / object indices into one-hot vectors # state_binary = torch.zeros(batch_size, self.map_dim**2, self.state_vocab).cuda() # object_binary = torch.zeros(batch_size, self.map_dim**2, self.object_vocab).cuda() # state_binary.scatter_(2, state, 1) # object_binary.scatter_(2, obj, 1) # state_binary = Variable( state_binary ) # object_binary = Variable( object_binary ) # ## add in agent position # ## < batch x 100 x 2 > # ## < batch x 100 x 100 x 2 > # # state_binary = state_binary.unsqueeze(1).repeat(1,self.map_dim**2,1,1) # # state_binary = self.__repeat_position(state_binary) # ## < batch*100 x 100 x 2 > # # state_binary = state_binary.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.state_vocab) # ## add agent position # # state_binary = torch.cat( (state_binary, self.positions), -1) # # pdb.set_trace() # # print(state_binary.size(), object_binary.size()) # ## reshape to (batched) vectors for input to MLPs # ## turn back into Variables for backprop # state_binary = state_binary.view(-1, self.state_dim) # object_binary = object_binary.view(-1, self.object_dim) # print('state: ', state_binary.size(), object_binary.size()) # state_out = self.state_mlp(state_binary) # object_out = self.object_mlp(object_binary) # # print(state_out.size(), object_out.size()) # lstm_out = self.lstm.forward(text, hidden) # # object_out = self.__repeat_position(object_out).view(-1, self.rank) # # lstm_out = self.__repeat_position(lstm_out).view(-1, self.rank) # # print(state_out.size(), object_out.size(), lstm_out.size()) # values = state_out * object_out * lstm_out # # map_pred = values.sum(1).view(self.batch_size, self.map_dim**2) # values = values.sum(1) # # print(values.size()) # map_pred = values.unsqueeze(-1).repeat(1,self.map_dim,self.map_dim) # print(values.size(), map_pred.size()) # return map_pred

36.169643

111

0.620464

torch.nn.functional as F from torch.autograd import Variable import spatial_reasoning.models as models import spatial_reasoning.utils as utils class UVFA_text(nn.Module): def __init__(self, lstm, state_vocab, object_vocab, args, map_dim = 10, batch_size = 32): super(UVFA_text, self).__init__() self.state_vocab = state_vocab self.object_vocab = object_vocab self.lstm = lstm self.rank = args.rank self.map_dim = map_dim self.batch_size = batch_size self.positions = self.__agent_pos() elf.state_vocab+1) * (map_dim**2) self.state_layers = [self.state_dim, 128, 128, args.rank] self.state_mlp = models.MLP(self.state_layers) self.object_dim = self.object_vocab * (map_dim**2) self.object_layers = [self.object_dim, 128, 128, args.rank] self.object_mlp = models.MLP(self.object_layers) def __agent_pos(self): size = self.map_dim**2 positions = torch.zeros(self.batch_size*size, 100, 1) for ind in range(size): positions[ind:self.batch_size*size:size, ind] = 1 return Variable( positions.cuda() ) def __repeat_position(self, x): if x.size() == 2: return x.unsqueeze(1).repeat(1,self.map_dim**2,1) else: return x.unsqueeze(1).repeat(1,self.map_dim**2,1,1) def __construct_inp(self, state, obj, text): state = self.__repeat_position(state) state = state.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.state_vocab) ch.cat( (state, self.positions), -1) obj = self.__repeat_position(obj) obj = obj.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.object_vocab) obj = obj.view(-1, self.object_dim) instr_length = text.size(1) n(text) w(self.batch_size*self.map_dim**2,instr_length) nspose(0,1) , obj, text def forward(self, inp): (state, obj, text) = inp batch_size = state.size(0) hidden = self.lstm.init_hidden(batch_size * self.map_dim**2) if batch_size != self.batch_size: self.batch_size = batch_size self.positions = self.__agent_pos() dim**2, 1) obj = obj.data.view(-1, self.map_dim**2, 1) ## make state / object indices into one-hot vectors state_binary = torch.zeros(batch_size, self.map_dim**2, self.state_vocab).cuda() object_binary = torch.zeros(batch_size, self.map_dim**2, self.object_vocab).cuda() state_binary.scatter_(2, state, 1) object_binary.scatter_(2, obj, 1) state_binary = Variable( state_binary ) object_binary = Variable( object_binary ) state_binary, object_binary, text = self.__construct_inp(state_binary, object_binary, text) # print(state_binary.size(), object_binary.size(), text.size()) # object_binary = self.__repeat_position(object_binary) # object_binary = object_binary.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.object_vocab) ## add in agent position ## < batch x 100 x 2 > ## < batch x 100 x 100 x 2 > # state_binary = self.__repeat_position(state_binary) ## < batch*100 x 100 x 2 > # state_binary = state_binary.view(self.batch_size*self.map_dim**2,self.map_dim**2,self.state_vocab) ## add agent position # state_binary = torch.cat( (state_binary, self.positions), -1) # pdb.set_trace() # print(state_binary.size(), object_binary.size()) ## reshape to (batched) vectors for input to MLPs ## turn back into Variables for backprop # state_binary = state_binary.view(-1, self.state_dim) # object_binary = object_binary.view(-1, self.object_dim) ## < batch*100 x rank > state_out = self.state_mlp(state_binary) object_out = self.object_mlp(object_binary) lstm_out = self.lstm.forward(text, hidden) # print(lstm_out.size()) values = state_out * object_out * lstm_out map_pred = values.sum(1, keepdim=True).view(self.batch_size, self.map_dim, self.map_dim) return map_pred # def forward(self, inp): # (state, obj, text) = inp # batch_size = state.size(0) # text = text.transpose(0,1) # hidden = self.lstm.init_hidden(batch_size) # if batch_size != self.batch_size: # self.batch_size = batch_size # # self.positions = self.__agent_pos() # ## reshape to (batched) vectors # ## can't scatter Variables

true

1c383171fd75508cefec057633d20ca9a2f06f7e

8,220

py

Python

bzt/modules/java/tools.py

YajanaRao/taurus

c08e5b90063bf4f7904e8ec4eb5f3c50a8e89ac0

[ "Apache-2.0" ]

null

bzt/modules/java/tools.py

YajanaRao/taurus

c08e5b90063bf4f7904e8ec4eb5f3c50a8e89ac0

[ "Apache-2.0" ]

null

bzt/modules/java/tools.py

YajanaRao/taurus

c08e5b90063bf4f7904e8ec4eb5f3c50a8e89ac0

[ "Apache-2.0" ]

null

""" Copyright 2018 BlazeMeter Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import re import subprocess from bzt import ToolError from bzt.utils import shell_exec, sync_run, RequiredTool, parse_java_version class JarTool(RequiredTool): VERSION = "" URL = "{remote_addr}{remote_path}" REMOTE_ADDR = "http://search.maven.org/remotecontent?filepath=" REMOTE_PATH = "" LOCAL_PATH = "~/.bzt/selenium-taurus/{tool_file}" def __init__(self, tool_name, local_path, tool_file): tool_file = tool_file.format(version=self.VERSION) remote_path = self.REMOTE_PATH.format(version=self.VERSION) if not local_path: local_path = self.LOCAL_PATH local_path = local_path.format(tool_file=tool_file) download_link = self.URL.format(remote_addr=self.REMOTE_ADDR, remote_path=remote_path) super(JarTool, self).__init__(tool_name=tool_name, tool_path=local_path, download_link=download_link) class JavaC(RequiredTool): def __init__(self, tool_path="javac"): super(JavaC, self).__init__("JavaC", tool_path) def _get_version(self, output): versions = re.findall("javac\ ([\d\._]*)", output) version = parse_java_version(versions) if not version: self.log.warning("Tool version parsing error: %s", output) return version def check_if_installed(self): cmd = [self.tool_path, '-version'] self.log.debug("Trying %s: %s", self.tool_name, cmd) try: output = sync_run(cmd) self.log.debug("%s output: %s", self.tool_name, output) self.version = self._get_version(output) return True except (subprocess.CalledProcessError, OSError) as exc: self.log.debug("Failed to check %s: %s", self.tool_name, exc) return False def install(self): raise ToolError("The %s is not operable or not available. Consider installing it" % self.tool_name) class SeleniumServer(JarTool): VERSION = "3.6" REMOTE_ADDR = "http://selenium-release.storage.googleapis.com/" REMOTE_PATH = "{version}/selenium-server-standalone-{version}.0.jar" def __init__(self, local_path=""): tool_file = "selenium-server-{version}.jar" super(SeleniumServer, self).__init__("Selenium server", local_path, tool_file) def check_if_installed(self): self.log.debug("%s path: %s", self.tool_name, self.tool_path) selenium_launch_command = ["java", "-jar", self.tool_path, "-help"] selenium_subproc = shell_exec(selenium_launch_command, stderr=subprocess.STDOUT) output = selenium_subproc.communicate() self.log.debug("%s output: %s", self.tool_name, output) if selenium_subproc.returncode == 0: self.already_installed = True return True else: return False class Json(JarTool): REMOTE_PATH = "org/json/json/20160810/json-20160810.jar" def __init__(self, tool_path=""): tool_file = "json.jar" super(Json, self).__init__("Json", tool_path, tool_file) class TestNG(JarTool): VERSION = "6.8.5" REMOTE_PATH = "org/testng/testng/{version}/testng-{version}.jar" def __init__(self, tool_path=""): tool_file = "testng-{version}.jar" super(TestNG, self).__init__("TestNG", tool_path, tool_file) class Hamcrest(JarTool): VERSION = "1.3" REMOTE_PATH = "org/hamcrest/hamcrest-core/{version}/hamcrest-core-{version}.jar" def __init__(self, tool_path=""): tool_file = "hamcrest-core-{version}.jar" super(Hamcrest, self).__init__("HamcrestJar", tool_path, tool_file) class JUnitJupiterApi(JarTool): VERSION = "5.2.0" REMOTE_PATH = "org/junit/jupiter/junit-jupiter-api/{version}/junit-jupiter-api-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-jupiter-api-{version}.jar" super(JUnitJupiterApi, self).__init__("JUnitJupiterApi", tool_path, tool_file) class JUnitJupiterEngine(JarTool): VERSION = "5.2.0" REMOTE_PATH = "org/junit/jupiter/junit-jupiter-engine/{version}/junit-jupiter-engine-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-jupiter-engine-{version}.jar" super(JUnitJupiterEngine, self).__init__("JUnitJupiterEngine", tool_path, tool_file) class JUnitVintageEngine(JarTool): VERSION = "5.2.0" REMOTE_PATH = "org/junit/vintage/junit-vintage-engine/{version}/junit-vintage-engine-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-vintage-engine-{version}.jar" super(JUnitVintageEngine, self).__init__("JUnitVintageEngine", tool_path, tool_file) class JUnitPlatformCommons(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-commons/{version}/junit-platform-commons-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-commons-{version}.jar" super(JUnitPlatformCommons, self).__init__("JUnitPlatformCommons", tool_path, tool_file) class JUnitPlatformEngine(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-engine/{version}/junit-platform-engine-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-engine-{version}.jar" super(JUnitPlatformEngine, self).__init__("JUnitPlatformEngine", tool_path, tool_file) class JUnitPlatformLauncher(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-launcher/{version}/junit-platform-launcher-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-launcher-{version}.jar" super(JUnitPlatformLauncher, self).__init__("JUnitPlatformLauncher", tool_path, tool_file) class JUnitPlatformRunner(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-runner/{version}/junit-platform-runner-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-runner-{version}.jar" super(JUnitPlatformRunner, self).__init__("JUnitPlatformRunner", tool_path, tool_file) class JUnitPlatformSuiteApi(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-suite-api/{version}/junit-platform-suite-api-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-suite-api-{version}.jar" super(JUnitPlatformSuiteApi, self).__init__("JUnitPlatformSuiteApi", tool_path, tool_file) class ApiGuardian(JarTool): VERSION = "1.0.0" REMOTE_PATH = "org/apiguardian/apiguardian-api/{version}/apiguardian-api-{version}.jar" def __init__(self, tool_path=""): tool_file = "apiguardian-api-{version}.jar" super(ApiGuardian, self).__init__("ApiGuardian", tool_path, tool_file) class OpenTest4j(JarTool): VERSION = "1.1.0" REMOTE_PATH = "org/opentest4j/opentest4j/{version}/opentest4j-{version}.jar" def __init__(self, tool_path=""): tool_file = "opentest4j-{version}.jar" super(OpenTest4j, self).__init__("OpenTest4j", tool_path, tool_file) class JUnit(JarTool): VERSION = "4.12" REMOTE_PATH = "junit/junit/{version}/junit-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-{version}.jar" super(JUnit, self).__init__("JUnit", tool_path, tool_file) class TaurusJavaHelper(JarTool): VERSION = "1.4" REMOTE_PATH = "com/blazemeter/taurus-java-helpers/{version}/taurus-java-helpers-{version}.jar" def __init__(self, tool_path=""): tool_file = "taurus-java-helpers-{version}.jar" super(TaurusJavaHelper, self).__init__("TaurusJavaHelper", tool_path, tool_file)

36.533333

112

0.693309

import re import subprocess from bzt import ToolError from bzt.utils import shell_exec, sync_run, RequiredTool, parse_java_version class JarTool(RequiredTool): VERSION = "" URL = "{remote_addr}{remote_path}" REMOTE_ADDR = "http://search.maven.org/remotecontent?filepath=" REMOTE_PATH = "" LOCAL_PATH = "~/.bzt/selenium-taurus/{tool_file}" def __init__(self, tool_name, local_path, tool_file): tool_file = tool_file.format(version=self.VERSION) remote_path = self.REMOTE_PATH.format(version=self.VERSION) if not local_path: local_path = self.LOCAL_PATH local_path = local_path.format(tool_file=tool_file) download_link = self.URL.format(remote_addr=self.REMOTE_ADDR, remote_path=remote_path) super(JarTool, self).__init__(tool_name=tool_name, tool_path=local_path, download_link=download_link) class JavaC(RequiredTool): def __init__(self, tool_path="javac"): super(JavaC, self).__init__("JavaC", tool_path) def _get_version(self, output): versions = re.findall("javac\ ([\d\._]*)", output) version = parse_java_version(versions) if not version: self.log.warning("Tool version parsing error: %s", output) return version def check_if_installed(self): cmd = [self.tool_path, '-version'] self.log.debug("Trying %s: %s", self.tool_name, cmd) try: output = sync_run(cmd) self.log.debug("%s output: %s", self.tool_name, output) self.version = self._get_version(output) return True except (subprocess.CalledProcessError, OSError) as exc: self.log.debug("Failed to check %s: %s", self.tool_name, exc) return False def install(self): raise ToolError("The %s is not operable or not available. Consider installing it" % self.tool_name) class SeleniumServer(JarTool): VERSION = "3.6" REMOTE_ADDR = "http://selenium-release.storage.googleapis.com/" REMOTE_PATH = "{version}/selenium-server-standalone-{version}.0.jar" def __init__(self, local_path=""): tool_file = "selenium-server-{version}.jar" super(SeleniumServer, self).__init__("Selenium server", local_path, tool_file) def check_if_installed(self): self.log.debug("%s path: %s", self.tool_name, self.tool_path) selenium_launch_command = ["java", "-jar", self.tool_path, "-help"] selenium_subproc = shell_exec(selenium_launch_command, stderr=subprocess.STDOUT) output = selenium_subproc.communicate() self.log.debug("%s output: %s", self.tool_name, output) if selenium_subproc.returncode == 0: self.already_installed = True return True else: return False class Json(JarTool): REMOTE_PATH = "org/json/json/20160810/json-20160810.jar" def __init__(self, tool_path=""): tool_file = "json.jar" super(Json, self).__init__("Json", tool_path, tool_file) class TestNG(JarTool): VERSION = "6.8.5" REMOTE_PATH = "org/testng/testng/{version}/testng-{version}.jar" def __init__(self, tool_path=""): tool_file = "testng-{version}.jar" super(TestNG, self).__init__("TestNG", tool_path, tool_file) class Hamcrest(JarTool): VERSION = "1.3" REMOTE_PATH = "org/hamcrest/hamcrest-core/{version}/hamcrest-core-{version}.jar" def __init__(self, tool_path=""): tool_file = "hamcrest-core-{version}.jar" super(Hamcrest, self).__init__("HamcrestJar", tool_path, tool_file) class JUnitJupiterApi(JarTool): VERSION = "5.2.0" REMOTE_PATH = "org/junit/jupiter/junit-jupiter-api/{version}/junit-jupiter-api-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-jupiter-api-{version}.jar" super(JUnitJupiterApi, self).__init__("JUnitJupiterApi", tool_path, tool_file) class JUnitJupiterEngine(JarTool): VERSION = "5.2.0" REMOTE_PATH = "org/junit/jupiter/junit-jupiter-engine/{version}/junit-jupiter-engine-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-jupiter-engine-{version}.jar" super(JUnitJupiterEngine, self).__init__("JUnitJupiterEngine", tool_path, tool_file) class JUnitVintageEngine(JarTool): VERSION = "5.2.0" REMOTE_PATH = "org/junit/vintage/junit-vintage-engine/{version}/junit-vintage-engine-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-vintage-engine-{version}.jar" super(JUnitVintageEngine, self).__init__("JUnitVintageEngine", tool_path, tool_file) class JUnitPlatformCommons(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-commons/{version}/junit-platform-commons-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-commons-{version}.jar" super(JUnitPlatformCommons, self).__init__("JUnitPlatformCommons", tool_path, tool_file) class JUnitPlatformEngine(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-engine/{version}/junit-platform-engine-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-engine-{version}.jar" super(JUnitPlatformEngine, self).__init__("JUnitPlatformEngine", tool_path, tool_file) class JUnitPlatformLauncher(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-launcher/{version}/junit-platform-launcher-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-launcher-{version}.jar" super(JUnitPlatformLauncher, self).__init__("JUnitPlatformLauncher", tool_path, tool_file) class JUnitPlatformRunner(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-runner/{version}/junit-platform-runner-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-runner-{version}.jar" super(JUnitPlatformRunner, self).__init__("JUnitPlatformRunner", tool_path, tool_file) class JUnitPlatformSuiteApi(JarTool): VERSION = "1.2.0" REMOTE_PATH = "org/junit/platform/junit-platform-suite-api/{version}/junit-platform-suite-api-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-platform-suite-api-{version}.jar" super(JUnitPlatformSuiteApi, self).__init__("JUnitPlatformSuiteApi", tool_path, tool_file) class ApiGuardian(JarTool): VERSION = "1.0.0" REMOTE_PATH = "org/apiguardian/apiguardian-api/{version}/apiguardian-api-{version}.jar" def __init__(self, tool_path=""): tool_file = "apiguardian-api-{version}.jar" super(ApiGuardian, self).__init__("ApiGuardian", tool_path, tool_file) class OpenTest4j(JarTool): VERSION = "1.1.0" REMOTE_PATH = "org/opentest4j/opentest4j/{version}/opentest4j-{version}.jar" def __init__(self, tool_path=""): tool_file = "opentest4j-{version}.jar" super(OpenTest4j, self).__init__("OpenTest4j", tool_path, tool_file) class JUnit(JarTool): VERSION = "4.12" REMOTE_PATH = "junit/junit/{version}/junit-{version}.jar" def __init__(self, tool_path=""): tool_file = "junit-{version}.jar" super(JUnit, self).__init__("JUnit", tool_path, tool_file) class TaurusJavaHelper(JarTool): VERSION = "1.4" REMOTE_PATH = "com/blazemeter/taurus-java-helpers/{version}/taurus-java-helpers-{version}.jar" def __init__(self, tool_path=""): tool_file = "taurus-java-helpers-{version}.jar" super(TaurusJavaHelper, self).__init__("TaurusJavaHelper", tool_path, tool_file)

true

1c383228303b44befe4c767af4340793fe11954c

7,109

py

Python

chemfiles/trajectory.py

chemfiles/Chemharp.py

45b8a02b7a0f07d6dcafa52db39df6a39f6f496c

[ "BSD-3-Clause" ]

2

2019-04-17T11:13:13.000Z

2021-04-28T20:34:49.000Z

chemfiles/trajectory.py

chemfiles/Chemharp.py

45b8a02b7a0f07d6dcafa52db39df6a39f6f496c

[ "BSD-3-Clause" ]

15

2016-02-19T21:51:33.000Z

2021-07-21T09:01:52.000Z

chemfiles/trajectory.py

chemfiles/Chemharp.py

45b8a02b7a0f07d6dcafa52db39df6a39f6f496c

[ "BSD-3-Clause" ]

3

2020-06-16T08:41:24.000Z

2021-07-22T14:51:33.000Z

# -*- coding=utf-8 -*- from __future__ import absolute_import, print_function, unicode_literals from ctypes import c_uint64, c_char_p import sys from .utils import CxxPointer, _call_with_growing_buffer from .frame import Frame, Topology from .misc import ChemfilesError # Python 2 compatibility if sys.hexversion >= 0x03000000: unicode_string = str bytes_string = bytes else: unicode_string = unicode # noqa bytes_string = str class BaseTrajectory(CxxPointer): def __init__(self, ptr): self.__closed = False super(BaseTrajectory, self).__init__(ptr, is_const=False) def __check_opened(self): if self.__closed: raise ChemfilesError("Can not use a closed Trajectory") def __del__(self): if not self.__closed: self.close() def __enter__(self): self.__check_opened() return self def __exit__(self, *args): self.close() def __iter__(self): self.__check_opened() for step in range(self.nsteps): yield self.read_step(step) def read(self): """ Read the next step of this :py:class:`Trajectory` and return the corresponding :py:class:`Frame`. """ self.__check_opened() frame = Frame() self.ffi.chfl_trajectory_read(self.mut_ptr, frame.mut_ptr) return frame def read_step(self, step): """ Read a specific ``step`` in this :py:class:`Trajectory` and return the corresponding :py:class:`Frame`. """ self.__check_opened() frame = Frame() self.ffi.chfl_trajectory_read_step(self.mut_ptr, c_uint64(step), frame.mut_ptr) return frame def write(self, frame): """Write a :py:class:`Frame` to this :py:class:`Trajectory`.""" self.__check_opened() self.ffi.chfl_trajectory_write(self.mut_ptr, frame.ptr) def set_topology(self, topology, format=""): """ Set the :py:class:`Topology` associated with this :py:class:`Trajectory`. The new topology will be used when reading and writing the files, replacing any topology in the frames or files. If the ``topology`` parameter is a :py:class:`Topology` instance, it is used directly. If the ``topology`` parameter is a string, the first :py:class:`Frame` of the corresponding file is read, and the topology of this frame is used. When reading from a file, if ``format`` is not the empty string, it is used as the file format instead of guessing it from the file extension. """ self.__check_opened() if isinstance(topology, Topology): self.ffi.chfl_trajectory_set_topology(self.mut_ptr, topology.ptr) else: self.ffi.chfl_trajectory_topology_file( self.mut_ptr, topology.encode("utf8"), format.encode("utf8") ) def set_cell(self, cell): """ Set the :py:class:`UnitCell` associated with this :py:class:`Trajectory` to a copy of ``cell``. This :py:class:`UnitCell` will be used when reading and writing the files, replacing any unit cell in the frames or files. """ self.__check_opened() self.ffi.chfl_trajectory_set_cell(self.mut_ptr, cell.ptr) @property def nsteps(self): """Get the current number of steps in this :py:class:`Trajectory`.""" self.__check_opened() nsteps = c_uint64() self.ffi.chfl_trajectory_nsteps(self.mut_ptr, nsteps) return nsteps.value @property def path(self): """Get the path used to open this :py:class:`Trajectory`.""" self.__check_opened() return _call_with_growing_buffer( lambda buffer, size: self.ffi.chfl_trajectory_path(self.ptr, buffer, size), initial=256, ) def close(self): """ Close this :py:class:`Trajectory` and write any buffered content to the file. """ self.__check_opened() self.__closed = True self.ffi.chfl_trajectory_close(self.ptr) class Trajectory(BaseTrajectory): """ A :py:class:`Trajectory` represent a physical file from which we can read :py:class:`Frame`. """ def __init__(self, path, mode="r", format=""): """ Open the file at the given ``path`` using the given ``mode`` and optional file ``format``. Valid modes are ``'r'`` for read, ``'w'`` for write and ``'a'`` for append. The ``format`` parameter is needed when the file format does not match the extension, or when there is not standard extension for this format. If `format` is an empty string, the format will be guessed from the file extension. """ ptr = self.ffi.chfl_trajectory_with_format( path.encode("utf8"), mode.encode("utf8"), format.encode("utf8") ) # Store mode and format for __repr__ self.__mode = mode self.__format = format super(Trajectory, self).__init__(ptr) def __repr__(self): return "Trajectory('{}', '{}', '{}')".format( self.path, self.__mode, self.__format ) class MemoryTrajectory(BaseTrajectory): """ A :py:class:`MemoryTrajectory` allow to read/write in-memory data as though it was a formatted file. """ def __init__(self, data="", mode="r", format=""): """ The ``format`` parameter is always required. When reading (``mode`` is ``'r'``), the ``data`` parameter will be used as the formatted file. When writing (``mode`` is ``'w'``), the ``data`` parameter is ignored. To get the memory buffer containing everything already written, use the :py:func:`buffer` function. """ if not format: raise ChemfilesError( "'format' is required when creating a MemoryTrajectory" ) if mode == "r": if isinstance(data, unicode_string): data = data.encode("utf8") elif not isinstance(data, bytes_string): raise ChemfilesError("the 'data' parameter must be a string") ptr = self.ffi.chfl_trajectory_memory_reader( data, len(data), format.encode("utf8") ) elif mode == "w": ptr = self.ffi.chfl_trajectory_memory_writer(format.encode("utf8")) else: raise ChemfilesError( "invalid mode '{}' passed to MemoryTrajectory".format(mode) ) super(MemoryTrajectory, self).__init__(ptr) def __repr__(self): return "MemoryTrajectory({}', '{}')".format(self.__mode, self.__format) def buffer(self): """ Get the data written to this in-memory trajectory. This is not valid to call when reading in-memory data. """ buffer = c_char_p() size = c_uint64() self.ffi.chfl_trajectory_memory_buffer(self.ptr, buffer, size) return buffer.value

32.760369

87

0.60965

from __future__ import absolute_import, print_function, unicode_literals from ctypes import c_uint64, c_char_p import sys from .utils import CxxPointer, _call_with_growing_buffer from .frame import Frame, Topology from .misc import ChemfilesError if sys.hexversion >= 0x03000000: unicode_string = str bytes_string = bytes else: unicode_string = unicode bytes_string = str class BaseTrajectory(CxxPointer): def __init__(self, ptr): self.__closed = False super(BaseTrajectory, self).__init__(ptr, is_const=False) def __check_opened(self): if self.__closed: raise ChemfilesError("Can not use a closed Trajectory") def __del__(self): if not self.__closed: self.close() def __enter__(self): self.__check_opened() return self def __exit__(self, *args): self.close() def __iter__(self): self.__check_opened() for step in range(self.nsteps): yield self.read_step(step) def read(self): self.__check_opened() frame = Frame() self.ffi.chfl_trajectory_read(self.mut_ptr, frame.mut_ptr) return frame def read_step(self, step): self.__check_opened() frame = Frame() self.ffi.chfl_trajectory_read_step(self.mut_ptr, c_uint64(step), frame.mut_ptr) return frame def write(self, frame): self.__check_opened() self.ffi.chfl_trajectory_write(self.mut_ptr, frame.ptr) def set_topology(self, topology, format=""): self.__check_opened() if isinstance(topology, Topology): self.ffi.chfl_trajectory_set_topology(self.mut_ptr, topology.ptr) else: self.ffi.chfl_trajectory_topology_file( self.mut_ptr, topology.encode("utf8"), format.encode("utf8") ) def set_cell(self, cell): self.__check_opened() self.ffi.chfl_trajectory_set_cell(self.mut_ptr, cell.ptr) @property def nsteps(self): self.__check_opened() nsteps = c_uint64() self.ffi.chfl_trajectory_nsteps(self.mut_ptr, nsteps) return nsteps.value @property def path(self): self.__check_opened() return _call_with_growing_buffer( lambda buffer, size: self.ffi.chfl_trajectory_path(self.ptr, buffer, size), initial=256, ) def close(self): self.__check_opened() self.__closed = True self.ffi.chfl_trajectory_close(self.ptr) class Trajectory(BaseTrajectory): def __init__(self, path, mode="r", format=""): ptr = self.ffi.chfl_trajectory_with_format( path.encode("utf8"), mode.encode("utf8"), format.encode("utf8") ) self.__mode = mode self.__format = format super(Trajectory, self).__init__(ptr) def __repr__(self): return "Trajectory('{}', '{}', '{}')".format( self.path, self.__mode, self.__format ) class MemoryTrajectory(BaseTrajectory): def __init__(self, data="", mode="r", format=""): if not format: raise ChemfilesError( "'format' is required when creating a MemoryTrajectory" ) if mode == "r": if isinstance(data, unicode_string): data = data.encode("utf8") elif not isinstance(data, bytes_string): raise ChemfilesError("the 'data' parameter must be a string") ptr = self.ffi.chfl_trajectory_memory_reader( data, len(data), format.encode("utf8") ) elif mode == "w": ptr = self.ffi.chfl_trajectory_memory_writer(format.encode("utf8")) else: raise ChemfilesError( "invalid mode '{}' passed to MemoryTrajectory".format(mode) ) super(MemoryTrajectory, self).__init__(ptr) def __repr__(self): return "MemoryTrajectory({}', '{}')".format(self.__mode, self.__format) def buffer(self): buffer = c_char_p() size = c_uint64() self.ffi.chfl_trajectory_memory_buffer(self.ptr, buffer, size) return buffer.value

true

1c38332477ade3863d6275a24700253942626083

29,157

py

Python

core/domain/collection_domain_test.py

alexewu/oppia

57c3c660ab7974835ec068d7c7f5ce5b5f1f25ae

[ "Apache-2.0" ]

null

core/domain/collection_domain_test.py

alexewu/oppia

57c3c660ab7974835ec068d7c7f5ce5b5f1f25ae

[ "Apache-2.0" ]

null

core/domain/collection_domain_test.py

alexewu/oppia

57c3c660ab7974835ec068d7c7f5ce5b5f1f25ae

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2015 The Oppia Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS-IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for collection domain objects and methods defined on them.""" from core.domain import collection_domain from core.domain import collection_services from core.tests import test_utils import feconf import utils # Dictionary-like data structures within sample YAML must be formatted # alphabetically to match string equivalence with the YAML generation # methods tested below. # # If evaluating differences in YAML, conversion to dict form via # utils.dict_from_yaml can isolate differences quickly. SAMPLE_YAML_CONTENT = ("""category: A category language_code: en nodes: - exploration_id: an_exploration_id objective: An objective schema_version: %d tags: [] title: A title """) % (feconf.CURRENT_COLLECTION_SCHEMA_VERSION) class CollectionChangeTests(test_utils.GenericTestBase): def test_collection_change_object_with_invalid_change_dict(self): # Raises exception as 'cmd' command is not found in change_dict. with self.assertRaisesRegexp(Exception, 'Invalid change_dict:'): collection_domain.CollectionChange({'invalid_cmd': 'data'}) # Raises exception due to invalid property name. with self.assertRaisesRegexp(Exception, 'Invalid change_dict:'): collection_domain.CollectionChange({ 'cmd': 'edit_collection_property', 'property_name': 'invalid_property_name', }) # Raises exception due to invalid command. with self.assertRaisesRegexp(Exception, 'Invalid change_dict:'): collection_domain.CollectionChange({ 'cmd': 'invalid_cmd' }) def test_collection_change_object_with_swap_nodes(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'swap_nodes', 'first_index': 'first_index', 'second_index': 'second_index' }) self.assertEqual(col_change_object.first_index, 'first_index') self.assertEqual(col_change_object.second_index, 'second_index') def test_collection_change_object_with_edit_collection_node_property(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'edit_collection_node_property', 'exploration_id': 'exploration_id', 'property_name': 'property_name', 'new_value': 'new_value', 'old_value': 'old_value' }) self.assertEqual(col_change_object.exploration_id, 'exploration_id') self.assertEqual(col_change_object.property_name, 'property_name') self.assertEqual(col_change_object.new_value, 'new_value') self.assertEqual(col_change_object.old_value, 'old_value') def test_collection_change_object_with_add_collection_skill(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'add_collection_skill', 'name': 'name' }) self.assertEqual(col_change_object.name, 'name') def test_collection_change_object_with_add_question_id_to_skill(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'add_question_id_to_skill', 'skill_id': 'skill_id', 'question_id': 'question_id' }) self.assertEqual(col_change_object.skill_id, 'skill_id') self.assertEqual(col_change_object.question_id, 'question_id') def test_collection_change_object_with_remove_question_id_from_skill(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'remove_question_id_from_skill', 'skill_id': 'skill_id', 'question_id': 'question_id' }) self.assertEqual(col_change_object.skill_id, 'skill_id') self.assertEqual(col_change_object.question_id, 'question_id') def test_collection_change_object_with_delete_collection_skill(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'delete_collection_skill', 'skill_id': 'skill_id', }) self.assertEqual(col_change_object.skill_id, 'skill_id') class CollectionDomainUnitTests(test_utils.GenericTestBase): """Test the collection domain object.""" COLLECTION_ID = 'collection_id' EXPLORATION_ID = 'exp_id_0' def setUp(self): super(CollectionDomainUnitTests, self).setUp() self.save_new_valid_collection( self.COLLECTION_ID, 'user@example.com', title='Title', category='Category', objective='Objective', exploration_id=self.EXPLORATION_ID) self.collection = collection_services.get_collection_by_id( self.COLLECTION_ID) def _assert_validation_error(self, expected_error_substring): """Checks that the collection passes strict validation.""" with self.assertRaisesRegexp( utils.ValidationError, expected_error_substring): self.collection.validate() def test_initial_validation(self): """Test validating a new, valid collection.""" self.collection.validate() def test_title_validation(self): self.collection.title = 0 self._assert_validation_error('Expected title to be a string') def test_category_validation(self): self.collection.category = 0 self._assert_validation_error('Expected category to be a string') def test_objective_validation(self): self.collection.objective = '' self._assert_validation_error('objective must be specified') self.collection.objective = 0 self._assert_validation_error('Expected objective to be a string') def test_language_code_validation(self): self.collection.language_code = '' self._assert_validation_error('language must be specified') self.collection.language_code = 0 self._assert_validation_error('Expected language code to be a string') self.collection.language_code = 'xz' self._assert_validation_error('Invalid language code') def test_tags_validation(self): self.collection.tags = 'abc' self._assert_validation_error('Expected tags to be a list') self.collection.tags = [2, 3] self._assert_validation_error('Expected each tag to be a string') self.collection.tags = ['', 'tag'] self._assert_validation_error('Tags should be non-empty') self.collection.tags = ['234'] self._assert_validation_error( 'Tags should only contain lowercase letters and spaces') self.collection.tags = [' abc'] self._assert_validation_error( 'Tags should not start or end with whitespace') self.collection.tags = ['abc def'] self._assert_validation_error( 'Adjacent whitespace in tags should be collapsed') self.collection.tags = ['abc', 'abc'] self._assert_validation_error( 'Expected tags to be unique, but found duplicates') def test_schema_version_validation(self): self.collection.schema_version = 'some_schema_version' self._assert_validation_error('Expected schema version to be an int') self.collection.schema_version = 100 self._assert_validation_error( 'Expected schema version to be %s' % feconf.CURRENT_COLLECTION_SCHEMA_VERSION) def test_nodes_validation(self): self.collection.nodes = {} self._assert_validation_error('Expected nodes to be a list') self.collection.nodes = [ collection_domain.CollectionNode.from_dict({ 'exploration_id': '0' }), collection_domain.CollectionNode.from_dict({ 'exploration_id': '0' }) ] self._assert_validation_error( 'There are explorations referenced in the collection more than ' 'once.') def test_initial_explorations_validation(self): # Having no collection nodes is fine for non-strict validation. self.collection.nodes = [] self.collection.validate(strict=False) # But it's not okay for strict validation. self._assert_validation_error( 'Expected to have at least 1 exploration in the collection.') def test_metadata_validation(self): self.collection.title = '' self.collection.objective = '' self.collection.category = '' self.collection.nodes = [] # Having no title is fine for non-strict validation. self.collection.validate(strict=False) # But it's not okay for strict validation. self._assert_validation_error( 'A title must be specified for the collection.') self.collection.title = 'A title' # Having no objective is fine for non-strict validation. self.collection.validate(strict=False) # But it's not okay for strict validation. self._assert_validation_error( 'An objective must be specified for the collection.') self.collection.objective = 'An objective' # Having no category is fine for non-strict validation. self.collection.validate(strict=False) # But it's not okay for strict validation. self._assert_validation_error( 'A category must be specified for the collection.') self.collection.category = 'A category' # Having no exploration is fine for non-strict validation. self.collection.validate(strict=False) # But it's not okay for strict validation. self._assert_validation_error( 'Expected to have at least 1 exploration in the collection.') self.collection.add_node('exp_id_1') # Now the collection passes both strict and non-strict validation. self.collection.validate(strict=False) self.collection.validate(strict=True) def test_collection_node_exploration_id_validation(self): # Validate CollectionNode's exploration_id. collection_node0 = self.collection.get_node('exp_id_0') collection_node0.exploration_id = 2 self._assert_validation_error('Expected exploration ID to be a string') def test_is_demo_property(self): """Test the is_demo property.""" demo = collection_domain.Collection.create_default_collection('0') self.assertEqual(demo.is_demo, True) notdemo1 = collection_domain.Collection.create_default_collection('a') self.assertEqual(notdemo1.is_demo, False) notdemo2 = collection_domain.Collection.create_default_collection( 'abcd') self.assertEqual(notdemo2.is_demo, False) def test_collection_export_import(self): """Test that to_dict and from_dict preserve all data within an collection. """ self.save_new_valid_exploration( '0', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.create_default_collection( '0', title='title', category='category', objective='objective') collection_dict = collection.to_dict() collection_from_dict = collection_domain.Collection.from_dict( collection_dict) self.assertEqual(collection_from_dict.to_dict(), collection_dict) def test_add_delete_swap_nodes(self): """Test that add_node, delete_node and swap_nodes fail in the correct situations. """ collection = collection_domain.Collection.create_default_collection( '0') self.assertEqual(len(collection.nodes), 0) collection.add_node('test_exp') self.assertEqual(len(collection.nodes), 1) with self.assertRaisesRegexp( ValueError, 'Exploration is already part of this collection: test_exp' ): collection.add_node('test_exp') collection.add_node('another_exp') self.assertEqual(len(collection.nodes), 2) collection.swap_nodes(0, 1) self.assertEqual(collection.nodes[0].exploration_id, 'another_exp') self.assertEqual(collection.nodes[1].exploration_id, 'test_exp') with self.assertRaisesRegexp( ValueError, 'Both indices point to the same collection node.' ): collection.swap_nodes(0, 0) collection.delete_node('another_exp') self.assertEqual(len(collection.nodes), 1) with self.assertRaisesRegexp( ValueError, 'Exploration is not part of this collection: another_exp' ): collection.delete_node('another_exp') collection.delete_node('test_exp') self.assertEqual(len(collection.nodes), 0) def test_update_collection_contents_from_model(self): versioned_collection_contents = { 'schema_version': 1, 'collection_contents': {} } collection_domain.Collection.update_collection_contents_from_model( versioned_collection_contents, 1) self.assertEqual(versioned_collection_contents['schema_version'], 2) self.assertEqual( versioned_collection_contents['collection_contents'], {}) def test_update_collection_contents_from_model_with_invalid_schema_version( self): versioned_collection_contents = { 'schema_version': feconf.CURRENT_COLLECTION_SCHEMA_VERSION, 'collection_contents': {} } with self.assertRaisesRegexp( Exception, 'Collection is version .+ but current collection schema version ' 'is %d' % feconf.CURRENT_COLLECTION_SCHEMA_VERSION): collection_domain.Collection.update_collection_contents_from_model( versioned_collection_contents, feconf.CURRENT_COLLECTION_SCHEMA_VERSION) class ExplorationGraphUnitTests(test_utils.GenericTestBase): """Test the general structure of explorations within a collection.""" def test_initial_explorations(self): """Any exploration without prerequisites should be an initial exploration. """ collection = collection_domain.Collection.create_default_collection( 'collection_id') # If there are no explorations in the collection, there can be no # initial explorations. self.assertEqual(collection.nodes, []) self.assertEqual(collection.first_exploration_id, None) # A freshly added exploration will be an initial one. collection.add_node('exp_id_0') self.assertEqual(collection.first_exploration_id, 'exp_id_0') # Having prerequisites will make an exploration no longer initial. collection.add_node('exp_id_1') self.assertEqual(len(collection.nodes), 2) self.assertEqual(collection.first_exploration_id, 'exp_id_0') def test_next_explorations(self): """Explorations should be suggested based on their index in the node list. """ collection = collection_domain.Collection.create_default_collection( 'collection_id') # There should be no next explorations for an empty collection. self.assertEqual(collection.get_next_exploration_id([]), None) # If a new exploration is added, the next exploration IDs should be the # same as the initial exploration. collection.add_node('exp_id_0') self.assertEqual(collection.get_next_exploration_id([]), 'exp_id_0') self.assertEqual( collection.first_exploration_id, collection.get_next_exploration_id([])) # Completing the only exploration of the collection should lead to no # available explorations thereafter. self.assertEqual( collection.get_next_exploration_id(['exp_id_0']), None) # If another exploration has been added, then the first exploration # should be the next one to complete. collection.add_node('exp_id_1') self.assertEqual(collection.get_next_exploration_id( ['exp_id_0']), 'exp_id_1') # If another exploration is added, then based on explorations # completed, the correct exploration should be shown as the next one. collection.add_node('exp_id_2') self.assertEqual( collection.get_next_exploration_id([]), 'exp_id_0') self.assertEqual( collection.get_next_exploration_id(['exp_id_0']), 'exp_id_1') self.assertEqual( collection.get_next_exploration_id(['exp_id_0', 'exp_id_1']), 'exp_id_2') # If all explorations have been completed, none should be suggested. self.assertEqual( collection.get_next_exploration_id( ['exp_id_0', 'exp_id_1', 'exp_id_2']), None) def test_next_explorations_in_sequence(self): collection = collection_domain.Collection.create_default_collection( 'collection_id') exploration_id = 'exp_id_0' collection.add_node(exploration_id) # Completing the only exploration of the collection should lead to no # available explorations thereafter. self.assertEqual( collection.get_next_exploration_id_in_sequence(exploration_id), None) collection.add_node('exp_id_1') collection.add_node('exp_id_2') self.assertEqual( collection.get_next_exploration_id_in_sequence(exploration_id), 'exp_id_1') self.assertEqual( collection.get_next_exploration_id_in_sequence('exp_id_1'), 'exp_id_2') def test_nodes_are_in_playble_order(self): # Create collection. collection = collection_domain.Collection.create_default_collection( 'collection_id') # There should be an empty node list in playable order for an empty # collection. self.assertEqual(collection.nodes, []) # Add nodes to collection. collection.add_node('exp_id_0') collection.add_node('exp_id_1') collection.add_node('exp_id_2') sorted_nodes = collection.nodes expected_explorations_ids = ['exp_id_0', 'exp_id_1', 'exp_id_2'] observed_exploration_ids = [ node.exploration_id for node in sorted_nodes] self.assertEqual(expected_explorations_ids, observed_exploration_ids) def test_next_explorations_with_invalid_exploration_ids(self): collection = collection_domain.Collection.create_default_collection( 'collection_id') collection.add_node('exp_id_1') # There should be one suggested exploration to complete by default. self.assertEqual(collection.get_next_exploration_id([]), 'exp_id_1') # If an invalid exploration ID is passed to get_next_exploration_id(), # it should be ignored. This tests the situation where an exploration # is deleted from a collection after being completed by a user. self.assertEqual( collection.get_next_exploration_id(['fake_exp_id']), 'exp_id_1') class YamlCreationUnitTests(test_utils.GenericTestBase): """Test creation of collections from YAML files.""" COLLECTION_ID = 'a_collection_id' EXPLORATION_ID = 'an_exploration_id' def test_yaml_import_and_export(self): """Test the from_yaml() and to_yaml() methods.""" self.save_new_valid_exploration( self.EXPLORATION_ID, 'user@example.com', end_state_name='End') collection = collection_domain.Collection.create_default_collection( self.COLLECTION_ID, title='A title', category='A category', objective='An objective') collection.add_node(self.EXPLORATION_ID) self.assertEqual(len(collection.nodes), 1) collection.validate() yaml_content = collection.to_yaml() self.assertEqual(yaml_content, SAMPLE_YAML_CONTENT) collection2 = collection_domain.Collection.from_yaml( 'collection2', yaml_content) self.assertEqual(len(collection2.nodes), 1) yaml_content_2 = collection2.to_yaml() self.assertEqual(yaml_content_2, yaml_content) # Should not be able to create a collection from no YAML content. with self.assertRaises(Exception): collection_domain.Collection.from_yaml('collection3', None) def test_from_yaml_with_no_schema_version_specified_raises_error(self): collection = collection_domain.Collection( self.COLLECTION_ID, 'title', 'category', 'objective', 'en', [], None, [], 0) yaml_content = collection.to_yaml() with self.assertRaisesRegexp( Exception, 'Invalid YAML file: no schema version specified.'): collection_domain.Collection.from_yaml( self.COLLECTION_ID, yaml_content) def test_from_yaml_with_invalid_schema_version_raises_error(self): collection = collection_domain.Collection( self.COLLECTION_ID, 'title', 'category', 'objective', 'en', [], 0, [], 0) yaml_content = collection.to_yaml() with self.assertRaisesRegexp( Exception, 'Sorry, we can only process v1 to .+ collection YAML files at ' 'present.'): collection_domain.Collection.from_yaml( self.COLLECTION_ID, yaml_content) class SchemaMigrationMethodsUnitTests(test_utils.GenericTestBase): """Tests the presence of appropriate schema migration methods in the Collection domain object class. """ def test_correct_collection_contents_schema_conversion_methods_exist(self): """Test that the right collection_contents schema conversion methods exist. """ current_collection_schema_version = ( feconf.CURRENT_COLLECTION_SCHEMA_VERSION) for version_num in range(1, current_collection_schema_version): self.assertTrue(hasattr( collection_domain.Collection, '_convert_collection_contents_v%s_dict_to_v%s_dict' % ( version_num, version_num + 1))) self.assertFalse(hasattr( collection_domain.Collection, '_convert_collection_contents_v%s_dict_to_v%s_dict' % ( current_collection_schema_version, current_collection_schema_version + 1))) def test_correct_collection_schema_conversion_methods_exist(self): """Test that the right collection schema conversion methods exist.""" current_collection_schema_version = ( feconf.CURRENT_COLLECTION_SCHEMA_VERSION) for version_num in range(1, current_collection_schema_version): self.assertTrue(hasattr( collection_domain.Collection, '_convert_v%s_dict_to_v%s_dict' % ( version_num, version_num + 1))) self.assertFalse(hasattr( collection_domain.Collection, '_convert_v%s_dict_to_v%s_dict' % ( current_collection_schema_version, current_collection_schema_version + 1))) class SchemaMigrationUnitTests(test_utils.GenericTestBase): """Test migration methods for yaml content.""" YAML_CONTENT_V1 = ("""category: A category nodes: - acquired_skills: - Skill1 - Skill2 exploration_id: Exp1 prerequisite_skills: [] - acquired_skills: [] exploration_id: Exp2 prerequisite_skills: - Skill1 objective: '' schema_version: 1 title: A title """) YAML_CONTENT_V2 = ("""category: A category language_code: en nodes: - acquired_skills: - Skill1 - Skill2 exploration_id: Exp1 prerequisite_skills: [] - acquired_skills: [] exploration_id: Exp2 prerequisite_skills: - Skill1 objective: '' schema_version: 2 tags: [] title: A title """) YAML_CONTENT_V3 = ("""category: A category language_code: en nodes: - acquired_skills: - Skill1 - Skill2 exploration_id: Exp1 prerequisite_skills: [] - acquired_skills: [] exploration_id: Exp2 prerequisite_skills: - Skill1 objective: '' schema_version: 2 tags: [] title: A title """) YAML_CONTENT_V4 = ("""category: A category language_code: en next_skill_id: 2 nodes: - acquired_skill_ids: - skill0 - skill1 exploration_id: Exp1 prerequisite_skill_ids: [] - acquired_skill_ids: [] exploration_id: Exp2 prerequisite_skill_ids: - skill0 objective: '' schema_version: 4 skills: skill0: name: Skill1 question_ids: [] skill1: name: Skill2 question_ids: [] tags: [] title: A title """) YAML_CONTENT_V5 = ("""category: A category language_code: en next_skill_index: 2 nodes: - acquired_skill_ids: - skill0 - skill1 exploration_id: Exp1 prerequisite_skill_ids: [] - acquired_skill_ids: [] exploration_id: Exp2 prerequisite_skill_ids: - skill0 objective: '' schema_version: 5 skills: skill0: name: Skill1 question_ids: [] skill1: name: Skill2 question_ids: [] tags: [] title: A title """) YAML_CONTENT_V6 = ("""category: A category language_code: en nodes: - exploration_id: Exp1 - exploration_id: Exp2 objective: '' schema_version: 6 tags: [] title: A title """) _LATEST_YAML_CONTENT = YAML_CONTENT_V6 def test_load_from_v1(self): """Test direct loading from a v1 yaml file.""" self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V1) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v2(self): """Test direct loading from a v2 yaml file.""" self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V2) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v3(self): """Test direct loading from a v3 yaml file.""" self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V3) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v4(self): """Test direct loading from a v4 yaml file.""" self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V4) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v5(self): """Test direct loading from a v5 yaml file.""" self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V5) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v6(self): """Test direct loading from a v6 yaml file.""" self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V6) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) class CollectionSummaryTests(test_utils.GenericTestBase): def test_collection_summary_gets_created(self): collection_summary_dict = { 'category': 'category', 'status': 'status', 'community_owned': 'True', 'viewer_ids': ['viewer_id'], 'version': 1, 'editor_ids': ['editor_id'], 'title': 'title', 'collection_model_created_on': {}, 'tags': [], 'collection_model_last_updated': {}, 'contributor_ids': ['contributor_id'], 'language_code': 'en', 'objective': 'objective', 'contributors_summary': {}, 'id': 'col_id', 'owner_ids': ['owner_id'] } collection_summary = collection_domain.CollectionSummary( 'col_id', 'title', 'category', 'objective', 'en', [], 'status', 'True', ['owner_id'], ['editor_id'], ['viewer_id'], ['contributor_id'], {}, 1, 1, {}, {}) self.assertEqual(collection_summary.to_dict(), collection_summary_dict)

36.629397

79

0.673732

from core.domain import collection_domain from core.domain import collection_services from core.tests import test_utils import feconf import utils SAMPLE_YAML_CONTENT = ("""category: A category language_code: en nodes: - exploration_id: an_exploration_id objective: An objective schema_version: %d tags: [] title: A title """) % (feconf.CURRENT_COLLECTION_SCHEMA_VERSION) class CollectionChangeTests(test_utils.GenericTestBase): def test_collection_change_object_with_invalid_change_dict(self): with self.assertRaisesRegexp(Exception, 'Invalid change_dict:'): collection_domain.CollectionChange({'invalid_cmd': 'data'}) with self.assertRaisesRegexp(Exception, 'Invalid change_dict:'): collection_domain.CollectionChange({ 'cmd': 'edit_collection_property', 'property_name': 'invalid_property_name', }) with self.assertRaisesRegexp(Exception, 'Invalid change_dict:'): collection_domain.CollectionChange({ 'cmd': 'invalid_cmd' }) def test_collection_change_object_with_swap_nodes(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'swap_nodes', 'first_index': 'first_index', 'second_index': 'second_index' }) self.assertEqual(col_change_object.first_index, 'first_index') self.assertEqual(col_change_object.second_index, 'second_index') def test_collection_change_object_with_edit_collection_node_property(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'edit_collection_node_property', 'exploration_id': 'exploration_id', 'property_name': 'property_name', 'new_value': 'new_value', 'old_value': 'old_value' }) self.assertEqual(col_change_object.exploration_id, 'exploration_id') self.assertEqual(col_change_object.property_name, 'property_name') self.assertEqual(col_change_object.new_value, 'new_value') self.assertEqual(col_change_object.old_value, 'old_value') def test_collection_change_object_with_add_collection_skill(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'add_collection_skill', 'name': 'name' }) self.assertEqual(col_change_object.name, 'name') def test_collection_change_object_with_add_question_id_to_skill(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'add_question_id_to_skill', 'skill_id': 'skill_id', 'question_id': 'question_id' }) self.assertEqual(col_change_object.skill_id, 'skill_id') self.assertEqual(col_change_object.question_id, 'question_id') def test_collection_change_object_with_remove_question_id_from_skill(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'remove_question_id_from_skill', 'skill_id': 'skill_id', 'question_id': 'question_id' }) self.assertEqual(col_change_object.skill_id, 'skill_id') self.assertEqual(col_change_object.question_id, 'question_id') def test_collection_change_object_with_delete_collection_skill(self): col_change_object = collection_domain.CollectionChange({ 'cmd': 'delete_collection_skill', 'skill_id': 'skill_id', }) self.assertEqual(col_change_object.skill_id, 'skill_id') class CollectionDomainUnitTests(test_utils.GenericTestBase): COLLECTION_ID = 'collection_id' EXPLORATION_ID = 'exp_id_0' def setUp(self): super(CollectionDomainUnitTests, self).setUp() self.save_new_valid_collection( self.COLLECTION_ID, 'user@example.com', title='Title', category='Category', objective='Objective', exploration_id=self.EXPLORATION_ID) self.collection = collection_services.get_collection_by_id( self.COLLECTION_ID) def _assert_validation_error(self, expected_error_substring): with self.assertRaisesRegexp( utils.ValidationError, expected_error_substring): self.collection.validate() def test_initial_validation(self): self.collection.validate() def test_title_validation(self): self.collection.title = 0 self._assert_validation_error('Expected title to be a string') def test_category_validation(self): self.collection.category = 0 self._assert_validation_error('Expected category to be a string') def test_objective_validation(self): self.collection.objective = '' self._assert_validation_error('objective must be specified') self.collection.objective = 0 self._assert_validation_error('Expected objective to be a string') def test_language_code_validation(self): self.collection.language_code = '' self._assert_validation_error('language must be specified') self.collection.language_code = 0 self._assert_validation_error('Expected language code to be a string') self.collection.language_code = 'xz' self._assert_validation_error('Invalid language code') def test_tags_validation(self): self.collection.tags = 'abc' self._assert_validation_error('Expected tags to be a list') self.collection.tags = [2, 3] self._assert_validation_error('Expected each tag to be a string') self.collection.tags = ['', 'tag'] self._assert_validation_error('Tags should be non-empty') self.collection.tags = ['234'] self._assert_validation_error( 'Tags should only contain lowercase letters and spaces') self.collection.tags = [' abc'] self._assert_validation_error( 'Tags should not start or end with whitespace') self.collection.tags = ['abc def'] self._assert_validation_error( 'Adjacent whitespace in tags should be collapsed') self.collection.tags = ['abc', 'abc'] self._assert_validation_error( 'Expected tags to be unique, but found duplicates') def test_schema_version_validation(self): self.collection.schema_version = 'some_schema_version' self._assert_validation_error('Expected schema version to be an int') self.collection.schema_version = 100 self._assert_validation_error( 'Expected schema version to be %s' % feconf.CURRENT_COLLECTION_SCHEMA_VERSION) def test_nodes_validation(self): self.collection.nodes = {} self._assert_validation_error('Expected nodes to be a list') self.collection.nodes = [ collection_domain.CollectionNode.from_dict({ 'exploration_id': '0' }), collection_domain.CollectionNode.from_dict({ 'exploration_id': '0' }) ] self._assert_validation_error( 'There are explorations referenced in the collection more than ' 'once.') def test_initial_explorations_validation(self): self.collection.nodes = [] self.collection.validate(strict=False) self._assert_validation_error( 'Expected to have at least 1 exploration in the collection.') def test_metadata_validation(self): self.collection.title = '' self.collection.objective = '' self.collection.category = '' self.collection.nodes = [] # Having no title is fine for non-strict validation. self.collection.validate(strict=False) # But it's not okay for strict validation. self._assert_validation_error( 'A title must be specified for the collection.') self.collection.title = 'A title' self.collection.validate(strict=False) self._assert_validation_error( 'An objective must be specified for the collection.') self.collection.objective = 'An objective' # Having no category is fine for non-strict validation. self.collection.validate(strict=False) # But it's not okay for strict validation. self._assert_validation_error( 'A category must be specified for the collection.') self.collection.category = 'A category' self.collection.validate(strict=False) self._assert_validation_error( 'Expected to have at least 1 exploration in the collection.') self.collection.add_node('exp_id_1') # Now the collection passes both strict and non-strict validation. self.collection.validate(strict=False) self.collection.validate(strict=True) def test_collection_node_exploration_id_validation(self): # Validate CollectionNode's exploration_id. collection_node0 = self.collection.get_node('exp_id_0') collection_node0.exploration_id = 2 self._assert_validation_error('Expected exploration ID to be a string') def test_is_demo_property(self): demo = collection_domain.Collection.create_default_collection('0') self.assertEqual(demo.is_demo, True) notdemo1 = collection_domain.Collection.create_default_collection('a') self.assertEqual(notdemo1.is_demo, False) notdemo2 = collection_domain.Collection.create_default_collection( 'abcd') self.assertEqual(notdemo2.is_demo, False) def test_collection_export_import(self): self.save_new_valid_exploration( '0', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.create_default_collection( '0', title='title', category='category', objective='objective') collection_dict = collection.to_dict() collection_from_dict = collection_domain.Collection.from_dict( collection_dict) self.assertEqual(collection_from_dict.to_dict(), collection_dict) def test_add_delete_swap_nodes(self): collection = collection_domain.Collection.create_default_collection( '0') self.assertEqual(len(collection.nodes), 0) collection.add_node('test_exp') self.assertEqual(len(collection.nodes), 1) with self.assertRaisesRegexp( ValueError, 'Exploration is already part of this collection: test_exp' ): collection.add_node('test_exp') collection.add_node('another_exp') self.assertEqual(len(collection.nodes), 2) collection.swap_nodes(0, 1) self.assertEqual(collection.nodes[0].exploration_id, 'another_exp') self.assertEqual(collection.nodes[1].exploration_id, 'test_exp') with self.assertRaisesRegexp( ValueError, 'Both indices point to the same collection node.' ): collection.swap_nodes(0, 0) collection.delete_node('another_exp') self.assertEqual(len(collection.nodes), 1) with self.assertRaisesRegexp( ValueError, 'Exploration is not part of this collection: another_exp' ): collection.delete_node('another_exp') collection.delete_node('test_exp') self.assertEqual(len(collection.nodes), 0) def test_update_collection_contents_from_model(self): versioned_collection_contents = { 'schema_version': 1, 'collection_contents': {} } collection_domain.Collection.update_collection_contents_from_model( versioned_collection_contents, 1) self.assertEqual(versioned_collection_contents['schema_version'], 2) self.assertEqual( versioned_collection_contents['collection_contents'], {}) def test_update_collection_contents_from_model_with_invalid_schema_version( self): versioned_collection_contents = { 'schema_version': feconf.CURRENT_COLLECTION_SCHEMA_VERSION, 'collection_contents': {} } with self.assertRaisesRegexp( Exception, 'Collection is version .+ but current collection schema version ' 'is %d' % feconf.CURRENT_COLLECTION_SCHEMA_VERSION): collection_domain.Collection.update_collection_contents_from_model( versioned_collection_contents, feconf.CURRENT_COLLECTION_SCHEMA_VERSION) class ExplorationGraphUnitTests(test_utils.GenericTestBase): def test_initial_explorations(self): collection = collection_domain.Collection.create_default_collection( 'collection_id') self.assertEqual(collection.nodes, []) self.assertEqual(collection.first_exploration_id, None) collection.add_node('exp_id_0') self.assertEqual(collection.first_exploration_id, 'exp_id_0') collection.add_node('exp_id_1') self.assertEqual(len(collection.nodes), 2) self.assertEqual(collection.first_exploration_id, 'exp_id_0') def test_next_explorations(self): collection = collection_domain.Collection.create_default_collection( 'collection_id') self.assertEqual(collection.get_next_exploration_id([]), None) collection.add_node('exp_id_0') self.assertEqual(collection.get_next_exploration_id([]), 'exp_id_0') self.assertEqual( collection.first_exploration_id, collection.get_next_exploration_id([])) self.assertEqual( collection.get_next_exploration_id(['exp_id_0']), None) collection.add_node('exp_id_1') self.assertEqual(collection.get_next_exploration_id( ['exp_id_0']), 'exp_id_1') collection.add_node('exp_id_2') self.assertEqual( collection.get_next_exploration_id([]), 'exp_id_0') self.assertEqual( collection.get_next_exploration_id(['exp_id_0']), 'exp_id_1') self.assertEqual( collection.get_next_exploration_id(['exp_id_0', 'exp_id_1']), 'exp_id_2') self.assertEqual( collection.get_next_exploration_id( ['exp_id_0', 'exp_id_1', 'exp_id_2']), None) def test_next_explorations_in_sequence(self): collection = collection_domain.Collection.create_default_collection( 'collection_id') exploration_id = 'exp_id_0' collection.add_node(exploration_id) self.assertEqual( collection.get_next_exploration_id_in_sequence(exploration_id), None) collection.add_node('exp_id_1') collection.add_node('exp_id_2') self.assertEqual( collection.get_next_exploration_id_in_sequence(exploration_id), 'exp_id_1') self.assertEqual( collection.get_next_exploration_id_in_sequence('exp_id_1'), 'exp_id_2') def test_nodes_are_in_playble_order(self): collection = collection_domain.Collection.create_default_collection( 'collection_id') self.assertEqual(collection.nodes, []) collection.add_node('exp_id_0') collection.add_node('exp_id_1') collection.add_node('exp_id_2') sorted_nodes = collection.nodes expected_explorations_ids = ['exp_id_0', 'exp_id_1', 'exp_id_2'] observed_exploration_ids = [ node.exploration_id for node in sorted_nodes] self.assertEqual(expected_explorations_ids, observed_exploration_ids) def test_next_explorations_with_invalid_exploration_ids(self): collection = collection_domain.Collection.create_default_collection( 'collection_id') collection.add_node('exp_id_1') self.assertEqual(collection.get_next_exploration_id([]), 'exp_id_1') self.assertEqual( collection.get_next_exploration_id(['fake_exp_id']), 'exp_id_1') class YamlCreationUnitTests(test_utils.GenericTestBase): COLLECTION_ID = 'a_collection_id' EXPLORATION_ID = 'an_exploration_id' def test_yaml_import_and_export(self): self.save_new_valid_exploration( self.EXPLORATION_ID, 'user@example.com', end_state_name='End') collection = collection_domain.Collection.create_default_collection( self.COLLECTION_ID, title='A title', category='A category', objective='An objective') collection.add_node(self.EXPLORATION_ID) self.assertEqual(len(collection.nodes), 1) collection.validate() yaml_content = collection.to_yaml() self.assertEqual(yaml_content, SAMPLE_YAML_CONTENT) collection2 = collection_domain.Collection.from_yaml( 'collection2', yaml_content) self.assertEqual(len(collection2.nodes), 1) yaml_content_2 = collection2.to_yaml() self.assertEqual(yaml_content_2, yaml_content) with self.assertRaises(Exception): collection_domain.Collection.from_yaml('collection3', None) def test_from_yaml_with_no_schema_version_specified_raises_error(self): collection = collection_domain.Collection( self.COLLECTION_ID, 'title', 'category', 'objective', 'en', [], None, [], 0) yaml_content = collection.to_yaml() with self.assertRaisesRegexp( Exception, 'Invalid YAML file: no schema version specified.'): collection_domain.Collection.from_yaml( self.COLLECTION_ID, yaml_content) def test_from_yaml_with_invalid_schema_version_raises_error(self): collection = collection_domain.Collection( self.COLLECTION_ID, 'title', 'category', 'objective', 'en', [], 0, [], 0) yaml_content = collection.to_yaml() with self.assertRaisesRegexp( Exception, 'Sorry, we can only process v1 to .+ collection YAML files at ' 'present.'): collection_domain.Collection.from_yaml( self.COLLECTION_ID, yaml_content) class SchemaMigrationMethodsUnitTests(test_utils.GenericTestBase): def test_correct_collection_contents_schema_conversion_methods_exist(self): current_collection_schema_version = ( feconf.CURRENT_COLLECTION_SCHEMA_VERSION) for version_num in range(1, current_collection_schema_version): self.assertTrue(hasattr( collection_domain.Collection, '_convert_collection_contents_v%s_dict_to_v%s_dict' % ( version_num, version_num + 1))) self.assertFalse(hasattr( collection_domain.Collection, '_convert_collection_contents_v%s_dict_to_v%s_dict' % ( current_collection_schema_version, current_collection_schema_version + 1))) def test_correct_collection_schema_conversion_methods_exist(self): current_collection_schema_version = ( feconf.CURRENT_COLLECTION_SCHEMA_VERSION) for version_num in range(1, current_collection_schema_version): self.assertTrue(hasattr( collection_domain.Collection, '_convert_v%s_dict_to_v%s_dict' % ( version_num, version_num + 1))) self.assertFalse(hasattr( collection_domain.Collection, '_convert_v%s_dict_to_v%s_dict' % ( current_collection_schema_version, current_collection_schema_version + 1))) class SchemaMigrationUnitTests(test_utils.GenericTestBase): YAML_CONTENT_V1 = ("""category: A category nodes: - acquired_skills: - Skill1 - Skill2 exploration_id: Exp1 prerequisite_skills: [] - acquired_skills: [] exploration_id: Exp2 prerequisite_skills: - Skill1 objective: '' schema_version: 1 title: A title """) YAML_CONTENT_V2 = ("""category: A category language_code: en nodes: - acquired_skills: - Skill1 - Skill2 exploration_id: Exp1 prerequisite_skills: [] - acquired_skills: [] exploration_id: Exp2 prerequisite_skills: - Skill1 objective: '' schema_version: 2 tags: [] title: A title """) YAML_CONTENT_V3 = ("""category: A category language_code: en nodes: - acquired_skills: - Skill1 - Skill2 exploration_id: Exp1 prerequisite_skills: [] - acquired_skills: [] exploration_id: Exp2 prerequisite_skills: - Skill1 objective: '' schema_version: 2 tags: [] title: A title """) YAML_CONTENT_V4 = ("""category: A category language_code: en next_skill_id: 2 nodes: - acquired_skill_ids: - skill0 - skill1 exploration_id: Exp1 prerequisite_skill_ids: [] - acquired_skill_ids: [] exploration_id: Exp2 prerequisite_skill_ids: - skill0 objective: '' schema_version: 4 skills: skill0: name: Skill1 question_ids: [] skill1: name: Skill2 question_ids: [] tags: [] title: A title """) YAML_CONTENT_V5 = ("""category: A category language_code: en next_skill_index: 2 nodes: - acquired_skill_ids: - skill0 - skill1 exploration_id: Exp1 prerequisite_skill_ids: [] - acquired_skill_ids: [] exploration_id: Exp2 prerequisite_skill_ids: - skill0 objective: '' schema_version: 5 skills: skill0: name: Skill1 question_ids: [] skill1: name: Skill2 question_ids: [] tags: [] title: A title """) YAML_CONTENT_V6 = ("""category: A category language_code: en nodes: - exploration_id: Exp1 - exploration_id: Exp2 objective: '' schema_version: 6 tags: [] title: A title """) _LATEST_YAML_CONTENT = YAML_CONTENT_V6 def test_load_from_v1(self): self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V1) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v2(self): self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V2) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v3(self): self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V3) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v4(self): self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V4) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v5(self): self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V5) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) def test_load_from_v6(self): self.save_new_valid_exploration( 'Exp1', 'user@example.com', end_state_name='End') collection = collection_domain.Collection.from_yaml( 'cid', self.YAML_CONTENT_V6) self.assertEqual(collection.to_yaml(), self._LATEST_YAML_CONTENT) class CollectionSummaryTests(test_utils.GenericTestBase): def test_collection_summary_gets_created(self): collection_summary_dict = { 'category': 'category', 'status': 'status', 'community_owned': 'True', 'viewer_ids': ['viewer_id'], 'version': 1, 'editor_ids': ['editor_id'], 'title': 'title', 'collection_model_created_on': {}, 'tags': [], 'collection_model_last_updated': {}, 'contributor_ids': ['contributor_id'], 'language_code': 'en', 'objective': 'objective', 'contributors_summary': {}, 'id': 'col_id', 'owner_ids': ['owner_id'] } collection_summary = collection_domain.CollectionSummary( 'col_id', 'title', 'category', 'objective', 'en', [], 'status', 'True', ['owner_id'], ['editor_id'], ['viewer_id'], ['contributor_id'], {}, 1, 1, {}, {}) self.assertEqual(collection_summary.to_dict(), collection_summary_dict)

true

1c3833377ac441e8cf3d5885d193dc72b8a361ad

64

py

Python

bokeh_metaplot/__init__.py

doomsplayer/bokeh-metaplot

e5f74017a6ed6c60b6a37a87a071ea54ab882f9c

[ "MIT" ]

null

bokeh_metaplot/__init__.py

doomsplayer/bokeh-metaplot

e5f74017a6ed6c60b6a37a87a071ea54ab882f9c

[ "MIT" ]

null

bokeh_metaplot/__init__.py

doomsplayer/bokeh-metaplot

e5f74017a6ed6c60b6a37a87a071ea54ab882f9c

[ "MIT" ]

null

from .metaplot import MetaPlot, MetaChart, MetaFigure, metaize

21.333333

62

0.8125

from .metaplot import MetaPlot, MetaChart, MetaFigure, metaize

true

1c38333a94e6ed4e43e327d6343bda2f1d029216

10,159

py

Python

prompt_toolkit/input/vt100.py

mark64/python-prompt-toolkit

df768003df167382f3a11a73d800a4555ca1157c

[ "BSD-3-Clause" ]

2

2020-05-29T17:51:10.000Z

2020-05-29T17:52:26.000Z

prompt_toolkit/input/vt100.py

vxgmichel/python-prompt-toolkit

a49c486fa5ae6ad5a38ff3c1050f9c9609acac1d

[ "BSD-3-Clause" ]

null

prompt_toolkit/input/vt100.py

vxgmichel/python-prompt-toolkit

a49c486fa5ae6ad5a38ff3c1050f9c9609acac1d

[ "BSD-3-Clause" ]

null

import contextlib import io import os import sys import termios import tty from asyncio import AbstractEventLoop, get_event_loop from typing import ( Callable, ContextManager, Dict, Generator, List, Optional, Set, TextIO, Tuple, Union, ) from prompt_toolkit.utils import is_dumb_terminal from ..key_binding import KeyPress from .base import Input from .posix_utils import PosixStdinReader from .vt100_parser import Vt100Parser __all__ = [ "Vt100Input", "raw_mode", "cooked_mode", ] class Vt100Input(Input): """ Vt100 input for Posix systems. (This uses a posix file descriptor that can be registered in the event loop.) """ # For the error messages. Only display "Input is not a terminal" once per # file descriptor. _fds_not_a_terminal: Set[int] = set() def __init__(self, stdin: TextIO) -> None: # Test whether the given input object has a file descriptor. # (Idle reports stdin to be a TTY, but fileno() is not implemented.) try: # This should not raise, but can return 0. stdin.fileno() except io.UnsupportedOperation: if "idlelib.run" in sys.modules: raise io.UnsupportedOperation( "Stdin is not a terminal. Running from Idle is not supported." ) else: raise io.UnsupportedOperation("Stdin is not a terminal.") # Even when we have a file descriptor, it doesn't mean it's a TTY. # Normally, this requires a real TTY device, but people instantiate # this class often during unit tests as well. They use for instance # pexpect to pipe data into an application. For convenience, we print # an error message and go on. isatty = stdin.isatty() fd = stdin.fileno() if not isatty and fd not in Vt100Input._fds_not_a_terminal: msg = "Warning: Input is not a terminal (fd=%r).\n" sys.stderr.write(msg % fd) sys.stderr.flush() Vt100Input._fds_not_a_terminal.add(fd) # self.stdin = stdin # Create a backup of the fileno(). We want this to work even if the # underlying file is closed, so that `typeahead_hash()` keeps working. self._fileno = stdin.fileno() self._buffer: List[KeyPress] = [] # Buffer to collect the Key objects. self.stdin_reader = PosixStdinReader(self._fileno, encoding=stdin.encoding) self.vt100_parser = Vt100Parser( lambda key_press: self._buffer.append(key_press) ) @property def responds_to_cpr(self) -> bool: # When the input is a tty, we assume that CPR is supported. # It's not when the input is piped from Pexpect. if os.environ.get("PROMPT_TOOLKIT_NO_CPR", "") == "1": return False if is_dumb_terminal(): return False try: return self.stdin.isatty() except ValueError: return False # ValueError: I/O operation on closed file def attach(self, input_ready_callback: Callable[[], None]) -> ContextManager[None]: """ Return a context manager that makes this input active in the current event loop. """ return _attached_input(self, input_ready_callback) def detach(self) -> ContextManager[None]: """ Return a context manager that makes sure that this input is not active in the current event loop. """ return _detached_input(self) def read_keys(self) -> List[KeyPress]: " Read list of KeyPress. " # Read text from stdin. data = self.stdin_reader.read() # Pass it through our vt100 parser. self.vt100_parser.feed(data) # Return result. result = self._buffer self._buffer = [] return result def flush_keys(self) -> List[KeyPress]: """ Flush pending keys and return them. (Used for flushing the 'escape' key.) """ # Flush all pending keys. (This is most important to flush the vt100 # 'Escape' key early when nothing else follows.) self.vt100_parser.flush() # Return result. result = self._buffer self._buffer = [] return result @property def closed(self) -> bool: return self.stdin_reader.closed def raw_mode(self) -> ContextManager[None]: return raw_mode(self.stdin.fileno()) def cooked_mode(self) -> ContextManager[None]: return cooked_mode(self.stdin.fileno()) def fileno(self) -> int: return self.stdin.fileno() def typeahead_hash(self) -> str: return "fd-%s" % (self._fileno,) _current_callbacks: Dict[ Tuple[AbstractEventLoop, int], Optional[Callable[[], None]] ] = {} # (loop, fd) -> current callback @contextlib.contextmanager def _attached_input( input: Vt100Input, callback: Callable[[], None] ) -> Generator[None, None, None]: """ Context manager that makes this input active in the current event loop. :param input: :class:`~prompt_toolkit.input.Input` object. :param callback: Called when the input is ready to read. """ loop = get_event_loop() fd = input.fileno() previous = _current_callbacks.get((loop, fd)) loop.add_reader(fd, callback) _current_callbacks[loop, fd] = callback try: yield finally: loop.remove_reader(fd) if previous: loop.add_reader(fd, previous) _current_callbacks[loop, fd] = previous else: del _current_callbacks[loop, fd] @contextlib.contextmanager def _detached_input(input: Vt100Input) -> Generator[None, None, None]: loop = get_event_loop() fd = input.fileno() previous = _current_callbacks.get((loop, fd)) if previous: loop.remove_reader(fd) _current_callbacks[loop, fd] = None try: yield finally: if previous: loop.add_reader(fd, previous) _current_callbacks[loop, fd] = previous class raw_mode: """ :: with raw_mode(stdin): ''' the pseudo-terminal stdin is now used in raw mode ''' We ignore errors when executing `tcgetattr` fails. """ # There are several reasons for ignoring errors: # 1. To avoid the "Inappropriate ioctl for device" crash if somebody would # execute this code (In a Python REPL, for instance): # # import os; f = open(os.devnull); os.dup2(f.fileno(), 0) # # The result is that the eventloop will stop correctly, because it has # to logic to quit when stdin is closed. However, we should not fail at # this point. See: # https://github.com/jonathanslenders/python-prompt-toolkit/pull/393 # https://github.com/jonathanslenders/python-prompt-toolkit/issues/392 # 2. Related, when stdin is an SSH pipe, and no full terminal was allocated. # See: https://github.com/jonathanslenders/python-prompt-toolkit/pull/165 def __init__(self, fileno: int) -> None: self.fileno = fileno self.attrs_before: Optional[List[Union[int, List[bytes]]]] try: self.attrs_before = termios.tcgetattr(fileno) except termios.error: # Ignore attribute errors. self.attrs_before = None def __enter__(self) -> None: # NOTE: On os X systems, using pty.setraw() fails. Therefor we are using this: try: newattr = termios.tcgetattr(self.fileno) except termios.error: pass else: newattr[tty.LFLAG] = self._patch_lflag(newattr[tty.LFLAG]) newattr[tty.IFLAG] = self._patch_iflag(newattr[tty.IFLAG]) # VMIN defines the number of characters read at a time in # non-canonical mode. It seems to default to 1 on Linux, but on # Solaris and derived operating systems it defaults to 4. (This is # because the VMIN slot is the same as the VEOF slot, which # defaults to ASCII EOT = Ctrl-D = 4.) newattr[tty.CC][termios.VMIN] = 1 # type: ignore termios.tcsetattr(self.fileno, termios.TCSANOW, newattr) # Put the terminal in cursor mode. (Instead of application mode.) os.write(self.fileno, b"\x1b[?1l") @classmethod def _patch_lflag(cls, attrs): return attrs & ~(termios.ECHO | termios.ICANON | termios.IEXTEN | termios.ISIG) @classmethod def _patch_iflag(cls, attrs): return attrs & ~( # Disable XON/XOFF flow control on output and input. # (Don't capture Ctrl-S and Ctrl-Q.) # Like executing: "stty -ixon." termios.IXON | termios.IXOFF | # Don't translate carriage return into newline on input. termios.ICRNL | termios.INLCR | termios.IGNCR ) def __exit__(self, *a: object) -> None: if self.attrs_before is not None: try: termios.tcsetattr(self.fileno, termios.TCSANOW, self.attrs_before) except termios.error: pass # # Put the terminal in application mode. # self._stdout.write('\x1b[?1h') class cooked_mode(raw_mode): """ The opposite of ``raw_mode``, used when we need cooked mode inside a `raw_mode` block. Used in `Application.run_in_terminal`.:: with cooked_mode(stdin): ''' the pseudo-terminal stdin is now used in cooked mode. ''' """ @classmethod def _patch_lflag(cls, attrs): return attrs | (termios.ECHO | termios.ICANON | termios.IEXTEN | termios.ISIG) @classmethod def _patch_iflag(cls, attrs): # Turn the ICRNL flag back on. (Without this, calling `input()` in # run_in_terminal doesn't work and displays ^M instead. Ptpython # evaluates commands using `run_in_terminal`, so it's important that # they translate ^M back into ^J.) return attrs | termios.ICRNL

32.353503

87

0.617974

import contextlib import io import os import sys import termios import tty from asyncio import AbstractEventLoop, get_event_loop from typing import ( Callable, ContextManager, Dict, Generator, List, Optional, Set, TextIO, Tuple, Union, ) from prompt_toolkit.utils import is_dumb_terminal from ..key_binding import KeyPress from .base import Input from .posix_utils import PosixStdinReader from .vt100_parser import Vt100Parser __all__ = [ "Vt100Input", "raw_mode", "cooked_mode", ] class Vt100Input(Input): _fds_not_a_terminal: Set[int] = set() def __init__(self, stdin: TextIO) -> None: try: stdin.fileno() except io.UnsupportedOperation: if "idlelib.run" in sys.modules: raise io.UnsupportedOperation( "Stdin is not a terminal. Running from Idle is not supported." ) else: raise io.UnsupportedOperation("Stdin is not a terminal.") isatty = stdin.isatty() fd = stdin.fileno() if not isatty and fd not in Vt100Input._fds_not_a_terminal: msg = "Warning: Input is not a terminal (fd=%r).\n" sys.stderr.write(msg % fd) sys.stderr.flush() Vt100Input._fds_not_a_terminal.add(fd) self.stdin = stdin self._fileno = stdin.fileno() self._buffer: List[KeyPress] = [] self.stdin_reader = PosixStdinReader(self._fileno, encoding=stdin.encoding) self.vt100_parser = Vt100Parser( lambda key_press: self._buffer.append(key_press) ) @property def responds_to_cpr(self) -> bool: if os.environ.get("PROMPT_TOOLKIT_NO_CPR", "") == "1": return False if is_dumb_terminal(): return False try: return self.stdin.isatty() except ValueError: return False # ValueError: I/O operation on closed file def attach(self, input_ready_callback: Callable[[], None]) -> ContextManager[None]: return _attached_input(self, input_ready_callback) def detach(self) -> ContextManager[None]: return _detached_input(self) def read_keys(self) -> List[KeyPress]: # Read text from stdin. data = self.stdin_reader.read() # Pass it through our vt100 parser. self.vt100_parser.feed(data) # Return result. result = self._buffer self._buffer = [] return result def flush_keys(self) -> List[KeyPress]: # Flush all pending keys. (This is most important to flush the vt100 # 'Escape' key early when nothing else follows.) self.vt100_parser.flush() # Return result. result = self._buffer self._buffer = [] return result @property def closed(self) -> bool: return self.stdin_reader.closed def raw_mode(self) -> ContextManager[None]: return raw_mode(self.stdin.fileno()) def cooked_mode(self) -> ContextManager[None]: return cooked_mode(self.stdin.fileno()) def fileno(self) -> int: return self.stdin.fileno() def typeahead_hash(self) -> str: return "fd-%s" % (self._fileno,) _current_callbacks: Dict[ Tuple[AbstractEventLoop, int], Optional[Callable[[], None]] ] = {} # (loop, fd) -> current callback @contextlib.contextmanager def _attached_input( input: Vt100Input, callback: Callable[[], None] ) -> Generator[None, None, None]: loop = get_event_loop() fd = input.fileno() previous = _current_callbacks.get((loop, fd)) loop.add_reader(fd, callback) _current_callbacks[loop, fd] = callback try: yield finally: loop.remove_reader(fd) if previous: loop.add_reader(fd, previous) _current_callbacks[loop, fd] = previous else: del _current_callbacks[loop, fd] @contextlib.contextmanager def _detached_input(input: Vt100Input) -> Generator[None, None, None]: loop = get_event_loop() fd = input.fileno() previous = _current_callbacks.get((loop, fd)) if previous: loop.remove_reader(fd) _current_callbacks[loop, fd] = None try: yield finally: if previous: loop.add_reader(fd, previous) _current_callbacks[loop, fd] = previous class raw_mode: # There are several reasons for ignoring errors: # 1. To avoid the "Inappropriate ioctl for device" crash if somebody would # execute this code (In a Python REPL, for instance): # # import os; f = open(os.devnull); os.dup2(f.fileno(), 0) # # The result is that the eventloop will stop correctly, because it has # to logic to quit when stdin is closed. However, we should not fail at # this point. See: # https://github.com/jonathanslenders/python-prompt-toolkit/pull/393 # https://github.com/jonathanslenders/python-prompt-toolkit/issues/392 # 2. Related, when stdin is an SSH pipe, and no full terminal was allocated. # See: https://github.com/jonathanslenders/python-prompt-toolkit/pull/165 def __init__(self, fileno: int) -> None: self.fileno = fileno self.attrs_before: Optional[List[Union[int, List[bytes]]]] try: self.attrs_before = termios.tcgetattr(fileno) except termios.error: # Ignore attribute errors. self.attrs_before = None def __enter__(self) -> None: # NOTE: On os X systems, using pty.setraw() fails. Therefor we are using this: try: newattr = termios.tcgetattr(self.fileno) except termios.error: pass else: newattr[tty.LFLAG] = self._patch_lflag(newattr[tty.LFLAG]) newattr[tty.IFLAG] = self._patch_iflag(newattr[tty.IFLAG]) # VMIN defines the number of characters read at a time in # non-canonical mode. It seems to default to 1 on Linux, but on # Solaris and derived operating systems it defaults to 4. (This is # because the VMIN slot is the same as the VEOF slot, which # defaults to ASCII EOT = Ctrl-D = 4.) newattr[tty.CC][termios.VMIN] = 1 # type: ignore termios.tcsetattr(self.fileno, termios.TCSANOW, newattr) # Put the terminal in cursor mode. (Instead of application mode.) os.write(self.fileno, b"\x1b[?1l") @classmethod def _patch_lflag(cls, attrs): return attrs & ~(termios.ECHO | termios.ICANON | termios.IEXTEN | termios.ISIG) @classmethod def _patch_iflag(cls, attrs): return attrs & ~( # Disable XON/XOFF flow control on output and input. # (Don't capture Ctrl-S and Ctrl-Q.) termios.IXON | termios.IXOFF | termios.ICRNL | termios.INLCR | termios.IGNCR ) def __exit__(self, *a: object) -> None: if self.attrs_before is not None: try: termios.tcsetattr(self.fileno, termios.TCSANOW, self.attrs_before) except termios.error: pass # # Put the terminal in application mode. # self._stdout.write('\x1b[?1h') class cooked_mode(raw_mode): @classmethod def _patch_lflag(cls, attrs): return attrs | (termios.ECHO | termios.ICANON | termios.IEXTEN | termios.ISIG) @classmethod def _patch_iflag(cls, attrs): # Turn the ICRNL flag back on. (Without this, calling `input()` in # run_in_terminal doesn't work and displays ^M instead. Ptpython # they translate ^M back into ^J.) return attrs | termios.ICRNL

true

1c3833a131a1ebfe57e0a4626e989e801f84a185

480

py

Python

pdf_first_page_to_jpg.py

Pakequis/Python_scripts

b0de2e2647ecaed331e7f8df2422b548df4b348c

[ "MIT" ]

null

pdf_first_page_to_jpg.py

Pakequis/Python_scripts

b0de2e2647ecaed331e7f8df2422b548df4b348c

[ "MIT" ]

null

pdf_first_page_to_jpg.py

Pakequis/Python_scripts

b0de2e2647ecaed331e7f8df2422b548df4b348c

[ "MIT" ]

null

# Library: pdf2image and poppler-0.68.0 import os from pdf2image import convert_from_path pdf_dir = "PATH" #Path to the pdf folder os.chdir(pdf_dir) print("PDF to image start...") for pdf_file in os.listdir(pdf_dir): if pdf_file.endswith(".pdf"): pages = convert_from_path(pdf_file, 150,None,1,1) #second argument is the image quality pdf_file = pdf_file[:-4] pages[0].save("%s.jpg" % (pdf_file), "JPEG") print(pdf_file) print("End...")

22.857143

95

0.66875

import os from pdf2image import convert_from_path pdf_dir = "PATH" os.chdir(pdf_dir) print("PDF to image start...") for pdf_file in os.listdir(pdf_dir): if pdf_file.endswith(".pdf"): pages = convert_from_path(pdf_file, 150,None,1,1) pdf_file = pdf_file[:-4] pages[0].save("%s.jpg" % (pdf_file), "JPEG") print(pdf_file) print("End...")

true

1c38359cb800082b537261c0842402b9fd7bf36f

84

py

Python

backend/fms_core/template_importer/row_handlers/container_rename/__init__.py

c3g/freezeman

bc4b6c8a2876e888ce41b7d14127cc22bc2b2143

[ "W3C" ]

2

2021-07-31T13:20:08.000Z

2021-09-28T13:18:55.000Z

backend/fms_core/template_importer/row_handlers/container_creation/__init__.py

c3g/freezeman

bc4b6c8a2876e888ce41b7d14127cc22bc2b2143

[ "W3C" ]

71

2021-03-12T22:08:19.000Z

2022-03-25T15:24:40.000Z

backend/fms_core/template_importer/row_handlers/container_rename/__init__.py

c3g/freezeman

bc4b6c8a2876e888ce41b7d14127cc22bc2b2143

[ "W3C" ]

null

from .container import ContainerRowHandler __all__ = [ "ContainerRowHandler", ]

16.8

42

0.761905

from .container import ContainerRowHandler __all__ = [ "ContainerRowHandler", ]

true

1c3836841e8cbea4eab578bd595d3c962670d427

4,334

py

Python

senlin-7.0.0/senlin/objects/profile.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

45

2015-10-18T02:56:50.000Z

2022-03-01T15:28:02.000Z

senlin-7.0.0/senlin/objects/profile.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

5

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

senlin-7.0.0/senlin/objects/profile.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

45

2015-10-19T02:35:57.000Z

2021-09-28T09:01:42.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Profile object.""" from oslo_utils import uuidutils from senlin.common import exception from senlin.common import utils from senlin.db import api as db_api from senlin.objects import base from senlin.objects import fields @base.SenlinObjectRegistry.register class Profile(base.SenlinObject, base.VersionedObjectDictCompat): """Senlin profile object.""" fields = { 'id': fields.UUIDField(), 'name': fields.StringField(), 'type': fields.StringField(), 'context': fields.JsonField(), 'spec': fields.JsonField(), 'created_at': fields.DateTimeField(), 'updated_at': fields.DateTimeField(nullable=True), 'user': fields.StringField(), 'project': fields.StringField(), 'domain': fields.StringField(nullable=True), 'permission': fields.StringField(nullable=True), 'metadata': fields.JsonField(nullable=True), } @classmethod def create(cls, context, values): values = cls._transpose_metadata(values) obj = db_api.profile_create(context, values) return cls._from_db_object(context, cls(context), obj) @classmethod def find(cls, context, identity, **kwargs): """Find a profile with the given identity. :param context: An instance of the request context. :param identity: The UUID, name or short-id of a profile. :param project_safe: A boolean indicating whether profile from projects other than the requesting one can be returned. :return: A DB object of profile or an exception `ResourceNotFound` if no matching object is found. """ if uuidutils.is_uuid_like(identity): profile = cls.get(context, identity, **kwargs) if not profile: profile = cls.get_by_name(context, identity, **kwargs) else: profile = cls.get_by_name(context, identity, **kwargs) if not profile: profile = cls.get_by_short_id(context, identity, **kwargs) if not profile: raise exception.ResourceNotFound(type='profile', id=identity) return profile @classmethod def get(cls, context, profile_id, **kwargs): obj = db_api.profile_get(context, profile_id, **kwargs) return cls._from_db_object(context, cls(), obj) @classmethod def get_by_name(cls, context, name, **kwargs): obj = db_api.profile_get_by_name(context, name, **kwargs) return cls._from_db_object(context, cls(), obj) @classmethod def get_by_short_id(cls, context, short_id, **kwargs): obj = db_api.profile_get_by_short_id(context, short_id, **kwargs) return cls._from_db_object(context, cls(), obj) @classmethod def get_all(cls, context, **kwargs): objs = db_api.profile_get_all(context, **kwargs) return [cls._from_db_object(context, cls(), obj) for obj in objs] @classmethod def update(cls, context, obj_id, values): values = cls._transpose_metadata(values) obj = db_api.profile_update(context, obj_id, values) return cls._from_db_object(context, cls(), obj) @classmethod def delete(cls, context, obj_id): db_api.profile_delete(context, obj_id) def to_dict(self): profile_dict = { 'id': self.id, 'name': self.name, 'type': self.type, 'user': self.user, 'project': self.project, 'domain': self.domain, 'spec': self.spec, 'metadata': self.metadata, 'created_at': utils.isotime(self.created_at), 'updated_at': utils.isotime(self.updated_at) } return profile_dict

36.728814

75

0.643747

from oslo_utils import uuidutils from senlin.common import exception from senlin.common import utils from senlin.db import api as db_api from senlin.objects import base from senlin.objects import fields @base.SenlinObjectRegistry.register class Profile(base.SenlinObject, base.VersionedObjectDictCompat): fields = { 'id': fields.UUIDField(), 'name': fields.StringField(), 'type': fields.StringField(), 'context': fields.JsonField(), 'spec': fields.JsonField(), 'created_at': fields.DateTimeField(), 'updated_at': fields.DateTimeField(nullable=True), 'user': fields.StringField(), 'project': fields.StringField(), 'domain': fields.StringField(nullable=True), 'permission': fields.StringField(nullable=True), 'metadata': fields.JsonField(nullable=True), } @classmethod def create(cls, context, values): values = cls._transpose_metadata(values) obj = db_api.profile_create(context, values) return cls._from_db_object(context, cls(context), obj) @classmethod def find(cls, context, identity, **kwargs): if uuidutils.is_uuid_like(identity): profile = cls.get(context, identity, **kwargs) if not profile: profile = cls.get_by_name(context, identity, **kwargs) else: profile = cls.get_by_name(context, identity, **kwargs) if not profile: profile = cls.get_by_short_id(context, identity, **kwargs) if not profile: raise exception.ResourceNotFound(type='profile', id=identity) return profile @classmethod def get(cls, context, profile_id, **kwargs): obj = db_api.profile_get(context, profile_id, **kwargs) return cls._from_db_object(context, cls(), obj) @classmethod def get_by_name(cls, context, name, **kwargs): obj = db_api.profile_get_by_name(context, name, **kwargs) return cls._from_db_object(context, cls(), obj) @classmethod def get_by_short_id(cls, context, short_id, **kwargs): obj = db_api.profile_get_by_short_id(context, short_id, **kwargs) return cls._from_db_object(context, cls(), obj) @classmethod def get_all(cls, context, **kwargs): objs = db_api.profile_get_all(context, **kwargs) return [cls._from_db_object(context, cls(), obj) for obj in objs] @classmethod def update(cls, context, obj_id, values): values = cls._transpose_metadata(values) obj = db_api.profile_update(context, obj_id, values) return cls._from_db_object(context, cls(), obj) @classmethod def delete(cls, context, obj_id): db_api.profile_delete(context, obj_id) def to_dict(self): profile_dict = { 'id': self.id, 'name': self.name, 'type': self.type, 'user': self.user, 'project': self.project, 'domain': self.domain, 'spec': self.spec, 'metadata': self.metadata, 'created_at': utils.isotime(self.created_at), 'updated_at': utils.isotime(self.updated_at) } return profile_dict

true

1c38375926e5e1f1169c96ab24e03ac84e1f9163

1,355

py

Python

python/week9.py

Coding-Dojo-ACC/group-a-project

5c5dbf5eb124f78a239de1f80f8b2178e6734e6a

[ "MIT" ]

null

python/week9.py

Coding-Dojo-ACC/group-a-project

5c5dbf5eb124f78a239de1f80f8b2178e6734e6a

[ "MIT" ]

null

python/week9.py

Coding-Dojo-ACC/group-a-project

5c5dbf5eb124f78a239de1f80f8b2178e6734e6a

[ "MIT" ]

3

2021-02-13T01:20:23.000Z

2021-02-26T22:29:13.000Z

# Count by 5's # for x in range(5,1000,1) # start at 5 go till 1000 and count every number # for x in range(5, 101, 5): # start at 5 go till 1000 and add 5 each iteration # print(x) # for y in range(5, 101, 1): # if y % 5 == 0: # print(y) def flexCount(lowNum, hiNum, mult): for i in range(lowNum, hiNum +1): if i % mult == 0: print(i) # flexCount(2,9,3) # l = 2 # h = 10 # m = 3 # for i in range(2,10): # if i % m == 0: # print(i) def add(a,b): x = a + b print(x) return x # add(2,4) def a(): return 5 # print(a()) def a(): return 5 # print(a()+a()) def a(b,c): print(b + c) # return b + c # adding in this line would allow it to actually concatonate the print # print(a(1,2) + a(2,3)) # Return statement means STOP no more instructions will follow me def a(): return 5 print(10) # print(a()) # if we change the above to the following it will print 10 and 5 def a(): print(10) return 5 # print(a()) def dojoWay(): for i in range(1, 101): if i % 10 == 0: # if true follow my instructions if false move on print("Coding Dojo") elif i % 5 == 0: # if true follow my instructions if false move on print("Coding") else: # if all else fails just follow me print(i) dojoWay()

17.828947

90

0.551292

# for x in range(5,1000,1) # start at 5 go till 1000 and count every number # for x in range(5, 101, 5): # start at 5 go till 1000 and add 5 each iteration # print(x) # for y in range(5, 101, 1): # if y % 5 == 0: # print(y) def flexCount(lowNum, hiNum, mult): for i in range(lowNum, hiNum +1): if i % mult == 0: print(i) # flexCount(2,9,3) # l = 2 # h = 10 # m = 3 # for i in range(2,10): # if i % m == 0: # print(i) def add(a,b): x = a + b print(x) return x # add(2,4) def a(): return 5 # print(a()) def a(): return 5 # print(a()+a()) def a(b,c): print(b + c) # return b + c # adding in this line would allow it to actually concatonate the print # print(a(1,2) + a(2,3)) # Return statement means STOP no more instructions will follow me def a(): return 5 print(10) # print(a()) # if we change the above to the following it will print 10 and 5 def a(): print(10) return 5 # print(a()) def dojoWay(): for i in range(1, 101): if i % 10 == 0: # if true follow my instructions if false move on print("Coding Dojo") elif i % 5 == 0: # if true follow my instructions if false move on print("Coding") else: # if all else fails just follow me print(i) dojoWay()

true

1c3838f06848ee092c71412bf25ae399d92d7be1

7,375

py

Python

napari/_vispy/vispy_image_layer.py

glyg/napari

be196dc979aed663a484f3a424dacc3227f7c958

[ "BSD-3-Clause" ]

null

napari/_vispy/vispy_image_layer.py

glyg/napari

be196dc979aed663a484f3a424dacc3227f7c958

[ "BSD-3-Clause" ]

null

napari/_vispy/vispy_image_layer.py

glyg/napari

be196dc979aed663a484f3a424dacc3227f7c958

[ "BSD-3-Clause" ]

null

from vispy.scene.visuals import Image as ImageNode from vispy.scene.visuals import Volume as VolumeNode from vispy.color import Colormap import numpy as np from .vispy_base_layer import VispyBaseLayer texture_dtypes = [ np.dtype(np.int8), np.dtype(np.uint8), np.dtype(np.int16), np.dtype(np.uint16), np.dtype(np.float32), ] class VispyImageLayer(VispyBaseLayer): def __init__(self, layer): node = ImageNode(None, method='auto') super().__init__(layer, node) self.layer.events.rendering.connect( lambda e: self._on_rendering_change() ) self.layer.events.interpolation.connect( lambda e: self._on_interpolation_change() ) self.layer.events.colormap.connect( lambda e: self._on_colormap_change() ) self.layer.events.contrast_limits.connect( lambda e: self._on_contrast_limits_change() ) self.layer.events.gamma.connect(lambda e: self._on_gamma_change()) self._on_display_change() self._on_data_change() def _on_display_change(self): parent = self.node.parent self.node.parent = None if self.layer.dims.ndisplay == 2: self.node = ImageNode(None, method='auto') else: self.node = VolumeNode(np.zeros((1, 1, 1))) self.node.parent = parent self.reset() def _on_data_change(self): # Check if ndisplay has changed current node type needs updating if ( self.layer.dims.ndisplay == 3 and not isinstance(self.node, VolumeNode) ) or ( self.layer.dims.ndisplay == 2 and not isinstance(self.node, ImageNode) ): self._on_display_change() data = self.layer._data_view dtype = np.dtype(data.dtype) if dtype not in texture_dtypes: try: dtype = dict( i=np.int16, f=np.float32, u=np.uint16, b=np.uint8 )[dtype.kind] except KeyError: # not an int or float raise TypeError( f'type {dtype} not allowed for texture; must be one of {set(texture_dtypes)}' # noqa: E501 ) data = data.astype(dtype) if self.layer.dims.ndisplay == 3 and self.layer.dims.ndim == 2: data = np.expand_dims(data, axis=0) if self.layer.dims.ndisplay == 2: self.node._need_colortransform_update = True self.node.set_data(data) else: self.node.set_data(data, clim=self.layer.contrast_limits) self.node.update() def _on_interpolation_change(self): if self.layer.dims.ndisplay == 2: self.node.interpolation = self.layer.interpolation def _on_rendering_change(self): if not self.layer.dims.ndisplay == 2: self.node.method = self.layer.rendering def _on_colormap_change(self): cmap = self.layer.colormap[1] if self.layer.gamma != 1: # when gamma!=1, we instantiate a new colormap # with 256 control points from 0-1 cmap = Colormap(cmap[np.linspace(0, 1, 256) ** self.layer.gamma]) # Below is fixed in #1712 if not self.layer.dims.ndisplay == 2: self.node.view_program['texture2D_LUT'] = ( cmap.texture_lut() if (hasattr(cmap, 'texture_lut')) else None ) self.node.cmap = cmap def _on_contrast_limits_change(self): if self.layer.dims.ndisplay == 2: self.node.clim = self.layer.contrast_limits else: self._on_data_change() def _on_gamma_change(self): self._on_colormap_change() def _on_scale_change(self): self.scale = [ self.layer.scale[d] * self.layer._scale_view[d] for d in self.layer.dims.displayed[::-1] ] if self.layer.is_pyramid: self.layer.top_left = self.find_top_left() self.layer.position = self._transform_position(self._position) def _on_translate_change(self): self.translate = [ self.layer.translate[d] + self.layer._translate_view[d] + self.layer.translate_grid[d] for d in self.layer.dims.displayed[::-1] ] self.layer.position = self._transform_position(self._position) def compute_data_level(self, size): """Computed what level of the pyramid should be viewed given the current size of the requested field of view. Parameters ---------- size : 2-tuple Requested size of field of view in image coordinates Returns ---------- level : int Level of the pyramid to be viewing. """ # Convert requested field of view from the camera into log units size = np.log2(np.max(size)) # Max allowed tile in log units max_size = np.log2(self.layer._max_tile_shape) # Allow for more than 2x coverage of field of view with max tile diff = size - max_size + 1.25 # Find closed downsample level to diff ds = self.layer.level_downsamples[:, self.layer.dims.displayed].max( axis=1 ) level = np.argmin(abs(np.log2(ds) - diff)) return level def find_top_left(self): """Finds the top left pixel of the canvas. Depends on the current pan and zoom position Returns ---------- top_left : tuple of int Coordinates of top left pixel. """ nd = self.layer.dims.ndisplay # Find image coordinate of top left canvas pixel if self.node.canvas is not None: transform = self.node.canvas.scene.node_transform(self.node) pos = ( transform.map([0, 0])[:nd] + self.translate[:nd] / self.scale[:nd] ) else: pos = [0] * nd top_left = np.zeros(self.layer.ndim, dtype=int) for i, d in enumerate(self.layer.dims.displayed[::-1]): top_left[d] = pos[i] # Clip according to the max image shape top_left = np.clip( top_left, 0, np.subtract(self.layer.level_shapes[0], 1) ) # Convert to offset for image array rounding_factor = self.layer._max_tile_shape / 4 top_left = rounding_factor * np.floor(top_left / rounding_factor) return top_left.astype(int) def on_draw(self, event): """Called whenever the canvas is drawn, which happens whenever new data is sent to the canvas or the camera is moved. """ self.layer.scale_factor = self.scale_factor if self.layer.is_pyramid: self.layer.scale_factor = self.scale_factor size = self.camera.rect.size data_level = self.compute_data_level(size) if data_level != self.layer.data_level: self.layer.data_level = data_level else: self.layer.top_left = self.find_top_left() def reset(self): self._reset_base() self._on_interpolation_change() self._on_rendering_change() self._on_colormap_change() if self.layer.dims.ndisplay == 2: self._on_contrast_limits_change()

33.371041

111

0.591051

from vispy.scene.visuals import Image as ImageNode from vispy.scene.visuals import Volume as VolumeNode from vispy.color import Colormap import numpy as np from .vispy_base_layer import VispyBaseLayer texture_dtypes = [ np.dtype(np.int8), np.dtype(np.uint8), np.dtype(np.int16), np.dtype(np.uint16), np.dtype(np.float32), ] class VispyImageLayer(VispyBaseLayer): def __init__(self, layer): node = ImageNode(None, method='auto') super().__init__(layer, node) self.layer.events.rendering.connect( lambda e: self._on_rendering_change() ) self.layer.events.interpolation.connect( lambda e: self._on_interpolation_change() ) self.layer.events.colormap.connect( lambda e: self._on_colormap_change() ) self.layer.events.contrast_limits.connect( lambda e: self._on_contrast_limits_change() ) self.layer.events.gamma.connect(lambda e: self._on_gamma_change()) self._on_display_change() self._on_data_change() def _on_display_change(self): parent = self.node.parent self.node.parent = None if self.layer.dims.ndisplay == 2: self.node = ImageNode(None, method='auto') else: self.node = VolumeNode(np.zeros((1, 1, 1))) self.node.parent = parent self.reset() def _on_data_change(self): if ( self.layer.dims.ndisplay == 3 and not isinstance(self.node, VolumeNode) ) or ( self.layer.dims.ndisplay == 2 and not isinstance(self.node, ImageNode) ): self._on_display_change() data = self.layer._data_view dtype = np.dtype(data.dtype) if dtype not in texture_dtypes: try: dtype = dict( i=np.int16, f=np.float32, u=np.uint16, b=np.uint8 )[dtype.kind] except KeyError: raise TypeError( f'type {dtype} not allowed for texture; must be one of {set(texture_dtypes)}' ) data = data.astype(dtype) if self.layer.dims.ndisplay == 3 and self.layer.dims.ndim == 2: data = np.expand_dims(data, axis=0) if self.layer.dims.ndisplay == 2: self.node._need_colortransform_update = True self.node.set_data(data) else: self.node.set_data(data, clim=self.layer.contrast_limits) self.node.update() def _on_interpolation_change(self): if self.layer.dims.ndisplay == 2: self.node.interpolation = self.layer.interpolation def _on_rendering_change(self): if not self.layer.dims.ndisplay == 2: self.node.method = self.layer.rendering def _on_colormap_change(self): cmap = self.layer.colormap[1] if self.layer.gamma != 1: cmap = Colormap(cmap[np.linspace(0, 1, 256) ** self.layer.gamma]) if not self.layer.dims.ndisplay == 2: self.node.view_program['texture2D_LUT'] = ( cmap.texture_lut() if (hasattr(cmap, 'texture_lut')) else None ) self.node.cmap = cmap def _on_contrast_limits_change(self): if self.layer.dims.ndisplay == 2: self.node.clim = self.layer.contrast_limits else: self._on_data_change() def _on_gamma_change(self): self._on_colormap_change() def _on_scale_change(self): self.scale = [ self.layer.scale[d] * self.layer._scale_view[d] for d in self.layer.dims.displayed[::-1] ] if self.layer.is_pyramid: self.layer.top_left = self.find_top_left() self.layer.position = self._transform_position(self._position) def _on_translate_change(self): self.translate = [ self.layer.translate[d] + self.layer._translate_view[d] + self.layer.translate_grid[d] for d in self.layer.dims.displayed[::-1] ] self.layer.position = self._transform_position(self._position) def compute_data_level(self, size): size = np.log2(np.max(size)) max_size = np.log2(self.layer._max_tile_shape) diff = size - max_size + 1.25 ds = self.layer.level_downsamples[:, self.layer.dims.displayed].max( axis=1 ) level = np.argmin(abs(np.log2(ds) - diff)) return level def find_top_left(self): nd = self.layer.dims.ndisplay if self.node.canvas is not None: transform = self.node.canvas.scene.node_transform(self.node) pos = ( transform.map([0, 0])[:nd] + self.translate[:nd] / self.scale[:nd] ) else: pos = [0] * nd top_left = np.zeros(self.layer.ndim, dtype=int) for i, d in enumerate(self.layer.dims.displayed[::-1]): top_left[d] = pos[i] top_left = np.clip( top_left, 0, np.subtract(self.layer.level_shapes[0], 1) ) rounding_factor = self.layer._max_tile_shape / 4 top_left = rounding_factor * np.floor(top_left / rounding_factor) return top_left.astype(int) def on_draw(self, event): self.layer.scale_factor = self.scale_factor if self.layer.is_pyramid: self.layer.scale_factor = self.scale_factor size = self.camera.rect.size data_level = self.compute_data_level(size) if data_level != self.layer.data_level: self.layer.data_level = data_level else: self.layer.top_left = self.find_top_left() def reset(self): self._reset_base() self._on_interpolation_change() self._on_rendering_change() self._on_colormap_change() if self.layer.dims.ndisplay == 2: self._on_contrast_limits_change()

true

1c3839b056d2f071491f44d4a6e908496eab21cc

979

py

Python

setup.py

spell00/pytorch-fmri

42abd1e5a9a54cd4f4b7e0e89579301203d0aeb0

[ "MIT" ]

null

setup.py

spell00/pytorch-fmri

42abd1e5a9a54cd4f4b7e0e89579301203d0aeb0

[ "MIT" ]

null

setup.py

spell00/pytorch-fmri

42abd1e5a9a54cd4f4b7e0e89579301203d0aeb0

[ "MIT" ]

null

import os import sys from distutils.sysconfig import get_python_lib from distutils.core import setup from setuptools import find_packages CURRENT_PYTHON = sys.version_info[:2] setup(name="fmri", version="0.2", description="A package for learning to classify raw audio according to a user's self-definied " "scores of appreciation", url="https://github.com/pytorch/audio", packages=find_packages(), author="Simon J Pelletier", author_email="simonjpelletier@gmail.com", install_requires=['torch', 'matplotlib', 'numpy', 'scipy', 'Unidecode', 'nibabel', 'nilearn', 'tqdm', 'torchvision', 'ax-platform', 'tensorboardX' # 'apex' ] )

31.580645

101

0.490296

import os import sys from distutils.sysconfig import get_python_lib from distutils.core import setup from setuptools import find_packages CURRENT_PYTHON = sys.version_info[:2] setup(name="fmri", version="0.2", description="A package for learning to classify raw audio according to a user's self-definied " "scores of appreciation", url="https://github.com/pytorch/audio", packages=find_packages(), author="Simon J Pelletier", author_email="simonjpelletier@gmail.com", install_requires=['torch', 'matplotlib', 'numpy', 'scipy', 'Unidecode', 'nibabel', 'nilearn', 'tqdm', 'torchvision', 'ax-platform', 'tensorboardX' # 'apex' ] )

true

1c383a6063e6bece097dd6a9255159222e9b8380

70,322

py

Python

tensorflow_model_analysis/writers/metrics_plots_and_validations_writer_test.py

jaymessina3/model-analysis

8638ad375d860a97df5938850c59c72b0def995a

[ "Apache-2.0" ]

1,118

2018-03-30T02:01:17.000Z

2022-03-22T07:25:58.000Z

tensorflow_model_analysis/writers/metrics_plots_and_validations_writer_test.py

jaymessina3/model-analysis

8638ad375d860a97df5938850c59c72b0def995a

[ "Apache-2.0" ]

82

2018-04-12T12:54:05.000Z

2022-03-31T11:14:31.000Z

tensorflow_model_analysis/writers/metrics_plots_and_validations_writer_test.py

jaymessina3/model-analysis

8638ad375d860a97df5938850c59c72b0def995a

[ "Apache-2.0" ]

280

2018-03-30T15:58:02.000Z

2022-03-21T07:41:53.000Z

# Lint as: python3 # Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Test for using the MetricsPlotsAndValidationsWriter API.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import string import tempfile from absl.testing import parameterized import apache_beam as beam from apache_beam.testing import util import numpy as np import tensorflow as tf from tensorflow_model_analysis import constants from tensorflow_model_analysis import types from tensorflow_model_analysis.api import model_eval_lib from tensorflow_model_analysis.eval_saved_model import testutil from tensorflow_model_analysis.eval_saved_model.example_trainers import fixed_prediction_estimator from tensorflow_model_analysis.evaluators import legacy_metrics_and_plots_evaluator from tensorflow_model_analysis.evaluators import metrics_plots_and_validations_evaluator from tensorflow_model_analysis.extractors import example_weights_extractor from tensorflow_model_analysis.extractors import features_extractor from tensorflow_model_analysis.extractors import labels_extractor from tensorflow_model_analysis.extractors import legacy_predict_extractor from tensorflow_model_analysis.extractors import predictions_extractor from tensorflow_model_analysis.extractors import slice_key_extractor from tensorflow_model_analysis.extractors import unbatch_extractor from tensorflow_model_analysis.metrics import attributions from tensorflow_model_analysis.metrics import binary_confusion_matrices from tensorflow_model_analysis.metrics import metric_types from tensorflow_model_analysis.post_export_metrics import metric_keys from tensorflow_model_analysis.post_export_metrics import post_export_metrics from tensorflow_model_analysis.proto import config_pb2 from tensorflow_model_analysis.proto import metrics_for_slice_pb2 from tensorflow_model_analysis.proto import validation_result_pb2 from tensorflow_model_analysis.slicer import slicer_lib as slicer from tensorflow_model_analysis.writers import metrics_plots_and_validations_writer from tfx_bsl.tfxio import raw_tf_record from tfx_bsl.tfxio import tensor_adapter from tfx_bsl.tfxio import test_util from google.protobuf import text_format from tensorflow_metadata.proto.v0 import schema_pb2 def _make_slice_key(*args): if len(args) % 2 != 0: raise ValueError('number of arguments should be even') result = [] for i in range(0, len(args), 2): result.append((args[i], args[i + 1])) result = tuple(result) return result class MetricsPlotsAndValidationsWriterTest(testutil.TensorflowModelAnalysisTest, parameterized.TestCase): def setUp(self): super(MetricsPlotsAndValidationsWriterTest, self).setUp() self.longMessage = True # pylint: disable=invalid-name def _getTempDir(self): return tempfile.mkdtemp() def _getExportDir(self): return os.path.join(self._getTempDir(), 'export_dir') def _getBaselineDir(self): return os.path.join(self._getTempDir(), 'baseline_export_dir') def _build_keras_model(self, model_dir, mul): input_layer = tf.keras.layers.Input(shape=(1,), name='input_1') output_layer = tf.keras.layers.Lambda( lambda x, mul: x * mul, output_shape=(1,), arguments={'mul': mul})( input_layer) model = tf.keras.models.Model([input_layer], output_layer) model.compile( optimizer=tf.keras.optimizers.Adam(lr=.001), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy']) model.fit(x=[[0], [1]], y=[[0], [1]], steps_per_epoch=1) model.save(model_dir, save_format='tf') return self.createTestEvalSharedModel( eval_saved_model_path=model_dir, tags=[tf.saved_model.SERVING]) def testConvertSlicePlotsToProto(self): slice_key = _make_slice_key('fruit', 'apple') plot_key = metric_types.PlotKey( name='calibration_plot', output_name='output_name') calibration_plot = text_format.Parse( """ buckets { lower_threshold_inclusive: -inf upper_threshold_exclusive: 0.0 num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } buckets { lower_threshold_inclusive: 0.0 upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction { value: 0.3 } } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { value: 1.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.7 } } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } """, metrics_for_slice_pb2.CalibrationHistogramBuckets()) expected_plots_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: 'fruit' bytes_value: 'apple' } } plot_keys_and_values { key { output_name: "output_name" } value { calibration_histogram_buckets { buckets { lower_threshold_inclusive: -inf upper_threshold_exclusive: 0.0 num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } buckets { lower_threshold_inclusive: 0.0 upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction { value: 0.3 } } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { value: 1.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.7 } } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } } } } """, metrics_for_slice_pb2.PlotsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_plots_to_proto( (slice_key, { plot_key: calibration_plot }), None) self.assertProtoEquals(expected_plots_for_slice, got) def testConvertSlicePlotsToProtoLegacyStringKeys(self): slice_key = _make_slice_key('fruit', 'apple') tfma_plots = { metric_keys.CALIBRATION_PLOT_MATRICES: np.array([ [0.0, 0.0, 0.0], [0.3, 1.0, 1.0], [0.7, 0.0, 1.0], [0.0, 0.0, 0.0], ]), metric_keys.CALIBRATION_PLOT_BOUNDARIES: np.array([0.0, 0.5, 1.0]), } expected_plot_data = """ slice_key { single_slice_keys { column: 'fruit' bytes_value: 'apple' } } plots { key: "post_export_metrics" value { calibration_histogram_buckets { buckets { lower_threshold_inclusive: -inf upper_threshold_exclusive: 0.0 num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } buckets { lower_threshold_inclusive: 0.0 upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction { value: 0.3 } } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { value: 1.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.7 } } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } } } } """ calibration_plot = ( post_export_metrics.calibration_plot_and_prediction_histogram()) got = metrics_plots_and_validations_writer.convert_slice_plots_to_proto( (slice_key, tfma_plots), [calibration_plot]) self.assertProtoEquals(expected_plot_data, got) def testConvertSlicePlotsToProtoEmptyPlot(self): slice_key = _make_slice_key('fruit', 'apple') tfma_plots = {metric_keys.ERROR_METRIC: 'error_message'} actual_plot = metrics_plots_and_validations_writer.convert_slice_plots_to_proto( (slice_key, tfma_plots), []) expected_plot = metrics_for_slice_pb2.PlotsForSlice() expected_plot.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key)) expected_plot.plots[ metric_keys.ERROR_METRIC].debug_message = 'error_message' self.assertProtoEquals(expected_plot, actual_plot) def testConvertSliceMetricsToProto(self): slice_key = _make_slice_key('age', 5, 'language', 'english', 'price', 0.3) slice_metrics = { metric_types.MetricKey(name='accuracy', output_name='output_name'): 0.8 } expected_metrics_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: 'age' int64_value: 5 } single_slice_keys { column: 'language' bytes_value: 'english' } single_slice_keys { column: 'price' float_value: 0.3 } } metric_keys_and_values { key { name: "accuracy" output_name: "output_name" } value { double_value { value: 0.8 } } }""", metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), None) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoConfusionMatrices(self): slice_key = _make_slice_key() slice_metrics = { metric_types.MetricKey(name='confusion_matrix_at_thresholds'): binary_confusion_matrices.Matrices( thresholds=[0.25, 0.75, 1.00], fn=[0.0, 1.0, 2.0], tn=[1.0, 1.0, 1.0], fp=[0.0, 0.0, 0.0], tp=[2.0, 1.0, 0.0]) } expected_metrics_for_slice = text_format.Parse( """ slice_key {} metric_keys_and_values { key: { name: "confusion_matrix_at_thresholds" } value { confusion_matrix_at_thresholds { matrices { threshold: 0.25 false_negatives: 0.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 2.0 precision: 1.0 recall: 1.0 } matrices { threshold: 0.75 false_negatives: 1.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 1.0 precision: 1.0 recall: 0.5 } matrices { threshold: 1.00 false_negatives: 2.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 0.0 precision: 1.0 recall: 0.0 } } } } """, metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), add_metrics_callbacks=[]) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoConfusionMatricesPostExport(self): slice_key = _make_slice_key() thresholds = [0.25, 0.75, 1.00] matrices = [[0.0, 1.0, 0.0, 2.0, 1.0, 1.0], [1.0, 1.0, 0.0, 1.0, 1.0, 0.5], [2.0, 1.0, 0.0, 0.0, float('nan'), 0.0]] slice_metrics = { metric_keys.CONFUSION_MATRIX_AT_THRESHOLDS_MATRICES: matrices, metric_keys.CONFUSION_MATRIX_AT_THRESHOLDS_THRESHOLDS: thresholds, } expected_metrics_for_slice = text_format.Parse( """ slice_key {} metrics { key: "post_export_metrics/confusion_matrix_at_thresholds" value { confusion_matrix_at_thresholds { matrices { threshold: 0.25 false_negatives: 0.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 2.0 precision: 1.0 recall: 1.0 bounded_false_negatives { value { value: 0.0 } } bounded_true_negatives { value { value: 1.0 } } bounded_true_positives { value { value: 2.0 } } bounded_false_positives { value { value: 0.0 } } bounded_precision { value { value: 1.0 } } bounded_recall { value { value: 1.0 } } t_distribution_false_negatives { unsampled_value { value: 0.0 } } t_distribution_true_negatives { unsampled_value { value: 1.0 } } t_distribution_true_positives { unsampled_value { value: 2.0 } } t_distribution_false_positives { unsampled_value { value: 0.0 } } t_distribution_precision { unsampled_value { value: 1.0 } } t_distribution_recall { unsampled_value { value: 1.0 } } } matrices { threshold: 0.75 false_negatives: 1.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 1.0 precision: 1.0 recall: 0.5 bounded_false_negatives { value { value: 1.0 } } bounded_true_negatives { value { value: 1.0 } } bounded_true_positives { value { value: 1.0 } } bounded_false_positives { value { value: 0.0 } } bounded_precision { value { value: 1.0 } } bounded_recall { value { value: 0.5 } } t_distribution_false_negatives { unsampled_value { value: 1.0 } } t_distribution_true_negatives { unsampled_value { value: 1.0 } } t_distribution_true_positives { unsampled_value { value: 1.0 } } t_distribution_false_positives { unsampled_value { value: 0.0 } } t_distribution_precision { unsampled_value { value: 1.0 } } t_distribution_recall { unsampled_value { value: 0.5 } } } matrices { threshold: 1.00 false_negatives: 2.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 0.0 precision: nan recall: 0.0 bounded_false_negatives { value { value: 2.0 } } bounded_true_negatives { value { value: 1.0 } } bounded_true_positives { value { value: 0.0 } } bounded_false_positives { value { value: 0.0 } } bounded_precision { value { value: nan } } bounded_recall { value { value: 0.0 } } t_distribution_false_negatives { unsampled_value { value: 2.0 } } t_distribution_true_negatives { unsampled_value { value: 1.0 } } t_distribution_true_positives { unsampled_value { value: 0.0 } } t_distribution_false_positives { unsampled_value { value: 0.0 } } t_distribution_precision { unsampled_value { value: nan } } t_distribution_recall { unsampled_value { value: 0.0 } } } } } } """, metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), [post_export_metrics.confusion_matrix_at_thresholds(thresholds)]) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoMetricsRanges(self): slice_key = _make_slice_key('age', 5, 'language', 'english', 'price', 0.3) slice_metrics = { 'accuracy': types.ValueWithTDistribution(0.8, 0.1, 9, 0.8), metric_keys.AUPRC: 0.1, metric_keys.lower_bound_key(metric_keys.AUPRC): 0.05, metric_keys.upper_bound_key(metric_keys.AUPRC): 0.17, metric_keys.AUC: 0.2, metric_keys.lower_bound_key(metric_keys.AUC): 0.1, metric_keys.upper_bound_key(metric_keys.AUC): 0.3 } expected_metrics_for_slice = text_format.Parse( string.Template(""" slice_key { single_slice_keys { column: 'age' int64_value: 5 } single_slice_keys { column: 'language' bytes_value: 'english' } single_slice_keys { column: 'price' float_value: 0.3 } } metrics { key: "accuracy" value { bounded_value { value { value: 0.8 } lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } methodology: POISSON_BOOTSTRAP } } } metrics { key: "$auc" value { bounded_value { lower_bound { value: 0.1 } upper_bound { value: 0.3 } value { value: 0.2 } methodology: RIEMANN_SUM } } } metrics { key: "$auprc" value { bounded_value { lower_bound { value: 0.05 } upper_bound { value: 0.17 } value { value: 0.1 } methodology: RIEMANN_SUM } } }""").substitute(auc=metric_keys.AUC, auprc=metric_keys.AUPRC), metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), [post_export_metrics.auc(), post_export_metrics.auc(curve='PR')]) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoFromLegacyStrings(self): slice_key = _make_slice_key('age', 5, 'language', 'english', 'price', 0.3) slice_metrics = { 'accuracy': 0.8, metric_keys.AUPRC: 0.1, metric_keys.lower_bound_key(metric_keys.AUPRC): 0.05, metric_keys.upper_bound_key(metric_keys.AUPRC): 0.17, metric_keys.AUC: 0.2, metric_keys.lower_bound_key(metric_keys.AUC): 0.1, metric_keys.upper_bound_key(metric_keys.AUC): 0.3 } expected_metrics_for_slice = text_format.Parse( string.Template(""" slice_key { single_slice_keys { column: 'age' int64_value: 5 } single_slice_keys { column: 'language' bytes_value: 'english' } single_slice_keys { column: 'price' float_value: 0.3 } } metrics { key: "accuracy" value { double_value { value: 0.8 } } } metrics { key: "$auc" value { bounded_value { lower_bound { value: 0.1 } upper_bound { value: 0.3 } value { value: 0.2 } methodology: RIEMANN_SUM } } } metrics { key: "$auprc" value { bounded_value { lower_bound { value: 0.05 } upper_bound { value: 0.17 } value { value: 0.1 } methodology: RIEMANN_SUM } } }""").substitute(auc=metric_keys.AUC, auprc=metric_keys.AUPRC), metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), [post_export_metrics.auc(), post_export_metrics.auc(curve='PR')]) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoEmptyMetrics(self): slice_key = _make_slice_key('age', 5, 'language', 'english', 'price', 0.3) slice_metrics = {metric_keys.ERROR_METRIC: 'error_message'} actual_metrics = ( metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), [post_export_metrics.auc(), post_export_metrics.auc(curve='PR')])) expected_metrics = metrics_for_slice_pb2.MetricsForSlice() expected_metrics.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key)) expected_metrics.metrics[ metric_keys.ERROR_METRIC].debug_message = 'error_message' self.assertProtoEquals(expected_metrics, actual_metrics) def testConvertSliceMetricsToProtoStringMetrics(self): slice_key = _make_slice_key() slice_metrics = { 'valid_ascii': b'test string', 'valid_unicode': b'\xF0\x9F\x90\x84', # U+1F404, Cow 'invalid_unicode': b'\xE2\x28\xA1', } expected_metrics_for_slice = metrics_for_slice_pb2.MetricsForSlice() expected_metrics_for_slice.slice_key.SetInParent() expected_metrics_for_slice.metrics[ 'valid_ascii'].bytes_value = slice_metrics['valid_ascii'] expected_metrics_for_slice.metrics[ 'valid_unicode'].bytes_value = slice_metrics['valid_unicode'] expected_metrics_for_slice.metrics[ 'invalid_unicode'].bytes_value = slice_metrics['invalid_unicode'] got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), []) self.assertProtoEquals(expected_metrics_for_slice, got) def testCombineValidationsValidationOk(self): input_validations = [ text_format.Parse( """ validation_ok: true metric_validations_per_slice { slice_key { single_slice_keys { column: "x" bytes_value: "x1" } } } validation_details { slicing_details { slicing_spec {} num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 1 } }""", validation_result_pb2.ValidationResult()), text_format.Parse( """ validation_ok: true validation_details { slicing_details { slicing_spec { feature_keys: ["x"] } num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 2 } }""", validation_result_pb2.ValidationResult()) ] eval_config = config_pb2.EvalConfig( model_specs=[ config_pb2.ModelSpec(name='candidate'), config_pb2.ModelSpec(name='baseline', is_baseline=True) ], slicing_specs=[config_pb2.SlicingSpec()], metrics_specs=[ config_pb2.MetricsSpec( metrics=[ config_pb2.MetricConfig( class_name='AUC', per_slice_thresholds=[ config_pb2.PerSliceMetricThreshold( slicing_specs=[config_pb2.SlicingSpec()], threshold=config_pb2.MetricThreshold( value_threshold=config_pb2 .GenericValueThreshold( lower_bound={'value': 0.7}))) ]), ], model_names=['candidate', 'baseline']), ]) expected_validation = text_format.Parse( """ validation_ok: true metric_validations_per_slice { slice_key { single_slice_keys { column: "x" bytes_value: "x1" } } } validation_details { slicing_details { slicing_spec {} num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 3 } slicing_details { slicing_spec { feature_keys: ["x"] } num_matching_slices: 1 } }""", validation_result_pb2.ValidationResult()) def verify_fn(result): self.assertLen(result, 1) self.assertProtoEquals(expected_validation, result[0]) with beam.Pipeline() as pipeline: result = ( pipeline | 'Create' >> beam.Create(input_validations) | 'CombineValidations' >> beam.CombineGlobally( metrics_plots_and_validations_writer.CombineValidations( eval_config))) util.assert_that(result, verify_fn) def testCombineValidationsMissingSlices(self): input_validations = [ text_format.Parse( """ validation_ok: false metric_validations_per_slice { slice_key { single_slice_keys { column: "x" bytes_value: "x1" } } failures { metric_key { name: "auc" model_name: "candidate" is_diff: true } metric_threshold { value_threshold { lower_bound { value: 0.7 } } } metric_value { double_value { value: 0.6 } } } } validation_details { slicing_details { slicing_spec {} num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 1 } }""", validation_result_pb2.ValidationResult()), text_format.Parse( """ validation_ok: true validation_details { slicing_details { slicing_spec { feature_keys: ["x"] } num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 2 } }""", validation_result_pb2.ValidationResult()) ] slicing_specs = [ config_pb2.SlicingSpec(), config_pb2.SlicingSpec(feature_keys=['x']), config_pb2.SlicingSpec(feature_keys=['x', 'y']), config_pb2.SlicingSpec(feature_keys=['z']), ] eval_config = config_pb2.EvalConfig( model_specs=[ config_pb2.ModelSpec(name='candidate'), config_pb2.ModelSpec(name='baseline', is_baseline=True) ], slicing_specs=slicing_specs, metrics_specs=[ config_pb2.MetricsSpec( metrics=[ config_pb2.MetricConfig( class_name='AUC', per_slice_thresholds=[ config_pb2.PerSliceMetricThreshold( slicing_specs=slicing_specs, threshold=config_pb2.MetricThreshold( value_threshold=config_pb2 .GenericValueThreshold( lower_bound={'value': 0.7}))) ]), ], model_names=['candidate', 'baseline']), ]) expected_validation = text_format.Parse( """ validation_ok: false metric_validations_per_slice { slice_key { single_slice_keys { column: "x" bytes_value: "x1" } } failures { metric_key { name: "auc" model_name: "candidate" is_diff: true } metric_threshold { value_threshold { lower_bound { value: 0.7 } } } metric_value { double_value { value: 0.6 } } } } missing_slices { feature_keys: "z" } validation_details { slicing_details { slicing_spec {} num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 3 } slicing_details { slicing_spec { feature_keys: ["x"] } num_matching_slices: 1 } }""", validation_result_pb2.ValidationResult()) def verify_fn(result): self.assertLen(result, 1) self.assertProtoEquals(expected_validation, result[0]) with beam.Pipeline() as pipeline: result = ( pipeline | 'Create' >> beam.Create(input_validations) | 'CombineValidations' >> beam.CombineGlobally( metrics_plots_and_validations_writer.CombineValidations( eval_config))) util.assert_that(result, verify_fn) def testUncertaintyValuedMetrics(self): slice_key = _make_slice_key() slice_metrics = { 'one_dim': types.ValueWithTDistribution(2.0, 1.0, 3, 2.0), 'nans': types.ValueWithTDistribution( float('nan'), float('nan'), -1, float('nan')), } expected_metrics_for_slice = text_format.Parse( """ slice_key {} metrics { key: "one_dim" value { bounded_value { value { value: 2.0 } lower_bound { value: -1.1824463 } upper_bound { value: 5.1824463 } methodology: POISSON_BOOTSTRAP } } } metrics { key: "nans" value { bounded_value { value { value: nan } lower_bound { value: nan } upper_bound { value: nan } methodology: POISSON_BOOTSTRAP } } } """, metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), []) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoTensorValuedMetrics(self): slice_key = _make_slice_key() slice_metrics = { 'one_dim': np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32), 'two_dims': np.array([['two', 'dims', 'test'], ['TWO', 'DIMS', 'TEST']]), 'three_dims': np.array([[[100, 200, 300]], [[500, 600, 700]]], dtype=np.int64), } expected_metrics_for_slice = text_format.Parse( """ slice_key {} metrics { key: "one_dim" value { array_value { data_type: FLOAT32 shape: 4 float32_values: [1.0, 2.0, 3.0, 4.0] } } } metrics { key: "two_dims" value { array_value { data_type: BYTES shape: [2, 3] bytes_values: ["two", "dims", "test", "TWO", "DIMS", "TEST"] } } } metrics { key: "three_dims" value { array_value { data_type: INT64 shape: [2, 1, 3] int64_values: [100, 200, 300, 500, 600, 700] } } } """, metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), []) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceAttributionsToProto(self): slice_key = _make_slice_key('language', 'english', 'price', 0.3) slice_attributions = { metric_types.AttributionsKey(name='mean', output_name='output_name'): { 'age': 0.8, 'language': 1.2, 'price': 2.3, }, } expected_attributions_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: 'language' bytes_value: 'english' } single_slice_keys { column: 'price' float_value: 0.3 } } attributions_keys_and_values { key { name: "mean" output_name: "output_name" } values { key: "age" value: { double_value { value: 0.8 } } } values { key: "language" value: { double_value { value: 1.2 } } } values { key: "price" value: { double_value { value: 2.3 } } } }""", metrics_for_slice_pb2.AttributionsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_attributions_to_proto( (slice_key, slice_attributions)) self.assertProtoEquals(expected_attributions_for_slice, got) _OUTPUT_FORMAT_PARAMS = [('without_output_file_format', ''), ('tfrecord_file_format', 'tfrecord'), ('parquet_file_format', 'parquet')] @parameterized.named_parameters(_OUTPUT_FORMAT_PARAMS) def testWriteValidationResults(self, output_file_format): model_dir, baseline_dir = self._getExportDir(), self._getBaselineDir() eval_shared_model = self._build_keras_model(model_dir, mul=0) baseline_eval_shared_model = self._build_keras_model(baseline_dir, mul=1) validations_file = os.path.join(self._getTempDir(), constants.VALIDATIONS_KEY) schema = text_format.Parse( """ tensor_representation_group { key: "" value { tensor_representation { key: "input_1" value { dense_tensor { column_name: "input_1" shape { dim { size: 1 } } } } } } } feature { name: "input_1" type: FLOAT } feature { name: "label" type: FLOAT } feature { name: "example_weight" type: FLOAT } feature { name: "extra_feature" type: BYTES } """, schema_pb2.Schema()) tfx_io = test_util.InMemoryTFExampleRecord( schema=schema, raw_record_column_name=constants.ARROW_INPUT_COLUMN) tensor_adapter_config = tensor_adapter.TensorAdapterConfig( arrow_schema=tfx_io.ArrowSchema(), tensor_representations=tfx_io.TensorRepresentations()) examples = [ self._makeExample( input_1=0.0, label=1.0, example_weight=1.0, extra_feature='non_model_feature'), self._makeExample( input_1=1.0, label=0.0, example_weight=0.5, extra_feature='non_model_feature'), ] slicing_specs = [ config_pb2.SlicingSpec(), config_pb2.SlicingSpec(feature_keys=['slice_does_not_exist']) ] cross_slicing_specs = [ config_pb2.CrossSlicingSpec( baseline_spec=config_pb2.SlicingSpec( feature_keys=['slice_does_not_exist']), slicing_specs=[ config_pb2.SlicingSpec(feature_keys=['slice_does_not_exist']) ]) ] eval_config = config_pb2.EvalConfig( model_specs=[ config_pb2.ModelSpec( name='candidate', label_key='label', example_weight_key='example_weight'), config_pb2.ModelSpec( name='baseline', label_key='label', example_weight_key='example_weight', is_baseline=True) ], slicing_specs=slicing_specs, cross_slicing_specs=cross_slicing_specs, metrics_specs=[ config_pb2.MetricsSpec( metrics=[ config_pb2.MetricConfig( class_name='WeightedExampleCount', per_slice_thresholds=[ config_pb2.PerSliceMetricThreshold( slicing_specs=slicing_specs, # 1.5 < 1, NOT OK. threshold=config_pb2.MetricThreshold( value_threshold=config_pb2 .GenericValueThreshold( upper_bound={'value': 1}))) ], # missing cross slice cross_slice_thresholds=[ config_pb2.CrossSliceMetricThreshold( cross_slicing_specs=cross_slicing_specs, threshold=config_pb2.MetricThreshold( value_threshold=config_pb2 .GenericValueThreshold( upper_bound={'value': 1}))) ]), config_pb2.MetricConfig( class_name='ExampleCount', # 2 > 10, NOT OK. threshold=config_pb2.MetricThreshold( value_threshold=config_pb2.GenericValueThreshold( lower_bound={'value': 10}))), config_pb2.MetricConfig( class_name='MeanLabel', # 0.5 > 1 and 0.5 > 1?: NOT OK. threshold=config_pb2.MetricThreshold( change_threshold=config_pb2.GenericChangeThreshold( direction=config_pb2.MetricDirection .HIGHER_IS_BETTER, relative={'value': 1}, absolute={'value': 1}))), config_pb2.MetricConfig( # MeanPrediction = (0+0)/(1+0.5) = 0 class_name='MeanPrediction', # -.01 < 0 < .01, OK. # Diff% = -.333/.333 = -100% < -99%, OK. # Diff = 0 - .333 = -.333 < 0, OK. threshold=config_pb2.MetricThreshold( value_threshold=config_pb2.GenericValueThreshold( upper_bound={'value': .01}, lower_bound={'value': -.01}), change_threshold=config_pb2.GenericChangeThreshold( direction=config_pb2.MetricDirection .LOWER_IS_BETTER, relative={'value': -.99}, absolute={'value': 0}))) ], model_names=['candidate', 'baseline']), ], options=config_pb2.Options( disabled_outputs={'values': ['eval_config_pb2.json']}), ) slice_spec = [ slicer.SingleSliceSpec(spec=s) for s in eval_config.slicing_specs ] eval_shared_models = { 'candidate': eval_shared_model, 'baseline': baseline_eval_shared_model } extractors = [ features_extractor.FeaturesExtractor(eval_config), labels_extractor.LabelsExtractor(eval_config), example_weights_extractor.ExampleWeightsExtractor(eval_config), predictions_extractor.PredictionsExtractor( eval_shared_model=eval_shared_models, eval_config=eval_config, tensor_adapter_config=tensor_adapter_config), unbatch_extractor.UnbatchExtractor(), slice_key_extractor.SliceKeyExtractor(slice_spec=slice_spec) ] evaluators = [ metrics_plots_and_validations_evaluator .MetricsPlotsAndValidationsEvaluator( eval_config=eval_config, eval_shared_model=eval_shared_models) ] output_paths = { constants.VALIDATIONS_KEY: validations_file, } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=[], output_file_format=output_file_format) ] with beam.Pipeline() as pipeline: # pylint: disable=no-value-for-parameter _ = ( pipeline | 'Create' >> beam.Create([e.SerializeToString() for e in examples]) | 'BatchExamples' >> tfx_io.BeamSource() | 'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts() | 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate( extractors=extractors, evaluators=evaluators) | 'WriteResults' >> model_eval_lib.WriteResults(writers=writers)) # pylint: enable=no-value-for-parameter validation_result = ( metrics_plots_and_validations_writer .load_and_deserialize_validation_result( os.path.dirname(validations_file), output_file_format)) expected_validations = [ text_format.Parse( """ metric_key { name: "weighted_example_count" model_name: "candidate" } metric_threshold { value_threshold { upper_bound { value: 1.0 } } } metric_value { double_value { value: 1.5 } } """, validation_result_pb2.ValidationFailure()), text_format.Parse( """ metric_key { name: "example_count" model_name: "candidate" } metric_threshold { value_threshold { lower_bound { value: 10.0 } } } metric_value { double_value { value: 2.0 } } """, validation_result_pb2.ValidationFailure()), text_format.Parse( """ metric_key { name: "mean_label" model_name: "candidate" is_diff: true } metric_threshold { change_threshold { absolute { value: 1.0 } relative { value: 1.0 } direction: HIGHER_IS_BETTER } } metric_value { double_value { value: 0.0 } } """, validation_result_pb2.ValidationFailure()), ] self.assertFalse(validation_result.validation_ok) self.assertFalse(validation_result.missing_thresholds) self.assertLen(validation_result.metric_validations_per_slice, 1) self.assertCountEqual( expected_validations, validation_result.metric_validations_per_slice[0].failures) expected_missing_slices = [ config_pb2.SlicingSpec(feature_keys=['slice_does_not_exist']) ] self.assertLen(validation_result.missing_slices, 1) self.assertCountEqual(expected_missing_slices, validation_result.missing_slices) expected_missing_cross_slices = [ config_pb2.CrossSlicingSpec( baseline_spec=config_pb2.SlicingSpec( feature_keys=['slice_does_not_exist']), slicing_specs=[ config_pb2.SlicingSpec(feature_keys=['slice_does_not_exist']) ]) ] self.assertLen(validation_result.missing_cross_slices, 1) self.assertCountEqual(expected_missing_cross_slices, validation_result.missing_cross_slices) expected_slicing_details = [ text_format.Parse( """ slicing_spec { } num_matching_slices: 1 """, validation_result_pb2.SlicingDetails()), ] self.assertLen(validation_result.validation_details.slicing_details, 1) self.assertCountEqual(expected_slicing_details, validation_result.validation_details.slicing_details) @parameterized.named_parameters(_OUTPUT_FORMAT_PARAMS) def testWriteValidationResultsNoThresholds(self, output_file_format): model_dir, baseline_dir = self._getExportDir(), self._getBaselineDir() eval_shared_model = self._build_keras_model(model_dir, mul=0) baseline_eval_shared_model = self._build_keras_model(baseline_dir, mul=1) validations_file = os.path.join(self._getTempDir(), constants.VALIDATIONS_KEY) schema = text_format.Parse( """ tensor_representation_group { key: "" value { tensor_representation { key: "input_1" value { dense_tensor { column_name: "input_1" shape { dim { size: 1 } } } } } } } feature { name: "input_1" type: FLOAT } feature { name: "label" type: FLOAT } feature { name: "example_weight" type: FLOAT } feature { name: "extra_feature" type: BYTES } """, schema_pb2.Schema()) tfx_io = test_util.InMemoryTFExampleRecord( schema=schema, raw_record_column_name=constants.ARROW_INPUT_COLUMN) tensor_adapter_config = tensor_adapter.TensorAdapterConfig( arrow_schema=tfx_io.ArrowSchema(), tensor_representations=tfx_io.TensorRepresentations()) examples = [ self._makeExample( input_1=0.0, label=1.0, example_weight=1.0, extra_feature='non_model_feature'), self._makeExample( input_1=1.0, label=0.0, example_weight=0.5, extra_feature='non_model_feature'), ] slicing_specs = [ config_pb2.SlicingSpec(), ] eval_config = config_pb2.EvalConfig( model_specs=[ config_pb2.ModelSpec( name='candidate', label_key='label', example_weight_key='example_weight'), config_pb2.ModelSpec( name='baseline', label_key='label', example_weight_key='example_weight', is_baseline=True) ], slicing_specs=slicing_specs, metrics_specs=[ config_pb2.MetricsSpec( metrics=[ config_pb2.MetricConfig(class_name='WeightedExampleCount'), config_pb2.MetricConfig(class_name='ExampleCount'), config_pb2.MetricConfig(class_name='MeanLabel') ], model_names=['candidate', 'baseline']), ], options=config_pb2.Options( disabled_outputs={'values': ['eval_config_pb2.json']}), ) slice_spec = [ slicer.SingleSliceSpec(spec=s) for s in eval_config.slicing_specs ] eval_shared_models = { 'candidate': eval_shared_model, 'baseline': baseline_eval_shared_model } extractors = [ features_extractor.FeaturesExtractor(eval_config), labels_extractor.LabelsExtractor(eval_config), example_weights_extractor.ExampleWeightsExtractor(eval_config), predictions_extractor.PredictionsExtractor( eval_shared_model=eval_shared_models, eval_config=eval_config, tensor_adapter_config=tensor_adapter_config), unbatch_extractor.UnbatchExtractor(), slice_key_extractor.SliceKeyExtractor(slice_spec=slice_spec) ] evaluators = [ metrics_plots_and_validations_evaluator .MetricsPlotsAndValidationsEvaluator( eval_config=eval_config, eval_shared_model=eval_shared_models) ] output_paths = { constants.VALIDATIONS_KEY: validations_file, } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=[], output_file_format=output_file_format) ] with beam.Pipeline() as pipeline: # pylint: disable=no-value-for-parameter _ = ( pipeline | 'Create' >> beam.Create([e.SerializeToString() for e in examples]) | 'BatchExamples' >> tfx_io.BeamSource() | 'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts() | 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate( extractors=extractors, evaluators=evaluators) | 'WriteResults' >> model_eval_lib.WriteResults(writers=writers)) # pylint: enable=no-value-for-parameter validation_result = ( metrics_plots_and_validations_writer .load_and_deserialize_validation_result( os.path.dirname(validations_file), output_file_format)) self.assertFalse(validation_result.validation_ok) self.assertTrue(validation_result.missing_thresholds) self.assertEmpty(validation_result.metric_validations_per_slice) # Add rubber stamp would make validation ok. writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=[], output_file_format=output_file_format, rubber_stamp=True) ] with beam.Pipeline() as pipeline: # pylint: disable=no-value-for-parameter _ = ( pipeline | 'Create' >> beam.Create([e.SerializeToString() for e in examples]) | 'BatchExamples' >> tfx_io.BeamSource() | 'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts() | 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate( extractors=extractors, evaluators=evaluators) | 'WriteResults' >> model_eval_lib.WriteResults(writers=writers)) # pylint: enable=no-value-for-parameter validation_result = ( metrics_plots_and_validations_writer .load_and_deserialize_validation_result( os.path.dirname(validations_file), output_file_format)) self.assertTrue(validation_result.validation_ok) self.assertFalse(validation_result.missing_thresholds) self.assertEmpty(validation_result.metric_validations_per_slice) self.assertTrue(validation_result.rubber_stamp) @parameterized.named_parameters(_OUTPUT_FORMAT_PARAMS) def testWriteMetricsAndPlots(self, output_file_format): metrics_file = os.path.join(self._getTempDir(), 'metrics') plots_file = os.path.join(self._getTempDir(), 'plots') temp_eval_export_dir = os.path.join(self._getTempDir(), 'eval_export_dir') _, eval_export_dir = ( fixed_prediction_estimator.simple_fixed_prediction_estimator( None, temp_eval_export_dir)) eval_config = config_pb2.EvalConfig( model_specs=[config_pb2.ModelSpec()], options=config_pb2.Options( disabled_outputs={'values': ['eval_config_pb2.json']})) eval_shared_model = self.createTestEvalSharedModel( eval_saved_model_path=eval_export_dir, add_metrics_callbacks=[ post_export_metrics.example_count(), post_export_metrics.calibration_plot_and_prediction_histogram( num_buckets=2) ]) extractors = [ legacy_predict_extractor.PredictExtractor( eval_shared_model, eval_config=eval_config), unbatch_extractor.UnbatchExtractor(), slice_key_extractor.SliceKeyExtractor() ] evaluators = [ legacy_metrics_and_plots_evaluator.MetricsAndPlotsEvaluator( eval_shared_model) ] output_paths = { constants.METRICS_KEY: metrics_file, constants.PLOTS_KEY: plots_file } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=eval_shared_model.add_metrics_callbacks, output_file_format=output_file_format) ] tfx_io = raw_tf_record.RawBeamRecordTFXIO( physical_format='inmemory', raw_record_column_name=constants.ARROW_INPUT_COLUMN, telemetry_descriptors=['TFMATest']) with beam.Pipeline() as pipeline: example1 = self._makeExample(prediction=0.0, label=1.0) example2 = self._makeExample(prediction=1.0, label=1.0) # pylint: disable=no-value-for-parameter _ = ( pipeline | 'Create' >> beam.Create([ example1.SerializeToString(), example2.SerializeToString(), ]) | 'BatchExamples' >> tfx_io.BeamSource() | 'ExtractEvaluateAndWriteResults' >> model_eval_lib.ExtractEvaluateAndWriteResults( eval_config=eval_config, eval_shared_model=eval_shared_model, extractors=extractors, evaluators=evaluators, writers=writers)) # pylint: enable=no-value-for-parameter expected_metrics_for_slice = text_format.Parse( """ slice_key {} metrics { key: "average_loss" value { double_value { value: 0.5 } } } metrics { key: "post_export_metrics/example_count" value { double_value { value: 2.0 } } } """, metrics_for_slice_pb2.MetricsForSlice()) metric_records = list( metrics_plots_and_validations_writer.load_and_deserialize_metrics( metrics_file, output_file_format)) self.assertLen(metric_records, 1, 'metrics: %s' % metric_records) self.assertProtoEquals(expected_metrics_for_slice, metric_records[0]) expected_plots_for_slice = text_format.Parse( """ slice_key {} plots { key: "post_export_metrics" value { calibration_histogram_buckets { buckets { lower_threshold_inclusive: -inf num_weighted_examples {} total_weighted_label {} total_weighted_refined_prediction {} } buckets { upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction {} } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { } total_weighted_label {} total_weighted_refined_prediction {} } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction { value: 1.0 } } } } } """, metrics_for_slice_pb2.PlotsForSlice()) plot_records = list( metrics_plots_and_validations_writer.load_and_deserialize_plots( plots_file, output_file_format)) self.assertLen(plot_records, 1, 'plots: %s' % plot_records) self.assertProtoEquals(expected_plots_for_slice, plot_records[0]) @parameterized.named_parameters(('parquet_file_format', 'parquet')) def testLoadAndDeserializeFilteredMetricsAndPlots(self, output_file_format): metrics_file = os.path.join(self._getTempDir(), 'metrics') plots_file = os.path.join(self._getTempDir(), 'plots') temp_eval_export_dir = os.path.join(self._getTempDir(), 'eval_export_dir') _, eval_export_dir = ( fixed_prediction_estimator.simple_fixed_prediction_estimator( None, temp_eval_export_dir)) eval_config = config_pb2.EvalConfig( model_specs=[config_pb2.ModelSpec()], slicing_specs=[ config_pb2.SlicingSpec(), config_pb2.SlicingSpec(feature_keys=['prediction']) ], options=config_pb2.Options( disabled_outputs={'values': ['eval_config_pb2.json']})) eval_shared_model = self.createTestEvalSharedModel( eval_saved_model_path=eval_export_dir, add_metrics_callbacks=[ post_export_metrics.example_count(), post_export_metrics.calibration_plot_and_prediction_histogram( num_buckets=2) ]) extractors = [ legacy_predict_extractor.PredictExtractor( eval_shared_model, eval_config=eval_config), unbatch_extractor.UnbatchExtractor(), slice_key_extractor.SliceKeyExtractor( eval_config=eval_config, materialize=False) ] evaluators = [ legacy_metrics_and_plots_evaluator.MetricsAndPlotsEvaluator( eval_shared_model) ] output_paths = { constants.METRICS_KEY: metrics_file, constants.PLOTS_KEY: plots_file } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=eval_shared_model.add_metrics_callbacks, output_file_format=output_file_format) ] tfx_io = raw_tf_record.RawBeamRecordTFXIO( physical_format='inmemory', raw_record_column_name=constants.ARROW_INPUT_COLUMN, telemetry_descriptors=['TFMATest']) with beam.Pipeline() as pipeline: example1 = self._makeExample(prediction=0.0, label=1.0, country='US') example2 = self._makeExample(prediction=1.0, label=1.0, country='CA') # pylint: disable=no-value-for-parameter _ = ( pipeline | 'Create' >> beam.Create([ example1.SerializeToString(), example2.SerializeToString(), ]) | 'BatchExamples' >> tfx_io.BeamSource() | 'ExtractEvaluateAndWriteResults' >> model_eval_lib.ExtractEvaluateAndWriteResults( eval_config=eval_config, eval_shared_model=eval_shared_model, extractors=extractors, evaluators=evaluators, writers=writers)) # pylint: enable=no-value-for-parameter # only read the metrics with slice keys that match the following spec slice_keys_filter = [slicer.SingleSliceSpec(features=[('prediction', 0)])] expected_metrics_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: "prediction" float_value: 0 } } metrics { key: "average_loss" value { double_value { value: 1.0 } } } metrics { key: "post_export_metrics/example_count" value { double_value { value: 1.0 } } } """, metrics_for_slice_pb2.MetricsForSlice()) metric_records = list( metrics_plots_and_validations_writer.load_and_deserialize_metrics( metrics_file, output_file_format, slice_keys_filter)) self.assertLen(metric_records, 1, 'metrics: %s' % metric_records) self.assertProtoEquals(expected_metrics_for_slice, metric_records[0]) expected_plots_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: "prediction" float_value: 0 } } plots { key: "post_export_metrics" value { calibration_histogram_buckets { buckets { lower_threshold_inclusive: -inf num_weighted_examples {} total_weighted_label {} total_weighted_refined_prediction {} } buckets { upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction {} } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { } total_weighted_label {} total_weighted_refined_prediction {} } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } } } } """, metrics_for_slice_pb2.PlotsForSlice()) plot_records = list( metrics_plots_and_validations_writer.load_and_deserialize_plots( plots_file, output_file_format, slice_keys_filter)) self.assertLen(plot_records, 1, 'plots: %s' % plot_records) self.assertProtoEquals(expected_plots_for_slice, plot_records[0]) @parameterized.named_parameters(_OUTPUT_FORMAT_PARAMS) def testWriteAttributions(self, output_file_format): attributions_file = os.path.join(self._getTempDir(), 'attributions') eval_config = config_pb2.EvalConfig( model_specs=[config_pb2.ModelSpec()], metrics_specs=[ config_pb2.MetricsSpec(metrics=[ config_pb2.MetricConfig(class_name=attributions .TotalAttributions().__class__.__name__) ]) ], options=config_pb2.Options( disabled_outputs={'values': ['eval_config.json']})) extractors = [slice_key_extractor.SliceKeyExtractor()] evaluators = [ metrics_plots_and_validations_evaluator .MetricsPlotsAndValidationsEvaluator(eval_config=eval_config) ] output_paths = { constants.ATTRIBUTIONS_KEY: attributions_file, } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, output_file_format=output_file_format) ] example1 = { 'features': {}, 'attributions': { 'feature1': 1.1, 'feature2': 1.2 } } example2 = { 'features': {}, 'attributions': { 'feature1': 2.1, 'feature2': 2.2 } } example3 = { 'features': {}, 'attributions': { 'feature1': np.array([3.1]), 'feature2': np.array([3.2]) } } with beam.Pipeline() as pipeline: # pylint: disable=no-value-for-parameter _ = ( pipeline | 'Create' >> beam.Create([example1, example2, example3]) | 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate( extractors=extractors, evaluators=evaluators) | 'WriteResults' >> model_eval_lib.WriteResults(writers=writers)) # pylint: enable=no-value-for-parameter expected_attributions_for_slice = text_format.Parse( """ slice_key {} attributions_keys_and_values { key { name: "total_attributions" } values { key: "feature1" value: { double_value { value: 6.3 } } } values { key: "feature2" value: { double_value { value: 6.6 } } } }""", metrics_for_slice_pb2.AttributionsForSlice()) attribution_records = list( metrics_plots_and_validations_writer.load_and_deserialize_attributions( attributions_file, output_file_format)) self.assertLen(attribution_records, 1) self.assertProtoEquals(expected_attributions_for_slice, attribution_records[0]) if __name__ == '__main__': tf.compat.v1.enable_v2_behavior() tf.test.main()

33.695256

98

0.543998

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import string import tempfile from absl.testing import parameterized import apache_beam as beam from apache_beam.testing import util import numpy as np import tensorflow as tf from tensorflow_model_analysis import constants from tensorflow_model_analysis import types from tensorflow_model_analysis.api import model_eval_lib from tensorflow_model_analysis.eval_saved_model import testutil from tensorflow_model_analysis.eval_saved_model.example_trainers import fixed_prediction_estimator from tensorflow_model_analysis.evaluators import legacy_metrics_and_plots_evaluator from tensorflow_model_analysis.evaluators import metrics_plots_and_validations_evaluator from tensorflow_model_analysis.extractors import example_weights_extractor from tensorflow_model_analysis.extractors import features_extractor from tensorflow_model_analysis.extractors import labels_extractor from tensorflow_model_analysis.extractors import legacy_predict_extractor from tensorflow_model_analysis.extractors import predictions_extractor from tensorflow_model_analysis.extractors import slice_key_extractor from tensorflow_model_analysis.extractors import unbatch_extractor from tensorflow_model_analysis.metrics import attributions from tensorflow_model_analysis.metrics import binary_confusion_matrices from tensorflow_model_analysis.metrics import metric_types from tensorflow_model_analysis.post_export_metrics import metric_keys from tensorflow_model_analysis.post_export_metrics import post_export_metrics from tensorflow_model_analysis.proto import config_pb2 from tensorflow_model_analysis.proto import metrics_for_slice_pb2 from tensorflow_model_analysis.proto import validation_result_pb2 from tensorflow_model_analysis.slicer import slicer_lib as slicer from tensorflow_model_analysis.writers import metrics_plots_and_validations_writer from tfx_bsl.tfxio import raw_tf_record from tfx_bsl.tfxio import tensor_adapter from tfx_bsl.tfxio import test_util from google.protobuf import text_format from tensorflow_metadata.proto.v0 import schema_pb2 def _make_slice_key(*args): if len(args) % 2 != 0: raise ValueError('number of arguments should be even') result = [] for i in range(0, len(args), 2): result.append((args[i], args[i + 1])) result = tuple(result) return result class MetricsPlotsAndValidationsWriterTest(testutil.TensorflowModelAnalysisTest, parameterized.TestCase): def setUp(self): super(MetricsPlotsAndValidationsWriterTest, self).setUp() self.longMessage = True def _getTempDir(self): return tempfile.mkdtemp() def _getExportDir(self): return os.path.join(self._getTempDir(), 'export_dir') def _getBaselineDir(self): return os.path.join(self._getTempDir(), 'baseline_export_dir') def _build_keras_model(self, model_dir, mul): input_layer = tf.keras.layers.Input(shape=(1,), name='input_1') output_layer = tf.keras.layers.Lambda( lambda x, mul: x * mul, output_shape=(1,), arguments={'mul': mul})( input_layer) model = tf.keras.models.Model([input_layer], output_layer) model.compile( optimizer=tf.keras.optimizers.Adam(lr=.001), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy']) model.fit(x=[[0], [1]], y=[[0], [1]], steps_per_epoch=1) model.save(model_dir, save_format='tf') return self.createTestEvalSharedModel( eval_saved_model_path=model_dir, tags=[tf.saved_model.SERVING]) def testConvertSlicePlotsToProto(self): slice_key = _make_slice_key('fruit', 'apple') plot_key = metric_types.PlotKey( name='calibration_plot', output_name='output_name') calibration_plot = text_format.Parse( """ buckets { lower_threshold_inclusive: -inf upper_threshold_exclusive: 0.0 num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } buckets { lower_threshold_inclusive: 0.0 upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction { value: 0.3 } } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { value: 1.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.7 } } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } """, metrics_for_slice_pb2.CalibrationHistogramBuckets()) expected_plots_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: 'fruit' bytes_value: 'apple' } } plot_keys_and_values { key { output_name: "output_name" } value { calibration_histogram_buckets { buckets { lower_threshold_inclusive: -inf upper_threshold_exclusive: 0.0 num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } buckets { lower_threshold_inclusive: 0.0 upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction { value: 0.3 } } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { value: 1.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.7 } } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } } } } """, metrics_for_slice_pb2.PlotsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_plots_to_proto( (slice_key, { plot_key: calibration_plot }), None) self.assertProtoEquals(expected_plots_for_slice, got) def testConvertSlicePlotsToProtoLegacyStringKeys(self): slice_key = _make_slice_key('fruit', 'apple') tfma_plots = { metric_keys.CALIBRATION_PLOT_MATRICES: np.array([ [0.0, 0.0, 0.0], [0.3, 1.0, 1.0], [0.7, 0.0, 1.0], [0.0, 0.0, 0.0], ]), metric_keys.CALIBRATION_PLOT_BOUNDARIES: np.array([0.0, 0.5, 1.0]), } expected_plot_data = """ slice_key { single_slice_keys { column: 'fruit' bytes_value: 'apple' } } plots { key: "post_export_metrics" value { calibration_histogram_buckets { buckets { lower_threshold_inclusive: -inf upper_threshold_exclusive: 0.0 num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } buckets { lower_threshold_inclusive: 0.0 upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction { value: 0.3 } } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { value: 1.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.7 } } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } } } } """ calibration_plot = ( post_export_metrics.calibration_plot_and_prediction_histogram()) got = metrics_plots_and_validations_writer.convert_slice_plots_to_proto( (slice_key, tfma_plots), [calibration_plot]) self.assertProtoEquals(expected_plot_data, got) def testConvertSlicePlotsToProtoEmptyPlot(self): slice_key = _make_slice_key('fruit', 'apple') tfma_plots = {metric_keys.ERROR_METRIC: 'error_message'} actual_plot = metrics_plots_and_validations_writer.convert_slice_plots_to_proto( (slice_key, tfma_plots), []) expected_plot = metrics_for_slice_pb2.PlotsForSlice() expected_plot.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key)) expected_plot.plots[ metric_keys.ERROR_METRIC].debug_message = 'error_message' self.assertProtoEquals(expected_plot, actual_plot) def testConvertSliceMetricsToProto(self): slice_key = _make_slice_key('age', 5, 'language', 'english', 'price', 0.3) slice_metrics = { metric_types.MetricKey(name='accuracy', output_name='output_name'): 0.8 } expected_metrics_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: 'age' int64_value: 5 } single_slice_keys { column: 'language' bytes_value: 'english' } single_slice_keys { column: 'price' float_value: 0.3 } } metric_keys_and_values { key { name: "accuracy" output_name: "output_name" } value { double_value { value: 0.8 } } }""", metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), None) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoConfusionMatrices(self): slice_key = _make_slice_key() slice_metrics = { metric_types.MetricKey(name='confusion_matrix_at_thresholds'): binary_confusion_matrices.Matrices( thresholds=[0.25, 0.75, 1.00], fn=[0.0, 1.0, 2.0], tn=[1.0, 1.0, 1.0], fp=[0.0, 0.0, 0.0], tp=[2.0, 1.0, 0.0]) } expected_metrics_for_slice = text_format.Parse( """ slice_key {} metric_keys_and_values { key: { name: "confusion_matrix_at_thresholds" } value { confusion_matrix_at_thresholds { matrices { threshold: 0.25 false_negatives: 0.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 2.0 precision: 1.0 recall: 1.0 } matrices { threshold: 0.75 false_negatives: 1.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 1.0 precision: 1.0 recall: 0.5 } matrices { threshold: 1.00 false_negatives: 2.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 0.0 precision: 1.0 recall: 0.0 } } } } """, metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), add_metrics_callbacks=[]) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoConfusionMatricesPostExport(self): slice_key = _make_slice_key() thresholds = [0.25, 0.75, 1.00] matrices = [[0.0, 1.0, 0.0, 2.0, 1.0, 1.0], [1.0, 1.0, 0.0, 1.0, 1.0, 0.5], [2.0, 1.0, 0.0, 0.0, float('nan'), 0.0]] slice_metrics = { metric_keys.CONFUSION_MATRIX_AT_THRESHOLDS_MATRICES: matrices, metric_keys.CONFUSION_MATRIX_AT_THRESHOLDS_THRESHOLDS: thresholds, } expected_metrics_for_slice = text_format.Parse( """ slice_key {} metrics { key: "post_export_metrics/confusion_matrix_at_thresholds" value { confusion_matrix_at_thresholds { matrices { threshold: 0.25 false_negatives: 0.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 2.0 precision: 1.0 recall: 1.0 bounded_false_negatives { value { value: 0.0 } } bounded_true_negatives { value { value: 1.0 } } bounded_true_positives { value { value: 2.0 } } bounded_false_positives { value { value: 0.0 } } bounded_precision { value { value: 1.0 } } bounded_recall { value { value: 1.0 } } t_distribution_false_negatives { unsampled_value { value: 0.0 } } t_distribution_true_negatives { unsampled_value { value: 1.0 } } t_distribution_true_positives { unsampled_value { value: 2.0 } } t_distribution_false_positives { unsampled_value { value: 0.0 } } t_distribution_precision { unsampled_value { value: 1.0 } } t_distribution_recall { unsampled_value { value: 1.0 } } } matrices { threshold: 0.75 false_negatives: 1.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 1.0 precision: 1.0 recall: 0.5 bounded_false_negatives { value { value: 1.0 } } bounded_true_negatives { value { value: 1.0 } } bounded_true_positives { value { value: 1.0 } } bounded_false_positives { value { value: 0.0 } } bounded_precision { value { value: 1.0 } } bounded_recall { value { value: 0.5 } } t_distribution_false_negatives { unsampled_value { value: 1.0 } } t_distribution_true_negatives { unsampled_value { value: 1.0 } } t_distribution_true_positives { unsampled_value { value: 1.0 } } t_distribution_false_positives { unsampled_value { value: 0.0 } } t_distribution_precision { unsampled_value { value: 1.0 } } t_distribution_recall { unsampled_value { value: 0.5 } } } matrices { threshold: 1.00 false_negatives: 2.0 true_negatives: 1.0 false_positives: 0.0 true_positives: 0.0 precision: nan recall: 0.0 bounded_false_negatives { value { value: 2.0 } } bounded_true_negatives { value { value: 1.0 } } bounded_true_positives { value { value: 0.0 } } bounded_false_positives { value { value: 0.0 } } bounded_precision { value { value: nan } } bounded_recall { value { value: 0.0 } } t_distribution_false_negatives { unsampled_value { value: 2.0 } } t_distribution_true_negatives { unsampled_value { value: 1.0 } } t_distribution_true_positives { unsampled_value { value: 0.0 } } t_distribution_false_positives { unsampled_value { value: 0.0 } } t_distribution_precision { unsampled_value { value: nan } } t_distribution_recall { unsampled_value { value: 0.0 } } } } } } """, metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), [post_export_metrics.confusion_matrix_at_thresholds(thresholds)]) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoMetricsRanges(self): slice_key = _make_slice_key('age', 5, 'language', 'english', 'price', 0.3) slice_metrics = { 'accuracy': types.ValueWithTDistribution(0.8, 0.1, 9, 0.8), metric_keys.AUPRC: 0.1, metric_keys.lower_bound_key(metric_keys.AUPRC): 0.05, metric_keys.upper_bound_key(metric_keys.AUPRC): 0.17, metric_keys.AUC: 0.2, metric_keys.lower_bound_key(metric_keys.AUC): 0.1, metric_keys.upper_bound_key(metric_keys.AUC): 0.3 } expected_metrics_for_slice = text_format.Parse( string.Template(""" slice_key { single_slice_keys { column: 'age' int64_value: 5 } single_slice_keys { column: 'language' bytes_value: 'english' } single_slice_keys { column: 'price' float_value: 0.3 } } metrics { key: "accuracy" value { bounded_value { value { value: 0.8 } lower_bound { value: 0.5737843 } upper_bound { value: 1.0262157 } methodology: POISSON_BOOTSTRAP } } } metrics { key: "$auc" value { bounded_value { lower_bound { value: 0.1 } upper_bound { value: 0.3 } value { value: 0.2 } methodology: RIEMANN_SUM } } } metrics { key: "$auprc" value { bounded_value { lower_bound { value: 0.05 } upper_bound { value: 0.17 } value { value: 0.1 } methodology: RIEMANN_SUM } } }""").substitute(auc=metric_keys.AUC, auprc=metric_keys.AUPRC), metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), [post_export_metrics.auc(), post_export_metrics.auc(curve='PR')]) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoFromLegacyStrings(self): slice_key = _make_slice_key('age', 5, 'language', 'english', 'price', 0.3) slice_metrics = { 'accuracy': 0.8, metric_keys.AUPRC: 0.1, metric_keys.lower_bound_key(metric_keys.AUPRC): 0.05, metric_keys.upper_bound_key(metric_keys.AUPRC): 0.17, metric_keys.AUC: 0.2, metric_keys.lower_bound_key(metric_keys.AUC): 0.1, metric_keys.upper_bound_key(metric_keys.AUC): 0.3 } expected_metrics_for_slice = text_format.Parse( string.Template(""" slice_key { single_slice_keys { column: 'age' int64_value: 5 } single_slice_keys { column: 'language' bytes_value: 'english' } single_slice_keys { column: 'price' float_value: 0.3 } } metrics { key: "accuracy" value { double_value { value: 0.8 } } } metrics { key: "$auc" value { bounded_value { lower_bound { value: 0.1 } upper_bound { value: 0.3 } value { value: 0.2 } methodology: RIEMANN_SUM } } } metrics { key: "$auprc" value { bounded_value { lower_bound { value: 0.05 } upper_bound { value: 0.17 } value { value: 0.1 } methodology: RIEMANN_SUM } } }""").substitute(auc=metric_keys.AUC, auprc=metric_keys.AUPRC), metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), [post_export_metrics.auc(), post_export_metrics.auc(curve='PR')]) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoEmptyMetrics(self): slice_key = _make_slice_key('age', 5, 'language', 'english', 'price', 0.3) slice_metrics = {metric_keys.ERROR_METRIC: 'error_message'} actual_metrics = ( metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), [post_export_metrics.auc(), post_export_metrics.auc(curve='PR')])) expected_metrics = metrics_for_slice_pb2.MetricsForSlice() expected_metrics.slice_key.CopyFrom(slicer.serialize_slice_key(slice_key)) expected_metrics.metrics[ metric_keys.ERROR_METRIC].debug_message = 'error_message' self.assertProtoEquals(expected_metrics, actual_metrics) def testConvertSliceMetricsToProtoStringMetrics(self): slice_key = _make_slice_key() slice_metrics = { 'valid_ascii': b'test string', 'valid_unicode': b'\xF0\x9F\x90\x84', 'invalid_unicode': b'\xE2\x28\xA1', } expected_metrics_for_slice = metrics_for_slice_pb2.MetricsForSlice() expected_metrics_for_slice.slice_key.SetInParent() expected_metrics_for_slice.metrics[ 'valid_ascii'].bytes_value = slice_metrics['valid_ascii'] expected_metrics_for_slice.metrics[ 'valid_unicode'].bytes_value = slice_metrics['valid_unicode'] expected_metrics_for_slice.metrics[ 'invalid_unicode'].bytes_value = slice_metrics['invalid_unicode'] got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), []) self.assertProtoEquals(expected_metrics_for_slice, got) def testCombineValidationsValidationOk(self): input_validations = [ text_format.Parse( """ validation_ok: true metric_validations_per_slice { slice_key { single_slice_keys { column: "x" bytes_value: "x1" } } } validation_details { slicing_details { slicing_spec {} num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 1 } }""", validation_result_pb2.ValidationResult()), text_format.Parse( """ validation_ok: true validation_details { slicing_details { slicing_spec { feature_keys: ["x"] } num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 2 } }""", validation_result_pb2.ValidationResult()) ] eval_config = config_pb2.EvalConfig( model_specs=[ config_pb2.ModelSpec(name='candidate'), config_pb2.ModelSpec(name='baseline', is_baseline=True) ], slicing_specs=[config_pb2.SlicingSpec()], metrics_specs=[ config_pb2.MetricsSpec( metrics=[ config_pb2.MetricConfig( class_name='AUC', per_slice_thresholds=[ config_pb2.PerSliceMetricThreshold( slicing_specs=[config_pb2.SlicingSpec()], threshold=config_pb2.MetricThreshold( value_threshold=config_pb2 .GenericValueThreshold( lower_bound={'value': 0.7}))) ]), ], model_names=['candidate', 'baseline']), ]) expected_validation = text_format.Parse( """ validation_ok: true metric_validations_per_slice { slice_key { single_slice_keys { column: "x" bytes_value: "x1" } } } validation_details { slicing_details { slicing_spec {} num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 3 } slicing_details { slicing_spec { feature_keys: ["x"] } num_matching_slices: 1 } }""", validation_result_pb2.ValidationResult()) def verify_fn(result): self.assertLen(result, 1) self.assertProtoEquals(expected_validation, result[0]) with beam.Pipeline() as pipeline: result = ( pipeline | 'Create' >> beam.Create(input_validations) | 'CombineValidations' >> beam.CombineGlobally( metrics_plots_and_validations_writer.CombineValidations( eval_config))) util.assert_that(result, verify_fn) def testCombineValidationsMissingSlices(self): input_validations = [ text_format.Parse( """ validation_ok: false metric_validations_per_slice { slice_key { single_slice_keys { column: "x" bytes_value: "x1" } } failures { metric_key { name: "auc" model_name: "candidate" is_diff: true } metric_threshold { value_threshold { lower_bound { value: 0.7 } } } metric_value { double_value { value: 0.6 } } } } validation_details { slicing_details { slicing_spec {} num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 1 } }""", validation_result_pb2.ValidationResult()), text_format.Parse( """ validation_ok: true validation_details { slicing_details { slicing_spec { feature_keys: ["x"] } num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 2 } }""", validation_result_pb2.ValidationResult()) ] slicing_specs = [ config_pb2.SlicingSpec(), config_pb2.SlicingSpec(feature_keys=['x']), config_pb2.SlicingSpec(feature_keys=['x', 'y']), config_pb2.SlicingSpec(feature_keys=['z']), ] eval_config = config_pb2.EvalConfig( model_specs=[ config_pb2.ModelSpec(name='candidate'), config_pb2.ModelSpec(name='baseline', is_baseline=True) ], slicing_specs=slicing_specs, metrics_specs=[ config_pb2.MetricsSpec( metrics=[ config_pb2.MetricConfig( class_name='AUC', per_slice_thresholds=[ config_pb2.PerSliceMetricThreshold( slicing_specs=slicing_specs, threshold=config_pb2.MetricThreshold( value_threshold=config_pb2 .GenericValueThreshold( lower_bound={'value': 0.7}))) ]), ], model_names=['candidate', 'baseline']), ]) expected_validation = text_format.Parse( """ validation_ok: false metric_validations_per_slice { slice_key { single_slice_keys { column: "x" bytes_value: "x1" } } failures { metric_key { name: "auc" model_name: "candidate" is_diff: true } metric_threshold { value_threshold { lower_bound { value: 0.7 } } } metric_value { double_value { value: 0.6 } } } } missing_slices { feature_keys: "z" } validation_details { slicing_details { slicing_spec {} num_matching_slices: 1 } slicing_details { slicing_spec { feature_keys: ["x", "y"] } num_matching_slices: 3 } slicing_details { slicing_spec { feature_keys: ["x"] } num_matching_slices: 1 } }""", validation_result_pb2.ValidationResult()) def verify_fn(result): self.assertLen(result, 1) self.assertProtoEquals(expected_validation, result[0]) with beam.Pipeline() as pipeline: result = ( pipeline | 'Create' >> beam.Create(input_validations) | 'CombineValidations' >> beam.CombineGlobally( metrics_plots_and_validations_writer.CombineValidations( eval_config))) util.assert_that(result, verify_fn) def testUncertaintyValuedMetrics(self): slice_key = _make_slice_key() slice_metrics = { 'one_dim': types.ValueWithTDistribution(2.0, 1.0, 3, 2.0), 'nans': types.ValueWithTDistribution( float('nan'), float('nan'), -1, float('nan')), } expected_metrics_for_slice = text_format.Parse( """ slice_key {} metrics { key: "one_dim" value { bounded_value { value { value: 2.0 } lower_bound { value: -1.1824463 } upper_bound { value: 5.1824463 } methodology: POISSON_BOOTSTRAP } } } metrics { key: "nans" value { bounded_value { value { value: nan } lower_bound { value: nan } upper_bound { value: nan } methodology: POISSON_BOOTSTRAP } } } """, metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), []) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceMetricsToProtoTensorValuedMetrics(self): slice_key = _make_slice_key() slice_metrics = { 'one_dim': np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32), 'two_dims': np.array([['two', 'dims', 'test'], ['TWO', 'DIMS', 'TEST']]), 'three_dims': np.array([[[100, 200, 300]], [[500, 600, 700]]], dtype=np.int64), } expected_metrics_for_slice = text_format.Parse( """ slice_key {} metrics { key: "one_dim" value { array_value { data_type: FLOAT32 shape: 4 float32_values: [1.0, 2.0, 3.0, 4.0] } } } metrics { key: "two_dims" value { array_value { data_type: BYTES shape: [2, 3] bytes_values: ["two", "dims", "test", "TWO", "DIMS", "TEST"] } } } metrics { key: "three_dims" value { array_value { data_type: INT64 shape: [2, 1, 3] int64_values: [100, 200, 300, 500, 600, 700] } } } """, metrics_for_slice_pb2.MetricsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_metrics_to_proto( (slice_key, slice_metrics), []) self.assertProtoEquals(expected_metrics_for_slice, got) def testConvertSliceAttributionsToProto(self): slice_key = _make_slice_key('language', 'english', 'price', 0.3) slice_attributions = { metric_types.AttributionsKey(name='mean', output_name='output_name'): { 'age': 0.8, 'language': 1.2, 'price': 2.3, }, } expected_attributions_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: 'language' bytes_value: 'english' } single_slice_keys { column: 'price' float_value: 0.3 } } attributions_keys_and_values { key { name: "mean" output_name: "output_name" } values { key: "age" value: { double_value { value: 0.8 } } } values { key: "language" value: { double_value { value: 1.2 } } } values { key: "price" value: { double_value { value: 2.3 } } } }""", metrics_for_slice_pb2.AttributionsForSlice()) got = metrics_plots_and_validations_writer.convert_slice_attributions_to_proto( (slice_key, slice_attributions)) self.assertProtoEquals(expected_attributions_for_slice, got) _OUTPUT_FORMAT_PARAMS = [('without_output_file_format', ''), ('tfrecord_file_format', 'tfrecord'), ('parquet_file_format', 'parquet')] @parameterized.named_parameters(_OUTPUT_FORMAT_PARAMS) def testWriteValidationResults(self, output_file_format): model_dir, baseline_dir = self._getExportDir(), self._getBaselineDir() eval_shared_model = self._build_keras_model(model_dir, mul=0) baseline_eval_shared_model = self._build_keras_model(baseline_dir, mul=1) validations_file = os.path.join(self._getTempDir(), constants.VALIDATIONS_KEY) schema = text_format.Parse( """ tensor_representation_group { key: "" value { tensor_representation { key: "input_1" value { dense_tensor { column_name: "input_1" shape { dim { size: 1 } } } } } } } feature { name: "input_1" type: FLOAT } feature { name: "label" type: FLOAT } feature { name: "example_weight" type: FLOAT } feature { name: "extra_feature" type: BYTES } """, schema_pb2.Schema()) tfx_io = test_util.InMemoryTFExampleRecord( schema=schema, raw_record_column_name=constants.ARROW_INPUT_COLUMN) tensor_adapter_config = tensor_adapter.TensorAdapterConfig( arrow_schema=tfx_io.ArrowSchema(), tensor_representations=tfx_io.TensorRepresentations()) examples = [ self._makeExample( input_1=0.0, label=1.0, example_weight=1.0, extra_feature='non_model_feature'), self._makeExample( input_1=1.0, label=0.0, example_weight=0.5, extra_feature='non_model_feature'), ] slicing_specs = [ config_pb2.SlicingSpec(), config_pb2.SlicingSpec(feature_keys=['slice_does_not_exist']) ] cross_slicing_specs = [ config_pb2.CrossSlicingSpec( baseline_spec=config_pb2.SlicingSpec( feature_keys=['slice_does_not_exist']), slicing_specs=[ config_pb2.SlicingSpec(feature_keys=['slice_does_not_exist']) ]) ] eval_config = config_pb2.EvalConfig( model_specs=[ config_pb2.ModelSpec( name='candidate', label_key='label', example_weight_key='example_weight'), config_pb2.ModelSpec( name='baseline', label_key='label', example_weight_key='example_weight', is_baseline=True) ], slicing_specs=slicing_specs, cross_slicing_specs=cross_slicing_specs, metrics_specs=[ config_pb2.MetricsSpec( metrics=[ config_pb2.MetricConfig( class_name='WeightedExampleCount', per_slice_thresholds=[ config_pb2.PerSliceMetricThreshold( slicing_specs=slicing_specs, threshold=config_pb2.MetricThreshold( value_threshold=config_pb2 .GenericValueThreshold( upper_bound={'value': 1}))) ], cross_slice_thresholds=[ config_pb2.CrossSliceMetricThreshold( cross_slicing_specs=cross_slicing_specs, threshold=config_pb2.MetricThreshold( value_threshold=config_pb2 .GenericValueThreshold( upper_bound={'value': 1}))) ]), config_pb2.MetricConfig( class_name='ExampleCount', threshold=config_pb2.MetricThreshold( value_threshold=config_pb2.GenericValueThreshold( lower_bound={'value': 10}))), config_pb2.MetricConfig( class_name='MeanLabel', threshold=config_pb2.MetricThreshold( change_threshold=config_pb2.GenericChangeThreshold( direction=config_pb2.MetricDirection .HIGHER_IS_BETTER, relative={'value': 1}, absolute={'value': 1}))), config_pb2.MetricConfig( class_name='MeanPrediction', threshold=config_pb2.MetricThreshold( value_threshold=config_pb2.GenericValueThreshold( upper_bound={'value': .01}, lower_bound={'value': -.01}), change_threshold=config_pb2.GenericChangeThreshold( direction=config_pb2.MetricDirection .LOWER_IS_BETTER, relative={'value': -.99}, absolute={'value': 0}))) ], model_names=['candidate', 'baseline']), ], options=config_pb2.Options( disabled_outputs={'values': ['eval_config_pb2.json']}), ) slice_spec = [ slicer.SingleSliceSpec(spec=s) for s in eval_config.slicing_specs ] eval_shared_models = { 'candidate': eval_shared_model, 'baseline': baseline_eval_shared_model } extractors = [ features_extractor.FeaturesExtractor(eval_config), labels_extractor.LabelsExtractor(eval_config), example_weights_extractor.ExampleWeightsExtractor(eval_config), predictions_extractor.PredictionsExtractor( eval_shared_model=eval_shared_models, eval_config=eval_config, tensor_adapter_config=tensor_adapter_config), unbatch_extractor.UnbatchExtractor(), slice_key_extractor.SliceKeyExtractor(slice_spec=slice_spec) ] evaluators = [ metrics_plots_and_validations_evaluator .MetricsPlotsAndValidationsEvaluator( eval_config=eval_config, eval_shared_model=eval_shared_models) ] output_paths = { constants.VALIDATIONS_KEY: validations_file, } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=[], output_file_format=output_file_format) ] with beam.Pipeline() as pipeline: _ = ( pipeline | 'Create' >> beam.Create([e.SerializeToString() for e in examples]) | 'BatchExamples' >> tfx_io.BeamSource() | 'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts() | 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate( extractors=extractors, evaluators=evaluators) | 'WriteResults' >> model_eval_lib.WriteResults(writers=writers)) validation_result = ( metrics_plots_and_validations_writer .load_and_deserialize_validation_result( os.path.dirname(validations_file), output_file_format)) expected_validations = [ text_format.Parse( """ metric_key { name: "weighted_example_count" model_name: "candidate" } metric_threshold { value_threshold { upper_bound { value: 1.0 } } } metric_value { double_value { value: 1.5 } } """, validation_result_pb2.ValidationFailure()), text_format.Parse( """ metric_key { name: "example_count" model_name: "candidate" } metric_threshold { value_threshold { lower_bound { value: 10.0 } } } metric_value { double_value { value: 2.0 } } """, validation_result_pb2.ValidationFailure()), text_format.Parse( """ metric_key { name: "mean_label" model_name: "candidate" is_diff: true } metric_threshold { change_threshold { absolute { value: 1.0 } relative { value: 1.0 } direction: HIGHER_IS_BETTER } } metric_value { double_value { value: 0.0 } } """, validation_result_pb2.ValidationFailure()), ] self.assertFalse(validation_result.validation_ok) self.assertFalse(validation_result.missing_thresholds) self.assertLen(validation_result.metric_validations_per_slice, 1) self.assertCountEqual( expected_validations, validation_result.metric_validations_per_slice[0].failures) expected_missing_slices = [ config_pb2.SlicingSpec(feature_keys=['slice_does_not_exist']) ] self.assertLen(validation_result.missing_slices, 1) self.assertCountEqual(expected_missing_slices, validation_result.missing_slices) expected_missing_cross_slices = [ config_pb2.CrossSlicingSpec( baseline_spec=config_pb2.SlicingSpec( feature_keys=['slice_does_not_exist']), slicing_specs=[ config_pb2.SlicingSpec(feature_keys=['slice_does_not_exist']) ]) ] self.assertLen(validation_result.missing_cross_slices, 1) self.assertCountEqual(expected_missing_cross_slices, validation_result.missing_cross_slices) expected_slicing_details = [ text_format.Parse( """ slicing_spec { } num_matching_slices: 1 """, validation_result_pb2.SlicingDetails()), ] self.assertLen(validation_result.validation_details.slicing_details, 1) self.assertCountEqual(expected_slicing_details, validation_result.validation_details.slicing_details) @parameterized.named_parameters(_OUTPUT_FORMAT_PARAMS) def testWriteValidationResultsNoThresholds(self, output_file_format): model_dir, baseline_dir = self._getExportDir(), self._getBaselineDir() eval_shared_model = self._build_keras_model(model_dir, mul=0) baseline_eval_shared_model = self._build_keras_model(baseline_dir, mul=1) validations_file = os.path.join(self._getTempDir(), constants.VALIDATIONS_KEY) schema = text_format.Parse( """ tensor_representation_group { key: "" value { tensor_representation { key: "input_1" value { dense_tensor { column_name: "input_1" shape { dim { size: 1 } } } } } } } feature { name: "input_1" type: FLOAT } feature { name: "label" type: FLOAT } feature { name: "example_weight" type: FLOAT } feature { name: "extra_feature" type: BYTES } """, schema_pb2.Schema()) tfx_io = test_util.InMemoryTFExampleRecord( schema=schema, raw_record_column_name=constants.ARROW_INPUT_COLUMN) tensor_adapter_config = tensor_adapter.TensorAdapterConfig( arrow_schema=tfx_io.ArrowSchema(), tensor_representations=tfx_io.TensorRepresentations()) examples = [ self._makeExample( input_1=0.0, label=1.0, example_weight=1.0, extra_feature='non_model_feature'), self._makeExample( input_1=1.0, label=0.0, example_weight=0.5, extra_feature='non_model_feature'), ] slicing_specs = [ config_pb2.SlicingSpec(), ] eval_config = config_pb2.EvalConfig( model_specs=[ config_pb2.ModelSpec( name='candidate', label_key='label', example_weight_key='example_weight'), config_pb2.ModelSpec( name='baseline', label_key='label', example_weight_key='example_weight', is_baseline=True) ], slicing_specs=slicing_specs, metrics_specs=[ config_pb2.MetricsSpec( metrics=[ config_pb2.MetricConfig(class_name='WeightedExampleCount'), config_pb2.MetricConfig(class_name='ExampleCount'), config_pb2.MetricConfig(class_name='MeanLabel') ], model_names=['candidate', 'baseline']), ], options=config_pb2.Options( disabled_outputs={'values': ['eval_config_pb2.json']}), ) slice_spec = [ slicer.SingleSliceSpec(spec=s) for s in eval_config.slicing_specs ] eval_shared_models = { 'candidate': eval_shared_model, 'baseline': baseline_eval_shared_model } extractors = [ features_extractor.FeaturesExtractor(eval_config), labels_extractor.LabelsExtractor(eval_config), example_weights_extractor.ExampleWeightsExtractor(eval_config), predictions_extractor.PredictionsExtractor( eval_shared_model=eval_shared_models, eval_config=eval_config, tensor_adapter_config=tensor_adapter_config), unbatch_extractor.UnbatchExtractor(), slice_key_extractor.SliceKeyExtractor(slice_spec=slice_spec) ] evaluators = [ metrics_plots_and_validations_evaluator .MetricsPlotsAndValidationsEvaluator( eval_config=eval_config, eval_shared_model=eval_shared_models) ] output_paths = { constants.VALIDATIONS_KEY: validations_file, } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=[], output_file_format=output_file_format) ] with beam.Pipeline() as pipeline: _ = ( pipeline | 'Create' >> beam.Create([e.SerializeToString() for e in examples]) | 'BatchExamples' >> tfx_io.BeamSource() | 'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts() | 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate( extractors=extractors, evaluators=evaluators) | 'WriteResults' >> model_eval_lib.WriteResults(writers=writers)) validation_result = ( metrics_plots_and_validations_writer .load_and_deserialize_validation_result( os.path.dirname(validations_file), output_file_format)) self.assertFalse(validation_result.validation_ok) self.assertTrue(validation_result.missing_thresholds) self.assertEmpty(validation_result.metric_validations_per_slice) writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=[], output_file_format=output_file_format, rubber_stamp=True) ] with beam.Pipeline() as pipeline: _ = ( pipeline | 'Create' >> beam.Create([e.SerializeToString() for e in examples]) | 'BatchExamples' >> tfx_io.BeamSource() | 'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts() | 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate( extractors=extractors, evaluators=evaluators) | 'WriteResults' >> model_eval_lib.WriteResults(writers=writers)) validation_result = ( metrics_plots_and_validations_writer .load_and_deserialize_validation_result( os.path.dirname(validations_file), output_file_format)) self.assertTrue(validation_result.validation_ok) self.assertFalse(validation_result.missing_thresholds) self.assertEmpty(validation_result.metric_validations_per_slice) self.assertTrue(validation_result.rubber_stamp) @parameterized.named_parameters(_OUTPUT_FORMAT_PARAMS) def testWriteMetricsAndPlots(self, output_file_format): metrics_file = os.path.join(self._getTempDir(), 'metrics') plots_file = os.path.join(self._getTempDir(), 'plots') temp_eval_export_dir = os.path.join(self._getTempDir(), 'eval_export_dir') _, eval_export_dir = ( fixed_prediction_estimator.simple_fixed_prediction_estimator( None, temp_eval_export_dir)) eval_config = config_pb2.EvalConfig( model_specs=[config_pb2.ModelSpec()], options=config_pb2.Options( disabled_outputs={'values': ['eval_config_pb2.json']})) eval_shared_model = self.createTestEvalSharedModel( eval_saved_model_path=eval_export_dir, add_metrics_callbacks=[ post_export_metrics.example_count(), post_export_metrics.calibration_plot_and_prediction_histogram( num_buckets=2) ]) extractors = [ legacy_predict_extractor.PredictExtractor( eval_shared_model, eval_config=eval_config), unbatch_extractor.UnbatchExtractor(), slice_key_extractor.SliceKeyExtractor() ] evaluators = [ legacy_metrics_and_plots_evaluator.MetricsAndPlotsEvaluator( eval_shared_model) ] output_paths = { constants.METRICS_KEY: metrics_file, constants.PLOTS_KEY: plots_file } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=eval_shared_model.add_metrics_callbacks, output_file_format=output_file_format) ] tfx_io = raw_tf_record.RawBeamRecordTFXIO( physical_format='inmemory', raw_record_column_name=constants.ARROW_INPUT_COLUMN, telemetry_descriptors=['TFMATest']) with beam.Pipeline() as pipeline: example1 = self._makeExample(prediction=0.0, label=1.0) example2 = self._makeExample(prediction=1.0, label=1.0) _ = ( pipeline | 'Create' >> beam.Create([ example1.SerializeToString(), example2.SerializeToString(), ]) | 'BatchExamples' >> tfx_io.BeamSource() | 'ExtractEvaluateAndWriteResults' >> model_eval_lib.ExtractEvaluateAndWriteResults( eval_config=eval_config, eval_shared_model=eval_shared_model, extractors=extractors, evaluators=evaluators, writers=writers)) expected_metrics_for_slice = text_format.Parse( """ slice_key {} metrics { key: "average_loss" value { double_value { value: 0.5 } } } metrics { key: "post_export_metrics/example_count" value { double_value { value: 2.0 } } } """, metrics_for_slice_pb2.MetricsForSlice()) metric_records = list( metrics_plots_and_validations_writer.load_and_deserialize_metrics( metrics_file, output_file_format)) self.assertLen(metric_records, 1, 'metrics: %s' % metric_records) self.assertProtoEquals(expected_metrics_for_slice, metric_records[0]) expected_plots_for_slice = text_format.Parse( """ slice_key {} plots { key: "post_export_metrics" value { calibration_histogram_buckets { buckets { lower_threshold_inclusive: -inf num_weighted_examples {} total_weighted_label {} total_weighted_refined_prediction {} } buckets { upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction {} } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { } total_weighted_label {} total_weighted_refined_prediction {} } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction { value: 1.0 } } } } } """, metrics_for_slice_pb2.PlotsForSlice()) plot_records = list( metrics_plots_and_validations_writer.load_and_deserialize_plots( plots_file, output_file_format)) self.assertLen(plot_records, 1, 'plots: %s' % plot_records) self.assertProtoEquals(expected_plots_for_slice, plot_records[0]) @parameterized.named_parameters(('parquet_file_format', 'parquet')) def testLoadAndDeserializeFilteredMetricsAndPlots(self, output_file_format): metrics_file = os.path.join(self._getTempDir(), 'metrics') plots_file = os.path.join(self._getTempDir(), 'plots') temp_eval_export_dir = os.path.join(self._getTempDir(), 'eval_export_dir') _, eval_export_dir = ( fixed_prediction_estimator.simple_fixed_prediction_estimator( None, temp_eval_export_dir)) eval_config = config_pb2.EvalConfig( model_specs=[config_pb2.ModelSpec()], slicing_specs=[ config_pb2.SlicingSpec(), config_pb2.SlicingSpec(feature_keys=['prediction']) ], options=config_pb2.Options( disabled_outputs={'values': ['eval_config_pb2.json']})) eval_shared_model = self.createTestEvalSharedModel( eval_saved_model_path=eval_export_dir, add_metrics_callbacks=[ post_export_metrics.example_count(), post_export_metrics.calibration_plot_and_prediction_histogram( num_buckets=2) ]) extractors = [ legacy_predict_extractor.PredictExtractor( eval_shared_model, eval_config=eval_config), unbatch_extractor.UnbatchExtractor(), slice_key_extractor.SliceKeyExtractor( eval_config=eval_config, materialize=False) ] evaluators = [ legacy_metrics_and_plots_evaluator.MetricsAndPlotsEvaluator( eval_shared_model) ] output_paths = { constants.METRICS_KEY: metrics_file, constants.PLOTS_KEY: plots_file } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, add_metrics_callbacks=eval_shared_model.add_metrics_callbacks, output_file_format=output_file_format) ] tfx_io = raw_tf_record.RawBeamRecordTFXIO( physical_format='inmemory', raw_record_column_name=constants.ARROW_INPUT_COLUMN, telemetry_descriptors=['TFMATest']) with beam.Pipeline() as pipeline: example1 = self._makeExample(prediction=0.0, label=1.0, country='US') example2 = self._makeExample(prediction=1.0, label=1.0, country='CA') _ = ( pipeline | 'Create' >> beam.Create([ example1.SerializeToString(), example2.SerializeToString(), ]) | 'BatchExamples' >> tfx_io.BeamSource() | 'ExtractEvaluateAndWriteResults' >> model_eval_lib.ExtractEvaluateAndWriteResults( eval_config=eval_config, eval_shared_model=eval_shared_model, extractors=extractors, evaluators=evaluators, writers=writers)) slice_keys_filter = [slicer.SingleSliceSpec(features=[('prediction', 0)])] expected_metrics_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: "prediction" float_value: 0 } } metrics { key: "average_loss" value { double_value { value: 1.0 } } } metrics { key: "post_export_metrics/example_count" value { double_value { value: 1.0 } } } """, metrics_for_slice_pb2.MetricsForSlice()) metric_records = list( metrics_plots_and_validations_writer.load_and_deserialize_metrics( metrics_file, output_file_format, slice_keys_filter)) self.assertLen(metric_records, 1, 'metrics: %s' % metric_records) self.assertProtoEquals(expected_metrics_for_slice, metric_records[0]) expected_plots_for_slice = text_format.Parse( """ slice_key { single_slice_keys { column: "prediction" float_value: 0 } } plots { key: "post_export_metrics" value { calibration_histogram_buckets { buckets { lower_threshold_inclusive: -inf num_weighted_examples {} total_weighted_label {} total_weighted_refined_prediction {} } buckets { upper_threshold_exclusive: 0.5 num_weighted_examples { value: 1.0 } total_weighted_label { value: 1.0 } total_weighted_refined_prediction {} } buckets { lower_threshold_inclusive: 0.5 upper_threshold_exclusive: 1.0 num_weighted_examples { } total_weighted_label {} total_weighted_refined_prediction {} } buckets { lower_threshold_inclusive: 1.0 upper_threshold_exclusive: inf num_weighted_examples { value: 0.0 } total_weighted_label { value: 0.0 } total_weighted_refined_prediction { value: 0.0 } } } } } """, metrics_for_slice_pb2.PlotsForSlice()) plot_records = list( metrics_plots_and_validations_writer.load_and_deserialize_plots( plots_file, output_file_format, slice_keys_filter)) self.assertLen(plot_records, 1, 'plots: %s' % plot_records) self.assertProtoEquals(expected_plots_for_slice, plot_records[0]) @parameterized.named_parameters(_OUTPUT_FORMAT_PARAMS) def testWriteAttributions(self, output_file_format): attributions_file = os.path.join(self._getTempDir(), 'attributions') eval_config = config_pb2.EvalConfig( model_specs=[config_pb2.ModelSpec()], metrics_specs=[ config_pb2.MetricsSpec(metrics=[ config_pb2.MetricConfig(class_name=attributions .TotalAttributions().__class__.__name__) ]) ], options=config_pb2.Options( disabled_outputs={'values': ['eval_config.json']})) extractors = [slice_key_extractor.SliceKeyExtractor()] evaluators = [ metrics_plots_and_validations_evaluator .MetricsPlotsAndValidationsEvaluator(eval_config=eval_config) ] output_paths = { constants.ATTRIBUTIONS_KEY: attributions_file, } writers = [ metrics_plots_and_validations_writer.MetricsPlotsAndValidationsWriter( output_paths, eval_config=eval_config, output_file_format=output_file_format) ] example1 = { 'features': {}, 'attributions': { 'feature1': 1.1, 'feature2': 1.2 } } example2 = { 'features': {}, 'attributions': { 'feature1': 2.1, 'feature2': 2.2 } } example3 = { 'features': {}, 'attributions': { 'feature1': np.array([3.1]), 'feature2': np.array([3.2]) } } with beam.Pipeline() as pipeline: _ = ( pipeline | 'Create' >> beam.Create([example1, example2, example3]) | 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate( extractors=extractors, evaluators=evaluators) | 'WriteResults' >> model_eval_lib.WriteResults(writers=writers)) expected_attributions_for_slice = text_format.Parse( """ slice_key {} attributions_keys_and_values { key { name: "total_attributions" } values { key: "feature1" value: { double_value { value: 6.3 } } } values { key: "feature2" value: { double_value { value: 6.6 } } } }""", metrics_for_slice_pb2.AttributionsForSlice()) attribution_records = list( metrics_plots_and_validations_writer.load_and_deserialize_attributions( attributions_file, output_file_format)) self.assertLen(attribution_records, 1) self.assertProtoEquals(expected_attributions_for_slice, attribution_records[0]) if __name__ == '__main__': tf.compat.v1.enable_v2_behavior() tf.test.main()

true

1c383a645bd26f9fa6a5955ec222204a3829f31e

5,276

py

Python

tests/filters/path_filter.py

pyllyukko/plaso

7533db2d1035ca71d264d6281ebd5db2d073c587

[ "Apache-2.0" ]

1,253

2015-01-02T13:58:02.000Z

2022-03-31T08:43:39.000Z

tests/filters/path_filter.py

pyllyukko/plaso

7533db2d1035ca71d264d6281ebd5db2d073c587

[ "Apache-2.0" ]

3,388

2015-01-02T11:17:58.000Z

2022-03-30T10:21:45.000Z

tests/filters/path_filter.py

pyllyukko/plaso

7533db2d1035ca71d264d6281ebd5db2d073c587

[ "Apache-2.0" ]

376

2015-01-20T07:04:54.000Z

2022-03-04T23:53:00.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """Tests for the scan tree-based path filter.""" import unittest from plaso.filters import path_filter from tests.filters import test_lib class PathFilterScanTreeTest(test_lib.FilterTestCase): """Tests for the path filter scan tree.""" # pylint: disable=protected-access def testInitialize(self): """Tests the initialize function.""" scan_tree = path_filter.PathFilterScanTree([]) self.assertIsNone(scan_tree._root_node) paths = [ 'HKEY_CURRENT_USER\\Software\\WinRAR\\ArcHistory', 'HKEY_CURRENT_USER\\Software\\WinRAR\\DialogEditHistory\\ArcName', 'HKEY_CURRENT_USER\\Software\\WinRAR\\DialogEditHistory\\ExtrPath', ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{FA99DFC7-6AC2-453A-A5E2-5E2AFF4507BD}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{F4E57C4B-2036-45F0-A9AB-443BCFE33D9F}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{F2A1CB5A-E3CC-4A2E-AF9D-505A7009D442}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{CEBFF5CD-ACE2-4F4F-9178-9926F41749EA}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{CAA59E3C-4792-41A5-9909-6A6A8D32490E}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{B267E3AD-A825-4A09-82B9-EEC22AA3B847}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{A3D53349-6E61-4557-8FC7-0028EDCEEBF6}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{9E04CAB2-CC14-11DF-BB8C-A2F1DED72085}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{75048700-EF1F-11D0-9888-006097DEACF9}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{5E6AB780-7743-11CF-A12B-00AA004AE837}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{0D6D4F41-2994-4BA0-8FEF-620E43CD2812}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{BCB48336-4DDD-48FF-BB0B-D3190DACB3E2}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Internet Explorer\\' 'TypedURLs'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\TypedPaths'), ('HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatibility'), ('HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache'), ('HKEY_LOCAL_MACHINE\\Software\\Microsoft\\Windows NT\\' 'CurrentVersion'), 'HKEY_LOCAL_MACHINE\\SAM\\Domains\\Account\\Users', ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Internet Settings\\Lockdown_Zones'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Internet Settings\\Zones'), ('HKEY_LOCAL_MACHINE\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Internet Settings\\Lockdown_Zones'), ('HKEY_LOCAL_MACHINE\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Internet Settings\\Zones'), ] scan_tree = path_filter.PathFilterScanTree( paths, path_segment_separator='\\') self.assertIsNotNone(scan_tree._root_node) path = 'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\Windows' self.assertFalse(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache') self.assertTrue(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache') self.assertFalse(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE/System/CurrentControlSet/Control/' 'Session Manager/AppCompatCache') self.assertTrue(scan_tree.CheckPath(path, path_segment_separator='/')) scan_tree = path_filter.PathFilterScanTree( paths, case_sensitive=False, path_segment_separator='\\') self.assertIsNotNone(scan_tree._root_node) path = 'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\Windows' self.assertFalse(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache') self.assertTrue(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache') self.assertTrue(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE/System/CurrentControlSet/Control/' 'Session Manager/AppCompatCache') self.assertTrue(scan_tree.CheckPath(path, path_segment_separator='/')) if __name__ == '__main__': unittest.main()

44.711864

77

0.69674

import unittest from plaso.filters import path_filter from tests.filters import test_lib class PathFilterScanTreeTest(test_lib.FilterTestCase): def testInitialize(self): scan_tree = path_filter.PathFilterScanTree([]) self.assertIsNone(scan_tree._root_node) paths = [ 'HKEY_CURRENT_USER\\Software\\WinRAR\\ArcHistory', 'HKEY_CURRENT_USER\\Software\\WinRAR\\DialogEditHistory\\ArcName', 'HKEY_CURRENT_USER\\Software\\WinRAR\\DialogEditHistory\\ExtrPath', ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{FA99DFC7-6AC2-453A-A5E2-5E2AFF4507BD}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{F4E57C4B-2036-45F0-A9AB-443BCFE33D9F}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{F2A1CB5A-E3CC-4A2E-AF9D-505A7009D442}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{CEBFF5CD-ACE2-4F4F-9178-9926F41749EA}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{CAA59E3C-4792-41A5-9909-6A6A8D32490E}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{B267E3AD-A825-4A09-82B9-EEC22AA3B847}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{A3D53349-6E61-4557-8FC7-0028EDCEEBF6}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{9E04CAB2-CC14-11DF-BB8C-A2F1DED72085}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{75048700-EF1F-11D0-9888-006097DEACF9}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{5E6AB780-7743-11CF-A12B-00AA004AE837}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{0D6D4F41-2994-4BA0-8FEF-620E43CD2812}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\UserAssist\\{BCB48336-4DDD-48FF-BB0B-D3190DACB3E2}'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Internet Explorer\\' 'TypedURLs'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Explorer\\TypedPaths'), ('HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatibility'), ('HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache'), ('HKEY_LOCAL_MACHINE\\Software\\Microsoft\\Windows NT\\' 'CurrentVersion'), 'HKEY_LOCAL_MACHINE\\SAM\\Domains\\Account\\Users', ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Internet Settings\\Lockdown_Zones'), ('HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Internet Settings\\Zones'), ('HKEY_LOCAL_MACHINE\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Internet Settings\\Lockdown_Zones'), ('HKEY_LOCAL_MACHINE\\Software\\Microsoft\\Windows\\CurrentVersion\\' 'Internet Settings\\Zones'), ] scan_tree = path_filter.PathFilterScanTree( paths, path_segment_separator='\\') self.assertIsNotNone(scan_tree._root_node) path = 'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\Windows' self.assertFalse(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache') self.assertTrue(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache') self.assertFalse(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE/System/CurrentControlSet/Control/' 'Session Manager/AppCompatCache') self.assertTrue(scan_tree.CheckPath(path, path_segment_separator='/')) scan_tree = path_filter.PathFilterScanTree( paths, case_sensitive=False, path_segment_separator='\\') self.assertIsNotNone(scan_tree._root_node) path = 'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\Windows' self.assertFalse(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache') self.assertTrue(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Control\\' 'Session Manager\\AppCompatCache') self.assertTrue(scan_tree.CheckPath(path)) path = ( 'HKEY_LOCAL_MACHINE/System/CurrentControlSet/Control/' 'Session Manager/AppCompatCache') self.assertTrue(scan_tree.CheckPath(path, path_segment_separator='/')) if __name__ == '__main__': unittest.main()

true

1c383c031f1ec6570de8548f7cf0b056ff16dca3

1,000

py

Python

face.py

yahya-idriss/Python-Personal-assistant

97ebac124441f3fbcb6d74a4e33da14e4632afc0

[ "MIT" ]

null

face.py

yahya-idriss/Python-Personal-assistant

97ebac124441f3fbcb6d74a4e33da14e4632afc0

[ "MIT" ]

null

face.py

yahya-idriss/Python-Personal-assistant

97ebac124441f3fbcb6d74a4e33da14e4632afc0

[ "MIT" ]

null

#!/usr/bin/env python import os import cv2 import numpy as np import sqlite3 from function import function class face_detect(): def __init__(self): self.cam = cv2.VideoCapture(0) self.detector=cv2.CascadeClassifier("haarcascade_frontalface_default.xml") server = function() self.id = server.get_last_id() def new(self): Id=str(self.id+1) sampleNum=0 while(True): ret, img = self.cam.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = self.detector.detectMultiScale(gray, 1.3, 5) for (x,y,w,h) in faces: cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) sampleNum=sampleNum+1 cv2.imwrite("dataSet/User."+Id +'.'+ str(sampleNum) + ".jpg", gray[y:y+h,x:x+w]) # cv2.imshow('frame',img) if cv2.waitKey(100) & 0xFF == ord('q'): break elif sampleNum>20: break self.cam.release() cv2.destroyAllWindows()

25

94

0.583

import os import cv2 import numpy as np import sqlite3 from function import function class face_detect(): def __init__(self): self.cam = cv2.VideoCapture(0) self.detector=cv2.CascadeClassifier("haarcascade_frontalface_default.xml") server = function() self.id = server.get_last_id() def new(self): Id=str(self.id+1) sampleNum=0 while(True): ret, img = self.cam.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = self.detector.detectMultiScale(gray, 1.3, 5) for (x,y,w,h) in faces: cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) sampleNum=sampleNum+1 cv2.imwrite("dataSet/User."+Id +'.'+ str(sampleNum) + ".jpg", gray[y:y+h,x:x+w]) cv2.imshow('frame',img) if cv2.waitKey(100) & 0xFF == ord('q'): break elif sampleNum>20: break self.cam.release() cv2.destroyAllWindows()

true

1c383c2418e352a46d37f1383de7f35a738196ea

190

py

Python

grid_tuple.py

ErnestoPena/Game-Of-Life.-Python

046bca4ae1c69e08efed4f45057b097653410ec8

[ "MIT" ]

null

grid_tuple.py

ErnestoPena/Game-Of-Life.-Python

046bca4ae1c69e08efed4f45057b097653410ec8

[ "MIT" ]

null

grid_tuple.py

ErnestoPena/Game-Of-Life.-Python

046bca4ae1c69e08efed4f45057b097653410ec8

[ "MIT" ]

null

import random def construct_main_tuple(): grid_list = [] for y in range(0,100): for x in range(0,100): grid_list.append([x*10, y*10, False]) return grid_list

23.75

49

0.605263

import random def construct_main_tuple(): grid_list = [] for y in range(0,100): for x in range(0,100): grid_list.append([x*10, y*10, False]) return grid_list

true

1c383d4343abe437b12bcd988019af09a77e5979

3,664

py

Python

tests/components/garmin_connect/test_config_flow.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

4

2020-07-29T17:47:10.000Z

2020-09-16T13:39:13.000Z

tests/components/garmin_connect/test_config_flow.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

6

2020-11-08T19:40:10.000Z

2022-03-01T11:11:07.000Z

tests/components/garmin_connect/test_config_flow.py

klauern/home-assistant-core

c18ba6aec0627e6afb6442c678edb5ff2bb17db6

[ "Apache-2.0" ]

5

2020-03-29T00:29:13.000Z

2021-09-06T20:58:40.000Z

"""Test the Garmin Connect config flow.""" from garminconnect import ( GarminConnectAuthenticationError, GarminConnectConnectionError, GarminConnectTooManyRequestsError, ) import pytest from homeassistant import data_entry_flow from homeassistant.components.garmin_connect.const import DOMAIN from homeassistant.const import CONF_ID, CONF_PASSWORD, CONF_USERNAME from tests.async_mock import patch from tests.common import MockConfigEntry MOCK_CONF = { CONF_ID: "First Lastname", CONF_USERNAME: "my@email.address", CONF_PASSWORD: "mypassw0rd", } @pytest.fixture(name="mock_garmin_connect") def mock_garmin(): """Mock Garmin.""" with patch("homeassistant.components.garmin_connect.config_flow.Garmin",) as garmin: garmin.return_value.get_full_name.return_value = MOCK_CONF[CONF_ID] yield garmin.return_value async def test_show_form(hass): """Test that the form is served with no input.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"} ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["step_id"] == "user" async def test_step_user(hass, mock_garmin_connect): """Test registering an integration and finishing flow works.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_CREATE_ENTRY assert result["data"] == MOCK_CONF async def test_connection_error(hass, mock_garmin_connect): """Test for connection error.""" mock_garmin_connect.login.side_effect = GarminConnectConnectionError("errormsg") result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {"base": "cannot_connect"} async def test_authentication_error(hass, mock_garmin_connect): """Test for authentication error.""" mock_garmin_connect.login.side_effect = GarminConnectAuthenticationError("errormsg") result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {"base": "invalid_auth"} async def test_toomanyrequest_error(hass, mock_garmin_connect): """Test for toomanyrequests error.""" mock_garmin_connect.login.side_effect = GarminConnectTooManyRequestsError( "errormsg" ) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {"base": "too_many_requests"} async def test_unknown_error(hass, mock_garmin_connect): """Test for unknown error.""" mock_garmin_connect.login.side_effect = Exception result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {"base": "unknown"} async def test_abort_if_already_setup(hass, mock_garmin_connect): """Test abort if already setup.""" entry = MockConfigEntry(domain=DOMAIN, data=MOCK_CONF, unique_id=MOCK_CONF[CONF_ID]) entry.add_to_hass(hass) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result["reason"] == "already_configured"

36.64

88

0.728985

from garminconnect import ( GarminConnectAuthenticationError, GarminConnectConnectionError, GarminConnectTooManyRequestsError, ) import pytest from homeassistant import data_entry_flow from homeassistant.components.garmin_connect.const import DOMAIN from homeassistant.const import CONF_ID, CONF_PASSWORD, CONF_USERNAME from tests.async_mock import patch from tests.common import MockConfigEntry MOCK_CONF = { CONF_ID: "First Lastname", CONF_USERNAME: "my@email.address", CONF_PASSWORD: "mypassw0rd", } @pytest.fixture(name="mock_garmin_connect") def mock_garmin(): with patch("homeassistant.components.garmin_connect.config_flow.Garmin",) as garmin: garmin.return_value.get_full_name.return_value = MOCK_CONF[CONF_ID] yield garmin.return_value async def test_show_form(hass): result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"} ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["step_id"] == "user" async def test_step_user(hass, mock_garmin_connect): result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_CREATE_ENTRY assert result["data"] == MOCK_CONF async def test_connection_error(hass, mock_garmin_connect): mock_garmin_connect.login.side_effect = GarminConnectConnectionError("errormsg") result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {"base": "cannot_connect"} async def test_authentication_error(hass, mock_garmin_connect): mock_garmin_connect.login.side_effect = GarminConnectAuthenticationError("errormsg") result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {"base": "invalid_auth"} async def test_toomanyrequest_error(hass, mock_garmin_connect): mock_garmin_connect.login.side_effect = GarminConnectTooManyRequestsError( "errormsg" ) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {"base": "too_many_requests"} async def test_unknown_error(hass, mock_garmin_connect): mock_garmin_connect.login.side_effect = Exception result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_FORM assert result["errors"] == {"base": "unknown"} async def test_abort_if_already_setup(hass, mock_garmin_connect): entry = MockConfigEntry(domain=DOMAIN, data=MOCK_CONF, unique_id=MOCK_CONF[CONF_ID]) entry.add_to_hass(hass) result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": "user"}, data=MOCK_CONF ) assert result["type"] == data_entry_flow.RESULT_TYPE_ABORT assert result["reason"] == "already_configured"

true

1c383d5095d9c1f64070cb21511e8a323a60d196

1,832

py

Python

ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py

tspen/MLOpsPython

dee07128e92c37ed6105938825530277672c8339

[ "MIT" ]

null

ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py

tspen/MLOpsPython

dee07128e92c37ed6105938825530277672c8339

[ "MIT" ]

null

ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py

tspen/MLOpsPython

dee07128e92c37ed6105938825530277672c8339

[ "MIT" ]

null

from azureml.pipeline.steps import PythonScriptStep from azureml.pipeline.core import Pipeline from azureml.core import Workspace from azureml.core.runconfig import RunConfiguration, CondaDependencies from ml_service.util.attach_compute import get_compute from ml_service.util.env_variables import Env def main(): e = Env() # Get Azure machine learning workspace aml_workspace = Workspace.get( name=e.workspace_name, subscription_id=e.subscription_id, resource_group=e.resource_group ) print("get_workspace:") print(aml_workspace) # Get Azure machine learning cluster aml_compute = get_compute( aml_workspace, e.compute_name, e.vm_size) if aml_compute is not None: print("aml_compute:") print(aml_compute) # Create a run configuration environment conda_deps_file = "diabetes_regression/training_dependencies.yml" conda_deps = CondaDependencies(conda_deps_file) run_config = RunConfiguration(conda_dependencies=conda_deps) run_config.environment.docker.enabled = True train_step = PythonScriptStep( name="Train Model", script_name="train_with_r.py", compute_target=aml_compute, source_directory="diabetes_regression/training/R", runconfig=run_config, allow_reuse=False, ) print("Step Train created") steps = [train_step] train_pipeline = Pipeline(workspace=aml_workspace, steps=steps) train_pipeline.validate() published_pipeline = train_pipeline.publish( name=e.pipeline_name, description="Model training/retraining pipeline", version=e.build_id ) print(f'Published pipeline: {published_pipeline.name}') print(f'for build {published_pipeline.version}') if __name__ == '__main__': main()

30.533333

70

0.718341

from azureml.pipeline.steps import PythonScriptStep from azureml.pipeline.core import Pipeline from azureml.core import Workspace from azureml.core.runconfig import RunConfiguration, CondaDependencies from ml_service.util.attach_compute import get_compute from ml_service.util.env_variables import Env def main(): e = Env() aml_workspace = Workspace.get( name=e.workspace_name, subscription_id=e.subscription_id, resource_group=e.resource_group ) print("get_workspace:") print(aml_workspace) aml_compute = get_compute( aml_workspace, e.compute_name, e.vm_size) if aml_compute is not None: print("aml_compute:") print(aml_compute) conda_deps_file = "diabetes_regression/training_dependencies.yml" conda_deps = CondaDependencies(conda_deps_file) run_config = RunConfiguration(conda_dependencies=conda_deps) run_config.environment.docker.enabled = True train_step = PythonScriptStep( name="Train Model", script_name="train_with_r.py", compute_target=aml_compute, source_directory="diabetes_regression/training/R", runconfig=run_config, allow_reuse=False, ) print("Step Train created") steps = [train_step] train_pipeline = Pipeline(workspace=aml_workspace, steps=steps) train_pipeline.validate() published_pipeline = train_pipeline.publish( name=e.pipeline_name, description="Model training/retraining pipeline", version=e.build_id ) print(f'Published pipeline: {published_pipeline.name}') print(f'for build {published_pipeline.version}') if __name__ == '__main__': main()

true

1c383e37261953fcc99197b12f66d9f5d4fdffcd

13,484

py

Python

manila/tests/share/drivers/quobyte/test_quobyte.py

nidhimittalhada/access_group_repo

62f3365bc5fb728fcca692a9b3977690fabcd78f

[ "Apache-2.0" ]

1

2015-05-28T22:28:08.000Z

manila/tests/share/drivers/quobyte/test_quobyte.py

nidhimittalhada/access_group_repo

62f3365bc5fb728fcca692a9b3977690fabcd78f

[ "Apache-2.0" ]

5

2015-08-13T15:17:28.000Z

2016-08-02T02:55:01.000Z

manila/tests/share/drivers/quobyte/test_quobyte.py

nidhimittalhada/access_group_repo

62f3365bc5fb728fcca692a9b3977690fabcd78f

[ "Apache-2.0" ]

2

2015-08-29T08:19:58.000Z

2016-08-02T02:46:10.000Z

# Copyright (c) 2015 Quobyte, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock from oslo_config import cfg import six from manila import context from manila import exception from manila.share import configuration as config from manila.share import driver from manila.share.drivers.quobyte import jsonrpc from manila.share.drivers.quobyte import quobyte from manila import test from manila.tests import fake_share CONF = cfg.CONF def fake_rpc_handler(name, *args): if name == 'resolveVolumeName': return None elif name == 'createVolume': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': 'fake_location', 'nfs_export_path': '/fake_share'} class QuobyteShareDriverTestCase(test.TestCase): """Tests QuobyteShareDriver.""" def setUp(self): super(QuobyteShareDriverTestCase, self).setUp() self._context = context.get_admin_context() CONF.set_default('driver_handles_share_servers', False) self.fake_conf = config.Configuration(None) self._driver = quobyte.QuobyteShareDriver(configuration=self.fake_conf) self._driver.rpc = mock.Mock() self.share = fake_share.fake_share(share_proto='NFS') self.access = fake_share.fake_access() @mock.patch('manila.share.drivers.quobyte.jsonrpc.JsonRpc', mock.Mock()) def test_do_setup_success(self): self._driver.rpc.call = mock.Mock(return_value=None) self._driver.do_setup(self._context) self._driver.rpc.call.assert_called_with('getInformation', {}) @mock.patch('manila.share.drivers.quobyte.jsonrpc.JsonRpc.__init__', mock.Mock(return_value=None)) @mock.patch.object(jsonrpc.JsonRpc, 'call', side_effect=exception.QBRpcException) def test_do_setup_failure(self, mock_call): self.assertRaises(exception.QBException, self._driver.do_setup, self._context) def test_create_share_new_volume(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) result = self._driver.create_share(self._context, self.share) self.assertEqual(self.share['export_location'], result) self._driver.rpc.call.assert_has_calls([ mock.call('createVolume', dict( name=self.share['name'], tenant_domain=self.share['project_id'], root_user_id=self.fake_conf.quobyte_default_volume_user, root_group_id=self.fake_conf.quobyte_default_volume_group, configuration_name=self.fake_conf.quobyte_volume_configuration )), mock.call('exportVolume', dict(protocol='NFS', volume_uuid='voluuid'))]) def test_create_share_existing_volume(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self._driver.create_share(self._context, self.share) self._driver.rpc.call.assert_called_with( 'exportVolume', dict(protocol='NFS', volume_uuid='voluuid')) def test_create_share_wrong_protocol(self): share = {'share_proto': 'WRONG_PROTOCOL'} self.assertRaises(exception.QBException, self._driver.create_share, context=None, share=share) def test_delete_share_existing_volume(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {} self._driver.configuration.quobyte_delete_shares = True self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) self._driver.rpc.call.assert_has_calls([ mock.call('resolveVolumeName', {'volume_name': 'fakename', 'tenant_domain': 'fake_project_uuid'}), mock.call('deleteVolume', {'volume_uuid': 'voluuid'}), mock.call('exportVolume', {'volume_uuid': 'voluuid', 'remove_export': True})]) def test_delete_share_existing_volume_disabled(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {} CONF.set_default('quobyte_delete_shares', False) self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'remove_export': True}) @mock.patch.object(quobyte.LOG, 'warning') def test_delete_share_nonexisting_volume(self, mock_warning): def rpc_handler(name, *args): if name == 'resolveVolumeName': return None self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) mock_warning.assert_called_with( 'No volume found for share fake_project_uuid/fakename') def test_allow_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.allow_access(self._context, self.share, self.access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'read_only': False, 'add_allow_ip': '10.0.0.1'}) def test_allow_ro_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) ro_access = fake_share.fake_access(access_level='ro') self._driver.allow_access(self._context, self.share, ro_access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'read_only': True, 'add_allow_ip': '10.0.0.1'}) def test_allow_access_nonip(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.access = fake_share.fake_access(**{"access_type": "non_existant_access_type"}) self.assertRaises(exception.InvalidShareAccess, self._driver.allow_access, self._context, self.share, self.access) def test_deny_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.deny_access(self._context, self.share, self.access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'remove_allow_ip': '10.0.0.1'}) @mock.patch.object(quobyte.LOG, 'debug') def test_deny_access_nonip(self, mock_debug): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.access = fake_share.fake_access( access_type="non_existant_access_type") self._driver.deny_access(self._context, self.share, self.access) mock_debug.assert_called_with( 'Quobyte driver only supports ip access control. ' 'Ignoring deny access call for %s , %s', 'fakename', 'fake_project_uuid') def test_resolve_volume_name(self): self._driver.rpc.call = mock.Mock( return_value={'volume_uuid': 'fake_uuid'}) self._driver._resolve_volume_name('fake_vol_name', 'fake_domain_name') self._driver.rpc.call.assert_called_with( 'resolveVolumeName', {'volume_name': 'fake_vol_name', 'tenant_domain': 'fake_domain_name'}) def test_resolve_volume_name_NOENT(self): self._driver.rpc.call = mock.Mock( return_value=None) self.assertIsNone( self._driver._resolve_volume_name('fake_vol_name', 'fake_domain_name')) def test_resolve_volume_name_other_error(self): self._driver.rpc.call = mock.Mock( side_effect=exception.QBRpcException( result='fubar', qbcode=666)) self.assertRaises(exception.QBRpcException, self._driver._resolve_volume_name, volume_name='fake_vol_name', tenant_domain='fake_domain_name') @mock.patch.object(driver.ShareDriver, '_update_share_stats') def test_update_share_stats(self, mock_uss): self._driver._get_capacities = mock.Mock(return_value=[42, 23]) self._driver._update_share_stats() mock_uss.assert_called_once_with( dict(storage_protocol='NFS', vendor_name='Quobyte', share_backend_name=self._driver.backend_name, driver_version=self._driver.DRIVER_VERSION, total_capacity_gb=42, free_capacity_gb=23, reserved_percentage=0)) def test_get_capacities_gb(self): capval = 42115548133 useval = 19695128917 self._driver.rpc.call = mock.Mock( return_value={'total_logical_capacity': six.text_type(capval), 'total_logical_usage': six.text_type(useval)}) self.assertEqual((39.223160718, 20.880642548), self._driver._get_capacities()) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value="fake_uuid") def test_ensure_share(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) result = self._driver.ensure_share(self._context, self.share, None) self.assertEqual(self.share["export_location"], result) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) self._driver.rpc.call.assert_has_calls([ mock.call('exportVolume', dict( volume_uuid="fake_uuid", protocol='NFS' ))]) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value=None) def test_ensure_deleted_share(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.assertRaises(exception.ShareResourceNotFound, self._driver.ensure_share, self._context, self.share, None) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) @mock.patch.object(quobyte.QuobyteShareDriver, "_resize_share") def test_extend_share(self, mock_qsd_resize_share): self._driver.extend_share(ext_share=self.share, ext_size=2, share_server=None) mock_qsd_resize_share.assert_called_once_with(share=self.share, new_size=2) def test_resize_share(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self._driver._resize_share(share=self.share, new_size=7) self._driver.rpc.call.assert_has_calls([ mock.call('setQuota', {"consumer": {"type": 3, "identifier": self.share["name"]}, "limits": {"type": 5, "value": 7}})]) @mock.patch.object(quobyte.QuobyteShareDriver, "_resize_share") def test_shrink_share(self, mock_qsd_resize_share): self._driver.shrink_share(shrink_share=self.share, shrink_size=3, share_server=None) mock_qsd_resize_share.assert_called_once_with(share=self.share, new_size=3)

39.426901

79

0.610427

import mock from oslo_config import cfg import six from manila import context from manila import exception from manila.share import configuration as config from manila.share import driver from manila.share.drivers.quobyte import jsonrpc from manila.share.drivers.quobyte import quobyte from manila import test from manila.tests import fake_share CONF = cfg.CONF def fake_rpc_handler(name, *args): if name == 'resolveVolumeName': return None elif name == 'createVolume': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': 'fake_location', 'nfs_export_path': '/fake_share'} class QuobyteShareDriverTestCase(test.TestCase): def setUp(self): super(QuobyteShareDriverTestCase, self).setUp() self._context = context.get_admin_context() CONF.set_default('driver_handles_share_servers', False) self.fake_conf = config.Configuration(None) self._driver = quobyte.QuobyteShareDriver(configuration=self.fake_conf) self._driver.rpc = mock.Mock() self.share = fake_share.fake_share(share_proto='NFS') self.access = fake_share.fake_access() @mock.patch('manila.share.drivers.quobyte.jsonrpc.JsonRpc', mock.Mock()) def test_do_setup_success(self): self._driver.rpc.call = mock.Mock(return_value=None) self._driver.do_setup(self._context) self._driver.rpc.call.assert_called_with('getInformation', {}) @mock.patch('manila.share.drivers.quobyte.jsonrpc.JsonRpc.__init__', mock.Mock(return_value=None)) @mock.patch.object(jsonrpc.JsonRpc, 'call', side_effect=exception.QBRpcException) def test_do_setup_failure(self, mock_call): self.assertRaises(exception.QBException, self._driver.do_setup, self._context) def test_create_share_new_volume(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) result = self._driver.create_share(self._context, self.share) self.assertEqual(self.share['export_location'], result) self._driver.rpc.call.assert_has_calls([ mock.call('createVolume', dict( name=self.share['name'], tenant_domain=self.share['project_id'], root_user_id=self.fake_conf.quobyte_default_volume_user, root_group_id=self.fake_conf.quobyte_default_volume_group, configuration_name=self.fake_conf.quobyte_volume_configuration )), mock.call('exportVolume', dict(protocol='NFS', volume_uuid='voluuid'))]) def test_create_share_existing_volume(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self._driver.create_share(self._context, self.share) self._driver.rpc.call.assert_called_with( 'exportVolume', dict(protocol='NFS', volume_uuid='voluuid')) def test_create_share_wrong_protocol(self): share = {'share_proto': 'WRONG_PROTOCOL'} self.assertRaises(exception.QBException, self._driver.create_share, context=None, share=share) def test_delete_share_existing_volume(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {} self._driver.configuration.quobyte_delete_shares = True self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) self._driver.rpc.call.assert_has_calls([ mock.call('resolveVolumeName', {'volume_name': 'fakename', 'tenant_domain': 'fake_project_uuid'}), mock.call('deleteVolume', {'volume_uuid': 'voluuid'}), mock.call('exportVolume', {'volume_uuid': 'voluuid', 'remove_export': True})]) def test_delete_share_existing_volume_disabled(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {} CONF.set_default('quobyte_delete_shares', False) self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'remove_export': True}) @mock.patch.object(quobyte.LOG, 'warning') def test_delete_share_nonexisting_volume(self, mock_warning): def rpc_handler(name, *args): if name == 'resolveVolumeName': return None self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) mock_warning.assert_called_with( 'No volume found for share fake_project_uuid/fakename') def test_allow_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.allow_access(self._context, self.share, self.access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'read_only': False, 'add_allow_ip': '10.0.0.1'}) def test_allow_ro_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) ro_access = fake_share.fake_access(access_level='ro') self._driver.allow_access(self._context, self.share, ro_access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'read_only': True, 'add_allow_ip': '10.0.0.1'}) def test_allow_access_nonip(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.access = fake_share.fake_access(**{"access_type": "non_existant_access_type"}) self.assertRaises(exception.InvalidShareAccess, self._driver.allow_access, self._context, self.share, self.access) def test_deny_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.deny_access(self._context, self.share, self.access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'remove_allow_ip': '10.0.0.1'}) @mock.patch.object(quobyte.LOG, 'debug') def test_deny_access_nonip(self, mock_debug): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.access = fake_share.fake_access( access_type="non_existant_access_type") self._driver.deny_access(self._context, self.share, self.access) mock_debug.assert_called_with( 'Quobyte driver only supports ip access control. ' 'Ignoring deny access call for %s , %s', 'fakename', 'fake_project_uuid') def test_resolve_volume_name(self): self._driver.rpc.call = mock.Mock( return_value={'volume_uuid': 'fake_uuid'}) self._driver._resolve_volume_name('fake_vol_name', 'fake_domain_name') self._driver.rpc.call.assert_called_with( 'resolveVolumeName', {'volume_name': 'fake_vol_name', 'tenant_domain': 'fake_domain_name'}) def test_resolve_volume_name_NOENT(self): self._driver.rpc.call = mock.Mock( return_value=None) self.assertIsNone( self._driver._resolve_volume_name('fake_vol_name', 'fake_domain_name')) def test_resolve_volume_name_other_error(self): self._driver.rpc.call = mock.Mock( side_effect=exception.QBRpcException( result='fubar', qbcode=666)) self.assertRaises(exception.QBRpcException, self._driver._resolve_volume_name, volume_name='fake_vol_name', tenant_domain='fake_domain_name') @mock.patch.object(driver.ShareDriver, '_update_share_stats') def test_update_share_stats(self, mock_uss): self._driver._get_capacities = mock.Mock(return_value=[42, 23]) self._driver._update_share_stats() mock_uss.assert_called_once_with( dict(storage_protocol='NFS', vendor_name='Quobyte', share_backend_name=self._driver.backend_name, driver_version=self._driver.DRIVER_VERSION, total_capacity_gb=42, free_capacity_gb=23, reserved_percentage=0)) def test_get_capacities_gb(self): capval = 42115548133 useval = 19695128917 self._driver.rpc.call = mock.Mock( return_value={'total_logical_capacity': six.text_type(capval), 'total_logical_usage': six.text_type(useval)}) self.assertEqual((39.223160718, 20.880642548), self._driver._get_capacities()) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value="fake_uuid") def test_ensure_share(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) result = self._driver.ensure_share(self._context, self.share, None) self.assertEqual(self.share["export_location"], result) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) self._driver.rpc.call.assert_has_calls([ mock.call('exportVolume', dict( volume_uuid="fake_uuid", protocol='NFS' ))]) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value=None) def test_ensure_deleted_share(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.assertRaises(exception.ShareResourceNotFound, self._driver.ensure_share, self._context, self.share, None) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) @mock.patch.object(quobyte.QuobyteShareDriver, "_resize_share") def test_extend_share(self, mock_qsd_resize_share): self._driver.extend_share(ext_share=self.share, ext_size=2, share_server=None) mock_qsd_resize_share.assert_called_once_with(share=self.share, new_size=2) def test_resize_share(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self._driver._resize_share(share=self.share, new_size=7) self._driver.rpc.call.assert_has_calls([ mock.call('setQuota', {"consumer": {"type": 3, "identifier": self.share["name"]}, "limits": {"type": 5, "value": 7}})]) @mock.patch.object(quobyte.QuobyteShareDriver, "_resize_share") def test_shrink_share(self, mock_qsd_resize_share): self._driver.shrink_share(shrink_share=self.share, shrink_size=3, share_server=None) mock_qsd_resize_share.assert_called_once_with(share=self.share, new_size=3)

true

1c383f0c22546fc778beaef16dcd29f57adc3e00

4,309

py

Python

roles/api/files/scripts/export-fworch-config.py

NilsPur/firewall-orchestrator

1e2dd61abc99b8dc72903abe9ac9c3172420b39f

[ "Apache-2.0" ]

null

roles/api/files/scripts/export-fworch-config.py

NilsPur/firewall-orchestrator

1e2dd61abc99b8dc72903abe9ac9c3172420b39f

[ "Apache-2.0" ]

null

roles/api/files/scripts/export-fworch-config.py

NilsPur/firewall-orchestrator

1e2dd61abc99b8dc72903abe9ac9c3172420b39f

[ "Apache-2.0" ]

null

#!/usr/bin/python3 # export-fworch-config.py: export the full config of the product itself for later import # does not contain any firewall config data, just the device config plus fworch user config import sys, logging import json, requests, requests.packages, argparse base_dir = "/usr/local/fworch" importer_base_dir = base_dir + '/importer' sys.path.append(importer_base_dir) import common, fwo_api parser = argparse.ArgumentParser( description='Export fworch configuration into encrypted json file') parser.add_argument('-o', '--out', metavar='output_file', required=True, help='filename to write output in json format to') parser.add_argument('-u', '--user', metavar='user_name', default='admin', help='username for getting fworch config (default=admin') parser.add_argument('-p', '--password', metavar='password_file', default=base_dir + '/etc/secrets/ui_admin_pwd', help='username for getting fworch config (default=$FWORCH_HOME/etc/secrets/ui_admin_pwd') parser.add_argument('-d', '--debug', metavar='debug_level', default='0', help='Debug Level: 0=off, 1=send debug to console, 2=send debug to file, 3=keep temporary config files; default=0') parser.add_argument('-x', '--proxy', metavar='proxy_string', help='proxy server string to use, e.g. http://1.2.3.4:8080') parser.add_argument('-s', '--ssl', metavar='ssl_verification_mode', default='', help='[ca]certfile, if value not set, ssl check is off"; default=empty/off') args = parser.parse_args() if len(sys.argv) == 1: parser.print_help(sys.stderr) sys.exit(1) fwo_config_filename = base_dir + '/etc/fworch.json' if args.ssl == '' or args.ssl == 'off': requests.packages.urllib3.disable_warnings() # suppress ssl warnings only debug_level = int(args.debug) common.set_log_level(log_level=debug_level, debug_level=debug_level) # read fwo config (API URLs) with open(fwo_config_filename, "r") as fwo_config: fwo_config = json.loads(fwo_config.read()) user_management_api_base_url = fwo_config['middleware_uri'] fwo_api_base_url = fwo_config['api_uri'] method = 'api/AuthenticationToken/Get' ssl_mode = args.ssl # authenticate to get JWT with open(args.password, 'r') as file: exporter_pwd = file.read().replace('\n', '') if 'proxy' in args: jwt = fwo_api.login(args.user, exporter_pwd, user_management_api_base_url, method, ssl_verification=ssl_mode, proxy_string=args.proxy) else: jwt = fwo_api.login(args.user, exporter_pwd, user_management_api_base_url, method, ssl_verification=ssl_mode) config_json = {} # get device details mgm_query = """ query getFullDeviceDetails { management { mgm_id mgm_name ssh_hostname ssh_port ssh_private_key ssh_public_key ssh_user dev_typ_id config_path do_not_import force_initial_import hide_in_gui importer_hostname mgm_comment debug_level mgm_create mgm_update last_import_md5_complete_config } device { dev_id dev_name dev_typ_id mgm_id local_rulebase_name global_rulebase_name package_name dev_comment do_not_import force_initial_import hide_in_gui dev_create dev_update } } """ api_call_result = fwo_api.call(fwo_api_base_url, jwt, mgm_query, query_variables={}, role='admin') if 'data' in api_call_result: config_json.update({ 'device_configuration': api_call_result['data'] }) else: logging.error('did not succeed in getting device details from API') sys.exit(1) # todo: use a single source for fwo_api between this script and importer # todo: use a single source for graphql queries between importer, config im/exporter, C# # todo: get more config data # get user related data: # ldap servers # tenants # uiusers including roles & groups & tenants # todo: encrypt config before writing to file with open(args.out, 'w') as file: file.write(json.dumps(config_json, indent=3)) sys.exit(0)

36.516949

202

0.668369

import sys, logging import json, requests, requests.packages, argparse base_dir = "/usr/local/fworch" importer_base_dir = base_dir + '/importer' sys.path.append(importer_base_dir) import common, fwo_api parser = argparse.ArgumentParser( description='Export fworch configuration into encrypted json file') parser.add_argument('-o', '--out', metavar='output_file', required=True, help='filename to write output in json format to') parser.add_argument('-u', '--user', metavar='user_name', default='admin', help='username for getting fworch config (default=admin') parser.add_argument('-p', '--password', metavar='password_file', default=base_dir + '/etc/secrets/ui_admin_pwd', help='username for getting fworch config (default=$FWORCH_HOME/etc/secrets/ui_admin_pwd') parser.add_argument('-d', '--debug', metavar='debug_level', default='0', help='Debug Level: 0=off, 1=send debug to console, 2=send debug to file, 3=keep temporary config files; default=0') parser.add_argument('-x', '--proxy', metavar='proxy_string', help='proxy server string to use, e.g. http://1.2.3.4:8080') parser.add_argument('-s', '--ssl', metavar='ssl_verification_mode', default='', help='[ca]certfile, if value not set, ssl check is off"; default=empty/off') args = parser.parse_args() if len(sys.argv) == 1: parser.print_help(sys.stderr) sys.exit(1) fwo_config_filename = base_dir + '/etc/fworch.json' if args.ssl == '' or args.ssl == 'off': requests.packages.urllib3.disable_warnings() # suppress ssl warnings only debug_level = int(args.debug) common.set_log_level(log_level=debug_level, debug_level=debug_level) # read fwo config (API URLs) with open(fwo_config_filename, "r") as fwo_config: fwo_config = json.loads(fwo_config.read()) user_management_api_base_url = fwo_config['middleware_uri'] fwo_api_base_url = fwo_config['api_uri'] method = 'api/AuthenticationToken/Get' ssl_mode = args.ssl # authenticate to get JWT with open(args.password, 'r') as file: exporter_pwd = file.read().replace('\n', '') if 'proxy' in args: jwt = fwo_api.login(args.user, exporter_pwd, user_management_api_base_url, method, ssl_verification=ssl_mode, proxy_string=args.proxy) else: jwt = fwo_api.login(args.user, exporter_pwd, user_management_api_base_url, method, ssl_verification=ssl_mode) config_json = {} # get device details mgm_query = """ query getFullDeviceDetails { management { mgm_id mgm_name ssh_hostname ssh_port ssh_private_key ssh_public_key ssh_user dev_typ_id config_path do_not_import force_initial_import hide_in_gui importer_hostname mgm_comment debug_level mgm_create mgm_update last_import_md5_complete_config } device { dev_id dev_name dev_typ_id mgm_id local_rulebase_name global_rulebase_name package_name dev_comment do_not_import force_initial_import hide_in_gui dev_create dev_update } } """ api_call_result = fwo_api.call(fwo_api_base_url, jwt, mgm_query, query_variables={}, role='admin') if 'data' in api_call_result: config_json.update({ 'device_configuration': api_call_result['data'] }) else: logging.error('did not succeed in getting device details from API') sys.exit(1) # todo: use a single source for fwo_api between this script and importer # todo: use a single source for graphql queries between importer, config im/exporter, C# # todo: get more config data # get user related data: # ldap servers # tenants # uiusers including roles & groups & tenants # todo: encrypt config before writing to file with open(args.out, 'w') as file: file.write(json.dumps(config_json, indent=3)) sys.exit(0)

true

1c383f218af36d693de4546a8854dc5ea2264b12

785

py

Python

resource_group.py

gfortil/hpcc-internship

8061771bcb4791fca54e6b1d74f0c019ad69bca4

[ "MIT" ]

null

resource_group.py

gfortil/hpcc-internship

8061771bcb4791fca54e6b1d74f0c019ad69bca4

[ "MIT" ]

null

resource_group.py

gfortil/hpcc-internship

8061771bcb4791fca54e6b1d74f0c019ad69bca4

[ "MIT" ]

1

2021-06-10T22:07:15.000Z

import subprocess from config import region_codes, resource_prefix, spot_region_map def create_resource_group(regions, prefix): for location in regions: resource_group_name = prefix + str(location) command = 'az group create --name {} --location {}'.format(resource_group_name, location) status, result = subprocess.getstatusoutput(command) print(status, result) def delete_resource_group(regions, prefix): for location in regions: resource_group_name = prefix + str(location) command = 'az group delete --name {} --yes'.format(resource_group_name) status, result = subprocess.getstatusoutput(command) print(status, result) if __name__ == '__main__': create_resource_group(region_codes, resource_prefix)

37.380952

98

0.719745

import subprocess from config import region_codes, resource_prefix, spot_region_map def create_resource_group(regions, prefix): for location in regions: resource_group_name = prefix + str(location) command = 'az group create --name {} --location {}'.format(resource_group_name, location) status, result = subprocess.getstatusoutput(command) print(status, result) def delete_resource_group(regions, prefix): for location in regions: resource_group_name = prefix + str(location) command = 'az group delete --name {} --yes'.format(resource_group_name) status, result = subprocess.getstatusoutput(command) print(status, result) if __name__ == '__main__': create_resource_group(region_codes, resource_prefix)

true

1c383f8b680f153f22ac31cf1562d4db70ad8920

5,771

py

Python

pychron/experiment/automated_run/factory_view.py

UManPychron/pychron

b84c9fd70072f9cbda30abe2c471e64fe3dd75d8

[ "Apache-2.0" ]

null

pychron/experiment/automated_run/factory_view.py

UManPychron/pychron

b84c9fd70072f9cbda30abe2c471e64fe3dd75d8

[ "Apache-2.0" ]

11

2015-10-08T14:21:39.000Z

2022-01-26T14:19:21.000Z

pychron/experiment/automated_run/factory_view.py

UManPychron/pychron

b84c9fd70072f9cbda30abe2c471e64fe3dd75d8

[ "Apache-2.0" ]

null

# =============================================================================== # Copyright 2013 Jake Ross # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =============================================================================== # ============= enthought library imports ======================= from traits.api import HasTraits, Instance from traitsui.api import View, Item, VGroup, Spring, HGroup, ButtonEditor from pychron.core.pychron_traits import BorderVGroup from pychron.core.ui.enum_editor import myEnumEditor from pychron.envisage.icon_button_editor import icon_button_editor POSITION_TOOLTIP = '''Set the position for this analysis or group of analyses. Examples: 1. 4 or p4 (goto position 4) 2. 3,4,5 (goto positions 3,4,5. treat as one analysis) 3. 7-12 (goto positions 7,8,9,10,11,12. treat as individual analyses) 4. 7:12 (same as #3) 5. 10:16:2 (goto positions 10,12,14,16. treat as individual analyses) 6. D1 (drill position 1) 7. T1-2 (goto named position T1-2 i.e transect 1, point 2) 8. L3 (trace path L3) 9. 1-6;9;11;15-20 (combination of rules 2. and 3. treat all positions as individual analyses) 10. 1.0,2.0 (goto the point defined by x,y[,z]. Use ";" to treat multiple points as one analysis e.g 1.0,2.0;3.0,4.0) ''' PATTERN_TOOLTIP = 'Select a pattern from Remote or Local Patterns. \ If unsure from which group to choice use a "Remote" pattern' class FactoryView(HasTraits): model = Instance('pychron.experiment.automated_run.factory.AutomatedRunFactory') def trait_context(self): return {'object': self.model} def traits_view(self): v = View(self._get_group()) return v def _get_group(self): sspring = lambda width=17: Spring(springy=False, width=width) extract_grp = BorderVGroup(HGroup(sspring(width=33), Item('extract_value', label='Extract', tooltip='Set the extract value in extract units', enabled_when='extractable'), Item('extract_units', show_label=False, editor=myEnumEditor(name='extract_units_names')), Item('ramp_duration', label='Ramp Dur. (s)'), ), HGroup(Item('use_cdd_warming', label='CDD Warm', tooltip='Use the CDD warming routine at end of measurement'), # Item('collection_time_zero_offset', # label='T_o offset (s)', # tooltip='# of seconds afer inlet opens to set time zero'), Item('overlap', label='Overlap (s)', tooltip='Duration to wait before staring next run')), self._step_heat_group(), HGroup(Item('duration', label='Duration (s)', tooltip='Set the number of seconds to run the extraction device.'), Item('pre_cleanup', label='Pre Cleanup (s)'), Item('cleanup', label='Cleanup (s)', tooltip='Set the number of seconds to getter the sample gas'), Item('post_cleanup', label='Post Cleanup (s)')), HGroup(Item('beam_diameter'), Item('light_value', label='Lighting')), self._position_group(), label='Extract') post_measurement_group = BorderVGroup(Item('delay_after'), label='Post Measurement') grp = VGroup(extract_grp, post_measurement_group) return grp def _position_group(self): grp = HGroup(Item('position', tooltip=POSITION_TOOLTIP), Item('pattern', show_label=False, tooltip=PATTERN_TOOLTIP, editor=myEnumEditor(name='patterns')), Item('edit_pattern', show_label=False, editor=ButtonEditor(label_value='edit_pattern_label'))) return grp def _step_heat_group(self): grp = HGroup(Item('template', label='Step Heat Template', editor=myEnumEditor(name='templates'), show_label=False, ), Item('edit_template', show_label=False, editor=ButtonEditor(label_value='edit_template_label')), icon_button_editor('apply_stepheat', 'arrow_right', enabled_when='_selected_runs', tooltip='Apply step heat template to selected')) return grp # ============= EOF =============================================

51.990991

117

0.515162

from traits.api import HasTraits, Instance from traitsui.api import View, Item, VGroup, Spring, HGroup, ButtonEditor from pychron.core.pychron_traits import BorderVGroup from pychron.core.ui.enum_editor import myEnumEditor from pychron.envisage.icon_button_editor import icon_button_editor POSITION_TOOLTIP = '''Set the position for this analysis or group of analyses. Examples: 1. 4 or p4 (goto position 4) 2. 3,4,5 (goto positions 3,4,5. treat as one analysis) 3. 7-12 (goto positions 7,8,9,10,11,12. treat as individual analyses) 4. 7:12 (same as #3) 5. 10:16:2 (goto positions 10,12,14,16. treat as individual analyses) 6. D1 (drill position 1) 7. T1-2 (goto named position T1-2 i.e transect 1, point 2) 8. L3 (trace path L3) 9. 1-6;9;11;15-20 (combination of rules 2. and 3. treat all positions as individual analyses) 10. 1.0,2.0 (goto the point defined by x,y[,z]. Use ";" to treat multiple points as one analysis e.g 1.0,2.0;3.0,4.0) ''' PATTERN_TOOLTIP = 'Select a pattern from Remote or Local Patterns. \ If unsure from which group to choice use a "Remote" pattern' class FactoryView(HasTraits): model = Instance('pychron.experiment.automated_run.factory.AutomatedRunFactory') def trait_context(self): return {'object': self.model} def traits_view(self): v = View(self._get_group()) return v def _get_group(self): sspring = lambda width=17: Spring(springy=False, width=width) extract_grp = BorderVGroup(HGroup(sspring(width=33), Item('extract_value', label='Extract', tooltip='Set the extract value in extract units', enabled_when='extractable'), Item('extract_units', show_label=False, editor=myEnumEditor(name='extract_units_names')), Item('ramp_duration', label='Ramp Dur. (s)'), ), HGroup(Item('use_cdd_warming', label='CDD Warm', tooltip='Use the CDD warming routine at end of measurement'), Item('overlap', label='Overlap (s)', tooltip='Duration to wait before staring next run')), self._step_heat_group(), HGroup(Item('duration', label='Duration (s)', tooltip='Set the number of seconds to run the extraction device.'), Item('pre_cleanup', label='Pre Cleanup (s)'), Item('cleanup', label='Cleanup (s)', tooltip='Set the number of seconds to getter the sample gas'), Item('post_cleanup', label='Post Cleanup (s)')), HGroup(Item('beam_diameter'), Item('light_value', label='Lighting')), self._position_group(), label='Extract') post_measurement_group = BorderVGroup(Item('delay_after'), label='Post Measurement') grp = VGroup(extract_grp, post_measurement_group) return grp def _position_group(self): grp = HGroup(Item('position', tooltip=POSITION_TOOLTIP), Item('pattern', show_label=False, tooltip=PATTERN_TOOLTIP, editor=myEnumEditor(name='patterns')), Item('edit_pattern', show_label=False, editor=ButtonEditor(label_value='edit_pattern_label'))) return grp def _step_heat_group(self): grp = HGroup(Item('template', label='Step Heat Template', editor=myEnumEditor(name='templates'), show_label=False, ), Item('edit_template', show_label=False, editor=ButtonEditor(label_value='edit_template_label')), icon_button_editor('apply_stepheat', 'arrow_right', enabled_when='_selected_runs', tooltip='Apply step heat template to selected')) return grp

true

1c384065f5f0c168b0082b1c67a44eacd1b94183

1,376

py

Python

service/__init__.py

nyu-devops-wishlist/wishlist

b0a99a6f9905dc8547ccbc6375e5d6db512102cf

[ "Apache-2.0" ]

1

2020-03-05T21:44:09.000Z

service/__init__.py

nyu-devops-wishlist/wishlist

b0a99a6f9905dc8547ccbc6375e5d6db512102cf

[ "Apache-2.0" ]

79

2020-02-25T23:55:32.000Z

2020-05-12T17:44:53.000Z

service/__init__.py

nyu-devops-wishlist/wishlist

b0a99a6f9905dc8547ccbc6375e5d6db512102cf

[ "Apache-2.0" ]

1

2020-03-10T21:58:13.000Z

""" Package: service Package for the application models and service routes This module creates and configures the Flask app and sets up the logging and SQL database """ import os import sys import logging from flask import Flask # Create Flask application app = Flask(__name__) app.config.from_object('config') # Import the rutes After the Flask app is created from service import service, models # Set up logging for production if __name__ != '__main__': gunicorn_logger = logging.getLogger('gunicorn.error') app.logger.handlers = gunicorn_logger.handlers app.logger.setLevel(gunicorn_logger.level) app.logger.propagate = False # Make all log formats consistent formatter = logging.Formatter("[%(asctime)s] [%(levelname)s] [%(module)s] %(message)s", "%Y-%m-%d %H:%M:%S %z") for handler in app.logger.handlers: handler.setFormatter(formatter) app.logger.info('Logging handler established') app.logger.info(70 * "*") app.logger.info(" TEST S E R V I C E R U N N I N G ".center(70, "*")) app.logger.info(70 * "*") try: service.init_db() # make our sqlalchemy tables except Exception as error: app.logger.critical("%s: Cannot continue", error) # gunicorn requires exit code 4 to stop spawning workers when they die sys.exit(4) app.logger.info("Service inititalized!")

32

116

0.694767

import os import sys import logging from flask import Flask app = Flask(__name__) app.config.from_object('config') from service import service, models if __name__ != '__main__': gunicorn_logger = logging.getLogger('gunicorn.error') app.logger.handlers = gunicorn_logger.handlers app.logger.setLevel(gunicorn_logger.level) app.logger.propagate = False formatter = logging.Formatter("[%(asctime)s] [%(levelname)s] [%(module)s] %(message)s", "%Y-%m-%d %H:%M:%S %z") for handler in app.logger.handlers: handler.setFormatter(formatter) app.logger.info('Logging handler established') app.logger.info(70 * "*") app.logger.info(" TEST S E R V I C E R U N N I N G ".center(70, "*")) app.logger.info(70 * "*") try: service.init_db() except Exception as error: app.logger.critical("%s: Cannot continue", error) sys.exit(4) app.logger.info("Service inititalized!")

true

1c384344aae69c08b0bf6b297a873f04d8fd7c49

49,993

py

Python

parser/team23/interface.py

itsmjoe/tytus

3b0341cc854d67979b766c5c8b06ed172ce0c913

[ "MIT" ]

null

parser/team23/interface.py

itsmjoe/tytus

3b0341cc854d67979b766c5c8b06ed172ce0c913

[ "MIT" ]

null

parser/team23/interface.py

itsmjoe/tytus

3b0341cc854d67979b766c5c8b06ed172ce0c913

[ "MIT" ]

null

from tkinter import * from tkinter import ttk from tkinter import filedialog from PIL import Image, ImageTk import grammar.sql_grammar as gramatica from graphviz import Source from tools.console_text import * from error.errores import * from tools.tabla_simbolos import * import os class window: def __init__(self): #Variables self.ventana = Tk() #Ventana self.color = "darkgray" #Color editor self.dir_os = os.path.dirname(__file__) #Dir self.count_tabs = 1 #Conteo de tabs self.tabControl = ttk.Notebook(self.ventana,height=500) #Notebook contenedor tabs self.tab_salida = ttk.Notebook(self.ventana, height=100) #Notebook contenedor de salidas self.my_status = StringVar() #Configuracion ventana self.ventana.title("Interfaz para compiladores") self.ventana.geometry("900x700") self.ventana.config(bg=self.color) #Creando widgets self.create_menu_bar() self.create_tool_bar() self.add_tab("Untitled-"+str(self.count_tabs)) self.create_consola() self.create_status_bar() #Search and Replace vars self.search_text = "" self.replace_text = "" #Ejecución de la ventana def run(self): self.ventana.mainloop() #Widgets de la interfaz def create_menu_bar(self): my_menu = Menu(self.ventana) self.ventana.config(menu = my_menu) #Items del menu file_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="File", menu=file_menu) edit_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="Edit", menu=edit_menu) tools_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="Tools", menu=tools_menu) options_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="Options", menu=options_menu) help_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="Help", menu=help_menu) #Items de submenu file_menu.add_command(label="New File", command=self.new_file, accelerator="Ctrl+N") file_menu.add_separator() file_menu.add_command(label="Open File", command=self.open_file , accelerator="Ctrl+O") file_menu.add_separator() file_menu.add_command(label="Save File", command=self.save_file, accelerator="Ctrl+Shift-S") file_menu.add_command(label="Save As", command=self.save_as, accelerator="Ctrl+S") file_menu.add_separator() file_menu.add_command(label="Close Tab", command=self.delete_tab, accelerator="Ctrl+W") file_menu.add_command(label="Exit", command=self.ventana.quit) edit_menu.add_command(label="Copy", command=lambda:self.ventana.focus_get().event_generate('<<Copy>>')) edit_menu.add_command(label="Paste", command=lambda:self.ventana.focus_get().event_generate('<<Paste>>')) edit_menu.add_command(label="Cut", command=lambda:self.ventana.focus_get().event_generate('<<Cut>>')) edit_menu.add_separator() edit_menu.add_command(label="Search", command=self.find_popup, accelerator="Ctrl+F") edit_menu.add_command(label="Replace", command=self.replace_popup, accelerator="Ctrl+H") tools_menu.add_command(label="Ejecutar", command=self.ejecutar_codigo, accelerator="F5") tools_menu.add_separator() tools_menu.add_command(label="AST", command = self.compilar_AST_pdf) tools_menu.add_separator() tools_menu.add_command(label = "Errores Lexicos", command = self.compilar_lexico_pdf) tools_menu.add_command(label = "Errores Sintacticos", command = self.compilar_sintactico_pdf) tools_menu.add_command(label = "Errores Semanticos", command = self.compilar_semantico_pdf) tools_menu.add_command(label = "Todos los errores", command = self.compilar_Error_pdf) tools_menu.add_separator() tools_menu.add_command(label = "Reporte Gramatical", command = self.compilar_grammar_pdf) tools_menu.add_separator() tools_menu.add_command(label = "Tabla de Simbolos", command=self.compilar_ts_pdf) #tools_menu.add_command(label="Debug", command=self.open_file, accelerator="F5") theme_menu = Menu(options_menu, tearoff=0) options_menu.add_cascade(label="Theme", menu=theme_menu) #options_menu.add_command(label="Line Number", command=self.open_file) theme_menu.add_command(label="Default", command=self.default_theme) theme_menu.add_command(label="Light Gray", command=self.gray_theme) theme_menu.add_command(label="Dark Night", command=self.dark_night_theme) theme_menu.add_command(label="Light Blue", command=self.light_blue_theme) theme_menu.add_command(label="Dark", command=self.dark_theme) help_menu.add_command(label="Help") help_menu.add_command(label="About", command=self.popup_about) def popup_about(self): popup = Tk() popup.wm_title("About") popup.geometry("330x190") popup.resizable(False, False) label = ttk.Label(popup, text="------------------ EDITOR COMPILADORES 2 ------------------", relief="sunken") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Versión: 1.51.1 (system setup)") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Confirmación: -----------------------------------") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Fecha: 2020-12-10T08:44:32") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Sistema Operativo: Windows_NT x64 10.0.18363") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Developer: Luis Fernando Arana Arias - 201700988\nPedro Rolando Ordoñez Carrillo - 201701187\nSteven Aaron Sis Hernandez - 201706357\nDavis Francisco Edward Enriquez - 201700972") label.pack(side="top", fill="x", pady=3) B1 = ttk.Button(popup, text="Close", command = popup.destroy) B1.pack() popup.mainloop() def pop_alert(self, msg): gui = Tk() gui.title("Alerta") gui.geometry("230x60") gui.resizable(False,False) label = ttk.Label(gui, text=msg) label.pack(side="top", fill="x", pady=3) B1 = ttk.Button(gui, text="Close", command = gui.destroy) B1.pack() gui.mainloop() def find_popup(self): popup = Tk() popup.title("Buscar") popup.geometry("250x30") popup.resizable(False, False) Label(popup, text="> ").pack(side=LEFT) txtbox = Entry(popup) txtbox.pack(side = LEFT, fill = BOTH, expand = 1, pady=2) txtbox.focus_set() Find = Button(popup, text ='Buscar') Find.pack(side = LEFT) def find_func(): tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item text = txtbox.get() txt_box.tag_remove('found', '1.0', END) if (text): idx = '1.0' while 1: idx = txt_box.search(text, idx, nocase = 1, stopindex = END) if not idx: break lastidx = '% s+% dc' % (idx, len(text)) txt_box.tag_add('found', idx, lastidx) idx = lastidx txt_box.tag_config('found', foreground='red', background ='#CACACA') Find.config(command = find_func) def replace_popup(self): popup = Tk() popup.title("Buscar y Remplazar") popup.geometry("350x30") popup.resizable(False, False) Label(popup, text="> ").pack(side=LEFT) txtbox = Entry(popup) txtbox.pack(side = LEFT, fill = BOTH, expand = 1, pady=2) txtbox.focus_set() Label(popup, text="> ").pack(side=LEFT) txtbox2 = Entry(popup) txtbox2.pack(side = LEFT, fill = BOTH, expand = 1, pady=2) txtbox2.focus_set() Replace = Button(popup, text ='Remplazar') Replace.pack(side = LEFT) def replace_func(): tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item text_find = txtbox.get() text_replace = txtbox2.get() txt_box.tag_remove('found', '1.0', END) if (text_find): idx = '1.0' while 1: idx = txt_box.search(text_find, idx, nocase = 1, stopindex = END) if not idx: break lastidx = '% s+% dc' % (idx, len(text_find)) txt_box.delete(idx, lastidx) txt_box.insert(idx, text_replace) lastidx = '% s+% dc' % (idx, len(text_replace)) txt_box.tag_add('found', idx, lastidx) idx = lastidx txt_box.tag_config('found', foreground ='green', background = 'yellow') Replace.config(command = replace_func) def create_tool_bar(self): #Barra de herramientas myTool = Frame(self.ventana) #Botones de la barra de herramientas imgOpen = Image.open(self.dir_os +'/assets/open.png') imgOpen = imgOpen.resize((20,20), Image.ANTIALIAS) imgOpen = ImageTk.PhotoImage(imgOpen) OpenBtn = Button(myTool, image=imgOpen, command=self.open_file) OpenBtn.image = imgOpen OpenBtn.pack(side=LEFT, padx=2, pady=2) imgFile = Image.open(self.dir_os +'/assets/file.png') imgFile = imgFile.resize((20, 20), Image.ANTIALIAS) imgFile = ImageTk.PhotoImage(imgFile) FileBtn = Button(myTool, image=imgFile, command=self.new_file) FileBtn.image = imgFile FileBtn.pack(side=LEFT, padx=2, pady=2) imgSave = Image.open(self.dir_os +'/assets/save.png') imgSave = imgSave.resize((20, 20), Image.ANTIALIAS) imgSave = ImageTk.PhotoImage(imgSave) SaveBtn = Button(myTool, image=imgSave, command=self.save_as) SaveBtn.image = imgSave SaveBtn.pack(side=LEFT, padx=2, pady=2) imgSearch = Image.open(self.dir_os +'/assets/search.png') imgSearch = imgSearch.resize((20, 20), Image.ANTIALIAS) imgSearch = ImageTk.PhotoImage(imgSearch) SearchBtn = Button(myTool, image=imgSearch, command=self.find_popup) SearchBtn.image = imgSearch SearchBtn.pack(side=LEFT, padx=2, pady=2) imgDebug = Image.open(self.dir_os +'/assets/debug.png') imgDebug = imgDebug.resize((20, 20), Image.ANTIALIAS) imgDebug = ImageTk.PhotoImage(imgDebug) DebugBtn = Button(myTool, image=imgDebug, command=self.open_file) DebugBtn.image = imgDebug DebugBtn.pack(side=RIGHT, padx=2, pady=2) imgExecute = Image.open(self.dir_os +'/assets/execute.png') imgExecute = imgExecute.resize((20, 20), Image.ANTIALIAS) imgExecute = ImageTk.PhotoImage(imgExecute) ExecuteBtn = Button(myTool, image=imgExecute, command=self.ejecutar_codigo) ExecuteBtn.image = imgExecute ExecuteBtn.pack(side=RIGHT, padx=2, pady=2) imgAbout = Image.open(self.dir_os +'/assets/about.png') imgAbout = imgAbout.resize((20, 20), Image.ANTIALIAS) imgAbout = ImageTk.PhotoImage(imgAbout) AboutBtn = Button(myTool, image=imgAbout, command=self.popup_about) AboutBtn.image = imgAbout AboutBtn.pack(side=LEFT, padx=2, pady=2) imgClose = Image.open(self.dir_os +'/assets/close.png') imgClose = imgClose.resize((20, 20), Image.ANTIALIAS) imgClose = ImageTk.PhotoImage(imgClose) CloseBtn = Button(myTool, image=imgClose, command=self.delete_tab) CloseBtn.image = imgClose CloseBtn.pack(side=LEFT, padx=2, pady=2) imgClear = Image.open(self.dir_os +'/assets/clear.png') imgClear = imgClear.resize((20, 20), Image.ANTIALIAS) imgClear = ImageTk.PhotoImage(imgClear) ClearBtn = Button(myTool, image=imgClear, command=self.clear_consola) ClearBtn.image = imgClear ClearBtn.pack(side=LEFT, padx=2, pady=2) imgAst = Image.open(self.dir_os +'/assets/ast.png') imgAst = imgAst.resize((20, 20), Image.ANTIALIAS) imgAst = ImageTk.PhotoImage(imgAst) AstBtn = Button(myTool, image=imgAst, command=self.compilar_AST_pdf) AstBtn.image = imgAst AstBtn.pack(side=LEFT, padx=2, pady=2) imgErrores = Image.open(self.dir_os +'/assets/error.png') imgErrores = imgErrores.resize((20, 20), Image.ANTIALIAS) imgErrores = ImageTk.PhotoImage(imgErrores) ErroresBtn = Button(myTool, image=imgErrores, command=self.compilar_Error_pdf) ErroresBtn.image = imgErrores ErroresBtn.pack(side=LEFT, padx=2, pady=2) imgGrammar = Image.open(self.dir_os +'/assets/grammar.png') imgGrammar = imgGrammar.resize((20, 20), Image.ANTIALIAS) imgGrammar = ImageTk.PhotoImage(imgGrammar) GrammarBtn = Button(myTool, image=imgGrammar, command=self.compilar_grammar_pdf) GrammarBtn.image = imgGrammar GrammarBtn.pack(side=LEFT, padx=2, pady=2) imgSimbolo = Image.open(self.dir_os +'/assets/simbolos.png') imgSimbolo = imgSimbolo.resize((20, 20), Image.ANTIALIAS) imgSimbolo = ImageTk.PhotoImage(imgSimbolo) SimboloBtn = Button(myTool, image=imgSimbolo, command=self.compilar_ts_pdf) SimboloBtn.image = imgSimbolo SimboloBtn.pack(side=LEFT, padx=2, pady=2) myTool.pack(side=TOP, fill=X) def clear_consola(self): limpiar_consola() for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.delete('1.0', END) widget_item.insert(INSERT, get_contenido()) def create_consola(self): tab_consola = ttk.Frame(self.tab_salida) new_scroll = Scrollbar(tab_consola) font_spec = ("Consolas", 11) consola = Text(tab_consola, font=font_spec) consola.pack(side=LEFT, fill=BOTH, expand=TRUE) consola.config(yscrollcommand=new_scroll.set) consola.insert(INSERT, contenido_consola) new_scroll.pack(side=RIGHT, fill=Y) new_scroll.config(command=consola.yview) self.tab_salida.add(tab_consola, text="Consola") self.tab_salida.pack(side=TOP, fill=BOTH, expand=TRUE) def add_tab(self, title): new_tab = ttk.Frame(self.tabControl) font_spec = ("Consolas", 11) new_scroll = Scrollbar(new_tab) txt_numbers = self.set_line_numbers(new_tab) new_textarea = Text(new_tab, font=font_spec) def double_scroll(self, *args): txt_numbers.yview_moveto(*args) new_textarea.yview_moveto(*args) new_scroll.config(command=double_scroll) def update_scroll(first, last): txt_numbers.yview_moveto(first) new_scroll.set(first, last) txt_numbers.configure(yscrollcommand=update_scroll) new_textarea.configure(yscrollcommand=update_scroll) new_textarea.bind('<Return>', lambda event, txt_number=txt_numbers, txt_area=new_textarea: self.update_line_number(txt_number, new_textarea)) new_textarea.bind('<BackSpace>', lambda event, txt_number=txt_numbers, txt_area=new_textarea: self.update_line_number_back(txt_number, new_textarea)) new_textarea.bind('<Control-Return>', lambda event, txt_number=txt_numbers, txt_area=new_textarea: self.update_line_number_back(txt_number, new_textarea)) new_scroll.pack(side=RIGHT, fill=Y) new_textarea.pack(side=LEFT, fill=BOTH, expand=TRUE) self.update_line_number_back(txt_numbers, new_textarea) self.bind_shortcuts(new_textarea) self.color_font_config(new_textarea) self.tabControl.add(new_tab, text=title) self.tabControl.select(self.count_tabs-1) self.tabControl.pack(side=TOP, fill=BOTH, expand=TRUE) def create_status_bar(self): self.my_status.set("Editor - 0.1") font_spec = ("Consolas", 11) label = Label(self.ventana, textvariable=self.my_status, fg="black", bg="lightgrey", anchor="sw", font=font_spec) label.pack(side=BOTTOM, fill=BOTH) def set_line_numbers(self, tab_item): line_number = Text(tab_item, width=4) line_number.pack(side=LEFT, fill=Y) font_spec = ("Consolas", 11) line_number.config(font=font_spec) line_number.config(state=DISABLED) line_number.config(background="#BBBDCC") return line_number #Funciones def open_file(self, *args): file_name = None file_name = filedialog.askopenfilename( defaultextension = ".txt", filetypes = [("All Files", "*.*"), ("Text Files", "*.txt"), ("Python Scripts", "*.py")]) if file_name: index_tab = self.tab_libre() tab = None if index_tab != -1: self.tabControl.tab(index_tab, text=file_name) tabs_list = self.tabControl.winfo_children() tab = tabs_list[index_tab] self.tabControl.select(index_tab) else: self.count_tabs +=1 self.add_tab(file_name) tabs_list = self.tabControl.winfo_children() tab = tabs_list[self.count_tabs - 1] txt_box = None widget_list = tab.winfo_children() for widget_item in widget_list: if isinstance(widget_item, Text): txt_box = widget_item with open(file_name, "r") as f: txt_box.insert("1.0", f.read()) self.update_line_number_back(widget_list[1], widget_list[2]) self.color_font_config(widget_list[2]) def new_file(self, *args): self.count_tabs +=1 self.add_tab("Untitled-"+str(self.count_tabs)) def tab_libre(self): if self.count_tabs == 0: return -1 tab_ideal = 0 tabs_list = self.tabControl.winfo_children() for tab_item in tabs_list: widget_list = tab_item.winfo_children() for widget_item in widget_list: if isinstance(widget_item, Text): contenido_txt = widget_item.get("1.0",END) if contenido_txt == "\n": return tab_ideal tab_ideal += 1 return -1 def delete_tab(self, *args): for item in self.tabControl.winfo_children(): if str(item)==self.tabControl.select(): item.destroy() self.count_tabs -= 1 return def save_as(self, *args): try: new_file = filedialog.asksaveasfilename( initialfile="Untitled-" + str(self.tabControl.index(CURRENT) + 1 ), defaultextension = ".txt", filetypes = [("All Files", "*.*"), ("Text Files", "*.txt"), ("Python Scripts", "*.py")]) tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item contenido = txt_box.get(1.0, END) with open(new_file, "w") as f: f.write(contenido) self.tabControl.tab(self.tabControl.index(CURRENT), text=new_file) self.update_status_bar(0) except Exception as er: print(er) def save_file(self, *args): nombre_eval = "Untitled-" actual_name = self.tabControl.tab(CURRENT, "text") nombre_aux = actual_name[:-1] if nombre_aux != nombre_eval: try: tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item contenido = txt_box.get(1.0, END) with open(actual_name, "w") as f: f.write(contenido) self.update_status_bar(0) except Exception as er: print(er) else: self.save_as() def update_status_bar(self, *args): if args[0] == 0: self.my_status.set("Archivo Guardado con éxito") else: self.my_status.set("Editor - 0.1") #Configuraciones def bind_shortcuts(self, text_edit): text_edit.bind('<Control-n>', self.new_file) text_edit.bind('<Control-o>', self.open_file) text_edit.bind('<Control-s>', self.save_file) text_edit.bind('<Control-S>', self.save_as) text_edit.bind('<Control-w>', self.delete_tab) text_edit.bind('<Key>', self.update_status_bar) text_edit.bind('<Key>', lambda event, txt_area=text_edit: self.color_font_config(text_edit)) def update_line_number(self, txt_number, txt_area): txt_number.config(state=NORMAL) txt_number.delete("1.0","end") lineas = int(txt_area.index('end').split('.')[0]) for i in range(1, lineas+1): line_print = str(i) + "\n" txt_number.insert(INSERT, line_print) txt_number.config(state=DISABLED) def update_line_number_back(self, txt_number, txt_area): txt_number.config(state=NORMAL) txt_number.delete("1.0","end") lineas = int(txt_area.index('end').split('.')[0]) -1 for i in range(1, lineas+1): line_print = str(i) + "\n" txt_number.insert(INSERT, line_print) txt_number.config(state=DISABLED) def highlight_pattern(self, pattern, tag, txt_area, start="1.0", end="end", regexp=False, case_sensitive = 0): start = txt_area.index(start) end = txt_area.index(end) txt_area.mark_set("matchStart", start) txt_area.mark_set("matchEnd", start) txt_area.mark_set("searchLimit", end) count = IntVar() while True: index = txt_area.search(pattern, "matchEnd","searchLimit", count=count, regexp=regexp, nocase=case_sensitive) if index == "": break if count.get() == 0: break # degenerate pattern which matches zero-length strings txt_area.mark_set("matchStart", index) txt_area.mark_set("matchEnd", "%s+%sc" % (index, count.get())) txt_area.tag_add(tag, "matchStart", "matchEnd") def default_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#000000") widget_item.config(background="#FFFFFF") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#000000") widget_item.config(background="#FFFFFF") def gray_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#000000") widget_item.config(background="#BBBDCC") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#000000") widget_item.config(background="#BBBDCC") def dark_night_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#FFFFFF") widget_item.config(background="#252327") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#FFFFFF") widget_item.config(background="#252327") def light_blue_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#000000") widget_item.config(background="#4C57C8") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#000000") widget_item.config(background="#4C57C8") def dark_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#FFFFFF") widget_item.config(background="#2D163D") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#FFFFFF") widget_item.config(background="#2D163D") def color_font_config(self, txt_area): #Colores txt_area.tag_config("reservada", foreground="#A675B9") txt_area.tag_config("id", foreground="#759AF0") txt_area.tag_config("string", foreground="#7AC883") txt_area.tag_config("comentario", foreground="#9BA29C") txt_area.tag_config("item", foreground="#A675B9") txt_area.tag_config("important", foreground="#E7375C") txt_area.tag_config("function", foreground="#5182C9") txt_area.tag_config("boolean", foreground="#FA8C31") #Palabras self.highlight_pattern(r'/\*(.|\n)*?\*/', "comentario", txt_area, regexp=True) self.highlight_pattern(r'--.*\n', "comentario", txt_area, regexp=True) self.highlight_pattern("SELECT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("UPDATE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("WHERE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("JOIN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CREATE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DELETE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("COUNT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SUM", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FROM", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CASE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("THEN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ELSE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SMALLINT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INTEGER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("BIGINT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DECIMAL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("NUMERIC", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("REAL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("MONEY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CHAR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CHARACTER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("VARYING", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TIMESTAMP", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("WITHOUT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("WITH", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TIME", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ZONE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DATE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INTERVAL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FIELDS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("YEAR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("MONTH", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DAY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("HOUR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("MINUTE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SECOND", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TO", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("BOOLEAN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("AS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ENUM", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TYPE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("IS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ISNULL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("NOTNULL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("NOT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("AND", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("OR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("BETWEEN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LIKE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("IN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INLIKE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SIMILAR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("REPLACE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("MODE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("OWNER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("IF", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("EXISTS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ALTER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DATABASE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("RENAME", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DROP", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TABLE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("PRIMARY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FOREIGN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("KEY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("REFERENCES", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CONSTRAINT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CHECK", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SET", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INSERT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("BY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("GROUP", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("HAVING", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ORDER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("WHEN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("UNION", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("END", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("VALUES", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INTERSECT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LIMIT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INNER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LEFT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("RIGHT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("OUTER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ASC", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DESC", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("GREATEST", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LEAST", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("OFFSET", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FIRST", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LAST", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FULL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ALL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TRUE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FALSE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INHERITS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("NULL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SHOW", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DATABASES", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("USE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("VARCHAR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("==", "item", txt_area) self.highlight_pattern("!=", "item", txt_area) self.highlight_pattern(">=", "item", txt_area) self.highlight_pattern("<=", "item", txt_area) self.highlight_pattern(">", "item", txt_area) self.highlight_pattern("<", "item", txt_area) self.highlight_pattern("=", "item", txt_area) self.highlight_pattern("+", "item", txt_area) self.highlight_pattern("-", "item", txt_area) self.highlight_pattern("*", "item", txt_area) self.highlight_pattern("/", "item", txt_area) self.highlight_pattern("self", "important", txt_area) self.highlight_pattern("print", "function", txt_area) self.highlight_pattern("true", "boolean", txt_area) self.highlight_pattern("false", "boolean", txt_area) self.highlight_pattern(r'(\".*?\")|(\'.*?\')', "string", txt_area, regexp=True) def graficar_AST(self, ast_): if len(ast_) != 0: ast_str = 'digraph AST { \n node [shape=record];\n' count_nodos = 0 for instruccion_ in ast_: if count_nodos != 0: ast_str += 'node' + str(count_nodos + 1000000) + '[label =\" Instruccion \"];\n' ast_str += 'node' + str(count_nodos + 10000) + '[label =\" Instrucciones \"];\n' ast_str += 'node' + str(count_nodos + 10000 - 1) + ' -> node' + str(count_nodos + 1000000) + ';\n' ast_str += 'node' + str(count_nodos + 10000 - 1) + ' -> node' + str(count_nodos + 10000) + ';\n' ast_str += 'node' + str(count_nodos + 1000000) + ' -> node' + instruccion_.nodo.num + ';\n' ast_str += 'node' + instruccion_.nodo.num + '[label =\"'+ instruccion_.nodo.valor +"\"];\n" ast_str += self.graficar_AST_hijos(instruccion_.nodo) else: ast_str += 'node' + instruccion_.nodo.num + '[label =\"'+ instruccion_.nodo.valor +"\"];\n" ast_str += 'node' + str(count_nodos + 1000000) + '[label =\" Instruccion \"];\n' ast_str += 'node' + str(count_nodos + 10000) + '[label =\" Instrucciones \"];\n' ast_str += 'start_ast -> node' + str(count_nodos + 10000) + ';\n' ast_str += 'start_ast -> node' + str(count_nodos + 1000000) + ';\n' ast_str += 'node' + str(count_nodos + 1000000) + ' -> node' + instruccion_.nodo.num + ';\n' ast_str += self.graficar_AST_hijos(instruccion_.nodo) count_nodos += 1 ast_str += '\n}' with open('ast_reporte.dot', 'w', encoding='utf8') as f: f.write(ast_str) def graficar_AST_hijos(self, instr_): t = '' for instruc in instr_.hijos: t += 'node'+instruc.num+'[label=\"'+instruc.valor+'\"];\n' t += 'node'+instr_.num + ' -> node' + instruc.num+';\n' t += self.graficar_AST_hijos(instruc) return t def graficar_Gramatical(self, ast_): if len(ast_) != 0: grammar_str = 'digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n' grammar_str += 'arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>PRODUCCIÓN</TD><TD>ACCIONES</TD></TR>\n' grammar_str += '<TR><TD>INSTRUCCIONES ::= INSTRUCCION INSTRUCCIONES1</TD><TD>INSTRUCCIONES = INSTRUCCIONES1; INSTRUCCIONES.append(INSTRUCCION); </TD></TR>\n' grammar_str += '<TR><TD>INSTRUCCIONES ::= </TD><TD>INSTRUCCIONES = [];</TD></TR>\n' for instr in ast_: grammar_str += instr.grammar_ + '\n' grammar_str += '</TABLE>\n>, ];\n}' with open('grammar_reporte.dot', 'w', encoding='utf8') as f: f.write(grammar_str) def graficar_Errores(self): if len(errores) != 0: reporte_errores = "digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n" reporte_errores += "arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>Tipo Error</TD>\n<TD>Descripcion</TD>\n<TD>Linea</TD>\n<TD>Columna</TD>\n</TR>\n" for error_ in errores: reporte_errores += '<TR>' reporte_errores += '<TD>' + error_.descripcion + '</TD>' reporte_errores += '<TD>' + error_.valor +'</TD>' reporte_errores += '<TD>' + error_.line +'</TD>' reporte_errores += '<TD>' + error_.column +'</TD>' reporte_errores += '</TR>\n' reporte_errores += '</TABLE>\n>, ];\n}' with open('errores_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_errores) def graficar_errores_lexicos(self): if len(errores) != 0: reporte_errores = "digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n" reporte_errores += "arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>Tipo Error</TD>\n<TD>Descripcion</TD>\n<TD>Linea</TD>\n<TD>Columna</TD>\n</TR>\n" for error_ in errores: if error_.descripcion.lower() == "léxico": reporte_errores += '<TR>' reporte_errores += '<TD>' + error_.descripcion + '</TD>' reporte_errores += '<TD>' + error_.valor +'</TD>' reporte_errores += '<TD>' + error_.line +'</TD>' reporte_errores += '<TD>' + error_.column +'</TD>' reporte_errores += '</TR>' reporte_errores += '</TABLE>\n>, ];\n}' with open('lexico_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_errores) def graficar_errores_sintacticos(self): if len(errores) != 0: reporte_errores = "digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n" reporte_errores += "arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>Tipo Error</TD>\n<TD>Descripcion</TD>\n<TD>Linea</TD>\n<TD>Columna</TD>\n</TR>\n" for error_ in errores: if error_.descripcion.lower() == "sintáctico": reporte_errores += '<TR>' reporte_errores += '<TD>' + error_.descripcion + '</TD>' reporte_errores += '<TD>' + error_.valor +'</TD>' reporte_errores += '<TD>' + error_.line +'</TD>' reporte_errores += '<TD>' + error_.column +'</TD>' reporte_errores += '</TR>' reporte_errores += '</TABLE>\n>, ];\n}' with open('sintactico_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_errores) def graficar_errores_semanticos(self): if len(errores) != 0: reporte_errores = "digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n" reporte_errores += "arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>Tipo Error</TD>\n<TD>Descripcion</TD>\n<TD>Linea</TD>\n<TD>Columna</TD>\n</TR>\n" for error_ in errores: if error_.descripcion.lower() == "semántico": reporte_errores += '<TR>' reporte_errores += '<TD>' + error_.descripcion + '</TD>' reporte_errores += '<TD>' + error_.valor +'</TD>' reporte_errores += '<TD>' + error_.line +'</TD>' reporte_errores += '<TD>' + error_.column +'</TD>' reporte_errores += '</TR>' reporte_errores += '</TABLE>\n>, ];\n}' with open('semantico_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_errores) def graficar_TS(self): reporte_ts = ts.reporte_ts() with open('ts_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_ts) def compilar_ts_png(self): img = Source.from_file("ts_reporte.dot", format = "png", encoding="utf8") img.render() entrada = self.popup_reporte_png(self.ventana, "ts_reporte.dot.png") def compilar_ts_pdf(self): file_pdf = Source.from_file("ts_reporte.dot", format = "pdf", encoding="utf8") file_pdf.view() def compilar_grammar_png(self): img = Source.from_file("grammar_reporte.dot", format = "png", encoding="utf8") img.render() entrada = self.popup_reporte_png(self.ventana, "grammar_reporte.dot.png") def compilar_grammar_pdf(self): file_pdf = Source.from_file("grammar_reporte.dot", format = "pdf", encoding="utf8") file_pdf.view() def compilar_semantico_png(self): img = Source.from_file("semantico_reporte.dot", format = "png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "semantico_reporte.dot.png") def compilar_semantico_pdf(self): file_pdf = Source.from_file("semantico_reporte.dot", format = "pdf", encoding='utf8') file_pdf.view() def compilar_sintactico_png(self): img = Source.from_file("sintactico_reporte.dot", format = "png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "sintactico_reporte.dot.png") def compilar_sintactico_pdf(self): file_pdf = Source.from_file("sintactico_reporte.dot", format = "pdf", encoding='utf8') file_pdf.view() def compilar_lexico_png(self): img = Source.from_file("lexico_reporte.dot", format = "png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "lexico_reporte.dot.png") def compilar_lexico_pdf(self): file_pdf = Source.from_file("lexico_reporte.dot", format = "pdf", encoding='utf8') file_pdf.view() def compilar_Error_png(self): img = Source.from_file("errores_reporte.dot", format="png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "errores_reporte.dot.png") def compilar_Error_pdf(self): file_pdf = Source.from_file("errores_reporte.dot", format="pdf", encoding='utf8') file_pdf.view() def compilar_AST_png(self): img = Source.from_file("ast_reporte.dot", format="png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "ast_reporte.dot.png") def compilar_AST_pdf(self): file_pdf = Source.from_file("ast_reporte.dot", format="pdf", encoding='utf8') file_pdf.view() def popup_reporte_png(self, master, path): top = self.top = Toplevel(master) img = ImageTk.PhotoImage(Image.open(path)) panel = Label(top, image = img) panel.image = img panel.pack(side = "bottom", fill = "both", expand = "yes") def ejecutar_codigo(self): errores = [] tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item contenido = txt_box.get(1.0, END) instruccions = [] #try: instruccions = gramatica.parse(contenido) self.ejecutar_resultado(instruccions) #except: # if len(contenido) == 1: # add_text("No hay código para ejecutar") # else: # add_text("Error al ejecutar el código") #Imprimir consola for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.delete('1.0', END) add_text("\nPS C:\\Users\\Grupo 23> ") widget_item.insert(INSERT, get_contenido()) self.graficar_AST(instruccions) self.graficar_Errores() self.graficar_errores_lexicos() self.graficar_errores_sintacticos() self.graficar_errores_semanticos() self.graficar_Gramatical(instruccions) self.graficar_TS() def ejecutar_resultado(self,instrucciones_): for instruccion_ in instrucciones_: instruccion_.ejecutar() if __name__ == "__main__": index = window() index.run()

46.461896

218

0.597784

from tkinter import * from tkinter import ttk from tkinter import filedialog from PIL import Image, ImageTk import grammar.sql_grammar as gramatica from graphviz import Source from tools.console_text import * from error.errores import * from tools.tabla_simbolos import * import os class window: def __init__(self): self.ventana = Tk() self.color = "darkgray" self.dir_os = os.path.dirname(__file__) self.count_tabs = 1 self.tabControl = ttk.Notebook(self.ventana,height=500) self.tab_salida = ttk.Notebook(self.ventana, height=100) self.my_status = StringVar() self.ventana.title("Interfaz para compiladores") self.ventana.geometry("900x700") self.ventana.config(bg=self.color) self.create_menu_bar() self.create_tool_bar() self.add_tab("Untitled-"+str(self.count_tabs)) self.create_consola() self.create_status_bar() self.search_text = "" self.replace_text = "" def run(self): self.ventana.mainloop() def create_menu_bar(self): my_menu = Menu(self.ventana) self.ventana.config(menu = my_menu) file_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="File", menu=file_menu) edit_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="Edit", menu=edit_menu) tools_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="Tools", menu=tools_menu) options_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="Options", menu=options_menu) help_menu = Menu(my_menu, tearoff=0) my_menu.add_cascade(label="Help", menu=help_menu) file_menu.add_command(label="New File", command=self.new_file, accelerator="Ctrl+N") file_menu.add_separator() file_menu.add_command(label="Open File", command=self.open_file , accelerator="Ctrl+O") file_menu.add_separator() file_menu.add_command(label="Save File", command=self.save_file, accelerator="Ctrl+Shift-S") file_menu.add_command(label="Save As", command=self.save_as, accelerator="Ctrl+S") file_menu.add_separator() file_menu.add_command(label="Close Tab", command=self.delete_tab, accelerator="Ctrl+W") file_menu.add_command(label="Exit", command=self.ventana.quit) edit_menu.add_command(label="Copy", command=lambda:self.ventana.focus_get().event_generate('<<Copy>>')) edit_menu.add_command(label="Paste", command=lambda:self.ventana.focus_get().event_generate('<<Paste>>')) edit_menu.add_command(label="Cut", command=lambda:self.ventana.focus_get().event_generate('<<Cut>>')) edit_menu.add_separator() edit_menu.add_command(label="Search", command=self.find_popup, accelerator="Ctrl+F") edit_menu.add_command(label="Replace", command=self.replace_popup, accelerator="Ctrl+H") tools_menu.add_command(label="Ejecutar", command=self.ejecutar_codigo, accelerator="F5") tools_menu.add_separator() tools_menu.add_command(label="AST", command = self.compilar_AST_pdf) tools_menu.add_separator() tools_menu.add_command(label = "Errores Lexicos", command = self.compilar_lexico_pdf) tools_menu.add_command(label = "Errores Sintacticos", command = self.compilar_sintactico_pdf) tools_menu.add_command(label = "Errores Semanticos", command = self.compilar_semantico_pdf) tools_menu.add_command(label = "Todos los errores", command = self.compilar_Error_pdf) tools_menu.add_separator() tools_menu.add_command(label = "Reporte Gramatical", command = self.compilar_grammar_pdf) tools_menu.add_separator() tools_menu.add_command(label = "Tabla de Simbolos", command=self.compilar_ts_pdf) theme_menu = Menu(options_menu, tearoff=0) options_menu.add_cascade(label="Theme", menu=theme_menu) theme_menu.add_command(label="Default", command=self.default_theme) theme_menu.add_command(label="Light Gray", command=self.gray_theme) theme_menu.add_command(label="Dark Night", command=self.dark_night_theme) theme_menu.add_command(label="Light Blue", command=self.light_blue_theme) theme_menu.add_command(label="Dark", command=self.dark_theme) help_menu.add_command(label="Help") help_menu.add_command(label="About", command=self.popup_about) def popup_about(self): popup = Tk() popup.wm_title("About") popup.geometry("330x190") popup.resizable(False, False) label = ttk.Label(popup, text="------------------ EDITOR COMPILADORES 2 ------------------", relief="sunken") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Versión: 1.51.1 (system setup)") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Confirmación: -----------------------------------") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Fecha: 2020-12-10T08:44:32") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Sistema Operativo: Windows_NT x64 10.0.18363") label.pack(side="top", fill="x", pady=3) label = ttk.Label(popup, text="Developer: Luis Fernando Arana Arias - 201700988\nPedro Rolando Ordoñez Carrillo - 201701187\nSteven Aaron Sis Hernandez - 201706357\nDavis Francisco Edward Enriquez - 201700972") label.pack(side="top", fill="x", pady=3) B1 = ttk.Button(popup, text="Close", command = popup.destroy) B1.pack() popup.mainloop() def pop_alert(self, msg): gui = Tk() gui.title("Alerta") gui.geometry("230x60") gui.resizable(False,False) label = ttk.Label(gui, text=msg) label.pack(side="top", fill="x", pady=3) B1 = ttk.Button(gui, text="Close", command = gui.destroy) B1.pack() gui.mainloop() def find_popup(self): popup = Tk() popup.title("Buscar") popup.geometry("250x30") popup.resizable(False, False) Label(popup, text="> ").pack(side=LEFT) txtbox = Entry(popup) txtbox.pack(side = LEFT, fill = BOTH, expand = 1, pady=2) txtbox.focus_set() Find = Button(popup, text ='Buscar') Find.pack(side = LEFT) def find_func(): tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item text = txtbox.get() txt_box.tag_remove('found', '1.0', END) if (text): idx = '1.0' while 1: idx = txt_box.search(text, idx, nocase = 1, stopindex = END) if not idx: break lastidx = '% s+% dc' % (idx, len(text)) txt_box.tag_add('found', idx, lastidx) idx = lastidx txt_box.tag_config('found', foreground='red', background ='#CACACA') Find.config(command = find_func) def replace_popup(self): popup = Tk() popup.title("Buscar y Remplazar") popup.geometry("350x30") popup.resizable(False, False) Label(popup, text="> ").pack(side=LEFT) txtbox = Entry(popup) txtbox.pack(side = LEFT, fill = BOTH, expand = 1, pady=2) txtbox.focus_set() Label(popup, text="> ").pack(side=LEFT) txtbox2 = Entry(popup) txtbox2.pack(side = LEFT, fill = BOTH, expand = 1, pady=2) txtbox2.focus_set() Replace = Button(popup, text ='Remplazar') Replace.pack(side = LEFT) def replace_func(): tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item text_find = txtbox.get() text_replace = txtbox2.get() txt_box.tag_remove('found', '1.0', END) if (text_find): idx = '1.0' while 1: idx = txt_box.search(text_find, idx, nocase = 1, stopindex = END) if not idx: break lastidx = '% s+% dc' % (idx, len(text_find)) txt_box.delete(idx, lastidx) txt_box.insert(idx, text_replace) lastidx = '% s+% dc' % (idx, len(text_replace)) txt_box.tag_add('found', idx, lastidx) idx = lastidx txt_box.tag_config('found', foreground ='green', background = 'yellow') Replace.config(command = replace_func) def create_tool_bar(self): myTool = Frame(self.ventana) imgOpen = Image.open(self.dir_os +'/assets/open.png') imgOpen = imgOpen.resize((20,20), Image.ANTIALIAS) imgOpen = ImageTk.PhotoImage(imgOpen) OpenBtn = Button(myTool, image=imgOpen, command=self.open_file) OpenBtn.image = imgOpen OpenBtn.pack(side=LEFT, padx=2, pady=2) imgFile = Image.open(self.dir_os +'/assets/file.png') imgFile = imgFile.resize((20, 20), Image.ANTIALIAS) imgFile = ImageTk.PhotoImage(imgFile) FileBtn = Button(myTool, image=imgFile, command=self.new_file) FileBtn.image = imgFile FileBtn.pack(side=LEFT, padx=2, pady=2) imgSave = Image.open(self.dir_os +'/assets/save.png') imgSave = imgSave.resize((20, 20), Image.ANTIALIAS) imgSave = ImageTk.PhotoImage(imgSave) SaveBtn = Button(myTool, image=imgSave, command=self.save_as) SaveBtn.image = imgSave SaveBtn.pack(side=LEFT, padx=2, pady=2) imgSearch = Image.open(self.dir_os +'/assets/search.png') imgSearch = imgSearch.resize((20, 20), Image.ANTIALIAS) imgSearch = ImageTk.PhotoImage(imgSearch) SearchBtn = Button(myTool, image=imgSearch, command=self.find_popup) SearchBtn.image = imgSearch SearchBtn.pack(side=LEFT, padx=2, pady=2) imgDebug = Image.open(self.dir_os +'/assets/debug.png') imgDebug = imgDebug.resize((20, 20), Image.ANTIALIAS) imgDebug = ImageTk.PhotoImage(imgDebug) DebugBtn = Button(myTool, image=imgDebug, command=self.open_file) DebugBtn.image = imgDebug DebugBtn.pack(side=RIGHT, padx=2, pady=2) imgExecute = Image.open(self.dir_os +'/assets/execute.png') imgExecute = imgExecute.resize((20, 20), Image.ANTIALIAS) imgExecute = ImageTk.PhotoImage(imgExecute) ExecuteBtn = Button(myTool, image=imgExecute, command=self.ejecutar_codigo) ExecuteBtn.image = imgExecute ExecuteBtn.pack(side=RIGHT, padx=2, pady=2) imgAbout = Image.open(self.dir_os +'/assets/about.png') imgAbout = imgAbout.resize((20, 20), Image.ANTIALIAS) imgAbout = ImageTk.PhotoImage(imgAbout) AboutBtn = Button(myTool, image=imgAbout, command=self.popup_about) AboutBtn.image = imgAbout AboutBtn.pack(side=LEFT, padx=2, pady=2) imgClose = Image.open(self.dir_os +'/assets/close.png') imgClose = imgClose.resize((20, 20), Image.ANTIALIAS) imgClose = ImageTk.PhotoImage(imgClose) CloseBtn = Button(myTool, image=imgClose, command=self.delete_tab) CloseBtn.image = imgClose CloseBtn.pack(side=LEFT, padx=2, pady=2) imgClear = Image.open(self.dir_os +'/assets/clear.png') imgClear = imgClear.resize((20, 20), Image.ANTIALIAS) imgClear = ImageTk.PhotoImage(imgClear) ClearBtn = Button(myTool, image=imgClear, command=self.clear_consola) ClearBtn.image = imgClear ClearBtn.pack(side=LEFT, padx=2, pady=2) imgAst = Image.open(self.dir_os +'/assets/ast.png') imgAst = imgAst.resize((20, 20), Image.ANTIALIAS) imgAst = ImageTk.PhotoImage(imgAst) AstBtn = Button(myTool, image=imgAst, command=self.compilar_AST_pdf) AstBtn.image = imgAst AstBtn.pack(side=LEFT, padx=2, pady=2) imgErrores = Image.open(self.dir_os +'/assets/error.png') imgErrores = imgErrores.resize((20, 20), Image.ANTIALIAS) imgErrores = ImageTk.PhotoImage(imgErrores) ErroresBtn = Button(myTool, image=imgErrores, command=self.compilar_Error_pdf) ErroresBtn.image = imgErrores ErroresBtn.pack(side=LEFT, padx=2, pady=2) imgGrammar = Image.open(self.dir_os +'/assets/grammar.png') imgGrammar = imgGrammar.resize((20, 20), Image.ANTIALIAS) imgGrammar = ImageTk.PhotoImage(imgGrammar) GrammarBtn = Button(myTool, image=imgGrammar, command=self.compilar_grammar_pdf) GrammarBtn.image = imgGrammar GrammarBtn.pack(side=LEFT, padx=2, pady=2) imgSimbolo = Image.open(self.dir_os +'/assets/simbolos.png') imgSimbolo = imgSimbolo.resize((20, 20), Image.ANTIALIAS) imgSimbolo = ImageTk.PhotoImage(imgSimbolo) SimboloBtn = Button(myTool, image=imgSimbolo, command=self.compilar_ts_pdf) SimboloBtn.image = imgSimbolo SimboloBtn.pack(side=LEFT, padx=2, pady=2) myTool.pack(side=TOP, fill=X) def clear_consola(self): limpiar_consola() for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.delete('1.0', END) widget_item.insert(INSERT, get_contenido()) def create_consola(self): tab_consola = ttk.Frame(self.tab_salida) new_scroll = Scrollbar(tab_consola) font_spec = ("Consolas", 11) consola = Text(tab_consola, font=font_spec) consola.pack(side=LEFT, fill=BOTH, expand=TRUE) consola.config(yscrollcommand=new_scroll.set) consola.insert(INSERT, contenido_consola) new_scroll.pack(side=RIGHT, fill=Y) new_scroll.config(command=consola.yview) self.tab_salida.add(tab_consola, text="Consola") self.tab_salida.pack(side=TOP, fill=BOTH, expand=TRUE) def add_tab(self, title): new_tab = ttk.Frame(self.tabControl) font_spec = ("Consolas", 11) new_scroll = Scrollbar(new_tab) txt_numbers = self.set_line_numbers(new_tab) new_textarea = Text(new_tab, font=font_spec) def double_scroll(self, *args): txt_numbers.yview_moveto(*args) new_textarea.yview_moveto(*args) new_scroll.config(command=double_scroll) def update_scroll(first, last): txt_numbers.yview_moveto(first) new_scroll.set(first, last) txt_numbers.configure(yscrollcommand=update_scroll) new_textarea.configure(yscrollcommand=update_scroll) new_textarea.bind('<Return>', lambda event, txt_number=txt_numbers, txt_area=new_textarea: self.update_line_number(txt_number, new_textarea)) new_textarea.bind('<BackSpace>', lambda event, txt_number=txt_numbers, txt_area=new_textarea: self.update_line_number_back(txt_number, new_textarea)) new_textarea.bind('<Control-Return>', lambda event, txt_number=txt_numbers, txt_area=new_textarea: self.update_line_number_back(txt_number, new_textarea)) new_scroll.pack(side=RIGHT, fill=Y) new_textarea.pack(side=LEFT, fill=BOTH, expand=TRUE) self.update_line_number_back(txt_numbers, new_textarea) self.bind_shortcuts(new_textarea) self.color_font_config(new_textarea) self.tabControl.add(new_tab, text=title) self.tabControl.select(self.count_tabs-1) self.tabControl.pack(side=TOP, fill=BOTH, expand=TRUE) def create_status_bar(self): self.my_status.set("Editor - 0.1") font_spec = ("Consolas", 11) label = Label(self.ventana, textvariable=self.my_status, fg="black", bg="lightgrey", anchor="sw", font=font_spec) label.pack(side=BOTTOM, fill=BOTH) def set_line_numbers(self, tab_item): line_number = Text(tab_item, width=4) line_number.pack(side=LEFT, fill=Y) font_spec = ("Consolas", 11) line_number.config(font=font_spec) line_number.config(state=DISABLED) line_number.config(background="#BBBDCC") return line_number def open_file(self, *args): file_name = None file_name = filedialog.askopenfilename( defaultextension = ".txt", filetypes = [("All Files", "*.*"), ("Text Files", "*.txt"), ("Python Scripts", "*.py")]) if file_name: index_tab = self.tab_libre() tab = None if index_tab != -1: self.tabControl.tab(index_tab, text=file_name) tabs_list = self.tabControl.winfo_children() tab = tabs_list[index_tab] self.tabControl.select(index_tab) else: self.count_tabs +=1 self.add_tab(file_name) tabs_list = self.tabControl.winfo_children() tab = tabs_list[self.count_tabs - 1] txt_box = None widget_list = tab.winfo_children() for widget_item in widget_list: if isinstance(widget_item, Text): txt_box = widget_item with open(file_name, "r") as f: txt_box.insert("1.0", f.read()) self.update_line_number_back(widget_list[1], widget_list[2]) self.color_font_config(widget_list[2]) def new_file(self, *args): self.count_tabs +=1 self.add_tab("Untitled-"+str(self.count_tabs)) def tab_libre(self): if self.count_tabs == 0: return -1 tab_ideal = 0 tabs_list = self.tabControl.winfo_children() for tab_item in tabs_list: widget_list = tab_item.winfo_children() for widget_item in widget_list: if isinstance(widget_item, Text): contenido_txt = widget_item.get("1.0",END) if contenido_txt == "\n": return tab_ideal tab_ideal += 1 return -1 def delete_tab(self, *args): for item in self.tabControl.winfo_children(): if str(item)==self.tabControl.select(): item.destroy() self.count_tabs -= 1 return def save_as(self, *args): try: new_file = filedialog.asksaveasfilename( initialfile="Untitled-" + str(self.tabControl.index(CURRENT) + 1 ), defaultextension = ".txt", filetypes = [("All Files", "*.*"), ("Text Files", "*.txt"), ("Python Scripts", "*.py")]) tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item contenido = txt_box.get(1.0, END) with open(new_file, "w") as f: f.write(contenido) self.tabControl.tab(self.tabControl.index(CURRENT), text=new_file) self.update_status_bar(0) except Exception as er: print(er) def save_file(self, *args): nombre_eval = "Untitled-" actual_name = self.tabControl.tab(CURRENT, "text") nombre_aux = actual_name[:-1] if nombre_aux != nombre_eval: try: tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item contenido = txt_box.get(1.0, END) with open(actual_name, "w") as f: f.write(contenido) self.update_status_bar(0) except Exception as er: print(er) else: self.save_as() def update_status_bar(self, *args): if args[0] == 0: self.my_status.set("Archivo Guardado con éxito") else: self.my_status.set("Editor - 0.1") def bind_shortcuts(self, text_edit): text_edit.bind('<Control-n>', self.new_file) text_edit.bind('<Control-o>', self.open_file) text_edit.bind('<Control-s>', self.save_file) text_edit.bind('<Control-S>', self.save_as) text_edit.bind('<Control-w>', self.delete_tab) text_edit.bind('<Key>', self.update_status_bar) text_edit.bind('<Key>', lambda event, txt_area=text_edit: self.color_font_config(text_edit)) def update_line_number(self, txt_number, txt_area): txt_number.config(state=NORMAL) txt_number.delete("1.0","end") lineas = int(txt_area.index('end').split('.')[0]) for i in range(1, lineas+1): line_print = str(i) + "\n" txt_number.insert(INSERT, line_print) txt_number.config(state=DISABLED) def update_line_number_back(self, txt_number, txt_area): txt_number.config(state=NORMAL) txt_number.delete("1.0","end") lineas = int(txt_area.index('end').split('.')[0]) -1 for i in range(1, lineas+1): line_print = str(i) + "\n" txt_number.insert(INSERT, line_print) txt_number.config(state=DISABLED) def highlight_pattern(self, pattern, tag, txt_area, start="1.0", end="end", regexp=False, case_sensitive = 0): start = txt_area.index(start) end = txt_area.index(end) txt_area.mark_set("matchStart", start) txt_area.mark_set("matchEnd", start) txt_area.mark_set("searchLimit", end) count = IntVar() while True: index = txt_area.search(pattern, "matchEnd","searchLimit", count=count, regexp=regexp, nocase=case_sensitive) if index == "": break if count.get() == 0: break txt_area.mark_set("matchStart", index) txt_area.mark_set("matchEnd", "%s+%sc" % (index, count.get())) txt_area.tag_add(tag, "matchStart", "matchEnd") def default_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#000000") widget_item.config(background="#FFFFFF") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#000000") widget_item.config(background="#FFFFFF") def gray_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#000000") widget_item.config(background="#BBBDCC") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#000000") widget_item.config(background="#BBBDCC") def dark_night_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#FFFFFF") widget_item.config(background="#252327") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#FFFFFF") widget_item.config(background="#252327") def light_blue_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#000000") widget_item.config(background="#4C57C8") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#000000") widget_item.config(background="#4C57C8") def dark_theme(self): for tab_item in self.tabControl.winfo_children(): conteo = 0 for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): if conteo != 0: widget_item.config(foreground="#FFFFFF") widget_item.config(background="#2D163D") conteo += 1 for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.config(foreground="#FFFFFF") widget_item.config(background="#2D163D") def color_font_config(self, txt_area): txt_area.tag_config("reservada", foreground="#A675B9") txt_area.tag_config("id", foreground="#759AF0") txt_area.tag_config("string", foreground="#7AC883") txt_area.tag_config("comentario", foreground="#9BA29C") txt_area.tag_config("item", foreground="#A675B9") txt_area.tag_config("important", foreground="#E7375C") txt_area.tag_config("function", foreground="#5182C9") txt_area.tag_config("boolean", foreground="#FA8C31") self.highlight_pattern(r'/\*(.|\n)*?\*/', "comentario", txt_area, regexp=True) self.highlight_pattern(r'--.*\n', "comentario", txt_area, regexp=True) self.highlight_pattern("SELECT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("UPDATE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("WHERE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("JOIN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CREATE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DELETE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("COUNT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SUM", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FROM", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CASE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("THEN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ELSE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SMALLINT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INTEGER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("BIGINT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DECIMAL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("NUMERIC", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("REAL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("MONEY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CHAR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CHARACTER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("VARYING", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TIMESTAMP", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("WITHOUT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("WITH", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TIME", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ZONE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DATE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INTERVAL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FIELDS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("YEAR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("MONTH", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DAY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("HOUR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("MINUTE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SECOND", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TO", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("BOOLEAN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("AS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ENUM", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TYPE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("IS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ISNULL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("NOTNULL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("NOT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("AND", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("OR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("BETWEEN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LIKE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("IN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INLIKE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SIMILAR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("REPLACE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("MODE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("OWNER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("IF", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("EXISTS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ALTER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DATABASE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("RENAME", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DROP", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TABLE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("PRIMARY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FOREIGN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("KEY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("REFERENCES", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CONSTRAINT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("CHECK", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SET", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INSERT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("BY", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("GROUP", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("HAVING", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ORDER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("WHEN", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("UNION", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("END", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("VALUES", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INTERSECT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LIMIT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INNER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LEFT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("RIGHT", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("OUTER", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ASC", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DESC", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("GREATEST", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LEAST", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("OFFSET", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FIRST", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("LAST", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FULL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("ALL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("TRUE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("FALSE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("INHERITS", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("NULL", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("SHOW", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("DATABASES", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("USE", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("VARCHAR", "reservada", txt_area, case_sensitive=1) self.highlight_pattern("==", "item", txt_area) self.highlight_pattern("!=", "item", txt_area) self.highlight_pattern(">=", "item", txt_area) self.highlight_pattern("<=", "item", txt_area) self.highlight_pattern(">", "item", txt_area) self.highlight_pattern("<", "item", txt_area) self.highlight_pattern("=", "item", txt_area) self.highlight_pattern("+", "item", txt_area) self.highlight_pattern("-", "item", txt_area) self.highlight_pattern("*", "item", txt_area) self.highlight_pattern("/", "item", txt_area) self.highlight_pattern("self", "important", txt_area) self.highlight_pattern("print", "function", txt_area) self.highlight_pattern("true", "boolean", txt_area) self.highlight_pattern("false", "boolean", txt_area) self.highlight_pattern(r'(\".*?\")|(\'.*?\')', "string", txt_area, regexp=True) def graficar_AST(self, ast_): if len(ast_) != 0: ast_str = 'digraph AST { \n node [shape=record];\n' count_nodos = 0 for instruccion_ in ast_: if count_nodos != 0: ast_str += 'node' + str(count_nodos + 1000000) + '[label =\" Instruccion \"];\n' ast_str += 'node' + str(count_nodos + 10000) + '[label =\" Instrucciones \"];\n' ast_str += 'node' + str(count_nodos + 10000 - 1) + ' -> node' + str(count_nodos + 1000000) + ';\n' ast_str += 'node' + str(count_nodos + 10000 - 1) + ' -> node' + str(count_nodos + 10000) + ';\n' ast_str += 'node' + str(count_nodos + 1000000) + ' -> node' + instruccion_.nodo.num + ';\n' ast_str += 'node' + instruccion_.nodo.num + '[label =\"'+ instruccion_.nodo.valor +"\"];\n" ast_str += self.graficar_AST_hijos(instruccion_.nodo) else: ast_str += 'node' + instruccion_.nodo.num + '[label =\"'+ instruccion_.nodo.valor +"\"];\n" ast_str += 'node' + str(count_nodos + 1000000) + '[label =\" Instruccion \"];\n' ast_str += 'node' + str(count_nodos + 10000) + '[label =\" Instrucciones \"];\n' ast_str += 'start_ast -> node' + str(count_nodos + 10000) + ';\n' ast_str += 'start_ast -> node' + str(count_nodos + 1000000) + ';\n' ast_str += 'node' + str(count_nodos + 1000000) + ' -> node' + instruccion_.nodo.num + ';\n' ast_str += self.graficar_AST_hijos(instruccion_.nodo) count_nodos += 1 ast_str += '\n}' with open('ast_reporte.dot', 'w', encoding='utf8') as f: f.write(ast_str) def graficar_AST_hijos(self, instr_): t = '' for instruc in instr_.hijos: t += 'node'+instruc.num+'[label=\"'+instruc.valor+'\"];\n' t += 'node'+instr_.num + ' -> node' + instruc.num+';\n' t += self.graficar_AST_hijos(instruc) return t def graficar_Gramatical(self, ast_): if len(ast_) != 0: grammar_str = 'digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n' grammar_str += 'arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>PRODUCCIÓN</TD><TD>ACCIONES</TD></TR>\n' grammar_str += '<TR><TD>INSTRUCCIONES ::= INSTRUCCION INSTRUCCIONES1</TD><TD>INSTRUCCIONES = INSTRUCCIONES1; INSTRUCCIONES.append(INSTRUCCION); </TD></TR>\n' grammar_str += '<TR><TD>INSTRUCCIONES ::= </TD><TD>INSTRUCCIONES = [];</TD></TR>\n' for instr in ast_: grammar_str += instr.grammar_ + '\n' grammar_str += '</TABLE>\n>, ];\n}' with open('grammar_reporte.dot', 'w', encoding='utf8') as f: f.write(grammar_str) def graficar_Errores(self): if len(errores) != 0: reporte_errores = "digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n" reporte_errores += "arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>Tipo Error</TD>\n<TD>Descripcion</TD>\n<TD>Linea</TD>\n<TD>Columna</TD>\n</TR>\n" for error_ in errores: reporte_errores += '<TR>' reporte_errores += '<TD>' + error_.descripcion + '</TD>' reporte_errores += '<TD>' + error_.valor +'</TD>' reporte_errores += '<TD>' + error_.line +'</TD>' reporte_errores += '<TD>' + error_.column +'</TD>' reporte_errores += '</TR>\n' reporte_errores += '</TABLE>\n>, ];\n}' with open('errores_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_errores) def graficar_errores_lexicos(self): if len(errores) != 0: reporte_errores = "digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n" reporte_errores += "arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>Tipo Error</TD>\n<TD>Descripcion</TD>\n<TD>Linea</TD>\n<TD>Columna</TD>\n</TR>\n" for error_ in errores: if error_.descripcion.lower() == "léxico": reporte_errores += '<TR>' reporte_errores += '<TD>' + error_.descripcion + '</TD>' reporte_errores += '<TD>' + error_.valor +'</TD>' reporte_errores += '<TD>' + error_.line +'</TD>' reporte_errores += '<TD>' + error_.column +'</TD>' reporte_errores += '</TR>' reporte_errores += '</TABLE>\n>, ];\n}' with open('lexico_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_errores) def graficar_errores_sintacticos(self): if len(errores) != 0: reporte_errores = "digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n" reporte_errores += "arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>Tipo Error</TD>\n<TD>Descripcion</TD>\n<TD>Linea</TD>\n<TD>Columna</TD>\n</TR>\n" for error_ in errores: if error_.descripcion.lower() == "sintáctico": reporte_errores += '<TR>' reporte_errores += '<TD>' + error_.descripcion + '</TD>' reporte_errores += '<TD>' + error_.valor +'</TD>' reporte_errores += '<TD>' + error_.line +'</TD>' reporte_errores += '<TD>' + error_.column +'</TD>' reporte_errores += '</TR>' reporte_errores += '</TABLE>\n>, ];\n}' with open('sintactico_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_errores) def graficar_errores_semanticos(self): if len(errores) != 0: reporte_errores = "digraph test {\ngraph [ratio=fill];\nnode [label=\"\\N\", fontsize=15, shape=plaintext];\ngraph [bb=\"0,0,352,154\"];\n" reporte_errores += "arset [label=<\n<TABLE ALIGN=\"LEFT\">\n<TR>\n<TD>Tipo Error</TD>\n<TD>Descripcion</TD>\n<TD>Linea</TD>\n<TD>Columna</TD>\n</TR>\n" for error_ in errores: if error_.descripcion.lower() == "semántico": reporte_errores += '<TR>' reporte_errores += '<TD>' + error_.descripcion + '</TD>' reporte_errores += '<TD>' + error_.valor +'</TD>' reporte_errores += '<TD>' + error_.line +'</TD>' reporte_errores += '<TD>' + error_.column +'</TD>' reporte_errores += '</TR>' reporte_errores += '</TABLE>\n>, ];\n}' with open('semantico_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_errores) def graficar_TS(self): reporte_ts = ts.reporte_ts() with open('ts_reporte.dot', 'w', encoding='utf8') as f: f.write(reporte_ts) def compilar_ts_png(self): img = Source.from_file("ts_reporte.dot", format = "png", encoding="utf8") img.render() entrada = self.popup_reporte_png(self.ventana, "ts_reporte.dot.png") def compilar_ts_pdf(self): file_pdf = Source.from_file("ts_reporte.dot", format = "pdf", encoding="utf8") file_pdf.view() def compilar_grammar_png(self): img = Source.from_file("grammar_reporte.dot", format = "png", encoding="utf8") img.render() entrada = self.popup_reporte_png(self.ventana, "grammar_reporte.dot.png") def compilar_grammar_pdf(self): file_pdf = Source.from_file("grammar_reporte.dot", format = "pdf", encoding="utf8") file_pdf.view() def compilar_semantico_png(self): img = Source.from_file("semantico_reporte.dot", format = "png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "semantico_reporte.dot.png") def compilar_semantico_pdf(self): file_pdf = Source.from_file("semantico_reporte.dot", format = "pdf", encoding='utf8') file_pdf.view() def compilar_sintactico_png(self): img = Source.from_file("sintactico_reporte.dot", format = "png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "sintactico_reporte.dot.png") def compilar_sintactico_pdf(self): file_pdf = Source.from_file("sintactico_reporte.dot", format = "pdf", encoding='utf8') file_pdf.view() def compilar_lexico_png(self): img = Source.from_file("lexico_reporte.dot", format = "png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "lexico_reporte.dot.png") def compilar_lexico_pdf(self): file_pdf = Source.from_file("lexico_reporte.dot", format = "pdf", encoding='utf8') file_pdf.view() def compilar_Error_png(self): img = Source.from_file("errores_reporte.dot", format="png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "errores_reporte.dot.png") def compilar_Error_pdf(self): file_pdf = Source.from_file("errores_reporte.dot", format="pdf", encoding='utf8') file_pdf.view() def compilar_AST_png(self): img = Source.from_file("ast_reporte.dot", format="png", encoding='utf8') img.render() entrada = self.popup_reporte_png(self.ventana, "ast_reporte.dot.png") def compilar_AST_pdf(self): file_pdf = Source.from_file("ast_reporte.dot", format="pdf", encoding='utf8') file_pdf.view() def popup_reporte_png(self, master, path): top = self.top = Toplevel(master) img = ImageTk.PhotoImage(Image.open(path)) panel = Label(top, image = img) panel.image = img panel.pack(side = "bottom", fill = "both", expand = "yes") def ejecutar_codigo(self): errores = [] tab_list = self.tabControl.winfo_children() current_tab = tab_list[self.tabControl.index(CURRENT)] txt_box = None for widget_item in current_tab.winfo_children(): if isinstance(widget_item, Text): txt_box = widget_item contenido = txt_box.get(1.0, END) instruccions = [] instruccions = gramatica.parse(contenido) self.ejecutar_resultado(instruccions) for tab_item in self.tab_salida.winfo_children(): for widget_item in tab_item.winfo_children(): if isinstance(widget_item, Text): widget_item.delete('1.0', END) add_text("\nPS C:\\Users\\Grupo 23> ") widget_item.insert(INSERT, get_contenido()) self.graficar_AST(instruccions) self.graficar_Errores() self.graficar_errores_lexicos() self.graficar_errores_sintacticos() self.graficar_errores_semanticos() self.graficar_Gramatical(instruccions) self.graficar_TS() def ejecutar_resultado(self,instrucciones_): for instruccion_ in instrucciones_: instruccion_.ejecutar() if __name__ == "__main__": index = window() index.run()

true

1c38434c1e88f2c41b796918c5a9fbb25223104e

3,558

py

Python

src/cnn_text/exp10.py

numb3r33/dl_nlp

3b28b883de08efaa2eec4bb3d976906c94b926d6

[ "MIT" ]

null

src/cnn_text/exp10.py

numb3r33/dl_nlp

3b28b883de08efaa2eec4bb3d976906c94b926d6

[ "MIT" ]

11

2020-03-24T16:56:30.000Z

2022-03-11T23:45:08.000Z

src/cnn_text/exp10.py

numb3r33/dl_nlp

3b28b883de08efaa2eec4bb3d976906c94b926d6

[ "MIT" ]

null

import pandas as pd import numpy as np import os import argparse from utils import * from train import * from losses import * from models import * SAMPLE_BASEPATH = '../../data/jigsaw_toxic/processed/' BASEPATH = '../../data/jigsaw_toxic/raw/' SUB_PATH = '../../submissions/' MODEL_FP = '../../models/' SUB_FN = 'exp10_cnn.csv' def main(exp_name, run_mode, is_sample): if run_mode == 'train': if is_sample: train = read_csv(os.path.join(SAMPLE_BASEPATH, 'train_sample.csv')) else: train = read_csv(os.path.join(BASEPATH, 'train.csv')) TARGET_COLS = get_target_cols() train_tokenized_comments = get_tokenized_comments(train.comment_text) train['tokenized_comments'] = get_tokenized_comments_string(train.comment_text) words = get_vocab(train_tokenized_comments, exp_name) #embedding_matrix, UNK, PAD, UNK_IX, PAD_IX = load_glove_embedding_matrix(words) embedding_matrix, UNK, PAD, UNK_IX, PAD_IX = load_wv_embedding_matrix(words) token_to_id = get_token_to_id(words, UNK, PAD, UNK_IX, PAD_IX) print('Vocabulary |V|: {}'.format(len(token_to_id))) data_train, data_val = get_train_test_splits(train, exp_name) model = get_exp10_model(embedding_matrix, token_to_id, exp_name, PAD_IX) criterion = get_exp2_criterion() optimizer = get_exp2_optimizer(model, exp_name) model, preds = train_and_evaluate(model, criterion, optimizer, embedding_matrix, token_to_id, exp_name, data_train, data_val, TARGET_COLS, UNK_IX, PAD_IX, run_mode) else: train = read_csv(os.path.join(BASEPATH, 'train.csv')) test = read_csv(os.path.join(BASEPATH, 'test.csv')) test_labels = read_csv(os.path.join(BASEPATH, 'test_labels.csv')) TARGET_COLS = get_target_cols() train_tokenized_comments = get_tokenized_comments(train.comment_text) train['tokenized_comments'] = get_tokenized_comments_string(train.comment_text) test['tokenized_comments'] = get_tokenized_comments_string(test.comment_text) words = get_vocab(train_tokenized_comments, exp_name) #embedding_matrix, UNK, PAD, UNK_IX, PAD_IX = load_glove_embedding_matrix(words) embedding_matrix, UNK, PAD, UNK_IX, PAD_IX = load_wv_embedding_matrix(words) token_to_id = get_token_to_id(words, UNK, PAD, UNK_IX, PAD_IX) model = get_exp10_model(embedding_matrix, token_to_id, exp_name, PAD_IX) criterion = get_exp2_criterion() optimizer = get_exp2_optimizer(model, exp_name) model, preds = train_and_evaluate(model, criterion, optimizer, embedding_matrix, token_to_id, exp_name, train, test, TARGET_COLS, UNK_IX, PAD_IX, run_mode) prepare_submission(test_labels, preds, os.path.join(SUB_PATH, SUB_FN)) if __name__ == '__main__': parser = argparse.ArgumentParser(description='CNN for Text Classification ( Kaggle Jigsaw Toxic Comments Classification )') parser.add_argument('-run_mode', type=str, help='Run the model in train or eval mode') parser.add_argument('-exp_name', type=str, help='Name of the Experiment') parser.add_argument('-is_sample', type=bool, help='Do you want to run the experiment on the sample?') args = parser.parse_args() run_mode = args.run_mode exp_name = args.exp_name is_sample = args.is_sample main(exp_name, run_mode, is_sample)

40.431818

172

0.68606

import pandas as pd import numpy as np import os import argparse from utils import * from train import * from losses import * from models import * SAMPLE_BASEPATH = '../../data/jigsaw_toxic/processed/' BASEPATH = '../../data/jigsaw_toxic/raw/' SUB_PATH = '../../submissions/' MODEL_FP = '../../models/' SUB_FN = 'exp10_cnn.csv' def main(exp_name, run_mode, is_sample): if run_mode == 'train': if is_sample: train = read_csv(os.path.join(SAMPLE_BASEPATH, 'train_sample.csv')) else: train = read_csv(os.path.join(BASEPATH, 'train.csv')) TARGET_COLS = get_target_cols() train_tokenized_comments = get_tokenized_comments(train.comment_text) train['tokenized_comments'] = get_tokenized_comments_string(train.comment_text) words = get_vocab(train_tokenized_comments, exp_name) embedding_matrix, UNK, PAD, UNK_IX, PAD_IX = load_wv_embedding_matrix(words) token_to_id = get_token_to_id(words, UNK, PAD, UNK_IX, PAD_IX) print('Vocabulary |V|: {}'.format(len(token_to_id))) data_train, data_val = get_train_test_splits(train, exp_name) model = get_exp10_model(embedding_matrix, token_to_id, exp_name, PAD_IX) criterion = get_exp2_criterion() optimizer = get_exp2_optimizer(model, exp_name) model, preds = train_and_evaluate(model, criterion, optimizer, embedding_matrix, token_to_id, exp_name, data_train, data_val, TARGET_COLS, UNK_IX, PAD_IX, run_mode) else: train = read_csv(os.path.join(BASEPATH, 'train.csv')) test = read_csv(os.path.join(BASEPATH, 'test.csv')) test_labels = read_csv(os.path.join(BASEPATH, 'test_labels.csv')) TARGET_COLS = get_target_cols() train_tokenized_comments = get_tokenized_comments(train.comment_text) train['tokenized_comments'] = get_tokenized_comments_string(train.comment_text) test['tokenized_comments'] = get_tokenized_comments_string(test.comment_text) words = get_vocab(train_tokenized_comments, exp_name) embedding_matrix, UNK, PAD, UNK_IX, PAD_IX = load_wv_embedding_matrix(words) token_to_id = get_token_to_id(words, UNK, PAD, UNK_IX, PAD_IX) model = get_exp10_model(embedding_matrix, token_to_id, exp_name, PAD_IX) criterion = get_exp2_criterion() optimizer = get_exp2_optimizer(model, exp_name) model, preds = train_and_evaluate(model, criterion, optimizer, embedding_matrix, token_to_id, exp_name, train, test, TARGET_COLS, UNK_IX, PAD_IX, run_mode) prepare_submission(test_labels, preds, os.path.join(SUB_PATH, SUB_FN)) if __name__ == '__main__': parser = argparse.ArgumentParser(description='CNN for Text Classification ( Kaggle Jigsaw Toxic Comments Classification )') parser.add_argument('-run_mode', type=str, help='Run the model in train or eval mode') parser.add_argument('-exp_name', type=str, help='Name of the Experiment') parser.add_argument('-is_sample', type=bool, help='Do you want to run the experiment on the sample?') args = parser.parse_args() run_mode = args.run_mode exp_name = args.exp_name is_sample = args.is_sample main(exp_name, run_mode, is_sample)

true

1c38437f64fca9bd28d1e4f5a7c12568c004f30a

909

py

Python

receiver.py

lobdellb/brycemodem

d95431e93b54b08d07ebefc7b64b8c89764a4f12

[ "MIT" ]

null

receiver.py

lobdellb/brycemodem

d95431e93b54b08d07ebefc7b64b8c89764a4f12

[ "MIT" ]

null

receiver.py

lobdellb/brycemodem

d95431e93b54b08d07ebefc7b64b8c89764a4f12

[ "MIT" ]

null

import pyaudio import wave import time import sys if len(sys.argv) < 2: print("Plays a wave file.\n\nUsage: %s filename.wav" % sys.argv[0]) sys.exit(-1) wf = wave.open(sys.argv[1], 'rb') # instantiate PyAudio (1) p = pyaudio.PyAudio() # define callback (2) def callback(in_data, frame_count, time_info, status): print(frame_count) data = wf.readframes(frame_count) return (data, pyaudio.paContinue) # open stream using callback (3) stream = p.open(format=p.get_format_from_width(wf.getsampwidth()), channels=wf.getnchannels(), rate=wf.getframerate(), output=True, stream_callback=callback) # start the stream (4) stream.start_stream() # wait for stream to finish (5) while stream.is_active(): time.sleep(0.1) # stop stream (6) stream.stop_stream() stream.close() wf.close() # close PyAudio (7) p.terminate()

19.76087

71

0.656766

import pyaudio import wave import time import sys if len(sys.argv) < 2: print("Plays a wave file.\n\nUsage: %s filename.wav" % sys.argv[0]) sys.exit(-1) wf = wave.open(sys.argv[1], 'rb') p = pyaudio.PyAudio() def callback(in_data, frame_count, time_info, status): print(frame_count) data = wf.readframes(frame_count) return (data, pyaudio.paContinue) stream = p.open(format=p.get_format_from_width(wf.getsampwidth()), channels=wf.getnchannels(), rate=wf.getframerate(), output=True, stream_callback=callback) stream.start_stream() while stream.is_active(): time.sleep(0.1) stream.stop_stream() stream.close() wf.close() p.terminate()

true

1c384397f9a6a26b3987220dac9d9957db3815cd

2,482

py

Python

themes/prompt-toolkit/base16/base16-cupertino.py

base16-fork/base16-fork

79856b7e6195dde0874a9e6d191101ac6c5c74f5

[ "MIT" ]

null

themes/prompt-toolkit/base16/base16-cupertino.py

base16-fork/base16-fork

79856b7e6195dde0874a9e6d191101ac6c5c74f5

[ "MIT" ]

null

themes/prompt-toolkit/base16/base16-cupertino.py

base16-fork/base16-fork

79856b7e6195dde0874a9e6d191101ac6c5c74f5

[ "MIT" ]

null

# -*- coding: utf-8 -*- # base16-prompt-toolkit (https://github.com/memeplex/base16-prompt-toolkit) # Base16 Prompt Toolkit template by Carlos Pita (carlosjosepita@gmail.com # Cupertino scheme by Defman21 try: # older than v2 from prompt_toolkit.output.vt100 import _256_colors except ModuleNotFoundError: # version 2 from prompt_toolkit.formatted_text.ansi import _256_colors from pygments.style import Style from pygments.token import (Keyword, Name, Comment, String, Error, Text, Number, Operator, Literal, Token) # See http://chriskempson.com/projects/base16/ for a description of the role # of the different colors in the base16 palette. base00 = '#ffffff' base01 = '#c0c0c0' base02 = '#c0c0c0' base03 = '#808080' base04 = '#808080' base05 = '#404040' base06 = '#404040' base07 = '#5e5e5e' base08 = '#c41a15' base09 = '#eb8500' base0A = '#826b28' base0B = '#007400' base0C = '#318495' base0D = '#0000ff' base0E = '#a90d91' base0F = '#826b28' # See https://github.com/jonathanslenders/python-prompt-toolkit/issues/355 colors = (globals()['base0' + d] for d in '08BADEC5379F1246') for i, color in enumerate(colors): r, g, b = int(color[1:3], 16), int(color[3:5], 16), int(color[5:], 16) _256_colors[r, g, b] = i + 6 if i > 8 else i # See http://pygments.org/docs/tokens/ for a description of the different # pygments tokens. class Base16Style(Style): background_color = base00 highlight_color = base02 default_style = base05 styles = { Text: base05, Error: '%s bold' % base08, Comment: base03, Keyword: base0E, Keyword.Constant: base09, Keyword.Namespace: base0D, Name.Builtin: base0D, Name.Function: base0D, Name.Class: base0D, Name.Decorator: base0E, Name.Exception: base08, Number: base09, Operator: base0E, Literal: base0B, String: base0B } # See https://github.com/jonathanslenders/python-prompt-toolkit/blob/master/prompt_toolkit/styles/defaults.py # for a description of prompt_toolkit related pseudo-tokens. overrides = { Token.Prompt: base0B, Token.PromptNum: '%s bold' % base0B, Token.OutPrompt: base08, Token.OutPromptNum: '%s bold' % base08, Token.Menu.Completions.Completion: 'bg:%s %s' % (base01, base04), Token.Menu.Completions.Completion.Current: 'bg:%s %s' % (base04, base01), Token.MatchingBracket.Other: 'bg:%s %s' % (base03, base00) }

29.903614

109

0.672039

try: from prompt_toolkit.output.vt100 import _256_colors except ModuleNotFoundError: from prompt_toolkit.formatted_text.ansi import _256_colors from pygments.style import Style from pygments.token import (Keyword, Name, Comment, String, Error, Text, Number, Operator, Literal, Token) base00 = '#ffffff' base01 = '#c0c0c0' base02 = '#c0c0c0' base03 = '#808080' base04 = '#808080' base05 = '#404040' base06 = '#404040' base07 = '#5e5e5e' base08 = '#c41a15' base09 = '#eb8500' base0A = '#826b28' base0B = '#007400' base0C = '#318495' base0D = '#0000ff' base0E = '#a90d91' base0F = '#826b28' colors = (globals()['base0' + d] for d in '08BADEC5379F1246') for i, color in enumerate(colors): r, g, b = int(color[1:3], 16), int(color[3:5], 16), int(color[5:], 16) _256_colors[r, g, b] = i + 6 if i > 8 else i class Base16Style(Style): background_color = base00 highlight_color = base02 default_style = base05 styles = { Text: base05, Error: '%s bold' % base08, Comment: base03, Keyword: base0E, Keyword.Constant: base09, Keyword.Namespace: base0D, Name.Builtin: base0D, Name.Function: base0D, Name.Class: base0D, Name.Decorator: base0E, Name.Exception: base08, Number: base09, Operator: base0E, Literal: base0B, String: base0B } overrides = { Token.Prompt: base0B, Token.PromptNum: '%s bold' % base0B, Token.OutPrompt: base08, Token.OutPromptNum: '%s bold' % base08, Token.Menu.Completions.Completion: 'bg:%s %s' % (base01, base04), Token.Menu.Completions.Completion.Current: 'bg:%s %s' % (base04, base01), Token.MatchingBracket.Other: 'bg:%s %s' % (base03, base00) }

true

1c3844f5ad8864ed597c03a7ca8fc9a33d3aea8e

3,684

py

Python

examples/resnet/train_fleet_static_amp.py

sandyhouse/FleetX

b3d089cdb0f388c12ad95494ee98053d5bfa450b

[ "Apache-2.0" ]

170

2020-08-12T12:07:01.000Z

2022-03-07T02:38:26.000Z

examples/resnet/train_fleet_static_amp.py

sandyhouse/FleetX

b3d089cdb0f388c12ad95494ee98053d5bfa450b

[ "Apache-2.0" ]

195

2020-08-13T03:22:15.000Z

2022-03-30T07:40:25.000Z

examples/resnet/train_fleet_static_amp.py

sandyhouse/FleetX

b3d089cdb0f388c12ad95494ee98053d5bfa450b

[ "Apache-2.0" ]

67

2020-08-14T02:07:46.000Z

2022-03-28T10:05:33.000Z

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import argparse import ast import paddle from paddle.distributed import fleet import resnet_static as resnet import os base_lr = 0.1 momentum_rate = 0.9 l2_decay = 1e-4 epoch = 10 batch_size = 32 class_dim = 102 def optimizer_setting(parameter_list=None): optimizer = paddle.optimizer.Momentum( learning_rate=base_lr, momentum=momentum_rate, weight_decay=paddle.regularizer.L2Decay(l2_decay), parameters=parameter_list) return optimizer def get_train_loader(feed_list, place): def reader_decorator(reader): def __reader__(): for item in reader(): img = np.array(item[0]).astype('float32').reshape(3, 224, 224) label = np.array(item[1]).astype('int64').reshape(1) yield img, label return __reader__ train_reader = paddle.batch( reader_decorator(paddle.dataset.flowers.train(use_xmap=True)), batch_size=batch_size, drop_last=True) train_loader = paddle.io.DataLoader.from_generator( capacity=32, use_double_buffer=True, feed_list=feed_list, iterable=True) train_loader.set_sample_list_generator(train_reader, place) return train_loader def train_resnet(): paddle.enable_static() paddle.vision.set_image_backend('cv2') image = paddle.static.data(name="x", shape=[None, 3, 224, 224], dtype='float32') label= paddle.static.data(name="y", shape=[None, 1], dtype='int64') model = resnet.ResNet(layers=50) out = model.net(input=image, class_dim=class_dim) avg_cost = paddle.nn.functional.cross_entropy(input=out, label=label) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) place = paddle.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0))) train_loader = get_train_loader([image, label], place) strategy = fleet.DistributedStrategy() strategy.amp = True strategy.amp_configs = { "init_loss_scaling": 32768, "decr_every_n_nan_or_inf": 2, "incr_every_n_steps": 1000, "incr_ratio": 2.0, "use_dynamic_loss_scaling": True, "decr_ratio": 0.5, "custom_white_list": [], "custom_black_list": [], } fleet.init(is_collective=True, strategy=strategy) optimizer = optimizer_setting() optimizer = fleet.distributed_optimizer(optimizer) optimizer.minimize(avg_cost) exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) epoch = 10 step = 0 for eop in range(epoch): for batch_id, data in enumerate(train_loader()): loss, acc1, acc5 = exe.run(paddle.static.default_main_program(), feed=data, fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name]) if batch_id % 5 == 0: print("[Epoch %d, batch %d] loss: %.5f, acc1: %.5f, acc5: %.5f" % (eop, batch_id, loss, acc1, acc5)) if __name__ == '__main__': train_resnet()

33.798165

158

0.678882

import numpy as np import argparse import ast import paddle from paddle.distributed import fleet import resnet_static as resnet import os base_lr = 0.1 momentum_rate = 0.9 l2_decay = 1e-4 epoch = 10 batch_size = 32 class_dim = 102 def optimizer_setting(parameter_list=None): optimizer = paddle.optimizer.Momentum( learning_rate=base_lr, momentum=momentum_rate, weight_decay=paddle.regularizer.L2Decay(l2_decay), parameters=parameter_list) return optimizer def get_train_loader(feed_list, place): def reader_decorator(reader): def __reader__(): for item in reader(): img = np.array(item[0]).astype('float32').reshape(3, 224, 224) label = np.array(item[1]).astype('int64').reshape(1) yield img, label return __reader__ train_reader = paddle.batch( reader_decorator(paddle.dataset.flowers.train(use_xmap=True)), batch_size=batch_size, drop_last=True) train_loader = paddle.io.DataLoader.from_generator( capacity=32, use_double_buffer=True, feed_list=feed_list, iterable=True) train_loader.set_sample_list_generator(train_reader, place) return train_loader def train_resnet(): paddle.enable_static() paddle.vision.set_image_backend('cv2') image = paddle.static.data(name="x", shape=[None, 3, 224, 224], dtype='float32') label= paddle.static.data(name="y", shape=[None, 1], dtype='int64') model = resnet.ResNet(layers=50) out = model.net(input=image, class_dim=class_dim) avg_cost = paddle.nn.functional.cross_entropy(input=out, label=label) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) place = paddle.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0))) train_loader = get_train_loader([image, label], place) strategy = fleet.DistributedStrategy() strategy.amp = True strategy.amp_configs = { "init_loss_scaling": 32768, "decr_every_n_nan_or_inf": 2, "incr_every_n_steps": 1000, "incr_ratio": 2.0, "use_dynamic_loss_scaling": True, "decr_ratio": 0.5, "custom_white_list": [], "custom_black_list": [], } fleet.init(is_collective=True, strategy=strategy) optimizer = optimizer_setting() optimizer = fleet.distributed_optimizer(optimizer) optimizer.minimize(avg_cost) exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) epoch = 10 step = 0 for eop in range(epoch): for batch_id, data in enumerate(train_loader()): loss, acc1, acc5 = exe.run(paddle.static.default_main_program(), feed=data, fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name]) if batch_id % 5 == 0: print("[Epoch %d, batch %d] loss: %.5f, acc1: %.5f, acc5: %.5f" % (eop, batch_id, loss, acc1, acc5)) if __name__ == '__main__': train_resnet()

true

1c38451fb424e2b6a391207b83c511a087bace8a

6,177

py

Python

cli/scaleout/details.py

ScilifelabDataCentre/stackn

00a65a16ff271f04548b3ff475c72dacbfd916df

[ "Apache-2.0" ]

null

cli/scaleout/details.py

ScilifelabDataCentre/stackn

00a65a16ff271f04548b3ff475c72dacbfd916df

[ "Apache-2.0" ]

null

cli/scaleout/details.py

ScilifelabDataCentre/stackn

00a65a16ff271f04548b3ff475c72dacbfd916df

[ "Apache-2.0" ]

null

import platform import psutil import logging import json import os import sys from scaleout.cli.helpers import prompt def get_system_details(info): try: info['Platform'] = platform.system() #info['Platform version'] = platform.version() info['Architecture'] = platform.machine() info['Processor'] = platform.processor() info['RAM'] = str(round(psutil.virtual_memory().total / (1024.0 **3))) + " GB" info['Python version'] = platform.python_version() json_prep = json.dumps(info) return json.loads(json_prep) except Exception as e: print("Failed to retrieve details about your system.") logging.exception(e) def get_cpu_details(info): try: info['Physical cores'] = psutil.cpu_count(logical=False) info['Total cores'] = psutil.cpu_count(logical=True) for i, percentage in enumerate(psutil.cpu_percent(percpu=True, interval=1)): info[f'Core {i}'] = f'{percentage}%' info['Total CPU usage'] = f'{psutil.cpu_percent()}%' json_prep = json.dumps(info) return json.loads(json_prep) except Exception as e: print("Failed to retrieve details about the CPU of your machine.") logging.exception(e) # Function that pauses the run until the user either commits changed files in the repo, or tells the program to contnue training with uncommitted files # ---------------- Question ------------------- # Should all files be committed before training or is it enough if the user commits some files in the repo? # --------------------------------------------- def commit_helper(repo, exit_message): # This function needs to be tested and modified. Might note even be necessary to have this function print('WARNING: Uncommitted files exist in the current Git repository. Training the model with uncommitted files '\ + 'should be avoided for major experiments since this will negatively impact code versioning. To increase future ' \ + 'reproducibility of your experiment, please consider committing all files before training the model.\n') valid = ["1", "2"] while True: answer = input("What do you want to do? \n" \ + " 1) Continue training the model without committing my files (Not recommended). \n"\ + " 2) Put the training session on hold to commit my files (Highly recommended). \n"\ + "Choose an option [1 or 2]: ") if answer in valid: break else: print("\nPlease respond with '1' or '2'. \n") if answer == "1": print("\nThe training session will continue with uncommitted files in the repo. This might affect the reproducibility of your experiment.") question = "Are you sure you want to continue?" confirmed = prompt(question) if confirmed: return False else: sys.exit(exit_message.format("commit your files")) else: # The user wants to commit files before continuing model training. # We could let the user add and commit files here with a subprocess operation? E.g. subprocess.run("git add .", check=True, shell=True) answer = input("\nA good choice! After you commit your files, press enter to continue training the model "\ + "(or abort the current training session by pressing arbitrary key): ") if answer: sys.exit(exit_message.format("commit your files")) else: # Would be good to check here whether the files have been committed successfully. Maybe the user does not want to commit all files? print("Perfect, your files have been committed and the training session will continue.") #while True: # if not repo.is_dirty(): # break # else: return True def get_git_details(code_version): exit_message = "Aborting this training session. Please {} before running 'stackn train' again." try: import git except ImportError: print('Failed to import Git') return None try: # current_repo = git.Repo(os.getcwd()) # Which one of these should we use? Needs testing current_repo = git.Repo(search_parent_directories=True) is_committed = True if current_repo.is_dirty(): # This should be true if uncommitted files exist is_committed = commit_helper(current_repo, exit_message) latest_commit = current_repo.head.object.hexsha print("Code version {} will be tied to the Git commit hash '{}'.".format(code_version, latest_commit)) if not is_committed: print("Since uncommitted files exist in the current repo, it will be noted in the training log that the code " \ + "used to train the model in this run does not correspond to the recorded commit hash. " \ + "This is done mainly for the purpose of appropriate code versioning and future reproducibility.") except (git.InvalidGitRepositoryError, ValueError): latest_commit = "No recent Git commit to log" if git.InvalidGitRepositoryError: print('WARNING: Failed to extract Git repo. Check to see if you are currently working in a Git repository.') question = "Do you want to continue training the model anyways (not recommended)?" confirmed = prompt(question) if confirmed: current_repo = "No Git repository to log" else: sys.exit(exit_message.format('enter an active Git repo')) elif ValueError and not committed_files: print("WARNING: Failed to extract latest Git commit hash. No commits seem to have been made yet and you have chosen not to commit them. " \ + "The training session will continue.") return (current_repo, latest_commit) def get_run_details(code_version): system_details = get_system_details({}) cpu_details = get_cpu_details({}) git_details = get_git_details(code_version) return system_details, cpu_details, git_details

51.049587

151

0.646592

import platform import psutil import logging import json import os import sys from scaleout.cli.helpers import prompt def get_system_details(info): try: info['Platform'] = platform.system() info['Architecture'] = platform.machine() info['Processor'] = platform.processor() info['RAM'] = str(round(psutil.virtual_memory().total / (1024.0 **3))) + " GB" info['Python version'] = platform.python_version() json_prep = json.dumps(info) return json.loads(json_prep) except Exception as e: print("Failed to retrieve details about your system.") logging.exception(e) def get_cpu_details(info): try: info['Physical cores'] = psutil.cpu_count(logical=False) info['Total cores'] = psutil.cpu_count(logical=True) for i, percentage in enumerate(psutil.cpu_percent(percpu=True, interval=1)): info[f'Core {i}'] = f'{percentage}%' info['Total CPU usage'] = f'{psutil.cpu_percent()}%' json_prep = json.dumps(info) return json.loads(json_prep) except Exception as e: print("Failed to retrieve details about the CPU of your machine.") logging.exception(e) def commit_helper(repo, exit_message): print('WARNING: Uncommitted files exist in the current Git repository. Training the model with uncommitted files '\ + 'should be avoided for major experiments since this will negatively impact code versioning. To increase future ' \ + 'reproducibility of your experiment, please consider committing all files before training the model.\n') valid = ["1", "2"] while True: answer = input("What do you want to do? \n" \ + " 1) Continue training the model without committing my files (Not recommended). \n"\ + " 2) Put the training session on hold to commit my files (Highly recommended). \n"\ + "Choose an option [1 or 2]: ") if answer in valid: break else: print("\nPlease respond with '1' or '2'. \n") if answer == "1": print("\nThe training session will continue with uncommitted files in the repo. This might affect the reproducibility of your experiment.") question = "Are you sure you want to continue?" confirmed = prompt(question) if confirmed: return False else: sys.exit(exit_message.format("commit your files")) else: answer = input("\nA good choice! After you commit your files, press enter to continue training the model "\ + "(or abort the current training session by pressing arbitrary key): ") if answer: sys.exit(exit_message.format("commit your files")) else: print("Perfect, your files have been committed and the training session will continue.") return True def get_git_details(code_version): exit_message = "Aborting this training session. Please {} before running 'stackn train' again." try: import git except ImportError: print('Failed to import Git') return None try: rectories=True) is_committed = True if current_repo.is_dirty(): is_committed = commit_helper(current_repo, exit_message) latest_commit = current_repo.head.object.hexsha print("Code version {} will be tied to the Git commit hash '{}'.".format(code_version, latest_commit)) if not is_committed: print("Since uncommitted files exist in the current repo, it will be noted in the training log that the code " \ + "used to train the model in this run does not correspond to the recorded commit hash. " \ + "This is done mainly for the purpose of appropriate code versioning and future reproducibility.") except (git.InvalidGitRepositoryError, ValueError): latest_commit = "No recent Git commit to log" if git.InvalidGitRepositoryError: print('WARNING: Failed to extract Git repo. Check to see if you are currently working in a Git repository.') question = "Do you want to continue training the model anyways (not recommended)?" confirmed = prompt(question) if confirmed: current_repo = "No Git repository to log" else: sys.exit(exit_message.format('enter an active Git repo')) elif ValueError and not committed_files: print("WARNING: Failed to extract latest Git commit hash. No commits seem to have been made yet and you have chosen not to commit them. " \ + "The training session will continue.") return (current_repo, latest_commit) def get_run_details(code_version): system_details = get_system_details({}) cpu_details = get_cpu_details({}) git_details = get_git_details(code_version) return system_details, cpu_details, git_details

true

1c3845d71bd0808be9a45e1440e146be6fafa885

11,636

py

Python

pychron/experiment/experimentor.py

ASUPychron/pychron

dfe551bdeb4ff8b8ba5cdea0edab336025e8cc76

[ "Apache-2.0" ]

31

2016-03-07T02:38:17.000Z

2022-02-14T18:23:43.000Z

pychron/experiment/experimentor.py

ASUPychron/pychron

dfe551bdeb4ff8b8ba5cdea0edab336025e8cc76

[ "Apache-2.0" ]

1,626

2015-01-07T04:52:35.000Z

2022-03-25T19:15:59.000Z

pychron/experiment/experimentor.py

UIllinoisHALPychron/pychron

f21b79f4592a9fb9dc9a4cb2e4e943a3885ededc

[ "Apache-2.0" ]

26

2015-05-23T00:10:06.000Z

2022-03-07T16:51:57.000Z

# =============================================================================== # Copyright 2013 Jake Ross # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =============================================================================== # ============= enthought library imports ======================= from __future__ import absolute_import from traits.api import Instance, List, on_trait_change, Bool, Event from pychron.dvc.dvc_irradiationable import DVCIrradiationable from pychron.experiment.experiment_executor import ExperimentExecutor from pychron.experiment.factory import ExperimentFactory from pychron.experiment.queue.experiment_queue import ExperimentQueue class Experimentor(DVCIrradiationable): experiment_factory = Instance(ExperimentFactory) experiment_queue = Instance(ExperimentQueue) executor = Instance(ExperimentExecutor) experiment_queues = List # stats = Instance(StatsGroup, ()) mode = None # unique_executor_db = False save_enabled = Bool # =========================================================================== # permissions # =========================================================================== # max_allowable_runs = 10000 # can_edit_scripts = True # _last_ver_time = None # _ver_timeout = 10 # =========================================================================== # task events # =========================================================================== activate_editor_event = Event save_event = Event def prepare_destory(self): if self.executor: if self.executor.datahub: self.executor.datahub.prepare_destroy() if self.experiment_factory: if self.experiment_factory.run_factory: if self.experiment_factory.run_factory.datahub: self.experiment_factory.run_factory.datahub.prepare_destroy() def load(self): self.experiment_factory.queue_factory.db_refresh_needed = True self.experiment_factory.run_factory.db_refresh_needed = True return True def reset_run_generator(self): if self.executor.is_alive(): self.debug("Queue modified. Reset run generator") # self.executor.queue_modified = True self.executor.set_queue_modified() def refresh_executable(self, qs=None): if qs is None: qs = self.experiment_queues if self.executor.is_alive(): qs = (self.executor.experiment_queue,) self.executor.executable = all([ei.is_executable() for ei in qs]) self.debug("setting executable {}".format(self.executor.executable)) def update_queues(self): self._update_queues() def update_info(self): try: self._update() except BaseException as e: self.debug_exception() self.warning_dialog("Failed updating info: Error={}".format(e)) # =============================================================================== # info update # =============================================================================== def _get_all_automated_runs(self, qs=None): if qs is None: qs = self.experiment_queues return [ai for ei in qs for ai in ei.automated_runs if ai.executable] def _update(self, queues=None): self.debug("update runs") if queues is None: queues = self.experiment_queues queues = [qi for qi in queues if qi.is_updateable()] if not queues: return self.debug("executor executable {}".format(self.executor.executable)) self.debug("updating stats, ") self.executor.stats.experiment_queues = queues self.executor.stats.calculate() self.refresh_executable(queues) self._set_analysis_metadata() self.debug("info updated") for qi in queues: qi.refresh_table_needed = True def _set_analysis_metadata(self): cache = dict() db = self.get_database() aruns = self._get_all_automated_runs() with db.session_ctx(): for ai in aruns: if ai.skip: continue ln = ai.labnumber if ln == "dg": continue # is run in cache if ln not in cache: info = db.get_identifier_info(ln) self.debug("Info for {}={}".format(ln, info)) if not info: cache[ln] = dict(identifier_error=True) else: info["identifier_error"] = False cache[ln] = info ai.trait_set(**cache[ln]) def execute_queues(self, queues): names = ",".join([e.name for e in queues]) self.debug("queues: n={}, names={}".format(len(queues), names)) self.executor.trait_set(experiment_queues=queues, experiment_queue=queues[0]) return self.executor.execute() def verify_database_connection(self, inform=True): db = self.get_database() if db is not None: if db.connect(force=True): return True elif inform: self.warning_dialog("No Database available") def sync_queue(self, queue): ms = queue.mass_spectrometer ed = queue.extract_device db = self.get_database() with db.session_ctx(): next_pos = None for i, ai in enumerate(queue.automated_runs): if ai.skip or ai.is_special(): continue kw = { "identifier": ai.identifier, "position": ai.position, "mass_spectrometer": ms.lower(), "extract_device": ed, } if ai.is_step_heat(): kw["aliquot"] = ai.aliquot kw["extract_value"] = ai.extract_value self.debug("checking {}/{}. attr={}".format(i, ai.runid, kw)) aa = db.get_analysis_by_attr(**kw) if aa is None: self.debug("----- not found") if next_pos == ai: i -= 1 break elif not self.confirmation_dialog( "Found analyses up to {}. " "position={}, extract={}. " "Continue searching?".format( ai.runid, ai.extract_value, ai.position ) ): break next_pos = queue.automated_runs[i + 1] if i: if i == len(queue.automated_runs) - 1: self.information_dialog( "All Analyses from this experiment have been run" ) else: queue.automated_runs = queue.automated_runs[i:] else: self.information_dialog( "No Analyses from this experiment have been run" ) # =============================================================================== # handlers # =============================================================================== def _experiment_queue_changed(self, eq): if eq: self.experiment_factory.queue = eq self.experiment_factory.sync_queue_meta() self.experiment_factory.edit_enabled = True else: self.experiment_factory.edit_enabled = False @on_trait_change("executor:experiment_queue") def _activate_editor(self, eq): self.activate_editor_event = id(eq) @on_trait_change("experiment_queues[]") def _update_queues(self): qs = self.experiment_queues self.executor.stats.experiment_queues = qs @on_trait_change("experiment_factory:run_factory:changed") def _queue_dirty(self): self.experiment_queue.changed = True @on_trait_change("experiment_queue:dclicked") def _dclicked_changed(self, new): self.experiment_factory.run_factory.edit_mode = True self._set_factory_runs(self.experiment_queue.selected) @on_trait_change("experiment_factory:run_factory:update_info_needed") def _refresh3(self): self.debug("update info needed fired") self.update_info() @on_trait_change("executor:queue_modified") def _refresh5(self, new): if new: self.debug("queue modified fired") self.update_info() @on_trait_change("experiment_factory:run_factory:refresh_table_needed") def _refresh4(self): for qi in self.experiment_queues: qi.refresh_table_needed = True @on_trait_change("experiment_factory:save_button") def _save_update(self): self.save_event = True self.update_info() @on_trait_change("experiment_queue:refresh_info_needed") def _handle_refresh(self): self.update_info() @on_trait_change("experiment_queue:selected") def _selected_changed(self, new): ef = self.experiment_factory rf = ef.run_factory rf.edit_mode = False if new: self._set_factory_runs(new) # if self.executor.is_alive(): a = new[-1] if not a.skip: self.executor.stats.calculate_at(a, at_times=self.executor.is_alive()) # self.stats.calculate() @on_trait_change("experiment_factory:queue_factory:delay_between_analyses") def handle_delay_between_analyses(self, new): if self.executor.is_alive(): self.executor.experiment_queue.delay_between_analyses = new def _set_factory_runs(self, new): ef = self.experiment_factory rf = ef.run_factory # print 'set runs' # rf.special_labnumber = 'Special Labnumber' rf.suppress_update = True rf.set_selected_runs(new) rf.suppress_update = False def _executor_factory(self): e = ExperimentExecutor(mode=self.mode, application=self.application) e.bind_preferences() return e # =============================================================================== # defaults # =============================================================================== def _executor_default(self): return self._executor_factory() def _experiment_factory_default(self): dms = "Spectrometer" if self.application: p2 = ( "pychron.spectrometer.base_spectrometer_manager.BaseSpectrometerManager" ) spec = self.application.get_service(p2) if spec: dms = spec.name.capitalize() e = ExperimentFactory( application=self.application, dvc=self.dvc, default_mass_spectrometer=dms ) return e # ============= EOF =============================================

34.942943

88

0.544173

from __future__ import absolute_import from traits.api import Instance, List, on_trait_change, Bool, Event from pychron.dvc.dvc_irradiationable import DVCIrradiationable from pychron.experiment.experiment_executor import ExperimentExecutor from pychron.experiment.factory import ExperimentFactory from pychron.experiment.queue.experiment_queue import ExperimentQueue class Experimentor(DVCIrradiationable): experiment_factory = Instance(ExperimentFactory) experiment_queue = Instance(ExperimentQueue) executor = Instance(ExperimentExecutor) experiment_queues = List mode = None save_enabled = Bool activate_editor_event = Event save_event = Event def prepare_destory(self): if self.executor: if self.executor.datahub: self.executor.datahub.prepare_destroy() if self.experiment_factory: if self.experiment_factory.run_factory: if self.experiment_factory.run_factory.datahub: self.experiment_factory.run_factory.datahub.prepare_destroy() def load(self): self.experiment_factory.queue_factory.db_refresh_needed = True self.experiment_factory.run_factory.db_refresh_needed = True return True def reset_run_generator(self): if self.executor.is_alive(): self.debug("Queue modified. Reset run generator") self.executor.set_queue_modified() def refresh_executable(self, qs=None): if qs is None: qs = self.experiment_queues if self.executor.is_alive(): qs = (self.executor.experiment_queue,) self.executor.executable = all([ei.is_executable() for ei in qs]) self.debug("setting executable {}".format(self.executor.executable)) def update_queues(self): self._update_queues() def update_info(self): try: self._update() except BaseException as e: self.debug_exception() self.warning_dialog("Failed updating info: Error={}".format(e)) def _get_all_automated_runs(self, qs=None): if qs is None: qs = self.experiment_queues return [ai for ei in qs for ai in ei.automated_runs if ai.executable] def _update(self, queues=None): self.debug("update runs") if queues is None: queues = self.experiment_queues queues = [qi for qi in queues if qi.is_updateable()] if not queues: return self.debug("executor executable {}".format(self.executor.executable)) self.debug("updating stats, ") self.executor.stats.experiment_queues = queues self.executor.stats.calculate() self.refresh_executable(queues) self._set_analysis_metadata() self.debug("info updated") for qi in queues: qi.refresh_table_needed = True def _set_analysis_metadata(self): cache = dict() db = self.get_database() aruns = self._get_all_automated_runs() with db.session_ctx(): for ai in aruns: if ai.skip: continue ln = ai.labnumber if ln == "dg": continue if ln not in cache: info = db.get_identifier_info(ln) self.debug("Info for {}={}".format(ln, info)) if not info: cache[ln] = dict(identifier_error=True) else: info["identifier_error"] = False cache[ln] = info ai.trait_set(**cache[ln]) def execute_queues(self, queues): names = ",".join([e.name for e in queues]) self.debug("queues: n={}, names={}".format(len(queues), names)) self.executor.trait_set(experiment_queues=queues, experiment_queue=queues[0]) return self.executor.execute() def verify_database_connection(self, inform=True): db = self.get_database() if db is not None: if db.connect(force=True): return True elif inform: self.warning_dialog("No Database available") def sync_queue(self, queue): ms = queue.mass_spectrometer ed = queue.extract_device db = self.get_database() with db.session_ctx(): next_pos = None for i, ai in enumerate(queue.automated_runs): if ai.skip or ai.is_special(): continue kw = { "identifier": ai.identifier, "position": ai.position, "mass_spectrometer": ms.lower(), "extract_device": ed, } if ai.is_step_heat(): kw["aliquot"] = ai.aliquot kw["extract_value"] = ai.extract_value self.debug("checking {}/{}. attr={}".format(i, ai.runid, kw)) aa = db.get_analysis_by_attr(**kw) if aa is None: self.debug("----- not found") if next_pos == ai: i -= 1 break elif not self.confirmation_dialog( "Found analyses up to {}. " "position={}, extract={}. " "Continue searching?".format( ai.runid, ai.extract_value, ai.position ) ): break next_pos = queue.automated_runs[i + 1] if i: if i == len(queue.automated_runs) - 1: self.information_dialog( "All Analyses from this experiment have been run" ) else: queue.automated_runs = queue.automated_runs[i:] else: self.information_dialog( "No Analyses from this experiment have been run" ) def _experiment_queue_changed(self, eq): if eq: self.experiment_factory.queue = eq self.experiment_factory.sync_queue_meta() self.experiment_factory.edit_enabled = True else: self.experiment_factory.edit_enabled = False @on_trait_change("executor:experiment_queue") def _activate_editor(self, eq): self.activate_editor_event = id(eq) @on_trait_change("experiment_queues[]") def _update_queues(self): qs = self.experiment_queues self.executor.stats.experiment_queues = qs @on_trait_change("experiment_factory:run_factory:changed") def _queue_dirty(self): self.experiment_queue.changed = True @on_trait_change("experiment_queue:dclicked") def _dclicked_changed(self, new): self.experiment_factory.run_factory.edit_mode = True self._set_factory_runs(self.experiment_queue.selected) @on_trait_change("experiment_factory:run_factory:update_info_needed") def _refresh3(self): self.debug("update info needed fired") self.update_info() @on_trait_change("executor:queue_modified") def _refresh5(self, new): if new: self.debug("queue modified fired") self.update_info() @on_trait_change("experiment_factory:run_factory:refresh_table_needed") def _refresh4(self): for qi in self.experiment_queues: qi.refresh_table_needed = True @on_trait_change("experiment_factory:save_button") def _save_update(self): self.save_event = True self.update_info() @on_trait_change("experiment_queue:refresh_info_needed") def _handle_refresh(self): self.update_info() @on_trait_change("experiment_queue:selected") def _selected_changed(self, new): ef = self.experiment_factory rf = ef.run_factory rf.edit_mode = False if new: self._set_factory_runs(new) a = new[-1] if not a.skip: self.executor.stats.calculate_at(a, at_times=self.executor.is_alive()) @on_trait_change("experiment_factory:queue_factory:delay_between_analyses") def handle_delay_between_analyses(self, new): if self.executor.is_alive(): self.executor.experiment_queue.delay_between_analyses = new def _set_factory_runs(self, new): ef = self.experiment_factory rf = ef.run_factory rf.suppress_update = True rf.set_selected_runs(new) rf.suppress_update = False def _executor_factory(self): e = ExperimentExecutor(mode=self.mode, application=self.application) e.bind_preferences() return e def _executor_default(self): return self._executor_factory() def _experiment_factory_default(self): dms = "Spectrometer" if self.application: p2 = ( "pychron.spectrometer.base_spectrometer_manager.BaseSpectrometerManager" ) spec = self.application.get_service(p2) if spec: dms = spec.name.capitalize() e = ExperimentFactory( application=self.application, dvc=self.dvc, default_mass_spectrometer=dms ) return e

true

1c38463f66647da1607e6d5fc5115661bd36b39a

5,398

py

Python

backend/api/viewsets/model_year_report_consumer_sales.py

NavpreetGrewal/zeva

a2fc9241d471bb6a2099bcf48b9589ac9391cc64

[ "Apache-2.0" ]

null

backend/api/viewsets/model_year_report_consumer_sales.py

NavpreetGrewal/zeva

a2fc9241d471bb6a2099bcf48b9589ac9391cc64

[ "Apache-2.0" ]

null

backend/api/viewsets/model_year_report_consumer_sales.py

NavpreetGrewal/zeva

a2fc9241d471bb6a2099bcf48b9589ac9391cc64

[ "Apache-2.0" ]

null

from rest_framework import mixins, viewsets from rest_framework.response import Response from django.db.models import Q from django.shortcuts import get_object_or_404 from api.models.model_year_report_vehicle import ModelYearReportVehicle from api.models.model_year_report import ModelYearReport from api.models.model_year_report_confirmation import \ ModelYearReportConfirmation from api.models.model_year_report_history import ModelYearReportHistory from api.models.model_year import ModelYear from api.models.model_year_report_ldv_sales import \ ModelYearReportLDVSales from api.models.model_year_report_statuses import ModelYearReportStatuses from api.permissions.model_year_report import ModelYearReportPermissions from api.serializers.model_year_report_history import \ ModelYearReportHistorySerializer from api.serializers.model_year_report_vehicle import \ ModelYearReportVehicleSerializer, ModelYearReportVehicleSaveSerializer from api.serializers.model_year_report import ModelYearReportSerializer from api.serializers.vehicle import ModelYearSerializer from api.services.vehicle import vehicles_sales from api.serializers.vehicle import VehicleSalesSerializer class ModelYearReportConsumerSalesViewSet(mixins.ListModelMixin, mixins.CreateModelMixin, viewsets.GenericViewSet): permission_classes = (ModelYearReportPermissions,) http_method_names = ['get', 'post', 'put', 'patch'] queryset = ModelYearReport.objects.all() serializer_classes = { 'default': ModelYearReportSerializer, 'create': ModelYearReportVehicleSaveSerializer, } def get_serializer_class(self): if self.action in list(self.serializer_classes.keys()): return self.serializer_classes[self.action] return self.serializer_classes['default'] def create(self, request, *args, **kwargs): vehicles = request.data.get('data') model_year_report_id = request.data.get('model_year_report_id') confirmations = request.data.get('confirmation') report = ModelYearReport.objects.get(id=model_year_report_id) """ Save/Update vehicle information """ vehicles_delete = ModelYearReportVehicle.objects.filter( model_year_report_id=model_year_report_id ) vehicles_delete.delete() for vehicle in vehicles: serializer = ModelYearReportVehicleSaveSerializer( data=vehicle, context={'request': request} ) serializer.is_valid(raise_exception=True) model_year_report_vehicle = serializer.save() """ Save/Update confirmation """ for confirmation in confirmations: confirmation_delete = ModelYearReportConfirmation.objects.filter( signing_authority_assertion_id=confirmation).filter( model_year_report=report) confirmation_delete.delete() consumer_sales_confirmation = ModelYearReportConfirmation.objects.create( create_user=request.user.username, model_year_report=report, has_accepted=True, title=request.user.title, signing_authority_assertion_id=confirmation ) consumer_sales_confirmation.save() ModelYearReportHistory.objects.create( model_year_report_id=model_year_report_id, validation_status=ModelYearReportStatuses.DRAFT, update_user=request.user.username, create_user=request.user.username, ) return Response( {"status": "saved"} ) def retrieve(self, request, pk): vehicles = None queryset = self.get_queryset() report = get_object_or_404(queryset, pk=pk) model_year = ModelYearSerializer(report.model_year) organization = request.user.organization.id summary_param = request.GET.get('summary', None) summary = True if summary_param == "true" else None confirmation = ModelYearReportConfirmation.objects.filter( model_year_report_id=pk, signing_authority_assertion__module="consumer_sales" ).values_list( 'signing_authority_assertion_id', flat=True ).distinct() if not confirmation and not summary: vehicle = vehicles_sales(model_year, organization) vehicles_serializer = VehicleSalesSerializer(vehicle, many=True) else: vehicle = ModelYearReportVehicle.objects.filter( model_year_report_id=report.id) vehicles_serializer = ModelYearReportVehicleSerializer( vehicle, many=True) vehicles = vehicles_serializer.data history_list = ModelYearReportHistory.objects.filter( model_year_report_id=pk ) history = ModelYearReportHistorySerializer(history_list, many=True) return Response({ 'vehicle_list': vehicles, 'model_year_report_history': history.data, 'confirmations': confirmation, 'organization_name': request.user.organization.name, 'validation_status': report.validation_status.value, })

38.014085

85

0.686365

from rest_framework import mixins, viewsets from rest_framework.response import Response from django.db.models import Q from django.shortcuts import get_object_or_404 from api.models.model_year_report_vehicle import ModelYearReportVehicle from api.models.model_year_report import ModelYearReport from api.models.model_year_report_confirmation import \ ModelYearReportConfirmation from api.models.model_year_report_history import ModelYearReportHistory from api.models.model_year import ModelYear from api.models.model_year_report_ldv_sales import \ ModelYearReportLDVSales from api.models.model_year_report_statuses import ModelYearReportStatuses from api.permissions.model_year_report import ModelYearReportPermissions from api.serializers.model_year_report_history import \ ModelYearReportHistorySerializer from api.serializers.model_year_report_vehicle import \ ModelYearReportVehicleSerializer, ModelYearReportVehicleSaveSerializer from api.serializers.model_year_report import ModelYearReportSerializer from api.serializers.vehicle import ModelYearSerializer from api.services.vehicle import vehicles_sales from api.serializers.vehicle import VehicleSalesSerializer class ModelYearReportConsumerSalesViewSet(mixins.ListModelMixin, mixins.CreateModelMixin, viewsets.GenericViewSet): permission_classes = (ModelYearReportPermissions,) http_method_names = ['get', 'post', 'put', 'patch'] queryset = ModelYearReport.objects.all() serializer_classes = { 'default': ModelYearReportSerializer, 'create': ModelYearReportVehicleSaveSerializer, } def get_serializer_class(self): if self.action in list(self.serializer_classes.keys()): return self.serializer_classes[self.action] return self.serializer_classes['default'] def create(self, request, *args, **kwargs): vehicles = request.data.get('data') model_year_report_id = request.data.get('model_year_report_id') confirmations = request.data.get('confirmation') report = ModelYearReport.objects.get(id=model_year_report_id) vehicles_delete = ModelYearReportVehicle.objects.filter( model_year_report_id=model_year_report_id ) vehicles_delete.delete() for vehicle in vehicles: serializer = ModelYearReportVehicleSaveSerializer( data=vehicle, context={'request': request} ) serializer.is_valid(raise_exception=True) model_year_report_vehicle = serializer.save() for confirmation in confirmations: confirmation_delete = ModelYearReportConfirmation.objects.filter( signing_authority_assertion_id=confirmation).filter( model_year_report=report) confirmation_delete.delete() consumer_sales_confirmation = ModelYearReportConfirmation.objects.create( create_user=request.user.username, model_year_report=report, has_accepted=True, title=request.user.title, signing_authority_assertion_id=confirmation ) consumer_sales_confirmation.save() ModelYearReportHistory.objects.create( model_year_report_id=model_year_report_id, validation_status=ModelYearReportStatuses.DRAFT, update_user=request.user.username, create_user=request.user.username, ) return Response( {"status": "saved"} ) def retrieve(self, request, pk): vehicles = None queryset = self.get_queryset() report = get_object_or_404(queryset, pk=pk) model_year = ModelYearSerializer(report.model_year) organization = request.user.organization.id summary_param = request.GET.get('summary', None) summary = True if summary_param == "true" else None confirmation = ModelYearReportConfirmation.objects.filter( model_year_report_id=pk, signing_authority_assertion__module="consumer_sales" ).values_list( 'signing_authority_assertion_id', flat=True ).distinct() if not confirmation and not summary: vehicle = vehicles_sales(model_year, organization) vehicles_serializer = VehicleSalesSerializer(vehicle, many=True) else: vehicle = ModelYearReportVehicle.objects.filter( model_year_report_id=report.id) vehicles_serializer = ModelYearReportVehicleSerializer( vehicle, many=True) vehicles = vehicles_serializer.data history_list = ModelYearReportHistory.objects.filter( model_year_report_id=pk ) history = ModelYearReportHistorySerializer(history_list, many=True) return Response({ 'vehicle_list': vehicles, 'model_year_report_history': history.data, 'confirmations': confirmation, 'organization_name': request.user.organization.name, 'validation_status': report.validation_status.value, })

true

1c38486b5a0bdf40a741bd9dbc89b9eb9f489f78

1,828

py

Python

modules/exploits/example.py

BA7JCM/metasploit-framework

a977d485084aefda76aeac2b15a8f5a4607938fc

[ "BSD-2-Clause", "BSD-3-Clause" ]

2

2022-02-19T07:46:56.000Z

2022-02-22T02:42:09.000Z

modules/exploits/example.py

BA7JCM/metasploit-framework

a977d485084aefda76aeac2b15a8f5a4607938fc

[ "BSD-2-Clause", "BSD-3-Clause" ]

5

2020-10-08T15:04:39.000Z

2020-10-08T15:04:46.000Z

modules/exploits/example.py

BA7JCM/metasploit-framework

a977d485084aefda76aeac2b15a8f5a4607938fc

[ "BSD-2-Clause", "BSD-3-Clause" ]

1

2022-03-01T05:18:40.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # standard modules import logging # extra modules dependencies_missing = False try: import requests except ImportError: dependencies_missing = True from metasploit import module metadata = { 'name': 'Python Module Example', 'description': ''' Python communication with msfconsole. ''', 'authors': [ 'Jacob Robles' ], 'date': '2018-03-22', 'license': 'MSF_LICENSE', 'references': [ {'type': 'url', 'ref': 'https://www.rapid7.com/blog/post/2017/12/28/regifting-python-in-metasploit/'}, {'type': 'aka', 'ref': 'Coldstone'} ], 'type': 'remote_exploit_cmd_stager', 'targets': [ {'platform':'linux', 'arch': 'x86'} ], 'payload': { 'command_stager_flavor': 'curl', }, 'options': { 'targeturi': {'type': 'string', 'description': 'The base path', 'required': True, 'default': '/'}, 'rhost': {'type': 'address', 'description': 'Target address', 'required': True, 'default': None}, 'command': {'type': 'string', 'description': 'The command to execute via the q GET parameter', 'required': True} } } def run(args): module.LogHandler.setup(msg_prefix='{} - '.format(args['rhost'])) if dependencies_missing: logging.error('Module dependency (requests) is missing, cannot continue') return # Your code here try: # args['command'] is where the command stager command lives r = requests.get('https://{}/{}/?q={}'.format(args['rhost'], args['targeturi'], args['command']), verify=False) except requests.exceptions.RequestException as e: logging.error('{}'.format(e)) return logging.info('{}...'.format(r.text[0:50])) if __name__ == '__main__': module.run(metadata, run)

28.123077

120

0.599562

import logging dependencies_missing = False try: import requests except ImportError: dependencies_missing = True from metasploit import module metadata = { 'name': 'Python Module Example', 'description': ''' Python communication with msfconsole. ''', 'authors': [ 'Jacob Robles' ], 'date': '2018-03-22', 'license': 'MSF_LICENSE', 'references': [ {'type': 'url', 'ref': 'https://www.rapid7.com/blog/post/2017/12/28/regifting-python-in-metasploit/'}, {'type': 'aka', 'ref': 'Coldstone'} ], 'type': 'remote_exploit_cmd_stager', 'targets': [ {'platform':'linux', 'arch': 'x86'} ], 'payload': { 'command_stager_flavor': 'curl', }, 'options': { 'targeturi': {'type': 'string', 'description': 'The base path', 'required': True, 'default': '/'}, 'rhost': {'type': 'address', 'description': 'Target address', 'required': True, 'default': None}, 'command': {'type': 'string', 'description': 'The command to execute via the q GET parameter', 'required': True} } } def run(args): module.LogHandler.setup(msg_prefix='{} - '.format(args['rhost'])) if dependencies_missing: logging.error('Module dependency (requests) is missing, cannot continue') return try: r = requests.get('https://{}/{}/?q={}'.format(args['rhost'], args['targeturi'], args['command']), verify=False) except requests.exceptions.RequestException as e: logging.error('{}'.format(e)) return logging.info('{}...'.format(r.text[0:50])) if __name__ == '__main__': module.run(metadata, run)

true

1c384892c1b89f4a0ea6806632905115f06fdd7f

1,586

py

Python

var/spack/repos/builtin/packages/sqlcipher/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

1

2021-02-08T15:05:27.000Z

var/spack/repos/builtin/packages/sqlcipher/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/sqlcipher/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

1

2022-01-18T23:39:24.000Z

# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) class Sqlcipher(AutotoolsPackage): """SQLCipher is an SQLite extension that provides 256 bit AES encryption of database files. """ homepage = "https://www.zetetic.net/sqlcipher/" url = "https://github.com/sqlcipher/sqlcipher/archive/v4.4.1.tar.gz" git = "https://github.com/sqlcipher/sqlcipher.git" maintainers = ['rmsds'] version('4.4.1', sha256='a36ed7c879a5e9af1054942201c75fc56f1db22e46bf6c2bbae3975dfeb6782d') version('4.4.0', sha256='0924b2ae1079717954498bda78a30de20ce2a6083076b16214a711567821d148') version('4.3.0', sha256='fccb37e440ada898902b294d02cde7af9e8706b185d77ed9f6f4d5b18b4c305f') version('4.2.0', sha256='105c1b813f848da038c03647a8bfc9d42fb46865e6aaf4edfd46ff3b18cdccfc') version('4.1.0', sha256='65144ca3ba4c0f9cd4bae8c20bb42f2b84424bf29d1ebcf04c44a728903b1faa') version('4.0.1', sha256='2f803017378c7479cb791be59b7bad8392a15acddbcc094e4433581fe421f4ca') version('4.0.0', sha256='c8f5fc6d800aae6107bf23900144804db5510c2676c93fbb269e4a0700837d68') version('3.4.2', sha256='69897a5167f34e8a84c7069f1b283aba88cdfa8ec183165c4a5da2c816cfaadb') depends_on('openssl') depends_on('tcl', type=['build', ]) depends_on('zlib') def configure_args(self): args = [] args.append('--enable-tempstore=yes') args.append('CFLAGS=-DSQLITE_HAS_CODEC') return args

46.647059

95

0.748424

class Sqlcipher(AutotoolsPackage): homepage = "https://www.zetetic.net/sqlcipher/" url = "https://github.com/sqlcipher/sqlcipher/archive/v4.4.1.tar.gz" git = "https://github.com/sqlcipher/sqlcipher.git" maintainers = ['rmsds'] version('4.4.1', sha256='a36ed7c879a5e9af1054942201c75fc56f1db22e46bf6c2bbae3975dfeb6782d') version('4.4.0', sha256='0924b2ae1079717954498bda78a30de20ce2a6083076b16214a711567821d148') version('4.3.0', sha256='fccb37e440ada898902b294d02cde7af9e8706b185d77ed9f6f4d5b18b4c305f') version('4.2.0', sha256='105c1b813f848da038c03647a8bfc9d42fb46865e6aaf4edfd46ff3b18cdccfc') version('4.1.0', sha256='65144ca3ba4c0f9cd4bae8c20bb42f2b84424bf29d1ebcf04c44a728903b1faa') version('4.0.1', sha256='2f803017378c7479cb791be59b7bad8392a15acddbcc094e4433581fe421f4ca') version('4.0.0', sha256='c8f5fc6d800aae6107bf23900144804db5510c2676c93fbb269e4a0700837d68') version('3.4.2', sha256='69897a5167f34e8a84c7069f1b283aba88cdfa8ec183165c4a5da2c816cfaadb') depends_on('openssl') depends_on('tcl', type=['build', ]) depends_on('zlib') def configure_args(self): args = [] args.append('--enable-tempstore=yes') args.append('CFLAGS=-DSQLITE_HAS_CODEC') return args

true

1c3849e06afbbb9b21ae5df801c2962db406e6ba

91,397

py

Python

tests/test_bert_ner.py

bond005/elmo_ner

c6135cfca5d7bf817a22c8c8631e7f81f6f05f94

[ "Apache-2.0" ]

80

2019-03-21T13:04:32.000Z

2021-09-27T16:53:34.000Z

tests/test_bert_ner.py

bond005/elmo_ner

c6135cfca5d7bf817a22c8c8631e7f81f6f05f94

[ "Apache-2.0" ]

7

2019-06-06T13:49:54.000Z

2022-02-10T01:05:18.000Z

tests/test_bert_ner.py

bond005/elmo_ner

c6135cfca5d7bf817a22c8c8631e7f81f6f05f94

[ "Apache-2.0" ]

16

2019-03-20T06:54:40.000Z

2021-09-23T17:40:24.000Z

import copy import gc import os import pickle import re import sys import tempfile import unittest import numpy as np from sklearn.exceptions import NotFittedError from spacy_udpipe.language import UDPipeLanguage try: from deep_ner.bert_ner import BERT_NER from deep_ner.utils import load_dataset_from_json, set_total_seed from deep_ner.quality import calculate_prediction_quality from deep_ner.udpipe_data import UNIVERSAL_DEPENDENCIES, UNIVERSAL_POS_TAGS except: sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from deep_ner.bert_ner import BERT_NER from deep_ner.utils import load_dataset_from_json, set_total_seed from deep_ner.quality import calculate_prediction_quality from deep_ner.udpipe_data import UNIVERSAL_DEPENDENCIES, UNIVERSAL_POS_TAGS class TestBertNer(unittest.TestCase): @classmethod def setUpClass(cls): set_total_seed(0) def tearDown(self): if hasattr(self, 'ner'): del self.ner if hasattr(self, 'another_ner'): del self.another_ner if hasattr(self, 'temp_file_name'): if os.path.isfile(self.temp_file_name): os.remove(self.temp_file_name) def test_creation(self): self.ner = BERT_NER(udpipe_lang='en') self.assertIsInstance(self.ner, BERT_NER) self.assertTrue(hasattr(self.ner, 'udpipe_lang')) self.assertTrue(hasattr(self.ner, 'use_shapes')) self.assertTrue(hasattr(self.ner, 'use_nlp_features')) self.assertTrue(hasattr(self.ner, 'batch_size')) self.assertTrue(hasattr(self.ner, 'lstm_units')) self.assertTrue(hasattr(self.ner, 'lr')) self.assertTrue(hasattr(self.ner, 'l2_reg')) self.assertTrue(hasattr(self.ner, 'clip_norm')) self.assertTrue(hasattr(self.ner, 'bert_hub_module_handle')) self.assertTrue(hasattr(self.ner, 'finetune_bert')) self.assertTrue(hasattr(self.ner, 'max_epochs')) self.assertTrue(hasattr(self.ner, 'patience')) self.assertTrue(hasattr(self.ner, 'random_seed')) self.assertTrue(hasattr(self.ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.ner, 'max_seq_length')) self.assertTrue(hasattr(self.ner, 'validation_fraction')) self.assertTrue(hasattr(self.ner, 'verbose')) self.assertIsInstance(self.ner.batch_size, int) self.assertIsInstance(self.ner.lstm_units, int) self.assertIsInstance(self.ner.lr, float) self.assertIsInstance(self.ner.l2_reg, float) self.assertIsInstance(self.ner.clip_norm, float) self.assertIsInstance(self.ner.bert_hub_module_handle, str) self.assertIsInstance(self.ner.udpipe_lang, str) self.assertIsInstance(self.ner.finetune_bert, bool) self.assertIsInstance(self.ner.max_epochs, int) self.assertIsInstance(self.ner.patience, int) self.assertIsNone(self.ner.random_seed) self.assertIsInstance(self.ner.gpu_memory_frac, float) self.assertIsInstance(self.ner.max_seq_length, int) self.assertIsInstance(self.ner.validation_fraction, float) self.assertIsInstance(self.ner.verbose, bool) self.assertIsInstance(self.ner.use_shapes, bool) self.assertIsInstance(self.ner.use_nlp_features, bool) def test_check_params_positive(self): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.0, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=None, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) self.assertTrue(True) def test_check_params_negative001(self): true_err_msg = re.escape('`bert_hub_module_handle` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=False, udpipe_lang='en' ) def test_check_params_negative002(self): true_err_msg = re.escape('`bert_hub_module_handle` is wrong! Expected `{0}`, got `{1}`.'.format( type('abc'), type(123))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle=1, finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=False, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative003(self): true_err_msg = re.escape('`batch_size` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative004(self): true_err_msg = re.escape('`batch_size` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size='32', max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative005(self): true_err_msg = re.escape('`batch_size` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=-3, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative006(self): true_err_msg = re.escape('`max_epochs` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative007(self): true_err_msg = re.escape('`max_epochs` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs='10', patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative008(self): true_err_msg = re.escape('`max_epochs` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=-3, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative009(self): true_err_msg = re.escape('`patience` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative010(self): true_err_msg = re.escape('`patience` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience='3', gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative011(self): true_err_msg = re.escape('`patience` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=-3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative012(self): true_err_msg = re.escape('`max_seq_length` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative013(self): true_err_msg = re.escape('`max_seq_length` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length='512', lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative014(self): true_err_msg = re.escape('`max_seq_length` is wrong! Expected a positive integer value, but -3 is not ' 'positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=-3, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative015(self): true_err_msg = re.escape('`validation_fraction` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative016(self): true_err_msg = re.escape('`validation_fraction` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction='0.1', max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative017(self): true_err_msg = '`validation_fraction` is wrong! Expected a positive floating-point value greater than or ' \ 'equal to 0.0, but {0} is not positive.'.format(-0.1) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=-0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative018(self): true_err_msg = '`validation_fraction` is wrong! Expected a positive floating-point value less than 1.0, but ' \ '{0} is not less than 1.0.'.format(1.1) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=1.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative019(self): true_err_msg = re.escape('`gpu_memory_frac` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative020(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac='1.0', verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative021(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected a floating-point value in the (0.0, 1.0], ' 'but {0} is not proper.'.format(-1.0)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=-1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative022(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected a floating-point value in the (0.0, 1.0], ' 'but {0} is not proper.'.format(1.3)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.3, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative023(self): true_err_msg = re.escape('`lr` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative024(self): true_err_msg = re.escape('`lr` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr='1e-3', l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative025(self): true_err_msg = re.escape('`lr` is wrong! Expected a positive floating-point value, but {0} is not ' 'positive.'.format(0.0)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=0.0, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative026(self): true_err_msg = re.escape('`lr` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative027(self): true_err_msg = re.escape('`lr` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr='1e-3', l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative028(self): true_err_msg = re.escape('`lr` is wrong! Expected a positive floating-point value, but {0} is not ' 'positive.'.format(0.0)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=0.0, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative029(self): true_err_msg = re.escape('`l2_reg` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative030(self): true_err_msg = re.escape('`l2_reg` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg='1e-4', clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative031(self): true_err_msg = re.escape('`l2_reg` is wrong! Expected a non-negative floating-point value, but {0} is ' 'negative.'.format(-2.0)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=-2.0, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative032(self): true_err_msg = re.escape('`finetune_bert` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, clip_norm=5.0, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative033(self): true_err_msg = re.escape('`finetune_bert` is wrong! Expected `{0}`, got `{1}`.'.format( type(True), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert='True', batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative034(self): true_err_msg = re.escape('`verbose` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative035(self): true_err_msg = re.escape('`verbose` is wrong! Expected `{0}`, got `{1}`.'.format( type(True), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose='False', random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative036(self): true_err_msg = re.escape('`lstm_units` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative037(self): true_err_msg = re.escape('`lstm_units` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', lstm_units='128', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative038(self): true_err_msg = re.escape('`lstm_units` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=-3, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_X_positive(self): X = ['abc', 'defgh', '4wdffg'] BERT_NER.check_X(X, 'X_train') self.assertTrue(True) def test_check_X_negative01(self): X = {'abc', 'defgh', '4wdffg'} true_err_msg = re.escape('`X_train` is wrong, because it is not list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_X(X, 'X_train') def test_check_X_negative02(self): X = np.random.uniform(-1.0, 1.0, (10, 2)) true_err_msg = re.escape('`X_train` is wrong, because it is not 1-D list!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_X(X, 'X_train') def test_check_X_negative03(self): X = ['abc', 23, '4wdffg'] true_err_msg = re.escape('Item 1 of `X_train` is wrong, because it is not string-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_X(X, 'X_train') def text_check_Xy_positive(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_classes_list = ('LOC', 'ORG', 'PER') self.assertEqual(true_classes_list, BERT_NER.check_Xy(X, 'X_train', y, 'y_train')) def text_check_Xy_negative01(self): X = { 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' } y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('`X_train` is wrong, because it is not list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative02(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = { '1': { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, '2': { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } } true_err_msg = re.escape('`y_train` is wrong, because it is not a list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative03(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = np.random.uniform(-1.0, 1.0, (10, 2)) true_err_msg = re.escape('`y_train` is wrong, because it is not 1-D list!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative04(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] }, { 'LOC': [(17, 24), (117, 130)] } ] true_err_msg = re.escape('Length of `X_train` does not correspond to length of `y_train`! 2 != 3') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative05(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, 4 ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because it is not a dictionary-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative06(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 1: [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because its key `1` is not a string-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative07(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'O': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `O` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative08(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], '123': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `123` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative09(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'loc': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `loc` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative10(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': {1, 2} }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because its value `{0}` is not a list-like ' 'object!'.format(y[0]['PER'])) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative11(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), 63], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because named entity bounds `63` are not specified as ' 'list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative12(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77, 81)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because named entity bounds `{0}` are not specified as ' '2-D list!'.format((63, 77, 81))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative13(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (219, 196)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((219, 196))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative14(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 519)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((196, 519))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative15(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(-1, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((-1, 137))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def test_detect_token_labels_positive01(self): # source_text = 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози.' tokenized_text = ['Ба', '##рак', 'Об', '##ама', 'принимает', 'в', 'Б', '##елом', 'доме', 'своего', 'французского', 'кол', '##ле', '##гу', 'Н', '##ико', '##ля', 'Са', '##рко', '##зи', '.'] token_bounds = [(0, 2), (2, 5), (6, 8), (8, 11), (12, 21), (22, 23), (24, 25), (25, 29), (30, 34), (35, 41), (42, 54), (55, 58), (58, 60), (60, 62), (63, 64), (64, 67), (67, 69), (70, 72), (72, 75), (75, 77), (77, 78)] indices_of_named_entities = np.array( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0], dtype=np.int32 ) label_IDs = {1: 1, 2: 2, 3: 1} y_true = np.array( [0, 2, 1, 1, 1, 0, 0, 4, 3, 3, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) y_pred = BERT_NER.detect_token_labels(tokenized_text, token_bounds, indices_of_named_entities, label_IDs, 32) self.assertIsInstance(y_pred, np.ndarray) self.assertEqual(y_true.shape, y_pred.shape) self.assertEqual(y_true.tolist(), y_pred.tolist()) def test_detect_token_labels_positive02(self): # source_text = 'С 1876 г Павлов ассистирует профессору К. Н. Устимовичу в Медико-хирургической академии и ' \ # 'параллельно изучает физиологию кровообращения.' tokenized_text = ['С', '1876', 'г', 'Павло', '##в', 'а', '##сси', '##сти', '##рует', 'профессор', '##у', 'К', '.', 'Н', '.', 'У', '##сти', '##мов', '##ич', '##у', 'в', 'М', '##еди', '##ко', '-', 'х', '##ир', '##ург', '##ической', 'академии', 'и', 'пара', '##лл', '##ельно', 'из', '##уч', '##ает', 'ф', '##из', '##ио', '##логи', '##ю', 'к', '##рово', '##об', '##ращения', '.'] token_bounds = [(0, 1), (2, 6), (7, 8), (9, 14), (14, 15), (16, 17), (17, 20), (20, 23), (23, 27), (28, 37), (37, 38), (39, 40), (40, 41), (42, 43), (43, 44), (45, 46), (46, 49), (49, 52), (52, 54), (54, 55), (56, 57), (58, 59), (59, 62), (62, 64), (64, 65), (65, 66), (66, 68), (68, 71), (71, 78), (79, 87), (88, 89), (90, 94), (94, 96), (96, 101), (102, 104), (104, 106), (106, 109), (110, 111), (111, 113), (113, 115), (115, 119), (119, 120), (121, 122), (122, 126), (126, 128), (128, 135), (135, 136)] indices_of_named_entities = np.array( [0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) label_IDs = {1: 1, 2: 2, 3: 3, 4: 2, 5: 4} y_true = np.array( [0, 0, 2, 1, 4, 3, 0, 0, 0, 0, 6, 5, 4, 3, 3, 3, 3, 3, 3, 3, 3, 0, 8, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) y_pred = BERT_NER.detect_token_labels(tokenized_text, token_bounds, indices_of_named_entities, label_IDs, 64) self.assertIsInstance(y_pred, np.ndarray) self.assertEqual(y_true.shape, y_pred.shape) self.assertEqual(y_true.tolist(), y_pred.tolist()) def test_detect_token_labels_positive03(self): # source_text = 'Весной 1890 года Варшавский и Томский университеты избирают его профессором.' tokenized_text = ['В', '##есной', '1890', 'года', 'В', '##ар', '##ша', '##вский', 'и', 'Томск', '##ий', 'университет', '##ы', 'из', '##бира', '##ют', 'его', 'профессором', '.'] token_bounds = [(0, 1), (1, 6), (7, 11), (12, 16), (17, 18), (18, 20), (20, 22), (22, 27), (28, 29), (30, 35), (35, 37), (38, 49), (49, 50), (51, 52), (53, 57), (57, 59), (60, 63), (64, 75), (75, 76)] indices_of_named_entities = np.array( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) label_IDs = {1: 1, 2: 2, 3: 2} y_true = np.array( [0, 2, 1, 1, 1, 4, 3, 3, 3, 3, 4, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) y_pred = BERT_NER.detect_token_labels(tokenized_text, token_bounds, indices_of_named_entities, label_IDs, 32) self.assertIsInstance(y_pred, np.ndarray) self.assertEqual(y_true.shape, y_pred.shape) self.assertEqual(y_true.tolist(), y_pred.tolist()) def test_calculate_indices_of_named_entities(self): source_text = 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози.' classes_list = ('LOCATION', 'ORG', 'PERSON') named_entities = {'PERSON': [(0, 11), (63, 77)], 'LOCATION': [(24, 34)]} true_indices = np.array( [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0], dtype=np.int32 ) true_labels_to_classes = {1: 1, 2: 3, 3: 3} indices, labels_to_classes = BERT_NER.calculate_indices_of_named_entities(source_text, classes_list, named_entities) self.assertIsInstance(indices, np.ndarray) self.assertIsInstance(labels_to_classes, dict) self.assertEqual(true_indices.shape, indices.shape) self.assertEqual(true_indices.tolist(), indices.tolist()) self.assertEqual(set(true_labels_to_classes.keys()), set(labels_to_classes.keys())) for label_ID in true_labels_to_classes: self.assertEqual(true_labels_to_classes[label_ID], labels_to_classes[label_ID]) def test_fit_positive01(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=3, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, lstm_units=32, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lstm_units, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.clip_norm, float) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertTrue(hasattr(res, 'universal_pos_tags_dict_')) self.assertTrue(hasattr(res, 'universal_dependencies_dict_')) self.assertTrue(hasattr(res, 'nlp_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) self.assertIsInstance(res.universal_pos_tags_dict_, dict) self.assertIsInstance(res.universal_dependencies_dict_, dict) self.assertIsInstance(res.nlp_, UDPipeLanguage) self.assertEqual(len(res.universal_pos_tags_dict_), len(UNIVERSAL_POS_TAGS)) self.assertEqual(len(res.universal_dependencies_dict_), len(UNIVERSAL_DEPENDENCIES)) def test_fit_positive02(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=True, max_epochs=3, batch_size=2, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=42, lstm_units=32, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lstm_units, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.clip_norm, float) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertEqual(res.random_seed, 42) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) def test_fit_positive03(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=3, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, lstm_units=None, clip_norm=None, udpipe_lang='ru', use_shapes=False, use_nlp_features=True) X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsNone(res.lstm_units) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsNone(res.clip_norm, None) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) def test_fit_predict(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=5, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=42, udpipe_lang='ru', use_shapes=True, use_nlp_features=False) X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lstm_units, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.clip_norm, float) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) y_pred = res.predict(X_train) self.assertIsInstance(y_pred, list) self.assertEqual(len(X_train), len(y_pred)) for sample_idx in range(len(y_pred)): self.assertIsInstance(y_pred[sample_idx], dict) f1, precision, recall, _ = calculate_prediction_quality(y_train, y_pred, res.classes_list_) self.assertGreater(f1, 0.0) self.assertGreater(precision, 0.0) self.assertGreater(recall, 0.0) def test_predict_negative(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=3, batch_size=4, random_seed=None, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) with self.assertRaises(NotFittedError): _ = self.ner.predict(X_train) def test_tokenize_all_01(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=1, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, udpipe_lang='ru', use_nlp_features=True, use_shapes=True) X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) self.ner.fit(X_train, y_train) res = self.ner.tokenize_all(X_train, y_train, shapes_vocabulary=self.ner.shapes_list_) self.assertIsInstance(res, tuple) self.assertEqual(len(res), 4) X_train_tokenized, y_train_tokenized, shapes_list, bounds_of_tokens_for_training = res self.assertIsInstance(X_train_tokenized, list) self.assertIsInstance(y_train_tokenized, np.ndarray) self.assertIs(self.ner.shapes_list_, shapes_list) self.assertIsInstance(bounds_of_tokens_for_training, np.ndarray) self.assertEqual(len(X_train_tokenized), 4) self.assertEqual(y_train_tokenized.shape, (len(y_train), self.ner.max_seq_length)) for data_idx in range(3): self.assertIsInstance(X_train_tokenized[data_idx], np.ndarray) self.assertEqual(X_train_tokenized[data_idx].shape, (len(X_train), self.ner.max_seq_length)) data_idx = 3 self.assertIsInstance(X_train_tokenized[data_idx], np.ndarray) self.assertEqual(len(X_train_tokenized[data_idx].shape), 3) self.assertEqual(X_train_tokenized[data_idx].shape[0], len(X_train)) self.assertEqual(X_train_tokenized[data_idx].shape[1], self.ner.max_seq_length) self.assertGreater(X_train_tokenized[data_idx].shape[2], 4 + len(UNIVERSAL_POS_TAGS) + len(UNIVERSAL_DEPENDENCIES)) for sample_idx in range(X_train_tokenized[data_idx].shape[0]): n = 0 for token_idx in range(X_train_tokenized[data_idx].shape[1]): if X_train_tokenized[data_idx][sample_idx][token_idx].sum() < 1e-3: break n += 1 self.assertGreater(n, 0, msg='Sample {0}: additional features are not defined!'.format(sample_idx)) for token_idx in range(n): self.assertAlmostEqual(X_train_tokenized[data_idx][sample_idx][token_idx][0:4].sum(), 1.0, msg='Sample {0}, token {1}: additional features are wrong!'.format( sample_idx, token_idx)) for token_idx in range(1, n - 1): start_pos = 4 end_pos = 4 + len(UNIVERSAL_POS_TAGS) self.assertAlmostEqual(X_train_tokenized[data_idx][sample_idx][token_idx][start_pos:end_pos].sum(), 1.0, msg='Sample {0}, token {1}: part of speech is not defined!'.format( sample_idx, token_idx)) start_pos = 4 + len(UNIVERSAL_POS_TAGS) end_pos = 4 + len(UNIVERSAL_POS_TAGS) + len(UNIVERSAL_DEPENDENCIES) self.assertGreaterEqual(X_train_tokenized[data_idx][sample_idx][token_idx][start_pos:end_pos].sum(), 1.0, msg='Sample {0}, token {1}: dependency tag is not defined!'.format( sample_idx, token_idx)) def test_tokenize_all_02(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=1, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, udpipe_lang='ru', use_shapes=False, use_nlp_features=False) X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) self.ner.fit(X_train, y_train) res = self.ner.tokenize_all(X_train, y_train, shapes_vocabulary=self.ner.shapes_list_) self.assertIsInstance(res, tuple) self.assertEqual(len(res), 4) X_train_tokenized, y_train_tokenized, shapes_list, bounds_of_tokens_for_training = res self.assertIsInstance(X_train_tokenized, list) self.assertIsInstance(y_train_tokenized, np.ndarray) self.assertIs(self.ner.shapes_list_, shapes_list) self.assertIsInstance(bounds_of_tokens_for_training, np.ndarray) self.assertEqual(len(X_train_tokenized), 3) self.assertEqual(y_train_tokenized.shape, (len(y_train), self.ner.max_seq_length)) for data_idx in range(3): self.assertIsInstance(X_train_tokenized[data_idx], np.ndarray) self.assertEqual(X_train_tokenized[data_idx].shape, (len(X_train), self.ner.max_seq_length)) def test_serialize_positive01(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=5, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=42, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lstm_units, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.clip_norm, float) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) y_pred1 = res.predict(X_train) self.assertIsInstance(y_pred1, list) self.assertEqual(len(X_train), len(y_pred1)) for sample_idx in range(len(y_pred1)): self.assertIsInstance(y_pred1[sample_idx], dict) f1, precision, recall, _ = calculate_prediction_quality(y_train, y_pred1, res.classes_list_) self.assertGreater(f1, 0.0) self.assertGreater(precision, 0.0) self.assertGreater(recall, 0.0) with tempfile.NamedTemporaryFile(mode='w', delete=True) as fp: self.temp_file_name = fp.name with open(self.temp_file_name, mode='wb') as fp: pickle.dump(res, fp) del res, self.ner gc.collect() with open(self.temp_file_name, mode='rb') as fp: self.ner = pickle.load(fp) y_pred2 = self.ner.predict(X_train) self.assertIsInstance(y_pred2, list) self.assertEqual(len(y_pred2), len(y_pred2)) for sample_idx in range(len(y_pred2)): self.assertIsInstance(y_pred2[sample_idx], dict) self.assertEqual(set(y_pred1[sample_idx]), set(y_pred2[sample_idx])) for ne_type in y_pred1[sample_idx]: self.assertEqual(y_pred1[sample_idx][ne_type], y_pred2[sample_idx][ne_type]) def test_serialize_positive02(self): self.ner = BERT_NER(random_seed=31, udpipe_lang='ru') old_udpipe_lang = self.ner.udpipe_lang old_batch_size = self.ner.batch_size old_lstm_units = self.ner.lstm_units old_lr = self.ner.lr old_l2_reg = self.ner.l2_reg old_clip_norm = self.ner.clip_norm old_bert_hub_module_handle = self.ner.bert_hub_module_handle old_finetune_bert = self.ner.finetune_bert old_max_epochs = self.ner.max_epochs old_patience = self.ner.patience old_random_seed = self.ner.random_seed old_gpu_memory_frac = self.ner.gpu_memory_frac old_max_seq_length = self.ner.max_seq_length old_validation_fraction = self.ner.validation_fraction old_verbose = self.ner.verbose old_use_shapes = self.ner.use_shapes old_use_nlp_features = self.ner.use_nlp_features with tempfile.NamedTemporaryFile(mode='w', delete=True) as fp: self.temp_file_name = fp.name with open(self.temp_file_name, mode='wb') as fp: pickle.dump(self.ner, fp) del self.ner gc.collect() with open(self.temp_file_name, mode='rb') as fp: self.ner = pickle.load(fp) self.assertIsInstance(self.ner, BERT_NER) self.assertTrue(hasattr(self.ner, 'udpipe_lang')) self.assertTrue(hasattr(self.ner, 'batch_size')) self.assertTrue(hasattr(self.ner, 'lstm_units')) self.assertTrue(hasattr(self.ner, 'lr')) self.assertTrue(hasattr(self.ner, 'l2_reg')) self.assertTrue(hasattr(self.ner, 'clip_norm')) self.assertTrue(hasattr(self.ner, 'bert_hub_module_handle')) self.assertTrue(hasattr(self.ner, 'finetune_bert')) self.assertTrue(hasattr(self.ner, 'max_epochs')) self.assertTrue(hasattr(self.ner, 'patience')) self.assertTrue(hasattr(self.ner, 'random_seed')) self.assertTrue(hasattr(self.ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.ner, 'max_seq_length')) self.assertTrue(hasattr(self.ner, 'validation_fraction')) self.assertTrue(hasattr(self.ner, 'verbose')) self.assertTrue(hasattr(self.ner, 'use_shapes')) self.assertTrue(hasattr(self.ner, 'use_nlp_features')) self.assertEqual(self.ner.udpipe_lang, old_udpipe_lang) self.assertEqual(self.ner.batch_size, old_batch_size) self.assertEqual(self.ner.lstm_units, old_lstm_units) self.assertAlmostEqual(self.ner.lr, old_lr) self.assertAlmostEqual(self.ner.l2_reg, old_l2_reg) self.assertAlmostEqual(self.ner.clip_norm, old_clip_norm) self.assertEqual(self.ner.bert_hub_module_handle, old_bert_hub_module_handle) self.assertEqual(self.ner.finetune_bert, old_finetune_bert) self.assertEqual(self.ner.max_epochs, old_max_epochs) self.assertEqual(self.ner.patience, old_patience) self.assertAlmostEqual(self.ner.gpu_memory_frac, old_gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, old_max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, old_validation_fraction) self.assertEqual(self.ner.verbose, old_verbose) self.assertEqual(self.ner.use_shapes, old_use_shapes) self.assertEqual(self.ner.use_nlp_features, old_use_nlp_features) self.assertEqual(self.ner.random_seed, old_random_seed) def test_copy_positive01(self): self.ner = BERT_NER(random_seed=0, udpipe_lang='ru', use_shapes=False, use_nlp_features=True) self.another_ner = copy.copy(self.ner) self.assertIsInstance(self.another_ner, BERT_NER) self.assertIsNot(self.ner, self.another_ner) self.assertTrue(hasattr(self.another_ner, 'udpipe_lang')) self.assertTrue(hasattr(self.another_ner, 'batch_size')) self.assertTrue(hasattr(self.another_ner, 'lstm_units')) self.assertTrue(hasattr(self.another_ner, 'lr')) self.assertTrue(hasattr(self.another_ner, 'l2_reg')) self.assertTrue(hasattr(self.another_ner, 'clip_norm')) self.assertTrue(hasattr(self.another_ner, 'bert_hub_module_handle')) self.assertTrue(hasattr(self.another_ner, 'finetune_bert')) self.assertTrue(hasattr(self.another_ner, 'max_epochs')) self.assertTrue(hasattr(self.another_ner, 'patience')) self.assertTrue(hasattr(self.another_ner, 'random_seed')) self.assertTrue(hasattr(self.another_ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.another_ner, 'max_seq_length')) self.assertTrue(hasattr(self.another_ner, 'validation_fraction')) self.assertTrue(hasattr(self.another_ner, 'verbose')) self.assertTrue(hasattr(self.another_ner, 'use_shapes')) self.assertTrue(hasattr(self.another_ner, 'use_nlp_features')) self.assertEqual(self.ner.udpipe_lang, self.another_ner.udpipe_lang) self.assertEqual(self.ner.batch_size, self.another_ner.batch_size) self.assertEqual(self.ner.lstm_units, self.another_ner.lstm_units) self.assertAlmostEqual(self.ner.lr, self.another_ner.lr) self.assertAlmostEqual(self.ner.l2_reg, self.another_ner.l2_reg) self.assertAlmostEqual(self.ner.clip_norm, self.another_ner.clip_norm) self.assertEqual(self.ner.bert_hub_module_handle, self.another_ner.bert_hub_module_handle) self.assertEqual(self.ner.finetune_bert, self.another_ner.finetune_bert) self.assertEqual(self.ner.max_epochs, self.another_ner.max_epochs) self.assertEqual(self.ner.patience, self.another_ner.patience) self.assertEqual(self.ner.random_seed, self.another_ner.random_seed) self.assertAlmostEqual(self.ner.gpu_memory_frac, self.another_ner.gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, self.another_ner.max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, self.another_ner.validation_fraction) self.assertEqual(self.ner.verbose, self.another_ner.verbose) self.assertEqual(self.ner.use_shapes, self.another_ner.use_shapes) self.assertEqual(self.ner.use_nlp_features, self.another_ner.use_nlp_features) def test_copy_positive02(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=3, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) self.ner.fit(X_train, y_train) self.another_ner = copy.copy(self.ner) self.assertIsInstance(self.another_ner, BERT_NER) self.assertIsNot(self.ner, self.another_ner) self.assertTrue(hasattr(self.another_ner, 'udpipe_lang')) self.assertTrue(hasattr(self.another_ner, 'batch_size')) self.assertTrue(hasattr(self.another_ner, 'lstm_units')) self.assertTrue(hasattr(self.another_ner, 'lr')) self.assertTrue(hasattr(self.another_ner, 'l2_reg')) self.assertTrue(hasattr(self.another_ner, 'clip_norm')) self.assertTrue(hasattr(self.another_ner, 'bert_hub_module_handle')) self.assertTrue(hasattr(self.another_ner, 'finetune_bert')) self.assertTrue(hasattr(self.another_ner, 'max_epochs')) self.assertTrue(hasattr(self.another_ner, 'patience')) self.assertTrue(hasattr(self.another_ner, 'random_seed')) self.assertTrue(hasattr(self.another_ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.another_ner, 'max_seq_length')) self.assertTrue(hasattr(self.another_ner, 'validation_fraction')) self.assertTrue(hasattr(self.another_ner, 'verbose')) self.assertTrue(hasattr(self.another_ner, 'use_shapes')) self.assertTrue(hasattr(self.another_ner, 'use_nlp_features')) self.assertTrue(hasattr(self.another_ner, 'classes_list_')) self.assertTrue(hasattr(self.another_ner, 'shapes_list_')) self.assertTrue(hasattr(self.another_ner, 'tokenizer_')) self.assertTrue(hasattr(self.another_ner, 'sess_')) self.assertEqual(self.ner.udpipe_lang, self.another_ner.udpipe_lang) self.assertEqual(self.ner.batch_size, self.another_ner.batch_size) self.assertEqual(self.ner.lstm_units, self.another_ner.lstm_units) self.assertAlmostEqual(self.ner.lr, self.another_ner.lr) self.assertAlmostEqual(self.ner.l2_reg, self.another_ner.l2_reg) self.assertAlmostEqual(self.ner.clip_norm, self.another_ner.clip_norm) self.assertEqual(self.ner.bert_hub_module_handle, self.another_ner.bert_hub_module_handle) self.assertEqual(self.ner.finetune_bert, self.another_ner.finetune_bert) self.assertEqual(self.ner.max_epochs, self.another_ner.max_epochs) self.assertEqual(self.ner.patience, self.another_ner.patience) self.assertEqual(self.ner.random_seed, self.another_ner.random_seed) self.assertAlmostEqual(self.ner.gpu_memory_frac, self.another_ner.gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, self.another_ner.max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, self.another_ner.validation_fraction) self.assertEqual(self.ner.verbose, self.another_ner.verbose) self.assertEqual(self.ner.use_shapes, self.another_ner.use_shapes) self.assertEqual(self.ner.use_nlp_features, self.another_ner.use_nlp_features) self.assertIs(self.ner.classes_list_, self.another_ner.classes_list_) self.assertIs(self.ner.shapes_list_, self.another_ner.shapes_list_) self.assertIs(self.ner.tokenizer_, self.another_ner.tokenizer_) self.assertIs(self.ner.sess_, self.another_ner.sess_) def test_calculate_bounds_of_named_entities(self): bounds_of_tokens = [(0, 2), (2, 5), (5, 8), (8, 10), (11, 16), (17, 20), (20, 22), (22, 26), (26, 27), (28, 31), (31, 34), (34, 37), (38, 48), (49, 52), (52, 54), (55, 57), (58, 59), (59, 61), (61, 63), (64, 70), (71, 83), (84, 87), (87, 90), (90, 93), (93, 95), (95, 98), (98, 99)] classes_list = ('LOCATION', 'ORG', 'PERSON') labels_of_tokens = [0, 0, 2, 1, 1, 2, 1, 0, 0, 0, 4, 3, 0, 6, 5, 5, 5, 0, 5, 5, 0, 2, 2, 3, 3, 6, 5] true_entities = { 'LOCATION': [(5, 16), (17, 22), (84, 87), (87, 90)], 'ORG': [(31, 37), (90, 95)], 'PERSON': [(49, 59), (61, 70), (95, 99)] } calc_entities = BERT_NER.calculate_bounds_of_named_entities(bounds_of_tokens, classes_list, labels_of_tokens) self.assertIsInstance(calc_entities, dict) self.assertEqual(set(true_entities.keys()), set(calc_entities.keys())) for entity_type in true_entities: self.assertEqual(true_entities[entity_type], calc_entities[entity_type]) def test_get_shape_of_string_positive01(self): src = '##чники' dst = 'a' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive02(self): src = 'уже' dst = 'a' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive03(self): src = 'К' dst = 'A' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive04(self): src = 'Однако' dst = 'Aa' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive05(self): src = '66–67' dst = 'D-D' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive06(self): src = '[UNK]' dst = '[UNK]' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive07(self): src = '…' dst = 'U' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive08(self): src = ',' dst = 'P' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_negative(self): src = '' dst = '' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_subword_ID_positive01(self): src = '##чники' dst = 2 self.assertEqual(dst, BERT_NER.get_subword_ID(src)) def test_get_subword_ID_positive02(self): src = 'Однако' dst = 3 self.assertEqual(dst, BERT_NER.get_subword_ID(src)) def test_get_subword_ID_positive03(self): src = '[CLS]' dst = 0 self.assertEqual(dst, BERT_NER.get_subword_ID(src)) def test_get_subword_ID_positive04(self): src = '[SEP]' dst = 1 self.assertEqual(dst, BERT_NER.get_subword_ID(src)) if __name__ == '__main__': unittest.main(verbosity=2)

56.980673

120

0.633642

import copy import gc import os import pickle import re import sys import tempfile import unittest import numpy as np from sklearn.exceptions import NotFittedError from spacy_udpipe.language import UDPipeLanguage try: from deep_ner.bert_ner import BERT_NER from deep_ner.utils import load_dataset_from_json, set_total_seed from deep_ner.quality import calculate_prediction_quality from deep_ner.udpipe_data import UNIVERSAL_DEPENDENCIES, UNIVERSAL_POS_TAGS except: sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from deep_ner.bert_ner import BERT_NER from deep_ner.utils import load_dataset_from_json, set_total_seed from deep_ner.quality import calculate_prediction_quality from deep_ner.udpipe_data import UNIVERSAL_DEPENDENCIES, UNIVERSAL_POS_TAGS class TestBertNer(unittest.TestCase): @classmethod def setUpClass(cls): set_total_seed(0) def tearDown(self): if hasattr(self, 'ner'): del self.ner if hasattr(self, 'another_ner'): del self.another_ner if hasattr(self, 'temp_file_name'): if os.path.isfile(self.temp_file_name): os.remove(self.temp_file_name) def test_creation(self): self.ner = BERT_NER(udpipe_lang='en') self.assertIsInstance(self.ner, BERT_NER) self.assertTrue(hasattr(self.ner, 'udpipe_lang')) self.assertTrue(hasattr(self.ner, 'use_shapes')) self.assertTrue(hasattr(self.ner, 'use_nlp_features')) self.assertTrue(hasattr(self.ner, 'batch_size')) self.assertTrue(hasattr(self.ner, 'lstm_units')) self.assertTrue(hasattr(self.ner, 'lr')) self.assertTrue(hasattr(self.ner, 'l2_reg')) self.assertTrue(hasattr(self.ner, 'clip_norm')) self.assertTrue(hasattr(self.ner, 'bert_hub_module_handle')) self.assertTrue(hasattr(self.ner, 'finetune_bert')) self.assertTrue(hasattr(self.ner, 'max_epochs')) self.assertTrue(hasattr(self.ner, 'patience')) self.assertTrue(hasattr(self.ner, 'random_seed')) self.assertTrue(hasattr(self.ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.ner, 'max_seq_length')) self.assertTrue(hasattr(self.ner, 'validation_fraction')) self.assertTrue(hasattr(self.ner, 'verbose')) self.assertIsInstance(self.ner.batch_size, int) self.assertIsInstance(self.ner.lstm_units, int) self.assertIsInstance(self.ner.lr, float) self.assertIsInstance(self.ner.l2_reg, float) self.assertIsInstance(self.ner.clip_norm, float) self.assertIsInstance(self.ner.bert_hub_module_handle, str) self.assertIsInstance(self.ner.udpipe_lang, str) self.assertIsInstance(self.ner.finetune_bert, bool) self.assertIsInstance(self.ner.max_epochs, int) self.assertIsInstance(self.ner.patience, int) self.assertIsNone(self.ner.random_seed) self.assertIsInstance(self.ner.gpu_memory_frac, float) self.assertIsInstance(self.ner.max_seq_length, int) self.assertIsInstance(self.ner.validation_fraction, float) self.assertIsInstance(self.ner.verbose, bool) self.assertIsInstance(self.ner.use_shapes, bool) self.assertIsInstance(self.ner.use_nlp_features, bool) def test_check_params_positive(self): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.0, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=None, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) self.assertTrue(True) def test_check_params_negative001(self): true_err_msg = re.escape('`bert_hub_module_handle` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=False, udpipe_lang='en' ) def test_check_params_negative002(self): true_err_msg = re.escape('`bert_hub_module_handle` is wrong! Expected `{0}`, got `{1}`.'.format( type('abc'), type(123))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle=1, finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=False, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative003(self): true_err_msg = re.escape('`batch_size` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative004(self): true_err_msg = re.escape('`batch_size` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size='32', max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative005(self): true_err_msg = re.escape('`batch_size` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=-3, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative006(self): true_err_msg = re.escape('`max_epochs` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative007(self): true_err_msg = re.escape('`max_epochs` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs='10', patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative008(self): true_err_msg = re.escape('`max_epochs` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=-3, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative009(self): true_err_msg = re.escape('`patience` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative010(self): true_err_msg = re.escape('`patience` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience='3', gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative011(self): true_err_msg = re.escape('`patience` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=-3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative012(self): true_err_msg = re.escape('`max_seq_length` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative013(self): true_err_msg = re.escape('`max_seq_length` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length='512', lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative014(self): true_err_msg = re.escape('`max_seq_length` is wrong! Expected a positive integer value, but -3 is not ' 'positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=-3, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative015(self): true_err_msg = re.escape('`validation_fraction` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative016(self): true_err_msg = re.escape('`validation_fraction` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction='0.1', max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative017(self): true_err_msg = '`validation_fraction` is wrong! Expected a positive floating-point value greater than or ' \ 'equal to 0.0, but {0} is not positive.'.format(-0.1) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=-0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative018(self): true_err_msg = '`validation_fraction` is wrong! Expected a positive floating-point value less than 1.0, but ' \ '{0} is not less than 1.0.'.format(1.1) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=1.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative019(self): true_err_msg = re.escape('`gpu_memory_frac` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative020(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac='1.0', verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative021(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected a floating-point value in the (0.0, 1.0], ' 'but {0} is not proper.'.format(-1.0)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=-1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative022(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected a floating-point value in the (0.0, 1.0], ' 'but {0} is not proper.'.format(1.3)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.3, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative023(self): true_err_msg = re.escape('`lr` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative024(self): true_err_msg = re.escape('`lr` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr='1e-3', l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative025(self): true_err_msg = re.escape('`lr` is wrong! Expected a positive floating-point value, but {0} is not ' 'positive.'.format(0.0)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=0.0, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative026(self): true_err_msg = re.escape('`lr` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative027(self): true_err_msg = re.escape('`lr` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr='1e-3', l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative028(self): true_err_msg = re.escape('`lr` is wrong! Expected a positive floating-point value, but {0} is not ' 'positive.'.format(0.0)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=0.0, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative029(self): true_err_msg = re.escape('`l2_reg` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative030(self): true_err_msg = re.escape('`l2_reg` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg='1e-4', clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative031(self): true_err_msg = re.escape('`l2_reg` is wrong! Expected a non-negative floating-point value, but {0} is ' 'negative.'.format(-2.0)) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=-2.0, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative032(self): true_err_msg = re.escape('`finetune_bert` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, clip_norm=5.0, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative033(self): true_err_msg = re.escape('`finetune_bert` is wrong! Expected `{0}`, got `{1}`.'.format( type(True), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert='True', batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative034(self): true_err_msg = re.escape('`verbose` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative035(self): true_err_msg = re.escape('`verbose` is wrong! Expected `{0}`, got `{1}`.'.format( type(True), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose='False', random_seed=42, lstm_units=128, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative036(self): true_err_msg = re.escape('`lstm_units` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative037(self): true_err_msg = re.escape('`lstm_units` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', lstm_units='128', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_params_negative038(self): true_err_msg = re.escape('`lstm_units` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_params( bert_hub_module_handle='https://tfhub.dev/google/bert_multi_cased_L-12_H-768_A-12/1', finetune_bert=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, clip_norm=5.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42, lstm_units=-3, use_shapes=True, use_nlp_features=True, udpipe_lang='en' ) def test_check_X_positive(self): X = ['abc', 'defgh', '4wdffg'] BERT_NER.check_X(X, 'X_train') self.assertTrue(True) def test_check_X_negative01(self): X = {'abc', 'defgh', '4wdffg'} true_err_msg = re.escape('`X_train` is wrong, because it is not list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_X(X, 'X_train') def test_check_X_negative02(self): X = np.random.uniform(-1.0, 1.0, (10, 2)) true_err_msg = re.escape('`X_train` is wrong, because it is not 1-D list!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_X(X, 'X_train') def test_check_X_negative03(self): X = ['abc', 23, '4wdffg'] true_err_msg = re.escape('Item 1 of `X_train` is wrong, because it is not string-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_X(X, 'X_train') def text_check_Xy_positive(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_classes_list = ('LOC', 'ORG', 'PER') self.assertEqual(true_classes_list, BERT_NER.check_Xy(X, 'X_train', y, 'y_train')) def text_check_Xy_negative01(self): X = { 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' } y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('`X_train` is wrong, because it is not list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative02(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = { '1': { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, '2': { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } } true_err_msg = re.escape('`y_train` is wrong, because it is not a list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative03(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = np.random.uniform(-1.0, 1.0, (10, 2)) true_err_msg = re.escape('`y_train` is wrong, because it is not 1-D list!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative04(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] }, { 'LOC': [(17, 24), (117, 130)] } ] true_err_msg = re.escape('Length of `X_train` does not correspond to length of `y_train`! 2 != 3') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative05(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, 4 ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because it is not a dictionary-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative06(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 1: [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because its key `1` is not a string-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative07(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'O': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `O` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative08(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], '123': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `123` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative09(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'loc': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `loc` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative10(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': {1, 2} }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because its value `{0}` is not a list-like ' 'object!'.format(y[0]['PER'])) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative11(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), 63], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because named entity bounds `63` are not specified as ' 'list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative12(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77, 81)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because named entity bounds `{0}` are not specified as ' '2-D list!'.format((63, 77, 81))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative13(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (219, 196)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((219, 196))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative14(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 519)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((196, 519))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative15(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(-1, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((-1, 137))) with self.assertRaisesRegex(ValueError, true_err_msg): BERT_NER.check_Xy(X, 'X_train', y, 'y_train') def test_detect_token_labels_positive01(self): tokenized_text = ['Ба', '##рак', 'Об', '##ама', 'принимает', 'в', 'Б', '##елом', 'доме', 'своего', 'французского', 'кол', '##ле', '##гу', 'Н', '##ико', '##ля', 'Са', '##рко', '##зи', '.'] token_bounds = [(0, 2), (2, 5), (6, 8), (8, 11), (12, 21), (22, 23), (24, 25), (25, 29), (30, 34), (35, 41), (42, 54), (55, 58), (58, 60), (60, 62), (63, 64), (64, 67), (67, 69), (70, 72), (72, 75), (75, 77), (77, 78)] indices_of_named_entities = np.array( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0], dtype=np.int32 ) label_IDs = {1: 1, 2: 2, 3: 1} y_true = np.array( [0, 2, 1, 1, 1, 0, 0, 4, 3, 3, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) y_pred = BERT_NER.detect_token_labels(tokenized_text, token_bounds, indices_of_named_entities, label_IDs, 32) self.assertIsInstance(y_pred, np.ndarray) self.assertEqual(y_true.shape, y_pred.shape) self.assertEqual(y_true.tolist(), y_pred.tolist()) def test_detect_token_labels_positive02(self): tokenized_text = ['С', '1876', 'г', 'Павло', '##в', 'а', '##сси', '##сти', '##рует', 'профессор', '##у', 'К', '.', 'Н', '.', 'У', '##сти', '##мов', '##ич', '##у', 'в', 'М', '##еди', '##ко', '-', 'х', '##ир', '##ург', '##ической', 'академии', 'и', 'пара', '##лл', '##ельно', 'из', '##уч', '##ает', 'ф', '##из', '##ио', '##логи', '##ю', 'к', '##рово', '##об', '##ращения', '.'] token_bounds = [(0, 1), (2, 6), (7, 8), (9, 14), (14, 15), (16, 17), (17, 20), (20, 23), (23, 27), (28, 37), (37, 38), (39, 40), (40, 41), (42, 43), (43, 44), (45, 46), (46, 49), (49, 52), (52, 54), (54, 55), (56, 57), (58, 59), (59, 62), (62, 64), (64, 65), (65, 66), (66, 68), (68, 71), (71, 78), (79, 87), (88, 89), (90, 94), (94, 96), (96, 101), (102, 104), (104, 106), (106, 109), (110, 111), (111, 113), (113, 115), (115, 119), (119, 120), (121, 122), (122, 126), (126, 128), (128, 135), (135, 136)] indices_of_named_entities = np.array( [0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) label_IDs = {1: 1, 2: 2, 3: 3, 4: 2, 5: 4} y_true = np.array( [0, 0, 2, 1, 4, 3, 0, 0, 0, 0, 6, 5, 4, 3, 3, 3, 3, 3, 3, 3, 3, 0, 8, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) y_pred = BERT_NER.detect_token_labels(tokenized_text, token_bounds, indices_of_named_entities, label_IDs, 64) self.assertIsInstance(y_pred, np.ndarray) self.assertEqual(y_true.shape, y_pred.shape) self.assertEqual(y_true.tolist(), y_pred.tolist()) def test_detect_token_labels_positive03(self): tokenized_text = ['В', '##есной', '1890', 'года', 'В', '##ар', '##ша', '##вский', 'и', 'Томск', '##ий', 'университет', '##ы', 'из', '##бира', '##ют', 'его', 'профессором', '.'] token_bounds = [(0, 1), (1, 6), (7, 11), (12, 16), (17, 18), (18, 20), (20, 22), (22, 27), (28, 29), (30, 35), (35, 37), (38, 49), (49, 50), (51, 52), (53, 57), (57, 59), (60, 63), (64, 75), (75, 76)] indices_of_named_entities = np.array( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) label_IDs = {1: 1, 2: 2, 3: 2} y_true = np.array( [0, 2, 1, 1, 1, 4, 3, 3, 3, 3, 4, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) y_pred = BERT_NER.detect_token_labels(tokenized_text, token_bounds, indices_of_named_entities, label_IDs, 32) self.assertIsInstance(y_pred, np.ndarray) self.assertEqual(y_true.shape, y_pred.shape) self.assertEqual(y_true.tolist(), y_pred.tolist()) def test_calculate_indices_of_named_entities(self): source_text = 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози.' classes_list = ('LOCATION', 'ORG', 'PERSON') named_entities = {'PERSON': [(0, 11), (63, 77)], 'LOCATION': [(24, 34)]} true_indices = np.array( [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0], dtype=np.int32 ) true_labels_to_classes = {1: 1, 2: 3, 3: 3} indices, labels_to_classes = BERT_NER.calculate_indices_of_named_entities(source_text, classes_list, named_entities) self.assertIsInstance(indices, np.ndarray) self.assertIsInstance(labels_to_classes, dict) self.assertEqual(true_indices.shape, indices.shape) self.assertEqual(true_indices.tolist(), indices.tolist()) self.assertEqual(set(true_labels_to_classes.keys()), set(labels_to_classes.keys())) for label_ID in true_labels_to_classes: self.assertEqual(true_labels_to_classes[label_ID], labels_to_classes[label_ID]) def test_fit_positive01(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=3, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, lstm_units=32, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lstm_units, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.clip_norm, float) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertTrue(hasattr(res, 'universal_pos_tags_dict_')) self.assertTrue(hasattr(res, 'universal_dependencies_dict_')) self.assertTrue(hasattr(res, 'nlp_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) self.assertIsInstance(res.universal_pos_tags_dict_, dict) self.assertIsInstance(res.universal_dependencies_dict_, dict) self.assertIsInstance(res.nlp_, UDPipeLanguage) self.assertEqual(len(res.universal_pos_tags_dict_), len(UNIVERSAL_POS_TAGS)) self.assertEqual(len(res.universal_dependencies_dict_), len(UNIVERSAL_DEPENDENCIES)) def test_fit_positive02(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=True, max_epochs=3, batch_size=2, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=42, lstm_units=32, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lstm_units, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.clip_norm, float) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertEqual(res.random_seed, 42) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) def test_fit_positive03(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=3, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, lstm_units=None, clip_norm=None, udpipe_lang='ru', use_shapes=False, use_nlp_features=True) X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsNone(res.lstm_units) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsNone(res.clip_norm, None) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) def test_fit_predict(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=5, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=42, udpipe_lang='ru', use_shapes=True, use_nlp_features=False) X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lstm_units, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.clip_norm, float) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) y_pred = res.predict(X_train) self.assertIsInstance(y_pred, list) self.assertEqual(len(X_train), len(y_pred)) for sample_idx in range(len(y_pred)): self.assertIsInstance(y_pred[sample_idx], dict) f1, precision, recall, _ = calculate_prediction_quality(y_train, y_pred, res.classes_list_) self.assertGreater(f1, 0.0) self.assertGreater(precision, 0.0) self.assertGreater(recall, 0.0) def test_predict_negative(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=3, batch_size=4, random_seed=None, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) with self.assertRaises(NotFittedError): _ = self.ner.predict(X_train) def test_tokenize_all_01(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=1, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, udpipe_lang='ru', use_nlp_features=True, use_shapes=True) X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) self.ner.fit(X_train, y_train) res = self.ner.tokenize_all(X_train, y_train, shapes_vocabulary=self.ner.shapes_list_) self.assertIsInstance(res, tuple) self.assertEqual(len(res), 4) X_train_tokenized, y_train_tokenized, shapes_list, bounds_of_tokens_for_training = res self.assertIsInstance(X_train_tokenized, list) self.assertIsInstance(y_train_tokenized, np.ndarray) self.assertIs(self.ner.shapes_list_, shapes_list) self.assertIsInstance(bounds_of_tokens_for_training, np.ndarray) self.assertEqual(len(X_train_tokenized), 4) self.assertEqual(y_train_tokenized.shape, (len(y_train), self.ner.max_seq_length)) for data_idx in range(3): self.assertIsInstance(X_train_tokenized[data_idx], np.ndarray) self.assertEqual(X_train_tokenized[data_idx].shape, (len(X_train), self.ner.max_seq_length)) data_idx = 3 self.assertIsInstance(X_train_tokenized[data_idx], np.ndarray) self.assertEqual(len(X_train_tokenized[data_idx].shape), 3) self.assertEqual(X_train_tokenized[data_idx].shape[0], len(X_train)) self.assertEqual(X_train_tokenized[data_idx].shape[1], self.ner.max_seq_length) self.assertGreater(X_train_tokenized[data_idx].shape[2], 4 + len(UNIVERSAL_POS_TAGS) + len(UNIVERSAL_DEPENDENCIES)) for sample_idx in range(X_train_tokenized[data_idx].shape[0]): n = 0 for token_idx in range(X_train_tokenized[data_idx].shape[1]): if X_train_tokenized[data_idx][sample_idx][token_idx].sum() < 1e-3: break n += 1 self.assertGreater(n, 0, msg='Sample {0}: additional features are not defined!'.format(sample_idx)) for token_idx in range(n): self.assertAlmostEqual(X_train_tokenized[data_idx][sample_idx][token_idx][0:4].sum(), 1.0, msg='Sample {0}, token {1}: additional features are wrong!'.format( sample_idx, token_idx)) for token_idx in range(1, n - 1): start_pos = 4 end_pos = 4 + len(UNIVERSAL_POS_TAGS) self.assertAlmostEqual(X_train_tokenized[data_idx][sample_idx][token_idx][start_pos:end_pos].sum(), 1.0, msg='Sample {0}, token {1}: part of speech is not defined!'.format( sample_idx, token_idx)) start_pos = 4 + len(UNIVERSAL_POS_TAGS) end_pos = 4 + len(UNIVERSAL_POS_TAGS) + len(UNIVERSAL_DEPENDENCIES) self.assertGreaterEqual(X_train_tokenized[data_idx][sample_idx][token_idx][start_pos:end_pos].sum(), 1.0, msg='Sample {0}, token {1}: dependency tag is not defined!'.format( sample_idx, token_idx)) def test_tokenize_all_02(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=1, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, udpipe_lang='ru', use_shapes=False, use_nlp_features=False) X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) self.ner.fit(X_train, y_train) res = self.ner.tokenize_all(X_train, y_train, shapes_vocabulary=self.ner.shapes_list_) self.assertIsInstance(res, tuple) self.assertEqual(len(res), 4) X_train_tokenized, y_train_tokenized, shapes_list, bounds_of_tokens_for_training = res self.assertIsInstance(X_train_tokenized, list) self.assertIsInstance(y_train_tokenized, np.ndarray) self.assertIs(self.ner.shapes_list_, shapes_list) self.assertIsInstance(bounds_of_tokens_for_training, np.ndarray) self.assertEqual(len(X_train_tokenized), 3) self.assertEqual(y_train_tokenized.shape, (len(y_train), self.ner.max_seq_length)) for data_idx in range(3): self.assertIsInstance(X_train_tokenized[data_idx], np.ndarray) self.assertEqual(X_train_tokenized[data_idx].shape, (len(X_train), self.ner.max_seq_length)) def test_serialize_positive01(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=5, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=42, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, BERT_NER) self.assertTrue(hasattr(res, 'udpipe_lang')) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lstm_units')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'clip_norm')) self.assertTrue(hasattr(res, 'bert_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_bert')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertTrue(hasattr(res, 'use_shapes')) self.assertTrue(hasattr(res, 'use_nlp_features')) self.assertIsInstance(res.udpipe_lang, str) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lstm_units, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.clip_norm, float) self.assertIsInstance(res.bert_hub_module_handle, str) self.assertIsInstance(res.finetune_bert, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertIsInstance(res.use_shapes, bool) self.assertIsInstance(res.use_nlp_features, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'tokenizer_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 3) self.assertEqual(res.shapes_list_[-3:], ('[CLS]', '[SEP]', '[UNK]')) y_pred1 = res.predict(X_train) self.assertIsInstance(y_pred1, list) self.assertEqual(len(X_train), len(y_pred1)) for sample_idx in range(len(y_pred1)): self.assertIsInstance(y_pred1[sample_idx], dict) f1, precision, recall, _ = calculate_prediction_quality(y_train, y_pred1, res.classes_list_) self.assertGreater(f1, 0.0) self.assertGreater(precision, 0.0) self.assertGreater(recall, 0.0) with tempfile.NamedTemporaryFile(mode='w', delete=True) as fp: self.temp_file_name = fp.name with open(self.temp_file_name, mode='wb') as fp: pickle.dump(res, fp) del res, self.ner gc.collect() with open(self.temp_file_name, mode='rb') as fp: self.ner = pickle.load(fp) y_pred2 = self.ner.predict(X_train) self.assertIsInstance(y_pred2, list) self.assertEqual(len(y_pred2), len(y_pred2)) for sample_idx in range(len(y_pred2)): self.assertIsInstance(y_pred2[sample_idx], dict) self.assertEqual(set(y_pred1[sample_idx]), set(y_pred2[sample_idx])) for ne_type in y_pred1[sample_idx]: self.assertEqual(y_pred1[sample_idx][ne_type], y_pred2[sample_idx][ne_type]) def test_serialize_positive02(self): self.ner = BERT_NER(random_seed=31, udpipe_lang='ru') old_udpipe_lang = self.ner.udpipe_lang old_batch_size = self.ner.batch_size old_lstm_units = self.ner.lstm_units old_lr = self.ner.lr old_l2_reg = self.ner.l2_reg old_clip_norm = self.ner.clip_norm old_bert_hub_module_handle = self.ner.bert_hub_module_handle old_finetune_bert = self.ner.finetune_bert old_max_epochs = self.ner.max_epochs old_patience = self.ner.patience old_random_seed = self.ner.random_seed old_gpu_memory_frac = self.ner.gpu_memory_frac old_max_seq_length = self.ner.max_seq_length old_validation_fraction = self.ner.validation_fraction old_verbose = self.ner.verbose old_use_shapes = self.ner.use_shapes old_use_nlp_features = self.ner.use_nlp_features with tempfile.NamedTemporaryFile(mode='w', delete=True) as fp: self.temp_file_name = fp.name with open(self.temp_file_name, mode='wb') as fp: pickle.dump(self.ner, fp) del self.ner gc.collect() with open(self.temp_file_name, mode='rb') as fp: self.ner = pickle.load(fp) self.assertIsInstance(self.ner, BERT_NER) self.assertTrue(hasattr(self.ner, 'udpipe_lang')) self.assertTrue(hasattr(self.ner, 'batch_size')) self.assertTrue(hasattr(self.ner, 'lstm_units')) self.assertTrue(hasattr(self.ner, 'lr')) self.assertTrue(hasattr(self.ner, 'l2_reg')) self.assertTrue(hasattr(self.ner, 'clip_norm')) self.assertTrue(hasattr(self.ner, 'bert_hub_module_handle')) self.assertTrue(hasattr(self.ner, 'finetune_bert')) self.assertTrue(hasattr(self.ner, 'max_epochs')) self.assertTrue(hasattr(self.ner, 'patience')) self.assertTrue(hasattr(self.ner, 'random_seed')) self.assertTrue(hasattr(self.ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.ner, 'max_seq_length')) self.assertTrue(hasattr(self.ner, 'validation_fraction')) self.assertTrue(hasattr(self.ner, 'verbose')) self.assertTrue(hasattr(self.ner, 'use_shapes')) self.assertTrue(hasattr(self.ner, 'use_nlp_features')) self.assertEqual(self.ner.udpipe_lang, old_udpipe_lang) self.assertEqual(self.ner.batch_size, old_batch_size) self.assertEqual(self.ner.lstm_units, old_lstm_units) self.assertAlmostEqual(self.ner.lr, old_lr) self.assertAlmostEqual(self.ner.l2_reg, old_l2_reg) self.assertAlmostEqual(self.ner.clip_norm, old_clip_norm) self.assertEqual(self.ner.bert_hub_module_handle, old_bert_hub_module_handle) self.assertEqual(self.ner.finetune_bert, old_finetune_bert) self.assertEqual(self.ner.max_epochs, old_max_epochs) self.assertEqual(self.ner.patience, old_patience) self.assertAlmostEqual(self.ner.gpu_memory_frac, old_gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, old_max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, old_validation_fraction) self.assertEqual(self.ner.verbose, old_verbose) self.assertEqual(self.ner.use_shapes, old_use_shapes) self.assertEqual(self.ner.use_nlp_features, old_use_nlp_features) self.assertEqual(self.ner.random_seed, old_random_seed) def test_copy_positive01(self): self.ner = BERT_NER(random_seed=0, udpipe_lang='ru', use_shapes=False, use_nlp_features=True) self.another_ner = copy.copy(self.ner) self.assertIsInstance(self.another_ner, BERT_NER) self.assertIsNot(self.ner, self.another_ner) self.assertTrue(hasattr(self.another_ner, 'udpipe_lang')) self.assertTrue(hasattr(self.another_ner, 'batch_size')) self.assertTrue(hasattr(self.another_ner, 'lstm_units')) self.assertTrue(hasattr(self.another_ner, 'lr')) self.assertTrue(hasattr(self.another_ner, 'l2_reg')) self.assertTrue(hasattr(self.another_ner, 'clip_norm')) self.assertTrue(hasattr(self.another_ner, 'bert_hub_module_handle')) self.assertTrue(hasattr(self.another_ner, 'finetune_bert')) self.assertTrue(hasattr(self.another_ner, 'max_epochs')) self.assertTrue(hasattr(self.another_ner, 'patience')) self.assertTrue(hasattr(self.another_ner, 'random_seed')) self.assertTrue(hasattr(self.another_ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.another_ner, 'max_seq_length')) self.assertTrue(hasattr(self.another_ner, 'validation_fraction')) self.assertTrue(hasattr(self.another_ner, 'verbose')) self.assertTrue(hasattr(self.another_ner, 'use_shapes')) self.assertTrue(hasattr(self.another_ner, 'use_nlp_features')) self.assertEqual(self.ner.udpipe_lang, self.another_ner.udpipe_lang) self.assertEqual(self.ner.batch_size, self.another_ner.batch_size) self.assertEqual(self.ner.lstm_units, self.another_ner.lstm_units) self.assertAlmostEqual(self.ner.lr, self.another_ner.lr) self.assertAlmostEqual(self.ner.l2_reg, self.another_ner.l2_reg) self.assertAlmostEqual(self.ner.clip_norm, self.another_ner.clip_norm) self.assertEqual(self.ner.bert_hub_module_handle, self.another_ner.bert_hub_module_handle) self.assertEqual(self.ner.finetune_bert, self.another_ner.finetune_bert) self.assertEqual(self.ner.max_epochs, self.another_ner.max_epochs) self.assertEqual(self.ner.patience, self.another_ner.patience) self.assertEqual(self.ner.random_seed, self.another_ner.random_seed) self.assertAlmostEqual(self.ner.gpu_memory_frac, self.another_ner.gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, self.another_ner.max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, self.another_ner.validation_fraction) self.assertEqual(self.ner.verbose, self.another_ner.verbose) self.assertEqual(self.ner.use_shapes, self.another_ner.use_shapes) self.assertEqual(self.ner.use_nlp_features, self.another_ner.use_nlp_features) def test_copy_positive02(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = BERT_NER(finetune_bert=False, max_epochs=3, batch_size=4, max_seq_length=128, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, udpipe_lang='ru') X_train, y_train = load_dataset_from_json(os.path.join(base_dir, 'true_named_entities.json')) self.ner.fit(X_train, y_train) self.another_ner = copy.copy(self.ner) self.assertIsInstance(self.another_ner, BERT_NER) self.assertIsNot(self.ner, self.another_ner) self.assertTrue(hasattr(self.another_ner, 'udpipe_lang')) self.assertTrue(hasattr(self.another_ner, 'batch_size')) self.assertTrue(hasattr(self.another_ner, 'lstm_units')) self.assertTrue(hasattr(self.another_ner, 'lr')) self.assertTrue(hasattr(self.another_ner, 'l2_reg')) self.assertTrue(hasattr(self.another_ner, 'clip_norm')) self.assertTrue(hasattr(self.another_ner, 'bert_hub_module_handle')) self.assertTrue(hasattr(self.another_ner, 'finetune_bert')) self.assertTrue(hasattr(self.another_ner, 'max_epochs')) self.assertTrue(hasattr(self.another_ner, 'patience')) self.assertTrue(hasattr(self.another_ner, 'random_seed')) self.assertTrue(hasattr(self.another_ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.another_ner, 'max_seq_length')) self.assertTrue(hasattr(self.another_ner, 'validation_fraction')) self.assertTrue(hasattr(self.another_ner, 'verbose')) self.assertTrue(hasattr(self.another_ner, 'use_shapes')) self.assertTrue(hasattr(self.another_ner, 'use_nlp_features')) self.assertTrue(hasattr(self.another_ner, 'classes_list_')) self.assertTrue(hasattr(self.another_ner, 'shapes_list_')) self.assertTrue(hasattr(self.another_ner, 'tokenizer_')) self.assertTrue(hasattr(self.another_ner, 'sess_')) self.assertEqual(self.ner.udpipe_lang, self.another_ner.udpipe_lang) self.assertEqual(self.ner.batch_size, self.another_ner.batch_size) self.assertEqual(self.ner.lstm_units, self.another_ner.lstm_units) self.assertAlmostEqual(self.ner.lr, self.another_ner.lr) self.assertAlmostEqual(self.ner.l2_reg, self.another_ner.l2_reg) self.assertAlmostEqual(self.ner.clip_norm, self.another_ner.clip_norm) self.assertEqual(self.ner.bert_hub_module_handle, self.another_ner.bert_hub_module_handle) self.assertEqual(self.ner.finetune_bert, self.another_ner.finetune_bert) self.assertEqual(self.ner.max_epochs, self.another_ner.max_epochs) self.assertEqual(self.ner.patience, self.another_ner.patience) self.assertEqual(self.ner.random_seed, self.another_ner.random_seed) self.assertAlmostEqual(self.ner.gpu_memory_frac, self.another_ner.gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, self.another_ner.max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, self.another_ner.validation_fraction) self.assertEqual(self.ner.verbose, self.another_ner.verbose) self.assertEqual(self.ner.use_shapes, self.another_ner.use_shapes) self.assertEqual(self.ner.use_nlp_features, self.another_ner.use_nlp_features) self.assertIs(self.ner.classes_list_, self.another_ner.classes_list_) self.assertIs(self.ner.shapes_list_, self.another_ner.shapes_list_) self.assertIs(self.ner.tokenizer_, self.another_ner.tokenizer_) self.assertIs(self.ner.sess_, self.another_ner.sess_) def test_calculate_bounds_of_named_entities(self): bounds_of_tokens = [(0, 2), (2, 5), (5, 8), (8, 10), (11, 16), (17, 20), (20, 22), (22, 26), (26, 27), (28, 31), (31, 34), (34, 37), (38, 48), (49, 52), (52, 54), (55, 57), (58, 59), (59, 61), (61, 63), (64, 70), (71, 83), (84, 87), (87, 90), (90, 93), (93, 95), (95, 98), (98, 99)] classes_list = ('LOCATION', 'ORG', 'PERSON') labels_of_tokens = [0, 0, 2, 1, 1, 2, 1, 0, 0, 0, 4, 3, 0, 6, 5, 5, 5, 0, 5, 5, 0, 2, 2, 3, 3, 6, 5] true_entities = { 'LOCATION': [(5, 16), (17, 22), (84, 87), (87, 90)], 'ORG': [(31, 37), (90, 95)], 'PERSON': [(49, 59), (61, 70), (95, 99)] } calc_entities = BERT_NER.calculate_bounds_of_named_entities(bounds_of_tokens, classes_list, labels_of_tokens) self.assertIsInstance(calc_entities, dict) self.assertEqual(set(true_entities.keys()), set(calc_entities.keys())) for entity_type in true_entities: self.assertEqual(true_entities[entity_type], calc_entities[entity_type]) def test_get_shape_of_string_positive01(self): src = '##чники' dst = 'a' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive02(self): src = 'уже' dst = 'a' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive03(self): src = 'К' dst = 'A' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive04(self): src = 'Однако' dst = 'Aa' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive05(self): src = '66–67' dst = 'D-D' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive06(self): src = '[UNK]' dst = '[UNK]' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive07(self): src = '…' dst = 'U' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive08(self): src = ',' dst = 'P' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_shape_of_string_negative(self): src = '' dst = '' self.assertEqual(dst, BERT_NER.get_shape_of_string(src)) def test_get_subword_ID_positive01(self): src = '##чники' dst = 2 self.assertEqual(dst, BERT_NER.get_subword_ID(src)) def test_get_subword_ID_positive02(self): src = 'Однако' dst = 3 self.assertEqual(dst, BERT_NER.get_subword_ID(src)) def test_get_subword_ID_positive03(self): src = '[CLS]' dst = 0 self.assertEqual(dst, BERT_NER.get_subword_ID(src)) def test_get_subword_ID_positive04(self): src = '[SEP]' dst = 1 self.assertEqual(dst, BERT_NER.get_subword_ID(src)) if __name__ == '__main__': unittest.main(verbosity=2)

true

1c384a7676714a112d2709b7de2e06ce6bb4ddd8

856

py

Python

tests/test_packages/test_connections/test_tcp/__init__.py

ejfitzgerald/agents-aea

6411fcba8af2cdf55a3005939ae8129df92e8c3e

[ "Apache-2.0" ]

126

2019-09-07T09:32:44.000Z

2022-03-29T14:28:41.000Z

tests/test_packages/test_connections/test_tcp/__init__.py

ejfitzgerald/agents-aea

6411fcba8af2cdf55a3005939ae8129df92e8c3e

[ "Apache-2.0" ]

1,814

2019-08-24T10:08:07.000Z

2022-03-31T14:28:36.000Z

tests/test_packages/test_connections/test_tcp/__init__.py

ejfitzgerald/agents-aea

6411fcba8af2cdf55a3005939ae8129df92e8c3e

[ "Apache-2.0" ]

46

2019-09-03T22:13:58.000Z

2022-03-22T01:25:16.000Z

# -*- coding: utf-8 -*- # ------------------------------------------------------------------------------ # # Copyright 2018-2019 Fetch.AI Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # ------------------------------------------------------------------------------ """This module contains the tests of the TCP connection."""

40.761905

80

0.58528

true

1c384c91f36d0a8c490fb7657c500258ebef08fc

2,882

py

Python

utils/common.py

projekgallus/gallus-gui

c7effdcbc68d64d60a96b68a1064d0f590b449b8

[ "BSD-3-Clause" ]

null

utils/common.py

projekgallus/gallus-gui

c7effdcbc68d64d60a96b68a1064d0f590b449b8

[ "BSD-3-Clause" ]

null

utils/common.py

projekgallus/gallus-gui

c7effdcbc68d64d60a96b68a1064d0f590b449b8

[ "BSD-3-Clause" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- ## Copyright (c) 2018, Projek Gallus ## Copyright (c) 2017, The Sumokoin Project (www.sumokoin.org) ''' Misc utility classes/functions for application ''' import os, sys, string try: from cStringIO import StringIO except ImportError: from StringIO import StringIO class DummyStream: ''' dummyStream behaves like a stream but does nothing. ''' def __init__(self): pass def write(self,data): pass def read(self,data): pass def flush(self): pass def close(self): pass def getAppPath(): '''Get the path to this script no matter how it's run.''' #Determine if the application is a py/pyw or a frozen exe. if hasattr(sys, 'frozen'): # If run from exe dir_path = os.path.dirname(unicode(sys.executable, sys.getfilesystemencoding())) elif '__file__' in locals(): # If run from py dir_path = os.path.dirname(unicode(__file__, sys.getfilesystemencoding())) else: # If run from command line #dir_path = sys.path[0] dir_path = os.getcwdu() return dir_path def getHomeDir(): if sys.platform == 'win32': import winpaths homedir = winpaths.get_common_appdata() # = e.g 'C:\ProgramData' else: homedir = os.path.expanduser("~") return homedir def getSockDir(): if sys.platform == 'win32': import winpaths homedir = winpaths.get_appdata() # = e.g 'C:\ProgramData' else: homedir = os.path.expanduser("~") return homedir def makeDir(d): if not os.path.exists(d): os.makedirs(d) return d def ensureDir(f): d = os.path.dirname(f) if not os.path.exists(d): os.makedirs(d) return f def _xorData(data): """Xor Method, Take a data Xor all bytes and return""" data = [chr(ord(c) ^ 10) for c in data] return string.join(data, '') def readFile(path, offset=0, size=-1, xor_data=False): """Read specified block from file, using the given size and offset""" fd = open(path, 'rb') fd.seek(offset) data = fd.read(size) fd.close() return _xorData(data) if xor_data else data def writeFile(path, buf, offset=0, xor_data=False): """Write specified block on file at the given offset""" if xor_data: buf = _xorData(buf) fd = open(path, 'wb') fd.seek(offset) fd.write(buf) fd.close() return len(buf) def print_money(amount): try: amount = int(amount) except: raise Exception("Error parsing amount. Money amount must be an integer.") return "%s <small>GAC</small>" % ("{:,.9f}".format(amount/1000000000.)) def print_money2(amount): try: amount = int(amount) except: raise Exception("Error parsing amount. Money amount must be an integer.") return "%s" % ("{:,.9f}".format(amount/1000000000.))

27.711538

88

0.62526

StringIO import StringIO class DummyStream: def __init__(self): pass def write(self,data): pass def read(self,data): pass def flush(self): pass def close(self): pass def getAppPath(): if hasattr(sys, 'frozen'): dir_path = os.path.dirname(unicode(sys.executable, sys.getfilesystemencoding())) elif '__file__' in locals(): dir_path = os.path.dirname(unicode(__file__, sys.getfilesystemencoding())) else: dir_path = os.getcwdu() return dir_path def getHomeDir(): if sys.platform == 'win32': import winpaths homedir = winpaths.get_common_appdata() else: homedir = os.path.expanduser("~") return homedir def getSockDir(): if sys.platform == 'win32': import winpaths homedir = winpaths.get_appdata() else: homedir = os.path.expanduser("~") return homedir def makeDir(d): if not os.path.exists(d): os.makedirs(d) return d def ensureDir(f): d = os.path.dirname(f) if not os.path.exists(d): os.makedirs(d) return f def _xorData(data): data = [chr(ord(c) ^ 10) for c in data] return string.join(data, '') def readFile(path, offset=0, size=-1, xor_data=False): fd = open(path, 'rb') fd.seek(offset) data = fd.read(size) fd.close() return _xorData(data) if xor_data else data def writeFile(path, buf, offset=0, xor_data=False): if xor_data: buf = _xorData(buf) fd = open(path, 'wb') fd.seek(offset) fd.write(buf) fd.close() return len(buf) def print_money(amount): try: amount = int(amount) except: raise Exception("Error parsing amount. Money amount must be an integer.") return "%s <small>GAC</small>" % ("{:,.9f}".format(amount/1000000000.)) def print_money2(amount): try: amount = int(amount) except: raise Exception("Error parsing amount. Money amount must be an integer.") return "%s" % ("{:,.9f}".format(amount/1000000000.))

true

1c384cb0654489f18fed0bfc27fa098d462fab70

2,131

py

Python

core/tests.py

Wanderer2436/django_pharmacy

2e12c41e30f2f2e2c0f3abdaded98a917420f5b8

[ "MIT" ]

null

core/tests.py

Wanderer2436/django_pharmacy

2e12c41e30f2f2e2c0f3abdaded98a917420f5b8

[ "MIT" ]

2

2022-03-31T14:34:44.000Z

2022-03-31T14:35:17.000Z

core/tests.py

Wanderer2436/django_pharmacy

2e12c41e30f2f2e2c0f3abdaded98a917420f5b8

[ "MIT" ]

null

from django.test import TestCase, Client, RequestFactory from django.urls import reverse from core import models class ProductSearchTestCase(TestCase): def setUp(self): self.client = Client() category1 = models.Category.objects.create(name='TestCat1') category2 = models.Category.objects.create(name='TestCat2') self.product1 = models.Product.objects.create(name='Test1', category=category1, price=10) self.product2 = models.Product.objects.create(name='Test2', category=category2, price=25) def testWithoutParams(self): response = self.client.get(reverse('core:catalog')) self.assertSequenceEqual( list(response.context['products']), list(models.Product.objects.all()), 'При поиске без параметров должны выводиться все продукты', ) def testSearchByName(self): response = self.client.get(reverse('core:catalog'), data={'name': 'Test1'}) self.assertEqual(1, response.context['products'].count()) self.assertEqual( 'Test1', response.context['object_list'].first().name, ) def testProductDetail(self): response = self.client.get('/catalog/product/1/') self.assertEqual(response.status_code, 200) class PharmacySearchTestCase(TestCase): def setUp(self): self.client = Client() self.pharmacy1 = models.Pharmacy.objects.create(name='Test1') self.pharmacy2 = models.Pharmacy.objects.create(name='Test2') def testWithoutParams(self): response = self.client.get(reverse('core:pharmacy')) self.assertSequenceEqual( list(response.context['object_list']), list(models.Pharmacy.objects.all()), 'При поиске без параметров должны выводиться все аптеки', ) def testSearchByName(self): response = self.client.get(reverse('core:pharmacy'), data={'name': 'Test1'}) self.assertEqual(2, response.context['object_list'].count()) self.assertEqual( 'Test1', response.context['object_list'].first().name, )

38.053571

97

0.650399

from django.test import TestCase, Client, RequestFactory from django.urls import reverse from core import models class ProductSearchTestCase(TestCase): def setUp(self): self.client = Client() category1 = models.Category.objects.create(name='TestCat1') category2 = models.Category.objects.create(name='TestCat2') self.product1 = models.Product.objects.create(name='Test1', category=category1, price=10) self.product2 = models.Product.objects.create(name='Test2', category=category2, price=25) def testWithoutParams(self): response = self.client.get(reverse('core:catalog')) self.assertSequenceEqual( list(response.context['products']), list(models.Product.objects.all()), 'При поиске без параметров должны выводиться все продукты', ) def testSearchByName(self): response = self.client.get(reverse('core:catalog'), data={'name': 'Test1'}) self.assertEqual(1, response.context['products'].count()) self.assertEqual( 'Test1', response.context['object_list'].first().name, ) def testProductDetail(self): response = self.client.get('/catalog/product/1/') self.assertEqual(response.status_code, 200) class PharmacySearchTestCase(TestCase): def setUp(self): self.client = Client() self.pharmacy1 = models.Pharmacy.objects.create(name='Test1') self.pharmacy2 = models.Pharmacy.objects.create(name='Test2') def testWithoutParams(self): response = self.client.get(reverse('core:pharmacy')) self.assertSequenceEqual( list(response.context['object_list']), list(models.Pharmacy.objects.all()), 'При поиске без параметров должны выводиться все аптеки', ) def testSearchByName(self): response = self.client.get(reverse('core:pharmacy'), data={'name': 'Test1'}) self.assertEqual(2, response.context['object_list'].count()) self.assertEqual( 'Test1', response.context['object_list'].first().name, )

true

1c384ccc0e50dbc39e26da9575b6526332b32a5b

1,454

py

Python

jdcloud_sdk/services/antipro/apis/DescribeAttackSourceRequest.py

jdcloud-apigateway/jdcloud-sdk-python

0886769bcf1fb92128a065ff0f4695be099571cc

[ "Apache-2.0" ]

null

jdcloud_sdk/services/antipro/apis/DescribeAttackSourceRequest.py

jdcloud-apigateway/jdcloud-sdk-python

0886769bcf1fb92128a065ff0f4695be099571cc

[ "Apache-2.0" ]

null

jdcloud_sdk/services/antipro/apis/DescribeAttackSourceRequest.py

jdcloud-apigateway/jdcloud-sdk-python

0886769bcf1fb92128a065ff0f4695be099571cc

[ "Apache-2.0" ]

null

# coding=utf8 # Copyright 2018 JDCLOUD.COM # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # NOTE: This class is auto generated by the jdcloud code generator program. from jdcloud_sdk.core.jdcloudrequest import JDCloudRequest class DescribeAttackSourceRequest(JDCloudRequest): """ 查询攻击来源 """ def __init__(self, parameters, header=None, version="v1"): super(DescribeAttackSourceRequest, self).__init__( '/attacklog/{attackLogId}:describeAttackSource', 'GET', header, version) self.parameters = parameters class DescribeAttackSourceParameters(object): def __init__(self, attackLogId, ip): """ :param attackLogId: 攻击记录 Id :param ip: DDoS 防护包已防护的公网 IP. <br>- 使用 <a href='http://docs.jdcloud.com/anti-ddos-protection-package/api/describeprotectediplist'>describeProtectedIpList</a> 接口查询 DDoS 防护包已防护的公网 IP """ self.attackLogId = attackLogId self.ip = ip

33.045455

188

0.722146

from jdcloud_sdk.core.jdcloudrequest import JDCloudRequest class DescribeAttackSourceRequest(JDCloudRequest): def __init__(self, parameters, header=None, version="v1"): super(DescribeAttackSourceRequest, self).__init__( '/attacklog/{attackLogId}:describeAttackSource', 'GET', header, version) self.parameters = parameters class DescribeAttackSourceParameters(object): def __init__(self, attackLogId, ip): self.attackLogId = attackLogId self.ip = ip

true

1c384d63920592db9836d8b889c140103e7fa797

1,867

py

Python

pdf_render/helpers/dados_testes.py

mauriciolongato/costurApp

7a700ef43e94023ee94c962873f9e2324e0f7530

[ "MIT" ]

1

2019-05-12T19:16:07.000Z

pdf_render/helpers/dados_testes.py

mauriciolongato/costurApp

7a700ef43e94023ee94c962873f9e2324e0f7530

[ "MIT" ]

null

pdf_render/helpers/dados_testes.py

mauriciolongato/costurApp

7a700ef43e94023ee94c962873f9e2324e0f7530

[ "MIT" ]

null

from helpers import jobs, functions def informacao_caixa(): data = {'header': {'nome_cliente': 'Polo', 'nome_caixa': 1, 'pedido_op': 'REF: COD10000001', 'referencia_id': 1, 'nome_referencia': 'Camisa Polo com Bolso', 'nome_sequencia': 'Fazer Camisa Polo Com Bolso' }, 'body': [{'maquina': 'Maquina de Costura', 'ordem_execucao': '1', 'nome_acao': 'Costura Bolso', 'tempo_medio': '1000', 'cod_bar': 987654321, # 'cod_bar_address': '{}'.format(jobs.create_barcode(987654321)), 'cod_bar_address': functions.path2url('static/{}'.format(jobs.create_barcode(987654321))), }, {'maquina': 'Maquina de Costura', 'ordem_execucao': '2', 'nome_acao': 'Costura Tronco', 'tempo_medio': '1000', 'cod_bar': 123456789, # 'cod_bar_address': '{}'.format(jobs.create_barcode(123456789)), 'cod_bar_address': functions.path2url('static/{}'.format(jobs.create_barcode(123456789))), }, {'maquina': 'Maquina de Costura', 'ordem_execucao': '1', 'nome_acao': 'Costura Bolso', 'tempo_medio': '1000', 'cod_bar': 987654321, # 'cod_bar_address': '{}'.format(jobs.create_barcode(987654321)), 'cod_bar_address': functions.path2url('static/{}'.format(jobs.create_barcode(987654321))), } ] } return data

47.871795

112

0.447242

from helpers import jobs, functions def informacao_caixa(): data = {'header': {'nome_cliente': 'Polo', 'nome_caixa': 1, 'pedido_op': 'REF: COD10000001', 'referencia_id': 1, 'nome_referencia': 'Camisa Polo com Bolso', 'nome_sequencia': 'Fazer Camisa Polo Com Bolso' }, 'body': [{'maquina': 'Maquina de Costura', 'ordem_execucao': '1', 'nome_acao': 'Costura Bolso', 'tempo_medio': '1000', 'cod_bar': 987654321, 'cod_bar_address': functions.path2url('static/{}'.format(jobs.create_barcode(987654321))), }, {'maquina': 'Maquina de Costura', 'ordem_execucao': '2', 'nome_acao': 'Costura Tronco', 'tempo_medio': '1000', 'cod_bar': 123456789, 'cod_bar_address': functions.path2url('static/{}'.format(jobs.create_barcode(123456789))), }, {'maquina': 'Maquina de Costura', 'ordem_execucao': '1', 'nome_acao': 'Costura Bolso', 'tempo_medio': '1000', 'cod_bar': 987654321, 'cod_bar_address': functions.path2url('static/{}'.format(jobs.create_barcode(987654321))), } ] } return data

true

1c384d76c4cde6b1c2953f675082b314778a5507

966

py

Python

pdb2pqr/tests/propka_test.py

ashermancinelli/apbs-pdb2pqr

0b1bc0126331cf3f1e08667ccc70dae8eda5cd00

[ "BSD-3-Clause" ]

null

pdb2pqr/tests/propka_test.py

ashermancinelli/apbs-pdb2pqr

0b1bc0126331cf3f1e08667ccc70dae8eda5cd00

[ "BSD-3-Clause" ]

null

pdb2pqr/tests/propka_test.py

ashermancinelli/apbs-pdb2pqr

0b1bc0126331cf3f1e08667ccc70dae8eda5cd00

[ "BSD-3-Clause" ]

null

"""Tests for PROPKA functionality.""" import logging from pathlib import Path import pytest import common _LOGGER = logging.getLogger(__name__) @pytest.mark.parametrize("input_pdb", ["1K1I", "1AFS", "1FAS", "5DV8", "5D8V"], ids=str) def test_propka_apo(input_pdb, tmp_path): """PROPKA non-regression tests on proteins without ligands.""" args = "--log-level=INFO --ff=AMBER --drop-water --titration-state-method=propka" output_pqr = Path(input_pdb).stem + ".pqr" common.run_pdb2pqr(args=args, input_pdb=input_pdb, output_pqr=output_pqr, tmp_path=tmp_path) # @pytest.mark.parametrize("input_pdb", ["1K1I", "1FAS"], ids=str) # def test_propka_apo(input_pdb, tmp_path): # """PROPKA titration of proteins without ligands.""" # args = "--log-level=INFO --ff=AMBER --drop-water --titration-state-method=propka" # output_pqr = Path(input_pdb).stem + ".pqr" # run_pdb2pqr(args, input_pdb, output_pqr, tmp_path)

35.777778

88

0.693582

import logging from pathlib import Path import pytest import common _LOGGER = logging.getLogger(__name__) @pytest.mark.parametrize("input_pdb", ["1K1I", "1AFS", "1FAS", "5DV8", "5D8V"], ids=str) def test_propka_apo(input_pdb, tmp_path): args = "--log-level=INFO --ff=AMBER --drop-water --titration-state-method=propka" output_pqr = Path(input_pdb).stem + ".pqr" common.run_pdb2pqr(args=args, input_pdb=input_pdb, output_pqr=output_pqr, tmp_path=tmp_path)

true

1c384db9b4b4396fd411433687e067b395fac623

1,681

py

Python

pakaman/config/targets.py

mikonse/pakaman

4b6ac2ec550bfd642d34dce1fc406f2bd230e8de

[ "MIT" ]

1

2020-10-18T17:57:30.000Z

pakaman/config/targets.py

mikonse/pakaman

4b6ac2ec550bfd642d34dce1fc406f2bd230e8de

[ "MIT" ]

null

pakaman/config/targets.py

mikonse/pakaman

4b6ac2ec550bfd642d34dce1fc406f2bd230e8de

[ "MIT" ]

null

from abc import ABCMeta from typing import List, Dict import schema TARGETS = {} class TargetMeta(ABCMeta): def __init__(cls, name, bases, classdict): super().__init__(name, bases, classdict) TARGETS[cls.name] = cls class Target(metaclass=TargetMeta): name = "unknown" schema = schema.Schema({"name": str, schema.Optional("dependencies"): [str]}, ignore_extra_keys=True) def __init__(self, dependencies: List[str]): self.dependencies = dependencies @classmethod def from_config(cls, config: Dict) -> "Target": data = cls.schema.validate(config) if data["name"] not in TARGETS: raise KeyError(f"unknown target {data['name']} given in config file") data["dependencies"] = data.get("dependencies", []) target = TARGETS[data.pop("name")] target_data = target.schema.validate(config) return target(**data, **target_data) class Debian(Target): name = "debian" schema = schema.Schema( {"dist_version": str, schema.Optional("compat_version", default=11): int, "architecture": str}, ignore_extra_keys=True) def __init__(self, dependencies: List[str], dist_version: str, compat_version: int, architecture: str): super().__init__(dependencies) self.dist_version = dist_version self.compat_version = compat_version self.architecture = architecture class Arch(Target): name = "arch" schema = schema.Schema({"architecture": str}, ignore_extra_keys=True) def __init__(self, dependencies: List[str], architecture: str): super().__init__(dependencies) self.architecture = architecture

30.017857

107

0.66627

from abc import ABCMeta from typing import List, Dict import schema TARGETS = {} class TargetMeta(ABCMeta): def __init__(cls, name, bases, classdict): super().__init__(name, bases, classdict) TARGETS[cls.name] = cls class Target(metaclass=TargetMeta): name = "unknown" schema = schema.Schema({"name": str, schema.Optional("dependencies"): [str]}, ignore_extra_keys=True) def __init__(self, dependencies: List[str]): self.dependencies = dependencies @classmethod def from_config(cls, config: Dict) -> "Target": data = cls.schema.validate(config) if data["name"] not in TARGETS: raise KeyError(f"unknown target {data['name']} given in config file") data["dependencies"] = data.get("dependencies", []) target = TARGETS[data.pop("name")] target_data = target.schema.validate(config) return target(**data, **target_data) class Debian(Target): name = "debian" schema = schema.Schema( {"dist_version": str, schema.Optional("compat_version", default=11): int, "architecture": str}, ignore_extra_keys=True) def __init__(self, dependencies: List[str], dist_version: str, compat_version: int, architecture: str): super().__init__(dependencies) self.dist_version = dist_version self.compat_version = compat_version self.architecture = architecture class Arch(Target): name = "arch" schema = schema.Schema({"architecture": str}, ignore_extra_keys=True) def __init__(self, dependencies: List[str], architecture: str): super().__init__(dependencies) self.architecture = architecture

true

1c384df1fb49a37e50cba49829227f17caf3f362

1,404

py

Python

AutoTriageBot/DataTypes.py

jaydave/AutoTriageBot

d87898ec11d6057797697ffa867ccb52770e65ec

[ "BSD-3-Clause" ]

58

2017-09-20T16:50:25.000Z

2021-05-15T10:22:53.000Z

AutoTriageBot/DataTypes.py

jaydave/AutoTriageBot

d87898ec11d6057797697ffa867ccb52770e65ec

[ "BSD-3-Clause" ]

2

2017-09-21T17:04:10.000Z

2017-09-25T23:29:20.000Z

AutoTriageBot/DataTypes.py

jaydave/AutoTriageBot

d87898ec11d6057797697ffa867ccb52770e65ec

[ "BSD-3-Clause" ]

16

2017-09-21T00:48:37.000Z

2021-01-02T20:22:39.000Z

""" Copyright (c) 2017, salesforce.com, inc. All rights reserved. Licensed under the BSD 3-Clause license. For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause """ from typing import NamedTuple, Mapping, Any from datetime import datetime # Represents the results of a vulnerability test VulnTestInfo = NamedTuple("VulnTestInfo", [('reproduced', bool), ('info', Mapping[str, Any]), ('message', str), ('type', str)]) # A simple representation of a report (used for duplicate testing purposes) ReportData = NamedTuple('ReportData', [('title', str), ('body', str), ('time', datetime), ('state', str), ('id', str), ('weakness', str)]) # Used to represent a parsed and normalized URL URLParts = NamedTuple("URLParts", [('domain', str), ('path', str), ('queries', Mapping[str, str])]) # Used to hold information about expected bounties BountyInfo = NamedTuple('BountyInfo', [('average', float), ('std', float)])

42.545455

109

0.487179

from typing import NamedTuple, Mapping, Any from datetime import datetime VulnTestInfo = NamedTuple("VulnTestInfo", [('reproduced', bool), ('info', Mapping[str, Any]), ('message', str), ('type', str)]) ReportData = NamedTuple('ReportData', [('title', str), ('body', str), ('time', datetime), ('state', str), ('id', str), ('weakness', str)]) URLParts = NamedTuple("URLParts", [('domain', str), ('path', str), ('queries', Mapping[str, str])]) BountyInfo = NamedTuple('BountyInfo', [('average', float), ('std', float)])

true

1c384df681c217a87980cd5a3d393f24b508e6e5

884

py

Python

annotate_updated.py

VictoriaSugrue/sheepclock

b0eaec0b96afcc35f0d60982eb3d1215ea329d64

[ "MIT" ]

null

annotate_updated.py

VictoriaSugrue/sheepclock

b0eaec0b96afcc35f0d60982eb3d1215ea329d64

[ "MIT" ]

null

annotate_updated.py

VictoriaSugrue/sheepclock

b0eaec0b96afcc35f0d60982eb3d1215ea329d64

[ "MIT" ]

null

import os import glob import numpy as np import pandas as pd filenames = [os.path.basename(x) for x in glob.glob("analysis_raw_results/*.bed")] metadata = pd.read_table('human_factor_full_QC.txt') for file in filenames: temp_name = os.path.splitext(file)[0] df_temp = pd.read_csv("analysis_raw_results/"+str(file),sep="\t",header=None) df_temp.columns = ['chromCG','CGstart','CGend','CGid','filename','bed_col_1','bed_col_2','bed_col_3','bed_col_4','bed_col_5','bed_col_6','bed_col_7','bed_col_8','bed_col_9','bed_col_10'] df_temp["DCid"] = df_temp["filename"].str.replace('human_factor_split/human_factor_[0-9][0-9][0-9]/|_sort_peaks.narrowPeak.bed','').astype('int64') df_output = df_temp.merge(metadata, on='DCid', how='left') df_output = df_output.sort_values(by=['CGid']) df_output.to_csv("analysis_results/" + str(temp_name) + ".csv",index=False)

49.111111

190

0.717195

import os import glob import numpy as np import pandas as pd filenames = [os.path.basename(x) for x in glob.glob("analysis_raw_results/*.bed")] metadata = pd.read_table('human_factor_full_QC.txt') for file in filenames: temp_name = os.path.splitext(file)[0] df_temp = pd.read_csv("analysis_raw_results/"+str(file),sep="\t",header=None) df_temp.columns = ['chromCG','CGstart','CGend','CGid','filename','bed_col_1','bed_col_2','bed_col_3','bed_col_4','bed_col_5','bed_col_6','bed_col_7','bed_col_8','bed_col_9','bed_col_10'] df_temp["DCid"] = df_temp["filename"].str.replace('human_factor_split/human_factor_[0-9][0-9][0-9]/|_sort_peaks.narrowPeak.bed','').astype('int64') df_output = df_temp.merge(metadata, on='DCid', how='left') df_output = df_output.sort_values(by=['CGid']) df_output.to_csv("analysis_results/" + str(temp_name) + ".csv",index=False)

true

1c384e0d64e076b98463cdbac0355c84829ca88c

1,706

py

Python

pastey/config.py

WolfwithSword/pastey

89b8c7a4da8a51a25270a2c9f3e6d7526c8211ad

[ "BSD-3-Clause" ]

65

2021-04-13T19:10:03.000Z

2022-02-03T01:36:04.000Z

pastey/config.py

WolfwithSword/pastey

89b8c7a4da8a51a25270a2c9f3e6d7526c8211ad

[ "BSD-3-Clause" ]

15

2021-04-14T11:57:43.000Z

2022-03-22T02:47:15.000Z

pastey/config.py

WolfwithSword/pastey

89b8c7a4da8a51a25270a2c9f3e6d7526c8211ad

[ "BSD-3-Clause" ]

8

2021-04-15T19:18:00.000Z

2022-02-03T03:02:27.000Z

# Data directory data_directory = "./data" # Listen address listen_address = "0.0.0.0" # Listen port listen_port = 5000 # Use whitelisting # Whitelisted IPs can view recent pastes on the home page, as well as delete pastes # For limiting pasting to whitelisted users, enable the "restrict_pasting" option below use_whitelist = True # Whitelist CIDR whitelist_cidr = ['127.0.0.1/32', '10.0.0.0/8', '172.16.0.0/12', '192.168.0.0/16'] # Blacklist CIDR blacklist_cidr = [] # Restrict pasting functionality to whitelisted IPs restrict_pasting = False # Rate limit for pasting (ignored for whitelisted users) rate_limit = "5/hour" # Guess threshold for automatic language detection guess_threshold = 0.20 # Number of recent pastes to show on the home page recent_pastes = 10 # Try to use X-Real-IP or X-Forwarded-For HTTP headers behind_proxy = False # Default theme to display to users default_theme = "Light" # Purge interval (in seconds) for checking expired pastes purge_interval = 3600 # Show recent pastes, even to non-whitelisted users (without a delete button) force_show_recent = False # Ignore these classifications for language detection ignore_guess = ['TeX', 'SQL'] # Show CLI button on home page show_cli_button = True # Include https in the generated links instead of http # This assumes you are behind something else doing the SSL # termination, but want the users to see https links # # This is normally handled by the HTTP headers now force_https_links = False # This can be used to specify a different domain for generated links # # Note: exclude the http:// or https:// prefix, as well as anything # following the domain (except the port, if applicable) override_domain = ""

27.516129

87

0.759086

data_directory = "./data" listen_address = "0.0.0.0" listen_port = 5000 use_whitelist = True whitelist_cidr = ['127.0.0.1/32', '10.0.0.0/8', '172.16.0.0/12', '192.168.0.0/16'] blacklist_cidr = [] restrict_pasting = False rate_limit = "5/hour" guess_threshold = 0.20 recent_pastes = 10 behind_proxy = False default_theme = "Light" purge_interval = 3600 force_show_recent = False ignore_guess = ['TeX', 'SQL'] show_cli_button = True force_https_links = False override_domain = ""

true

1c384e41969475a7829dcd8b5e539e84df26a381

1,102

py

Python

25aapairs.py

5ophia5/Homework

7b756e5dd36bf5425a5ba67b7263a45ec2648510

[ "MIT" ]

null

25aapairs.py

5ophia5/Homework

7b756e5dd36bf5425a5ba67b7263a45ec2648510

[ "MIT" ]

null

25aapairs.py

5ophia5/Homework

7b756e5dd36bf5425a5ba67b7263a45ec2648510

[ "MIT" ]

null

#!/usr/bin/env python3 # Print out all the unique pairwise amino acid combinations # AC is the same as CA # Skip AA, CC etc. # Also print out how many combinations there are aa = 'ACDEFGHIKLMNPQRSTVWY' combo = 0 for aa1 in aa: for aa2 in aa: if aa2 > aa1: print (aa1, aa2) combo +=1 print(combo) """ python3 25aapairs.py A C A D A E A F A G A H A I A K A L A M A N A P A Q A R A S A T A V A W A Y C D C E C F C G C H C I C K C L C M C N C P C Q C R C S C T C V C W C Y D E D F D G D H D I D K D L D M D N D P D Q D R D S D T D V D W D Y E F E G E H E I E K E L E M E N E P E Q E R E S E T E V E W E Y F G F H F I F K F L F M F N F P F Q F R F S F T F V F W F Y G H G I G K G L G M G N G P G Q G R G S G T G V G W G Y H I H K H L H M H N H P H Q H R H S H T H V H W H Y I K I L I M I N I P I Q I R I S I T I V I W I Y K L K M K N K P K Q K R K S K T K V K W K Y L M L N L P L Q L R L S L T L V L W L Y M N M P M Q M R M S M T M V M W M Y N P N Q N R N S N T N V N W N Y P Q P R P S P T P V P W P Y Q R Q S Q T Q V Q W Q Y R S R T R V R W R Y S T S V S W S Y T V T W T Y V W V Y W Y 190 """

5.222749

59

0.557169

aa = 'ACDEFGHIKLMNPQRSTVWY' combo = 0 for aa1 in aa: for aa2 in aa: if aa2 > aa1: print (aa1, aa2) combo +=1 print(combo)

true

1c384e66487612e80be41bfcb84c82317974ff4d

3,353

py

Python

db/migrations/replaceableObjects.py

DeschutesBrewery/brewerypi

5459dfc6b1ed415920c13a8a7c9a2d3d3c82099f

[ "MIT" ]

27

2017-11-27T05:01:05.000Z

2020-11-14T19:52:26.000Z

db/migrations/replaceableObjects.py

DeschutesBrewery/brewerypi

5459dfc6b1ed415920c13a8a7c9a2d3d3c82099f

[ "MIT" ]

259

2017-11-23T00:43:26.000Z

2020-11-03T01:07:30.000Z

db/migrations/replaceableObjects.py

DeschutesBrewery/brewerypi

5459dfc6b1ed415920c13a8a7c9a2d3d3c82099f

[ "MIT" ]

8

2018-10-29T04:39:29.000Z

2020-10-01T22:18:12.000Z

from alembic.operations import Operations, MigrateOperation # Concepts from: http://alembic.zzzcomputing.com/en/latest/cookbook.html#replaceable-objects. class ReplaceableObject(object): def __init__(self, name, parameters, returns, sqlText): self.name = name self.parameters = parameters self.returns = returns self.sqlText = sqlText class ReversibleOperation(MigrateOperation): def __init__(self, target): self.target = target @classmethod def invokeForTarget(cls, operations, target): op = cls(target) return operations.invoke(op) def reverse(self): raise NotImplementedError() @classmethod def _get_object_from_version(cls, operations, ident): version, objname = ident.split(".") module = operations.get_context().script.get_revision(version).module obj = getattr(module, objname) return obj @classmethod def replace(cls, operations, target, replaces = None, replaceWith = None): if replaces: oldObject = cls._get_object_from_version(operations, replaces) dropOld = cls(oldObject).reverse() createNew = cls(target) elif replaceWith: oldObject = cls._get_object_from_version(operations, replaceWith) dropOld = cls(target).reverse() createNew = cls(oldObject) else: raise TypeError("replaces or replaceWith is required") operations.invoke(dropOld) operations.invoke(createNew) @Operations.register_operation("createStoredProcedure", "invokeForTarget") @Operations.register_operation("replaceStoredProcedure", "replace") class CreateStoredProcedureOperation(ReversibleOperation): def reverse(self): return DropStoredProcedureOperation(self.target) @Operations.register_operation("dropStoredProcedure", "invokeForTarget") class DropStoredProcedureOperation(ReversibleOperation): def reverse(self): return CreateStoredProcedureOperation(self.target) @Operations.implementation_for(CreateStoredProcedureOperation) def createStoredProcedure(operations, operation): operations.execute("CREATE PROCEDURE %s(%s) %s" % (operation.target.name, operation.target.parameters, operation.target.sqlText)) @Operations.implementation_for(DropStoredProcedureOperation) def dropStoredProcedure(operations, operation): operations.execute("DROP PROCEDURE %s" % operation.target.name) @Operations.register_operation("createFunction", "invokeForTarget") @Operations.register_operation("replaceFunction", "replace") class CreateFunctionOperation(ReversibleOperation): def reverse(self): return DropFunctionOperation(self.target) @Operations.register_operation("dropFunction", "invokeForTarget") class DropFunctionOperation(ReversibleOperation): def reverse(self): return CreateFunctionOperation(self.target) @Operations.implementation_for(CreateFunctionOperation) def createFunction(operations, operation): operations.execute("CREATE FUNCTION %s(%s) RETURNS %s %s" % (operation.target.name, operation.target.parameters, operation.target.returns, operation.target.sqlText)) @Operations.implementation_for(DropFunctionOperation) def dropFunction(operations, operation): operations.execute("DROP FUNCTION %s" % operation.target.name)

39.447059

142

0.742917

from alembic.operations import Operations, MigrateOperation ject(object): def __init__(self, name, parameters, returns, sqlText): self.name = name self.parameters = parameters self.returns = returns self.sqlText = sqlText class ReversibleOperation(MigrateOperation): def __init__(self, target): self.target = target @classmethod def invokeForTarget(cls, operations, target): op = cls(target) return operations.invoke(op) def reverse(self): raise NotImplementedError() @classmethod def _get_object_from_version(cls, operations, ident): version, objname = ident.split(".") module = operations.get_context().script.get_revision(version).module obj = getattr(module, objname) return obj @classmethod def replace(cls, operations, target, replaces = None, replaceWith = None): if replaces: oldObject = cls._get_object_from_version(operations, replaces) dropOld = cls(oldObject).reverse() createNew = cls(target) elif replaceWith: oldObject = cls._get_object_from_version(operations, replaceWith) dropOld = cls(target).reverse() createNew = cls(oldObject) else: raise TypeError("replaces or replaceWith is required") operations.invoke(dropOld) operations.invoke(createNew) @Operations.register_operation("createStoredProcedure", "invokeForTarget") @Operations.register_operation("replaceStoredProcedure", "replace") class CreateStoredProcedureOperation(ReversibleOperation): def reverse(self): return DropStoredProcedureOperation(self.target) @Operations.register_operation("dropStoredProcedure", "invokeForTarget") class DropStoredProcedureOperation(ReversibleOperation): def reverse(self): return CreateStoredProcedureOperation(self.target) @Operations.implementation_for(CreateStoredProcedureOperation) def createStoredProcedure(operations, operation): operations.execute("CREATE PROCEDURE %s(%s) %s" % (operation.target.name, operation.target.parameters, operation.target.sqlText)) @Operations.implementation_for(DropStoredProcedureOperation) def dropStoredProcedure(operations, operation): operations.execute("DROP PROCEDURE %s" % operation.target.name) @Operations.register_operation("createFunction", "invokeForTarget") @Operations.register_operation("replaceFunction", "replace") class CreateFunctionOperation(ReversibleOperation): def reverse(self): return DropFunctionOperation(self.target) @Operations.register_operation("dropFunction", "invokeForTarget") class DropFunctionOperation(ReversibleOperation): def reverse(self): return CreateFunctionOperation(self.target) @Operations.implementation_for(CreateFunctionOperation) def createFunction(operations, operation): operations.execute("CREATE FUNCTION %s(%s) RETURNS %s %s" % (operation.target.name, operation.target.parameters, operation.target.returns, operation.target.sqlText)) @Operations.implementation_for(DropFunctionOperation) def dropFunction(operations, operation): operations.execute("DROP FUNCTION %s" % operation.target.name)

true

1c384ed35a686e610c02507262b0e05c5705abb5

599

py

Python

Curso_de_Python/Mundo_02/Aula_14/Exercicios/ex058.py

tarcisioribeiro/Python

69b8fee218a6c9b93ae7a6ea36eb903d5ac36955

[ "MIT" ]

null

Curso_de_Python/Mundo_02/Aula_14/Exercicios/ex058.py

tarcisioribeiro/Python

69b8fee218a6c9b93ae7a6ea36eb903d5ac36955

[ "MIT" ]

null

Curso_de_Python/Mundo_02/Aula_14/Exercicios/ex058.py

tarcisioribeiro/Python

69b8fee218a6c9b93ae7a6ea36eb903d5ac36955

[ "MIT" ]

null

from random import randint print() print('Tente adivinhar o número que estou pensando!') print() npc = randint(0, 10) nuser = -1 palpite = 0 while (npc != nuser): nuser = int(input('Qual o número que eu pensei? ')) print() if(nuser == npc): print('Parabéns! Você acertou!') print() elif(nuser < npc): print('Mais! Tente novamente.') print() palpite += 1 elif(nuser > npc): print('Menos! Tente novamente.') print() palpite += 1 print('Foram necessárias {} tentativas para acertar o número.'.format(palpite)) print()

24.958333

79

0.597663

from random import randint print() print('Tente adivinhar o número que estou pensando!') print() npc = randint(0, 10) nuser = -1 palpite = 0 while (npc != nuser): nuser = int(input('Qual o número que eu pensei? ')) print() if(nuser == npc): print('Parabéns! Você acertou!') print() elif(nuser < npc): print('Mais! Tente novamente.') print() palpite += 1 elif(nuser > npc): print('Menos! Tente novamente.') print() palpite += 1 print('Foram necessárias {} tentativas para acertar o número.'.format(palpite)) print()

true

1c384f35d72b0cdb3ecdc5e040b85c709490a00d

3,271

py

Python

python/pyarrow/tests/test_misc.py

tanyaschlusser/arrow

77c6cd3237a2b305cf9c086cbf8d0a49be016701

[ "Apache-2.0", "CC0-1.0" ]

1

2021-01-28T17:30:06.000Z

python/pyarrow/tests/test_misc.py

ivanyu/arrow

5d1934fc3f5c65f70a3966b71c68941b2fd8d362

[ "Apache-2.0", "CC0-1.0" ]

null

python/pyarrow/tests/test_misc.py

ivanyu/arrow

5d1934fc3f5c65f70a3966b71c68941b2fd8d362

[ "Apache-2.0", "CC0-1.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import os import pytest import pyarrow as pa def test_get_include(): include_dir = pa.get_include() assert os.path.exists(os.path.join(include_dir, 'arrow', 'api.h')) @pytest.mark.skipif('sys.platform != "win32"') def test_get_library_dirs_win32(): library_dirs = pa.get_library_dirs() library_lib = library_dirs[-1] assert os.path.exists(os.path.join(library_lib, 'arrow.lib')) def test_cpu_count(): n = pa.cpu_count() assert n > 0 try: pa.set_cpu_count(n + 5) assert pa.cpu_count() == n + 5 finally: pa.set_cpu_count(n) @pytest.mark.parametrize('klass', [ pa.Field, pa.Schema, pa.Column, pa.ChunkedArray, pa.RecordBatch, pa.Table, pa.Buffer, pa.Array, pa.Tensor, pa.lib.DataType, pa.lib.ListType, pa.lib.UnionType, pa.lib.StructType, pa.lib.Time32Type, pa.lib.Time64Type, pa.lib.TimestampType, pa.lib.Decimal128Type, pa.lib.DictionaryType, pa.lib.FixedSizeBinaryType, pa.NullArray, pa.NumericArray, pa.IntegerArray, pa.FloatingPointArray, pa.BooleanArray, pa.Int8Array, pa.Int16Array, pa.Int32Array, pa.Int64Array, pa.UInt8Array, pa.UInt16Array, pa.UInt32Array, pa.UInt64Array, pa.ListArray, pa.UnionArray, pa.BinaryArray, pa.StringArray, pa.FixedSizeBinaryArray, pa.DictionaryArray, pa.Date32Array, pa.Date64Array, pa.TimestampArray, pa.Time32Array, pa.Time64Array, pa.Decimal128Array, pa.StructArray, pa.ArrayValue, pa.BooleanValue, pa.Int8Value, pa.Int16Value, pa.Int32Value, pa.Int64Value, pa.UInt8Value, pa.UInt16Value, pa.UInt32Value, pa.UInt64Value, pa.HalfFloatValue, pa.FloatValue, pa.DoubleValue, pa.DecimalValue, pa.Date32Value, pa.Date64Value, pa.Time32Value, pa.Time64Value, pa.TimestampValue, pa.StringValue, pa.BinaryValue, pa.FixedSizeBinaryValue, pa.ListValue, pa.UnionValue, pa.StructValue, pa.DictionaryValue, pa.ipc.Message, pa.ipc.MessageReader, pa.MemoryPool, pa.LoggingMemoryPool, pa.ProxyMemoryPool, ]) def test_extension_type_constructor_errors(klass): # ARROW-2638: prevent calling extension class constructors directly msg = "Do not call {cls}'s constructor directly, use .* instead." with pytest.raises(TypeError, match=msg.format(cls=klass.__name__)): klass()

25.161538

72

0.690614

import os import pytest import pyarrow as pa def test_get_include(): include_dir = pa.get_include() assert os.path.exists(os.path.join(include_dir, 'arrow', 'api.h')) @pytest.mark.skipif('sys.platform != "win32"') def test_get_library_dirs_win32(): library_dirs = pa.get_library_dirs() library_lib = library_dirs[-1] assert os.path.exists(os.path.join(library_lib, 'arrow.lib')) def test_cpu_count(): n = pa.cpu_count() assert n > 0 try: pa.set_cpu_count(n + 5) assert pa.cpu_count() == n + 5 finally: pa.set_cpu_count(n) @pytest.mark.parametrize('klass', [ pa.Field, pa.Schema, pa.Column, pa.ChunkedArray, pa.RecordBatch, pa.Table, pa.Buffer, pa.Array, pa.Tensor, pa.lib.DataType, pa.lib.ListType, pa.lib.UnionType, pa.lib.StructType, pa.lib.Time32Type, pa.lib.Time64Type, pa.lib.TimestampType, pa.lib.Decimal128Type, pa.lib.DictionaryType, pa.lib.FixedSizeBinaryType, pa.NullArray, pa.NumericArray, pa.IntegerArray, pa.FloatingPointArray, pa.BooleanArray, pa.Int8Array, pa.Int16Array, pa.Int32Array, pa.Int64Array, pa.UInt8Array, pa.UInt16Array, pa.UInt32Array, pa.UInt64Array, pa.ListArray, pa.UnionArray, pa.BinaryArray, pa.StringArray, pa.FixedSizeBinaryArray, pa.DictionaryArray, pa.Date32Array, pa.Date64Array, pa.TimestampArray, pa.Time32Array, pa.Time64Array, pa.Decimal128Array, pa.StructArray, pa.ArrayValue, pa.BooleanValue, pa.Int8Value, pa.Int16Value, pa.Int32Value, pa.Int64Value, pa.UInt8Value, pa.UInt16Value, pa.UInt32Value, pa.UInt64Value, pa.HalfFloatValue, pa.FloatValue, pa.DoubleValue, pa.DecimalValue, pa.Date32Value, pa.Date64Value, pa.Time32Value, pa.Time64Value, pa.TimestampValue, pa.StringValue, pa.BinaryValue, pa.FixedSizeBinaryValue, pa.ListValue, pa.UnionValue, pa.StructValue, pa.DictionaryValue, pa.ipc.Message, pa.ipc.MessageReader, pa.MemoryPool, pa.LoggingMemoryPool, pa.ProxyMemoryPool, ]) def test_extension_type_constructor_errors(klass): msg = "Do not call {cls}'s constructor directly, use .* instead." with pytest.raises(TypeError, match=msg.format(cls=klass.__name__)): klass()

true

1c3851b3d43779cdb1f4c93db026666f01001b41

111

py

Python

tiingo_prices/__init__.py

robren/tiingo_prices

3e7f45728b0c5a4a4e60f552b2fda97aa8340a20

[ "MIT" ]

null

tiingo_prices/__init__.py

robren/tiingo_prices

3e7f45728b0c5a4a4e60f552b2fda97aa8340a20

[ "MIT" ]

1

2020-08-11T18:16:20.000Z

tiingo_prices/__init__.py

robren/tiingo_prices

3e7f45728b0c5a4a4e60f552b2fda97aa8340a20

[ "MIT" ]

null

# -*- coding: utf-8 -*- __author__ = """Robert Rennison""" __email__ = 'rob@robren.net' __version__ = '0.3.1'

18.5

34

0.621622

__author__ = """Robert Rennison""" __email__ = 'rob@robren.net' __version__ = '0.3.1'

true

1c38532383da6d8d572acc687d189e7ed6fd9a0d

3,795

py

Python

snake/base/map.py

AuthurExcalbern/AI-Snake

5dee76fcecd40add58a432ca8b88880b00ac45b9

[ "MIT" ]

null

snake/base/map.py

AuthurExcalbern/AI-Snake

5dee76fcecd40add58a432ca8b88880b00ac45b9

[ "MIT" ]

1

2018-03-24T08:50:32.000Z

2018-03-28T11:11:50.000Z

snake/base/map.py

AuthurExcalbern/AI-Snake

5dee76fcecd40add58a432ca8b88880b00ac45b9

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # pylint: disable=C0103,C0111,W0201,W0212 """Definition of class Map.""" import random from snake.base.point import Point, PointType from snake.base.pos import Pos class Map: """2D game map.""" def __init__(self, num_rows, num_cols): """Initialize a Map object.""" if not isinstance(num_rows, int) or not isinstance(num_cols, int): raise TypeError("\'num_rows\' and \'num_cols\' must be integers") if num_rows < 5 or num_cols < 5: raise ValueError("\'num_rows\' and \'num_cols\' must >= 5") self._num_rows = num_rows self._num_cols = num_cols self._capacity = (num_rows - 2) * (num_cols - 2)#去除墙后地图的容量 self._content = [[Point() for _ in range(num_cols)] for _ in range(num_rows)] self.reset() def reset(self): self._food = None for i in range(self._num_rows): for j in range(self._num_cols): if i == 0 or i == self._num_rows - 1 or \ j == 0 or j == self._num_cols - 1: self._content[i][j].type = PointType.WALL else: self._content[i][j].type = PointType.EMPTY def copy(self): m_copy = Map(self._num_rows, self._num_cols) for i in range(self._num_rows): for j in range(self._num_cols): m_copy._content[i][j].type = self._content[i][j].type return m_copy @property def num_rows(self): return self._num_rows @property def num_cols(self): return self._num_cols @property def capacity(self): return self._capacity @property def food(self): return self._food def point(self, pos): """Return a point on the map. DO NOT directly modify the point type to PointType.FOOD and vice versa. Use {add|rm}_food() methods instead. Args: pos (base.pos.Pos): The position of the point to be fetched Returns: snake.point.Point: The point at the given position. """ return self._content[pos.x][pos.y] def is_inside(self, pos): return pos.x > 0 and pos.x < self.num_rows - 1 \ and pos.y > 0 and pos.y < self.num_cols - 1 def is_empty(self, pos): return self.is_inside(pos) and self.point(pos).type == PointType.EMPTY def is_safe(self, pos): return self.is_inside(pos) and (self.point(pos).type == PointType.EMPTY or \ self.point(pos).type == PointType.FOOD) def is_full(self): """Check if the map is filled with the snake's bodies.""" for i in range(1, self.num_rows - 1): for j in range(1, self.num_cols - 1): t = self._content[i][j].type if t.value < PointType.HEAD_L.value: return False return True def has_food(self): return self._food is not None def rm_food(self): if self.has_food(): self.point(self._food).type = PointType.EMPTY self._food = None def create_food(self, pos): self.point(pos).type = PointType.FOOD self._food = pos return self._food def create_rand_food(self): empty_pos = [] for i in range(1, self._num_rows - 1): for j in range(1, self._num_cols - 1): t = self._content[i][j].type if t == PointType.EMPTY: empty_pos.append(Pos(i, j)) elif t == PointType.FOOD: return None # Stop if food exists if empty_pos: return self.create_food(random.choice(empty_pos)) else: return None

30.853659

85

0.560474

import random from snake.base.point import Point, PointType from snake.base.pos import Pos class Map: def __init__(self, num_rows, num_cols): if not isinstance(num_rows, int) or not isinstance(num_cols, int): raise TypeError("\'num_rows\' and \'num_cols\' must be integers") if num_rows < 5 or num_cols < 5: raise ValueError("\'num_rows\' and \'num_cols\' must >= 5") self._num_rows = num_rows self._num_cols = num_cols self._capacity = (num_rows - 2) * (num_cols - 2) self._content = [[Point() for _ in range(num_cols)] for _ in range(num_rows)] self.reset() def reset(self): self._food = None for i in range(self._num_rows): for j in range(self._num_cols): if i == 0 or i == self._num_rows - 1 or \ j == 0 or j == self._num_cols - 1: self._content[i][j].type = PointType.WALL else: self._content[i][j].type = PointType.EMPTY def copy(self): m_copy = Map(self._num_rows, self._num_cols) for i in range(self._num_rows): for j in range(self._num_cols): m_copy._content[i][j].type = self._content[i][j].type return m_copy @property def num_rows(self): return self._num_rows @property def num_cols(self): return self._num_cols @property def capacity(self): return self._capacity @property def food(self): return self._food def point(self, pos): return self._content[pos.x][pos.y] def is_inside(self, pos): return pos.x > 0 and pos.x < self.num_rows - 1 \ and pos.y > 0 and pos.y < self.num_cols - 1 def is_empty(self, pos): return self.is_inside(pos) and self.point(pos).type == PointType.EMPTY def is_safe(self, pos): return self.is_inside(pos) and (self.point(pos).type == PointType.EMPTY or \ self.point(pos).type == PointType.FOOD) def is_full(self): for i in range(1, self.num_rows - 1): for j in range(1, self.num_cols - 1): t = self._content[i][j].type if t.value < PointType.HEAD_L.value: return False return True def has_food(self): return self._food is not None def rm_food(self): if self.has_food(): self.point(self._food).type = PointType.EMPTY self._food = None def create_food(self, pos): self.point(pos).type = PointType.FOOD self._food = pos return self._food def create_rand_food(self): empty_pos = [] for i in range(1, self._num_rows - 1): for j in range(1, self._num_cols - 1): t = self._content[i][j].type if t == PointType.EMPTY: empty_pos.append(Pos(i, j)) elif t == PointType.FOOD: return None if empty_pos: return self.create_food(random.choice(empty_pos)) else: return None

true

1c385327f6e8f54f165abb2360d61facae4ffb2f

2,063

py

Python

tasks/UDEMY/100_days/L003/day-3-1.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

2

2022-01-19T18:01:35.000Z

2022-02-06T06:54:38.000Z

tasks/UDEMY/100_days/L003/day-3-1.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

null

tasks/UDEMY/100_days/L003/day-3-1.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

null

# 🚨 Don't change the code below 👇 number = int(input("Which number do you want to check? ")) # 🚨 Don't change the code above 👆 print("This is an even number." if number % 2 == 0 else "This is an odd number.") #First *fork* your copy. Then copy-paste your code below this line 👇 #Finally click "Run" to execute the tests #SOLUTION # #If the number can be divided by 2 with 0 remainder. # if number % 2 == 0: # print("This is an even number.") # #Otherwise (number cannot be divided by 2 with 0 remainder). # else: # print("This is an odd number.") #SOLUTION #Write your code above this line 👆 # 🚨 Do NOT modify the code below this line 👇 with open('testing_copy.py', 'w') as file: file.write('def test_func():\n') with open('day-3-1.py', 'r') as original: f2 = original.readlines()[0:40] for x in f2: file.write(" " + x) import testing_copy import unittest from unittest.mock import patch from io import StringIO import os class MyTest(unittest.TestCase): def run_test(self, given_answer, expected_print): with patch('builtins.input', return_value=given_answer), patch('sys.stdout', new=StringIO()) as fake_out: testing_copy.test_func() self.assertEqual(fake_out.getvalue(), expected_print) def test_1(self): self.run_test(given_answer='10', expected_print='This is an even number.\n') def test_2(self): self.run_test(given_answer='12', expected_print="This is an even number.\n") def test_3(self): self.run_test(given_answer='90', expected_print='This is an even number.\n') def test_4(self): self.run_test(given_answer='13', expected_print='This is an odd number.\n') print("\n\n\n.\n.\n.") print('Checking what your code prints for several different numbers.\nFor the number 8 it should print this *exactly*:\n') print('This is an even number.') print('\nRunning some tests on your code:') print(".\n.\n.") unittest.main(verbosity=1, exit=False) os.remove("testing_copy.py")

21.05102

122

0.663112

number = int(input("Which number do you want to check? ")) # 🚨 Don't change the code above 👆 print("This is an even number." if number % 2 == 0 else "This is an odd number.") t_func():\n') with open('day-3-1.py', 'r') as original: f2 = original.readlines()[0:40] for x in f2: file.write(" " + x) import testing_copy import unittest from unittest.mock import patch from io import StringIO import os class MyTest(unittest.TestCase): def run_test(self, given_answer, expected_print): with patch('builtins.input', return_value=given_answer), patch('sys.stdout', new=StringIO()) as fake_out: testing_copy.test_func() self.assertEqual(fake_out.getvalue(), expected_print) def test_1(self): self.run_test(given_answer='10', expected_print='This is an even number.\n') def test_2(self): self.run_test(given_answer='12', expected_print="This is an even number.\n") def test_3(self): self.run_test(given_answer='90', expected_print='This is an even number.\n') def test_4(self): self.run_test(given_answer='13', expected_print='This is an odd number.\n') print("\n\n\n.\n.\n.") print('Checking what your code prints for several different numbers.\nFor the number 8 it should print this *exactly*:\n') print('This is an even number.') print('\nRunning some tests on your code:') print(".\n.\n.") unittest.main(verbosity=1, exit=False) os.remove("testing_copy.py")

true

1c3854aecee3c4aeeb666f82155b1d9948d1b537

13,148

py

Python

maskrcnn_benchmark-dota/modeling/rpn/rfcos/smallerRF_wo_clsloss.py

RSIA-LIESMARS-WHU/AxisLearning

5a108860c959a200811f9643d567ca7883c74875

[ "BSD-2-Clause" ]

3

2021-01-26T07:03:26.000Z

2021-02-03T12:14:05.000Z

maskrcnn_benchmark/modeling/rpn/rfcos/smallerRF_wo_clsloss.py

RSIA-LIESMARS-WHU/AxisLearning

5a108860c959a200811f9643d567ca7883c74875

[ "BSD-2-Clause" ]

null

maskrcnn_benchmark/modeling/rpn/rfcos/smallerRF_wo_clsloss.py

RSIA-LIESMARS-WHU/AxisLearning

5a108860c959a200811f9643d567ca7883c74875

[ "BSD-2-Clause" ]

null

""" This file contains specific functions for computing losses of FCOS file """ import torch from torch.nn import functional as F from torch import nn from ..utils import concat_box_prediction_layers from maskrcnn_benchmark.layers import IOULoss from maskrcnn_benchmark.layers import SigmoidFocalLoss, smooth_l1_loss from maskrcnn_benchmark.modeling.matcher import Matcher from maskrcnn_benchmark.modeling.utils import cat from maskrcnn_benchmark.structures.rboxlist_ops import targets_for_locations from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist import time INF = 100000000 # def onehot(label, cls_num): # onehot = np.zeros(self.__num_classes, dtype=np.float) # onehot[bbox_class_ind] = 1.0 # uniform_distribution = np.full(self.__num_classes, 1.0 / self.__num_classes) # deta = 0.01 # smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution class FCOSLossComputation(object): """ This class computes the FCOS losses. """ def __init__(self, cfg): self.cls_loss_func = SigmoidFocalLoss( cfg.MODEL.FCOS.LOSS_GAMMA, cfg.MODEL.FCOS.LOSS_ALPHA ) # self.cls_loss_func = nn.CrossEntropyLoss(size_average=False, reduce=True) self.cfg = cfg # we make use of IOU Loss for bounding boxes regression, # but we found that L1 in log scale can yield a similar performance # self.box_reg_loss_func = IOULoss() self.centerness_loss_func = nn.BCEWithLogitsLoss() self.num_pts = cfg.MODEL.FCOS.NUM_PTS # 2 def prepare_targets(self, points, targets): # FoveaBox # strides=[8, 16, 32, 64, 128], # base_edge_list=[16, 32, 64, 128, 256], # scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), if self.cfg.MODEL.FCOS.SELECT_FEATURE_METHOD == "fcos": # FCOS object_sizes_of_interest = [ [-1, 64], [64, 128], [128, 256], [256, 512], [512, INF], ] # object_sizes_of_interest = [ # [-1, 32], # [32, 64], # [64, 128], # [128, 256], # [256, INF], # ] elif self.cfg.MODEL.FCOS.SELECT_FEATURE_METHOD == "foveabox": object_sizes_of_interest = [ [-1, 64], [32, 128], [64, 256], [128, 512], [256, INF], ] elif self.cfg.MODEL.FCOS.SELECT_FEATURE_METHOD == "all": object_sizes_of_interest = [ [-1, 64], [-1, 128], [-1, 256], [-1, 512], [-1, INF], ] normal_factor = [16, 32, 64, 128, 256] # normal_factor = [16, 48, 96, 192, 384] expanded_object_sizes_of_interest = [] expanded_normal_factor=[] # p3 - p7 for l, points_per_level in enumerate(points): # 2 object_sizes_of_interest_per_level = \ points_per_level.new_tensor(object_sizes_of_interest[l]) # 1 2 -> len(points_per_level) 2 expanded_object_sizes_of_interest.append( object_sizes_of_interest_per_level[None].expand(len(points_per_level), -1) ) normal_factor_per_level = \ points_per_level.new_tensor(normal_factor[l]) # 1 2 -> len(points_per_level) 2 expanded_normal_factor.append( normal_factor_per_level.expand(len(points_per_level)) ) expanded_object_sizes_of_interest = torch.cat(expanded_object_sizes_of_interest, dim=0) expanded_normal_factor = torch,cat(expanded_normal_factor, dim=0) num_points_per_level = [len(points_per_level) for points_per_level in points] points_all_level = torch.cat(points, dim=0) # batch len(locations) 1 batch len(locations) 6 labels, reg_targets = self.compute_targets_for_locations( points_all_level, targets, expanded_object_sizes_of_interest, expanded_normal_factor ) # 对每一张图片进行处理 for i in range(len(labels)): labels[i] = torch.split(labels[i], num_points_per_level, dim=0) reg_targets[i] = torch.split(reg_targets[i], num_points_per_level, dim=0) labels_level_first = [] reg_targets_level_first = [] for level in range(len(points)): labels_level_first.append( torch.cat([labels_per_im[level] for labels_per_im in labels], dim=0) ) reg_targets_level_first.append( torch.cat([reg_targets_per_im[level] for reg_targets_per_im in reg_targets], dim=0) ) return labels_level_first, reg_targets_level_first # 3 def compute_targets_for_locations(self, locations, targets, object_sizes_of_interest, normal_factor): labels = [] reg_targets = [] # xs, ys = locations[:, 0], locations[:, 1] for im_i in range(len(targets)): # 第i张图片 targets_per_im = targets[im_i] # assert targets_per_im.mode == "xyxy" bboxes = targets_per_im.bbox labels_per_im = targets_per_im.get_field("labels")#.cpu() # print(labels_per_im) reg_targets_per_im = targets_for_locations(bboxes, locations)#.cpu() # torch.cuda.empty_cache() # max_reg_targets_per_im = reg_targets_per_im.max(dim=2)[0] max_reg_targets_per_im = torch.abs(reg_targets_per_im[:,2:6]).max(dim=1)[0] # distance dist_1 = torch.sqrt(torch.pow(reg_targets_per_im[:,2],2) + torch.pow(reg_targets_per_im[:,3],2)) dist_2 = torch.sqrt(torch.pow(reg_targets_per_im[:,4],2) + torch.pow(reg_targets_per_im[:,5],2)) target_h = reg_targets_per_im[:,5] max_reg_targets_per_im = torch.stack([dist_1, dist_2, target_h], dim=1).max(dim=1)[0] # limit the regression range for each location 上下左右都要在感兴趣范围之内 # len(locations) len(locations), 1 object_sizes_of_interest= object_sizes_of_interest#.cpu() is_cared_in_the_level = \ (max_reg_targets_per_im >= object_sizes_of_interest[:, 0]) & \ (max_reg_targets_per_im <= object_sizes_of_interest[:, 1]) # print("labels_per_im", len(labels_per_im), len(bboxes), torch.min(reg_targets_per_im[:, 0].long()), torch.max(reg_targets_per_im[:, 0].long()), reg_targets_per_im[:, 0].sum()) labels_per_im = labels_per_im[reg_targets_per_im[:, 0].long()] # 落在目标框外面label为0 labels_per_im[reg_targets_per_im[:, 1] < 0.5 ] = 0#bg # 或者落在外面且s感受野不够 labels_per_im[is_cared_in_the_level == 0] = 0#no reg # 落在框内但是感受野不够 labels_per_im[(reg_targets_per_im[:, 1] > 0.5) * (is_cared_in_the_level == 0)] = -1#ignore # detax1 detay1 detax2 detay2 h ones = torch.ones_like(reg_targets_per_im[:,2:7]) one_minusone = torch.where(reg_targets_per_im[:,2:7]>=0, ones, -ones)#.cpu() reg_targets_per_im[:,2:7] = one_minusone*torch.pow(torch.abs(reg_targets_per_im[:,2:7])/normal_factor[1][:,None], 1/3)#.cpu()#.cpu() labels.append(labels_per_im) reg_targets.append(reg_targets_per_im[:,2:]) return labels, reg_targets def compute_centerness_targets(self, reg_targets): left_right = reg_targets[:, [0, 2]] top_bottom = reg_targets[:, [1, 3]] centerness = (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * \ (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]) return torch.sqrt(centerness) # 1 def __call__(self, locations, box_cls, box_regression, centerness, targets): """ Arguments: locations (list[BoxList]) box_cls (list[Tensor]) box_regression (list[Tensor]) centerness (list[Tensor]) targets (list[BoxList]) Returns: cls_loss (Tensor) reg_loss (Tensor) centerness_loss (Tensor) """ # 0 fpn 第一层 N = box_cls[0].size(0) num_classes = box_cls[0].size(1)#//self.num_pts # level first labels, reg_targets = self.prepare_targets(locations, targets) box_cls_flatten = [] box_regression_flatten = [] centerness_flatten = [] labels_flatten = [] reg_targets_flatten = [] # for level for l in range(len(labels)): # batch*num_pos num_classes box_cls_flatten.append(box_cls[l].permute(0, 2, 3, 1).reshape(-1, num_classes)) box_regression_flatten.append(box_regression[l].permute(0, 2, 3, 1).reshape(-1, 5)) # layer_h, layer_w = box_cls[l].size(2), box_cls[l].size(3) # box_cls_flatten.append(box_cls[l].permute(0, 2, 3, 1).reshape(N, layer_h, layer_w, self.num_pts, num_classes).permute(0, 3, 1, 2, 4).reshape(-1,num_classes)) # box_regression_flatten.append(box_regression[l].permute(0, 2, 3, 1).reshape(N, layer_h, layer_w, self.num_pts, 5).permute(0, 3, 1, 2, 4).reshape(-1,5)) labels_flatten.append(labels[l].reshape(-1)) reg_targets_flatten.append(reg_targets[l].reshape(-1, 6)) centerness_flatten.append(centerness[l].reshape(-1)) # level batch*num_pos num_classes box_cls_flatten = torch.cat(box_cls_flatten, dim=0) box_regression_flatten = torch.cat(box_regression_flatten, dim=0) centerness_flatten = torch.cat(centerness_flatten, dim=0) labels_flatten = torch.cat(labels_flatten, dim=0) reg_targets_flatten = torch.cat(reg_targets_flatten, dim=0) pos_inds = torch.nonzero(labels_flatten > 0).squeeze(1) valid_inds = torch.nonzero(labels_flatten > -1).squeeze(1) ignore_inds = torch.nonzero(labels_flatten == -1).squeeze(1) # wrong # cls_weight=torch.where(centerness_flatten==0, torch.ones_like(centerness_flatten), centerness_flatten).unsqueeze(-1) # cls_loss = self.cls_loss_func( # box_cls_flatten,#.cpu() # labels_flatten.int(),#,#.cpu() # weight = cls_weight # ) / (pos_inds.numel() + N) # add N to avoid dividing by a zero # true all_centerness_targets = reg_targets_flatten[:, -1] # # torch.sqrt( cls_weight = torch.where(all_centerness_targets==0, torch.ones_like(all_centerness_targets), all_centerness_targets).unsqueeze(-1) cls_weight[ignore_inds] = 0.05 # # cls_weight=torch.where(all_centerness_targets==0, torch.full_like(all_centerness_targets, 1), all_centerness_targets).unsqueeze(-1) ''' 涉及到将感受野不够或者超过的点看做负样本/忽略样本/正样本看成忽略样本涉及到定位不准确　冗余检测看成负样本 conf is low''' # # focal loss 2* # cls_loss = self.cls_loss_func( # box_cls_flatten[valid_inds],#.cpu() # labels_flatten[valid_inds].int(),#.cpu() # weight = cls_weight[valid_inds] # ) / (pos_inds.numel() + N) # add N to avoid dividing by a zero # weight = cl labels_flatten[labels_flatten==-1]=0 cls_loss = self.cls_loss_func( box_cls_flatten,#.cpu() labels_flatten.int(),#.cpu() weight = cls_weight ) / (pos_inds.numel() + N) # add N to avoid dividing by a zero # s_weight, # self.cls_loss_func = nn.CrossEntropyLoss(size_average=False, reduce=True) # cls_loss = self.cls_loss_func( # box_cls_flatten[pos_inds],# # labels_flatten[pos_inds]-1,# # )/ (pos_inds.numel() + N) box_regression_pos = box_regression_flatten[pos_inds] reg_targets_pos = reg_targets_flatten[pos_inds] if pos_inds.numel() > 0: centerness_targets_pos = reg_targets_pos[:, -1] #只预测 reg_loss = smooth_l1_loss( box_regression_pos,#.cpu() reg_targets_pos[:, :-1],#.cpu() weight = centerness_targets_pos.unsqueeze(-1)#cls_weight # ) # 一定要回归center ness # all # centerness_flatten[ignore_inds] = 0 centerness_loss = self.centerness_loss_func( centerness_flatten,#.cpu() reg_targets_flatten[:,-1]#.cpu() ) # centerness_loss = self.centerness_loss_func( # centerness_flatten[pos_inds],#.cpu() # centerness_targets_pos#.cpu() # ) else: reg_loss = box_regression_flatten.sum() centerness_loss = centerness_flatten.sum() # .cuda() return cls_loss, reg_loss, centerness_loss#*0 def make_fcos_loss_evaluator(cfg): loss_evaluator = FCOSLossComputation(cfg) return loss_evaluator

40.580247

189

0.599255

import torch from torch.nn import functional as F from torch import nn from ..utils import concat_box_prediction_layers from maskrcnn_benchmark.layers import IOULoss from maskrcnn_benchmark.layers import SigmoidFocalLoss, smooth_l1_loss from maskrcnn_benchmark.modeling.matcher import Matcher from maskrcnn_benchmark.modeling.utils import cat from maskrcnn_benchmark.structures.rboxlist_ops import targets_for_locations from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist import time INF = 100000000 class FCOSLossComputation(object): def __init__(self, cfg): self.cls_loss_func = SigmoidFocalLoss( cfg.MODEL.FCOS.LOSS_GAMMA, cfg.MODEL.FCOS.LOSS_ALPHA ) self.cfg = cfg self.centerness_loss_func = nn.BCEWithLogitsLoss() self.num_pts = cfg.MODEL.FCOS.NUM_PTS def prepare_targets(self, points, targets): if self.cfg.MODEL.FCOS.SELECT_FEATURE_METHOD == "fcos": object_sizes_of_interest = [ [-1, 64], [64, 128], [128, 256], [256, 512], [512, INF], ] elif self.cfg.MODEL.FCOS.SELECT_FEATURE_METHOD == "foveabox": object_sizes_of_interest = [ [-1, 64], [32, 128], [64, 256], [128, 512], [256, INF], ] elif self.cfg.MODEL.FCOS.SELECT_FEATURE_METHOD == "all": object_sizes_of_interest = [ [-1, 64], [-1, 128], [-1, 256], [-1, 512], [-1, INF], ] normal_factor = [16, 32, 64, 128, 256] expanded_object_sizes_of_interest = [] expanded_normal_factor=[] for l, points_per_level in enumerate(points): object_sizes_of_interest_per_level = \ points_per_level.new_tensor(object_sizes_of_interest[l]) expanded_object_sizes_of_interest.append( object_sizes_of_interest_per_level[None].expand(len(points_per_level), -1) ) normal_factor_per_level = \ points_per_level.new_tensor(normal_factor[l]) expanded_normal_factor.append( normal_factor_per_level.expand(len(points_per_level)) ) expanded_object_sizes_of_interest = torch.cat(expanded_object_sizes_of_interest, dim=0) expanded_normal_factor = torch,cat(expanded_normal_factor, dim=0) num_points_per_level = [len(points_per_level) for points_per_level in points] points_all_level = torch.cat(points, dim=0) labels, reg_targets = self.compute_targets_for_locations( points_all_level, targets, expanded_object_sizes_of_interest, expanded_normal_factor ) for i in range(len(labels)): labels[i] = torch.split(labels[i], num_points_per_level, dim=0) reg_targets[i] = torch.split(reg_targets[i], num_points_per_level, dim=0) labels_level_first = [] reg_targets_level_first = [] for level in range(len(points)): labels_level_first.append( torch.cat([labels_per_im[level] for labels_per_im in labels], dim=0) ) reg_targets_level_first.append( torch.cat([reg_targets_per_im[level] for reg_targets_per_im in reg_targets], dim=0) ) return labels_level_first, reg_targets_level_first def compute_targets_for_locations(self, locations, targets, object_sizes_of_interest, normal_factor): labels = [] reg_targets = [] for im_i in range(len(targets)): targets_per_im = targets[im_i] bboxes = targets_per_im.bbox labels_per_im = targets_per_im.get_field("labels") reg_targets_per_im = targets_for_locations(bboxes, locations) max_reg_targets_per_im = torch.abs(reg_targets_per_im[:,2:6]).max(dim=1)[0] dist_1 = torch.sqrt(torch.pow(reg_targets_per_im[:,2],2) + torch.pow(reg_targets_per_im[:,3],2)) dist_2 = torch.sqrt(torch.pow(reg_targets_per_im[:,4],2) + torch.pow(reg_targets_per_im[:,5],2)) target_h = reg_targets_per_im[:,5] max_reg_targets_per_im = torch.stack([dist_1, dist_2, target_h], dim=1).max(dim=1)[0] object_sizes_of_interest= object_sizes_of_interest is_cared_in_the_level = \ (max_reg_targets_per_im >= object_sizes_of_interest[:, 0]) & \ (max_reg_targets_per_im <= object_sizes_of_interest[:, 1]) labels_per_im = labels_per_im[reg_targets_per_im[:, 0].long()] labels_per_im[reg_targets_per_im[:, 1] < 0.5 ] = 0 labels_per_im[is_cared_in_the_level == 0] = 0 labels_per_im[(reg_targets_per_im[:, 1] > 0.5) * (is_cared_in_the_level == 0)] = -1 ones = torch.ones_like(reg_targets_per_im[:,2:7]) one_minusone = torch.where(reg_targets_per_im[:,2:7]>=0, ones, -ones) reg_targets_per_im[:,2:7] = one_minusone*torch.pow(torch.abs(reg_targets_per_im[:,2:7])/normal_factor[1][:,None], 1/3) labels.append(labels_per_im) reg_targets.append(reg_targets_per_im[:,2:]) return labels, reg_targets def compute_centerness_targets(self, reg_targets): left_right = reg_targets[:, [0, 2]] top_bottom = reg_targets[:, [1, 3]] centerness = (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * \ (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]) return torch.sqrt(centerness) def __call__(self, locations, box_cls, box_regression, centerness, targets): N = box_cls[0].size(0) num_classes = box_cls[0].size(1) labels, reg_targets = self.prepare_targets(locations, targets) box_cls_flatten = [] box_regression_flatten = [] centerness_flatten = [] labels_flatten = [] reg_targets_flatten = [] for l in range(len(labels)): box_cls_flatten.append(box_cls[l].permute(0, 2, 3, 1).reshape(-1, num_classes)) box_regression_flatten.append(box_regression[l].permute(0, 2, 3, 1).reshape(-1, 5)) labels_flatten.append(labels[l].reshape(-1)) reg_targets_flatten.append(reg_targets[l].reshape(-1, 6)) centerness_flatten.append(centerness[l].reshape(-1)) box_cls_flatten = torch.cat(box_cls_flatten, dim=0) box_regression_flatten = torch.cat(box_regression_flatten, dim=0) centerness_flatten = torch.cat(centerness_flatten, dim=0) labels_flatten = torch.cat(labels_flatten, dim=0) reg_targets_flatten = torch.cat(reg_targets_flatten, dim=0) pos_inds = torch.nonzero(labels_flatten > 0).squeeze(1) valid_inds = torch.nonzero(labels_flatten > -1).squeeze(1) ignore_inds = torch.nonzero(labels_flatten == -1).squeeze(1) argets = reg_targets_flatten[:, -1] weight = torch.where(all_centerness_targets==0, torch.ones_like(all_centerness_targets), all_centerness_targets).unsqueeze(-1) cls_weight[ignore_inds] = 0.05 labels_flatten.int(), weight = cls_weight ) / (pos_inds.numel() + N) box_regression_pos = box_regression_flatten[pos_inds] reg_targets_pos = reg_targets_flatten[pos_inds] if pos_inds.numel() > 0: centerness_targets_pos = reg_targets_pos[:, -1] reg_loss = smooth_l1_loss( box_regression_pos, reg_targets_pos[:, :-1], weight = centerness_targets_pos.unsqueeze(-1) ) centerness_loss = self.centerness_loss_func( centerness_flatten, reg_targets_flatten[:,-1] ) else: reg_loss = box_regression_flatten.sum() centerness_loss = centerness_flatten.sum() return cls_loss, reg_loss, centerness_loss def make_fcos_loss_evaluator(cfg): loss_evaluator = FCOSLossComputation(cfg) return loss_evaluator

true

1c385522dc3ad879da84161cb05818f4cd356848

8,193

py

Python

powerline_shell/colortrans.py

vivekkrish/powerline-shell

ad27ed4ccc28f314d7985776b8e8663a33a67102

[ "MIT" ]

2,656

2017-11-28T05:11:53.000Z

2022-03-31T14:10:18.000Z

powerline_shell/colortrans.py

vivekkrish/powerline-shell

ad27ed4ccc28f314d7985776b8e8663a33a67102

[ "MIT" ]

193

2017-11-28T23:20:17.000Z

2022-03-25T12:57:55.000Z

powerline_shell/colortrans.py

vivekkrish/powerline-shell

ad27ed4ccc28f314d7985776b8e8663a33a67102

[ "MIT" ]

387

2017-11-29T22:33:39.000Z

2022-03-31T18:24:35.000Z

#! /usr/bin/env python """ Code is modified (fairly heavily) by hryanjones@gmail.com from https://gist.github.com/MicahElliott/719710 Convert values between RGB tuples and xterm-256 color codes. Nice long listing of all 256 colors and their codes. Useful for developing console color themes, or even script output schemes. Resources: * http://en.wikipedia.org/wiki/8-bit_color * http://en.wikipedia.org/wiki/ANSI_escape_code * /usr/share/X11/rgb.txt I'm not sure where this script was inspired from. I think I must have written it from scratch, though it's been several years now. """ __author__ = 'Micah Elliott http://MicahElliott.com' __version__ = '0.1' __copyright__ = 'Copyright (C) 2011 Micah Elliott. All rights reserved.' __license__ = 'WTFPL http://sam.zoy.org/wtfpl/' #--------------------------------------------------------------------- def hexstr2num(hexstr): return int(hexstr, 16) def rgbstring2tuple(s): return tuple([hexstr2num(h) for h in (s[:2], s[2:4], s[4:])]) RGB2SHORT_DICT = { (0, 0, 0): 16, (0, 0, 95): 17, (0, 0, 128): 4, (0, 0, 135): 18, (0, 0, 175): 19, (0, 0, 215): 20, (0, 0, 255): 12, (0, 95, 0): 22, (0, 95, 95): 23, (0, 95, 135): 24, (0, 95, 175): 25, (0, 95, 215): 26, (0, 95, 255): 27, (0, 128, 0): 2, (0, 128, 128): 6, (0, 135, 0): 28, (0, 135, 95): 29, (0, 135, 135): 30, (0, 135, 175): 31, (0, 135, 215): 32, (0, 135, 255): 33, (0, 175, 0): 34, (0, 175, 95): 35, (0, 175, 135): 36, (0, 175, 175): 37, (0, 175, 215): 38, (0, 175, 255): 39, (0, 215, 0): 40, (0, 215, 95): 41, (0, 215, 135): 42, (0, 215, 175): 43, (0, 215, 215): 44, (0, 215, 255): 45, (0, 255, 0): 46, (0, 255, 95): 47, (0, 255, 135): 48, (0, 255, 175): 49, (0, 255, 215): 50, (0, 255, 255): 14, (8, 8, 8): 232, (18, 18, 18): 233, (28, 28, 28): 234, (38, 38, 38): 235, (48, 48, 48): 236, (58, 58, 58): 237, (68, 68, 68): 238, (78, 78, 78): 239, (88, 88, 88): 240, (95, 0, 0): 52, (95, 0, 95): 53, (95, 0, 135): 54, (95, 0, 175): 55, (95, 0, 215): 56, (95, 0, 255): 57, (95, 95, 0): 58, (95, 95, 95): 59, (95, 95, 135): 60, (95, 95, 175): 61, (95, 95, 215): 62, (95, 95, 255): 63, (95, 135, 0): 64, (95, 135, 95): 65, (95, 135, 135): 66, (95, 135, 175): 67, (95, 135, 215): 68, (95, 135, 255): 69, (95, 175, 0): 70, (95, 175, 95) : 71, (95, 175, 135): 72, (95, 175, 175): 73, (95, 175, 215): 74, (95, 175, 255): 75, (95, 215, 0): 76, (95, 215, 95) : 77, (95, 215, 135): 78, (95, 215, 175): 79, (95, 215, 215): 80, (95, 215, 255): 81, (95, 255, 0): 82, (95, 255, 95) : 83, (95, 255, 135): 84, (95, 255, 175): 85, (95, 255, 215): 86, (95, 255, 255): 87, (98, 98, 98): 241, (108, 108, 108): 242, (118, 118, 118): 243, (128, 0, 0): 1, (128, 0, 128): 5, (128, 128, 0): 3, (128, 128, 128): 244, (135, 0, 0): 88, (135, 0, 95): 89, (135, 0, 135): 90, (135, 0, 175): 91, (135, 0, 215): 92, (135, 0, 255): 93, (135, 95, 0): 94, (135, 95, 95): 95, (135, 95, 135): 96, (135, 95, 175): 97, (135, 95, 215): 98, (135, 95, 255): 99, (135, 135, 0): 100, (135, 135, 95): 101, (135, 135, 135): 102, (135, 135, 175): 103, (135, 135, 215): 104, (135, 135, 255): 105, (135, 175, 0): 106, (135, 175, 95): 107, (135, 175, 135): 108, (135, 175, 175): 109, (135, 175, 215): 110, (135, 175, 255): 111, (135, 215, 0): 112, (135, 215, 95): 113, (135, 215, 135): 114, (135, 215, 175): 115, (135, 215, 215): 116, (135, 215, 255): 117, (135, 255, 0): 118, (135, 255, 95): 119, (135, 255, 135): 120, (135, 255, 175): 121, (135, 255, 215): 122, (135, 255, 255): 123, (138, 138, 138): 245, (148, 148, 148): 246, (158, 158, 158): 247, (168, 168, 168): 248, (175, 0, 0): 124, (175, 0, 95): 125, (175, 0, 135): 126, (175, 0, 175): 127, (175, 0, 215): 128, (175, 0, 255): 129, (175, 95, 0): 130, (175, 95, 95): 131, (175, 95, 135): 132, (175, 95, 175): 133, (175, 95, 215): 134, (175, 95, 255): 135, (175, 135, 0): 136, (175, 135, 95): 137, (175, 135, 135): 138, (175, 135, 175): 139, (175, 135, 215): 140, (175, 135, 255): 141, (175, 175, 0): 142, (175, 175, 95): 143, (175, 175, 135): 144, (175, 175, 175): 145, (175, 175, 215): 146, (175, 175, 255): 147, (175, 215, 0): 148, (175, 215, 95): 149, (175, 215, 135): 150, (175, 215, 175): 151, (175, 215, 215): 152, (175, 215, 255): 153, (175, 255, 0): 154, (175, 255, 95): 155, (175, 255, 135): 156, (175, 255, 175): 157, (175, 255, 215): 158, (175, 255, 255): 159, (178, 178, 178): 249, (188, 188, 188): 250, (192, 192, 192): 7, (198, 198, 198): 251, (208, 208, 208): 252, (215, 0, 0): 160, (215, 0, 95): 161, (215, 0, 135): 162, (215, 0, 175): 163, (215, 0, 215): 164, (215, 0, 255): 165, (215, 95, 0): 166, (215, 95, 95): 167, (215, 95, 135): 168, (215, 95, 175): 169, (215, 95, 215): 170, (215, 95, 255): 171, (215, 135, 0): 172, (215, 135, 95): 173, (215, 135, 135): 174, (215, 135, 175): 175, (215, 135, 215): 176, (215, 135, 255): 177, (215, 175, 0): 178, (215, 175, 95): 179, (215, 175, 135): 180, (215, 175, 175): 181, (215, 175, 215): 182, (215, 175, 255): 183, (215, 215, 0): 184, (215, 215, 95): 185, (215, 215, 135): 186, (215, 215, 175): 187, (215, 215, 215): 188, (215, 215, 255): 189, (215, 255, 0): 190, (215, 255, 95): 191, (215, 255, 135): 192, (215, 255, 175): 193, (215, 255, 215): 194, (215, 255, 255): 195, (218, 218, 218): 253, (228, 228, 228): 254, (238, 238, 238): 255, (255, 0, 0): 196, (255, 0, 95): 197, (255, 0, 135): 198, (255, 0, 175): 199, (255, 0, 215): 200, (255, 0, 255): 13, (255, 95, 0): 202, (255, 95, 95): 203, (255, 95, 135): 204, (255, 95, 175): 205, (255, 95, 215): 206, (255, 95, 255): 207, (255, 135, 0): 208, (255, 135, 95): 209, (255, 135, 135): 210, (255, 135, 175): 211, (255, 135, 215): 212, (255, 135, 255): 213, (255, 175, 0): 214, (255, 175, 95): 215, (255, 175, 135): 216, (255, 175, 175): 217, (255, 175, 215): 218, (255, 175, 255): 219, (255, 215, 0): 220, (255, 215, 95): 221, (255, 215, 135): 222, (255, 215, 175): 223, (255, 215, 215): 224, (255, 215, 255): 225, (255, 255, 0): 11, (255, 255, 95): 227, (255, 255, 135): 228, (255, 255, 175): 229, (255, 255, 215): 230, (255, 255, 255): 231} def rgb2short(r, g, b): """ Find the closest xterm-256 approximation to the given RGB value. @param r,g,b: each is a number between 0-255 for the Red, Green, and Blue values @returns: integer between 0 and 255, compatible with xterm. >>> rgb2short(18, 52, 86) 23 >>> rgb2short(255, 255, 255) 231 >>> rgb2short(13, 173, 214) # vimeo logo 38 """ incs = (0x00, 0x5f, 0x87, 0xaf, 0xd7, 0xff) # Break 6-char RGB code into 3 integer vals. parts = [ r, g, b] res = [] for part in parts: i = 0 while i < len(incs)-1: s, b = incs[i], incs[i+1] # smaller, bigger if s <= part <= b: s1 = abs(s - part) b1 = abs(b - part) if s1 < b1: closest = s else: closest = b res.append(closest) break i += 1 #print '***', res return RGB2SHORT_DICT[tuple(res)] #--------------------------------------------------------------------- if __name__ == '__main__': import doctest doctest.testmod()

25.683386

84

0.45368

__author__ = 'Micah Elliott http://MicahElliott.com' __version__ = '0.1' __copyright__ = 'Copyright (C) 2011 Micah Elliott. All rights reserved.' __license__ = 'WTFPL http://sam.zoy.org/wtfpl/' def hexstr2num(hexstr): return int(hexstr, 16) def rgbstring2tuple(s): return tuple([hexstr2num(h) for h in (s[:2], s[2:4], s[4:])]) RGB2SHORT_DICT = { (0, 0, 0): 16, (0, 0, 95): 17, (0, 0, 128): 4, (0, 0, 135): 18, (0, 0, 175): 19, (0, 0, 215): 20, (0, 0, 255): 12, (0, 95, 0): 22, (0, 95, 95): 23, (0, 95, 135): 24, (0, 95, 175): 25, (0, 95, 215): 26, (0, 95, 255): 27, (0, 128, 0): 2, (0, 128, 128): 6, (0, 135, 0): 28, (0, 135, 95): 29, (0, 135, 135): 30, (0, 135, 175): 31, (0, 135, 215): 32, (0, 135, 255): 33, (0, 175, 0): 34, (0, 175, 95): 35, (0, 175, 135): 36, (0, 175, 175): 37, (0, 175, 215): 38, (0, 175, 255): 39, (0, 215, 0): 40, (0, 215, 95): 41, (0, 215, 135): 42, (0, 215, 175): 43, (0, 215, 215): 44, (0, 215, 255): 45, (0, 255, 0): 46, (0, 255, 95): 47, (0, 255, 135): 48, (0, 255, 175): 49, (0, 255, 215): 50, (0, 255, 255): 14, (8, 8, 8): 232, (18, 18, 18): 233, (28, 28, 28): 234, (38, 38, 38): 235, (48, 48, 48): 236, (58, 58, 58): 237, (68, 68, 68): 238, (78, 78, 78): 239, (88, 88, 88): 240, (95, 0, 0): 52, (95, 0, 95): 53, (95, 0, 135): 54, (95, 0, 175): 55, (95, 0, 215): 56, (95, 0, 255): 57, (95, 95, 0): 58, (95, 95, 95): 59, (95, 95, 135): 60, (95, 95, 175): 61, (95, 95, 215): 62, (95, 95, 255): 63, (95, 135, 0): 64, (95, 135, 95): 65, (95, 135, 135): 66, (95, 135, 175): 67, (95, 135, 215): 68, (95, 135, 255): 69, (95, 175, 0): 70, (95, 175, 95) : 71, (95, 175, 135): 72, (95, 175, 175): 73, (95, 175, 215): 74, (95, 175, 255): 75, (95, 215, 0): 76, (95, 215, 95) : 77, (95, 215, 135): 78, (95, 215, 175): 79, (95, 215, 215): 80, (95, 215, 255): 81, (95, 255, 0): 82, (95, 255, 95) : 83, (95, 255, 135): 84, (95, 255, 175): 85, (95, 255, 215): 86, (95, 255, 255): 87, (98, 98, 98): 241, (108, 108, 108): 242, (118, 118, 118): 243, (128, 0, 0): 1, (128, 0, 128): 5, (128, 128, 0): 3, (128, 128, 128): 244, (135, 0, 0): 88, (135, 0, 95): 89, (135, 0, 135): 90, (135, 0, 175): 91, (135, 0, 215): 92, (135, 0, 255): 93, (135, 95, 0): 94, (135, 95, 95): 95, (135, 95, 135): 96, (135, 95, 175): 97, (135, 95, 215): 98, (135, 95, 255): 99, (135, 135, 0): 100, (135, 135, 95): 101, (135, 135, 135): 102, (135, 135, 175): 103, (135, 135, 215): 104, (135, 135, 255): 105, (135, 175, 0): 106, (135, 175, 95): 107, (135, 175, 135): 108, (135, 175, 175): 109, (135, 175, 215): 110, (135, 175, 255): 111, (135, 215, 0): 112, (135, 215, 95): 113, (135, 215, 135): 114, (135, 215, 175): 115, (135, 215, 215): 116, (135, 215, 255): 117, (135, 255, 0): 118, (135, 255, 95): 119, (135, 255, 135): 120, (135, 255, 175): 121, (135, 255, 215): 122, (135, 255, 255): 123, (138, 138, 138): 245, (148, 148, 148): 246, (158, 158, 158): 247, (168, 168, 168): 248, (175, 0, 0): 124, (175, 0, 95): 125, (175, 0, 135): 126, (175, 0, 175): 127, (175, 0, 215): 128, (175, 0, 255): 129, (175, 95, 0): 130, (175, 95, 95): 131, (175, 95, 135): 132, (175, 95, 175): 133, (175, 95, 215): 134, (175, 95, 255): 135, (175, 135, 0): 136, (175, 135, 95): 137, (175, 135, 135): 138, (175, 135, 175): 139, (175, 135, 215): 140, (175, 135, 255): 141, (175, 175, 0): 142, (175, 175, 95): 143, (175, 175, 135): 144, (175, 175, 175): 145, (175, 175, 215): 146, (175, 175, 255): 147, (175, 215, 0): 148, (175, 215, 95): 149, (175, 215, 135): 150, (175, 215, 175): 151, (175, 215, 215): 152, (175, 215, 255): 153, (175, 255, 0): 154, (175, 255, 95): 155, (175, 255, 135): 156, (175, 255, 175): 157, (175, 255, 215): 158, (175, 255, 255): 159, (178, 178, 178): 249, (188, 188, 188): 250, (192, 192, 192): 7, (198, 198, 198): 251, (208, 208, 208): 252, (215, 0, 0): 160, (215, 0, 95): 161, (215, 0, 135): 162, (215, 0, 175): 163, (215, 0, 215): 164, (215, 0, 255): 165, (215, 95, 0): 166, (215, 95, 95): 167, (215, 95, 135): 168, (215, 95, 175): 169, (215, 95, 215): 170, (215, 95, 255): 171, (215, 135, 0): 172, (215, 135, 95): 173, (215, 135, 135): 174, (215, 135, 175): 175, (215, 135, 215): 176, (215, 135, 255): 177, (215, 175, 0): 178, (215, 175, 95): 179, (215, 175, 135): 180, (215, 175, 175): 181, (215, 175, 215): 182, (215, 175, 255): 183, (215, 215, 0): 184, (215, 215, 95): 185, (215, 215, 135): 186, (215, 215, 175): 187, (215, 215, 215): 188, (215, 215, 255): 189, (215, 255, 0): 190, (215, 255, 95): 191, (215, 255, 135): 192, (215, 255, 175): 193, (215, 255, 215): 194, (215, 255, 255): 195, (218, 218, 218): 253, (228, 228, 228): 254, (238, 238, 238): 255, (255, 0, 0): 196, (255, 0, 95): 197, (255, 0, 135): 198, (255, 0, 175): 199, (255, 0, 215): 200, (255, 0, 255): 13, (255, 95, 0): 202, (255, 95, 95): 203, (255, 95, 135): 204, (255, 95, 175): 205, (255, 95, 215): 206, (255, 95, 255): 207, (255, 135, 0): 208, (255, 135, 95): 209, (255, 135, 135): 210, (255, 135, 175): 211, (255, 135, 215): 212, (255, 135, 255): 213, (255, 175, 0): 214, (255, 175, 95): 215, (255, 175, 135): 216, (255, 175, 175): 217, (255, 175, 215): 218, (255, 175, 255): 219, (255, 215, 0): 220, (255, 215, 95): 221, (255, 215, 135): 222, (255, 215, 175): 223, (255, 215, 215): 224, (255, 215, 255): 225, (255, 255, 0): 11, (255, 255, 95): 227, (255, 255, 135): 228, (255, 255, 175): 229, (255, 255, 215): 230, (255, 255, 255): 231} def rgb2short(r, g, b): incs = (0x00, 0x5f, 0x87, 0xaf, 0xd7, 0xff) parts = [ r, g, b] res = [] for part in parts: i = 0 while i < len(incs)-1: s, b = incs[i], incs[i+1] if s <= part <= b: s1 = abs(s - part) b1 = abs(b - part) if s1 < b1: closest = s else: closest = b res.append(closest) break i += 1 return RGB2SHORT_DICT[tuple(res)] if __name__ == '__main__': import doctest doctest.testmod()

true

1c38553cfee6e8a970919d085bb6fbe062063cc1

773

py

Python

src/qgis_ros/core/translators/json_transport.py

acfrmarine/qgis_ros

01dd107f963b87df063bf2f11b0a484f4323cae9

[ "MIT" ]

31

2018-09-11T17:50:17.000Z

2021-09-13T11:48:47.000Z

src/qgis_ros/core/translators/json_transport.py

acfrmarine/qgis_ros

01dd107f963b87df063bf2f11b0a484f4323cae9

[ "MIT" ]

9

2018-09-11T00:45:23.000Z

2022-01-07T13:19:20.000Z

src/qgis_ros/core/translators/json_transport.py

acfrmarine/qgis_ros

01dd107f963b87df063bf2f11b0a484f4323cae9

[ "MIT" ]

9

2018-10-12T12:31:38.000Z

2021-06-05T02:52:44.000Z

import json_transport from .translator import Translator, VectorTranslatorMixin class JSONTransportTranslator(Translator, VectorTranslatorMixin): messageType = json_transport.PackedJson # geomType = Translator.GeomTypes.Unknown # Need to detect this from the first message. geomType = Translator.GeomTypes.Polygon # TODO: revert this. @staticmethod def translate(msg): # Attempt to detect GeoJSON in a JSON message. msg = msg.data if isinstance(msg, list): geojson_msg = msg elif isinstance(msg, dict) and msg.get('type') == 'FeatureCollection': geojson_msg = msg.get('features') else: raise ValueError('JSON message is not valid GeoJSON.') return geojson_msg

30.92

92

0.683053

import json_transport from .translator import Translator, VectorTranslatorMixin class JSONTransportTranslator(Translator, VectorTranslatorMixin): messageType = json_transport.PackedJson @staticmethod def translate(msg): msg = msg.data if isinstance(msg, list): geojson_msg = msg elif isinstance(msg, dict) and msg.get('type') == 'FeatureCollection': geojson_msg = msg.get('features') else: raise ValueError('JSON message is not valid GeoJSON.') return geojson_msg

true

1c3855af57ee7989c4d5d05a2f3435cc8c8f1ddf

16,700

py

Python

unicorn_binance_websocket_api/restclient.py

HrTran/unicorn-binance-websocket-api

64da01868a8c251a486993b868f0de5083922d64

[ "MIT" ]

null

unicorn_binance_websocket_api/restclient.py

HrTran/unicorn-binance-websocket-api

64da01868a8c251a486993b868f0de5083922d64

[ "MIT" ]

null

unicorn_binance_websocket_api/restclient.py

HrTran/unicorn-binance-websocket-api

64da01868a8c251a486993b868f0de5083922d64

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # File: unicorn_binance_websocket_api/restclient.py # # Part of ‘UNICORN Binance WebSocket API’ # Project website: https://github.com/LUCIT-Systems-and-Development/unicorn-binance-websocket-api # Documentation: https://lucit-systems-and-development.github.io/unicorn-binance-websocket-api # PyPI: https://pypi.org/project/unicorn-binance-websocket-api/ # # Author: LUCIT Systems and Development # # Copyright (c) 2019-2022, LUCIT Systems and Development (https://www.lucit.tech) and Oliver Zehentleitner # All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, dis- # tribute, sublicense, and/or sell copies of the Software, and to permit # persons to whom the Software is furnished to do so, subject to the fol- # lowing conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABIL- # ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT # SHALL THE AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. import json import logging import requests import socket import threading import time logger = logging.getLogger(__name__) class BinanceWebSocketApiRestclient(object): def __init__(self, manager): """ Create a restclient instance! :param manager: provide `self` of `BinanceWebsocketApiManager()` :type manager: object """ self.manager = manager self.api_key = False self.api_secret = False self.symbol = False self.listen_key = False self.last_static_ping_listen_key = False self.listen_key_output = False self.threading_lock = threading.Lock() if self.manager.exchange == "binance.com": self.restful_base_uri = "https://api.binance.com/" self.path_userdata = "api/v3/userDataStream" elif self.manager.exchange == "binance.com-testnet": self.restful_base_uri = "https://testnet.binance.vision/" self.path_userdata = "api/v3/userDataStream" elif self.manager.exchange == "binance.com-margin": self.restful_base_uri = "https://api.binance.com/" self.path_userdata = "sapi/v1/userDataStream" elif self.manager.exchange == "binance.com-margin-testnet": self.restful_base_uri = "https://testnet.binance.vision/" self.path_userdata = "sapi/v1/userDataStream" elif self.manager.exchange == "binance.com-isolated_margin": self.restful_base_uri = "https://api.binance.com/" self.path_userdata = "sapi/v1/userDataStream/isolated" elif self.manager.exchange == "binance.com-isolated_margin-testnet": self.restful_base_uri = "https://testnet.binance.vision/" self.path_userdata = "sapi/v1/userDataStream/isolated" elif self.manager.exchange == "binance.com-futures": self.restful_base_uri = "https://fapi.binance.com/" self.path_userdata = "fapi/v1/listenKey" elif self.manager.exchange == "binance.com-futures-testnet": self.restful_base_uri = "https://testnet.binancefuture.com/" self.path_userdata = "fapi/v1/listenKey" elif self.manager.exchange == "binance.com-coin-futures" or self.manager.exchange == "binance.com-coin_futures": self.restful_base_uri = "https://dapi.binance.com/" self.path_userdata = "dapi/v1/listenKey" elif self.manager.exchange == "binance.je": self.restful_base_uri = "https://api.binance.je/" self.path_userdata = "api/v1/userDataStream" elif self.manager.exchange == "binance.us": self.restful_base_uri = "https://api.binance.us/" self.path_userdata = "api/v1/userDataStream" elif self.manager.exchange == "trbinance.com": self.restful_base_uri = "https://api.binance.cc/" self.path_userdata = "api/v1/userDataStream" elif self.manager.exchange == "jex.com": self.restful_base_uri = "https://www.jex.com/" self.path_userdata = "api/v1/userDataStream" def _do_request(self, action=False): """ Do a request! :param action: choose "delete" or "keepalive" :type action: str :return: the response :rtype: str or False """ if action == "keepalive": logger.info(f"BinanceWebSocketApiRestclient.keepalive_listen_key({str(self.listen_key_output)})") method = "put" try: response = self._request(method, self.path_userdata, False, {'listenKey': str(self.listen_key)}) self.last_static_ping_listen_key = time.time() return response except KeyError: return False except TypeError: return False elif action == "delete": logger.info(f"BinanceWebSocketApiRestclient.delete_listen_key({str(self.listen_key_output)})") method = "delete" try: response = self._request(method, self.path_userdata, False, {'listenKey': str(self.listen_key)}) self.listen_key = False return response except KeyError as error_msg: logger.error(f"BinanceWebSocketApiRestclient.delete_listen_key({str(self.listen_key_output)}) - " f"KeyError - error_msg: {str(error_msg)}") return False except TypeError as error_msg: logger.error(f"BinanceWebSocketApiRestclient.delete_listen_key({str(self.listen_key_output)}) - " f"KeyError - error_msg: {str(error_msg)}") return False else: return False def _init_vars(self, stream_id, api_key=False, api_secret=False, symbol=False, listen_key=False, last_static_ping_listen_key=False): """ set default values and load values from stream_list :param stream_id: provide a stream_id (only needed for userData Streams (acquiring a listenKey) :type stream_id: str :param api_key: provide a valid Binance API key :type api_key: str :param api_secret: provide a valid Binance API secret :type api_secret: str :param symbol: provide the symbol for isolated_margin user_data listen_key :type symbol: str :param listen_key: provide the listen_key :type listen_key: str :param listen_key: the `last_static_ping_listen_key` variable of the `listen_key` you want to keepalive :type listen_key: int :return: bool """ self.api_key = api_key self.api_secret = api_secret self.listen_key = listen_key self.symbol = symbol self.last_static_ping_listen_key = last_static_ping_listen_key self.listen_key_output = self.manager.replacement_text try: if self.api_key is False: self.api_key = self.manager.stream_list[stream_id]['api_key'] if self.api_secret is False: self.api_secret = self.manager.stream_list[stream_id]['api_secret'] if self.symbol is False: self.symbol = self.manager.stream_list[stream_id]['symbols'] if self.listen_key is False: self.listen_key = self.manager.stream_list[stream_id]['listen_key'] if self.last_static_ping_listen_key is False: self.last_static_ping_listen_key = self.manager.stream_list[stream_id]['last_static_ping_listen_key'] if self.manager.show_secrets_in_logs is True: self.listen_key_output = self.listen_key except KeyError as error_msg: logger.error(f"BinanceWebSocketApiRestclient.init_vars() - TypeError - error_msg: {str(error_msg)}") return False return True def _request(self, method, path, query=False, data=False): """ Do the request :param method: choose the method to use (post, put or delete) :type method: str :param path: choose the path to use :type path: str :param query: choose the query to use :type query: str :param data: the payload for the post method :type data: str :return: the response :rtype: str or False """ requests_headers = {'Accept': 'application/json', 'User-Agent': str(self.manager.get_user_agent()), 'X-MBX-APIKEY': str(self.api_key)} if query is not False: uri = self.restful_base_uri + path + "?" + query else: uri = self.restful_base_uri + path try: if method == "post": if data is False: request_handler = requests.post(uri, headers=requests_headers) else: request_handler = requests.post(uri, headers=requests_headers, data=data) elif method == "put": request_handler = requests.put(uri, headers=requests_headers, data=data) elif method == "delete": request_handler = requests.delete(uri, headers=requests_headers) else: request_handler = False except requests.exceptions.ConnectionError as error_msg: logger.critical(f"BinanceWebSocketApiRestclient._request() - error: 9 - error_msg: {str(error_msg)}") return False except socket.gaierror as error_msg: logger.critical(f"BinanceWebSocketApiRestclient._request() - error: 10 - error_msg: {str(error_msg)}") return False if request_handler.status_code == "418": logger.critical("BinanceWebSocketApiRestclient._request() - error_msg: received status_code 418 from binance! You got" "banned from the binance api! Read this: https://github.com/binance-exchange/binance-" "official-api-sphinx/blob/master/rest-api.md#limits") elif request_handler.status_code == "429": logger.critical("BinanceWebSocketApiRestclient._request() - error_msg: received status_code 429 from " "binance! Back off or you are going to get banned! Read this: " "https://github.com/binance-exchange/binance-official-api-sphinx/blob/master/" "rest-api.md#limits") try: respond = request_handler.json() except json.decoder.JSONDecodeError as error_msg: logger.error(f"BinanceWebSocketApiRestclient._request() - error_msg: {str(error_msg)}") return False self.manager.binance_api_status['weight'] = request_handler.headers.get('X-MBX-USED-WEIGHT') self.manager.binance_api_status['timestamp'] = time.time() self.manager.binance_api_status['status_code'] = request_handler.status_code request_handler.close() return respond def get_listen_key(self, stream_id=False, api_key=False, api_secret=False, last_static_ping_listen_key=False, symbol=False): """ Request a valid listen_key from binance :param stream_id: provide a stream_id :type stream_id: str :param api_key: provide a valid Binance API key :type api_key: str :param api_secret: provide a valid Binance API secret :type api_secret: str :param last_static_ping_listen_key: the `last_static_ping_listen_key` variable of the `listen_key` you want to keepalive :type last_static_ping_listen_key: int :param symbol: provide the symbol for isolated_margin user_data listen_key :type symbol: str :return: listen_key :rtype: str or False """ logger.info(f"BinanceWebSocketApiRestclient.get_listen_key() symbol='{str(self.symbol)}' " f"stream_id='{str(stream_id)}')") if stream_id is False: return False with self.threading_lock: self._init_vars(stream_id, api_key=api_key, api_secret=api_secret, symbol=symbol, last_static_ping_listen_key=last_static_ping_listen_key) method = "post" if self.manager.exchange == "binance.com-isolated_margin" or \ self.manager.exchange == "binance.com-isolated_margin-testnet": if self.symbol is False: logger.critical("BinanceWebSocketApiRestclient.get_listen_key() - error_msg: Parameter " "`symbol` is missing!") return False else: response = self._request(method, self.path_userdata, False, {'symbol': str(self.symbol)}) else: try: response = self._request(method, self.path_userdata) except AttributeError as error_msg: logger.critical(f"BinanceWebSocketApiRestclient.get_listen_key() - error: 8 - " f"error_msg: {error_msg} - Can not acquire listen_key!") return False try: self.listen_key = response['listenKey'] self.last_static_ping_listen_key = time.time() return response except KeyError: return response except TypeError: return False def delete_listen_key(self, stream_id=False, api_key=False, api_secret=False, listen_key=False): """ Delete a specific listen key :param stream_id: provide a stream_id :type stream_id: str :param api_key: provide a valid Binance API key :type api_key: str :param api_secret: provide a valid Binance API secret :type api_secret: str :param listen_key: the listenkey you want to delete :type listen_key: str or bool :return: the response :rtype: str or False """ logger.info(f"BinanceWebSocketApiRestclient.delete_listen_key() stream_id='{str(stream_id)}')") if stream_id is False: return False with self.threading_lock: self._init_vars(stream_id, api_key, api_secret, listen_key) return self._do_request("delete") def keepalive_listen_key(self, stream_id=False, api_key=False, api_secret=False, listen_key=False, last_static_ping_listen_key=False): """ Ping a listenkey to keep it alive :param stream_id: provide a stream_id :type stream_id: str :param api_key: provide a valid Binance API key :type api_key: str :param api_secret: provide a valid Binance API secret :type api_secret: str :param listen_key: the listenkey you want to keepalive :type listen_key: str :param last_static_ping_listen_key: the `last_static_ping_listen_key` variable of the `listen_key` you want to keepalive :type last_static_ping_listen_key: int :return: the response :rtype: str or False """ logger.info(f"BinanceWebSocketApiRestclient.get_listen_key() stream_id='{str(stream_id)}')") if stream_id is False: return False with self.threading_lock: self._init_vars(stream_id, api_key, api_secret, listen_key, last_static_ping_listen_key) return self._do_request("keepalive")

46.648045

130

0.614731

import json import logging import requests import socket import threading import time logger = logging.getLogger(__name__) class BinanceWebSocketApiRestclient(object): def __init__(self, manager): self.manager = manager self.api_key = False self.api_secret = False self.symbol = False self.listen_key = False self.last_static_ping_listen_key = False self.listen_key_output = False self.threading_lock = threading.Lock() if self.manager.exchange == "binance.com": self.restful_base_uri = "https://api.binance.com/" self.path_userdata = "api/v3/userDataStream" elif self.manager.exchange == "binance.com-testnet": self.restful_base_uri = "https://testnet.binance.vision/" self.path_userdata = "api/v3/userDataStream" elif self.manager.exchange == "binance.com-margin": self.restful_base_uri = "https://api.binance.com/" self.path_userdata = "sapi/v1/userDataStream" elif self.manager.exchange == "binance.com-margin-testnet": self.restful_base_uri = "https://testnet.binance.vision/" self.path_userdata = "sapi/v1/userDataStream" elif self.manager.exchange == "binance.com-isolated_margin": self.restful_base_uri = "https://api.binance.com/" self.path_userdata = "sapi/v1/userDataStream/isolated" elif self.manager.exchange == "binance.com-isolated_margin-testnet": self.restful_base_uri = "https://testnet.binance.vision/" self.path_userdata = "sapi/v1/userDataStream/isolated" elif self.manager.exchange == "binance.com-futures": self.restful_base_uri = "https://fapi.binance.com/" self.path_userdata = "fapi/v1/listenKey" elif self.manager.exchange == "binance.com-futures-testnet": self.restful_base_uri = "https://testnet.binancefuture.com/" self.path_userdata = "fapi/v1/listenKey" elif self.manager.exchange == "binance.com-coin-futures" or self.manager.exchange == "binance.com-coin_futures": self.restful_base_uri = "https://dapi.binance.com/" self.path_userdata = "dapi/v1/listenKey" elif self.manager.exchange == "binance.je": self.restful_base_uri = "https://api.binance.je/" self.path_userdata = "api/v1/userDataStream" elif self.manager.exchange == "binance.us": self.restful_base_uri = "https://api.binance.us/" self.path_userdata = "api/v1/userDataStream" elif self.manager.exchange == "trbinance.com": self.restful_base_uri = "https://api.binance.cc/" self.path_userdata = "api/v1/userDataStream" elif self.manager.exchange == "jex.com": self.restful_base_uri = "https://www.jex.com/" self.path_userdata = "api/v1/userDataStream" def _do_request(self, action=False): if action == "keepalive": logger.info(f"BinanceWebSocketApiRestclient.keepalive_listen_key({str(self.listen_key_output)})") method = "put" try: response = self._request(method, self.path_userdata, False, {'listenKey': str(self.listen_key)}) self.last_static_ping_listen_key = time.time() return response except KeyError: return False except TypeError: return False elif action == "delete": logger.info(f"BinanceWebSocketApiRestclient.delete_listen_key({str(self.listen_key_output)})") method = "delete" try: response = self._request(method, self.path_userdata, False, {'listenKey': str(self.listen_key)}) self.listen_key = False return response except KeyError as error_msg: logger.error(f"BinanceWebSocketApiRestclient.delete_listen_key({str(self.listen_key_output)}) - " f"KeyError - error_msg: {str(error_msg)}") return False except TypeError as error_msg: logger.error(f"BinanceWebSocketApiRestclient.delete_listen_key({str(self.listen_key_output)}) - " f"KeyError - error_msg: {str(error_msg)}") return False else: return False def _init_vars(self, stream_id, api_key=False, api_secret=False, symbol=False, listen_key=False, last_static_ping_listen_key=False): self.api_key = api_key self.api_secret = api_secret self.listen_key = listen_key self.symbol = symbol self.last_static_ping_listen_key = last_static_ping_listen_key self.listen_key_output = self.manager.replacement_text try: if self.api_key is False: self.api_key = self.manager.stream_list[stream_id]['api_key'] if self.api_secret is False: self.api_secret = self.manager.stream_list[stream_id]['api_secret'] if self.symbol is False: self.symbol = self.manager.stream_list[stream_id]['symbols'] if self.listen_key is False: self.listen_key = self.manager.stream_list[stream_id]['listen_key'] if self.last_static_ping_listen_key is False: self.last_static_ping_listen_key = self.manager.stream_list[stream_id]['last_static_ping_listen_key'] if self.manager.show_secrets_in_logs is True: self.listen_key_output = self.listen_key except KeyError as error_msg: logger.error(f"BinanceWebSocketApiRestclient.init_vars() - TypeError - error_msg: {str(error_msg)}") return False return True def _request(self, method, path, query=False, data=False): requests_headers = {'Accept': 'application/json', 'User-Agent': str(self.manager.get_user_agent()), 'X-MBX-APIKEY': str(self.api_key)} if query is not False: uri = self.restful_base_uri + path + "?" + query else: uri = self.restful_base_uri + path try: if method == "post": if data is False: request_handler = requests.post(uri, headers=requests_headers) else: request_handler = requests.post(uri, headers=requests_headers, data=data) elif method == "put": request_handler = requests.put(uri, headers=requests_headers, data=data) elif method == "delete": request_handler = requests.delete(uri, headers=requests_headers) else: request_handler = False except requests.exceptions.ConnectionError as error_msg: logger.critical(f"BinanceWebSocketApiRestclient._request() - error: 9 - error_msg: {str(error_msg)}") return False except socket.gaierror as error_msg: logger.critical(f"BinanceWebSocketApiRestclient._request() - error: 10 - error_msg: {str(error_msg)}") return False if request_handler.status_code == "418": logger.critical("BinanceWebSocketApiRestclient._request() - error_msg: received status_code 418 from binance! You got" "banned from the binance api! Read this: https://github.com/binance-exchange/binance-" "official-api-sphinx/blob/master/rest-api.md#limits") elif request_handler.status_code == "429": logger.critical("BinanceWebSocketApiRestclient._request() - error_msg: received status_code 429 from " "binance! Back off or you are going to get banned! Read this: " "https://github.com/binance-exchange/binance-official-api-sphinx/blob/master/" "rest-api.md#limits") try: respond = request_handler.json() except json.decoder.JSONDecodeError as error_msg: logger.error(f"BinanceWebSocketApiRestclient._request() - error_msg: {str(error_msg)}") return False self.manager.binance_api_status['weight'] = request_handler.headers.get('X-MBX-USED-WEIGHT') self.manager.binance_api_status['timestamp'] = time.time() self.manager.binance_api_status['status_code'] = request_handler.status_code request_handler.close() return respond def get_listen_key(self, stream_id=False, api_key=False, api_secret=False, last_static_ping_listen_key=False, symbol=False): logger.info(f"BinanceWebSocketApiRestclient.get_listen_key() symbol='{str(self.symbol)}' " f"stream_id='{str(stream_id)}')") if stream_id is False: return False with self.threading_lock: self._init_vars(stream_id, api_key=api_key, api_secret=api_secret, symbol=symbol, last_static_ping_listen_key=last_static_ping_listen_key) method = "post" if self.manager.exchange == "binance.com-isolated_margin" or \ self.manager.exchange == "binance.com-isolated_margin-testnet": if self.symbol is False: logger.critical("BinanceWebSocketApiRestclient.get_listen_key() - error_msg: Parameter " "`symbol` is missing!") return False else: response = self._request(method, self.path_userdata, False, {'symbol': str(self.symbol)}) else: try: response = self._request(method, self.path_userdata) except AttributeError as error_msg: logger.critical(f"BinanceWebSocketApiRestclient.get_listen_key() - error: 8 - " f"error_msg: {error_msg} - Can not acquire listen_key!") return False try: self.listen_key = response['listenKey'] self.last_static_ping_listen_key = time.time() return response except KeyError: return response except TypeError: return False def delete_listen_key(self, stream_id=False, api_key=False, api_secret=False, listen_key=False): logger.info(f"BinanceWebSocketApiRestclient.delete_listen_key() stream_id='{str(stream_id)}')") if stream_id is False: return False with self.threading_lock: self._init_vars(stream_id, api_key, api_secret, listen_key) return self._do_request("delete") def keepalive_listen_key(self, stream_id=False, api_key=False, api_secret=False, listen_key=False, last_static_ping_listen_key=False): logger.info(f"BinanceWebSocketApiRestclient.get_listen_key() stream_id='{str(stream_id)}')") if stream_id is False: return False with self.threading_lock: self._init_vars(stream_id, api_key, api_secret, listen_key, last_static_ping_listen_key) return self._do_request("keepalive")

true

1c38572ef8d952a91c39c221f8b1ab75118fae3b

536

py

Python

helloWorldApp/app/__init__.py

OscarPalominoC/cursoFlask

8f87b8165a45c1c0124184e9866a505def463e44

[ "MIT" ]

null

helloWorldApp/app/__init__.py

OscarPalominoC/cursoFlask

8f87b8165a45c1c0124184e9866a505def463e44

[ "MIT" ]

null

helloWorldApp/app/__init__.py

OscarPalominoC/cursoFlask

8f87b8165a45c1c0124184e9866a505def463e44

[ "MIT" ]

null

from flask import Flask from flask_bootstrap import Bootstrap from flask_login import LoginManager from .config import Config from .auth import auth from .models import UserModel login_manager = LoginManager() login_manager.login_view = 'auth.login' @login_manager.user_loader def load_user(username): return UserModel.query(username) def create_app(): app = Flask(__name__) bootstrap = Bootstrap(app) app.config.from_object(Config) login_manager.init_app(app) app.register_blueprint(auth) return app

20.615385

39

0.774254

from flask import Flask from flask_bootstrap import Bootstrap from flask_login import LoginManager from .config import Config from .auth import auth from .models import UserModel login_manager = LoginManager() login_manager.login_view = 'auth.login' @login_manager.user_loader def load_user(username): return UserModel.query(username) def create_app(): app = Flask(__name__) bootstrap = Bootstrap(app) app.config.from_object(Config) login_manager.init_app(app) app.register_blueprint(auth) return app

true

1c385763e55768956dcf5c96c892d9d54ca34977

31,252

py

Python

libqtile/backend/wayland/window.py

flexagoon/qtile

ecf79a91da058dc9f8dd032a42e13eac9c2a270d

[ "MIT" ]

null

libqtile/backend/wayland/window.py

flexagoon/qtile

ecf79a91da058dc9f8dd032a42e13eac9c2a270d

[ "MIT" ]

null

libqtile/backend/wayland/window.py

flexagoon/qtile

ecf79a91da058dc9f8dd032a42e13eac9c2a270d

[ "MIT" ]

null

# Copyright (c) 2021 Matt Colligan # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from __future__ import annotations import functools import typing import cairocffi import pywayland from wlroots import ffi from wlroots.util.edges import Edges from wlroots.wlr_types import Box, Texture from wlroots.wlr_types.layer_shell_v1 import LayerSurfaceV1 from wlroots.wlr_types.xdg_shell import ( XdgPopup, XdgSurface, XdgTopLevelSetFullscreenEvent, ) from libqtile import hook, utils from libqtile.backend import base from libqtile.backend.base import FloatStates from libqtile.backend.wayland.drawer import Drawer from libqtile.backend.wayland.wlrq import DRM_FORMAT_ARGB8888, HasListeners from libqtile.command.base import CommandError from libqtile.log_utils import logger if typing.TYPE_CHECKING: from typing import Dict, List, Optional, Tuple, Union from wlroots.wlr_types.surface import SubSurface as WlrSubSurface from libqtile.backend.wayland.core import Core from libqtile.backend.wayland.output import Output from libqtile.core.manager import Qtile from libqtile.group import _Group from libqtile.utils import ColorType EDGES_TILED = Edges.TOP | Edges.BOTTOM | Edges.LEFT | Edges.RIGHT EDGES_FLOAT = Edges.NONE @functools.lru_cache() def _rgb(color: ColorType) -> ffi.CData: """Helper to create and cache float[4] arrays for border painting""" if isinstance(color, ffi.CData): return color return ffi.new("float[4]", utils.rgb(color)) # Window manages XdgSurfaces, Static manages XdgSurfaces and LayerSurfaceV1s SurfaceType = typing.Union[XdgSurface, LayerSurfaceV1] class Window(base.Window, HasListeners): def __init__(self, core: Core, qtile: Qtile, surface: SurfaceType, wid: int): base.Window.__init__(self) self.core = core self.qtile = qtile self.surface = surface self._group: Optional[_Group] = None self.popups: List[XdgPopupWindow] = [] self.subsurfaces: List[SubSurface] = [] self._wid = wid self._mapped: bool = False self.x = 0 self.y = 0 self.bordercolor: List[ffi.CData] = [_rgb((0, 0, 0, 1))] self.opacity: float = 1.0 self._outputs: List[Output] = [] # These start as None and are set in the first place() call self._width: Optional[int] = None self._height: Optional[int] = None assert isinstance(surface, XdgSurface) if surface.toplevel.title: self.name = surface.toplevel.title self._app_id: Optional[str] = surface.toplevel.app_id surface.set_tiled(EDGES_TILED) self._float_state = FloatStates.NOT_FLOATING self.float_x: Optional[int] = None self.float_y: Optional[int] = None self._float_width: int = self.width self._float_height: int = self.height self.add_listener(surface.map_event, self._on_map) self.add_listener(surface.unmap_event, self._on_unmap) self.add_listener(surface.destroy_event, self._on_destroy) self.add_listener(surface.new_popup_event, self._on_new_popup) self.add_listener(surface.toplevel.request_fullscreen_event, self._on_request_fullscreen) self.add_listener(surface.toplevel.set_title_event, self._on_set_title) self.add_listener(surface.toplevel.set_app_id_event, self._on_set_app_id) self.add_listener(surface.surface.commit_event, self._on_commit) self.add_listener(surface.surface.new_subsurface_event, self._on_new_subsurface) def finalize(self): self.finalize_listeners() for subsurface in self.subsurfaces: subsurface.finalize() @property def wid(self): return self._wid @property def width(self) -> int: if self._width is None: return self.surface.surface.current.width return self._width @width.setter def width(self, width: int) -> None: self._width = width @property def height(self) -> int: if self._height is None: return self.surface.surface.current.height return self._height @height.setter def height(self, height: int) -> None: self._height = height @property def group(self) -> Optional[_Group]: return self._group @group.setter def group(self, group: Optional[_Group]) -> None: self._group = group @property def mapped(self) -> bool: return self._mapped @mapped.setter def mapped(self, mapped: bool) -> None: """We keep track of which windows are mapped to we know which to render""" if mapped == self._mapped: return self._mapped = mapped if mapped: self.core.mapped_windows.append(self) else: self.core.mapped_windows.remove(self) self.core.stack_windows() def _on_map(self, _listener, _data): logger.debug("Signal: window map") if self in self.core.pending_windows: self.core.pending_windows.remove(self) logger.debug(f"Managing new top-level window with window ID: {self.wid}") self.qtile.manage(self) if self.group.screen: self.mapped = True self.core.focus_window(self) def _on_unmap(self, _listener, _data): logger.debug("Signal: window unmap") self.mapped = False self.damage() seat = self.core.seat if not seat.destroyed: if self.surface.surface == seat.keyboard_state.focused_surface: seat.keyboard_clear_focus() def _on_destroy(self, _listener, _data): logger.debug("Signal: window destroy") if self.mapped: logger.warning("Window destroyed before unmap event.") self.mapped = False self.qtile.unmanage(self.wid) self.finalize() def _on_new_popup(self, _listener, xdg_popup: XdgPopup): logger.debug("Signal: window new_popup") self.popups.append(XdgPopupWindow(self, xdg_popup)) def _on_request_fullscreen(self, _listener, event: XdgTopLevelSetFullscreenEvent): logger.debug("Signal: window request_fullscreen") if self.qtile.config.auto_fullscreen: self.fullscreen = event.fullscreen def _on_set_title(self, _listener, _data): logger.debug("Signal: window set_title") self.name = self.surface.toplevel.title hook.fire('client_name_updated', self) def _on_set_app_id(self, _listener, _data): logger.debug("Signal: window set_app_id") self._app_id = self.surface.toplevel.app_id def _on_commit(self, _listener, _data): self.damage() def _on_new_subsurface(self, _listener, subsurface: WlrSubSurface): self.subsurfaces.append(SubSurface(self, subsurface)) def has_fixed_size(self) -> bool: assert isinstance(self.surface, XdgSurface) state = self.surface.toplevel._ptr.current return ( 0 < state.min_width == state.max_width and 0 < state.min_height == state.max_height ) def is_transient_for(self) -> Optional[base.WindowType]: """What window is this window a transient window for?""" assert isinstance(self.surface, XdgSurface) parent = self.surface.toplevel.parent if parent: for win in self.qtile.windows_map.values(): if not isinstance(win, Internal) and win.surface == parent: # type: ignore return win return None def _find_outputs(self): """Find the outputs on which this window can be seen.""" self._outputs = [o for o in self.core.outputs if o.contains(self)] def damage(self) -> None: for output in self._outputs: output.damage() def hide(self): if self.mapped: self.surface.unmap_event.emit() def unhide(self): if not self.mapped: self.surface.map_event.emit() def kill(self): self.surface.send_close() def get_pid(self) -> int: pid = pywayland.ffi.new("pid_t *") pywayland.lib.wl_client_get_credentials( self.surface._ptr.client.client, pid, ffi.NULL, ffi.NULL ) return pid[0] def get_wm_class(self) -> Optional[List]: if self._app_id: return [self._app_id] return None def togroup(self, group_name=None, *, switch_group=False): """Move window to a specified group Also switch to that group if switch_group is True. """ if group_name is None: group = self.qtile.current_group else: group = self.qtile.groups_map.get(group_name) if group is None: raise CommandError("No such group: %s" % group_name) if self.group is not group: self.hide() if self.group: if self.group.screen: # for floats remove window offset self.x -= self.group.screen.x self.group.remove(self) if group.screen and self.x < group.screen.x: self.x += group.screen.x group.add(self) if switch_group: group.cmd_toscreen(toggle=False) def paint_borders(self, color: Union[ColorType, List[ColorType]], width) -> None: if color: if isinstance(color, list): if len(color) > width: color = color[:width] self.bordercolor = [_rgb(c) for c in color] else: self.bordercolor = [_rgb(color)] self.borderwidth = width @property def floating(self): return self._float_state != FloatStates.NOT_FLOATING @floating.setter def floating(self, do_float): if do_float and self._float_state == FloatStates.NOT_FLOATING: if self.group and self.group.screen: screen = self.group.screen if not self._float_width: # These might start as 0 self._float_width = self.width self._float_height = self.height self._enablefloating( screen.x + self.float_x, screen.y + self.float_y, self._float_width, self._float_height ) else: # if we are setting floating early, e.g. from a hook, we don't have a screen yet self._float_state = FloatStates.FLOATING elif (not do_float) and self._float_state != FloatStates.NOT_FLOATING: if self._float_state == FloatStates.FLOATING: # store last size self._float_width = self.width self._float_height = self.height self._float_state = FloatStates.NOT_FLOATING self.group.mark_floating(self, False) hook.fire('float_change') @property def fullscreen(self): return self._float_state == FloatStates.FULLSCREEN @fullscreen.setter def fullscreen(self, do_full): self.surface.set_fullscreen(do_full) if do_full: screen = self.group.screen or \ self.qtile.find_closest_screen(self.x, self.y) self._enablefloating( screen.x, screen.y, screen.width, screen.height, new_float_state=FloatStates.FULLSCREEN ) return if self._float_state == FloatStates.FULLSCREEN: self.floating = False @property def maximized(self): return self._float_state == FloatStates.MAXIMIZED @maximized.setter def maximized(self, do_maximize): if do_maximize: screen = self.group.screen or \ self.qtile.find_closest_screen(self.x, self.y) self._enablefloating( screen.dx, screen.dy, screen.dwidth, screen.dheight, new_float_state=FloatStates.MAXIMIZED ) else: if self._float_state == FloatStates.MAXIMIZED: self.floating = False @property def minimized(self): return self._float_state == FloatStates.MINIMIZED @minimized.setter def minimized(self, do_minimize): if do_minimize: if self._float_state != FloatStates.MINIMIZED: self._enablefloating(new_float_state=FloatStates.MINIMIZED) else: if self._float_state == FloatStates.MINIMIZED: self.floating = False def focus(self, warp: bool) -> None: self.core.focus_window(self) if isinstance(self, base.Internal): # self.core.focus_window is enough for internal windows return if warp and self.qtile.config.cursor_warp: self.core.warp_pointer( self.x + self.width // 2, self.y + self.height // 2, ) hook.fire("client_focus", self) def place(self, x, y, width, height, borderwidth, bordercolor, above=False, margin=None, respect_hints=False): # Adjust the placement to account for layout margins, if there are any. if margin is not None: if isinstance(margin, int): margin = [margin] * 4 x += margin[3] y += margin[0] width -= margin[1] + margin[3] height -= margin[0] + margin[2] if respect_hints: state = self.surface.toplevel._ptr.current width = max(width, state.min_width) height = max(height, state.min_height) if state.max_width: width = min(width, state.max_width) if state.max_height: height = min(height, state.max_height) # save x and y float offset if self.group is not None and self.group.screen is not None: self.float_x = x - self.group.screen.x self.float_y = y - self.group.screen.y self.x = x self.y = y self.surface.set_size(int(width), int(height)) self._width = int(width) self._height = int(height) self.paint_borders(bordercolor, borderwidth) if above and self._mapped: self.core.mapped_windows.remove(self) self.core.mapped_windows.append(self) self.core.stack_windows() self._find_outputs() self.damage() def _tweak_float(self, x=None, y=None, dx=0, dy=0, w=None, h=None, dw=0, dh=0): if x is None: x = self.x x += dx if y is None: y = self.y y += dy if w is None: w = self.width w += dw if h is None: h = self.height h += dh if h < 0: h = 0 if w < 0: w = 0 screen = self.qtile.find_closest_screen( self.x + self.width // 2, self.y + self.height // 2 ) if self.group and screen is not None and screen != self.group.screen: self.group.remove(self, force=True) screen.group.add(self, force=True) self.qtile.focus_screen(screen.index) self._reconfigure_floating(x, y, w, h) def _enablefloating(self, x=None, y=None, w=None, h=None, new_float_state=FloatStates.FLOATING): self._reconfigure_floating(x, y, w, h, new_float_state) def _reconfigure_floating(self, x, y, w, h, new_float_state=FloatStates.FLOATING): if new_float_state == FloatStates.MINIMIZED: self.hide() else: self.place( x, y, w, h, self.borderwidth, self.bordercolor, above=True, respect_hints=True ) if self._float_state != new_float_state: self._float_state = new_float_state if self.group: # may be not, if it's called from hook self.group.mark_floating(self, True) hook.fire('float_change') def info(self) -> Dict: """Return a dictionary of info.""" float_info = { "x": self.float_x, "y": self.float_y, "width": self._float_width, "height": self._float_height, } return dict( name=self.name, x=self.x, y=self.y, width=self.width, height=self.height, group=self.group.name if self.group else None, id=self.wid, float_info=float_info, floating=self._float_state != FloatStates.NOT_FLOATING, maximized=self._float_state == FloatStates.MAXIMIZED, minimized=self._float_state == FloatStates.MINIMIZED, fullscreen=self._float_state == FloatStates.FULLSCREEN ) def _items(self, name: str) -> ItemT: if name == "group": return True, [] elif name == "layout": return True, list(range(len(self.group.layouts))) elif name == "screen" and self.group.screen is not None: return True, [] return None def _select(self, name, sel): if name == "group": return self.group elif name == "layout": if sel is None: return self.group.layout else: return utils.lget(self.group.layouts, sel) elif name == "screen": return self.group.screen def cmd_focus(self, warp: bool = True) -> None: """Focuses the window.""" self.focus(warp) def cmd_move_floating(self, dx: int, dy: int) -> None: self._tweak_float(dx=dx, dy=dy) def cmd_resize_floating(self, dw: int, dh: int) -> None: self._tweak_float(dw=dw, dh=dh) def cmd_set_position_floating(self, x: int, y: int) -> None: self._tweak_float(x=x, y=y) def cmd_set_size_floating(self, w: int, h: int) -> None: self._tweak_float(w=w, h=h) def cmd_place(self, x, y, width, height, borderwidth, bordercolor, above=False, margin=None): self.place(x, y, width, height, borderwidth, bordercolor, above, margin) def cmd_get_position(self) -> Tuple[int, int]: return self.x, self.y def cmd_get_size(self) -> Tuple[int, int]: return self.width, self.height def cmd_toggle_floating(self) -> None: self.floating = not self.floating def cmd_enable_floating(self): self.floating = True def cmd_disable_floating(self): self.floating = False def cmd_toggle_maximize(self) -> None: self.maximized = not self.maximized def cmd_toggle_minimize(self) -> None: self.minimized = not self.minimized def cmd_toggle_fullscreen(self) -> None: self.fullscreen = not self.fullscreen def cmd_enable_fullscreen(self) -> None: self.fullscreen = True def cmd_disable_fullscreen(self) -> None: self.fullscreen = False def cmd_bring_to_front(self) -> None: if self.mapped: self.core.mapped_windows.remove(self) self.core.mapped_windows.append(self) self.core.stack_windows() def cmd_kill(self) -> None: self.kill() class Internal(base.Internal, Window): """ Internal windows are simply textures controlled by the compositor. """ texture: Texture def __init__( self, core: Core, qtile: Qtile, x: int, y: int, width: int, height: int ): self.core = core self.qtile = qtile self._group: Optional[_Group] = None self._mapped: bool = False self._wid: int = self.core.new_wid() self.x: int = x self.y: int = y self.opacity: float = 1.0 self._width: int = width self._height: int = height self._outputs: List[Output] = [] self._find_outputs() self._reset_texture() def finalize(self): self.hide() def _reset_texture(self): clear = cairocffi.ImageSurface(cairocffi.FORMAT_ARGB32, self.width, self.height) with cairocffi.Context(clear) as context: context.set_source_rgba(*utils.rgb("#000000")) context.paint() self.texture = Texture.from_pixels( self.core.renderer, DRM_FORMAT_ARGB8888, cairocffi.ImageSurface.format_stride_for_width(cairocffi.FORMAT_ARGB32, self.width), self.width, self.height, cairocffi.cairo.cairo_image_surface_get_data(clear._pointer), ) def create_drawer(self, width: int, height: int) -> Drawer: """Create a Drawer that draws to this window.""" return Drawer(self.qtile, self, width, height) @property def width(self) -> int: return self._width @width.setter def width(self, value: int) -> None: self._width = value @property def height(self) -> int: return self._height @height.setter def height(self, value: int) -> None: self._height = value def hide(self) -> None: self.mapped = False self.damage() def unhide(self) -> None: self.mapped = True self.damage() def kill(self) -> None: self.hide() del self.qtile.windows_map[self.wid] def place(self, x, y, width, height, borderwidth, bordercolor, above=False, margin=None, respect_hints=False): if above and self._mapped: self.core.mapped_windows.remove(self) self.core.mapped_windows.append(self) self.core.stack_windows() self.x = x self.y = y needs_reset = width != self.width or height != self.height self.width = width self.height = height if needs_reset: self._reset_texture() self._find_outputs() self.damage() def info(self) -> Dict: """Return a dictionary of info.""" return dict( x=self.x, y=self.y, width=self.width, height=self.height, id=self.wid, ) class Static(base.Static, Window): """ Static windows represent both regular windows made static by the user and layer surfaces created as part of the wlr layer shell protocol. """ def __init__( self, core: Core, qtile: Qtile, surface: SurfaceType, wid: int, ): base.Static.__init__(self) self.core = core self.qtile = qtile self._group: Optional[_Group] = None self.surface = surface self.subsurfaces: List[SubSurface] = [] self._wid = wid self._mapped: bool = False self.x = 0 self.y = 0 self.borderwidth: int = 0 self.bordercolor: List[ffi.CData] = [_rgb((0, 0, 0, 1))] self.opacity: float = 1.0 self._outputs: List[Output] = [] self._float_state = FloatStates.FLOATING self.defunct = True self.is_layer = False self.screen = qtile.current_screen self.add_listener(surface.map_event, self._on_map) self.add_listener(surface.unmap_event, self._on_unmap) self.add_listener(surface.destroy_event, self._on_destroy) self.add_listener(surface.surface.commit_event, self._on_commit) if isinstance(surface, LayerSurfaceV1): self.is_layer = True if surface.output is None: surface.output = core.output_layout.output_at(core.cursor.x, core.cursor.y) self.output = core.output_from_wlr_output(surface.output) self.screen = self.output.screen self.mapped = True self._outputs.append(self.output) else: self._find_outputs() @property def mapped(self) -> bool: # This is identical to the parent class' version but mypy has a bug that # triggers a false positive: https://github.com/python/mypy/issues/1465 return self._mapped @mapped.setter def mapped(self, mapped: bool) -> None: if mapped == self._mapped: return self._mapped = mapped if isinstance(self.surface, LayerSurfaceV1): layer = self.output.layers[self.surface.client_pending.layer] if mapped: layer.append(self) else: layer.remove(self) if self.reserved_space: self.qtile.free_reserved_space(self.reserved_space, self.screen) self.output.organise_layers() else: if mapped: self.core.mapped_windows.append(self) else: self.core.mapped_windows.remove(self) self.core.stack_windows() def _on_map(self, _listener, data): logger.debug("Signal: window map") self.mapped = True if self.is_layer: self.output.organise_layers() self.core.focus_window(self, self.surface.surface) def _on_unmap(self, _listener, data): logger.debug("Signal: window unmap") self.mapped = False if self.surface.surface == self.core.seat.keyboard_state.focused_surface: group = self.qtile.current_screen.group if group.current_window: group.focus(group.current_window, warp=self.qtile.config.cursor_warp) else: self.core.seat.keyboard_clear_focus() if self.is_layer: self.output.organise_layers() self.damage() def has_fixed_size(self) -> bool: return False def kill(self): if self.is_layer: self.surface.close() else: self.surface.send_close() def place(self, x, y, width, height, borderwidth, bordercolor, above=False, margin=None, respect_hints=False): self.x = x self.y = y if self.is_layer: self.surface.configure(width, height) else: self.surface.set_size(int(width), int(height)) self.paint_borders(bordercolor, borderwidth) self.damage() def cmd_bring_to_front(self) -> None: if self.mapped and isinstance(self.surface, XdgSurface): self.core.mapped_windows.remove(self) self.core.mapped_windows.append(self) self.core.stack_windows() WindowType = typing.Union[Window, Internal, Static] class XdgPopupWindow(HasListeners): """ This represents a single `struct wlr_xdg_popup` object and is owned by a single parent window (of `Union[WindowType, XdgPopupWindow]`). wlroots does most of the work for us, but we need to listen to certain events so that we know when to render frames and we need to unconstrain the popups so they are completely visible. """ def __init__(self, parent: Union[WindowType, XdgPopupWindow], xdg_popup: XdgPopup): self.parent = parent self.xdg_popup = xdg_popup self.core: Core = parent.core self.popups: List[XdgPopupWindow] = [] # Keep on output if isinstance(parent, XdgPopupWindow): # This is a nested XdgPopup self.output: Output = parent.output self.output_box: Box = parent.output_box else: # Parent is an XdgSurface; This is a first-level XdgPopup box = xdg_popup.base.get_geometry() lx, ly = self.core.output_layout.closest_point(parent.x + box.x, parent.y + box.y) wlr_output = self.core.output_layout.output_at(lx, ly) self.output = wlr_output.data box = Box(*self.output.get_geometry()) box.x = round(box.x - lx) box.y = round(box.y - ly) self.output_box = box xdg_popup.unconstrain_from_box(self.output_box) self.add_listener(xdg_popup.base.map_event, self._on_map) self.add_listener(xdg_popup.base.unmap_event, self._on_unmap) self.add_listener(xdg_popup.base.destroy_event, self._on_destroy) self.add_listener(xdg_popup.base.new_popup_event, self._on_new_popup) self.add_listener(xdg_popup.base.surface.commit_event, self._on_commit) def _on_map(self, _listener, _data): logger.debug("Signal: popup map") self.output.damage() def _on_unmap(self, _listener, _data): logger.debug("Signal: popup unmap") self.output.damage() def _on_destroy(self, _listener, _data): logger.debug("Signal: popup destroy") self.finalize_listeners() self.output.damage() def _on_new_popup(self, _listener, xdg_popup: XdgPopup): logger.debug("Signal: popup new_popup") self.popups.append(XdgPopupWindow(self, xdg_popup)) def _on_commit(self, _listener, _data): self.output.damage() class SubSurface(HasListeners): """ This represents a single `struct wlr_subsurface` object and is owned by a single parent window (of `Union[WindowType, SubSurface]`). We only need to track them so that we can listen to their commit events and render accordingly. """ def __init__(self, parent: Union[WindowType, SubSurface], subsurface: WlrSubSurface): self.parent = parent self.subsurfaces: List[SubSurface] = [] self.add_listener(subsurface.destroy_event, self._on_destroy) self.add_listener(subsurface.surface.commit_event, parent._on_commit) self.add_listener(subsurface.surface.new_subsurface_event, self._on_new_subsurface) def finalize(self): self.finalize_listeners() for subsurface in self.subsurfaces: subsurface.finalize() self.parent.subsurfaces.remove(self) def _on_destroy(self, _listener, _data): self.finalize() def _on_commit(self, _listener, _data): self.parent._on_commit(None, None) def _on_new_subsurface(self, _listener, subsurface: WlrSubSurface): self.subsurfaces.append(SubSurface(self, subsurface))

34.043573

97

0.615001

from __future__ import annotations import functools import typing import cairocffi import pywayland from wlroots import ffi from wlroots.util.edges import Edges from wlroots.wlr_types import Box, Texture from wlroots.wlr_types.layer_shell_v1 import LayerSurfaceV1 from wlroots.wlr_types.xdg_shell import ( XdgPopup, XdgSurface, XdgTopLevelSetFullscreenEvent, ) from libqtile import hook, utils from libqtile.backend import base from libqtile.backend.base import FloatStates from libqtile.backend.wayland.drawer import Drawer from libqtile.backend.wayland.wlrq import DRM_FORMAT_ARGB8888, HasListeners from libqtile.command.base import CommandError from libqtile.log_utils import logger if typing.TYPE_CHECKING: from typing import Dict, List, Optional, Tuple, Union from wlroots.wlr_types.surface import SubSurface as WlrSubSurface from libqtile.backend.wayland.core import Core from libqtile.backend.wayland.output import Output from libqtile.core.manager import Qtile from libqtile.group import _Group from libqtile.utils import ColorType EDGES_TILED = Edges.TOP | Edges.BOTTOM | Edges.LEFT | Edges.RIGHT EDGES_FLOAT = Edges.NONE @functools.lru_cache() def _rgb(color: ColorType) -> ffi.CData: if isinstance(color, ffi.CData): return color return ffi.new("float[4]", utils.rgb(color)) SurfaceType = typing.Union[XdgSurface, LayerSurfaceV1] class Window(base.Window, HasListeners): def __init__(self, core: Core, qtile: Qtile, surface: SurfaceType, wid: int): base.Window.__init__(self) self.core = core self.qtile = qtile self.surface = surface self._group: Optional[_Group] = None self.popups: List[XdgPopupWindow] = [] self.subsurfaces: List[SubSurface] = [] self._wid = wid self._mapped: bool = False self.x = 0 self.y = 0 self.bordercolor: List[ffi.CData] = [_rgb((0, 0, 0, 1))] self.opacity: float = 1.0 self._outputs: List[Output] = [] self._width: Optional[int] = None self._height: Optional[int] = None assert isinstance(surface, XdgSurface) if surface.toplevel.title: self.name = surface.toplevel.title self._app_id: Optional[str] = surface.toplevel.app_id surface.set_tiled(EDGES_TILED) self._float_state = FloatStates.NOT_FLOATING self.float_x: Optional[int] = None self.float_y: Optional[int] = None self._float_width: int = self.width self._float_height: int = self.height self.add_listener(surface.map_event, self._on_map) self.add_listener(surface.unmap_event, self._on_unmap) self.add_listener(surface.destroy_event, self._on_destroy) self.add_listener(surface.new_popup_event, self._on_new_popup) self.add_listener(surface.toplevel.request_fullscreen_event, self._on_request_fullscreen) self.add_listener(surface.toplevel.set_title_event, self._on_set_title) self.add_listener(surface.toplevel.set_app_id_event, self._on_set_app_id) self.add_listener(surface.surface.commit_event, self._on_commit) self.add_listener(surface.surface.new_subsurface_event, self._on_new_subsurface) def finalize(self): self.finalize_listeners() for subsurface in self.subsurfaces: subsurface.finalize() @property def wid(self): return self._wid @property def width(self) -> int: if self._width is None: return self.surface.surface.current.width return self._width @width.setter def width(self, width: int) -> None: self._width = width @property def height(self) -> int: if self._height is None: return self.surface.surface.current.height return self._height @height.setter def height(self, height: int) -> None: self._height = height @property def group(self) -> Optional[_Group]: return self._group @group.setter def group(self, group: Optional[_Group]) -> None: self._group = group @property def mapped(self) -> bool: return self._mapped @mapped.setter def mapped(self, mapped: bool) -> None: if mapped == self._mapped: return self._mapped = mapped if mapped: self.core.mapped_windows.append(self) else: self.core.mapped_windows.remove(self) self.core.stack_windows() def _on_map(self, _listener, _data): logger.debug("Signal: window map") if self in self.core.pending_windows: self.core.pending_windows.remove(self) logger.debug(f"Managing new top-level window with window ID: {self.wid}") self.qtile.manage(self) if self.group.screen: self.mapped = True self.core.focus_window(self) def _on_unmap(self, _listener, _data): logger.debug("Signal: window unmap") self.mapped = False self.damage() seat = self.core.seat if not seat.destroyed: if self.surface.surface == seat.keyboard_state.focused_surface: seat.keyboard_clear_focus() def _on_destroy(self, _listener, _data): logger.debug("Signal: window destroy") if self.mapped: logger.warning("Window destroyed before unmap event.") self.mapped = False self.qtile.unmanage(self.wid) self.finalize() def _on_new_popup(self, _listener, xdg_popup: XdgPopup): logger.debug("Signal: window new_popup") self.popups.append(XdgPopupWindow(self, xdg_popup)) def _on_request_fullscreen(self, _listener, event: XdgTopLevelSetFullscreenEvent): logger.debug("Signal: window request_fullscreen") if self.qtile.config.auto_fullscreen: self.fullscreen = event.fullscreen def _on_set_title(self, _listener, _data): logger.debug("Signal: window set_title") self.name = self.surface.toplevel.title hook.fire('client_name_updated', self) def _on_set_app_id(self, _listener, _data): logger.debug("Signal: window set_app_id") self._app_id = self.surface.toplevel.app_id def _on_commit(self, _listener, _data): self.damage() def _on_new_subsurface(self, _listener, subsurface: WlrSubSurface): self.subsurfaces.append(SubSurface(self, subsurface)) def has_fixed_size(self) -> bool: assert isinstance(self.surface, XdgSurface) state = self.surface.toplevel._ptr.current return ( 0 < state.min_width == state.max_width and 0 < state.min_height == state.max_height ) def is_transient_for(self) -> Optional[base.WindowType]: assert isinstance(self.surface, XdgSurface) parent = self.surface.toplevel.parent if parent: for win in self.qtile.windows_map.values(): if not isinstance(win, Internal) and win.surface == parent: return win return None def _find_outputs(self): self._outputs = [o for o in self.core.outputs if o.contains(self)] def damage(self) -> None: for output in self._outputs: output.damage() def hide(self): if self.mapped: self.surface.unmap_event.emit() def unhide(self): if not self.mapped: self.surface.map_event.emit() def kill(self): self.surface.send_close() def get_pid(self) -> int: pid = pywayland.ffi.new("pid_t *") pywayland.lib.wl_client_get_credentials( self.surface._ptr.client.client, pid, ffi.NULL, ffi.NULL ) return pid[0] def get_wm_class(self) -> Optional[List]: if self._app_id: return [self._app_id] return None def togroup(self, group_name=None, *, switch_group=False): if group_name is None: group = self.qtile.current_group else: group = self.qtile.groups_map.get(group_name) if group is None: raise CommandError("No such group: %s" % group_name) if self.group is not group: self.hide() if self.group: if self.group.screen: self.x -= self.group.screen.x self.group.remove(self) if group.screen and self.x < group.screen.x: self.x += group.screen.x group.add(self) if switch_group: group.cmd_toscreen(toggle=False) def paint_borders(self, color: Union[ColorType, List[ColorType]], width) -> None: if color: if isinstance(color, list): if len(color) > width: color = color[:width] self.bordercolor = [_rgb(c) for c in color] else: self.bordercolor = [_rgb(color)] self.borderwidth = width @property def floating(self): return self._float_state != FloatStates.NOT_FLOATING @floating.setter def floating(self, do_float): if do_float and self._float_state == FloatStates.NOT_FLOATING: if self.group and self.group.screen: screen = self.group.screen if not self._float_width: self._float_width = self.width self._float_height = self.height self._enablefloating( screen.x + self.float_x, screen.y + self.float_y, self._float_width, self._float_height ) else: self._float_state = FloatStates.FLOATING elif (not do_float) and self._float_state != FloatStates.NOT_FLOATING: if self._float_state == FloatStates.FLOATING: # store last size self._float_width = self.width self._float_height = self.height self._float_state = FloatStates.NOT_FLOATING self.group.mark_floating(self, False) hook.fire('float_change') @property def fullscreen(self): return self._float_state == FloatStates.FULLSCREEN @fullscreen.setter def fullscreen(self, do_full): self.surface.set_fullscreen(do_full) if do_full: screen = self.group.screen or \ self.qtile.find_closest_screen(self.x, self.y) self._enablefloating( screen.x, screen.y, screen.width, screen.height, new_float_state=FloatStates.FULLSCREEN ) return if self._float_state == FloatStates.FULLSCREEN: self.floating = False @property def maximized(self): return self._float_state == FloatStates.MAXIMIZED @maximized.setter def maximized(self, do_maximize): if do_maximize: screen = self.group.screen or \ self.qtile.find_closest_screen(self.x, self.y) self._enablefloating( screen.dx, screen.dy, screen.dwidth, screen.dheight, new_float_state=FloatStates.MAXIMIZED ) else: if self._float_state == FloatStates.MAXIMIZED: self.floating = False @property def minimized(self): return self._float_state == FloatStates.MINIMIZED @minimized.setter def minimized(self, do_minimize): if do_minimize: if self._float_state != FloatStates.MINIMIZED: self._enablefloating(new_float_state=FloatStates.MINIMIZED) else: if self._float_state == FloatStates.MINIMIZED: self.floating = False def focus(self, warp: bool) -> None: self.core.focus_window(self) if isinstance(self, base.Internal): # self.core.focus_window is enough for internal windows return if warp and self.qtile.config.cursor_warp: self.core.warp_pointer( self.x + self.width // 2, self.y + self.height // 2, ) hook.fire("client_focus", self) def place(self, x, y, width, height, borderwidth, bordercolor, above=False, margin=None, respect_hints=False): # Adjust the placement to account for layout margins, if there are any. if margin is not None: if isinstance(margin, int): margin = [margin] * 4 x += margin[3] y += margin[0] width -= margin[1] + margin[3] height -= margin[0] + margin[2] if respect_hints: state = self.surface.toplevel._ptr.current width = max(width, state.min_width) height = max(height, state.min_height) if state.max_width: width = min(width, state.max_width) if state.max_height: height = min(height, state.max_height) # save x and y float offset if self.group is not None and self.group.screen is not None: self.float_x = x - self.group.screen.x self.float_y = y - self.group.screen.y self.x = x self.y = y self.surface.set_size(int(width), int(height)) self._width = int(width) self._height = int(height) self.paint_borders(bordercolor, borderwidth) if above and self._mapped: self.core.mapped_windows.remove(self) self.core.mapped_windows.append(self) self.core.stack_windows() self._find_outputs() self.damage() def _tweak_float(self, x=None, y=None, dx=0, dy=0, w=None, h=None, dw=0, dh=0): if x is None: x = self.x x += dx if y is None: y = self.y y += dy if w is None: w = self.width w += dw if h is None: h = self.height h += dh if h < 0: h = 0 if w < 0: w = 0 screen = self.qtile.find_closest_screen( self.x + self.width // 2, self.y + self.height // 2 ) if self.group and screen is not None and screen != self.group.screen: self.group.remove(self, force=True) screen.group.add(self, force=True) self.qtile.focus_screen(screen.index) self._reconfigure_floating(x, y, w, h) def _enablefloating(self, x=None, y=None, w=None, h=None, new_float_state=FloatStates.FLOATING): self._reconfigure_floating(x, y, w, h, new_float_state) def _reconfigure_floating(self, x, y, w, h, new_float_state=FloatStates.FLOATING): if new_float_state == FloatStates.MINIMIZED: self.hide() else: self.place( x, y, w, h, self.borderwidth, self.bordercolor, above=True, respect_hints=True ) if self._float_state != new_float_state: self._float_state = new_float_state if self.group: # may be not, if it's called from hook self.group.mark_floating(self, True) hook.fire('float_change') def info(self) -> Dict: float_info = { "x": self.float_x, "y": self.float_y, "width": self._float_width, "height": self._float_height, } return dict( name=self.name, x=self.x, y=self.y, width=self.width, height=self.height, group=self.group.name if self.group else None, id=self.wid, float_info=float_info, floating=self._float_state != FloatStates.NOT_FLOATING, maximized=self._float_state == FloatStates.MAXIMIZED, minimized=self._float_state == FloatStates.MINIMIZED, fullscreen=self._float_state == FloatStates.FULLSCREEN ) def _items(self, name: str) -> ItemT: if name == "group": return True, [] elif name == "layout": return True, list(range(len(self.group.layouts))) elif name == "screen" and self.group.screen is not None: return True, [] return None def _select(self, name, sel): if name == "group": return self.group elif name == "layout": if sel is None: return self.group.layout else: return utils.lget(self.group.layouts, sel) elif name == "screen": return self.group.screen def cmd_focus(self, warp: bool = True) -> None: self.focus(warp) def cmd_move_floating(self, dx: int, dy: int) -> None: self._tweak_float(dx=dx, dy=dy) def cmd_resize_floating(self, dw: int, dh: int) -> None: self._tweak_float(dw=dw, dh=dh) def cmd_set_position_floating(self, x: int, y: int) -> None: self._tweak_float(x=x, y=y) def cmd_set_size_floating(self, w: int, h: int) -> None: self._tweak_float(w=w, h=h) def cmd_place(self, x, y, width, height, borderwidth, bordercolor, above=False, margin=None): self.place(x, y, width, height, borderwidth, bordercolor, above, margin) def cmd_get_position(self) -> Tuple[int, int]: return self.x, self.y def cmd_get_size(self) -> Tuple[int, int]: return self.width, self.height def cmd_toggle_floating(self) -> None: self.floating = not self.floating def cmd_enable_floating(self): self.floating = True def cmd_disable_floating(self): self.floating = False def cmd_toggle_maximize(self) -> None: self.maximized = not self.maximized def cmd_toggle_minimize(self) -> None: self.minimized = not self.minimized def cmd_toggle_fullscreen(self) -> None: self.fullscreen = not self.fullscreen def cmd_enable_fullscreen(self) -> None: self.fullscreen = True def cmd_disable_fullscreen(self) -> None: self.fullscreen = False def cmd_bring_to_front(self) -> None: if self.mapped: self.core.mapped_windows.remove(self) self.core.mapped_windows.append(self) self.core.stack_windows() def cmd_kill(self) -> None: self.kill() class Internal(base.Internal, Window): texture: Texture def __init__( self, core: Core, qtile: Qtile, x: int, y: int, width: int, height: int ): self.core = core self.qtile = qtile self._group: Optional[_Group] = None self._mapped: bool = False self._wid: int = self.core.new_wid() self.x: int = x self.y: int = y self.opacity: float = 1.0 self._width: int = width self._height: int = height self._outputs: List[Output] = [] self._find_outputs() self._reset_texture() def finalize(self): self.hide() def _reset_texture(self): clear = cairocffi.ImageSurface(cairocffi.FORMAT_ARGB32, self.width, self.height) with cairocffi.Context(clear) as context: context.set_source_rgba(*utils.rgb("#000000")) context.paint() self.texture = Texture.from_pixels( self.core.renderer, DRM_FORMAT_ARGB8888, cairocffi.ImageSurface.format_stride_for_width(cairocffi.FORMAT_ARGB32, self.width), self.width, self.height, cairocffi.cairo.cairo_image_surface_get_data(clear._pointer), ) def create_drawer(self, width: int, height: int) -> Drawer: return Drawer(self.qtile, self, width, height) @property def width(self) -> int: return self._width @width.setter def width(self, value: int) -> None: self._width = value @property def height(self) -> int: return self._height @height.setter def height(self, value: int) -> None: self._height = value def hide(self) -> None: self.mapped = False self.damage() def unhide(self) -> None: self.mapped = True self.damage() def kill(self) -> None: self.hide() del self.qtile.windows_map[self.wid] def place(self, x, y, width, height, borderwidth, bordercolor, above=False, margin=None, respect_hints=False): if above and self._mapped: self.core.mapped_windows.remove(self) self.core.mapped_windows.append(self) self.core.stack_windows() self.x = x self.y = y needs_reset = width != self.width or height != self.height self.width = width self.height = height if needs_reset: self._reset_texture() self._find_outputs() self.damage() def info(self) -> Dict: return dict( x=self.x, y=self.y, width=self.width, height=self.height, id=self.wid, ) class Static(base.Static, Window): def __init__( self, core: Core, qtile: Qtile, surface: SurfaceType, wid: int, ): base.Static.__init__(self) self.core = core self.qtile = qtile self._group: Optional[_Group] = None self.surface = surface self.subsurfaces: List[SubSurface] = [] self._wid = wid self._mapped: bool = False self.x = 0 self.y = 0 self.borderwidth: int = 0 self.bordercolor: List[ffi.CData] = [_rgb((0, 0, 0, 1))] self.opacity: float = 1.0 self._outputs: List[Output] = [] self._float_state = FloatStates.FLOATING self.defunct = True self.is_layer = False self.screen = qtile.current_screen self.add_listener(surface.map_event, self._on_map) self.add_listener(surface.unmap_event, self._on_unmap) self.add_listener(surface.destroy_event, self._on_destroy) self.add_listener(surface.surface.commit_event, self._on_commit) if isinstance(surface, LayerSurfaceV1): self.is_layer = True if surface.output is None: surface.output = core.output_layout.output_at(core.cursor.x, core.cursor.y) self.output = core.output_from_wlr_output(surface.output) self.screen = self.output.screen self.mapped = True self._outputs.append(self.output) else: self._find_outputs() @property def mapped(self) -> bool: # triggers a false positive: https://github.com/python/mypy/issues/1465 return self._mapped @mapped.setter def mapped(self, mapped: bool) -> None: if mapped == self._mapped: return self._mapped = mapped if isinstance(self.surface, LayerSurfaceV1): layer = self.output.layers[self.surface.client_pending.layer] if mapped: layer.append(self) else: layer.remove(self) if self.reserved_space: self.qtile.free_reserved_space(self.reserved_space, self.screen) self.output.organise_layers() else: if mapped: self.core.mapped_windows.append(self) else: self.core.mapped_windows.remove(self) self.core.stack_windows() def _on_map(self, _listener, data): logger.debug("Signal: window map") self.mapped = True if self.is_layer: self.output.organise_layers() self.core.focus_window(self, self.surface.surface) def _on_unmap(self, _listener, data): logger.debug("Signal: window unmap") self.mapped = False if self.surface.surface == self.core.seat.keyboard_state.focused_surface: group = self.qtile.current_screen.group if group.current_window: group.focus(group.current_window, warp=self.qtile.config.cursor_warp) else: self.core.seat.keyboard_clear_focus() if self.is_layer: self.output.organise_layers() self.damage() def has_fixed_size(self) -> bool: return False def kill(self): if self.is_layer: self.surface.close() else: self.surface.send_close() def place(self, x, y, width, height, borderwidth, bordercolor, above=False, margin=None, respect_hints=False): self.x = x self.y = y if self.is_layer: self.surface.configure(width, height) else: self.surface.set_size(int(width), int(height)) self.paint_borders(bordercolor, borderwidth) self.damage() def cmd_bring_to_front(self) -> None: if self.mapped and isinstance(self.surface, XdgSurface): self.core.mapped_windows.remove(self) self.core.mapped_windows.append(self) self.core.stack_windows() WindowType = typing.Union[Window, Internal, Static] class XdgPopupWindow(HasListeners): def __init__(self, parent: Union[WindowType, XdgPopupWindow], xdg_popup: XdgPopup): self.parent = parent self.xdg_popup = xdg_popup self.core: Core = parent.core self.popups: List[XdgPopupWindow] = [] # Keep on output if isinstance(parent, XdgPopupWindow): # This is a nested XdgPopup self.output: Output = parent.output self.output_box: Box = parent.output_box else: # Parent is an XdgSurface; This is a first-level XdgPopup box = xdg_popup.base.get_geometry() lx, ly = self.core.output_layout.closest_point(parent.x + box.x, parent.y + box.y) wlr_output = self.core.output_layout.output_at(lx, ly) self.output = wlr_output.data box = Box(*self.output.get_geometry()) box.x = round(box.x - lx) box.y = round(box.y - ly) self.output_box = box xdg_popup.unconstrain_from_box(self.output_box) self.add_listener(xdg_popup.base.map_event, self._on_map) self.add_listener(xdg_popup.base.unmap_event, self._on_unmap) self.add_listener(xdg_popup.base.destroy_event, self._on_destroy) self.add_listener(xdg_popup.base.new_popup_event, self._on_new_popup) self.add_listener(xdg_popup.base.surface.commit_event, self._on_commit) def _on_map(self, _listener, _data): logger.debug("Signal: popup map") self.output.damage() def _on_unmap(self, _listener, _data): logger.debug("Signal: popup unmap") self.output.damage() def _on_destroy(self, _listener, _data): logger.debug("Signal: popup destroy") self.finalize_listeners() self.output.damage() def _on_new_popup(self, _listener, xdg_popup: XdgPopup): logger.debug("Signal: popup new_popup") self.popups.append(XdgPopupWindow(self, xdg_popup)) def _on_commit(self, _listener, _data): self.output.damage() class SubSurface(HasListeners): def __init__(self, parent: Union[WindowType, SubSurface], subsurface: WlrSubSurface): self.parent = parent self.subsurfaces: List[SubSurface] = [] self.add_listener(subsurface.destroy_event, self._on_destroy) self.add_listener(subsurface.surface.commit_event, parent._on_commit) self.add_listener(subsurface.surface.new_subsurface_event, self._on_new_subsurface) def finalize(self): self.finalize_listeners() for subsurface in self.subsurfaces: subsurface.finalize() self.parent.subsurfaces.remove(self) def _on_destroy(self, _listener, _data): self.finalize() def _on_commit(self, _listener, _data): self.parent._on_commit(None, None) def _on_new_subsurface(self, _listener, subsurface: WlrSubSurface): self.subsurfaces.append(SubSurface(self, subsurface))

true

1c3857f5ef43f46f8e2416b7d4d3c2c0e9b2c929

3,596

py

Python

ec2instanceconnectcli/mops.py

Hallian/aws-ec2-instance-connect-cli

95be70889d8503964071dac227507c090a1bc980

[ "Apache-2.0" ]

null

ec2instanceconnectcli/mops.py

Hallian/aws-ec2-instance-connect-cli

95be70889d8503964071dac227507c090a1bc980

[ "Apache-2.0" ]

null

ec2instanceconnectcli/mops.py

Hallian/aws-ec2-instance-connect-cli

95be70889d8503964071dac227507c090a1bc980

[ "Apache-2.0" ]

null

# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. import sys import argparse from ec2instanceconnectcli.EC2InstanceConnectCLI import EC2InstanceConnectCLI from ec2instanceconnectcli.EC2InstanceConnectKey import EC2InstanceConnectKey from ec2instanceconnectcli.EC2InstanceConnectCommand import EC2InstanceConnectCommand from ec2instanceconnectcli.EC2InstanceConnectLogger import EC2InstanceConnectLogger from ec2instanceconnectcli import input_parser DEFAULT_INSTANCE = '' DEFAULT_PROFILE = None def main(program, mode): """ Parses system arguments and sets defaults Calls `ssh` or `sftp` to SSH into the Instance or transfer files. :param program: Client program to be used for SSH/SFTP operations. :type program: basestring :param mode: Identifies either SSH/SFTP operation. :type mode: basestring """ usage = "" if mode == "ssh": usage=""" mssh [-t instance_id] [-u profile] [-z availability_zone] [-r region] [supported ssh flags] target [command] target => [user@]instance_id | [user@]hostname [supported ssh flags] => [-l login_name] [-p port] """ elif mode == "sftp": usage=""" msftp [-u aws_profile] [-z availability_zone] [supported sftp flags] target target => [user@]instance_id[:file ...][:dir[/]] | [user@]hostname[:file ...][:dir[/]] [supported sftp flags] => [-P port] [-b batchfile] """ parser = argparse.ArgumentParser(usage=usage) parser.add_argument('-r', '--region', action='store', help='AWS region', type=str, metavar='') parser.add_argument('-z', '--zone', action='store', help='Availability zone', type=str, metavar='') parser.add_argument('-u', '--profile', action='store', help='AWS Config Profile', type=str, default=DEFAULT_PROFILE, metavar='') parser.add_argument('-t', '--instance_id', action='store', help='EC2 Instance ID. Required if target is hostname', type=str, default=DEFAULT_INSTANCE, metavar='') parser.add_argument('-J', '--jumphost', action='store', help='EC2 Jump host Instance ID.', type=str, default=DEFAULT_INSTANCE, metavar='') parser.add_argument('-d', '--debug', action="store_true", help='Turn on debug logging') args = parser.parse_known_args() logger = EC2InstanceConnectLogger(args[0].debug) try: instance_bundles, flags, program_command = input_parser.parseargs(args, mode) except Exception as e: print(str(e)) parser.print_help() sys.exit(1) #Generate temp key cli_key = EC2InstanceConnectKey(logger.get_logger()) cli_command = EC2InstanceConnectCommand(program, instance_bundles, cli_key.get_priv_key_file(), flags, program_command, logger.get_logger()) try: # TODO: Handling for if the '-i' flag is passed cli = EC2InstanceConnectCLI(instance_bundles, cli_key.get_pub_key(), cli_command, logger.get_logger()) cli.invoke_command() except Exception as e: print('Failed with:\n' + str(e)) sys.exit(1)

44.395062

166

0.689655

import sys import argparse from ec2instanceconnectcli.EC2InstanceConnectCLI import EC2InstanceConnectCLI from ec2instanceconnectcli.EC2InstanceConnectKey import EC2InstanceConnectKey from ec2instanceconnectcli.EC2InstanceConnectCommand import EC2InstanceConnectCommand from ec2instanceconnectcli.EC2InstanceConnectLogger import EC2InstanceConnectLogger from ec2instanceconnectcli import input_parser DEFAULT_INSTANCE = '' DEFAULT_PROFILE = None def main(program, mode): usage = "" if mode == "ssh": usage=""" mssh [-t instance_id] [-u profile] [-z availability_zone] [-r region] [supported ssh flags] target [command] target => [user@]instance_id | [user@]hostname [supported ssh flags] => [-l login_name] [-p port] """ elif mode == "sftp": usage=""" msftp [-u aws_profile] [-z availability_zone] [supported sftp flags] target target => [user@]instance_id[:file ...][:dir[/]] | [user@]hostname[:file ...][:dir[/]] [supported sftp flags] => [-P port] [-b batchfile] """ parser = argparse.ArgumentParser(usage=usage) parser.add_argument('-r', '--region', action='store', help='AWS region', type=str, metavar='') parser.add_argument('-z', '--zone', action='store', help='Availability zone', type=str, metavar='') parser.add_argument('-u', '--profile', action='store', help='AWS Config Profile', type=str, default=DEFAULT_PROFILE, metavar='') parser.add_argument('-t', '--instance_id', action='store', help='EC2 Instance ID. Required if target is hostname', type=str, default=DEFAULT_INSTANCE, metavar='') parser.add_argument('-J', '--jumphost', action='store', help='EC2 Jump host Instance ID.', type=str, default=DEFAULT_INSTANCE, metavar='') parser.add_argument('-d', '--debug', action="store_true", help='Turn on debug logging') args = parser.parse_known_args() logger = EC2InstanceConnectLogger(args[0].debug) try: instance_bundles, flags, program_command = input_parser.parseargs(args, mode) except Exception as e: print(str(e)) parser.print_help() sys.exit(1) cli_key = EC2InstanceConnectKey(logger.get_logger()) cli_command = EC2InstanceConnectCommand(program, instance_bundles, cli_key.get_priv_key_file(), flags, program_command, logger.get_logger()) try: cli = EC2InstanceConnectCLI(instance_bundles, cli_key.get_pub_key(), cli_command, logger.get_logger()) cli.invoke_command() except Exception as e: print('Failed with:\n' + str(e)) sys.exit(1)

true

1c38582751f28bdc2dffc5c4099f3b071797762e

3,755

py

Python

torch_geometric/datasets/flickr.py

beneisner/pytorch_geometric

53d44a96bd2de2753b1ab1d7153c026c92606a81

[ "MIT" ]

1

2022-02-17T09:40:17.000Z

torch_geometric/datasets/flickr.py

beneisner/pytorch_geometric

53d44a96bd2de2753b1ab1d7153c026c92606a81

[ "MIT" ]

null

torch_geometric/datasets/flickr.py

beneisner/pytorch_geometric

53d44a96bd2de2753b1ab1d7153c026c92606a81

[ "MIT" ]

1

2021-06-16T11:40:43.000Z

from typing import Optional, Callable, List import json import os.path as osp import torch import numpy as np import scipy.sparse as sp from google_drive_downloader import GoogleDriveDownloader as gdd from torch_geometric.data import InMemoryDataset, Data class Flickr(InMemoryDataset): r"""The Flickr dataset from the `"GraphSAINT: Graph Sampling Based Inductive Learning Method" <https://arxiv.org/abs/1907.04931>`_ paper, containing descriptions and common properties of images. Args: root (string): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an :obj:`torch_geometric.data.Data` object and returns a transformed version. The data object will be transformed before every access. (default: :obj:`None`) pre_transform (callable, optional): A function/transform that takes in an :obj:`torch_geometric.data.Data` object and returns a transformed version. The data object will be transformed before being saved to disk. (default: :obj:`None`) """ adj_full_id = '1crmsTbd1-2sEXsGwa2IKnIB7Zd3TmUsy' feats_id = '1join-XdvX3anJU_MLVtick7MgeAQiWIZ' class_map_id = '1uxIkbtg5drHTsKt-PAsZZ4_yJmgFmle9' role_id = '1htXCtuktuCW8TR8KiKfrFDAxUgekQoV7' def __init__(self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None): super().__init__(root, transform, pre_transform) self.data, self.slices = torch.load(self.processed_paths[0]) @property def raw_file_names(self) -> List[str]: return ['adj_full.npz', 'feats.npy', 'class_map.json', 'role.json'] @property def processed_file_names(self) -> str: return 'data.pt' def download(self): path = osp.join(self.raw_dir, 'adj_full.npz') gdd.download_file_from_google_drive(self.adj_full_id, path) path = osp.join(self.raw_dir, 'feats.npy') gdd.download_file_from_google_drive(self.feats_id, path) path = osp.join(self.raw_dir, 'class_map.json') gdd.download_file_from_google_drive(self.class_map_id, path) path = osp.join(self.raw_dir, 'role.json') gdd.download_file_from_google_drive(self.role_id, path) def process(self): f = np.load(osp.join(self.raw_dir, 'adj_full.npz')) adj = sp.csr_matrix((f['data'], f['indices'], f['indptr']), f['shape']) adj = adj.tocoo() row = torch.from_numpy(adj.row).to(torch.long) col = torch.from_numpy(adj.col).to(torch.long) edge_index = torch.stack([row, col], dim=0) x = np.load(osp.join(self.raw_dir, 'feats.npy')) x = torch.from_numpy(x).to(torch.float) ys = [-1] * x.size(0) with open(osp.join(self.raw_dir, 'class_map.json')) as f: class_map = json.load(f) for key, item in class_map.items(): ys[int(key)] = item y = torch.tensor(ys) with open(osp.join(self.raw_dir, 'role.json')) as f: role = json.load(f) train_mask = torch.zeros(x.size(0), dtype=torch.bool) train_mask[torch.tensor(role['tr'])] = True val_mask = torch.zeros(x.size(0), dtype=torch.bool) val_mask[torch.tensor(role['va'])] = True test_mask = torch.zeros(x.size(0), dtype=torch.bool) test_mask[torch.tensor(role['te'])] = True data = Data(x=x, edge_index=edge_index, y=y, train_mask=train_mask, val_mask=val_mask, test_mask=test_mask) data = data if self.pre_transform is None else self.pre_transform(data) torch.save(self.collate([data]), self.processed_paths[0])

38.71134

79

0.656724

from typing import Optional, Callable, List import json import os.path as osp import torch import numpy as np import scipy.sparse as sp from google_drive_downloader import GoogleDriveDownloader as gdd from torch_geometric.data import InMemoryDataset, Data class Flickr(InMemoryDataset): adj_full_id = '1crmsTbd1-2sEXsGwa2IKnIB7Zd3TmUsy' feats_id = '1join-XdvX3anJU_MLVtick7MgeAQiWIZ' class_map_id = '1uxIkbtg5drHTsKt-PAsZZ4_yJmgFmle9' role_id = '1htXCtuktuCW8TR8KiKfrFDAxUgekQoV7' def __init__(self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None): super().__init__(root, transform, pre_transform) self.data, self.slices = torch.load(self.processed_paths[0]) @property def raw_file_names(self) -> List[str]: return ['adj_full.npz', 'feats.npy', 'class_map.json', 'role.json'] @property def processed_file_names(self) -> str: return 'data.pt' def download(self): path = osp.join(self.raw_dir, 'adj_full.npz') gdd.download_file_from_google_drive(self.adj_full_id, path) path = osp.join(self.raw_dir, 'feats.npy') gdd.download_file_from_google_drive(self.feats_id, path) path = osp.join(self.raw_dir, 'class_map.json') gdd.download_file_from_google_drive(self.class_map_id, path) path = osp.join(self.raw_dir, 'role.json') gdd.download_file_from_google_drive(self.role_id, path) def process(self): f = np.load(osp.join(self.raw_dir, 'adj_full.npz')) adj = sp.csr_matrix((f['data'], f['indices'], f['indptr']), f['shape']) adj = adj.tocoo() row = torch.from_numpy(adj.row).to(torch.long) col = torch.from_numpy(adj.col).to(torch.long) edge_index = torch.stack([row, col], dim=0) x = np.load(osp.join(self.raw_dir, 'feats.npy')) x = torch.from_numpy(x).to(torch.float) ys = [-1] * x.size(0) with open(osp.join(self.raw_dir, 'class_map.json')) as f: class_map = json.load(f) for key, item in class_map.items(): ys[int(key)] = item y = torch.tensor(ys) with open(osp.join(self.raw_dir, 'role.json')) as f: role = json.load(f) train_mask = torch.zeros(x.size(0), dtype=torch.bool) train_mask[torch.tensor(role['tr'])] = True val_mask = torch.zeros(x.size(0), dtype=torch.bool) val_mask[torch.tensor(role['va'])] = True test_mask = torch.zeros(x.size(0), dtype=torch.bool) test_mask[torch.tensor(role['te'])] = True data = Data(x=x, edge_index=edge_index, y=y, train_mask=train_mask, val_mask=val_mask, test_mask=test_mask) data = data if self.pre_transform is None else self.pre_transform(data) torch.save(self.collate([data]), self.processed_paths[0])

true

1c3858d364bdb885edc680b7942cf284a92dde06

192

py

Python

filer_addons/filer_gui/admin/__init__.py

benzkji/django-filer-addons

0220b1c75520dcda9ec1fe6a46e62a66735c7699

[ "MIT" ]

null

filer_addons/filer_gui/admin/__init__.py

benzkji/django-filer-addons

0220b1c75520dcda9ec1fe6a46e62a66735c7699

[ "MIT" ]

23

2017-06-17T07:11:07.000Z

2020-11-06T17:05:05.000Z

filer_addons/filer_gui/admin/__init__.py

benzkji/django-filer-addons

0220b1c75520dcda9ec1fe6a46e62a66735c7699

[ "MIT" ]

2

2017-06-20T09:25:09.000Z

2017-08-03T07:38:17.000Z

from __future__ import unicode_literals from django.contrib import admin from .api import FilerGuiAdmin from ..models import FilerGuiFile admin.site.register(FilerGuiFile, FilerGuiAdmin)

17.454545

48

0.833333

from __future__ import unicode_literals from django.contrib import admin from .api import FilerGuiAdmin from ..models import FilerGuiFile admin.site.register(FilerGuiFile, FilerGuiAdmin)

true

1c38592f9643bb88ae18a29c8b362c5be017dfa0

430

py

Python

jms_oidc_rp/admin.py

BaiJiangJie/jumpserver-django-oidc-rp

b2d6a63dd82214263b8971412a9043268c31ba7f

[ "MIT" ]

20

2018-04-16T13:17:35.000Z

2021-06-05T00:08:33.000Z

jms_oidc_rp/admin.py

BaiJiangJie/jumpserver-django-oidc-rp

b2d6a63dd82214263b8971412a9043268c31ba7f

[ "MIT" ]

9

2018-07-20T18:19:13.000Z

2021-12-22T08:57:18.000Z

oidc_rp/admin.py

bcgov/django-oidc-rp

50e6fa143e61b04849b4c66beef078be0d7669de

[ "MIT" ]

21

2018-07-10T16:05:44.000Z

2022-01-24T05:57:09.000Z

""" OpenID Connect relying party (RP) model admin definitions ========================================================= This module defines admin classes used to populate the Django administration dashboard. """ from django.contrib import admin from .models import OIDCUser @admin.register(OIDCUser) class UserAdmin(admin.ModelAdmin): """ The OIDC user model admin. """ list_display = ('sub', 'user', )

22.631579

91

0.616279

from django.contrib import admin from .models import OIDCUser @admin.register(OIDCUser) class UserAdmin(admin.ModelAdmin): list_display = ('sub', 'user', )

true

1c38594b131a1b460726372bc8e9479ebc4c1a0f

44,677

py

Python

classic_heuristics/gapvrp.py

mschmidt87/VeRyPy

3086288cf203bbe6dc488de72fb6f8ea2a1382e6

[ "MIT" ]

1

2020-07-14T15:47:07.000Z

classic_heuristics/gapvrp.py

mrwright313/VeRyPy

eaac6e210d861441071565575750a2f0e25dfb72

[ "MIT" ]

null

classic_heuristics/gapvrp.py

mrwright313/VeRyPy

eaac6e210d861441071565575750a2f0e25dfb72

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ############################################################################### """ This file is a part of the VeRyPy classical vehicle routing problem heuristic library and provides an implementation of the Fisher&Jaikumar (1981) heuristic, which generates an approximate solution for a VRP via solving it as an generalized assignment problem (GAP). The script is callable and can be used as a standalone solver for TSPLIB formatted CVRPs. It has extensive dependencies: MIP solver Gurobi, built-in TSP solver, and numpy and scipy for reading and preparing the problem instance.""" ############################################################################### # Written in Python 2.7, but try to maintain Python 3+ compatibility from __future__ import print_function from __future__ import division from signal import signal, SIGINT, default_int_handler from collections import namedtuple from math import pi, ceil from logging import log, DEBUG, WARNING import numpy as np from gurobipy import Model, GRB, LinExpr, GurobiError #from tsp_solvers.tsp_solver_ropt import solve_tsp_ropt as solve_tsp #from tsp_solvers.tsp_solver_lkh import solve_tsp_lkh as solve_tsp from tsp_solvers.tsp_solver_gurobi import solve_tsp_gurobi as solve_tsp from sweep import get_sweep_from_cartesian_coordinates, bisect_angle from cvrp_io import calculate_D from util import is_better_sol, totald from config import MAX_MIP_SOLVER_RUNTIME, MIP_SOLVER_THREADS from config import CAPACITY_EPSILON as C_EPS from config import COST_EPSILON as S_EPS __author__ = "Jussi Rasku" __copyright__ = "Copyright 2018, Jussi Rasku" __credits__ = ["Jussi Rasku"] __license__ = "MIT" __maintainer__ = "Jussi Rasku" __email__ = "jussi.rasku@jyu.fi" __status__ = "Development" # These hard coded parameters define how the relaxation is adjusted if the # GAP solution is not L feasible. L_MPLR_DEFAULT = 1.0 L_ADAPTIVE_MPLR_INIT = 0.85 L_ADAPTIVE_MPLR_INC = 0.85 L_ADAPTIVE_MPLR_MAX_TRIES = 3 INCREASE_K_ON_FAILURE_UPTO = 1.1 # = 10% increase to K (or min of 1) def _decision_variables_to_assignments(m, Y_ik, N, K): """ Convert the decision variables in m for keys Y_ik to assignments of customers i==2..N (0 is the depot) to the routes k=1..K . TODO: there is probably a neat numpy trick to get node and k indices out of the decision variable array. For now just use nested loops, but a cleverer way would problably be faster. However, "premature optimization is the root of all evil", so profile first, and modify only after verifying it to be a real bottleneck.""" assignments = [] Y_ik_values = m.getAttr('x', Y_ik) for k in range(K): route_nodes = [] for i in range(1, N): if Y_ik_values[i,k]: route_nodes.append(i) assignments.append(route_nodes) return assignments def _solve_gap(N, D_s, d, C, K, L=None, L_ctr_multipiler=1.0): """A helper function that Solves VRP as a Generalized Assignment Problem to assign customers to vehicles with a objective function that the delivery cost as described in (Fisher & Jaikumar 1981). D_s is the distance matrix complemented with distances to K seed points. That is: [D_0] D_s = [S ], where D_0 is the first row of the full distance matrix and S is the distances from seed points to node points d is the list of customer demands with d[0]=0 being the depot node C is the capacity of the K identical trucks also, additional (and optional) constraints can be given: L is the maximum tour cost/duration/length L_ctr_multipiler allows iteratively adjusting the max route cost approximation constraint in order to avoid producing assignments that are ruled infeasible by the feasibility checker. -- Fisher, M. L. and Jaikumar, R. (1981), A generalized assignment heuristic for vehicle routing. Networks, 11: 109-124. """ ## build the cost approximation matrix "insertion_cost" # it is ~ a insertion cost matrix, where each coefficient is the cost # of inserting customer i to the route consisting visit to seed k. # # we assume that distances are symmetric, but if asymmetric # distances are to be used, take min # d_{ik} = min(c_{0i}+c_{i{i_k}}+c_{i{i_k}}, # c_{0{i_k}}+c_[{i_k}i}+c_{i0}) # -(c_{0{i_k}}+c_{{i_k}0}) m = Model("GAPCVRP") # the order of the keys is important when we interpret the results Y_ik_keys = [(i,k) for k in range(K) for i in range(1,N)] # delivery cost approximation coefficients for the objective function insertion_cost = {(i,k): D_s[0,i]+D_s[k,i]-D_s[k,0] \ for i,k in Y_ik_keys} # variables and the objective Y_ik = m.addVars(Y_ik_keys, obj=insertion_cost, vtype=GRB.BINARY, name='y') ## constraints # c1, the capacity constraint and optional tour cost constraint cl approx_route_cost_constraints = [] if C: c1_coeffs = d[1:] for k in range(K): ck_vars = [Y_ik[i,k] for i in range(1,N)] if C: c1_lhs = LinExpr(c1_coeffs,ck_vars) #c1_lhs = Y_ik.prod(c1_coeffs, '*', k) m.addConstr(c1_lhs <= C, "c1_k%d"%k) # ct = optional tour cost constraints # it is a bit hidden, but the additional side constraint can be found # from Fisher & Jaikumar (1981) p121, 2. paragraph. # However, for whatever reason, this does not seem to produce the # same results as reported in their paper as the constraint easily # starts to make the problem infeasible and the exact mechanism to # recover that is not specified in the paper. if L: ct_coeffs = [insertion_cost[(i,k)]*L_ctr_multipiler for i in range(1,N)] ct_lhs = LinExpr(ct_coeffs,ck_vars) #ct_lhs = Y_ik.prod(ct_coeffs, '*', k) constr_l = m.addConstr(ct_lhs <= L, "cl_k%d"%k) approx_route_cost_constraints.append(constr_l) # c2, the assignment constraints for i in range(1,N): # c2_1..N every node assigned only to 1 route m.addConstr(Y_ik.sum(i, '*') == 1, "c1_i%d"%i) ## update the model and solve m._vars = Y_ik m.modelSense = GRB.MINIMIZE m.update() #m.write("gapvrp_model.lp") # disable output m.setParam('OutputFlag', 0) m.setParam('Threads', MIP_SOLVER_THREADS) # REMOVEME m.setParam('MIPFocus', 3) m.setParam('TimeLimit', MAX_MIP_SOLVER_RUNTIME) m.optimize() # restore SIGINT callback handler which is changed by gurobipy signal(SIGINT, default_int_handler) if __debug__: log(DEBUG-1,"Gurobi runtime = %.2f"%m.Runtime) if m.Status == GRB.OPTIMAL: return _decision_variables_to_assignments(m, Y_ik, N, K) elif m.Status == GRB.INFEASIBLE and L: # relax the model and allow violating minimal number of the approximate # route length constraints pens = [1.0]*len(approx_route_cost_constraints) m.feasRelax(1, True, None, None, None, approx_route_cost_constraints, pens) # TODO: not sure if feasRelax can change Status, test it someday if m.Status == GRB.INTERRUPTED: raise KeyboardInterrupt() # pass it on m.optimize() # restore SIGINT callback handler which is changed by gurobipy signal(SIGINT, default_int_handler) status = m.Status if __debug__: log(DEBUG-1, "Relaxed problem Gurobi runtime = %.2f"%m.Runtime) if status == GRB.OPTIMAL: return _decision_variables_to_assignments(m, Y_ik, N, K) elif status == GRB.TIME_LIMIT: raise GurobiError(10023, "Gurobi timeout reached when attempting to solve relaxed SCPCVRP") elif m.Status == GRB.INTERRUPTED: raise KeyboardInterrupt() # pass it on return None elif m.Status == GRB.TIME_LIMIT: raise GurobiError(10023, "Gurobi timeout reached when attempting to solve GAP") elif m.Status == GRB.INTERRUPTED: raise KeyboardInterrupt() # pass it on return None _Cone = namedtuple('_Cone', ['phi1', 'phi2', 'demand', 'nodes']) def _sweep_seed_points(points, D, d, C, K, trial=0): """A seed point generation function that implements the rule used in Fisher and Jaikumar (1981) to select the seed customers for the delivery cost approximation calculation in their VRP heuristic. It is assumed that all customers are located on a plane with euclidean distances D between them and that the truck capacity C is the the same for all vehicles. """ ## Assume planar case and convert to a sweep sweep = get_sweep_from_cartesian_coordinates(points) ## Append each of the K customer cones into K groups of concecutive cones if C: alpha = sum(d)/float(K*C) group_target = alpha*C # = sum(d)/K EPS = C_EPS else: #only L set? total_sweep_len = sum( D[int(sweep[2][i-1]),int(sweep[2][i])] for i in range(len(sweep[2])) ) group_target = total_sweep_len/K EPS = S_EPS if __debug__: log(DEBUG-2,"Cone group demand/cost target = %.2f"%group_target ) #for start_cone in range(len(cones)): start_cone_idx = trial grouped_cones = [] group_start_ray = None group_end_ray = None group_cum = 0.0 group_nodes = [] prev_node_i = None prev_node_rho = None prev_node_phi = sweep[0][start_cone_idx-1] if start_cone_idx==0: prev_node_phi-=2*pi prev_ray = None # iterate over all (phi,rho,node_idx) staring from start_cone_idx # and doing it twice for circle_view in (sweep.T[start_cone_idx:], sweep.T[:start_cone_idx+1]): for node_phi,node_rho,i in circle_view: i = int(i) # is numpy float if (node_phi<prev_node_phi): node_phi+=2*pi ray = bisect_angle(prev_node_phi,node_phi) if prev_ray is None: group_start_ray = ray if __debug__: log(DEBUG-2,"First node %d cone sets group_start_ray=%.2f"%(i,group_start_ray)) else: # calculate if the entire cone (~customer) can be added to the group # or if only a fraction is needed to fill the group. if C: cone_fraction = 1.0 if d[prev_node_i]!=0: cone_fraction = min(1.0, (group_target-group_cum)/d[prev_node_i]) cone_wt = cone_fraction*d[prev_node_i] else: cone_fraction = min(1.0, (group_target-group_cum)/(D[prev_node_i,i])) cone_wt = cone_fraction*D[prev_node_i,i] group_cum+=cone_wt group_nodes.append( (prev_node_rho,prev_node_i, d[prev_node_i] if C else D[prev_node_i,i]) ) if __debug__: if C: log(DEBUG-3,"Node %d, added %.2f %% of demand (%.2f)" %\ (prev_node_i, cone_fraction*100, d[prev_node_i])) else: log(DEBUG-3,"Node %d, added %.2f %% of cost (%.2f)" %\ (prev_node_i, cone_fraction*100, 0.5*D[prev_node_i,i])) log(DEBUG-2,"Group %.2f %% full"%\ (group_cum/group_target*100.0)) if (group_target-group_cum)<EPS: group_end_ray = bisect_angle(prev_ray, ray, cone_fraction) # group is full, store it grouped_cones.append( _Cone(group_start_ray,group_end_ray, group_cum, group_nodes) ) if __debug__: log(DEBUG-2,"Node %d cone sets group_end_ray=%.2f"%\ (prev_node_i,group_end_ray)) log(DEBUG-2,"Group completed!\n") # next group group_start_ray = group_end_ray group_nodes = [] group_cum = 0 if cone_fraction<1.0: if C: rmdr_wt = (1.0-cone_fraction)*d[prev_node_i] else: rmdr_wt = (1.0-cone_fraction)*D[prev_node_i,i] group_cum += rmdr_wt group_nodes.append((prev_node_rho,prev_node_i, d[prev_node_i] if C else D[prev_node_i,i])) if __debug__: if len(grouped_cones)<K: log(DEBUG-2,"Node %d cone sets group_start_ray=%.2f"%\ (prev_node_i,group_start_ray)) # the group now spans upto this group_end_ray = ray if __debug__: if len(grouped_cones)<K: log(DEBUG-2,"Node %d cone grows group to ray=%.2f"%\ (prev_node_i,group_end_ray)) prev_ray = ray prev_node_i = i prev_node_rho = node_rho prev_node_phi = node_phi ## get seed form the resulting K merged cones seed_points = np.zeros((K,2), dtype=np.float64) depot_x = points[0][0] depot_y = points[0][1] for k, grouped_cone in enumerate(grouped_cones): if __debug__: log(DEBUG-3," ===========================================") log(DEBUG-3," #%d %s"%(k, str(grouped_cone))) log(DEBUG-3," ===========================================\n") # Find an arc that splits the k-cone in a way that the linear demand # under the arc is "around" 0.75 (the exact definition is in the # Fisher & Jaikumar (1981) paper. Begin by sorting by distance from # the depot and grow arc as long as weight sum is under the limit. seed_rho = 0 grow_arc_wt = 0 weight_target = 0.75*group_target # 0.75{\labmda}b for cr,ci,cwt in sorted(grouped_cone.nodes): if grow_arc_wt+cwt>weight_target: # take a fraction of the weight just outside the arc seed_rho+=((weight_target-grow_arc_wt)/cwt)*(cr-seed_rho) break else: grow_arc_wt+=cwt seed_rho=cr # Calculate the actual seed point position seed_phi = bisect_angle(grouped_cone.phi1,grouped_cone.phi2) seed_points[k,0] = depot_x+seed_rho*np.cos(seed_phi) seed_points[k,1] = depot_y+seed_rho*np.sin(seed_phi) return seed_points.tolist() def _kmeans_seed_points(points, D, d, C, K, trial=0): """A seed point generation function that puts the seed points at customer node point cluster centers using k-Means clustering.""" from sklearn.cluster import KMeans kmeans = KMeans(n_clusters=K, random_state=trial).fit(points[1:]) return kmeans.cluster_centers_.tolist() def _end_of_thoroughfares_seed_points(points, D, d, C, K, trial=0): """A seed point generation function that automates the human assisted idea presented in Fisher and Jaikumar (1981) involving placing the seed points to the end of throughtfares leaving from the depot. A DBSCAN clustering is made and the seeds are selected among non-core points. Non- core points should be, due to the operating principle of DBSCAN, at the ends of long cluster "arms". By selecting the non-core points farthest from the depot and previously selected seeds, we should get a set of seed points closely following the Fisher and Jaikumar (1981) idea: "customers often lie along radial corridors corresponding to major thoroughfares, and the most distant ... along these corridors are natural seed customers". Fisher and Jaikumar (1981) presented the idea interactive computer systems in mind, whereas this implementation is automatic. TODO: in practice, the results are underwhelming. Instead, one should do 1d clustering for phis and then choose the farthest point of each "Sweep cluster". parameters: - points, D, d, C, K as before - trial can be used to get different clusterings from the DBSCAN algorithm. the DBSCAN min_size is 2,2,3,3,4,4,... for trial 0,1,2,3,4,5... . The inititial eps is determined by getting the median distance of the nn=2., 3., 2., 3., 3., 4., 3,... nearest neightbour of all nodes depending if the trial is 0,1,2,3,4,5,6,7.. following the formula nn=2+trial%2+int(trial/4)) The seed points are selected among the non-core points S_nc by maximizing the squared distances . If it happens that |S_nc|<K, all non-core points are included and the rest of the seed points clustered points are enough non-core points are found. WARNING: This seed heuristic may return None seeds as the existence of non- core points cannot be guranteed. """ from sklearn.cluster import DBSCAN from util import produce_nn_list # use a heuristic to get eps that finds all 2. closest nodes and # uses the median distance of those as the eps N = len(d) nnD = produce_nn_list(D) nn = 2+trial%2+int(trial/4) nn2l = [nnS[nn][0] for nnS in nnD] nn2l.sort() min_size = 3#+int(trial/2) eps = nn2l[int(N/2)] ## Get non-core DBSCAN points if __debug__: log(DEBUG-2,"Doing DBSCAN with eps =", eps, " min_size =",min_size) db = DBSCAN(eps=eps, min_samples=min_size).fit(points) outliers_mask = db.labels_ == -1 clustered_mask = db.labels_ != -1 core_samples_mask = np.zeros(N, dtype=bool) core_samples_mask[db.core_sample_indices_] = True # we are interested of the nodes at the fringes of the clusters candidate_mask = clustered_mask^core_samples_mask candidate_idxs = np.where(candidate_mask)[0].tolist() candidates_type = "cluster non-core" if __debug__: log(DEBUG-3,"DBSCAN labels = %s"%str(zip(range(N),db.labels_))) log(DEBUG-3,"DBSCAN core = %s"%str(db.core_sample_idxs_)) log(DEBUG-2,"Select %d seed nodes from non-core nodes %s."% (min(len(candidate_idxs),K), str(candidate_idxs))) seeds = [] selected_seeds_mask = np.zeros(N, dtype=bool) # make depot like a seed -> maximize distance from it selected_seeds_mask[0] = True if len(candidate_idxs)<=K: # if all candidates are needed, add them without checking the distances for seed_idx in candidate_idxs: seeds.append( points[seed_idx] ) if __debug__: log(DEBUG-2,"Selecting n%d (%.2f, %.2f) that is a %s point to be a seed"% (seed_idx,points[seed_idx][0],points[seed_idx][1],candidates_type)) selected_seeds_mask[seed_idx] = True candidate_idxs = [] used_core_points = False while len(seeds)<K: if not candidate_idxs: if not used_core_points: # ran out of non-core candidates. Use clustered as candidates candidate_mask = core_samples_mask candidate_idxs = np.where(core_samples_mask)[0].tolist() candidates_type = "cluster core" used_core_points = True if __debug__: log(DEBUG-3,"Ran out of non-core nodes, select %d seed nodes from core nodes %s"% (min(len(candidate_idxs), K-len(seeds)), str(candidate_idxs))) else: candidate_mask = outliers_mask candidate_idxs = np.where(outliers_mask)[0].tolist() candidates_type = "outliers" if __debug__: log(DEBUG-3, "Ran out of core and non-core nodes, select %d seed nodes from outlier nodes %s"% (K-len(seeds), str(candidate_idxs))) # maximize the distance to other seeds and depot if not seeds: D_to_seeds = D[selected_seeds_mask,candidate_mask] else: D_to_seeds = np.sum( np.sqrt((D[selected_seeds_mask,:])[:,candidate_mask]), axis=0) seed_idx = candidate_idxs[np.argmax( D_to_seeds )] selected_seeds_mask[seed_idx] = True seeds.append( points[seed_idx] ) if __debug__: log(DEBUG-2, "Selecting n%d (%.2f, %.2f) that is a %s point to be a seed"% (seed_idx,points[seed_idx][0],points[seed_idx][1], candidates_type)) # prevent selecting it again candidate_mask[seed_idx] = False candidate_idxs.remove(seed_idx) return seeds def _large_demand_seed_points(points, D, d, C, K, trial=0): """A seed point generation function that automates the human assisted idea presented in Fisher and Jaikumar (1981) """ # make sure we are dealing with np arrays here np_d = np.array(d) N = len(d) # we are look mainly the large d nodes where only 1 fits on a route can_fit_only_1_mask = np_d > (0.5*C) candidate_d_mask = can_fit_only_1_mask.copy() candidate_d_idxs = np.where(can_fit_only_1_mask)[0].tolist() if trial: # in addition, add as many OTHER largest d ones as trial is not_over_half_idxs = np.where( ~candidate_d_mask )[0].tolist() sorted_d = [(d[i], i) for i in not_over_half_idxs] sorted_d.sort(reverse=True) sorted_d_idxs = list(zip(*sorted_d)[1]) additional_large_d_idxs = sorted_d_idxs[max(0, trial-N):min(N,trial)] candidate_d_idxs+=additional_large_d_idxs candidate_d_mask[additional_large_d_idxs] = True large_d_mask = np.copy(candidate_d_mask) if __debug__: log(DEBUG-2, "Select %d seed nodes from large demand nodes %s"% (min(len(candidate_d_idxs),K), str(candidate_d_idxs))) seeds = [] selected_seeds_mask = np.zeros(len(d), dtype=bool) # make depot like a seed -> maximize distance from it selected_seeds_mask[0] = True if len(candidate_d_idxs)<=K: # if all candidates are needed, add them without checking the distances for seed_idx in candidate_d_idxs: seeds.append( points[seed_idx] ) selected_seeds_mask[seed_idx] = True if __debug__: log(DEBUG-2,"Selecting n%d (%.2f, %.2f) that %s to be a seed"%\ (seed_idx,points[seed_idx][0],points[seed_idx][1], "fills over the half of the capacity" if can_fit_only_1_mask[seed_idx] else "is within "+str(trial)+" largest demands")) candidate_d_idxs = [] select_from_non_large = False while len(seeds)<K: if not candidate_d_idxs: candidate_d_mask = ~large_d_mask candidate_d_mask[0]=False candidate_d_idxs = np.where(candidate_d_mask)[0].tolist() select_from_non_large = True if __debug__: log(DEBUG-2,"Ran out of nodes with large demand, select %d seed nodes from rest of the nodes %s using inter seed distances weighted by the node demand"% (min(len(candidate_d_idxs), K-len(seeds)), str(candidate_d_idxs))) # maximize the distance to other seeds and depot if not seeds: D_to_seeds = D[selected_seeds_mask,candidate_d_mask] else: D_to_seeds = np.sum( np.sqrt((D[selected_seeds_mask,:])[:,candidate_d_mask]), axis=0) if select_from_non_large: # multiply by demand D_to_seeds = np.multiply(D_to_seeds,np_d[candidate_d_mask]/C) seed_idx = candidate_d_idxs[np.argmax( D_to_seeds )] selected_seeds_mask[seed_idx] = True seeds.append( points[seed_idx] ) if __debug__: if can_fit_only_1_mask[seed_idx]: candidates_type = "fills over the half of the capacity" elif large_d_mask[seed_idx]: candidates_type = "is within "+str(trial)+" largest demands" else: candidates_type = "when weighted by demand has largest distance from other seeds" log(DEBUG-2,"Selecting a node n%d (%.2f, %.2f) that %s to be a seed"%\ (seed_idx,points[seed_idx][0],points[seed_idx][1], candidates_type)) # prevent selecting it again candidate_d_mask[seed_idx] = False candidate_d_idxs.remove(seed_idx) return seeds def gap_init(points, D, d, C, L=None, st=None, K=None, minimize_K=True, find_optimal_seeds=True, seed_method="cones", seed_edge_weight_type='EUC_2D', use_adaptive_L_constraint_weights=True, increase_K_on_failure=False): #REMOVEME, disable! #increase_K_on_failure=True): """ An implementation of a three phase cluster-first-route-second CVRP construction / route initialization algorithm. The first two phases involve the clustering. First, a seed point is generated for each route, which is then used in approximating customer node service costs in solving generalized assignment problem (GAP) relaxation of the VRP. The resulting assignments are then routed using a TSP solver. The algorithm has been first proposed in (Fisher and Jaikumar 1981). The algorithm assumes that the problem is planar and this implementation allows seed in two ways: * seed_method="cones", the initialization method of Fisher and Jaikumar (1981) which can be described as Sweep with fractional distribution of customer demand and placing the seed points approximately to the center of demand mass of created sectors. * seed_method="kmeans", intialize seed points to k-means cluster centers. * seed_method="large_demands", according to Fisher and Jaikumar (1981) "Customers for which d_i > 1/2 C can also be made seed customers". However applying this rule relies on human operator who then decides the intuitively best seed points. This implementation selects the seed points satisfying the criteria d_i>mC, where m is the fractional capacity multipier, that are farthest from the depot and each other. The m is made iteratively smaller if there are no at least K seed point candidates. * seed_method="ends_of_thoroughfares", this option was descibed in (Fisher and Jaikumar 1981) as "Most distant customers at the end of thoroughfares leaving from the depot are natural seed customers". They relied on human operator. To automate this selection we make a DBSCAN clustering with eps = median 2. nearest neighbor of all nodes and min_samples of 3. The other parameters are: * points is a list of x,y coordinates of the depot [0] and the customers. * D is a numpy ndarray (or equvalent) of the full 2D distance matrix. including the service times (st/2.0 for leaving and entering nodes). * d is a list of demands. d[0] should be 0.0 as it is the depot. * C is the capacity constraint limit for the identical vehicles. * L is the optional constraint for the maximum route length/duration/cost. * st is the service time. However, also the D should be modified with service times to allow straight computation of the TSP solutions (see above) * K is the optional parameter specifying the required number of vehicles. The algorithm is only allowed to find solutions with this many vehicles. * minimize_K, if set to True (default), makes the minimum number of routes the primary and the solution cost the secondary objective. If set False the algorithm optimizes for mimimum solution / route cost by increasing K as long as it seems beneficial. WARNING: the algorithm suits this use case (cost at the objective) poorly and setting this option to False may significantly increase the required CPU time. * find_optimal_seeds if set to True, tries all possible Sweep start positions / k-Means with N different seeds. If False, only one sweep from the node closest to the depot is done / k-Means clustering is done only once with one random seed value. * seed_edge_weight_type specifies how to round off the distances from the customer nodes (points) to the seed points. Supports all TSPLIB edge weight types. Note1: The GAP is optimized using Gurobi solver. If L constraint is set, the side constraints may make the GAP instance tricky to solve and it is advisable to set a sensible timeout with config.MAX_MIP_SOLVER_RUNTIME * use_adaptive_L_constraint_weights if set True, and the L constraint is set, the algorithm adaptively adjusts the route cost approximation of the relevant side constraints so that a solution which is not L infeasible or GAP infeasible is found. The exact handling of L consraint is vague in (Fisher and Jaikumar 1981) and this was our best guess on how the feasible region of the problem can be found. Note that if GAP solver is terminated due to a timeout, the adaptive multipier is increased and GAP solution is attempted again. However, if increase_K_on_failure is set, (see below) it takes priority over this. * increase_K_on_failure (default False) is another countermeasure against long running GAP solving attempts for problem instances without L constraint (if there is L constraint, and use_adaptive_L_constraint_- weights is enabled, this is ignored) or instances where K estimation does not work and it takes excessively long time to check all initial seed configurations before increasing K. If Gurobi timeout is encountered or the solution is GAP infeasible, and this option is enabled, the K is temporately increased, new seeds points generated for current sweep start location and another GAP solution attempt is made. K is allowed to increased temporarely up to 10% of the mimimum K allowed (or 1, whichever is larger). Note2: logger controls the debug level but running the script with Python -O option disables all debug output. Fisher, M. L. and Jaikumar, R. (1981), A generalized assignment heuristic for vehicle routing. Networks, 11: 109-124. doi:10.1002/net.3230110205 """ #TODO: other alternatives # customers with maximum demand or most distant customer from origin if seed_method=="cones": seed_f = _sweep_seed_points if seed_method=="kmeans": seed_f = _kmeans_seed_points if seed_method=="large_demands": if not C: raise ValueError("""The "large_demands" seed initialization method requires demands and C constraint to be known.""") seed_f = _large_demand_seed_points if seed_method=="ends_of_thoroughfares": seed_f = _end_of_thoroughfares_seed_points int_dists = issubclass(D.dtype.type, np.integer) if seed_edge_weight_type=="EXPLICIT": seed_edge_weight_type = "EUC_2D" if int_dists else "EXACT_2D" if not points: raise ValueError("The algorithm requires 2D coordinates for the points") N = len(D) if K: startK = K maxK = K else: # start from the smallest K possible if C: startK = int(ceil(sum(d)/C)) elif L: # find a lower bound by checking how many visits from the TSP # tour need to add to have any chance of making this L feasible. _,tsp_f = solve_tsp(D, range(1,N)) shortest_depot_edges = list(D[0,1:]) shortest_depot_edges.sort() startK = int(ceil(tsp_f/L)) while True: if tsp_f+sum(shortest_depot_edges[:startK*2])<=startK*L: break startK+=1 else: raise ValueError("If C and L have not been set, K is required") maxK = N-1 # We only need first row of the distance matrix to calculcate insertion # costs for GAP objective function D_0 = np.copy( D[0,:] ) best_sol = None best_f = None best_K = None seed_trial = 0 incK = 0 maxKinc = max(startK+1, int(startK*INCREASE_K_ON_FAILURE_UPTO)) L_ctr_multipiler = L_MPLR_DEFAULT if L and use_adaptive_L_constraint_weights: # Adaptive L constraint multipier L_ctr_multipiler = L_ADAPTIVE_MPLR_INIT L_ctr_multipiler_tries = 0 try: for currentK in range(startK, maxK+1): found_improving_solution_for_this_K = False seed_trial=0 while True: if __debug__: log(DEBUG, "ITERATION:K=%d, trial=%d, L_ctr_mul=%.6f\n"% (currentK+incK,seed_trial,L_ctr_multipiler)) log(DEBUG-1, "Getting %d seed points...\n"%(currentK+incK)) # Get seed points seed_points = seed_f(points, D, d, C, currentK+incK, seed_trial) if __debug__: log(DEBUG-1, "...got seed points %s\n"%str(seed_points)) # Extend the distance matrix with seed distances S = calculate_D(seed_points, points, seed_edge_weight_type) if st: # include the "leaving half" of the service_time in the # distances (the other half is already added to the D # prior to gapvrp_init) halftst = int(st/2) if int_dists else st/2.0 S[:,1:] += halftst D_s = np.vstack( (D_0, S) ) GAP_infeasible = False L_infeasible = False solution = [0] sol_f = 0 solved = False sol_K = 0 take_next_seed = False try: # Distribute the nodes to vehicles using the approxmate # service costs in D_s and by solving it as GAP # #TODO: the model has the same dimensions for all iterations # with the same K and only the weights differ. Consider # replacing the coefficient matrix e.g. via C interface #https://stackoverflow.com/questions/33461329 assignments = _solve_gap(N, D_s, d, C, currentK+incK, L, L_ctr_multipiler) if not assignments: if __debug__: log(DEBUG, "INFEASIBILITY: GAP infeasible solution") corrective_action = "try with another seed = %d"%seed_trial GAP_infeasible = True else: if __debug__: log(DEBUG-1, "Assignments = %s"%str(assignments)) # Due to floating point inaccuracies in L constrained # cases the feasrelax may be used, which, in turn, can # in some corner cases return solutions that are not # really feasible. Make sure it is not the case if L: served = set([0]) for route_nodes in assignments: if not route_nodes: continue route,route_l = solve_tsp(D, [0]+route_nodes) # Check for feasibility violations due to feasrelax if L: served |= set(route_nodes) if C and d and totald(route,d)-C_EPS>C: if __debug__: log(DEBUG, "INFEASIBILITY: feasRelax "+ "caused GAP infeasible solution "+ " (capacity constraint violation)") GAP_infeasible = True break # the route loop solution += route[1:] sol_f += route_l sol_K += 1 if __debug__: log(DEBUG-2, "DEBUG: Got TSP solution %s (%.2f)"% (str(route),route_l)) if L and route_l-S_EPS>L: if __debug__: log(DEBUG, "INFEASIBILITY: L infeasible solution") L_infeasible = True break # break route for loop # Check for feasibility violations due to feasrelax. # Have all customers been served? if not GAP_infeasible and not L_infeasible and\ L and len(served)<len(D): if __debug__: log(DEBUG, "INFEASIBILITY: feasRelax caused GAP "+ "infeasible solution (all customers "+ "are not served)") GAP_infeasible = True if not GAP_infeasible and not L_infeasible: if __debug__: log(DEBUG, "Yielded feasible solution = %s (%.2f)"%(str(solution), sol_f)) solved = True except GurobiError as grbe: if __debug__: log(WARNING, str(grbe)) if L and use_adaptive_L_constraint_weights and \ L_ctr_multipiler_tries<L_ADAPTIVE_MPLR_MAX_TRIES: L_ctr_multipiler+=L_ADAPTIVE_MPLR_INC L_ctr_multipiler_tries+=1 if __debug__: corrective_action = "Gurobi timeout, try with another L_ctr_multipiler = %.2f"%L_ctr_multipiler elif increase_K_on_failure and currentK+incK+1<=maxKinc: if L and use_adaptive_L_constraint_weights and\ L_ctr_multipiler_tries>=L_ADAPTIVE_MPLR_MAX_TRIES: # try with all multiplier values for larger K L_ctr_multipiler = L_ADAPTIVE_MPLR_INIT L_ctr_multipiler_tries = 0 incK+=1 if __debug__: corrective_action = "Gurobi timeout, temporarely increase K by %d"%incK elif find_optimal_seeds: take_next_seed = True else: grbe.message+=", consider increasing the MAX_MIP_SOLVER_RUNTIME in config.py" raise grbe else: if L and use_adaptive_L_constraint_weights: ## Adaptive GAP/L constraint multiplier reset # reset multiplier in case it the L feasibility was not violated # or it has reached the max_value. if solved or L_ctr_multipiler_tries>=L_ADAPTIVE_MPLR_MAX_TRIES: L_ctr_multipiler = L_ADAPTIVE_MPLR_INIT L_ctr_multipiler_tries = 0 take_next_seed = True if not solved and increase_K_on_failure and currentK+incK+1<=maxKinc: incK+=1 take_next_seed = False if __debug__: corrective_action = "temporarely increase K by %d"%incK else: if __debug__: corrective_action = "try with another seed = %d"%seed_trial ## Adaptive GAP/L constraint multiplier update else: L_ctr_multipiler+=L_ADAPTIVE_MPLR_INC L_ctr_multipiler_tries+=1 if __debug__: corrective_action = "try with another L_ctr_multipiler = %.2f"%L_ctr_multipiler else: if not solved and increase_K_on_failure and currentK+incK+1<=maxKinc: incK+=1 if __debug__: corrective_action = "temporarely increase K by %d"%incK else: take_next_seed = True # Store the best so far if solved: if is_better_sol(best_f, best_K, sol_f, sol_K, minimize_K): best_sol = solution best_f = sol_f best_K = sol_K found_improving_solution_for_this_K = True else: # No feasible solution was found for this trial (max route cost # or capacity constraint was violated). if __debug__: if GAP_infeasible or L_infeasible: log(DEBUG, "Constraint is violated, "+corrective_action) else: log(DEBUG, "Continuing search, "+corrective_action) if take_next_seed: incK = 0 seed_trial+=1 if not find_optimal_seeds: break # seed loop, possibly try next K if seed_trial==N: incK = 0 break # seed loop, possibly try next K if minimize_K: # do not try different K if we found a solution if best_sol: break # K loop else: # not minimize_K # We already have an feasible solution for K<K_current, and could # not find a better solution than that on K_current. Therefore, it # is improbable we will find one even if we increase K and we # should stop here. if best_sol and not found_improving_solution_for_this_K: break except KeyboardInterrupt: #or SIGINT # pass on the current best_sol raise KeyboardInterrupt(best_sol) return best_sol # --------------------------------------------------------------------- # Wrapper for the command line user interface (CLI) def get_gap_algorithm(seed_method="cones"): algo_name = "FJ81-GAP" algo_desc = "Fisher & Jaikumar (1981) generalized assignment problem heuristic" def call_init(points, D, d, C, L, st, wtt, single, minimize_K): return gap_init(points, D, d, C, L=L, st=st, K=None, minimize_K=minimize_K, seed_edge_weight_type=wtt, find_optimal_seeds=(not single), seed_method=seed_method) return (algo_name, algo_desc, call_init) if __name__=="__main__": from shared_cli import cli cli(*get_gap_algorithm())

47.327331

168

0.583298

emand (%.2f)" %\ (prev_node_i, cone_fraction*100, d[prev_node_i])) else: log(DEBUG-3,"Node %d, added %.2f %% of cost (%.2f)" %\ (prev_node_i, cone_fraction*100, 0.5*D[prev_node_i,i])) log(DEBUG-2,"Group %.2f %% full"%\ (group_cum/group_target*100.0)) if (group_target-group_cum)<EPS: group_end_ray = bisect_angle(prev_ray, ray, cone_fraction) grouped_cones.append( _Cone(group_start_ray,group_end_ray, group_cum, group_nodes) ) if __debug__: log(DEBUG-2,"Node %d cone sets group_end_ray=%.2f"%\ (prev_node_i,group_end_ray)) log(DEBUG-2,"Group completed!\n") group_start_ray = group_end_ray group_nodes = [] group_cum = 0 if cone_fraction<1.0: if C: rmdr_wt = (1.0-cone_fraction)*d[prev_node_i] else: rmdr_wt = (1.0-cone_fraction)*D[prev_node_i,i] group_cum += rmdr_wt group_nodes.append((prev_node_rho,prev_node_i, d[prev_node_i] if C else D[prev_node_i,i])) if __debug__: if len(grouped_cones)<K: log(DEBUG-2,"Node %d cone sets group_start_ray=%.2f"%\ (prev_node_i,group_start_ray)) group_end_ray = ray if __debug__: if len(grouped_cones)<K: log(DEBUG-2,"Node %d cone grows group to ray=%.2f"%\ (prev_node_i,group_end_ray)) prev_ray = ray prev_node_i = i prev_node_rho = node_rho prev_node_phi = node_phi float64) depot_x = points[0][0] depot_y = points[0][1] for k, grouped_cone in enumerate(grouped_cones): if __debug__: log(DEBUG-3," ===========================================") log(DEBUG-3," #%d %s"%(k, str(grouped_cone))) log(DEBUG-3," ===========================================\n") seed_rho = 0 grow_arc_wt = 0 weight_target = 0.75*group_target for cr,ci,cwt in sorted(grouped_cone.nodes): if grow_arc_wt+cwt>weight_target: seed_rho+=((weight_target-grow_arc_wt)/cwt)*(cr-seed_rho) break else: grow_arc_wt+=cwt seed_rho=cr seed_phi = bisect_angle(grouped_cone.phi1,grouped_cone.phi2) seed_points[k,0] = depot_x+seed_rho*np.cos(seed_phi) seed_points[k,1] = depot_y+seed_rho*np.sin(seed_phi) return seed_points.tolist() def _kmeans_seed_points(points, D, d, C, K, trial=0): from sklearn.cluster import KMeans kmeans = KMeans(n_clusters=K, random_state=trial).fit(points[1:]) return kmeans.cluster_centers_.tolist() def _end_of_thoroughfares_seed_points(points, D, d, C, K, trial=0): from sklearn.cluster import DBSCAN from util import produce_nn_list N = len(d) nnD = produce_nn_list(D) nn = 2+trial%2+int(trial/4) nn2l = [nnS[nn][0] for nnS in nnD] nn2l.sort() min_size = 3 eps = nn2l[int(N/2)] EBUG-2,"Doing DBSCAN with eps =", eps, " min_size =",min_size) db = DBSCAN(eps=eps, min_samples=min_size).fit(points) outliers_mask = db.labels_ == -1 clustered_mask = db.labels_ != -1 core_samples_mask = np.zeros(N, dtype=bool) core_samples_mask[db.core_sample_indices_] = True candidate_mask = clustered_mask^core_samples_mask candidate_idxs = np.where(candidate_mask)[0].tolist() candidates_type = "cluster non-core" if __debug__: log(DEBUG-3,"DBSCAN labels = %s"%str(zip(range(N),db.labels_))) log(DEBUG-3,"DBSCAN core = %s"%str(db.core_sample_idxs_)) log(DEBUG-2,"Select %d seed nodes from non-core nodes %s."% (min(len(candidate_idxs),K), str(candidate_idxs))) seeds = [] selected_seeds_mask = np.zeros(N, dtype=bool) selected_seeds_mask[0] = True if len(candidate_idxs)<=K: for seed_idx in candidate_idxs: seeds.append( points[seed_idx] ) if __debug__: log(DEBUG-2,"Selecting n%d (%.2f, %.2f) that is a %s point to be a seed"% (seed_idx,points[seed_idx][0],points[seed_idx][1],candidates_type)) selected_seeds_mask[seed_idx] = True candidate_idxs = [] used_core_points = False while len(seeds)<K: if not candidate_idxs: if not used_core_points: candidate_mask = core_samples_mask candidate_idxs = np.where(core_samples_mask)[0].tolist() candidates_type = "cluster core" used_core_points = True if __debug__: log(DEBUG-3,"Ran out of non-core nodes, select %d seed nodes from core nodes %s"% (min(len(candidate_idxs), K-len(seeds)), str(candidate_idxs))) else: candidate_mask = outliers_mask candidate_idxs = np.where(outliers_mask)[0].tolist() candidates_type = "outliers" if __debug__: log(DEBUG-3, "Ran out of core and non-core nodes, select %d seed nodes from outlier nodes %s"% (K-len(seeds), str(candidate_idxs))) if not seeds: D_to_seeds = D[selected_seeds_mask,candidate_mask] else: D_to_seeds = np.sum( np.sqrt((D[selected_seeds_mask,:])[:,candidate_mask]), axis=0) seed_idx = candidate_idxs[np.argmax( D_to_seeds )] selected_seeds_mask[seed_idx] = True seeds.append( points[seed_idx] ) if __debug__: log(DEBUG-2, "Selecting n%d (%.2f, %.2f) that is a %s point to be a seed"% (seed_idx,points[seed_idx][0],points[seed_idx][1], candidates_type)) candidate_mask[seed_idx] = False candidate_idxs.remove(seed_idx) return seeds def _large_demand_seed_points(points, D, d, C, K, trial=0): np_d = np.array(d) N = len(d) can_fit_only_1_mask = np_d > (0.5*C) candidate_d_mask = can_fit_only_1_mask.copy() candidate_d_idxs = np.where(can_fit_only_1_mask)[0].tolist() if trial: not_over_half_idxs = np.where( ~candidate_d_mask )[0].tolist() sorted_d = [(d[i], i) for i in not_over_half_idxs] sorted_d.sort(reverse=True) sorted_d_idxs = list(zip(*sorted_d)[1]) additional_large_d_idxs = sorted_d_idxs[max(0, trial-N):min(N,trial)] candidate_d_idxs+=additional_large_d_idxs candidate_d_mask[additional_large_d_idxs] = True large_d_mask = np.copy(candidate_d_mask) if __debug__: log(DEBUG-2, "Select %d seed nodes from large demand nodes %s"% (min(len(candidate_d_idxs),K), str(candidate_d_idxs))) seeds = [] selected_seeds_mask = np.zeros(len(d), dtype=bool) selected_seeds_mask[0] = True if len(candidate_d_idxs)<=K: for seed_idx in candidate_d_idxs: seeds.append( points[seed_idx] ) selected_seeds_mask[seed_idx] = True if __debug__: log(DEBUG-2,"Selecting n%d (%.2f, %.2f) that %s to be a seed"%\ (seed_idx,points[seed_idx][0],points[seed_idx][1], "fills over the half of the capacity" if can_fit_only_1_mask[seed_idx] else "is within "+str(trial)+" largest demands")) candidate_d_idxs = [] select_from_non_large = False while len(seeds)<K: if not candidate_d_idxs: candidate_d_mask = ~large_d_mask candidate_d_mask[0]=False candidate_d_idxs = np.where(candidate_d_mask)[0].tolist() select_from_non_large = True if __debug__: log(DEBUG-2,"Ran out of nodes with large demand, select %d seed nodes from rest of the nodes %s using inter seed distances weighted by the node demand"% (min(len(candidate_d_idxs), K-len(seeds)), str(candidate_d_idxs))) if not seeds: D_to_seeds = D[selected_seeds_mask,candidate_d_mask] else: D_to_seeds = np.sum( np.sqrt((D[selected_seeds_mask,:])[:,candidate_d_mask]), axis=0) if select_from_non_large: D_to_seeds = np.multiply(D_to_seeds,np_d[candidate_d_mask]/C) seed_idx = candidate_d_idxs[np.argmax( D_to_seeds )] selected_seeds_mask[seed_idx] = True seeds.append( points[seed_idx] ) if __debug__: if can_fit_only_1_mask[seed_idx]: candidates_type = "fills over the half of the capacity" elif large_d_mask[seed_idx]: candidates_type = "is within "+str(trial)+" largest demands" else: candidates_type = "when weighted by demand has largest distance from other seeds" log(DEBUG-2,"Selecting a node n%d (%.2f, %.2f) that %s to be a seed"%\ (seed_idx,points[seed_idx][0],points[seed_idx][1], candidates_type)) candidate_d_mask[seed_idx] = False candidate_d_idxs.remove(seed_idx) return seeds def gap_init(points, D, d, C, L=None, st=None, K=None, minimize_K=True, find_optimal_seeds=True, seed_method="cones", seed_edge_weight_type='EUC_2D', use_adaptive_L_constraint_weights=True, increase_K_on_failure=False): if seed_method=="cones": seed_f = _sweep_seed_points if seed_method=="kmeans": seed_f = _kmeans_seed_points if seed_method=="large_demands": if not C: raise ValueError("""The "large_demands" seed initialization method requires demands and C constraint to be known.""") seed_f = _large_demand_seed_points if seed_method=="ends_of_thoroughfares": seed_f = _end_of_thoroughfares_seed_points int_dists = issubclass(D.dtype.type, np.integer) if seed_edge_weight_type=="EXPLICIT": seed_edge_weight_type = "EUC_2D" if int_dists else "EXACT_2D" if not points: raise ValueError("The algorithm requires 2D coordinates for the points") N = len(D) if K: startK = K maxK = K else: if C: startK = int(ceil(sum(d)/C)) elif L: _,tsp_f = solve_tsp(D, range(1,N)) shortest_depot_edges = list(D[0,1:]) shortest_depot_edges.sort() startK = int(ceil(tsp_f/L)) while True: if tsp_f+sum(shortest_depot_edges[:startK*2])<=startK*L: break startK+=1 else: raise ValueError("If C and L have not been set, K is required") maxK = N-1 D_0 = np.copy( D[0,:] ) best_sol = None best_f = None best_K = None seed_trial = 0 incK = 0 maxKinc = max(startK+1, int(startK*INCREASE_K_ON_FAILURE_UPTO)) L_ctr_multipiler = L_MPLR_DEFAULT if L and use_adaptive_L_constraint_weights: L_ctr_multipiler = L_ADAPTIVE_MPLR_INIT L_ctr_multipiler_tries = 0 try: for currentK in range(startK, maxK+1): found_improving_solution_for_this_K = False seed_trial=0 while True: if __debug__: log(DEBUG, "ITERATION:K=%d, trial=%d, L_ctr_mul=%.6f\n"% (currentK+incK,seed_trial,L_ctr_multipiler)) log(DEBUG-1, "Getting %d seed points...\n"%(currentK+incK)) seed_points = seed_f(points, D, d, C, currentK+incK, seed_trial) if __debug__: log(DEBUG-1, "...got seed points %s\n"%str(seed_points)) S = calculate_D(seed_points, points, seed_edge_weight_type) if st: halftst = int(st/2) if int_dists else st/2.0 S[:,1:] += halftst D_s = np.vstack( (D_0, S) ) GAP_infeasible = False L_infeasible = False solution = [0] sol_f = 0 solved = False sol_K = 0 take_next_seed = False try: assignments = _solve_gap(N, D_s, d, C, currentK+incK, L, L_ctr_multipiler) if not assignments: if __debug__: log(DEBUG, "INFEASIBILITY: GAP infeasible solution") corrective_action = "try with another seed = %d"%seed_trial GAP_infeasible = True else: if __debug__: log(DEBUG-1, "Assignments = %s"%str(assignments)) if L: served = set([0]) for route_nodes in assignments: if not route_nodes: continue route,route_l = solve_tsp(D, [0]+route_nodes) if L: served |= set(route_nodes) if C and d and totald(route,d)-C_EPS>C: if __debug__: log(DEBUG, "INFEASIBILITY: feasRelax "+ "caused GAP infeasible solution "+ " (capacity constraint violation)") GAP_infeasible = True break solution += route[1:] sol_f += route_l sol_K += 1 if __debug__: log(DEBUG-2, "DEBUG: Got TSP solution %s (%.2f)"% (str(route),route_l)) if L and route_l-S_EPS>L: if __debug__: log(DEBUG, "INFEASIBILITY: L infeasible solution") L_infeasible = True break if not GAP_infeasible and not L_infeasible and\ L and len(served)<len(D): if __debug__: log(DEBUG, "INFEASIBILITY: feasRelax caused GAP "+ "infeasible solution (all customers "+ "are not served)") GAP_infeasible = True if not GAP_infeasible and not L_infeasible: if __debug__: log(DEBUG, "Yielded feasible solution = %s (%.2f)"%(str(solution), sol_f)) solved = True except GurobiError as grbe: if __debug__: log(WARNING, str(grbe)) if L and use_adaptive_L_constraint_weights and \ L_ctr_multipiler_tries<L_ADAPTIVE_MPLR_MAX_TRIES: L_ctr_multipiler+=L_ADAPTIVE_MPLR_INC L_ctr_multipiler_tries+=1 if __debug__: corrective_action = "Gurobi timeout, try with another L_ctr_multipiler = %.2f"%L_ctr_multipiler elif increase_K_on_failure and currentK+incK+1<=maxKinc: if L and use_adaptive_L_constraint_weights and\ L_ctr_multipiler_tries>=L_ADAPTIVE_MPLR_MAX_TRIES: L_ctr_multipiler = L_ADAPTIVE_MPLR_INIT L_ctr_multipiler_tries = 0 incK+=1 if __debug__: corrective_action = "Gurobi timeout, temporarely increase K by %d"%incK elif find_optimal_seeds: take_next_seed = True else: grbe.message+=", consider increasing the MAX_MIP_SOLVER_RUNTIME in config.py" raise grbe else: if L and use_adaptive_L_constraint_weights: if solved or L_ctr_multipiler_tries>=L_ADAPTIVE_MPLR_MAX_TRIES: L_ctr_multipiler = L_ADAPTIVE_MPLR_INIT L_ctr_multipiler_tries = 0 take_next_seed = True if not solved and increase_K_on_failure and currentK+incK+1<=maxKinc: incK+=1 take_next_seed = False if __debug__: corrective_action = "temporarely increase K by %d"%incK else: if __debug__: corrective_action = "try with another seed = %d"%seed_trial L_ctr_multipiler+=L_ADAPTIVE_MPLR_INC L_ctr_multipiler_tries+=1 if __debug__: corrective_action = "try with another L_ctr_multipiler = %.2f"%L_ctr_multipiler else: if not solved and increase_K_on_failure and currentK+incK+1<=maxKinc: incK+=1 if __debug__: corrective_action = "temporarely increase K by %d"%incK else: take_next_seed = True if solved: if is_better_sol(best_f, best_K, sol_f, sol_K, minimize_K): best_sol = solution best_f = sol_f best_K = sol_K found_improving_solution_for_this_K = True else: if __debug__: if GAP_infeasible or L_infeasible: log(DEBUG, "Constraint is violated, "+corrective_action) else: log(DEBUG, "Continuing search, "+corrective_action) if take_next_seed: incK = 0 seed_trial+=1 if not find_optimal_seeds: break if seed_trial==N: incK = 0 break if minimize_K: if best_sol: break else: if best_sol and not found_improving_solution_for_this_K: break except KeyboardInterrupt: raise KeyboardInterrupt(best_sol) return best_sol def get_gap_algorithm(seed_method="cones"): algo_name = "FJ81-GAP" algo_desc = "Fisher & Jaikumar (1981) generalized assignment problem heuristic" def call_init(points, D, d, C, L, st, wtt, single, minimize_K): return gap_init(points, D, d, C, L=L, st=st, K=None, minimize_K=minimize_K, seed_edge_weight_type=wtt, find_optimal_seeds=(not single), seed_method=seed_method) return (algo_name, algo_desc, call_init) if __name__=="__main__": from shared_cli import cli cli(*get_gap_algorithm())

true

1c385a3f212c43b7b143336820cc95534fcd766b

1,761

py

Python

src/api/authentication/social_login_signup.py

ThaDeveloper/grind

fa90b65d12e6d9b3d658b132874801ecda08c57f

[ "MIT" ]

1

2019-11-06T22:26:26.000Z

src/api/authentication/social_login_signup.py

ThaDeveloper/grind

fa90b65d12e6d9b3d658b132874801ecda08c57f

[ "MIT" ]

5

2021-03-19T02:49:44.000Z

2021-06-10T19:13:00.000Z

src/api/authentication/social_login_signup.py

ThaDeveloper/grind

fa90b65d12e6d9b3d658b132874801ecda08c57f

[ "MIT" ]

null

import uuid from rest_framework.response import Response from rest_framework import status from api.models import User from api.helpers.get_response import custom_reponse class SocialAuth: """ Logs in/ registers a new social user. """ def social_login_signup(self, user_info, **kwargs): """ If user exists, authenticate user with their `social account` info else register user using their `social accounts` info. Returns: API access token and/or user data. """ try: user = User.objects.get(email=user_info.get('email')) token = user.token return custom_reponse('success', 200, toke=token, message='Logged in successfully.') except User.DoesNotExist: password = User.objects.make_random_password() user = User( username=str(user_info.get('first_name')) + str(uuid.uuid1().int)[: 3], email=user_info.get('email'), first_name=user_info.get('first_name'), last_name=user_info.get('last_name'), active=True) user.set_password(password) user.save() user_details = { 'id': user.id, 'username': user.username, 'email': user.email, 'first_name': user.first_name, 'last_name': user.last_name } token = user.token return Response({ 'status': 'success', 'token': token, 'data': user_details, 'message': 'Account created successfully ' }, status=status.HTTP_201_CREATED)

33.865385

74

0.545713

import uuid from rest_framework.response import Response from rest_framework import status from api.models import User from api.helpers.get_response import custom_reponse class SocialAuth: def social_login_signup(self, user_info, **kwargs): try: user = User.objects.get(email=user_info.get('email')) token = user.token return custom_reponse('success', 200, toke=token, message='Logged in successfully.') except User.DoesNotExist: password = User.objects.make_random_password() user = User( username=str(user_info.get('first_name')) + str(uuid.uuid1().int)[: 3], email=user_info.get('email'), first_name=user_info.get('first_name'), last_name=user_info.get('last_name'), active=True) user.set_password(password) user.save() user_details = { 'id': user.id, 'username': user.username, 'email': user.email, 'first_name': user.first_name, 'last_name': user.last_name } token = user.token return Response({ 'status': 'success', 'token': token, 'data': user_details, 'message': 'Account created successfully ' }, status=status.HTTP_201_CREATED)

true

1c385a8aa041e66422af67145f40f8881410313a

4,766

py

Python

gcloud/core/apis/drf/viewsets/utils.py

qqqqqie/bk-sops

f2e734c2cdac76f89d2e4f0fd7de36168e452141

[ "Apache-2.0" ]

881

2019-03-25T02:45:42.000Z

2022-03-30T09:10:49.000Z

gcloud/core/apis/drf/viewsets/utils.py

m0re-work/bk-sops

d03ba8a4ee0781c6daaf0dd38a7369dc82669f7d

[ "Apache-2.0" ]

3,303

2019-03-25T04:18:03.000Z

2022-03-31T11:52:03.000Z

gcloud/core/apis/drf/viewsets/utils.py

m0re-work/bk-sops

d03ba8a4ee0781c6daaf0dd38a7369dc82669f7d

[ "Apache-2.0" ]

395

2019-03-25T02:53:36.000Z

2022-03-31T08:37:28.000Z

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community Edition) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import logging from iam import Subject, Action, MultiActionRequest from iam.shortcuts import allow_or_raise_auth_failed from rest_framework import status from rest_framework.exceptions import ErrorDetail from rest_framework.response import Response from rest_framework.viewsets import GenericViewSet from gcloud import err_code from gcloud.iam_auth import IAMMeta, get_iam_client iam = get_iam_client() iam_logger = logging.getLogger("iam") class ApiMixin(GenericViewSet): EXEMPT_STATUS_CODES = {status.HTTP_204_NO_CONTENT} def finalize_response(self, request, response, *args, **kwargs): # 对rest_framework的Response进行统一处理 if isinstance(response, Response): if response.exception is True: error = response.data.get( "detail", ErrorDetail("Error from API exception", err_code.UNKNOWN_ERROR.code) ) response.data = {"result": False, "data": response.data, "code": error.code, "message": str(error)} elif response.status_code not in self.EXEMPT_STATUS_CODES: response.data = {"result": True, "data": response.data, "code": err_code.SUCCESS.code, "message": ""} return super(ApiMixin, self).finalize_response(request, response, *args, **kwargs) class IAMMixin: @staticmethod def iam_auth_check(request, action, resources): allow_or_raise_auth_failed( iam=iam, system=IAMMeta.SYSTEM_ID, subject=Subject("user", request.user.username), action=Action(action), resources=resources, ) def iam_get_instances_auth_actions(self, request, instances): helper = getattr(self, "iam_resource_helper", None) if not helper: return None # 1. collect resources resources_list = [] for instance in instances: resources_list.append(helper.get_resources(instance)) if not resources_list: return None # 2. make request request = MultiActionRequest( helper.system, helper.get_subject_for_alter_list(request, instances), [Action(action) for action in helper.actions], [], helper.get_environment_for_alter_list(request, instances), ) resource_actions_allowed = helper.iam.batch_resource_multi_actions_allowed(request, resources_list) iam_logger.debug( "[drf iam_get_instances_auth_actions] batch_resource_multi_actions_allowed request({}) result: {}".format( request.to_dict(), resource_actions_allowed ) ) # 3. assemble action allowed data auth_actions = dict() for instance in instances: rid = str(helper.get_resources_id(instance)) auth_actions[instance.id] = [ action for action, allowed in resource_actions_allowed.get(rid, {}).items() if allowed ] return auth_actions def iam_get_instance_auth_actions(self, request, instance): helper = getattr(self, "iam_resource_helper", None) if not helper: return None # 1. get resources resources = helper.get_resources(instance) # 2. make request request = MultiActionRequest( helper.system, helper.get_subject_for_alter_detail(request, instance), [Action(action) for action in helper.actions], resources, helper.get_environment_for_alter_detail(request, instance), ) actions_allowed = helper.iam.resource_multi_actions_allowed(request) iam_logger.debug( "[drf iam_get_instance_auth_actions] resource_multi_actions_allowed request({}) result: {}".format( request.to_dict(), actions_allowed ) ) # 3. assemble action allowed data auth_actions = [action for action, allowed in actions_allowed.items() if allowed] return auth_actions

38.128

118

0.672472

import logging from iam import Subject, Action, MultiActionRequest from iam.shortcuts import allow_or_raise_auth_failed from rest_framework import status from rest_framework.exceptions import ErrorDetail from rest_framework.response import Response from rest_framework.viewsets import GenericViewSet from gcloud import err_code from gcloud.iam_auth import IAMMeta, get_iam_client iam = get_iam_client() iam_logger = logging.getLogger("iam") class ApiMixin(GenericViewSet): EXEMPT_STATUS_CODES = {status.HTTP_204_NO_CONTENT} def finalize_response(self, request, response, *args, **kwargs): if isinstance(response, Response): if response.exception is True: error = response.data.get( "detail", ErrorDetail("Error from API exception", err_code.UNKNOWN_ERROR.code) ) response.data = {"result": False, "data": response.data, "code": error.code, "message": str(error)} elif response.status_code not in self.EXEMPT_STATUS_CODES: response.data = {"result": True, "data": response.data, "code": err_code.SUCCESS.code, "message": ""} return super(ApiMixin, self).finalize_response(request, response, *args, **kwargs) class IAMMixin: @staticmethod def iam_auth_check(request, action, resources): allow_or_raise_auth_failed( iam=iam, system=IAMMeta.SYSTEM_ID, subject=Subject("user", request.user.username), action=Action(action), resources=resources, ) def iam_get_instances_auth_actions(self, request, instances): helper = getattr(self, "iam_resource_helper", None) if not helper: return None resources_list = [] for instance in instances: resources_list.append(helper.get_resources(instance)) if not resources_list: return None request = MultiActionRequest( helper.system, helper.get_subject_for_alter_list(request, instances), [Action(action) for action in helper.actions], [], helper.get_environment_for_alter_list(request, instances), ) resource_actions_allowed = helper.iam.batch_resource_multi_actions_allowed(request, resources_list) iam_logger.debug( "[drf iam_get_instances_auth_actions] batch_resource_multi_actions_allowed request({}) result: {}".format( request.to_dict(), resource_actions_allowed ) ) auth_actions = dict() for instance in instances: rid = str(helper.get_resources_id(instance)) auth_actions[instance.id] = [ action for action, allowed in resource_actions_allowed.get(rid, {}).items() if allowed ] return auth_actions def iam_get_instance_auth_actions(self, request, instance): helper = getattr(self, "iam_resource_helper", None) if not helper: return None resources = helper.get_resources(instance) request = MultiActionRequest( helper.system, helper.get_subject_for_alter_detail(request, instance), [Action(action) for action in helper.actions], resources, helper.get_environment_for_alter_detail(request, instance), ) actions_allowed = helper.iam.resource_multi_actions_allowed(request) iam_logger.debug( "[drf iam_get_instance_auth_actions] resource_multi_actions_allowed request({}) result: {}".format( request.to_dict(), actions_allowed ) ) auth_actions = [action for action, allowed in actions_allowed.items() if allowed] return auth_actions

true

1c385d6490b25ea69dde00beb4f6ee638b4d42f2

2,032

py

Python

corus/sources/taiga/nplus1.py

Ilseyar/corus

61a4776f5e534469bb9df1e451b6a6d5fc0e991b

[ "MIT" ]

205

2019-05-01T07:38:01.000Z

2022-03-30T04:02:54.000Z

corus/sources/taiga/nplus1.py

Ilseyar/corus

61a4776f5e534469bb9df1e451b6a6d5fc0e991b

[ "MIT" ]

78

2019-04-29T06:53:53.000Z

2021-09-20T14:51:25.000Z

corus/sources/taiga/nplus1.py

Ilseyar/corus

61a4776f5e534469bb9df1e451b6a6d5fc0e991b

[ "MIT" ]

18

2019-06-19T09:56:10.000Z

2022-01-30T14:55:14.000Z

from datetime import datetime from .common import ( Author, Meta, load_tar_metas, load_tar_texts, patch_month, merge_metas, ) # {'author': 'Владимир Королев', # 'authorreaders': '', # 'authortexts': '', # 'date': '21 Янв. 2017', # 'magazine': '', # 'segment': 'nplus1', # 'source': 'https://nplus1.ru/news/2017/01/21/Asphaltene-3d', # 'tags': '', # 'textdiff': '5.2', # 'textid': '20170121Asphaltene-3d', # 'textname': '«Архипелаги» асфальтенов ощупали в 3D', # 'textregion': '', # 'textrubric': 'Наука', # 'time': '17:34'}, NPLUS1_MONTHS = { 'Янв.': 'Jan', 'Фев.': 'Feb', 'Март': 'Mar', 'Апр.': 'Apr', 'Май': 'May', 'Июнь': 'Jun', 'Июль': 'Jul', 'Авг.': 'Aug', 'Сен.': 'Sep', 'Окт.': 'Oct', 'Нояб.': 'Nov', 'Дек.': 'Dec', } def parse_metas(items): for item in items: id = item['textid'] timestamp, date, time = None, item['date'], item['time'] if date and time: timestamp = patch_month(date, NPLUS1_MONTHS) + time timestamp = datetime.strptime(timestamp, '%d %b %Y%H:%M') name = item['author'] or None author = Author(name=name) title = item['textname'] rubric = item['textrubric'] or None url = item['source'] yield Meta( id=id, timestamp=timestamp, author=author, title=title, rubric=rubric, url=url ) def load_taiga_nplus1_metas(path, offset=0, count=1): items = load_tar_metas(path, '*/newmetadata.csv', offset, count) return parse_metas(items) # home/tsha/NPlus1/texts/20150320drone.txt # home/tsha/NPlus1/texts/20150320nitrogen.txt # home/tsha/NPlus1/texts/20150320silica.txt def load_taiga_nplus1(path, metas=None, offset=1919488, count=7696): records = load_tar_texts(path, '*/texts/*.txt', offset, count) return merge_metas(records, metas) __all__ = [ 'load_taiga_nplus1_metas', 'load_taiga_nplus1' ]

22.577778

69

0.572835

from datetime import datetime from .common import ( Author, Meta, load_tar_metas, load_tar_texts, patch_month, merge_metas, ) NPLUS1_MONTHS = { 'Янв.': 'Jan', 'Фев.': 'Feb', 'Март': 'Mar', 'Апр.': 'Apr', 'Май': 'May', 'Июнь': 'Jun', 'Июль': 'Jul', 'Авг.': 'Aug', 'Сен.': 'Sep', 'Окт.': 'Oct', 'Нояб.': 'Nov', 'Дек.': 'Dec', } def parse_metas(items): for item in items: id = item['textid'] timestamp, date, time = None, item['date'], item['time'] if date and time: timestamp = patch_month(date, NPLUS1_MONTHS) + time timestamp = datetime.strptime(timestamp, '%d %b %Y%H:%M') name = item['author'] or None author = Author(name=name) title = item['textname'] rubric = item['textrubric'] or None url = item['source'] yield Meta( id=id, timestamp=timestamp, author=author, title=title, rubric=rubric, url=url ) def load_taiga_nplus1_metas(path, offset=0, count=1): items = load_tar_metas(path, '*/newmetadata.csv', offset, count) return parse_metas(items) def load_taiga_nplus1(path, metas=None, offset=1919488, count=7696): records = load_tar_texts(path, '*/texts/*.txt', offset, count) return merge_metas(records, metas) __all__ = [ 'load_taiga_nplus1_metas', 'load_taiga_nplus1' ]

true

1c385f91a9b3b361993612243a63eebb6335184b

6,325

py

Python

tests.py

badalraina31/snappass

f16106acc7cada1b3f77fddd312faa963e0b6e20

[ "MIT" ]

null

tests.py

badalraina31/snappass

f16106acc7cada1b3f77fddd312faa963e0b6e20

[ "MIT" ]

null

tests.py

badalraina31/snappass

f16106acc7cada1b3f77fddd312faa963e0b6e20

[ "MIT" ]

null

import re import time import unittest import uuid from unittest import TestCase from unittest import mock from urllib.parse import unquote from cryptography.fernet import Fernet from freezegun import freeze_time from werkzeug.exceptions import BadRequest from fakeredis import FakeStrictRedis # noinspection PyPep8Naming import snappass.main as snappass __author__ = 'davedash' class SnapPassTestCase(TestCase): @mock.patch('redis.client.StrictRedis', FakeStrictRedis) def test_get_password(self): password = "melatonin overdose 1337!$" key = snappass.set_password(password, 30) self.assertEqual(password, snappass.get_password(key)) # Assert that we can't look this up a second time. self.assertIsNone(snappass.get_password(key)) def test_password_is_not_stored_in_plaintext(self): password = "trustno1" token = snappass.set_password(password, 30) redis_key = token.split(snappass.TOKEN_SEPARATOR)[0] stored_password_text = snappass.redis_client.get(redis_key).decode('utf-8') self.assertNotIn(password, stored_password_text) def test_returned_token_format(self): password = "trustsome1" token = snappass.set_password(password, 30) token_fragments = token.split(snappass.TOKEN_SEPARATOR) self.assertEqual(2, len(token_fragments)) redis_key, encryption_key = token_fragments self.assertEqual(32 + len(snappass.REDIS_PREFIX), len(redis_key)) try: Fernet(encryption_key.encode('utf-8')) except ValueError: self.fail('the encryption key is not valid') def test_encryption_key_is_returned(self): password = "trustany1" token = snappass.set_password(password, 30) token_fragments = token.split(snappass.TOKEN_SEPARATOR) redis_key, encryption_key = token_fragments stored_password = snappass.redis_client.get(redis_key) fernet = Fernet(encryption_key.encode('utf-8')) decrypted_password = fernet.decrypt(stored_password).decode('utf-8') self.assertEqual(password, decrypted_password) def test_unencrypted_passwords_still_work(self): unencrypted_password = "trustevery1" storage_key = uuid.uuid4().hex snappass.redis_client.setex(storage_key, 30, unencrypted_password) retrieved_password = snappass.get_password(storage_key) self.assertEqual(unencrypted_password, retrieved_password) def test_password_is_decoded(self): password = "correct horse battery staple" key = snappass.set_password(password, 30) self.assertFalse(isinstance(snappass.get_password(key), bytes)) def test_clean_input(self): # Test Bad Data with snappass.app.test_request_context( "/", data={'password': 'foo', 'ttl': 'bar'}, method='POST'): self.assertRaises(BadRequest, snappass.clean_input) # No Password with snappass.app.test_request_context( "/", method='POST'): self.assertRaises(BadRequest, snappass.clean_input) # No TTL with snappass.app.test_request_context( "/", data={'password': 'foo'}, method='POST'): self.assertRaises(BadRequest, snappass.clean_input) with snappass.app.test_request_context( "/", data={'password': 'foo', 'ttl': 'hour'}, method='POST'): self.assertEqual((3600, 'foo'), snappass.clean_input()) def test_password_before_expiration(self): password = 'fidelio' key = snappass.set_password(password, 1) self.assertEqual(password, snappass.get_password(key)) def test_password_after_expiration(self): password = 'open sesame' key = snappass.set_password(password, 1) time.sleep(1.5) self.assertIsNone(snappass.get_password(key)) class SnapPassRoutesTestCase(TestCase): # noinspection PyPep8Naming def setUp(self): snappass.app.config['TESTING'] = True self.app = snappass.app.test_client() def test_preview_password(self): password = "I like novelty kitten statues!" key = snappass.set_password(password, 30) rv = self.app.get('/{0}'.format(key)) self.assertNotIn(password, rv.get_data(as_text=True)) def test_show_password(self): password = "I like novelty kitten statues!" key = snappass.set_password(password, 30) rv = self.app.post('/{0}'.format(key)) self.assertIn(password, rv.get_data(as_text=True)) def test_url_prefix(self): password = "I like novelty kitten statues!" snappass.URL_PREFIX = "/test/prefix" rv = self.app.post('/', data={'password': password, 'ttl': 'hour'}) self.assertIn("localhost/test/prefix/", rv.get_data(as_text=True)) def test_set_password(self): with freeze_time("2020-05-08 12:00:00") as frozen_time: password = 'my name is my passport. verify me.' rv = self.app.post('/', data={'password': password, 'ttl': 'two weeks'}) html_content = rv.data.decode("ascii") key = re.search(r'id="password-link" value="https://localhost/([^"]+)', html_content).group(1) key = unquote(key) frozen_time.move_to("2020-05-22 11:59:59") self.assertEqual(snappass.get_password(key), password) frozen_time.move_to("2020-05-22 12:00:00") self.assertIsNone(snappass.get_password(key)) def test_set_password_json(self): with freeze_time("2020-05-08 12:00:00") as frozen_time: password = 'my name is my passport. verify me.' rv = self.app.post( '/', headers={'Accept': 'application/json'}, data={'password': password, 'ttl': 'two weeks'}, ) json_content = rv.get_json() key = re.search(r'https://localhost/([^"]+)', json_content['link']).group(1) key = unquote(key) frozen_time.move_to("2020-05-22 11:59:59") self.assertEqual(snappass.get_password(key), password) frozen_time.move_to("2020-05-22 12:00:00") self.assertIsNone(snappass.get_password(key)) if __name__ == '__main__': unittest.main()

38.567073

106

0.655178

import re import time import unittest import uuid from unittest import TestCase from unittest import mock from urllib.parse import unquote from cryptography.fernet import Fernet from freezegun import freeze_time from werkzeug.exceptions import BadRequest from fakeredis import FakeStrictRedis import snappass.main as snappass __author__ = 'davedash' class SnapPassTestCase(TestCase): @mock.patch('redis.client.StrictRedis', FakeStrictRedis) def test_get_password(self): password = "melatonin overdose 1337!$" key = snappass.set_password(password, 30) self.assertEqual(password, snappass.get_password(key)) self.assertIsNone(snappass.get_password(key)) def test_password_is_not_stored_in_plaintext(self): password = "trustno1" token = snappass.set_password(password, 30) redis_key = token.split(snappass.TOKEN_SEPARATOR)[0] stored_password_text = snappass.redis_client.get(redis_key).decode('utf-8') self.assertNotIn(password, stored_password_text) def test_returned_token_format(self): password = "trustsome1" token = snappass.set_password(password, 30) token_fragments = token.split(snappass.TOKEN_SEPARATOR) self.assertEqual(2, len(token_fragments)) redis_key, encryption_key = token_fragments self.assertEqual(32 + len(snappass.REDIS_PREFIX), len(redis_key)) try: Fernet(encryption_key.encode('utf-8')) except ValueError: self.fail('the encryption key is not valid') def test_encryption_key_is_returned(self): password = "trustany1" token = snappass.set_password(password, 30) token_fragments = token.split(snappass.TOKEN_SEPARATOR) redis_key, encryption_key = token_fragments stored_password = snappass.redis_client.get(redis_key) fernet = Fernet(encryption_key.encode('utf-8')) decrypted_password = fernet.decrypt(stored_password).decode('utf-8') self.assertEqual(password, decrypted_password) def test_unencrypted_passwords_still_work(self): unencrypted_password = "trustevery1" storage_key = uuid.uuid4().hex snappass.redis_client.setex(storage_key, 30, unencrypted_password) retrieved_password = snappass.get_password(storage_key) self.assertEqual(unencrypted_password, retrieved_password) def test_password_is_decoded(self): password = "correct horse battery staple" key = snappass.set_password(password, 30) self.assertFalse(isinstance(snappass.get_password(key), bytes)) def test_clean_input(self): # Test Bad Data with snappass.app.test_request_context( "/", data={'password': 'foo', 'ttl': 'bar'}, method='POST'): self.assertRaises(BadRequest, snappass.clean_input) # No Password with snappass.app.test_request_context( "/", method='POST'): self.assertRaises(BadRequest, snappass.clean_input) # No TTL with snappass.app.test_request_context( "/", data={'password': 'foo'}, method='POST'): self.assertRaises(BadRequest, snappass.clean_input) with snappass.app.test_request_context( "/", data={'password': 'foo', 'ttl': 'hour'}, method='POST'): self.assertEqual((3600, 'foo'), snappass.clean_input()) def test_password_before_expiration(self): password = 'fidelio' key = snappass.set_password(password, 1) self.assertEqual(password, snappass.get_password(key)) def test_password_after_expiration(self): password = 'open sesame' key = snappass.set_password(password, 1) time.sleep(1.5) self.assertIsNone(snappass.get_password(key)) class SnapPassRoutesTestCase(TestCase): # noinspection PyPep8Naming def setUp(self): snappass.app.config['TESTING'] = True self.app = snappass.app.test_client() def test_preview_password(self): password = "I like novelty kitten statues!" key = snappass.set_password(password, 30) rv = self.app.get('/{0}'.format(key)) self.assertNotIn(password, rv.get_data(as_text=True)) def test_show_password(self): password = "I like novelty kitten statues!" key = snappass.set_password(password, 30) rv = self.app.post('/{0}'.format(key)) self.assertIn(password, rv.get_data(as_text=True)) def test_url_prefix(self): password = "I like novelty kitten statues!" snappass.URL_PREFIX = "/test/prefix" rv = self.app.post('/', data={'password': password, 'ttl': 'hour'}) self.assertIn("localhost/test/prefix/", rv.get_data(as_text=True)) def test_set_password(self): with freeze_time("2020-05-08 12:00:00") as frozen_time: password = 'my name is my passport. verify me.' rv = self.app.post('/', data={'password': password, 'ttl': 'two weeks'}) html_content = rv.data.decode("ascii") key = re.search(r'id="password-link" value="https://localhost/([^"]+)', html_content).group(1) key = unquote(key) frozen_time.move_to("2020-05-22 11:59:59") self.assertEqual(snappass.get_password(key), password) frozen_time.move_to("2020-05-22 12:00:00") self.assertIsNone(snappass.get_password(key)) def test_set_password_json(self): with freeze_time("2020-05-08 12:00:00") as frozen_time: password = 'my name is my passport. verify me.' rv = self.app.post( '/', headers={'Accept': 'application/json'}, data={'password': password, 'ttl': 'two weeks'}, ) json_content = rv.get_json() key = re.search(r'https://localhost/([^"]+)', json_content['link']).group(1) key = unquote(key) frozen_time.move_to("2020-05-22 11:59:59") self.assertEqual(snappass.get_password(key), password) frozen_time.move_to("2020-05-22 12:00:00") self.assertIsNone(snappass.get_password(key)) if __name__ == '__main__': unittest.main()

true

1c3860194879062b51704087b47e5edb5d95420a

11,828

py

Python

src/probnum/randprocs/markov/integrator/_iwp.py

alpiges/probnum

2e4153cb0df559984e09ec74487ef6c9d3f6d464

[ "MIT" ]

null

src/probnum/randprocs/markov/integrator/_iwp.py

alpiges/probnum

2e4153cb0df559984e09ec74487ef6c9d3f6d464

[ "MIT" ]

40

2021-04-12T07:56:29.000Z

2022-03-28T00:18:18.000Z

src/probnum/randprocs/markov/integrator/_iwp.py

alpiges/probnum

2e4153cb0df559984e09ec74487ef6c9d3f6d464

[ "MIT" ]

null

"""Integrated Brownian motion.""" try: # cached_property is only available in Python >=3.8 from functools import cached_property except ImportError: from cached_property import cached_property import warnings import numpy as np import scipy.special from probnum import config, linops, randvars from probnum.randprocs.markov import _markov_process, continuous, discrete from probnum.randprocs.markov.integrator import _integrator, _preconditioner class IntegratedWienerProcess(_markov_process.MarkovProcess): r"""Integrated Wiener process. Convenience access to :math:`\nu` times integrated (:math:`d` dimensional) Wiener processes. Parameters ---------- initarg Initial time point. num_derivatives Number of modelled derivatives of the integrated process (''order'', ''number of integrations''). Optional. Default is :math:`\nu=1`. wiener_process_dimension Dimension of the underlying Wiener process. Optional. Default is :math:`d=1`. The dimension of the integrated Wiener process itself is :math:`d(\nu + 1)`. initrv Law of the integrated Wiener process at the initial time point. Optional. Default is a :math:`d(\nu + 1)` dimensional standard-normal distribution. diffuse Whether to instantiate a diffuse prior. A diffuse prior has large initial variances. Optional. Default is `False`. If `True`, and if an initial random variable is not passed, an initial random variable is created, where the initial covariance is of the form :math:`\kappa I_{d(\nu + 1)}` with :math:`\kappa=10^6`. Diffuse priors are used when initial distributions are not known. They are common for filtering-based probabilistic ODE solvers. forward_implementation Implementation of the forward-propagation in the underlying transitions. Optional. Default is `classic`. `sqrt` implementation is more computationally expensive, but also more stable. backward_implementation Implementation of the backward-conditioning in the underlying transitions. Optional. Default is `classic`. `sqrt` implementation is more computationally expensive, but also more stable. Raises ------ Warning If `initrv` is not None and `diffuse` is True. Examples -------- >>> iwp1 = IntegratedWienerProcess(initarg=0.) >>> print(iwp1) <IntegratedWienerProcess with input_dim=1, output_dim=2, dtype=float64> >>> iwp2 = IntegratedWienerProcess(initarg=0., num_derivatives=2) >>> print(iwp2) <IntegratedWienerProcess with input_dim=1, output_dim=3, dtype=float64> >>> iwp3 = IntegratedWienerProcess(initarg=0., wiener_process_dimension=10) >>> print(iwp3) <IntegratedWienerProcess with input_dim=1, output_dim=20, dtype=float64> >>> iwp4 = IntegratedWienerProcess(initarg=0., num_derivatives=4, wiener_process_dimension=1) >>> print(iwp4) <IntegratedWienerProcess with input_dim=1, output_dim=5, dtype=float64> """ def __init__( self, initarg, num_derivatives=1, wiener_process_dimension=1, initrv=None, diffuse=False, forward_implementation="classic", backward_implementation="classic", ): iwp_transition = IntegratedWienerTransition( num_derivatives=num_derivatives, wiener_process_dimension=wiener_process_dimension, forward_implementation=forward_implementation, backward_implementation=backward_implementation, ) if initrv is not None and diffuse: warnings.warn( "Parameter `diffuse` has no effect, because an `initrv` has been provided." ) if initrv is None: if diffuse: scale_cholesky = 1e3 else: scale_cholesky = 1.0 zeros = np.zeros(iwp_transition.state_dimension) cov_cholesky = scale_cholesky * np.eye(iwp_transition.state_dimension) initrv = randvars.Normal( mean=zeros, cov=cov_cholesky ** 2, cov_cholesky=cov_cholesky ) super().__init__(transition=iwp_transition, initrv=initrv, initarg=initarg) class IntegratedWienerTransition(_integrator.IntegratorTransition, continuous.LTISDE): """Integrated Brownian motion in :math:`d` dimensions.""" def __init__( self, num_derivatives, wiener_process_dimension, forward_implementation="classic", backward_implementation="classic", ): # initialise BOTH superclasses' inits. # I don't like it either, but it does the job. _integrator.IntegratorTransition.__init__( self, num_derivatives=num_derivatives, wiener_process_dimension=wiener_process_dimension, ) continuous.LTISDE.__init__( self, drift_matrix=self._drift_matrix, force_vector=self._force_vector, dispersion_matrix=self._dispersion_matrix, forward_implementation=forward_implementation, backward_implementation=backward_implementation, ) @cached_property def _drift_matrix(self): drift_matrix_1d = np.diag(np.ones(self.num_derivatives), 1) if config.matrix_free: return linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.Matrix(A=drift_matrix_1d), ) return np.kron(np.eye(self.wiener_process_dimension), drift_matrix_1d) @cached_property def _force_vector(self): return np.zeros((self.wiener_process_dimension * (self.num_derivatives + 1))) @cached_property def _dispersion_matrix(self): dispersion_matrix_1d = np.zeros(self.num_derivatives + 1) dispersion_matrix_1d[-1] = 1.0 # Unit diffusion if config.matrix_free: return linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.Matrix(A=dispersion_matrix_1d.reshape(-1, 1)), ) return np.kron(np.eye(self.wiener_process_dimension), dispersion_matrix_1d).T @cached_property def equivalent_discretisation_preconditioned(self): """Discretised IN THE PRECONDITIONED SPACE. The preconditioned state transition is the flipped Pascal matrix. The preconditioned process noise covariance is the flipped Hilbert matrix. The shift is always zero. Reference: https://arxiv.org/abs/2012.10106 """ state_transition_1d = np.flip( scipy.linalg.pascal(self.num_derivatives + 1, kind="lower", exact=False) ) if config.matrix_free: state_transition = linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.aslinop(state_transition_1d), ) else: state_transition = np.kron( np.eye(self.wiener_process_dimension), state_transition_1d ) process_noise_1d = np.flip(scipy.linalg.hilbert(self.num_derivatives + 1)) if config.matrix_free: process_noise = linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.aslinop(process_noise_1d), ) else: process_noise = np.kron( np.eye(self.wiener_process_dimension), process_noise_1d ) empty_shift = np.zeros( self.wiener_process_dimension * (self.num_derivatives + 1) ) process_noise_cholesky_1d = np.linalg.cholesky(process_noise_1d) if config.matrix_free: process_noise_cholesky = linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.aslinop(process_noise_cholesky_1d), ) else: process_noise_cholesky = np.kron( np.eye(self.wiener_process_dimension), process_noise_cholesky_1d ) return discrete.LTIGaussian( state_trans_mat=state_transition, shift_vec=empty_shift, proc_noise_cov_mat=process_noise, proc_noise_cov_cholesky=process_noise_cholesky, forward_implementation=self.forward_implementation, backward_implementation=self.backward_implementation, ) def forward_rv( self, rv, t, dt=None, compute_gain=False, _diffusion=1.0, **kwargs, ): if dt is None: raise ValueError( "Continuous-time transitions require a time-increment ``dt``." ) rv = _preconditioner.apply_precon(self.precon.inverse(dt), rv) rv, info = self.equivalent_discretisation_preconditioned.forward_rv( rv, t, compute_gain=compute_gain, _diffusion=_diffusion ) info["crosscov"] = self.precon(dt) @ info["crosscov"] @ self.precon(dt).T if "gain" in info: info["gain"] = self.precon(dt) @ info["gain"] @ self.precon.inverse(dt).T return _preconditioner.apply_precon(self.precon(dt), rv), info def backward_rv( self, rv_obtained, rv, rv_forwarded=None, gain=None, t=None, dt=None, _diffusion=1.0, **kwargs, ): if dt is None: raise ValueError( "Continuous-time transitions require a time-increment ``dt``." ) rv_obtained = _preconditioner.apply_precon(self.precon.inverse(dt), rv_obtained) rv = _preconditioner.apply_precon(self.precon.inverse(dt), rv) rv_forwarded = ( _preconditioner.apply_precon(self.precon.inverse(dt), rv_forwarded) if rv_forwarded is not None else None ) gain = ( self.precon.inverse(dt) @ gain @ self.precon.inverse(dt).T if gain is not None else None ) rv, info = self.equivalent_discretisation_preconditioned.backward_rv( rv_obtained=rv_obtained, rv=rv, rv_forwarded=rv_forwarded, gain=gain, t=t, _diffusion=_diffusion, ) return _preconditioner.apply_precon(self.precon(dt), rv), info def discretise(self, dt): """Equivalent discretisation of the process. Overwrites matrix-fraction decomposition in the super-class. Only present for user's convenience and to maintain a clean interface. Not used for forward_rv, etc.. """ state_trans_mat = ( self.precon(dt) @ self.equivalent_discretisation_preconditioned.state_trans_mat @ self.precon.inverse(dt) ) proc_noise_cov_mat = ( self.precon(dt) @ self.equivalent_discretisation_preconditioned.proc_noise_cov_mat @ self.precon(dt).T ) zero_shift = np.zeros(state_trans_mat.shape[0]) # The Cholesky factor of the process noise covariance matrix of the IBM # always exists, even for non-square root implementations. proc_noise_cov_cholesky = ( self.precon(dt) @ self.equivalent_discretisation_preconditioned.proc_noise_cov_cholesky ) return discrete.LTIGaussian( state_trans_mat=state_trans_mat, shift_vec=zero_shift, proc_noise_cov_mat=proc_noise_cov_mat, proc_noise_cov_cholesky=proc_noise_cov_cholesky, forward_implementation=self.forward_implementation, backward_implementation=self.forward_implementation, )

37.07837

118

0.644234

try: from functools import cached_property except ImportError: from cached_property import cached_property import warnings import numpy as np import scipy.special from probnum import config, linops, randvars from probnum.randprocs.markov import _markov_process, continuous, discrete from probnum.randprocs.markov.integrator import _integrator, _preconditioner class IntegratedWienerProcess(_markov_process.MarkovProcess): def __init__( self, initarg, num_derivatives=1, wiener_process_dimension=1, initrv=None, diffuse=False, forward_implementation="classic", backward_implementation="classic", ): iwp_transition = IntegratedWienerTransition( num_derivatives=num_derivatives, wiener_process_dimension=wiener_process_dimension, forward_implementation=forward_implementation, backward_implementation=backward_implementation, ) if initrv is not None and diffuse: warnings.warn( "Parameter `diffuse` has no effect, because an `initrv` has been provided." ) if initrv is None: if diffuse: scale_cholesky = 1e3 else: scale_cholesky = 1.0 zeros = np.zeros(iwp_transition.state_dimension) cov_cholesky = scale_cholesky * np.eye(iwp_transition.state_dimension) initrv = randvars.Normal( mean=zeros, cov=cov_cholesky ** 2, cov_cholesky=cov_cholesky ) super().__init__(transition=iwp_transition, initrv=initrv, initarg=initarg) class IntegratedWienerTransition(_integrator.IntegratorTransition, continuous.LTISDE): def __init__( self, num_derivatives, wiener_process_dimension, forward_implementation="classic", backward_implementation="classic", ): # I don't like it either, but it does the job. _integrator.IntegratorTransition.__init__( self, num_derivatives=num_derivatives, wiener_process_dimension=wiener_process_dimension, ) continuous.LTISDE.__init__( self, drift_matrix=self._drift_matrix, force_vector=self._force_vector, dispersion_matrix=self._dispersion_matrix, forward_implementation=forward_implementation, backward_implementation=backward_implementation, ) @cached_property def _drift_matrix(self): drift_matrix_1d = np.diag(np.ones(self.num_derivatives), 1) if config.matrix_free: return linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.Matrix(A=drift_matrix_1d), ) return np.kron(np.eye(self.wiener_process_dimension), drift_matrix_1d) @cached_property def _force_vector(self): return np.zeros((self.wiener_process_dimension * (self.num_derivatives + 1))) @cached_property def _dispersion_matrix(self): dispersion_matrix_1d = np.zeros(self.num_derivatives + 1) dispersion_matrix_1d[-1] = 1.0 if config.matrix_free: return linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.Matrix(A=dispersion_matrix_1d.reshape(-1, 1)), ) return np.kron(np.eye(self.wiener_process_dimension), dispersion_matrix_1d).T @cached_property def equivalent_discretisation_preconditioned(self): state_transition_1d = np.flip( scipy.linalg.pascal(self.num_derivatives + 1, kind="lower", exact=False) ) if config.matrix_free: state_transition = linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.aslinop(state_transition_1d), ) else: state_transition = np.kron( np.eye(self.wiener_process_dimension), state_transition_1d ) process_noise_1d = np.flip(scipy.linalg.hilbert(self.num_derivatives + 1)) if config.matrix_free: process_noise = linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.aslinop(process_noise_1d), ) else: process_noise = np.kron( np.eye(self.wiener_process_dimension), process_noise_1d ) empty_shift = np.zeros( self.wiener_process_dimension * (self.num_derivatives + 1) ) process_noise_cholesky_1d = np.linalg.cholesky(process_noise_1d) if config.matrix_free: process_noise_cholesky = linops.Kronecker( A=linops.Identity(self.wiener_process_dimension), B=linops.aslinop(process_noise_cholesky_1d), ) else: process_noise_cholesky = np.kron( np.eye(self.wiener_process_dimension), process_noise_cholesky_1d ) return discrete.LTIGaussian( state_trans_mat=state_transition, shift_vec=empty_shift, proc_noise_cov_mat=process_noise, proc_noise_cov_cholesky=process_noise_cholesky, forward_implementation=self.forward_implementation, backward_implementation=self.backward_implementation, ) def forward_rv( self, rv, t, dt=None, compute_gain=False, _diffusion=1.0, **kwargs, ): if dt is None: raise ValueError( "Continuous-time transitions require a time-increment ``dt``." ) rv = _preconditioner.apply_precon(self.precon.inverse(dt), rv) rv, info = self.equivalent_discretisation_preconditioned.forward_rv( rv, t, compute_gain=compute_gain, _diffusion=_diffusion ) info["crosscov"] = self.precon(dt) @ info["crosscov"] @ self.precon(dt).T if "gain" in info: info["gain"] = self.precon(dt) @ info["gain"] @ self.precon.inverse(dt).T return _preconditioner.apply_precon(self.precon(dt), rv), info def backward_rv( self, rv_obtained, rv, rv_forwarded=None, gain=None, t=None, dt=None, _diffusion=1.0, **kwargs, ): if dt is None: raise ValueError( "Continuous-time transitions require a time-increment ``dt``." ) rv_obtained = _preconditioner.apply_precon(self.precon.inverse(dt), rv_obtained) rv = _preconditioner.apply_precon(self.precon.inverse(dt), rv) rv_forwarded = ( _preconditioner.apply_precon(self.precon.inverse(dt), rv_forwarded) if rv_forwarded is not None else None ) gain = ( self.precon.inverse(dt) @ gain @ self.precon.inverse(dt).T if gain is not None else None ) rv, info = self.equivalent_discretisation_preconditioned.backward_rv( rv_obtained=rv_obtained, rv=rv, rv_forwarded=rv_forwarded, gain=gain, t=t, _diffusion=_diffusion, ) return _preconditioner.apply_precon(self.precon(dt), rv), info def discretise(self, dt): state_trans_mat = ( self.precon(dt) @ self.equivalent_discretisation_preconditioned.state_trans_mat @ self.precon.inverse(dt) ) proc_noise_cov_mat = ( self.precon(dt) @ self.equivalent_discretisation_preconditioned.proc_noise_cov_mat @ self.precon(dt).T ) zero_shift = np.zeros(state_trans_mat.shape[0]) proc_noise_cov_cholesky = ( self.precon(dt) @ self.equivalent_discretisation_preconditioned.proc_noise_cov_cholesky ) return discrete.LTIGaussian( state_trans_mat=state_trans_mat, shift_vec=zero_shift, proc_noise_cov_mat=proc_noise_cov_mat, proc_noise_cov_cholesky=proc_noise_cov_cholesky, forward_implementation=self.forward_implementation, backward_implementation=self.forward_implementation, )

true

1c386043c55d45b1b88ea0a2646c4901ed16ed5a

950

py

Python

distribulator/engine/mode/Mode.py

adace74/test

b7b45fae6c4ba6ffe8a04c45b6e2bf23a918226b

[ "BSD-3-Clause" ]

null

distribulator/engine/mode/Mode.py

adace74/test

b7b45fae6c4ba6ffe8a04c45b6e2bf23a918226b

[ "BSD-3-Clause" ]

null

distribulator/engine/mode/Mode.py

adace74/test

b7b45fae6c4ba6ffe8a04c45b6e2bf23a918226b

[ "BSD-3-Clause" ]

null

###################################################################### # # $Id$ # # (c) Copyright 2004 Orbitz, Inc. All Rights Reserved. # Please see the accompanying LICENSE file for license information. # ###################################################################### # Pydoc comments """This class is the base abstract class for all runtime modes.""" # Version tag __version__= '$Revision$'[11:-2] # Standard modules # Custom modules ###################################################################### class Mode: """This class is the base abstract class for all runtime modes.""" def __init__(self, PassedGlobalConfig): """Constructor.""" pass ###################################################################### def invoke(self): """This method is the main entry point into tons of custom logic.""" pass ######################################################################

25

76

0.413684

true

1c38615b6e5a27005bd983f0f82fb8dfb4efac93

425

py

Python

rest/notification/list-get-example-1/list-get-example-1.5.x.py

azaddeveloper/api-snippets

f88b153cd7186fa70b33733b205886502db0d1f2

[ "MIT" ]

2

2017-11-23T11:31:20.000Z

2018-01-22T04:14:02.000Z

rest/notification/list-get-example-1/list-get-example-1.5.x.py

azaddeveloper/api-snippets

f88b153cd7186fa70b33733b205886502db0d1f2

[ "MIT" ]

null

rest/notification/list-get-example-1/list-get-example-1.5.x.py

azaddeveloper/api-snippets

f88b153cd7186fa70b33733b205886502db0d1f2

[ "MIT" ]

2

2020-05-22T23:31:21.000Z

2021-06-10T18:33:45.000Z

# Download the Python helper library from twilio.com/docs/python/install from twilio.rest import TwilioRestClient # Your Account Sid and Auth Token from twilio.com/user/account account_sid = "ACXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" auth_token = "your_auth_token" client = TwilioRestClient(account_sid, auth_token) # A list of notification objects with the properties described above notifications = client.notifications.list()

38.636364

72

0.828235

from twilio.rest import TwilioRestClient account_sid = "ACXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" auth_token = "your_auth_token" client = TwilioRestClient(account_sid, auth_token) notifications = client.notifications.list()

true

1c38619eee36a6d8e0be87801ad8d87b80681fc2

6,617

py

Python

assignment1/cs231n/classifiers/fc_net.py

qiaw99/CS231n-Convolutional-Neural-Networks-for-Visual-Recognition

5949b4f68f04e23751879dbc679e7708735d313f

[ "Apache-2.0" ]

null

assignment1/cs231n/classifiers/fc_net.py

qiaw99/CS231n-Convolutional-Neural-Networks-for-Visual-Recognition

5949b4f68f04e23751879dbc679e7708735d313f

[ "Apache-2.0" ]

null

assignment1/cs231n/classifiers/fc_net.py

qiaw99/CS231n-Convolutional-Neural-Networks-for-Visual-Recognition

5949b4f68f04e23751879dbc679e7708735d313f

[ "Apache-2.0" ]

null

from builtins import range from builtins import object import numpy as np from ..layers import * from ..layer_utils import * class TwoLayerNet(object): """ A two-layer fully-connected neural network with ReLU nonlinearity and softmax loss that uses a modular layer design. We assume an input dimension of D, a hidden dimension of H, and perform classification over C classes. The architecure should be affine - relu - affine - softmax. Note that this class does not implement gradient descent; instead, it will interact with a separate Solver object that is responsible for running optimization. The learnable parameters of the model are stored in the dictionary self.params that maps parameter names to numpy arrays. """ def __init__( self, input_dim=3 * 32 * 32, hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0, ): """ Initialize a new network. Inputs: - input_dim: An integer giving the size of the input - hidden_dim: An integer giving the size of the hidden layer - num_classes: An integer giving the number of classes to classify - weight_scale: Scalar giving the standard deviation for random initialization of the weights. - reg: Scalar giving L2 regularization strength. """ self.params = {} self.reg = reg ############################################################################ # TODO: Initialize the weights and biases of the two-layer net. Weights # # should be initialized from a Gaussian centered at 0.0 with # # standard deviation equal to weight_scale, and biases should be # # initialized to zero. All weights and biases should be stored in the # # dictionary self.params, with first layer weights # # and biases using the keys 'W1' and 'b1' and second layer # # weights and biases using the keys 'W2' and 'b2'. # ############################################################################ # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** self.params = {"W1": np.random.normal(0, weight_scale, size=(input_dim, hidden_dim)), "b1": np.zeros(hidden_dim), "W2": np.random.normal(0, weight_scale, size=(hidden_dim, num_classes)), "b2": np.zeros(num_classes) } # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** ############################################################################ # END OF YOUR CODE # ############################################################################ def loss(self, X, y=None): """ Compute loss and gradient for a minibatch of data. Inputs: - X: Array of input data of shape (N, d_1, ..., d_k) - y: Array of labels, of shape (N,). y[i] gives the label for X[i]. Returns: If y is None, then run a test-time forward pass of the model and return: - scores: Array of shape (N, C) giving classification scores, where scores[i, c] is the classification score for X[i] and class c. If y is not None, then run a training-time forward and backward pass and return a tuple of: - loss: Scalar value giving the loss - grads: Dictionary with the same keys as self.params, mapping parameter names to gradients of the loss with respect to those parameters. """ scores = None ############################################################################ # TODO: Implement the forward pass for the two-layer net, computing the # # class scores for X and storing them in the scores variable. # ############################################################################ # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** relu_output, relu_cache = affine_relu_forward(X, self.params['W1'], self.params['b1']) scores, cache = affine_forward(relu_output, self.params['W2'], self.params['b2']) # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** ############################################################################ # END OF YOUR CODE # ############################################################################ # If y is None then we are in test mode so just return scores if y is None: return scores loss, grads = 0, {} ############################################################################ # TODO: Implement the backward pass for the two-layer net. Store the loss # # in the loss variable and gradients in the grads dictionary. Compute data # # loss using softmax, and make sure that grads[k] holds the gradients for # # self.params[k]. Don't forget to add L2 regularization! # # # # NOTE: To ensure that your implementation matches ours and you pass the # # automated tests, make sure that your L2 regularization includes a factor # # of 0.5 to simplify the expression for the gradient. # ############################################################################ # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** loss, d_scores = softmax_loss(scores, y) loss += 0.5 * self.reg * np.sum(self.params['W1'] * self.params['W1']) + 0.5 * self.reg * np.sum(self.params['W2'] * self.params['W2']) dx, dw, db = affine_backward(d_scores, cache) grads["W2"] = dw grads["b2"] = db dx, dw, db = affine_relu_backward(dx, relu_cache) grads["W1"] = dw grads["b1"] = db grads["W1"] += self.reg * self.params['W1'] grads["W2"] += self.reg * self.params['W2'] # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)***** ############################################################################ # END OF YOUR CODE # ############################################################################ return loss, grads

47.264286

143

0.484207

from builtins import range from builtins import object import numpy as np from ..layers import * from ..layer_utils import * class TwoLayerNet(object): def __init__( self, input_dim=3 * 32 * 32, hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0, ): self.params = {} self.reg = reg

true

1c3861b42892e0760a2b57f037101b3f9ff8dd62

11,482

py

Python

pcdet/models/roi_heads/voxelrcnn_head.py

EmiyaNing/OpenPCDet

41ff28209cb000b51626a0ed8593b0adbe3dd447

[ "Apache-2.0" ]

null

pcdet/models/roi_heads/voxelrcnn_head.py

EmiyaNing/OpenPCDet

41ff28209cb000b51626a0ed8593b0adbe3dd447

[ "Apache-2.0" ]

null

pcdet/models/roi_heads/voxelrcnn_head.py

EmiyaNing/OpenPCDet

41ff28209cb000b51626a0ed8593b0adbe3dd447

[ "Apache-2.0" ]

null

import torch import torch.nn as nn from ...ops.pointnet2.pointnet2_stack import voxel_pool_modules as voxelpool_stack_modules from ...utils import common_utils from .roi_head_template import RoIHeadTemplate class VoxelRCNNHead(RoIHeadTemplate): def __init__(self, backbone_channels, model_cfg, point_cloud_range, voxel_size, num_class=1, **kwargs): super().__init__(num_class=num_class, model_cfg=model_cfg) self.model_cfg = model_cfg self.pool_cfg = model_cfg.ROI_GRID_POOL LAYER_cfg = self.pool_cfg.POOL_LAYERS self.point_cloud_range = point_cloud_range self.voxel_size = voxel_size c_out = 0 self.roi_grid_pool_layers = nn.ModuleList() for src_name in self.pool_cfg.FEATURES_SOURCE: mlps = LAYER_cfg[src_name].MLPS for k in range(len(mlps)): mlps[k] = [backbone_channels[src_name]] + mlps[k] pool_layer = voxelpool_stack_modules.NeighborVoxelSAModuleMSG( query_ranges=LAYER_cfg[src_name].QUERY_RANGES, nsamples=LAYER_cfg[src_name].NSAMPLE, radii=LAYER_cfg[src_name].POOL_RADIUS, mlps=mlps, pool_method=LAYER_cfg[src_name].POOL_METHOD, ) self.roi_grid_pool_layers.append(pool_layer) c_out += sum([x[-1] for x in mlps]) GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE # c_out = sum([x[-1] for x in mlps]) pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * c_out shared_fc_list = [] for k in range(0, self.model_cfg.SHARED_FC.__len__()): shared_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.SHARED_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]), nn.ReLU(inplace=True) ]) pre_channel = self.model_cfg.SHARED_FC[k] if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.shared_fc_layer = nn.Sequential(*shared_fc_list) cls_fc_list = [] for k in range(0, self.model_cfg.CLS_FC.__len__()): cls_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.CLS_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.CLS_FC[k]), nn.ReLU() ]) pre_channel = self.model_cfg.CLS_FC[k] if k != self.model_cfg.CLS_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: cls_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.cls_fc_layers = nn.Sequential(*cls_fc_list) self.cls_pred_layer = nn.Linear(pre_channel, self.num_class, bias=True) reg_fc_list = [] for k in range(0, self.model_cfg.REG_FC.__len__()): reg_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.REG_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.REG_FC[k]), nn.ReLU() ]) pre_channel = self.model_cfg.REG_FC[k] if k != self.model_cfg.REG_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: reg_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.reg_fc_layers = nn.Sequential(*reg_fc_list) self.reg_pred_layer = nn.Linear(pre_channel, self.box_coder.code_size * self.num_class, bias=True) self.init_weights() def init_weights(self): init_func = nn.init.xavier_normal_ for module_list in [self.shared_fc_layer, self.cls_fc_layers, self.reg_fc_layers]: for m in module_list.modules(): if isinstance(m, nn.Linear): init_func(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) nn.init.normal_(self.cls_pred_layer.weight, 0, 0.01) nn.init.constant_(self.cls_pred_layer.bias, 0) nn.init.normal_(self.reg_pred_layer.weight, mean=0, std=0.001) nn.init.constant_(self.reg_pred_layer.bias, 0) # def _init_weights(self): # init_func = nn.init.xavier_normal_ # for m in self.modules(): # if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear): # init_func(m.weight) # if m.bias is not None: # nn.init.constant_(m.bias, 0) # nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001) def roi_grid_pool(self, batch_dict): """ Args: batch_dict: batch_size: rois: (B, num_rois, 7 + C) point_coords: (num_points, 4) [bs_idx, x, y, z] point_features: (num_points, C) point_cls_scores: (N1 + N2 + N3 + ..., 1) point_part_offset: (N1 + N2 + N3 + ..., 3) Returns: """ rois = batch_dict['rois'] batch_size = batch_dict['batch_size'] with_vf_transform = batch_dict.get('with_voxel_feature_transform', False) roi_grid_xyz, _ = self.get_global_grid_points_of_roi( rois, grid_size=self.pool_cfg.GRID_SIZE ) # (BxN, 6x6x6, 3) # roi_grid_xyz: (B, Nx6x6x6, 3) roi_grid_xyz = roi_grid_xyz.view(batch_size, -1, 3) # compute the voxel coordinates of grid points roi_grid_coords_x = (roi_grid_xyz[:, :, 0:1] - self.point_cloud_range[0]) // self.voxel_size[0] roi_grid_coords_y = (roi_grid_xyz[:, :, 1:2] - self.point_cloud_range[1]) // self.voxel_size[1] roi_grid_coords_z = (roi_grid_xyz[:, :, 2:3] - self.point_cloud_range[2]) // self.voxel_size[2] # roi_grid_coords: (B, Nx6x6x6, 3) roi_grid_coords = torch.cat([roi_grid_coords_x, roi_grid_coords_y, roi_grid_coords_z], dim=-1) batch_idx = rois.new_zeros(batch_size, roi_grid_coords.shape[1], 1) for bs_idx in range(batch_size): batch_idx[bs_idx, :, 0] = bs_idx # roi_grid_coords: (B, Nx6x6x6, 4) # roi_grid_coords = torch.cat([batch_idx, roi_grid_coords], dim=-1) # roi_grid_coords = roi_grid_coords.int() roi_grid_batch_cnt = rois.new_zeros(batch_size).int().fill_(roi_grid_coords.shape[1]) pooled_features_list = [] for k, src_name in enumerate(self.pool_cfg.FEATURES_SOURCE): pool_layer = self.roi_grid_pool_layers[k] cur_stride = batch_dict['multi_scale_3d_strides'][src_name] cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name] if with_vf_transform: cur_sp_tensors = batch_dict['multi_scale_3d_features_post'][src_name] else: cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name] # compute voxel center xyz and batch_cnt cur_coords = cur_sp_tensors.indices cur_voxel_xyz = common_utils.get_voxel_centers( cur_coords[:, 1:4], downsample_times=cur_stride, voxel_size=self.voxel_size, point_cloud_range=self.point_cloud_range ) cur_voxel_xyz_batch_cnt = cur_voxel_xyz.new_zeros(batch_size).int() for bs_idx in range(batch_size): cur_voxel_xyz_batch_cnt[bs_idx] = (cur_coords[:, 0] == bs_idx).sum() # get voxel2point tensor v2p_ind_tensor = common_utils.generate_voxel2pinds(cur_sp_tensors) # compute the grid coordinates in this scale, in [batch_idx, x y z] order cur_roi_grid_coords = roi_grid_coords // cur_stride cur_roi_grid_coords = torch.cat([batch_idx, cur_roi_grid_coords], dim=-1) cur_roi_grid_coords = cur_roi_grid_coords.int() # voxel neighbor aggregation pooled_features = pool_layer( xyz=cur_voxel_xyz.contiguous(), xyz_batch_cnt=cur_voxel_xyz_batch_cnt, new_xyz=roi_grid_xyz.contiguous().view(-1, 3), new_xyz_batch_cnt=roi_grid_batch_cnt, new_coords=cur_roi_grid_coords.contiguous().view(-1, 4), features=cur_sp_tensors.features.contiguous(), voxel2point_indices=v2p_ind_tensor ) pooled_features = pooled_features.view( -1, self.pool_cfg.GRID_SIZE ** 3, pooled_features.shape[-1] ) # (BxN, 6x6x6, C) pooled_features_list.append(pooled_features) ms_pooled_features = torch.cat(pooled_features_list, dim=-1) return ms_pooled_features def get_global_grid_points_of_roi(self, rois, grid_size): rois = rois.view(-1, rois.shape[-1]) batch_size_rcnn = rois.shape[0] local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size) # (B, 6x6x6, 3) global_roi_grid_points = common_utils.rotate_points_along_z( local_roi_grid_points.clone(), rois[:, 6] ).squeeze(dim=1) global_center = rois[:, 0:3].clone() global_roi_grid_points += global_center.unsqueeze(dim=1) return global_roi_grid_points, local_roi_grid_points @staticmethod def get_dense_grid_points(rois, batch_size_rcnn, grid_size): faked_features = rois.new_ones((grid_size, grid_size, grid_size)) dense_idx = faked_features.nonzero() # (N, 3) [x_idx, y_idx, z_idx] dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float() # (B, 6x6x6, 3) local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6] roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \ - (local_roi_size.unsqueeze(dim=1) / 2) # (B, 6x6x6, 3) return roi_grid_points def forward(self, batch_dict): """ :param input_data: input dict :return: """ targets_dict = self.proposal_layer( batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST'] ) if self.training: targets_dict = self.assign_targets(batch_dict) batch_dict['rois'] = targets_dict['rois'] batch_dict['roi_labels'] = targets_dict['roi_labels'] # RoI aware pooling pooled_features = self.roi_grid_pool(batch_dict) # (BxN, 6x6x6, C) # Box Refinement pooled_features = pooled_features.view(pooled_features.size(0), -1) shared_features = self.shared_fc_layer(pooled_features) rcnn_cls = self.cls_pred_layer(self.cls_fc_layers(shared_features)) rcnn_reg = self.reg_pred_layer(self.reg_fc_layers(shared_features)) if not self.training: batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg ) batch_dict['batch_cls_preds'] = batch_cls_preds batch_dict['batch_box_preds'] = batch_box_preds batch_dict['cls_preds_normalized'] = False else: targets_dict['rcnn_cls'] = rcnn_cls targets_dict['rcnn_reg'] = rcnn_reg batch_dict['rcnn_cls'] = rcnn_cls batch_dict['rcnn_reg'] = rcnn_reg self.forward_ret_dict = targets_dict return batch_dict

45.027451

116

0.620536

import torch import torch.nn as nn from ...ops.pointnet2.pointnet2_stack import voxel_pool_modules as voxelpool_stack_modules from ...utils import common_utils from .roi_head_template import RoIHeadTemplate class VoxelRCNNHead(RoIHeadTemplate): def __init__(self, backbone_channels, model_cfg, point_cloud_range, voxel_size, num_class=1, **kwargs): super().__init__(num_class=num_class, model_cfg=model_cfg) self.model_cfg = model_cfg self.pool_cfg = model_cfg.ROI_GRID_POOL LAYER_cfg = self.pool_cfg.POOL_LAYERS self.point_cloud_range = point_cloud_range self.voxel_size = voxel_size c_out = 0 self.roi_grid_pool_layers = nn.ModuleList() for src_name in self.pool_cfg.FEATURES_SOURCE: mlps = LAYER_cfg[src_name].MLPS for k in range(len(mlps)): mlps[k] = [backbone_channels[src_name]] + mlps[k] pool_layer = voxelpool_stack_modules.NeighborVoxelSAModuleMSG( query_ranges=LAYER_cfg[src_name].QUERY_RANGES, nsamples=LAYER_cfg[src_name].NSAMPLE, radii=LAYER_cfg[src_name].POOL_RADIUS, mlps=mlps, pool_method=LAYER_cfg[src_name].POOL_METHOD, ) self.roi_grid_pool_layers.append(pool_layer) c_out += sum([x[-1] for x in mlps]) GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * c_out shared_fc_list = [] for k in range(0, self.model_cfg.SHARED_FC.__len__()): shared_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.SHARED_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]), nn.ReLU(inplace=True) ]) pre_channel = self.model_cfg.SHARED_FC[k] if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.shared_fc_layer = nn.Sequential(*shared_fc_list) cls_fc_list = [] for k in range(0, self.model_cfg.CLS_FC.__len__()): cls_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.CLS_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.CLS_FC[k]), nn.ReLU() ]) pre_channel = self.model_cfg.CLS_FC[k] if k != self.model_cfg.CLS_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: cls_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.cls_fc_layers = nn.Sequential(*cls_fc_list) self.cls_pred_layer = nn.Linear(pre_channel, self.num_class, bias=True) reg_fc_list = [] for k in range(0, self.model_cfg.REG_FC.__len__()): reg_fc_list.extend([ nn.Linear(pre_channel, self.model_cfg.REG_FC[k], bias=False), nn.BatchNorm1d(self.model_cfg.REG_FC[k]), nn.ReLU() ]) pre_channel = self.model_cfg.REG_FC[k] if k != self.model_cfg.REG_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: reg_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.reg_fc_layers = nn.Sequential(*reg_fc_list) self.reg_pred_layer = nn.Linear(pre_channel, self.box_coder.code_size * self.num_class, bias=True) self.init_weights() def init_weights(self): init_func = nn.init.xavier_normal_ for module_list in [self.shared_fc_layer, self.cls_fc_layers, self.reg_fc_layers]: for m in module_list.modules(): if isinstance(m, nn.Linear): init_func(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) nn.init.normal_(self.cls_pred_layer.weight, 0, 0.01) nn.init.constant_(self.cls_pred_layer.bias, 0) nn.init.normal_(self.reg_pred_layer.weight, mean=0, std=0.001) nn.init.constant_(self.reg_pred_layer.bias, 0) def roi_grid_pool(self, batch_dict): rois = batch_dict['rois'] batch_size = batch_dict['batch_size'] with_vf_transform = batch_dict.get('with_voxel_feature_transform', False) roi_grid_xyz, _ = self.get_global_grid_points_of_roi( rois, grid_size=self.pool_cfg.GRID_SIZE ) roi_grid_xyz = roi_grid_xyz.view(batch_size, -1, 3) roi_grid_coords_x = (roi_grid_xyz[:, :, 0:1] - self.point_cloud_range[0]) // self.voxel_size[0] roi_grid_coords_y = (roi_grid_xyz[:, :, 1:2] - self.point_cloud_range[1]) // self.voxel_size[1] roi_grid_coords_z = (roi_grid_xyz[:, :, 2:3] - self.point_cloud_range[2]) // self.voxel_size[2] roi_grid_coords = torch.cat([roi_grid_coords_x, roi_grid_coords_y, roi_grid_coords_z], dim=-1) batch_idx = rois.new_zeros(batch_size, roi_grid_coords.shape[1], 1) for bs_idx in range(batch_size): batch_idx[bs_idx, :, 0] = bs_idx roi_grid_batch_cnt = rois.new_zeros(batch_size).int().fill_(roi_grid_coords.shape[1]) pooled_features_list = [] for k, src_name in enumerate(self.pool_cfg.FEATURES_SOURCE): pool_layer = self.roi_grid_pool_layers[k] cur_stride = batch_dict['multi_scale_3d_strides'][src_name] cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name] if with_vf_transform: cur_sp_tensors = batch_dict['multi_scale_3d_features_post'][src_name] else: cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name] cur_coords = cur_sp_tensors.indices cur_voxel_xyz = common_utils.get_voxel_centers( cur_coords[:, 1:4], downsample_times=cur_stride, voxel_size=self.voxel_size, point_cloud_range=self.point_cloud_range ) cur_voxel_xyz_batch_cnt = cur_voxel_xyz.new_zeros(batch_size).int() for bs_idx in range(batch_size): cur_voxel_xyz_batch_cnt[bs_idx] = (cur_coords[:, 0] == bs_idx).sum() v2p_ind_tensor = common_utils.generate_voxel2pinds(cur_sp_tensors) cur_roi_grid_coords = roi_grid_coords // cur_stride cur_roi_grid_coords = torch.cat([batch_idx, cur_roi_grid_coords], dim=-1) cur_roi_grid_coords = cur_roi_grid_coords.int() pooled_features = pool_layer( xyz=cur_voxel_xyz.contiguous(), xyz_batch_cnt=cur_voxel_xyz_batch_cnt, new_xyz=roi_grid_xyz.contiguous().view(-1, 3), new_xyz_batch_cnt=roi_grid_batch_cnt, new_coords=cur_roi_grid_coords.contiguous().view(-1, 4), features=cur_sp_tensors.features.contiguous(), voxel2point_indices=v2p_ind_tensor ) pooled_features = pooled_features.view( -1, self.pool_cfg.GRID_SIZE ** 3, pooled_features.shape[-1] ) pooled_features_list.append(pooled_features) ms_pooled_features = torch.cat(pooled_features_list, dim=-1) return ms_pooled_features def get_global_grid_points_of_roi(self, rois, grid_size): rois = rois.view(-1, rois.shape[-1]) batch_size_rcnn = rois.shape[0] local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size) global_roi_grid_points = common_utils.rotate_points_along_z( local_roi_grid_points.clone(), rois[:, 6] ).squeeze(dim=1) global_center = rois[:, 0:3].clone() global_roi_grid_points += global_center.unsqueeze(dim=1) return global_roi_grid_points, local_roi_grid_points @staticmethod def get_dense_grid_points(rois, batch_size_rcnn, grid_size): faked_features = rois.new_ones((grid_size, grid_size, grid_size)) dense_idx = faked_features.nonzero() dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float() local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6] roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \ - (local_roi_size.unsqueeze(dim=1) / 2) return roi_grid_points def forward(self, batch_dict): targets_dict = self.proposal_layer( batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST'] ) if self.training: targets_dict = self.assign_targets(batch_dict) batch_dict['rois'] = targets_dict['rois'] batch_dict['roi_labels'] = targets_dict['roi_labels'] pooled_features = self.roi_grid_pool(batch_dict) pooled_features = pooled_features.view(pooled_features.size(0), -1) shared_features = self.shared_fc_layer(pooled_features) rcnn_cls = self.cls_pred_layer(self.cls_fc_layers(shared_features)) rcnn_reg = self.reg_pred_layer(self.reg_fc_layers(shared_features)) if not self.training: batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg ) batch_dict['batch_cls_preds'] = batch_cls_preds batch_dict['batch_box_preds'] = batch_box_preds batch_dict['cls_preds_normalized'] = False else: targets_dict['rcnn_cls'] = rcnn_cls targets_dict['rcnn_reg'] = rcnn_reg batch_dict['rcnn_cls'] = rcnn_cls batch_dict['rcnn_reg'] = rcnn_reg self.forward_ret_dict = targets_dict return batch_dict

true

1c3861f9352bd1c49f29f5b231e79694c4ccbaae

1,148

py

Python

setup.py

anoadragon453/matrix-nio

e157c5fed96d75797bbb0fadaa0c473fe11b5e6a

[ "Apache-2.0" ]

null

setup.py

anoadragon453/matrix-nio

e157c5fed96d75797bbb0fadaa0c473fe11b5e6a

[ "Apache-2.0" ]

null

setup.py

anoadragon453/matrix-nio

e157c5fed96d75797bbb0fadaa0c473fe11b5e6a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from os import path from io import open from setuptools import find_packages, setup this_directory = path.abspath(path.dirname(__file__)) with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f: long_description = f.read() setup( name="matrix-nio", version="0.5", url="https://github.com/poljar/matrix-nio", author='Damir Jelić', author_email="poljar@termina.org.uk", description=("A Python Matrix client library, designed according to sans " "I/O principles."), long_description=long_description, long_description_content_type="text/markdown", license="ISC", packages=find_packages(), install_requires=[ "attrs", "future", "aiohttp;python_version>'3.5'", "typing;python_version<'3.5'", "h11", "h2", "logbook", "jsonschema", "unpaddedbase64", "pycryptodome", ], extras_require={ "e2e": [ "python-olm>=3.1.0", "peewee>=3.9.5", "cachetools", "atomicwrites", ] }, zip_safe=False )

25.511111

78

0.585366

from os import path from io import open from setuptools import find_packages, setup this_directory = path.abspath(path.dirname(__file__)) with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f: long_description = f.read() setup( name="matrix-nio", version="0.5", url="https://github.com/poljar/matrix-nio", author='Damir Jelić', author_email="poljar@termina.org.uk", description=("A Python Matrix client library, designed according to sans " "I/O principles."), long_description=long_description, long_description_content_type="text/markdown", license="ISC", packages=find_packages(), install_requires=[ "attrs", "future", "aiohttp;python_version>'3.5'", "typing;python_version<'3.5'", "h11", "h2", "logbook", "jsonschema", "unpaddedbase64", "pycryptodome", ], extras_require={ "e2e": [ "python-olm>=3.1.0", "peewee>=3.9.5", "cachetools", "atomicwrites", ] }, zip_safe=False )

true

1c386306cbf996ad6e0e5a309be4b1867d2be2d2

127

py

Python

iot_kernel/__main__.py

iot49/iot-kernel

5eb3e32a7e63b6ce5297cbab3df48d50d45cad50

[ "MIT" ]

2

2021-07-01T12:32:43.000Z

2021-12-16T22:21:20.000Z

iot_kernel/__main__.py

iot49/iot-kernel

5eb3e32a7e63b6ce5297cbab3df48d50d45cad50

[ "MIT" ]

null

iot_kernel/__main__.py

iot49/iot-kernel

5eb3e32a7e63b6ce5297cbab3df48d50d45cad50

[ "MIT" ]

null

from ipykernel.kernelapp import IPKernelApp from .kernel import IoTKernel IPKernelApp.launch_instance(kernel_class=IoTKernel)

25.4

51

0.874016

from ipykernel.kernelapp import IPKernelApp from .kernel import IoTKernel IPKernelApp.launch_instance(kernel_class=IoTKernel)

true

1c3864289d1471dfe5e1c5439cfcea90e3d1c2a2

1,438

py

Python

tests/test_filename_checking.py

rcbops/flake8-filename

1bfb0f6174e11bf6d3750aecddcb8e0216ad645b

[ "Apache-2.0" ]

null

tests/test_filename_checking.py

rcbops/flake8-filename

1bfb0f6174e11bf6d3750aecddcb8e0216ad645b

[ "Apache-2.0" ]

4

2018-05-30T14:31:43.000Z

2020-08-10T08:23:28.000Z

tests/test_filename_checking.py

rcbops/flake8-filename

1bfb0f6174e11bf6d3750aecddcb8e0216ad645b

[ "Apache-2.0" ]

4

2018-05-25T15:21:38.000Z

2020-02-22T13:04:24.000Z

# -*- coding: utf-8 -*- import pytest # args to only use checks that raise an 'N' prefixed error extra_args = ['--select', 'N'] config = r""" [flake8] filename_check1 = filter_regex=test_.+ filename_regex=test_[\w-]+$ """ def test_pass_filter_and_match_filename(flake8dir): """Verify that no violations are raised when a file passes the filter and matches the desired filename.""" # Setup flake8dir.make_setup_cfg(config) flake8dir.make_file('test_File-10.py', 'import sys') # Test result = flake8dir.run_flake8(extra_args) assert result.out_lines == [] def test_fail_filter(flake8dir): """Verify that no violations are raised when a file fails the filter.""" # Setup flake8dir.make_setup_cfg(config) flake8dir.make_file('regular_file.py', 'import sys') # Test result = flake8dir.run_flake8(extra_args) assert result.out_lines == [] def test_pass_filter_and_fail_match(flake8dir): """Verify that a violation is raised when a file passes the filter and fails to match the desired filename.""" # Setup flake8dir.make_setup_cfg(config) flake8dir.make_file('test_not.allowed.py', 'import sys') expected = ["./test_not.allowed.py:0:1: N501 filename failed regex validation 'test_[\\w-]+$'"] # Test result = flake8dir.run_flake8(extra_args) observed = result.out_lines pytest.helpers.assert_lines(expected, observed)

27.132075

114

0.698192

import pytest extra_args = ['--select', 'N'] config = r""" [flake8] filename_check1 = filter_regex=test_.+ filename_regex=test_[\w-]+$ """ def test_pass_filter_and_match_filename(flake8dir): flake8dir.make_setup_cfg(config) flake8dir.make_file('test_File-10.py', 'import sys') result = flake8dir.run_flake8(extra_args) assert result.out_lines == [] def test_fail_filter(flake8dir): flake8dir.make_setup_cfg(config) flake8dir.make_file('regular_file.py', 'import sys') result = flake8dir.run_flake8(extra_args) assert result.out_lines == [] def test_pass_filter_and_fail_match(flake8dir): flake8dir.make_setup_cfg(config) flake8dir.make_file('test_not.allowed.py', 'import sys') expected = ["./test_not.allowed.py:0:1: N501 filename failed regex validation 'test_[\\w-]+$'"] result = flake8dir.run_flake8(extra_args) observed = result.out_lines pytest.helpers.assert_lines(expected, observed)

true

1c38649c2ee470c2fd8143faecb7a0b42c0a9261

3,292

py

Python

targets/waxwing/base.py

timvideos/litex-buildenv

a103774342c0265458979a79082b233d9ce08edb

[ "BSD-2-Clause" ]

198

2018-01-17T05:39:54.000Z

2022-03-15T08:59:16.000Z

targets/waxwing/base.py

timvideos/litex-buildenv

a103774342c0265458979a79082b233d9ce08edb

[ "BSD-2-Clause" ]

610

2017-12-31T01:32:32.000Z

2022-03-19T22:07:28.000Z

targets/waxwing/base.py

timvideos/litex-buildenv

a103774342c0265458979a79082b233d9ce08edb

[ "BSD-2-Clause" ]

85

2018-01-13T05:51:38.000Z

2022-02-11T18:54:14.000Z

# Support for the Numato Saturn (http://numato.com/product/saturn-spartan-6-fpga-development-board-with-ddr-sdram) from fractions import Fraction from migen import * from litex.soc.integration.soc_sdram import * from litex.soc.integration.builder import * from litex.soc.interconnect import wishbone from litedram.modules import MT46H32M16 from litedram.phy import s6ddrphy from litedram.core import ControllerSettings from targets.utils import dict_set_max #from gateware import cas from gateware import info from gateware import spi_flash from .crg import _CRG class BaseSoC(SoCSDRAM): mem_map = {**SoCSDRAM.mem_map, **{ 'spiflash': 0x20000000, }} def __init__(self, platform, **kwargs): if kwargs.get('cpu_type', None) == 'mor1kx': dict_set_max(kwargs, 'integrated_rom_size', 0x10000) else: dict_set_max(kwargs, 'integrated_rom_size', 0x8000) dict_set_max(kwargs, 'integrated_sram_size', 0x8000) sys_clk_freq = (31 + Fraction(1, 4))*1000*1000 # SoCSDRAM --------------------------------------------------------------------------------- SoCSDRAM.__init__(self, platform, clk_freq=sys_clk_freq, **kwargs) # CRG -------------------------------------------------------------------------------------- self.submodules.crg = _CRG(platform, sys_clk_freq) self.platform.add_period_constraint(self.crg.cd_sys.clk, 1e9/sys_clk_freq) # DDR2 SDRAM ------------------------------------------------------------------------------- if True: sdram_module = MT46H32M16(sys_clk_freq, "1:2") self.submodules.ddrphy = s6ddrphy.S6HalfRateDDRPHY( platform.request("ddram"), memtype = sdram_module.memtype, rd_bitslip = 2, wr_bitslip = 3, dqs_ddr_alignment="C1") self.add_csr("ddrphy") controller_settings = ControllerSettings( with_bandwidth=True) self.register_sdram( self.ddrphy, geom_settings = sdram_module.geom_settings, timing_settings = sdram_module.timing_settings, controller_settings=controller_settings) self.comb += [ self.ddrphy.clk4x_wr_strb.eq(self.crg.clk4x_wr_strb), self.ddrphy.clk4x_rd_strb.eq(self.crg.clk4x_rd_strb), ] # Basic peripherals ------------------------------------------------------------------------ # info module self.submodules.info = info.Info(platform, self.__class__.__name__) self.add_csr("info") # control and status module #self.submodules.cas = cas.ControlAndStatus(platform, sys_clk_freq) self.add_csr("cas") # Add debug interface if the CPU has one --------------------------------------------------- if hasattr(self.cpu, "debug_bus"): self.register_mem( name="vexriscv_debug", address=0xf00f0000, interface=self.cpu.debug_bus, size=0x100) # Memory mapped SPI Flash ------------------------------------------------------------------ # TODO: Add SPI flash. SoC = BaseSoC

38.729412

114

0.545869

from fractions import Fraction from migen import * from litex.soc.integration.soc_sdram import * from litex.soc.integration.builder import * from litex.soc.interconnect import wishbone from litedram.modules import MT46H32M16 from litedram.phy import s6ddrphy from litedram.core import ControllerSettings from targets.utils import dict_set_max from gateware import info from gateware import spi_flash from .crg import _CRG class BaseSoC(SoCSDRAM): mem_map = {**SoCSDRAM.mem_map, **{ 'spiflash': 0x20000000, }} def __init__(self, platform, **kwargs): if kwargs.get('cpu_type', None) == 'mor1kx': dict_set_max(kwargs, 'integrated_rom_size', 0x10000) else: dict_set_max(kwargs, 'integrated_rom_size', 0x8000) dict_set_max(kwargs, 'integrated_sram_size', 0x8000) sys_clk_freq = (31 + Fraction(1, 4))*1000*1000 SoCSDRAM.__init__(self, platform, clk_freq=sys_clk_freq, **kwargs) self.submodules.crg = _CRG(platform, sys_clk_freq) self.platform.add_period_constraint(self.crg.cd_sys.clk, 1e9/sys_clk_freq) if True: sdram_module = MT46H32M16(sys_clk_freq, "1:2") self.submodules.ddrphy = s6ddrphy.S6HalfRateDDRPHY( platform.request("ddram"), memtype = sdram_module.memtype, rd_bitslip = 2, wr_bitslip = 3, dqs_ddr_alignment="C1") self.add_csr("ddrphy") controller_settings = ControllerSettings( with_bandwidth=True) self.register_sdram( self.ddrphy, geom_settings = sdram_module.geom_settings, timing_settings = sdram_module.timing_settings, controller_settings=controller_settings) self.comb += [ self.ddrphy.clk4x_wr_strb.eq(self.crg.clk4x_wr_strb), self.ddrphy.clk4x_rd_strb.eq(self.crg.clk4x_rd_strb), ] self.submodules.info = info.Info(platform, self.__class__.__name__) self.add_csr("info") self.add_csr("cas") if hasattr(self.cpu, "debug_bus"): self.register_mem( name="vexriscv_debug", address=0xf00f0000, interface=self.cpu.debug_bus, size=0x100) SoC = BaseSoC

true

1c3864a2202941f66300375795f86ea08723517c

9,270

py

Python

sdk/synapse/azure-mgmt-synapse/azure/mgmt/synapse/operations/_sql_pool_metadata_sync_configs_operations.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/synapse/azure-mgmt-synapse/azure/mgmt/synapse/operations/_sql_pool_metadata_sync_configs_operations.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/synapse/azure-mgmt-synapse/azure/mgmt/synapse/operations/_sql_pool_metadata_sync_configs_operations.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpRequest, HttpResponse from azure.mgmt.core.exceptions import ARMErrorFormat from .. import models as _models if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Callable, Dict, Generic, Optional, TypeVar T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] class SqlPoolMetadataSyncConfigsOperations(object): """SqlPoolMetadataSyncConfigsOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.synapse.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def get( self, resource_group_name, # type: str workspace_name, # type: str sql_pool_name, # type: str **kwargs # type: Any ): # type: (...) -> Optional["_models.MetadataSyncConfig"] """Get SQL pool metadata sync config. Get the metadata sync configuration for a SQL pool. :param resource_group_name: The name of the resource group. The name is case insensitive. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param sql_pool_name: SQL pool name. :type sql_pool_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: MetadataSyncConfig, or the result of cls(response) :rtype: ~azure.mgmt.synapse.models.MetadataSyncConfig or None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.MetadataSyncConfig"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2021-06-01" accept = "application/json" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str', min_length=1), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'sqlPoolName': self._serialize.url("sql_pool_name", sql_pool_name, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 404]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('MetadataSyncConfig', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Synapse/workspaces/{workspaceName}/sqlPools/{sqlPoolName}/metadataSync/config'} # type: ignore def create( self, resource_group_name, # type: str workspace_name, # type: str sql_pool_name, # type: str metadata_sync_configuration, # type: "_models.MetadataSyncConfig" **kwargs # type: Any ): # type: (...) -> Optional["_models.MetadataSyncConfig"] """Set SQL pool metadata sync config. Set the metadata sync configuration for a SQL pool. :param resource_group_name: The name of the resource group. The name is case insensitive. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param sql_pool_name: SQL pool name. :type sql_pool_name: str :param metadata_sync_configuration: Metadata sync configuration. :type metadata_sync_configuration: ~azure.mgmt.synapse.models.MetadataSyncConfig :keyword callable cls: A custom type or function that will be passed the direct response :return: MetadataSyncConfig, or the result of cls(response) :rtype: ~azure.mgmt.synapse.models.MetadataSyncConfig or None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.MetadataSyncConfig"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2021-06-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.create.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str', min_length=1), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'sqlPoolName': self._serialize.url("sql_pool_name", sql_pool_name, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(metadata_sync_configuration, 'MetadataSyncConfig') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 404]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('MetadataSyncConfig', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Synapse/workspaces/{workspaceName}/sqlPools/{sqlPoolName}/metadataSync/config'} # type: ignore

48.789474

213

0.68123

from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpRequest, HttpResponse from azure.mgmt.core.exceptions import ARMErrorFormat from .. import models as _models if TYPE_CHECKING: from typing import Any, Callable, Dict, Generic, Optional, TypeVar T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] class SqlPoolMetadataSyncConfigsOperations(object): models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def get( self, resource_group_name, workspace_name, sql_pool_name, **kwargs ): cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2021-06-01" accept = "application/json" url = self.get.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str', min_length=1), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'sqlPoolName': self._serialize.url("sql_pool_name", sql_pool_name, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 404]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('MetadataSyncConfig', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Synapse/workspaces/{workspaceName}/sqlPools/{sqlPoolName}/metadataSync/config'} def create( self, resource_group_name, workspace_name, sql_pool_name, metadata_sync_configuration, **kwargs ): cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2021-06-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self.create.metadata['url'] path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str', min_length=1), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1), 'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str'), 'sqlPoolName': self._serialize.url("sql_pool_name", sql_pool_name, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(metadata_sync_configuration, 'MetadataSyncConfig') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 404]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('MetadataSyncConfig', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Synapse/workspaces/{workspaceName}/sqlPools/{sqlPoolName}/metadataSync/config'}

true

1c3864ebd82b1039ffbfd4ca1a853f561a39ca03

15,279

py

Python

mol_dqn/experimental/multi_obj_opt.py

awesome-archive/google-research

6b3c751abbf658b33fb03e51d7b84105d2dbea68

[ "Apache-2.0" ]

1

2019-02-16T12:17:14.000Z

mol_dqn/experimental/multi_obj_opt.py

awesome-archive/google-research

6b3c751abbf658b33fb03e51d7b84105d2dbea68

[ "Apache-2.0" ]

null

mol_dqn/experimental/multi_obj_opt.py

awesome-archive/google-research

6b3c751abbf658b33fb03e51d7b84105d2dbea68

[ "Apache-2.0" ]

1

2019-01-27T13:03:12.000Z

# coding=utf-8 # Copyright 2018 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Maximizes the QED of the molecule while keep similarity. Multi-Objective optimization using multiple Q functions. Obj1: QED; Obj2: similarity. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import json import os import random import time from absl import app from absl import flags from absl import logging from baselines.common import schedules from baselines.deepq import replay_buffer import numpy as np from rdkit import Chem from rdkit import DataStructs from rdkit.Chem import AllChem from rdkit.Chem import Descriptors from rdkit.Chem import QED import tensorflow as tf from tensorflow import gfile from mol_dqn.chemgraph.mcts import deep_q_networks from mol_dqn.chemgraph.mcts import molecules as molecules_mdp from mol_dqn.chemgraph.py import molecules from mol_dqn.chemgraph.tensorflow import core flags.DEFINE_string('model_dir', '/namespace/gas/primary/zzp/dqn/r=3/exp2_bs_dqn', 'The directory to save data to.') flags.DEFINE_string('target_molecule', 'C1CCC2CCCCC2C1', 'The SMILES string of the target molecule.') flags.DEFINE_string('start_molecule', None, 'The SMILES string of the start molecule.') flags.DEFINE_string('hparams', None, 'Filename for serialized HParams.') flags.DEFINE_boolean('multi_objective', True, 'Whether to run multi objective DQN.') flags.DEFINE_integer('num_episodes', 2000, 'num episodes') flags.DEFINE_float('gamma', 0.999, 'discount') FLAGS = flags.FLAGS class TargetWeightMolecule(molecules_mdp.Molecule): """Defines the subclass of a molecule MDP with a target molecular weight.""" def __init__(self, target_weight, **kwargs): """Initializes the class. Args: target_weight: Float. the target molecular weight. **kwargs: The keyword arguments passed to the parent class. """ super(TargetWeightMolecule, self).__init__(**kwargs) self.target_weight = target_weight def _reward(self): """Calculates the reward of the current state. The reward is defined as the negative l2 distance between the current molecular weight and target molecular weight. Returns: Float. The negative distance. """ molecule = Chem.MolFromSmiles(self._state) if molecule is None: return -self.target_weight**2 return -(Descriptors.MolWt(molecule) - self.target_weight)**2 class MultiObjectiveRewardMolecule(molecules_mdp.Molecule): """Defines the subclass of generating a molecule with a specific reward. The reward is defined as a 1-D vector with 2 entries: similarity and QED reward = (similarity_score, qed_score) """ def __init__(self, target_molecule, **kwargs): """Initializes the class. Args: target_molecule: SMILES string. the target molecule against which we calculate the similarity. **kwargs: The keyword arguments passed to the parent class. """ super(MultiObjectiveRewardMolecule, self).__init__(**kwargs) target_molecule = Chem.MolFromSmiles(target_molecule) self._target_mol_fingerprint = self.get_fingerprint(target_molecule) self._target_mol_scaffold = molecules.get_scaffold(target_molecule) self.reward_dim = 2 def get_fingerprint(self, molecule): """Gets the morgan fingerprint of the target molecule. Args: molecule: Chem.Mol. The current molecule. Returns: rdkit.ExplicitBitVect. The fingerprint of the target. """ return AllChem.GetMorganFingerprint(molecule, radius=2) def get_similarity(self, smiles): """Gets the similarity between the current molecule and the target molecule. Args: smiles: String. The SMILES string for the current molecule. Returns: Float. The Tanimoto similarity. """ structure = Chem.MolFromSmiles(smiles) if structure is None: return 0.0 fingerprint_structure = self.get_fingerprint(structure) return DataStructs.TanimotoSimilarity(self._target_mol_fingerprint, fingerprint_structure) def _reward(self): """Calculates the reward of the current state. The reward is defined as a tuple of the similarity and QED value. Returns: A tuple of the similarity and qed value """ # calculate similarity. # if the current molecule does not contain the scaffold of the target, # similarity is zero. if self._state is None: return 0.0, 0.0 mol = Chem.MolFromSmiles(self._state) if mol is None: return 0.0, 0.0 if molecules.contains_scaffold(mol, self._target_mol_scaffold): similarity_score = self.get_similarity(self._state) else: similarity_score = 0.0 # calculate QED qed_value = QED.qed(mol) return similarity_score * FLAGS.gamma**( self.max_steps - self._counter), qed_value * FLAGS.gamma**( self.max_steps - self._counter) # TODO(zzp): use the tf.estimator interface. def run_training(hparams, environment, dqn): """Runs the training procedure. Briefly, the agent runs the action network to get an action to take in the environment. The state transition and reward are stored in the memory. Periodically the agent samples a batch of samples from the memory to update(train) its Q network. Note that the Q network and the action network share the same set of parameters, so the action network is also updated by the samples of (state, action, next_state, reward) batches. Args: hparams: tf.HParams. The hyper parameters of the model. environment: molecules.Molecule. The environment to run on. dqn: An instance of the DeepQNetwork class. Returns: None """ summary_writer = tf.summary.FileWriter(FLAGS.model_dir) tf.reset_default_graph() with tf.Session() as sess: dqn.build() model_saver = tf.Saver(max_to_keep=hparams.max_num_checkpoints) # The schedule for the epsilon in epsilon greedy policy. exploration = schedules.PiecewiseSchedule( [(0, 1.0), (int(hparams.num_episodes / 2), 0.1), (hparams.num_episodes, 0.01)], outside_value=0.01) if hparams.prioritized: memory = replay_buffer.PrioritizedReplayBuffer(hparams.replay_buffer_size, hparams.prioritized_alpha) beta_schedule = schedules.LinearSchedule( hparams.num_episodes, initial_p=hparams.prioritized_beta, final_p=0) else: memory = replay_buffer.ReplayBuffer(hparams.replay_buffer_size) beta_schedule = None sess.run(tf.global_variables_initializer()) sess.run(dqn.update_op) global_step = 0 for episode in range(FLAGS.num_episodes * 6): sim_weight = random.random() dqn.objective_weight = np.array([[sim_weight], [1 - sim_weight]]) logging.info('Episode %i, ObjWeight %s', episode, str(dqn.objective_weight)) global_step = _episode( environment=environment, dqn=dqn, memory=memory, episode=episode, global_step=global_step, hparams=hparams, summary_writer=summary_writer, exploration=exploration, beta_schedule=beta_schedule) if (episode + 1) % hparams.update_frequency == 0: sess.run(dqn.update_op) if (episode + 1) % hparams.save_frequency == 0: model_saver.save( sess, os.path.join(FLAGS.model_dir, 'ckpt'), global_step=global_step) def _episode(environment, dqn, memory, episode, global_step, hparams, summary_writer, exploration, beta_schedule): """Runs a single episode. Args: environment: molecules.Molecule; the environment to run on. dqn: DeepQNetwork used for estimating rewards. memory: ReplayBuffer used to store observations and rewards. episode: Integer episode number. global_step: Integer global step; the total number of steps across all episodes. hparams: HParams. summary_writer: FileWriter used for writing Summary protos. exploration: Schedule used for exploration in the environment. beta_schedule: Schedule used for prioritized replay buffers. Returns: Updated global_step. """ episode_start_time = time.time() environment.initialize() if hparams.num_bootstrap_heads: head = np.random.randint(hparams.num_bootstrap_heads) else: head = 0 for step in range(hparams.max_steps_per_episode): result = _step( environment=environment, dqn=dqn, memory=memory, episode=episode, hparams=hparams, exploration=exploration, head=head) if step == hparams.max_steps_per_episode - 1: episode_summary = dqn.log_result(result.state, result.reward) summary_writer.add_summary(episode_summary, global_step) logging.info('Episode %d/%d took %gs', episode + 1, hparams.num_episodes, time.time() - episode_start_time) logging.info('SMILES: %s\n', result.state) # Use %s since reward can be a tuple or a float number. logging.info('The reward is: %s', str(result.reward)) if (episode > min(50, hparams.num_episodes / 10)) and ( global_step % hparams.learning_frequency == 0): if hparams.prioritized: (state_t, _, reward_t, state_tp1, done_mask, weight, indices) = memory.sample( hparams.batch_size, beta=beta_schedule.value(episode)) else: (state_t, _, reward_t, state_tp1, done_mask) = memory.sample(hparams.batch_size) weight = np.ones([reward_t.shape[0]]) # np.atleast_2d cannot be used here because a new dimension will # be always added in the front and there is no way of changing this. if reward_t.ndim == 1: reward_t = np.expand_dims(reward_t, axis=1) td_error, error_summary, _ = dqn.train( states=state_t, rewards=reward_t, next_states=state_tp1, done=np.expand_dims(done_mask, axis=1), weight=np.expand_dims(weight, axis=1)) summary_writer.add_summary(error_summary, global_step) logging.info('Current TD error: %.4f', np.mean(np.abs(td_error))) if hparams.prioritized: memory.update_priorities( indices, np.abs(np.squeeze(td_error) + hparams.prioritized_epsilon).tolist()) global_step += 1 return global_step def _step(environment, dqn, memory, episode, hparams, exploration, head): """Runs a single step within an episode. Args: environment: molecules.Molecule; the environment to run on. dqn: DeepQNetwork used for estimating rewards. memory: ReplayBuffer used to store observations and rewards. episode: Integer episode number. hparams: HParams. exploration: Schedule used for exploration in the environment. head: Integer index of the DeepQNetwork head to use. Returns: molecules.Result object containing the result of the step. """ # Compute the encoding for each valid action from the current state. steps_left = hparams.max_steps_per_episode - environment.num_steps_taken valid_actions = list(environment.get_valid_actions()) observations = np.vstack([ np.append(deep_q_networks.get_fingerprint(act, hparams), steps_left) for act in valid_actions ]) action = valid_actions[dqn.get_action( observations, head=head, update_epsilon=exploration.value(episode))] result = environment.step(action) action_fingerprints = np.vstack([ np.append(deep_q_networks.get_fingerprint(act, hparams), steps_left) for act in environment.get_valid_actions() ]) # we store the fingerprint of the action in obs_t so action # does not matter here. memory.add( obs_t=np.append( deep_q_networks.get_fingerprint(action, hparams), steps_left), action=0, reward=result.reward, obs_tp1=action_fingerprints, done=float(result.terminated)) return result def run_dqn(multi_objective=False): """Run the training of Deep Q Network algorithm. Args: multi_objective: Boolean. Whether to run the multiobjective DQN. """ if FLAGS.hparams is not None: with gfile.Open(FLAGS.hparams, 'r') as f: hparams = deep_q_networks.get_hparams(**json.load(f)) else: hparams = deep_q_networks.get_hparams() logging.info( 'HParams:\n%s', '\n'.join([ '\t%s: %s' % (key, value) for key, value in sorted(hparams.values().iteritems()) ])) # TODO(zzp): merge single objective DQN to multi objective DQN. if multi_objective: environment = MultiObjectiveRewardMolecule( target_molecule=FLAGS.target_molecule, atom_types=set(hparams.atom_types), init_mol=FLAGS.start_molecule, allow_removal=hparams.allow_removal, allow_no_modification=hparams.allow_no_modification, allow_bonds_between_rings=False, allowed_ring_sizes={3, 4, 5, 6}, max_steps=hparams.max_steps_per_episode) dqn = deep_q_networks.MultiObjectiveDeepQNetwork( objective_weight=np.array([[0.5], [0.5]]), input_shape=(hparams.batch_size, hparams.fingerprint_length + 1), q_fn=functools.partial( deep_q_networks.multi_layer_model, hparams=hparams), optimizer=hparams.optimizer, grad_clipping=hparams.grad_clipping, num_bootstrap_heads=hparams.num_bootstrap_heads, gamma=hparams.gamma, epsilon=1.0) else: environment = TargetWeightMolecule( target_weight=FLAGS.target_weight, atom_types=set(hparams.atom_types), init_mol=FLAGS.start_molecule, allow_removal=hparams.allow_removal, allow_no_modification=hparams.allow_no_modification, allow_bonds_between_rings=hparams.allow_bonds_between_rings, allowed_ring_sizes=set(hparams.allowed_ring_sizes), max_steps=hparams.max_steps_per_episode) dqn = deep_q_networks.DeepQNetwork( input_shape=(hparams.batch_size, hparams.fingerprint_length + 1), q_fn=functools.partial( deep_q_networks.multi_layer_model, hparams=hparams), optimizer=hparams.optimizer, grad_clipping=hparams.grad_clipping, num_bootstrap_heads=hparams.num_bootstrap_heads, gamma=hparams.gamma, epsilon=1.0) run_training( hparams=hparams, environment=environment, dqn=dqn, ) core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json')) def main(argv): del argv # unused. run_dqn(FLAGS.multi_objective) if __name__ == '__main__': app.run(main)

35.532558

80

0.70214

from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import json import os import random import time from absl import app from absl import flags from absl import logging from baselines.common import schedules from baselines.deepq import replay_buffer import numpy as np from rdkit import Chem from rdkit import DataStructs from rdkit.Chem import AllChem from rdkit.Chem import Descriptors from rdkit.Chem import QED import tensorflow as tf from tensorflow import gfile from mol_dqn.chemgraph.mcts import deep_q_networks from mol_dqn.chemgraph.mcts import molecules as molecules_mdp from mol_dqn.chemgraph.py import molecules from mol_dqn.chemgraph.tensorflow import core flags.DEFINE_string('model_dir', '/namespace/gas/primary/zzp/dqn/r=3/exp2_bs_dqn', 'The directory to save data to.') flags.DEFINE_string('target_molecule', 'C1CCC2CCCCC2C1', 'The SMILES string of the target molecule.') flags.DEFINE_string('start_molecule', None, 'The SMILES string of the start molecule.') flags.DEFINE_string('hparams', None, 'Filename for serialized HParams.') flags.DEFINE_boolean('multi_objective', True, 'Whether to run multi objective DQN.') flags.DEFINE_integer('num_episodes', 2000, 'num episodes') flags.DEFINE_float('gamma', 0.999, 'discount') FLAGS = flags.FLAGS class TargetWeightMolecule(molecules_mdp.Molecule): def __init__(self, target_weight, **kwargs): super(TargetWeightMolecule, self).__init__(**kwargs) self.target_weight = target_weight def _reward(self): molecule = Chem.MolFromSmiles(self._state) if molecule is None: return -self.target_weight**2 return -(Descriptors.MolWt(molecule) - self.target_weight)**2 class MultiObjectiveRewardMolecule(molecules_mdp.Molecule): def __init__(self, target_molecule, **kwargs): super(MultiObjectiveRewardMolecule, self).__init__(**kwargs) target_molecule = Chem.MolFromSmiles(target_molecule) self._target_mol_fingerprint = self.get_fingerprint(target_molecule) self._target_mol_scaffold = molecules.get_scaffold(target_molecule) self.reward_dim = 2 def get_fingerprint(self, molecule): return AllChem.GetMorganFingerprint(molecule, radius=2) def get_similarity(self, smiles): structure = Chem.MolFromSmiles(smiles) if structure is None: return 0.0 fingerprint_structure = self.get_fingerprint(structure) return DataStructs.TanimotoSimilarity(self._target_mol_fingerprint, fingerprint_structure) def _reward(self): if self._state is None: return 0.0, 0.0 mol = Chem.MolFromSmiles(self._state) if mol is None: return 0.0, 0.0 if molecules.contains_scaffold(mol, self._target_mol_scaffold): similarity_score = self.get_similarity(self._state) else: similarity_score = 0.0 qed_value = QED.qed(mol) return similarity_score * FLAGS.gamma**( self.max_steps - self._counter), qed_value * FLAGS.gamma**( self.max_steps - self._counter) def run_training(hparams, environment, dqn): summary_writer = tf.summary.FileWriter(FLAGS.model_dir) tf.reset_default_graph() with tf.Session() as sess: dqn.build() model_saver = tf.Saver(max_to_keep=hparams.max_num_checkpoints) exploration = schedules.PiecewiseSchedule( [(0, 1.0), (int(hparams.num_episodes / 2), 0.1), (hparams.num_episodes, 0.01)], outside_value=0.01) if hparams.prioritized: memory = replay_buffer.PrioritizedReplayBuffer(hparams.replay_buffer_size, hparams.prioritized_alpha) beta_schedule = schedules.LinearSchedule( hparams.num_episodes, initial_p=hparams.prioritized_beta, final_p=0) else: memory = replay_buffer.ReplayBuffer(hparams.replay_buffer_size) beta_schedule = None sess.run(tf.global_variables_initializer()) sess.run(dqn.update_op) global_step = 0 for episode in range(FLAGS.num_episodes * 6): sim_weight = random.random() dqn.objective_weight = np.array([[sim_weight], [1 - sim_weight]]) logging.info('Episode %i, ObjWeight %s', episode, str(dqn.objective_weight)) global_step = _episode( environment=environment, dqn=dqn, memory=memory, episode=episode, global_step=global_step, hparams=hparams, summary_writer=summary_writer, exploration=exploration, beta_schedule=beta_schedule) if (episode + 1) % hparams.update_frequency == 0: sess.run(dqn.update_op) if (episode + 1) % hparams.save_frequency == 0: model_saver.save( sess, os.path.join(FLAGS.model_dir, 'ckpt'), global_step=global_step) def _episode(environment, dqn, memory, episode, global_step, hparams, summary_writer, exploration, beta_schedule): episode_start_time = time.time() environment.initialize() if hparams.num_bootstrap_heads: head = np.random.randint(hparams.num_bootstrap_heads) else: head = 0 for step in range(hparams.max_steps_per_episode): result = _step( environment=environment, dqn=dqn, memory=memory, episode=episode, hparams=hparams, exploration=exploration, head=head) if step == hparams.max_steps_per_episode - 1: episode_summary = dqn.log_result(result.state, result.reward) summary_writer.add_summary(episode_summary, global_step) logging.info('Episode %d/%d took %gs', episode + 1, hparams.num_episodes, time.time() - episode_start_time) logging.info('SMILES: %s\n', result.state) logging.info('The reward is: %s', str(result.reward)) if (episode > min(50, hparams.num_episodes / 10)) and ( global_step % hparams.learning_frequency == 0): if hparams.prioritized: (state_t, _, reward_t, state_tp1, done_mask, weight, indices) = memory.sample( hparams.batch_size, beta=beta_schedule.value(episode)) else: (state_t, _, reward_t, state_tp1, done_mask) = memory.sample(hparams.batch_size) weight = np.ones([reward_t.shape[0]]) if reward_t.ndim == 1: reward_t = np.expand_dims(reward_t, axis=1) td_error, error_summary, _ = dqn.train( states=state_t, rewards=reward_t, next_states=state_tp1, done=np.expand_dims(done_mask, axis=1), weight=np.expand_dims(weight, axis=1)) summary_writer.add_summary(error_summary, global_step) logging.info('Current TD error: %.4f', np.mean(np.abs(td_error))) if hparams.prioritized: memory.update_priorities( indices, np.abs(np.squeeze(td_error) + hparams.prioritized_epsilon).tolist()) global_step += 1 return global_step def _step(environment, dqn, memory, episode, hparams, exploration, head): steps_left = hparams.max_steps_per_episode - environment.num_steps_taken valid_actions = list(environment.get_valid_actions()) observations = np.vstack([ np.append(deep_q_networks.get_fingerprint(act, hparams), steps_left) for act in valid_actions ]) action = valid_actions[dqn.get_action( observations, head=head, update_epsilon=exploration.value(episode))] result = environment.step(action) action_fingerprints = np.vstack([ np.append(deep_q_networks.get_fingerprint(act, hparams), steps_left) for act in environment.get_valid_actions() ]) memory.add( obs_t=np.append( deep_q_networks.get_fingerprint(action, hparams), steps_left), action=0, reward=result.reward, obs_tp1=action_fingerprints, done=float(result.terminated)) return result def run_dqn(multi_objective=False): if FLAGS.hparams is not None: with gfile.Open(FLAGS.hparams, 'r') as f: hparams = deep_q_networks.get_hparams(**json.load(f)) else: hparams = deep_q_networks.get_hparams() logging.info( 'HParams:\n%s', '\n'.join([ '\t%s: %s' % (key, value) for key, value in sorted(hparams.values().iteritems()) ])) if multi_objective: environment = MultiObjectiveRewardMolecule( target_molecule=FLAGS.target_molecule, atom_types=set(hparams.atom_types), init_mol=FLAGS.start_molecule, allow_removal=hparams.allow_removal, allow_no_modification=hparams.allow_no_modification, allow_bonds_between_rings=False, allowed_ring_sizes={3, 4, 5, 6}, max_steps=hparams.max_steps_per_episode) dqn = deep_q_networks.MultiObjectiveDeepQNetwork( objective_weight=np.array([[0.5], [0.5]]), input_shape=(hparams.batch_size, hparams.fingerprint_length + 1), q_fn=functools.partial( deep_q_networks.multi_layer_model, hparams=hparams), optimizer=hparams.optimizer, grad_clipping=hparams.grad_clipping, num_bootstrap_heads=hparams.num_bootstrap_heads, gamma=hparams.gamma, epsilon=1.0) else: environment = TargetWeightMolecule( target_weight=FLAGS.target_weight, atom_types=set(hparams.atom_types), init_mol=FLAGS.start_molecule, allow_removal=hparams.allow_removal, allow_no_modification=hparams.allow_no_modification, allow_bonds_between_rings=hparams.allow_bonds_between_rings, allowed_ring_sizes=set(hparams.allowed_ring_sizes), max_steps=hparams.max_steps_per_episode) dqn = deep_q_networks.DeepQNetwork( input_shape=(hparams.batch_size, hparams.fingerprint_length + 1), q_fn=functools.partial( deep_q_networks.multi_layer_model, hparams=hparams), optimizer=hparams.optimizer, grad_clipping=hparams.grad_clipping, num_bootstrap_heads=hparams.num_bootstrap_heads, gamma=hparams.gamma, epsilon=1.0) run_training( hparams=hparams, environment=environment, dqn=dqn, ) core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json')) def main(argv): del argv run_dqn(FLAGS.multi_objective) if __name__ == '__main__': app.run(main)

true