xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

695247f1a8c81f5a3710bde126195c617c12ea51

104

py

Python

system/configsupport.py

conny-lin/python_lib

f31a93aab7331b5d1112db4282e5ec71c93f8869

[ "MIT" ]

null

system/configsupport.py

conny-lin/python_lib

f31a93aab7331b5d1112db4282e5ec71c93f8869

[ "MIT" ]

null

system/configsupport.py

conny-lin/python_lib

f31a93aab7331b5d1112db4282e5ec71c93f8869

[ "MIT" ]

null

# module that helps with configuration import os, glob # find config file in the nearest parent folder

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py

Python

autograd/nn/__init__.py

willeagren/autograd

7e0d7b4735c4abe209cb964e4b3e8a3e5c017a00

[ "MIT" ]

null

autograd/nn/__init__.py

willeagren/autograd

7e0d7b4735c4abe209cb964e4b3e8a3e5c017a00

[ "MIT" ]

1

2022-03-12T16:29:33.000Z

2022-03-13T13:31:04.000Z

autograd/nn/__init__.py

willeagren/autograd

7e0d7b4735c4abe209cb964e4b3e8a3e5c017a00

[ "MIT" ]

null

from .nn import Module, Sequential from .dense import Dense from .activations import ReLU, LogSoftmax, Sigmoid __all__ = [ Module, Sequential, Dense, ReLU, LogSoftmax, Sigmoid ]

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15c9373a92740fa49f8e22faa10819139cfb492f

88

py

Python

police_api/utils.py

rkhleics/police-api-client-python

4d428a83e4bcf9571484b624a435d47deb2f71a6

[ "MIT" ]

29

2015-04-03T01:49:44.000Z

2021-12-08T13:10:06.000Z

police_api/utils.py

rkhleics/police-api-client-python

4d428a83e4bcf9571484b624a435d47deb2f71a6

[ "MIT" ]

3

2015-08-19T11:37:14.000Z

2021-10-31T20:47:08.000Z

police_api/utils.py

rkhleics/police-api-client-python

4d428a83e4bcf9571484b624a435d47deb2f71a6

[ "MIT" ]

10

2016-01-08T09:51:02.000Z

2020-06-25T00:11:57.000Z

def encode_polygon(points): return ':'.join(['{0},{1}'.format(*p) for p in points])

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py

Python

imgtag/tabs/__init__.py

pauljxtan/imgtag

abd0be1957c91a897572aeae3823555299feb9bc

[ "MIT" ]

null

imgtag/tabs/__init__.py

pauljxtan/imgtag

abd0be1957c91a897572aeae3823555299feb9bc

[ "MIT" ]

11

2019-11-24T18:12:23.000Z

2020-06-21T16:43:45.000Z

imgtag/tabs/__init__.py

pauljxtan/imgtag

abd0be1957c91a897572aeae3823555299feb9bc

[ "MIT" ]

null

from .file import FileTab from .gallery import GalleryTab

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15fe504480ff3c37dc332f977bea0a7e18ad21f4

3,227

py

Python

comet_chaser_api/database/comet_roster.py

chung-ejy/comet_chaser_api

e18a4b65d606bcf5106cff5095b1a3134901abff

[ "MIT" ]

null

comet_chaser_api/database/comet_roster.py

chung-ejy/comet_chaser_api

e18a4b65d606bcf5106cff5095b1a3134901abff

[ "MIT" ]

null

comet_chaser_api/database/comet_roster.py

chung-ejy/comet_chaser_api

e18a4b65d606bcf5106cff5095b1a3134901abff

[ "MIT" ]

null

from database.adatabase import ADatabase import pandas as pd from cryptography.fernet import Fernet import os header_key = os.getenv("ROSTERKEY") encryption_key = os.getenv("ENCRYPTIONKEY") class CometRoster(ADatabase): def __init__(self): super().__init__("comet_roster") def get_user_trade_params(self,version,user): try: db = self.client[self.name] table = db[f"{version}_trading_params"] data = table.find({"username":user},{"_id":0},show_record_id=False).sort("_id", -1).limit(10) return pd.DataFrame(list(data)) except Exception as e: print(self.name,"roster",str(e)) def get_secrets(self,user): try: db = self.client[self.name] table = db["coinbase_credentials"] data = table.find({"username":user},{"_id":0},show_record_id=False) return pd.DataFrame(list(data)) except Exception as e: print(self.name,"roster",str(e)) def update_roster(self,user,params): try: db = self.client[self.name] table = db["roster"] data = table.update_one({"username":user},{"$set":params}) return data except Exception as e: print(self.name,"roster",str(e)) def update_keys(self,user,params): try: db = self.client[self.name] table = db["coinbase_credentials"] fernet = Fernet(encryption_key.encode()) encoded_keys = {} for key in params.keys(): if "key" in key or "secret" in key or "pass" in key: encoded_keys[key] = fernet.encrypt(params[key].encode()) data = table.update_one({"username":user},{"$set":encoded_keys}) return data except Exception as e: print(self.name,"roster",str(e)) def update_subscription(self,user,params): try: db = self.client[self.name] table = db["paypal_subscriptions"] data = table.update_one({"username":user},{"$set":params}) return data except Exception as e: print(self.name,"roster",str(e)) def get_bot_status(self,user): try: db = self.client[self.name] table = db["roster"] data = table.find({"username":user},{"_id":0},show_record_id=False) return pd.DataFrame(list(data)) except Exception as e: print(self.name,"roster",str(e)) def get_subscription(self,user): try: db = self.client[self.name] table = db["paypal_subscriptions"] data = table.find({"username":user},{"_id":0},show_record_id=False) return pd.DataFrame(list(data)) except Exception as e: print(self.name,"roster",str(e)) def get_all_subscription(self): try: db = self.client[self.name] table = db["paypal_subscriptions"] data = table.find({},{"_id":0},show_record_id=False) return pd.DataFrame(list(data)) except Exception as e: print(self.name,"roster",str(e))

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c60c52b0bff65ff53a51a9b048aaadcbfa230b42

132

py

Python

demo_worker/__init__.py

selinon/demo-worker

052a4924219be006c626479156e35803ca5545af

[ "MIT" ]

null

demo_worker/__init__.py

selinon/demo-worker

052a4924219be006c626479156e35803ca5545af

[ "MIT" ]

4

2018-05-04T07:13:38.000Z

2022-03-17T19:04:18.000Z

demo_worker/__init__.py

selinon/demo-worker

052a4924219be006c626479156e35803ca5545af

[ "MIT" ]

1

2021-07-01T07:34:23.000Z

__name__ = 'app' __version__ = '0.0.1' __author__ = 'Fridolin Pokorny' from .utils import get_config_files from .utils import init

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c651e52e8d50f3b57871a53a74d5358c648574b5

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py

Python

bindings/python/tests/fingerprint_test.py

kamyu104/libtorrent-1

87ec445943324a243be2b9499b74dc0983a42af9

[ "BSL-1.0", "BSD-3-Clause" ]

9

2019-11-05T16:47:12.000Z

2022-03-05T15:21:25.000Z

bindings/python/tests/fingerprint_test.py

kamyu104/libtorrent-1

87ec445943324a243be2b9499b74dc0983a42af9

[ "BSL-1.0", "BSD-3-Clause" ]

null

bindings/python/tests/fingerprint_test.py

kamyu104/libtorrent-1

87ec445943324a243be2b9499b74dc0983a42af9

[ "BSL-1.0", "BSD-3-Clause" ]

null

import unittest import libtorrent as lt class GenerateFingerprintTest(unittest.TestCase): @unittest.skip("https://github.com/arvidn/libtorrent/issues/5985") def test_generate(self) -> None: # full version self.assertEqual( lt.generate_fingerprint_bytes(b"ABCD", 1, 2, 3, 4), # type: ignore b"-AB1234-", ) # short name self.assertEqual( lt.generate_fingerprint_bytes(b"A", 1, 2, 3, 4), # type: ignore b"-A\x001234-", ) # major.minor self.assertEqual( lt.generate_fingerprint_bytes(b"ABCD", 1, 2), # type: ignore b"-AB1200-", ) # high versions self.assertEqual( lt.generate_fingerprint_bytes( # type: ignore b"ABCD", 1000, 2000, 3000, 4000 ), b"unknown", ) # version < 0 self.assertEqual( lt.generate_fingerprint_bytes(b"ABCD", -1, -1, -1, -1), # type: ignore b"-AB0000-", ) @unittest.skip("https://github.com/arvidn/libtorrent/issues/5988") def test_deprecations(self) -> None: with self.assertWarns(DeprecationWarning): lt.generate_fingerprint("ABCD", 1, 2, 3, 4) def test_generate_str(self) -> None: # full version self.assertEqual(lt.generate_fingerprint("ABCD", 1, 2, 3, 4), "-AB1234-") # short name self.assertEqual(lt.generate_fingerprint("A", 1, 2, 3, 4), "---1234-") # version < 0 self.assertEqual(lt.generate_fingerprint("ABCD", 1, 2, -1, -1), "-AB1200-") class FingerprintTest(unittest.TestCase): @unittest.skip("https://github.com/arvidn/libtorrent/issues/5967") def test_deprecations(self) -> None: with self.assertWarns(DeprecationWarning): lt.fingerprint("AB", 1, 2, 3, 4) def test_fingerprint(self) -> None: fprint = lt.fingerprint("AB", 1, 2, 3, 4) with self.assertWarns(DeprecationWarning): self.assertEqual(str(fprint), "-AB1234-") # self.assertEqual(fprint.major_version, 1) # self.assertEqual(fprint.minor_version, 2) # self.assertEqual(fprint.revision_version, 3) # self.assertEqual(fprint.tag_version, 4) # short names behave differently fprint = lt.fingerprint("A", 1, 2, 3, 4) with self.assertWarns(DeprecationWarning): self.assertEqual(str(fprint), "-A\x001234-") @unittest.skip("fingerprint.<attr> broke") def test_fingerprint_broken(self) -> None: fprint = lt.fingerprint("AB", 1, 2, 3, 4) self.assertEqual(fprint.major_version, 1) self.assertEqual(fprint.minor_version, 2) self.assertEqual(fprint.revision_version, 3) self.assertEqual(fprint.tag_version, 4)

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d661d8e86726137439759903702685cde2f5e45e

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py

Python

users/admin.py

Ab1gor/cardsite

3da8b998d093fd2b788a28bf8bc0cf09a43023c3

[ "BSD-3-Clause" ]

1

2019-03-12T06:33:21.000Z

users/admin.py

Ab1gor/cardsite

3da8b998d093fd2b788a28bf8bc0cf09a43023c3

[ "BSD-3-Clause" ]

4

2021-03-18T20:48:41.000Z

2022-01-13T00:49:58.000Z

users/admin.py

Ab1gor/cardsite

3da8b998d093fd2b788a28bf8bc0cf09a43023c3

[ "BSD-3-Clause" ]

null

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

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d6c55ba995056b7f14dde5909a96116882286fb4

988

py

Python

tests/acceptance/test_project/__init__.py

wo0dyn/raincoat

56fb9624c92bc11690975b12efc192402ca334a4

[ "MIT" ]

18

2016-10-13T10:16:49.000Z

2017-10-20T07:54:11.000Z

tests/acceptance/test_project/__init__.py

wo0dyn/raincoat

56fb9624c92bc11690975b12efc192402ca334a4

[ "MIT" ]

18

2016-10-13T11:37:47.000Z

2017-10-20T20:59:34.000Z

tests/acceptance/test_project/__init__.py

wo0dyn/raincoat

56fb9624c92bc11690975b12efc192402ca334a4

[ "MIT" ]

2

2020-06-30T12:57:56.000Z

2020-07-16T08:02:21.000Z

""" Raincoat comments that are checked in acceptance tests """ def simple_function(): # Raincoat: pypi package: raincoat==0.1.4 path: raincoat/_acceptance_test.py element: use_umbrella # noqa # Raincoat: pypi package: raincoat==0.1.4 path: raincoat/_acceptance_test.py element: Umbrella.open # noqa # Raincoat: pypi package: raincoat==0.1.4 path: raincoat/_acceptance_test.py element: Umbrella # noqa # Raincoat: pypi package: raincoat==0.1.4 path: raincoat/_acceptance_test.py # noqa # Raincoat: pypi package: raincoat==0.1.4 path: raincoat/_acceptance_test.py element: non_existant # noqa # Raincoat: pypi package: raincoat==0.1.4 path: raincoat/non_existant.py # noqa # Raincoat: django ticket: #25981 # Raincoat: django ticket: #27754 # Raincoat: pygithub repo: peopledoc/raincoat@a35df1d path: raincoat/_acceptance_test.py element: Umbrella.open # noqa pass # this file should never be executed, only parsed. raise NotImplementedError

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44

py

Python

meloshare/ui/app.py

ocervell/meloshare

c42aaa72af178d6e8357381b3a9c3986fffdff6a

[ "MIT" ]

null

meloshare/ui/app.py

ocervell/meloshare

c42aaa72af178d6e8357381b3a9c3986fffdff6a

[ "MIT" ]

2

2018-01-28T00:11:20.000Z

2018-01-28T00:20:14.000Z

meloshare/ui/app.py

ocervell/meloshare

c42aaa72af178d6e8357381b3a9c3986fffdff6a

[ "MIT" ]

1

2018-02-26T01:37:41.000Z

from . import create_app app = create_app()

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ba54f229b0a67a173d8328a86895a9c88aa28c27

286

py

Python

products/admin.py

cactus-computing/product-recommendation

b5d9bb27205a4fb032fd19934ecab56a5a8c6d81

[ "MIT" ]

null

products/admin.py

cactus-computing/product-recommendation

b5d9bb27205a4fb032fd19934ecab56a5a8c6d81

[ "MIT" ]

null

products/admin.py

cactus-computing/product-recommendation

b5d9bb27205a4fb032fd19934ecab56a5a8c6d81

[ "MIT" ]

null

from django.contrib import admin from .models import ProductAttributes, OrderAttributes, CrossSellPredictions, UpSellPredictions admin.site.register(OrderAttributes) admin.site.register(ProductAttributes) admin.site.register(CrossSellPredictions) admin.site.register(UpSellPredictions)

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ba65cca17d4478f2c975092c47d7362d00a13f98

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py

Python

admit/util/continuumsubtraction/spectral/algorithms/__init__.py

astroumd/admit

bbf3d79bb6e1a6f7523553ed8ede0d358d106f2c

[ "MIT" ]

4

2017-03-01T17:26:28.000Z

2022-03-03T19:23:06.000Z

admit/util/continuumsubtraction/spectral/algorithms/__init__.py

teuben/admit

1cae54d1937c9af3f719102838df716e7e6d655c

[ "MIT" ]

48

2016-10-04T01:25:33.000Z

2021-09-08T14:51:10.000Z

admit/util/continuumsubtraction/spectral/algorithms/__init__.py

teuben/admit

1cae54d1937c9af3f719102838df716e7e6d655c

[ "MIT" ]

2

2016-11-10T14:10:22.000Z

2017-03-30T18:58:05.000Z

from PolyFit import PolyFit as PolyFit from SplineFit import SplineFit as SplineFit

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223

py

Python

eta/segmenters/__init__.py

ErfanTagh/eta

3aa51006439a89cc5e2c78bbe1f98234bbc347ea

[ "Apache-2.0" ]

25

2018-07-21T02:37:34.000Z

2022-03-30T12:57:54.000Z

eta/segmenters/__init__.py

ErfanTagh/eta

3aa51006439a89cc5e2c78bbe1f98234bbc347ea

[ "Apache-2.0" ]

183

2018-06-13T18:57:00.000Z

2022-02-24T14:35:49.000Z

eta/segmenters/__init__.py

ErfanTagh/eta

3aa51006439a89cc5e2c78bbe1f98234bbc347ea

[ "Apache-2.0" ]

13

2018-09-10T18:46:58.000Z

2022-02-07T02:25:31.000Z

""" Segmenters package declaration. Copyright 2017-2021, Voxel51, Inc. voxel51.com """ # Import all segmenters into the `eta.segmenters` namespace from .tf_segmenters import TFSemanticSegmenter, TFSemanticSegmenterConfig

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241153a470187d78e669c83f4fbb4874ebb49e6b

697

py

Python

Flow/generate_time.py

fainyang/EE_Project

0e487d139a6751ecc0ccf39a877965abd9320e9f

[ "MIT" ]

3

2018-03-13T09:31:32.000Z

2020-06-26T11:18:28.000Z

Flow/generate_time.py

fainyang/EE_Project

0e487d139a6751ecc0ccf39a877965abd9320e9f

[ "MIT" ]

null

Flow/generate_time.py

fainyang/EE_Project

0e487d139a6751ecc0ccf39a877965abd9320e9f

[ "MIT" ]

1

2019-12-06T08:31:02.000Z

def getime(date): list1=[] for hour in range(8): for minute in range(0,56,5): if minute<10: time=str(date)+'-0'+str(hour)+':0'+str(minute)+':01' else: time=str(date)+'-0'+str(hour)+':'+str(minute)+':01' list1.append(time) for hour in range(8,24): if hour<10: for minute in range(0,60): if minute<10: time=str(date)+'-0'+str(hour)+':0'+str(minute)+':01' else: time=str(date)+'-0'+str(hour)+':'+str(minute)+':01' list1.append(time) else: for minute in range(0,60): if minute<10: time=str(date)+'-'+str(hour)+':0'+str(minute)+':01' else: time=str(date)+'-'+str(hour)+':'+str(minute)+':01' list1.append(time) return list1

24.892857

57

0.572453

116

697

3.439655

0.198276

0.070175

0.165414

0.120301

0.884712

0.766917

0.684211

0

0.08042

0.17934

697

27

58

25.814815

0.617133

0

0.64

0

0.053161

0

1

0.04

false

0

0.08

0

null

0

1

0

1

0

null

0

5

243c99a43d825391fca4de21bb2094fb3cd6820f

34

py

Python

my_test.py

Athenian-ComputerScience-Fall2020/day-1-workspace-maleich

40e2787595d6a269f9d2deb40c9c1cf1dcc4bb4e

[ "Apache-2.0" ]

null

my_test.py

Athenian-ComputerScience-Fall2020/day-1-workspace-maleich

40e2787595d6a269f9d2deb40c9c1cf1dcc4bb4e

[ "Apache-2.0" ]

null

my_test.py

Athenian-ComputerScience-Fall2020/day-1-workspace-maleich

40e2787595d6a269f9d2deb40c9c1cf1dcc4bb4e

[ "Apache-2.0" ]

null

# No test code for this repository

34

0.794118

6

34

4.5

1

0

0.176471

34

1

34

0.964286

0.941176

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

03317154402de5bba10a93f556e6b41f1b99d29c

53

py

Python

tests/fixtures/exceptions/recursion.py

danieleades/cleo

76a4e64668670b4cbfe68ec3ec0ec592a3eadbbd

[ "MIT" ]

null

tests/fixtures/exceptions/recursion.py

danieleades/cleo

76a4e64668670b4cbfe68ec3ec0ec592a3eadbbd

[ "MIT" ]

null

tests/fixtures/exceptions/recursion.py

danieleades/cleo

76a4e64668670b4cbfe68ec3ec0ec592a3eadbbd

[ "MIT" ]

null

def recursion_error() -> None: recursion_error()

17.666667

30

0.698113

6

53

5.833333

0.666667

0.8

0

0.169811

53

2

31

26.5

0.795455

0

1

0.5

true

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

cef5233a8677dead0a10e116124db460245a35b4

200

py

Python

pudoku/SimpleStringifier.py

Hendrikto/Pudoku3

edc32066f7210fbb831002c0f6c364d939746e2d

[ "MIT" ]

null

pudoku/SimpleStringifier.py

Hendrikto/Pudoku3

edc32066f7210fbb831002c0f6c364d939746e2d

[ "MIT" ]

null

pudoku/SimpleStringifier.py

Hendrikto/Pudoku3

edc32066f7210fbb831002c0f6c364d939746e2d

[ "MIT" ]

null

# author: Hendrik Werner from .SudokuStringifier import SudokuStringifier class SimpleStringifier(SudokuStringifier): def stringify(self, sudoku): return "".join(map(str, sudoku.cells))

25

48

0.755

20

200

7.55

0.85

0

0.15

200

7

49

28.571429

0.888235

0.11

0

1

0.25

false

0

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

30555d6e04fb241ebe3a75f62510a59ab0ee3ca6

44

py

Python

src/superfit/datasetselector/__init__.py

awacha/superfit

a95d346c4b38f61173c7434eb7389e2cf1ccae9c

[ "BSD-3-Clause" ]

null

src/superfit/datasetselector/__init__.py

awacha/superfit

a95d346c4b38f61173c7434eb7389e2cf1ccae9c

[ "BSD-3-Clause" ]

null

src/superfit/datasetselector/__init__.py

awacha/superfit

a95d346c4b38f61173c7434eb7389e2cf1ccae9c

[ "BSD-3-Clause" ]

null

from .datasetselector import DataSetSelector

44

0.909091

4

44

10

0.75

0

0.068182

44

1

44

0.97561

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

061a581f50567ab03326a60a71bbd31e5de6e512

266

py

Python

app/services/BAC.py

izconcept/Turnt

28d25ebfbd43aa6472aa1f0eec7e73ec1b8d15d3

[ "Apache-2.0" ]

4

2018-01-29T05:51:32.000Z

2018-02-08T05:18:47.000Z

app/services/BAC.py

izconcept/Turnt

28d25ebfbd43aa6472aa1f0eec7e73ec1b8d15d3

[ "Apache-2.0" ]

3

2018-01-30T21:41:09.000Z

2018-01-31T18:20:01.000Z

app/services/BAC.py

izconcept/Turnt

28d25ebfbd43aa6472aa1f0eec7e73ec1b8d15d3

[ "Apache-2.0" ]

1

2019-03-29T19:32:28.000Z

from datetime import datetime def calculate_bac(drinks, weight, start_time, gender): return ((sum([drink['percentage'] * drink['amount'] for drink in drinks]) * 5.14) / weight * (.73 if gender == 'male' else .66)) - (0.15 * (datetime.now() - start_time).hour)

44.333333

178

0.669173

38

266

4.605263

0.763158

0.102857

0

0.044444

0.154135

266

5

179

53.2

0.733333

0

0.075188

0

1

0.333333

false

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

5

062c5915006e9c6b377aabd995102110bcb09527

126

py

Python

find-numbers-with-even-number-of-digits/find-numbers-with-even-number-of-digits.py

Dongfang1021/Leetcode

4ecdad3279300720e92eeac683962ebc52c98a12

[ "MIT" ]

1

2021-06-05T06:26:32.000Z

find-numbers-with-even-number-of-digits/find-numbers-with-even-number-of-digits.py

Dongfang1021/Leetcode

4ecdad3279300720e92eeac683962ebc52c98a12

[ "MIT" ]

null

find-numbers-with-even-number-of-digits/find-numbers-with-even-number-of-digits.py

Dongfang1021/Leetcode

4ecdad3279300720e92eeac683962ebc52c98a12

[ "MIT" ]

null

class Solution: def findNumbers(self, nums: List[int]) -> int: return [len(str(num)) % 2 for num in nums].count(0)

42

59

0.626984

20

126

3.95

0.85

0

0.020202

0.214286

126

3

59

42

0.777778

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

068982d99e55e28d199fa7683cdef793b1e60591

44,967

py

Python

tests/test_veracode.py

echohack/lantern

7a0aa7f1862639c558c20bcff0415b9c8c1b965c

[ "Apache-2.0" ]

13

2015-04-09T14:08:30.000Z

2017-04-07T10:58:56.000Z

tests/test_veracode.py

echohack/lantern

7a0aa7f1862639c558c20bcff0415b9c8c1b965c

[ "Apache-2.0" ]

10

2016-09-02T19:56:37.000Z

2021-03-23T07:53:00.000Z

tests/test_veracode.py

echohack/lantern

7a0aa7f1862639c558c20bcff0415b9c8c1b965c

[ "Apache-2.0" ]

2

2017-05-10T10:58:24.000Z

2018-05-30T17:50:10.000Z

import os import sys sys.path.append(os.getcwd()) from lantern import * import requests import nose.tools from mock import patch # mock data. These are real xml examples that have been scrubbed for personal information that represent actual xml responses from Veracode. mock_app_builds_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<applicationbuilds xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/applicationbuilds" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/applicationbuilds https://analysiscenter.veracode.com/resource/2.0/applicationbuilds.xsd" ' 'account_id="00001">' '<application app_name="TestApp" app_id="00001" industry_vertical="Software" assurance_level="High" business_criticality="High" origin="Internally Developed" ' 'modified_date="2010-09-17T11:43:22-04:00" cots="false" business_unit="Not Specified" tags="">' '<customfield name="Custom 1" value=""/>' '<customfield name="Custom 2" value=""/>' '<customfield name="Custom 3" value=""/>' '<customfield name="Custom 4" value=""/>' '<customfield name="Custom 5" value=""/>' '<build version="5.0.0.3232" build_id="12724" submitter="my submitter" platform="Windows" lifecycle_stage="External or Beta Testing" ' 'results_ready="true" policy_name="Veracode Transitional High" policy_version="1" policy_compliance_status="Did Not Pass" rules_status="Did Not Pass" ' 'grace_period_expired="false" scan_overdue="false">' '<analysis_unit analysis_type="Static" published_date="2008-11-26T14:43:43-05:00" published_date_sec="1227728623" status="Results Ready"/>' '</build></application>' '<application app_name="TestApp2" app_id="00002" industry_vertical="Technology" assurance_level="High" business_criticality="High" origin="Not Specified" ' 'modified_date="2012-11-21T09:47:57-05:00" cots="false" business_unit="Not Specified" tags="">' '<customfield name="Custom 1" value=""/>' '<customfield name="Custom 2" value=""/>' '<customfield name="Custom 3" value=""/>' '<customfield name="Custom 4" value=""/>' '<customfield name="Custom 5" value=""/>' '<build version="20121121" build_id="79970" submitter="Veracode" platform="Not Specified" lifecycle_stage="Not Specified" results_ready="true" ' 'policy_name="Veracode Transitional High" policy_version="1" policy_compliance_status="Did Not Pass" rules_status="Did Not Pass" ' 'grace_period_expired="false" scan_overdue="false">' '<analysis_unit analysis_type="Manual" published_date="2012-11-21T09:47:45-05:00" published_date_sec="1353509265" status="Results Ready"/>' '</build></application></applicationbuilds>') mock_app_info_xml = () mock_app_list_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<applist xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/applist" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/applist https://analysiscenter.veracode.com/resource/2.0/applist.xsd" account_id="00001">' '<app app_id="00001" app_name="TestApp"/>' '<app app_id="00002" app_name="TestApp2"/>' '</applist>') mock_build_info_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<buildinfo xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/4.0/buildinfo" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/4.0/buildinfo https://analysiscenter.veracode.com/resource/4.0/buildinfo.xsd" ' 'account_id="00001" app_id="00001" build_id="00001"> ' '<build version="TestApp 7.5.0.234" build_id="00001" submitter="Continuous Quality" platform="Not Specified" ' 'lifecycle_stage="Not Specified" results_ready="true" policy_name="Veracode Transitional Medium" policy_version="1" ' 'policy_compliance_status="Pass" rules_status="Pass" grace_period_expired="false" scan_overdue="false"> ' '<analysis_unit analysis_type="Static" published_date="2013-02-14T12:39:53-05:00" published_date_sec="1360863593" ' 'status="Results Ready"/> ' '</build></buildinfo>') mock_build_info_xml_create_build = ( '<?xml version="1.0" encoding="UTF-8"?>' '<buildinfo xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/4.0/buildinfo" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/4.0/buildinfo https://analysiscenter.veracode.com/resource/4.0/buildinfo.xsd" ' 'account_id="00001" app_id="00001" build_id="00001"> ' '<build version="TestCreateBuild" build_id="00002" submitter="Continuous Quality" platform="Not Specified" ' 'lifecycle_stage="Not Specified" results_ready="true" policy_name="Veracode Transitional Medium" policy_version="1" ' 'policy_compliance_status="Pass" rules_status="Pass" grace_period_expired="false" scan_overdue="false"> ' '<analysis_unit analysis_type="Static" published_date="2013-02-14T12:39:53-05:00" published_date_sec="1360863593" ' 'status="Results Ready"/> ' '</build></buildinfo>') mock_build_info_xml_prescan_success = ( '<?xml version="1.0" encoding="UTF-8"?>' '<buildinfo xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/4.0/buildinfo" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/4.0/buildinfo https://analysiscenter.veracode.com/resource/4.0/buildinfo.xsd" ' 'account_id="00001" app_id="00001" build_id="00001"> ' '<build version="TestApp 7.5.0.234" build_id="00001" submitter="Continuous Quality" platform="Not Specified" ' 'lifecycle_stage="Not Specified" results_ready="true" policy_name="Veracode Transitional Medium" policy_version="1" ' 'policy_compliance_status="Pass" rules_status="Pass" grace_period_expired="false" scan_overdue="false"> ' '<analysis_unit analysis_type="Static" published_date="2013-02-14T12:39:53-05:00" published_date_sec="1360863593" ' 'status="Pre-Scan Success"/> ' '</build></buildinfo>') mock_build_info_xml_prescan_in_progress = ( '<?xml version="1.0" encoding="UTF-8"?>' '<buildinfo xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/4.0/buildinfo" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/4.0/buildinfo https://analysiscenter.veracode.com/resource/4.0/buildinfo.xsd" ' 'account_id="00001" app_id="00001" build_id="00001"> ' '<build version="TestApp 7.5.0.234" build_id="00001" submitter="Continuous Quality" platform="Not Specified" ' 'lifecycle_stage="Not Specified" results_ready="true" policy_name="Veracode Transitional Medium" policy_version="1" ' 'policy_compliance_status="Pass" rules_status="Pass" grace_period_expired="false" scan_overdue="false"> ' '<analysis_unit analysis_type="Static" published_date="2013-02-14T12:39:53-05:00" published_date_sec="1360863593" ' 'status="Pre-Scan In Progress"/> ' '</build></buildinfo>') mock_build_list_xml = ( '<buildlist xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/buildlist" xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/buildlist https://analysiscenter.veracode.com/resource/2.0/buildlist.xsd" account_id="00001" app_id="00001" app_name="testApp">' '<build build_id="00001" version="TestApp 7.5.0.234"/>' '<build build_id="00002" version="TestCreateBuild"/>' '</buildlist>') mock_build_list_xml_create_build = ( '<buildlist xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/buildlist" xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/buildlist https://analysiscenter.veracode.com/resource/2.0/buildlist.xsd" account_id="00001" app_id="00001" app_name="testApp">' '<build build_id="00001" version="TestApp 7.5.0.234"/>' '</buildlist>') mock_call_stacks_xml = () mock_error_xml = ( '<error></error>') mock_file_list = ( ["TestFile01.jsp", "TestFile02.jsp", "TestFile03.class", "TestFile04.jsp", "TestFile05.htm", "TestFile06.class", "TestFile07.jsp", "TestFile08.jspi", "TestFile09.jsp"]) mock_file_list_blacklist = ( ["TestFile03.class", "TestFile05.htm", "TestFile06.class", "TestFile08.jspi"]) mock_file_list_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<filelist xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/filelist" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/filelist https://analysiscenter.veracode.com/resource/2.0/filelist.xsd" ' 'account_id="00001" app_id="00001" build_id="00002">' '<file file_id="21271739" file_name="TestFile01.jsp" file_status="Uploaded"/>' '<file file_id="21243504" file_name="TestFile02.jsp" file_status="Uploaded"/>' '<file file_id="21243519" file_name="TestFile03.class" file_status="Uploaded"/>' '<file file_id="21243523" file_name="TestFile04.jsp" file_status="Uploaded"/>' '<file file_id="21243525" file_name="TestFile05.htm" file_status="Uploaded"/>' '<file file_id="21243527" file_name="TestFile06.class" file_status="Uploaded"/>' '<file file_id="21265337" file_name="TestFile07.jsp" file_status="Uploaded"/>' '<file file_id="21265341" file_name="TestFile08.jspi" file_status="Uploaded"/>' '<file file_id="21265343" file_name="TestFile09.jsp" file_status="Uploaded"/>' '</filelist>') mock_file_list_blacklist_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<filelist xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/filelist" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/filelist https://analysiscenter.veracode.com/resource/2.0/filelist.xsd" ' 'account_id="00001" app_id="00001" build_id="00002">' '<file file_id="21243519" file_name="TestFile03.class" file_status="Uploaded"/>' '<file file_id="21243525" file_name="TestFile05.htm" file_status="Uploaded"/>' '<file file_id="21243527" file_name="TestFile06.class" file_status="Uploaded"/>' '<file file_id="21265341" file_name="TestFile08.jspi" file_status="Uploaded"/>' '</filelist>') mock_file_list_remove_file_by_name_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<filelist xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/filelist" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/filelist https://analysiscenter.veracode.com/resource/2.0/filelist.xsd" ' 'account_id="00001" app_id="00001" build_id="00002">' '<file file_id="21271739" file_name="TestFile01.jsp" file_status="Uploaded"/>' '<file file_id="21243504" file_name="TestFile02.jsp" file_status="Uploaded"/>' '<file file_id="21243519" file_name="TestFile03.class" file_status="Uploaded"/>' '<file file_id="21243523" file_name="TestFile04.jsp" file_status="Uploaded"/>' '<file file_id="21243525" file_name="TestFile05.htm" file_status="Uploaded"/>' '<file file_id="21243527" file_name="TestFile06.class" file_status="Uploaded"/>' '<file file_id="21265337" file_name="TestFile07.jsp" file_status="Uploaded"/>' '<file file_id="21265341" file_name="TestFile08.jspi" file_status="Uploaded"/>' '</filelist>') mock_file_list_empty_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<filelist xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/filelist" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/filelist https://analysiscenter.veracode.com/resource/2.0/filelist.xsd" ' 'account_id="00001" app_id="00001" build_id="00002">' '</filelist>') mock_module_list = [{'module_id': '00000059', 'module_name': 'PrescanTest01.class'}, {'module_id': '00000060', 'module_name': 'PrescanTest02.class'}, {'module_id': '00000061', 'module_name': 'PrescanTest03.class'}] mock_module_white_list = [{'module_id': '00000060', 'module_name': 'PrescanTest02.class'}, {'module_id': '00000061', 'module_name': 'PrescanTest03.class'}] mock_policy_list_xml = () mock_prescan_results_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<prescanresults xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/prescanresults" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/prescanresults https://analysiscenter.veracode.com/resource/2.0/prescanresults.xsd" ' 'account_id="00001" app_id="00001" build_id="00002">' '<module id="00000059" name="PrescanTest01.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="4KB" ' 'status="Missing Supporting Files - 1 File, Unsupported Framework - 1 File" has_fatal_errors="false">' '<issue details="Unsupported framework: Apache Axis"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest01Dependency01" details="Not Found (Optional)"/>' '</module>' '<module id="00000060" name="PrescanTest02.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="5KB" ' 'status="Missing Supporting Files - 2 Files, Unsupported Framework - 1 File" has_fatal_errors="false">' '<issue details="Unsupported framework: Apache Axis"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest02Dependency01" details="Not Found (Optional)"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest02Dependency02" details="Not Found (Optional)"/>' '</module>' '<module id="00000061" name="PrescanTest03.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="2KB" ' 'status="Missing Supporting Files - 2 Files" has_fatal_errors="false">' '<file_issue filename="com.mock.cmp.product.adhoc.PrescanTest03Dependency01" details="Not Found (Optional)"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest03Dependency02" details="Not Found (Optional)"/>' '</module></prescanresults>') mock_prescan_results_xml_all_fatal_errors = ( '<?xml version="1.0" encoding="UTF-8"?>' '<prescanresults xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/prescanresults" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/prescanresults https://analysiscenter.veracode.com/resource/2.0/prescanresults.xsd" ' 'account_id="00001" app_id="00001" build_id="00002">' '<module id="00000059" name="PrescanTest01.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="4KB" ' 'status="Missing Supporting Files - 1 File, Unsupported Framework - 1 File" has_fatal_errors="true">' '<issue details="Unsupported framework: Apache Axis"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest01Dependency01" details="Not Found (Optional)"/>' '</module>' '<module id="00000060" name="PrescanTest02.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="5KB" ' 'status="Missing Supporting Files - 2 Files, Unsupported Framework - 1 File" has_fatal_errors="true">' '<issue details="Unsupported framework: Apache Axis"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest02Dependency01" details="Not Found (Optional)"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest02Dependency02" details="Not Found (Optional)"/>' '</module>' '<module id="00000061" name="PrescanTest03.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="2KB" ' 'status="Missing Supporting Files - 2 Files" has_fatal_errors="true">' '<file_issue filename="com.mock.cmp.product.adhoc.PrescanTest03Dependency01" details="Not Found (Optional)"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest03Dependency02" details="Not Found (Optional)"/>' '</module></prescanresults>') mock_prescan_results_xml_mixed = ( '<?xml version="1.0" encoding="UTF-8"?>' '<prescanresults xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://analysiscenter.veracode.com/schema/2.0/prescanresults" ' 'xsi:schemaLocation="https://analysiscenter.veracode.com/schema/2.0/prescanresults https://analysiscenter.veracode.com/resource/2.0/prescanresults.xsd" ' 'account_id="00001" app_id="00001" build_id="00002">' '<module id="00000059" name="PrescanTest01.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="4KB" ' 'status="Missing Supporting Files - 1 File, Unsupported Framework - 1 File" has_fatal_errors="false">' '<issue details="Unsupported framework: Apache Axis"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest01Dependency01" details="Not Found (Optional)"/>' '</module>' '<module id="00000060" name="PrescanTest02.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="5KB" ' 'status="Missing Supporting Files - 2 Files, Unsupported Framework - 1 File" has_fatal_errors="true">' '<issue details="Unsupported framework: Apache Axis"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest02Dependency01" details="Not Found (Optional)"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest02Dependency02" details="Not Found (Optional)"/>' '</module>' '<module id="00000061" name="PrescanTest03.class" platform="JVM / Java J2SE 6 / JAVAC_6" size="2KB" ' 'status="Missing Supporting Files - 2 Files" has_fatal_errors="true">' '<file_issue filename="com.mock.cmp.product.adhoc.PrescanTest03Dependency01" details="Not Found (Optional)"/>' '<file_issue filename="com.mock.ws.common.v1.PrescanTest03Dependency02" details="Not Found (Optional)"/>' '</module></prescanresults>') mock_detailed_report_pdf = (b'') mock_detailed_report_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<detailedreport xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://www.veracode.com/schema/reports/export/1.0" ' 'xsi:schemaLocation="https://www.veracode.com/schema/reports/export/1.0 https://analysiscenter.veracode.com/resource/detailedreport.xsd" ' 'report_format_version="1.1" app_name="testApp" app_id="00001" first_build_submitted_date="2012-12-20 22:24:36 UTC" ' 'version="1.0.1.0" build_id="00002" submitter="Continuous Quality" platform="Not Specified" ' 'assurance_level="3" business_criticality="3" generation_date="2013-02-20 20:01:33 UTC" veracode_level="VL3" ' 'total_flaws="4" flaws_not_mitigated="4" teams="Quality" life_cycle_stage="Not Specified" ' 'planned_deployment_date="" last_update_time="2013-02-19 23:45:37 UTC" is_latest_build="true" policy_name="Veracode Transitional Medium" ' 'policy_version="1" policy_compliance_status="Pass" policy_rules_status="Pass" grace_period_expired="false" scan_overdue="false" ' 'any_type_scan_due="2013-02-19 23:45:33 UTC" business_owner="Testing" business_unit="Not Specified" tags="App,Automation,CQ,Quality,Test">' '<static-analysis rating="A" score="98" submitted_date="2013-02-19 23:27:07 UTC" published_date="2013-02-19 23:45:33 UTC" ' 'analysis_size_bytes="330970">' '<modules>' '<module name="ActivityLogHelper.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" loc="00001" score="99" ' 'numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="2" numflawssev4="0" numflawssev5="0"/>' '<module name="AccessFilter.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" loc="0002" score="99" ' 'numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="1" numflawssev4="0" numflawssev5="0"/>' '<module name="JobRunner.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" loc="00003" score="99" ' 'numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="1" numflawssev4="0" numflawssev5="0"/>' '<module name="EntityTypeHelper.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" loc="12545" ' 'score="100" numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="0" numflawssev4="0" numflawssev5="0"/>' '<module name="IntegrationCodeSearchCriteria.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" ' 'loc="00004" score="100" numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="0" numflawssev4="0" numflawssev5="0"/>' '</modules>' '</static-analysis>' '<severity level="5"/>' '<severity level="4"/>' '<severity level="3">' '<category categoryid="21" categoryname="CRLF Injection" pcirelated="true">' '<desc>' '<para text="The acronym CRLF stands for "Carriage Return, Line Feed" and refers to the sequence of characters "' '"used to denote the end of a line of text. CRLF injection vulnerabilities occur when data enters an application from an untrusted "' '"source and is not properly validated before being used. For example, if an attacker is able to inject a CRLF into a log file, he "' '"could append falsified log entries, thereby misleading administrators or cover traces of the attack. If an attacker is able to inject "' '"CRLFs into an HTTP response header, he can use this ability to carry out other attacks such as cache poisoning. CRLF vulnerabilities "' '"primarily affect data integrity. "/>' '</desc>' '<recommendations>' '<para text="Apply robust input filtering for all user-supplied data, using centralized data validation routines when possible. "' '"Use output filters to sanitize all output derived from user-supplied input, replacing non-alphanumeric characters with their HTML entity equivalents."/>' '</recommendations></category></severity>' '<severity level="2"/>' '<severity level="1"/>' '<severity level="0"/>' '<flaw-status new="4" reopen="0" open="0" fixed="0" total="4" not_mitigated="4" sev-1-change="0" sev-2-change="0" sev-3-change="4" sev-4-change="0" sev-5-change="0"/>' '</detailedreport>') mock_summary_report_pdf = (b'') mock_summary_report_xml = ( '<?xml version="1.0" encoding="UTF-8"?>' '<summaryreport xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="https://www.veracode.com/schema/reports/export/1.0" ' 'xsi:schemaLocation="https://www.veracode.com/schema/reports/export/1.0 https://analysiscenter.veracode.com/resource/summaryreport.xsd" ' 'report_format_version="1.1" app_name="testApp" app_id="00001" first_build_submitted_date="2012-12-20 22:24:36 UTC" ' 'version="1.0.1.0" build_id="00002" submitter="Continuous Quality" platform="Not Specified" assurance_level="3" ' 'business_criticality="3" generation_date="2013-02-20 19:24:37 UTC" veracode_level="VL3" total_flaws="4" flaws_not_mitigated="4" ' 'teams="Quality" life_cycle_stage="Not Specified" planned_deployment_date="" last_update_time="2013-02-19 23:45:37 UTC" ' 'is_latest_build="true" policy_name="Veracode Transitional Medium" policy_version="1" policy_compliance_status="Pass" ' 'policy_rules_status="Pass" grace_period_expired="false" scan_overdue="false" any_type_scan_due="2013-02-19 23:45:33 UTC" ' 'business_owner="Testing" business_unit="Not Specified" tags="App,Automation,CQ,Quality,Test">' '<static-analysis rating="A" score="98" submitted_date="2013-02-19 23:27:07 UTC" published_date="2013-02-19 23:45:33 UTC" analysis_size_bytes="0">' '<modules>' '<module name="ActivityLogHelper.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" loc="00001" score="99" ' 'numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="2" numflawssev4="0" numflawssev5="0"/>' '<module name="AccessFilter.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" loc="0002" score="99" ' 'numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="1" numflawssev4="0" numflawssev5="0"/>' '<module name="JobRunner.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" loc="00003" score="99" ' 'numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="1" numflawssev4="0" numflawssev5="0"/>' '<module name="EntityTypeHelper.class" compiler="JAVAC_6" os="Java J2SE 6" architecture="JVM" loc="12545" ' 'score="100" numflawssev0="0" numflawssev1="0" numflawssev2="0" numflawssev3="0" numflawssev4="0" numflawssev5="0"/>' '</modules>' '</static-analysis>' '<severity level="5"/><severity level="4"/><severity level="3"><category categoryname="CRLF Injection" ' 'severity="Medium" count="2"/><category categoryname="Code Quality" severity="Medium" count="1"/><category ' 'categoryname="Session Fixation" severity="Medium" count="1"/></severity><severity level="2"/><severity level="1"/>' '<severity level="0"/><flaw-status new="4" reopen="0" open="0" fixed="0" total="4" not_mitigated="4" sev-1-change="0" ' 'sev-2-change="0" sev-3-change="4" sev-4-change="0" sev-5-change="0"/></summaryreport>') mock_third_party_report_xml = () mock_third_party_report_pdf = (b'') mock_update_build_xml = () mock_vendor_list_xml = () class TestVeracode(): test_instance = None @classmethod def setup_class(cls): cls.test_instance = AbstractAPI("myTestUsername", "myTestPassword") @classmethod def teardown_class(cls): cls.test_instance = None def test_begin_prescan(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_build_info_xml mock_method.return_value = r begin_prescan_xml = self.test_instance.begin_prescan(34, 52) assert begin_prescan_xml == mock_build_info_xml def test_begin_scan(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_build_info_xml mock_method.return_value = r begin_scan_xml = self.test_instance.begin_scan(34, ["something", "something1", "something2"], False) assert begin_scan_xml == mock_build_info_xml def test_create_app(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_app_info_xml mock_method.return_value = r create_app_xml = self.test_instance.create_app( "myApp", "High", "This is an app description.", 11, "testPolicy", "testBusinessUnit", "testBusinessOwner", "testBusinessOwnerEmail@example.com", "testTeam", "testOrigin", "testIndustry", "testAppType", "testDeploymentType", "testWebApplication", "testArcherAppName", "testTags") assert create_app_xml == mock_app_info_xml def test_create_build(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_build_info_xml mock_method.return_value = r create_build_xml = self.test_instance.create_build(34, "testVersion", "testLifecycleStage", 19, "2013-22-01") assert create_build_xml == mock_build_info_xml def test_delete_app(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_app_list_xml mock_method.return_value = r delete_app_xml = self.test_instance.delete_app(34) assert delete_app_xml == mock_app_list_xml def test_delete_build(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_app_list_xml mock_method.return_value = r delete_build_xml = self.test_instance.delete_build(34) assert delete_build_xml == mock_app_list_xml def test_detailed_report_pdf(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.content = mock_detailed_report_pdf mock_method.return_value = r detailed_report_pdf = self.test_instance.detailed_report_pdf(52) assert detailed_report_pdf == mock_detailed_report_pdf def test_get_app_builds(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_app_builds_xml mock_method.return_value = r app_builds_xml = self.test_instance.get_app_builds() assert app_builds_xml == mock_app_builds_xml def test_get_app_info(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_app_info_xml mock_method.return_value = r app_info_xml = self.test_instance.get_app_info(52) assert app_info_xml == mock_app_info_xml def test_get_app_list(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_app_list_xml mock_method.return_value = r app_list_xml = self.test_instance.get_app_list() assert app_list_xml == mock_app_list_xml def test_get_build_info(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_build_info_xml mock_method.return_value = r build_info_xml = self.test_instance.get_build_info(34, 52) assert build_info_xml == mock_build_info_xml def test_get_build_list(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_app_info_xml mock_method.return_value = r build_list_xml = self.test_instance.get_build_list(52) assert build_list_xml == mock_app_info_xml def test_get_call_stacks(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_call_stacks_xml mock_method.return_value = r call_stacks_xml = self.test_instance.get_call_stacks(34, 975) assert call_stacks_xml == mock_call_stacks_xml def test_get_file_list(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_file_list_xml mock_method.return_value = r file_list_xml = self.test_instance.get_file_list(34, 52) assert file_list_xml == mock_file_list_xml def test_get_policy_list(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_policy_list_xml mock_method.return_value = r policy_list_xml = self.test_instance.get_policy_list() assert policy_list_xml == mock_policy_list_xml def test_get_prescan_results(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_prescan_results_xml mock_method.return_value = r prescan_results_xml = self.test_instance.get_prescan_results(34, 52) assert prescan_results_xml == mock_prescan_results_xml def test_get_vendor_list(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_vendor_list_xml mock_method.return_value = r vendor_list_xml = self.test_instance.get_vendor_list() assert vendor_list_xml == mock_vendor_list_xml def test_remove_file(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_file_list_xml mock_method.return_value = r remove_file_xml = self.test_instance.remove_file(34, 3420) assert remove_file_xml == mock_file_list_xml def test_third_party_report_pdf(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.content = mock_third_party_report_pdf mock_method.return_value = r third_party_report_pdf = self.test_instance.third_party_report_pdf(52) assert third_party_report_pdf == mock_third_party_report_pdf def test_summary_report_pdf(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.content = mock_summary_report_pdf mock_method.return_value = r summary_report_pdf = self.test_instance.summary_report_pdf(52) assert summary_report_pdf == mock_summary_report_pdf def test_upload_file(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_file_list_xml mock_method.return_value = r fileName = "myUploadFile.txt" with open(fileName, "w+") as f: f.write("test") upload_file_xml = self.test_instance.upload_file(34, fileName) os.remove(fileName) assert upload_file_xml == mock_file_list_xml def test_update_app(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_app_info_xml mock_method.return_value = r update_app_xml = self.test_instance.update_app( 34, "myApp", "High", "testPolicy", "testBusinessUnit", "testBusinessOwner", "testBusinessOwnerEmail@example.com", "testTeam", "testOrigin", "testIndustry", "testAppType", "testDeploymentType", "testArcherAppName", "testTags", "testCustomFieldName", "testCustomFieldValue") assert update_app_xml == mock_app_info_xml def test_update_build(self): with patch.object(requests, "request") as mock_method: r = requests.Response r.text = mock_update_build_xml mock_method.return_value = r update_build_xml = self.test_instance.update_build(34, 52, "testVersion", "testLifecycleStage", "2013-22-01") assert update_build_xml == mock_update_build_xml class TestAPI(): test_instance = None @classmethod def setup_class(cls): with patch("lantern.AbstractAPI.get_app_list") as get_app_list: get_app_list.return_value = mock_app_list_xml with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml with patch("lantern.AbstractAPI.get_build_list") as get_build_list: get_build_list.return_value = mock_build_list_xml cls.test_instance = API("myTestUsername", "myTestPassword", "TestApp", "TestApp 7.5.0.234") @classmethod def teardown_class(cls): cls.test_instance = None def test_begin_prescan(self): with patch("lantern.AbstractAPI.begin_prescan") as begin_prescan: begin_prescan.return_value = mock_build_info_xml result = self.test_instance.begin_prescan() assert result == mock_build_info_xml def test_begin_scan(self): with patch("lantern.AbstractAPI.begin_scan") as begin_scan: begin_scan.return_value = mock_build_info_xml begin_scan_xml = self.test_instance.begin_scan(mock_prescan_results_xml, ["PrescanTest01.*"]) assert begin_scan_xml == mock_build_info_xml def test_create_module_white_list(self): module_white_list = self.test_instance.create_module_white_list(mock_module_list, ["PrescanTest01.*"]) assert module_white_list == mock_module_white_list def test_compare_file_list(self): with patch("lantern.AbstractAPI.get_file_list") as get_file_list: get_file_list.return_value = mock_file_list_blacklist_xml positive_result = self.test_instance.compare_file_list(os.getcwd() + "/ext/", ["*.jsp"]) assert positive_result == [] negative_result = self.test_instance.compare_file_list(os.getcwd(), ["*.jsp"]) assert negative_result != [] def test_compare_module_list(self): module_white_list = self.test_instance.compare_module_list(mock_prescan_results_xml, ["PrescanTest01.*"]) assert module_white_list == mock_module_white_list def test_create_new_build(self): with patch("lantern.AbstractAPI.get_build_list") as get_build_list: get_build_list.return_value = mock_build_list_xml_create_build with patch("lantern.AbstractAPI.create_build") as create_build: create_build.return_value = mock_build_info_xml_create_build self.test_instance.set_build_id("TestCreateBuild") result = self.test_instance.build_id assert result == "00002" def test_use_existing_build(self): with patch("lantern.AbstractAPI.get_build_list") as get_build_list: get_build_list.return_value = mock_build_list_xml self.test_instance.set_build_id("TestCreateBuild") result = self.test_instance.build_id assert result == "00002" def test_delete_build(self): with patch("lantern.AbstractAPI.delete_build") as delete_build: delete_build.return_value = mock_app_list_xml result = self.test_instance.delete_build() assert result == mock_app_list_xml def test_detailed_report(self): with patch("lantern.AbstractAPI.detailed_report") as detailed_report: detailed_report.return_value = mock_detailed_report_xml with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml result = self.test_instance.detailed_report(4, 0.001, 0.001, 2) assert result == mock_detailed_report_xml def test_detailed_report_exceeds_retries(self): with patch("lantern.AbstractAPI.detailed_report") as detailed_report: detailed_report.return_value = mock_detailed_report_xml with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml_prescan_success with nose.tools.assert_raises(ExceededRetries): self.test_instance.detailed_report(4, 0.001, 0.001, 2) def test_get_app_builds(self): with patch("lantern.AbstractAPI.get_app_builds") as get_app_builds: get_app_builds.return_value = mock_app_builds_xml result = self.test_instance.get_app_builds() assert result == mock_app_builds_xml def test_get_build_info_status(self): result = self.test_instance.get_build_info_status(mock_build_info_xml) assert result == "Results Ready" def test_get_build_version(self): result = self.test_instance.get_build_version(mock_build_info_xml) assert result == "TestApp 7.5.0.234" def test_get_module_list(self): module_list = self.test_instance.get_module_list(mock_prescan_results_xml) assert module_list == mock_module_list def test_get_prescan_results(self): with patch("lantern.AbstractAPI.get_prescan_results") as get_prescan_results: get_prescan_results.return_value = mock_prescan_results_xml with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml_prescan_success result = self.test_instance.get_prescan_results(1, 0, 0.001, 2) assert result == mock_prescan_results_xml def test_get_prescan_results_error_status_not_ready(self): with patch("lantern.AbstractAPI.get_prescan_results") as get_prescan_results: get_prescan_results.return_value = mock_prescan_results_xml with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml_prescan_in_progress with nose.tools.assert_raises(ExceededRetries): self.test_instance.get_prescan_results(4, 0, 0.001, 2) def test_get_xml_attrib_error(self): with patch.object(API, 'get_xml_attrib') as mock: with nose.tools.assert_raises(ReceivedErrorXML): mock.side_effect = ReceivedErrorXML API.get_xml_attrib("<error>This is an error</error>") def test_poll_detailed_report(self): with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml with patch("lantern.AbstractAPI.detailed_report") as detailed_report: detailed_report.return_value = mock_detailed_report_xml result = self.test_instance.detailed_report(1, 0, 0.001, 2) assert result == mock_detailed_report_xml def test_poll_detailed_report_pdf(self): with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml with patch("lantern.AbstractAPI.detailed_report_pdf") as detailed_report_pdf: detailed_report_pdf.return_value = mock_detailed_report_pdf result = self.test_instance.detailed_report_pdf(1, 0, 0.001, 2) assert result == mock_detailed_report_pdf def test_poll_summary_report(self): with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml with patch("lantern.AbstractAPI.summary_report") as summary_report: summary_report.return_value = mock_summary_report_xml result = self.test_instance.summary_report(1, 0, 0.001, 2) assert result == mock_summary_report_xml def test_poll_summary_report_pdf(self): with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml with patch("lantern.AbstractAPI.summary_report_pdf") as summary_report_pdf: summary_report_pdf.return_value = mock_summary_report_pdf result = self.test_instance.summary_report_pdf(1, 0, 0.001, 2) assert result == mock_summary_report_pdf def test_poll_third_party_report_pdf(self): with patch("lantern.AbstractAPI.get_build_info") as get_build_info: get_build_info.return_value = mock_build_info_xml with patch("lantern.AbstractAPI.third_party_report_pdf") as third_party_report_pdf: third_party_report_pdf.return_value = mock_third_party_report_pdf result = self.test_instance.third_party_report_pdf(1, 0, 0.001, 2) assert result == mock_third_party_report_pdf def test_remove_file_by_name(self): with patch("lantern.AbstractAPI.get_file_list") as get_file_list: get_file_list.return_value = mock_file_list_xml with patch("lantern.AbstractAPI.remove_file") as remove_file: remove_file.return_value = mock_file_list_remove_file_by_name_xml file_list_remove_file_by_name_xml = self.test_instance.remove_file_by_name("TestFile09.jsp") assert file_list_remove_file_by_name_xml == mock_file_list_remove_file_by_name_xml nose.tools.assert_raises(FileNotFound, self.test_instance.remove_file_by_name, "foo.txt") def test_remove_file_retry(self): with patch("lantern.AbstractAPI.get_file_list") as get_file_list: get_file_list.return_value = mock_file_list_xml with patch("lantern.AbstractAPI.remove_file") as remove_file: remove_file.return_value = mock_file_list_empty_xml file_list_remove_file_retry_xml = self.test_instance.remove_file_retry() assert file_list_remove_file_retry_xml == mock_file_list_empty_xml def test_set_build_id_attribute_error(self): with patch.object(API, 'set_build_id') as mock: with nose.tools.assert_raises(AttributeError): mock.side_effect = AttributeError self.test_instance.set_build_id("this will cause a attribute error!") def test_upload_file_with_blacklist(self): with patch("lantern.AbstractAPI.upload_file") as upload_file: upload_file.return_value = mock_file_list_blacklist_xml with patch("lantern.AbstractAPI.get_file_list") as get_file_list: get_file_list.return_value = mock_file_list_empty_xml file_list_upload_file_xml = self.test_instance.upload_file(os.getcwd() + "/ext/", ["*.jsp"]) assert file_list_upload_file_xml == mock_file_list_blacklist_xml def test_xml_attrib_for_error_xml(self): with nose.tools.assert_raises(ReceivedErrorXML): self.test_instance.get_build_info_status(mock_error_xml)

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Python

asgi/__init__.py

ischaojie/learn-py

b24ec70c776fbc7176bdffbbd1b9ce46e6a25916

[ "MIT" ]

null

asgi/__init__.py

ischaojie/learn-py

b24ec70c776fbc7176bdffbbd1b9ce46e6a25916

[ "MIT" ]

null

asgi/__init__.py

ischaojie/learn-py

b24ec70c776fbc7176bdffbbd1b9ce46e6a25916

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ ASGI """

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Python

test/gcp_reader.py

davidraleigh/epl-imagery-reader

8edafab6797943355ec7661e7330db5200811a1d

[ "Apache-2.0" ]

null

test/gcp_reader.py

davidraleigh/epl-imagery-reader

8edafab6797943355ec7661e7330db5200811a1d

[ "Apache-2.0" ]

null

test/gcp_reader.py

davidraleigh/epl-imagery-reader

8edafab6797943355ec7661e7330db5200811a1d

[ "Apache-2.0" ]

null

import py_compile import unittest from datetime import date import numpy as np import pyproj import requests import shapely.geometry from lxml import etree from osgeo import gdal from shapely.wkt import loads from epl.native.imagery import PLATFORM_PROVIDER from epl.native.imagery.reader import MetadataService, Landsat, Storage, RasterMetadata, DataType, FunctionDetails from epl.native.imagery.metadata_helpers import LandsatQueryFilters, SpacecraftID, BandMap, Band from test.tools.test_helpers import xml_compare class TestGCPMetadataSQL(unittest.TestCase): def test_all_sat_data(self): metadata_service = MetadataService() landsat_filters = LandsatQueryFilters() landsat_filters.cloud_cover.set_value(0) d_start = date(2004, 6, 24) d_end = date(2008, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -114.31054687499999, 35.84029065139799) landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = metadata_service.search( data_filters=landsat_filters) rows = list(rows) first_item = rows[0] self.assertEqual(len(rows), 10) rows = metadata_service.search( satellite_id=SpacecraftID.UNKNOWN_SPACECRAFT, data_filters=landsat_filters) rows = list(rows) other_item = rows[0] self.assertEqual(len(rows), 10) self.assertEqual(first_item.scene_id, other_item.scene_id) def test_no_bounding_box(self): d_start = date(2003, 4, 4) d_end = date(2003, 4, 7) landsat_filters = LandsatQueryFilters() landsat_filters.wrs_path.set_value(125) landsat_filters.wrs_row.set_value(49) # sql_filters = ['wrs_row=49', 'wrs_path=125'] metadata_service = MetadataService() landsat_filters.acquired.set_range(d_start, True, d_end, True) rows = metadata_service.search( satellite_id=None, data_filters=landsat_filters) rows = list(rows) self.assertEqual(len(rows), 3) def test_metatdata_file_list(self): wkt = "POLYGON((136.2469482421875 -27.57843813308233,138.6639404296875 -27.57843813308233," \ "138.6639404296875 -29.82351878748485,136.2469482421875 -29.82351878748485,136." \ "2469482421875 -27.57843813308233))" polygon = loads(wkt) metadata_service = MetadataService() # sql_filters = ['cloud_cover=0'] d_start = date(2006, 8, 4) d_end = date(2006, 8, 5) bounding_box = polygon.bounds # sql_filters = ['wrs_row=79'] landsat_filters = LandsatQueryFilters() # landsat_filters.wrs_path.set_value(125) landsat_filters.wrs_row.set_value(79) landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = metadata_service.search( SpacecraftID.LANDSAT_5, data_filters=landsat_filters) rows = list(rows) metadata = rows[0] self.assertEqual(len(metadata.get_file_list()), 0) @unittest.skip("not sure why I put this test in or when it last passed.") def test_get_file(self): d_start = date(2015, 6, 24) d_end = date(2016, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -78.31054687499999, 44.84029065139799) landsat_filters = LandsatQueryFilters() landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) metadata_service = MetadataService() rows = metadata_service.search(SpacecraftID.LANDSAT_8, data_filters=landsat_filters, limit=1) rows = list(rows) metadata = rows[0] landsat = Landsat(metadata) self.assertIsNotNone(landsat) vrt = landsat.get_vrt([4, 3, 2]) self.assertIsNotNone(vrt) dataset = landsat.get_dataset([4, 3, 2], DataType.UINT16) self.assertIsNotNone(dataset) # 'gs://gcp-public-data-landsat/LC08/PRE/037/036/LC80370362016082LGN00' class TestGCPLandsat(unittest.TestCase): base_mount_path = '/imagery' metadata_service = None metadata_set = [] r = requests.get("https://raw.githubusercontent.com/johan/world.geo.json/master/countries/USA/NM/Taos.geo.json") taos_geom = r.json() taos_shape = shapely.geometry.shape(taos_geom['features'][0]['geometry']) def setUp(self): d_start = date(2017, 3, 12) # 2017-03-12 d_end = date(2017, 3, 19) # 2017-03-20, epl api is inclusive self.metadata_service = MetadataService() landsat_filters = LandsatQueryFilters() landsat_filters.collection_number.set_value("PRE") landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*self.taos_shape.bounds) rows = self.metadata_service.search( SpacecraftID.LANDSAT_8, limit=10, data_filters=landsat_filters) rows = list(rows) # mounted directory in docker container base_mount_path = '/imagery' for row in rows: self.metadata_set.append(row) def test_gdal_info(self): d_start = date(2015, 6, 24) d_end = date(2016, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -78.31054687499999, 44.84029065139799) landsat_filters = LandsatQueryFilters() landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search(SpacecraftID.LANDSAT_8, data_filters=landsat_filters, limit=1) rows = list(rows) metadata = rows[0] storage = Storage(metadata.bucket_name) b_mounted = storage.mount_sub_folder(metadata, "generic") self.assertTrue(b_mounted) b_deleted = storage.unmount_sub_folder(metadata, "generic") self.assertTrue(b_deleted) def test_landsat5_vrt(self): # 5th Place: Lake Eyre Landsat 5 Acquired August 5, 2006 wkt = "POLYGON((136.2469482421875 -27.57843813308233,138.6639404296875 -27.57843813308233," \ "138.6639404296875 -29.82351878748485,136.2469482421875 -29.82351878748485,136." \ "2469482421875 -27.57843813308233))" polygon = loads(wkt) # sql_filters = ['cloud_cover=0'] d_start = date(2006, 8, 4) d_end = date(2006, 8, 5) bounding_box = polygon.bounds landsat_filters = LandsatQueryFilters() landsat_filters.wrs_row.set_value(79) landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search( SpacecraftID.LANDSAT_5, data_filters=landsat_filters) rows = list(rows) self.assertEqual(len(rows), 1) # data structure that contains all fields from Google's Landsat BigQuery Database metadata = rows[0] # GDAL helper functions for generating VRT landsat = Landsat(metadata) vrt = landsat.get_vrt([3, 2, 1]) with open('testlandsat5.vrt', 'r') as myfile: data = myfile.read() expected = etree.XML(data) actual = etree.XML(vrt) result, message = xml_compare(expected, actual, {"GeoTransform": 1e-10}) self.assertTrue(result, message) def test_australia(self): # 5th Place: Lake Eyre Landsat 5 Acquired August 5, 2006 wkt = "POLYGON((136.2469482421875 -27.57843813308233,138.6639404296875 -27.57843813308233," \ "138.6639404296875 -29.82351878748485,136.2469482421875 -29.82351878748485,136." \ "2469482421875 -27.57843813308233))" polygon = loads(wkt) # sql_filters = ['cloud_cover=0'] d_start = date(2006, 8, 4) d_end = date(2006, 8, 7) bounding_box = polygon.bounds landsat_filters = LandsatQueryFilters() landsat_filters.wrs_row.set_value(79) landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search( SpacecraftID.LANDSAT_5, data_filters=landsat_filters) rows = list(rows) self.assertEqual(len(rows), 1) metadata = rows[0] landsat = Landsat(metadata) # get a numpy.ndarray from bands for specified imagery band_numbers = [3, 2, 1] scale_params = [[0.0, 65535], [0.0, 65535], [0.0, 65535]] # nda = landsat.__get_ndarray(band_numbers, metadata, scale_params) nda = landsat.fetch_imagery_array(band_numbers, scale_params) self.assertEqual((3581, 4046, 3), nda.shape) def test_unmount_destructor(self): wkt = "POLYGON((136.2469482421875 -27.57843813308233,138.6639404296875 -27.57843813308233," \ "138.6639404296875 -29.82351878748485,136.2469482421875 -29.82351878748485,136." \ "2469482421875 -27.57843813308233))" polygon = loads(wkt) # sql_filters = ['cloud_cover=0'] d_start = date(2006, 8, 4) d_end = date(2006, 8, 7) bounding_box = polygon.bounds landsat_filters = LandsatQueryFilters() landsat_filters.wrs_row.set_value(79) landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search( SpacecraftID.LANDSAT_5, data_filters=landsat_filters) rows = list(rows) metadata = rows[0] landsat = Landsat(metadata) vrt = landsat.get_vrt([4]) # storage = Storage("gcp-public-data-landsat") # del landsat # self.assertFalse(storage.is_mounted(metadata)) def test_unmount_destructor_conflict(self): wkt = "POLYGON((136.2469482421875 -27.57843813308233,138.6639404296875 -27.57843813308233," \ "138.6639404296875 -29.82351878748485,136.2469482421875 -29.82351878748485,136." \ "2469482421875 -27.57843813308233))" polygon = loads(wkt) # sql_filters = ['cloud_cover=0'] d_start = date(2006, 8, 4) d_end = date(2006, 8, 7) bounding_box = polygon.bounds # sql_filters = ['wrs_row=79'] landsat_filters = LandsatQueryFilters() landsat_filters.wrs_row.set_value(79) landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search( SpacecraftID.LANDSAT_5, data_filters=landsat_filters) rows = list(rows) metadata = rows[0] landsat = Landsat(metadata) vrt = landsat.get_vrt([4]) storage = Storage("gcp-public-data-landsat") landsat_2 = Landsat(metadata) vrt = landsat_2.get_vrt([4]) del landsat self.assertTrue(storage.is_mounted(metadata)) def test_vrt(self): d_start = date(2015, 6, 24) d_end = date(2016, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -78.31054687499999, 44.84029065139799) # sql_filters = ['scene_id="LC80400312016103LGN00"'] landsat_filters = LandsatQueryFilters() landsat_filters.scene_id.set_value("LC80400312016103LGN00") landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search(SpacecraftID.LANDSAT_8, limit=1, data_filters=landsat_filters) rows = list(rows) metadata = rows[0] landsat = Landsat(metadata) vrt = landsat.get_vrt([4, 3, 2]) with open('test_1.vrt', 'r') as myfile: data = myfile.read() expected = etree.XML(data) actual = etree.XML(vrt) result, message = xml_compare(expected, actual) self.assertTrue(result, message) dataset = gdal.Open(vrt) self.assertIsNotNone(dataset) ds_band_1 = dataset.GetRasterBand(1) self.assertIsNotNone(ds_band_1) self.assertEqual(ds_band_1.XSize, 7631) ds_band_2 = dataset.GetRasterBand(2) self.assertIsNotNone(ds_band_2) self.assertEqual(ds_band_2.YSize, 7771) ds_band_3 = dataset.GetRasterBand(3) self.assertIsNotNone(ds_band_3) self.assertEqual(ds_band_3.YSize, 7771) class TestStorage(unittest.TestCase): base_mount_path = '/imagery' def test_storage_create(self): metadata_service = MetadataService() d_start = date(2015, 6, 24) d_end = date(2016, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -78.31054687499999, 44.84029065139799) landsat_filters = LandsatQueryFilters() landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = metadata_service.search(SpacecraftID.LANDSAT_8, data_filters=landsat_filters, limit=1) rows = list(rows) metadata = rows[0] storage = Storage(metadata.bucket_name) metadata = rows[0] self.assertTrue(storage.mount_sub_folder(metadata, "generic")) self.assertTrue(storage.unmount_sub_folder(metadata, "generic")) def test_singleton(self): metadata_service = MetadataService() d_start = date(2015, 6, 24) d_end = date(2016, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -78.31054687499999, 44.84029065139799) landsat_filters = LandsatQueryFilters() landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = metadata_service.search(SpacecraftID.LANDSAT_8, data_filters=landsat_filters, limit=1) rows = list(rows) metadata = rows[0] storage_1 = Storage(metadata.bucket_name) storage_2 = Storage(metadata.bucket_name) self.assertTrue(storage_1 is storage_2) def test_delete_storage(self): metadata_service = MetadataService() d_start = date(2015, 6, 24) d_end = date(2016, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -78.31054687499999, 44.84029065139799) landsat_filters = LandsatQueryFilters() landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = metadata_service.search(SpacecraftID.LANDSAT_8, data_filters=landsat_filters, limit=1) rows = list(rows) metadata = rows[0] # storage = Storage(metadata.bucket_name) # # # self.assertTrue(storage.mount_sub_folder(metadata, "generic")) # files = [f for f in os.listdir(metadata.full_mount_path) if # os.path.isfile(os.path.join(metadata.full_mount_path, f))] # self.assertTrue(len(files) > 0) # # self.assertTrue(storage.unmount_sub_folder(metadata, "generic")) # files = [f for f in os.listdir(metadata.full_mount_path) if # os.path.isfile(os.path.join(metadata.full_mount_path, f))] # self.assertEqual(len(files), 0) # # self.assertTrue(storage.mount_sub_folder(metadata, "generic")) # files = [f for f in os.listdir(metadata.full_mount_path) if # os.path.isfile(os.path.join(metadata.full_mount_path, f))] # self.assertTrue(len(files) > 0) # self.assertTrue(storage.unmount_sub_folder(metadata, "generic")) def test_platform_provider(self): self.assertEqual("GCP", PLATFORM_PROVIDER) class TestGCPPixelFunctions(unittest.TestCase): m_metadata = None base_mount_path = '/imagery' metadata_service = MetadataService() iowa_polygon = None metadata_set = [] r = requests.get("https://raw.githubusercontent.com/johan/world.geo.json/master/countries/USA/NM/Taos.geo.json") taos_geom = r.json() taos_shape = shapely.geometry.shape(taos_geom['features'][0]['geometry']) def setUp(self): metadata_service = MetadataService() d_start = date(2015, 6, 24) d_end = date(2016, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -78.31054687499999, 44.84029065139799) # sql_filters = ['scene_id="LC80400312016103LGN00"'] landsat_filters = LandsatQueryFilters() landsat_filters.scene_id.set_value("LC80400312016103LGN00") landsat_filters.aoi.set_bounds(*bounding_box) landsat_filters.acquired.set_range(d_start, True, d_end, True) rows = metadata_service.search(SpacecraftID.LANDSAT_8, limit=1, data_filters=landsat_filters) rows = list(rows) self.m_metadata = rows[0] wkt_iowa = "POLYGON((-93.76075744628906 42.32707774458643,-93.47854614257812 42.32707774458643," \ "-93.47854614257812 42.12674735753131,-93.76075744628906 42.12674735753131," \ "-93.76075744628906 42.32707774458643))" self.iowa_polygon = loads(wkt_iowa) gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', "YES") d_start = date(2017, 3, 12) # 2017-03-12 d_end = date(2017, 3, 19) # 2017-03-20, epl api is inclusive landsat_filters = LandsatQueryFilters() landsat_filters.collection_number.set_value("PRE") landsat_filters.aoi.set_bounds(*self.taos_shape.bounds) landsat_filters.acquired.set_range(start=d_start, end=d_end) rows = self.metadata_service.search( SpacecraftID.LANDSAT_8, limit=10, data_filters=landsat_filters) rows = list(rows) for row in rows: self.metadata_set.append(row) def test_pixel_1(self): metadata = self.m_metadata landsat = Landsat(metadata) # , gsurl[2]) code = """import numpy as np def multiply_rounded(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs): factor = float(kwargs['factor']) out_ar[:] = np.round_(np.clip(in_ar[0] * factor,0,255))""" function_arguments = {"factor": "1.5"} pixel_function_details = FunctionDetails(name="multiply_rounded", band_definitions=[2], data_type=DataType.FLOAT32, code=code, arguments=function_arguments) vrt = landsat.get_vrt([pixel_function_details, 3, 2]) with open('pixel_1.vrt', 'r') as myfile: data = myfile.read() expected = etree.XML(data) actual = etree.XML(vrt) result, message = xml_compare(expected, actual, {"GeoTransform": 1e-10}) self.assertTrue(result, message) def test_pixel_ndvi(self): """ http://grindgis.com/blog/vegetation-indices-arcgis NDVI = (NIR - RED) / (NIR + RED) NDVI = (5 - 4) / (5 + 4) :return: """ landsat = Landsat(self.m_metadata) # , gsurl[2]) code = """import numpy as np def ndvi_numpy(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs): with np.errstate(divide = 'ignore', invalid = 'ignore'): output = np.divide((in_ar[1] - in_ar[0]), (in_ar[1] + in_ar[0])) output[np.isnan(output)] = 0.0 # shift range from -1.0-1.0 to 0.0-2.0 output += 1.0 # scale up from 0.0-2.0 to 0 to 255 by multiplying by 255/2 # https://stackoverflow.com/a/1735122/445372 output *= 65535/2.0 # https://stackoverflow.com/a/10622758/445372 # in place type conversion out_ar[:] = output.astype(np.int16, copy=False)""" pixel_function_details = FunctionDetails(name="ndvi_numpy", band_definitions=[4, 5], data_type=DataType.UINT16, code=code) vrt = landsat.get_vrt([pixel_function_details, 3, 2]) with open('ndvi_numpy.vrt', 'r') as myfile: data = myfile.read() expected = etree.XML(data) actual = etree.XML(vrt) result, message = xml_compare(expected, actual, {"GeoTransform": 1e-10}) self.assertTrue(result, message) gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', "YES") ds = gdal.Open(vrt) self.assertIsNotNone(ds) arr_ndvi = ds.GetRasterBand(1).ReadAsArray() ds = None self.assertIsNotNone(arr_ndvi) scale_params = [[0.0, 65535], [0.0, 65535], [0.0, 65535]] band_definitions = [pixel_function_details, 3, 2] nda = landsat.fetch_imagery_array(band_definitions, scale_params) self.assertIsNotNone(nda) @staticmethod def ndvi_numpy(nir, red): with np.errstate(divide='ignore', invalid='ignore'): out_ar = np.divide((nir.astype(float) - red.astype(float)), (nir.astype(float) + red.astype(float))) out_ar[np.isnan(out_ar)] = 0.0 return out_ar def test_iowa_ndarray(self): d_start = date(2016, 4, 4) d_end = date(2016, 8, 7) bounding_box = self.iowa_polygon.bounds # sql_filters = ["cloud_cover<=15"] landsat_filters = LandsatQueryFilters() landsat_filters.cloud_cover.set_range(end=15, end_inclusive=15) landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search( SpacecraftID.LANDSAT_8, data_filters=landsat_filters) rows = list(rows) metadata = rows[0] landsat = Landsat(metadata) code = """import numpy as np def ndvi_numpy(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs): with np.errstate(divide = 'ignore', invalid = 'ignore'): out_ar[:] = np.divide((in_ar[1] - in_ar[0]), (in_ar[1] + in_ar[0])) out_ar[np.isnan(out_ar)] = 0.0 out_ar """ pixel_function_details = FunctionDetails(name="ndvi_numpy", band_definitions=[4, 5], code=code, data_type=DataType.FLOAT32) # pixel_function_details = { # "band_numbers": [4, 5], # "function_code": code, # "function_type": "ndvi_numpy", # "data_type": DataType.FLOAT32, # } band_definitions = [pixel_function_details, 4, 5] vrt = landsat.get_vrt(band_definitions) ds = gdal.Open(vrt) self.assertIsNotNone(ds) arr_4 = ds.GetRasterBand(2).ReadAsArray() arr_5 = ds.GetRasterBand(3).ReadAsArray() arr_ndvi = ds.GetRasterBand(1).ReadAsArray() del ds del landsat print(np.ndarray.max(arr_ndvi)) print(np.ndarray.min(arr_ndvi)) self.assertFalse(np.any(np.isinf(arr_ndvi))) self.assertIsNotNone(arr_ndvi) local_ndvi = self.ndvi_numpy(arr_5, arr_4) del arr_4 del arr_5 self.assertFalse(np.any(np.isinf(local_ndvi))) np.testing.assert_almost_equal(arr_ndvi, local_ndvi) def test_iowa_scaled(self): d_start = date(2016, 4, 4) d_end = date(2016, 8, 7) bounding_box = self.iowa_polygon.bounds # sql_filters = ["cloud_cover<=15"] landsat_filters = LandsatQueryFilters() landsat_filters.cloud_cover.set_range(end=15, end_inclusive=True) landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search( SpacecraftID.LANDSAT_8, data_filters=landsat_filters) rows = list(rows) metadata = rows[0] landsat = Landsat(metadata) code = """import numpy as np def ndvi_numpy(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs): with np.errstate(divide = 'ignore', invalid = 'ignore'): factor = float(kwargs['factor']) out_ar[:] = np.divide((in_ar[1] - in_ar[0]), (in_ar[1] + in_ar[0])) out_ar[np.isnan(out_ar)] = 0.0 # shift range from -1.0-1.0 to 0.0-2.0 out_ar += 1.0 # scale up from 0.0-2.0 to 0 to 255 by multiplying by 255/2 out_ar *= factor/2.0""" # pixel_function_details = { # "function_arguments": {"factor": 255}, # "band_numbers": [4, Band.NIR], # "function_code": code, # "function_type": "ndvi_numpy", # "data_type": DataType.FLOAT32, # } pixel_function_details = FunctionDetails(name="ndvi_numpy", band_definitions=[4, Band.NIR], code=code, arguments={"factor": 255}, data_type=DataType.FLOAT32) band_definitions = [pixel_function_details, Band.RED, 5] vrt = landsat.get_vrt(band_definitions) ds = gdal.Open(vrt) self.assertIsNotNone(ds) arr_4 = ds.GetRasterBand(2).ReadAsArray() arr_5 = ds.GetRasterBand(3).ReadAsArray() arr_ndvi = ds.GetRasterBand(1).ReadAsArray() del ds del landsat print(np.ndarray.max(arr_ndvi)) # print(np.ndarray.min(arr_ndvi)) # self.assertFalse(np.any(np.isinf(arr_ndvi))) self.assertIsNotNone(arr_ndvi) local_ndvi = self.ndvi_numpy(arr_5, arr_4) del arr_4 del arr_5 local_ndvi += 1.0 local_ndvi *= float(pixel_function_details.arguments['factor']) / 2.0 self.assertFalse(np.any(np.isinf(local_ndvi))) np.floor(arr_ndvi, out=arr_ndvi) np.floor(local_ndvi, out=local_ndvi) np.testing.assert_almost_equal(arr_ndvi, local_ndvi, decimal=0) def test_malformed_funciton(self): code = """import numpy as np def ndvi_numpy(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs): with np.errstate(divide = 'ignore', invalid = 'ignore'): factor = float(kwargs['factor']) out_ar[:] = np.divide((in_ar[1] - in_ar[0]), (in_ar[1] + in_ar[0])) out_ar[np.isnan(out_ar)] = 0.0 # shift range from -1.0-1.0 to 0.0-2.0 out_ar += 1.0 # scale up from 0.0-2.0 to 0 to 255 by multiplying by 255/2 out_ar *= factor/2.0 """ # pixel_function_details = { # "function_arguments": {"factor": 255}, # "band_numbers": [4, 5], # "function_code": code, # "function_type": "ndvi_numpy", # "data_type": DataType.FLOAT32, # } self.assertRaises(py_compile.PyCompileError, lambda: FunctionDetails(name="ndvi_numpy", code=code, band_definitions=[4, 5], data_type=DataType.FLOAT32, arguments={"factor": 255})) # def test_translate_vrt(self): # # LC80390332016208LGN00 """ gdalbuildvrt -vrtnodata 0 0 0 -separate rgb_35.vrt /imagery/gcp-public-data-landsat/LC08/PRE/033/035/LC80330352017072LGN00/LC80330352017072LGN00_B4.TIF /imagery/gcp-public-data-landsat/LC08/PRE/033/035/LC80330352017072LGN00/LC80330352017072LGN00_B3.TIF /imagery/gcp-public-data-landsat/LC08/PRE/033/035/LC80330352017072LGN00/LC80330352017072LGN00_B2.TIF gdalbuildvrt -separate rgb_34.vrt /imagery/gcp-public-data-landsat/LC08/PRE/033/034/LC80330342017072LGN00/LC80330342017072LGN00_B4.TIF /imagery/gcp-public-data-landsat/LC08/PRE/033/034/LC80330342017072LGN00/LC80330342017072LGN00_B3.TIF /imagery/gcp-public-data-landsat/LC08/PRE/033/034/LC80330342017072LGN00/LC80330342017072LGN00_B2.TIF """ # # gdal_translate -of VRT -ot Byte -scale -tr 60 60 rgb.vrt rgb_byte_scaled.vrt # # self.assertTrue(True) # sql_filters = ['scene_id="LC80330342017072LGN00"'] # metadata_service = MetadataService() # rows = metadata_service.search(SpacecraftID.LANDSAT_8, data_filters=landsat_filters) #rows = list(rows) # # # metadata = rows[0] # gsurl = urlparse(metadata.base_url) # storage = Storage(gsurl[1]) # # b_mounted = storage.mount_sub_folder(gsurl[2], self.base_mount_path) # landsat = Landsat(base_mount_path, gsurl[2]) # vrt = landsat.get_vrt(metadata, [5, 4, 3]) # # with open('gdalbuildvrt_LC80390332016208LGN00.vrt', 'r') as myfile: # data = myfile.read() # expected = etree.XML(data) # actual = etree.XML(vrt) # result, message = xml_compare(expected, actual) # self.assertTrue(result, message) def test_ndvi_taos(self): code = """import numpy as np def ndvi_numpy(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs): with np.errstate(divide = 'ignore', invalid = 'ignore'): factor = float(kwargs['factor']) output = np.divide((in_ar[1] - in_ar[0]), (in_ar[1] + in_ar[0])) output[np.isnan(output)] = 0.0 # shift range from -1.0-1.0 to 0.0-2.0 output += 1.0 # scale up from 0.0-2.0 to 0 to 255 by multiplying by 255/2 # https://stackoverflow.com/a/1735122/445372 output *= factor/2.0 # https://stackoverflow.com/a/10622758/445372 # in place type conversion out_ar[:] = output.astype(np.int16, copy=False)""" code2 = """import numpy as np def ndvi_numpy2(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs): with np.errstate(divide = 'ignore', invalid = 'ignore'): output = (in_ar[1] - in_ar[0]) / (in_ar[1] + in_ar[0]) output[np.isnan(output)] = 0.0 out_ar[:] = output""" landsat = Landsat(self.metadata_set) scale_params = [[0, DataType.UINT16.range_max, -1.0, 1.0]] pixel_function_details = FunctionDetails(name="ndvi_numpy", band_definitions=[Band.RED, Band.NIR], code=code, arguments={"factor": DataType.UINT16.range_max}, data_type=DataType.UINT16) gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', "YES") nda = landsat.fetch_imagery_array([pixel_function_details], scale_params=scale_params, polygon_boundary_wkb=self.taos_shape.wkb, output_type=DataType.FLOAT32) self.assertIsNotNone(nda) self.assertGreaterEqual(1.0, nda.max()) self.assertLessEqual(-1.0, nda.min()) pixel_function_details = FunctionDetails(name="ndvi_numpy2", band_definitions=[Band.RED, Band.NIR], code=code2, data_type=DataType.FLOAT32) nda2 = landsat.fetch_imagery_array([pixel_function_details], polygon_boundary_wkb=self.taos_shape.wkb, output_type=DataType.FLOAT32) self.assertIsNotNone(nda2) self.assertGreaterEqual(1.0, nda2.max()) self.assertLessEqual(-1.0, nda2.min()) def test_fail_1_to_1(self): code = """import numpy as np def ndvi_numpy(in_ar, out_ar, xoff, yoff, xsize, ysize, raster_xsize, raster_ysize, buf_radius, gt, **kwargs): out_ar[:] = in_ar[0]""" landsat = Landsat(self.metadata_set) scale_params = [[0, 40000], [0, 40000], [0, 40000]] pixel_function_details = FunctionDetails(name="ndvi_numpy", band_definitions=[Band.RED], code=code, arguments={"factor": DataType.UINT16.range_max}, data_type=DataType.UINT16) gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', "YES") nda = landsat.fetch_imagery_array([pixel_function_details, Band.GREEN, Band.BLUE], scale_params=scale_params, polygon_boundary_wkb=self.taos_shape.wkb, output_type=DataType.BYTE) nda2 = landsat.fetch_imagery_array([Band.RED, Band.GREEN, Band.BLUE], scale_params=scale_params, polygon_boundary_wkb=self.taos_shape.wkb, output_type=DataType.BYTE) self.assertIsNotNone(nda) np.testing.assert_almost_equal(nda, nda2) np.testing.assert_equal(nda, nda2) @unittest.skip("failing for some reason. unknown.") def test_native_vs_custom(self): landsat = Landsat(self.metadata_set) gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', "YES") pixel_native = FunctionDetails(name="sqrt", band_definitions=[Band.RED], data_type=DataType.UINT16, transfer_type=DataType.FLOAT32) nda = landsat.fetch_imagery_array([pixel_native], polygon_boundary_wkb=self.taos_shape.wkb, output_type=DataType.FLOAT32) self.assertIsNotNone(nda) # TODO add own sqrt function here class TestRasterMetadata(unittest.TestCase): base_mount_path = '/imagery' metadata_service = None def setUp(self): self.metadata_service = MetadataService() def test_add_metadata_error(self): d_start = date(2015, 6, 24) d_end = date(2016, 6, 24) bounding_box = (-115.927734375, 34.52466147177172, -78.31054687499999, 44.84029065139799) landsat_filters = LandsatQueryFilters() landsat_filters.data_type.set_value("L1T") landsat_filters.acquired.set_range(d_start, True, d_end, True) landsat_filters.aoi.set_bounds(*bounding_box) rows = self.metadata_service.search(SpacecraftID.LANDSAT_8, limit=2, data_filters=landsat_filters) rows = list(rows) metadata_1 = rows[0] metadata_2 = rows[1] bands = [Band.RED, Band.BLUE, Band.GREEN] band_map = BandMap(SpacecraftID.LANDSAT_8) raster_metadata = RasterMetadata() storage = Storage() storage.mount_sub_folder(metadata_1) storage.mount_sub_folder(metadata_2) second = False for band in bands: band_number = band_map.get_number(band) if second: self.assertRaises(Exception, lambda: raster_metadata.add_metadata(band_number, metadata_2)) raster_metadata.add_metadata(band_number, metadata_1) second = True # @unittest.skip("changed how bounds are queried") def test_bounds(self): metadata_service = MetadataService() landsat_filters = LandsatQueryFilters() landsat_filters.scene_id.set_value("LC80330342017072LGN00") landsat_filters.collection_number.set_value("PRE") rows = metadata_service.search( SpacecraftID.LANDSAT_8, data_filters=landsat_filters) rows = list(rows) self.assertEqual(len(rows), 1) metadata = rows[0] bands = [Band.RED, Band.BLUE, Band.GREEN] band_map = BandMap(SpacecraftID.LANDSAT_8) raster_metadata = RasterMetadata() storage = Storage() storage.mount_sub_folder(metadata) for band in bands: band_number = band_map.get_number(band) raster_metadata.add_metadata(band_number, metadata) boundary = raster_metadata.bounds self.assertIsNotNone(boundary) r = requests.get("https://raw.githubusercontent.com/johan/world.geo.json/master/countries/USA/NM/Taos.geo.json") taos_geom = r.json() taos_shape = shapely.geometry.shape(taos_geom['features'][0]['geometry']) clipped_raster = raster_metadata.calculate_clipped(taos_shape.bounds, pyproj.Proj(init='epsg:4326')) self.assertIsNotNone(clipped_raster.bounds) big_box = shapely.geometry.box(*boundary) small_box = shapely.geometry.box(*clipped_raster.bounds) self.assertTrue(big_box.contains(small_box)) def test_metadata_extent(self): r = requests.get("https://raw.githubusercontent.com/johan/world.geo.json/master/countries/USA/NM/Taos.geo.json") taos_geom = r.json() print(taos_geom) taos_shape = shapely.geometry.shape(taos_geom['features'][0]['geometry']) metadata_service = MetadataService() # sql_filters = ['scene_id="LC80330342017072LGN00"', 'collection_number="PRE"'] landsat_filters = LandsatQueryFilters() landsat_filters.scene_id.set_value("LC80330342017072LGN00") landsat_filters.collection_number.set_value("PRE") rows = metadata_service.search( SpacecraftID.LANDSAT_8, data_filters=landsat_filters) rows = list(rows) self.assertEqual(len(rows), 1) metadata = rows[0] # GDAL helper functions for generating VRT landsat = Landsat(metadata) # get a numpy.ndarray from bands for specified imagery band_numbers = [Band.RED, Band.GREEN, Band.BLUE] scale_params = [[0.0, 65535], [0.0, 65535], [0.0, 65535]] vrt = landsat.get_vrt(band_numbers, envelope_boundary=taos_shape.bounds) with open('clipped_LC80330342017072LGN00.vrt', 'r') as myfile: data = myfile.read() expected = etree.XML(data) actual = etree.XML(vrt) result, message = xml_compare(expected, actual, {"GeoTransform": 1e-10, "xOff": 1e-10, "yOff": 1e-10}) self.assertTrue(result, message) dataset = gdal.Open(vrt) geo_transform = dataset.GetGeoTransform() # self.assertEqual(geo_transform, raster_metadata.get_geotransform(taos_shape.bounds)) # self.assertNotEqual(geo_transform, raster_metadata.get_geotransform()) """ gdal command for creating test data--/Users/davidraleigh/code/echopark/gcp-landsat-reader/test/clipped_LC80330342017072LGN00.vrt gdalbuildvrt -te 404696.67322238116 4028985.0 482408.22401454527 4094313.7809402538 -separate rgb_clipped.vrt /imagery/gcp-public-data-landsat/LC08/PRE/033/034/LC80330342017072LGN00/LC80330342017072LGN00_B4.TIF /imagery/gcp-public-data-landsat/LC08/PRE/033/034/LC80330342017072LGN00/LC80330342017072LGN00_B3.TIF /imagery/gcp-public-data-landsat/LC08/PRE/033/034/LC80330342017072LGN00/LC80330342017072LGN00_B2.TIF gdal command for creating test data-- gdal_translate -ot Byte -tr 60 60 -of VRT -scale 0 65535 0 255 /opt/src/gcp-imagery-reader/rgb_clipped.vrt /opt/src/gcp-imagery-reader/rgb_clipped_translated.vrt """ # TODO test band values for SrcRect # TODO test vs. something that autatically clips by extent and exports to vrt # TODO test by getting extent of vrt, projecting back to wgs 84 and making sure it is contained by taos_geom

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stackstate_checks_dev/stackstate_checks/dev/tooling/__main__.py

CTAC-unixsupport/stackstate-agent-integrations

9ac167e182e69d035a09bedec9bbceb135e2727b

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2020-03-10T13:21:37.000Z

2021-04-01T07:52:16.000Z

stackstate_checks_dev/stackstate_checks/dev/tooling/__main__.py

DennisLoos/stackstate-agent-integrations

8a8cc1607a8f1b8560e450d15cefa0d8d1227674

[ "BSD-3-Clause" ]

33

2020-02-05T16:18:32.000Z

2022-03-21T14:08:04.000Z

stackstate_checks_dev/stackstate_checks/dev/tooling/__main__.py

DennisLoos/stackstate-agent-integrations

8a8cc1607a8f1b8560e450d15cefa0d8d1227674

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mafipy/function/black_scholes.py

i05nagai/mafipy

ea7312065b8abea4c7054203176269637ff346ca

[ "MIT" ]

6

2017-01-15T05:05:09.000Z

2020-12-29T20:03:37.000Z

mafipy/function/black_scholes.py

i05nagai/mafipy

ea7312065b8abea4c7054203176269637ff346ca

[ "MIT" ]

77

2016-12-03T12:54:42.000Z

2018-06-15T14:44:14.000Z

mafipy/function/black_scholes.py

i05nagai/mafipy

ea7312065b8abea4c7054203176269637ff346ca

[ "MIT" ]

3

2016-12-17T11:09:38.000Z

2017-11-05T09:15:02.000Z

from __future__ import division, print_function, absolute_import import math import numpy as np import scipy.special import mafipy.function # ---------------------------------------------------------------------------- # Black scholes european call/put # ---------------------------------------------------------------------------- def _is_d1_or_d2_infinity(underlying, strike, vol): """is_d1_or_d2_infinity :param float underlying: :param float strike: :param float vol: :return: check whether :math:`d_{1}` and :math:`d_{2}` is infinity or not. :rtype: bool """ return (np.isclose(underlying, 0.0) or strike < 0.0 or vol < 0.0) def func_d1(underlying, strike, rate, maturity, vol): """func_d1 calculate :math:`d_{1}` in black scholes formula. See :py:func:`black_scholes_call_formula`. :param float underlying: underlying/strike must be non-negative. :param float strike: underlying/strike must be non-negative. :param float rate: :param float maturity: must be non-negative. :param float vol: must be non-negative. :return: :math:`d_{1}`. :rtype: float """ assert(underlying / strike >= 0.0) assert(maturity >= 0.0) assert(vol >= 0.0) numerator = ( math.log(underlying / strike) + (rate + vol * vol * 0.5) * maturity) denominator = vol * math.sqrt(maturity) return numerator / denominator def func_d2(underlying, strike, rate, maturity, vol): """func_d2 calculate :math:`d_{2}` in black scholes formula. See :py:func:`black_scholes_call_formula`. :param float underlying: underlying/strike must be non-negative. :param float strike: underlying/strike must be non-negative. :param float rate: :param float maturity: must be non-negative. :param float vol: must be non-negative. :return: :math:`d_{2}`. :rtype: float. """ assert(underlying / strike >= 0.0) assert(maturity >= 0.0) assert(vol >= 0.0) numerator = ( math.log(underlying / strike) + (rate - vol * vol * 0.5) * maturity) denominator = vol * math.sqrt(maturity) return numerator / denominator def d_fprime_by_strike(underlying, strike, rate, maturity, vol): """d_fprime_by_strike derivative of :math:`d_{1}` with respect to :math:`K` where :math:`K` is strike. See :py:func:`func_d1`. .. math:: \\frac{\partial }{\partial K} d_{1}(K) = \\frac{K}{\sigma S \sqrt{T}}. Obviously, derivative of :math:`d_{1}` and :math:`d_{2}` is same. That is .. math:: \\frac{\partial }{\partial K} d_{1}(K) = \\frac{\partial }{\partial K} d_{2}(K). :param float underlying: :param float strike: :param float rate: :param float maturity: must be non-negative. :param float vol: :return: value of derivative. :rtype: float """ assert(maturity > 0.0) return - 1.0 / (math.sqrt(maturity) * vol * strike) def d_fhess_by_strike(underlying, strike, rate, maturity, vol): """d_fhess_by_strike second derivative of :math:`d_{i}\ (i = 1, 2)` with respect to :math:`K`, where :math:`K` is strike. .. math:: \\frac{\partial^{2}}{\partial K^{2}} d_{1}(K) = \\frac{1}{S \sigma \sqrt{T} }, where :math:`S` is underlying, :math:`\sigma` is vol, :math:`T` is maturity. :param float underlying: :param float strike: :param float rate: :param float maturity: :param float vol: :return: value of second derivative of :math:`d_{1}` or :math:`d_{2}`. :rtype: float """ assert(maturity > 0.0) return 1.0 / (math.sqrt(maturity) * vol * strike * strike) def black_scholes_call_formula(underlying, strike, rate, maturity, vol): """black_scholes_call_formula calculate well known black scholes formula for call option. .. math:: c(S, K, r, T, \sigma) := S N(d_{1}) - K e^{-rT} N(d_{2}), where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is vol, :math:`N(\cdot)` is standard normal distribution, and :math:`d_{1}` and :math:`d_{2}` are defined as follows: .. math:: \\begin{eqnarray} d_{1} & = & \\frac{\ln(S/K) + (r + \sigma^{2}/2)T}{\sigma \sqrt{T}}, \\ d_{2} & = & \\frac{\ln(S/K) + (r - \sigma^{2}/2)T} {\sigma \sqrt{T}}, \end{eqnarray} :param float underlying: value of underlying. :param float strike: strike of call option. :param float rate: risk free rate. :param float maturity: year fraction to maturity. :param float vol: volatility. :return: call value. :rtype: float """ d1 = func_d1(underlying, strike, rate, maturity, vol) d2 = func_d2(underlying, strike, rate, maturity, vol) return (underlying * scipy.special.ndtr(d1) - strike * math.exp(-rate * maturity) * scipy.special.ndtr(d2)) def black_scholes_put_formula(underlying, strike, rate, maturity, vol): """black_scholes_put_formula calculate well known black scholes formula for put option. Here value of put option is calculated by put-call parity. .. math:: \\begin{array}{cccl} & e^{-rT}(S - K) & = & c(S, K, r, T, \sigma) - p(S, K, r, T, \sigma) \\\\ \iff & p(S, K, r, T, \sigma) & = & c(S, K, r, T, \sigma) - e^{-rT}(S - K) \end{array} where :math:`c(\cdot)` denotes value of call option, :math:`p(\cdot)` denotes value of put option, :math:`S` is value of underlying at today, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is vol. :math:`c(\cdot)` is calculated by :py:func:`black_scholes_call_formula`. :param float underlying: value of underlying. :param float strike: strike of put option. :param float rate: risk free rate. :param float maturity: year fraction to maturity. :param float vol: volatility. :return: put value. :rtype: float """ call_value = black_scholes_call_formula( underlying, strike, rate, maturity, vol) discount = math.exp(-rate * maturity) return call_value - (underlying - strike * discount) def black_scholes_call_value( underlying, strike, rate, maturity, vol, today=0.0): """black_scholes_call_value calculate call value in the case of today is not zero. (`maturity` - `today`) is treated as time to expiry. See :py:func:`black_scholes_call_formula`. * case :math:`S > 0, K < 0` * return :math:`S - e^{-rT} K` * case :math:`S < 0, K > 0` * return 0 * case :math:`S < 0, K < 0` * return :math:`S - e^{-rT}K + E[(-(S - K))^{+}]` * case :math:`T \le 0` * return 0 :param float underlying: :param float strike: :param float rate: :param float maturity: :param float vol: volatility. This must be positive. :param float today: :return: call value. :rtype: float """ assert(vol >= 0.0) time = maturity - today # option is expired if time < 0.0 or np.isclose(time, 0.0): return 0.0 elif np.isclose(underlying, 0.0): return math.exp(-rate * time) * max(-strike, 0.0) elif np.isclose(strike, 0.0) and underlying > 0.0: return math.exp(-rate * today) * underlying elif np.isclose(strike, 0.0) and underlying < 0.0: return 0.0 # never below strike elif strike < 0.0 and underlying > 0.0: return underlying - math.exp(-rate * time) * strike # never beyond strike elif strike > 0.0 and underlying < 0.0: return 0.0 elif underlying < 0.0: # max(S - K, 0) = (S - K) + max(-(S - K), 0) value = black_scholes_call_formula( -underlying, -strike, rate, time, vol) return (underlying - strike) + value return black_scholes_call_formula( underlying, strike, rate, time, vol) def black_scholes_put_value( underlying, strike, rate, maturity, vol, today=0.0): """black_scholes_put_value evaluates value of put option using put-call parity so that this function calls :py:func:`black_scholes_call_value`. See :py:func:`black_scholes_put_formula`. :param float underlying: :param float strike: :param float rate: :param float maturity: :param float vol: :param float today: :return: put value. :rtype: float """ time = maturity - today # option is expired if time < 0.0 or np.isclose(time, 0.0): return 0.0 elif np.isclose(strike, 0.0) and underlying > 0.0: return 0.0 elif np.isclose(strike, 0.0) and underlying < 0.0: return underlying * math.exp(-rate * today) call_value = black_scholes_call_value( underlying, strike, rate, maturity, vol, today) discount = math.exp(-rate * time) return call_value - (underlying - strike * discount) def black_scholes_call_value_fprime_by_strike( underlying, strike, rate, maturity, vol): """black_scholes_call_value_fprime_by_strike First derivative of value of call option with respect to strike under black scholes model. See :py:func:`black_scholes_call_formula`. .. math:: \\frac{\partial }{\partial K} c(K; S, r, T, \sigma) = - e^{-rT} \Phi(d_{1}(K)) where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is vol, :math:`d_{1}, d_{2}` is defined in :py:func:`black_scholes_call_formula`, :math:`\Phi(\cdot)` is c.d.f. of standard normal distribution, :math:`\phi(\cdot)` is p.d.f. of standard normal distribution. :param float underlying: :param float strike: :param float rate: :param float maturity: must be non-negative. :param float vol: volatility. must be non-negative. :return: value of derivative. :rtype: float """ norm = scipy.stats.norm assert(maturity > 0.0) d2 = func_d2(underlying, strike, rate, maturity, vol) discount = math.exp(-rate * maturity) return -discount * norm.cdf(d2) def black_scholes_call_value_fhess_by_strike( underlying, strike, rate, maturity, vol): """black_scholes_call_value_fhess_by_strike Second derivative of value of call option with respect to strike under black scholes model. See :py:func:`black_scholes_call_formula` and :py:func:`black_scholes_call_value_fprime_by_strike`. .. math:: \\begin{array}{ccl} \\frac{\partial^{2}}{\partial K^{2}} c(0, S; T, K) & = & -e^{-rT} \phi(d_{2}(K)) d^{\prime}(K) \end{array} where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is vol, :math:`d_{1}, d_{2}` is defined in :py:func:`black_scholes_call_formula`, :math:`\Phi(\cdot)` is c.d.f. of standard normal distribution, :math:`\phi(\cdot)` is p.d.f. of standard normal distribution. :param float underlying: :param float strike: :param float rate: :param float maturity: non-negative. :param float vol: volatility. non-negative. :return: value of second derivative. :rtype: float. """ norm = scipy.stats.norm # option is expired if maturity < 0.0 or np.isclose(maturity, 0.0): return 0.0 # never below strike elif strike <= 0.0 and underlying > 0.0: return 0.0 # never beyond strike elif strike > 0.0 and underlying < 0.0: return 0.0 elif underlying < 0.0 and strike < 0.0: underlying = -underlying strike = -strike discount = math.exp(-rate * maturity) d2 = func_d2(underlying, strike, rate, maturity, vol) d_fprime = d_fprime_by_strike(underlying, strike, rate, maturity, vol) d2_density = norm.pdf(d2) return -discount * d2_density * d_fprime def black_scholes_call_value_third_by_strike( underlying, strike, rate, maturity, vol): """black_scholes_call_value_third_by_strike Third derivative of value of call option with respect to strike under black scholes model. See :py:func:`black_scholes_call_formula` and :py:func:`black_scholes_call_value_fprime_by_strike`, and :py:func:`black_scholes_call_value_fhess_by_strike`. .. math:: \\begin{array}{ccl} \\frac{\partial^{3}}{\partial K^{3}} c(0, S; T, K) & = & -e^{-rT} \left( \phi^{\prime}(d_{2}(K))(d^{\prime}(K))^{2} + \phi(d_{2}(K))d^{\prime\prime}(K) \\right) \end{array} where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is vol, :math:`d_{1}, d_{2}` is defined in :py:func:`black_scholes_call_formula`, :math:`\Phi(\cdot)` is c.d.f. of standard normal distribution, :math:`\phi(\cdot)` is p.d.f. of standard normal distribution. :param float underlying: :param float strike: :param float rate: :param float maturity: non-negative. :param float vol: volatility. non-negative. :return: value of third derivative. :rtype: float. """ norm = scipy.stats.norm assert(vol > 0.0) # option is expired if maturity < 0.0 or np.isclose(maturity, 0.0): return 0.0 discount = math.exp(-rate * maturity) d2 = func_d2(underlying, strike, rate, maturity, vol) d_fprime = d_fprime_by_strike(underlying, strike, rate, maturity, vol) d_fhess = d_fhess_by_strike(underlying, strike, rate, maturity, vol) d2_density = norm.pdf(d2) d2_density_fprime = mafipy.function.norm_pdf_fprime(d2) term1 = d2_density_fprime * d_fprime * d_fprime term2 = d2_density * d_fhess return -discount * (term1 + term2) # ---------------------------------------------------------------------------- # Black scholes greeks # ---------------------------------------------------------------------------- def black_scholes_call_delta(underlying, strike, rate, maturity, vol): """black_scholes_call_delta calculates black scholes delta. .. math:: \\frac{\partial}{\partial S} c(S, K, r, T, \sigma) = \Phi(d_{1}(S)) where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is volatility, :math:`\Phi` is standard normal c.d.f, :math:`d_{1}` is defined in :py:func:`func_d1`. :param float underlying: :param float strike: :param float rate: :param float maturity: if maturity <= 0, this function returns 0. :param float vol: volatility. This must be positive. :return: value of delta. :rtype: float. """ assert(vol >= 0.0) if maturity <= 0.0: return 0.0 d1 = func_d1(underlying, strike, rate, maturity, vol) return scipy.stats.norm.cdf(d1) def black_scholes_call_gamma(underlying, strike, rate, maturity, vol): """black_scholes_call_gamma calculates black scholes gamma. .. math:: \\frac{\partial^{2}}{\partial S^{2}} c(S, K, r, T, \sigma) = -\phi(d_{1}(S, K, r, T, \sigma)) \\frac{1}{S^{2}\sigma\sqrt{T}} where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is volatility, :math:`\Phi` is standard normal c.d.f, :math:`d_{1}` is defined in :py:func:`func_d1`. See :py:func:`black_scholes_call_value`. :param float underlying: :param float strike: :param float rate: :param float maturity: if maturity is not positive, this function returns 0.0. :param float vol: volatility. This must be positive. :return: value of gamma. :rtype: float. """ assert(vol >= 0.0) if maturity <= 0.0: return 0.0 d1 = func_d1(underlying, strike, rate, maturity, vol) denominator = underlying * vol * math.sqrt(maturity) return scipy.stats.norm.pdf(d1) / denominator def black_scholes_call_vega(underlying, strike, rate, maturity, vol): """black_scholes_call_vega calculates black scholes vega. .. math:: \\frac{\partial}{\partial \sigma} c(S, K, r, T, \sigma) = \sqrt{T}S\phi(d_{1}(S, K, r, T, \sigma)) where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is volatility, :math:`\phi` is standard normal p.d.f, :math:`d_{1}` is defined in :py:func:`func_d1`. See :py:func:`black_scholes_call_value`. :param float underlying: :param float strike: :param float rate: :param float maturity: if maturity <= 0.0, this function returns 0. :param float vol: volatility. This must be positive. :return: value of vega. :rtype: float. """ assert(vol >= 0.0) if maturity <= 0.0: return 0.0 d1 = func_d1(underlying, strike, rate, maturity, vol) return math.sqrt(maturity) * underlying * scipy.stats.norm.pdf(d1) def black_scholes_call_volga(underlying, strike, rate, maturity, vol): """black_scholes_call_volg calculates black scholes volga. .. math:: \\frac{\partial^{2}}{\partial \sigma^{2}} c(S, K, r, T, \sigma) S \phi^{\prime}(d_{1}(\sigma)) \\frac{ (\\frac{1}{2} \sigma^{2} - r)T }{ \sigma^{2} } where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is volatility, :math:`\phi` is standard normal p.d.f, :math:`d_{1}` is defined in :py:func:`func_d1`. See :py:func:`black_scholes_call_value`. :param float underlying: :param float strike: :param float rate: :param float maturity: must be non-negative. :param float vol: volatility. This must be positive. :return: value of volga. :rtype: float. """ assert(vol >= 0.0) if maturity < 0.0: return 0.0 d1 = func_d1(underlying, strike, rate, maturity, vol) pdf_fprime = mafipy.function.norm_pdf_fprime(d1) ln_moneyness = math.log(underlying / strike) numerator = -ln_moneyness + (0.5 * vol * vol - rate) * maturity factor = numerator / (vol * vol) return underlying * pdf_fprime * factor def black_scholes_call_theta(underlying, strike, rate, maturity, vol, today): """black_scholes_call_theta calculates black scholes theta. .. math:: \\frac{\partial}{\partial t} c(t, S, K, r, T, \sigma) = - S * \phi(d_{1}) \left( \\frac{\sigma}{2\sqrt{T - t}} \\right) - r e^{-r(T - t)} K \Phi(d_{2}) where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is volatility, :math:`\phi` is standard normal p.d.f, :math:`d_{1}` is defined in :py:func:`func_d1`. See :py:func:`black_scholes_call_value`. :param float underlying: :param float strike: :param float rate: :param float maturity: must be non-negative. :param float vol: volatility. This must be positive. :return: value of theta. :rtype: float. """ assert(maturity >= 0.0) assert(vol >= 0.0) norm = scipy.stats.norm time = maturity - today d1 = func_d1(underlying, strike, rate, time, vol) d2 = func_d2(underlying, strike, rate, time, vol) term1 = underlying * norm.pdf(d1) * (vol / (2.0 * math.sqrt(time))) term2 = rate * math.exp(-rate * time) * strike * norm.cdf(d2) return - term1 - term2 def black_scholes_call_rho(underlying, strike, rate, maturity, vol, today): """black_scholes_call_rho calculates black scholes rho. .. math:: \\frac{\partial}{\partial t} c(t, S, K, r, T, \sigma) = (T - t) e^{-r (T - t)} K \Phi(d_{2}) where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is volatility, :math:`\phi` is standard normal p.d.f, :math:`d_{2}` is defined in :py:func:`func_d2`. See :py:func:`black_scholes_call_value`. :param float underlying: :param float strike: :param float rate: :param float maturity: must be non-negative. :param float vol: volatility. This must be positive. :return: value of rho. :rtype: float. """ assert(maturity >= 0.0) assert(vol >= 0.0) norm = scipy.stats.norm time = maturity - today d2 = func_d2(underlying, strike, rate, time, vol) return time * math.exp(-rate * time) * strike * norm.cdf(d2) def black_scholes_call_vega_fprime_by_strike( underlying, strike, rate, maturity, vol): """black_scholes_call_vega_fprime_by_strike calculates derivative of black scholes vega with respect to strike. This is required for :py:func:`sabr_pdf`. .. math:: \\frac{\partial}{\partial K} \mathrm{Vega}{\mathrm{BSCall}}(S, K, r, T, \sigma) = S\phi^{\prime}(d_{1}(S, K, r, T, \sigma)) \\frac{1}{\sigma K} where :math:`S` is underlying, :math:`K` is strike, :math:`r` is rate, :math:`T` is maturity, :math:`\sigma` is volatility, :math:`\phi` is standard normal p.d.f, :math:`d_{1}` is defined in :py:func:`func_d1`. See :py:func:`black_scholes_call_value`. :param float underlying: :param float strike: :param float rate: :param float maturity: if maturity <= 0.0, this function returns 0. :param float vol: volatility. This must be positive. :return: derivative of vega with respect to strike. :rtype: float. """ assert(vol >= 0.0) if maturity <= 0.0: return 0.0 d1 = func_d1(underlying, strike, rate, maturity, vol) density_fprime = mafipy.function.norm_pdf_fprime(d1) return -underlying * density_fprime / (vol * strike) # ---------------------------------------------------------------------------- # Black scholes distributions # ---------------------------------------------------------------------------- def black_scholes_cdf(underlying, strike, rate, maturity, vol): """black_scholes_cdf calculates value of c.d.f. of black scholes model. :param float underlying: :param float strike: :param float rate: :param float maturity: :param float vol: must be positive. :return: value of p.d.f. of black scholes model. :rtype: float. """ assert(vol > 0.0) return (1.0 + black_scholes_call_value_fprime_by_strike( underlying, strike, rate, maturity, vol) * math.exp(rate * maturity)) def black_scholes_pdf(underlying, strike, rate, maturity, vol): """black_scholes_pdf calculates value of p.d.f. of black scholes model. :param float underlying: :param float strike: :param float rate: :param float maturity: :param float vol: must be positive. :return: value of p.d.f. of black scholes model. :rtype: float. """ assert(vol > 0.0) return (black_scholes_call_value_fhess_by_strike( underlying, strike, rate, maturity, vol) * math.exp(rate * maturity))

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2317314f7c09f8f3a1a8f02519fe5fbfb22682c4

581

py

Python

impls/Implements.py

SyouEthernet/AutoExecutor

deeeea389a70aa5519a0b957df8dd7f345ef0645

[ "Apache-2.0" ]

null

impls/Implements.py

SyouEthernet/AutoExecutor

deeeea389a70aa5519a0b957df8dd7f345ef0645

[ "Apache-2.0" ]

null

impls/Implements.py

SyouEthernet/AutoExecutor

deeeea389a70aa5519a0b957df8dd7f345ef0645

[ "Apache-2.0" ]

null

from exceptionHanlder import Handler as Handler from executor import Executor as Executor # 此文件中完成具体操作逻辑 class ExecutorImpl(Executor.Executor): def preExecute(self): # ... return True def onExecute(self): # ... return True def onFinished(self): # ... return True class ExceptionHandler(Handler.ErrorHandler): def handlePreExecuteFail(self): # ... return True def handleExecuteError(self): # ... return True def handleFinishedError(self): # ... return True

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py

Python

tests/test.py

Philinphiladelphia/uwu

531843b8663d3342ccaf0089bfe8734d95fdecb9

[ "MIT" ]

null

tests/test.py

Philinphiladelphia/uwu

531843b8663d3342ccaf0089bfe8734d95fdecb9

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null

tests/test.py

Philinphiladelphia/uwu

531843b8663d3342ccaf0089bfe8734d95fdecb9

[ "MIT" ]

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import uwuizer uwuizer.owoize("hello")

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Python

survae/flows/__init__.py

robert-giaquinto/survae_flows

4d7dc638f77c48ad3c8393b967c33ac9dbad60fe

[ "MIT" ]

2

2021-03-06T19:37:39.000Z

2022-01-09T11:19:45.000Z

survae/flows/__init__.py

robert-giaquinto/survae_flows

4d7dc638f77c48ad3c8393b967c33ac9dbad60fe

[ "MIT" ]

null

survae/flows/__init__.py

robert-giaquinto/survae_flows

4d7dc638f77c48ad3c8393b967c33ac9dbad60fe

[ "MIT" ]

null

from .flow import Flow from .inverse_flow import InverseFlow from .cond_flow import ConditionalFlow from .cond_inverse_flow import ConditionalInverseFlow from .compressive_flow import CompressiveFlow from .boosted_flow import BoostedFlow from .cond_boosted_flow import ConditionalBoostedFlow

29.4

53

0.87415

36

294

6.916667

0.361111

0.281125

0.136546

0

0.102041

294

9

54

32.666667

0.943182

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

2349dc34566363d9569b016599a47588b0bca93d

72

py

Python

BZOJ/BZOJ1876.py

HeRaNO/OI-ICPC-Codes

4a4639cd3e347b472520065ca6ab8caadde6906d

[ "MIT" ]

18

2019-01-01T13:16:59.000Z

2022-02-28T04:51:50.000Z

BZOJ/BZOJ1876.py

HeRaNO/OI-ICPC-Codes

4a4639cd3e347b472520065ca6ab8caadde6906d

[ "MIT" ]

null

BZOJ/BZOJ1876.py

HeRaNO/OI-ICPC-Codes

4a4639cd3e347b472520065ca6ab8caadde6906d

[ "MIT" ]

5

2019-09-13T08:48:17.000Z

2022-02-19T06:59:03.000Z

a,b=input(),input() c=a%b while c!=0L: a=b b=c c=a%b print b

10.285714

19

0.5

19

72

1.894737

0.368421

0.222222

0.166667

0

0.019231

0.277778

72

7

20

10.285714

0.673077

0

0.285714

0

null

0

null

0.142857

1

0

1

null

1

0

1

0

null

0

1

0

5

2382369da638fba9be17f80ecde5950c84ddefe4

154

py

Python

tests/test_project/app_correct/models.py

christianbundy/django-migration-linter

3f0531f349c4c237a5ff1afd594956a58103dc5d

[ "Apache-2.0" ]

357

2017-04-05T20:50:31.000Z

2022-03-16T01:37:13.000Z

tests/test_project/app_correct/models.py

christianbundy/django-migration-linter

3f0531f349c4c237a5ff1afd594956a58103dc5d

[ "Apache-2.0" ]

146

2017-04-06T14:14:26.000Z

2022-03-28T18:02:53.000Z

tests/test_project/app_correct/models.py

christianbundy/django-migration-linter

3f0531f349c4c237a5ff1afd594956a58103dc5d

[ "Apache-2.0" ]

45

2017-10-31T16:25:22.000Z

2022-02-24T22:13:37.000Z

from django.db import models class A(models.Model): null_field = models.IntegerField(null=True) new_null_field = models.IntegerField(null=True)

22

51

0.75974

22

154

5.181818

0.590909

0.157895

0.263158

0.473684

0.614035

0

0.142857

154

6

52

25.666667

0.863636

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

5

2382700a0794a70ab4ca71302094a9eda41d6538

21

py

Python

leetcode/p4.py

holmescn/practice

edd992a309d1413baf7e2b9bed31ece6cb242fde

[ "MIT" ]

null

leetcode/p4.py

holmescn/practice

edd992a309d1413baf7e2b9bed31ece6cb242fde

[ "MIT" ]

null

leetcode/p4.py

holmescn/practice

edd992a309d1413baf7e2b9bed31ece6cb242fde

[ "MIT" ]

null

def f(m, n, mb, me):

10.5

20

0.47619

6

21

1.666667

1

0

0.238095

21

1

21

0.625

0

null

0

null

0

1

null

0

1

0

null

0

1

0

5

000dd4e8e0525cc12c0feb402dc883a8300f08ab

44

py

Python

run.py

alexchamberlain/city-api

f29c41c08dc7c130f5a66c1ca2c1a1d2b87f3755

[ "MIT" ]

3

2018-06-12T08:52:45.000Z

2019-04-18T04:54:28.000Z

run.py

alexchamberlain/city-api

f29c41c08dc7c130f5a66c1ca2c1a1d2b87f3755

[ "MIT" ]

null

run.py

alexchamberlain/city-api

f29c41c08dc7c130f5a66c1ca2c1a1d2b87f3755

[ "MIT" ]

null

from cities import app app.run(debug=True)

11

22

0.772727

8

44

4.25

0.875

0

0.136364

44

3

23

14.666667

0.894737

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

cc608549aa378f917ffa8aaa50d42d3a4a077c80

585

py

Python

data-hub-api/apps/cdms_api/tests/integration/test_env.py

uktrade/data-hub-api-old

5ecf093d88692870982a638ced45de6a82d55672

[ "MIT" ]

null

data-hub-api/apps/cdms_api/tests/integration/test_env.py

uktrade/data-hub-api-old

5ecf093d88692870982a638ced45de6a82d55672

[ "MIT" ]

18

2016-04-04T12:42:45.000Z

2016-09-01T07:21:05.000Z

data-hub-api/apps/cdms_api/tests/integration/test_env.py

uktrade/data-hub-api-old

5ecf093d88692870982a638ced45de6a82d55672

[ "MIT" ]

1

2016-06-01T15:45:21.000Z

from django.conf import settings from django.test import TestCase from ..decorators import skipIntegration @skipIntegration class TestEnv(TestCase): def test_happy(self): """ Non-Default values for settings are loaded Vanilla MSDCRM11 needs custom configuration - if the defaults from settings are in place then tests will fail. """ self.assertNotEqual(settings.CDMS_BASE_URL, 'https://example.com') self.assertNotEqual(settings.CDMS_USERNAME, 'username') self.assertNotEqual(settings.CDMS_PASSWORD, 'password')

29.25

74

0.71453

67

585

6.164179

0.641791

0.130751

0.188862

0.217918

0

0.00432

0.208547

585

19

75

30.789474

0.887689

0.263248

0

0.089514

0

0.333333

1

0.111111

false

0.111111

0.333333

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

5

aea46a66886160cabda7cd27fc945f42d2890ebf

20

py

Python

hello word.py

fmweendyy/Mi-Primer-Repositorio-3A

e71e30624160de220ac5af41d80ffff1dc8cce86

[ "MIT" ]

null

hello word.py

fmweendyy/Mi-Primer-Repositorio-3A

e71e30624160de220ac5af41d80ffff1dc8cce86

[ "MIT" ]

null

hello word.py

fmweendyy/Mi-Primer-Repositorio-3A

e71e30624160de220ac5af41d80ffff1dc8cce86

[ "MIT" ]

null

print(holamundo3a)

10

19

0.8

2

20

8

1

0

0.055556

0.1

20

1

20

0.833333

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

4e04dd3326130f71adeb70764a8787c5fda42cc4

55,772

py

Python

trendvisualizer/sector_mappings.py

GBERESEARCH/trendvisualizer

b7f3ecdb8a0a7421fa60beda32d629a813315897

[ "MIT" ]

2

2021-09-10T04:31:10.000Z

2021-11-15T11:02:11.000Z

trendvisualizer/sector_mappings.py

GBERESEARCH/trendvisualizer

b7f3ecdb8a0a7421fa60beda32d629a813315897

[ "MIT" ]

null

trendvisualizer/sector_mappings.py

GBERESEARCH/trendvisualizer

b7f3ecdb8a0a7421fa60beda32d629a813315897

[ "MIT" ]

1

2021-09-10T04:31:11.000Z

# Commodity and Equity Sector mappings sectmap = { 'commodity_sector_mappings':{ '&6A_CCB':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'AUD'), # AUD '&6B_CCB':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'GBP'), # GBP '&6C_CCB':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'CAD'), # CAD '&6E_CCB':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'EUR'), # EUR '&6J_CCB':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'JPY'), # JPY '&6M_CCB':('Currencies', 'EM Currencies', 'EM Currencies', 'EM Currencies', 'MXN'), # MXN '&6N_CCB':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'NZD'), # NZD '&6S_CCB':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'CHF'), # CHF '&AFB_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Eastern Australia Feed Barley'), # Eastern Australia Feed Barley '&AWM_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Eastern Australia Wheat'), # Eastern Australia Wheat '&BAX_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Canadian Bankers Acceptance'), # Canadian Bankers Acceptance '&BRN_CCB':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Brent Crude Oil'), # Brent Crude Oil '&BTC_CCB':('Currencies', 'Crypto Currencies', 'Crypto Currencies', 'Crypto Currencies', 'Bitcoin'), # Bitcoin '&CC_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Cocoa'), # Cocoa '&CGB_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Canadian 10y'), # Canadian 10 Yr Govt Bond '&CL_CCB':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Crude Oil - Light Sweet'), # Crude Oil - Light Sweet '&CT_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Cotton #2'), # Cotton #2 '&DC_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Milk - Class III'), # Milk - Class III '&DX_CCB':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'Benchmark'), # US Dollar Index '&EH_CCB':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Ethanol'), # Ethanol '&EMD_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'S&P MidCap 400 E-mini'), # S&P MidCap 400 E-mini '&ES_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'S&P 500 E-mini'), # S&P 500 E-mini '&EUA_CCB':('Commodities', 'Energy', 'Energy', 'Energy', 'EUA (Carbon Emissions)'), # EUA (Carbon Emissions) '&FBTP_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-BTP Long Term'), # Euro-BTP Long Term '&FCE_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'CAC 40'), # CAC 40 '&FDAX_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'DAX'), # DAX '&FDAX9_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'DAX'), # DAX, Last in Close field '&FESX_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'EURO STOXX 50'), # EURO STOXX 50 '&FESX9_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'EURO STOXX 50'), # EURO STOXX 50, Last in Close field '&FGBL_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-Bund - 10 Yr'), # Euro-Bund - 10 Yr '&FGBM_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-Bobl - 5 Yr'), # Euro-Bobl - 5 Yr '&FGBS_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-Schatz - 2 Yr'), # Euro-Schatz - 2 Yr '&FGBX_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-Buxl - 30 Yr'), # Euro-Buxl - 30 Yr '&FOAT_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euro-OAT Continuous Contract'), # Euro-OAT Continuous Contract '&FOAT9_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euro-OAT(L) Continuous Contract'), # Euro-OAT(L) Continuous Contract '&FSMI_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Swiss Market Index'), # Swiss Market Index '&FTDX_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'TecDAX'), # TecDAX '&GAS_CCB':('Commodities', 'Energy', 'Energy', 'Energy', 'Gas Oil'), # Gas Oil '&GC_CCB':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Gold'), # Gold '&GD_CCB':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # GS&P GSCI '&GE_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Eurodollar'), # Eurodollar '&GF_CCB':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Feeder Cattle'), # Feeder Cattle '&GWM_CCB':('Commodities', 'Energy', 'Energy', 'Energy', 'UK Natural Gas'), # UK Natural Gas '&HE_CCB':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Lean Hogs'), # Lean Hogs '&HG_CCB':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Copper'), # Copper '&HO_CCB':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'NY Harbor ULSD'), # NY Harbor ULSD '&HSI_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Hang Seng Index'), # Hang Seng Index '&HTW_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'MSCI Taiwan Index'), # MSCI Taiwan Index '&KC_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Coffee C'), # Coffee C '&KE_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'KC HRW Wheat'), # KC HRW Wheat '&KOS_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'KOSPI 200'), # KOSPI 200 '&LBS_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Lumber'), # Lumber '&LCC_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'London Cocoa'), # London Cocoa '&LE_CCB':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Live Cattle'), # Live Cattle '&LES_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euro Swiss'), # Euro Swiss '&LEU_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euribor'), # Euribor '&LEU9_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euribor'), # Euribor, Official Close '&LFT_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'FTSE 100'), # FTSE 100 '&LFT9_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'FTSE 100'), # FTSE 100, Official Close '&LLG_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Long Gilt'), # Long Gilt '&LRC_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Robusta Coffee'), # Robusta Coffee '&LSS_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Short Sterling'), # Short Sterling '&LSU_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'White Sugar'), # White Sugar '&LWB_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Feed Wheat'), # Feed Wheat '&MHI_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Hang Seng Index'), # Hang Seng Index - Mini '&MWE_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Hard Red Spring Wheat'), # Hard Red Spring Wheat '&NG_CCB':('Commodities', 'Energy', 'Energy', 'Energy', 'Henry Hub Natural Gas'), # Henry Hub Natural Gas '&NIY_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Nikkei 225'), # Nikkei 225 Yen '&NKD_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Nikkei 225'), # Nikkei 225 Dollar '&NQ_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Nasdaq-100 - E-mini'), # Nasdaq-100 - E-mini '&OJ_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Frozen Concentrated Orange Juice'), # Frozen Concentrated Orange Juice '&PA_CCB':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Palladium'), # Palladium '&PL_CCB':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Platinum'), # Platinum '&RB_CCB':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'RBOB Gasoline'), # RBOB Gasoline '&RS_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Canola'), # Canola '&RTY_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Russell 2000 - E-mini'), # Russell 2000 - E-mini '&SB_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Sugar No. 11'), # Sugar No. 11 '&SCN_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'FTSE China A50 Index'), # FTSE China A50 Index '&SI_CCB':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Silver'), # Silver '&SIN_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'SGX Nifty 50 Index'), # SGX Nifty 50 Index '&SJB_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Japanese Govt Bond - Mini'), # Japanese Govt Bond - Mini '&SNK_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Nikkei 225'), # Nikkei 225 (SGX) '&SP_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'S&P 500'), # S&P 500 '&SR3_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', '3M SOFR Continuous Contract'), # 3M SOFR Continuous Contract '&SSG_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'MSCI Singapore Index'), # MSCI Singapore Index '&STW_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'MSCI Taiwan Index'), # MSCI Taiwan Index, Discontinued '&SXF_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'S&P/TSX 60 Index'), # S&P/TSX 60 Index '&TN_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Ultra 10 Year U.S. T-Note'), # Ultra 10 Year U.S. T-Note '&UB_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Ultra U.S. T-Bond'), # Ultra U.S. T-Bond '&VX_CCB':('Volatility', 'Volatility', 'Volatility', 'Volatility', 'Cboe Volatility Index'), # Cboe Volatility Index '&WBS_CCB':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'WTI Crude Oil'), # WTI Crude Oil '&YAP_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'ASX SPI 200'), # ASX SPI 200 '&YAP4_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'ASX SPI 200'), # ASX SPI 200, Day '&YAP10_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'ASX SPI 200'), # ASX SPI 200, Night '&YG_CCB':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Gold - Mini'), # Gold - Mini '&YI_CCB':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Silver - Mini'), # Silver - Mini '&YIB_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'ASX 30 Day Interbank Cash Rate'), # ASX 30 Day Interbank Cash Rate '&YIR_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'ASX 90 Day Bank Accepted Bills'), # ASX 90 Day Bank Accepted Bills '&YM_CCB':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'E-mini Dow'), # E-mini Dow '&YXT_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'ASX 10 Year Treasury Bond'), # ASX 10 Year Treasury Bond '&YYT_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'ASX 3 Year Treasury Bond'), # ASX 3 Year Treasury Bond '&ZB_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'U.S. T-Bond'), # U.S. T-Bond '&ZC_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Corn'), # Corn '&ZF_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', '5-Year US T-Note'), # 5-Year US T-Note '&ZG_CCB':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Gold'), # Gold 100oz, Discountinued '&ZI_CCB':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Silver'), # Silver 5000oz, Discontinued '&ZL_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Soybean Oil'), # Soybean Oil '&ZM_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Soybean Meal'), # Soybean Meal '&ZN_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', '10-Year US T-Note'), # 10-Year US T-Note '&ZO_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Oats'), # Oats '&ZQ_CCB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', '30 Day Federal Funds'), # 30 Day Federal Funds '&ZR_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Rough Rice'), # Rough Rice '&ZS_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Soybeans'), # Soybeans '&ZT_CCB':('Bonds','Government Bonds','Government Bonds','Government Bonds', '2-Year US T-Note'), # 2-Year US T-Note '&ZW_CCB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Chicago SRW Wheat'), # Chicago SRW Wheat '&6A':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'AUD'), # AUD '&6B':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'GBP'), # GBP '&6C':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'CAD'), # CAD '&6E':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'EUR'), # EUR '&6J':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'JPY'), # JPY '&6M':('Currencies', 'EM Currencies', 'EM Currencies', 'EM Currencies', 'MXN'), # MXN '&6N':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'NZD'), # NZD '&6S':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'CHF'), # CHF '&AFB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Eastern Australia Feed Barley'), # Eastern Australia Feed Barley '&AWM':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Eastern Australia Wheat'), # Eastern Australia Wheat '&BAX':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Canadian Bankers Acceptance'), # Canadian Bankers Acceptance '&BRN':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Brent Crude Oil'), # Brent Crude Oil '&BTC':('Currencies', 'Crypto Currencies', 'Crypto Currencies', 'Crypto Currencies', 'Bitcoin'), # Bitcoin '&CC':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Cocoa'), # Cocoa '&CGB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Canadian 10y'), # Canadian 10 Yr Govt Bond '&CL':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Crude Oil - Light Sweet'), # Crude Oil - Light Sweet '&CT':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Cotton #2'), # Cotton #2 '&DC':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Milk - Class III'), # Milk - Class III '&DX':('Currencies', 'G10 Currencies', 'G10 Currencies', 'G10 Currencies', 'Benchmark'), # US Dollar Index '&EH':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Ethanol'), # Ethanol '&EMD':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'S&P MidCap 400 E-mini'), # S&P MidCap 400 E-mini '&ES':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'S&P 500 E-mini'), # S&P 500 E-mini '&EUA':('Commodities', 'Energy', 'Energy', 'Energy', 'EUA (Carbon Emissions)'), # EUA (Carbon Emissions) '&FBTP':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-BTP Long Term'), # Euro-BTP Long Term '&FCE':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'CAC 40'), # CAC 40 '&FDAX':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'DAX'), # DAX '&FDAX9':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'DAX'), # DAX, Last in Close field '&FESX':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'EURO STOXX 50'), # EURO STOXX 50 '&FESX9':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'EURO STOXX 50'), # EURO STOXX 50, Last in Close field '&FGBL':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-Bund - 10 Yr'), # Euro-Bund - 10 Yr '&FGBM':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-Bobl - 5 Yr'), # Euro-Bobl - 5 Yr '&FGBS':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-Schatz - 2 Yr'), # Euro-Schatz - 2 Yr '&FGBX':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Euro-Buxl - 30 Yr'), # Euro-Buxl - 30 Yr '&FOAT':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euro-OAT Continuous Contract'), # Euro-OAT Continuous Contract '&FOAT9':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euro-OAT(L) Continuous Contract'), # Euro-OAT(L) Continuous Contract '&FSMI':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Swiss Market Index'), # Swiss Market Index '&FTDX':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'TecDAX'), # TecDAX '&GAS':('Commodities', 'Energy', 'Energy', 'Energy', 'Gas Oil'), # Gas Oil '&GC':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Gold'), # Gold '&GD':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # GS&P GSCI '&GE':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Eurodollar'), # Eurodollar '&GF':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Feeder Cattle'), # Feeder Cattle '&GWM':('Commodities', 'Energy', 'Energy', 'Energy', 'UK Natural Gas'), # UK Natural Gas '&HE':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Lean Hogs'), # Lean Hogs '&HG':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Copper'), # Copper '&HO':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'NY Harbor ULSD'), # NY Harbor ULSD '&HSI':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Hang Seng Index'), # Hang Seng Index '&HTW':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'MSCI Taiwan Index'), # MSCI Taiwan Index '&KC':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Coffee C'), # Coffee C '&KE':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'KC HRW Wheat'), # KC HRW Wheat '&KOS':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'KOSPI 200'), # KOSPI 200 '&LBS':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Lumber'), # Lumber '&LCC':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'London Cocoa'), # London Cocoa '&LE':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Live Cattle'), # Live Cattle '&LES':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euro Swiss'), # Euro Swiss '&LEU':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euribor'), # Euribor '&LEU9':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Euribor'), # Euribor, Official Close '&LFT':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'FTSE 100'), # FTSE 100 '&LFT9':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'FTSE 100'), # FTSE 100, Official Close '&LLG':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Long Gilt'), # Long Gilt '&LRC':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Robusta Coffee'), # Robusta Coffee '&LSS':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'Short Sterling'), # Short Sterling '&LSU':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'White Sugar'), # White Sugar '&LWB':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Feed Wheat'), # Feed Wheat '&MHI':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Hang Seng Index'), # Hang Seng Index - Mini '&MWE':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Hard Red Spring Wheat'), # Hard Red Spring Wheat '&NG':('Commodities', 'Energy', 'Energy', 'Energy', 'Henry Hub Natural Gas'), # Henry Hub Natural Gas '&NIY':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Nikkei 225'), # Nikkei 225 Yen '&NKD':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Nikkei 225'), # Nikkei 225 Dollar '&NQ':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Nasdaq-100 - E-mini'), # Nasdaq-100 - E-mini '&OJ':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Frozen Concentrated Orange Juice'), # Frozen Concentrated Orange Juice '&PA':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Palladium'), # Palladium '&PL':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Platinum'), # Platinum '&RB':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'RBOB Gasoline'), # RBOB Gasoline '&RS':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Canola'), # Canola '&RTY':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Russell 2000 - E-mini'), # Russell 2000 - E-mini '&SB':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Sugar No. 11'), # Sugar No. 11 '&SCN':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'FTSE China A50 Index'), # FTSE China A50 Index '&SI':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Silver'), # Silver '&SIN':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'SGX Nifty 50 Index'), # SGX Nifty 50 Index '&SJB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Japanese Govt Bond - Mini'), # Japanese Govt Bond - Mini '&SNK':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'Nikkei 225'), # Nikkei 225 (SGX) '&SP':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'S&P 500'), # S&P 500 '&SR3':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', '3M SOFR Continuous Contract'), # 3M SOFR Continuous Contract '&SSG':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'MSCI Singapore Index'), # MSCI Singapore Index '&STW':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'MSCI Taiwan Index'), # MSCI Taiwan Index, Discontinued '&SXF':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'S&P/TSX 60 Index'), # S&P/TSX 60 Index '&TN':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Ultra 10 Year U.S. T-Note'), # Ultra 10 Year U.S. T-Note '&UB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'Ultra U.S. T-Bond'), # Ultra U.S. T-Bond '&VX':('Volatility', 'Volatility', 'Volatility', 'Volatility', 'Cboe Volatility Index'), # Cboe Volatility Index '&WBS':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'WTI Crude Oil'), # WTI Crude Oil '&YAP':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'ASX SPI 200'), # ASX SPI 200 '&YAP4':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'ASX SPI 200'), # ASX SPI 200, Day '&YAP10':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'ASX SPI 200'), # ASX SPI 200, Night '&YG':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Gold - Mini'), # Gold - Mini '&YI':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Silver - Mini'), # Silver - Mini '&YIB':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'ASX 30 Day Interbank Cash Rate'), # ASX 30 Day Interbank Cash Rate '&YIR':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', 'ASX 90 Day Bank Accepted Bills'), # ASX 90 Day Bank Accepted Bills '&YM':('Equity Indices', 'Equity Indices','Equity Indices','Equity Indices', 'E-mini Dow'), # E-mini Dow '&YXT':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'ASX 10 Year Treasury Bond'), # ASX 10 Year Treasury Bond '&YYT':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'ASX 3 Year Treasury Bond'), # ASX 3 Year Treasury Bond '&ZB':('Bonds','Government Bonds','Government Bonds','Government Bonds', 'U.S. T-Bond'), # U.S. T-Bond '&ZC':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Corn'), # Corn '&ZF':('Bonds','Government Bonds','Government Bonds','Government Bonds', '5-Year US T-Note'), # 5-Year US T-Note '&ZG':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Gold'), # Gold 100oz, Discountinued '&ZI':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Silver'), # Silver 5000oz, Discontinued '&ZL':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Soybean Oil'), # Soybean Oil '&ZM':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Soybean Meal'), # Soybean Meal '&ZN':('Bonds','Government Bonds','Government Bonds','Government Bonds', '10-Year US T-Note'), # 10-Year US T-Note '&ZO':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Oats'), # Oats '&ZQ':('Interest Rates', 'Interest Rates', 'Interest Rates', 'Interest Rates', '30 Day Federal Funds'), # 30 Day Federal Funds '&ZR':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Rough Rice'), # Rough Rice '&ZS':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Soybeans'), # Soybeans '&ZT':('Bonds','Government Bonds','Government Bonds','Government Bonds', '2-Year US T-Note'), # 2-Year US T-Note '&ZW':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Chicago SRW Wheat'), # Chicago SRW Wheat '#GSR':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Benchmark'), # Gold/Silver Ratio '$BCOM':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # Bloomberg Commodity Index '$BCOMAG':('Commodities','Diversified Agriculture', 'Agriculture', 'Agriculture', 'Benchmark'), # Bloomberg Agriculture Sub-Index '$BCOMEN':('Commodities', 'Energy', 'Energy', 'Energy', 'Benchmark'), # Bloomberg Energy Sub-Index '$BCOMGR':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Benchmark'), # Bloomberg Grains Sub-Index '$BCOMIN':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Benchmark'), # Bloomberg Industrial Metals Sub-Index '$BCOMLI':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Benchmark'), # Bloomberg Livestock Sub-Index '$BCOMPE':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Benchmark') , # Bloomberg Petroleum Sub-Index '$BCOMPR':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Benchmark'), # Bloomberg Precious Metals Sub-Index '$BCOMSO':('Commodities','Diversified Agriculture', 'Agriculture', 'Softs', 'Benchmark'), # Bloomberg Softs Sub-Index '$BCOMTR':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # Bloomberg Commodity Total Return Index '$BCOMXE':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # Bloomberg Ex-Energy Sub-Index '$CRB':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # Refinitiv/CoreCommodity CRB Index '$FC':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Feeder Cattle'), # CME Feeder Cattle Index '$LH':('Commodities','Diversified Agriculture', 'Livestock', 'Livestock', 'Lean Hogs'), # CME Lean Hogs Index '$LMEX':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Benchmark'), # LMEX Index '$RBABCA':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # RBA Bulk Commodities Sub-Index (AUD) '$RBABCU':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # RBA Bulk Commodities Sub-Index (USD) '$RBABMA':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Benchmark'), # RBA Base Metals Sub-Index (AUD) '$RBABMU':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Benchmark'), # RBA Base Metals Sub-Index (USD) '$RBACPA':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # RBA Commodity Prices Index (AUD) '$RBACPU':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # RBA Commodity Prices Index (USD) '$RBANRCPA':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # RBA Non-Rural Commodity Prices Sub-Index (AUD) '$RBANRCPU':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # RBA Non-Rural Commodity Prices Sub-Index (USD) '$RBARCPA':('Commodities', 'Diversified Agriculture', 'Diversified Agriculture', 'Diversified Agriculture', 'Benchmark'), # RBA Rural Commodity Prices Sub-Index (AUD) '$RBARCPU':('Commodities', 'Diversified Agriculture', 'Diversified Agriculture', 'Diversified Agriculture', 'Benchmark'), # RBA Rural Commodity Prices Sub-Index (USD) '$SPGSCI':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # S&P GSCI Spot Index '$SPGSCITR':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # S&P GSCI Total Return Index '$SPGSEW':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # S&P GSCI Select Equal Weight Spot Index '$SPGSEWTR':('Commodities', 'Commodities', 'Commodities', 'Commodities', 'Benchmark'), # S&P GSCI Select Equal Weight Total Return Index '@AA':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium Alloy - LME Official Cash '@AA03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium Alloy - LME 03 Months Seller '@AAWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium Alloy - LME Warehouse Opening Stocks '@AL':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium - LME Official Cash '@AL03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium - LME 03 Months Seller '@ALAUD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium - LME Official Cash (AUD) '@ALCAD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium - LME Official Cash (CAD) '@ALWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium - LME Warehouse Opening Stocks '@BFOE':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Crude Oil'), # Brent Crude Europe FOB Spot '@C2Y':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Corn'), # Corn #2 Yellow Central Illinois Average Price Spot '@CO':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Cobalt'), # Cobalt - LME Official Cash '@CO03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Cobalt'), # Cobalt - LME 03 Months Seller '@CO15S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Cobalt'), # Cobalt - LME 15 Months Seller '@COWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Cobalt'), # Cobalt - LME Warehouse Opening Stocks '@CU':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Copper'), # Copper - LME Official Cash '@CU03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Copper'), # Copper - LME 03 Months Seller '@CUAUD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Copper'), # Copper - LME Official Cash (AUD) '@CUCAD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Copper'), # Copper - LME Official Cash (CAD) '@CUWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Copper'), # Copper - LME Warehouse Opening Stocks '@FE':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Iron Ore'), # Iron Ore CFR China 62% Fe Spot '@FEAUD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Iron Ore'), # Iron Ore CFR China 62% Fe Spot (AUD) '@FECAD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Iron Ore'), # Iron Ore CFR China 62% Fe Spot (CAD) '@GC':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Gold'), # Gold - London PM Fix '@HHNG':('Commodities', 'Energy', 'Energy', 'Energy', 'Natural Gas'), # Henry Hub Natural Gas Spot '@HO':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Heating Oil'), # Heating Oil Spot '@NA':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium Alloy (NASAAC) - LME Official Cash '@NA03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium Alloy (NASAAC) - LME 03 Months Seller '@NAWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Aluminium'), # Aluminium Alloy (NASAAC) - LME Warehouse Opening Stocks '@NI':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Nickel'), # Nickel - LME Official Cash '@NI03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Nickel'), # Nickel - LME 03 Months Seller '@NIAUD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Nickel'), # Nickel - LME Official Cash (AUD) '@NICAD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Nickel'), # Nickel - LME Official Cash (CAD) '@NIWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Nickel'), # Nickel - LME Warehouse Opening Stocks '@PA':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Palladium'), # Palladium - London PM Fix '@PAAUD':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Palladium'), # Palladium - London PM Fix (AUD) '@PACAD':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Palladium'), # Palladium - London PM Fix (CAD) '@PB':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Lead'), # Lead - LME Official Cash '@PB03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Lead'), # Lead - LME 03 Months Seller '@PBAUD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Lead'), # Lead - LME Official Cash (AUD) '@PBCAD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Lead'), # Lead - LME Official Cash (CAD) '@PBWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Lead'), # Lead - LME Warehouse Opening Stocks '@PL':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Platinum'), # Platinum - London PM Fix '@PLAUD':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Platinum'), # Platinum - London PM Fix (AUD) '@PLCAD':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Platinum'), # Platinum - London PM Fix (CAD) '@RBOB':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'RBOB Gasoline'), # RBOB Gasoline Spot '@S1Y':('Commodities','Diversified Agriculture', 'Agriculture', 'Grains', 'Soybeans'), # Soybeans #1 Yellow Central Illinois Average Price Spot '@SI':('Commodities', 'Metals', 'Precious Metals', 'Precious Metals', 'Silver'), # Silver - London Fix '@SN':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Tin'), # Tin - LME Official Cash '@SN03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Tin'), # Tin - LME 03 Months Seller '@SN15S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Tin'), # Tin - LME 15 Months Seller '@SNAUD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Tin'), # Tin - LME Official Cash (AUD) '@SNCAD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Tin'), # Tin - LME Official Cash (CAD) '@SNWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Tin'), # Tin - LME Warehouse Opening Stocks '@U3O8':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Uranium'), # Uranium Spot '@U3O8AUD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Uranium'), # Uranium Spot (AUD) '@U3O8CAD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Uranium'), # Uranium Spot (CAD) '@WTI':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Crude Oil'), # West Texas Intermediate Crude Oil Spot '@WTIAUD':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Crude Oil'), # West Texas Intermediate Crude Oil Spot (AUD) '@WTICAD':('Commodities', 'Energy', 'Petroleum', 'Petroleum', 'Crude Oil'), # West Texas Intermediate Crude Oil Spot (CAD) '@YCX':('Commodities', 'Energy', 'Energy', 'Energy', 'Thermal Coal'), # Thermal Coal Spot '@YCXAUD':('Commodities', 'Energy', 'Energy', 'Energy', 'Thermal Coal'), # Thermal Coal Spot (AUD) '@YCXCAD':('Commodities', 'Energy', 'Energy', 'Energy', 'Thermal Coal'), # Thermal Coal Spot (CAD) '@ZN':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Zinc'), # Zinc - LME Official Cash '@ZN03S':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Zinc'), # Zinc - LME 03 Months Seller '@ZNAUD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Zinc'), # Zinc - LME Official Cash (AUD) '@ZNCAD':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Zinc'), # Zinc - LME Official Cash (CAD) '@ZNWS':('Commodities', 'Metals', 'Industrial Metals', 'Industrial Metals', 'Zinc'), # Zinc - LME Warehouse Opening Stocks }, 'equity_sector_mappings':{ 'Oil & Gas Drilling':('Energy','Energy','Energy Equipment & Services'), 'Oil & Gas Equipment & Services':('Energy','Energy','Energy Equipment & Services'), 'Integrated Oil & Gas':('Energy','Energy','Oil, Gas & Consumable Fuels'), 'Oil & Gas Exploration & Production':('Energy','Energy','Oil, Gas & Consumable Fuels'), 'Oil & Gas Refining & Marketing':('Energy','Energy','Oil, Gas & Consumable Fuels'), 'Oil & Gas Storage & Transportation':('Energy','Energy','Oil, Gas & Consumable Fuels'), 'Coal & Consumable Fuels':('Energy','Energy','Oil, Gas & Consumable Fuels'), 'Commodity Chemicals':('Materials','Materials','Chemicals'), 'Diversified Chemicals':('Materials','Materials','Chemicals'), 'Fertilizers & Agricultural Chemicals':('Materials','Materials','Chemicals'), 'Industrial Gases':('Materials','Materials','Chemicals'), 'Specialty Chemicals':('Materials','Materials','Chemicals'), 'Construction Materials':('Materials','Materials','Construction Materials'), 'Metal & Glass Containers':('Materials','Materials','Containers & Packaging'), 'Paper Packaging':('Materials','Materials','Containers & Packaging'), 'Aluminum':('Materials','Materials','Metals & Mining'), 'Diversified Metals & Mining':('Materials','Materials','Metals & Mining'), 'Copper':('Materials','Materials','Metals & Mining'), 'Gold':('Materials','Materials','Metals & Mining'), 'Precious Metals & Minerals':('Materials','Materials','Metals & Mining'), 'Silver':('Materials','Materials','Metals & Mining'), 'Steel':('Materials','Materials','Metals & Mining'), 'Forest Products':('Materials','Materials','Paper & Forest Products'), 'Paper Products':('Materials','Materials','Paper & Forest Products'), 'Aerospace & Defense':('Industrials','Capital Goods','Aerospace & Defense'), 'Building Products':('Industrials','Capital Goods','Building Products'), 'Construction & Engineering':('Industrials','Capital Goods','Construction & Engineering'), 'Electrical Components & Equipment':('Industrials','Capital Goods','Electrical Equipment'), 'Heavy Electrical Equipment':('Industrials','Capital Goods','Electrical Equipment'), 'Industrial Conglomerates':('Industrials','Capital Goods','Industrial Conglomerates'), 'Construction Machinery & Heavy Trucks':('Industrials','Capital Goods','Machinery'), 'Agricultural & Farm Machinery':('Industrials','Capital Goods','Machinery'), 'Industrial Machinery':('Industrials','Capital Goods','Machinery'), 'Trading Companies & Distributors':('Industrials','Capital Goods','Trading Companies & Distributors'), 'Commercial Printing':('Industrials','Commercial & Professional Services','Commercial Services & Supplies'), 'Environmental & Facilities Services':('Industrials','Commercial & Professional Services','Commercial Services & Supplies'), 'Office Services & Supplies':('Industrials','Commercial & Professional Services','Commercial Services & Supplies'), 'Diversified Support Services':('Industrials','Commercial & Professional Services','Commercial Services & Supplies'), 'Security & Alarm Services':('Industrials','Commercial & Professional Services','Commercial Services & Supplies'), 'Human Resource & Employment Services':('Industrials','Commercial & Professional Services','Professional Services'), 'Research & Consulting Services':('Industrials','Commercial & Professional Services','Professional Services'), 'Air Freight & Logistics':('Industrials','Transportation','Air Freight & Logistics'), 'Airlines':('Industrials','Transportation','Airlines'), 'Marine':('Industrials','Transportation','Marine'), 'Railroads':('Industrials','Transportation','Road & Rail'), 'Trucking':('Industrials','Transportation','Road & Rail'), 'Airport Services':('Industrials','Transportation','Transportation Infrastructure'), 'Highways & Railtracks':('Industrials','Transportation','Transportation Infrastructure'), 'Marine Ports & Services':('Industrials','Transportation','Transportation Infrastructure'), 'Auto Parts & Equipment':('Consumer Discretionary','Automobiles & Components','Auto Components'), 'Tires & Rubber':('Consumer Discretionary','Automobiles & Components','Auto Components'), 'Automobile Manufacturers':('Consumer Discretionary','Automobiles & Components','Automobiles'), 'Motorcycle Manufacturers':('Consumer Discretionary','Automobiles & Components','Automobiles'), 'Consumer Electronics':('Consumer Discretionary','Consumer Durables & Apparel','Household Durables'), 'Home Furnishings':('Consumer Discretionary','Consumer Durables & Apparel','Household Durables'), 'Homebuilding':('Consumer Discretionary','Consumer Durables & Apparel','Household Durables'), 'Household Appliances':('Consumer Discretionary','Consumer Durables & Apparel','Household Durables'), 'Housewares & Specialties':('Consumer Discretionary','Consumer Durables & Apparel','Household Durables'), 'Leisure Products':('Consumer Discretionary','Consumer Durables & Apparel','Leisure Products'), 'Apparel, Accessories & Luxury Goods':('Consumer Discretionary','Consumer Durables & Apparel','Textiles, Apparel & Luxury Goods'), 'Footwear':('Consumer Discretionary','Consumer Durables & Apparel','Textiles, Apparel & Luxury Goods'), 'Textiles':('Consumer Discretionary','Consumer Durables & Apparel','Textiles, Apparel & Luxury Goods'), 'Casinos & Gaming':('Consumer Discretionary','Consumer Services','Hotels, Restaurants & Leisure'), 'Hotels, Resorts & Cruise Lines':('Consumer Discretionary','Consumer Services','Hotels, Restaurants & Leisure'), 'Leisure Facilities':('Consumer Discretionary','Consumer Services','Hotels, Restaurants & Leisure'), 'Restaurants':('Consumer Discretionary','Consumer Services','Hotels, Restaurants & Leisure'), 'Education Services':('Consumer Discretionary','Consumer Services','Diversified Consumer Services'), 'Specialized Consumer Services':('Consumer Discretionary','Consumer Services','Diversified Consumer Services'), 'Distributors':('Consumer Discretionary','Retailing','Distributors'), 'Internet & Direct Marketing Retail':('Consumer Discretionary','Retailing','Internet & Direct Marketing Retail'), 'Department Stores':('Consumer Discretionary','Retailing','Multiline Retail'), 'General Merchandise Stores':('Consumer Discretionary','Retailing','Multiline Retail'), 'Apparel Retail':('Consumer Discretionary','Retailing','Specialty Retail'), 'Computer & Electronics Retail':('Consumer Discretionary','Retailing','Specialty Retail'), 'Home Improvement Retail':('Consumer Discretionary','Retailing','Specialty Retail'), 'Specialty Stores':('Consumer Discretionary','Retailing','Specialty Retail'), 'Automotive Retail':('Consumer Discretionary','Retailing','Specialty Retail'), 'Homefurnishing Retail':('Consumer Discretionary','Retailing','Specialty Retail'), 'Drug Retail':('Consumer Staples','Food & Staples Retailing','Food & Staples Retailing'), 'Food Distributors':('Consumer Staples','Food & Staples Retailing','Food & Staples Retailing'), 'Food Retail':('Consumer Staples','Food & Staples Retailing','Food & Staples Retailing'), 'Hypermarkets & Super Centers':('Consumer Staples','Food & Staples Retailing','Food & Staples Retailing'), 'Brewers':('Consumer Staples','Food, Beverage & Tobacco','Beverages'), 'Distillers & Vintners':('Consumer Staples','Food, Beverage & Tobacco','Beverages'), 'Soft Drinks':('Consumer Staples','Food, Beverage & Tobacco','Beverages'), 'Agricultural Products':('Consumer Staples','Food, Beverage & Tobacco','Food Products'), 'Packaged Foods & Meats':('Consumer Staples','Food, Beverage & Tobacco','Food Products'), 'Tobacco':('Consumer Staples','Food, Beverage & Tobacco','Tobacco'), 'Household Products':('Consumer Staples','Household & Personal Products','Household Products'), 'Personal Products':('Consumer Staples','Household & Personal Products','Personal Products'), 'Health Care Equipment':('Health Care','Health Care Equipment & Services','Health Care Equipment & Supplies'), 'Health Care Supplies':('Health Care','Health Care Equipment & Services','Health Care Equipment & Supplies'), 'Health Care Distributors':('Health Care','Health Care Equipment & Services','Health Care Providers & Services'), 'Health Care Services':('Health Care','Health Care Equipment & Services','Health Care Providers & Services'), 'Health Care Facilities':('Health Care','Health Care Equipment & Services','Health Care Providers & Services'), 'Managed Health Care':('Health Care','Health Care Equipment & Services','Health Care Providers & Services'), 'Health Care Technology':('Health Care','Health Care Equipment & Services','Health Care Technology'), 'Biotechnology':('Health Care','Pharmaceuticals, Biotechnology & Life Sciences','Biotechnology'), 'Pharmaceuticals':('Health Care','Pharmaceuticals, Biotechnology & Life Sciences','Pharmaceuticals'), 'Life Sciences Tools & Services':('Health Care','Pharmaceuticals, Biotechnology & Life Sciences','Life Sciences Tools & Services'), 'Diversified Banks':('Financials','Banks','Banks'), 'Regional Banks':('Financials','Banks','Banks'), 'Thrifts & Mortgage Finance':('Financials','Banks','Thrifts & Mortgage Finance'), 'Other Diversified Financial Services':('Financials','Diversified Financials','Diversified Financial Services'), 'Multi-Sector Holdings':('Financials','Diversified Financials','Diversified Financial Services'), 'Specialized Finance':('Financials','Diversified Financials','Diversified Financial Services'), 'Consumer Finance':('Financials','Diversified Financials','Consumer Finance'), 'Asset Management & Custody Banks':('Financials','Diversified Financials','Capital Markets'), 'Investment Banking & Brokerage':('Financials','Diversified Financials','Capital Markets'), 'Diversified Capital Markets':('Financials','Diversified Financials','Capital Markets'), 'Financial Exchanges & Data':('Financials','Diversified Financials','Capital Markets'), 'Mortgage REITs':('Financials','Diversified Financials','Mortgage Real Estate Investment Trusts (REITs)'), 'Insurance Brokers':('Financials','Insurance','Insurance'), 'Life & Health Insurance':('Financials','Insurance','Insurance'), 'Multi-line Insurance':('Financials','Insurance','Insurance'), 'Property & Casualty Insurance':('Financials','Insurance','Insurance'), 'Reinsurance':('Financials','Insurance','Insurance'), 'IT Consulting & Other Services':('Information Technology','Software & Services','IT Services'), 'Data Processing & Outsourced Services':('Information Technology','Software & Services','IT Services'), 'Internet Services & Infrastructure':('Information Technology','Software & Services','IT Services'), 'Application Software':('Information Technology','Software & Services','Software'), 'Systems Software':('Information Technology','Software & Services','Software'), 'Communications Equipment':('Information Technology','Technology Hardware & Equipment','Communications Equipment'), 'Technology Hardware, Storage & Peripherals':('Information Technology','Technology Hardware & Equipment','Technology Hardware, Storage & Peripherals'), 'Electronic Equipment & Instruments':('Information Technology','Technology Hardware & Equipment','Electronic Equipment, Instruments & Components'), 'Electronic Components':('Information Technology','Technology Hardware & Equipment','Electronic Equipment, Instruments & Components'), 'Electronic Manufacturing Services':('Information Technology','Technology Hardware & Equipment','Electronic Equipment, Instruments & Components'), 'Technology Distributors':('Information Technology','Technology Hardware & Equipment','Electronic Equipment, Instruments & Components'), 'Semiconductor Equipment':('Information Technology','Semiconductors & Semiconductor Equipment','Semiconductors & Semiconductor Equipment'), 'Semiconductors':('Information Technology','Semiconductors & Semiconductor Equipment','Semiconductors & Semiconductor Equipment'), 'Alternative Carriers':('Communication Services','Communication Services','Diversified Telecommunication Services'), 'Integrated Telecommunication Services':('Communication Services','Communication Services','Diversified Telecommunication Services'), 'Wireless Telecommunication Services':('Communication Services','Communication Services','Wireless Telecommunication Services'), 'Advertising':('Communication Services','Media & Entertainment','Media'), 'Broadcasting':('Communication Services','Media & Entertainment','Media'), 'Cable & Satellite':('Communication Services','Media & Entertainment','Media'), 'Publishing':('Communication Services','Media & Entertainment','Media'), 'Movies & Entertainment':('Communication Services','Media & Entertainment','Entertainment'), 'Interactive Home Entertainment':('Communication Services','Media & Entertainment','Entertainment'), 'Interactive Media & Services':('Communication Services','Media & Entertainment','Interactive Media & Services'), 'Electric Utilities':('Utilities','Utilities','Electric Utilities'), 'Gas Utilities':('Utilities','Utilities','Gas Utilities'), 'Multi-Utilities':('Utilities','Utilities','Multi-Utilities'), 'Water Utilities':('Utilities','Utilities','Water Utilities'), 'Independent Power Producers & Energy Traders':('Utilities','Utilities','Independent Power and Renewable Electricity Producers'), 'Renewable Electricity':('Utilities','Utilities','Independent Power and Renewable Electricity Producers'), 'Diversified REITs':('Real Estate','Real Estate','Equity Real Estate Investment Trusts (REITs)'), 'Industrial REITs':('Real Estate','Real Estate','Equity Real Estate Investment Trusts (REITs)'), 'Hotel & Resort REITs':('Real Estate','Real Estate','Equity Real Estate Investment Trusts (REITs)'), 'Office REITs':('Real Estate','Real Estate','Equity Real Estate Investment Trusts (REITs)'), 'Health Care REITs':('Real Estate','Real Estate','Equity Real Estate Investment Trusts (REITs)'), 'Residential REITs':('Real Estate','Real Estate','Equity Real Estate Investment Trusts (REITs)'), 'Retail REITs':('Real Estate','Real Estate','Equity Real Estate Investment Trusts (REITs)'), 'Specialized REITs':('Real Estate','Real Estate','Equity Real Estate Investment Trusts (REITs)'), 'Diversified Real Estate Activities':('Real Estate','Real Estate','Real Estate Management & Development'), 'Real Estate Operating Companies':('Real Estate','Real Estate','Real Estate Management & Development'), 'Real Estate Development':('Real Estate','Real Estate','Real Estate Management & Development'), 'Real Estate Services':('Real Estate','Real Estate','Real Estate Management & Development'), }, }

112.670707

174

0.660887

5,713

55,772

6.430947

0.120952

0.084921

0.093087

0.127382

0.841481

0.807975

0.755144

0.699102

0.646053

0.556369

0

0.011345

0.15922

55,772

494

175

112.898785

0.772157

0.121262

0

0.701331

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1

0

false

0

null

0

1

0

1

0

1

0

null

0

5

4e0e25c801ebf3f2d9ead4fd258f19fb18985ea2

39

py

Python

inference/__init__.py

kamodulin/TRAILMAP

1700eca3db070b02132ac1d9db8b9a80323d02cb

[ "MIT" ]

29

2019-11-12T22:36:51.000Z

2021-12-16T00:11:44.000Z

inference/__init__.py

kamodulin/TRAILMAP

1700eca3db070b02132ac1d9db8b9a80323d02cb

[ "MIT" ]

14

2019-11-06T19:19:00.000Z

2022-01-25T21:14:13.000Z

inference/__init__.py

kamodulin/TRAILMAP

1700eca3db070b02132ac1d9db8b9a80323d02cb

[ "MIT" ]

13

2019-10-22T12:53:33.000Z

2022-03-15T20:15:52.000Z

from inference.segment_brain import *

13

37

0.820513

5

39

6.2

1

0

0.128205

39

2

38

19.5

0.911765

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9dd4d38622ed2407d8cc7444c47602b73db1172d

45

py

Python

pyEpiabm/pyEpiabm/tests/test_property/__init__.py

Saketkr21/epiabm

3ec0dcbc78d3fd4114ed3c6bdd78ef39f0013d2f

[ "BSD-3-Clause" ]

11

2021-12-02T15:24:02.000Z

2022-03-10T14:02:13.000Z

pyEpiabm/pyEpiabm/tests/test_property/__init__.py

Saketkr21/epiabm

3ec0dcbc78d3fd4114ed3c6bdd78ef39f0013d2f

[ "BSD-3-Clause" ]

119

2021-11-24T13:56:48.000Z

2022-03-30T11:52:07.000Z

pyEpiabm/pyEpiabm/tests/test_unit/test_property/__init__.py

SABS-R3-Epidemiology/epiabm

8eb83fd2de84104f6f77929e3771095f7b033ddc

[ "BSD-3-Clause" ]

3

2022-01-13T03:05:19.000Z

2022-03-11T22:00:17.000Z

# # Tests for subpackage pyEpiabm.property #

11.25

40

0.755556

5

45

6.8

1

0

0.155556

45

3

41

15

0.894737

0.844444

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

d1b9852fb2e958e5f64404e0ede3da7fbca1d3c4

309

py

Python

units/prefixes/small.py

misspellted/enlightened

59fac50bb17a5a3236a9b4561b5ed0107825fb85

[ "Unlicense" ]

2

2021-03-03T05:57:51.000Z

2021-07-06T06:26:07.000Z

units/prefixes/small.py

misspellted/enlightened

59fac50bb17a5a3236a9b4561b5ed0107825fb85

[ "Unlicense" ]

6

2021-04-09T00:30:24.000Z

2022-03-14T16:08:37.000Z

units/prefixes/small.py

misspellted/enlightened

59fac50bb17a5a3236a9b4561b5ed0107825fb85

[ "Unlicense" ]

1

2021-03-03T06:00:46.000Z

from units.prefixes import Prefix class Milli(Prefix): def __init__(self): Prefix.__init__(self, "m", "milli", 1e-3) class Micro(Prefix): def __init__(self): Prefix.__init__(self, "μ", "micro", 1e-6) class Nano(Prefix): def __init__(self): Prefix.__init__(self, "ns", "nano", 1e-9)

15.45

45

0.653722

44

309

4.045455

0.431818

0.269663

0.219101

0.286517

0.522472

0

0.023622

0.177994

309

19

46

16.263158

0.677165

0

0.3

0

0.058824

0

1

0.3

false

0

0.1

0

0.7

0

null

1

0

null

0

1

0

1

0

5

ae0a703df04c1a245dba88843845ded8e8589bda

711

py

Python

jdit/trainer/instances/__init__.py

dingguanglei/jdit

ef878e696c9e2fad5069f106496289d4e4cc6154

[ "Apache-2.0" ]

28

2019-06-18T15:56:53.000Z

2021-11-09T13:11:13.000Z

jdit/trainer/instances/__init__.py

dingguanglei/jdit

ef878e696c9e2fad5069f106496289d4e4cc6154

[ "Apache-2.0" ]

2

2018-10-24T01:09:56.000Z

2018-11-08T07:13:48.000Z

jdit/trainer/instances/__init__.py

dingguanglei/jdit

ef878e696c9e2fad5069f106496289d4e4cc6154

[ "Apache-2.0" ]

8

2019-01-11T01:12:15.000Z

2021-03-12T10:15:43.000Z

from .fashionClassification import FashionClassTrainer, start_fashionClassTrainer from .fashionGenerateGan import FashionGenerateGanTrainer, start_fashionGenerateGanTrainer from .cifarPix2pixGan import start_cifarPix2pixGanTrainer from .fashionClassParallelTrainer import start_fashionClassPrarallelTrainer from .fashionAutoencoder import FashionAutoEncoderTrainer, start_fashionAutoencoderTrainer __all__ = ['FashionClassTrainer', 'start_fashionClassTrainer', 'FashionGenerateGanTrainer', 'start_fashionGenerateGanTrainer', 'cifarPix2pixGan', 'start_cifarPix2pixGanTrainer', 'start_fashionClassPrarallelTrainer', 'start_fashionAutoencoderTrainer', 'FashionAutoEncoderTrainer']

71.1

99

0.853727

43

711

13.790698

0.325581

0.080944

0.145025

0

0.006211

0.094233

711

9

100

79

0.914596

0

1

0

0.327707

0.279887

0

1

0

false

0

0.555556

0

0.555556

0

1

null

0

1

0

1

0

null

0

1

0

1

0

5

ae27587556ffd7ff853ae00896a8b61ada032522

174

py

Python

ex.024.identificandoPalavras.py

romulorm/cev-python

b5c6844956c131a9e4e02355459c218739ebf8c5

[ "MIT" ]

null

ex.024.identificandoPalavras.py

romulorm/cev-python

b5c6844956c131a9e4e02355459c218739ebf8c5

[ "MIT" ]

null

ex.024.identificandoPalavras.py

romulorm/cev-python

b5c6844956c131a9e4e02355459c218739ebf8c5

[ "MIT" ]

null

cidade = str(input('Informe a cidade em que nasceu: ').strip()) print('O nome da cidade em que nasceu começa com a palavra Santo: {}'.format(cidade[:5].upper() == 'SANTO'))

43.5

108

0.678161

28

174

4.214286

0.714286

0.135593

0.186441

0.288136

0

0.006711

0.143678

174

3

109

58

0.785235

0

0.566474

0

1

0

false

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

5

ae2ab85badcfff462c032b273254622c19ecceb2

181

py

Python

migrate.py

dannluciano/django-mini-system-monitor

977feec09b7c64a03f4d6edd0cc39fab61f29f43

[ "MIT" ]

9

2021-01-31T20:51:56.000Z

2022-01-30T23:56:07.000Z

migrate.py

dannluciano/django-mini-system-monitor

977feec09b7c64a03f4d6edd0cc39fab61f29f43

[ "MIT" ]

3

2021-01-31T21:59:33.000Z

2021-09-01T19:18:33.000Z

migrate.py

dannluciano/django-mini-system-monitor

977feec09b7c64a03f4d6edd0cc39fab61f29f43

[ "MIT" ]

1

2022-03-20T04:10:54.000Z

#!/usr/bin/env python # migrate.py from django.core.management import call_command from boot_django import boot_django boot_django() call_command("migrate", "mini_system_monitor")

22.625

47

0.812155

27

181

5.185185

0.62963

0.214286

0

0.088398

181

8

48

22.625

0.848485

0.171271

0

0.174497

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

ae2d2d99b4acba40f8a984a2f5ae910424f896ec

240

py

Python

subdir1/__init__.py

cramraj8/RamPyPackage

6f4e046e63860a5894bdbe3b4a3059a4f835a8da

[ "Apache-2.0" ]

null

subdir1/__init__.py

cramraj8/RamPyPackage

6f4e046e63860a5894bdbe3b4a3059a4f835a8da

[ "Apache-2.0" ]

null

subdir1/__init__.py

cramraj8/RamPyPackage

6f4e046e63860a5894bdbe3b4a3059a4f835a8da

[ "Apache-2.0" ]

null

from .intermediate_from_subdir1 import intermediate_from_subdir1 from .intermediate_2_from_subdir1 import intermediate_2_from_subdir1 __all__ = ( # sub-packages 'intermediate_from_subdir1', 'intermediate_2_from_subdir1', )

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null

1

0

null

0

1

0

5

ae33f67c3dd64927b4a6ce06f577c50082cc4ba0

9,393

py

Python

function/python/brightics/function/regression/test/ada_boost_regression_test.py

parkjh80/studio

6d8d8384272e5e1b2838b12e5557272a19408e89

[ "Apache-2.0" ]

202

2018-10-23T04:37:35.000Z

2022-01-27T05:51:10.000Z

function/python/brightics/function/regression/test/ada_boost_regression_test.py

data-weirdo/studio

48852c4f097f773ce3d408b59f79fda2e2d60470

[ "Apache-2.0" ]

444

2018-11-07T08:41:14.000Z

2022-03-16T06:48:57.000Z

function/python/brightics/function/regression/test/ada_boost_regression_test.py

data-weirdo/studio

48852c4f097f773ce3d408b59f79fda2e2d60470

[ "Apache-2.0" ]

99

2018-11-08T04:12:13.000Z

2022-03-30T05:36:27.000Z

""" Copyright 2019 Samsung SDS Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from brightics.function.regression.ada_boost_regression import ada_boost_regression_train from brightics.function.regression.ada_boost_regression import ada_boost_regression_predict from brightics.common.datasets import load_iris import unittest import pandas as pd import numpy as np import HtmlTestRunner import os class ADABoostRegression(unittest.TestCase): def setUp(self): print("*** ADA Boost Regression UnitTest Start ***") self.testdata = load_iris() def tearDown(self): print("*** ADA Boost Regression UnitTest End ***") def test(self): ada_train = ada_boost_regression_train(self.testdata, feature_cols=['sepal_length', 'sepal_width', 'petal_length', ], label_col='petal_width', random_state=12345) ada_model = ada_train['model']['regressor'] estimator_weights = ada_model.estimator_weights_ if hasattr(ada_model, 'estimator_weights_') else None estimator_errors = ada_model.estimator_errors_ if hasattr(ada_model, 'estimator_errors_') else None feature_importances = ada_model.feature_importances_ if hasattr(ada_model, 'feature_importances_') else None np.testing.assert_array_equal([round(x, 15) for x in estimator_weights], [1.413875270282188, 0.842690804421279, 0.745744569599211, 0.849855966774747, 0.873567992253140, 0.412870149785776, 0.735336038549665, 0.948762342244301, 0.119925737139696, 1.084352707003922, 0.215508369140552, 1.261341652523880, 0.693756215973489, 0.815705197279502, 0.107744343744492, 0.892721151562112, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000, 0.000000000000000]) np.testing.assert_array_equal([round(x, 15) for x in estimator_errors], [0.195623543954738, 0.300968372309124, 0.321749243945297, 0.299463072654870, 0.294512419786350, 0.398224113163979, 0.324024864473794, 0.279133793052534, 0.470054447315167, 0.252683191393177, 0.446330465454831, 0.220743020672426, 0.333198005871043, 0.306676087054173, 0.473089941916390, 0.290548596637435, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000, 1.000000000000000]) np.testing.assert_array_equal([round(x, 15) for x in feature_importances], [0.060563052789016, 0.057352925738117, 0.882084021472867]) predict = ada_boost_regression_predict(self.testdata, ada_train['model']) species = predict['out_table']['species'] prediction = predict['out_table']['prediction'] np.testing.assert_array_equal(species, ['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica']) np.testing.assert_array_equal([round(x, 15) for x in prediction], [0.271428571428571 , 0.194444444444444 , 0.205555555555556 , 0.194444444444444 , 0.271428571428571 , 0.375000000000000 , 0.233333333333333 , 0.247619047619048 , 0.194444444444444 , 0.194444444444444 , 0.275000000000000 , 0.250000000000000 , 0.194444444444444 , 0.194444444444444 , 0.300000000000000 , 0.275000000000000 , 0.275000000000000 , 0.271428571428571 , 0.400000000000000 , 0.271428571428571 , 0.357142857142857 , 0.271428571428571 , 0.271428571428571 , 0.316666666666667 , 0.250000000000000 , 0.271428571428571 , 0.271428571428571 , 0.271428571428571 , 0.247619047619048 , 0.230000000000000 , 0.230000000000000 , 0.247619047619048 , 0.257894736842105 , 0.270000000000000 , 0.194444444444444 , 0.233333333333333 , 0.300000000000000 , 0.194444444444444 , 0.194444444444444 , 0.247619047619048 , 0.271428571428571 , 0.205555555555556 , 0.205555555555556 , 0.366666666666667 , 0.366666666666667 , 0.194444444444444 , 0.316666666666667 , 0.205555555555556 , 0.275000000000000 , 0.233333333333333 , 1.494444444444444 , 1.494444444444444 , 1.933999999999999 , 1.205882352941176 , 1.460000000000000 , 1.460000000000000 , 1.494444444444444 , 1.100000000000000 , 1.494444444444444 , 1.205263157894737 , 1.100000000000000 , 1.350000000000000 , 1.205882352941176 , 1.494444444444444 , 1.242105263157894 , 1.421428571428571 , 1.494444444444444 , 1.205882352941176 , 1.460000000000000 , 1.205263157894737 , 1.620000000000000 , 1.242105263157894 , 1.626315789473684 , 1.460000000000000 , 1.357142857142857 , 1.421428571428571 , 1.823333333333333 , 1.933999999999999 , 1.494444444444444 , 1.093333333333333 , 1.160000000000000 , 1.160000000000000 , 1.205263157894737 , 1.823333333333333 , 1.494444444444444 , 1.494444444444444 , 1.494444444444444 , 1.251851851851852 , 1.280000000000000 , 1.205882352941176 , 1.251851851851852 , 1.494444444444444 , 1.205882352941176 , 1.100000000000000 , 1.242105263157894 , 1.350000000000000 , 1.280000000000000 , 1.357142857142857 , 1.100000000000000 , 1.242105263157894 , 2.197916666666667 , 1.907843137254902 , 1.998387096774192 , 1.933999999999999 , 1.998387096774192 , 1.998387096774192 , 1.494444444444444 , 1.998387096774192 , 1.933999999999999 , 2.197916666666667 , 1.998387096774192 , 1.907843137254902 , 1.998387096774192 , 1.907843137254902 , 1.985882352941177 , 1.998701298701298 , 1.998387096774192 , 2.197916666666667 , 1.985882352941177 , 1.823333333333333 , 2.012903225806451 , 1.823333333333333 , 1.998387096774192 , 1.626315789473684 , 2.197916666666667 , 2.012903225806451 , 1.592000000000000 , 1.823333333333333 , 1.985882352941177 , 1.998387096774192 , 1.998387096774192 , 2.197916666666667 , 1.985882352941177 , 1.875342465753425 , 1.825000000000001 , 1.998387096774192 , 2.127500000000000 , 1.998701298701298 , 1.620000000000000 , 1.998701298701298 , 1.998701298701298 , 1.998387096774192 , 1.907843137254902 , 2.012903225806451 , 2.197916666666667 , 1.985882352941177 , 1.823333333333333 , 1.985882352941177 , 2.127500000000000 , 1.933999999999999]) if __name__ == '__main__': filepath = os.path.dirname(os.path.abspath(__file__)) reportFoler = filepath + "/../../../../../../../reports" unittest.main(testRunner=HtmlTestRunner.HTMLTestRunner(combine_reports=True, output=reportFoler))

161.948276

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992

9,393

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0.082829

0.121707

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null

0

1

0

1

0

null

0

1

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5

ae65c059f0116aeabe64833abaad136d794c0427

83

py

Python

kutils/urls.py

gluwer/przepisymm

dc83fdc4068fb0102a87081bd519807fd66397c2

[ "BSD-3-Clause" ]

null

kutils/urls.py

gluwer/przepisymm

dc83fdc4068fb0102a87081bd519807fd66397c2

[ "BSD-3-Clause" ]

null

kutils/urls.py

gluwer/przepisymm

dc83fdc4068fb0102a87081bd519807fd66397c2

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # index.urls def make_rules(): return [] all_views = {}

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11.857143

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1

0.333333

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null

0

1

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null

0

1

0

1

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5

88897cb95ceea7a38cdd7c606ae0e58465ed89dd

26

py

Python

myNumber.py

Walker-00/ph_no_track

1604c435e22dc917543ba7f0b1a57f527e9b5e94

[ "BSL-1.0" ]

null

myNumber.py

Walker-00/ph_no_track

1604c435e22dc917543ba7f0b1a57f527e9b5e94

[ "BSL-1.0" ]

null

myNumber.py

Walker-00/ph_no_track

1604c435e22dc917543ba7f0b1a57f527e9b5e94

[ "BSL-1.0" ]

null

number = "+959252160714"

8.666667

24

0.692308

2

26

9

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0.545455

0.153846

26

2

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null

0

1

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1

0

1

0

null

0

5

ee0ff8e4a514bf1011d9aaf3cb032dad5ee1bb6e

239

py

Python

pvae/models/__init__.py

cll27/pvae

7abbb4604a1acec2332b1b4dfe21267834b505cc

[ "MIT" ]

1

2021-06-17T13:47:38.000Z

pvae/models/__init__.py

cll27/pvae

7abbb4604a1acec2332b1b4dfe21267834b505cc

[ "MIT" ]

null

pvae/models/__init__.py

cll27/pvae

7abbb4604a1acec2332b1b4dfe21267834b505cc

[ "MIT" ]

null

from .vae_tree import Tree as VAE_tree from .vae_hyp_tree import Tree as VAE_hyp_tree from .vae_mnist import Mnist as VAE_mnist from .vae_hyp_mnist import Mnist as VAE_hyp_mnist __all__ = [VAE_tree, VAE_hyp_tree, VAE_mnist, VAE_hyp_mnist]

39.833333

60

0.832636

47

239

3.765957

0.170213

0.20339

0.169492

0.180791

0.451977

0

0.125523

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39.833333

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false

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0.8

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1

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null

0

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1

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5

ee8dfdfefc4733796d78124b9fb526ca6ebf53c4

418

py

Python

django/alumnos/Equipo 3/historial_medico_mujeres_priv-master/project/my_web/my_web/padecimientos/models.py

R3SWebDevelopment/CeroUnoApprenticeshipProgramPython

b00b3dce329240889401627e99b72d3d9cadb7d9

[ "MIT" ]

1

2019-11-29T21:34:42.000Z

django/alumnos/Equipo 3/historial_medico_mujeres_priv-master/project/my_web/my_web/padecimientos/models.py

R3SWebDevelopment/CeroUnoApprenticeshipProgramPython

b00b3dce329240889401627e99b72d3d9cadb7d9

[ "MIT" ]

null

django/alumnos/Equipo 3/historial_medico_mujeres_priv-master/project/my_web/my_web/padecimientos/models.py

R3SWebDevelopment/CeroUnoApprenticeshipProgramPython

b00b3dce329240889401627e99b72d3d9cadb7d9

[ "MIT" ]

1

2019-11-30T17:51:50.000Z

from django.db import models class Padecimiento(models.Model): nombre_padecimiento = models.CharField(max_length=200, null=False, blank=False) descripcion_padecimiento = models.TextField(null=False, blank=False, max_length=300) intensidad_padecimiento = models.IntegerField(null=False, blank=False) def __str__(self): return "Padecimiento: {nombre}".format(nombre = self.nombre_padecimiento)

41.8

88

0.770335

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418

6.24

0.52

0.230769

0.134615

0.182692

0

0.016438

0.126794

418

9

89

46.444444

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false

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null

1

0

1

0

1

0

null

0

1

0

5

c98e843f842e35b3689d57b57c4a6b7fecfcf674

279

py

Python

psdl/__init__.py

ankith26/psdl

29846fe4e8054c413fc7b9f864d6def60f28da72

[ "MIT" ]

1

2021-02-06T06:52:28.000Z

psdl/__init__.py

ankith26/psdl

29846fe4e8054c413fc7b9f864d6def60f28da72

[ "MIT" ]

null

psdl/__init__.py

ankith26/psdl

29846fe4e8054c413fc7b9f864d6def60f28da72

[ "MIT" ]

2

2021-02-06T06:52:33.000Z

2021-02-06T11:26:10.000Z

""" This file is a part of the psdl package. Copyright (C) 2021 Ankith (ankith26) Distributed under the MIT license. """ from psdl.sdl import * from psdl.version import * from psdl.video import * from psdl.clipboard import * from psdl.cpuinfo import * from psdl.events import *

21.461538

40

0.749104

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4.860465

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0.229665

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null

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5

c9c081368a0ad62a478c8fe0ee1bd5b28f509675

1,509

py

Python

survol/sources_types/rabbitmq/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

9

2017-10-05T23:36:23.000Z

2021-08-09T15:40:03.000Z

survol/sources_types/rabbitmq/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

21

2018-01-02T09:33:03.000Z

2018-08-27T11:09:52.000Z

survol/sources_types/rabbitmq/__init__.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

4

2018-06-23T09:05:45.000Z

2021-01-22T15:36:50.000Z

""" RabbitMQ concepts """ def Graphic_shape(): return "none" def Graphic_colorfill(): return "#FFCC66" def Graphic_colorbg(): return "#FFCC66" def Graphic_border(): return 2 def Graphic_is_rounded(): return True # managementUrl = rabbitmq.ManagementUrlPrefix(configNam) # managementUrl = rabbitmq.ManagementUrlPrefix(configNam,"users",namUser) # managementUrl = rabbitmq.ManagementUrlPrefix(configNam,"vhosts",namVHost) # managementUrl = rabbitmq.ManagementUrlPrefix(configNam,"exchanges",namVHost,namExchange) # managementUrl = rabbitmq.ManagementUrlPrefix(configNam,"queues",namVHost,namQ) # managementUrl = "http://" + configNam + "/#/queues/" + "%2F" + "/" + namQueue # managementUrl = "http://" + configNam + "/#/vhosts/" + "%2F" # managementUrl = "http://" + configNam + "/#/users/" + namUser # managementUrl = "http://" + configNam + "/#/users/" + namUser def ManagementUrlPrefix(config_nam, key="vhosts", name_key1="", name_key2=""): pre_prefix = "http://" + config_nam + "/#/" if not key: return pre_prefix if key == "users": return pre_prefix + "users/" + name_key1 # It is a virtual host name. if name_key1 == "/": effective_v_host = "%2F" else: effective_v_host = name_key1 effective_v_host = effective_v_host.lower() # RFC4343 vhost_prefix = pre_prefix + key + "/" + effective_v_host if key in ["vhosts", "connections"]: return vhost_prefix return vhost_prefix + "/" + name_key2

26.946429

90

0.667329

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1,509

6.216561

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0.251025

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1,509

55

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0.451955

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0.24

false

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null

0

1

0

null

0

1

0

1

0

5

c9d34eec409d6e8348fcd8226051e9f8c16fc913

206

py

Python

profiles_api/admin.py

kalenshi/profiles-rest-api

b1840b4387cc7ee8744f0b70ceb046b674cc84b4

[ "MIT" ]

null

profiles_api/admin.py

kalenshi/profiles-rest-api

b1840b4387cc7ee8744f0b70ceb046b674cc84b4

[ "MIT" ]

null

profiles_api/admin.py

kalenshi/profiles-rest-api

b1840b4387cc7ee8744f0b70ceb046b674cc84b4

[ "MIT" ]

null

from django.contrib import admin from profiles_api.models import Book,Author,UserProfile # Register your models here. admin.site.register(UserProfile) admin.site.register(Book) admin.site.register(Author)

25.75

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0.3

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1

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null

0

1

0

1

0

5

a022ea1fbec0bfcd5ea6b3a8c37db8fc18c19ac4

163

py

Python

graduate_main/test/root_setup.py

mxito3/graduate_pro

7fca6e0387f741b8fc887cddd7e7fd8c9953a330

[ "MIT" ]

1

2020-01-02T01:40:57.000Z

graduate_main/test/root_setup.py

mxito3/graduate_pro

7fca6e0387f741b8fc887cddd7e7fd8c9953a330

[ "MIT" ]

1

2021-06-02T01:18:04.000Z

graduate_main/test/root_setup.py

mxito3/graduate_pro

7fca6e0387f741b8fc887cddd7e7fd8c9953a330

[ "MIT" ]

null

import sys import os.path root_directory=os.path.abspath(os.path.join(os.path.abspath(__file__),"../../../")) print(root_directory) sys.path.append(root_directory)

32.6

83

0.766871

25

163

4.72

0.44

0.20339

0.220339

0

0.03681

163

5

84

32.6

0.751592

0

0.054878

0

1

0

false

0

0.4

0

0.4

0.2

1

0

null

1

0

1

0

null

0

1

0

5

4e64bc63448a1de096d082cb2c97c554ebbe5870

46

py

Python

daceml/transformation/__init__.py

TizianoDeMatteis/daceml

d11ab15a8681ec0e0c52a68b838fb70e26ea6559

[ "BSD-3-Clause" ]

null

daceml/transformation/__init__.py

TizianoDeMatteis/daceml

d11ab15a8681ec0e0c52a68b838fb70e26ea6559

[ "BSD-3-Clause" ]

null

daceml/transformation/__init__.py

TizianoDeMatteis/daceml

d11ab15a8681ec0e0c52a68b838fb70e26ea6559

[ "BSD-3-Clause" ]

null

from .constant_folding import ConstantFolding

23

45

0.891304

5

46

8

1

0

0.086957

46

1

46

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

4e83dc5a790c587e62667ababccb48beb61bbff0

164

py

Python

ebl/fragmentarium/application/fragment_pager_info_schema.py

ElectronicBabylonianLiterature/dictionary

5977a57314cf57f94f75cd12520f178b1d6a6555

[ "MIT" ]

4

2020-04-12T14:24:51.000Z

2020-10-15T15:48:15.000Z

ebl/fragmentarium/application/fragment_pager_info_schema.py

ElectronicBabylonianLiterature/dictionary

5977a57314cf57f94f75cd12520f178b1d6a6555

[ "MIT" ]

200

2019-12-04T09:53:20.000Z

2022-03-30T20:11:31.000Z

ebl/fragmentarium/application/fragment_pager_info_schema.py

ElectronicBabylonianLiterature/dictionary

5977a57314cf57f94f75cd12520f178b1d6a6555

[ "MIT" ]

1

2021-09-06T16:22:39.000Z

from marshmallow import Schema, fields class FragmentPagerInfoSchema(Schema): previous = fields.String(required=True) next = fields.String(required=True)

23.428571

43

0.77439

18

164

7.055556

0.666667

0.188976

0.314961

0.377953

0

0.140244

164

6

44

27.333333

0.900709

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

5

4e9318d8f6e3ee9cd3a3ec14f83e44b6b8c4c547

154

py

Python

project/account/admin.py

leehe228/Ecoverse

1554e4f44c8ba3cc60a0b7f2509f4d9b7a94099a

[ "CC-BY-4.0" ]

null

project/account/admin.py

leehe228/Ecoverse

1554e4f44c8ba3cc60a0b7f2509f4d9b7a94099a

[ "CC-BY-4.0" ]

null

project/account/admin.py

leehe228/Ecoverse

1554e4f44c8ba3cc60a0b7f2509f4d9b7a94099a

[ "CC-BY-4.0" ]

null

from django.contrib import admin from .models import User, Ingame # Register your models here. admin.site.register(User) admin.site.register(Ingame) #

15.4

32

0.779221

22

154

5.454545

0.545455

0.15

0.283333

0

0.12987

154

9

33

17.111111

0.895522

0.168831

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

4eb3ae5369bcaede734db944101db2aed1345d9c

3,958

py

Python

tests/test_frame_unmarshaling_errors.py

annuupadhyayPS/pamqp

cf7b3d2ba367ff8226b1a6a1fbf6931162de3574

[ "BSD-3-Clause" ]

38

2015-08-24T06:52:59.000Z

2022-02-06T09:48:15.000Z

tests/test_frame_unmarshaling_errors.py

annuupadhyayPS/pamqp

cf7b3d2ba367ff8226b1a6a1fbf6931162de3574

[ "BSD-3-Clause" ]

33

2015-01-05T19:28:05.000Z

2022-02-13T22:31:36.000Z

tests/test_frame_unmarshaling_errors.py

annuupadhyayPS/pamqp

cf7b3d2ba367ff8226b1a6a1fbf6931162de3574

[ "BSD-3-Clause" ]

21

2015-05-06T08:33:28.000Z

2022-02-19T19:39:33.000Z

# coding=utf-8 import struct import unittest from pamqp import constants, exceptions, frame class TestCase(unittest.TestCase): def test_invalid_protocol_header(self): with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(b'AMQP\x00\x00\t') self.assertTrue(str(err).startswith( "Could not unmarshal <class 'pamqp.header.ProtocolHeader'> " 'frame: Data did not match the ProtocolHeader format')) def test_invalid_frame_header(self): frame_data = struct.pack('>BI', 255, 0) with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(frame_data) self.assertEqual( str(err), 'Could not unmarshal Unknown frame: No frame size') def test_frame_with_no_length(self): frame_data = (b'\x01\x00\x01\x00\x00\x00\x00\x00<\x00P\x00\x00\x00\x00' b'\x00\x00\x00\x01\x00\xce') with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(frame_data) self.assertEqual( str(err), 'Could not unmarshal Unknown frame: No frame size') def test_frame_malformed_length(self): frame_data = (b'\x01\x00\x01\x00\x00\x00\x0c\x00<\x00P\x00\x00\x00\x00' b'\x00\x00\x00\xce') with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(frame_data) self.assertEqual( str(err), 'Could not unmarshal Unknown frame: Not all data received') def test_frame_malformed_end_byte(self): frame_data = (b'\x01\x00\x01\x00\x00\x00\r\x00<\x00P\x00\x00\x00\x00' b'\x00\x00\x00\x01\x00\x00') with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(frame_data) self.assertEqual( str(err), 'Could not unmarshal Unknown frame: Last byte error') def test_malformed_frame_content(self): payload = struct.pack('>HxxQ', 8192, 32768) frame_value = b''.join([struct.pack('>BHI', 5, 0, len(payload)), payload, constants.FRAME_END_CHAR]) with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(frame_value) self.assertEqual( str(err), 'Could not unmarshal Unknown frame: Unknown frame type: 5') def test_invalid_method_frame_index(self): payload = struct.pack('>L', 42949) frame_value = b''.join([struct.pack('>BHI', 1, 0, len(payload)), payload, constants.FRAME_END_CHAR]) with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(frame_value) self.assertEqual( str(err), ('Could not unmarshal Unknown frame: ' 'Unknown method index: 42949')) def test_invalid_method_frame_content(self): payload = struct.pack('>L', 0x000A0029) frame_value = b''.join([struct.pack('>BHI', 1, 0, len(payload)), payload, constants.FRAME_END_CHAR]) with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(frame_value) self.assertTrue(str(err).startswith( 'Could not unmarshal <pamqp.specification.Connection.OpenOk')) def test_invalid_content_header_frame(self): payload = struct.pack('>L', 0x000A0029) frame_value = b''.join([struct.pack('>BHI', 2, 0, len(payload)), payload, constants.FRAME_END_CHAR]) with self.assertRaises(exceptions.UnmarshalingException) as err: frame.unmarshal(frame_value) self.assertTrue(str(err).startswith( 'Could not unmarshal ContentHeader frame:'))

44.977273

79

0.614452

454

3,958

5.23348

0.185022

0.063131

0.05303

0.113636

0.785354

0.757155

0.738215

0.726431

0.70665

0.677189

0

0.054584

0.277918

3,958

87

80

45.494253

0.776767

0.003032

0

0.540541

0

0.040541

0.201572

0.06998

0

0.005071

0

0.243243

1

0.121622

false

0

0.040541

0

0.175676

0

null

0

1

0

null

0

5

4ed9cc2a600c1d17b17624862d8eafc6a548e2be

272

py

Python

goopylib/imports.py

BhavyeMathur/goopylib

f9eb1458e9218a8dd4add6693ce70b804624bf91

[ "MIT" ]

25

2020-07-09T10:57:16.000Z

2022-02-06T10:31:34.000Z

goopylib/imports.py

BhavyeMathur/goopy

f9eb1458e9218a8dd4add6693ce70b804624bf91

[ "MIT" ]

48

2020-07-02T20:08:40.000Z

2020-07-06T16:09:25.000Z

goopylib/imports.py

BhavyeMathur/goopy

f9eb1458e9218a8dd4add6693ce70b804624bf91

[ "MIT" ]

1

2020-12-01T13:45:53.000Z

from goopylib.styles import * from goopylib.util import * from goopylib.constants import * from goopylib.colours import * from goopylib.Window import Window from goopylib.objects.imports import * from goopylib.maths.imports import * from goopylib.sound.imports import *

24.727273

38

0.808824

36

272

6.111111

0.333333

0.436364

0.490909

0.227273

0

0.125

272

10

39

27.2

0.92437

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

5

14f851ff92e887a0afc83031923373bfc6a58480

162

py

Python

impress/json.py

somia/impress

6ebacd0cc7c5e089364d5d37be071e4768c273b1

[ "BSD-2-Clause" ]

1

2020-09-02T04:02:17.000Z

impress/json.py

somia/impress

6ebacd0cc7c5e089364d5d37be071e4768c273b1

[ "BSD-2-Clause" ]

null

impress/json.py

somia/impress

6ebacd0cc7c5e089364d5d37be071e4768c273b1

[ "BSD-2-Clause" ]

null

from __future__ import absolute_import import json from json import loads separators = ",", ":" def dumps(obj): return json.dumps(obj, separators=separators)

16.2

46

0.759259

21

162

5.619048

0.52381

0.135593

0

0.141975

162

9

47

18

0.848921

0

0.012346

0

1

0.166667

false

0

0.5

0.166667

0.833333

0

1

0

null

0

1

0

null

0

1

0

5

09001a5c6774a7a9b4f373a9b5243603938274b1

75

py

Python

vformer/common/__init__.py

aditya-agrawal-30502/vformer

e1f4950f980238442ff1dc39a8f0791e4fbc9dac

[ "MIT" ]

90

2021-09-08T10:21:19.000Z

2022-03-26T18:11:47.000Z

vformer/common/__init__.py

aditya-agrawal-30502/vformer

e1f4950f980238442ff1dc39a8f0791e4fbc9dac

[ "MIT" ]

72

2021-09-09T06:54:50.000Z

2022-03-31T09:23:31.000Z

vformer/common/__init__.py

aditya-agrawal-30502/vformer

e1f4950f980238442ff1dc39a8f0791e4fbc9dac

[ "MIT" ]

21

2021-09-09T05:56:03.000Z

2022-03-20T08:22:09.000Z

from .base_model import BaseClassificationModel from .blocks import DWConv

25

47

0.866667

9

75

7.111111

0.777778

0

0.106667

75

2

48

37.5

0.955224

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0947636560364d62cfa092e946c32ba0bc3eb728

115

py

Python

src/rbls/admin.py

speedlight/rblmonitor

40e51398c7395c5b123e24eb4829529492a4343b

[ "MIT" ]

4

2017-05-04T05:14:54.000Z

2018-09-12T07:52:50.000Z

src/rbls/admin.py

speedlight/rblmonitor

40e51398c7395c5b123e24eb4829529492a4343b

[ "MIT" ]

null

src/rbls/admin.py

speedlight/rblmonitor

40e51398c7395c5b123e24eb4829529492a4343b

[ "MIT" ]

null

from django.contrib import admin from .models import Rbllist admin.site.register(Rbllist, list_display = ['name'])

28.75

53

0.791304

16

115

5.625

0.75

0

0.104348

115

4

53

28.75

0.873786

0

0.034483

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

095dbfdcd8405ba7d1f37f9a7ecd050f1c2184ca

92

py

Python

__init__.py

zags4life/read_only_collections

6e5af00115c27a53a92c796f2ab7ea10afb19d68

[ "Apache-2.0" ]

null

__init__.py

zags4life/read_only_collections

6e5af00115c27a53a92c796f2ab7ea10afb19d68

[ "Apache-2.0" ]

null

__init__.py

zags4life/read_only_collections

6e5af00115c27a53a92c796f2ab7ea10afb19d68

[ "Apache-2.0" ]

null

# __init__.py from .readonlydict import ReadOnlyDict from .readonlylist import ReadOnlyList

23

38

0.847826

10

92

7.4

0.6

0

0.108696

92

4

39

23

0.902439

0.119565

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1182ed63ccdf29b648fbd7e2a539566932559358

36

py

Python

Day_1_Scientific_Python/numpys/_solutions/02_dataset_intro_2.py

rth/data-science-workshop-2021

4a048d9732c60b6015c324212abdb4c51041263c

[ "BSD-3-Clause" ]

null

Day_1_Scientific_Python/numpys/_solutions/02_dataset_intro_2.py

rth/data-science-workshop-2021

4a048d9732c60b6015c324212abdb4c51041263c

[ "BSD-3-Clause" ]

1

2021-05-17T08:43:36.000Z

Day_1_Scientific_Python/numpys/_solutions/02_dataset_intro_2.py

rth/data-science-workshop-2021

4a048d9732c60b6015c324212abdb4c51041263c

[ "BSD-3-Clause" ]

1

2021-05-13T12:06:35.000Z

b = a[2, [0, 2, 3]] print(b) type(b)

12

19

0.472222

10

36

1.7

0.7

0

0.137931

0.194444

36

3

20

12

0.448276

0

1

0

false

0

0.333333

1

null

0

1

0

1

0

null

0

5

1186483a35df40a409a4597ea2fc8b51e009fb7a

276

py

Python

pyngsi/tests/test_iso8601.py

Orange-OpenSource/pyngsi

86bdb3218850b82d219278b831a3e96b0fb4655b

[ "Apache-2.0" ]

1

2021-11-05T16:45:04.000Z

pyngsi/tests/test_iso8601.py

Orange-OpenSource/pyngsi

86bdb3218850b82d219278b831a3e96b0fb4655b

[ "Apache-2.0" ]

null

pyngsi/tests/test_iso8601.py

Orange-OpenSource/pyngsi

86bdb3218850b82d219278b831a3e96b0fb4655b

[ "Apache-2.0" ]

1

2021-06-22T09:14:15.000Z

#!/usr/bin/env python3 from datetime import datetime, timezone from pyngsi.utils.iso8601 import datetime_to_iso8601 def test_datetime_to_iso8601(): dt = datetime(2021, 5, 18, 17, 45, 00, tzinfo=timezone.utc) assert datetime_to_iso8601(dt) == "2021-05-18T17:45:00Z"

27.6

63

0.75

43

276

4.651163

0.627907

0.15

0.255

0.19

0

0.1841

0.134058

276

9

64

30.666667

0.65272

0.076087

0

0.07874

0

0.2

1

0.2

false

0

0.4

0

0.6

0

null

0

1

0

1

0

null

0

1

0

1

0

5

1192921f8a7b1eab92f87703fe5f132598af6d6e

89

py

Python

bitmovin_api_sdk/account/organizations/groups/tenants/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

11

2019-07-03T10:41:16.000Z

2022-02-25T21:48:06.000Z

bitmovin_api_sdk/account/organizations/groups/tenants/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

8

2019-11-23T00:01:25.000Z

2021-04-29T12:30:31.000Z

bitmovin_api_sdk/account/organizations/groups/tenants/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

13

2020-01-02T14:58:18.000Z

2022-03-26T12:10:30.000Z

from bitmovin_api_sdk.account.organizations.groups.tenants.tenants_api import TenantsApi

44.5

88

0.898876

12

89

6.416667

0.833333

0

0.044944

89

1

89

0.905882

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

11a28788004bae67f07c0f599f0c3b14e7e2e891

165

py

Python

core/admin.py

ktowen/python.pizza.2020-project

795b80ddf2f94cd1e51e1504df4f3e21e279fa24

[ "MIT" ]

null

core/admin.py

ktowen/python.pizza.2020-project

795b80ddf2f94cd1e51e1504df4f3e21e279fa24

[ "MIT" ]

null

core/admin.py

ktowen/python.pizza.2020-project

795b80ddf2f94cd1e51e1504df4f3e21e279fa24

[ "MIT" ]

null

from django.contrib import admin from core.models import Libro, Autor, Articulo admin.site.register(Libro) admin.site.register(Autor) admin.site.register(Articulo)

23.571429

46

0.818182

24

165

5.625

0.5

0.2

0.377778

0

0.084848

165

7

47

23.571429

0.89404

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

11a887b41fddc41b1cb6691bb035c937d90e6aca

2,205

py

Python

tests/conftest.py

fuzzmz/vspheretools

10890423bfbba976e3ddee61204e9eed4b73fe92

[ "MIT" ]

22

2016-05-30T15:43:44.000Z

2022-02-06T02:56:42.000Z

tests/conftest.py

fuzzmz/vspheretools

10890423bfbba976e3ddee61204e9eed4b73fe92

[ "MIT" ]

7

2016-05-19T16:01:55.000Z

2017-07-11T11:50:32.000Z

tests/conftest.py

fuzzmz/vspheretools

10890423bfbba976e3ddee61204e9eed4b73fe92

[ "MIT" ]

4

2018-10-12T09:47:13.000Z

2021-06-21T01:53:51.000Z

# -*- coding: utf-8 -*- import pysphere def pytest_sessionstart(session): class VMInstanceWrapper(object): def __init__(self, status='POWERED OFF'): self.status = status def get_status(self, *args, **kwargs): return self.status def power_on(self, *args, **kwargs): return 'TEST POWER ON' def power_off(self, *args, **kwargs): return 'TEST POWER OFF' def wait_for_tools(self, *args, **kwargs): return 'Waiting until OS started...' def get_properties(self, *args, **kwargs): return {'ip_address': '0.0.0.0', 'test': 123, 'testSub': {'subName': {'subSubName': 'qqq'}}} def get_current_snapshot_name(self, *args, **kwargs): return '' def get_snapshots(self, *args, **kwargs): return ['current snapshot', 'another snapshot'] def revert_to_snapshot(self, *args, **kwargs): return 'reverting to current snapshot...' def revert_to_named_snapshot(self, *args, **kwargs): return 'reverting to named snapshot...' def delete_named_snapshot(self, *args, **kwargs): return 'deleting named snapshot...' def create_snapshot(self, *args, **kwargs): return 'creating new snapshot...' def clone(self, *args, **kwargs): return 'cloning vm...' def login_in_guest(self, *args, **kwargs): return 'login in guest' def send_file(self, *args, **kwargs): return 'sending file...' def get_file(self, *args, **kwargs): return 'geting file...' def make_directory(self, *args, **kwargs): return 'making directory...' class VIServerWrapper(object): def connect(self, *args, **kwargs): return 'CONNECTED' def get_vm_by_name(self, *args, **kwargs): if 'FAKE' in args: raise Exception('No Name found for CloneVM test') else: return VMInstanceWrapper() def delete_vm_by_name(self, *args, **kwargs): return True, 'DELETED' pysphere.VIServer = VIServerWrapper

28.636364

104

0.5678

240

2,205

5.070833

0.341667

0.124897

0.21857

0.295809

0.266228

0.179129

0.064092

0

0.005198

0.302041

2,205

76

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null

0

1

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1

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5

11da0582c2df389685e16cf7708ee2c2fbab05fe

84

py

Python

__init__.py

mszsorondo/marcosz_distributions

12c7a32014e85bc2d4ade9b01bb7603d89c46300

[ "MIT" ]

null

__init__.py

mszsorondo/marcosz_distributions

12c7a32014e85bc2d4ade9b01bb7603d89c46300

[ "MIT" ]

null

__init__.py

mszsorondo/marcosz_distributions

12c7a32014e85bc2d4ade9b01bb7603d89c46300

[ "MIT" ]

null

from Gaussiandistribution import Gaussian from Binomialdistribution import Binomial

28

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84

9.5

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null

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1

0

1

0

5

11e594445a0795c68b55db2252ca44078b4d05d7

3,845

py

Python

data/processing/resizer.py

IsmaelMekene/meteor-object-detector

0c385b04e63e73c0f9cee21fee361bdbbc5ef300

[ "MIT" ]

null

data/processing/resizer.py

IsmaelMekene/meteor-object-detector

0c385b04e63e73c0f9cee21fee361bdbbc5ef300

[ "MIT" ]

null

data/processing/resizer.py

IsmaelMekene/meteor-object-detector

0c385b04e63e73c0f9cee21fee361bdbbc5ef300

[ "MIT" ]

null

import numpy as np import pandas as pd import matplotlib.pyplot as plt import glob2 import PIL try: import Image except ImportError: from PIL import Image import cv2 from skimage import io, color from tensorflow import keras import tensorflow as tf tf.__version__ from keras.layers import * from tqdm import tqdm import ast import shutil def resize_image(image_path): al = plt.imread(image_path) #print('the initial shape is:',al.shape) if len(al.shape) == 2: al = cv2.merge((al,al,al)) elif al.shape[2] == 4: al = cv2.cvtColor(al, cv2.COLOR_BGRA2BGR) else: al = al w = al.shape[1] #store the width h = al.shape[0] #store the height c = al.shape[2] #store the number of channels(3 in this case) #print(c) # In case image is horizontally orientated if w > h: combine = np.zeros((w,w,c)) combino = combine for i in range(c): combino[int((w-h)/2):int(((w-h)/2)+h) ,: ,i] = al[:,:,i] resized = cv2.resize(combino, (480, 480), interpolation=cv2.INTER_NEAREST) # In case image is vertically orientated elif w < h: combine = np.zeros((h,h,c)) combino = combine for i in range(c): combino[: ,int((h-w)/2):int(((h-w)/2)+w) ,i] = al[:,:,i] resized = cv2.resize(combino, (480, 480), interpolation=cv2.INTER_NEAREST) # In case image is squared else: resized = cv2.resize(al, (480, 480), interpolation=cv2.INTER_NEAREST) al = resized #plt.imshow(al.astype(np.uint8)) # to Clip input data to the valid range for imshow with RGB data. #plt.show() #print('the final shape is:',al.shape) return al def resize_mask(ali): w = ali.shape[1] #store the width h = ali.shape[0] #store the height #c = al.shape[2] #store the number of channels(3 in this case) #print(c) al = ali.reshape((h,w)) #print('the initial shape is:',al.shape) # In case image is horizontally orientated if w > h: combine = np.zeros((w,w)) combine[int((w-h)/2):int(((w-h)/2)+h) ,:] = al[:,:] resized = cv2.resize(combine, (480, 480), interpolation=cv2.INTER_NEAREST) # In case image is vertically orientated elif w < h: combine = np.zeros((h,h)) combine[: ,int((h-w)/2):int(((h-w)/2)+w)] = al[:,:] resized = cv2.resize(combine, (480, 480), interpolation=cv2.INTER_NEAREST) # In case image is squared else: resized = cv2.resize(al, (480, 480), interpolation=cv2.INTER_NEAREST) #print('the final shape of resized is:',resized.shape) alou = np.reshape(resized, (480, 480, 1)) #plt.imshow(resized) # to Clip input data to the valid range for imshow with RGB data. #plt.show() #print('the final shape is:',alai.shape) return alou def resize_distancegeo(npy_path): al = npy_path #al = np.load(npy_path) #print('the initial shape is:',al.shape) w = al.shape[1] #store the width h = al.shape[0] #store the height c = al.shape[2] #store the number of channels(3 in this case) #print(c) # In case image is horizontally orientated if w > h: combine = np.zeros((w,w,c)) combino = combine for i in range(c): combino[int((w-h)/2):int(((w-h)/2)+h) ,: ,i] = al[:,:,i] resized = cv2.resize(combino, (480, 480), interpolation=cv2.INTER_NEAREST) # In case image is vertically orientated elif w < h: combine = np.zeros((h,h,c)) combino = combine for i in range(c): combino[: ,int((h-w)/2):int(((h-w)/2)+w) ,i] = al[:,:,i] resized = cv2.resize(combino, (480, 480), interpolation=cv2.INTER_NEAREST) # In case image is squared else: resized = cv2.resize(al, (480, 480), interpolation=cv2.INTER_NEAREST) al = resized #plt.imshow(al.astype(np.uint8)) # to Clip input data to the valid range for imshow with RGB data. #plt.show() #print('the final shape is:',al.shape) return al

23.023952

101

0.637971

626

3,845

3.883387

0.158147

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0.040724

0.048128

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0

null

0

1

0

null

0

5

eea9a3f0003c8cc9767d6dda37f264b12004cb02

126

py

Python

codeswiftr/home/admin.py

bogdan-veliscu/dev-portfolio-website

43eb323c67f3fd691388e79039e32479c1bc0974

[ "Apache-2.0" ]

null

codeswiftr/home/admin.py

bogdan-veliscu/dev-portfolio-website

43eb323c67f3fd691388e79039e32479c1bc0974

[ "Apache-2.0" ]

4

2021-03-30T13:40:00.000Z

2021-09-22T19:12:56.000Z

codeswiftr/home/admin.py

bogdan-veliscu/dev-portfolio-website

43eb323c67f3fd691388e79039e32479c1bc0974

[ "Apache-2.0" ]

null

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

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32

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126

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0

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5

33

25.2

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true

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null

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1

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1

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1

0

5

eec5455d76da71445aa46376151ca32b7d3c840d

75

py

Python

extensions/.stubs/clrclasses/System/Runtime/InteropServices/Expando/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

1

2020-03-25T03:27:24.000Z

extensions/.stubs/clrclasses/System/Runtime/InteropServices/Expando/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

null

extensions/.stubs/clrclasses/System/Runtime/InteropServices/Expando/__init__.py

vicwjb/Pycad

7391cd694b7a91ad9f9964ec95833c1081bc1f84

[ "MIT" ]

null

from __clrclasses__.System.Runtime.InteropServices.Expando import IExpando

37.5

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75

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1

0

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1

75

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1

0

1

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1

0

null

0

1

0

1

0

1

0

5

eee637439bf259836d2ad4e6155806916157600f

52

py

Python

plugins/__init__.py

trishume/VintageousPlus

1dd62435138234979fe5bb413e1731119b017daf

[ "MIT" ]

6

2017-04-01T05:30:08.000Z

2017-04-05T14:17:40.000Z

plugins/__init__.py

trishume/VintageousPlus

1dd62435138234979fe5bb413e1731119b017daf

[ "MIT" ]

1

2017-04-04T06:47:13.000Z

2017-04-04T14:26:32.000Z

plugins/__init__.py

trishume/VintageousPlus

1dd62435138234979fe5bb413e1731119b017daf

[ "MIT" ]

null

from VintageousPlus.plugins.plugins import register

26

51

0.884615

6

52

7.666667

0.833333

0

0.076923

52

1

52

0.958333

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true

0

1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

011ea5e17e4b463463a18f2369a90b362da29def

39

py

Python

jxaas/__init__.py

jxaas/cli

e28932722c571b03658a9979f62a5c9f92def8c5

[ "Apache-2.0" ]

null

jxaas/__init__.py

jxaas/cli

e28932722c571b03658a9979f62a5c9f92def8c5

[ "Apache-2.0" ]

2

2015-01-19T23:13:46.000Z

2015-01-19T23:14:16.000Z

jxaas/__init__.py

jxaas/cli

e28932722c571b03658a9979f62a5c9f92def8c5

[ "Apache-2.0" ]

null

from main import * from simple import *

19.5

20

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39

5

0.666667

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39

2

20

19.5

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true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

5

0131aa40b1873d0f59d3196eea601b68241072b8

78

py

Python

scripts/start_daemon.py

jsannemo/programming-battle

6db807836bcb046ceb02c9ee02ac2f7b023c6635

[ "BSD-2-Clause" ]

1

2016-05-31T00:23:29.000Z

scripts/start_daemon.py

simonlindholm/programming-battle

2fa7d52d3db0b511d6cbef7fcf4b18966c6d97eb

[ "BSD-2-Clause" ]

null

scripts/start_daemon.py

simonlindholm/programming-battle

2fa7d52d3db0b511d6cbef7fcf4b18966c6d97eb

[ "BSD-2-Clause" ]

1

2019-07-08T04:52:04.000Z

#!/usr/bin/env python3 from battle.main import start_backend start_backend()

15.6

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4

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1

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null

0

1

0

1

0

5

01477ef4ceab8be6ced71eef3eba8a5bc9066637

67

py

Python

waveglow_inference/__init__.py

narumiruna/waveglow-inference

47dbc10ed7d7e87caff8262e5783b5c0f2d4e519

[ "BSD-3-Clause" ]

1

2020-08-06T16:18:18.000Z

waveglow_inference/__init__.py

narumiruna/waveglow-inference

47dbc10ed7d7e87caff8262e5783b5c0f2d4e519

[ "BSD-3-Clause" ]

null

waveglow_inference/__init__.py

narumiruna/waveglow-inference

47dbc10ed7d7e87caff8262e5783b5c0f2d4e519

[ "BSD-3-Clause" ]

null

from .inference import synthesize from .layers import TacotronSTFT

22.333333

33

0.850746

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67

7.125

0.75

0

0.119403

67

2

34

33.5

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true

0

1

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1

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null

0

1

0

1

0

1

0

5

6da4cf82e63b24678babb9c3abcab6b8e486c44a

2,187

py

Python

idl2py/star/vactoair.py

RapidLzj/idl2py

193051cd8d01db0d125b8975713b885ad521a992

[ "MIT" ]

null

idl2py/star/vactoair.py

RapidLzj/idl2py

193051cd8d01db0d125b8975713b885ad521a992

[ "MIT" ]

null

idl2py/star/vactoair.py

RapidLzj/idl2py

193051cd8d01db0d125b8975713b885ad521a992

[ "MIT" ]

null

""" By Dr Jie Zheng -Q, NAOC v1 2019-04-27 """ import numpy as np from..util import * def vactoair(): pass #pro vactoair,wave_vac, wave_air #;+ #; NAME: #; VACTOAIR #; PURPOSE: #; Convert vacuum wavelengths to air wavelengths #; EXPLANATION: #; Corrects for the index of refraction of air under standard conditions. #; Wavelength values below 2000 A will not be altered. Accurate to #; about 10 m/s. #; #; CALLING SEQUENCE: #; VACTOAIR, WAVE_VAC, [WAVE_AIR] #; #; INPUT/OUTPUT: #; WAVE_VAC - Vacuum Wavelength in Angstroms, scalar or vector #; If the second parameter is not supplied, then this will be #; updated on output to contain double precision air wavelengths. #; #; OPTIONAL OUTPUT: #; WAVE_AIR - Air wavelength in Angstroms, same number of elements as #; WAVE_VAC, double precision #; #; EXAMPLE: #; If the vacuum wavelength is W = 2000, then #; #; IDL> VACTOAIR, W #; #; yields an air wavelength of W = 1999.353 Angstroms #; #; METHOD: #; Formula from Ciddor 1996 Applied Optics , 35, 1566 #; #; REVISION HISTORY #; Written, D. Lindler 1982 #; Documentation W. Landsman Feb. 1989 #; Use Ciddor (1996) formula for better accuracy in the infrared #; Added optional output vector, W Landsman Mar 2011 #;- # On_error,2 # compile_opt idl2 # # if N_params() EQ 0 then begin # print,'Syntax - VACTOAIR, Wave_Vac, [Wave_Air]' # return # endif # # wave_air = double(wave_vac) # g = where(wave_vac GE 2000, Ng) ;Only modify above 2000 A # # if Ng GT 0 then begin # # sigma2 = (1d4/double(wave_vac[g]) )^2. ;Convert to wavenumber squared # #; Compute conversion factor # # fact = 1.D + 5.792105D-2/(238.0185D0 - sigma2) + $ # 1.67917D-3/( 57.362D0 - sigma2) # # #; Convert wavelengths # # wave_air[g] = wave_vac[g]/fact # if N_Params() eq 1 then wave_vac = wave_air # endif # # return # end

26.349398

82

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2,187

4.529412

0.551471

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null

0

null

0

1

0

1

0

5

6da8643b830a5d053cc64f2d9c0e194bc073beee

82

py

Python

src/elaspic2/plugins/protbert/types.py

elaspic/elaspic2

edabe98de79b720715b798d3c5d33b613f978788

[ "MIT" ]

3

2021-07-12T21:38:56.000Z

2021-11-04T01:39:40.000Z

src/elaspic2/plugins/protbert/types.py

elaspic/elaspic2

edabe98de79b720715b798d3c5d33b613f978788

[ "MIT" ]

2

2021-02-23T08:30:01.000Z

2021-06-12T12:49:56.000Z

src/elaspic2/plugins/protbert/types.py

elaspic/elaspic2

edabe98de79b720715b798d3c5d33b613f978788

[ "MIT" ]

2

2021-07-12T21:38:58.000Z

2021-11-04T01:39:43.000Z

from typing import NamedTuple class ProtBertData(NamedTuple): sequence: str

13.666667

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0

1

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1

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1

0

5

6db393236b08485a015be81fbebbeacb3f0b2770

19

py

Python

startup.py

gwolf0719/python_ufs

08dd005c71b8e66ed07ee2a65fbc7aa09f124b93

[ "BSD-3-Clause" ]

null

startup.py

gwolf0719/python_ufs

08dd005c71b8e66ed07ee2a65fbc7aa09f124b93

[ "BSD-3-Clause" ]

15

2021-02-02T23:02:23.000Z

2021-08-09T02:21:43.000Z

startup.py

gwolf0719/python_ufs

08dd005c71b8e66ed07ee2a65fbc7aa09f124b93

[ "BSD-3-Clause" ]

1

2020-03-16T10:27:28.000Z

from . import wsgi2

19

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5

1

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19

1

19

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0

1

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1

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1

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0

1

0

null

0

1

0

1

0

5

6de60261329210d9d51d0a2e28b13d0976bf29b4

182

py

Python

algorithms/pickingNumbers.py

marismarcosta/hackerrank

3580b4fe0094e2a13f9a7efeeb0e072810be9ebf

[ "MIT" ]

null

algorithms/pickingNumbers.py

marismarcosta/hackerrank

3580b4fe0094e2a13f9a7efeeb0e072810be9ebf

[ "MIT" ]

3

2020-09-27T22:57:05.000Z

2020-09-29T23:07:44.000Z

algorithms/pickingNumbers.py

marismarcosta/hackerrank-challenges

3580b4fe0094e2a13f9a7efeeb0e072810be9ebf

[ "MIT" ]

1

2020-11-06T21:16:19.000Z

def pickingNumbers(a): solution = 0 for num1 in a: if a.count(num1) + a.count(num1 + 1) > solution: solution = a.count(num1) + a.count(num1 + 1) return solution

22.75

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1

0

1

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null

0

5

0992cee1165362724405810c82cb40901b1a6ca6

93

py

Python

db_table_creation.py

fs714/sqlalchemy-example

397e3fe2e53f6a10ebc00d4a0dc354e45b005c91

[ "Apache-2.0" ]

null

db_table_creation.py

fs714/sqlalchemy-example

397e3fe2e53f6a10ebc00d4a0dc354e45b005c91

[ "Apache-2.0" ]

null

db_table_creation.py

fs714/sqlalchemy-example

397e3fe2e53f6a10ebc00d4a0dc354e45b005c91

[ "Apache-2.0" ]

null

from db_objects import Base from db_session import engine Base.metadata.create_all(engine)

15.5

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null

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1

0

1

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1

0

5

09972a181368a40ebf716ce0d02ad3dc73dd61fe

13,381

py

Python

examples/extinction.py

zhiweijt/pyLRT

58efa005fdb32b4d4261411972f685bb18f1ebc8

[ "BSD-3-Clause" ]

7

2020-10-16T18:04:02.000Z

2022-03-23T11:35:07.000Z

examples/extinction.py

GermanZz/pyLRT

58efa005fdb32b4d4261411972f685bb18f1ebc8

[ "BSD-3-Clause" ]

2

2020-10-12T08:34:52.000Z

2021-03-31T10:15:45.000Z

examples/extinction.py

GermanZz/pyLRT

58efa005fdb32b4d4261411972f685bb18f1ebc8

[ "BSD-3-Clause" ]

9

2020-10-16T20:02:22.000Z

2022-03-23T06:31:28.000Z

from pyLRT import RadTran, get_lrt_folder from pyLRT.misc import planck_function import matplotlib.pyplot as plt import copy import numpy as np import scipy import scipy.interpolate LIBRADTRAN_FOLDER = get_lrt_folder() slrt = RadTran(LIBRADTRAN_FOLDER) slrt.options['rte_solver'] = 'disort' slrt.options['source'] = 'solar' slrt.options['wavelength'] = '200 2600' slrt.options['output_user'] = 'lambda eglo eup edn edir' slrt.options['zout'] = '0 5 TOA' slrt.options['albedo'] = '0' slrt.options['umu'] = '-1.0 1.0' slrt.options['quiet'] = '' slrt.options['sza'] = '0' tlrt = copy.deepcopy(slrt) tlrt.options['rte_solver'] = 'disort' tlrt.options['source'] = 'thermal' tlrt.options['output_user'] = 'lambda edir eup uu' tlrt.options['wavelength'] = '2500 80000' tlrt.options['mol_abs_param'] = 'reptran fine' tlrt.options['sza'] = '0' ############## # Run the RT # ############## print('Initial RT') sdata, sverb = slrt.run(verbose=True) tdata, tverb = tlrt.run(verbose=True) print('Done RT') ########################### # Setup some plot details # ########################### wvlticks = [(list(range(200, 1000, 100))+ list(range(1000, 10000, 1000))+ list(range(10000, 71000, 10000))), (['0.2']+['']*7+ ['1']+['']*8+ ['10']+['']*5+['70'])] trans_ticks = [[0, 0.25, 0.5, 0.75, 1], [0, 25, 50, 75, 100]] tclearsurf = scipy.interpolate.interp1d(np.log(tdata[::3, 0]), tdata[::3, 2]) tcleartoa = scipy.interpolate.interp1d(np.log(tdata[2::3, 0]), tdata[2::3, 2]) xtlocs = np.linspace(np.log(tdata[0, 0]), np.log(tdata[-1, 0]), 1000) vars = [['rayleigh_dtau', 'rayleigh', 'Rayleigh'], ['o3', 'o3', r'O$_3$'], ['o2', 'o2', r'O$_2$'], ['h2o', 'h2o', r'H$_2$O'], ['co2', 'co2', r'CO$_2$'], ['ch4', 'ch4', r'CH$_4$']] ########################################################## # Total Extinction, Planck function and major components # ########################################################## fig = plt.gcf() toa = 0 # Top of atmosphere index # Atmospheric transmittance swvl = sverb['gases']['wvl'][::10] swvl = np.concatenate((swvl, np.linspace(2500, 5000, 100))) plt.subplot2grid((7, 1), (0, 0), rowspan=2) # Extinction is calculated from optical depth (sum of molecular and rayleigh components) plt.fill_between(np.log(sverb['gases']['wvl'][::10]), 1-np.exp((-sverb['gases']['mol_abs'] - sverb['gases']['rayleigh_dtau'])[::10, toa:].sum(axis=-1)), color='grey') plt.plot(np.log(sverb['gases']['wvl'][::10]), 1-np.exp((-sverb['gases']['mol_abs'] - sverb['gases']['rayleigh_dtau'])[::10, toa:].sum(axis=-1)), c='k', lw=0.5) # Included rayleigh for the LW section, although the contribution is small! plt.fill_between(np.log(tverb['gases']['wvl'][::10]), 1-np.exp((-tverb['gases']['mol_abs'] - tverb['gases']['rayleigh_dtau'])[::10, toa:].sum(axis=-1)), color='grey') plt.plot(np.log(tverb['gases']['wvl'][::10]), 1-np.exp((-tverb['gases']['mol_abs'] - tverb['gases']['rayleigh_dtau'])[::10, toa:].sum(axis=-1)), c='k', lw=0.5) # Calculate some Planck functions at representative temperatures planck5800 = planck_function(5800, wavelength=swvl*1e-9) planck5800 = planck5800*swvl plt.plot(np.log(swvl), planck5800/planck5800.max(), c='b', lw=2, label='5800K') planck255 = planck_function(210, wavelength=tverb['gases']['wvl'][::10]*1e-9) planck255 = planck255*tverb['gases']['wvl'][::10] plt.plot(np.log(tverb['gases']['wvl'][::10]), planck255/planck255.max(), c='r', lw=2, label='210K') planck255 = planck_function(255, wavelength=tverb['gases']['wvl'][::10]*1e-9) planck255 = planck255*tverb['gases']['wvl'][::10] plt.plot(np.log(tverb['gases']['wvl'][::10]), planck255/planck255.max(), c='C1', lw=2, label='255K') planck255 = planck_function(310, wavelength=tverb['gases']['wvl'][::10]*1e-9) planck255 = planck255*tverb['gases']['wvl'][::10] plt.plot(np.log(tverb['gases']['wvl'][::10]), planck255/planck255.max(), c='y', lw=2, label='310K') plt.legend() plt.xticks(np.log(wvlticks[0]), ['']*len(wvlticks[1])) plt.xlim(np.log(wvlticks[0][0]), np.log(wvlticks[0][-1])) plt.yticks(*trans_ticks) plt.ylim(0, 1) plt.ylabel(r'Extinction') ax2 = plt.gca().twinx() plt.ylabel(r'$\lambda$B$_{\lambda}$ (normalised)') plt.yticks([], []) # Plot the extinction for a selection of important gases for v, var in enumerate(vars[:-1]): plt.subplot2grid((7, 1), (v+2, 0)) plt.fill_between(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases'][var[0]].sum(axis=-1))[::10], color='grey') sol, = plt.plot(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases'][var[0]].sum(axis=-1))[::10], c='k', lw=0.5) plt.fill_between(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases'][var[0]].sum(axis=-1))[::10], color='grey') plt.plot(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases'][var[0]].sum(axis=-1))[::10], c='k', lw=0.5) plt.text(np.log(30000), 0.5, var[2], verticalalignment='center') plt.xticks([], []) plt.xlim(np.log(wvlticks[0][0]), np.log(wvlticks[0][-1])) plt.yticks(trans_ticks[0], ['', '', '', '', '']) plt.ylim(0, 1) plt.xticks(np.log(wvlticks[0]), wvlticks[1]) plt.xlabel(r'Wavelength ($\mu$m)') plt.tight_layout(h_pad=0.1) fig.set_size_inches(6, 5) fig.savefig('output/as_complete.png', bbox_inches='tight') fig.clf() del(fig) ######################################### # Total extinction and Planck functions # ######################################### fig = plt.gcf() toa = 0 # Atmospheric transmittance swvl = sverb['gases']['wvl'][::10] swvl = np.concatenate((swvl, np.linspace(2500, 5000, 100))) plt.fill_between(np.log(sverb['gases']['wvl'][::10]), 1-np.exp((-sverb['gases']['mol_abs'] - sverb['gases']['rayleigh_dtau'])[::10, toa:].sum(axis=-1)), color='grey') plt.plot(np.log(sverb['gases']['wvl'][::10]), 1-np.exp((-sverb['gases']['mol_abs'] - sverb['gases']['rayleigh_dtau'])[::10, toa:].sum(axis=-1)), c='k', lw=0.5) plt.fill_between(np.log(tverb['gases']['wvl'][::10]), 1-np.exp((-tverb['gases']['mol_abs'] - tverb['gases']['rayleigh_dtau'])[::10, toa:].sum(axis=-1)), color='grey') plt.plot(np.log(tverb['gases']['wvl'][::10]), 1-np.exp((-tverb['gases']['mol_abs'] - tverb['gases']['rayleigh_dtau'])[::10, toa:].sum(axis=-1)), c='k', lw=0.5) planck5800 = planck_function(5800, wavelength=swvl*1e-9) planck5800 = planck5800*swvl plt.plot(np.log(swvl), planck5800/planck5800.max(), c='b', lw=2, label='5800K') planck255 = planck_function(210, wavelength=tverb['gases']['wvl'][::10]*1e-9) planck255 = planck255*tverb['gases']['wvl'][::10] plt.plot(np.log(tverb['gases']['wvl'][::10]), planck255/planck255.max(), c='r', lw=2, label='210K') planck255 = planck_function(255, wavelength=tverb['gases']['wvl'][::10]*1e-9) planck255 = planck255*tverb['gases']['wvl'][::10] plt.plot(np.log(tverb['gases']['wvl'][::10]), planck255/planck255.max(), c='C1', lw=2, label='255K') planck255 = planck_function(310, wavelength=tverb['gases']['wvl'][::10]*1e-9) planck255 = planck255*tverb['gases']['wvl'][::10] plt.plot(np.log(tverb['gases']['wvl'][::10]), planck255/planck255.max(), c='y', lw=2, label='310K') plt.legend() plt.xticks(np.log(wvlticks[0]), wvlticks[1]) plt.xlim(np.log(wvlticks[0][0]), np.log(wvlticks[0][-1])) plt.yticks(*trans_ticks) plt.ylim(0, 1) plt.xlabel(r'Wavelength ($\mu$m)') plt.ylabel(r'Extinction') ax2 = plt.gca().twinx() plt.ylabel(r'$\lambda$B$_{\lambda}$ (normalised)') plt.yticks([], []) plt.tight_layout(h_pad=0) fig.set_size_inches((8, 3)) fig.savefig('output/as_total.png') fig.clf() ############################# # Extinction by height plot # ############################# fig = plt.gcf() for k, toa in enumerate([-1, -5, -10]): print(k, toa) plt.subplot(3, 1, k+1) plt.fill_between(np.log(sverb['gases']['wvl'][::10]), 1-np.exp((-sverb['gases']['mol_abs'] - sverb['gases']['rayleigh_dtau'])[::10, :toa].sum(axis=-1)), color='grey') plt.plot(np.log(sverb['gases']['wvl'][::10]), 1-np.exp((-sverb['gases']['mol_abs'] - sverb['gases']['rayleigh_dtau'])[::10, :toa].sum(axis=-1)), c='k', lw=0.5) plt.fill_between(np.log(tverb['gases']['wvl'][::10]), 1-np.exp((-tverb['gases']['mol_abs'] - tverb['gases']['rayleigh_dtau'])[::10, :toa].sum(axis=-1)), color='grey') plt.plot(np.log(tverb['gases']['wvl'][::10]), 1-np.exp((-tverb['gases']['mol_abs'] - tverb['gases']['rayleigh_dtau'])[::10, :toa].sum(axis=-1)), c='k', lw=0.5) if k == 2: plt.xlabel(r'Wavelength ($\mu$m)') plt.xticks(np.log(wvlticks[0]), wvlticks[1]) else: plt.xlabel('') plt.xticks(np.log(wvlticks[0]), ['']) plt.ylabel('Extinction\n'+['TOA-Surf', 'TOA-5km', 'TOA-10km'][k]) plt.yticks(*trans_ticks) plt.xlim(np.log(wvlticks[0][0]), np.log(wvlticks[0][-1])) plt.ylim(0, 1) plt.tight_layout(h_pad=0) fig = plt.gcf() fig.set_size_inches((8, 4)) fig.savefig('output/as_total_heights.png') fig.clf() ################################# # Extinction by component plots # ################################# for var in vars: plt.fill_between(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases'][var[0]][::10, toa:].sum(axis=-1)), color='grey') plt.plot(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases'][var[0]][::10, toa:].sum(axis=-1)), c='k', lw=0.5) plt.fill_between(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases'][var[0]][::10, toa:].sum(axis=-1)), color='grey') plt.plot(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases'][var[0]][::10, toa:].sum(axis=-1)), c='k', lw=0.5) plt.xticks(np.log(wvlticks[0]), wvlticks[1]) plt.xlim(np.log(wvlticks[0][0]), np.log(wvlticks[0][-1])) plt.yticks(*trans_ticks) plt.ylim(0, 1) plt.xlabel(r'Wavelength ($\mu$m)') plt.ylabel(r'Extinction') plt.tight_layout() fig = plt.gcf() fig.set_size_inches((8, 3)) fig.savefig('output/as_{}.png'.format(var[1])) fig.clf() ############################################## # Extinction and contribution to total plots # ############################################## for var in vars: plt.subplot(211) plt.fill_between(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases']['mol_abs'].sum(axis=-1) - sverb['gases']['rayleigh_dtau'].sum(axis=-1))[::10], color='r') plt.fill_between(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases']['mol_abs'].sum(axis=-1) - tverb['gases']['rayleigh_dtau'].sum(axis=-1))[::10], color='r') plt.fill_between(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases']['mol_abs'].sum(axis=-1) - sverb['gases']['rayleigh_dtau'].sum(axis=-1) + sverb['gases'][var[0]].sum(axis=-1))[::10], color='grey') sol, = plt.plot(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases']['mol_abs'].sum(axis=-1) - sverb['gases']['rayleigh_dtau'].sum(axis=-1) + sverb['gases'][var[0]].sum(axis=-1))[::10], c='k', lw=0.5) plt.fill_between(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases']['mol_abs'].sum(axis=-1) - tverb['gases']['rayleigh_dtau'].sum(axis=-1) + tverb['gases'][var[0]].sum(axis=-1))[::10], color='grey') plt.plot(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases']['mol_abs'].sum(axis=-1) - tverb['gases']['rayleigh_dtau'].sum(axis=-1) + tverb['gases'][var[0]].sum(axis=-1))[::10], c='k', lw=0.5) plt.xticks([], []) plt.xlim(np.log(wvlticks[0][0]), np.log(wvlticks[0][-1])) plt.yticks(*trans_ticks) plt.ylim(0, 1) plt.ylabel(r'Extinction') plt.subplot(212) plt.fill_between(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases'][var[0]].sum(axis=-1))[::10], color='grey') sol, = plt.plot(np.log(sverb['gases']['wvl'][::10]), 1-np.exp(-sverb['gases'][var[0]].sum(axis=-1))[::10], c='k', lw=0.5) plt.fill_between(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases'][var[0]].sum(axis=-1))[::10], color='grey') plt.plot(np.log(tverb['gases']['wvl'][::10]), 1-np.exp(-tverb['gases'][var[0]].sum(axis=-1))[::10], c='k', lw=0.5) plt.xticks(np.log(wvlticks[0]), wvlticks[1]) plt.xlim(np.log(wvlticks[0][0]), np.log(wvlticks[0][-1])) plt.yticks(*trans_ticks) plt.ylim(0, 1) plt.xlabel(r'Wavelength ($\mu$m)') plt.ylabel(r'Extinction') plt.tight_layout() fig = plt.gcf() fig.set_size_inches((8, 3)) fig.savefig('output/as2_{}.png'.format(var[1])) fig.clf()

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lightly/api/__init__.py

laurenmoos/lightly

2e9ae8bbf433b09c89d666eee0358935d7f9eb9d

[ "MIT" ]

null

lightly/api/__init__.py

laurenmoos/lightly

2e9ae8bbf433b09c89d666eee0358935d7f9eb9d

[ "MIT" ]

null

lightly/api/__init__.py

laurenmoos/lightly

2e9ae8bbf433b09c89d666eee0358935d7f9eb9d

[ "MIT" ]

null

""" The lightly.api module provides access to the Lightly web-app. """ # Copyright (c) 2020. Lightly AG and its affiliates. # All Rights Reserved from lightly.api import routes from lightly.api.routes.pip import get_version # noqa: F401 from lightly.api.upload import upload_images_from_folder # noqa: F401 from lightly.api.upload import upload_embeddings_from_csv # noqa: F401 from lightly.api.upload import upload_file_with_signed_url # noqa: F401 from lightly.api.download import get_samples_by_tag # noqa: F401

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py

Python

data/__init__.py

Jabb0/FastFlow3D

cdc2a547268b85d0c851cf87786d80fcde4e8487

[ "MIT" ]

6

2021-10-14T03:30:32.000Z

2022-03-25T07:16:03.000Z

data/__init__.py

Jabb0/FastFlow3D

cdc2a547268b85d0c851cf87786d80fcde4e8487

[ "MIT" ]

2

2021-10-08T09:06:24.000Z

2022-03-26T10:37:22.000Z

data/__init__.py

Jabb0/FastFlow3D

cdc2a547268b85d0c851cf87786d80fcde4e8487

[ "MIT" ]

null

from .WaymoDataModule import WaymoDataModule from .RandomDataModule import RandomDataModule

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py

Python

categories/migrations/0009_product_name.py

pmaigutyak/mp-shop

14ea67f71fd91a282d2070414924708214fc6464

[ "0BSD" ]

2

2018-03-14T11:32:36.000Z

2021-09-25T14:31:36.000Z

categories/migrations/0009_product_name.py

pmaigutyak/mp-shop

14ea67f71fd91a282d2070414924708214fc6464

[ "0BSD" ]

null

categories/migrations/0009_product_name.py

pmaigutyak/mp-shop

14ea67f71fd91a282d2070414924708214fc6464

[ "0BSD" ]

null

# Generated by Django 3.0.13 on 2021-05-22 09:32 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('categories', '0008_mptt'), ] operations = [ migrations.AddField( model_name='category', name='product_name', field=models.CharField(blank=True, max_length=255, verbose_name='Product name'), ), migrations.AddField( model_name='category', name='product_name_ru', field=models.CharField(blank=True, max_length=255, null=True, verbose_name='Product name'), ), migrations.AddField( model_name='category', name='product_name_uk', field=models.CharField(blank=True, max_length=255, null=True, verbose_name='Product name'), ), ]

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py

Python

pugh_torch/modules/__init__.py

BrianPugh/pugh_torch

d620a518d78ec03556c5089bfc76e4cf7bd0cd70

[ "MIT" ]

4

2020-09-15T17:30:31.000Z

2021-08-07T02:32:22.000Z

pugh_torch/modules/__init__.py

BrianPugh/pugh_torch

d620a518d78ec03556c5089bfc76e4cf7bd0cd70

[ "MIT" ]

null

pugh_torch/modules/__init__.py

BrianPugh/pugh_torch

d620a518d78ec03556c5089bfc76e4cf7bd0cd70

[ "MIT" ]

1

2020-11-02T22:46:32.000Z

from .conv import conv3x3, conv1x1 from .activation import Activation, ActivationModule import pugh_torch.modules.init from .load_state_dict_mixin import LoadStateDictMixin import pugh_torch.modules.meta try: import pytorch_lightning except ImportError: pass else: from .lightning_module import LightningModule

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py

Python

GG/__init__.py

Dmunch04/GGLogger

517d80981c23eb98353313fabe2c5149e828377a

[ "MIT" ]

1

2019-07-01T10:07:42.000Z

GG/__init__.py

Dmunch04/GGLogger

517d80981c23eb98353313fabe2c5149e828377a

[ "MIT" ]

null

GG/__init__.py

Dmunch04/GGLogger

517d80981c23eb98353313fabe2c5149e828377a

[ "MIT" ]

null

from GG.GG import Log, Print, Check, Int, Float, String, List, Dict, Tuple, Combine

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5

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231

py

Python

tcpcs/state.py

matrix65537/lab

f1d9e0d7aa93083d493ccbad2439726f5c0f93c3

[ "MIT" ]

null

tcpcs/state.py

matrix65537/lab

f1d9e0d7aa93083d493ccbad2439726f5c0f93c3

[ "MIT" ]

null

tcpcs/state.py

matrix65537/lab

f1d9e0d7aa93083d493ccbad2439726f5c0f93c3

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- from __future__ import unicode_literals _var_dict = None def set_var_dict(var_dict): global _var_dict _var_dict = var_dict def get_var_dict(): return _var_dict

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0.285714

false

0

0.142857

0.571429

0

null

1

0

null

0

1

0

1

0

5

fedf7e98d701b14393c04f9e2f23610b98fc4774

304

pyde

Python

Squares1_py.pyde

chernayMitifa/2019-fall-polytech-cs

d1aadb8f47a5252529162f3d394665c9b553fe36

[ "MIT" ]

null

Squares1_py.pyde

chernayMitifa/2019-fall-polytech-cs

d1aadb8f47a5252529162f3d394665c9b553fe36

[ "MIT" ]

null

Squares1_py.pyde

chernayMitifa/2019-fall-polytech-cs

d1aadb8f47a5252529162f3d394665c9b553fe36

[ "MIT" ]

null

def setup(): size (500,500) background (100) smooth() noLoop() strokeWeight(15) str(100) def draw (): fill (250) rect (100,100, 100,100) fill (50) rect (200,200, 50,100)

21.714286

36

0.375

29

304

3.931034

0.586207

0.157895

0

0.285714

0.516447

304

13

37

23.384615

0.489796

0

1

0.166667

true

0

0.166667

0

1

0

null

0

1

0

1

0

null

0

1

0

5

fef30fc929582ec76b04d18f63ef5678b2ca6732

255

py

Python

generated-libraries/python/netapp/snapmirror_policy/policy_owner.py

radekg/netapp-ontap-lib-get

6445ebb071ec147ea82a486fbe9f094c56c5c40d

[ "MIT" ]

2

2017-03-28T15:31:26.000Z

2018-08-16T22:15:18.000Z

generated-libraries/python/netapp/snapmirror_policy/policy_owner.py

radekg/netapp-ontap-lib-get

6445ebb071ec147ea82a486fbe9f094c56c5c40d

[ "MIT" ]

null

generated-libraries/python/netapp/snapmirror_policy/policy_owner.py

radekg/netapp-ontap-lib-get

6445ebb071ec147ea82a486fbe9f094c56c5c40d

[ "MIT" ]

null

class PolicyOwner(basestring): """ cluster-admin|vserver-admin Possible values: <ul> <li> "cluster_admin" , <li> "vserver_admin" </ul> """ @staticmethod def get_api_name(): return "policy-owner"

17

31

0.556863

25

255

5.52

0.72

0.173913

0

0.309804

255

14

32

18.214286

0.784091

0.388235

0

0.1

0

1

0.25

true

0

0.25

0.75

0

1

0

null

0

null

0

1

0

1

0

5

3a2f266690a0229a0e37be697b6bf86a59481fdb

120

py

Python

example.py

namdevel/LK21-downloader

b566bf8994352074ffd31615de9371bbe927de6a

[ "MIT" ]

null

example.py

namdevel/LK21-downloader

b566bf8994352074ffd31615de9371bbe927de6a

[ "MIT" ]

null

example.py

namdevel/LK21-downloader

b566bf8994352074ffd31615de9371bbe927de6a

[ "MIT" ]

null

from src.LK21 import LK21Downloader movie_url = input("\nPaste LK21 Movie URL : ") LK21Downloader().generate(movie_url)

30

46

0.783333

16

120

5.75

0.625

0.26087

0

0.074766

0.108333

120

4

47

30

0.785047

0

1

0

0.206612

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

1

0

null

0

1

0

5

3a3ae2e8cf8900be3e8dc2f095939ddad911abe7

27

py

Python

src/control/autonomous.py

ncl-ROVers/surface-2019-20

209c06008803971d0430fd3993ef36f9a4686646

[ "MIT" ]

3

2021-01-21T07:18:30.000Z

2021-12-20T11:09:29.000Z

src/control/autonomous.py

ncl-ROVers/surface-2019-20

209c06008803971d0430fd3993ef36f9a4686646

[ "MIT" ]

null

src/control/autonomous.py

ncl-ROVers/surface-2019-20

209c06008803971d0430fd3993ef36f9a4686646

[ "MIT" ]

3

2020-11-24T11:46:23.000Z

2021-08-05T18:02:07.000Z

""" TODO: Write code ;) """

9

19

0.481481

3

27

4.333333

1

0

0.185185

27

3

20

9

0.590909

0.703704

0

null

0

null

0

null

0

0.333333

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

1

0

5

3a54d115cd679bfa8d359818d5d4d48d76aee2bc

150

py

Python

pyexcel_io/fileformat/__init__.py

AverkinSergei/pyexcel-io

a611a69cf7c2fa75f226b7879aba61bcfdaceda1

[ "BSD-3-Clause" ]

null

pyexcel_io/fileformat/__init__.py

AverkinSergei/pyexcel-io

a611a69cf7c2fa75f226b7879aba61bcfdaceda1

[ "BSD-3-Clause" ]

null

pyexcel_io/fileformat/__init__.py

AverkinSergei/pyexcel-io

a611a69cf7c2fa75f226b7879aba61bcfdaceda1

[ "BSD-3-Clause" ]

1

2019-04-27T04:40:14.000Z

from . import _csv as csv from . import csvz from . import tsv from . import tsvz exports = csv.exports + csvz.exports + tsv.exports + tsvz.exports

18.75

65

0.726667

23

150

4.695652

0.347826

0.37037

0

0.193333

150

7

66

21.428571

0.892562

0

1

0

false

0

0.8

0

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

28a901a1aa5a1bddf0e27c9c7d678b2b9daeb996

217

py

Python

octavvs/io/__init__.py

ctroein/octavvs

5a68ed61d01640f377bda116769b7cf783d3b668

[ "MIT" ]

7

2020-02-19T13:05:11.000Z

2021-08-28T05:23:33.000Z

octavvs/io/__init__.py

ctroein/octavvs

5a68ed61d01640f377bda116769b7cf783d3b668

[ "MIT" ]

2

2020-02-19T14:39:28.000Z

2020-03-23T15:13:38.000Z

octavvs/io/__init__.py

ctroein/octavvs

5a68ed61d01640f377bda116769b7cf783d3b668

[ "MIT" ]

2

2019-12-09T12:16:38.000Z

2021-01-11T02:58:01.000Z

from .spectraldata import SpectralData from .decompositiondata import DecompositionData from .opusreader import OpusReader from .ptirreader import PtirReader from .image import Image from .parameters import Parameters

36.166667

48

0.866359

24

217

7.833333

0.333333

0

0.105991

217

6

49

36.166667

0.969072

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

28ebb50ed7fa8931a6d5781c9163cd7472a041f1

233

py

Python

leet_code_array/mid_questions/unique_path.py

IvanFan/leetcode-python

72a12a107681cc5f09f1f88537c5b0741f0818a4

[ "MIT" ]

null

leet_code_array/mid_questions/unique_path.py

IvanFan/leetcode-python

72a12a107681cc5f09f1f88537c5b0741f0818a4

[ "MIT" ]

null

leet_code_array/mid_questions/unique_path.py

IvanFan/leetcode-python

72a12a107681cc5f09f1f88537c5b0741f0818a4

[ "MIT" ]

null

class Solution: def uniquePaths(self, m, n): """ :type m: int :type n: int :rtype: int """ return int(math.factorial(m+n-2)/ (math.factorial(m-1)* math.factorial(n-1)))

25.888889

85

0.480687

30

233

3.733333

0.5

0.348214

0.25

0

0.020134

0.360515

233

9

86

25.888889

0.731544

0.158798

0

1

0.333333

false

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

e913febb2b9f78511da1911e7dd283c3e90f623f

29

py

Python

py4xs/__init__.py

NSLS-II-LIX/py4xs

cc2102bd852a7ade1c1969fb5faf2ad361550617

[ "BSD-3-Clause" ]

4

2019-10-23T21:00:35.000Z

2021-02-09T15:57:31.000Z

py4xs/__init__.py

NSLS-II-LIX/py4xs

cc2102bd852a7ade1c1969fb5faf2ad361550617

[ "BSD-3-Clause" ]

null

py4xs/__init__.py

NSLS-II-LIX/py4xs

cc2102bd852a7ade1c1969fb5faf2ad361550617

[ "BSD-3-Clause" ]

2

2018-09-27T15:16:02.000Z

2021-02-09T15:23:36.000Z

__version__ = '2021.8.10.0'

9.666667

27

0.655172

5

29

3

1

0

0.32

0.137931

29

2

28

14.5

0.28

0

0.392857

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

e921d1bccde350a1e6f799873f0b4e901f577f7e

6,838

py

Python

intro/part06-05_course_grading_part_2/test/test_course_grading_part_2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

intro/part06-05_course_grading_part_2/test/test_course_grading_part_2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

intro/part06-05_course_grading_part_2/test/test_course_grading_part_2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

import unittest from unittest.mock import patch from tmc import points from tmc.utils import load, load_module, reload_module, get_stdout from functools import reduce import os import textwrap from random import choice, randint exercise = 'src.course_grading_part_2' def f(d): return '\n'.join(d) def w(x): return [f"test/{i}" for i in x] @points('6.course_gradind_part_2') class CourseGradingPart2Test(unittest.TestCase): @classmethod def setUpClass(cls): with patch('builtins.input', side_effect=['test/students1.csv', 'test/exercises1.csv', 'test/exam_points1.csv']): cls.module = load_module(exercise, 'fi') def test_1_works_with_file_1(self): words = ['students1.csv', 'exercises1.csv', 'exam_points1.csv'] with patch('builtins.input', side_effect =w(words) + [ AssertionError("Too many inputs.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Ensure that your program works with input\n{f(words)}") exp = """pekka peloton 0 jaana javanainen 1 liisa virtanen 3""" expRows = exp.split('\n') mssage = """\nPlease note, that in this program NO CODE should be included inside if __name__ == "__main__": block """ #\n{mssage}") self.assertTrue(len(output_all)>0, f"Your program does not output anything with input\n{f(words)}\n{mssage}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expRows), len(output), f"Instead of {len(expRows)} rows, your program outputs {len(output)} rows:\n{output_all}\nwith input:\n{f(words)}\nOutput should contain the following lines:\n{exp}") for i in range(len(expRows)): line = output[i] self.assertTrue(line.strip() in exp, f"Your program does not work correctly with input\n{f(words)}\nLine {line} is not what expected\nOutput should contain the following lines:\n{exp}\nThe whole output is:\n{output_all}") def test_2_works_with_file_2(self): words = ['students2.csv', 'exercises2.csv', 'exam_points2.csv'] with patch('builtins.input', side_effect =w(words) + [ AssertionError("Too many inputs.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Ensure that your program works with input\n{f(words)}") exp = """pekka peloton 1 jaana javanainen 1 liisa virtanen 0 donald frump 1 john doe 3 angela tarkel 3 karkki eila 0 alan turing 4 ada lovelace 5""" expRows = exp.split('\n') mssage = """\nPlease note, that in this program NO CODE should be included inside if __name__ == "__main__": block """ #\n{mssage}") self.assertTrue(len(output_all)>0, f"Your program does not output anything with input\n{f(words)}\n{mssage}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expRows), len(output), f"Instead of {len(expRows)} rows, your program outputs {len(output)} rows:\n{output_all}\nwith input:\n{f(words)}\nOutput should contain the following lines:\n{exp}") for i in range(len(expRows)): line = output[i] self.assertTrue(line.strip() in exp, f"Your program does not work correctly with input\n{f(words)}\nLine {line} is not what expected\nOutput should contain the following lines:\n{exp}\nThe whole output is:\n{output_all}") def test_3_works_with_file_3(self): words = ['students3.csv', 'exercises3.csv', 'exam_points3.csv'] with patch('builtins.input', side_effect =w(words) + [ AssertionError("Too many inputs.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Ensure that your program works with input\n{f(words)}") exp = """pekka peloton 1 jaana javanainen 2 liisa virtanen 3 donald frump 0 john doe 2 angela tarkel 1 karkki eila 1 alan turing 3 ada lovelace 5""" expRows = exp.split('\n') mssage = """\nPlease note, that in this program NO CODE should be included inside if __name__ == "__main__": block """ #\n{mssage}") self.assertTrue(len(output_all)>0, f"Your program does not output anything with input\n{f(words)}\n{mssage}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expRows), len(output), f"Instead of {len(expRows)} rows, your program outputs {len(output)} rows:\n{output_all}\nwith input:\n{f(words)}\nOutput should contain the following lines:\n{exp}") for i in range(len(expRows)): line = output[i] self.assertTrue(line.strip() in exp, f"Your program does not work correctly with input\n{f(words)}\nLine {line} is not what expected\nOutput should contain the following lines:\n{exp}\nThe whole output is:\n{output_all}") def test_4_works_with_file_4(self): words = ['students4.csv', 'exercises4.csv', 'exam_points4.csv'] with patch('builtins.input', side_effect =w(words) + [ AssertionError("Too many inputs.")]): try: reload_module(self.module) output_all = get_stdout() except: self.assertTrue(False, f"Ensure that your program works with input\n{f(words)}") exp = """pekka pelokas 0 mirja virtanen 1 jane doe 3 donald frump 4 john doe 5 kalle paakkola 0 eila kaisla 4 antti tuuri 0 leena lempinen 1 eero honkela 1""" expRows = exp.split('\n') mssage = """\nPlease note, that in this program NO CODE should be included inside if __name__ == "__main__": block """ #\n{mssage}") self.assertTrue(len(output_all)>0, f"Your program does not output anything with input\n{f(words)}\n{mssage}") output = [line.strip() for line in output_all.split("\n") if len(line) > 0] self.assertEqual(len(expRows), len(output), f"Instead of {len(expRows)} rows, your program outputs {len(output)} rows:\n{output_all}\nwith input:\n{f(words)}\nOutput should contain the following lines:\n{exp}") for i in range(len(expRows)): line = output[i] self.assertTrue(line.strip() in exp, f"Your program does not work correctly with input\n{f(words)}\nLine {line} is not what expected\nOutput should contain the following lines:\n{exp}\nThe whole output is:\n{output_all}") if __name__ == '__main__': unittest.main()

43.278481

237

0.634104

961

6,838

4.399584

0.168574

0.042573

0.02649

0.045412

0.776963

0.766793

0.759224

0

0.012736

0.242176

6,838

157

238

43.55414

0.803165

0.007458

0

0.578125

0

0.0625

0.470215

0.069743

0

0.15625

1

0.054688

false

0

0.0625

0.015625

0.140625

0

null

0

1

0

null

0

5

3a8dc25780b378716829c7515f679cc226cfd5ee

118

py

Python

tasks_app.py

jhparkinfinyx/fabric_server

45caa1504eba4344ab5706c0d061dd325231766a

[ "MIT" ]

null

tasks_app.py

jhparkinfinyx/fabric_server

45caa1504eba4344ab5706c0d061dd325231766a

[ "MIT" ]

null

tasks_app.py

jhparkinfinyx/fabric_server

45caa1504eba4344ab5706c0d061dd325231766a

[ "MIT" ]

null

from tasks import add result = add.delay(30000, 1337) print(result.ready()) print(result.get()) print(result.ready())

19.666667

31

0.737288

18

118

4.833333

0.611111

0.37931

0.367816

0

0.084112

0.09322

118

6

32

19.666667

0.728972

0

0.4

0

1

0

false

0

0.2

0

0.2

0.6

1

0

null

1

0

1

0

null

0

1

0

5

3a92e3c8f88a3b34b050d5feaac449fed1cf5e57

42

py

Python

firstStep.py

liliangbin/first-step-python

3c4afe6ed54068cb4101d28a1ed4bbeb6e0d43fd

[ "MIT" ]

1

2018-04-10T16:12:39.000Z

firstStep.py

liliangbin/first-step-python

3c4afe6ed54068cb4101d28a1ed4bbeb6e0d43fd

[ "MIT" ]

null

firstStep.py

liliangbin/first-step-python

3c4afe6ed54068cb4101d28a1ed4bbeb6e0d43fd

[ "MIT" ]

null

#!/usr/bin/env python print('nihao')

5.25

21

0.595238

6

42

4.166667

1

0

0.190476

42

7

22

6

0.735294

0.47619

0

0.277778

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c914522635f06ac630b22b5e7ec3bc4b8a9d04e7

126

py

Python

VAE_GAN/__init__.py

TheDudeFromCI/VAE-GAN

d14f65f64897a701a0a94a75b42d0e70bb1fd98a

[ "MIT" ]

null

VAE_GAN/__init__.py

TheDudeFromCI/VAE-GAN

d14f65f64897a701a0a94a75b42d0e70bb1fd98a

[ "MIT" ]

null

VAE_GAN/__init__.py

TheDudeFromCI/VAE-GAN

d14f65f64897a701a0a94a75b42d0e70bb1fd98a

[ "MIT" ]

null

from .model import Model, ModelParameters from .optimizer import optimize __all__ = ['Model', 'ModelParameters', 'optimize']

25.2

50

0.769841

13

126

7.153846

0.538462

0.430108

0

0.119048

126

4

51

31.5

0.837838

0

0.222222

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

c91eb1034226682ebd286dbb38af6e1bfca20d2a

80

py

Python

testFile.py

tohabyuraev/calculation-of-pneumatic-installation

dd283132665d8a3c92eb5b52f129e79e639d9fdc

[ "BSD-3-Clause" ]

null

testFile.py

tohabyuraev/calculation-of-pneumatic-installation

dd283132665d8a3c92eb5b52f129e79e639d9fdc

[ "BSD-3-Clause" ]

null

testFile.py

tohabyuraev/calculation-of-pneumatic-installation

dd283132665d8a3c92eb5b52f129e79e639d9fdc

[ "BSD-3-Clause" ]

null

print("Установлено соединение с GitHub!") print("Получилось закомитить файл...")

40

41

0.775

9

80

6.888889

0.888889

0

0.075

80

2

42

40

0.837838

0

0.753086

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

c9270a4a7a7dfa505a6a228211bcd3dbfca0f99a

32

py

Python

requests_lsp/__init__.py

abitrolly/requests-lsp

65fef15af22a78e5873d5eaff10c36eee74c9394

[ "Unlicense" ]

null

requests_lsp/__init__.py

abitrolly/requests-lsp

65fef15af22a78e5873d5eaff10c36eee74c9394

[ "Unlicense" ]

null

requests_lsp/__init__.py

abitrolly/requests-lsp

65fef15af22a78e5873d5eaff10c36eee74c9394

[ "Unlicense" ]

null

from .adapter import LSPAdapter

16

31

0.84375

4

32

6.75

1

0

0.125

32

1

32

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c9856eed1250a6e4007fa6f8d5ada6e92e214394

255

py

Python

datastore/objects/util.py

datastore/datastore.objects

226960312e1f34fbdf08eef40b90ffe90baf2678

[ "MIT" ]

2

2016-07-09T04:09:36.000Z

2021-12-10T20:06:26.000Z

datastore/objects/util.py

datastore/datastore.objects

226960312e1f34fbdf08eef40b90ffe90baf2678

[ "MIT" ]

null

datastore/objects/util.py

datastore/datastore.objects

226960312e1f34fbdf08eef40b90ffe90baf2678

[ "MIT" ]

3

2015-01-23T17:03:31.000Z

2021-04-04T03:10:46.000Z

class classproperty(object): '''Implements both @property and @classmethod behavior.''' def __init__(self, getter): self.getter = getter def __get__(self, instance, owner): return self.getter(instance) if instance else self.getter(owner)

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5