xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

7f78b3e71640fb7eac704e0b8c9153589e150331

1,732

py

Python

tensorflow_transform/beam/__init__.py

rtg0795/transform

ee1a769f0e359a8722dca7b434a3b499396a140f

[ "Apache-2.0" ]

null

tensorflow_transform/beam/__init__.py

rtg0795/transform

ee1a769f0e359a8722dca7b434a3b499396a140f

[ "Apache-2.0" ]

null

tensorflow_transform/beam/__init__.py

rtg0795/transform

ee1a769f0e359a8722dca7b434a3b499396a140f

[ "Apache-2.0" ]

null

# Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Module level imports for tensorflow_transform.beam.""" # pylint: disable=wildcard-import # The doc-generator's `explicit_package_contents_filter` requires that # sub-modules you want documented are explicitly imported. # Also: analyzer_impls registers implementation of analyzers. from tensorflow_transform.beam import analyzer_cache from tensorflow_transform.beam import analyzer_impls from tensorflow_transform.beam import experimental from tensorflow_transform.beam.context import Context from tensorflow_transform.beam.impl import AnalyzeAndTransformDataset from tensorflow_transform.beam.impl import AnalyzeDataset from tensorflow_transform.beam.impl import AnalyzeDatasetWithCache from tensorflow_transform.beam.impl import TransformDataset from tensorflow_transform.beam.tft_beam_io import * # pylint: enable=wildcard-import # TF 2.6 split support for filesystems such as Amazon S3 out to the # `tensorflow_io` package. Hence, this import is needed wherever we touch the # filesystem. try: import tensorflow_io as _ # pytype: disable=import-error # pylint: disable=g-import-not-at-top except ModuleNotFoundError: pass

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py

Python

src/wgan/plotting.py

chrismolli/redes_neuronales_semester_project

3309d102b809b395af39f7b570927e23d10db5ea

[ "MIT" ]

null

src/wgan/plotting.py

chrismolli/redes_neuronales_semester_project

3309d102b809b395af39f7b570927e23d10db5ea

[ "MIT" ]

null

src/wgan/plotting.py

chrismolli/redes_neuronales_semester_project

3309d102b809b395af39f7b570927e23d10db5ea

[ "MIT" ]

null

import matplotlib.pyplot as plt import pandas as pd import matplotlib.ticker as ticker def plot_csv_log(model_directory): # read log log = pd.read_csv(model_directory+"/log.csv", sep=",") fig, ax1 = plt.subplots(figsize=(5,2)) ax1.set_xlabel('Epoch') ax1.set_ylabel('Wasserstein Distance', color="C0") ax1.plot(log["epoch"], log["w_distance"], label="c_loss_real", color="C0") ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis ax2.set_ylabel('Generator Loss', color="C2") # we already handled the x-label with ax1 ax2.plot(log["epoch"],log["g_loss"],label="g_loss", color="C2") fig.tight_layout() # otherwise the right y-label is slightly clipped plt.savefig(model_directory+"/loss.pdf") plt.close() def plot_csv_log_v2(model_directory, max_epoch=None): # read log log = pd.read_csv(model_directory+"/log.csv", sep=",") if max_epoch: log = log[:max_epoch] fig, ax1 = plt.subplots(figsize=(5,3)) ax1.set_xlabel('Epoch') ax1.set_ylabel('Generator Loss', color="C2") # we already handled the x-label with ax1 ax1.plot(log["epoch"],log["g_loss"],label="g_loss", color="C2") ax1.yaxis.set_major_formatter(ticker.FormatStrFormatter("%.1e")) fig.tight_layout() # otherwise the right y-label is slightly clipped if max_epoch: ax1.set_xlim([0,max_epoch]) plt.savefig(model_directory+"/generator_loss.pdf") plt.close() fig, ax1 = plt.subplots(figsize=(5, 2)) ax1.set_xlabel('Epoch') ax1.set_ylabel('Wasserstein Distance', color="C0") ax1.plot(log["epoch"], log["w_distance"], label="c_loss_real", color="C0") ax1.yaxis.set_major_formatter(ticker.FormatStrFormatter("%.1e")) fig.tight_layout() # otherwise the right y-label is slightly clipped if max_epoch: ax1.set_xlim([0,max_epoch]) plt.savefig(model_directory + "/wassertstein_distance.pdf") plt.close()

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py

Python

moto/cloudformation/__init__.py

symroe/moto

4e106995af6f2820273528fca8a4e9ee288690a5

[ "Apache-2.0" ]

null

moto/cloudformation/__init__.py

symroe/moto

4e106995af6f2820273528fca8a4e9ee288690a5

[ "Apache-2.0" ]

1

2022-02-19T02:10:45.000Z

2022-02-19T02:15:52.000Z

moto/cloudformation/__init__.py

symroe/moto

4e106995af6f2820273528fca8a4e9ee288690a5

[ "Apache-2.0" ]

null

from .models import cloudformation_backends from ..core.models import base_decorator cloudformation_backend = cloudformation_backends["us-east-1"] mock_cloudformation = base_decorator(cloudformation_backends)

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py

Python

Step 2 The Fundamentals/Algorithms/Sorting/sorting_examples.py

jyeh20/interview-prep

031cead76a68ee5ade184628c4096300b6ff5df3

[ "MIT" ]

null

Step 2 The Fundamentals/Algorithms/Sorting/sorting_examples.py

jyeh20/interview-prep

031cead76a68ee5ade184628c4096300b6ff5df3

[ "MIT" ]

null

Step 2 The Fundamentals/Algorithms/Sorting/sorting_examples.py

jyeh20/interview-prep

031cead76a68ee5ade184628c4096300b6ff5df3

[ "MIT" ]

1

2021-03-19T02:20:21.000Z

# Sorting Examples

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py

Python

shopify/orders/objects/note_attribute.py

alikhan126/python-shopify-api

656cdf1af99485b25be545e2ed527bcb653076fd

[ "Unlicense" ]

10

2016-12-29T06:53:21.000Z

2022-03-01T10:35:32.000Z

shopify/orders/objects/note_attribute.py

alikhan126/python-shopify-api

656cdf1af99485b25be545e2ed527bcb653076fd

[ "Unlicense" ]

4

2016-12-30T15:12:47.000Z

2021-07-24T07:14:20.000Z

shopify/orders/objects/note_attribute.py

alikhan126/python-shopify-api

656cdf1af99485b25be545e2ed527bcb653076fd

[ "Unlicense" ]

8

2016-12-29T19:13:39.000Z

2022-03-22T18:02:58.000Z

from ...base import BaseParser class NoteAttribute(BaseParser): @property def name(self): return self._dict.get('name') @property def value(self): return self._dict.get('value')

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py

Python

toontown/pets/PetDCImportsAI.py

LittleNed/toontown-stride

1252a8f9a8816c1810106006d09c8bdfe6ad1e57

[ "Apache-2.0" ]

3

2020-01-02T08:43:36.000Z

2020-07-05T08:59:02.000Z

toontown/pets/PetDCImportsAI.py

NoraTT/Historical-Commits-Project-Altis-Source

fe88e6d07edf418f7de6ad5b3d9ecb3d0d285179

[ "Apache-2.0" ]

null

toontown/pets/PetDCImportsAI.py

NoraTT/Historical-Commits-Project-Altis-Source

fe88e6d07edf418f7de6ad5b3d9ecb3d0d285179

[ "Apache-2.0" ]

4

2019-06-20T23:45:23.000Z

2020-10-14T20:30:15.000Z

if hasattr(simbase, 'wantPets') and simbase.wantPets: from toontown.pets import DistributedPetAI

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py

Python

accesslink-API/accesslink/endpoints/resource.py

mendelson/polar-data-analysis

04c7b8615d88e3966e8a71c4353ad23c61ff022d

[ "MIT" ]

115

2017-10-26T16:59:51.000Z

2022-03-29T13:56:48.000Z

accesslink-API/accesslink/endpoints/resource.py

mendelson/polar-data-analysis

04c7b8615d88e3966e8a71c4353ad23c61ff022d

[ "MIT" ]

14

2018-01-08T10:02:05.000Z

2022-02-17T16:05:01.000Z

accesslink-API/accesslink/endpoints/resource.py

mendelson/polar-data-analysis

04c7b8615d88e3966e8a71c4353ad23c61ff022d

[ "MIT" ]

61

2017-10-27T10:38:17.000Z

2022-03-11T20:03:52.000Z

#!/usr/bin/env python class Resource(object): def __init__(self, oauth): self.oauth = oauth def _get(self, *args, **kwargs): return self.oauth.get(*args, **kwargs) def _post(self, *args, **kwargs): return self.oauth.post(*args, **kwargs) def _put(self, *args, **kwargs): return self.oauth.put(*args, **kwargs) def _delete(self, *args, **kwargs): return self.oauth.delete(*args, **kwargs)

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d63652614733a0fbafc795ca6354efb802f1c806

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py

Python

image_matting/modules/salient_object_detector/u2net/__init__.py

image-matting/backend

bbf502539cf70822dadb5eded31529d5e66c6276

[ "Apache-2.0" ]

1

2022-01-22T04:12:48.000Z

image_matting/modules/salient_object_detector/u2net/__init__.py

image-matting/backend

bbf502539cf70822dadb5eded31529d5e66c6276

[ "Apache-2.0" ]

4

2021-12-23T14:02:17.000Z

2022-01-26T18:44:06.000Z

image_matting/modules/salient_object_detector/u2net/__init__.py

image-matting/backend

bbf502539cf70822dadb5eded31529d5e66c6276

[ "Apache-2.0" ]

null

from u2net.u2_salient_object_detector import U2NetSalientObjectDetector

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py

Python

train_lib/clients/fhir/__init__.py

PHT-EU/train-container-library

b0c94c3a543fad48681b4c7f2f16f56f32054e71

[ "MIT" ]

1

2021-12-16T12:06:30.000Z

train_lib/clients/fhir/__init__.py

PHT-EU/train-container-library

b0c94c3a543fad48681b4c7f2f16f56f32054e71

[ "MIT" ]

35

2021-11-02T09:19:39.000Z

2022-03-31T13:24:33.000Z

train_lib/clients/fhir/__init__.py

PHT-Medic/train-container-library

b0c94c3a543fad48681b4c7f2f16f56f32054e71

[ "MIT" ]

null

from .fhir_client import PHTFhirClient from .fhir_query_builder import build_query_string, load_query_file

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c3cbfd3ec13ce6af561114a8707599f9e6da5516

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py

Python

api/GetStudentInfo.py

hackxg/CQUPT-SDK

cb2d1b93a813561e924c1d9eb6c4acfdbabc9185

[ "MIT" ]

4

2020-06-23T17:03:21.000Z

2021-12-19T05:04:00.000Z

api/GetStudentInfo.py

hackxg/CQUPT-SDK

cb2d1b93a813561e924c1d9eb6c4acfdbabc9185

[ "MIT" ]

null

api/GetStudentInfo.py

hackxg/CQUPT-SDK

cb2d1b93a813561e924c1d9eb6c4acfdbabc9185

[ "MIT" ]

null

import requests import GetCookie import LoginApi import GetUrl # @author Longm # @date 2020/6/23 16:34 # Blog https://Longm.top from lxml import etree def getinfo1(username,password):#学生基本信息 if LoginApi.getlogin(username, password)== '登录成功': url=GetUrl.jwzx() cookies=GetCookie.get(username) seesion = requests.session() seesion.cookies['PHPSESSID'] = cookies try: headers = { "User-Agent": "Mozilla/5.0 (Windows NT 6.3; WOW64; rv:32.0) Gecko/20100101 Firefox/32.0", "Accept": "*/*", "Accept-Language": "zh-cn,zh;q=0.8,en-us;q=0.5,en;q=0.3", "Accept-Encoding": "gzip, deflate", "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Referer": "https://ids.cqupt.edu.cn/authserver/login?service=http%3A%2F%2Fjwc.cqupt.edu.cn%2Ftysfrz%2Findex.php", "X-Requested-With": "XMLHttpRequest", "Connection": "keep-alive", "Pragma": "no-cache", "Cache-Control": "no-cache" } req=seesion.get(url+'/user.php',headers=headers) json = getdata1(req.text) return json except: data = { "data": {}, "code": "1" } return data else: data = { "data": {}, "code": "1" } return data def getinfo2(username,password): if LoginApi.getlogin(username, password)== '登录成功': url=GetUrl.jwzx() cookies=GetCookie.get(username) seesion = requests.session() seesion.cookies['PHPSESSID'] = cookies try: headers = { "User-Agent": "Mozilla/5.0 (Windows NT 6.3; WOW64; rv:32.0) Gecko/20100101 Firefox/32.0", "Accept": "*/*", "Accept-Language": "zh-cn,zh;q=0.8,en-us;q=0.5,en;q=0.3", "Accept-Encoding": "gzip, deflate", "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Referer": "https://ids.cqupt.edu.cn/authserver/login?service=http%3A%2F%2Fjwc.cqupt.edu.cn%2Ftysfrz%2Findex.php", "X-Requested-With": "XMLHttpRequest", "Connection": "keep-alive", "Pragma": "no-cache", "Cache-Control": "no-cache" } req=seesion.get(url+'/student/xj.php',headers=headers) json = getdata2(req.text) return json except: data = { "data": {}, "code": "1" } return data else: data = { "data": {}, "code": "1" } return data def getinfo3(username,password): if LoginApi.getlogin(username, password)== '登录成功': StudentId=GetCookie.getStudentId(username) url = GetUrl.jwzx() cookies = GetCookie.get(username) seesion = requests.session() seesion.cookies['PHPSESSID'] = cookies try: headers = { "User-Agent": "Mozilla/5.0 (Windows NT 6.3; WOW64; rv:32.0) Gecko/20100101 Firefox/32.0", "Accept": "*/*", "Accept-Language": "zh-cn,zh;q=0.8,en-us;q=0.5,en;q=0.3", "Accept-Encoding": "gzip, deflate", "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Referer": "https://ids.cqupt.edu.cn/authserver/login?service=http%3A%2F%2Fjwc.cqupt.edu.cn%2Ftysfrz%2Findex.php", "X-Requested-With": "XMLHttpRequest", "Connection": "keep-alive", "Pragma": "no-cache", "Cache-Control": "no-cache" } req = seesion.get(url + '/showstupic.php?xh='+StudentId, headers=headers) base64_data = req.content # 使用base64进行加密 return base64_data except: pass def getdata1(text): html = etree.HTML(text) temp = html.xpath(r'//tbody//tr//td/text()') stuNumber = temp[1] realName = temp[3] grade = temp[5] academy = temp[9] data = {"data":{ "stuNumber": stuNumber, "realName": realName, "grade": grade, "academy": academy}, "code":"0" } return data def getdata2(text): html = etree.HTML(text) idtemp = html.xpath(r'//*[@id="xjTabs-xjInfo"]/table/tr[8]//text()') #身份证号 2 出生年月 4 colleg = html.xpath(r'//*[@id="xjTabs-xjInfo"]/table/tr[14]//text()') # 考生号 2 通知书号 4 id=idtemp[2] Bdate=idtemp[5] ksh=colleg[2] tzsh=colleg[5] data = { "data":{ "id": id, "Bdate": Bdate, "ksh": ksh, "tzsh": tzsh }, "code":"0" } return data

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Python

models/__init__.py

gfoo/fastapi-demo

44ceb9e94fa833841756136c3b446f192a311dde

[ "Unlicense" ]

null

models/__init__.py

gfoo/fastapi-demo

44ceb9e94fa833841756136c3b446f192a311dde

[ "Unlicense" ]

null

models/__init__.py

gfoo/fastapi-demo

44ceb9e94fa833841756136c3b446f192a311dde

[ "Unlicense" ]

null

# import models for autogenerate alembic stuffs # do ont change order from .user import DBUser # noqa from .project import DBProject # noqa

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Python

default_encode_settings.py

nick3633/multibitrate-video-encode

174c0a9b75976c045bfb7fb4e647b979d677ee19

[ "Apache-2.0" ]

null

default_encode_settings.py

nick3633/multibitrate-video-encode

174c0a9b75976c045bfb7fb4e647b979d677ee19

[ "Apache-2.0" ]

null

default_encode_settings.py

nick3633/multibitrate-video-encode

174c0a9b75976c045bfb7fb4e647b979d677ee19

[ "Apache-2.0" ]

null

encode_settings = ''' { "video_encode_list": { "2160p.hevc": { "codec": "hevc", "dr": "sdr", "codded_width": "3840", "codded_height": "2160", "maxrate": "30000", "bufsize": "40000", "encode_speed": "medium", "encode_profile": "main10", "encode_extra_settings": "--aq-mode 3", "crf": "20" }, "1080p.hevc": { "codec": "hevc", "dr": "sdr", "codded_width": "1920", "codded_height": "1080", "maxrate": "12000", "bufsize": "16000", "encode_speed": "medium", "encode_profile": "main10", "encode_extra_settings": "--aq-mode 3", "crf": "18" }, "480p.hevc": { "codec": "hevc", "dr": "sdr", "codded_width": "854", "codded_height": "480", "maxrate": "3750", "bufsize": "5000", "encode_speed": "medium", "encode_profile": "main10", "encode_extra_settings": "--aq-mode 3", "crf": "16" }, "2160p.hevc.hdr": { "codec": "hevc", "dr": "hdr", "codded_width": "3840", "codded_height": "2160", "maxrate": "30000", "bufsize": "40000", "encode_speed": "medium", "encode_profile": "main10", "encode_extra_settings": "--aq-mode 3", "crf": "17" }, "1080p.hevc.hdr": { "codec": "hevc", "dr": "hdr", "codded_width": "1920", "codded_height": "1080", "maxrate": "12000", "bufsize": "16000", "encode_speed": "medium", "encode_profile": "main10", "encode_extra_settings": "--aq-mode 3", "crf": "15" }, "1080p.avc": { "codec": "avc", "dr": "sdr", "codded_width": "1920", "codded_height": "1080", "maxrate": "12000", "bufsize": "16000", "encode_speed": "medium", "encode_profile": "high", "encode_extra_settings": "", "crf": "18" }, "480p.avc": { "codec": "avc", "dr": "sdr", "codded_width": "854", "codded_height": "480", "maxrate": "3750", "bufsize": "5000", "encode_speed": "medium", "encode_profile": "main", "encode_extra_settings": "", "crf": "16" } }, "video_other_settings": { "chunked_encoding": true, "sdr_highest_res_only": false, "hdr_highest_res_only": true, "replace_sdr_with_hdr": true, "hls_compatible": true, "hls_compatible_settings": { "keyint_second": 6 } } } '''

24.67

45

0.498987

241

2,467

4.854772

0.253112

0.065812

0.101709

0.137607

0.760684

0.704274

0.668376

0

0.099032

0.287799

2,467

100

46

24.67

0.566875

0

0.65

0

0.98987

0.115883

0

1

0

false

0

null

0

1

0

1

0

1

0

null

0

5

613387317dc05f4afcd105f121ff1b666878e113

33

py

Python

fitnessfirstsg/__init__.py

terencelimzhengwei/fitnessfirstsg-api

72fe27ea7befaf3cd13ffab48c888fd7b18531a9

[ "MIT" ]

null

fitnessfirstsg/__init__.py

terencelimzhengwei/fitnessfirstsg-api

72fe27ea7befaf3cd13ffab48c888fd7b18531a9

[ "MIT" ]

null

fitnessfirstsg/__init__.py

terencelimzhengwei/fitnessfirstsg-api

72fe27ea7befaf3cd13ffab48c888fd7b18531a9

[ "MIT" ]

1

2021-01-24T03:04:34.000Z

from fitnessfirstsg.api import *

16.5

32

0.818182

4

33

6.75

1

0

0.121212

33

1

33

0.931034

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

6149bb405df705adf31203c6415f86be31e95182

378

py

Python

core/mutation_fuzzer/data_generators/__init__.py

ShreyasTheOne/Super-Duper-Fuzzer

b667e2dca3e49a370634ad4b0bd826aca06136b7

[ "MIT" ]

null

core/mutation_fuzzer/data_generators/__init__.py

ShreyasTheOne/Super-Duper-Fuzzer

b667e2dca3e49a370634ad4b0bd826aca06136b7

[ "MIT" ]

null

core/mutation_fuzzer/data_generators/__init__.py

ShreyasTheOne/Super-Duper-Fuzzer

b667e2dca3e49a370634ad4b0bd826aca06136b7

[ "MIT" ]

null

from core.mutation_fuzzer.data_generators.bool_generator import BoolGenerator from core.mutation_fuzzer.data_generators.int_generator import IntGenerator from core.mutation_fuzzer.data_generators.str_generator import StrGenerator from core.mutation_fuzzer.data_generators.list_generator import ListGenerator __all__ = [BoolGenerator, IntGenerator, StrGenerator, ListGenerator]

54

77

0.891534

45

378

7.133333

0.377778

0.099688

0.199377

0.274143

0.448598

0

0.060847

378

6

78

63

0.904225

0

1

0

false

0

0.8

0

0.8

0

null

0

1

0

1

0

null

0

1

0

1

0

5

614f984c887badcd24599e07f60834e91bfd6157

113

py

Python

sqlite_utils/__init__.py

seattleflu/sqlite-utils

d0cdaaaf00249230e847be3a3b393ee2689fbfe4

[ "Apache-2.0" ]

729

2018-07-15T02:13:50.000Z

2022-03-28T21:54:15.000Z

sqlite_utils/__init__.py

seattleflu/sqlite-utils

d0cdaaaf00249230e847be3a3b393ee2689fbfe4

[ "Apache-2.0" ]

396

2018-08-12T22:50:57.000Z

2022-03-29T23:52:39.000Z

sqlite_utils/__init__.py

seattleflu/sqlite-utils

d0cdaaaf00249230e847be3a3b393ee2689fbfe4

[ "Apache-2.0" ]

65

2019-02-01T17:32:18.000Z

2022-03-10T09:55:46.000Z

from .db import Database from .utils import suggest_column_types __all__ = ["Database", "suggest_column_types"]

22.6

46

0.79646

15

113

5.466667

0.6

0.317073

0.439024

0

0.115044

113

4

47

28.25

0.82

0

0.247788

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

5

615e8393cfdd4c82793888d827ce172ba732c73b

42

py

Python

cifar/step2/tensor_compression/__init__.py

chatzikon/DNN-COMPRESSION

5c19ab740048052426a77eb5bc7a56ab3fae93e9

[ "MIT" ]

9

2020-05-06T10:14:11.000Z

2021-07-09T10:12:22.000Z

ilscvr/step2/tensor_compression/__init__.py

chatzikon/DNN-COMPRESSION

5c19ab740048052426a77eb5bc7a56ab3fae93e9

[ "MIT" ]

null

ilscvr/step2/tensor_compression/__init__.py

chatzikon/DNN-COMPRESSION

5c19ab740048052426a77eb5bc7a56ab3fae93e9

[ "MIT" ]

null

from .compress import get_compressed_model

42

0.904762

6

42

6

1

0

0.071429

42

1

42

0.923077

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

617d64c03a1ba4046d214f339c05ea45d08f4651

139

py

Python

sanic_template/error/exc.py

aragentum/sanic-template

c73c874e4612f0eaca8a6174999f76239b4a2078

[ "MIT" ]

2

2020-09-03T17:46:31.000Z

2021-08-08T02:15:56.000Z

sanic_template/error/exc.py

aragentum/sanic-template

c73c874e4612f0eaca8a6174999f76239b4a2078

[ "MIT" ]

null

sanic_template/error/exc.py

aragentum/sanic-template

c73c874e4612f0eaca8a6174999f76239b4a2078

[ "MIT" ]

null

from sanic.exceptions import SanicException, add_status_code @add_status_code(400) class SQLOperationException(SanicException): pass

19.857143

60

0.834532

16

139

7

0.75

0.160714

0.232143

0

0.024194

0.107914

139

6

61

23.166667

0.879032

0

1

0

true

0.25

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

5

617e9936a0e55bb2dd26d63454d15a34224bc7dd

116

py

Python

example0/ex.py

cjaques/pybind_examples

d0756e7150ef238e80020d5ce8d4c28c17d267e5

[ "MIT" ]

16

2019-02-18T14:46:36.000Z

2022-02-19T18:28:28.000Z

example0/ex.py

cjaques/pybind_examples

d0756e7150ef238e80020d5ce8d4c28c17d267e5

[ "MIT" ]

null

example0/ex.py

cjaques/pybind_examples

d0756e7150ef238e80020d5ce8d4c28c17d267e5

[ "MIT" ]

5

2018-05-03T00:28:29.000Z

2022-02-10T10:59:38.000Z

import numpy as np import example a = np.ones((10,3)) b = np.ones((10,3))*3 c = example.add_arrays(a, b) print(c)

12.888889

28

0.646552

25

116

2.96

0.56

0.162162

0.216216

0.243243

0

0.072165

0.163793

116

8

29

14.5

0.690722

0

1

0

false

0

0.333333

0

0.333333

0.166667

1

0

null

0

1

0

1

0

null

0

1

0

5

4ee973be808c5a952b221f7b2bb83001444b07ef

219

py

Python

public_html/API/util.py

jlambert23/COP4331

49ae9563d596d16d0aaa2095da43047f353b052c

[ "MIT" ]

null

public_html/API/util.py

jlambert23/COP4331

49ae9563d596d16d0aaa2095da43047f353b052c

[ "MIT" ]

null

public_html/API/util.py

jlambert23/COP4331

49ae9563d596d16d0aaa2095da43047f353b052c

[ "MIT" ]

null

import json def throwErr(message): print(json.dumps({'error': "" + message + ""})) def getjson(): import sys return json.load(sys.stdin) def sendjson(message): print(json.dumps(message))

16.846154

51

0.611872

26

219

5.153846

0.538462

0.179104

0.238806

0.313433

0

0.232877

219

12

52

18.25

0.797619

0

0.022831

0

1

0.375

false

0

0.25

0

0.75

0.25

1

0

null

0

1

0

null

0

1

0

1

0

5

f63ea86b11b24bf2e320d1ed2a671c2faa1a7fc3

14,632

py

Python

autotest/t022_test.py

hansonmcoombs/flopy

49398983c36d381992621d5bf698ea7f78fc0014

[ "CC0-1.0", "BSD-3-Clause" ]

null

autotest/t022_test.py

hansonmcoombs/flopy

49398983c36d381992621d5bf698ea7f78fc0014

[ "CC0-1.0", "BSD-3-Clause" ]

null

autotest/t022_test.py

hansonmcoombs/flopy

49398983c36d381992621d5bf698ea7f78fc0014

[ "CC0-1.0", "BSD-3-Clause" ]

null

# Test SWR binary read functionality import os import flopy pth = os.path.join("..", "examples", "data", "swr_test") files = ( "SWR004.stg", "SWR004.flow", "SWR004.vel", "swr005.qaq", "SWR004.str", "SWR004.obs", ) def test_swr_binary_stage(ipos=0): fpth = os.path.join(pth, files[ipos]) sobj = flopy.utils.SwrStage(fpth) assert isinstance( sobj, flopy.utils.SwrStage ), "SwrStage object not created" nrecords = sobj.get_nrecords() assert nrecords == (18, 0), "SwrStage records does not equal (18, 0)" ntimes = sobj.get_ntimes() assert ntimes == 336, "SwrStage ntimes does not equal 336" for idx in range(ntimes): r = sobj.get_data(idx=idx) assert ( r is not None ), "SwrStage could not read data with get_data(idx=)" assert r.shape == ( 18, ), "SwrStage stage data shape does not equal (18,)" assert ( len(r.dtype.names) == 2 ), "SwrStage stage data dtype does not have 2 entries" kswrkstpkper = sobj.get_kswrkstpkper() assert kswrkstpkper.shape == ( 336, 3, ), "SwrStage kswrkstpkper shape does not equal (336, 3)" for kkk in kswrkstpkper: r = sobj.get_data(kswrkstpkper=kkk) assert ( r is not None ), "SwrStage could not read data with get_data(kswrkstpkper=)" assert r.shape == ( 18, ), "SwrStage stage data shape does not equal (18,)" assert ( len(r.dtype.names) == 2 ), "SwrStage stage data dtype does not have 2 entries" times = sobj.get_times() assert len(times) == 336, "SwrStage times length does not equal 336" for time in times: r = sobj.get_data(totim=time) assert ( r is not None ), "SwrStage could not read data with get_data(tottim=)" assert r.shape == ( 18, ), "SwrStage stage data shape does not equal (18,)" assert ( len(r.dtype.names) == 2 ), "SwrStage stage data dtype does not have 2 entries" ts = sobj.get_ts(irec=17) assert ts.shape == ( 336, ), "SwrStage stage timeseries shape does not equal (336,)" assert ( len(ts.dtype.names) == 2 ), "SwrStage stage time series stage data dtype does not have 2 entries" # plt.plot(ts['totim'], ts['stage']) # plt.show() return def test_swr_binary_budget(ipos=1): fpth = os.path.join(pth, files[ipos]) sobj = flopy.utils.SwrBudget(fpth) assert isinstance( sobj, flopy.utils.SwrBudget ), "SwrBudget object not created" nrecords = sobj.get_nrecords() assert nrecords == (18, 0), "SwrBudget records does not equal (18, 0)" ntimes = sobj.get_ntimes() assert ntimes == 336, "SwrBudget ntimes does not equal 336" for idx in range(ntimes): r = sobj.get_data(idx=idx) assert ( r is not None ), "SwrBudget could not read data with get_data(idx=)" assert r.shape == ( 18, ), "SwrBudget budget data shape does not equal (18,)" assert ( len(r.dtype.names) == 15 ), "SwrBudget data dtype does not have 15 entries" # plt.bar(range(18), r['inf-out']) # plt.show() kswrkstpkper = sobj.get_kswrkstpkper() assert kswrkstpkper.shape == ( 336, 3, ), "SwrBudget kswrkstpkper shape does not equal (336, 3)" for kkk in kswrkstpkper: r = sobj.get_data(kswrkstpkper=kkk) assert ( r is not None ), "SwrBudget could not read data with get_data(kswrkstpkper=)" assert r.shape == ( 18, ), "SwrBudget budget data shape does not equal (18,)" assert ( len(r.dtype.names) == 15 ), "SwrBudget budget data dtype does not have 15 entries" times = sobj.get_times() assert len(times) == 336, "SwrBudget times length does not equal 336" for time in times: r = sobj.get_data(totim=time) assert ( r is not None ), "SwrBudget could not read data with get_data(tottim=)" assert r.shape == ( 18, ), "SwrBudget budget data shape does not equal (18,)" assert ( len(r.dtype.names) == 15 ), "SwrBudget budget data dtype does not have 15 entries" ts = sobj.get_ts(irec=17) assert ts.shape == ( 336, ), "SwrBudget budget timeseries shape does not equal (336,)" assert ( len(ts.dtype.names) == 15 ), "SwrBudget time series budget data dtype does not have 15 entries" # plt.plot(ts['totim'], ts['qbcflow']) # plt.show() return def test_swr_binary_qm(ipos=2): fpth = os.path.join(pth, files[ipos]) sobj = flopy.utils.SwrFlow(fpth) assert isinstance(sobj, flopy.utils.SwrFlow), "SwrFlow object not created" nrecords = sobj.get_nrecords() assert nrecords == (40, 18), "SwrFlow records does not equal (40, 18)" connect = sobj.get_connectivity() assert connect.shape == ( 40, 3, ), "SwrFlow connectivity shape does not equal (40, 3)" ntimes = sobj.get_ntimes() assert ntimes == 336, "SwrFlow ntimes does not equal 336" for idx in range(ntimes): r = sobj.get_data(idx=idx) assert r is not None, "SwrFlow could not read data with get_data(idx=)" assert r.shape == (40,), "SwrFlow qm data shape does not equal (40,)" assert ( len(r.dtype.names) == 3 ), "SwrFlow qm data dtype does not have 3 entries" # plt.bar(range(40), r['flow']) # plt.show() kswrkstpkper = sobj.get_kswrkstpkper() assert kswrkstpkper.shape == ( 336, 3, ), "SwrFlow kswrkstpkper shape does not equal (336, 3)" for kkk in kswrkstpkper: r = sobj.get_data(kswrkstpkper=kkk) assert ( r is not None ), "SwrFlow could not read data with get_data(kswrkstpkper=)" assert r.shape == (40,), "SwrFlow qm data shape does not equal (40,)" assert ( len(r.dtype.names) == 3 ), "SwrFlow qm data dtype does not have 3 entries" times = sobj.get_times() assert len(times) == 336, "SwrFlow times length does not equal 336" for time in times: r = sobj.get_data(totim=time) assert ( r is not None ), "SwrFlow could not read data with get_data(tottim=)" assert r.shape == (40,), "SwrFlow qm data shape does not equal (40,)" assert ( len(r.dtype.names) == 3 ), "SwrFlow qm data dtype does not have 3 entries" ts = sobj.get_ts(irec=17, iconn=16) assert ts.shape == ( 336, ), "SwrFlow qm timeseries shape does not equal (336,)" assert ( len(ts.dtype.names) == 3 ), "SwrFlow time series qm data dtype does not have 3 entries" ts2 = sobj.get_ts(irec=16, iconn=17) assert ts2.shape == ( 336, ), "SwrFlow qm timeseries shape does not equal (336,)" assert ( len(ts2.dtype.names) == 3 ), "SwrFlow time series qm data dtype does not have 3 entries" # plt.plot(ts['totim'], ts['velocity']) # plt.plot(ts2['totim'], ts2['velocity']) # plt.show() return def test_swr_binary_qaq(ipos=3): fpth = os.path.join(pth, files[ipos]) sobj = flopy.utils.SwrExchange(fpth, verbose=True) assert isinstance( sobj, flopy.utils.SwrExchange ), "SwrExchange object not created" nrecords = sobj.get_nrecords() assert nrecords == (19, 0), "SwrExchange records does not equal (19, 0)" ntimes = sobj.get_ntimes() assert ntimes == 350, "SwrExchange ntimes does not equal 350" for idx in range(ntimes): r = sobj.get_data(idx=idx) assert ( r is not None ), "SwrExchange could not read data with get_data(idx=)" assert r.shape == ( 21, ), "SwrExchange qaq data shape does not equal (21,)" assert ( len(r.dtype.names) == 11 ), "SwrExchange qaq data dtype does not have 11 entries" # plt.bar(range(21), r['qaq']) # plt.show() kswrkstpkper = sobj.get_kswrkstpkper() assert kswrkstpkper.shape == ( 350, 3, ), "SwrExchange kswrkstpkper shape does not equal (350, 3)" for kkk in kswrkstpkper: r = sobj.get_data(kswrkstpkper=kkk) assert ( r is not None ), "SwrExchange could not read data with get_data(kswrkstpkper=)" assert r.shape == ( 21, ), "SwrExchange qaq data shape does not equal (21,)" assert ( len(r.dtype.names) == 11 ), "SwrExchange qaq data dtype does not have 11 entries" times = sobj.get_times() assert len(times) == 350, "SwrExchange times length does not equal 350" for time in times: r = sobj.get_data(totim=time) assert ( r is not None ), "SwrExchange could not read data with get_data(tottim=)" assert r.shape == ( 21, ), "SwrExchange qaq data shape does not equal (21,)" assert ( len(r.dtype.names) == 11 ), "SwrExchange qaq data dtype does not have 11 entries" ts = sobj.get_ts(irec=17, klay=0) assert ts.shape == ( 350, ), "SwrExchange timeseries shape does not equal (350,)" assert ( len(ts.dtype.names) == 11 ), "SwrExchange time series qaq data dtype does not have 11 entries" # plt.plot(ts['totim'], ts['qaq']) # plt.show() return def test_swr_binary_structure(ipos=4): fpth = os.path.join(pth, files[ipos]) sobj = flopy.utils.SwrStructure(fpth, verbose=True) assert isinstance( sobj, flopy.utils.SwrStructure ), "SwrStructure object not created" nrecords = sobj.get_nrecords() assert nrecords == (18, 0), "SwrStructure records does not equal (18, 0)" ntimes = sobj.get_ntimes() assert ntimes == 336, "SwrStructure ntimes does not equal 336" for idx in range(ntimes): r = sobj.get_data(idx=idx) assert ( r is not None ), "SwrStructure could not read data with get_data(idx=)" assert r.shape == ( 2, ), "SwrStructure structure data shape does not equal (2,)" assert ( len(r.dtype.names) == 8 ), "SwrStructure structure data dtype does not have 8 entries" kswrkstpkper = sobj.get_kswrkstpkper() assert kswrkstpkper.shape == ( 336, 3, ), "SwrStructure kswrkstpkper shape does not equal (336, 3)" for kkk in kswrkstpkper: r = sobj.get_data(kswrkstpkper=kkk) assert ( r is not None ), "SwrStructure could not read data with get_data(kswrkstpkper=)" assert r.shape == ( 2, ), "SwrStructure structure data shape does not equal (2,)" assert ( len(r.dtype.names) == 8 ), "SwrStructure structure data dtype does not have 8 entries" times = sobj.get_times() assert len(times) == 336, "SwrStructure times length does not equal 336" for time in times: r = sobj.get_data(totim=time) assert ( r is not None ), "SwrStructure could not read data with get_data(tottim=)" assert r.shape == ( 2, ), "SwrStructure structure data shape does not equal (2,)" assert ( len(r.dtype.names) == 8 ), "SwrStructure structure data dtype does not have 8 entries" ts = sobj.get_ts(irec=17, istr=0) assert ts.shape == ( 336, ), "SwrStructure timeseries shape does not equal (336,)" assert ( len(ts.dtype.names) == 8 ), "SwrStructure time series structure data dtype does not have 8 entries" # plt.plot(ts['totim'], ts['strflow']) # plt.show() obs3 = sobj.get_ts(irec=17, istr=0) return def test_swr_binary_obs(ipos=5): fpth = os.path.join(pth, files[ipos]) sobj = flopy.utils.SwrObs(fpth) assert isinstance(sobj, flopy.utils.SwrObs), "SwrObs object not created" nobs = sobj.get_nobs() assert nobs == 9, "SwrObs numobs does not equal 9" obsnames = sobj.get_obsnames() assert len(obsnames) == 9, "SwrObs number of obsnames does not equal 9" ntimes = sobj.get_ntimes() assert ntimes == 336, "SwrObs numtimes does not equal 336" times = sobj.get_times() assert len(times) == 336, "SwrFile times length does not equal 336" ts = sobj.get_data() assert ts.shape == ( 336, ), "SwrObs length of data array does not equal (336,)" assert ( len(ts.dtype.names) == 10 ), "SwrObs data does not have totim + 9 observations" ts = sobj.get_data(obsname="OBS5") assert ts.shape == ( 336, ), "SwrObs length of data array does not equal (336,)" assert ( len(ts.dtype.names) == 2 ), "SwrObs data does not have totim + 1 observation" # plt.plot(ts['totim'], ts['OBS5']) # plt.show() for idx in range(ntimes): d = sobj.get_data(idx=idx) assert d.shape == ( 1, ), "SwrObs length of data array does not equal (1,)" assert ( len(d.dtype.names) == nobs + 1 ), "SwrObs data does not have nobs + 1" for time in times: d = sobj.get_data(totim=time) assert d.shape == ( 1, ), "SwrObs length of data array does not equal (1,)" assert ( len(d.dtype.names) == nobs + 1 ), "SwrObs data does not have nobs + 1" # test get_dataframes() try: import pandas as pd for idx in range(ntimes): df = sobj.get_dataframe(idx=idx, timeunit="S") assert isinstance(df, pd.DataFrame), "A DataFrame was not returned" assert df.shape == (1, nobs + 1), "data shape is not (1, 10)" for time in times: df = sobj.get_dataframe(totim=time, timeunit="S") assert isinstance(df, pd.DataFrame), "A DataFrame was not returned" assert df.shape == (1, nobs + 1), "data shape is not (1, 10)" df = sobj.get_dataframe(timeunit="S") assert isinstance(df, pd.DataFrame), "A DataFrame was not returned" assert df.shape == (336, nobs + 1), "data shape is not (336, 10)" except ImportError: print("pandas not available...") return if __name__ == "__main__": test_swr_binary_obs() test_swr_binary_stage() test_swr_binary_budget() test_swr_binary_qm() test_swr_binary_qaq() test_swr_binary_structure()

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f64b0de484e60e1e3c084444c766e530a122c9f9

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py

Python

changes.py

ukBaz/webhook-test

7846e9142bbe5ec1dc72c7b9644f845112acbeb1

[ "MIT" ]

null

changes.py

ukBaz/webhook-test

7846e9142bbe5ec1dc72c7b9644f845112acbeb1

[ "MIT" ]

1

2019-08-11T11:24:01.000Z

2019-08-11T11:36:17.000Z

changes.py

ukBaz/webhook-test

7846e9142bbe5ec1dc72c7b9644f845112acbeb1

[ "MIT" ]

null

# Modification 1 for PR

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76

py

Python

collectibles/collectible.py

csvoss/todo-collector

51469a7471da062f0d991872cb932e20d642393e

[ "MIT" ]

null

collectibles/collectible.py

csvoss/todo-collector

51469a7471da062f0d991872cb932e20d642393e

[ "MIT" ]

null

collectibles/collectible.py

csvoss/todo-collector

51469a7471da062f0d991872cb932e20d642393e

[ "MIT" ]

2

2018-03-27T01:09:38.000Z

2021-07-02T00:55:55.000Z

class Collectible(object): # TODO: write template methods here pass

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9c9c00d87ace682595041f97850134cfc4990758

36

py

Python

tests/components/plex/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/plex/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/plex/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Tests for the Plex component."""

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9ce83ec3e19a563e9b51d243cade1a1cd32c0896

42

py

Python

002-kernel-and-systemcall/hello_python.py

satoru-takeuchi/youtube-sample

bae4c675445ab3cd363a17f2d1b3433993ec5bbb

[ "MIT" ]

5

2020-07-11T14:00:58.000Z

2020-11-30T15:11:27.000Z

002-kernel-and-systemcall/hello_python.py

satoru-takeuchi/youtube-sample

bae4c675445ab3cd363a17f2d1b3433993ec5bbb

[ "MIT" ]

null

002-kernel-and-systemcall/hello_python.py

satoru-takeuchi/youtube-sample

bae4c675445ab3cd363a17f2d1b3433993ec5bbb

[ "MIT" ]

1

2021-12-18T17:48:38.000Z

#!/usr/bin/python3 print("hello Python")

10.5

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9ced749abde51d38bc6fffc1d4a2838ba56d0883

302

py

Python

{{ cookiecutter.app_name }}/factories.py

epicserve/cookiecutter-django-base-app

789afe726d2a14d980fb270147bfdc95c23df4bc

[ "BSD-3-Clause" ]

1

2015-01-05T06:52:41.000Z

{{ cookiecutter.app_name }}/factories.py

epicserve/cookiecutter-django-base-app

789afe726d2a14d980fb270147bfdc95c23df4bc

[ "BSD-3-Clause" ]

null

{{ cookiecutter.app_name }}/factories.py

epicserve/cookiecutter-django-base-app

789afe726d2a14d980fb270147bfdc95c23df4bc

[ "BSD-3-Clause" ]

null

import factory from . import models class {{ cookiecutter.model_name }}Factory(factory.DjangoModelFactory): FACTORY_FOR = models.{{ cookiecutter.model_name }} title = factory.Sequence(lambda n: 'Title {0}'.format(n)) description = factory.Sequence(lambda n: 'Description {0}'.format(n))

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null

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5

147ab2aa23abee91f81e86038f61990003431558

55

py

Python

altair/vegalite/schema.py

zjffdu/altair

cd34b03ce011f16616f7c6c59a3c60436b679302

[ "BSD-3-Clause" ]

1

2021-03-10T00:36:53.000Z

altair/vegalite/schema.py

zjffdu/altair

cd34b03ce011f16616f7c6c59a3c60436b679302

[ "BSD-3-Clause" ]

null

altair/vegalite/schema.py

zjffdu/altair

cd34b03ce011f16616f7c6c59a3c60436b679302

[ "BSD-3-Clause" ]

1

2017-08-23T17:52:59.000Z

"""Altair schema wrappers""" from .v2.schema import *

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1

0

5

1ad430b7e52fce76e235ec384ed7efa3efd1ac1b

43

py

Python

ilovethrice.py

Flowlesyewitahe/my-sister-is-yelling-while-i-am-coding

af1fda85a678583482d2842922f7a6c6f7ff263a

[ "Apache-2.0" ]

null

ilovethrice.py

Flowlesyewitahe/my-sister-is-yelling-while-i-am-coding

af1fda85a678583482d2842922f7a6c6f7ff263a

[ "Apache-2.0" ]

null

ilovethrice.py

Flowlesyewitahe/my-sister-is-yelling-while-i-am-coding

af1fda85a678583482d2842922f7a6c6f7ff263a

[ "Apache-2.0" ]

null

import time import meow meow time.sleep(3)

8.6

13

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8

43

4.25

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0.139535

43

4

14

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null

0

1

0

1

0

5

212fe7eb567438c5285a05eaeab524dd48622597

2,718

py

Python

tasrif/test_scripts/test_pipeline_SlidingWindowOperator.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

20

2021-12-06T10:41:54.000Z

2022-03-13T16:25:43.000Z

tasrif/test_scripts/test_pipeline_SlidingWindowOperator.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

33

2021-12-06T08:27:18.000Z

2022-03-14T05:07:53.000Z

tasrif/test_scripts/test_pipeline_SlidingWindowOperator.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

2

2022-02-07T08:06:48.000Z

2022-02-14T07:13:42.000Z

# + import pandas as pd from tasrif.processing_pipeline.custom import SlidingWindowOperator df = pd.DataFrame( [ ["2020-02-16 11:45:00", 27, 102.5], ["2020-02-16 12:00:00", 27, 68.5], ["2020-02-16 12:15:00", 27, 40.0], ["2020-02-16 15:15:00", 27, 282.5], ["2020-02-16 15:30:00", 27, 275.0], ["2020-02-16 15:45:00", 27, 250.0], ["2020-02-16 16:00:00", 27, 235.0], ["2020-02-16 16:15:00", 27, 206.5], ["2020-02-16 16:30:00", 27, 191.0], ["2020-02-16 16:45:00", 27, 166.5], ["2020-02-16 17:00:00", 27, 171.5], ["2020-02-16 17:15:00", 27, 152.0], ["2020-02-16 17:30:00", 27, 124.0], ["2020-02-16 17:45:00", 27, 106.0], ["2020-02-16 18:00:00", 27, 96.5], ["2020-02-16 18:15:00", 27, 86.5], ["2020-02-16 18:30:00", 27, 78.0], ["2020-02-16 18:45:00", 27, 71.5], ["2020-02-16 19:00:00", 27, 64.5], ["2020-02-16 19:15:00", 27, 51.0], ["2020-02-16 19:30:00", 27, 50.666668], ["2020-02-16 19:45:00", 27, 41.0], ["2020-02-16 20:00:00", 27, 40.0], ["2020-02-16 20:15:00", 27, 40.0], ["2020-02-16 20:30:00", 27, 40.0], ["2020-02-16 14:45:00", 31, 125.0], ["2020-02-16 15:00:00", 31, 140.5], ["2020-02-16 15:15:00", 31, 183.0], ["2020-02-16 15:30:00", 31, 222.0], ["2020-02-16 15:45:00", 31, 234.5], ["2020-02-16 16:00:00", 31, 249.0], ["2020-02-16 16:15:00", 31, 245.5], ["2020-02-16 16:30:00", 31, 236.0], ["2020-02-16 16:45:00", 31, 223.0], ["2020-02-16 17:00:00", 31, 208.0], ["2020-02-16 17:15:00", 31, 194.0], ["2020-02-16 17:30:00", 31, 186.0], ["2020-02-16 17:45:00", 31, 177.0], ["2020-02-16 18:00:00", 31, 171.0], ["2020-02-16 18:15:00", 31, 164.0], ["2020-02-16 18:30:00", 31, 156.0], ["2020-02-16 18:45:00", 31, 157.0], ["2020-02-16 19:00:00", 31, 158.0], ["2020-02-16 19:15:00", 31, 158.5], ["2020-02-16 19:30:00", 31, 150.0], ["2020-02-16 19:45:00", 31, 145.0], ["2020-02-16 20:00:00", 31, 137.0], ["2020-02-16 20:15:00", 31, 141.0], ["2020-02-16 20:45:00", 31, 146.0], ["2020-02-16 21:00:00", 31, 141.0], ], columns=["dateTime", "patientID", "CGM"], ) df["dateTime"] = pd.to_datetime(df["dateTime"]) df # + op = SlidingWindowOperator( winsize="1h15t", time_col="dateTime", label_col="CGM", participant_identifier="patientID", ) df_timeseries, df_labels, df_label_time, df_pids = op.process(df)[0] df_timeseries

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0d03016bfc357a0c430b8a7e2d7a97034e3eeb38

250

py

Python

random_attendee.py

oakoneric/programmierung-ss19

819a789020d7e280b1cb54f14494674e6772adce

[ "MIT" ]

9

2019-04-10T21:32:59.000Z

2019-07-29T14:58:17.000Z

random_attendee.py

oakoneric/programmierung-ss19

819a789020d7e280b1cb54f14494674e6772adce

[ "MIT" ]

null

random_attendee.py

oakoneric/programmierung-ss19

819a789020d7e280b1cb54f14494674e6772adce

[ "MIT" ]

1

2021-07-19T14:07:26.000Z

import random print('-----------------------------------------------') num = int(input('number of attendees: ')) print('the next task is reserved for student no. ' + str(random.randint(1,num))) print('-----------------------------------------------')

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0d34b13772c5f53593d2a77ee5b045784d7cadf8

216

py

Python

gammapy/catalog/__init__.py

QRemy/gammapy

fe799e8a8e792d216fdb11fb7abcb64d58f273dd

[ "BSD-3-Clause" ]

null

gammapy/catalog/__init__.py

QRemy/gammapy

fe799e8a8e792d216fdb11fb7abcb64d58f273dd

[ "BSD-3-Clause" ]

null

gammapy/catalog/__init__.py

QRemy/gammapy

fe799e8a8e792d216fdb11fb7abcb64d58f273dd

[ "BSD-3-Clause" ]

null

# Licensed under a 3-clause BSD style license - see LICENSE.rst """Source catalogs.""" from .core import * from .fermi import * from .gammacat import * from .hawc import * from .hess import * from .registry import *

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0d38607e07e5b0bdcf75c5cc5d3736d4f580ae8e

89

py

Python

phonotactics/onsets/__init__.py

shlomo-Kallner/coventreiya

aa0773693220025f8d2c23644a2c5d9d884773e9

[ "Apache-2.0" ]

null

phonotactics/onsets/__init__.py

shlomo-Kallner/coventreiya

aa0773693220025f8d2c23644a2c5d9d884773e9

[ "Apache-2.0" ]

null

phonotactics/onsets/__init__.py

shlomo-Kallner/coventreiya

aa0773693220025f8d2c23644a2c5d9d884773e9

[ "Apache-2.0" ]

null

__package__ = "onsets" __all__ = [ "onsets" , "ver_1_5_1" , "ver_1_5_5" , "ver_1_5_7" ]

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py

Python

cityyouthmatrix/apps/accounts/views.py

johnathaningle/CityYouthMatrix

b4ad244d92c97f3e20a923e18babf1ed1d278a87

[ "MIT" ]

1

2020-06-13T11:26:31.000Z

cityyouthmatrix/apps/accounts/views.py

jingle1000/CityYouthMatrix

b4ad244d92c97f3e20a923e18babf1ed1d278a87

[ "MIT" ]

2

2021-03-30T13:37:26.000Z

2021-04-08T21:01:26.000Z

cityyouthmatrix/apps/accounts/views.py

johnathaningle/CityYouthMatrix

b4ad244d92c97f3e20a923e18babf1ed1d278a87

[ "MIT" ]

null

from django.shortcuts import redirect, render, resolve_url from django.contrib.auth.decorators import login_required from django.http import HttpRequest # Create your views here. def login_success(request): if request.user.is_anonymous: return redirect('/') if request.user.is_superuser: return redirect('/admin') try: if request.user.driver: return redirect('driver/driver') else: return redirect("family-info") #this needs to be changed except: return redirect("driver/driver") def dispatcher(request: HttpRequest): return render(request, "accounts/dispatcher/dispatcher.html") def home(request: HttpRequest): return render(request, "accounts/home.html") def manage_drivers(request: HttpRequest): return render(request, "accounts/dispatcher/manage-drivers.html") def manage_families(request: HttpRequest): return render(request, "accounts/dispatcher/manage-families.html") def family_info(request: HttpRequest): return render(request, "accounts/dispatcher/family-info.html") def driver_info(request: HttpRequest): return render(request, "accounts/dispatcher/driver-info.html") def manage_trips(request: HttpRequest): return render(request, "accounts/dispatcher/manage-trips.html") def trip_info(request: HttpRequest): return render(request, "accounts/dispatcher/trip-info.html") def broadcast(request: HttpRequest): return render(request, "accounts/dispatcher/broadcast.html") def notifications(request: HttpRequest): return render(request, "accounts/dispatcher/notifications.html") def dispatcher_profile(request: HttpRequest): return render(request, "accounts/dispatcher/profile.html") def manage_rules(request: HttpRequest): return render(request, "accounts/dispatcher/rules.html") def new_trip(request: HttpRequest): return render(request, "accounts/dispatcher/new-trip.html") #driver views @login_required(login_url="") def driver(request: HttpRequest): return render(request, "accounts/driver/driver.html") @login_required(login_url="") def driver_profile(request: HttpRequest): return render(request, "accounts/driver/driver-profile.html") @login_required(login_url="") def driver_notifications(request: HttpRequest): return render(request, "accounts/driver/notifications.html") @login_required(login_url="") def driver_site_rules(request: HttpRequest): return render(request, "accounts/driver/site-rules.html") @login_required(login_url="") def driver_trip_info(request: HttpRequest): return render(request, "accounts/driver/trip-info.html")

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70

0.756283

312

2,626

6.269231

0.176282

0.165644

0.220859

0.276074

0.628834

0.590491

0.271472

0

0.12719

2,626

80

71

32.825

0.853403

0.022468

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0.089286

0

0.250878

0.226687

0

1

0.339286

false

0

0.053571

0.321429

0.803571

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null

0

1

0

null

0

1

0

1

0

5

b4c17f942fa059c7585a6643066701e9c0dd71e9

31

py

Python

ui/custom_pb/__init__.py

magnusjwatson2786/pyDM

0551a6365a07d336f7dd1d6713c3891954666278

[ "MIT" ]

null

ui/custom_pb/__init__.py

magnusjwatson2786/pyDM

0551a6365a07d336f7dd1d6713c3891954666278

[ "MIT" ]

null

ui/custom_pb/__init__.py

magnusjwatson2786/pyDM

0551a6365a07d336f7dd1d6713c3891954666278

[ "MIT" ]

null

from . custompb import CustomPb

31

0.83871

4

31

6.5

0.75

0

0.129032

31

1

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

b4c63c54c056420a5ba181bbde6af71700a5fe18

37

py

Python

loutilities/flask/user/__init__.py

louking/loutilities

7a7bb27b09b8d6e3a411153b604858aaec397fc6

[ "Apache-2.0" ]

1

2020-03-16T12:47:08.000Z

loutilities/flask/user/__init__.py

louking/loutilities

7a7bb27b09b8d6e3a411153b604858aaec397fc6

[ "Apache-2.0" ]

35

2015-07-11T14:57:30.000Z

2022-03-12T00:53:44.000Z

loutilities/flask/user/__init__.py

louking/loutilities

7a7bb27b09b8d6e3a411153b604858aaec397fc6

[ "Apache-2.0" ]

null

from loutilities.user.tables import *

37

0.837838

5

37

6.2

1

0

0.081081

37

1

37

0.911765

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

b4ed0e39bcbae9f1790f742d07466bd82c14d320

66

py

Python

planningpoker/views/__init__.py

not-raspberry/planningpoker

75113821d479f9973b41c39ad77940801c5e9525

[ "MIT" ]

null

planningpoker/views/__init__.py

not-raspberry/planningpoker

75113821d479f9973b41c39ad77940801c5e9525

[ "MIT" ]

null

planningpoker/views/__init__.py

not-raspberry/planningpoker

75113821d479f9973b41c39ad77940801c5e9525

[ "MIT" ]

null

from planningpoker.views import status, moderator, player # noqa

33

65

0.80303

8

66

6.625

1

0

0.136364

66

1

66

0.929825

0.060606

0

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true

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1

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1

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

3713c75621f670b4526bc031e734e4b7453a0c35

85

py

Python

tccli/services/tiw/__init__.py

ivandksun/tencentcloud-cli-intl-en

41b84e339918961b8bc92f7498e56347d21e16d3

[ "Apache-2.0" ]

47

2018-05-31T11:26:25.000Z

2022-03-08T02:12:45.000Z

tccli/services/tiw/__init__.py

ivandksun/tencentcloud-cli-intl-en

41b84e339918961b8bc92f7498e56347d21e16d3

[ "Apache-2.0" ]

23

2018-06-14T10:46:30.000Z

2022-02-28T02:53:09.000Z

tccli/services/tiw/__init__.py

ivandksun/tencentcloud-cli-intl-en

41b84e339918961b8bc92f7498e56347d21e16d3

[ "Apache-2.0" ]

22

2018-10-22T09:49:45.000Z

2022-03-30T08:06:04.000Z

# -*- coding: utf-8 -*- from tccli.services.tiw.tiw_client import action_caller

21.25

55

0.694118

12

85

4.75

0.916667

0

0.014085

0.164706

85

4

56

21.25

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0

1

0

true

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null

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null

0

1

0

1

0

1

0

5

2e926f614d626aed0c3dfb6308c9fe3b7cc55ba6

48,035

py

Python

tests/io/hdf_utils/test_model.py

sulaymandesai/pyUSID

fa4d152856e4717c92b1fbe34222eb2e1c042707

[ "MIT" ]

null

tests/io/hdf_utils/test_model.py

sulaymandesai/pyUSID

fa4d152856e4717c92b1fbe34222eb2e1c042707

[ "MIT" ]

null

tests/io/hdf_utils/test_model.py

sulaymandesai/pyUSID

fa4d152856e4717c92b1fbe34222eb2e1c042707

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:07:16 2017 @author: Suhas Somnath """ from __future__ import division, print_function, unicode_literals, absolute_import import unittest import os import sys import h5py import numpy as np import dask.array as da import shutil sys.path.append("../../pyUSID/") from pyUSID.io import hdf_utils, write_utils, USIDataset from tests.io import data_utils if sys.version_info.major == 3: unicode = str class TestModel(unittest.TestCase): def setUp(self): data_utils.make_beps_file() data_utils.make_sparse_sampling_file() data_utils.make_incomplete_measurement_file() data_utils.make_relaxation_file() def tearDown(self): for file_path in [data_utils.std_beps_path, data_utils.sparse_sampling_path, data_utils.incomplete_measurement_path, data_utils.relaxation_path]: data_utils.delete_existing_file(file_path) class TestGetDimensionality(TestModel): def test_legal_no_sort(self): self.__helper_no_sort(hdf_dsets=True) self.__helper_no_sort(hdf_dsets=False) def __helper_no_sort(self, hdf_dsets=True): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_dsets = [h5_f['/Raw_Measurement/Spectroscopic_Indices'], h5_f['/Raw_Measurement/source_main-Fitter_000/Spectroscopic_Indices'], h5_f['/Raw_Measurement/Position_Indices']] expected_shapes = [[7, 2], [7], [5, 3]] for h5_dset, exp_shape in zip(h5_dsets, expected_shapes): if not hdf_dsets: h5_dset = h5_dset[()] self.assertTrue(np.all(exp_shape == hdf_utils.get_dimensionality(h5_dset))) def test_legal_w_sort(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_dsets = [h5_f['/Raw_Measurement/Spectroscopic_Indices'], h5_f['/Raw_Measurement/source_main-Fitter_000/Spectroscopic_Indices'], h5_f['/Raw_Measurement/Position_Indices']] expected_shapes = [[2, 7], [7], [3, 5]] sort_orders = [[1, 0], [0], [1, 0]] for h5_dset, s_oder, exp_shape in zip(h5_dsets, sort_orders, expected_shapes): self.assertTrue(np.all(exp_shape == hdf_utils.get_dimensionality(h5_dset, index_sort=s_oder))) def test_not_hdf_dset(self): for obj in [15, 'srds']: with self.assertRaises(TypeError): _ = hdf_utils.get_dimensionality(obj) def test_invalid_sort(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_dset = h5_f['/Raw_Measurement/Spectroscopic_Indices'] with self.assertRaises(ValueError): _ = hdf_utils.get_dimensionality(h5_dset, index_sort=[3, 4]) _ = hdf_utils.get_dimensionality(h5_dset, index_sort=['a', np.arange(5)]) class TestGetSortOrder(TestModel): def test_invalid_types(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: for obj in ['fdfdfd', h5_f]: with self.assertRaises(TypeError): _ = hdf_utils.get_sort_order(obj) def test_simple(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_dsets = [h5_f['/Raw_Measurement/Spectroscopic_Indices'], h5_f['/Raw_Measurement/source_main-Fitter_000/Spectroscopic_Indices'], h5_f['/Raw_Measurement/Position_Indices']] expected_order = [[0, 1], [0], [0, 1]] for h5_dset, exp_order in zip(h5_dsets, expected_order): self.assertTrue(np.all(exp_order == hdf_utils.get_sort_order(h5_dset))) def test_reversed(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_dsets = [np.flipud(h5_f['/Raw_Measurement/Spectroscopic_Indices']), h5_f['/Raw_Measurement/source_main-Fitter_000/Spectroscopic_Indices'], np.fliplr(h5_f['/Raw_Measurement/Position_Indices'])] expected_order = [[1, 0], [0], [1, 0]] for h5_dset, exp_order in zip(h5_dsets, expected_order): self.assertTrue(np.all(exp_order == hdf_utils.get_sort_order(h5_dset))) class TestGetUnitValues(TestModel): def test_source_spec_all(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_inds = h5_f['/Raw_Measurement/Spectroscopic_Indices'] h5_vals = h5_f['/Raw_Measurement/Spectroscopic_Values'] expected = {} for dim_name in ['Bias', 'Cycle']: expected[dim_name] = h5_f['/Raw_Measurement/' + dim_name][()] ret_val = hdf_utils.get_unit_values(h5_inds, h5_vals) self.assertEqual(len(expected), len(ret_val)) for key, exp in expected.items(): self.assertTrue(np.allclose(exp, ret_val[key])) def test_source_spec_all_explicit(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_inds = h5_f['/Raw_Measurement/Spectroscopic_Indices'] h5_vals = h5_f['/Raw_Measurement/Spectroscopic_Values'] expected = {} for dim_name in ['Bias', 'Cycle']: expected[dim_name] = h5_f['/Raw_Measurement/' + dim_name][()] ret_val = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names=['Cycle', 'Bias']) self.assertEqual(len(expected), len(ret_val)) for key, exp in expected.items(): self.assertTrue(np.allclose(exp, ret_val[key])) def test_illegal_key(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_inds = h5_f['/Raw_Measurement/Spectroscopic_Indices'] h5_vals = h5_f['/Raw_Measurement/Spectroscopic_Values'] with self.assertRaises(KeyError): _ = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names=['Cycle', 'Does not exist']) def test_illegal_dset(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_inds = h5_f['/Raw_Measurement/Spectroscopic_Indices'] h5_vals = h5_f['/Raw_Measurement/Ancillary'] with self.assertRaises(ValueError): _ = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names=['Cycle', 'Bias']) def test_source_spec_single(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_inds = h5_f['/Raw_Measurement/Spectroscopic_Indices'] h5_vals = h5_f['/Raw_Measurement/Spectroscopic_Values'] expected = {'Bias': h5_f['/Raw_Measurement/Bias'][()]} ret_val = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names='Bias') self.assertEqual(len(expected), len(ret_val)) for key, exp in expected.items(): self.assertTrue(np.allclose(exp, ret_val[key])) def test_source_pos_all(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_inds = h5_f['/Raw_Measurement/Position_Indices'] h5_vals = h5_f['/Raw_Measurement/Position_Values'] expected = {} for dim_name in ['X', 'Y']: expected[dim_name] = h5_f['/Raw_Measurement/' + dim_name][()] ret_val = hdf_utils.get_unit_values(h5_inds, h5_vals) self.assertEqual(len(expected), len(ret_val)) for key, exp in expected.items(): self.assertTrue(np.allclose(exp, ret_val[key])) def test_source_pos_single(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_inds = h5_f['/Raw_Measurement/Position_Indices'] h5_vals = h5_f['/Raw_Measurement/Position_Values'] expected = {'Y': h5_f['/Raw_Measurement/Y'][()]} ret_val = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names='Y') self.assertEqual(len(expected), len(ret_val)) for key, exp in expected.items(): self.assertTrue(np.allclose(exp, ret_val[key])) def test_all_dim_names_not_provided(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_inds = h5_f['/Raw_Measurement/Position_Indices'][()] h5_vals = h5_f['/Raw_Measurement/Position_Values'][()] with self.assertRaises(TypeError): _ = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names=['Y']) def test_dependent_dim(self): with h5py.File(data_utils.relaxation_path, mode='r') as h5_f: h5_inds = h5_f['/Measurement_000/Channel_000/Spectroscopic_Indices'] h5_vals = h5_f['/Measurement_000/Channel_000/Spectroscopic_Values'] spec_dim_names = hdf_utils.get_attr(h5_inds, 'labels') ret_dict = hdf_utils.get_unit_values(h5_inds, h5_vals) for dim_ind, dim_name in enumerate(spec_dim_names): exp_val = hdf_utils.get_attr(h5_inds, 'unit_vals_dim_' + str(dim_ind)) act_val = ret_dict[dim_name] self.assertTrue(np.allclose(exp_val, act_val)) def test_sparse_samp_no_attr(self): # What should the user expect this function to do? throw an error. # Without the attribute, this function will have no idea that it is looking at a sparse sampling case # it will return the first and second columns of vals blindly with h5py.File(data_utils.sparse_sampling_path, mode='r') as h5_f: h5_inds = h5_f['/Measurement_000/Channel_000/Position_Indices'] h5_vals = h5_f['/Measurement_000/Channel_000/Position_Values'] dim_names = hdf_utils.get_attr(h5_inds, 'labels') ret_dict = hdf_utils.get_unit_values(h5_inds, h5_vals) for dim_ind, dim_name in enumerate(dim_names): exp_val = h5_vals[:, dim_ind] act_val = ret_dict[dim_name] self.assertTrue(np.allclose(exp_val, act_val)) def test_sparse_samp_w_attr(self): # What should the user expect this function to do? throw an error. with h5py.File(data_utils.sparse_sampling_path, mode='r') as h5_f: h5_inds = h5_f['/Measurement_000/Channel_001/Position_Indices'] h5_vals = h5_f['/Measurement_000/Channel_001/Position_Values'] with self.assertRaises(ValueError): _ = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names=['Y']) def test_incomp_dim_no_attr(self): # What should the user expect this function to do? throw an error. # Given that the unit values for each tile are different, it should throw a ValueError for X. # Even though we know Y is incomplete, it won't know since it wasn't looking at X. # However, now this function will automatically find unit values for ALL dimensions just to catch such scenarios with h5py.File(data_utils.incomplete_measurement_path, mode='r') as h5_f: h5_inds = h5_f['/Measurement_000/Channel_000/Position_Indices'] h5_vals = h5_f['/Measurement_000/Channel_000/Position_Values'] with self.assertRaises(ValueError): _ = hdf_utils.get_unit_values(h5_inds, h5_vals) with self.assertRaises(ValueError): _ = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names=['X']) with self.assertRaises(ValueError): _ = hdf_utils.get_unit_values(h5_inds, h5_vals, dim_names=['Y']) class TestReshapeToNDims(TestModel): def test_h5_already_sorted(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: nd_slow_to_fast = h5_f['/Raw_Measurement/n_dim_form'][()] h5_main = h5_f['/Raw_Measurement/source_main'] # Data is always slowest to fastest # Anc dims arranged from fastest to slowest # Expecting data dims to be arranged according to anc dims order n_dim, success, labels = hdf_utils.reshape_to_n_dims(h5_main, get_labels=True, sort_dims=False, lazy=False, verbose=True) self.assertTrue(np.all([x == y for x, y in zip(labels, ['X', 'Y', 'Bias', 'Cycle'])])) self.assertTrue(success) nd_fast_to_slow = nd_slow_to_fast.transpose(1, 0, 3, 2) self.assertTrue(np.allclose(nd_fast_to_slow, n_dim)) # Anc dims arranged from fastest to slowest # Expecting data dims to be arranged according to slow to fast n_dim, success, labels = hdf_utils.reshape_to_n_dims(h5_main, get_labels=True, sort_dims=True, lazy=False, verbose=True) self.assertTrue(success) self.assertTrue(np.all([x == y for x, y in zip(labels, ['Y', 'X', 'Cycle', 'Bias'])])) self.assertTrue(np.allclose(nd_slow_to_fast, n_dim)) def test_h5_manually_provided_anc_dsets_h5(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: nd_slow_to_fast = h5_f['/Raw_Measurement/n_dim_form'][()] nd_fast_to_slow = nd_slow_to_fast.transpose(1, 0, 3, 2) exp_labs = ['X', 'Y', 'Bias', 'Cycle'] h5_main = h5_f['/Raw_Measurement/source_main'] h5_pos_inds = h5_f['/Raw_Measurement/Position_Indices'] h5_spec_inds = h5_f['/Raw_Measurement/Spectroscopic_Indices'] # BOTH POS AND SPEC n_dim, success, labels = hdf_utils.reshape_to_n_dims(h5_main, h5_pos=h5_pos_inds, h5_spec=h5_spec_inds, get_labels=True, sort_dims=False, lazy=False, verbose=True) self.assertTrue(np.all([x == y for x, y in zip(labels, exp_labs)])) self.assertTrue(success) self.assertTrue(np.allclose(nd_fast_to_slow, n_dim)) # ONLY POS: n_dim, success, labels = hdf_utils.reshape_to_n_dims(h5_main, h5_pos=h5_pos_inds, h5_spec=None, get_labels=True, sort_dims=False, lazy=False, verbose=True) self.assertTrue(np.all([x == y for x, y in zip(labels, exp_labs)])) self.assertTrue(success) self.assertTrue(np.allclose(nd_fast_to_slow, n_dim)) # ONLY SPEC n_dim, success, labels = hdf_utils.reshape_to_n_dims(h5_main, h5_pos=None, h5_spec=h5_spec_inds, get_labels=True, sort_dims=False, lazy=False, verbose=True) self.assertTrue(np.all([x == y for x, y in zip(labels, exp_labs)])) self.assertTrue(success) self.assertTrue(np.allclose(nd_fast_to_slow, n_dim)) def test_h5_not_main_dset(self): with h5py.File(data_utils.std_beps_path, mode='r') as h5_f: h5_main = h5_f['/Raw_Measurement/Ancillary'] h5_pos = h5_f['/Raw_Measurement/Position_Indices'] h5_spec = h5_f['/Raw_Measurement/Spectroscopic_Indices'] # Not main with self.assertRaises(ValueError): _ = hdf_utils.reshape_to_n_dims(h5_main) # Not main and not helping that we are supplign incompatible ancillary datasets with self.assertRaises(ValueError): _ = hdf_utils.reshape_to_n_dims(h5_main, h5_pos=h5_pos, h5_spec=h5_spec) # main but we are supplign incompatible ancillary datasets h5_main = h5_f['/Raw_Measurement/source_main-Fitter_000/results_main'] with self.assertRaises(ValueError): _ = hdf_utils.reshape_to_n_dims(h5_main, h5_pos=h5_pos, h5_spec=h5_spec) def build_main_anc_4d(self): num_rows = 3 num_cols = 5 num_cycles = 2 num_cycle_pts = 7 # arrange as fast, slow pos_inds = np.vstack((np.tile(np.arange(num_cols), num_rows), np.repeat(np.arange(num_rows), num_cols))).T # arrange as fast, slow spec_inds = np.vstack((np.tile(np.arange(num_cycle_pts), num_cycles), np.repeat(np.arange(num_cycles), num_cycle_pts))) # Data is arranged from slowest to fastest main_nd = np.zeros(shape=(num_rows, num_cols, num_cycles, num_cycle_pts), dtype=np.uint8) for row_ind in range(num_rows): for col_ind in range(num_cols): for cycle_ind in range(num_cycles): # for bias_ind in range(num_cycle_pts): val = 1E+3*row_ind + 1E+2*col_ind + 1E+1*cycle_ind + np.arange(num_cycle_pts) main_nd[row_ind, col_ind, cycle_ind] = val return main_nd, pos_inds, spec_inds def base_comparison_4d(self, flip_pos_inds, flip_spec_inds, lazy_in=False, lazy_out=False, verbose=False): # Generated Data dims from slowest to fastest exp_nd_s2f, pos_inds, spec_inds = self.build_main_anc_4d() # nd (Y, X, Cycle, Bias) main_2d = exp_nd_s2f.reshape(np.prod(exp_nd_s2f.shape[:2]), np.prod(exp_nd_s2f.shape[2:])) # Dimension names arranged from slowest to fastest labs_s2f = ['Position Dimension 1', 'Position Dimension 0', 'Spectral Dimension 1', 'Spectral Dimension 0'] # Generated ancillary dimensions are arranged from fastest to slowest # Unless any flipping is requested, as-is order should be fast to slow as_is_nd_order = [1, 0, 3, 2] # Unless any flipping is requested, s2f order is already in place s2f_lab_order = [0, 1, 2, 3] if flip_pos_inds: # arranged as slow to fast pos_inds = np.fliplr(pos_inds) as_is_nd_order = as_is_nd_order[:2][::-1] + as_is_nd_order[2:] s2f_lab_order = [1, 0] + s2f_lab_order[2:] if flip_spec_inds: # arranged as slow to fast as_is_nd_order = as_is_nd_order[:2] + as_is_nd_order[2:][::-1] s2f_lab_order = s2f_lab_order[:2] + [3, 2] spec_inds = np.flipud(spec_inds) if lazy_in: main_2d = da.from_array(main_2d, chunks=main_2d.shape) pos_inds = da.from_array(pos_inds, chunks=pos_inds.shape) spec_inds = da.from_array(spec_inds, chunks=spec_inds.shape) n_dim, suc, labs = hdf_utils.reshape_to_n_dims(main_2d, h5_pos=pos_inds, h5_spec=spec_inds, sort_dims=True, get_labels=True, lazy=lazy_out, verbose=verbose) if lazy_out: self.assertIsInstance(n_dim, da.core.Array) self.assertTrue(np.allclose(exp_nd_s2f, n_dim)) self.assertTrue(suc) # labels were auto-generated and these will be flipped blindly exp_labs = np.array(labs_s2f)[s2f_lab_order] self.assertTrue(np.all([x == y for x, y in zip(labs, exp_labs)])) if verbose: print('~~~~~~~~~~~~~~~~~~~~~~ UNSORTED ~~~~~~~~~~~~~~~~~~~~~~~~~') n_dim, suc, labs = hdf_utils.reshape_to_n_dims(main_2d, h5_pos=pos_inds, h5_spec=spec_inds, sort_dims=False, get_labels=True, lazy=lazy_out, verbose=verbose) if lazy_out: self.assertIsInstance(n_dim, da.core.Array) # Rearrange the dim labels and N-dim form from slow-to-fast to: if verbose: print('N-dim order will be permuted as: {}'.format(as_is_nd_order)) print('Labels will be permuted as: {}'.format([1, 0, 3, 2])) exp_nd = exp_nd_s2f.transpose(tuple(as_is_nd_order)) """ This is sort of confusing: No matter how the pos / spec dims are ordered, the names will always start as P0, P1, S0, S1 """ exp_labs = np.array(labs_s2f)[[1, 0, 3, 2]] if verbose: print('Expected N-dim shape: {} and labels: {}' ''.format(exp_nd.shape, exp_labs)) self.assertTrue(np.allclose(exp_nd, n_dim)) self.assertTrue(suc) self.assertTrue(np.all([x == y for x, y in zip(labs, exp_labs)])) def test_numpy_ordinary(self): self.base_comparison_4d(False, False) def test_dask_input(self): self.base_comparison_4d(False, False, lazy_in=True, lazy_out=False) def test_dask_output(self): self.base_comparison_4d(False, False, lazy_in=False, lazy_out=True) def test_dask_all(self): self.base_comparison_4d(False, False, lazy_in=True, lazy_out=True) def test_numpy_pos_inds_order_flipped(self): self.base_comparison_4d(True, False) def test_numpy_spec_inds_order_flipped(self): # This is the same situation as in BEPS self.base_comparison_4d(False, True) def test_numpy_both_inds_order_flipped(self): self.base_comparison_4d(True, True) def test_dask_all_both_inds_order_flipped(self): self.base_comparison_4d(True, True, lazy_in=True, lazy_out=True) def build_main_anc_1_2d(self, is_2d=True, is_spec=False): num_rows = 2 num_cols = 3 # arrange as fast, slow pos_inds = np.vstack((np.tile(np.arange(num_cols), num_rows), np.repeat(np.arange(num_rows), num_cols))).T # Data is arranged from slowest to fastest main_nd = np.random.randint(0, high=255, size=(num_rows, num_cols), dtype=np.uint8) if not is_2d: pos_inds = np.expand_dims(np.arange(num_rows), axis=1) main_nd = np.random.randint(0, high=255, size=num_rows, dtype=np.uint8) spec_inds= np.expand_dims([0], axis=0) if is_spec: return main_nd, spec_inds, pos_inds.T return main_nd, pos_inds, spec_inds def base_comparison_1_2d(self, is_2d, is_spec, flip_inds, lazy_in=False, lazy_out=False): # Data is always stored from fastest to slowest # By default the ancillary dimensions are arranged from fastest to slowest main_nd, pos_inds, spec_inds = self.build_main_anc_1_2d(is_2d=is_2d, is_spec=is_spec) main_2d = main_nd.reshape(-1, 1) main_nd_w_sing = np.expand_dims(main_nd, axis=-1) if is_spec: main_2d = main_2d.T main_nd_w_sing = np.expand_dims(main_nd, axis=0) # nd (Y, X) order = [1, 0, 2] if is_spec: order = [0, 2, 1] if flip_inds: # arranged as slow to fast if is_spec: spec_inds = np.flipud(spec_inds) order = [0] + order[1:][::-1] else: pos_inds = np.fliplr(pos_inds) order = order[:2][::-1] + [2] print('2D: {}, Spec: {}, Flip: {}'.format(is_2d, is_spec, flip_inds)) print('Main data shapes ND: {}, 2D: {}'.format(main_nd.shape, main_2d.shape)) print(main_nd) print(main_2d) if lazy_in: main_2d = da.from_array(main_2d, chunks=main_2d.shape) n_dim, success = hdf_utils.reshape_to_n_dims(main_2d, h5_pos=pos_inds, h5_spec=spec_inds, sort_dims=True, get_labels=False, lazy=lazy_out, verbose=True) if lazy_out: self.assertIsInstance(n_dim, da.core.Array) self.assertTrue(np.allclose(main_nd_w_sing, n_dim)) print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~') n_dim, success = hdf_utils.reshape_to_n_dims(main_2d, h5_pos=pos_inds, h5_spec=spec_inds, sort_dims=False, get_labels=False, lazy=lazy_out, verbose=True) if lazy_out: self.assertIsInstance(n_dim, da.core.Array) if is_2d: main_nd_w_sing = main_nd_w_sing.transpose(order) self.assertTrue(np.allclose(main_nd_w_sing, n_dim)) def test_numpy_ordinary_1d_pos(self): self.base_comparison_1_2d(False, False, False) def test_dask_in_ordinary_1d_pos(self): self.base_comparison_1_2d(False, False, False, lazy_in=True, lazy_out=False) def test_dask_out_ordinary_1d_pos(self): self.base_comparison_1_2d(False, False, False, lazy_in=False, lazy_out=True) def test_dask_all_ordinary_1d_pos(self): self.base_comparison_1_2d(False, False, False, lazy_in=True, lazy_out=True) def test_numpy_ordinary_1d_spec(self): self.base_comparison_1_2d(False, True, False) def test_dask_in_ordinary_1d_spec(self): self.base_comparison_1_2d(False, True, False, lazy_in=True, lazy_out=False) def test_dask_out_ordinary_1d_spec(self): self.base_comparison_1_2d(False, True, False, lazy_in=False, lazy_out=True) def test_dask_all_ordinary_1d_spec(self): self.base_comparison_1_2d(False, True, False, lazy_in=True, lazy_out=True) def test_numpy_ordinary_2d_pos(self): self.base_comparison_1_2d(True, False, False) def test_numpy_ordinary_2d_spec(self): self.base_comparison_1_2d(True, True, False) def test_h5_both_inds_flipped(self): # Flipping both the spec and pos dimensions means that the order in which # the data is stored is the same order in which dimensions are arranged # In other words, sort should make no difference at all! file_path = 'reshape_to_n_dim_sort_required.h5' data_utils.delete_existing_file(file_path) with h5py.File(file_path, mode='w') as h5_f: h5_raw_grp = h5_f.create_group('Raw_Measurement') main_nd, source_pos_data, source_spec_data = self.build_main_anc_4d() # arrange as slow, fast instead of fast, slow source_pos_data = np.fliplr(source_pos_data) # make spectroscopic slow, fast instead of fast, slow source_spec_data = np.flipud(source_spec_data) source_dset_name = 'source_main' # Arrange from slow to fast pos_attrs = {'units': ['nm', 'um'], 'labels': ['Y', 'X']} #def build_ind_val_dsets(name, inds, attrs, is_spec): h5_pos_inds = h5_raw_grp.create_dataset('Position_Indices', data=source_pos_data, dtype=np.uint16) data_utils.write_aux_reg_ref(h5_pos_inds, pos_attrs['labels'], is_spec=False) data_utils.write_string_list_as_attr(h5_pos_inds, pos_attrs) h5_pos_vals = h5_raw_grp.create_dataset('Position_Values', data=source_pos_data, dtype=np.float32) data_utils.write_aux_reg_ref(h5_pos_vals, pos_attrs['labels'], is_spec=False) data_utils.write_string_list_as_attr(h5_pos_vals, pos_attrs) source_main_data = main_nd.reshape(np.prod(main_nd.shape[:2]), np.prod(main_nd.shape[2:])) h5_source_main = h5_raw_grp.create_dataset(source_dset_name, data=source_main_data) data_utils.write_safe_attrs(h5_source_main, {'units': 'A', 'quantity': 'Current'}) # Remember to set from slow to faset source_spec_attrs = {'units': ['', 'V'], 'labels': ['Cycle', 'Bias']} h5_source_spec_inds = h5_raw_grp.create_dataset('Spectroscopic_Indices', data=source_spec_data, dtype=np.uint16) data_utils.write_aux_reg_ref(h5_source_spec_inds, source_spec_attrs['labels'], is_spec=True) data_utils.write_string_list_as_attr(h5_source_spec_inds, source_spec_attrs) h5_source_spec_vals = h5_raw_grp.create_dataset('Spectroscopic_Values', data=source_spec_data, dtype=np.float32) data_utils.write_aux_reg_ref(h5_source_spec_vals, source_spec_attrs['labels'], is_spec=True) data_utils.write_string_list_as_attr(h5_source_spec_vals, source_spec_attrs) # Now need to link as main! for dset in [h5_pos_inds, h5_pos_vals, h5_source_spec_inds, h5_source_spec_vals]: h5_source_main.attrs[dset.name.split('/')[-1]] = dset.ref n_dim, success, labels = hdf_utils.reshape_to_n_dims(h5_source_main, get_labels=True, sort_dims=True, lazy=False, verbose=False) self.assertTrue(np.all([x == y for x, y in zip(labels, ['Y', 'X', 'Cycle', 'Bias'])])) self.assertTrue(np.allclose(main_nd, n_dim)) expected_n_dim = main_nd # np.transpose(main_nd, [1, 0, 3, 2]) n_dim, success, labels = hdf_utils.reshape_to_n_dims( h5_source_main, get_labels=True, sort_dims=False, lazy=False, verbose=False) self.assertTrue(np.all([x == y for x, y in zip(labels, ['Y', 'X', 'Cycle', 'Bias'])])) self.assertTrue(np.allclose(expected_n_dim, n_dim)) os.remove(file_path) def test_h5_beps_field(self): # Flipping both the spec and pos dimensions means that the order in which # the data is stored is the same order in which dimensions are arranged # In other words, sort should make no difference at all! file_path = 'reshape_to_n_dim_sort_required.h5' data_utils.delete_existing_file(file_path) with h5py.File(file_path, mode='w') as h5_f: h5_raw_grp = h5_f.create_group('Raw_Measurement') num_rows = 3 num_cols = 5 num_fields = 2 num_cycle_pts = 7 # arrange as fast, slow source_pos_data = np.vstack( (np.tile(np.arange(num_cols), num_rows), np.repeat(np.arange(num_rows), num_cols))).T # arrange as fast, slow source_spec_data = np.vstack( (np.tile(np.arange(num_fields), num_cycle_pts), np.repeat(np.arange(num_cycle_pts), num_fields),)) # Data is arranged from slowest to fastest test = np.vstack((np.arange(num_cycle_pts) * -1 - 1, np.arange(num_cycle_pts) + 1)) main_nd = np.zeros( shape=(num_rows, num_cols, num_fields, num_cycle_pts), dtype=np.float16) for row_ind in range(num_rows): for col_ind in range(num_cols): main_nd[ row_ind, col_ind] = 1E+3 * row_ind + 1E+2 * col_ind + test main_nd = main_nd.transpose(0, 1, 3, 2) source_dset_name = 'source_main' # Arrange from fast to slow pos_attrs = {'units': ['nm', 'um'], 'labels': ['X', 'Y']} h5_pos_inds = h5_raw_grp.create_dataset('Position_Indices', data=source_pos_data, dtype=np.uint16) data_utils.write_aux_reg_ref(h5_pos_inds, pos_attrs['labels'], is_spec=False) data_utils.write_string_list_as_attr(h5_pos_inds, pos_attrs) h5_pos_vals = h5_raw_grp.create_dataset('Position_Values', data=source_pos_data, dtype=np.float32) data_utils.write_aux_reg_ref(h5_pos_vals, pos_attrs['labels'], is_spec=False) data_utils.write_string_list_as_attr(h5_pos_vals, pos_attrs) source_main_data = main_nd.reshape(np.prod(main_nd.shape[:2]), np.prod(main_nd.shape[2:])) h5_source_main = h5_raw_grp.create_dataset(source_dset_name, data=source_main_data) data_utils.write_safe_attrs(h5_source_main, {'units': 'A', 'quantity': 'Current'}) # Remember to set from fast to slow source_spec_attrs = {'units': ['', 'V'], 'labels': ['Field', 'Bias']} h5_source_spec_inds = h5_raw_grp.create_dataset( 'Spectroscopic_Indices', data=source_spec_data, dtype=np.uint16) data_utils.write_aux_reg_ref(h5_source_spec_inds, source_spec_attrs['labels'], is_spec=True) data_utils.write_string_list_as_attr(h5_source_spec_inds, source_spec_attrs) h5_source_spec_vals = h5_raw_grp.create_dataset( 'Spectroscopic_Values', data=source_spec_data, dtype=np.float32) data_utils.write_aux_reg_ref(h5_source_spec_vals, source_spec_attrs['labels'], is_spec=True) data_utils.write_string_list_as_attr(h5_source_spec_vals, source_spec_attrs) # Now need to link as main! for dset in [h5_pos_inds, h5_pos_vals, h5_source_spec_inds, h5_source_spec_vals]: h5_source_main.attrs[dset.name.split('/')[-1]] = dset.ref n_dim, success, labels = hdf_utils.reshape_to_n_dims( h5_source_main, get_labels=True, sort_dims=True, lazy=False, verbose=False) self.assertTrue(np.all( [x == y for x, y in zip(labels, ['Y', 'X', 'Bias', 'Field'])])) self.assertTrue(np.allclose(main_nd, n_dim)) expected_n_dim = np.transpose(main_nd, [1, 0, 3, 2]) n_dim, success, labels = hdf_utils.reshape_to_n_dims( h5_source_main, get_labels=True, sort_dims=False, lazy=False, verbose=False) self.assertTrue(np.all( [x == y for x, y in zip(labels, ['X', 'Y', 'Field', 'Bias'])])) self.assertTrue(np.allclose(expected_n_dim, n_dim)) os.remove(file_path) class TestReshapeFromNDims(TestModel): def test_pos_and_spec_provided(self): num_rows = 3 num_cols = 5 num_cycles = 2 num_cycle_pts = 7 # the N dimensional dataset should be arranged in the following order: # [positions slowest to fastest, spectroscopic slowest to fastest] source_nd = np.zeros(shape=(num_rows, num_cols, num_cycles, num_cycle_pts), dtype=np.float16) expected_2d = np.zeros(shape=(num_rows * num_cols, num_cycle_pts * num_cycles), dtype=np.float16) for row_ind in range(num_rows): for col_ind in range(num_cols): for cycle_ind in range(num_cycles): for bias_ind in range(num_cycle_pts): val = 1E+3 * row_ind + 1E+2 * col_ind + 1E+1 * cycle_ind + bias_ind expected_2d[row_ind * num_cols + col_ind, cycle_ind * num_cycle_pts + bias_ind] = val source_nd[row_ind, col_ind, cycle_ind, bias_ind] = val # case 1: Pos and Spec both arranged as slow to fast: source_pos_data = np.vstack((np.repeat(np.arange(num_rows), num_cols), np.tile(np.arange(num_cols), num_rows))).T source_spec_data = np.vstack((np.repeat(np.arange(num_cycles), num_cycle_pts), np.tile(np.arange(num_cycle_pts), num_cycles))) ret_2d, success = hdf_utils.reshape_from_n_dims(source_nd, h5_pos=source_pos_data, h5_spec=source_spec_data) self.assertTrue(success) self.assertTrue(np.allclose(ret_2d, expected_2d)) # case 2: Only Pos arranged as slow to fast: main_pos_sorted = np.transpose(source_nd, (0, 1, 3, 2)) source_pos_data = np.vstack((np.repeat(np.arange(num_rows), num_cols), np.tile(np.arange(num_cols), num_rows))).T source_spec_data = np.vstack((np.tile(np.arange(num_cycle_pts), num_cycles), np.repeat(np.arange(num_cycles), num_cycle_pts),)) ret_2d, success = hdf_utils.reshape_from_n_dims(main_pos_sorted, h5_pos=source_pos_data, h5_spec=source_spec_data) self.assertTrue(success) self.assertTrue(np.allclose(ret_2d, expected_2d)) # case 3: only Spec arranged as slow to fast: main_spec_sorted = np.transpose(source_nd, (1, 0, 2, 3)) source_pos_data = np.vstack((np.tile(np.arange(num_cols), num_rows), np.repeat(np.arange(num_rows), num_cols))).T source_spec_data = np.vstack((np.repeat(np.arange(num_cycles), num_cycle_pts), np.tile(np.arange(num_cycle_pts), num_cycles))) ret_2d, success = hdf_utils.reshape_from_n_dims(main_spec_sorted, h5_pos=source_pos_data, h5_spec=source_spec_data) self.assertTrue(success) self.assertTrue(np.allclose(ret_2d, expected_2d)) # case 4: neither pos nor spec arranged as slow to fast: main_not_sorted = np.transpose(source_nd, (1, 0, 3, 2)) source_pos_data = np.vstack((np.tile(np.arange(num_cols), num_rows), np.repeat(np.arange(num_rows), num_cols))).T source_spec_data = np.vstack((np.tile(np.arange(num_cycle_pts), num_cycles), np.repeat(np.arange(num_cycles), num_cycle_pts),)) ret_2d, success = hdf_utils.reshape_from_n_dims(main_not_sorted, h5_pos=source_pos_data, h5_spec=source_spec_data) self.assertTrue(success) self.assertTrue(np.allclose(ret_2d, expected_2d)) def test_pos_and_spec_may_may_not_be_provided(self): num_rows = 3 num_cols = 5 num_cycles = 2 num_cycle_pts = 7 # the N dimensional dataset should be arranged in the following order: # [positions slowest to fastest, spectroscopic slowest to fastest] source_nd = np.zeros(shape=(num_rows, num_cols, num_cycles, num_cycle_pts), dtype=np.float16) expected_2d = np.zeros(shape=(num_rows * num_cols, num_cycle_pts * num_cycles), dtype=np.float16) for row_ind in range(num_rows): for col_ind in range(num_cols): for cycle_ind in range(num_cycles): for bias_ind in range(num_cycle_pts): val = 1E+3 * row_ind + 1E+2 * col_ind + 1E+1 * cycle_ind + bias_ind expected_2d[row_ind * num_cols + col_ind, cycle_ind * num_cycle_pts + bias_ind] = val source_nd[row_ind, col_ind, cycle_ind, bias_ind] = val source_pos_data = np.vstack((np.repeat(np.arange(num_rows), num_cols), np.tile(np.arange(num_cols), num_rows))).T source_spec_data = np.vstack((np.repeat(np.arange(num_cycles), num_cycle_pts), np.tile(np.arange(num_cycle_pts), num_cycles))) # case 1: only pos provided: ret_2d, success = hdf_utils.reshape_from_n_dims(source_nd, h5_pos=source_pos_data) self.assertTrue(success) self.assertTrue(np.allclose(ret_2d, expected_2d)) # case 2: only spec provided: ret_2d, success = hdf_utils.reshape_from_n_dims(source_nd, h5_spec=source_spec_data) self.assertTrue(success) self.assertTrue(np.allclose(ret_2d, expected_2d)) # case 3: neither pos nor spec provided: with self.assertRaises(ValueError): _ = hdf_utils.reshape_from_n_dims(source_nd) class TestWriteMainDataset(TestModel): def base_write(self, lazy_main=False, empty_main=False, pre_pos=False, pre_spec=False, to_new_file=False): file_path = 'test.h5' new_file_path = 'new.h5' data_utils.delete_existing_file(file_path) main_data = np.random.rand(15, 14) main_data_name = 'Test_Main' quantity = 'Current' dset_units = 'nA' pos_sizes = [5, 3] pos_names = ['X', 'Y'] pos_units = ['nm', 'um'] pos_dims = [] for length, name, units in zip(pos_sizes, pos_names, pos_units): pos_dims.append(write_utils.Dimension(name, units, np.arange(length))) pos_data = np.vstack((np.tile(np.arange(5), 3), np.repeat(np.arange(3), 5))).T spec_sizes = [7, 2] spec_names = ['Bias', 'Cycle'] spec_units = ['V', ''] spec_dims = [] for length, name, units in zip(spec_sizes, spec_names, spec_units): spec_dims.append(write_utils.Dimension(name, units, np.arange(length))) spec_data = np.vstack((np.tile(np.arange(7), 2), np.repeat(np.arange(2), 7))) input_data = main_data kwargs = {} if lazy_main: input_data = da.from_array(main_data, chunks=main_data.shape) if empty_main: input_data = main_data.shape kwargs.update({'dtype': np.float16}) with h5py.File(file_path, mode='w') as h5_f: if pre_spec: h5_spec_inds, h5_spec_vals = hdf_utils.write_ind_val_dsets( h5_f, spec_dims, is_spectral=True) spec_dims = None kwargs.update({'h5_spec_inds': h5_spec_inds, 'h5_spec_vals': h5_spec_vals}) if pre_pos: h5_pos_inds, h5_pos_vals = hdf_utils.write_ind_val_dsets(h5_f, pos_dims, is_spectral=False) pos_dims = None kwargs.update({'h5_pos_inds': h5_pos_inds, 'h5_pos_vals': h5_pos_vals}) targ_loc = h5_f if to_new_file: h5_f_2 = h5py.File(new_file_path, mode='w') targ_loc = h5_f_2 usid_main = hdf_utils.write_main_dataset(targ_loc, input_data, main_data_name, quantity, dset_units, pos_dims, spec_dims, main_dset_attrs=None, slow_to_fast=False, verbose=True, **kwargs) self.assertIsInstance(usid_main, USIDataset) self.assertEqual(usid_main.name.split('/')[-1], main_data_name) self.assertEqual(usid_main.parent, targ_loc) if not empty_main: self.assertTrue(np.allclose(main_data, usid_main[()])) data_utils.validate_aux_dset_pair(self, targ_loc, usid_main.h5_pos_inds, usid_main.h5_pos_vals, pos_names, pos_units, pos_data, h5_main=usid_main, is_spectral=False, slow_to_fast=False) data_utils.validate_aux_dset_pair(self, targ_loc, usid_main.h5_spec_inds, usid_main.h5_spec_vals, spec_names, spec_units, spec_data, h5_main=usid_main, is_spectral=True, slow_to_fast=False) if to_new_file: os.remove(new_file_path) os.remove(file_path) def test_numpy_small(self): self.base_write() def test_dask_small(self): self.base_write(lazy_main=True) def test_empty_main(self): self.base_write(empty_main=True) def test_write_main_existing_pos_aux(self): self.base_write(pre_pos=True, pre_spec=False) def test_write_main_existing_pos_aux_diff_file(self): self.base_write(pre_pos=True, pre_spec=False, to_new_file=True) def test_write_main_existing_spec_aux(self): self.base_write(pre_pos=False, pre_spec=True) def test_write_main_existing_spec_aux_diff_file(self): self.base_write(pre_pos=False, pre_spec=True, to_new_file=True) def test_write_main_both_existing_aux(self): self.base_write(pre_pos=True, pre_spec=True) def test_write_main_both_existing_aux_diff_file(self): self.base_write(pre_pos=True, pre_spec=True, to_new_file=True) def test_prod_sizes_mismatch(self): file_path = 'test.h5' data_utils.delete_existing_file(file_path) main_data = np.random.rand(15, 14) main_data_name = 'Test_Main' quantity = 'Current' dset_units = 'nA' pos_sizes = [5, 15] # too many steps in the Y direction pos_names = ['X', 'Y'] pos_units = ['nm', 'um'] pos_dims = [] for length, name, units in zip(pos_sizes, pos_names, pos_units): pos_dims.append(write_utils.Dimension(name, units, np.arange(length))) spec_sizes = [7, 2] spec_names = ['Bias', 'Cycle'] spec_units = ['V', ''] spec_dims = [] for length, name, units in zip(spec_sizes, spec_names, spec_units): spec_dims.append(write_utils.Dimension(name, units, np.arange(length))) with h5py.File(file_path, mode='w') as h5_f: with self.assertRaises(ValueError): _ = hdf_utils.write_main_dataset(h5_f, main_data, main_data_name, quantity, dset_units, pos_dims, spec_dims) os.remove(file_path) if __name__ == '__main__': unittest.main()

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2efc89bb6947e4617f353fcbc4a4133aef37f473

38

py

Python

test.py

dickensas/demodatetime

f9d08b2d3b22ceb94fdb256a53097db5e5525514

[ "MIT" ]

2

2020-10-24T20:14:42.000Z

2022-01-14T10:18:40.000Z

test.py

dickensas/demodatetime

f9d08b2d3b22ceb94fdb256a53097db5e5525514

[ "MIT" ]

null

test.py

dickensas/demodatetime

f9d08b2d3b22ceb94fdb256a53097db5e5525514

[ "MIT" ]

1

2021-09-17T21:08:16.000Z

import myModule myModule.helloworld()

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258d8d02d53a7773fde42703ba3efc7b573c73d1

3,416

py

Python

application/common/httpclient.py

teomoney1999/fn_backend

ba10c1aa2a46a4b5bd3a51212eba335991b173b0

[ "MIT" ]

null

application/common/httpclient.py

teomoney1999/fn_backend

ba10c1aa2a46a4b5bd3a51212eba335991b173b0

[ "MIT" ]

null

application/common/httpclient.py

teomoney1999/fn_backend

ba10c1aa2a46a4b5bd3a51212eba335991b173b0

[ "MIT" ]

null

import aiohttp import asyncio import ujson import json as json_load import requests from gatco.response import json from application.server import app class HTTPClient(object): # def __init__(self, url=None): # pass #self._url = url @staticmethod async def get(url, params=None, headers={}): #resp = None headers["Content-Type"] = "application/json" async with aiohttp.ClientSession(headers=headers) as session: async with session.get(url, params=params) as response: if (response.status == 200) or (response.status == 201): try: resp = await response.json() return resp except: return {"error_code": "HTTP_ERROR", "error_message": await response.text()} else: return {"error_code": "HTTP_ERROR", "error_message": await response.text()} return {"error_code": "UNKNOWN_ERROR", "error_message": ""} @staticmethod async def post(url, data, headers={}): #resp = None # if not ("private_replies" in url): headers["Content-Type"] = "application/json" async with aiohttp.ClientSession(headers=headers, json_serialize=ujson.dumps) as session: async with session.post(url, json=data) as response: if (response.status == 200) or (response.status == 201): try: resp = await response.json() return resp except: return {"error_code": "HTTP_ERROR", "error_message": await response.text()} else: return {"error_code": "HTTP_ERROR", "error_message": await response.text()} return {"error_code": "UNKNOWN_ERROR", "error_message": ""} @staticmethod def sync_post(url, data, headers={}): try: requests.post(url, data=json_load.dumps(data), headers=headers) return {'ok': True} except: return {'ok': False} @staticmethod async def put(url, data, headers={}): #resp = None headers["Content-Type"] = "application/json" async with aiohttp.ClientSession(headers=headers, json_serialize=ujson.dumps) as session: async with session.put(url, json=data) as response: if (response.status == 200) or (response.status == 201): resp = await response.json() return resp else: return {"error_code": "HTTP_ERROR", "error_message": await response.text()} return {"error_code": "UNKNOWN_ERROR", "error_message": "unknown_error"} @staticmethod async def delete(url, params=None, headers={}): #resp = None headers["Content-Type"] = "application/json" async with aiohttp.ClientSession(headers=headers, json_serialize=ujson.dumps) as session: async with session.delete(url, params=params) as response: if (response.status == 200) or (response.status == 201): resp = await response.json() return resp else: return {"error_code": "HTTP_ERROR", "error_message": await response.text()} return {"error_code": "UNKNOWN_ERROR", "error_message": ""}

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1

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null

0

5

2591e75db33a366e50c9e1e036fe36736bd9b6f5

96

py

Python

tests/conftest.py

ChrisPappalardo/python-devtools

fb0021b3e6815348a28c1d2bf11b50b8f0bd511a

[ "MIT" ]

487

2017-08-21T11:59:24.000Z

2022-03-30T09:39:55.000Z

tests/conftest.py

ChrisPappalardo/python-devtools

fb0021b3e6815348a28c1d2bf11b50b8f0bd511a

[ "MIT" ]

92

2017-09-08T17:50:50.000Z

2022-02-28T09:22:19.000Z

tests/conftest.py

ChrisPappalardo/python-devtools

fb0021b3e6815348a28c1d2bf11b50b8f0bd511a

[ "MIT" ]

26

2019-07-26T15:36:00.000Z

2022-03-31T11:59:39.000Z

import os def pytest_sessionstart(session): os.environ.pop('PY_DEVTOOLS_HIGHLIGHT', None)

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0

5

2598f3376017cd77c56c77c1514b488b896a4022

191

py

Python

office365/sharepoint/fields/field_multi_choice.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

544

2016-08-04T17:10:16.000Z

2022-03-31T07:17:20.000Z

office365/sharepoint/fields/field_multi_choice.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

438

2016-10-11T12:24:22.000Z

2022-03-31T19:30:35.000Z

office365/sharepoint/fields/field_multi_choice.py

theodoriss/Office365-REST-Python-Client

3bd7a62dadcd3f0a0aceeaff7584fff3fd44886e

[ "MIT" ]

202

2016-08-22T19:29:40.000Z

2022-03-30T20:26:15.000Z

from office365.sharepoint.fields.field import Field class FieldMultiChoice(Field): """Specifies a field (2) that contains one or more values from a set of specified values.""" pass

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25d9ae6145952a23c1f2562b132ce916cf1467b7

36

py

Python

tests/__init__.py

gpp-rnd/gpplot

627a2feb398fe8de5539ee6d0ae3150079578a7a

[ "MIT" ]

2

2020-06-19T19:35:14.000Z

2020-07-22T17:24:02.000Z

tests/__init__.py

gpp-rnd/gpplot

627a2feb398fe8de5539ee6d0ae3150079578a7a

[ "MIT" ]

1

2020-08-23T21:47:57.000Z

tests/__init__.py

gpp-rnd/gpplot

627a2feb398fe8de5539ee6d0ae3150079578a7a

[ "MIT" ]

null

"""Unit test package for gpplot."""

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25f6397805e4f929cfded83227fe22b77ab7f330

38

py

Python

torch/distributed/_shard/sharding_plan/__init__.py

lkct/pytorch

ec62901a2c38b63d12843e0f079bdeb7644d8714

[ "Intel" ]

null

torch/distributed/_shard/sharding_plan/__init__.py

lkct/pytorch

ec62901a2c38b63d12843e0f079bdeb7644d8714

[ "Intel" ]

null

torch/distributed/_shard/sharding_plan/__init__.py

lkct/pytorch

ec62901a2c38b63d12843e0f079bdeb7644d8714

[ "Intel" ]

null

from .api import ( ShardingPlan )

9.5

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null

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5

d335ce1175e84d687aac1c07426eb1c872cff61c

120

py

Python

sshsysmon/lib/plugins/__init__.py

zix99/sshsysmon

091a28f2d28795f05e12a158bef22a10c87de8ff

[ "MIT" ]

46

2016-03-13T20:57:24.000Z

2022-03-21T13:37:04.000Z

sshsysmon/lib/plugins/__init__.py

zix99/sshmon

091a28f2d28795f05e12a158bef22a10c87de8ff

[ "MIT" ]

5

2016-03-15T10:00:54.000Z

2021-04-30T01:41:02.000Z

sshsysmon/lib/plugins/__init__.py

zix99/sshmon

091a28f2d28795f05e12a158bef22a10c87de8ff

[ "MIT" ]

9

2016-09-23T09:37:31.000Z

2021-05-11T11:26:46.000Z

from .driver import Driver from .inspector import Inspector from .channel import Channel from .loader import loadPlugin

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120

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5

d372fc68d4c19f4295ea58429d8bbd9fdfc3437e

177

py

Python

quiz/models/__init__.py

manikagarg/iQuiz

99b2550eeedb92134a631d71fdb017844f81ef78

[ "MIT" ]

null

quiz/models/__init__.py

manikagarg/iQuiz

99b2550eeedb92134a631d71fdb017844f81ef78

[ "MIT" ]

null

quiz/models/__init__.py

manikagarg/iQuiz

99b2550eeedb92134a631d71fdb017844f81ef78

[ "MIT" ]

1

2021-09-26T14:10:28.000Z

from .quiz import * from .lti_user import * from .question import * from .response import * from .outcome_service_data import * from .oauth_nonce import * from .answer import *

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5

d3a05faaadf1d86f12d9704bf4885fe804ee0a07

45

py

Python

app/publisher/exceptions.py

petechd/eq-questionnaire-runner

1c5b182a7f8bc878cfdd767ae080410fa679abd6

[ "MIT" ]

27

2015-10-02T17:27:54.000Z

2021-04-05T12:39:16.000Z

app/publisher/exceptions.py

petechd/eq-questionnaire-runner

1c5b182a7f8bc878cfdd767ae080410fa679abd6

[ "MIT" ]

1,836

2015-09-16T09:59:03.000Z

2022-03-30T14:27:06.000Z

app/publisher/exceptions.py

petechd/eq-questionnaire-runner

1c5b182a7f8bc878cfdd767ae080410fa679abd6

[ "MIT" ]

20

2016-09-09T16:56:12.000Z

2021-11-12T06:09:27.000Z

class PublicationFailed(Exception): pass

15

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null

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5

6c9709748658d5cc69a8fd185e8f9860fb6c2c62

20

py

Python

subtl/__init__.py

packetloop/subtl

983f7647f3ca9d1bf3aa8dab38ff88b0a0fe4f5f

[ "BSD-3-Clause" ]

1

2017-05-31T08:53:49.000Z

subtl/__init__.py

packetloop/subtl

983f7647f3ca9d1bf3aa8dab38ff88b0a0fe4f5f

[ "BSD-3-Clause" ]

null

subtl/__init__.py

packetloop/subtl

983f7647f3ca9d1bf3aa8dab38ff88b0a0fe4f5f

[ "BSD-3-Clause" ]

null

from subtl import *

10

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null

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1

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5

6ce1958064b7f5a02834182e9979d4bcec213a33

31

py

Python

by_requests/direct_scrappy.py

118020071/bilibili_scrapper

56e5c854bb141d34bf6b228aafcc3044e56906e9

[ "Apache-2.0" ]

null

by_requests/direct_scrappy.py

118020071/bilibili_scrapper

56e5c854bb141d34bf6b228aafcc3044e56906e9

[ "Apache-2.0" ]

null

by_requests/direct_scrappy.py

118020071/bilibili_scrapper

56e5c854bb141d34bf6b228aafcc3044e56906e9

[ "Apache-2.0" ]

null

import requests, bs4, openpyxl

15.5

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9f0664fe4f446048650b3430a48faf8202e600bf

774

py

Python

src/comodash_api_client_lowlevel/test/test_query.py

ComotionLabs/dash-sdk

8ab532dd58cbcb85969bb84503678cd54b3b2bfe

[ "Apache-2.0" ]

1

2021-06-19T18:44:31.000Z

src/comodash_api_client_lowlevel/test/test_query.py

ComotionLabs/dash-sdk

8ab532dd58cbcb85969bb84503678cd54b3b2bfe

[ "Apache-2.0" ]

null

src/comodash_api_client_lowlevel/test/test_query.py

ComotionLabs/dash-sdk

8ab532dd58cbcb85969bb84503678cd54b3b2bfe

[ "Apache-2.0" ]

3

2021-06-25T14:50:50.000Z

2021-09-16T13:00:29.000Z

""" Comotion Dash API Comotion Dash API # noqa: E501 The version of the OpenAPI document: 2.0 Generated by: https://openapi-generator.tech """ import sys import unittest import comodash_api_client_lowlevel from comodash_api_client_lowlevel.model.query_status import QueryStatus globals()['QueryStatus'] = QueryStatus from comodash_api_client_lowlevel.model.query import Query class TestQuery(unittest.TestCase): """Query unit test stubs""" def setUp(self): pass def tearDown(self): pass def testQuery(self): """Test Query""" # FIXME: construct object with mandatory attributes with example values # model = Query() # noqa: E501 pass if __name__ == '__main__': unittest.main()

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py

Python

nexinfosys/ie_exports/ilcd_lca.py

MAGIC-nexus/nis-backend

dd425925321134f66884f60b202a59b38b7786a0

[ "BSD-3-Clause" ]

6

2019-05-31T23:02:30.000Z

2022-01-07T22:56:50.000Z

nexinfosys/ie_exports/ilcd_lca.py

ENVIRO-Module/nis-backend

fd86cf30f79f53cdccddd2a5479507d32f914d4e

[ "BSD-3-Clause" ]

2

2021-12-03T18:22:42.000Z

2021-12-13T19:57:15.000Z

nexinfosys/ie_exports/ilcd_lca.py

ENVIRO-Module/nis-backend

fd86cf30f79f53cdccddd2a5479507d32f914d4e

[ "BSD-3-Clause" ]

3

2019-04-05T16:45:09.000Z

2021-03-17T12:05:44.000Z

""" Export to European LifeCycle Data (ELCD) """

12.25

40

0.673469

6

49

5.5

1

0

0.163265

49

4

41

12.25

0.804878

0.816327

0

null

0

null

0

null

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true

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0

1

0

1

0

null

0

1

0

5

9f254da899f2fc731bc0abc721eb9e3bb20c2a17

405

py

Python

mintools/minql/impl/sql/client.py

bitstein/elements-explorer

ec9e5caa1bc6f209d2fbcea9d9d240075b7a179e

[ "MIT" ]

null

mintools/minql/impl/sql/client.py

bitstein/elements-explorer

ec9e5caa1bc6f209d2fbcea9d9d240075b7a179e

[ "MIT" ]

null

mintools/minql/impl/sql/client.py

bitstein/elements-explorer

ec9e5caa1bc6f209d2fbcea9d9d240075b7a179e

[ "MIT" ]

null

from ...interface import MinqlBaseClient class SqlMinqlClient(MinqlBaseClient): def update(self, table_name, row): raise NotImplementedError def insert(self, table_name, row): raise NotImplementedError def put(self, table_name, row): if self._get(table_name, row['id']): return self.update(table_name, row) return self.insert(table_name, row)

23.823529

47

0.676543

48

405

5.5625

0.416667

0.202247

0.269663

0.179775

0.322097

0

0.22963

405

16

48

25.3125

0.855769

0

0.2

0

0.004951

0

1

0.3

false

0

0.1

0

0.7

0

null

1

0

null

0

1

0

1

0

5

9f25db3c135d6a27d728058ea84b93fa007ca642

176

py

Python

api/edge_api/identities/exceptions.py

SolidStateGroup/Bullet-Train-API

ea47ccbdadf665a806ae4e0eff6ad1a2f1b0ba19

[ "BSD-3-Clause" ]

126

2019-12-13T18:41:43.000Z

2020-11-10T13:33:55.000Z

api/edge_api/identities/exceptions.py

SolidStateGroup/Bullet-Train-API

ea47ccbdadf665a806ae4e0eff6ad1a2f1b0ba19

[ "BSD-3-Clause" ]

30

2019-12-12T16:52:01.000Z

2020-11-09T18:55:29.000Z

api/edge_api/identities/exceptions.py

SolidStateGroup/Bullet-Train-API

ea47ccbdadf665a806ae4e0eff6ad1a2f1b0ba19

[ "BSD-3-Clause" ]

20

2020-02-14T21:55:36.000Z

2020-11-03T22:29:03.000Z

from rest_framework import status from rest_framework.exceptions import APIException class TraitPersistenceError(APIException): status_code = status.HTTP_400_BAD_REQUEST

25.142857

50

0.857955

21

176

6.904762

0.666667

0.110345

0.234483

0

0.019108

0.107955

176

6

51

29.333333

0.904459

0

1

0

false

0

0.5

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

9f638b6e74c30030ec1333193fcf82598a3da603

48

py

Python

buildscripts/tests/resmokelib/powercycle/__init__.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

buildscripts/tests/resmokelib/powercycle/__init__.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

buildscripts/tests/resmokelib/powercycle/__init__.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

"""Empty init file to make the linter happy."""

24

47

0.6875

8

48

4.125

1

0

0.166667

48

1

48

0.825

0.854167

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

9f7bebac78f76b2da6cffc220fe92a37de56007d

315

py

Python

app/endpoints/__init__.py

H1bro/library

0a444ba2be117a92dd3b881ff46bc7b38d0d8d71

[ "BSD-2-Clause" ]

null

app/endpoints/__init__.py

H1bro/library

0a444ba2be117a92dd3b881ff46bc7b38d0d8d71

[ "BSD-2-Clause" ]

1

2019-12-22T23:38:29.000Z

app/endpoints/__init__.py

H1bro/library

0a444ba2be117a92dd3b881ff46bc7b38d0d8d71

[ "BSD-2-Clause" ]

null

from .library import books_blueprint def register_blueprints_books(app): app.register_blueprint(books_blueprint) ##################################################### from .library import litterateurs_blueprint def register_blueprints_litterateurs(app): app.register_blueprint(litterateurs_blueprint)

22.5

53

0.695238

30

315

6.966667

0.333333

0.105263

0.162679

0.287081

0

0.092063

315

13

54

24.230769

0.730769

0

1

0.333333

false

0

0.333333

0

0.666667

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9fcc8142cdc65677928f5a3138aa3704a2068f1c

28

py

Python

spendee/__init__.py

dionysio/spendee

6c66e528115d6219434607e46c2cc24e9fbeb790

[ "MIT" ]

12

2019-12-30T02:15:20.000Z

2022-03-26T09:25:59.000Z

spendee/__init__.py

dionysio/spendee

6c66e528115d6219434607e46c2cc24e9fbeb790

[ "MIT" ]

4

2020-12-02T19:52:32.000Z

2021-11-15T11:16:24.000Z

spendee/__init__.py

dionysio/spendee

6c66e528115d6219434607e46c2cc24e9fbeb790

[ "MIT" ]

3

2020-05-02T19:02:18.000Z

2021-11-06T05:58:52.000Z

from .spendee import Spendee

28

0.857143

4

28

6

0.75

0

0.107143

28

1

28

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

4c95064192269967b8601ad9c0fc50cb867e519d

61

py

Python

wgm/cli/privacybot/__init__.py

Skylaski-VPN/WireGuard-Gateway-Manager

5cacbbc2318fdf662cd4793a786e3c7b9b74c5c4

[ "MIT" ]

1

2021-11-28T21:26:58.000Z

wgm/cli/privacybot/__init__.py

Skylaski-VPN/WireGuard-Gateway-Manager

5cacbbc2318fdf662cd4793a786e3c7b9b74c5c4

[ "MIT" ]

2

2021-04-07T18:10:07.000Z

2021-04-07T21:41:35.000Z

wgm/cli/privacybot/__init__.py

Skylaski-VPN/WireGuard-Gateway-Manager

5cacbbc2318fdf662cd4793a786e3c7b9b74c5c4

[ "MIT" ]

2

2021-04-07T16:13:55.000Z

2021-04-23T18:33:22.000Z

# Privacybot init from .privacybot import get_repeer_list

10.166667

39

0.803279

8

61

5.875

0.875

0

0.163934

61

5

40

12.2

0.921569

0.245902

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4cc438aa4aa7959412c380286a037b9fc8fa7ffb

234

py

Python

Lab_5/Test Task_3.py

spencerperley/CPE_101

9ae3c5a0042780f824de5edee275b35cdb0bbaec

[ "MIT" ]

1

2022-01-12T21:48:23.000Z

Lab_5/Test Task_3.py

spencerperley/CPE_101

9ae3c5a0042780f824de5edee275b35cdb0bbaec

[ "MIT" ]

null

Lab_5/Test Task_3.py

spencerperley/CPE_101

9ae3c5a0042780f824de5edee275b35cdb0bbaec

[ "MIT" ]

null

from Task_3 import * def testFilterPalendrome(): assert filter_pallendromes(["abdba","abcde","ddddd"]) == ["abdba","ddddd"] assert filter_pallendromes(["abdka","abcde","dddkd"]) == [] testFilterPalendrome() print("Pass")

29.25

78

0.675214

23

234

6.73913

0.695652

0.154839

0.309677

0

0.004878

0.123932

234

8

79

29.25

0.75122

0

0.187234

0

0.333333

1

0.166667

true

0.166667

0

0.333333

0.166667

1

0

null

0

1

0

1

0

null

0

1

0

5

e2199daba72680167ccbc0335e5409f57084699d

2,371

py

Python

solutions/day3.py

qkleinfelter/AdventOfCode2021

fdb72e35d3d8a0971b3f8914d4f9b034e6acbbc8

[ "MIT" ]

null

solutions/day3.py

qkleinfelter/AdventOfCode2021

fdb72e35d3d8a0971b3f8914d4f9b034e6acbbc8

[ "MIT" ]

null

solutions/day3.py

qkleinfelter/AdventOfCode2021

fdb72e35d3d8a0971b3f8914d4f9b034e6acbbc8

[ "MIT" ]

null

from collections import Counter def solution(): data = [x for x in open(r'inputs\day3.in').read().strip().split("\n")] print('Part 1 result: ' + str(part1(data))) print('Part 2 result: ' + str(part2(data))) def part1(data): gamma = "" epsilon = "" for i in range(len(data[0])): # counts the amount of times each character appears at this bit in data common = Counter([x[i] for x in data]) # more common bit goes into gamma, less common bit goes into epsilon if common['0'] > common['1']: gamma += "0" epsilon += "1" else: gamma += "1" epsilon += "0" # convert gamma and epsilon from binary to decimal, and multiply for result return int(gamma, 2) * int(epsilon, 2) def part2(data): oxygen = "" co2 = "" for i in range(len(data[0])): # counts the amount of times each character appears at this bit in data common = Counter([x[i] for x in data]) if common['0'] > common['1']: # only keep pieces of data if there is a 0 at the current bit, since it is the most common here data = [x for x in data if x[i] == '0'] else: # only keep pieces of data if there is a 1 at the current bit, since it is the most common here data = [x for x in data if x[i] == '1'] # stop when we only have 1 piece remaining if len(data) == 1: oxygen = data[0] break data = [x for x in open(r'inputs\day3.in').read().strip().split("\n")] for i in range(len(data[0])): # counts the amount of times each character appears at this bit in data common = Counter([x[i] for x in data]) if common['0'] > common['1']: # only keep pieces of data if there is a 0 at the current bit, since it is the least common here data = [x for x in data if x[i] == '1'] else: # only keep pieces of data if there is a 1 at the current bit, since it is the least common here data = [x for x in data if x[i] == '0'] # stop when we only have 1 piece remaining if len(data) == 1: c02 = data[0] break # convert oxygen and c02 from binary to decimal, and multiply for result return (int(oxygen, 2) * int(c02, 2)) solution()

35.924242

108

0.561788

375

2,371

3.552

0.213333

0.045045

0.040541

0.052553

0.748499

0.736486

0

0.02884

0.327288

2,371

66

109

35.924242

0.80627

0.371573

0

0.536585

0

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0

1

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false

0

0.02439

0

0.146341

0.04878

0

null

0

1

0

null

0

5

e23248af07e5b6a53ce60bde9b5e82f3b6677bd3

20,738

py

Python

pria_lifechem/models/stage_cross_validation.py

chao1224/pria_lifechem

1fd892505a45695c6197f8d711a8a37589cd7097

[ "MIT" ]

5

2018-05-14T10:15:13.000Z

2021-03-15T17:18:10.000Z

pria_lifechem/models/stage_cross_validation.py

chao1224/pria_lifechem

1fd892505a45695c6197f8d711a8a37589cd7097

[ "MIT" ]

5

2018-05-05T21:04:11.000Z

2019-06-24T22:05:35.000Z

pria_lifechem/models/stage_cross_validation.py

chao1224/pria_lifechem

1fd892505a45695c6197f8d711a8a37589cd7097

[ "MIT" ]

2

2019-10-18T23:42:27.000Z

2020-07-08T19:46:14.000Z

import argparse import pandas as pd import csv import numpy as np import json import keras import sys from keras.models import Sequential from keras.layers import Dense, Dropout from keras.layers.normalization import BatchNormalization from keras.optimizers import SGD, Adam from sklearn.cross_validation import StratifiedShuffleSplit from sklearn.grid_search import ParameterGrid from pria_lifechem.function import * from pria_lifechem.evaluation import * from pria_lifechem.models.CallBacks import * from pria_lifechem.models.deep_classification import * from pria_lifechem.models.deep_regression import * from pria_lifechem.models.vanilla_lstm import * from pria_lifechem.models.tree_net import * def run_single_classification(running_index, use_duplicate=False): if running_index >= cross_validation_upper_bound: raise ValueError('Process number out of limit. At most {}.'.format(cross_validation_upper_bound-1)) with open(config_json_file, 'r') as f: conf = json.load(f) label_name_list = conf['label_name_list'] print 'label_name_list ', label_name_list # specify dataset k = 5 directory = '../../dataset/fixed_dataset/fold_{}/'.format(k) file_list = [] for i in range(k): file_list.append('{}file_{}.csv'.format(directory, i)) file_list = np.array(file_list) # read data test_index = running_index / 4 val_index = running_index % 4 + (running_index % 4 >= test_index) complete_index = np.arange(k) train_index = np.where((complete_index != test_index) & (complete_index != val_index))[0] print train_index train_file_list = file_list[train_index] val_file_list = file_list[val_index:val_index+1] test_file_list = file_list[test_index:test_index+1] print 'train files ', train_file_list print 'val files ', val_file_list print 'test files ', test_file_list train_pd = filter_out_missing_values(read_merged_data(train_file_list), label_list=label_name_list) val_pd = filter_out_missing_values(read_merged_data(val_file_list), label_list=label_name_list) test_pd = filter_out_missing_values(read_merged_data(test_file_list), label_list=label_name_list) # extract data, and split training data into training and val X_train, y_train = extract_feature_and_label(train_pd, feature_name='Fingerprints', label_name_list=label_name_list) X_val, y_val = extract_feature_and_label(val_pd, feature_name='Fingerprints', label_name_list=label_name_list) X_test, y_test = extract_feature_and_label(test_pd, feature_name='Fingerprints', label_name_list=label_name_list) print 'done data preparation' if use_duplicate: X_complement = [] y_complement = [] pos_count = 0 for index in range(y_train.shape[0]): label = y_train[index, 0] if label == 1: pos_count += 1 for _ in range(500): X_complement.append(X_train[index]) y_complement.append(y_train[index]) X_complement = np.array(X_complement) y_complement = np.array(y_complement) X_train = np.vstack((X_train, X_complement)) y_train = np.vstack((y_train, y_complement)) task = SingleClassification(conf=conf) task.train_and_predict(X_train, y_train, X_val, y_val, X_test, y_test, PMTNN_weight_file) store_data(transform_json_to_csv(config_json_file), config_csv_file) return def run_single_regression(running_index): if running_index >= cross_validation_upper_bound: raise ValueError('Process number out of limit. At most {}.'.format(cross_validation_upper_bound-1)) with open(config_json_file, 'r') as f: conf = json.load(f) label_name_list = conf['label_name_list'] print 'label_name_list ', label_name_list # specify dataset k = 5 directory = '../../dataset/fixed_dataset/fold_{}/'.format(k) file_list = [] for i in range(k): file_list.append('{}file_{}.csv'.format(directory, i)) file_list = np.array(file_list) # read data test_index = running_index / 4 val_index = running_index % 4 + (running_index % 4 >= test_index) complete_index = np.arange(k) train_index = np.where((complete_index != test_index) & (complete_index != val_index))[0] print train_index train_file_list = file_list[train_index] val_file_list = file_list[val_index:val_index+1] test_file_list = file_list[test_index:test_index+1] print 'train files ', train_file_list print 'val files ', val_file_list print 'test files ', test_file_list train_pd = filter_out_missing_values(read_merged_data(train_file_list), label_list=label_name_list) val_pd = filter_out_missing_values(read_merged_data(val_file_list), label_list=label_name_list) test_pd = filter_out_missing_values(read_merged_data(test_file_list), label_list=label_name_list) # extract data, and split training data into training and val X_train, y_train = extract_feature_and_label(train_pd, feature_name='Fingerprints', label_name_list=label_name_list) X_val, y_val = extract_feature_and_label(val_pd, feature_name='Fingerprints', label_name_list=label_name_list) X_test, y_test = extract_feature_and_label(test_pd, feature_name='Fingerprints', label_name_list=label_name_list) y_train_classification = reshape_data_into_2_dim(y_train[:, 0]) y_train_regression = reshape_data_into_2_dim(y_train[:, 1]) y_val_classification = reshape_data_into_2_dim(y_val[:, 0]) y_val_regression = reshape_data_into_2_dim(y_val[:, 1]) y_test_classification = reshape_data_into_2_dim(y_test[:, 0]) y_test_regression = reshape_data_into_2_dim(y_test[:, 1]) print 'done data preparation' task = SingleRegression(conf=conf) task.train_and_predict(X_train, y_train_regression, y_train_classification, X_val, y_val_regression, y_val_classification, X_test, y_test_regression, y_test_classification, PMTNN_weight_file) store_data(transform_json_to_csv(config_json_file), config_csv_file) return def run_vanilla_lstm(running_index): if running_index >= cross_validation_upper_bound: raise ValueError('Process number out of limit. At most {}.'.format(cross_validation_upper_bound-1)) with open(config_json_file, 'r') as f: conf = json.load(f) label_name_list = conf['label_name_list'] print 'label_name_list ', label_name_list # specify dataset k = 5 directory = '../../dataset/fixed_dataset/fold_{}/'.format(k) file_list = [] for i in range(k): file_list.append('{}file_{}.csv'.format(directory, i)) file_list = np.array(file_list) # read data test_index = running_index / 4 val_index = running_index % 4 + (running_index % 4 >= test_index) complete_index = np.arange(k) train_index = np.where((complete_index != test_index) & (complete_index != val_index))[0] print train_index train_file_list = file_list[train_index] val_file_list = file_list[val_index:val_index + 1] test_file_list = file_list[test_index:test_index + 1] print 'train files ', train_file_list print 'val files ', val_file_list print 'test files ', test_file_list # TODO: No validation set for LSTM, may merge with train set train_pd = filter_out_missing_values(read_merged_data(train_file_list), label_list=label_name_list) val_pd = filter_out_missing_values(read_merged_data(val_file_list), label_list=label_name_list) test_pd = filter_out_missing_values(read_merged_data(test_file_list), label_list=label_name_list) # extract data, and split training data into training and val X_train, y_train = extract_SMILES_and_label(train_pd, feature_name='SMILES', label_name_list=label_name_list, SMILES_mapping_json_file=SMILES_mapping_json_file) X_val, y_val = extract_SMILES_and_label(val_pd, feature_name='SMILES', label_name_list=label_name_list, SMILES_mapping_json_file=SMILES_mapping_json_file) X_test, y_test = extract_SMILES_and_label(test_pd, feature_name='SMILES', label_name_list=label_name_list, SMILES_mapping_json_file=SMILES_mapping_json_file) print 'done data preparation' task = VanillaLSTM(conf) X_train = sequence.pad_sequences(X_train, maxlen=task.padding_length) X_val = sequence.pad_sequences(X_val, maxlen=task.padding_length) X_test = sequence.pad_sequences(X_test, maxlen=task.padding_length) task.train_and_predict(X_train, y_train, X_val, y_val, X_test, y_test, PMTNN_weight_file) store_config(conf, config_csv_file) return def run_tree_net(running_index): if running_index >= cross_validation_upper_bound: raise ValueError('Process number out of limit. At most {}.'.format(cross_validation_upper_bound-1)) with open(config_json_file, 'r') as f: conf = json.load(f) label_name_list = conf['label_name_list'] print 'label_name_list ', label_name_list k = 5 directory = '../../dataset/fixed_dataset/fold_{}/'.format(k) file_list = [] for i in range(k): file_list.append('{}file_{}.csv'.format(directory, i)) file_list = np.array(file_list) # read data test_index = running_index / 4 val_index = running_index % 4 + (running_index % 4 >= test_index) complete_index = np.arange(k) train_index = np.where((complete_index != test_index) & (complete_index != val_index))[0] print train_index train_file_list = file_list[train_index] val_file_list = file_list[val_index:val_index+1] test_file_list = file_list[test_index:test_index+1] print 'train files ', train_file_list print 'val files ', val_file_list print 'test files ', test_file_list train_pd = read_merged_data(train_file_list) val_pd = read_merged_data(val_file_list) test_pd = read_merged_data(test_file_list) # extract data, and split training data into training and val X_train, y_train = extract_feature_and_label(train_pd, feature_name='Fingerprints', label_name_list=label_name_list) X_val, y_val = extract_feature_and_label(val_pd, feature_name='Fingerprints', label_name_list=label_name_list) X_test, y_test = extract_feature_and_label(test_pd, feature_name='Fingerprints', label_name_list=label_name_list) y_train_classification = reshape_data_into_2_dim(y_train[:, 0]) y_train_regression = reshape_data_into_2_dim(y_train[:, 1]) y_val_classification = reshape_data_into_2_dim(y_val[:, 0]) y_val_regression = reshape_data_into_2_dim(y_val[:, 1]) y_test_classification = reshape_data_into_2_dim(y_test[:, 0]) y_test_regression = reshape_data_into_2_dim(y_test[:, 1]) print 'done data preparation' task = TreeNet(conf) task.train_and_predict_ensemble(X_train, y_train_regression, y_train_classification, X_val, y_val_regression, y_val_classification, X_test, y_test_regression, y_test_classification, PMTNN_weight_file) return def run_multiple_classification(running_index): if running_index >= cross_validation_upper_bound: raise ValueError('Process number out of limit. At most {}.'.format(cross_validation_upper_bound-1)) with open(config_json_file, 'r') as f: conf = json.load(f) label_name_list = conf['label_name_list'] print 'label_name_list ', label_name_list # specify dataset k = 5 directory = '../../dataset/keck_pcba/fold_{}/'.format(k) file_list = [] for i in range(k): file_list.append('{}file_{}.csv'.format(directory, i)) file_list = np.array(file_list) # read data test_index = running_index / 4 val_index = running_index % 4 + (running_index % 4 >= test_index) complete_index = np.arange(k) train_index = np.where((complete_index != test_index) & (complete_index != val_index))[0] print train_index train_file_list = file_list[train_index] val_file_list = file_list[val_index:val_index + 1] test_file_list = file_list[test_index:test_index + 1] print 'train files ', train_file_list print 'val files ', val_file_list print 'test files ', test_file_list train_pd = read_merged_data(train_file_list) train_pd.fillna(0, inplace=True) val_pd = read_merged_data(val_file_list) val_pd.fillna(0, inplace=True) # TODO: may only consider Keck label test_pd = read_merged_data(test_file_list) test_pd.fillna(0, inplace=True) multi_name_list = train_pd.columns[-128:].tolist() multi_name_list.extend(label_name_list) print 'multi_name_list ', multi_name_list X_train, y_train = extract_feature_and_label(train_pd, feature_name='Fingerprints', label_name_list=multi_name_list) X_val, y_val = extract_feature_and_label(val_pd, feature_name='Fingerprints', label_name_list=multi_name_list) X_test, y_test = extract_feature_and_label(test_pd, feature_name='Fingerprints', label_name_list=multi_name_list) sample_weight_dir = '../../dataset/sample_weights/keck_pcba/fold_5/' file_list = [] for i in range(k): file_list.append('sample_weight_{}.csv'.format(i)) sample_weight_file = [sample_weight_dir + f_ for f_ in file_list] sample_weight_file = np.array(sample_weight_file) sample_weight_pd = read_merged_data(sample_weight_file[train_index]) _, sample_weight = extract_feature_and_label(sample_weight_pd, feature_name='Fingerprints', label_name_list=multi_name_list) print 'done data preparation' task = MultiClassification(conf=conf) task.train_and_predict(X_train, y_train, X_val, y_val, X_test, y_test, sample_weight=sample_weight, PMTNN_weight_file=PMTNN_weight_file, score_file=score_file) store_data(transform_json_to_csv(config_json_file), config_csv_file) return def run_dnn_rf(running_index): if running_index >= cross_validation_upper_bound: raise ValueError('Process number out of limit. At most {}.'.format(cross_validation_upper_bound-1)) with open(config_json_file, 'r') as f: conf = json.load(f) # TODO: debug conf['fitting']['nb_epoch'] = 200 conf['fitting']['early_stopping']['patience'] = 50 label_name_list = conf['label_name_list'] print 'label_name_list ', label_name_list # specify dataset k = 5 directory = '../../dataset/fixed_dataset/fold_{}/'.format(k) file_list = [] for i in range(k): file_list.append('{}file_{}.csv'.format(directory, i)) file_list = np.array(file_list) # read data test_index = running_index / 4 val_index = running_index % 4 + (running_index % 4 >= test_index) complete_index = np.arange(k) train_index = np.where((complete_index != test_index) & (complete_index != val_index))[0] print train_index train_file_list = file_list[train_index] val_file_list = file_list[val_index:val_index+1] test_file_list = file_list[test_index:test_index+1] print 'train files ', train_file_list print 'val files ', val_file_list print 'test files ', test_file_list train_pd = read_merged_data(train_file_list) val_pd = read_merged_data(val_file_list) test_pd = read_merged_data(test_file_list) # extract data, and split training data into training and val X_train, y_train = extract_feature_and_label(train_pd, feature_name='Fingerprints', label_name_list=label_name_list) X_val, y_val = extract_feature_and_label(val_pd, feature_name='Fingerprints', label_name_list=label_name_list) X_test, y_test = extract_feature_and_label(test_pd, feature_name='Fingerprints', label_name_list=label_name_list) print 'done data preparation' # TODO: remove debugging info # conf['fitting']['nb_epoch'] = 1 task = DNN_RF(conf=conf) print print 'This is STNN' task.train_and_predict(X_train, y_train, X_val, y_val, X_test, y_test, PMTNN_weight_file) print print 'This is STNN+RF' task.get_rf(X_train, y_train, X_val, y_val, X_test, y_test) return if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--config_json_file', dest="config_json_file", action="store", required=True) parser.add_argument('--PMTNN_weight_file', dest="PMTNN_weight_file", action="store", required=True) parser.add_argument('--config_csv_file', dest="config_csv_file", action="store", required=True) parser.add_argument('--process_num', dest='process_num', type=int, action='store', required=True) parser.add_argument('--SMILES_mapping_json_file', dest='SMILES_mapping_json_file', action='store', required=False, default= '../../json/SMILES_mapping.json') parser.add_argument('--score_file', dest='score_file', action='store', required=False) parser.add_argument('--model', dest='model', action='store', required=True) parser.add_argument('--cross_validation_upper_bound', dest='cross_validation_upper_bound', type=int, action='store', required=False, default=20) parser.add_argument('--seed', dest='seed', type=int, action='store', required=False, default=None) given_args = parser.parse_args() print 'Seed is {}'.format(given_args.seed) if given_args.seed is not None: np.random.seed(given_args.seed) config_json_file = given_args.config_json_file PMTNN_weight_file = given_args.PMTNN_weight_file config_csv_file = given_args.config_csv_file cross_validation_upper_bound = given_args.cross_validation_upper_bound process_num = int(given_args.process_num) model = given_args.model if model == 'single_classification': run_single_classification(process_num) elif model == 'single_regression': run_single_regression(process_num) elif model == 'vanilla_lstm': SMILES_mapping_json_file = given_args.SMILES_mapping_json_file run_vanilla_lstm(process_num) elif model == 'multi_classification': score_file = given_args.score_file run_multiple_classification(process_num) elif model == 'tree_net': run_tree_net(process_num) elif model == 'single_dnn_rf': run_dnn_rf(process_num) else: raise Exception('No such model! Should be among [{}, {}, {}, {}, {}].'.format( 'single_classification', 'single_regression', 'vanilla_lstm', 'multi_classification', 'tree_net', 'single_dnn_rf' ))

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5

e23619ea08a235266f7322b699a5160ec301f4bf

343

py

Python

myfinalproject/museos/admin.py

apayol/X-Serv-Practica-Museos

ca17d12418273f718c006ba9d9f33624ccb1f008

[ "Apache-2.0" ]

null

myfinalproject/museos/admin.py

apayol/X-Serv-Practica-Museos

ca17d12418273f718c006ba9d9f33624ccb1f008

[ "Apache-2.0" ]

null

myfinalproject/museos/admin.py

apayol/X-Serv-Practica-Museos

ca17d12418273f718c006ba9d9f33624ccb1f008

[ "Apache-2.0" ]

1

2021-07-03T09:05:59.000Z

from museos.models import Seleccionado from museos.models import ConfigUsuario from museos.models import Comentario from django.contrib import admin # Register your models here. from museos.models import Museo admin.site.register(Museo) admin.site.register(Comentario) admin.site.register(ConfigUsuario) admin.site.register(Seleccionado)

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1

0

5

e2990a46c896e8d8a11220e2cd5f312f6093f4d5

283

py

Python

synonym_dict/__init__.py

bkuczenski/synonym_dict

0968e63c3dc37f1ff383befc9c2805cd9014a3b6

[ "BSD-3-Clause" ]

null

synonym_dict/__init__.py

bkuczenski/synonym_dict

0968e63c3dc37f1ff383befc9c2805cd9014a3b6

[ "BSD-3-Clause" ]

5

2020-12-29T07:38:25.000Z

2021-03-17T18:27:17.000Z

synonym_dict/__init__.py

bkuczenski/synonym_dict

0968e63c3dc37f1ff383befc9c2805cd9014a3b6

[ "BSD-3-Clause" ]

null

from .lower_dict import LowerDict from .synonym_dict import SynonymDict, MergeError, TermExists from .synonym_set import SynonymSet from .compartments import Compartment, CompartmentManager, InconsistentLineage, NonSpecificCompartment from .flowables import Flowable, FlowablesDict

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1

0

1

0

5

2c5e014ee79145ced3d8b00a852b5ed1e67c057b

156

py

Python

src/foobar/widgets/__init__.py

jwodder/mypy-bug-20220227

257c65d2944e5589414aeaa24d0290a45aa2159a

[ "MIT" ]

null

src/foobar/widgets/__init__.py

jwodder/mypy-bug-20220227

257c65d2944e5589414aeaa24d0290a45aa2159a

[ "MIT" ]

null

src/foobar/widgets/__init__.py

jwodder/mypy-bug-20220227

257c65d2944e5589414aeaa24d0290a45aa2159a

[ "MIT" ]

null

from .base import Widget, WidgetSpec from .blue import BlueWidget from .red import RedWidget __all__ = ["BlueWidget", "RedWidget", "Widget", "WidgetSpec"]

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null

0

1

0

1

0

5

2c5f06f6e6b733a7008306aa567549cab3e70d5c

43

py

Python

Ex3.py

akhilsambasivan/PythonPractices

887a26ca172329e9d232dcdf2b993b2a6bb6584b

[ "MIT" ]

1

2019-07-26T16:30:15.000Z

Ex3.py

akhilsambasivan/PythonPractices

887a26ca172329e9d232dcdf2b993b2a6bb6584b

[ "MIT" ]

null

Ex3.py

akhilsambasivan/PythonPractices

887a26ca172329e9d232dcdf2b993b2a6bb6584b

[ "MIT" ]

null

name = "Akhil Sambasivan" print(name[:-1])c

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null

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1

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5

2c72810ff99f63dfc7d7899c9b90eb1a961e6f6b

249

py

Python

ex008.py

jefernathan/Python

2f840a625e8d46d41ab36df07ef50ae15a03c5ab

[ "MIT" ]

null

ex008.py

jefernathan/Python

2f840a625e8d46d41ab36df07ef50ae15a03c5ab

[ "MIT" ]

null

ex008.py

jefernathan/Python

2f840a625e8d46d41ab36df07ef50ae15a03c5ab

[ "MIT" ]

null

# Escreva um programa que leia um valor em metros e o exiba convertido em centímetros e milímetros. metros = float(input('Digite um valor em metros: ')) print(f'{metros} metros tem {metros * 100:.0f} centímetros e {metros * 1000:.0f} milímetros')

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4.692308

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0.160643

249

5

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null

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5

2c789017a4f07084881aac80168c647e92e92b89

171

py

Python

test/__init__.py

NREL/wp3-precon

c1c163007d16986d04bc34deefbf1c1e1c754aa8

[ "BSD-3-Clause" ]

null

test/__init__.py

NREL/wp3-precon

c1c163007d16986d04bc34deefbf1c1e1c754aa8

[ "BSD-3-Clause" ]

null

test/__init__.py

NREL/wp3-precon

c1c163007d16986d04bc34deefbf1c1e1c754aa8

[ "BSD-3-Clause" ]

null

from pathlib import Path example_data_path = Path(__file__).parents[1].resolve() / "examples" / "data" / "la_haute_borne" example_data_path_str = str(example_data_path)

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0

5

2ccd04ef9d5bc6648e55566c5b83b0bb1e334972

39,725

py

Python

compiler/parsetab.py

olefran/Pound

737a245602df600a6de8df9b749cddf17f4127f6

[ "MIT" ]

1

2020-04-21T09:30:17.000Z

compiler/parsetab.py

olefran/Patitoplusplus

737a245602df600a6de8df9b749cddf17f4127f6

[ "MIT" ]

null

compiler/parsetab.py

olefran/Patitoplusplus

737a245602df600a6de8df9b749cddf17f4127f6

[ "MIT" ]

null

# parsetab.py # This file is automatically generated. Do not edit. # pylint: disable=W,C,R _tabversion = '3.10' _lr_method = 'LALR' _lr_signature = 'PROGRAMleftPLUSMINUSleftMULTDIVMODrightEQUALleftANDORAND CHAR COMA COMMENT COMPARE CTE_CH CTE_F CTE_I CTE_STRING DESDE DET_ARR DIFFERENT DIV DOTCOMA ENTONCES EQUAL ESCRIBE FLOAT FUNCION HACER HASTA HAZ ID INT INV_ARR LBRACKET LEE LESS LESSEQUAL LPAREN LSTAPLE MIENTRAS MINUS MOD MORE MOREEQUAL MULT NOT NULL OR PLUS PRINCIPAL PROGRAMA RBRACKET REGRESA RPAREN RSTAPLE SI SINO STRING TRANS_ARR VAR VOIDempty :PROGRAM : PROGRAMA r_goto_main ID DOTCOMA VARS r_save_vars FUNCTIONS MAIN r_print_constantsMAIN : PRINCIPAL r_save_func LPAREN RPAREN r_register_princ r_save_param_func VARS r_save_vars r_end_princ r_func_set BLOQUE r_func_end VARS : VAR VAR_AUX\n | emptyVAR_AUX : TIPO IDS VAR_AUX\n | emptyTIPO : INT r_save_type\n | FLOAT r_save_type\n | CHAR r_save_type\n | STRING r_save_typeIDS : ID r_register_var ARRDIM r_populate_r DOTCOMA\n | ID r_register_var ARRDIM r_populate_r COMA IDSARRDIM : r_register_arr LSTAPLE CTE_I r_register_dim ARRDIM_AUX RSTAPLE ARRDIM\n | emptyARRDIM_AUX : COMA CTE_I r_register_dim ARRDIM_AUX\n | emptyFUNCTIONS : FUNCTION FUNCTIONS\n | emptyFUNCTION : FUNCION TIPO ID r_save_func r_register_func LPAREN PARAM RPAREN r_save_param_func VARS r_save_vars r_func_set BLOQUE r_func_end\n | FUNCION VOID r_save_type ID r_save_func r_register_func LPAREN PARAM RPAREN r_save_param_func VARS r_save_vars r_func_set BLOQUE r_func_endPARAM : TIPO ID r_register_var PARAM_AUX\n | emptyPARAM_AUX : COMA PARAM\n | emptyBLOQUE : LBRACKET ESTATUTOS RBRACKETESTATUTOS : ESTATUTO ESTATUTOS\n | emptyESTATUTO : ASIGNACION DOTCOMA\n | FUN DOTCOMA\n | COND\n | WRITE DOTCOMA\n | READ DOTCOMA\n | RETURN DOTCOMAASIGNACION : ID r_seen_operand_id ARRACC EQUAL r_seen_operator EXPRESION r_seen_equalARRACC : LSTAPLE r_check_dim EXPRESION r_create_quad ARRACC_AUX RSTAPLE r_close_arracc\n | emptyARRACC_AUX : COMA r_add_dim EXPRESION r_create_quad ARRACC_AUX\n | emptyEXPRESION : SUBEXP r_seen_subexp EXPRESION_AUXEXPRESION_AUX : AND r_seen_operator EXPRESION\n | OR r_seen_operator EXPRESION\n | emptySUBEXP : EXP r_seen_exp SUBEXP_AUXSUBEXP_AUX : COMPARACION SUBEXP\n | emptyCOMPARACION : MORE r_seen_operator\n | LESS r_seen_operator\n | COMPARE r_seen_operator\n | DIFFERENT r_seen_operator\n | MOREEQUAL r_seen_operator\n | LESSEQUAL r_seen_operatorEXP : TERMINO r_seen_term EXP_AUXEXP_AUX : PLUS r_seen_operator EXP\n | MINUS r_seen_operator EXP\n | emptyTERMINO : FACTOR r_seen_factor TERMINO_AUXTERMINO_AUX : MULT r_seen_operator TERMINO r_seen_term\n | DIV r_seen_operator TERMINO r_seen_term\n | MOD r_seen_operator TERMINO r_seen_term\n | emptyFACTOR : NOT r_seen_unary_operator FACTOR_AUX\n | FACTOR_AUXFACTOR_AUX : SIGN LPAREN r_seen_operator EXPRESION RPAREN r_pop_fake_bottom\n | SIGN CTE ARROPSIGN : PLUS r_seen_unary_operator\n | MINUS r_seen_unary_operator\n | emptyCTE : CTE_I r_seen_operand\n | CTE_F r_seen_operand\n | CTE_CH r_seen_operand\n | CTE_STRING r_seen_operand\n | FUN\n | ID r_seen_operand_id ARRACC ARROP : DET_ARR r_seen_operator_mat\n | TRANS_ARR r_seen_operator_mat\n | INV_ARR r_seen_operator_mat\n | emptyFUN : ID r_check_func LPAREN r_create_bottom FUN_AUX RPAREN r_pop_fake_bottom r_go_subFUN_AUX : EXPRESION r_check_param COMA FUN_AUX\n | EXPRESION r_check_param\n | emptyCOND : IF\n | FOR\n | WHILEIF : SI LPAREN EXPRESION r_check_int RPAREN ENTONCES IF2 r_if_endIF2 : BLOQUE IF_AUX\n | CONDIF_AUX : SINO r_else_start BLOQUE\n | emptyWHILE : MIENTRAS r_set_while LPAREN EXPRESION r_check_int RPAREN WHILE_AUX WHILE2 r_while_endWHILE2 : BLOQUE\n | CONDWHILE_AUX : HAZ\n | empty FOR : DESDE ASIGNACION r_set_for HASTA EXPRESION r_for_gen HACER FOR2 r_for_endFOR2 : BLOQUE\n | CONDWRITE : ESCRIBE LPAREN WRITE_AUX RPARENWRITE_AUX : EXPRESION r_escribe WRITE_AUXSUBWRITE_AUXSUB : COMA WRITE_AUX\n | emptyREAD : LEE LPAREN READ_AUX RPARENREAD_AUX : ID r_seen_operand_id ARRDIM r_lee READ_AUXSUBREAD_AUXSUB : COMA READ_AUX\n | emptyRETURN : REGRESA LPAREN EXPRESION RPAREN r_regresa\n | REGRESA LPAREN NULL RPARENr_save_type : r_save_func : r_register_func : r_register_var : r_register_arr : r_register_dim : r_populate_r : r_check_dim : r_create_quad : r_add_dim : r_close_arracc : r_register_princ : r_end_princ : r_seen_operand : r_seen_operand_id :r_seen_operator : r_seen_unary_operator : r_seen_operator_mat : r_seen_equal : r_seen_subexp : r_seen_exp : r_seen_term : r_seen_factor : r_pop_fake_bottom : r_create_bottom : r_check_int : r_if_end : r_else_start : r_set_while : r_while_end : r_set_for : r_for_gen : r_for_end : r_save_param_func : r_save_vars : r_func_set : r_func_end : r_check_func : r_check_param : r_go_sub : r_goto_main : r_regresa : r_escribe : r_lee : r_print_constants : ' _lr_action_items = 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_lr_action = {} for _k, _v in _lr_action_items.items(): for _x,_y in zip(_v[0],_v[1]): if not _x in _lr_action: _lr_action[_x] = {} _lr_action[_x][_k] = _y del _lr_action_items _lr_goto_items = {'PROGRAM':([0,],[1,]),'r_goto_main':([2,],[3,]),'VARS':([5,57,73,82,],[6,65,81,87,]),'empty':([5,7,9,18,21,33,53,56,57,61,69,72,73,76,79,82,89,94,121,123,125,126,154,157,158,160,161,162,163,164,165,167,177,181,183,189,196,214,224,231,235,236,244,245,246,247,248,254,257,263,276,283,306,],[8,12,19,19,12,40,60,64,8,60,40,80,8,64,60,8,95,95,134,147,147,147,147,147,187,190,194,197,207,212,147,219,40,147,147,147,147,147,134,261,147,147,147,147,147,147,147,274,281,187,294,147,261,]),'r_save_vars':([6,65,81,87,],[9,71,86,91,]),'VAR_AUX':([7,21,],[10,32,]),'TIPO':([7,20,21,53,61,79,],[11,30,11,58,58,58,]),'FUNCTIONS':([9,18,],[17,29,]),'FUNCTION':([9,18,],[18,18,]),'IDS':([11,50,],[21,55,]),'r_save_type':([13,14,15,16,31,],[23,24,25,26,37,]),'MAIN':([17,],[27,]),'r_register_var':([22,66,],[33,72,]),'r_print_constants':([27,],[34,]),'r_save_func':([28,36,43,],[35,42,48,]),'ARRDIM':([33,69,177,],[38,75,225,]),'r_register_arr':([33,69,177,],[39,39,39,]),'r_populate_r':([38,],[44,]),'r_register_func':([42,48,],[47,54,]),'r_register_princ':([46,],[52,]),'r_register_dim':([51,70,],[56,76,]),'r_save_param_func':([52,67,74,],[57,73,82,]),'PARAM':([53,61,79,],[59,68,85,]),'ARRDIM_AUX':([56,76,],[62,83,]),'r_end_princ':([71,],[77,]),'PARAM_AUX':([72,],[78,]),'r_func_set':([77,86,91,],[84,90,113,]),'BLOQUE':([84,90,113,255,278,279,303,],[88,112,131,276,296,299,307,]),'r_func_end':([88,112,131,],[92,130,155,]),'ESTATUTOS':([89,94,],[93,115,]),'ESTATUTO':([89,94,],[94,94,]),'ASIGNACION':([89,94,110,],[96,96,127,]),'FUN':([89,94,144,],[97,97,172,]),'COND':([89,94,255,278,279,],[98,98,277,297,300,]),'WRITE':([89,94,],[99,99,]),'READ':([89,94,],[100,100,]),'RETURN':([89,94,],[101,101,]),'IF':([89,94,255,278,279,],[103,103,103,103,103,]),'FOR':([89,94,255,278,279,],[104,104,104,104,104,]),'WHILE':([89,94,255,278,279,],[105,105,105,105,105,]),'r_seen_operand_id':([102,128,149,173,],[121,121,177,224,]),'r_check_func':([102,173,],[122,122,]),'r_set_while':([111,],[129,]),'ARRACC':([121,224,],[132,253,]),'WRITE_AUX':([123,189,],[136,234,]),'EXPRESION':([123,125,126,154,157,158,181,183,189,214,235,236,263,283,],[137,150,152,182,184,186,228,230,137,249,264,265,186,302,]),'SUBEXP':([123,125,126,154,157,158,181,183,189,196,214,235,236,263,283,],[138,138,138,138,138,138,138,138,138,237,138,138,138,138,138,]),'EXP':([123,125,126,154,157,158,181,183,189,196,214,235,236,244,245,263,283,],[139,139,139,139,139,139,139,139,139,139,139,139,139,266,267,139,139,]),'TERMINO':([123,125,126,154,157,158,181,183,189,196,214,235,236,244,245,246,247,248,263,283,],[140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,268,269,270,140,140,]),'FACTOR':([123,125,126,154,157,158,181,183,189,196,214,235,236,244,245,246,247,248,263,283,],[141,141,141,141,141,141,141,141,141,141,141,141,141,141,141,141,141,141,141,141,]),'FACTOR_AUX':([123,125,126,154,157,158,165,181,183,189,196,214,235,236,244,245,246,247,248,263,283,],[143,143,143,143,143,143,213,143,143,143,143,143,143,143,143,143,143,143,143,143,143,]),'SIGN':([123,125,126,154,157,158,165,181,183,189,196,214,235,236,244,245,246,247,248,263,283,],[144,144,144,144,144,144,144,144,144,144,144,144,144,144,144,144,144,144,144,144,144,]),'READ_AUX':([124,273,],[148,290,]),'r_set_for':([127,],[153,]),'r_check_dim':([133,],[157,]),'r_create_bottom':([135,],[158,]),'r_escribe':([137,],[160,]),'r_seen_subexp':([138,],[161,]),'r_seen_exp':([139,],[162,]),'r_seen_term':([140,268,269,270,],[163,286,287,288,]),'r_seen_factor':([141,],[164,]),'r_seen_unary_operator':([142,145,146,],[165,174,175,]),'CTE':([144,],[167,]),'r_check_int':([152,182,],[180,229,]),'r_seen_operator':([156,166,192,193,198,199,200,201,202,203,205,206,209,210,211,],[183,214,235,236,238,239,240,241,242,243,244,245,246,247,248,]),'FUN_AUX':([158,263,],[185,285,]),'WRITE_AUXSUB':([160,],[188,]),'EXPRESION_AUX':([161,],[191,]),'SUBEXP_AUX':([162,],[195,]),'COMPARACION':([162,],[196,]),'EXP_AUX':([163,],[204,]),'TERMINO_AUX':([164,],[208,]),'ARROP':([167,],[215,]),'r_seen_operand':([168,169,170,171,],[220,221,222,223,]),'r_regresa':([178,],[226,]),'r_create_quad':([184,302,],[231,306,]),'r_check_param':([186,],[233,]),'r_seen_operator_mat':([216,217,218,],[250,251,252,]),'r_lee':([225,],[254,]),'r_for_gen':([228,],[256,]),'r_seen_equal':([230,],[258,]),'ARRACC_AUX':([231,306,],[259,308,]),'r_pop_fake_bottom':([232,271,],[262,289,]),'READ_AUXSUB':([254,],[272,]),'IF2':([255,],[275,]),'WHILE_AUX':([257,],[279,]),'r_add_dim':([260,],[283,]),'r_go_sub':([262,],[284,]),'r_if_end':([275,],[291,]),'IF_AUX':([276,],[292,]),'FOR2':([278,],[295,]),'WHILE2':([279,],[298,]),'r_close_arracc':([282,],[301,]),'r_else_start':([293,],[303,]),'r_for_end':([295,],[304,]),'r_while_end':([298,],[305,]),} _lr_goto = {} for _k, _v in _lr_goto_items.items(): for _x, _y in zip(_v[0], _v[1]): if not _x in _lr_goto: _lr_goto[_x] = {} _lr_goto[_x][_k] = _y del _lr_goto_items _lr_productions = [ ("S' -> PROGRAM","S'",1,None,None,None), ('empty -> <empty>','empty',0,'p_empty','parser.py',25), ('PROGRAM -> PROGRAMA r_goto_main ID DOTCOMA VARS r_save_vars FUNCTIONS MAIN r_print_constants','PROGRAM',9,'p_PROGRAM','parser.py',33), ('MAIN -> PRINCIPAL r_save_func LPAREN RPAREN r_register_princ r_save_param_func VARS r_save_vars r_end_princ r_func_set BLOQUE r_func_end','MAIN',12,'p_MAIN','parser.py',45), ('VARS -> VAR VAR_AUX','VARS',2,'p_VARS','parser.py',50), ('VARS -> empty','VARS',1,'p_VARS','parser.py',51), ('VAR_AUX -> TIPO IDS VAR_AUX','VAR_AUX',3,'p_VAR_AUX','parser.py',56), ('VAR_AUX -> empty','VAR_AUX',1,'p_VAR_AUX','parser.py',57), ('TIPO -> INT r_save_type','TIPO',2,'p_TIPO','parser.py',63), ('TIPO -> FLOAT r_save_type','TIPO',2,'p_TIPO','parser.py',64), ('TIPO -> CHAR r_save_type','TIPO',2,'p_TIPO','parser.py',65), ('TIPO -> STRING r_save_type','TIPO',2,'p_TIPO','parser.py',66), ('IDS -> ID r_register_var ARRDIM r_populate_r DOTCOMA','IDS',5,'p_IDS','parser.py',73), ('IDS -> ID r_register_var ARRDIM r_populate_r COMA IDS','IDS',6,'p_IDS','parser.py',74), ('ARRDIM -> r_register_arr LSTAPLE CTE_I r_register_dim ARRDIM_AUX RSTAPLE ARRDIM','ARRDIM',7,'p_ARRDIM','parser.py',81), ('ARRDIM -> empty','ARRDIM',1,'p_ARRDIM','parser.py',82), ('ARRDIM_AUX -> COMA CTE_I r_register_dim ARRDIM_AUX','ARRDIM_AUX',4,'p_ARRDIM_AUX','parser.py',88), ('ARRDIM_AUX -> empty','ARRDIM_AUX',1,'p_ARRDIM_AUX','parser.py',89), ('FUNCTIONS -> FUNCTION FUNCTIONS','FUNCTIONS',2,'p_FUNCTIONS','parser.py',94), ('FUNCTIONS -> empty','FUNCTIONS',1,'p_FUNCTIONS','parser.py',95), ('FUNCTION -> FUNCION TIPO ID r_save_func r_register_func LPAREN PARAM RPAREN r_save_param_func VARS r_save_vars r_func_set BLOQUE r_func_end','FUNCTION',14,'p_FUNCTION','parser.py',107), ('FUNCTION -> FUNCION VOID r_save_type ID r_save_func r_register_func LPAREN PARAM RPAREN r_save_param_func VARS r_save_vars r_func_set BLOQUE r_func_end','FUNCTION',15,'p_FUNCTION','parser.py',108), ('PARAM -> TIPO ID r_register_var PARAM_AUX','PARAM',4,'p_PARAM','parser.py',115), ('PARAM -> empty','PARAM',1,'p_PARAM','parser.py',116), ('PARAM_AUX -> COMA PARAM','PARAM_AUX',2,'p_PARAM_AUX','parser.py',121), ('PARAM_AUX -> empty','PARAM_AUX',1,'p_PARAM_AUX','parser.py',122), ('BLOQUE -> LBRACKET ESTATUTOS RBRACKET','BLOQUE',3,'p_BLOQUE','parser.py',127), ('ESTATUTOS -> ESTATUTO ESTATUTOS','ESTATUTOS',2,'p_ESTATUTOS','parser.py',132), ('ESTATUTOS -> empty','ESTATUTOS',1,'p_ESTATUTOS','parser.py',133), ('ESTATUTO -> ASIGNACION DOTCOMA','ESTATUTO',2,'p_ESTATUTO','parser.py',138), ('ESTATUTO -> FUN DOTCOMA','ESTATUTO',2,'p_ESTATUTO','parser.py',139), ('ESTATUTO -> COND','ESTATUTO',1,'p_ESTATUTO','parser.py',140), ('ESTATUTO -> WRITE DOTCOMA','ESTATUTO',2,'p_ESTATUTO','parser.py',141), ('ESTATUTO -> READ DOTCOMA','ESTATUTO',2,'p_ESTATUTO','parser.py',142), ('ESTATUTO -> RETURN DOTCOMA','ESTATUTO',2,'p_ESTATUTO','parser.py',143), ('ASIGNACION -> ID r_seen_operand_id ARRACC EQUAL r_seen_operator EXPRESION r_seen_equal','ASIGNACION',7,'p_ASIGNACION','parser.py',151), ('ARRACC -> LSTAPLE r_check_dim EXPRESION r_create_quad ARRACC_AUX RSTAPLE r_close_arracc','ARRACC',7,'p_ARRACC','parser.py',159), ('ARRACC -> empty','ARRACC',1,'p_ARRACC','parser.py',160), ('ARRACC_AUX -> COMA r_add_dim EXPRESION r_create_quad ARRACC_AUX','ARRACC_AUX',5,'p_ARRACC_AUX','parser.py',167), ('ARRACC_AUX -> empty','ARRACC_AUX',1,'p_ARRACC_AUX','parser.py',168), ('EXPRESION -> SUBEXP r_seen_subexp EXPRESION_AUX','EXPRESION',3,'p_EXPRESION','parser.py',174), ('EXPRESION_AUX -> AND r_seen_operator EXPRESION','EXPRESION_AUX',3,'p_EXPRESION_AUX','parser.py',180), ('EXPRESION_AUX -> OR r_seen_operator EXPRESION','EXPRESION_AUX',3,'p_EXPRESION_AUX','parser.py',181), ('EXPRESION_AUX -> empty','EXPRESION_AUX',1,'p_EXPRESION_AUX','parser.py',182), ('SUBEXP -> EXP r_seen_exp SUBEXP_AUX','SUBEXP',3,'p_SUBEXP','parser.py',188), ('SUBEXP_AUX -> COMPARACION SUBEXP','SUBEXP_AUX',2,'p_SUBEXP_AUX','parser.py',193), ('SUBEXP_AUX -> empty','SUBEXP_AUX',1,'p_SUBEXP_AUX','parser.py',194), ('COMPARACION -> MORE r_seen_operator','COMPARACION',2,'p_COMPARACION','parser.py',200), ('COMPARACION -> LESS r_seen_operator','COMPARACION',2,'p_COMPARACION','parser.py',201), ('COMPARACION -> COMPARE r_seen_operator','COMPARACION',2,'p_COMPARACION','parser.py',202), ('COMPARACION -> DIFFERENT r_seen_operator','COMPARACION',2,'p_COMPARACION','parser.py',203), ('COMPARACION -> MOREEQUAL r_seen_operator','COMPARACION',2,'p_COMPARACION','parser.py',204), ('COMPARACION -> LESSEQUAL r_seen_operator','COMPARACION',2,'p_COMPARACION','parser.py',205), ('EXP -> TERMINO r_seen_term EXP_AUX','EXP',3,'p_EXP','parser.py',211), ('EXP_AUX -> PLUS r_seen_operator EXP','EXP_AUX',3,'p_EXP_AUX','parser.py',217), ('EXP_AUX -> MINUS r_seen_operator EXP','EXP_AUX',3,'p_EXP_AUX','parser.py',218), ('EXP_AUX -> empty','EXP_AUX',1,'p_EXP_AUX','parser.py',219), ('TERMINO -> FACTOR r_seen_factor TERMINO_AUX','TERMINO',3,'p_TERMINO','parser.py',225), ('TERMINO_AUX -> MULT r_seen_operator TERMINO r_seen_term','TERMINO_AUX',4,'p_TERMINO_AUX','parser.py',232), ('TERMINO_AUX -> DIV r_seen_operator TERMINO r_seen_term','TERMINO_AUX',4,'p_TERMINO_AUX','parser.py',233), ('TERMINO_AUX -> MOD r_seen_operator TERMINO r_seen_term','TERMINO_AUX',4,'p_TERMINO_AUX','parser.py',234), ('TERMINO_AUX -> empty','TERMINO_AUX',1,'p_TERMINO_AUX','parser.py',235), ('FACTOR -> NOT r_seen_unary_operator FACTOR_AUX','FACTOR',3,'p_FACTOR','parser.py',242), ('FACTOR -> FACTOR_AUX','FACTOR',1,'p_FACTOR','parser.py',243), ('FACTOR_AUX -> SIGN LPAREN r_seen_operator EXPRESION RPAREN r_pop_fake_bottom','FACTOR_AUX',6,'p_FACTOR_AUX','parser.py',253), ('FACTOR_AUX -> SIGN CTE ARROP','FACTOR_AUX',3,'p_FACTOR_AUX','parser.py',254), ('SIGN -> PLUS r_seen_unary_operator','SIGN',2,'p_SIGN','parser.py',262), ('SIGN -> MINUS r_seen_unary_operator','SIGN',2,'p_SIGN','parser.py',263), ('SIGN -> empty','SIGN',1,'p_SIGN','parser.py',264), ('CTE -> CTE_I r_seen_operand','CTE',2,'p_CTE','parser.py',270), ('CTE -> CTE_F r_seen_operand','CTE',2,'p_CTE','parser.py',271), ('CTE -> CTE_CH r_seen_operand','CTE',2,'p_CTE','parser.py',272), ('CTE -> CTE_STRING r_seen_operand','CTE',2,'p_CTE','parser.py',273), ('CTE -> FUN','CTE',1,'p_CTE','parser.py',274), ('CTE -> ID r_seen_operand_id ARRACC','CTE',3,'p_CTE','parser.py',275), ('ARROP -> DET_ARR r_seen_operator_mat','ARROP',2,'p_ARROP','parser.py',281), ('ARROP -> TRANS_ARR r_seen_operator_mat','ARROP',2,'p_ARROP','parser.py',282), ('ARROP -> INV_ARR r_seen_operator_mat','ARROP',2,'p_ARROP','parser.py',283), ('ARROP -> empty','ARROP',1,'p_ARROP','parser.py',284), ('FUN -> ID r_check_func LPAREN r_create_bottom FUN_AUX RPAREN r_pop_fake_bottom r_go_sub','FUN',8,'p_FUN','parser.py',291), ('FUN_AUX -> EXPRESION r_check_param COMA FUN_AUX','FUN_AUX',4,'p_FUN_AUX','parser.py',297), ('FUN_AUX -> EXPRESION r_check_param','FUN_AUX',2,'p_FUN_AUX','parser.py',298), ('FUN_AUX -> empty','FUN_AUX',1,'p_FUN_AUX','parser.py',299), ('COND -> IF','COND',1,'p_COND','parser.py',304), ('COND -> FOR','COND',1,'p_COND','parser.py',305), ('COND -> WHILE','COND',1,'p_COND','parser.py',306), ('IF -> SI LPAREN EXPRESION r_check_int RPAREN ENTONCES IF2 r_if_end','IF',8,'p_IF','parser.py',313), ('IF2 -> BLOQUE IF_AUX','IF2',2,'p_IF2','parser.py',318), ('IF2 -> COND','IF2',1,'p_IF2','parser.py',319), ('IF_AUX -> SINO r_else_start BLOQUE','IF_AUX',3,'p_IF_AUX','parser.py',325), ('IF_AUX -> empty','IF_AUX',1,'p_IF_AUX','parser.py',326), ('WHILE -> MIENTRAS r_set_while LPAREN EXPRESION r_check_int RPAREN WHILE_AUX WHILE2 r_while_end','WHILE',9,'p_WHILE','parser.py',334), ('WHILE2 -> BLOQUE','WHILE2',1,'p_WHILE2','parser.py',339), ('WHILE2 -> COND','WHILE2',1,'p_WHILE2','parser.py',340), ('WHILE_AUX -> HAZ','WHILE_AUX',1,'p_WHILE_AUX','parser.py',345), ('WHILE_AUX -> empty','WHILE_AUX',1,'p_WHILE_AUX','parser.py',346), ('FOR -> DESDE ASIGNACION r_set_for HASTA EXPRESION r_for_gen HACER FOR2 r_for_end','FOR',9,'p_FOR','parser.py',354), ('FOR2 -> BLOQUE','FOR2',1,'p_FOR2','parser.py',359), ('FOR2 -> COND','FOR2',1,'p_FOR2','parser.py',360), ('WRITE -> ESCRIBE LPAREN WRITE_AUX RPAREN','WRITE',4,'p_WRITE','parser.py',365), ('WRITE_AUX -> EXPRESION r_escribe WRITE_AUXSUB','WRITE_AUX',3,'p_WRITE_AUX','parser.py',371), ('WRITE_AUXSUB -> COMA WRITE_AUX','WRITE_AUXSUB',2,'p_WRITE_AUXSUB','parser.py',376), ('WRITE_AUXSUB -> empty','WRITE_AUXSUB',1,'p_WRITE_AUXSUB','parser.py',377), ('READ -> LEE LPAREN READ_AUX RPAREN','READ',4,'p_READ','parser.py',382), ('READ_AUX -> ID r_seen_operand_id ARRDIM r_lee READ_AUXSUB','READ_AUX',5,'p_READ_AUX','parser.py',389), ('READ_AUXSUB -> COMA READ_AUX','READ_AUXSUB',2,'p_READ_AUXSUB','parser.py',394), ('READ_AUXSUB -> empty','READ_AUXSUB',1,'p_READ_AUXSUB','parser.py',395), ('RETURN -> REGRESA LPAREN EXPRESION RPAREN r_regresa','RETURN',5,'p_RETURN','parser.py',401), ('RETURN -> REGRESA LPAREN NULL RPAREN','RETURN',4,'p_RETURN','parser.py',402), ('r_save_type -> <empty>','r_save_type',0,'p_r_save_type','parser.py',419), ('r_save_func -> <empty>','r_save_func',0,'p_r_save_func','parser.py',425), ('r_register_func -> <empty>','r_register_func',0,'p_r_register_func','parser.py',431), ('r_register_var -> <empty>','r_register_var',0,'p_r_register_var','parser.py',451), ('r_register_arr -> <empty>','r_register_arr',0,'p_r_register_arr','parser.py',478), ('r_register_dim -> <empty>','r_register_dim',0,'p_r_register_dim','parser.py',489), ('r_populate_r -> <empty>','r_populate_r',0,'p_r_populate_r','parser.py',509), ('r_check_dim -> <empty>','r_check_dim',0,'p_r_check_dim','parser.py',537), ('r_create_quad -> <empty>','r_create_quad',0,'p_r_create_quad','parser.py',551), ('r_add_dim -> <empty>','r_add_dim',0,'p_r_add_dim','parser.py',594), ('r_close_arracc -> <empty>','r_close_arracc',0,'p_r_close_arracc','parser.py',602), ('r_register_princ -> <empty>','r_register_princ',0,'p_r_register_princ','parser.py',642), ('r_end_princ -> <empty>','r_end_princ',0,'p_r_end_princ','parser.py',650), ('r_seen_operand -> <empty>','r_seen_operand',0,'p_r_seen_operand','parser.py',657), ('r_seen_operand_id -> <empty>','r_seen_operand_id',0,'p_r_seen_operand_id','parser.py',664), ('r_seen_operator -> <empty>','r_seen_operator',0,'p_r_seen_operator','parser.py',671), ('r_seen_unary_operator -> <empty>','r_seen_unary_operator',0,'p_r_seen_unary_operator','parser.py',678), ('r_seen_operator_mat -> <empty>','r_seen_operator_mat',0,'p_r_seen_operator_mat','parser.py',692), ('r_seen_equal -> <empty>','r_seen_equal',0,'p_r_seen_equal','parser.py',700), ('r_seen_subexp -> <empty>','r_seen_subexp',0,'p_r_seen_subexp','parser.py',707), ('r_seen_exp -> <empty>','r_seen_exp',0,'p_r_seen_exp','parser.py',714), ('r_seen_term -> <empty>','r_seen_term',0,'p_r_seen_term','parser.py',721), ('r_seen_factor -> <empty>','r_seen_factor',0,'p_r_seen_factor','parser.py',728), ('r_pop_fake_bottom -> <empty>','r_pop_fake_bottom',0,'p_r_pop_fake_bottom','parser.py',735), ('r_create_bottom -> <empty>','r_create_bottom',0,'p_r_create_bottom','parser.py',741), ('r_check_int -> <empty>','r_check_int',0,'p_r_check_int','parser.py',748), ('r_if_end -> <empty>','r_if_end',0,'p_r_if_end','parser.py',755), ('r_else_start -> <empty>','r_else_start',0,'p_r_else_start','parser.py',762), ('r_set_while -> <empty>','r_set_while',0,'p_r_set_while','parser.py',769), ('r_while_end -> <empty>','r_while_end',0,'p_r_while_end','parser.py',776), ('r_set_for -> <empty>','r_set_for',0,'p_r_set_for','parser.py',783), ('r_for_gen -> <empty>','r_for_gen',0,'p_r_for_gen','parser.py',790), ('r_for_end -> <empty>','r_for_end',0,'p_r_for_end','parser.py',797), ('r_save_param_func 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e2c9ebf48a2f930a70bce8e702fc0b264badab6f

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py

Python

docs/_build/jupyter_execute/vignette_example.py

connixu/ny_artsy_date

212a83086e8ca046bc6eda5ebecc5aacad3f63e6

[ "MIT" ]

1

2021-12-21T21:14:04.000Z

docs/_build/jupyter_execute/vignette_example.py

connixu/ny_artsy_date

212a83086e8ca046bc6eda5ebecc5aacad3f63e6

[ "MIT" ]

null

docs/_build/jupyter_execute/vignette_example.py

connixu/ny_artsy_date

212a83086e8ca046bc6eda5ebecc5aacad3f63e6

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # # Example usage # # To use `ny_artsy_date` in a project: # In[1]: import ny_artsy_date print(ny_artsy_date.__version__)

11.133333

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167

3.785714

0.75

0.198113

0.311321

0

0.014493

0.173653

167

14

39

11.928571

0.753623

0.562874

0

1

0

true

0

0.5

0

0.5

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

e2f757494dea94643e41c72bc77817cb9afbe220

165

py

Python

budgetcenter/budgetcenter/doctype/auroville_maintenance_item_table/test_auroville_maintenance_item_table.py

yuvabedev/budgetcenter

d3307500fa9fa71c0d292951282f1f6f135ff510

[ "MIT" ]

null

budgetcenter/budgetcenter/doctype/auroville_maintenance_item_table/test_auroville_maintenance_item_table.py

yuvabedev/budgetcenter

d3307500fa9fa71c0d292951282f1f6f135ff510

[ "MIT" ]

3

2022-01-04T06:30:57.000Z

2022-01-19T06:42:40.000Z

budgetcenter/budgetcenter/doctype/auroville_maintenance_item_table/test_auroville_maintenance_item_table.py

yuvabedev/budgetcenter

d3307500fa9fa71c0d292951282f1f6f135ff510

[ "MIT" ]

null

18.333333

59

0.806061

18

165

7.388889

0.888889

0

0.027778

0.127273

165

8

60

20.625

0.895833

0.448485

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

39077c53fc87c57c3ab243a037c79342656727e6

85

py

Python

venddb/views.py

PHSCRC/vendingserver

2f3e8920a58f11d241b5fe862aba3eeedd57c9e3

[ "Apache-2.0" ]

null

venddb/views.py

PHSCRC/vendingserver

2f3e8920a58f11d241b5fe862aba3eeedd57c9e3

[ "Apache-2.0" ]

null

venddb/views.py

PHSCRC/vendingserver

2f3e8920a58f11d241b5fe862aba3eeedd57c9e3

[ "Apache-2.0" ]

null

from django.shortcuts import render from . import models # Create your views here.

14.166667

35

0.776471

12

85

5.5

0.833333

0

0.176471

85

5

36

17

0.942857

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0

1

0

true

0

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1

0

null

0

1

0

1

0

1

0

5

1a309402d864a31e994ffddb348a018bcad1fd82

190

py

Python

python_modules/libraries/dagstermill/dagstermill_tests/test_repository.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

1

2021-04-27T19:49:59.000Z

python_modules/libraries/dagstermill/dagstermill_tests/test_repository.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

7

2022-03-16T06:55:04.000Z

2022-03-18T07:03:25.000Z

python_modules/libraries/dagstermill/dagstermill_tests/test_repository.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

1

2020-08-20T14:20:31.000Z

from dagstermill.examples.repository import notebook_repo from dagster import RepositoryDefinition def test_dagstermill_repo(): assert isinstance(notebook_repo, RepositoryDefinition)

23.75

58

0.852632

20

190

7.9

0.65

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0

0.105263

190

7

59

27.142857

0.929412

0

0.25

1

0.25

true

0

0.5

0

0.75

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1

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null

0

1

0

null

0

1

0

1

0

5

1a519329eb4ae5d7f7b589948e2782d816f96ba8

257

py

Python

backend/apps/cmdb/serializers/__init__.py

codelieche/erp

96861ff63a63a93918fbd5181ffb2646446d0eec

[ "MIT" ]

null

backend/apps/cmdb/serializers/__init__.py

codelieche/erp

96861ff63a63a93918fbd5181ffb2646446d0eec

[ "MIT" ]

29

2020-06-05T19:57:11.000Z

2022-02-26T13:42:36.000Z

backend/apps/cmdb/serializers/__init__.py

codelieche/erp

96861ff63a63a93918fbd5181ffb2646446d0eec

[ "MIT" ]

null

# -*- coding:utf-8 -*- from .model import ModelSerializer, ModelInfoSerializer from .field import FieldModelSerializer from .instance import InstanceModelSerializer from .value import ValueModelSerializer from .permission import PermissionModelSerializer

28.555556

55

0.836576

24

257

8.958333

0.666667

0

0.004348

0.105058

257

8

56

32.125

0.930435

0.077821

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1a55878182214db9af28b16a925071cd8590d067

384

py

Python

api/service/dao/demand.py

feiwencaho/sharezone

0a72cef8d9c1b6fa8e007c2df55d32fbdb43fa23

[ "Apache-2.0" ]

null

api/service/dao/demand.py

feiwencaho/sharezone

0a72cef8d9c1b6fa8e007c2df55d32fbdb43fa23

[ "Apache-2.0" ]

6

2021-03-18T21:23:50.000Z

2022-03-11T23:32:30.000Z

api/service/dao/demand.py

hifeiwenchao/sharezone

0a72cef8d9c1b6fa8e007c2df55d32fbdb43fa23

[ "Apache-2.0" ]

null

from api.models import Demand from django.db.models import Count def create(**kwargs): return Demand.objects.create(**kwargs) def get_demand(**kwargs): return Demand.objects.filter(**kwargs).first() def get_demands(**kwargs): return Demand.objects.filter(**kwargs).all() def demand_count(user): return Demand.objects.all().annotate(demand_count=Count("user"))

20.210526

68

0.726563

52

384

5.288462

0.384615

0.174545

0.276364

0.272727

0.269091

0

0.125

384

18

69

21.333333

0.818452

0

0.010417

0

1

0.4

false

0

0.2

0.4

1

0

null

0

1

0

null

0

1

0

1

0

5

1aa8161a54e886314d199d5040a01206b88e29be

181

py

Python

mysite/editor/admin.py

dkaimekin/zhana-aygerim-backend

306f28b019fe6bce982bbd10a8f50c1d7da9bfe4

[ "MIT" ]

null

mysite/editor/admin.py

dkaimekin/zhana-aygerim-backend

306f28b019fe6bce982bbd10a8f50c1d7da9bfe4

[ "MIT" ]

null

mysite/editor/admin.py

dkaimekin/zhana-aygerim-backend

306f28b019fe6bce982bbd10a8f50c1d7da9bfe4

[ "MIT" ]

null

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

25.857143

38

0.801105

27

181

5.37037

0.481481

0.186207

0.351724

0

0.099448

181

7

39

25.857143

0.889571

0.143646

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

46b9b3aac93acec1d59e41dcbf9e55a686f617ac

77

py

Python

src/dcllottery/__init__.py

deepcloudlabs/lottery

06185e68bc4168b306108890dea02fa23a14106d

[ "MIT" ]

null

src/dcllottery/__init__.py

deepcloudlabs/lottery

06185e68bc4168b306108890dea02fa23a14106d

[ "MIT" ]

null

src/dcllottery/__init__.py

deepcloudlabs/lottery

06185e68bc4168b306108890dea02fa23a14106d

[ "MIT" ]

1

2021-09-07T21:45:16.000Z

print("dcllottery package is loaded!") # all initialization code goes here

25.666667

39

0.766234

10

77

5.9

1

0

0.155844

77

2

40

38.5

0.907692

0.428571

0

0.725

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

204833f44fdc1cdbb2c535a00990a8166815824e

50

py

Python

neupy/plots/__init__.py

FrostByte266/neupy

4b7127e5e4178b0cce023ba36542f5ad3f1d798c

[ "MIT" ]

801

2015-09-23T09:24:47.000Z

2022-03-29T19:19:03.000Z

neupy/plots/__init__.py

FrostByte266/neupy

4b7127e5e4178b0cce023ba36542f5ad3f1d798c

[ "MIT" ]

277

2015-09-22T19:48:50.000Z

2022-03-11T23:25:32.000Z

neupy/plots/__init__.py

FrostByte266/neupy

4b7127e5e4178b0cce023ba36542f5ad3f1d798c

[ "MIT" ]

194

2015-09-23T15:03:57.000Z

2022-03-31T13:54:46.000Z

from .hinton import * from .saliency_map import *

16.666667

27

0.76

7

50

5.285714

0.714286

0

0.16

50

2

28

25

0.880952

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2059cefa76346776224cacf36bdb3d0d57d8af8c

189

py

Python

pysdds/__init__.py

nikitakuklev/pySDDS

3f5a104712813b7494b56ff959a5feef1271d889

[ "MIT" ]

null

pysdds/__init__.py

nikitakuklev/pySDDS

3f5a104712813b7494b56ff959a5feef1271d889

[ "MIT" ]

null

pysdds/__init__.py

nikitakuklev/pySDDS

3f5a104712813b7494b56ff959a5feef1271d889

[ "MIT" ]

null

__author__ = "Nikita Kuklev" from .readers import read from .writers import write from .structures import SDDSFile from . import _version __version__ = _version.get_versions()['version']

21

48

0.78836

23

189

6

0.608696

0.202899

0

0.132275

189

8

49

23.625

0.841463

0

0.10582

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

6489ef6c9e076cdf2e75ccca30d4a6b07b5d710f

141

py

Python

ex2.py

AyeNandar/python-exercises

63936b53a48dafd96b7e9b222167faf5124bb86c

[ "MIT" ]

null

ex2.py

AyeNandar/python-exercises

63936b53a48dafd96b7e9b222167faf5124bb86c

[ "MIT" ]

null

ex2.py

AyeNandar/python-exercises

63936b53a48dafd96b7e9b222167faf5124bb86c

[ "MIT" ]

null

print("hello world") print("hello again") print("I like typing this") print("This is fun") print('Yay!Printing') print("Hello to mandalay")

17.625

27

0.70922

22

141

4.545455

0.636364

0.3

0

0.113475

141

7

28

20.142857

0.8

0

0.571429

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

649501c5d0d34f0b765ec2e4f2ac40fd42a786b6

477

py

Python

Python/creational_patterns/prototype/framework/manager.py

ploukareas/Design-Patterns

8effde38d73ae9058c3028c97ef395644a90d55b

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

28

2018-09-28T07:45:35.000Z

2022-02-12T12:25:05.000Z

Python/creational_patterns/prototype/framework/manager.py

ploukareas/Design-Patterns

8effde38d73ae9058c3028c97ef395644a90d55b

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

null

Python/creational_patterns/prototype/framework/manager.py

ploukareas/Design-Patterns

8effde38d73ae9058c3028c97ef395644a90d55b

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

5

2021-05-10T23:19:55.000Z

2022-03-04T20:26:35.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # ˅ # ˄ class Manager(object): # ˅ # ˄ def __init__(self): self.__display = {} # ˅ pass # ˄ def register_display(self, display_name, display): # ˅ self.__display[display_name] = display # ˄ def get_display(self, display_name): # ˅ d = self.__display[display_name] return d.clone() # ˄ # ˅ # ˄ # ˅ # ˄

11.925

54

0.454927

56

477

3.839286

0.410714

0.255814

0.167442

0.204651

0

0.00346

0.39413

477

39

55

12.230769

0.692042

0.146751

0

1

0.333333

false

0.111111

0

0.555556

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

6496ac9adfad2d47a85aa69ea7cbd929d7137633

640

py

Python

examples/custom_env_basic/custom_env.py

ZimmermanGroup/conformer-rl

beb98cbee6ba6efba686d7c6eebbf33fd737f279

[ "MIT" ]

9

2021-09-03T18:46:46.000Z

2022-03-22T05:47:20.000Z

examples/custom_env_basic/custom_env.py

ZimmermanGroup/conformer-rl

beb98cbee6ba6efba686d7c6eebbf33fd737f279

[ "MIT" ]

4

2021-07-15T03:57:26.000Z

2021-08-03T06:27:28.000Z

examples/custom_env_basic/custom_env.py

ZimmermanGroup/conformer-rl

beb98cbee6ba6efba686d7c6eebbf33fd737f279

[ "MIT" ]

1

2022-03-17T01:59:36.000Z

from conformer_rl.environments import ConformerEnv from conformer_rl.environments.environment_components.action_mixins import DiscreteActionMixin from conformer_rl.environments.environment_components.obs_mixins import AtomTypeGraphObsMixin from conformer_rl.environments.environment_components.reward_mixins import GibbsPruningRewardMixin import gym # construct custom environment from pre-built environment mixins class TestEnv(DiscreteActionMixin, AtomTypeGraphObsMixin, GibbsPruningRewardMixin, ConformerEnv): pass # register the environment with OpenAI gym gym.register( id='TestEnv-v0', entry_point='custom_env:TestEnv' )

42.666667

98

0.864063

69

640

7.84058

0.463768

0.096118

0.110906

0.19963

0.266174

0

0.001712

0.0875

640

15

99

42.666667

0.924658

0.160938

0

0.052336

0

1

0

true

0.090909

0.454545

0

0.545455

0

null

0

1

0

null

0

1

0

1

0

5

64a506c7696d74384c4bb78560f091d6cc4141e4

153

py

Python

KID/agent/__init__.py

microsoft/KID

a23e9d819b53605b6426170124feed10288c6f8b

[ "MIT" ]

13

2022-03-22T11:45:54.000Z

2022-03-30T18:16:46.000Z

KID/agent/__init__.py

microsoft/KID

a23e9d819b53605b6426170124feed10288c6f8b

[ "MIT" ]

null

KID/agent/__init__.py

microsoft/KID

a23e9d819b53605b6426170124feed10288c6f8b

[ "MIT" ]

null

"""Agent package""" from KID.agent.base_agent import BaseAgent from KID.agent.kid_agent import KIDAgent __all__ = [ 'BaseAgent', 'KIDAgent', ]

15.3

42

0.705882

19

153

5.368421

0.473684

0.235294

0

0.169935

153

9

43

17

0.80315

0.084967

0

0.126866

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

b3c92ac05ad01920971a443a2829b8d348c79573

123

py

Python

tests/plugin/testplugin_invalid.py

xcgx/streamlink

b635e0d9d0fe9363817a96ec7d31faefed95cb57

[ "BSD-2-Clause" ]

10

2017-04-10T18:25:41.000Z

2021-09-15T20:14:58.000Z

tests/plugin/testplugin_invalid.py

xcgx/streamlink

b635e0d9d0fe9363817a96ec7d31faefed95cb57

[ "BSD-2-Clause" ]

9

2020-04-04T09:49:52.000Z

2020-04-21T01:52:02.000Z

tests/plugin/testplugin_invalid.py

xcgx/streamlink

b635e0d9d0fe9363817a96ec7d31faefed95cb57

[ "BSD-2-Clause" ]

12

2022-01-30T23:34:18.000Z

2022-03-26T17:09:43.000Z

class TestPluginInvalid: pass # does not inherit from streamlink.plugin.plugin.Plugin __plugin__ = TestPluginInvalid

17.571429

55

0.804878

13

123

7.307692

0.692308

0.378947

0

0.146341

123

6

56

20.5

0.904762

0.430894

0

1

0

false

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

b3e3db85ce6777c44676ed9a253b57311494863c

67

py

Python

KD_Lib/KD/text/BERT2LSTM/__init__.py

PiaCuk/KD_Lib

153299d484e4c6b33793749709dbb0f33419f190

[ "MIT" ]

360

2020-05-11T08:18:20.000Z

2022-03-31T01:48:43.000Z

KD_Lib/KD/text/BERT2LSTM/__init__.py

PiaCuk/KD_Lib

153299d484e4c6b33793749709dbb0f33419f190

[ "MIT" ]

91

2020-05-11T08:14:56.000Z

2022-03-30T05:29:03.000Z

KD_Lib/KD/text/BERT2LSTM/__init__.py

PiaCuk/KD_Lib

153299d484e4c6b33793749709dbb0f33419f190

[ "MIT" ]

39

2020-05-11T08:06:47.000Z

2022-03-29T05:11:18.000Z

from .bert2lstm import BERT2LSTM from .utils import get_essentials

22.333333

33

0.850746

9

67

6.222222

0.666667

0

0.033898

0.119403

67

2

34

33.5

0.915254

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

b3f9a5ad3a652326685a3fb2ac94fb05eae616cb

43

py

Python

tests/components/ambiclimate/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/ambiclimate/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/ambiclimate/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Tests for the Ambiclimate component."""

21.5

42

0.72093

5

43

6.2

1

0

0.116279

43

1

43

0.815789

0.837209

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

37480f6229f0dff1491ea05d0545a86de3f674fa

43

py

Python

pythonpower.py

Vemassa/hacktoberfest-1

1979b83285650e6ed642330314b2273052ee8b5f

[ "MIT" ]

null

pythonpower.py

Vemassa/hacktoberfest-1

1979b83285650e6ed642330314b2273052ee8b5f

[ "MIT" ]

null

pythonpower.py

Vemassa/hacktoberfest-1

1979b83285650e6ed642330314b2273052ee8b5f

[ "MIT" ]

null

#!/usr/bin/env python3 print("LAst ONEEE")

14.333333

22

0.697674

7

43

4.285714

1

0

0.025641

0.093023

43

3

23

14.333333

0.74359

0.488372

0

0.454545

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

3764b33b10d08545206ae966fc9832419595fdd3

174

py

Python

pdfop/pdfopex.py

szintakacseva/PythonTutorial

c32c8a7e00871e542d74c4c5bd56ee499bf0b6c3

[ "Apache-2.0" ]

null

pdfop/pdfopex.py

szintakacseva/PythonTutorial

c32c8a7e00871e542d74c4c5bd56ee499bf0b6c3

[ "Apache-2.0" ]

null

pdfop/pdfopex.py

szintakacseva/PythonTutorial

c32c8a7e00871e542d74c4c5bd56ee499bf0b6c3

[ "Apache-2.0" ]

null

''' def pdfmerge(): with file('C:/'+filename, 'rb') as fd: merger.append(PdfFileReader(fd)) #if os.path.exists('C:/'+filename): os.remove('C:/'+filename) '''

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37a435956fa79af223a599be1487119094642d71

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py

Python

Code/datasets.py

shikhar-srivastava/CCMCL

52283b2b046e8e389a16fb67d0f45f33f7a22768

[ "Apache-2.0" ]

1

2021-10-06T16:19:20.000Z

Code/datasets.py

shikhar-srivastava/CCMCL

52283b2b046e8e389a16fb67d0f45f33f7a22768

[ "Apache-2.0" ]

null

Code/datasets.py

shikhar-srivastava/CCMCL

52283b2b046e8e389a16fb67d0f45f33f7a22768

[ "Apache-2.0" ]

1

2021-09-02T03:36:48.000Z

""" This file contains data sets for continual learning. """ import tensorflow as tf import tensorflow_datasets as tfds import abc # Disable progress bar tfds.disable_progress_bar() class DataSet(abc.ABC): # Base class for data set classes @abc.abstractmethod def __init__(self): pass def filter_fn(self, batch, classes): return tf.reduce_any(tf.math.equal(batch["label"], classes)) def get_split(self, classes): train_data = self.train_data.filter(lambda x: self.filter_fn(x, classes)) val_data = self.val_data.filter(lambda x: self.filter_fn(x, classes)) test_data = self.test_data.filter(lambda x: self.filter_fn(x, classes)) return train_data, val_data, test_data def get_all(self): return self.train_data, self.val_data, self.test_data class SplitMNIST(DataSet): def __init__(self, num_validation): self.train_data = tfds.load(name="mnist", split="train[{:d}:]".format(int(num_validation))) self.val_data = tfds.load(name="mnist", split="train[:{:d}]".format(int(num_validation))) self.test_data = tfds.load(name="mnist", split="test") class SplitEMNIST(DataSet): def __init__(self, num_validation): self.train_data = tfds.load(name="emnist/letters", split="train[{:d}:]".format(int(num_validation))) self.val_data = tfds.load(name="emnist/letters", split="train[:{:d}]".format(int(num_validation))) self.test_data = tfds.load(name="emnist/letters", split="test") class SplitFashionMNIST(DataSet): def __init__(self, num_validation): self.train_data = tfds.load(name="fashion_mnist", split="train[{:d}:]".format(int(num_validation))) self.val_data = tfds.load(name="fashion_mnist", split="train[:{:d}]".format(int(num_validation))) self.test_data = tfds.load(name="fashion_mnist", split="test") class SplitCIFAR10(DataSet): def __init__(self, num_validation): self.train_data = tfds.load(name="cifar10", split="train[{:d}:]".format(int(num_validation))) self.val_data = tfds.load(name="cifar10", split="train[:{:d}]".format(int(num_validation))) self.test_data = tfds.load(name="cifar10", split="test") class SplitCIFAR100(DataSet): def __init__(self, num_validation): self.train_data = tfds.load(name="cifar100", split="train[{:d}:]".format(int(num_validation))) self.val_data = tfds.load(name="cifar100", split="train[:{:d}]".format(int(num_validation))) self.test_data = tfds.load(name="cifar100", split="test") class SplitOMNIGLOT(DataSet): def __init__(self, num_validation): self.train_data = tfds.load(name="omniglot", split="train[{:d}:]".format(int(num_validation))) self.val_data = tfds.load(name="omniglot", split="train[:{:d}]".format(int(num_validation))) self.test_data = tfds.load(name="omniglot", split="test") class SplitSVHN(DataSet): def __init__(self, num_validation): self.train_data = tfds.load(name="svhn_cropped", split="train[{:d}:]".format(int(num_validation))) self.val_data = tfds.load(name="svhn_cropped", split="train[:{:d}]".format(int(num_validation))) self.test_data = tfds.load(name="svhn_cropped", split="test") class SplitCaltech101(DataSet): def __init__(self, num_validation): self.train_data = tfds.load(name="caltech101", split="train[{:d}:]".format(int(num_validation))) self.val_data = tfds.load(name="caltech101", split="train[:{:d}]".format(int(num_validation))) self.test_data = tfds.load(name="caltech101", split="test")

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37b370ab4bbc99054b46f9195ac6f5106a78ff67

123

py

Python

test_gpsimage.py

dima-kov/gpsimage

29faa4922071f3b2ccc2ac630bfb43d16095453e

[ "Apache-2.0" ]

4

2017-03-20T03:15:19.000Z

2022-01-09T08:42:33.000Z

test_gpsimage.py

dima-kov/gpsimage

29faa4922071f3b2ccc2ac630bfb43d16095453e

[ "Apache-2.0" ]

3

2017-03-06T18:09:09.000Z

2022-03-11T23:19:17.000Z

test_gpsimage.py

dima-kov/gpsimage

29faa4922071f3b2ccc2ac630bfb43d16095453e

[ "Apache-2.0" ]

5

2017-03-06T17:48:08.000Z

2021-04-25T08:40:23.000Z

#!/usr/bin/python # coding: utf8 import gpsimage import pytest import unittest def test_entry_points(): gpsimage.open

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809ec4bdd948caaabf9750201f9f1323941de17b

160

py

Python

classifier/utilities.py

ilhamadun/chili-quality-classifier

5501f59b6e1f23659acfb0c34f5dcb35ec7c17b9

[ "MIT" ]

null

classifier/utilities.py

ilhamadun/chili-quality-classifier

5501f59b6e1f23659acfb0c34f5dcb35ec7c17b9

[ "MIT" ]

null

classifier/utilities.py

ilhamadun/chili-quality-classifier

5501f59b6e1f23659acfb0c34f5dcb35ec7c17b9

[ "MIT" ]

null

import re def _atoi(text): return int(text) if text.isdigit() else text def natural_keys(text): return [_atoi(c) for c in re.split(r'(\d+)', text)]

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80bba13abb99537985ceb44e7b7c05d3df5bab41

138

py

Python

resilience/actions/__init__.py

vishalbelsare/resilience

596a9b8224fc6168bd6ee5718ea6c57425b7f273

[ "Apache-2.0" ]

7

2020-04-04T20:55:21.000Z

2021-11-15T10:42:44.000Z

resilience/actions/__init__.py

vishalbelsare/resilience

596a9b8224fc6168bd6ee5718ea6c57425b7f273

[ "Apache-2.0" ]

1

2020-06-19T04:26:35.000Z

2020-09-25T04:50:00.000Z

resilience/actions/__init__.py

vishalbelsare/resilience

596a9b8224fc6168bd6ee5718ea6c57425b7f273

[ "Apache-2.0" ]

4

2020-04-16T19:10:33.000Z

2020-09-24T17:54:21.000Z

from .PullFunding import PullFunding from .RedeemShares import RedeemShares from .PayLoan import PayLoan from .SellAsset import SellAsset

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80d1b7dd4883befc428939509ace4cd8180af211

174

py

Python

sw/groundstation/gs/test/test_geo_decimal_degrees.py

nzjrs/wasp

b763309af59e7784811baa6dd80e17dba1b27d81

[ "MIT" ]

2

2021-07-11T13:47:17.000Z

2021-11-08T11:21:51.000Z

sw/groundstation/gs/test/test_geo_decimal_degrees.py

nzjrs/wasp

b763309af59e7784811baa6dd80e17dba1b27d81

[ "MIT" ]

null

sw/groundstation/gs/test/test_geo_decimal_degrees.py

nzjrs/wasp

b763309af59e7784811baa6dd80e17dba1b27d81

[ "MIT" ]

2

2015-10-03T06:24:07.000Z

2016-01-21T11:36:20.000Z

import doctest import gs.geo.decimaldegrees as decimaldegrees # Run doctest def _test(): return doctest.testmod(decimaldegrees) if __name__ == "__main__": _test()

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5

03d096bb12152027bd03cf9276beae76b2baebf8

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py

Python

machine_learning/optimize_architecture.py

Yannick947/deep_bau

878cbc452cee8f1e7832ccdd54e5c3ef598702e1

[ "MIT" ]

2

2021-04-23T20:11:32.000Z

2021-04-25T21:33:31.000Z

machine_learning/optimize_architecture.py

Yannick947/deep_bau

878cbc452cee8f1e7832ccdd54e5c3ef598702e1

[ "MIT" ]

null

machine_learning/optimize_architecture.py

Yannick947/deep_bau

878cbc452cee8f1e7832ccdd54e5c3ef598702e1

[ "MIT" ]

1

2021-04-25T22:04:20.000Z

import datetime from machine_learning.classfication_models import create_bayesian_classifier, create_bayesian_dummy_classifier import numpy as np import pandas as pd import kerastuner as kt from machine_learning.utils import get_datagen_split import machine_learning.models import machine_learning.classfication_models from machine_learning.models import create_hyperband_model from machine_learning.data_generator import BauGenerator BATCH_SIZE = 256 LOOK_AHEAD_SIZE = 1 LOOK_BACK_WINDOW_SIZE = 10 def dummy_classification(df: pd.DataFrame): df_train, df_val = get_datagen_split(df) datagen_train = BauGenerator(df=df_train, batch_size=BATCH_SIZE, window_size=LOOK_BACK_WINDOW_SIZE, look_ahead_steps=LOOK_AHEAD_SIZE) datagen_val = BauGenerator(df=df_val, batch_size=BATCH_SIZE, window_size=LOOK_BACK_WINDOW_SIZE, look_ahead_steps=LOOK_AHEAD_SIZE) machine_learning.classfication_models.HYPER_NUM_ROWS_DF = datagen_train.X_batches.shape[2] machine_learning.classfication_models.HYPER_NUM_OUTPUT_FIELDS = datagen_train.Y_batches.shape[ 1] machine_learning.classfication_models.HYPER_WINDOW_SIZE = LOOK_BACK_WINDOW_SIZE machine_learning.classfication_models.HYPER_LOOK_AHEAD_SIZE = LOOK_AHEAD_SIZE tuner = kt.BayesianOptimization(create_bayesian_dummy_classifier, objective='val_accuracy', max_trials=100, project_name="arch_opt_") tuner.search(datagen_train, validation_data=datagen_val, epochs=60, callbacks=[], workers=16) best_model = tuner.get_best_models(1)[0] best_hyperparameters = tuner.get_best_hyperparameters(1)[0] print(best_hyperparameters) def bayesian_classification_optimization(df: pd.DataFrame): df_train, df_val = get_datagen_split(df) datagen_train = BauGenerator(df=df_train, batch_size=BATCH_SIZE, window_size=LOOK_BACK_WINDOW_SIZE, look_ahead_steps=LOOK_AHEAD_SIZE) datagen_val = BauGenerator(df=df_val, batch_size=BATCH_SIZE, window_size=LOOK_BACK_WINDOW_SIZE, look_ahead_steps=LOOK_AHEAD_SIZE) machine_learning.classfication_models.HYPER_NUM_ROWS_DF = datagen_train.X_batches.shape[2] machine_learning.classfication_models.HYPER_NUM_OUTPUT_FIELDS = datagen_train.Y_batches.shape[ 1] machine_learning.classfication_models.HYPER_WINDOW_SIZE = LOOK_BACK_WINDOW_SIZE machine_learning.classfication_models.HYPER_LOOK_AHEAD_SIZE = LOOK_AHEAD_SIZE tuner = kt.BayesianOptimization(create_bayesian_classifier, objective='val_loss', max_trials=200) tuner.search(datagen_train, validation_data=datagen_val, epochs=150, callbacks=[], workers=16) best_model = tuner.get_best_models(1)[0] best_hyperparameters = tuner.get_best_hyperparameters(1)[0] print(best_hyperparameters) def hyperband_optimization(df: pd.DataFrame): df_train, df_val = get_datagen_split(df) datagen_train = BauGenerator(df=df_train, binarize_activity_hours=False, batch_size=BATCH_SIZE, window_size=LOOK_BACK_WINDOW_SIZE, look_ahead_steps=LOOK_AHEAD_SIZE) datagen_val = BauGenerator(df=df_val, binarize_activity_hours=False, batch_size=BATCH_SIZE, window_size=LOOK_BACK_WINDOW_SIZE, look_ahead_steps=LOOK_AHEAD_SIZE) machine_learning.models.HYPER_NUM_ROWS_DF = datagen_train.X_batches.shape[2] machine_learning.models.HYPER_NUM_OUTPUT_FIELDS = datagen_train.Y_batches.shape[2] machine_learning.models.HYPER_WINDOW_SIZE = LOOK_BACK_WINDOW_SIZE machine_learning.models.HYPER_LOOK_AHEAD_SIZE = LOOK_AHEAD_SIZE tuner = kt.BayesianOptimization(create_hyperband_model, objective='val_binary_accuracy', max_trials=200) tuner.search(datagen_train, validation_data=datagen_val, epochs=70, callbacks=[], workers=16) best_model = tuner.get_best_models(1)[0] best_hyperparameters = tuner.get_best_hyperparameters(1)[0] print(best_hyperparameters) if __name__ == '__main__': PERCENTAGE_USED_DATA = 0.7 working_hours = pd.read_csv( "./data/preprocessed/df_deep_bau.csv", error_bad_lines=False, sep=',', index_col=False) start_index = int((1 - PERCENTAGE_USED_DATA) * working_hours.shape[0]) working_hours = working_hours[start_index:] df = working_hours.select_dtypes([np.number]) dummy_classification(df)

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03ddec181c20f22f44755ec04b2fa018e9de71e1

199

py

Python

pydecorator/__init__.py

luciancooper/pydecorator

4617699378782dfd917f2d9c41ad3162bb1fb2ef

[ "MIT" ]

2

2019-01-18T02:13:47.000Z

2019-01-18T02:14:21.000Z

pydecorator/__init__.py

luciancooper/pydecorator

4617699378782dfd917f2d9c41ad3162bb1fb2ef

[ "MIT" ]

null

pydecorator/__init__.py

luciancooper/pydecorator

4617699378782dfd917f2d9c41ad3162bb1fb2ef

[ "MIT" ]

null

from .generator import _list as list,_set as set,_tuple as tuple,_dict as dict,_str as str from .sort import * from .transform import * from .file import * from .numpy import * from .pandas import *

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5

03f9cbf3c216c7c09671425c5c8f23316b0c3842

208

py

Python

inconnu/vr/__init__.py

tiltowait/inconnu

6cca5fed520899d159537701b695c94222d8dc45

[ "MIT" ]

4

2021-09-06T20:18:13.000Z

2022-02-05T17:08:44.000Z

inconnu/vr/__init__.py

tiltowait/inconnu

6cca5fed520899d159537701b695c94222d8dc45

[ "MIT" ]

7

2021-09-13T00:46:57.000Z

2022-01-11T06:38:50.000Z

inconnu/vr/__init__.py

tiltowait/inconnu

6cca5fed520899d159537701b695c94222d8dc45

[ "MIT" ]

2

2021-11-27T22:24:53.000Z

2022-03-16T21:05:00.000Z

"""Defines the imported interfaces for performing rolls.""" from . import dicemoji from .parse import parse, perform_roll, display_outcome, prepare_roll, needs_character from .rolldisplay import RollDisplay

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ff1afae7144bee166a489613ca8bf55cac700aab

78

py

Python

setup.py

x522758754/XlsTools

2e0fc0e66acaee28e64072b251fb956bb65d1474

[ "MIT" ]

null

setup.py

x522758754/XlsTools

2e0fc0e66acaee28e64072b251fb956bb65d1474

[ "MIT" ]

null

setup.py

x522758754/XlsTools

2e0fc0e66acaee28e64072b251fb956bb65d1474

[ "MIT" ]

null

from distutils.core import setup import py2exe setup (console=['xlsToTxt.py'])

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1

0

1

0

1

0

5

455163f67c5ec5155e23f789e0310c90e1e29be2

336

py

Python

mlp/interfaces.py

guidj/mlp

06e60cffde8c8a7f60cebf0e329efae116042e61

[ "Apache-2.0" ]

null

mlp/interfaces.py

guidj/mlp

06e60cffde8c8a7f60cebf0e329efae116042e61

[ "Apache-2.0" ]

null

mlp/interfaces.py

guidj/mlp

06e60cffde8c8a7f60cebf0e329efae116042e61

[ "Apache-2.0" ]

null

""" Interfaces """ class Model(object): def fit(self, X, y): raise NotImplemented('%s is not implemented' % self.__name__) def transform(self, X): raise NotImplemented('%s is not implemented' % self.__name__) def coefficients(self): raise NotImplemented('%s is not implemented' % self.__name__)

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null

0

1

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1

0

5

458544a783382596b32991b3d343a5f6d5eda117

46

py

Python

App/config.py

inishchith/HelpOff

8e0e58f1b9172ab4081ceefabe926399d0f72bee

[ "MIT" ]

5

2018-04-01T12:27:08.000Z

2018-04-03T06:56:25.000Z

App/config.py

inishchith/HelpOff

8e0e58f1b9172ab4081ceefabe926399d0f72bee

[ "MIT" ]

null

App/config.py

inishchith/HelpOff

8e0e58f1b9172ab4081ceefabe926399d0f72bee

[ "MIT" ]

1

2018-04-01T12:35:18.000Z

YOUR_API_KEY = "" # Enter your API Key here.

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0

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1

0

1

0

null

0

5

458562f5d3922617fccf08df00a2adce28ef299a

105

py

Python

profile_api/admin.py

Rajmohanraj/profiles-rest-api

ccb8d6d90a62bc3603041940bf9e2e10a89899c2

[ "MIT" ]

null

profile_api/admin.py

Rajmohanraj/profiles-rest-api

ccb8d6d90a62bc3603041940bf9e2e10a89899c2

[ "MIT" ]

null

profile_api/admin.py

Rajmohanraj/profiles-rest-api

ccb8d6d90a62bc3603041940bf9e2e10a89899c2

[ "MIT" ]

null

from django.contrib import admin from profile_api import models admin.site.register(models.UserProfile)

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tests/__init__.py

souravaich/pyquote

ac48640d238f4e8d5c20b206180144b82d47cb20

[ "MIT" ]

1

2020-12-31T19:54:00.000Z

tests/__init__.py

souravaich/pyquote

ac48640d238f4e8d5c20b206180144b82d47cb20

[ "MIT" ]

null

tests/__init__.py

souravaich/pyquote

ac48640d238f4e8d5c20b206180144b82d47cb20

[ "MIT" ]

null

"""Unit test package for pyquote."""

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b34af5614c63b7d01424a5db056ec4ed016eeb3d

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Python

tests/test_bulk_select_model_dicts.py

solismaam/django-bulk-load

c8a4fd3f67b7087ddfd9afc311c8807548df4fdc

[ "MIT" ]

4

2022-01-27T05:40:17.000Z

2022-03-09T09:27:48.000Z

tests/test_bulk_select_model_dicts.py

solismaam/django-bulk-load

c8a4fd3f67b7087ddfd9afc311c8807548df4fdc

[ "MIT" ]

null

tests/test_bulk_select_model_dicts.py

solismaam/django-bulk-load

c8a4fd3f67b7087ddfd9afc311c8807548df4fdc

[ "MIT" ]

2

2022-03-04T21:31:25.000Z

2022-03-13T18:06:08.000Z

from datetime import datetime, timezone from django.test import TestCase from django_bulk_load import bulk_select_model_dicts from .test_project.models import ( TestComplexModel, TestForeignKeyModel, ) class E2ETestBulkInsertChangedModels(TestCase): def test_empty_get(self): self.assertEqual( bulk_select_model_dicts( model_class=TestComplexModel, filter_field_names=[], filter_data=[], select_field_names=[], ), [], ) def test_single_select(self): foreign = TestForeignKeyModel() foreign.save() saved_model = TestComplexModel( integer_field=123, string_field="hello", json_field=dict(fun="run"), datetime_field=datetime(2018, 1, 5, 3, 4, 5, tzinfo=timezone.utc), test_foreign=foreign, ) saved_model.save() result_dicts = bulk_select_model_dicts( model_class=TestComplexModel, filter_field_names=["integer_field"], filter_data=[(123,)], select_field_names=["string_field", "json_field", "test_foreign_id"], ) self.assertEqual(len(result_dicts), 1) for attr in ["integer_field", "string_field", "json_field", "test_foreign_id"]: self.assertEqual(getattr(saved_model, attr), result_dicts[0][attr]) def test_multi_select(self): saved_model1 = TestComplexModel( integer_field=1, json_field=None, string_field="hello1" ) saved_model1.save() saved_model2 = TestComplexModel( integer_field=2, json_field=None, string_field="hello2" ) saved_model2.save() saved_model3 = TestComplexModel( integer_field=3, json_field=None, string_field="hello3" ) saved_model3.save() result_dicts = bulk_select_model_dicts( model_class=TestComplexModel, filter_field_names=["id"], filter_data=[(saved_model1.id,), (saved_model2.id,), (saved_model3.id,)], select_field_names=["integer_field", "string_field", "json_field"], ) # Sort the results, so the order is the same result_dicts.sort(key=lambda result_dict: result_dict["id"]) self.assertEqual(len(result_dicts), 3) for attr in ["integer_field", "string_field", "json_field"]: self.assertEqual(getattr(saved_model1, attr), result_dicts[0][attr]) self.assertEqual(getattr(saved_model2, attr), result_dicts[1][attr]) self.assertEqual(getattr(saved_model3, attr), result_dicts[2][attr]) def test_multi_model_get_pk_fields(self): saved_model1 = TestComplexModel( integer_field=1, json_field=None, string_field="hello1" ) saved_model1.save() saved_model2 = TestComplexModel( integer_field=2, json_field=None, string_field="hello2" ) saved_model2.save() saved_model3 = TestComplexModel( integer_field=3, json_field=None, string_field="hello3" ) saved_model3.save() result_dicts = bulk_select_model_dicts( model_class=TestComplexModel, filter_field_names=["integer_field", "string_field"], filter_data=[ (saved_model1.integer_field, saved_model1.string_field), (saved_model2.integer_field, saved_model2.string_field), (saved_model3.integer_field, saved_model3.string_field), ], select_field_names=["json_field"], ) # Sort the results, so the order is the same result_dicts.sort(key=lambda result_dict: result_dict["integer_field"]) self.assertEqual(len(result_dicts), 3) for attr in ["integer_field", "string_field", "json_field"]: self.assertEqual(getattr(saved_model1, attr), result_dicts[0][attr]) self.assertEqual(getattr(saved_model2, attr), result_dicts[1][attr]) self.assertEqual(getattr(saved_model3, attr), result_dicts[2][attr]) def test_multi_model_matches_multiple(self): saved_model1 = TestComplexModel( integer_field=1, json_field=dict(a="b"), string_field="hello1" ) saved_model1.save() # Same as model above except different json_field saved_model2 = TestComplexModel( integer_field=1, json_field=None, string_field="hello1" ) saved_model2.save() saved_model3 = TestComplexModel( integer_field=2, json_field=None, string_field="hello2" ) saved_model3.save() result_dicts = bulk_select_model_dicts( model_class=TestComplexModel, filter_field_names=["integer_field", "string_field"], select_field_names=["json_field", "id"], filter_data=[(saved_model1.integer_field, saved_model1.string_field)], ) # Sort the results, so the order is the same result_dicts.sort(key=lambda result_dict: result_dict["id"]) self.assertEqual(len(result_dicts), 2) for attr in ["integer_field", "string_field", "json_field"]: self.assertEqual(getattr(saved_model1, attr), result_dicts[0][attr]) self.assertEqual(getattr(saved_model2, attr), result_dicts[1][attr])

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2fd6d05cb7b38f794e5f8f35b3c0cc9f9adb4ad3

5,853

py

Python

or_suite/envs/inventory_control_multiple_suppliers/inventory_control_test.py

JasmineSamadi/ORSuite

e2b2b0a5b497ea6566e794dcef1f176081fca4ce

[ "MIT" ]

4

2021-12-01T10:56:17.000Z

2022-02-06T17:07:43.000Z

or_suite/envs/inventory_control_multiple_suppliers/inventory_control_test.py

JasmineSamadi/ORSuite

e2b2b0a5b497ea6566e794dcef1f176081fca4ce

[ "MIT" ]

2

2021-08-11T13:25:01.000Z

2022-03-20T19:23:23.000Z

or_suite/envs/inventory_control_multiple_suppliers/inventory_control_test.py

JasmineSamadi/ORSuite

e2b2b0a5b497ea6566e794dcef1f176081fca4ce

[ "MIT" ]

2

2021-07-27T02:39:37.000Z

2022-02-14T21:03:15.000Z

import gym import numpy as np import sys from scipy.stats import poisson from .. import env_configs import pytest from stable_baselines3.common.env_checker import check_env # These tests are for 2 suppliers CONFIG = env_configs.inventory_control_multiple_suppliers_default_config env = gym.make('MultipleSuppliers-v0', config=CONFIG) lead_times = CONFIG['lead_times'] sum_L = 0 # Sum of all leadtimes for x in range(len(lead_times)): sum_L += lead_times[x] CONFIG3 = {'lead_times': [5, 1, 8], 'demand_dist': lambda x: np.random.poisson(10), 'supplier_costs': [100, 105, 90], 'hold_cost': 1, 'backorder_cost': 19, 'max_inventory': 1000, 'max_order': 20, 'epLen': 500, 'starting_state': None, 'neg_inventory': True} env3 = gym.make('MultipleSuppliers-v0', config=CONFIG3) L3 = CONFIG3['lead_times'] sum_L3 = 0 # Sum of all leadtimes for x in range(len(L3)): sum_L3 += L3[x] def test_initial_state(): # Testing state is correct length assert len(env.state) == sum_L + \ 1, "State array is not the same as the sum of all leading times plus one" # Testing that state has all 0s as starting values. for i in range(sum_L): assert env.state[i] == 0, "State array has not been initialized to all zeros" assert env.state[-1] == env.max_inventory, "Last index is not max" # Test to see if timestep starts at zero assert env.timestep == 0, "Timestep does not start at 0" # Testing if starting state is part of observation space assert env.observation_space.contains( env.state), "Starting state is not present in given observation space" def test_step(): np.random.seed(10) newState, reward, done, info = env.step([1, 15]) # Test if new state is part of observation space assert env.observation_space.contains( newState), "Returned state is not part of given observation space after step" # Test to see if returned reward is a float assert type(reward) == float, "Reward is not a float" assert reward == -1852.0 expected_state = [1, 0, 0, 0, 0, 15, 987] for i in range(sum_L + 1): assert env.state[i] == expected_state[i], "New state does not match expected state at index {}".format( i) # Do step again newState, reward, done, info = env.step([1, 15]) # Test if new state is part of observation space assert env.observation_space.contains( newState), "Returned state is not part of given observation space after step" assert reward == -2042.0 expected_state = [1, 0, 0, 0, 15, 15, 977] for i in range(sum_L + 1): assert env.state[i] == expected_state[i], "New state does not match expected state at index {}".format( i) check_env(env, skip_render_check=True) def test_bad_action(): # Testing to see if action not in action space raises an exception with pytest.raises(AssertionError): env.step( [0, 0, 0]) def test_reset(): env.reset() assert env.timestep == 0, "Timestep not set to 0 on reset" for i in range(sum_L): assert env.state[i] == env.starting_state[i], "State not set back to starting state on reset at index {}".format( i) assert env.state[-1] == env.max_inventory # These tests are for three suppliers def test_initial_state_three(): # Testing state is correct length assert len(env3.state) == sum_L3 + \ 1, "State array is not the same as the sum of all leading times plus one" # Testing that state has all 0s as starting values. for i in range(sum_L3): assert env3.state[i] == 0, "State array has not been initialized to all zeros" assert env3.state[-1] == env3.max_inventory, "Last index is not max" # Test to see if timestep starts at zero assert env3.timestep == 0, "Timestep does not start at 0" # Testing if starting state is part of observation space assert env3.observation_space.contains( env3.state), "Starting state is not present in given observation space" def test_step_three(): np.random.seed(10) newState, reward, done, info = env3.step([1, 15, 4]) # Test if new state is part of observation space assert env3.observation_space.contains( newState), "Returned state is not part of given observation space after step" # Test to see if returned reward is a float assert type(reward) == float, "Reward is not a float" assert reward == -2282.0 expected_state = [0, 0, 0, 0, 1, 15, 0, 0, 0, 0, 0, 0, 0, 4, 987] for i in range(sum_L3 + 1): assert env3.state[i] == expected_state[i], "New state does not match expected state at index {}".format( i) # Do step again newState, reward, done, info = env3.step([1, 15, 4]) # Test if new state is part of observation space assert env3.observation_space.contains( newState), "Returned state is not part of given observation space after step" assert reward == -2206.0 expected_state = [0, 0, 0, 1, 1, 15, 0, 0, 0, 0, 0, 0, 4, 4, 991] for i in range(sum_L3 + 1): assert env3.state[i] == expected_state[i], "New state does not match expected state at index {}".format( i) check_env(env3, skip_render_check=True) def test_bad_action_three(): # Testing to see if action not in action space raises an exception with pytest.raises(AssertionError): env3.step( []) def test_reset_three(): env3.reset() assert env.timestep == 0, "Timestep not set to 0 on reset" for i in range(sum_L3): assert env3.state[i] == env3.starting_state[i], "State not set back to starting state on reset at index {}".format( i) assert env3.state[-1] == env3.max_inventory

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