ytzi/the-stack-dedup-python-filtered · Datasets at Hugging Face

eb20be04422ba85fc708db252613db55adc1f7a9

359

py

Python

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/scripts/vulture/whitelist.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

3

2021-12-15T04:58:18.000Z

2022-02-06T12:15:37.000Z

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/scripts/vulture/whitelist.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

null

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/scripts/vulture/whitelist.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

1

2019-01-02T14:38:50.000Z

#!/bin/env python # Vulture often detects false positives when analyzing a code # base. If there are particular things you wish to ignore, # add them below. This file is consumed by # scripts/dead_code/find-dead-code.sh from vulture.whitelist_utils import Whitelist view_whitelilst = Whitelist() # Example: # view_whitelist.name_of_function_to_whitelist

23.933333

61

0.793872

eb212bcaed139e5c9db595186ee8e16677921512

8,088

py

Python

mmdet/utils/memory.py

Youth-Got/mmdetection

2e0a02599804da6e07650dde37b9df538e15d646

[ "Apache-2.0" ]

1

2021-12-10T15:08:22.000Z

mmdet/utils/memory.py

q3394101/mmdetection

ca11860f4f3c3ca2ce8340e2686eeaec05b29111

[ "Apache-2.0" ]

null

mmdet/utils/memory.py

q3394101/mmdetection

ca11860f4f3c3ca2ce8340e2686eeaec05b29111

[ "Apache-2.0" ]

null

# Copyright (c) OpenMMLab. All rights reserved. import warnings from collections import abc from contextlib import contextmanager from functools import wraps import torch from mmdet.utils import get_root_logger def cast_tensor_type(inputs, src_type=None, dst_type=None): """Recursively convert Tensor in inputs from ``src_type`` to ``dst_type``. Args: inputs: Inputs that to be casted. src_type (torch.dtype | torch.device): Source type. src_type (torch.dtype | torch.device): Destination type. Returns: The same type with inputs, but all contained Tensors have been cast. """ assert dst_type is not None if isinstance(inputs, torch.Tensor): if isinstance(dst_type, torch.device): # convert Tensor to dst_device if hasattr(inputs, 'to') and \ hasattr(inputs, 'device') and \ (inputs.device == src_type or src_type is None): return inputs.to(dst_type) else: return inputs else: # convert Tensor to dst_dtype if hasattr(inputs, 'to') and \ hasattr(inputs, 'dtype') and \ (inputs.dtype == src_type or src_type is None): return inputs.to(dst_type) else: return inputs # we need to ensure that the type of inputs to be casted are the same # as the argument `src_type`. elif isinstance(inputs, abc.Mapping): return type(inputs)({ k: cast_tensor_type(v, src_type=src_type, dst_type=dst_type) for k, v in inputs.items() }) elif isinstance(inputs, abc.Iterable): return type(inputs)( cast_tensor_type(item, src_type=src_type, dst_type=dst_type) for item in inputs) # TODO: Currently not supported # elif isinstance(inputs, InstanceData): # for key, value in inputs.items(): # inputs[key] = cast_tensor_type( # value, src_type=src_type, dst_type=dst_type) # return inputs else: return inputs # To use AvoidOOM as a decorator AvoidCUDAOOM = AvoidOOM()

37.794393

103

0.574679

eb213849d6f5cbf00a64871c3293e7fb777f9ff4

2,278

py

Python

game.py

YeonjuKim05/Kim_Y_RPS_Fall2020

031bfeec09f663686ae2c9418185ab5070af3b7a

[ "MIT" ]

null

game.py

YeonjuKim05/Kim_Y_RPS_Fall2020

031bfeec09f663686ae2c9418185ab5070af3b7a

[ "MIT" ]

1

2020-11-28T16:29:28.000Z

game.py

YeonjuKim05/Kim_Y_RPS_Fall2020

031bfeec09f663686ae2c9418185ab5070af3b7a

[ "MIT" ]

null

# import packages to extend python (just like we extend sublime, or Atom, or VSCode) from random import randint from gameComponents import gameVars, chooseWinner while gameVars.player is False: print("=======================*/ RPS CONTEST /*=======================") print("Computer Lives: ", gameVars.ai_lives, "/", gameVars.total_lives) print("Player Lives: ", gameVars.player_lives, "/", gameVars.total_lives) print("==============================================") print("Choose your weapon! or type quit to leave\n") gameVars.player = input("Choose rock, paper or scissors: \n") # if the player chose to quit then exit the game if gameVars.player == "quit": print("You chose to quit") exit() #player = True -> it has a value (rock, paper, or scissors) # this will be the AI choice -> a random pick from the choices array computer = gameVars.choices[randint(0, 2)] # check to see what the user input # print outputs whatever is in the round brackets -> in this case it outputs player to the command prompt window print("user chose: " + gameVars.player) # validate that the random choice worked for the AI print("AI chose: " + computer) #--------------------------- MOVE THIS CHUNK OF CODE TO A PACKAGE - START HERE -------------------- if (computer == gameVars.player): print("tie") # always check for negative conditions first (the losing case) elif (computer == "rock"): if (gameVars.player == "scissors"): print("you lose") gameVars.player_lives -= 1 else: print("you win!") gameVars.ai_lives -= 1 elif (computer == "paper"): if (gameVars.player == "rock"): print("you lose") gameVars.player_lives -= 1 else: print("you win!") gameVars.ai_lives -= 1 elif (computer == "scissors"): if (gameVars.player == "paper"): print("you lose") gameVars.player_lives -= 1 else: print("you win!") gameVars.ai_lives -= 1 #--------------------------- stop here - all of the above needs to move ----------------------- if gameVars.player_lives is 0: chooseWinner.winorlose("lost") if gameVars.ai_lives is 0: chooseWinner.winorlose("won") print("Player has", gameVars.player_lives, "lives left") print("AI has", gameVars.ai_lives, "lives left") gameVars.player = False

26.183908

113

0.6295

eb21b87b5bc6c350c9c4db10e19ca1430b1bd7c2

1,227

py

Python

dataset/utils.py

tarun-bisht/mlpipe

0cd1f0b57a7788222228dc08f0c8a21ed51a7cc1

[ "MIT" ]

null

dataset/utils.py

tarun-bisht/mlpipe

0cd1f0b57a7788222228dc08f0c8a21ed51a7cc1

[ "MIT" ]

null

dataset/utils.py

tarun-bisht/mlpipe

0cd1f0b57a7788222228dc08f0c8a21ed51a7cc1

[ "MIT" ]

null

import pandas as pd import os

36.088235

109

0.597392

eb2259b4263e5697783bf6849627924369449a0f

1,222

py

Python

THreading.py

asd86826/OpticalFlow_Test

f4d621994871b4913b95a18f59cb171526d786ae

[ "MIT" ]

null

THreading.py

asd86826/OpticalFlow_Test

f4d621994871b4913b95a18f59cb171526d786ae

[ "MIT" ]

null

THreading.py

asd86826/OpticalFlow_Test

f4d621994871b4913b95a18f59cb171526d786ae

[ "MIT" ]

null

import time from threading import Timer i = 0 if __name__ == "__main__": print("Starting...") rt = RepeatedTimer(0.05, timeTest) # it auto start ,so dont need rt.start() try: ST = time.time() time.sleep(5) except Exception as e: raise e finally: rt.stop() print(time.time() - ST)

24.44

85

0.531097

eb22d571bce236b4e4b07269afd4c1273f92107f

721

py

Python

src/main/PyCodes/deep_versions.py

panditu2015/DL-Lab-7th-Semester

59a64d9c219cbed8cc4a75517f46c7f551a95a5a

[ "MIT" ]

null

src/main/PyCodes/deep_versions.py

panditu2015/DL-Lab-7th-Semester

59a64d9c219cbed8cc4a75517f46c7f551a95a5a

[ "MIT" ]

null

src/main/PyCodes/deep_versions.py

panditu2015/DL-Lab-7th-Semester

59a64d9c219cbed8cc4a75517f46c7f551a95a5a

[ "MIT" ]

null

# coding: utf-8 # In[1]: import keras # In[2]: # scipy import scipy print( ' scipy: %s ' % scipy.__version__) # numpy import numpy print( ' numpy: %s ' % numpy.__version__) # matplotlib import matplotlib print( ' matplotlib: %s ' % matplotlib.__version__) # pandas import pandas print( ' pandas: %s ' % pandas.__version__) # statsmodels import statsmodels print( ' statsmodels: %s ' % statsmodels.__version__) # scikit-learn import sklearn print( ' sklearn: %s ' % sklearn.__version__) # In[3]: # theano import theano print( ' theano: %s ' % theano.__version__) # tensorflow import tensorflow print( ' tensorflow: %s ' % tensorflow.__version__) # keras import keras print( ' keras: %s ' % keras.__version__)

15.673913

53

0.694868

eb2601a12ac399bfb0e416993c3a1b51cb79ad73

577

py

Python

graph_help/colorschemes/DarkColorScheme.py

jgurhem/Graph_Generator

d60f4451feef0c530389bfc4bc6978bda3d4c0cb

[ "MIT" ]

null

graph_help/colorschemes/DarkColorScheme.py

jgurhem/Graph_Generator

d60f4451feef0c530389bfc4bc6978bda3d4c0cb

[ "MIT" ]

null

graph_help/colorschemes/DarkColorScheme.py

jgurhem/Graph_Generator

d60f4451feef0c530389bfc4bc6978bda3d4c0cb

[ "MIT" ]

null

from .DefaultColorScheme import DefaultColorScheme

30.368421

50

0.636049

eb266bf3b2f0517ce3d9501b3cfc011f8ded2d3e

3,817

bzl

Python

defs.bzl

attilaolah/bazel-tools

823216936ee93ab6884c6111a8e60e9a836fa7cc

[ "Apache-2.0" ]

2

2021-09-02T18:59:09.000Z

2021-09-20T23:13:17.000Z

defs.bzl

attilaolah/bazel-tools

823216936ee93ab6884c6111a8e60e9a836fa7cc

[ "Apache-2.0" ]

null

defs.bzl

attilaolah/bazel-tools

823216936ee93ab6884c6111a8e60e9a836fa7cc

[ "Apache-2.0" ]

null

# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. load("@bazel_skylib//lib:shell.bzl", "shell") json_extract = rule( implementation = _json_extract_impl, attrs = { "srcs": attr.label_list( mandatory = True, allow_files = [".json"], doc = "List of inputs. Must all be valid JSON files.", ), "suffix": attr.string( default = "", doc = ("Output file extensions. Each input file will be renamed " + "from basename.json to basename+suffix."), ), "raw": attr.bool( default = False, doc = ("Whether or not to pass -r to jq. Passing -r will result " + "in raw data being extracted, i.e. non-JSQN output."), ), "query": attr.string( default = ".", doc = ("Query to pass to the jq binary. The default is '.', " + "meaning just copy the validated input."), ), "flags": attr.string_list( allow_empty = True, doc = "List of flags to pass to the jq binary.", ), "_jq": attr.label( executable = True, cfg = "host", default = Label("@jq"), ), }, ) json_test = rule( implementation = _json_test_impl, attrs = { "srcs": attr.label_list( mandatory = True, allow_files = [".json"], doc = ("List of inputs. The test will verify that they are " + "valid JSON files."), ), "_jq": attr.label( executable = True, cfg = "host", default = Label("@jq"), ), }, outputs = {"test": "%{name}.sh"}, test = True, )

31.545455

79

0.556196

eb26e6350d60cf3d97e04c6da4b6ad1b56768020

554

py

Python

Psi_Phi/plot.py

Twinstar2/Phython_scripts

19f88420bca64014585e87747d01737afe074400

[ "MIT" ]

null

Psi_Phi/plot.py

Twinstar2/Phython_scripts

19f88420bca64014585e87747d01737afe074400

[ "MIT" ]

1

2018-02-14T15:19:07.000Z

Psi_Phi/plot.py

TobiasJu/Python_Master_scripts

19f88420bca64014585e87747d01737afe074400

[ "MIT" ]

null

import matplotlib.pyplot as plt plt.switch_backend('agg') import seaborn as sns sns_plot = \ (sns.jointplot(psi, phi, size=12, space=0, xlim=(-190, 190), ylim=(-190, 190)).plot_joint(sns.kdeplot, zorder=0, n_levels=6)) # sns_plot = sns.jointplot(psi_list_numpy, phi_list_numpy, kind="hex", color="#4CB391") # stat_func=kendalltau # sns_plot.ylim(-180, 180) print "plotting: ", pfam sns_plot.savefig("Ramachandranplot_scatter/ramachandranplot_" + pfam + ".png")

39.571429

112

0.617329

eb289039ceb1e6cb9ff0bbb176aa1f763781e163

692

py

Python

tests/test_instrumentation/test_base.py

cloudchacho/hedwig-python

1e4ca5472fe661ffd9d3cedd10a9ddc2daa0926b

[ "Apache-2.0" ]

null

tests/test_instrumentation/test_base.py

cloudchacho/hedwig-python

1e4ca5472fe661ffd9d3cedd10a9ddc2daa0926b

[ "Apache-2.0" ]

3

2021-06-25T20:52:50.000Z

2021-11-30T16:22:30.000Z

tests/test_instrumentation/test_base.py

cloudchacho/hedwig-python

1e4ca5472fe661ffd9d3cedd10a9ddc2daa0926b

[ "Apache-2.0" ]

null

from unittest import mock import pytest get_tracer = pytest.importorskip('opentelemetry.trace.get_tracer')

40.705882

99

0.789017

eb2a05506a2d5dac21a3a7230d334f572006e5b5

42

py

Python

logic/start_game.py

sparkingdark/Project

fdd521407d788d1945275148337992a795ebdf0c

[ "MIT" ]

null

logic/start_game.py

sparkingdark/Project

fdd521407d788d1945275148337992a795ebdf0c

[ "MIT" ]

null

logic/start_game.py

sparkingdark/Project

fdd521407d788d1945275148337992a795ebdf0c

[ "MIT" ]

5

2020-11-28T13:13:15.000Z

2020-12-07T16:32:36.000Z

from logic import *

8.4

19

0.619048

eb2a6dfadfc03cbe4b08fd33a47e0c0b3e370224

1,184

py

Python

Leetcode/SwapNodesInPairs.py

tswsxk/CodeBook

01b976418d64f5f94257ae0e2b36751afb93c105

[ "MIT" ]

null

Leetcode/SwapNodesInPairs.py

tswsxk/CodeBook

01b976418d64f5f94257ae0e2b36751afb93c105

[ "MIT" ]

1

2019-09-24T22:04:03.000Z

Leetcode/SwapNodesInPairs.py

tswsxk/CodeBook

01b976418d64f5f94257ae0e2b36751afb93c105

[ "MIT" ]

null

# Definition for singly-linked list. def initlist(listnum): head = ListNode(listnum[0]) tail = head for num in listnum[1:]: tail.next = ListNode(num) tail = tail.next return head if __name__ == "__main__": sol = Solution() sol.swapPairs(initlist([1,2,3,4]))

24.163265

44

0.47973

eb2b0a445ecc0e541307b4aff935b22d4cc3183d

939

py

Python

hello.py

ookcode/CodingSpider

eac57ef8b41be841a8366f3cc376ff259d01e27f

[ "MIT" ]

null

hello.py

ookcode/CodingSpider

eac57ef8b41be841a8366f3cc376ff259d01e27f

[ "MIT" ]

null

hello.py

ookcode/CodingSpider

eac57ef8b41be841a8366f3cc376ff259d01e27f

[ "MIT" ]

1

2022-02-23T07:12:23.000Z

#!/usr/bin/python #coding=utf-8 import os from flask import Flask from flask import Response from flask import request app = Flask(__name__) if __name__ == "__main__": app.run()

30.290323

97

0.652822

eb2c8b8b8d777e9a0438515ac0aea6cd01f5301b

2,696

py

Python

chess-board-0.2.0/chessboard/pieces.py

fshelobolin/irohbot

4ad4c554ecff1e1005fbecf26ee097c387bf357d

[ "MIT" ]

null

chess-board-0.2.0/chessboard/pieces.py

fshelobolin/irohbot

4ad4c554ecff1e1005fbecf26ee097c387bf357d

[ "MIT" ]

null

chess-board-0.2.0/chessboard/pieces.py

fshelobolin/irohbot

4ad4c554ecff1e1005fbecf26ee097c387bf357d

[ "MIT" ]

null

""" Ahira Justice, ADEFOKUN justiceahira@gmail.com """ import os import pygame BASE_DIR = os.path.dirname(os.path.abspath(__file__)) IMAGE_DIR = os.path.join(BASE_DIR, "images") BLACK = "BLACK" WHITE = "WHITE" BISHOP = "BISHOP" KING = "KING" KNGHT = "KNIGHT" PAWN = "PAWN" QUEEN = "QUEEN" ROOK = "ROOK"

29.304348

66

0.582715

eb2cab16d3d0736d863c283be6817d00ab5e890d

3,993

py

Python

stacks/XIAOMATECH/1.0/services/ROCKETMQ/package/scripts/namesrv.py

tvorogme/dataops

acfa21df42a20768c004c6630a064f4e38e280b2

[ "Apache-2.0" ]

3

2019-08-13T01:44:16.000Z

2019-12-10T04:05:56.000Z

stacks/XIAOMATECH/1.0/services/ROCKETMQ/package/scripts/namesrv.py

tvorogme/dataops

acfa21df42a20768c004c6630a064f4e38e280b2

[ "Apache-2.0" ]

null

stacks/XIAOMATECH/1.0/services/ROCKETMQ/package/scripts/namesrv.py

tvorogme/dataops

acfa21df42a20768c004c6630a064f4e38e280b2

[ "Apache-2.0" ]

7

2019-05-29T17:35:25.000Z

2021-12-04T07:55:10.000Z

from resource_management.core.resources.system import Execute from resource_management.libraries.script import Script from resource_management.core.resources.system import Directory from resource_management.core.resources.system import File from resource_management.core.source import InlineTemplate from resource_management.libraries.functions.check_process_status import check_process_status import os if __name__ == "__main__": Rocketmq().execute()

35.651786

146

0.643877

eb361ceecffd166eeb0b6b3ee13b8be48e6f4d86

819

py

Python

setup.py

ktvng/cue

5f31c8898f3bc53a18956220f609489cd2bbe590

[ "MIT" ]

null

setup.py

ktvng/cue

5f31c8898f3bc53a18956220f609489cd2bbe590

[ "MIT" ]

null

setup.py

ktvng/cue

5f31c8898f3bc53a18956220f609489cd2bbe590

[ "MIT" ]

null

"""Cue: Script Orchestration for Data Analysis Cue lets your package your data analysis into simple actions which can be connected into a dynamic data analysis pipeline with coverage over even complex data sets. """ DOCLINES = (__doc__ or '').split('\n') from setuptools import find_packages, setup setup( name='py-cue', package_dir={'cue/cue': 'cue'}, packages=find_packages(include=['cue']), version='0.1.0', description=DOCLINES[0], long_description="\n".join(DOCLINES[2:]), project_urls={ "Source Code": "https://github.com/ktvng/cue" }, author='ktvng', license='MIT', python_requires='>=3.8', install_requires=['pyyaml>=5.2'], entry_points={ 'console_scripts': { 'cue=cue.cli:run' } } )

26.419355

85

0.616606

eb3657629d59fdcbd7874c2822fc0707cfc70c45

1,689

py

Python

tests/getz.py

deflax/steinvord

709326ff219159a78f644c0adf3c5b224ed42804

[ "Zlib" ]

1

2021-06-02T19:51:26.000Z

tests/getz.py

deflax/steinvord

709326ff219159a78f644c0adf3c5b224ed42804

[ "Zlib" ]

null

tests/getz.py

deflax/steinvord

709326ff219159a78f644c0adf3c5b224ed42804

[ "Zlib" ]

null

#!/usr/bin/python3.2 # # Zabbix API Python usage example # Christoph Haas <email@christoph-haas.de> # username='' password='1' hostgroup='' item_name='system.cpu.load[,avg1]' zabbix_url='' import zabbix_api import sys # Connect to Zabbix server z=zabbix_api.ZabbixAPI(server=zabbix_url) z.login(user=username, password=password) # Get hosts in the hostgroup hostgroup = z.hostgroup.get( { 'filter': { 'name':hostgroup }, 'sortfield': 'name', 'sortorder': 'ASC', 'limit':2, 'select_hosts':'extend' }) print(hostgroup[0]) print("\n") for host in hostgroup[0]['name']: hostname = host['host'] print("Host:", hostname) print("Host-ID:", host['hostid']) item = z.item.get({ 'output':'extend', 'hostids':host['hostid'], 'filter':{'key_':item_name}}) if item: print(item[0]['lastvalue']) print("Item-ID:", item[0]['itemid']) # Get history lastvalue = z.history.get({ 'history': item[0]['value_type'], 'itemids': item[0]['itemid'], 'output': 'extend', # Sort by timestamp from new to old 'sortfield':'clock', 'sortorder':'DESC', # Get only the first (=newest) entry 'limit': 1, }) # CAVEAT! The history.get function must be told which type the # values are (float, text, etc.). The item.value_type contains # the number that needs to be passed to history.get. if lastvalue: lastvalue = lastvalue[0]['value'] print("Last value:", lastvalue) else: print("No item....") print("---------------------------")

23.788732

70

0.562463

eb3b035d6a2b960bc0d338d7dd3785c2208f99f5

11,813

py

Python

server.py

uanthwal/starter-snake-python

6eff23ac9b9b0cfb9dbbf6d756a92a677bbf0417

[ "MIT" ]

null

server.py

uanthwal/starter-snake-python

6eff23ac9b9b0cfb9dbbf6d756a92a677bbf0417

[ "MIT" ]

null

server.py

uanthwal/starter-snake-python

6eff23ac9b9b0cfb9dbbf6d756a92a677bbf0417

[ "MIT" ]

null

import copy import math import os import random import cherrypy """ This is a simple Battlesnake server written in Python. For instructions see https://github.com/BattlesnakeOfficial/starter-snake-python/README.md """ if __name__ == "__main__": server = Battlesnake() cherrypy.config.update({"server.socket_host": "0.0.0.0"}) cherrypy.config.update({ "server.socket_port": int(os.environ.get("PORT", "8080")), }) print("Starting Battlesnake Server...") cherrypy.quickstart(server)

31.501333

108

0.632439

eb3bba063d98bf83051c3973141cbbea653626d3

342

py

Python

EventIntegrityLib.py

fermi-lat/EventIntegrity

600c64c7b9be57e1008d12b7bd28ef0d260d7973

[ "BSD-3-Clause" ]

null

EventIntegrityLib.py

fermi-lat/EventIntegrity

600c64c7b9be57e1008d12b7bd28ef0d260d7973

[ "BSD-3-Clause" ]

null

EventIntegrityLib.py

fermi-lat/EventIntegrity

600c64c7b9be57e1008d12b7bd28ef0d260d7973

[ "BSD-3-Clause" ]

null

# $Header: /nfs/slac/g/glast/ground/cvs/GlastRelease-scons/EventIntegrity/EventIntegrityLib.py,v 1.2 2008/08/28 21:50:54 ecephas Exp $

38

134

0.681287

eb3c0fe9fe75281912b7403d1e9af8679184f59d

107

py

Python

mr4mp/__init__.py

lapets/mr4mp

3f3d6ec01272d4b450eda536b37bcd76851a57d2

[ "MIT" ]

5

2019-06-28T17:36:37.000Z

2022-03-08T18:59:01.000Z

mr4mp/__init__.py

lapets/mr4mp

3f3d6ec01272d4b450eda536b37bcd76851a57d2

[ "MIT" ]

null

mr4mp/__init__.py

lapets/mr4mp

3f3d6ec01272d4b450eda536b37bcd76851a57d2

[ "MIT" ]

null

"""Gives users direct access to class and functions.""" from mr4mp.mr4mp import pool, mapreduce, mapconcat

35.666667

55

0.775701

eb3c1435400a880f8b3833ff6b37ef02c5237e11

59,098

py

Python

google/devtools/testing/v1/devtools-testing-v1-py/google/devtools/testing_v1/types/test_execution.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

7

2021-02-21T10:39:41.000Z

2021-12-07T07:31:28.000Z

google/devtools/testing/v1/devtools-testing-v1-py/google/devtools/testing_v1/types/test_execution.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

6

2021-02-02T23:46:11.000Z

2021-11-15T01:46:02.000Z

google/devtools/testing/v1/devtools-testing-v1-py/google/devtools/testing_v1/types/test_execution.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

4

2021-01-28T23:25:45.000Z

2021-08-30T01:55:16.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import proto # type: ignore from google.protobuf import duration_pb2 # type: ignore from google.protobuf import timestamp_pb2 # type: ignore __protobuf__ = proto.module( package='google.devtools.testing.v1', manifest={ 'OrchestratorOption', 'RoboActionType', 'InvalidMatrixDetails', 'TestState', 'OutcomeSummary', 'TestMatrix', 'TestExecution', 'TestSpecification', 'SystraceSetup', 'TestSetup', 'IosTestSetup', 'EnvironmentVariable', 'Account', 'GoogleAuto', 'Apk', 'AppBundle', 'DeviceFile', 'ObbFile', 'RegularFile', 'IosDeviceFile', 'AndroidTestLoop', 'IosXcTest', 'IosTestLoop', 'AndroidInstrumentationTest', 'AndroidRoboTest', 'RoboDirective', 'RoboStartingIntent', 'LauncherActivityIntent', 'StartActivityIntent', 'EnvironmentMatrix', 'AndroidDeviceList', 'IosDeviceList', 'AndroidMatrix', 'ClientInfo', 'ClientInfoDetail', 'ResultStorage', 'ToolResultsHistory', 'ToolResultsExecution', 'ToolResultsStep', 'GoogleCloudStorage', 'FileReference', 'Environment', 'AndroidDevice', 'IosDevice', 'TestDetails', 'InvalidRequestDetail', 'ShardingOption', 'UniformSharding', 'ManualSharding', 'TestTargetsForShard', 'Shard', 'CreateTestMatrixRequest', 'GetTestMatrixRequest', 'CancelTestMatrixRequest', 'CancelTestMatrixResponse', }, ) __all__ = tuple(sorted(__protobuf__.manifest))

30.029472

108

0.619953

eb3c4ae70f222dd8a499b8678c9508db3922f5b5

1,457

py

Python

CONTENT/Resources/guides/__UNSORTED/244_shortest_word_distance_ii/shortest.py

impastasyndrome/DS-ALGO-OFFICIAL

c85ec9cf0af0009f038b7a571a7ac1fb466b7f3a

[ "Apache-2.0" ]

13

2021-03-11T00:25:22.000Z

2022-03-19T00:19:23.000Z

CONTENT/Resources/guides/__UNSORTED/244_shortest_word_distance_ii/shortest.py

impastasyndrome/DS-ALGO-OFFICIAL

c85ec9cf0af0009f038b7a571a7ac1fb466b7f3a

[ "Apache-2.0" ]

162

2021-03-09T01:52:11.000Z

2022-03-12T01:09:07.000Z

CONTENT/Resources/guides/__UNSORTED/244_shortest_word_distance_ii/shortest.py

impastasyndrome/DS-ALGO-OFFICIAL

c85ec9cf0af0009f038b7a571a7ac1fb466b7f3a

[ "Apache-2.0" ]

12

2021-04-26T19:43:01.000Z

2022-01-31T08:36:29.000Z

from collections import defaultdict # Your WordDistance object will be instantiated and called as such: # wordDistance = WordDistance(words) # wordDistance.shortest("word1", "word2") # wordDistance.shortest("anotherWord1", "anotherWord2")

29.734694

67

0.539465

eb41c235a81322c2905a0154804ac4a18d5c346c

1,060

py

Python

src/sentimentClassification.py

MaxPowerScience/EnglishSentiment

119eeb6e1ee9f24805fbad6650d1a9c3e305f952

[ "Apache-2.0" ]

null

src/sentimentClassification.py

MaxPowerScience/EnglishSentiment

119eeb6e1ee9f24805fbad6650d1a9c3e305f952

[ "Apache-2.0" ]

null

src/sentimentClassification.py

MaxPowerScience/EnglishSentiment

119eeb6e1ee9f24805fbad6650d1a9c3e305f952

[ "Apache-2.0" ]

null

from perceptron import train_network, create_perceptron, test_network from preprocessingData import get_ids_matrix, separate_test_and_training_data, read_word_list from extractRawData import get_raw_data from lstm import create_lstm, create_lstm_with_tensorflow if __name__ == "__main__": main()

37.857143

93

0.766038

eb41c51ce9970b54d5b685bba4f5e3319c3b6398

33,225

py

Python

Developer-Essentials-Capstone/Python/Includes/Capstone-Setup.py

databricks-academy/developer-essentials-capstone

77e70b1eb5b49b5f6779495fac7d14f5fadded9d

[ "CC0-1.0" ]

1

2022-02-08T03:56:32.000Z

Developer-Essentials-Capstone/Python/Includes/Capstone-Setup.py

databricks-academy/developer-essentials-capstone

77e70b1eb5b49b5f6779495fac7d14f5fadded9d

[ "CC0-1.0" ]

null

Developer-Essentials-Capstone/Python/Includes/Capstone-Setup.py

databricks-academy/developer-essentials-capstone

77e70b1eb5b49b5f6779495fac7d14f5fadded9d

[ "CC0-1.0" ]

4

2022-01-01T09:41:31.000Z

2022-02-17T09:48:05.000Z

# Databricks notebook source import builtins as BI # Setup the capstone import re, uuid from pyspark.sql.types import StructType, StringType, IntegerType, TimestampType, DoubleType from pyspark.sql.functions import col, to_date, weekofyear from pyspark.sql import DataFrame static_tests = None bronze_tests = None silver_tests = None gold_tests = None registration_id = None final_passed = False course_name = "Core Partner Enablement" username = spark.sql("SELECT current_user()").first()[0] clean_username = re.sub("[^a-zA-Z0-9]", "_", username) user_db = f"dbacademy_{clean_username}_dev_ess_cap" working_dir = f"dbfs:/user/{username}/dbacademy/dev-ess-cap" outputPathBronzeTest = f"{working_dir}/bronze_test" outputPathSilverTest = f"{working_dir}/silver_test" outputPathGoldTest = f"{working_dir}/gold_test" source_path = f"wasbs://courseware@dbacademy.blob.core.windows.net/developer-essentials-capstone/v01" eventSchema = ( StructType() .add('eventName', StringType()) .add('eventParams', StructType() .add('game_keyword', StringType()) .add('app_name', StringType()) .add('scoreAdjustment', IntegerType()) .add('platform', StringType()) .add('app_version', StringType()) .add('device_id', StringType()) .add('client_event_time', TimestampType()) .add('amount', DoubleType()) ) ) print(f"Declared the following variables:") print(f" * user_db: {user_db}") print(f" * working_dir: {working_dir}") print() print(f"Declared the following function:") print(f" * realityCheckBronze(..)") print(f" * realityCheckStatic(..)") print(f" * realityCheckSilver(..)") print(f" * realityCheckGold(..)") print(f" * realityCheckFinal()") # COMMAND ---------- try: reinstall = dbutils.widgets.get("reinstall").lower() == "true" except: reinstall = False install_exercise_datasets(reinstall) print(f"\nYour Registration ID is {registration_id}") # COMMAND ---------- # Setup Bronze from pyspark.sql import DataFrame import time None # COMMAND ---------- # Setup Static None # COMMAND ---------- # Setup Silver None # COMMAND ---------- # Setup Gold None # COMMAND ---------- html_passed = f""" <html> <body> <h2>Congratulations! You're all done!</h2> While the preliminary evaluation of your project indicates that you have passed, we have a few more validation steps to run on the back-end:<br/> <ul style="margin:0"> <li> Code & statistical analysis of your capstone project</li> <li> Correlation of your account in our LMS via your email address, <b>{username}</b></li> <li> Final preparation of your badge </ul> <p>Assuming there are no issues with our last few steps, you will receive your <b>Databricks Developer Essentials Badge</b> within 2 weeks. Notification will be made by email to <b>{username}</b> regarding the availability of your digital badge via <b>Accredible</b>. Should we have any issues, such as not finding your email address in our LMS, we will do our best to resolve the issue using the email address provided here. </p> <p>Your digital badge will be available in a secure, verifiable, and digital format that you can easily retrieve via <b>Accredible</b>. You can then share your achievement via any number of different social media platforms.</p> <p>If you have questions about the status of your badge after the initial two-week window, or if the email address listed above is incorrect, please <a href="https://help.databricks.com/s/contact-us?ReqType=training" target="_blank">submit a ticket</a> with the subject "Core Capstone" and your Registration ID (<b>{registration_id}</b>) in the message body. Please allow us 3-5 business days to respond.</p> One final note: In order to comply with <a href="https://oag.ca.gov/privacy/ccpa" target="_blank">CCPA</a> and <a href="https://gdpr.eu/" target="_blank">GDPR</a>, which regulate the collection of your personal information, the status of this capstone and its correlation to your email address will be deleted within 30 days of its submission. </body> </html> """ html_failed = f""" <html> <body> <h2>Almost There!</h2> <p>Our preliminary evaluation of your project indicates that you have not passed.</p> <p>In order for your project to be submitted <b>all</b> reality checks must pass.</p> <p>In some cases this problem can be resolved by simply clearning the notebook's state (<b>Clear State & Results</b>) and then selecting <b>Run All</b> from the toolbar above.</p> <p>If your project continues to fail validation, please review each step above to ensure that you are have properly addressed all the corresponding requirements.</p> </body> </html> """ # Setup Final None # COMMAND ---------- daLogger = CapstoneLogger() None # COMMAND ---------- # These imports are OK to provide for students import pyspark from typing import Callable, Any, Iterable, List, Set, Tuple import uuid ############################################# # Test Suite classes ############################################# # Test case # Test result # Decorator to lazy evaluate - used by TestSuite def lazy_property(fn): '''Decorator that makes a property lazy-evaluated. ''' attr_name = '_lazy_' + fn.__name__ return _lazy_property testResultsStyle = """ <style> table { text-align: left; border-collapse: collapse; margin: 1em; caption-side: bottom; font-family: Sans-Serif; font-size: 16px} caption { text-align: left; padding: 5px } th, td { border: 1px solid #ddd; padding: 5px } th { background-color: #ddd } .passed { background-color: #97d897 } .failed { background-color: #e2716c } .skipped { background-color: #f9d275 } .results .points { display: none } .results .message { display: none } .results .passed::before { content: "Passed" } .results .failed::before { content: "Failed" } .results .skipped::before { content: "Skipped" } .grade .passed .message:empty::before { content:"Passed" } .grade .failed .message:empty::before { content:"Failed" } .grade .skipped .message:empty::before { content:"Skipped" } </style> """.strip() # Test suite class class __TestResultsAggregator(object): testResults = dict() def displayResults(self): displayHTML(testResultsStyle + f""" <table class='results'> <tr><th colspan="2">Test Summary</th></tr> <tr><td>Number of Passing Tests</td><td style="text-align:right">{self.score}</td></tr> <tr><td>Number of Failing Tests</td><td style="text-align:right">{self.maxScore-self.score}</td></tr> <tr><td>Percentage Passed</td><td style="text-align:right">{self.percentage}%</td></tr> </table> """) # Lazy-man's singleton TestResultsAggregator = __TestResultsAggregator() None # COMMAND ---------- from pyspark.sql import Row, DataFrame None # COMMAND ---------- from pyspark.sql import DataFrame from pyspark.sql.functions import col, sum import os print("Finished setting up the capstone environment.")

38.544084

408

0.669586

eb424108a96bf604264def77319d83c190ad7040

12,658

py

Python

scraper/Scraper.py

tiskutis/Capstone24Scraper

3182463e129f37f0f895a440d2285a51e0cfb9a2

[ "MIT" ]

null

scraper/Scraper.py

tiskutis/Capstone24Scraper

3182463e129f37f0f895a440d2285a51e0cfb9a2

[ "MIT" ]

null

scraper/Scraper.py

tiskutis/Capstone24Scraper

3182463e129f37f0f895a440d2285a51e0cfb9a2

[ "MIT" ]

null

import requests from bs4 import BeautifulSoup as bs, BeautifulSoup import pandas as pd import numpy as np import re import logging def get_houses_in_location( self, location_url_: str, houses_in_location: set = set(), page_limit: int = 1, page_number: int = 1, ) -> list: """ Accepts location url and goes through pages in that location scraping every house until page limit is reached. Returns list of dicts with scraped information about every house in that location. :param location_url_: string with link to specific location in California state :param houses_in_location: set with already scraped links. Since retrieved links can be repetitive, there is no need to go to the same link which has already been scraped. Set is used for faster search :param page_limit: how many pages to scraped. If not passed by the user, default is 1 :param page_number: Current page to scrape. Starting number is 1 :return: list of dictionaries """ houses_information = [] try: new_url = self.basic_url + location_url_ + f"?page={page_number}" page_ = self.get_page(new_url) if page_.find_all("li", class_="lslide"): for elem in page_.find_all("li", class_="lslide"): link = elem.find("a")["href"] if link.startswith("/US") and link not in houses_in_location: houses_information.append( self.scrape_info_one_house( self.get_page(self.basic_url + link) ) ) houses_in_location.add(link) if page_number <= page_limit: page_number += 1 self.get_houses_in_location( location_url_, houses_in_location, page_limit, page_number=page_number, ) except Exception as err: logging.error(f"Error occurred while scraping locations. Message: {err}") return houses_information def scrape_platform(self, page_limit: int = 1) -> None: """ Main scraping function. Accepts page limit - how many pages to scrape, default is 1. The flow: - First, all California areas (locations) are extracted and put into a list. - Area list is iterated over. Each area has a number of pages with real estate descriptions. User can select how many pages he wants to go through. - Scraper visits every real estate link in the page and scrapes required information. After all houses are scraped, scraper moves to the next page. When no more pages are left or user denoted page limit is reached, scraper moves to the next category. :param page_limit: how many pages to scrape per area :return: None. """ starting_url = "https://www.point2homes.com/US/Real-Estate-Listings/CA.html" houses = [] starting_page = self.get_page(starting_url) locations = self.get_location_urls(starting_page) for location in locations: houses.extend( self.get_houses_in_location(location, set(), page_limit=page_limit) ) self.to_dataframe(houses).to_csv("California Housing.csv")

38.241692

123

0.586902

eb42e8c815ef79c9ee2b0e9d574f89c917610639

693

py

Python

ArticleSpider/ArticleSpider/utils/selenium_spider.py

ms-wu/Scrapy_projects

376eb5e1c6eca54bcfb781170513c8e9d3476fec

[ "MIT" ]

null

ArticleSpider/ArticleSpider/utils/selenium_spider.py

ms-wu/Scrapy_projects

376eb5e1c6eca54bcfb781170513c8e9d3476fec

[ "MIT" ]

null

ArticleSpider/ArticleSpider/utils/selenium_spider.py

ms-wu/Scrapy_projects

376eb5e1c6eca54bcfb781170513c8e9d3476fec

[ "MIT" ]

null

from selenium import webdriver from scrapy.selector import Selector import time chrome_opt = webdriver.ChromeOptions() prefs = {"profile.managed_default_content_settings.images": 2} chrome_opt.add_experimental_option("prefs", prefs) browser = webdriver.Chrome(executable_path="H:\chromedriver.exe", chrome_options=chrome_opt) browser.get("https://www.taobao.com") # time.sleep(5) # browser.find_element_by_css_selector() # t_selector = Selector(text=browser.page_source) # t_selector.css() # for i in range(3): # browser.execute_script("window.scrollTo(0, document.body.scrollHeight); var lenOfPage=document.body.scrollHeight; return lenOfPage;") # time.sleep(3) # browser.quit()

31.5

139

0.780664

eb4407cbcc3f00735c03c065582c4a89413734d8

1,678

py

Python

launcher.py

dlario/PyFlow

b53b9d14b37aa586426d85842c6cd9a9c35443f2

[ "MIT" ]

null

launcher.py

dlario/PyFlow

b53b9d14b37aa586426d85842c6cd9a9c35443f2

[ "MIT" ]

null

launcher.py

dlario/PyFlow

b53b9d14b37aa586426d85842c6cd9a9c35443f2

[ "MIT" ]

null

from nine import str from Qt.QtWidgets import QApplication, QStyleFactory from Qt import QtGui from Qt import QtCore import sys import os from PyFlow.App import PyFlow FILE_DIR = os.path.abspath(os.path.dirname(__file__)) SETTINGS_PATH = os.path.join(FILE_DIR, "PyFlow", "appConfig.ini") STYLE_PATH = os.path.join(FILE_DIR, "PyFlow", "style.css") app = QApplication(sys.argv) app.setStyle(QStyleFactory.create("plastique")) dark_palette = app.palette() dark_palette.setColor(QtGui.QPalette.Window, QtGui.QColor(53, 53, 53)) dark_palette.setColor(QtGui.QPalette.WindowText, QtCore.Qt.white) dark_palette.setColor(QtGui.QPalette.Base, QtGui.QColor(25, 25, 25)) dark_palette.setColor(QtGui.QPalette.AlternateBase, QtGui.QColor(53, 53, 53)) dark_palette.setColor(QtGui.QPalette.ToolTipBase, QtCore.Qt.white) dark_palette.setColor(QtGui.QPalette.ToolTipText, QtCore.Qt.white) dark_palette.setColor(QtGui.QPalette.Text, QtCore.Qt.black) dark_palette.setColor(QtGui.QPalette.Button, QtGui.QColor(53, 53, 53)) dark_palette.setColor(QtGui.QPalette.ButtonText, QtCore.Qt.black) dark_palette.setColor(QtGui.QPalette.BrightText, QtCore.Qt.red) dark_palette.setColor(QtGui.QPalette.Link, QtGui.QColor(42, 130, 218)) dark_palette.setColor(QtGui.QPalette.Highlight, QtGui.QColor(42, 130, 218)) dark_palette.setColor(QtGui.QPalette.HighlightedText, QtCore.Qt.black) app.setPalette(dark_palette) try: with open(STYLE_PATH, 'r') as f: styleString = f.read() app.setStyleSheet(styleString) except Exception as e: print(e) instance = PyFlow.instance() app.setActiveWindow(instance) instance.show() try: sys.exit(app.exec_()) except Exception as e: print(e)

33.56

77

0.781883

eb444f1d2f4c6079bc153578e3e68294eef319a0

4,344

py

Python

src/gapminder_challenge/dashboard/dash_app2.py

UBC-MDS/gapminder_challenge

bbc8132a475d483e7c6c46572c8efca40b506afc

[ "MIT" ]

1

2022-03-19T03:31:49.000Z

src/gapminder_challenge/dashboard/dash_app2.py

imtvwy/gapminder_challenge

0f7d9816b0c5baf6422baff24e0413c800d6e62a

[ "MIT" ]

39

2022-02-17T05:04:48.000Z

2022-03-19T21:37:20.000Z

src/gapminder_challenge/dashboard/dash_app2.py

imtvwy/gapminder_challenge

0f7d9816b0c5baf6422baff24e0413c800d6e62a

[ "MIT" ]

1

2022-03-19T03:30:08.000Z

import pandas as pd from dash import Dash, html, dcc, Input, Output import altair as alt df = pd.read_csv('../../data/raw/world-data-gapminder_raw.csv') # local run # df = pd.read_csv('data/raw/world-data-gapminder_raw.csv') # heroku deployment url = '/dash_app2/' def add_dash(server): """ It creates a Dash app that plots a line chart of children per woman from gapminder dataset with 2 widgets : rangeslider for years and dropdown for filter :param server: The Flask app object :return: A Dash server """ app = Dash(server=server, url_base_pathname=url) app.layout = html.Div([ html.Iframe( id='line_children', style={'border-width': '0', 'width': '600px', 'height': '400px', 'display': 'block', 'margin-left': 'auto', 'margin-right': 'auto'}), html.Label([ 'Zoom in years: ', dcc.RangeSlider(1918, 2018, 10, value=[1918, 2018], id='year_range_slider', marks={str(year): str(year) for year in range(1918, 2028, 10)}), ]), html.Label([ 'See breakdown number by: ', dcc.Dropdown(options=[ {'label': 'All', 'value': 'all'}, {'label': 'Income Group', 'value': 'income_group'}, {'label': 'Region', 'value': 'region'} ], value='', id='filter_dropdown') ]), html.Div(id="data_card_2", **{'data-card_2_data': []}) ]) # Set up callbacks/backend return app.server

42.174757

113

0.575506

eb448a448b8928b4d93cd021756f058d5d672505

4,595

py

Python

emulator/utils/common.py

Harry45/emuPK

c5cd8a4ab7ef593b196ee58d9df5d826d444a2b9

[ "MIT" ]

2

2021-05-10T16:59:34.000Z

2021-05-19T16:10:24.000Z

emulator/utils/common.py

Harry45/emuPK

c5cd8a4ab7ef593b196ee58d9df5d826d444a2b9

[ "MIT" ]

null

emulator/utils/common.py

Harry45/emuPK

c5cd8a4ab7ef593b196ee58d9df5d826d444a2b9

[ "MIT" ]

2

2021-04-16T23:55:16.000Z

2021-09-09T12:48:41.000Z

# Author: Arrykrishna Mootoovaloo # Collaborators: Alan Heavens, Andrew Jaffe, Florent Leclercq # Email : a.mootoovaloo17@imperial.ac.uk # Affiliation : Imperial Centre for Inference and Cosmology # Status : Under Development ''' Perform all additional operations such as interpolations ''' import os import logging import numpy as np import scipy.interpolate as itp from typing import Tuple def indices(nzmax: int) -> Tuple[list, tuple]: ''' Generates indices for double sum power spectra :param: nzmax (int) - the maximum number of redshifts (assuming first redshift is zero) :return: di_ee (list), idx_gi (tuple) - double indices for EE and indices for GI ''' # create emty lists to recod all indices # for EE power spectrum di_ee = [] # for GI power spectrum # ab means alpha, beta Lab_1 = [] Lab_2 = [] Lba_1 = [] Lba_2 = [] for i in range(1, nzmax + 1): for j in range(1, nzmax + 1): di_ee.append(np.min([i, j])) if i > j: Lab_1.append(i) Lab_2.append(j) elif j > i: Lba_1.append(i) Lba_2.append(j) Lab_1 = np.asarray(Lab_1) Lab_2 = np.asarray(Lab_2) Lba_1 = np.asarray(Lba_1) Lba_2 = np.asarray(Lba_2) di_ee = np.asarray(di_ee) idx_gi = (Lab_1, Lab_2, Lba_1, Lba_2) return di_ee, idx_gi def dvalues(d: dict) -> np.ndarray: ''' Returns an array of values instead of dictionary format :param: d (dict) - a dictionary with keys and values :return: v (np.ndarray) - array of values ''' v = np.array(list(d.values())) return v def like_interp_2d(inputs: list, int_type: str = 'cubic') -> object: ''' We want to predict the function for any new point of k and z (example) :param: inputs (list) - a list containing x, y, f(x,y) :param: int_type (str) - interpolation type (default: 'cubic') :return: f (object) - the interpolator ''' k, z, f_kz = np.log(inputs[0]), inputs[1], inputs[2] inputs_trans = [k, z, f_kz] f = itp.interp2d(*inputs_trans) return f def two_dims_interpolate(inputs: list, grid: list) -> np.ndarray: ''' Function to perform 2D interpolation using interpolate.interp2d :param: inputs (list) : inputs to the interpolation module, that is, we need to specify the following: - x - y - f(x,y) - 'linear', 'cubic', 'quintic' :param: grid (list) : a list containing xnew and ynew :return: pred_new (np.ndarray) : the predicted values on the 2D grid ''' # check that all elements are greater than 0 for log-transformation to be used condition = np.all(inputs[2] > 0) if condition: # transform k and f to log k, z, f_kz, int_type = np.log(inputs[0]), inputs[1], np.log(inputs[2]), inputs[3] else: # transform in k to log k, z, f_kz, int_type = np.log(inputs[0]), inputs[1], inputs[2], inputs[3] inputs_trans = [k, z, f_kz, int_type] # tranform the grid to log knew, znew = np.log(grid[0]), grid[1] grid_trans = [knew, znew] f = itp.interp2d(*inputs_trans) if condition: pred_new = np.exp(f(*grid_trans)) else: pred_new = f(*grid_trans) return pred_new def interpolate(inputs: list) -> np.ndarray: ''' Function to interpolate the power spectrum along the redshift axis :param: inputs (list or tuple) : x values, y values and new values of x :return: ynew (np.ndarray) : an array of the interpolated power spectra ''' x, y, xnew = inputs[0], inputs[1], inputs[2] spline = itp.splrep(x, y) ynew = itp.splev(xnew, spline) return ynew def get_logger(name: str, log_name: str, folder_name: str = 'logs'): ''' Create a log file for each Python scrip :param: name (str) - name of the Python script :param: log_name (str) - name of the output log file ''' # create the folder if it does not exist if not os.path.exists(folder_name): os.makedirs(folder_name) log_format = '%(asctime)s %(name)8s %(levelname)5s %(message)s' logging.basicConfig(level=logging.DEBUG, format=log_format, filename=folder_name + '/' + log_name + '.log', filemode='w') console = logging.StreamHandler() console.setLevel(logging.DEBUG) console.setFormatter(logging.Formatter(log_format)) logging.getLogger(name).addHandler(console) return logging.getLogger(name)

24.972826

106

0.618498

eb458b4c5c0f75854528fff96d2061d078c5cbe7

2,984

py

Python

pypy/translator/microbench/pybench/Imports.py

camillobruni/pygirl

ddbd442d53061d6ff4af831c1eab153bcc771b5a

[ "MIT" ]

12

2016-01-06T07:10:28.000Z

2021-05-13T23:02:02.000Z

pypy/translator/microbench/pybench/Imports.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

null

pypy/translator/microbench/pybench/Imports.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

2

2016-07-29T07:09:50.000Z

2016-10-16T08:50:26.000Z

from pybench import Test # First imports: import os import package.submodule

21.314286

37

0.515416

de16d40373757db432c5c7a3e7d57eeddc1025cc

1,745

py

Python

tests/test_generators_rst.py

dbaty/soho

3fe67d3dc52919751217d6e73be436c3e291ab04

[ "BSD-3-Clause" ]

null

tests/test_generators_rst.py

dbaty/soho

3fe67d3dc52919751217d6e73be436c3e291ab04

[ "BSD-3-Clause" ]

1

2015-10-11T10:34:08.000Z

tests/test_generators_rst.py

dbaty/soho

3fe67d3dc52919751217d6e73be436c3e291ab04

[ "BSD-3-Clause" ]

null

from unittest import TestCase

37.12766

79

0.581089

de170bec53f0702af41038f426ab0305ba516d45

206

py

Python

wagtail_ab_testing/test/apps.py

alxbridge/wagtail-ab-testing

1e959cc4ea1fa9b6d9adda2525fc3aae8e8b7807

[ "BSD-3-Clause" ]

14

2021-02-19T08:52:37.000Z

2022-03-16T05:16:38.000Z

wagtail_ab_testing/test/apps.py

alxbridge/wagtail-ab-testing

1e959cc4ea1fa9b6d9adda2525fc3aae8e8b7807

[ "BSD-3-Clause" ]

10

2021-04-09T16:16:17.000Z

2022-03-31T17:30:18.000Z

wagtail_ab_testing/test/apps.py

alxbridge/wagtail-ab-testing

1e959cc4ea1fa9b6d9adda2525fc3aae8e8b7807

[ "BSD-3-Clause" ]

11

2021-04-23T15:19:06.000Z

2022-03-28T16:15:14.000Z

from django.apps import AppConfig

25.75

47

0.771845

de188ec6c9675e889154db140be0ba41e013c1c2

835

py

Python

shc/__init__.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

5

2021-07-02T21:48:45.000Z

2021-12-12T21:55:42.000Z

shc/__init__.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

49

2020-09-18T20:05:55.000Z

2022-03-05T19:51:33.000Z

shc/__init__.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

1

2021-12-10T14:50:43.000Z

# Copyright 2020 Michael Thies <mail@mhthies.de> # # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the # specific language governing permissions and limitations under the License. from . import base from . import supervisor from . import variables from . import datatypes from . import conversion from . import timer from .base import handler, blocking_handler from .variables import Variable from .supervisor import main

34.791667

120

0.777246

de1a03c3bf2d4b4418706f4fb2057bc7977a7251

777

py

Python

client.py

juzejunior/HttpBasicServer

7e77b49f693d9cfe0d782e93026d8f9261368b69

[ "MIT" ]

null

client.py

juzejunior/HttpBasicServer

7e77b49f693d9cfe0d782e93026d8f9261368b69

[ "MIT" ]

null

client.py

juzejunior/HttpBasicServer

7e77b49f693d9cfe0d782e93026d8f9261368b69

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Simple Http Client, to request html files Modification: 11/09/2017 Author: J. Jnior ''' import httplib import sys #get http server ip - pass in the command line http_server = sys.argv[1] #create a connection with the server conn = httplib.HTTPConnection(http_server) while 1: cmd = raw_input('input command (ex. GET index.html): ') cmd = cmd.split() if cmd[0] == 'exit': #type exit to end it break #request command to server conn.request(cmd[0], cmd[1]) #get response from server rsp = conn.getresponse() #print server response and data print(rsp.status, rsp.reason) print(rsp.getheaders()) data_received = rsp.read() print(data_received) #close connection conn.close()

22.852941

58

0.679537

de1d5ad5042762573fde2a3a38799da995504ae1

6,881

py

Python

pyssh/crypto/asymmetric.py

beckjake/pyssh

d6b7a6cca7e38d0835f84386723ec10ac5ad621f

[ "CC0-1.0" ]

null

pyssh/crypto/asymmetric.py

beckjake/pyssh

d6b7a6cca7e38d0835f84386723ec10ac5ad621f

[ "CC0-1.0" ]

null

pyssh/crypto/asymmetric.py

beckjake/pyssh

d6b7a6cca7e38d0835f84386723ec10ac5ad621f

[ "CC0-1.0" ]

null

"""Implement asymmetric cryptography. """ from __future__ import print_function, division, absolute_import from __future__ import unicode_literals from cryptography.hazmat.primitives import hashes, serialization from cryptography.hazmat.primitives.asymmetric import rsa, dsa, utils, padding from cryptography.hazmat.primitives.asymmetric.padding import PKCS1v15 from cryptography.hazmat.backends import default_backend from collections import OrderedDict import io from builtins import int #pylint: disable=redefined-builtin from pyssh.constants import ENC_SSH_RSA, ENC_SSH_DSS from pyssh.base_types import String, MPInt # pylint:disable=invalid-name #TODO: ECDSA (RFC 5656) def pack_pubkey(self): """Pack a public key into bytes.""" raise NotImplementedError('not implemented') def verify_signature(self, signature, data): """Verify the signature against the given data. Pubkey must be set.""" raise NotImplementedError('not implemented') def sign(self, data): """Sign some data. Privkey must be set.""" raise NotImplementedError('not implemented') def read_pubkey(self, data): """Read a public key from data in the ssh public key format. :param bytes data: the data to read. Sets self.pubkey. """ pubkey = serialization.load_ssh_public_key(data, default_backend()) assert isinstance(pubkey.public_numbers(), self.PUBKEY_CLASS) self.pubkey = pubkey def read_privkey(self, data, password=None): """Read a PEM-encoded private key from data. If a password is set, it will be used to decode the key. :param bytes data: the data to read :param bytes password: The password. Sets self.privkey. """ privkey = serialization.load_pem_private_key(data, password, default_backend()) assert isinstance(privkey.private_numbers(), self.PRIVKEY_CLASS) self.privkey = privkey class RSAAlgorithm(BaseAlgorithm): """Support for the RSA algorithm.""" FORMAT_STR = String(ENC_SSH_RSA) PRIVKEY_CLASS = rsa.RSAPrivateNumbers PUBKEY_CLASS = rsa.RSAPublicNumbers class DSAAlgorithm(BaseAlgorithm): """Support for the DSA.""" FORMAT_STR = String(ENC_SSH_DSS) PRIVKEY_CLASS = dsa.DSAPrivateNumbers PUBKEY_CLASS = dsa.DSAPublicNumbers PUBLIC_KEY_PROTOCOLS = OrderedDict(( (ENC_SSH_RSA, RSAAlgorithm), (ENC_SSH_DSS, DSAAlgorithm) )) def get_asymmetric_algorithm(keytype): """Get the referenced public key type. If a signature_blob blob is included, validate it. """ try: handler = PUBLIC_KEY_PROTOCOLS[keytype] except KeyError: raise UnsupportedKeyProtocol(keytype) return handler()

31.277273

83

0.636826

de1dfa963d73dc87e79e92fa3fe653f6462539c8

1,230

py

Python

books/李航-统计学习/machine_learning_algorithm-master/naive_bayes/naive_bayes.py

haohonglin/DeepLearning-1

c00eee4738d322f6eb5d61d5bafbcfa7b20152a0

[ "Apache-2.0" ]

1

2020-12-01T06:13:21.000Z

books/李航-统计学习/machine_learning_algorithm-master/naive_bayes/naive_bayes.py

idonashino/DeepLearning

c00eee4738d322f6eb5d61d5bafbcfa7b20152a0

[ "Apache-2.0" ]

null

books/李航-统计学习/machine_learning_algorithm-master/naive_bayes/naive_bayes.py

idonashino/DeepLearning

c00eee4738d322f6eb5d61d5bafbcfa7b20152a0

[ "Apache-2.0" ]

1

2021-01-01T15:28:36.000Z

""" @ jetou @ cart decision_tree @ date 2017 10 31 """ import numpy as np

28.604651

89

0.585366

de1e40b74da53919bbdc4c6c8dda38d5aba2c247

27

py

Python

src/__init__.py

natrodrigues/face-recognition

00c78bea55d2738913cf5475056c2faf05fe960e

[ "MIT" ]

null

src/__init__.py

natrodrigues/face-recognition

00c78bea55d2738913cf5475056c2faf05fe960e

[ "MIT" ]

null

src/__init__.py

natrodrigues/face-recognition

00c78bea55d2738913cf5475056c2faf05fe960e

[ "MIT" ]

null

from . import frame_manager

27

0.851852

de1e4247762eb410a1475e5659c71d8d5fb3aa3a

276

py

Python

sparweltbitool/config.py

checkout-charlie/bitool

e41ce66ab2b88992dbfc08d79372bf3965724f3e

[ "MIT" ]

null

sparweltbitool/config.py

checkout-charlie/bitool

e41ce66ab2b88992dbfc08d79372bf3965724f3e

[ "MIT" ]

null

sparweltbitool/config.py

checkout-charlie/bitool

e41ce66ab2b88992dbfc08d79372bf3965724f3e

[ "MIT" ]

1

2015-07-22T16:53:42.000Z

import os import sys if sys.version_info[:2] >= (3, 4): import configparser config = configparser.ConfigParser() else: import ConfigParser config = ConfigParser.ConfigParser() config.readfp(open('app/config/config_%s.cfg' % os.environ.get('APP_ENV', 'dev')))

25.090909

82

0.706522

de2067c1459291384093f5c6102e9ab0301ade68

3,164

py

Python

src/rsa_decryption_125/app.py

seanballais/rsa-decryption-125

df2ad27d055469e7c58a811f40cfc2c8a6171298

[ "MIT" ]

null

src/rsa_decryption_125/app.py

seanballais/rsa-decryption-125

df2ad27d055469e7c58a811f40cfc2c8a6171298

[ "MIT" ]

null

src/rsa_decryption_125/app.py

seanballais/rsa-decryption-125

df2ad27d055469e7c58a811f40cfc2c8a6171298

[ "MIT" ]

null

import tkinter from tkinter import * from rsa_decryption_125 import decryptor if __name__ == '__main__': main()

34.391304

102

0.631163

de207e25aa9bca185c57928c53cd749f04d47818

2,031

py

Python

model.py

starinsun/multiagent-particle-envs

23b1c47fad4d71347ba3de7a5e8cec910f08382d

[ "MIT" ]

null

model.py

starinsun/multiagent-particle-envs

23b1c47fad4d71347ba3de7a5e8cec910f08382d

[ "MIT" ]

null

model.py

starinsun/multiagent-particle-envs

23b1c47fad4d71347ba3de7a5e8cec910f08382d

[ "MIT" ]

null

import paddle.fluid as fluid import parl from parl import layers

27.445946

68

0.573609

de20802d519423344cda6384cb09a94946775ee1

724

py

Python

src/fmWidgets/FmColorEdit.py

ComputerArchitectureGroupPWr/Floorplan-Maker

8f2922cdab16501d3bb00f93c3130d3f2c593698

[ "MIT" ]

null

src/fmWidgets/FmColorEdit.py

ComputerArchitectureGroupPWr/Floorplan-Maker

8f2922cdab16501d3bb00f93c3130d3f2c593698

[ "MIT" ]

null

src/fmWidgets/FmColorEdit.py

ComputerArchitectureGroupPWr/Floorplan-Maker

8f2922cdab16501d3bb00f93c3130d3f2c593698

[ "MIT" ]

null

from PyQt4.QtGui import QPalette, QColor __author__ = 'pawel' from PyQt4 import QtGui from PyQt4.QtCore import Qt

25.857143

57

0.672652

de269b1d0a4fe87a69767fba8b3e00ccf68b4d65

6,543

py

Python

admin.py

ericholscher/pypi

4c7c13bd2061d99bbf11a803ac7a7afe3740e365

[ "BSD-3-Clause" ]

1

2015-11-08T11:31:07.000Z

admin.py

ericholscher/pypi

4c7c13bd2061d99bbf11a803ac7a7afe3740e365

[ "BSD-3-Clause" ]

null

admin.py

ericholscher/pypi

4c7c13bd2061d99bbf11a803ac7a7afe3740e365

[ "BSD-3-Clause" ]

null

import sys, os, urllib, StringIO, traceback, cgi, binascii, getopt, shutil import zipfile, gzip, tarfile #sys.path.append('/usr/local/pypi/lib') import store, config def set_password(store, name, pw): """ Reset the user's password and send an email to the address given. """ user = store.get_user(name.strip()) if user is None: raise ValueError, 'user name unknown to me' store.store_user(user['name'], pw.strip(), user['email'], None) print 'done' def remove_package(store, name): ''' Remove a package from the database ''' store.remove_package(name) print 'done' def add_classifier(st, classifier): ''' Add a classifier to the trove_classifiers list ''' cursor = st.get_cursor() cursor.execute("select max(id) from trove_classifiers") id = cursor.fetchone()[0] if id: id = int(id) + 1 else: id = 1 fields = [f.strip() for f in classifier.split('::')] for f in fields: assert ':' not in f levels = [] for l in range(2, len(fields)): c2 = ' :: '.join(fields[:l]) store.safe_execute(cursor, 'select id from trove_classifiers where classifier=%s', (c2,)) l = cursor.fetchone() if not l: raise ValueError, c2 + " is not a known classifier" levels.append(l[0]) levels += [id] + [0]*(3-len(levels)) store.safe_execute(cursor, 'insert into trove_classifiers (id, classifier, l2, l3, l4, l5) ' 'values (%s,%s,%s,%s,%s,%s)', [id, classifier]+levels) def rename_package(store, old, new): ''' Rename a package. ''' if not store.has_package(old): raise ValueError, 'no such package' if store.has_package(new): raise ValueError, new+' exists' store.rename_package(old, new) print "Please give www-data permissions to all files of", new def add_mirror(store, root, user): ''' Add a mirror to the mirrors list ''' store.add_mirror(root, user) print 'done' def delete_mirror(store, root): ''' Delete a mirror ''' store.delete_mirror(root) print 'done' def delete_old_docs(config, store): '''Delete documentation directories for packages that have been deleted''' for i in os.listdir(config.database_docs_dir): if not store.has_package(i): path = os.path.join(config.database_docs_dir, i) print "Deleting", path shutil.rmtree(path) if __name__ == '__main__': config = config.Config('/data/pypi/config.ini') st = store.Store(config) st.open() command = sys.argv[1] args = (st, ) + tuple(sys.argv[2:]) try: if command == 'password': set_password(*args) elif command == 'rmpackage': remove_package(*args) elif command == 'addclass': add_classifier(*args) print 'done' elif command == 'addowner': add_owner(*args) elif command == 'delowner': delete_owner(*args) elif command == 'rename': rename_package(*args) elif command == 'addmirror': add_mirror(*args) elif command == 'delmirror': delete_mirror(*args) elif command == 'delolddocs': delete_old_docs(config, *args) elif command == 'send_comments': send_comments(*args) elif command == 'mergeuser': merge_user(*args) elif command == 'nuke_nested_lists': nuke_nested_lists(*args) else: print "unknown command '%s'!"%command st.changed() finally: st.close()

35.367568

97

0.599419

de26d7fc8c223d9eef08edc2aa50933adc8cafe1

1,777

py

Python

scripts/geodata/address_expansions/equivalence.py

Fillr/libpostal

bce153188aff9fbe65aef12c3c639d8069e707fc

[ "MIT" ]

3,489

2015-03-03T00:21:38.000Z

2022-03-29T09:03:05.000Z

scripts/geodata/address_expansions/equivalence.py

StephenHildebrand/libpostal

d8c9847c5686a1b66056e65128e1774f060ff36f

[ "MIT" ]

488

2015-05-29T23:04:28.000Z

2022-03-29T11:20:24.000Z

scripts/geodata/address_expansions/equivalence.py

StephenHildebrand/libpostal

d8c9847c5686a1b66056e65128e1774f060ff36f

[ "MIT" ]

419

2015-11-24T16:53:07.000Z

2022-03-27T06:51:28.000Z

import random import re import six from itertools import izip from geodata.address_expansions.gazetteers import * from geodata.encoding import safe_decode, safe_encode from geodata.text.normalize import normalized_tokens from geodata.text.tokenize import tokenize_raw, token_types from geodata.text.utils import non_breaking_dash_regex def equivalent(s1, s2, gazetteer, language): ''' Address/place equivalence ------------------------- OSM discourages abbreviations, but to make our training data map better to real-world input, we can safely replace the canonical phrase with an abbreviated version and retain the meaning of the words ''' tokens_s1 = normalized_tokens(s1) tokens_s2 = normalized_tokens(s2) abbreviated_s1 = list(abbreviations_gazetteer.filter(tokens_s1)) abbreviated_s2 = list(abbreviations_gazetteer.filter(tokens_s2)) if len(abbreviated_s1) != len(abbreviated_s2): return False for ((t1, c1, l1, d1), (t2, c2, l2, d2)) in izip(abbreviated_s1, abbreviated_s2): if c1 != token_types.PHRASE and c2 != token_types.PHRASE: if t1 != t2: return False elif c2 == token_types.PHRASE and c2 == token_types.PHRASE: canonicals_s1 = canonicals_for_language(d1, language) canonicals_s2 = canonicals_for_language(d2, language) if not canonicals_s1 & canonicals_s2: return False else: return False return True

31.175439

85

0.68655

de27afb959d2cb13e74aaad06b80a65da178a7e6

170

py

Python

Language Skills/Python/Unit 08 Loops/01 Loops/Step Up 'For's/While Loops/3-While You're at it.py

rhyep/Python_tutorials

f5c8a64b91802b005dfe7dd9035f8d8daae8c3e3

[ "MIT" ]

346

2016-02-22T20:21:10.000Z

2022-01-27T20:55:53.000Z

Language Skills/Python/Unit 8/1-Loops/While Loops/3-While You're at it.py

vpstudios/Codecademy-Exercise-Answers

ebd0ee8197a8001465636f52c69592ea6745aa0c

[ "MIT" ]

55

2016-04-07T13:58:44.000Z

2020-06-25T12:20:24.000Z

Language Skills/Python/Unit 8/1-Loops/While Loops/3-While You're at it.py

vpstudios/Codecademy-Exercise-Answers

ebd0ee8197a8001465636f52c69592ea6745aa0c

[ "MIT" ]

477

2016-02-21T06:17:02.000Z

2021-12-22T10:08:01.000Z

num = 1 while num <= 10: # Fill in the condition x = num ** 2# Print num squared num = num + 1# Increment num (make sure to do this!) print x print num

21.25

56

0.594118

de2838f69cfe04090e0142bb22b24b01a4243cd5

948

py

Python

setup.py

povilasb/udptest

3d16d2e6509e008b37775e7784af54b6edb6633e

[ "MIT" ]

2

2017-11-17T09:10:41.000Z

2019-09-20T21:50:08.000Z

setup.py

povilasb/udptest

3d16d2e6509e008b37775e7784af54b6edb6633e

[ "MIT" ]

null

setup.py

povilasb/udptest

3d16d2e6509e008b37775e7784af54b6edb6633e

[ "MIT" ]

null

from setuptools import setup, find_packages setup( name='udptest', version='0.1.0', description='UDP benchmarking/testing tool.', long_description=open('README.rst').read(), url='https://github.com/povilasb/httpmeter', author='Povilas Balciunas', author_email='balciunas90@gmail.com', license='MIT', packages=find_packages(exclude=('tests')), entry_points={ 'console_scripts': [ 'udptestd = udptest.server:main', 'udptest = udptest.client:main', ] }, classifiers=[ 'Programming Language :: Python :: 3.6', 'Operating System :: POSIX :: Linux', 'Natural Language :: English', 'Development Status :: 3 - Alpha', 'Topic :: System :: Networking', 'Topic :: Internet :: UDP', ], install_requires=requirements(), )

25.621622

49

0.582278

de28f51f7fb4db9f4c4cfed3b53384caa7188918

3,200

py

Python

ssanchors/utilities.py

IoSR-Surrey/source-separation-anchors

c2c73312bdc7f08f37c088fa3986168813f13799

[ "MIT" ]

4

2018-07-06T14:35:29.000Z

2019-08-28T17:13:11.000Z

ssanchors/utilities.py

nd1511/source-separation-anchors

c2c73312bdc7f08f37c088fa3986168813f13799

[ "MIT" ]

1

2018-06-18T17:08:28.000Z

2018-06-19T10:45:58.000Z

ssanchors/utilities.py

nd1511/source-separation-anchors

c2c73312bdc7f08f37c088fa3986168813f13799

[ "MIT" ]

1

2018-11-05T19:56:17.000Z

from __future__ import division import numpy as np from untwist import data from untwist import transforms def target_accompaniment(target, others, sample_rate=None): """ Given a target source and list of 'other' sources, this function returns the target and accompaniment as untwist.data.audio.Wave objects. The accompaniment is defined as the sum of the other sources. Parameters ---------- target : np.ndarray or Wave, shape=(num_samples, num_channels) The true target source. others : List or single np.ndarray or Wave object Each object should have the shape=(num_samples, num_channels) If a single array is given, this should correspond to the accompaniment. sample_rate : int, optional Only needed if Wave objects not provided. Returns ------- target : Wave, shape=(num_samples, num_channels) accompaniment : Wave, shape=(num_samples, num_channels) """ if isinstance(others, list): if not isinstance(others[0], data.audio.Wave): others = [data.audio.Wave(_, sample_rate) for _ in others] accompaniment = sum(other for other in others) else: if not isinstance(others, data.audio.Wave): others = data.audio.Wave(others, sample_rate) accompaniment = others if not isinstance(target, data.audio.Wave): target = data.audio.Wave(target, sample_rate) return target, accompaniment def stft_istft(num_points=2048, window='hann'): """ Returns an STFT and an ISTFT Processor object, both configured with the same window and transform length. These objects are to be used as follows: >>> stft, istft = stft_istft() >>> x = untwist.data.audio.Wave.tone() # Or some Wave >>> y = stft.process(x) >>> x = istft.process(y) Parameters ---------- num_points : int The number of points to use for the window and the fft transform. window : str The type of window to use. Returns ------- stft : untwist.transforms.stft.STFT An STFT processor. itft : untwist.transforms.stft.ITFT An ISTFT processor. """ stft = transforms.STFT(window, num_points, num_points // 2) istft = transforms.ISTFT(window, num_points, num_points // 2) return stft, istft def ensure_audio_doesnt_clip(list_of_arrays): """ Takes a list of arrays and scales them by the same factor such that none clip. Parameters ---------- list_of_arrays : list A list of array_like objects Returns ------- new_list_of_arrays : list A list of scaled array_like objects. """ max_peak = 1 for audio in list_of_arrays: audio_peak = np.max(np.abs(audio)) if audio_peak > max_peak: max_peak = audio_peak if max_peak >= 1: print('Warning: Audio has been attenuated to prevent clipping') gain = 0.999 / max_peak new_list_of_arrays = [] for audio in list_of_arrays: new_list_of_arrays.append(audio * gain) else: new_list_of_arrays = list_of_arrays return new_list_of_arrays

25.806452

78

0.64625

de296667231d2bd75b621d94c889fd2ea3b5afb5

812

py

Python

bids_events/Events.py

InstitutoDOr/bids_events

c00d76e1f62e5b647f94609acbc9e173a356aef7

[ "MIT" ]

null

bids_events/Events.py

InstitutoDOr/bids_events

c00d76e1f62e5b647f94609acbc9e173a356aef7

[ "MIT" ]

null

bids_events/Events.py

InstitutoDOr/bids_events

c00d76e1f62e5b647f94609acbc9e173a356aef7

[ "MIT" ]

null

import os import re

31.230769

62

0.571429

de2bfdafb52bf7f86a472b4af4f49451d709be07

87

py

Python

tests/fixtures/abcd_package/test_a.py

venmo/nose-randomly

39db5db71a226ffdb6572d5785638e0a16379cfb

[ "BSD-3-Clause" ]

19

2015-07-30T17:27:56.000Z

2021-08-10T07:19:43.000Z

tests/fixtures/abcd_package/test_a.py

venmo/nose-randomly

39db5db71a226ffdb6572d5785638e0a16379cfb

[ "BSD-3-Clause" ]

11

2016-02-14T10:33:44.000Z

2016-10-28T12:38:35.000Z

tests/fixtures/abcd_package/test_a.py

adamchainz/nose-randomly

8a3fbeaf7cc5452c44da8c7e7573fe89391c8260

[ "BSD-3-Clause" ]

4

2016-06-01T06:04:46.000Z

2016-10-26T11:41:53.000Z

from unittest import TestCase

12.428571

29

0.666667

de2d96eb9081272f5172b90d540db88b204c04b4

427

py

Python

Python_Challenge_115/6/F.py

LIkelion-at-KOREATECH/LikeLion_Django_Study_Summary

c788182af5bcfd16bdd4b57235a48659758e494b

[ "MIT" ]

28

2019-10-15T13:15:26.000Z

2021-11-08T08:23:45.000Z

Python_Challenge_115/6/F.py

jhleed/LikeLion_Django_Study_Summary

c788182af5bcfd16bdd4b57235a48659758e494b

[ "MIT" ]

null

Python_Challenge_115/6/F.py

jhleed/LikeLion_Django_Study_Summary

c788182af5bcfd16bdd4b57235a48659758e494b

[ "MIT" ]

17

2019-09-09T00:15:36.000Z

2021-01-28T13:08:51.000Z

''' Statement Fibonacci numbers are the numbers in the integer sequence starting with 1, 1 where every number after the first two is the sum of the two preceding ones: 1, 1, 2, 3, 5, 8, 13, 21, 34, ... Given a positive integer n, print the nth Fibonacci number. Example input 6 Example output 8 ''' num = int(input()) before, curr, i = 0, 1, 1 while num > i: before, curr = curr, curr + before i += 1 print(curr)

18.565217

153

0.676815

de2edc2bbe1eee14e878fa5bd6b3104c3a6af8ad

144

py

Python

test/test_sum_up.py

marco-a-wagner/nirvana

325756ec5f208994767b4909ed217ce716f5fcfb

[ "CC0-1.0" ]

null

test/test_sum_up.py

marco-a-wagner/nirvana

325756ec5f208994767b4909ed217ce716f5fcfb

[ "CC0-1.0" ]

null

test/test_sum_up.py

marco-a-wagner/nirvana

325756ec5f208994767b4909ed217ce716f5fcfb

[ "CC0-1.0" ]

null

from src.sum_up import *

16

30

0.583333

de2ffb901bbfbc3af2061583ab91b8842066be1f

1,376

py

Python

cluster.py

YektaDmrc/UW_GEMSEC

b9e0c995e34f098fdb607fa35a3fe47663839086

[ "MIT" ]

1

2018-07-10T23:37:47.000Z

cluster.py

YektaDmrc/UW_GEMSEC

b9e0c995e34f098fdb607fa35a3fe47663839086

[ "MIT" ]

null

cluster.py

YektaDmrc/UW_GEMSEC

b9e0c995e34f098fdb607fa35a3fe47663839086

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Jul 13 15:38:11 2018 @author: Yekta """ import csv import numpy as np from sklearn.cluster import KMeans clon = list(csv.reader(open("C:/Users/Yekta/Desktop/stajvol3/MoS2BP Binding Characterization_07-11-17_DY.csv"))) for k in range(1,15): fin=[] for m in range(1,13): dataFromCSV = list(csv.reader(open("C:/Users/Yekta/Desktop/stajvol3/573x96/recon/location"+str(m)+"/PCA"+str(k)+".csv"))) dataFromCSV=np.asarray(dataFromCSV) dataFromCSV=dataFromCSV.T temp=dataFromCSV[1:,1:] temp=temp.astype(np.float) #clusters according to properties kmeans = KMeans(n_clusters = 3, init = 'k-means++', random_state = 42) y_kmeans = kmeans.fit_predict(temp) fin.append(y_kmeans) fin=np.asarray(fin) fin=fin.T matrix = [[0 for x in range(13)] for y in range(97)] matrix[0][0]="Index" for z in range(1,97): matrix[z][0]=clon[z+1][11] for x in range(1,13): matrix[0][x]=x for y in range(1,97): matrix[y][x]=fin[y-1,x-1] matrix=np.asarray(matrix) with open("C:/Users/Yekta/Desktop/stajvol3/573x96/cluster/clusteredPCA"+str(k)+".csv", 'w', newline='') as myfile: wr = csv.writer(myfile) wr.writerows(matrix)

32.761905

130

0.588663

de319a3d0a027f8b448c09d0528c44c359822d8e

1,440

py

Python

test_collision/test_discretedynamicsworld.py

Klumhru/boost-python-bullet

d9ffae09157280f60cb469d8c9c9fa4c1920e3ce

[ "MIT" ]

2

2015-09-16T15:24:39.000Z

2015-11-18T11:53:51.000Z

test_collision/test_discretedynamicsworld.py

Klumhru/boost-python-bullet

d9ffae09157280f60cb469d8c9c9fa4c1920e3ce

[ "MIT" ]

1

2018-04-04T15:33:20.000Z

test_collision/test_discretedynamicsworld.py

Klumhru/boost-python-bullet

d9ffae09157280f60cb469d8c9c9fa4c1920e3ce

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ test_collision.test_discretedynamicsworld """ from __future__ import unicode_literals, print_function, absolute_import import unittest import bullet from .test_worlds import WorldTestDataMixin

28.8

72

0.634028

de31e808778594864eecf61a23f3d4e16b0f2a4b

820

py

Python

force_wfmanager/notifications/tests/test_ui_notification_hooks_factory.py

force-h2020/force-wfmanager

bcd488cd37092cacd9d0c81b544ee8c1654d1d92

[ "BSD-2-Clause" ]

1

2019-08-19T16:02:20.000Z

force_wfmanager/notifications/tests/test_ui_notification_hooks_factory.py

force-h2020/force-wfmanager

bcd488cd37092cacd9d0c81b544ee8c1654d1d92

[ "BSD-2-Clause" ]

396

2017-07-18T15:19:55.000Z

2021-05-03T06:23:06.000Z

force_wfmanager/notifications/tests/test_ui_notification_hooks_factory.py

force-h2020/force-wfmanager

bcd488cd37092cacd9d0c81b544ee8c1654d1d92

[ "BSD-2-Clause" ]

2

2019-03-05T16:23:10.000Z

2020-04-16T08:59:11.000Z

# (C) Copyright 2010-2020 Enthought, Inc., Austin, TX # All rights reserved. import unittest from force_wfmanager.notifications.ui_notification_hooks_manager \ import \ UINotificationHooksManager from force_wfmanager.notifications.ui_notification_plugin import \ UINotificationPlugin

31.538462

68

0.74878

de31ea78bbeb185adcdcced18fcb297d6af4dc71

447

py

Python

phrasebook/middleware.py

DanCatchpole/phrasebook-django

4f85ec40626cbb97c659448ee06f2291c8f2918b

[ "MIT" ]

1

2020-11-10T17:31:56.000Z

phrasebook/middleware.py

DanCatchpole/phrasebook-django

4f85ec40626cbb97c659448ee06f2291c8f2918b

[ "MIT" ]

null

phrasebook/middleware.py

DanCatchpole/phrasebook-django

4f85ec40626cbb97c659448ee06f2291c8f2918b

[ "MIT" ]

null

from django.shortcuts import redirect from .models import UserLanguage

27.9375

66

0.680089

de346180214f310ac4c427bc250a7eb3f75732e4

113

py

Python

PROGATE/PYTHON_I_page07.py

vox256/Codes

c408ef0fbc25af46dacef93b3496985feb98dd5c

[ "MIT" ]

null

PROGATE/PYTHON_I_page07.py

vox256/Codes

c408ef0fbc25af46dacef93b3496985feb98dd5c

[ "MIT" ]

null

PROGATE/PYTHON_I_page07.py

vox256/Codes

c408ef0fbc25af46dacef93b3496985feb98dd5c

[ "MIT" ]

null

money = 2000 print(money) # money5000money money += 5000 # money print (money)

14.125

36

0.787611

de3486ad1b0724a14e6330a44ee92a956bf5ee2e

380

py

Python

quokka/modules/accounts/views.py

yencchen/quokka_epus

d64aeb9c5ca59ee4bdcd84381f9bb0504680f5f5

[ "MIT" ]

null

quokka/modules/accounts/views.py

yencchen/quokka_epus

d64aeb9c5ca59ee4bdcd84381f9bb0504680f5f5

[ "MIT" ]

null

quokka/modules/accounts/views.py

yencchen/quokka_epus

d64aeb9c5ca59ee4bdcd84381f9bb0504680f5f5

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from flask import redirect, request, url_for from flask.views import MethodView from flask.ext.security import current_user

22.352941

59

0.692105

de34fea664d85474bd07e69ca7917ce3402fb32e

142

py

Python

nolina/__init__.py

JohnReid/nolina

23894517ac60d27d167447871ef85a4a78cad630

[ "MIT" ]

null

nolina/__init__.py

JohnReid/nolina

23894517ac60d27d167447871ef85a4a78cad630

[ "MIT" ]

null

nolina/__init__.py

JohnReid/nolina

23894517ac60d27d167447871ef85a4a78cad630

[ "MIT" ]

null

"""Randomised linear algebra.""" import numpy.linalg as la

15.777778

39

0.640845

de35289eea69e5ceb7febfc7fa32b43c5609a79c

887

py

Python

src/commands/reload.py

zaanposni/umfrageBot

3e19dc0629cde394da2ae8706e6e043b4e87059d

[ "MIT" ]

6

2019-08-15T20:19:38.000Z

2021-02-28T21:33:19.000Z

src/commands/reload.py

zaanposni/umfrageBot

3e19dc0629cde394da2ae8706e6e043b4e87059d

[ "MIT" ]

31

2019-08-14T08:42:08.000Z

2020-05-07T13:43:43.000Z

src/commands/reload.py

zaanposni/umfrageBot

3e19dc0629cde394da2ae8706e6e043b4e87059d

[ "MIT" ]

5

2019-08-17T13:39:53.000Z

2020-04-01T07:25:51.000Z

from bt_utils.console import Console from bt_utils.config import cfg from bt_utils.embed_templates import SuccessEmbed, WarningEmbed from bt_utils.handle_sqlite import DatabaseHandler SHL = Console('BundestagsBot Reload') DB = DatabaseHandler() settings = { 'name': 'reload', 'channels': ['team'], 'mod_cmd': True }

27.71875

92

0.713641

de3555aacf51f612d0e7cb4e5d614fc7db59f6c9

4,022

py

Python

scanner.py

Darchiv/scambus

0a81a67b76a5ec5117d56a4c05c4392696eb3f06

[ "MIT" ]

22

2015-08-21T11:58:20.000Z

2021-12-28T04:50:05.000Z

scanner.py

Darchiv/scambus

0a81a67b76a5ec5117d56a4c05c4392696eb3f06

[ "MIT" ]

5

2017-02-26T14:22:53.000Z

2021-02-11T00:47:48.000Z

scanner.py

Darchiv/scambus

0a81a67b76a5ec5117d56a4c05c4392696eb3f06

[ "MIT" ]

14

2015-04-13T08:02:18.000Z

2021-12-16T14:08:54.000Z

#! /usr/bin/env python2.7 import getopt, sys, time, util from wmbus import WMBusFrame from Crypto.Cipher import AES if __name__ == "__main__": main(sys.argv[1:]) ''' Class Scanner(threading.Thread): def __init__(self,dev): #something here that initialize serial port def run(): while True: def pack(self): #something def checksum(self): #something def write(self): #something '''

31.421875

92

0.458478

de35b41f521bfe20dfbbf60f134cdbe2d7425715

2,080

py

Python

pyy1/.pycharm_helpers/python_stubs/-1550516950/gi/_gi/BaseInfo.py

pyy1988/pyy_test1

6bea878409e658aa87441384419be51aaab061e7

[ "Apache-2.0" ]

null

pyy1/.pycharm_helpers/python_stubs/-1550516950/gi/_gi/BaseInfo.py

pyy1988/pyy_test1

6bea878409e658aa87441384419be51aaab061e7

[ "Apache-2.0" ]

null

pyy1/.pycharm_helpers/python_stubs/-1550516950/gi/_gi/BaseInfo.py

pyy1988/pyy_test1

6bea878409e658aa87441384419be51aaab061e7

[ "Apache-2.0" ]

null

# encoding: utf-8 # module gi._gi # from /usr/lib/python3/dist-packages/gi/_gi.cpython-35m-x86_64-linux-gnu.so # by generator 1.145 # no doc # imports import _gobject as _gobject # <module '_gobject'> import _glib as _glib # <module '_glib'> import gi as __gi import gobject as __gobject from .object import object

25.679012

76

0.610577

de3618687057494d918d8f6f783dfd78edbb7ce5

828

py

Python

setup.py

ntamas/python-selecta

bc9a11f288df427ceb126aa994ac3810685e2d94

[ "MIT" ]

1

2019-02-21T14:47:40.000Z

setup.py

ntamas/python-selecta

bc9a11f288df427ceb126aa994ac3810685e2d94

[ "MIT" ]

2

2015-07-11T03:32:35.000Z

2015-08-26T09:29:40.000Z

setup.py

ntamas/python-selecta

bc9a11f288df427ceb126aa994ac3810685e2d94

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from selecta import __version__ from setuptools import setup options = dict( name='python-selecta', version=__version__, url='http://github.com/ntamas/python-selecta', description='Python port of @garybernhardt/selecta', license='MIT', author='Tamas Nepusz', author_email='ntamas@gmail.com', package_dir={'selecta': 'selecta'}, packages=['selecta'], entry_points={ "console_scripts": [ 'selecta = selecta.__main__:main' ] }, test_suite="tests", platforms='ALL', classifiers=[ # TODO 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Operating System :: OS Independent', 'Programming Language :: Python' ] ) setup(**options)

20.195122

56

0.607488

de37ff05a0046e06ac61cbc292e777a426c175fb

525

py

Python

graphsaint/setup.py

alexs131/GraphSAINT

20ac0dce1bdad0505b98ab117aaca84d1aa0bcd8

[ "MIT" ]

null

graphsaint/setup.py

alexs131/GraphSAINT

20ac0dce1bdad0505b98ab117aaca84d1aa0bcd8

[ "MIT" ]

null

graphsaint/setup.py

alexs131/GraphSAINT

20ac0dce1bdad0505b98ab117aaca84d1aa0bcd8

[ "MIT" ]

null

# cython: language_level=3 from distutils.core import setup, Extension from Cython.Build import cythonize import numpy # import cython_utils import os os.environ["CC"] = "/opt/homebrew/Cellar/gcc/11.2.0_3/bin/g++-11" os.environ["CXX"] = "/opt/homebrew/Cellar/gcc/11.2.0_3/bin/g++-11" setup(ext_modules=cythonize(["graphsaint/cython_sampler.pyx", "graphsaint/cython_utils.pyx", "graphsaint/norm_aggr.pyx"]), include_dirs=[numpy.get_include()]) # to compile: python graphsaint/setup.py build_ext --inplace

37.5

93

0.737143

de3854551e9e60f025c395d03bedb3f5b3cb6f38

4,958

py

Python

models/get_networks.py

kingqiuol/pytorch-template

8bc78f996fbbc15ae54a3055cd3d33199b4a96d8

[ "MIT" ]

null

models/get_networks.py

kingqiuol/pytorch-template

8bc78f996fbbc15ae54a3055cd3d33199b4a96d8

[ "MIT" ]

null

models/get_networks.py

kingqiuol/pytorch-template

8bc78f996fbbc15ae54a3055cd3d33199b4a96d8

[ "MIT" ]

null

import sys def get_network(args): """ return given network """ if args.MODEL.NAME == 'vgg16': from models.vgg import vgg16_bn net = vgg16_bn() elif args.MODEL.NAME == 'vgg13': from models.vgg import vgg13_bn net = vgg13_bn() elif args.MODEL.NAME == 'vgg11': from models.vgg import vgg11_bn net = vgg11_bn() elif args.MODEL.NAME == 'vgg19': from models.vgg import vgg19_bn net = vgg19_bn() elif args.MODEL.NAME == 'densenet121': from models.densenet import densenet121 net = densenet121() elif args.MODEL.NAME == 'densenet161': from models.densenet import densenet161 net = densenet161() elif args.MODEL.NAME == 'densenet169': from models.densenet import densenet169 net = densenet169() elif args.MODEL.NAME == 'densenet201': from models.densenet import densenet201 net = densenet201() elif args.MODEL.NAME == 'googlenet': from models.googlenet import googlenet net = googlenet() elif args.MODEL.NAME == 'inceptionv3': from models.inceptionv3 import inceptionv3 net = inceptionv3() elif args.MODEL.NAME == 'inceptionv4': from models.inceptionv4 import inceptionv4 net = inceptionv4() elif args.MODEL.NAME == 'inceptionresnetv2': from models.inceptionv4 import inception_resnet_v2 net = inception_resnet_v2() elif args.MODEL.NAME == 'xception': from models.xception import xception net = xception() elif args.MODEL.NAME == 'resnet18': from models.resnet import resnet18 net = resnet18() elif args.MODEL.NAME == 'resnet34': from models.resnet import resnet34 net = resnet34() elif args.MODEL.NAME == 'resnet50': from models.resnet import resnet50 net = resnet50() elif args.MODEL.NAME == 'resnet101': from models.resnet import resnet101 net = resnet101() elif args.MODEL.NAME == 'resnet152': from models.resnet import resnet152 net = resnet152() elif args.MODEL.NAME == 'preactresnet18': from models.preactresnet import preactresnet18 net = preactresnet18() elif args.MODEL.NAME == 'preactresnet34': from models.preactresnet import preactresnet34 net = preactresnet34() elif args.MODEL.NAME == 'preactresnet50': from models.preactresnet import preactresnet50 net = preactresnet50() elif args.MODEL.NAME == 'preactresnet101': from models.preactresnet import preactresnet101 net = preactresnet101() elif args.MODEL.NAME == 'preactresnet152': from models.preactresnet import preactresnet152 net = preactresnet152() elif args.MODEL.NAME == 'resnext50': from models.resnext import resnext50 net = resnext50() elif args.MODEL.NAME == 'resnext101': from models.resnext import resnext101 net = resnext101() elif args.MODEL.NAME == 'resnext152': from models.resnext import resnext152 net = resnext152() elif args.MODEL.NAME == 'shufflenet': from models.shufflenet import shufflenet net = shufflenet() elif args.MODEL.NAME == 'shufflenetv2': from models.shufflenetv2 import shufflenetv2 net = shufflenetv2() elif args.MODEL.NAME == 'squeezenet': from models.squeezenet import squeezenet net = squeezenet() elif args.MODEL.NAME == 'mobilenet': from models.mobilenet import mobilenet net = mobilenet() elif args.MODEL.NAME == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2() elif args.MODEL.NAME == 'nasnet': from models.nasnet import nasnet net = nasnet() elif args.MODEL.NAME == 'attention56': from models.attention import attention56 net = attention56() elif args.MODEL.NAME == 'attention92': from models.attention import attention92 net = attention92() elif args.MODEL.NAME == 'seresnet18': from models.senet import seresnet18 net = seresnet18() elif args.MODEL.NAME == 'seresnet34': from models.senet import seresnet34 net = seresnet34() elif args.MODEL.NAME == 'seresnet50': from models.senet import seresnet50 net = seresnet50() elif args.MODEL.NAME == 'seresnet101': from models.senet import seresnet101 net = seresnet101() elif args.MODEL.NAME == 'seresnet152': from models.senet import seresnet152 net = seresnet152() elif args.MODEL.NAME == 'wideresnet': from models.wideresidual import wideresnet net = wideresnet() elif args.MODEL.NAME == 'stochasticdepth18': from models.stochasticdepth import stochastic_depth_resnet18 net = stochastic_depth_resnet18() elif args.MODEL.NAME == 'stochasticdepth34': from models.stochasticdepth import stochastic_depth_resnet34 net = stochastic_depth_resnet34() elif args.MODEL.NAME == 'stochasticdepth50': from models.stochasticdepth import stochastic_depth_resnet50 net = stochastic_depth_resnet50() elif args.MODEL.NAME == 'stochasticdepth101': from models.stochasticdepth import stochastic_depth_resnet101 net = stochastic_depth_resnet101() elif args.MODEL.NAME == 'vit': from models.vit import vit net =vit() else: print('the network name you have entered is not supported yet') sys.exit() if args.MODEL.USE_GPU: # use_gpu net = net.cuda() return net

32.834437

65

0.740621

de38b348a7c3f728ca43e602a33e53edfd8f033d

10,812

py

Python

tests/eth2/beacon/state_machines/forks/test_serenity_block_attestation_validation.py

hwwhww/trinity

614b083a637c665f84b1af228541f37c25d9c665

[ "MIT" ]

2

2020-01-30T21:51:00.000Z

2020-07-22T14:51:05.000Z

tests/eth2/beacon/state_machines/forks/test_serenity_block_attestation_validation.py

hwwhww/trinity

614b083a637c665f84b1af228541f37c25d9c665

[ "MIT" ]

null

tests/eth2/beacon/state_machines/forks/test_serenity_block_attestation_validation.py

hwwhww/trinity

614b083a637c665f84b1af228541f37c25d9c665

[ "MIT" ]

null

import pytest from hypothesis import ( given, settings, strategies as st, ) from eth_utils import ( ValidationError, ) from eth.constants import ( ZERO_HASH32, ) from eth2.beacon.committee_helpers import ( get_crosslink_committees_at_slot, ) from eth2.beacon.state_machines.forks.serenity.block_validation import ( validate_attestation_aggregate_signature, validate_attestation_latest_crosslink_root, validate_attestation_justified_block_root, validate_attestation_justified_epoch, validate_attestation_crosslink_data_root, validate_attestation_slot, ) from eth2.beacon.tools.builder.validator import ( create_mock_signed_attestation, ) from eth2.beacon.types.attestation_data import AttestationData from eth2.beacon.types.crosslink_records import CrosslinkRecord

31.068966

117

0.561321

de3966c1044750e98c8968c82831f55e24112044

13,679

py

Python

SeqtaSDSBridge.py

jacobcurulli/SeqtaSDSBridge

19b8da95462d1e0aa8a059c9f8075d8f7ce1b417

[ "CC-BY-4.0" ]

null

SeqtaSDSBridge.py

jacobcurulli/SeqtaSDSBridge

19b8da95462d1e0aa8a059c9f8075d8f7ce1b417

[ "CC-BY-4.0" ]

1

2021-05-21T04:52:28.000Z

2021-05-21T05:00:10.000Z

SeqtaSDSBridge.py

jacobcurulli/SeqtaSDSBridge

19b8da95462d1e0aa8a059c9f8075d8f7ce1b417

[ "CC-BY-4.0" ]

1

2021-04-07T13:50:43.000Z

########################################################################################################### ########################################################################################################### ## SeqtaToSDS ## ## Jacob Curulli ## ## This code is shared as is, under Creative Commons Attribution Non-Commercial 4.0 License ## ## Permissions beyond the scope of this license may be available at http://creativecommons.org/ns ## ########################################################################################################### # Read Me # This script will likely not work out of the box and will need to be customised # 1. The approvedClassesCSV is a list of classes in Seqta that will be exported, # the list is checked against the 'name' column in the public.classunit table. # 2. A directory called 'sds' will need to be created in the root of where the script is run. # 3. This script allows for an admin user to be added to every class (section) # import required modules # psycopg2 isn't usually included with python and may need to be installed separately # see www.psycopg.org for instructions import psycopg2 import csv import os.path import configparser from datetime import datetime # Get the date dateNow = datetime.now() # Read the config.ini file config = configparser.ConfigParser() config.read('config.ini') # read config file for seqta database connection details db_user=config['db']['user'] db_port=config['db']['port'] db_password=config['db']['password'] db_database=config['db']['database'] db_host=config['db']['host'] db_sslmode=config['db']['sslmode'] # read config file for school details teamsAdminUsername=config['school']['teamsAdminUsername'] teamsAdminFirstName=config['school']['teamsAdminFirstName'] teamsAdminLastName=config['school']['teamsAdminLastName'] teamsAdminID=config['school']['teamsAdminID'] schoolName =config['school']['schoolName'] schoolSISId=config['school']['schoolSISId'] classTermName=config['school']['classTermName'] # declare some variables here so we can make sure they are present staffList = set() studentList = set() classArray = tuple() currentYear = dateNow.strftime("%Y") print("current year is:", currentYear) # file locations, this can be changed to suit your environment csvApprovedClasses = "approved_classes.csv" csvSchoolFilename = "sds/School.csv" csvSectionFileName = "sds/Section.csv" csvStudentFileName = "sds/Student.csv" csvTeacherFileName = "sds/Teacher.csv" csvTeacherRosterFileName = "sds/TeacherRoster.csv" csvStudentEnrollmentFileName = "sds/StudentEnrollment.csv" # remove the csv files if they already exist. This is a messy way of doing it but I learnt python 2 days ago so whatever if os.path.exists(csvSchoolFilename): os.remove(csvSchoolFilename) if os.path.exists(csvSectionFileName): os.remove(csvSectionFileName) if os.path.exists(csvStudentFileName): os.remove(csvStudentFileName) if os.path.exists(csvTeacherFileName): os.remove(csvTeacherFileName) if os.path.exists(csvTeacherRosterFileName): os.remove(csvTeacherRosterFileName) if os.path.exists(csvStudentEnrollmentFileName): os.remove(csvStudentEnrollmentFileName) try: # Import CSV file for approved class lists with open(csvApprovedClasses, newline='', encoding='utf-8-sig') as csvfile: classList = list(csv.reader(csvfile)) print (type(classList)) print (classList) print ("Number of classes imported from csv list: ",len(classList)) except: print("***************************") print("Error importing csv file") # Open connection to Seqta try: connection = psycopg2.connect(user=db_user, port=db_port, password=db_password, database=db_database, host = db_host, sslmode = db_sslmode) cursor = connection.cursor() print(connection.get_dsn_parameters(), "\n") except (Exception, psycopg2.Error) as error: print("Error while connecting to PostgreSQL", error) # Fetch data for classlists try: for i in classList: className = str(('[%s]' % ', '.join(map(str, (i))))[1:-1]) print ("**") print (className) # Print PostgreSQL version cursor.execute("SELECT version();") record = cursor.fetchone() # Lookup classID from Class name in Seqta sq_classUnitQuery = "SELECT * FROM public.classunit WHERE name = (%s);" cursor.execute(sq_classUnitQuery,(className,)) classUnitPull = cursor.fetchall() print("Getting class information for:", (className)) for row in classUnitPull: classUnitID = row[0] classSubjectID = row[4] classTermID = row[7] print("Class unit ID (classUnitID) is:", classUnitID) print("Class subject ID (classSubjectID) is:", classSubjectID) print("Class term ID (classTermID) is:", classTermID) # Check if class has a staff member or students # If they don't we need to stop processing the class and drop it gracefully # Get subject description for Class sq_classSubjectQuery = "SELECT * FROM subject WHERE id = (%s);" cursor.execute(sq_classSubjectQuery, (classSubjectID,)) classSubjectPull = cursor.fetchall() for row in classSubjectPull: classSubjectDescription = row[3] classSubjectName = row[2] classTeamName = (className + " - " + classSubjectDescription) print("Class subject Description (classSubjectDescription) is:", classSubjectDescription) print("Class team name (classTeamName) is:", classTeamName) print("Class subject Name (classSubjectName) is:", classSubjectName) # Get StaffID in this classUnit sq_staffIDQuery = "SELECT staff from public.classinstance WHERE classunit = (%s) and date <= current_date ORDER BY id DESC LIMIT 1;" cursor.execute(sq_staffIDQuery, (classUnitID,)) staffID_pre = cursor.fetchone() if staffID_pre is None: print("Couldn't find a class today or previously for classunit:", classUnitID) print("Checking for a class up to 14 days in the future and selecting the closest date to today") sq_staffIDQuery = "SELECT staff from public.classinstance WHERE classunit = (%s) date = current_date + interval '14 day' ORDER BY id DESC LIMIT 1;" cursor.execute(sq_staffIDQuery, (classUnitID,)) staffID_pre = cursor.fetchone() staffID = int(staffID_pre[0]) print("Staff ID is:", (staffID)) # Write to teacher ID list staffList.add(staffID) else: staffID = int(staffID_pre[0]) print("Staff ID is:", (staffID)) # Write to teacher ID list staffList.add(staffID) # Get Student ID's for this classUnit sq_studentIDListQuery = "SELECT student from \"classunitStudent\" WHERE classunit = (%s) and removed is NULL;" cursor.execute(sq_studentIDListQuery, (classUnitID,)) studentIDArray = tuple([r[0] for r in cursor.fetchall()]) print("List of students in class name:", className) print(studentIDArray) for row in studentIDArray: studentList.add(row) # Check if the csv section file exists csvSectionFileExists = os.path.isfile(csvSectionFileName) # Write to the section csv file with open(csvSectionFileName, 'a', newline='') as csvSection: writer = csv.writer(csvSection) # If the csv doesn't exist already we'll need to put in the headers if not csvSectionFileExists: writer.writerow(["SIS ID", "School SIS ID", "Section Name", "Section Number", "Term SIS ID", "Term Name", "Course SIS ID", "Course Name", "Course Description"]) writer.writerow([(classUnitID), (schoolSISId), (classTeamName), (classUnitID), (classTermID), (classTermName), (classUnitID), (classSubjectName), (classSubjectDescription)]) print ("Writing class section row") # Check if the csv teacher roster file exists csvTeacherRosterFileExists = os.path.isfile(csvTeacherRosterFileName) # Write to the teacher roster csv file with open(csvTeacherRosterFileName, 'a', newline='') as csvTeacherRoster: writer = csv.writer(csvTeacherRoster) # If the csv doesn't exist already we'll need to put in the headers if not csvTeacherRosterFileExists: writer.writerow(["Section SIS ID", "SIS ID"]) writer.writerow([(classUnitID), (staffID)]) # Also include the Teams Admin account as a teacher writer.writerow([(classUnitID), (teamsAdminID)]) print("Written staff to roster") # Check if the csv student enrollment file exists csvStudentEnrollmentFileNameExists = os.path.isfile(csvStudentEnrollmentFileName) # Write to the student enrollment csv file with open(csvStudentEnrollmentFileName, 'a', newline='') as csvStudentEnrollment: writer = csv.writer(csvStudentEnrollment) # If the csv doesn't exist already we'll need to put in the headers if not csvStudentEnrollmentFileNameExists: writer.writerow(["Section SIS ID", "SIS ID"]) for studentInArray in studentIDArray: writer.writerow([(classUnitID), (studentInArray)]) except: print("") print("***************************") print("Error fetching class list data") print("") # Now we will fetch the staff information try: print("Print the staff lists now") print(staffList) for staff in staffList: # Now get the staff information sq_staffQuery = "SELECT * from public.staff WHERE id = (%s);" cursor.execute(sq_staffQuery, (staff,)) staffPull = cursor.fetchall() for row in staffPull: staffFirstName = row[4] staffLastName = row[7] staffUsername = row[21] print("Staff First Name (staffFirstName) is:", staffFirstName) print("Staff Last Name (staffLastName) is:", staffLastName) print("Staff username (staffUsername) is:", staffUsername) print("Staff ID is (staff) is:", staff) # Now we write this information to the Teacher.csv file # Check if the csv teacher file exists csvTeacherFileNameExists = os.path.isfile(csvTeacherFileName) # Write to the teacher csv file with open(csvTeacherFileName, 'a', newline='') as csvTeacher: writer = csv.writer(csvTeacher) # If the csv doesn't exist already we'll need to put in the headers if not csvTeacherFileNameExists: writer.writerow(["SIS ID", "School SIS ID", "First Name", "Last Name", "Username", "Teacher Number"]) # Also include the Teams Admin user as a teacher writer.writerow( [(teamsAdminID), (schoolSISId), (teamsAdminFirstName), (teamsAdminLastName), (teamsAdminUsername), (teamsAdminID)]) writer.writerow([(staff), (schoolSISId), (staffFirstName), (staffLastName), (staffUsername), (staff)]) except: print("something went wrong getting the staff data") # Now we will fetch the student information try: print("Print the student lists now") print(studentList) for student in studentList: # Now get the student information sq_studentQuery = "SELECT * from student WHERE id = (%s) AND status = 'FULL';" cursor.execute(sq_studentQuery, (student,)) studentPull = cursor.fetchall() for row in studentPull: studentFirstName = row[3] studentLastName = row[6] studentUsername = row[47] print("Student First Name (studentFirstName) is:", studentFirstName) print("Student Last Name (studentLastName) is:", studentLastName) print("Student username (studentUsername) is:", studentUsername) print("Student ID is (student) is:", student) # Now we write this information to the Student.csv file # Check if the csv Student file exists csvStudentFileNameExists = os.path.isfile(csvStudentFileName) # Write to the student enrollment csv file with open(csvStudentFileName, 'a', newline='') as csvStudent: writer = csv.writer(csvStudent) # If the csv doesn't exist already we'll need to put in the headers if not csvStudentFileNameExists: writer.writerow(["SIS ID", "School SIS ID", "First Name", "Last Name", "Username", "Student Number"]) writer.writerow([(student), (schoolSISId), (studentFirstName), (studentLastName), (studentUsername), (student)]) except: print("something went wrong getting the student data") # write the School.csv file try: with open('sds/School.csv', 'a', newline='') as csvSchool: writer = csv.writer(csvSchool) writer.writerow(["SIS ID","Name"]) writer.writerow([(schoolSISId),(schoolName)]) except: print("something went wrong writing the school csv file") finally: # closing database connection. if (connection): cursor.close() connection.close() print("PostgreSQL connection is closed")

45.445183

185

0.635865

de3b514aae1619036f4e6044f0e8e9c86052e8a3

457

py

Python

Chapter 1/imtools.py

ai-distill/PythonVisionProgramming

15a432b34d4ca43ab0a0bc765dbcaa9bc8de3d8e

[ "Apache-2.0" ]

null

Chapter 1/imtools.py

ai-distill/PythonVisionProgramming

15a432b34d4ca43ab0a0bc765dbcaa9bc8de3d8e

[ "Apache-2.0" ]

null

Chapter 1/imtools.py

ai-distill/PythonVisionProgramming

15a432b34d4ca43ab0a0bc765dbcaa9bc8de3d8e

[ "Apache-2.0" ]

null

""" """ import os from PIL import Image from numpy import * def get_imlist(path): """ JPG :param path: :return: """ return [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.jpg')] def imresize(im, sz): """ :param im: :param sz: :return: """ pil_im = Image.fromarray(uint8(im)) return array(pil_im.resize(sz)) print(get_imlist('.'))

16.925926

82

0.610503

de3ba9c03d6171d2fbdd34396181dfc69aedd8a7

5,190

py

Python

cart/views.py

lbacon17/lb-fitness

16f78841c834ca0e45317285b6c3b05ad97501f6

[ "W3C" ]

null

cart/views.py

lbacon17/lb-fitness

16f78841c834ca0e45317285b6c3b05ad97501f6

[ "W3C" ]

null

cart/views.py

lbacon17/lb-fitness

16f78841c834ca0e45317285b6c3b05ad97501f6

[ "W3C" ]

1

2021-03-31T10:55:51.000Z

from django.shortcuts import ( render, redirect, reverse, get_object_or_404, HttpResponse) from django.contrib import messages from shop.models import Product from members.models import Member def load_cart(request): """This view render's the user's cart contents""" return render(request, 'cart/cart.html') def add_item_to_cart(request, item_id): """This view lets the user add an item to their shopping cart""" item = get_object_or_404(Product, pk=item_id) quantity = int(request.POST.get('quantity')) redirect_url = request.POST.get('redirect_url') size = None if 'item_size' in request.POST: size = request.POST['item_size'] cart = request.session.get('cart', {}) if size: if item_id in list(cart.keys()): if size in cart[item_id]['items_by_size'].keys(): cart[item_id]['items_by_size'][size] += quantity messages.success(request, f'Updated size {size.upper()} ' f'of {item.friendly_name} to ' f'{cart[item_id]["items_by_size"][size]}') else: cart[item_id]['items_by_size'][size] = quantity messages.success(request, f'Added {quantity}x ' f'{item.friendly_name} in {size.upper()}') else: cart[item_id] = {'items_by_size': {size: quantity}} messages.success(request, f'Added {quantity}x {item.friendly_name}' f' in size {size.upper()}') else: if item_id in list(cart.keys()): cart[item_id] += quantity messages.success(request, f'Added {quantity}x {item.friendly_name}' f' to your cart. You now have {cart[item_id]} of' f' {item.friendly_name} in your cart') else: cart[item_id] = quantity messages.success(request, f'{cart[item_id]}x {item.friendly_name} ' f'was added to your cart') request.session['cart'] = cart return redirect(redirect_url) def update_cart(request, item_id): """This view lets the user update the quantity of an item in their cart""" item = get_object_or_404(Product, pk=item_id) quantity = int(request.POST.get('quantity')) size = None if 'item_size' in request.POST: size = request.POST['item_size'] cart = request.session.get('cart', {}) if size: if quantity > 99: messages.error(request, 'You cannot add this many units of a ' 'product. The maximum possible quantity is 99. ' 'Please enter a quantity within the accepted ' 'range.') elif quantity > 0: cart[item_id]['items_by_size'][size] = quantity messages.success(request, f'Updated quantity of ' f'{item.friendly_name} in size {size.upper()} ' f'to to {cart[item_id]["items_by_size"][size]}.') else: del cart[item_id]['items_by_size'][size] if not cart[item_id]['items_by_size']: cart.pop(item_id) messages.success(request, f'Removed {item.friendly_name} in size ' f'{size.upper()} from your cart.') else: if quantity > 99: messages.error(request, 'You cannot add this many units of a ' 'product. The maximum possible quantity is 99. ' 'Please enter a quantity within the accepted ' 'range.') elif quantity > 0: cart[item_id] = quantity messages.success(request, f'Successfully updated quantity of ' f'{item.friendly_name} to {cart[item_id]}.') else: cart.pop(item_id) messages.success(request, f'{item.friendly_name} was removed from ' 'your cart.') request.session['cart'] = cart return redirect(reverse('load_cart')) def remove_item_from_cart(request, item_id): """This view lets the user delete an item from their shopping cart""" try: item = get_object_or_404(Product, pk=item_id) size = None if 'item_size' in request.POST: size = request.POST['item_size'] cart = request.session.get('cart', {}) if size: del cart[item_id]['items_by_size'][size] if not cart[item_id]['items_by_size']: cart.pop(item_id) messages.success(request, f'Removed {item.friendly_name} in size ' f'{size.upper()} from your cart.') else: cart.pop(item_id) messages.success(request, f'{item.friendly_name} was deleted from ' 'your cart.') request.session['cart'] = cart return HttpResponse(status=200) except Exception as e: messages.error(request, f'There was a a problem removing the item.' '{e}') return HttpResponse(status=500)

41.854839

79

0.559152

de3d6c63aa40e3dc9ff43cbc7c4deca001d8d40e

172

py

Python

runserver.py

revalo/hush.mit.edu

e47c28c934dcfb94c52f6e12367869389e8ed7a8

[ "MIT" ]

21

2017-10-30T20:55:48.000Z

2021-09-03T14:06:58.000Z

runserver.py

revalo/hush.mit.edu

e47c28c934dcfb94c52f6e12367869389e8ed7a8

[ "MIT" ]

1

2021-11-08T02:05:34.000Z

2021-11-08T06:54:41.000Z

runserver.py

revalo/hush.mit.edu

e47c28c934dcfb94c52f6e12367869389e8ed7a8

[ "MIT" ]

3

2017-11-15T23:18:00.000Z

2018-01-01T06:44:03.000Z

from confess import app from confess.config import PORT, DEBUG if __name__ == '__main__': app.run( host='0.0.0.0', port=PORT, debug=DEBUG )

19.111111

38

0.593023

de3daa1f9c197f223b8adf05ac9c7b5634367d5c

5,945

py

Python

bin/plot_examples/plot_vars_barchart.py

gonzalorodrigo/ScSFWorkload

2301dacf486df8ed783c0ba33cbbde6e9978c17e

[ "BSD-3-Clause-LBNL" ]

1

2019-03-18T18:27:49.000Z

bin/plot_examples/plot_vars_barchart.py

gonzalorodrigo/ScSFWorkload

2301dacf486df8ed783c0ba33cbbde6e9978c17e

[ "BSD-3-Clause-LBNL" ]

1

2020-12-17T21:33:15.000Z

2020-12-17T21:35:41.000Z

bin/plot_examples/plot_vars_barchart.py

gonzalorodrigo/ScSFWorkload

2301dacf486df8ed783c0ba33cbbde6e9978c17e

[ "BSD-3-Clause-LBNL" ]

1

2021-01-05T08:23:20.000Z

""" Plots analysis on the workflow variables for experiments with different workflow types and different %of workflow core hours in the workload. Resuls are plotted as barchars that show how much the vas deviate in single and multi from aware. """ import matplotlib from orchestration import get_central_db from orchestration.definition import ExperimentDefinition from plot import (plot_multi_bars, produce_plot_config, extract_results, gen_trace_ids_exps, calculate_diffs, get_args, join_rows, replace) from stats.trace import ResultTrace # remote use no Display matplotlib.use('Agg') base_trace_id_percent, lim = get_args(2459, True) print("Base Exp", base_trace_id_percent) print("Using analysis of limited workflows:", lim) db_obj = get_central_db() edge_keys= {0: "[0,48] core.h", 48*3600:"(48, 960] core.h", 960*3600:"(960, inf.) core.h"} trace_id_rows = [] base_exp=170 exp=ExperimentDefinition() exp.load(db_obj, base_exp) core_seconds_edges=exp.get_machine().get_core_seconds_edges() # trace_id_rows = [ # [ 4166, 4167, 4168, 4184, 4185, 4186, 4202, 4203, 4204, # 4220, 4221, 4222, 4238, 4239, 4240 ], # [ 4169, 4170, 4171, 4187, 4188, 4189, 4205, 4206, 4207, # 4223, 4224, 4225, 4241, 4242, 4243 ], # [ 4172, 4173, 4174, 4190, 4191, 4192, 4208, 4209, 4210, # 4226, 4227, 4228, 4244, 4245, 4246 ], # [ 4175, 4176, 4177, 4193, 4194, 4195, 4211, 4212, 4213, # 4229, 4230, 4231, 4247, 4248, 4249], # [ 4178, 4179, 4180, 4196, 4197, 4198, 4214, 4215, 4216, # 4232, 4233, 4234, 4250, 4251, 4252], # [ 4181, 4182, 4183, 4199, 4200, 4201, 4217, 4218, 4219, # 4235, 4236, 4237, 4253, 4254, 4255], # ] pre_base_trace_id_percent = 2549+18 trace_id_rows= join_rows( gen_trace_ids_exps(pre_base_trace_id_percent, inverse=False, group_jump=18, block_count=6, base_exp_group=None, group_count=1), gen_trace_ids_exps(base_trace_id_percent, inverse=False, group_jump=18, block_count=6, base_exp_group=None, group_count=5) ) trace_id_colors=join_rows( gen_trace_ids_exps(pre_base_trace_id_percent+1, inverse=False, skip=1, group_jump=18, block_count=6, base_exp_group=None, group_count=1, group_size=2), gen_trace_ids_exps(base_trace_id_percent+1, inverse=False,skip=1, group_jump=18, block_count=6, base_exp_group=None, group_count=5, group_size=2) ) print("IDS", trace_id_rows) trace_id_rows=replace(trace_id_rows, [2489, 2490, 2491, 2507, 2508, 2509, 2525, 2526, 2527], [2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809]) print("IDS", trace_id_rows) print("COLORS", trace_id_colors) time_labels = ["", "5%", "", "10%", "", "25%", "", "50%", "", "75%", "", "100%"] manifest_label=["floodP", "longW", "wideL", "cybers", "sipht", "montage"] y_limits_dic={"[0,48] core.h": (1, 1000), "(48, 960] core.h":(1,100), "(960, inf.) core.h":(1,20)} target_dir="percent" grouping_types = [["bar", "bar"], ["bar", "bar"], ["bar", "bar"], ["bar", "bar"], ["bar", "bar"], ["bar", "bar"]] colors, hatches, legend = produce_plot_config(db_obj, trace_id_colors) #head_file_name="percent" head_file_name="wf_percent-b{0}".format(base_trace_id_percent) for (name, result_type) in zip(["Turnaround speedup", "wait time(h.)", "runtime (h.)", "stretch factor"], ["wf_turnaround", "wf_waittime", "wf_runtime", "wf_stretch_factor"]): if lim: result_type="lim_{0}".format(result_type) print("Loading: {0}".format(name)) factor=1.0/3600.0 if result_type in ("wf_stretch_factor", "lim_wf_stretch_factor"): factor=None edge_plot_results = extract_results(db_obj, trace_id_rows, result_type, factor=factor, second_pass=lim) diffs_results = calculate_diffs(edge_plot_results, base_index=0, group_count=3, speedup=True) # for res_row in edge_plot_results: # print [ x._get("median") for x in res_row] title="{0}".format(name) y_limits=(0,4) print("Plotting figure") ref_level=1.0 plot_multi_bars( name=title, file_name=target_dir+"/{0}-{1}-bars.png".format(head_file_name, result_type), title=title, exp_rows=diffs_results, y_axis_labels=manifest_label, x_axis_labels=time_labels, y_axis_general_label=name, type_rows=grouping_types, colors=colors, hatches=hatches, y_limits=y_limits, y_log_scale=False, legend=legend, y_tick_count=3, subtitle="% workflow workload", ncols=2, ref_line=ref_level )

36.030303

75

0.518923

de3df638310dcbe32c189284547dca83d1fe51a7

410

py

Python

devpotato_bot/commands/daily_titles/models/inevitable_title.py

cl0ne/cryptopotato-bot

af62d794adffe186a4f6a4b0aa7ecd4f7e8700a1

[ "MIT" ]

1

2021-05-15T23:41:29.000Z

devpotato_bot/commands/daily_titles/models/inevitable_title.py

cl0ne/cryptopotato-bot

af62d794adffe186a4f6a4b0aa7ecd4f7e8700a1

[ "MIT" ]

1

2022-02-19T20:38:33.000Z

2022-02-19T23:53:39.000Z

devpotato_bot/commands/daily_titles/models/inevitable_title.py

cl0ne/cryptopotato-bot

af62d794adffe186a4f6a4b0aa7ecd4f7e8700a1

[ "MIT" ]

1

2021-05-15T23:42:21.000Z

from __future__ import annotations from .title import TitleFromGroupChat, Base

27.333333

63

0.660976

de3e64921cbcc4e464aa3d32a70cc4b3179f2705

1,034

py

Python

matplotlib/gas_price_overtime.py

MatveiAleksandrovich/Artificial-Intelligence

d3d6f253e7c2256f6f9d490b077bdb50ca1da229

[ "MIT" ]

null

matplotlib/gas_price_overtime.py

MatveiAleksandrovich/Artificial-Intelligence

d3d6f253e7c2256f6f9d490b077bdb50ca1da229

[ "MIT" ]

null

matplotlib/gas_price_overtime.py

MatveiAleksandrovich/Artificial-Intelligence

d3d6f253e7c2256f6f9d490b077bdb50ca1da229

[ "MIT" ]

null

import requests import pandas as pd import matplotlib.pyplot as plt url_gas_data = 'https://raw.githubusercontent.com/KeithGalli/matplotlib_tutorial/master/gas_prices.csv' res1 = requests.get(url_gas_data, allow_redirects=True) with open('gas_prices.csv', 'wb') as file: file.write(res1.content) plt.figure(figsize=(12, 5)) gas = pd.read_csv('gas_prices.csv') plt.title('Gas prices overtime (in USD)', fontdict={ 'fontweight': 'bold', 'fontsize': 16 }) countries_to_look_at = ['USA', 'Australia', 'South Korea', 'Canada'] for country in gas: if country in countries_to_look_at: plt.plot(gas.Year, gas[country], label=country, marker='.') """ Other way to pass data: plt.plot(gas.Year, gas.USA, 'b.-', label='United States') plt.plot(gas.Year, gas.Canada, 'r.-', label='Canada') plt.plot(gas.Year, gas['South Korea'], 'g.-', label='South Korea') plt.plot(gas.Year, gas.Australia, 'y.-', label='Australia') """ plt.xticks(gas.Year[::3]) plt.xlabel('Year') plt.ylabel('US Dollars') plt.legend() plt.show()

23.5

103

0.698259

de40955063f239619674a2b5ecbf4dbaa910621e

2,305

py

Python

integration_tests/test_surveys.py

ONSdigital/sdx-tester

df193867c0d5e9dbf39790c85c41b07a9efed756

[ "MIT" ]

null

integration_tests/test_surveys.py

ONSdigital/sdx-tester

df193867c0d5e9dbf39790c85c41b07a9efed756

[ "MIT" ]

null

integration_tests/test_surveys.py

ONSdigital/sdx-tester

df193867c0d5e9dbf39790c85c41b07a9efed756

[ "MIT" ]

null

import unittest import uuid from app import survey_loader from app import message_manager from app.tester import run_survey

37.786885

109

0.572668

de42aa506b54f4487685cb532dc908e5f790e4a5

509

py

Python

shared/app_business_logic.py

c-w/python-loadtests

3ffd3dc89780b9372a5d20a71b2becec121ff3d2

[ "Apache-2.0" ]

2

2020-02-12T23:03:09.000Z

2020-02-12T23:09:42.000Z

shared/app_business_logic.py

c-w/python-loadtests

3ffd3dc89780b9372a5d20a71b2becec121ff3d2

[ "Apache-2.0" ]

null

shared/app_business_logic.py

c-w/python-loadtests

3ffd3dc89780b9372a5d20a71b2becec121ff3d2

[ "Apache-2.0" ]

null

from os import environ from azure.storage.table import TableService azure_account_name = environ['AZURE_ACCOUNT_NAME'] azure_account_key = environ['AZURE_ACCOUNT_KEY'] azure_table_name = environ['AZURE_TABLE_NAME'] table = TableService(azure_account_name, azure_account_key) get_entity = table.get_entity

28.277778

65

0.776031

de44446f8526c9f2e48dd37b76b2ac71ae33e71b

3,424

py

Python

csrank/dataset_reader/objectranking/letor_object_ranking_dataset_reader.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

null

csrank/dataset_reader/objectranking/letor_object_ranking_dataset_reader.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

null

csrank/dataset_reader/objectranking/letor_object_ranking_dataset_reader.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

1

2018-10-30T08:57:14.000Z

import logging import h5py import numpy as np from sklearn.utils import check_random_state from csrank.constants import OBJECT_RANKING from csrank.dataset_reader.letor_dataset_reader import LetorDatasetReader from csrank.dataset_reader.objectranking.util import sub_sampling NAME = "LetorObjectRankingDatasetReader" # if __name__ == '__main__': # import sys # import os # import inspect # dirname = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) # logging.basicConfig(filename=os.path.join(dirname, 'log.log'), level=logging.DEBUG, # format='%(asctime)s %(name)s %(levelname)-8s %(message)s', # datefmt='%Y-%m-%d %H:%M:%S') # logger = logging.getLogger(name='letor') # sys.path.append("..") # for n in [2008, 2007]: # ds = LetorObjectRankingDatasetReader(year=n) # logger.info(ds.X_train.shape) # logger.info(np.array(ds.X_test.keys).shape)

39.356322

104

0.629965

de44c06366bdb1cf83f5f3bb8ad925cefb959cf0

1,222

py

Python

app/wqFull/dev/trans.py

fkwai/geolearn

30cb4353d22af5020a48100d07ab04f465a315b0

[ "MIT" ]

null

app/wqFull/dev/trans.py

fkwai/geolearn

30cb4353d22af5020a48100d07ab04f465a315b0

[ "MIT" ]

null

app/wqFull/dev/trans.py

fkwai/geolearn

30cb4353d22af5020a48100d07ab04f465a315b0

[ "MIT" ]

2

2021-04-04T02:45:59.000Z

2022-03-19T09:41:39.000Z

from sklearn.preprocessing import QuantileTransformer, PowerTransformer from hydroDL.data import usgs, gageII, gridMET, ntn, GLASS, transform, dbBasin import numpy as np import matplotlib.pyplot as plt from hydroDL.post import axplot, figplot from hydroDL import kPath import json import os import importlib importlib.reload(axplot) importlib.reload(figplot) dm = dbBasin.DataFrameBasin('weathering') # subset dm.saveSubset('B10', ed='2009-12-31') dm.saveSubset('A10', sd='2010-01-01') yrIn = np.arange(1985, 2020, 5).tolist() t1 = dbBasin.func.pickByYear(dm.t, yrIn, pick=False) t2 = dbBasin.func.pickByYear(dm.t, yrIn) dm.createSubset('rmYr5', dateLst=t1) dm.createSubset('pkYr5', dateLst=t2) codeSel = ['00915', '00925', '00930', '00935', '00940', '00945', '00955'] d1 = dbBasin.DataModelBasin(dm, varY=codeSel, subset='rmYr5') d2 = dbBasin.DataModelBasin(dm, varY=codeSel, subset='pkYr5') mtdY = ['QT' for var in codeSel] d1.trans(mtdY=mtdY) d1.saveStat('temp') # d2.borrowStat(d1) d2.loadStat('temp') yy = d2.y yP = d2.transOutY(yy) yO = d2.Y # TS indS = 1 fig, axes = figplot.multiTS(d1.t, [yO[:, indS, :], yP[:, indS, :]]) fig.show() indS = 1 fig, axes = figplot.multiTS(d1.t, [yy[:, indS, :]]) fig.show()

25.458333

78

0.714403

de463062073e4c38b0ef746845b5c9b821ed145e

659

py

Python

pysad/statistics/__init__.py

selimfirat/pysad

dff2ff38258eb8a85c9d34cf5f0b876fc1dc9ede

[ "BSD-3-Clause" ]

155

2020-08-17T12:52:38.000Z

2022-03-19T02:59:26.000Z

pysad/statistics/__init__.py

shubhsoni/pysad

dff2ff38258eb8a85c9d34cf5f0b876fc1dc9ede

[ "BSD-3-Clause" ]

2

2020-10-22T09:50:28.000Z

2021-02-15T02:01:44.000Z

pysad/statistics/__init__.py

shubhsoni/pysad

dff2ff38258eb8a85c9d34cf5f0b876fc1dc9ede

[ "BSD-3-Clause" ]

14

2020-10-09T17:08:23.000Z

2022-03-25T11:30:12.000Z

""" The :mod:`pysad.statistics` module contains methods to keep track of statistics on streaming data. """ from .abs_statistic import AbsStatistic from .average_meter import AverageMeter from .count_meter import CountMeter from .max_meter import MaxMeter from .median_meter import MedianMeter from .min_meter import MinMeter from .running_statistic import RunningStatistic from .sum_meter import SumMeter from .sum_squares_meter import SumSquaresMeter from .variance_meter import VarianceMeter __all__ = ["AbsStatistic", "AverageMeter", "CountMeter", "MaxMeter", "MedianMeter", "MinMeter", "RunningStatistic", "SumMeter", "SumSquaresMeter", "VarianceMeter"]

41.1875

163

0.814871

de481c317eb312cc809e4b8eb2f8383abd96ba97

324

py

Python

src/elrados/views.py

IamShobe/elrados

dd2523e1523591c7a3213dfd062b376f41bb9f18

[ "MIT" ]

2

2018-07-20T11:03:42.000Z

2019-06-06T06:00:12.000Z

src/elrados/views.py

IamShobe/elrados

dd2523e1523591c7a3213dfd062b376f41bb9f18

[ "MIT" ]

null

src/elrados/views.py

IamShobe/elrados

dd2523e1523591c7a3213dfd062b376f41bb9f18

[ "MIT" ]

2

2018-12-18T16:00:34.000Z

2019-04-08T14:29:02.000Z

"""Global index view.""" import pkg_resources from django.shortcuts import render def index(request): """Basic view.""" plugins = \ [plugin.load() for plugin in pkg_resources.iter_entry_points(group='elrados.plugins')] return render(request, "index.html", { "plugins": plugins })

21.6

66

0.641975

de48207667680d4095ac834e7b25417f0ab4f83a

2,274

py

Python

examples/old/zipline_momentun.py

sherrytp/TradingEvolved

4bc9cc18244954bff37a80f67cce658bd0802b5d

[ "Apache-2.0" ]

null

examples/old/zipline_momentun.py

sherrytp/TradingEvolved

4bc9cc18244954bff37a80f67cce658bd0802b5d

[ "Apache-2.0" ]

null

examples/old/zipline_momentun.py

sherrytp/TradingEvolved

4bc9cc18244954bff37a80f67cce658bd0802b5d

[ "Apache-2.0" ]

1

2022-03-26T07:11:18.000Z

import pandas as pd import matplotlib.pyplot as plt from zipline.finance.commission import PerShare from zipline.api import set_commission, symbol, order_target_percent import zipline from models.live_momentum import LiveMomentum with open('/Users/landey/Desktop/Eonum/live_model/eouniverse/stock_list.txt', 'r') as f: data = f.read().split() tickers = data[:20] etf_list = tickers[15:] start = pd.Timestamp('2020-3-22', tz='utc') end = pd.Timestamp('2020-4-28', tz='utc') perf = zipline.run_algorithm(start=start, end=end, initialize=initialize, capital_base=100000, handle_data=handle_data, bundle='sep') perf.portfolio_value.plot() plt.show()

30.72973

95

0.647757

de4860345de948d81c21b1062677ea640e28f033

10,120

py

Python

packages/robotControl/scripts/intercept.py

Falcons-Robocup/code

2281a8569e7f11cbd3238b7cc7341c09e2e16249

[ "Apache-2.0" ]

2

2021-01-15T13:27:19.000Z

2021-08-04T08:40:52.000Z

packages/robotControl/scripts/intercept.py

Falcons-Robocup/code

2281a8569e7f11cbd3238b7cc7341c09e2e16249

[ "Apache-2.0" ]

null

packages/robotControl/scripts/intercept.py

Falcons-Robocup/code

2281a8569e7f11cbd3238b7cc7341c09e2e16249

[ "Apache-2.0" ]

5

2018-05-01T10:39:31.000Z

2022-03-25T03:02:35.000Z

# Copyright 2020 Jan Feitsma (Falcons) # SPDX-License-Identifier: Apache-2.0 #!/usr/bin/env python3 # Jan Feitsma, March 2020 # Robot will continuously intercept around current position. # # For description and usage hints, execute with '-h' import sys, os import time import logging, signal logging.basicConfig(level=logging.INFO) import math, random import argparse import falconspy import rtdb2tools from robotLibrary import RobotLibrary from worldState import WorldState from FalconsCoordinates import * def calcCirclePos(robotIdx, numRobots, radius=3, center=(0,0)): """ Helper function to distribute robot positions on a circle. """ gamma = 2*math.pi / numRobots x = radius * math.cos(gamma * robotIdx) + center[0] y = radius * math.sin(gamma * robotIdx) + center[1] phi = gamma * robotIdx - math.pi return (x, y, phi) if __name__ == '__main__': args = parse_arguments() if args.robot == 0 or args.robot == None: raise RuntimeError("Error: could not determine robot ID, this script should run on a robot") main(args)

42.700422

305

0.619368

de4f135b4907a9ad1ee036150f5775fba0b81256

4,859

py

Python

arpym/tools/plc.py

dpopadic/arpmRes

ddcc4de713b46e3e9dcb77cc08c502ce4df54f76

[ "MIT" ]

6

2021-04-10T13:24:30.000Z

2022-03-26T08:20:42.000Z

arpym/tools/plc.py

dpopadic/arpmRes

ddcc4de713b46e3e9dcb77cc08c502ce4df54f76

[ "MIT" ]

null

arpym/tools/plc.py

dpopadic/arpmRes

ddcc4de713b46e3e9dcb77cc08c502ce4df54f76

[ "MIT" ]

6

2019-08-13T22:02:17.000Z

2022-02-09T17:49:12.000Z

# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec, GridSpecFromSubplotSpec from matplotlib.ticker import FuncFormatter def plot_dynamic_strats(t, v_t_strat, v_t_risky, w_t_risky, h_t_risky, num, j_sel): """For details, see here. Parameters ---------- t : array, shape (t_,) v_t_strat : array, shape (j_,t_) v_t_risky : array, shape (j_,t_) w_t_risky : array, shape (j_,t_) h_t_risky: array, shape (j_,t_) num: int j_sel: int """ # adjust v_t_risky so that it has the same initial value as v_t_strat v_t_risky = v_t_risky * v_t_strat[0, 0] / v_t_risky[0, 0] mu_risky = np.mean(v_t_risky, axis=0, keepdims=True).reshape(-1) sig_risky = np.std(v_t_risky, axis=0, keepdims=True).reshape(-1) mu_strat = np.mean(v_t_strat, axis=0, keepdims=True).reshape(-1) sig_strat = np.std(v_t_strat, axis=0, keepdims=True).reshape(-1) plt.style.use('arpm') fig = plt.figure() gs = GridSpec(1, 2) gs1 = GridSpecFromSubplotSpec(3, 1, subplot_spec=gs[0]) num_bins = int(round(100 * np.log(v_t_strat.shape[1]))) lgrey = [0.8, 0.8, 0.8] # light grey dgrey = [0.4, 0.4, 0.4] # dark grey j_ = v_t_risky.shape[0] x_min = t[0] x_max = 1.25 * t[-1] y_min = v_t_strat[0, 0] / 4 y_max = v_t_strat[0, 0] * 2.25 # scatter plot ax4 = plt.subplot(gs[1]) plt.scatter(v_t_risky[:, -1], v_t_strat[:, -1], marker='.', s=2) so = np.sort(v_t_risky[:, -1]) plt.plot(so, so, label='100% risky instrument', color='r') plt.plot([y_min, v_t_risky[j_sel, -1], v_t_risky[j_sel, -1]], [v_t_strat[j_sel, -1], v_t_strat[j_sel, -1], y_min], 'b--') plt.plot(v_t_risky[j_sel, -1], v_t_strat[j_sel, -1], 'bo') ax4.set_xlim(y_min, y_max) ax4.set_ylim(y_min, y_max) ax4.xaxis.set_major_formatter(FuncFormatter(tick_label_func)) ax4.yaxis.set_major_formatter(FuncFormatter(tick_label_func)) plt.xlabel('Strategy') plt.ylabel('Risky instrument') plt.legend() # weights and holdings ax3 = plt.subplot(gs1[2]) y_min_3 = np.min(h_t_risky[j_sel, : -1]) y_max_3 = np.max(h_t_risky[j_sel, : -1]) plt.sca(ax3) plt.plot(t, w_t_risky[j_sel, :], color='b') plt.axis([x_min, x_max, 0, 1]) plt.xticks(np.linspace(t[0], 1.2 * t[-1], 7)) plt.yticks(np.linspace(0, 1, 3), color='b') plt.ylabel('Weights', color='b') plt.xlabel('Time') ax3_2 = ax3.twinx() plt.plot(t, h_t_risky[j_sel, :], color='black') plt.ylabel('Holdings', color='black') plt.axis([x_min, x_max, y_min_3 - 1, y_max_3 + 1]) plt.yticks(np.linspace(y_min_3, y_max_3, 3)) ax3_2.yaxis.set_major_formatter(FuncFormatter(tick_label_func_1)) ax1 = plt.subplot(gs1[0], sharex=ax3, sharey=ax4) # simulated path, standard deviation of strategy for j in range(j_ - num, j_): plt.plot(t, v_t_strat[j, :], color=lgrey) plt.plot(t, v_t_strat[j_sel, :], color='b') plt.plot(t, mu_strat + sig_strat, color='orange') plt.plot(t, mu_strat - sig_strat, color='orange') plt.xticks(np.linspace(t[0], 1.2 * t[-1], 7)) # histogram y_hist, x_hist = np.histogram(v_t_strat[:, -1], num_bins) scale = 0.25 * t[-1] / np.max(y_hist) y_hist = y_hist * scale plt.barh(x_hist[: -1], y_hist, height=(max(x_hist) - min(x_hist)) / (len(x_hist) - 1), left=t[-1], facecolor=dgrey, edgecolor=dgrey) plt.setp(ax1.get_xticklabels(), visible=False) plt.ylabel('Strategy') ax1.set_ylim(y_min, y_max) ax1.yaxis.set_major_formatter(FuncFormatter(tick_label_func)) # risky instrument ax2 = plt.subplot(gs1[1], sharex=ax3, sharey=ax4) # simulated path, standard deviation of risky instrument for j in range(j_ - num, j_): plt.plot(t, v_t_risky[j, :], color=lgrey) plt.plot(t, v_t_risky[j_sel, :], color='b') plt.plot(t, mu_risky + sig_risky, color='orange') plt.plot(t, mu_risky - sig_risky, color='orange') plt.xticks(np.linspace(t[0], 1.2 * t[-1], 7)) # histogram y_hist, x_hist = np.histogram(v_t_risky[:, -1], num_bins) scale = 0.25 * t[-1] / np.max(y_hist) y_hist = y_hist * scale plt.barh(x_hist[: -1], y_hist, height=(max(x_hist) - min(x_hist)) / (len(x_hist) - 1), left=t[-1], facecolor=dgrey, edgecolor=dgrey) plt.setp(ax2.get_xticklabels(), visible=False) plt.ylabel('Risky instrument') ax2.set_ylim(y_min, y_max) ax2.yaxis.set_major_formatter(FuncFormatter(tick_label_func)) plt.grid(True) plt.tight_layout() return fig, gs

35.210145

106

0.61844

de4f23bfb5a827684724b1fa6940e53745dbb142

1,166

py

Python

krpc_client.py

janismac/ksp_rtls_launch_to_rendezvous

195ebfb5aacf1a857aaaf0a69bf071d93d887efd

[ "Apache-2.0" ]

1

2020-11-07T15:53:19.000Z

krpc_client.py

janismac/ksp_rtls_launch_to_rendezvous

195ebfb5aacf1a857aaaf0a69bf071d93d887efd

[ "Apache-2.0" ]

null

krpc_client.py

janismac/ksp_rtls_launch_to_rendezvous

195ebfb5aacf1a857aaaf0a69bf071d93d887efd

[ "Apache-2.0" ]

1

2020-11-07T15:56:06.000Z

import sys import subprocess import time import json import krpc import math import scipy.integrate import numpy as np from PrePlanningChecklist import PrePlanningChecklist from PlannerUiPanel import PlannerUiPanel from MainUiPanel import MainUiPanel from ConfigUiPanel import ConfigUiPanel from AutopilotUiPanel import AutopilotUiPanel from predict_orbit_BCBF import predict_orbit_BCBF while True: try: main() #time.sleep(2.0) except krpc.error.RPCError: time.sleep(4.0) #except ValueError: # time.sleep(4.0)

25.911111

76

0.736707

de4fbddd1a8e5c3c47f15c39acb99e707f22e65b

617

py

Python

src/alerter.py

Jawgo/DiscordBot

43dccce80aa8d8bd51b44c0de732fd70d9194672

[ "MIT" ]

null

src/alerter.py

Jawgo/DiscordBot

43dccce80aa8d8bd51b44c0de732fd70d9194672

[ "MIT" ]

null

src/alerter.py

Jawgo/DiscordBot

43dccce80aa8d8bd51b44c0de732fd70d9194672

[ "MIT" ]

null

import os from discord import Webhook, RequestsWebhookAdapter, Colour, Embed

36.294118

110

0.666126

de50a4c4fb04e2350cc10caa2aea9a7a75fcac8c

4,593

py

Python

dataset_preproc/preproc_video/face_extract.py

RicardoP0/multimodal-matchmap

aa44c574a57073833004172734394882889d8d3b

[ "MIT" ]

null

dataset_preproc/preproc_video/face_extract.py

RicardoP0/multimodal-matchmap

aa44c574a57073833004172734394882889d8d3b

[ "MIT" ]

null

dataset_preproc/preproc_video/face_extract.py

RicardoP0/multimodal-matchmap

aa44c574a57073833004172734394882889d8d3b

[ "MIT" ]

null

#%% #https://github.com/timesler/facenet-pytorch from facenet_pytorch import MTCNN, extract_face import torch import numpy as np import mmcv, cv2 import os import matplotlib.pyplot as plt from PIL import Image # %% #%% device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') print('Running on device: {}'.format(device)) print(os.getcwd()) mtcnn = MTCNN(keep_all=True, device=device,image_size=100) video_dir = "VIDEO_FILES/" dest_path = 'VIDEO_PROCESSED/' dir_list = os.listdir(video_dir) dir_list.sort() if not os.path.exists(dest_path): os.makedirs(dest_path) #%% # %% #iemocap k = 1 #session to process video_dir = "IEMOCAP_full_release.tar/IEMOCAP_full_release/Session{}/dialog/avi/DivX".format(k) dir_list = os.listdir(video_dir) dir_list.sort() dir_list = [x for x in dir_list if x[0] =='S'] i=0 #%% dir_list path = 'datasets/IEMOCAP/CLIPPED_VIDEOS/' + 'Session{}/'.format(k) if not os.path.exists(path): os.makedirs(path) dir_list #%% #divide each video and manually crop around face video_dir = "IEMOCAP_full_release.tar/IEMOCAP_full_release/Session{}/dialog/avi/DivX".format(k) dir_list = os.listdir(video_dir) dir_list.sort() dir_list = [x for x in dir_list if x[0] =='S'] path = 'IEMOCAP/CLIPPED_VIDEOS/' + 'Session{}/'.format(k) if not os.path.exists(path): os.makedirs(path) for file_name in dir_list: print(file_name) video = mmcv.VideoReader(video_dir + '/'+file_name) if 'F_' in file_name: new_file_left = path + file_name[:-4] + '_F.avi' new_file_right = path +file_name[:-4] + '_M.avi' else: new_file_left = path +file_name[:-4] + '_M.avi' new_file_right = path + file_name[:-4] + '_F.avi' h,w,c = video[0].shape dim = (300,280) fourcc = cv2.VideoWriter_fourcc(*'FMP4') #left video_tracked = cv2.VideoWriter(new_file_left, fourcc, 25.0, dim) i=0 for frame in video: h,w,c = frame.shape #left #different boxes for each session #box (left, upper, right, lower)-tuple #ses1 [120:int(h- 690),120:int(w/2.4)] #ses2 [150:int(h - 660),120:int(w/2.4)] #ses5 [120:int(h - 690),120:int(w/2.4)] #[130:int(h/2.18),120:int(w/2.4)] video_tracked.write(frame[100:h-100,:300]) video_tracked.release() del video_tracked print(h,w,c) dim = (370,280) # #right video_tracked = cv2.VideoWriter(new_file_right, fourcc, 25.0, dim) for frame in video: h,w,c = frame.shape #right #ses1 [150:int(h - 660),int(w/1.5):int(w-60)] #ses2 [150:int(h - 660),int(w/1.5):int(w-60)] #ses5 [150:int(h - 660),int(w/1.5):int(w-60)] video_tracked.write(frame[100:h-100,350:]) video_tracked.release() del video, video_tracked #%% device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') print('Running on device: {}'.format(device)) print(os.getcwd()) mtcnn = MTCNN(keep_all=True, device=device,image_size=2000,margin=5) i = 1 video_dir = "../../../../datasets/IEMOCAP/CLIPPED_VIDEOS/Session{}/".format(i) dir_list = os.listdir(video_dir) dir_list.sort() dir_list = [x for x in dir_list if x[0] =='S'] dir_list #%% file_list = dir_list path = '../datasets/IEMOCAP/FACE_VIDEOS/Session{}/'.format(i) if not os.path.exists(path): os.makedirs(path) #%% #%% #track using mtcnn for file_name in file_list: video = mmcv.VideoReader(video_dir + file_name) frames = [Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) for frame in video] frames_tracked = [] for x, frame in enumerate(frames): #print('\rTracking frame: {}'.format(i + 1), end='') # Detect faces boxes, _ = mtcnn.detect(frame) if not boxes is None: # print(boxes[0]) im_array = extract_face(frame, boxes[0],image_size=112,margin=50) #im_array = im_array.permute(1,2,0) img = im_array #Image.fromar ray(np.uint8(im_array.numpy())) # Add to frame list frames_tracked.append(img) else: frames_tracked.append(img) dim = frames_tracked[0].size print(len(frames),len(frames_tracked)) new_file = path + '/' + file_name print(new_file) fourcc = cv2.VideoWriter_fourcc(*'FMP4') video_tracked = cv2.VideoWriter(new_file, fourcc, 25.0, dim) for frame in frames_tracked: video_tracked.write(cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR)) video_tracked.release() del video, video_tracked, frames_tracked, frames

29.254777

95

0.642717

de51709d96e27d7e3576d5ee6ad6f2ebabdc7ebc

1,441

py

Python

launch/gazebo.launch.py

fly4future/fog_gazebo_resources

1af1aa2d3a5e7c67bf39605655ca96a154daa4b3

[ "BSD-3-Clause" ]

null

launch/gazebo.launch.py

fly4future/fog_gazebo_resources

1af1aa2d3a5e7c67bf39605655ca96a154daa4b3

[ "BSD-3-Clause" ]

null

launch/gazebo.launch.py

fly4future/fog_gazebo_resources

1af1aa2d3a5e7c67bf39605655ca96a154daa4b3

[ "BSD-3-Clause" ]

null

"""Launch Gazebo server and client with command line arguments.""" from launch import LaunchDescription from launch.substitutions import LaunchConfiguration from launch.actions import DeclareLaunchArgument from launch.actions import IncludeLaunchDescription from launch.actions import ExecuteProcess from launch.conditions import IfCondition from launch.launch_description_sources import PythonLaunchDescriptionSource from ament_index_python.packages import get_package_share_directory

38.945946

117

0.696738

de5241403b212e20d0b5a9c1eb86d5461e49bad7

957

py

Python

hlrl/torch/utils/contexts/training.py

Chainso/HLRL

584f4ed2fa4d8b311a21dbd862ec9434833dd7cd

[ "MIT" ]

null

hlrl/torch/utils/contexts/training.py

Chainso/HLRL

584f4ed2fa4d8b311a21dbd862ec9434833dd7cd

[ "MIT" ]

null

hlrl/torch/utils/contexts/training.py

Chainso/HLRL

584f4ed2fa4d8b311a21dbd862ec9434833dd7cd

[ "MIT" ]

null

from contextlib import contextmanager import torch.nn as nn

20.804348

66

0.6186

de53cfe343832488633720622d964252c48b5617

3,180

py

Python

test/test_postfix.py

JoseTomasTocino/toptal-calculator

baeb69fdeca81699d655e1f2f11f03f2a3972ab7

[ "Unlicense" ]

null

test/test_postfix.py

JoseTomasTocino/toptal-calculator

baeb69fdeca81699d655e1f2f11f03f2a3972ab7

[ "Unlicense" ]

null

test/test_postfix.py

JoseTomasTocino/toptal-calculator

baeb69fdeca81699d655e1f2f11f03f2a3972ab7

[ "Unlicense" ]

null

import unittest from calculator import tokens, evaluator from calculator.parser import tokenize, infix_to_postfix

31.485149

70

0.646226

de55352cff35ae8596924966eb4c23a46054b461

1,124

py

Python

Weather API/app.py

TanushreeShaw/Weather

0bebe029536f579bbd9d28c07d3e33f3438a1a56

[ "MIT" ]

null

Weather API/app.py

TanushreeShaw/Weather

0bebe029536f579bbd9d28c07d3e33f3438a1a56

[ "MIT" ]

null

Weather API/app.py

TanushreeShaw/Weather

0bebe029536f579bbd9d28c07d3e33f3438a1a56

[ "MIT" ]

null

from flask import Flask, render_template, request import requests import json import os app = Flask(__name__) picfolder = os.path.join('static','pics') app.config['UPLOAD_FOLDER'] = picfolder if __name__ == '__main__': app.run(debug=True)

33.058824

152

0.670819

de559c2b5884fa9c7d514b793b602e0875f672ea

561

py

Python

core/urls.py

cybernetisk/internsystem

b81faa0deef08153032e56d5740173e5a6cf3ad9

[ "MIT" ]

null

core/urls.py

cybernetisk/internsystem

b81faa0deef08153032e56d5740173e5a6cf3ad9

[ "MIT" ]

38

2017-12-21T10:10:54.000Z

2022-03-07T20:54:37.000Z

core/urls.py

cybernetisk/internsystem

b81faa0deef08153032e56d5740173e5a6cf3ad9

[ "MIT" ]

6

2018-06-01T21:04:34.000Z

2020-01-14T15:26:26.000Z

from django.conf.urls import url from core.views import me from core.rest import CardViewSet, UserViewSet, NfcCardViewSet, GroupViewSet from core.utils import SharedAPIRootRouter # SharedAPIRootRouter is automatically imported in global urls config router = SharedAPIRootRouter() router.register(r"core/users", UserViewSet, basename="users") router.register(r"core/cards", CardViewSet, basename="voucher_cards") router.register(r"core/nfc", NfcCardViewSet) router.register(r"core/groups", GroupViewSet) urlpatterns = [ url(r"^api/me$", me, name="me"), ]

33

76

0.787879

de5df9efa200676cbee6ac7078451697101f76eb

2,931

py

Python

flora_tools/experiments/measure_time_irq_process.py

Atokulus/flora-tools

6f878a4495e4dcb6b9bc19a75aaac37b9dfb16b0

[ "MIT" ]

1

2020-11-20T16:36:17.000Z

flora_tools/experiments/measure_time_irq_process.py

Atokulus/flora-tools

6f878a4495e4dcb6b9bc19a75aaac37b9dfb16b0

[ "MIT" ]

null

flora_tools/experiments/measure_time_irq_process.py

Atokulus/flora-tools

6f878a4495e4dcb6b9bc19a75aaac37b9dfb16b0

[ "MIT" ]

null

from flora_tools.experiment import *

37.576923

111

0.588536

de5e91c132fdc9f05dd13b11b8708a82b0c0f470

213

py

Python

6P/REDES/restAPI/main/serializers.py

rwnicholas/fluffy-potato

52ccd25cf77f8cebce1420e7fe9028a277811986

[ "MIT" ]

null

6P/REDES/restAPI/main/serializers.py

rwnicholas/fluffy-potato

52ccd25cf77f8cebce1420e7fe9028a277811986

[ "MIT" ]

null

6P/REDES/restAPI/main/serializers.py

rwnicholas/fluffy-potato

52ccd25cf77f8cebce1420e7fe9028a277811986

[ "MIT" ]

null

from rest_framework import serializers from main.models import Suco

26.625

55

0.704225

de5f40f2fa117e9d234c38567381795609e6e892

183

py

Python

gpytorch/kernels/keops/__init__.py

wjmaddox/gpytorch

679f437fa71f8e15d98b3d256924ecf4b52c0448

[ "MIT" ]

1

2019-09-16T16:58:54.000Z

gpytorch/kernels/keops/__init__.py

wjmaddox/gpytorch

679f437fa71f8e15d98b3d256924ecf4b52c0448

[ "MIT" ]

null

gpytorch/kernels/keops/__init__.py

wjmaddox/gpytorch

679f437fa71f8e15d98b3d256924ecf4b52c0448

[ "MIT" ]

null

from .matern_kernel import MaternKernel from .rbf_kernel import RBFKernel from .spectralgp_kernel import SpectralGPKernel __all__ = ["MaternKernel", "RBFKernel", "SpectralGPKernel"]

30.5

59

0.825137

de61a2c63bd8bf8c89dfa8db3b212f5ada8c9268

271

py

Python

bc/recruitment/migrations/0018_merge_20200324_1630.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

1

2021-02-27T07:27:17.000Z

bc/recruitment/migrations/0018_merge_20200324_1630.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

null

bc/recruitment/migrations/0018_merge_20200324_1630.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

1

2021-06-09T15:56:54.000Z

# Generated by Django 2.2.10 on 2020-03-24 16:30 from django.db import migrations

19.357143

52

0.664207

de61aeb69172f0bbf84a85482ba65c30efe863a2

1,901

py

Python

main.py

SHGoldfarb/fantastic-barnacle

64650155ef8172530a6f88be6e7361bfc7e6bfa2

[ "MIT" ]

null

main.py

SHGoldfarb/fantastic-barnacle

64650155ef8172530a6f88be6e7361bfc7e6bfa2

[ "MIT" ]

null

main.py

SHGoldfarb/fantastic-barnacle

64650155ef8172530a6f88be6e7361bfc7e6bfa2

[ "MIT" ]

null

import requests import os from datetime import datetime import pandas as pd if __name__ == "__main__": main()

23.7625

78

0.711731

de6435cdbc67360ee94636dc50bd704495e2b720

382

py

Python

dump/yoloCarAccident/generate.py

lovishchopra/ITRI-Car-Accident

96a1ffa25eacfb2885ea1fa0852a91c8bb5ec95d

[ "MIT" ]

null

dump/yoloCarAccident/generate.py

lovishchopra/ITRI-Car-Accident

96a1ffa25eacfb2885ea1fa0852a91c8bb5ec95d

[ "MIT" ]

null

dump/yoloCarAccident/generate.py

lovishchopra/ITRI-Car-Accident

96a1ffa25eacfb2885ea1fa0852a91c8bb5ec95d

[ "MIT" ]

null

import os import yoloCarAccident as yc # yc.find('test.txt') f1 = open('result2.txt','r') i = 0 s = "" for lines in f1: if(i<80000): s += lines i+=1 else: f2 = open('test.txt','w') f2.write(s) f2.close() try: yc.find('test.txt') except ValueError: pass s = "" i = 0 # break # f2 = open('test.txt','w') # f2.write(s) # f2.close() # yc.find('test.txt')

13.172414

28

0.557592

de65eb26862ea6588043a83de4e49020ae4daf2c

1,853

py

Python

socketserver_extra.py

sim642/pyqwebirc

cd0cc120eacd3eea60b827ff7b2b157ab4a5dd1e

[ "MIT" ]

null

socketserver_extra.py

sim642/pyqwebirc

cd0cc120eacd3eea60b827ff7b2b157ab4a5dd1e

[ "MIT" ]

2

2017-01-04T18:24:00.000Z

2017-01-04T18:50:32.000Z

socketserver_extra.py

sim642/pyqwebirc

cd0cc120eacd3eea60b827ff7b2b157ab4a5dd1e

[ "MIT" ]

null

import socketserver import socket

33.089286

88

0.652455

de681128c0eb4ded13f92d6720603223e15efc17

4,560

py

Python

train_n_test/train_decoder.py

kamieen03/style-transfer-net

c9f56aa579553be8c72f37ce975ba88dbd775605

[ "BSD-2-Clause" ]

2

2019-12-14T14:59:22.000Z

2020-01-30T16:17:28.000Z

train_n_test/train_decoder.py

kamieen03/style-transfer-net

c9f56aa579553be8c72f37ce975ba88dbd775605

[ "BSD-2-Clause" ]

null

train_n_test/train_decoder.py

kamieen03/style-transfer-net

c9f56aa579553be8c72f37ce975ba88dbd775605

[ "BSD-2-Clause" ]

1

2020-01-16T20:03:35.000Z

#!/usr/bin/env python3 import os, sys sys.path.append(os.path.abspath(__file__ + "/../../")) # just so we can use 'libs' import torch.utils.data import torch.optim as optim from torch import nn import numpy as np import torch from libs.Loader import Dataset from libs.shufflenetv2 import ShuffleNetV2AutoEncoder BATCH_SIZE = 32 CROP_SIZE = 400 ENCODER_SAVE_PATH = f'models/regular/shufflenetv2_x1_encoder.pth' DECODER_SAVE_PATH = f'models/regular/shufflenetv2_x1_decoder.pth' EPOCHS = 20 if __name__ == '__main__': main()

35.905512

84

0.53114

de69814605b1835959a1ffdafc1b9774d60d18ad

75

py

Python

utils/__init__.py

bitst0rm-st3/AutomaticPackageReloader

b48699420ccadb3c1a8796a1a7275f70089f0934

[ "MIT" ]

null

utils/__init__.py

bitst0rm-st3/AutomaticPackageReloader

b48699420ccadb3c1a8796a1a7275f70089f0934

[ "MIT" ]

null

utils/__init__.py

bitst0rm-st3/AutomaticPackageReloader

b48699420ccadb3c1a8796a1a7275f70089f0934

[ "MIT" ]

null

from .progress_bar import ProgressBar from .read_config import read_config

25

37

0.866667

de6c1a64c58a8aca902a8fc78dd2204b84031a65

2,871

py

Python

src/main/create/c_chains_user_json.py

WikiCommunityHealth/wikimedia-revert

b584044d8b6a61a79d98656db356bf1f74d23ee0

[ "MIT" ]

null

src/main/create/c_chains_user_json.py

WikiCommunityHealth/wikimedia-revert

b584044d8b6a61a79d98656db356bf1f74d23ee0

[ "MIT" ]

null

src/main/create/c_chains_user_json.py

WikiCommunityHealth/wikimedia-revert

b584044d8b6a61a79d98656db356bf1f74d23ee0

[ "MIT" ]

null

#%% # PAGE EXAMPLE # {'title': 'Zuppa_di_pesce_(film)', # 'chains': [{'revisions': ['95861493', '95861612', '95973728'], # 'users': {'93.44.99.33': '', 'Kirk39': '63558', 'AttoBot': '482488'}, # 'len': 3, # 'start': '2018-04-01 04:54:40.0', # 'end': '2018-04-05 07:36:26.0'}], # 'n_chains': 1, # 'n_reverts': 3, # 'mean': 3.0, # 'longest': 3, # 'M': 0, # 'lunghezze': {'3': 1}} import json from datetime import datetime import numpy as np import pandas as pd import os import shutil from utils import utils import sys language = sys.argv[1] dataset_folder = f'/home/gandelli/dev/data/{language}/chains/page/' output = f'/home/gandelli/dev/data/{language}/chains/user/' #%% get users from the json page # input a dict of users with the chains joined #%% shutil.rmtree(output) os.mkdir(output) users = get_users() compute_users(users) # %%

26.1

183

0.563915

de6c4ab063a946c3b3fd6bbb89fa20997b2be723

5,105

py

Python

src/carts/views.py

dhaval6552/ecommerce-2

ab80fbbf15c0fbd37db94cfd7aa9a3ac0b46c737

[ "MIT" ]

null

src/carts/views.py

dhaval6552/ecommerce-2

ab80fbbf15c0fbd37db94cfd7aa9a3ac0b46c737

[ "MIT" ]

null

src/carts/views.py

dhaval6552/ecommerce-2

ab80fbbf15c0fbd37db94cfd7aa9a3ac0b46c737

[ "MIT" ]

null

from django.contrib.auth.forms import AuthenticationForm from django.core.urlresolvers import reverse from django.views.generic.base import View from django.views.generic.detail import SingleObjectMixin,DetailView from django.shortcuts import render,get_object_or_404,redirect from django.http import HttpResponseRedirect,Http404,JsonResponse from django.views.generic.edit import FormMixin from orders.forms import GuestCheckoutForm from products.models import Variation from carts.models import Cart,CartItem # Create your views here.

33.807947

92

0.582174

de71e1c800cd0628725b2dd49b907881044e1b6d

721

py

Python

Python/PythonCgiMock03/src/maincgi/test/TestCgiMainXml.py

tduoth/JsObjects

eb3e2a8b1f47d0da53c8b1a85a7949269711932f

[ "MIT" ]

22

2015-02-26T09:07:18.000Z

2020-05-10T16:22:05.000Z

Python/PythonCgiMock03/src/maincgi/test/TestCgiMainXml.py

tduoth/JsObjects

eb3e2a8b1f47d0da53c8b1a85a7949269711932f

[ "MIT" ]

123

2016-04-05T18:32:41.000Z

2022-03-13T21:09:21.000Z

Python/PythonCgiMock03/src/maincgi/test/TestCgiMainXml.py

tduoth/JsObjects

eb3e2a8b1f47d0da53c8b1a85a7949269711932f

[ "MIT" ]

56

2015-03-19T22:26:37.000Z

2021-12-06T02:52:02.000Z

#!/usr/bin/python ''' Created on May 23, 2012 @author: Charlie ''' import unittest from mock import patch import xml.etree.ElementTree as ET from TestCgiMainBase import TestCgiMainBase if __name__ == "__main__": unittest.main()

24.862069

71

0.647712