xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

a7c125db9c000988b3b97088aac18a3ae9aabaa6

127

py

Python

venv/Lib/site-packages/dask_ml/cluster/__init__.py

ZhangQingsen/CISC849Proj

ae89693648ead79d97805d663c1db58dfc0786a0

[ "MIT" ]

803

2017-06-16T02:08:30.000Z

2022-03-28T14:02:25.000Z

venv/Lib/site-packages/dask_ml/cluster/__init__.py

ZhangQingsen/CISC849Proj

ae89693648ead79d97805d663c1db58dfc0786a0

[ "MIT" ]

748

2017-09-24T20:32:33.000Z

2022-03-28T18:49:27.000Z

venv/Lib/site-packages/dask_ml/cluster/__init__.py

ZhangQingsen/CISC849Proj

ae89693648ead79d97805d663c1db58dfc0786a0

[ "MIT" ]

250

2017-06-15T15:57:18.000Z

2022-03-25T08:31:02.000Z

"""Unsupervised Clustering Algorithms""" from .k_means import KMeans # noqa from .spectral import SpectralClustering # noqa

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ac0b99af4ee2abd4bd6323acd4469ecf5cd9eb17

132

py

Python

telegram_bot/keyboard/inline/__init__.py

alenworld/django_telegram_bot

aa9a3570787feaaf474086a8cee66155f749983e

[ "MIT" ]

3

2021-07-07T02:30:56.000Z

2021-12-19T07:48:35.000Z

telegram_bot/keyboard/inline/__init__.py

alenworld/django_telegram_bot

aa9a3570787feaaf474086a8cee66155f749983e

[ "MIT" ]

null

telegram_bot/keyboard/inline/__init__.py

alenworld/django_telegram_bot

aa9a3570787feaaf474086a8cee66155f749983e

[ "MIT" ]

1

2021-07-07T02:42:23.000Z

from .utils import make_addresses_inline_keyboard, keyboard_confirm_decline_broadcasting from .faq import make_faq_inline_keyboard

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5

ac19781ccdeeff3922279d7686041a4361e2e3f6

106

py

Python

4_src/3_other/1_surasura-python/q5-4/q5-4.py

hirobel/todoapp

834e6dcdd3e6c227a79004c89430c6853935b23c

[ "Apache-2.0" ]

null

4_src/3_other/1_surasura-python/q5-4/q5-4.py

hirobel/todoapp

834e6dcdd3e6c227a79004c89430c6853935b23c

[ "Apache-2.0" ]

null

4_src/3_other/1_surasura-python/q5-4/q5-4.py

hirobel/todoapp

834e6dcdd3e6c227a79004c89430c6853935b23c

[ "Apache-2.0" ]

null

alpha_num_dict = { 'a':1, 'b':2, 'c':3 } alpha_num_dict['a'] = 10 print(alpha_num_dict['a'])

11.777778

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5

ac2d4bfeaa6d340b56548c5b5c8ad9ce99c96b72

174

py

Python

mocodo/dynamic.py

JeanHenri79/mocodo

2c9e68f81bb5528134fdb4ee3cb6fc8a4042c73a

[ "MIT" ]

158

2015-09-01T13:49:22.000Z

2022-03-05T19:57:06.000Z

mocodo/dynamic.py

JeanHenri79/mocodo

2c9e68f81bb5528134fdb4ee3cb6fc8a4042c73a

[ "MIT" ]

43

2015-09-01T08:46:39.000Z

2022-01-07T18:50:10.000Z

mocodo/dynamic.py

JeanHenri79/mocodo

2c9e68f81bb5528134fdb4ee3cb6fc8a4042c73a

[ "MIT" ]

45

2015-10-02T21:15:22.000Z

2022-03-17T16:49:23.000Z

#!/usr/bin/env python # encoding: utf-8 class Dynamic(str): """Wrapper for the strings that need to be dynamically interpreted by the generated Python files.""" pass

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5

ac5273dbce439b8b19234d66d14e10e37f55e6c3

325

py

Python

toolcli/__init__.py

sslivkoff/toolcli

7f6cb50bdb3eab5118d4fe4dd256c68a206f1df2

[ "MIT" ]

null

toolcli/__init__.py

sslivkoff/toolcli

7f6cb50bdb3eab5118d4fe4dd256c68a206f1df2

[ "MIT" ]

null

toolcli/__init__.py

sslivkoff/toolcli

7f6cb50bdb3eab5118d4fe4dd256c68a206f1df2

[ "MIT" ]

null

"""toolcli makes it easy to create structured hierarchical cli tools""" from .command_utils import * from .capture_utils import * from .file_edit_utils import * from .file_validate_utils import * from .input_utils import * from .style_utils import * from .terminal_utils import * from .spec import * __version__ = '0.5.3'

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null

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1

0

5

ac572c0fa27ad1869c265f291345074bb701cfb5

75

py

Python

users/models.py

sh4rpy/foodgram

4ebc9655f9a68e05ebb83e7f2f2a2e04128d6713

[ "BSD-3-Clause" ]

null

users/models.py

sh4rpy/foodgram

4ebc9655f9a68e05ebb83e7f2f2a2e04128d6713

[ "BSD-3-Clause" ]

9

2021-04-08T20:01:45.000Z

2022-03-12T00:48:46.000Z

users/models.py

sh4rpy/foodgram

4ebc9655f9a68e05ebb83e7f2f2a2e04128d6713

[ "BSD-3-Clause" ]

null

from django.contrib.auth import get_user_model # Create your models here.

18.75

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1

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1

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1

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5

ac6619269b99b6f008f0fb8351119b590985d0fc

38

py

Python

13.range.py

shaunakganorkar/PythonMeetup-2014

a845b1612b5755eeb3b91ba34f3339327763fdfe

[ "MIT" ]

null

13.range.py

shaunakganorkar/PythonMeetup-2014

a845b1612b5755eeb3b91ba34f3339327763fdfe

[ "MIT" ]

null

13.range.py

shaunakganorkar/PythonMeetup-2014

a845b1612b5755eeb3b91ba34f3339327763fdfe

[ "MIT" ]

null

for i in range(0,100): print i,

12.666667

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null

0

1

0

1

0

5

3ba3215cea34cf50d1d2fb7c72dba8687b35394a

132

py

Python

example/blog/types_.py

njncalub/apistar-mongoengine

c6bff844449ebc910bf3a85c075d760204606047

[ "MIT" ]

null

example/blog/types_.py

njncalub/apistar-mongoengine

c6bff844449ebc910bf3a85c075d760204606047

[ "MIT" ]

6

2018-05-17T15:52:38.000Z

2018-05-27T04:31:32.000Z

example/blog/types_.py

njncalub/apistar-mongoengine

c6bff844449ebc910bf3a85c075d760204606047

[ "MIT" ]

null

from apistar import validators from apistar_mongoengine.types import Type class PostType(Type): message = validators.String()

18.857143

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0.795455

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132

6.5

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null

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1

0

1

0

5

3ba3cb5da5b09e4cd19b67f5e8fe68f926725129

6,649

py

Python

SubgraphCountingMatching/utils/scheduler.py

HKUST-KnowComp/DualMessagePassing

d29d627be2a8c8f24b52e3db2c383e33a059aaa7

[ "MIT" ]

12

2021-12-06T02:31:17.000Z

2022-03-11T15:17:57.000Z

SubgraphCountingMatching/utils/scheduler.py

HKUST-KnowComp/DualMessagePassing

d29d627be2a8c8f24b52e3db2c383e33a059aaa7

[ "MIT" ]

null

SubgraphCountingMatching/utils/scheduler.py

HKUST-KnowComp/DualMessagePassing

d29d627be2a8c8f24b52e3db2c383e33a059aaa7

[ "MIT" ]

null

import math from torch.optim.lr_scheduler import LambdaLR PI = 3.141592653589793 INIT_STEPS = 600 SCHEDULE_STEPS = 10000 NUM_CYCLES = 2 MIN_PERCENT = 1e-3 class ConstantScheduler(LambdaLR): def __init__(self): pass def set_optimizer(self, optimizer): super(ConstantScheduler, self).__init__(optimizer, self.lr_lambda) def lr_lambda(self, current_step): return 1.0 class ConstantWarmupScheduler(LambdaLR): def __init__( self, num_warmup_steps=INIT_STEPS ): self.num_warmup_steps = num_warmup_steps def set_optimizer(self, optimizer): super(ConstantWarmupScheduler, self).__init__(optimizer, self.lr_lambda) def lr_lambda(self, current_step): if current_step < self.num_warmup_steps: return float(current_step) / max(1.0, float(self.num_warmup_steps)) return 1.0 class LinearScheduler(LambdaLR): def __init__( self, num_schedule_steps=SCHEDULE_STEPS, min_percent=MIN_PERCENT ): self.num_schedule_steps = num_schedule_steps self.min_percent = min_percent def set_optimizer(self, optimizer): super(LinearScheduler, self).__init__(optimizer, self.lr_lambda) def lr_lambda(self, current_step): return max( self.min_percent, float(self.num_schedule_steps - current_step) / \ float(max(1, self.num_schedule_steps)) ) class LinearWarmupScheduler(LambdaLR): def __init__( self, num_warmup_steps=INIT_STEPS, num_schedule_steps=SCHEDULE_STEPS, min_percent=MIN_PERCENT ): self.num_warmup_steps = num_warmup_steps self.num_schedule_steps = num_schedule_steps self.min_percent = min_percent def set_optimizer(self, optimizer): super(LinearWarmupScheduler, self).__init__(optimizer, self.lr_lambda) def lr_lambda(self, current_step): if current_step < self.num_warmup_steps: return float(current_step) / float(max(1, self.num_warmup_steps)) return max( self.min_percent, float(self.num_schedule_steps - current_step) / \ float(max(1, self.num_schedule_steps - self.num_warmup_steps)) ) class LinearWarmupRestartScheduler(LambdaLR): def __init__( self, num_warmup_steps=INIT_STEPS, num_schedule_steps=SCHEDULE_STEPS, num_cycles=NUM_CYCLES, min_percent=MIN_PERCENT ): self.num_warmup_steps = num_warmup_steps self.num_schedule_steps = num_schedule_steps self.num_cycles = num_cycles self.min_percent = min_percent def set_optimizer(self, optimizer): super(LinearWarmupRestartScheduler, self).__init__(optimizer, self.lr_lambda) def lr_lambda(self, current_step): if current_step < self.num_warmup_steps: return float(current_step) / float(max(1, self.num_warmup_steps)) progress = float(current_step - self.num_warmup_steps) / \ float(max(1, self.num_schedule_steps - self.num_warmup_steps)) if progress >= 1.0: return self.min_percent return max(self.min_percent, 1 - (float(self.num_cycles) * progress) % 1.0) class CosineScheduler(LambdaLR): def __init__( self, num_schedule_steps=SCHEDULE_STEPS, num_cycles=NUM_CYCLES, min_percent=MIN_PERCENT ): self.num_schedule_steps = num_schedule_steps self.num_cycles = num_cycles self.min_percent = min_percent def set_optimizer(self, optimizer): super(CosineScheduler, self).__init__(optimizer, self.lr_lambda) def lr_lambda(self, current_step): progress = float(current_step) / float(max(1, self.num_schedule_steps)) return max(self.min_percent, 0.5 * (1.0 + math.cos(PI * float(self.num_cycles) * 2.0 * progress))) class CosineWarmupScheduler(LambdaLR): def __init__( self, num_warmup_steps=INIT_STEPS, num_schedule_steps=SCHEDULE_STEPS, num_cycles=NUM_CYCLES, min_percent=MIN_PERCENT ): self.num_warmup_steps = num_warmup_steps self.num_schedule_steps = num_schedule_steps self.num_cycles = num_cycles self.min_percent = min_percent def set_optimizer(self, optimizer): super(CosineWarmupScheduler, self).__init__(optimizer, self.lr_lambda) def lr_lambda(self, current_step): if current_step < self.num_warmup_steps: return float(current_step) / float(max(1, self.num_warmup_steps)) progress = float(current_step - self.num_warmup_steps) / \ float(max(1, self.num_schedule_steps - self.num_warmup_steps)) return max(self.min_percent, 0.5 * (1.0 + math.cos(PI * float(self.num_cycles) * 2.0 * progress))) class CosineWarmupRestartScheduler(LambdaLR): def __init__( self, num_warmup_steps=INIT_STEPS, num_schedule_steps=SCHEDULE_STEPS, num_cycles=NUM_CYCLES, min_percent=MIN_PERCENT ): self.num_warmup_steps = num_warmup_steps self.num_schedule_steps = num_schedule_steps self.num_cycles = num_cycles self.min_percent = min_percent def set_optimizer(self, optimizer): super(CosineWarmupRestartScheduler, self).__init__(optimizer, self.lr_lambda) def lr_lambda(self, current_step): if current_step < self.num_warmup_steps: return float(current_step) / float(max(1, self.num_warmup_steps)) progress = float(current_step - self.num_warmup_steps) / \ float(max(1, self.num_schedule_steps - self.num_warmup_steps)) if progress >= 1.0: return self.min_percent return max(self.min_percent, 0.5 * (1.0 + math.cos(PI * ((float(self.num_cycles) * progress) % 1.0)))) supported_schedulers = { "constant": ConstantScheduler(), "constant_with_warmup": ConstantWarmupScheduler(), "linear": LinearScheduler(), "linear_with_warmup": LinearWarmupScheduler(), "linear_with_warmup_and_restart": LinearWarmupRestartScheduler(), "cosine": CosineScheduler(), "cosine_with_warmup": CosineWarmupScheduler(), "cosine_with_warmup_and_restart": CosineWarmupRestartScheduler(), } def map_scheduler_str_to_scheduler(scheduler, **kw): if scheduler not in supported_schedulers: raise NotImplementedError sdlr = supported_schedulers[scheduler] for k, v in kw.items(): if hasattr(sdlr, k): try: setattr(sdlr, k, v) except: pass return sdlr

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null

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1

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null

0

5

3ba987e4e2e10be17c68bd09d6df5a1a5707460d

47

py

Python

SciFiReaders/__version__.py

sumner-harris/SciFiReaders

4494b7e7350ad2a6198c87590d193393566ad470

[ "MIT" ]

null

SciFiReaders/__version__.py

sumner-harris/SciFiReaders

4494b7e7350ad2a6198c87590d193393566ad470

[ "MIT" ]

null

SciFiReaders/__version__.py

sumner-harris/SciFiReaders

4494b7e7350ad2a6198c87590d193393566ad470

[ "MIT" ]

1

2021-09-02T11:39:57.000Z

version = '0.0.1' time = '2021-02-07 10:00:25'

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null

0

5

3bac834ffb995d7d061a7b3f4a823e89b6e9d515

591

py

Python

app/models.py

Nexus357ZA/upgraded-couscous

3e4ec3e02f589565bfa49e0e4d2d2ec7122aee21

[ "Apache-2.0" ]

null

app/models.py

Nexus357ZA/upgraded-couscous

3e4ec3e02f589565bfa49e0e4d2d2ec7122aee21

[ "Apache-2.0" ]

null

app/models.py

Nexus357ZA/upgraded-couscous

3e4ec3e02f589565bfa49e0e4d2d2ec7122aee21

[ "Apache-2.0" ]

null

from flask import current_app, url_for import json #from app import db class Article(): # #TODO - This is just a skeleton # id = db.Column(db.Integer, primary_key=True) # source = db.Column(db.String(160)) # author = db.Column(db.String(160)) # title = db.Column(db.String(160)) # description = db.Column(db.String(160)) # url = db.Column(db.String()) # urlToImage = db.Column(db.String()) # publishedAt = db.Column(db.String(160)) # content = db.Column(db.String()) # # def __repr__(self): # return '<Article {}>'.format(self.title)

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591

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5

3bbb01baddf4b73ec8a083273fe9999f3407ba7d

211

py

Python

src/mbi/__init__.py

siddhant-pradhan/private-pgm

f60734e444175e78e748e5aaab63ba7c3354a7f3

[ "Apache-2.0" ]

null

src/mbi/__init__.py

siddhant-pradhan/private-pgm

f60734e444175e78e748e5aaab63ba7c3354a7f3

[ "Apache-2.0" ]

null

src/mbi/__init__.py

siddhant-pradhan/private-pgm

f60734e444175e78e748e5aaab63ba7c3354a7f3

[ "Apache-2.0" ]

null

from mbi.domain import Domain from mbi.dataset import Dataset from mbi.factor import Factor from mbi.graphical_model import GraphicalModel from mbi.inference import FactoredInference from mbi.lbp import LBP

35.166667

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5.677419

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0.238636

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211

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35.166667

0.961749

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null

0

1

0

1

0

1

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5

3bcfccd82c906d1d36540db295694840f6957797

155

py

Python

conftest.py

omri374/openvino-textspotting-docker

1f63e3fbe8a40acd0c4fb12c184646b4f1aa985f

[ "Unlicense" ]

null

conftest.py

omri374/openvino-textspotting-docker

1f63e3fbe8a40acd0c4fb12c184646b4f1aa985f

[ "Unlicense" ]

null

conftest.py

omri374/openvino-textspotting-docker

1f63e3fbe8a40acd0c4fb12c184646b4f1aa985f

[ "Unlicense" ]

null

import pytest import os @pytest.fixture(scope='session') def app(request): from text_spotting import Server server = Server() return server.app

22.142857

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0.735484

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155

5.380952

0.666667

0.212389

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155

7

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0

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0.714286

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null

0

1

0

1

0

5

3bde682a1eb7775e2d6493f7ca7431ea7ff8cc2a

80

py

Python

celery_repro/repro/__init__.py

Evzdrop/celery-2619-repro

97a8e8f3786658f06c14a1813a9043640e63f2b8

[ "MIT" ]

null

celery_repro/repro/__init__.py

Evzdrop/celery-2619-repro

97a8e8f3786658f06c14a1813a9043640e63f2b8

[ "MIT" ]

2

2020-02-11T23:00:18.000Z

2020-06-05T17:07:30.000Z

celery_repro/repro/__init__.py

Evzdrop/celery-2619-repro

97a8e8f3786658f06c14a1813a9043640e63f2b8

[ "MIT" ]

null

from __future__ import absolute_import from .celeryApp import app as celery_app

26.666667

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0.8625

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80

5.25

0.666667

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0.125

80

3

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26.666667

0.9

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true

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0

5

3bde9926aa28bc4a5bb9861476d37caf5f616a68

11,419

py

Python

tests/test_question_category.py

samsungnlp/semeval2022-task9

2d44d9ebc6224bf7a3f70182bf7b81a7ab356370

[ "Apache-2.0" ]

null

tests/test_question_category.py

samsungnlp/semeval2022-task9

2d44d9ebc6224bf7a3f70182bf7b81a7ab356370

[ "Apache-2.0" ]

null

tests/test_question_category.py

samsungnlp/semeval2022-task9

2d44d9ebc6224bf7a3f70182bf7b81a7ab356370

[ "Apache-2.0" ]

null

import unittest from src.pipeline.question_category import QuestionCategory, GetCategoryFromQuestionStructure from src.unpack_data import QuestionAnswerRecipe, Recipe, Q_A class TestQuestionCategory(unittest.TestCase): def test_determine_description(self): qc = QuestionCategory("counting_times") self.assertIn("Counting times? A: Number", qc.description) def test_determine_description_str(self): qc = QuestionCategory("event_ordering") self.assertIn("X, Y which comes first?", qc.description) def test_no_description(self): qc = QuestionCategory("whatever") self.assertEqual("N/A", qc.description) class TestGetCategoryFromQuestionStructure(unittest.TestCase): def test_regex_classifier_class_counting_times(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = How many times is the bowl used?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("counting_times", a_class.category) def test_regex_classifier_class_counting_actions(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = How many actions does it take to process the tomato?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("counting_actions", a_class.category) def test_regex_classifier_class_counting_uses(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = How many spoons are used?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("counting_uses", a_class.category) def test_regex_classifier_class_ellipsis(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(qa=Q_A("# question 20-9 = What should be served?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("ellipsis", a_class.category) def test_regex_classifier_class_location_crl(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = Where should you add the chopped vegetables?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("location_crl", a_class.category) def test_regex_classifier_class_how_1(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = How do you brush the salad dressing?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("method", a_class.category) def test_regex_classifier_class_how_2(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(qa=Q_A("# question 20-9 = How did you get the cooked vegetable?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("lifespan_how", a_class.category) def test_regex_classifier_class_lifespan_what(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(qa=Q_A("# question 20-9 = What's in the lentil salad?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("lifespan_what", a_class.category) def test_regex_classifier_class_event_ordering(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(qa=Q_A( "# question 20-9 = Cutting the stem into bite - size pieces into bite - size pieces and sauting minced " "meat in a separate pan, which comes first?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("event_ordering", a_class.category) def test_regex_classifier_class_result(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = To what extent do you cut carrots and zucchini?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("result", a_class.category) def test_regex_classifier_class_how_3(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(qa=Q_A("# question 20-9 = How do you prick the dough slightly?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("method", a_class.category) def test_regex_classifier_class_time(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = For how long do you boil the potatoes until cooked?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("time", a_class.category) def test_regex_classifier_class_location_srl(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = Where do you season the trout with salt and pepper?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("location_srl", a_class.category) def test_regex_classifier_class_extent(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = By how much do you cover the beans with water in a pot?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("extent", a_class.category) def test_regex_classifier_class_how_4(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = How do you coat hot syrup mixture the popcorn nut mixture?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("method", a_class.category) def test_regex_classifier_class_purpose1(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(qa=Q_A("# question 20-9 = Why do you use gas?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("purpose", a_class.category) def test_regex_classifier_class_copatient1(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = What do you mix the oil in a small bowl with?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("copatient", a_class.category) def test_regex_classifier_class_copatient2(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = What do you put the raspberries into a liqudizer with?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("copatient", a_class.category) def test_regex_classifier_class_how_5(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(qa=Q_A("# question 20-9 = How do you use the same pot of water??"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("method", a_class.category) def test_regex_classifier_class_purpose2(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(qa=Q_A("# question 20-9 = Why do you pinch the pizza dough?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("purpose", a_class.category) def test_regex_classifier_class_source(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = From where do you remove the spinach and shallots mix?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("source", a_class.category) def test_regex_classifier_class_location_change(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = Where was the stuffed mushroom before it was garnished?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("location_change", a_class.category) def test_regex_classifier_class_result_na(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe( qa=Q_A("# question 20-9 = To what extent do you cut the shortening in?", "answer = a"), recipe=None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("result", a_class.category) def test_regex_classifier_class_how_preheat_1(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(Q_A("# question 20-9 = How do you preheat your oven?"), None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("method_preheat", a_class.category) def test_regex_classifier_class_how_preheat__alt_spelling(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(Q_A("# question 20-9 = How do you pre - heat the oven?", "answer = a"), None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("method_preheat", a_class.category) def test_regex_classifier_not_recognized(self): engine = GetCategoryFromQuestionStructure() question = QuestionAnswerRecipe(Q_A("# question XYZ-ABC = Is this question recognizable?", "answer = No"), None) a_class = engine.predict_category(question) self.assertIsNotNone(a_class) self.assertEqual("not_recognized", a_class.category)

49.008584

120

0.685962

1,282

11,419

5.893136

0.144306

0.06274

0.041297

0.075711

0.798676

0.793779

0.761482

0.704169

0

0.009573

0.222436

11,419

232

121

49.219828

0.841311

0

0.580808

0

0.005051

0.191348

0

0.277778

1

0.146465

false

0

0.015152

0

0.171717

0

null

0

1

0

null

0

5

3bea08882e0adb610c2d7732761910faa2bf79e5

222

py

Python

Test_address.py

KaivnD/Noa-Core

82a7f65289df4f703da0bfebc8cb3d453a9cf7bd

[ "MIT" ]

null

Test_address.py

KaivnD/Noa-Core

82a7f65289df4f703da0bfebc8cb3d453a9cf7bd

[ "MIT" ]

4

2020-03-24T17:35:25.000Z

2021-06-02T00:30:23.000Z

Test_address.py

KaivnD/Noa-Core

82a7f65289df4f703da0bfebc8cb3d453a9cf7bd

[ "MIT" ]

null

from AdressConvertApiTasker.AdressConvertApiTasker import * import_data={'location':'31.225696563611,121.49884033194',} output_data={'file_name':'test.txt'} a=AdressConvertApiTasker(import_data,output_data) print(a.run())

37

59

0.81982

26

222

6.807692

0.653846

0.316384

0

0.130841

0.036036

222

5

60

44.4

0.696262

0

0.252252

0.13964

0

1

0

false

0

0.6

0

0.6

0.2

1

0

null

1

0

1

0

null

0

1

0

1

0

5

ce0d9c677862920dd6c5eb3654daff22915695a0

228

py

Python

students/k33402/Sholomov_Dan/lab34/lab3/order/admin.py

heidamn/ITMO_ICT_WebDevelopment_2020-2021

47eb0cdf7c7dbe8d071bc4fd3f1ac94848475e7b

[ "MIT" ]

null

students/k33402/Sholomov_Dan/lab34/lab3/order/admin.py

heidamn/ITMO_ICT_WebDevelopment_2020-2021

47eb0cdf7c7dbe8d071bc4fd3f1ac94848475e7b

[ "MIT" ]

null

students/k33402/Sholomov_Dan/lab34/lab3/order/admin.py

heidamn/ITMO_ICT_WebDevelopment_2020-2021

47eb0cdf7c7dbe8d071bc4fd3f1ac94848475e7b

[ "MIT" ]

null

from django.contrib import admin from .models import Order, OrderedItem @admin.register(Order) class ItemAdmin(admin.ModelAdmin): pass @admin.register(OrderedItem) class ItemAdmin(admin.ModelAdmin): pass

17.538462

39

0.736842

26

228

6.461538

0.5

0.154762

0.22619

0.345238

0.392857

0

0.179825

228

12

40

19

0.898396

0

0.5

0

1

0

true

0.25

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

ce1620eace121783dc65dbb266550baa5f0f2077

864

py

Python

server/openapi_server/models/__init__.py

hubmapconsortium/ontology-api

f7fadf31de028acdd9cabbb5e9d6e48b9863ffac

[ "MIT" ]

2

2021-10-03T15:31:55.000Z

2021-10-04T08:55:23.000Z

server/openapi_server/models/__init__.py

hubmapconsortium/ontology-api

f7fadf31de028acdd9cabbb5e9d6e48b9863ffac

[ "MIT" ]

105

2020-12-11T13:03:31.000Z

2022-03-31T17:08:03.000Z

server/openapi_server/models/__init__.py

hubmapconsortium/ontology-api

f7fadf31de028acdd9cabbb5e9d6e48b9863ffac

[ "MIT" ]

2

2021-07-08T14:49:25.000Z

2022-02-14T20:12:20.000Z

# coding: utf-8 # flake8: noqa from __future__ import absolute_import # import models into model package from openapi_server.models.codes_codes_obj import CodesCodesObj from openapi_server.models.concept_detail import ConceptDetail from openapi_server.models.concept_term import ConceptTerm from openapi_server.models.full_capacity_term import FullCapacityTerm from openapi_server.models.qqst import QQST from openapi_server.models.sab_code_term import SabCodeTerm from openapi_server.models.sab_definition import SabDefinition from openapi_server.models.sab_relationship_concept_prefterm import SabRelationshipConceptPrefterm from openapi_server.models.semantic_stn import SemanticStn from openapi_server.models.sty_tui_stn import StyTuiStn from openapi_server.models.term_resp_obj import TermRespObj from openapi_server.models.termtype_code import TermtypeCode

48

98

0.890046

117

864

6.273504

0.384615

0.179837

0.277929

0.376022

0.188011

0

0.002503

0.075231

864

17

99

50.823529

0.916145

0.068287

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

025238bd5558ffe79241c57f24def7add70a520a

8,783

py

Python

tools/moduletests/unit/test_selinuxpermissive.py

stivesso/aws-ec2rescue-linux

63fb350ba65d3d67c25c0ecc367793adef6cebbd

[ "Apache-2.0" ]

178

2017-07-18T18:58:36.000Z

2022-03-31T03:12:52.000Z

tools/moduletests/unit/test_selinuxpermissive.py

stivesso/aws-ec2rescue-linux

63fb350ba65d3d67c25c0ecc367793adef6cebbd

[ "Apache-2.0" ]

45

2017-07-18T23:19:06.000Z

2021-11-30T17:31:51.000Z

tools/moduletests/unit/test_selinuxpermissive.py

stivesso/aws-ec2rescue-linux

63fb350ba65d3d67c25c0ecc367793adef6cebbd

[ "Apache-2.0" ]

72

2017-07-18T18:57:59.000Z

2022-03-29T06:14:06.000Z

# Copyright 2016-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """ Unit tests for the selinuxpermissive module """ import os import sys import unittest import mock import moduletests.src.selinuxpermissive try: # Python 2.x from cStringIO import StringIO except ImportError: # Python 3.x from io import StringIO if sys.hexversion >= 0x3040000: # contextlib.redirect_stdout was introduced in Python 3.4 import contextlib else: # contextlib2 is a backport of contextlib from Python 3.5 and is compatible with Python2/3 import contextlib2 as contextlib class Testselinuxpermissive(unittest.TestCase): config_file_path = "/etc/selinux/config" def setUp(self): self.output = StringIO() def tearDown(self): self.output.close() @mock.patch("moduletests.src.selinuxpermissive.os.path.isfile", return_value=False) def test_detect_no_selinux(self, isfile_mock): self.assertFalse(moduletests.src.selinuxpermissive.detect(self.config_file_path)) self.assertTrue(isfile_mock.called) @mock.patch("moduletests.src.selinuxpermissive.os.path.isfile", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.open", mock.mock_open(read_data="SELINUX=enforcing")) def test_detect_problem(self, isfile_mock): self.assertTrue(moduletests.src.selinuxpermissive.detect(self.config_file_path)) self.assertTrue(isfile_mock.called) @mock.patch("moduletests.src.selinuxpermissive.os.path.isfile", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.open", mock.mock_open(read_data="SELINUX=permissive")) def test_detect_noproblem(self, isfile_mock): self.assertFalse(moduletests.src.selinuxpermissive.detect(self.config_file_path)) self.assertTrue(isfile_mock.called) @mock.patch("moduletests.src.selinuxpermissive.open", mock.mock_open(read_data="SELINUX=enforcing")) def test_fix_success(self): self.assertTrue(moduletests.src.selinuxpermissive.fix(self.config_file_path)) @mock.patch("moduletests.src.selinuxpermissive.open", side_effect=IOError) def test_fix_exception(self, open_mock): with contextlib.redirect_stdout(self.output): self.assertRaises(IOError, moduletests.src.selinuxpermissive.fix, self.config_file_path) self.assertEqual(self.output.getvalue(), "[WARN] Unable to replace contents of /etc/selinux/config\n") self.assertTrue(open_mock.called) @mock.patch("moduletests.src.selinuxpermissive.get_config_dict") @mock.patch("moduletests.src.selinuxpermissive.detect", side_effect=(True, False)) @mock.patch("moduletests.src.selinuxpermissive.os.path.isfile", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.backup", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.fix", return_value=True) def test_run_success_fixed(self, fix_mock, backup_mock, isfile_mock, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": dict(), "REMEDIATE": True} with contextlib.redirect_stdout(self.output): self.assertTrue(moduletests.src.selinuxpermissive.run()) self.assertTrue("[SUCCESS] selinux set to permissive" in self.output.getvalue()) self.assertTrue(fix_mock.called) self.assertTrue(backup_mock.called) self.assertTrue(isfile_mock.called) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.selinuxpermissive.get_config_dict", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.detect", return_value=False) def test_run_success(self, detect_mock, config_mock): with contextlib.redirect_stdout(self.output): self.assertTrue(moduletests.src.selinuxpermissive.run()) self.assertTrue("[SUCCESS] selinux is not set to enforcing" in self.output.getvalue()) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.selinuxpermissive.get_config_dict") @mock.patch("moduletests.src.selinuxpermissive.detect", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.os.path.isfile", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.backup", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.fix", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.restore", return_value=True) def test_run_failure_isfile(self, restore_mock, fix_mock, backup_mock, isfile_mock, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": {self.config_file_path: "/some/path"}, "REMEDIATE": True, "SUDO": True} with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.selinuxpermissive.run()) self.assertTrue("[FAILURE] failed to set selinux set to permissive" in self.output.getvalue()) self.assertTrue(restore_mock.called) self.assertTrue(fix_mock.called) self.assertTrue(backup_mock.called) self.assertTrue(isfile_mock.called) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.selinuxpermissive.get_config_dict") @mock.patch("moduletests.src.selinuxpermissive.detect", return_value=True) @mock.patch("moduletests.src.selinuxpermissive.os.path.isfile", return_value=False) @mock.patch("moduletests.src.selinuxpermissive.fix", return_value=True) def test_run_failure(self, fix_mock, isfile_mock, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": dict(), "REMEDIATE": True, "SUDO": True} with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.selinuxpermissive.run()) self.assertTrue("[FAILURE] failed to set selinux set to permissive" in self.output.getvalue()) self.assertTrue(fix_mock.called) self.assertTrue(isfile_mock.called) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.selinuxpermissive.get_config_dict") @mock.patch("moduletests.src.selinuxpermissive.detect", side_effect=IOError) @mock.patch("moduletests.src.selinuxpermissive.restore", return_value=True) def test_run_failure_exception(self, restore_mock, detect_mock, config_mock): config_mock.return_value = {"BACKUP_DIR": "/var/tmp/ec2rl", "LOG_DIR": "/var/tmp/ec2rl", "BACKED_FILES": {self.config_file_path: "/some/path"}, "REMEDIATE": True} with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.selinuxpermissive.run()) self.assertTrue(self.output.getvalue().endswith("Review the logs to determine the cause of the issue.\n")) self.assertTrue(restore_mock.called) self.assertTrue(detect_mock.called) self.assertTrue(config_mock.called) @mock.patch("moduletests.src.selinuxpermissive.get_config_dict", side_effect=IOError) def test_run_failure_config_exception(self, config_mock): with contextlib.redirect_stdout(self.output): self.assertFalse(moduletests.src.selinuxpermissive.run()) self.assertTrue(self.output.getvalue().endswith("Review the logs to determine the cause of the issue.\n")) self.assertTrue(config_mock.called)

51.063953

114

0.683138

1,016

8,783

5.747047

0.165354

0.095907

0.212365

0.110293

0.773934

0.754239

0.742422

0.737113

0.699092

0.695667

0

0.005472

0.209382

8,783

171

115

51.362573

0.835397

0.086189

0

0.621212

0

0.23869

0.153462

0

0.001125

0

0.325758

1

0.098485

false

0

0.075758

0

0.189394

0

null

0

1

0

1

0

1

0

null

0

5

5a1f5bbe05981d0eb994d7e823249c4d51b796e1

177

py

Python

app/admin.py

Kanogaelias/neighbourhoodwatch

0dea00bdf83e5045b3cfaba55e483bdd72343a1d

[ "Unlicense" ]

1

2020-09-28T03:51:33.000Z

app/admin.py

Kanogaelias/neighbourhoodwatch

0dea00bdf83e5045b3cfaba55e483bdd72343a1d

[ "Unlicense" ]

null

app/admin.py

Kanogaelias/neighbourhoodwatch

0dea00bdf83e5045b3cfaba55e483bdd72343a1d

[ "Unlicense" ]

null

from django.contrib import admin from .models import Neighbourhood,Profile,Business admin.site.register(Neighbourhood) admin.site.register(Profile) admin.site.register(Business)

35.4

50

0.853107

23

177

6.565217

0.478261

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true

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null

0

1

0

1

0

5

5a51fbe326c398e262cbdb3bebbaebe4d2376347

57

py

Python

gym_forest/envs/__init__.py

kmckiern/gym-forest

12f7c8c1ab64d18983c8817bec4efe627e298bde

[ "Apache-2.0" ]

2

2020-01-14T07:47:29.000Z

2020-04-16T13:50:03.000Z

gym_forest/envs/__init__.py

kmckiern/gym-forest

12f7c8c1ab64d18983c8817bec4efe627e298bde

[ "Apache-2.0" ]

null

gym_forest/envs/__init__.py

kmckiern/gym-forest

12f7c8c1ab64d18983c8817bec4efe627e298bde

[ "Apache-2.0" ]

null

from gym_forest.envs.gym_forest import ForestDiscreteEnv

28.5

56

0.894737

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57

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true

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null

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1

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1

0

5

ce556fd1cb299c8c110043a6231cf66a106e6003

168

py

Python

Lib/site-packages/QtModularUiPack/Widgets/VideoExtensions/__init__.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

[ "bzip2-1.0.6", "0BSD" ]

3

2019-11-11T12:09:23.000Z

2022-02-17T10:02:55.000Z

QtModularUiPack/Widgets/VideoExtensions/__init__.py

dowerner/QtModularUiPack

de2ce6ba3a1cd52ca00eaea3ea3bb2247fe76ba3

[ "Apache-2.0" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

Lib/site-packages/QtModularUiPack/Widgets/VideoExtensions/__init__.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

[ "bzip2-1.0.6", "0BSD" ]

2

2019-11-11T12:09:31.000Z

2019-11-11T12:09:42.000Z

from .image_render_widget import ImageRenderWidget, ImageCircle, ImageEllipse, ImageLayer, ImageRectangle, ImageShape from .video_frame_grabber import VideoFrameGrabber

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0

1

0

1

0

5

ce593babf7fb43d18d84500bd381972dac1e100c

3,427

py

Python

models/unetp/layers.py

qgking/DASC_COVID19

3300516b1d0e9896e2fb2ffda8527e0e1a1fcf2c

[ "MIT" ]

4

2021-04-21T05:09:49.000Z

2022-01-17T13:02:45.000Z

models/unetp/layers.py

qgking/DASC_COVID19

3300516b1d0e9896e2fb2ffda8527e0e1a1fcf2c

[ "MIT" ]

null

models/unetp/layers.py

qgking/DASC_COVID19

3300516b1d0e9896e2fb2ffda8527e0e1a1fcf2c

[ "MIT" ]

1

2021-07-08T02:20:43.000Z

# -*- coding: utf-8 -*- # @Time : 20/7/2 11:08 # @Author : qgking # @Email : qgking@tju.edu.cn # @Software: PyCharm # @Desc : layers.py import torch import torch.nn as nn import torch.nn.functional as F from models.unetp.init_weights import init_weights class unetConv2(nn.Module): def __init__(self, in_size, out_size, is_batchnorm, n=2, ks=3, stride=1, padding=1): super(unetConv2, self).__init__() self.n = n self.ks = ks self.stride = stride self.padding = padding s = stride p = padding if is_batchnorm: for i in range(1, n + 1): conv = nn.Sequential(nn.Conv2d(in_size, out_size, ks, s, p), nn.BatchNorm2d(out_size), nn.ReLU(inplace=True), ) setattr(self, 'conv%d' % i, conv) in_size = out_size else: for i in range(1, n + 1): conv = nn.Sequential(nn.Conv2d(in_size, out_size, ks, s, p), nn.ReLU(inplace=True), ) setattr(self, 'conv%d' % i, conv) in_size = out_size # initialise the blocks for m in self.children(): init_weights(m, init_type='kaiming') def forward(self, inputs): x = inputs for i in range(1, self.n + 1): conv = getattr(self, 'conv%d' % i) x = conv(x) return x class unetUp(nn.Module): def __init__(self, in_size, out_size, is_deconv, n_concat=2): super(unetUp, self).__init__() # self.conv = unetConv2(in_size + (n_concat - 2) * out_size, out_size, False) self.conv = unetConv2(out_size * 2, out_size, False) if is_deconv: self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=4, stride=2, padding=1) else: self.up = nn.UpsamplingBilinear2d(scale_factor=2) # initialise the blocks for m in self.children(): if m.__class__.__name__.find('unetConv2') != -1: continue init_weights(m, init_type='kaiming') def forward(self, inputs0, *input): # print(self.n_concat) # print(input) outputs0 = self.up(inputs0) for i in range(len(input)): outputs0 = torch.cat([outputs0, input[i]], 1) return self.conv(outputs0) class unetUp_origin(nn.Module): def __init__(self, in_size, out_size, is_deconv, n_concat=2): super(unetUp_origin, self).__init__() # self.conv = unetConv2(out_size*2, out_size, False) if is_deconv: self.conv = unetConv2(in_size + (n_concat - 2) * out_size, out_size, False) self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=4, stride=2, padding=1) else: self.conv = unetConv2(in_size + (n_concat - 2) * out_size, out_size, False) self.up = nn.UpsamplingBilinear2d(scale_factor=2) # initialise the blocks for m in self.children(): if m.__class__.__name__.find('unetConv2') != -1: continue init_weights(m, init_type='kaiming') def forward(self, inputs0, *input): # print(self.n_concat) # print(input) outputs0 = self.up(inputs0) for i in range(len(input)): outputs0 = torch.cat([outputs0, input[i]], 1) return self.conv(outputs0)

35.329897

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455

3,427

4.046154

0.213187

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0.063552

0.768061

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0.710483

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0.314269

3,427

96

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0.238806

0

null

0

1

0

null

0

5

ce8c5d32aa7deb64d060bb2911a4ad28bae94c9a

1,630

py

Python

onap_tests/unit/components/sdnc.py

Orange-OpenSource/xtesting-onap-tests

ce4237f49089a91c81f5fad552f78fec384fd504

[ "Apache-2.0" ]

null

onap_tests/unit/components/sdnc.py

Orange-OpenSource/xtesting-onap-tests

ce4237f49089a91c81f5fad552f78fec384fd504

[ "Apache-2.0" ]

null

onap_tests/unit/components/sdnc.py

Orange-OpenSource/xtesting-onap-tests

ce4237f49089a91c81f5fad552f78fec384fd504

[ "Apache-2.0" ]

2

2018-06-08T15:49:51.000Z

2021-06-22T10:06:30.000Z

#!/usr/bin/env python # Copyright (c) 2017 Orange and others. # # All rights reserved. This program and the accompanying materials # are made available under the terms of the Apache License, Version 2.0 # which accompanies this distribution, and is available at # http://www.apache.org/licenses/LICENSE-2.0 # pylint: disable=missing-docstring import unittest class SdncTestingBase(unittest.TestCase): pass # {"input": {"sdnc-request-header": # {"svc-notification-url": # "http:\\/\\/onap.org:8080\\/adapters\\/rest\\/SDNCNotify", # "svc-request-id": "test", "svc-action": "reserve"}, # "request-information": # {"request-action": "PreloadVNFRequest", "order-version": "1", # "notification-url": "onap.org", "order-number": "1", "request-id": "test"}, # "vnf-topology-information": {"vnf-assignments": {"vnf-vms": [], # "availability-zones": [], "vnf-networks": []}, # "vnf-parameters": # [{"vnf-parameter-name": "netconf_user_1", # "vnf-parameter-value": "netconfuser1"}, # {"vnf-parameter-name": "netconf_password_1", # "vnf-parameter-value": "ncuser1Pass"}, # {"vnf-parameter-name": "netconf_ssh_public_key_1", # "vnf-parameter-value": "vmrf_key_pair"}], # "vnf-topology-identifier": # {"service-type": "a674f0ce-3f7e-4f75-96f7-39830e9a1b61", # "generic-vnf-type": "vMRFaaS3/vMRF3 0", # "vnf-name": "be1e0d5e-4c89-4467-b2ef-c1c3f8a5b136", # "generic-vnf-name": "vMRFaaS3-service-instance-0DP8AF", # "vnf-type": "vmrf30..Vmrf3..base_swms..module-0"}}}} # # SDNC url: /restconf/operations/VNF-API:preload-vnf-topology-operation # # {"output":{"svc-request-id":"test", # "response-code":"200","ack-final-indicator":"Y"}}

36.222222

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1

0

1

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5

cea9d3d45019f38b2af20d0ed28e7db843971d6a

301

py

Python

dpctl/tests/_helper.py

reazulhoque/dpctl

27634efff7bcaf2096d3e236d9739e1a25e0d99e

[ "Apache-2.0" ]

1

2020-08-09T13:55:34.000Z

dpctl/tests/_helper.py

PokhodenkoSA/pydppl

9b8644b167a2bc9d1067e5c0d13d3abef0bff82b

[ "Apache-2.0" ]

1

2021-07-30T09:01:28.000Z

dpctl/tests/_helper.py

PokhodenkoSA/dpctl

9b8644b167a2bc9d1067e5c0d13d3abef0bff82b

[ "Apache-2.0" ]

null

import dpctl def has_gpu(backend="opencl"): return bool(dpctl.get_num_devices(backend=backend, device_type="gpu")) def has_cpu(backend="opencl"): return bool(dpctl.get_num_devices(backend=backend, device_type="cpu")) def has_sycl_platforms(): return bool(len(dpctl.get_platforms()))

21.5

74

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301

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0.409091

0.084112

0.17757

0.214953

0.607477

0

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301

13

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23.153846

0.801498

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false

0

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null

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1

0

1

0

null

0

1

0

1

0

5

cebb3410e55a48d88294300e9e43bf648df548a0

8,633

py

Python

lotlan_scheduler/parser/LoTLanParserListener.py

iml130/lotlan-scheduler

b576f853706d614a918dccd9572cc2c2b666bbe4

[ "Apache-2.0" ]

null

lotlan_scheduler/parser/LoTLanParserListener.py

iml130/lotlan-scheduler

b576f853706d614a918dccd9572cc2c2b666bbe4

[ "Apache-2.0" ]

null

lotlan_scheduler/parser/LoTLanParserListener.py

iml130/lotlan-scheduler

b576f853706d614a918dccd9572cc2c2b666bbe4

[ "Apache-2.0" ]

null

# Generated from LoTLanParser.g4 by ANTLR 4.8 from antlr4 import * if __name__ is not None and "." in __name__: from .LoTLanParser import LoTLanParser else: from LoTLanParser import LoTLanParser # This class defines a complete listener for a parse tree produced by LoTLanParser. class LoTLanParserListener(ParseTreeListener): # Enter a parse tree produced by LoTLanParser#program. def enterProgram(self, ctx:LoTLanParser.ProgramContext): pass # Exit a parse tree produced by LoTLanParser#program. def exitProgram(self, ctx:LoTLanParser.ProgramContext): pass # Enter a parse tree produced by LoTLanParser#template. def enterTemplate(self, ctx:LoTLanParser.TemplateContext): pass # Exit a parse tree produced by LoTLanParser#template. def exitTemplate(self, ctx:LoTLanParser.TemplateContext): pass # Enter a parse tree produced by LoTLanParser#templateStart. def enterTemplateStart(self, ctx:LoTLanParser.TemplateStartContext): pass # Exit a parse tree produced by LoTLanParser#templateStart. def exitTemplateStart(self, ctx:LoTLanParser.TemplateStartContext): pass # Enter a parse tree produced by LoTLanParser#instance. def enterInstance(self, ctx:LoTLanParser.InstanceContext): pass # Exit a parse tree produced by LoTLanParser#instance. def exitInstance(self, ctx:LoTLanParser.InstanceContext): pass # Enter a parse tree produced by LoTLanParser#instanceStart. def enterInstanceStart(self, ctx:LoTLanParser.InstanceStartContext): pass # Exit a parse tree produced by LoTLanParser#instanceStart. def exitInstanceStart(self, ctx:LoTLanParser.InstanceStartContext): pass # Enter a parse tree produced by LoTLanParser#memberVariable. def enterMemberVariable(self, ctx:LoTLanParser.MemberVariableContext): pass # Exit a parse tree produced by LoTLanParser#memberVariable. def exitMemberVariable(self, ctx:LoTLanParser.MemberVariableContext): pass # Enter a parse tree produced by LoTLanParser#value. def enterValue(self, ctx:LoTLanParser.ValueContext): pass # Exit a parse tree produced by LoTLanParser#value. def exitValue(self, ctx:LoTLanParser.ValueContext): pass # Enter a parse tree produced by LoTLanParser#transportOrderStep. def enterTransportOrderStep(self, ctx:LoTLanParser.TransportOrderStepContext): pass # Exit a parse tree produced by LoTLanParser#transportOrderStep. def exitTransportOrderStep(self, ctx:LoTLanParser.TransportOrderStepContext): pass # Enter a parse tree produced by LoTLanParser#tosStart. def enterTosStart(self, ctx:LoTLanParser.TosStartContext): pass # Exit a parse tree produced by LoTLanParser#tosStart. def exitTosStart(self, ctx:LoTLanParser.TosStartContext): pass # Enter a parse tree produced by LoTLanParser#tosStatement. def enterTosStatement(self, ctx:LoTLanParser.TosStatementContext): pass # Exit a parse tree produced by LoTLanParser#tosStatement. def exitTosStatement(self, ctx:LoTLanParser.TosStatementContext): pass # Enter a parse tree produced by LoTLanParser#locationStatement. def enterLocationStatement(self, ctx:LoTLanParser.LocationStatementContext): pass # Exit a parse tree produced by LoTLanParser#locationStatement. def exitLocationStatement(self, ctx:LoTLanParser.LocationStatementContext): pass # Enter a parse tree produced by LoTLanParser#optTosStatement. def enterOptTosStatement(self, ctx:LoTLanParser.OptTosStatementContext): pass # Exit a parse tree produced by LoTLanParser#optTosStatement. def exitOptTosStatement(self, ctx:LoTLanParser.OptTosStatementContext): pass # Enter a parse tree produced by LoTLanParser#eventStatement. def enterEventStatement(self, ctx:LoTLanParser.EventStatementContext): pass # Exit a parse tree produced by LoTLanParser#eventStatement. def exitEventStatement(self, ctx:LoTLanParser.EventStatementContext): pass # Enter a parse tree produced by LoTLanParser#onDoneStatement. def enterOnDoneStatement(self, ctx:LoTLanParser.OnDoneStatementContext): pass # Exit a parse tree produced by LoTLanParser#onDoneStatement. def exitOnDoneStatement(self, ctx:LoTLanParser.OnDoneStatementContext): pass # Enter a parse tree produced by LoTLanParser#parameterStatement. def enterParameterStatement(self, ctx:LoTLanParser.ParameterStatementContext): pass # Exit a parse tree produced by LoTLanParser#parameterStatement. def exitParameterStatement(self, ctx:LoTLanParser.ParameterStatementContext): pass # Enter a parse tree produced by LoTLanParser#task. def enterTask(self, ctx:LoTLanParser.TaskContext): pass # Exit a parse tree produced by LoTLanParser#task. def exitTask(self, ctx:LoTLanParser.TaskContext): pass # Enter a parse tree produced by LoTLanParser#taskStart. def enterTaskStart(self, ctx:LoTLanParser.TaskStartContext): pass # Exit a parse tree produced by LoTLanParser#taskStart. def exitTaskStart(self, ctx:LoTLanParser.TaskStartContext): pass # Enter a parse tree produced by LoTLanParser#taskStatement. def enterTaskStatement(self, ctx:LoTLanParser.TaskStatementContext): pass # Exit a parse tree produced by LoTLanParser#taskStatement. def exitTaskStatement(self, ctx:LoTLanParser.TaskStatementContext): pass # Enter a parse tree produced by LoTLanParser#constraintsStatement. def enterConstraintsStatement(self, ctx:LoTLanParser.ConstraintsStatementContext): pass # Exit a parse tree produced by LoTLanParser#constraintsStatement. def exitConstraintsStatement(self, ctx:LoTLanParser.ConstraintsStatementContext): pass # Enter a parse tree produced by LoTLanParser#transportOrder. def enterTransportOrder(self, ctx:LoTLanParser.TransportOrderContext): pass # Exit a parse tree produced by LoTLanParser#transportOrder. def exitTransportOrder(self, ctx:LoTLanParser.TransportOrderContext): pass # Enter a parse tree produced by LoTLanParser#fromStatement. def enterFromStatement(self, ctx:LoTLanParser.FromStatementContext): pass # Exit a parse tree produced by LoTLanParser#fromStatement. def exitFromStatement(self, ctx:LoTLanParser.FromStatementContext): pass # Enter a parse tree produced by LoTLanParser#toStatement. def enterToStatement(self, ctx:LoTLanParser.ToStatementContext): pass # Exit a parse tree produced by LoTLanParser#toStatement. def exitToStatement(self, ctx:LoTLanParser.ToStatementContext): pass # Enter a parse tree produced by LoTLanParser#parameters. def enterParameters(self, ctx:LoTLanParser.ParametersContext): pass # Exit a parse tree produced by LoTLanParser#parameters. def exitParameters(self, ctx:LoTLanParser.ParametersContext): pass # Enter a parse tree produced by LoTLanParser#repeatStatement. def enterRepeatStatement(self, ctx:LoTLanParser.RepeatStatementContext): pass # Exit a parse tree produced by LoTLanParser#repeatStatement. def exitRepeatStatement(self, ctx:LoTLanParser.RepeatStatementContext): pass # Enter a parse tree produced by LoTLanParser#expression. def enterExpression(self, ctx:LoTLanParser.ExpressionContext): pass # Exit a parse tree produced by LoTLanParser#expression. def exitExpression(self, ctx:LoTLanParser.ExpressionContext): pass # Enter a parse tree produced by LoTLanParser#binOperation. def enterBinOperation(self, ctx:LoTLanParser.BinOperationContext): pass # Exit a parse tree produced by LoTLanParser#binOperation. def exitBinOperation(self, ctx:LoTLanParser.BinOperationContext): pass # Enter a parse tree produced by LoTLanParser#unOperation. def enterUnOperation(self, ctx:LoTLanParser.UnOperationContext): pass # Exit a parse tree produced by LoTLanParser#unOperation. def exitUnOperation(self, ctx:LoTLanParser.UnOperationContext): pass # Enter a parse tree produced by LoTLanParser#con. def enterCon(self, ctx:LoTLanParser.ConContext): pass # Exit a parse tree produced by LoTLanParser#con. def exitCon(self, ctx:LoTLanParser.ConContext): pass del LoTLanParser

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null

0

1

0

1

0

5

0c9b7e6cbf3bfc9e02bf5d7b89e503bbfdb26688

2,119

py

Python

matrix_ops.py

rkibria/display3dfile

d1ea6d369d4ebee60b33734f94292f18f0754c2d

[ "MIT" ]

null

matrix_ops.py

rkibria/display3dfile

d1ea6d369d4ebee60b33734f94292f18f0754c2d

[ "MIT" ]

null

matrix_ops.py

rkibria/display3dfile

d1ea6d369d4ebee60b33734f94292f18f0754c2d

[ "MIT" ]

null

import math def matrixMult(v, m): return ( m[ 0] * v[0] + m[ 1] * v[1] + m[ 2] * v[2] + m[ 3] * v[3], m[ 4] * v[0] + m[ 5] * v[1] + m[ 6] * v[2] + m[ 7] * v[3], m[ 8] * v[0] + m[ 9] * v[1] + m[10] * v[2] + m[11] * v[3], m[12] * v[0] + m[13] * v[1] + m[14] * v[2] + m[15] * v[3], ) def getTranslationMatrix(dx, dy, dz): return [ 1.0, 0.0, 0.0, float(dx), 0.0, 1.0, 0.0, float(dy), 0.0, 0.0, 1.0, float(dz), 0.0, 0.0, 0.0, 1.0, ] def getRotateXMatrix(phi): cos_phi = math.cos(phi) sin_phi = math.sin(phi) return [ 1.0, 0.0, 0.0, 0.0, 0.0, cos_phi, -sin_phi, 0.0, 0.0, sin_phi, cos_phi, 0.0, 0.0, 0.0, 0.0, 1.0, ] def getRotateYMatrix(phi): cos_phi = math.cos(phi) sin_phi = math.sin(phi) return [ cos_phi, 0.0, sin_phi, 0.0, 0.0, 1.0, 0.0, 0.0, -sin_phi, 0.0, cos_phi, 0.0, 0.0, 0.0, 0.0, 1.0, ] def getRotateZMatrix(phi): cos_phi = math.cos(phi) sin_phi = math.sin(phi) return [ cos_phi, -sin_phi, 0.0, 0.0, sin_phi, cos_phi, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, ] def getScaleMatrix(sx, sy, sz): return [ float(sx), 0.0, 0.0, 0.0, 0.0, float(sy), 0.0, 0.0, 0.0, 0.0, float(sz), 0.0, 0.0, 0.0, 0.0, 1.0, ]

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2.045614

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0.35506

0.363636

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0.463122

0

0.177437

0.569136

2,119

55

75

38.527273

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false

0

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0.265306

0

null

1

0

1

0

1

0

null

0

5

0b7432bfb99d5cfbdeba9186f7d72161c81fc535

108

py

Python

py/Utility.SetData.py

mathematicalmichael/SpringNodes

3ff4034b6e57ee6efa55c963e1819f3d30a2c4ab

[ "MIT" ]

51

2015-09-25T09:30:57.000Z

2022-01-19T14:16:44.000Z

py/Utility.SetData.py

sabeelcoder/SpringNodes

e21a24965474d54369e74d23c06f8c42a7b926b5

[ "MIT" ]

66

2015-09-30T02:43:32.000Z

2022-03-31T02:26:52.000Z

py/Utility.SetData.py

sabeelcoder/SpringNodes

e21a24965474d54369e74d23c06f8c42a7b926b5

[ "MIT" ]

48

2015-11-19T01:34:47.000Z

2022-02-25T17:26:48.000Z

import System dataKey, data = IN System.AppDomain.CurrentDomain.SetData("_Dyn_Wireless_%s" % dataKey, data)

27

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14

108

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true

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0b8d9b5f54af7a02bfebe6bb369e3b7b52e4fff6

259

py

Python

src/airfly/_vendor/airflow/contrib/operators/gcp_text_to_speech_operator.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

7

2021-09-27T11:38:48.000Z

2022-02-01T06:06:24.000Z

src/airfly/_vendor/airflow/contrib/operators/gcp_text_to_speech_operator.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

null

src/airfly/_vendor/airflow/contrib/operators/gcp_text_to_speech_operator.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

null

# Auto generated by 'inv collect-airflow' from airfly._vendor.airflow.providers.google.cloud.operators.text_to_speech import ( CloudTextToSpeechSynthesizeOperator, ) class GcpTextToSpeechSynthesizeOperator(CloudTextToSpeechSynthesizeOperator): pass

28.777778

84

0.837838

23

259

9.304348

0.913043

0

0.096525

259

8

85

32.375

0.91453

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0

1

0

1

0

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null

0

1

0

1

0

null

0

1

0

5

0b95ea2572caea42cfef156c084df6e66bc2a75b

105

py

Python

vnpy/app/realtime_monitor/ui/__init__.py

xyh888/vnpy

7b51716928ab9574f171a2eda190b37b4f393bb1

[ "MIT" ]

5

2019-05-24T05:19:55.000Z

2020-07-29T13:21:49.000Z

vnpy/app/realtime_monitor/ui/__init__.py

xyh888/vnpy

7b51716928ab9574f171a2eda190b37b4f393bb1

[ "MIT" ]

null

vnpy/app/realtime_monitor/ui/__init__.py

xyh888/vnpy

7b51716928ab9574f171a2eda190b37b4f393bb1

[ "MIT" ]

2

2019-07-01T02:14:04.000Z

2020-07-29T13:21:53.000Z

#!/usr/bin/python # -*- coding:utf-8 -*- """ @author:Hadrianl """ from .widget import CandleChartWidget

13.125

37

0.657143

12

105

5.75

1

0

0.01087

0.12381

105

8

37

13.125

0.73913

0.514286

0

1

0

true

0

1

0

1

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1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0bc07f714464b22a45e71ba4f3cc3e49b022e34b

214

py

Python

yourcfp/proposals/admin.py

sujay0399/CFP

cc39d4322fa1e1f1867e96c6208fd52ba55b3e8e

[ "MIT" ]

2

2019-06-10T11:30:48.000Z

2019-08-17T21:19:12.000Z

yourcfp/proposals/admin.py

sujay0399/CFP

cc39d4322fa1e1f1867e96c6208fd52ba55b3e8e

[ "MIT" ]

32

2019-05-22T19:38:43.000Z

2019-12-12T07:48:18.000Z

yourcfp/proposals/admin.py

sujay0399/CFP

cc39d4322fa1e1f1867e96c6208fd52ba55b3e8e

[ "MIT" ]

1

2019-05-19T14:07:50.000Z

from django.contrib import admin from .models import Proposal, ProposalStatus, Feedback # Register your models here. admin.site.register(Proposal) admin.site.register(ProposalStatus) admin.site.register(Feedback)

26.75

54

0.827103

27

214

6.555556

0.481481

0.152542

0.288136

0

0.088785

214

7

55

30.571429

0.907692

0.121495

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

e7f6c3a12191b3ddcadaf1fb8c25c606407c6263

25

py

Python

RUNFILE.py

AbhilashPal/IETHackathon18

78a8240ff0e5e16156f72956991c9c2510ab7c7f

[ "MIT" ]

null

RUNFILE.py

AbhilashPal/IETHackathon18

78a8240ff0e5e16156f72956991c9c2510ab7c7f

[ "MIT" ]

null

RUNFILE.py

AbhilashPal/IETHackathon18

78a8240ff0e5e16156f72956991c9c2510ab7c7f

[ "MIT" ]

1

2019-03-13T10:24:40.000Z

import py1 py1.func1()

8.333333

12

0.68

4

25

4.25

0.75

0

0.15

0.2

25

2

13

12.5

0.7

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

e7f7a5e47c572cd74e2ca8502f25e6892a5705da

149

py

Python

DateTime/TimeExample1.py

suprit08/PythonAssignments

6cab78660d8c77cf573cbea82e4dada19b0fc08c

[ "MIT" ]

null

DateTime/TimeExample1.py

suprit08/PythonAssignments

6cab78660d8c77cf573cbea82e4dada19b0fc08c

[ "MIT" ]

null

DateTime/TimeExample1.py

suprit08/PythonAssignments

6cab78660d8c77cf573cbea82e4dada19b0fc08c

[ "MIT" ]

null

#TimeExample1.py import time #Printing the no.of ticks spent since 12AM, 1st January 1970 print("No.of total ticks since 1970 : ",time.time())

24.833333

61

0.724832

24

149

4.5

0.708333

0.074074

0

0.097561

0.174497

149

6

62

24.833333

0.780488

0.496644

0

0.449275

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f00b3ff3621b2aec8525d673155e5394b229c758

65

py

Python

src/core/uv_edit/helpers/__init__.py

Epihaius/panda3dstudio

f5c62ca49617cae1aa5aa5b695200027da99e242

[ "BSD-3-Clause" ]

63

2016-01-02T16:28:47.000Z

2022-01-19T11:29:51.000Z

src/core/uv_edit/helpers/__init__.py

Epihaius/panda3dstudio

f5c62ca49617cae1aa5aa5b695200027da99e242

[ "BSD-3-Clause" ]

12

2016-06-12T14:14:15.000Z

2020-12-18T16:11:45.000Z

src/core/uv_edit/helpers/__init__.py

Epihaius/panda3dstudio

f5c62ca49617cae1aa5aa5b695200027da99e242

[ "BSD-3-Clause" ]

17

2016-05-23T00:02:27.000Z

2021-04-25T17:48:27.000Z

from .grid import Grid from .trnsf_gizmo import UVTransformGizmo

21.666667

41

0.846154

9

65

6

0.666667

0

0.123077

65

2

42

32.5

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f02297c941f2ac0c91c1553310250eea23e91813

78

py

Python

scripts/field/go50000.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

2

2020-04-15T03:16:07.000Z

2020-08-12T23:28:32.000Z

scripts/field/go50000.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

null

scripts/field/go50000.py

Snewmy/swordie

ae01ed4ec0eb20a18730e8cd209eea0b84a8dd17

[ "MIT" ]

3

2020-08-25T06:55:25.000Z

2020-12-01T13:07:43.000Z

# Inside Dangerous Forest sm.showEffect("Map/Effect.img/maplemap/enter/50000")

39

52

0.807692

11

78

5.727273

1

0

0.067568

0.051282

78

2

52

39

0.783784

0.294872

0

0.648148

0

1

0

true

0

1

0

null

0

1

0

1

null

0

1

0

5

f02949e0cd24e0b7a8f6a0e5fa240c297e91ba9a

151

py

Python

task/admin.py

suvajitsarkar/taskManagement

0054c20fba8dd8eb3c4c83abdded8fc778a8b62b

[ "Apache-2.0" ]

null

task/admin.py

suvajitsarkar/taskManagement

0054c20fba8dd8eb3c4c83abdded8fc778a8b62b

[ "Apache-2.0" ]

1

2021-06-10T23:00:14.000Z

task/admin.py

suvajitsarkar/taskManagement

0054c20fba8dd8eb3c4c83abdded8fc778a8b62b

[ "Apache-2.0" ]

null

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

18.875

32

0.794702

22

151

5.454545

0.545455

0.15

0.283333

0

0.119205

151

7

33

21.571429

0.902256

0.172185

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

f02ef548c818c988f4a4155f301151d124148863

162

py

Python

fmridenoise/interfaces/__init__.py

wiheto/fmridenoise

cc544264806418618861f0ee93fff71a0fa83eca

[ "Apache-2.0" ]

null

fmridenoise/interfaces/__init__.py

wiheto/fmridenoise

cc544264806418618861f0ee93fff71a0fa83eca

[ "Apache-2.0" ]

null

fmridenoise/interfaces/__init__.py

wiheto/fmridenoise

cc544264806418618861f0ee93fff71a0fa83eca

[ "Apache-2.0" ]

null

from .quality_measures import (QualityMeasures, PipelinesQualityMeasures, MergeGroupQualityMeasures)

54

57

0.555556

7

162

12.714286

1

0

0.41358

162

3

57

54

0.936842

0

1

0

true

0

0.333333

0

0.333333

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

f059f2d51603c41a48b96eb9cf19790fe7bf6136

2,011

py

Python

src/utils/logger.py

gdevos010/ml_supervised_learning

ffe7ce99b0fdd346df6324a55370548873bc7b72

[ "MIT" ]

null

src/utils/logger.py

gdevos010/ml_supervised_learning

ffe7ce99b0fdd346df6324a55370548873bc7b72

[ "MIT" ]

null

src/utils/logger.py

gdevos010/ml_supervised_learning

ffe7ce99b0fdd346df6324a55370548873bc7b72

[ "MIT" ]

null

import inspect import logging import os from datetime import datetime logger = logging.getLogger(__name__) def info(msg): level = "INFO" frame, filename, line_number, function_name, lines, index = inspect.getouterframes( inspect.currentframe())[1] line = lines[0] indentation_level = line.find(line.lstrip()) now = datetime.now().time().strftime("%H:%M:%S") prefix = f'[{now} {os.path.basename(filename)}:{line_number} - {function_name}'.ljust(45) + f'] {level}:' logger.info('{prefix}{i}\t{m}'.format( prefix=prefix, i=' ' * max(0, indentation_level - 8), m=msg )) def debug(msg): level = "DEBUG" frame, filename, line_number, function_name, lines, index = inspect.getouterframes( inspect.currentframe())[1] line = lines[0] indentation_level = line.find(line.lstrip()) now = datetime.now().time().strftime("%H:%M:%S") prefix = f'[{now} {os.path.basename(filename)}:{line_number} - {function_name}'.ljust(45) + f'] {level}:' logger.info('{prefix}{i}\t{m}'.format( prefix=prefix, i=' ' * max(0, indentation_level - 8), m=msg )) def error(msg): level = "ERROR" frame, filename, line_number, function_name, lines, index = inspect.getouterframes( inspect.currentframe())[1] line = lines[0] indentation_level = line.find(line.lstrip()) now = datetime.now().time().strftime("%H:%M:%S") prefix = f'[{now} {os.path.basename(filename)}:{line_number} - {function_name}'.ljust(45) + f'] {level}:' logger.info('{prefix}{i}\t{m}'.format( prefix=prefix, i=' ' * max(0, indentation_level - 8), m=msg )) def init_logger(): logger = logging.getLogger(__name__) logging.basicConfig(format="", handlers=[ logging.FileHandler("ml_supervised.log", 'a'), logging.StreamHandler() ]) logger.setLevel(logging.DEBUG)

30.938462

109

0.593237

238

2,011

4.894958

0.235294

0.061803

0.092704

0.133906

0.751931

0

0.011711

0.235704

2,011

64

110

31.421875

0.746259

0

0.673077

0

0.168076

0.062655

0

1

0.076923

false

0

0.076923

0

0.153846

0

null

0

1

0

null

0

5

f06131eff75ddc6fde7cf77ba3295bb9165315a1

93

py

Python

exercicios-turtle/.history/conversor_temp_20210624131757.py

Aleff13/poo-ufsc

bc1574df26f840a3c0fd5b1e0c72e5d69f61493d

[ "MIT" ]

1

2021-11-28T18:49:21.000Z

exercicios-turtle/.history/conversor_temp_20210624131757.py

Aleff13/poo-ufsc

bc1574df26f840a3c0fd5b1e0c72e5d69f61493d

[ "MIT" ]

null

exercicios-turtle/.history/conversor_temp_20210624131757.py

Aleff13/poo-ufsc

bc1574df26f840a3c0fd5b1e0c72e5d69f61493d

[ "MIT" ]

null

print("Abaixo digite o valor da temperatura em graus para saber sua equivalencia em farhent")

93

0.817204

15

93

5.066667

0.933333

0

0.139785

93

1

93

0.95

0

0.893617

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

f066d159f049879af57017c36c170c23e929fbc9

84

py

Python

vidsz/opencv/writer/__init__.py

BlueMirrors/vidsz

c47f09a6b8cb8da9a0b6c97caf99bc2baab6fee7

[ "Apache-2.0" ]

10

2021-06-13T07:09:42.000Z

2022-02-03T16:29:13.000Z

vidsz/opencv/writer/__init__.py

BlueMirrors/vidsz

c47f09a6b8cb8da9a0b6c97caf99bc2baab6fee7

[ "Apache-2.0" ]

3

2021-09-30T18:40:57.000Z

2022-01-31T08:09:31.000Z

vidsz/opencv/writer/__init__.py

BlueMirrors/vidsz

c47f09a6b8cb8da9a0b6c97caf99bc2baab6fee7

[ "Apache-2.0" ]

1

2021-09-30T21:02:55.000Z

"""Implements vidsz's Writer for Opencv Backend """ from .base_writer import Writer

21

47

0.77381

12

84

5.333333

0.833333

0

0.130952

84

3

48

28

0.876712

0.52381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6525057b487c01026e2ea1ca287e70baa93193fa

105

py

Python

office365/sharepoint/social/socialRestActor.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

544

2016-08-04T17:10:16.000Z

2022-03-31T07:17:20.000Z

office365/sharepoint/social/socialRestActor.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

438

2016-10-11T12:24:22.000Z

2022-03-31T19:30:35.000Z

office365/sharepoint/social/socialRestActor.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

202

2016-08-22T19:29:40.000Z

2022-03-30T20:26:15.000Z

from office365.runtime.client_object import ClientObject class SocialRestActor(ClientObject): pass

17.5

56

0.828571

11

105

7.818182

0.909091

0

0.032609

0.12381

105

5

57

21

0.902174

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

652f17d43938dcf31e21ec8792cc0d07ba906cb6

111

py

Python

module.py

damnkk/cycle-GAN

fbf84eb67ef0a1ba909e95c6862f72419c6f4185

[ "MIT" ]

null

module.py

damnkk/cycle-GAN

fbf84eb67ef0a1ba909e95c6862f72419c6f4185

[ "MIT" ]

null

module.py

damnkk/cycle-GAN

fbf84eb67ef0a1ba909e95c6862f72419c6f4185

[ "MIT" ]

null

import tensorflow.compat.v1 as tf import ops import utils from reader import Reader from gen import Generator

15.857143

33

0.828829

18

111

5.111111

0.666667

0

0.010638

0.153153

111

6

34

18.5

0.968085

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e8f1217d476c4e333500c56daec82417a2932258

58

py

Python

src/petronia/defimpl/configuration/__init__.py

groboclown/petronia

486338023d19cee989e92f0c5692680f1a37811f

[ "MIT" ]

19

2017-06-21T10:28:24.000Z

2021-12-31T11:49:28.000Z

src/petronia/defimpl/configuration/__init__.py

groboclown/petronia

486338023d19cee989e92f0c5692680f1a37811f

[ "MIT" ]

10

2016-11-11T18:57:57.000Z

2021-02-01T15:33:43.000Z

src/petronia/defimpl/configuration/__init__.py

groboclown/petronia

486338023d19cee989e92f0c5692680f1a37811f

[ "MIT" ]

3

2017-09-17T03:29:35.000Z

2019-06-03T10:43:08.000Z

""" Initial extension configuration implementations. """

11.6

48

0.758621

4

58

11

1

0

0.12069

58

4

49

14.5

0.862745

0.827586

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

1

null

0

1

0

1

0

null

0

1

0

5

3310c438f5ddc574530b96b1e372b8b133d33d06

7,284

py

Python

tests/algorithms/test_pbr.py

UCL/scikit-surgeryfredwebapp

3e22fc8b9d0898502a5f8a6c8cc813dc62fc3fd5

[ "BSD-3-Clause" ]

5

2020-10-22T01:41:33.000Z

2022-01-07T08:55:39.000Z

tests/algorithms/test_pbr.py

UCL/scikit-surgeryfred

3e60a67fced1ca38f54920ccf37043588bdb1401

[ "BSD-3-Clause" ]

62

2020-06-05T10:54:04.000Z

2021-05-18T19:31:27.000Z

tests/algorithms/test_pbr.py

UCL/scikit-surgeryfredwebapp

3e22fc8b9d0898502a5f8a6c8cc813dc62fc3fd5

[ "BSD-3-Clause" ]

1

2020-06-25T09:59:53.000Z

# coding=utf-8 """Fiducial Registration Educational Demonstration tests""" import math import numpy as np from scipy.stats import linregress import pytest from sksurgeryfred.algorithms.errors import expected_absolute_value import sksurgeryfred.algorithms.point_based_reg as pbreg def _make_circle_fiducials(no_fids, centre, radius, fixed_stddevs, moving_stddevs): fixed_fids = np.zeros(shape=(no_fids, 3), dtype=np.float64) moving_fids = np.zeros(shape=(no_fids, 3), dtype=np.float64) angle_inc = math.pi * 2.0 / float(no_fids) for fid in range(no_fids): fixed_fids[fid] = ([radius * math.cos(angle_inc*fid), radius * math.sin(angle_inc*fid), 0.0] + np.random.normal(scale=fixed_stddevs) + centre) moving_fids[fid] = ([radius * math.cos(angle_inc*fid), radius * math.sin(angle_inc*fid), 0.0] + np.random.normal(scale=moving_stddevs) + centre) return fixed_fids, moving_fids def _run_registrations (pbr, no_fids, centre, radius, fixed_stddevs, moving_stddevs, repeats): tres=np.empty(repeats, dtype=np.float64) fres=np.empty(repeats, dtype=np.float64) np.random.seed(0) for i in range(repeats): fixed_fids, moving_fids = _make_circle_fiducials(no_fids, centre, radius, fixed_stddevs, moving_stddevs) [_success, fres[i], _mean_fle, expected_tre_squared, expected_fre, _transformed_target_2d, tres[i], _no_fids] = pbr.register( fixed_fids, moving_fids) ave_tre = np.average(tres * tres) ave_fre = np.average(fres * fres) _slope, _intercept, _r_value, p_value, _std_err = linregress(tres, fres) return ave_tre, ave_fre, expected_tre_squared, expected_fre, p_value def test_init_with_moving_fle(): """ Init pbr with moving fle should yield non implemented error """ fixed_fle_std_dev = np.array([1.0, 1.0, 1.0], dtype=np.float64) moving_fle_std_dev = np.array([1.0, 1.0, 1.0], dtype=np.float64) fixed_fle_easv = expected_absolute_value(fixed_fle_std_dev) moving_fle_easv = expected_absolute_value(moving_fle_std_dev) target = np.array([[0.0, 0.0, 0.0]], dtype=np.float64) with pytest.raises(NotImplementedError): pbreg.PointBasedRegistration(target, fixed_fle_easv, moving_fle_easv) def test_pbr_3_fids(): """ Tests for tre_from_fle_2d """ fixed_fle_std_dev = np.array([1.0, 1.0, 1.0], dtype=np.float64) moving_fle_std_dev = np.array([0.0, 0.0, 0.0], dtype=np.float64) fixed_fle_easv = expected_absolute_value(fixed_fle_std_dev) moving_fle_easv = expected_absolute_value(moving_fle_std_dev) target = np.array([[0.0, 0.0, 0.0]], dtype=np.float64) pbr = pbreg.PointBasedRegistration(target, fixed_fle_easv, moving_fle_easv) centre = np.array([0.0, 0.0, 0.0], dtype=np.float64) radius = 20.0 expected_tre_squared = 0 expected_fre = 0 repeats = 100 no_fids = 3 ave_tresq, ave_fresq, expected_tre_squared, expected_fre, p_value = \ _run_registrations(pbr, no_fids, centre, radius, fixed_fle_std_dev, moving_fle_std_dev, repeats) assert np.isclose(ave_tresq, expected_tre_squared, atol=0.0, rtol=0.10) assert np.isclose(ave_fresq, expected_fre, atol=0.0, rtol=0.05) assert p_value > 0.05 def test_pbr_10_fids(): """ Tests for tre_from_fle_2d """ fixed_fle_std_dev = np.array([1.0, 1.0, 1.0], dtype=np.float64) moving_fle_std_dev = np.array([0.0, 0.0, 0.0], dtype=np.float64) fixed_fle_easv = expected_absolute_value(fixed_fle_std_dev) moving_fle_easv = expected_absolute_value(moving_fle_std_dev) target = np.array([[0.0, 0.0, 0.0]], dtype=np.float64) pbr = pbreg.PointBasedRegistration(target, fixed_fle_easv, moving_fle_easv) centre = np.array([0.0, 0.0, 0.0], dtype=np.float64) radius = 2.0 repeats = 200 no_fids = 10 ave_tresq, ave_fresq, expected_tre_squared, expected_fre, p_value = \ _run_registrations(pbr, no_fids, centre, radius, fixed_fle_std_dev, moving_fle_std_dev, repeats) assert np.isclose(ave_tresq, expected_tre_squared, atol=0.0, rtol=0.10) assert np.isclose(ave_fresq, expected_fre, atol=0.0, rtol=0.05) assert p_value > 0.05 def test_pbr_10_fids_offset_target(): """ Tests for tre_from_fle_2d """ fixed_fle_std_dev = np.array([1.0, 1.0, 1.0], dtype=np.float64) moving_fle_std_dev = np.array([0.0, 0.0, 0.0], dtype=np.float64) fixed_fle_easv = expected_absolute_value(fixed_fle_std_dev) moving_fle_easv = expected_absolute_value(moving_fle_std_dev) target = np.array([[2.0, 1.0, 0.0]], dtype=np.float64) pbr = pbreg.PointBasedRegistration(target, fixed_fle_easv, moving_fle_easv) centre = np.array([0.0, 0.0, 0.0], dtype=np.float64) radius = 2.0 repeats = 200 no_fids = 10 ave_tresq, ave_fresq, expected_tre_squared, expected_fre, p_value = \ _run_registrations(pbr, no_fids, centre, radius, fixed_fle_std_dev, moving_fle_std_dev, repeats) assert np.isclose(ave_tresq, expected_tre_squared, atol=0.0, rtol=0.10) assert np.isclose(ave_fresq, expected_fre, atol=0.0, rtol=0.05) assert p_value > 0.05 def test_pbr_20_fids_offset_target(): """ Tests for tre_from_fle_2d """ fixed_fle_std_dev = np.array([1.0, 1.0, 1.0], dtype=np.float64) moving_fle_std_dev = np.array([0.0, 0.0, 0.0], dtype=np.float64) fixed_fle_easv = expected_absolute_value(fixed_fle_std_dev) moving_fle_easv = expected_absolute_value(moving_fle_std_dev) target = np.array([[2.0, 1.0, 0.0]], dtype=np.float64) pbr = pbreg.PointBasedRegistration(target, fixed_fle_easv, moving_fle_easv) centre = np.array([0.0, 0.0, 0.0], dtype=np.float64) radius = 20.0 repeats = 200 no_fids = 20 #test get transformed target before registration status, transformed_target = pbr.get_transformed_target() assert not status assert transformed_target is None ave_tresq, ave_fresq, expected_tre_squared, expected_fre, p_value = \ _run_registrations(pbr, no_fids, centre, radius, fixed_fle_std_dev, moving_fle_std_dev, repeats) assert np.isclose(ave_tresq, expected_tre_squared, atol=0.0, rtol=0.10) assert np.isclose(ave_fresq, expected_fre, atol=0.0, rtol=0.05) assert p_value > 0.05 #test get transformed target after registration status, transformed_target = pbr.get_transformed_target() assert status assert np.allclose(np.transpose(transformed_target), target, atol=1.0)

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3315cb7e01e834143b1e6aeae4bdc43863b4c1d1

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Python

examples/official/trial/fashion_mnist_tf_keras/data.py

ybt195/determined

913fdc3b81ef33c2760bdb128c8ce9179e4ab9b2

[ "Apache-2.0" ]

3

2020-04-30T03:56:15.000Z

2020-04-30T04:01:24.000Z

examples/official/trial/fashion_mnist_tf_keras/data.py

ybt195/determined

913fdc3b81ef33c2760bdb128c8ce9179e4ab9b2

[ "Apache-2.0" ]

1

2022-02-10T07:31:44.000Z

examples/official/trial/fashion_mnist_tf_keras/data.py

ybt195/determined

913fdc3b81ef33c2760bdb128c8ce9179e4ab9b2

[ "Apache-2.0" ]

2

2020-07-10T23:08:23.000Z

2021-01-13T10:01:59.000Z

""" This files mimics keras.dataset download's function. For parallel and distributed training, we need to account for multiple processes (one per GPU) per agent. For more information on data in Determined, read our data-access tutorial. """ import gzip import tempfile import numpy as np from tensorflow.python.keras.utils.data_utils import get_file def load_training_data(): """Loads the Fashion-MNIST dataset. Returns: Tuple of Numpy arrays: `(x_train, y_train)`. License: The copyright for Fashion-MNIST is held by Zalando SE. Fashion-MNIST is licensed under the [MIT license]( https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE). """ download_directory = tempfile.mkdtemp() base = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/" files = [ "train-labels-idx1-ubyte.gz", "train-images-idx3-ubyte.gz", ] paths = [] for fname in files: paths.append(get_file(fname, origin=base + fname, cache_subdir=download_directory)) with gzip.open(paths[0], "rb") as lbpath: y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8) with gzip.open(paths[1], "rb") as imgpath: x_train = np.frombuffer(imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28) return x_train, y_train def load_validation_data(): """Loads the Fashion-MNIST dataset. Returns: Tuple of Numpy arrays: `(x_test, y_test)`. License: The copyright for Fashion-MNIST is held by Zalando SE. Fashion-MNIST is licensed under the [MIT license]( https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE). """ download_directory = tempfile.mkdtemp() base = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/" files = [ "t10k-labels-idx1-ubyte.gz", "t10k-images-idx3-ubyte.gz", ] paths = [] for fname in files: paths.append(get_file(fname, origin=base + fname, cache_subdir=download_directory)) with gzip.open(paths[0], "rb") as lbpath: y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8) with gzip.open(paths[1], "rb") as imgpath: x_test = np.frombuffer(imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28) return x_test, y_test

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Python

ee/api/chalicelib/blueprints/bp_ee_crons.py

nogamenofun98/openreplay

543384496fbfd5bd95482bd51b15865acba78bda

[ "MIT" ]

3,614

2021-05-22T08:23:31.000Z

2022-03-31T19:46:01.000Z

ee/api/chalicelib/blueprints/bp_ee_crons.py

aayushgautam/openreplay

3298230c3a04fe537794bf396bdaf695c81301c6

[ "MIT" ]

245

2021-05-25T14:49:35.000Z

2022-03-30T22:15:28.000Z

ee/api/chalicelib/blueprints/bp_ee_crons.py

aayushgautam/openreplay

3298230c3a04fe537794bf396bdaf695c81301c6

[ "MIT" ]

151

2021-05-22T07:57:17.000Z

2022-03-29T00:37:31.000Z

from chalice import Blueprint from chalice import Cron from chalicelib import _overrides app = Blueprint(__name__) _overrides.chalice_app(app)

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Python

phonotactics/codas/__init__.py

shlomo-Kallner/coventreiya

aa0773693220025f8d2c23644a2c5d9d884773e9

[ "Apache-2.0" ]

null

phonotactics/codas/__init__.py

shlomo-Kallner/coventreiya

aa0773693220025f8d2c23644a2c5d9d884773e9

[ "Apache-2.0" ]

null

phonotactics/codas/__init__.py

shlomo-Kallner/coventreiya

aa0773693220025f8d2c23644a2c5d9d884773e9

[ "Apache-2.0" ]

null

__package__ = "codas" __all__ = [ "codas" , "ver_1_5_1" , "ver_1_5_7" ]

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6851ed96358cf7394f0adf782e5cb09b7ad29a20

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py

Python

test/test_data/recursive_test_extension/__init__.py

CuteFwan/dango.py

315d74ab32a512a5e54043ebbd1ff8559e592c52

[ "MIT" ]

30

2017-07-12T11:40:58.000Z

2021-09-05T21:15:44.000Z

test/test_data/recursive_test_extension/__init__.py

CuteFwan/dango.py

315d74ab32a512a5e54043ebbd1ff8559e592c52

[ "MIT" ]

11

2017-12-25T00:08:49.000Z

2020-10-29T05:30:14.000Z

test/test_data/recursive_test_extension/__init__.py

CuteFwan/dango.py

315d74ab32a512a5e54043ebbd1ff8559e592c52

[ "MIT" ]

9

2017-09-15T14:58:52.000Z

2021-03-17T08:32:18.000Z

from dango import dcog, Cog from .cmds import SubModule # noqa pylint: disable=unused-import @dcog() class InModule(Cog): def __init__(self, config): pass

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py

Python

tests/test_bql.py

almartin82/bayeslite

a27f243b5f16cc6a01e84336a829e5b65d665b7b

[ "Apache-2.0" ]

964

2015-09-24T15:02:05.000Z

2022-03-29T21:41:21.000Z

tests/test_bql.py

almartin82/bayeslite

a27f243b5f16cc6a01e84336a829e5b65d665b7b

[ "Apache-2.0" ]

435

2015-09-23T16:46:58.000Z

2020-04-19T12:32:03.000Z

tests/test_bql.py

almartin82/bayeslite

a27f243b5f16cc6a01e84336a829e5b65d665b7b

[ "Apache-2.0" ]

86

2015-10-24T20:08:30.000Z

2021-08-09T13:53:00.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2010-2016, MIT Probabilistic Computing Project # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import StringIO import apsw import pytest import struct import bayeslite import bayeslite.ast as ast import bayeslite.compiler as compiler import bayeslite.core as core import bayeslite.guess as guess import bayeslite.backends.troll_rng as troll import bayeslite.parse as parse from bayeslite.exception import BQLError from bayeslite.math_util import relerr from bayeslite.backends.cgpm_backend import CGPM_Backend from bayeslite.util import cursor_value import test_core import test_csv from stochastic import stochastic def bql2sql(string, setup=None): with bayeslite.bayesdb_open(':memory:') as bdb: test_core.t1_schema(bdb) test_core.t1_data(bdb) bdb.execute(''' create population p1 for t1 ( id ignore; label nominal; age numerical; weight numerical ) ''') if setup is not None: setup(bdb) phrases = parse.parse_bql_string(string) out = compiler.Output(0, {}, ()) for phrase in phrases: assert ast.is_query(phrase) compiler.compile_query(bdb, phrase, out) out.write(';') return out.getvalue() # XXX Kludgey mess. Please reorganize. def bql2sqlparam(string): with bayeslite.bayesdb_open(':memory:') as bdb: test_core.t1_schema(bdb) test_core.t1_data(bdb) bdb.execute(''' create population p1 for t1 ( id ignore; label nominal; age numerical; weight numerical ) ''') phrases = parse.parse_bql_string(string) out0 = StringIO.StringIO() for phrase in phrases: out = None if isinstance(phrase, ast.Parametrized): bindings = (None,) * phrase.n_numpar out = compiler.Output(phrase.n_numpar, phrase.nampar_map, bindings) phrase = phrase.phrase else: out = StringIO.StringIO() assert ast.is_query(phrase) compiler.compile_query(bdb, phrase, out) # XXX Do something about the parameters. out0.write(out.getvalue()) out0.write(';') return out0.getvalue() def bql_execute(bdb, string, bindings=()): return map(tuple, bdb.execute(string, bindings)) def empty(cursor): assert cursor is not None assert cursor.description is not None assert len(cursor.description) == 0 with pytest.raises(StopIteration): cursor.next() def test_trivial_population(): with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) # XXX if (not) exists bdb.execute(''' create population p for t ( guess stattypes of (*); age numerical ) ''') bdb.execute('drop population p') def test_population_invalid_numerical(): with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) with pytest.raises(BQLError): bdb.execute(''' create population p for t ( guess stattypes of (*); gender numerical ) ''') def test_population_invalid_numerical_alterpop_addvar(): with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) bdb.execute(''' create population p for t ( guess stattypes of (*); ignore gender ) ''') with pytest.raises(BQLError): bdb.execute('alter population p add variable gender numerical') bdb.execute('drop population p') def test_population_invalid_numerical_alterpop_stattype(): with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) bdb.execute(''' create population p for t ( guess stattypes of (*); gender nominal ) ''') with pytest.raises(BQLError): bdb.execute(''' alter population p set stattype of gender to numerical ''') bdb.execute('drop population p') def test_similarity_identity(): with test_core.t1() as (bdb, population_id, _generator_id): bdb.execute('initialize 6 models for p1_cc;') rowids = bdb.sql_execute('select rowid from t1') for rowid in rowids: c = bdb.execute(''' estimate similarity of (rowid=?) to (rowid=?) in the context of age by p1 ''', (rowid[0], rowid[0])).fetchall() assert len(c) == 1 assert c[0][0] == 1 def test_predictive_relevance(): assert bql2sql(''' estimate predictive relevance of (label = 'Uganda') to existing rows (rowid < 4) and hypothetical rows with values ( ("age" = 82, "weight" = 14), ("age" = 74, label = 'Europe', "weight" = 7) ) in the context of "weight" by p1 ''') == \ 'SELECT bql_row_predictive_relevance(1, NULL, NULL, ' \ '(SELECT _rowid_ FROM "t1" WHERE ("label" = \'Uganda\')), '\ '\'[1, 2, 3]\', 3, '\ '2, 82, 3, 14, NULL, 2, 74, 1, \'Europe\', 3, 7, NULL);' assert bql2sql(''' estimate predictive relevance of (label = 'mumble') to existing rows (label = 'frotz' or age <= 4) in the context of "label" by p1 ''') == \ 'SELECT bql_row_predictive_relevance(1, NULL, NULL, ' \ '(SELECT _rowid_ FROM "t1" WHERE ("label" = \'mumble\')), '\ '\'[5, 8]\', 1);' assert bql2sql(''' estimate label, predictive relevance to hypothetical rows with values ( ("age" = 82, "weight" = 14), ("age" = 74, label = 'hunf', "weight" = 7) ) in the context of "age", _rowid_ + 1 from p1 ''') == \ 'SELECT "label", bql_row_predictive_relevance(1, NULL, NULL, _rowid_, '\ '\'[]\', 2, 2, 82, 3, 14, NULL, 2, 74, 1, \'hunf\', 3, 7, NULL), '\ '("_rowid_" + 1) FROM "t1";' # No matching rows should still compile. assert bql2sql(''' estimate label, predictive relevance to existing rows (rowid < 0) in the context of "age" from p1 ''') == \ 'SELECT "label", bql_row_predictive_relevance(1, NULL, NULL, _rowid_, '\ '\'[]\', 2) FROM "t1";' # When using `BY`, require OF to be specified. with pytest.raises(BQLError): bql2sql(''' estimate predictive relevance to hypothetical rows with values ( ("age" = 82, "weight" = 14), ("age" = 74, label = 'Europe', "weight" = 7) ) in the context of "age" by p1 ''') # When using `FROM`, require OF to be unspecified. with pytest.raises(BQLError): bql2sql(''' estimate predictive relevance of (name = 'mansour') to hypothetical rows with values ( ("age" = 82, "weight" = 14) ) in the context of "age" from p1 ''') assert bql2sql(''' estimate label from p1 where (predictive relevance to existing rows (label = 'quux' and age < 5) in the context of "weight") > 1 order by predictive relevance to hypothetical rows with values ((label='zot')) in the context of "age" ''') == \ 'SELECT "label" FROM "t1" WHERE '\ '(bql_row_predictive_relevance(1, NULL, NULL, '\ '_rowid_, \'[5]\', 3) > 1) '\ 'ORDER BY bql_row_predictive_relevance(1, NULL, NULL, '\ '_rowid_, \'[]\', 2, 1, \'zot\', NULL);' @stochastic(max_runs=2, min_passes=1) def test_conditional_probability(seed): with test_core.t1(seed=seed) as (bdb, _population_id, _generator_id): bdb.execute('drop generator p1_cc') bdb.execute('drop population p1') bdb.execute(''' create population p1 for t1 ( ignore id, label; set stattype of age to numerical; set stattype of weight to numerical ) ''') bdb.execute(''' create generator p1_cond_prob_cc for p1; ''') bdb.execute('initialize 1 model for p1_cond_prob_cc') bdb.execute('alter generator p1_cond_prob_cc ' 'ensure variables * dependent') bdb.execute('analyze p1_cond_prob_cc for 1 iteration') q0 = 'estimate probability density of age = 8 by p1' q1 = 'estimate probability density of age = 8 given () by p1' age_is_8 = bdb.execute(q0).fetchvalue() assert age_is_8 == bdb.execute(q1).fetchvalue() q2 = 'estimate probability density of age = 8 given (weight = 16)' \ ' by p1' age_is_8_given_weight_is_16 = bdb.execute(q2).fetchvalue() assert age_is_8 < age_is_8_given_weight_is_16 probs = bdb.execute( 'estimate probability density of value 8 given (weight = 16)' ' from columns of p1 where v.name != \'weight\'').fetchall() assert [(age_is_8_given_weight_is_16,)] == probs @stochastic(max_runs=2, min_passes=1) def test_joint_probability(seed): with test_core.t1(seed=seed) as (bdb, _population_id, _generator_id): bdb.execute('initialize 10 models for p1_cc') bdb.execute('analyze p1_cc for 10 iterations') q0 = 'estimate probability density of age = 8 by p1' q1 = 'estimate probability density of (age = 8) by p1' assert bdb.execute(q0).fetchvalue() == bdb.execute(q1).fetchvalue() q1 = 'estimate probability density of (age = 8) given () by p1' assert bdb.execute(q0).fetchvalue() == bdb.execute(q1).fetchvalue() q2 = 'estimate probability density of age = 8 given (weight = 16)' \ ' by p1' assert bdb.execute(q0).fetchvalue() < bdb.execute(q2).fetchvalue() q0 = 'estimate probability density of age = 8 by p1' q1 = 'estimate probability density of (age = 8, weight = 16) by p1' assert bdb.execute(q1).fetchvalue() < bdb.execute(q0).fetchvalue() q2 = 'estimate probability density of (age = 8, weight = 16)' \ " given (label = 'mumble') by p1" assert bdb.execute(q1).fetchvalue() < bdb.execute(q2).fetchvalue() def test_badbql(): with test_core.t1() as (bdb, _population_id, _generator_id): with pytest.raises(ValueError): bdb.execute('') with pytest.raises(ValueError): bdb.execute(';') with pytest.raises(ValueError): bdb.execute('select 0; select 1') def test_select_trivial(): assert bql2sql('select null;') == 'SELECT NULL;' assert bql2sql("select 'x';") == "SELECT 'x';" assert bql2sql("select 'x''y';") == "SELECT 'x''y';" assert bql2sql('select "x";') == 'SELECT "x";' assert bql2sql('select "x""y";') == 'SELECT "x""y";' assert bql2sql('select 0;') == 'SELECT 0;' assert bql2sql('select 0.;') == 'SELECT 0.0;' assert bql2sql('select .0;') == 'SELECT 0.0;' assert bql2sql('select 0.0;') == 'SELECT 0.0;' assert bql2sql('select 1e0;') == 'SELECT 1.0;' assert bql2sql('select 1e+1;') == 'SELECT 10.0;' assert bql2sql('select 1e-1;') == 'SELECT 0.1;' assert bql2sql('select -1e+1;') == 'SELECT (- 10.0);' assert bql2sql('select +1e-1;') == 'SELECT (+ 0.1);' assert bql2sql('select SQRT(1-EXP(-2*value)) FROM bm_mi;') == \ 'SELECT "SQRT"((1 - "EXP"(((- 2) * "value")))) FROM "bm_mi";' assert bql2sql('select .1e0;') == 'SELECT 0.1;' assert bql2sql('select 1.e10;') == 'SELECT 10000000000.0;' assert bql2sql('select all 0;') == 'SELECT 0;' assert bql2sql('select distinct 0;') == 'SELECT DISTINCT 0;' assert bql2sql('select 0 as z;') == 'SELECT 0 AS "z";' assert bql2sql('select * from t;') == 'SELECT * FROM "t";' assert bql2sql('select t.* from t;') == 'SELECT "t".* FROM "t";' assert bql2sql('select c from t;') == 'SELECT "c" FROM "t";' assert bql2sql('select c as d from t;') == 'SELECT "c" AS "d" FROM "t";' assert bql2sql('select t.c as d from t;') == \ 'SELECT "t"."c" AS "d" FROM "t";' assert bql2sql('select t.c as d, p as q, x from t;') == \ 'SELECT "t"."c" AS "d", "p" AS "q", "x" FROM "t";' assert bql2sql('select * from t, u;') == 'SELECT * FROM "t", "u";' assert bql2sql('select * from t as u;') == 'SELECT * FROM "t" AS "u";' assert bql2sql('select * from (select 0);') == 'SELECT * FROM (SELECT 0);' assert bql2sql('select t.c from (select d as c from u) as t;') == \ 'SELECT "t"."c" FROM (SELECT "d" AS "c" FROM "u") AS "t";' assert bql2sql('select * where x;') == 'SELECT * WHERE "x";' assert bql2sql('select * from t where x;') == \ 'SELECT * FROM "t" WHERE "x";' assert bql2sql('select * group by x;') == 'SELECT * GROUP BY "x";' assert bql2sql('select * from t where x group by y;') == \ 'SELECT * FROM "t" WHERE "x" GROUP BY "y";' assert bql2sql('select * from t where x group by y, z;') == \ 'SELECT * FROM "t" WHERE "x" GROUP BY "y", "z";' assert bql2sql('select * from t where x group by y having sum(z) < 1') == \ 'SELECT * FROM "t" WHERE "x" GROUP BY "y" HAVING ("sum"("z") < 1);' assert bql2sql('select * order by x;') == 'SELECT * ORDER BY "x";' assert bql2sql('select * order by x asc;') == 'SELECT * ORDER BY "x";' assert bql2sql('select * order by x desc;') == \ 'SELECT * ORDER BY "x" DESC;' assert bql2sql('select * order by x, y;') == 'SELECT * ORDER BY "x", "y";' assert bql2sql('select * order by x desc, y;') == \ 'SELECT * ORDER BY "x" DESC, "y";' assert bql2sql('select * order by x, y asc;') == \ 'SELECT * ORDER BY "x", "y";' assert bql2sql('select * limit 32;') == 'SELECT * LIMIT 32;' assert bql2sql('select * limit 32 offset 16;') == \ 'SELECT * LIMIT 32 OFFSET 16;' assert bql2sql('select * limit 16, 32;') == 'SELECT * LIMIT 32 OFFSET 16;' assert bql2sql('select (select0);') == 'SELECT "select0";' assert bql2sql('select (select 0);') == 'SELECT (SELECT 0);' assert bql2sql('select f(f(), f(x), y);') == \ 'SELECT "f"("f"(), "f"("x"), "y");' assert bql2sql('select a and b or c or not d is e is not f like j;') == \ 'SELECT ((("a" AND "b") OR "c") OR' \ + ' (NOT ((("d" IS "e") IS NOT "f") LIKE "j")));' assert bql2sql('select a like b not like c like d escape e;') == \ 'SELECT ((("a" LIKE "b") NOT LIKE "c") LIKE "d" ESCAPE "e");' assert bql2sql('select a like b escape c glob d not glob e;') == \ 'SELECT ((("a" LIKE "b" ESCAPE "c") GLOB "d") NOT GLOB "e");' assert bql2sql('select a not glob b glob c escape d;') == \ 'SELECT (("a" NOT GLOB "b") GLOB "c" ESCAPE "d");' assert bql2sql('select a glob b escape c regexp e not regexp f;') == \ 'SELECT ((("a" GLOB "b" ESCAPE "c") REGEXP "e") NOT REGEXP "f");' assert bql2sql('select a not regexp b regexp c escape d;') == \ 'SELECT (("a" NOT REGEXP "b") REGEXP "c" ESCAPE "d");' assert bql2sql('select a regexp b escape c not regexp d escape e;') == \ 'SELECT (("a" REGEXP "b" ESCAPE "c") NOT REGEXP "d" ESCAPE "e");' assert bql2sql('select a not regexp b escape c match e not match f;') == \ 'SELECT ((("a" NOT REGEXP "b" ESCAPE "c") MATCH "e") NOT MATCH "f");' assert bql2sql('select a not match b match c escape d;') == \ 'SELECT (("a" NOT MATCH "b") MATCH "c" ESCAPE "d");' assert bql2sql('select a match b escape c not match d escape e;') == \ 'SELECT (("a" MATCH "b" ESCAPE "c") NOT MATCH "d" ESCAPE "e");' assert bql2sql('select a not match b escape c between d and e;') == \ 'SELECT (("a" NOT MATCH "b" ESCAPE "c") BETWEEN "d" AND "e");' assert bql2sql('select a between b and c and d;') == \ 'SELECT (("a" BETWEEN "b" AND "c") AND "d");' assert bql2sql('select a like b like c escape d between e and f;') == \ 'SELECT ((("a" LIKE "b") LIKE "c" ESCAPE "d") BETWEEN "e" AND "f");' assert bql2sql('select a between b and c not between d and e;') == \ 'SELECT (("a" BETWEEN "b" AND "c") NOT BETWEEN "d" AND "e");' assert bql2sql('select a not between b and c in (select f);') == \ 'SELECT (("a" NOT BETWEEN "b" AND "c") IN (SELECT "f"));' assert bql2sql('select a in (select b) and c not in (select d);') == \ 'SELECT (("a" IN (SELECT "b")) AND ("c" NOT IN (SELECT "d")));' assert bql2sql("select a in (1 + 2, '3') and b not in (select c);") == \ 'SELECT (("a" IN ((1 + 2), \'3\')) AND ("b" NOT IN (SELECT "c")));' assert bql2sql('select a in (select b) isnull notnull!=c<>d<e<=f>g;') == \ 'SELECT ((((("a" IN (SELECT "b")) ISNULL) NOTNULL) != "c") !=' \ + ' ((("d" < "e") <= "f") > "g"));' assert bql2sql('select a>b>=c<<d>>e&f|g+h-i*j/k;') == \ 'SELECT (("a" > "b") >= (((("c" << "d") >> "e") & "f") |' \ + ' (("g" + "h") - (("i" * "j") / "k"))));' assert bql2sql('select a/b%c||~~d collate e collate\'f\'||1;') == \ 'SELECT (("a" / "b") % (("c" || (((~ (~ "d")) COLLATE "e")' \ + ' COLLATE "f")) || 1));' assert bql2sql('select cast(f(x) as binary blob);') == \ 'SELECT CAST("f"("x") AS "binary" "blob");' assert bql2sql('select cast(42 as varint(73));') == \ 'SELECT CAST(42 AS "varint"(73));' assert bql2sql('select cast(f(x, y, z) as varchar(12 ,34));') == \ 'SELECT CAST("f"("x", "y", "z") AS "varchar"(12, 34));' assert bql2sql('select exists (select a) and not exists (select b);') == \ 'SELECT (EXISTS (SELECT "a") AND (NOT EXISTS (SELECT "b")));' assert bql2sql('select case when a - b then c else d end from t;') == \ 'SELECT CASE WHEN ("a" - "b") THEN "c" ELSE "d" END FROM "t";' assert bql2sql('select case f(a) when b + c then d else e end from t;') \ == \ 'SELECT CASE "f"("a") WHEN ("b" + "c") THEN "d" ELSE "e" END FROM "t";' def test_estimate_bql(): # PREDICTIVE PROBABILITY assert bql2sql('estimate predictive probability of weight from p1;') == \ 'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\ '\'[3]\', \'[]\')' \ ' FROM "t1";' assert bql2sql('estimate predictive probability of (age, weight) ' 'from p1;') == \ 'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\ '\'[2, 3]\', \'[]\')' \ ' FROM "t1";' assert bql2sql('estimate predictive probability of (age, weight) given ' '(label) from p1;') == \ 'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\ '\'[2, 3]\', \'[1]\')' \ ' FROM "t1";' assert bql2sql('estimate predictive probability of (*) from p1;') == \ 'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\ '\'[1, 2, 3]\', \'[]\')' \ ' FROM "t1";' assert bql2sql('estimate predictive probability of (*) given (age, weight) ' 'from p1;') == \ 'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\ '\'[1]\', \'[2, 3]\')' \ ' FROM "t1";' assert bql2sql('estimate predictive probability of age given (*) ' 'from p1;') == \ 'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\ '\'[2]\', \'[1, 3]\')' \ ' FROM "t1";' assert bql2sql('estimate label, predictive probability of weight' ' from p1;') \ == \ 'SELECT "label", ' \ 'bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\ '\'[3]\', \'[]\')' \ ' FROM "t1";' assert bql2sql('estimate predictive probability of weight, label' ' from p1;') \ == \ 'SELECT bql_row_column_predictive_probability(1, NULL, NULL, _rowid_, '\ '\'[3]\', \'[]\'),' \ ' "label"' \ ' FROM "t1";' assert bql2sql('estimate predictive probability of weight + 1' ' from p1;') == \ 'SELECT (bql_row_column_predictive_probability(1, NULL, NULL, '\ '_rowid_, \'[3]\', \'[]\') + 1)' \ ' FROM "t1";' assert bql2sql('estimate predictive probability of weight given (*) + 1' ' from p1;') == \ 'SELECT (bql_row_column_predictive_probability(1, NULL, NULL, '\ '_rowid_, \'[3]\', \'[1, 2]\') + 1)' \ ' FROM "t1";' # PREDICTIVE PROBABILITY parse and compilation errors. with pytest.raises(parse.BQLParseError): # Need a table. bql2sql('estimate predictive probability of weight;') with pytest.raises(parse.BQLParseError): # Need at most one generator. bql2sql('estimate predictive probability of weight' ' from p1, p1;') with pytest.raises(parse.BQLParseError): # Need a generator name, not a subquery. bql2sql('estimate predictive probability of weight' ' from (select 0);') with pytest.raises(parse.BQLParseError): # Need a column. bql2sql('estimate predictive probability from p1;') with pytest.raises(bayeslite.BQLError): # Using (*) in both targets and constraints. bql2sql('estimate predictive probability of (*) given (*) from p1;') with pytest.raises(bayeslite.BQLError): # Using (weight, *) in targets. bql2sql('estimate predictive probability of (weight, *) given (age) ' 'from p1;') with pytest.raises(bayeslite.BQLError): # Using (age, *) in constraints. bql2sql('estimate predictive probability of weight given (*, age) ' 'from p1;') with pytest.raises(bayeslite.BQLError): # Using duplicate column age. bql2sql('estimate predictive probability of age given (weight, age) ' 'from p1;') # PROBABILITY DENISTY. assert bql2sql('estimate probability density of weight = 20 from p1;') == \ 'SELECT bql_pdf_joint(1, NULL, NULL, 3, 20) FROM "t1";' assert bql2sql('estimate probability density of weight = 20' ' given (age = 8)' ' from p1;') == \ 'SELECT bql_pdf_joint(1, NULL, NULL, 3, 20, NULL, 2, 8) FROM "t1";' assert bql2sql('estimate probability density of (weight = 20, age = 8)' ' from p1;') == \ 'SELECT bql_pdf_joint(1, NULL, NULL, 3, 20, 2, 8) FROM "t1";' assert bql2sql('estimate probability density of (weight = 20, age = 8)' " given (label = 'mumble') from p1;") == \ "SELECT bql_pdf_joint(1, NULL, NULL, 3, 20, 2, 8, NULL, 1, 'mumble')" \ ' FROM "t1";' assert bql2sql('estimate probability density of weight = (c + 1)' ' from p1;') == \ 'SELECT bql_pdf_joint(1, NULL, NULL, 3, ("c" + 1)) FROM "t1";' assert bql2sql('estimate probability density of weight = f(c)' ' from p1;') == \ 'SELECT bql_pdf_joint(1, NULL, NULL, 3, "f"("c")) FROM "t1";' assert bql2sql('estimate similarity to (rowid = 5) ' 'in the context of weight from p1;') == \ 'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,' \ ' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)), 3) FROM "t1";' assert bql2sql( 'estimate similarity of (rowid = 12) to (rowid = 5) ' 'in the context of weight from p1;') == \ 'SELECT bql_row_similarity(1, NULL, NULL,' \ ' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 12)),' \ ' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)), 3) FROM "t1";' assert bql2sql('estimate similarity to (rowid = 5) in the context of age' ' from p1') == \ 'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,' \ ' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)), 2) FROM "t1";' assert bql2sql( 'estimate similarity of (rowid = 5) to (height = 7 and age < 10)' ' in the context of weight from p1;') == \ 'SELECT bql_row_similarity(1, NULL, NULL,' \ ' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)),' \ ' (SELECT _rowid_ FROM "t1" WHERE (("height" = 7) AND ("age" < 10))),' \ ' 3) FROM "t1";' with pytest.raises(bayeslite.BQLError): # Cannot use all variables for similarity. bql2sql( 'estimate similarity to (rowid = 5) in the context of * from p1;') assert bql2sql('estimate similarity to (rowid = 5)' ' in the context of age from p1;') == \ 'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,' \ ' (SELECT _rowid_ FROM "t1" WHERE ("rowid" = 5)), 2) FROM "t1";' assert bql2sql('estimate dependence probability of age with weight' ' from p1;') == \ 'SELECT bql_column_dependence_probability(1, NULL, NULL, 2, 3) '\ 'FROM "t1";' with pytest.raises(bayeslite.BQLError): # Need both rows fixed. bql2sql('estimate similarity to (rowid=2) in the context of r by p1') with pytest.raises(bayeslite.BQLError): # Need both rows fixed. bql2sql('estimate similarity in the context of r within p1') with pytest.raises(bayeslite.BQLError): # Need both columns fixed. bql2sql('estimate dependence probability with age from p1;') with pytest.raises(bayeslite.BQLError): # Need both columns fixed. bql2sql('estimate dependence probability from p1;') assert bql2sql('estimate mutual information of age with weight' + ' from p1;') == \ 'SELECT bql_column_mutual_information('\ '1, NULL, NULL, \'[2]\', \'[3]\', NULL)'\ ' FROM "t1";' assert bql2sql('estimate mutual information of age with weight' + ' using 42 samples from p1;') == \ 'SELECT bql_column_mutual_information('\ '1, NULL, NULL, \'[2]\', \'[3]\', 42)'\ ' FROM "t1";' with pytest.raises(bayeslite.BQLError): # Need both columns fixed. bql2sql('estimate mutual information with age from p1;') with pytest.raises(bayeslite.BQLError): # Need both columns fixed. bql2sql('estimate mutual information from p1;') with pytest.raises(bayeslite.BQLError): # Need both columns fixed. bql2sql('estimate mutual information with age using 42 samples' ' from p1;') with pytest.raises(bayeslite.BQLError): # Need both columns fixed. bql2sql('estimate mutual information using 42 samples from p1;') # XXX Should be SELECT, not ESTIMATE, here? assert bql2sql('estimate correlation of age with weight from p1;') == \ 'SELECT bql_column_correlation(1, NULL, NULL, 2, 3) FROM "t1";' with pytest.raises(bayeslite.BQLError): # Need both columns fixed. bql2sql('estimate correlation with age from p1;') with pytest.raises(bayeslite.BQLError): # Need both columns fixed. bql2sql('estimate correlation from p1;') with pytest.raises(BQLError): # Variable must exist. bql2sql('estimate correlation with agee from variables of p1') def test_predict_outside_infer(): with pytest.raises(bayeslite.BQLError): # No PREDICT outside INFER. bql2sql('estimate predict age with confidence 0.9 from p1;') def test_infer_explicit_predict_confidence(): assert bql2sql('infer explicit predict age with confidence 0.9' ' from p1;') == \ 'SELECT bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, NULL) FROM "t1";' def test_infer_explicit_predict_confidence_nsamples(): assert bql2sql('infer explicit' ' predict age with confidence 0.9 using 42 samples' ' from p1;') == \ 'SELECT bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, 42) FROM "t1";' def test_infer_explicit_verbatim_and_predict_confidence(): assert bql2sql('infer explicit rowid, age,' ' predict age confidence age_conf from p1') == \ 'SELECT c0 AS "rowid", c1 AS "age",' \ ' bql_json_get(c2, \'value\') AS "age",' \ ' bql_json_get(c2, \'confidence\') AS "age_conf"' \ ' FROM (SELECT "rowid" AS c0, "age" AS c1,' \ ' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, NULL)' \ ' AS c2 FROM "t1");' def test_infer_explicit_verbatim_and_predict_noconfidence(): assert bql2sql('infer explicit rowid, age,' ' predict age from p1') == \ 'SELECT c0 AS "rowid", c1 AS "age",' \ ' bql_json_get(c2, \'value\') AS "age"' \ ' FROM (SELECT "rowid" AS c0, "age" AS c1,' \ ' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, NULL)' \ ' AS c2 FROM "t1");' def test_infer_explicit_verbatim_and_predict_confidence_nsamples(): assert bql2sql('infer explicit rowid, age,' ' predict age confidence age_conf using 42 samples from p1') == \ 'SELECT c0 AS "rowid", c1 AS "age",' \ ' bql_json_get(c2, \'value\') AS "age",' \ ' bql_json_get(c2, \'confidence\') AS "age_conf"' \ ' FROM (SELECT "rowid" AS c0, "age" AS c1,' \ ' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, 42)' \ ' AS c2 FROM "t1");' def test_infer_explicit_verbatim_and_predict_noconfidence_nsamples(): assert bql2sql('infer explicit rowid, age,' ' predict age using 42 samples from p1') == \ 'SELECT c0 AS "rowid", c1 AS "age",' \ ' bql_json_get(c2, \'value\') AS "age"' \ ' FROM (SELECT "rowid" AS c0, "age" AS c1,' \ ' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, 42)' \ ' AS c2 FROM "t1");' def test_infer_explicit_verbatim_and_predict_confidence_as(): assert bql2sql('infer explicit rowid, age,' ' predict age as age_inf confidence age_conf from p1') == \ 'SELECT c0 AS "rowid", c1 AS "age",' \ ' bql_json_get(c2, \'value\') AS "age_inf",' \ ' bql_json_get(c2, \'confidence\') AS "age_conf"' \ ' FROM (SELECT "rowid" AS c0, "age" AS c1,' \ ' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, NULL)' \ ' AS c2 FROM "t1");' def test_infer_explicit_verbatim_and_predict_noconfidence_as(): assert bql2sql('infer explicit rowid, age,' ' predict age as age_inf from p1') == \ 'SELECT c0 AS "rowid", c1 AS "age",' \ ' bql_json_get(c2, \'value\') AS "age_inf"' \ ' FROM (SELECT "rowid" AS c0, "age" AS c1,' \ ' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, NULL)' \ ' AS c2 FROM "t1");' def test_infer_explicit_verbatim_and_predict_confidence_as_nsamples(): assert bql2sql('infer explicit rowid, age,' ' predict age as age_inf confidence age_conf using 87 samples' ' from p1') == \ 'SELECT c0 AS "rowid", c1 AS "age",' \ ' bql_json_get(c2, \'value\') AS "age_inf",' \ ' bql_json_get(c2, \'confidence\') AS "age_conf"' \ ' FROM (SELECT "rowid" AS c0, "age" AS c1,' \ ' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, 87)' \ ' AS c2 FROM "t1");' def test_infer_explicit_verbatim_and_predict_noconfidence_as_nsamples(): assert bql2sql('infer explicit rowid, age,' ' predict age as age_inf using 87 samples' ' from p1') == \ 'SELECT c0 AS "rowid", c1 AS "age",' \ ' bql_json_get(c2, \'value\') AS "age_inf"' \ ' FROM (SELECT "rowid" AS c0, "age" AS c1,' \ ' bql_predict_confidence(1, NULL, NULL, _rowid_, 2, 87)' \ ' AS c2 FROM "t1");' def test_infer_auto(): assert bql2sql('infer rowid, age, weight from p1') \ == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0, NULL))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0, NULL))' \ ' AS "weight"' \ ' FROM "t1";' def test_infer_auto_nsamples(): assert bql2sql('infer rowid, age, weight using (1+2) samples from p1') \ == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0, (1 + 2)))' \ ' AS "age",' \ ' "IFNULL"("weight",'\ ' bql_predict(1, NULL, NULL, _rowid_, 3, 0, (1 + 2)))' \ ' AS "weight"' \ ' FROM "t1";' def test_infer_auto_with_confidence(): assert bql2sql('infer rowid, age, weight with confidence 0.9 from p1') \ == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, NULL))' \ ' AS "age",' \ ' "IFNULL"("weight",'\ ' bql_predict(1, NULL, NULL, _rowid_, 3, 0.9, NULL))' \ ' AS "weight"' \ ' FROM "t1";' def test_infer_auto_with_confidence_nsamples(): assert bql2sql('infer rowid, age, weight with confidence 0.9' ' using sqrt(2) samples' ' from p1') \ == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9,' \ ' "sqrt"(2)))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9,' \ ' "sqrt"(2)))' \ ' AS "weight"' \ ' FROM "t1";' def test_infer_auto_with_confidence_where(): assert bql2sql('infer rowid, age, weight with confidence 0.9 from p1' ' where label = \'foo\'') \ == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, NULL))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9,'\ ' NULL))' \ ' AS "weight"' \ ' FROM "t1"' \ ' WHERE ("label" = \'foo\');' def test_infer_auto_with_confidence_nsamples_where(): assert bql2sql('infer rowid, age, weight with confidence 0.9' ' using 42 samples' ' from p1' ' where label = \'foo\'') \ == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, 42))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9, 42))' \ ' AS "weight"' \ ' FROM "t1"' \ ' WHERE ("label" = \'foo\');' def test_infer_auto_with_confidence_nsamples_where_predict(): assert bql2sql('infer rowid, age, weight with confidence 0.9 from p1' ' where ifnull(label, predict label with confidence 0.7)' ' = \'foo\'') \ == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, NULL))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9,' \ ' NULL))' \ ' AS "weight"' \ ' FROM "t1"' \ ' WHERE ("ifnull"("label",' \ ' bql_predict(1, NULL, NULL, _rowid_, 1, 0.7, NULL))' \ ' = \'foo\');' def test_infer_auto_with_confidence_nsamples_where_predict_nsamples(): assert bql2sql('infer rowid, age, weight with confidence 0.9' ' using 42 samples' ' from p1' ' where ifnull(label, predict label with confidence 0.7' ' using 73 samples)' ' = \'foo\'') \ == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0.9, 42))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0.9, 42))' \ ' AS "weight"' \ ' FROM "t1"' \ ' WHERE ("ifnull"("label",' \ ' bql_predict(1, NULL, NULL, _rowid_, 1, 0.7, 73))' \ ' = \'foo\');' def test_infer_auto_star(): assert bql2sql('infer rowid, * from p1') == \ 'SELECT "rowid" AS "rowid", "id" AS "id",' \ ' "IFNULL"("label", bql_predict(1, NULL, NULL, _rowid_, 1, 0, NULL))' \ ' AS "label",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0, NULL))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0, NULL))' \ ' AS "weight"' \ ' FROM "t1";' def test_infer_auto_star_nsamples(): assert bql2sql('infer rowid, * using 1 samples from p1') == \ 'SELECT "rowid" AS "rowid", "id" AS "id",' \ ' "IFNULL"("label", bql_predict(1, NULL, NULL, _rowid_, 1, 0, 1))' \ ' AS "label",' \ ' "IFNULL"("age", bql_predict(1, NULL, NULL, _rowid_, 2, 0, 1))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, NULL, NULL, _rowid_, 3, 0, 1))' \ ' AS "weight"' \ ' FROM "t1";' def test_estimate_columns_trivial(): prefix0 = 'SELECT v.name AS name' prefix1 = ' FROM bayesdb_variable AS v' \ ' WHERE v.population_id = 1' \ ' AND v.generator_id IS NULL' prefix = prefix0 + prefix1 assert bql2sql('estimate * from columns of p1;') == \ prefix + ';' assert bql2sql('estimate * from columns of p1 where' + ' (probability density of value 42) > 0.5') == \ prefix + \ ' AND (bql_column_value_probability(1, NULL, NULL, v.colno, 42) > 0.5);' assert bql2sql('estimate * from columns of p1' ' where (probability density of value 8)' ' > (probability density of age = 16)') == \ prefix + \ ' AND (bql_column_value_probability(1, NULL, NULL, v.colno, 8) >' \ ' bql_pdf_joint(1, NULL, NULL, 2, 16));' assert bql2sql('estimate *, probability density of value 8 given (age = 8)' ' from columns of p1;') == \ prefix0 + \ ', bql_column_value_probability(1, NULL, NULL, v.colno, 8, 2, 8)' + \ prefix1 + ';' with pytest.raises(bayeslite.BQLError): bql2sql('estimate probability density of value 8 given (agee = 8)' ' from columns of p1') with pytest.raises(bayeslite.BQLError): # PREDICTIVE PROBABILITY makes no sense without row. bql2sql('estimate * from columns of p1 where' + ' predictive probability of x > 0;') with pytest.raises(bayeslite.BQLError): # SIMILARITY makes no sense without row. bql2sql('estimate * from columns of p1 where' + ' similarity to (rowid = x) in the context of c > 0;') assert bql2sql('estimate * from columns of p1 where' + ' dependence probability with age > 0.5;') == \ prefix + \ ' AND (bql_column_dependence_probability(1, NULL, NULL, 2, v.colno)' \ ' > 0.5);' with pytest.raises(bayeslite.BQLError): # Must omit exactly one column. bql2sql('estimate * from columns of p1 where' + ' dependence probability of age with weight > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit exactly one column. bql2sql('estimate * from columns of p1' ' where dependence probability > 0.5;') assert bql2sql('estimate * from columns of p1 order by' + ' mutual information with age;') == \ prefix + \ ' ORDER BY bql_column_mutual_information(1, NULL, NULL, \'[2]\','\ ' \'[\' || v.colno || \']\', NULL);' assert bql2sql('estimate * from columns of p1 order by' + ' mutual information with (age, label) using 42 samples;') == \ prefix + \ ' ORDER BY bql_column_mutual_information(1, NULL, NULL, \'[2, 1]\','\ ' \'[\' || v.colno || \']\', 42);' assert bql2sql('estimate * from columns of p1 order by' + ' mutual information with (age, label)' ' given (weight=12) using 42 samples;') == \ prefix + \ ' ORDER BY bql_column_mutual_information(1, NULL, NULL, \'[2, 1]\','\ ' \'[\' || v.colno || \']\', 42, 3, 12);' with pytest.raises(bayeslite.BQLError): # Must omit exactly one column. bql2sql('estimate * from columns of p1 order by' + ' mutual information of age with weight;') with pytest.raises(bayeslite.BQLError): # Must omit exactly one column. bql2sql('estimate * from columns of p1' ' where mutual information > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit exactly one column. bql2sql('estimate * from columns of p1 order by' + ' mutual information of age with weight using 42 samples;') with pytest.raises(bayeslite.BQLError): # Must omit exactly one column. bql2sql('estimate * from columns of p1 where' + ' mutual information using 42 samples > 0.5;') assert bql2sql('estimate * from columns of p1 order by' + ' correlation with age desc;') == \ prefix + ' ORDER BY bql_column_correlation(1, NULL, NULL, 2, v.colno)' \ ' DESC;' with pytest.raises(bayeslite.BQLError): # Must omit exactly one column. bql2sql('estimate * from columns of p1 order by' + ' correlation of age with weight;') with pytest.raises(bayeslite.BQLError): # Must omit exactly one column. bql2sql('estimate * from columns of p1 where correlation > 0.5;') with pytest.raises(bayeslite.BQLError): # Makes no sense. bql2sql('estimate * from columns of p1' ' where predict age with confidence 0.9 > 30;') assert bql2sql('estimate' ' *, dependence probability with weight as depprob,' ' mutual information with weight as mutinf' ' from columns of p1' ' where depprob > 0.5 order by mutinf desc') == \ prefix0 + \ ', bql_column_dependence_probability(1, NULL, NULL, 3, v.colno)' \ ' AS "depprob"' \ ', bql_column_mutual_information(1, NULL, NULL, \'[3]\',' \ ' \'[\' || v.colno || \']\', NULL) AS "mutinf"' \ + prefix1 + \ ' AND ("depprob" > 0.5)' \ ' ORDER BY "mutinf" DESC;' assert bql2sql('estimate' ' *, dependence probability with weight as depprob,' ' mutual information with (age, weight) as mutinf' ' from columns of p1' ' where depprob > 0.5 order by mutinf desc') == \ prefix0 + \ ', bql_column_dependence_probability(1, NULL, NULL, 3, v.colno)' \ ' AS "depprob"' \ ', bql_column_mutual_information(1, NULL, NULL, \'[2, 3]\',' \ ' \'[\' || v.colno || \']\', NULL) AS "mutinf"' \ + prefix1 + \ ' AND ("depprob" > 0.5)' \ ' ORDER BY "mutinf" DESC;' # XXX This mixes up target and reference variables, which is OK, # because MI is symmetric, but...oops. assert bql2sql('estimate * from variables of p1' ' where probability of (mutual information with age < 0.1)' ' > 0.8') == \ prefix + \ ' AND ((SELECT "AVG"("x") FROM (SELECT ("v0" < 0.1) AS "x"' \ ' FROM (SELECT mi AS "v0" FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[' || v.colno || ']'))) > 0.8);" assert bql2sql('estimate * from variables of p1' ' order by probability of (mutual information with age < 0.1)') ==\ prefix + \ ' ORDER BY (SELECT "AVG"("x") FROM (SELECT ("v0" < 0.1) AS "x"' \ ' FROM (SELECT mi AS "v0" FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[' || v.colno || ']')));" def test_estimate_pairwise_trivial(): prefix = 'SELECT 1 AS population_id, v0.name AS name0, v1.name AS name1, ' infix = ' AS value' infix0 = ' FROM bayesdb_population AS p,' infix0 += ' bayesdb_variable AS v0,' infix0 += ' bayesdb_variable AS v1' infix0 += ' WHERE p.id = 1' infix0 += ' AND v0.population_id = p.id AND v1.population_id = p.id' infix0 += ' AND v0.generator_id IS NULL' infix0 += ' AND v1.generator_id IS NULL' infix += infix0 assert bql2sql('estimate dependence probability' ' from pairwise columns of p1;') == \ prefix + \ 'bql_column_dependence_probability(1, NULL, NULL, v0.colno,'\ ' v1.colno)' + \ infix + ';' assert bql2sql('estimate mutual information' ' from pairwise columns of p1 where' ' (probability density of age = 0) > 0.5;') == \ prefix + \ 'bql_column_mutual_information(1, NULL, NULL, '\ '\'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', NULL)' + \ infix + \ ' AND (bql_pdf_joint(1, NULL, NULL, 2, 0) > 0.5);' assert bql2sql('estimate mutual information given (label=\'go\', weight)' ' from pairwise columns of p1 where' ' (probability density of age = 0) > 0.5;') == \ prefix + \ 'bql_column_mutual_information(1, NULL, NULL,'\ ' \'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', NULL,'\ ' 1, \'go\', 3, NULL)' + \ infix + \ ' AND (bql_pdf_joint(1, NULL, NULL, 2, 0) > 0.5);' with pytest.raises(bayeslite.BQLError): # PROBABILITY DENSITY OF VALUE is 1-column. bql2sql('estimate correlation from pairwise columns of p1 where' + ' (probability density of value 0) > 0.5;') with pytest.raises(bayeslite.BQLError): # PREDICTIVE PROBABILITY OF is a row function. bql2sql('estimate dependence probability' ' from pairwise columns of p1' + ' where predictive probability of x > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate dependence probability' ' from pairwise columns of p1' ' where dependence probability of age with weight > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate mutual information from pairwise columns of p1' ' where dependence probability with weight > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate mutual information using 42 samples' ' from pairwise columns of p1' ' where dependence probability with weight > 0.5;') assert bql2sql('estimate correlation from pairwise columns of p1' ' where dependence probability > 0.5;') == \ prefix + 'bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno)' + \ infix + ' AND' \ ' (bql_column_dependence_probability(1, NULL, NULL, v0.colno,' \ ' v1.colno)' \ ' > 0.5);' with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate dependence probability' ' from pairwise columns of p1' ' where mutual information of age with weight > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate dependence probability' ' from pairwise columns of p1' ' where mutual information of age with weight using 42 samples' ' > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate mutual information from pairwise columns of p1' ' where mutual information with weight > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate mutual information using 42 samples' ' from pairwise columns of p1' ' where mutual information with weight using 42 samples > 0.5;') assert bql2sql('estimate correlation from pairwise columns of p1' + ' where mutual information > 0.5;') == \ prefix + 'bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno)' + \ infix + ' AND' + \ ' (bql_column_mutual_information(1, NULL, NULL,'\ ' \'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', NULL) > 0.5);' assert bql2sql('estimate correlation from pairwise columns of p1' + ' where mutual information using 42 samples > 0.5;') == \ prefix + 'bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno)' + \ infix + ' AND' + \ ' (bql_column_mutual_information(1, NULL, NULL,'\ ' \'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', 42) > 0.5);' with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate dependence probability' ' from pairwise columns of p1' ' where correlation of age with weight > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate mutual information from pairwise columns of p1' ' where correlation with weight > 0.5;') with pytest.raises(bayeslite.BQLError): # Must omit both columns. bql2sql('estimate mutual information using 42 samples' ' from pairwise columns of p1' ' where correlation with weight > 0.5;') assert bql2sql('estimate correlation from pairwise columns of p1' ' where correlation > 0.5;') == \ prefix + 'bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno)' + \ infix + ' AND' + \ ' (bql_column_correlation(1, NULL, NULL, v0.colno, v1.colno) > 0.5);' with pytest.raises(bayeslite.BQLError): # Makes no sense. bql2sql('estimate dependence probability' ' from pairwise columns of p1' ' where predict age with confidence 0.9 > 30;') assert bql2sql('estimate dependence probability as depprob,' ' mutual information as mutinf' ' from pairwise columns of p1' ' where depprob > 0.5 order by mutinf desc') == \ prefix + \ 'bql_column_dependence_probability(1, NULL, NULL, v0.colno, v1.colno)' \ ' AS "depprob",' \ ' bql_column_mutual_information(1, NULL, NULL,'\ ' \'[\' || v0.colno || \']\', \'[\' || v1.colno || \']\', NULL)'\ ' AS "mutinf"' \ + infix0 + \ ' AND ("depprob" > 0.5)' \ ' ORDER BY "mutinf" DESC;' def test_estimate_pairwise_row(): prefix = 'SELECT r0._rowid_ AS rowid0, r1._rowid_ AS rowid1' infix = ' AS value FROM "t1" AS r0, "t1" AS r1' assert bql2sql('estimate similarity in the context of age' + ' from pairwise p1;') == \ prefix + ', bql_row_similarity(1, NULL, NULL,'\ ' r0._rowid_, r1._rowid_, 2)' + \ infix + ';' with pytest.raises(bayeslite.BQLError): # PREDICT is a 1-row function. bql2sql('estimate predict age with confidence 0.9 from pairwise t1;') def test_estimate_pairwise_selected_columns(): assert bql2sql('estimate dependence probability' ' from pairwise columns of p1 for label, age') == \ 'SELECT 1 AS population_id, v0.name AS name0, v1.name AS name1,' \ ' bql_column_dependence_probability(1, NULL, NULL,' \ ' v0.colno, v1.colno)' \ ' AS value' \ ' FROM bayesdb_population AS p,' \ ' bayesdb_variable AS v0,' \ ' bayesdb_variable AS v1' \ ' WHERE p.id = 1' \ ' AND v0.population_id = p.id AND v1.population_id = p.id' \ ' AND v0.generator_id IS NULL AND v1.generator_id IS NULL' \ ' AND v0.colno IN (1, 2) AND v1.colno IN (1, 2);' assert bql2sql('estimate dependence probability' ' from pairwise columns of p1' ' for (ESTIMATE * FROM COLUMNS OF p1' ' ORDER BY name DESC LIMIT 2)') == \ 'SELECT 1 AS population_id, v0.name AS name0, v1.name AS name1,' \ ' bql_column_dependence_probability(1, NULL, NULL, v0.colno,' \ ' v1.colno)' \ ' AS value' \ ' FROM bayesdb_population AS p,' \ ' bayesdb_variable AS v0,' \ ' bayesdb_variable AS v1' \ ' WHERE p.id = 1' \ ' AND v0.population_id = p.id AND v1.population_id = p.id' \ ' AND v0.generator_id IS NULL AND v1.generator_id IS NULL' \ ' AND v0.colno IN (3, 1) AND v1.colno IN (3, 1);' def test_select_columns_subquery(): assert bql2sql('select id, t1.(estimate * from columns of p1' ' order by name asc limit 2) from t1') == \ 'SELECT "id", "t1"."age", "t1"."label" FROM "t1";' @pytest.mark.xfail(strict=True, reason='no simulate vars from models of') def test_simulate_models_columns_subquery(): assert bql2sql('simulate weight, t1.(estimate * from columns of p1' ' order by name asc limit 2) from models of p1') == \ 'SELECT * FROM "bayesdb_temp_0";' assert bql2sql('simulate 0, weight, t1.(estimate * from columns of p1' ' order by name asc limit 2) from models of p1') == \ 'SELECT 0, "v0" AS "weight", "v1" AS "age", "v2" AS "label" FROM' \ ' (SELECT * FROM "bayesdb_temp_0");' assert bql2sql('simulate weight + 1, t1.(estimate * from columns of p1' ' order by name asc limit 2) from models of p1') == \ 'SELECT ("v0" + 1), "v1" AS "age", "v2" AS "label" FROM' \ ' (SELECT * FROM "bayesdb_temp_0");' assert bql2sql('simulate weight + 1 AS wp1,' ' t1.(estimate * from columns of p1' ' order by name asc limit 2) from models of p1') == \ 'SELECT ("v0" + 1) AS "wp1", "v1" AS "age", "v2" AS "label" FROM' \ ' (SELECT * FROM "bayesdb_temp_0");' def test_simulate_columns_subquery(): # XXX This test is a little unsatisfactory -- we do not get to see # what the variables in the result are named... assert bql2sql('simulate weight, t1.(estimate * from columns of p1' ' order by name asc limit 2) from p1 limit 10') == \ 'SELECT * FROM "bayesdb_temp_0";' with pytest.raises(parse.BQLParseError): # Compound columns not yet implemented for SIMULATE. bql2sql('simulate weight + 1, t1.(estimate * from columns of p1' ' order by name asc limit 2) from p1 limit 10') def test_simulate_models(): # Base case. assert bql2sql('simulate mutual information of age with weight' ' from models of p1') == \ 'SELECT mi FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[3]';" # Multiple target variables. assert bql2sql('simulate mutual information of (label, age) with weight' ' from models of p1') == \ 'SELECT mi FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[1, 2]'" \ " AND reference_vars = '[3]';" # Multiple reference variables. assert bql2sql('simulate mutual information of age with (label, weight)' ' from models of p1') == \ 'SELECT mi FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[1, 3]';" # Specified number of samples. assert bql2sql('simulate mutual information of age with weight' ' using 42 samples from models of p1') == \ 'SELECT mi FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[3]'" \ ' AND nsamples = 42;' # Conditional. assert bql2sql('simulate mutual information of age with weight' " given (label = 'foo') from models of p1") == \ 'SELECT mi FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[3]'" \ " AND conditions = '{\"1\": \"foo\"}';" # Modeled by a specific generator. assert bql2sql('simulate mutual information of age with weight' ' from models of p1 modeled by g1', lambda bdb: bdb.execute('create generator g1 for p1')) == \ 'SELECT mi FROM bql_mutinf' \ ' WHERE population_id = 1' \ ' AND generator_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[3]';" # Two mutual informations. assert bql2sql('simulate mutual information of age with weight AS "mi(aw)",' ' mutual information of label with weight AS "mi(lw)"' ' from models of p1') == \ 'SELECT t0."mi(aw)" AS "mi(aw)", t1."mi(lw)" AS "mi(lw)"' \ ' FROM (SELECT _rowid_, mi AS "mi(aw)" FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[3]') AS t0," \ ' (SELECT _rowid_, mi AS "mi(lw)" FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[1]'" \ " AND reference_vars = '[3]') AS t1" \ ' WHERE t0._rowid_ = t1._rowid_;' def test_probability_of_mutinf(): assert bql2sql('estimate probability of' ' (mutual information of age with weight < 0.1) > 0.5' ' within p1') == \ 'SELECT ((SELECT "AVG"("x") FROM (SELECT ("v0" < 0.1) AS "x"' \ ' FROM (SELECT mi AS "v0" FROM bql_mutinf' \ ' WHERE population_id = 1' \ " AND target_vars = '[2]'" \ " AND reference_vars = '[3]'))) > 0.5);" def test_modeledby_usingmodels_trival(): def setup(bdb): bdb.execute('create generator m1 for p1 using cgpm;') assert bql2sql('estimate predictive probability of weight + 1' ' from p1 modeled by m1 using models 1-3, 5;', setup=setup) == \ 'SELECT (bql_row_column_predictive_probability(1, 1, \'[1, 2, 3, 5]\','\ ' _rowid_, \'[3]\', \'[]\') + 1)' \ ' FROM "t1";' assert bql2sql( 'infer rowid, age, weight from p1 modeled by m1 using model 7', setup=setup) == \ 'SELECT "rowid" AS "rowid",' \ ' "IFNULL"("age", bql_predict(1, 1, \'[7]\', _rowid_, 2, 0, NULL))' \ ' AS "age",' \ ' "IFNULL"("weight", bql_predict(1, 1, \'[7]\', _rowid_, 3, 0, NULL))' \ ' AS "weight"' \ ' FROM "t1";' assert bql2sql('infer explicit predict age with confidence 0.9' ' from p1 using models 0, 3-5;', setup=setup) == \ 'SELECT bql_predict(1, NULL, \'[0, 3, 4, 5]\', _rowid_, 2, 0.9, NULL)'\ ' FROM "t1";' assert bql2sql(''' estimate predictive relevance of (label = 'Uganda') to existing rows (rowid < 4) and hypothetical rows with values ( ("age" = 82, "weight" = 14), ("age" = 74, label = 'Europe', "weight" = 7) ) in the context of "weight" by p1 modeled by m1 using models 8, 10-12 ''', setup=setup) == \ 'SELECT bql_row_predictive_relevance(1, 1, \'[8, 10, 11, 12]\', ' \ '(SELECT _rowid_ FROM "t1" WHERE ("label" = \'Uganda\')), '\ '\'[1, 2, 3]\', 3, '\ '2, 82, 3, 14, NULL, 2, 74, 1, \'Europe\', 3, 7, NULL);' assert bql2sql(''' estimate dependence probability from pairwise columns of p1 for label, age modeled by m1 using models 1, 4, 12 ''', setup=setup) == \ 'SELECT 1 AS population_id, v0.name AS name0, v1.name AS name1,' \ ' bql_column_dependence_probability(1, 1, \'[1, 4, 12]\',' \ ' v0.colno, v1.colno)' \ ' AS value' \ ' FROM bayesdb_population AS p,' \ ' bayesdb_variable AS v0,' \ ' bayesdb_variable AS v1' \ ' WHERE p.id = 1' \ ' AND v0.population_id = p.id AND v1.population_id = p.id' \ ' AND (v0.generator_id IS NULL OR v0.generator_id = 1)' \ ' AND (v1.generator_id IS NULL OR v1.generator_id = 1)' \ ' AND v0.colno IN (1, 2) AND v1.colno IN (1, 2);' assert bql2sql(''' estimate mutual information of age with weight from p1 modeled by m1 using model 1; ''', setup=setup) == \ 'SELECT bql_column_mutual_information('\ '1, 1, \'[1]\', \'[2]\', \'[3]\', NULL)'\ ' FROM "t1";' def test_simulate_columns_all(): with pytest.raises(parse.BQLParseError): bql2sql('simulate * from p1 limit 1') def test_trivial_commands(): with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): # XXX Query parameters! with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) with open(fname, 'rU') as f: with pytest.raises(ValueError): bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True, ifnotexists=True) guess.bayesdb_guess_population(bdb, 'p', 't') with pytest.raises(ValueError): guess.bayesdb_guess_population(bdb, 'p', 't') guess.bayesdb_guess_population(bdb, 'p', 't', ifnotexists=True) bdb.execute('create generator p_cc for p;') bdb.execute('initialize 2 models for p_cc') with pytest.raises(bayeslite.BQLError): bdb.execute('initialize 2 models for p_cc') bdb.execute('drop models from p_cc') bdb.execute('drop models from p_cc') bdb.execute('initialize 2 models for p_cc') with pytest.raises(bayeslite.BQLError): bdb.execute('initialize 2 models for p_cc') with pytest.raises(bayeslite.BQLError): bdb.execute('drop models 0-2 from p_cc') bdb.execute('drop models 0-1 from p_cc') with bdb.savepoint(): bdb.execute('initialize 2 models for p_cc') bdb.execute('drop models 0-1 from p_cc') with pytest.raises(bayeslite.BQLError): bdb.execute('drop models 0-1 from p_cc') bdb.execute('initialize 2 models for p_cc') bdb.execute('initialize 1 model if not exists for p_cc') bdb.execute('initialize 2 models if not exists for p_cc') population_id = core.bayesdb_get_population(bdb, 'p') generator_id = core.bayesdb_get_generator(bdb, population_id, 'p_cc') assert core.bayesdb_generator_table(bdb, generator_id) == 't' bdb.execute('alter table t rename to t') assert core.bayesdb_generator_table(bdb, generator_id) == 't' bdb.execute('alter table t rename to T') assert core.bayesdb_generator_table(bdb, generator_id) == 'T' bdb.execute('alter population p rename to p') assert core.bayesdb_population_name(bdb, population_id) == 'p' bdb.execute('alter population p rename to p2') assert core.bayesdb_population_name(bdb, population_id) == 'p2' bdb.execute('alter population p2 rename to p') assert core.bayesdb_population_name(bdb, population_id) == 'p' bdb.execute('estimate count(*) from p').fetchall() bdb.execute('alter table t rename to t') assert core.bayesdb_generator_table(bdb, generator_id) == 't' bdb.execute('alter generator p_cc rename to p0_cc') assert core.bayesdb_generator_name(bdb, generator_id) == 'p0_cc' bdb.execute('alter generator p0_cc rename to zot, rename to P0_CC') assert core.bayesdb_generator_name(bdb, generator_id) == 'P0_CC' bdb.execute('alter generator P0_cc rename to P0_cc') assert core.bayesdb_generator_name(bdb, generator_id) == 'P0_cc' bdb.execute('alter generator p0_CC rename to p0_cc') assert core.bayesdb_generator_name(bdb, generator_id) == 'p0_cc' bdb.execute('estimate count(*) from p').fetchall() with pytest.raises(bayeslite.BQLError): bdb.execute('estimate count(*) from p_cc') bdb.execute('alter generator p0_cc rename to P0_cc') bdb.execute('analyze p0_cc for 1 iteration') colno = core.bayesdb_variable_number(bdb, population_id, generator_id, 'gender') with pytest.raises(parse.BQLParseError): # Rename the table's columns, not the generator's columns. bdb.execute('alter generator p0_cc rename gender to sex') with pytest.raises(NotImplementedError): # XXX bdb.execute('alter table t rename to t0, rename gender to sex') assert core.bayesdb_variable_number( bdb, population_id, generator_id, 'sex') \ == colno bdb.execute('analyze p0_cc model 0 for 1 iteration') bdb.execute('alter generator p0_cc rename to p_cc') assert core.bayesdb_variable_number( bdb, population_id, generator_id, 'sex') \ == colno bdb.execute('select sex from t0').fetchall() with pytest.raises(AssertionError): # XXX bdb.execute('select gender from t0') assert False, 'Need to fix quoting of unknown columns!' with pytest.raises(bayeslite.BQLError): bdb.execute('estimate predict sex with confidence 0.9' ' from p').fetchall() bdb.execute('infer explicit predict sex with confidence 0.9' ' from p').fetchall() with pytest.raises(bayeslite.BQLError): bdb.execute('estimate predict gender with confidence 0.9' ' from p') with pytest.raises(bayeslite.BQLError): bdb.execute('infer explicit predict gender with confidence 0.9' ' from p') bdb.execute('alter table t0 rename sex to gender') assert core.bayesdb_variable_number( bdb, population_id, generator_id, 'gender') \ == colno bdb.execute('alter generator p0_cc rename to p_cc') # XXX bdb.execute('alter table t rename to T0') # XXX bdb.sql_execute('create table t0_temp(x)') bdb.execute('alter table T0 rename to t0') assert bdb.execute('select count(*) from t0_temp').fetchvalue() == 0 assert bdb.execute('select count(*) from t0').fetchvalue() > 0 with pytest.raises(bayeslite.BQLError): # Cannot specify models with rename. bdb.execute('alter generator p_cc models (1) rename to p_cc_fail') bdb.execute('drop table T0_TEMP') bdb.execute('analyze p_cc model 0 for 1 iteration') bdb.execute('analyze p_cc model 1 for 1 iteration') bdb.execute('analyze p_cc models 0-1 for 1 iteration') bdb.execute('analyze p_cc models 0,1 for 1 iteration') bdb.execute('analyze p_cc for 1 iteration') bdb.execute('select * from t0').fetchall() bdb.execute('select * from T0').fetchall() bdb.execute('estimate * from p').fetchall() bdb.execute('estimate * from P').fetchall() # SIMIARITY IN THE CONTEXT OF requires exactly 1 variable. with pytest.raises(bayeslite.BQLError): bdb.execute('estimate similarity in the context of * ' 'from pairwise p').fetchall() bdb.execute('estimate similarity in the context of age ' 'from pairwise p').fetchall() bdb.execute('alter population p rename to p2') assert core.bayesdb_population_name(bdb, population_id) == 'p2' bdb.execute('estimate similarity to (rowid=1) in the context of rank ' 'from p2').fetchall() bdb.execute('select value from' ' (estimate correlation from pairwise columns of p2)').fetchall() bdb.execute('infer explicit predict age with confidence 0.9' ' from p2').fetchall() bdb.execute('infer explicit predict AGE with confidence 0.9' ' from P2').fetchall() bdb.execute('infer explicit predict aGe with confidence 0.9' ' from P2').fetchall() with pytest.raises(bayeslite.BQLError): bdb.execute('estimate predict agee with confidence 0.9 from p2') with pytest.raises(bayeslite.BQLError): bdb.execute('infer explicit predict agee with confidence 0.9' ' from p2') guess.bayesdb_guess_population(bdb, 'pe', 't0', overrides=[ ('age', 'numerical'), ('rank', 'numerical'), ]) bdb.execute('create generator pe_cc for pe;') with pytest.raises(bayeslite.BQLError): # No models to analyze. bdb.execute('analyze pe_cc for 1 iteration') bdb.execute('initialize 1 model if not exists for pe_cc') bdb.execute('analyze pe_cc for 1 iteration') bdb.execute('estimate correlation' ' from pairwise columns of pe').fetchall() with pytest.raises(bayeslite.BQLError): bdb.execute('initialize 4 models if not exists for t') with pytest.raises(bayeslite.BQLError): bdb.execute('analyze t0 for 1 iteration') with pytest.raises(bayeslite.BQLError): bdb.execute('estimate * from t') with pytest.raises(bayeslite.BQLError): bdb.execute('estimate * from columns of t') with pytest.raises(bayeslite.BQLError): bdb.execute('estimate correlation from pairwise columns of t') with pytest.raises(bayeslite.BQLError): bdb.execute('estimate similarity in the context of age ' 'from pairwise t') bdb.execute('initialize 6 models if not exists for p_cc') bdb.execute('analyze p_cc for 1 iteration') def test_trivial_deadline(): with test_core.t1() as (bdb, _population_id, _generator_id): bdb.execute('initialize 1 model for p1_cc') bdb.execute('analyze p1_cc for 1 second') def test_parametrized(): assert bql2sqlparam('select * from t where id = ?') == \ 'SELECT * FROM "t" WHERE ("id" = ?1);' assert bql2sqlparam('select * from t where id = :foo') == \ 'SELECT * FROM "t" WHERE ("id" = ?1);' assert bql2sqlparam('select * from t where id = $foo') == \ 'SELECT * FROM "t" WHERE ("id" = ?1);' assert bql2sqlparam('select * from t where id = @foo') == \ 'SELECT * FROM "t" WHERE ("id" = ?1);' assert bql2sqlparam('select * from t where id = ?123') == \ 'SELECT * FROM "t" WHERE ("id" = ?1);' assert bql2sqlparam('select * from t where a = $foo and b = ?1;') == \ 'SELECT * FROM "t" WHERE (("a" = ?1) AND ("b" = ?1));' assert bql2sqlparam('select * from t' + ' where a = ?123 and b = :foo and c = ?124') == \ 'SELECT * FROM "t" WHERE' + \ ' ((("a" = ?1) AND ("b" = ?2)) AND ("c" = ?2));' with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) assert bql_execute(bdb, 'select count(*) from t') == [(7,)] assert bql_execute(bdb, 'select count(distinct division) from t') == \ [(6,)] assert bql_execute(bdb, 'select * from t where height > ?', (70,)) == \ [ (41, 'M', 65600, 72, 'marketing', 4), (30, 'M', 70000, 73, 'sales', 4), (30, 'F', 81000, 73, 'engineering', 3), ] assert bql_execute(bdb, 'select * from t where height > ?123', (0,)*122 + (70,)) == \ [ (41, 'M', 65600, 72, 'marketing', 4), (30, 'M', 70000, 73, 'sales', 4), (30, 'F', 81000, 73, 'engineering', 3), ] assert bql_execute(bdb, 'select age from t where division = :division', {':division': 'sales'}) == \ [(34,), (30,)] assert bql_execute(bdb, 'select division from t' + ' where age < @age and rank > ?;', (40, 4)) == \ [('accounting',)] assert bql_execute(bdb, 'select division from t' + ' where age < @age and rank > :rank;', {':RANK': 4, '@aGe': 40}) == \ [('accounting',)] with pytest.raises(ValueError): bdb.execute('select * from t where age < ? and rank > :r', {':r': 4}) def traced_execute(query, *args): bql = [] def trace(string, _bindings): bql.append(' '.join(string.split())) bdb.trace(trace) with bdb.savepoint(): bdb.execute(query, *args) bdb.untrace(trace) return bql def sqltraced_execute(query, *args): sql = [] def trace(string, _bindings): sql.append(' '.join(string.split())) bdb.sql_trace(trace) with bdb.savepoint(): bdb.execute(query, *args) bdb.sql_untrace(trace) return sql guess.bayesdb_guess_population(bdb, 'p', 't') bdb.execute('create generator p_cc for p;') bdb.execute('initialize 1 model for p_cc;') assert traced_execute('estimate similarity to (rowid = 1)' ' in the context of (estimate * from columns of p limit 1)' ' from p;') == [ 'estimate similarity to (rowid = 1)' \ ' in the context of (estimate * from columns of p limit 1)' \ ' from p;', ] assert sqltraced_execute('estimate similarity to (rowid = 1)' ' in the context of (estimate * from columns of p limit 1)' ' from p;') == [ 'SELECT COUNT(*) FROM bayesdb_population WHERE name = ?', 'SELECT id FROM bayesdb_population WHERE name = ?', 'SELECT tabname FROM bayesdb_population WHERE id = ?', 'SELECT COUNT(*) FROM bayesdb_population WHERE name = ?', 'SELECT id FROM bayesdb_population WHERE name = ?', 'SELECT v.name AS name FROM bayesdb_variable AS v' ' WHERE v.population_id = 1' ' AND v.generator_id IS NULL' ' LIMIT 1', 'SELECT colno FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT tabname FROM bayesdb_population' ' WHERE id = ?', 'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,' ' (SELECT _rowid_ FROM "t" WHERE ("rowid" = 1)), 0) FROM "t"', 'SELECT id FROM bayesdb_generator WHERE population_id = ?', 'SELECT backend FROM bayesdb_generator WHERE id = ?', 'SELECT cgpm_rowid FROM bayesdb_cgpm_individual' ' WHERE generator_id = ? AND table_rowid = ?', 'SELECT cgpm_rowid FROM bayesdb_cgpm_individual ' 'WHERE generator_id = ? AND table_rowid = ?', 'SELECT engine_stamp FROM bayesdb_cgpm_generator ' 'WHERE generator_id = ?' ] assert sqltraced_execute('estimate similarity to (rowid = 1)' ' in the context of (estimate * from columns of p limit ?)' ' from p;', (1,)) == [ 'SELECT COUNT(*) FROM bayesdb_population' ' WHERE name = ?', 'SELECT id FROM bayesdb_population' ' WHERE name = ?', 'SELECT tabname FROM bayesdb_population WHERE id = ?', 'SELECT COUNT(*) FROM bayesdb_population' ' WHERE name = ?', 'SELECT id FROM bayesdb_population' ' WHERE name = ?', # ESTIMATE * FROM COLUMNS OF: 'SELECT v.name AS name' ' FROM bayesdb_variable AS v' ' WHERE v.population_id = 1' ' AND v.generator_id IS NULL' ' LIMIT ?1', 'SELECT colno FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT tabname FROM bayesdb_population WHERE id = ?', # ESTIMATE SIMILARITY TO (rowid=1): 'SELECT bql_row_similarity(1, NULL, NULL, _rowid_,' ' (SELECT _rowid_ FROM "t" WHERE ("rowid" = 1)), 0) FROM "t"', 'SELECT id FROM bayesdb_generator WHERE population_id = ?', 'SELECT backend FROM bayesdb_generator WHERE id = ?', 'SELECT cgpm_rowid FROM bayesdb_cgpm_individual' ' WHERE generator_id = ? AND table_rowid = ?', 'SELECT cgpm_rowid FROM bayesdb_cgpm_individual' ' WHERE generator_id = ? AND table_rowid = ?', 'SELECT engine_stamp FROM bayesdb_cgpm_generator' ' WHERE generator_id = ?' ] assert sqltraced_execute( 'create temp table if not exists sim as ' 'simulate age, RANK, division ' 'from p given gender = \'F\' limit 4') == [ 'PRAGMA table_info("sim")', 'PRAGMA table_info("bayesdb_temp_0")', 'SELECT COUNT(*) FROM bayesdb_population WHERE name = ?', 'SELECT id FROM bayesdb_population WHERE name = ?', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT CAST(4 AS INTEGER), \'F\'', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT colno FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT colno FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT colno FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT colno FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT tabname FROM bayesdb_population WHERE id = ?', 'SELECT MAX(_rowid_) FROM "t"', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT id FROM bayesdb_generator' ' WHERE population_id = ?', 'SELECT backend FROM bayesdb_generator WHERE id = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT tabname FROM bayesdb_population WHERE id = ?', 'SELECT 1 FROM "t" WHERE oid = ?', 'SELECT 1 FROM bayesdb_cgpm_individual' ' WHERE generator_id = ? AND table_rowid = ? LIMIT 1', 'SELECT cgpm_rowid FROM bayesdb_cgpm_individual' ' WHERE generator_id = ? AND table_rowid = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT code FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND value = ?', 'SELECT engine_stamp FROM bayesdb_cgpm_generator' ' WHERE generator_id = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ? AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'CREATE TEMP TABLE "bayesdb_temp_0"' ' ("age","RANK","division")', 'INSERT INTO "bayesdb_temp_0" ("age","RANK","division")' ' VALUES (?,?,?)', 'INSERT INTO "bayesdb_temp_0" ("age","RANK","division")' ' VALUES (?,?,?)', 'INSERT INTO "bayesdb_temp_0" ("age","RANK","division")' ' VALUES (?,?,?)', 'INSERT INTO "bayesdb_temp_0" ("age","RANK","division")' ' VALUES (?,?,?)', 'CREATE TEMP TABLE IF NOT EXISTS "sim" AS' ' SELECT * FROM "bayesdb_temp_0"', 'DROP TABLE "bayesdb_temp_0"' ] assert sqltraced_execute( 'select * from (simulate age from p ' 'given gender = \'F\' limit 4)') == [ 'PRAGMA table_info("bayesdb_temp_1")', 'SELECT COUNT(*) FROM bayesdb_population WHERE name = ?', 'SELECT id FROM bayesdb_population WHERE name = ?', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT CAST(4 AS INTEGER), \'F\'', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT colno FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT COUNT(*) FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT colno FROM bayesdb_variable' ' WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?)' ' AND name = ?', 'SELECT tabname FROM bayesdb_population WHERE id = ?', 'SELECT MAX(_rowid_) FROM "t"', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT token FROM bayesdb_rowid_tokens', 'SELECT id FROM bayesdb_generator WHERE population_id = ?', 'SELECT backend FROM bayesdb_generator WHERE id = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT tabname FROM bayesdb_population WHERE id = ?', 'SELECT 1 FROM "t" WHERE oid = ?', 'SELECT 1 FROM bayesdb_cgpm_individual' ' WHERE generator_id = ? AND table_rowid = ? LIMIT 1', 'SELECT cgpm_rowid FROM bayesdb_cgpm_individual' ' WHERE generator_id = ? AND table_rowid = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT code FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND value = ?', 'SELECT engine_stamp FROM bayesdb_cgpm_generator' ' WHERE generator_id = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT stattype FROM bayesdb_variable WHERE population_id = ?' ' AND (generator_id IS NULL OR generator_id = ?) AND colno = ?', 'SELECT value FROM bayesdb_cgpm_category' ' WHERE generator_id = ? AND colno = ? AND code = ?', 'CREATE TEMP TABLE "bayesdb_temp_1" ("age")', 'INSERT INTO "bayesdb_temp_1" ("age") VALUES (?)', 'INSERT INTO "bayesdb_temp_1" ("age") VALUES (?)', 'INSERT INTO "bayesdb_temp_1" ("age") VALUES (?)', 'INSERT INTO "bayesdb_temp_1" ("age") VALUES (?)', 'SELECT * FROM (SELECT * FROM "bayesdb_temp_1")', 'DROP TABLE "bayesdb_temp_1"', ] bdb.execute(''' create population q for t ( age NUMERICAL; gender NOMINAL; -- Not binary! salary NUMERICAL; height NUMERICAL; division NOMINAL; rank NOMINAL; ) ''') bdb.execute('create generator q_cc for q;') bdb.execute('initialize 1 model for q_cc;') assert sqltraced_execute('analyze q_cc for 1 iteration;') == [ 'SELECT COUNT(*) FROM bayesdb_generator WHERE name = ?', 'SELECT id FROM bayesdb_generator WHERE name = ?', 'SELECT backend FROM bayesdb_generator WHERE id = ?', 'SELECT engine_json, engine_stamp FROM bayesdb_cgpm_generator' ' WHERE generator_id = ?', 'SELECT population_id FROM bayesdb_generator WHERE id = ?', 'SELECT engine_stamp FROM bayesdb_cgpm_generator' ' WHERE generator_id = ?', 'UPDATE bayesdb_cgpm_generator' ' SET engine_json = :engine_json, engine_stamp = :engine_stamp' ' WHERE generator_id = :generator_id'] def test_create_table_ifnotexists_as_simulate(): with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) # If not exists table tests guess.bayesdb_guess_population(bdb, 'p', 't', overrides=[('age', 'numerical')]) bdb.execute('create generator p_cc for p;') bdb.execute('initialize 1 model for p_cc') bdb.execute('analyze p_cc for 1 iteration') bdb.execute(''' create table if not exists u as simulate age from p limit 10 ''') bdb.execute("drop table u") bdb.execute(''' create table if not exists w as simulate age from p given division='sales' limit 10 ''') bdb.execute("drop table w") bdb.execute("create table u as simulate age from p limit 10") x = bdb.execute("select count (*) from u").fetchvalue() bdb.execute(''' create table if not exists u as simulate age from p limit 10 ''') bdb.execute(''' create table if not exists u as simulate age from p given division='sales' limit 10 ''') assert x == bdb.execute("select count (*) from u").fetchvalue() def test_createtab(): with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): with pytest.raises(apsw.SQLError): bdb.execute('drop table t') bdb.execute('drop table if exists t') with pytest.raises(bayeslite.BQLError): bdb.execute('drop population p') bdb.execute('drop population if exists p') with pytest.raises(bayeslite.BQLError): bdb.execute('drop generator p_cc') bdb.execute('drop generator if exists p_cc') with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) with bdb.savepoint(): # Savepoint because we don't actually want the new data to # be inserted. with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True, ifnotexists=True) guess.bayesdb_guess_population(bdb, 'p', 't', overrides=[('age', 'numerical')]) bdb.execute('create generator p_cc for p;') with pytest.raises(bayeslite.BQLError): # Redefining population. bdb.execute('create population p for t (age numerical)') with pytest.raises(bayeslite.BQLError): # Redefining generator. bdb.execute('create generator p_cc for p;') # Make sure ignore columns work. # # XXX Also check key columns. guess.bayesdb_guess_population(bdb, 'p0', 't', overrides=[('age', 'ignore')]) bdb.execute('drop population p0') population_id = core.bayesdb_get_population(bdb, 'p') colno = core.bayesdb_variable_number(bdb, population_id, None, 'age') assert core.bayesdb_variable_stattype( bdb, population_id, None, colno) == 'numerical' bdb.execute('initialize 1 model for p_cc') with pytest.raises(bayeslite.BQLError): bdb.execute('drop table t') with pytest.raises(bayeslite.BQLError): bdb.execute('drop population p') bdb.execute('drop generator p_cc') with pytest.raises(bayeslite.BQLError): bdb.execute('drop generator p_cc') with pytest.raises(bayeslite.BQLError): bdb.execute('drop table t') bdb.execute('drop generator if exists p_cc') bdb.execute('drop population p') bdb.execute('drop population if exists p') bdb.execute('drop table t') bdb.execute('drop table if exists t') with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) guess.bayesdb_guess_population(bdb, 'p', 't') bdb.execute("create table u as select * from t where gender = 'F'") assert bql_execute(bdb, 'select * from u') == [ (23, 'F', 81000, 67, 'data science', 3), (36, 'F', 96000, 70, 'management', 2), (30, 'F', 81000, 73, 'engineering', 3), ] with pytest.raises(bayeslite.BQLError): bdb.execute("create table u as select * from t where gender = 'F'") bdb.execute('drop table u') with pytest.raises(apsw.SQLError): bql_execute(bdb, 'select * from u') bdb.execute("create temp table u as" " select * from t where gender = 'F'") assert bql_execute(bdb, 'select * from u') == [ (23, 'F', 81000, 67, 'data science', 3), (36, 'F', 96000, 70, 'management', 2), (30, 'F', 81000, 73, 'engineering', 3), ] # XXX Test to make sure TEMP is passed through, and the table # doesn't persist on disk. def test_alterpop_addvar(): with bayeslite.bayesdb_open() as bdb: bayeslite.bayesdb_read_csv( bdb, 't', StringIO.StringIO(test_csv.csv_data), header=True, create=True) bdb.execute(''' create population p for t with schema( age numerical; gender nominal; salary numerical; height ignore; division ignore; rank ignore; ) ''') population_id = core.bayesdb_get_population(bdb, 'p') bdb.execute('create generator m for p;') # Fail when variable does not exist in base table. with pytest.raises(bayeslite.BQLError): bdb.execute('alter population p add variable quux;') # Fail when variable already in population. with pytest.raises(bayeslite.BQLError): bdb.execute('alter population p add variable age numerical;') # Fail when given invalid statistical type. with pytest.raises(bayeslite.BQLError): bdb.execute('alter population p add variable heigh numr;') # Alter pop with stattype. assert not core.bayesdb_has_variable(bdb, population_id, None, 'height') bdb.execute('alter population p add variable height numerical;') assert core.bayesdb_has_variable(bdb, population_id, None, 'height') # Alter pop multiple without stattype. assert not core.bayesdb_has_variable(bdb, population_id, None, 'rank') assert not core.bayesdb_has_variable( bdb, population_id, None, 'division') bdb.execute(''' alter population p add variable rank, add variable division; ''') assert core.bayesdb_has_variable(bdb, population_id, None, 'rank') assert core.bayesdb_has_variable(bdb, population_id, None, 'division') # Add a new column weight to the base table. bdb.sql_execute('alter table t add column weight real;') # Fail when no values in new column. with pytest.raises(bayeslite.BQLError): bdb.execute('alter population p add variable weight numerical;') assert not core.bayesdb_has_variable(bdb, population_id, None, 'weight') # Update a single value and update the population. bdb.sql_execute('update t set weight = 1 where oid = 1;') bdb.execute('alter population p add variable weight numerical;') assert core.bayesdb_has_variable(bdb, population_id, None, 'weight') def test_txn(): with test_csv.bayesdb_csv_file(test_csv.csv_data) as (bdb, fname): # Make sure rollback and commit fail outside a transaction. with pytest.raises(bayeslite.BayesDBTxnError): bdb.execute('ROLLBACK') with pytest.raises(bayeslite.BayesDBTxnError): bdb.execute('COMMIT') # Open a transaction which we'll roll back. bdb.execute('BEGIN') try: # Make sure transactions don't nest. (Use savepoints.) with pytest.raises(bayeslite.BayesDBTxnError): bdb.execute('BEGIN') finally: bdb.execute('ROLLBACK') # Make sure rollback and commit still fail outside a transaction. with pytest.raises(bayeslite.BayesDBTxnError): bdb.execute('ROLLBACK') with pytest.raises(bayeslite.BayesDBTxnError): bdb.execute('COMMIT') # Open a transaction which we'll commit. bdb.execute('BEGIN') try: with pytest.raises(bayeslite.BayesDBTxnError): bdb.execute('BEGIN') finally: bdb.execute('COMMIT') with pytest.raises(bayeslite.BayesDBTxnError): bdb.execute('ROLLBACK') with pytest.raises(bayeslite.BayesDBTxnError): bdb.execute('COMMIT') # Make sure ROLLBACK undoes the effects of the transaction. bdb.execute('BEGIN') try: with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) bdb.execute('SELECT * FROM t').fetchall() guess.bayesdb_guess_population(bdb, 'p', 't') bdb.execute('ESTIMATE * FROM p').fetchall() finally: bdb.execute('ROLLBACK') with pytest.raises(apsw.SQLError): bdb.execute('SELECT * FROM t') with pytest.raises(bayeslite.BQLError): bdb.execute('ESTIMATE * FROM p') # Make sure CREATE and DROP both work in the transaction. bdb.execute('BEGIN') try: with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) bdb.execute('SELECT * FROM t').fetchall() guess.bayesdb_guess_population(bdb, 'p', 't') bdb.execute('ESTIMATE * FROM p').fetchall() with pytest.raises(bayeslite.BQLError): bdb.execute('DROP TABLE t') bdb.execute('DROP POPULATION p') with pytest.raises(bayeslite.BQLError): bdb.execute('ESTIMATE * FROM p') bdb.execute('DROP TABLE t') with pytest.raises(apsw.SQLError): bdb.execute('SELECT * FROM t') finally: bdb.execute('ROLLBACK') with pytest.raises(bayeslite.BQLError): bdb.execute('ESTIMATE * FROM p') with pytest.raises(apsw.SQLError): bdb.execute('SELECT * FROM t') # Make sure CREATE and DROP work even if we commit. bdb.execute('BEGIN') try: with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) bdb.execute('SELECT * FROM t').fetchall() guess.bayesdb_guess_population(bdb, 'p', 't') bdb.execute('ESTIMATE * FROM p').fetchall() with pytest.raises(bayeslite.BQLError): bdb.execute('DROP TABLE t') bdb.execute('DROP POPULATION p') with pytest.raises(bayeslite.BQLError): bdb.execute('ESTIMATE * FROM p') bdb.execute('DROP TABLE t') with pytest.raises(apsw.SQLError): bdb.execute('SELECT * FROM t') finally: bdb.execute('COMMIT') with pytest.raises(bayeslite.BQLError): bdb.execute('ESTIMATE * FROM p') with pytest.raises(apsw.SQLError): bdb.execute('SELECT * FROM t') # Make sure CREATE persists if we commit. bdb.execute('BEGIN') try: with open(fname, 'rU') as f: bayeslite.bayesdb_read_csv(bdb, 't', f, header=True, create=True) bdb.execute('SELECT * FROM t').fetchall() guess.bayesdb_guess_population(bdb, 'p', 't') bdb.execute('ESTIMATE * FROM p').fetchall() finally: bdb.execute('COMMIT') bdb.execute('SELECT * FROM t').fetchall() bdb.execute('ESTIMATE * FROM p').fetchall() # Make sure bdb.transaction works, rolls back on exception, # and handles nesting correctly in the context of savepoints. try: with bdb.transaction(): bdb.sql_execute('create table quagga(x)') raise StopIteration except StopIteration: pass with pytest.raises(apsw.SQLError): bdb.execute('select * from quagga') with bdb.transaction(): with bdb.savepoint(): with bdb.savepoint(): pass with bdb.savepoint(): with pytest.raises(bayeslite.BayesDBTxnError): with bdb.transaction(): pass # XXX To do: Make sure other effects (e.g., analysis) get # rolled back by ROLLBACK. def test_predprob_null(): backend = CGPM_Backend({}, multiprocess=False) with test_core.bayesdb(backend=backend) as bdb: bdb.sql_execute(''' create table foo ( id integer primary key not null, x numeric, y numeric, z numeric ) ''') bdb.sql_execute("insert into foo values (1, 1, 'strange', 3)") bdb.sql_execute("insert into foo values (2, 1.2, 'strange', 1)") bdb.sql_execute("insert into foo values (3, 0.8, 'strange', 3)") bdb.sql_execute("insert into foo values (4, NULL, 'strange', 9)") bdb.sql_execute("insert into foo values (5, 73, 'up', 11)") bdb.sql_execute("insert into foo values (6, 80, 'up', -1)") bdb.sql_execute("insert into foo values (7, 60, 'up', NULL)") bdb.sql_execute("insert into foo values (8, 67, NULL, NULL)") bdb.sql_execute("insert into foo values (9, 3.1415926, 'down', 1)") bdb.sql_execute("insert into foo values (10, 1.4142135, 'down', 0)") bdb.sql_execute("insert into foo values (11, 2.7182818, 'down', -1)") bdb.sql_execute("insert into foo values (12, NULL, 'down', 10)") bdb.execute(''' create population pfoo for foo ( id ignore; x numerical; y nominal; z numerical; ) ''') bdb.execute('create generator pfoo_cc for pfoo using cgpm;') bdb.execute('initialize 1 model for pfoo_cc') bdb.execute('analyze pfoo_cc for 1 iteration') # Null value => null predictive probability. assert bdb.execute('estimate predictive probability of x' ' from pfoo where id = 4;').fetchall() == \ [(None,)] # Nonnull value => nonnull predictive probability. x = bdb.execute('estimate predictive probability of x' ' from pfoo where id = 5').fetchall() assert len(x) == 1 assert len(x[0]) == 1 assert isinstance(x[0][0], (int, float)) # All null values => null predictive probability. assert bdb.execute('estimate predictive probability of (y, z)' ' from pfoo where id = 8;').fetchall() == \ [(None,)] # Some nonnull values => nonnull predictive probability. x = bdb.execute('estimate predictive probability of (x, z)' ' from pfoo where id = 8;').fetchall() assert len(x) == 1 assert len(x[0]) == 1 assert isinstance(x[0][0], (int, float)) # All NULL constraints => same result regardless of given clause. c0 = bdb.execute('estimate predictive probability of x' ' from pfoo where id = 8;') v0 = cursor_value(c0) assert v0 is not None c1 = bdb.execute('estimate predictive probability of x given (y, z)' ' from pfoo where id = 8;') v1 = cursor_value(c1) assert relerr(v0, v1) < 0.0001 def test_guess_all(): with test_core.bayesdb() as bdb: bdb.sql_execute('create table foo (x numeric, y numeric, z numeric)') bdb.sql_execute('insert into foo values (1, 2, 3)') bdb.sql_execute('insert into foo values (4, 5, 6)') # XXX GUESS(*) guess.bayesdb_guess_population(bdb, 'pfoo', 'foo') def test_misc_errors(): with test_core.t1() as (bdb, _population_id, _generator_id): with pytest.raises(bayeslite.BQLError): bdb.execute('create table t1 as SELECT 1 FROM t1' # t1 already exists as a table. ' limit 1') with pytest.raises(bayeslite.BQLError): # t1 already exists as a table. bdb.execute('create table t1 as simulate weight from p1' ' limit 1') with pytest.raises(bayeslite.BQLError): # t1x does not exist as a population. bdb.execute('create table t1_sim as simulate weight from t1x' ' limit 1') with pytest.raises(bayeslite.BQLError): # p1 does not have a variable waught. bdb.execute('create table t1_sim as simulate waught from p1' ' limit 1') with pytest.raises(bayeslite.BQLError): # p1 does not have a variable agee. bdb.execute('create table t1_sim as simulate weight from p1' ' given agee = 42 limit 1') with bdb.savepoint(): bdb.sql_execute('create table t2(x)') with pytest.raises(bayeslite.BQLError): # t1 already exists as a table. bdb.execute('alter table t2 rename to t1') with pytest.raises(NotImplementedError): # Renaming columns is not yet implemented. bdb.execute('alter table t1 rename weight to mass') with pytest.raises(bayeslite.BQLError): # xcat does not exist as a backend. bdb.execute('create generator p1_xc for p1 using xcat()') with pytest.raises(bayeslite.BQLError): # p1 already exists as a population. bdb.execute('create generator p1_cc for p1;') with pytest.raises(bayeslite.BQLError): # multinomial is not a known statistical type. bdb.execute(''' create population q1 for t1( ignore id, label, weight; weight multinomial ) ''') with pytest.raises(bayeslite.BQLError): # p1_xc does not exist as a generator. bdb.execute('alter generator p1_xc rename to p1_xcat') with bdb.savepoint(): bdb.execute('create generator p1_xc for p1;') with pytest.raises(bayeslite.BQLError): # p1_xc already exists as a generator. bdb.execute('alter generator p1_cc rename to p1_xc') with pytest.raises(bayeslite.BQLParseError): # WAIT is not allowed. bdb.execute('analyze p1_cc for 1 iteration wait') with bdb.savepoint(): bdb.execute('initialize 1 model for p1_cc') bdb.execute('analyze p1_cc for 1 iteration') bdb.execute('initialize 1 model for p1_xc') bdb.execute('analyze p1_xc for 1 iteration') with pytest.raises(apsw.SQLError): bdb.execute('select' ' nonexistent((simulate age from p1 limit 1));') with pytest.raises(ValueError): bdb.execute('select :x', {'y': 42}) with pytest.raises(ValueError): bdb.execute('select :x', {'x': 53, 'y': 42}) with pytest.raises(ValueError): bdb.execute('select ?, ?', (1,)) with pytest.raises(ValueError): bdb.execute('select ?', (1, 2)) with pytest.raises(TypeError): bdb.execute('select ?', 42) with pytest.raises(NotImplementedError): bdb.execute('infer explicit predict age confidence ac, *' ' from p1') with pytest.raises(NotImplementedError): bdb.execute('infer explicit predict age confidence ac,' ' t1.(select age from t1 limit 1) from p1') with pytest.raises(bayeslite.BQLError): try: bdb.execute('estimate similarity to (rowid=1)' ' in the context of agee from p1') except bayeslite.BQLError as e: assert 'No such columns in population:' in str(e) raise def test_nested_simulate(): with test_core.t1() as (bdb, _population_id, _generator_id): bdb.execute('initialize 1 model for p1_cc') bdb.execute('analyze p1_cc for 1 iteration') bdb.execute('select (simulate age from p1 limit 1),' ' (simulate weight from p1 limit 1)').fetchall() assert bdb.temp_table_name() == 'bayesdb_temp_2' assert not core.bayesdb_has_table(bdb, 'bayesdb_temp_0') assert not core.bayesdb_has_table(bdb, 'bayesdb_temp_1') bdb.execute('simulate weight from p1' ' given age = (simulate age from p1 limit 1)' ' limit 1').fetchall() # Make sure unwinding doesn't raise an exception. Calling # __del__ directly, rather than via del(), has two effects: # # (a) It actually raises any exceptions in the method, unlike # del(), which suppresses them. # # (b) It may cause a subsequent __del__ to fail and raise an # exception, so that a subsequent del(), including an implicit # one at the end of a scope, may print a message to stderr. # # Effect (a) is what we are actually trying to test. Effect # (b) is a harmless consequence as far as pytest is concerned, # as long as the test otherwise passes. bdb.execute('simulate weight from p1' ' given age = (simulate age from p1 limit 1)' ' limit 1').__del__() def test_checkpoint__ci_slow(): with test_core.t1() as (bdb, population_id, generator_id): bdb.execute('initialize 1 model for p1_cc') bdb.execute('analyze p1_cc for 10 iterations checkpoint 1 iteration') # No checkpoint by seconds. with pytest.raises(NotImplementedError): bdb.execute('analyze p1_cc for 5 seconds checkpoint 1 second') bdb.execute('drop models from p1_cc') bdb.execute('initialize 1 model for p1_cc') # No checkpoint by seconds. with pytest.raises(NotImplementedError): bdb.execute('analyze p1_cc for 5 iterations checkpoint 1 second') bdb.execute('drop models from p1_cc') bdb.execute('initialize 1 model for p1_cc') bdb.execute('analyze p1_cc for 1 iteration checkpoint 2 iterations') def test_infer_confidence__ci_slow(): with test_core.t1() as (bdb, _population_id, _generator_id): bdb.execute('initialize 1 model for p1_cc') bdb.execute('analyze p1_cc for 1 iteration') bdb.execute('infer explicit rowid, rowid as another_rowid, 4,' ' age, predict age as age_inf confidence age_conf' ' from p1').fetchall() def test_infer_as_estimate(): with test_core.t1() as (bdb, _population_id, _generator_id): bdb.execute('initialize 1 model for p1_cc') bdb.execute('analyze p1_cc for 1 iteration') bdb.execute('infer explicit predictive probability of age' ' from p1').fetchall() def test_infer_error(): with test_core.t1() as (bdb, _population_id, _generator_id): bdb.execute('initialize 1 model for p1_cc') bdb.execute('infer explicit predict age confidence age_conf' ' from p1').fetchall() with pytest.raises(bayeslite.BQLError): bdb.execute('infer explicit predict agee confidence age_conf' ' from p1').fetchall() def test_estimate_by(): with test_core.t1() as (bdb, _population_id, _generator_id): bdb.execute('initialize 1 model for p1_cc') bdb.execute('analyze p1_cc for 1 iteration') with pytest.raises(bayeslite.BQLError): bdb.execute('estimate predictive probability of age' ' by p1') with pytest.raises(bayeslite.BQLError): bdb.execute('estimate similarity to (rowid=1) ' 'in the context of age by p1') def check(x, bindings=None): assert len(bdb.execute(x, bindings=bindings).fetchall()) == 1 check('estimate probability density of age = 42 by p1') check('estimate dependence probability of age with weight by p1') check('estimate mutual information of age with weight by p1') check('estimate correlation of age with weight by p1') check('estimate correlation pvalue of age with weight by p1') rowid = bdb.execute('select min(rowid) from t1').fetchall()[0][0] check(''' estimate similarity of (rowid=?) to (rowid=?) in the context of weight by p1 ''', (rowid, rowid,)) def test_empty_cursor(): with bayeslite.bayesdb_open() as bdb: assert bdb.execute('SELECT 0').connection == bdb empty(bdb.execute('BEGIN')) empty(bdb.execute('COMMIT')) empty(bdb.sql_execute('CREATE TABLE t(x, y, z)')) empty(bdb.sql_execute('INSERT INTO t VALUES(1,2,3)')) empty(bdb.sql_execute('INSERT INTO t VALUES(4,5,6)')) empty(bdb.sql_execute('INSERT INTO t VALUES(7,8,9)')) empty(bdb.execute('CREATE POPULATION p FOR t ' '(IGNORE z,y; x NOMINAL)')) empty(bdb.execute('CREATE GENERATOR p_cc FOR p;')) empty(bdb.execute('INITIALIZE 1 MODEL FOR p_cc')) empty(bdb.execute('DROP GENERATOR p_cc')) empty(bdb.execute('DROP POPULATION p')) empty(bdb.execute('DROP TABLE t')) def test_create_generator_ifnotexists(): # XXX Test other backends too, because they have a role in ensuring that # this works. Their create_generator will still be called. # # [TRC 20160627: The above comment appears to be no longer true -- # if it was ever true.] for using_clause in ('cgpm()',): with bayeslite.bayesdb_open() as bdb: bdb.sql_execute('CREATE TABLE t(x, y, z)') bdb.sql_execute('INSERT INTO t VALUES(1,2,3)') bdb.execute(''' CREATE POPULATION p FOR t ( x NUMERICAL; y NUMERICAL; z NOMINAL; ) ''') for _i in (0, 1): bdb.execute('CREATE GENERATOR IF NOT EXISTS p_cc FOR p USING ' + using_clause) try: bdb.execute('CREATE GENERATOR p_cc FOR p USING ' + using_clause) assert False # Should have said it exists. except bayeslite.BQLError: pass def test_bql_rand(): with bayeslite.bayesdb_open() as bdb: bdb.sql_execute('CREATE TABLE frobotz(x)') for _ in range(10): bdb.sql_execute('INSERT INTO frobotz VALUES(2)') cursor = bdb.execute('SELECT bql_rand() FROM frobotz LIMIT 10;') rands = cursor.fetchall() # These are "the" random numbers (internal PRNG is seeded to 0) ans = [(0.28348770982811367,), (0.4789774612650598,), (0.07824908989551316,), (0.6091223239372148,), (0.03906608409906187,), (0.3690599096081546,), (0.8223420512129717,), (0.7777771914916722,), (0.061856771629497986,), (0.6492586781908201,)] assert rands == ans def test_bql_rand2(): seed = struct.pack('<QQQQ', 0, 0, 0, 3) with bayeslite.bayesdb_open(seed=seed) as bdb: bdb.sql_execute('CREATE TABLE frobotz(x)') for _ in range(10): bdb.sql_execute('INSERT INTO frobotz VALUES(2)') cursor = bdb.execute('SELECT bql_rand() FROM frobotz LIMIT 10;') rands = cursor.fetchall() ans = [(0.8351877951287725,), (0.9735099617243271,), (0.026142315910925418,), (0.09380653289687524,), (0.1097050387582088,), (0.33154896906379605,), (0.4579314980719317,), (0.09072802203491703,), (0.5276180968829105,), (0.9993280772797679,)] assert rands == ans class MockTracerOneQuery(bayeslite.IBayesDBTracer): def __init__(self, q, qid): self.q = q self.qid = qid self.start_calls = 0 self.ready_calls = 0 self.error_calls = 0 self.finished_calls = 0 self.abandoned_calls = 0 def start(self, qid, query, bindings): assert qid == self.qid assert query == self.q assert bindings == () self.start_calls += 1 def ready(self, qid, _cursor): assert qid == self.qid self.ready_calls += 1 def error(self, qid, _e): assert qid == self.qid self.error_calls += 1 def finished(self, qid): assert qid == self.qid self.finished_calls += 1 def abandoned(self, qid): assert qid == self.qid self.abandoned_calls += 1 def test_tracing_smoke(): with test_core.t1() as (bdb, _population_id, _generator_id): q = 'SELECT * FROM t1' tracer = MockTracerOneQuery(q, 1) bdb.trace(tracer) cursor = bdb.execute(q) assert tracer.start_calls == 1 assert tracer.ready_calls == 1 assert tracer.error_calls == 0 assert tracer.finished_calls == 0 assert tracer.abandoned_calls == 0 cursor.fetchall() assert tracer.start_calls == 1 assert tracer.ready_calls == 1 assert tracer.error_calls == 0 assert tracer.finished_calls == 1 assert tracer.abandoned_calls == 0 del cursor assert tracer.start_calls == 1 assert tracer.ready_calls == 1 assert tracer.error_calls == 0 assert tracer.finished_calls == 1 assert tracer.abandoned_calls == 1 bdb.untrace(tracer) # XXX Make sure the whole cursor API works. q = 'SELECT 42' tracer = MockTracerOneQuery(q, 2) bdb.trace(tracer) cursor = bdb.execute(q) assert tracer.start_calls == 1 assert tracer.ready_calls == 1 assert tracer.error_calls == 0 assert tracer.finished_calls == 0 assert tracer.abandoned_calls == 0 assert cursor.fetchvalue() == 42 assert tracer.start_calls == 1 assert tracer.ready_calls == 1 assert tracer.error_calls == 0 assert tracer.finished_calls == 1 assert tracer.abandoned_calls == 0 del cursor assert tracer.start_calls == 1 assert tracer.ready_calls == 1 assert tracer.error_calls == 0 assert tracer.finished_calls == 1 assert tracer.abandoned_calls == 1 def test_tracing_error_smoke(): with test_core.t1() as (bdb, _population_id, _generator_id): q = 'SELECT * FROM wrong' tracer = MockTracerOneQuery(q, 1) bdb.trace(tracer) with pytest.raises(apsw.SQLError): bdb.execute(q) assert tracer.start_calls == 1 assert tracer.ready_calls == 0 assert tracer.error_calls == 1 assert tracer.finished_calls == 0 assert tracer.abandoned_calls == 0 class Boom(Exception): pass class ErroneousBackend(troll.TrollBackend): def __init__(self): self.call_ct = 0 def name(self): return 'erroneous' def logpdf_joint(self, *_args, **_kwargs): if self.call_ct > 10: # Wait to avoid raising during sqlite's prefetch raise Boom() self.call_ct += 1 return 0 def test_tracing_execution_error_smoke(): with test_core.t1() as (bdb, _population_id, _generator_id): bayeslite.bayesdb_register_backend(bdb, ErroneousBackend()) bdb.execute('DROP GENERATOR p1_cc') bdb.execute('CREATE GENERATOR p1_err FOR p1 USING erroneous()') q = 'ESTIMATE PREDICTIVE PROBABILITY OF age FROM p1' tracer = MockTracerOneQuery(q, 1) bdb.trace(tracer) cursor = bdb.execute(q) assert tracer.start_calls == 1 assert tracer.ready_calls == 1 assert tracer.error_calls == 0 assert tracer.finished_calls == 0 assert tracer.abandoned_calls == 0 with pytest.raises(Boom): cursor.fetchall() assert tracer.start_calls == 1 assert tracer.ready_calls == 1 assert tracer.error_calls == 1 assert tracer.finished_calls == 0 assert tracer.abandoned_calls == 0 def test_pdf_var(): with test_core.t1() as (bdb, population_id, _generator_id): bdb.execute('initialize 6 models for p1_cc;') c = bdb.execute( 'estimate probability density of label = label from p1') c.fetchall() assert bql2sql( 'estimate probability density of label = label from p1') == \ 'SELECT bql_pdf_joint(1, NULL, NULL, 1, "label") FROM "t1";'

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68a71839020ce7ad671d78acb47c03813f6d2f0f

177

py

Python

django_private_beta/urls.py

andytwoods/Django-Private-Beta

aafebab96bc5126c78f76de68780fa03f9825191

[ "MIT" ]

null

django_private_beta/urls.py

andytwoods/Django-Private-Beta

aafebab96bc5126c78f76de68780fa03f9825191

[ "MIT" ]

null

django_private_beta/urls.py

andytwoods/Django-Private-Beta

aafebab96bc5126c78f76de68780fa03f9825191

[ "MIT" ]

null

from django.conf.urls import url from . import views app_name = 'private_beta' urlpatterns = [ url(r'^private_beta/', views.PrivateBeta.as_view(), name='private_beta'), ]

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py

Python

tests/schema/data/__init__.py

datapio/klander

d862bb1640a6cf4c0010246e1d53316103321a4d

[ "Apache-2.0" ]

2

2021-05-14T22:00:55.000Z

2021-09-17T20:09:17.000Z

tests/schema/data/__init__.py

datapio/klander

d862bb1640a6cf4c0010246e1d53316103321a4d

[ "Apache-2.0" ]

null

tests/schema/data/__init__.py

datapio/klander

d862bb1640a6cf4c0010246e1d53316103321a4d

[ "Apache-2.0" ]

1

2021-07-16T08:35:43.000Z

from .state_reconciler import * from .response import *

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d7a23bc8b1c197bcfdfd5c1b5353d51faa932b74

64

py

Python

videotracker/functions/__init__.py

lysogeny/videotracker

581e7e461525ed83c47fcbf7ff94749e6185691a

[ "MIT" ]

1

2021-02-24T00:02:53.000Z

videotracker/functions/__init__.py

lysogeny/videotracker

581e7e461525ed83c47fcbf7ff94749e6185691a

[ "MIT" ]

null

videotracker/functions/__init__.py

lysogeny/videotracker

581e7e461525ed83c47fcbf7ff94749e6185691a

[ "MIT" ]

null

from .functions import * from . import abc from . import params

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d7e84f699f8edcb6ab82c3fe4a4ac65f9041434b

81

py

Python

crudlib/__init__.py

zxyle/TinyCRUD

1cdf858f435f0bab89b0ce423c0259d073cc371b

[ "MIT" ]

2

2019-07-25T23:35:10.000Z

2019-08-14T13:09:41.000Z

crudlib/__init__.py

zxyle/TinyCRUD

1cdf858f435f0bab89b0ce423c0259d073cc371b

[ "MIT" ]

8

2019-12-16T07:28:06.000Z

2020-09-13T10:29:06.000Z

crudlib/__init__.py

zxyle/TinyCRUD

1cdf858f435f0bab89b0ce423c0259d073cc371b

[ "MIT" ]

null

from .mysql import MySQL from .sqlite import SQLite from .mariadb import MariaDB

20.25

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d7efe8d5e4c2b2afe3b3f9460a06c41317be8270

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py

Python

testdocker/cli/__init__.py

sip-li/testdocker

ab01ed57d052386017dcf33310c52766d6b3a3fb

[ "Apache-2.0" ]

null

testdocker/cli/__init__.py

sip-li/testdocker

ab01ed57d052386017dcf33310c52766d6b3a3fb

[ "Apache-2.0" ]

null

testdocker/cli/__init__.py

sip-li/testdocker

ab01ed57d052386017dcf33310c52766d6b3a3fb

[ "Apache-2.0" ]

null

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041772f7dc8d537acde9472a75cc08332defcaa2

173

py

Python

projects/TextDet/textdetection/__init__.py

AzeroGYH/detectron2_crpn

617d6a67a95945915e36e0fad4a7739331881bbe

[ "Apache-2.0" ]

null

projects/TextDet/textdetection/__init__.py

AzeroGYH/detectron2_crpn

617d6a67a95945915e36e0fad4a7739331881bbe

[ "Apache-2.0" ]

null

projects/TextDet/textdetection/__init__.py

AzeroGYH/detectron2_crpn

617d6a67a95945915e36e0fad4a7739331881bbe

[ "Apache-2.0" ]

null

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0

1

0

5

041bab6ee68b38136895b05d676af598dec6d4bb

10,708

py

Python

main.py

Aus-miner/Miner-Model

f7abc9f74cec00f82a2df6e359363670a64ad72f

[ "MIT" ]

18

2021-04-18T03:51:22.000Z

2022-03-16T13:14:36.000Z

main.py

Aus-miner/Miner-Model

f7abc9f74cec00f82a2df6e359363670a64ad72f

[ "MIT" ]

1

2021-05-04T14:27:02.000Z

main.py

Aus-miner/Miner-Model

f7abc9f74cec00f82a2df6e359363670a64ad72f

[ "MIT" ]

8

2021-05-03T19:24:19.000Z

2022-02-20T22:20:18.000Z

import plotly.express as px import plotly.io as pio import plotly.graph_objects as go from plotly.subplots import make_subplots import pandas as pd import numpy as np from agents import * from generators import * from CMDataLoader import CMDataLoader from Simulator import Simulator from plotutils import update_layout_wrapper import config import constants import random # my_palette = ["#264653","#9D1DC8","#287271", "#645DAC","#636EFA", "#ECA400","#FE484E","#8484E8", "#03b800" ,"#9251e1","#F4A261"] # my_palette = ["#54478c","#9D1DC8","#2c699a","#048ba8","#0db39e","#16db93","#83e377","#b9e769","#efea5a","#f1c453","#f29e4c"] my_palette = ["#1f00a7","#9d1dc8","#00589f","#009b86","#00a367","#67a300","#645dac","#eca400","#fd7e00","#b6322b", "#FE484E"] hardware_palette = ["#009b86", "#9D1DC8"] opex_palette = ["#9D1DC8","#264653","#8484E8"] primary_color = ["#9d1dc8"] def save_csvs(prices, global_hash_rate, n_trials, user_positions, file_suffix): pd.DataFrame({'price': prices, 'hashrate': global_hash_rate, 'trials': n_trials}).to_csv(f"plots/{file_suffix}/env_values_{file_suffix}.csv", index = False) user_positions.to_csv(f"plots/{file_suffix}/user_values_{file_suffix}.csv", index = False) def get_environment_plots(prices, global_hash_rate, n_trials, title_suffix): price_fig = update_layout_wrapper(px.line(x = list(range(len(prices))), y = prices, labels = {"y": "Price (USD)", "x": "Day"}, title = f"Simulated Bitcoin Price over {n_trials} Trials {title_suffix}", color_discrete_sequence = primary_color, width=1600, height=900)) hashrate_fig = update_layout_wrapper(px.line(x = list(range(len(global_hash_rate))), y = global_hash_rate, labels = {"y": "Hash Rate (EH/s)", "x": "Day"}, title = f"Simulated Bitcoin Network Hash Rate over {n_trials} Trials {title_suffix}", color_discrete_sequence = primary_color, width=1600, height=900)) return (price_fig, hashrate_fig) def get_user_plots(user_positions, n_trials, title_suffix, elec_cost, palette): user_positions_e_c = user_positions.loc[user_positions.elec_cost == elec_cost] long_btc_fig = update_layout_wrapper(px.line(user_positions_e_c.loc[user_positions_e_c.strategy == constants.Strategy.LONG_BTC.value].sort_values(by=['day']), x = "day", y = "total_position_usd", color = "machine_type", labels = {"total_position_usd": "Simulated Position (USD)", "day": "Day", "machine_type": "Machine Type "}, title = f"Simulated Position Value over {n_trials} Trials {title_suffix}, Long BTC, ${elec_cost} per kWh", color_discrete_sequence = palette, width=1600, height=900)) sell_daily_fig = update_layout_wrapper(px.line(user_positions_e_c.loc[user_positions_e_c.strategy == constants.Strategy.SELL_DAILY.value].sort_values(by=['day']), x = "day", y = "total_position_usd", color = "machine_type", labels = {"total_position_usd": "Simulated Position (USD)", "day": "Day", "machine_type": "Machine Type "}, title = f"Simulated Position Value over {n_trials} Trials {title_suffix}, Selling Daily, ${elec_cost} per kWh", color_discrete_sequence = palette, width=1600, height=900)) return (long_btc_fig, sell_daily_fig) def get_summary_plots(price_params, fee_params, block_subsidy, n_trials, title_suffix, file_suffix, user_machine_prices = config.machine_prices, elec_costs = [0.04, 0.07], palette = my_palette): init_prices = PriceGenerator(price_params).generate_prices() user_miners_long_btc, user_miners_sell_daily = UserMinerGenerator().generate_user_miners(machine_prices = user_machine_prices, elec_costs = elec_costs) env_miners = MinerGenerator().generate_miner_distribution() sim = Simulator(env_miners = env_miners, user_miners_long_btc = user_miners_long_btc, user_miners_sell_daily = user_miners_sell_daily, prices = init_prices, price_params = price_params, fee_params = fee_params, block_subsidy = block_subsidy) sim.run_simulation_n_trials(n_trials) user_positions = sim.get_avg_user_positions() prices = sim.get_avg_prices() global_hash_rate = sim.get_avg_global_hash_rate() price_fig, hashrate_fig = get_environment_plots(prices, global_hash_rate, n_trials, title_suffix) price_fig.write_image(f"plots/{file_suffix}/price_plot_{file_suffix}.png", scale=8) hashrate_fig.write_image(f"plots/{file_suffix}/hashrate_plot_{file_suffix}.png", scale=8) for elec_cost in user_positions.elec_cost.unique(): user_figs = get_user_plots(user_positions, n_trials, title_suffix, elec_cost, palette) user_figs[0].write_image(f"plots/{file_suffix}/long_btc_plot_{file_suffix}_{int(elec_cost * 100)}.png", scale=8) user_figs[1].write_image(f"plots/{file_suffix}/sell_daily_plot_{file_suffix}_{int(elec_cost * 100)}.png", scale=8) save_csvs(prices, global_hash_rate, n_trials, user_positions, file_suffix) def get_user_opex_plots(user_positions, n_trials, title_suffix, machine_type, palette): user_positions_m_t = user_positions.loc[user_positions.machine_type == machine_type.value] long_btc_fig = update_layout_wrapper(px.line(user_positions_m_t.loc[user_positions_m_t.strategy == constants.Strategy.LONG_BTC.value].sort_values(by=['day']), x = "day", y = "total_position_usd", color = "elec_cost", labels = {"total_position_usd": "Simulated Position (USD)", "day": "Day", "elec_cost": "Electricity Cost (USD/kWh) "}, title = f"Simulated Position Value over {n_trials} Trials using {machine_type.value} {title_suffix}, Long BTC", color_discrete_sequence = palette, width=1600, height=900)) sell_daily_fig = update_layout_wrapper(px.line(user_positions_m_t.loc[user_positions_m_t.strategy == constants.Strategy.SELL_DAILY.value].sort_values(by=['day']), x = "day", y = "total_position_usd", color = "elec_cost", labels = {"total_position_usd": "Simulated Position (USD)", "day": "Day", "elec_cost": "Electricity Cost (USD/kWh) "}, title = f"Simulated Position Value over {n_trials} Trials using {machine_type.value} {title_suffix}, Selling Daily", color_discrete_sequence = palette, width=1600, height=900)) return (long_btc_fig, sell_daily_fig) def get_summary_plots_opex(price_params, fee_params, block_subsidy, n_trials, title_suffix, file_suffix, user_machine_prices = config.machine_prices, elec_costs = [0.04, 0.07], palette = opex_palette): init_prices = PriceGenerator(price_params).generate_prices() user_miners_long_btc, user_miners_sell_daily = UserMinerGenerator().generate_user_miners(machine_prices = user_machine_prices, elec_costs = elec_costs) env_miners = MinerGenerator().generate_miner_distribution() sim = Simulator(env_miners = env_miners, user_miners_long_btc = user_miners_long_btc, user_miners_sell_daily = user_miners_sell_daily, prices = init_prices, price_params = price_params, fee_params = fee_params, block_subsidy = block_subsidy) sim.run_simulation_n_trials(n_trials) user_positions = sim.get_avg_user_positions() prices = sim.get_avg_prices() global_hash_rate = sim.get_avg_global_hash_rate() price_fig, hashrate_fig = get_environment_plots(prices, global_hash_rate, n_trials, title_suffix) price_fig.write_image(f"plots/{file_suffix}/price_plot_{file_suffix}.png", scale=8) hashrate_fig.write_image(f"plots/{file_suffix}/hashrate_plot_{file_suffix}.png", scale=8) for machine_type in user_machine_prices: user_figs = get_user_opex_plots(user_positions, n_trials, title_suffix, machine_type, palette) user_figs[0].write_image(f"plots/{file_suffix}/long_btc_plot_{file_suffix}_{machine_type.value}.png", scale=8) user_figs[1].write_image(f"plots/{file_suffix}/sell_daily_plot_{file_suffix}_{machine_type.value}.png", scale=8) save_csvs(prices, global_hash_rate, n_trials, user_positions, file_suffix) if __name__ == '__main__': random.seed(1032009) np.random.seed(1032009) n_trials = 25 fee_params = CMDataLoader.get_historical_fee_params() block_subsidy = 6.25 historical_price_params = CMDataLoader.get_historical_price_params() get_summary_plots(historical_price_params, fee_params, block_subsidy, n_trials, "with Historical Parameters", "historical") bearish_price_params = (historical_price_params[0], -1 * abs(historical_price_params[1]), historical_price_params[2]) get_summary_plots(bearish_price_params, fee_params, block_subsidy, n_trials, "with Bearish Parameters", "bearish") corrections_price_params = (historical_price_params[0], 0, historical_price_params[2] * 1.25) get_summary_plots(corrections_price_params, fee_params, block_subsidy, n_trials, "in Bull Market with Corrections", "corrections") s9_s19_prices = {key: config.machine_prices[key] for key in [constants.MachineName.ANTMINER_S9, constants.MachineName.ANTMINER_S19]} get_summary_plots(historical_price_params, fee_params, block_subsidy, n_trials, "with Historical Parameters", "historical-machines", s9_s19_prices, [0.03], hardware_palette) get_summary_plots_opex(bearish_price_params, fee_params, block_subsidy, n_trials, "with Bearish Parameters", "bearish-opex", s9_s19_prices, [0.03, 0.04, 0.05], opex_palette)

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0.147081

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0.028086

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0.764352

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0

null

0

1

0

null

0

5

045d818a85f7e417a15d81255c6e0bba62cc720a

26

py

Python

json_dbindex/models.py

peopledoc/django-json-dbindex

5fb497b22d185e349ac9485bd6217c5bccb295c4

[ "BSD-3-Clause" ]

3

2015-04-19T11:56:53.000Z

2016-07-07T19:38:31.000Z

json_dbindex/models.py

peopledoc/django-json-dbindex

5fb497b22d185e349ac9485bd6217c5bccb295c4

[ "BSD-3-Clause" ]

5

2015-04-01T14:51:06.000Z

2016-09-15T14:22:06.000Z

json_dbindex/models.py

peopledoc/django-json-dbindex

5fb497b22d185e349ac9485bd6217c5bccb295c4

[ "BSD-3-Clause" ]

1

2015-10-26T14:04:29.000Z

# No models for this apps

13

25

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1

0

null

0

1

0

5

f095f83d0fb998742b62734022052168214a7e83

87

py

Python

classroom/admin.py

HelloYeew/ta_assistant_django

c72af9ae260c917d4835892811240894602ac454

[ "MIT" ]

null

classroom/admin.py

HelloYeew/ta_assistant_django

c72af9ae260c917d4835892811240894602ac454

[ "MIT" ]

null

classroom/admin.py

HelloYeew/ta_assistant_django

c72af9ae260c917d4835892811240894602ac454

[ "MIT" ]

null

from django.contrib import admin from .models import Class admin.site.register(Class)

17.4

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87

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5

f0b335de65dfd9a6e266026338baff29e86c20da

7,078

py

Python

a10sdk/core/cgnv6/cgnv6_ddos_protection.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

16

2015-05-20T07:26:30.000Z

2021-01-23T11:56:57.000Z

a10sdk/core/cgnv6/cgnv6_ddos_protection.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

6

2015-03-24T22:07:11.000Z

2017-03-28T21:31:18.000Z

a10sdk/core/cgnv6/cgnv6_ddos_protection.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

23

2015-03-29T15:43:01.000Z

2021-06-02T17:12:01.000Z

from a10sdk.common.A10BaseClass import A10BaseClass class PacketsPerSecond(A10BaseClass): """This class does not support CRUD Operations please use parent. :param ip: {"description": "Configure packets-per-second threshold per IP(default 3000)", "format": "number", "default": 3000, "maximum": 30000000, "minimum": 0, "type": "number"} :param udp: {"description": "Configure packets-per-second threshold per UDP port (default: 3000)", "format": "number", "default": 3000, "maximum": 30000000, "minimum": 0, "type": "number"} :param other: {"description": "Configure packets-per-second threshold for other L4 protocols(default 10000)", "format": "number", "default": 10000, "maximum": 30000000, "minimum": 0, "type": "number"} :param tcp: {"description": "Configure packets-per-second threshold per TCP port (default: 3000)", "format": "number", "default": 3000, "maximum": 30000000, "minimum": 0, "type": "number"} :param DeviceProxy: The device proxy for REST operations and session handling. Refer to `common/device_proxy.py` """ def __init__(self, **kwargs): self.ERROR_MSG = "" self.b_key = "packets-per-second" self.DeviceProxy = "" self.ip = "" self.udp = "" self.other = "" self.tcp = "" for keys, value in kwargs.items(): setattr(self,keys, value) class Logging(A10BaseClass): """This class does not support CRUD Operations please use parent. :param logging_toggle: {"default": "enable", "enum": ["enable", "disable"], "type": "string", "description": "'enable': Enable CGNV6 NAT pool DDoS protection logging (default); 'disable': Disable CGNV6 NAT pool DDoS protection logging; ", "format": "enum"} :param DeviceProxy: The device proxy for REST operations and session handling. Refer to `common/device_proxy.py` """ def __init__(self, **kwargs): self.ERROR_MSG = "" self.b_key = "logging" self.DeviceProxy = "" self.logging_toggle = "" for keys, value in kwargs.items(): setattr(self,keys, value) class SamplingEnable(A10BaseClass): """This class does not support CRUD Operations please use parent. :param counters1: {"enum": ["all", "entry_added", "entry_deleted", "entry_added_to_hw", "entry_removed_from_hw", "hw_out_of_entries", "entry_match_drop", "entry_match_drop_hw", "entry_list_alloc", "entry_list_free", "entry_list_alloc_failure", "ip_node_alloc", "ip_node_free", "ip_node_alloc_failure", "ip_port_block_alloc", "ip_port_block_free", "ip_port_block_alloc_failure", "ip_other_block_alloc", "ip_other_block_free", "ip_other_block_alloc_failure", "entry_added_shadow", "entry_invalidated"], "type": "string", "description": "'all': all; 'entry_added': entry_added; 'entry_deleted': entry_deleted; 'entry_added_to_hw': entry_added_to_hw; 'entry_removed_from_hw': entry_removed_from_hw; 'hw_out_of_entries': hw_out_of_entries; 'entry_match_drop': entry_match_drop; 'entry_match_drop_hw': entry_match_drop_hw; 'entry_list_alloc': entry_list_alloc; 'entry_list_free': entry_list_free; 'entry_list_alloc_failure': entry_list_alloc_failure; 'ip_node_alloc': ip_node_alloc; 'ip_node_free': ip_node_free; 'ip_node_alloc_failure': ip_node_alloc_failure; 'ip_port_block_alloc': ip_port_block_alloc; 'ip_port_block_free': ip_port_block_free; 'ip_port_block_alloc_failure': ip_port_block_alloc_failure; 'ip_other_block_alloc': ip_other_block_alloc; 'ip_other_block_free': ip_other_block_free; 'ip_other_block_alloc_failure': ip_other_block_alloc_failure; 'entry_added_shadow': entry_added_shadow; 'entry_invalidated': entry_invalidated; ", "format": "enum"} :param DeviceProxy: The device proxy for REST operations and session handling. Refer to `common/device_proxy.py` """ def __init__(self, **kwargs): self.ERROR_MSG = "" self.b_key = "sampling-enable" self.DeviceProxy = "" self.counters1 = "" for keys, value in kwargs.items(): setattr(self,keys, value) class DdosProtection(A10BaseClass): """ :param toggle: {"description": "'enable': Enable CGNV6 NAT pool DDoS protection (default); 'disable': Disable CGNV6 NAT pool DDoS protection; ", "format": "enum", "default": "enable", "type": "string", "enum": ["enable", "disable"], "optional": true} :param uuid: {"description": "uuid of the object", "format": "string", "minLength": 1, "modify-not-allowed": 1, "optional": true, "maxLength": 64, "type": "string"} :param sampling_enable: {"minItems": 1, "items": {"type": "object"}, "uniqueItems": true, "type": "array", "array": [{"properties": {"optional": true, "counters1": {"enum": ["all", "entry_added", "entry_deleted", "entry_added_to_hw", "entry_removed_from_hw", "hw_out_of_entries", "entry_match_drop", "entry_match_drop_hw", "entry_list_alloc", "entry_list_free", "entry_list_alloc_failure", "ip_node_alloc", "ip_node_free", "ip_node_alloc_failure", "ip_port_block_alloc", "ip_port_block_free", "ip_port_block_alloc_failure", "ip_other_block_alloc", "ip_other_block_free", "ip_other_block_alloc_failure", "entry_added_shadow", "entry_invalidated"], "type": "string", "description": "'all': all; 'entry_added': entry_added; 'entry_deleted': entry_deleted; 'entry_added_to_hw': entry_added_to_hw; 'entry_removed_from_hw': entry_removed_from_hw; 'hw_out_of_entries': hw_out_of_entries; 'entry_match_drop': entry_match_drop; 'entry_match_drop_hw': entry_match_drop_hw; 'entry_list_alloc': entry_list_alloc; 'entry_list_free': entry_list_free; 'entry_list_alloc_failure': entry_list_alloc_failure; 'ip_node_alloc': ip_node_alloc; 'ip_node_free': ip_node_free; 'ip_node_alloc_failure': ip_node_alloc_failure; 'ip_port_block_alloc': ip_port_block_alloc; 'ip_port_block_free': ip_port_block_free; 'ip_port_block_alloc_failure': ip_port_block_alloc_failure; 'ip_other_block_alloc': ip_other_block_alloc; 'ip_other_block_free': ip_other_block_free; 'ip_other_block_alloc_failure': ip_other_block_alloc_failure; 'entry_added_shadow': entry_added_shadow; 'entry_invalidated': entry_invalidated; ", "format": "enum"}}}]} :param DeviceProxy: The device proxy for REST operations and session handling. Refer to `common/device_proxy.py` Class Description:: Configure CGNV6 DDoS Protection. Class ddos-protection supports CRUD Operations and inherits from `common/A10BaseClass`. This class is the `"PARENT"` class for this module.` URL for this object:: `https://<Hostname|Ip address>//axapi/v3/cgnv6/ddos-protection`. """ def __init__(self, **kwargs): self.ERROR_MSG = "" self.required=[] self.b_key = "ddos-protection" self.a10_url="/axapi/v3/cgnv6/ddos-protection" self.DeviceProxy = "" self.packets_per_second = {} self.toggle = "" self.logging = {} self.uuid = "" self.sampling_enable = [] for keys, value in kwargs.items(): setattr(self,keys, value)

62.087719

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0.132979

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0.053938

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0

1

0

1

0

1

0

null

0

5

f0cf54098ed541ac733e9342aca4e178b689ee5d

166

py

Python

graphormer/data/__init__.py

shawnwang-tech/Graphormer

49286ac8093dcc165076c2a6cd1a5380749a48a7

[ "MIT" ]

858

2021-06-12T14:50:56.000Z

2022-03-31T18:56:05.000Z

graphormer/data/__init__.py

ericdoug-qi/Graphormer

2e48f3fb52d25d505d0950f74a6016c5f9967c13

[ "MIT" ]

77

2021-06-16T21:49:45.000Z

2022-03-31T06:27:40.000Z

graphormer/data/__init__.py

ericdoug-qi/Graphormer

2e48f3fb52d25d505d0950f74a6016c5f9967c13

[ "MIT" ]

150

2021-06-12T15:11:42.000Z

2022-03-30T13:34:59.000Z

DATASET_REGISTRY = {} def register_dataset(name: str): def register_dataset_func(func): DATASET_REGISTRY[name] = func() return register_dataset_func

23.714286

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0.728916

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0.394737

0.315789

0

0.180723

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0.4

false

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0

null

1

0

1

0

null

0

1

0

5

f0e74c38028faeaf907ae03a7d395847719222f3

123

py

Python

gears/compressors/__init__.py

gears/gears

5729c2525a8c04c185e998bd9a86233708972921

[ "0BSD" ]

9

2015-03-23T15:34:04.000Z

2021-03-19T03:03:48.000Z

gears/compressors/__init__.py

gears/gears

5729c2525a8c04c185e998bd9a86233708972921

[ "0BSD" ]

2

2015-08-31T03:19:27.000Z

2016-01-20T09:54:01.000Z

gears/compressors/__init__.py

gears/gears

5729c2525a8c04c185e998bd9a86233708972921

[ "0BSD" ]

3

2015-02-01T06:21:24.000Z

2015-07-30T02:31:31.000Z

from .base import BaseCompressor, ExecCompressor from .cssmin import CSSMinCompressor from .slimit import SlimItCompressor

30.75

48

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123

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123

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true

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null

0

1

0

1

0

1

0

5

0b0ed3365bb8d7ffd3d84d792e2c43f7bc00ba2e

189

py

Python

pythonexercicios/ex008-mtr-cent-mil.py

marroni1103/exercicios-pyton

734162cc4b63ed30d754a6efe4c5622baaa1a50b

[ "MIT" ]

null

pythonexercicios/ex008-mtr-cent-mil.py

marroni1103/exercicios-pyton

734162cc4b63ed30d754a6efe4c5622baaa1a50b

[ "MIT" ]

null

pythonexercicios/ex008-mtr-cent-mil.py

marroni1103/exercicios-pyton

734162cc4b63ed30d754a6efe4c5622baaa1a50b

[ "MIT" ]

null

m = float(input('Informe os metros: ')) print(f'{m} metros equivale a: \n{m*0.001}km\n{m*0.01}hm\n{m*0.1:.1f}dam\n{m*10:.0f}dm\n{m*100:.0f}cm\n{m*1000:.0f}mm') #km, hm, dam, m, dm, cm, mm

37.8

119

0.597884

50

189

2.26

0.52

0.106195

0.079646

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0.129412

0.100529

189

5

120

37.8

0.535294

0.137566

0

0.5

0.785276

0.527607

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1

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false

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0.5

0

null

0

1

0

1

null

0

1

0

5

9bc9fbb641a409c467750b8954175089dcd45894

83

py

Python

tests/tests/base_tests/__init__.py

zhouhanjiang/aws-device-farm-appium-python-tests-for-android-sample-app

d43b892baf5cc202732a59967ec258cbafef3c37

[ "Apache-2.0" ]

null

tests/tests/base_tests/__init__.py

zhouhanjiang/aws-device-farm-appium-python-tests-for-android-sample-app

d43b892baf5cc202732a59967ec258cbafef3c37

[ "Apache-2.0" ]

null

tests/tests/base_tests/__init__.py

zhouhanjiang/aws-device-farm-appium-python-tests-for-android-sample-app

d43b892baf5cc202732a59967ec258cbafef3c37

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from native_test import NativeTest

13.833333

34

0.674699

12

83

4.583333

1

0

0.014286

0.156627

83

5

35

16.6

0.771429

0.506024

0

1

0

true

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null

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1

0

null

0

1

0

1

0

1

0

5

50207f9b9e42811e1d80f379d8051c0e63891e18

81

py

Python

brainda/algorithms/deep_learning/__init__.py

Mrswolf/brainda

cbd2fa6334d9e6243324dbaf086be4eb4047e801

[ "MIT" ]

24

2021-03-05T14:33:43.000Z

2022-03-20T01:23:40.000Z

brainda/algorithms/deep_learning/__init__.py

ccc65535/brainda

366a1288bc0f1b835f78fe8dd6c53bcde631c1a5

[ "MIT" ]

2

2021-03-10T05:34:05.000Z

2021-12-16T05:22:18.000Z

brainda/algorithms/deep_learning/__init__.py

ccc65535/brainda

366a1288bc0f1b835f78fe8dd6c53bcde631c1a5

[ "MIT" ]

4

2021-04-02T12:33:04.000Z

2022-03-03T01:38:05.000Z

from .base import * from .eegnet import EEGNet from .shallownet import ShallowNet

27

34

0.814815

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81

6

0.454545

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1

0

1

0

1

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5

ac955887490ebdf12f0d48dc349f3c103239ddea

553

py

Python

projectreport/__init__.py

whoopnip/project-report

b08e78fd864ebd7f48443b2d58c89661c1adcab7

[ "MIT" ]

null

projectreport/__init__.py

whoopnip/project-report

b08e78fd864ebd7f48443b2d58c89661c1adcab7

[ "MIT" ]

null

projectreport/__init__.py

whoopnip/project-report

b08e78fd864ebd7f48443b2d58c89661c1adcab7

[ "MIT" ]

null

""" A set of tools for describing software projects. Finds software projects, analyzes them, and outputs reports. """ from projectreport.analyzer.project import Project from projectreport.analyzer.ts.github import GithubAnalysis from projectreport.config import DEFAULT_IGNORE_PATHS from projectreport.finder.combine import CombinedFinder from projectreport.finder.git import GitFinder from projectreport.finder.js import JavaScriptPackageFinder from projectreport.finder.python import PythonPackageFinder from projectreport.report.report import Report

42.538462

88

0.862568

66

553

7.19697

0.545455

0.286316

0.193684

0

0.090416

553

12

89

46.083333

0.944334

0.197107

0

1

0

true

0

1

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1

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1

0

null

0

1

0

1

0

1

0

5

ac9d90119133c88f80ee7a88fbb57a4aa23edebc

326

py

Python

franki/exceptions.py

cr0hn/franki

f375ad9f5f4fc233fc007242076d15063e754b2b

[ "BSD-3-Clause" ]

1

2020-08-08T11:57:12.000Z

franki/exceptions.py

cr0hn/franki

f375ad9f5f4fc233fc007242076d15063e754b2b

[ "BSD-3-Clause" ]

2

2020-07-20T22:39:30.000Z

2021-09-02T12:00:29.000Z

franki/exceptions.py

cr0hn/franki

f375ad9f5f4fc233fc007242076d15063e754b2b

[ "BSD-3-Clause" ]

1

2020-08-08T11:57:14.000Z

class FrankiException(Exception): pass class FrankiInvalidFormatException(Exception): pass class FrankiFileNotFound(Exception): pass class FrankiInvalidFileFormat(Exception): pass __all__ = ("FrankiInvalidFormatException", "FrankiFileNotFound", "FrankiInvalidFileFormat", "FrankiException")

17.157895

64

0.757669

21

326

11.571429

0.380952

0.213992

0.222222

0

0.162577

326

18

65

18.111111

0.89011

0

0.4

0

0.257669

0.156442

0

1

0

false

0.4

0

0.4

0

1

0

1

null

1

0

null

0

1

0

5

acc1f5d57300368832fd5db3bd81bb55024f950b

200

py

Python

GPyOpt/util/stats.py

zhenwendai/GPyOpt

fd96875e7ec0cb0f78014d96813ece400648827d

[ "BSD-3-Clause" ]

850

2015-05-31T21:12:41.000Z

2022-03-24T17:25:37.000Z

GPyOpt/util/stats.py

lakshaykc/GPyOpt

097ba66e81c7e22b5bf9fdbe64fd135753bc4a67

[ "BSD-3-Clause" ]

340

2015-09-10T14:08:06.000Z

2022-03-28T20:35:26.000Z

GPyOpt/util/stats.py

lakshaykc/GPyOpt

097ba66e81c7e22b5bf9fdbe64fd135753bc4a67

[ "BSD-3-Clause" ]

299

2015-07-30T13:18:37.000Z

2022-03-22T21:27:31.000Z

33.333333

78

0.79

29

200

5.37931

0.896552

0

0.028902

0.135

200

5

79

40

0.872832

0.865

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

acff259283e1eec5e8740c189d715e031471ec8c

1,613

py

Python

SR/model/SRCNN.py

AntonyYX/Super-Resolution

9a5a55169b08849be39a42f0ee955feb60527fbf

[ "MIT" ]

null

SR/model/SRCNN.py

AntonyYX/Super-Resolution

9a5a55169b08849be39a42f0ee955feb60527fbf

[ "MIT" ]

null

SR/model/SRCNN.py

AntonyYX/Super-Resolution

9a5a55169b08849be39a42f0ee955feb60527fbf

[ "MIT" ]

1

2021-10-02T11:03:49.000Z

import math from torch import nn import torch from torch.nn.modules.activation import ReLU from torchvision import transforms from PIL import Image class SRCNN(nn.Module): def __init__(self, in_channel: int = 3): super(SRCNN, self).__init__() self.body = nn.Sequential( nn.Conv2d(in_channels=in_channel, out_channels=64, kernel_size=9, padding=9//2), nn.ReLU(True), nn.Conv2d(in_channels=64, out_channels=32, kernel_size=5, padding=5//2), nn.ReLU(True), nn.Conv2d(in_channels=32, out_channels=in_channel, kernel_size=5, padding=5//2), nn.ReLU(True), ) def forward(self, inputs): return self.body(inputs) class SRCNN_BN(nn.Module): def __init__(self, in_channel: int = 3): super(SRCNN_BN, self).__init__() self.body = nn.Sequential( nn.Conv2d(in_channels=in_channel, out_channels=64, kernel_size=9, padding=9//2), nn.ReLU(True), nn.BatchNorm2d(64), nn.Conv2d(in_channels=64, out_channels=32, kernel_size=5, padding=5//2), nn.ReLU(True), nn.BatchNorm2d(32), nn.Conv2d(in_channels=32, out_channels=in_channel, kernel_size=5, padding=5//2), nn.ReLU(True), ) def forward(self, inputs): return self.body(inputs) if __name__ == "__main__": model = SRCNN_BN(3) img = torch.rand((1, 3, 600, 600)) print(model(img).shape)

29.87037

62

0.568506

209

1,613

4.143541

0.248804

0.062356

0.069284

0.124711

0.752887

0.727483

0

0.051444

0.313081

1,613

53

63

30.433962

0.730144

0

0.590909

0

0.00496

0

1

0.090909

false

0

0.136364

0.045455

0.318182

0.022727

0

null

0

1

0

null

0

5

4a1d3fa2d343712d70873e8247660aa7840c2930

58

py

Python

tests/package1/subpackage1/__init__.py

sizrailev/py2reqs

f09f8b808b310c27860a273660dedd50d3c7bea3

[ "MIT" ]

null

tests/package1/subpackage1/__init__.py

sizrailev/py2reqs

f09f8b808b310c27860a273660dedd50d3c7bea3

[ "MIT" ]

null

tests/package1/subpackage1/__init__.py

sizrailev/py2reqs

f09f8b808b310c27860a273660dedd50d3c7bea3

[ "MIT" ]

null

from .module3 import foo3 as bar3 def foo(): bar3()

9.666667

33

0.637931

9

58

4.111111

0.888889

0

0.093023

0.258621

58

5

34

11.6

0.767442

0

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c5857b7f958ed44156d7ace0958ab3175a5d143a

119

py

Python

dsa_stl/testdsa.py

aman2000jaiswal14/dsa_stl

925485913a783ac7dfa8c59e30b24e5be3f76a2e

[ "MIT" ]

null

dsa_stl/testdsa.py

aman2000jaiswal14/dsa_stl

925485913a783ac7dfa8c59e30b24e5be3f76a2e

[ "MIT" ]

null

dsa_stl/testdsa.py

aman2000jaiswal14/dsa_stl

925485913a783ac7dfa8c59e30b24e5be3f76a2e

[ "MIT" ]

null

def test(): print("test successful...") def update(): print("Updating DSA") if __name__=='__main__': pass

14.875

31

0.613445

14

119

4.642857

0.785714

0

0.201681

119

8

32

14.875

0.684211

0

0.316667

0

1

0.333333

true

0.166667

0

0.333333

1

0

null

0

1

0

null

0

1

0

5

c5a0a8324ab48013a779c91b647b3bd3ef2863a0

25,437

py

Python

consultantform/forms.py

rajeshgupta14/pathscriptfinal

1a0b933d00b902588dfe30b9bea62c3e0c7ec4a2

[ "Apache-2.0" ]

null

consultantform/forms.py

rajeshgupta14/pathscriptfinal

1a0b933d00b902588dfe30b9bea62c3e0c7ec4a2

[ "Apache-2.0" ]

null

consultantform/forms.py

rajeshgupta14/pathscriptfinal

1a0b933d00b902588dfe30b9bea62c3e0c7ec4a2

[ "Apache-2.0" ]

null

from django import forms from consultantform.models import Relatedcompany,Article,Backgroundcheck,Backgroundcheckb, Problemsolving, Problemsolvingp, Digitalization, Digitalizationp, Miom, Miomp, Duediligence, Script, Strategy,Duediligencep, Scriptp, Strategyp, Branch,Subsidiary from myapp.models import Project,Client,User,Product from django.utils.translation import ugettext_lazy as _ class ArticleForm(forms.ModelForm):#kyc form founding_date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Article fields = ('founding_date','headquarter_location', 'areas_served','no_of_employees','type_of_company','type_of_industry','type_of_activity','warehouse_addresses', 'factory_addresses','number_of_owners_and_officers','officers_and_roles', 'registered_address','telephone','email','website', 'services_opted','upload_Doc1','upload_Doc2','upload_Doc3','upload_Doc4','upload_Doc5','upload_Doc6','upload_Doc7','upload_Doc8','upload_Doc9','upload_Doc10','upload_Doc11','upload_Doc12','notes') labels = { 'services_opted' : _('Services Opted (Hold Ctrl + select for alternate choices, Hold Shift + select for continuous choices)'), } def __init__(self,request,*args, **kwargs): super(ArticleForm, self).__init__(*args, **kwargs) # self.fields['company_name'].queryset = Client.objects.filter( # userid=request.user.id) class BranchForm(forms.ModelForm):#branch form branch_founding_date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Branch fields = ( 'branch_name','branch_founding_date','branch_location', 'areas_served_by_branch','no_of_employees_in_branch','type_of_business_by_branch', 'number_of_owners_and_officers_in_branch','officers_and_roles_in_branch', 'branch_registered_address','branch_telephone','branch_email','branch_website','upload_Doc1','upload_Doc2','upload_Doc3','upload_Doc4','upload_Doc5') def __init__(self,request,*args, **kwargs): super(BranchForm, self).__init__(*args, **kwargs) #self.fields['company_name'].queryset = Client.objects.filter( # userid=request.user.id) class SubsidiaryForm(forms.ModelForm): subsidiary_founding_date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Subsidiary fields = ( 'subsidiary_name','subsidiary_founding_date','subsidiary_location', 'areas_served_by_subsidiary','no_of_employees_in_subsidiary','type_of_business_by_subsidiary', 'subsidiary_warehouse_addresses','subsidiary_factory_addresses','number_of_owners_and_officers_in_subsidiary','officers_and_roles_in_subsidiary', 'subsidiary_registered_address','subsidiary_telephone','subsidiary_email','subsidiary_website','upload_Doc1','upload_Doc2','upload_Doc3','upload_Doc4','upload_Doc5') def __init__(self,request,*args, **kwargs): super(SubsidiaryForm, self).__init__(*args, **kwargs) #self.fields['company_name'].queryset = Client.objects.filter( # userid=request.user.id) class RelatedcompanyForm(forms.ModelForm): class Meta: model = Relatedcompany fields = ( 'related_company_name','relation','related_company_registered_address','related_company_telephone','related_company_email','related_company_website','upload_Doc1','upload_Doc2','upload_Doc3','upload_Doc4','upload_Doc5','upload_Doc6','upload_Doc7') def __init__(self,request,*args, **kwargs): super(RelatedcompanyForm, self).__init__(*args, **kwargs) #self.fields['company_name'].queryset = Client.objects.filter( # userid=request.user.id) class BackgroundcheckForm(forms.ModelForm): class Meta: model = Backgroundcheck fields = ('c1','rOC_Certificates','c2','mOA','c3', 'current_List_of_Directors_including_Photo_ID','c4', 'term_Sheets','c5','current_Bankers_and_Auditors_and_Company_Secretary','c6', 'sales_Tax_Registration_Certificate','c7','last_Filed_Sales_Tax_Certificate','c8', 'municipal_Certificate','c9','last_2_years_Audited_Books_of_Accounts','c10', 'last_Paid_Tax_Receipt','c11','employee_List_Statement','c12', 'last_Provident_Fund_Receipt','c13','list_of_Competitors') labels = { 'company_name': _('Company Name'), 'c1': _('Currently with'), 'rOC_Certificates': _('ROC Certificates'), 'c2': _('Currently with'), 'mOA': _('MOA'), 'c3': _('Currently with'), 'current_List_of_Directors_including_Photo_ID': _('Current List of Directors including Photo ID'), 'c4': _('Currently with'), 'term_Sheets': _('Term Sheets'), 'c5': _('Currently with'), 'current_Bankers_and_Auditors_and_Company_Secretary': _('Current Bankers, Auditors and Company Secretary'), 'c6': _('Currently with'), 'sales_Tax_Registration_Certificate': _('Sales Tax Registration Certificate'), 'c7': _('Currently with'), 'last_Filed_Sales_Tax_Certificate': _('Last Filed Sales Tax Certificate'), 'c8': _('Currently with'), 'municipal_Certificate': _('Municipal Certificate'), 'c9': _('Currently with'), 'last_2_years_Audited_Books_of_Accounts': _('Last 2 years Audited Books of Accounts'), 'c10': _('Currently with'), 'last_Paid_Tax_Receipt': _('Last Paid Tax Receipt'), 'c11': _('Currently with'), 'employee_List_Statement': _('Employee List Statement'), 'c12': _('Currently with'), 'last_Provident_Fund_Receipt': _('last Provident Fund Receipt'), 'c13': _('Currently with'), 'list_of_Competitors': _('List of Competitors'), } def __init__(self,request,*args, **kwargs): super(BackgroundcheckForm, self).__init__(*args, **kwargs) #self.fields['company_name'].queryset = Client.objects.filter(userid=request.user.id) self.fields['c1'].queryset = User.objects.filter(is_staff=True) self.fields['c2'].queryset = User.objects.filter(is_staff=True) self.fields['c3'].queryset = User.objects.filter(is_staff=True) self.fields['c4'].queryset = User.objects.filter(is_staff=True) self.fields['c5'].queryset = User.objects.filter(is_staff=True) self.fields['c6'].queryset = User.objects.filter(is_staff=True) self.fields['c7'].queryset = User.objects.filter(is_staff=True) self.fields['c8'].queryset = User.objects.filter(is_staff=True) self.fields['c9'].queryset = User.objects.filter(is_staff=True) self.fields['c10'].queryset = User.objects.filter(is_staff=True) self.fields['c11'].queryset = User.objects.filter(is_staff=True) self.fields['c12'].queryset = User.objects.filter(is_staff=True) self.fields['c13'].queryset = User.objects.filter(is_staff=True) class BackgroundcheckbForm(forms.ModelForm): class Meta: model = Backgroundcheckb fields = ('c1','rOC_Certificates','c2','mOA','c3', 'current_List_of_Directors_including_Photo_ID','c4', 'term_Sheets','c5','current_Bankers_and_Auditors_and_Company_Secretary','c6', 'sales_Tax_Registration_Certificate','c7','last_Filed_Sales_Tax_Certificate','c8', 'municipal_Certificate','c9','last_2_years_Audited_Books_of_Accounts','c10', 'last_Paid_Tax_Receipt','c11','employee_List_Statement','c12', 'last_Provident_Fund_Receipt','c13','list_of_Competitors') # self.fields['company_name'].queryset = Client.objects.filter(id=User.objects.get(clientid=request.user.clientid).clientid) class ProjectForm(forms.ModelForm):#create, view project form class Meta: model = Project fields=('name','client','product','user') def __init__(self,request,*args, **kwargs): super(ProjectForm, self).__init__(*args, **kwargs) self.fields['client'].queryset = Client.objects.filter( userid=request.user.id) class ProductForm(forms.ModelForm):#create , view product form class Meta: model = Product fields=('name','description','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(ProductForm, self).__init__(*args, **kwargs) class CustomerForm(forms.ModelForm):#kyc form in client view class Meta: model = Article fields = ('founding_date','headquarter_location', 'areas_served','no_of_employees','type_of_company','type_of_industry','type_of_activity','warehouse_addresses', 'factory_addresses','number_of_owners_and_officers','officers_and_roles', 'registered_address','telephone','email','website','upload_Doc1','upload_Doc2','upload_Doc3','upload_Doc4','upload_Doc5','upload_Doc6','upload_Doc7','upload_Doc8','upload_Doc9','upload_Doc10','upload_Doc11','upload_Doc12') def __init__(self,request,*args, **kwargs): super(CustomerForm, self).__init__(*args, **kwargs) #self.fields['company_name'].queryset = Client.objects.filter( # userid=request.user.id) class DuediligencetForm(forms.ModelForm):#duediligence create form date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Duediligence fields = ('date','version', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','what_is_the_key_need_you_are_providing_for_your_customer', 'evidences_that_show_need_stated_above_for_customer_is_fulfilled', 'what_are_some_of_the_aspects_you_are_facing_a_challenge_with','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(DuediligencetForm, self).__init__(*args, **kwargs) class DuediligenceForm(forms.ModelForm):#duediligence temporary form date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Duediligence fields = ('date', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','what_is_the_key_need_you_are_providing_for_your_customer', 'evidences_that_show_need_stated_above_for_customer_is_fulfilled', 'what_are_some_of_the_aspects_you_are_facing_a_challenge_with','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(DuediligenceForm, self).__init__(*args, **kwargs) #self.fields['project'].queryset = Project.objects.filter( #user=request.user.id) class DuediligencepForm(forms.ModelForm):#duediligence permanent date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Duediligencep fields = ('date', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','what_is_the_key_need_you_are_providing_for_your_customer', 'evidences_that_show_need_stated_above_for_customer_is_fulfilled', 'what_are_some_of_the_aspects_you_are_facing_a_challenge_with','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(DuediligencepForm, self).__init__(*args, **kwargs) #self.fields['project'].queryset = Project.objects.filter( #user=request.user.id) class ScripttForm(forms.ModelForm):#script create form date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Script fields = ('date','version', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','what_is_the_idea_you_are_looking_to_implement', 'why_do_you_think_that_the_idea_should_be_implemented','was_this_idea_previously_executed_and_if_yes_state_the_method', 'reasons_for_failure_of_previous_implementation_methods','other_methods_of_implementation_that_you_would_suggest', 'is_the_level_of_implementation_generic_or_specific','deadline_by_which_you_need_the_idea_to_be_implemented','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(ScripttForm, self).__init__(*args, **kwargs) #self.fields['project'].queryset = Project.objects.filter( # user=request.user.id) class ScriptForm(forms.ModelForm):#script temporary date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Script fields = ('date', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','what_is_the_idea_you_are_looking_to_implement', 'why_do_you_think_that_the_idea_should_be_implemented','was_this_idea_previously_executed_and_if_yes_state_the_method', 'reasons_for_failure_of_previous_implementation_methods','other_methods_of_implementation_that_you_would_suggest', 'is_the_level_of_implementation_generic_or_specific','deadline_by_which_you_need_the_idea_to_be_implemented','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(ScriptForm, self).__init__(*args, **kwargs) #self.fields['project'].queryset = Project.objects.filter( # user=request.user.id) class ScriptpForm(forms.ModelForm):#script permanent date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Scriptp fields = ('date', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','what_is_the_idea_you_are_looking_to_implement', 'why_do_you_think_that_the_idea_should_be_implemented','was_this_idea_previously_executed_and_if_yes_state_the_method', 'reasons_for_failure_of_previous_implementation_methods','other_methods_of_implementation_that_you_would_suggest', 'is_the_level_of_implementation_generic_or_specific','deadline_by_which_you_need_the_idea_to_be_implemented','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(ScriptpForm, self).__init__(*args, **kwargs) #self.fields['project'].queryset = Project.objects.filter( # user=request.user.id) class StrategytForm(forms.ModelForm):#strategy create date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Strategy fields = ('date','version', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','business_strategies_that_are_already_deployed_in_your_company', 'what_are_the_strategies_that_were_deployed_but_failed','limitations_of_previous_strategies', 'factors_to_be_considered_before_planning_new_strategies','deadline_by_which_strategy_needs_to_be_deployed','upload_Doc1','upload_Doc2','upload_Doc3','upload_Doc4','upload_Doc5') def __init__(self,request,*args, **kwargs): super(StrategytForm, self).__init__(*args, **kwargs) #self.fields['project'].queryset = Project.objects.filter( # user=request.user.id) class StrategyForm(forms.ModelForm):#strategy temporary date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Strategy fields = ('date', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','business_strategies_that_are_already_deployed_in_your_company', 'what_are_the_strategies_that_were_deployed_but_failed','limitations_of_previous_strategies', 'factors_to_be_considered_before_planning_new_strategies','deadline_by_which_strategy_needs_to_be_deployed','upload_Doc1','upload_Doc2','upload_Doc3','upload_Doc4','upload_Doc5') def __init__(self,request,*args, **kwargs): super(StrategyForm, self).__init__(*args, **kwargs) # self.fields['project'].queryset = Project.objects.filter( # user=request.user.id) class StrategypForm(forms.ModelForm):#strategy permanent date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Strategyp fields = ('date', 'please_select_the_growth_stage_of_your_company','please_tick_the_type_of_company', 'stock_exchange','ticker_ID','business_strategies_that_are_already_deployed_in_your_company', 'what_are_the_strategies_that_were_deployed_but_failed','limitations_of_previous_strategies', 'factors_to_be_considered_before_planning_new_strategies','deadline_by_which_strategy_needs_to_be_deployed','upload_Doc1','upload_Doc2','upload_Doc3','upload_Doc4','upload_Doc5') def __init__(self,request,*args, **kwargs): super(StrategypForm, self).__init__(*args, **kwargs) #self.fields['project'].queryset = Project.objects.filter( # user=request.user.id) class ProblemSolvingtForm(forms.ModelForm):#problem solving create date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Problemsolving fields = ('date','version', 'what_is_the_issue_that_needs_to_be_addressed','what_is_its_effect_on_the_company', 'researches_that_have_been_done_on_the_possible_solutions','what_are_the_solutions_that_have_already_been_tried', 'what_are_the_solutions_that_failed_and_the_reasons_for_failure','what_are_the_parameters_to_be_considered','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(ProblemSolvingtForm, self).__init__(*args, **kwargs) class ProblemSolvingForm(forms.ModelForm):#problem solving temporary date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Problemsolving fields = ('date', 'what_is_the_issue_that_needs_to_be_addressed','what_is_its_effect_on_the_company', 'researches_that_have_been_done_on_the_possible_solutions','what_are_the_solutions_that_have_already_been_tried', 'what_are_the_solutions_that_failed_and_the_reasons_for_failure','what_are_the_parameters_to_be_considered','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(ProblemSolvingForm, self).__init__(*args, **kwargs) class ProblemSolvingpForm(forms.ModelForm):#problem solving permanent date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Problemsolvingp fields = ('date', 'what_is_the_issue_that_needs_to_be_addressed','what_is_its_effect_on_the_company', 'researches_that_have_been_done_on_the_possible_solutions','what_are_the_solutions_that_have_already_been_tried', 'what_are_the_solutions_that_failed_and_the_reasons_for_failure','what_are_the_parameters_to_be_considered','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(ProblemSolvingpForm, self).__init__(*args, **kwargs) class DigitalizationtForm(forms.ModelForm):#digitalization create date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Digitalization fields = ('date','version', 'what_are_the_departments_that_need_to_be_digitalized_and_why', 'please_mention_if_they_are_new_or_preexisting_departments', 'what_is_the_budget_allocated_for_the_digitlization_process', 'the_priority_in_which_the_deaprtments_need_to_be_digitalized', 'what_are_the_limitations_that_need_to_be_considered','the_deadline_by_which_digitalization_needs_to_be_done','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(DigitalizationtForm, self).__init__(*args, **kwargs) class DigitalizationForm(forms.ModelForm):#digitalization temporary date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Digitalization fields = ('date', 'what_are_the_departments_that_need_to_be_digitalized_and_why', 'please_mention_if_they_are_new_or_preexisting_departments', 'what_is_the_budget_allocated_for_the_digitlization_process', 'the_priority_in_which_the_deaprtments_need_to_be_digitalized', 'what_are_the_limitations_that_need_to_be_considered','the_deadline_by_which_digitalization_needs_to_be_done','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(DigitalizationForm, self).__init__(*args, **kwargs) class DigitalizationpForm(forms.ModelForm):#digitalization permanent date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Digitalizationp fields = ('date', 'what_are_the_departments_that_need_to_be_digitalized_and_why', 'please_mention_if_they_are_new_or_preexisting_departments', 'what_is_the_budget_allocated_for_the_digitlization_process', 'the_priority_in_which_the_deaprtments_need_to_be_digitalized', 'what_are_the_limitations_that_need_to_be_considered','the_deadline_by_which_digitalization_needs_to_be_done','upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(DigitalizationpForm, self).__init__(*args, **kwargs) class MiomtForm(forms.ModelForm):#min of meeting create date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Miom fields = ('date','version', 'meeting_description','main_concerns','restrictions', 'plan_of_action_for_Pathscript','plan_of_action_for_Client', 'upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(MiomtForm, self).__init__(*args, **kwargs) class MiomForm(forms.ModelForm):#min of meeting temporary date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Miom fields = ('date', 'meeting_description','main_concerns','restrictions', 'plan_of_action_for_Pathscript','plan_of_action_for_Client', 'upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(MiomForm, self).__init__(*args, **kwargs) class MiompForm(forms.ModelForm):#min of meeting permanent date=forms.DateField(widget=forms.SelectDateWidget(years=range(1900, 2100))) class Meta: model = Miomp fields = ('date', 'meeting_description','main_concerns','restrictions', 'plan_of_action_for_Pathscript','plan_of_action_for_Client', 'upload_Doc1','upload_Doc2') def __init__(self,request,*args, **kwargs): super(MiompForm, self).__init__(*args, **kwargs)

52.232033

265

0.667689

2,807

25,437

5.556466

0.109369

0.03334

0.024235

0.030006

0.795666

0.761685

0.756107

0.733282

0.725396

0.686735

0

0.016917

0.223847

25,437

486

266

52.339506

0.773084

0.080788

0

0.60241

0

0.41177

0.2825

0

1

0.078313

false

0

0.012048

0

0.316265

0

null

0

1

0

null

0

5

c5d264b666ec206bb38235e6b929269a680d4c3f

201

py

Python

moceansdk/modules/command/button/wa_call_button.py

d3no/mocean-sdk-python

cbc215a0eb8aa26c04afb940eab6482f23150c75

[ "MIT" ]

null

moceansdk/modules/command/button/wa_call_button.py

d3no/mocean-sdk-python

cbc215a0eb8aa26c04afb940eab6482f23150c75

[ "MIT" ]

null

moceansdk/modules/command/button/wa_call_button.py

d3no/mocean-sdk-python

cbc215a0eb8aa26c04afb940eab6482f23150c75

[ "MIT" ]

null

from moceansdk.modules.command.button.wa_button_basic import WaButtonBasic class WaCallButton(WaButtonBasic): def type(self): return "call" def required_key(self): return []

20.1

74

0.711443

23

201

6.086957

0.782609

0.142857

0

0.20398

201

9

75

22.333333

0.875

0

0.019901

0

1

0.333333

false

0

0.166667

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

a85d9f6adc75abade2df1b9fb782e28ad3153356

91

py

Python

Ejercicio3/Gato.py

carlotamartin/Ejercicio-de-POO-entrega

454362e86ba28b12c7853391fda2820212ab5a70

[ "Apache-2.0" ]

null

Ejercicio3/Gato.py

carlotamartin/Ejercicio-de-POO-entrega

454362e86ba28b12c7853391fda2820212ab5a70

[ "Apache-2.0" ]

null

Ejercicio3/Gato.py

carlotamartin/Ejercicio-de-POO-entrega

454362e86ba28b12c7853391fda2820212ab5a70

[ "Apache-2.0" ]

null

from Mamifero import Mamifero class Gato (Mamifero): def __init__ (self): pass

18.2

29

0.681319

11

91

5.272727

0.818182

0

0.252747

91

5

30

18.2

0.852941

0

1

0.25

false

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

a8604acc6869a5557580d6852ac8ca8c051c0cef

82

py

Python

build/lib/geonomics/utils/__init__.py

AnushaPB/geonomics-1

deee0c377e81f509463eaf6f9d0b2f0809f2ddc3

[ "MIT" ]

8

2020-08-27T17:06:04.000Z

2021-09-17T22:55:07.000Z

build/lib/geonomics/utils/__init__.py

AnushaPB/geonomics-1

deee0c377e81f509463eaf6f9d0b2f0809f2ddc3

[ "MIT" ]

null

build/lib/geonomics/utils/__init__.py

AnushaPB/geonomics-1

deee0c377e81f509463eaf6f9d0b2f0809f2ddc3

[ "MIT" ]

2

2020-08-28T23:45:28.000Z

2021-01-25T21:47:40.000Z

from . import io from . import viz from . import spatial from . import _str_repr_

16.4

24

0.756098

13

82

4.538462

0.538462

0.677966

0

0.195122

82

4

25

20.5

0.893939

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

a87fc71bc99f8160644a8f466ca6c7f7a4dce4ea

3,874

py

Python

jmetal/problem/multiobjective/constrained.py

LuckysonKhaidem/ProjectAlpha

e4b4779a8968a83f1e8add3490a4d2c4ad145d55

[ "MIT" ]

1

2020-05-26T18:57:31.000Z

jmetal/problem/multiobjective/constrained.py

LuckysonKhaidem/ProjectAlpha

e4b4779a8968a83f1e8add3490a4d2c4ad145d55

[ "MIT" ]

null

jmetal/problem/multiobjective/constrained.py

LuckysonKhaidem/ProjectAlpha

e4b4779a8968a83f1e8add3490a4d2c4ad145d55

[ "MIT" ]

2

2019-01-08T11:52:52.000Z

2020-05-25T13:21:26.000Z

from math import pi, cos, atan from jmetal.core.solution import FloatSolution from jmetal.core.problem import FloatProblem """ .. module:: constrained :platform: Unix, Windows :synopsis: Constrained test problems for multi-objective optimization .. moduleauthor:: Antonio J. Nebro <antonio@lcc.uma.es> """ class Srinivas(FloatProblem): """ Class representing problem Srinivas. """ def __init__(self, rf_path: str=None): super(Srinivas, self).__init__(rf_path=rf_path) self.number_of_objectives = 2 self.number_of_variables = 2 self.number_of_constraints = 2 self.obj_directions = [self.MINIMIZE, self.MINIMIZE] self.obj_labels = ['f(x)', 'f(y)'] self.lower_bound = [-20.0 for _ in range(self.number_of_variables)] self.upper_bound = [20.0 for _ in range(self.number_of_variables)] FloatSolution.lower_bound = self.lower_bound FloatSolution.upper_bound = self.upper_bound def evaluate(self, solution: FloatSolution) -> FloatSolution: x1 = solution.variables[0] x2 = solution.variables[1] solution.objectives[0] = 2.0 + (x1 - 2.0) * (x1 - 2.0) + (x2 - 1.0) * (x2 - 1.0) solution.objectives[1] = 9.0 * x1 - (x2 - 1.0) * (x2 - 1.0) return solution def evaluate_constraints(self, solution: FloatSolution) -> None: constraints = [0.0 for _ in range(self.number_of_constraints)] x1 = solution.variables[0] x2 = solution.variables[1] constraints[0] = 1.0 - (x1 * x1 + x2 * x2) / 225.0 constraints[1] = (3.0 * x2 - x1) / 10.0 - 1.0 overall_constraint_violation = 0.0 number_of_violated_constraints = 0.0 for constrain in constraints: if constrain < 0.0: overall_constraint_violation += constrain number_of_violated_constraints += 1 solution.attributes['overall_constraint_violation'] = overall_constraint_violation solution.attributes['number_of_violated_constraints'] = number_of_violated_constraints def get_name(self): return 'Srinivas' class Tanaka(FloatProblem): """ Class representing problem Tanaka """ def __init__(self, rf_path: str=None): super(Tanaka, self).__init__(rf_path=rf_path) self.number_of_objectives = 2 self.number_of_variables = 2 self.number_of_constraints = 2 self.obj_directions = [self.MINIMIZE, self.MINIMIZE] self.obj_labels = ['f(x)', 'f(y)'] self.lower_bound = [10e-5 for _ in range(self.number_of_variables)] self.upper_bound = [pi for _ in range(self.number_of_variables)] FloatSolution.lower_bound = self.lower_bound FloatSolution.upper_bound = self.upper_bound def evaluate(self, solution: FloatSolution) -> FloatSolution: solution.objectives[0] = solution.variables[0] solution.objectives[1] = solution.variables[1] return solution def evaluate_constraints(self, solution: FloatSolution) -> None: constraints = [0.0 for _ in range(self.number_of_constraints)] x1 = solution.variables[0] x2 = solution.variables[1] constraints[0] = (x1 * x1 + x2 * x2 - 1.0 - 0.1 * cos(16.0 * atan(x1 / x2))) constraints[1] = -2.0 * ((x1 - 0.5) * (x1 - 0.5) + (x2 - 0.5) * (x2 - 0.5) - 0.5) overall_constraint_violation = 0.0 number_of_violated_constraints = 0.0 for constrain in constraints: if constrain < 0.0: overall_constraint_violation += constrain number_of_violated_constraints += 1 solution.attributes['overall_constraint_violation'] = overall_constraint_violation solution.attributes['number_of_violated_constraints'] = number_of_violated_constraints def get_name(self): return 'Tanaka'

34.589286

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0.651007

485

3,874

4.969072

0.173196

0.06639

0.059751

0.089627

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0.742739

0.702075

0

0.042755

0.239288

3,874

111

95

34.900901

0.775025

0.018327

0

0.695652

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0.032222

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1

0.115942

false

0

0.043478

0.028986

0.246377

0

null

0

1

0

null

0

5

a8c4916dbe0cc67a85ce77eda0d08d4f06d98fd1

1,086

py

Python

utils/types.py

fomula91/todoapp

b1a9ca40af92f7bb0f5054760c13c3089f430440

[ "MIT" ]

4

2022-03-07T12:37:11.000Z

2022-03-13T21:30:26.000Z

utils/types.py

fomula91/todoapp

b1a9ca40af92f7bb0f5054760c13c3089f430440

[ "MIT" ]

3

2022-03-09T16:19:24.000Z

2022-03-27T15:09:58.000Z

utils/types.py

fomula91/todoapp

b1a9ca40af92f7bb0f5054760c13c3089f430440

[ "MIT" ]

1

2022-03-10T23:40:46.000Z

# Annotaion(함수나 클래스의 인자값 또는 반환값의 형태를 알려주기 위해 타입을 지정하는 방법)을 위한 클래스 # bool 타입의 ok class BooleanOk: @staticmethod def __type__(): return bool # dict 타입의 uses class DictionaryUser: @staticmethod def __type__(): fields = { "_id": str, "user_id": str, "user_name": str, "user_passwd": str } return fields # dict 타입의 payload class DictionaryPayload: @staticmethod def __type__(): fields = { "user_id": str, "user_name": str, } return fields # string 타입의 token class StringToken: @staticmethod def __type__(): return str # string 타입의 user_id class StringUserId: @staticmethod def __type__(): return str # string 타입의 message class StringMessage: @staticmethod def __type__(): return str # string 타입의 objectId class StringObjectId: @staticmethod def __type__(): return str # list 타입의 words class ArrayWords: @staticmethod def __type__(): return str

16.208955

65

0.586556

115

1,086

5.2

0.4

0.200669

0.254181

0.250836

0.346154

0.252508

0.185619

0

0.339779

1,086

66

16.454545

0.834031

0.180479

0

0.690476

0

0.052273

0

1

0.190476

false

0.02381

0

0.142857

0.571429

0

null

1

0

null

0

1

0

5

a8d40ac38a43b09224c3c2a2c47dc0491a3acaa5

103

py

Python

terrascript/dnsimple/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

null

terrascript/dnsimple/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

null

terrascript/dnsimple/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

null

# terrascript/dnsimple/r.py import terrascript class dnsimple_record(terrascript.Resource): pass

14.714286

44

0.796117

12

103

6.75

0.75

0

0.126214

103

6

45

17.166667

0.9

0.242718

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

76592530eed6588636cb3a63be38df604139394b

88

py

Python

VPhys_MET.py

JCCPort/Tests

821e7df2426e964d67b59d7fd32eba5796930a84

[ "Apache-2.0" ]

null

VPhys_MET.py

JCCPort/Tests

821e7df2426e964d67b59d7fd32eba5796930a84

[ "Apache-2.0" ]

null

VPhys_MET.py

JCCPort/Tests

821e7df2426e964d67b59d7fd32eba5796930a84

[ "Apache-2.0" ]

null

import pandas as pd import numpy as np import scipy as sc import math import numba as nb

17.6

19

0.806818

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76b8982e2d78d698128fa58c2163c2287af41b75

140

py

Python

sphinxcontrib/needs/api/__init__.py

gregegg/sphinxcontrib-needs

b0c10a44756bb8f16313dcf52e17fd87cf47e780

[ "MIT" ]

1

2021-12-31T03:55:12.000Z

sphinxcontrib/needs/api/__init__.py

gregegg/sphinxcontrib-needs

b0c10a44756bb8f16313dcf52e17fd87cf47e780

[ "MIT" ]

null

sphinxcontrib/needs/api/__init__.py

gregegg/sphinxcontrib-needs

b0c10a44756bb8f16313dcf52e17fd87cf47e780

[ "MIT" ]

1

2021-12-31T03:55:44.000Z

from .configuration import get_need_types, add_need_type, add_extra_option, add_dynamic_function from .need import add_need, make_hashed_id

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4f93660a90fbe86460d75b52aff46460d5f1a182

37

py

Python

classification_scale.py

jrctrabuco/Assay-Multiparameter-Dash

54c9a4b8818a75db1a7a4607c1532440fda34ec3

[ "MIT" ]

null

classification_scale.py

jrctrabuco/Assay-Multiparameter-Dash

54c9a4b8818a75db1a7a4607c1532440fda34ec3

[ "MIT" ]

null

classification_scale.py

jrctrabuco/Assay-Multiparameter-Dash

54c9a4b8818a75db1a7a4607c1532440fda34ec3

[ "MIT" ]

null

#Scale for classification of Devices

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96e92ec98ec68d59fcae79863a30d219d38ea4c1

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py

Python

speedysvc/serialisation/ArrowSerialisation.py

mcyph/shmrpc

4e0e972657f677a845eb6e7acbf788535c07117a

[ "Unlicense", "MIT" ]

4

2020-02-11T04:20:57.000Z

2021-06-20T10:03:52.000Z

speedysvc/serialisation/ArrowSerialisation.py

mcyph/shmrpc

4e0e972657f677a845eb6e7acbf788535c07117a

[ "Unlicense", "MIT" ]

1

2020-09-16T23:18:30.000Z

2020-09-21T10:07:22.000Z

speedysvc/serialisation/ArrowSerialisation.py

mcyph/shmrpc

4e0e972657f677a845eb6e7acbf788535c07117a

[ "Unlicense", "MIT" ]

null

import pyarrow class ArrowSerialisation: """ TODO! """ mimetype = 'application/octet-stream' @staticmethod def dumps(o): return pyarrow.serialize(o).to_buffer() @staticmethod def loads(o): return pyarrow.deserialize(o)

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1

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8c4c30ab6088043d02ff78f28b99f66e1a557792

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py

Python

maximum-depth-of-binary-tree/Solution.6583982.py

rahul-ramadas/leetcode

6c84c2333a613729361c5cdb63dc3fc80203b340

[ "MIT" ]

null

maximum-depth-of-binary-tree/Solution.6583982.py

rahul-ramadas/leetcode

6c84c2333a613729361c5cdb63dc3fc80203b340

[ "MIT" ]

1

2016-09-11T22:26:17.000Z

2016-09-13T01:49:48.000Z

maximum-depth-of-binary-tree/Solution.6583982.py

rahul-ramadas/leetcode

6c84c2333a613729361c5cdb63dc3fc80203b340

[ "MIT" ]

null

class Solution: def maxDepth(self, root): if root is None: return 0 return 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))

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1

0

1

0

null

0

1

0

5

4ffd57362eafe3d62bf32eaaabf954e3fa4fe4ba

72

py

Python

parser.py

danesjenovdan/ajdovscina-parser

b28dd46d6d0379d47f0508b5c7bb29f87f5bb8e1

[ "CC0-1.0" ]

null

parser.py

danesjenovdan/ajdovscina-parser

b28dd46d6d0379d47f0508b5c7bb29f87f5bb8e1

[ "CC0-1.0" ]

null

parser.py

danesjenovdan/ajdovscina-parser

b28dd46d6d0379d47f0508b5c7bb29f87f5bb8e1

[ "CC0-1.0" ]

null

from parlaparser.parser import Parser parser = Parser() parser.parse()

14.4

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null

0

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5

8b413ea1f58f2f4341b112b997adc24b0819de7d

1,168

py

Python

tests/utilities/test_sha3.py

Arachnid/web3.py

4a0b4adc292981958c899ae731ee60014fd94775

[ "MIT" ]

4

2018-02-04T22:06:20.000Z

2021-04-14T22:09:43.000Z

tests/utilities/test_sha3.py

gkapkowski/web3.py

cd0cf580119e4afa41c511eb35ee31840a2fd321

[ "MIT" ]

null

tests/utilities/test_sha3.py

gkapkowski/web3.py

cd0cf580119e4afa41c511eb35ee31840a2fd321

[ "MIT" ]

1

2018-10-04T09:13:28.000Z

from __future__ import unicode_literals import pytest @pytest.mark.parametrize( 'value,expected,encoding', ( ( '', '0xc5d2460186f7233c927e7db2dcc703c0e500b653ca82273b7bfad8045d85a470', None, ), ( 'test123', '0xf81b517a242b218999ec8eec0ea6e2ddbef2a367a14e93f4a32a39e260f686ad', None, ), ( 'test(int)', '0xf4d03772bec1e62fbe8c5691e1a9101e520e8f8b5ca612123694632bf3cb51b1', None, ), ( '0x80', '0x56e81f171bcc55a6ff8345e692c0f86e5b48e01b996cadc001622fb5e363b421', 'hex', ), ( '0x80', '0x6b03a5eef7706e3fb52a61c19ab1122fad7237726601ac665bd4def888f0e4a0', None, ), ( '0x3c9229289a6125f7fdf1885a77bb12c37a8d3b4962d936f7e3084dece32a3ca1', '0x82ff40c0a986c6a5cfad4ddf4c3aa6996f1a7837f9c398e17e5de5cbd5a12b28', 'hex', ) ) ) def test_sha3(web3, value, expected, encoding): actual = web3.sha3(value, encoding=encoding) assert expected == actual

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null

0

1

0

5

8b45f05cfe6f673b7b32233c7415eeca2ed5d2ba

38

py

Python

ssk/helpers/__init__.py

jobliz/solid-state-kinetics

c5767b400b19bd0256c806001664f0b369718bab

[ "MIT" ]

2

2017-03-08T21:32:11.000Z

2017-07-19T03:27:18.000Z

ssk/helpers/__init__.py

jobliz/solid-state-kinetics

c5767b400b19bd0256c806001664f0b369718bab

[ "MIT" ]

null

ssk/helpers/__init__.py

jobliz/solid-state-kinetics

c5767b400b19bd0256c806001664f0b369718bab

[ "MIT" ]

null

from api import * from excel import *

12.666667

19

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38

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0.210526

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2

20

19

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1

0

null

0

1

0

1

0

5

8ca1958105bef5127b31f8fbf44e21e0f54e3042

12,458

py

Python

win_animation.py

corbanmailloux/MysteryMansion

21086550bcd9de7f6df440bc62b80ece2cc0156f

[ "MIT" ]

1

2021-09-16T17:31:36.000Z

win_animation.py

corbanmailloux/MysteryMansion

21086550bcd9de7f6df440bc62b80ece2cc0156f

[ "MIT" ]

7

2016-01-10T06:39:19.000Z

2022-01-16T15:25:00.000Z

win_animation.py

corbanmailloux/MysteryMansion

21086550bcd9de7f6df440bc62b80ece2cc0156f

[ "MIT" ]

1

2016-03-29T22:27:01.000Z

"""Win animation frames for Mystery Mansion. Animation from: http://www.angelfire.com/ca/mathcool/fireworks.html """ win_animation = [ """ .| | | |'| ._____ ___ | | |. |' .---"| _ .-' '-. | | .--'| || | _| | .-'| _.| | || '-__ | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ .| | | |'| ' ._____ ___ | | . |. |' .---"| _ .-' '-. | | . .--'| || | _| | .-'| _.| | || '-__ | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ .| _\\/_ | | /\\ |'| ' ._____ ___ | | . |. |' .---"| _ .-' '-. | | . .--'| || | _| | .-'| _.| | || '-__ | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ * * .| *_\\/_* | | * /\\ * |'| * * ._____ ___ | | |. |' .---"| _ .-' '-. | | .--'| || | _| | .-'| _.| | || '-__ | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ * * .| * * | | * * _\\/_ _\\/_ |'| * * /\\ ._____ /\\ ___ | | |. |' .---"| _ .-' '-. | | .--'| || | _| | .-'| _.| | || '-__ | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ * * _\\/_ .| * * .::. .''. /\\ | | * :_\\/_: :_\\/_: |'| * * : /\\ :_____ : /\\ :___ | | o '::'|. |' .---"| _ '..-' '-. | | .--'| || | _| | .-'| _.| | || '-__ | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ .''. * :_\\/_: .| .::. .''.: /\\ : | | : : : :'..' |'| \\'/ : :_____ : :___ | | = o = '::'|. |' .---"| _ '..-' '-. | | /.\\.--'| || | _| | .-'| _.| | || '-__ | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ _\\)/_ .''. /(\\ : : .| _\\/_ .''.: : | | : /\\ : :'..' |'|'.\\'/.' ._____ : :___ | |-= o =- |. |' .---"| _ '..-' '-. | |.'/.\\:--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ . _\\)/_ .''. /(\\ .''. : : .| ' :_\\/_: : : | | : : /\\ : '..' |'|'. ' .' '..'._____ ___ | |-= =- |. |' .---"| _ .-' '-. | |.' . :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ _\\/_ .''. /\\ .| : : | | : : |'| '..'._____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ \\'/ * * = o = *_\\/_* /.\\ .''. * /\\ * .| : : * * | | : : |'| '..'._____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ '.\\'/.' * * -= o =- * * .'/.\\'. * * .| : * * | | |'| ._____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ '.\\'/.' -= =- o .'/.\\'. o .| : | | .:. |'| ':' ._____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ '. ' .' \\'/ - - \\'/ = o = .' . '. = o = /.\\ .| : .:::. /.\\ | | ::::::: |'| ':::'_____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ : : '.\\'/.' '.\\'/.'-= o =- .:::. -= o =-.'/.\\'..| ::::::: .'/.\\'. : | | ::::::: : |'| ':::'_____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ : : '.\\'/.' '.\\'/.'-= =- * .:::. -= =-.'/.\\'..| ::' ':: .'/.\\'. : | | ::. .:: : |'| ':::'_____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ : . : '. ' .' _\\)/_ '. ' .'- - /(\\ .'''. - -.' . '..| ' : : .' . '. : | | : : : |'| '...'_____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ . _\\)/_ _\\/_ /(\\ _\\/_ /\\ .| ' /\\ | | |'| ._____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ . .''. _\\)/_ .''. :_\\/_: /(\\ :_\\/_:: /\\ : .| ' : /\\ : '..' o | | '..' |'| ._____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ .''. .''. : : _\\/_ : :: : \\'/ .| /\\ : : '..' = o = | | _\\/_ '..' /.\\ |'| /\\ ._____ ___ | | |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ .''. : :_\\/_: '.\\'/.' .| : /\\.:'. -= o =- | | '.:_\\/_: .'/.\\'. |'| : /\\ : ._____ :__ | | '..' |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ .''. : : : '.\\'/.' .| : .:'. -= =- | | '.: : .'/.\\'. |'| : : ._____ :__ | | '..' |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """, """ .''. : : : '. ' .' .| : .:'. - - | | '.: : .' . '. |'| : : ._____ :__ | | '..' |. |' .---"| _ .-' '-. | | :--'| || | _| | .-'| _.| | || '-__: | | | || | |' | |. | || | | | | || | ___| '-' ' "" '-' '-.' '` |____ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """]

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8cab05020b7533aeed8137c8d563d8752e3ca0fe

659

py

Python

example_1/Batuhan/workspace/build/meturone_egitim/cmake/meturone_egitim-genmsg-context.py

tekmen0/ROS-intro

7f85bcfc2e8897ac80a045c40682698563418ab1

[ "MIT" ]

3

2020-09-11T08:14:10.000Z

2020-09-27T14:58:30.000Z

example_1/Batuhan/workspace/build/meturone_egitim/cmake/meturone_egitim-genmsg-context.py

tekmen0/ROS-intro

7f85bcfc2e8897ac80a045c40682698563418ab1

[ "MIT" ]

1

2020-09-11T08:09:31.000Z

example_1/Batuhan/workspace/build/meturone_egitim/cmake/meturone_egitim-genmsg-context.py

tekmen0/ROS-intro

7f85bcfc2e8897ac80a045c40682698563418ab1

[ "MIT" ]

9

2020-09-10T21:57:17.000Z

2021-02-23T15:17:43.000Z

# generated from genmsg/cmake/pkg-genmsg.context.in messages_str = "/home/tekmen0/meturone_egitim/src/meturone_egitim/msg/Dummy.msg;/home/tekmen0/meturone_egitim/src/meturone_egitim/msg/answer.msg" services_str = "" pkg_name = "meturone_egitim" dependencies_str = "std_msgs" langs = "gencpp;geneus;genlisp;gennodejs;genpy" dep_include_paths_str = "meturone_egitim;/home/tekmen0/meturone_egitim/src/meturone_egitim/msg;std_msgs;/opt/ros/noetic/share/std_msgs/cmake/../msg" PYTHON_EXECUTABLE = "/usr/bin/python3" package_has_static_sources = '' == 'TRUE' genmsg_check_deps_script = "/opt/ros/noetic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"

54.916667

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null

1

0

1

null

0

5

506d04e3bbb3c998df47b52d997c24ec5437c976

196

py

Python

service/models.py

acslaszlo/docker-test

6fd0fd290292a45aea8f87afa05db45e5d0eaf02

[ "MIT" ]

null

service/models.py

acslaszlo/docker-test

6fd0fd290292a45aea8f87afa05db45e5d0eaf02

[ "MIT" ]

4

2018-11-26T08:15:06.000Z

2018-11-30T06:47:05.000Z

service/models.py

acslaszlo/docker-test

6fd0fd290292a45aea8f87afa05db45e5d0eaf02

[ "MIT" ]

null

from flywheel import Field, Model class Data(Model): id = Field(data_type=str, hash_key=True) val1 = Field(data_type=str) val2 = Field(data_type=int) val3 = Field(data_type=str)

21.777778

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1

0

1

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null

0

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5

508bc2080641d573b31146ad67e0647857e93fc9

158

py

Python

python-web/FORM/form_workshop/form_workshop/create_form/urls.py

yosif88/SoftUni

ca1778ae9eb796b82e8d9f5882b6e7fdb0a96372

[ "MIT" ]

null

python-web/FORM/form_workshop/form_workshop/create_form/urls.py

yosif88/SoftUni

ca1778ae9eb796b82e8d9f5882b6e7fdb0a96372

[ "MIT" ]

null

python-web/FORM/form_workshop/form_workshop/create_form/urls.py

yosif88/SoftUni

ca1778ae9eb796b82e8d9f5882b6e7fdb0a96372

[ "MIT" ]

null

from django.urls import path from form_workshop.create_form.views import show_form_data urlpatterns = [ path('', show_form_data, name='show form') ]

26.333333

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1

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null

0

1

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5

509428202f6cba98dd60d77f3200afddf9bb51a2

156

py

Python

audioPlayer.py

Razpudding/rpi-duckling

c2a240c148f0a1188a2563b74a053549a08b6ab8

[ "MIT" ]

null

audioPlayer.py

Razpudding/rpi-duckling

c2a240c148f0a1188a2563b74a053549a08b6ab8

[ "MIT" ]

null

audioPlayer.py

Razpudding/rpi-duckling

c2a240c148f0a1188a2563b74a053549a08b6ab8

[ "MIT" ]

null

import pygame pygame.mixer.init() pygame.mixer.music.load("myFile.wav") pygame.mixer.music.play() while pygame.mixer.music.get_busy() == True: continue

22.285714

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5.043478

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0.37931

0.413793

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0.089744

156

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0.064103

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1

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1

0

null

0

1

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5

50e4b2049bb0de21aed66f361ce51ab77caa0e72

124

py

Python

modules/__init__.py

tinnguyen96/partition-coupling

1078171465e61bee5bca3d2c2f1bf0fc86c0d865

[ "MIT" ]

null

modules/__init__.py

tinnguyen96/partition-coupling

1078171465e61bee5bca3d2c2f1bf0fc86c0d865

[ "MIT" ]

null

modules/__init__.py

tinnguyen96/partition-coupling

1078171465e61bee5bca3d2c2f1bf0fc86c0d865

[ "MIT" ]

null

# todos # add a compress function (triple Mode can create over 1000 files in a directory, which # put strain on Supercloud).

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20

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4.75

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124

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41.333333

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null

0

1

0

1

0

5

50eb8d14e83cb5896661f256781852fadb4fc98e

88

py

Python

push_deployment.py

eyalatox/Tableau-dummy

7bdab8dc5c078a35b2667445fcfdbe3babf14244

[ "MIT" ]

null

push_deployment.py

eyalatox/Tableau-dummy

7bdab8dc5c078a35b2667445fcfdbe3babf14244

[ "MIT" ]

null

push_deployment.py

eyalatox/Tableau-dummy

7bdab8dc5c078a35b2667445fcfdbe3babf14244

[ "MIT" ]

null

import os def validate_helm_chart(helm_chart): os.system(f'helm lint {helm_chart}')

22

40

0.761364

15

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4.2

0.6

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0.333333

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0.333333

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null

0

1

0

1

0

5

0fdc4d769d0236a4f41312871e0cf34c6fb76151

4,198

py

Python

tests/generate_examples.py

hsolbrig/avidreader

71d98c91a7954b3aa3e2a7fe20a0f23c66e7bdb9

[ "CC0-1.0" ]

3

2021-02-18T18:32:25.000Z

2021-02-19T19:59:13.000Z

tests/generate_examples.py

hsolbrig/avidreader

71d98c91a7954b3aa3e2a7fe20a0f23c66e7bdb9

[ "CC0-1.0" ]

10

2021-02-19T16:50:20.000Z

2021-05-09T22:14:25.000Z

tests/generate_examples.py

hsolbrig/hbreader

71d98c91a7954b3aa3e2a7fe20a0f23c66e7bdb9

[ "CC0-1.0" ]

null

import os from hbreader import FileInfo, hbopen, hbread # This removes any absolute paths from the output -- not generally used FileInfo.rel_offset = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')) # Open a vanilla file metadata = FileInfo() with hbopen('../tests/data/test data 1.txt', metadata) as f: print(f.read()) print(metadata) # I'm some friendly test data # # FileInfo(source_file='hbreader/tests/data/test data 1.txt', source_file_date='Wed Feb 17 17:01:09 2021', source_file_size=28, base_path='hbreader/tests/data') # Open a file using a base address data_file_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../tests/data')) with hbopen('test data 1.txt', base_path=data_file_dir) as f: print(f.read()) # I'm some friendly test data # Open an absolute URL FileInfo.rel_offset = None url = "https://raw.githubusercontent.com/hsolbrig/hbreader/master/tests/data/test data 1.txt" with hbopen("https://raw.githubusercontent.com/hsolbrig/hbreader/master/tests/data/test data 1.txt", metadata.clear()) as f: print(f.read()) print(metadata) # I'm some friendly test data # # FileInfo(source_file='https://raw.githubusercontent.com/hsolbrig/hbreader/master/tests/data/test%20data%201.txt', source_file_date='Thu, 18 Feb 2021 16:02:50 GMT', source_file_size='28', base_path='https://raw.githubusercontent.com/hsolbrig/hbreader/master/tests/data') # Open a relative URL base_address = metadata.base_path print(f"Base: {base_address}") # Base: https://raw.githubusercontent.com/hsolbrig/hbreader/master/tests/data with hbopen('test data 1.txt', base_path=base_address) as f: print(f.read()) # I'm some friendly test data # Open a file handle with open('../tests/data/test data 1.txt') as fhandle: with hbopen(fhandle, metadata.clear()) as f: print(f.read()) print(metadata) # I'm some friendly test data # FileInfo(source_file='../tests/data/test data 1.txt', source_file_date='Wed Feb 17 17:01:09 2021', source_file_size=28, base_path='../tests/data') # Open an 'latin-1' encoded file with hbopen('test_8859.txt', base_path=data_file_dir, read_codec='latin-1') as f: print(f.read()) # Some Text With weird ÒtextÓ And single ÔquotesÕ # Open a bytes file handle -- still reads as text with open('data/test data 1.txt', 'rb') as fhandle: with hbopen(fhandle) as f: print(f.read()) # I'm some friendly test data # Open a block of text as a file some_text = """ This is the honey badger. Watch it run in slow motion. It's pretty badass. Look. It runs all over the place. "Whoa! Watch out!" says that bird. Eew, it's got a snake! Oh! It's chasing a jackal! Oh my gosh! Oh, the honey badger is just crazy! The honey badger has been referred to by the Guiness Book of World Records as the most fearless animal in the animal kingdom. It really doesn't give a shit. If it's hungry, it's hungry. """ with hbopen(some_text, metadata.clear()) as f: print(f.read()) print(metadata) # # This is the honey badger. Watch it run in slow motion. # # It's pretty badass. Look. It runs all over the place. "Whoa! Watch out!" says that bird. # # Eew, it's got a snake! Oh! It's chasing a jackal! Oh my gosh! # # Oh, the honey badger is just crazy! # # The honey badger has been referred to by the Guiness Book of World Records as the most fearless animal in the animal kingdom. It really doesn't give a shit. If it's hungry, it's hungry. # hbopen doesn't require 'with' f = hbopen('l1\nl2\nl3\n') for l in f: print(l, end='') f.close() # l1 # l2 # l3 # hpread returns the content rather than a file handle print(hbread('test_8859.txt', base_path=data_file_dir, read_codec='latin-1')) # Some Text With weird ÒtextÓ And single ÔquotesÕ print(hbread("https://raw.githubusercontent.com/hsolbrig/hbreader/master/tests/data/test data 1.txt", metadata.clear())) # I'm some friendly test data print(metadata) # FileInfo(source_file='https://raw.githubusercontent.com/hsolbrig/hbreader/master/tests/data/test%20data%201.txt', source_file_date='Thu, 18 Feb 2021 16:28:37 GMT', source_file_size='28', base_path='https://raw.githubusercontent.com/hsolbrig/hbreader/master/tests/data')

40.757282

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715

4,198

4.186014

0.226573

0.045439

0.03007

0.040094

0.781824

0.754761

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0.72703

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0.144116

4,198

102

272

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0.377778

0

null

0

1

0

null

0

5

ba00b0922bc7761f169afaef7c623049b3f97d57

43

py

Python

hive_attention_tokens/chain/consensus/peers.py

imwatsi/hive-attention-tokens

87b02b1b6fa6dc75f2cdf25d92f0a79cbeeb7e5f

[ "MIT" ]

null

hive_attention_tokens/chain/consensus/peers.py

imwatsi/hive-attention-tokens

87b02b1b6fa6dc75f2cdf25d92f0a79cbeeb7e5f

[ "MIT" ]

null

hive_attention_tokens/chain/consensus/peers.py

imwatsi/hive-attention-tokens

87b02b1b6fa6dc75f2cdf25d92f0a79cbeeb7e5f

[ "MIT" ]

null

"""Consensus on the state of peer nodes."""

43

0.697674

7

43

4.285714

1

0

0.139535

43

1

43

0.810811

0.860465

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

ba1f1dc0a769a993fc3bd73fd9dcffbfd685a82e

568

py

Python

Utils/RandamData.py

yasserfaraazkhan/Selenium-Python-Pytest

748f01809b8b6ff1e89973d67ffd36514f6b77b0

[ "MIT" ]

null

Utils/RandamData.py

yasserfaraazkhan/Selenium-Python-Pytest

748f01809b8b6ff1e89973d67ffd36514f6b77b0

[ "MIT" ]

null

Utils/RandamData.py

yasserfaraazkhan/Selenium-Python-Pytest

748f01809b8b6ff1e89973d67ffd36514f6b77b0

[ "MIT" ]

null

import random class Utils(): @classmethod def _get_random_alphanumeric_string(cls): return ''.join(random.choice('ABCDSFGEHIJK123456') for i in range(5)) @classmethod def _get_random_numeric_string(cls): return ''.join(random.choice('1234567890') for i in range(10)) @classmethod def _get_random_five_number_string(cls): return ''.join(random.choice('123456789') for i in range(5)) @classmethod def _get_random_alphabetic_string(cls): return ''.join(random.choice('ABCDSFGEHIJK') for i in range(5))

28.4

77

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568

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0.149333

0.181333

0.245333

0.549333

0.517333

0.186667

0

0.065359

0.191901

568

19

78

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null

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0

null

0

1

0

1

0

5

ba1ff244633d82344157c37f23fa167df1410ade

175

py

Python

tests/context.py

itsdaveba/rubik-solver

eebae6cffc9f91e64d5f3e49d556a78df0e703f5

[ "MIT" ]

null

tests/context.py

itsdaveba/rubik-solver

eebae6cffc9f91e64d5f3e49d556a78df0e703f5

[ "MIT" ]

null

tests/context.py

itsdaveba/rubik-solver

eebae6cffc9f91e64d5f3e49d556a78df0e703f5

[ "MIT" ]

null

import sys import os sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import rubik_solver from rubik_solver.defs import available_moves

29.166667

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0.765714

28

175

4.535714

0.571429

0.141732

0

0.006369

0.102857

175

6

84

29.166667

0.802548

0

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true

0

0.8

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0.8

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null

0

1

0

null

0

1

0

1

0

1

0

5

e87031dd4f9df978b67ef30e680bfb04430b26ab

91

py

Python

pl2.py

kwadrat/pl_py

ed8526df6dd813ae028c37ca07c8ba03cd11a5b2

[ "MIT" ]

null

pl2.py

kwadrat/pl_py

ed8526df6dd813ae028c37ca07c8ba03cd11a5b2

[ "MIT" ]

null

pl2.py

kwadrat/pl_py

ed8526df6dd813ae028c37ca07c8ba03cd11a5b2

[ "MIT" ]

null

#!/usr/bin/env python2 import common_pl if __name__ == '__main__': common_pl.main()

11.375

26

0.681319

13

91

4

0.769231

0.307692

0

0.013333

0.175824

91

7

27

13

0.68

0.230769

0

0.115942

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

e87d602b972abbd672a9e5b351bdfbb7549eb780

34

py

Python

Kalecgos/config.py

Raka-loah/Kalecgos

2d63c2b01af0beecac1c270830dd32d4bdef4153

[ "MIT" ]

1

2020-09-29T09:47:48.000Z

Kalecgos/config.py

Raka-loah/Kalecgos

2d63c2b01af0beecac1c270830dd32d4bdef4153

[ "MIT" ]

null

Kalecgos/config.py

Raka-loah/Kalecgos

2d63c2b01af0beecac1c270830dd32d4bdef4153

[ "MIT" ]

null

base_url = 'http://127.0.0.1:5700'

34

0.647059

8

34

2.625

0.875

0

0.3125

0.058824

34

1

34

0.34375

0

0.6

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

e8a4be889e29281377e2d7a5e30c1bf5c4ae440c

158

py

Python

tweetable/admin.py

kkiyama117/django-HP

90c25a6d0597abf364f5b51ca2cd192cf8b998a2

[ "Apache-2.0" ]

1

2020-12-08T16:22:36.000Z

tweetable/admin.py

kkiyama117/django-HP

90c25a6d0597abf364f5b51ca2cd192cf8b998a2

[ "Apache-2.0" ]

44

2018-04-09T02:30:30.000Z

2018-10-15T15:53:43.000Z

tweetable/admin.py

kkiyama117/django-HP

90c25a6d0597abf364f5b51ca2cd192cf8b998a2

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import * class TrainAdmin(admin.ModelAdmin): pass admin.site.register(User) admin.site.register(Tweet)

13.166667

35

0.765823

21

158

5.761905

0.666667

0.14876

0.280992

0

0.139241

158

11

36

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0.889706

0

1

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true

0.166667

0.333333

0

0.5

0

1

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null

0

1

0

null

0

1

0

5

e8abfc2f97490e8723a87588d02e8cd7dc932294

1,397

py

Python

fibonacci/test_fibonacci.py

codenameyau/python-recursion

4067a48abe9f9ec56c6fbfcbcc573b7677419394

[ "MIT" ]

null

fibonacci/test_fibonacci.py

codenameyau/python-recursion

4067a48abe9f9ec56c6fbfcbcc573b7677419394

[ "MIT" ]

null

fibonacci/test_fibonacci.py

codenameyau/python-recursion

4067a48abe9f9ec56c6fbfcbcc573b7677419394

[ "MIT" ]

1

2018-09-19T13:45:13.000Z

import unittest import fibonacci class TestFibonacci(unittest.TestCase): def test_fib(self): self.assertEqual(fibonacci.fib(1), 1) self.assertEqual(fibonacci.fib(2), 1) self.assertEqual(fibonacci.fib(3), 2) self.assertEqual(fibonacci.fib(4), 3) self.assertEqual(fibonacci.fib(5), 5) self.assertEqual(fibonacci.fib(6), 8) self.assertEqual(fibonacci.fib(7), 13) self.assertEqual(fibonacci.fib(8), 21) def test_fib_rec(self): self.assertEqual(fibonacci.fib_rec(1), 1) self.assertEqual(fibonacci.fib_rec(2), 1) self.assertEqual(fibonacci.fib_rec(3), 2) self.assertEqual(fibonacci.fib_rec(4), 3) self.assertEqual(fibonacci.fib_rec(5), 5) self.assertEqual(fibonacci.fib_rec(6), 8) self.assertEqual(fibonacci.fib_rec(7), 13) self.assertEqual(fibonacci.fib_rec(8), 21) def test_fib_binet(self): self.assertEqual(fibonacci.fib_binet(1), 1) self.assertEqual(fibonacci.fib_binet(2), 1) self.assertEqual(fibonacci.fib_binet(3), 2) self.assertEqual(fibonacci.fib_binet(4), 3) self.assertEqual(fibonacci.fib_binet(5), 5) self.assertEqual(fibonacci.fib_binet(6), 8) self.assertEqual(fibonacci.fib_binet(7), 13) self.assertEqual(fibonacci.fib_binet(8), 21) if __name__ == '__main__': unittest.main()

36.763158

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0.198998

1,397

37

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