xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

3a6fe42e30bacd42644e94608a0b68b5909b1f52

176

py

Python

RemoveDuplicate/remove_duplicate_char_from_string.py

Akash671/Algorithms

f71be624bc49e686087c8dd22a09b9cf343b0634

[ "MIT" ]

1

2021-03-25T18:29:07.000Z

RemoveDuplicate/remove_duplicate_char_from_string.py

Akash671/Algorithms

f71be624bc49e686087c8dd22a09b9cf343b0634

[ "MIT" ]

null

RemoveDuplicate/remove_duplicate_char_from_string.py

Akash671/Algorithms

f71be624bc49e686087c8dd22a09b9cf343b0634

[ "MIT" ]

null

#author : @akash kumar from collections import OrderedDict def remove_duplicate(str1): return "".join(OrderedDict.fromkeys(str1)) s=str(input()) print(remove_duplicate(s))

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3a7a1165ca56d7ca983ef071373ea494c7681d5c

267

py

Python

static/downloads/1000GP/rename.py

raqsilva/VCFDataExporter

b5eaabd82ade95a837fd98dff5e1600046eeb029

[ "Apache-2.0" ]

null

static/downloads/1000GP/rename.py

raqsilva/VCFDataExporter

b5eaabd82ade95a837fd98dff5e1600046eeb029

[ "Apache-2.0" ]

null

static/downloads/1000GP/rename.py

raqsilva/VCFDataExporter

b5eaabd82ade95a837fd98dff5e1600046eeb029

[ "Apache-2.0" ]

null

import os names = os.listdir('.') for old_name in names: if old_name.endswith('.vcf.gz'): os.rename(old_name, old_name.split('.')[1]+'.vcf.gz') elif old_name.endswith('.vcf.gz.tbi'): os.rename(old_name, old_name.split('.')[1]+'.vcf.gz.tbi')

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null

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3a858bdcf1bd8ea86fa98d712434b57d22465c35

257

py

Python

waab/assets.py

clld/waab

9693da8887cf8498a47bc41250a2a048595f89f3

[ "Apache-2.0" ]

2

2015-05-11T13:29:04.000Z

2017-12-23T04:15:02.000Z

waab/assets.py

clld/waab

9693da8887cf8498a47bc41250a2a048595f89f3

[ "Apache-2.0" ]

null

waab/assets.py

clld/waab

9693da8887cf8498a47bc41250a2a048595f89f3

[ "Apache-2.0" ]

1

2015-12-06T22:03:18.000Z

from clld.web.assets import environment from clldutils.path import Path import waab environment.append_path( Path(waab.__file__).parent.joinpath('static').as_posix(), url='/waab:static/') environment.load_path = list(reversed(environment.load_path))

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4

3a94069899bc67972275a4949c911d6dbf78b698

250

py

Python

neodroid/__init__.py

cnHeider/neo

30c03bb142bbe25f6d7b61f22f66747076f08aa6

[ "Apache-2.0" ]

null

neodroid/__init__.py

cnHeider/neo

30c03bb142bbe25f6d7b61f22f66747076f08aa6

[ "Apache-2.0" ]

null

neodroid/__init__.py

cnHeider/neo

30c03bb142bbe25f6d7b61f22f66747076f08aa6

[ "Apache-2.0" ]

1

2018-09-27T14:31:20.000Z

""" .. module:: neodroid :platform: Unix, Windows :synopsis: An API for communicating with a Unity Game process. .. moduleauthor:: Christian Heider Nielsen <chrini13@student.aau.dk> """ from .neodroid_environment import NeodroidEnvironment

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3a998479c05de0b92de6ecc40860da340e40016a

228

py

Python

nec_calendar/urls.py

lindychi/mnec

3d18f257ed3b0bc327340de988e7552e035dbec1

[ "MIT" ]

1

2018-02-20T13:46:41.000Z

nec_calendar/urls.py

lindychi/mnec

3d18f257ed3b0bc327340de988e7552e035dbec1

[ "MIT" ]

53

2017-10-10T02:43:22.000Z

2022-03-11T23:15:05.000Z

nec_calendar/urls.py

lindychi/mnec

3d18f257ed3b0bc327340de988e7552e035dbec1

[ "MIT" ]

null

from django.conf.urls import url from . import views urlpatterns = [ url(r'^$', views.index, name='calendar_today'), url(r'^$monthly/(?P<year>[^/]+)/(?P<month>[^/]+)/$', views.index, name='calendar_monthly'), ]

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null

0

4

3a9d2d064cdbfc102e4a3ab658861ad8dd940d50

128

py

Python

pythonCode/sum.py

eatmore/python_practice

c6a773c8d24182b23a86fd9b66b27b5ff948b258

[ "MIT" ]

null

pythonCode/sum.py

eatmore/python_practice

c6a773c8d24182b23a86fd9b66b27b5ff948b258

[ "MIT" ]

null

pythonCode/sum.py

eatmore/python_practice

c6a773c8d24182b23a86fd9b66b27b5ff948b258

[ "MIT" ]

1

2020-03-12T06:05:38.000Z

s = 0 for i in range(2,100): for j in range(2,i): if i % j == 0: break else: s += i print(s)

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null

1

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null

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4

3aaced5bd1f08ac5f98a404166a66e4fe488c592

91

py

Python

lesson09/lixuebin/devops/adminlte/apps.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

1

2017-07-25T01:31:31.000Z

lesson09/lixuebin/devops/adminlte/apps.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

2

2021-03-09T01:22:11.000Z

2021-03-09T01:23:11.000Z

lesson09/lixuebin/devops/adminlte/apps.py

herrywen-nanj/51reboot

1130c79a360e1b548a6eaad176eb60f8bed22f40

[ "Apache-2.0" ]

5

2017-10-17T06:05:21.000Z

2020-12-10T03:04:28.000Z

from django.apps import AppConfig class AdminlteConfig(AppConfig): name = 'adminlte'

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3ac1311d6c592bacca0ff5b7b7847336b19cc237

219

py

Python

application/role/views.py

roklem314/PotilasArkisto

b005dcad4442820a265b62156ddfe61abb5b9707

[ "MIT" ]

2

2018-03-23T08:45:10.000Z

2021-01-22T11:17:14.000Z

application/role/views.py

roklem314/PotilasArkisto

b005dcad4442820a265b62156ddfe61abb5b9707

[ "MIT" ]

1

2018-05-07T18:56:00.000Z

2019-02-11T21:10:44.000Z

application/role/views.py

roklem314/Laakari-palvelu

b005dcad4442820a265b62156ddfe61abb5b9707

[ "MIT" ]

null

from flask import Flask, render_template, request, redirect, url_for, flash from flask_login import login_user, logout_user, current_user from application import app from application.registration.models import Accounts

43.8

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1

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4

3af1bd828764c3b64e7e4a0e8cc05b69c64931d5

1,832

py

Python

problem011/solution.py

andysnell/project-euler

43d92e59d247dfc319c6fe4c22ecc7962e2283ca

[ "FTL" ]

null

problem011/solution.py

andysnell/project-euler

43d92e59d247dfc319c6fe4c22ecc7962e2283ca

[ "FTL" ]

null

problem011/solution.py

andysnell/project-euler

43d92e59d247dfc319c6fe4c22ecc7962e2283ca

[ "FTL" ]

null

import numpy s = ''' 8 2 22 97 38 15 0 40 0 75 4 5 7 78 52 12 50 77 91 8; 49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 4 56 62 0; 81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 3 49 13 36 65; 52 70 95 23 4 60 11 42 69 24 68 56 1 32 56 71 37 2 36 91; 22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80; 24 47 32 60 99 3 45 2 44 75 33 53 78 36 84 20 35 17 12 50; 32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70; 67 26 20 68 2 62 12 20 95 63 94 39 63 8 40 91 66 49 94 21; 24 55 58 5 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72; 21 36 23 8 75 0 76 44 20 45 35 14 0 61 33 97 34 31 33 95; 78 17 53 28 22 75 31 67 15 94 3 80 4 62 16 14 8 53 56 92; 16 39 5 42 96 35 31 47 55 58 88 24 0 17 54 24 36 29 85 57; 86 56 0 48 35 71 89 7 5 44 44 37 44 60 21 58 51 54 17 58; 19 80 81 68 5 94 47 69 28 73 92 13 86 52 17 77 4 89 55 40; 4 52 8 83 97 35 99 16 7 97 57 32 16 26 26 79 33 27 98 66; 88 36 68 87 57 62 20 72 3 46 33 67 46 55 12 32 63 93 53 69; 4 42 16 73 38 25 39 11 24 94 72 18 8 46 29 32 40 62 76 36; 20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 4 36 16; 20 73 35 29 78 31 90 1 74 31 49 71 48 86 81 16 23 57 5 54; 1 70 54 71 83 51 54 69 16 92 33 48 61 43 52 1 89 19 67 48 ''' matrix = numpy.matrix(s) print(matrix[1, 0]) for i in matrix: print(i) def getHorizontalProduct(matrix, row, col): return matrix[row, col] \ * matrix[row, col + 1] \ * matrix[row, col + 2] \ * matrix[row, col + 3] def getVerticalProduct(matrix, row, col): return matrix[row, col] \ * matrix[row + 1, col] \ * matrix[row + 2, col] \ * matrix[row + 3, col] def getDiagonalProduct(matrix, row, col): return matrix[row, col] \ * matrix[row + 1, col + 1] \ * matrix[row + 2, col + 2] \ * matrix[row + 3, col + 3]

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4

c90fe7a851365feafe7dccd2eb41b4f8853f6e33

152

py

Python

test/pytest/profiler_utils/resnet_custom.py

dumpmemory/serve

a21e28fc6e775612b2eab2f74333a606e71692fd

[ "Apache-2.0" ]

null

test/pytest/profiler_utils/resnet_custom.py

dumpmemory/serve

a21e28fc6e775612b2eab2f74333a606e71692fd

[ "Apache-2.0" ]

3

2022-03-12T01:08:09.000Z

2022-03-15T10:56:14.000Z

test/pytest/profiler_utils/resnet_custom.py

dumpmemory/serve

a21e28fc6e775612b2eab2f74333a606e71692fd

[ "Apache-2.0" ]

null

from ts.torch_handler.image_classifier import ImageClassifier class ResnetHandler(ImageClassifier): def __init__(self): super().__init__()

25.333333

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4

c910d4078f561abf1ed8694f33875d45e8e67a5c

927

py

Python

api/light_processing.py

RogerTangos/aurum_debug

fa3afd96c795e9b674a5951430635d43aa3f5c78

[ "MIT" ]

null

api/light_processing.py

RogerTangos/aurum_debug

fa3afd96c795e9b674a5951430635d43aa3f5c78

[ "MIT" ]

6

2020-06-05T17:52:29.000Z

2021-06-10T19:44:58.000Z

api/light_processing.py

RogerTangos/aurum_debug

fa3afd96c795e9b674a5951430635d43aa3f5c78

[ "MIT" ]

null

from api.apiutils import compute_field_id as id_from import pandas as pd def __obtain_dataframe(path): # TODO: analyze path format so that we can choose the appropriate read method df = pd.read_csv(path) return df def head(dbname, sname, fname=False): # TODO: return def tail(dbname, sname, fname=False): # TODO: return def random(dbname, sname, fname=False): # TODO: return def sample(dbname, sname, fname=False): # TODO: return def pandas_handler(store_handler, hit): """ Just obtain a handler to a pandas DataFrame, without exposing the format of the underlying data source :param store_handler: :param nid: :return: """ nid = hit.nid sname = hit.source_name path = store_handler.get_path_of(nid) + sname df = __obtain_dataframe(path) return df if __name__ == "__main__": print("Lightweight processing layer here")

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c91abdb6ef678d5016def0a4ac81b1cd966209eb

88

py

Python

code/log_msg/apps.py

AlanJui/DjangoApp-DevTemplate

da39db79439a3e94ced5e853af4aa8b6ebf52191

[ "PostgreSQL" ]

null

code/log_msg/apps.py

AlanJui/DjangoApp-DevTemplate

da39db79439a3e94ced5e853af4aa8b6ebf52191

[ "PostgreSQL" ]

2

2021-03-30T13:48:40.000Z

2021-04-08T20:43:31.000Z

code/log_msg/apps.py

AlanJui/DjangoApp-DevTemplate

da39db79439a3e94ced5e853af4aa8b6ebf52191

[ "PostgreSQL" ]

null

from django.apps import AppConfig class LogMsgConfig(AppConfig): name = 'log_msg'

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c925d7c3ccb50e26740803389ee0d9a4a7dd6941

296

py

Python

cda/dataset_info.py

alisoltanirad/CDA

b83950d89bfd7b8fef7d50640469b1772bfc45d0

[ "MIT" ]

null

cda/dataset_info.py

alisoltanirad/CDA

b83950d89bfd7b8fef7d50640469b1772bfc45d0

[ "MIT" ]

null

cda/dataset_info.py

alisoltanirad/CDA

b83950d89bfd7b8fef7d50640469b1772bfc45d0

[ "MIT" ]

null

from .college_scorecard import MetaData class DatasetInfo: def __init__(self, path=None): if path == None: self._data = MetaData() else: self._data = MetaData(path) def get_attribute_names(self): return self._data.get_attribute_names()

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a3a097765eceaa8161406ee5e7662afd4c669ddc

124

py

Python

locale/pot/api/utilities/_autosummary/pyvista-ParametricKlein-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

4

2020-08-07T08:19:19.000Z

2020-12-04T09:51:11.000Z

locale/pot/api/utilities/_autosummary/pyvista-ParametricKlein-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

19

2020-08-06T00:24:30.000Z

2022-03-30T19:22:24.000Z

locale/pot/api/utilities/_autosummary/pyvista-ParametricKlein-1.py

tkoyama010/pyvista-doc-translations

23bb813387b7f8bfe17e86c2244d5dd2243990db

[ "MIT" ]

1

2021-03-09T07:50:40.000Z

# Create a ParametricKlein mesh # import pyvista mesh = pyvista.ParametricKlein() mesh.plot(color='w', smooth_shading=True)

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6e6b27b19f0bfd7ff008433799009a02c0602987

29

py

Python

test1.py

nesquik91/TCCG

fca08d99227bd39746d0a0e47cae4126f2761e0e

[ "MIT" ]

null

test1.py

nesquik91/TCCG

fca08d99227bd39746d0a0e47cae4126f2761e0e

[ "MIT" ]

null

test1.py

nesquik91/TCCG

fca08d99227bd39746d0a0e47cae4126f2761e0e

[ "MIT" ]

null

import numpy as np a=[3, 4]

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6e75bded8ce81fb3877b7bd6bed84e92d84af3d4

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py

Python

PROJ/LEVY/Swing_Options/PROJ_Swing_ConstantRecovery_Aug.py

mattslezak-shell/PROJ_Option_Pricing_Matlab

6105bd00ba3471802180c122fdf81e90833a91c4

[ "MIT" ]

null

PROJ/LEVY/Swing_Options/PROJ_Swing_ConstantRecovery_Aug.py

mattslezak-shell/PROJ_Option_Pricing_Matlab

6105bd00ba3471802180c122fdf81e90833a91c4

[ "MIT" ]

null

PROJ/LEVY/Swing_Options/PROJ_Swing_ConstantRecovery_Aug.py

mattslezak-shell/PROJ_Option_Pricing_Matlab

6105bd00ba3471802180c122fdf81e90833a91c4

[ "MIT" ]

1

2022-01-07T15:31:45.000Z

# Generated with SMOP 0.41-beta try: from smop.libsmop import * except ImportError: raise ImportError('File compiled with `smop3`, please install `smop3` to run it.') from None # PROJ_Swing_ConstantRecovery_Aug.m @function def PROJ_Swing_ConstantRecovery_Aug(r=None,S_0=None,Dmax=None,T_0=None,T=None,tau1=None,Mtau=None,N=None,alpha=None,rnSYMB=None,Ks=None,*args,**kwargs): varargin = PROJ_Swing_ConstantRecovery_Aug.varargin nargin = PROJ_Swing_ConstantRecovery_Aug.nargin # rnSYMB is risk-neutral Levy symbol # Ks = [K1,K2,K3,K4] K1=Ks(1) # PROJ_Swing_ConstantRecovery_Aug.m:5 K2=Ks(2) # PROJ_Swing_ConstantRecovery_Aug.m:5 K3=Ks(3) # PROJ_Swing_ConstantRecovery_Aug.m:5 K4=Ks(4) # PROJ_Swing_ConstantRecovery_Aug.m:5 w=log(Ks / S_0) # PROJ_Swing_ConstantRecovery_Aug.m:6 xmin=- alpha / 2 + (w(3) + w(2)) / 2 # PROJ_Swing_ConstantRecovery_Aug.m:7 Gx=lambda x=None: G_func_swing(x,K1,K2,K3,K4,S_0) # PROJ_Swing_ConstantRecovery_Aug.m:9 K=N / 2 # PROJ_Swing_ConstantRecovery_Aug.m:11 dt=tau1 / Mtau # PROJ_Swing_ConstantRecovery_Aug.m:12 nrdt=dot(- r,dt) # PROJ_Swing_ConstantRecovery_Aug.m:13 p=floor((T - T_0) / tau1) # PROJ_Swing_ConstantRecovery_Aug.m:15 Ttil_p=T - dot(p,tau1) # PROJ_Swing_ConstantRecovery_Aug.m:16 Mtau_pr=floor((Ttil_p - T_0) / dt) # PROJ_Swing_ConstantRecovery_Aug.m:17 #Ttil_pp1 = Ttil_p -Mtau_pr*dt; #If a final European step is needed M=dot(p,Mtau) + Mtau_pr # PROJ_Swing_ConstantRecovery_Aug.m:20 dxtil=dot(2,alpha) / (N - 1) # PROJ_Swing_ConstantRecovery_Aug.m:22 #================ Determine xGrid ======================================== nbars=floor((w - xmin) / dxtil + 1) # PROJ_Swing_ConstantRecovery_Aug.m:25 xnbars=xmin + dot(dxtil,(nbars - 1)) # PROJ_Swing_ConstantRecovery_Aug.m:26 diffs=w - xnbars # PROJ_Swing_ConstantRecovery_Aug.m:28 nbars[diffs < diffs(1)]=nbars(diffs < diffs(1)) - 1 # PROJ_Swing_ConstantRecovery_Aug.m:29 dx=(w(4) - w(1)) / (nbars(4) - nbars(1)) # PROJ_Swing_ConstantRecovery_Aug.m:31 a=1 / dx # PROJ_Swing_ConstantRecovery_Aug.m:31 xmin=w(1) - dot((nbars(1) - 1),dx) # PROJ_Swing_ConstantRecovery_Aug.m:32 nbars[arange(2,3)]=floor((w(arange(2,3)) - xmin) / dx + 1) # PROJ_Swing_ConstantRecovery_Aug.m:34 rhos=w - xnbars # PROJ_Swing_ConstantRecovery_Aug.m:36 zetastar=dot(a,rhos) # PROJ_Swing_ConstantRecovery_Aug.m:37 nnot=floor(1 - dot(xmin,a)) # PROJ_Swing_ConstantRecovery_Aug.m:38 #========================================================================== #################################################################### ######## Gaussian 3-point #################################################################### ###### Gaussian Quad Constants q_plus=(1 + sqrt(3 / 5)) / 2 # PROJ_Swing_ConstantRecovery_Aug.m:46 q_minus=(1 - sqrt(3 / 5)) / 2 # PROJ_Swing_ConstantRecovery_Aug.m:46 b3=sqrt(15) # PROJ_Swing_ConstantRecovery_Aug.m:47 b4=b3 / 10 # PROJ_Swing_ConstantRecovery_Aug.m:47 #### PAYOFF CONSTANTS----------------------------------- varthet_01=dot(exp(dot(0.5,dx)),(dot(5,cosh(dot(b4,dx))) - dot(b3,sinh(dot(b4,dx))) + 4)) / 18 # PROJ_Swing_ConstantRecovery_Aug.m:50 zetas2=zetastar ** 2 # PROJ_Swing_ConstantRecovery_Aug.m:51 edn=exp(- dx) # PROJ_Swing_ConstantRecovery_Aug.m:52 ###---------------------------------------- ### Initialize the dstars used in psis function rhos_plus=dot(rhos,q_plus) # PROJ_Swing_ConstantRecovery_Aug.m:56 rhos_minus=dot(rhos,q_minus) # PROJ_Swing_ConstantRecovery_Aug.m:56 zetas_plus=dot(a,rhos_plus) # PROJ_Swing_ConstantRecovery_Aug.m:57 zetas_minus=dot(a,rhos_minus) # PROJ_Swing_ConstantRecovery_Aug.m:57 eds1=exp(rhos_minus) # PROJ_Swing_ConstantRecovery_Aug.m:58 eds2=exp(rhos / 2) # PROJ_Swing_ConstantRecovery_Aug.m:58 eds3=exp(rhos_plus) # PROJ_Swing_ConstantRecovery_Aug.m:58 dbars_1=zetas2 / 2 # PROJ_Swing_ConstantRecovery_Aug.m:60 dbars_0=zetastar - dbars_1 # PROJ_Swing_ConstantRecovery_Aug.m:61 ds_0=multiply(zetastar,(dot(5,(multiply((1 - zetas_minus),eds1) + multiply((1 - zetas_plus),eds3))) + multiply(dot(4,(2 - zetastar)),eds2))) / 18 # PROJ_Swing_ConstantRecovery_Aug.m:62 ds_1=multiply(dot(edn,zetastar),(dot(5,(multiply(zetas_minus,eds1) + multiply(zetas_plus,eds3))) + multiply(dot(4,zetastar),eds2))) / 18 # PROJ_Swing_ConstantRecovery_Aug.m:63 dstars=zeros(1,4) # PROJ_Swing_ConstantRecovery_Aug.m:65 dstars[1]=dbars_0(2) # PROJ_Swing_ConstantRecovery_Aug.m:66 dstars[2]=ds_0(2) # PROJ_Swing_ConstantRecovery_Aug.m:66 dstars[3]=ds_1(3) # PROJ_Swing_ConstantRecovery_Aug.m:67 dstars[4]=dbars_1(3) # PROJ_Swing_ConstantRecovery_Aug.m:67 #========================================================================== ThetaG=GetThetaG_swing(xmin,K,dx,K1,K2,K3,K4,S_0) # PROJ_Swing_ConstantRecovery_Aug.m:70 THET=zeros(K,M) # PROJ_Swing_ConstantRecovery_Aug.m:72 THET[arange(),M]=dot(Dmax,ThetaG) # PROJ_Swing_ConstantRecovery_Aug.m:73 E=dot(S_0,exp(xmin + dot(dx,(arange(0,K - 1))))) # PROJ_Swing_ConstantRecovery_Aug.m:74 ##################################### ########### PHASE I ################ ##################################### #################################################################### ###### T^dt #################################################################### a2=a ** 2 # PROJ_Swing_ConstantRecovery_Aug.m:83 zmin=dot((1 - K),dx) # PROJ_Swing_ConstantRecovery_Aug.m:84 dw=dot(dot(2,pi),a) / N # PROJ_Swing_ConstantRecovery_Aug.m:86 DW=dot(dw,(arange(1,N - 1))) # PROJ_Swing_ConstantRecovery_Aug.m:87 grand1=multiply(exp(dot(dot(- 1j,zmin),DW)),(sin(DW / (dot(2,a))) / DW) ** 2.0) / (2 + cos(DW / a)) # PROJ_Swing_ConstantRecovery_Aug.m:88 Cons1=dot(24,a2) / N # PROJ_Swing_ConstantRecovery_Aug.m:89 ###------------------------------------------------------------------ chfpoints=rnSYMB(DW) # PROJ_Swing_ConstantRecovery_Aug.m:91 Cons2=dot(Cons1,exp(nrdt)) # PROJ_Swing_ConstantRecovery_Aug.m:93 grand=multiply(grand1,exp(dot(dt,chfpoints))) # PROJ_Swing_ConstantRecovery_Aug.m:94 beta=dot(Cons2,real(fft(concat([1 / (dot(24,a2)),grand])))) # PROJ_Swing_ConstantRecovery_Aug.m:95 toepM=concat([[beta(arange(K,1,- 1)).T],[0],[beta(arange(dot(2,K) - 1,K,- 1),+ 1).T]]) # PROJ_Swing_ConstantRecovery_Aug.m:97 toepM=fft(toepM) # PROJ_Swing_ConstantRecovery_Aug.m:97 ###------------------------------------------------------------------ ### initialize for search nms=zeros(1,2) # PROJ_Swing_ConstantRecovery_Aug.m:101 nms[1]=nbars(2) + 1 # PROJ_Swing_ConstantRecovery_Aug.m:102 nms[2]=nbars(3) - 1 # PROJ_Swing_ConstantRecovery_Aug.m:103 ###---------------------------------------- Cons4=1 / 12 # PROJ_Swing_ConstantRecovery_Aug.m:105 ###---------------------------------------- xbars=zeros(1,2) # PROJ_Swing_ConstantRecovery_Aug.m:108 ###---------------------------------------- G=dot(Dmax,Gx(xmin + dot(dx,(arange(0,K - 1)))).T) # PROJ_Swing_ConstantRecovery_Aug.m:111 ###---------------------------------------- edn=exp(- dx) # PROJ_Swing_ConstantRecovery_Aug.m:114 dK21=(K2 - K1) # PROJ_Swing_ConstantRecovery_Aug.m:115 dK43=(K4 - K3) # PROJ_Swing_ConstantRecovery_Aug.m:116 Thetbar_dt=dot(dK43,cumsum(beta(arange(dot(2,K),K + 1,- 1))).T) + dot(dK21,concat([[fliplr(cumsum(beta(arange(1,K - 1,1)))).T],[0]])) # PROJ_Swing_ConstantRecovery_Aug.m:118 for m in arange(M - 1,M - (Mtau - 1),- 1).reshape(-1): pp=ifft(multiply(toepM,fft(concat([[THET(arange(1,K),m + 1)],[zeros(K,1)]])))) # PROJ_Swing_ConstantRecovery_Aug.m:121 Cont_dt=pp(arange(1,K)) + Thetbar_dt # PROJ_Swing_ConstantRecovery_Aug.m:122 while (nms(1) > 2) and (Cont_dt(nms(1)) > G(nms(1))): nms[1]=nms(1) - 1 # PROJ_Swing_ConstantRecovery_Aug.m:126 nms[2]=nms(2) + 1 # PROJ_Swing_ConstantRecovery_Aug.m:129 while nms(2) < K - 2 and Cont_dt(nms(2)) > G(nms(2)): nms[2]=nms(2) + 1 # PROJ_Swing_ConstantRecovery_Aug.m:131 nms[2]=nms(2) - 1 # PROJ_Swing_ConstantRecovery_Aug.m:133 xnbars=xmin + dot(dx,(nms - 1)) # PROJ_Swing_ConstantRecovery_Aug.m:135 tmp1=Cont_dt(nms(1)) - G(nms(1)) # PROJ_Swing_ConstantRecovery_Aug.m:138 tmp2=Cont_dt(nms(1) + 1) - G(nms(1) + 1) # PROJ_Swing_ConstantRecovery_Aug.m:138 xbars[1]=(dot((xnbars(1) + dx),tmp1) - dot(xnbars(1),tmp2)) / (tmp1 - tmp2) # PROJ_Swing_ConstantRecovery_Aug.m:139 tmp1=Cont_dt(nms(2)) - G(nms(2)) # PROJ_Swing_ConstantRecovery_Aug.m:142 tmp2=Cont_dt(nms(2) + 1) - G(nms(2) + 1) # PROJ_Swing_ConstantRecovery_Aug.m:142 xbars[2]=(dot((xnbars(2) + dx),tmp1) - dot(xnbars(2),tmp2)) / (tmp1 - tmp2) # PROJ_Swing_ConstantRecovery_Aug.m:143 rhos=xbars - xnbars # PROJ_Swing_ConstantRecovery_Aug.m:145 zetas=dot(a,rhos) # PROJ_Swing_ConstantRecovery_Aug.m:146 psis=Get_psis_swing_VER2(rhos,zetas,q_plus,q_minus,Ks,a,varthet_01,E,nms,nbars,edn,zetastar,dstars) # PROJ_Swing_ConstantRecovery_Aug.m:148 varths_dt=Get_Varths_swing(zetas,nms,Cont_dt) # PROJ_Swing_ConstantRecovery_Aug.m:149 THET[arange(1,nms(1) - 1),m]=THET(arange(1,nms(1) - 1),M) # PROJ_Swing_ConstantRecovery_Aug.m:152 THET[nms(1),m]=THET(nms(1),M) - dot(Dmax,psis(1)) + varths_dt(1) # PROJ_Swing_ConstantRecovery_Aug.m:153 THET[nms(1) + 1,m]=dot(Dmax,psis(2)) + varths_dt(2) # PROJ_Swing_ConstantRecovery_Aug.m:154 THET[arange(nms(1) + 2,nms(2) - 1),m]=dot(Cons4,(Cont_dt(arange(nms(1) + 1,nms(2) - 2)) + dot(10,Cont_dt(arange(nms(1) + 2,nms(2) - 1))) + Cont_dt(arange(nms(1) + 3,nms(2))))) # PROJ_Swing_ConstantRecovery_Aug.m:156 THET[nms(2),m]=dot(Dmax,psis(3)) + varths_dt(3) # PROJ_Swing_ConstantRecovery_Aug.m:158 THET[nms(2) + 1,m]=THET(nms(2) + 1,M) - dot(Dmax,psis(4)) + varths_dt(4) # PROJ_Swing_ConstantRecovery_Aug.m:159 THET[arange(nms(2) + 2,K),m]=THET(arange(nms(2) + 2,K),M) # PROJ_Swing_ConstantRecovery_Aug.m:160 ##################################### ##################################### ########### PHASE II ############### ##################################### ##################################### Cons3=dot(Cons1,exp(dot(- r,tau1))) # PROJ_Swing_ConstantRecovery_Aug.m:171 grand=multiply(grand1,exp(dot(tau1,chfpoints))) # PROJ_Swing_ConstantRecovery_Aug.m:172 beta=dot(Cons3,real(fft(concat([1 / (dot(24,a2)),grand])))) # PROJ_Swing_ConstantRecovery_Aug.m:173 toepMD=concat([[beta(arange(K,1,- 1)).T],[0],[beta(arange(dot(2,K) - 1,K,- 1),+ 1).T]]) # PROJ_Swing_ConstantRecovery_Aug.m:174 toepMD=fft(toepMD) # PROJ_Swing_ConstantRecovery_Aug.m:174 evec_D1=cumsum(beta(arange(dot(2,K),K + 1,- 1))).T # PROJ_Swing_ConstantRecovery_Aug.m:176 evec_D2=concat([[fliplr(cumsum(beta(arange(1,K - 1,1)))).T],[0]]) # PROJ_Swing_ConstantRecovery_Aug.m:177 count=copy(Mtau) # PROJ_Swing_ConstantRecovery_Aug.m:179 for m in arange(M - Mtau,1,- 1).reshape(-1): pp=ifft(multiply(toepM,fft(concat([[THET(arange(1,K),m + 1)],[zeros(K,1)]])))) # PROJ_Swing_ConstantRecovery_Aug.m:182 Cont_dt=pp(arange(1,K)) # PROJ_Swing_ConstantRecovery_Aug.m:183 pp=ifft(multiply(toepMD,fft(concat([[THET(arange(1,K),m + Mtau)],[zeros(K,1)]])))) # PROJ_Swing_ConstantRecovery_Aug.m:185 Cont_D=pp(arange(1,K)) + dot(evec_D2,THET(1,m + Mtau)) + dot(evec_D1,THET(K,m + Mtau)) # PROJ_Swing_ConstantRecovery_Aug.m:186 PSI=G + Cont_D # PROJ_Swing_ConstantRecovery_Aug.m:187 if rem(count,Mtau) == 0: nms[1]=nbars(2) # PROJ_Swing_ConstantRecovery_Aug.m:191 nms[2]=nbars(3) + 1 # PROJ_Swing_ConstantRecovery_Aug.m:191 count=count + 1 # PROJ_Swing_ConstantRecovery_Aug.m:193 while nms(1) > 2 and Cont_dt(nms(1)) > PSI(nms(1)): nms[1]=nms(1) - 1 # PROJ_Swing_ConstantRecovery_Aug.m:196 while nms(2) < K - 2 and Cont_dt(nms(2)) > PSI(nms(2)): nms[2]=nms(2) + 1 # PROJ_Swing_ConstantRecovery_Aug.m:200 nms[2]=nms(2) - 1 # PROJ_Swing_ConstantRecovery_Aug.m:204 xnbars=xmin + dot(dx,(nms - 1)) # PROJ_Swing_ConstantRecovery_Aug.m:206 tmp1=Cont_dt(nms(1)) - PSI(nms(1)) # PROJ_Swing_ConstantRecovery_Aug.m:209 tmp2=Cont_dt(nms(1) + 1) - PSI(nms(1) + 1) # PROJ_Swing_ConstantRecovery_Aug.m:209 xbars[1]=xnbars(1) + max(0,dot(dx,tmp1) / (tmp1 - tmp2)) # PROJ_Swing_ConstantRecovery_Aug.m:210 tmp1=Cont_dt(nms(2)) - PSI(nms(2)) # PROJ_Swing_ConstantRecovery_Aug.m:213 tmp2=Cont_dt(nms(2) + 1) - PSI(nms(2) + 1) # PROJ_Swing_ConstantRecovery_Aug.m:213 xbars[2]=xnbars(2) + max(0,dot(dx,tmp1) / (tmp1 - tmp2)) # PROJ_Swing_ConstantRecovery_Aug.m:214 rhos=xbars - xnbars # PROJ_Swing_ConstantRecovery_Aug.m:217 zetas=dot(a,rhos) # PROJ_Swing_ConstantRecovery_Aug.m:218 psis=Get_psis_swing_VER2(rhos,zetas,q_plus,q_minus,Ks,a,varthet_01,E,nms,nbars,edn,zetastar,dstars) # PROJ_Swing_ConstantRecovery_Aug.m:220 varths_dt=Get_Varths_swing(zetas,nms,Cont_dt) # PROJ_Swing_ConstantRecovery_Aug.m:222 varths_D=Get_VarthsDD_swing(zetas,nms,Cont_D) # PROJ_Swing_ConstantRecovery_Aug.m:223 THET[arange(2,nms(1) - 1),m]=THET(arange(2,nms(1) - 1),M) + dot(Cons4,(Cont_D(arange(1,nms(1) - 2)) + dot(10,Cont_D(arange(2,nms(1) - 1))) + Cont_D(arange(3,nms(1))))) # PROJ_Swing_ConstantRecovery_Aug.m:227 THET[1,m]=THET(2,m) # PROJ_Swing_ConstantRecovery_Aug.m:228 THET[nms(1),m]=THET(nms(1),M) - dot(Dmax,psis(1)) + varths_dt(1) + varths_D(3) # PROJ_Swing_ConstantRecovery_Aug.m:230 THET[nms(1) + 1,m]=dot(Dmax,psis(2)) + varths_dt(2) + varths_D(4) # PROJ_Swing_ConstantRecovery_Aug.m:231 THET[arange(nms(1) + 2,nms(2) - 1),m]=dot(Cons4,(Cont_dt(arange(nms(1) + 1,nms(2) - 2)) + dot(10,Cont_dt(arange(nms(1) + 2,nms(2) - 1))) + Cont_dt(arange(nms(1) + 3,nms(2))))) # PROJ_Swing_ConstantRecovery_Aug.m:233 THET[nms(2),m]=dot(Dmax,psis(3)) + varths_dt(3) + varths_D(1) # PROJ_Swing_ConstantRecovery_Aug.m:235 THET[nms(2) + 1,m]=THET(nms(2) + 1,M) - dot(Dmax,psis(4)) + varths_dt(4) + varths_D(2) # PROJ_Swing_ConstantRecovery_Aug.m:236 THET[arange(nms(2) + 2,K - 1),m]=THET(arange(nms(2) + 2,K - 1),M) + dot(Cons4,(Cont_D(arange(nms(2) + 1,K - 2)) + dot(10,Cont_D(arange(nms(2) + 2,K - 1))) + Cont_D(arange(nms(2) + 3,K)))) # PROJ_Swing_ConstantRecovery_Aug.m:238 THET[K,m]=THET(K - 1,m) # PROJ_Swing_ConstantRecovery_Aug.m:239 ###---------------------------------------- pp=ifft(multiply(toepM,fft(concat([[THET(arange(1,K),1)],[zeros(K,1)]])))) # PROJ_Swing_ConstantRecovery_Aug.m:243 Cont_dt=pp(arange(1,K)) # PROJ_Swing_ConstantRecovery_Aug.m:244 pp=ifft(multiply(toepMD,fft(concat([[THET(arange(1,K),Mtau)],[zeros(K,1)]])))) # PROJ_Swing_ConstantRecovery_Aug.m:246 Cont_D=pp(arange(1,K)) + dot(evec_D2,THET(1,Mtau)) + dot(evec_D1,THET(K,Mtau)) # PROJ_Swing_ConstantRecovery_Aug.m:247 PSI=G + Cont_D # PROJ_Swing_ConstantRecovery_Aug.m:248 xnot=xmin + dot((nnot - 1),dx) # PROJ_Swing_ConstantRecovery_Aug.m:250 xs=concat([xnot - dx,xnot,xnot + dx,xnot + dot(2,dx)]) # PROJ_Swing_ConstantRecovery_Aug.m:252 inds=concat([nnot - 1,nnot,nnot + 1,nnot + 2]) # PROJ_Swing_ConstantRecovery_Aug.m:253 ys=max(PSI(inds),Cont_dt(inds)) # PROJ_Swing_ConstantRecovery_Aug.m:254 price=spline(xs,ys,0) # PROJ_Swing_ConstantRecovery_Aug.m:256 return price if __name__ == '__main__': pass

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0.457106

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1

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null

0

4

6e848cd05bc34462c5586214a8b7f206aaf1e512

83

py

Python

projectShaun.py

rantingdemon/project_shaun

28e7e538912840cfafcbdff7898405b4a7d04fe5

[ "MIT" ]

null

projectShaun.py

rantingdemon/project_shaun

28e7e538912840cfafcbdff7898405b4a7d04fe5

[ "MIT" ]

null

projectShaun.py

rantingdemon/project_shaun

28e7e538912840cfafcbdff7898405b4a7d04fe5

[ "MIT" ]

null

from app import app app.run(ssl_context='adhoc', host="0.0.0.0", threaded=True)

13.833333

59

0.698795

16

83

3.5625

0.6875

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6e9199334dfc777deab08fb32b84f93f9fe2efe7

229

py

Python

08_numeric/main.py

jmmedel/Python-tutorial-Youtube

d3319a8744cc636ad6a4fb3d90889278806a6cff

[ "MIT" ]

null

08_numeric/main.py

jmmedel/Python-tutorial-Youtube

d3319a8744cc636ad6a4fb3d90889278806a6cff

[ "MIT" ]

null

08_numeric/main.py

jmmedel/Python-tutorial-Youtube

d3319a8744cc636ad6a4fb3d90889278806a6cff

[ "MIT" ]

2

2020-07-16T05:10:35.000Z

2020-09-08T01:43:10.000Z

#Python number # int # float # complex x = 1 y = 13.23 z = 1j print(x) print(y) print(z) print("-----------------------------------") print(type(x)) print(type(y)) print(type(z)) print("-----------------------------------")

10.904762

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1

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6eac2adab30624a3e98779ab04548c9280d1da65

423

py

Python

python/src/main/python/pyalink/alink/common/types/bases/model_stream_scan_params.py

wenwei8268/Alink

c00702538c95a32403985ebd344eb6aeb81749a7

[ "Apache-2.0" ]

null

python/src/main/python/pyalink/alink/common/types/bases/model_stream_scan_params.py

wenwei8268/Alink

c00702538c95a32403985ebd344eb6aeb81749a7

[ "Apache-2.0" ]

null

python/src/main/python/pyalink/alink/common/types/bases/model_stream_scan_params.py

wenwei8268/Alink

c00702538c95a32403985ebd344eb6aeb81749a7

[ "Apache-2.0" ]

null

from abc import ABC from .with_params import WithParams class ModelStreamScanParams(WithParams, ABC): def setModelStreamFilePath(self, val): return self._add_param('modelStreamFilePath', val) def setModelStreamScanInterval(self, val): return self._add_param('modelStreamScanInterval', val) def setModelStreamStartTime(self, val): return self._add_param('modelStreamStartTime', val)

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null

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1

0

1

0

4

6ec4439ade5365add87ec5b530a255527d6f16f4

270

py

Python

takeaway/settings/fastapi/extension.py

ShahinZeynalov/startapp

3470666114dad3563393ea976cac8daa03611c41

[ "MIT" ]

1

2020-06-08T06:54:08.000Z

takeaway/settings/fastapi/extension.py

ShahinZeynalov/startapp

3470666114dad3563393ea976cac8daa03611c41

[ "MIT" ]

null

takeaway/settings/fastapi/extension.py

ShahinZeynalov/startapp

3470666114dad3563393ea976cac8daa03611c41

[ "MIT" ]

null

extension = ''' # from sqlalchemy.ext.declarative import declarative_base # # from core.factories import Session from sqlalchemy import MetaData from gino.ext.starlette import Gino from core.factories import settings db: MetaData = Gino(dsn=settings.DATABASE_URL) '''

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0.122222

270

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58

24.545455

0.898734

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0.929889

0.210332

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false

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null

0

1

0

1

0

null

0

1

0

1

0

4

6ec657166be770cdd48ac03618293d3bdcf15b31

97

py

Python

qsbk/start.py

chlbupt/scrapy_demp

6324cb4bdd7fc714f829c7f74c47dbf57244a0c0

[ "bzip2-1.0.6" ]

null

qsbk/start.py

chlbupt/scrapy_demp

6324cb4bdd7fc714f829c7f74c47dbf57244a0c0

[ "bzip2-1.0.6" ]

null

qsbk/start.py

chlbupt/scrapy_demp

6324cb4bdd7fc714f829c7f74c47dbf57244a0c0

[ "bzip2-1.0.6" ]

null

# encoding:utf-8 from scrapy import cmdline cmdline.execute('scrapy crawl qsbk_spider'.split())

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97

5.357143

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0.011494

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97

5

51

19.4

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0.292683

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0

0.5

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null

0

1

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null

0

1

0

1

0

4

6ed9f62937f9fba7af848dd48bfb315bbdcd46dd

27

py

Python

pythongui/__init__.py

timmo001/python-gui

6bac25f814a980f6ed40e49edccf302d9848e9e6

[ "MIT" ]

1

2022-01-13T07:06:58.000Z

pythongui/__init__.py

timmo001/python-gui

6bac25f814a980f6ed40e49edccf302d9848e9e6

[ "MIT" ]

1

2022-03-21T14:24:23.000Z

pythongui/__init__.py

timmo001/python-gui

6bac25f814a980f6ed40e49edccf302d9848e9e6

[ "MIT" ]

null

"""Python GUI GUI: Init"""

13.5

26

0.592593

4

27

4

0.75

0

0.148148

27

1

27

0.695652

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null

0

null

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null

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null

0

1

0

null

0

1

0

4

42cb3eb3cefd98eb8037f9e957db43e5cbb01c58

140

py

Python

example_app.py

sneawo/aiohttp-apispec

a8a12346fa802dc40a471b2c645642a083804f3b

[ "MIT" ]

198

2017-12-18T02:26:12.000Z

2022-03-19T17:49:20.000Z

example_app.py

sneawo/aiohttp-apispec

a8a12346fa802dc40a471b2c645642a083804f3b

[ "MIT" ]

109

2018-03-15T14:41:56.000Z

2022-02-25T20:15:17.000Z

example_app.py

sneawo/aiohttp-apispec

a8a12346fa802dc40a471b2c645642a083804f3b

[ "MIT" ]

64

2018-03-01T09:05:41.000Z

2022-02-07T10:02:56.000Z

from aiohttp import web from example.app import create_app if __name__ == "__main__": web_app = create_app() web.run_app(web_app)

17.5

34

0.728571

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140

4.045455

0.5

0.202247

0

0.185714

140

7

35

20

0.780702

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0

1

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1

0

null

0

1

0

4

42d5f4ca01f1871389b5164313f51412688612f1

111

py

Python

simpleflow/runtime/__init__.py

David-Wobrock/simpleflow

09f59105b48ae79aef37b506bbde0cd1f2c360d1

[ "MIT" ]

69

2015-02-24T00:49:40.000Z

2022-02-05T02:35:04.000Z

simpleflow/runtime/__init__.py

David-Wobrock/simpleflow

09f59105b48ae79aef37b506bbde0cd1f2c360d1

[ "MIT" ]

295

2015-02-06T11:02:00.000Z

2022-03-21T11:01:34.000Z

simpleflow/runtime/__init__.py

David-Wobrock/simpleflow

09f59105b48ae79aef37b506bbde0cd1f2c360d1

[ "MIT" ]

27

2015-08-31T22:14:42.000Z

2022-02-08T07:25:01.000Z

import logging from simpleflow.log import setup_logging setup_logging() logger = logging.getLogger(__name__)

15.857143

40

0.828829

14

111

6.142857

0.642857

0.27907

0

0.108108

111

6

41

18.5

0.868687

0

1

0

false

0

0.5

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0.5

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0

null

1

0

1

0

null

0

1

0

4

42e49d7b0a14a89a526b8fd336a726df0e395878

245

py

Python

index_scraper/index_scraper/items.py

ralphqq/pse-indices-scoreboard

f11a01915ac5a17b663db604afe996c5ed928fb9

[ "MIT" ]

1

2019-10-08T16:54:07.000Z

index_scraper/index_scraper/items.py

ralphqq/pse-indices-scoreboard

f11a01915ac5a17b663db604afe996c5ed928fb9

[ "MIT" ]

8

2020-02-12T01:17:41.000Z

2021-12-13T20:06:12.000Z

index_scraper/index_scraper/items.py

ralphqq/pse-indices-scoreboard

f11a01915ac5a17b663db604afe996c5ed928fb9

[ "MIT" ]

null

# -*- coding: utf-8 -*- import scrapy class IndexScraperItem(scrapy.Item): name = scrapy.Field() current_value = scrapy.Field() points_change = scrapy.Field() percent_change = scrapy.Field() market_status = scrapy.Field()

20.416667

36

0.673469

28

245

5.75

0.607143

0.341615

0.21118

0

0.005051

0.191837

245

11

37

22.272727

0.808081

0.085714

0

1

0

false

0

0.142857

0

1

0

1

0

null

1

0

null

0

1

0

4

42e5542d36b90d3fb4cf6c4f893b6dbb88fb6706

330

py

Python

scripts/end_to_end.py

PatrickAlphaC/vrf_pizza

c39c03ea707234a814d3d57419111c8e049d87a9

[ "MIT" ]

9

2021-03-13T03:47:12.000Z

2021-09-01T13:49:58.000Z

scripts/end_to_end.py

PatrickAlphaC/vrf_pizza

c39c03ea707234a814d3d57419111c8e049d87a9

[ "MIT" ]

1

2021-03-07T21:32:55.000Z

2021-03-29T00:34:22.000Z

scripts/end_to_end.py

PatrickAlphaC/vrf_pizza

c39c03ea707234a814d3d57419111c8e049d87a9

[ "MIT" ]

1

2021-09-01T13:49:58.000Z

#!/usr/bin/python3 import os from brownie import VRF_Pizza, VRF_Pizza_RNG, accounts, network, config, interface from .a1_deploy import deploy_pizza_contracts from .a2_order_random_pizza import order_pizza def main(): end_to_end() def end_to_end(): vrf_pizza, vrf_pizza_rng = deploy_pizza_contracts() order_pizza()

22

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330

4.666667

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0.092437

0.134454

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330

14

83

23.571429

0.827465

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0.222222

true

0

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null

0

null

0

1

0

1

0

1

0

4

6e255ae0096bd993a6f773ddb30a47d4f00cd309

431

py

Python

part5/enum_4.py

MADTeacher/python_basics

06ae43d8063c1c8426a4fbb53443b6d1ee727951

[ "MIT" ]

null

part5/enum_4.py

MADTeacher/python_basics

06ae43d8063c1c8426a4fbb53443b6d1ee727951

[ "MIT" ]

null

part5/enum_4.py

MADTeacher/python_basics

06ae43d8063c1c8426a4fbb53443b6d1ee727951

[ "MIT" ]

4

2020-10-04T12:24:14.000Z

2022-01-16T17:01:59.000Z

from enum import IntFlag class TestFlag(IntFlag): A = 1 B = 2 C = 3 D = 4 if __name__ == "__main__": print(repr(TestFlag.A | TestFlag.B)) RW = TestFlag.A | TestFlag.D print(repr(RW)) print(TestFlag.A in RW) print(repr(TestFlag.D & TestFlag.C)) print(repr(TestFlag.D ^ TestFlag.C)) print(bool(TestFlag.D & TestFlag.C)) print(repr(TestFlag.D ^ 12)) print(repr(TestFlag.A | 6))

20.52381

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431

3.923077

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0.211765

0.333333

0.211765

0.372549

0.282353

0

0.021472

0.243619

431

21

41

20.52381

0.760736

0

0.018519

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1

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false

0

0.0625

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0.375

0.5

0

null

1

0

null

0

1

0

4

6e3361eda180b866b83a533942741fa89d2fe609

484

py

Python

app/domain/commands.py

zmoog/concierge

5f77b979c34182d5933e8a3d26e97ee1e5ad8339

[ "MIT" ]

1

2020-02-13T16:32:01.000Z

app/domain/commands.py

zmoog/concierge

5f77b979c34182d5933e8a3d26e97ee1e5ad8339

[ "MIT" ]

389

2019-09-06T06:02:45.000Z

2022-03-31T04:07:25.000Z

app/domain/commands.py

zmoog/concierge

5f77b979c34182d5933e8a3d26e97ee1e5ad8339

[ "MIT" ]

2

2020-02-24T13:04:46.000Z

2020-02-24T13:16:43.000Z

# pylint: disable=too-few-public-methods from datetime import date from typing import List # Optional # from dataclasses import dataclass # from pydantic import BaseModel # from pydantic.dataclasses import dataclass from pydantic import BaseModel class Command(BaseModel): pass # @dataclass class Summarize(Command): day: date # @dataclass class DownloadIFQ(Command): day: date # @dataclass class CheckRefurbished(Command): store: str products: List[str]

17.285714

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0.754132

57

484

6.403509

0.473684

0.09863

0.142466

0.164384

0.443836

0.290411

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0.173554

484

27

45

17.925926

0.9125

0.38843

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0.166667

0

1

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true

0.083333

0.25

0

0.916667

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null

0

1

0

null

0

1

0

1

0

4

282bf4d3cd6b812862cb76de3382da22ee4496ac

3,267

py

Python

result/result.py

nkeyes/result

47821b4d3fa3aa12a482b04ef99ca7f216be725b

[ "MIT" ]

null

result/result.py

nkeyes/result

47821b4d3fa3aa12a482b04ef99ca7f216be725b

[ "MIT" ]

null

result/result.py

nkeyes/result

47821b4d3fa3aa12a482b04ef99ca7f216be725b

[ "MIT" ]

null

from typing import Generic, TypeVar, Union, Any, Optional, cast, overload E = TypeVar("E") T = TypeVar("T") A = TypeVar("A") class Result(Generic[E, T]): """ A simple `Result` type inspired by Rust. Not all methods (https://doc.rust-lang.org/std/result/enum.Result.html) have been implemented, only the ones that make sense in the Python context. """ def __init__(self, is_ok: bool, value: Union[E, T], force: bool = False) -> None: """Do not call this constructor, use the Ok or Err class methods instead. There are no type guarantees on the value if this is called directly. Args: is_ok: If this represents an ok result value: The value inside the result force: Force creation of the object. This is false by default to prevent accidentally creating instance of a Result in an unsafe way. """ if force is not True: raise RuntimeError("Don't instantiate a Result directly. " "Use the Ok(value) and Err(error) class methods instead.") else: self._is_ok = is_ok self._value = value def __eq__(self, other: Any) -> bool: return (self.__class__ == other.__class__ and self.is_ok() == cast(Result, other).is_ok() and self._value == other._value) def __ne__(self, other: Any) -> bool: return not (self == other) def __hash__(self) -> int: return hash((self.is_ok(), self._value)) def __repr__(self) -> str: if self.is_ok(): return 'Ok({})'.format(repr(self._value)) else: return 'Err({})'.format(repr(self._value)) @classmethod @overload def Ok(cls) -> 'Result[E, bool]': pass @classmethod @overload def Ok(cls, value: T) -> 'Result[E, T]': pass @classmethod def Ok(cls, value: Any = True) -> 'Result[E, Any]': return cls(is_ok=True, value=value, force=True) @classmethod def Err(cls, error: E) -> 'Result[E, T]': return cls(is_ok=False, value=error, force=True) def is_ok(self) -> bool: return self._is_ok def is_err(self) -> bool: return not self._is_ok def ok(self) -> Optional[T]: """ Return the value if it is an `Ok` type. Return `None` if it is an `Err`. """ return cast(T, self._value) if self.is_ok() else None def err(self) -> Optional[E]: """ Return the error if this is an `Err` type. Return `None` otherwise. """ return cast(E, self._value) if self.is_err() else None @property def value(self) -> Union[E, T]: """ Return the inner value. This might be either the ok or the error type. """ return self._value # TODO: Implement __iter__ for destructuring @overload def Ok() -> Result[E, bool]: pass @overload def Ok(value: T) -> Result[E, T]: pass def Ok(value: Any = True) -> Result[E, Any]: """ Shortcut function to create a new Result. """ return Result.Ok(value) def Err(error: E) -> Result[E, T]: """ Shortcut function to create a new Result. """ return Result.Err(error)

27.453782

97

0.579431

452

3,267

4.05531

0.263274

0.030551

0.034915

0.014184

0.181124

0.093835

0.050191

0

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118

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0

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false

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null

0

null

0

1

0

1

0

1

0

4

283069f44e96dffc65cd9e07a26ffe0240931a3f

128

py

Python

tests/test_select_vts_to_plot.py

burkesquires/FeaVar

68695bff105fbabbda18f27b0d296f7f446ca0bb

[ "CC0-1.0" ]

1

2019-10-28T16:58:34.000Z

tests/test_select_vts_to_plot.py

burkesquires/nvariant

68695bff105fbabbda18f27b0d296f7f446ca0bb

[ "CC0-1.0" ]

1

2021-09-03T13:37:57.000Z

2021-09-03T20:40:55.000Z

tests/test_select_vts_to_plot.py

burkesquires/ombre

68695bff105fbabbda18f27b0d296f7f446ca0bb

[ "CC0-1.0" ]

null

from unittest import TestCase class TestSelectVtsToPlot(TestCase): def test_select_vts_to_plot(self): self.fail()

18.285714

38

0.75

16

128

5.75

0.875

0

0.179688

128

6

39

21.333333

0.87619

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

95401e2294bedcdc9af8c6c14610ea85b599982a

1,262

py

Python

models/layers/softmax.py

milesgray/ImageFunctions

35e4423b94149b0ba291eafb0cd98260a70d5f31

[ "Apache-2.0" ]

null

models/layers/softmax.py

milesgray/ImageFunctions

35e4423b94149b0ba291eafb0cd98260a70d5f31

[ "Apache-2.0" ]

null

models/layers/softmax.py

milesgray/ImageFunctions

35e4423b94149b0ba291eafb0cd98260a70d5f31

[ "Apache-2.0" ]

null

import torch import torch.nn as nn class SpatialSoftmax2d(nn.Module): def __init__(self, temp: float=1.0, requires_grad: bool=True): super().__init__() self.temp = nn.Parameter(torch.FloatTensor([temp]), requires_grad=requires_grad) self.softmax = nn.Softmax2d() def forward(self, x): x = self.softmax(x) return x * self.temp.to(x.device) class ChannelSoftmax2d(nn.Module): def __init__(self, temp: float=1.0, requires_grad: bool=True): super().__init__() self.temp = nn.Parameter(torch.FloatTensor([temp]), requires_grad=requires_grad) self.softmax = nn.Softmax(dim=1) def forward(self, x): x = self.softmax(x) return x * self.temp.to(x.device) class ChannelGumbelMax2d(nn.Module): def __init__(self, tau: float=1.0, hard: bool=False, dim: int=-1, requires_grad: bool=True): super().__init__() self.tau = tau self.hard = hard self.dim = dim self.temp = nn.Parameter(torch.FloatTensor([temp]), requires_grad=requires_grad) self.softmax = nn.Softmax(dim=1) def forward(self, x): x = F.gumbel_softmax(x, tau=self.tau, hard=self.hard, dim=self.dim) return x * self.temp.to(x.device)

35.055556

96

0.637876

177

1,262

4.355932

0.20904

0.140078

0.062257

0.058366

0.744488

0.719844

0.645914

0

0.013265

0.223455

1,262

36

97

35.055556

0.773469

0

0.62069

0

1

0.206897

false

0

0.068966

0

0.482759

0

null

0

1

0

1

0

null

0

1

0

4

957fc749564cef728c84b25cdd57c72a4e950822

285

py

Python

main.py

marcelloamr/Acompanhar-site-em-python

4058a54d2a8e58c2f7b2a3f252fde2da98977254

[ "MIT" ]

null

main.py

marcelloamr/Acompanhar-site-em-python

4058a54d2a8e58c2f7b2a3f252fde2da98977254

[ "MIT" ]

null

main.py

marcelloamr/Acompanhar-site-em-python

4058a54d2a8e58c2f7b2a3f252fde2da98977254

[ "MIT" ]

null

from time import sleep from text import * while(True): # page("","") page("https://www.cebraspe.org.br/concursos/SERPRO_21","Serpro_21") page("https://www.transparencia.serpro.gov.br/acesso-a-informacao/servidores/concurso-publico/concurso-2021","Serpro_21_2")

35.625

128

0.698246

39

285

5

0.666667

0.123077

0

0.044355

0.129825

285

7

129

40.714286

0.741935

0.038596

0

0.2

0.633962

0

1

0

true

0

0.4

0

0.4

0

null

0

1

0

1

0

null

0

1

0

1

0

4

958317b3e41a2669beb96aa8b1e434b04803f943

243

py

Python

polyhymnia/utils/synonyms.py

luoy2/polyhymnia

7de33e7dd816ec2c75d395c35d5ddc46a21ce94c

[ "Apache-2.0" ]

2

2022-03-22T12:49:21.000Z

2022-03-22T12:55:01.000Z

polyhymnia/utils/synonyms.py

luoy2/polyhymnia

7de33e7dd816ec2c75d395c35d5ddc46a21ce94c

[ "Apache-2.0" ]

null

polyhymnia/utils/synonyms.py

luoy2/polyhymnia

7de33e7dd816ec2c75d395c35d5ddc46a21ce94c

[ "Apache-2.0" ]

null

__all__ = ['get_synonyms'] import os import pathlib # os.environ["SYNONYMS_WORD2VEC_BIN_MODEL_ZH_CN"] = str(pathlib.Path(__file__).parents[1] / 'data/words.vector') import synonyms def get_synonyms(word): return synonyms.nearby(word)[0]

24.3

112

0.761317

35

243

4.857143

0.714286

0.129412

0

0.013761

0.102881

243

9

113

27

0.766055

0.452675

0

0.091603

0

1

0.166667

false

0

0.5

0.166667

0.833333

0

null

0

1

0

null

0

1

0

4

959aec2216fda8c6e1a6cdf6ec2c88c1c6a7e137

217

py

Python

plangclassifier/__init__.py

arnauorriols/plangclassifier

8e061e59e6a916ebb70b22ec6509daceb8c7c0a8

[ "BSD-3-Clause" ]

1

2017-06-14T17:06:30.000Z

plangclassifier/__init__.py

arnauorriols/plangclassifier

8e061e59e6a916ebb70b22ec6509daceb8c7c0a8

[ "BSD-3-Clause" ]

null

plangclassifier/__init__.py

arnauorriols/plangclassifier

8e061e59e6a916ebb70b22ec6509daceb8c7c0a8

[ "BSD-3-Clause" ]

null

from .classifier import Classifier from .samples import DATA def classify_langs(code, languages=None): if languages is None: languages = [] return Classifier.classify(DATA, code, languages=languages)

27.125

63

0.741935

26

217

6.153846

0.538462

0.1625

0

0.179724

217

7

64

31

0.898876

0

1

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959aec7de3b24dff564b56dd06eb2d216f98f5d5

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py

Python

hdp/store/excel.py

Open-Health-Data-Project/ohdp

9b7e339ecf09cb9319ee4d507327701619b70317

[ "BSD-3-Clause" ]

10

2020-05-06T17:59:37.000Z

2021-02-15T23:11:05.000Z

hdp/store/excel.py

Open-Health-Data-Project/ohdp

9b7e339ecf09cb9319ee4d507327701619b70317

[ "BSD-3-Clause" ]

2

2020-07-02T16:36:23.000Z

2020-07-14T21:51:07.000Z

hdp/store/excel.py

Open-Health-Data-Project/ohdp

9b7e339ecf09cb9319ee4d507327701619b70317

[ "BSD-3-Clause" ]

11

2020-05-08T18:49:55.000Z

2020-06-29T17:33:10.000Z

# Team 5 def save_to_excel(datatables: list, directory=None): pass def open_excel(): pass

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95a0d1c7850fffaa5550fe2c318e4631fbd6a8e4

83

py

Python

moex/apps.py

ghostforpy/bonds-docker

fda77225b85264cb4ba06b15ff63bc807858425a

[ "MIT" ]

2

2020-09-08T12:51:56.000Z

2021-08-18T15:27:52.000Z

moex/apps.py

nrsharip/iss-web

e8ca66ade3933dfac4795ba7c44e067c26a079e2

[ "MIT" ]

1

2021-12-13T20:43:35.000Z

moex/apps.py

nrsharip/iss-web

e8ca66ade3933dfac4795ba7c44e067c26a079e2

[ "MIT" ]

null

from django.apps import AppConfig class MoexConfig(AppConfig): name = 'moex'

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py

Python

filer/apps.py

namanwfhsolve/tempfiler

dfce98f85e7313e42df74c96cb2f84d1836017c1

[ "MIT" ]

1

2022-02-01T22:40:21.000Z

filer/apps.py

namanwfhsolve/tempfiler

dfce98f85e7313e42df74c96cb2f84d1836017c1

[ "MIT" ]

2

2021-07-05T14:28:59.000Z

2021-07-05T15:41:19.000Z

filer/apps.py

namanwfhsolve/tempfiler

dfce98f85e7313e42df74c96cb2f84d1836017c1

[ "MIT" ]

null

from django.apps import AppConfig class FilerConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'filer' def ready(self): from filer import signals

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95ee4fbebcaa75c7422656a3f1fb679039e991d4

357

py

Python

budgetme/apps/transactions/renderers.py

poblouin/budgetme-rest-api

74d9237bc7b0a118255a659029637c5ed1a8b7a1

[ "MIT" ]

2

2018-03-07T09:43:07.000Z

2018-03-11T04:50:41.000Z

budgetme/apps/transactions/renderers.py

poblouin/budgetme-rest-api

74d9237bc7b0a118255a659029637c5ed1a8b7a1

[ "MIT" ]

13

2017-12-28T02:44:09.000Z

2020-06-05T21:13:13.000Z

budgetme/apps/transactions/renderers.py

poblouin/budgetme-rest-api

74d9237bc7b0a118255a659029637c5ed1a8b7a1

[ "MIT" ]

null

from budgetme.apps.core.renderers import BudgetMeJSONRenderer class TransactionJSONRenderer(BudgetMeJSONRenderer): object_label = 'transaction' pagination_object_label = 'transactions' class ScheduledTransactionJSONRenderer(BudgetMeJSONRenderer): object_label = 'scheduled_transaction' pagination_object_label = 'scheduled_transactions'

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251f51905608bf1b30bb0893d30ac54b141aa68d

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py

Python

experiment4_1/snn_cvx.py

worden1/ICTAI_snn

b0955c4c0bf22056f46c78a3b15438baafbd490f

[ "MIT" ]

null

experiment4_1/snn_cvx.py

worden1/ICTAI_snn

b0955c4c0bf22056f46c78a3b15438baafbd490f

[ "MIT" ]

null

experiment4_1/snn_cvx.py

worden1/ICTAI_snn

b0955c4c0bf22056f46c78a3b15438baafbd490f

[ "MIT" ]

null

import numpy as np import numba as nb @nb.jit(nopython=True) def run_snn_trial(x_sample, F_weights, omega, thresholds, dt, leak, mu=0., sigma_v=0. ): """ Function to simulate the spiking network for defined connectivity parameters, thresholds and time parameters. It returns the instantaneous firing rates of neurons for the whole simulation time Parameters ---------- x_sample: array Input array (shape=[K, num_bins]) F_weights: array Feed-forward weights (shape=[N, K]) omega: array Recurrent weights (shape=[N, N]) thresholds: array Neurons thresholds (shape=[N,]) dt: float time step leak: float membrane leak time-constant mu: float controls spike cost sigma_v: float controls variance of voltage noise Returns ------- array network instantaneous firing rates (shape=[N x num_bins]) """ # initialize system N = F_weights.shape[0] # number of neurons num_bins = x_sample.shape[1] # number of time bins firing_rates = np.zeros((N, num_bins)) V_membrane = np.zeros(N) # implement the Euler method to solve the differential equations for t in range(num_bins - 1): # compute command signal command_x = (x_sample[:, t + 1] - x_sample[:, t]) / dt + leak * x_sample[:, t] # update membrane potential V_membrane += dt * (-leak * V_membrane + np.dot(F_weights, command_x) ) + np.sqrt(2 * dt * leak) * sigma_v * np.random.randn(N) # update firing rates firing_rates[:, t + 1] = (1 - leak * dt) * firing_rates[:, t] # Check if any neurons are past their threshold during the last time-step diff_voltage_thresh = V_membrane - thresholds spiking_neurons_indices = np.arange(N)[diff_voltage_thresh >= 0] if spiking_neurons_indices.size > 0: # Pick the neuron which likely would have spiked first, by max distance from threshold to_pick = np.argmax(V_membrane[spiking_neurons_indices] - thresholds[spiking_neurons_indices]) s = spiking_neurons_indices[to_pick] # Update membrane potential V_membrane[s] -= mu V_membrane += omega[:, s] # Update firing rates firing_rates[s, t + 1] += 1 else: pass return firing_rates @nb.jit(nopython=True) def update_weights(x_sample, y_target_sample, F_weights, G_weights, omega, thresholds, buffer_bins, dt, leak, leak_thresh, alpha_thresh, alpha_F, mu=0., sigma_v=0., ): """ Train the network in one trial with one presented input-target pair (x_sample, y_target_sample) The function returns the updated thresholds and feed-forward weights after that trial Parameters ---------- x_sample: array Input array (shape=[K, num_bins]) y_target_sample: array target sample (shape=[M,]) F_weights: array Feed-forward weights (shape=[N, K]) G_weights: array Encoder weights (shape=[N, M]) omega: array Recurrent weights (shape=[N, N]) thresholds: array Neurons thresholds (shape=[N,]) buffer_bins: int Number of bins before learning starts dt: float time step leak: float membrane leak time-constant leak_thresh: float controls the speed of drift in thresholds alpha_thresh: float learning rate of thresholds (>> leak thresh) alpha_F: float learning rate for forward weights mu: float controls spike cost sigma_v: float controls variance of voltage noise Returns ------- array updated thresholds array array updated feed-forward weights array """ # initialize system beta = 1 / 500 N = F_weights.shape[0] num_bins = x_sample.shape[1] firing_rates = np.zeros((N, num_bins)) V_membrane = np.zeros(N) # implement the Euler method to solve the differential equations for t in range(num_bins - 1): # compute command signal command_x = (x_sample[:, t + 1] - x_sample[:, t]) / dt + leak * x_sample[:, t] # update membrane potential V_membrane += dt * (-leak * V_membrane + np.dot(F_weights, command_x) ) + np.sqrt(2 * dt * leak) * sigma_v * np.random.randn(N) # update rates firing_rates[:, t + 1] = (1 - leak * dt) * firing_rates[:, t] # Check if any neurons are past their threshold during the last time-step diff_voltage_thresh = V_membrane - thresholds spiking_neurons_indices = np.arange(N)[diff_voltage_thresh >= 0] if spiking_neurons_indices.size > 0: # Pick the neuron which likely would have spiked first, by max distance from threshold to_pick = np.argmax(V_membrane[spiking_neurons_indices] - thresholds[spiking_neurons_indices]) s = spiking_neurons_indices[to_pick] # Update membrane potential V_membrane[s] -= mu V_membrane += omega[:, s] # Update rates with spikes firing_rates[s, t + 1] += 1 # !! Update weights if t >= buffer_bins: proj_error_neuron = F_weights[s, :] @ x_sample[:, t] - thresholds[ s] - G_weights[s, :] @ y_target_sample - beta * (F_weights[s, :] @ x_sample[:, t] - thresholds[s] ) dLdthresh = -proj_error_neuron dLdf_weights = proj_error_neuron * x_sample[:, t] thresholds[s] -= alpha_thresh * dLdthresh F_weights[s, :] -= alpha_F * dLdf_weights else: pass # drift thresholds if t >= buffer_bins: thresholds -= dt * leak_thresh return thresholds, F_weights @nb.jit(nopython=True) def update_weights_2(x_sample, y_target_sample, F_weights,F_weights_2, G_weights,G_weights_2, omega,omega_2, thresholds,thresholds_2, buffer_bins, dt, leak, leak_thresh, alpha_thresh, alpha_F, mu=0., sigma_v=0., ): """ Train the network in one trial with one presented input-target pair (x_sample, y_target_sample) The function returns the updated thresholds and feed-forward weights after that trial Parameters ---------- x_sample: array Input array (shape=[K, num_bins]) y_target_sample: array target sample (shape=[M,]) F_weights: array Feed-forward weights (shape=[N, K]) G_weights: array Encoder weights (shape=[N, M]) omega: array Recurrent weights (shape=[N, N]) thresholds: array Neurons thresholds (shape=[N,]) buffer_bins: int Number of bins before learning starts dt: float time step leak: float membrane leak time-constant leak_thresh: float controls the speed of drift in thresholds alpha_thresh: float learning rate of thresholds (>> leak thresh) alpha_F: float learning rate for forward weights mu: float controls spike cost sigma_v: float controls variance of voltage noise Returns ------- array updated thresholds array array updated feed-forward weights array """ # initialize system beta = 1 / 500 N = F_weights.shape[0] num_bins = x_sample.shape[1] firing_rates = np.zeros((N, num_bins)) V_membrane = np.zeros(N) V_membrane_2 = np.zeros(N) firing_rates_2 = np.zeros((N, num_bins)) # implement the Euler method to solve the differential equations for t in range(num_bins - 1): # compute command signal command_x = (x_sample[:, t + 1] - x_sample[:, t]) / dt + leak * x_sample[:, t] # update membrane potential V_membrane += dt * (-leak * V_membrane + np.dot(F_weights, command_x) ) + np.sqrt(2 * dt * leak) * sigma_v * np.random.randn(N) # update rates firing_rates[:, t + 1] = (1 - leak * dt) * firing_rates[:, t] # Check if any neurons are past their threshold during the last time-step diff_voltage_thresh = V_membrane - thresholds spiking_neurons_indices = np.arange(N)[diff_voltage_thresh >= 0] if spiking_neurons_indices.size > 0: # Pick the neuron which likely would have spiked first, by max distance from threshold to_pick = np.argmax(V_membrane[spiking_neurons_indices] - thresholds[spiking_neurons_indices]) s = spiking_neurons_indices[to_pick] # Update membrane potential V_membrane[s] -= mu V_membrane += omega[:, s] # Update rates with spikes firing_rates[s, t + 1] += 1 # !! Update weights if t >= buffer_bins: proj_error_neuron = F_weights[s, :] @ x_sample[:, t] - thresholds[ s] - G_weights[s, :] @ y_target_sample - beta * (F_weights[s, :] @ x_sample[:, t] - thresholds[s] ) dLdthresh = -proj_error_neuron dLdf_weights = proj_error_neuron * x_sample[:, t] thresholds[s] -= alpha_thresh * dLdthresh F_weights[s, :] -= alpha_F * dLdf_weights else: pass #layer 2 V_membrane_2 += dt * (-leak * V_membrane_2 + F_weights_2 @ firing_rates[:, t] ) + np.sqrt(2 * dt * leak) * sigma_v * np.random.randn(N) firing_rates_2[:, t + 1] = (1 - leak * dt) * firing_rates_2[:, t] diff_voltage_thresh_2 = V_membrane_2 - thresholds_2 spiking_neurons_indices_2 = np.arange(N)[diff_voltage_thresh_2 >= 0] if spiking_neurons_indices_2.size > 0: # Pick the neuron which likely would have spiked first, by max distance from threshold to_pick_2 = np.argmax(V_membrane_2[spiking_neurons_indices_2] - thresholds_2[spiking_neurons_indices_2]) s_2 = spiking_neurons_indices_2[to_pick_2] # Update membrane potential V_membrane_2[s_2] -= mu V_membrane_2 += omega_2[:, s_2] # Update rates with spikes firing_rates_2[s_2, t + 1] += 1 # !! Update weights if t >= buffer_bins: proj_error_neuron_2 = F_weights_2[s_2, :] @ firing_rates[:, t] - thresholds_2[ s_2] - G_weights_2[s_2, :] @ y_target_sample - beta * (F_weights_2[s_2, :] @ firing_rates[:, t] - thresholds_2[s_2] ) dLdthresh_2 = -proj_error_neuron_2 dLdf_weights_2 = proj_error_neuron_2 * x_sample[:, t] thresholds_2[s_2] -= alpha_thresh * dLdthresh_2 F_weights_2[s_2, :] -= alpha_F * dLdf_weights_2 else: pass # drift thresholds if t >= buffer_bins: thresholds -= dt * leak_thresh thresholds_2 -= dt * leak_thresh return thresholds, F_weights, thresholds_2, F_weights_2 def run_snn(x, F_weights, omega, thresholds, dt, leak, mu=0., sigma_v=0., silence_T=None, silence_prop=0, delay=0 ): """ Function to simulate the spiking network for defined connectivity parameters, thresholds and time parameters. It returns the firing rates, voltages, currents, and spikes. Parameters ---------- x: array Input array (shape=[K, num_bins]) F_weights: array Feed-forward weights (shape=[N, K]) omega: array Recurrent weights (shape=[N, N]) thresholds: array Neurons thresholds (shape=[N,]) dt: float time step leak: float membrane leak time-constant mu: float controls spike cost sigma_v: float controls variance of voltage noise silence_T: int From which time-point to silence neurons silence_prop: float Which proportion of the population to silence delay: int Synaptic delay in recurrent connections in number of timesteps Returns ------- array network instantaneous firing rates (shape=[N x num_bins]) array network spikes (shape=[N x num_bins]) array network voltages (shape=[N x num_bins]) array network excitatory currents (shape=[N x num_bins]) array network inhibitory currents (shape=[N x num_bins]) """ # initialize system N = F_weights.shape[0] # number of neurons num_bins = x.shape[1] # number of time bins firing_rates = np.zeros((N, num_bins)) spikes = np.zeros((N, num_bins)) V_membrane = np.zeros((N, num_bins)) I_E = np.zeros((N, num_bins)) I_I = np.zeros((N, num_bins)) # split connectivity into recurrent and self-connections omega_self = np.copy(omega) omega_rec = np.copy(omega) omega_rec[np.eye(N) == 1] = 0 omega_self[np.eye(N) == 0] = 0 # separate recurrent weights into inhibitory and excitatory omega_e, omega_i = omega.copy(), omega.copy() omega_e[omega < 0] = 0 omega_i[omega > 0] = 0 omega_i[range(N), range(N)] = 0 # remove self-resets omega_e[range(N), range(N)] = 0 # remove self-resets # separate feed-forward weights into positive and negative F_pos, F_neg = F_weights.copy(), F_weights.copy() F_pos[F_weights < 0] = 0 F_neg[F_weights > 0] = 0 # if not given, set silence point at end if silence_T is None: silence_T = num_bins + 1 # implement the Euler method to solve the differential equations for t in range(num_bins - 1): # compute command signal command_x = (x[:, t + 1] - x[:, t]) / dt + leak * x[:, t] # update membrane potential V_membrane[:, t + 1] = V_membrane[:, t] + dt * (-leak * V_membrane[:, t] + np.dot(F_weights, command_x) + np.dot(omega_self, spikes[:, t]/dt) ) + np.sqrt(2 * dt * leak) * sigma_v * np.random.randn(N) if t >= delay: V_membrane[:, t + 1] = V_membrane[:, t + 1] + np.dot(omega_rec, spikes[:, t-delay]) # get positive/negative inputs command_x_pos, command_x_neg = command_x.copy(), command_x.copy() command_x_pos[command_x < 0] = 0 command_x_neg[command_x > 0] = 0 # update currents dI_I = np.dot(F_neg, command_x_pos) + np.dot(F_pos, command_x_neg) # inhibitory inputs if t >= delay: dI_I += np.dot(omega_i, spikes[:, t-delay] / dt) I_I[:, t+1] = I_I[:, t] + dt*(-I_I[:, t]*leak + dI_I) dI_E = np.dot(F_pos, command_x_pos) + np.dot(F_neg, command_x_neg) # excitatory inputs if t >= delay: dI_E += np.dot(omega_e, spikes[:, t-delay] / dt) I_E[:, t+1] = I_E[:, t] + dt*(-I_E[:, t]*leak + dI_E) # update firing rates firing_rates[:, t + 1] = (1 - leak * dt) * firing_rates[:, t] # silence neurons if t > silence_T: V_membrane[int(N * (1 - silence_prop)):, t + 1] = -100 # Check if any neurons are past their threshold during the last time-step diff_voltage_thresh = V_membrane[:, t + 1] - thresholds spiking_neurons_indices = np.arange(N)[diff_voltage_thresh >= 0] if spiking_neurons_indices.size > 0: if delay == 0: # Pick the neuron which likely would have spiked first, by max distance from threshold to_pick = np.argmax(diff_voltage_thresh[spiking_neurons_indices]) s = spiking_neurons_indices[to_pick] # Update membrane potential V_membrane[s, t + 1] -= mu spikes[s, t + 1] = 1 # Update firing rates firing_rates[s, t + 1] += 1 else: # Update membrane potential V_membrane[spiking_neurons_indices, t + 1] -= mu # V_membrane[:, t + 1] += omega[:, s] spikes[spiking_neurons_indices, t + 1] = 1 # Update firing rates firing_rates[spiking_neurons_indices, t + 1] += 1 else: pass return firing_rates, spikes, V_membrane, I_E, I_I @nb.jit(nopython=True) def run_maxout(x_sample, F_weights, G_weights, thresholds): # calculate activities neural_boundary = np.zeros((G_weights.shape[1], G_weights.shape[0] + 1)) neural_boundary[:, 1:] = F_weights @ x_sample / G_weights.ravel() - thresholds / G_weights.ravel() y_out = neural_boundary.max() nactive = neural_boundary.argmax() return y_out, nactive

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Python

Service/migrations/0013_auto_20191229_1254.py

Bixbar/Bixbar-Backend

f69e0e96ea4bcc0c57000fdf1e24c66ead59df3f

[ "MIT" ]

1

2021-04-17T16:19:19.000Z

Service/migrations/0013_auto_20191229_1254.py

co3oing/Bixbar-Backend

f69e0e96ea4bcc0c57000fdf1e24c66ead59df3f

[ "MIT" ]

null

Service/migrations/0013_auto_20191229_1254.py

co3oing/Bixbar-Backend

f69e0e96ea4bcc0c57000fdf1e24c66ead59df3f

[ "MIT" ]

1

2021-04-15T14:56:49.000Z

# Generated by Django 2.2.4 on 2019-12-29 03:54 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('bixbar', '0012_auto_20191229_1250'), ] operations = [ migrations.RenameField( model_name='cocktail', old_name='profile', new_name='baseSpirit', ), migrations.RenameField( model_name='cocktail', old_name='Garnish', new_name='garnish', ), migrations.RenameField( model_name='cocktail', old_name='Glass', new_name='glass', ), migrations.RenameField( model_name='cocktail', old_name='riquor', new_name='liquor', ), migrations.RenameField( model_name='cocktail', old_name='riquorml', new_name='liquorml', ), migrations.AddField( model_name='cocktail', name='brands', field=models.CharField(max_length=200, null=True), ), migrations.AddField( model_name='cocktail', name='cocktailType', field=models.CharField(max_length=200, null=True), ), migrations.AddField( model_name='cocktail', name='difficulty', field=models.CharField(max_length=200, null=True), ), migrations.AddField( model_name='cocktail', name='flavor', field=models.CharField(max_length=200, null=True), ), migrations.AddField( model_name='cocktail', name='hours', field=models.CharField(max_length=200, null=True), ), migrations.AddField( model_name='cocktail', name='preparation', field=models.CharField(max_length=200, null=True), ), migrations.AddField( model_name='cocktail', name='served', field=models.CharField(max_length=200, null=True), ), migrations.AddField( model_name='cocktail', name='strength', field=models.CharField(max_length=200, null=True), ), ]

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255025f5a8dc42f20eed8dba2ef3c7c4e14a96f4

31

py

Python

esmvaltool/cmor/_fixes/obs4mips/__init__.py

yifatdzigan/ESMValTool

83320b0e0b24ddde965599961bb80428e180a731

[ "Apache-2.0" ]

26

2019-06-07T07:50:07.000Z

2022-03-22T21:04:01.000Z

esmvaltool/cmor/_fixes/obs4mips/__init__.py

yifatdzigan/ESMValTool

83320b0e0b24ddde965599961bb80428e180a731

[ "Apache-2.0" ]

1,370

2019-06-06T09:03:07.000Z

2022-03-31T04:37:20.000Z

esmvaltool/cmor/_fixes/obs4mips/__init__.py

yifatdzigan/ESMValTool

83320b0e0b24ddde965599961bb80428e180a731

[ "Apache-2.0" ]

26

2019-07-03T13:08:48.000Z

2022-03-02T16:08:47.000Z

"""Fixes for obs4mips data."""

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0

1

0

null

0

1

0

4

2553fa6a7db412f77adaac7c61b075ffc291008d

704

py

Python

setup/setup.py

Elijah-glitch/The-TikTok-Bot

48a3b1b3b9df9b4f6b4cb1fb1f692fd3f2029bc9

[ "MIT" ]

56

2020-08-13T12:10:13.000Z

2022-03-25T23:42:22.000Z

setup/setup.py

Elijah-glitch/The-TikTok-Bot

48a3b1b3b9df9b4f6b4cb1fb1f692fd3f2029bc9

[ "MIT" ]

13

2020-08-13T14:01:52.000Z

2021-07-19T19:11:35.000Z

setup/setup.py

Elijah-glitch/The-TikTok-Bot

48a3b1b3b9df9b4f6b4cb1fb1f692fd3f2029bc9

[ "MIT" ]

15

2020-09-22T01:43:25.000Z

2022-03-08T19:05:17.000Z

import os def installLibraries(): print(f'Installing Libraries....') os.system('pip install requests') os.system('pip install threading') os.system('pip install bs4') os.system('pip install time') os.system('pip install math') os.system('pip install selenium') os.system('pip install Keys') os.system('pip install ActionsChains') os.system('pip install sys') os.system('pip install multiprocessing') os.system('pip install logging') os.system('pip install glob') if __name__ == "__main__": installLibraries() print(f'\n'+'All Required Libraries are now installed.')

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0.605114

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704

5.160494

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0.315789

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0

0.111111

0

null

1

0

null

0

1

0

4

c2631ffe6eba42cb522892ab740ab3bf0334d921

201

py

Python

gpio_test.py

eldon/netSignalGauge

11f97d09f20decab41ffd7b2bbbc33fb14b7650e

[ "Apache-2.0" ]

null

gpio_test.py

eldon/netSignalGauge

11f97d09f20decab41ffd7b2bbbc33fb14b7650e

[ "Apache-2.0" ]

null

gpio_test.py

eldon/netSignalGauge

11f97d09f20decab41ffd7b2bbbc33fb14b7650e

[ "Apache-2.0" ]

null

#!/usr/bin/python import CHIP_IO.GPIO as GPIO try: GPIO.setup("CSID7", GPIO.OUT) GPIO.output("CSID7", GPIO.HIGH) raw_input() GPIO.output("CSID7", GPIO.LOW) finally: GPIO.cleanup()

18.272727

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201

4.3

0.633333

0.209302

0.232558

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0

0.018182

0.179104

201

10

36

20.1

0.763636

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0.081522

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1

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true

0

0.125

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0.125

0

null

1

0

null

0

1

0

4

c27dcc848d8e5b695cb2bc21dcc0266d229f6924

587

py

Python

Models/import_batches.py

wboughattas/UCI-CIFAR-Various-Analyses

e9cd6454653718cd93992c2c3803f408918f8663

[ "MIT" ]

null

Models/import_batches.py

wboughattas/UCI-CIFAR-Various-Analyses

e9cd6454653718cd93992c2c3803f408918f8663

[ "MIT" ]

null

Models/import_batches.py

wboughattas/UCI-CIFAR-Various-Analyses

e9cd6454653718cd93992c2c3803f408918f8663

[ "MIT" ]

null

import os import pickle def import_batches(): dataset = [unpickle_file('batches.meta'), unpickle_file('data_batch_1'), unpickle_file('data_batch_2'), unpickle_file('data_batch_3'), unpickle_file('data_batch_4'), unpickle_file('data_batch_5'), unpickle_file('test_batch')] return dataset def unpickle_file(filename): data_path = os.path.join(os.path.abspath(os.path.dirname(os.path.dirname(__file__))), 'Data', 'CIFAR-10', filename) with open(data_path, 'rb') as file: data = pickle.load(file, encoding='bytes') return data

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81

587

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0.170358

587

18

120

32.611111

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false

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0

null

1

0

null

0

1

0

4

c2a9efb832770fad79d2354dbe1b90c7b55b7a4e

17

py

Python

platzi/Prueba.py

diegosish/Introduction-Python

b40dde602c0b5ca4ec4f6b13303ad6d3831fab73

[ "MIT" ]

null

platzi/Prueba.py

diegosish/Introduction-Python

b40dde602c0b5ca4ec4f6b13303ad6d3831fab73

[ "MIT" ]

null

platzi/Prueba.py

diegosish/Introduction-Python

b40dde602c0b5ca4ec4f6b13303ad6d3831fab73

[ "MIT" ]

null

print('Hola') x=2

8.5

13

0.647059

4

17

2.75

1

0

0.0625

0.058824

17

2

14

8.5

0.625

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0.222222

0

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0

0.5

1

0

null

0

1

0

null

0

1

0

4

c2ac4ba54426e3d29a2311a80b8fcfdd32ddfed5

96

py

Python

media_tools/apps.py

zeyneloz/django-media-tools

b961739541e42581729f7f2cff4b538ecb73658e

[ "MIT" ]

3

2018-03-12T12:46:42.000Z

2018-03-14T12:32:36.000Z

media_tools/apps.py

zeyneloz/django-media-tools

b961739541e42581729f7f2cff4b538ecb73658e

[ "MIT" ]

null

media_tools/apps.py

zeyneloz/django-media-tools

b961739541e42581729f7f2cff4b538ecb73658e

[ "MIT" ]

null

from django.apps import AppConfig class MediaToolsConfig(AppConfig): name = 'media_tools'

16

34

0.770833

11

96

6.636364

0.909091

0

0.15625

96

5

35

19.2

0.901235

0

0.114583

0

1

0

false

0

0.333333

0

1

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1

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null

0

1

0

null

0

1

0

1

0

4

c2b9e776c0b89dc171cb23d05ab33656c42abb3d

169

py

Python

dp_tornado/engine/template/example/config/server/version.py

donghak-shin/dp-tornado

095bb293661af35cce5f917d8a2228d273489496

[ "MIT" ]

18

2015-04-07T14:28:39.000Z

2020-02-08T14:03:38.000Z

dp_tornado/engine/template/example/config/server/version.py

donghak-shin/dp-tornado

095bb293661af35cce5f917d8a2228d273489496

[ "MIT" ]

7

2016-10-05T05:14:06.000Z

2021-05-20T02:07:22.000Z

dp_tornado/engine/template/example/config/server/version.py

donghak-shin/dp-tornado

095bb293661af35cce5f917d8a2228d273489496

[ "MIT" ]

11

2015-12-15T09:49:39.000Z

2021-09-06T18:38:21.000Z

# -*- coding: utf-8 -*- from dp_tornado.engine.config import Config as dpConfig class VersionConfig(dpConfig): def index(self): self.conf.master = '1.0'

16.9

55

0.662722

23

169

4.826087

0.869565

0

0.022222

0.201183

169

9

56

18.777778

0.8

0.12426

0

0.020548

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c2c6a4c261a5c2707c287d61583322a995660fe6

203

py

Python

API PILOT 1/test.py

EricChiquitoG/NegotiationEngine

91075f9b085d54f008b84aaa6fcc2dd20eb88e71

[ "MIT" ]

null

API PILOT 1/test.py

EricChiquitoG/NegotiationEngine

91075f9b085d54f008b84aaa6fcc2dd20eb88e71

[ "MIT" ]

null

API PILOT 1/test.py

EricChiquitoG/NegotiationEngine

91075f9b085d54f008b84aaa6fcc2dd20eb88e71

[ "MIT" ]

1

2022-01-17T15:08:42.000Z

from bson import ObjectId dicti= {'sellersign':{'val':['ok']}} dicti['sellersign']['val'][0]='camaron' dict2={'sellersign':22,'camaron':2345} print(dict2) dict2['eric']=223 print(dict2['eric'])

22.555556

40

0.650246

26

203

5.076923

0.615385

0.227273

0.272727

0

0.076503

0.098522

203

9

41

22.555556

0.644809

0

0.306122

0

1

0

false

0

0.142857

0

0.142857

0.285714

1

0

null

1

0

1

0

null

0

4

c2c856caec2ab046cea6589e5b85e1b86d44f95c

192

py

Python

joplin/pages/news_page/factories.py

cityofaustin/joplin

01424e46993e9b1c8e57391d6b7d9448f31d596b

[ "MIT" ]

15

2018-09-27T07:36:30.000Z

2021-08-03T16:01:21.000Z

joplin/pages/news_page/factories.py

cityofaustin/joplin

01424e46993e9b1c8e57391d6b7d9448f31d596b

[ "MIT" ]

183

2017-11-16T23:30:47.000Z

2020-12-18T21:43:36.000Z

joplin/pages/news_page/factories.py

cityofaustin/joplin

01424e46993e9b1c8e57391d6b7d9448f31d596b

[ "MIT" ]

12

2017-12-12T22:48:05.000Z

2021-03-01T18:01:24.000Z

from pages.news_page.models import NewsPage from pages.topic_page.factories import JanisBasePageFactory class NewsPageFactory(JanisBasePageFactory): class Meta: model = NewsPage

24

59

0.802083

21

192

7.238095

0.666667

0.118421

0

0.151042

192

7

60

27.428571

0.932515

0

1

0

false

0

0.4

0

0.8

0

1

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null

0

1

0

null

0

1

0

1

0

4

6c01d583414b8e4c2c7c2942ebeb7ef09622d490

503

py

Python

code/experience.py

rahul-dhavalikar/dqn-predator-prey-dynamics

58c26d71c87bcfaaf729d1f710b9c4458ca4f473

[ "MIT" ]

1

2021-12-15T10:58:09.000Z

code/experience.py

rahul-dhavalikar/dqn-predator-prey-dynamics

58c26d71c87bcfaaf729d1f710b9c4458ca4f473

[ "MIT" ]

null

code/experience.py

rahul-dhavalikar/dqn-predator-prey-dynamics

58c26d71c87bcfaaf729d1f710b9c4458ca4f473

[ "MIT" ]

2

2018-10-04T02:39:23.000Z

2021-12-15T10:59:16.000Z

import numpy as np import random class experience_buffer(): def __init__(self, buffer_size=50000): self.buffer = [] self.buffer_size = buffer_size def add(self, experience): if len(self.buffer) + len(experience) >= self.buffer_size: self.buffer[0:(len(experience) + len(self.buffer)) - self.buffer_size] = [] self.buffer.extend(experience) def sample(self, size): return np.reshape(np.array(random.sample(self.buffer, size)), [size, 5])

33.533333

87

0.650099

66

503

4.80303

0.348485

0.315457

0.22082

0.126183

0.258675

0

0.017722

0.214712

503

15

88

33.533333

0.78481

0

1

0.25

false

0

0.166667

0.083333

0.583333

0

null

1

0

null

0

1

0

1

0

4

6c1308acaa19db2927830360c4a884dba13f7741

279

py

Python

welleng/__init__.py

kwinkunks/welleng

d0669b9b5164671ff4861a4efd33666c3fc9758f

[ "Apache-2.0" ]

1

2020-12-26T14:42:51.000Z

welleng/__init__.py

kwinkunks/welleng

d0669b9b5164671ff4861a4efd33666c3fc9758f

[ "Apache-2.0" ]

null

welleng/__init__.py

kwinkunks/welleng

d0669b9b5164671ff4861a4efd33666c3fc9758f

[ "Apache-2.0" ]

null

import welleng.clearance import welleng.io import welleng.error import welleng.survey import welleng.utils import welleng.mesh import welleng.visual import welleng.version import welleng.errors.iscwsa_mwd import welleng.exchange.wbp import welleng.target import welleng.connector

23.25

32

0.867384

39

279

6.179487

0.435897

0.647303

0

0.082437

279

12

33

23.25

0.941406

0

1

0

true

0

1

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1

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null

1

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null

0

1

0

1

0

1

0

4

6c1a67a5a8f9f1a3b8679d80b3dd08492a05c7de

99

py

Python

app_reRepeat/apps.py

bbajcetic/reRepeat

c5f6e2534a7baf20f9fc0345f84598877c93b869

[ "BSD-3-Clause" ]

null

app_reRepeat/apps.py

bbajcetic/reRepeat

c5f6e2534a7baf20f9fc0345f84598877c93b869

[ "BSD-3-Clause" ]

null

app_reRepeat/apps.py

bbajcetic/reRepeat

c5f6e2534a7baf20f9fc0345f84598877c93b869

[ "BSD-3-Clause" ]

null

from django.apps import AppConfig class app_reRepeatConfig(AppConfig): name = 'app_reRepeat'

16.5

36

0.777778

12

99

6.25

0.833333

0

0.151515

99

5

37

19.8

0.892857

0

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1

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false

0

0.333333

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1

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null

0

1

0

1

0

4

6c54b53ad527b7c644c39c4602a9fdcd45aab622

10,303

py

Python

archives/model_prep.py

d-v-dlee/super_liga_xg

e3054cadf00755a347d31ce335c567db91e433a7

[ "MIT" ]

3

2019-01-24T07:30:45.000Z

2019-11-28T15:52:44.000Z

archives/model_prep.py

d-v-dlee/super_liga_xg

e3054cadf00755a347d31ce335c567db91e433a7

[ "MIT" ]

null

archives/model_prep.py

d-v-dlee/super_liga_xg

e3054cadf00755a347d31ce335c567db91e433a7

[ "MIT" ]

null

import numpy as np import pandas as pd from sklearn.metrics import log_loss import matplotlib.pyplot as plt # def manual_train_split(df, train_sample_size=80): # """input dataframe of shots, return train_test_split by game_id # input dataframe -> get unique game ids -> take sample of 80/159 (may be some repeasts from # np.random.choice) # first for loop: take those game ids and then add them to games_to_train_on # second for loop: drop those games from the master 'possible_games' list # return dataframe of shots (in specific games) to train model on # -------------------------------------------------------------------- # holdout_test, train = manual_train_split(shots_df) # returns: game_ids to hold out/predict on, list to train on # """ # possible_games = list(df['game_id'].unique()) # game_numbers = len(possible_games) # games_to_sample = np.random.choice(game_numbers, train_sample_size) # games_to_train_on = [] # for i in games_to_sample: # games_to_train_on.append(possible_games[i]) # for game in games_to_train_on: # if game in possible_games: # possible_games.remove(game) # return possible_games, games_to_train_on # def manual_test_split(possible_games, test_sample_size=50): # """input: possible_games which is game_ids minus the game_ids used for training, # test_sample_size is 50 games to predict on, usually less due to random sample # output: list of games_ids to predict and remaining holdout set # first for loop: takes random sample, then corresponding game_ids in list (by position) # second for loop: removes those sample games from the possible_games list, leaving final holdout_list""" # games_left = len(possible_games) # games_to_sample = np.random.choice(games_left, test_sample_size) # games_to_predict = [] # for i in games_to_sample: # games_to_predict.append(possible_games[i]) # for game in games_to_predict: # if game in possible_games: # possible_games.remove(game) # holdout_games = possible_games.copy() # return holdout_games, games_to_predict # def create_training_df(df, train_sample_size=90): # """input total shot df and return training data split into train_data (x) and train_y (y) # train_sample_size is the number of games (will sample 90 with some possible repeats) # ex: train_data, train_y, indices, hold_test = training_df(shots_df) # """ # rf_columns = ['player_id', 'shot_distance', 'shot_angle', 'assisted_shot', 'is_penalty_attempt'] # hold_test, train = manual_train_split(df) # shots_to_train_on = df[df['game_id'].isin(np.array(train))].copy() # train_data = shots_to_train_on[rf_columns].astype(float) # train_y = shots_to_train_on['is_goal'].astype(float) # indices = shots_to_train_on.index.values # return train_data, train_y, indices, hold_test # def create_test_df(df, hold_test): # """input df, and previous hold_test from training_df to return # test_data and test_y to be run through rf""" # rf_columns = ['player_id', 'shot_distance', 'shot_angle', 'assisted_shot', 'is_penalty_attempt'] # holdout, test = manual_test_split(hold_test) # shots_to_predict = df[df['game_id'].isin(np.array(test))].copy() # test_data = shots_to_predict[rf_columns].astype(float) # test_y = shots_to_predict['is_goal'].astype(float) # indices1 = shots_to_predict.index.values # return test_data, test_y, indices1, holdout, test # def use_holdout_df(df, holdout): # """insert df and holdout (game_ids not yet predicted) and return df to predict on""" # rf_columns = ['player_id', 'shot_distance', 'shot_angle', 'assisted_shot', 'is_penalty_attempt'] # shots_to_predict = df[df['game_id'].isin(np.array(holdout))].copy() # test_data = shots_to_predict[rf_columns].astype(float) # test_y = shots_to_predict['is_goal'].astype(float) # indices1 = shots_to_predict.index.values # return test_data, test_y, indices1 # def create_xG_df(test_data, test_y, model_predictions): # """create new dataframe with predicted probas and actual goals for predicted shots""" # df = pd.DataFrame(test_data) # df['is_goal'] = test_y # df['xG'] = model_predictions[:, 1] # df['xA'] = df['assisted_shot'] * df['xG'] # return df # def create_hypothetical_df(test_data, model_predictions): # """create new dataframe with predicted probas and actual goals for predicted shots""" # df = pd.DataFrame(test_data) # df['xG'] = model_predictions[:, 1] # return df # def create_summed_xG_df(df): # """input xg_df and return dataframe of summed xg and xa for each player""" # unique_players = df['player_id'].unique() # contributions = [] # for player in unique_players: # xgsum = round(df[df['player_id'] == player]['xG'].sum(), 2) # xasum = round(df[df['player_id'] == player]['xA'].sum(), 2) # xgxasum = round(xgsum + xasum, 2) # goals = df[df['player_id'] == player]['is_goal'].sum() # pen_attempts = df[df['player_id'] == player]['is_penalty_attempt'].sum() # contributions.append([player, xgsum, xasum, xgxasum, pen_attempts, goals]) # by_xG = sorted(contributions, key=lambda x: x[1], reverse=True) # contribution_df = pd.DataFrame(by_xG, columns=['player_id', 'total_xG', 'total_xA', 'total_xG+xA', 'pen_attempts', 'goals']) # return contribution_df # ### moving from shots to players and minutes # def create_test_min_df(player_df, test): # """input player_df and the list of games that were predited on to return # the players and minuts played in the predicted games""" # min_df = player_df[player_df['game_id'].isin(np.array(test))].copy() # players = min_df['player_id'].unique() # player_minutes = [] # for player in players: # total_minutes = min_df[min_df['player_id'] == player]['minutes_played'].sum() # name = min_df[min_df['player_id'] == player]['name'].iloc[0] # player_minutes.append([player, total_minutes, name]) # player_minutes_df = pd.DataFrame(player_minutes, columns=['player_id', 'total_minutes_played', 'player_name']) # return player_minutes_df # def merged_dataframes(player_df, contribution_df): # columns = ['player_name', 'player_id', 'total_xG', 'total_xA', 'total_xG+xA', 'goals', 'xG+xA/90', 'total_minutes_played'] # xg_min = pd.merge(contribution_df, player_df, on='player_id', how='outer') # xg_min['xG+xA/90'] = xg_min['total_xG+xA'].copy() / (xg_min['total_minutes_played'] / 90) # xg_final = xg_min[columns] # return xg_final # def player_minutes_total(players_minutes_df): # """input player_minutes_df from create_test_min_df so that each row is a unique player""" # players = players_minutes_df['player_id'].unique() # player_minutes = [] # for player in players: # total_minutes = players_minutes_df[players_minutes_df['player_id'] == player]['minutes_played'].sum() # name = players_minutes_df[players_minutes_df['player_id'] == player]['name'].iloc[0] # squad_num = players_minutes_df[players_minutes_df['player_id'] == player]['squad_number'].iloc[0] # club_brev = players_minutes_df[players_minutes_df['player_id'] == player]['club_brev'].iloc[0] # position_id = players_minutes_df[players_minutes_df['player_id'] == player]['position_id'].iloc[0] # player_minutes.append([player, total_minutes, name, squad_num, club_brev, position_id]) # summed_player_min = pd.DataFrame(player_minutes, columns=['player_id', 'total_minutes_played', 'player_name', 'squad_num', 'club_brev', 'position_id']) # return summed_player_min # def create_rf_prep(df): # """input df, return the appropriate columns to be run through rf""" # rf_columns = ['shot_distance', 'shot_angle', 'assisted_shot'] # return df[rf_columns].astype(float) # #use to tune classifiers # def stage_score_plot(estimator, X_train, y_train, X_test, y_test): # ''' # Parameters: estimator: GradientBoostingClassifier or xgBoostClassifier # X_train: pandas dataframe # y_train: 1d panda dataframe # X_test: pandas dataframe # y_test: 1d panda dataframe # Returns: A plot of the number of iterations vs the log loss for the model for # both the training set and test set. # ''' # # fit estimator # estimator.fit(X_train, y_train) # train_logloss_at_stages = [] # test_logloss_at_stages = [] # # iterate through all stages for test and train and record log loss lists # for y1, y2 in zip(estimator.staged_predict_proba(X_train), estimator.staged_predict_proba(X_test)): # train_logloss = log_loss(y_train, y1) # train_logloss_at_stages.append(train_logloss) # test_logloss = log_loss(y_test, y2) # test_logloss_at_stages.append(test_logloss) # # find the # of trees at which test error is the lowest # lowest_test_error = np.min(test_logloss_at_stages) # num_trees_lowest_test_error = np.argmin(test_logloss_at_stages) # # create xs in order to plot. each x represents n_estimators. # xs = range(0, len(test_logloss_at_stages)) # fig, ax = plt.subplots(figsize=(8, 6)) # ax.plot(xs, train_logloss_at_stages, # label="{} Train".format(estimator.__class__.__name__)) # ax.plot(xs, test_logloss_at_stages, # label="{} Test".format(estimator.__class__.__name__)) # ax.axvline(num_trees_lowest_test_error) # ax.legend() # return lowest_test_error, num_trees_lowest_test_error # # print(f'lowest test error(log loss): {lowest_test_error}') # # print(f'num_trees at lowest test error: {num_trees_lowest_test_error}') # # example of how to use: # # fig, ax = plt.subplots(figsize=(12, 8)) # # stage_score_plot(gdbr_model, X_train, y_train, X_test, y_test) # # stage_score_plot(gdbr_model_2, X_train, y_train, X_test, y_test) # # ax.legend() # # plt.show() # def add_xg_shotdf(shot_df, model_pred): # """add predicted xG to shot_df""" # shot_df['xG'] = model_pred[:, 1]

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6c625f538953f61bc13a2781a6d48b73d2b046ed

218

py

Python

terrascript/resource/rancher/rke.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/resource/rancher/rke.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/resource/rancher/rke.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/resource/rancher/rke.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:25:51 UTC) import terrascript class rke_cluster(terrascript.Resource): pass __all__ = [ "rke_cluster", ]

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6663d3d9b9f03b3bf53d7aa80800270386bed1a9

11,855

py

Python

tongyou/tongyou/apps/carts/views.py

hellowangziyi/TongYou-Web

82e9e89e0dcced08e921aeb15d55e7c449af30d1

[ "MIT" ]

null

tongyou/tongyou/apps/carts/views.py

hellowangziyi/TongYou-Web

82e9e89e0dcced08e921aeb15d55e7c449af30d1

[ "MIT" ]

null

tongyou/tongyou/apps/carts/views.py

hellowangziyi/TongYou-Web

82e9e89e0dcced08e921aeb15d55e7c449af30d1

[ "MIT" ]

null

from django.shortcuts import render from django.views import View import json, base64, pickle from django import http from django_redis import get_redis_connection from django.shortcuts import render from goods.models import SKU from tongyou.utils.response_code import RETCODE # Create your views here. class CartsView(View): """购物车""" def post(self, request): """添加购物车""" # 接收参数 json_dict = json.loads(request.body) sku_id = json_dict.get('sku_id') count = json_dict.get('count') selected = json_dict.get('selected', True) # 校验参数 if all([sku_id, count]) is False: return http.HttpResponseForbidden('缺少必传参数') try: sku = SKU.objects.get(id=sku_id, is_launched=True) except Exception as e: return http.HttpResponseForbidden('sku_id不存在') try: count = int(count) except Exception as e: return http.HttpResponseForbidden('参数count有误') if isinstance(selected, bool) is False: return http.HttpResponseForbidden('参数selected有误') user = request.user # 判断用户是否登录 if user.is_authenticated: # 登录用户，操作redis购物车 """ hash:{sku_id:count} map:{sku_id} """ redis_conn = get_redis_connection('carts') pl = redis_conn.pipeline() # 新增购物车数据 pl.hincrby('carts_%s' % user.id, sku_id, count) # 新增选中的状态 if selected: pl.sadd('selected_%s' % user.id, sku_id) # 执行管道 pl.execute() # 响应结果 return http.JsonResponse({'code': RETCODE.OK, 'errmsg': '添加购物车成功'}) else: # 未登录用户，操作cookie购物车 cart_str = request.COOKIES.get('carts') if cart_str: # 将cart_str转成bytes,再将bytes转成base64的bytes,最后将bytes转字典 bytes_str = cart_str.encode() bytes_un = base64.b64decode(bytes_str) cart_dict = pickle.loads(bytes_un) if sku_id in cart_dict: origin_count = cart_dict[sku_id]['count'] count += origin_count else: # 用户从没有操作过cookie购物车 cart_dict = {} # 更新购物车 cart_dict[sku_id] = {'count': count, 'selected': selected} # 将字典重新转字符串 bytes_un = pickle.dumps(cart_dict) bytes_str = base64.b64encode(bytes_un) cart_str = bytes_str.decode() # 创建响应对象 response = http.JsonResponse({'code': RETCODE.OK, 'errmsg': '添加购物车成功'}) # 响应结果并将购物车数据写入到cookie response.set_cookie('carts', cart_str) return response def get(self, request): """展示购物车""" user = request.user # 判断用户是否登录 if user.is_authenticated: # 登录用户，操作redis购物车 redis_conn = get_redis_connection('carts') # 获取redis.hash中的购物车数据 redis_cart = redis_conn.hgetall('carts_%s' % user.id) # 获取redis.map中的选中状态 cart_selected = redis_conn.smembers('selected_%s' % user.id) # 将redis中的数据包装成大字典 cart_dict = {} for sku_id in redis_cart: cart_dict[int(sku_id)] = { 'count': int(redis_cart[sku_id]), 'selected': sku_id in cart_selected } else: # 未登录用户，查询cookie购物车 cart_str = request.COOKIES.get('carts') if cart_str: bytes_str = cart_str.encode() bytes_un = base64.b64decode(bytes_str) cart_dict = pickle.loads(bytes_un) else: return render(request, 'cart.html') # 查询sku模型 sku_qs = SKU.objects.filter(id__in=cart_dict.keys()) sku_list = [] for sku_model in sku_qs: count = cart_dict[sku_model.id]['count'] sku_list.append({ 'id': sku_model.id, 'name': sku_model.name, 'default_image_url': sku_model.default_image.url, 'price': str(sku_model.price), 'count': count, 'amount': str(sku_model.price * count), 'selected': str(cart_dict[sku_model.id]['selected']) }) return render(request, 'cart.html', {'cart_skus': sku_list}) def put(self, request): """修改购物车""" # 接收参数 json_dict = json.loads(request.body) sku_id = json_dict.get('sku_id') count = json_dict.get('count') selected = json_dict.get('selected') # 校验参数 if not all([sku_id, count]): return http.HttpResponseForbidden('缺少必传参数') try: sku = SKU.objects.get(id=sku_id) except Exception as e: return http.HttpResponseForbidden('商品sku_id不存在') try: count = int(count) except Exception as e: return http.HttpResponseForbidden('参数count有误') if isinstance(selected, bool) is False: return http.HttpResponseForbidden('参数selected有误') cart_sku = { 'id': sku_id, 'name': sku.name, 'count': count, 'selected': selected, 'price': sku.price, 'amount': sku.price * count, 'default_image_url': sku.default_image.url } # 创建响应对象 response = http.JsonResponse({'code': RETCODE.OK, 'errmsg': 'OK', 'cart_sku': cart_sku}) user = request.user # 判断用户是否登录 if user.is_authenticated: # 登录用户，操作redis购物车 redis_conn = get_redis_connection('carts') pl = redis_conn.pipeline() pl.hset('carts_%s' % user.id, sku_id, count) if selected: pl.sadd('selected_%s' % user.id, sku_id) else: pl.srem('selected_%s' % user.id, sku_id) pl.execute() else: # 未登录用户，修改cookie购物车 cart_str = request.COOKIES.get('carts') if cart_str: # 将cart_str转成bytes,再将bytes转成base64的bytes,最后将bytes转字典 bytes_str = cart_str.encode() bytes_un = base64.b64decode(bytes_str) cart_dict = pickle.loads(bytes_un) if sku_id in cart_dict: origin_count = cart_dict[sku_id]['count'] count += origin_count else: return http.JsonResponse({'code': RETCODE.DBERR, 'errmsg': 'cookie数据没有获取到'}) cart_dict[sku_id] = {'count': count, 'selected': selected} # 将字典重新转字符串 bytes_un = pickle.dumps(cart_dict) bytes_str = base64.b64encode(bytes_un) cart_str = bytes_str.decode() # 响应结果并将购物车数据写入到cookie response.set_cookie('carts', cart_str) return response def delete(self, request): """删除购物车""" # 接收和校验参数 json_dict = json.loads(request.body) sku_id = json_dict.get('sku_id') try: sku = SKU.objects.get(id=sku_id) except Exception as e: return http.HttpResponseForbidden('sku_id不存在') user = request.user # 判断用户是否登录 if user.is_authenticated: # 登录用户，删除redis购物车 redis_conn = get_redis_connection('carts') pl = redis_conn.pipeline() pl.hdel('carts_%s' % user.id, sku_id) pl.srem('selected_%s' % user.id, sku_id) pl.execute() return http.JsonResponse({'code': RETCODE.OK, 'errmsg': "删除购物车成功"}) else: # 未登录用户，删除cookie购物车 cart_str = request.COOKIES.get('carts') if cart_str: bytes_str = cart_str.encode() bytes_un = base64.b64decode(bytes_str) cart_dict = pickle.loads(bytes_un) else: return http.JsonResponse({'code': RETCODE.DBERR, 'errmsg': 'cookie数据没获取到'}) if sku_id in cart_dict: del cart_dict[sku_id] # 判断当前字典是为空,如果为空将cookie删除 response = http.JsonResponse({'code': RETCODE.OK, 'errmsg': "删除购物车成功"}) if not cart_dict: response.delete_cookie('carts') else: # 将字典重新转字符串 bytes_un = pickle.dumps(cart_dict) bytes_str = base64.b64encode(bytes_un) cart_str = bytes_str.decode() response.set_cookie('carts', cart_str) return response class CartsSelectAllView(View): """全选购物车""" def put(self, request): # 接收参数 json_dict = json.loads(request.body.decode()) selected = json_dict.get('selected', True) # 校验参数 if selected: if not isinstance(selected, bool): return http.HttpResponseForbidden('参数selected有误') # 判断用户是否登录 user = request.user if user.is_authenticated: # 登录用户，操作redis购物车 redis_conn = get_redis_connection('carts') sku_ids = redis_conn.hgetall('carts_%s' % user.id) sku_id_list = sku_ids.keys() if selected: # 全选 for sku_id in sku_id_list: redis_conn.sadd('selected_%s' % user.id, sku_id) else: # 取消全选 redis_conn.delete('selected_%s' % user.id) return http.JsonResponse({'code': RETCODE.OK, 'errmsg': 'OK'}) else: # 未登录用户，操作cookie购物车 cart_str = request.COOKIES.get('carts') if not cart_str: return http.JsonResponse({'code': RETCODE.DBERR, 'errmsg': 'cookie没有获取到'}) # 将cart_str转成bytes,再将bytes转成base64的bytes,最后将bytes转字典 bytes_str = cart_str.encode() bytes_un = base64.b64decode(bytes_str) cart_dict = pickle.loads(bytes_un) # 遍历cookie购物车大字典,将内部的每一个selected修改为True 或 False for sku_id in cart_dict: cart_dict[sku_id]['selected'] = selected # 将字典重新转字符串 bytes_un = pickle.dumps(cart_dict) bytes_str = base64.b64encode(bytes_un) cart_str = bytes_str.decode() response = http.JsonResponse({'code': RETCODE.OK, 'errmsg': 'OK'}) response.set_cookie('carts', cart_str) return response class CartsSimpleView(View): """商品页面右上角购物车""" def get(self, request): """展示商品页面简单购物车""" user = request.user # 判断用户是否登录 if user.is_authenticated: # 登录用户，操作redis购物车 redis_conn = get_redis_connection('carts') # 获取redis.hash中的购物车数据 redis_cart = redis_conn.hgetall('carts_%s' % user.id) # 将redis中的数据包装成大字典 cart_dict = {} for sku_id in redis_cart: cart_dict[int(sku_id)] = { 'count': int(redis_cart[sku_id]), } else: # 未登录用户，查询cookie购物车 cart_str = request.COOKIES.get('carts') if cart_str: bytes_str = cart_str.encode() bytes_un = base64.b64decode(bytes_str) cart_dict = pickle.loads(bytes_un) else: cart_dict = {} # 查询sku模型 sku_qs = SKU.objects.filter(id__in=cart_dict.keys()) sku_list = [] for sku_model in sku_qs: sku_list.append({ 'id': sku_model.id, 'name': sku_model.name, 'default_image_url': sku_model.default_image.url, 'count': cart_dict[sku_model.id]['count'] }) return http.JsonResponse({'code': RETCODE.OK, 'errmsg': 'OK', 'cart_skus': sku_list})

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1

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null

0

4

668289b485829df79ee76495e17996966433525e

102

py

Python

animal/admin.py

rankrh/soli

8f19945a175106064591d09a53d07fcbfa26b7da

[ "MIT" ]

null

animal/admin.py

rankrh/soli

8f19945a175106064591d09a53d07fcbfa26b7da

[ "MIT" ]

null

animal/admin.py

rankrh/soli

8f19945a175106064591d09a53d07fcbfa26b7da

[ "MIT" ]

2

2019-09-07T15:10:14.000Z

2020-09-04T01:51:19.000Z

from django.contrib import admin from animal.models.animal import Animal admin.site.register(Animal)

20.4

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102

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4

66e1ff079b6e42165164db21eb7de184b3d79c61

348

py

Python

src/IsingRegisterAllocator/get_solution_limited_unit.py

kumagaimasahito/IsingRegisterAllocator

7d20f56ee035fcaff456ab7641e51bad4b68144f

[ "MIT" ]

1

2021-05-04T06:56:42.000Z

src/IsingRegisterAllocator/get_solution_limited_unit.py

kumagaimasahito/IsingRegisterAllocator

7d20f56ee035fcaff456ab7641e51bad4b68144f

[ "MIT" ]

1

2021-03-31T14:56:27.000Z

src/IsingRegisterAllocator/get_solution_limited_unit.py

kumagaimasahito/IsingRegisterAllocator

7d20f56ee035fcaff456ab7641e51bad4b68144f

[ "MIT" ]

null

from .util import split_unit, get_qubo from .util.solve_qubo import by_amplify as solve_qubo def get_solution_limited_unit(interference, num_registers, limitation, token): response = get_qubo.by_amplify_limited(interference, num_registers, limitation) solution = solve_qubo(response["qubits"], response["model"], token) return solution

49.714286

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4

dd00ffffca73cd739e6b37faee8d363216c81ef9

167

py

Python

main.py

szoz/fast-boardgames

76d6f6135a4d872c03535f13e547ea2deb4f338a

[ "MIT" ]

null

main.py

szoz/fast-boardgames

76d6f6135a4d872c03535f13e547ea2deb4f338a

[ "MIT" ]

null

main.py

szoz/fast-boardgames

76d6f6135a4d872c03535f13e547ea2deb4f338a

[ "MIT" ]

null

from fastapi import FastAPI from routers import misc, items app = FastAPI() app.include_router(misc.router, tags=['miscellaneous']) app.include_router(items.router)

20.875

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null

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4

dd5399c33dbbbc3463d61ee55b0c03c39b727c67

154

py

Python

django_google_optimize/apps.py

adinhodovic/django-google-optimize

d6849b1563a9ba98bca40213f84b911a828df4fd

[ "MIT" ]

37

2019-12-06T11:36:21.000Z

2022-03-16T14:10:17.000Z

django_google_optimize/apps.py

adinhodovic/django-google-optimize

d6849b1563a9ba98bca40213f84b911a828df4fd

[ "MIT" ]

25

2020-01-08T19:58:42.000Z

2021-08-25T16:20:22.000Z

django_google_optimize/apps.py

adinhodovic/django-google-optimize

d6849b1563a9ba98bca40213f84b911a828df4fd

[ "MIT" ]

5

2020-01-07T12:41:38.000Z

2021-12-23T08:05:27.000Z

from django.apps import AppConfig class DjangoGoogleOptimizeConfig(AppConfig): name = "django_google_optimize" verbose_name = "Google Optimize"

22

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0

4

dd5e56e7b972076a34512e42444079b3b32bcb0e

253

py

Python

unplugged/signals.py

fakegit/unplugged

755227a5319ea443c3d8f1356380981430c80419

[ "MIT" ]

2

2019-07-17T15:45:25.000Z

2021-08-10T18:27:43.000Z

unplugged/signals.py

fakegit/unplugged

755227a5319ea443c3d8f1356380981430c80419

[ "MIT" ]

null

unplugged/signals.py

fakegit/unplugged

755227a5319ea443c3d8f1356380981430c80419

[ "MIT" ]

1

2019-11-23T08:30:40.000Z

import django.dispatch plugin_loaded = django.dispatch.Signal(providing_args=["plugin"]) plugin_unloaded = django.dispatch.Signal(providing_args=["plugin"]) wamp_realm_created = django.dispatch.Signal() wamp_realm_discarded = django.dispatch.Signal()

31.625

67

0.814229

31

253

6.387097

0.419355

0.353535

0.40404

0.292929

0.393939

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253

7

68

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0.2

0

null

1

0

1

0

null

0

4

dd9a5dd9f345c9c05b5fe74daf8a589e53e527dd

239

py

Python

icecream/serializers.py

sreeo/ice_cream_api

c9dbccb3360855145b1d48881756e786092f9a62

[ "MIT" ]

null

icecream/serializers.py

sreeo/ice_cream_api

c9dbccb3360855145b1d48881756e786092f9a62

[ "MIT" ]

null

icecream/serializers.py

sreeo/ice_cream_api

c9dbccb3360855145b1d48881756e786092f9a62

[ "MIT" ]

null

from rest_framework import serializers from icecream.models import IceCream class IceCreamModelSerializer(serializers.ModelSerializer): class Meta: model = IceCream fields = ("cone_wafer", "base_flavour", "toppings")

26.555556

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0.748954

24

239

7.333333

0.75

0

0.175732

239

8

60

29.875

0.893401

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0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

dd9f45232a8f2dc7252ed17288d7bb0159d1e148

575

py

Python

python-variables/08-type-conversion.py

oviniciusoliveira/python-bootcamp

cd08ec4ec30049822c283a656307a5dcb25b5d99

[ "MIT" ]

null

python-variables/08-type-conversion.py

oviniciusoliveira/python-bootcamp

cd08ec4ec30049822c283a656307a5dcb25b5d99

[ "MIT" ]

null

python-variables/08-type-conversion.py

oviniciusoliveira/python-bootcamp

cd08ec4ec30049822c283a656307a5dcb25b5d99

[ "MIT" ]

null

# int() # float() # str() # bool() number_input = input("number: ") print(type(number_input)) number_input = int(number_input) print(type(number_input)) # Falsy Values print(bool(0)) print(bool(0.0)) print(bool('')) print(bool(())) print(bool([])) print(bool({})) print(bool(set())) print(bool(complex())) print(bool(range())) print(bool(False)) print(bool(None)) # Truthy Values print(bool(1)) print(bool(1.0)) print(bool('a')) print(bool((1,))) print(bool([1])) print(bool({1})) print(bool(set([1]))) print(bool(complex(1))) print(bool(range(1))) print(bool(True))

15.972222

32

0.652174

89

575

4.157303

0.224719

0.510811

0.189189

0.194595

0.281081

0.227027

0

0.022945

0.090435

575

35

33

16.428571

0.684512

0.092174

0

0.08

0

0.017476

0

1

0

false

0

0.92

0

null

1

0

null

0

1

0

4

06d4360390b0a5a17a300cc5b1414dedc3667eb8

70

py

Python

enthought/pyface/workbench/workbench_window_layout.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/pyface/workbench/workbench_window_layout.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/pyface/workbench/workbench_window_layout.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from pyface.workbench.workbench_window_layout import *

23.333333

54

0.842857

9

70

6.333333

0.888889

0

0.1

70

2

55

35

0.904762

0.171429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

06dd830f40ff6ffab0c6c0bc544dcf36f91bfa18

106

py

Python

icrawler/storage/__init__.py

DevinWangGZ/picture_crawler

00b49e4c7d16adb0bc520c3ce78ec016cdd778d9

[ "MIT" ]

1

2017-08-10T12:47:25.000Z

icrawler/storage/__init__.py

DevinWangGZ/picture_crawler

00b49e4c7d16adb0bc520c3ce78ec016cdd778d9

[ "MIT" ]

null

icrawler/storage/__init__.py

DevinWangGZ/picture_crawler

00b49e4c7d16adb0bc520c3ce78ec016cdd778d9

[ "MIT" ]

null

from .base import BaseStorage from .filesystem import FileSystem __all__ = ['BaseStorage', 'FileSystem']

21.2

39

0.783019

11

106

7.181818

0.545455

0

0.122642

106

4

40

26.5

0.849462

0

0.198113

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

66124d75b356ed334d18dbde44dcbad412737ff2

59

py

Python

strutil.py

jgurtowski/ectools

031eb0300c82392915d8393a5fedb4d3452b15bf

[ "BSD-3-Clause" ]

33

2015-01-21T18:34:49.000Z

2020-05-21T13:06:39.000Z

strutil.py

jgurtowski/ectools

031eb0300c82392915d8393a5fedb4d3452b15bf

[ "BSD-3-Clause" ]

7

2015-05-08T09:35:26.000Z

2019-09-27T05:35:58.000Z

strutil.py

jgurtowski/ectools

031eb0300c82392915d8393a5fedb4d3452b15bf

[ "BSD-3-Clause" ]

11

2015-09-08T09:40:02.000Z

2021-08-17T08:02:57.000Z

def strAppend(suffix): return lambda x : x + suffix

9.833333

32

0.644068

8

59

4.75

0.75

0

0.271186

59

5

33

11.8

0.883721

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

663e52160e6e09606bed54578b2e948faa7af714

704

py

Python

msghandle/spec/no_utoken.py

RaenonX/Jelly-Bot-API

c7da1e91783dce3a2b71b955b3a22b68db9056cf

[ "MIT" ]

5

2020-08-26T20:12:00.000Z

2020-12-11T16:39:22.000Z

msghandle/spec/no_utoken.py

RaenonX/Jelly-Bot

c7da1e91783dce3a2b71b955b3a22b68db9056cf

[ "MIT" ]

234

2019-12-14T03:45:19.000Z

2020-08-26T18:55:19.000Z

msghandle/spec/no_utoken.py

RaenonX/Jelly-Bot-API

c7da1e91783dce3a2b71b955b3a22b68db9056cf

[ "MIT" ]

2

2019-10-23T15:21:15.000Z

2020-05-22T09:35:55.000Z

from typing import List from ttldict import TTLOrderedDict from JellyBot.systemconfig import System from msghandle.models import MessageEventObject, HandledMessageEvent, HandledMessageEventText from strres.msghandle import HandledResult _sent_cache_ = TTLOrderedDict(System.NoUserTokenNotificationSeconds) def handle_no_user_token(e: MessageEventObject) -> List[HandledMessageEvent]: if e.is_test_event: return [HandledMessageEventText(content=HandledResult.TestFailedNoToken)] if e.channel_oid not in _sent_cache_: _sent_cache_[e.channel_oid] = True return [HandledMessageEventText(content=HandledResult.ErrorNoToken, bypass_multiline_check=True)] return []

30.608696

105

0.81108

73

704

7.575342

0.561644

0.048825

0.130199

0.177215

0

0.130682

704

22

106

32

0.903595

0

1

0.076923

false

0.076923

0.384615

0

0.692308

0

null

0

1

0

null

0

1

0

1

0

4

663f621d5aa29f8f188a032bf378b6d36927da21

153

py

Python

setup.py

bengisug/yzv_abet_src

b012cde7d1492d1af8cdbd25bb1c42e8362518a8

[ "MIT" ]

null

setup.py

bengisug/yzv_abet_src

b012cde7d1492d1af8cdbd25bb1c42e8362518a8

[ "MIT" ]

null

setup.py

bengisug/yzv_abet_src

b012cde7d1492d1af8cdbd25bb1c42e8362518a8

[ "MIT" ]

null

from setuptools import setup, find_packages setup(name='yzv_abet', version='0.1', author='Bengisu Guresti', packages=find_packages())

21.857143

43

0.686275

19

153

5.368421

0.789474

0.235294

0

0.016129

0.189542

153

6

44

25.5

0.806452

0

0.169935

0

1

0

true

0

0.2

0

0.2

0

1

0

null

1

0

1

0

null

0

1

0

4

b082b4b63bdb7da581876dfd7d8ebf83b838c87b

43

py

Python

src/matching/src/__init__.py

njounkengdaizem/UCMigrantFinder

ddbeee0595a60ceceb392b12641933d6d4a77711

[ "MIT" ]

1

2021-10-10T04:56:50.000Z

src/matching/src/__init__.py

njounkengdaizem/UCMigrantFinder

ddbeee0595a60ceceb392b12641933d6d4a77711

[ "MIT" ]

null

src/matching/src/__init__.py

njounkengdaizem/UCMigrantFinder

ddbeee0595a60ceceb392b12641933d6d4a77711

[ "MIT" ]

2

2021-10-10T07:23:38.000Z

2021-11-14T09:29:58.000Z

""" Directory for the matching system. """

10.75

34

0.674419

5

43

5.8

1

0

0.162791

43

3

35

14.333333

0.805556

0.790698

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

b088ca500b588f79f41f3331377a10839a49c62d

647

py

Python

xu/src/python/Model/XFile.py

sonnts996/XuCompa-Request

f343e7bfd1b4263eb76438c96d347c549cc75ce3

[ "Apache-2.0" ]

null

xu/src/python/Model/XFile.py

sonnts996/XuCompa-Request

f343e7bfd1b4263eb76438c96d347c549cc75ce3

[ "Apache-2.0" ]

null

xu/src/python/Model/XFile.py

sonnts996/XuCompa-Request

f343e7bfd1b4263eb76438c96d347c549cc75ce3

[ "Apache-2.0" ]

null

import os class XFile: def __init__(self, path): self.path = path self.unsavedData = "" def getPath(self): return self.path def setPath(self, path): self.path = path def name(self): return os.path.splitext(os.path.basename(self.path)) def parent(self): return os.path.basename(os.path.dirname(self.path)) def dir(self): return os.path.dirname(self.path) def __eq__(self, other): if isinstance(other, XFile): return other.path == self.path elif isinstance(other, str): return other == self.path return False

21.566667

60

0.588872

82

647

4.54878

0.304878

0.214477

0.117962

0.128686

0.235925

0.128686

0

0.299845

647

29

61

22.310345

0.8234

0

0.095238

0

1

0.333333

false

0

0.047619

0.190476

0.761905

0

null

1

0

null

0

1

0

1

0

4

b08b3c4c2e7f0002eac0aa9eff22b4ab2858afb6

192

py

Python

Day 3.py

Passionate-coder997/HackerRank-s

bc6c01d8a51ccdecf660400ac33bb9d1cd8f4a3a

[ "Unlicense" ]

null

Day 3.py

Passionate-coder997/HackerRank-s

bc6c01d8a51ccdecf660400ac33bb9d1cd8f4a3a

[ "Unlicense" ]

null

Day 3.py

Passionate-coder997/HackerRank-s

bc6c01d8a51ccdecf660400ac33bb9d1cd8f4a3a

[ "Unlicense" ]

null

n = int(input()) if n%2!=0: print('Weird') elif 2<n<=5 and n%2==0: print('Not Weird') elif 6<n<=20 and n%2==0: print('Weird') elif n>20 and n%2==0: print('Not Weird')

19.2

25

0.520833

40

192

2.5

0.35

0.08

0.12

0.32

0.81

0.55

0

0.104167

0.25

192

10

26

19.2

0.590278

0

0.444444

0

0.152174

0

1

0

false

0

0.444444

0

null

0

1

0

null

0

1

0

4

b0a29f7381b4409b68efd62779da30edffea5d5e

153

py

Python

exploits/staff/utils/invalid_response_exception.py

amreo/ructfe-2019

19064842acc57243f16143a85c06a5613378ebec

[ "MIT" ]

23

2019-11-23T19:53:10.000Z

2021-02-19T06:13:28.000Z

exploits/staff/utils/invalid_response_exception.py

amreo/ructfe-2019

19064842acc57243f16143a85c06a5613378ebec

[ "MIT" ]

1

2019-11-30T16:10:52.000Z

2019-12-01T15:23:39.000Z

exploits/staff/utils/invalid_response_exception.py

amreo/ructfe-2019

19064842acc57243f16143a85c06a5613378ebec

[ "MIT" ]

3

2019-11-24T09:35:43.000Z

2021-02-19T06:13:29.000Z

class InvalidResponseException(Exception): def __init__(self, message): self.message = f'Request was not success: {message}' super()

30.6

60

0.679739

16

153

6.25

0.8125

0.22

0

0.215686

153

4

61

38.25

0.833333

0

0.222222

0

1

0.25

false

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

b0a44bb2f2325fcc510069a0535c957a649e5252

606

py

Python

connect/__init__.py

Adrian-IM/connect.py

72a9dccedbaadb5241bcfd6d46e45b26835a1dd3

[ "Apache-2.0" ]

null

connect/__init__.py

Adrian-IM/connect.py

72a9dccedbaadb5241bcfd6d46e45b26835a1dd3

[ "Apache-2.0" ]

null

connect/__init__.py

Adrian-IM/connect.py

72a9dccedbaadb5241bcfd6d46e45b26835a1dd3

[ "Apache-2.0" ]

null

from connect.autogen import connect_Collection as Collection from connect.autogen import connect_Config as Config from connect.autogen import connect_Dictionary as Dictionary from connect.autogen import connect_Env as Env from connect.autogen import connect_Inflection as Inflection from connect.autogen import connect_Logger as Logger from connect.autogen import connect_LoggerWriter as LoggerWriter from connect.autogen import connect_Processor as Processor __all__ = [ 'Collection', 'Config', 'Dictionary', 'Env', 'Inflection', 'Logger', 'LoggerWriter', 'Processor', ]

28.857143

64

0.787129

73

606

6.369863

0.191781

0.189247

0.309677

0.412903

0.533333

0

0.155116

606

20

65

30.3

0.908203

0

1

0

0.108911

0

1

0

false

0

0.444444

0

0.444444

0

null

0

1

0

1

0

null

0

1

0

4

9fd5ae65430e6fcdaa8d68ce906daa310aed62c1

76

py

Python

dash_worldbankapp/worldbank.py

santamm/worldbankapp

177d62cf58f83067eba30d87164101a3e6aa0c34

[ "MIT" ]

null

dash_worldbankapp/worldbank.py

santamm/worldbankapp

177d62cf58f83067eba30d87164101a3e6aa0c34

[ "MIT" ]

null

dash_worldbankapp/worldbank.py

santamm/worldbankapp

177d62cf58f83067eba30d87164101a3e6aa0c34

[ "MIT" ]

1

2020-12-30T12:41:39.000Z

from worldbankapp import app app.run(host='0.0.0.0', port=3002, debug=True)

25.333333

46

0.736842

15

76

3.733333

0.733333

0.107143

0

0.115942

0.092105

76

2

47

38

0.695652

0

0.092105

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

9fd6a1988773b152e2a8ee40967287091810c309

115

py

Python

johnnyv/tests/test_verification.py

lincolnreese/JohnnyV

bce5a9f11796090754de8dbac91a6ea080fe4236

[ "MIT" ]

null

johnnyv/tests/test_verification.py

lincolnreese/JohnnyV

bce5a9f11796090754de8dbac91a6ea080fe4236

[ "MIT" ]

null

johnnyv/tests/test_verification.py

lincolnreese/JohnnyV

bce5a9f11796090754de8dbac91a6ea080fe4236

[ "MIT" ]

null

from unittest import TestCase class TestVerification(TestCase): def test_validate(self): self.fail()

16.428571

33

0.721739

13

115

6.307692

0.846154

0

0.2

115

6

34

19.166667

0.891304

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

9febd49613b097f27f46131ca22f69a0533cdbf8

385

py

Python

Python-3/basic_examples/abs_examples.py

ghiloufibelgacem/jornaldev

b9b27f9f7da595892520314b4ed1d2675556310a

[ "MIT" ]

1,139

2018-05-09T11:54:36.000Z

2022-03-31T06:52:50.000Z

Python-3/basic_examples/abs_examples.py

iamharshverma/journaldev

af24242a1ac1b7dc3e8e2404ec916b77ccf5044a

[ "MIT" ]

56

2018-06-20T03:52:53.000Z

2022-02-09T22:57:41.000Z

Python-3/basic_examples/abs_examples.py

iamharshverma/journaldev

af24242a1ac1b7dc3e8e2404ec916b77ccf5044a

[ "MIT" ]

2,058

2018-05-09T09:32:17.000Z

2022-03-29T13:19:42.000Z

import sys x = 5 # int print(abs(x)) x = sys.maxsize # long print(abs(x)) x = 50.23434 # float print(abs(x)) x = 10 - 4j # complex print(abs(x)) x = complex(10, 2) # another complex example print(abs(x)) # numbers in different formats x = 10.23e1/2 # exponential print(abs(x)) x = 0b1010 # binary print(abs(x)) x = 0o15 # octal print(abs(x)) x = 0xF # hexadecimal print(abs(x))

13.75

44

0.646753

69

385

3.608696

0.434783

0.289157

0.325301

0.281125

0

0.092949

0.18961

385

28

45

13.75

0.705128

0.290909

0

0.473684

0

0.011407

0

1

0

false

0

0.052632

0

0.052632

0.473684

0

null

1

0

null

0

1

0

4

9ffeb1b05b19ba323765537e554ddeec7fa6e537

416

py

Python

redbookmating/animals/managers.py

maximovd/redbook-mating

f8186f2f08c869e71856230d0250e2ca3a8e47d2

[ "MIT" ]

null

redbookmating/animals/managers.py

maximovd/redbook-mating

f8186f2f08c869e71856230d0250e2ca3a8e47d2

[ "MIT" ]

null

redbookmating/animals/managers.py

maximovd/redbook-mating

f8186f2f08c869e71856230d0250e2ca3a8e47d2

[ "MIT" ]

null

from django.db import models class BaseManager(models.Manager): def get_queryset(self): return super().get_queryset().filter(is_deleted=False) class AllManager(models.Manager): def get_queryset(self): return super().get_queryset().filter() class OrderedManager(models.Manager): def get_queryset(self): return super().get_queryset().exclude(is_deleted=False).order_by('-id')

24.470588

79

0.716346

53

416

5.45283

0.45283

0.228374

0.16609

0.197232

0.591696

0

0.153846

416

16

80

26

0.821023

0

0.3

0

0.007212

0

1

0.3

false

0

0.1

0.3

1

0

null

1

0

1

0

null

0

1

0

1

0

4

b007a2e33023695da540cc0617a786c67f1e83da

231

py

Python

cart/urls.py

AnthonyNicklin/newage-auctions

f829c9761e4fef0c084cf0244a4617a4bda8e0c2

[ "FSFAP" ]

1

2021-07-29T07:47:10.000Z

cart/urls.py

AnthonyNicklin/newage-auctions

f829c9761e4fef0c084cf0244a4617a4bda8e0c2

[ "FSFAP" ]

9

2019-12-19T21:27:23.000Z

2022-01-13T01:59:10.000Z

cart/urls.py

AnthonyNicklin/newage-auctions

f829c9761e4fef0c084cf0244a4617a4bda8e0c2

[ "FSFAP" ]

1

2020-02-11T19:50:45.000Z

from django.urls import path from .views import view_cart, remove_from_cart urlpatterns = [ path('', view_cart, name='view_cart'), path('remove_from_cart/<int:auction_id>/', remove_from_cart, name='remove_from_cart'), ]

23.1

90

0.735931

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null

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4

b00caf3c8b776d03c0bef6c71c037ac3a50f3bce

192

py

Python

Python/mysamples/workspace/myimport/main/app.py

KagenoMoheji/MyMiniLibraries

9494025b24ad18368d8f9fdf2c29fdc9c0616aa3

[ "MIT" ]

null

Python/mysamples/workspace/myimport/main/app.py

KagenoMoheji/MyMiniLibraries

9494025b24ad18368d8f9fdf2c29fdc9c0616aa3

[ "MIT" ]

1

2022-01-13T03:50:59.000Z

Python/mysamples/workspace/myimport/main/app.py

KagenoMoheji/MyMiniLibraries

9494025b24ad18368d8f9fdf2c29fdc9c0616aa3

[ "MIT" ]

null

import inspect import os import sys PYPATH = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) + "/" sys.path.append(PYPATH + ".") from modules.md_a.a import funcA

32

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0.114583

192

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32

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0

1

0

null

0

1

0

1

0

4

b0271d26e1536447c15c3f1f4a20b11dcace0b41

177

py

Python

OpenGLCffi/GL/EXT/NVX/conditional_render.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/GL/EXT/NVX/conditional_render.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/GL/EXT/NVX/conditional_render.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

from OpenGLCffi.GL import params @params(api='gl', prms=['id']) def glBeginConditionalRenderNVX(id): pass @params(api='gl', prms=[]) def glEndConditionalRenderNVX(): pass

14.75

36

0.723164

21

177

6.095238

0.571429

0.140625

0.171875

0.234375

0

0.112994

177

11

37

16.090909

0.815287

0

0.285714

0

0.034286

0

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0.285714

false

0.285714

0.142857

0

0.428571

0

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0

null

0

1

0

null

0

1

0

1

0

4

b04b5766f6267d39fae724e6185cb305eb51c6ad

395

py

Python

solutions/03_04_array_indexing.py

PyDataMallorca/PyConES2018_Introduccion_a_data_science_en_Python

71d0c6f86afdc992e3b2ba1d6862eb34dead5698

[ "MIT" ]

15

2018-09-03T16:45:42.000Z

2020-02-16T13:50:06.000Z

solutions/03_04_array_indexing.py

NachoAG76/PyConES2018_Introduccion_a_data_science_en_Python

71d0c6f86afdc992e3b2ba1d6862eb34dead5698

[ "MIT" ]

null

solutions/03_04_array_indexing.py

NachoAG76/PyConES2018_Introduccion_a_data_science_en_Python

71d0c6f86afdc992e3b2ba1d6862eb34dead5698

[ "MIT" ]

9

2018-09-08T10:06:00.000Z

2020-05-09T05:09:38.000Z

from IPython.display import HTML HTML(""" <img src="../../images/03_05_arraygraphics_2.png" width=200px /> <img src="../../images/03_07_arraygraphics_3.png" width=200px /> <img src="../../images/03_09_arraygraphics_4.png" width=200px /> <img src="../../images/03_11_arraygraphics_5.png" width=200px /> (imágenes extraídas de [aquí](https://github.com/gertingold/euroscipy-numpy-tutorial)) """)

39.5

86

0.721519

57

395

4.789474

0.561404

0.087912

0.175824

0.205128

0.296703

0

0.086957

0.068354

395

10

87

39.5

0.654891

0

0.125

0.881313

0.444444

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true

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0.125

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null

0

1

0

1

null

0

1

0

4

c671e044e9ad0b0e2ec652dae147a9feaeef9b59

97

py

Python

tv_shows/admin.py

sks444/awesome100

ae9f16d4101ffc2be9140e77cec1f0b67b725af0

[ "MIT" ]

2

2018-10-30T05:30:50.000Z

2020-05-22T14:42:35.000Z

tv_shows/admin.py

sks444/awesome100

ae9f16d4101ffc2be9140e77cec1f0b67b725af0

[ "MIT" ]

34

2018-04-13T11:01:44.000Z

2018-04-18T23:06:20.000Z

tv_shows/admin.py

sks444/awesome100

ae9f16d4101ffc2be9140e77cec1f0b67b725af0

[ "MIT" ]

null

from django.contrib import admin from tv_shows.models import TvShow admin.site.register(TvShow)

19.4

34

0.835052

15

97

5.333333

0.733333

0

0.103093

97

4

35

24.25

0.91954

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true

0

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null

0

1

0

null

0

1

0

1

0

4

c682475f71dc25f9cd5ca5d649fbc9c5749af42e

189

py

Python

Air_Quality_Index/test.py

kalpeshsnaik09/data_science_portfolio

bc55cd50060d08be2e90a504bfffc4f3cee0dcba

[ "Apache-2.0" ]

null

Air_Quality_Index/test.py

kalpeshsnaik09/data_science_portfolio

bc55cd50060d08be2e90a504bfffc4f3cee0dcba

[ "Apache-2.0" ]

null

Air_Quality_Index/test.py

kalpeshsnaik09/data_science_portfolio

bc55cd50060d08be2e90a504bfffc4f3cee0dcba

[ "Apache-2.0" ]

null

import pandas as pd df=pd.read_csv('Data/main_data/air_quality_index.csv') print(df.shape) print(df.isna().sum()) df.dropna(inplace=True) print(df.shape) print(df.isna().sum()) print(df)

18.9

54

0.73545

35

189

3.857143

0.542857

0.259259

0.177778

0.251852

0.385185

0

0.068783

189

9

55

21

0.767045

0

0.5

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0.190476

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false

0

0.125

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0.125

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null

1

0

1

0

1

0

null

0

1

0

4

c6890bdfdf04bbef8203793b6d551a7b43e13d66

138

py

Python

arbitrage/public_markets/gdaxeur.py

fiona-pet/bitcoin-arbitrage

a955b5930997b2ad46d2bbfb29db4dcad5e6c3e7

[ "MIT" ]

1,633

2015-01-05T00:47:41.000Z

2022-03-27T04:05:03.000Z

arbitrage/public_markets/gdaxeur.py

fiona-pet/bitcoin-arbitrage

a955b5930997b2ad46d2bbfb29db4dcad5e6c3e7

[ "MIT" ]

22

2015-02-16T09:31:32.000Z

2020-10-14T20:14:26.000Z

arbitrage/public_markets/gdaxeur.py

fiona-pet/bitcoin-arbitrage

a955b5930997b2ad46d2bbfb29db4dcad5e6c3e7

[ "MIT" ]

531

2015-01-02T10:13:01.000Z

2022-03-26T16:06:19.000Z

from arbitrage.public_markets._gdax import GDAX class GDAXEUR(GDAX): def __init__(self): super().__init__("EUR", "BTC-EUR")

19.714286

47

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18

138

4.722222

0.777778

0

0.173913

138

6

48

23

0.745614

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0

1

0.25

false

0

0.25

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0.75

0

1

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null

0

1

0

null

0

1

0

1

0

4

c6a5e018cd44fd349dd867f3c964d778ef54906b

203

py

Python

devel.py

vanjaBijalkovic/test

cbd853002b796359f49434754a9db3dab92d8c45

[ "BSD-2-Clause" ]

1

2019-12-24T19:41:37.000Z

devel.py

vanjaBijalkovic/test

cbd853002b796359f49434754a9db3dab92d8c45

[ "BSD-2-Clause" ]

null

devel.py

vanjaBijalkovic/test

cbd853002b796359f49434754a9db3dab92d8c45

[ "BSD-2-Clause" ]

null

from importlib import import_module from config import configs from name import app_name application = import_module(f'{app_name}') config = configs['development'] app = application.create_app(config)

22.555556

42

0.807882

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203

5.678571

0.428571

0.150943

0

0.1133

203

8

43

25.375

0.883333

0

0.103448

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c6c1001a60ac659f3d63d133a647b31975d7f1b1

1,453

py

Python

7segment/countdown_seven_segment.py

leonardoandrade/embedded

b6b82a59bf5ff822fa497a9123a1f099f587f07e

[ "MIT" ]

null

7segment/countdown_seven_segment.py

leonardoandrade/embedded

b6b82a59bf5ff822fa497a9123a1f099f587f07e

[ "MIT" ]

null

7segment/countdown_seven_segment.py

leonardoandrade/embedded

b6b82a59bf5ff822fa497a9123a1f099f587f07e

[ "MIT" ]

null

import RPi.GPIO as GPIO import time # map of pins pin_map = [16, #0 12, #1 19, #2 13, #3 6, #4 20, #5 21] #6 segment_to_pin_map = [ [GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.LOW], #0 [GPIO.LOW, GPIO.HIGH, GPIO.HIGH, GPIO.LOW, GPIO.LOW, GPIO.LOW, GPIO.LOW], #1 [GPIO.HIGH, GPIO.HIGH, GPIO.LOW, GPIO.HIGH, GPIO.HIGH, GPIO.LOW, GPIO.HIGH], #2 [GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.LOW, GPIO.LOW, GPIO.HIGH], #3 [GPIO.LOW, GPIO.HIGH, GPIO.HIGH, GPIO.LOW, GPIO.LOW, GPIO.HIGH, GPIO.HIGH], #4 [GPIO.HIGH, GPIO.LOW, GPIO.HIGH, GPIO.HIGH, GPIO.LOW, GPIO.HIGH, GPIO.HIGH], #5 [GPIO.HIGH, GPIO.LOW, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH], #6 [GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.LOW, GPIO.LOW, GPIO.LOW, GPIO.LOW], #7 [GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH], #8 [GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.HIGH, GPIO.LOW, GPIO.HIGH, GPIO.HIGH] #9 ] GPIO.setmode(GPIO.BCM) GPIO.setwarnings(False) def countdown(interval): for p in pin_map: GPIO.setup(p, GPIO.OUT) for x in reversed(range(9+1)): print "displaying:", x for i, s in enumerate(segment_to_pin_map[x]): GPIO.output(pin_map[i], s) time.sleep(interval) for pin in pin_map: GPIO.output(pin, GPIO.LOW) countdown(0.5)

32.288889

87

0.601514

242

1,453

3.570248

0.210744

0.453704

0.583333

0.574074

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0.614583

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0

0.030521

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1,453

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0.007824

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null

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0

null

1

0

null

0

1

0

4

c6e8d270c5d493591c54159614d9f117a7520893

66

py

Python

src/python/track/__init__.py

ml-starter-packs/ml-monorepo

c8c9e7406faf640cd151e3ad5c715f6c2d3979a8

[ "MIT" ]

null

src/python/track/__init__.py

ml-starter-packs/ml-monorepo

c8c9e7406faf640cd151e3ad5c715f6c2d3979a8

[ "MIT" ]

7

2022-02-15T23:59:05.000Z

2022-03-30T19:47:14.000Z

src/python/track/__init__.py

ml-starter-packs/ml-monorepo

c8c9e7406faf640cd151e3ad5c715f6c2d3979a8

[ "MIT" ]

null

from .core import simple_tracking __all__ = ("simple_tracking",)

16.5

33

0.772727

8

66

5.625

0.75

0.622222

0

0.121212

66

3

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22

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1

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1

0

null

0

1

0

4

05b5ed2ffd768f46649af0b4bb8e315d7a3fa632

1,142

py

Python

p1_basic/day16_21module/day17/4.分组在re模块中的使用.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

1

2020-04-03T01:32:05.000Z

p1_basic/day16_21module/day17/4.分组在re模块中的使用.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

null

p1_basic/day16_21module/day17/4.分组在re模块中的使用.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

null

import re # s = '<a>wahaha</a>' # 标签语言 html 网页 # ret = re.search('<(\w+)>(\w+)</(\w+)>',s) # print(ret.group()) # 所有的结果 # print(ret.group(1)) # 数字参数代表的是取对应分组中的内容 # print(ret.group(2)) # print(ret.group(3)) # 为了findall也可以顺利取到分组中的内容,有一个特殊的语法,就是优先显示分组中的内容 # ret = re.findall('(\w+)',s) # print(ret) # ret = re.findall('>(\w+)<',s) # print(ret) # 取消分组优先(?:正则表达式) # ret = re.findall('\d+(\.\d+)?','1.234*4') # print(ret) # 关于分组 # 对于正则表达式来说有些时候我们需要进行分组,来整体约束某一组字符出现的次数 # (\.[\w]+)? # 对于python语言来说分组可以帮助你更好更精准的找到你真正需要的内容 # <(\w+)>(\w+)</(\w+)> # split # ret = re.split('\d+','alex83taibai40egon25') # print(ret) # ret = re.split('(\d+)','alex83taibai40egon25aa') # print(ret) # python 和正则表达式之间的特殊的约定 # 分组命名 (?P<这个组的名字>正则表达式) # s = '<a>wahaha</a>' # ret = re.search('>(?P<con>\w+)<',s) # print(ret.group(1)) # print(ret.group('con')) # s = '<a>wahaha</a>' # pattern = '<(\w+)>(\w+)</(\w+)>' # ret = re.search(pattern,s) # print(ret.group(1) == ret.group(3)) # 使用前面的分组要求使用这个名字的分组和前面同名分组中的内容匹配的必须一致 # pattern = '<(?P<tab>\w+)>(\w+)</(?P=tab)>' # ret = re.search(pattern,s) # print(ret) # 2018-12-06 # 2018.12.6 # 2018 12 06 # 12:30:30

21.148148

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4.12963

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1

0

null

0

1

0

1

0

1

0

4

05c2771088d435783ab1800306e3c79b5f03d48a

281

py

Python

AlfaTesting Resources/DummyVnfmDriver2.py

ViniGarcia/EMSPlatform

f51e1c5ce1b75016bc7f453e125a37909ac9b566

[ "MIT" ]

1

2021-05-31T15:44:25.000Z

AlfaTesting Resources/DummyVnfmDriver2.py

ViniGarcia/HoLMES

f51e1c5ce1b75016bc7f453e125a37909ac9b566

[ "MIT" ]

null

AlfaTesting Resources/DummyVnfmDriver2.py

ViniGarcia/HoLMES

f51e1c5ce1b75016bc7f453e125a37909ac9b566

[ "MIT" ]

null

import sys sys.path.insert(0,'Access Subsystem/Ve-Vnfm-em') import VnfmDriverTemplate class DummyVnfmDriver2(VnfmDriverTemplate.VnfmDriverTemplate): def __init__(self, vnfmId, vnfmAddress, vnfmCredentials): super().__init__(vnfmId, vnfmAddress, vnfmCredentials)

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7.535714

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1

0

null

0

1

0

1

0

4

af0c4b3f99c10362d466264d0ce982cd1c842d0f

45

py

Python

v_programmer.py

fossabot/ava

e8541c43b7bc4e92f2cfb21f64f0167057d8b565

[ "Apache-2.0" ]

null

v_programmer.py

fossabot/ava

e8541c43b7bc4e92f2cfb21f64f0167057d8b565

[ "Apache-2.0" ]

null

v_programmer.py

fossabot/ava

e8541c43b7bc4e92f2cfb21f64f0167057d8b565

[ "Apache-2.0" ]

null

""" The verbal programmer module for AVA """

11.25

36

0.688889

6

45

5.166667

1

0

0.177778

45

3

37

15

0.837838

0.8

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

af85636edd0258cb860df9141d88a2cc392a7826

10,306

py

Python

backend/django/test/core_utils/test_core_utils_annotate.py

RTIInternational/SMART

c95eaa088df33bac95aababdc560c10422eed6e3

[ "MIT" ]

185

2018-10-01T16:38:35.000Z

2022-03-16T14:39:04.000Z

backend/django/test/core_utils/test_core_utils_annotate.py

RTIInternational/SMART

c95eaa088df33bac95aababdc560c10422eed6e3

[ "MIT" ]

93

2018-12-07T15:33:44.000Z

2022-03-09T15:19:08.000Z

backend/django/test/core_utils/test_core_utils_annotate.py

RTIInternational/SMART

c95eaa088df33bac95aababdc560c10422eed6e3

[ "MIT" ]

30

2019-03-04T05:44:44.000Z

2022-03-19T17:07:14.000Z

from test.conftest import TEST_QUEUE_LEN from test.util import assert_obj_exists from core.models import AssignedData, Data, DataLabel, DataQueue, Label from core.utils.utils_annotate import ( assign_datum, get_assignments, label_data, move_skipped_to_admin_queue, unassign_datum, ) from core.utils.utils_queue import fill_queue def test_assign_datum_project_queue_returns_datum( db, test_queue, test_profile, test_redis ): """Assign a datum from a project-wide queue (null profile ID).""" fill_queue(test_queue, orderby="random") datum = assign_datum(test_profile, test_queue.project) # Make sure we got the datum assert isinstance(datum, Data) def test_assign_datum_project_queue_correct_assignment( db, test_queue, test_profile, test_redis ): fill_queue(test_queue, orderby="random") datum = assign_datum(test_profile, test_queue.project) # Make sure the assignment is correct assignment = AssignedData.objects.filter(data=datum) assert len(assignment) == 1 assert assignment[0].profile == test_profile assert assignment[0].queue == test_queue assert assignment[0].assigned_timestamp is not None def test_assign_datum_project_queue_pops_queues( db, test_queue, test_profile, test_redis ): fill_queue(test_queue, orderby="random") datum = assign_datum(test_profile, test_queue.project) # Make sure the datum was removed from queues but not set assert test_redis.llen("queue:" + str(test_queue.pk)) == test_queue.length - 1 assert test_redis.scard("set:" + str(test_queue.pk)) == test_queue.length # but not from the db queue assert test_queue.data.count() == test_queue.length assert datum in test_queue.data.all() def test_assign_datum_profile_queue_returns_correct_datum( db, test_profile_queue, test_profile, test_profile_queue2, test_profile2, test_redis ): fill_queue(test_profile_queue, orderby="random") fill_queue(test_profile_queue2, orderby="random") datum = assign_datum(test_profile, test_profile_queue.project) assert isinstance(datum, Data) def test_assign_datum_profile_queue_correct_assignment( db, test_profile_queue, test_profile, test_profile_queue2, test_profile2, test_redis ): fill_queue(test_profile_queue, orderby="random") fill_queue(test_profile_queue2, orderby="random") datum = assign_datum(test_profile, test_profile_queue.project) assignment = AssignedData.objects.filter(data=datum) assert len(assignment) == 1 assert assignment[0].profile == test_profile assert assignment[0].queue == test_profile_queue assert assignment[0].assigned_timestamp is not None def test_assign_datum_profile_queue_pops_queues( db, test_profile_queue, test_profile, test_profile_queue2, test_profile2, test_redis ): fill_queue(test_profile_queue, orderby="random") fill_queue(test_profile_queue2, orderby="random") datum = assign_datum(test_profile, test_profile_queue.project) # Make sure the datum was removed from the correct queues but not sets assert ( test_redis.llen("queue:" + str(test_profile_queue.pk)) == test_profile_queue.length - 1 ) assert ( test_redis.scard("set:" + str(test_profile_queue.pk)) == test_profile_queue.length ) # ...but not the other queues assert test_profile_queue.data.count() == test_profile_queue.length assert datum in test_profile_queue.data.all() assert ( test_redis.llen("queue:" + str(test_profile_queue2.pk)) == test_profile_queue2.length ) assert ( test_redis.scard("set:" + str(test_profile_queue2.pk)) == test_profile_queue2.length ) assert test_profile_queue2.data.count() == test_profile_queue2.length def test_label_data(db, test_profile, test_queue, test_redis): fill_queue(test_queue, orderby="random") datum = assign_datum(test_profile, test_queue.project) test_label = Label.objects.create(name="test", project=test_queue.project) label_data(test_label, datum, test_profile, 3) # Make sure the label was properly recorded assert datum in test_profile.labeled_data.all() assert_obj_exists( DataLabel, { "data": datum, "profile": test_profile, "label": test_label, "time_to_label": 3, }, ) # Make sure the assignment was removed assert not AssignedData.objects.filter( profile=test_profile, data=datum, queue=test_queue ).exists() def test_get_assignments_no_existing_assignment_one_assignment( db, test_profile, test_project_data, test_queue, test_redis ): fill_queue(test_queue, orderby="random") assert AssignedData.objects.count() == 0 data = get_assignments(test_profile, test_project_data, 1) assert len(data) == 1 assert isinstance(data[0], Data) assert_obj_exists(AssignedData, {"data": data[0], "profile": test_profile}) def test_get_assignments_no_existing_assignment_half_max_queue_length( db, test_profile, test_project_data, test_queue, test_redis ): fill_queue(test_queue, orderby="random") assert AssignedData.objects.count() == 0 data = get_assignments(test_profile, test_project_data, TEST_QUEUE_LEN // 2) assert len(data) == TEST_QUEUE_LEN // 2 for datum in data: assert isinstance(datum, Data) assert_obj_exists(AssignedData, {"data": datum, "profile": test_profile}) def test_get_assignments_no_existing_assignment_max_queue_length( db, test_profile, test_project_data, test_queue, test_redis ): fill_queue(test_queue, orderby="random") assert AssignedData.objects.count() == 0 data = get_assignments(test_profile, test_project_data, TEST_QUEUE_LEN) assert len(data) == TEST_QUEUE_LEN for datum in data: assert isinstance(datum, Data) assert_obj_exists(AssignedData, {"data": datum, "profile": test_profile}) def test_get_assignments_no_existing_assignment_over_max_queue_length( db, test_profile, test_project_data, test_queue, test_redis ): fill_queue(test_queue, orderby="random") assert AssignedData.objects.count() == 0 data = get_assignments(test_profile, test_project_data, TEST_QUEUE_LEN + 10) assert len(data) == TEST_QUEUE_LEN for datum in data: assert isinstance(datum, Data) assert_obj_exists(AssignedData, {"data": datum, "profile": test_profile}) def test_get_assignments_one_existing_assignment( db, test_profile, test_project_data, test_queue, test_redis ): fill_queue(test_queue, orderby="random") assigned_datum = assign_datum(test_profile, test_project_data) data = get_assignments(test_profile, test_project_data, 1) assert isinstance(data[0], Data) # We should just get the datum that was already assigned assert data[0] == assigned_datum def test_get_assignments_multiple_existing_assignments( db, test_profile, test_project_data, test_queue, test_redis ): fill_queue(test_queue, orderby="random") assigned_data = [] for i in range(5): assigned_data.append(assign_datum(test_profile, test_project_data)) data = get_assignments(test_profile, test_project_data, 5) assert len(data) == 5 assert len(data) == len(assigned_data) for datum, assigned_datum in zip(data, assigned_data): assert isinstance(datum, Data) # We should just get the data that was already assigned assert len(data) == len(assigned_data) def test_unassign(db, test_profile, test_project_data, test_queue, test_redis): fill_queue(test_queue, orderby="random") assert test_redis.llen("queue:" + str(test_queue.pk)) == test_queue.length assert test_redis.scard("set:" + str(test_queue.pk)) == test_queue.length datum = get_assignments(test_profile, test_project_data, 1)[0] assert test_redis.llen("queue:" + str(test_queue.pk)) == (test_queue.length - 1) assert test_redis.scard("set:" + str(test_queue.pk)) == test_queue.length assert AssignedData.objects.filter(data=datum, profile=test_profile).exists() unassign_datum(datum, test_profile) assert test_redis.llen("queue:" + str(test_queue.pk)) == test_queue.length assert test_redis.scard("set:" + str(test_queue.pk)) == test_queue.length assert not AssignedData.objects.filter(data=datum, profile=test_profile).exists() # The unassigned datum should be the next to be assigned reassigned_datum = get_assignments(test_profile, test_project_data, 1)[0] assert reassigned_datum == datum def test_unassign_after_fillqueue( db, test_profile, test_project_data, test_queue, test_labels, test_redis ): fill_queue(test_queue, "random") assert test_redis.llen("queue:" + str(test_queue.pk)) == test_queue.length assert test_redis.scard("set:" + str(test_queue.pk)) == test_queue.length data = get_assignments(test_profile, test_project_data, 10) assert test_redis.llen("queue:" + str(test_queue.pk)) == (test_queue.length - 10) assert test_redis.scard("set:" + str(test_queue.pk)) == test_queue.length test_label = test_labels[0] for i in range(5): label_data(test_label, data[i], test_profile, 3) assert test_redis.llen("queue:" + str(test_queue.pk)) == (test_queue.length - 10) assert test_redis.scard("set:" + str(test_queue.pk)) == (test_queue.length - 5) fill_queue(test_queue, "random") assert test_redis.llen("queue:" + str(test_queue.pk)) == test_queue.length - 5 assert test_redis.scard("set:" + str(test_queue.pk)) == test_queue.length def test_skip_data(db, test_profile, test_queue, test_admin_queue, test_redis): fill_queue(test_queue, orderby="random") project = test_queue.project datum = assign_datum(test_profile, project) move_skipped_to_admin_queue(datum, test_profile, project) # Make sure the assignment was removed assert not AssignedData.objects.filter( profile=test_profile, data=datum, queue=test_queue ).exists() # make sure the item was re-assigned to the admin queue assert DataQueue.objects.filter(data=datum, queue=test_admin_queue).exists() # make sure not in normal queue assert not DataQueue.objects.filter(data=datum, queue=test_queue).exists()

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pertussis/__init__.py

DeanLa/israel_pertussis

dd1f6bb00dfac5a2f580da72e60bf32c3ce228c7

[ "MIT" ]

null

pertussis/__init__.py

DeanLa/israel_pertussis

dd1f6bb00dfac5a2f580da72e60bf32c3ce228c7

[ "MIT" ]

null

pertussis/__init__.py

DeanLa/israel_pertussis

dd1f6bb00dfac5a2f580da72e60bf32c3ce228c7

[ "MIT" ]

null

# INITIAL from .config import * from .funcs import * # MODEL from .parameters import * from .data import * #Requires Parameters from .model import * # Requires Parameters from .predict import * # Requires Parameters from .sampling import * # POST from .charts import * from .report import * from .analysis import *

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Python

app/main/__init__.py

chen940303/Diaosier_home

58e0713703d7f902739c7fe3495c0d964151bcc8

[ "MIT" ]

2

2016-09-05T15:46:23.000Z

2016-09-05T15:46:27.000Z

app/main/__init__.py

chen940303/Diaosier_home

58e0713703d7f902739c7fe3495c0d964151bcc8

[ "MIT" ]

null

app/main/__init__.py

chen940303/Diaosier_home

58e0713703d7f902739c7fe3495c0d964151bcc8

[ "MIT" ]

null

from flask import Blueprint from ..models import Permission main=Blueprint('main',__name__) @main.app_context_processor def inject_permissions(): return dict(Permission=Permission) from . import views, errors

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filter-plugin/logstash-filter-pubsub-mysql-guardium/PubSubMySQLPackage/sqlFormatter/sqlFormatter.py

IBM-klahn/universal-connectors

a68c6054dfdbd999064a02e4b0e33a174d51e731

[ "Apache-2.0" ]

null

filter-plugin/logstash-filter-pubsub-mysql-guardium/PubSubMySQLPackage/sqlFormatter/sqlFormatter.py

IBM-klahn/universal-connectors

a68c6054dfdbd999064a02e4b0e33a174d51e731

[ "Apache-2.0" ]

null

filter-plugin/logstash-filter-pubsub-mysql-guardium/PubSubMySQLPackage/sqlFormatter/sqlFormatter.py

IBM-klahn/universal-connectors

a68c6054dfdbd999064a02e4b0e33a174d51e731

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import sys def formattedQuery(q): return " ".join([s.strip() for s in q.splitlines()]) sql = sys.stdin.read().rstrip() print(formattedQuery(sql))

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Python

dynamicserialize/dstypes/com/raytheon/uf/common/auth/__init__.py

srcarter3/python-awips

d981062662968cf3fb105e8e23d955950ae2497e

[ "BSD-3-Clause" ]

33

2016-03-17T01:21:18.000Z

2022-02-08T10:41:06.000Z

dynamicserialize/dstypes/com/raytheon/uf/common/auth/__init__.py

srcarter3/python-awips

d981062662968cf3fb105e8e23d955950ae2497e

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2016-04-19T16:34:08.000Z

2020-09-09T19:57:54.000Z

dynamicserialize/dstypes/com/raytheon/uf/common/auth/__init__.py

Unidata/python-awips

8459aa756816e5a45d2e5bea534d23d5b1dd1690

[ "BSD-3-Clause" ]

20

2016-03-12T01:46:58.000Z

2022-02-08T06:53:22.000Z

__all__ = [ 'resp', 'user' ]

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py

Python

examples/statemachines/sources/do_state_action.py

DmitryBogomolov/aws-cloudformation-sample

f0454b203973e07027a4cdf5f36468d137d310fd

[ "MIT" ]

null

examples/statemachines/sources/do_state_action.py

DmitryBogomolov/aws-cloudformation-sample

f0454b203973e07027a4cdf5f36468d137d310fd

[ "MIT" ]

36

2018-04-20T06:11:41.000Z

2018-07-07T21:55:55.000Z

examples/statemachines/sources/do_state_action.py

DmitryBogomolov/aws-cloudformation-sample

f0454b203973e07027a4cdf5f36468d137d310fd

[ "MIT" ]

null

import os def mix(value): return (value * 7 + 29) % 17 def handler(event, context): return { 'count': event['count'] - 1, 'value': mix(event['value']) }

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Python

user_login.py

foozimachine/football

a71c6217619a3f73714b2039c6b0c8ec36919237

[ "Apache-2.0" ]

null

user_login.py

foozimachine/football

a71c6217619a3f73714b2039c6b0c8ec36919237

[ "Apache-2.0" ]

null

user_login.py

foozimachine/football

a71c6217619a3f73714b2039c6b0c8ec36919237

[ "Apache-2.0" ]

null

Mein neuer Code... Noch mehr Code

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Python

buzzni/ai/reco/mlserving/_state.py

BuzzniAILab/mlserving

8b8add9dbe5cdd6392e0c87ee789492de0a1c70e

[ "MIT" ]

13

2020-08-23T17:35:53.000Z

2022-02-10T14:14:03.000Z

mlserving/_state.py

orlevi111/ganesha

137cc388806fc98f7768298da01ebeddf03f9464

[ "MIT" ]

3

2020-08-20T21:09:01.000Z

2021-06-25T15:33:54.000Z

mlserving/_state.py

orlevi111/ganesha

137cc388806fc98f7768298da01ebeddf03f9464

[ "MIT" ]

3

2021-04-12T01:56:22.000Z

2021-10-05T12:50:12.000Z

class Status(object): SHUTTING_DOWN = 'Shutting Down' RUNNING = 'Running' class __State(object): def __init__(self): self._shutting_down = False def set_to_shutting_down(self): self._shutting_down = True def is_shutting_down(self): return self._shutting_down @property def status(self): return Status.SHUTTING_DOWN if self.is_shutting_down() else Status.RUNNING runtime_state = __State()

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py

Python

pywi/__init__.py

jeremiedecock/mrif

094b0dd81ff2be0e24bf3871caab48da1b5d138b

[ "MIT" ]

1

2021-07-06T06:02:45.000Z

pywi/__init__.py

jeremiedecock/mrif

094b0dd81ff2be0e24bf3871caab48da1b5d138b

[ "MIT" ]

null

pywi/__init__.py

jeremiedecock/mrif

094b0dd81ff2be0e24bf3871caab48da1b5d138b

[ "MIT" ]

1

2019-01-07T10:50:38.000Z

"""Python Wavelet Imaging PyWI is an image filtering library aimed at removing additive background noise from raster graphics images. * Input: a FITS file containing the raster graphics to clean (i.e. an image defined as a classic rectangular lattice of square pixels). * Output: a FITS file containing the cleaned raster graphics. The image filter relies on multiresolution analysis methods (Wavelet transforms) that remove some scales (frequencies) locally in space. These methods are particularly efficient when signal and noise are located at different scales (or frequencies). Optional features improve the SNR ratio when the (clean) signal constitute a single cluster of pixels on the image (e.g. electromagnetic showers produced with Imaging Atmospheric Cherenkov Telescopes). This library is written in Python and is based on the existing Cosmostat tools iSAp (Interactive Sparse Astronomical data analysis Packages http://www.cosmostat.org/software/isap/). The PyWI library also contains a dedicated package to optimize the image filter parameters for a given set of images (i.e. to adapt the filter to a specific problem). From a given training set of images (containing pairs of noised and clean images) and a given performance estimator (a function that assess the image filter parameters comparing the cleaned image to the actual clean image), the optimizer can determine the optimal filtering level for each scale. The PyWI library contains: * wavelet transform and wavelet filtering functions for image multiresolution analysis and filtering; * additional filter to remove some image components (non-significant pixels clusters); * a set of generic filtering performance estimators (MSE, NRMSE, SSIM, PSNR, image moment's difference), some relying on the scikit-image Python library (supplementary estimators can be easily added to meet particular needs); * a graphical user interface to visualize the filtering process in the wavelet transformed space; * an Evolution Strategies (ES) algorithm known in the mathematical optimization community for its good convergence rate on generic derivative-free continuous global optimization problems (Beyer, H. G. (2013) "The theory of evolution strategies", Springer Science & Business Media); * additional tools to manage and monitor the parameter optimization. Note: This project is in beta stage. Viewing documentation using IPython ----------------------------------- To see which functions are available in `pywi`, type ``pywi.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use ``pywi.*transform*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow down the list. To view the docstring for a function, use ``pywi.transform?<ENTER>`` (to view the docstring) and ``pywi.transform??<ENTER>`` (to view the source code). """ # PEP0440 compatible formatted version, see: # https://www.python.org/dev/peps/pep-0440/ # # Generic release markers: # X.Y # X.Y.Z # For bugfix releases # # Admissible pre-release markers: # X.YaN # Alpha release # X.YbN # Beta release # X.YrcN # Release Candidate # X.Y # Final release # # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' __version__ = '0.3.dev12' def get_version(): return __version__ # The following lines are temporary commented to avoid BUG#2 (c.f. BUGS.md) #from . import benchmark #from . import data #from . import io #from . import optimization #from . import processing #from . import ui

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Exporter Options/merged/DELETE THIS PLS.py

Kraypex/Exporter-Options

f75d5222762d03f090b97c72077b5712cf807b34

[ "MIT" ]

3

2021-11-22T11:55:15.000Z

2022-03-25T11:52:07.000Z

Exporter Options/merged/DELETE THIS PLS.py

Kraypex/Exporter-Options

f75d5222762d03f090b97c72077b5712cf807b34

[ "MIT" ]

null

Exporter Options/merged/DELETE THIS PLS.py

Kraypex/Exporter-Options

f75d5222762d03f090b97c72077b5712cf807b34

[ "MIT" ]

3

2021-11-22T10:25:27.000Z

2021-12-27T16:37:21.000Z

from time import sleep print("Hello! Please delete this, also use code Kraypex :))") sleep(5)

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py

Python

tests/test_pagination.py

MichalMilewicz/tungsten-ci-dashboard

67ca8e39855009ab7b4d603b691ea910ee6efbbc

[ "Apache-2.0" ]

null

tests/test_pagination.py

MichalMilewicz/tungsten-ci-dashboard

67ca8e39855009ab7b4d603b691ea910ee6efbbc

[ "Apache-2.0" ]

null

tests/test_pagination.py

MichalMilewicz/tungsten-ci-dashboard

67ca8e39855009ab7b4d603b691ea910ee6efbbc

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import unittest from dashboard.history import Paginator, pagination class TestPagination(unittest.TestCase): def test_zero_buildsests_should_return_empty_paginator(self): paginator = pagination(number_of_buildsets=0, page=1, per_page=10, page_links=5) expected = Paginator(pages=[], previous_page=None, next_page=None, current_page=1) self.assertEqual(paginator, expected) def test_negative_buildsets_should_raise_value_error(self): with self.assertRaises(ValueError): pagination(number_of_buildsets=-1, page=1, per_page=10, page_links=5) def test_current_page_first_should_return_no_previous_page(self): paginator = pagination(number_of_buildsets=100, page=1, per_page=20, page_links=4) expected = Paginator(pages=[1, 2, 3, 4, 5], previous_page=None, next_page=2, current_page=1) self.assertEqual(paginator, expected) def test_current_page_last_should_return_no_next_page(self): paginator = pagination(number_of_buildsets=100, page=5, per_page=20, page_links=4) expected = Paginator(pages=[1, 2, 3, 4, 5], previous_page=4, next_page=None, current_page=5) self.assertEqual(paginator, expected) def test_200_buildsets_20_per_page_should_return_10_pages(self): paginator = pagination(number_of_buildsets=200, page=1, per_page=20, page_links=20) expected = Paginator(pages=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], previous_page=None, next_page=2, current_page=1) self.assertEqual(paginator, expected) def test_201_buildsets_20_per_page_should_return_11_pages(self): paginator = pagination(number_of_buildsets=201, page=1, per_page=20, page_links=20) expected = Paginator(pages=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], previous_page=None, next_page=2, current_page=1) self.assertEqual(paginator, expected) def test_40_buildsets_10_per_page_should_return_4_pages(self): paginator = pagination(number_of_buildsets=40, page=1, per_page=10, page_links=10) expected = Paginator(pages=[1, 2, 3, 4], previous_page=None, next_page=2, current_page=1) self.assertEqual(paginator, expected) def test_45_buildsets_10_per_page_should_return_5_pages(self): paginator = pagination(number_of_buildsets=45, page=1, per_page=10, page_links=10) expected = Paginator(pages=[1, 2, 3, 4, 5], previous_page=None, next_page=2, current_page=1) self.assertEqual(paginator, expected) def test_current_page_5_of_10_should_return_previus_and_next_page(self): paginator = pagination(number_of_buildsets=100, page=5, per_page=10, page_links=5) expected = Paginator(pages=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], previous_page=4, next_page=6, current_page=5) self.assertEqual(paginator, expected) def test_5_page_links_on_page_10_should_return_5_prev_and_next_pages(self): paginator = pagination(number_of_buildsets=400, page=10, per_page=20, page_links=5) expected = Paginator(pages=[1, None, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, None, 20], previous_page=9, next_page=11, current_page=10) self.assertEqual(paginator, expected) def test_3_pages_links_on_page_5_should_return_3_prev_and_next_pages(self): paginator = pagination(number_of_buildsets=400, page=5, per_page=20, page_links=3) expected = Paginator(pages=[1, None, 2, 3, 4, 5, 6, 7, 8, None, 20], previous_page=4, next_page=6, current_page=5) self.assertEqual(paginator, expected) def test_current_page_zero_should_raise_value_error(self): with self.assertRaises(ValueError): pagination(number_of_buildsets=200, page=0, per_page=20, page_links=5) def test_per_page_is_zero_should_raise_value_error(self): with self.assertRaises(ValueError): pagination(number_of_buildsets=200, page=1, per_page=0, page_links=5) def test_per_page_is_negative_should_raise_value_error(self): with self.assertRaises(ValueError): pagination(number_of_buildsets=200, page=1, per_page=-5, page_links=5) def test_page_is_none_should_raise_type_error(self): with self.assertRaises(TypeError): pagination(number_of_buildsets=200, page=None, per_page=10, page_links=5) def test_number_of_buildsets_is_none_should_raise_type_error(self): with self.assertRaises(TypeError): pagination(number_of_buildsets=None, page=1, per_page=10, page_links=5) def test_per_page_is_none_should_raise_type_error(self): with self.assertRaises(TypeError): pagination(number_of_buildsets=200, page=1, per_page=None, page_links=5) def test_page_links_is_none_should_raise_type_error(self): with self.assertRaises(TypeError): pagination(number_of_buildsets=200, page=1, per_page=10, page_links=None)

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