xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

d211f9f70b797554c340157b8758a3ad51e66320

170

py

Python

movement_assistant/models.py

davidwickerhf/movement-assistant

570380adf440faa36993ab8f52e386584a90fec8

[ "MIT" ]

3

2020-06-11T13:06:21.000Z

2020-06-11T21:35:41.000Z

movement_assistant/models.py

davidwickerhf/movement-assistant

570380adf440faa36993ab8f52e386584a90fec8

[ "MIT" ]

25

2020-04-29T16:44:05.000Z

2020-06-11T08:18:47.000Z

movement_assistant/models.py

davidwickerhf/fff-transparency-wg

570380adf440faa36993ab8f52e386584a90fec8

[ "MIT" ]

1

2020-12-23T09:33:05.000Z

from movement_assistant.bots.telebot.activate import is_subgroup, parent_group, purpose from movement_assistant import db # https://www.youtube.com/watch?v=juPQ04_twtA

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null

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0

1

0

6

d22b24091bfb015dc2acb000ca344d333973f210

30

py

Python

root_utils/bmn/__init__.py

t3hseus/ariadne

b4471a37741000e22281c4d6ff647d65ab9e1914

[ "MIT" ]

6

2020-08-28T22:44:07.000Z

2022-01-24T20:53:00.000Z

root_utils/bmn/__init__.py

t3hseus/ariadne

b4471a37741000e22281c4d6ff647d65ab9e1914

[ "MIT" ]

1

2021-02-20T09:38:46.000Z

root_utils/bmn/__init__.py

t3hseus/ariadne

b4471a37741000e22281c4d6ff647d65ab9e1914

[ "MIT" ]

2

2021-10-04T09:25:06.000Z

2022-02-09T09:09:09.000Z

from .utils import root2pandas

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0.866667

4

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0.037037

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30

1

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1

0

1

0

6

d2508d5673ef1a39ce332c042c87a8ac7aadb0e4

32

py

Python

kernelml/hdre/__init__.py

Freedomtowin/kernel_optimizer

2676044e0f287cd8dda8f9f92a6d3813544965e4

[ "MIT" ]

9

2019-10-03T18:02:29.000Z

2021-08-09T09:30:33.000Z

hdre/hdre_bycython/__init__.py

freedomtowin/high-density-region-estimator

a9c4d30c32d8f6ce16d2bc0712bdcc588124ed61

[ "MIT" ]

1

2019-12-11T09:46:09.000Z

2021-06-17T00:45:16.000Z

hdre/hdre_bycython/__init__.py

freedomtowin/high-density-region-estimator

a9c4d30c32d8f6ce16d2bc0712bdcc588124ed61

[ "MIT" ]

3

2020-04-18T10:41:56.000Z

2021-06-17T02:06:14.000Z

from .region_estimator import *

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6

d26ae3d61cbcd3e5ef2685ce9712c2114b3a9da8

14,662

py

Python

api/api.py

mcass19/moving_features_grupo8

fad76c6c71506977f2a56d5a3432ed1832515493

[ "Apache-2.0" ]

null

api/api.py

mcass19/moving_features_grupo8

fad76c6c71506977f2a56d5a3432ed1832515493

[ "Apache-2.0" ]

null

api/api.py

mcass19/moving_features_grupo8

fad76c6c71506977f2a56d5a3432ed1832515493

[ "Apache-2.0" ]

null

import flask # import psycopg2 # from postgis.psycopg import register # from mobilitydb.psycopg import register from flask_cors import CORS app = flask.Flask(__name__) CORS(app) app.config["DEBUG"] = True @app.route('/', methods=['GET']) def home(): connectionObject = None try: # IMPORTANT !!!!!!!!!!!!!! # The following endpoint it's retuning a hardcoded czml that you can find on sampleData too # Commented it's the connection with mobilitydb # What is missing is store data accordingly in the DB, get it from there and return it as czml # Set the connection parameters to PostgreSQL # connection = psycopg2.connect(host='localhost', database='test', user='user', password='pw') # connection.autocommit = True # # Register MobilityDB data types # register(connection) # # Open a cursor to perform database operations # cursor = connection.cursor() # # Query the database and obtain data as Python objects # select_query = "SELECT * FROM tbl_tfloatseq ORDER BY k LIMIT 10" # cursor.execute(select_query) # rows = cursor.fetchall() # # Print the obtained rows and call a method on the instances # for row in rows: # print("key =", row[0]) # print("tfloatseq =", row[1]) # if not row[1]: # print("") # else: # print("startTimestamp =", row[1].startTimestamp(), "\n") return "[{\"id\":\"document\",\"name\":\"SampleFlight\",\"version\":\"1.0\",\"clock\":{\"interval\":\"20170711T16Z/20170711T1620Z\",\"currentTime\":\"20170711T16Z\",\"multiplier\":2,\"range\":\"LOOP_STOP\",\"step\":\"SYSTEM_CLOCK_MULTIPLIER\"}},{\"id\":\"Aircraft/Aircraft1\",\"name\":\"Aircraft1\",\"availability\":\"20170711T16Z/20170711T162001.65996549888041Z\",\"billboard\":{\"color\":{\"rgba\":[0,255,255,255]},\"eyeOffset\":{\"cartesian\":[0,0,0]},\"horizontalOrigin\":\"CENTER\",\"image\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAICAYAAADED76LAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAAjSURBVChTYyAa/EcDUGEIgIphAKg0XRSAAFQMDqDChAADAwDC13+BJ+0oDwAAAABJRU5ErkJgggAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\",\"pixelOffset\":{\"cartesian2\":[0,0]},\"scale\":1,\"show\":true,\"verticalOrigin\":\"CENTER\"},\"path\":{\"show\":[{\"interval\":\"20170711T16Z/20170711T162001.65996549900046Z\",\"boolean\":true}],\"width\":1,\"material\":{\"solidColor\":{\"color\":{\"rgba\":[0,255,255,255]}}},\"resolution\":1200},\"position\":{\"interpolationAlgorithm\":\"LAGRANGE\",\"interpolationDegree\":1,\"epoch\":\"20170711T16Z\",\"cartesian\":[0,1334148.2169703,-4654069.88053539,4138607.58771276,91.86139701440516,1335484.54584316,-4653037.9376884,4139331.98105418,93.60663214893611,1335509.53110048,-4653018.09267666,4139346.13232191,95.3518666688924,1335533.68948835,-4652997.79399382,4139361.054846,97.09710055725736,1335556.9915735,-4652977.06637069,4139376.73044565,98.84233379994839,1335579.4089659,-4652955.93506065,4139393.14002251,100.58756638604427,1335600.91435342,-4652934.42580893,4139410.26358403,102.33279830799074,1335621.48153503,-4652912.5648212,4139428.08026776,104.07802956102933,1335641.08545279,-4652890.37873169,4139446.56836678,105.82326014431783,1335659.70222232,-4652867.8945707,4139465.70535617,107.56849005969525,1335677.30916195,-4652845.13973169,4139485.46792042,109.31371931234753,1335693.88482034,-4652822.14193792,4139505.83198188,111.05894791114406,1335709.40900257,-4652798.9292086,4139526.77273004,112.80417586771182,1335723.86279481,-4652775.52982487,4139548.26465179,114.54940319672824,1335737.22858734,-4652751.97229524,4139570.28156253,116.29462991641412,1335749.49009599,-4652728.2853209,4139592.796638,118.03985604742957,1335760.63238199,-4652704.49776076,4139615.78244704,119.78508161359969,1335770.64187018,-4652680.63859629,4139639.21098494,121.53030664152175,1335779.50636551,-4652656.73689616,4139663.05370763,123.27553115996125,1335787.21506798,-4652632.82178092,4139687.28156639,125.02075520111794,1335793.75858569,-4652608.92238742,4139711.86504329,126.76597879858491,1335799.12894639,-4652585.06783337,4139736.77418713,128.5112019882581,1335803.3196071,-4652561.28718186,4139761.97864994,130.25642480871102,1335806.32546215,-4652537.60940593,4139787.44772395,132.00164729948665,1335808.14284935,-4652514.0633533,4139813.15037899,133.746869502158,1335808.76955452,-4652490.67771119,4139839.05530032,135.49209145963323,1335808.20481409,-4652467.4809714,4139865.13092678,137.23731321593914,1335806.44931611,-4652444.50139558,4139891.34548922,138.98253481613938,1335803.5051994,-4652421.76698078,4139917.66704922,140.7277563061889,1335799.37605089,-4652399.3054254,4139944.06353802,142.4729777321918,1335794.06690132,-4652377.14409537,4139970.50279554,144.21819914111074,1335787.58421908,-4652355.30999087,4139996.95260963,145.22293343280762,1335783.32298762,-4652342.89918393,4140012.1716459,212.66752307694514,1335484.32291699,-4651513.76084887,4141033.28753812,214.41274449847333,1335477.16655719,-4651492.12168627,4141059.72386,216.15796590960963,1335471.17927914,-4651470.13966118,4141086.1679337,217.9031873570366,1335466.36837741,-4651447.84155533,4141112.58754121,219.64840888731123,1335462.73971337,-4651425.2545355,4141138.95049428,221.3936305465577,1335460.29770796,-4651402.40612053,4141165.22467373,223.13885238045987,1335459.04533642,-4651379.32414768,4141191.37806853,224.88407443361575,1335458.98412455,-4651356.03673881,4141217.37881478,226.6292967495683,1335460.11414695,-4651332.57226604,4141243.1952346,228.3745193707473,1335462.43402685,-4651308.95931723,4141268.79587466,230.1197423380636,1335465.94093785,-4651285.22666113,4141294.14954454,231.8649656905509,1335470.63060731,-4651261.40321237,4141319.2253547,233.61018946567492,1335476.49732161,-4651237.51799614,4141343.99275415,235.35541369857856,1335483.53393305,-4651213.60011293,4141368.42156763,237.1006384222619,1335491.73186862,-4651189.67870303,4141392.48203242,238.84586366726944,1335501.08114041,-4651165.782911,4141416.14483457,240.5910894618828,1335511.57035776,-4651141.94185021,4141439.38114461,242.33631583129136,1335523.18674118,-4651118.18456736,4141462.1626527,244.08154279826886,1335535.91613792,-4651094.54000705,4141484.46160309,245.8267703824531,1335549.74303916,-4651071.03697656,4141506.25082799,247.57199860042965,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except (Exception, psycopg2.Error) as error: print("Error while connecting to PostgreSQL", error) finally: # Close the connection if connectionObject: connectionObject.close() app.run()

266.581818

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0

1

0

1

null

0

6

d273ab7aaa63fdb4d56fc5f96393a1fcef993aff

438

py

Python

project/mainapp/permissions.py

Gustutu/django-auth-project

587308f4afa9eea0b901ccbcd05a0c7348020e83

[ "Apache-2.0" ]

null

project/mainapp/permissions.py

Gustutu/django-auth-project

587308f4afa9eea0b901ccbcd05a0c7348020e83

[ "Apache-2.0" ]

null

project/mainapp/permissions.py

Gustutu/django-auth-project

587308f4afa9eea0b901ccbcd05a0c7348020e83

[ "Apache-2.0" ]

null

from rest_framework import permissions class GerantPermission(permissions.BasePermission): message = 'not allowed.' def has_permission(self, request, view): return request.user.has_perm("mainapp.gerant_default_new") class AgentPermission(permissions.BasePermission): message = 'not allowed' def has_permission(self, request, view): return request.user.has_perm("mainapp.agent_default_new") pass

24.333333

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1

0

null

0

1

0

1

0

6

967a97722e79cdc0eef42b8c3a7594d457dcec7c

263

py

Python

multilens/ext/db/commands.py

uesleicarvalhoo/Multilens

677cf00c07e9b4ce7a5d3efd7be04b6d13dd09b8

[ "MIT" ]

null

multilens/ext/db/commands.py

uesleicarvalhoo/Multilens

677cf00c07e9b4ce7a5d3efd7be04b6d13dd09b8

[ "MIT" ]

null

multilens/ext/db/commands.py

uesleicarvalhoo/Multilens

677cf00c07e9b4ce7a5d3efd7be04b6d13dd09b8

[ "MIT" ]

1

2020-11-02T23:30:02.000Z

from werkzeug.security import check_password_hash from multilens.ext.db import db from multilens.ext.db.models import User def create_db(): """Cria o banco de dados""" db.create_all() def drop_db(): """Limpa o banco de dados""" db.drop_all()

17.533333

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4.238095

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18.785714

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null

0

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0

null

0

1

0

1

0

6

967c6c264768cdc95dbab668b460607c9d6ce2b9

83

py

Python

codigo/Live102/app.py

cassiasamp/live-de-python

00b5e51793097544ba9b75c97a0d30e63970bf45

[ "MIT" ]

572

2018-04-03T03:17:08.000Z

2022-03-31T19:05:32.000Z

codigo/Live102/app.py

cassiasamp/live-de-python

00b5e51793097544ba9b75c97a0d30e63970bf45

[ "MIT" ]

176

2018-05-18T15:56:16.000Z

2022-03-28T20:39:07.000Z

codigo/Live102/app.py

cassiasamp/live-de-python

00b5e51793097544ba9b75c97a0d30e63970bf45

[ "MIT" ]

140

2018-04-18T13:59:11.000Z

2022-03-29T00:43:49.000Z

def concats(x, y): return x + y def concatx(x, y): return concats(x, y)

10.375

24

0.566265

15

83

3.133333

0.4

0.170213

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7

25

11.857143

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0

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1

0

1

0

null

0

1

0

1

0

6

9685bd02b44d4aa7c0d6b14ff15141fd332a9014

45

py

Python

marklogic_client/__init__.py

HanKruiger/marklogic_client

689e5ba4b36edf2f9c5f2940d19ebcea22ff54c3

[ "MIT" ]

1

2020-04-22T23:33:44.000Z

marklogic_client/__init__.py

quangis/marklogic_client

689e5ba4b36edf2f9c5f2940d19ebcea22ff54c3

[ "MIT" ]

null

marklogic_client/__init__.py

quangis/marklogic_client

689e5ba4b36edf2f9c5f2940d19ebcea22ff54c3

[ "MIT" ]

null

from .marklogic_client import MarkLogicClient

45

0.911111

5

45

8

1

0

0.066667

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45

0.952381

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0

1

0

1

0

1

0

6

7376832879a01c0faf5bd886347e1f770f4e8a6d

4,477

py

Python

tests/template_tests/test_library.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

16

2019-08-10T12:24:06.000Z

2020-05-21T09:11:14.000Z

tests/template_tests/test_library.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

12

2019-08-10T11:55:29.000Z

2020-05-21T04:46:30.000Z

tests/template_tests/test_library.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

3

2019-08-20T13:29:34.000Z

2020-01-30T22:05:10.000Z

import functools from django.template import Library from django.template.base import Node from django.test import SimpleTestCase class FilterRegistrationTests(SimpleTestCase): def setUp(self): self.library = Library() def test_filter(self): @self.library.filter def func(): return "" self.assertEqual(self.library.filters["func"], func) def test_filter_parens(self): @self.library.filter() def func(): return "" self.assertEqual(self.library.filters["func"], func) def test_filter_name_arg(self): @self.library.filter("name") def func(): return "" self.assertEqual(self.library.filters["name"], func) def test_filter_name_kwarg(self): @self.library.filter(name="name") def func(): return "" self.assertEqual(self.library.filters["name"], func) def test_filter_call(self): def func(): return "" self.library.filter("name", func) self.assertEqual(self.library.filters["name"], func) def test_filter_invalid(self): msg = "Unsupported arguments to Library.filter: (None, '')" with self.assertRaisesMessage(ValueError, msg): self.library.filter(None, "") class InclusionTagRegistrationTests(SimpleTestCase): def setUp(self): self.library = Library() def test_inclusion_tag(self): @self.library.inclusion_tag("template.html") def func(): return "" self.assertIn("func", self.library.tags) def test_inclusion_tag_name(self): @self.library.inclusion_tag("template.html", name="name") def func(): return "" self.assertIn("name", self.library.tags) def test_inclusion_tag_wrapped(self): @self.library.inclusion_tag("template.html") @functools.lru_cache(maxsize=32) def func(): return "" func_wrapped = self.library.tags["func"].__wrapped__ self.assertIs(func_wrapped, func) self.assertTrue(hasattr(func_wrapped, "cache_info")) class SimpleTagRegistrationTests(SimpleTestCase): def setUp(self): self.library = Library() def test_simple_tag(self): @self.library.simple_tag def func(): return "" self.assertIn("func", self.library.tags) def test_simple_tag_parens(self): @self.library.simple_tag() def func(): return "" self.assertIn("func", self.library.tags) def test_simple_tag_name_kwarg(self): @self.library.simple_tag(name="name") def func(): return "" self.assertIn("name", self.library.tags) def test_simple_tag_invalid(self): msg = "Invalid arguments provided to simple_tag" with self.assertRaisesMessage(ValueError, msg): self.library.simple_tag("invalid") def test_simple_tag_wrapped(self): @self.library.simple_tag @functools.lru_cache(maxsize=32) def func(): return "" func_wrapped = self.library.tags["func"].__wrapped__ self.assertIs(func_wrapped, func) self.assertTrue(hasattr(func_wrapped, "cache_info")) class TagRegistrationTests(SimpleTestCase): def setUp(self): self.library = Library() def test_tag(self): @self.library.tag def func(parser, token): return Node() self.assertEqual(self.library.tags["func"], func) def test_tag_parens(self): @self.library.tag() def func(parser, token): return Node() self.assertEqual(self.library.tags["func"], func) def test_tag_name_arg(self): @self.library.tag("name") def func(parser, token): return Node() self.assertEqual(self.library.tags["name"], func) def test_tag_name_kwarg(self): @self.library.tag(name="name") def func(parser, token): return Node() self.assertEqual(self.library.tags["name"], func) def test_tag_call(self): def func(parser, token): return Node() self.library.tag("name", func) self.assertEqual(self.library.tags["name"], func) def test_tag_invalid(self): msg = "Unsupported arguments to Library.tag: (None, '')" with self.assertRaisesMessage(ValueError, msg): self.library.tag(None, "")

26.96988

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0.614697

504

4,477

5.309524

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0.001212

0.262676

4,477

165

68

27.133333

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0

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0

null

0

1

0

null

0

1

0

1

0

6

737db5479eae82de2ec951ed335c69526686732d

125

py

Python

app/bayescmd/abc/__init__.py

Jignesh1996/bcmd-web

2444352ef235b162531c4995dbef3907b0d83719

[ "MIT" ]

1

2018-07-16T15:39:41.000Z

app/bayescmd/abc/__init__.py

Jignesh1996/bcmd-web

2444352ef235b162531c4995dbef3907b0d83719

[ "MIT" ]

6

2018-07-16T15:55:25.000Z

2018-11-01T15:14:10.000Z

app/bayescmd/abc/__init__.py

Jignesh1996/bcmd-web

2444352ef235b162531c4995dbef3907b0d83719

[ "MIT" ]

1

2018-07-16T15:34:54.000Z

from .rejection import Rejection from .distances import get_distance from .data_import import import_actual_data, inputParse

31.25

55

0.864

17

125

6.117647

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0.104

125

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41.666667

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0

1

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0

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null

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1

0

1

0

1

0

6

73874b8f186e1e66a51769ba1ac53d9b0be5e087

163

py

Python

PwnTheBox/Base_re.py

Don2025/CTFwriteUp

41e0a5bf87a1a02dd1548e621853c145ff64cedb

[ "MIT" ]

2

2022-03-20T02:27:59.000Z

2022-03-20T02:28:02.000Z

PwnTheBox/Base_re.py

Don2025/CTFwriteUp

41e0a5bf87a1a02dd1548e621853c145ff64cedb

[ "MIT" ]

null

PwnTheBox/Base_re.py

Don2025/CTFwriteUp

41e0a5bf87a1a02dd1548e621853c145ff64cedb

[ "MIT" ]

null

from base64 import * flag = b64decode('ZmxhZ3tiNTljNjdiZjE5NmE0NzU4MTkxZTQyZjc2NjcwY2ViYX0=').decode('utf-8') print(flag) # flag{b59c67bf196a4758191e42f76670ceba}

40.75

88

0.828221

13

163

10.384615

0.846154

0

0.215686

0.06135

163

4

89

40.75

0.666667

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0

0.459677

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0

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0.333333

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0.333333

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null

0

1

0

1

0

1

null

0

1

0

6

73f459be96b11f4ce2469d90d2b045ed5b07dbac

143

py

Python

config.py

anthonyattard/item-catalog

08885e8e98dc126311f45e0d810dbfc879cd1a06

[ "MIT" ]

null

config.py

anthonyattard/item-catalog

08885e8e98dc126311f45e0d810dbfc879cd1a06

[ "MIT" ]

null

config.py

anthonyattard/item-catalog

08885e8e98dc126311f45e0d810dbfc879cd1a06

[ "MIT" ]

null

GOOGLE_CLIENT_ID = "163087330440-2dv41o1fh9hiqp476ate0c2tq4bcgo7n.apps.googleusercontent.com" GOOGLE_CLIENT_SECRET = "7RQ1XLSYQR2Vi6aprkUJuu0g"

71.5

93

0.895105

12

143

10.333333

0.833333

0.193548

0

0.210145

0.034965

143

2

94

71.5

0.688406

0

0.666667

0

1

0

false

0

1

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1

null

0

1

0

1

0

1

null

0

6

fb435cb5f43f5c550f0371d91da54bd43a38ef84

134

py

Python

tikplay/utils/__init__.py

tietokilta-saato/tikplay

8061451c21f06bd07129a8a42543ea86b7518d4a

[ "MIT" ]

2

2015-01-15T14:14:50.000Z

2015-10-23T05:37:34.000Z

tikplay/utils/__init__.py

tietokilta-saato/tikplay

8061451c21f06bd07129a8a42543ea86b7518d4a

[ "MIT" ]

8

2015-01-12T10:27:27.000Z

2015-05-11T12:05:03.000Z

tikplay/utils/__init__.py

tietokilta-saato/tikplay

8061451c21f06bd07129a8a42543ea86b7518d4a

[ "MIT" ]

null

def is_uri(uri): return uri.find(":") != -1 and len(uri) > 3 def is_url(uri): return uri.startswith(("http://", "https://"))

22.333333

50

0.567164

21

134

3.52381

0.619048

0.135135

0.324324

0

0.018182

0.179104

134

6

50

22.333333

0.654545

0

0.118519

0

1

0.5

false

0

0.5

1

0

1

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null

0

1

0

1

0

null

0

1

0

1

0

6

fb43a45eab1b6e6662246abe3f458eb22e45f409

65

py

Python

tests/basics/dict_get.py

learnforpractice/micropython-cpp

004bc8382f74899e7b876cc29bfa6a9cc976ba10

[ "MIT" ]

13,648

2015-01-01T01:34:51.000Z

2022-03-31T16:19:53.000Z

tests/basics/dict_get.py

learnforpractice/micropython-cpp

004bc8382f74899e7b876cc29bfa6a9cc976ba10

[ "MIT" ]

7,092

2015-01-01T07:59:11.000Z

2022-03-31T23:52:18.000Z

tests/basics/dict_get.py

learnforpractice/micropython-cpp

004bc8382f74899e7b876cc29bfa6a9cc976ba10

[ "MIT" ]

4,942

2015-01-02T11:48:50.000Z

2022-03-31T19:57:10.000Z

for d in {}, {42:2}: print(d.get(42)) print(d.get(42,2))

16.25

22

0.492308

14

65

2.285714

0.5

0.1875

0.5625

0.6875

0

0.16

0.230769

65

3

23

21.666667

0.48

0

1

0

false

0

0.666667

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

fb7f74e8f76f05d5e772ff317795b10aab119852

148

py

Python

leo/test/unittest/at-path-test1.py

ATikhonov2/leo-editor

225aac990a9b2804aaa9dea29574d6e072e30474

[ "MIT" ]

2

2020-01-19T18:11:05.000Z

2020-01-19T18:12:07.000Z

leo/test/unittest/at-path-test1.py

ATikhonov2/leo-editor

225aac990a9b2804aaa9dea29574d6e072e30474

[ "MIT" ]

1

2020-06-19T02:28:25.000Z

leo/test/unittest/at-path-test1.py

ATikhonov2/leo-editor

225aac990a9b2804aaa9dea29574d6e072e30474

[ "MIT" ]

null

#@+leo-ver=5-thin #@+node:ekr.20120228145505.4834: * @thin ../test/unittest/at-path-test1.py #@@language python # unittest/at-path-test1.py #@-leo

24.666667

74

0.695946

23

148

4.478261

0.695652

0.194175

0.271845

0.368932

0.407767

0

0.153285

0.074324

148

5

75

29.6

0.59854

0.932432

0

null

0

null

0

null

0

null

1

null

true

0

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1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

fbd7ea4e768fdaf98462cc0956906537b9481e95

263

py

Python

Python/libraries/datatypes-timex-expression/datatypes_timex_expression/timex_convert.py

ahmedabuamra/Recognizers-Text

31193d89d3532839742992a2755c1d8539c68116

[ "MIT" ]

2

2017-08-22T11:21:19.000Z

2017-09-17T20:06:00.000Z

Python/libraries/datatypes-timex-expression/datatypes_timex_expression/timex_convert.py

ahmedabuamra/Recognizers-Text

31193d89d3532839742992a2755c1d8539c68116

[ "MIT" ]

76

2018-11-09T18:19:44.000Z

2019-08-20T20:29:53.000Z

Python/libraries/datatypes-timex-expression/datatypes_timex_expression/timex_convert.py

ahmedabuamra/Recognizers-Text

31193d89d3532839742992a2755c1d8539c68116

[ "MIT" ]

6

2017-05-04T17:24:59.000Z

2019-07-23T15:48:44.000Z

from .english import * class TimexConvert: @staticmethod def convert_timex_to_string(timex): return convert_timex_to_string(timex) @staticmethod def convert_timex_set_to_string(timex): return convert_timex_set_to_string(timex)

20.230769

49

0.745247

33

263

5.515152

0.393939

0.263736

0.285714

0.296703

0.648352

0.510989

0

0.197719

263

12

50

21.916667

0.862559

0

0.25

0

1

0.25

false

0

0.125

0.25

0.75

0

null

1

0

null

0

1

0

1

0

6

836e8b995c1c7bbba5b64ce9a65cd441df2629c5

44

py

Python

python/fastscore/utils/__init__.py

modelop/fastscore-sdk

2206a4b9294cd83b6b8c2470193070bdc35a9061

[ "Apache-2.0" ]

2

2018-06-05T19:14:30.000Z

2019-02-06T17:15:10.000Z

python/fastscore/utils/__init__.py

modelop/fastscore-sdk

2206a4b9294cd83b6b8c2470193070bdc35a9061

[ "Apache-2.0" ]

2

2018-02-20T21:58:43.000Z

2018-10-07T10:10:54.000Z

python/fastscore/utils/__init__.py

modelop/fastscore-sdk

2206a4b9294cd83b6b8c2470193070bdc35a9061

[ "Apache-2.0" ]

1

2017-12-29T20:38:06.000Z

from .utils import * from .secrets import *

14.666667

22

0.727273

6

44

5.333333

0.666667

0

0.181818

44

2

23

22

0.888889

0

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0

1

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1

0

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0

6

83d1d0f13a9624aa9566ae150eed6e38967db718

23

py

Python

utils/resume_training.py

dumpmemory/SPPR

0df749d000e50a64ae13c606072a902f19ecb251

[ "MIT" ]

62

2021-08-01T09:32:32.000Z

2022-03-22T06:40:40.000Z

utils/resume_training.py

dumpmemory/SPPR

0df749d000e50a64ae13c606072a902f19ecb251

[ "MIT" ]

3

2021-10-17T10:51:07.000Z

2022-02-05T12:44:39.000Z

utils/resume_training.py

dumpmemory/SPPR

0df749d000e50a64ae13c606072a902f19ecb251

[ "MIT" ]

9

2021-08-02T03:22:10.000Z

2022-02-24T00:54:54.000Z

def resume(): pass

7.666667

13

0.565217

3

23

4.333333

1

0

0.304348

23

2

14

11.5

0.8125

0

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0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

f7d938319c1237ab553b82c9722404ed7b114c1a

8,839

py

Python

traces/microbenchmark/micro_bm_gen.py

netx-repo/NetLock

b4f44efd7b01bca2ecd661a5c7876ada4d7af5fb

[ "Apache-2.0" ]

12

2020-07-31T13:51:00.000Z

2022-01-02T08:03:19.000Z

traces/microbenchmark/micro_bm_gen.py

netx-repo/NetLock

b4f44efd7b01bca2ecd661a5c7876ada4d7af5fb

[ "Apache-2.0" ]

null

traces/microbenchmark/micro_bm_gen.py

netx-repo/NetLock

b4f44efd7b01bca2ecd661a5c7876ada4d7af5fb

[ "Apache-2.0" ]

2

2020-09-02T18:26:36.000Z

2021-04-21T06:14:11.000Z

import os,sys import csv lib_path = os.path.abspath(os.path.join('../../client')) sys.path.append(lib_path) from config import * import random from random import randint class MicroBenchmark: def __init__(self, lock_type = SHARED_LOCK, max_lock_id = 100000, server_number = 10, threads_per_server = 2): self.lock_per_server = max_lock_id / server_number if (lock_type == SHARED_LOCK): self.lock_type = 1 elif (lock_type == EXCLUSIVE_LOCK): self.lock_type = 2 self.max_lock_id = max_lock_id self.server_number = server_number self.threads_per_server = threads_per_server #for i in range(1, server_number + 1): def main(): micro_benchmark = MicroBenchmark(SHARED_LOCK, 120000, 12) for i in range(1, micro_benchmark.server_number + 1): with open('shared/micro_bm_s'+str(i)+'.csv', mode='w') as output_file: csv_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['** on machine #'+str(i)]) csv_writer.writerow(["** txn_id", "action", "target_lm_id", "target_obj_idx", "lock_type"]) for j in range(1, micro_benchmark.lock_per_server + 1): txn_id = j % 1000 action = ACQUIRE_LOCK target_lm_id = 2 #lock_id = (i-1) * micro_benchmark.lock_per_server + j lock_id = i - 1 lock_type = micro_benchmark.lock_type csv_writer.writerow([txn_id, action, target_lm_id, lock_id, lock_type]) ## exclusive locks contention on client itself (between threads) hold by switch (2) micro_benchmark = MicroBenchmark(EXCLUSIVE_LOCK, 54000, 12) for i in range(1, micro_benchmark.server_number + 1): with open('ex_old/micro_bm_x'+str(i)+'.csv', mode='w') as output_file: csv_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['** on machine #'+str(i)]) csv_writer.writerow(["** txn_id", "action", "target_lm_id", "target_obj_idx", "lock_type"]) for j in range(1, micro_benchmark.lock_per_server + 1): txn_id = j % 1000 action = ACQUIRE_LOCK target_lm_id = 2 lock_id = (i-1) * micro_benchmark.lock_per_server + j lock_type = micro_benchmark.lock_type csv_writer.writerow([txn_id, action, target_lm_id, lock_id, lock_type]) ## exclusive locks contention on clients (not on threads) hold by switch (2) micro_benchmark = MicroBenchmark(EXCLUSIVE_LOCK, 54000, 12) for i in range(1, micro_benchmark.server_number + 1): for l in range(1, 3): with open('ex_old/micro_bm_x'+str(i)+"_lc"+str(l+5)+'.csv', mode='w') as output_file: csv_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['** on machine #'+str(i)+' contention_degree: 2']) csv_writer.writerow(["** txn_id", "action", "target_lm_id", "target_obj_idx", "lock_type"]) for j in range((l-1) * micro_benchmark.lock_per_server + 1, l * micro_benchmark.lock_per_server + 1): txn_id = j % 1000 action = ACQUIRE_LOCK target_lm_id = 2 lock_id = ((i-1) * micro_benchmark.lock_per_server + j + 55000 - 1) % 55000 + 1 lock_type = micro_benchmark.lock_type csv_writer.writerow([txn_id, action, target_lm_id, lock_id, lock_type]) ## exclusive locks contention on clients (not on threads) can't hold by switch for contention_degree in range(1,7): micro_benchmark = MicroBenchmark(EXCLUSIVE_LOCK, 54000, 12) for i in range(1, micro_benchmark.server_number + 1): for l in range(1, 3): with open('contention/queue_size_2/micro_bm_x'+str(i)+"_cd"+str(contention_degree)+"_lc"+str(l+5)+'.csv', mode='w') as output_file: csv_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['** on machine #'+str(i)+' contention_degree: '+str(contention_degree)]) csv_writer.writerow(["** txn_id", "action", "target_lm_id", "target_obj_idx", "lock_type"]) for j in range((l-1) * micro_benchmark.lock_per_server * contention_degree / micro_benchmark.threads_per_server + 1, l * micro_benchmark.lock_per_server * contention_degree / micro_benchmark.threads_per_server + 1): txn_id = j % 1000 action = ACQUIRE_LOCK target_lm_id = 2 lock_id = ((i-1) * micro_benchmark.lock_per_server + j + 55000 - 1) % 55000 + 1 lock_type = micro_benchmark.lock_type csv_writer.writerow([txn_id, action, target_lm_id, lock_id, lock_type]) return ## exclusive locks, test different contention (decide by number clients*threads, switch can hold) client_num = 1200 # lock_nums = [1, 2, 3, 6, 10, 12, 20, 24, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150, 200, 250, 300] # lock_nums = [150, 200, 250, 300, 350] lock_nums = [] server_num = 12 micro_benchmark = MicroBenchmark(EXCLUSIVE_LOCK, 55000, server_num, client_num / server_num) for lk in lock_nums: for i in range(1, micro_benchmark.server_number + 1): for j in range(0, micro_benchmark.threads_per_server): with open('contention/lk'+str(lk)+'/micro_bm_x'+str(i)+"_t"+str(j)+"_lk"+str(lk)+".csv", mode = 'w') as output_file: csv_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['** on machine #'+str(i)+' client: '+str(j)]) csv_writer.writerow(["** txn_id", "action", "target_lm_id", "target_obj_idx", "lock_type"]) for l in range(1200): lock_id = randint(0, lk - 1) txn_id = l % 1000 action = ACQUIRE_LOCK target_lm_id = 2 lock_type = micro_benchmark.lock_type csv_writer.writerow([txn_id, action, target_lm_id, lock_id, lock_type]) ## exclusive locks, test different contention (decide by number of locks, switch can hold) client_num = 24 # lock_nums = [1, 2, 3, 6, 10, 12, 20, 24, 30, 40, 50, 60, 70, 80, 90, 100, 120, 150, 200, 250, 300] # lock_nums = [150, 200, 250, 300, 350] # lock_nums = [2, 10, 100, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 8000, 10000] lock_nums = [2, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000] server_num = 12 micro_benchmark = MicroBenchmark(EXCLUSIVE_LOCK, 55000, server_num, client_num / server_num) for lk in lock_nums: for i in range(1, micro_benchmark.server_number + 1): lk_list = range(lk) random.shuffle(lk_list) os.system("mkdir -p contention_shuffle; mkdir -p contention_shuffle/lk"+str(lk)) for j in range(0, micro_benchmark.threads_per_server): ## cache miss shard_list = lk_list[j*lk / micro_benchmark.threads_per_server:(j+1)*lk / micro_benchmark.threads_per_server] shard_list.sort() with open('contention_shuffle/lk'+str(lk)+'/micro_bm_x'+str(i)+"_t"+str(j)+"_lk"+str(lk)+".csv", mode = 'w') as output_file: csv_writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['** on machine #'+str(i)+' client: '+str(j)]) csv_writer.writerow(["** txn_id", "action", "target_lm_id", "target_obj_idx", "lock_type"]) for l in range(lk / micro_benchmark.threads_per_server): # lock_id = j * lk / micro_benchmark.threads_per_server + lk_list[l + j * j * lk / micro_benchmark.threads_per_server] lock_id = lk_list[l + j * lk / micro_benchmark.threads_per_server] #lock_id = shard_list[l] txn_id = l % 1000 action = ACQUIRE_LOCK target_lm_id = 2 lock_type = micro_benchmark.lock_type csv_writer.writerow([txn_id, action, target_lm_id, lock_id, lock_type]) if __name__ == '__main__': main()

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null

0

6

7906a8c3f5fdac8427b4722b6aa4f5168db10968

9,411

py

Python

climatespider/climatespider/spiders/AO_wugspider.py

burnman108/climateSpider

434ba25b6a30fe6d07231b7758cbc64d8243bf4c

[ "Apache-2.0" ]

null

climatespider/climatespider/spiders/AO_wugspider.py

burnman108/climateSpider

434ba25b6a30fe6d07231b7758cbc64d8243bf4c

[ "Apache-2.0" ]

null

climatespider/climatespider/spiders/AO_wugspider.py

burnman108/climateSpider

434ba25b6a30fe6d07231b7758cbc64d8243bf4c

[ "Apache-2.0" ]

null

#-*- coding: utf-8 -*- from scrapy.spiders import CrawlSpider, Rule from scrapy.linkextractors import LinkExtractor from climatespider.items import ClimatespiderItem from scrapy.selector import Selector from dateutil.parser import parse import re import datetime from scrapy.exceptions import CloseSpider def getyesterdaty(): today_date = datetime.date.today() yesterday_date = today_date - datetime.timedelta(days=1) return yesterday_date.strftime('%Y/%m/%d') class wugSpider(CrawlSpider): name = "WUGCrawlSpider_AO" #today_date = datetime.now().strftime('%Y/%m/%d') allowed_domains = ['www.wunderground.com'] start_urls = [ 'https://www.wunderground.com/history/airport/ZBAA/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/54618/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZBTJ/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZBYN/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZSSS/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/50888/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/50136/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZYHB/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/50854/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZSOF/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZLXY/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/54602/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/VMMC/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/54401/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/58506/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZGHA/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZSHC/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZHHH/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/58606/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZGGG/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZGSZ/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/53798/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZYTL/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/airport/ZUUU/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/50774/{0}/DailyHistory.html'.format(getyesterdaty()), 'https://www.wunderground.com/history/station/50949/{0}/DailyHistory.html'.format(getyesterdaty()) ] def parse(self, response): sel = Selector(response) indexlist = list(map(lambda x: x.replace(' ','').replace('.',''),sel.xpath('//table[@id="obsTable"]/thead/tr/th/text()').extract())) date = re.match(r'.*(\d{4}\/\d{1,2}\/\d{1,2}).*', response.url).group(1) datatable = sel.xpath('//tr[@class="no-metars"]') # items = [] for each in datatable: item = ClimatespiderItem() item['area'] = re.match(r'.*history/(.*)/2\d{3}/.*', response.url).group(1) # item['date'] = date if len(indexlist) == 13: item['the_date'] = date item['the_time'] = parse(each.xpath('td[1]/text()').extract()[0]).strftime('%H:%M') item['qx_Humidity'] = each.xpath('td[5]/text()').extract()[0] item['qx_WindDir'] = each.xpath('td[8]/text()').extract()[0] item['qx_Precip'] = each.xpath('td[11]/text()').extract()[0] item['qx_Events'] = each.xpath('td[12]/text()').extract()[0].strip() try: item['qx_Condition'] = each.xpath('td[13]/text()').extract()[0] except Exception as e: item['qx_Condition'] = '' try: item['qx_Temp'] = each.xpath('td[2]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_Temp'] = each.xpath('td[2]/text()').extract()[0].strip().replace('-','') try: item['qx_WindChill_HeatIndex'] = each.xpath('td[3]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_WindChill_HeatIndex'] = each.xpath('td[3]/text()').extract()[0].strip().replace('-','') try: item['qx_DewPoint'] = each.xpath('td[4]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_DewPoint'] = each.xpath('td[4]/text()').extract()[0].strip().replace('-','') try: item['qx_Pressure'] = each.xpath('td[6]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_Pressure'] = each.xpath('td[6]/text()').extract()[0].strip().replace('-','') try: item['qx_Visibility'] = each.xpath('td[7]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_Visibility'] = each.xpath('td[7]/text()').extract()[0].strip().replace('-','') try: item['qx_WindSpeed'] = each.xpath('td[9]/span[1]/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_WindSpeed'] = each.xpath('td[9]/text()').extract()[0].strip().replace('-','') try: item['qx_GustSpeed'] = each.xpath('td[10]/span[1]/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_GustSpeed'] = each.xpath('td[10]/text()').extract()[0].strip().replace('-','') yield item else: item['the_date'] = date item['the_time'] = parse(each.xpath('td[1]/text()').extract()[0]).strftime('%H:%M') item['qx_Humidity'] = each.xpath('td[4]/text()').extract()[0] item['qx_WindDir'] = each.xpath('td[7]/text()').extract()[0] item['qx_Precip'] = each.xpath('td[10]/text()').extract()[0] item['qx_Events'] = each.xpath('td[11]/text()').extract()[0].strip() try: item['qx_Condition'] = each.xpath('td[12]/text()').extract()[0] except Exception as e: item['qx_Condition'] = '' try: item['qx_Temp'] = each.xpath('td[2]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_Temp'] = each.xpath('td[2]/text()').extract()[0].strip().replace('-','') # try: # item['WindChill_HeatIndex'] = each.xpath('td[3]/span/span[@class="wx-value"]/text()').extract()[0] # except Exception as e: # item['WindChill_HeatIndex'] = each.xpath('td[3]/text()').extract()[0].strip().replace('-', '') try: item['qx_DewPoint'] = each.xpath('td[3]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_DewPoint'] = each.xpath('td[3]/text()').extract()[0].strip().replace('-', '') try: item['qx_Pressure'] = each.xpath('td[5]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_Pressure'] = each.xpath('td[5]/text()').extract()[0].strip().replace('-', '') try: item['qx_Visibility'] = each.xpath('td[6]/span/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_Visibility'] = each.xpath('td[6]/text()').extract()[0].strip().replace('-', '') try: item['qx_WindSpeed'] = each.xpath('td[8]/span[1]/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_WindSpeed'] = each.xpath('td[8]/text()').extract()[0].strip().replace('-', '') try: item['qx_GustSpeed'] = each.xpath('td[9]/span[1]/span[@class="wx-value"]/text()').extract()[0] except Exception as e: item['qx_GustSpeed'] = each.xpath('td[9]/text()').extract()[0].strip().replace('-', '') yield item # for index in range(len(indexlist)):

66.274648

140

0.568165

1,087

9,411

4.869365

0.143514

0.085207

0.083129

0.112979

0.822218

0.815417

0.808426

0.795579

0.795201

0.750236

0

0.025459

0.223674

9,411

141

66.744681

0.699015

0.038891

0

0.330709

0

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0.078473

0

1

0.015748

false

0

0.062992

0

0.11811

0

null

0

1

0

null

0

6

790b14439046bd301a529a673057b56fe6681eb9

315

py

Python

sprint/core/parser/args.py

ii-Python/Sprint-v2

2579b7f9a36ac5c5ec541ca3dce6cf61357db948

[ "MIT" ]

null

sprint/core/parser/args.py

ii-Python/Sprint-v2

2579b7f9a36ac5c5ec541ca3dce6cf61357db948

[ "MIT" ]

null

sprint/core/parser/args.py

ii-Python/Sprint-v2

2579b7f9a36ac5c5ec541ca3dce6cf61357db948

[ "MIT" ]

null

class Argument(object): def __init__(self, argument = None, base: bool = False): self.arg = argument self.is_base = base def __repr__(self): return self.arg def __str__(self): return self.arg def is_pipe(self): return self.arg == ">>" or self.arg == "<<"

21

60

0.571429

40

315

4.15

0.425

0.210843

0.253012

0.307229

0.240964

0

0.301587

315

14

61

22.5

0.754545

0

0.2

0

0.012698

0

1

0.4

false

0

0.3

0.8

0

null

1

0

null

0

1

0

1

0

6

7917eb82c91f982f3274c65a99d949a0b683cbbe

46

py

Python

src/django_secrecy/test.py

Cyxapic/django-secrecy

7f3fcd42afc1cc7c49dba92530c171e7ed2edac8

[ "MIT" ]

null

src/django_secrecy/test.py

Cyxapic/django-secrecy

7f3fcd42afc1cc7c49dba92530c171e7ed2edac8

[ "MIT" ]

1

2021-02-15T09:37:44.000Z

src/django_secrecy/test.py

Cyxapic/django-secrecy

7f3fcd42afc1cc7c49dba92530c171e7ed2edac8

[ "MIT" ]

null

import datetime print(datetime.datetime.now())

23

30

0.826087

6

46

6.333333

0.666667

0

0.043478

46

2

30

23

0.863636

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true

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0.5

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null

0

1

0

null

0

1

0

1

0

1

0

6

f7095169f139557a45e67599ce006c31ba8e3471

157

py

Python

python/pip_package/__init__.py

kanishkg/lab

a9a3b5c38ad160ffd2e77a3af3e13c6e66eed457

[ "CC-BY-4.0" ]

16

2019-02-27T22:37:25.000Z

2021-11-08T12:39:33.000Z

python/pip_package/__init__.py

kanishkg/lab

a9a3b5c38ad160ffd2e77a3af3e13c6e66eed457

[ "CC-BY-4.0" ]

null

python/pip_package/__init__.py

kanishkg/lab

a9a3b5c38ad160ffd2e77a3af3e13c6e66eed457

[ "CC-BY-4.0" ]

7

2019-05-28T06:26:26.000Z

2021-11-27T16:33:55.000Z

"""Loads deepmind_lab.so.""" import imp import pkg_resources imp.load_dynamic(__name__, pkg_resources.resource_filename( __name__, 'deepmind_lab.so'))

19.625

59

0.77707

21

157

5.142857

0.619048

0.203704

0.240741

0

0.101911

157

7

60

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0.765957

0.140127

0

0.116279

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true

0

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0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

f70ea28bcf4185dc675728f2549fd2e9447346be

77,417

py

Python

Graphing_Summaries.py

GrantRoss-Tenki/Malawi-CQC-CSC-OSU-Work

a720e0451579945ba10eafdafe2e0d59a86d5cfb

[ "MIT" ]

null

Graphing_Summaries.py

GrantRoss-Tenki/Malawi-CQC-CSC-OSU-Work

a720e0451579945ba10eafdafe2e0d59a86d5cfb

[ "MIT" ]

null

Graphing_Summaries.py

GrantRoss-Tenki/Malawi-CQC-CSC-OSU-Work

a720e0451579945ba10eafdafe2e0d59a86d5cfb

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np import pandas as pd #from pylab import plot, show, xlim,figure,hold, ylim,legend, boxplot, setup, axes import seaborn as sns # Is this a personal or work computer # Are you graphing for hood or no hood Computer = 'personal' #or 'personal' or 'work' Hood_or_no = 'no_hood' # 'no_hood' or 'hood' #what household do you want to remove make sure it is in ascending order # if there is nothing, then put a placeholder of 1045 or higher Household_removal = [1045] #Household_removal = Household_removal.sort(reverse=False) Household_removal_NO_Hood_fuel_day_adult = [1045] Household_removal_Hood_fuel_day_adult = [2020] Household_removal_NO_Hood_PM = [1045] Household_removal_Hood_PM = [2020] pd.set_option('display.max_rows', 500) pd.set_option('display.max_columns', 500) pd.set_option('display.width', 1000) if Hood_or_no == 'hood': C_Place_holder = 2001 else: C_Place_holder = 1001 if Computer == 'personal' and Hood_or_no == 'no_hood': # 1N datafile_path_day_1N ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_Summary_Day_1_exact.csv" Day_1N = pd.read_csv(datafile_path_day_1N, skiprows=2) datafile_path_event_1N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_Summary_Event_1_exact.csv" Event_1N = pd.read_csv(datafile_path_event_1N, skiprows=2) # there is no second exact in phase 1N #1N Survey datafile_path_survey_1N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_1H_Survey_summary_.csv" Filter_1n_survey = pd.read_csv(datafile_path_survey_1N, skiprows=0) #print(Filter_1n_survey.iloc[0:40, :]) Survey_1N = Filter_1n_survey.iloc[0:40,:] #24 hour Kitchen pm breakdown data_file_path_24_PM_1N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_24_hour_Kitchen_PM.csv" Kit_PM_1N_24hr = pd.read_csv(data_file_path_24_PM_1N, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_Fuel_1N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_24_hour_Fuel_removal.csv" Fuel_remove_1N_24hr = pd.read_csv(data_file_path_24_Fuel_1N, skiprows=0) #2N datafile_path_day_2N ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_Summary_Day_1_exact.csv" Day_2N = pd.read_csv(datafile_path_day_2N, skiprows=2) datafile_path_event_2N_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_Summary_Event_1_exact.csv" Event_2N_1 = pd.read_csv(datafile_path_event_2N_1, skiprows=2) #2N second Exact datafile_path_event_2N_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_Summary_Event_2_exact.csv" Event_2N_2 = pd.read_csv(datafile_path_event_2N_2, skiprows=2) #2N Survey datafile_path_survey_2N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_Survey_summary_.csv" Survey_2N = pd.read_csv(datafile_path_survey_2N, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_2N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_24_hour_Kitchen_PM.csv" Kit_PM_2N_24hr = pd.read_csv(data_file_path_24_PM_2N, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_Fuel_2N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_24_hour_Fuel_removal.csv" Fuel_remove_2N_24hr = pd.read_csv(data_file_path_24_Fuel_2N, skiprows=0) #3N datafile_path_day_3N ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_Summary_Day_1_exact.csv" Day_3N = pd.read_csv(datafile_path_day_3N, skiprows=2) datafile_path_event_3N_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_Summary_Event_1_exact.csv" Event_3N_1 = pd.read_csv(datafile_path_event_3N_1, skiprows=2) #3N second Exact datafile_path_event_3N_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_Summary_Event_2_exact.csv" Event_3N_2 = pd.read_csv(datafile_path_event_3N_2, skiprows=2) #3N Survey datafile_path_survey_3N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_Survey_summary_.csv" Survey_3N = pd.read_csv(datafile_path_survey_3N, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_3N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_24_hour_Kitchen_PM.csv" Kit_PM_3N_24hr = pd.read_csv(data_file_path_24_PM_3N, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_Fuel_3N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_24_hour_Fuel_removal.csv" Fuel_remove_3N_24hr = pd.read_csv(data_file_path_24_Fuel_3N, skiprows=0) #4N datafile_path_day_4N ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_Summary_Day_1_exact.csv" Day_4N = pd.read_csv(datafile_path_day_4N, skiprows=2) datafile_path_event_4N_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_Summary_Event_1_exact.csv" Event_4N_1 = pd.read_csv(datafile_path_event_4N_1, skiprows=2) #4N second Exact datafile_path_event_4N_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_Summary_Event_2_exact.csv" Event_4N_2 = pd.read_csv(datafile_path_event_4N_2, skiprows=2) #4N Survey datafile_path_survey_4N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_Survey_summary_.csv" Survey_4N = pd.read_csv(datafile_path_survey_4N, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_4N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_24_hour_Kitchen_PM.csv" Kit_PM_4N_24hr = pd.read_csv(data_file_path_24_PM_4N, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_Fuel_4N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_24_hour_Fuel_removal.csv" Fuel_remove_4N_24hr = pd.read_csv(data_file_path_24_Fuel_4N, skiprows=0) elif Computer == 'personal' and Hood_or_no == 'hood': #1H datafile_path_day_1H ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1H/1H_Summary_Day_1_exact.csv" Day_1H = pd.read_csv(datafile_path_day_1H, skiprows=2) datafile_path_event_1H ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1H/1H_Summary_Event_1_exact.csv" Event_1H = pd.read_csv(datafile_path_event_1H, skiprows=2) #there is no second exact in phase 1H #1H Survey (row 40 or so afterward is Hood portion column 1 is houshold number) datafile_path_survey_1H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_1H_Survey_summary_.csv" Survey_1H = pd.read_csv(datafile_path_survey_1H, skiprows=40) #24 hour Kitchen pm breakdown data_file_path_24_PM_1H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1H/1H_24_hour_Kitchen_PM.csv" Kit_PM_1H_24hr = pd.read_csv(data_file_path_24_PM_1H, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_fuel_1H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1H/1H_24_hour_Fuel_removal.csv" Fuel_remove_1H_24hr = pd.read_csv(data_file_path_24_fuel_1H, skiprows=0) #2H datafile_path_day_2H ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_Summary_Day_1_exact.csv" Day_2H = pd.read_csv(datafile_path_day_2H, skiprows=2) datafile_path_event_2H_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_Summary_Event_1_exact.csv" Event_2H_1 = pd.read_csv(datafile_path_event_2H_1, skiprows=2) #2H second Exact datafile_path_event_2H_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_Summary_Event_2_exact.csv" Event_2H_2 = pd.read_csv(datafile_path_event_2H_2, skiprows=2) #2H survey datafile_path_survey_2H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_Survey_summary_.csv" Survey_2H = pd.read_csv(datafile_path_survey_2H, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_2H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_24_hour_Kitchen_PM.csv" Kit_PM_2H_24hr = pd.read_csv(data_file_path_24_PM_2H, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_fuel_2H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_24_hour_Fuel_removal.csv" Fuel_remove_2H_24hr = pd.read_csv(data_file_path_24_fuel_2H, skiprows=0) #3H datafile_path_day_3H ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_Summary_Day_1_exact.csv" Day_3H = pd.read_csv(datafile_path_day_3H, skiprows=2) datafile_path_event_3N_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_Summary_Event_1_exact.csv" Event_3H_1 = pd.read_csv(datafile_path_event_3N_1, skiprows=2) #3H second Exact datafile_path_event_3H_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_Summary_Event_2_exact.csv" Event_3H_2 = pd.read_csv(datafile_path_event_3H_2, skiprows=2) #3H survey datafile_path_survey_3H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_Survey_summary_.csv" Survey_3H = pd.read_csv(datafile_path_survey_3H, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_3H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_24_hour_Kitchen_PM.csv" Kit_PM_3H_24hr = pd.read_csv(data_file_path_24_PM_3H, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_fuel_3H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_24_hour_Fuel_removal.csv" Fuel_remove_3H_24hr = pd.read_csv(data_file_path_24_fuel_3H, skiprows=0) #work uses box information and not local data elif Computer == 'work' and Hood_or_no == 'no_hood': # 1N for box file system datafile_path_day_1N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_Summary_Day_1_exact.csv" Day_1N = pd.read_csv(datafile_path_day_1N, skiprows=2) datafile_path_event_1N ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_Summary_Event_1_exact.csv" Event_1N = pd.read_csv(datafile_path_event_1N, skiprows=2) # there is no second exact in phase 1N #1N Survey datafile_path_survey_1N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_1H_Survey_summary_.csv" Filter_1n_survey = pd.read_csv(datafile_path_survey_1N, skiprows=0) #print(Filter_1n_survey.iloc[0:40, :]) Survey_1N = Filter_1n_survey.iloc[0:40,:] #24 hour Kitchen pm breakdown data_file_path_24_PM_1N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_24_hour_Kitchen_PM.csv" Kit_PM_1N_24hr = pd.read_csv(data_file_path_24_PM_1N, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_Fuel_1N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_24_hour_Fuel_removal.csv" Fuel_remove_1N_24hr = pd.read_csv(data_file_path_24_Fuel_1N, skiprows=0) #2N datafile_path_day_2N ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_Summary_Day_1_exact.csv" Day_2N = pd.read_csv(datafile_path_day_2N, skiprows=2) datafile_path_event_2N_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_Summary_Event_1_exact.csv" Event_2N_1 = pd.read_csv(datafile_path_event_2N_1, skiprows=2) #2N second Exact datafile_path_event_2N_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_Summary_Event_2_exact.csv" Event_2N_2 = pd.read_csv(datafile_path_event_2N_2, skiprows=2) #2N Survey datafile_path_survey_2N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_Survey_summary_.csv" Survey_2N = pd.read_csv(datafile_path_survey_2N, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_2N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_24_hour_Kitchen_PM.csv" Kit_PM_2N_24hr = pd.read_csv(data_file_path_24_PM_2N, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_Fuel_2N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_24_hour_Fuel_removal.csv" Fuel_remove_2N_24hr = pd.read_csv(data_file_path_24_Fuel_2N, skiprows=0) #3N datafile_path_day_3N ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_Summary_Day_1_exact.csv" Day_3N = pd.read_csv(datafile_path_day_3N, skiprows=2) datafile_path_event_3N_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_Summary_Event_1_exact.csv" Event_3N_1 = pd.read_csv(datafile_path_event_3N_1, skiprows=2) #3N second Exact datafile_path_event_3N_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_Summary_Event_2_exact.csv" Event_3N_2 = pd.read_csv(datafile_path_event_3N_2, skiprows=2) #3N survey datafile_path_survey_3N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_Survey_summary_.csv" Survey_3N = pd.read_csv(datafile_path_survey_3N, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_3N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_24_hour_Kitchen_PM.csv" Kit_PM_3N_24hr = pd.read_csv(data_file_path_24_PM_3N, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_Fuel_3N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_24_hour_Fuel_removal.csv" Fuel_remove_3N_24hr = pd.read_csv(data_file_path_24_Fuel_3N, skiprows=0) #4N datafile_path_day_4N ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_Summary_Day_1_exact.csv" Day_4N = pd.read_csv(datafile_path_day_4N, skiprows=2) datafile_path_event_4N_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_Summary_Event_1_exact.csv" Event_4N_1 = pd.read_csv(datafile_path_event_4N_1, skiprows=2) #4N second Exact datafile_path_event_4N_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_Summary_Event_2_exact.csv" Event_4N_2 = pd.read_csv(datafile_path_event_4N_2, skiprows=2) #4N Survey datafile_path_survey_4N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_Survey_summary_.csv" Survey_4N = pd.read_csv(datafile_path_survey_4N, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_4N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_24_hour_Kitchen_PM.csv" Kit_PM_4N_24hr = pd.read_csv(data_file_path_24_PM_4N, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_Fuel_4N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_24_hour_Fuel_removal.csv" Fuel_remove_4N_24hr = pd.read_csv(data_file_path_24_Fuel_4N, skiprows=0) else: #1H datafile_path_day_1H ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1H/1H_Summary_Day_1_exact.csv" Day_1H = pd.read_csv(datafile_path_day_1H, skiprows=2) datafile_path_event_1H ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1H/1H_Summary_Event_1_exact.csv" Event_1H = pd.read_csv(datafile_path_event_1H, skiprows=2) #there is no second exact in phase 1H #1H Survey (row 40 or so afterward is Hood portion column 1 is houshold number) datafile_path_survey_1H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_1H_Survey_summary_.csv" Survey_1H = pd.read_csv(datafile_path_survey_1H, skiprows=40) #24 hour Kitchen pm breakdown data_file_path_24_PM_1H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1H/1H_24_hour_Kitchen_PM.csv" Kit_PM_1H_24hr = pd.read_csv(data_file_path_24_PM_1H, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_fuel_1H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1H/1H_24_hour_Fuel_removal.csv" Fuel_remove_1H_24hr = pd.read_csv(data_file_path_24_fuel_1H, skiprows=0) #2H datafile_path_day_2H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_Summary_Day_1_exact.csv" Day_2H = pd.read_csv(datafile_path_day_2H, skiprows=2) datafile_path_event_2H_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_Summary_Event_1_exact.csv" Event_2H_1 = pd.read_csv(datafile_path_event_2H_1, skiprows=2) #2H second Exact datafile_path_event_2H_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_Summary_Event_2_exact.csv" Event_2H_2 = pd.read_csv(datafile_path_event_2H_2, skiprows=2) #2H survey datafile_path_survey_2H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_Survey_summary_.csv" Survey_2H = pd.read_csv(datafile_path_survey_2H, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_2H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_24_hour_Kitchen_PM.csv" Kit_PM_2H_24hr = pd.read_csv(data_file_path_24_PM_2H, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_fuel_2H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_24_hour_Fuel_removal.csv" Fuel_remove_2H_24hr = pd.read_csv(data_file_path_24_fuel_2H, skiprows=0) #3H datafile_path_day_3H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_Summary_Day_1_exact.csv" Day_3H = pd.read_csv(datafile_path_day_3H, skiprows=2) datafile_path_event_3N_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_Summary_Event_1_exact.csv" Event_3H_1 = pd.read_csv(datafile_path_event_3N_1, skiprows=2) #3H second Exact datafile_path_event_3H_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_Summary_Event_2_exact.csv" Event_3H_2 = pd.read_csv(datafile_path_event_3H_2, skiprows=2) #3H survey datafile_path_survey_3H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_Survey_summary_.csv" Survey_3H = pd.read_csv(datafile_path_survey_3H, skiprows=0) #24 hour Kitchen pm breakdown data_file_path_24_PM_3H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_24_hour_Kitchen_PM.csv" Kit_PM_3H_24hr = pd.read_csv(data_file_path_24_PM_3H, skiprows=0) #24 hour Fuel Removal breakdown data_file_path_24_fuel_3H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_24_hour_Fuel_removal.csv" Fuel_remove_3H_24hr = pd.read_csv(data_file_path_24_fuel_3H, skiprows=0) #time to start ploting fun things #1st starting with the fuel per day per adult histogram and box plot NO_hood_counter = np.arange(0,39) hood_counter = np.arange(0,14) #what household do you want to remove from the graphs (1046 is a dummy spacer) print('---------------Fuel per Day per Adult No-Hood Phase---------------------') if Hood_or_no == 'no_hood': Fuel_per_day_per_adult_1N = [] f_d_a_1N = [] Fuel_per_day_per_adult_2N = [] f_d_a_2N = [] Fuel_per_day_per_adult_3N = [] f_d_a_3N = [] Fuel_per_day_per_adult_4N = [] f_d_a_4N =[] count_t = 0 count_f = 0 for c in NO_hood_counter: if c == (Household_removal[count_t] - C_Place_holder): count_t = count_t + 1 if count_t == len(Household_removal): count_t = 0 continue if c == (Household_removal_NO_Hood_fuel_day_adult[count_f] - C_Place_holder): count_f = count_f + 1 if count_f == len(Household_removal_NO_Hood_fuel_day_adult): count_f = 0 continue if Fuel_remove_1N_24hr.iloc[c,6]!= -1.00: Fuel_per_day_per_adult_1N.append(Fuel_remove_1N_24hr.iloc[c,6]/Survey_1N.iloc[c,7]) f_d_a_1N.append(Day_1N.iloc[c,0]) if Fuel_remove_2N_24hr.iloc[c,6] != -1.00: Fuel_per_day_per_adult_2N.append(Fuel_remove_2N_24hr.iloc[c,6] / Survey_2N.iloc[c, 7]) f_d_a_2N.append(Day_2N.iloc[c,0]) if Fuel_remove_3N_24hr.iloc[c,6] != -1.00: Fuel_per_day_per_adult_3N.append(Fuel_remove_3N_24hr.iloc[c,6]/ Survey_3N.iloc[c, 7]) f_d_a_3N.append(Day_3N.iloc[c, 0]) if Fuel_remove_4N_24hr.iloc[c,6] != -1.00: Fuel_per_day_per_adult_4N.append(Fuel_remove_4N_24hr.iloc[c,6] / Survey_4N.iloc[c, 7]) f_d_a_4N.append(Day_3N.iloc[c, 0]) # percentage Change of Fuel per day between the phases Fuel_per_day_per_adult_2N_1N = [] f_d_a_2N_1N = [] Fuel_per_day_per_adult_3N_1N = [] f_d_a_3N_1N = [] Fuel_per_day_per_adult_4N_1N = [] f_d_a_4N_1N = [] Fuel_per_day_per_adult_3N_2N = [] f_d_a_3N_2N = [] Fuel_per_day_per_adult_4N_3N = [] f_d_a_4N_3N = [] Fuel_per_day_per_adult_4N_2N = [] f_d_a_4N_2N = [] count_t = 0 count_f = 0 for c in NO_hood_counter: if c == (Household_removal[count_t] - C_Place_holder): count_t = count_t + 1 if count_t == len(Household_removal): count_t = 0 continue if c == (Household_removal_NO_Hood_fuel_day_adult[count_f] - C_Place_holder): count_f = count_f + 1 if count_f == len(Household_removal_NO_Hood_fuel_day_adult): count_f = 0 continue if (len(Fuel_per_day_per_adult_2N)-1) >= c and (len(Fuel_per_day_per_adult_1N)-1) >= c: if Day_1N.iloc[c,13] > 0 and Day_2N.iloc[c,13] > 0 and Day_1N.iloc[c,0] == Day_2N.iloc[c,0]: Fuel_per_day_per_adult_2N_1N.append(Fuel_per_day_per_adult_2N[c]/Fuel_per_day_per_adult_1N[c]) f_d_a_2N_1N.append(Day_1N.iloc[c,0]) if (len(Fuel_per_day_per_adult_3N)-1) >= c and (len(Fuel_per_day_per_adult_1N)-1) >= c: if Day_3N.iloc[c,13] > 0 and Day_1N.iloc[c,13] > 0 and Day_3N.iloc[c,0] == Day_1N.iloc[c,0]: Fuel_per_day_per_adult_3N_1N.append(Fuel_per_day_per_adult_3N[c]/Fuel_per_day_per_adult_1N[c]) f_d_a_3N_1N.append(Day_1N.iloc[c,0]) if (len(Fuel_per_day_per_adult_4N)-1) >= c and (len(Fuel_per_day_per_adult_1N)-1) >= c: if Day_4N.iloc[c,13] > 0 and Day_1N.iloc[c,13] > 0 and Day_4N.iloc[c,0] == Day_1N.iloc[c,0]: Fuel_per_day_per_adult_4N_1N.append(Fuel_per_day_per_adult_4N[c]/Fuel_per_day_per_adult_1N[c]) f_d_a_4N_1N.append(Day_1N.iloc[c,0]) if (len(Fuel_per_day_per_adult_3N)-1) >= c and (len(Fuel_per_day_per_adult_2N)-1) >= c: if Day_3N.iloc[c,13] > 0 and Day_2N.iloc[c,13] > 0 and Day_3N.iloc[c,0] == Day_2N.iloc[c,0]: Fuel_per_day_per_adult_3N_2N.append(Fuel_per_day_per_adult_3N[c]/Fuel_per_day_per_adult_2N[c]) f_d_a_3N_2N.append(Day_2N.iloc[c,0]) if (len(Fuel_per_day_per_adult_4N)-1) >= c and (len(Fuel_per_day_per_adult_3N)-1) >= c: if Day_4N.iloc[c,13] > 0 and Day_3N.iloc[c,13] > 0 and Day_4N.iloc[c,0] == Day_3N.iloc[c,0]: Fuel_per_day_per_adult_4N_3N.append(Fuel_per_day_per_adult_4N[c]/Fuel_per_day_per_adult_3N[c]) f_d_a_4N_3N.append(Day_3N.iloc[c,0]) if (len(Fuel_per_day_per_adult_4N)-1) >= c and (len(Fuel_per_day_per_adult_2N)-1) >= c: if Day_4N.iloc[c,13] > 0 and Day_2N.iloc[c,13] > 0 and Day_4N.iloc[c,0] == Day_2N.iloc[c,0]: Fuel_per_day_per_adult_4N_2N.append(Fuel_per_day_per_adult_4N[c]/Fuel_per_day_per_adult_2N[c]) f_d_a_4N_2N.append(Day_4N.iloc[c,0]) # now for box plotting for Fuel per day beteen Phases #1N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Fuel_per_day_per_adult_1N, ax=ax_box, color='b') sns.distplot(Fuel_per_day_per_adult_1N, ax=ax_hist, color='b') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('1N Fuel per Day per Adult') plt.ylim(top=2) plt.ylim(bottom = 0) #2N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Fuel_per_day_per_adult_2N, ax=ax_box, color='g') sns.distplot(Fuel_per_day_per_adult_2N, ax=ax_hist, color='g') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('2N Fuel per Day per Adult') plt.ylim(top=2) plt.ylim(bottom = 0) #3N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Fuel_per_day_per_adult_3N, ax=ax_box, color='r') sns.distplot(Fuel_per_day_per_adult_3N, ax=ax_hist, color='r') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('3N Fuel per Day per Adult') plt.ylim(top=2) plt.ylim(bottom = 0) #4N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Fuel_per_day_per_adult_4N, ax=ax_box, color='y') sns.distplot(Fuel_per_day_per_adult_4N, ax=ax_hist, color='y') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('4N Fuel per Day per Adult') plt.ylim(top=2) plt.ylim(bottom = 0) #Plotting on the same graph fig, ax = plt.subplots() plt.title('No-Hood Fuel per Day per Adult') #plt.hold(True) #1N quant_1_1N = np.percentile(Fuel_per_day_per_adult_1N, [25,50,75]) Top_lim_1_1N = quant_1_1N[2] + 1.5*(quant_1_1N[2] - quant_1_1N[0]) Low_lim_1_1N = quant_1_1N[0] - 1.5*(quant_1_1N[2] - quant_1_1N[0]) bp_1 = plt.boxplot(Fuel_per_day_per_adult_1N, positions = [1], widths = 0.6) Fuel_D_A_1N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_1N): if a > Top_lim_1_1N or a < Low_lim_1_1N: Fuel_D_A_1N_outlier.append(f_d_a_1N[v]) plt.text(1,a,f_d_a_1N[v]) plt.text(1,0.1,'1N',color='b') #2N quant_1_2N = np.percentile(Fuel_per_day_per_adult_2N, [25,50,75]) Top_lim_1_2N = quant_1_2N[2] + 1.5*(quant_1_2N[2] - quant_1_2N[0]) Low_lim_1_2N = quant_1_2N[0] - 1.5*(quant_1_2N[2] - quant_1_2N[0]) bp_1 = plt.boxplot(Fuel_per_day_per_adult_2N,positions = [2], widths = 0.6) Fuel_D_A_2N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_2N): if a > Top_lim_1_2N or a < Low_lim_1_2N: Fuel_D_A_2N_outlier.append(f_d_a_2N[v]) plt.text(2,a,f_d_a_2N[v]) plt.text(2,0.1,'2N', color= 'g') #3N quant_1_3N = np.percentile(Fuel_per_day_per_adult_3N, [25,50,75]) Top_lim_1_3N = quant_1_3N[2] + 1.5*(quant_1_3N[2] - quant_1_3N[0]) Low_lim_1_3N = quant_1_3N[0] - 1.5*(quant_1_3N[2] - quant_1_3N[0]) bp_1 = plt.boxplot(Fuel_per_day_per_adult_3N,positions = [3], widths = 0.6) count = 0 Fuel_D_A_3N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_3N): if a > Top_lim_1_3N or a < Low_lim_1_3N: Fuel_D_A_3N_outlier.append(f_d_a_3N[v]) count = count + 1 if count == 2: plt.text(3,a,f_d_a_3N[v],ha='left',va='bottom') elif count != 2: plt.text(3,a,f_d_a_3N[v],ha='right',va='bottom') plt.text(3,0.1,'3N', color='r') #4N quant_1_4N = np.percentile(Fuel_per_day_per_adult_4N, [25,50,75]) Top_lim_1_4N = quant_1_4N[2] + 1.5*(quant_1_4N[2] - quant_1_4N[0]) Low_lim_1_4N = quant_1_4N[0] - 1.5*(quant_1_4N[2] - quant_1_4N[0]) bp_1 = plt.boxplot(Fuel_per_day_per_adult_4N,positions = [4], widths = 0.6) Fuel_D_A_4N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_4N): if a > Top_lim_1_4N or a < Low_lim_1_4N: Fuel_D_A_4N_outlier.append(f_d_a_4N[v]) plt.text(4,a,f_d_a_4N[v]) plt.text(4,0.1,'4N', color='y') plt.xlim(0,5) plt.ylim(0,2.3) print('Fuel/Day/Adult 1N had these values as outliers ', Fuel_D_A_1N_outlier) print('Fuel/Day/Adult 2N had these values as outliers ', Fuel_D_A_2N_outlier) print('Fuel/Day/Adult 3N had these values as outliers ', Fuel_D_A_3N_outlier) print('Fuel/Day/Adult 4N had these values as outliers ', Fuel_D_A_4N_outlier) plt.show() # % change of fuel per day per adult between each phase fig_2, ax2 = plt.subplots() plt.title('% No_hood Change from Fuel per Day per Adult' ) #plt.hold(True) #2N to 1N quant_1_2N_1N = np.percentile(Fuel_per_day_per_adult_2N_1N, [25,50,75]) Top_lim_1_2N_1N = quant_1_2N_1N[2] + 1.5*(quant_1_2N_1N[2]-quant_1_2N_1N[0]) Low_lim_1_2N_1N = quant_1_2N_1N[0] - 1.5*(quant_1_2N_1N[2]-quant_1_2N_1N[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_2N_1N, positions=[1], widths= 0.6) Fuel_D_A_2N_1N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_2N_1N): if a > Top_lim_1_2N_1N or a < Low_lim_1_2N_1N: Fuel_D_A_2N_1N_outlier.append(f_d_a_2N_1N[v]) plt.text(1, a, f_d_a_2N_1N[v]) plt.text(0.5, 0, '2N / 1N', color= 'g') #3N to 1N quant_1_3N_1N = np.percentile(Fuel_per_day_per_adult_3N_1N, [25,50,75]) Top_lim_1_3N_1N = quant_1_3N_1N[2] + 1.5*(quant_1_3N_1N[2]-quant_1_3N_1N[0]) Low_lim_1_3N_1N = quant_1_3N_1N[0] - 1.5*(quant_1_3N_1N[2]-quant_1_3N_1N[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_3N_1N, positions=[2], widths= 0.6) Fuel_D_A_3N_1N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_3N_1N): if a > Top_lim_1_3N_1N or a < Low_lim_1_3N_1N: Fuel_D_A_3N_1N_outlier.append(f_d_a_3N_1N[v]) plt.text(2, a, f_d_a_3N_1N[v]) plt.text(1.5, 0, '3N / 1N', color= 'r') #4N to 1N quant_1_4N_1N = np.percentile(Fuel_per_day_per_adult_4N_1N, [25,50,75]) Top_lim_1_4N_1N = quant_1_4N_1N[2] + 1.5*(quant_1_4N_1N[2]-quant_1_4N_1N[0]) Low_lim_1_4N_1N = quant_1_4N_1N[0] - 1.5*(quant_1_4N_1N[2]-quant_1_4N_1N[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_4N_1N, positions=[3], widths= 0.6) Fuel_D_A_4N_1N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_4N_1N): if a > Top_lim_1_4N_1N or a < Low_lim_1_4N_1N: Fuel_D_A_4N_1N_outlier.append(f_d_a_4N_1N[v]) plt.text(3, a, f_d_a_4N_1N[v]) plt.text(2.5, 0, '4N / 1N', color= 'y') #3N to 2N quant_1_3N_2N = np.percentile(Fuel_per_day_per_adult_3N_2N, [25,50,75]) Top_lim_1_3N_2N = quant_1_3N_2N[2] + 1.5*(quant_1_3N_2N[2]-quant_1_3N_2N[0]) Low_lim_1_3N_2N = quant_1_3N_2N[0] - 1.5*(quant_1_3N_2N[2]-quant_1_3N_2N[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_3N_2N, positions=[4], widths= 0.6) Fuel_D_A_3N_2N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_3N_2N): if a > Top_lim_1_3N_2N or a < Low_lim_1_3N_2N: Fuel_D_A_3N_2N_outlier.append(f_d_a_3N_2N[v]) plt.text(4, a, f_d_a_3N_2N[v]) plt.text(3.5, 0, '3N / 2N', color= 'm') #4N to 3N quant_1_4N_3N = np.percentile(Fuel_per_day_per_adult_4N_3N, [25,50,75]) Top_lim_1_4N_3N = quant_1_4N_3N[2] + 1.5*(quant_1_4N_3N[2]-quant_1_4N_3N[0]) Low_lim_1_4N_3N = quant_1_4N_3N[0] - 1.5*(quant_1_4N_3N[2]-quant_1_4N_3N[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_4N_3N, positions=[5], widths= 0.6) Fuel_D_A_4N_3N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_4N_3N): if a > Top_lim_1_4N_3N or a < Low_lim_1_4N_3N: Fuel_D_A_4N_3N_outlier.append(f_d_a_4N_3N[v]) plt.text(5, a, f_d_a_4N_3N[v]) plt.text(4.5, 0, '4N / 3N', color= 'k') #4N to 2N quant_1_4N_2N = np.percentile(Fuel_per_day_per_adult_4N_2N, [25,50,75]) Top_lim_1_4N_2N = quant_1_4N_2N[2] + 1.5*(quant_1_4N_2N[2]-quant_1_4N_2N[0]) Low_lim_1_4N_2N = quant_1_4N_2N[0] - 1.5*(quant_1_4N_2N[2]-quant_1_4N_2N[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_4N_2N, positions=[6], widths= 0.6) Fuel_D_A_4N_2N_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_4N_2N): if a > Top_lim_1_4N_2N or a < Low_lim_1_4N_2N: Fuel_D_A_4N_2N_outlier.append(f_d_a_4N_2N[v]) plt.text(6, a, f_d_a_4N_2N[v]) plt.text(5.5, 0, '4N / 2N', color= 'tab:orange') plt.xlim(0,7) plt.ylim(-0.5,4) print('Fuel/Day/Adult 2N/1N had these values as outliers ', Fuel_D_A_2N_1N_outlier) print('Fuel/Day/Adult 3N/1N had these values as outliers ', Fuel_D_A_3N_1N_outlier) print('Fuel/Day/Adult 4N/1N had these values as outliers ', Fuel_D_A_4N_1N_outlier) print('Fuel/Day/Adult 3N/2N had these values as outliers ', Fuel_D_A_3N_2N_outlier) print('Fuel/Day/Adult 4N/3N had these values as outliers ', Fuel_D_A_4N_3N_outlier) print('Fuel/Day/Adult 4N/2N had these values as outliers ', Fuel_D_A_4N_2N_outlier) plt.show() #adding averages to the tables quant_1_1N = np.append(quant_1_1N, np.average(Fuel_per_day_per_adult_1N)) quant_1_2N = np.append(quant_1_2N, np.average(Fuel_per_day_per_adult_2N)) quant_1_3N = np.append(quant_1_3N, np.average(Fuel_per_day_per_adult_3N)) quant_1_4N = np.append(quant_1_4N, np.average(Fuel_per_day_per_adult_4N)) D_50_quant_phase_f_d_a = {'Percentile %': ['25','50','75', 'Avg'], '1N': quant_1_1N, '2N': quant_1_2N,'3N' : quant_1_3N,'4N': quant_1_4N} F_D_A_50_phase_no_hood = pd.DataFrame(data=D_50_quant_phase_f_d_a, columns=['Percentile %','1N', '2N', '3N','4N']) quant_1_2N_1N = np.append(quant_1_2N_1N , np.average(Fuel_per_day_per_adult_2N_1N)) quant_1_3N_1N = np.append(quant_1_3N_1N , np.average(Fuel_per_day_per_adult_3N_1N)) quant_1_4N_1N = np.append(quant_1_4N_1N , np.average(Fuel_per_day_per_adult_4N_1N)) quant_1_3N_2N = np.append(quant_1_3N_2N , np.average(Fuel_per_day_per_adult_3N_2N)) quant_1_4N_3N = np.append(quant_1_4N_3N , np.average(Fuel_per_day_per_adult_4N_3N)) quant_1_4N_2N = np.append(quant_1_4N_2N , np.average(Fuel_per_day_per_adult_4N_2N)) D_50_quant_percent_f_d_a ={'Percentile %': ['25','50','75', 'Avg'],'2N / 1N': quant_1_2N_1N,'3N / 1N': quant_1_3N_1N,'4N / 1N': quant_1_4N_1N, '3N / 2N': quant_1_3N_2N,'4N / 3N': quant_1_4N_3N,'4N / 2N': quant_1_4N_2N} F_D_A_50_percent_change_no_hood = pd.DataFrame(data=D_50_quant_percent_f_d_a, columns=['Percentile %','2N / 1N','3N / 1N', '4N / 1N' ,'3N / 2N','4N / 3N','4N / 2N']) print(F_D_A_50_phase_no_hood) print(F_D_A_50_percent_change_no_hood) # add more print ('-------------------Fuel per Day per Adult Hood Phase -------------------') if Hood_or_no == 'hood': Fuel_per_day_per_adult_1H = [] f_d_a_1H = [] Fuel_per_day_per_adult_2H = [] f_d_a_2H = [] Fuel_per_day_per_adult_3H = [] f_d_a_3H = [] count_t = 0 count_f = 0 for c in hood_counter: if c == (Household_removal[count_t] - C_Place_holder): count_t = count_t + 1 if count_t == len(Household_removal): count_t = 0 continue if c == (Household_removal_Hood_fuel_day_adult[count_f] - C_Place_holder): count_f = count_f + 1 if count_f == len(Household_removal_Hood_fuel_day_adult): count_f = 0 continue if Fuel_remove_1H_24hr.iloc[c,6] != -1.00: Fuel_per_day_per_adult_1H.append(Fuel_remove_1H_24hr.iloc[c,6]/Survey_1H.iloc[c,7]) f_d_a_1H.append(Day_1H.iloc[c,0]) if Fuel_remove_2H_24hr.iloc[c,6] != -1.00: Fuel_per_day_per_adult_2H.append(Fuel_remove_2H_24hr.iloc[c,6] / Survey_2H.iloc[c, 7]) f_d_a_2H.append(Day_2H.iloc[c,0]) if Fuel_remove_3H_24hr.iloc[c,6] != -1.00: Fuel_per_day_per_adult_3H.append(Fuel_remove_3H_24hr.iloc[c,6]/ Survey_3H.iloc[c, 7]) f_d_a_3H.append(Day_3H.iloc[c, 0]) # percentage Change of Fuel per day between the phases Fuel_per_day_per_adult_2H_1H = [] f_d_a_2H_1H = [] Fuel_per_day_per_adult_3H_1H = [] f_d_a_3H_1H = [] Fuel_per_day_per_adult_3H_2H = [] f_d_a_3H_2H = [] count_t = 0 count_f = 0 for c in hood_counter: if c == (Household_removal[count_t] - C_Place_holder): count_t = count_t + 1 if count_t == len(Household_removal): count_t = 0 continue if c == (Household_removal_Hood_fuel_day_adult[count_f] - C_Place_holder): count_f = count_f + 1 if count_f == len(Household_removal_Hood_fuel_day_adult): count_f = 0 continue if (len(Fuel_per_day_per_adult_2H)-1) >= c and (len(Fuel_per_day_per_adult_1H)-1) >= c: if Day_1H.iloc[c,13] > 0 and Day_2H.iloc[c,13] > 0 and Day_1H.iloc[c,0] == Day_2H.iloc[c,0]: Fuel_per_day_per_adult_2H_1H.append(Fuel_per_day_per_adult_2H[c]/Fuel_per_day_per_adult_1H[c]) f_d_a_2H_1H.append(Day_1H.iloc[c,0]) if (len(Fuel_per_day_per_adult_3H)-1) >= c and (len(Fuel_per_day_per_adult_1H)-1) >= c: if Day_3H.iloc[c,13] > 0 and Day_1H.iloc[c,13] > 0 and Day_3H.iloc[c,0] == Day_1H.iloc[c,0]: Fuel_per_day_per_adult_3H_1H.append(Fuel_per_day_per_adult_3H[c]/Fuel_per_day_per_adult_1H[c]) f_d_a_3H_1H.append(Day_1H.iloc[c,0]) if (len(Fuel_per_day_per_adult_3H)-1) >= c and (len(Fuel_per_day_per_adult_2H)-1) >= c: if Day_3H.iloc[c,13] > 0 and Day_2H.iloc[c,13] > 0 and Day_3H.iloc[c,0] == Day_2H.iloc[c,0]: Fuel_per_day_per_adult_3H_2H.append(Fuel_per_day_per_adult_3H[c]/Fuel_per_day_per_adult_2H[c]) f_d_a_3H_2H.append(Day_1H.iloc[c,0]) # now for plotting #1H sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Fuel_per_day_per_adult_1H, ax=ax_box, color='b') sns.distplot(Fuel_per_day_per_adult_1H, ax=ax_hist, color='b') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('1H Fuel per Day per Adult') plt.ylim(top=2) plt.ylim(bottom = 0) #2H sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Fuel_per_day_per_adult_2H, ax=ax_box, color='g') sns.distplot(Fuel_per_day_per_adult_2H, ax=ax_hist, color='g') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('2H Fuel per Day per Adult') plt.ylim(top=2) plt.ylim(bottom = 0) #3H sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Fuel_per_day_per_adult_3H, ax=ax_box, color='r') sns.distplot(Fuel_per_day_per_adult_3H, ax=ax_hist, color='r') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('3H Fuel per Day per Adult') plt.ylim(top=2) plt.ylim(bottom = 0) fig_2, ax_2 = plt.subplots() plt.title('Hood Fuel per Day per Adult') #plt.hold(True) quant_1_1H = np.percentile(Fuel_per_day_per_adult_1H, [25,50,75]) Top_lim_1_1H = quant_1_1H[2] + 1.5*(quant_1_1H[2] - quant_1_1H[0]) Low_lim_1_1H = quant_1_1H[0] - 1.5*(quant_1_1H[2] - quant_1_1H[0]) bp_1 = plt.boxplot(Fuel_per_day_per_adult_1H, positions = [1], widths = 0.6) Fuel_D_A_1H_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_1H): if a > Top_lim_1_1H or a < Low_lim_1_1H: Fuel_D_A_1H_outlier.append(f_d_a_1H[v]) plt.text(1,a,f_d_a_1H[v]) plt.text(1,0,'1H',color='b') quant_1_2H = np.percentile(Fuel_per_day_per_adult_2H, [25,50,75]) Top_lim_1_2H = quant_1_2H[2] + 1.5*(quant_1_2H[2] - quant_1_2H[0]) Low_lim_1_2H = quant_1_2H[0] - 1.5*(quant_1_2H[2] - quant_1_2H[0]) bp_1 = plt.boxplot(Fuel_per_day_per_adult_2H,positions = [2], widths = 0.6) count = 0 Fuel_D_A_2H_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_2H): if a > Top_lim_1_2H or a < Low_lim_1_2H: Fuel_D_A_2H_outlier.append(f_d_a_2H[v]) count = count + 1 if count == 1: plt.text(2,a,f_d_a_2H[v],ha='left',va='bottom') elif count !=1: plt.text(2,a,f_d_a_2H[v],ha='right',va='bottom') plt.text(2,0,'2H', color= 'g') quant_1_3H = np.percentile(Fuel_per_day_per_adult_3H, [25,50,75]) Top_lim_1_3H = quant_1_3H[2] + 1.5*(quant_1_3H[2] - quant_1_3H[0]) Low_lim_1_3H = quant_1_3H[0] - 1.5*(quant_1_3H[2] - quant_1_3H[0]) bp_1 = plt.boxplot(Fuel_per_day_per_adult_3H,positions = [3], widths = 0.6) count = 0 Fuel_D_A_3H_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_3H): if a > Top_lim_1_3H or a < Low_lim_1_3H: Fuel_D_A_3H_outlier.append(f_d_a_3H[v]) count = count + 1 if count == 3: plt.text(3,a,f_d_a_3H[v],ha='left',va='bottom') elif count != 1: plt.text(3,a,f_d_a_3H[v],ha='right',va='bottom') plt.text(3,0,'3H', color='r') plt.xlim(-0,4) plt.ylim(-0.25,2.5) print('Fuel/Day/Adult 1H had these values as outliers ', Fuel_D_A_1H_outlier) print('Fuel/Day/Adult 2H had these values as outliers ', Fuel_D_A_2H_outlier) print('Fuel/Day/Adult 3H had these values as outliers ', Fuel_D_A_3H_outlier) plt.show() #% change of fuel perday per adult between each phase fig_2, ax2 = plt.subplots() plt.title('% No_hood Change from Fuel per Day per Adult' ) #plt.hold(True) #2H to 1H quant_1_2H_1H = np.percentile(Fuel_per_day_per_adult_2H_1H, [25,50,75]) Top_lim_1_2H_1H = quant_1_2H_1H[2] + 1.5*(quant_1_2H_1H[2]-quant_1_2H_1H[0]) Low_lim_1_2H_1H = quant_1_2H_1H[0] - 1.5*(quant_1_2H_1H[2]-quant_1_2H_1H[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_2H_1H, positions=[1], widths= 0.6) Fuel_D_A_2H_1H_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_2H_1H): if a > Top_lim_1_2H_1H or a < Low_lim_1_2H_1H: Fuel_D_A_2H_1H_outlier.append(f_d_a_2H_1H[v]) plt.text(1, a, f_d_a_2H_1H[v]) plt.text(0.75, -0.25, '2H / 1H', color= 'g') #3H to 1H quant_1_3H_1H = np.percentile(Fuel_per_day_per_adult_3H_1H, [25,50,75]) Top_lim_1_3H_1H = quant_1_3H_1H[2] + 1.5*(quant_1_3H_1H[2]-quant_1_3H_1H[0]) Low_lim_1_3H_1H = quant_1_3H_1H[0] - 1.5*(quant_1_3H_1H[2]-quant_1_3H_1H[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_3H_1H, positions=[2], widths= 0.6) Fuel_D_A_3H_1H_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_3H_1H): if a > Top_lim_1_3H_1H or a < Low_lim_1_3H_1H: Fuel_D_A_3H_1H_outlier.append(f_d_a_3H_1H[v]) plt.text(2, a, f_d_a_3H_1H[v]) plt.text(1.75, -0.25, '3H / 1H', color= 'r') #3H to 2H quant_1_3H_2H = np.percentile(Fuel_per_day_per_adult_3H_2H, [25,50,75]) Top_lim_1_3H_2H = quant_1_3H_2H[2] + 1.5*(quant_1_3H_2H[2]-quant_1_3H_2H[0]) Low_lim_1_3H_2H = quant_1_3H_2H[0] - 1.5*(quant_1_3H_2H[2]-quant_1_3H_2H[0]) bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_3H_2H, positions=[3], widths= 0.6) Fuel_D_A_3H_2H_outlier = [] for v,a in enumerate(Fuel_per_day_per_adult_3H_2H): if a > Top_lim_1_3H_2H or a < Low_lim_1_3H_2H: Fuel_D_A_3H_2H_outlier.append(f_d_a_3H_2H[v]) plt.text(3, a, f_d_a_3H_2H[v]) plt.text(2.75, -0.25, '2H / 1H', color= 'm') plt.xlim(-0,4) plt.ylim(-0.25,6) print('Fuel/Day/Adult 2H/1H had these values as outliers ', Fuel_D_A_2H_1H_outlier) print('Fuel/Day/Adult 3H/1H had these values as outliers ', Fuel_D_A_3H_1H_outlier) print('Fuel/Day/Adult 3H/2H had these values as outliers ', Fuel_D_A_3H_2H_outlier) plt.show() quant_1_1H = np.append(quant_1_1H, np.average(Fuel_per_day_per_adult_1H)) quant_1_2H = np.append(quant_1_2H, np.average(Fuel_per_day_per_adult_2H)) quant_1_3H = np.append(quant_1_3H, np.average(Fuel_per_day_per_adult_3H)) D_50_quant_phase_f_d_a_hood = {'Percentile %': ['25','50','75', 'Avg'], '1H': quant_1_1H, '2H': quant_1_2H,'3H' : quant_1_3H} F_D_A_50_phase_hood = pd.DataFrame(data=D_50_quant_phase_f_d_a_hood, columns=['Percentile %','1H', '2H','3H'] ) quant_1_2H_1H = np.append(quant_1_2H_1H , np.average(Fuel_per_day_per_adult_2H_1H)) quant_1_3H_1H = np.append(quant_1_3H_1H , np.average(Fuel_per_day_per_adult_3H_1H)) quant_1_3H_2H = np.append(quant_1_3H_2H , np.average(Fuel_per_day_per_adult_3H_2H)) D_50_quant_percent_f_d_a_hood ={'Percentile %': ['25','50','75', 'Avg'],'2H / 1H': quant_1_2H_1H,'3H / 1H': quant_1_3H_1H,'3H / 2H': quant_1_3H_2H} F_D_A_50_percent_change_hood = pd.DataFrame(data=D_50_quant_percent_f_d_a_hood, columns=['Percentile %','2H / 1H','3H / 1H','3H / 2H']) print(F_D_A_50_phase_hood) print(F_D_A_50_percent_change_hood) print('----------------------- Kitchen PM per Day -----------------------------') if Hood_or_no == 'no_hood': Kit_PM_per_day_1N = [] K_PM_D_1N = [] Kit_PM_per_day_2N = [] K_PM_D_2N = [] Kit_PM_per_day_3N = [] K_PM_D_3N = [] Kit_PM_per_day_4N = [] K_PM_D_4N = [] count_t = 0 count_pm = 0 for c in NO_hood_counter: if c == (Household_removal[count_t] - C_Place_holder): count_t = count_t + 1 if count_t == len(Household_removal): count_t = 0 continue if c == (Household_removal_NO_Hood_PM[count_pm] - C_Place_holder): count_pm = count_pm + 1 if count_pm == len(Household_removal_NO_Hood_PM): count_pm = 0 continue # if Day_1N.iloc[c,7] != -1.00: # Kit_PM_per_day_1N.append(Day_1N.iloc[c,7]/Day_1N.iloc[c,1]) # K_PM_D_1N.append(Day_1N.iloc[c,0]) if Kit_PM_1N_24hr.iloc[c,6] != -1.00: Kit_PM_per_day_1N.append(Kit_PM_1N_24hr.iloc[c,6]) K_PM_D_1N.append(Kit_PM_1N_24hr.iloc[c, 0]) #if Day_2N.iloc[c, 7] != -1.00: # Kit_PM_per_day_2N.append(Day_2N.iloc[c,7]/Day_2N.iloc[c,1]) # K_PM_D_2N.append(Day_2N.iloc[c,0]) if Kit_PM_2N_24hr.iloc[c, 6] != -1.00: Kit_PM_per_day_2N.append(Kit_PM_2N_24hr.iloc[c, 6]) K_PM_D_2N.append(Kit_PM_2N_24hr.iloc[c, 0]) # if Day_3N.iloc[c, 7] != -1.00: # Kit_PM_per_day_3N.append(Day_3N.iloc[c,7]/Day_3N.iloc[c,1]) # K_PM_D_3N.append(Day_3N.iloc[c, 0]) if Kit_PM_3N_24hr.iloc[c, 6] != -1.00: Kit_PM_per_day_3N.append(Kit_PM_3N_24hr.iloc[c, 6]) K_PM_D_3N.append(Kit_PM_3N_24hr.iloc[c, 0]) # if Day_4N.iloc[c, 7] != -1.00: # Kit_PM_per_day_4N.append(Day_4N.iloc[c,7]/Day_4N.iloc[c,1]) # K_PM_D_4N.append(Day_4N.iloc[c, 0]) if Kit_PM_4N_24hr.iloc[c, 6] != -1.00: Kit_PM_per_day_4N.append(Kit_PM_4N_24hr.iloc[c, 6]) K_PM_D_4N.append(Kit_PM_4N_24hr.iloc[c, 0]) # percentages Between Phases of kitchen PM per day Kit_per_day_2N_1N = [] K_PM_D_2N_1N = [] Kit_per_day_3N_1N = [] K_PM_D_3N_1N = [] Kit_per_day_4N_1N = [] K_PM_D_4N_1N = [] Kit_per_day_3N_2N = [] K_PM_D_3N_2N = [] Kit_per_day_4N_3N = [] K_PM_D_4N_3N = [] Kit_per_day_4N_2N = [] K_PM_D_4N_2N = [] count_t = 0 count_pm = 0 for c in NO_hood_counter: if c == (Household_removal[count_t] - C_Place_holder): count_t = count_t + 1 if count_t == len(Household_removal): count_t = 0 continue if c == (Household_removal_NO_Hood_PM[count_pm] - C_Place_holder): count_pm = count_pm + 1 if count_pm == len(Household_removal_NO_Hood_PM): count_pm = 0 continue if (len(Kit_PM_per_day_2N)-1) >= c and (len(Kit_PM_per_day_1N)-1) >= c: #if Day_1N.iloc[c,7] > 0 and Day_2N.iloc[c,7] > 0 and Day_1N.iloc[c,0] == Day_2N.iloc[c,0]: if Kit_PM_1N_24hr.iloc[c,6] > 0 and Kit_PM_2N_24hr.iloc[c,6] > 0 and Kit_PM_1N_24hr.iloc[c,0] == Kit_PM_2N_24hr.iloc[c,0]: Kit_per_day_2N_1N.append(Kit_PM_per_day_2N[c]/Kit_PM_per_day_1N[c]) K_PM_D_2N_1N.append(Day_1N.iloc[c,0]) if (len(Kit_PM_per_day_3N)-1) >= c and (len(Kit_PM_per_day_1N)-1) >= c: #if Day_3N.iloc[c,7] > 0 and Day_1N.iloc[c,7] > 0 and Day_3N.iloc[c,0] == Day_1N.iloc[c,0]: if Kit_PM_3N_24hr.iloc[c, 6] > 0 and Kit_PM_1N_24hr.iloc[c, 6] > 0 and Kit_PM_3N_24hr.iloc[c, 0] == \ Kit_PM_1N_24hr.iloc[c, 0]: Kit_per_day_3N_1N.append(Kit_PM_per_day_3N[c]/Kit_PM_per_day_1N[c]) K_PM_D_3N_1N.append(Day_1N.iloc[c,0]) if (len(Kit_PM_per_day_4N)-1) >= c and (len(Kit_PM_per_day_1N)-1) >= c: #if Day_4N.iloc[c,7] > 0 and Day_1N.iloc[c,7] > 0 and Day_4N.iloc[c,0] == Day_1N.iloc[c,0]: if Kit_PM_4N_24hr.iloc[c, 6] > 0 and Kit_PM_1N_24hr.iloc[c, 6] > 0 and Kit_PM_4N_24hr.iloc[c, 0] == \ Kit_PM_1N_24hr.iloc[c, 0]: Kit_per_day_4N_1N.append(Kit_PM_per_day_4N[c]/Kit_PM_per_day_1N[c]) K_PM_D_4N_1N.append(Day_1N.iloc[c,0]) if (len(Kit_PM_per_day_3N)-1) >= c and (len(Kit_PM_per_day_2N)-1) >= c: #if Day_3N.iloc[c,7] > 0 and Day_2N.iloc[c,7] > 0 and Day_3N.iloc[c,0] == Day_2N.iloc[c,0]: if Kit_PM_3N_24hr.iloc[c, 6] > 0 and Kit_PM_2N_24hr.iloc[c, 6] > 0 and Kit_PM_3N_24hr.iloc[c, 0] == \ Kit_PM_2N_24hr.iloc[c, 0]: Kit_per_day_3N_2N.append(Kit_PM_per_day_3N[c]/Kit_PM_per_day_2N[c]) K_PM_D_3N_2N.append(Day_2N.iloc[c,0]) if (len(Kit_PM_per_day_4N)-1) >= c and (len(Kit_PM_per_day_3N)-1) >= c: #if Day_4N.iloc[c,7] > 0 and Day_3N.iloc[c,7] > 0 and Day_4N.iloc[c,0] == Day_3N.iloc[c,0]: if Kit_PM_4N_24hr.iloc[c, 6] > 0 and Kit_PM_3N_24hr.iloc[c, 6] > 0 and Kit_PM_3N_24hr.iloc[c, 0] == \ Kit_PM_4N_24hr.iloc[c, 0]: Kit_per_day_4N_3N.append(Kit_PM_per_day_4N[c]/Kit_PM_per_day_3N[c]) K_PM_D_4N_3N.append(Day_3N.iloc[c,0]) if (len(Kit_PM_per_day_4N)-1) >= c and (len(Kit_PM_per_day_2N)-1) >= c: #if Day_4N.iloc[c,7] > 0 and Day_2N.iloc[c,7] > 0 and Day_4N.iloc[c,0] == Day_2N.iloc[c,0]: if Kit_PM_4N_24hr.iloc[c, 6] > 0 and Kit_PM_4N_24hr.iloc[c, 6] > 0 and Kit_PM_4N_24hr.iloc[c, 0] == \ Kit_PM_2N_24hr.iloc[c, 0]: Kit_per_day_4N_2N.append(Kit_PM_per_day_4N[c]/Kit_PM_per_day_2N[c]) K_PM_D_4N_2N.append(Day_4N.iloc[c,0]) # now for box plotting for Kitchen PM per day percent changes #2N to 1N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_2N_1N, ax=ax_box, color='g') sns.distplot(Kit_per_day_2N_1N, ax=ax_hist, color='g') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 2N/1N (Kitchen PM per Day)') plt.ylim(top=2) plt.ylim(bottom = 0) #3N to 1N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_3N_1N, ax=ax_box, color='r') sns.distplot(Kit_per_day_3N_1N, ax=ax_hist, color='r') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 3N/1N (Kitchen PM per Day)') plt.ylim(top=2) plt.ylim(bottom = 0) #4N to 1N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_4N_1N, ax=ax_box, color='y') sns.distplot(Kit_per_day_4N_1N, ax=ax_hist, color='y') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 4N/1N (Kitchen PM per Day)') plt.ylim(top=2) plt.ylim(bottom = 0) #3N to 2N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_3N_2N, ax=ax_box, color='m') sns.distplot(Kit_per_day_3N_2N, ax=ax_hist, color='m') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 3N/2N (Kitchen PM per Day)') plt.ylim(top=2) plt.ylim(bottom = 0) #4N to 3N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_4N_3N, ax=ax_box, color='k') sns.distplot(Kit_per_day_4N_3N, ax=ax_hist, color='k') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 4N/3N (Kitchen PM per Day)') plt.ylim(top=2) plt.ylim(bottom = 0) #4N to 2N sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_4N_2N, ax=ax_box, color='tab:orange') sns.distplot(Kit_per_day_4N_2N, ax=ax_hist, color='tab:orange') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 4N/2N (Kitchen PM per Day)') plt.ylim(top=2) plt.ylim(bottom = 0) #Plotting on the same graph fig, ax = plt.subplots() plt.title('No-Hood Kitchen PM per day') #plt.hold() #1N quant_1_1N = np.percentile(Kit_PM_per_day_1N, [25,50,75]) Top_lim_1_1N = quant_1_1N[2] + 1.5*(quant_1_1N[2] - quant_1_1N[0]) Low_lim_1_1N = quant_1_1N[0] - 1.5*(quant_1_1N[2] - quant_1_1N[0]) bp_1 = plt.boxplot(Kit_PM_per_day_1N, positions = [1], widths = 0.6) kitchen_pm_1N_outlier = [] for v,a in enumerate(Kit_PM_per_day_1N): if a > Top_lim_1_1N or a < Low_lim_1_1N: kitchen_pm_1N_outlier.append(K_PM_D_1N[v]) plt.text(1,a,K_PM_D_1N[v]) plt.text(1,0.1,'1N',color='b') #2N quant_1_2N = np.percentile(Kit_PM_per_day_2N, [25,50,75]) Top_lim_1_2N = quant_1_2N[2] + 1.5*(quant_1_2N[2] - quant_1_2N[0]) Low_lim_1_2N = quant_1_2N[0] - 1.5*(quant_1_2N[2] - quant_1_2N[0]) bp_1 = plt.boxplot(Kit_PM_per_day_2N,positions = [2], widths = 0.6) kitchen_pm_2N_outlier = [] for v,a in enumerate(Kit_PM_per_day_2N): if a > Top_lim_1_2N or a < Low_lim_1_2N: kitchen_pm_2N_outlier.append(K_PM_D_2N[v]) plt.text(2,a,K_PM_D_2N[v]) plt.text(2,0.1,'2N', color= 'g') #3N quant_1_3N = np.percentile(Kit_PM_per_day_3N, [25,50,75]) Top_lim_1_3N = quant_1_3N[2] + 1.5*(quant_1_3N[2] - quant_1_3N[0]) Low_lim_1_3N = quant_1_3N[0] - 1.5*(quant_1_3N[2] - quant_1_3N[0]) kitchen_pm_3N_outlier = [] bp_1 = plt.boxplot(Kit_PM_per_day_3N,positions = [3], widths = 0.6) count = 0 for v,a in enumerate(Kit_PM_per_day_3N): if a > Top_lim_1_3N or a < Low_lim_1_3N: kitchen_pm_3N_outlier.append(K_PM_D_3N[v]) count = count + 1 if count == (3): plt.text(3,a,K_PM_D_3N[v],ha='left', va='bottom') if count == (1): plt.text(3,a,K_PM_D_3N[v],ha='left', va='top') else: plt.text(3,a,K_PM_D_3N[v],ha='right', va='bottom') plt.text(3,0.1,'3N', color='r') #4N quant_1_4N = np.percentile(Kit_PM_per_day_4N, [25,50,75]) Top_lim_1_4N = quant_1_4N[2] + 1.5*(quant_1_4N[2] - quant_1_4N[0]) Low_lim_1_4N = quant_1_4N[0] - 1.5*(quant_1_4N[2] - quant_1_4N[0]) bp_1 = plt.boxplot(Kit_PM_per_day_4N,positions = [4], widths = 0.6) kitchen_pm_4N_outlier = [] for v,a in enumerate(Kit_PM_per_day_4N): if a > Top_lim_1_4N or a < Low_lim_1_4N: kitchen_pm_4N_outlier.append(K_PM_D_4N[v]) plt.text(4,a,K_PM_D_4N[v]) plt.text(4,0.1,'4N', color='y') plt.xlim(0,5) plt.ylim(0,1200) print('Kitchen PM 1N had these values as outliers ', kitchen_pm_1N_outlier) print('Kitchen PM 2N had these values as outliers ', kitchen_pm_2N_outlier) print('Kitchen PM 3N had these values as outliers ', kitchen_pm_3N_outlier) print('Kitchen PM 4N had these values as outliers ', kitchen_pm_4N_outlier) plt.show() # % change of PM per day fig_2, ax2 = plt.subplots() plt.title('% No_hood PM per Day Change' ) #plt.hold(True) #2N to 1N quant_1_2N_1N = np.percentile(Kit_per_day_2N_1N, [25,50,75]) Top_lim_1_2N_1N = quant_1_2N_1N[2] + 1.5*(quant_1_2N_1N[2]-quant_1_2N_1N[0]) Low_lim_1_2N_1N = quant_1_2N_1N[0] - 1.5*(quant_1_2N_1N[2]-quant_1_2N_1N[0]) bp_1_1 = plt.boxplot(Kit_per_day_2N_1N, positions=[1], widths= 0.6) kitchen_pm_2N_1N_outlier = [] for v,a in enumerate(Kit_per_day_2N_1N): if a > Top_lim_1_2N_1N or a < Low_lim_1_2N_1N: kitchen_pm_2N_1N_outlier.append(K_PM_D_2N_1N[v]) plt.text(1, a, K_PM_D_2N_1N[v]) plt.text(0.5, -0.25, '2N / 1N', color= 'g') #3N to 1N quant_1_3N_1N = np.percentile(Kit_per_day_3N_1N, [25,50,75]) Top_lim_1_3N_1N = quant_1_3N_1N[2] + 1.5*(quant_1_3N_1N[2]-quant_1_3N_1N[0]) Low_lim_1_3N_1N = quant_1_3N_1N[0] - 1.5*(quant_1_3N_1N[2]-quant_1_3N_1N[0]) bp_1_1 = plt.boxplot(Kit_per_day_3N_1N, positions=[2], widths= 0.6) kitchen_pm_3N_1N_outlier = [] for v,a in enumerate(Kit_per_day_3N_1N): if a > Top_lim_1_3N_1N or a < Low_lim_1_3N_1N: kitchen_pm_3N_1N_outlier.append(K_PM_D_3N_1N[v]) plt.text(2, a, K_PM_D_3N_1N[v]) plt.text(1.5, -0.25, '3N / 1N', color= 'r') #4N to 1N quant_1_4N_1N = np.percentile(Kit_per_day_4N_1N, [25,50,75]) Top_lim_1_4N_1N = quant_1_4N_1N[2] + 1.5*(quant_1_4N_1N[2]-quant_1_4N_1N[0]) Low_lim_1_4N_1N = quant_1_4N_1N[0] - 1.5*(quant_1_4N_1N[2]-quant_1_4N_1N[0]) bp_1_1 = plt.boxplot(Kit_per_day_4N_1N, positions=[3], widths= 0.6) kitchen_pm_4N_1N_outlier = [] for v,a in enumerate(Kit_per_day_4N_1N): if a > Top_lim_1_4N_1N or a < Low_lim_1_4N_1N: kitchen_pm_4N_1N_outlier.append(K_PM_D_4N_1N[v]) plt.text(3, a, K_PM_D_4N_1N[v]) plt.text(2.5, -0.25, '4N / 1N', color= 'y') #3N to 2N quant_1_3N_2N = np.percentile(Kit_per_day_3N_2N, [25,50,75]) Top_lim_1_3N_2N = quant_1_3N_2N[2] + 1.5*(quant_1_3N_2N[2]-quant_1_3N_2N[0]) Low_lim_1_3N_2N = quant_1_3N_2N[0] - 1.5*(quant_1_3N_2N[2]-quant_1_3N_2N[0]) bp_1_1 = plt.boxplot(Kit_per_day_3N_2N, positions=[4], widths= 0.6) kitchen_pm_3N_2N_outlier = [] for v,a in enumerate(Kit_per_day_3N_2N): if a > Top_lim_1_3N_2N or a < Low_lim_1_3N_2N: kitchen_pm_3N_2N_outlier.append(K_PM_D_3N_2N[v]) plt.text(4, a, K_PM_D_3N_2N[v]) plt.text(3.5, -0.25, '3N / 2N', color= 'm') #4N to 3N quant_1_4N_3N = np.percentile(Kit_per_day_4N_3N, [25,50,75]) Top_lim_1_4N_3N = quant_1_4N_3N[2] + 1.5*(quant_1_4N_3N[2]-quant_1_4N_3N[0]) Low_lim_1_4N_3N = quant_1_4N_3N[0] - 1.5*(quant_1_4N_3N[2]-quant_1_4N_3N[0]) bp_1_1 = plt.boxplot(Kit_per_day_4N_3N, positions=[5], widths= 0.6) kitchen_pm_4N_3N_outlier = [] for v,a in enumerate(Kit_per_day_4N_3N): if a > Top_lim_1_4N_3N or a < Low_lim_1_4N_3N: kitchen_pm_4N_3N_outlier.append(K_PM_D_4N_3N[v]) plt.text(5, a, K_PM_D_4N_3N[v]) plt.text(4.5, -0.25, '4N / 3N', color= 'k') #4N to 2N quant_1_4N_2N = np.percentile(Kit_per_day_4N_2N, [25,50,75]) Top_lim_1_4N_2N = quant_1_4N_2N[2] + 1.5*(quant_1_4N_2N[2]-quant_1_4N_2N[0]) Low_lim_1_4N_2N = quant_1_4N_2N[0] - 1.5*(quant_1_4N_2N[2]-quant_1_4N_2N[0]) bp_1_1 = plt.boxplot(Kit_per_day_4N_2N, positions=[6], widths= 0.6) kitchen_pm_4N_2N_outlier = [] for v,a in enumerate(Kit_per_day_4N_2N): if a > Top_lim_1_4N_2N or a < Low_lim_1_4N_2N: kitchen_pm_4N_2N_outlier.append(K_PM_D_4N_2N[v]) plt.text(6, a, K_PM_D_4N_2N[v]) plt.text(5.5, -0.25, '4N / 2N', color= 'tab:orange') plt.xlim(0,7) plt.ylim(-0.5,5) print('Kitchen PM 2N/1N had these values as outliers ', kitchen_pm_2N_1N_outlier) print('Kitchen PM 3N/1N had these values as outliers ', kitchen_pm_3N_1N_outlier) print('Kitchen PM 4N/1N had these values as outliers ', kitchen_pm_4N_1N_outlier) print('Kitchen PM 3N/2N had these values as outliers ', kitchen_pm_3N_2N_outlier) print('Kitchen PM 4N/3N had these values as outliers ', kitchen_pm_4N_3N_outlier) print('Kitchen PM 4N/2N had these values as outliers ', kitchen_pm_4N_2N_outlier) plt.show() #adding averages to the tables quant_1_1N = np.append(quant_1_1N, np.average(Kit_PM_per_day_1N)) quant_1_2N = np.append(quant_1_2N, np.average(Kit_PM_per_day_2N)) quant_1_3N = np.append(quant_1_3N, np.average(Kit_PM_per_day_3N)) quant_1_4N = np.append(quant_1_4N, np.average(Kit_PM_per_day_4N)) D_50_quant_phase_PM_d = {'Percentile %': ['25','50','75', 'Avg'], '1N': quant_1_1N, '2N': quant_1_2N,'3N' : quant_1_3N,'4N': quant_1_4N} PM_D_50_phase_no_hood = pd.DataFrame(data=D_50_quant_phase_PM_d,columns=['Percentile %','1N', '2N', '3N','4N']) quant_1_2N_1N = np.append(quant_1_2N_1N , np.average(Kit_per_day_2N_1N)) quant_1_3N_1N = np.append(quant_1_3N_1N , np.average(Kit_per_day_3N_1N)) quant_1_4N_1N = np.append(quant_1_4N_1N , np.average(Kit_per_day_4N_1N)) quant_1_3N_2N = np.append(quant_1_3N_2N , np.average(Kit_per_day_3N_2N)) quant_1_4N_3N = np.append(quant_1_4N_3N , np.average(Kit_per_day_4N_3N)) quant_1_4N_2N = np.append(quant_1_4N_2N , np.average(Kit_per_day_4N_2N)) D_50_quant_percent_PM_d ={'Percentile %': ['25','50','75', 'Avg'],'2N / 1N': quant_1_2N_1N,'3N / 1N': quant_1_3N_1N,'4N / 1N': quant_1_4N_1N, '3N / 2N': quant_1_3N_2N,'4N / 3N': quant_1_4N_3N,'4N / 2N': quant_1_4N_2N} PM_D_50_percent_change_no_hood = pd.DataFrame(data=D_50_quant_percent_PM_d, columns=['Percentile %','2N / 1N','3N / 1N', '4N / 1N' ,'3N / 2N','4N / 3N','4N / 2N']) print(PM_D_50_phase_no_hood) print(PM_D_50_percent_change_no_hood) # hood Pm per day if Hood_or_no == 'hood': Kit_PM_per_day_1H = [] K_PM_D_1H = [] Kit_PM_per_day_2H = [] K_PM_D_2H = [] Kit_PM_per_day_3H = [] K_PM_D_3H = [] count_t = 0 count_pm = 0 for c in hood_counter: if c == (Household_removal[count_t] - C_Place_holder): count_t = count_t + 1 if count_t == len(Household_removal): count_t = 0 continue if c == (Household_removal_Hood_PM[count_pm] - C_Place_holder): count_pm = count_pm + 1 if count_pm == len(Household_removal_Hood_PM): count_pm = 0 continue # if Day_1H.iloc[c,7] != -1.00: # Kit_PM_per_day_1H.append(Day_1H.iloc[c,7]/Day_1H.iloc[c,1]) # K_PM_D_1H.append(Day_1H.iloc[c,0]) if Kit_PM_1H_24hr.iloc[c, 6] != -1.00: Kit_PM_per_day_1H.append(Kit_PM_1H_24hr.iloc[c,6]) K_PM_D_1H.append(Kit_PM_1H_24hr.iloc[c,0]) # if Day_2H.iloc[c, 7] != -1.00: # Kit_PM_per_day_2H.append(Day_2H.iloc[c,7]/Day_2H.iloc[c,1]) # K_PM_D_2H.append(Day_2H.iloc[c,0]) if Kit_PM_2H_24hr.iloc[c, 6] != -1.00: Kit_PM_per_day_2H.append(Kit_PM_2H_24hr.iloc[c,6]) K_PM_D_2H.append(Kit_PM_2H_24hr.iloc[c,0]) # if Day_3H.iloc[c, 7] != -1.00: # Kit_PM_per_day_3H.append(Day_3H.iloc[c,7]/Day_3H.iloc[c,1]) # K_PM_D_3H.append(Day_3H.iloc[c, 0]) if Kit_PM_3H_24hr.iloc[c, 6] != -1.00: Kit_PM_per_day_3H.append(Kit_PM_3H_24hr.iloc[c,6]) K_PM_D_3H.append(Kit_PM_3H_24hr.iloc[c,0]) # percentages Between Phases of kitchen PM per day Kit_per_day_2H_1H = [] K_PM_D_2H_1H = [] Kit_per_day_3H_1H = [] K_PM_D_3H_1H = [] Kit_per_day_3H_2H = [] K_PM_D_3H_2H = [] count_t = 0 count_pm = 0 for c in NO_hood_counter: if c == (Household_removal[count_t] - C_Place_holder): count_t = count_t + 1 if count_t == len(Household_removal): count_t = 0 continue if c == (Household_removal_Hood_PM[count_pm] - C_Place_holder): count_pm = count_pm + 1 if count_pm == len(Household_removal_Hood_PM): count_pm = 0 continue if (len(Kit_PM_per_day_2H)-1) >= c and (len(Kit_PM_per_day_1H)-1) >= c: #if Day_1H.iloc[c,7] > 0 and Day_2H.iloc[c,7] > 0 and Day_1H.iloc[c,0] == Day_2H.iloc[c,0]: if Kit_PM_1H_24hr.iloc[c, 6] > 0 and Kit_PM_2H_24hr.iloc[c, 6] > 0 and Kit_PM_1H_24hr.iloc[c, 0] == Kit_PM_2H_24hr.iloc[c, 0]: Kit_per_day_2H_1H.append(Kit_PM_per_day_2H[c]/Kit_PM_per_day_1H[c]) K_PM_D_2H_1H.append(Day_1H.iloc[c,0]) if (len(Kit_PM_per_day_3H)-1) >= c and (len(Kit_PM_per_day_1H)-1) >= c: #if Day_3H.iloc[c,7] > 0 and Day_1H.iloc[c,7] > 0 and Day_3H.iloc[c,0] == Day_1H.iloc[c,0]: if Kit_PM_3H_24hr.iloc[c, 6] > 0 and Kit_PM_1H_24hr.iloc[c, 6] > 0 and Kit_PM_1H_24hr.iloc[c, 0] == \ Kit_PM_3H_24hr.iloc[c, 0]: Kit_per_day_3H_1H.append(Kit_PM_per_day_3H[c]/Kit_PM_per_day_1H[c]) K_PM_D_3H_1H.append(Day_1H.iloc[c,0]) if (len(Kit_PM_per_day_3H)-1) >= c and (len(Kit_PM_per_day_2H)-1) >= c: #if Day_3H.iloc[c,7] > 0 and Day_2H.iloc[c,7] > 0 and Day_3H.iloc[c,0] == Day_2H.iloc[c,0]: if Kit_PM_3H_24hr.iloc[c, 6] > 0 and Kit_PM_2H_24hr.iloc[c, 6] > 0 and Kit_PM_3H_24hr.iloc[c, 0] == \ Kit_PM_2H_24hr.iloc[c, 0]: Kit_per_day_3H_2H.append(Kit_PM_per_day_3H[c]/Kit_PM_per_day_2H[c]) K_PM_D_3H_2H.append(Day_2H.iloc[c,0]) # now for box plotting for Kitchen PM per day percent changes #2H to 1H sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_2H_1H, ax=ax_box, color='g') sns.distplot(Kit_per_day_2H_1H, ax=ax_hist, color='g') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 2H/1H (Kitchen PM per Day)') plt.ylim(top=1.5) plt.ylim(bottom = 0) #3H to 1H sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_3H_1H, ax=ax_box, color='r') sns.distplot(Kit_per_day_3H_1H, ax=ax_hist, color='r') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 3H/1H (Kitchen PM per Day)') plt.ylim(top=2) plt.ylim(bottom = 0) #3H to 2H sns.set(style="ticks") f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)}) sns.boxplot(Kit_per_day_3H_2H, ax=ax_box, color='m') sns.distplot(Kit_per_day_3H_2H, ax=ax_hist, color='m') ax_box.set(yticks=[]) sns.despine(ax=ax_hist) sns.despine(ax=ax_box, left=True) plt.title('% 3H/2H (Kitchen PM per Day)') plt.ylim(top=2) plt.ylim(bottom = 0) #Plotting on the same graph fig, ax = plt.subplots() plt.title('Hood Kitchen PM per day') #1H quant_1_1H = np.percentile(Kit_PM_per_day_1H, [25,50,75]) Top_lim_1_1H = quant_1_1H[2] + 1.5*(quant_1_1H[2] - quant_1_1H[0]) Low_lim_1_1H = quant_1_1H[0] - 1.5*(quant_1_1H[2] - quant_1_1H[0]) bp_1 = plt.boxplot(Kit_PM_per_day_1H, positions = [1], widths = 0.6) kitchen_pm_1H_outlier = [] for v,a in enumerate(Kit_PM_per_day_1H): if a > Top_lim_1_1H or a < Low_lim_1_1H: kitchen_pm_1H_outlier.append(K_PM_D_1H[v]) plt.text(1,a,K_PM_D_1H[v]) plt.text(0.5,0.1,'1H',color='b') #2N quant_1_2H = np.percentile(Kit_PM_per_day_2H, [25,50,75]) Top_lim_1_2N = quant_1_2H[2] + 1.5*(quant_1_2H[2] - quant_1_2H[0]) Low_lim_1_2N = quant_1_2H[0] - 1.5*(quant_1_2H[2] - quant_1_2H[0]) bp_1 = plt.boxplot(Kit_PM_per_day_2H,positions = [2], widths = 0.6) kitchen_pm_2H_outlier = [] for v,a in enumerate(Kit_PM_per_day_2H): if a > Top_lim_1_2N or a < Low_lim_1_2N: kitchen_pm_2H_outlier.append(K_PM_D_2H[v]) plt.text(2,a,K_PM_D_2H[v]) plt.text(1.5,0.1,'2H', color= 'g') #3H quant_1_3H = np.percentile(Kit_PM_per_day_3H, [25,50,75]) Top_lim_1_3N = quant_1_3H[2] + 1.5*(quant_1_3H[2] - quant_1_3H[0]) Low_lim_1_3N = quant_1_3H[0] - 1.5*(quant_1_3H[2] - quant_1_3H[0]) kitchen_3H_outlier = [] bp_1 = plt.boxplot(Kit_PM_per_day_3H,positions = [3], widths = 0.6) count = 0 kitchen_pm_3H_outlier = [] for v,a in enumerate(Kit_PM_per_day_3H): if a > Top_lim_1_3N or a < Low_lim_1_3N: kitchen_pm_3H_outlier.append(K_PM_D_3H[v]) plt.text(3,a,K_PM_D_3H[v]) # kitchen_3N_outlier.append(K_PM_D_3N[v]) # count = count + 1 # if count == (3): # plt.text(3,a,K_PM_D_3N[v],ha='left', va='bottom') # if count == (1): # plt.text(3,a,K_PM_D_3N[v],ha='left', va='top') # else: # plt.text(3,a,K_PM_D_3N[v],ha='right', va='bottom') plt.text(2.5,0.1,'3H', color='r') plt.xlim(0,4) plt.ylim(0,1200) print('Kitchen PM 1H had these values as outliers ', kitchen_pm_1H_outlier) print('Kitchen PM 2H had these values as outliers ', kitchen_pm_2H_outlier) print('Kitchen PM 3H had these values as outliers ', kitchen_pm_3H_outlier) plt.show() #print('3N had these values as outliers ' , kitchen_3N_outlier) # % change of PM per day fig_2, ax2 = plt.subplots() plt.title('% hood PM per Day Change' ) #plt.hold(True) #2H to 1H quant_1_2H_1H = np.percentile(Kit_per_day_2H_1H, [25,50,75]) Top_lim_1_2N_1N = quant_1_2H_1H[2] + 1.5*(quant_1_2H_1H[2]-quant_1_2H_1H[0]) Low_lim_1_2N_1N = quant_1_2H_1H[0] - 1.5*(quant_1_2H_1H[2]-quant_1_2H_1H[0]) bp_1_1 = plt.boxplot(Kit_per_day_2H_1H, positions=[1], widths= 0.6) kitchen_pm_2H_1H_outlier = [] for v,a in enumerate(Kit_per_day_2H_1H): if a > Top_lim_1_2N_1N or a < Low_lim_1_2N_1N: kitchen_pm_2H_1H_outlier.append(K_PM_D_2H_1H[v]) plt.text(1, a, K_PM_D_2H_1H[v]) plt.text(0.75, -0.25, '2H / 1H', color= 'g') #3H to 1H quant_1_3H_1H = np.percentile(Kit_per_day_3H_1H, [25,50,75]) Top_lim_1_3N_1N = quant_1_3H_1H[2] + 1.5*(quant_1_3H_1H[2]-quant_1_3H_1H[0]) Low_lim_1_3N_1N = quant_1_3H_1H[0] - 1.5*(quant_1_3H_1H[2]-quant_1_3H_1H[0]) bp_1_1 = plt.boxplot(Kit_per_day_3H_1H, positions=[2], widths= 0.6) kitchen_pm_3H_1H_outlier = [] for v,a in enumerate(Kit_per_day_3H_1H): if a > Top_lim_1_3N_1N or a < Low_lim_1_3N_1N: kitchen_pm_3H_1H_outlier.append(K_PM_D_3H_1H[v]) plt.text(2, a, K_PM_D_3H_1H[v]) plt.text(1.75, -0.25, '3H / 1H', color= 'r') #3H to 2H quant_1_3H_2H = np.percentile(Kit_per_day_3H_2H, [25,50,75]) Top_lim_1_3N_2N = quant_1_3H_2H[2] + 1.5*(quant_1_3H_2H[2]-quant_1_3H_2H[0]) Low_lim_1_3N_2N = quant_1_3H_2H[0] - 1.5*(quant_1_3H_2H[2]-quant_1_3H_2H[0]) bp_1_1 = plt.boxplot(Kit_per_day_3H_2H, positions=[3], widths= 0.6) kitchen_pm_3H_2H_outlier = [] for v,a in enumerate(Kit_per_day_3H_2H): if a > Top_lim_1_3N_2N or a < Low_lim_1_3N_2N: kitchen_pm_3H_2H_outlier.append(K_PM_D_3H_2H[v]) plt.text(3, a, K_PM_D_3H_2H[v]) plt.text(2.75, -0.25, '3H / 2H', color= 'm') plt.xlim(0,4) plt.ylim(-0.5,5) print('Kitchen PM 2H/1H had these values as outliers ', kitchen_pm_2H_1H_outlier) print('Kitchen PM 3H/1H had these values as outliers ', kitchen_pm_3H_1H_outlier) print('Kitchen PM 3H/2H had these values as outliers ', kitchen_pm_3H_2H_outlier) plt.show() quant_1_1H = np.append(quant_1_1H, np.average(Kit_PM_per_day_1H)) quant_1_2H = np.append(quant_1_2H, np.average(Kit_PM_per_day_2H)) quant_1_3H = np.append(quant_1_3H, np.average(Kit_PM_per_day_3H)) D_50_quant_phase_PM_D_hood = {'Percentile %': ['25','50','75', 'Avg'], '1H': quant_1_1H, '2H': quant_1_2H,'3H' : quant_1_3H} PM_D_50_phase_hood = pd.DataFrame(data=D_50_quant_phase_PM_D_hood, columns= ['Percentile %','1H','2H','3H' ]) quant_1_2H_1H = np.append(quant_1_2H_1H , np.average(Kit_per_day_2H_1H)) quant_1_3H_1H = np.append(quant_1_3H_1H , np.average(Kit_per_day_3H_1H)) quant_1_3H_2H = np.append(quant_1_3H_2H , np.average(Kit_per_day_3H_2H)) D_50_quant_percent_PM_D_hood ={'Percentile %': ['25','50','75', 'Avg'],'2H / 1H': quant_1_2H_1H,'3H / 1H': quant_1_3H_1H,'3H / 2H': quant_1_3H_2H} PM_D_50_percent_change_hood = pd.DataFrame(data=D_50_quant_percent_PM_D_hood, columns=['Percentile %','2H / 1H','3H / 1H','3H / 2H']) print(PM_D_50_phase_hood) print(PM_D_50_percent_change_hood) # when i am ready to transfer to a data frame and get the differences #histograms for the comparison if Hood_or_no == 'no_hood': plt.title('Histogram of Fuel per 24 Hours per Person - No Hood' ) plt.hist([Fuel_per_day_per_adult_1N], color=['b'], alpha=0.5, label='1N') plt.hist([Fuel_per_day_per_adult_2N], color=['g'], alpha=0.5, label='2N') plt.hist([Fuel_per_day_per_adult_3N], color=['r'], alpha=0.5, label='3N') plt.hist([Fuel_per_day_per_adult_4N], color=['y'], alpha=0.5, label='4N') plt.legend(loc='upper right') plt.show() plt.title('Histogram of Kitchen PM 24 Hours - No Hood' ) plt.hist([Kit_PM_per_day_1N], color=['b'], alpha=0.5, label='1N') plt.hist([Kit_PM_per_day_2N], color=['g'], alpha=0.5, label='2N') plt.hist([Kit_PM_per_day_3N], color=['r'], alpha=0.5, label='3N') plt.hist([Kit_PM_per_day_4N], color=['y'], alpha=0.5, label='4N') plt.legend(loc='upper right') plt.show() if Hood_or_no == 'hood': plt.title('Histogram of Fuel per 24 Hours per Person - Hood' ) plt.hist([Fuel_per_day_per_adult_1H], color=['b'], alpha=0.5, label='1H') plt.hist([Fuel_per_day_per_adult_2H], color=['g'], alpha=0.5, label='2H') plt.hist([Fuel_per_day_per_adult_3H], color=['r'], alpha=0.5, label='3H') plt.legend(loc='upper right') plt.show() plt.title('Histogram of Kitchen PM 24 Hours - Hood' ) plt.hist([Kit_PM_per_day_1H], color=['b'], alpha=0.5, label='1H') plt.hist([Kit_PM_per_day_2H], color=['g'], alpha=0.5, label='2H') plt.hist([Kit_PM_per_day_3H], color=['r'], alpha=0.5, label='3H') plt.legend(loc='upper right') plt.show()

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null

0

1

0

null

0

6

f71e4ba69c62e1f2c427e125b8d3019de0eb2970

28

py

Python

try.py

zf-nobody/pyaudio_portaudio

8f703866e6b3d9aad30792fbd07fa63d504505f2

[ "MIT" ]

null

try.py

zf-nobody/pyaudio_portaudio

8f703866e6b3d9aad30792fbd07fa63d504505f2

[ "MIT" ]

null

try.py

zf-nobody/pyaudio_portaudio

8f703866e6b3d9aad30792fbd07fa63d504505f2

[ "MIT" ]

null

print("I am having a try.")

14

27

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6

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28

1

28

0.782609

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0.642857

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null

0

1

0

1

0

6

f71fb01cbdb1f124478ac2b092b7ac4885231833

119

py

Python

examples/test_error.py

ak1ra24/pytest-md-report

9d861a9237176e9dd1e6872c197f5bb5985ee049

[ "MIT" ]

9

2020-05-06T20:54:29.000Z

2022-03-27T04:11:38.000Z

examples/test_error.py

solisa986/pytest-md-report

a6cdeda92ef8f1ab64c346a86a085ce9e1585880

[ "MIT" ]

null

examples/test_error.py

solisa986/pytest-md-report

a6cdeda92ef8f1ab64c346a86a085ce9e1585880

[ "MIT" ]

3

2021-05-05T19:58:33.000Z

2021-08-12T07:14:52.000Z

def test_error(invalid_fixture): pass class Test: def test_error(self, invalid_fixture): assert True

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6

f72f8af5b3ccf2010b8feadf774b09fd508c9661

32,775

py

Python

WhoopClient.py

lcintron/WhoopClient

46ccc6c3e3b98f4b6c82cf8938056d72a22bd6b6

[ "MIT" ]

null

WhoopClient.py

lcintron/WhoopClient

46ccc6c3e3b98f4b6c82cf8938056d72a22bd6b6

[ "MIT" ]

null

WhoopClient.py

lcintron/WhoopClient

46ccc6c3e3b98f4b6c82cf8938056d72a22bd6b6

[ "MIT" ]

null

import requests import pandas as pd import numpy as np import configparser from datetime import datetime from dateutil import relativedelta, parser, rrule from dateutil.rrule import WEEKLY class WhoopClient: '''A class to allow a user to login and store their authorization code, then perform pulls using the code in order to access different types of data''' def __init__(self, auth_code=None, whoop_id=None, current_datetime=datetime.utcnow()): self.auth_code = auth_code self.whoop_id = whoop_id self.current_datetime = current_datetime self.start_datetime = None self.all_data = None self.all_activities = None self.sport_dict = None self.all_sleep = None self.all_sleep_events = None def reset(self): self.auth_code = None self.whoop_id = None self.current_datetime = datetime.utcnow() self.start_datetime = None self.all_data = None self.all_activities = None self.sport_dict = None self.all_sleep = None self.all_sleep_events = None def pull_api(self, url, df=False): auth_code = self.auth_code headers = {'authorization': auth_code} pull = requests.get(url, headers=headers) if pull.status_code == 200 and len(pull.content) > 1: if df: d = pd.json_normalize(pull.json()) return d else: return pull.json() else: return "no response" def pull_sleep_main(self, sleep_id): athlete_id = self.whoop_id sleep = self.pull_api( 'https://api-7.whoop.com/users/{}/sleeps/{}'.format( athlete_id, sleep_id)) main_df = pd.json_normalize(sleep) return main_df def pull_sleep_events(self, sleep_id): athlete_id = self.whoop_id sleep = self.pull_api( 'https://api-7.whoop.com/users/{}/sleeps/{}'.format( athlete_id, sleep_id)) events_df = pd.json_normalize(sleep['events']) events_df['id'] = sleep_id return events_df def get_authorization(self, user_ini): ''' Function to get the authorization token and user id. This must be completed before a user can query the api ''' config = configparser.ConfigParser() config.read(user_ini) username = config['whoop']['username'] password = config['whoop']['password'] headers = { "username": username, "password": password, "grant_type": "password", "issueRefresh": False } auth = requests.post("https://api-7.whoop.com/oauth/token", json=headers) if auth.status_code == 200: content = auth.json() user_id = content['user']['id'] token = content['access_token'] start_time = content['user']['profile']['createdAt'] self.whoop_id = user_id self.auth_code = 'bearer ' + token self.start_datetime = start_time print("Whoop: Authentication successful") else: print( "Authentication failed - please double check your credentials") def get_keydata_all(self): ''' This function returns a dataframe of WHOOP metrics for each day of WHOOP membership. In the resulting dataframe, each day is a row and contains strain, recovery, and sleep information ''' if self.start_datetime: if self.all_data is not None: ## All data already pulled return self.all_data else: start_date = parser.isoparse( self.start_datetime).replace(tzinfo=None) end_time = 'T23:59:59.999Z' start_time = 'T00:00:00.000Z' intervals = rrule.rrule(freq=WEEKLY, interval=1, until=self.current_datetime, dtstart=start_date) date_range = [[ d.strftime('%Y-%m-%d') + start_time, (d + relativedelta.relativedelta(weeks=1)).strftime('%Y-%m-%d') + end_time ] for d in intervals] all_data = pd.DataFrame() for dates in date_range: cycle_url = 'https://api-7.whoop.com/users/{}/cycles?end={}&start={}'.format( self.whoop_id, dates[1], dates[0]) data = self.pull_api(cycle_url, df=True) all_data = pd.concat([all_data, data]) all_data.reset_index(drop=True, inplace=True) ## fixing the day column so it's not a list all_data['days'] = all_data['days'].map(lambda d: d[0]) all_data.rename(columns={"days": 'day'}, inplace=True) ## Putting all time into minutes instead of milliseconds sleep_cols = [ 'qualityDuration', 'needBreakdown.baseline', 'needBreakdown.debt', 'needBreakdown.naps', 'needBreakdown.strain', 'needBreakdown.total' ] for sleep_col in sleep_cols: all_data['sleep.' + sleep_col] = all_data[ 'sleep.' + sleep_col].astype(float).apply( lambda x: np.nan if np.isnan(x) else x / 60000) ## Making nap variable all_data['nap_duration'] = all_data['sleep.naps'].apply( lambda x: x[0]['qualityDuration'] / 60000 if len(x) == 1 else (sum([ y['qualityDuration'] for y in x if y['qualityDuration'] is not None ]) / 60000 if len(x) > 1 else 0)) all_data.drop(['sleep.naps'], axis=1, inplace=True) ## dropping duplicates subsetting because of list columns all_data.drop_duplicates(subset=['day', 'sleep.id'], inplace=True) self.all_data = all_data return all_data else: print("Please run the authorization function first") def get_activities_all(self): ''' Activity data is pulled through the get_keydata functions so if the data pull is present, this function just transforms the activity column into a dataframe of activities, where each activity is a row. If it has not been pulled, this function runs the key data function then returns the activity dataframe''' if self.sport_dict: sport_dict = self.sport_dict else: sports = self.pull_api('https://api-7.whoop.com/sports') sport_dict = {sport['id']: sport['name'] for sport in sports} self.sport_dict = self.sport_dict if self.start_datetime: ## process activity data if self.all_data is not None: ## use existing data = self.all_data else: ## pull all data to process activities data = self.get_keydata_all() ## now process activities data act_data = pd.json_normalize( data[data['strain.workouts'].apply(len) > 0] ['strain.workouts'].apply(lambda x: x[0])) act_data[['during.upper', 'during.lower' ]] = act_data[['during.upper', 'during.lower']].apply(pd.to_datetime) act_data['total_minutes'] = act_data.apply( lambda x: (x['during.upper'] - x['during.lower']).total_seconds() / 60.0, axis=1) for z in range(0, 6): act_data['zone{}_minutes'.format( z + 1)] = act_data['zones'].apply(lambda x: x[z] / 60000.) act_data['sport_name'] = act_data.sportId.apply( lambda x: sport_dict[x]) act_data['day'] = act_data['during.lower'].dt.strftime('%Y-%m-%d') act_data.drop(['zones', 'during.bounds'], axis=1, inplace=True) act_data.drop_duplicates(inplace=True) self.all_activities = act_data return act_data else: print("Whoop: Please run the authorization function first") def get_sleep_all(self): ''' This function returns all sleep metrics in a data frame, for the duration of user's WHOOP membership. Each row in the data frame represents one night of sleep ''' if self.auth_code: if self.all_data is not None: ## use existing data = self.all_data else: ## pull timeframe data data = self.get_keydata_all() ## getting all the sleep ids if self.all_sleep is not None: ## All sleep data already pulled return self.all_sleep else: sleep_ids = data['sleep.id'].values.tolist() sleep_list = [int(x) for x in sleep_ids if pd.isna(x) == False] all_sleep = pd.DataFrame() for s in sleep_list: m = self.pull_sleep_main(s) all_sleep = pd.concat([all_sleep, m]) ## Cleaning sleep data sleep_update = [ 'qualityDuration', 'latency', 'debtPre', 'debtPost', 'needFromStrain', 'sleepNeed', 'habitualSleepNeed', 'timeInBed', 'lightSleepDuration', 'slowWaveSleepDuration', 'remSleepDuration', 'wakeDuration', 'arousalTime', 'noDataDuration', 'creditFromNaps', 'projectedSleep' ] for col in sleep_update: all_sleep[col] = all_sleep[col].astype(float).apply( lambda x: np.nan if np.isnan(x) else x / 60000) all_sleep.drop(['during.bounds'], axis=1, inplace=True) self.all_sleep = all_sleep.copy(deep=True) all_sleep.drop(['events'], axis=1, inplace=True) return all_sleep else: print("Whoop: Please run the authorization function first") def get_sleep_events_all(self): ''' This function returns all sleep events in a data frame, for the duration of user's WHOOP membership. Each row in the data frame represents an individual sleep event within an individual night of sleep. Sleep events can be joined against the sleep or main datasets by sleep id. All sleep times are returned in minutes. ''' if self.auth_code: if self.all_data is not None: ## use existing data = self.all_data else: ## pull timeframe data data = self.get_keydata_all() ## getting all the sleep ids if self.all_sleep_events is not None: ## All sleep data already pulled return self.all_sleep_events else: if self.all_sleep is not None: sleep_events = self.all_sleep[['activityId', 'events']] all_sleep_events = pd.concat([ pd.concat([ pd.json_normalize(events), pd.DataFrame({'id': len(events) * [sleep]}) ], axis=1) for events, sleep in zip(sleep_events['events'], sleep_events['activityId']) ]) else: sleep_ids = data['sleep.id'].values.tolist() sleep_list = [ int(x) for x in sleep_ids if pd.isna(x) == False ] all_sleep_events = pd.DataFrame() for s in sleep_list: events = self.pull_sleep_events(s) all_sleep_events = pd.concat( [all_sleep_events, events]) ## Cleaning sleep events data all_sleep_events['during.lower'] = pd.to_datetime( all_sleep_events['during.lower']) all_sleep_events['during.upper'] = pd.to_datetime( all_sleep_events['during.upper']) all_sleep_events.drop(['during.bounds'], axis=1, inplace=True) all_sleep_events['total_minutes'] = all_sleep_events.apply( lambda x: (x['during.upper'] - x['during.lower'] ).total_seconds() / 60.0, axis=1) self.all_sleep_events = all_sleep_events return all_sleep_events else: print("Whoop: Please run the authorization function first") #returnTYpe = df, json def get_hr_all(self, returnType=None): ''' This function will pull every heart rate measurement recorded for the life of WHOOP membership. The default return for this function is a list of lists, where each "row" contains the date, time, and hr value. The measurements are spaced out every ~6 seconds on average. To return a dataframe, set df=True. This will take a bit longer, but will return a data frame. NOTE: This api pull takes about 6 seconds per week of data ... or 1 minutes for 10 weeks of data, so be careful when you pull, it may take a while. ''' if self.start_datetime: athlete_id = self.whoop_id start_date = parser.isoparse( self.start_datetime).replace(tzinfo=None) end_time = 'T23:59:59.999Z' start_time = 'T00:00:00.000Z' intervals = rrule.rrule(freq=WEEKLY, interval=1, until=self.current_datetime, dtstart=start_date) date_range = [[ d.strftime('%Y-%m-%d') + start_time, (d + relativedelta.relativedelta(weeks=1)).strftime('%Y-%m-%d') + end_time ] for d in intervals] hr_list = [] for dates in date_range: start = dates[0] end = dates[1] ul = '''https://api-7.whoop.com/users/{}/metrics/heart_rate?end={}&order=t&start={}&step=6'''.format( athlete_id, end, start) hr_vals = self.pull_api(ul)['values'] hr_values = [[ datetime.utcfromtimestamp(h['time'] / 1e3).date(), datetime.utcfromtimestamp(h['time'] / 1e3).time(), h['data'] ] for h in hr_vals] hr_list.extend(hr_values) if returnType == "df": hr_df = pd.DataFrame(hr_list) hr_df.columns = ['date', 'time', 'hr'] return hr_df elif returnType == "json": hr_json = [{ 'datetime': str(h[0]) + 'T' + str(h[1]), 'hr': h[2] } for h in hr_list] return hr_json else: return hr_list else: print("Please run the authorization function first") def get_keydata_timeframe(self, start, end=datetime.strftime(datetime.utcnow(), "%Y-%m-%d")): ''' This function returns a dataframe of WHOOP metrics for each day in a specified time period. To use this function, provide a start and end date in string format as follows "YYYY-MM-DD". If no end date is specified, it will default to today's date. In the resulting dataframe, each day is a row and contains strain, recovery, and sleep information ''' st = datetime.strptime(start, '%Y-%m-%d') e = datetime.strptime(end, '%Y-%m-%d') if st > e: if e > datetime.today(): print("Please enter an end date earlier than tomorrow") else: print( "Please enter a start date that is earlier than your end date" ) else: if self.auth_code: end_time = 'T23:59:59.999Z' start_time = 'T00:00:00.000Z' intervals = rrule.rrule(freq=WEEKLY, interval=1, until=e, dtstart=st) date_range = [[ d.strftime('%Y-%m-%d') + start_time, (d + relativedelta.relativedelta(weeks=1)).strftime('%Y-%m-%d') + end_time ] for d in intervals if d <= e] time_data = pd.DataFrame() for dates in date_range: cycle_url = 'https://api-7.whoop.com/users/{}/cycles?end={}&start={}'.format( self.whoop_id, dates[1], dates[0]) data = self.pull_api(cycle_url, df=True) time_data = pd.concat([time_data, data]) time_data.reset_index(drop=True, inplace=True) ## fixing the day column so it's not a list time_data['days'] = time_data['days'].map(lambda d: d[0]) time_data.rename(columns={"days": 'day'}, inplace=True) ## Putting all time into minutes instead of milliseconds sleep_cols = [ 'qualityDuration', 'needBreakdown.baseline', 'needBreakdown.debt', 'needBreakdown.naps', 'needBreakdown.strain', 'needBreakdown.total' ] for sleep_col in sleep_cols: time_data['sleep.' + sleep_col] = time_data[ 'sleep.' + sleep_col].astype(float).apply( lambda x: np.nan if np.isnan(x) else x / 60000) ## Making nap variable time_data['nap_duration'] = time_data['sleep.naps'].apply( lambda x: x[0]['qualityDuration'] / 60000 if len(x) == 1 else (sum([ y['qualityDuration'] for y in x if y['qualityDuration'] is not None ]) / 60000 if len(x) > 1 else 0)) time_data.drop(['sleep.naps'], axis=1, inplace=True) ## removing duplicates time_data.drop_duplicates(subset=['day', 'sleep.id'], inplace=True) return time_data else: print("Whoop: Please run the authorization function first") def get_activities_timeframe(self, start, end=datetime.strftime(datetime.utcnow(), "%Y-%m-%d")): ''' Activity data is pulled through the get_keydata functions so if the data pull is present, this function just transforms the activity column into a dataframe of activities, where each activity is a row. If it has not been pulled, this function runs the key data function then returns the activity dataframe If no end date is specified, it will default to today's date. ''' st = datetime.strptime(start, '%Y-%m-%d') e = datetime.strptime(end, '%Y-%m-%d') if st > e: if e > datetime.today(): print("Please enter an end date earlier than tomorrow") else: print( "Please enter a start date that is earlier than your end date" ) else: if self.auth_code: if self.sport_dict: sport_dict = self.sport_dict else: sports = self.pull_api('https://api-7.whoop.com/sports') sport_dict = { sport['id']: sport['name'] for sport in sports } self.sport_dict = self.sport_dict ## process activity data if self.all_data is not None: ## use existing data = self.all_data data = data[(data.day >= start) & (data.day <= end)].copy(deep=True) else: ## pull timeframe data data = self.get_keydata_timeframe(start, end) ## now process activities data act_data = pd.json_normalize( data[data['strain.workouts'].apply(len) > 0] ['strain.workouts'].apply(lambda x: x[0])) act_data[['during.upper', 'during.lower' ]] = act_data[['during.upper', 'during.lower']].apply(pd.to_datetime) act_data['total_minutes'] = act_data.apply( lambda x: (x['during.upper'] - x['during.lower'] ).total_seconds() / 60.0, axis=1) for z in range(0, 6): act_data['zone{}_minutes'.format( z + 1)] = act_data['zones'].apply(lambda x: x[z] / 60000.) act_data['sport_name'] = act_data.sportId.apply( lambda x: sport_dict[x]) act_data['day'] = act_data['during.lower'].dt.strftime( '%Y-%m-%d') act_data.drop(['zones', 'during.bounds'], axis=1, inplace=True) act_data.drop_duplicates(inplace=True) self.all_activities = act_data return act_data else: print("Whoop: Please run the authorization function first") def get_sleep_timeframe(self, start, end=datetime.strftime(datetime.utcnow(), "%Y-%m-%d")): ''' This function returns sleep metrics in a data frame, for timeframe specified by the user. Each row in the data frame represents one night of sleep. If no end date is specified, it will default to today's date. All sleep times are returned in minutes. ''' st = datetime.strptime(start, '%Y-%m-%d') e = datetime.strptime(end, '%Y-%m-%d') if st > e: if e > datetime.today(): print("Whoop: Please enter an end date earlier than tomorrow") else: print( "Whoop: Please enter a start date that is earlier than your end date" ) else: if self.auth_code: if self.all_data is not None: ## use existing data = self.all_data data = data[(data.day >= start) & (data.day <= end)].copy(deep=True) else: ## pull timeframe data data = self.get_keydata_timeframe(start, end) ## getting all the sleep ids sleep_ids = data['sleep.id'].values.tolist() sleep_list = [int(x) for x in sleep_ids if pd.isna(x) == False] if self.all_sleep is not None: ## All sleep data already pulled so just filter all_sleep = self.all_sleep time_sleep = all_sleep[all_sleep.activityId.isin( sleep_list)] return time_sleep else: time_sleep = pd.DataFrame() for s in sleep_list: m = self.pull_sleep_main(s) time_sleep = pd.concat([time_sleep, m]) ## Cleaning sleep data sleep_update = [ 'qualityDuration', 'latency', 'debtPre', 'debtPost', 'needFromStrain', 'sleepNeed', 'habitualSleepNeed', 'timeInBed', 'lightSleepDuration', 'slowWaveSleepDuration', 'remSleepDuration', 'wakeDuration', 'arousalTime', 'noDataDuration', 'creditFromNaps', 'projectedSleep' ] for col in sleep_update: time_sleep[col] = time_sleep[col].astype(float).apply( lambda x: np.nan if np.isnan(x) else x / 60000) time_sleep.drop(['during.bounds', 'events'], axis=1, inplace=True) return time_sleep else: print("Whoop: Please run the authorization function first") def get_sleep_events_timeframe(self, start, end=datetime.strftime( datetime.utcnow(), "%Y-%m-%d")): ''' This function returns sleep events in a data frame, for the time frame specified by the user. Each row in the data frame represents an individual sleep event within an individual night of sleep. Sleep events can be joined against the sleep or main datasets by sleep id. If no end date is specified, it will default to today's date. ''' st = datetime.strptime(start, '%Y-%m-%d') e = datetime.strptime(end, '%Y-%m-%d') if st > e: if e > datetime.today(): print("Whoop: Please enter an end date earlier than tomorrow") else: print( "Whoop: Please enter a start date that is earlier than your end date" ) else: if self.auth_code: if self.all_data is not None: ## use existing data = self.all_data data = data[(data.day >= start) & (data.day <= end)].copy(deep=True) else: ## pull timeframe data data = self.get_keydata_timeframe(start, end) ## getting all the sleep ids sleep_ids = data['sleep.id'].values.tolist() sleep_list = [int(x) for x in sleep_ids if pd.isna(x) == False] if self.all_sleep_events is not None: ## All sleep data already pulled so just filter all_sleep_events = self.all_sleep_events time_sleep_events = all_sleep_events[ all_sleep_events.id.isin(sleep_list)] return time_sleep_events else: if self.all_sleep is not None: sleep_events = self.all_sleep[['activityId', 'events']] time_sleep = sleep_events[sleep_events.id.isin( sleep_list)] time_sleep_events = pd.concat([ pd.concat([ pd.json_normalize(events), pd.DataFrame({'id': len(events) * [sleep]}) ], axis=1) for events, sleep in zip(time_sleep['events'], time_sleep['activityId']) ]) else: time_sleep_events = pd.DataFrame() for s in sleep_list: events = self.pull_sleep_events(s) time_sleep_events = pd.concat( [time_sleep_events, events]) ## Cleaning sleep events data time_sleep_events['during.lower'] = pd.to_datetime( time_sleep_events['during.lower']) time_sleep_events['during.upper'] = pd.to_datetime( time_sleep_events['during.upper']) time_sleep_events.drop(['during.bounds'], axis=1, inplace=True) time_sleep_events[ 'total_minutes'] = time_sleep_events.apply( lambda x: (x['during.upper'] - x['during.lower'] ).total_seconds() / 60.0, axis=1) return time_sleep_events else: print("Whoop: Please run the authorization function first") def get_hr_timeframe(self, start, end=datetime.strftime(datetime.utcnow(), "%Y-%m-%d"), returnType=None): ''' This function will pull every heart rate measurement recorded, for the time frame specified by the user. The default return for this function is a list of lists, where each "row" contains the date, time, and hr value. The measurements are spaced out every ~6 seconds on average. To return a dataframe, set df=True. This will take a bit longer, but will return a data frame. If no end date is specified, it will default to today's date. NOTE: This api pull takes about 6 seconds per week of data ... or 1 minutes for 10 weeks of data, so be careful when you pull, it may take a while. ''' st = datetime.strptime(start, '%Y-%m-%d') e = datetime.strptime(end, '%Y-%m-%d') if st > e: if e > datetime.today(): print("Whoop: Please enter an end date earlier than tomorrow") else: print( "Whoop: Please enter a start date that is earlier than your end date" ) else: if self.start_datetime: athlete_id = self.whoop_id start_date = parser.isoparse( self.start_datetime).replace(tzinfo=None) end_time = 'T23:59:59.999Z' start_time = 'T00:00:00.000Z' ## using the st and e since it needs the datetime formatted date intervals = rrule.rrule(freq=WEEKLY, interval=1, until=e, dtstart=st) date_range = [[ d.strftime('%Y-%m-%d') + start_time, (d + relativedelta.relativedelta(weeks=1)).strftime('%Y-%m-%d') + end_time ] for d in intervals] hr_list = [] for dates in date_range: start = dates[0] end = dates[1] ul = '''https://api-7.whoop.com/users/{}/metrics/heart_rate?end={}&order=t&start={}&step=6'''.format( athlete_id, end, start) hr_vals = self.pull_api(ul)['values'] hr_values = [[ str(datetime.utcfromtimestamp(h['time'] / 1e3).date()), str(datetime.utcfromtimestamp(h['time'] / 1e3).time()), h['data'] ] for h in hr_vals] hr_list.extend(hr_values) if returnType == "df": hr_df = pd.DataFrame(hr_list) hr_df.columns = ['date', 'time', 'hr'] return hr_df elif returnType == "json": hr_json = [{ 'datetime': str(h[0]) + 'T' + str(h[1]), 'hr': h[2] } for h in hr_list] return hr_json else: return hr_list else: print("Whoop: Please run the authorization function first")

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f7422731979ad4853b3ed8984d505258dff7f132

24,927

py

Python

pybind/nos/v7_0_1b/interface/port_channel/ip/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/nos/v7_0_1b/interface/port_channel/ip/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/nos/v7_0_1b/interface/port_channel/ip/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ import ip_config import arp import interface_po_dhcp_conf import icmp import igmp_po_intf_cfg import interface_PO_ospf_conf import pim_intf_po_cont class ip(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-interface - based on the path /interface/port-channel/ip. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: The IP configurations for an interface. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__ip_config','__arp','__interface_po_dhcp_conf','__icmp','__igmp_po_intf_cfg','__interface_PO_ospf_conf','__pim_intf_po_cont',) _yang_name = 'ip' _rest_name = 'ip' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__arp = YANGDynClass(base=arp.arp, is_container='container', presence=False, yang_name="arp", rest_name="arp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure ARP', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-dai', defining_module='brocade-dai', yang_type='container', is_config=True) self.__igmp_po_intf_cfg = YANGDynClass(base=igmp_po_intf_cfg.igmp_po_intf_cfg, is_container='container', presence=False, yang_name="igmp-po-intf-cfg", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'IgmpPo', u'sort-priority': u'122'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='container', is_config=True) self.__interface_po_dhcp_conf = YANGDynClass(base=interface_po_dhcp_conf.interface_po_dhcp_conf, is_container='container', presence=False, yang_name="interface-po-dhcp-conf", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None}}, namespace='urn:brocade.com:mgmt:brocade-dhcp', defining_module='brocade-dhcp', yang_type='container', is_config=True) self.__pim_intf_po_cont = YANGDynClass(base=pim_intf_po_cont.pim_intf_po_cont, is_container='container', presence=False, yang_name="pim-intf-po-cont", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'PimPoIntfCallpoint', u'sort-priority': u'121'}}, namespace='urn:brocade.com:mgmt:brocade-pim', defining_module='brocade-pim', yang_type='container', is_config=True) self.__interface_PO_ospf_conf = YANGDynClass(base=interface_PO_ospf_conf.interface_PO_ospf_conf, is_container='container', presence=False, yang_name="interface-PO-ospf-conf", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'OSPFPoInterfaceCallPoint'}}, namespace='urn:brocade.com:mgmt:brocade-ospf', defining_module='brocade-ospf', yang_type='container', is_config=True) self.__ip_config = YANGDynClass(base=ip_config.ip_config, is_container='container', presence=False, yang_name="ip-config", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'intf-po-ip-cfg-cp', u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG'}}, namespace='urn:brocade.com:mgmt:brocade-ip-config', defining_module='brocade-ip-config', yang_type='container', is_config=True) self.__icmp = YANGDynClass(base=icmp.icmp, is_container='container', presence=False, yang_name="icmp", rest_name="icmp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Internet Control Message Protocol(ICMP)', u'sort-priority': u'117', u'display-when': u'/vcsmode/vcs-mode = "true"', u'cli-incomplete-no': None, u'callpoint': u'IcmpPoIntfConfigCallpoint'}}, namespace='urn:brocade.com:mgmt:brocade-icmp', defining_module='brocade-icmp', yang_type='container', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'interface', u'port-channel', u'ip'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'interface', u'Port-channel', u'ip'] def _get_ip_config(self): """ Getter method for ip_config, mapped from YANG variable /interface/port_channel/ip/ip_config (container) """ return self.__ip_config def _set_ip_config(self, v, load=False): """ Setter method for ip_config, mapped from YANG variable /interface/port_channel/ip/ip_config (container) If this variable is read-only (config: false) in the source YANG file, then _set_ip_config is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ip_config() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=ip_config.ip_config, is_container='container', presence=False, yang_name="ip-config", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'intf-po-ip-cfg-cp', u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG'}}, namespace='urn:brocade.com:mgmt:brocade-ip-config', defining_module='brocade-ip-config', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """ip_config must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=ip_config.ip_config, is_container='container', presence=False, yang_name="ip-config", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'intf-po-ip-cfg-cp', u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG'}}, namespace='urn:brocade.com:mgmt:brocade-ip-config', defining_module='brocade-ip-config', yang_type='container', is_config=True)""", }) self.__ip_config = t if hasattr(self, '_set'): self._set() def _unset_ip_config(self): self.__ip_config = YANGDynClass(base=ip_config.ip_config, is_container='container', presence=False, yang_name="ip-config", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'intf-po-ip-cfg-cp', u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG'}}, namespace='urn:brocade.com:mgmt:brocade-ip-config', defining_module='brocade-ip-config', yang_type='container', is_config=True) def _get_arp(self): """ Getter method for arp, mapped from YANG variable /interface/port_channel/ip/arp (container) """ return self.__arp def _set_arp(self, v, load=False): """ Setter method for arp, mapped from YANG variable /interface/port_channel/ip/arp (container) If this variable is read-only (config: false) in the source YANG file, then _set_arp is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_arp() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=arp.arp, is_container='container', presence=False, yang_name="arp", rest_name="arp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure ARP', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-dai', defining_module='brocade-dai', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """arp must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=arp.arp, is_container='container', presence=False, yang_name="arp", rest_name="arp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure ARP', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-dai', defining_module='brocade-dai', yang_type='container', is_config=True)""", }) self.__arp = t if hasattr(self, '_set'): self._set() def _unset_arp(self): self.__arp = YANGDynClass(base=arp.arp, is_container='container', presence=False, yang_name="arp", rest_name="arp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure ARP', u'cli-incomplete-no': None}}, namespace='urn:brocade.com:mgmt:brocade-dai', defining_module='brocade-dai', yang_type='container', is_config=True) def _get_interface_po_dhcp_conf(self): """ Getter method for interface_po_dhcp_conf, mapped from YANG variable /interface/port_channel/ip/interface_po_dhcp_conf (container) """ return self.__interface_po_dhcp_conf def _set_interface_po_dhcp_conf(self, v, load=False): """ Setter method for interface_po_dhcp_conf, mapped from YANG variable /interface/port_channel/ip/interface_po_dhcp_conf (container) If this variable is read-only (config: false) in the source YANG file, then _set_interface_po_dhcp_conf is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_interface_po_dhcp_conf() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=interface_po_dhcp_conf.interface_po_dhcp_conf, is_container='container', presence=False, yang_name="interface-po-dhcp-conf", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None}}, namespace='urn:brocade.com:mgmt:brocade-dhcp', defining_module='brocade-dhcp', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """interface_po_dhcp_conf must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=interface_po_dhcp_conf.interface_po_dhcp_conf, is_container='container', presence=False, yang_name="interface-po-dhcp-conf", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None}}, namespace='urn:brocade.com:mgmt:brocade-dhcp', defining_module='brocade-dhcp', yang_type='container', is_config=True)""", }) self.__interface_po_dhcp_conf = t if hasattr(self, '_set'): self._set() def _unset_interface_po_dhcp_conf(self): self.__interface_po_dhcp_conf = YANGDynClass(base=interface_po_dhcp_conf.interface_po_dhcp_conf, is_container='container', presence=False, yang_name="interface-po-dhcp-conf", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None}}, namespace='urn:brocade.com:mgmt:brocade-dhcp', defining_module='brocade-dhcp', yang_type='container', is_config=True) def _get_icmp(self): """ Getter method for icmp, mapped from YANG variable /interface/port_channel/ip/icmp (container) """ return self.__icmp def _set_icmp(self, v, load=False): """ Setter method for icmp, mapped from YANG variable /interface/port_channel/ip/icmp (container) If this variable is read-only (config: false) in the source YANG file, then _set_icmp is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_icmp() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=icmp.icmp, is_container='container', presence=False, yang_name="icmp", rest_name="icmp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Internet Control Message Protocol(ICMP)', u'sort-priority': u'117', u'display-when': u'/vcsmode/vcs-mode = "true"', u'cli-incomplete-no': None, u'callpoint': u'IcmpPoIntfConfigCallpoint'}}, namespace='urn:brocade.com:mgmt:brocade-icmp', defining_module='brocade-icmp', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """icmp must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=icmp.icmp, is_container='container', presence=False, yang_name="icmp", rest_name="icmp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Internet Control Message Protocol(ICMP)', u'sort-priority': u'117', u'display-when': u'/vcsmode/vcs-mode = "true"', u'cli-incomplete-no': None, u'callpoint': u'IcmpPoIntfConfigCallpoint'}}, namespace='urn:brocade.com:mgmt:brocade-icmp', defining_module='brocade-icmp', yang_type='container', is_config=True)""", }) self.__icmp = t if hasattr(self, '_set'): self._set() def _unset_icmp(self): self.__icmp = YANGDynClass(base=icmp.icmp, is_container='container', presence=False, yang_name="icmp", rest_name="icmp", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Internet Control Message Protocol(ICMP)', u'sort-priority': u'117', u'display-when': u'/vcsmode/vcs-mode = "true"', u'cli-incomplete-no': None, u'callpoint': u'IcmpPoIntfConfigCallpoint'}}, namespace='urn:brocade.com:mgmt:brocade-icmp', defining_module='brocade-icmp', yang_type='container', is_config=True) def _get_igmp_po_intf_cfg(self): """ Getter method for igmp_po_intf_cfg, mapped from YANG variable /interface/port_channel/ip/igmp_po_intf_cfg (container) """ return self.__igmp_po_intf_cfg def _set_igmp_po_intf_cfg(self, v, load=False): """ Setter method for igmp_po_intf_cfg, mapped from YANG variable /interface/port_channel/ip/igmp_po_intf_cfg (container) If this variable is read-only (config: false) in the source YANG file, then _set_igmp_po_intf_cfg is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_igmp_po_intf_cfg() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=igmp_po_intf_cfg.igmp_po_intf_cfg, is_container='container', presence=False, yang_name="igmp-po-intf-cfg", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'IgmpPo', u'sort-priority': u'122'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """igmp_po_intf_cfg must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=igmp_po_intf_cfg.igmp_po_intf_cfg, is_container='container', presence=False, yang_name="igmp-po-intf-cfg", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'IgmpPo', u'sort-priority': u'122'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='container', is_config=True)""", }) self.__igmp_po_intf_cfg = t if hasattr(self, '_set'): self._set() def _unset_igmp_po_intf_cfg(self): self.__igmp_po_intf_cfg = YANGDynClass(base=igmp_po_intf_cfg.igmp_po_intf_cfg, is_container='container', presence=False, yang_name="igmp-po-intf-cfg", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'IgmpPo', u'sort-priority': u'122'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='container', is_config=True) def _get_interface_PO_ospf_conf(self): """ Getter method for interface_PO_ospf_conf, mapped from YANG variable /interface/port_channel/ip/interface_PO_ospf_conf (container) """ return self.__interface_PO_ospf_conf def _set_interface_PO_ospf_conf(self, v, load=False): """ Setter method for interface_PO_ospf_conf, mapped from YANG variable /interface/port_channel/ip/interface_PO_ospf_conf (container) If this variable is read-only (config: false) in the source YANG file, then _set_interface_PO_ospf_conf is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_interface_PO_ospf_conf() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=interface_PO_ospf_conf.interface_PO_ospf_conf, is_container='container', presence=False, yang_name="interface-PO-ospf-conf", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'OSPFPoInterfaceCallPoint'}}, namespace='urn:brocade.com:mgmt:brocade-ospf', defining_module='brocade-ospf', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """interface_PO_ospf_conf must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=interface_PO_ospf_conf.interface_PO_ospf_conf, is_container='container', presence=False, yang_name="interface-PO-ospf-conf", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'OSPFPoInterfaceCallPoint'}}, namespace='urn:brocade.com:mgmt:brocade-ospf', defining_module='brocade-ospf', yang_type='container', is_config=True)""", }) self.__interface_PO_ospf_conf = t if hasattr(self, '_set'): self._set() def _unset_interface_PO_ospf_conf(self): self.__interface_PO_ospf_conf = YANGDynClass(base=interface_PO_ospf_conf.interface_PO_ospf_conf, is_container='container', presence=False, yang_name="interface-PO-ospf-conf", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'OSPFPoInterfaceCallPoint'}}, namespace='urn:brocade.com:mgmt:brocade-ospf', defining_module='brocade-ospf', yang_type='container', is_config=True) def _get_pim_intf_po_cont(self): """ Getter method for pim_intf_po_cont, mapped from YANG variable /interface/port_channel/ip/pim_intf_po_cont (container) """ return self.__pim_intf_po_cont def _set_pim_intf_po_cont(self, v, load=False): """ Setter method for pim_intf_po_cont, mapped from YANG variable /interface/port_channel/ip/pim_intf_po_cont (container) If this variable is read-only (config: false) in the source YANG file, then _set_pim_intf_po_cont is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_pim_intf_po_cont() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=pim_intf_po_cont.pim_intf_po_cont, is_container='container', presence=False, yang_name="pim-intf-po-cont", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'PimPoIntfCallpoint', u'sort-priority': u'121'}}, namespace='urn:brocade.com:mgmt:brocade-pim', defining_module='brocade-pim', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """pim_intf_po_cont must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=pim_intf_po_cont.pim_intf_po_cont, is_container='container', presence=False, yang_name="pim-intf-po-cont", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'PimPoIntfCallpoint', u'sort-priority': u'121'}}, namespace='urn:brocade.com:mgmt:brocade-pim', defining_module='brocade-pim', yang_type='container', is_config=True)""", }) self.__pim_intf_po_cont = t if hasattr(self, '_set'): self._set() def _unset_pim_intf_po_cont(self): self.__pim_intf_po_cont = YANGDynClass(base=pim_intf_po_cont.pim_intf_po_cont, is_container='container', presence=False, yang_name="pim-intf-po-cont", rest_name="", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-drop-node-name': None, u'callpoint': u'PimPoIntfCallpoint', u'sort-priority': u'121'}}, namespace='urn:brocade.com:mgmt:brocade-pim', defining_module='brocade-pim', yang_type='container', is_config=True) ip_config = __builtin__.property(_get_ip_config, _set_ip_config) arp = __builtin__.property(_get_arp, _set_arp) interface_po_dhcp_conf = __builtin__.property(_get_interface_po_dhcp_conf, _set_interface_po_dhcp_conf) icmp = __builtin__.property(_get_icmp, _set_icmp) igmp_po_intf_cfg = __builtin__.property(_get_igmp_po_intf_cfg, _set_igmp_po_intf_cfg) interface_PO_ospf_conf = __builtin__.property(_get_interface_PO_ospf_conf, _set_interface_PO_ospf_conf) pim_intf_po_cont = __builtin__.property(_get_pim_intf_po_cont, _set_pim_intf_po_cont) _pyangbind_elements = {'ip_config': ip_config, 'arp': arp, 'interface_po_dhcp_conf': interface_po_dhcp_conf, 'icmp': icmp, 'igmp_po_intf_cfg': igmp_po_intf_cfg, 'interface_PO_ospf_conf': interface_PO_ospf_conf, 'pim_intf_po_cont': pim_intf_po_cont, }

72.885965

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0.741565

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24,927

4.880313

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0.04011

0.048132

0.035927

0.883509

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0.830908

0.823573

0.80816

0.798533

0

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0.142055

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false

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0.297674

0

null

0

1

0

null

0

6

f7674b5ae471a8633ed272cff20d4b73ad8b36b6

23

py

Python

exoatlas/telescopes/__init__.py

zkbt/exopop

5e8b9d391fe9e2d39c623d7ccd7eca8fd0f0f3f8

[ "MIT" ]

4

2020-06-24T16:38:27.000Z

2022-01-23T01:57:19.000Z

exoatlas/telescopes/__init__.py

zkbt/exopop

5e8b9d391fe9e2d39c623d7ccd7eca8fd0f0f3f8

[ "MIT" ]

4

2018-09-20T23:12:30.000Z

2019-05-15T15:31:58.000Z

exoatlas/telescopes/__init__.py

zkbt/exopop

5e8b9d391fe9e2d39c623d7ccd7eca8fd0f0f3f8

[ "MIT" ]

null

from .buckets 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

e3914d855698b3d924403a03e74e44abdbacb0b7

24

py

Python

lefi/ws/__init__.py

Shom770/Lefi

7d9d45a8356605d82e2b7247715db4992d21c377

[ "MIT" ]

null

lefi/ws/__init__.py

Shom770/Lefi

7d9d45a8356605d82e2b7247715db4992d21c377

[ "MIT" ]

null

lefi/ws/__init__.py

Shom770/Lefi

7d9d45a8356605d82e2b7247715db4992d21c377

[ "MIT" ]

null

from .wsclient import *

12

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6

e3dca70c836598a47c545d259722eb2d727b88cc

7,567

py

Python

picbackend/tests/views/v2/patient_assist_scheduler_views_tests.py

bbcawodu/careadvisors-backend

5ebd3c0fc189b2486cea92b2a13c0bd8a0ee3838

[ "MIT" ]

null

picbackend/tests/views/v2/patient_assist_scheduler_views_tests.py

bbcawodu/careadvisors-backend

5ebd3c0fc189b2486cea92b2a13c0bd8a0ee3838

[ "MIT" ]

null

picbackend/tests/views/v2/patient_assist_scheduler_views_tests.py

bbcawodu/careadvisors-backend

5ebd3c0fc189b2486cea92b2a13c0bd8a0ee3838

[ "MIT" ]

null

""" Defines tests for version 1 of the patient assist consumer appointment scheduler API for the picbackend app """ from django.test import TestCase from .base_v2_api_tests import BaseV2RqstTests import json class PatientAssistSchedulerAPITests(TestCase, BaseV2RqstTests): def setUp(self): self.base_url += "patient_assist_apt_mgr/" def test_view_next_available_navigator_appointments(self): post_data = {"Preferred Times": [],} post_json = json.dumps(post_data) response = self.client_object.post(self.base_url, post_json, content_type="application/json") response_json = response.content.decode('utf-8') response_data = json.loads(response_json) # Test for valid decoded json data from response body self.assertIsNotNone(response_data) # Test decoded JSON data for correct API version self.assertEqual(response_data["Status"]["Version"], 2.0) status_data = response_data["Status"] # Test decoded JSON data for "Status" key self.assertIsNotNone(status_data) # Test decoded JSON data for non empty "Next Available Appointments" data next_available_appointment_data = response_data["Data"]["Next Available Appointments"] self.assertNotEqual(len(next_available_appointment_data), 0) preferred_appointments_data = response_data["Data"]["Preferred Appointments"] # Test that length of "Preferred Appointments" in decoded JSON data is equal to length of request # "Preferred Times" list self.assertEqual(len(preferred_appointments_data), len(post_data["Preferred Times"])) # Test decoded JSON data for empty "Preferred Appointments" data self.assertEqual(len(preferred_appointments_data), 0) self.assertNotIn("Errors", status_data) self.assertEqual(status_data["Error Code"], 0) self.assertIn("Data", response_data) self.assertNotEqual(len(response_data["Data"]), 0) # def test_view_preferred_navigator_appointments(self): # post_data = {"Preferred Times": ["2018-01-04T20:00:00"],} # post_json = json.dumps(post_data) # response = self.client_object.post(self.base_url, post_json, content_type="application/json") # response_json = response.content.decode('utf-8') # response_data = json.loads(response_json) # # # Test for valid decoded json data from response body # self.assertIsNotNone(response_data) # # # Test decoded JSON data for correct API version # self.assertEqual(response_data["Status"]["Version"], 2.0) # # status_data = response_data["Status"] # # # Test decoded JSON data for "Status" key # self.assertIsNotNone(status_data) # # # Test decoded JSON data for non empty "Next Available Appointments" data # next_available_appointment_data = response_data["Data"]["Next Available Appointments"] # self.assertEqual(len(next_available_appointment_data), 0) # # preferred_appointments_data = response_data["Data"]["Preferred Appointments"] # # # Test that length of "Preferred Appointments" in decoded JSON data is equal to length of request # # "Preferred Times" list # self.assertEqual(len(preferred_appointments_data), len(post_data["Preferred Times"])) # # # Test decoded JSON data for non empty preferred appointment # self.assertNotEqual(len(preferred_appointments_data[0]), 0) # # self.assertNotIn("Errors", status_data) # self.assertEqual(status_data["Error Code"], 0) # self.assertIn("Data", response_data) # self.assertNotEqual(len(response_data["Data"]), 0) def test_add_consumer_apt_with_nav(self): post_data = {"navigator_id": 1, "Appointment Date and Time": '2019-03-08T16:00:00', "Consumer Info": { "first_name": "calkfndy", "middle_name": "ljhvjhgjhgjhgoli", "last_name": "pophgfthcdfgcgh", "email": "[email protected]", "phone": "2813308004", "household_size": 11, "plan": "String (Can be empty)", "preferred_language": "English", "address_line_1": "6540 N Glenwood", "address_line_2": "", "city": "", "state_province": "", "zipcode": "" } } post_json = json.dumps(post_data) response = self.client_object.put(self.base_url, post_json, content_type="application/json") response_json = response.content.decode('utf-8') response_data = json.loads(response_json) # Test for valid decoded json data from response body self.assertIsNotNone(response_data) # Test decoded JSON data for correct API version self.assertEqual(response_data["Status"]["Version"], 2.0) status_data = response_data["Status"] # Test decoded JSON data for "Status" key self.assertIsNotNone(status_data) self.assertNotIn("Errors", status_data) self.assertEqual(status_data["Error Code"], 0) self.assertIn("Data", response_data) self.assertNotEqual(len(response_data["Data"]), 0) def test_view_navigators_scheduled_appointments(self): self.base_url += "?nav_id=1" response = self.client_object.get(self.base_url) response_json = response.content.decode('utf-8') response_data = json.loads(response_json) # Test for valid decoded json data from response body self.assertIsNotNone(response_data) # Test decoded JSON data for correct API version self.assertEqual(response_data["Status"]["Version"], 2.0) status_data = response_data["Status"] # Test decoded JSON data for "Status" key self.assertIsNotNone(status_data) self.assertNotIn("Errors", status_data) self.assertEqual(status_data["Error Code"], 0) self.assertIn("Data", response_data) self.assertNotEqual(len(response_data["Data"]), 0) def test_delete_consumer_apt_with_nav(self): post_data = {"Navigator ID": 1, "Appointment Date and Time": '2019-03-08T16:00:00', } post_json = json.dumps(post_data) response = self.client_object.delete(self.base_url, post_json, content_type="application/json") response_json = response.content.decode('utf-8') response_data = json.loads(response_json) # Test for valid decoded json data from response body self.assertIsNotNone(response_data) # Test decoded JSON data for correct API version self.assertEqual(response_data["Status"]["Version"], 2.0) status_data = response_data["Status"] # Test decoded JSON data for "Status" key self.assertIsNotNone(status_data) self.assertNotIn("Errors", status_data) self.assertEqual(status_data["Error Code"], 0) self.assertIn("Data", response_data) self.assertNotEqual(len(response_data["Data"]), 0)

43.24

107

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836

7,567

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7,567

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0

0.105882

0

null

0

1

0

null

0

6

e3ec1d204bfbbc9358ffefb99b135fd36c283e17

28

py

Python

ot/externals/funcsigs.py

SpaceLearner/torch_ot

7663d0e2f66891f6019db870760e6f27bc76a437

[ "MIT" ]

5

2020-12-02T12:19:44.000Z

2021-07-13T12:22:56.000Z

ot/externals/funcsigs.py

SpaceLearner/torch_ot

7663d0e2f66891f6019db870760e6f27bc76a437

[ "MIT" ]

1

2021-04-25T15:53:24.000Z

ot/externals/funcsigs.py

SpaceLearner/torch_ot

7663d0e2f66891f6019db870760e6f27bc76a437

[ "MIT" ]

1

2021-06-03T17:07:39.000Z

def signature(): pass

5.6

16

0.571429

3

28

5.333333

1

0

0.321429

28

5

17

5.6

0.842105

0

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true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

581cdbc479610ad23dcf3a0ee0ade69338ba0253

30

py

Python

src/SpoutGL/enums.py

worosom/Python-SpoutGL

7ec5f2b992c3512104960136db74b7d956e0b5a7

[ "BSD-3-Clause" ]

5

2021-12-30T15:03:52.000Z

2022-03-08T14:34:39.000Z

src/SpoutGL/enums.py

worosom/Python-SpoutGL

7ec5f2b992c3512104960136db74b7d956e0b5a7

[ "BSD-3-Clause" ]

1

2021-12-08T01:41:17.000Z

src/SpoutGL/enums.py

worosom/Python-SpoutGL

7ec5f2b992c3512104960136db74b7d956e0b5a7

[ "BSD-3-Clause" ]

1

2021-11-24T23:12:49.000Z

from ._spoutgl.enums import *

15

29

0.766667

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30

5.5

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30

1

30

0.846154

0

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true

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null

0

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0

null

0

1

0

1

0

1

0

6

584d87b17e8c9da02266a10ac00245f5c3efc14c

2,502

py

Python

userbot/plugins/pro_nub.py

RiderFA/Dark_Userbot

480df539bfeae994d59649a54d2478ed24b445bb

[ "MIT" ]

null

userbot/plugins/pro_nub.py

RiderFA/Dark_Userbot

480df539bfeae994d59649a54d2478ed24b445bb

[ "MIT" ]

null

userbot/plugins/pro_nub.py

RiderFA/Dark_Userbot

480df539bfeae994d59649a54d2478ed24b445bb

[ "MIT" ]

null

import asyncio from mafiabot.utils import admin_cmd, sudo_cmd, edit_or_reply @bot.on(admin_cmd(pattern="unoob$", outgoing=True)) @bot.on(sudo_cmd(pattern="unoob$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.5 animation_ttl = range(0, 9) await edit_or_reply(event, "You Noob") animation_chars = [ "EvErYbOdY", "iZ", "BiGGeSt", "NoOoB", "uNtiL", "YoU", "aRriVe", "😈", "EvErYbOdY iZ BiGGeSt NoOoB uNtiL YoU aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 9]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="menoob$", outgoing=True)) @bot.on(sudo_cmd(pattern="menoob$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.5 animation_ttl = range(0, 9) await edit_or_reply(event, "Me Noob") animation_chars = [ "EvErYbOdY", "iZ", "BiGGeSt", "NoOoB", "uNtiL", "i", "aRriVe", "😈", "EvErYbOdY iZ BiGGeSt NoOoB uNtiL i aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 9]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="uproo$", outgoing=True)) @bot.on(sudo_cmd(pattern="uproo$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.5 animation_ttl = range(0, 8) await edit_or_reply(event, "You Pro") animation_chars = [ "EvErYbOdY", "iZ", "PeRu", "uNtiL", "YoU", "aRriVe", "😈", "EvErYbOdY iZ PeRu uNtiL YoU aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 8]) await asyncio.sleep(animation_interval) @bot.on(admin_cmd(pattern="mepro$", outgoing=True)) @bot.on(sudo_cmd(pattern="mepro$", allow_sudo=True)) async def _(event): if event.fwd_from: return animation_interval = 0.5 animation_ttl = range(0, 8) await edit_or_reply(event, "Me Pro") animation_chars = [ "EvErYbOdY", "iZ", "PeRu", "uNtiL", "i", "aRriVe", "😈", "EvErYbOdY iZ PeRu uNtiL i aRriVe 😈", ] for i in animation_ttl: await event.edit(animation_chars[i % 8]) await asyncio.sleep(animation_interval)

19.246154

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313

2,502

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0.039286

0.037143

0.925714

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0.617143

0

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2,502

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0

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1

0

1

0

null

0

6

5873813e4c26db04e5d2f063dc53058c984fa54d

160

py

Python

relief/admin.py

shivkiyer/covid-sahyog

bf88c800abd61d4f203f7e6bb46315dee6d08dac

[ "MIT" ]

1

2021-05-13T16:17:47.000Z

relief/admin.py

shivkiyer/covid-sahyog

bf88c800abd61d4f203f7e6bb46315dee6d08dac

[ "MIT" ]

null

relief/admin.py

shivkiyer/covid-sahyog

bf88c800abd61d4f203f7e6bb46315dee6d08dac

[ "MIT" ]

2

2021-05-12T05:10:38.000Z

2021-05-12T05:12:20.000Z

from django.contrib import admin from . import models # Register your models here. admin.site.register(models.State) admin.site.register(models.RequestHelp)

17.777778

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0.1125

160

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1

0

1

0

null

0

1

0

1

0

6

547b73092b407d97f139e226a0409bb1028d7e55

274

py

Python

django_query_profiler/django/db/backends/postgresql/base.py

sonej/django-query-profiler

4afe3694ded26d7ba0b435f5666e990b668d85b5

[ "BSD-3-Clause" ]

97

2020-03-03T01:20:35.000Z

2022-03-23T14:06:09.000Z

django_query_profiler/django/db/backends/postgresql/base.py

sonej/django-query-profiler

4afe3694ded26d7ba0b435f5666e990b668d85b5

[ "BSD-3-Clause" ]

24

2020-03-06T17:35:08.000Z

2022-02-09T20:06:05.000Z

django_query_profiler/django/db/backends/postgresql/base.py

sonej/django-query-profiler

4afe3694ded26d7ba0b435f5666e990b668d85b5

[ "BSD-3-Clause" ]

9

2020-03-22T18:17:09.000Z

2022-01-31T18:59:11.000Z

import django.db.backends.postgresql.base as postgresql_base from django_query_profiler.django.db.backends.database_wrapper_mixin import QueryProfilerDatabaseWrapperMixin class DatabaseWrapper(postgresql_base.DatabaseWrapper, QueryProfilerDatabaseWrapperMixin): pass

34.25

109

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0.135593

0

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274

7

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0

1

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true

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null

0

1

0

null

0

1

0

1

0

6

548541e74101893456f355f1dfc70167f7f88750

13,311

py

Python

nemo/collections/nlp/data/glue_benchmark/data_processors.py

eesungkim/NeMo

461a8668bd713af11c98b68a75866dccb2df175d

[ "Apache-2.0" ]

null

nemo/collections/nlp/data/glue_benchmark/data_processors.py

eesungkim/NeMo

461a8668bd713af11c98b68a75866dccb2df175d

[ "Apache-2.0" ]

null

nemo/collections/nlp/data/glue_benchmark/data_processors.py

eesungkim/NeMo

461a8668bd713af11c98b68a75866dccb2df175d

[ "Apache-2.0" ]

null

# Copyright 2018 The Google AI Language Team Authors and # The HuggingFace Inc. team. # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from nemo.collections.nlp.data.data_utils.data_preprocessing import DataProcessor from nemo.utils import logging __all__ = [ 'ColaProcessor', 'MnliProcessor', 'MnliMismatchedProcessor', 'MrpcProcessor', 'Sst2Processor', 'StsbProcessor', 'QqpProcessor', 'QnliProcessor', 'RteProcessor', 'WnliProcessor', ] class MrpcProcessor(DataProcessor): """Processor for the MRPC data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" logging.info(f'LOOKING AT {os.path.join(data_dir, "train.tsv")}') return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, i) text_a = line[3] text_b = line[4] label = line[0] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): return f"mrpc sentence1: {text_a} sentence2: {text_b}" def label2string(self, label): return "equivalent" if label == "1" else "not equivalent" class MnliProcessor(DataProcessor): """Processor for the MultiNLI data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched") def get_labels(self): """See base class.""" return ["contradiction", "entailment", "neutral"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[8] text_b = line[9] label = line[-1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): return f"mnli hypothesis: {text_a} premise: {text_b}" def label2string(self, label): return label class MnliMismatchedProcessor(MnliProcessor): """Processor for the MultiNLI Mismatched data set (GLUE version).""" def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_matched") class ColaProcessor(DataProcessor): """Processor for the CoLA data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) text_a = line[3] label = line[1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): assert text_b is None return f"cola sentence: {text_a}" def label2string(self, label): return "acceptable" if label == "1" else "not acceptable" class Sst2Processor(DataProcessor): """Processor for the SST-2 data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, i) text_a = line[0] label = line[1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): assert text_b is None return f"sst2 sentence: {text_a}" def label2string(self, label): return "positive" if label == "1" else "negative" class StsbProcessor(DataProcessor): """Processor for the STS-B data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return [None] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[7] text_b = line[8] label = line[-1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): return f"stsb sentence1: {text_a} sentence2: {text_b}" def label2string(self, label): return '%.1f' % float(label) class QqpProcessor(DataProcessor): """Processor for the QQP data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) try: text_a = line[3] text_b = line[4] label = line[5] except IndexError: continue examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): return f"qqp question1: {text_a} question2: {text_b}" def label2string(self, label): return "duplicate" if label == "1" else "not_duplicate" class QnliProcessor(DataProcessor): """Processor for the QNLI data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["entailment", "not_entailment"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[1] text_b = line[2] label = line[-1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): return f"qnli question: {text_a} sentence: {text_b}" def label2string(self, label): return label class RteProcessor(DataProcessor): """Processor for the RTE data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["entailment", "not_entailment"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[1] text_b = line[2] label = line[-1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): return f"rte sentence1: {text_a} sentence2: {text_b}" def label2string(self, label): return label class WnliProcessor(DataProcessor): """Processor for the WNLI data set (GLUE version).""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[1] text_b = line[2] label = line[-1] examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def get_t5_prompted_query(self, text_a, text_b): raise NotImplementedError("NeMo-Megatron T5 does not support WNLI at the moment.") def label2string(self, label): raise NotImplementedError("NeMo-Megatron T5 does not support WNLI at the moment.") class InputExample(object): """A single training/test example for simple sequence classification. Args: guid: Unique id for the example. text_a: The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: The untokenized text of the second sequence. Only must be specified for sequence pair tasks. label:The label of the example. This should be specified for train and dev examples, but not for test examples. """ def __init__(self, guid: int, text_a: str, text_b: str = None, label: str = None): """Constructs a InputExample.""" self.guid = guid self.text_a = text_a self.text_b = text_b self.label = label def __repr__(self): return ( f"InputExample(guid='{self.guid}', text_a='{self.text_a}', text_b='{self.text_b}', label='{self.label}')" )

34.574026

117

0.615581

1,766

13,311

4.446206

0.121178

0.031839

0.03324

0.061895

0.722618

0.709246

0.706954

0.699312

0.68403

0.671294

0

0.009916

0.257531

13,311

384

118

34.664063

0.78458

0.190594

0

0.72807

0

0.004386

0.113296

0.01384

0

0.008772

1

0.25

false

0

0.013158

0.065789

0.548246

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

54ba5fbcf6c70c01a1e3b29e3bce63a001137f53

7,249

py

Python

tests/test_destination_weather_api.py

baffolobill/HerePy

c6655e9dfde7a5888cc231d7f9f9e8a888f54dcd

[ "MIT" ]

null

tests/test_destination_weather_api.py

baffolobill/HerePy

c6655e9dfde7a5888cc231d7f9f9e8a888f54dcd

[ "MIT" ]

null

tests/test_destination_weather_api.py

baffolobill/HerePy

c6655e9dfde7a5888cc231d7f9f9e8a888f54dcd

[ "MIT" ]

null

#!/usr/bin/env python import os import time import unittest import json import responses import herepy from herepy.here_enum import WeatherProductType class DestinationWeatherApiTest(unittest.TestCase): def setUp(self): api = herepy.DestinationWeatherApi('app_id', 'app_code') self._api = api def test_initiation(self): self.assertIsInstance(self._api, herepy.DestinationWeatherApi) self.assertEqual(self._api._app_id, 'app_id') self.assertEqual(self._api._app_code, 'app_code') self.assertEqual(self._api._base_url, 'https://weather.api.here.com/weather/1.0/report.json') @responses.activate def test_invalid_request_is_thrown(self): with open('testdata/models/destination_weather_error_invalid_request.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_7days name = "Berlin" with self.assertRaises(herepy.InvalidRequestError): self._api.weather_for_location_name(name, product) @responses.activate def test_unauthorized_is_thrown(self): with open('testdata/models/destination_weather_error_unauthorized.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_7days name = "Berlin" with self.assertRaises(herepy.UnauthorizedError): self._api.weather_for_location_name(name, product) @responses.activate def test_weather_for_location_name(self): with open('testdata/models/destination_weather_forecasts.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_7days name = "Berlin" response = self._api.weather_for_location_name(name, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse) @responses.activate def test_weather_for_coordinates(self): with open('testdata/models/destination_weather_forecasts.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_7days latitude = 52.51784 longitude = 13.38736 response = self._api.weather_for_coordinates(latitude, longitude, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse) @responses.activate def test_weather_for_zip_code(self): with open('testdata/models/destination_weather_forecasts.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_7days zip_code = "10025" response = self._api.weather_for_zip_code(zip_code, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse) @responses.activate def test_weather_product_type_alerts(self): with open('testdata/models/destination_weather_alerts.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.alerts zip_code = "10025" response = self._api.weather_for_zip_code(zip_code, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse) @responses.activate def test_weather_product_type_forecast_7days(self): with open('testdata/models/destination_weather_forecasts.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_7days zip_code = "10025" response = self._api.weather_for_zip_code(zip_code, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse) @responses.activate def test_weather_product_type_forecast_7days_simple(self): with open('testdata/models/destination_weather_forecasts_simple.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_7days_simple zip_code = "10025" response = self._api.weather_for_zip_code(zip_code, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse) @responses.activate def test_weather_product_type_forecast_astronomy(self): with open('testdata/models/destination_weather_forecasts_astronomy.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_astronomy zip_code = "10025" response = self._api.weather_for_zip_code(zip_code, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse) @responses.activate def test_weather_product_type_forecast_hourly(self): with open('testdata/models/destination_weather_forecasts_hourly.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.forecast_hourly zip_code = "10025" response = self._api.weather_for_zip_code(zip_code, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse) @responses.activate def test_weather_product_type_nws_alerts(self): with open('testdata/models/destination_weather_forecasts_nsw_alerts.json', 'r') as f: expectedResponse = f.read() responses.add(responses.GET, 'https://weather.api.here.com/weather/1.0/report.json', expectedResponse, status=200) product = herepy.WeatherProductType.nws_alerts zip_code = "10025" response = self._api.weather_for_zip_code(zip_code, product) self.assertTrue(response) self.assertIsInstance(response, herepy.DestinationWeatherResponse)

47.379085

101

0.698855

807

7,249

6.075589

0.110285

0.031409

0.036712

0.046502

0.871303

0.854375

0.852335

0.790944

0

0.019831

0.200028

7,249

152

102

47.690789

0.82566

0.002759

0

0.669118

0

0.182485

0.083426

0

0.176471

1

0.095588

false

0

0.051471

0

0.154412

0

null

0

1

0

null

0

6

b725bd81ce5b2689bff2fdc3737040d564303e20

392

py

Python

mango/dataset.py

gabrielelanaro/mango-ml

59b6063c72aef084f632aed7d0a0d19b6b86deb9

[ "MIT" ]

null

mango/dataset.py

gabrielelanaro/mango-ml

59b6063c72aef084f632aed7d0a0d19b6b86deb9

[ "MIT" ]

2

2018-04-20T23:54:43.000Z

2018-04-30T13:40:49.000Z

mango/dataset.py

gabrielelanaro/mango

59b6063c72aef084f632aed7d0a0d19b6b86deb9

[ "MIT" ]

null

from .base import Parameterized class Dataset(Parameterized): pass class SplitDataset(Dataset): def build(self): raise NotImplementedError() def train(self): raise NotImplementedError() def test(self): raise NotImplementedError() def transform_train(self, data): return data def transform_test(self, data): return data

16.333333

36

0.658163

40

392

6.4

0.45

0.105469

0.328125

0.363281

0

0.265306

392

23

37

17.043478

0.888889

0

0.357143

0

1

0.357143

false

0.071429

0.142857

0.714286

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3f9462f3fdabe76952310e8cfd579e89d18c8175

69

py

Python

pyp3d/v18446497929133817856/__init__.py

pengdi-cabr/pyp3d

3b5981257838a60837b38106dc39d66bbad38767

[ "MIT" ]

22

2021-11-16T05:54:05.000Z

2021-12-03T12:16:46.000Z

pyp3d/v18446497929133817856/__init__.py

pengdi-cabr/pyp3d

3b5981257838a60837b38106dc39d66bbad38767

[ "MIT" ]

null

pyp3d/v18446497929133817856/__init__.py

pengdi-cabr/pyp3d

3b5981257838a60837b38106dc39d66bbad38767

[ "MIT" ]

4

2021-12-01T07:38:07.000Z

2022-01-18T13:01:54.000Z

from .p3d_type import * from .dll import * from .component import *

23

24

0.724638

10

69

4.9

0.6

0.408163

0

0.017857

0.188406

69

3

25

23

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

3f97202e28b20e2c3d5549504429400b9b7cc98a

124

py

Python

genelang/bricks/OP1L.py

GabrielAmare/Genelang

af5294e900d2f79ff54375f9759c156a4b5a098a

[ "MIT" ]

null

genelang/bricks/OP1L.py

GabrielAmare/Genelang

af5294e900d2f79ff54375f9759c156a4b5a098a

[ "MIT" ]

null

genelang/bricks/OP1L.py

GabrielAmare/Genelang

af5294e900d2f79ff54375f9759c156a4b5a098a

[ "MIT" ]

null

from .OP1 import OP1 class OP1L(OP1): def __str__(self): return f"{str(self.symbols[0])}{str(self.items[0])}"

17.714286

60

0.620968

20

124

3.65

0.65

0.287671

0

0.06

0.193548

124

6

61

20.666667

0.67

0

0.33871

0

1

0.25

false

0

0.25

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

3fa1adbd8cd0d6fb05904c87f6eb8483be04ef57

10,545

py

Python

dao/test_moonstream.py

bugout-dev/dao

d6e089d32ecd54a5bfd3b31f98e582528b201f15

[ "Apache-2.0" ]

9

2021-12-18T16:48:36.000Z

2022-02-15T17:54:07.000Z

dao/test_moonstream.py

bugout-dev/dao

d6e089d32ecd54a5bfd3b31f98e582528b201f15

[ "Apache-2.0" ]

20

2021-12-16T13:47:42.000Z

2022-03-17T17:39:49.000Z

dao/test_moonstream.py

bugout-dev/dao

d6e089d32ecd54a5bfd3b31f98e582528b201f15

[ "Apache-2.0" ]

null

import unittest from brownie import accounts import brownie from . import ERC20Facet, ERC20Initializer from .core import ZERO_ADDRESS, facet_cut from .test_core import MoonstreamDAOSingleContractTestCase, MoonstreamTokenTestCase class TestDeployment(MoonstreamDAOSingleContractTestCase): def test_add_and_replace(self): initializer = ERC20Initializer.ERC20Initializer(None) initializer.deploy({"from": accounts[0]}) erc20_facet = ERC20Facet.ERC20Facet(None) erc20_facet.deploy({"from": accounts[0]}) diamond_address = self.contracts["Diamond"] facet_cut( diamond_address, "ERC20Facet", erc20_facet.address, "add", {"from": accounts[0]}, initializer.address, initializer_params=["Moonstream DAO", "MNSTR"], ) diamond_erc20 = ERC20Facet.ERC20Facet(diamond_address) name = diamond_erc20.name() expected_name = "Moonstream DAO" self.assertEqual(name, expected_name) symbol = diamond_erc20.symbol() expected_symbol = "MNSTR" self.assertEqual(symbol, expected_symbol) decimals = diamond_erc20.decimals() expected_decimals = 18 self.assertEqual(decimals, expected_decimals) new_erc20_facet = ERC20Facet.ERC20Facet(None) new_erc20_facet.deploy({"from": accounts[0]}) facet_cut( diamond_address, "ERC20Facet", new_erc20_facet.address, "replace", {"from": accounts[0]}, initializer.address, initializer_params=["ROFL", "LOL"], ) name = diamond_erc20.name() expected_name = "ROFL" self.assertEqual(name, expected_name) symbol = diamond_erc20.symbol() expected_symbol = "LOL" self.assertEqual(symbol, expected_symbol) class TestRemoveFacet(MoonstreamDAOSingleContractTestCase): def test_remove_facet(self): initializer = ERC20Initializer.ERC20Initializer(None) initializer.deploy({"from": accounts[0]}) erc20_facet = ERC20Facet.ERC20Facet(None) erc20_facet.deploy({"from": accounts[0]}) diamond_address = self.contracts["Diamond"] facet_cut( diamond_address, "ERC20Facet", erc20_facet.address, "add", {"from": accounts[0]}, initializer.address, initializer_params=["Moonstream DAO", "MNSTR"], ) diamond_erc20 = ERC20Facet.ERC20Facet(diamond_address) name = diamond_erc20.name() expected_name = "Moonstream DAO" self.assertEqual(name, expected_name) symbol = diamond_erc20.symbol() expected_symbol = "MNSTR" self.assertEqual(symbol, expected_symbol) decimals = diamond_erc20.decimals() expected_decimals = 18 self.assertEqual(decimals, expected_decimals) facet_cut( diamond_address, "ERC20Facet", ZERO_ADDRESS, "remove", {"from": accounts[0]}, ) with self.assertRaises(Exception): name = diamond_erc20.name() with self.assertRaises(Exception): symbol = diamond_erc20.symbol() class TestERC20(MoonstreamTokenTestCase): def test_mint_fails_if_not_controller(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) with self.assertRaises(Exception): diamond.mint(accounts[1].address, 1000, {"from": accounts[1]}) def test_mint_to_another_address(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) initial_balance = diamond.balance_of(accounts[1].address) diamond.mint(accounts[1].address, 1000, {"from": accounts[0]}) final_balance = diamond.balance_of(accounts[1].address) self.assertEqual(final_balance, initial_balance + 1000) def test_transfer(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) diamond.mint(accounts[1].address, 1000, {"from": accounts[0]}) initial_sender_balance = diamond.balance_of(accounts[1].address) initial_receiver_balance = diamond.balance_of(accounts[2].address) diamond.transfer(accounts[2].address, 500, {"from": accounts[1]}) final_sender_balance = diamond.balance_of(accounts[1].address) final_receiver_balance = diamond.balance_of(accounts[2].address) self.assertEqual(final_sender_balance, initial_sender_balance - 500) self.assertEqual(final_receiver_balance, initial_receiver_balance + 500) def test_transfer_insufficient_balance(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) initial_sender_balance = diamond.balance_of(accounts[1].address) initial_receiver_balance = diamond.balance_of(accounts[2].address) with self.assertRaises(Exception): diamond.transfer( accounts[2].address, initial_sender_balance + 1, {"from": accounts[1]} ) final_sender_balance = diamond.balance_of(accounts[1].address) final_receiver_balance = diamond.balance_of(accounts[2].address) self.assertEqual(final_sender_balance, initial_sender_balance) self.assertEqual(final_receiver_balance, initial_receiver_balance) def test_transfer_from_with_approval(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) diamond.mint(accounts[1].address, 1000, {"from": accounts[0]}) initial_sender_balance = diamond.balance_of(accounts[1].address) initial_receiver_balance = diamond.balance_of(accounts[2].address) diamond.approve(accounts[2].address, 500, {"from": accounts[1]}) diamond.transfer_from( accounts[1].address, accounts[2].address, 500, {"from": accounts[2]} ) final_sender_balance = diamond.balance_of(accounts[1].address) final_receiver_balance = diamond.balance_of(accounts[2].address) self.assertEqual(final_sender_balance, initial_sender_balance - 500) self.assertEqual(final_receiver_balance, initial_receiver_balance + 500) def test_transfer_with_approval_insufficient_balance(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) initial_sender_balance = diamond.balance_of(accounts[1].address) initial_receiver_balance = diamond.balance_of(accounts[2].address) diamond.approve( accounts[2].address, initial_sender_balance + 1, {"from": accounts[1]} ) with self.assertRaises(Exception): diamond.transfer_from( accounts[1].address, accounts[2].address, initial_sender_balance + 1, {"from": accounts[2]}, ) final_sender_balance = diamond.balance_of(accounts[1].address) final_receiver_balance = diamond.balance_of(accounts[2].address) self.assertEqual(final_sender_balance, initial_sender_balance) self.assertEqual(final_receiver_balance, initial_receiver_balance) def test_transfer_from_with_approval_insufficient_allowance_sufficient_balance( self, ): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) diamond.mint(accounts[1].address, 1000, {"from": accounts[0]}) diamond.approve(accounts[2].address, 500, {"from": accounts[1]}) initial_sender_balance = diamond.balance_of(accounts[1].address) initial_receiver_balance = diamond.balance_of(accounts[2].address) with self.assertRaises(Exception): diamond.transfer_from( accounts[1].address, accounts[2].address, 501, {"from": accounts[2]}, ) final_sender_balance = diamond.balance_of(accounts[1].address) final_receiver_balance = diamond.balance_of(accounts[2].address) self.assertEqual(final_sender_balance, initial_sender_balance) self.assertEqual(final_receiver_balance, initial_receiver_balance) def test_not_burnable(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) diamond.mint(accounts[1].address, 1000, {"from": accounts[0]}) with self.assertRaises(Exception): diamond.transfer(brownie.ZERO_ADDRESS, 500, {"from": accounts[1]}) def test_approve_and_allowance(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) diamond.approve(accounts[2].address, 500, {"from": accounts[1]}) allowance = diamond.allowance(accounts[1].address, accounts[2].address) self.assertEqual(allowance, 500) def test_increase_allowance(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) initial_allowance = diamond.allowance(accounts[1].address, accounts[2].address) diamond.increase_allowance(accounts[2].address, 500, {"from": accounts[1]}) final_allowance = diamond.allowance(accounts[1].address, accounts[2].address) self.assertEqual(final_allowance, initial_allowance + 500) def test_decrease_allowance(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) initial_allowance = diamond.allowance(accounts[1].address, accounts[2].address) diamond.decrease_allowance(accounts[2].address, 500, {"from": accounts[1]}) final_allowance = diamond.allowance(accounts[1].address, accounts[2].address) self.assertEqual(final_allowance, initial_allowance - 500) def test_mint_total_supply(self): diamond_address = self.contracts["Diamond"] diamond = ERC20Facet.ERC20Facet(diamond_address) initial_total_supply = diamond.total_supply() diamond.mint(accounts[1].address, 1000, {"from": accounts[0]}) final_total_supply = diamond.total_supply() self.assertEqual(final_total_supply, initial_total_supply + 1000) if __name__ == "__main__": unittest.main()

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3fa299b6ba4753a9546e85b5fbe0cce1ed5357d0

215

py

Python

pmtour/models/__init__.py

sunoru/pokemon_tournament

920bc980c7021a433c46e30c248de1d9ba90871a

[ "MIT" ]

3

2016-12-05T03:33:44.000Z

2019-11-06T18:05:28.000Z

pmtour/models/__init__.py

sunoru/pokemon_tournament

920bc980c7021a433c46e30c248de1d9ba90871a

[ "MIT" ]

2

2016-01-02T15:09:07.000Z

2021-07-15T23:02:48.000Z

pmtour/models/__init__.py

sunoru/pokemon_tournament

920bc980c7021a433c46e30c248de1d9ba90871a

[ "MIT" ]

null

# coding=utf-8 from pmtour.models.bases import BaseModel from pmtour.models.tournament import Tournament from pmtour.models.player import Player from pmtour.models.turn import Turn from pmtour.models.log import Log

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3fc8078d753a0c0513e6c6366aef79f54a70b9c5

4,831

py

Python

draw.py

djrrb/drawbotlab

663da0beb43a4638458a92e7b43a2235585ef1b7

[ "MIT" ]

29

2015-06-21T09:50:44.000Z

2021-11-03T10:04:42.000Z

draw.py

djrrb/drawbotlab

663da0beb43a4638458a92e7b43a2235585ef1b7

[ "MIT" ]

1

2020-04-23T14:43:13.000Z

2020-05-06T20:24:06.000Z

draw.py

djrrb/drawbotlab

663da0beb43a4638458a92e7b43a2235585ef1b7

[ "MIT" ]

4

2015-08-24T21:07:54.000Z

2019-10-11T19:22:28.000Z

import drawBot def imageBox(im, box, fit="fill", clip=False, center=None, alpha=1): """ Draw an image object in a given rectangle. """ # if given a string, make an image object if isinstance(im, str): im = ImageObject(im) boxX, boxY, boxW, boxH = box # get the image dimensions imW, imH = im.size() imX, imY = boxX, boxY # if a center is not provided, use the center of the image if center is None: center = imW/2, imH/2 # get the relative scale of image to box in both directions fitScaleX = boxW/imW fitScaleY = boxH/imH # make the transformations with savedState(): translate(boxX, boxY) # if fit is "cover", make a clipping path if clip or (fit == "cover" and clip is None): b = BezierPath() b.rect(0, 0, boxW, boxH) clipPath(b) # use the center of the box as a starting point offsetX = boxW/2 offsetY = boxH/2 # the scale we will actually use scaleX = 1 scaleY = 1 # if fit is "cover", use the maximum fit if fit == 'cover': scaleX = scaleY = max(fitScaleX, fitScaleY) # if fit is "contain", use the minimum fit elif fit == "contain": scaleX = scaleY = min(fitScaleX, fitScaleY) elif fit == "scale-down": contain = min(fitScaleX, fitScaleY) scaleX = scaleY = min(contain, 1) # if fit is "none", do nothing elif fit == "none": pass else: # by default, fit in both directions scaleX = fitScaleX scaleY = fitScaleY # move to the center translate(offsetX, offsetY) # scale depending on fit scale(scaleX, scaleY) # draw the image centered on the center point image(im, (-center[0], -center[1]), alpha=alpha) # draw the center point DEBUG = False if DEBUG: with savedState(): fill(0, 1, 0) oval(boxX+offsetX-5, boxY+offsetY-5, 10, 10) def pathBox(path, box, fit="fill", clip=False, center=None): """ Draw a BezierPath in a given rectangle. """ boxX, boxY, boxW, boxH = box # get the image dimensions xMin, yMin, xMax, yMax = path.bounds() imW = xMax - xMin imH = yMax - yMin imX, imY = xMin, yMin # if a center is not provided, use the center of the image if center is None: center = imW/2, imH/2 # get the relative scale of image to box in both directions fitScaleX = boxW/imW fitScaleY = boxH/imH # make the transormations with savedState(): translate(boxX, boxY) # if fit is "cover", make a clipping path if clip: b = BezierPath() b.rect(0, 0, boxW, boxH) clipPath(b) # use the center of the box as a starting point offsetX = boxW/2 offsetY = boxH/2 # the scale we will actually use scaleX = 1 scaleY = 1 # if fit is "cover", use the maximum fit if fit == 'cover': scaleX = scaleY = max(fitScaleX, fitScaleY) # if fit is "contain", use the minimum fit elif fit == "contain": scaleX = scaleY = min(fitScaleX, fitScaleY) elif fit == "scale-down": contain = min(fitScaleX, fitScaleY) scaleX = scaleY = min(contain, 1) # if fit is "none", do nothing elif fit == "none": pass else: # by default, fit in both directions scaleX = fitScaleX scaleY = fitScaleY # move to the center translate(offsetX, offsetY) # scale depending on fit scale(scaleX, scaleY) # draw the image centered on the center point translate(-imX-center[0], -imY-center[1]) drawPath(path) DEBUG = False if DEBUG: with savedState(): fill(0, 1, 0) oval(boxX+offsetX-5, boxY+offsetY-5, 10, 10) if __name__ == "__main__": path = "image.png" for fit in ['fill', 'contain', 'scale-down', 'cover', 'none']: newPage(1000, 500) r = (200, 125, 200, 200) im = ImageObject(path) imageBox(im, r, fit=fit) with savedState(): fill(None) stroke(1, 0, 0) strokeWidth(2) rect(*r) r = (600, 125, 200, 200) fs = FormattedString('a', fontSize=800, font='Condor Variable') b = BezierPath() b.text(fs) fill(0, 1, 0) pathBox(b, r, fit=fit) with savedState(): fill(None) stroke(1, 0, 0) strokeWidth(2) rect(*r) fontSize(30) fill(0) text(fit, (width()/2, 380), align="center")

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py

Python

ramda/split_at_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

56

2018-08-06T08:44:58.000Z

2022-03-17T09:49:03.000Z

ramda/split_at_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

28

2019-06-17T11:09:52.000Z

2022-02-18T16:59:21.000Z

ramda/split_at_test.py

jakobkolb/ramda.py

982b2172f4bb95b9a5b09eff8077362d6f2f0920

[ "MIT" ]

5

2019-09-18T09:24:38.000Z

2021-07-21T08:40:23.000Z

from ramda import * from ramda.private.asserts import * def split_at_test(): assert_equal(split_at(1, [1, 2, 3]), [[1], [2, 3]]) assert_equal(split_at(5, "hello world"), ["hello", " world"]) assert_equal(split_at(-1, "foobar"), ["fooba", "r"])

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6

b7559cd3a85f88f92fae65af87cfa9053eb9fc0b

79

py

Python

source/main.py

ItsSeaJay/jinja-generator

c2f36ad796ec1cf88e8d08a4a1469c251530415e

[ "MIT" ]

null

source/main.py

ItsSeaJay/jinja-generator

c2f36ad796ec1cf88e8d08a4a1469c251530415e

[ "MIT" ]

null

source/main.py

ItsSeaJay/jinja-generator

c2f36ad796ec1cf88e8d08a4a1469c251530415e

[ "MIT" ]

null

from generator import Generator generator = Generator() generator.generate()

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b7a9725ea9a914b488c3fc9b7be6c9da5106d89e

135

py

Python

audtorch/__init__.py

hagenw/audtorch

d82ae7f7f8c7edb7b7180b83442224e9a68483bd

[ "MIT" ]

81

2019-05-22T16:39:46.000Z

2022-03-01T04:11:38.000Z

audtorch/__init__.py

hagenw/audtorch

d82ae7f7f8c7edb7b7180b83442224e9a68483bd

[ "MIT" ]

33

2019-05-24T09:04:06.000Z

2021-12-06T12:11:56.000Z

audtorch/__init__.py

hagenw/audtorch

d82ae7f7f8c7edb7b7180b83442224e9a68483bd

[ "MIT" ]

12

2019-05-23T09:48:15.000Z

2021-04-02T16:12:47.000Z

from . import collate from . import datasets from . import metrics from . import samplers from . import transforms from . import utils

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6

b7cac8d060c77710990c340129d9d3f04e74daf0

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py

Python

tests/test_units.py

csdms/bmi-tester

1bece69ecda922d047fc1df5dc1c562c97b4354b

[ "MIT" ]

null

tests/test_units.py

csdms/bmi-tester

1bece69ecda922d047fc1df5dc1c562c97b4354b

[ "MIT" ]

4

2016-10-06T03:52:04.000Z

2020-09-10T16:44:38.000Z

tests/test_units.py

csdms/bmi-tester

1bece69ecda922d047fc1df5dc1c562c97b4354b

[ "MIT" ]

2

2016-09-19T17:32:20.000Z

2020-09-24T17:16:16.000Z

from bmi_tester.api import ( check_unit_is_dimensionless, check_unit_is_time, check_unit_is_valid, ) def test_check_valid_units(): assert check_unit_is_valid("m") assert check_unit_is_valid("m / s") assert check_unit_is_valid("m s-1") assert check_unit_is_valid("N m") assert check_unit_is_valid("N.m") assert check_unit_is_valid("m^2") assert check_unit_is_valid("m2") assert check_unit_is_valid("") assert check_unit_is_valid("1") def test_check_invalid_units(): assert not check_unit_is_valid("foo") assert not check_unit_is_valid("m ** 2") assert not check_unit_is_valid("-") def test_dimensionless_units(): assert check_unit_is_dimensionless("") assert check_unit_is_dimensionless("1") assert not check_unit_is_dimensionless("m") # assert not check_unit_is_dimensionless("-") def test_time_units(): assert check_unit_is_time("s") assert check_unit_is_time("d") assert check_unit_is_time("yr") assert check_unit_is_time("seconds since 1970-01-01") assert check_unit_is_time("seconds since 1970-01-01 00:00:00 UTC") assert check_unit_is_time("days since 1970-01-01 00:00:00 UTC") assert check_unit_is_time("years since 1970-01-01 00:00:00 UTC") assert not check_unit_is_time("m")

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6

4d24de4443a3932232514d09ca335e5c9a2a21ee

34

py

Python

src/archiver/__init__.py

StarovoitovNik/archivator

f7b306e270e327f0a122faed159c569ee519e10a

[ "MIT" ]

null

src/archiver/__init__.py

StarovoitovNik/archivator

f7b306e270e327f0a122faed159c569ee519e10a

[ "MIT" ]

null

src/archiver/__init__.py

StarovoitovNik/archivator

f7b306e270e327f0a122faed159c569ee519e10a

[ "MIT" ]

null

from src.archiver.arciv import arc

34

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0

6

4d363f98ea895722ae26b1531e8c2ef8e5293e2f

244

py

Python

lib/JumpScale/baselib/serializers/SerializerDict.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

1

2015-10-26T10:38:13.000Z

lib/JumpScale/baselib/serializers/SerializerDict.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

4

2016-08-25T12:08:39.000Z

2018-04-12T12:36:01.000Z

lib/JumpScale/baselib/serializers/SerializerDict.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

3

2016-03-08T07:49:34.000Z

2018-10-19T13:56:43.000Z

import blosc class SerializerDict(object): def dumps(self,obj): # from IPython import embed # print "DEBUG NOW dict serializer" # embed() ##TODO pass def loads(self,s): return s

18.769231

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6

4d47dff9146088a33f8b61bbd687baf53210b85b

120

py

Python

tests/test_version.py

andrewmilligan/fec-filing-iterator

7ac2da9561ea9f346f316dffc72c1a42b2c914eb

[ "0BSD" ]

null

tests/test_version.py

andrewmilligan/fec-filing-iterator

7ac2da9561ea9f346f316dffc72c1a42b2c914eb

[ "0BSD" ]

null

tests/test_version.py

andrewmilligan/fec-filing-iterator

7ac2da9561ea9f346f316dffc72c1a42b2c914eb

[ "0BSD" ]

null

from fec_filing_iterator import _version as version def test_version(): assert len(version.__version_info__) == 3

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4d802c5be4c55e7abd73ca411d27649e2f9fdbb9

123

py

Python

python/fastquant/__init__.py

rafmacalaba/fastquant

b3436c8737a4ab1b5d555f7cd34fba9c406cad0a

[ "MIT" ]

3

2021-03-28T07:55:46.000Z

2021-03-29T04:52:12.000Z

python/fastquant/__init__.py

rafmacalaba/fastquant

b3436c8737a4ab1b5d555f7cd34fba9c406cad0a

[ "MIT" ]

null

python/fastquant/__init__.py

rafmacalaba/fastquant

b3436c8737a4ab1b5d555f7cd34fba9c406cad0a

[ "MIT" ]

null

from .fastquant import * from .disclosures import * from .strategies import * from .network import * from .config import *

20.5

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6.2

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4dfb60e3d6a308fdcaaa78bb3395cf04f3b6592a

229

py

Python

Unimodal/Eval/__init__.py

hasamkhalid/FakeAVCeleb

0d8d22a44e1750dd2885c5741d7b0a7796304a99

[ "MIT" ]

9

2021-09-20T02:07:38.000Z

2022-01-03T07:54:01.000Z

Unimodal/Eval/__init__.py

alsgkals2/FakeAVCeleb

0d8d22a44e1750dd2885c5741d7b0a7796304a99

[ "MIT" ]

null

Unimodal/Eval/__init__.py

alsgkals2/FakeAVCeleb

0d8d22a44e1750dd2885c5741d7b0a7796304a99

[ "MIT" ]

4

2021-09-12T09:30:42.000Z

2021-12-30T10:34:01.000Z

__all__ = ['Eval_MesoInceptionNet','Eval_MesoNet','Eval_Xception','Eval_F3Net','Eval_EfficientB0','Eval_VGG16']#TO BE MODIFIED WITH BOTTOM TEXT # __all__ = ['Eval_MesoInceptionNet','Eval_MesoNet','Eval_Headpose','Eval_Xception']

76.333333

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1

0

1

0

null

0

6

12eb664861eba4fea78f79aea4baf5b96c2059d1

39

py

Python

CSGM/__init__.py

PSCLab-ASU/OpenICS

e8f639f9278ce88c98f14daf026a56395cb64ca9

[ "CC0-1.0" ]

13

2021-03-03T13:13:34.000Z

2022-01-13T12:02:59.000Z

LDAMP/__init__.py

PSCLab-ASU/OpenICS

e8f639f9278ce88c98f14daf026a56395cb64ca9

[ "CC0-1.0" ]

null

LDAMP/__init__.py

PSCLab-ASU/OpenICS

e8f639f9278ce88c98f14daf026a56395cb64ca9

[ "CC0-1.0" ]

2

2021-03-04T12:16:27.000Z

2021-05-09T03:07:44.000Z

from . import * from .main import main

13

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null

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6

420355a8e34070fe81b798bae1fee36abf381b35

56,717

py

Python

ee/clickhouse/queries/funnels/test/test_funnel_correlation.py

rightlyip/posthog

c00ad7a2b02df68930ca332675fc04ce4ed83a60

[ "MIT" ]

null

ee/clickhouse/queries/funnels/test/test_funnel_correlation.py

rightlyip/posthog

c00ad7a2b02df68930ca332675fc04ce4ed83a60

[ "MIT" ]

null

ee/clickhouse/queries/funnels/test/test_funnel_correlation.py

rightlyip/posthog

c00ad7a2b02df68930ca332675fc04ce4ed83a60

[ "MIT" ]

null

import unittest from typing import List from uuid import uuid4 from rest_framework.exceptions import ValidationError from ee.clickhouse.models.event import create_event from ee.clickhouse.models.group import create_group from ee.clickhouse.queries.funnels.funnel_correlation import EventContingencyTable, EventStats, FunnelCorrelation from ee.clickhouse.queries.funnels.funnel_correlation_persons import FunnelCorrelationPersons from ee.clickhouse.util import ClickhouseTestMixin, snapshot_clickhouse_queries from posthog.constants import INSIGHT_FUNNELS from posthog.models.element import Element from posthog.models.filters import Filter from posthog.models.group_type_mapping import GroupTypeMapping from posthog.models.person import Person from posthog.models.property import Property from posthog.test.base import APIBaseTest, test_with_materialized_columns def _create_person(**kwargs): person = Person.objects.create(**kwargs) return Person(id=person.uuid, uuid=person.uuid) def _create_event(**kwargs): kwargs.update({"event_uuid": uuid4()}) create_event(**kwargs) class TestClickhouseFunnelCorrelation(ClickhouseTestMixin, APIBaseTest): maxDiff = None def _get_people_for_event(self, filter: Filter, event_name: str, properties=None, success=True): person_filter = filter.with_data( { "funnel_correlation_person_entity": {"id": event_name, "type": "events", "properties": properties}, "funnel_correlation_person_converted": "TrUe" if success else "falSE", } ) results, _ = FunnelCorrelationPersons(person_filter, self.team).run() return [row["uuid"] for row in results] def _get_people_for_property(self, filter: Filter, property_values: list, success=True): person_filter = filter.with_data( { "funnel_correlation_property_values": [ {"key": prop, "value": value, "type": "person"} for prop, value in property_values ], "funnel_correlation_person_converted": "TrUe" if success else "falSE", } ) results, _ = FunnelCorrelationPersons(person_filter, self.team).run() return [row["uuid"] for row in results] def test_basic_funnel_correlation_with_events(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "events", } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) for i in range(10): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) if i % 2 == 0: _create_event( team=self.team, event="positively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) for i in range(10, 20): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) if i % 2 == 0: _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore expected_odds_ratios = [11, 1 / 11] for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual( result, [ { "event": "positively_related", "success_count": 5, "failure_count": 0, # "odds_ratio": 11.0, "correlation_type": "success", }, { "event": "negatively_related", "success_count": 0, "failure_count": 5, # "odds_ratio": 1 / 11, "correlation_type": "failure", }, ], ) self.assertEqual(len(self._get_people_for_event(filter, "positively_related")), 5) self.assertEqual(len(self._get_people_for_event(filter, "positively_related", success=False)), 0) self.assertEqual(len(self._get_people_for_event(filter, "negatively_related", success=False)), 5) self.assertEqual(len(self._get_people_for_event(filter, "negatively_related")), 0) # Now exclude positively_related filter = filter.with_data({"funnel_correlation_exclude_event_names": ["positively_related"]}) correlation = FunnelCorrelation(filter, self.team) result = correlation._run()[0] odds_ratio = result[0].pop("odds_ratio") # type: ignore expected_odds_ratio = 1 / 11 self.assertAlmostEqual(odds_ratio, expected_odds_ratio) self.assertEqual( result, [ { "event": "negatively_related", "success_count": 0, "failure_count": 5, # "odds_ratio": 1 / 11, "correlation_type": "failure", }, ], ) # Getting specific people isn't affected by exclude_events self.assertEqual(len(self._get_people_for_event(filter, "positively_related")), 5) self.assertEqual(len(self._get_people_for_event(filter, "positively_related", success=False)), 0) self.assertEqual(len(self._get_people_for_event(filter, "negatively_related", success=False)), 5) self.assertEqual(len(self._get_people_for_event(filter, "negatively_related")), 0) @snapshot_clickhouse_queries def test_funnel_correlation_with_events_and_groups(self): GroupTypeMapping.objects.create(team=self.team, group_type="organization", group_type_index=0) create_group(team_id=self.team.pk, group_type_index=0, group_key="org:5", properties={"industry": "finance"}) create_group(team_id=self.team.pk, group_type_index=0, group_key="org:7", properties={"industry": "finance"}) for i in range(10, 20): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", properties={"$group_0": f"org:{i}"}, ) if i % 2 == 0: _create_event( team=self.team, event="positively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={"$group_0": f"org:{i}"}, ) # this event shouldn't show up when dealing with groups _create_event( team=self.team, event="positively_related_without_group", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", properties={"$group_0": f"org:{i}"}, ) # one fail group _create_person(distinct_ids=[f"user_fail"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_fail", timestamp="2020-01-02T14:00:00Z", properties={"$group_0": f"org:5"}, ) _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={"$group_0": f"org:5"}, ) # one success group with same filter property _create_person(distinct_ids=[f"user_succ"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_succ", timestamp="2020-01-02T14:00:00Z", properties={"$group_0": f"org:7"}, ) _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={"$group_0": f"org:7"}, ) _create_event( team=self.team, event="paid", distinct_id=f"user_succ", timestamp="2020-01-04T14:00:00Z", properties={"$group_0": f"org:7"}, ) filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "events", "aggregation_group_type_index": 0, } filter = Filter(data=filters) result = FunnelCorrelation(filter, self.team)._run()[0] odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore expected_odds_ratios = [12 / 7, 1 / 11] for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual( result, [ { "event": "positively_related", "success_count": 5, "failure_count": 0, # "odds_ratio": 12/7, "correlation_type": "success", }, { "event": "negatively_related", "success_count": 1, "failure_count": 1, # "odds_ratio": 1 / 11, "correlation_type": "failure", }, ], ) # Now exclude all groups in positive filter = filter.with_data( {"properties": [{"key": "industry", "value": "finance", "type": "group", "group_type_index": 0}],} ) result = FunnelCorrelation(filter, self.team)._run()[0] odds_ratio = result[0].pop("odds_ratio") # type: ignore expected_odds_ratio = 1 # success total and failure totals remove other groups too self.assertAlmostEqual(odds_ratio, expected_odds_ratio) self.assertEqual( result, [ { "event": "negatively_related", "success_count": 1, "failure_count": 1, # "odds_ratio": 1, "correlation_type": "failure", }, ], ) @test_with_materialized_columns(event_properties=[], person_properties=["$browser"]) @snapshot_clickhouse_queries def test_basic_funnel_correlation_with_properties(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "properties", "funnel_correlation_names": ["$browser"], } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) for i in range(10): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Positive"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) for i in range(10, 20): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Negative"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) if i % 2 == 0: _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) # One Positive with failure _create_person(distinct_ids=[f"user_fail"], team_id=self.team.pk, properties={"$browser": "Positive"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_fail", timestamp="2020-01-02T14:00:00Z", ) # One Negative with success _create_person(distinct_ids=[f"user_succ"], team_id=self.team.pk, properties={"$browser": "Negative"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_succ", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_succ", timestamp="2020-01-04T14:00:00Z", ) result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore # Success Total = 11, Failure Total = 11 # # Browser::Positive # Success: 10 # Failure: 1 # Browser::Negative # Success: 1 # Failure: 10 prior_count = 1 expected_odds_ratios = [ ((10 + prior_count) / (1 + prior_count)) * ((11 - 1 + prior_count) / (11 - 10 + prior_count)), ((1 + prior_count) / (10 + prior_count)) * ((11 - 10 + prior_count) / (11 - 1 + prior_count)), ] for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual( result, [ { "event": "$browser::Positive", "success_count": 10, "failure_count": 1, # "odds_ratio": 121/4, "correlation_type": "success", }, { "event": "$browser::Negative", "success_count": 1, "failure_count": 10, # "odds_ratio": 4/121, "correlation_type": "failure", }, ], ) self.assertEqual(len(self._get_people_for_property(filter, [("$browser", "Positive")])), 10) self.assertEqual(len(self._get_people_for_property(filter, [("$browser", "Positive")], False)), 1) self.assertEqual(len(self._get_people_for_property(filter, [("$browser", "Negative")])), 1) self.assertEqual(len(self._get_people_for_property(filter, [("$browser", "Negative")], False)), 10) @test_with_materialized_columns(event_properties=[], person_properties=["$browser"]) @snapshot_clickhouse_queries def test_funnel_correlation_with_properties_and_groups(self): GroupTypeMapping.objects.create(team=self.team, group_type="organization", group_type_index=0) for i in range(10): create_group( team_id=self.team.pk, group_type_index=0, group_key=f"org:{i}", properties={"industry": "positive"} ) _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Positive"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", properties={"$group_0": f"org:{i}"}, ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", properties={"$group_0": f"org:{i}"}, ) for i in range(10, 20): create_group( team_id=self.team.pk, group_type_index=0, group_key=f"org:{i}", properties={"industry": "negative"} ) _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Negative"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", properties={"$group_0": f"org:{i}"}, ) if i % 2 == 0: _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={"$group_0": f"org:{i}"}, ) # One Positive with failure create_group( team_id=self.team.pk, group_type_index=0, group_key=f"org:fail", properties={"industry": "positive"} ) _create_person(distinct_ids=[f"user_fail"], team_id=self.team.pk, properties={"$browser": "Positive"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_fail", timestamp="2020-01-02T14:00:00Z", properties={"$group_0": f"org:fail"}, ) # One Negative with success create_group( team_id=self.team.pk, group_type_index=0, group_key=f"org:succ", properties={"industry": "negative"} ) _create_person(distinct_ids=[f"user_succ"], team_id=self.team.pk, properties={"$browser": "Negative"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_succ", timestamp="2020-01-02T14:00:00Z", properties={"$group_0": f"org:succ"}, ) _create_event( team=self.team, event="paid", distinct_id=f"user_succ", timestamp="2020-01-04T14:00:00Z", properties={"$group_0": f"org:succ"}, ) filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "properties", "funnel_correlation_names": ["industry"], "aggregation_group_type_index": 0, } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore # Success Total = 11, Failure Total = 11 # # Industry::Positive # Success: 10 # Failure: 1 # Industry::Negative # Success: 1 # Failure: 10 prior_count = 1 expected_odds_ratios = [ ((10 + prior_count) / (1 + prior_count)) * ((11 - 1 + prior_count) / (11 - 10 + prior_count)), ((1 + prior_count) / (10 + prior_count)) * ((11 - 10 + prior_count) / (11 - 1 + prior_count)), ] for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual( result, [ { "event": "industry::positive", "success_count": 10, "failure_count": 1, # "odds_ratio": 121/4, "correlation_type": "success", }, { "event": "industry::negative", "success_count": 1, "failure_count": 10, # "odds_ratio": 4/121, "correlation_type": "failure", }, ], ) # test with `$all` as property # _run property correlation with filter on all properties filter = filter.with_data({"funnel_correlation_names": ["$all"]}) correlation = FunnelCorrelation(filter, self.team) new_result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in new_result] # type: ignore for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual(new_result, result) def test_no_divide_by_zero_errors(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) for i in range(2): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Positive"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) # failure count for this event is 0 _create_event( team=self.team, event="positive", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) for i in range(2, 4): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Negative"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) if i % 2 == 0: # success count for this event is 0 _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) results = correlation._run() self.assertFalse(results[1]) result = results[0] odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore expected_odds_ratios = [9, 1 / 3] for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual( result, [ { "event": "positive", "success_count": 2, "failure_count": 0, # "odds_ratio": 9.0, "correlation_type": "success", }, { "event": "negatively_related", "success_count": 0, "failure_count": 1, # "odds_ratio": 1 / 3, "correlation_type": "failure", }, ], ) def test_correlation_with_properties_raises_validation_error(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "properties", # "funnel_correlation_names": ["$browser"], missing value } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) _create_person(distinct_ids=[f"user_1"], team_id=self.team.pk, properties={"$browser": "Positive"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_1", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="rick", distinct_id=f"user_1", timestamp="2020-01-03T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_1", timestamp="2020-01-04T14:00:00Z", ) with self.assertRaises(ValidationError): correlation._run() filter = filter.with_data({"funnel_correlation_type": "event_with_properties"}) # missing "funnel_correlation_event_names": ["rick"], with self.assertRaises(ValidationError): FunnelCorrelation(filter, self.team)._run() @test_with_materialized_columns(event_properties=[], person_properties=["$browser"], verify_no_jsonextract=False) def test_correlation_with_multiple_properties(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "properties", "funnel_correlation_names": ["$browser", "$nice"], } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) # 5 successful people with both properties for i in range(5): _create_person( distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Positive", "$nice": "very"} ) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) # 10 successful people with some different properties for i in range(5, 15): _create_person( distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Positive", "$nice": "not"} ) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) # 5 Unsuccessful people with some common properties for i in range(15, 20): _create_person( distinct_ids=[f"user_{i}"], team_id=self.team.pk, properties={"$browser": "Negative", "$nice": "smh"} ) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) # One Positive with failure, no $nice property _create_person(distinct_ids=[f"user_fail"], team_id=self.team.pk, properties={"$browser": "Positive"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_fail", timestamp="2020-01-02T14:00:00Z", ) # One Negative with success, no $nice property _create_person(distinct_ids=[f"user_succ"], team_id=self.team.pk, properties={"$browser": "Negative"}) _create_event( team=self.team, event="user signed up", distinct_id=f"user_succ", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_succ", timestamp="2020-01-04T14:00:00Z", ) result = correlation._run()[0] # Success Total = 5 + 10 + 1 = 16 # Failure Total = 5 + 1 = 6 # Add 1 for priors odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore expected_odds_ratios = [ (16 / 2) * ((7 - 1) / (17 - 15)), (11 / 1) * ((7 - 0) / (17 - 10)), (6 / 1) * ((7 - 0) / (17 - 5)), (1 / 6) * ((7 - 5) / (17 - 0)), (2 / 6) * ((7 - 5) / (17 - 1)), (2 / 2) * ((7 - 1) / (17 - 1)), ] # (success + 1) / (failure + 1) for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) expected_result = [ { "event": "$browser::Positive", "success_count": 15, "failure_count": 1, # "odds_ratio": 24, "correlation_type": "success", }, { "event": "$nice::not", "success_count": 10, "failure_count": 0, # "odds_ratio": 11, "correlation_type": "success", }, { "event": "$nice::very", "success_count": 5, "failure_count": 0, # "odds_ratio": 3.5, "correlation_type": "success", }, { "event": "$nice::smh", "success_count": 0, "failure_count": 5, # "odds_ratio": 0.0196078431372549, "correlation_type": "failure", }, { "event": "$browser::Negative", "success_count": 1, "failure_count": 5, # "odds_ratio": 0.041666666666666664, "correlation_type": "failure", }, { "event": "$nice::", "success_count": 1, "failure_count": 1, # "odds_ratio": 0.375, "correlation_type": "failure", }, ] self.assertEqual(result, expected_result) # _run property correlation with filter on all properties filter = filter.with_data({"funnel_correlation_names": ["$all"]}) correlation = FunnelCorrelation(filter, self.team) new_result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in new_result] # type: ignore new_expected_odds_ratios = expected_odds_ratios[:-1] new_expected_result = expected_result[:-1] # When querying all properties, we don't consider properties that don't exist for part of the data # since users aren't explicitly asking for that property. Thus, # We discard $nice:: because it's an empty result set for odds, expected_odds in zip(odds_ratios, new_expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual(new_result, new_expected_result) filter = filter.with_data({"funnel_correlation_exclude_names": ["$browser"]}) # search for $all but exclude $browser correlation = FunnelCorrelation(filter, self.team) new_result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in new_result] # type: ignore new_expected_odds_ratios = expected_odds_ratios[1:4] # choosing the $nice property values new_expected_result = expected_result[1:4] for odds, expected_odds in zip(odds_ratios, new_expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual(new_result, new_expected_result) self.assertEqual(len(self._get_people_for_property(filter, [("$nice", "not")])), 10) self.assertEqual(len(self._get_people_for_property(filter, [("$nice", "")], False)), 1) self.assertEqual(len(self._get_people_for_property(filter, [("$nice", "very")])), 5) def test_discarding_insignificant_events(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "events", } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) for i in range(10): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) if i % 2 == 0: _create_event( team=self.team, event="positively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) if i % 10 == 0: _create_event( team=self.team, event="low_sig_positively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:20:00Z", ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) for i in range(10, 20): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) if i % 2 == 0: _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) if i % 5 == 0: _create_event( team=self.team, event="low_sig_negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", ) # Total 10 positive, 10 negative # low sig count = 1 and 2, high sig count >= 5 # Thus, to discard the low sig count, % needs to be >= 10%, or count >= 2 # Discard both due to % FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.11 FunnelCorrelation.MIN_PERSON_COUNT = 25 result = correlation._run()[0] self.assertEqual(len(result), 2) def test_events_within_conversion_window_for_correlation(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "funnel_window_interval": "10", "funnel_window_interval_unit": "minute", "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "events", } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) _create_person(distinct_ids=["user_successful"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id="user_successful", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="positively_related", distinct_id="user_successful", timestamp="2020-01-02T14:02:00Z", ) _create_event( team=self.team, event="paid", distinct_id="user_successful", timestamp="2020-01-02T14:06:00Z", ) _create_person(distinct_ids=["user_dropoff"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id="user_dropoff", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="NOT_negatively_related", distinct_id="user_dropoff", timestamp="2020-01-02T14:15:00Z", # event happened outside conversion window ) result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore expected_odds_ratios = [4] for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual( result, [ { "event": "positively_related", "success_count": 1, "failure_count": 0, # "odds_ratio": 4.0, "correlation_type": "success", }, ], ) @test_with_materialized_columns(["blah", "signup_source"], verify_no_jsonextract=False) def test_funnel_correlation_with_event_properties(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "event_with_properties", "funnel_correlation_event_names": ["positively_related", "negatively_related"], } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) for i in range(10): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) if i % 2 == 0: _create_event( team=self.team, event="positively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={"signup_source": "facebook" if i % 4 == 0 else "email", "blah": "value_bleh"}, ) # source: email occurs only twice, so would be discarded from result set _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) for i in range(10, 20): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) if i % 2 == 0: _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={"signup_source": "shazam" if i % 6 == 0 else "email"}, ) # source: shazam occurs only once, so would be discarded from result set result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore expected_odds_ratios = [11, 5.5, 2 / 11] for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual( result, [ { "event": "positively_related::blah::value_bleh", "success_count": 5, "failure_count": 0, # "odds_ratio": 11.0, "correlation_type": "success", }, { "event": "positively_related::signup_source::facebook", "success_count": 3, "failure_count": 0, # "odds_ratio": 5.5, "correlation_type": "success", }, { "event": "negatively_related::signup_source::email", "success_count": 0, "failure_count": 3, # "odds_ratio": 0.18181818181818182, "correlation_type": "failure", }, ], ) self.assertEqual(len(self._get_people_for_event(filter, "positively_related", {"blah": "value_bleh"})), 5) self.assertEqual( len(self._get_people_for_event(filter, "positively_related", {"signup_source": "facebook"})), 3 ) self.assertEqual( len(self._get_people_for_event(filter, "positively_related", {"signup_source": "facebook"}, False)), 0 ) self.assertEqual( len(self._get_people_for_event(filter, "negatively_related", {"signup_source": "email"}, False)), 3 ) @test_with_materialized_columns(["blah", "signup_source"], verify_no_jsonextract=False) @snapshot_clickhouse_queries def test_funnel_correlation_with_event_properties_and_groups(self): # same test as test_funnel_correlation_with_event_properties but with events attached to groups GroupTypeMapping.objects.create(team=self.team, group_type="organization", group_type_index=1) for i in range(10): create_group( team_id=self.team.pk, group_type_index=1, group_key=f"org:{i}", properties={"industry": "positive"} ) _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", properties={"$group_1": f"org:{i}"}, ) if i % 2 == 0: _create_event( team=self.team, event="positively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={ "signup_source": "facebook" if i % 4 == 0 else "email", "blah": "value_bleh", "$group_1": f"org:{i}", }, ) # source: email occurs only twice, so would be discarded from result set _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", properties={"$group_1": f"org:{i}"}, ) for i in range(10, 20): create_group( team_id=self.team.pk, group_type_index=1, group_key=f"org:{i}", properties={"industry": "positive"} ) _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", properties={"$group_1": f"org:{i}"}, ) if i % 2 == 0: _create_event( team=self.team, event="negatively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={"signup_source": "shazam" if i % 6 == 0 else "email", "$group_1": f"org:{i}"}, ) # source: shazam occurs only once, so would be discarded from result set filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "aggregation_group_type_index": 1, "funnel_correlation_type": "event_with_properties", "funnel_correlation_event_names": ["positively_related", "negatively_related"], } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) result = correlation._run()[0] odds_ratios = [item.pop("odds_ratio") for item in result] # type: ignore expected_odds_ratios = [11, 5.5, 2 / 11] for odds, expected_odds in zip(odds_ratios, expected_odds_ratios): self.assertAlmostEqual(odds, expected_odds) self.assertEqual( result, [ { "event": "positively_related::blah::value_bleh", "success_count": 5, "failure_count": 0, # "odds_ratio": 11.0, "correlation_type": "success", }, { "event": "positively_related::signup_source::facebook", "success_count": 3, "failure_count": 0, # "odds_ratio": 5.5, "correlation_type": "success", }, { "event": "negatively_related::signup_source::email", "success_count": 0, "failure_count": 3, # "odds_ratio": 0.18181818181818182, "correlation_type": "failure", }, ], ) def test_funnel_correlation_with_event_properties_exclusions(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "event_with_properties", "funnel_correlation_event_names": ["positively_related"], "funnel_correlation_event_exclude_property_names": ["signup_source"], } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) # Need more than 2 events to get a correlation for i in range(3): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="positively_related", distinct_id=f"user_{i}", timestamp="2020-01-03T14:00:00Z", properties={"signup_source": "facebook", "blah": "value_bleh"}, ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) # Atleast one person that fails, to ensure we get results _create_person(distinct_ids=[f"user_fail"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_fail", timestamp="2020-01-02T14:00:00Z", ) result = correlation._run()[0] self.assertEqual( result, [ { "event": "positively_related::blah::value_bleh", "success_count": 3, "failure_count": 0, "odds_ratio": 8, "correlation_type": "success", }, # missing signup_source, as expected ], ) self.assertEqual(len(self._get_people_for_event(filter, "positively_related", {"blah": "value_bleh"})), 3) # If you search for persons with a specific property, even if excluded earlier, you should get them self.assertEqual( len(self._get_people_for_event(filter, "positively_related", {"signup_source": "facebook"})), 3 ) @test_with_materialized_columns(["$event_type", "signup_source"]) def test_funnel_correlation_with_event_properties_autocapture(self): filters = { "events": [ {"id": "user signed up", "type": "events", "order": 0}, {"id": "paid", "type": "events", "order": 1}, ], "insight": INSIGHT_FUNNELS, "date_from": "2020-01-01", "date_to": "2020-01-14", "funnel_correlation_type": "event_with_properties", "funnel_correlation_event_names": ["$autocapture"], } filter = Filter(data=filters) correlation = FunnelCorrelation(filter, self.team) # Need a minimum of 3 hits to get a correlation result for i in range(6): _create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_{i}", timestamp="2020-01-02T14:00:00Z", ) _create_event( team=self.team, event="$autocapture", distinct_id=f"user_{i}", elements=[Element(nth_of_type=1, nth_child=0, tag_name="a", href="/movie")], timestamp="2020-01-03T14:00:00Z", properties={"signup_source": "email", "$event_type": "click"}, ) # Test two different types of autocapture elements, with different counts, so we can accurately test results if i % 2 == 0: _create_event( team=self.team, event="$autocapture", distinct_id=f"user_{i}", elements=[Element(nth_of_type=1, nth_child=0, tag_name="button", text="Pay $10")], timestamp="2020-01-03T14:00:00Z", properties={"signup_source": "facebook", "$event_type": "submit"}, ) _create_event( team=self.team, event="paid", distinct_id=f"user_{i}", timestamp="2020-01-04T14:00:00Z", ) # Atleast one person that fails, to ensure we get results _create_person(distinct_ids=[f"user_fail"], team_id=self.team.pk) _create_event( team=self.team, event="user signed up", distinct_id=f"user_fail", timestamp="2020-01-02T14:00:00Z", ) result = correlation._run()[0] # $autocapture results only return elements chain self.assertEqual( result, [ { "event": '$autocapture::elements_chain::click__~~__a:href="/movie"nth-child="0"nth-of-type="1"', "success_count": 6, "failure_count": 0, "odds_ratio": 14.0, "correlation_type": "success", }, { "event": '$autocapture::elements_chain::submit__~~__button:nth-child="0"nth-of-type="1"text="Pay $10"', "success_count": 3, "failure_count": 0, "odds_ratio": 2.0, "correlation_type": "success", }, ], ) self.assertEqual(len(self._get_people_for_event(filter, "$autocapture", {"signup_source": "facebook"})), 3) self.assertEqual(len(self._get_people_for_event(filter, "$autocapture", {"$event_type": "click"})), 6) self.assertEqual( len( self._get_people_for_event( filter, "$autocapture", [ {"key": "tag_name", "operator": "exact", "type": "element", "value": "button"}, {"key": "text", "operator": "exact", "type": "element", "value": "Pay $10"}, ], ) ), 3, ) self.assertEqual( len( self._get_people_for_event( filter, "$autocapture", [ {"key": "tag_name", "operator": "exact", "type": "element", "value": "a"}, {"key": "href", "operator": "exact", "type": "element", "value": "/movie"}, ], ) ), 6, ) class TestCorrelationFunctions(unittest.TestCase): def test_are_results_insignificant(self): # Same setup as above test: test_discarding_insignificant_events contingency_tables = [ EventContingencyTable( event="negatively_related", visited=EventStats(success_count=0, failure_count=5), success_total=10, failure_total=10, ), EventContingencyTable( event="positively_related", visited=EventStats(success_count=5, failure_count=0), success_total=10, failure_total=10, ), EventContingencyTable( event="low_sig_negatively_related", visited=EventStats(success_count=0, failure_count=2), success_total=10, failure_total=10, ), EventContingencyTable( event="low_sig_positively_related", visited=EventStats(success_count=1, failure_count=0), success_total=10, failure_total=10, ), ] # Discard both low_sig due to % FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.11 FunnelCorrelation.MIN_PERSON_COUNT = 25 result = [ 1 for contingency_table in contingency_tables if not FunnelCorrelation.are_results_insignificant(contingency_table) ] self.assertEqual(len(result), 2) # Discard one low_sig due to % FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.051 FunnelCorrelation.MIN_PERSON_COUNT = 25 result = [ 1 for contingency_table in contingency_tables if not FunnelCorrelation.are_results_insignificant(contingency_table) ] self.assertEqual(len(result), 3) # Discard both due to count FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.5 FunnelCorrelation.MIN_PERSON_COUNT = 3 result = [ 1 for contingency_table in contingency_tables if not FunnelCorrelation.are_results_insignificant(contingency_table) ] self.assertEqual(len(result), 2) # Discard one due to count FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.5 FunnelCorrelation.MIN_PERSON_COUNT = 2 result = [ 1 for contingency_table in contingency_tables if not FunnelCorrelation.are_results_insignificant(contingency_table) ] self.assertEqual(len(result), 3) # Discard everything due to % FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.5 FunnelCorrelation.MIN_PERSON_COUNT = 100 result = [ 1 for contingency_table in contingency_tables if not FunnelCorrelation.are_results_insignificant(contingency_table) ] self.assertEqual(len(result), 0) # Discard everything due to count FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.5 FunnelCorrelation.MIN_PERSON_COUNT = 6 result = [ 1 for contingency_table in contingency_tables if not FunnelCorrelation.are_results_insignificant(contingency_table) ] self.assertEqual(len(result), 0)

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42216e3109924961c75fd036232e1bdfd3fffa75

241

py

Python

RecoVertex/BeamSpotProducer/python/BeamSpotNominalCollision_IntDB_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

RecoVertex/BeamSpotProducer/python/BeamSpotNominalCollision_IntDB_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

RecoVertex/BeamSpotProducer/python/BeamSpotNominalCollision_IntDB_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms from RecoVertex.BeamSpotProducer.BeamSpotNominalCollision_cfi import * BeamSpotNominal.connect = 'frontier://cms_conditions_data/CMS_COND_20X_BEAMSPOT' ##cms_conditions_data/CMS_COND_20X_BEAMSPOT"

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423cfe0d6b5dd54f8f80ae0720cd6a5f989f59bc

224

py

Python

holobot/extensions/moderation/reactive/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

1

2021-05-24T00:17:46.000Z

holobot/extensions/moderation/reactive/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

41

2021-03-24T22:50:09.000Z

2021-12-17T12:15:13.000Z

holobot/extensions/moderation/reactive/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

null

from .log_on_moderation_command_used import LogOnModerationCommandUsed from .log_on_moderation_menu_item_used import LogOnModerationMenuItemUsed from .punish_on_enough_warns_accumulated import PunishOnEnoughWarnsAccumulated

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4289367c645608f84ff4da465338d39173dda85c

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py

Python

plugins/jinja2/date.py

Saevon/saevon.github.io

65609f9902740d6b873e4b5c5e8c295c9a0b62cb

[ "MIT" ]

null

plugins/jinja2/date.py

Saevon/saevon.github.io

65609f9902740d6b873e4b5c5e8c295c9a0b62cb

[ "MIT" ]

null

plugins/jinja2/date.py

Saevon/saevon.github.io

65609f9902740d6b873e4b5c5e8c295c9a0b62cb

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: UTF-8 -*- from __future__ import unicode_literals def date_to_xmlschema(date): return date.isoformat()

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null

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1

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1

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6

429c9ed0be93699a5978fdb2eeec5435608be410

22

py

Python

dashboard/influxs/__init__.py

mr2cef/open_data_tyrol

87ff1d8e00b65c26995c9ed3fa69be1a8698746c

[ "MIT" ]

1

2021-08-20T18:17:41.000Z

dashboard/influxs/__init__.py

mr2cef/open_data_tyrol

87ff1d8e00b65c26995c9ed3fa69be1a8698746c

[ "MIT" ]

null

dashboard/influxs/__init__.py

mr2cef/open_data_tyrol

87ff1d8e00b65c26995c9ed3fa69be1a8698746c

[ "MIT" ]

null

from ._influx import *

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1

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6

42a764e5d0d39f5967ebdc4e38d7dcdb6db5fad4

67

py

Python

Python/imports/test.py

elaelheni/INF5190-H22

51f29399fd865b6a8de7dc65865fd1a99b9e5664

[ "Apache-2.0" ]

2

2022-02-14T21:19:01.000Z

2022-03-25T04:32:18.000Z

Python/imports/test.py

elaelheni/INF5190-H22

51f29399fd865b6a8de7dc65865fd1a99b9e5664

[ "Apache-2.0" ]

null

Python/imports/test.py

elaelheni/INF5190-H22

51f29399fd865b6a8de7dc65865fd1a99b9e5664

[ "Apache-2.0" ]

3

2022-02-20T16:34:09.000Z

2022-03-23T01:55:13.000Z

def test(): print("je teste des trucs") test() print(__name__)

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35ebe747c5dfe75209f7d1cb306d0f4ceb925048

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py

Python

src/euler_python_package/euler_python/medium/p417.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

src/euler_python_package/euler_python/medium/p417.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

src/euler_python_package/euler_python/medium/p417.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

def problem417(): pass

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35f46cd5096216a052cdf792b0f06bfef3f9d55c

86

py

Python

hypergeo/utils/__init__.py

jsleb333/hypergeometric_tail_inversion

7141d2bfec97f8d2e162a389531f01ce6e931b7c

[ "MIT" ]

null

hypergeo/utils/__init__.py

jsleb333/hypergeometric_tail_inversion

7141d2bfec97f8d2e162a389531f01ce6e931b7c

[ "MIT" ]

null

hypergeo/utils/__init__.py

jsleb333/hypergeometric_tail_inversion

7141d2bfec97f8d2e162a389531f01ce6e931b7c

[ "MIT" ]

null

from hypergeo.utils.utils import * from hypergeo.utils.func_to_cmd import func_to_cmd

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c40628b13a0f5a436b98498b7e662b20f50df183

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py

Python

strym/multimode/__init__.py

jmscslgroup/canviz

27a43277b4f8b265d2fd9961cade26fbf0415a45

[ "Unlicense" ]

7

2020-02-13T06:44:11.000Z

2022-01-31T00:43:55.000Z

strym/multimode/__init__.py

jmscslgroup/canviz

27a43277b4f8b265d2fd9961cade26fbf0415a45

[ "Unlicense" ]

27

2020-03-31T23:11:29.000Z

2022-03-30T00:09:19.000Z

strym/multimode/__init__.py

jmscslgroup/canviz

27a43277b4f8b265d2fd9961cade26fbf0415a45

[ "Unlicense" ]

6

2020-03-11T18:19:31.000Z

2022-01-24T23:13:43.000Z

from .platoons import platoons

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6

c4126b19471973090ef62013fc115832e9e22228

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py

Python

src/.ipynb_checkpoints/losses-checkpoint.py

pherrusa7/Traffic4cast_NeurIPS_2019

a5f1ce2bdbf116d29bd6b7810e164f895a30997e

[ "Apache-2.0" ]

2

2020-07-26T20:55:42.000Z

2020-07-28T22:35:22.000Z

src/losses.py

pherrusa7/Traffic4cast_NeurIPS_2019

a5f1ce2bdbf116d29bd6b7810e164f895a30997e

[ "Apache-2.0" ]

1

2021-09-14T07:15:36.000Z

src/.ipynb_checkpoints/losses-checkpoint.py

pherrusa7/Traffic4cast_NeurIPS_2019

a5f1ce2bdbf116d29bd6b7810e164f895a30997e

[ "Apache-2.0" ]

1

2020-05-28T08:26:06.000Z

import os from keras import models, activations, losses, optimizers from keras import backend as K import numpy as np from src.data import get_generators, format_bytes, data_postprocess, data_preprocess, exchange_HEADING, vec2tensor from src.data import write_data, create_directory_structure, data_2_submission_format EXTRA_DATA_MODEL, EXTRA_DATA_MODELwIN, RAEwSCwWSwINwCLF = "RAE_w_SC_WS", "RAE_w_SC_WS_wIN", "RAEwSCwWSwINwCLF" ###################################################### LOSS DEFINITIONS def softmax_axis(axis=2): """ axis=2 refers to dim=5 in tensor [None, 3, 5, 495, 436] """ def soft(x): return activations.softmax(x, axis=axis) return soft def cross_entropy_with_axis(axis_softmax=2): def ce_axis(y_true, y_pred): return K.categorical_crossentropy(y_true, y_pred, axis=axis_softmax) return ce_axis def get_recurrent_embedding_loss(y_true_z, y_pred_z, loss_weights): def r_emb_loss(y_true, y_pred): """ Embedding loss """ return loss_weights['predicted_emb']*losses.mean_squared_error(y_true_z, y_pred_z) return r_emb_loss def loss_with_latent_term(y_true_z, y_pred_z, loss_weights={'predicted_frames':1., 'predicted_emb':1.}): """ returns a loss function that takes into account 2 terms: 1. predicted vs. true loss 2. latent space manifold input: y_true_z : latent representation of future frames y_pred_z : latent prediction of current frames into the future """ def seq2seq_recurrent_loss(y_true, y_pred): # Reconstruction loss predicted_frames_loss = losses.mean_squared_error(y_true, y_pred) # Embedding loss predicted_emb_loss = losses.mean_squared_error(y_true_z, y_pred_z) print('y_true:', y_true.shape, 'y_pred:', y_pred.shape) print('y_true_z:', y_true_z.shape, 'y_pred_z:', y_pred_z.shape) print('predicted_frames_loss:', predicted_frames_loss.shape, 'predicted_emb_loss:', predicted_emb_loss.shape) return loss_weights['predicted_frames']*predicted_frames_loss #+ loss_weights['predicted_emb']*predicted_emb_loss return seq2seq_recurrent_loss def loss_with_latent_term_2(y_true_z, y_pred_z, loss_weights): """ returns a loss function that takes into account 2 terms: 1. predicted vs. true loss 2. latent space manifold input: y_true_z : latent representation of future frames y_pred_z : latent prediction of current frames into the future """ def seq2seq_recurrent_loss(y_true, y_pred): # Reconstruction loss predicted_frames_loss = K.mean(losses.mean_squared_error(y_true, y_pred)) # Embedding loss predicted_emb_loss = K.mean(losses.mean_squared_error(y_true_z, y_pred_z)) return loss_weights['predicted_frames']*predicted_frames_loss + loss_weights['predicted_emb']*predicted_emb_loss return seq2seq_recurrent_loss ###################################################### METRIC DEFINITIONS def MSE(x, y): """ MSE pixel-wise, preserving time-slots and channels input shape example: (48, 3, 3, 495, 436), first 3 is the number of time-slots, last three is the number of channels output shape example: (3, 3) """ return np.mean((x-y)**2, axis=(0, -1, -2)) def add_info(mse): mean = np.vstack((np.asarray([[' Speed ', ' Volume ', ' Heading ']]), mse.mean(axis=0))) mean = np.hstack((np.asarray([[' ', ' 5 minutes', '10 minutes', '15 minutes']]).T, mean)) return mean def print_eval(city, mse, mse_server, log_path): print('city:', city, 'shape:', mse.shape) print('mean mse:', mse.mean()) print(add_info(mse)) print('-----------------------------') print('mean mse like submission:', mse_server.mean()) print(add_info(mse_server)) # save all info as .npy np.save(log_path + city+'_mse_val.npy', mse) np.save(log_path + city+'_mse_server_val.npy', mse_server) # save mean by days info as csv for easy access np.savetxt(log_path + city+'_mse_val.csv', add_info(mse), delimiter=",", fmt='%s') np.savetxt(log_path + city+'_mse_server_val.csv', add_info(mse_server), delimiter=",", fmt='%s') def save_y_hat(y, y_hat): np.save('/home/pherruzo/projects/nips_traffic/models/y', y) np.save('/home/pherruzo/projects/nips_traffic/models/y_hat', y_hat) def model_evaluate(dataset, model, log_path, city, model_type, mask_path='/home/pherruzo/projects/nips_traffic/models/'): # Use a mask to make zero areas where no road pass-through binary_mask = np.moveaxis(np.load(mask_path+city+'_mask.npy'), -1, -3) # channels first binary_mask = np.expand_dims(np.expand_dims(binary_mask, axis=0), axis=0) # create dimension for samples and time-slots city_days_mse, city_days_mse_server = [], [] city_days_mse_server_clf, heading_acc, heading_acc_clf = [], [], [] total_batches = len(dataset) conv_and_clf, exchange_heading_for_last_seen_heading = "ConvLSTM+Clf", False clf_as_heading = False for i, sample in enumerate(dataset): print("Evaluating time-bin/time-slot batch {}/{}".format(i+1, total_batches)) ######## load data if model_type in ["ConvLSTM", "ConvLSTM+Clf"]: x, y, sample_weights = sample[0], sample[1]['convlstm_3'], sample[2] elif model_type in [EXTRA_DATA_MODEL, EXTRA_DATA_MODELwIN ]:#=='RAE_w_SC_WS': x, y, sample_weights = sample[0], sample[1]['Concat_predicted_frames'], sample[2] x['future_frames'] = x['prev_frames'][:, -3:] # take the last three elif model_type in [RAEwSCwWSwINwCLF]: x, y, sample_weights = sample[0], sample[1], sample[2] x['future_frames'] = x['prev_frames'][:, -3:] # take the last three y_clf = y['softmax_clf'] y = y['Concat_predicted_frames'] clf_as_heading = True else: x, y, sample_weights = sample[0]['prev_frames'], sample[1]['Concat_predicted_frames'], sample[2] # extra data depending on model if model_type == conv_and_clf: #ConvLSTM y_clf = sample[1]['softmax_clf'] clf_as_heading = True ######## predict and compute mse if model_type == conv_and_clf: #ConvLSTM y_hat, y_hat_clf = model.predict(x) elif model_type == 'RAE_w_SC': y_hat = model.predict([x, x]) # put in the same shape as all models y_hat = np.transpose(y_hat, (0, 1, 4, 2, 3)) y = np.transpose(y, (0, 1, 4, 2, 3)) elif model_type in [EXTRA_DATA_MODEL, EXTRA_DATA_MODELwIN ]:#=='RAE_w_SC_WS': y_hat = model.predict(x) # put in the same shape as all models (sadly it means channel first) y_hat = np.transpose(y_hat, (0, 1, 4, 2, 3)) y = np.transpose(y, (0, 1, 4, 2, 3)) elif model_type in [RAEwSCwWSwINwCLF]: y_hat, y_hat_clf = model.predict(x) # put in the same shape as all models (sadly it means channel first) y_hat = np.transpose(y_hat, (0, 1, 4, 2, 3)) y = np.transpose(y, (0, 1, 4, 2, 3)) else: y_hat = model.predict(x) if exchange_heading_for_last_seen_heading: # from the last frame in the sequence, get heading heading_last_frame = x[:, -1, -1] heading_last_frame = np.moveaxis(np.array([heading_last_frame, heading_last_frame, heading_last_frame]), 0, 1) # assign it as the heading for all predicted frames y_hat[:, :, -1] = heading_last_frame mse = MSE(y_hat, y) city_days_mse.append(mse) ###################### imitate evaluation in server to compute mse # 1. save output in range 0, 255 (integer) y_hat = data_postprocess(y_hat, binary_mask) #save_y_hat(y, y_hat) # 1. load input and rescale to 0, 1 (float) y_hat = data_preprocess(y_hat) mse = MSE(y_hat, y) city_days_mse_server.append(mse) # compute acc for the heading channel heading_acc.append((np.sum(y[:,:,-1]==y_hat[:,:,-1]))/y[:,:,-1].size) # print("Acc y_hat:", np.sum(y[:,:,-1]==y_hat[:,:,-1])/y[:,:,-1].size, "rmse:", np.mean((y[:,:,-1]-y_hat[:,:,-1])**2), mse.mean()) ###################### compute mse with clf if clf_as_heading: #ConvLSTM # transform HEADING vector to image and exchange HEADING dimension y_hat_clf = vec2tensor(y_hat_clf) y_hat_clf = exchange_HEADING(y_hat.copy(), y_hat_clf) mse = MSE(y_hat_clf, y) city_days_mse_server_clf.append(mse) # compute acc for the heading channel heading_acc_clf.append((np.sum(y[:,:,-1]==y_hat_clf[:,:,-1]))/y_hat_clf[:,:,-1].size) # print("Acc y_hat_clf:", np.sum(y[:,:,-1]==y_hat_clf[:,:,-1])/y_hat_clf[:,:,-1].size, "rmse:", np.mean((y[:,:,-1]-y_hat_clf[:,:,-1])**2), mse.mean()) # return y_hat, y_hat_clf, x, y, y_clf # convert arrays to numpy city_days_mse, city_days_mse_server = np.asarray(city_days_mse), np.asarray(city_days_mse_server) print_eval(city, city_days_mse, city_days_mse_server, log_path) if clf_as_heading: city_days_mse_server_clf = np.asarray(city_days_mse_server_clf) print('-----------------------------') print('mean mse with HEADING as clf:', city_days_mse_server_clf.mean()) print(add_info(city_days_mse_server_clf)) print("=========") # we compute average of average since all batches have the same number of samples (mb except the last one) print("Acc y_hat in HEADING:", np.asarray(heading_acc).mean()) if clf_as_heading: print("Acc y_hat_clf in HEADING:", np.asarray(heading_acc_clf).mean()) ###################################################### OUTPUT FILES GENERATION def write_submission_files(dataset, model, output_path, city, model_type, mask_path='/home/pherruzo/projects/nips_traffic/models/'): create_directory_structure(output_path, city) # Use a mask to make zero areas where no road pass-through binary_mask = np.moveaxis(np.load(mask_path+city+'_mask.npy'), -1, -3) # channels first binary_mask = np.expand_dims(np.expand_dims(binary_mask, axis=0), axis=0) # create dimension for samples and time-slots # params total_batches = len(dataset) conv_and_clf, exchange_heading_for_last_seen_heading, use_clf_as_heading = "ConvLSTM+Clf", False, False for i, sample in enumerate(dataset): # get name of the file and data f = sample[1] sample = sample[0] # 1. load data if model_type in ["ConvLSTM", "ConvLSTM+Clf"]: x, y, sample_weights = sample[0], sample[1]['convlstm_3'], sample[2] elif model_type in [EXTRA_DATA_MODEL, EXTRA_DATA_MODELwIN ]:#=='RAE_w_SC_WS': x, y, sample_weights = sample[0], sample[1]['Concat_predicted_frames'], sample[2] x['future_frames'] = x['prev_frames'][:, -3:] # take the last three elif model_type in [RAEwSCwWSwINwCLF]: x, y, sample_weights = sample[0], sample[1], sample[2] x['future_frames'] = x['prev_frames'][:, -3:] # take the last three y_clf = y['softmax_clf'] y = y['Concat_predicted_frames'] clf_as_heading = True else: x, y, sample_weights = sample[0]['prev_frames'], sample[1]['Concat_predicted_frames'], sample[2] # extra data depending on model if model_type == conv_and_clf: #ConvLSTM y_clf = sample[1]['softmax_clf'] clf_as_heading = True # 2. predict and compute mse if model_type == conv_and_clf: #ConvLSTM y_hat, y_hat_clf = model.predict(x) elif model_type == 'RAE_w_SC': y_hat = model.predict([x, x]) # put in the same shape as all models y_hat = np.transpose(y_hat, (0, 1, 4, 2, 3)) y = np.transpose(y, (0, 1, 4, 2, 3)) elif model_type in [EXTRA_DATA_MODEL, EXTRA_DATA_MODELwIN ]:#=='RAE_w_SC_WS': y_hat = model.predict(x) # put in the same shape as all models (sadly it means channel first) y_hat = np.transpose(y_hat, (0, 1, 4, 2, 3)) y = np.transpose(y, (0, 1, 4, 2, 3)) elif model_type in [RAEwSCwWSwINwCLF]: y_hat, y_hat_clf = model.predict(x) # put in the same shape as all models (sadly it means channel first) y_hat = np.transpose(y_hat, (0, 1, 4, 2, 3)) y = np.transpose(y, (0, 1, 4, 2, 3)) else: y_hat = model.predict(x) ###################### different heading predictions if exchange_heading_for_last_seen_heading: # from the last frame in the sequence, get heading heading_last_frame = x[:, -1, -1] heading_last_frame = np.moveaxis(np.array([heading_last_frame, heading_last_frame, heading_last_frame]), 0, 1) # assign it as the heading for all predicted frames y_hat[:, :, -1] = heading_last_frame print("Using last known heading for prediction") if use_clf_as_heading: #ConvLSTM # transform HEADING vector to image and exchange HEADING dimension y_hat_clf = vec2tensor(y_hat_clf) y_hat = exchange_HEADING(y_hat.copy(), y_hat_clf) print("Using clf as heading") # 3. transform data into submission format y_hat = data_2_submission_format(y_hat, binary_mask) # 4. generate output file path outfile = os.path.join(output_path, city, city+'_test', f.split('/')[-1]) write_data(y_hat, outfile) print("City:{}, just wrote file {}/{}: {}".format(city, i+1, total_batches, outfile)) def write_submission_files_bu(dataset, model, output_path, city, model_type, mask_path='/home/pherruzo/projects/nips_traffic/models/'): create_directory_structure(output_path, city) # Use a mask to make zero areas where no road pass-through binary_mask = np.moveaxis(np.load(mask_path+city+'_mask.npy'), -1, -3) # channels first binary_mask = np.expand_dims(np.expand_dims(binary_mask, axis=0), axis=0) # create dimension for samples and time-slots # params total_batches = len(dataset) conv_and_clf, exchange_heading_for_last_seen_heading, use_clf_as_heading = "ConvLSTM+Clf", False, False for i, sample in enumerate(dataset): # get name of the file and data f = sample[1] sample = sample[0] # 1. load data if model_type in ["ConvLSTM", "ConvLSTM+Clf"]: x, y, sample_weights = sample[0], sample[1]['convlstm_3'], sample[2] elif model_type in [EXTRA_DATA_MODEL, EXTRA_DATA_MODELwIN ]:#=='RAE_w_SC_WS': x, y, sample_weights = sample[0], sample[1]['Concat_predicted_frames'], sample[2] x['future_frames'] = x['prev_frames'][:, -3:] # take the last three else: x, y, sample_weights = sample[0]['prev_frames'], sample[1]['Concat_predicted_frames'], sample[2] # extra data depending on model if model_type == conv_and_clf: #ConvLSTM y_clf = sample[1]['softmax_clf'] # 2. predict and compute mse if model_type == conv_and_clf: #ConvLSTM y_hat, y_hat_clf = model.predict(x) elif model_type == 'RAE_w_SC': y_hat = model.predict([x, x]) # put in the same shape as all models y_hat = np.transpose(y_hat, (0, 1, 4, 2, 3)) y = np.transpose(y, (0, 1, 4, 2, 3)) elif model_type in [EXTRA_DATA_MODEL, EXTRA_DATA_MODELwIN ]:#=='RAE_w_SC_WS': y_hat = model.predict(x) # put in the same shape as all models (sadly it means channel first) y_hat = np.transpose(y_hat, (0, 1, 4, 2, 3)) y = np.transpose(y, (0, 1, 4, 2, 3)) else: y_hat = model.predict(x) ###################### different heading predictions if exchange_heading_for_last_seen_heading: # from the last frame in the sequence, get heading heading_last_frame = x[:, -1, -1] heading_last_frame = np.moveaxis(np.array([heading_last_frame, heading_last_frame, heading_last_frame]), 0, 1) # assign it as the heading for all predicted frames y_hat[:, :, -1] = heading_last_frame print("Using last known heading for prediction") if use_clf_as_heading: #ConvLSTM # transform HEADING vector to image and exchange HEADING dimension y_hat_clf = vec2tensor(y_hat_clf) y_hat = exchange_HEADING(y_hat.copy(), y_hat_clf) print("Using clf as heading") # 3. transform data into submission format y_hat = data_2_submission_format(y_hat, binary_mask) # 4. generate output file path outfile = os.path.join(output_path, city, city+'_test', f.split('/')[-1]) write_data(y_hat, outfile) print("City:{}, just wrote file {}/{}: {}".format(city, i+1, total_batches, outfile)) def write_submission_files_backup(dataset, model, output_path, city, model_type, mask_path='/home/pherruzo/projects/nips_traffic/models/'): create_directory_structure(output_path, city) # Use a mask to make zero areas where no road pass-through binary_mask = np.moveaxis(np.load(mask_path+city+'_mask.npy'), -1, -3) # channels first binary_mask = np.expand_dims(np.expand_dims(binary_mask, axis=0), axis=0) # create dimension for samples and time-slots # params total_batches = len(dataset) conv_and_clf, exchange_heading_for_last_seen_heading, use_clf_as_heading = "ConvLSTM+Clf", False, False for i, sample in enumerate(dataset): # get name of the file and data f = sample[1] sample = sample[0] # 1. load data x, y, sample_weights = sample[0], sample[1]['convlstm_3'], sample[2] if model_type == conv_and_clf: y_clf = sample[1]['softmax_clf'] # 2. predict if model_type == conv_and_clf: #ConvLSTM y_hat, y_hat_clf = model.predict(x) else: y_hat = model.predict(x) ###################### different heading predictions if exchange_heading_for_last_seen_heading: # from the last frame in the sequence, get heading heading_last_frame = x[:, -1, -1] heading_last_frame = np.moveaxis(np.array([heading_last_frame, heading_last_frame, heading_last_frame]), 0, 1) # assign it as the heading for all predicted frames y_hat[:, :, -1] = heading_last_frame print("Using last known heading for prediction") if use_clf_as_heading: #ConvLSTM # transform HEADING vector to image and exchange HEADING dimension y_hat_clf = vec2tensor(y_hat_clf) y_hat = exchange_HEADING(y_hat.copy(), y_hat_clf) print("Using clf as heading") # 3. transform data into submission format y_hat = data_2_submission_format(y_hat, binary_mask) # 4. generate output file path outfile = os.path.join(output_path, city, city+'_test', f.split('/')[-1]) write_data(y_hat, outfile) print("City:{}, just wrote file {}/{}: {}".format(city, i+1, total_batches, outfile))

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py

Python

granite/__init__.py

raplonu/granite

b11c3ff89c983d35bcd8fa793ffb704e6f2d38ab

[ "MIT" ]

null

granite/__init__.py

raplonu/granite

b11c3ff89c983d35bcd8fa793ffb704e6f2d38ab

[ "MIT" ]

null

granite/__init__.py

raplonu/granite

b11c3ff89c983d35bcd8fa793ffb704e6f2d38ab

[ "MIT" ]

null

from .command.app import GraniteApp

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py

Python

coral/analysis/_sequencing/substitution_matrices/dna.py

klavinslab/coral

17f59591211562a59a051f474cd6cecba4829df9

[ "MIT" ]

34

2015-12-26T22:13:51.000Z

2021-11-17T11:46:37.000Z

coral/analysis/_sequencing/substitution_matrices/dna.py

klavinslab/coral

17f59591211562a59a051f474cd6cecba4829df9

[ "MIT" ]

13

2015-09-11T23:27:51.000Z

2018-06-25T20:44:28.000Z

coral/analysis/_sequencing/substitution_matrices/dna.py

klavinslab/coral

17f59591211562a59a051f474cd6cecba4829df9

[ "MIT" ]

14

2015-10-08T17:08:48.000Z

2022-02-22T04:25:54.000Z

import numpy as np from .substitution_matrix import SubstitutionMatrix DNA = SubstitutionMatrix( np.array([[5, -4, -4, -4, -4, 1, 1, -4, -4, 1, -4, -1, -1, -1, -2, -4], [-4, 5, -4, -4, -4, 1, -4, 1, 1, -4, -1, -4, -1, -1, -2, 5], [-4, -4, 5, -4, 1, -4, 1, -4, 1, -4, -1, -1, -4, -1, -2, -4], [-4, -4, -4, 5, 1, -4, -4, 1, -4, 1, -1, -1, -1, -4, -2, -4], [-4, -4, 1, 1, -1, -4, -2, -2, -2, -2, -1, -1, -3, -3, -1, -4], [1, 1, -4, -4, -4, -1, -2, -2, -2, -2, -3, -3, -1, -1, -1, 1], [1, -4, 1, -4, -2, -2, -1, -4, -2, -2, -3, -1, -3, -1, -1, -4], [-4, 1, -4, 1, -2, -2, -4, -1, -2, -2, -1, -3, -1, -3, -1, 1], [-4, 1, 1, -4, -2, -2, -2, -2, -1, -4, -1, -3, -3, -1, -1, 1], [1, -4, -4, 1, -2, -2, -2, -2, -4, -1, -3, -1, -1, -3, -1, -4], [-4, -1, -1, -1, -1, -3, -3, -1, -1, -3, -1, -2, -2, -2, -1, -1], [-1, -4, -1, -1, -1, -3, -1, -3, -3, -1, -2, -1, -2, -2, -1, -4], [-1, -1, -4, -1, -3, -1, -3, -1, -3, -1, -2, -2, -1, -2, -1, -1], [-1, -1, -1, -4, -3, -1, -1, -3, -1, -3, -2, -2, -2, -1, -1, -1], [-2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -2], [-4, 5, -4, -4, -4, 1, -4, 1, 1, -4, -1, -4, -1, -1, -2, 5]]), 'ATGCSWRYKMBVHDNU')

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c46bddfafb7753a9a01d00486f81d80e617bd60d

250

py

Python

resolwe/test_helpers/management/__init__.py

plojyon/resolwe

1bee6f0860fdd087534adf1680e9350d79ab97cf

[ "Apache-2.0" ]

27

2015-12-07T18:29:12.000Z

2022-03-16T08:01:47.000Z

resolwe/test_helpers/management/__init__.py

plojyon/resolwe

1bee6f0860fdd087534adf1680e9350d79ab97cf

[ "Apache-2.0" ]

681

2015-12-01T11:52:24.000Z

2022-03-21T07:43:37.000Z

resolwe/test_helpers/management/__init__.py

plojyon/resolwe

1bee6f0860fdd087534adf1680e9350d79ab97cf

[ "Apache-2.0" ]

28

2015-12-01T08:32:57.000Z

2021-12-14T00:04:16.000Z

""".. Ignore pydocstyle D400. ============ Test Helpers ============ .. automodule:: resolwe.test_helpers.management.commands.list_process_tags :members: .. automodule:: resolwe.test_helpers.management.commands.show_profile :members: """

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6

c46c0b700f9aa3729b8df02c5eb0572cfe2e80f2

109

py

Python

test/__init__.py

Lewis-Trowbridge/Go-To-Sleep-Revengeance

2036381f86d482efdd944b30a1e065410f59fa0d

[ "MIT" ]

null

test/__init__.py

Lewis-Trowbridge/Go-To-Sleep-Revengeance

2036381f86d482efdd944b30a1e065410f59fa0d

[ "MIT" ]

5

2021-11-11T07:18:37.000Z

2022-03-21T07:21:43.000Z

test/__init__.py

Lewis-Trowbridge/Go-To-Sleep-Revengeance

2036381f86d482efdd944b30a1e065410f59fa0d

[ "MIT" ]

1

2020-05-30T11:33:56.000Z

from .test_argparser import TestArgParser from .test_times import TestTimes from .test_pings import TestPings

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675ad6a9d14625f6f8a8acace85e87476d1ae9c6

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py

Python

src/autotrainer/autotrainer/blob/models/labelled_blob.py

JasonTheDeveloper/Custom-Vision-Autotrainer

9a4cc786f116dccce747e47c1a804b03e8b52cc6

[ "MIT" ]

10

2019-05-21T04:10:11.000Z

2021-12-15T05:47:11.000Z

src/autotrainer/autotrainer/blob/models/labelled_blob.py

JasonTheDeveloper/Custom-Vision-Autotrainer

9a4cc786f116dccce747e47c1a804b03e8b52cc6

[ "MIT" ]

12

2019-02-24T21:51:06.000Z

2019-03-30T04:00:17.000Z

src/autotrainer/autotrainer/blob/models/labelled_blob.py

xtellurian/Custom-Vision-Autotrainer

6ede6d8d1dd4dc7fd4ffba8bfe0b19d2ce0569fe

[ "MIT" ]

7

2019-11-06T21:01:59.000Z

2021-09-13T12:50:55.000Z

class LabelledBlob: download_url: str labels: [str] def __init__(self, download_url: str, labels: [str]): self.download_url = download_url self.labels = labels

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6

67615efa8586bd37b9c8d68f9af855e12503679d

38

py

Python

src/textacy/spacier/__init__.py

techthiyanes/textacy

c7a5e1f881a3df63a89991accefcbd375ede5353

[ "Apache-2.0" ]

null

src/textacy/spacier/__init__.py

techthiyanes/textacy

c7a5e1f881a3df63a89991accefcbd375ede5353

[ "Apache-2.0" ]

null

src/textacy/spacier/__init__.py

techthiyanes/textacy

c7a5e1f881a3df63a89991accefcbd375ede5353

[ "Apache-2.0" ]

null

from . import core, extensions, utils

19

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null

0

1

0

1

0

1

0

6

679013889346666d300db89f7ed47ec9c905bf6c

47

py

Python

app/routers/__init__.py

Simple2B/ClipWeb

12861c14433ef655ae3a1156dace4a6ab91bf367

[ "MIT" ]

null

app/routers/__init__.py

Simple2B/ClipWeb

12861c14433ef655ae3a1156dace4a6ab91bf367

[ "MIT" ]

null

app/routers/__init__.py

Simple2B/ClipWeb

12861c14433ef655ae3a1156dace4a6ab91bf367

[ "MIT" ]

null

# flake8: noqa F401 from .visit import router

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2,640

py

Python

epytope/Data/pssms/smm/mat/B_58_01_11.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smm/mat/B_58_01_11.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smm/mat/B_58_01_11.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

B_58_01_11 = {0: {'A': 0.0, 'C': 0.0, 'E': 0.0, 'D': 0.0, 'G': 0.0, 'F': 0.0, 'I': -0.0, 'H': 0.0, 'K': -0.0, 'M': 0.0, 'L': -0.0, 'N': 0.0, 'Q': -0.0, 'P': 0.0, 'S': 0.0, 'R': 0.0, 'T': 0.0, 'W': 0.0, 'V': 0.0, 'Y': 0.0}, 1: {'A': -0.089, 'C': -0.068, 'E': 0.266, 'D': -0.059, 'G': 0.0, 'F': 0.158, 'I': 0.028, 'H': 0.0, 'K': 0.0, 'M': 0.006, 'L': -0.043, 'N': 0.0, 'Q': 0.078, 'P': 0.011, 'S': -0.631, 'R': 0.184, 'T': -0.024, 'W': 0.0, 'V': 0.149, 'Y': 0.034}, 2: {'A': 0.075, 'C': 0.008, 'E': 0.0, 'D': -0.095, 'G': 0.026, 'F': -0.209, 'I': -0.159, 'H': 0.0, 'K': 0.058, 'M': 0.0, 'L': 0.003, 'N': -0.033, 'Q': 0.125, 'P': 0.026, 'S': 0.161, 'R': 0.01, 'T': -0.07, 'W': 0.0, 'V': 0.096, 'Y': -0.022}, 3: {'A': 0.038, 'C': 0.024, 'E': -0.004, 'D': 0.028, 'G': 0.002, 'F': -0.069, 'I': 0.0, 'H': 0.0, 'K': 0.0, 'M': 0.0, 'L': -0.01, 'N': 0.0, 'Q': 0.0, 'P': -0.012, 'S': -0.075, 'R': 0.015, 'T': -0.003, 'W': 0.03, 'V': 0.019, 'Y': 0.017}, 4: {'A': 0.003, 'C': 0.008, 'E': 0.001, 'D': -0.058, 'G': -0.018, 'F': -0.013, 'I': 0.076, 'H': 0.0, 'K': 0.039, 'M': 0.0, 'L': 0.031, 'N': -0.184, 'Q': 0.014, 'P': 0.011, 'S': 0.079, 'R': 0.0, 'T': 0.0, 'W': 0.0, 'V': 0.005, 'Y': 0.004}, 5: {'A': 0.013, 'C': 0.004, 'E': -0.015, 'D': 0.004, 'G': 0.0, 'F': 0.014, 'I': 0.004, 'H': 0.001, 'K': -0.013, 'M': 0.005, 'L': -0.006, 'N': 0.0, 'Q': 0.025, 'P': -0.009, 'S': 0.006, 'R': -0.005, 'T': -0.027, 'W': 0.0, 'V': 0.0, 'Y': 0.0}, 6: {'A': -0.014, 'C': 0.004, 'E': 0.017, 'D': 0.015, 'G': -0.004, 'F': -0.008, 'I': 0.012, 'H': 0.0, 'K': 0.0, 'M': 0.0, 'L': -0.002, 'N': 0.011, 'Q': -0.009, 'P': 0.002, 'S': 0.018, 'R': 0.0, 'T': 0.003, 'W': 0.016, 'V': -0.025, 'Y': -0.037}, 7: {'A': -0.931, 'C': 0.0, 'E': 0.0, 'D': 0.0, 'G': 0.235, 'F': 0.01, 'I': -0.603, 'H': 0.424, 'K': -0.119, 'M': 0.0, 'L': -0.201, 'N': 0.045, 'Q': 0.028, 'P': -0.09, 'S': 0.184, 'R': -0.24, 'T': 0.56, 'W': 0.108, 'V': 0.48, 'Y': 0.11}, 8: {'A': 0.269, 'C': 0.126, 'E': 0.088, 'D': -0.124, 'G': -0.266, 'F': -0.108, 'I': 0.087, 'H': 0.0, 'K': 0.385, 'M': 0.0, 'L': -0.04, 'N': 0.025, 'Q': 0.044, 'P': -0.161, 'S': 0.053, 'R': -0.106, 'T': -0.514, 'W': 0.131, 'V': 0.041, 'Y': 0.071}, 9: {'A': 0.282, 'C': 0.0, 'E': 0.103, 'D': 0.0, 'G': 0.199, 'F': 0.126, 'I': 0.175, 'H': -0.117, 'K': 0.059, 'M': -0.468, 'L': -0.716, 'N': 0.467, 'Q': -0.289, 'P': 0.184, 'S': 0.0, 'R': 0.126, 'T': 0.033, 'W': -0.201, 'V': 0.018, 'Y': 0.019}, 10: {'A': 0.0, 'C': 0.0, 'E': 0.0, 'D': 0.0, 'G': 0.0, 'F': -0.421, 'I': 0.125, 'H': 0.0, 'K': 0.866, 'M': 0.0, 'L': 0.322, 'N': 0.0, 'Q': 0.0, 'P': 0.0, 'S': 0.0, 'R': 0.131, 'T': 0.067, 'W': -1.77, 'V': 0.627, 'Y': 0.052}, -1: {'con': 4.04406}}

2,640

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2,640

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67b6868e1c6c689968fa7dcf62931438ee1b85de

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py

Python

src/ostorlab/cli/scan/stop/__init__.py

bbhunter/ostorlab

968fe4e5b927c0cd159594c13b73f95b71150154

[ "Apache-2.0" ]

113

2022-02-21T09:30:14.000Z

2022-03-31T21:54:26.000Z

src/ostorlab/cli/scan/stop/__init__.py

bbhunter/ostorlab

968fe4e5b927c0cd159594c13b73f95b71150154

[ "Apache-2.0" ]

2

2022-02-25T10:56:55.000Z

2022-03-24T13:08:06.000Z

src/ostorlab/cli/scan/stop/__init__.py

bbhunter/ostorlab

968fe4e5b927c0cd159594c13b73f95b71150154

[ "Apache-2.0" ]

20

2022-02-28T14:25:04.000Z

2022-03-30T23:01:11.000Z

"""Module for sub-command of the command scan: stop.""" from ostorlab.cli.scan.stop import stop

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6

e1e30ef4204bf5134a64ed6a6086498c57c92073

12,330

py

Python

model.py

styler00dollar/Colab-CSA-pytorch

5e569581bc3d1870f0a9fe40fdb8254627fac184

[ "MIT" ]

29

2019-08-15T12:12:46.000Z

2022-02-20T11:30:25.000Z

model.py

styler00dollar/Colab-CSA-pytorch

5e569581bc3d1870f0a9fe40fdb8254627fac184

[ "MIT" ]

3

2019-10-15T06:48:34.000Z

2020-12-17T11:26:58.000Z

model.py

styler00dollar/Colab-CSA-pytorch

5e569581bc3d1870f0a9fe40fdb8254627fac184

[ "MIT" ]

8

2019-09-09T06:18:03.000Z

2020-10-21T07:16:38.000Z

import torch from torch import nn import torch.nn.functional as F def get_norm(name, out_channels): if name == 'batch': norm = nn.BatchNorm2d(out_channels) elif name == 'instance': norm = nn.InstanceNorm2d(out_channels) else: norm = None return norm def get_act(name): if name == 'relu': activation = nn.ReLU(inplace=True) elif name == 'elu': activation == nn.ELU(inplace=True) elif name == 'leaky_relu': activation = nn.LeakyReLU(negative_slope=0.2, inplace=True) elif name == 'tanh': activation = nn.Tanh() elif name == 'sigmoid': activation = nn.Sigmoid() else: activation = None return activation class CoarseEncodeBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, normalization=None, activation=None): super().__init__() layers = [] if activation: layers.append(get_act(activation)) layers.append( nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=1)) if normalization: layers.append(get_norm(normalization, out_channels)) self.encode = nn.Sequential(*layers) def forward(self, x): return self.encode(x) class CoarseDecodeBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, normalization=None, activation=None): super().__init__() layers = [] if activation: layers.append(get_act(activation)) layers.append( nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding=1)) if normalization: layers.append(get_norm(normalization, out_channels)) self.decode = nn.Sequential(*layers) def forward(self, x): return self.decode(x) class CoarseNet(nn.Module): def __init__(self, c_img=3, norm='instance', act_en='leaky_relu', act_de='relu'): super().__init__() cnum = 64 self.en_1 = nn.Conv2d(c_img, cnum, 4, 2, padding=1) self.en_2 = CoarseEncodeBlock(cnum, cnum*2, 4, 2, normalization=norm, activation=act_en) self.en_3 = CoarseEncodeBlock(cnum*2, cnum*4, 4, 2, normalization=norm, activation=act_en) self.en_4 = CoarseEncodeBlock(cnum*4, cnum*8, 4, 2, normalization=norm, activation=act_en) self.en_5 = CoarseEncodeBlock(cnum*8, cnum*8, 4, 2, normalization=norm, activation=act_en) self.en_6 = CoarseEncodeBlock(cnum*8, cnum*8, 4, 2, normalization=norm, activation=act_en) self.en_7 = CoarseEncodeBlock(cnum*8, cnum*8, 4, 2, normalization=norm, activation=act_en) self.en_8 = CoarseEncodeBlock(cnum*8, cnum*8, 4, 2, activation=act_en) self.de_8 = CoarseDecodeBlock(cnum*8, cnum*8, 4, 2, normalization=norm, activation=act_de) self.de_7 = CoarseDecodeBlock(cnum*8*2, cnum*8, 4, 2, normalization=norm, activation=act_de) self.de_6 = CoarseDecodeBlock(cnum*8*2, cnum*8, 4, 2, normalization=norm, activation=act_de) self.de_5 = CoarseDecodeBlock(cnum*8*2, cnum*8, 4, 2, normalization=norm, activation=act_de) self.de_4 = CoarseDecodeBlock(cnum*8*2, cnum*4, 4, 2, normalization=norm, activation=act_de) self.de_3 = CoarseDecodeBlock(cnum*4*2, cnum*2, 4, 2, normalization=norm, activation=act_de) self.de_2 = CoarseDecodeBlock(cnum*2*2, cnum, 4, 2, normalization=norm, activation=act_de) self.de_1 = nn.Sequential( get_act(act_de), nn.ConvTranspose2d(cnum*2, c_img, 4, 2, padding=1), get_act('tanh')) def forward(self, x): out_1 = self.en_1(x) out_2 = self.en_2(out_1) out_3 = self.en_3(out_2) out_4 = self.en_4(out_3) out_5 = self.en_5(out_4) out_6 = self.en_6(out_5) out_7 = self.en_7(out_6) out_8 = self.en_8(out_7) dout_8 = self.de_8(out_8) dout_8_out_7 = torch.cat([dout_8, out_7], 1) dout_7 = self.de_7(dout_8_out_7) dout_7_out_6 = torch.cat([dout_7, out_6], 1) dout_6 = self.de_6(dout_7_out_6) dout_6_out_5 = torch.cat([dout_6, out_5], 1) dout_5 = self.de_5(dout_6_out_5) dout_5_out_4 = torch.cat([dout_5, out_4], 1) dout_4 = self.de_4(dout_5_out_4) dout_4_out_3 = torch.cat([dout_4, out_3], 1) dout_3 = self.de_3(dout_4_out_3) dout_3_out_2 = torch.cat([dout_3, out_2], 1) dout_2 = self.de_2(dout_3_out_2) dout_2_out_1 = torch.cat([dout_2, out_1], 1) dout_1 = self.de_1(dout_2_out_1) return dout_1 class RefineEncodeBlock(nn.Module): def __init__(self, in_channels, out_channels, normalization=None, activation=None): super().__init__() layers = [] if activation: layers.append(get_act(activation)) layers.append( nn.Conv2d(in_channels, in_channels, 4, 2, dilation=2, padding=3)) if normalization: layers.append(get_norm(normalization, out_channels)) if activation: layers.append(get_act(activation)) layers.append( nn.Conv2d(in_channels, out_channels, 3, 1, padding=1)) if normalization: layers.append(get_norm(normalization, out_channels)) self.encode = nn.Sequential(*layers) def forward(self, x): return self.encode(x) class RefineDecodeBlock(nn.Module): def __init__(self, in_channels, out_channels, normalization=None, activation=None): super().__init__() layers = [] if activation: layers.append(get_act(activation)) layers.append( nn.ConvTranspose2d(in_channels, out_channels, 3, 1, padding=1)) if normalization: layers.append(get_norm(normalization, out_channels)) if activation: layers.append(get_act(activation)) layers.append( nn.ConvTranspose2d(out_channels, out_channels, 4, 2, padding=1)) if normalization: layers.append(get_norm(normalization, out_channels)) self.decode = nn.Sequential(*layers) def forward(self, x): return self.decode(x) class RefineNet(nn.Module): def __init__(self, c_img=3, norm='instance', act_en='leaky_relu', act_de='relu'): super().__init__() c_in = c_img + c_img cnum = 64 self.en_1 = nn.Conv2d(c_in, cnum, 3, 1, padding=1) self.en_2 = RefineEncodeBlock(cnum, cnum*2, normalization=norm, activation=act_en) self.en_3 = RefineEncodeBlock(cnum*2, cnum*4, normalization=norm, activation=act_en) self.en_4 = RefineEncodeBlock(cnum*4, cnum*8, normalization=norm, activation=act_en) self.en_5 = RefineEncodeBlock(cnum*8, cnum*8, normalization=norm, activation=act_en) self.en_6 = RefineEncodeBlock(cnum*8, cnum*8, normalization=norm, activation=act_en) self.en_7 = RefineEncodeBlock(cnum*8, cnum*8, normalization=norm, activation=act_en) self.en_8 = RefineEncodeBlock(cnum*8, cnum*8, normalization=norm, activation=act_en) self.en_9 = nn.Sequential( get_act(act_en), nn.Conv2d(cnum*8, cnum*8, 4, 2, padding=1)) self.de_9 = nn.Sequential( get_act(act_de), nn.ConvTranspose2d(cnum*8, cnum*8, 4, 2, padding=1), get_norm(norm, cnum*8)) self.de_8 = RefineDecodeBlock(cnum*8*2, cnum*8, normalization=norm, activation=act_de) self.de_7 = RefineDecodeBlock(cnum*8*2, cnum*8, normalization=norm, activation=act_de) self.de_6 = RefineDecodeBlock(cnum*8*2, cnum*8, normalization=norm, activation=act_de) self.de_5 = RefineDecodeBlock(cnum*8*2, cnum*8, normalization=norm, activation=act_de) self.de_4 = RefineDecodeBlock(cnum*8*2, cnum*4, normalization=norm, activation=act_de) self.de_3 = RefineDecodeBlock(cnum*4*2, cnum*2, normalization=norm, activation=act_de) self.de_2 = RefineDecodeBlock(cnum*2*2, cnum, normalization=norm, activation=act_de) self.de_1 = nn.Sequential( get_act(act_de), nn.ConvTranspose2d(cnum*2, c_img, 3, 1, padding=1)) def forward(self, x1, x2): x = torch.cat([x1, x2], 1) out_1 = self.en_1(x) out_2 = self.en_2(out_1) out_3 = self.en_3(out_2) out_4 = self.en_4(out_3) out_5 = self.en_5(out_4) out_6 = self.en_6(out_5) out_7 = self.en_7(out_6) out_8 = self.en_8(out_7) out_9 = self.en_9(out_8) dout_9 = self.de_9(out_9) dout_9_out_8 = torch.cat([dout_9, out_8], 1) dout_8 = self.de_8(dout_9_out_8) dout_8_out_7 = torch.cat([dout_8, out_7], 1) dout_7 = self.de_7(dout_8_out_7) dout_7_out_6 = torch.cat([dout_7, out_6], 1) dout_6 = self.de_6(dout_7_out_6) dout_6_out_5 = torch.cat([dout_6, out_5], 1) dout_5 = self.de_5(dout_6_out_5) dout_5_out_4 = torch.cat([dout_5, out_4], 1) dout_4 = self.de_4(dout_5_out_4) dout_4_out_3 = torch.cat([dout_4, out_3], 1) dout_3 = self.de_3(dout_4_out_3) dout_3_out_2 = torch.cat([dout_3, out_2], 1) dout_2 = self.de_2(dout_3_out_2) dout_2_out_1 = torch.cat([dout_2, out_1], 1) dout_1 = self.de_1(dout_2_out_1) return dout_1, out_4, dout_5 class CSA(nn.Module): def __init__(self): super().__init__() def forward(self, x, mask): return x class InpaintNet(nn.Module): def __init__(self): super().__init__() self.coarse = CoarseNet() self.refine = RefineNet() def forward(self, image, mask): out_c = self.coarse(image) out_c = image * (1. - mask) + out_c * mask out_r, csa, csa_d = self.refine(out_c, image) out_r = image * (1. - mask) + out_r * mask return out_c, out_r, csa, csa_d class PatchDiscriminator(nn.Module): def __init__(self, c_img=3, norm='instance', act='leaky_relu'): super().__init__() c_in = c_img + c_img cnum = 64 self.discriminator = nn.Sequential( nn.Conv2d(c_in, cnum, 4, 2, 1), get_act(act), nn.Conv2d(cnum, cnum*2, 4, 2, 1), get_norm(norm, cnum*2), get_act(act), nn.Conv2d(cnum*2, cnum*4, 4, 2, 1), get_norm(norm, cnum*4), get_act(act), nn.Conv2d(cnum*4, cnum*8, 4, 1, 1), get_norm(norm, cnum*8), get_act(act), nn.Conv2d(cnum*8, 1, 4, 1, 1)) def forward(self, x1, x2): x = torch.cat([x1, x2], 1) return self.discriminator(x) class FeaturePatchDiscriminator(nn.Module): def __init__(self, c_img=3, norm='instance', act='leaky_relu'): super().__init__() c_in = c_img + c_img cnum = 64 self.discriminator = nn.Sequential( # VGG-16 up to 3rd pooling nn.Conv2d(c_in, cnum, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(cnum, cnum, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(cnum, cnum*2, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(cnum*2, cnum*2, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(cnum*2, cnum*4, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(cnum*4, cnum*4, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(cnum*4, cnum*4, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # Discriminator nn.Conv2d(cnum*4, cnum*8, 4, 2, 1), get_act(act), nn.Conv2d(cnum*8, cnum*8, 4, 1, 1), get_norm(norm, cnum*8), get_act(act), nn.Conv2d(cnum*8, cnum*8, 4, 1, 1)) def forward(self, x1, x2): x = torch.cat([x1, x2], 1) return self.discriminator(x)

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6

e1e31b82c2570c9a0de317ff102a5ecd7e7ac2b0

22,354

py

Python

webapp/tests/forms/steps/lotse/test_personal_data_steps.py

digitalservice4germany/steuerlotse

ef3e094e4d7d4768431a50ac4be60672cd03221d

[ "MIT" ]

20

2021-07-02T07:49:08.000Z

2022-03-18T22:26:10.000Z

webapp/tests/forms/steps/lotse/test_personal_data_steps.py

digitalservice4germany/steuerlotse

ef3e094e4d7d4768431a50ac4be60672cd03221d

[ "MIT" ]

555

2021-06-28T15:35:15.000Z

2022-03-31T11:51:55.000Z

webapp/tests/forms/steps/lotse/test_personal_data_steps.py

digitalservice4germany/steuerlotse

ef3e094e4d7d4768431a50ac4be60672cd03221d

[ "MIT" ]

1

2021-07-04T20:34:12.000Z

import datetime from unittest.mock import patch, MagicMock import pytest from flask.sessions import SecureCookieSession from flask_babel import ngettext, _ from pydantic import ValidationError from werkzeug.datastructures import MultiDict, ImmutableMultiDict from app.forms.steps.lotse.personal_data import StepSteuernummer, StepPersonA, StepPersonB, ShowPersonBPrecondition, \ StepTelephoneNumber from app.forms.flows.lotse_step_chooser import _LOTSE_DATA_KEY, LotseStepChooser from tests.elster_client.mock_erica import MockErica from tests.utils import create_session_form_data class SummaryStep: pass def new_step_with_bufa_choices(form_data): step = LotseStepChooser().get_correct_step( StepSteuernummer.name, True, ImmutableMultiDict(form_data)) return step @pytest.mark.usefixtures('test_request_context') class TestStepSteuernummer: def test_if_steuernummer_exists_and_hessen_and_tax_number_10_digits_then_fail_validation(self): data = MultiDict({'steuernummer_exists': 'yes', 'bundesland': 'HE', 'steuernummer': '9811310010', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is False def test_if_steuernummer_exists_missing_then_fail_validation(self): data = MultiDict({'bundesland': 'BY', 'steuernummer': '19811310010', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is False def test_if_steuernummer_exists_and_bundesland_missing_then_fail_validation(self): data = MultiDict({'steuernummer_exists': 'yes', 'steuernummer': '19811310010', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is False def test_if_steuernummer_exists_and_steuernummer_missing_then_fail_validation(self): data = MultiDict({'steuernummer_exists': 'yes', 'bundesland': 'BY', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is False def test_if_steuernummer_exists_and_nothing_is_missing_then_succeed_validation(self): data = MultiDict({'steuernummer_exists': 'yes', 'bundesland': 'BY', 'steuernummer': '19811310010', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is True def test_if_no_steuernummer_and_bundesland_missing_then_fail_validation(self): data = MultiDict({'steuernummer_exists': 'no', 'bufa_nr': '9201', 'request_new_tax_number': 'y', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is False def test_if_no_steuernummer_and_bufa_nr_missing_then_fail_validation(self): data = MultiDict({'steuernummer_exists': 'no', 'bundesland': 'BY', 'request_new_tax_number': 'y', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is False def test_if_no_steuernummer_and_request_new_tax_number_missing_then_fail_validation(self): data = MultiDict({'steuernummer_exists': 'no', 'bundesland': 'BY', 'bufa_nr': '9201', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is False def test_if_no_steuernummer_and_nothing_is_missing_then_succeed_validation(self): data = MultiDict({'steuernummer_exists': 'no', 'bundesland': 'BY', 'bufa_nr': '9201', 'request_new_tax_number': 'y', }) form = new_step_with_bufa_choices(form_data=data).render_info.form assert form.validate() is True def test_if_multiple_users_then_show_multiple_text(self, app): session_data = { 'familienstand': 'married', 'familienstand_date': datetime.date(2000, 1, 31), 'familienstand_married_lived_separated': 'no', 'familienstand_confirm_zusammenveranlagung': True, } expected_number_of_users = 2 expected_steuernummer_exists_label = ngettext('form.lotse.steuernummer_exists', 'form.lotse.steuernummer_exists', num=expected_number_of_users) expected_request_new_tax_number_label = ngettext('form.lotse.steuernummer.request_new_tax_number', 'form.lotse.steuernummer.request_new_tax_number', num=expected_number_of_users) with app.test_request_context(method='GET') as req: req.session = SecureCookieSession( {_LOTSE_DATA_KEY: create_session_form_data(session_data)}) step = LotseStepChooser(endpoint='lotse').get_correct_step(StepSteuernummer.name, False, ImmutableMultiDict({})) step._pre_handle() assert expected_steuernummer_exists_label == step.render_info.form.steuernummer_exists.label.text assert expected_request_new_tax_number_label == step.render_info.form.request_new_tax_number.label.text def test_if_single_user_then_show_single_text(self, app): session_data = { 'familienstand': 'single', } expected_number_of_users = 1 expected_steuernummer_exists_label = ngettext('form.lotse.steuernummer_exists', 'form.lotse.steuernummer_exists', num=expected_number_of_users) expected_request_new_tax_number_label = ngettext('form.lotse.steuernummer.request_new_tax_number', 'form.lotse.steuernummer.request_new_tax_number', num=expected_number_of_users) with app.test_request_context(method='GET') as req: req.session = SecureCookieSession( {_LOTSE_DATA_KEY: create_session_form_data(session_data)}) step = LotseStepChooser(endpoint='lotse').get_correct_step(StepSteuernummer.name, False, ImmutableMultiDict({})) step._pre_handle() assert expected_steuernummer_exists_label == step.render_info.form.steuernummer_exists.label.text assert expected_request_new_tax_number_label == step.render_info.form.request_new_tax_number.label.text class TestStepSteuernummerInputFormInit: def test_if_init_called_then_set_tax_offices_attribute_correctly(self): expected_tax_offices = [ {"state_abbreviation": "vu", "name": "Vulcan", "tax_offices": [{"name": "Finanzamt Ni'Var", "bufa_nr": "2801"}] }, {"state_abbreviation": "tr", "name": "Terra", "tax_offices": [{"name": "Finanzamt Klingon Arbeitnehmerbereich (101)", "bufa_nr": "9101"}, {"name": "Finanzamt Klingon Arbeitgeberbereich (102)", "bufa_nr": "9102"}] } ] with patch('app.forms.steps.lotse.personal_data.request_tax_offices', MagicMock(return_value=expected_tax_offices)): created_form = StepSteuernummer.InputForm() assert created_form.tax_offices == expected_tax_offices def test_if_init_called_then_set_bufa_nr_choices_correctly(self): tax_offices = [ {"state_abbreviation": "vu", "name": "Vulcan", "tax_offices": [{"name": "Finanzamt Ni'Var", "bufa_nr": "2801"}] }, {"state_abbreviation": "tr", "name": "Terra", "tax_offices": [{"name": "Finanzamt Klingon Arbeitnehmerbereich (101)", "bufa_nr": "9101"}, {"name": "Finanzamt Klingon Arbeitgeberbereich (102)", "bufa_nr": "9102"}] } ] with patch('app.forms.steps.lotse.personal_data.request_tax_offices', MagicMock(return_value=tax_offices)): created_form = StepSteuernummer.InputForm() assert created_form.bufa_nr.choices == [("2801", "Finanzamt Ni'Var"), ("9101", "Finanzamt Klingon Arbeitnehmerbereich (101)"), ("9102", "Finanzamt Klingon Arbeitgeberbereich (102)") ] class TestStepSteuernummerValidate: @pytest.mark.usefixtures("test_request_context") def test_if_erica_returns_invalid_tax_number_then_flash_error(self, app): MockErica.tax_number_is_invalid = True bundesland_abbreviation = 'BY' steuernummer = '19811310010' input_data = {'steuernummer_exists': 'yes', 'bundesland': bundesland_abbreviation, 'steuernummer': steuernummer} try: with patch('app.forms.steps.lotse.personal_data.flash') as mock_flash: StepSteuernummer.prepare_render_info( stored_data={}, input_data=ImmutableMultiDict(input_data), should_update_data=True) finally: MockErica.tax_number_is_invalid = False mock_flash.assert_called_once_with( _('form.lotse.tax-number.invalid-tax-number-error'), 'warn') @pytest.mark.usefixtures("test_request_context") def test_if_valid_number_given_then_flash_no_error(self, app): bundesland_abbreviation = 'BY' steuernummer = '19811310010' input_data = {'steuernummer_exists': 'yes', 'bundesland': bundesland_abbreviation, 'steuernummer': steuernummer} with patch('app.forms.steps.lotse.personal_data.flash') as mock_flash: StepSteuernummer.prepare_render_info( stored_data={}, input_data=ImmutableMultiDict(input_data), should_update_data=True) mock_flash.assert_not_called() @pytest.mark.usefixtures("test_request_context") def test_if_invalid_number_given_then_flash_error(self, app): bundesland_abbreviation = 'BY' steuernummer = '11111111111' input_data = {'steuernummer_exists': 'yes', 'bundesland': bundesland_abbreviation, 'steuernummer': steuernummer} with patch('app.forms.steps.lotse.personal_data.flash') as mock_flash: StepSteuernummer.prepare_render_info( stored_data={}, input_data=ImmutableMultiDict(input_data), should_update_data=True) mock_flash.assert_called_once_with( _('form.lotse.tax-number.invalid-tax-number-error'), 'warn') @pytest.mark.usefixtures("test_request_context") def test_if_no_number_given_then_flash_no_error(self, app): bundesland_abbreviation = 'BY' steuernummer = '' input_data = {'steuernummer_exists': 'yes', 'bundesland': bundesland_abbreviation, 'steuernummer': steuernummer} with patch('app.forms.steps.lotse.personal_data.flash') as mock_flash: StepSteuernummer.prepare_render_info( stored_data={}, input_data=ImmutableMultiDict(input_data), should_update_data=True) mock_flash.assert_not_called() @pytest.mark.usefixtures("test_request_context") def test_if_no_bundesland_given_then_flash_no_error(self, app): bundesland_abbreviation = '' steuernummer = '11111111111' input_data = {'steuernummer_exists': 'yes', 'bundesland': bundesland_abbreviation, 'steuernummer': steuernummer} with patch('app.forms.steps.lotse.personal_data.flash') as mock_flash: StepSteuernummer.prepare_render_info( stored_data={}, input_data=ImmutableMultiDict(input_data), should_update_data=True) mock_flash.assert_not_called() class TestStepPersonATexts: def test_if_multiple_users_then_show_multiple_text(self, app): session_data = { 'familienstand': 'married', 'familienstand_date': datetime.date(2000, 1, 31), 'familienstand_married_lived_separated': 'no', 'familienstand_confirm_zusammenveranlagung': True, } expected_number_of_users = 2 expected_step_title = ngettext('form.lotse.person-a-title', 'form.lotse.person-a-title', num=expected_number_of_users) expected_step_intro = _( 'form.lotse.person-a-intro') if expected_number_of_users > 1 else None with app.test_request_context(method='GET') as req: req.session = SecureCookieSession( {_LOTSE_DATA_KEY: create_session_form_data(session_data)}) step = LotseStepChooser(endpoint='lotse').get_correct_step(StepPersonA.name, False, ImmutableMultiDict({})) step._pre_handle() assert step.render_info.step_title == expected_step_title assert step.render_info.step_intro == expected_step_intro def test_if_single_user_then_show_single_text(self, app): session_data = { 'familienstand': 'single', } expected_number_of_users = 1 expected_step_title = ngettext('form.lotse.person-a-title', 'form.lotse.person-a-title', num=expected_number_of_users) expected_step_intro = _( 'form.lotse.person-a-intro') if expected_number_of_users > 1 else None with app.test_request_context(method='GET') as req: req.session = SecureCookieSession( {_LOTSE_DATA_KEY: create_session_form_data(session_data)}) step = LotseStepChooser(endpoint='lotse').get_correct_step(StepPersonA.name, False, ImmutableMultiDict({})) step._pre_handle() assert step.render_info.step_title == expected_step_title assert step.render_info.step_intro == expected_step_intro class TestStepPersonAGetLabel: def test_if_single_user_then_return_single_text(self): session_data = { 'familienstand': 'single', } expected_label = ngettext( 'form.lotse.step_person_a.label', 'form.lotse.step_person_a.label', num=1) returned_label = StepPersonA.get_label(session_data) assert returned_label == expected_label def test_if_multiple_users_then_return_multiple_text(self): session_data = { 'familienstand': 'married', 'familienstand_date': datetime.date(2000, 1, 31), 'familienstand_married_lived_separated': 'no', 'familienstand_confirm_zusammenveranlagung': True, } expected_label = ngettext( 'form.lotse.step_person_a.label', 'form.lotse.step_person_a.label', num=2) returned_label = StepPersonA.get_label(session_data) assert returned_label == expected_label def new_person_a_step(form_data): return LotseStepChooser().get_correct_step(StepPersonA.name, True, ImmutableMultiDict(form_data)) @pytest.mark.usefixtures('test_request_context') class TestPersonAValidation: @pytest.fixture() def valid_form_data(self): return {'person_a_idnr': '04452397687', 'person_a_first_name': 'Hermine', 'person_a_last_name': 'Granger', 'person_a_dob': ['01', '01', '1985'], 'person_a_street': 'Hogwartsstraße', 'person_a_street_number': '7', 'person_a_plz': '12345', 'person_a_town': 'Hogsmeade', 'person_a_religion': 'none'} def test_if_plz_starts_with_zero_then_succ_validation(self, valid_form_data): data = MultiDict({**valid_form_data, ** {'person_a_plz': '01234'}}) form = new_person_a_step(form_data=data).render_info.form assert form.validate() is True def test_if_plz_has_5_digits_then_succ_validation(self, valid_form_data): data = MultiDict({**valid_form_data, **{'person_a_plz': '12345'}}) form = new_person_a_step(form_data=data).render_info.form assert form.validate() is True def test_if_plz_has_too_little_digits_then_fail_validation(self, valid_form_data): data = MultiDict({**valid_form_data, **{'person_a_plz': '1234'}}) form = new_person_a_step(form_data=data).render_info.form assert form.validate() is False def test_if_plz_has_too_many_digits_then_fail_validation(self, valid_form_data): data = MultiDict({**valid_form_data, **{'person_a_plz': '123456'}}) form = new_person_a_step(form_data=data).render_info.form assert form.validate() is False def new_person_b_step(form_data): return LotseStepChooser().get_correct_step(StepPersonB.name, True, ImmutableMultiDict(form_data)) class TestShowPersonBPrecondition: def test_if_show_person_b_false_then_raise_validation_error(self): with patch('app.model.form_data.JointTaxesModel.show_person_b', return_value=False), \ pytest.raises(ValidationError): ShowPersonBPrecondition.parse_obj({'familienstand': 'single'}) def test_if_show_person_b_true_then_do_not_raise_validation_error(self): with patch('app.model.form_data.JointTaxesModel.show_person_b', return_value=True): ShowPersonBPrecondition.parse_obj({'familienstand': 'single'}) class TestPersonBValidation: valid_stored_data = {'familienstand': 'married', 'familienstand_date': datetime.date(2000, 1, 31), 'familienstand_married_lived_separated': 'no', 'familienstand_confirm_zusammenveranlagung': True} @pytest.fixture() def valid_form_data(self): return {'person_b_idnr': '04452397687', 'person_b_first_name': 'Hermine', 'person_b_last_name': 'Granger', 'person_b_dob': ['01', '01', '1985'], 'person_b_same_address': 'yes', 'person_b_religion': 'none'} def test_if_plz_starts_with_zero_then_succ_validation(self, valid_form_data, new_test_request_context): data = MultiDict({**valid_form_data, ** {'person_b_plz': '01234'}}) with new_test_request_context(stored_data=self.valid_stored_data, form_data=data): form = new_person_b_step(form_data=data).render_info.form assert form.validate() is True def test_if_plz_has_5_digits_then_succ_validation(self, valid_form_data, new_test_request_context): data = MultiDict({**valid_form_data, **{'person_b_plz': '12345'}}) with new_test_request_context(stored_data=self.valid_stored_data, form_data=data): form = new_person_b_step(form_data=data).render_info.form assert form.validate() is True def test_if_plz_has_too_little_digits_then_fail_validation(self, valid_form_data, new_test_request_context): data = MultiDict({**valid_form_data, **{'person_b_plz': '1234'}}) with new_test_request_context(stored_data=self.valid_stored_data, form_data=data): form = new_person_b_step(form_data=data).render_info.form assert form.validate() is False def test_if_plz_has_too_many_digits_then_fail_validation(self, valid_form_data, new_test_request_context): data = MultiDict({**valid_form_data, **{'person_b_plz': '123456'}}) with new_test_request_context(stored_data=self.valid_stored_data, form_data=data): form = new_person_b_step(form_data=data).render_info.form assert form.validate() is False def test_if_same_address_yes_then_validation_succ_without_address(self, valid_form_data, new_test_request_context): data = MultiDict( {**valid_form_data, **{'person_b_same_address': 'yes'}}) with new_test_request_context(stored_data=self.valid_stored_data, form_data=data): form = new_person_b_step(form_data=data).render_info.form assert form.validate() is True def test_if_same_address_no_and_no_address_set_then_fail_validation(self, valid_form_data, new_test_request_context): data = MultiDict( {**valid_form_data, **{'person_b_same_address': 'no'}}) with new_test_request_context(stored_data=self.valid_stored_data, form_data=data): form = new_person_b_step(form_data=data).render_info.form assert form.validate() is False def test_if_same_address_no_and_address_set_then_succ_validation(self, valid_form_data, new_test_request_context): data = MultiDict({**valid_form_data, **{'person_b_same_address': 'no', 'person_b_street': 'Diagon Alley', 'person_b_street_number': '7', 'person_b_plz': '12345', 'person_b_town': 'Hogsmeade'}}) with new_test_request_context(stored_data=self.valid_stored_data, form_data=data): form = new_person_b_step(form_data=data).render_info.form assert form.validate() is True class TestTelephoneNumberValidation: def test_if_number_max_25_chars_then_succ_validation(self, new_test_request_context): data = MultiDict({'telephone_number': 'Lorem ipsum dolor sit ame'}) with new_test_request_context(form_data=data): step = LotseStepChooser().get_correct_step( StepTelephoneNumber.name, True, ImmutableMultiDict(data)) form = step.render_info.form assert form.validate() is True def test_if_number_over_25_chars_then_succ_validation(self, new_test_request_context): data = MultiDict({'telephone_number': 'Lorem ipsum dolor sit amet'}) with new_test_request_context(stored_data=data): step = LotseStepChooser().get_correct_step( StepTelephoneNumber.name, True, ImmutableMultiDict(data)) form = step.render_info.form assert form.validate() is False

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Python

auto/wpt_interface_test/case_suite/reprieve_loan.py

Strugglingrookie/oldboy2

8ed6723cab1f54f2ff8ea0947c6f982aef7e1b47

[ "Apache-2.0" ]

1

2021-06-15T07:01:23.000Z

auto/wpt_interface_test/case_suite/reprieve_loan.py

Strugglingrookie/oldboy2

8ed6723cab1f54f2ff8ea0947c6f982aef7e1b47

[ "Apache-2.0" ]

3

2020-02-13T14:35:36.000Z

2021-06-10T21:27:14.000Z

auto/wpt_interface_test/case_suite/reprieve_loan.py

Strugglingrookie/oldboy2

8ed6723cab1f54f2ff8ea0947c6f982aef7e1b47

[ "Apache-2.0" ]

1

2020-04-09T02:13:12.000Z

#!/usr/bin/python3 # -*- coding: utf-8 -*- # @Time : 2019/8/30 8:23 # @Author : TangYong # @Email : [email protected] # @File : reprieve_loan.py # @Software: PyCharm import sys import requests import unittest from settings import config from fun import public from fun import fatp_db_server class ReprieveLoan(unittest.TestCase): ''' 暂缓放款 ''' def test_get_send_info(self): ''' 批量获取发标信息''' # 未记账/未暂缓状态数据 putout_status = { 'defer_pay_status': '0', 'put_out_status': '0' } fatp = fatp_db_server.ApplyToContractRepository() query_result = fatp.handel_query(('t2.apply_serial_no'),**putout_status) if query_result is None: public.log_record('当前查询条件【%s】下未存在有效数据'%putout_status, sys._getframe().f_lineno, query_result) return None public.log_record('当前查询条件【%s】下查询结果数据' % putout_status, sys._getframe().f_lineno, query_result) #获取记账状态下的apply_id apply_id = query_result['apply_serial_no'] #批量获取发标信息请求数据 send_info_req_data = { 'applyIdList':[apply_id] } public.log_record('批量获取发标信息请求数据' , sys._getframe().f_lineno, send_info_req_data) send_info_res_data = requests.post( url=config.wk_send_info_default_url, json=send_info_req_data ) if send_info_res_data.json()['code']: public.log_record('批量获取发标信息响应数据', sys._getframe().f_lineno, send_info_res_data.text) self.assertEqual(send_info_res_data.json()['code'],'000000') else: public.log_record('批量获取发标信息失败响应数据', sys._getframe().f_lineno, send_info_res_data.text) def test_add_reprieve(self): ''' 添加暂缓''' # # 未记账/未暂缓状态数据 putout_status = { 'defer_pay_status':'0', 'put_out_status':'0' } fatp = fatp_db_server.LoadApplyRepository() query_result = fatp.handel_query('1',('serial_no'),**putout_status) if query_result is None: public.log_record('当前查询条件【%s】下未存在有效数据' % putout_status, sys._getframe().f_lineno, query_result) return None public.log_record('当前查询条件【%s】下查询结果数据' % putout_status, sys._getframe().f_lineno, query_result) # 获取记账状态下的借据编号 serial_no = query_result['serial_no'] # 新增暂缓请求数据 add_reprieve_req_data = { 'loanNo': serial_no, 'type':'0' } public.log_record('新增暂缓请求数据', sys._getframe().f_lineno, add_reprieve_req_data) add_reprieve_res_data = requests.post( url=config.fatp_notify_default_url, json=add_reprieve_req_data ) if add_reprieve_res_data.json(): public.log_record('新增暂缓响应数据', sys._getframe().f_lineno, add_reprieve_res_data.text) #检测资金数据库中暂缓状态是否为0(未暂缓) reprieve_status = fatp.handel_query('1',('defer_pay_status'),**{'serial_no':serial_no}) public.log_record('检测资金数据库中暂缓状态是否为0(未暂缓)', sys._getframe().f_lineno,reprieve_status['defer_pay_status']) self.assertEqual(str(reprieve_status['defer_pay_status']),'0') else: public.log_record('新增暂缓失败响应数据', sys._getframe().f_lineno, add_reprieve_res_data.text) def test_submit_reprieve(self): ''' 提交暂缓''' # # 未记账/未暂缓状态数据 putout_status = { 'defer_pay_status': '0', 'put_out_status': '0' } fatp = fatp_db_server.LoadApplyRepository() query_result = fatp.handel_query('1', ('serial_no'), **putout_status) if query_result is None: public.log_record('当前查询条件【%s】下未存在有效数据' % putout_status, sys._getframe().f_lineno, query_result) return None public.log_record('当前查询条件【%s】下查询结果数据' % putout_status, sys._getframe().f_lineno, query_result) # 获取记账状态下的借据编号 serial_no = query_result['serial_no'] # 提交暂缓请求数据 submit_reprieve_req_data = { 'loanNo': serial_no, 'type': '1' } public.log_record('提交暂缓请求数据', sys._getframe().f_lineno, submit_reprieve_req_data) submit_reprieve_res_data = requests.post( url=config.fatp_notify_default_url, json=submit_reprieve_req_data ) if submit_reprieve_res_data.json(): public.log_record('提交暂缓响应数据', sys._getframe().f_lineno, submit_reprieve_res_data.text) # 检测资金数据库中暂缓状态是否为0(已暂缓) reprieve_status = fatp.handel_query('1', ('defer_pay_status'), **{'serial_no': serial_no}) public.log_record('检测资金数据库中暂缓状态是否为1(已暂缓)', sys._getframe().f_lineno, reprieve_status['defer_pay_status']) self.assertEqual(str(reprieve_status['defer_pay_status']), '1') else: public.log_record('提交暂缓失败响应数据', sys._getframe().f_lineno, submit_reprieve_res_data.text) def test_cancel_reprieve(self): ''' 取消暂缓''' # 未记账/已暂缓状态数据 putout_status = { 'defer_pay_status': '1', 'put_out_status': '0' } fatp = fatp_db_server.LoadApplyRepository() query_result = fatp.handel_query( ('t1.serial_no'), **putout_status) if query_result is None: public.log_record('当前查询条件【%s】下未存在有效数据' % putout_status, sys._getframe().f_lineno, query_result) return None public.log_record('当前查询条件【%s】下查询结果数据' % putout_status, sys._getframe().f_lineno, query_result) # 获取借据编号 serial_no = query_result['serial_no'] # 取消暂缓请求数据 cancel_reprieve_req_data = { 'loanNo': serial_no, 'type': '0' } public.log_record('取消暂缓请求数据', sys._getframe().f_lineno, cancel_reprieve_req_data) cancel_reprieve_res_data = requests.post( url=config.fatp_notify_default_url, json=cancel_reprieve_req_data ) if cancel_reprieve_res_data.json(): public.log_record('取消暂缓请响应数据', sys._getframe().f_lineno, cancel_reprieve_res_data.text) # 检测资金数据库中暂缓状态是否为0(未暂缓) reprieve_status = fatp.handel_query('1', ('defer_pay_status'), **{'serial_no': serial_no}) public.log_record('检测资金数据库中暂缓状态是否为0(未暂缓)', sys._getframe().f_lineno, reprieve_status['defer_pay_status']) self.assertEqual(str(reprieve_status['defer_pay_status']), '0') else: public.log_record('取消暂缓失败响应数据', sys._getframe().f_lineno, cancel_reprieve_res_data.text) def test_stop_apply(self): ''' 终止申请''' # 未记账/未暂缓状态数据 putout_status = { 'defer_pay_status': '0', 'put_out_status': '0' } fatp = fatp_db_server.LoadApplyRepository() query_result = fatp.handel_query('1', ('serial_no'), **putout_status) if query_result is None: public.log_record('当前查询条件【%s】下未存在有效数据' % putout_status, sys._getframe().f_lineno, query_result) return None public.log_record('当前查询条件【%s】下查询结果数据' % putout_status, sys._getframe().f_lineno, query_result) # 获取借据编号 serial_no = query_result['serial_no'] # 终止申请请求数据 stop_apply_req_data = { 'loanNo': serial_no, 'type': '1' } public.log_record('终止申请请求数据', sys._getframe().f_lineno, stop_apply_req_data) stop_apply_res_data = requests.post( url=config.fatp_notify_default_url, json=stop_apply_req_data ) if stop_apply_res_data.json(): public.log_record('终止申请响应数据', sys._getframe().f_lineno, stop_apply_res_data.text) # 检测资金数据库中暂缓状态是否为1(已暂缓) reprieve_status = fatp.handel_query('1', ('defer_pay_status'), **{'serial_no': serial_no}) public.log_record('检测资金数据库中暂缓状态是否为1(已暂缓)', sys._getframe().f_lineno, reprieve_status['defer_pay_status']) self.assertEqual(str(reprieve_status['defer_pay_status']), '1') else: public.log_record('终止申请失败响应数据', sys._getframe().f_lineno, stop_apply_res_data.text) def test_cancel_stop_apply(self): ''' 取消终止申请''' # 未记账/未暂缓状态数据 putout_status = { 'defer_pay_status': '1', 'put_out_status': '0' } fatp = fatp_db_server.LoadApplyRepository() query_result = fatp.handel_query('1', ('serial_no'), **putout_status) if query_result is None: public.log_record('当前查询条件【%s】下未存在有效数据' % putout_status, sys._getframe().f_lineno, query_result) return None public.log_record('当前查询条件【%s】下查询结果数据' % putout_status, sys._getframe().f_lineno, query_result) # 获取借据编号 serial_no = query_result['serial_no'] # 取消终止申请请求数据 cancel_stop_req_data = { 'loanNo': serial_no, 'type': '0' } public.log_record('取消终止申请请求数据', sys._getframe().f_lineno, cancel_stop_req_data) cancel_stop_res_data = requests.post( url=config.fatp_notify_default_url, json=cancel_stop_req_data ) if cancel_stop_res_data.json(): public.log_record('取消终止申请响应数据', sys._getframe().f_lineno, cancel_stop_res_data.text) # 检测资金数据库中暂缓状态是否为0(未暂缓) reprieve_status = fatp.handel_query('1', ('defer_pay_status'), **{'serial_no': serial_no}) public.log_record('检测资金数据库中暂缓状态是否为1(未暂缓)', sys._getframe().f_lineno, reprieve_status['defer_pay_status']) self.assertEqual(str(reprieve_status['defer_pay_status']), '0') else: public.log_record('取消终止申请失败响应数据', sys._getframe().f_lineno, cancel_stop_res_data.text) def test_stop_loan(self): ''' 终止放款''' # 未记账/已暂缓状态数据 putout_status = { 'defer_pay_status': '1', 'put_out_status': '0' } fatp = fatp_db_server.LoadApplyRepository() query_result = fatp.handel_query('1', ('serial_no'), **putout_status) if query_result is None: public.log_record('当前查询条件【%s】下未存在有效数据' % putout_status, sys._getframe().f_lineno, query_result) return None public.log_record('当前查询条件【%s】下查询结果数据' % putout_status, sys._getframe().f_lineno, query_result) # 获取借据编号 serial_no = query_result['serial_no'] # 终止放款请求数据 stop_loan_req_data = { 'loanNo': serial_no, 'type': '2' } public.log_record('终止放款请求数据', sys._getframe().f_lineno, stop_loan_req_data) stop_loan_res_data = requests.post( url=config.fatp_notify_default_url, json=stop_loan_req_data ) if stop_loan_res_data.json(): public.log_record('终止放款响应数据', sys._getframe().f_lineno, stop_loan_res_data.text) # 检测资金数据库中暂缓状态是否为2(已终止) reprieve_status = fatp.handel_query('1', ('defer_pay_status'), **{'serial_no': serial_no}) public.log_record('检测资金数据库中暂缓状态是否为2(已终止)', sys._getframe().f_lineno, reprieve_status['defer_pay_status']) self.assertEqual(str(reprieve_status['defer_pay_status']), '2') else: public.log_record('终止放款失败响应数据', sys._getframe().f_lineno, stop_loan_res_data.text) def test_cancel_stop_loan(self): ''' 取消终止放款''' # 未记账/已暂缓状态数据 putout_status = { 'defer_pay_status': '1', 'put_out_status': '0' } fatp = fatp_db_server.LoadApplyRepository() query_result = fatp.handel_query('1', ('serial_no'), **putout_status) if query_result is None: public.log_record('当前查询条件【%s】下未存在有效数据' % putout_status, sys._getframe().f_lineno, query_result) return None public.log_record('当前查询条件【%s】下查询结果数据' % putout_status, sys._getframe().f_lineno, query_result) # 获取借据编号 serial_no = query_result['serial_no'] # 取消终止放款请求数据 cancel_stop_req_data = { 'loanNo': serial_no, 'type': '1' } public.log_record('取消终止放款请求数据', sys._getframe().f_lineno, cancel_stop_req_data) stop_loan_res_data = requests.post( url=config.fatp_notify_default_url, json=cancel_stop_req_data ) if stop_loan_res_data.json(): public.log_record('取消终止放款响应数据', sys._getframe().f_lineno, stop_loan_res_data.text) # 检测资金数据库中暂缓状态是否为1(已暂缓) reprieve_status = fatp.handel_query('1', ('defer_pay_status'), **{'serial_no': serial_no}) public.log_record('检测资金数据库中暂缓状态是否为1(已暂缓)', sys._getframe().f_lineno, reprieve_status['defer_pay_status']) self.assertEqual(str(reprieve_status['defer_pay_status']), '1') else: public.log_record('取消终止放款失败响应数据', sys._getframe().f_lineno, stop_loan_res_data.text) if __name__ == '__main__': unittest.main()

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0.83358

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12,882

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33.9

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0

0.1

0

null

0

1

0

null

0

6

c04c9c42cf5836a1eff63a7d533e9e572094b517

222

py

Python

exceptions.py

rafagonc/django-amqp-consumer

9a14a3d62fbf533f2f340495f0037cc5a0799e6b

[ "MIT" ]

null

exceptions.py

rafagonc/django-amqp-consumer

9a14a3d62fbf533f2f340495f0037cc5a0799e6b

[ "MIT" ]

null

exceptions.py

rafagonc/django-amqp-consumer

9a14a3d62fbf533f2f340495f0037cc5a0799e6b

[ "MIT" ]

null

class CannotFindQueueException(Exception): def __init__(self, queue_name): self.queue_name = queue_name def __str__(self): return "Cannot find queue: " + self.queue_name + " on django settings"

22.2

78

0.684685

26

222

5.384615

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0

0.225225

222

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0.4

false

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null

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0

1

0

null

0

1

0

1

0

6

c05847a970427ca9b7b78a9bd5d689741b3e088c

40

py

Python

torpido/exceptions/__init__.py

AP-Atul/Torpido

a646b4d6de7f2e2c96de4c64ce3113f53e3931c2

[ "Unlicense" ]

21

2020-12-23T07:13:10.000Z

2022-01-12T10:32:22.000Z

torpido/exceptions/__init__.py

AP-Atul/Torpido

a646b4d6de7f2e2c96de4c64ce3113f53e3931c2

[ "Unlicense" ]

2

2020-12-30T10:45:42.000Z

2021-09-25T09:52:00.000Z

torpido/exceptions/__init__.py

AP-Atul/Torpido

a646b4d6de7f2e2c96de4c64ce3113f53e3931c2

[ "Unlicense" ]

1

2021-02-06T21:39:41.000Z

from torpido.exceptions.custom import *

20

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5

40

6.6

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null

0

1

0

1

0

1

0

6

c05a4c2bba8c9f85a65eca0b40600c663ee19113

202

py

Python

components/camera.py

nancynobody/teddy

ac2bde38d2f9891e83b644e9092af63b9b33e705

[ "MIT" ]

null

components/camera.py

nancynobody/teddy

ac2bde38d2f9891e83b644e9092af63b9b33e705

[ "MIT" ]

null

components/camera.py

nancynobody/teddy

ac2bde38d2f9891e83b644e9092af63b9b33e705

[ "MIT" ]

null

"""CAMERA """ import picamera class Camera: def __init__(self): self.camera = picamera.PiCamera() def start_recording(self): pass def stop_recording(self): pass

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1

0

1

0

6

c0699e70f27a08712282b97f1c28869c3ea3e6ab

38,207

py

Python

cryosat_toolkit/read_cryosat_L2.py

Sibada/read-cryosat-2

3267a0bb52857feb142a67cbb0e352160415c28f

[ "MIT" ]

null

cryosat_toolkit/read_cryosat_L2.py

Sibada/read-cryosat-2

3267a0bb52857feb142a67cbb0e352160415c28f

[ "MIT" ]

null

cryosat_toolkit/read_cryosat_L2.py

Sibada/read-cryosat-2

3267a0bb52857feb142a67cbb0e352160415c28f

[ "MIT" ]

null

#!/usr/bin/env python u""" read_cryosat_L2.py Written by Tyler Sutterley (10/2018) Reads CryoSat Level-2 data products from baselines A, B and C Supported CryoSat Modes: LRM, SAR, SARin, FDM, SID, GDR INPUTS: full_filename: full path of CryoSat .DBL file OUTPUTS: Data_1Hz: Time and Orbit Parameters Corrections: Elevation Corrections and Flags Data_20Hz: Geolocation and Elevation Measurements with Quality Parameters METADATA: MPH, SPH and DSD Header data UPDATE HISTORY: Updated 10/2018: updated header read functions for python3 Updated 11/2016: added Abs_Orbit and Ascending_Flg to Data_1Hz outputs Abs_Orbit should be same as in read_cryosat_ground_tracks.py Ascending_Flg can use in surface regression fits following McMillan (2014) Updated 05/2016: using __future__ print and division functions Written 03/2016 """ from __future__ import print_function from __future__ import division import os import re import numpy as np #-- PURPOSE: Initiate L2 MDS variables for CryoSat Baselines A and B def cryosat_baseline_AB(fid,record_size,n_records): #-- CryoSat-2 1 Hz data fields (Location Group) #-- Time and Orbit Parameters plus Measurement Mode L2_1Hz_parameters = {} #-- Time: day part L2_1Hz_parameters['Day'] = np.zeros((n_records),dtype=np.int32) #-- Time: second part L2_1Hz_parameters['Second'] = np.zeros((n_records),dtype=np.int32) #-- Time: microsecond part L2_1Hz_parameters['Micsec'] = np.zeros((n_records),dtype=np.int32) #-- SIRAL mode L2_1Hz_parameters['Siral_mode'] = np.zeros((n_records),dtype=np.uint64) #-- Lat_1Hz: packed units (0.1 micro-degree, 1e-7 degrees) L2_1Hz_parameters['Lat_1Hz'] = np.zeros((n_records),dtype=np.int32) #-- Lon_1Hz: packed units (0.1 micro-degree, 1e-7 degrees) L2_1Hz_parameters['Lon_1Hz'] = np.zeros((n_records),dtype=np.int32) #-- Alt_1Hz: packed units (mm, 1e-3 m) #-- Altitude of COG above reference ellipsoid (interpolated value) L2_1Hz_parameters['Alt_1Hz'] = np.zeros((n_records),dtype=np.int32) #-- Mispointing: packed units (millidegrees, 1e-3 degrees) L2_1Hz_parameters['Mispointing'] = np.zeros((n_records),dtype=np.int16) #-- Number of valid records in the block of twenty that contain data #-- Last few records of the last block of a dataset may be blank blocks #-- inserted to bring the file up to a multiple of twenty. L2_1Hz_parameters['N_valid'] = np.zeros((n_records),dtype=np.int16) #-- CryoSat-2 geophysical corrections (External Corrections Group) L2_final_corrections = {} #-- Dry Tropospheric Correction packed units (mm, 1e-3 m) L2_final_corrections['dryTrop'] = np.zeros((n_records),dtype=np.int16) #-- Wet Tropospheric Correction packed units (mm, 1e-3 m) L2_final_corrections['wetTrop'] = np.zeros((n_records),dtype=np.int16) #-- Inverse Barometric Correction packed units (mm, 1e-3 m) L2_final_corrections['InvBar'] = np.zeros((n_records),dtype=np.int16) #-- Dynamic Atmosphere Correction packed units (mm, 1e-3 m) L2_final_corrections['DynAtm'] = np.zeros((n_records),dtype=np.int16) #-- Ionospheric Correction packed units (mm, 1e-3 m) L2_final_corrections['Iono'] = np.zeros((n_records),dtype=np.int16) #-- Sea State Bias Correction packed units (mm, 1e-3 m) L2_final_corrections['SSB'] = np.zeros((n_records),dtype=np.int16) #-- Ocean tide Correction packed units (mm, 1e-3 m) L2_final_corrections['ocTideElv'] = np.zeros((n_records),dtype=np.int16) #-- Long period equilibrium ocean tide Correction packed units (mm, 1e-3 m) L2_final_corrections['lpeTideElv'] = np.zeros((n_records),dtype=np.int16) #-- Ocean loading tide Correction packed units (mm, 1e-3 m) L2_final_corrections['olTideElv'] = np.zeros((n_records),dtype=np.int16) #-- Solid Earth tide Correction packed units (mm, 1e-3 m) L2_final_corrections['seTideElv'] = np.zeros((n_records),dtype=np.int16) #-- Geocentric Polar tide Correction packed units (mm, 1e-3 m) L2_final_corrections['gpTideElv'] = np.zeros((n_records),dtype=np.int16) L2_final_corrections['Spare1'] = np.zeros((n_records),dtype=np.int16) #-- Surface Type: Packed in groups of three bits for each of the 20 records L2_final_corrections['Surf_type'] = np.zeros((n_records),dtype=np.uint64) #-- Mean Sea Surface or Geoid packed units (mm, 1e-3 m) L2_final_corrections['MSS_Geoid'] = np.zeros((n_records),dtype=np.int32) #-- Ocean Depth/Land Elevation Model (ODLE) packed units (mm, 1e-3 m) L2_final_corrections['ODLE'] = np.zeros((n_records),dtype=np.int32) #-- Ice Concentration packed units (%/100) L2_final_corrections['Ice_conc'] = np.zeros((n_records),dtype=np.int16) #-- Snow Depth packed units (mm, 1e-3 m) L2_final_corrections['Snow_depth'] = np.zeros((n_records),dtype=np.int16) #-- Snow Density packed units (kg/m^3) L2_final_corrections['Snow_density'] = np.zeros((n_records),dtype=np.int16) L2_final_corrections['Spare2'] = np.zeros((n_records),dtype=np.int16) #-- Corrections Status Flag L2_final_corrections['C_status'] = np.zeros((n_records),dtype=np.uint32) #-- Significant Wave Height (SWH) packed units (mm, 1e-3) L2_final_corrections['SWH'] = np.zeros((n_records),dtype=np.int16) #-- Wind Speed packed units (mm/s, 1e-3 m/s) L2_final_corrections['Wind_speed'] = np.zeros((n_records),dtype=np.uint16) L2_final_corrections['Spare3'] = np.zeros((n_records),dtype=np.int16) L2_final_corrections['Spare4'] = np.zeros((n_records),dtype=np.int16) L2_final_corrections['Spare5'] = np.zeros((n_records),dtype=np.int16) L2_final_corrections['Spare6'] = np.zeros((n_records),dtype=np.int16) #-- CryoSat-2 20 Hz data fields (Measurement Group) #-- Derived from instrument measurement parameters n_blocks = 20 L2_final_measurements = {} #-- Delta between the timestamps for 20Hz record and the 1Hz record #-- D_time_mics packed units (microseconds) L2_final_measurements['D_time_mics'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Lat: packed units (0.1 micro-degree, 1e-7 degrees) L2_final_measurements['Lat'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Lon: packed units (0.1 micro-degree, 1e-7 degrees) L2_final_measurements['Lon'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Measured elevation above ellipsoid from retracker: packed units (mm, 1e-3 m) L2_final_measurements['Elev'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Interpolated Sea Surface Height Anomaly: packed units (mm, 1e-3 m) L2_final_measurements['SSHA_interp'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Interpolated Sea Surface Height measurement count L2_final_measurements['SSHA_num'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Interpolation quality estimate RSS: packed units (mm, 1e-3 m) L2_final_measurements['SSHA_qual'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Sigma Zero Backscatter for retracker: packed units (1e-2 dB) L2_final_measurements['Sig0'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Peakiness: packed units (1e-2) L2_final_measurements['Peakiness'] = np.zeros((n_records,n_blocks),dtype=np.uint16) #-- Freeboard: packed units (mm, 1e-3 m) #-- -9999 default value indicates computation has not been performed L2_final_measurements['Freeboard'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Number of averaged echoes or beams L2_final_measurements['N_avg'] = np.zeros((n_records,n_blocks),dtype=np.int16) L2_final_measurements['Spare1'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Quality flags L2_final_measurements['Quality_Flg'] = np.zeros((n_records,n_blocks),dtype=np.uint32) L2_final_measurements['Spare2'] = np.zeros((n_records,n_blocks),dtype=np.int16) L2_final_measurements['Spare3'] = np.zeros((n_records,n_blocks),dtype=np.int16) L2_final_measurements['Spare4'] = np.zeros((n_records,n_blocks),dtype=np.int16) L2_final_measurements['Spare5'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- for each record in the CryoSat file for r in range(n_records): #-- CryoSat-2 Location Group for record r L2_1Hz_parameters['Day'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_1Hz_parameters['Second'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_1Hz_parameters['Micsec'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_1Hz_parameters['Siral_mode'][r] = np.fromfile(fid,dtype='>u8',count=1) L2_1Hz_parameters['Lat_1Hz'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_1Hz_parameters['Lon_1Hz'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_1Hz_parameters['Alt_1Hz'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_1Hz_parameters['Mispointing'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_1Hz_parameters['N_valid'][r] = np.fromfile(fid,dtype='>i2',count=1) #-- CryoSat-2 External Corrections Group for record r L2_final_corrections['dryTrop'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['wetTrop'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['InvBar'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['DynAtm'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Iono'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['SSB'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['ocTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['lpeTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['olTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['seTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['gpTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Spare1'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Surf_type'][r] = np.fromfile(fid,dtype='>u8',count=1) L2_final_corrections['MSS_Geoid'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_final_corrections['ODLE'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_final_corrections['Ice_conc'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Snow_depth'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Snow_density'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Spare2'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['C_status'][r] = np.fromfile(fid,dtype='>u4',count=1) L2_final_corrections['SWH'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Wind_speed'][r] = np.fromfile(fid,dtype='>u2',count=1) L2_final_corrections['Spare3'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Spare4'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Spare5'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_final_corrections['Spare6'][r] = np.fromfile(fid,dtype='>i2',count=1) #-- CryoSat-2 Measurements Group for record r and block b for b in range(n_blocks): L2_final_measurements['D_time_mics'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_final_measurements['Lat'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_final_measurements['Lon'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_final_measurements['Elev'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_final_measurements['SSHA_interp'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['SSHA_num'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['SSHA_qual'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['Sig0'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['Peakiness'][r,b] = np.fromfile(fid,dtype='>u2',count=1) L2_final_measurements['Freeboard'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['N_avg'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['Spare1'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['Quality_Flg'][r,b] = np.fromfile(fid,dtype='>u4',count=1) L2_final_measurements['Spare2'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['Spare3'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['Spare4'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_final_measurements['Spare5'][r,b] = np.fromfile(fid,dtype='>i2',count=1) #-- Bind all the bits of the l2_mds together into a single dictionary CS_l2_mds = {} CS_l2_mds['Data_1Hz'] = L2_1Hz_parameters CS_l2_mds['Corrections'] = L2_final_corrections CS_l2_mds['Data_20Hz'] = L2_final_measurements #-- return the output dictionary return CS_l2_mds #-- PURPOSE: Initiate L2 MDS variables for CryoSat Baseline C def cryosat_baseline_C(fid,record_size,n_records): #-- CryoSat-2 1 Hz data fields (Location Group) #-- Time and Orbit Parameters plus Measurement Mode L2_c_1Hz_parameters = {} #-- Time: day part L2_c_1Hz_parameters['Day'] = np.zeros((n_records),dtype=np.int32) #-- Time: second part L2_c_1Hz_parameters['Second'] = np.zeros((n_records),dtype=np.int32) #-- Time: microsecond part L2_c_1Hz_parameters['Micsec'] = np.zeros((n_records),dtype=np.int32) #-- SIRAL mode L2_c_1Hz_parameters['Siral_mode'] = np.zeros((n_records),dtype=np.uint64) #-- Lat_1Hz: packed units (0.1 micro-degree, 1e-7 degrees) L2_c_1Hz_parameters['Lat_1Hz'] = np.zeros((n_records),dtype=np.int32) #-- Lon_1Hz: packed units (0.1 micro-degree, 1e-7 degrees) L2_c_1Hz_parameters['Lon_1Hz'] = np.zeros((n_records),dtype=np.int32) #-- Alt_1Hz: packed units (mm, 1e-3 m) #-- Altitude of COG above reference ellipsoid (interpolated value) L2_c_1Hz_parameters['Alt_1Hz'] = np.zeros((n_records),dtype=np.int32) #-- Roll: packed units (0.1 micro-degree, 1e-7 degrees) L2_c_1Hz_parameters['Roll'] = np.zeros((n_records),dtype=np.int32) #-- Pitch: packed units (0.1 micro-degree, 1e-7 degrees) L2_c_1Hz_parameters['Pitch'] = np.zeros((n_records),dtype=np.int32) #-- Yaw: packed units (0.1 micro-degree, 1e-7 degrees) L2_c_1Hz_parameters['Yaw'] = np.zeros((n_records),dtype=np.int32) L2_c_1Hz_parameters['Spare'] = np.zeros((n_records),dtype=np.int16) #-- Number of valid records in the block of twenty that contain data #-- Last few records of the last block of a dataset may be blank blocks #-- inserted to bring the file up to a multiple of twenty. L2_c_1Hz_parameters['N_valid'] = np.zeros((n_records),dtype=np.int16) #-- CryoSat-2 geophysical corrections (External Corrections Group) L2_c_final_corrections = {} #-- Dry Tropospheric Correction packed units (mm, 1e-3 m) L2_c_final_corrections['dryTrop'] = np.zeros((n_records),dtype=np.int16) #-- Wet Tropospheric Correction packed units (mm, 1e-3 m) L2_c_final_corrections['wetTrop'] = np.zeros((n_records),dtype=np.int16) #-- Inverse Barometric Correction packed units (mm, 1e-3 m) L2_c_final_corrections['InvBar'] = np.zeros((n_records),dtype=np.int16) #-- Dynamic Atmosphere Correction packed units (mm, 1e-3 m) L2_c_final_corrections['DynAtm'] = np.zeros((n_records),dtype=np.int16) #-- Ionospheric Correction packed units (mm, 1e-3 m) L2_c_final_corrections['Iono'] = np.zeros((n_records),dtype=np.int16) #-- Sea State Bias Correction packed units (mm, 1e-3 m) L2_c_final_corrections['SSB'] = np.zeros((n_records),dtype=np.int16) #-- Ocean tide Correction packed units (mm, 1e-3 m) L2_c_final_corrections['ocTideElv'] = np.zeros((n_records),dtype=np.int16) #-- Long period equilibrium ocean tide Correction packed units (mm, 1e-3 m) L2_c_final_corrections['lpeTideElv'] = np.zeros((n_records),dtype=np.int16) #-- Ocean loading tide Correction packed units (mm, 1e-3 m) L2_c_final_corrections['olTideElv'] = np.zeros((n_records),dtype=np.int16) #-- Solid Earth tide Correction packed units (mm, 1e-3 m) L2_c_final_corrections['seTideElv'] = np.zeros((n_records),dtype=np.int16) #-- Geocentric Polar tide Correction packed units (mm, 1e-3 m) L2_c_final_corrections['gpTideElv'] = np.zeros((n_records),dtype=np.int16) L2_c_final_corrections['Spare1'] = np.zeros((n_records),dtype=np.int16) #-- Surface Type: Packed in groups of three bits for each of the 20 records L2_c_final_corrections['Surf_type'] = np.zeros((n_records),dtype=np.uint64) #-- Mean Sea Surface or Geoid packed units (mm, 1e-3 m) L2_c_final_corrections['MSS_Geoid'] = np.zeros((n_records),dtype=np.int32) #-- Ocean Depth/Land Elevation Model (ODLE) packed units (mm, 1e-3 m) L2_c_final_corrections['ODLE'] = np.zeros((n_records),dtype=np.int32) #-- Ice Concentration packed units (%/100) L2_c_final_corrections['Ice_conc'] = np.zeros((n_records),dtype=np.int16) #-- Snow Depth packed units (mm, 1e-3 m) L2_c_final_corrections['Snow_depth'] = np.zeros((n_records),dtype=np.int16) #-- Snow Density packed units (kg/m^3) L2_c_final_corrections['Snow_density'] = np.zeros((n_records),dtype=np.int16) L2_c_final_corrections['Spare2'] = np.zeros((n_records),dtype=np.int16) #-- Corrections Status Flag L2_c_final_corrections['C_status'] = np.zeros((n_records),dtype=np.uint32) #-- Significant Wave Height (SWH) packed units (mm, 1e-3) L2_c_final_corrections['SWH'] = np.zeros((n_records),dtype=np.int16) #-- Wind Speed packed units (mm/s, 1e-3 m/s) L2_c_final_corrections['Wind_speed'] = np.zeros((n_records),dtype=np.uint16) L2_c_final_corrections['Spare3'] = np.zeros((n_records),dtype=np.int16) L2_c_final_corrections['Spare4'] = np.zeros((n_records),dtype=np.int16) L2_c_final_corrections['Spare5'] = np.zeros((n_records),dtype=np.int16) L2_c_final_corrections['Spare6'] = np.zeros((n_records),dtype=np.int16) #-- CryoSat-2 20 Hz data fields (Measurement Group) #-- Derived from instrument measurement parameters n_blocks = 20 L2_c_final_measurements = {} #-- Delta between the timestamps for 20Hz record and the 1Hz record #-- D_time_mics packed units (microseconds) L2_c_final_measurements['D_time_mics'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Lat: packed units (0.1 micro-degree, 1e-7 degrees) L2_c_final_measurements['Lat'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Lon: packed units (0.1 micro-degree, 1e-7 degrees) L2_c_final_measurements['Lon'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Measured elevation above ellipsoid from retracker 1: packed units (mm, 1e-3 m) L2_c_final_measurements['Elev_1'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Measured elevation above ellipsoid from retracker 2: packed units (mm, 1e-3 m) L2_c_final_measurements['Elev_2'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Measured elevation above ellipsoid from retracker 3: packed units (mm, 1e-3 m) L2_c_final_measurements['Elev_3'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Sigma Zero Backscatter for retracker 1: packed units (1e-2 dB) L2_c_final_measurements['Sig0_1'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Sigma Zero Backscatter for retracker 2: packed units (1e-2 dB) L2_c_final_measurements['Sig0_2'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Sigma Zero Backscatter for retracker 3: packed units (1e-2 dB) L2_c_final_measurements['Sig0_3'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Freeboard: packed units (mm, 1e-3 m) #-- -9999 default value indicates computation has not been performed L2_c_final_measurements['Freeboard'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Interpolated Sea Surface Height Anomaly: packed units (mm, 1e-3 m) L2_c_final_measurements['SSHA_interp'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Interpolated Sea Surface Height measurement count L2_c_final_measurements['SSHA_num'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Interpolation quality estimate RSS: packed units (mm, 1e-3 m) L2_c_final_measurements['SSHA_qual'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Peakiness: packed units (1e-2) L2_c_final_measurements['Peakiness'] = np.zeros((n_records,n_blocks),dtype=np.uint16) #-- Number of averaged echoes or beams L2_c_final_measurements['N_avg'] = np.zeros((n_records,n_blocks),dtype=np.int16) L2_c_final_measurements['Spare1'] = np.zeros((n_records,n_blocks),dtype=np.int16) #-- Quality flags L2_c_final_measurements['Quality_Flg'] = np.zeros((n_records,n_blocks),dtype=np.uint32) #-- Corrections Application Flag L2_c_final_measurements['Corrections_Flg'] = np.zeros((n_records,n_blocks),dtype=np.uint32) #-- Quality metric for retracker 1 L2_c_final_measurements['Quality_1'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Quality metric for retracker 2 L2_c_final_measurements['Quality_2'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- Quality metric for retracker 3 L2_c_final_measurements['Quality_3'] = np.zeros((n_records,n_blocks),dtype=np.int32) #-- for each record in the CryoSat file for r in range(n_records): #-- CryoSat-2 Location Group for record r L2_c_1Hz_parameters['Day'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Second'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Micsec'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Siral_mode'][r] = np.fromfile(fid,dtype='>u8',count=1) L2_c_1Hz_parameters['Lat_1Hz'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Lon_1Hz'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Alt_1Hz'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Roll'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Pitch'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Yaw'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_1Hz_parameters['Spare'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_1Hz_parameters['N_valid'][r] = np.fromfile(fid,dtype='>i2',count=1) #-- CryoSat-2 External Corrections Group for record r L2_c_final_corrections['dryTrop'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['wetTrop'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['InvBar'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['DynAtm'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Iono'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['SSB'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['ocTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['lpeTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['olTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['seTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['gpTideElv'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Spare1'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Surf_type'][r] = np.fromfile(fid,dtype='>u8',count=1) L2_c_final_corrections['MSS_Geoid'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_corrections['ODLE'][r] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_corrections['Ice_conc'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Snow_depth'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Snow_density'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Spare2'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['C_status'][r] = np.fromfile(fid,dtype='>u4',count=1) L2_c_final_corrections['SWH'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Wind_speed'][r] = np.fromfile(fid,dtype='>u2',count=1) L2_c_final_corrections['Spare3'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Spare4'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Spare5'][r] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_corrections['Spare6'][r] = np.fromfile(fid,dtype='>i2',count=1) #-- CryoSat-2 Measurements Group for record r and block b for b in range(n_blocks): L2_c_final_measurements['D_time_mics'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_measurements['Lat'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_measurements['Lon'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_measurements['Elev_1'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_measurements['Elev_2'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_measurements['Elev_3'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_measurements['Sig0_1'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['Sig0_2'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['Sig0_3'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['Freeboard'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['SSHA_interp'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['SSHA_num'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['SSHA_qual'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['Peakiness'][r,b] = np.fromfile(fid,dtype='>u2',count=1) L2_c_final_measurements['N_avg'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['Spare1'][r,b] = np.fromfile(fid,dtype='>i2',count=1) L2_c_final_measurements['Quality_Flg'][r,b] = np.fromfile(fid,dtype='>u4',count=1) L2_c_final_measurements['Corrections_Flg'][r,b] = np.fromfile(fid,dtype='>u4',count=1) L2_c_final_measurements['Quality_1'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_measurements['Quality_2'][r,b] = np.fromfile(fid,dtype='>i4',count=1) L2_c_final_measurements['Quality_3'][r,b] = np.fromfile(fid,dtype='>i4',count=1) #-- Bind all the bits of the l2_mds together into a single dictionary CS_l2_c_mds = {} CS_l2_c_mds['Data_1Hz'] = L2_c_1Hz_parameters CS_l2_c_mds['Corrections'] = L2_c_final_corrections CS_l2_c_mds['Data_20Hz'] = L2_c_final_measurements #-- return the output dictionary return CS_l2_c_mds #-- PURPOSE: Read ASCII Main Product Header (MPH) block from an ESA PDS file def read_MPH(full_filename): #-- read input data file with open(full_filename, 'rb') as fid: file_contents = fid.read().splitlines() #-- Define constant values associated with PDS file formats #-- number of text lines in standard MPH n_MPH_lines = 41 #-- check that first line of header matches PRODUCT if not bool(re.match(b'PRODUCT\=\"(.*)(?=\")',file_contents[0])): raise IOError('File does not start with a valid PDS MPH') #-- read MPH header text s_MPH_fields = {} for i in range(n_MPH_lines): #-- use regular expression operators to read headers if bool(re.match(b'(.*?)\=\"(.*)(?=\")',file_contents[i])): #-- data fields within quotes field,value=re.findall(b'(.*?)\=\"(.*)(?=\")',file_contents[i]).pop() s_MPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() elif bool(re.match(b'(.*?)\=(.*)',file_contents[i])): #-- data fields without quotes field,value=re.findall(b'(.*?)\=(.*)',file_contents[i]).pop() s_MPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() #-- Return block name array to calling function return s_MPH_fields #-- PURPOSE: Read ASCII Specific Product Header (SPH) block from a PDS file def read_SPH(full_filename,j_sph_size): #-- read input data file with open(full_filename, 'rb') as fid: file_contents = fid.read().splitlines() #-- Define constant values associated with PDS file formats #-- number of text lines in standard MPH n_MPH_lines = 41 #-- compile regular expression operator for reading headers rx = re.compile(b'(.*?)\=\"?(.*)',re.VERBOSE) #-- check first line of header matches SPH_DESCRIPTOR if not bool(re.match(b'SPH\_DESCRIPTOR\=',file_contents[n_MPH_lines+1])): raise IOError('File does not have a valid PDS DSD') #-- read SPH header text (no binary control characters) s_SPH_lines = [li for li in file_contents[n_MPH_lines+1:] if rx.match(li) and not re.search(b'[^\x20-\x7e]+',li)] #-- extract SPH header text s_SPH_fields = {} c = 0 while (c < len(s_SPH_lines)): #-- check if line is within DS_NAME portion of SPH header if bool(re.match(b'DS_NAME',s_SPH_lines[c])): #-- add dictionary for DS_NAME field,value=re.findall(b'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c]).pop() key = value.decode('utf-8').rstrip() s_SPH_fields[key] = {} for line in s_SPH_lines[c+1:c+7]: if bool(re.match(b'(.*?)\=\"(.*)(?=\")',line)): #-- data fields within quotes dsfield,dsvalue=re.findall(b'(.*?)\=\"(.*)(?=\")',line).pop() s_SPH_fields[key][dsfield.decode('utf-8')] = dsvalue.decode('utf-8').rstrip() elif bool(re.match(b'(.*?)\=(.*)',line)): #-- data fields without quotes dsfield,dsvalue=re.findall(b'(.*?)\=(.*)',line).pop() s_SPH_fields[key][dsfield.decode('utf-8')] = dsvalue.decode('utf-8').rstrip() #-- add 6 to counter to go to next entry c += 6 #-- use regular expression operators to read headers elif bool(re.match(b'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c])): #-- data fields within quotes field,value=re.findall(b'(.*?)\=\"(.*)(?=\")',s_SPH_lines[c]).pop() s_SPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() elif bool(re.match(b'(.*?)\=(.*)',s_SPH_lines[c])): #-- data fields without quotes field,value=re.findall(b'(.*?)\=(.*)',s_SPH_lines[c]).pop() s_SPH_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() #-- add 1 to counter to go to next line c += 1 #-- Return block name array to calling function return s_SPH_fields #-- PURPOSE: Read ASCII Data Set Descriptors (DSD) block from a PDS file def read_DSD(full_filename): #-- read input data file with open(full_filename, 'rb') as fid: file_contents = fid.read().splitlines() #-- Define constant values associated with PDS file formats #-- number of text lines in standard MPH n_MPH_lines = 41 #-- number of text lines in a DSD header n_DSD_lines = 8 #-- Level-2 CryoSat DS_NAMES within files regex_patterns = [] regex_patterns.append(b'DS_NAME\="SIR_LRM_L2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SAR_L2B[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SAR_L2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_FDM_L2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SARIL2B[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SARIL2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SAR_L2B_I[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SAR_L2A[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SIN_L2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SID_L2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_LRMIL2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_LRM_L2_I[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SARIL2A[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SAR_L2A_I[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SAR_L2_I[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SINIL2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SIN_L2_I[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SIDIL2[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_SID_L2_I[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_GDR_2A[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_GDR_2B[\s+]*"') regex_patterns.append(b'DS_NAME\="SIR_GDR_2[\s+]*"') #-- find the DSD starting line within the SPH header c = 0 Flag = False while ((Flag is False) and (c < len(regex_patterns))): #-- find indice within indice = [i for i,line in enumerate(file_contents[n_MPH_lines+1:]) if re.search(regex_patterns[c],line)] if indice: Flag = True else: c+=1 #-- check that valid indice was found within header if not indice: raise IOError('Can not find correct DSD field') #-- extract s_DSD_fields info DSD_START = n_MPH_lines + indice[0] + 1 s_DSD_fields = {} for i in range(DSD_START,DSD_START+n_DSD_lines): #-- use regular expression operators to read headers if bool(re.match(b'(.*?)\=\"(.*)(?=\")',file_contents[i])): #-- data fields within quotes field,value=re.findall(b'(.*?)\=\"(.*)(?=\")',file_contents[i]).pop() s_DSD_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() elif bool(re.match(b'(.*?)\=(.*)',file_contents[i])): #-- data fields without quotes field,value=re.findall(b'(.*?)\=(.*)',file_contents[i]).pop() s_DSD_fields[field.decode('utf-8')] = value.decode('utf-8').rstrip() #-- Return block name array to calling function return s_DSD_fields #-- PURPOSE: read CryoSat Level-2 data def read_cryosat_L2(full_filename, VERBOSE=False): #-- file basename and file extension of input file fileBasename,fileExtension=os.path.splitext(os.path.basename(full_filename)) #-- CryoSat file class #-- OFFL (Off Line Processing/Systematic) #-- NRT_ (Near Real Time) #-- RPRO (ReProcessing) #-- TEST (Testing) #-- LTA_ (Long Term Archive) regex_class = 'OFFL|NRT_|RPRO|TEST|LTA_' #-- CryoSat mission products #-- SIR_LRM_2 L2 Product from Low Resolution Mode Processing #-- SIR_FDM_2 L2 Product from Fast Delivery Marine Mode Processing #-- SIR_SIN_2 L2 Product from SAR Interferometric Processing #-- SIR_SID_2 L2 Product from SIN Degraded Processing #-- SIR_SAR_2 L2 Product from SAR Processing #-- SIR_GDR_2 L2 Consolidated Product #-- SIR_LRMI2 In-depth L2 Product from LRM Processing #-- SIR_SINI2 In-depth L2 Product from SIN Processing #-- SIR_SIDI2 In-depth L2 Product from SIN Degraded Process. #-- SIR_SARI2 In-depth L2 Product from SAR Processing regex_products = ('SIR_LRM_2|SIR_FDM_2|SIR_SIN_2|SIR_SID_2|' 'SIR_SAR_2|SIR_GDR_2|SIR_LRMI2|SIR_SINI2|SIR_SIDI2|SIR_SARI2') #-- CRYOSAT LEVEL-2 PRODUCTS NAMING RULES #-- Mission Identifier #-- File Class #-- File Product #-- Validity Start Date and Time #-- Validity Stop Date and Time #-- Baseline Identifier #-- Version Number regex_pattern = '(.*?)_({0})_({1})__(\d+T?\d+)_(\d+T?\d+)_(.*?)(\d+)'.format( regex_class, regex_products) rx = re.compile(regex_pattern, re.VERBOSE) #-- extract file information from filename MI,CLASS,PRODUCT,START,STOP,BASELINE,VERSION=rx.findall(fileBasename).pop() #-- Extract Date information start_yr,start_mon,start_day=np.array([START[:4],START[4:6],START[6:8]],dtype=np.uint16) start_hh,start_mm,start_ss=np.array([START[-6:-4],START[-4:-2],START[-2:]],dtype=np.uint8) stop_yr,stop_mon,stop_day=np.array([STOP[:4],STOP[4:6],STOP[6:8]],dtype=np.uint16) stop_hh,stop_mm,stop_ss=np.array([STOP[-6:-4],STOP[-4:-2],STOP[-2:]],dtype=np.uint8) #-- Record sizes CS_L2_MDS_REC_SIZE = 980 CS_L2_C_MDS_REC_SIZE = 1392 #-- check baseline from file to set i_record_size and allocation function if (BASELINE == 'C'): i_record_size = CS_L2_C_MDS_REC_SIZE read_cryosat_variables = cryosat_baseline_C else: i_record_size = CS_L2_MDS_REC_SIZE read_cryosat_variables = cryosat_baseline_AB #-- read the input file to get file information fid = os.open(os.path.expanduser(full_filename),os.O_RDONLY) file_info = os.fstat(fid) os.close(fid) #-- num DSRs from SPH j_num_DSR = np.int32(file_info.st_size//i_record_size) #-- print file information if VERBOSE: print(fileBasename) print('{0:d} {1:d} {2:d}'.format(j_num_DSR,file_info.st_size,i_record_size)) #-- Check if MPH/SPH/DSD headers if (j_num_DSR*i_record_size == file_info.st_size): print('No Header on file') print('The number of DSRs is: {0:d}'.format(j_num_DSR)) else: print('Header on file') #-- Check if MPH/SPH/DSD headers if (j_num_DSR*i_record_size != file_info.st_size): #-- If there are MPH/SPH/DSD headers s_MPH_fields = read_MPH(full_filename) j_sph_size = np.int32(re.findall('[-+]?\d+',s_MPH_fields['SPH_SIZE']).pop()) s_SPH_fields = read_SPH(full_filename,j_sph_size) #-- extract information from DSD fields s_DSD_fields = read_DSD(full_filename) #-- extract DS_OFFSET j_DS_start = np.int32(re.findall('[-+]?\d+',s_DSD_fields['DS_OFFSET']).pop()) #-- extract number of DSR in the file j_num_DSR = np.int32(re.findall('[-+]?\d+',s_DSD_fields['NUM_DSR']).pop()) #-- check the record size j_DSR_size = np.int32(re.findall('[-+]?\d+',s_DSD_fields['DSR_SIZE']).pop()) #-- minimum size is start of the read plus number of records to read j_check_size = j_DS_start +(j_DSR_size*j_num_DSR) if VERBOSE: print('The offset of the DSD is: {0:d} bytes'.format(j_DS_start)) print('The number of DSRs is {0:d}'.format(j_num_DSR)) print('The size of the DSR is {0:d}'.format(j_DSR_size)) #-- check if invalid file size if (j_check_size > file_info.st_size): raise IOError('File size error') #-- extract binary data from input CryoSat data file (skip headers) fid = open(full_filename, 'rb') cryosat_header = fid.read(j_DS_start) #-- iterate through CryoSat file and fill output variables CS_L2_mds = read_cryosat_variables(fid,i_record_size,j_num_DSR) #-- add headers to output dictionary as METADATA CS_L2_mds['METADATA'] = {} CS_L2_mds['METADATA']['MPH'] = s_MPH_fields CS_L2_mds['METADATA']['SPH'] = s_SPH_fields CS_L2_mds['METADATA']['DSD'] = s_DSD_fields #-- add absolute orbit number to 1Hz data CS_L2_mds['Data_1Hz']['Abs_Orbit']=np.zeros((j_num_DSR),dtype=np.uint32) CS_L2_mds['Data_1Hz']['Abs_Orbit'][:]=np.uint32(s_MPH_fields['ABS_ORBIT']) #-- add ascending/descending flag to 1Hz data (A=ascending,D=descending) CS_L2_mds['Data_1Hz']['Ascending_Flg']=np.zeros((j_num_DSR),dtype=np.bool) if (s_SPH_fields['ASCENDING_FLAG'] == 'A'): CS_L2_mds['Data_1Hz']['Ascending_Flg'][:] = True #-- close the input CryoSat binary file fid.close() else: #-- If there are not MPH/SPH/DSD headers #-- extract binary data from input CryoSat data file fid = open(full_filename, 'rb') #-- iterate through CryoSat file and fill output variables CS_L2_mds = read_cryosat_variables(fid,i_record_size,j_num_DSR) #-- close the input CryoSat binary file fid.close() #-- return the data and headers return CS_L2_mds

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fbf12203c00167a6583c5273bfe342886b08eaa3

16,928

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Python

tests/gui/steps/oneprovider/file_browser.py

aoxiangflysky/onedata

5fe5783f4fb23e90e6567d638a165a0bfcc2f663

[ "Apache-2.0" ]

2

2017-09-15T10:38:56.000Z

2017-09-20T12:48:55.000Z

tests/gui/steps/oneprovider/file_browser.py

aoxiangflysky/onedata

5fe5783f4fb23e90e6567d638a165a0bfcc2f663

[ "Apache-2.0" ]

31

2016-09-07T11:50:15.000Z

2017-10-31T11:47:50.000Z

tests/gui/steps/oneprovider/file_browser.py

aoxiangflysky/onedata

5fe5783f4fb23e90e6567d638a165a0bfcc2f663

[ "Apache-2.0" ]

1

2017-08-31T11:55:09.000Z

"""Steps used for file list handling in various GUI testing scenarios """ from time import time from datetime import datetime import pytest from pytest_bdd import when, then, parsers from tests.gui.conftest import WAIT_BACKEND, SELENIUM_IMPLICIT_WAIT, WAIT_FRONTEND from tests.gui.utils.generic import parse_seq, repeat_failed, implicit_wait __author__ = "Bartek Walkowicz" __copyright__ = "Copyright (C) 2017 ACK CYFRONET AGH" __license__ = "This software is released under the MIT license cited in " \ "LICENSE.txt" @when(parsers.parse('user of {browser_id} sees "{msg}" ' 'instead of file browser')) @then(parsers.parse('user of {browser_id} sees "{msg}" ' 'instead of file browser')) @repeat_failed(timeout=WAIT_BACKEND) def assert_msg_instead_of_browser(browser_id, msg, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] displayed_msg = browser.empty_dir_msg assert displayed_msg == msg, 'displayed {} does not match expected ' \ '{}'.format(displayed_msg, msg) @when(parsers.parse('user of {browser_id} does not see {tool_type} ' 'icon for "{item_name}" in file browser')) @then(parsers.parse('user of {browser_id} does not see {tool_type} ' 'icon for "{item_name}" in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def assert_not_tool_icon_for_file_in_file_browser(browser_id, tool_type, item_name, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] err_msg = '{} tool for {} in file browser visible, ' \ 'while should not be'.format(tool_type, item_name) assert not browser[item_name].is_tool_visible(tool_type), err_msg @when(parsers.parse('user of {browser_id} sees {tool_type} ' 'icon for "{item_name}" in file browser')) @then(parsers.parse('user of {browser_id} sees {tool_type} ' 'icon for "{item_name}" in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def assert_tool_icon_for_file_in_file_browser(browser_id, tool_type, item_name, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] err_msg = '{} tool for {} in file browser not visible'.format(tool_type, item_name) assert browser[item_name].is_tool_visible(tool_type), err_msg @when(parsers.parse('user of {browser_id} clicks on {tool_type} tool icon ' 'in file row for "{item_name}" in file browser')) @then(parsers.parse('user of {browser_id} clicks on {tool_type} tool icon ' 'in file row for "{item_name}" in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def click_on_tool_icon_for_file_in_file_browser(browser_id, tool_type, item_name, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] browser[item_name].click_on_tool(tool_type) @when(parsers.parse('user of {browser_id} sees that item named {item_list} ' 'has disappeared from files browser')) @then(parsers.parse('user of {browser_id} sees that item named {item_list} ' 'has disappeared from files browser')) @when(parsers.parse('user of {browser_id} sees that items named {item_list} ' 'have disappeared from files browser')) @then(parsers.parse('user of {browser_id} sees that items named {item_list} ' 'have disappeared from files browser')) @when(parsers.parse('user of {browser_id} does not see any item(s) named ' '{item_list} in file browser')) @then(parsers.parse('user of {browser_id} does not see any item(s) named ' '{item_list} in file browser')) @repeat_failed(timeout=WAIT_BACKEND) def assert_items_absence_in_file_browser(selenium, browser_id, item_list, tmp_memory): driver = selenium[browser_id] browser = tmp_memory[browser_id]['file_browser'] with implicit_wait(driver, 0.1, SELENIUM_IMPLICIT_WAIT): for item_name in parse_seq(item_list): with pytest.raises(RuntimeError): _ = browser[item_name] @when(parsers.parse('user of {browser_id} sees item(s) ' 'named {item_list} in file browser')) @then(parsers.parse('user of {browser_id} sees item(s) ' 'named {item_list} in file browser')) @when(parsers.parse('user of {browser_id} sees that item named ' '{item_list} has appeared in file browser')) @then(parsers.parse('user of {browser_id} sees that item named ' '{item_list} has appeared in file browser')) @when(parsers.parse('user of {browser_id} sees that items named ' '{item_list} have appeared in file browser')) @then(parsers.parse('user of {browser_id} sees that items named ' '{item_list} have appeared in file browser')) @repeat_failed(timeout=WAIT_BACKEND) def assert_items_presence_in_file_browser(browser_id, item_list, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] for item_name in parse_seq(item_list): _ = browser[item_name] @when(parsers.parse('user of {browser_id} sees item(s) named ' '{item_list} in file browser in given order')) @then(parsers.parse('user of {browser_id} sees item(s) named ' '{item_list} in file browser in given order')) @repeat_failed(timeout=WAIT_BACKEND) def assert_presence_in_file_browser_with_order(browser_id, item_list, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] items = iter(parse_seq(item_list)) curr_item = next(items) for item in browser: if item.name == curr_item: try: curr_item = next(items) except StopIteration: return raise RuntimeError('item(s) not in browser or not in specified order ' '{order} starting from {item}'.format(order=item_list, item=curr_item)) @when(parsers.parse('user of {browser_id} sees that modification date of item ' 'named "{item_name}" is not earlier than {err_time:d} ' 'seconds ago in file browser')) @then(parsers.parse('user of {browser_id} sees that modification date of item ' 'named "{item_name}" is not earlier than {err_time:d} ' 'seconds ago in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def assert_item_in_file_browser_is_of_mdate(browser_id, item_name, err_time, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] date_fmt = '%Y-%m-%d %H:%M' item_date = datetime.strptime(browser[item_name].modification_date, date_fmt) expected_date = datetime.fromtimestamp(time()) err_msg = 'displayed mod time {} for {} does not match expected {}' assert abs(expected_date - item_date).seconds < err_time, \ err_msg.format(item_date, item_name, expected_date) @when(parsers.parse('user of {browser_id} sees that item named "{item_name}" ' 'is of {size} size in file browser')) @then(parsers.parse('user of {browser_id} sees that item named "{item_name}" ' 'is of {size} size in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def assert_item_in_file_browser_is_of_size(browser_id, item_name, size, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] item_size = browser[item_name].size err_msg = 'displayed size {} for {} does not match expected {}' assert size == item_size, err_msg.format(item_size, item_name, size) @when(parsers.parse('user of {browser_id} scrolls to the bottom ' 'of file browser')) @then(parsers.parse('user of {browser_id} scrolls to the bottom ' 'of file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def scroll_to_bottom_of_file_browser(browser_id, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] browser.scroll_to_bottom() # @when(parsers.parse('user of {browser_id} sees that there is(are) {num:d} ' # 'item(s) in file browser')) # @then(parsers.parse('user of {browser_id} sees that there is(are) {num:d} ' # 'item(s) in file browser')) @when(parsers.re('user of (?P<browser_id>.+?) sees that there ' '(is 1|are (?P<num>\d+)) items? in file browser')) @then(parsers.re('user of (?P<browser_id>.+?) sees that there ' '(is 1|are (?P<num>\d+)) items? in file browser')) @repeat_failed(timeout=WAIT_BACKEND) def assert_num_of_files_are_displayed_in_file_browser(browser_id, num, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] err_msg = 'displayed number of files {} does not match expected {}' files_num = browser.files_count num = int(num) if num is not None else 1 assert files_num == num, err_msg.format(files_num, num) @when(parsers.parse('user of {browser_id} sees that item named "{item_name}" ' 'is {item_attr} in file browser')) @then(parsers.parse('user of {browser_id} sees that item named "{item_name}" ' 'is {item_attr} in file browser')) @repeat_failed(timeout=WAIT_BACKEND) def assert_item_in_file_browser_is_of_type(browser_id, item_name, item_attr, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] action = getattr(browser[item_name], 'is_{}'.format(item_attr)) assert action(), '"{}" is not {}, while it should'.format(item_name, item_attr) @when(parsers.parse('user of {browser_id} double clicks on item ' 'named "{item_name}" in file browser')) @then(parsers.parse('user of {browser_id} double clicks on item ' 'named "{item_name}" in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def double_click_on_item_in_file_browser(browser_id, item_name, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] browser[item_name].double_click() @when(parsers.parse('user of {browser_id} clicks once on item ' 'named "{item_name}" in file browser')) @then(parsers.parse('user of {browser_id} clicks once on item ' 'named "{item_name}" in file browser')) @repeat_failed(timeout=WAIT_BACKEND) def click_on_item_in_file_browser(browser_id, item_name, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] browser[item_name].click() @when(parsers.parse('user of {browser_id} selects {item_list} ' 'item(s) from file browser with pressed shift')) @then(parsers.parse('user of {browser_id} selects {item_list} ' 'item(s) from file browser with pressed shift')) def select_files_from_file_list_using_shift(browser_id, item_list, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] with browser.select_files() as selector: selector.shift_down() _select_files(browser, selector, item_list) selector.shift_up() @when(parsers.parse('user of {browser_id} selects {item_list} ' 'item(s) from file browser with pressed ctrl')) @then(parsers.parse('user of {browser_id} selects {item_list} ' 'item(s) from file browser with pressed ctrl')) @repeat_failed(timeout=WAIT_FRONTEND) def select_files_from_file_list_using_ctrl(browser_id, item_list, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] with browser.select_files() as selector: selector.ctrl_or_cmd_down() _select_files(browser, selector, item_list) selector.ctrl_or_cmd_up() @when(parsers.parse('user of {browser_id} deselects {item_list} ' 'item(s) from file browser')) @then(parsers.parse('user of {browser_id} deselects {item_list} ' 'item(s) from file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def deselect_items_from_file_browser(browser_id, item_list, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] with browser.select_files() as selector: selector.ctrl_or_cmd_down() _deselect_files(browser, selector, item_list) selector.ctrl_or_cmd_up() def _select_files(browser, selector, item_list): for item_name in parse_seq(item_list): item = browser[item_name] if not item.is_selected(): selector.select(item) def _deselect_files(browser, selector, item_list): for item_name in parse_seq(item_list): item = browser[item_name] if item.is_selected(): selector.select(item) @when(parsers.parse('user of {browser_id} deselects all ' 'selected items from file browser')) @then(parsers.parse('user of {browser_id} deselects all ' 'selected items from file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def deselect_all_items_from_file_browser(browser_id, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] item = browser[0] item.click() if item.is_selected(): item.click() @when(parsers.parse('user of {browser_id} sees that {item_list} ' 'item is selected in file browser')) @then(parsers.parse('user of {browser_id} sees that {item_list} ' 'item is selected in file browser')) @when(parsers.parse('user of {browser_id} sees that {item_list} ' 'items are selected in file browser')) @then(parsers.parse('user of {browser_id} sees that {item_list} ' 'items are selected in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def assert_items_are_selected_in_file_browser(browser_id, item_list, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] err_msg = 'item "{name}" is not selected while it should be' for item_name in parse_seq(item_list): item = browser[item_name] assert item.is_selected(), err_msg.format(name=item_name) @when(parsers.parse('user of {browser_id} sees that {item_list} ' 'item is not selected in file browser')) @then(parsers.parse('user of {browser_id} sees that {item_list} ' 'item is not selected in file browser')) @when(parsers.parse('user of {browser_id} sees that {item_list} ' 'items are not selected in file browser')) @then(parsers.parse('user of {browser_id} sees that {item_list} ' 'items are not selected in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def assert_items_are_not_selected_in_file_browser(browser_id, item_list, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] err_msg = 'item "{name}" is selected while it should not be' for item_name in parse_seq(item_list): item = browser[item_name] assert not item.is_selected(), err_msg.format(name=item_name) @when(parsers.parse('user of {browser_id} sees that none ' 'item is selected in file browser')) @then(parsers.parse('user of {browser_id} sees that none ' 'item is selected in file browser')) @repeat_failed(timeout=WAIT_FRONTEND) def assert_none_item_is_selected_in_file_browser(browser_id, item_list, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] err_msg = 'item "{name}" is selected while it should not be' for item_name in parse_seq(item_list): item = browser[item_name] assert not item.is_selected(), err_msg.format(name=item_name) @when(parsers.parse('user of {browser_id} sees empty directory message ' 'in file browser')) @then(parsers.parse('user of {browser_id} sees empty directory message ' 'in file browser')) @repeat_failed(timeout=WAIT_BACKEND) def assert_empty_dir_msg_in_file_browser(browser_id, tmp_memory): browser = tmp_memory[browser_id]['file_browser'] expected_msg = 'Nothing here yet.\n' \ 'Drop your files here or use the button in toolbar' displayed_msg = browser.empty_dir_msg assert expected_msg == displayed_msg, 'Displayed empty dir msg "{}" ' \ 'does not match expected one ' \ '"{}"'.format(displayed_msg, expected_msg)

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fbfc223779491c516d4a00188f3cd001a39089dc

258

py

Python

omoide_index/domain/infra/__init__.py

IgorZyktin/omoide-index

b64cdc9e661b0b3d3b25a460f8bb0ef689ea81ad

[ "MIT" ]

null

omoide_index/domain/infra/__init__.py

IgorZyktin/omoide-index

b64cdc9e661b0b3d3b25a460f8bb0ef689ea81ad

[ "MIT" ]

16

2021-12-22T02:27:24.000Z

2022-03-31T02:26:07.000Z

omoide_index/domain/infra/__init__.py

IgorZyktin/omoide-index

b64cdc9e661b0b3d3b25a460f8bb0ef689ea81ad

[ "MIT" ]

null

# -*- coding: utf-8 -*- from omoide_index.domain.infra.abstract_clock import AbstractClock from omoide_index.domain.infra.abstract_config import AbstractConfig from omoide_index.domain.infra.abstract_memory_calculator import ( AbstractMemoryCalculator )

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1

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223621529b2849d9a663d8fcfa232fd37ee2c856

5,808

py

Python

tests/vmss/test_vmss_fetcher.py

ianalderman/chaostoolkit-azure

1ed41aa19b005cd05faffe3a11446e13d53b781a

[ "Apache-2.0" ]

null

tests/vmss/test_vmss_fetcher.py

ianalderman/chaostoolkit-azure

1ed41aa19b005cd05faffe3a11446e13d53b781a

[ "Apache-2.0" ]

null

tests/vmss/test_vmss_fetcher.py

ianalderman/chaostoolkit-azure

1ed41aa19b005cd05faffe3a11446e13d53b781a

[ "Apache-2.0" ]

2

2020-09-20T11:07:40.000Z

2020-10-19T14:48:58.000Z

from unittest.mock import patch import pytest from chaoslib.exceptions import FailedActivity import chaosazure from chaosazure.vmss.actions import delete_vmss from chaosazure.vmss.fetcher import fetch_vmss, fetch_instances from tests.data import vmss_provider @patch('chaosazure.vmss.fetcher.fetch_resources', autospec=True) def test_succesful_fetch_vmss(mocked_fetch_vmss): scale_set = vmss_provider.provide_scale_set() scale_sets = [scale_set] mocked_fetch_vmss.return_value = scale_sets result = fetch_vmss(None, None, None) assert len(result) == 1 assert result[0].get('name') == 'chaos-pool' @patch('chaosazure.vmss.fetcher.fetch_resources', autospec=True) def test_empty_fetch_vmss(mocked_fetch_vmss): with pytest.raises(FailedActivity) as x: mocked_fetch_vmss.return_value = [] fetch_vmss(None, None, None) assert "No VMSS" in str(x.value) @patch.object(chaosazure.vmss.fetcher, '__fetch_vmss_instances', autospec=True) def test_succesful_fetch_instances_without_instance_criteria(mocked_fetch_instances): instance = vmss_provider.provide_instance() instances = [instance] mocked_fetch_instances.return_value = instances scale_set = vmss_provider.provide_scale_set() result = fetch_instances(scale_set, None, None, None) assert len(result) == 1 assert result[0].get('name') == 'chaos-pool_0' assert result[0].get('instance_id') == '0' @patch.object(chaosazure.vmss.fetcher, '__fetch_vmss_instances', autospec=True) def test_empty_fetch_instances_without_instance_criteria(mocked_fetch_instances): with pytest.raises(FailedActivity) as x: mocked_fetch_instances.return_value = [] scale_set = vmss_provider.provide_scale_set() fetch_instances(scale_set, None, None, None) assert "No VMSS instances" in str(x.value) @patch.object(chaosazure.vmss.fetcher, '__fetch_vmss_instances', autospec=True) def test_succesful_fetch_instances_with_instance_criteria_for_instance0(mocked_fetch_instances): # arrange instance_0 = vmss_provider.provide_instance() instance_0['instance_id'] = '0' instance_1 = vmss_provider.provide_instance() instance_1['instance_id'] = '1' instance_2 = vmss_provider.provide_instance() instance_2['instance_id'] = '2' instances = [instance_0, instance_1, instance_2] mocked_fetch_instances.return_value = instances scale_set = vmss_provider.provide_scale_set() # fire result = fetch_instances(scale_set, [{'instance_id': '0'}], None, None) # assert assert len(result) == 1 assert result[0].get('name') == 'chaos-pool_0' assert result[0].get('instance_id') == '0' @patch.object(chaosazure.vmss.fetcher, '__fetch_vmss_instances', autospec=True) def test_succesful_fetch_instances_with_instance_criteria_for_instance0_instance_2(mocked_fetch_instances): # arrange instance_0 = vmss_provider.provide_instance() instance_0['instance_id'] = '0' instance_0['name'] = 'chaos-pool_0' instance_1 = vmss_provider.provide_instance() instance_1['instance_id'] = '1' instance_1['name'] = 'chaos-pool_1' instance_2 = vmss_provider.provide_instance() instance_2['instance_id'] = '2' instance_2['name'] = 'chaos-pool_2' instances = [instance_0, instance_1, instance_2] mocked_fetch_instances.return_value = instances scale_set = vmss_provider.provide_scale_set() # fire result = fetch_instances(scale_set, [{'instance_id': '0'}, {'instance_id': '2'}], None, None) # assert assert len(result) == 2 assert result[0].get('name') == 'chaos-pool_0' assert result[0].get('instance_id') == '0' assert result[1].get('name') == 'chaos-pool_2' assert result[1].get('instance_id') == '2' @patch.object(chaosazure.vmss.fetcher, '__fetch_vmss_instances', autospec=True) def test_succesful_fetch_instances_with_instance_criteria_for_all_instances(mocked_fetch_instances): # arrange instance_0 = vmss_provider.provide_instance() instance_0['instance_id'] = '0' instance_0['name'] = 'chaos-pool_0' instance_1 = vmss_provider.provide_instance() instance_1['instance_id'] = '1' instance_1['name'] = 'chaos-pool_1' instance_2 = vmss_provider.provide_instance() instance_2['instance_id'] = '2' instance_2['name'] = 'chaos-pool_2' instances = [instance_0, instance_1, instance_2] mocked_fetch_instances.return_value = instances scale_set = vmss_provider.provide_scale_set() # fire result = fetch_instances( scale_set, [{'instance_id': '0'}, {'instance_id': '1'}, {'instance_id': '2'}], None, None) # assert assert len(result) == 3 assert result[0].get('name') == 'chaos-pool_0' assert result[0].get('instance_id') == '0' assert result[1].get('name') == 'chaos-pool_1' assert result[1].get('instance_id') == '1' assert result[2].get('name') == 'chaos-pool_2' assert result[2].get('instance_id') == '2' @patch.object(chaosazure.vmss.fetcher, '__fetch_vmss_instances', autospec=True) def test_empty_fetch_instances_with_instance_criteria(mocked_fetch_instances): # arrange instance_0 = vmss_provider.provide_instance() instance_0['instance_id'] = '0' instance_1 = vmss_provider.provide_instance() instance_1['instance_id'] = '1' instance_2 = vmss_provider.provide_instance() instance_2['instance_id'] = '2' instances = [instance_0, instance_1, instance_2] mocked_fetch_instances.return_value = instances scale_set = vmss_provider.provide_scale_set() # fire with pytest.raises(FailedActivity) as x: fetch_instances( scale_set, [{'instance_id': '99'}, {'instance_id': '100'}, {'instance_id': '101'}], None, None) assert "No VMSS instance" in x.value

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false

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0

null

0

1

0

null

0

6

97d47ac5be712c13a3a8b67a284823eb26d57d2a

1,825

py

Python

tasks/docs.py

tmbb/playfair

cad2491c955259e0482a443cea94f5d334b6e05e

[ "MIT" ]

null

tasks/docs.py

tmbb/playfair

cad2491c955259e0482a443cea94f5d334b6e05e

[ "MIT" ]

null

tasks/docs.py

tmbb/playfair

cad2491c955259e0482a443cea94f5d334b6e05e

[ "MIT" ]

null

def make_example_image_1(): from playfair.compare import add_comparisons_to_axes, Comparison, stars from matplotlib import pyplot as plt import numpy as np # Generate some data d1 = np.linspace(1, 2, 55) d2 = np.linspace(2, 2.5, 34) # Create a comparison marker between populations at the positions 1 and 2. comparison_marker = Comparison("$p < 0.01$", d1, d2, 1, 2) fig, ax = plt.subplots(1) # Add a normal boxplot ax.boxplot([d1, d2], labels=["Left", "Right"]) add_comparisons_to_axes(ax, [comparison_marker]) # Set the ylims manually because matplotlib isn't smart enough # to scale things such that the markers fit in the plot ax.set_ylim(0, 3.5) fig.savefig('docs/_static/images/example1.png') def make_example_image_2(): from playfair.compare import add_comparisons_to_axes, Comparison, stars from matplotlib import pyplot as plt import numpy as np # Generate some data d1 = np.linspace(1, 2, 55) d2 = np.linspace(2, 2.5, 34) d3 = np.linspace(1.35, 1.70, 55) # Create a comparison marker between populations at the positions 1 and 2. comparison_marker_1 = Comparison("$p < 0.01$", d1, d2, 1, 2) # Create a comparison marker between populations at the positions 1 and 3. comparison_marker_2 = Comparison("$p < 0.05$", d1, d2, 1, 3) fig, ax = plt.subplots(1) # Add a normal boxplot ax.boxplot([d1, d2, d3], labels=["A", "B", "C"]) add_comparisons_to_axes(ax, [comparison_marker_1, comparison_marker_2]) # Set the ylims manually because matplotlib isn't smart enough # to scale things such that the markers fit in the plot ax.set_ylim(0, 4) fig.savefig('docs/_static/images/example2.png') if __name__ == '__main__': make_example_image_1() make_example_image_2()

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0.684384

288

1,825

4.177083

0.298611

0.119701

0.0532

0.0665

0.823774

0.780549

0.717373

0.684123

0

0.055517

0.210411

1,825

52

79

35.096154

0.77932

0.289315

0

0.413793

1

0

0.088716

0.049805

0

1

0.068966

false

0

0.206897

0

0.275862

0

null

0

1

0

1

0

1

0

null

0

6

97ede6c37c4d381c2d5119717b7c6ab3912f1e2d

22

py

Python

python/app/thirdparty/dirsearch/thirdparty/requests/packages/__init__.py

taomujian/linbing

fe772a58f41e3b046b51a866bdb7e4655abaf51a

[ "MIT" ]

351

2020-02-26T05:23:26.000Z

2022-03-26T12:39:19.000Z

python/app/thirdparty/dirsearch/thirdparty/requests/packages/__init__.py

taomujian/linbing

fe772a58f41e3b046b51a866bdb7e4655abaf51a

[ "MIT" ]

15

2020-03-26T07:31:49.000Z

2022-03-09T02:12:17.000Z

python/app/thirdparty/dirsearch/thirdparty/requests/packages/__init__.py

taomujian/linbing

fe772a58f41e3b046b51a866bdb7e4655abaf51a

[ "MIT" ]

99

2020-02-28T07:30:46.000Z

2022-03-16T16:41:09.000Z

from . import urllib3

11

21

0.772727

3

22

5.666667

1

0

0.055556

0.181818

22

1

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

451f93db6e8045db8d799feecc51a41bba6dd94d

46,395

py

Python

font_6x8_src.py

azorg/font_6x8

6c889e1c89b1dc1902782057b5b151119ad6f379

[ "BSD-3-Clause" ]

1

2021-09-12T19:28:36.000Z

font_6x8_src.py

azorg/font_6x8

6c889e1c89b1dc1902782057b5b151119ad6f379

[ "BSD-3-Clause" ]

null

font_6x8_src.py

azorg/font_6x8

6c889e1c89b1dc1902782057b5b151119ad6f379

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: UTF8 -*- font_6x8_koi8r_src = [tuple(' ' * 12 for i in range(8)) for j in range(256)] # ' ' "\u0020" font_6x8_koi8r_src[0x20] = ( " ", " ", " ", " ", " ", " ", " ", " ") # '!' "\u0021" font_6x8_koi8r_src[0x21] = ( " ", " ## ", " ## ", " ## ", " ## ", " ## ", " ", " ## ") # '"' "\u0022" font_6x8_koi8r_src[0x22] = ( " ", " ## ## ", " ## ## ", " ", " ", " ", " ", " ") # '#' "\u0023" font_6x8_koi8r_src[0x23] = ( " ", " ## ## ", " ## ## ", "########## ", " ## ## ", "########## ", " ## ## ", " ## ## ") # '$' "\u0024" font_6x8_koi8r_src[0x24] = ( " ", " ## ", " ######## ", "## ## ", " ###### ", " ## ## ", "######## ", " ## ") # '%' "\u0025" font_6x8_koi8r_src[0x25] = ( " ", "#### ", "#### ## ", " ## ", " ## ", " ## ", "## #### ", " #### ") # '&' "\u0026" font_6x8_koi8r_src[0x26] = ( " ", " #### ", "## ## ", "## ## ", " ## ", "## ## ## ", "## ## ", " #### ## ") # ''' "\u0027" font_6x8_koi8r_src[0x27] = ( " ", " ## ", " ## ", " ", " ", " ", " ", " ") # '(' "\u0028" font_6x8_koi8r_src[0x28] = ( " ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ") # ')' "\u0029" font_6x8_koi8r_src[0x29] = ( " ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ") # '*' "\u002A" font_6x8_koi8r_src[0x2A] = ( " ", " ", " ## ", "## ## ## ", " ###### ", "## ## ## ", " ## ", " ") # '+' "\u002B" font_6x8_koi8r_src[0x2B] = ( " ", " ", " ## ", " ## ", "########## ", " ## ", " ## ", " ") # ',' "\u002C" font_6x8_koi8r_src[0x2C] = ( " ", " ", " ", " ", " ", " #### ", " ## ", " ## ") # '-' "\u002D" font_6x8_koi8r_src[0x2D] = ( " ", " ", " ", " ", "########## ", " ", " ", " ") # '.' "\u002E" font_6x8_koi8r_src[0x2E] = ( " ", " ", " ", " ", " ", " ", " #### ", " #### ") # '/' "\u002F" font_6x8_koi8r_src[0x2F] = ( " ", " ", " ## ", " ## ", " ## ", " ## ", "## ", " ") # '0' "\u0030" font_6x8_koi8r_src[0x30] = ( " ", " ###### ", "## ## ", "## #### ", "## ## ## ", "#### ## ", "## ## ", " ###### ") # '1' "\u0031" font_6x8_koi8r_src[0x31] = ( " ", " ## ", " #### ", " ## ", " ## ", " ## ", " ## ", " ###### ") # '2' "\u0032" font_6x8_koi8r_src[0x32] = ( " ", " ###### ", "## ## ", " ## ", " ###### ", "## ", "## ", "########## ") # '3' "\u0033" font_6x8_koi8r_src[0x33] = ( " ", "########## ", " ## ", " ## ", " ## ", " ## ", "## ## ", " ###### ") # '4' "\u0034" font_6x8_koi8r_src[0x34] = ( " ", " ## ", " #### ", " ## ## ", "## ## ", "########## ", " ## ", " ## ") # '5' "\u0035" font_6x8_koi8r_src[0x35] = ( " ", "########## ", "## ", "######## ", " ## ", " ## ", "## ## ", " ###### ") # '6' "\u0036" font_6x8_koi8r_src[0x36] = ( " ", " ###### ", "## ## ", "## ", "######## ", "## ## ", "## ## ", " ###### ") # '7' "\u0037" font_6x8_koi8r_src[0x37] = ( " ", "########## ", "## ## ", " ## ", " ## ", " ## ", " ## ", " ## ") # '8' "\u0038" font_6x8_koi8r_src[0x38] = ( " ", " ###### ", "## ## ", "## ## ", " ###### ", "## ## ", "## ## ", " ###### ") # '9' "\u0039" font_6x8_koi8r_src[0x39] = ( " ", " ###### ", "## ## ", "## ## ", " ######## ", " ## ", "## ## ", " ###### ") # ':' "\u003A" font_6x8_koi8r_src[0x3A] = ( " ", " ", " #### ", " #### ", " ", " #### ", " #### ", " ") # ';' "\u003B" font_6x8_koi8r_src[0x3B] = ( " ", " ", " #### ", " #### ", " ", " #### ", " #### ", " ## ") # '<' "\u003C" font_6x8_koi8r_src[0x3C] = ( " ", " ## ", " ## ", " ## ", "## ", " ## ", " ## ", " ## ") # '=' "\u003D" font_6x8_koi8r_src[0x3D] = ( " ", " ", " ", "########## ", " ", "########## ", " ", " ") # '>' "\u003E" font_6x8_koi8r_src[0x3E] = ( " ", "## ", " ## ", " ## ", " ## ", " ## ", " ## ", "## ") # '?' "\u003F" font_6x8_koi8r_src[0x3F] = ( " ", " ###### ", "## ## ", " ## ", " ## ", " ## ", " ", " ## ") # '@' "\u0040" font_6x8_koi8r_src[0x40] = ( " ", " ###### ", "## ###### ", "#### ## ", "#### ## ", "## ###### ", "## ", " ###### ") # 'A' "\u0041" font_6x8_koi8r_src[0x41] = ( " ", " ###### ", " ## ## ", "## ## ", "########## ", "## ## ", "## ## ", "## ## ") # 'B' "\u0042" font_6x8_koi8r_src[0x42] = ( " ", "######## ", "## ## ", "## ## ", "######## ", "## ## ", "## ## ", "######## ") # 'C' "\u0043" font_6x8_koi8r_src[0x43] = ( " ", " ###### ", "## ## ", "## ", "## ", "## ", "## ## ", " ###### ") # 'D' "\u0044" font_6x8_koi8r_src[0x44] = ( " ", "###### ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ", "###### ") # 'E' "\u0045" font_6x8_koi8r_src[0x45] = ( " ", "########## ", "## ", "## ", "######## ", "## ", "## ", "########## ") # 'F' "\u0046" font_6x8_koi8r_src[0x46] = ( " ", "########## ", "## ", "## ", "######## ", "## ", "## ", "## ") # 'G' "\u0047" font_6x8_koi8r_src[0x47] = ( " ", " ###### ", "## ## ", "## ", "## ", "## #### ", "## ## ", " ###### ") # 'H' "\u0048" font_6x8_koi8r_src[0x48] = ( " ", "## ## ", "## ## ", "## ## ", "########## ", "## ## ", "## ## ", "## ## ") # 'I' "\u0049" font_6x8_koi8r_src[0x49] = ( " ", " ###### ", " ## ", " ## ", " ## ", " ## ", " ## ", " ###### ") # 'J' "\u004A" font_6x8_koi8r_src[0x4A] = ( " ", " ###### ", " ## ", " ## ", " ## ", " ## ", "## ## ", " #### ") # 'K' "\u004B" font_6x8_koi8r_src[0x4B] = ( " ", "## ## ", "## ## ", "## ## ", "#### ", "## ## ", "## ## ", "## ## ") # 'L' "\u004C" font_6x8_koi8r_src[0x4C] = ( " ", "## ", "## ", "## ", "## ", "## ", "## ", "########## ") # 'M' "\u004D" font_6x8_koi8r_src[0x4D] = ( " ", "## ## ", "#### #### ", "## ## ## ", "## ## ", "## ## ", "## ## ", "## ## ") # 'N' "\u004E" font_6x8_koi8r_src[0x4E] = ( " ", "## ## ", "## ## ", "#### ## ", "## ## ## ", "## #### ", "## ## ", "## ## ") # 'O' "\u004F" font_6x8_koi8r_src[0x4F] = ( " ", " ###### ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ", " ###### ") # 'P' "\u0050" font_6x8_koi8r_src[0x50] = ( " ", "######## ", "## ## ", "## ## ", "######## ", "## ", "## ", "## ") # 'Q' "\u0051" font_6x8_koi8r_src[0x51] = ( " ", " ###### ", "## ## ", "## ## ", "## ## ", "## ## ## ", "## ## ", " #### ## ") # 'R' "\u0052" font_6x8_koi8r_src[0x52] = ( " ", "######## ", "## ## ", "## ## ", "######## ", "## ## ", "## ## ", "## ## ") # 'S' "\u0053" font_6x8_koi8r_src[0x53] = ( " ", " ###### ", "## ## ", "## ", " ###### ", " ## ", "## ## ", " ###### ") # 'T' "\u0054" font_6x8_koi8r_src[0x54] = ( " ", "########## ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ") # 'U' "\u0055" font_6x8_koi8r_src[0x55] = ( " ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ", " ###### ") # 'V' "\u0056" font_6x8_koi8r_src[0x56] = ( " ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ", " ## ## ", " ## ") # 'W' "\u0057" font_6x8_koi8r_src[0x57] = ( " ", "## ## ", "## ## ", "## ## ", "## ## ## ", "## ## ## ", "## ## ## ", " ## ## ") # 'X' "\u0058" font_6x8_koi8r_src[0x58] = ( " ", "## ## ", "## ## ", " ## ## ", " ## ", " ## ## ", "## ## ", "## ## ") # 'Y' "\u0059" font_6x8_koi8r_src[0x59] = ( " ", "## ## ", "## ## ", "## ## ", " ## ## ", " ## ", " ## ", " ## ") # 'Z' "\u005A" font_6x8_koi8r_src[0x5A] = ( " ", "########## ", " ## ", " ## ", " ## ", " ## ", "## ", "########## ") # '[' "\u005B" font_6x8_koi8r_src[0x5B] = ( " ", " ###### ", " ## ", " ## ", " ## ", " ## ", " ## ", " ###### ") # '\' "\u005C" font_6x8_koi8r_src[0x5C] = ( " ", " ", "## ", " ## ", " ## ", " ## ", " ## ", " ") # ']' "\u005D" font_6x8_koi8r_src[0x5D] = ( " ", " ###### ", " ## ", " ## ", " ## ", " ## ", " ## ", " ###### ") # '^' "\u005E" font_6x8_koi8r_src[0x5E] = ( " ", " ## ", " ## ## ", "## ## ", " ", " ", " ", " ") # '_' "\u005F" font_6x8_koi8r_src[0x5F] = ( " ", " ", " ", " ", " ", " ", " ", "############") # '`' "\u0060" font_6x8_koi8r_src[0x60] = ( " ", " ## ", " ## ", " ", " ", " ", " ", " ") # 'a' "\u0061" font_6x8_koi8r_src[0x61] = ( " ", " ", " ", " ###### ", "## ## ", "## ## ", "## ## ", " #### ## ") # 'b' "\u0062" font_6x8_koi8r_src[0x62] = ( " ", "## ", "## ", "######## ", "## ## ", "## ## ", "## ## ", "######## ") # 'c' "\u0063" font_6x8_koi8r_src[0x63] = ( " ", " ", " ", " ###### ", "## ## ", "## ", "## ## ", " ###### ") # 'd' "\u0064" font_6x8_koi8r_src[0x64] = ( " ", " ## ", " ## ", " ######## ", "## ## ", "## ## ", "## ## ", " ######## ") # 'e' "\u0065" font_6x8_koi8r_src[0x65] = ( " ", " ", " ", " ###### ", "## ## ", "########## ", "## ", " ###### ") # 'f' "\u0066" font_6x8_koi8r_src[0x66] = ( " ", " #### ", " ## ## ", " ## ", "###### ", " ## ", " ## ", " ## ") # 'g' "\u0067" font_6x8_koi8r_src[0x67] = ( " ", " ", " ###### ", "## ## ", "## ## ", " ######## ", " ## ", " ###### ") # 'h' "\u0068" font_6x8_koi8r_src[0x68] = ( " ", "## ", "## ", "## #### ", "#### ## ", "## ## ", "## ## ", "## ## ") # 'i' "\u0069" font_6x8_koi8r_src[0x69] = ( " ", " ## ", " ", " #### ", " ## ", " ## ", " ## ", " ###### ") # 'j' "\u006A" font_6x8_koi8r_src[0x6A] = ( " ", " ## ", " ", " #### ", " ## ", " ## ", "## ## ", " #### ") # 'k' "\u006B" font_6x8_koi8r_src[0x6B] = ( " ", " ## ", " ## ", " ## ## ", " ## ## ", " #### ", " ## ## ", " ## ## ") # 'l' "\u006C" font_6x8_koi8r_src[0x6C] = ( " ", " #### ", " ## ", " ## ", " ## ", " ## ", " ## ", " ###### ") # 'm' "\u006D" font_6x8_koi8r_src[0x6D] = ( " ", " ", " ", "#### ## ", "## ## ## ", "## ## ## ", "## ## ## ", "## ## ## ") # 'n' "\u006E" font_6x8_koi8r_src[0x6E] = ( " ", " ", " ", "## #### ", "#### ## ", "## ## ", "## ## ", "## ## ") # 'o' "\u006F" font_6x8_koi8r_src[0x6F] = ( " ", " ", " ", " ###### ", "## ## ", "## ## ", "## ## ", " ###### ") # 'p' "\u0070" font_6x8_koi8r_src[0x70] = ( " ", " ", " ", "######## ", "## ## ", "## ## ", "######## ", "## ") # 'q' "\u0071" font_6x8_koi8r_src[0x71] = ( " ", " ", " ", " ######## ", "## ## ", "## ## ", " ######## ", " ## ") # 'r' "\u0072" font_6x8_koi8r_src[0x72] = ( " ", " ", " ", "## #### ", "#### ## ", "## ", "## ", "## ") # 's' "\u0073" font_6x8_koi8r_src[0x73] = ( " ", " ", " ", " ###### ", "## ", " ###### ", " ## ", " ###### ") # 't' "\u0074" font_6x8_koi8r_src[0x74] = ( " ", " ## ", " ## ", "###### ", " ## ", " ## ", " ## ## ", " #### ") # 'u' "\u0075" font_6x8_koi8r_src[0x75] = ( " ", " ", " ", "## ## ", "## ## ", "## ## ", "## #### ", " #### ## ") # 'v' "\u0076" font_6x8_koi8r_src[0x76] = ( " ", " ", " ", "## ## ", "## ## ", "## ## ", " ## ## ", " ## ") # 'w' "\u0077" font_6x8_koi8r_src[0x77] = ( " ", " ", " ", "## ## ", "## ## ", "## ## ", "## ## ## ", " ## ## ") # 'x' "\u0078" font_6x8_koi8r_src[0x78] = ( " ", " ", " ", "## ## ", " ## ## ", " ## ", " ## ## ", "## ## ") # 'y' "\u0079" font_6x8_koi8r_src[0x79] = ( " ", " ", " ", "## ## ", "## ## ", " ######## ", " ## ", " ###### ") # 'z' "\u007A" font_6x8_koi8r_src[0x7A] = ( " ", " ", " ", "########## ", " ## ", " ## ", " ## ", "########## ") # '{' "\u007B" font_6x8_koi8r_src[0x7B] = ( " ", " #### ", " ## ", " ## ", " ## ", " ## ", " ## ", " #### ") # '|' "\u007C" font_6x8_koi8r_src[0x7C] = ( " ", " ## ", " ## ", " ## ", " ", " ## ", " ## ", " ## ") # '}' "\u007D" font_6x8_koi8r_src[0x7D] = ( " ", " #### ", " ## ", " ## ", " ## ", " ## ", " ## ", " #### ") # '~' "\u007E" font_6x8_koi8r_src[0x7E] = ( " ", " ", " ", " ## ", "## ## ## ", " ## ", " ", " ") # [DEL] "\u007F" font_6x8_koi8r_src[0x7F] = ( " ", " ## ", " ## ", " ## ", "########## ", " ## ", " ## ", " ## ") # '─' "\u2500" font_6x8_koi8r_src[0x80] = ( " ", " ", " ", " ", "############", " ", " ", " ") # '│' "\u2502" font_6x8_koi8r_src[0x81] = ( " ## ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ") # '┌' "\u250C" font_6x8_koi8r_src[0x82] = ( " ", " ", " ", " ", " ########", " ## ", " ## ", " ## ") # '┐' "\u2510" font_6x8_koi8r_src[0x83] = ( " ", " ", " ", " ", "###### ", " ## ", " ## ", " ## ") # '└' "\u2514" font_6x8_koi8r_src[0x84] = ( " ## ", " ## ", " ## ", " ## ", " ########", " ", " ", " ") # '┘' "\u2518" font_6x8_koi8r_src[0x85] = ( " ## ", " ## ", " ## ", " ## ", "###### ", " ", " ", " ") # '├' "\u251C" font_6x8_koi8r_src[0x86] = ( " ## ", " ## ", " ## ", " ## ", " ########", " ## ", " ## ", " ## ") # '┤' "\u2524" font_6x8_koi8r_src[0x87] = ( " ## ", " ## ", " ## ", " ## ", "###### ", " ## ", " ## ", " ## ") # '┬' "\u252C" font_6x8_koi8r_src[0x88] = ( " ", " ", " ", " ", "############", " ## ", " ## ", " ## ") # '┴' "\u2534" font_6x8_koi8r_src[0x89] = ( " ## ", " ## ", " ## ", " ## ", "############", " ", " ", " ") # '┼' "\u253C" font_6x8_koi8r_src[0x8A] = ( " ## ", " ## ", " ## ", " ## ", "############", " ## ", " ## ", " ## ") # '▀' "\u2580" font_6x8_koi8r_src[0x8B] = ( "############", "############", "############", "############", "############", " ", " ", " ") # '▄' "\u2584" font_6x8_koi8r_src[0x8C] = ( " ", " ", " ", " ", "############", "############", "############", "############") # '█' "\u2588" font_6x8_koi8r_src[0x8D] = ( "############", "############", "############", "############", "############", "############", "############", "############") # '▌' "\u258C" font_6x8_koi8r_src[0x8E] = ( "###### ", "###### ", "###### ", "###### ", "###### ", "###### ", "###### ", "###### ") # '▐' "\u2590" font_6x8_koi8r_src[0x8F] = ( " ########", " ########", " ########", " ########", " ########", " ########", " ########", " ########") # '░' "\u2591" font_6x8_koi8r_src[0x90] = ( " ", "## ## ", " ", " ## ## ", " ", "## ## ", " ", " ## ## ") # '▒' "\u2592" font_6x8_koi8r_src[0x91] = ( " ## ##", "## ## ", " ## ## ", " ## ##", "## ## ", " ## ## ", " ## ##", "## ## ") # '▓' "\u2593" font_6x8_koi8r_src[0x92] = ( " ## ## ##", "## ## ## ", " ## ## ##", "## ## ## ", " ## ## ##", "## ## ## ", " ## ## ##", "## ## ## ") # '⌠' "\u2320" font_6x8_koi8r_src[0x93] = ( " ## ", " ## ## ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ") # '■' "\u25A0" font_6x8_koi8r_src[0x94] = ( " ", " ", "########## ", "########## ", "########## ", "########## ", "########## ", "########## ") # '∙' "\u2219" font_6x8_koi8r_src[0x95] = ( " ", " ", " ", " ###### ", " ###### ", " ###### ", " ", " ") # '√' "\u221A" font_6x8_koi8r_src[0x96] = ( " ", " ## ", "## ## ", " ## ## ", " ## ", " ## ", " ## ", " ## ") # '≈' "\u2248" font_6x8_koi8r_src[0x97] = ( " ", " ", " ## ## ", " ## ## ", " ", " ## ## ", " ## ## ", " ") # '≤' "\u2264" font_6x8_koi8r_src[0x98] = ( " ", " ## ", " ## ", " ## ", " ## ", " ## ", " ", " ###### ") # '≥' "\u2265" font_6x8_koi8r_src[0x99] = ( " ", " ## ", " ## ", " ## ", " ## ", " ## ", " ", " ###### ") # ' ' "\u00A0" font_6x8_koi8r_src[0x9A] = ( " ", " ", " ", " ", " ", " ", " ", " ") # '⌡' "\u2321" font_6x8_koi8r_src[0x9B] = ( " ## ", " ## ", " ## ", " ## ", " ## ", " ## ", "## ## ", " ## ") # '°' "\u00B0" font_6x8_koi8r_src[0x9C] = ( " ", " #### ", " ## ## ", " ## ## ", " #### ", " ", " ", " ") # '²' "\u00B2" font_6x8_koi8r_src[0x9D] = ( " ", " ###### ", " ## ## ", " #### ", " ## ", " ###### ", " ", " ") # '·' "\u00B7" font_6x8_koi8r_src[0x9E] = ( " ", " ", " ", " ## ", " ###### ", " ## ", " ", " ") # '÷' "\u00F7" font_6x8_koi8r_src[0x9F] = ( " ", " ", " ## ", " ", "########## ", " ", " ## ", " ") # '═' "\u2550" font_6x8_koi8r_src[0xA0] = ( " ", " ", " ", "############", " ", "############", " ", " ") # '║' "\u2551" font_6x8_koi8r_src[0xA1] = ( " ## ## ", " ## ## ", " ## ## ", " ## ## ", " ## ## ", " ## ## ", " ## ## ", " ## ## ") # '╒' "\u2552" font_6x8_koi8r_src[0xA2] = ( " ", " ", " ", " ########", " ## ", " ########", " ## ", " ## ") # 'ё' "\u0451" font_6x8_koi8r_src[0xA3] = ( " ", " ## ## ", " ", " ###### ", "## ## ", "########## ", "## ", " ###### ") # '╓' "\u2553" font_6x8_koi8r_src[0xA4] = ( " ", " ", " ", " ", " ##########", " ## ## ", " ## ## ", " ## ## ") # '╔' "\u2554" font_6x8_koi8r_src[0xA5] = ( " ", " ", " ", " ##########", " ## ", " ## ######", " ## ## ", " ## ## ") # '╕' "\u2555" font_6x8_koi8r_src[0xA6] = ( " ", " ", " ", "###### ", " ## ", "###### ", " ## ", " ## ") # '╖' "\u2556" font_6x8_koi8r_src[0xA7] = ( " ", " ", " ", " ", "######## ", " ## ## ", " ## ## ", " ## ## ") # '╗' "\u2557" font_6x8_koi8r_src[0xA8] = ( " ", " ", " ", "######## ", " ## ", "#### ## ", " ## ## ", " ## ## ") # '╘' "\u2558" font_6x8_koi8r_src[0xA9] = ( " ## ", " ## ", " ## ", " ########", " ## ", " ########", " ", " ") # '╙' "\u2559" font_6x8_koi8r_src[0xAA] = ( " ## ## ", " ## ## ", " ## ## ", " ## ## ", " ##########", " ", " ", " ") # '╚' "\u255A" font_6x8_koi8r_src[0xAB] = ( " ## ## ", " ## ## ", " ## ## ", " ## ######", " ## ", " ##########", " ", " ") # '╛' "\u255B" font_6x8_koi8r_src[0xAC] = ( " ## ", " ## ", " ## ", "###### ", " ## ", "###### ", " ", " ") # '╜' "\u255C" font_6x8_koi8r_src[0xAD] = ( " ## ## ", " ## ## ", " ## ## ", " ## ## ", "######## ", " ", " ", " ") # '╝' "\u255D" font_6x8_koi8r_src[0xAE] = ( " ## ## ", " ## ## ", " ## ## ", "#### ## ", " ## ", "######## ", " ", " ") # '╞' "\u255E" font_6x8_koi8r_src[0xAF] = ( " ## ", " ## ", " ## ", " ########", " ## ", " ########", " ## ", " ## ") # '╟' "\u255F" font_6x8_koi8r_src[0xB0] = ( " ## ## ", " ## ## ", " ## ## ", " ## ## ", " ## ######", " ## ## ", " ## ## ", " ## ## ") # '╠' "\u2560" font_6x8_koi8r_src[0xB1] = ( " ## ## ", " ## ## ", " ## ## ", " ## ######", " ## ", " ## ######", " ## ## ", " ## ## ") # '╡' "\u2561" font_6x8_koi8r_src[0xB2] = ( " ", " ## ", " ## ", "###### ", " ## ", "###### ", " ## ", " ## ") # 'Ё' "\u0401" font_6x8_koi8r_src[0xB3] = ( " ", " ## ## ", "########## ", "## ", "######## ", "## ", "## ", "########## ") # '╢' "\u2562" font_6x8_koi8r_src[0xB4] = ( " ## ## ", " ## ## ", " ## ## ", " ## ## ", "#### ## ", " ## ## ", " ## ## ", " ## ## ") # '╣' "\u2563" font_6x8_koi8r_src[0xB5] = ( " ## ## ", " ## ## ", " ## ## ", "#### ## ", " ## ", "#### ## ", " ## ## ", " ## ## ") # '╤' "\u2564" font_6x8_koi8r_src[0xB6] = ( " ", " ", " ", "############", " ", "#### ######", " ## ## ", " ## ## ") # '╥' "\u2565" font_6x8_koi8r_src[0xB7] = ( " ", " ", " ", " ", "############", " ## ## ", " ## ## ", " ## ## ") # '╦' "\u2566" font_6x8_koi8r_src[0xB8] = ( " ", " ", " ", "############", " ", "#### ######", " ## ## ", " ## ## ") # '╧' "\u2567" font_6x8_koi8r_src[0xB9] = ( " ## ", " ## ", " ## ", "############", " ", "############", " ", " ") # '╨' "\u2568" font_6x8_koi8r_src[0xBA] = ( " ## ## ", " ## ## ", " ## ## ", " ## ## ", "############", " ", " ", " ") # '╩' "\u2569" font_6x8_koi8r_src[0xBB] = ( " ## ## ", " ## ## ", " ## ## ", "#### ######", " ", "############", " ", " ") # '╪' "\u256A" font_6x8_koi8r_src[0xBC] = ( " ## ", " ## ", " ## ", "############", " ", "############", " ## ", " ## ") # '╫' "\u256B" font_6x8_koi8r_src[0xBD] = ( " ## ## ", " ## ## ", " ## ## ", " ## ## ", "############", " ## ## ", " ## ## ", " ## ## ") # '╬' "\u256C" font_6x8_koi8r_src[0xBE] = ( " ## ## ", " ## ## ", " ## ## ", "#### ######", " ", "#### ######", " ## ## ", " ## ## ") # '©' "\u00A9" font_6x8_koi8r_src[0xBF] = ( " ", " ######## ", "## ##", "## #### ##", "## ## ##", "## #### ##", "## ##", " ######## ") # 'ю' "\u044E" font_6x8_koi8r_src[0xC0] = ( " ", " ", " ", "## ## ", "## ## ## ", "###### ## ", "## ## ## ", "## ## ") # 'а' "\u0430" font_6x8_koi8r_src[0xC1] = ( " ", " ", " ", " ###### ", "## ## ", "## ## ", "## ## ", " #### ## ") # 'б' "\u0431" font_6x8_koi8r_src[0xC2] = ( " ", " ## ", " ###### ", "## ", " ###### ", "## ## ", "## ## ", " ###### ") # 'ц' "\u0446" font_6x8_koi8r_src[0xC3] = ( " ", " ", " ", "## ## ", "## ## ", "## ## ", "########## ", " ## ") # 'д' "\u0434" font_6x8_koi8r_src[0xC4] = ( " ", " ", " ", " #### ", " ## ## ", " ## ## ", "########## ", "## ## ") # 'е' "\u0435" font_6x8_koi8r_src[0xC5] = ( " ", " ", " ", " ###### ", "## ## ", "########## ", "## ", " ###### ") # 'ф' "\u0444" font_6x8_koi8r_src[0xC6] = ( " ", " ", " ## ", " ###### ", "## ## ## ", " ###### ", " ## ", " ## ") # 'г' "\u0433" font_6x8_koi8r_src[0xC7] = ( " ", " ", " ", " ######## ", " ## ", " ## ", " ## ", " ## ") # 'х' "\u0445" font_6x8_koi8r_src[0xC8] = ( " ", " ", " ", "## ## ", " ## ## ", " ## ", " ## ## ", "## ## ") # 'и' "\u0438" font_6x8_koi8r_src[0xC9] = ( " ", " ", " ", "## ## ", "## ## ", "## ## ", "## #### ", " #### ## ") # 'й' "\u0439" font_6x8_koi8r_src[0xCA] = ( " ", " ## ", " ## ", "## ## ", "## ## ", "## ## ", "## #### ", " #### ## ") # 'к' "\u043A" font_6x8_koi8r_src[0xCB] = ( " ", " ", " ", "## #### ", "## ## ", "###### ", "## ## ", "## ## ") # 'л' "\u043B" font_6x8_koi8r_src[0xCC] = ( " ", " ", " ", " ###### ", " ## ## ", " ## ## ", " ## ## ", "## ## ") # 'м' "\u043C" font_6x8_koi8r_src[0xCD] = ( " ", " ", " ", "## ## ", "#### #### ", "## ## ## ", "## ## ", "## ## ") # 'н' "\u043D" font_6x8_koi8r_src[0xCE] = ( " ", " ", " ", "## ## ", "## ## ", "########## ", "## ## ", "## ## ") # 'о' "\u043E" font_6x8_koi8r_src[0xCF] = ( " ", " ", " ", " ###### ", "## ## ", "## ## ", "## ## ", " ###### ") # 'п' "\u043F" font_6x8_koi8r_src[0xD0] = ( " ", " ", " ", "########## ", "## ## ", "## ## ", "## ## ", "## ## ") # 'я' "\u044F" font_6x8_koi8r_src[0xD1] = ( " ", " ", " ", " ######## ", "## ## ", " ######## ", " ## ## ", "#### ## ") # 'р' "\u0440" font_6x8_koi8r_src[0xD2] = ( " ", " ", " ", "########## ", "## ## ", "## ## ", "########## ", "## ") # 'с' "\u0441" font_6x8_koi8r_src[0xD3] = ( " ", " ", " ", " ###### ", "## ## ", "## ", "## ## ", " ###### ") # 'т' "\u0442" font_6x8_koi8r_src[0xD4] = ( " ", " ", " ", "########## ", " ## ", " ## ", " ## ", " ## ") # 'у' "\u0443" font_6x8_koi8r_src[0xD5] = ( " ", " ", " ", "## ## ", "## ## ", " ######## ", " ## ", " ###### ") # 'ж' "\u0436" font_6x8_koi8r_src[0xD6] = ( " ", " ", " ", "## ## ## ", "## ## ## ", " ###### ", "## ## ## ", "## ## ## ") # 'в' "\u0432" font_6x8_koi8r_src[0xD7] = ( " ", " ", " ", " ###### ", " ## ## ", " ###### ", " ## ## ", " ###### ") # 'ь' "\u044C" font_6x8_koi8r_src[0xD8] = ( " ", " ", " ", " ## ", " ## ", " ###### ", " ## ## ", " ###### ") # 'ы' "\u044B" font_6x8_koi8r_src[0xD9] = ( " ", " ", " ", "## ## ", "## ## ", "###### ## ", "## ## ## ", "###### ## ") # 'з' "\u0437" font_6x8_koi8r_src[0xDA] = ( " ", " ", " ", " ###### ", " ## ", " #### ", " ## ", " ###### ") # 'ш' "\u0448" font_6x8_koi8r_src[0xDB] = ( " ", " ", " ", "## ## ## ", "## ## ## ", "## ## ## ", "## ## ## ", "########## ") # 'э' "\u044D" font_6x8_koi8r_src[0xDC] = ( " ", " ", " ", " ###### ", " ## ", " ###### ", " ## ", " ###### ") # 'щ' "\u0449" font_6x8_koi8r_src[0xDD] = ( " ", " ", " ", "## ## ## ", "## ## ## ", "## ## ## ", "########## ", " ## ") # 'ч' "\u0447" font_6x8_koi8r_src[0xDE] = ( " ", " ", " ", "## ## ", "## ## ", " ######## ", " ## ", " ## ") # 'ъ' "\u044A" font_6x8_koi8r_src[0xDF] = ( " ", " ", " ", "#### ", " ## ", " ###### ", " ## ## ", " ###### ") # 'Ю' "\u042E" font_6x8_koi8r_src[0xE0] = ( " ", "## ## ", "## ## ## ", "## ## ## ", "###### ## ", "## ## ## ", "## ## ## ", "## ## ") # 'А' "\u0410" font_6x8_koi8r_src[0xE1] = ( " ", " ###### ", " ## ## ", "## ## ", "########## ", "## ## ", "## ## ", "## ## ") # 'Б' "\u0411" font_6x8_koi8r_src[0xE2] = ( " ", "######## ", "## ", "## ", "######## ", "## ## ", "## ## ", "######## ") # 'Ц' "\u0426" font_6x8_koi8r_src[0xE3] = ( " ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ", "########## ", " ## ") # 'Д' "\u0414" font_6x8_koi8r_src[0xE4] = ( " ", " #### ", " ## ## ", " ## ## ", " ## ## ", " ## ## ", "########## ", "## ## ") # 'Е' "\u0415" font_6x8_koi8r_src[0xE5] = ( " ", "########## ", "## ", "## ", "######## ", "## ", "## ", "########## ") # 'Ф' "\u0424" font_6x8_koi8r_src[0xE6] = ( " ", " ###### ", "## ## ## ", "## ## ## ", "## ## ## ", " ###### ", " ## ", " ## ") # 'Г' "\u0413" font_6x8_koi8r_src[0xE7] = ( " ", "########## ", "## ", "## ", "## ", "## ", "## ", "## ") # 'Х' "\u0425" font_6x8_koi8r_src[0xE8] = ( " ", "## ## ", "## ## ", " ## ## ", " ## ", " ## ## ", "## ## ", "## ## ") # 'И' "\u0418" font_6x8_koi8r_src[0xE9] = ( " ", "## ## ", "## ## ", "## ## ", "## #### ", "## ## ## ", "#### ## ", "## ## ") # 'Й' "\u0419" font_6x8_koi8r_src[0xEA] = ( " ", " ## ## ", "## ## ## ", "## ## ", "## #### ", "## ## ## ", "#### ## ", "## ## ") # 'К' "\u041A" font_6x8_koi8r_src[0xEB] = ( " ", "## ## ", "## ## ", "## ## ", "#### ", "## ## ", "## ## ", "## ## ") # 'Л' "\u041B" font_6x8_koi8r_src[0xEC] = ( " ", " #### ", " ## ## ", " ## ## ", " ## ## ", " ## ## ", " ## ## ", "## ## ") # 'М' "\u041C" font_6x8_koi8r_src[0xED] = ( " ", "## ## ", "#### #### ", "## ## ## ", "## ## ", "## ## ", "## ## ", "## ## ") # 'Н' "\u041D" font_6x8_koi8r_src[0xEE] = ( " ", "## ## ", "## ## ", "## ## ", "########## ", "## ## ", "## ## ", "## ## ") # 'О' "\u041E" font_6x8_koi8r_src[0xEF] = ( " ", " ###### ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ", " ###### ") # 'П' "\u041F" font_6x8_koi8r_src[0xF0] = ( " ", "########## ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ", "## ## ") # 'Я' "\u042F" font_6x8_koi8r_src[0xF1] = ( " ", " ######## ", "## ## ", "## ## ", " ######## ", " ## ## ", " ## ## ", "## ## ") # 'Р' "\u0420" font_6x8_koi8r_src[0xF2] = ( " ", "######## ", "## ## ", "## ## ", "######## ", "## ", "## ", "## ") # 'С' "\u0421" font_6x8_koi8r_src[0xF3] = ( " ", " ###### ", "## ## ", "## ", "## ", "## ", "## ## ", " ###### ") # 'Т' "\u0422" font_6x8_koi8r_src[0xF4] = ( " ", "########## ", " ## ", " ## ", " ## ", " ## ", " ## ", " ## ") # 'У' "\u0423" font_6x8_koi8r_src[0xF5] = ( " ", "## ## ", "## ## ", "## ## ", " ######## ", " ## ", "## ## ", " ###### ") # 'Ж' "\u0416" font_6x8_koi8r_src[0xF6] = ( " ", "## ## ## ", "## ## ## ", "## ## ## ", " ###### ", "## ## ## ", "## ## ## ", "## ## ## ") # 'В' "\u0412" font_6x8_koi8r_src[0xF7] = ( " ", "######## ", "## ## ", "## ## ", "######## ", "## ## ", "## ## ", "######## ") # 'Ь' "\u042C" font_6x8_koi8r_src[0xF8] = ( " ", "## ", "## ", "## ", "######## ", "## ## ", "## ## ", "######## ") # 'Ы' "\u042B" font_6x8_koi8r_src[0xF9] = ( " ", "## ## ", "## ## ", "## ## ", "#### ## ", "## ## ## ", "## ## ## ", "#### ## ") # 'З' "\u0417" font_6x8_koi8r_src[0xFA] = ( " ", " ###### ", "## ## ", " ## ", " ## ", " ## ", "## ## ", " ###### ") # 'Ш' "\u0428" font_6x8_koi8r_src[0xFB] = ( " ", "## ## ## ", "## ## ## ", "## ## ## ", "## ## ## ", "## ## ## ", "## ## ## ", "########## ") # 'Э' "\u042D" font_6x8_koi8r_src[0xFC] = ( " ", " ###### ", "## ## ", " ## ", " ######## ", " ## ", "## ## ", " ###### ") # 'Щ' "\u0429" font_6x8_koi8r_src[0xFD] = ( " ", "## ## ## ", "## ## ## ", "## ## ## ", "## ## ## ", "## ## ## ", "########## ", " ## ") # 'Ч' "\u0427" font_6x8_koi8r_src[0xFE] = ( " ", "## ## ", "## ## ", "## ## ", " ######## ", " ## ", " ## ", " ## ") # 'Ъ' "\u042A" font_6x8_koi8r_src[0xFF] = ( " ", "#### ", " ## ", " ## ", " ###### ", " ## ## ", " ## ## ", " ###### ") def f1(s): retv = "" for i in range(6): retv += "#" if s[i * 2] != " " else " " return '"' + retv + '"' def f2(s): retv = "" for i in range(6): retv += "()" if s[i * 2] != " " else " " return '"' + retv + '"' def f3(s): retv = "" for i in range(5): retv += "1, " if s[i * 2] != " " else "0, " retv += "1" if s[5 * 2] != " " else "0" return '(' + retv + ')' def f4(s): retv = "0b00" for i in range(6): if s[i * 2] != " ": retv += '1' else: retv += '0' return retv def f5(v, x): retv = 0 for i in range(8): if v[i][x * 2] != " ": retv |= 0x80 >> i return "%02X" % retv def fhex(s): retv = 0 for i in range(6): if s[i * 2] != " ": retv |= 0x20 >> i return "%02X" % retv for i in range(0x20, 0x100): ustr = bytes((i, )).decode(encoding="koi8-r") uchr = ord(ustr) sym = "'" + ustr + "'" if i != 0x7F else "[D]" #uni = True # unicode uni = False # KOI8-R var = 6 if var == 1: # demo print print("# " + sym + " " + (('"\\u%04X"') % uchr) + (" - 0x%02X" % i)) if uni: print(("font_6x8[0x%04X] = (" % uchr)) else: print(("font_6x8_koi8r[0x%02X] = (" % i)) f = f2 # f1, f2, f3, f4 for j in range(7): print(' ' + f(font_6x8_koi8r_src[i][j]) + ',') print(' ' + f(font_6x8_koi8r_src[i][7]) + ')') print() elif var == 2: # bad idea if uni: str = "font_6x8[0x%04X] = (" % uchr else: str = "font_6x8_koi8r[0x%2X] = (" % i for j in range(7): str += '0x' + fhex(font_6x8_koi8r_src[i][j]) + ", " str += fhex(font_6x8_koi8r_src[i][7]) + ")" str += " # " + sym + " " + (('"\\u%04X"') % uchr) +(" - 0x%02X" % i) print(str) elif var == 3: # hex mode (bad idea in Python, may be for C) str = " (" for j in range(7): str += '0x' + fhex(font_6x8_koi8r_src[i][j]) + ", " str += '0x' + fhex(font_6x8_koi8r_src[i][7]) + ")," str += " # " + sym + " " + (('"\\u%04X"') % uchr) str += (" - 0x%02X" % i) print(str) elif var == 4: # KOI8-R bytes (good idea) str = ' b"' for j in range(7): str += '\\x' + fhex(font_6x8_koi8r_src[i][j]) str += '\\x' + fhex(font_6x8_koi8r_src[i][7]) + '",' str += " # " + sym + " " + (('"\\u%04X"') % uchr) str += (" - 0x%02X" % i) print(str) elif var == 5: # Unicode dict (good idea) str = ' 0x%04X: b"' % uchr for j in range(7): str += '\\x' + fhex(font_6x8_koi8r_src[i][j]) str += '\\x' + fhex(font_6x8_koi8r_src[i][7]) + '",' str += " # " + sym + " " + (('"\\u%04X"') % uchr) str += (" - 0x%02X" % i) print(str) elif var == 6: # Unicode dict (best idea !!!) str = ' 0x%04X: b"' % uchr for j in range(6): str += '\\x' + f5(font_6x8_koi8r_src[i], j) str += '", # ' + sym + " " + (('"\\u%04X"') % uchr) str += (" - 0x%02X" % i) print(str) elif var == 7: # KOI8-R matrix (good idea) print(" # " + sym + " " + (('"\\u%04X"') % uchr) + (" - 0x%02X" % i)) print(" (", end="") print(f3(font_6x8_koi8r_src[i][0]) + ',') for j in range(1, 7): print(' ' + f3(font_6x8_koi8r_src[i][j]) + ',') print(' ' + f3(font_6x8_koi8r_src[i][7]) + '),') print() elif var == 8: # Unicode matrix (good idea) print((" 0x%04X:" % uchr) + " # " + sym + (" - 0x%02X" % i)) print(' (' + f3(font_6x8_koi8r_src[i][0]) + ',') for j in range(1, 7): print(' ' + f3(font_6x8_koi8r_src[i][j]) + ',') print(' ' + f3(font_6x8_koi8r_src[i][7]) + '),') print()

17.892403

78

0.169005

2,092

46,395

3.426386

0.305927

0.240234

0.408482

0.506417

0.146066

0.13923

0.128488

0.120675

0.112026

0.107003

0

0.09907

0.518095

46,395

2,592

79

17.899306

0.218848

0.068801

0

0.869238

0

0.510276

0.001001

0

0.021344

0

1

0.002822

false

0

0.005644

0.010348

0

null

1

0

1

0

1

0

1

0

null

0

6

18aeb369ef13926aa8f76586750af7fae5287599

10,362

py

Python

pycity_calc/test/test_chp.py

RWTH-EBC/pyCity_calc

99fd0dab7f9a9030fd84ba4715753364662927ec

[ "MIT" ]

4

2020-06-22T14:14:25.000Z

2021-11-08T11:47:01.000Z

pycity_calc/test/test_chp.py

RWTH-EBC/pyCity_calc

99fd0dab7f9a9030fd84ba4715753364662927ec

[ "MIT" ]

4

2019-08-28T19:42:28.000Z

2019-08-28T19:43:44.000Z

pycity_calc/test/test_chp.py

RWTH-EBC/pyCity_calc

99fd0dab7f9a9030fd84ba4715753364662927ec

[ "MIT" ]

null

#!/usr/bin/env python # coding=utf-8 """ Test script for BatteryExtended class """ from __future__ import division from decimal import * import pycity_calc.energysystems.chp as Chp import pycity_calc.economic.energy_sys_cost.chp_cost as chp_cost from pycity_calc.test.pycity_calc_fixtures import fixture_environment, \ fixture_chp_el, fixture_chp_th class Test_Chp(): def test_chp_init(self, fixture_environment): chp = Chp.ChpExtended(environment=fixture_environment, q_nominal=1000, p_nominal=300) assert chp._kind == 'chp' assert chp.chp_type == 'ASUE_2015' # TODO test both operation modi def test_thOperation_calc_chp_th_power_output(self, fixture_chp_th): # thermal power is 10000 W # lower_activation_limit is 0.6 -> 6000 W control_signal = 5000 th_power = fixture_chp_th.thOperation_calc_chp_th_power_output( control_signal) assert th_power == 0 control_signal = 6000 th_power = fixture_chp_th.thOperation_calc_chp_th_power_output( control_signal) assert th_power == 6000 control_signal = 12000 th_power = fixture_chp_th.thOperation_calc_chp_th_power_output( control_signal) assert th_power == 10000 def test_thOperation_calc_chp_el_power_output(self, fixture_chp_th): # thermal power is 10000 W # lower_activation_limit is 0.6 -> 6000 W control_signal = 5000 el_power = fixture_chp_th.thOperation_calc_chp_el_power_output( control_signal) assert el_power == 0 control_signal = 6000 el_power = fixture_chp_th.thOperation_calc_chp_el_power_output( control_signal) assert round(el_power, 2) == 2262.05 control_signal = 12000 el_power = fixture_chp_th.thOperation_calc_chp_el_power_output( control_signal) assert round(el_power, 2) == 4127.95 def test_thOperation_calc_chp_fuel_power_input(self, fixture_chp_th): control_signal = 5000 fuel_power = fixture_chp_th.thOperation_calc_chp_fuel_power_input( control_signal) assert fuel_power == 0 control_signal = 6000 fuel_power = fixture_chp_th.thOperation_calc_chp_fuel_power_input( control_signal) assert round(fuel_power, 2) == 9496.61 control_signal = 12000 fuel_power = fixture_chp_th.thOperation_calc_chp_fuel_power_input( control_signal) assert round(fuel_power, 2) == 16239.03 def test_thOperation_calc_chp_th_efficiency(self, fixture_chp_th): control_signal = 5000 th_eff = fixture_chp_th.thOperation_calc_chp_th_efficiency( control_signal) assert th_eff == 0 control_signal = 6000 th_eff = fixture_chp_th.thOperation_calc_chp_th_efficiency( control_signal) assert round(th_eff, 4) == 0.6318 control_signal = 12000 th_eff = fixture_chp_th.thOperation_calc_chp_th_efficiency( control_signal) assert round(th_eff, 4) == 0.6158 def test_thOperation_calc_chp_el_efficiency(self, fixture_chp_th): control_signal = 5000 el_eff = fixture_chp_th.thOperation_calc_chp_el_efficiency( control_signal) assert el_eff == 0 control_signal = 6000 el_eff = fixture_chp_th.thOperation_calc_chp_el_efficiency( control_signal) assert round(el_eff, 4) == 0.2382 control_signal = 12000 el_eff = fixture_chp_th.thOperation_calc_chp_el_efficiency( control_signal) assert round(el_eff, 4) == 0.2542 def test_elOperation_calc_chp_el_power_output(self, fixture_chp_el): # electrical power is 4500 W # lower_activation_limit is 0.6 -> 2700 W control_signal = 2500 th_power = fixture_chp_el.elOperation_calc_chp_el_power_output( control_signal) assert th_power == 0 control_signal = 3000 th_power = fixture_chp_el.elOperation_calc_chp_el_power_output( control_signal) assert th_power == 3000 control_signal = 6000 th_power = fixture_chp_el.elOperation_calc_chp_el_power_output( control_signal) assert th_power == 4500 def test_elOperation_calc_chp_th_power_output(self, fixture_chp_el): # electrical power is 4500 W # lower_activation_limit is 0.6 -> 2700 W control_signal = 2500 el_power = fixture_chp_el.elOperation_calc_chp_th_power_output( control_signal) assert el_power == 0 control_signal = 3000 el_power = fixture_chp_el.elOperation_calc_chp_th_power_output( control_signal) assert round(el_power, 2) == 7635.91 control_signal = 6000 el_power = fixture_chp_el.elOperation_calc_chp_th_power_output( control_signal) assert round(el_power, 2) == 10770.31 def test_elOperation_calc_chp_fuel_power_input(self, fixture_chp_el): # electrical power is 4500 W # lower_activation_limit is 0.6 -> 2700 W control_signal = 2500 fuel_power = fixture_chp_el.elOperation_calc_chp_fuel_power_input( control_signal) assert fuel_power == 0 control_signal = 3000 fuel_power = fixture_chp_el.elOperation_calc_chp_fuel_power_input( control_signal) assert round(fuel_power, 2) == 12225.18 control_signal = 6000 fuel_power = fixture_chp_el.elOperation_calc_chp_fuel_power_input( control_signal) assert round(fuel_power, 2) == 17552.08 def test_elOperation_calc_chp_th_efficiency(self, fixture_chp_el): # electrical power is 4500 W # lower_activation_limit is 0.6 -> 2700 W control_signal = 2500 th_eff = fixture_chp_el.elOperation_calc_chp_th_efficiency( control_signal) assert th_eff == 0 control_signal = 3000 th_eff = fixture_chp_el.elOperation_calc_chp_th_efficiency( control_signal) assert round(th_eff, 4) == 0.6246 control_signal = 6000 th_eff = fixture_chp_el.elOperation_calc_chp_th_efficiency( control_signal) assert round(th_eff, 4) == 0.6136 def test_elOperation_calc_chp_el_efficiency(self, fixture_chp_el): # electrical power is 4500 W # lower_activation_limit is 0.6 -> 2700 W control_signal = 2500 el_eff = fixture_chp_el.elOperation_calc_chp_el_efficiency( control_signal) assert el_eff == 0 control_signal = 3000 el_eff = fixture_chp_el.elOperation_calc_chp_el_efficiency( control_signal) assert round(el_eff, 4) == 0.2454 control_signal = 6000 el_eff = fixture_chp_el.elOperation_calc_chp_el_efficiency( control_signal) assert round(el_eff, 4) == 0.2564 def test_chp_cost(self): p_el_nom = 900 # in kW chp_cost.calc_invest_cost_chp(p_el_nom, method='asue2015', with_inst=True, use_el_input=True, q_th_nom=None) p_el_nom = 1100 # in kW chp_cost.calc_invest_cost_chp(p_el_nom, method='asue2015', with_inst=True, use_el_input=True, q_th_nom=None) chp_cost.calc_invest_cost_chp(p_el_nom, method='asue2015', with_inst=True, use_el_input=False, q_th_nom=12) p_el_nom = 5 # in kW chp_cost.calc_invest_cost_chp(p_el_nom, method='spieker', with_inst=True, use_el_input=True, q_th_nom=None) p_el_nom = 50 # in kW chp_cost.calc_invest_cost_chp(p_el_nom, method='spieker', with_inst=True, use_el_input=True, q_th_nom=None) p_el_nom = 250 # in kW chp_cost.calc_invest_cost_chp(p_el_nom, method='spieker', with_inst=True, use_el_input=True, q_th_nom=None) p_el_nom = 450 # in kW chp_cost.calc_invest_cost_chp(p_el_nom, method='spieker', with_inst=True, use_el_input=True, q_th_nom=None) p_el_nom = 550 # in kW chp_cost.calc_invest_cost_chp(p_el_nom, method='spieker', with_inst=True, use_el_input=True, q_th_nom=None) p_el_nom = 800 # in kW chp_cost.calc_invest_cost_chp(p_el_nom, method='spieker', with_inst=True, use_el_input=True, q_th_nom=None) def test_nb_switches(self, fixture_environment): """ Test checks returning of number of switching events """ chp = Chp.ChpExtended(environment=fixture_environment, q_nominal=1000) # Manipulate results array (8 switching events) chp.totalQOutput[1] = 1 chp.totalQOutput[2] = 1 chp.totalQOutput[3] = 2 chp.totalQOutput[4] = 3 chp.totalQOutput[25] = 3 chp.totalQOutput[27] = 3.9 chp.totalQOutput[50] = 0.5 nb_switch = chp.calc_nb_on_off_switching() assert nb_switch == 8

35.244898

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null

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null

0

6

18b4c48d63e145af6c4c3ae24ea037a18aa0ee5d

148

py

Python

app/main/errors.py

Abzed/pitches-python

28c9b1462f929ae4b84ca76d8d72092d460ba380

[ "MIT" ]

null

app/main/errors.py

Abzed/pitches-python

28c9b1462f929ae4b84ca76d8d72092d460ba380

[ "MIT" ]

null

app/main/errors.py

Abzed/pitches-python

28c9b1462f929ae4b84ca76d8d72092d460ba380

[ "MIT" ]

null

from flask import render_template from . import main @main.app_errorhandler(404) def fo_o_fo(error): return render_template('fo_o_fo.html'),404

24.666667

46

0.790541

25

148

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0.6

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148

6

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24.666667

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1

0

1

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null

0

1

0

6

18ca96d80812360b0d02f2e1f9d5dbb3519fe7ad

10,160

py

Python

testcases/security_scan/connect.py

rski/functest-mirror

7a2538438eab7a406c821acd7c72352f4a6ba364

[ "Apache-2.0" ]

null

testcases/security_scan/connect.py

rski/functest-mirror

7a2538438eab7a406c821acd7c72352f4a6ba364

[ "Apache-2.0" ]

null

testcases/security_scan/connect.py

rski/functest-mirror

7a2538438eab7a406c821acd7c72352f4a6ba364

[ "Apache-2.0" ]

null

#!/usr/bin/python # # Copyright (c) 2016 Red Hat # Luke Hinds ([email protected]) # This program and the accompanying materials # are made available under the terms of the Apache License, Version 2.0 # which accompanies this distribution, and is available at # # http://www.apache.org/licenses/LICENSE-2.0 # # 0.1: OpenSCAP paramiko connection functions import os import socket import paramiko import functest.utils.functest_logger as ft_logger # add installer IP from env INSTALLER_IP = os.getenv('INSTALLER_IP') # Set up loggers logger = ft_logger.Logger("security_scan").getLogger() paramiko.util.log_to_file("/var/log/paramiko.log") class SetUp: def __init__(self, *args): self.args = args def keystonepass(self): com = self.args[0] client = paramiko.SSHClient() privatekeyfile = os.path.expanduser('/root/.ssh/id_rsa') selectedkey = paramiko.RSAKey.from_private_key_file(privatekeyfile) client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) try: client.connect(INSTALLER_IP, port=22, username='stack', pkey=selectedkey) except paramiko.SSHException: logger.error("Password is invalid for " "undercloud host: {0}".format(INSTALLER_IP)) except paramiko.AuthenticationException: logger.error("Authentication failed for " "undercloud host: {0}".format(INSTALLER_IP)) except socket.error: logger.error("Socker Connection failed for " "undercloud host: {0}".format(INSTALLER_IP)) stdin, stdout, stderr = client.exec_command(com) return stdout.read() client.close() def getockey(self): remotekey = self.args[0] localkey = self.args[1] privatekeyfile = os.path.expanduser('/root/.ssh/id_rsa') selectedkey = paramiko.RSAKey.from_private_key_file(privatekeyfile) transport = paramiko.Transport((INSTALLER_IP, 22)) transport.connect(username='stack', pkey=selectedkey) try: sftp = paramiko.SFTPClient.from_transport(transport) except paramiko.SSHException: logger.error("Authentication failed for " "host: {0}".format(INSTALLER_IP)) except paramiko.AuthenticationException: logger.error("Authentication failed for " "host: {0}".format(INSTALLER_IP)) except socket.error: logger.error("Socker Connection failed for " "undercloud host: {0}".format(INSTALLER_IP)) sftp.get(remotekey, localkey) sftp.close() transport.close() class ConnectionManager: def __init__(self, host, port, user, localkey, *args): self.host = host self.port = port self.user = user self.localkey = localkey self.args = args def remotescript(self): localpath = self.args[0] remotepath = self.args[1] com = self.args[2] client = paramiko.SSHClient() privatekeyfile = os.path.expanduser('/root/.ssh/id_rsa') selectedkey = paramiko.RSAKey.from_private_key_file(privatekeyfile) client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Connection to undercloud try: client.connect(INSTALLER_IP, port=22, username='stack', pkey=selectedkey) except paramiko.SSHException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except paramiko.AuthenticationException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except socket.error: logger.error("Socker Connection failed for " "undercloud host: {0}".format(self.host)) transport = client.get_transport() local_addr = ('127.0.0.1', 0) channel = transport.open_channel("direct-tcpip", (self.host, int(self.port)), (local_addr)) remote_client = paramiko.SSHClient() remote_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Tunnel to overcloud try: remote_client.connect('127.0.0.1', port=22, username=self.user, key_filename=self.localkey, sock=channel) sftp = remote_client.open_sftp() sftp.put(localpath, remotepath) except paramiko.SSHException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except paramiko.AuthenticationException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except socket.error: logger.error("Socker Connection failed for " "undercloud host: {0}".format(self.host)) output = "" stdin, stdout, stderr = remote_client.exec_command(com) stdout = stdout.readlines() # remove script sftp.remove(remotepath) remote_client.close() client.close() # Pipe back stout for line in stdout: output = output + line if output != "": return output def remotecmd(self): com = self.args[0] client = paramiko.SSHClient() privatekeyfile = os.path.expanduser('/root/.ssh/id_rsa') selectedkey = paramiko.RSAKey.from_private_key_file(privatekeyfile) client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Connection to undercloud try: client.connect(INSTALLER_IP, port=22, username='stack', pkey=selectedkey) except paramiko.SSHException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except paramiko.AuthenticationException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except socket.error: logger.error("Socker Connection failed for " "undercloud host: {0}".format(self.host)) transport = client.get_transport() local_addr = ('127.0.0.1', 0) # 0 denotes choose random port channel = transport.open_channel("direct-tcpip", (self.host, int(self.port)), (local_addr)) remote_client = paramiko.SSHClient() remote_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Tunnel to overcloud try: remote_client.connect('127.0.0.1', port=22, username=self.user, key_filename=self.localkey, sock=channel) except paramiko.SSHException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except paramiko.AuthenticationException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except socket.error: logger.error("Socker Connection failed for " "undercloud host: {0}".format(self.host)) chan = remote_client.get_transport().open_session() chan.get_pty() feed = chan.makefile() chan.exec_command(com) print feed.read() remote_client.close() client.close() def download_reports(self): dl_folder = self.args[0] reportfile = self.args[1] reportname = self.args[2] resultsname = self.args[3] client = paramiko.SSHClient() privatekeyfile = os.path.expanduser('/root/.ssh/id_rsa') selectedkey = paramiko.RSAKey.from_private_key_file(privatekeyfile) client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Connection to overcloud try: client.connect(INSTALLER_IP, port=22, username='stack', pkey=selectedkey) except paramiko.SSHException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except paramiko.AuthenticationException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except socket.error: logger.error("Socker Connection failed for " "undercloud host: {0}".format(self.host)) transport = client.get_transport() local_addr = ('127.0.0.1', 0) # 0 denotes choose random port channel = transport.open_channel("direct-tcpip", (self.host, int(self.port)), (local_addr)) remote_client = paramiko.SSHClient() remote_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Tunnel to overcloud try: remote_client.connect('127.0.0.1', port=22, username=self.user, key_filename=self.localkey, sock=channel) except paramiko.SSHException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except paramiko.AuthenticationException: logger.error("Authentication failed for " "host: {0}".format(self.host)) except socket.error: logger.error("Socker Connection failed for " "undercloud host: {0}".format(self.host)) # Download the reports sftp = remote_client.open_sftp() logger.info("Downloading \"{0}\"...".format(reportname)) sftp.get(reportfile, ('{0}/{1}'.format(dl_folder, reportname))) logger.info("Downloading \"{0}\"...".format(resultsname)) sftp.get(reportfile, ('{0}/{1}'.format(dl_folder, resultsname))) sftp.close() transport.close()

41.469388

75

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0.710441

0.695183

0

0.015376

0.308661

10,160

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41.639344

0.815063

0.06063

0

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0

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0.002206

0

null

0.00995

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null

0.004975

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null

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1

0

1

0

null

0

1

0

6

18f237b1bdcfc940cf58e3a2abd3ce6c3dbd7088

32,955

py

Python

zfit/_loss/binnedloss.py

nsahoo/zfit

fcad2578f31138f5383f7fa5de6c0f8c6b1dbaa4

[ "BSD-3-Clause" ]

null

zfit/_loss/binnedloss.py

nsahoo/zfit

fcad2578f31138f5383f7fa5de6c0f8c6b1dbaa4

[ "BSD-3-Clause" ]

null

zfit/_loss/binnedloss.py

nsahoo/zfit

fcad2578f31138f5383f7fa5de6c0f8c6b1dbaa4

[ "BSD-3-Clause" ]

null

# Copyright (c) 2021 zfit from typing import Iterable, Optional, Set import numpy as np import tensorflow as tf from uhi.typing.plottable import PlottableHistogram from .. import z from ..core.interfaces import ZfitBinnedData, ZfitBinnedPDF from ..core.loss import BaseLoss from ..util import ztyping from ..util.checks import NONE from ..util.container import convert_to_container from ..util.warnings import warn_advanced_feature from ..util.ztyping import OptionsInputType, ConstraintsInputType from ..z import numpy as znp @z.function(wraps='tensor') def _spd_transform(values, probs, variances): # Scaled Poisson distribution from Bohm and Zech, NIMA 748 (2014) 1-6 scale = values * tf.math.reciprocal_no_nan(variances) return values * scale, probs * scale @z.function(wraps='tensor') def poisson_loss_calc(probs, values, log_offset=None, variances=None): if variances is not None: values, probs = _spd_transform(values, probs, variances=variances) values += znp.asarray(1e-307, dtype=znp.float64) probs += znp.asarray(1e-307, dtype=znp.float64) poisson_term = tf.nn.log_poisson_loss(values, # TODO: correct offset znp.log( probs), compute_full_loss=False) # TODO: optimization? if log_offset is not None: poisson_term += log_offset return poisson_term class BaseBinned(BaseLoss): def __init__(self, model: ztyping.BinnedPDFInputType, data: ztyping.BinnedDataInputType, constraints: ConstraintsInputType = None, options: OptionsInputType = None): model = convert_to_container(model) data = convert_to_container(data) from zfit._data.binneddatav1 import BinnedData data = [ BinnedData.from_hist(d) if (isinstance(d, PlottableHistogram) and not isinstance(d, ZfitBinnedData)) else d for d in data ] not_binned_pdf = [mod for mod in model if not isinstance(mod, ZfitBinnedPDF)] not_binned_data = [dat for dat in data if not isinstance(dat, ZfitBinnedData)] not_binned_pdf_msg = ("The following PDFs are not binned but need to be. They can be wrapped in an " f"BinnedFromUnbinnedPDF. {not_binned_pdf} ") not_binned_data_msg = ( "The following datasets are not binned but need to be. They can be converted to a binned " f"using the `to_binned` method. {not_binned_data}") error_msg = "" if not_binned_pdf: error_msg += not_binned_pdf_msg if not_binned_data: error_msg += not_binned_data_msg if error_msg: raise ValueError(error_msg) super().__init__(model=model, data=data, constraints=constraints, fit_range=None, options=options) def create_new(self, model: ztyping.BinnedPDFInputType = NONE, data: ztyping.BinnedDataInputType = NONE, constraints: ConstraintsInputType = NONE, options: OptionsInputType = NONE): r"""Create a new binned loss of this type. This is preferrable over creating a new instance in most cases. Internals, such as certain optimizations will be shared and therefore the loss is made comparable. If something is not given, it will be taken from the current loss. Args: model: |@doc:loss.binned.init.model| Binned PDF(s) that return the normalized probability (`rel_counts` or `counts`) for *data* under the given parameters. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.model| data: |@doc:loss.binned.init.data| Binned dataset that will be given to the *model*. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.data| constraints: |@doc:loss.init.constraints| Auxiliary measurements ("constraints") that add a likelihood term to the loss. .. math:: \mathcal{L}(\theta) = \mathcal{L}_{unconstrained} \prod_{i} f_{constr_i}(\theta) Usually, an auxiliary measurement -- by its very nature -S should only be added once to the loss. zfit does not automatically deduplicate constraints if they are given multiple times, leaving the freedom for arbitrary constructs. Constraints can also be used to restrict the loss by adding any kinds of penalties. |@docend:loss.init.constraints| options: |@doc:loss.init.options| Additional options (as a dict) for the loss. Current possibilities include: - 'subtr_const' (default True): subtract from each points log probability density a constant that is approximately equal to the average log probability density in the very first evaluation before the summation. This brings the initial loss value closer to 0 and increases, especially for large datasets, the numerical stability. The value will be stored ith 'subtr_const_value' and can also be given directly. The subtraction should not affect the minimum as the absolute value of the NLL is meaningless. However, with this switch on, one cannot directly compare different likelihoods ablolute value as the constant may differ! Use `create_new` in order to have a comparable likelihood between different losses These settings may extend over time. In order to make sure that a loss is the same under the same data, make sure to use `create_new` instead of instantiating a new loss as the former will automatically overtake any relevant constants and behavior. |@docend:loss.init.options| Returns: """ if model is NONE: model = self.model if data is NONE: data = self.data if constraints is NONE: constraints = self.constraints if constraints is not None: constraints = constraints.copy() if options is NONE: options = self._options if isinstance(options, dict): options = options.copy() return type(self)(model=model, data=data, constraints=constraints, options=options) class ExtendedBinnedNLL(BaseBinned): def __init__(self, model: ztyping.BinnedPDFInputType, data: ztyping.BinnedDataInputType, constraints: ConstraintsInputType = None, options: OptionsInputType = None): r"""Extended binned likelihood using the expected number of events per bin with a poisson probability. |@doc:loss.init.explain.spdtransform| A scaled Poisson distribution is used as described by Bohm and Zech, NIMA 748 (2014) 1-6 |@docend:loss.init.explain.spdtransform| The binned likelihood is defined as .. math:: \mathcal{L} = \product \mathcal{poiss}(N_{modelbin_i}, N_{databin_i}) = N_{databin_i}^{N_{modelbin_i}} \frac{e^{- N_{databin_i}}}{N_{modelbin_i}!} where :math:`databin_i` is the :math:`i^{th}` bin in the data and :math:`modelbin_i` is the :math:`i^{th}` bin of the model, the expected counts. |@doc:loss.init.explain.simultaneous| A simultaneous fit can be performed by giving one or more `model`, `data`, to the loss. The length of each has to match the length of the others .. math:: \mathcal{L}_{simultaneous}(\theta | {data_0, data_1, ..., data_n}) = \prod_{i} \mathcal{L}(\theta_i, data_i) where :math:`\theta_i` is a set of parameters and a subset of :math:`\theta` |@docend:loss.init.explain.simultaneous| |@doc:loss.init.explain.negativelog| For optimization purposes, it is often easier to minimize a function and to use a log transformation. The actual loss is given by .. math:: \mathcal{L} = - \sum_{i}^{n} ln(f(\theta|x_i)) and therefore being called "negative log ..." |@docend:loss.init.explain.negativelog| Args: model: |@doc:loss.binned.init.model| Binned PDF(s) that return the normalized probability (`rel_counts` or `counts`) for *data* under the given parameters. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.model| data: |@doc:loss.binned.init.data| Binned dataset that will be given to the *model*. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.data| constraints: |@doc:loss.init.constraints| Auxiliary measurements ("constraints") that add a likelihood term to the loss. .. math:: \mathcal{L}(\theta) = \mathcal{L}_{unconstrained} \prod_{i} f_{constr_i}(\theta) Usually, an auxiliary measurement -- by its very nature -S should only be added once to the loss. zfit does not automatically deduplicate constraints if they are given multiple times, leaving the freedom for arbitrary constructs. Constraints can also be used to restrict the loss by adding any kinds of penalties. |@docend:loss.init.constraints| options: |@doc:loss.init.options| Additional options (as a dict) for the loss. Current possibilities include: - 'subtr_const' (default True): subtract from each points log probability density a constant that is approximately equal to the average log probability density in the very first evaluation before the summation. This brings the initial loss value closer to 0 and increases, especially for large datasets, the numerical stability. The value will be stored ith 'subtr_const_value' and can also be given directly. The subtraction should not affect the minimum as the absolute value of the NLL is meaningless. However, with this switch on, one cannot directly compare different likelihoods ablolute value as the constant may differ! Use `create_new` in order to have a comparable likelihood between different losses These settings may extend over time. In order to make sure that a loss is the same under the same data, make sure to use `create_new` instead of instantiating a new loss as the former will automatically overtake any relevant constants and behavior. |@docend:loss.init.options| """ self._errordef = 0.5 super().__init__(model=model, data=data, constraints=constraints, options=options) @z.function(wraps='loss') def _loss_func(self, model: Iterable[ZfitBinnedPDF], data: Iterable[ZfitBinnedData], fit_range, constraints, log_offset): poisson_terms = [] for mod, dat in zip(model, data): values = dat.values( # TODO: right order of model and data? # obs=mod.obs ) variances = dat.variances() probs = mod.counts(dat) poisson_term = poisson_loss_calc(probs, values, log_offset, variances) poisson_terms.append(poisson_term) # TODO: change None nll = znp.sum(poisson_terms) if constraints: constraints = z.reduce_sum([c.value() for c in constraints]) nll += constraints return nll @property def is_extended(self): return True def _get_params(self, floating: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True) -> Set["ZfitParameter"]: return super()._get_params(floating, is_yield, extract_independent) class BinnedNLL(BaseBinned): def __init__(self, model: ztyping.BinnedPDFInputType, data: ztyping.BinnedDataInputType, constraints: ConstraintsInputType = None, options: OptionsInputType = None): r"""Binned negative log likelihood. |@doc:loss.init.explain.spdtransform| A scaled Poisson distribution is used as described by Bohm and Zech, NIMA 748 (2014) 1-6 |@docend:loss.init.explain.spdtransform| The binned likelihood is the binned version of :py:class:`~zfit.loss.UnbinnedNLL`. It is defined as .. math:: \\mathcal{L} = \\product \\mathcal{poiss}(N_{modelbin_i}, N_{databin_i}) = N_{databin_i}^{N_{modelbin_i}} \frac{e^{- N_{databin_i}}}{N_{modelbin_i}!} where :math:`databin_i` is the :math:`i^{th}` bin in the data and :math:`modelbin_i` is the :math:`i^{th}` bin of the model multiplied by the total number of events in data. |@doc:loss.init.explain.simultaneous| A simultaneous fit can be performed by giving one or more `model`, `data`, to the loss. The length of each has to match the length of the others .. math:: \mathcal{L}_{simultaneous}(\theta | {data_0, data_1, ..., data_n}) = \prod_{i} \mathcal{L}(\theta_i, data_i) where :math:`\theta_i` is a set of parameters and a subset of :math:`\theta` |@docend:loss.init.explain.simultaneous| |@doc:loss.init.explain.negativelog| For optimization purposes, it is often easier to minimize a function and to use a log transformation. The actual loss is given by .. math:: \mathcal{L} = - \sum_{i}^{n} ln(f(\theta|x_i)) and therefore being called "negative log ..." |@docend:loss.init.explain.negativelog| Args: model: |@doc:loss.binned.init.model| Binned PDF(s) that return the normalized probability (`rel_counts` or `counts`) for *data* under the given parameters. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.model| data: |@doc:loss.binned.init.data| Binned dataset that will be given to the *model*. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.data| constraints: |@doc:loss.init.constraints| Auxiliary measurements ("constraints") that add a likelihood term to the loss. .. math:: \mathcal{L}(\theta) = \mathcal{L}_{unconstrained} \prod_{i} f_{constr_i}(\theta) Usually, an auxiliary measurement -- by its very nature -S should only be added once to the loss. zfit does not automatically deduplicate constraints if they are given multiple times, leaving the freedom for arbitrary constructs. Constraints can also be used to restrict the loss by adding any kinds of penalties. |@docend:loss.init.constraints| options: |@doc:loss.init.options| Additional options (as a dict) for the loss. Current possibilities include: - 'subtr_const' (default True): subtract from each points log probability density a constant that is approximately equal to the average log probability density in the very first evaluation before the summation. This brings the initial loss value closer to 0 and increases, especially for large datasets, the numerical stability. The value will be stored ith 'subtr_const_value' and can also be given directly. The subtraction should not affect the minimum as the absolute value of the NLL is meaningless. However, with this switch on, one cannot directly compare different likelihoods ablolute value as the constant may differ! Use `create_new` in order to have a comparable likelihood between different losses These settings may extend over time. In order to make sure that a loss is the same under the same data, make sure to use `create_new` instead of instantiating a new loss as the former will automatically overtake any relevant constants and behavior. |@docend:loss.init.options| """ self._errordef = 0.5 super().__init__(model=model, data=data, constraints=constraints, options=options) extended_pdfs = [pdf for pdf in self.model if pdf.is_extended] if extended_pdfs and type(self) == BinnedNLL: warn_advanced_feature(f"Extended PDFs ({extended_pdfs}) are given to a normal BinnedNLL. " f" This won't take the yield " "into account and simply treat the PDFs as non-extended PDFs. To create an " "extended NLL, use the `ExtendedBinnedNLL`.", identifier='extended_in_BinnedNLL') @z.function(wraps='loss') def _loss_func(self, model: Iterable[ZfitBinnedPDF], data: Iterable[ZfitBinnedData], fit_range, constraints, log_offset): poisson_terms = [] for mod, dat in zip(model, data): values = dat.values( # TODO: right order of model and data? # obs=mod.obs ) variances = dat.variances() probs = mod.rel_counts(dat) probs *= znp.sum(values) poisson_term = poisson_loss_calc(probs, values, log_offset, variances) poisson_terms.append(poisson_term) nll = znp.sum(poisson_terms) if constraints: constraints = z.reduce_sum([c.value() for c in constraints]) nll += constraints return nll @property def is_extended(self): return False def _get_params(self, floating: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True) -> Set["ZfitParameter"]: if not self.is_extended: is_yield = False # the loss does not depend on the yields return super()._get_params(floating, is_yield, extract_independent) @z.function(wraps='tensor') def chi2_loss_calc(probs, values, variances, log_offset=None, ignore_empty=None): if ignore_empty is None: ignore_empty = True chi2_term = tf.math.squared_difference(probs, values) if ignore_empty: one_over_var = tf.math.reciprocal_no_nan(variances) else: one_over_var = tf.math.reciprocal(variances) chi2_term *= one_over_var chi2_term = znp.sum(chi2_term) if log_offset is not None: chi2_term += log_offset return chi2_term def _check_small_counts_chi2(data, ignore_empty): for dat in data: variances = dat.variances() smaller_than_six = dat.values() < 6 if variances is None: raise ValueError(f"variances cannot be None for Chi2: {dat}") elif np.any(variances <= 0) and not ignore_empty: raise ValueError(f"Variances of {dat} contains zeros or negative numbers, cannot calculate chi2." f" {variances}") elif np.any(smaller_than_six): warn_advanced_feature(f"Some values in {dat} are < 6, the chi2 assumption of gaussian distributed" f" uncertainties most likely won't hold anymore. Use Chi2 for large samples." f"For smaller samples, consider using (Extended)BinnedNLL (or an unbinned fit).", identifier='chi2_counts_small') class BinnedChi2(BaseBinned): def __init__(self, model: ztyping.BinnedPDFInputType, data: ztyping.BinnedDataInputType, constraints: ConstraintsInputType = None, options: OptionsInputType = None): r"""Binned Chi2 loss, using the :math:`N_{tot} from the data. .. math:: \chi^2 = \sum_{\mathrm{bins}} \left( \frac{N_\mathrm{PDF,bin} - N_\mathrm{Data,bin}}{\sigma_\mathrm{Data,bin}} \right)^2 where .. math:: N_\mathrm{PDF,bin} = \mathrm{pdf}(\text{integral}) \cdot N_\mathrm{Data,tot} \sigma_\mathrm{bin} = \text{variance} with `variance` the value of :class:`~zfit.data.BinnedData.variances` of the binned data. |@doc:loss.init.binned.explain.chi2zeros| If the dataset has empty bins, the errors will be zero and :math:`\chi^2` is undefined. Two possibilities are available and can be given as an option: - "empty": "ignore" will ignore all bins with zero entries and won't count to the loss - "errors": "expected" will use the expected counts from the model with a Poissonian uncertainty |@docend:loss.init.binned.explain.chi2zeros| Args: model: |@doc:loss.binned.init.model| Binned PDF(s) that return the normalized probability (`rel_counts` or `counts`) for *data* under the given parameters. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.model| data: |@doc:loss.binned.init.data| Binned dataset that will be given to the *model*. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.data| constraints: |@doc:loss.init.constraints| Auxiliary measurements ("constraints") that add a likelihood term to the loss. .. math:: \mathcal{L}(\theta) = \mathcal{L}_{unconstrained} \prod_{i} f_{constr_i}(\theta) Usually, an auxiliary measurement -- by its very nature -S should only be added once to the loss. zfit does not automatically deduplicate constraints if they are given multiple times, leaving the freedom for arbitrary constructs. Constraints can also be used to restrict the loss by adding any kinds of penalties. |@docend:loss.init.constraints| options: |@doc:loss.init.options| Additional options (as a dict) for the loss. Current possibilities include: - 'subtr_const' (default True): subtract from each points log probability density a constant that is approximately equal to the average log probability density in the very first evaluation before the summation. This brings the initial loss value closer to 0 and increases, especially for large datasets, the numerical stability. The value will be stored ith 'subtr_const_value' and can also be given directly. The subtraction should not affect the minimum as the absolute value of the NLL is meaningless. However, with this switch on, one cannot directly compare different likelihoods ablolute value as the constant may differ! Use `create_new` in order to have a comparable likelihood between different losses These settings may extend over time. In order to make sure that a loss is the same under the same data, make sure to use `create_new` instead of instantiating a new loss as the former will automatically overtake any relevant constants and behavior. |@docend:loss.init.options| """ self._errordef = 1. if options is None: options = {} if options.get('empty') is None: options['empty'] = "ignore" if options.get('errors') is None: options['errors'] = "data" super().__init__(model=model, data=data, constraints=constraints, options=options) extended_pdfs = [pdf for pdf in self.model if pdf.is_extended] if extended_pdfs and type(self) == BinnedChi2: warn_advanced_feature(f"Extended PDFs ({extended_pdfs}) are given to a normal BinnedChi2. " f" This won't take the yield " "into account and simply treat the PDFs as non-extended PDFs. To create an " "extended loss, use the `ExtendedBinnedChi2`.", identifier='extended_in_BinnedChi2') def _precompile(self): super()._precompile() ignore_empty = self._options.get('empty') == "ignore" or self._options.get('errors') == 'expected' data = self.data _check_small_counts_chi2(data, ignore_empty) @z.function(wraps='loss') def _loss_func(self, model: Iterable[ZfitBinnedPDF], data: Iterable[ZfitBinnedData], fit_range, constraints, log_offset): del fit_range ignore_empty = self._options.get('empty') == 'ignore' chi2_terms = [] for mod, dat in zip(model, data): values = dat.values( # TODO: right order of model and data? # obs=mod.obs ) probs = mod.rel_counts(dat) probs *= znp.sum(values) variance_method = self._options.get('errors') if variance_method == 'expected': variances = znp.sqrt(probs + znp.asarray(1e-307, dtype=znp.float64)) elif variance_method == 'data': variances = dat.variances() else: raise ValueError() if variances is None: raise ValueError(f"variances cannot be None for Chi2: {dat}") chi2_term = chi2_loss_calc(probs, values, variances, log_offset, ignore_empty=ignore_empty) chi2_terms.append(chi2_term) chi2_term = znp.sum(chi2_terms) if constraints: constraints = z.reduce_sum([c.value() for c in constraints]) chi2_term += constraints return chi2_term @property def is_extended(self): return False def _get_params(self, floating: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True) -> Set["ZfitParameter"]: if not self.is_extended: is_yield = False # the loss does not depend on the yields return super()._get_params(floating, is_yield, extract_independent) class ExtendedBinnedChi2(BaseBinned): def __init__(self, model: ztyping.BinnedPDFInputType, data: ztyping.BinnedDataInputType, constraints: ConstraintsInputType = None, options: OptionsInputType = None): r"""Binned Chi2 loss, using the :math:`N_{tot} from the PDF. .. math:: \chi^2 = \sum_{\mathrm{bins}} \left( \frac{N_\mathrm{PDF,bin} - N_\mathrm{Data,bin}}{\sigma_\mathrm{Data,bin}} \right)^2 where .. math:: N_\mathrm{PDF,bin} = \mathrm{pdf}(\text{integral}) \cdot N_\mathrm{PDF,expected} \sigma_\mathrm{bin} = \text{variance} with `variance` the value of :class:`~zfit.data.BinnedData.variances` of the binned data. |@doc:loss.init.binned.explain.chi2zeros| If the dataset has empty bins, the errors will be zero and :math:`\chi^2` is undefined. Two possibilities are available and can be given as an option: - "empty": "ignore" will ignore all bins with zero entries and won't count to the loss - "errors": "expected" will use the expected counts from the model with a Poissonian uncertainty |@docend:loss.init.binned.explain.chi2zeros| Args: model: |@doc:loss.binned.init.model| Binned PDF(s) that return the normalized probability (`rel_counts` or `counts`) for *data* under the given parameters. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.model| data: |@doc:loss.binned.init.data| Binned dataset that will be given to the *model*. If multiple model and data are given, they will be used in the same order to do a simultaneous fit. |@docend:loss.binned.init.data| constraints: |@doc:loss.init.constraints| Auxiliary measurements ("constraints") that add a likelihood term to the loss. .. math:: \mathcal{L}(\theta) = \mathcal{L}_{unconstrained} \prod_{i} f_{constr_i}(\theta) Usually, an auxiliary measurement -- by its very nature -S should only be added once to the loss. zfit does not automatically deduplicate constraints if they are given multiple times, leaving the freedom for arbitrary constructs. Constraints can also be used to restrict the loss by adding any kinds of penalties. |@docend:loss.init.constraints| options: |@doc:loss.init.options| Additional options (as a dict) for the loss. Current possibilities include: - 'subtr_const' (default True): subtract from each points log probability density a constant that is approximately equal to the average log probability density in the very first evaluation before the summation. This brings the initial loss value closer to 0 and increases, especially for large datasets, the numerical stability. The value will be stored ith 'subtr_const_value' and can also be given directly. The subtraction should not affect the minimum as the absolute value of the NLL is meaningless. However, with this switch on, one cannot directly compare different likelihoods ablolute value as the constant may differ! Use `create_new` in order to have a comparable likelihood between different losses These settings may extend over time. In order to make sure that a loss is the same under the same data, make sure to use `create_new` instead of instantiating a new loss as the former will automatically overtake any relevant constants and behavior. |@docend:loss.init.options| """ self._errordef = 1. if options is None: options = {} if options.get('empty') is None: options['empty'] = "ignore" if options.get('errors') is None: options['errors'] = "data" super().__init__(model=model, data=data, constraints=constraints, options=options) def _precompile(self): super()._precompile() ignore_empty = self._options.get('empty') == "ignore" or self._options.get('errors') == 'expected' data = self.data _check_small_counts_chi2(data, ignore_empty) @z.function(wraps='loss') def _loss_func(self, model: Iterable[ZfitBinnedPDF], data: Iterable[ZfitBinnedData], fit_range, constraints, log_offset): del fit_range ignore_empty = self._options.get('empty') == "ignore" chi2_terms = [] for mod, dat in zip(model, data): values = dat.values( # TODO: right order of model and data? # obs=mod.obs ) probs = mod.counts(dat) variance_method = self._options.get('errors') if variance_method == 'expected': variances = znp.sqrt(probs + znp.asarray(1e-307, dtype=znp.float64)) elif variance_method == 'data': variances = dat.variances() else: raise ValueError(f"Variance method {variance_method} not supported") if variances is None: raise ValueError(f"variances cannot be None for Chi2: {dat}") chi2_term = chi2_loss_calc(probs, values, variances, log_offset, ignore_empty=ignore_empty) chi2_terms.append(chi2_term) chi2_term = znp.sum(chi2_terms) if constraints: constraints = z.reduce_sum([c.value() for c in constraints]) chi2_term += constraints return chi2_term @property def is_extended(self): return True

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py

Python

mistral/db/sqlalchemy/migration/alembic_migrations/versions/001_kilo.py

shubhamdang/mistral

3c83837f6ce1e4ab74fb519a63e82eaae70f9d2d

[ "Apache-2.0" ]

205

2015-06-21T11:51:47.000Z

2022-03-05T04:00:04.000Z

mistral/db/sqlalchemy/migration/alembic_migrations/versions/001_kilo.py

shubhamdang/mistral

3c83837f6ce1e4ab74fb519a63e82eaae70f9d2d

[ "Apache-2.0" ]

8

2015-06-23T14:47:58.000Z

2021-01-28T06:06:44.000Z

mistral/db/sqlalchemy/migration/alembic_migrations/versions/001_kilo.py

shubhamdang/mistral

3c83837f6ce1e4ab74fb519a63e82eaae70f9d2d

[ "Apache-2.0" ]

110

2015-06-14T03:34:38.000Z

2021-11-11T12:12:56.000Z

# Copyright 2015 OpenStack Foundation. # # 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. """Kilo release Revision ID: 001 Revises: None Create Date: 2015-03-31 12:02:51.935368 """ # revision identifiers, used by Alembic. revision = '001' down_revision = None from alembic import op import sqlalchemy as sa from mistral.db.sqlalchemy import types as st def upgrade(): op.create_table( 'workbooks_v2', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('scope', sa.String(length=80), nullable=True), sa.Column('project_id', sa.String(length=80), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=80), nullable=True), sa.Column('definition', sa.Text(), nullable=True), sa.Column('spec', st.JsonEncoded(), nullable=True), sa.Column('tags', st.JsonEncoded(), nullable=True), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name', 'project_id') ) op.create_table( 'tasks', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=80), nullable=True), sa.Column('requires', st.JsonEncoded(), nullable=True), sa.Column('workbook_name', sa.String(length=80), nullable=True), sa.Column('execution_id', sa.String(length=36), nullable=True), sa.Column('description', sa.String(length=200), nullable=True), sa.Column('task_spec', st.JsonEncoded(), nullable=True), sa.Column('action_spec', st.JsonEncoded(), nullable=True), sa.Column('state', sa.String(length=20), nullable=True), sa.Column('tags', st.JsonEncoded(), nullable=True), sa.Column('in_context', st.JsonEncoded(), nullable=True), sa.Column('parameters', st.JsonEncoded(), nullable=True), sa.Column('output', st.JsonEncoded(), nullable=True), sa.Column('task_runtime_context', st.JsonEncoded(), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_table( 'action_definitions_v2', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('scope', sa.String(length=80), nullable=True), sa.Column('project_id', sa.String(length=80), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=80), nullable=True), sa.Column('definition', sa.Text(), nullable=True), sa.Column('spec', st.JsonEncoded(), nullable=True), sa.Column('tags', st.JsonEncoded(), nullable=True), sa.Column('description', sa.Text(), nullable=True), sa.Column('input', sa.Text(), nullable=True), sa.Column('action_class', sa.String(length=200), nullable=True), sa.Column('attributes', st.JsonEncoded(), nullable=True), sa.Column('is_system', sa.Boolean(), nullable=True), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name', 'project_id') ) op.create_table( 'workflow_definitions_v2', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('scope', sa.String(length=80), nullable=True), sa.Column('project_id', sa.String(length=80), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=80), nullable=True), sa.Column('definition', sa.Text(), nullable=True), sa.Column('spec', st.JsonEncoded(), nullable=True), sa.Column('tags', st.JsonEncoded(), nullable=True), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name', 'project_id') ) op.create_table( 'executions_v2', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('scope', sa.String(length=80), nullable=True), sa.Column('project_id', sa.String(length=80), nullable=True), sa.Column('type', sa.String(length=50), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=80), nullable=True), sa.Column('workflow_name', sa.String(length=80), nullable=True), sa.Column('spec', st.JsonEncoded(), nullable=True), sa.Column('state', sa.String(length=20), nullable=True), sa.Column('state_info', sa.String(length=1024), nullable=True), sa.Column('tags', st.JsonEncoded(), nullable=True), sa.Column('accepted', sa.Boolean(), nullable=True), sa.Column('input', st.JsonEncoded(), nullable=True), sa.Column('output', st.JsonLongDictType(), nullable=True), sa.Column('params', st.JsonEncoded(), nullable=True), sa.Column('context', st.JsonEncoded(), nullable=True), sa.Column('action_spec', st.JsonEncoded(), nullable=True), sa.Column('processed', sa.BOOLEAN(), nullable=True), sa.Column('in_context', st.JsonLongDictType(), nullable=True), sa.Column('published', st.JsonEncoded(), nullable=True), sa.Column('runtime_context', st.JsonEncoded(), nullable=True), sa.Column('task_execution_id', sa.String(length=36), nullable=True), sa.Column( 'workflow_execution_id', sa.String(length=36), nullable=True ), sa.ForeignKeyConstraint( ['task_execution_id'], ['executions_v2.id'], ), sa.ForeignKeyConstraint( ['workflow_execution_id'], ['executions_v2.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table( 'workbooks', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=80), nullable=False), sa.Column('definition', sa.Text(), nullable=True), sa.Column('description', sa.String(length=200), nullable=True), sa.Column('tags', st.JsonEncoded(), nullable=True), sa.Column('scope', sa.String(length=80), nullable=True), sa.Column('project_id', sa.String(length=80), nullable=True), sa.Column('trust_id', sa.String(length=80), nullable=True), sa.PrimaryKeyConstraint('id', 'name'), sa.UniqueConstraint('name') ) op.create_table( 'environments_v2', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('scope', sa.String(length=80), nullable=True), sa.Column('project_id', sa.String(length=80), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=200), nullable=True), sa.Column('description', sa.Text(), nullable=True), sa.Column('variables', st.JsonEncoded(), nullable=True), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name', 'project_id') ) op.create_table( 'triggers', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=80), nullable=False), sa.Column('pattern', sa.String(length=20), nullable=False), sa.Column('next_execution_time', sa.DateTime(), nullable=False), sa.Column('workbook_name', sa.String(length=80), nullable=False), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name') ) op.create_table( 'delayed_calls_v2', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column( 'factory_method_path', sa.String(length=200), nullable=True ), sa.Column('target_method_name', sa.String(length=80), nullable=False), sa.Column('method_arguments', st.JsonEncoded(), nullable=True), sa.Column('serializers', st.JsonEncoded(), nullable=True), sa.Column('auth_context', st.JsonEncoded(), nullable=True), sa.Column('execution_time', sa.DateTime(), nullable=False), sa.PrimaryKeyConstraint('id') ) op.create_table( 'workflow_executions', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('workbook_name', sa.String(length=80), nullable=True), sa.Column('task', sa.String(length=80), nullable=True), sa.Column('state', sa.String(length=20), nullable=True), sa.Column('context', st.JsonEncoded(), nullable=True), sa.PrimaryKeyConstraint('id') ) op.create_table( 'cron_triggers_v2', sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('scope', sa.String(length=80), nullable=True), sa.Column('project_id', sa.String(length=80), nullable=True), sa.Column('id', sa.String(length=36), nullable=False), sa.Column('name', sa.String(length=200), nullable=True), sa.Column('pattern', sa.String(length=100), nullable=True), sa.Column('next_execution_time', sa.DateTime(), nullable=False), sa.Column('workflow_name', sa.String(length=80), nullable=True), sa.Column('remaining_executions', sa.Integer(), nullable=True), sa.Column('workflow_id', sa.String(length=36), nullable=True), sa.Column('workflow_input', st.JsonEncoded(), nullable=True), sa.Column('workflow_input_hash', sa.CHAR(length=64), nullable=True), sa.Column('trust_id', sa.String(length=80), nullable=True), sa.ForeignKeyConstraint( ['workflow_id'], ['workflow_definitions_v2.id'], ), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name', 'project_id'), sa.UniqueConstraint( 'workflow_input_hash', 'workflow_name', 'pattern', 'project_id' ) )

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7a15c8b10b6be644cfeed0310573c5374df35e24

27

py

Python

limix_ext/gcta/__init__.py

glimix/limix-ext

7cf7a3b2b02f6a73cbba90f1945a06b9295b7357

[ "MIT" ]

null

limix_ext/gcta/__init__.py

glimix/limix-ext

7cf7a3b2b02f6a73cbba90f1945a06b9295b7357

[ "MIT" ]

2

2017-06-05T08:29:22.000Z

2017-06-07T16:54:54.000Z

limix_ext/gcta/__init__.py

glimix/limix-ext

7cf7a3b2b02f6a73cbba90f1945a06b9295b7357

[ "MIT" ]

null

from . import heritability

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6

e1b2782c8ce4b0270809102f21fc90c1f96879b8

189

py

Python

tax_calc/filing_status.py

dayfine/tax_calc

4315e79c10f117cb56603d3e232792e01d37781e

[ "MIT" ]

null

tax_calc/filing_status.py

dayfine/tax_calc

4315e79c10f117cb56603d3e232792e01d37781e

[ "MIT" ]

null

tax_calc/filing_status.py

dayfine/tax_calc

4315e79c10f117cb56603d3e232792e01d37781e

[ "MIT" ]

null

import enum class FilingStatus(enum.Enum): SINGLE = enum.auto() MARRIED_FILING_JOINTLY = enum.auto() MARRIED_FILING_SEPARATELY = enum.auto() HEAD_OF_HOUSEHOLD = enum.auto()

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e1bbeb4f4d91df2cacca1635c97a67bf7db70b8f

157

py

Python

pdml2flow-new-plugin.py

Enteee/pdml2flow

2e5da6f03bc799f0e8ef77dd987031b969d4a5df

[ "Apache-2.0" ]

12

2016-04-01T10:59:14.000Z

2022-01-27T04:05:43.000Z

pdml2flow-new-plugin.py

Enteee/pdml2flow

2e5da6f03bc799f0e8ef77dd987031b969d4a5df

[ "Apache-2.0" ]

16

2016-03-18T10:44:00.000Z

2019-08-12T05:52:24.000Z

pdml2flow-new-plugin.py

Enteee/pdml2flow

2e5da6f03bc799f0e8ef77dd987031b969d4a5df

[ "Apache-2.0" ]

2

2016-09-08T11:49:39.000Z

2020-09-09T04:39:15.000Z

#!/usr/bin/env python3 # vim: set fenc=utf8 ts=4 sw=4 et : from pdml2flow import pdml2flow_new_plugin if __name__ == '__main__': pdml2flow_new_plugin()

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6

bed61d0301287edb78f1bb38264d567bbbb2b789

5,370

py

Python

tests/test_transition_masks.py

sagnik/baseline

8d75616e04c1cca509dbebbb6d08ad7e1a7b9f88

[ "Apache-2.0" ]

241

2016-04-25T20:02:31.000Z

2019-09-03T05:44:09.000Z

tests/test_transition_masks.py

sagnik/baseline

8d75616e04c1cca509dbebbb6d08ad7e1a7b9f88

[ "Apache-2.0" ]

131

2019-10-12T10:53:17.000Z

2021-12-03T19:52:47.000Z

tests/test_transition_masks.py

sagnik/baseline

8d75616e04c1cca509dbebbb6d08ad7e1a7b9f88

[ "Apache-2.0" ]

75

2016-06-28T01:18:58.000Z

2019-08-29T06:47:22.000Z

import pytest from eight_mile.utils import transition_mask IOBv = {"<PAD>": 0, "<GO>": 1, "<EOS>": 2, "B-X": 3, "I-X": 4, "B-X-Y": 5, "I-X-Y": 6, "O": 7} BIOv = IOBv IOBESv = { "<PAD>": 0, "<GO>": 1, "<EOS>": 2, "B-X": 3, "I-X": 4, "E-X": 5, "S-X": 6, "B-X-Y": 7, "I-X-Y": 8, "E-X-Y": 9, "S-X-Y": 10, "O": 11, } @pytest.fixture def IOB(): return transition_mask(IOBv, "IOB", IOBv["<GO>"], IOBv["<EOS>"], IOBv["<PAD>"]) @pytest.fixture def BIO(): return transition_mask(IOBv, "BIO", IOBv["<GO>"], IOBv["<EOS>"], IOBv["<PAD>"]) @pytest.fixture def IOBES(): return transition_mask(IOBESv, "IOBES", IOBESv["<GO>"], IOBESv["<EOS>"], IOBESv["<PAD>"]) def test_IOB_shape(IOB): assert IOB.shape == (len(IOBv), len(IOBv)) def test_BIO_shape(BIO): assert BIO.shape == (len(IOBv), len(IOBv)) mask = transition_mask(IOBv, "IOB2", IOBv["<GO>"], IOBv["<EOS>"], IOBv["<PAD>"]) assert mask.shape == (len(IOBv), len(IOBv)) def test_IOBES_shape(IOBES): assert IOBES.shape == (len(IOBESv), len(IOBESv)) def test_IOB_I_B_mismatch(IOB): assert IOB[IOBv["B-X"], IOBv["I-X-Y"]] == 0 def test_ION_I_I_match(IOB): assert IOB[IOBv["I-X"], IOBv["I-X"]] == 1 def test_IOB_I_I_mismatch(IOB): assert IOB[IOBv["I-X-Y"], IOBv["I-X"]] == 1 def test_IOB_to_pad(IOB): assert IOB[IOBv["<PAD>"], IOBv["O"]] == 1 assert IOB[IOBv["<PAD>"], IOBv["I-X"]] == 1 assert IOB[IOBv["<PAD>"], IOBv["B-X"]] == 1 def test_IOB_to_end(IOB): assert IOB[IOBv["<EOS>"], IOBv["O"]] == 1 assert IOB[IOBv["<EOS>"], IOBv["I-X"]] == 1 assert IOB[IOBv["<EOS>"], IOBv["B-X"]] == 1 def test_BIO_from_start(BIO): assert BIO[BIOv["B-X"], BIOv["<GO>"]] == 1 assert BIO[BIOv["I-X"], BIOv["<GO>"]] == 0 assert BIO[BIOv["O"], BIOv["<GO>"]] == 1 def test_IOBES_to_start(IOBES): assert IOBES[IOBESv["<GO>"], IOBESv["B-X"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["I-X"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["E-X"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["S-X"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["O"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["<EOS>"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["<PAD>"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["<GO>"]] == 0 def test_IOBES_from_end(IOBES): assert IOBES[IOBESv["B-X"], IOBESv["<EOS>"]] == 0 assert IOBES[IOBESv["I-X"], IOBESv["<EOS>"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["<EOS>"]] == 0 assert IOBES[IOBESv["S-X"], IOBESv["<EOS>"]] == 0 assert IOBES[IOBESv["O"], IOBESv["<EOS>"]] == 0 assert IOBES[IOBESv["<PAD>"], IOBESv["<EOS>"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["<EOS>"]] == 0 assert IOBES[IOBESv["<EOS>"], IOBESv["<EOS>"]] == 0 def test_IOBES_from_pad(IOBES): assert IOBES[IOBESv["B-X"], IOBESv["<PAD>"]] == 0 assert IOBES[IOBESv["I-X"], IOBESv["<PAD>"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["<PAD>"]] == 0 assert IOBES[IOBESv["S-X"], IOBESv["<PAD>"]] == 0 assert IOBES[IOBESv["O"], IOBESv["<PAD>"]] == 0 assert IOBES[IOBESv["<GO>"], IOBESv["<PAD>"]] == 0 assert IOBES[IOBESv["<PAD>"], IOBESv["<PAD>"]] == 1 assert IOBES[IOBESv["<EOS>"], IOBESv["<PAD>"]] == 1 def test_IOBES_O(IOBES): assert IOBES[IOBESv["B-X"], IOBESv["O"]] == 1 assert IOBES[IOBESv["I-X"], IOBESv["O"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["O"]] == 0 assert IOBES[IOBESv["S-X"], IOBESv["O"]] == 1 assert IOBES[IOBESv["O"], IOBESv["O"]] == 1 def test_IOBES_B(IOBES): assert IOBES[IOBESv["I-X"], IOBESv["B-X"]] == 1 assert IOBES[IOBESv["E-X"], IOBESv["B-X"]] == 1 assert IOBES[IOBESv["I-X-Y"], IOBESv["B-X"]] == 0 assert IOBES[IOBESv["E-X-Y"], IOBESv["B-X"]] == 0 assert IOBES[IOBESv["S-X"], IOBESv["B-X"]] == 0 assert IOBES[IOBESv["B-X"], IOBESv["B-X"]] == 0 assert IOBES[IOBESv["O"], IOBESv["B-X"]] == 0 def test_IOBES_I(IOBES): assert IOBES[IOBESv["I-X"], IOBESv["I-X"]] == 1 assert IOBES[IOBESv["E-X"], IOBESv["I-X"]] == 1 assert IOBES[IOBESv["I-X-Y"], IOBESv["I-X"]] == 0 assert IOBES[IOBESv["E-X-Y"], IOBESv["I-X"]] == 0 assert IOBES[IOBESv["S-X"], IOBESv["I-X"]] == 0 assert IOBES[IOBESv["B-X"], IOBESv["I-X"]] == 0 assert IOBES[IOBESv["O"], IOBESv["I-X"]] == 0 def test_IOBES_from_E(IOBES): assert IOBES[IOBESv["I-X"], IOBESv["E-X"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["E-X"]] == 0 assert IOBES[IOBESv["S-X"], IOBESv["E-X"]] == 1 assert IOBES[IOBESv["B-X"], IOBESv["E-X"]] == 1 assert IOBES[IOBESv["O"], IOBESv["E-X"]] == 1 def test_IOBES_to_E(IOBES): assert IOBES[IOBESv["E-X"], IOBESv["B-X"]] == 1 assert IOBES[IOBESv["E-X"], IOBESv["I-X"]] == 1 assert IOBES[IOBESv["E-X"], IOBESv["E-X"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["B-X-Y"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["I-X-Y"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["E-X-Y"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["S-X"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["S-X-Y"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["O"]] == 0 def test_IOBES_S(IOBES): assert IOBES[IOBESv["B-X"], IOBESv["S-X"]] == 1 assert IOBES[IOBESv["I-X"], IOBESv["S-X"]] == 0 assert IOBES[IOBESv["E-X"], IOBESv["S-X"]] == 0 assert IOBES[IOBESv["S-X"], IOBESv["S-X"]] == 1 assert IOBES[IOBESv["O"], IOBESv["S-X"]] == 1

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null

1

0

null

0

1

0

6

befb3a0760cc473c85eb3f0e827c3857d7ca2b8e

26

py

Python

mail_to_sms/__init__.py

naschorr/mail-to-sms

40acd30b1ebccf350713c6b17d5b9b313e3f39d6

[ "MIT" ]

5

2017-08-15T03:57:14.000Z

2022-01-24T00:37:27.000Z

mail_to_sms/__init__.py

agcashdaum/mail-to-sms

b1d9bcaf20570192b82a0684e595f9ed000335e1

[ "MIT" ]

2

2021-05-03T06:01:17.000Z

2021-10-30T07:53:34.000Z

mail_to_sms/__init__.py

agcashdaum/mail-to-sms

b1d9bcaf20570192b82a0684e595f9ed000335e1

[ "MIT" ]

4

2018-01-25T09:14:18.000Z

2021-09-21T06:25:22.000Z

from .mail_to_sms import *

26

0.807692

5

26

3.8

1

0

0.115385

26

1

26

0.826087

0

1

0

true

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1

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1

0

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1

0

null

0

1

0

1

0

1

0

6

8366bc46f113764791ef128e075b6f9df98a13cd

27

py

Python

2018-01/2018-01-23/animal/animal.py

wenjuanchendora/Python_Study

02d08229210602edf4e1fa96fd7167356275e316

[ "MIT" ]

null

2018-01/2018-01-23/animal/animal.py

wenjuanchendora/Python_Study

02d08229210602edf4e1fa96fd7167356275e316

[ "MIT" ]

null

2018-01/2018-01-23/animal/animal.py

wenjuanchendora/Python_Study

02d08229210602edf4e1fa96fd7167356275e316

[ "MIT" ]

null

def run(): print("run")

13.5

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0.518519

4

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true

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0

1

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null

0

1

0

1

0

6

3607b17a7ea85fbdf681d26efbd8046602b81f6b

258,376

py

Python

instances/passenger_demand/pas-20210422-1717-int1/92.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int1/92.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int1/92.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 19309 passenger_arriving = ( (5, 2, 2, 5, 2, 1, 4, 1, 1, 2, 0, 1, 0, 7, 7, 2, 1, 8, 0, 1, 2, 2, 4, 1, 1, 0), # 0 (4, 11, 5, 2, 2, 4, 6, 0, 3, 1, 0, 1, 0, 5, 3, 3, 0, 6, 4, 2, 2, 2, 0, 1, 1, 0), # 1 (8, 8, 1, 6, 2, 2, 2, 3, 1, 0, 0, 1, 0, 8, 7, 4, 5, 6, 6, 0, 1, 3, 1, 0, 2, 0), # 2 (6, 4, 4, 4, 3, 3, 2, 1, 2, 1, 0, 0, 0, 4, 5, 4, 2, 3, 1, 3, 1, 0, 2, 1, 0, 0), # 3 (7, 8, 5, 9, 5, 1, 4, 1, 4, 1, 0, 1, 0, 10, 8, 5, 3, 6, 3, 1, 1, 1, 0, 0, 1, 0), # 4 (3, 5, 4, 3, 12, 4, 4, 3, 2, 1, 4, 0, 0, 9, 3, 3, 4, 7, 2, 2, 1, 3, 1, 3, 0, 0), # 5 (6, 5, 4, 5, 3, 8, 1, 6, 3, 0, 2, 2, 0, 2, 10, 4, 2, 4, 3, 2, 1, 1, 2, 0, 1, 0), # 6 (9, 8, 6, 7, 7, 1, 3, 2, 3, 0, 0, 2, 0, 8, 3, 4, 4, 5, 4, 5, 1, 2, 2, 2, 0, 0), # 7 (5, 8, 10, 8, 7, 3, 4, 3, 4, 2, 1, 0, 0, 11, 10, 5, 4, 5, 2, 2, 3, 2, 2, 0, 1, 0), # 8 (11, 3, 7, 9, 3, 5, 1, 4, 1, 1, 0, 0, 0, 4, 13, 6, 3, 7, 7, 4, 2, 3, 3, 3, 0, 0), # 9 (4, 6, 4, 2, 9, 1, 2, 5, 4, 0, 2, 1, 0, 8, 9, 5, 5, 4, 5, 3, 0, 5, 4, 3, 1, 0), # 10 (7, 5, 8, 7, 3, 0, 2, 3, 3, 2, 3, 0, 0, 7, 9, 4, 4, 6, 5, 3, 4, 2, 2, 0, 0, 0), # 11 (11, 9, 9, 9, 8, 3, 3, 3, 6, 0, 4, 2, 0, 5, 6, 3, 4, 5, 6, 3, 2, 1, 2, 2, 0, 0), # 12 (7, 8, 5, 6, 5, 5, 8, 7, 6, 2, 0, 0, 0, 9, 12, 12, 5, 6, 5, 3, 1, 2, 4, 0, 0, 0), # 13 (14, 5, 5, 6, 5, 3, 2, 5, 3, 1, 1, 0, 0, 11, 8, 12, 8, 6, 4, 3, 4, 2, 2, 1, 0, 0), # 14 (6, 10, 10, 8, 7, 3, 2, 7, 5, 3, 0, 1, 0, 7, 10, 7, 5, 15, 5, 6, 0, 4, 6, 4, 0, 0), # 15 (10, 11, 9, 10, 5, 4, 5, 5, 2, 6, 1, 1, 0, 5, 8, 8, 5, 6, 3, 5, 5, 4, 6, 1, 2, 0), # 16 (6, 9, 6, 9, 14, 5, 5, 5, 5, 2, 4, 0, 0, 11, 7, 6, 4, 10, 7, 3, 4, 8, 3, 1, 1, 0), # 17 (14, 10, 10, 9, 7, 5, 4, 5, 4, 0, 2, 0, 0, 8, 8, 10, 6, 8, 3, 5, 2, 4, 0, 2, 1, 0), # 18 (6, 8, 10, 6, 5, 4, 2, 4, 2, 0, 1, 0, 0, 3, 11, 6, 7, 7, 4, 2, 2, 7, 4, 0, 0, 0), # 19 (17, 9, 6, 13, 7, 2, 4, 1, 1, 4, 1, 2, 0, 9, 10, 6, 13, 8, 7, 0, 2, 6, 8, 1, 1, 0), # 20 (7, 14, 10, 8, 6, 5, 6, 4, 4, 4, 1, 2, 0, 12, 8, 12, 8, 5, 1, 3, 5, 3, 2, 4, 0, 0), # 21 (16, 8, 5, 14, 12, 2, 4, 3, 9, 1, 1, 2, 0, 12, 7, 5, 7, 5, 6, 3, 1, 2, 4, 2, 1, 0), # 22 (12, 5, 7, 11, 8, 5, 3, 5, 5, 3, 0, 0, 0, 9, 9, 9, 7, 11, 7, 3, 1, 3, 2, 5, 0, 0), # 23 (8, 12, 10, 12, 6, 1, 3, 5, 6, 4, 0, 1, 0, 4, 6, 10, 4, 7, 5, 6, 1, 5, 7, 1, 2, 0), # 24 (13, 10, 12, 8, 4, 0, 3, 8, 4, 1, 3, 2, 0, 12, 11, 10, 8, 7, 7, 5, 0, 4, 0, 0, 0, 0), # 25 (11, 9, 5, 11, 12, 6, 4, 7, 3, 5, 2, 1, 0, 12, 6, 12, 3, 3, 10, 4, 1, 4, 5, 1, 1, 0), # 26 (7, 8, 9, 5, 7, 2, 6, 5, 4, 3, 1, 1, 0, 9, 9, 7, 4, 8, 6, 5, 2, 4, 1, 3, 0, 0), # 27 (10, 9, 11, 9, 2, 2, 4, 4, 7, 3, 2, 2, 0, 14, 6, 3, 8, 4, 10, 3, 3, 2, 4, 0, 0, 0), # 28 (12, 9, 8, 8, 17, 6, 11, 4, 4, 1, 4, 0, 0, 13, 4, 7, 9, 4, 6, 5, 2, 2, 5, 1, 0, 0), # 29 (22, 13, 10, 7, 9, 2, 2, 5, 7, 1, 3, 0, 0, 15, 5, 2, 8, 9, 10, 5, 1, 7, 3, 2, 0, 0), # 30 (8, 10, 15, 12, 4, 9, 3, 5, 5, 4, 2, 0, 0, 9, 6, 6, 6, 4, 4, 2, 2, 4, 4, 3, 0, 0), # 31 (16, 8, 4, 7, 8, 4, 3, 2, 2, 2, 4, 2, 0, 6, 11, 5, 7, 4, 5, 5, 2, 4, 3, 0, 1, 0), # 32 (13, 14, 8, 14, 6, 5, 7, 5, 2, 0, 0, 1, 0, 7, 8, 11, 6, 10, 6, 11, 2, 2, 2, 1, 1, 0), # 33 (9, 8, 7, 8, 6, 1, 2, 4, 2, 6, 0, 1, 0, 13, 10, 5, 8, 11, 5, 4, 0, 2, 1, 0, 1, 0), # 34 (4, 14, 10, 11, 10, 1, 1, 2, 4, 0, 1, 3, 0, 10, 9, 3, 4, 8, 4, 5, 2, 8, 8, 2, 2, 0), # 35 (12, 10, 7, 1, 8, 4, 6, 4, 0, 0, 1, 2, 0, 10, 16, 4, 6, 7, 9, 5, 2, 4, 6, 1, 2, 0), # 36 (8, 15, 6, 11, 7, 5, 5, 5, 3, 2, 1, 1, 0, 8, 9, 5, 4, 6, 8, 4, 5, 1, 2, 0, 1, 0), # 37 (6, 5, 12, 7, 7, 6, 3, 3, 6, 2, 0, 0, 0, 14, 7, 9, 6, 10, 5, 2, 3, 3, 5, 2, 3, 0), # 38 (13, 17, 9, 10, 9, 6, 2, 0, 4, 3, 1, 0, 0, 10, 7, 9, 5, 8, 3, 1, 3, 5, 5, 4, 0, 0), # 39 (13, 13, 13, 11, 13, 3, 3, 6, 3, 1, 0, 1, 0, 9, 7, 5, 5, 7, 7, 5, 1, 1, 2, 2, 0, 0), # 40 (10, 8, 6, 7, 8, 6, 7, 5, 6, 2, 1, 0, 0, 12, 9, 8, 4, 7, 7, 2, 1, 3, 0, 1, 1, 0), # 41 (9, 10, 10, 11, 10, 2, 4, 1, 5, 3, 0, 1, 0, 14, 14, 3, 4, 12, 3, 3, 1, 3, 3, 2, 2, 0), # 42 (16, 8, 6, 19, 9, 3, 2, 5, 4, 2, 0, 1, 0, 14, 9, 9, 10, 8, 4, 4, 2, 2, 2, 1, 0, 0), # 43 (10, 8, 7, 6, 9, 3, 6, 8, 7, 2, 1, 1, 0, 6, 13, 5, 6, 12, 3, 3, 4, 5, 3, 2, 3, 0), # 44 (9, 8, 5, 14, 5, 4, 0, 2, 1, 5, 2, 0, 0, 11, 13, 5, 9, 11, 6, 9, 4, 7, 4, 1, 1, 0), # 45 (8, 9, 8, 4, 7, 1, 1, 1, 2, 2, 2, 1, 0, 14, 6, 6, 5, 6, 3, 4, 3, 11, 1, 1, 1, 0), # 46 (11, 10, 12, 17, 10, 6, 1, 2, 3, 0, 1, 0, 0, 11, 8, 8, 14, 9, 6, 3, 1, 3, 1, 3, 1, 0), # 47 (11, 9, 10, 5, 4, 3, 3, 4, 6, 0, 0, 0, 0, 15, 10, 7, 4, 3, 6, 6, 0, 1, 2, 3, 2, 0), # 48 (8, 11, 2, 8, 5, 1, 4, 3, 1, 3, 2, 0, 0, 12, 8, 7, 5, 11, 5, 3, 3, 3, 1, 2, 0, 0), # 49 (8, 12, 12, 7, 8, 4, 4, 2, 6, 3, 0, 0, 0, 8, 14, 2, 3, 9, 3, 3, 2, 8, 2, 2, 3, 0), # 50 (13, 9, 4, 4, 9, 2, 5, 1, 3, 2, 1, 1, 0, 13, 12, 6, 4, 10, 7, 5, 4, 3, 2, 1, 2, 0), # 51 (8, 13, 7, 5, 11, 1, 3, 0, 4, 2, 0, 2, 0, 9, 8, 8, 9, 8, 5, 4, 5, 1, 2, 2, 1, 0), # 52 (6, 10, 7, 13, 8, 7, 5, 2, 5, 2, 1, 1, 0, 10, 8, 5, 2, 5, 7, 2, 4, 4, 1, 1, 1, 0), # 53 (11, 5, 6, 17, 9, 5, 5, 1, 3, 4, 2, 2, 0, 12, 10, 7, 5, 10, 1, 5, 3, 3, 4, 0, 0, 0), # 54 (12, 10, 10, 9, 3, 6, 3, 2, 3, 1, 1, 0, 0, 8, 5, 9, 4, 6, 4, 5, 5, 2, 5, 3, 3, 0), # 55 (11, 9, 13, 8, 9, 5, 4, 2, 4, 1, 3, 1, 0, 6, 10, 4, 7, 7, 3, 4, 1, 4, 5, 3, 0, 0), # 56 (14, 18, 11, 10, 12, 2, 7, 5, 9, 1, 1, 0, 0, 7, 13, 6, 4, 11, 6, 3, 2, 3, 6, 1, 0, 0), # 57 (6, 8, 11, 11, 5, 5, 5, 5, 1, 1, 4, 1, 0, 14, 6, 13, 8, 6, 3, 3, 2, 5, 3, 3, 2, 0), # 58 (9, 8, 11, 15, 4, 0, 4, 5, 6, 4, 2, 1, 0, 11, 9, 8, 6, 11, 5, 4, 0, 3, 0, 1, 1, 0), # 59 (10, 12, 7, 5, 7, 2, 3, 4, 2, 3, 0, 4, 0, 14, 3, 4, 5, 10, 3, 5, 0, 4, 6, 1, 1, 0), # 60 (6, 11, 9, 7, 3, 3, 8, 3, 8, 2, 1, 0, 0, 9, 5, 9, 10, 8, 7, 3, 4, 5, 2, 2, 1, 0), # 61 (7, 5, 9, 9, 5, 6, 5, 2, 5, 1, 1, 0, 0, 11, 10, 9, 8, 9, 4, 5, 3, 3, 2, 3, 1, 0), # 62 (9, 5, 10, 7, 7, 8, 4, 2, 1, 1, 2, 1, 0, 13, 5, 3, 7, 12, 3, 5, 2, 7, 4, 0, 2, 0), # 63 (5, 9, 10, 10, 8, 4, 3, 2, 5, 2, 2, 2, 0, 7, 5, 11, 3, 8, 3, 3, 3, 4, 2, 4, 1, 0), # 64 (7, 11, 8, 10, 10, 2, 5, 2, 4, 5, 1, 0, 0, 16, 10, 5, 6, 9, 5, 5, 0, 1, 1, 2, 0, 0), # 65 (17, 9, 9, 10, 8, 2, 4, 2, 5, 4, 1, 0, 0, 8, 13, 9, 1, 10, 6, 3, 3, 2, 4, 3, 1, 0), # 66 (8, 3, 10, 15, 6, 3, 4, 1, 3, 3, 2, 0, 0, 16, 9, 9, 3, 9, 1, 2, 4, 2, 4, 0, 2, 0), # 67 (14, 11, 4, 5, 8, 2, 2, 7, 9, 0, 1, 1, 0, 7, 11, 11, 3, 11, 5, 2, 3, 3, 4, 2, 1, 0), # 68 (15, 9, 7, 6, 5, 3, 6, 5, 3, 3, 2, 1, 0, 11, 7, 9, 9, 9, 3, 8, 2, 5, 2, 3, 0, 0), # 69 (8, 11, 4, 8, 11, 4, 2, 2, 7, 2, 1, 0, 0, 5, 7, 6, 4, 9, 8, 2, 0, 4, 1, 3, 0, 0), # 70 (9, 8, 5, 13, 6, 1, 1, 2, 3, 0, 1, 1, 0, 12, 6, 7, 9, 6, 2, 5, 5, 5, 2, 2, 0, 0), # 71 (15, 7, 7, 9, 10, 7, 3, 0, 8, 5, 1, 0, 0, 12, 13, 8, 8, 5, 5, 7, 1, 3, 3, 1, 2, 0), # 72 (13, 8, 9, 11, 8, 2, 4, 3, 3, 2, 1, 1, 0, 13, 9, 6, 7, 8, 7, 3, 5, 4, 5, 0, 0, 0), # 73 (13, 10, 8, 6, 7, 5, 3, 3, 4, 1, 2, 2, 0, 12, 10, 7, 4, 6, 4, 5, 7, 4, 5, 2, 0, 0), # 74 (11, 7, 6, 8, 10, 5, 2, 3, 4, 3, 3, 0, 0, 12, 13, 9, 6, 6, 4, 4, 3, 6, 3, 3, 1, 0), # 75 (14, 10, 11, 11, 7, 4, 3, 2, 2, 0, 0, 0, 0, 9, 12, 10, 4, 8, 5, 5, 1, 3, 1, 0, 1, 0), # 76 (12, 4, 9, 9, 6, 2, 2, 2, 4, 0, 2, 1, 0, 12, 8, 6, 6, 7, 2, 5, 2, 4, 2, 1, 1, 0), # 77 (10, 10, 8, 11, 7, 3, 1, 3, 3, 0, 2, 0, 0, 13, 9, 9, 5, 5, 4, 7, 1, 1, 3, 4, 1, 0), # 78 (10, 16, 6, 12, 5, 6, 9, 3, 3, 1, 1, 1, 0, 6, 6, 2, 3, 8, 4, 7, 1, 2, 2, 2, 0, 0), # 79 (7, 7, 11, 5, 5, 2, 4, 2, 4, 1, 1, 1, 0, 9, 3, 4, 5, 11, 8, 5, 3, 3, 3, 2, 0, 0), # 80 (6, 6, 9, 6, 10, 4, 4, 4, 3, 1, 3, 0, 0, 11, 9, 4, 6, 5, 4, 3, 2, 4, 4, 2, 0, 0), # 81 (7, 7, 13, 8, 3, 2, 6, 0, 5, 1, 1, 0, 0, 8, 7, 1, 8, 7, 2, 2, 2, 2, 1, 1, 0, 0), # 82 (7, 7, 3, 6, 2, 3, 2, 1, 2, 2, 1, 0, 0, 11, 8, 4, 5, 7, 1, 4, 3, 7, 1, 3, 0, 0), # 83 (8, 8, 10, 10, 11, 3, 3, 5, 10, 1, 0, 4, 0, 10, 11, 6, 7, 9, 3, 4, 6, 4, 4, 3, 3, 0), # 84 (5, 10, 5, 8, 9, 5, 1, 7, 1, 5, 1, 0, 0, 6, 6, 9, 9, 8, 8, 7, 3, 7, 5, 1, 1, 0), # 85 (12, 2, 13, 7, 12, 2, 4, 2, 5, 0, 3, 2, 0, 10, 13, 8, 4, 10, 4, 1, 3, 2, 4, 2, 0, 0), # 86 (14, 12, 5, 6, 5, 2, 4, 9, 3, 2, 0, 1, 0, 15, 9, 7, 7, 7, 2, 2, 2, 3, 4, 0, 1, 0), # 87 (13, 5, 14, 8, 10, 0, 2, 4, 8, 1, 1, 1, 0, 9, 13, 9, 4, 6, 3, 7, 5, 3, 3, 3, 0, 0), # 88 (12, 7, 3, 6, 10, 6, 6, 1, 3, 0, 0, 1, 0, 13, 9, 5, 4, 9, 3, 4, 3, 5, 4, 1, 1, 0), # 89 (9, 9, 14, 11, 8, 4, 1, 2, 5, 2, 1, 1, 0, 7, 14, 11, 6, 4, 3, 1, 5, 1, 5, 1, 0, 0), # 90 (13, 8, 6, 9, 7, 3, 4, 5, 4, 2, 3, 0, 0, 9, 5, 4, 5, 8, 1, 4, 3, 3, 3, 0, 0, 0), # 91 (14, 9, 9, 7, 11, 5, 3, 4, 5, 3, 1, 0, 0, 15, 11, 7, 7, 11, 4, 5, 3, 2, 4, 2, 0, 0), # 92 (13, 7, 10, 9, 4, 6, 3, 3, 7, 4, 2, 0, 0, 7, 10, 4, 5, 8, 4, 2, 6, 4, 1, 1, 1, 0), # 93 (17, 3, 10, 7, 8, 8, 4, 4, 7, 3, 0, 0, 0, 15, 9, 5, 2, 5, 2, 3, 0, 3, 1, 3, 1, 0), # 94 (8, 11, 9, 6, 10, 6, 5, 3, 2, 3, 1, 0, 0, 11, 4, 7, 4, 6, 8, 3, 0, 4, 3, 0, 4, 0), # 95 (5, 7, 21, 11, 5, 4, 4, 0, 4, 3, 1, 0, 0, 12, 7, 9, 5, 5, 7, 1, 2, 3, 4, 0, 0, 0), # 96 (6, 6, 12, 13, 9, 4, 1, 1, 7, 1, 1, 2, 0, 10, 8, 11, 5, 5, 3, 2, 4, 2, 0, 1, 2, 0), # 97 (17, 5, 11, 6, 4, 5, 5, 4, 5, 1, 0, 1, 0, 8, 10, 4, 5, 6, 2, 3, 2, 3, 2, 1, 1, 0), # 98 (9, 12, 6, 3, 6, 3, 5, 0, 4, 3, 1, 0, 0, 15, 10, 3, 3, 11, 6, 6, 3, 4, 1, 1, 0, 0), # 99 (8, 9, 10, 9, 11, 3, 5, 1, 4, 3, 1, 0, 0, 11, 9, 9, 4, 5, 4, 4, 3, 3, 2, 2, 2, 0), # 100 (12, 7, 11, 6, 8, 6, 3, 5, 6, 4, 1, 0, 0, 10, 12, 4, 5, 7, 5, 4, 3, 2, 4, 2, 1, 0), # 101 (8, 12, 7, 10, 5, 2, 1, 3, 4, 5, 1, 2, 0, 9, 9, 4, 2, 8, 3, 3, 5, 3, 2, 3, 4, 0), # 102 (14, 5, 6, 11, 8, 5, 1, 1, 5, 1, 2, 1, 0, 9, 6, 8, 0, 6, 3, 1, 2, 7, 5, 1, 1, 0), # 103 (9, 8, 10, 9, 3, 0, 3, 3, 2, 1, 2, 1, 0, 9, 10, 5, 8, 8, 3, 3, 3, 6, 3, 2, 1, 0), # 104 (6, 8, 7, 10, 6, 5, 4, 2, 4, 1, 1, 0, 0, 5, 7, 3, 8, 4, 1, 5, 1, 3, 3, 2, 1, 0), # 105 (16, 0, 5, 10, 5, 0, 1, 1, 1, 1, 1, 1, 0, 16, 4, 7, 6, 4, 0, 2, 3, 2, 0, 1, 0, 0), # 106 (13, 8, 7, 3, 7, 3, 2, 2, 3, 1, 2, 1, 0, 10, 7, 10, 6, 6, 4, 4, 5, 5, 2, 3, 1, 0), # 107 (13, 3, 5, 13, 2, 3, 2, 1, 3, 1, 1, 0, 0, 11, 14, 4, 2, 8, 2, 4, 2, 5, 2, 4, 2, 0), # 108 (13, 7, 4, 7, 3, 5, 3, 1, 5, 5, 3, 1, 0, 7, 5, 9, 4, 7, 2, 2, 1, 6, 1, 4, 0, 0), # 109 (11, 7, 8, 8, 12, 3, 5, 2, 3, 4, 2, 0, 0, 10, 8, 2, 6, 3, 2, 4, 5, 4, 1, 1, 1, 0), # 110 (10, 12, 11, 8, 2, 3, 4, 1, 4, 2, 1, 0, 0, 14, 10, 6, 3, 6, 5, 1, 6, 4, 4, 2, 0, 0), # 111 (4, 9, 4, 8, 8, 1, 3, 5, 3, 2, 1, 0, 0, 8, 6, 4, 5, 9, 1, 3, 2, 5, 3, 2, 1, 0), # 112 (7, 4, 5, 5, 6, 4, 2, 6, 4, 0, 0, 1, 0, 10, 8, 5, 5, 10, 3, 3, 4, 2, 4, 2, 2, 0), # 113 (16, 6, 6, 3, 15, 2, 1, 0, 3, 0, 0, 1, 0, 9, 5, 7, 8, 9, 3, 3, 1, 3, 3, 1, 2, 0), # 114 (10, 11, 9, 7, 6, 7, 3, 1, 2, 1, 2, 1, 0, 5, 11, 7, 3, 6, 5, 2, 2, 2, 1, 1, 0, 0), # 115 (9, 10, 8, 4, 7, 1, 3, 8, 5, 2, 0, 0, 0, 8, 10, 7, 10, 6, 4, 4, 5, 1, 2, 2, 1, 0), # 116 (7, 9, 7, 8, 3, 3, 3, 2, 6, 1, 1, 1, 0, 15, 2, 4, 6, 6, 5, 5, 3, 3, 3, 0, 1, 0), # 117 (14, 5, 8, 4, 9, 2, 3, 2, 2, 1, 2, 0, 0, 12, 6, 2, 4, 4, 2, 2, 2, 3, 2, 0, 1, 0), # 118 (7, 2, 5, 6, 6, 0, 1, 3, 2, 1, 1, 0, 0, 7, 5, 7, 6, 6, 3, 5, 2, 6, 2, 1, 1, 0), # 119 (4, 3, 10, 7, 9, 4, 2, 5, 5, 0, 2, 0, 0, 7, 7, 3, 7, 7, 1, 2, 4, 1, 4, 0, 0, 0), # 120 (6, 8, 5, 8, 9, 3, 5, 2, 5, 1, 2, 0, 0, 7, 5, 6, 3, 8, 7, 4, 1, 2, 1, 0, 0, 0), # 121 (12, 3, 10, 4, 5, 2, 2, 3, 3, 1, 2, 0, 0, 14, 8, 6, 8, 3, 0, 3, 5, 1, 3, 0, 0, 0), # 122 (12, 7, 7, 12, 11, 2, 2, 3, 7, 0, 1, 0, 0, 10, 9, 6, 3, 6, 2, 6, 5, 8, 3, 2, 0, 0), # 123 (10, 8, 8, 2, 9, 3, 0, 1, 3, 2, 0, 1, 0, 10, 8, 5, 9, 8, 1, 4, 3, 8, 4, 0, 0, 0), # 124 (8, 6, 7, 3, 4, 4, 0, 3, 3, 3, 2, 2, 0, 4, 5, 4, 2, 6, 3, 3, 3, 3, 1, 1, 2, 0), # 125 (9, 10, 8, 5, 6, 8, 5, 5, 2, 2, 1, 0, 0, 7, 13, 4, 5, 7, 4, 2, 1, 3, 4, 2, 2, 0), # 126 (22, 4, 5, 10, 10, 3, 0, 2, 3, 1, 2, 1, 0, 11, 7, 7, 5, 7, 3, 3, 0, 3, 5, 1, 1, 0), # 127 (8, 4, 10, 10, 11, 8, 3, 4, 7, 0, 2, 0, 0, 11, 7, 7, 7, 9, 1, 1, 0, 3, 2, 4, 0, 0), # 128 (14, 8, 5, 4, 3, 6, 3, 1, 5, 0, 2, 1, 0, 5, 7, 7, 3, 5, 3, 2, 0, 4, 3, 2, 0, 0), # 129 (10, 10, 3, 9, 7, 7, 3, 2, 5, 3, 0, 2, 0, 10, 11, 3, 2, 5, 4, 2, 1, 0, 2, 3, 1, 0), # 130 (5, 9, 9, 11, 7, 3, 5, 2, 1, 4, 1, 1, 0, 6, 4, 6, 2, 4, 4, 2, 1, 3, 2, 1, 1, 0), # 131 (9, 11, 9, 8, 4, 4, 2, 3, 5, 0, 0, 1, 0, 5, 4, 8, 6, 12, 4, 2, 1, 1, 2, 1, 0, 0), # 132 (5, 2, 6, 9, 7, 2, 5, 2, 2, 2, 1, 0, 0, 9, 1, 4, 6, 5, 2, 3, 1, 2, 1, 6, 1, 0), # 133 (8, 9, 10, 13, 8, 5, 4, 1, 6, 1, 0, 1, 0, 9, 12, 2, 4, 13, 2, 4, 4, 4, 2, 2, 1, 0), # 134 (11, 8, 5, 10, 6, 4, 2, 2, 2, 2, 1, 0, 0, 5, 5, 1, 6, 4, 3, 2, 2, 3, 4, 0, 1, 0), # 135 (8, 7, 7, 8, 4, 4, 2, 0, 8, 3, 0, 0, 0, 8, 10, 11, 1, 10, 4, 2, 5, 3, 4, 2, 1, 0), # 136 (6, 2, 10, 7, 3, 4, 2, 2, 2, 1, 1, 1, 0, 8, 6, 6, 3, 8, 4, 5, 4, 2, 2, 0, 0, 0), # 137 (7, 1, 9, 7, 7, 1, 2, 2, 3, 0, 0, 0, 0, 5, 4, 4, 4, 6, 3, 2, 4, 6, 2, 3, 0, 0), # 138 (13, 5, 11, 7, 7, 1, 5, 1, 3, 0, 0, 1, 0, 8, 7, 7, 2, 8, 1, 1, 0, 3, 0, 2, 0, 0), # 139 (11, 7, 6, 10, 4, 7, 2, 3, 3, 1, 1, 0, 0, 10, 5, 6, 4, 1, 4, 5, 3, 4, 1, 1, 1, 0), # 140 (6, 11, 6, 7, 6, 3, 0, 1, 4, 3, 2, 0, 0, 9, 7, 5, 7, 4, 3, 3, 3, 5, 1, 1, 2, 0), # 141 (13, 4, 5, 6, 4, 4, 2, 3, 3, 0, 1, 0, 0, 12, 8, 5, 4, 9, 2, 6, 3, 4, 7, 2, 0, 0), # 142 (8, 7, 7, 8, 4, 6, 4, 1, 5, 3, 0, 2, 0, 5, 10, 11, 2, 10, 3, 2, 1, 3, 0, 3, 2, 0), # 143 (9, 6, 6, 7, 10, 3, 0, 3, 4, 3, 1, 0, 0, 6, 7, 2, 4, 6, 4, 3, 3, 6, 1, 1, 1, 0), # 144 (4, 3, 7, 11, 9, 7, 2, 4, 8, 2, 0, 1, 0, 11, 5, 8, 9, 9, 4, 3, 1, 2, 3, 2, 1, 0), # 145 (6, 4, 13, 7, 5, 1, 1, 2, 4, 0, 0, 0, 0, 11, 4, 5, 6, 4, 4, 6, 2, 5, 4, 2, 0, 0), # 146 (6, 5, 9, 12, 5, 4, 0, 2, 5, 1, 0, 1, 0, 6, 14, 4, 3, 4, 3, 1, 4, 5, 3, 0, 0, 0), # 147 (10, 5, 4, 3, 10, 2, 0, 1, 2, 0, 2, 0, 0, 14, 11, 1, 5, 8, 2, 3, 3, 4, 1, 3, 0, 0), # 148 (7, 9, 8, 6, 4, 3, 2, 0, 5, 2, 3, 0, 0, 11, 4, 6, 2, 4, 4, 3, 2, 2, 1, 0, 0, 0), # 149 (12, 4, 7, 6, 10, 0, 1, 3, 2, 2, 1, 1, 0, 14, 8, 5, 5, 6, 5, 6, 1, 2, 2, 2, 1, 0), # 150 (8, 9, 7, 11, 4, 2, 3, 5, 1, 1, 2, 0, 0, 12, 3, 3, 0, 4, 3, 2, 2, 5, 2, 2, 0, 0), # 151 (14, 9, 6, 10, 11, 4, 2, 3, 4, 2, 0, 0, 0, 9, 6, 2, 4, 5, 3, 3, 1, 5, 2, 1, 2, 0), # 152 (8, 7, 11, 12, 3, 2, 3, 3, 7, 1, 2, 1, 0, 8, 2, 3, 3, 2, 4, 4, 2, 3, 2, 2, 0, 0), # 153 (5, 7, 5, 7, 4, 2, 2, 4, 6, 1, 2, 0, 0, 8, 6, 5, 6, 9, 3, 1, 4, 2, 2, 0, 0, 0), # 154 (9, 3, 5, 3, 4, 1, 0, 2, 1, 2, 3, 0, 0, 9, 6, 7, 3, 8, 2, 4, 4, 1, 1, 1, 0, 0), # 155 (8, 4, 1, 9, 8, 1, 4, 0, 7, 0, 1, 0, 0, 6, 5, 3, 5, 9, 4, 5, 3, 1, 0, 0, 1, 0), # 156 (7, 6, 5, 9, 5, 4, 4, 5, 2, 2, 1, 0, 0, 5, 9, 5, 2, 10, 8, 1, 2, 2, 4, 1, 0, 0), # 157 (8, 5, 6, 6, 10, 2, 1, 3, 8, 1, 5, 0, 0, 10, 16, 7, 2, 2, 2, 1, 3, 6, 3, 3, 0, 0), # 158 (10, 3, 7, 9, 5, 1, 5, 3, 4, 1, 1, 0, 0, 9, 5, 1, 4, 5, 2, 1, 2, 3, 5, 1, 1, 0), # 159 (4, 8, 6, 8, 7, 3, 0, 2, 1, 2, 0, 0, 0, 3, 4, 3, 3, 6, 3, 5, 1, 2, 3, 0, 0, 0), # 160 (12, 2, 5, 11, 5, 3, 2, 1, 4, 3, 1, 1, 0, 5, 4, 7, 3, 4, 1, 3, 4, 1, 0, 0, 0, 0), # 161 (0, 4, 10, 7, 6, 3, 5, 4, 1, 1, 4, 1, 0, 9, 6, 7, 3, 9, 3, 3, 5, 3, 1, 0, 0, 0), # 162 (9, 4, 6, 6, 6, 2, 5, 3, 5, 1, 3, 1, 0, 8, 4, 6, 1, 5, 3, 10, 2, 2, 1, 2, 2, 0), # 163 (8, 5, 7, 8, 4, 3, 0, 5, 0, 0, 1, 0, 0, 10, 4, 4, 1, 8, 2, 0, 3, 3, 1, 0, 0, 0), # 164 (8, 3, 3, 3, 5, 6, 1, 1, 2, 2, 1, 1, 0, 4, 7, 8, 3, 4, 2, 1, 0, 1, 3, 1, 0, 0), # 165 (12, 5, 6, 2, 7, 4, 3, 1, 1, 0, 1, 0, 0, 10, 5, 7, 3, 4, 3, 1, 0, 3, 4, 1, 1, 0), # 166 (9, 6, 6, 5, 5, 5, 1, 1, 2, 2, 0, 0, 0, 9, 6, 2, 2, 8, 3, 1, 2, 2, 2, 2, 0, 0), # 167 (7, 1, 4, 7, 6, 1, 2, 1, 5, 2, 1, 1, 0, 6, 4, 1, 2, 7, 5, 1, 2, 1, 2, 1, 0, 0), # 168 (14, 2, 4, 4, 4, 4, 0, 1, 2, 1, 2, 1, 0, 5, 5, 7, 1, 10, 3, 1, 0, 2, 2, 0, 0, 0), # 169 (3, 5, 7, 3, 3, 3, 0, 2, 2, 1, 1, 0, 0, 11, 2, 3, 1, 1, 6, 2, 2, 3, 3, 1, 0, 0), # 170 (3, 1, 7, 3, 2, 1, 1, 2, 3, 0, 2, 1, 0, 7, 3, 7, 1, 6, 3, 1, 0, 4, 2, 1, 0, 0), # 171 (3, 2, 7, 6, 3, 2, 5, 1, 1, 0, 0, 0, 0, 7, 4, 3, 1, 9, 4, 1, 2, 1, 6, 2, 0, 0), # 172 (8, 2, 4, 2, 3, 1, 0, 0, 0, 0, 1, 1, 0, 7, 3, 5, 0, 5, 2, 1, 2, 0, 1, 0, 1, 0), # 173 (9, 5, 3, 4, 2, 2, 2, 3, 1, 1, 0, 0, 0, 7, 3, 0, 2, 6, 1, 1, 1, 1, 1, 1, 0, 0), # 174 (8, 2, 4, 4, 7, 0, 1, 1, 3, 3, 0, 0, 0, 6, 5, 3, 1, 5, 1, 2, 3, 1, 0, 0, 0, 0), # 175 (4, 5, 5, 3, 4, 3, 3, 1, 1, 0, 0, 0, 0, 2, 1, 3, 0, 4, 1, 1, 1, 1, 1, 2, 0, 0), # 176 (6, 3, 7, 3, 7, 2, 0, 1, 0, 2, 0, 2, 0, 7, 6, 3, 3, 7, 0, 1, 1, 3, 4, 0, 2, 0), # 177 (5, 2, 4, 1, 1, 2, 1, 1, 2, 1, 1, 0, 0, 6, 2, 3, 1, 2, 0, 1, 1, 2, 1, 1, 0, 0), # 178 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179 ) station_arriving_intensity = ( (5.020865578371768, 5.525288559693166, 5.211283229612507, 6.214667773863432, 5.554685607609612, 3.1386549320373387, 4.146035615373915, 4.653176172979423, 6.090099062168007, 3.9580150155223697, 4.205265163885603, 4.897915078306173, 5.083880212578363), # 0 (5.354327152019974, 5.890060694144759, 5.555346591330152, 6.625144253276616, 5.922490337474237, 3.3459835840425556, 4.419468941263694, 4.959513722905708, 6.492245326332909, 4.21898069227715, 4.483096135956131, 5.221216660814354, 5.419791647439855), # 1 (5.686723008979731, 6.253385170890979, 5.8980422855474135, 7.033987704664794, 6.288962973749744, 3.5524851145124448, 4.691818507960704, 5.264625247904419, 6.892786806877549, 4.478913775020546, 4.759823148776313, 5.543232652053055, 5.75436482820969), # 2 (6.016757793146562, 6.613820501936447, 6.238010869319854, 7.439576407532074, 6.652661676001902, 3.757340622585113, 4.962003641647955, 5.567301157494507, 7.290135160921093, 4.736782698426181, 5.0343484118273825, 5.862685684930461, 6.086272806254225), # 3 (6.343136148415981, 6.9699251992857745, 6.573892899703036, 7.840288641382569, 7.012144603796492, 3.9597312073986677, 5.2289436685084585, 5.866331861194915, 7.682702045582707, 4.991555897167679, 5.305574134590575, 6.178298392354764, 6.414188632939817), # 4 (6.66456271868351, 7.320257774943588, 6.9043289337525175, 8.234502685720393, 7.36596991669928, 4.158837968091214, 5.491557914725224, 6.160507768524592, 8.068899117981559, 5.242201805918663, 5.572402526547132, 6.488793407234148, 6.736785359632827), # 5 (6.979742147844666, 7.663376740914501, 7.227959528523866, 8.620596820049652, 7.712695774276043, 4.353842003800864, 5.7487657064812625, 6.4486192890024885, 8.447138035236815, 5.487688859352758, 5.833735797178282, 6.792893362476808, 7.052736037699606), # 6 (7.2873790797949685, 7.997840609203132, 7.543425241072635, 8.996949323874462, 8.050880336092554, 4.543924413665721, 5.999486369959585, 6.729456832147552, 8.815830454467644, 5.726985492143586, 6.088476155965268, 7.089320890990929, 7.360713718506519), # 7 (7.586178158429934, 8.322207891814099, 7.849366628454396, 9.361938476698928, 8.379081761714586, 4.7282662968238895, 6.2426392313431975, 7.001810807478725, 9.173388032793206, 5.959060138964774, 6.335525812389321, 7.376798625684702, 7.659391453419917), # 8 (7.874844027645085, 8.635037100752022, 8.144424247724704, 9.713942558027169, 8.69585821070791, 4.906048752413484, 6.47714361681512, 7.264471624514963, 9.518222427332674, 6.182881234489941, 6.573786975931678, 7.654049199466313, 7.947442293806162), # 9 (8.152081331335932, 8.934886748021516, 8.427238655939124, 10.051339847363288, 8.9997678426383, 5.076452879572607, 6.701918852558355, 7.516229692775211, 9.848745295205214, 6.397417213392714, 6.802161856073574, 7.919795245243952, 8.22353929103161), # 10 (8.416594713398005, 9.220315345627206, 8.696450410153215, 10.372508624211397, 9.289368817071534, 5.238659777439368, 6.915884264755916, 7.7558754217784145, 10.163368293529993, 6.601636510346719, 7.019552662296249, 8.17275939592581, 8.486355496462611), # 11 (8.667088817726812, 9.489881405573698, 8.95070006742254, 10.675827168075612, 9.563219293573377, 5.391850545151869, 7.1179591795908115, 7.982199221043521, 10.460503079426179, 6.794507560025572, 7.224861604080934, 8.411664284420068, 8.734563961465534), # 12 (8.902268288217876, 9.74214343986562, 9.188628184802662, 10.959673758460044, 9.819877431709601, 5.5352062818482235, 7.307062923246056, 8.193991500089481, 10.738561310012932, 6.974998797102904, 7.416990890908869, 8.63523254363492, 8.966837737406735), # 13 (9.120837768766716, 9.975659960507588, 9.408875319349146, 11.222426674868792, 10.05790139104599, 5.667908086666534, 7.482114821904661, 8.390042668435246, 10.995954642409421, 7.142078656252334, 7.594842732261284, 8.84218680647856, 9.181849875652563), # 14 (9.321501903268855, 10.188989479504217, 9.610082028117542, 11.462464196805985, 10.275849331148308, 5.789137058744912, 7.642034201749626, 8.569143135599756, 11.23109473373482, 7.29471557214749, 7.757319337619419, 9.031249705859171, 9.37827342756938), # 15 (9.5029653356198, 10.380690508860132, 9.790888868163425, 11.678164603775716, 10.472279411582333, 5.898074297221459, 7.785740388963976, 8.73008331110196, 11.442393241108286, 7.431877979461996, 7.9033229164645125, 9.20114387468494, 9.554781444523545), # 16 (9.663932709715075, 10.549321560579946, 9.949936396542352, 11.867906175282112, 10.645749791913838, 5.993900901234285, 7.9121527097307105, 8.871653604460818, 11.628261821648984, 7.552534312869467, 8.031755678277799, 9.350591945864055, 9.710046977881415), # 17 (9.803108669450204, 10.693441146668274, 10.08586517030988, 12.030067190829278, 10.794818631708589, 6.075797969921503, 8.020190490232851, 8.99264442519526, 11.787112132476096, 7.6556530070435365, 8.141519832540508, 9.478316552304715, 9.842743079009345), # 18 (9.919197858720699, 10.811607779129744, 10.197315746521578, 12.163025929921314, 10.918044090532366, 6.142946602421208, 8.108773056653394, 9.091846182824245, 11.917355830708779, 7.740202496657828, 8.231517588733878, 9.583040326915096, 9.951542799273696), # 19 (10.010904921422082, 10.902379969968962, 10.282928682233003, 12.265160672062354, 11.013984327950944, 6.194527897871518, 8.176819735175362, 9.168049286866717, 12.017404573466198, 7.805151216385958, 8.30065115633915, 9.66348590260339, 10.035119190040824), # 20 (10.076934501449866, 10.964316231190558, 10.341344534499719, 12.334849696756486, 11.081197503530088, 6.229722955410535, 8.223249851981759, 9.220044146841623, 12.085670017867521, 7.849467600901555, 8.34782274483756, 9.718375912277793, 10.092145302677078), # 21 (10.115991242699579, 10.995975074799144, 10.371203860377285, 12.370471283507836, 11.118241776835575, 6.247712874176367, 8.2469827332556, 9.246621172267915, 12.120563821031915, 7.872120084878242, 8.37193456371034, 9.74643298884649, 10.121294188548827), # 22 (10.13039336334264, 10.999723593964335, 10.374923182441702, 12.374930812757203, 11.127732056032597, 6.25, 8.249804002259339, 9.249493827160494, 12.124926234567901, 7.874792272519433, 8.37495803716174, 9.749897576588934, 10.125), # 23 (10.141012413034153, 10.997537037037038, 10.374314814814815, 12.374381944444446, 11.133107613614852, 6.25, 8.248253812636166, 9.2455, 12.124341666666666, 7.87315061728395, 8.37462457912458, 9.749086419753086, 10.125), # 24 (10.15140723021158, 10.993227023319616, 10.373113854595337, 12.373296039094651, 11.138364945594503, 6.25, 8.24519890260631, 9.237654320987655, 12.123186728395062, 7.869918838591678, 8.373963399426362, 9.747485139460448, 10.125), # 25 (10.161577019048034, 10.986859396433472, 10.371336762688616, 12.37168544238683, 11.143503868421105, 6.25, 8.240686718308721, 9.226104938271606, 12.1214762345679, 7.865150708733425, 8.372980483850855, 9.745115683584821, 10.125), # 26 (10.171520983716636, 10.978499999999999, 10.369, 12.369562499999999, 11.148524198544214, 6.25, 8.234764705882354, 9.211, 12.119225, 7.858899999999999, 8.371681818181818, 9.742, 10.125), # 27 (10.181238328390501, 10.968214677640603, 10.366120027434842, 12.366939557613168, 11.153425752413401, 6.25, 8.22748031146615, 9.192487654320988, 12.116447839506172, 7.851220484682213, 8.370073388203018, 9.73816003657979, 10.125), # 28 (10.19072825724275, 10.95606927297668, 10.362713305898492, 12.36382896090535, 11.15820834647822, 6.25, 8.218880981199066, 9.170716049382715, 12.113159567901235, 7.842165935070874, 8.368161179698216, 9.733617741197987, 10.125), # 29 (10.199989974446497, 10.94212962962963, 10.358796296296296, 12.360243055555555, 11.162871797188236, 6.25, 8.209014161220043, 9.145833333333332, 12.109375, 7.83179012345679, 8.365951178451178, 9.728395061728394, 10.125), # 30 (10.209022684174858, 10.926461591220852, 10.354385459533608, 12.356194187242798, 11.167415920993008, 6.25, 8.19792729766804, 9.117987654320988, 12.105108950617284, 7.820146822130773, 8.363449370245666, 9.722513946044812, 10.125), # 31 (10.217825590600954, 10.909131001371742, 10.349497256515773, 12.35169470164609, 11.171840534342095, 6.25, 8.185667836681999, 9.087327160493828, 12.100376234567902, 7.807289803383631, 8.360661740865444, 9.715996342021034, 10.125), # 32 (10.226397897897897, 10.890203703703703, 10.344148148148149, 12.346756944444444, 11.176145453685063, 6.25, 8.172283224400871, 9.054, 12.095191666666667, 7.793272839506173, 8.357594276094275, 9.708864197530863, 10.125), # 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139 (8.444997677025897, 6.801298785613425, 8.647308796115487, 10.012835745039444, 9.92156467458478, 5.445445909628379, 5.400580673463397, 5.795978747590996, 10.388476938898332, 5.420555393811186, 6.358719326686294, 7.562716378308592, 8.849082182878314), # 140 (8.40124789791083, 6.758192090297021, 8.61893536244316, 9.973361639464553, 9.885718431458253, 5.431951548851015, 5.370940751340795, 5.78451993660327, 10.36727094769768, 5.396366207481251, 6.331940092293238, 7.532266740021525, 8.816572659637913), # 141 (8.356443573718156, 6.714206505295466, 8.58972123390407, 9.93279651265672, 9.848855495829087, 5.418015949208927, 5.340622296506126, 5.772639944200211, 10.345333550752942, 5.371546573421828, 6.304441160030697, 7.500982076994594, 8.783144913695466), # 142 (8.310548338670674, 6.669281647641981, 8.559626999172925, 9.891087380676975, 9.810945743940529, 5.403611506868106, 5.3095798374875685, 5.760292330179432, 10.322615447166147, 5.3460546332438525, 6.276174032075593, 7.4688164188730894, 8.748768651462617), # 143 (8.263525826991184, 6.623357134369786, 8.528613246924428, 9.848181259586356, 9.771959052035829, 5.388710617994547, 5.277767902813299, 5.747430654338549, 10.29906733603931, 5.31984852855826, 6.247090210604851, 7.435723795302299, 8.713413579351014), # 144 (8.215339672902477, 6.576372582512099, 8.496640565833289, 9.804025165445895, 9.731865296358233, 5.3732856787542405, 5.245141021011493, 5.734008476475176, 10.274639916474454, 5.292886400975988, 6.217141197795395, 7.401658235927513, 8.6770494037723), # 145 (8.16595351062735, 6.528267609102142, 8.463669544574216, 9.758566114316626, 9.690634353150992, 5.35730908531318, 5.21165372061033, 5.719979356386927, 10.249283887573606, 5.2651263921079705, 6.186278495824149, 7.3665737703940195, 8.639645831138118), # 146 (8.1153309743886, 6.47898183117313, 8.42966077182191, 9.71175112225958, 9.648236098657351, 5.340753233837358, 5.177260530137981, 5.705296853871415, 10.22294994843879, 5.236526643565146, 6.154453606868036, 7.3304244283471105, 8.601172567860118), # 147 (8.063435698409021, 6.428454865758288, 8.394574836251083, 9.663527205335797, 9.604640409120561, 5.323590520492767, 5.1419159781226265, 5.689914528726257, 10.195588798172029, 5.207045296958447, 6.1216180331039824, 7.29316423943207, 8.561599320349941), # 148 (8.010231316911412, 6.37662632989083, 8.358372326536443, 9.613841379606303, 9.55981716078387, 5.3057933414453995, 5.105574593092441, 5.673785940749067, 10.167151135875338, 5.176640493898813, 6.08772327670891, 7.254747233294191, 8.520895795019237), # 149 (7.955681464118564, 6.323435840603979, 8.321013831352694, 9.562640661132138, 9.513736229890526, 5.287334092861249, 5.0681909035756005, 5.656864649737456, 10.137587660650752, 5.1452703759971765, 6.0527208398597425, 7.215127439578763, 8.479031698279647), # 150 (7.899749774253275, 6.268823014930954, 8.282459939374542, 9.50987206597433, 9.466367492683776, 5.268185170906305, 5.029719438100283, 5.639104215489043, 10.106849071600289, 5.112893084864478, 6.016562224733405, 7.174258887931072, 8.435976736542818), # 151 (7.842399881538343, 6.212727469904973, 8.242671239276701, 9.455482610193918, 9.417680825406869, 5.2483189717465635, 4.9901147251946645, 5.620458197801441, 10.07488606782597, 5.079466762111649, 5.979198933506821, 7.132095607996409, 8.391700616220398), # 152 (7.78359542019656, 6.155088822559256, 8.201608319733868, 9.399419309851933, 9.367646104303056, 5.2277078915480155, 4.949331293386919, 5.600880156472262, 10.041649348429823, 5.044949549349629, 5.940582468356916, 7.088591629420064, 8.346173043724027), # 153 (7.723300024450729, 6.095846689927024, 8.159231769420758, 9.34162918100941, 9.31623320561558, 5.206324326476654, 4.907323671205228, 5.580323651299123, 10.007089612513866, 5.009299588189353, 5.900664331460612, 7.043700981847325, 8.299363725465357), # 154 (7.6614773285236355, 6.034940689041495, 8.115502177012075, 9.282059239727378, 9.263412005587696, 5.184140672698471, 4.864046387177761, 5.558742242079636, 9.971157559180128, 4.972475020241754, 5.859396024994833, 6.997377694923482, 8.251242367856026), # 155 (7.598090966638081, 5.972310436935888, 8.070380131182526, 9.220656502066875, 9.209152380462648, 5.161129326379461, 4.8194539698327, 5.5360894886114185, 9.933803887530626, 4.934433987117773, 5.816729051136504, 6.949575798293822, 8.201778677307685), # 156 (7.533104573016862, 5.907895550643423, 8.023826220606818, 9.157367984088937, 9.153424206483685, 5.137262683685614, 4.773500947698219, 5.512318950692082, 9.894979296667389, 4.895134630428341, 5.772614912062549, 6.900249321603637, 8.150942360231976), # 157 (7.464680946405239, 5.840453120772258, 7.973591953902355, 9.089769581651243, 9.093681105870997, 5.11102447631711, 4.725106720927857, 5.485796952349372, 9.851662091599097, 4.8533659162911436, 5.7255957525389425, 6.847599564194339, 8.096485859415345), # 158 (7.382286766978402, 5.763065319599478, 7.906737818402988, 9.003977158788453, 9.015191309781628, 5.073689648007103, 4.668212763385716, 5.4472135327643825, 9.786427261222144, 4.802280994098745, 5.667416935618994, 6.781362523683108, 8.025427646920194), # 159 (7.284872094904309, 5.675096728540714, 7.821920957955888, 8.89857751040886, 8.916420131346795, 5.024341296047684, 4.602243748383784, 5.3955991895273465, 9.697425227228651, 4.741205651862893, 5.59725950860954, 6.700501948887847, 7.93642060889358), # 160 (7.17322205458596, 5.577120868080469, 7.720046971910309, 8.774572503756728, 8.798393124282113, 4.963577241570314, 4.527681446006876, 5.33160053310978, 9.585829766999018, 4.6706581931709374, 5.515741654599707, 6.605767468907571, 7.830374044819097), # 161 (7.048121770426357, 5.469711258703239, 7.602021459615496, 8.632964006076326, 8.662135842303204, 4.891995305706455, 4.445007626339809, 5.255864173983202, 9.452814657913637, 4.5911569216102315, 5.42348155667862, 6.497908712841293, 7.708197254180333), # 162 (6.9103563668284975, 5.353441420893524, 7.468750020420702, 8.474753884611934, 8.508673839125688, 4.810193309587572, 4.354704059467401, 5.169036722619125, 9.299553677352906, 4.503220140768125, 5.321097397935408, 6.3776753097880325, 7.570799536460879), # 163 (6.760710968195384, 5.228884875135821, 7.321138253675176, 8.300944006607818, 8.339032668465189, 4.718769074345129, 4.257252515474466, 5.071764789489069, 9.127220602697223, 4.407366154231968, 5.209207361459196, 6.245816888846803, 7.419090191144328), # 164 (6.599970698930017, 5.096615141914632, 7.160091758728169, 8.112536239308252, 8.154237884037324, 4.618320421110586, 4.153134764445822, 4.964694985064546, 8.93698921132698, 4.3041132655891134, 5.088429630339111, 6.10308307911662, 7.25397851771427), # 165 (6.428920683435397, 4.957205741714454, 6.9865161349289275, 7.910532449957501, 7.955315039557714, 4.509445171015408, 4.042832576466286, 4.848473919817077, 8.730033280622573, 4.193979778426912, 4.959382387664279, 5.950223509696501, 7.0763738156542955), # 166 (6.248346046114523, 4.811230195019787, 6.801316981626704, 7.695934505799843, 7.74328968874198, 4.392741145191058, 3.9268277216206746, 4.723748204218176, 8.5075265879644, 4.077483996332714, 4.822683816523827, 5.7879878096854585, 6.887185384447996), # 167 (6.059031911370395, 4.659262022315128, 6.605399898170748, 7.469744274079546, 7.519187385305742, 4.268806164768999, 3.805601969993804, 4.5911644487393595, 8.270642910732855, 3.955144222893872, 4.678952100006881, 5.617125608182511, 6.6873225235789615), # 168 (5.861763403606015, 4.501874744084979, 6.399670483910309, 7.232963622040883, 7.28403368296462, 4.138238050880695, 3.6796370916704917, 4.451369263852145, 8.020556026308338, 3.8274787616977366, 4.528805421202568, 5.438386534286672, 6.477694532530785), # 169 (5.657325647224384, 4.339641880813837, 6.185034338194635, 6.98659441692812, 7.038854135434233, 4.001634624657607, 3.549414856735553, 4.305009260028047, 7.7584397120712385, 3.6950059163316578, 4.372861963200016, 5.252520217096959, 6.259210710787055), # 170 (5.4465037666285, 4.173136952986201, 5.962397060372978, 6.731638525985535, 6.784674296430206, 3.8595937072311983, 3.4154170352738054, 4.152731047738583, 7.485467745401956, 3.5582439903829886, 4.211739909088348, 5.060276285712386, 6.032780357831365), # 171 (5.230082886221365, 4.002933481086569, 5.7326642497945866, 6.4690978164573965, 6.5225197196681535, 3.7127131197329337, 3.2781253973700655, 3.9951812374552707, 7.202813903680886, 3.41771128743908, 4.046057441956694, 4.862404369231971, 5.799312773147303), # 172 (5.00884813040598, 3.8296049855994423, 5.4967415058087115, 6.1999741555879755, 6.253415958863702, 3.5615906832942748, 3.1380217131091497, 3.8330064396496235, 6.911651964288422, 3.2739261110872815, 3.8764327448941778, 4.659654096754725, 5.5597172562184625), # 173 (4.783584623585344, 3.653724987009318, 5.2555344277646014, 5.9252694106215404, 5.978388567732466, 3.406824219046685, 2.9955877525758754, 3.6668532647931604, 6.613155704604964, 3.1274067649149466, 3.7034840009899277, 4.452775097379668, 5.314903106528433), # 174 (4.555077490162455, 3.4758670058006946, 5.009948615011508, 5.645985448802367, 5.698463099990069, 3.2490115481216284, 2.851305285855058, 3.497368323357396, 6.308498902010905, 2.9786715525094243, 3.5278293933330693, 4.242517000205814, 5.0657796235608075), # 175 (4.324111854540319, 3.296604562458073, 4.760889666898678, 5.363124137374725, 5.41466510935213, 3.0887504916505666, 2.705656083031515, 3.325198225813849, 5.998855333886642, 2.828238777458067, 3.35008710501273, 4.029629434332179, 4.813256106799174), # 176 (4.0914728411219325, 3.1165111774659513, 4.5092631827753635, 5.077687343582883, 5.128020149534273, 2.9266388707649633, 2.5591219141900625, 3.1509895826340326, 5.68539877761257, 2.6766267433482245, 3.1708753191180357, 3.8148620288577786, 4.5582418557271245), # 177 (3.8579455743102966, 2.9361603713088282, 4.255974761990814, 4.790676934671116, 4.8395537742521135, 2.7632745065962827, 2.4121845494155174, 2.9753890042894655, 5.3693030105690855, 2.52435375376725, 2.9908122187381125, 3.598964412881627, 4.301646169828252), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_arriving_acc = ( (5, 2, 2, 5, 2, 1, 4, 1, 1, 2, 0, 1, 0, 7, 7, 2, 1, 8, 0, 1, 2, 2, 4, 1, 1, 0), # 0 (9, 13, 7, 7, 4, 5, 10, 1, 4, 3, 0, 2, 0, 12, 10, 5, 1, 14, 4, 3, 4, 4, 4, 2, 2, 0), # 1 (17, 21, 8, 13, 6, 7, 12, 4, 5, 3, 0, 3, 0, 20, 17, 9, 6, 20, 10, 3, 5, 7, 5, 2, 4, 0), # 2 (23, 25, 12, 17, 9, 10, 14, 5, 7, 4, 0, 3, 0, 24, 22, 13, 8, 23, 11, 6, 6, 7, 7, 3, 4, 0), # 3 (30, 33, 17, 26, 14, 11, 18, 6, 11, 5, 0, 4, 0, 34, 30, 18, 11, 29, 14, 7, 7, 8, 7, 3, 5, 0), # 4 (33, 38, 21, 29, 26, 15, 22, 9, 13, 6, 4, 4, 0, 43, 33, 21, 15, 36, 16, 9, 8, 11, 8, 6, 5, 0), # 5 (39, 43, 25, 34, 29, 23, 23, 15, 16, 6, 6, 6, 0, 45, 43, 25, 17, 40, 19, 11, 9, 12, 10, 6, 6, 0), # 6 (48, 51, 31, 41, 36, 24, 26, 17, 19, 6, 6, 8, 0, 53, 46, 29, 21, 45, 23, 16, 10, 14, 12, 8, 6, 0), # 7 (53, 59, 41, 49, 43, 27, 30, 20, 23, 8, 7, 8, 0, 64, 56, 34, 25, 50, 25, 18, 13, 16, 14, 8, 7, 0), # 8 (64, 62, 48, 58, 46, 32, 31, 24, 24, 9, 7, 8, 0, 68, 69, 40, 28, 57, 32, 22, 15, 19, 17, 11, 7, 0), # 9 (68, 68, 52, 60, 55, 33, 33, 29, 28, 9, 9, 9, 0, 76, 78, 45, 33, 61, 37, 25, 15, 24, 21, 14, 8, 0), # 10 (75, 73, 60, 67, 58, 33, 35, 32, 31, 11, 12, 9, 0, 83, 87, 49, 37, 67, 42, 28, 19, 26, 23, 14, 8, 0), # 11 (86, 82, 69, 76, 66, 36, 38, 35, 37, 11, 16, 11, 0, 88, 93, 52, 41, 72, 48, 31, 21, 27, 25, 16, 8, 0), # 12 (93, 90, 74, 82, 71, 41, 46, 42, 43, 13, 16, 11, 0, 97, 105, 64, 46, 78, 53, 34, 22, 29, 29, 16, 8, 0), # 13 (107, 95, 79, 88, 76, 44, 48, 47, 46, 14, 17, 11, 0, 108, 113, 76, 54, 84, 57, 37, 26, 31, 31, 17, 8, 0), # 14 (113, 105, 89, 96, 83, 47, 50, 54, 51, 17, 17, 12, 0, 115, 123, 83, 59, 99, 62, 43, 26, 35, 37, 21, 8, 0), # 15 (123, 116, 98, 106, 88, 51, 55, 59, 53, 23, 18, 13, 0, 120, 131, 91, 64, 105, 65, 48, 31, 39, 43, 22, 10, 0), # 16 (129, 125, 104, 115, 102, 56, 60, 64, 58, 25, 22, 13, 0, 131, 138, 97, 68, 115, 72, 51, 35, 47, 46, 23, 11, 0), # 17 (143, 135, 114, 124, 109, 61, 64, 69, 62, 25, 24, 13, 0, 139, 146, 107, 74, 123, 75, 56, 37, 51, 46, 25, 12, 0), # 18 (149, 143, 124, 130, 114, 65, 66, 73, 64, 25, 25, 13, 0, 142, 157, 113, 81, 130, 79, 58, 39, 58, 50, 25, 12, 0), # 19 (166, 152, 130, 143, 121, 67, 70, 74, 65, 29, 26, 15, 0, 151, 167, 119, 94, 138, 86, 58, 41, 64, 58, 26, 13, 0), # 20 (173, 166, 140, 151, 127, 72, 76, 78, 69, 33, 27, 17, 0, 163, 175, 131, 102, 143, 87, 61, 46, 67, 60, 30, 13, 0), # 21 (189, 174, 145, 165, 139, 74, 80, 81, 78, 34, 28, 19, 0, 175, 182, 136, 109, 148, 93, 64, 47, 69, 64, 32, 14, 0), # 22 (201, 179, 152, 176, 147, 79, 83, 86, 83, 37, 28, 19, 0, 184, 191, 145, 116, 159, 100, 67, 48, 72, 66, 37, 14, 0), # 23 (209, 191, 162, 188, 153, 80, 86, 91, 89, 41, 28, 20, 0, 188, 197, 155, 120, 166, 105, 73, 49, 77, 73, 38, 16, 0), # 24 (222, 201, 174, 196, 157, 80, 89, 99, 93, 42, 31, 22, 0, 200, 208, 165, 128, 173, 112, 78, 49, 81, 73, 38, 16, 0), # 25 (233, 210, 179, 207, 169, 86, 93, 106, 96, 47, 33, 23, 0, 212, 214, 177, 131, 176, 122, 82, 50, 85, 78, 39, 17, 0), # 26 (240, 218, 188, 212, 176, 88, 99, 111, 100, 50, 34, 24, 0, 221, 223, 184, 135, 184, 128, 87, 52, 89, 79, 42, 17, 0), # 27 (250, 227, 199, 221, 178, 90, 103, 115, 107, 53, 36, 26, 0, 235, 229, 187, 143, 188, 138, 90, 55, 91, 83, 42, 17, 0), # 28 (262, 236, 207, 229, 195, 96, 114, 119, 111, 54, 40, 26, 0, 248, 233, 194, 152, 192, 144, 95, 57, 93, 88, 43, 17, 0), # 29 (284, 249, 217, 236, 204, 98, 116, 124, 118, 55, 43, 26, 0, 263, 238, 196, 160, 201, 154, 100, 58, 100, 91, 45, 17, 0), # 30 (292, 259, 232, 248, 208, 107, 119, 129, 123, 59, 45, 26, 0, 272, 244, 202, 166, 205, 158, 102, 60, 104, 95, 48, 17, 0), # 31 (308, 267, 236, 255, 216, 111, 122, 131, 125, 61, 49, 28, 0, 278, 255, 207, 173, 209, 163, 107, 62, 108, 98, 48, 18, 0), # 32 (321, 281, 244, 269, 222, 116, 129, 136, 127, 61, 49, 29, 0, 285, 263, 218, 179, 219, 169, 118, 64, 110, 100, 49, 19, 0), # 33 (330, 289, 251, 277, 228, 117, 131, 140, 129, 67, 49, 30, 0, 298, 273, 223, 187, 230, 174, 122, 64, 112, 101, 49, 20, 0), # 34 (334, 303, 261, 288, 238, 118, 132, 142, 133, 67, 50, 33, 0, 308, 282, 226, 191, 238, 178, 127, 66, 120, 109, 51, 22, 0), # 35 (346, 313, 268, 289, 246, 122, 138, 146, 133, 67, 51, 35, 0, 318, 298, 230, 197, 245, 187, 132, 68, 124, 115, 52, 24, 0), # 36 (354, 328, 274, 300, 253, 127, 143, 151, 136, 69, 52, 36, 0, 326, 307, 235, 201, 251, 195, 136, 73, 125, 117, 52, 25, 0), # 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165 (1607, 1286, 1287, 1359, 1154, 578, 530, 494, 658, 292, 210, 112, 0, 1583, 1329, 1012, 833, 1177, 662, 584, 406, 572, 449, 259, 132, 0), # 166 (1616, 1292, 1293, 1364, 1159, 583, 531, 495, 660, 294, 210, 112, 0, 1592, 1335, 1014, 835, 1185, 665, 585, 408, 574, 451, 261, 132, 0), # 167 (1623, 1293, 1297, 1371, 1165, 584, 533, 496, 665, 296, 211, 113, 0, 1598, 1339, 1015, 837, 1192, 670, 586, 410, 575, 453, 262, 132, 0), # 168 (1637, 1295, 1301, 1375, 1169, 588, 533, 497, 667, 297, 213, 114, 0, 1603, 1344, 1022, 838, 1202, 673, 587, 410, 577, 455, 262, 132, 0), # 169 (1640, 1300, 1308, 1378, 1172, 591, 533, 499, 669, 298, 214, 114, 0, 1614, 1346, 1025, 839, 1203, 679, 589, 412, 580, 458, 263, 132, 0), # 170 (1643, 1301, 1315, 1381, 1174, 592, 534, 501, 672, 298, 216, 115, 0, 1621, 1349, 1032, 840, 1209, 682, 590, 412, 584, 460, 264, 132, 0), # 171 (1646, 1303, 1322, 1387, 1177, 594, 539, 502, 673, 298, 216, 115, 0, 1628, 1353, 1035, 841, 1218, 686, 591, 414, 585, 466, 266, 132, 0), # 172 (1654, 1305, 1326, 1389, 1180, 595, 539, 502, 673, 298, 217, 116, 0, 1635, 1356, 1040, 841, 1223, 688, 592, 416, 585, 467, 266, 133, 0), # 173 (1663, 1310, 1329, 1393, 1182, 597, 541, 505, 674, 299, 217, 116, 0, 1642, 1359, 1040, 843, 1229, 689, 593, 417, 586, 468, 267, 133, 0), # 174 (1671, 1312, 1333, 1397, 1189, 597, 542, 506, 677, 302, 217, 116, 0, 1648, 1364, 1043, 844, 1234, 690, 595, 420, 587, 468, 267, 133, 0), # 175 (1675, 1317, 1338, 1400, 1193, 600, 545, 507, 678, 302, 217, 116, 0, 1650, 1365, 1046, 844, 1238, 691, 596, 421, 588, 469, 269, 133, 0), # 176 (1681, 1320, 1345, 1403, 1200, 602, 545, 508, 678, 304, 217, 118, 0, 1657, 1371, 1049, 847, 1245, 691, 597, 422, 591, 473, 269, 135, 0), # 177 (1686, 1322, 1349, 1404, 1201, 604, 546, 509, 680, 305, 218, 118, 0, 1663, 1373, 1052, 848, 1247, 691, 598, 423, 593, 474, 270, 135, 0), # 178 (1686, 1322, 1349, 1404, 1201, 604, 546, 509, 680, 305, 218, 118, 0, 1663, 1373, 1052, 848, 1247, 691, 598, 423, 593, 474, 270, 135, 0), # 179 ) passenger_arriving_rate = ( (5.020865578371768, 5.064847846385402, 4.342736024677089, 4.661000830397574, 3.7031237384064077, 1.8308820436884476, 2.0730178076869574, 1.938823405408093, 2.030033020722669, 0.9895037538805926, 0.7008775273142672, 0.4081595898588478, 0.0, 5.083880212578363, 4.489755488447325, 3.5043876365713356, 2.968511261641777, 4.060066041445338, 2.7143527675713304, 2.0730178076869574, 1.3077728883488913, 1.8515618692032039, 1.5536669434658585, 0.8685472049354179, 0.4604407133077639, 0.0), # 0 (5.354327152019974, 5.399222302966028, 4.629455492775127, 4.968858189957462, 3.948326891649491, 1.9518237573581576, 2.209734470631847, 2.066464051210712, 2.164081775444303, 1.0547451730692876, 0.7471826893260219, 0.4351013884011963, 0.0, 5.419791647439855, 4.786115272413158, 3.73591344663011, 3.164235519207862, 4.328163550888606, 2.8930496716949965, 2.209734470631847, 1.3941598266843982, 1.9741634458247455, 1.6562860633191545, 0.9258910985550255, 0.49083839117872996, 0.0), # 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28 (10.19072825724275, 10.043063500228623, 8.635594421582077, 9.272871720679012, 7.438805564318813, 3.6458333333333335, 4.109440490599533, 3.821131687242798, 4.037719855967078, 1.9605414837677189, 1.3946935299497027, 0.811134811766499, 0.0, 10.125, 8.922482929431489, 6.973467649748514, 5.881624451303155, 8.075439711934155, 5.349584362139917, 4.109440490599533, 2.604166666666667, 3.7194027821594067, 3.0909572402263383, 1.7271188843164156, 0.9130057727480568, 0.0), # 29 (10.199989974446497, 10.03028549382716, 8.63233024691358, 9.270182291666666, 7.441914531458824, 3.6458333333333335, 4.104507080610022, 3.8107638888888884, 4.036458333333333, 1.957947530864198, 1.39432519640853, 0.8106995884773662, 0.0, 10.125, 8.917695473251028, 6.9716259820426485, 5.873842592592593, 8.072916666666666, 5.335069444444444, 4.104507080610022, 2.604166666666667, 3.720957265729412, 3.0900607638888897, 1.7264660493827162, 0.9118441358024693, 0.0), # 30 (10.209022684174858, 10.01592312528578, 8.62865454961134, 9.267145640432098, 7.444943947328672, 3.6458333333333335, 4.09896364883402, 3.799161522633745, 4.035036316872428, 1.9550367055326936, 1.3939082283742779, 0.8102094955037343, 0.0, 10.125, 8.912304450541077, 6.969541141871389, 5.865110116598079, 8.070072633744855, 5.318826131687243, 4.09896364883402, 2.604166666666667, 3.722471973664336, 3.0890485468107003, 1.7257309099222682, 0.910538465935071, 0.0), # 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34 (10.242847531796807, 9.943837162780063, 8.610110882487428, 9.25171199845679, 7.456263650767246, 3.6458333333333335, 4.071164165254579, 3.741640946502058, 4.0278420781893, 1.9404935413808875, 1.3917739639190256, 0.807737006553879, 0.0, 10.125, 8.88510707209267, 6.958869819595128, 5.821480624142661, 8.0556841563786, 5.238297325102881, 4.071164165254579, 2.604166666666667, 3.728131825383623, 3.0839039994855972, 1.7220221764974855, 0.9039851966163696, 0.0), # 35 (10.250723266745005, 9.922458333333331, 8.604583333333334, 9.247078125, 7.45889347478189, 3.6458333333333335, 4.062926470588235, 3.724791666666667, 4.025691666666666, 1.9362000000000004, 1.391128787878788, 0.8070000000000002, 0.0, 10.125, 8.877, 6.95564393939394, 5.8086, 8.051383333333332, 5.214708333333334, 4.062926470588235, 2.604166666666667, 3.729446737390945, 3.0823593750000007, 1.7209166666666669, 0.9020416666666666, 0.0), # 36 (10.258365219256524, 9.89985728166438, 8.598726566072246, 9.242152584876543, 7.4614430133246135, 3.6458333333333335, 4.054221092552247, 3.707078189300412, 4.023410390946502, 1.931670244627344, 1.3904409631292352, 0.8062190976985216, 0.0, 10.125, 8.868410074683737, 6.952204815646175, 5.79501073388203, 8.046820781893004, 5.189909465020577, 4.054221092552247, 2.604166666666667, 3.7307215066623067, 3.080717528292182, 1.7197453132144491, 0.8999870256058529, 0.0), # 37 (10.265772593504476, 9.876094364426155, 8.592554298125286, 9.23694463734568, 7.46391214402846, 3.6458333333333335, 4.04507175421609, 3.6885622427983544, 4.021003189300411, 1.92691771833562, 1.3897114873009937, 0.8053961286389272, 0.0, 10.125, 8.859357415028198, 6.948557436504967, 5.780753155006859, 8.042006378600822, 5.163987139917697, 4.04507175421609, 2.604166666666667, 3.73195607201423, 3.078981545781894, 1.7185108596250571, 0.8978267604023779, 0.0), # 38 (10.272944593661986, 9.851229938271604, 8.586080246913582, 9.231463541666667, 7.466300744526468, 3.6458333333333335, 4.035502178649238, 3.6693055555555554, 4.0184750000000005, 1.9219558641975314, 1.3889413580246914, 0.8045329218106996, 0.0, 10.125, 8.849862139917693, 6.944706790123457, 5.765867592592593, 8.036950000000001, 5.137027777777778, 4.035502178649238, 2.604166666666667, 3.733150372263234, 3.07715451388889, 1.7172160493827164, 0.8955663580246914, 0.0), # 39 (10.279880423902163, 9.82532435985368, 8.579318129858253, 9.225718557098766, 7.468608692451679, 3.6458333333333335, 4.025536088921165, 3.649369855967079, 4.015830761316872, 1.9167981252857802, 1.3881315729309558, 0.8036313062033228, 0.0, 10.125, 8.83994436823655, 6.940657864654778, 5.750394375857339, 8.031661522633744, 5.1091177983539104, 4.025536088921165, 2.604166666666667, 3.7343043462258394, 3.0752395190329227, 1.7158636259716507, 0.8932113054412438, 0.0), # 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115 (9.273179873237634, 7.0850892578507265, 7.648776824986561, 8.077999612699802, 7.036792350922519, 3.330080178417474, 3.0133024087639466, 2.5272417970412473, 3.6020604464092765, 1.480198339612387, 1.1519343218785802, 0.6802102664572789, 0.0, 9.43260725975589, 7.482312931030067, 5.7596716093929015, 4.44059501883716, 7.204120892818553, 3.5381385158577463, 3.0133024087639466, 2.3786286988696244, 3.5183961754612594, 2.6926665375666015, 1.5297553649973124, 0.6440990234409752, 0.0), # 116 (9.243829442823772, 7.04830504435266, 7.632093362938321, 8.056111029444182, 7.02313718419674, 3.323555141118853, 2.9998041194738763, 2.5208740813181603, 3.5959129388307343, 1.4747943502270324, 1.1479250164705472, 0.6781935872119792, 0.0, 9.413047004858225, 7.46012945933177, 5.739625082352736, 4.424383050681096, 7.1918258776614685, 3.5292237138454245, 2.9998041194738763, 2.3739679579420376, 3.51156859209837, 2.6853703431480613, 1.5264186725876645, 0.6407550040320601, 0.0), # 117 (9.214297245985211, 7.0120436072457135, 7.615497548340306, 8.03435789116525, 7.009286202577227, 3.317159168158581, 2.9865122214896576, 2.51479527536254, 3.5899160365213114, 1.46948389924369, 1.143989752709904, 0.6761992632811126, 0.0, 9.393295573379024, 7.438191896092237, 5.71994876354952, 4.40845169773107, 7.179832073042623, 3.5207133855075567, 2.9865122214896576, 2.369399405827558, 3.5046431012886137, 2.678119297055084, 1.5230995096680613, 0.6374585097496104, 0.0), # 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157 (7.464680946405239, 5.353748694041236, 6.644659961585297, 6.817327186238432, 6.062454070580665, 2.9814309445183143, 2.3625533604639286, 2.285748730145572, 3.2838873638663655, 1.213341479072786, 0.9542659587564906, 0.570633297016195, 0.0, 8.096485859415345, 6.276966267178143, 4.771329793782452, 3.640024437218358, 6.567774727732731, 3.200048222203801, 2.3625533604639286, 2.129593531798796, 3.0312270352903323, 2.2724423954128112, 1.3289319923170593, 0.48670442673102154, 0.0), # 158 (7.382286766978402, 5.282809876299521, 6.58894818200249, 6.7529828690913405, 6.010127539854418, 2.95965229467081, 2.334106381692858, 2.2696723053184926, 3.2621424204073812, 1.2005702485246865, 0.9445694892698324, 0.5651135436402591, 0.0, 8.025427646920194, 6.216248980042849, 4.722847446349162, 3.601710745574059, 6.5242848408147625, 3.17754122744589, 2.334106381692858, 2.114037353336293, 3.005063769927209, 2.250994289697114, 1.3177896364004982, 0.4802554432999565, 0.0), # 159 (7.284872094904309, 5.202172001162321, 6.51826746496324, 6.673933132806645, 5.94428008756453, 2.9308657560278157, 2.301121874191892, 2.248166328969728, 3.2324750757428835, 1.1853014129657236, 0.9328765847682567, 0.5583751624073207, 0.0, 7.93642060889358, 6.142126786480525, 4.664382923841283, 3.55590423889717, 6.464950151485767, 3.147432860557619, 2.301121874191892, 2.0934755400198686, 2.972140043782265, 2.2246443776022153, 1.3036534929926482, 0.47292472737839286, 0.0), # 160 (7.17322205458596, 5.11236079574043, 6.4333724765919245, 6.5809293778175455, 5.865595416188075, 2.895420057582683, 2.263840723003438, 2.2215002221290754, 3.1952765889996724, 1.1676645482927346, 0.9192902757666179, 0.5504806224089643, 0.0, 7.830374044819097, 6.055286846498606, 4.596451378833089, 3.5029936448782033, 6.390553177999345, 3.1101003109807053, 2.263840723003438, 2.0681571839876307, 2.9327977080940375, 2.1936431259391824, 1.2866744953183848, 0.46476007234003913, 0.0), # 161 (7.048121770426357, 5.013901987144635, 6.335017883012913, 6.474723004557244, 5.7747572282021356, 2.853663928328766, 2.2225038131699044, 2.1899434058263343, 3.150938219304545, 1.147789230402558, 0.9039135927797701, 0.5414923927367745, 0.0, 7.708197254180333, 5.956416320104519, 4.519567963898851, 3.4433676912076736, 6.30187643860909, 3.065920768156868, 2.2225038131699044, 2.03833137737769, 2.8873786141010678, 2.158241001519082, 1.2670035766025827, 0.4558092715586033, 0.0), # 162 (6.9103563668284975, 4.90732130248573, 6.223958350350585, 6.35606541345895, 5.672449226083792, 2.8059460972594175, 2.1773520297337003, 2.153765301091302, 3.0998512257843016, 1.1258050351920315, 0.8868495663225682, 0.5314729424823361, 0.0, 7.570799536460879, 5.846202367305696, 4.43424783161284, 3.3774151055760937, 6.199702451568603, 3.015271421527823, 2.1773520297337003, 2.0042472123281554, 2.836224613041896, 2.118688471152984, 1.2447916700701172, 0.4461201184077937, 0.0), # 163 (6.760710968195384, 4.793144468874502, 6.100948544729314, 6.225708004955863, 5.559355112310126, 2.752615293367992, 2.128626257737233, 2.113235328953779, 3.0424068675657407, 1.1018415385579923, 0.8682012269098661, 0.5204847407372336, 0.0, 7.419090191144328, 5.725332148109569, 4.34100613454933, 3.305524615673976, 6.0848137351314815, 2.9585294605352903, 2.128626257737233, 1.9661537809771372, 2.779677556155063, 2.075236001651955, 1.2201897089458629, 0.43574040626131844, 0.0), # 164 (6.599970698930017, 4.671897213421746, 5.966743132273474, 6.084402179481189, 5.436158589358215, 2.694020245647842, 2.076567382222911, 2.068622910443561, 2.9789964037756596, 1.0760283163972786, 0.8480716050565187, 0.5085902565930517, 0.0, 7.25397851771427, 5.594492822523568, 4.2403580252825925, 3.2280849491918353, 5.957992807551319, 2.8960720746209856, 2.076567382222911, 1.9243001754627442, 2.7180792946791077, 2.0281340598270634, 1.1933486264546949, 0.42471792849288603, 0.0), # 165 (6.428920683435397, 4.54410526323825, 5.82209677910744, 5.932899337468126, 5.3035433597051425, 2.630509683092322, 2.021416288233143, 2.020197466590449, 2.9100110935408576, 1.0484949446067282, 0.8265637312773799, 0.49585195914137514, 0.0, 7.0763738156542955, 5.454371550555126, 4.1328186563869, 3.145484833820184, 5.820022187081715, 2.8282764532266285, 2.021416288233143, 1.8789354879230868, 2.6517716798525712, 1.9776331124893758, 1.1644193558214881, 0.41310047847620457, 0.0), # 166 (6.248346046114523, 4.410294345434805, 5.667764151355587, 5.771950879349882, 5.1621931258279865, 2.562432334694784, 1.9634138608103373, 1.9682284184242402, 2.835842195988133, 1.0193709990831787, 0.8037806360873045, 0.48233231747378824, 0.0, 6.887185384447996, 5.30565549221167, 4.0189031804365225, 3.058112997249536, 5.671684391976266, 2.755519785793936, 1.9634138608103373, 1.8303088104962744, 2.5810965629139933, 1.9239836264499612, 1.1335528302711175, 0.4009358495849823, 0.0), # 167 (6.059031911370395, 4.270990187122201, 5.50449991514229, 5.60230820555966, 5.012791590203827, 2.490136929448583, 1.902800984996902, 1.9129851869747332, 2.7568809702442847, 0.9887860557234682, 0.7798253500011468, 0.468093800681876, 0.0, 6.6873225235789615, 5.149031807500635, 3.8991267500057343, 2.9663581671704042, 5.513761940488569, 2.6781792617646265, 1.902800984996902, 1.7786692353204163, 2.5063957951019136, 1.867436068519887, 1.100899983028458, 0.3882718351929274, 0.0), # 168 (5.861763403606015, 4.1267185154112305, 5.333058736591924, 5.4247227165306615, 4.856022455309747, 2.413972196347072, 1.8398185458352458, 1.8547371932717271, 2.6735186754361124, 0.9568696904244344, 0.7548009035337614, 0.45319887785722274, 0.0, 6.477694532530785, 4.985187656429449, 3.774004517668807, 2.8706090712733023, 5.347037350872225, 2.596632070580418, 1.8398185458352458, 1.724265854533623, 2.4280112276548733, 1.808240905510221, 1.066611747318385, 0.3751562286737483, 0.0), # 169 (5.657325647224384, 3.978005057412684, 5.154195281828863, 5.23994581269609, 4.692569423622822, 2.334286864383604, 1.7747074283677764, 1.7937538583450197, 2.5861465706904125, 0.9237514790829147, 0.7288103272000027, 0.4377100180914133, 0.0, 6.259210710787055, 4.814810199005545, 3.6440516360000137, 2.7712544372487433, 5.172293141380825, 2.5112554016830275, 1.7747074283677764, 1.6673477602740028, 2.346284711811411, 1.7466486042320304, 1.0308390563657726, 0.36163682340115316, 0.0), # 170 (5.4465037666285, 3.82537554023735, 4.968664216977482, 5.048728894489152, 4.523116197620137, 2.2514296625515327, 1.7077085176369027, 1.7303046032244096, 2.495155915133985, 0.8895609975957474, 0.7019566515147247, 0.4216896904760322, 0.0, 6.032780357831365, 4.638586595236354, 3.509783257573624, 2.6686829927872413, 4.99031183026797, 2.4224264445141737, 1.7077085176369027, 1.6081640446796661, 2.2615580988100685, 1.6829096314963843, 0.9937328433954964, 0.3477614127488501, 0.0), # 171 (5.230082886221365, 3.6693556909960217, 4.777220208162156, 4.851823362343048, 4.348346479778769, 2.1657493198442115, 1.6390626986850327, 1.664658848939696, 2.4009379678936282, 0.8544278218597702, 0.6743429069927823, 0.4052003641026643, 0.0, 5.799312773147303, 4.457204005129307, 3.3717145349639117, 2.56328346557931, 4.8018759357872565, 2.3305223885155746, 1.6390626986850327, 1.5469637998887225, 2.1741732398893845, 1.6172744541143496, 0.9554440416324312, 0.3335777900905475, 0.0), # 172 (5.00884813040598, 3.510471236799489, 4.58061792150726, 4.649980616690982, 4.168943972575801, 2.077594565254994, 1.5690108565545748, 1.5970860165206766, 2.303883988096141, 0.8184815277718206, 0.6460721241490297, 0.3883045080628938, 0.0, 5.5597172562184625, 4.271349588691831, 3.2303606207451483, 2.4554445833154612, 4.607767976192282, 2.235920423128947, 1.5690108565545748, 1.483996118039281, 2.0844719862879004, 1.5499935388969943, 0.916123584301452, 0.31913374879995354, 0.0), # 173 (4.783584623585344, 3.349247904758541, 4.3796120231371685, 4.443952057966156, 3.9855923784883105, 1.987314127777233, 1.4977938762879377, 1.5278555269971503, 2.204385234868321, 0.7818516912287369, 0.6172473334983214, 0.37106459144830567, 0.0, 5.314903106528433, 4.081710505931362, 3.0862366674916064, 2.34555507368621, 4.408770469736642, 2.1389977377960103, 1.4977938762879377, 1.4195100912694523, 1.9927961892441552, 1.4813173526553853, 0.8759224046274336, 0.3044770822507765, 0.0), # 174 (4.555077490162455, 3.18621142198397, 4.174957179176257, 4.2344890866017755, 3.7989753999933793, 1.8952567364042834, 1.425652642927529, 1.457236801398915, 2.102832967336968, 0.7446678881273562, 0.5879715655555117, 0.35354308335048457, 0.0, 5.0657796235608075, 3.8889739168553294, 2.939857827777558, 2.234003664382068, 4.205665934673936, 2.040131521958481, 1.425652642927529, 1.3537548117173452, 1.8994876999966896, 1.411496362200592, 0.8349914358352515, 0.28965558381672457, 0.0), # 175 (4.324111854540319, 3.0218875155865668, 3.9674080557488987, 4.0223431030310435, 3.609776739568087, 1.8017711201294973, 1.3528280415157574, 1.3854992607557703, 1.9996184446288805, 0.7070596943645169, 0.558347850835455, 0.33580245286101496, 0.0, 4.813256106799174, 3.693826981471164, 2.791739254177275, 2.1211790830935504, 3.999236889257761, 1.9396989650580787, 1.3528280415157574, 1.2869793715210696, 1.8048883697840434, 1.3407810343436815, 0.7934816111497798, 0.2747170468715061, 0.0), # 176 (4.0914728411219325, 2.856801912677122, 3.7577193189794698, 3.808265507687162, 3.4186800996895155, 1.7072060079462288, 1.2795609570950313, 1.3129123260975137, 1.8951329258708567, 0.6691566858370562, 0.528479219853006, 0.3179051690714816, 0.0, 4.5582418557271245, 3.496956859786297, 2.6423960992650297, 2.0074700575111684, 3.7902658517417134, 1.838077256536519, 1.2795609570950313, 1.2194328628187348, 1.7093400498447577, 1.269421835895721, 0.751543863795894, 0.25970926478882933, 0.0), # 177 (3.8579455743102966, 2.6914803403664256, 3.5466456349923448, 3.593007701003337, 3.226369182834742, 1.6119101288478317, 1.2060922747077587, 1.239745418453944, 1.7897676701896952, 0.6310884384418126, 0.49846870312301883, 0.299913701073469, 0.0, 4.301646169828252, 3.299050711808158, 2.4923435156150937, 1.8932653153254375, 3.5795353403793904, 1.7356435858355217, 1.2060922747077587, 1.1513643777484512, 1.613184591417371, 1.1976692336677792, 0.7093291269984691, 0.24468003094240237, 0.0), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_allighting_rate = ( (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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4 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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91 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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178 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 8991598675325360468762009371570610170 #index for seed sequence child child_seed_index = ( 1, # 0 91, # 1 )

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Edward-TL/vsbuy_backend

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Edward-TL/vsbuy_backend

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vsbuy_backend/products/models/__init__.py

Edward-TL/vsbuy_backend

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