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

0c084907ab6f7a66d8c89aefdff3de051c8499ac

1,406

py

Python

encryption_client.py

salmanhiro/fernet-rabbitmq

8130514e6d21b7df9c78a28130c603512f500a23

[ "MIT" ]

null

encryption_client.py

salmanhiro/fernet-rabbitmq

8130514e6d21b7df9c78a28130c603512f500a23

[ "MIT" ]

null

encryption_client.py

salmanhiro/fernet-rabbitmq

8130514e6d21b7df9c78a28130c603512f500a23

[ "MIT" ]

null

import pika import uuid import time import json message = {"text":"kulikuli"} fernet_result = FernetRpc() print(" [x] Requesting user") start = time.time() response = fernet_result.call(message) end = time.time() - start print(" [v] Got %r" % response) print(" [.] Time elapsed %r s" %end)

26.528302

69

0.619488

0c0862941d8ae706603317f21fde751ca0bd01fb

3,225

py

Python

services/cert_server/project/tests/test_cert_server.py

EvaldoNeto/openvpn-http

73d75a990d5d7ed7f89a526c0ce324db42c37f1f

[ "MIT" ]

5

2019-11-19T02:54:05.000Z

2020-03-03T19:48:41.000Z

services/cert_server/project/tests/test_cert_server.py

EvaldoNeto/openvpn-http

73d75a990d5d7ed7f89a526c0ce324db42c37f1f

[ "MIT" ]

23

2019-10-31T12:00:37.000Z

2019-11-22T21:00:28.000Z

services/cert_server/project/tests/test_cert_server.py

EvaldoNeto/openvpn-http

73d75a990d5d7ed7f89a526c0ce324db42c37f1f

[ "MIT" ]

null

# services/ovpn_server/project/tests/test_ovpn_server.py import os import json import io from flask import current_app from project.tests.base import BaseTestCase

37.068966

78

0.556279

0c08971682b47651e14df294d06cff25310ada7b

956

py

Python

powerline/lib/watcher/stat.py

MrFishFinger/powerline

361534bafecf836e100eaff257c93eb4805f48db

[ "MIT" ]

11,435

2015-01-01T03:32:34.000Z

2022-03-31T20:39:05.000Z

powerline/lib/watcher/stat.py

ritiek/powerline

82c1373ba0b424c57e8c12cb5f6f1a7ee3829c27

[ "MIT" ]

879

2015-01-02T11:59:30.000Z

2022-03-24T09:52:17.000Z

powerline/lib/watcher/stat.py

ritiek/powerline

82c1373ba0b424c57e8c12cb5f6f1a7ee3829c27

[ "MIT" ]

1,044

2015-01-05T22:37:53.000Z

2022-03-17T19:43:16.000Z

# vim:fileencoding=utf-8:noet from __future__ import (unicode_literals, division, absolute_import, print_function) import os from threading import RLock from powerline.lib.path import realpath

21.244444

84

0.706067

0c08a69ecbe4701e579ed0c55e6c61397156d087

2,531

py

Python

refData/mlpy/mlpy-3.5.0/mlpy/bordacount/borda.py

xrick/DTW-Tutorial

bbbce1c2beff91384cdcb7dbf503f93ad2fa285c

[ "MIT" ]

null

refData/mlpy/mlpy-3.5.0/mlpy/bordacount/borda.py

xrick/DTW-Tutorial

bbbce1c2beff91384cdcb7dbf503f93ad2fa285c

[ "MIT" ]

null

refData/mlpy/mlpy-3.5.0/mlpy/bordacount/borda.py

xrick/DTW-Tutorial

bbbce1c2beff91384cdcb7dbf503f93ad2fa285c

[ "MIT" ]

null

## This code is written by Davide Albanese, <albanese@fbk.eu>. ## (C) 2010 mlpy Developers. ## This program is free software: you can redistribute it and/or modify ## it under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## This program is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## You should have received a copy of the GNU General Public License ## along with this program. If not, see <http://www.gnu.org/licenses/>. __all__ = ['borda_count'] import numpy as np import sys if sys.version >= '3': from . import cborda else: import cborda def borda_count(x, k=None): """Given N ranked ids lists of length P compute the number of extractions on top-k positions and the mean position for each id. Sort the element ids with decreasing number of extractions, and element ids with equal number of extractions will be sorted with increasing mean positions. :Parameters: x : 2d array_like object integer (N, P) ranked ids lists. For each list ids must be unique in [0, P-1]. k : None or integer compute borda on top-k position (None -> k = P) :Returns: borda : 1d numpy array objects sorted-ids, number of extractions, mean positions Example: >>> import numpy as np >>> import mlpy >>> x = [[2,4,1,3,0], # first ranked list ... [3,4,1,2,0], # second ranked list ... [2,4,3,0,1], # third ranked list ... [0,1,4,2,3]] # fourth ranked list >>> mlpy.borda_count(x=x, k=3) (array([4, 1, 2, 3, 0]), array([4, 3, 2, 2, 1]), array([ 1.25 , 1.66666667, 0. , 1. , 0. ])) * Id 4 is in the first position with 4 extractions and mean position 1.25. * Id 1 is in the first position with 3 extractions and mean position 1.67. * ... """ x_arr = np.asarray(x, dtype=np.int) n, p = x_arr.shape if k == None: k = p if k < 1 or k > p: raise ValueError('k must be in [1, %d]' % p) ext, pos = cborda.core(x_arr, k) invpos = (pos + 1)**(-1) # avoid zero division idx = np.lexsort(keys=(invpos, ext))[::-1] return idx, ext[idx], pos[idx]

32.448718

126

0.614382

0c08ae96e8b31b452042a012ea2cbfe21f5f54d5

2,641

py

Python

envs/base_mujoco_env.py

zaynahjaved/AWAC

e225eeb8c0cd3498ab55ce15a9de60cb4e957c50

[ "MIT" ]

null

envs/base_mujoco_env.py

zaynahjaved/AWAC

e225eeb8c0cd3498ab55ce15a9de60cb4e957c50

[ "MIT" ]

null

envs/base_mujoco_env.py

zaynahjaved/AWAC

e225eeb8c0cd3498ab55ce15a9de60cb4e957c50

[ "MIT" ]

null

''' All cartgripper env modules built on cartrgipper implementation in https://github.com/SudeepDasari/visual_foresight ''' from abc import ABC from mujoco_py import load_model_from_path, MjSim import numpy as np from base_env import BaseEnv

29.344444

96

0.632336

0c097274adeceb2e1e44250ea00c4016e23c60ed

191

py

Python

Desafios/desafio009.py

LucasHenrique-dev/Exercicios-Python

b1f6ca56ea8e197a89a044245419dc6079bdb9c7

[ "MIT" ]

1

2020-04-09T23:18:03.000Z

Desafios/desafio009.py

LucasHenrique-dev/Exercicios-Python

b1f6ca56ea8e197a89a044245419dc6079bdb9c7

[ "MIT" ]

null

Desafios/desafio009.py

LucasHenrique-dev/Exercicios-Python

b1f6ca56ea8e197a89a044245419dc6079bdb9c7

[ "MIT" ]

null

n1 = int(input('Digite um nmero e veja qual a sua tabuada: ')) n = 0 print('{} X {:2} = {:2}'.format(n1, 0, n1*n)) while n < 10: n += 1 print('{} X {:2} = {:2}'.format(n1, n, n1*n))

27.285714

63

0.502618

0c09891ffb40760a1dcac5e46984a7d055ce0caf

2,587

py

Python

web/app/djrq/admin/admin.py

bmillham/djrq2

c84283b75a7c15da1902ebfc32b7d75159c09e20

[ "MIT" ]

1

2016-11-23T20:50:00.000Z

web/app/djrq/admin/admin.py

bmillham/djrq2

c84283b75a7c15da1902ebfc32b7d75159c09e20

[ "MIT" ]

15

2017-01-15T04:18:40.000Z

2017-02-25T04:13:06.000Z

web/app/djrq/admin/admin.py

bmillham/djrq2

c84283b75a7c15da1902ebfc32b7d75159c09e20

[ "MIT" ]

null

# encoding: utf-8 from web.ext.acl import when from ..templates.admin.admintemplate import page as _page from ..templates.admin.requests import requeststemplate, requestrow from ..templates.requests import requestrow as rr from ..send_update import send_update import cinje

43.847458

198

0.709702

0c0c0154d635c140279cd61ef15b6dfc6c89cd23

755

py

Python

test_knot_hasher.py

mmokko/aoc2017

0732ac440775f9e6bd4a8447c665c9b0e6969f74

[ "MIT" ]

null

test_knot_hasher.py

mmokko/aoc2017

0732ac440775f9e6bd4a8447c665c9b0e6969f74

[ "MIT" ]

null

test_knot_hasher.py

mmokko/aoc2017

0732ac440775f9e6bd4a8447c665c9b0e6969f74

[ "MIT" ]

null

from unittest import TestCase from day10 import KnotHasher

30.2

72

0.658278

0c0c55cfe0bc18dae70bf566cb7d439dd048fafe

602

py

Python

udp/src/server.py

matthewchute/net-prot

82d2d92b3c88afb245161780fdd7909d7bf15eb1

[ "MIT" ]

null

udp/src/server.py

matthewchute/net-prot

82d2d92b3c88afb245161780fdd7909d7bf15eb1

[ "MIT" ]

null

udp/src/server.py

matthewchute/net-prot

82d2d92b3c88afb245161780fdd7909d7bf15eb1

[ "MIT" ]

null

import constants, helpers, os temp_msg = None whole_msg = b'' file_path = None helpers.sock.bind(constants.IP_PORT) print "Server Ready" # recieve while temp_msg != constants.EOF: datagram = helpers.sock.recvfrom(constants.BUFFER_SIZE) temp_msg = datagram[0] if file_path is None: print("Receiving " + temp_msg.decode() + "...") file_path = os.path.join(constants.SERVER_FILE_PATH, temp_msg.decode()) else: whole_msg += temp_msg whole_msg = whole_msg.strip(constants.EOF) with open(file_path, 'wb') as sFile: sFile.write(whole_msg) print "Received"

22.296296

79

0.696013

0c0dcfc232bbe604e854e762de0825bd246ecc01

3,697

py

Python

sdk/apimanagement/azure-mgmt-apimanagement/azure/mgmt/apimanagement/models/hostname_configuration.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

1

2021-09-07T18:36:04.000Z

sdk/apimanagement/azure-mgmt-apimanagement/azure/mgmt/apimanagement/models/hostname_configuration.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

2

2019-10-02T23:37:38.000Z

2020-10-02T01:17:31.000Z

sdk/apimanagement/azure-mgmt-apimanagement/azure/mgmt/apimanagement/models/hostname_configuration.py

tzhanl/azure-sdk-for-python

18cd03f4ab8fd76cc0498f03e80fbc99f217c96e

[ "MIT" ]

1

2019-06-17T22:18:23.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model

48.012987

94

0.678117

0c0decf0160c2c2495315ba2014b0b8cb06458ac

4,717

py

Python

src/interactive_conditional_samples.py

50417/gpt-2

0e0b3c97efb0048abffb2947aaa8573a783706ed

[ "MIT" ]

null

src/interactive_conditional_samples.py

50417/gpt-2

0e0b3c97efb0048abffb2947aaa8573a783706ed

[ "MIT" ]

null

src/interactive_conditional_samples.py

50417/gpt-2

0e0b3c97efb0048abffb2947aaa8573a783706ed

[ "MIT" ]

null

#!/usr/bin/env python3 import fire import json import os import numpy as np import tensorflow as tf import model, sample, encoder def interact_model( model_name='117M', seed=None, nsamples=1000, batch_size=1, length=None, temperature=1, top_k=0, top_p=0.0 ): """ Interactively run the model :model_name=117M : String, which model to use :seed=None : Integer seed for random number generators, fix seed to reproduce results :nsamples=1 : Number of samples to return total :batch_size=1 : Number of batches (only affects speed/memory). Must divide nsamples. :length=None : Number of tokens in generated text, if None (default), is determined by model hyperparameters :temperature=1 : Float value controlling randomness in boltzmann distribution. Lower temperature results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. Higher temperature results in more random completions. :top_k=0 : Integer value controlling diversity. 1 means only 1 word is considered for each step (token), resulting in deterministic completions, while 40 means 40 words are considered at each step. 0 (default) is a special setting meaning no restrictions. 40 generally is a good value. :top_p=0.0 : Float value controlling diversity. Implements nucleus sampling, overriding top_k if set to a value > 0. A good setting is 0.9. """ if batch_size is None: batch_size = 1 assert nsamples % batch_size == 0 enc = encoder.get_encoder(model_name) hparams = model.default_hparams() with open(os.path.join('models', model_name, 'hparams.json')) as f: hparams.override_from_dict(json.load(f)) if length is None: length = hparams.n_ctx // 2 print(length) #elif length > hparams.n_ctx: # raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx) #config = tf.ConfigProto(device_count={'GPU': 0}) config = tf.ConfigProto() with tf.Session(graph=tf.Graph(),config=config) as sess: context = tf.placeholder(tf.int32, [batch_size, None]) np.random.seed(seed) tf.set_random_seed(seed) raw_text = """Model {""" #input("Model prompt >>> ") context_tokens = enc.encode(raw_text) output = sample.sample_sequence( hparams=hparams, length=length, context=context, batch_size=batch_size, temperature=temperature, top_k=top_k, top_p=top_p ) saver = tf.train.Saver() ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name)) saver.restore(sess, ckpt) from datetime import datetime #while True: generated = 0 import time grand_start = time.time() for cnt in range(nsamples // batch_size): start_per_sample = time.time() output_text = raw_text text = raw_text context_tokens = enc.encode(text) #raw_text = input("Model prompt >>> ") # while not raw_text: # print('Prompt should not be empty!') # raw_text = input("Model prompt >>> ") #print(context_tokens) #file_to_save.write(raw_text) #for cnt in range(nsamples // batch_size): while "<|endoftext|>" not in text: out = sess.run(output, feed_dict={context: [context_tokens for _ in range(batch_size)]})[:, len(context_tokens):] for i in range(batch_size): #generated += 1 text = enc.decode(out[i]) if "<|endoftext|>" in text: sep = "<|endoftext|>" rest = text.split(sep, 1)[0] output_text += rest break context_tokens = enc.encode(text) output_text += text print("=" * 40 + " SAMPLE " + str(cnt+12) + " " + "=" * 40) minutes, seconds = divmod(time.time() - start_per_sample, 60) print("Output Done : {:0>2}:{:05.2f}".format(int(minutes),seconds) ) print("=" * 80) with open("Simulink_sample/sample__"+str(cnt+12)+".mdl","w+") as f: f.write(output_text) elapsed_total = time.time()-grand_start hours, rem = divmod(elapsed_total,3600) minutes, seconds = divmod(rem, 60) print("Total time to generate 1000 samples :{:0>2}:{:0>2}:{:05.2f}".format(int(hours),int(minutes),seconds)) if __name__ == '__main__': fire.Fire(interact_model)

38.349593

116

0.606954

0c0e5be12d46a3b1b4e3d634643649fcf6a3f4da

291

py

Python

todofy/tests/conftest.py

bokiex/eti_todo

1c636d0973c57d4253440b4528185dba0ecb9d05

[ "BSD-3-Clause" ]

1

2019-11-29T09:52:19.000Z

todofy/tests/conftest.py

bokiex/eti_todo

1c636d0973c57d4253440b4528185dba0ecb9d05

[ "BSD-3-Clause" ]

28

2019-11-28T20:02:48.000Z

2022-02-10T14:04:45.000Z

todofy/tests/conftest.py

bokiex/eti_todo

1c636d0973c57d4253440b4528185dba0ecb9d05

[ "BSD-3-Clause" ]

null

import pytest

16.166667

46

0.704467

0c0e6124651142c0387644ad144cc2392388c0c5

33

py

Python

Fase 4 - Temas avanzados/Tema 11 - Modulos/Leccion 01 - Modulos/Saludos/test.py

ruben69695/python-course

a3d3532279510fa0315a7636c373016c7abe4f0a

[ "MIT" ]

1

2019-01-27T20:44:53.000Z

Fase 4 - Temas avanzados/Tema 11 - Modulos/Leccion 01 - Modulos/Saludos/test.py

ruben69695/python-course

a3d3532279510fa0315a7636c373016c7abe4f0a

[ "MIT" ]

null

Fase 4 - Temas avanzados/Tema 11 - Modulos/Leccion 01 - Modulos/Saludos/test.py

ruben69695/python-course

a3d3532279510fa0315a7636c373016c7abe4f0a

[ "MIT" ]

null

import saludos saludos.saludar()

11

17

0.818182

0c0ea1386a3f6993039b27ca1ae2f4e56ebc457c

1,033

py

Python

question_bank/split-array-into-fibonacci-sequence/split-array-into-fibonacci-sequence.py

yatengLG/leetcode-python

5d48aecb578c86d69835368fad3d9cc21961c226

[ "Apache-2.0" ]

9

2020-08-12T10:01:00.000Z

2022-01-05T04:37:48.000Z

question_bank/split-array-into-fibonacci-sequence/split-array-into-fibonacci-sequence.py

yatengLG/leetcode-python

5d48aecb578c86d69835368fad3d9cc21961c226

[ "Apache-2.0" ]

1

2021-02-16T10:19:31.000Z

question_bank/split-array-into-fibonacci-sequence/split-array-into-fibonacci-sequence.py

yatengLG/leetcode-python

5d48aecb578c86d69835368fad3d9cc21961c226

[ "Apache-2.0" ]

4

2020-08-12T10:13:31.000Z

2021-11-05T01:26:58.000Z

# -*- coding: utf-8 -*- # @Author : LG """ 148 ms, Python3 35.57% 13.7 MB, Python3 36.81% """

30.382353

159

0.460794

0c111c07238e7921c9ce9cb0615b8ac96b16babf

2,771

py

Python

convert_bootswatch_vurple.py

douglaskastle/bootswatch

cb8f368c8d3671afddae487736d7cba6509b7f5b

[ "MIT" ]

null

convert_bootswatch_vurple.py

douglaskastle/bootswatch

cb8f368c8d3671afddae487736d7cba6509b7f5b

[ "MIT" ]

null

convert_bootswatch_vurple.py

douglaskastle/bootswatch

cb8f368c8d3671afddae487736d7cba6509b7f5b

[ "MIT" ]

null

import re import os values = { 'uc': 'Vurple', 'lc': 'vurple', 'cl': '#116BB7', } if __name__ == '__main__': main()

35.075949

139

0.587153

0c117a09b3c94bdc715dd3e404e0bc7ed330ac20

721

py

Python

python/interface_getPixel.py

BulliB/PixelTable

f08ff3a7908857583f3cbc1b689abf2e8739f7d8

[ "BSD-2-Clause" ]

2

2019-10-28T14:33:31.000Z

2019-10-30T10:08:58.000Z

python/interface_getPixel.py

BulliB/PixelTable

f08ff3a7908857583f3cbc1b689abf2e8739f7d8

[ "BSD-2-Clause" ]

33

2019-10-28T14:17:26.000Z

2020-02-22T11:04:02.000Z

python/interface_getPixel.py

BulliB/PixelTable

f08ff3a7908857583f3cbc1b689abf2e8739f7d8

[ "BSD-2-Clause" ]

2

2019-11-08T11:14:33.000Z

2019-11-19T21:22:54.000Z

#!/usr/bin/python3 from validData import * from command import * from readback import * import sys import time # Expected Input # 1: Row -> 0 to 9 # 2: Column -> 0 to 19 if ( isInt(sys.argv[1]) and strLengthIs(sys.argv[1],1) and isInt(sys.argv[2]) and (strLengthIs(sys.argv[2],1) or strLengthIs(sys.argv[2],2)) ): command = ["PixelToWeb"] command.append(sys.argv[1]) command.append(sys.argv[2]) setNewCommand(command) time.sleep(.3) print(readbackGet()) readbackClear() else: f = open("/var/www/pixel/python/.error", "a") f.write(sys.argv[0] + '\n') f.write(sys.argv[1] + '\n') f.write(sys.argv[2] + '\n') f.write(sys.argv[3] + '\n') f.close()

21.848485

89

0.601942

0c11d6edd1fa7404e67e7a29c7dcaef50cd598a8

1,834

py

Python

FunTOTP/interface.py

Z33DD/FunTOTP

912c1a4a307af6a495f12a82305ae7dbf49916a2

[ "Unlicense" ]

3

2020-01-19T17:10:37.000Z

2022-02-19T18:39:20.000Z

FunTOTP/interface.py

Z33DD/FunTOTP

912c1a4a307af6a495f12a82305ae7dbf49916a2

[ "Unlicense" ]

null

FunTOTP/interface.py

Z33DD/FunTOTP

912c1a4a307af6a495f12a82305ae7dbf49916a2

[ "Unlicense" ]

1

2020-01-19T20:25:18.000Z

from getpass import getpass from colorama import init, Fore, Back, Style yes = ['Y', 'y', 'YES', 'yes', 'Yes']

25.123288

76

0.490185

0c1456a33812aa7157896227520f3def0676ad91

885

py

Python

envdsys/envcontacts/apps.py

NOAA-PMEL/envDataSystem

4db4a3569d2329658799a3eef06ce36dd5c0597d

[ "Unlicense" ]

1

2021-11-06T19:22:53.000Z

envdsys/envcontacts/apps.py

NOAA-PMEL/envDataSystem

4db4a3569d2329658799a3eef06ce36dd5c0597d

[ "Unlicense" ]

25

2019-06-18T20:40:36.000Z

2021-07-23T20:56:48.000Z

envdsys/envcontacts/apps.py

NOAA-PMEL/envDataSystem

4db4a3569d2329658799a3eef06ce36dd5c0597d

[ "Unlicense" ]

null

from django.apps import AppConfig

29.5

64

0.523164

0c16dc36c44b72bd40c213bf05ac31ec7273fca3

8,053

py

Python

tests/interpreter.py

AndrejHatzi/Haya

31291142decf6a172149516f08a2f2d68115e2dc

[ "MIT" ]

null

tests/interpreter.py

AndrejHatzi/Haya

31291142decf6a172149516f08a2f2d68115e2dc

[ "MIT" ]

1

2019-02-14T16:47:10.000Z

tests/interpreter.py

AndrejHatzi/Haya

31291142decf6a172149516f08a2f2d68115e2dc

[ "MIT" ]

null

from sly import Lexer from sly import Parser import sys #-------------------------- # While Loop # Del Var # Print stmt # EQEQ, LEQ #-------------------------- #=> This version has parenthesis precedence! if __name__ == '__main__': lexer = BasicLexer() parser = BasicParser() env = {} try: file : str = sys.argv[1] try: with open(file, 'r', encoding="utf-8") as f: line : str for line in f: try: text = line except EOFError: break if text: tree = parser.parse(lexer.tokenize(text)) BasicExecute(tree, env) except: print('the specified file "{}" was not found!'.format(file)) except: while True: try: text = input('haya development edition > ') except EOFError: break if text: tree = parser.parse(lexer.tokenize(text)) BasicExecute(tree, env) #parsetree = parser.parse(lexer.tokenize(text)) #print(parsetree)

27.768966

83

0.46343

0c16fa03a21f3d8b261783bab62dd87a48e2c16d

1,012

py

Python

braintree/apple_pay_card.py

futureironman/braintree_python

26bb8a857bc29322a8bca2e8e0fe6d99cfe6a1ac

[ "MIT" ]

182

2015-01-09T05:26:46.000Z

2022-03-16T14:10:06.000Z

braintree/apple_pay_card.py

futureironman/braintree_python

26bb8a857bc29322a8bca2e8e0fe6d99cfe6a1ac

[ "MIT" ]

95

2015-02-24T23:29:56.000Z

2022-03-13T03:27:58.000Z

braintree/apple_pay_card.py

futureironman/braintree_python

26bb8a857bc29322a8bca2e8e0fe6d99cfe6a1ac

[ "MIT" ]

93

2015-02-19T17:59:06.000Z

2022-03-19T17:01:25.000Z

import braintree from braintree.resource import Resource

30.666667

132

0.662055

0c1758002a3f4c2e5686dc0e50493960b4c98bea

4,054

py

Python

src/lda_without_tf_idf_sports.py

mspkvp/MiningOpinionTweets

23f05b4cea22254748675e03a51844da1dff70ac

[ "MIT" ]

1

2016-01-18T14:30:31.000Z

src/lda_without_tf_idf_sports.py

mspkvp/MiningOpinionTweets

23f05b4cea22254748675e03a51844da1dff70ac

[ "MIT" ]

null

src/lda_without_tf_idf_sports.py

mspkvp/MiningOpinionTweets

23f05b4cea22254748675e03a51844da1dff70ac

[ "MIT" ]

null

from __future__ import print_function from time import time import csv import sys import os from sklearn.feature_extraction.text import CountVectorizer import numpy as np import lda import logging logging.basicConfig(filename='lda_analyser.log', level=logging.DEBUG) entities = ['jose_mourinho', 'cristiano_ronaldo', 'ruben_neves', 'pinto_da_costa', 'jorge_jesus', 'lionel_messi', 'eusebio', 'luisao', 'paulo_bento', 'iker_casillas', 'joao_moutinho', 'jorge_mendes', 'julen_lopetegui', 'rui_vitoria', 'ricardo', 'luis_figo', 'jose_socrates', 'antonio_costa', 'benfica', 'futebol_porto', 'sporting'] if not os.path.exists("results"): os.makedirs("results") for n_topics in [10, 20, 50, 100]: n_features = 10000 n_top_words = int(sys.argv[1]) + 1 corpus = [] topics_write_file = csv.writer(open("results/lda_topics_{}topics_{}words_{}.csv".format(n_topics, n_top_words - 1, "sports"), "wb"), delimiter="\t", quotechar='|', quoting=csv.QUOTE_MINIMAL) write_file = csv.writer(open("results/lda_topics_{}topics_{}words_mapping_{}.csv".format(n_topics, n_top_words - 1, "sports"), "wb"), delimiter="\t", quotechar='|', quoting=csv.QUOTE_MINIMAL) entity_day_dict = dict() # read all files and store their contents on a dictionary for i in os.listdir(os.getcwd() + "/filtered_tweets"): for filename in os.listdir(os.getcwd() + "/filtered_tweets" + "/" + i): if(filename.split(".")[0] in entities): entity_day_dict[i+" "+filename] = open(os.getcwd() + "/filtered_tweets" + "/" + i + "/" + filename, 'r').read() entity_day_key_index = dict() i = 0 for key in entity_day_dict: entity_day_key_index[i] = key.split(" ") corpus.append(entity_day_dict[key]) i += 1 # Use tf (raw term count) features for LDA. logging.info("Extracting tf features for LDA...") tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=n_features, stop_words='english') t0 = time() tf = tf_vectorizer.fit_transform(corpus) logging.info("done in %0.3fs." % (time() - t0)) logging.info("Fitting LDA models with tf") model = lda.LDA(n_topics=n_topics, n_iter=1500, random_state=1) #LatentDirichletAllocation(n_topics=n_topics, max_iter=5, learning_method='online', #learning_offset=50., random_state=0) t0 = time() model.fit(tf) logging.info("done in %0.3fs." % (time() - t0)) topic_word = model.topic_word_ doc_topic = model.doc_topic_ logging.info("\nTopics in LDA model:") tf_feature_names = tf_vectorizer.get_feature_names() print_top_words(topic_word, doc_topic, tf_feature_names, n_top_words, entity_day_key_index)

37.192661

129

0.561914

0c18dab0a973e417315a5c146525d7d91b9da0fe

4,476

py

Python

glab_common/allsummary.py

gentnerlab/glab-common-py

9ff87ac6ca5f07c0d550594da38080bd3ee916db

[ "BSD-3-Clause" ]

3

2016-03-07T19:51:32.000Z

2018-11-08T22:34:14.000Z

glab_common/allsummary.py

gentnerlab/glab-common-py

9ff87ac6ca5f07c0d550594da38080bd3ee916db

[ "BSD-3-Clause" ]

16

2015-02-19T04:32:01.000Z

2018-11-14T20:09:09.000Z

glab_common/allsummary.py

gentnerlab/glab-common-py

9ff87ac6ca5f07c0d550594da38080bd3ee916db

[ "BSD-3-Clause" ]

4

2015-04-01T23:55:25.000Z

2018-02-28T18:23:29.000Z

from __future__ import print_function import re import datetime as dt from behav.loading import load_data_pandas import warnings import subprocess import os import sys process_fname = "/home/bird/opdat/panel_subject_behavior" box_nums = [] bird_nums = [] processes = [] with open(process_fname, "rt") as psb_file: for line in psb_file.readlines(): if line.startswith("#") or not line.strip(): pass # skip comment lines & blank lines else: spl_line = line.split() if spl_line[1] == "1": # box enabled box_nums.append(spl_line[0]) bird_nums.append(int(spl_line[2])) processes.append(spl_line[4]) # rsync magpis hostname = os.uname()[1] if "magpi" in hostname: for box_num in box_nums: box_hostname = box_num rsync_src = "bird@{}:/home/bird/opdat/".format(box_hostname) rsync_dst = "/home/bird/opdat/" print("Rsync src: {}".format(rsync_src), file=sys.stderr) print("Rsync dest: {}".format(rsync_dst), file=sys.stderr) rsync_output = subprocess.run(["rsync", "-avz", "--exclude Generated_Songs/", rsync_src, rsync_dst]) subjects = ["B%d" % (bird_num) for bird_num in bird_nums] data_folder = "/home/bird/opdat" with open("/home/bird/all.summary", "w") as as_file: as_file.write( "this all.summary generated at %s\n" % (dt.datetime.now().strftime("%x %X")) ) as_file.write( "FeedErr(won't come up, won't go down, already up, resp during feed)\n" ) # Now loop through each bird and grab the error info from each summaryDAT file for (box, bird, proc) in zip(box_nums, bird_nums, processes): try: # make sure box is a string box = str(box) if proc in ("shape", "lights", "pylights", "lights.py"): as_file.write("%s\tB%d\t %s\n" % (box, bird, proc)) else: summaryfname = "/home/bird/opdat/B%d/%d.summaryDAT" % (bird, bird) with open(summaryfname, "rt") as sdat: sdata = sdat.read() m = re.search(r"failures today: (\w+)", sdata) hopper_failures = m.group(1) m = re.search(r"down failures today: (\w+)", sdata) godown_failures = m.group(1) m = re.search(r"up failures today: (\w+)", sdata) goup_failures = m.group(1) m = re.search(r"Responses during feed: (\w+)", sdata) resp_feed = m.group(1) subj = "B%d" % (bird) with warnings.catch_warnings(): warnings.simplefilter("ignore") behav_data = load_data_pandas([subj], data_folder) df = behav_data[subj] # df = df[~pd.isnull(data.index)] todays_data = df[ (df.index.date - dt.datetime.today().date()) == dt.timedelta(days=0) ] feeder_ops = sum(todays_data["reward"].values) trials_run = len(todays_data) noRs = sum(todays_data["response"].values == "none") TOs = trials_run - feeder_ops - noRs last_trial_time = todays_data.sort_index().tail().index[-1] if last_trial_time.day != dt.datetime.now().day: datediff = "(not today)" else: minutes_ago = (dt.datetime.now() - last_trial_time).seconds / 60 datediff = "(%d mins ago)" % (minutes_ago) outline = ( "%s\tB%d\t %s \ttrls=%s \tfeeds=%d \tTOs=%d \tnoRs=%d \tFeedErrs=(%s,%s,%s,%s) \tlast @ %s %s\n" % ( box, bird, proc, trials_run, feeder_ops, TOs, noRs, hopper_failures, godown_failures, goup_failures, resp_feed, last_trial_time.strftime("%x %X"), datediff, ) ) as_file.write(outline) except Exception as e: as_file.write( "%s\tB%d\t Error opening SummaryDat or incorrect format\n" % (box, bird) ) print(e)

38.921739

122

0.50849

0c194bbda6bf427b571869e7619f91e9298b8f04

2,239

py

Python

api/v1/circuits.py

tahoe/janitor

b6ce73bddc13c70079bdc7ba4c7a9b3ee0cad0bd

[ "Apache-2.0" ]

52

2019-08-14T10:48:26.000Z

2022-03-30T18:09:08.000Z

api/v1/circuits.py

tahoe/janitor

b6ce73bddc13c70079bdc7ba4c7a9b3ee0cad0bd

[ "Apache-2.0" ]

18

2019-08-20T04:13:37.000Z

2022-01-31T12:40:12.000Z

api/v1/circuits.py

tahoe/janitor

b6ce73bddc13c70079bdc7ba4c7a9b3ee0cad0bd

[ "Apache-2.0" ]

12

2019-08-14T10:49:11.000Z

2020-09-02T18:56:34.000Z

from app.models import Circuit, CircuitSchema, Provider from flask import make_response, jsonify from app import db def read_all(): """ This function responds to a request for /circuits with the complete lists of circuits :return: sorted list of circuits """ circuits = Circuit.query.all() schema = CircuitSchema(many=True) return schema.dump(circuits).data def create(circuit): """ creates a circuit! checks to see if the provider_cid is unique and that the provider exists. :return: circuit """ provider_cid = circuit.get('provider_cid') provider_id = circuit.get('provider_id') circuit_exists = Circuit.query.filter( Circuit.provider_cid == provider_cid ).one_or_none() provider_exists = Provider.query.filter(Provider.id == provider_id).one_or_none() if circuit_exists: text = f'Circuit {provider_cid} already exists' return make_response(jsonify(error=409, message=text), 409) if not provider_exists: text = f'Provider {provider_id} does not exist.' 'Unable to create circuit' return make_response(jsonify(error=403, message=text), 403) schema = CircuitSchema() new_circuit = schema.load(circuit, session=db.session).data db.session.add(new_circuit) db.session.commit() data = schema.dump(new_circuit).data return data, 201 def update(circuit_id, circuit): """ updates a circuit! :return: circuit """ c = Circuit.query.filter_by(id=circuit_id).one_or_none() if not c: text = f'Can not update a circuit that does not exist!' return make_response(jsonify(error=409, message=text), 404) schema = CircuitSchema() update = schema.load(circuit, session=db.session).data db.session.merge(update) db.session.commit() data = schema.dump(c).data return data, 201

26.341176

85

0.676195

0c1a66a69d47f4abcbb592a1b69142a384d2f89b

2,311

py

Python

youtube_related/client.py

kijk2869/youtube-related

daabefc60277653098e1d8e266258b71567796d8

[ "MIT" ]

7

2020-07-13T00:15:37.000Z

2021-12-06T14:35:14.000Z

youtube_related/client.py

kijk2869/youtube-related

daabefc60277653098e1d8e266258b71567796d8

[ "MIT" ]

11

2020-07-17T16:11:16.000Z

2022-03-01T23:02:54.000Z

youtube_related/client.py

kijk2869/youtube-related

daabefc60277653098e1d8e266258b71567796d8

[ "MIT" ]

3

2020-11-04T11:44:50.000Z

2022-01-11T04:21:01.000Z

import asyncio import json import re from collections import deque from typing import Deque, Dict, List, Match, Pattern import aiohttp from .error import RateLimited headers: dict = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko" } DATA_JSON: Pattern = re.compile( r'(?:window\["ytInitialData"\]|ytInitialData)\W?=\W?({.*?});' )

29.253165

89

0.63479

0c1b29cfd60d9ee7d4e6451a8264af9459d2ddcb

2,522

py

Python

app/request/migrations/0001_initial.py

contestcrew/2019SeoulContest-Backend

2e99cc6ec6a712911da3b79412ae84a9d35453e1

[ "MIT" ]

null

app/request/migrations/0001_initial.py

contestcrew/2019SeoulContest-Backend

2e99cc6ec6a712911da3b79412ae84a9d35453e1

[ "MIT" ]

32

2019-08-30T13:09:28.000Z

2021-06-10T19:07:56.000Z

app/request/migrations/0001_initial.py

contestcrew/2019SeoulContest-Backend

2e99cc6ec6a712911da3b79412ae84a9d35453e1

[ "MIT" ]

3

2019-09-19T10:12:50.000Z

2019-09-30T15:59:13.000Z

# Generated by Django 2.2.5 on 2019-09-24 09:11 from django.db import migrations, models import django.db.models.deletion

50.44

179

0.596352

0c1bfa28ddb2f6e0a2bc571eb9a019b7ef92cb0d

690

py

Python

field/FieldFactory.py

goph-R/NodeEditor

5cc4749785bbd348f3db01b27c1533b4caadb920

[ "Apache-2.0" ]

null

field/FieldFactory.py

goph-R/NodeEditor

5cc4749785bbd348f3db01b27c1533b4caadb920

[ "Apache-2.0" ]

null

field/FieldFactory.py

goph-R/NodeEditor

5cc4749785bbd348f3db01b27c1533b4caadb920

[ "Apache-2.0" ]

null

from PySide2.QtGui import QVector3D, QColor from field.ColorField import ColorField from field.FloatField import FloatField from field.StringField import StringField from field.Vector3Field import Vector3Field

23

44

0.618841

0c1cee1a04ba87b43d0454e7e5294887e53530fd

1,348

py

Python

scrapy/utils/engine.py

sulochanaviji/scrapy

6071c82e7ac80136e844b56a09d5d31aa8f41296

[ "BSD-3-Clause" ]

8

2021-02-01T07:55:19.000Z

2021-03-22T18:17:47.000Z

scrapy/utils/engine.py

sulochanaviji/scrapy

6071c82e7ac80136e844b56a09d5d31aa8f41296

[ "BSD-3-Clause" ]

30

2021-02-17T14:17:57.000Z

2021-03-03T16:57:16.000Z

scrapy/utils/engine.py

sulochanaviji/scrapy

6071c82e7ac80136e844b56a09d5d31aa8f41296

[ "BSD-3-Clause" ]

3

2021-08-21T04:09:17.000Z

2021-08-25T01:00:41.000Z

"""Some debugging functions for working with the Scrapy engine""" # used in global tests code from time import time # noqa: F401 def get_engine_status(engine): """Return a report of the current engine status""" tests = [ "time()-engine.start_time", "engine.has_capacity()", "len(engine.downloader.active)", "engine.scraper.is_idle()", "engine.spider.name", "engine.spider_is_idle(engine.spider)", "engine.slot.closing", "len(engine.slot.inprogress)", "len(engine.slot.scheduler.dqs or [])", "len(engine.slot.scheduler.mqs)", "len(engine.scraper.slot.queue)", "len(engine.scraper.slot.active)", "engine.scraper.slot.active_size", "engine.scraper.slot.itemproc_size", "engine.scraper.slot.needs_backout()", ] checks = [] for test in tests: try: checks += [(test, eval(test))] except Exception as e: checks += [(test, f"{type(e).__name__} (exception)")] return checks

27.510204

65

0.609792

0c1da110a449d15b92ca6653ffd9fc76029d3fee

2,588

py

Python

share/pegasus/init/population/scripts/full_res_pop_raster.py

hariharan-devarajan/pegasus

d0641541f2eccc69dd6cc5a09b0b51303686d3ac

[ "Apache-2.0" ]

null

share/pegasus/init/population/scripts/full_res_pop_raster.py

hariharan-devarajan/pegasus

d0641541f2eccc69dd6cc5a09b0b51303686d3ac

[ "Apache-2.0" ]

null

share/pegasus/init/population/scripts/full_res_pop_raster.py

hariharan-devarajan/pegasus

d0641541f2eccc69dd6cc5a09b0b51303686d3ac

[ "Apache-2.0" ]

null

#!/usr/bin/python3 from typing import Dict import optparse import numpy as np import rasterio from rasterio import features def main(county_pop_file, spatial_dist_file, fname_out, no_data_val=-9999): ''' county_pop_file: County level population estimates spatial_dist_file: Spatial projection of population distribution ''' # ------------------------------------- # Open and read raster file with county # level population estimates # ------------------------------------- with rasterio.open(county_pop_file) as rastf: county_pop = rastf.read() nodatacp = rastf.nodata # -------------------------------------------------------------- # Open and read raster file with spatial population distribution # -------------------------------------------------------------- with rasterio.open(spatial_dist_file) as rastf: pop_dist = rastf.read() nodatasp = rastf.nodata prf = rastf.profile county_pop = np.squeeze(county_pop) pop_dist = np.squeeze(pop_dist) pop_est = np.ones(pop_dist.shape)*no_data_val ind1 = np.where(county_pop.flatten() != nodatacp)[0] ind2 = np.where(pop_dist.flatten() != nodatasp)[0] ind = np.intersect1d(ind1, ind2) ind2d = np.unravel_index(ind, pop_dist.shape) pop_est[ind2d] = county_pop[ind2d] * pop_dist[ind2d] pop_est[ind2d] = np.round(pop_est[ind2d]) # Update raster meta-data prf.update(nodata=no_data_val) # Write out spatially distributed population estimate to raster with open(fname_out, "wb") as fout: with rasterio.open(fout.name, 'w', **prf) as out_raster: out_raster.write(pop_est.astype(rasterio.float32), 1) argparser = optparse.OptionParser() argparser.add_option('--population-file', action='store', dest='pop_file', help='County level population estimates') argparser.add_option('--dist-file', action='store', dest='dist_file', help='Spatial projection of population distribution') argparser.add_option('--out-file', action='store', dest='out_file', help='Filename of the output') (options, args) = argparser.parse_args() if not options.pop_file: print('Please specify a population file with --population-file') sys.exit(1) if not options.dist_file: print('Please specify a distribution file with --dist-file') sys.exit(1) if not options.out_file: print('Please specify the name of the output with --out-file') sys.exit(1) main(options.pop_file, options.dist_file, options.out_file)

33.179487

75

0.63524

0c1e7c3ccf6eceb66230761a4bde8362593a8064

9,557

py

Python

TestCase/pr_test_case.py

openeuler-mirror/ci-bot

c50056ff73670bc0382e72cf8c653c01e1aed5e1

[ "MulanPSL-1.0" ]

1

2020-01-12T07:35:34.000Z

TestCase/pr_test_case.py

openeuler-mirror/ci-bot

c50056ff73670bc0382e72cf8c653c01e1aed5e1

[ "MulanPSL-1.0" ]

null

TestCase/pr_test_case.py

openeuler-mirror/ci-bot

c50056ff73670bc0382e72cf8c653c01e1aed5e1

[ "MulanPSL-1.0" ]

2

2020-03-04T02:09:14.000Z

2020-03-07T03:00:40.000Z

import os import requests import subprocess import time import yaml if __name__ == '__main__': with open('config.yaml', 'r') as f: info = yaml.load(f.read())['test case'] owner = info[0]['owner'] repo = info[1]['repo'] local_owner = info[2]['local_owner'] pr = PullRequestOperation(owner, repo, local_owner) print('Prepare:') print('step 1/4: git clone') pr.git_clone() print('\nstep 2/4: change file') pr.change_file() print('\nstep 3/4: git push') pr.git_push() print('\nstep 4/4: pull request') number = pr.pull_request() print('the number of the pull request: {}'.format(number)) time.sleep(10) print('\n\nTest:') print('test case 1: without comments by contributor') comments = pr.get_all_comments(number) labels = pr.get_all_labels(number) print('labels: {}'.format(labels)) errors = 0 if len(comments) == 0: print('no "Welcome to ci-bot Community."') print('no "Thanks for your pull request."') else: if 'Welcome to ci-bot Community.' not in comments[0]: print('no "Welcome to ci-bot Community."') errors += 1 if 'Thanks for your pull request.' not in comments[-1]: print('no "Thanks for your pull request."') errors += 1 if len(labels) == 0: print('no label "ci-bot-cla/yes" or "ci-bot-cla/no"') errors += 1 elif len(labels) > 0: if 'ci-bot-cla/yes' not in labels: print('no label "ci-bot-cla/yes"') errors += 1 if 'ci-bot-cla/no' not in labels: print('no label "ci-bot-cla/no"') errors += 1 if errors == 0: print('test case 1 succeeded') else: print('test case 1 failed') print('\ntest case 2: /lgtm') pr.comment(number, '/lgtm') time.sleep(10) labels = pr.get_all_labels(number) print('labels: {}'.format(labels)) comments = pr.get_all_comments(number) if 'can not be added in your self-own pull request' in comments[-1]: print('test case 2 succeeded') else: print('test case 2 failed') print(comments[-1]) print('\ntest case 3: comment /lgtm by others') pr.comment_by_others(number, '/lgtm') time.sleep(10) labels = pr.get_all_labels(number) print('labels: {}'.format(labels)) comments = pr.get_all_comments(number) if 'Thanks for your review' in comments[-1]: print('test case 3 succeeded') else: print('test case 3 failed') print(comments[-1]) print('\ntest case 4: comment /approve by others') pr.comment_by_others(number, '/approve') time.sleep(10) labels = pr.get_all_labels(number) print('labels: {}'.format(labels)) comments = pr.get_all_comments(number) if 'has no permission to add' in comments[-1]: print('test case 4 succeeded') else: print('test case 4 failed') print(comments[-1]) print('\ntest case 5: /approve') pr.comment(number, '/approve') time.sleep(10) labels = pr.get_all_labels(number) print('labels: {}'.format(labels)) comments = pr.get_all_comments(number) if '***approved*** is added in this pull request by' in comments[-1]: print('test case 5 succeeded') else: print('test case 5 failed') print(comments[-1]) print('\ntest case 6: tag stat/need-squash') labels_before_commit = pr.get_all_labels(number) print('labels_before_commit: {}'.format(labels_before_commit)) pr.write_2_file() time.sleep(10) lables_after_commit = pr.get_all_labels(number) print('lables_after_commit: {}'.format(lables_after_commit)) if 'lgtm' not in labels and 'stat/need-squash' in lables_after_commit: print('test case 6 succeeded') else: print('test case 6 failed') print('\ntest case 7: add labels') pr.add_labels_2_pr(number, '["lgtm"]') time.sleep(10) labels = pr.get_all_labels(number) print('labels: {}'.format(labels)) if "lgtm" in labels: print('test case 7 succeeded') else: print('test case 7 failed') print('\ntest case 8: check-pr') pr.comment(number, '/check-pr') time.sleep(10) code = pr.get_pr_status(number) if code == 200: print('test case 8 succeeded') else: print('failed code: {}'.format(code)) print('test case 8 failed')

38.381526

206

0.548917

0c1eb2fd9329de0c031fe686c52f4c0e67ec1227

1,103

py

Python

tempest/api/hybrid_cloud/compute/flavors/test_flavors_operations.py

Hybrid-Cloud/hybrid-tempest

319e90c6fa6e46925b495c93cd5258f088a30ec0

[ "Apache-2.0" ]

null

tempest/api/hybrid_cloud/compute/flavors/test_flavors_operations.py

Hybrid-Cloud/hybrid-tempest

319e90c6fa6e46925b495c93cd5258f088a30ec0

[ "Apache-2.0" ]

null

tempest/api/hybrid_cloud/compute/flavors/test_flavors_operations.py

Hybrid-Cloud/hybrid-tempest

319e90c6fa6e46925b495c93cd5258f088a30ec0

[ "Apache-2.0" ]

null

import testtools from oslo_log import log from tempest.api.compute import base import tempest.api.compute.flavors.test_flavors as FlavorsV2Test import tempest.api.compute.flavors.test_flavors_negative as FlavorsListWithDetailsNegativeTest import tempest.api.compute.flavors.test_flavors_negative as FlavorDetailsNegativeTest from tempest.common.utils import data_utils from tempest.lib import exceptions as lib_exc from tempest.lib import decorators from tempest import test from tempest import config CONF = config.CONF LOG = log.getLogger(__name__)

36.766667

126

0.853128

0c1f09091be19e77ace869bcb2f31a8df0eb57b2

8,910

py

Python

dycall/exports.py

demberto/DyCall

b234e7ba535eae71234723bb3d645eb986f96a30

[ "MIT" ]

null

dycall/exports.py

demberto/DyCall

b234e7ba535eae71234723bb3d645eb986f96a30

[ "MIT" ]

null

dycall/exports.py

demberto/DyCall

b234e7ba535eae71234723bb3d645eb986f96a30

[ "MIT" ]

null

#!/usr/bin/env python3 """ dycall.exports ~~~~~~~~~~~~~~ Contains `ExportsFrame` and `ExportsTreeView`. """ from __future__ import annotations import logging import pathlib from typing import TYPE_CHECKING import ttkbootstrap as tk from ttkbootstrap import ttk from ttkbootstrap.dialogs import Messagebox from ttkbootstrap.localization import MessageCatalog as MsgCat from ttkbootstrap.tableview import Tableview from dycall._widgets import _TrLabelFrame from dycall.types import Export, PEExport from dycall.util import StaticThemedTooltip, get_img log = logging.getLogger(__name__)

34.534884

86

0.579237

0c1fb0aec727010060874060c9a7121a40357346

1,899

py

Python

src/homologs/filter_by_occupancy.py

jlanga/smsk_selection

08070c6d4a6fbd9320265e1e698c95ba80f81123

[ "MIT" ]

4

2021-07-18T05:20:20.000Z

2022-01-03T10:22:33.000Z

src/homologs/filter_by_occupancy.py

jlanga/smsk_selection

08070c6d4a6fbd9320265e1e698c95ba80f81123

[ "MIT" ]

1

2017-08-21T07:26:13.000Z

2018-11-08T13:59:48.000Z

src/homologs/filter_by_occupancy.py

jlanga/smsk_orthofinder

08070c6d4a6fbd9320265e1e698c95ba80f81123

[ "MIT" ]

2

2021-07-18T05:20:26.000Z

2022-03-31T18:23:31.000Z

#!/usr/bin/env python """ Filter a fasta alignment according to its occupancy: filter_by_occupancy.py fasta_raw.fa fasta_trimmed.fa 0.5 """ import os import sys from helpers import fasta_to_dict def filter_by_occupancy(filename_in, filename_out, min_occupancy=0.5): """ Filter an alignment in fasta format according to the occupancy of the columns. Store the results in fasta format. """ fasta_raw = fasta_to_dict(filename_in) n_sequences = len(fasta_raw.keys()) alignment_length = len(fasta_raw[tuple(fasta_raw.keys())[0]]) columns = tuple( "".join(fasta_raw[seqname][column_index] for seqname in fasta_raw.keys()) for column_index in range(alignment_length) ) columns_to_keep = [] for column_number, column in enumerate(columns): n_gaps = column.count("-") if 1 - float(n_gaps) / float(n_sequences) >= min_occupancy: columns_to_keep.append(column_number) fasta_trimmed = {} for seqname, sequence in fasta_raw.items(): fasta_trimmed[seqname] = "".join( fasta_raw[seqname][column_to_keep] for column_to_keep in columns_to_keep ) if not os.path.exists(os.path.dirname(filename_out)): os.makedirs(os.path.dirname(filename_out)) with open(filename_out, "w") as f_out: for seqname, sequence in fasta_trimmed.items(): f_out.write( ">{seqname}\n{sequence}\n".format(seqname=seqname, sequence=sequence) ) if __name__ == "__main__": if len(sys.argv) != 4: sys.stderr.write( "ERROR: incorrect number of arguments.\n" "python filter_by_occupancy.py fastain fastaout min_occupancy\n" ) sys.exit(1) FASTA_IN = sys.argv[1] FASTA_OUT = sys.argv[2] MIN_OCCUPANCY = float(sys.argv[3]) filter_by_occupancy(FASTA_IN, FASTA_OUT, MIN_OCCUPANCY)

28.772727

85

0.664034

0c20b529cd83a9fd598afa8e482ff4d521f8b78a

954

py

Python

setup.py

eppeters/xontrib-dotenv

f866f557592d822d1ecb2b607c63c4cdecb580e4

[ "BSD-2-Clause" ]

null

setup.py

eppeters/xontrib-dotenv

f866f557592d822d1ecb2b607c63c4cdecb580e4

[ "BSD-2-Clause" ]

null

setup.py

eppeters/xontrib-dotenv

f866f557592d822d1ecb2b607c63c4cdecb580e4

[ "BSD-2-Clause" ]

1

2020-03-16T00:39:57.000Z

#!/usr/bin/env python """ xontrib-dotenv ----- Automatically reads .env file from current working directory or parentdirectories and push variables to environment. """ from setuptools import setup setup( name='xontrib-dotenv', version='0.1', description='Reads .env files into environment', long_description=__doc__, license='BSD', url='https://github.com/urbaniak/xontrib-dotenv', author='Krzysztof Urbaniak', packages=['xontrib'], package_dir={'xontrib': 'xontrib'}, package_data={'xontrib': ['*.xsh']}, zip_safe=True, include_package_data=False, platforms='any', install_requires=[ 'xonsh>=0.4.6', ], classifiers=[ 'Environment :: Console', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: System :: Shells', 'Topic :: System :: System Shells', ] )

25.105263

60

0.631027

0c20b7c255ec391f7dad36b9c36ded1071de5e8b

222

py

Python

tests/sample_app/urls.py

dreipol/meta-tagger

c1a2f1f8b0c051018a5bb75d4e579d27bd2c27b2

[ "BSD-3-Clause" ]

3

2016-05-30T07:48:54.000Z

2017-02-08T21:16:03.000Z

tests/sample_app/urls.py

dreipol/meta-tagger

c1a2f1f8b0c051018a5bb75d4e579d27bd2c27b2

[ "BSD-3-Clause" ]

null

tests/sample_app/urls.py

dreipol/meta-tagger

c1a2f1f8b0c051018a5bb75d4e579d27bd2c27b2

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from django.conf.urls import url from tests.sample_app.views import NewsArticleDetailView urlpatterns = [ url(r'^(?P<pk>\d+)/$', NewsArticleDetailView.as_view(), name='news-article-detail'), ]

27.75

88

0.702703

0c23261891d98100b6ddda0879a36f77857b6f48

624

py

Python

utils/bbox_utils/center_to_corner.py

Jaskaran197/Red-blood-cell-detection-SSD

a33b330ad17454a7425aa7f57818c0a41b4e0ff9

[ "MIT" ]

null

utils/bbox_utils/center_to_corner.py

Jaskaran197/Red-blood-cell-detection-SSD

a33b330ad17454a7425aa7f57818c0a41b4e0ff9

[ "MIT" ]

null

utils/bbox_utils/center_to_corner.py

Jaskaran197/Red-blood-cell-detection-SSD

a33b330ad17454a7425aa7f57818c0a41b4e0ff9

[ "MIT" ]

null

import numpy as np def center_to_corner(boxes): """ Convert bounding boxes from center format (cx, cy, width, height) to corner format (xmin, ymin, xmax, ymax) Args: - boxes: numpy array of tensor containing all the boxes to be converted Returns: - A numpy array or tensor of converted boxes """ temp = boxes.copy() temp[..., 0] = boxes[..., 0] - (boxes[..., 2] / 2) # xmin temp[..., 1] = boxes[..., 1] - (boxes[..., 3] / 2) # ymin temp[..., 2] = boxes[..., 0] + (boxes[..., 2] / 2) # xmax temp[..., 3] = boxes[..., 1] + (boxes[..., 3] / 2) # ymax return temp

32.842105

115

0.535256

0c24e2918c9577a7b38b38b7b54cfb7d7c91ca26

337

py

Python

pytest/np.py

i0Ek3/disintegration

b59307f8166b93d76fab35af180a5cf3ffa51b09

[ "MIT" ]

null

pytest/np.py

i0Ek3/disintegration

b59307f8166b93d76fab35af180a5cf3ffa51b09

[ "MIT" ]

null

pytest/np.py

i0Ek3/disintegration

b59307f8166b93d76fab35af180a5cf3ffa51b09

[ "MIT" ]

null

import numpy as np list = [np.linspace([1,2,3], 3),\ np.array([1,2,3]),\ np.arange(3),\ np.arange(8).reshape(2,4),\ np.zeros((2,3)),\ np.zeros((2,3)).T,\ np.ones((3,1)),\ np.eye(3),\ np.full((3,3), 1),\ np.random.rand(3),\ np.random.rand(3,3),\ np.random.uniform(5,15,3),\ np.random.randn(3),\ np.random.normal(3, 2.5, 3)] print(list)

17.736842

33

0.590504

0c25a90b221d6137090c0e77b536a592e4921a3d

337

py

Python

api/data_explorer/models/__init__.py

karamalhotra/data-explorer

317f4d7330887969ab6bfe2ca23ec24163472c55

[ "BSD-3-Clause" ]

null

api/data_explorer/models/__init__.py

karamalhotra/data-explorer

317f4d7330887969ab6bfe2ca23ec24163472c55

[ "BSD-3-Clause" ]

null

api/data_explorer/models/__init__.py

karamalhotra/data-explorer

317f4d7330887969ab6bfe2ca23ec24163472c55

[ "BSD-3-Clause" ]

null

# coding: utf-8 # flake8: noqa from __future__ import absolute_import # import models into model package from data_explorer.models.dataset_response import DatasetResponse from data_explorer.models.facet import Facet from data_explorer.models.facet_value import FacetValue from data_explorer.models.facets_response import FacetsResponse

33.7

65

0.860534

0c2791187a63a4bcc6905cd731c3e9fbdcde2c2b

2,288

py

Python

seabird/cli.py

nicholas512/seabird

23073b2b9a550b86ec155cbe43be9b50e50b8310

[ "BSD-3-Clause" ]

38

2015-04-15T08:57:44.000Z

2022-03-13T02:51:53.000Z

seabird/cli.py

nicholas512/seabird

23073b2b9a550b86ec155cbe43be9b50e50b8310

[ "BSD-3-Clause" ]

54

2015-01-28T03:53:43.000Z

2021-12-11T07:37:24.000Z

seabird/cli.py

nicholas512/seabird

23073b2b9a550b86ec155cbe43be9b50e50b8310

[ "BSD-3-Clause" ]

22

2015-09-22T12:24:22.000Z

2022-01-31T22:27:16.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Command line utilities for package Seabird """ import click from seabird.exceptions import CNVError from .cnv import fCNV from .netcdf import cnv2nc

28.962025

68

0.645979

0c2931d41844c5cadfbc0f4d8cd12cf1c0991cb4

1,752

py

Python

main.py

Mitch-the-Fridge/pi

70ab24dab9b06722084e93f783dc541747d46720

[ "MIT" ]

null

main.py

Mitch-the-Fridge/pi

70ab24dab9b06722084e93f783dc541747d46720

[ "MIT" ]

null

main.py

Mitch-the-Fridge/pi

70ab24dab9b06722084e93f783dc541747d46720

[ "MIT" ]

1

2020-05-31T17:13:42.000Z

#!/usr/bin/env python3 #import face_recognition import cv2 import numpy as np from datetime import datetime, timedelta from buffer import Buffer from collections import deque import os from copy import copy import archive WEIGHT_EPS = 5 TIMEOUT = 5 # in seconds # with an fps we then have a "before" duration of 15 seconds video_buffer = Buffer(300) building = False clip = None previous_weight = poll_weight() last_weight_event = None cap = cv2.VideoCapture(0) while True: archive.try_upload_buffer() # if enough_diff is true we will actually start the recording weight = poll_weight() weight_diff = weight - previous_weight enough_diff = abs(weight_diff) >= WEIGHT_EPS ret, frame = cap.read() rgb_frame = cv2.resize(frame, (0, 0), fx=.5, fy=.5)[:, :, ::-1] #face_locations = face_recognition.face_locations(rgb_frame) print( len(video_buffer.q), len(clip) if clip is not None else 0, building, #face_locations ) point = { 'time': datetime.now(), #'face_locations': face_locations, 'frame': frame, 'current_weight': weight, } if building: clip.append(point) else: video_buffer.add(point) if not building and enough_diff: building = True clip = copy(video_buffer.q) video_buffer.clear() elif building and datetime.now() >= last_weight_event + timedelta(seconds=TIMEOUT): frames = list(clip) clip = None building = False print("creating clip of len", len(frames)) print(archive.create_from_clip(frames)) previous_weight = weight if enough_diff: last_weight_event = datetime.now()

23.36

87

0.660388

0c2a0afb31018189385f06e7bd9d48b8c0f6df9c

2,895

py

Python

OpenPNM/Network/models/pore_topology.py

Eng-RSMY/OpenPNM

a0a057d0f6346c515792459b1da97f05bab383c1

[ "MIT" ]

1

2021-03-30T21:38:26.000Z

OpenPNM/Network/models/pore_topology.py

Eng-RSMY/OpenPNM

a0a057d0f6346c515792459b1da97f05bab383c1

[ "MIT" ]

null

OpenPNM/Network/models/pore_topology.py

Eng-RSMY/OpenPNM

a0a057d0f6346c515792459b1da97f05bab383c1

[ "MIT" ]

null

r""" =============================================================================== pore_topology -- functions for monitoring and adjusting topology =============================================================================== """ import scipy as _sp def get_subscripts(network, shape, **kwargs): r""" Return the 3D subscripts (i,j,k) into the cubic network Parameters ---------- shape : list The (i,j,k) shape of the network in number of pores in each direction """ if network.num_pores('internal') != _sp.prod(shape): print('Supplied shape does not match Network size, cannot proceed') else: template = _sp.atleast_3d(_sp.empty(shape)) a = _sp.indices(_sp.shape(template)) i = a[0].flatten() j = a[1].flatten() k = a[2].flatten() ind = _sp.vstack((i, j, k)).T vals = _sp.ones((network.Np, 3))*_sp.nan vals[network.pores('internal')] = ind return vals def adjust_spacing(network, new_spacing, **kwargs): r""" Adjust the the pore-to-pore lattice spacing on a cubic network Parameters ---------- new_spacing : float The new lattice spacing to apply Notes ----- At present this method only applies a uniform spacing in all directions. This is a limiation of OpenPNM Cubic Networks in general, and not of the method. """ coords = network['pore.coords'] try: spacing = network._spacing coords = coords/spacing*new_spacing network._spacing = new_spacing except: pass return coords def reduce_coordination(network, z, mode='random', **kwargs): r""" Reduce the coordination number to the specified z value Parameters ---------- z : int The coordination number or number of throats connected a pore mode : string, optional Controls the logic used to trim connections. Options are: - 'random': (default) Throats will be randomly removed to achieve a coordination of z - 'max': All pores will be adjusted to have a maximum coordination of z (not implemented yet) Returns ------- A label array indicating which throats should be trimmed to achieve desired coordination. Notes ----- Pores with only 1 throat will be ignored in all calculations since these are generally boundary pores. """ T_trim = ~network['throat.all'] T_nums = network.num_neighbors(network.pores()) # Find protected throats T_keep = network.find_neighbor_throats(pores=(T_nums == 1)) if mode == 'random': z_ave = _sp.average(T_nums[T_nums > 1]) f_trim = (z_ave - z)/z_ave T_trim = _sp.rand(network.Nt) < f_trim T_trim = T_trim*(~network.tomask(throats=T_keep)) if mode == 'max': pass return T_trim

29.242424

79

0.587219

0c2a4b436c5eaf17c454eecf85f7cdb41e8c152f

9,559

py

Python

contrib/workflow/SpiffWorkflow/src/SpiffWorkflow/Tasks/Join.py

gonicus/clacks

da579f0acc4e48cf2e9451417ac6792282cf7ab6

[ "ZPL-2.1" ]

2

2015-01-26T07:15:19.000Z

2015-11-09T13:42:11.000Z

contrib/workflow/SpiffWorkflow/src/SpiffWorkflow/Tasks/Join.py

gonicus/clacks

da579f0acc4e48cf2e9451417ac6792282cf7ab6

[ "ZPL-2.1" ]

null

contrib/workflow/SpiffWorkflow/src/SpiffWorkflow/Tasks/Join.py

gonicus/clacks

da579f0acc4e48cf2e9451417ac6792282cf7ab6

[ "ZPL-2.1" ]

null

# Copyright (C) 2007 Samuel Abels # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA from SpiffWorkflow.Task import Task from SpiffWorkflow.Exception import WorkflowException from SpiffWorkflow.Operators import valueof from TaskSpec import TaskSpec

37.93254

80

0.61293

0c2c64f073f540439acf039ecdc1016885d5eb85

5,763

py

Python

covsirphy/visualization/bar_plot.py

ardhanii/covid19-sir

87881963c49a2fc5b6235c8b21269d216acaa941

[ "Apache-2.0" ]

97

2020-05-15T15:20:15.000Z

2022-03-18T02:55:54.000Z

covsirphy/visualization/bar_plot.py

ardhanii/covid19-sir

87881963c49a2fc5b6235c8b21269d216acaa941

[ "Apache-2.0" ]

970

2020-06-01T13:48:34.000Z

2022-03-29T08:20:49.000Z

covsirphy/visualization/bar_plot.py

ardhani31/Covid19-SIRV-v3

59d95156b375c41259c46ce4e656b86903f92ec2

[ "Apache-2.0" ]

36

2020-05-15T15:36:43.000Z

2022-02-25T17:59:08.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from matplotlib import pyplot as plt from matplotlib.ticker import ScalarFormatter import pandas as pd from covsirphy.util.argument import find_args from covsirphy.visualization.vbase import VisualizeBase def bar_plot(df, title=None, filename=None, show_legend=True, **kwargs): """ Wrapper function: show chronological change of the data. Args: data (pandas.DataFrame or pandas.Series): data to show Index Date (pandas.Timestamp) Columns variables to show title (str): title of the figure filename (str or None): filename to save the figure or None (display) show_legend (bool): whether show legend or not kwargs: keyword arguments of the following classes and methods. - covsirphy.BarPlot() and its methods, - matplotlib.pyplot.savefig(), matplotlib.pyplot.legend(), - pandas.DataFrame.plot() """ with BarPlot(filename=filename, **find_args(plt.savefig, **kwargs)) as bp: bp.title = title bp.plot(data=df, **find_args([BarPlot.plot, pd.DataFrame.plot], **kwargs)) # Axis bp.x_axis(**find_args([BarPlot.x_axis], **kwargs)) bp.y_axis(**find_args([BarPlot.y_axis], **kwargs)) # Vertical/horizontal lines bp.line(**find_args([BarPlot.line], **kwargs)) # Legend if show_legend: bp.legend(**find_args([BarPlot.legend, plt.legend], **kwargs)) else: bp.legend_hide()

36.942308

152

0.599167

0c2c8fc01f580afd1e737eea2d3f4a891285699e

3,342

py

Python

03-process-unsplash-dataset.py

l294265421/natural-language-image-search

71621f2208f345b922ed0f82d406526cef456d48

[ "MIT" ]

null

03-process-unsplash-dataset.py

l294265421/natural-language-image-search

71621f2208f345b922ed0f82d406526cef456d48

[ "MIT" ]

null

03-process-unsplash-dataset.py

l294265421/natural-language-image-search

71621f2208f345b922ed0f82d406526cef456d48

[ "MIT" ]

null

import os import math from pathlib import Path import clip import torch from PIL import Image import numpy as np import pandas as pd from common import common_path # Set the path to the photos # dataset_version = "lite" # Use "lite" or "full" # photos_path = Path("unsplash-dataset") / dataset_version / "photos" photos_path = os.path.join(common_path.project_dir, 'unsplash-dataset/lite/photos') # List all JPGs in the folder photos_files = list(Path(photos_path).glob("*.jpg")) # Print some statistics print(f"Photos found: {len(photos_files)}") # Load the open CLIP model device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = clip.load("ViT-B/32", device=device) # Function that computes the feature vectors for a batch of images # Define the batch size so that it fits on your GPU. You can also do the processing on the CPU, but it will be slower. batch_size = 16 # Path where the feature vectors will be stored features_path = os.path.join(common_path.project_dir, 'unsplash-dataset/lite/features') # Compute how many batches are needed batches = math.ceil(len(photos_files) / batch_size) # Process each batch for i in range(batches): print(f"Processing batch {i + 1}/{batches}") batch_ids_path = os.path.join(features_path, f"{i:010d}.csv") batch_features_path = os.path.join(features_path, f"{i:010d}.npy") # Only do the processing if the batch wasn't processed yet if not os.path.exists(batch_features_path): try: # Select the photos for the current batch batch_files = photos_files[i * batch_size: (i + 1) * batch_size] # Compute the features and save to a numpy file batch_features = compute_clip_features(batch_files) np.save(batch_features_path, batch_features) # Save the photo IDs to a CSV file photo_ids = [photo_file.name.split(".")[0] for photo_file in batch_files] photo_ids_data = pd.DataFrame(photo_ids, columns=['photo_id']) photo_ids_data.to_csv(batch_ids_path, index=False) except: # Catch problems with the processing to make the process more robust print(f'Problem with batch {i}') # Load all numpy files features_list = [np.load(features_file) for features_file in sorted(Path(features_path).glob("*.npy"))] # Concatenate the features and store in a merged file features = np.concatenate(features_list) np.save(os.path.join(features_path, "features.npy"), features) # Load all the photo IDs photo_ids = pd.concat([pd.read_csv(ids_file) for ids_file in sorted(Path(features_path).glob("*.csv"))]) photo_ids.to_csv(os.path.join(features_path, "photo_ids.csv"), index=False)

36.326087

118

0.718731

0c2caff0890d29c7f470b93cedd466717f34705f

4,612

py

Python

treadmill_pipeline/treadmill.py

ttngu207/project-treadmill

55b5241b1c0b2634da8c153bf9aaeb511f28b07f

[ "MIT" ]

null

treadmill_pipeline/treadmill.py

ttngu207/project-treadmill

55b5241b1c0b2634da8c153bf9aaeb511f28b07f

[ "MIT" ]

null

treadmill_pipeline/treadmill.py

ttngu207/project-treadmill

55b5241b1c0b2634da8c153bf9aaeb511f28b07f

[ "MIT" ]

4

2020-03-05T15:44:36.000Z

2020-03-18T15:18:11.000Z

import numpy as np import datajoint as dj from treadmill_pipeline import project_database_prefix from ephys.utilities import ingestion, time_sync from ephys import get_schema_name schema = dj.schema(project_database_prefix + 'treadmill_pipeline') reference = dj.create_virtual_module('reference', get_schema_name('reference')) acquisition = dj.create_virtual_module('acquisition', get_schema_name('acquisition')) behavior = dj.create_virtual_module('behavior', get_schema_name('behavior'))

50.681319

130

0.624892

0c3043c88aed8f6a40aafefe3d1e9548537a28e3

1,324

py

Python

management/api/v1/serializers.py

bwksoftware/cypetulip

4ea5c56d2d48a311220e144d094280a275109316

[ "MIT" ]

3

2019-08-03T12:00:22.000Z

2020-02-02T08:37:09.000Z

management/api/v1/serializers.py

basetwode/cypetulip

d6be294a288706c5661afb433215fe6c3ffea92b

[ "MIT" ]

47

2019-08-03T16:17:41.000Z

2022-03-11T23:15:48.000Z

management/api/v1/serializers.py

basetwode/cypetulip

d6be294a288706c5661afb433215fe6c3ffea92b

[ "MIT" ]

null

from rest_framework import serializers from management.models.main import MailSetting, LdapSetting, ShopSetting, LegalSetting, Header, CacheSetting, Footer

22.066667

116

0.702417

0c309ee4537295e1c6db342512009ad9c9a55328

9,854

py

Python

tests/test_optimal.py

craffer/fantasy-coty

08903cb138fa1c2d160b90fc028c8ec55901040b

[ "MIT" ]

null

tests/test_optimal.py

craffer/fantasy-coty

08903cb138fa1c2d160b90fc028c8ec55901040b

[ "MIT" ]

2

2019-12-21T18:48:40.000Z

2019-12-22T20:19:20.000Z

tests/test_optimal.py

craffer/fantasy-coty

08903cb138fa1c2d160b90fc028c8ec55901040b

[ "MIT" ]

null

"""Unit test optimal lineup functions.""" import unittest import copy import ff_espn_api # pylint: disable=import-error from collections import defaultdict from fantasy_coty.main import add_to_optimal if __name__ == "__main__": unittest.main()

46.046729

100

0.623909

0c31fa4744359e49cd3c719e5fe2aae79bc7f68a

5,391

py

Python

spark/explorer.py

Elavarasan17/Stack-Overflow-Data-Dump-Analysis

3742a1eef17b211ddcda4bd5f41d8a8c42ec228f

[ "zlib-acknowledgement", "RSA-MD" ]

null

spark/explorer.py

Elavarasan17/Stack-Overflow-Data-Dump-Analysis

3742a1eef17b211ddcda4bd5f41d8a8c42ec228f

[ "zlib-acknowledgement", "RSA-MD" ]

null

spark/explorer.py

Elavarasan17/Stack-Overflow-Data-Dump-Analysis

3742a1eef17b211ddcda4bd5f41d8a8c42ec228f

[ "zlib-acknowledgement", "RSA-MD" ]

null

from pyspark import SparkConf, SparkContext from pyspark.sql import SparkSession, functions, types from pyspark.sql.functions import date_format from pyspark.sql.functions import year, month, dayofmonth import sys import json import argparse assert sys.version_info >= (3, 5) # make sure we have Python 3.5+ # add more functions as necessary if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("-posts_src", action="store", dest="posts_src", type=str) parser.add_argument("-users_src", action="store", dest="users_src", type=str) parser.add_argument("-tempbucket_src", action="store", dest="tempbucket_src", type=str) parser.add_argument("-dataset_src", action="store", dest="dataset_src", type=str) args = parser.parse_args() posts_inputs = args.posts_src users_inputs = args.users_src temp_bucket_input = args.tempbucket_src dataset_input = args.dataset_src spark = SparkSession.builder.appName('Explorer DF').getOrCreate() assert spark.version >= '3.0' # make sure we have Spark 3.0+ spark.sparkContext.setLogLevel('WARN') main(posts_inputs, users_inputs,temp_bucket_input,dataset_input)

70.012987

325

0.725654

0c32795d8af79fcf1c3d723adbd4971a62b457ad

2,177

py

Python

self_supervised/loss.py

ravidziv/self-supervised-learning

f02c1639ce3c2119afa522e400d793e741fb68a0

[ "MIT" ]

null

self_supervised/loss.py

ravidziv/self-supervised-learning

f02c1639ce3c2119afa522e400d793e741fb68a0

[ "MIT" ]

null

self_supervised/loss.py

ravidziv/self-supervised-learning

f02c1639ce3c2119afa522e400d793e741fb68a0

[ "MIT" ]

null

"""Contrastive loss functions.""" from functools import partial import tensorflow as tf LARGE_NUM = 1e9 def add_supervised_loss(labels: tf.Tensor, logits: tf.Tensor): """Compute mean supervised loss over local batch.""" losses = tf.keras.losses.CategoricalCrossentropy( from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(labels, logits) return tf.reduce_mean(losses) def add_contrastive_loss(hidden: tf.Tensor, hidden_norm: bool = True, temperature: float = 1.0): """Compute loss for model. Args: hidden: hidden vector (`Tensor`) of shape (bsz, dim). hidden_norm: whether or not to use normalization on the hidden vector. temperature: a `floating` number for temperature scaling. Returns: A loss scalar. The logits for contrastive prediction task. The labels for contrastive prediction task. """ if hidden_norm: hidden = tf.math.l2_normalize(hidden, -1) hidden1, hidden2 = tf.split(hidden, 2, 0) batch_size = tf.shape(hidden1)[0] hidden1_large = hidden1 hidden2_large = hidden2 labels = tf.one_hot(tf.range(batch_size), batch_size * 2) masks = tf.one_hot(tf.range(batch_size), batch_size) logits_aa = tf.matmul(hidden1, hidden1_large, transpose_b=True) / temperature logits_aa = logits_aa - masks * LARGE_NUM logits_bb = tf.matmul(hidden2, hidden2_large, transpose_b=True) / temperature logits_bb = logits_bb - masks * LARGE_NUM logits_ab = tf.matmul(hidden1, hidden2_large, transpose_b=True) / temperature logits_ba = tf.matmul(hidden2, hidden1_large, transpose_b=True) / temperature loss_a = tf.nn.softmax_cross_entropy_with_logits( labels, tf.concat([logits_ab, logits_aa], 1)) loss_b = tf.nn.softmax_cross_entropy_with_logits( labels, tf.concat([logits_ba, logits_bb], 1)) loss = tf.reduce_mean(loss_a + loss_b) return loss, logits_ab, labels

36.283333

81

0.683969

0c336dedc298c3448acb41a9e995e66ab5dfe2bf

3,391

py

Python

suzieq/engines/pandas/tables.py

zxiiro/suzieq

eca92820201c05bc80081599f69e41cd6b991107

[ "Apache-2.0" ]

null

suzieq/engines/pandas/tables.py

zxiiro/suzieq

eca92820201c05bc80081599f69e41cd6b991107

[ "Apache-2.0" ]

null

suzieq/engines/pandas/tables.py

zxiiro/suzieq

eca92820201c05bc80081599f69e41cd6b991107

[ "Apache-2.0" ]

null

import pandas as pd from suzieq.engines.pandas.engineobj import SqPandasEngine from suzieq.sqobjects import get_sqobject

34.252525

76

0.529932

0c34007b8ed98fbad90350a4894f2960e309e1be

3,306

py

Python

connect_box/data.py

jtru/python-connect-box

2d26923e966fbb319760da82e3e71103018ded0b

[ "MIT" ]

null

connect_box/data.py

jtru/python-connect-box

2d26923e966fbb319760da82e3e71103018ded0b

[ "MIT" ]

null

connect_box/data.py

jtru/python-connect-box

2d26923e966fbb319760da82e3e71103018ded0b

[ "MIT" ]

null

"""Handle Data attributes.""" from datetime import datetime from ipaddress import IPv4Address, IPv6Address, ip_address as convert_ip from typing import Iterable, Union import attr

24.857143

82

0.635209

0c34fc08f42c8637a1d795a07180d44a6de5c252

4,946

py

Python

components/server/src/data_model/sources/jenkins.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

components/server/src/data_model/sources/jenkins.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

components/server/src/data_model/sources/jenkins.py

m-zakeri/quality-time

531931f0d8d4f5d262ea98445868158e41d268da

[ "Apache-2.0" ]

null

"""Jenkins source.""" from ..meta.entity import Color, EntityAttributeType from ..parameters import ( access_parameters, Days, FailureType, MultipleChoiceParameter, MultipleChoiceWithAdditionParameter, TestResult, ) from ..meta.source import Source def jenkins_access_parameters(*args, **kwargs): """Create Jenkins specific access parameters.""" kwargs["include"] = dict(private_token=False, landing_url=False) if "name" not in kwargs.setdefault("kwargs", {}).setdefault("url", {}): kwargs["kwargs"]["url"]["name"] = "URL to Jenkins job" kwargs["kwargs"]["password"] = dict( name="Password or API token for basic authentication", help_url="https://wiki.jenkins.io/display/JENKINS/Authenticating+scripted+clients", ) return access_parameters(*args, **kwargs) ALL_JENKINS_METRICS = ["failed_jobs", "source_up_to_dateness", "source_version", "unused_jobs"] JOB_ENTITY = dict( name="job", attributes=[ dict(name="Job name", key="name", url="url"), dict( name="Status of most recent build", key="build_status", color=dict(Success=Color.POSITIVE, Failure=Color.NEGATIVE, Aborted=Color.ACTIVE, Unstable=Color.WARNING), ), dict(name="Date of most recent build", key="build_date", type=EntityAttributeType.DATE), ], ) JENKINS = Source( name="Jenkins", description="Jenkins is an open source continuous integration/continuous deployment server.", url="https://jenkins.io/", parameters=dict( inactive_days=Days( name="Number of days without builds after which to consider CI-jobs unused.", short_name="number of days without builds", default_value="90", metrics=["unused_jobs"], ), jobs_to_include=MultipleChoiceWithAdditionParameter( name="Jobs to include (regular expressions or job names)", short_name="jobs to include", help="Jobs to include can be specified by job name or by regular expression. " "Use {parent job name}/{child job name} for the names of nested jobs.", placeholder="all", metrics=["failed_jobs", "source_up_to_dateness", "unused_jobs"], ), jobs_to_ignore=MultipleChoiceWithAdditionParameter( name="Jobs to ignore (regular expressions or job names)", short_name="jobs to ignore", help="Jobs to ignore can be specified by job name or by regular expression. " "Use {parent job name}/{child job name} for the names of nested jobs.", metrics=["failed_jobs", "source_up_to_dateness", "unused_jobs"], ), result_type=MultipleChoiceParameter( name="Build result types", short_name="result types", help="Limit which build result types to include.", placeholder="all result types", values=["Aborted", "Failure", "Not built", "Success", "Unstable"], metrics=["source_up_to_dateness"], ), failure_type=FailureType(values=["Aborted", "Failure", "Not built", "Unstable"]), **jenkins_access_parameters( ALL_JENKINS_METRICS, kwargs=dict( url=dict( name="URL", help="URL of the Jenkins instance, with port if necessary, but without path. For example, " "'https://jenkins.example.org'.", ) ), ) ), entities=dict( failed_jobs=JOB_ENTITY, source_up_to_dateness=JOB_ENTITY, unused_jobs=JOB_ENTITY, ), ) ALL_JENKINS_TEST_REPORT_METRICS = ["source_up_to_dateness", "tests"] JENKINS_TEST_REPORT = Source( name="Jenkins test report", description="A Jenkins job with test results.", url="https://plugins.jenkins.io/junit", parameters=dict( test_result=TestResult(values=["failed", "passed", "skipped"]), **jenkins_access_parameters( ALL_JENKINS_TEST_REPORT_METRICS, kwargs=dict( url=dict( help="URL to a Jenkins job with a test report generated by the JUnit plugin. For example, " "'https://jenkins.example.org/job/test' or https://jenkins.example.org/job/test/job/master' " "in case of a pipeline job." ) ), ) ), entities=dict( tests=dict( name="test", attributes=[ dict(name="Class name"), dict(name="Test case", key="name"), dict( name="Test result", color=dict(failed=Color.NEGATIVE, passed=Color.POSITIVE, skipped=Color.WARNING) ), dict(name="Number of builds the test has been failing", key="age", type=EntityAttributeType.INTEGER), ], ) ), )

38.640625

119

0.603114

0c3529b1f848cd4aef129f241e7149c8c46fd8c6

3,155

py

Python

tests/test_transducer.py

dandersonw/myouji-kenchi

6a373d8626995bf5d4383dcac4fc8e6372135640

[ "MIT" ]

7

2019-10-22T10:09:12.000Z

2022-01-31T07:49:07.000Z

tests/test_transducer.py

dandersonw/myouji-kenchi

6a373d8626995bf5d4383dcac4fc8e6372135640

[ "MIT" ]

null

tests/test_transducer.py

dandersonw/myouji-kenchi

6a373d8626995bf5d4383dcac4fc8e6372135640

[ "MIT" ]

1

2021-07-09T18:10:17.000Z

import myouji_kenchi # Given that the output depends on what goes into the attested myouji file I'm # hesitant to write too many tests in the blast radius of changes to that file

44.43662

78

0.66878

0c36ba1ececdebf56f9ae6696bc5d261578450ca

1,668

py

Python

algorithms/named/mergesort.py

thundergolfer/uni

e604d1edd8e5085f0ae1c0211015db38c07fc926

[ "MIT" ]

1

2022-01-06T04:50:09.000Z

algorithms/named/mergesort.py

thundergolfer/uni

e604d1edd8e5085f0ae1c0211015db38c07fc926

[ "MIT" ]

1

2022-01-23T06:09:21.000Z

2022-01-23T06:14:17.000Z

algorithms/named/mergesort.py

thundergolfer/uni

e604d1edd8e5085f0ae1c0211015db38c07fc926

[ "MIT" ]

null

import unittest from typing import List, Optional # Consider making generic with: # https://stackoverflow.com/a/47970232/4885590 if __name__ == "__main__": x = [10, 5, 9, 10, 3] print(x) merge_sort(x) print(x) unittest.main()

24.895522

80

0.576739

0c380570b168add317dd67b7037f3b6ec7e93c2b

392

py

Python

pages/main_page.py

thaidem/selenium-training-page-objects

1f37a2b5287a502295bb57050c95455d68c2d3eb

[ "Apache-2.0" ]

null

pages/main_page.py

thaidem/selenium-training-page-objects

1f37a2b5287a502295bb57050c95455d68c2d3eb

[ "Apache-2.0" ]

null

pages/main_page.py

thaidem/selenium-training-page-objects

1f37a2b5287a502295bb57050c95455d68c2d3eb

[ "Apache-2.0" ]

null

from selenium.webdriver.support.wait import WebDriverWait

23.058824

67

0.670918

0c39dce08e2639d2b8e9721a52545154b1694858

196

py

Python

src/frr/tests/lib/test_nexthop_iter.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

src/frr/tests/lib/test_nexthop_iter.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

src/frr/tests/lib/test_nexthop_iter.py

zhouhaifeng/vpe

9c644ffd561988e5740021ed26e0f7739844353d

[ "Apache-2.0" ]

null

import frrtest TestNexthopIter.onesimple("Simple test passed.") TestNexthopIter.onesimple("PRNG test passed.")

19.6

48

0.785714

0c3b154bc332d251c3e35e98a56001cf94c27a53

1,319

py

Python

setup.py

themis-project/themis-finals-checker-app-py

12e70102bcca3d6e4082d96e676e364176c0da67

[ "MIT" ]

null

setup.py

themis-project/themis-finals-checker-app-py

12e70102bcca3d6e4082d96e676e364176c0da67

[ "MIT" ]

null

setup.py

themis-project/themis-finals-checker-app-py

12e70102bcca3d6e4082d96e676e364176c0da67

[ "MIT" ]

null

# -*- coding: utf-8 -*- from setuptools import setup, find_packages import io import os about = {} about_filename = os.path.join( os.path.dirname(os.path.realpath(__file__)), 'themis', 'finals', 'checker', 'app', '__about__.py') with io.open(about_filename, 'rb') as fp: exec(fp.read(), about) setup( name='themis.finals.checker.app', version=about['__version__'], description='Themis Finals checker application', author='Alexander Pyatkin', author_email='aspyatkin@gmail.com', url='https://github.com/themis-project/themis-finals-checker-app-py', license='MIT', packages=find_packages('.'), install_requires=[ 'setuptools>=0.8', 'Flask>=0.11.1,<0.12', 'redis>=2.10.5,<2.11', 'hiredis>=0.2.0,<0.3', 'rq>=0.7.1,<0.8.0', 'requests>=2.11.0', 'python-dateutil>=2.5.3,<2.6', 'themis.finals.checker.result==1.1.0', 'raven>=5.26.0,<5.27.0', 'PyJWT>=1.5.0,<1.6.0', 'cryptography>=1.8.1,<1.9.0', 'PyYAML>=3.11' ], namespace_packages=[ 'themis', 'themis.finals', 'themis.finals.checker' ], entry_points=dict( console_scripts=[ 'themis-finals-checker-app-worker = themis.finals.checker.app:start_worker' ] ) )

26.918367

87

0.581501

0c3b1affbabd1c858deb93d0a0302a8d675091d1

8,090

py

Python

tools/xenserver/cleanup_sm_locks.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

tools/xenserver/cleanup_sm_locks.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

null

tools/xenserver/cleanup_sm_locks.py

bopopescu/nova-token

ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2

[ "Apache-2.0" ]

2

2017-07-20T17:31:34.000Z

2020-07-24T02:42:19.000Z

13.758503

495

0.591595

0c3c5abd70c1f21c01879c6ec3f584ca3464ae2e

13,324

py

Python

clifun.py

tdimiduk/clifun

d7e5acae0a76506d9440ae86a15341b6cc1cf25e

[ "MIT" ]

1

2022-01-04T17:58:19.000Z

clifun.py

tdimiduk/clifun

d7e5acae0a76506d9440ae86a15341b6cc1cf25e

[ "MIT" ]

4

2022-01-04T17:17:33.000Z

2022-01-04T17:26:12.000Z

clifun.py

tdimiduk/clifun

d7e5acae0a76506d9440ae86a15341b6cc1cf25e

[ "MIT" ]

null

import datetime as dt import importlib.util import inspect import itertools import json import os import pathlib import sys import types import typing from typing import ( Any, Callable, Dict, Generic, Iterable, Iterator, List, Optional, Set, Tuple, Type, TypeVar, Union, ) S = TypeVar("S") T = TypeVar("T") O = TypeVar("O", Any, None) StringInterpreters = Dict[Type[T], Callable[[str], T]] def call( c: Callable[..., T], args: Optional[List[str]] = None, string_interpreters: Optional[StringInterpreters] = None, ) -> T: """ Call a function from the command line Assembles the inputs to a function from command line arguments, environment variables, and config files and call it. """ argv = sys.argv if args is None else args interpreters = ( string_interpreters if string_interpreters is not None else default_string_interpreters() ) annotated = annotate_callable(c, interpreters, []) provided_inputs = assemble_input_sources(argv) if provided_inputs.args.help: print_usage(annotated, header=True) sys.exit(0) needed_inputs = all_needed_inputs(annotated) unknown = invalid_args(provided_inputs.args.keyword.keys(), needed_inputs) if unknown: print(f"Unknown arguments: {unknown}") print_usage(annotated) sys.exit(1) resolved_inputs, missing_inputs = resolve_inputs(needed_inputs, provided_inputs) if missing_inputs: print(f"Missing arguments: {missing_inputs}") print_usage(annotated) sys.exit(1) return annotated(resolved_inputs) ################################################################################ # Interpreting strings into python types ################################################################################ def interpret_bool(s: str) -> bool: """ Slightly more intuitive bool iterpretation Raw python's `bool("false")==True` since it is a non-empty string """ if s.lower() in {"t", "true", "yes", "y"}: return True elif s.lower() in {"f", "false", "no", "n"}: return False else: raise InterpretationError(s, bool) def interpret_datetime(s: str) -> dt.datetime: """ Date and time in isoformat """ if hasattr(dt.datetime, "fromisoformat"): return dt.datetime.fromisoformat(s) else: # for python 3.6 where `fromisoformat` doesn't exist import isodate # type: ignore return isodate.parse_datetime(s) def interpret_date(s: str) -> dt.date: """ Dates in YYYY-MM-DD format """ return dt.date(*[int(i) for i in s.split("-")]) ################################################################################ # Data classes # # these should really be dataclasses, and will be converted when clifun drops compatability # with python 3.6 ################################################################################ Annotated = Union["AnnotatedParameter", "AnnotatedCallable"] def __str__(self) -> str: return f"<parameter: {self.name}: {self.t}>" class InputSources: def __init__(self, args: Arguments, config_files: ConfigFiles): self.args = args self.config_files = config_files ################################################################################ # Assemble inputs from the "outside world" ################################################################################ NOT_SPECIFIED = inspect._empty ################################################################################ # Input validation and help ################################################################################ ################################################################################ # Determine what inputs a function needs ################################################################################ ################################################################################ # Make clifun.py usable as a script to call functions in any module ################################################################################ if __name__ == "__main__": print(sys.argv) if len(sys.argv) < 3: print("Usage: clifun.py path_to_module function_name ...") sys.exit(1) target = pathlib.Path(sys.argv[1]).resolve() function_name = sys.argv[2] arguments = sys.argv[2:] module = import_module_by_path(target) function = getattr(module, function_name) print(call(function, arguments))

29.283516

120

0.590513

0c3c8f51e10f9073a8d53d99be68ca016464578d

2,758

py

Python

pages/views.py

joshua-hashimoto/eigo-of-the-day-django

68ec7fe4257c67689de596cf34e991a3750b7f36

[ "MIT" ]

null

pages/views.py

joshua-hashimoto/eigo-of-the-day-django

68ec7fe4257c67689de596cf34e991a3750b7f36

[ "MIT" ]

8

2021-04-08T19:45:15.000Z

2022-03-12T00:49:25.000Z

pages/views.py

joshua-hashimoto/eigo-of-the-day-django

68ec7fe4257c67689de596cf34e991a3750b7f36

[ "MIT" ]

null

import os import json import uuid from django.conf import settings from django.http import HttpResponse from django.utils.translation import ugettext_lazy as _ from django.views.generic import View import cloudinary

34.049383

85

0.591008

0c3e673531e09903ae71e40dc82ffb45887a73df

1,776

py

Python

shc/log/in_memory.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

5

2021-07-02T21:48:45.000Z

2021-12-12T21:55:42.000Z

shc/log/in_memory.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

49

2020-09-18T20:05:55.000Z

2022-03-05T19:51:33.000Z

shc/log/in_memory.py

fabaff/smarthomeconnect

611cd0f372d03b5fc5798a2a9a5f962d1da72799

[ "Apache-2.0" ]

1

2021-12-10T14:50:43.000Z

import datetime from typing import Optional, Type, Generic, List, Tuple from ..base import T from .generic import PersistenceVariable

36.244898

96

0.606419

0c3ec0f29f7bce414073cc341dd9839fbf5fca06

1,393

py

Python

guts/api/contrib/type_actions.py

smallwormer/stable-liberty-guts

e635b710cdd210f70e9d50c3b85fffdeb53e8f01

[ "Apache-2.0" ]

null

guts/api/contrib/type_actions.py

smallwormer/stable-liberty-guts

e635b710cdd210f70e9d50c3b85fffdeb53e8f01

[ "Apache-2.0" ]

null

guts/api/contrib/type_actions.py

smallwormer/stable-liberty-guts

e635b710cdd210f70e9d50c3b85fffdeb53e8f01

[ "Apache-2.0" ]

1

2022-03-03T05:41:31.000Z

# Copyright (c) 2015 Aptira Pty Ltd. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from oslo_config import cfg from oslo_log import log as logging from guts.api import extensions from guts.api.openstack import wsgi CONF = cfg.CONF LOG = logging.getLogger(__name__) authorize = extensions.extension_authorizer('types', '')

29.638298

78

0.724336

0c4122d4f0b749136bdf171cdb6e696eecf404bd

8,579

py

Python

models/StyleTransfer/AdaIN.py

mtroym/pytorch-train

3b303b6c7e364a58cb88d7142da942a30cc2b255

[ "Apache-2.0" ]

2

2019-12-21T14:40:11.000Z

2020-05-26T09:26:52.000Z

models/StyleTransfer/AdaIN.py

mtroym/pytorch-train

3b303b6c7e364a58cb88d7142da942a30cc2b255

[ "Apache-2.0" ]

null

models/StyleTransfer/AdaIN.py

mtroym/pytorch-train

3b303b6c7e364a58cb88d7142da942a30cc2b255

[ "Apache-2.0" ]

1

2020-10-16T12:03:19.000Z

""" Author: Yiming Mao - mtroym@github Description: Transplant from "https://github.com/xunhuang1995/AdaIN-style/blob/master/train.lua" """ import functools import os from collections import OrderedDict import torch import torch.nn as nn from torchvision.models import vgg19 from datasets.utils import denorm from models.blocks import AdaptiveInstanceNorm2d from models.blocks.vgg import rename_sequential from models.helpers import init_weights if __name__ == '__main__': bs = 10 w, h = 128, 128 image = torch.rand((bs, 3, w, h)) # g = _Generator_ResizeConv() e = _Encoder() d = _Decoder(e) adain = AdaptiveInstanceNorm2d(e.out_channels) te = adain(e(image)["relu4_1"], e(image)["relu4_1"]) print(d) print(d(te).shape) # print(e(image).shape) # print(d(e(image)).shape) # print(.out_channels) # fak = g(z) # print(fak.shape) # print(d(fak).shape)

40.852381

115

0.602518

0c418b56746d824c2d98f37af03cc0b209cd7415

1,099

py

Python

airflow/migrations/versions/52d714495f0_job_id_indices.py

rubeshdcube/incubator-airflow

5419fbb78a2ea2388456c356d2f899ea1991b2de

[ "Apache-2.0" ]

6

2016-04-20T20:40:43.000Z

2022-02-20T10:32:00.000Z

airflow/migrations/versions/52d714495f0_job_id_indices.py

curest0x1021/incubator-airflow

e6d3160a061dbaa6042d524095dcd1cbc15e0bcd

[ "Apache-2.0" ]

13

2018-11-30T18:18:32.000Z

2021-02-19T17:04:12.000Z

airflow/migrations/versions/52d714495f0_job_id_indices.py

curest0x1021/incubator-airflow

e6d3160a061dbaa6042d524095dcd1cbc15e0bcd

[ "Apache-2.0" ]

9

2017-08-24T15:47:44.000Z

2022-02-14T03:30:49.000Z

# -*- coding: utf-8 -*- # # 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. """job_id indices Revision ID: 52d714495f0 Revises: 338e90f54d61 Create Date: 2015-10-20 03:17:01.962542 """ # revision identifiers, used by Alembic. revision = '52d714495f0' down_revision = '338e90f54d61' branch_labels = None depends_on = None from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import mysql

27.475

98

0.755232

0c42822d78adfa0e7f0264f5d356cb0270939941

7,836

py

Python

deep_semantic_similarity_keras.py

viksit/Deep-Semantic-Similarity-Model

1dc94346801e711125fb573284a1984ce17fb90e

[ "MIT" ]

3

2016-05-26T00:04:38.000Z

2019-10-22T09:52:39.000Z

deep_semantic_similarity_keras.py

viksit/Deep-Semantic-Similarity-Model

1dc94346801e711125fb573284a1984ce17fb90e

[ "MIT" ]

null

deep_semantic_similarity_keras.py

viksit/Deep-Semantic-Similarity-Model

1dc94346801e711125fb573284a1984ce17fb90e

[ "MIT" ]

1

2019-10-22T09:59:04.000Z

# Michael A. Alcorn (malcorn@redhat.com) # An implementation of the Deep Semantic Similarity Model (DSSM) found in [1]. # [1] Shen, Y., He, X., Gao, J., Deng, L., and Mesnil, G. 2014. A latent semantic model # with convolutional-pooling structure for information retrieval. In CIKM, pp. 101-110. # http://research.microsoft.com/pubs/226585/cikm2014_cdssm_final.pdf # [2] http://research.microsoft.com/en-us/projects/dssm/ # [3] http://research.microsoft.com/pubs/238873/wsdm2015.v3.pdf import numpy as np from keras import backend from keras.layers import Input, merge from keras.layers.core import Dense, Lambda, Reshape from keras.layers.convolutional import Convolution1D from keras.models import Model def R(vects): """ Calculates the cosine similarity of two vectors. :param vects: a list of two vectors. :return: the cosine similarity of two vectors. """ (x, y) = vects return backend.dot(x, backend.transpose(y)) / (x.norm(2) * y.norm(2)) # See equation (4) LETTER_GRAM_SIZE = 3 # See section 3.2. WINDOW_SIZE = 3 # See section 3.2. TOTAL_LETTER_GRAMS = int(3 * 1e4) # Determined from data. See section 3.2. WORD_DEPTH = WINDOW_SIZE * TOTAL_LETTER_GRAMS # See equation (1). K = 300 # Dimensionality of the max-pooling layer. See section 3.4. L = 128 # Dimensionality of latent semantic space. See section 3.5. J = 4 # Number of random unclicked documents serving as negative examples for a query. See section 4. FILTER_LENGTH = 1 # We only consider one time step for convolutions. # Input tensors holding the query, positive (clicked) document, and negative (unclicked) documents. # The first dimension is None because the queries and documents can vary in length. query = Input(shape = (None, WORD_DEPTH)) pos_doc = Input(shape = (None, WORD_DEPTH)) neg_docs = [Input(shape = (None, WORD_DEPTH)) for j in range(J)] # Query model. The paper uses separate neural nets for queries and documents (see section 5.2). # In this step, we transform each word vector with WORD_DEPTH dimensions into its # convolved representation with K dimensions. K is the number of kernels/filters # being used in the operation. Essentially, the operation is taking the dot product # of a single weight matrix (W_c) with each of the word vectors (l_t) from the # query matrix (l_Q), adding a bias vector (b_c), and then applying the tanh function. # That is, h_Q = tanh(W_c l_Q + b_c). With that being said, that's not actually # how the operation is being calculated here. To tie the weights of the weight # matrix (W_c) together, we have to use a one-dimensional convolutional layer. # Further, we have to transpose our query matrix (l_Q) so that time is the first # dimension rather than the second (as described in the paper). That is, l_Q[0, :] # represents our first word vector rather than l_Q[:, 0]. We can think of the weight # matrix (W_c) as being similarly transposed such that each kernel is a column # of W_c. Therefore, h_Q = tanh(l_Q W_c + b_c) with l_Q, W_c, and b_c being # the transposes of the matrices described in the paper. query_conv = Convolution1D(K, FILTER_LENGTH, border_mode = "same", input_shape = (None, WORD_DEPTH), activation = "tanh")(query) # See equation (2). # Next, we apply a max-pooling layer to the convolved query matrix. Keras provides # its own max-pooling layers, but they cannot handle variable length input (as # far as I can tell). As a result, I define my own max-pooling layer here. In the # paper, the operation selects the maximum value for each row of h_Q, but, because # we're using the transpose, we're selecting the maximum value for each column. query_max = Lambda(lambda x: x.max(axis = 1), output_shape = (K,))(query_conv) # See section 3.4. # In this step, we generate the semantic vector represenation of the query. This # is a standard neural network dense layer, i.e., y = tanh(W_s v + b_s). query_sem = Dense(L, activation = "tanh", input_dim = K)(query_max) # See section 3.5. # The document equivalent of the above query model. doc_conv = Convolution1D(K, FILTER_LENGTH, border_mode = "same", input_shape = (None, WORD_DEPTH), activation = "tanh") doc_max = Lambda(lambda x: x.max(axis = 1), output_shape = (K,)) doc_sem = Dense(L, activation = "tanh", input_dim = K) pos_doc_conv = doc_conv(pos_doc) neg_doc_convs = [doc_conv(neg_doc) for neg_doc in neg_docs] pos_doc_max = doc_max(pos_doc_conv) neg_doc_maxes = [doc_max(neg_doc_conv) for neg_doc_conv in neg_doc_convs] pos_doc_sem = doc_sem(pos_doc_max) neg_doc_sems = [doc_sem(neg_doc_max) for neg_doc_max in neg_doc_maxes] # This layer calculates the cosine similarity between the semantic representations of # a query and a document. R_layer = Lambda(R, output_shape = (1,)) # See equation (4). R_Q_D_p = R_layer([query_sem, pos_doc_sem]) # See equation (4). R_Q_D_ns = [R_layer([query_sem, neg_doc_sem]) for neg_doc_sem in neg_doc_sems] # See equation (4). concat_Rs = merge([R_Q_D_p] + R_Q_D_ns, mode = "concat") concat_Rs = Reshape((J + 1, 1))(concat_Rs) # In this step, we multiply each R(Q, D) value by gamma. In the paper, gamma is # described as a smoothing factor for the softmax function, and it's set empirically # on a held-out data set. We're going to learn gamma's value by pretending it's # a single, 1 x 1 kernel. with_gamma = Convolution1D(1, 1, border_mode = "same", input_shape = (J + 1, 1), activation = "linear")(concat_Rs) # See equation (5). # Next, we exponentiate each of the gamma x R(Q, D) values. exponentiated = Lambda(lambda x: backend.exp(x), output_shape = (J + 1,))(with_gamma) # See equation (5). exponentiated = Reshape((J + 1,))(exponentiated) # Finally, we use the softmax function to calculate the P(D+|Q). prob = Lambda(lambda x: x[0][0] / backend.sum(x[0]), output_shape = (1,))(exponentiated) # See equation (5). # We now have everything we need to define our model. model = Model(input = [query, pos_doc] + neg_docs, output = prob) model.compile(optimizer = "adadelta", loss = "binary_crossentropy") # Build a random data set. sample_size = 10 l_Qs = [] pos_l_Ds = [] for i in range(sample_size): query_len = np.random.randint(1, 10) l_Q = np.random.rand(1, query_len, WORD_DEPTH) l_Qs.append(l_Q) doc_len = np.random.randint(50, 500) l_D = np.random.rand(1, doc_len, WORD_DEPTH) pos_l_Ds.append(l_D) neg_l_Ds = [] for i in range(sample_size): possibilities = list(range(sample_size)) possibilities.remove(i) negatives = np.random.choice(possibilities, J) neg_l_Ds.append([pos_l_Ds[negative] for negative in negatives]) # Because we're using the "binary_crossentropy" loss function, we can pretend that # we're dealing with a binary classification problem and that every sample is a # member of the "1" class. y = np.ones(1) for i in range(sample_size): history = model.fit([l_Qs[i], pos_l_Ds[i]] + neg_l_Ds[i], y, nb_epoch = 1, verbose = 0) # Here, I walk through an example of how to define a function for calculating output # from the computational graph. Let's define a function that calculates R(Q, D+) # for a given query and clicked document. The function depends on two inputs, query # and pos_doc. That is, if you start at the point in the graph where R(Q, D+) is # calculated and then backtrack as far as possible, you'll end up at two different # starting points, query and pos_doc. As a result, we supply those inputs in a list # to the function. This particular function only calculates a single output, but # multiple outputs are possible (see the next example). get_R_Q_D_p = backend.function([query, pos_doc], R_Q_D_p) get_R_Q_D_p([l_Qs[0], pos_l_Ds[0]]) # A slightly more complex function. Notice that both neg_docs and the output are # lists. get_R_Q_D_ns = backend.function([query] + neg_docs, R_Q_D_ns) get_R_Q_D_ns([l_Qs[0]] + neg_l_Ds[0])

49.910828

148

0.732006

0c4483174d1c4ff711dd1bd4cb802a150131d7f7

469

py

Python

posthog/migrations/0087_fix_annotation_created_at.py

avoajaugochukwu/posthog

7e7fd42b0542ebc4734aedb926df11d462e3dd4f

[ "MIT" ]

7,409

2020-02-09T23:18:10.000Z

2022-03-31T22:36:25.000Z

posthog/migrations/0087_fix_annotation_created_at.py

avoajaugochukwu/posthog

7e7fd42b0542ebc4734aedb926df11d462e3dd4f

[ "MIT" ]

5,709

2020-02-09T23:26:13.000Z

2022-03-31T20:20:01.000Z

posthog/migrations/0087_fix_annotation_created_at.py

avoajaugochukwu/posthog

7e7fd42b0542ebc4734aedb926df11d462e3dd4f

[ "MIT" ]

647

2020-02-13T17:50:55.000Z

2022-03-31T11:24:19.000Z

# Generated by Django 3.0.7 on 2020-10-14 07:46 import django.utils.timezone from django.db import migrations, models

23.45

85

0.648188

0c476cbc9139db2d5b5477a2919a3f47a83b94b5

4,723

py

Python

tumorevo/tumorfig/main.py

pedrofale/tumorevo

cf43f3854f6815c822cf4df71be82fc6dbae065b

[ "MIT" ]

2

2022-02-08T12:54:58.000Z

2022-03-04T12:21:06.000Z

tumorevo/tumorfig/main.py

pedrofale/tumorevo

cf43f3854f6815c822cf4df71be82fc6dbae065b

[ "MIT" ]

null

tumorevo/tumorfig/main.py

pedrofale/tumorevo

cf43f3854f6815c822cf4df71be82fc6dbae065b

[ "MIT" ]

null

""" Create a cartoon of a tumor given the frequencies of different genotypes. """ from .util import * import pandas as pd import matplotlib.pyplot as plt import click import os from pathlib import Path from pymuller import muller if __name__ == "__main__": main()

28.624242

85

0.547957

0c48b673acc0ea7efa42fafb3fba6d032e5deab7

196

py

Python

src/brouwers/online_users/urls.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

6

2015-03-03T13:23:07.000Z

2021-12-19T18:12:41.000Z

src/brouwers/online_users/urls.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

95

2015-02-07T00:55:39.000Z

2022-02-08T20:22:05.000Z

src/brouwers/online_users/urls.py

modelbrouwers/modelbrouwers

e0ba4819bf726d6144c0a648fdd4731cdc098a52

[ "MIT" ]

2

2016-03-22T16:53:26.000Z

2019-02-09T22:46:04.000Z

from django.conf.urls import url from .views import get_online_users, set_online app_name = 'online_users' urlpatterns = [ url(r'^so/$', set_online), url(r'^ous/$', get_online_users), ]

19.6

47

0.704082

0c49d08e42802a84e6d6315644d21f43e88ce921

5,881

py

Python

dftb+/plot_spline.py

hsulab/DailyScripts

26b03cfb721fd66f39c86df50d2ec5866e651d6e

[ "MIT" ]

2

2020-06-08T21:39:44.000Z

2020-10-18T15:12:47.000Z

dftb+/plot_spline.py

hsulab/DailyScripts

26b03cfb721fd66f39c86df50d2ec5866e651d6e

[ "MIT" ]

null

dftb+/plot_spline.py

hsulab/DailyScripts

26b03cfb721fd66f39c86df50d2ec5866e651d6e

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import os import argparse import numpy as np import matplotlib as mpl mpl.use('Agg') #silent mode from matplotlib import pyplot as plt from mpl_toolkits.axes_grid1.inset_locator import inset_axes, InsetPosition, zoomed_inset_axes, mark_inset MAXLINE = 10000 def plot_spline(skf='Pt-Pt.skf', skf2=None, rmin=1.0, pic='spl.png'): """Plot the Spline Repulsive Potential...""" # read spline, turn into spline object SP_rep1 = read_spline(skf) # generate data rs = np.linspace(0.,SP_rep1.cutoff-0.01,1000) reps = [] for r in rs: reps.append(SP_rep1.calc_rep(r)) skf_name = os.path.basename(skf).split('.')[0] rs = np.array(rs) reps = np.array(reps) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12,8)) ax.set_title(r'$%s$ Spline Repulsive Potential' %skf_name, \ fontsize=24, fontweight='bold') ax.set_xlabel(r'$r$ / Bohr', fontsize=20) ax.set_ylabel(r'$V_{rep}(r)$ / Hartree', fontsize=20) skf1_curve, = ax.plot(rs, reps, \ color='g', linestyle='-', linewidth=2., \ label='Skf-1') # inset figure ax2 = plt.axes([0,0,1,1]) ip = InsetPosition(ax, [0.4,0.2,0.5,0.5]) ax2.set_axes_locator(ip) mark_inset(ax, ax2, loc1=1, loc2=3, fc="none", ec='0.5') #ax2 = zoomed_inset_axes(ax, 1, loc=4) r_min, r_max = rmin, SP_rep1.cutoff indices = np.where((rs>r_min) & (rs<r_max)) ax2.plot(rs[indices], reps[indices], color='g', linestyle='-', linewidth=2.) # skf2 for comparision if skf2: SP_rep2 = read_spline(skf2) # generate data rs = np.linspace(0.,SP_rep2.cutoff-0.01,1000) reps = [] for r in rs: reps.append(SP_rep2.calc_rep(r)) rs = np.array(rs) reps = np.array(reps) skf2_curve, = ax.plot(rs, reps, \ color='orange', linestyle='--', linewidth=2., \ label='Skf-2') ax2.plot(rs[indices], reps[indices], color='orange', linestyle='--', linewidth=2.) plt.legend(handles=[skf1_curve,skf2_curve]) else: plt.legend(handles=[skf1_curve,]) plt.savefig(pic) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-f', '--skf', required=True,\ help='Slater-Koster File') parser.add_argument('-f2', '--skf2', default=None, \ help='Second Slater-Koster File for Comparision') parser.add_argument('-p', '--pic', \ default='spl.png', help='Spline Repulsive Potential Figure') parser.add_argument('-rmin', '--radius_min', type=float,\ default=1.0, help='Minimum Radius for Zoom') args = parser.parse_args() plot_spline(args.skf, args.skf2, args.radius_min, args.pic) #plot_spline()

29.70202

106

0.530012

0c4b5ba22b3ba7761012b4918404bffd6258a269

370

py

Python

network_monitor/__init__.py

brennanhfredericks/network-monitor-client

618d222bb015662c3958f0100a965f3c71b29d32

[ "MIT" ]

null

network_monitor/__init__.py

brennanhfredericks/network-monitor-client

618d222bb015662c3958f0100a965f3c71b29d32

[ "MIT" ]

null

network_monitor/__init__.py

brennanhfredericks/network-monitor-client

618d222bb015662c3958f0100a965f3c71b29d32

[ "MIT" ]

null

import argparse import netifaces import sys import signal import os import asyncio from asyncio import CancelledError, Task from typing import Optional, List, Any from .services import ( Service_Manager, Packet_Parser, Packet_Submitter, Packet_Filter, ) from .configurations import generate_configuration_template, DevConfig, load_config_from_file

16.818182

93

0.802703

0c4c75e50a5aeb0f4d0c50388de64676ac264483

1,516

py

Python

investing_com/cs_pattern_list.py

filipecn/maldives

f20f17d817fc3dcad7f9674753744716d1d4c821

[ "MIT" ]

1

2021-09-17T18:04:33.000Z

investing_com/cs_pattern_list.py

filipecn/maldives

f20f17d817fc3dcad7f9674753744716d1d4c821

[ "MIT" ]

null

investing_com/cs_pattern_list.py

filipecn/maldives

f20f17d817fc3dcad7f9674753744716d1d4c821

[ "MIT" ]

3

2021-09-17T18:04:43.000Z

2022-03-18T20:04:07.000Z

#!/usr/bin/py import pandas as pd import os # Holds investing.com candlestick patterns

29.72549

73

0.513852

0c4cdf64475499e51798185a532224a138493103

1,113

py

Python

simpleTest04Client_.py

LaplaceKorea/APIClient

e772482c3d9cbedee98f46a3529dca5acc254f3c

[ "MIT" ]

null

simpleTest04Client_.py

LaplaceKorea/APIClient

e772482c3d9cbedee98f46a3529dca5acc254f3c

[ "MIT" ]

null

simpleTest04Client_.py

LaplaceKorea/APIClient

e772482c3d9cbedee98f46a3529dca5acc254f3c

[ "MIT" ]

null

from LaplaceWSAPIClient import * from MarkowitzSerde import * from TargetSerde import * from Operators import * from TargetOperators import * from RLStructure import * from ClientConfig import client_config query = RLQuery("default", datetime(2021,1,1), datetime(2021,1,21), { "BankAccount": 100000.0, "MMM":1.0, "AA":1.0, "AXP":1.0, "BA":1.0, "BAC":1.0, "C":1.0, "CAT":1.0, "CVX":1.0, "DD":1.0, "DIS":1.0, "GE":1.0, "GM":1.0, "HD":1.0, "HPQ":1.0, "IBM":1.0, "JNJ":1.0, "JPM":1.0, "KO":1.0, "MCD":1.0, "MRK":1.0, "PFE":1.0, "PG":1.0, "T":1.0, "UTX":1.0, "VZ":1.0, "WMT":1.0, "XOM":1.0 }, UserTokenSerde(client_config["user"],client_config["token"])) performQueryRLQuery(client_config["wss"], query, lambda x: print("yahoo: ", x.Steps[0][0], x.Steps[0][1], x.Steps[0][203]))

27.146341

123

0.442947

0c4fdea50a153837205a14c5c61c7d560b9d7a43

14,406

py

Python

vdisk.py

cookpan001/vdisk

1414e5c20eba3722ce99818fe48ddf0217fb25ca

[ "BSD-3-Clause" ]

1

2016-01-11T06:46:11.000Z

vdisk.py

cookpan001/vdisk

1414e5c20eba3722ce99818fe48ddf0217fb25ca

[ "BSD-3-Clause" ]

null

vdisk.py

cookpan001/vdisk

1414e5c20eba3722ce99818fe48ddf0217fb25ca

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # encoding: utf-8 # author: cookpan001 import sys import logging import time import mimetypes import urllib import urllib2 """ oauth2 client """ """ All the responses will be a Response object """ """ The vdisk(weipan) client. """

36.470886

115

0.521102

0c50ef47cd53ea48685602b6b3d98c7fea184c96

263

py

Python

setup.py

thevoxium/netspeed

9e16a49d64da90a173ef9eaf491d4245c1023105

[ "MIT" ]

null

setup.py

thevoxium/netspeed

9e16a49d64da90a173ef9eaf491d4245c1023105

[ "MIT" ]

null

setup.py

thevoxium/netspeed

9e16a49d64da90a173ef9eaf491d4245c1023105

[ "MIT" ]

null

from setuptools import setup setup( name='netspeed', version='0.1', py_modules=['netspeed'], install_requires=[ 'Click', 'pyspeedtest' ], entry_points=''' [console_scripts] netspeed=netspeed:cli ''', )

16.4375

29

0.558935

0c51490cf6a9e00d3f171f44d583a875d050c2af

244

py

Python

store/admin.py

salemzii/ChopFast

95ea88387ecfdb56bd643970b69425b1a1c6f388

[ "MIT" ]

null

store/admin.py

salemzii/ChopFast

95ea88387ecfdb56bd643970b69425b1a1c6f388

[ "MIT" ]

null

store/admin.py

salemzii/ChopFast

95ea88387ecfdb56bd643970b69425b1a1c6f388

[ "MIT" ]

null

from django.contrib import admin from .models import (Dish, Payments, Order, Delivery, OrderItem) admin.site.register(Dish) admin.site.register(Payments) admin.site.register(Order) admin.site.register(Delivery) admin.site.register(OrderItem)

24.4

64

0.807377

0c517d2c976cb6c4a933b0a237cbe0bcc83aaacb

31,109

py

Python

hpedockerplugin/request_context.py

renovate-bot/python-hpedockerplugin

b7fa6b3193fa6dd42574585b4c621ff6a16babc9

[ "Apache-2.0" ]

49

2016-06-14T22:25:40.000Z

2021-04-05T05:00:59.000Z

hpedockerplugin/request_context.py

imran-ansari/python-hpedockerplugin

e2726f48ac793dc894100e3772c40ce89bfe9bb8

[ "Apache-2.0" ]

550

2016-07-25T12:01:12.000Z

2021-11-15T17:52:40.000Z

hpedockerplugin/request_context.py

imran-ansari/python-hpedockerplugin

e2726f48ac793dc894100e3772c40ce89bfe9bb8

[ "Apache-2.0" ]

96

2016-06-01T22:07:03.000Z

2021-06-22T09:05:05.000Z

import abc import json import re from collections import OrderedDict from oslo_log import log as logging import hpedockerplugin.exception as exception from hpedockerplugin.hpe import share LOG = logging.getLogger(__name__) # To be implemented by derived class def _default_req_ctxt_creator(self, contents): pass def _check_is_valid_acl_string(self, fsMode): fsMode_list = fsMode.split(',') if len(fsMode_list) != 3: msg = "Passed acl string is not valid. "\ "Pass correct acl string." LOG.error(msg) raise exception.InvalidInput(reason=msg) for value in fsMode_list: self._check_valid_fsMode_string(value) return True # TODO: This is work in progress - can be taken up later if agreed upon # class VolumeRequestContextBuilder(RequestContextBuilder): # def __init__(self, backend_configs): # super(VolumeRequestContextBuilder, self).__init__(backend_configs) # # def _get_build_req_ctxt_map(self): # build_req_ctxt_map = OrderedDict() # build_req_ctxt_map['virtualCopyOf,scheduleName'] = \ # self._create_snap_schedule_req_ctxt, # build_req_ctxt_map['virtualCopyOf,scheduleFrequency'] = \ # self._create_snap_schedule_req_ctxt # build_req_ctxt_map['virtualCopyOf,snaphotPrefix'] = \ # self._create_snap_schedule_req_ctxt # build_req_ctxt_map['virtualCopyOf'] = \ # self._create_snap_req_ctxt # build_req_ctxt_map['cloneOf'] = \ # self._create_clone_req_ctxt # build_req_ctxt_map['importVol'] = \ # self._create_import_vol_req_ctxt # build_req_ctxt_map['replicationGroup'] = \ # self._create_rcg_req_ctxt # build_req_ctxt_map['help'] = self._create_help_req_ctxt # return build_req_ctxt_map # # def _default_req_ctxt_creator(self, contents): # return self._create_vol_create_req_ctxt(contents) # # @staticmethod # def _validate_mutually_exclusive_ops(contents): # mutually_exclusive_ops = ['virtualCopyOf', 'cloneOf', 'importVol', # 'replicationGroup'] # if 'Opts' in contents and contents['Opts']: # received_opts = contents.get('Opts').keys() # diff = set(mutually_exclusive_ops) - set(received_opts) # if len(diff) < len(mutually_exclusive_ops) - 1: # mutually_exclusive_ops.sort() # msg = "Operations %s are mutually exclusive and cannot be " \ # "specified together. Please check help for usage." % \ # mutually_exclusive_ops # raise exception.InvalidInput(reason=msg) # # @staticmethod # def _validate_opts(operation, contents, valid_opts, mandatory_opts=None): # if 'Opts' in contents and contents['Opts']: # received_opts = contents.get('Opts').keys() # # if mandatory_opts: # diff = set(mandatory_opts) - set(received_opts) # if diff: # # Print options in sorted manner # mandatory_opts.sort() # msg = "One or more mandatory options %s are missing " \ # "for operation %s" % (mandatory_opts, operation) # raise exception.InvalidInput(reason=msg) # # diff = set(received_opts) - set(valid_opts) # if diff: # diff = list(diff) # diff.sort() # msg = "Invalid option(s) %s specified for operation %s. " \ # "Please check help for usage." % \ # (diff, operation) # raise exception.InvalidInput(reason=msg) # # def _create_vol_create_req_ctxt(self, contents): # valid_opts = ['compression', 'size', 'provisioning', # 'flash-cache', 'qos-name', 'fsOwner', # 'fsMode', 'mountConflictDelay', 'cpg', # 'snapcpg', 'backend'] # self._validate_opts("create volume", contents, valid_opts) # return {'operation': 'create_volume', # '_vol_orchestrator': 'volume'} # # def _create_clone_req_ctxt(self, contents): # valid_opts = ['cloneOf', 'size', 'cpg', 'snapcpg', # 'mountConflictDelay'] # self._validate_opts("clone volume", contents, valid_opts) # return {'operation': 'clone_volume', # 'orchestrator': 'volume'} # # def _create_snap_req_ctxt(self, contents): # valid_opts = ['virtualCopyOf', 'retentionHours', 'expirationHours', # 'mountConflictDelay', 'size'] # self._validate_opts("create snapshot", contents, valid_opts) # return {'operation': 'create_snapshot', # '_vol_orchestrator': 'volume'} # # def _create_snap_schedule_req_ctxt(self, contents): # valid_opts = ['virtualCopyOf', 'scheduleFrequency', 'scheduleName', # 'snapshotPrefix', 'expHrs', 'retHrs', # 'mountConflictDelay', 'size'] # mandatory_opts = ['scheduleName', 'snapshotPrefix', # 'scheduleFrequency'] # self._validate_opts("create snapshot schedule", contents, # valid_opts, mandatory_opts) # return {'operation': 'create_snapshot_schedule', # 'orchestrator': 'volume'} # # def _create_import_vol_req_ctxt(self, contents): # valid_opts = ['importVol', 'backend', 'mountConflictDelay'] # self._validate_opts("import volume", contents, valid_opts) # # # Replication enabled backend cannot be used for volume import # backend = contents['Opts'].get('backend', 'DEFAULT') # if backend == '': # backend = 'DEFAULT' # # try: # config = self._backend_configs[backend] # except KeyError: # backend_names = list(self._backend_configs.keys()) # backend_names.sort() # msg = "ERROR: Backend '%s' doesn't exist. Available " \ # "backends are %s. Please use " \ # "a valid backend name and retry." % \ # (backend, backend_names) # raise exception.InvalidInput(reason=msg) # # if config.replication_device: # msg = "ERROR: Import volume not allowed with replication " \ # "enabled backend '%s'" % backend # raise exception.InvalidInput(reason=msg) # # volname = contents['Name'] # existing_ref = str(contents['Opts']['importVol']) # manage_opts = contents['Opts'] # return {'orchestrator': 'volume', # 'operation': 'import_volume', # 'args': (volname, # existing_ref, # backend, # manage_opts)} # # def _create_rcg_req_ctxt(self, contents): # valid_opts = ['replicationGroup', 'size', 'provisioning', # 'backend', 'mountConflictDelay', 'compression'] # self._validate_opts('create replicated volume', contents, valid_opts) # # # It is possible that the user configured replication in hpe.conf # # but didn't specify any options. In that case too, this operation # # must fail asking for "replicationGroup" parameter # # Hence this validation must be done whether "Opts" is there or not # options = contents['Opts'] # backend = self._get_str_option(options, 'backend', 'DEFAULT') # create_vol_args = self._get_create_volume_args(options) # rcg_name = create_vol_args['replicationGroup'] # try: # self._validate_rcg_params(rcg_name, backend) # except exception.InvalidInput as ex: # return json.dumps({u"Err": ex.msg}) # # return {'operation': 'create_volume', # 'orchestrator': 'volume', # 'args': create_vol_args} # # def _get_fs_owner(self, options): # val = self._get_str_option(options, 'fsOwner', None) # if val: # fs_owner = val.split(':') # if len(fs_owner) != 2: # msg = "Invalid value '%s' specified for fsOwner. Please " \ # "specify a correct value." % val # raise exception.InvalidInput(msg) # return fs_owner # return None # # def _get_fs_mode(self, options): # fs_mode_str = self._get_str_option(options, 'fsMode', None) # if fs_mode_str: # try: # int(fs_mode_str) # except ValueError as ex: # msg = "Invalid value '%s' specified for fsMode. Please " \ # "specify an integer value." % fs_mode_str # raise exception.InvalidInput(msg) # # if fs_mode_str[0] != '0': # msg = "Invalid value '%s' specified for fsMode. Please " \ # "specify an octal value." % fs_mode_str # raise exception.InvalidInput(msg) # # for mode in fs_mode_str: # if int(mode) > 7: # msg = "Invalid value '%s' specified for fsMode. Please"\ # " specify an octal value." % fs_mode_str # raise exception.InvalidInput(msg) # return fs_mode_str # # def _get_create_volume_args(self, options): # ret_args = dict() # ret_args['size'] = self._get_int_option( # options, 'size', volume.DEFAULT_SIZE) # ret_args['provisioning'] = self._get_str_option( # options, 'provisioning', volume.DEFAULT_PROV, # ['full', 'thin', 'dedup']) # ret_args['flash-cache'] = self._get_str_option( # options, 'flash-cache', volume.DEFAULT_FLASH_CACHE, # ['true', 'false']) # ret_args['qos-name'] = self._get_str_option( # options, 'qos-name', volume.DEFAULT_QOS) # ret_args['compression'] = self._get_str_option( # options, 'compression', volume.DEFAULT_COMPRESSION_VAL, # ['true', 'false']) # ret_args['fsOwner'] = self._get_fs_owner(options) # ret_args['fsMode'] = self._get_fs_mode(options) # ret_args['mountConflictDelay'] = self._get_int_option( # options, 'mountConflictDelay', # volume.DEFAULT_MOUNT_CONFLICT_DELAY) # ret_args['cpg'] = self._get_str_option(options, 'cpg', None) # ret_args['snapcpg'] = self._get_str_option(options, 'snapcpg', None) # ret_args['replicationGroup'] = self._get_str_option( # options, 'replicationGroup', None) # # return ret_args # # def _validate_rcg_params(self, rcg_name, backend_name): # LOG.info("Validating RCG: %s, backend name: %s..." % (rcg_name, # backend_name)) # hpepluginconfig = self._backend_configs[backend_name] # replication_device = hpepluginconfig.replication_device # # LOG.info("Replication device: %s" % six.text_type( # replication_device)) # # if rcg_name and not replication_device: # msg = "Request to create replicated volume cannot be fulfilled"\ # "without defining 'replication_device' entry defined in"\ # "hpe.conf for the backend '%s'. Please add it and execute"\ # "the request again." % backend_name # raise exception.InvalidInput(reason=msg) # # if replication_device and not rcg_name: # backend_names = list(self._backend_configs.keys()) # backend_names.sort() # # msg = "'%s' is a replication enabled backend. " \ # "Request to create replicated volume cannot be fulfilled "\ # "without specifying 'replicationGroup' option in the "\ # "request. Please either specify 'replicationGroup' or use"\ # "a normal backend and execute the request again. List of"\ # "backends defined in hpe.conf: %s" % (backend_name, # backend_names) # raise exception.InvalidInput(reason=msg) # # if rcg_name and replication_device: # # def _check_valid_replication_mode(mode): # valid_modes = ['synchronous', 'asynchronous', 'streaming'] # if mode.lower() not in valid_modes: # msg = "Unknown replication mode '%s' specified. Valid "\ # "values are 'synchronous | asynchronous | " \ # "streaming'" % mode # raise exception.InvalidInput(reason=msg) # # rep_mode = replication_device['replication_mode'].lower() # _check_valid_replication_mode(rep_mode) # if replication_device.get('quorum_witness_ip'): # if rep_mode.lower() != 'synchronous': # msg = "For Peer Persistence, replication mode must be "\ # "synchronous" # raise exception.InvalidInput(reason=msg) # # sync_period = replication_device.get('sync_period') # if sync_period and rep_mode == 'synchronous': # msg = "'sync_period' can be defined only for 'asynchronous'"\ # " and 'streaming' replicate modes" # raise exception.InvalidInput(reason=msg) # # if (rep_mode == 'asynchronous' or rep_mode == 'streaming')\ # and sync_period: # try: # sync_period = int(sync_period) # except ValueError as ex: # msg = "Non-integer value '%s' not allowed for " \ # "'sync_period'. %s" % ( # replication_device.sync_period, ex) # raise exception.InvalidInput(reason=msg) # else: # SYNC_PERIOD_LOW = 300 # SYNC_PERIOD_HIGH = 31622400 # if sync_period < SYNC_PERIOD_LOW or \ # sync_period > SYNC_PERIOD_HIGH: # msg = "'sync_period' must be between 300 and " \ # "31622400 seconds." # raise exception.InvalidInput(reason=msg) # # @staticmethod # def _validate_name(vol_name): # is_valid_name = re.match("^[A-Za-z0-9]+[A-Za-z0-9_-]+$", vol_name) # if not is_valid_name: # msg = 'Invalid volume name: %s is passed.' % vol_name # raise exception.InvalidInput(reason=msg)

43.387727

79

0.567746

0c51d4fd680a6be2f21491d3d55f99e1a13769ea

32,369

py

Python

scripts/train_image_.py

shafieelab/SPyDERMAN

1b3fe1d0fcb33dcaed85fb110c88575ffa6fb7b6

[ "MIT" ]

1

2021-01-26T18:07:56.000Z

scripts/train_image_.py

Deeksha-K/SPyDERMAN

8cb4a3efc2b8706133f81e7bf878439110402434

[ "MIT" ]

null

scripts/train_image_.py

Deeksha-K/SPyDERMAN

8cb4a3efc2b8706133f81e7bf878439110402434

[ "MIT" ]

3

2021-01-26T18:07:39.000Z

2021-04-07T22:07:01.000Z

import argparse import csv import os import os.path as osp import statistics import tqdm import time from datetime import datetime import copy import numpy as np import torch import torch.nn as nn import torch.optim as optim import helper_utils.network as network import helper_utils.loss as loss import helper_utils.pre_process as prep from sklearn.metrics import confusion_matrix from torch.utils.data import DataLoader import helper_utils.lr_schedule as lr_schedule from helper_utils.data_list_m import ImageList from helper_utils.logger import Logger from helper_utils.sampler import ImbalancedDatasetSampler if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--gpu_id', type=str, nargs='?', default='0', help="device id to run") parser.add_argument('--dset', type=str, default='COVID19', help="The dataset or source dataset used") parser.add_argument('--trail', type=str, default='mb', help="The dataset or source dataset used") parser.add_argument('--lr', type=float, default=0.005) args = parser.parse_args() seed = 0 now = datetime.now() dt_string = now.strftime("%d/%m/%Y %H:%M:%S") dt_string = dt_string.replace("/", "_").replace(" ", "_").replace(":", "_").replace(".", "_") dataset = args.dset valid_or_test = "" # "valid or "test" or "" if whole dataset test_on_source_ed3 = False is_training = True if valid_or_test == "test": is_training = False testing = "testing" # testing = "train" if testing == "testing": is_training = False print(dataset) os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id log_output_dir_root = '../logs/' + dataset + '/' results_output_dir_root = '../experimental results/' + dataset + '/' models_output_dir_root = '../models/' + dataset + '/' trial_number = args.trail + "_" + dataset + "_" + testing + "_" + dt_string if dataset == 'HCV': source_path = "../Data/HCV/txt_80_20_tile/HCV_train_80.txt" valid_source_path = "../Data/HCV/txt_80_20_tile/HCV_val_20.txt" target_path = "../Data/HCV/HCV_target_tile.txt" no_of_classes = 2 elif dataset == 'HIV': source_path = "../Data/HIV/txt_80_20_tile/HIV_train_80.txt" valid_source_path = "../Data/HIV/txt_80_20_tile/HIV_val_20.txt" target_path = "../Data/HIV/HIV_target_tile.txt" no_of_classes = 2 elif dataset == 'ZIKA': source_path = "../Data/ZIKA/txt_90_10_tile/ZIKA_train_90.txt" valid_source_path = "../Data/ZIKA/txt_90_10_tile/ZIKA_val_10.txt" target_path = "../Data/ZIKA/ZIKA_target_tile.txt" no_of_classes = 2 elif dataset == 'HBV': source_path = "../Data/HBV/txt_80_20_tile/HBV_train_80.txt" valid_source_path = "../Data/HBV/txt_80_20_tile/HBV_val_20.txt" target_path = "../Data/HBV/HBV_target_tile.txt" no_of_classes = 2 elif dataset == 'COVID19': source_path = "../Data/COVID19/txt_80_20_tile/COVID19_train_80.txt" valid_source_path = "../Data/COVID19/txt_80_20_tile/COVID19_val_20.txt" target_path = "../Data/COVID19/COVID19_target_tile.txt" no_of_classes = 2 elif dataset == 'CAS12': source_path = "../Data/CAS12/txt_80_20_tile/CAS12_train_80.txt" valid_source_path = "../Data/CAS12/txt_80_20_tile/CAS12_val_20.txt" target_path = "../Data/CAS12/CAS12_target_tile.txt" no_of_classes = 2 else: no_of_classes = None net = 'Xception' # net = 'ResNet50' dset = dataset lr_ = args.lr gamma = 0.001 power = 0.75 # power = 0.9 momentum = 0.9 weight_decay = 0.0005 nesterov = True optimizer = optim.Adam config = {} config['method'] = 'CDAN+E' config["gpu"] = '0' config["num_iterations"] = 10000 config["test_interval"] = 50 config["snapshot_interval"] = 5000 batch_size = 8 batch_size_test = 128 use_bottleneck = False bottleneck_dim = 256 adv_lay_random = False random_dim = 1024 new_cls = True if not is_training: valid_source_path = "../Data/Test/mb1/mb_test.txt" target_path = "../Data/Test/mb1/mb_test.txt" model_path_for_testing = "../Final Models/CDAN + GAN/COVID19/model_1600.pth.tar" config["num_iterations"] = 0 best_itr = "testing" print("Testing:") config["best_itr"] = "testing" print("num_iterations", config["num_iterations"]) header_list = ["trail no ", 'metric name', 'source_val_accuracy', 'source_val_loss', 'best_cm', # 'val_accuracy_target', 'val_loss_target', 'best_cm_target', "best_classifier_loss", "best_transfer_loss", "best_total_loss" , "best_itr"] + \ ["lr", "gamma", "power", "momentum", "weight_decay", "nesterov", "optimizer", "batch_size", "batch_size_test", "use_bottleneck", "bottleneck_dim", "adv_lay_random", "random_dim", "no_of_classes", "new_cls", "dset", "net", "source_path", "target_path", "output_path", "model_path" , "logs_path", "gpu", "test_interval", "seed"] log_output_path = log_output_dir_root + net + '/' + 'trial-' + trial_number + '/' trial_results_path = net + '/trial-' + trial_number + '/' config["output_path"] = results_output_dir_root + trial_results_path config["model_path"] = models_output_dir_root + trial_results_path config["logs_path"] = log_output_path if not os.path.exists(config["logs_path"]): os.makedirs(config["logs_path"]) if is_training: if not os.path.exists(config["model_path"]): os.makedirs(config["model_path"]) # if not os.path.exists(config["output_path"]): # os.makedirs(config["output_path"]) if not os.path.isfile(osp.join(log_output_dir_root, "log.csv")): with open(osp.join(log_output_dir_root, "log.csv"), mode='w') as param_log_file: param_log_writer = csv.writer(param_log_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) param_log_writer.writerow(header_list) config["out_file"] = open(osp.join(config["logs_path"], "log.txt"), "w") config["trial_parameters_log"] = csv.writer(open(osp.join(log_output_dir_root, "log.csv"), "a")) config["prep"] = {"test_10crop": False, 'params': {"resize_size": 224, "crop_size": 224, 'alexnet': False}} config["loss"] = {"trade_off": 1.0} if "Xception" in net: config["network"] = \ {"name": network.XceptionFc, "params": {"use_bottleneck": use_bottleneck, "bottleneck_dim": bottleneck_dim, "new_cls": new_cls}} elif "ResNet50" in net: config["network"] = {"name": network.ResNetFc, "params": {"resnet_name": net, "use_bottleneck": use_bottleneck, "bottleneck_dim": bottleneck_dim, "new_cls": new_cls}} config["loss"]["random"] = adv_lay_random config["loss"]["random_dim"] = random_dim if optimizer == optim.SGD: config["optimizer"] = {"type": optim.SGD, "optim_params": {'lr': lr_, "momentum": momentum, "weight_decay": weight_decay, "nesterov": nesterov}, "lr_type": "inv", "lr_param": {"lr": lr_, "gamma": gamma, "power": power}} elif optimizer == optim.Adam: config["optimizer"] = {"type": optim.Adam, "optim_params": {'lr': lr_, "weight_decay": weight_decay}, "lr_type": "inv", "lr_param": {"lr": lr_, "gamma": gamma, "power": power}} config["dataset"] = dset config["data"] = {"source": {"list_path": source_path, "batch_size": batch_size}, "target": {"list_path": target_path, "batch_size": batch_size}, "test": {"list_path": target_path, "batch_size": batch_size_test}, "valid_source": {"list_path": valid_source_path, "batch_size": batch_size}} config["optimizer"]["lr_param"]["lr"] = lr_ config["network"]["params"]["class_num"] = no_of_classes config["out_file"].write(str(config)) config["out_file"].flush() training_parameters = [lr_, gamma, power, momentum, weight_decay, nesterov, optimizer, batch_size, batch_size_test, use_bottleneck, bottleneck_dim, adv_lay_random, random_dim, no_of_classes, new_cls, dset, net, source_path, target_path, config["output_path"], config["model_path"] , config["logs_path"], config["gpu"], config["test_interval"], str(seed)] print("source_path", source_path) print("target_path", target_path) print("lr_", lr_) print('GPU', os.environ["CUDA_VISIBLE_DEVICES"], config["gpu"]) train(config)

42.646904

170

0.575489

0c51eb0b9b67869087426ffee62488bbc0029d3f

1,230

py

Python

src/freshchat/client/configuration.py

twyla-ai/python-freshchat

5bb0ea730f82b63292688be61315b6b880896e1f

[ "MIT" ]

4

2019-10-15T11:03:28.000Z

2021-08-19T01:14:12.000Z

src/freshchat/client/configuration.py

twyla-ai/python-freshchat

5bb0ea730f82b63292688be61315b6b880896e1f

[ "MIT" ]

137

2019-10-18T04:36:21.000Z

2022-03-21T04:11:18.000Z

src/freshchat/client/configuration.py

twyla-ai/python-freshchat

5bb0ea730f82b63292688be61315b6b880896e1f

[ "MIT" ]

1

2021-08-19T01:14:14.000Z

import os from dataclasses import dataclass, field from typing import AnyStr, Dict, Optional from urllib.parse import urljoin

28.604651

80

0.64065

0c521cb77fbca7152db05ece3eddd9a49ae59322

20,120

py

Python

get_headers.py

rupendrab/py_unstr_parse

3cece3fb7ca969734bf5e60fe5846a7148ce8be4

[ "MIT" ]

null

get_headers.py

rupendrab/py_unstr_parse

3cece3fb7ca969734bf5e60fe5846a7148ce8be4

[ "MIT" ]

null

get_headers.py

rupendrab/py_unstr_parse

3cece3fb7ca969734bf5e60fe5846a7148ce8be4

[ "MIT" ]

null

#!/usr/bin/env python3.5 import sys import re import os import csv import numpy as np from operator import itemgetter from time import time from multiprocessing import Pool from extract_toc import parseargs from predict_using_toc_mapper import Mapper, get_topic, read_model_file from find_topics import toc_entries, get_summary_map, read_topics import dateparser import check_309 import parse_name from get_ratings import Ratings, Ratings2 import delimiterwriter from predict_using_subtopic_mapper import SubtopicPredictor from analyze_topic import SubtopicReader from headerutil import * # NEWLINE_WITHIN_COLUMN = '\r\n' # NEWLINE_WITHIN_COLUMN = '\r\n' # CSV_LINE_TERMINATOR = '\r\n' # CSV_FIELD_DELIMITER = ',' # FD_REPLACED = None p_region = re.compile('(^|.*\s+)region(\s*[:\']\s*|\s+)(.*)?\s*$', re.IGNORECASE) p_region_with_other = re.compile('(.*)?\s{5,}(certificate\s+num[bh]e[ir]|certificate|charter\s+num[bh]er|charter|field\s+offic\s*e|url)\s*:?\s*(.*)?\s*$', re.IGNORECASE) p_blank = re.compile('^\s*$') p_from_first_uppercase_char = re.compile('^.*?([A-Z].*)$', re.MULTILINE) p_cert_direct = re.compile('(^|^.*\s+)(certificate\s+number)(\s*:\s*|\s+)(\w+).*$', re.IGNORECASE) p_region_direct = re.compile('(^|^.*\s+)(region)(\s*:\s*|\s+)(\w+).*$', re.IGNORECASE) p_patterns_str = { 'bank_name' : [ 'bank\s+name', 'institution\s+name', 'name' ], 'bank_location': [ 'location' ], 'examiner_in_charge': [ 'examiner[\s\-]*in[\s\-]*charge' ], 'exam_start_date': [ 'examination[\s\-]*start[\s\-]*date' ], 'exam_date': [ 'examination[\s\-]*date' ], 'exam_as_of_date': [ 'examination[\s\-]*as[\s\-]*of[\s\-]*date' ] } all_matched = {} for k,patterns in p_patterns_str.items(): all_matched[k] = [] p_patterns = {} for k,patterns in p_patterns_str.items(): p_patterns[k] = [re.compile('(^|.*\s+)' + p + '(\s*[:\'][\'\s\.]*|[\'\s\.]+)' + '(.*)?\s*$', re.IGNORECASE) for p in patterns] """ def best_match(pat): # print('In best match', pat) all_m = all_matched.get(pat) if (all_m): l = sorted(all_matched.get(pat), key=lambda x: (-1 * p_patterns_str[pat].index(x[0]), x[2]), reverse=True) # print('Best match sorted list', l) if (l): if (l[0][2] > 0): ## Quality more than zero return l[0] """ """ def best_match_text(pat): bm_tuple = best_match(pat) if (bm_tuple and len(bm_tuple) == 3): return bm_tuple[1] else: return "" """ if __name__ == '__main__': args = sys.argv[1:] main(args)

31.885895

174

0.655964

0c52238d0be9f0af598966fd7664c6c79e85f8cb

6,214

py

Python

dalle_pytorch/dalle_pytorch.py

tensorfork/DALLE-pytorch

0e8f5d9a7fe054c587ed91d9c9616c7a883f393b

[ "MIT" ]

1

2021-06-22T08:26:20.000Z

dalle_pytorch/dalle_pytorch.py

tensorfork/DALLE-pytorch

0e8f5d9a7fe054c587ed91d9c9616c7a883f393b

[ "MIT" ]

null

dalle_pytorch/dalle_pytorch.py

tensorfork/DALLE-pytorch

0e8f5d9a7fe054c587ed91d9c9616c7a883f393b

[ "MIT" ]

null

from math import log2 import torch from torch import nn, einsum import torch.nn.functional as F from einops import rearrange from x_transformers import Encoder, Decoder # helpers # classes # main classes

30.019324

117

0.587383

0c52795432861cbcf4e3ec45d893ec1acc331585

7,668

py

Python

aiida_phonopy/parsers/phonopy.py

giovannipizzi/aiida-phonopy

26e419c34415c68f815fa81ce2ac644aa387ae72

[ "MIT" ]

null

aiida_phonopy/parsers/phonopy.py

giovannipizzi/aiida-phonopy

26e419c34415c68f815fa81ce2ac644aa387ae72

[ "MIT" ]

null

aiida_phonopy/parsers/phonopy.py

giovannipizzi/aiida-phonopy

26e419c34415c68f815fa81ce2ac644aa387ae72

[ "MIT" ]

null

# -*- coding: utf-8 -*- from aiida.orm.data.folder import FolderData from aiida.parsers.parser import Parser from aiida.common.datastructures import calc_states from aiida.parsers.exceptions import OutputParsingError from aiida.common.exceptions import UniquenessError import numpy from aiida.orm.data.array import ArrayData from aiida.orm.data.array.bands import BandsData from aiida.orm.data.array.kpoints import KpointsData from aiida.orm.data.parameter import ParameterData from aiida.orm.data.structure import StructureData import json from aiida_phonopy.calculations.phonopy import PhonopyCalculation __copyright__ = u"Copyright (c), 2014-2015, cole Polytechnique Fdrale de Lausanne (EPFL), Switzerland, Laboratory of Theory and Simulation of Materials (THEOS). All rights reserved." __license__ = "Non-Commercial, End-User Software License Agreement, see LICENSE.txt file" __version__ = "0.4.1"

36.865385

185

0.594027

0c52883ec5869dd4ebaf9438c8845a04d78492ff

1,128

py

Python

bagua/bagua_define.py

jphgxq/bagua

3444f79b8fe9c9d2975a8994a1a613ebd14c3d33

[ "MIT" ]

1

2021-07-12T03:33:38.000Z

bagua/bagua_define.py

jphgxq/bagua

3444f79b8fe9c9d2975a8994a1a613ebd14c3d33

[ "MIT" ]

null

bagua/bagua_define.py

jphgxq/bagua

3444f79b8fe9c9d2975a8994a1a613ebd14c3d33

[ "MIT" ]

null

import enum from typing import List import sys if sys.version_info >= (3, 9): from typing import TypedDict # pytype: disable=not-supported-yet else: from typing_extensions import TypedDict # pytype: disable=not-supported-yet from pydantic import BaseModel def get_tensor_declaration_bytes(td: TensorDeclaration) -> int: dtype_unit_size = { TensorDtype.F32.value: 4, TensorDtype.F16.value: 2, TensorDtype.U8.value: 1, } return td["num_elements"] * dtype_unit_size[td["dtype"]]

22.56

80

0.656915

0c53db086eb0eb7f6a00e60d3b14eacbfe7ba92e

97

py

Python

instaphotos/apps.py

LekamCharity/insta-IG

0302440df3b2029297af54eb9c56090f82232973

[ "MIT" ]

null

instaphotos/apps.py

LekamCharity/insta-IG

0302440df3b2029297af54eb9c56090f82232973

[ "MIT" ]

null

instaphotos/apps.py

LekamCharity/insta-IG

0302440df3b2029297af54eb9c56090f82232973

[ "MIT" ]

null

from django.apps import AppConfig

16.166667

35

0.773196

0c5539475c0da1f3dfc53cbf5dc335c43077d9cf

2,835

py

Python

services/backend/expiring_links/tests/test_expiring_link_generator_serializer.py

patpio/drf_images_api

ef689bac10ce8b9d2f03d6b647fa4bbd70b02f1c

[ "Beerware" ]

1

2022-02-27T16:34:46.000Z

services/backend/expiring_links/tests/test_expiring_link_generator_serializer.py

patpio/drf_images_api

ef689bac10ce8b9d2f03d6b647fa4bbd70b02f1c

[ "Beerware" ]

null

services/backend/expiring_links/tests/test_expiring_link_generator_serializer.py

patpio/drf_images_api

ef689bac10ce8b9d2f03d6b647fa4bbd70b02f1c

[ "Beerware" ]

null

import pytest from expiring_links.serializers import ExpiringLinkGeneratorSerializer

42.954545

118

0.71358

0c553d8f4165e63fa177620f1fa3f79bb1b9cb45

91,609

py

Python

com/vmware/nsx/trust_management_client.py

adammillerio/vsphere-automation-sdk-python

c07e1be98615201139b26c28db3aa584c4254b66

[ "MIT" ]

null

com/vmware/nsx/trust_management_client.py

adammillerio/vsphere-automation-sdk-python

c07e1be98615201139b26c28db3aa584c4254b66

[ "MIT" ]

null

com/vmware/nsx/trust_management_client.py

adammillerio/vsphere-automation-sdk-python

c07e1be98615201139b26c28db3aa584c4254b66

[ "MIT" ]

null

# -*- coding: utf-8 -*- #--------------------------------------------------------------------------- # Copyright 2020 VMware, Inc. All rights reserved. # AUTO GENERATED FILE -- DO NOT MODIFY! # # vAPI stub file for package com.vmware.nsx.trust_management. #--------------------------------------------------------------------------- """ """ __author__ = 'VMware, Inc.' __docformat__ = 'restructuredtext en' import sys from vmware.vapi.bindings import type from vmware.vapi.bindings.converter import TypeConverter from vmware.vapi.bindings.enum import Enum from vmware.vapi.bindings.error import VapiError from vmware.vapi.bindings.struct import VapiStruct from vmware.vapi.bindings.stub import ( ApiInterfaceStub, StubFactoryBase, VapiInterface) from vmware.vapi.bindings.common import raise_core_exception from vmware.vapi.data.validator import (UnionValidator, HasFieldsOfValidator) from vmware.vapi.exception import CoreException from vmware.vapi.lib.constants import TaskType from vmware.vapi.lib.rest import OperationRestMetadata

44.40572

156

0.596492

0c5549700625606ae1bd959bf730c22c941eb303

4,255

py

Python

bottleneck/tests/list_input_test.py

stroxler/bottleneck

6e91bcb8a21170588ee9a3f2c425a4e307ae05de

[ "BSD-2-Clause" ]

2

2015-05-26T09:06:32.000Z

2015-05-26T09:06:46.000Z

bottleneck/tests/list_input_test.py

stroxler/bottleneck

6e91bcb8a21170588ee9a3f2c425a4e307ae05de

[ "BSD-2-Clause" ]

null

bottleneck/tests/list_input_test.py

stroxler/bottleneck

6e91bcb8a21170588ee9a3f2c425a4e307ae05de

[ "BSD-2-Clause" ]

null

"Test list input." # For support of python 2.5 from __future__ import with_statement import numpy as np from numpy.testing import assert_equal, assert_array_almost_equal import bottleneck as bn # --------------------------------------------------------------------------- # Check that functions can handle list input def unit_maker(func, func0, args=tuple()): "Test that bn.xxx gives the same output as bn.slow.xxx for list input." msg = '\nfunc %s | input %s | shape %s\n' msg += '\nInput array:\n%s\n' for i, arr in enumerate(lists()): argsi = tuple([list(arr)] + list(args)) actual = func(*argsi) desired = func0(*argsi) tup = (func.__name__, 'a'+str(i), str(np.array(arr).shape), arr) err_msg = msg % tup assert_array_almost_equal(actual, desired, err_msg=err_msg) def test_nn(): "Test nn." a = [[1, 2], [3, 4]] a0 = [1, 2] assert_equal(bn.nn(a, a0), bn.slow.nn(a, a0))

22.632979

77

0.636193

0c587de94c3ee270415110f012b7d77cb256c5a4

1,475

py

Python

hanzo/warcindex.py

ukwa/warctools

f74061382d6bc37b6eec889a3aec26c5748d90d3

[ "MIT" ]

1

2020-09-03T00:51:50.000Z

hanzo/warcindex.py

martinsbalodis/warc-tools

d9d5e708e00bd0f6d9d0c2d95cbc9332f51b05e4

[ "MIT" ]

null

hanzo/warcindex.py

martinsbalodis/warc-tools

d9d5e708e00bd0f6d9d0c2d95cbc9332f51b05e4

[ "MIT" ]

1

2021-04-12T01:45:14.000Z

#!/usr/bin/env python """warcindex - dump warc index""" import os import sys import sys import os.path from optparse import OptionParser from .warctools import WarcRecord, expand_files parser = OptionParser(usage="%prog [options] warc warc warc") parser.add_option("-l", "--limit", dest="limit") parser.add_option("-O", "--output-format", dest="output_format", help="output format (ignored)") parser.add_option("-o", "--output", dest="output_format", help="output file (ignored)") parser.add_option("-L", "--log-level", dest="log_level") parser.set_defaults(output=None, limit=None, log_level="info") if __name__ == '__main__': run()

23.412698

114

0.633898

0c599f149ff2c8a006a46a9e33e3ef181a3cc037

1,469

py

Python

tsdata/migrations/0001_initial.py

OpenDataPolicingNC/Traffic-Stops

74e0d16ad2ac32addca6f04d34c2ddf36d023990

[ "MIT" ]

25

2015-09-12T23:10:52.000Z

2021-03-24T08:39:46.000Z

tsdata/migrations/0001_initial.py

OpenDataPolicingNC/Traffic-Stops

74e0d16ad2ac32addca6f04d34c2ddf36d023990

[ "MIT" ]

159

2015-07-01T03:57:23.000Z

2021-04-17T21:09:19.000Z

tsdata/migrations/0001_initial.py

copelco/NC-Traffic-Stops

74e0d16ad2ac32addca6f04d34c2ddf36d023990

[ "MIT" ]

8

2015-10-02T16:56:40.000Z

2020-10-18T01:16:29.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models

40.805556

153

0.582709

0c5b11a856de6baa5333d1f6f60e74187acb3fcd

1,836

py

Python

api/tests/opentrons/protocol_engine/execution/test_run_control_handler.py

mrod0101/opentrons

6450edb0421f1c2484c292f8583602d8f6fd13b8

[ "Apache-2.0" ]

235

2017-10-27T20:37:27.000Z

2022-03-30T14:09:49.000Z

api/tests/opentrons/protocol_engine/execution/test_run_control_handler.py

koji/opentrons

0f339f45de238183b2c433e67f839363d5177582

[ "Apache-2.0" ]

8,425

2017-10-26T15:25:43.000Z

2022-03-31T23:54:26.000Z

api/tests/opentrons/protocol_engine/execution/test_run_control_handler.py

mrod0101/opentrons

6450edb0421f1c2484c292f8583602d8f6fd13b8

[ "Apache-2.0" ]

130

2017-11-09T21:02:37.000Z

2022-03-15T18:01:24.000Z

"""Run control side-effect handler.""" import pytest from decoy import Decoy from opentrons.protocol_engine.state import StateStore from opentrons.protocol_engine.actions import ActionDispatcher, PauseAction from opentrons.protocol_engine.execution.run_control import RunControlHandler from opentrons.protocol_engine.state import EngineConfigs async def test_pause( decoy: Decoy, state_store: StateStore, action_dispatcher: ActionDispatcher, subject: RunControlHandler, ) -> None: """It should be able to execute a pause.""" decoy.when(state_store.get_configs()).then_return(EngineConfigs(ignore_pause=False)) await subject.pause() decoy.verify( action_dispatcher.dispatch(PauseAction()), await state_store.wait_for(condition=state_store.commands.get_is_running), )

30.6

88

0.751634

0c5b7ae73a2b618a79092df65cc9600f76dbf5e0

510

py

Python

Datasets/Generator/Healthcare/mergedrug.py

undraaa/m2bench

b661b61ca04470ed1c9c50531ce760a2cd5000d9

[ "RSA-MD" ]

null

Datasets/Generator/Healthcare/mergedrug.py

undraaa/m2bench

b661b61ca04470ed1c9c50531ce760a2cd5000d9

[ "RSA-MD" ]

null

Datasets/Generator/Healthcare/mergedrug.py

undraaa/m2bench

b661b61ca04470ed1c9c50531ce760a2cd5000d9

[ "RSA-MD" ]

1

2021-11-29T10:31:36.000Z

import json import glob

26.842105

76

0.592157

0c5c225bea97b848df7068538bc1df5271634638

10,326

py

Python

tests/test_rundramatiq_command.py

BradleyKirton/django_dramatiq

93a4a9ae39aee643cc4a987b18030ad8d1fc8480

[ "Apache-2.0" ]

null

tests/test_rundramatiq_command.py

BradleyKirton/django_dramatiq

93a4a9ae39aee643cc4a987b18030ad8d1fc8480

[ "Apache-2.0" ]

null

tests/test_rundramatiq_command.py

BradleyKirton/django_dramatiq

93a4a9ae39aee643cc4a987b18030ad8d1fc8480

[ "Apache-2.0" ]

null

import os import sys from io import StringIO from unittest.mock import patch from django.core.management import call_command from django_dramatiq.management.commands import rundramatiq def test_rundramatiq_command_autodiscovers_additional_modules(settings): settings.DRAMATIQ_AUTODISCOVER_MODULES = ("services", ) assert rundramatiq.Command().discover_tasks_modules() == [ "django_dramatiq.setup", "django_dramatiq.tasks", "tests.testapp1.tasks", "tests.testapp2.tasks", "tests.testapp3.tasks.other_tasks", "tests.testapp3.tasks.tasks", "tests.testapp3.tasks.utils", "tests.testapp3.tasks.utils.not_a_task", "tests.testapp4.services", ]

35.122449

93

0.673833

0c5c924b0477b69417c6a0474627207f48573e2f

3,620

py

Python

WordRPG/data/states/new_game.py

ChristopherLBruce/WordRPG

e545cf313afc430e8191a7c813db9ee9759a6fd4

[ "Apache-2.0" ]

2

2018-12-15T15:06:35.000Z

2022-02-09T00:19:28.000Z

WordRPG/data/states/new_game.py

ChristopherLBruce/WordRPG

e545cf313afc430e8191a7c813db9ee9759a6fd4

[ "Apache-2.0" ]

null

WordRPG/data/states/new_game.py

ChristopherLBruce/WordRPG

e545cf313afc430e8191a7c813db9ee9759a6fd4

[ "Apache-2.0" ]

null

""" 'new_game' state. Includes character creation. """ from ...engine.gui.screen import const, Screen from ...engine.state_machine import State

27.424242

86

0.551381

0c5da302d0cfb597c70f8c34fe51028d86ae2e18

2,106

py

Python

guestbook.py

Tycx2ry/FKRTimeline

11e784f4a3800336abf19c42c15a06c86af970bd

[ "Apache-2.0" ]

null

guestbook.py

Tycx2ry/FKRTimeline

11e784f4a3800336abf19c42c15a06c86af970bd

[ "Apache-2.0" ]

null

guestbook.py

Tycx2ry/FKRTimeline

11e784f4a3800336abf19c42c15a06c86af970bd

[ "Apache-2.0" ]

null

#!/usr /bin/env python # -*- coding: utf-8 -*- __author__ = 'jiangge' from flask_sqlalchemy import SQLAlchemy from datetime import datetime from flask import Flask, request, render_template, redirect application = Flask(__name__) application.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///guestbook.db' application.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False db = SQLAlchemy(application) def save_data(name, comment, url, create_at): """ save data from form submitted """ db.session.add(posts(name, comment, url, create_at)) db.session.commit() def load_data(page): """ load saved data """ record_list = posts.query.paginate(page, per_page=5, error_out=True) return record_list if __name__ == '__main__': if True: db.drop_all() db.create_all() db.session.add(posts(text=application.config["FIRST_MESSAGE"])) db.session.commit() application.run('0.0.0.0', port=80, debug=True)

27.350649

73

0.632479

0c5db28673060acc0246927ee800263dd3a7f124

707

py

Python

dash_test_runner/testapp/migrations/0001_initial.py

Ilhasoft/dash

d9b900cc08d9238304a226d837a4c90dec6b46fc

[ "BSD-3-Clause" ]

null

dash_test_runner/testapp/migrations/0001_initial.py

Ilhasoft/dash

d9b900cc08d9238304a226d837a4c90dec6b46fc

[ "BSD-3-Clause" ]

null

dash_test_runner/testapp/migrations/0001_initial.py

Ilhasoft/dash

d9b900cc08d9238304a226d837a4c90dec6b46fc

[ "BSD-3-Clause" ]

1

2018-04-12T20:18:34.000Z

from django.db import migrations, models

32.136364

114

0.575672

0c5e4893a61a507b2525a971a14202b85e75581a

6,596

py

Python

tests/test_integration.py

Radico/business-rules

7dd0551e8b33234fcea0abaf04f9982eb6f3426f

[ "MIT" ]

null

tests/test_integration.py

Radico/business-rules

7dd0551e8b33234fcea0abaf04f9982eb6f3426f

[ "MIT" ]

null

tests/test_integration.py

Radico/business-rules

7dd0551e8b33234fcea0abaf04f9982eb6f3426f

[ "MIT" ]

null

from __future__ import absolute_import from business_rules.actions import rule_action, BaseActions from business_rules.engine import check_condition, run_all from business_rules.fields import FIELD_TEXT, FIELD_NUMERIC, FIELD_SELECT from business_rules.variables import BaseVariables, string_rule_variable, numeric_rule_variable, boolean_rule_variable from . import TestCase

27.256198

118

0.575045

0c5f5d9ac8242efc8ccf5bafaa6e567b8ee2cc86

5,808

py

Python

cog/cli/user_argparser.py

Demonware/cog

b206066ebfd5faae000b1a1708988db8ca592b94

[ "BSD-3-Clause" ]

2

2016-06-02T02:15:56.000Z

2016-08-16T08:37:27.000Z

cog/cli/user_argparser.py

Demonware/cog

b206066ebfd5faae000b1a1708988db8ca592b94

[ "BSD-3-Clause" ]

null

cog/cli/user_argparser.py

Demonware/cog

b206066ebfd5faae000b1a1708988db8ca592b94

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import sys import argparse arg_no = len(sys.argv) tool_parser = argparse.ArgumentParser(add_help=False) tool_subparsers = tool_parser.add_subparsers(help='commands', dest='command') # The rename command. rename_parser = tool_subparsers.add_parser('rename', help='rename an existing user account.') rename_parser.add_argument( 'name', action='store', metavar='<name>', help='account name' ) rename_parser.add_argument( '--new-name', '-n', action='store', dest='newName', metavar='<new account name>' ) # The add command. add_parser = tool_subparsers.add_parser('add', help='add new user account to the directory.') add_parser.add_argument( '--type', '-t', action='store', default='generic', dest='account_type', metavar='<type of account>' ) add_parser.add_argument( 'name', action='store', help='account name', metavar='<name>' ) group1_parser = add_parser.add_argument_group('account specific') group1_parser.add_argument( '--password', '-P', action='store', dest='userPassword', metavar='<account\'s owner password>' ) group1_parser.add_argument( '--home', action='store', dest='homeDirectory', metavar='<path to the home directory>' ) group1_parser.add_argument( '--shell', action='store', dest='loginShell', metavar='<path to the shell interpreter>' ) group1_parser = add_parser.add_argument_group('personal information') group1_parser.add_argument( '--phone-no', action='append', dest='telephoneNumber', metavar='<phone number>' ) group1_parser.add_argument( '--last-name', action='store', dest='sn', metavar='<account owner\'s last name>' ) group1_parser.add_argument( '--first-name', action='store', dest='givenName', metavar='<account owner\'s first name>' ) group1_parser.add_argument( '--organization', '-o', action='store', dest='o', metavar='<organization>' ) group1_parser.add_argument( '--email', action='append', dest='mail', metavar='<email>' ) group1_parser.add_argument( '--full-name', action='store', dest='cn', metavar='<account owner\'s full name>' ) group1_parser = add_parser.add_argument_group('uid and group management') group1_parser.add_argument( '--uid', action='store', dest='uid', metavar='<user\'s uid>' ) group1_parser.add_argument( '--add-group', action='append', dest='group', metavar='<secondary group>' ) group1_parser.add_argument( '--uid-number', action='store', dest='uidNumber', metavar='<user id number>' ) group1_parser.add_argument( '--gid', action='store', dest='gidNumber', metavar='<primary group id>' ) # The show command. show_parser = tool_subparsers.add_parser('show', help='show account data') show_parser.add_argument( 'name', action='append', nargs='*', help='account name' ) show_parser.add_argument( '--verbose', '-v', action='store_true', dest='verbose', help='be verbose about it' ) # The edit command. edit_parser = tool_subparsers.add_parser('edit', help='edit existing user data in the directory') edit_parser.add_argument( '--type', '-t', action='store', dest='account_type', metavar='<change account type>' ) edit_parser.add_argument( 'name', action='store', help='account name' ) group1_parser = edit_parser.add_argument_group('account specific') group1_parser.add_argument( '--reset-password', '-r', dest='resetPassword', action='store_true', help='<reset user\'s password>' ) group1_parser.add_argument( '--home', action='store', dest='homeDirectory', metavar='<new home directory path>' ) group1_parser.add_argument( '--shell', action='store', dest='loginShell', metavar='<new shell interpreter path>' ) group1_parser = edit_parser.add_argument_group('personal information') group1_parser.add_argument( '--first-name', action='store', dest='givenName', metavar='<new first name>' ) group1_parser.add_argument( '--del-email', action='append', dest='delMail', metavar='<remove email address>' ) group1_parser.add_argument( '--last-name', action='store', dest='sn', metavar='<new last name>' ) group1_parser.add_argument( '--add-email', action='append', dest='addMail', metavar='<add new email address>' ) group1_parser.add_argument( '--del-phone-no', action='append', dest='delTelephoneNumber', metavar='<phone number to remove>' ) group1_parser.add_argument( '--organization', '-o', action='store', dest='o', metavar='<organization>' ) group1_parser.add_argument( '--add-phone-no', action='append', dest='addTelephoneNumber', metavar='<phone number to add>' ) group1_parser.add_argument( '--full-name', action='store', dest='cn', metavar='<new full name>' ) group1_parser = edit_parser.add_argument_group('uid and group management') group1_parser.add_argument( '--del-group', action='append', dest='delgroup', metavar='<remove user from the group>' ) group1_parser.add_argument( '--group-id', action='store', dest='gidNumber', metavar='<change primary group ID>' ) group1_parser.add_argument( '--add-group', action='append', dest='addgroup', metavar='<add user to the group>' ) group1_parser.add_argument( '--uid-number', action='store', dest='uidNumber', metavar='<change user ID number>' ) group1_parser.add_argument( '--uid', action='store', dest='uid', metavar='<user\'s uid>' ) # The retire command. retire_parser = tool_subparsers.add_parser('retire', help='retire an existing account and remove all its privileges.') retire_parser.add_argument( 'name', action='store', metavar='<name>', help='account name' ) # The type command. type_parser = tool_subparsers.add_parser('type', help='manage user types') type_parser.add_argument( '--list', '-l', action='store_true', dest='list_types', help='list user types' ) # The remove command. remove_parser = tool_subparsers.add_parser('remove', help='remove an existing account.') remove_parser.add_argument( 'name', action='store', metavar='<name>', help='account name' )

35.2

118

0.722968

0c605a349671fad2588ca9a0e3c2afed9c2453f5

6,235

py

Python

custom_components/wisersmart/climate.py

tomtomfx/wiserSmartForHA

9878840b073250302e583bd2f6040a825de97803

[ "MIT" ]

1

2020-10-06T19:49:59.000Z

custom_components/wisersmart/climate.py

tomtomfx/wiserSmartForHA

9878840b073250302e583bd2f6040a825de97803

[ "MIT" ]

1

2020-10-06T20:18:32.000Z

2020-10-24T19:50:53.000Z

custom_components/wisersmart/climate.py

tomtomfx/wiserSmartForHA

9878840b073250302e583bd2f6040a825de97803

[ "MIT" ]

1

2021-04-12T16:37:40.000Z

""" Climate Platform Device for Wiser Smart https://github.com/tomtomfx/wiserSmartForHA thomas.fayoux@gmail.com """ import asyncio import logging import voluptuous as vol from functools import partial from ruamel.yaml import YAML as yaml from homeassistant.components.climate import ClimateEntity from homeassistant.core import callback from homeassistant.components.climate.const import ( SUPPORT_TARGET_TEMPERATURE, ATTR_CURRENT_TEMPERATURE, HVAC_MODE_HEAT, HVAC_MODE_OFF, ) from homeassistant.const import ( ATTR_TEMPERATURE, TEMP_CELSIUS, ) import homeassistant.helpers.config_validation as cv from homeassistant.helpers.dispatcher import async_dispatcher_connect from homeassistant.helpers.entity import Entity from .const import ( _LOGGER, DOMAIN, MANUFACTURER, ROOM, WISER_SMART_SERVICES, ) SUPPORT_FLAGS = SUPPORT_TARGET_TEMPERATURE """ Definition of WiserSmartRoom """

28.865741

94

0.645549

0c60917a4d7a8f1d00442aa352ab85caf9e37f11

4,765

py

Python

src/dataset/utils/process_df.py

Fkaneko/kaggle_g2net_gravitational_wave_detection-

8bb32cc675e6b56171da8a3754fffeda41e934bb

[ "Apache-2.0" ]

null

src/dataset/utils/process_df.py

Fkaneko/kaggle_g2net_gravitational_wave_detection-

8bb32cc675e6b56171da8a3754fffeda41e934bb

[ "Apache-2.0" ]

null

src/dataset/utils/process_df.py

Fkaneko/kaggle_g2net_gravitational_wave_detection-

8bb32cc675e6b56171da8a3754fffeda41e934bb

[ "Apache-2.0" ]

null

import os from functools import partial from multiprocessing import Pool from typing import Any, Callable, Dict, List, Optional import numpy as np import pandas as pd from tqdm import tqdm from src.dataset.utils.waveform_preprocessings import preprocess_strain def id_2_path( image_id: str, is_train: bool = True, data_dir: str = "../input/g2net-gravitational-wave-detection", ) -> str: """ modify from https://www.kaggle.com/ihelon/g2net-eda-and-modeling """ folder = "train" if is_train else "test" return "{}/{}/{}/{}/{}/{}.npy".format( data_dir, folder, image_id[0], image_id[1], image_id[2], image_id ) def get_site_metrics( df: pd.DataFrame, interp_psd: Optional[Callable] = None, psds: Optional[np.ndarray] = None, window: str = "tukey", fs: int = 2048, fband: List[int] = [10, 912], psd_cache_path_suffix: Optional[str] = None, num_workers: int = 8, ): """ Compute for each id the metrics for each site. df: the complete df modify from https://www.kaggle.com/andradaolteanu/g2net-searching-the-sky-pytorch-effnet-w-meta """ func_ = partial( get_agg_feats, interp_psd=interp_psd, psds=psds, window=window, fs=fs, fband=fband, psd_cache_path_suffix=psd_cache_path_suffix, ) if num_workers > 1: with Pool(processes=num_workers) as pool: agg_dicts = list( tqdm( pool.imap(func_, df["path"].tolist()), total=len(df), ) ) else: agg_dicts = [] for ID, path in tqdm(zip(df["id"].values, df["path"].values)): # First extract the cronological info agg_dict = func_(path=path) agg_dicts.append(agg_dict) agg_df = pd.DataFrame(agg_dicts) df = pd.merge(df, agg_df, on="id") return df

28.532934

87

0.570619

0c60c978bb3233d48fef80aac1fbd85b7650f54f

637

py

Python

sa/migrations/0051_managedobject_set_profile.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

sa/migrations/0051_managedobject_set_profile.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

sa/migrations/0051_managedobject_set_profile.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# ---------------------------------------------------------------------- # managedobject set profile # ---------------------------------------------------------------------- # Copyright (C) 2007-2019 The NOC Project # See LICENSE for details # ---------------------------------------------------------------------- # NOC modules from noc.core.migration.base import BaseMigration

37.470588

90

0.470958

0c6180591c4611e118c4ac0d8c026f5d2d7c99fa

305

py

Python

oct2py/compat.py

sdvillal/oct2py

f7aa89b909cbb5959ddedf3ab3e743898eac3d45

[ "MIT" ]

8

2015-10-16T23:28:16.000Z

2020-06-19T18:49:18.000Z

oct2py/compat.py

sdvillal/oct2py

f7aa89b909cbb5959ddedf3ab3e743898eac3d45

[ "MIT" ]

8

2015-06-25T20:57:56.000Z

2020-04-03T22:33:16.000Z

oct2py/compat.py

sdvillal/oct2py

f7aa89b909cbb5959ddedf3ab3e743898eac3d45

[ "MIT" ]

6

2015-04-21T12:23:44.000Z

2021-10-01T00:08:47.000Z

# -*- coding: utf-8 -*- import sys PY2 = sys.version[0] == '2' PY3 = sys.version[0] == '3' if PY2: unicode = unicode long = long from StringIO import StringIO import Queue as queue else: # pragma : no cover unicode = str long = int from io import StringIO import queue

16.944444

33

0.606557

0c61d0a37539223a22a77c96706aa91e5bab6637

1,563

py

Python

lambda_functions/compute/campaign/aws.py

pierrealixt/MapCampaigner

7845bda4b0f6ccb7d18905a8c77d91ba6a4f78ad

[ "BSD-3-Clause" ]

null

lambda_functions/compute/campaign/aws.py

pierrealixt/MapCampaigner

7845bda4b0f6ccb7d18905a8c77d91ba6a4f78ad

[ "BSD-3-Clause" ]

1

2018-07-24T13:57:03.000Z

lambda_functions/compute/campaign/aws.py

pierrealixt/MapCampaigner

7845bda4b0f6ccb7d18905a8c77d91ba6a4f78ad

[ "BSD-3-Clause" ]

null

import os import json import boto3

24.809524

78

0.515035