xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

8b005666f48a057038ece823912c6a7d400f17c6

79

py

Python

features_python/ImFEATbox/config/__init__.py

annikaliebgott/ImFEATbox

e7361303f11390cfe880b9db23472903f69ee3f2

[ "Apache-2.0" ]

22

2016-11-13T15:23:45.000Z

2022-03-02T06:40:51.000Z

features_python/ImFEATbox/config/__init__.py

alps1122/ImFEATbox

9bad2e47b363df8a55e97dc512cf77cbbac793f1

[ "Apache-2.0" ]

1

2017-03-20T11:49:06.000Z

2017-05-23T09:49:25.000Z

features_python/ImFEATbox/config/__init__.py

alps1122/ImFEATbox

9bad2e47b363df8a55e97dc512cf77cbbac793f1

[ "Apache-2.0" ]

16

2016-11-15T13:14:50.000Z

2022-03-05T01:51:04.000Z

# -*- coding: utf-8 -*- from ImFEATbox.config import parameters_ImFEATBox_def

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79

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null

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1

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1

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5

8b2bd04e27158131e726033edb25368e306d1e16

75

py

Python

Python/CursoEmVideo/exOlaMundo.py

araujobtc/my-progress

3583e1e12f22a4547e4b4167490e7c26914d4780

[ "MIT" ]

null

Python/CursoEmVideo/exOlaMundo.py

araujobtc/my-progress

3583e1e12f22a4547e4b4167490e7c26914d4780

[ "MIT" ]

null

Python/CursoEmVideo/exOlaMundo.py

araujobtc/my-progress

3583e1e12f22a4547e4b4167490e7c26914d4780

[ "MIT" ]

null

#Crie um programa que escreva "Olá, Mundo!" na tela. print('Olá, Mundo!')

18.75

52

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75

4.25

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53

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null

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1

0

5

8c8bf67416ff963e786149afa4de9d8049e0f760

835

py

Python

services/controller/src/plz/controller/images/local.py

neomatrix369/plz

12f05a8d071e9c1976c444d34161530ffa73eeae

[ "MIT" ]

1

2020-09-06T16:35:27.000Z

services/controller/src/plz/controller/images/local.py

neomatrix369/plz

12f05a8d071e9c1976c444d34161530ffa73eeae

[ "MIT" ]

null

services/controller/src/plz/controller/images/local.py

neomatrix369/plz

12f05a8d071e9c1976c444d34161530ffa73eeae

[ "MIT" ]

null

from typing import BinaryIO, Callable, Iterator import docker from plz.controller.images.images_base import Images class LocalImages(Images): def __init__(self, docker_api_client_creator: Callable[[], docker.APIClient], repository: str): super().__init__(docker_api_client_creator, repository) def build(self, fileobj: BinaryIO, tag: str) -> Iterator[bytes]: return self._build(fileobj, tag) def for_host(self, docker_url: str) -> 'LocalImages': def new_docker_api_client_creator(): return docker.APIClient(base_url=docker_url) return LocalImages(new_docker_api_client_creator, self.repository) def push(self, tag: str): pass def pull(self, tag: str): pass def can_pull(self, _) -> bool: return True

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0.113636

0.166667

0.159091

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835

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false

0.1

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null

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1

0

1

0

1

0

5

8cebfe6fc371b234e5079ab747c83e82d7033803

224

py

Python

agents/__init__.py

JacobChen258/AI-Markov-Probability

909696597850e746e1cd7eef06df4aee0ce67ef2

[ "MIT" ]

null

agents/__init__.py

JacobChen258/AI-Markov-Probability

909696597850e746e1cd7eef06df4aee0ce67ef2

[ "MIT" ]

null

agents/__init__.py

JacobChen258/AI-Markov-Probability

909696597850e746e1cd7eef06df4aee0ce67ef2

[ "MIT" ]

null

from .ai_agent import AIAgent from .random_agent import RandomAgent from .generic_agent import GenericAgent from .chase_agent import ChaseAgent from .markov_agent import MarkovAgent from .particle_agent import ParticleAgent

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0.107143

224

6

42

37.333333

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1

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1

0

5

506ca2cc1edc54af07224a52e7d8483b012156a5

1,128

py

Python

tests/conftest.py

floGik/RaceControl

55ecfd46e9e3bdfbfb73c373cf5578257689f5fc

[ "Apache-2.0" ]

2

2020-04-28T21:21:13.000Z

2021-04-24T18:10:54.000Z

tests/conftest.py

floGik/RaceControl

55ecfd46e9e3bdfbfb73c373cf5578257689f5fc

[ "Apache-2.0" ]

1

2021-04-25T10:32:50.000Z

2021-04-26T12:49:42.000Z

tests/conftest.py

cdbrkfxrpt/RaceControl

55ecfd46e9e3bdfbfb73c373cf5578257689f5fc

[ "Apache-2.0" ]

null

import pytest from connectedrace.globals import * from connectedrace.antenna import * from connectedrace.cannon import * from connectedrace.bucket import * from connectedrace.cable import * from connectedrace.logger import * @pytest.fixture(scope="session") def message(): return can.Message(data=[1,2,3,4,5,6,7,8]) @pytest.fixture(scope="session") def node(message): return Node('127.0.0.1', message) @pytest.fixture(scope="session") def listener(): return can.BufferedReader() @pytest.fixture(scope="session") def antenna(listener): return AntennaDaemon(listeners=[listener], node_ips=[]) @pytest.fixture(scope="session") def cannon(antenna): return Cannon(antenna) @pytest.fixture(scope="session") def bucket_handler(): return BucketHandler() @pytest.fixture(scope="session") def bucket(antenna): return Bucket(('', D_PORT), BucketHandler, antenna) @pytest.fixture(scope="session") def cable(): return CableDaemon() @pytest.fixture(scope="session") def logger(): return LoggingDaemon() @pytest.fixture(scope="session") def csv_logger(): return CSVLogger(FILEFORMAT + '.csv')

23.5

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0.218712

0.303767

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0.133657

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0.01407

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1,128

47

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false

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0.189189

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null

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null

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1

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1

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5

507133f459cd9a470d196ba64027899602eaf1c8

131

py

Python

odufrn_downloader/mixins/filters/__init__.py

Physsix27/odufrn-downloader

7ab1d9afb9f93ba620ee540d8e691c6ce3558271

[ "MIT" ]

33

2019-08-02T17:18:46.000Z

2021-02-20T03:41:15.000Z

odufrn_downloader/mixins/filters/__init__.py

Physsix27/odufrn-downloader

7ab1d9afb9f93ba620ee540d8e691c6ce3558271

[ "MIT" ]

62

2019-07-24T19:10:08.000Z

2019-11-01T18:21:21.000Z

odufrn_downloader/mixins/filters/__init__.py

Physsix27/odufrn-downloader

7ab1d9afb9f93ba620ee540d8e691c6ce3558271

[ "MIT" ]

2

2019-09-30T22:05:12.000Z

2019-10-05T19:03:39.000Z

from .LevenshteinMixin import LevenshteinMixin from .SimpleSearchMixin import SimpleSearchMixin from .YearsMixin import YearsMixin

32.75

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0.885496

12

131

9.666667

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0.091603

131

3

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43.666667

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true

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5

507302a039eb96966feb56e6f2907723f0513ef4

238

py

Python

problemsets/Codeforces/Python/A1345.py

juarezpaulino/coderemite

a4649d3f3a89d234457032d14a6646b3af339ac1

[ "Apache-2.0" ]

null

problemsets/Codeforces/Python/A1345.py

juarezpaulino/coderemite

a4649d3f3a89d234457032d14a6646b3af339ac1

[ "Apache-2.0" ]

null

problemsets/Codeforces/Python/A1345.py

juarezpaulino/coderemite

a4649d3f3a89d234457032d14a6646b3af339ac1

[ "Apache-2.0" ]

null

""" * * Author: Juarez Paulino(coderemite) * Email: juarez.paulino@gmail.com * """ for s in[*open(0)][1:]:a,b=map(int,s.split())print('YNEOS'[a+b<a*b::2]) exec(int(input())*"n,m=map(int,input().split());print('YNEOS'[n+m<n*m::2]);")

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3.204545

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0.042553

0.212766

0

0.018349

0.084034

238

8

77

29.75

0.62844

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null

0

1

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1

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5

50e683e3dbe371ed752413755c51d5c5f848a0a6

188

py

Python

service/test_common.py

bentondrew/demo_common

c0031c439c39c384cf8aa1f9452eb32c8033aee4

[ "Apache-2.0" ]

null

service/test_common.py

bentondrew/demo_common

c0031c439c39c384cf8aa1f9452eb32c8033aee4

[ "Apache-2.0" ]

null

service/test_common.py

bentondrew/demo_common

c0031c439c39c384cf8aa1f9452eb32c8033aee4

[ "Apache-2.0" ]

null

from flask import Flask app = Flask(__name__) @app.route('/') def index(): import demo_common return ('demo_common version installed: {}' .format(demo_common.__version__))

18.8

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0.691489

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5.173913

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0.252101

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0.180851

188

9

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null

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1

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5

50fcea02ae0bba785ac485b517d8b89522d53b25

37

py

Python

flappening/__init__.py

kawu/flappening

e4bd5bb94a1d2f6acca75b3eab32a4d2b30c4171

[ "MIT" ]

null

flappening/__init__.py

kawu/flappening

e4bd5bb94a1d2f6acca75b3eab32a4d2b30c4171

[ "MIT" ]

null

flappening/__init__.py

kawu/flappening

e4bd5bb94a1d2f6acca75b3eab32a4d2b30c4171

[ "MIT" ]

1

2020-07-17T09:27:42.000Z

# __init__.py from .game import Game

12.333333

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5

0feaba82a1e926214725d24559c54c6530663c6b

11,207

py

Python

display_lidar_packets.py

NizarTarabay/Extract_packets_os16

94ca8b58f035582b78c15ed105a57140a1eb82b2

[ "MIT" ]

1

2020-01-21T06:45:58.000Z

display_lidar_packets.py

NizarTarabay/Extract_packets_os16

94ca8b58f035582b78c15ed105a57140a1eb82b2

[ "MIT" ]

1

2020-02-11T15:31:55.000Z

2020-02-12T16:30:47.000Z

display_lidar_packets.py

NizarTarabay/Extract_packets_os16

94ca8b58f035582b78c15ed105a57140a1eb82b2

[ "MIT" ]

null

import numpy as np from helpers import get_signal, number_of_frames, first_last_frame_number import os import cv2 import seaborn as sns; sns.set() mode = 1024 fps = 20 signal_list = ['range', 'reflectivity', 'signal', 'ambient'] signal_name = input("Signal to display:") for idx, val in enumerate(signal_list): if signal_name == val: break # =============== Build the array of images =============== # os.chdir('/media/nizar/Transcend/test in the lab/Data/myFormat/Lidar') file_name_t = input("Time and date:") file_name = 'Lidar_myFormat_packet_' + str(file_name_t) + '.txt' img_array = get_signal(file_name, mode, signal_list[idx]) ######################################################################################################################## # # file_name = 'Lidar_myFormat_packet_' + str(file_name_t) # img_array_depth, list = number_of_frames(file_name , mode) # img_array = np.zeros((img_array_depth+1, int(mode/4)+17, 16)).astype(np.int) # this is the array the contains all the pixels acquired by the sensor # m, i, j, k, l = 0, 0, 0, 0, 0 # enc_list = [] # #find the smallest encoder number # for m in range(0, len(list)): # if m % 18 == 0: # encoder_count = ['0'] # s = 0 # for c in list[m]: # if s == 5 and (c != ' ' or c != '\n'): # s=5 for encoder count # encoder_count.append(c) # if c == ' ': # s += 1 # if s == 6: # s=6 for encoder count # break # enc = '' # enc = (int(enc.join(encoder_count))) # print(enc) # enc_list.append(enc) # # enc_min = min(enc_list) # framelist = frame_list(list)[0] # # ######################################### for: fill the array! ############################################## # for k in range(0, img_array_depth+1): # # # print(l) # for j in range(0, int(mode/4)+17): # for i in range(0, 18): # if l % 18 == 0: # encoder_count = ['0'] # s = 0 # for c in list[l]: # if s == 5 and (c != ' ' or c != '\n'): # s=3 encoder don't touch! # encoder_count.append(c) # if c == ' ': # s += 1 # if s == 6: # s=4 encoder don't touch! # break # enc = '' # enc = (int(enc.join(encoder_count))-enc_min)/(44*(2048/mode)) # real_frame = first_last_frame_number(list[l]) # else: # if (l+1)%18 == 0: # print (list[l]) # else: # signal = ['0'] # s = 0 # for c in list[l]: # if s == 2 and (c != ' ' or c != '\n'): # s=1 or 0 1 for reflectivity 0 for range # signal.append(c) # if c == ' ': # s += 1 # if s == 3: # s=1 or 2; 2 for reflectivity 1 for range # break # # print (l) # sig = '' # sig = int(sig.join(signal)) # img_array[real_frame - framelist][int(enc)][i-1] = sig # # # l += 1 # # print(l) # if l >= len(list): # break # if l >= len(list): # break # if l >= len(list): # break # # print (l) # # print (j) # # print (k) # print(enc) ######################################################################################################################## import matplotlib.pyplot as plt # ax = sns.heatmap(img_array[222][0:256], square=True, linewidth=0) # plt.show() k = number_of_frames(file_name, mode)[0] b = np.zeros((k, int(mode/4), 64)) for frame in range(0, k): for i in range(0, 16): for j in range(0, 4): b[frame][0:int(mode/4), i*4+j] = img_array[frame][0:int(mode / 4), i] # im = plt.imshow(b[1][0:int(mode/4)]) # for i in range(0, k): # # im.set_data(b[i][0:int(mode/4)]) # im = plt.imshow(np.flip(np.rot90(b[i][0:int(mode/4)], 3), 1)) # plt.axis('off') # plt.pause(0.01) # initialize water image height = 64 width = int(mode / 4) water_depth = np.zeros((height, width), dtype=float) # initialize video writer fourcc = cv2.VideoWriter_fourcc('M','J','P','G') video_filename = 'Lidar_myFormat_packet_' + str(file_name_t) + '_' + signal_list[idx] + '.avi' out = cv2.VideoWriter(video_filename, fourcc, fps, (width, height)) # new frame after each addition of water for i in range(k): #add this array to the video gray = cv2.normalize(np.flip(np.rot90(b[i], 1), 1), None, 255, 0, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U) gray_3c = cv2.merge([gray, gray, gray]) out.write(gray_3c) # close out the video writer out.release() # import numpy as np # import os # mode = 2048 # fps = 10 # # def number_of_frames(file, mode): # ''' # :arg: string; Name of the file with a specific format (like the one returned by the function "extract_data_txt_file") # :return: int; This function return the total number of frames in the txt file # ''' # # mode = 2048 # depend on the scanning mode of the lidar, it can take the following values: 512, 1024, 2048 # fn = 65536 # 2^16 max frame number (max reached) # # =============== open the file =============== # # file1 = open(file, 'r') # lineList = file1.readlines() # save all the lines in a list # file1.close() # # =============== close the file =============== # # # # =============== get the first frame number =============== # # for line in lineList: # if 'F' in line: # print(line) # i = 0 # l = [] # for c in line: # if i == 3 and c != ' ': # l.append(c) # if c == ' ': # i += 1 # if i == 4: # break # f1 = '' # f1 = int(f1.join(l)) # break # # =============== get the last frame number =============== # # for line in lineList[::-1]: # if 'F' in line: # # print(line) # i = 0 # l = [] # for c in line: # if i == 3 and c != ' ': # l.append(c) # if c == ' ': # i += 1 # if i == 4: # break # f2 = '' # f2 = int(f2.join(l)) # break # # =============== check if the max has been reached =============== # # max_frame_reach = 0 # i = 0 # for line in lineList: # if i % 18 == 0: # if 'F 65535' in line: # # print(line) # max_frame_reach += 1 # i += 1 # # # i = int(max_frame_reach/(mode/4)) # # print (i) # number_frames = int(f2 - f1 + i * fn) # # return number_frames, lineList # # # # =============== Build the array of images =============== # # os.chdir('/media/nizar/Transcend/test in the lab/Data/myFormat/Lidar') # file_name_t = input("Time and date:") # file_name = 'Lidar_myFormat_packet_' + str(file_name_t) # img_array_depth, list = number_of_frames(file_name + '.txt', mode) # img_array = np.zeros((img_array_depth, int(mode/4)+17, 16)).astype(np.int) # this is the array the contains all the pixels acquired by the sensor # m, i, j, k, l = 0 , 0 ,0 , 0, 0 # enc_list = [] # #find the smallest encoder number # for m in range(0, len(list)): # if m % 18 == 0: # encoder_count = ['0'] # s = 0 # for c in list[m]: # if s == 5 and (c != ' ' or c != '\n'): # s=5 for encoder count # encoder_count.append(c) # if c == ' ': # s += 1 # if s == 6: # s=6 for encoder count # break # enc = '' # enc = (int(enc.join(encoder_count))) # print(enc) # enc_list.append(enc) # # enc_min = min(enc_list) # ######################################### for: fill the array! ############################################## # for k in range(0, img_array_depth): # # print(l) # for j in range(0, int(mode/4)+17): # for i in range(0, 18): # if l % 18 == 0: # encoder_count = ['0'] # s = 0 # for c in list[l]: # if s == 5 and (c != ' ' or c != '\n'): # s=3 encoder don't touch! # encoder_count.append(c) # if c == ' ': # s += 1 # if s == 6: # s=4 encoder don't touch! # break # enc = '' # enc = (int(enc.join(encoder_count))-enc_min)/(44*(2048/mode)) # else: # if (l+1)%18 == 0: # print (list[l]) # else: # signal = ['0'] # s = 0 # for c in list[l]: # if s == 3 and (c != ' ' or c != '\n'): # s=1 or 0 1 for reflectivity 0 for range # signal.append(c) # if c == ' ': # s += 1 # if s == 4: # s=1 or 2; 2 for reflectivity 1 for range # break # # print (l) # sig = '' # sig = int(sig.join(signal)) # img_array[k][int(enc)][i-1] = sig # # # l += 1 # # print(l) # if l >= len(list): # break # if l >= len(list): # break # if l >= len(list): # break # # print (l) # # print (j) # # print (k) # print(enc) # import matplotlib.pyplot as plt # import seaborn as sns; sns.set() # # # ax = sns.heatmap(img_array[222][0:256], square=True, linewidth=0) # # plt.show() # # # b = np.zeros((k, int(mode/4), 64)) # for frame in range(0, k): # for i in range(0, 16): # for j in range(0, 4): # b[frame][0:int(mode/4), i*4+j] = img_array[frame][0:int(mode / 4), i] # # # # im = plt.imshow(b[1][0:int(mode/4)]) # for i in range(0, k): # # im.set_data(b[i][0:int(mode/4)]) # im = plt.imshow(np.flip(np.rot90(b[i][0:int(mode/4)], 3), 1)) # plt.axis('off') # plt.pause(0.01) # # import cv2 # # initialize water image # height = 64 # width = int(mode / 4) # water_depth = np.zeros((height, width), dtype=float) # # initialize video writer # fourcc = cv2.VideoWriter_fourcc('M','J','P','G') # video_filename = file_name + '_ambient.avi' # out = cv2.VideoWriter(video_filename, fourcc, fps, (width, height)) # # new frame after each addition of water # for i in range(k): # #add this array to the video # gray = cv2.normalize(np.flip(np.rot90(b[i], 1), 1), None, 255, 0, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U) # gray_3c = cv2.merge([gray, gray, gray]) # out.write(gray_3c) # # close out the video writer # out.release() #

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0

5

0ff45f614c83301daad5c7b49ea0ef5c7bf38047

126

py

Python

bin/diff.py

eddo888/McUnix

612babfe8f3a127e5f904dceb08d89e11923c053

[ "MIT" ]

null

bin/diff.py

eddo888/McUnix

612babfe8f3a127e5f904dceb08d89e11923c053

[ "MIT" ]

null

bin/diff.py

eddo888/McUnix

612babfe8f3a127e5f904dceb08d89e11923c053

[ "MIT" ]

null

#!/usr/bin/env python3 from McUnix.diff import argue, diff args = argue() diff(args.lhs.rstrip('/'), args.rhs.rstrip('/'))

15.75

48

0.666667

19

126

4.421053

0.684211

0.214286

0.309524

0

0.009009

0.119048

126

7

49

18

0.747748

0.166667

0

0.019417

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

ba00babf797e7f03bd763a2d4918bf0968336fc7

214

py

Python

paths.py

fin10/NeuralColorPainter

6add68cdc58dec6362596071edb6836c2c9b907a

[ "Apache-2.0" ]

null

paths.py

fin10/NeuralColorPainter

6add68cdc58dec6362596071edb6836c2c9b907a

[ "Apache-2.0" ]

null

paths.py

fin10/NeuralColorPainter

6add68cdc58dec6362596071edb6836c2c9b907a

[ "Apache-2.0" ]

1

2021-01-09T13:20:59.000Z

import os class Paths: ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__))) MODEL = os.path.join(ROOT, 'model') IMAGES = os.path.join(ROOT, 'images') OUTPUT = os.path.join(ROOT, 'out')

23.777778

67

0.649533

33

214

4.090909

0.424242

0.266667

0.296296

0.311111

0

0.172897

214

8

68

26.75

0.762712

0

0.065421

0

1

0

false

0

0.166667

0

1

0

null

1

0

1

0

null

0

1

0

5

ba156c7e9aae852604bf2f09aee33054b7030c07

198

py

Python

tests/testConverters.py

ttm/musicLegacy

106c0d55657c703a7afa42e230c645fb9a2874fe

[ "MIT" ]

2

2017-08-22T15:39:24.000Z

2019-12-23T10:48:28.000Z

tests/testConverters.py

ttm/musicLegacy

106c0d55657c703a7afa42e230c645fb9a2874fe

[ "MIT" ]

null

tests/testConverters.py

ttm/musicLegacy

106c0d55657c703a7afa42e230c645fb9a2874fe

[ "MIT" ]

null

import musicLegacy as m import importlib #from IPython.lib.deepreload import reload as dreload importlib.reload(m.converters) importlib.reload(m) #dreload(m,exclude="pytz") co=m.BasicConverter()

19.8

53

0.79798

28

198

5.642857

0.571429

0.189873

0.202532

0

0.09596

198

9

54

22

0.882682

0.388889

0

1

0

false

0

0.8

0

0.8

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

e841fe361824c4baf8aa600e7e5401ee6ce0849d

588

py

Python

webdev/users/tests/test_users_post.py

h-zanetti/jewelry-manager

74166b89f492303b8ebf5ff8af058f394eb2a28b

[ "MIT" ]

null

webdev/users/tests/test_users_post.py

h-zanetti/jewelry-manager

74166b89f492303b8ebf5ff8af058f394eb2a28b

[ "MIT" ]

103

2021-04-25T21:28:11.000Z

2022-03-15T01:36:31.000Z

webdev/users/tests/test_users_post.py

h-zanetti/jewelry-manager

74166b89f492303b8ebf5ff8af058f394eb2a28b

[ "MIT" ]

null

import pytest from django.urls import reverse from django.contrib.auth.models import User from pytest_django.asserts import assertRedirects @pytest.fixture def resposta(client, db): usr = User.objects.create_user(username='UserTest', password='minhaSenha123') resp = client.post(reverse('login'), data={'username': 'UserTest', 'password': 'minhaSenha123'}) return resp def test_user_autenticado(resposta): assert resposta.wsgi_request.user.is_authenticated == True def test_redirecionamento(resposta): assertRedirects(resposta, reverse('produtos:estoque_produtos'))

36.75

100

0.782313

70

588

6.457143

0.571429

0.044248

0.106195

0.163717

0

0.011429

0.107143

588

16

101

36.75

0.849524

0

0.149406

0.042445

0

0.230769

1

0.230769

false

0.153846

0.307692

0

0.615385

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e859cbcacca3b5175723c0305fc040239fe5e91b

158

py

Python

xmmdet/__init__.py

www516717402/edgeai-mmdetection

c5563434728da227678ba3588621b4b426cda43d

[ "BSD-3-Clause" ]

null

xmmdet/__init__.py

www516717402/edgeai-mmdetection

c5563434728da227678ba3588621b4b426cda43d

[ "BSD-3-Clause" ]

null

xmmdet/__init__.py

www516717402/edgeai-mmdetection

c5563434728da227678ba3588621b4b426cda43d

[ "BSD-3-Clause" ]

null

import mmcv from mmdet import * from .ops import * from .core import * from .datasets import * from .models import * from .utils import * from .apis import *

17.555556

23

0.727848

23

158

5

0.434783

0.521739

0

0.189873

158

8

24

19.75

0.898438

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

e876f40d3dfde66c53da91352c0fb2eed37f19fb

93

py

Python

HelloWorld.py

mvtuong/Yelp-Challenge

b9df3d4296e05bd33eeeda816191cf68a327a36d

[ "Apache-2.0", "BSD-3-Clause" ]

1

2019-09-14T07:06:13.000Z

HelloWorld.py

mvtuong/Yelp-Challenge

b9df3d4296e05bd33eeeda816191cf68a327a36d

[ "Apache-2.0", "BSD-3-Clause" ]

null

HelloWorld.py

mvtuong/Yelp-Challenge

b9df3d4296e05bd33eeeda816191cf68a327a36d

[ "Apache-2.0", "BSD-3-Clause" ]

1

2019-01-24T10:34:16.000Z

print "Hello World" a = 5 b = 8 c = a + b print("c=%d, c+1=%d" %(c, c+1)) print("hello")

15.5

32

0.483871

21

93

2.142857

0.47619

0.444444

0

0.057143

0.247312

93

6

33

15.5

0.585714

0

0.314607

0

null

0

null

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

5

e89072dc4e079913d4e3eceaa6f4fb3c616813a0

18,757

py

Python

tests/conductor.py

wooga/karajan

b0952f156d69206fdcb1d71bd42c227077da6fd2

[ "MIT" ]

null

tests/conductor.py

wooga/karajan

b0952f156d69206fdcb1d71bd42c227077da6fd2

[ "MIT" ]

null

tests/conductor.py

wooga/karajan

b0952f156d69206fdcb1d71bd42c227077da6fd2

[ "MIT" ]

2

2018-02-01T14:00:07.000Z

2022-03-26T18:09:14.000Z

# # Copyright 2017 Wooga GmbH # # Permission is hereby granted, free of charge, to any person obtaining a copy of # this software and associated documentation files (the "Software"), to deal in # the Software without restriction, including without limitation the rights to # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies # of the Software, and to permit persons to whom the Software is furnished to do # so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from datetime import datetime from unittest import TestCase from airflow.models import DagRun from mock import MagicMock from karajan.conductor import Conductor from tests.helpers import defaults from tests.helpers.config import ConfigHelper class TestConductor(TestCase): def setUp(self): self.engine = MagicMock() self.conf = ConfigHelper() self.dags = {} def build_dags(self): Conductor(self.conf).build('test_dag', engine=self.engine, output=self.dags) return self def dag_run(self, dag_id, external_trigger=False): return DagRun( dag_id=dag_id, run_id="karajan_run_%s" % datetime.now(), external_trigger=external_trigger, conf={'start_date': defaults.EXTERNAL_START_DATE, 'end_date': defaults.EXTERNAL_END_DATE} if external_trigger else None, execution_date=defaults.EXTERNAL_EXECUTION_DATE if external_trigger else datetime.now(), state='running' ) def context(self, dag_id, item=None, ds=defaults.EXECUTION_DATE, external_trigger=False): return { 'dag_run': self.dag_run(dag_id, external_trigger), 'ds': ds.strftime("%Y-%m-%d"), 'ds_nodash': ds.strftime("%Y%m%d"), 'dag': self.dags["%s_%s" % (dag_id, item) if item else dag_id] } def get_dag(self, dag_id, item=None): dag_id = "%s_%s" % (dag_id, item) if item else dag_id self.assertIn(dag_id, self.dags) return self.dags[dag_id] def get_operator(self, task_id, item=None, dag_id='test_dag'): dag = self.get_dag(dag_id, item) self.assertIn(task_id, dag.task_dict) return dag.get_task(task_id) def execute(self, task_id, item=None, dag_id='test_dag', external_trigger=False): op = self.get_operator(task_id, item) op.execute(self.context(dag_id, item, external_trigger=external_trigger)) return self def test_cleanup_operator(self): self.build_dags().execute('cleanup_test_aggregation') self.engine.cleanup.assert_called_with( defaults.TMP_TABLE_NAME, ) def test_cleanup_operator_with_parametrization(self): self.conf.parameterize_context() self.build_dags().execute('cleanup_test_aggregation', 'item') self.engine.cleanup.assert_called_with( defaults.TMP_ITEM_TABLE_NAME, ) def test_cleanup_operator_with_external_trigger(self): self.build_dags().execute('cleanup_test_aggregation', external_trigger=True) self.engine.cleanup.assert_called_with( defaults.EXTERNAL_TMP_TABLE_NAME, ) def test_aggregation_operator_without_parameterization(self): self.build_dags().execute('aggregate_test_aggregation') self.engine.aggregate.assert_called_with( defaults.TMP_TABLE_NAME, {'test_time_key', 'another_table_test_src_column', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM DUAL WHERE dt BETWEEN '2017-08-01' AND '2017-08-01'", None, ) def test_aggregation_operator_with_timeseries(self): self.conf.with_timeseries() self.build_dags().execute('aggregate_test_aggregation') self.engine.aggregate.assert_called_with( defaults.TMP_TABLE_NAME, {'test_time_key', 'another_table_test_src_column', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM DUAL WHERE dt BETWEEN '2017-08-01' AND '2017-08-01'", None, ) def test_aggregation_operator_with_other_timeseries(self): self.conf.with_timeseries(target_id='another_table') self.build_dags().execute('aggregate_another_aggregation') self.engine.aggregate.assert_called_with( 'test_dag_agg_another_aggregation_20170801', {'another_aggregation_test_src_column', 'key_column', 'another_test_time_key'}, u"SELECT everything FROM here", None, ) def test_aggregation_operator_with_parameterized_context(self): self.conf.parameterize_context() self.build_dags().execute('aggregate_test_aggregation', 'item') self.engine.aggregate.assert_called_with( defaults.TMP_ITEM_TABLE_NAME, {'another_table_test_src_column', 'item_column', 'test_time_key', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM DUAL WHERE dt BETWEEN '2017-08-01' AND '2017-08-01'", {'item_column': 'item'}, ) def test_aggregation_operator_with_parameterized_context_and_aggregation(self): self.conf.parameterize_context().parameterize_aggregation() self.build_dags().execute('aggregate_test_aggregation', 'item') self.engine.aggregate.assert_called_with( defaults.TMP_ITEM_TABLE_NAME, {'another_table_test_src_column', "'item' as item_column", 'test_time_key', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM item WHERE dt BETWEEN '2017-08-01' AND '2017-08-01'", None, ) def test_aggregation_operator_with_offset(self): self.conf.with_offset() self.build_dags().execute('aggregate_test_aggregation') self.engine.aggregate.assert_called_with( defaults.TMP_TABLE_NAME, {'test_time_key', 'another_table_test_src_column', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM DUAL WHERE dt BETWEEN '2017-07-31' AND '2017-07-31'", None, ) def test_aggregation_operator_with_reruns(self): self.conf.with_reruns() self.build_dags().execute('aggregate_test_aggregation') self.engine.aggregate.assert_called_with( defaults.TMP_TABLE_NAME, {'test_time_key', 'another_table_test_src_column', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM DUAL WHERE dt BETWEEN '2017-07-29' AND '2017-08-01'", None, ) def test_aggregation_operator_with_offset_and_reruns(self): self.conf.with_offset().with_reruns() self.build_dags().execute('aggregate_test_aggregation') self.engine.aggregate.assert_called_with( defaults.TMP_TABLE_NAME, {'test_time_key', 'another_table_test_src_column', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM DUAL WHERE dt BETWEEN '2017-07-28' AND '2017-07-31'", None, ) def test_aggregation_operator_with_external_trigger(self): self.build_dags().execute('aggregate_test_aggregation', external_trigger=True) self.engine.aggregate.assert_called_with( defaults.EXTERNAL_TMP_TABLE_NAME, {'test_time_key', 'another_table_test_src_column', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM DUAL WHERE dt BETWEEN '2016-08-01' AND '2016-09-01'", None, ) def test_aggregation_operator_with_external_trigger_reruns_and_offset(self): self.conf.with_offset().with_reruns() self.build_dags().execute('aggregate_test_aggregation', external_trigger=True) self.engine.aggregate.assert_called_with( defaults.EXTERNAL_TMP_TABLE_NAME, {'test_time_key', 'another_table_test_src_column', 'test_src_column', 'key_column', 'another_test_src_column'}, u"SELECT * FROM DUAL WHERE dt BETWEEN '2016-07-28' AND '2016-08-31'", None, ) def test_merge_operator_bootstrap(self): self.conf.parameterize_context() self.engine.describe.return_value = defaults.DESCRIBE_SRC_COLUMNS self.build_dags().execute('merge_test_aggregation_test_table', 'item') self.engine.describe.assert_called_with(defaults.TMP_ITEM_TABLE_NAME) self.engine.bootstrap.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.DESCRIBE_TARGET_COLUMNS_WITH_META) def test_merge_operator_bootstrap_with_timeseries(self): self.conf.parameterize_context().with_timeseries() self.engine.describe.return_value = defaults.DESCRIBE_SRC_COLUMNS self.build_dags().execute('merge_test_aggregation_test_table', 'item') self.engine.describe.assert_called_with(defaults.TMP_ITEM_TABLE_NAME) self.engine.bootstrap.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.DESCRIBE_TARGET_COLUMNS) def test_merge_operator_delete_existing_data_without_timeseries(self): self.build_dags().execute('merge_test_aggregation_test_table') self.engine.delete_timeseries.assert_not_called() def test_merge_operator_delete_existing_data_with_timeseries(self): self.conf.with_timeseries() self.build_dags().execute('merge_test_aggregation_test_table') self.engine.delete_timeseries.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: defaults.DATE_RANGE}) def test_merge_operator_delete_existing_data_with_timeseries_parameterization(self): self.conf.with_timeseries().parameterize_context() self.build_dags().execute('merge_test_aggregation_test_table', 'item') self.engine.delete_timeseries.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: defaults.DATE_RANGE, 'item_column': 'item'}) def test_merge_operator_delete_existing_data_with_timeseries_offsets_and_reruns(self): self.conf.with_timeseries().with_reruns().with_offset() self.build_dags().execute('merge_test_aggregation_test_table') self.engine.delete_timeseries.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: ('2017-07-28', '2017-07-31')}) def test_merge_operator_delete_existing_data_with_timeseries_and_external_trigger(self): self.conf.with_timeseries() self.build_dags().execute('merge_test_aggregation_test_table', external_trigger=True) self.engine.delete_timeseries.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: defaults.EXTERNAL_DATE_RANGE}) def test_merge_operator_delete_existing_data_with_timeseries_offsets_reruns_and_external_trigger(self): self.conf.with_timeseries().with_reruns().with_offset() self.build_dags().execute('merge_test_aggregation_test_table', external_trigger=True) self.engine.delete_timeseries.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: ('2016-07-28', '2016-08-31')}) def test_merge_operator_merge(self): self.build_dags().execute('merge_test_aggregation_test_table') self.engine.merge.assert_called_with(defaults.TMP_TABLE_NAME, defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, {'key_column': 'key_column'}, defaults.MERGE_VALUE_COLUMNS, defaults.MERGE_UPDATE_TYPES, 'test_time_key') def test_merge_operator_merge_with_parametrization(self): self.conf.parameterize_context() self.build_dags().execute('merge_test_aggregation_test_table', 'item') self.engine.merge.assert_called_with(defaults.TMP_ITEM_TABLE_NAME, defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, {'key_column': 'key_column', 'item_column': 'item_column'}, defaults.MERGE_VALUE_COLUMNS, defaults.MERGE_UPDATE_TYPES, 'test_time_key') def test_merge_operator_merge_with_timeseries(self): self.conf.with_timeseries() self.build_dags().execute('merge_test_aggregation_test_table') self.engine.merge.assert_called_with(defaults.TMP_TABLE_NAME, defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, {'key_column': 'key_column', 'timeseries_column': 'test_time_key'}, defaults.MERGE_VALUE_COLUMNS, None, None) def test_merge_operator_merge_with_timeseries_and_parametrization(self): self.conf.parameterize_context().with_timeseries() self.build_dags().execute('merge_test_aggregation_test_table', 'item') self.engine.merge.assert_called_with(defaults.TMP_ITEM_TABLE_NAME, defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, {'key_column': 'key_column', 'timeseries_column': 'test_time_key', 'item_column': 'item_column'}, defaults.MERGE_VALUE_COLUMNS, None, None) def test_merge_operator_merge_with_external_trigger(self): self.build_dags().execute('merge_test_aggregation_test_table', external_trigger=True) self.engine.merge.assert_called_with(defaults.EXTERNAL_TMP_TABLE_NAME, defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, {'key_column': 'key_column'}, defaults.MERGE_VALUE_COLUMNS, defaults.MERGE_UPDATE_TYPES, 'test_time_key') def test_finish_operator_purge_without_timeseries(self): self.build_dags().execute('finish_test_table') self.engine.purge.assert_not_called() def test_finish_operator_purge_with_timeseries(self): self.conf.with_timeseries() self.build_dags().execute('finish_test_table') self.engine.purge.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_ALL_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: defaults.DATE_RANGE}) def test_finish_operator_purge_with_timeseries_and_parametetrization(self): self.conf.with_timeseries().parameterize_context() self.build_dags().execute('finish_test_table', 'item') self.engine.purge.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_ALL_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: defaults.DATE_RANGE, 'item_column': 'item'}) def test_finish_operator_purge_with_timeseries_reruns_and_offsets(self): self.conf.with_timeseries().with_offset().with_reruns() self.build_dags().execute('finish_test_table') self.engine.purge.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_ALL_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: ('2017-07-28', '2017-08-01')}) def test_finish_operator_purge_with_timeseries_and_external_trigger(self): self.conf.with_timeseries() self.build_dags().execute('finish_test_table', external_trigger=True) self.engine.purge.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_ALL_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: defaults.EXTERNAL_DATE_RANGE}) def test_finish_operator_purge_with_timeseries_reruns_offsets_and_external_trigger(self): self.conf.with_timeseries().with_offset().with_reruns() self.build_dags().execute('finish_test_table', external_trigger=True) self.engine.purge.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.TARGET_ALL_VALUE_COLUMNS, {defaults.TIMESERIES_KEY: ('2016-07-28', '2016-09-01')}) def test_finish_operator_parameters_without_parameter_columns(self): self.build_dags().execute('finish_test_table') self.engine.parameters.assert_not_called() def test_finish_operator_parameters(self): self.conf.with_parameter_columns() self.build_dags().execute('finish_test_table') self.engine.parameters.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.PARAMETER_COLUMNS, None) def test_finish_operator_parameters_with_parametrization(self): self.conf.with_parameter_columns().parameterize_context() self.build_dags().execute('finish_test_table', 'item') self.engine.parameters.assert_called_with(defaults.TARGET_SCHEMA_NAME, defaults.TARGET_NAME, defaults.PARAMETER_COLUMNS, {'item_column': 'item'})

54.526163

150

0.669883

2,210

18,757

5.293665

0.095928

0.057441

0.038892

0.059834

0.808616

0.770493

0.75374

0.716471

0.673989

0.640909

0

0.015435

0.240124

18,757

343

151

54.685131

0.805374

0.055766

0

0.519856

0

0.032491

0.170831

0.075127

0

0.140794

1

0.151625

false

0

0.025271

0.00722

0.202166

0

null

0

1

0

1

0

null

0

5

2cdb9c555e7a91232cc3bd457abdd8b917abed0a

120

py

Python

sharing/admin.py

ssoumyajit/imgapi2

b2129f1d35d55e093a3d96272686ac25ea2cf7bb

[ "MIT" ]

null

sharing/admin.py

ssoumyajit/imgapi2

b2129f1d35d55e093a3d96272686ac25ea2cf7bb

[ "MIT" ]

null

sharing/admin.py

ssoumyajit/imgapi2

b2129f1d35d55e093a3d96272686ac25ea2cf7bb

[ "MIT" ]

null

from django.contrib import admin from .models import Sharing # Register your models here. admin.site.register(Sharing)

20

32

0.808333

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120

5.705882

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0.125

120

5

33

24

0.92381

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0

true

0

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1

0

null

0

1

0

1

0

1

0

5

2ce45b5a361499e8da2a16a49979a10abade1498

61

py

Python

modelscript/scripts/metamodels/all.py

ScribesZone/ModelScribes

a36be1047283f2e470dc2dd4353f2a714377bb7d

[ "MIT" ]

1

2019-02-22T14:27:06.000Z

modelscript/scripts/metamodels/all.py

ScribesZone/ModelScribes

a36be1047283f2e470dc2dd4353f2a714377bb7d

[ "MIT" ]

4

2019-02-12T07:49:15.000Z

2019-02-12T07:50:12.000Z

modelscript/scripts/metamodels/all.py

ScribesZone/ModelScribes

a36be1047283f2e470dc2dd4353f2a714377bb7d

[ "MIT" ]

null

# coding=utf-8 import modelscript.scripts.metamodels.parser

15.25

44

0.819672

8

61

6.25

1

0

0.017857

0.081967

61

3

45

20.333333

0.875

0.196721

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

fa0046dd2b7c6d5042e3c68e2632112e264d2aad

125

py

Python

w01/e03.py

Luccifer/PythonCoruseraHSE

653d6a24325789342f0d033717ba548dc6e90483

[ "Unlicense" ]

1

2020-01-12T12:55:07.000Z

w01/e03.py

Luccifer/PythonCourseraHSE

653d6a24325789342f0d033717ba548dc6e90483

[ "Unlicense" ]

null

w01/e03.py

Luccifer/PythonCourseraHSE

653d6a24325789342f0d033717ba548dc6e90483

[ "Unlicense" ]

null

# Дележ яблок-1 def apple_sharing(n, k): return print(k // n) n = int(input()) k = int(input()) apple_sharing(n, k)

10.416667

24

0.6

22

125

3.318182

0.545455

0.328767

0.356164

0.383562

0

0.010204

0.216

125

11

25

11.363636

0.734694

0.104

0

1

0.2

false

0

0.2

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0.2

1

0

null

1

0

null

0

1

0

5

fa0aed6428adaad385ea09454ceece28d3c47786

189

py

Python

device.py

peter0512lee/pytorch-YOLOv4

deaf4c054133fee8a556d76fdb1fe91aa06cea09

[ "Apache-2.0" ]

null

device.py

peter0512lee/pytorch-YOLOv4

deaf4c054133fee8a556d76fdb1fe91aa06cea09

[ "Apache-2.0" ]

null

device.py

peter0512lee/pytorch-YOLOv4

deaf4c054133fee8a556d76fdb1fe91aa06cea09

[ "Apache-2.0" ]

null

import torch import torch.nn as nn print(torch.cuda.get_device_name(0)) print(torch.cuda.is_available()) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(device)

31.5

69

0.772487

32

189

4.4375

0.46875

0.190141

0.197183

0.28169

0

0.00578

0.084656

189

6

70

31.5

0.815029

0

0.036842

0

1

0

false

0

0.333333

0

0.333333

0.5

0

null

0

1

0

1

0

null

0

1

0

1

0

5

fa28544c9990211c22cff979a74ac0a93b50cdee

229

py

Python

solutions/001/solution.py

jwmcgettigan/project-euler-solutions

f06b6551e713619d5fd1359ee2f96fcff61c425b

[ "FTL" ]

1

2020-08-21T00:30:17.000Z

solutions/001/solution.py

jwmcgettigan/project-euler-solutions

f06b6551e713619d5fd1359ee2f96fcff61c425b

[ "FTL" ]

2

2020-09-18T00:40:01.000Z

2020-09-21T04:13:05.000Z

solutions/001/solution.py

jwmcgettigan/project-euler-solutions

f06b6551e713619d5fd1359ee2f96fcff61c425b

[ "FTL" ]

null

def multiple_of(num, multiple): return num % multiple == 0 def sum_of_multiples(limit): return sum(x for x in range(limit) if multiple_of(x, 3) or multiple_of(x, 5)) if __name__ == "__main__": print(sum_of_multiples(1000))

28.625

79

0.724891

40

229

3.775

0.525

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0.18543

0

0.035897

0.148472

229

8

80

28.625

0.738462

0

0.034783

0

1

0.333333

false

0

0.333333

0.666667

0.166667

0

null

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1

0

1

0

null

0

1

0

1

0

5

fa2ab6580e90d1b64e579f40be9f53bcce93840b

130

py

Python

python-sdk/nuscenes/eval/tracking/data_classes.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

python-sdk/nuscenes/eval/tracking/data_classes.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

python-sdk/nuscenes/eval/tracking/data_classes.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

version https://git-lfs.github.com/spec/v1 oid sha256:5eca636366997d0ff94fe840e0f0c554bc845333a7adab625f67a63506b5617c size 13879

32.5

75

0.884615

13

130

8.846154

1

0

0.41129

0.046154

130

3

76

43.333333

0.516129

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

null

1

0

1

0

5

d71262e83d6196977ebf4f2086c7a160e553ac64

75

py

Python

dynamo2relion/__init__.py

EuanPyle/import_2_relion4_sta

d755be7f83c8f3837bac740429203929b1e8175a

[ "BSD-3-Clause" ]

3

2021-10-18T21:49:07.000Z

2022-01-17T11:10:14.000Z

dynamo2relion/__init__.py

EuanPyle/dynamo2relion

d755be7f83c8f3837bac740429203929b1e8175a

[ "BSD-3-Clause" ]

null

dynamo2relion/__init__.py

EuanPyle/dynamo2relion

d755be7f83c8f3837bac740429203929b1e8175a

[ "BSD-3-Clause" ]

null

from .dynamo2relion import dynamo2relion from .version import __version__

18.75

40

0.853333

8

75

7.5

0.5

0

0.030303

0.12

75

3

41

25

0.878788

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1

0

true

0

1

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1

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1

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1

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null

0

1

0

1

0

1

0

5

d72f53a26af6a73f2006f40005d475fa06100d90

109

py

Python

src/rios/__init__.py

prometheusresearch/rios.converter

59f46a6c88389285d42d8a060edf58df1bc0b386

[ "Apache-2.0" ]

null

src/rios/__init__.py

prometheusresearch/rios.converter

59f46a6c88389285d42d8a060edf58df1bc0b386

[ "Apache-2.0" ]

3

2021-09-08T01:37:59.000Z

2022-03-12T00:13:51.000Z

src/rios/__init__.py

prometheusresearch/rios.converter

59f46a6c88389285d42d8a060edf58df1bc0b386

[ "Apache-2.0" ]

null

15.571429

55

0.761468

12

109

6.083333

1

0

0.041237

0.110092

109

6

56

18.166667

0.71134

0.40367

0

0.213115

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d768d9c76a81da604593951269849eb396f6348d

79

py

Python

strategery/exceptions.py

rcgale/strategery

d1608ea59587d7e49db0bdf788e3243d4d42081a

[ "MIT" ]

null

strategery/exceptions.py

rcgale/strategery

d1608ea59587d7e49db0bdf788e3243d4d42081a

[ "MIT" ]

null

strategery/exceptions.py

rcgale/strategery

d1608ea59587d7e49db0bdf788e3243d4d42081a

[ "MIT" ]

null

class TaskError(Exception): pass class StrategyError(Exception): pass

13.166667

31

0.734177

8

79

7.25

0.625

0.448276

0

0.189873

79

5

32

15.8

0.90625

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

d794b4f6c38f5737d8771b16e1f782859e864577

50

py

Python

verifai/simulators/__init__.py

shromonag/VerifAI

ace214d1c3282ed5ea63ee3f52457e35f54ebb62

[ "BSD-3-Clause" ]

1

2020-07-27T13:32:01.000Z

verifai/simulators/__init__.py

shromonag/VerifAI

ace214d1c3282ed5ea63ee3f52457e35f54ebb62

[ "BSD-3-Clause" ]

null

verifai/simulators/__init__.py

shromonag/VerifAI

ace214d1c3282ed5ea63ee3f52457e35f54ebb62

[ "BSD-3-Clause" ]

null

from .openai_gym import * from .webots import *

16.666667

26

0.72

7

50

5

0.714286

0

0.2

50

2

27

25

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d14b2a834f35cfedd90cbf959ae71435837f6dc6

434

py

Python

freeldep/templates/__init__.py

MatthieuBlais/freeldep

092de3c603a28b9d12e9ad93d6c0cca773469c9f

[ "Apache-2.0" ]

null

freeldep/templates/__init__.py

MatthieuBlais/freeldep

092de3c603a28b9d12e9ad93d6c0cca773469c9f

[ "Apache-2.0" ]

null

freeldep/templates/__init__.py

MatthieuBlais/freeldep

092de3c603a28b9d12e9ad93d6c0cca773469c9f

[ "Apache-2.0" ]

null

from freeldep.templates.core import CoreDeployerTemplate # noqa from freeldep.templates.initialize import InitializeDeployerTemplate # noqa from freeldep.templates.project import ProjectTemplate # noqa from freeldep.templates.repository import DeployerRepositoryTemplate # noqa from freeldep.templates.service import ServiceDeployerTemplate # noqa from freeldep.templates.subscription import SubscriptionDeployerTemplate # noqa

62

80

0.861751

42

434

8.904762

0.404762

0.192513

0.336898

0.334225

0

0.096774

434

6

81

72.333333

0.954082

0.06682

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

0f1abcfe4f3c072e0de6f55f980b6e9eceea9760

259

py

Python

evaluation/__init__.py

aliyun/Self-Evolving-Keypoint-Demo

52a8bb312040bfbf5c2be02ac5d40ce3f0142026

[ "MIT" ]

25

2020-07-16T02:55:25.000Z

2021-12-25T03:37:09.000Z

evaluation/__init__.py

aliyun/Self-Evolving-Keypoint-Demo

52a8bb312040bfbf5c2be02ac5d40ce3f0142026

[ "MIT" ]

8

2020-08-20T04:36:51.000Z

2021-03-24T12:31:37.000Z

evaluation/__init__.py

aliyun/Self-Evolving-Keypoint-Demo

52a8bb312040bfbf5c2be02ac5d40ce3f0142026

[ "MIT" ]

2

2020-08-07T13:45:12.000Z

2021-03-09T01:54:59.000Z

# __init__.py from .extract_sekd import extract_sekd, extract_sekd_desc from .extract_opencv_features import extract_opencv_features, extract_opencv_desc __all__ = ['extract_sekd', 'extract_sekd_desc', 'extract_opencv_features', 'extract_opencv_desc']

28.777778

81

0.826255

34

259

5.558824

0.294118

0.291005

0.333333

0.232804

0.677249

0.402116

0

0.096525

259

8

82

32.375

0.807692

0.042471

0

0.289796

0.093878

0

1

0

false

0

0.5

0

0.5

0

null

1

0

1

0

null

0

1

0

5

0f37ed023a0ad28010f81eaf36391b94cfa83ec7

64

py

Python

jadepunk/engine/__init__.py

HarkonenBade/jadepunk-chargen

198590946b7192e78967de0788da4009f8454dd5

[ "MIT" ]

1

2020-05-28T13:06:43.000Z

jadepunk/engine/__init__.py

HarkonenBade/jadepunk-chargen

198590946b7192e78967de0788da4009f8454dd5

[ "MIT" ]

null

jadepunk/engine/__init__.py

HarkonenBade/jadepunk-chargen

198590946b7192e78967de0788da4009f8454dd5

[ "MIT" ]

null

from .base import EngineLoader from . import markdown, moinmoin

21.333333

32

0.8125

8

64

6.5

0.75

0

0.140625

64

2

33

32

0.945455

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0f6622ae20d04be8567fefa92478b2fbe05f89ab

103

py

Python

notifications/admin.py

briansok/derpi

0e111a84b17ce8caeb60d2899957a0a24cab47b3

[ "MIT" ]

null

notifications/admin.py

briansok/derpi

0e111a84b17ce8caeb60d2899957a0a24cab47b3

[ "MIT" ]

null

notifications/admin.py

briansok/derpi

0e111a84b17ce8caeb60d2899957a0a24cab47b3

[ "MIT" ]

1

2019-03-07T04:30:36.000Z

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

20.6

34

0.84466

13

103

6.692308

0.692308

0

0.097087

103

4

35

25.75

0.935484

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7e1495b5dfd5ab4623ce77d00788647d9fc5e413

136

py

Python

restaurants/admin.py

WorkShoft/python-developer-delectatech

f6ef284b156141289a8f141e90628b835bd186f5

[ "Apache-2.0" ]

null

restaurants/admin.py

WorkShoft/python-developer-delectatech

f6ef284b156141289a8f141e90628b835bd186f5

[ "Apache-2.0" ]

null

restaurants/admin.py

WorkShoft/python-developer-delectatech

f6ef284b156141289a8f141e90628b835bd186f5

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Segment, Restaurant admin.site.register(Segment) admin.site.register(Restaurant)

19.428571

39

0.823529

18

136

6.222222

0.555556

0.160714

0.303571

0

0.095588

136

6

40

22.666667

0.910569

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

7e21724402db16b29ba18070b85eb144985bab27

257

py

Python

Biopyutils/Protein.py

jingxinfu/Biopyutils

a04f86e3b12bcbb44bf317f3bb9c65ef5a6ab862

[ "BSD-3-Clause" ]

1

2022-03-15T03:45:28.000Z

Biopyutils/Protein.py

jingxinfu/Biopyutils

a04f86e3b12bcbb44bf317f3bb9c65ef5a6ab862

[ "BSD-3-Clause" ]

1

2020-09-05T18:10:41.000Z

Biopyutils/Protein.py

jingxinfu/Biopyutils

a04f86e3b12bcbb44bf317f3bb9c65ef5a6ab862

[ "BSD-3-Clause" ]

3

2020-09-04T17:05:46.000Z

2020-09-10T14:39:20.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # License : GPL3 # Author : Jingxin Fu <jingxinfu.tj@gmail.com> # Date : 11/02/2020 # Last Modified Date: 11/02/2020 # Last Modified By : Jingxin Fu <jingxinfu.tj@gmail.com>

28.555556

57

0.579767

35

257

4.257143

0.657143

0.120805

0.241611

0.268456

0.697987

0

0.101604

0.272374

257

8

58

32.125

0.695187

0.941634

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

1

0

null

0

1

0

5

7e2da13e8a9dfeb10d2df5ac05bc7be77119789b

49

py

Python

gdcdatamodel/models/notifications.py

NCI-GDC/gdcdatamodel

924fc8ab695b1cbb0131636ffcb6d3881db2e200

[ "Apache-2.0" ]

27

2016-06-24T20:32:44.000Z

2022-01-17T07:53:48.000Z

gdcdatamodel/models/notifications.py

NCI-GDC/gdcdatamodel

924fc8ab695b1cbb0131636ffcb6d3881db2e200

[ "Apache-2.0" ]

63

2016-07-20T21:40:11.000Z

2021-08-12T18:39:21.000Z

gdcdatamodel/models/notifications.py

NCI-GDC/gdcdatamodel

924fc8ab695b1cbb0131636ffcb6d3881db2e200

[ "Apache-2.0" ]

5

2016-10-20T20:00:09.000Z

2020-08-14T08:55:40.000Z

from gdc_ng_models.models.notifications import *

24.5

48

0.857143

7

49

5.714286

0.857143

0

0.081633

49

1

49

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

7e73266449b97ed3f492eab9b67f93b8fe263496

232

py

Python

code4/Item.py

weijie88/spider_files

9039f7ff0f4c76bc5aa80ca8b87b8280880392cc

[ "Apache-2.0" ]

null

code4/Item.py

weijie88/spider_files

9039f7ff0f4c76bc5aa80ca8b87b8280880392cc

[ "Apache-2.0" ]

null

code4/Item.py

weijie88/spider_files

9039f7ff0f4c76bc5aa80ca8b87b8280880392cc

[ "Apache-2.0" ]

null

class Stock(): def __init__(self,code,name,price): self.code = code self.name = name self.price = price def __str__(self): return 'code{},name{},price{}'.format(self.code,self.name,self.price)

33.142857

77

0.603448

31

232

4.258065

0.354839

0.181818

0.19697

0

0.241379

232

7

77

33.142857

0.75

0

0.090129

0

1

0.285714

false

0

0.142857

0.571429

0

null

0

1

0

1

0

null

0

1

0

1

0

5

7e74048a7f77be243e4286bf8cd8a428ea6ef557

80

py

Python

django/wsgi.py

a-rey/aaronmreyes_heroku

f397741ec33a35c318b6e4d51837b352183085f9

[ "MIT" ]

1

2022-03-12T22:23:44.000Z

django/wsgi.py

a-rey/docker_website

f397741ec33a35c318b6e4d51837b352183085f9

[ "MIT" ]

2

2020-04-07T22:09:50.000Z

django/wsgi.py

a-rey/docker_website

f397741ec33a35c318b6e4d51837b352183085f9

[ "MIT" ]

null

import django.core.wsgi application = django.core.wsgi.get_wsgi_application()

16

53

0.8125

11

80

5.727273

0.545455

0.31746

0.444444

0

0.0875

80

4

54

20

0.863014

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

0e575d418b654db5dc6507d8be41d83f56c240b6

4,321

py

Python

mfi_customization/mfi/doctype/communication.py

parimal-bizmap/mfi_customization

ddd2361898d5d873b06356c28990bf81e4e2745e

[ "MIT" ]

null

mfi_customization/mfi/doctype/communication.py

parimal-bizmap/mfi_customization

ddd2361898d5d873b06356c28990bf81e4e2745e

[ "MIT" ]

null

mfi_customization/mfi/doctype/communication.py

parimal-bizmap/mfi_customization

ddd2361898d5d873b06356c28990bf81e4e2745e

[ "MIT" ]

null

import frappe def after_insert_file(doc,method): if doc.attached_to_doctype=="Communication": if len(frappe.get_all('Issue',{'communication':doc.attached_to_name}))==0: cmm_doc=frappe.get_doc("Communication",doc.attached_to_name) domain_rule=email_rules_true_for_domain(cmm_doc.sender) email_rule=email_rules_true_for_emails_table(cmm_doc.sender) if (domain_rule.get('is_true') or email_rule.get('is_true')) and cmm_doc.sent_or_received=='Received': if "Re:" not in doc.subject: issue=frappe.new_doc("Issue") issue.subject=cmm_doc.subject issue.description=cmm_doc.content issue.raised_by=cmm_doc.sender issue.communication=doc.attached_to_name issue.customer=domain_rule.get('customer') if domain_rule.get('is_true') else email_rule.get('customer') issue.flags.ignore_mandatory=True issue.company=domain_rule.get('company') if domain_rule.get('is_true') else email_rule.get('company') issue.save() file_doc = frappe.new_doc("File") file_doc.file_name = doc.file_name file_doc.file_size = doc.file_size file_doc.folder = doc.folder file_doc.is_private = doc.is_private file_doc.file_url = doc.file_url file_doc.attached_to_doctype = "Issue" file_doc.attached_to_name=issue.get('name') file_doc.save() else: for d in frappe.get_all('Issue',{'communication':doc.attached_to_name},['name']): file_doc = frappe.new_doc("File") file_doc.file_name = doc.file_name file_doc.file_size = doc.file_size file_doc.folder = doc.folder file_doc.is_private = doc.is_private file_doc.file_url = doc.file_url file_doc.attached_to_doctype = "Issue" file_doc.attached_to_name=d.get('name') file_doc.save() def email_rules_true_for_domain(sender): resp={'is_ture':False,'customer':'','company':''} for d in frappe.get_all('Email Rules for Issue',{'group_by':'Domain'},['name','domain_name','customer']): if '@' in sender and d.get('domain_name').lower() in sender.split('@')[1]: customer=frappe.get_doc('Customer',d.customer) resp.update({'is_true':True,'customer':d.customer}) for cu in customer.get('accounts'): resp.update({'company':cu.company}) return resp return resp def email_rules_true_for_emails_table(sender): resp={'is_ture':False,'customer':'','company':''} for d in frappe.get_all('Email Rules for Issue',{'group_by':'Emails'},['name','customer']): rules=frappe.get_doc('Email Rules for Issue',d.name) emails=[] for tb in rules.get('email_list_for_issue'): emails.append(tb.get('email')) if sender in emails: customer=frappe.get_doc('Customer',d.customer) resp.update({'is_true':True,'customer':d.customer}) for cu in customer.get('accounts'): resp.update({'company':cu.company}) return resp return resp def after_insert(doc,method): if len(frappe.get_all('Issue',{'communication':doc.name}))==0: domain_rule=email_rules_true_for_domain(doc.sender) email_rule=email_rules_true_for_emails_table(doc.sender) if (domain_rule.get('is_true') or email_rule.get('is_true')) and doc.sent_or_received=='Received': if "Re:" not in doc.subject: issue=frappe.new_doc("Issue") issue.subject=doc.subject issue.description=doc.content issue.raised_by=doc.sender issue.communication=doc.name issue.customer=domain_rule.get('customer') if domain_rule.get('is_true') else email_rule.get('customer') issue.flags.ignore_mandatory=True issue.company=domain_rule.get('company') if domain_rule.get('is_true') else email_rule.get('company') issue.save()

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0.042328

0.835165

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0.707774

0.690273

0.652015

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0.000957

0.274242

4,321

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126

52.695122

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0

null

0

1

0

1

0

null

0

5

0e89b4f21a80f76fa1a6670a5f964bed58f712b6

321

py

Python

src/chemcalculator/__init__.py

UBC-MDS/chemcalculator

383b2d23fc7400ac62b98b5c06ff8ff28b8672e7

[ "MIT" ]

null

src/chemcalculator/__init__.py

UBC-MDS/chemcalculator

383b2d23fc7400ac62b98b5c06ff8ff28b8672e7

[ "MIT" ]

27

2022-01-13T21:35:12.000Z

2022-02-05T07:15:00.000Z

src/chemcalculator/__init__.py

UBC-MDS/chemcalculator

383b2d23fc7400ac62b98b5c06ff8ff28b8672e7

[ "MIT" ]

null

# read version from installed package from importlib.metadata import version __version__ = version("chemcalculator") # populate package namespace from chemcalculator.chemcalculator import compute_mass from chemcalculator.chemcalculator import moles_grams_converter from chemcalculator.chemcalculator import percent_mass

35.666667

63

0.872274

35

321

7.771429

0.485714

0.198529

0.352941

0.419118

0

0.093458

321

8

64

40.125

0.934708

0.193146

0

0.054688

0

1

0

false

0

0.8

0

0.8

0

null

0

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0

1

0

null

0

1

0

1

0

5

0ea4367a9b5ff317f5e82b2b7cbbd5493cf68a29

74

py

Python

cmdline/reportcachestatus.py

williamscraigm/arcrest

5a381988fe0035678dc94703d857c6ecb4194738

[ "Apache-2.0" ]

11

2015-02-06T23:35:49.000Z

2021-11-28T21:26:46.000Z

cmdline/reportcachestatus.py

williamscraigm/arcrest

5a381988fe0035678dc94703d857c6ecb4194738

[ "Apache-2.0" ]

1

2015-06-24T13:46:44.000Z

2015-07-01T07:46:28.000Z

cmdline/reportcachestatus.py

williamscraigm/arcrest

5a381988fe0035678dc94703d857c6ecb4194738

[ "Apache-2.0" ]

6

2015-02-23T22:51:53.000Z

2021-01-17T05:57:24.000Z

#! python import arcrest.admin arcrest.admin.cmdline.reportcachestatus()

14.8

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74

7.5

0.75

0.4

0

0.081081

74

4

42

18.5

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0

1

0

true

0

0.5

0

0.5

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null

1

0

1

0

null

0

1

0

1

0

5

7ed31f4f28e0dd77411d34b11ae6847ec4d91131

2,433

py

Python

tests/routes/test_api_searchLeases.py

Biosystems-Analytics-Lab/shellcast

8d578bfa3d66d75502f1a133fe6263d376694247

[ "CC-BY-4.0" ]

5

2021-03-24T19:19:48.000Z

2022-01-11T09:27:13.000Z

tests/routes/test_api_searchLeases.py

Biosystems-Analytics-Lab/shellcast

8d578bfa3d66d75502f1a133fe6263d376694247

[ "CC-BY-4.0" ]

1

2022-01-13T15:11:09.000Z

2022-01-13T21:16:10.000Z

tests/routes/test_api_searchLeases.py

Biosystems-Analytics-Lab/shellcast

8d578bfa3d66d75502f1a133fe6263d376694247

[ "CC-BY-4.0" ]

null

import pytest from models.User import User from models.UserLease import UserLease from models.NCDMFLease import NCDMFLease from firebase_admin import auth def test_search_leases(client, dbSession, addMockFbUser): # add a mock Firebase user addMockFbUser(dict(uid='3sH9so5Y3DP72QA1XqbWw9J6I8o1', email='blah@gmail.com'), 'validUser1') # add some NCDMF leases ncdmfLeases = [ NCDMFLease(ncdmf_lease_id='819401', grow_area_name='D11', cmu_name='U001', rainfall_thresh_in=2.5, geometry=(34.404497, -77.567573)), NCDMFLease(ncdmf_lease_id='123456', grow_area_name='B05', cmu_name='U002', rainfall_thresh_in=3.5, geometry=(35.923741, -76.239482)), NCDMFLease(ncdmf_lease_id='4-C-89', grow_area_name='A01', cmu_name='U003', rainfall_thresh_in=1.5, geometry=(36.303915, -75.864693)) ] dbSession.add_all(ncdmfLeases) dbSession.commit() res = client.post('/searchLeases', headers={'Authorization': 'validUser1'}, json={'search': '1'}) assert res.status_code == 200 json = res.get_json() assert len(json) == 2 assert json[0] == '819401' assert json[1] == '123456' def test_search_leases_with_existing_user_lease(client, dbSession, addMockFbUser): # add a mock Firebase user addMockFbUser(dict(uid='3sH9so5Y3DP72QA1XqbWw9J6I8o1', email='blah@gmail.com'), 'validUser1') # add the user to the db user = User(firebase_uid='3sH9so5Y3DP72QA1XqbWw9J6I8o1', email='blah@gmail.com') dbSession.add(user) dbSession.commit() # add some NCDMF leases ncdmfLeases = [ NCDMFLease(ncdmf_lease_id='819401', grow_area_name='D11', cmu_name='U001', rainfall_thresh_in=2.5, geometry=(34.404497, -77.567573)), NCDMFLease(ncdmf_lease_id='123456', grow_area_name='B05', cmu_name='U002', rainfall_thresh_in=3.5, geometry=(35.923741, -76.239482)), NCDMFLease(ncdmf_lease_id='4-C-89', grow_area_name='A01', cmu_name='U003', rainfall_thresh_in=1.5, geometry=(36.303915, -75.864693)) ] dbSession.add_all(ncdmfLeases) dbSession.commit() # add one existing lease for the user lease = UserLease(user_id=user.id, ncdmf_lease_id='123456', grow_area_name='B05', cmu_name='U002', rainfall_thresh_in=3.5, geometry=(35.923741, -76.239482)) dbSession.add(lease) dbSession.commit() res = client.post('/searchLeases', headers={'Authorization': 'validUser1'}, json={'search': '1'}) assert res.status_code == 200 json = res.get_json() assert len(json) == 1 assert json[0] == '819401'

39.885246

158

0.733251

343

2,433

5.008746

0.262391

0.040745

0.048894

0.076834

0.779977

0.752037

0

0.123428

0.11755

2,433

60

159

40.55

0.676758

0.062474

0

0.6

0

0.149956

0.036939

0

0.175

1

0.05

false

0

0.125

0

0.175

0

null

0

1

0

null

0

5

7ed8484936ccba5c069b5f29988d450e86b02e83

43

py

Python

hello.py

natesb3/HelloRobot

f13ac0d3db15d3ff7c75224961dc700452845d0a

[ "MIT" ]

null

hello.py

natesb3/HelloRobot

f13ac0d3db15d3ff7c75224961dc700452845d0a

[ "MIT" ]

null

hello.py

natesb3/HelloRobot

f13ac0d3db15d3ff7c75224961dc700452845d0a

[ "MIT" ]

null

from gopigo import * enc_tgt(1,1,72) fwd()

10.75

20

0.697674

9

43

3.222222

0.888889

0

0.108108

0.139535

43

4

21

10.75

0.675676

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

5

7ef10faa58db14cc68a83e0af0463f453b58cadc

3,717

py

Python

python-cli/tests/test_cli.py

treebeardtech/pytest-deepcov

972d13d36555d47afdf00a326cbf4d9534761c28

[ "Apache-2.0" ]

15

2021-03-31T11:17:03.000Z

2022-01-18T18:23:11.000Z

python-cli/tests/test_cli.py

treebeardtech/pytest-deepcov

972d13d36555d47afdf00a326cbf4d9534761c28

[ "Apache-2.0" ]

null

python-cli/tests/test_cli.py

treebeardtech/pytest-deepcov

972d13d36555d47afdf00a326cbf4d9534761c28

[ "Apache-2.0" ]

null

import json import os import shutil import sys from subprocess import CalledProcessError, check_output import pytest from click.testing import CliRunner from deepcov.cli import File from snapshottest.pytest import PyTestSnapshotTest from tests.util import RESOURCES from deepcov import cli # isort:skip pytest_plugins = "pytester" @pytest.fixture def tested_dir(): try: check_output(f"{sys.executable} -m pytest", cwd="tests/resources", shell=True) except CalledProcessError as err: assert err.returncode == 1 os.chdir(RESOURCES / ".deepcov") class TestCli: def test_when_test_file_then_success( self, tested_dir: object, snapshot: PyTestSnapshotTest ): runner = CliRunner() source = RESOURCES / "src" / "test_lib.py" assert source.exists() print(f"Running {source}") result = runner.invoke(cli.run, source.as_posix(), catch_exceptions=False) print(result.stdout) f = File(**json.loads(result.stdout)) [snapshot.assert_match({line: f.lines[line]}) for line in sorted(f.lines)] def test_when_src_file_then_success( self, tested_dir: object, snapshot: PyTestSnapshotTest ): runner = CliRunner() source = RESOURCES / "src" / "lib.py" assert source.exists() print(f"Running {source}") result = runner.invoke(cli.run, source.as_posix(), catch_exceptions=False) print(result.stdout) f = File(**json.loads(result.stdout)) [snapshot.assert_match({line: f.lines[line]}) for line in sorted(f.lines)] def test_when_no_junit_then_error( self, tested_dir: object, testdir: pytest.Testdir, ): shutil.copyfile(RESOURCES / ".deepcov" / ".coverage", ".coverage") runner = CliRunner() source = RESOURCES / "src" / "lib.py" assert source.exists() print(f"Running {source}") result = runner.invoke(cli.run, source.as_posix(), catch_exceptions=False) assert "error" in json.loads(result.stdout) def test_when_no_cov_then_error(self, tested_dir: object, testdir: pytest.Testdir): shutil.copyfile(RESOURCES / ".deepcov" / "junit.xml", "junit.xml") runner = CliRunner() source = RESOURCES / "src" / "lib.py" assert source.exists() print(f"Running {source}") result = runner.invoke(cli.run, source.as_posix(), catch_exceptions=False) assert "error" in json.loads(result.stdout) def test_when_unknown_file_then_error(self, tested_dir: object): runner = CliRunner() source = RESOURCES / "src" / "asdf.py" assert not source.exists() print(f"Running {source}") result = runner.invoke(cli.run, source.as_posix(), catch_exceptions=False) assert json.loads(result.stdout)["error"].startswith("No cov") def test_when_out_of_cov_scope_then_error(self, tested_dir: object): runner = CliRunner() source = RESOURCES / "out_of_cov_scope.py" assert source.exists() print(f"Running {source}") result = runner.invoke(cli.run, source.as_posix(), catch_exceptions=False) assert json.loads(result.stdout)["error"].startswith("No cov") def test_when_status_then_time_given(self, tested_dir: object): runner = CliRunner() result = runner.invoke(cli.run, catch_exceptions=False) assert json.loads(result.stdout)["time_since_run"] == "just now" def test_when_status_no_data_then_null(self, testdir: pytest.Testdir): runner = CliRunner() result = runner.invoke(cli.run, catch_exceptions=False) assert json.loads(result.stdout)["time_since_run"] == None

36.80198

87

0.665591

461

3,717

5.197397

0.21692

0.050083

0.036728

0.070117

0.72788

0.726628

0.718698

0

0.000343

0.216034

3,717

100

88

37.17

0.821894

0.00269

0

0.535714

0

0.092578

0

0.178571

1

0.107143

false

0

0.130952

0

0.25

0.095238

0

null

0

1

0

null

0

5

7ef9b5994c90ce96bcd77bec426b2fc0d31fe8a2

9,454

py

Python

tests/test_core.py

JEFuller/dataclasses-configobj

d8623713c81debe2d957f4776b3d3dac8f83abe2

[ "MIT" ]

null

tests/test_core.py

JEFuller/dataclasses-configobj

d8623713c81debe2d957f4776b3d3dac8f83abe2

[ "MIT" ]

2

2021-09-10T16:55:18.000Z

2021-10-15T18:49:52.000Z

tests/test_core.py

JEFuller/dataclasses-configobj

d8623713c81debe2d957f4776b3d3dac8f83abe2

[ "MIT" ]

null

import unittest from dataclasses import dataclass from typing import List, Optional, Type, TypeVar import configobj import validate from dataclasses_configobj import core class CoreTestCase(unittest.TestCase): def test_config(self): spec = list(map(str.strip, """\ [foo] bar = string pip = integer\ """.split('\n'))) infile = list(map(str.strip, """\ [foo] bar = one pip = 1\ """.split('\n'))) root = configobj.ConfigObj(infile=infile, configspec=spec) vtor = validate.Validator() res = root.validate(vtor, preserve_errors=True) self.assertEqual(res, True) foo = root['foo'] self.assertIsNotNone(foo) self.assertEqual(foo['bar'], 'one') self.assertEqual(foo['pip'], 1) def test_to_spec_1(self): @dataclass class Foo: bar: str pip: int @dataclass class Config: foo: Foo expectedSpec = list(map(str.strip, """\ [foo] bar = string pip = integer\ """.split('\n'))) root = configobj.ConfigObj() foo = configobj.Section(root, 1, root) root['foo'] = foo foo.__setitem__('bar', 'string') foo.__setitem__('pip', 'integer') self.assertEqual(expectedSpec, root.write()) spec = core.to_spec(Config) self.assertEqual(expectedSpec, spec.write()) def test_to_spec_2(self): @dataclass class Foo: a: str @dataclass class Bar: b: int @dataclass class Config: pip: str foo: Foo bar: Bar baz: str expectedSpec = list(map(str.strip, """\ pip = string baz = string [foo] a = string [bar] b = integer\ """.split('\n'))) root = configobj.ConfigObj() root['pip'] = 'string' root['baz'] = 'string' foo = configobj.Section(root, 1, root) root['foo'] = foo foo.__setitem__('a', 'string') bar = configobj.Section(root, 1, root) root['bar'] = bar bar.__setitem__('b', 'integer') self.assertEqual(expectedSpec, root.write()) spec = core.to_spec(Config) self.assertEqual(expectedSpec, spec.write()) def test_to_spec_3(self): @dataclass class Single: other: str @dataclass class OneOfMany: _name: str val: str @dataclass class Config: single: Single _many: List[OneOfMany] expectedSpec = list(map(str.strip, """\ [single] other = string [__many__] val = string\ """.split('\n'))) spec = core.to_spec(Config) self.assertEqual(expectedSpec, spec.write()) def test_to_spec_4(self): @dataclass class OneOfMany: _name: str val: str @dataclass class Wrapper: _many: List[OneOfMany] @dataclass class Config: wrapper: Wrapper expectedSpec = list(map(str.strip, """\ [wrapper] [[__many__]] val = string\ """.split('\n'))) spec = core.to_spec(Config) self.assertEqual(expectedSpec, spec.write()) def test_type(self): T = TypeVar('T') def doit(klass: Type[T]) -> T: vars = {'other': 'test'} return klass(**vars) @dataclass class Parent: other: str self.assertEqual(doit(Parent).other, 'test') def test_lift_1(self): @dataclass class Single: other: str @dataclass class OneOfMany: _name: str val: str @dataclass class Config: single: Single _many: List[OneOfMany] infile = list(map(str.strip, """\ [single] other = hello [one] val = apple [two] val = banana\ """.split('\n'))) expectedConfig = Config( single=Single(other = 'hello'), _many=[ OneOfMany(_name = 'one', val = 'apple'), OneOfMany(_name = 'two', val = 'banana') ] ) spec = core.to_spec(Config) root = configobj.ConfigObj(infile=infile, configspec=spec) config = core.lift(Config, root) self.assertEqual(expectedConfig, config) def test_lift_2(self): @dataclass class OneOfMany: _name: str val: str @dataclass class Wrapper: _many: List[OneOfMany] @dataclass class Config: wrapper: Wrapper infile = list(map(str.strip, """\ [wrapper] [[one]] val = apple [[two]] val = banana\ """.split('\n'))) expectedConfig = Config( wrapper=Wrapper( _many=[ OneOfMany(_name = 'one', val = 'apple'), OneOfMany(_name = 'two', val = 'banana') ] ) ) spec = core.to_spec(Config) root = configobj.ConfigObj(infile=infile, configspec=spec) config = core.lift(Config, root) self.assertEqual(expectedConfig, config) def test_lift_3(self): @dataclass class Foo: bar: str pip: int @dataclass class OneOfMany: _name: str val: str @dataclass class Wrapper: test: str foo: Foo _many: List[OneOfMany] @dataclass class Config: wrapper: Wrapper infile = list(map(str.strip, """\ [wrapper] test = yes [[foo]] bar = testing pip = 123 [[one]] val = apple [[two]] val = banana\ """.split('\n'))) expectedConfig = Config( wrapper=Wrapper( test='yes', foo=Foo('testing', 123), _many=[ OneOfMany(_name = 'one', val = 'apple'), OneOfMany(_name = 'two', val = 'banana') ] ) ) spec = core.to_spec(Config) root = configobj.ConfigObj(infile=infile, configspec=spec) vtor = validate.Validator() root.validate(vtor) config = core.lift(Config, root) self.assertEqual(expectedConfig, config) def test_optional_root(self): @dataclass class Config: required: str optional: Optional[str] = None expectedSpec = list(map(str.strip, """\ required = string optional = string(default=None)\ """.split('\n'))) spec = core.to_spec(Config) self.assertEqual(expectedSpec, spec.write()) here = configobj.ConfigObj(infile= ["required = yes", "optional = here"], configspec=spec) vtor = validate.Validator() here.validate(vtor) self.assertEqual(Config('yes', 'here'), core.lift(Config, here)) empty = configobj.ConfigObj(infile= ["required = yes"], configspec=spec) vtor = validate.Validator() empty.validate(vtor) self.assertEqual(Config('yes', None), core.lift(Config, empty)) def test_default_root(self): @dataclass class Config: required: str optional: str = 'defaultvalue' expectedSpec = list(map(str.strip, """\ required = string optional = string(default='defaultvalue')\ """.split('\n'))) spec = core.to_spec(Config) self.assertEqual(expectedSpec, spec.write()) here = configobj.ConfigObj(infile= ["required = yes", "optional = here"], configspec=spec) vtor = validate.Validator() here.validate(vtor) self.assertEqual(Config('yes', 'here'), core.lift(Config, here)) empty = configobj.ConfigObj(infile= ["required = yes"], configspec=spec) vtor = validate.Validator() empty.validate(vtor) self.assertEqual(Config('yes', 'defaultvalue'), core.lift(Config, empty)) def test_readme_example(self): @dataclass class Single: other: str @dataclass class OneOfMany: _name: str val: str @dataclass class Config: single: Single _many: List[OneOfMany] optional: Optional[str] = None withdefault: str = 'test123' infile = list(map(str.strip, """\ [single] other = hello [one] val = apple [two] val = banana\ """.split('\n'))) spec = core.to_spec(Config) root = configobj.ConfigObj(infile=infile, configspec=spec) validator = validate.Validator() root.validate(validator) expectedConfig = Config( single=Single(other='hello'), optional=None, withdefault='test123', _many=[ OneOfMany(_name='one', val='apple'), OneOfMany(_name='two', val='banana') ] ) config: Config = core.lift(Config, root) self.assertEqual(expectedConfig, config)

24.303342

98

0.51026

895

9,454

5.293855

0.098324

0.079781

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0

5

7d1bb21b4f6b96eb330b25dacd7db8dfe9819ea1

19

py

Python

lib/python3.6/site-packages/stripe/version.py

jdmueller/ArmoniaSaleor

1d7c1e9bb697325cee3d007b3ea811f25c4086d9

[ "BSD-3-Clause" ]

1

2019-07-18T13:16:09.000Z

lib/python3.6/site-packages/stripe/version.py

jdmueller/ArmoniaSaleor

1d7c1e9bb697325cee3d007b3ea811f25c4086d9

[ "BSD-3-Clause" ]

null

lib/python3.6/site-packages/stripe/version.py

jdmueller/ArmoniaSaleor

1d7c1e9bb697325cee3d007b3ea811f25c4086d9

[ "BSD-3-Clause" ]

null

VERSION = "2.32.0"

9.5

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19

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null

0

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null

0

5

7d4233bc97376d7ca10c02219f43c1578b3fd177

27

py

Python

docs/source/exercises/week7_trees/trees/__init__.py

Heroes-Academy/DataStructures_Winter2017

2dab3537af2810399b2dd1aa6a570d2b185e3661

[ "MIT" ]

null

docs/source/exercises/week7_trees/trees/__init__.py

Heroes-Academy/DataStructures_Winter2017

2dab3537af2810399b2dd1aa6a570d2b185e3661

[ "MIT" ]

null

docs/source/exercises/week7_trees/trees/__init__.py

Heroes-Academy/DataStructures_Winter2017

2dab3537af2810399b2dd1aa6a570d2b185e3661

[ "MIT" ]

null

from .binarytrees import *

13.5

26

0.777778

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0.913043

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null

0

1

0

1

0

5

ade733fdb3d6634efa2ebeb807462e445b2cff0b

148

py

Python

test_bots.py

alviproject/birdstorm

439ff97ee40c3dd93b8fa8bfca557bdee9c036e1

[ "MIT" ]

null

test_bots.py

alviproject/birdstorm

439ff97ee40c3dd93b8fa8bfca557bdee9c036e1

[ "MIT" ]

null

test_bots.py

alviproject/birdstorm

439ff97ee40c3dd93b8fa8bfca557bdee9c036e1

[ "MIT" ]

null

import bots bots.move( token='56f879426ad79431b15bbcc3300180a28c9d4fd2', ship=4, systems=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], )

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null

0

1

0

5

adfc9f6b8e342150dc30849441ef5c2507b9f384

3,595

py

Python

tests/test_health_contrib.py

RogerEMO/srd

40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5

[ "MIT" ]

1

2021-11-22T18:15:09.000Z

tests/test_health_contrib.py

RogerEMO/srd

40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5

[ "MIT" ]

3

2021-05-10T18:46:16.000Z

2021-06-01T16:51:48.000Z

tests/test_health_contrib.py

RogerEMO/srd

40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5

[ "MIT" ]

1

2021-05-05T17:20:06.000Z

import pytest from math import isclose import sys sys.path.append('/Users/pyann/Dropbox (CEDIA)/srd/Model') import srd from srd import quebec qc_form = quebec.form(2016) @pytest.mark.parametrize('nkids, net_inc', [(0, 18500), (1, 23800), (2, 27000)]) def test_cond_true_10_12_14(nkids, net_inc): p = srd.Person(age=45) hh = srd.Hhold(p, prov='qc') for _ in range(nkids): k = srd.Dependent(age=12) hh.add_dependent(k) qc_form.file(hh) p.prov_return['net_income'] = net_inc assert qc_form.health_contrib(p, hh) == 0 @pytest.mark.parametrize('nkids, net_inc', [(0, 19000), (1, 24000), (2, 28000)]) def test_cond_false_10_12_14(nkids, net_inc): p = srd.Person(age=45) hh = srd.Hhold(p, prov='qc') for _ in range(nkids): k = srd.Dependent(age=12) hh.add_dependent(k) qc_form.file(hh) p.prov_return['net_income'] = net_inc assert qc_form.health_contrib(p, hh) > 0 @pytest.mark.parametrize('nkids, net_inc', [(0, 23800), (1, 27000), (2, 29900)]) def test_cond_true_16_18_20(nkids, net_inc): p0 = srd.Person(age=45) p1 = srd.Person(age=45) hh = srd.Hhold(p0, p1, prov='qc') for _ in range(nkids): k = srd.Dependent(age=12) hh.add_dependent(k) qc_form.file(hh) p0.prov_return['net_income'] = net_inc assert qc_form.health_contrib(p0, hh) == 0 @pytest.mark.parametrize('nkids, net_inc', [(0, 24000), (1, 28000), (2, 30000)]) def test_cond_true_16_18_20(nkids, net_inc): p0 = srd.Person(age=45) p1 = srd.Person(age=45) hh = srd.Hhold(p0, p1, prov='qc') for _ in range(nkids): k = srd.Dependent(age=12) hh.add_dependent(k) qc_form.file(hh) p0.prov_return['net_income'] = net_inc assert qc_form.health_contrib(p0, hh) > 0 @pytest.mark.parametrize('inc_gis, amount', [(9300, 0), (9200, 50)]) def test_cond27(inc_gis, amount): p = srd.Person(age=78) hh = srd.Hhold(p, prov='qc') qc_form.file(hh) p.inc_gis = inc_gis p.prov_return['net_income'] = 41e3 assert qc_form.health_contrib(p, hh) == amount @pytest.mark.parametrize('inc_gis, amount', [(5850, 0), (5800, 50)]) def test_cond28(inc_gis, amount): p0 = srd.Person(age=78) p1 = srd.Person(age=78) hh = srd.Hhold(p0, p1, prov='qc') qc_form.file(hh) p0.inc_gis = inc_gis p0.prov_return['net_income'] = 41e3 assert qc_form.health_contrib(p0, hh) == amount @pytest.mark.parametrize('inc_gis, amount', [(5400, 0), (5300, 50)]) def test_cond29(inc_gis, amount): p0 = srd.Person(age=78) p1 = srd.Person(age=62) hh = srd.Hhold(p0, p1, prov='qc') qc_form.file(hh) p0.inc_gis = inc_gis p0.prov_return['net_income'] = 41e3 assert qc_form.health_contrib(p0, hh) == amount @pytest.mark.parametrize('inc_gis, amount', [(8700, 0), (8600, 50)]) def test_cond31(inc_gis, amount): p0 = srd.Person(age=78) p1 = srd.Person(age=59) hh = srd.Hhold(p0, p1, prov='qc') qc_form.file(hh) p0.inc_gis = inc_gis p0.prov_return['net_income'] = 41e3 assert qc_form.health_contrib(p0, hh) == amount @pytest.mark.parametrize('net_income, amount', [(18570, 0), (41e3, 50), (41265, 50), (134e3, 175), (200e3, 1000)]) def test_amount(net_income, amount): p = srd.Person(age=70, earn=50e3) hh = srd.Hhold(p, prov='qc') qc_form.file(hh) p.prov_return['net_income'] = net_income assert qc_form.health_contrib(p, hh) == amount

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null

0

1

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null

0

5

bc035b4b70d0a3429c3ff33bf4cc7dc17a4cdf3b

71

py

Python

django_src/dashboard/services/__init__.py

jup014/Walk-Data-Processing

5951df6e467702ab0bc3c2721cb5457b0a074aa4

[ "MIT" ]

null

django_src/dashboard/services/__init__.py

jup014/Walk-Data-Processing

5951df6e467702ab0bc3c2721cb5457b0a074aa4

[ "MIT" ]

null

django_src/dashboard/services/__init__.py

jup014/Walk-Data-Processing

5951df6e467702ab0bc3c2721cb5457b0a074aa4

[ "MIT" ]

null

from .CSVFileUploadService import * from .TaskExecutionService import *

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0.84507

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71

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35.5

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null

0

1

0

1

0

1

0

5

bc0936deee1aa189e858d5bcfc6c0b04b05f6fe3

60

py

Python

hello.py

Irene17/Python

9054771d7d388d4cfb03f7063ff60ad03cac708c

[ "MIT" ]

null

hello.py

Irene17/Python

9054771d7d388d4cfb03f7063ff60ad03cac708c

[ "MIT" ]

1

2020-05-14T08:40:19.000Z

2020-05-14T08:40:58.000Z

hello.py

Irene17/Python

9054771d7d388d4cfb03f7063ff60ad03cac708c

[ "MIT" ]

null

print("This line will be printed.") print("Goodbye, World!")

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null

0

1

0

1

0

5

70b9dfa2a333b9acfe7102b45a5b201511641b5f

323

py

Python

run.py

llzgsh/mykit-db-sync

09f4d512fbbb865cddc7357cb1a5f40929865237

[ "Apache-2.0" ]

null

run.py

llzgsh/mykit-db-sync

09f4d512fbbb865cddc7357cb1a5f40929865237

[ "Apache-2.0" ]

null

run.py

llzgsh/mykit-db-sync

09f4d512fbbb865cddc7357cb1a5f40929865237

[ "Apache-2.0" ]

null

import os jar="./mykit-db-transfer/target/mykit-db-transfer-1.0.0.jar;./mykit-db-common/target/mykit-db-common-1.0.0.jar;./lib/ojdbc8-full/ojdbc8.jar;" for f in os.listdir("./target/lib"): jar+='./target/lib/'+f+";" os.system("java -DisDelete=true -cp %s io.mykit.db.transfer.Main oracle_to_oracle_jobs.xml "%(jar,))

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0.69969

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3.779661

0.491525

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0.201794

0.053812

0

0.02649

0.065015

323

8

142

40.375

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0

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0.2

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null

0

1

0

1

0

1

null

0

5

70d6d46c966e79d1ca76edff7f3ebf7dd152d477

71

py

Python

day3/exercise/p4.py

AkshayManchanda/Python_Training

5a50472d118ac6d40145bf1dd60f26864bf9fb6c

[ "MIT" ]

null

day3/exercise/p4.py

AkshayManchanda/Python_Training

5a50472d118ac6d40145bf1dd60f26864bf9fb6c

[ "MIT" ]

null

day3/exercise/p4.py

AkshayManchanda/Python_Training

5a50472d118ac6d40145bf1dd60f26864bf9fb6c

[ "MIT" ]

null

mylist = [1,1,1,1,1,2,2,2,2,3,3,3,3] new_set=set(mylist) print(new_set)

23.666667

36

0.661972

21

71

2.142857

0.333333

0.177778

0.2

0.177778

0

0.19403

0.056338

71

3

37

23.666667

0.477612

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false

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0.333333

1

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null

0

1

0

1

0

1

0

null

0

5

70dc7273578e8bd38aa24ff7fc7f3ea580052e0b

66

py

Python

src/optimizer/__init__.py

Enchan1207/NeuralNetwork_Learning

e224c3a6cf109ae319f4248841dbb57f65bdfd4b

[ "MIT" ]

null

src/optimizer/__init__.py

Enchan1207/NeuralNetwork_Learning

e224c3a6cf109ae319f4248841dbb57f65bdfd4b

[ "MIT" ]

2

2022-02-13T07:41:29.000Z

2022-02-21T10:31:28.000Z

src/optimizer/__init__.py

Enchan1207/NeuralNetwork_Learning

e224c3a6cf109ae319f4248841dbb57f65bdfd4b

[ "MIT" ]

null

# # 学習オプティマイザ # from .base import Optimizer from .sgd import SGD

9.428571

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66

5.333333

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true

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1

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1

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5

70e410491c616db7892b61c8af78b0d65c7eed98

5,100

py

Python

customics/encoders/encoders.py

HakimBenkirane/customics

7c9c9df7a571fd0b6e54d9e17b05285f52269300

[ "MIT" ]

null

customics/encoders/encoders.py

HakimBenkirane/customics

7c9c9df7a571fd0b6e54d9e17b05285f52269300

[ "MIT" ]

null

customics/encoders/encoders.py

HakimBenkirane/customics

7c9c9df7a571fd0b6e54d9e17b05285f52269300

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed 01 Sept 2021 @author: Hakim Benkirane CentraleSupelec MICS laboratory 9 rue Juliot Curie, Gif-Sur-Yvette, 91190 France Build the Standard Encoder module. """ import torch.nn as nn from collections import OrderedDict from customics.tools import FullyConnectedLayer class Encoder(nn.Module): """ Standard Encoder Network. """ def __init__(self, input_dim, hidden_dim, latent_dim, norm_layer=nn.BatchNorm1d, leaky_slope=0.2, dropout=0, debug=False): """ Constructs the Standard Encoder network Parameters ---------- input_dim: int Dimension of the input tensor. hidden_dim: list List of dimensions for the multiple intermediate layers. latent_dim: int Dimension of the resulting latent representation. norm_layer: pytorch.nn Normalization Layer. leaky_slope: float Coefficient for the Leaky ReLU (must be between 0 and 1). dropout: float Dropout rate (must be between 0 and 1). debug: bool Debug parameter, prints the intermediate tensors during training. """ super(Encoder, self).__init__() self.dt_layers = OrderedDict() self.dt_layers['InputLayer'] = FullyConnectedLayer(input_dim, hidden_dim[0], norm_layer=norm_layer, leaky_slope=leaky_slope, dropout=dropout, activation=True) block_layer_num = len(hidden_dim) dropout_flag = True for num in range(1, block_layer_num): self.dt_layers['Layer{}'.format(num)] = FullyConnectedLayer(hidden_dim[num - 1], hidden_dim[num], norm_layer=norm_layer, leaky_slope=leaky_slope, dropout=dropout_flag*dropout, activation=True) # dropout for every other layer dropout_flag = not dropout_flag # the output fully-connected layer of the classifier self.dt_layers['OutputLayer']= FullyConnectedLayer(hidden_dim[-1], latent_dim, norm_layer=norm_layer, leaky_slope=leaky_slope, dropout=0, activation=False, normalization=False) self.net = nn.Sequential(self.dt_layers) def forward(self, x): h = self.net(x) return h def get_outputs(self, x): lt_output = [] for layer in self.net: lt_output.append(layer(x)) class ProbabilisticEncoder(nn.Module): """ Module that performs the inference step for the variational autoencoder """ def __init__(self, input_dim, hidden_dim, latent_dim, norm_layer=nn.BatchNorm1d, leaky_slope=0.2, dropout=0, debug=False): """ Constructs the inference network for the VAE architecture Parameters ---------- input_dim: int Dimension of the input tensor. hidden_dim: list List of dimensions for the multiple intermediate layers. latent_dim: int Dimension of the resulting latent representation. norm_layer: pytorch.nn Normalization Layer. leaky_slope: float Coefficient for the Leaky ReLU (must be between 0 and 1) dropout: float Dropout rate (must be between 0 and 1) debug: bool Debug parameter, prints the intermediate tensors during training """ super(ProbabilisticEncoder, self).__init__() self.dt_layers = OrderedDict() self.dt_layers['InputLayer'] = FullyConnectedLayer(input_dim, hidden_dim[0], norm_layer=norm_layer, leaky_slope=leaky_slope, dropout=dropout, activation=True) block_layer_num = len(hidden_dim) dropout_flag = True for num in range(1, block_layer_num): self.dt_layers['Layer{}'.format(num)] = FullyConnectedLayer(hidden_dim[num - 1], hidden_dim[num], norm_layer=norm_layer, leaky_slope=leaky_slope, dropout=dropout_flag*dropout, activation=True) # dropout for every other layer dropout_flag = not dropout_flag # the output fully-connected layer of the classifier self.net = nn.Sequential(self.dt_layers) self.mean_layer = FullyConnectedLayer(hidden_dim[-1], latent_dim, norm_layer=norm_layer, leaky_slope=leaky_slope, dropout=0, activation=False, normalization=False) self.log_var_layer = FullyConnectedLayer(hidden_dim[-1], latent_dim, norm_layer=norm_layer, leaky_slope=leaky_slope, dropout=0, activation=False, normalization=False) def forward(self, x): h = self.net(x) mean = self.mean_layer(h) log_var = self.log_var_layer(h) return mean, log_var

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0.60451

579

5,100

5.124352

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0.054601

0.045501

0.042467

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0

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0

null

0

1

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null

0

5

70f5fbdfad920909d244fc8df3bcff6048a44c68

2,918

py

Python

Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/python/estimator/estimator_lib.py

JustinACoder/H22-GR3-UnrealAI

361eb9ef1147f8a2991e5f98c4118cd823184adf

[ "MIT" ]

6

2022-02-04T18:12:24.000Z

2022-03-21T23:57:12.000Z

Lib/site-packages/tensorflow/python/estimator/estimator_lib.py

shfkdroal/Robot-Learning-in-Mixed-Adversarial-and-Collaborative-Settings

1fa4cd6a566c8745f455fc3d2273208f21f88ced

[ "bzip2-1.0.6" ]

null

Lib/site-packages/tensorflow/python/estimator/estimator_lib.py

shfkdroal/Robot-Learning-in-Mixed-Adversarial-and-Collaborative-Settings

1fa4cd6a566c8745f455fc3d2273208f21f88ced

[ "bzip2-1.0.6" ]

1

2022-02-08T03:53:23.000Z

# Copyright 2017 The TensorFlow Authors. 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. # ============================================================================== """Estimator: High level tools for working with models.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # pylint: disable=unused-import,line-too-long,wildcard-import from tensorflow.python.estimator.canned.baseline import BaselineClassifier from tensorflow.python.estimator.canned.baseline import BaselineRegressor from tensorflow.python.estimator.canned.boosted_trees import BoostedTreesClassifier from tensorflow.python.estimator.canned.boosted_trees import BoostedTreesRegressor from tensorflow.python.estimator.canned.dnn import DNNClassifier from tensorflow.python.estimator.canned.dnn import DNNRegressor from tensorflow.python.estimator.canned.dnn_linear_combined import DNNLinearCombinedClassifier from tensorflow.python.estimator.canned.dnn_linear_combined import DNNLinearCombinedRegressor from tensorflow.python.estimator.canned.linear import LinearClassifier from tensorflow.python.estimator.canned.linear import LinearRegressor from tensorflow.python.estimator.canned.parsing_utils import classifier_parse_example_spec from tensorflow.python.estimator.canned.parsing_utils import regressor_parse_example_spec from tensorflow.python.estimator.estimator import Estimator from tensorflow.python.estimator.estimator import VocabInfo from tensorflow.python.estimator.estimator import WarmStartSettings from tensorflow.python.estimator.export import export_lib as export from tensorflow.python.estimator.exporter import Exporter from tensorflow.python.estimator.exporter import FinalExporter from tensorflow.python.estimator.exporter import LatestExporter from tensorflow.python.estimator.inputs import inputs from tensorflow.python.estimator.keras import model_to_estimator from tensorflow.python.estimator.model_fn import EstimatorSpec from tensorflow.python.estimator.model_fn import ModeKeys from tensorflow.python.estimator.run_config import RunConfig from tensorflow.python.estimator.training import EvalSpec from tensorflow.python.estimator.training import train_and_evaluate from tensorflow.python.estimator.training import TrainSpec # pylint: enable=unused-import,line-too-long,wildcard-import

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null

0

1

0

1

0

1

0

5

cb037fd7b075dc46ceead68e0ebc9e3c6eb20d88

69

py

Python

crawlit/utils.py

ihor-nahuliak/crawlit

138d02968e88c14da6b441852d8e09ebb0c29140

[ "MIT" ]

null

crawlit/utils.py

ihor-nahuliak/crawlit

138d02968e88c14da6b441852d8e09ebb0c29140

[ "MIT" ]

null

crawlit/utils.py

ihor-nahuliak/crawlit

138d02968e88c14da6b441852d8e09ebb0c29140

[ "MIT" ]

null

def is_xpath_selector(selector): return selector.startswith('/')

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0.115942

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2

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null

0

1

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1

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5

cb3425f636bc3f9f02d6bbd712a32c7d54f8701f

3,276

py

Python

CircuitAnalysis/BuildTimes/binomial-15.py

isislovecruft/torflow

666689ad18d358d764a35d041a7b16adb8d3287c

[ "BSD-3-Clause" ]

null

CircuitAnalysis/BuildTimes/binomial-15.py

isislovecruft/torflow

666689ad18d358d764a35d041a7b16adb8d3287c

[ "BSD-3-Clause" ]

1

2018-12-18T15:58:40.000Z

2018-12-26T16:52:51.000Z

CircuitAnalysis/BuildTimes/binomial-15.py

isislovecruft/torflow

666689ad18d358d764a35d041a7b16adb8d3287c

[ "BSD-3-Clause" ]

null

#!/usr/bin/python # # Uses the binomial distribution to estimate the expected number of # circuit 15-circ groups before a false positive that discards all of our # 15-circ groups for a few different parameters. import math def fact(n): if n==1: return 1 return n*fact(n-1) def choose(n, k): return fact(n)/(fact(k)*fact(n-k)) def binomial(p, n, k): return choose(n,k)*math.pow(p,k)*math.pow(1-p,n-k) def BinomialF(p, n, k): F = 0.0 for i in xrange(k,n): F+=binomial(p,n,i) return F twenty_pct = BinomialF(.2, 15, 12) thirty_pct = BinomialF(.3, 15, 12) fourty_pct = BinomialF(.4, 15, 12) fifty_pct = BinomialF(.5, 15, 12) sixty_pct = BinomialF(.6, 15, 12) seventy_pct = BinomialF(.7, 15, 12) eighty_pct = BinomialF(.8, 15, 12) ninety_pct = BinomialF(.9, 15, 12) print "12 out of 15:" print "20% circ timeout rate expects "+str(1.0/twenty_pct)+" 15-circ groups" print "30% circ timeout rate expects "+str(1.0/thirty_pct) +" 15-circ groups" print "40% circ timeout rate expects "+str(1.0/fourty_pct) +" 15-circ groups" print "50% circ timeout rate expects "+str(1.0/fifty_pct)+" 15-circ groups" print "60% circ timeout rate expects "+str(1.0/sixty_pct)+" 15-circ groups" print "70% circ timeout rate expects "+str(1.0/seventy_pct)+" 15-circ groups" print "80% circ timeout rate expects "+str(1.0/eighty_pct)+" 20-circ groups" print "90% circ timeout rate expects "+str(1.0/ninety_pct)+" 20-circ groups" print twenty_pct = BinomialF(.2, 15, 13) thirty_pct = BinomialF(.3, 15, 13) fourty_pct = BinomialF(.4, 15, 13) fifty_pct = BinomialF(.5, 15, 13) sixty_pct = BinomialF(.6, 15, 13) seventy_pct = BinomialF(.7, 15, 13) eighty_pct = BinomialF(.8, 15, 13) ninety_pct = BinomialF(.9, 15, 13) print "13 out of 15:" print "20% circ timeout rate expects "+str(1.0/twenty_pct) +" 15-circ groups" print "30% circ timeout rate expects "+str(1.0/thirty_pct) +" 15-circ groups" print "40% circ timeout rate expects "+str(1.0/fourty_pct) +" 15-circ groups" print "50% circ timeout rate expects "+str(1.0/fifty_pct)+" 15-circ groups" print "60% circ timeout rate expects "+str(1.0/sixty_pct)+" 15-circ groups" print "70% circ timeout rate expects "+str(1.0/seventy_pct)+" 15-circ groups" print "80% circ timeout rate expects "+str(1.0/eighty_pct)+" 20-circ groups" print "90% circ timeout rate expects "+str(1.0/ninety_pct)+" 20-circ groups" print twenty_pct = BinomialF(.2, 15, 14) thirty_pct = BinomialF(.3, 15, 14) fourty_pct = BinomialF(.4, 15, 14) fifty_pct = BinomialF(.5, 15, 14) sixty_pct = BinomialF(.6, 15, 14) seventy_pct = BinomialF(.7, 15, 14) eighty_pct = BinomialF(.8, 15, 14) ninety_pct = BinomialF(.9, 15, 14) print "14 out of 15:" print "20% circ timeout rate expects "+str(1.0/twenty_pct)+" 15-circ groups" print "30% circ timeout rate expects "+str(1.0/thirty_pct) +" 15-circ groups" print "40% circ timeout rate expects "+str(1.0/fourty_pct) +" 15-circ groups" print "50% circ timeout rate expects "+str(1.0/fifty_pct)+" 15-circ groups" print "60% circ timeout rate expects "+str(1.0/sixty_pct)+" 15-circ groups" print "70% circ timeout rate expects "+str(1.0/seventy_pct)+" 15-circ groups" print "80% circ timeout rate expects "+str(1.0/eighty_pct)+" 20-circ groups" print "90% circ timeout rate expects "+str(1.0/ninety_pct)+" 20-circ groups" print

40.444444

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null

0

1

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1

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5

cb39ba9e29508f7383d385a01d0233ddc36e37ab

73

py

Python

data_visualization.py

liangtrevor/wkw-visualization

22541699ec74596b9e8916c18d12e1425fc1873c

[ "MIT" ]

null

data_visualization.py

liangtrevor/wkw-visualization

22541699ec74596b9e8916c18d12e1425fc1873c

[ "MIT" ]

null

data_visualization.py

liangtrevor/wkw-visualization

22541699ec74596b9e8916c18d12e1425fc1873c

[ "MIT" ]

null

import matplotlib as pl import numpy as np import pandas as pd import csv

18.25

23

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1

0

null

0

1

0

1

0

1

0

5

cb5a72227fab3d6927474efd9722ef9f7cee8017

123

py

Python

backend/properties/admin.py

crowdbotics-apps/confer-32440

8d631d4899c45ce6bac3ff355f7b87cd02a0271c

[ "FTL", "AML", "RSA-MD" ]

null

backend/properties/admin.py

crowdbotics-apps/confer-32440

8d631d4899c45ce6bac3ff355f7b87cd02a0271c

[ "FTL", "AML", "RSA-MD" ]

null

backend/properties/admin.py

crowdbotics-apps/confer-32440

8d631d4899c45ce6bac3ff355f7b87cd02a0271c

[ "FTL", "AML", "RSA-MD" ]

null

from django.contrib import admin from .models import Property admin.site.register(Property) # Register your models here.

17.571429

32

0.804878

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123

5.823529

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1

0

1

0

1

0

5

cb7afc3ff3404b11f1e610d50ce75a80f502d049

52

py

Python

configs/__init__.py

light1726/VAENAR-TTS

5ca9661cf3835e0f3f3e9bbab85fb95e6a9c7c7a

[ "MIT" ]

125

2021-07-01T20:08:42.000Z

2022-03-31T08:03:07.000Z

configs/__init__.py

light1726/VAENAR-TTS

5ca9661cf3835e0f3f3e9bbab85fb95e6a9c7c7a

[ "MIT" ]

10

2021-06-29T09:25:52.000Z

2022-03-31T07:45:38.000Z

configs/__init__.py

light1726/VAENAR-TTS

5ca9661cf3835e0f3f3e9bbab85fb95e6a9c7c7a

[ "MIT" ]

16

2021-06-29T02:49:41.000Z

2022-03-25T07:43:52.000Z

from .hparams import * from .logger import Logger

17.333333

27

0.75

7

52

5.571429

0.571429

0

0.192308

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2

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26

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true

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null

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1

0

1

0

1

0

5

cba7e5f983f177049d28507c7e8ecba193ca4683

313

py

Python

should_dsl/__init__.py

hugobr/should-dsl

f01210f0becc23173802061dc6652922d3e1845a

[ "MIT" ]

5

2015-01-28T19:17:22.000Z

2019-07-12T22:30:21.000Z

should_dsl/__init__.py

rodrigomanhaes/should-dsl

f01210f0becc23173802061dc6652922d3e1845a

[ "MIT" ]

2

2020-04-26T22:23:24.000Z

2021-04-20T13:43:53.000Z

should_dsl/__init__.py

rodrigomanhaes/should-dsl

f01210f0becc23173802061dc6652922d3e1845a

[ "MIT" ]

4

2015-08-24T18:15:48.000Z

2020-03-27T14:13:52.000Z

from should_dsl.dsl import (should, should_not, matcher, add_predicate_regex, matcher_configuration, aliases, ShouldNotSatisfied) from should_dsl import matchers

31.3

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313

6.333333

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313

9

48

34.777778

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null

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5

cbc5aad0186f66de637a4c5ebd16946b2222d91c

226

py

Python

integrationTest/src/main/resources/jsMacros/Macros/runTests.py

LeonXu98/multiconnect

650d4c071b63696e66c14674fc6390cf8d9f6b07

[ "MIT" ]

null

integrationTest/src/main/resources/jsMacros/Macros/runTests.py

LeonXu98/multiconnect

650d4c071b63696e66c14674fc6390cf8d9f6b07

[ "MIT" ]

null

integrationTest/src/main/resources/jsMacros/Macros/runTests.py

LeonXu98/multiconnect

650d4c071b63696e66c14674fc6390cf8d9f6b07

[ "MIT" ]

null

from net.earthcomputer.multiconnect.api import Protocols from net.earthcomputer.multiconnect.integrationtest import IntegrationTest ip = IntegrationTest.setupServer(Protocols.V1_16_5) Client.connect(ip) Client.waitTick(100)

28.25

74

0.858407

27

226

7.111111

0.62963

0.072917

0.208333

0.333333

0

0.033175

0.066372

226

7

75

32.285714

0.876777

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

5

1db972c3f02f97bfb04074d0630bae5025beba81

95

py

Python

Django_rest_project/books/books_api/admin.py

Beshkov/Python-web-fundamentals

6b0e9cc9725ea80a33c2ebde6e29f2ab585ab8d9

[ "MIT" ]

null

Django_rest_project/books/books_api/admin.py

Beshkov/Python-web-fundamentals

6b0e9cc9725ea80a33c2ebde6e29f2ab585ab8d9

[ "MIT" ]

null

Django_rest_project/books/books_api/admin.py

Beshkov/Python-web-fundamentals

6b0e9cc9725ea80a33c2ebde6e29f2ab585ab8d9

[ "MIT" ]

null

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

19

32

0.831579

13

95

6.076923

0.692308

0

0.105263

95

5

33

19

0.929412

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1dd702aa81b7eeb809ec883551284dfd6af1e42b

26

py

Python

share/python/tools/python/Core/__init__.py

lucas-bremond/spacer-core

08966016dc5d870a80a289a453396b038f61cc1b

[ "MIT" ]

null

share/python/tools/python/Core/__init__.py

lucas-bremond/spacer-core

08966016dc5d870a80a289a453396b038f61cc1b

[ "MIT" ]

1

2018-03-05T05:13:50.000Z

share/python/tools/python/Core/__init__.py

lucas-bremond/spacer-core

08966016dc5d870a80a289a453396b038f61cc1b

[ "MIT" ]

null

from SpacerCorePy import *

26

0.846154

3

26

7.333333

1

0

0.115385

26

1

26

0.956522

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

1de49377e792a2616a72a8e5fb58fdf0cd0f4af7

2,622

py

Python

utils/api_helper.py

MohammedWaasim/api-lite

f69367f3733a6db90df1ffe3e3a5e3f0d04ee6e0

[ "MIT" ]

null

utils/api_helper.py

MohammedWaasim/api-lite

f69367f3733a6db90df1ffe3e3a5e3f0d04ee6e0

[ "MIT" ]

null

utils/api_helper.py

MohammedWaasim/api-lite

f69367f3733a6db90df1ffe3e3a5e3f0d04ee6e0

[ "MIT" ]

null

import logging import pdb import json import requests import utils.custom_logger as cl class ApiHelper(): log=cl.customLogger(logging.DEBUG) def __init__(self,apikey): self.apikey=apikey def get(self,uri,params=None,header=None): try: if not header: header={} header["Content-Type"]="application/json" header["Authorization"]=self.apikey response=requests.get(url=uri,params=params,headers=header) if(response.status_code==200): return response.json() else: self.log.info("the requested url is not successful please check the url and params " + uri + " " + str(params)) self.log.info("response received is " + response.json()) except: self.log.error("unable to perform get call for url " + uri + " params " + str(params)) def post(self,uri,payload=None,header=None): try: if not header: header = {} header["Content-Type"] = "application/json" header["Authorization"] = self.apikey header['Accept']= 'text/plain' res = requests.post(url=uri,json=payload,headers=header) #here ideally it should be 201 status code if(res.status_code==200): self.log.info("the requested url is successful") return res.json() else: self.log.info("the requested url is not successful please check the url and params " + uri + " " + str(payload)) self.log.info("response received is "+res.json()) except Exception as e: self.log.error("unable to perform post call bcoz of "+e) def put(self,uri,payload=None,header=None): try: if not header: header = {} header["Content-Type"] = "application/json" header["Authorization"] = self.apikey header['Accept']= 'text/plain' res = requests.put(url=uri,json=payload,headers=header) #here ideally it should be 201 status code if(res.status_code==200): self.log.info("the requested url is successful") return res.json() else: self.log.info("the requested url is not successful please check the url and params " + uri + " " + str(payload)) self.log.info("response received is " + res.json()) except Exception as e: self.log.error("unable to perform post call bcoz of "+e)

42.290323

128

0.563692

311

2,622

4.726688

0.234727

0.052381

0.059864

0.047619

0.77551

0.737415

0

0.008508

0.327613

2,622

61

129

42.983607

0.825298

0.031274

0

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false

0

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0

0.254545

0

null

0

1

0

null

0

5

380dc32d46bd05fc0df91a53b601bffc6fbf113a

126

py

Python

06. Python Essentials/06. Functions/05. Calculate Rectangle Area.py

tdrv90/softuni-courses

ebf48083211f499050c04a237627c3a9c5367de7

[ "MIT" ]

null

06. Python Essentials/06. Functions/05. Calculate Rectangle Area.py

tdrv90/softuni-courses

ebf48083211f499050c04a237627c3a9c5367de7

[ "MIT" ]

2

2021-05-08T08:50:10.000Z

2021-05-08T08:50:53.000Z

06. Python Essentials/06. Functions/05. Calculate Rectangle Area.py

tdrv90/softuni-courses

ebf48083211f499050c04a237627c3a9c5367de7

[ "MIT" ]

null

a = int(input()) b = int(input()) def rectangle_area(a, b): return '{:.0f}'.format(a * b) print(rectangle_area(a, b))

12.6

33

0.587302

21

126

3.428571

0.52381

0.083333

0.388889

0.416667

0

0.009709

0.18254

126

9

34

14

0.68932

0

0.047619

0

1

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false

0

0.2

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null

0

1

0

1

0

null

0

1

0

5

69c99444146b57d4ce1f0083083a91171275c99e

49

py

Python

script.py

Funathon-Duvel/Chimay

c71a942627deb7ae42eb22607fbd97a75b447c9f

[ "MIT" ]

null

script.py

Funathon-Duvel/Chimay

c71a942627deb7ae42eb22607fbd97a75b447c9f

[ "MIT" ]

null

script.py

Funathon-Duvel/Chimay

c71a942627deb7ae42eb22607fbd97a75b447c9f

[ "MIT" ]

null

print("Hello world !") print("Who wants a beer?")

24.5

26

0.673469

8

49

4.125

0.875

0

0.122449

49

2

26

24.5

0.767442

0

0.6

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

38b6acebd31058341e6a6d8b075c43487cf7adaf

51

py

Python

common/exceptions.py

shapeshift-legacy/watchtower

c9cd5150f8549145f7de9b1ea820d548959350fe

[ "MIT" ]

null

common/exceptions.py

shapeshift-legacy/watchtower

c9cd5150f8549145f7de9b1ea820d548959350fe

[ "MIT" ]

null

common/exceptions.py

shapeshift-legacy/watchtower

c9cd5150f8549145f7de9b1ea820d548959350fe

[ "MIT" ]

null

class XPubNotRegisteredError(ValueError): pass

17

41

0.803922

4

51

10.25

1

0

0.137255

51

2

42

25.5

0.931818

0

1

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true

0.5

0

0.5

0

1

0

1

null

0

1

0

null

0

1

0

5

38c95db70bfb0d9bb5886b6c8980e4bfe5129ea0

185

py

Python

dataset/research/__init__.py

mikhailkin/dataset

7417483fdbe2e3743af4d614cb9036fd5b1375c0

[ "Apache-2.0" ]

null

dataset/research/__init__.py

mikhailkin/dataset

7417483fdbe2e3743af4d614cb9036fd5b1375c0

[ "Apache-2.0" ]

null

dataset/research/__init__.py

mikhailkin/dataset

7417483fdbe2e3743af4d614cb9036fd5b1375c0

[ "Apache-2.0" ]

null

""" Research module. """ from .grid import KV, Grid, Option, ConfigAlias from .distributor import Worker, Distributor from .workers import PipelineWorker from .research import Research

30.833333

47

0.789189

22

185

6.636364

0.545455

0

0.12973

185

5

48

37

0.906832

0.086486

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

38d3cffa899476fed96ed352a9bccd51cd4dc909

142

py

Python

functions.py

BONK1/Python

0690539ad33bf9d626b4fd005e2207ac5bba4ec1

[ "MIT" ]

null

functions.py

BONK1/Python

0690539ad33bf9d626b4fd005e2207ac5bba4ec1

[ "MIT" ]

null

functions.py

BONK1/Python

0690539ad33bf9d626b4fd005e2207ac5bba4ec1

[ "MIT" ]

null

#Functions def userFunction(): #Putting Function print("Hello, User :)") print("Have a nice day!") #Calling Function userFunction()

15.777778

37

0.683099

16

142

6.0625

0.8125

0

0.176056

142

8

38

17.75

0.82906

0.288732

0

0.306122

0

1

0.25

true

0

0.25

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

2a2c5f397c00032f7691b851a8768c8149381338

233

py

Python

account.py

victorlomi/Password-Locker

2bba04e3afa3e70304db8fcdb27557d87bf5a50b

[ "Unlicense" ]

null

account.py

victorlomi/Password-Locker

2bba04e3afa3e70304db8fcdb27557d87bf5a50b

[ "Unlicense" ]

null

account.py

victorlomi/Password-Locker

2bba04e3afa3e70304db8fcdb27557d87bf5a50b

[ "Unlicense" ]

null

class Account: """Store login information(username and password).""" def __init__(self, username='', password=''): """Store username and password.""" self.username = username self.password = password

29.125

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0.630901

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null

0

1

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2a2cc900cfef92f424a3a71d1499d90fa438e6c7

116

py

Python

mitm/__init__.py

zentooo/mitm

d023955d359f9f9b7cbaf6fb84f9517d6ec285dd

[ "MIT" ]

null

mitm/__init__.py

zentooo/mitm

d023955d359f9f9b7cbaf6fb84f9517d6ec285dd

[ "MIT" ]

null

mitm/__init__.py

zentooo/mitm

d023955d359f9f9b7cbaf6fb84f9517d6ec285dd

[ "MIT" ]

null

from .gen_keys import create_self_signed_cert from .server import ManInTheMiddle from .stream import EmulatedClient

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null

0

1

0

1

0

1

0

5

aa495973d8bfe962f48d76b21dad01dc5ab3e5ed

87

py

Python

SignalLib/tests/test_instantiation.py

mchiuminatto/MVA_Crossover

9ad581231a8339229a48a65c1dc9030f87eeefd2

[ "MIT" ]

null

SignalLib/tests/test_instantiation.py

mchiuminatto/MVA_Crossover

9ad581231a8339229a48a65c1dc9030f87eeefd2

[ "MIT" ]

null

SignalLib/tests/test_instantiation.py

mchiuminatto/MVA_Crossover

9ad581231a8339229a48a65c1dc9030f87eeefd2

[ "MIT" ]

null

from SignalLib.Signal import Signal def test_instantiation(): _sig = Signal()

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null

0

1

0

1

0

1

0

5

aa724d05deef21258010c21ab87421b159c908c2

434

py

Python

indexcreator.py

madeso/prettygood

ba09141bc61664253230d68f03b5a2de1f27ab75

[ "MIT" ]

null

indexcreator.py

madeso/prettygood

ba09141bc61664253230d68f03b5a2de1f27ab75

[ "MIT" ]

null

indexcreator.py

madeso/prettygood

ba09141bc61664253230d68f03b5a2de1f27ab75

[ "MIT" ]

null

#!/usr/bin/env python3 class IndexCreator: def __init__(self): self._index = 0 def generate(self): r = self._index self._index += 1 return r def clear(self): self._index = 0 if __name__ == "__main__": i = IndexCreator() print(i.generate(), i.generate(), i.generate(), i.generate()) i.clear() print(i.generate(), i.generate(), i.generate(), i.generate())

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434

4.240741

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0.305677

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0.362445

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0.012739

0.276498

434

19

66

22.842105

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0

null

1

0

1

0

null

0

1

0

5

aa7a7ae1fd0734b3d565d268cff0f4337db5cb23

18

py

Python

problog/version.py

HEmile/problog

576b6fd305f72b12125111c8d4d62cf8a7bbda0f

[ "Apache-2.0" ]

189

2019-05-27T08:20:10.000Z

2022-03-28T09:29:22.000Z

problog/version.py

HEmile/problog

576b6fd305f72b12125111c8d4d62cf8a7bbda0f

[ "Apache-2.0" ]

60

2019-06-11T15:07:48.000Z

2022-03-25T02:31:23.000Z

problog/version.py

HEmile/problog

576b6fd305f72b12125111c8d4d62cf8a7bbda0f

[ "Apache-2.0" ]

33

2019-07-03T13:14:24.000Z

2022-02-20T01:07:15.000Z

version = '2.2.2'

9

17

0.555556

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18

2.5

0.5

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1

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false

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null

1

0

1

0

1

0

null

0

5

aaaa9af5044444936c8f5d8b4e083ab7bb69b67c

497

py

Python

toontown/cogdominium/DistributedCogdoBattleBldg.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

99

2019-11-02T22:25:00.000Z

2022-02-03T03:48:00.000Z

toontown/cogdominium/DistributedCogdoBattleBldg.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

42

2019-11-03T05:31:08.000Z

2022-03-16T22:50:32.000Z

toontown/cogdominium/DistributedCogdoBattleBldg.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

57

2019-11-03T07:47:37.000Z

2022-03-22T00:41:49.000Z

from direct.directnotify import DirectNotifyGlobal from toontown.toonbase import TTLocalizer from toontown.battle import DistributedBattleBldg class DistributedCogdoBattleBldg(DistributedBattleBldg.DistributedBattleBldg): notify = DirectNotifyGlobal.directNotify.newCategory('DistributedCogdoBattleBldg') def __init__(self, cr): DistributedBattleBldg.DistributedBattleBldg.__init__(self, cr) def getBossBattleTaunt(self): return TTLocalizer.CogdoBattleBldgBossTaunt

38.230769

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0.333333

0.111111

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0

1

null

0

null

0

1

0

1

0

5

aab1d1591921bb31fae6fbdeecf63f2664a1844a

138

py

Python

application/notifications/__init__.py

QualiChain/qualichain_backend

cc6dbf1ae5d09e8d01cccde94326563b25d28b58

[ "MIT" ]

null

application/notifications/__init__.py

QualiChain/qualichain_backend

cc6dbf1ae5d09e8d01cccde94326563b25d28b58

[ "MIT" ]

null

application/notifications/__init__.py

QualiChain/qualichain_backend

cc6dbf1ae5d09e8d01cccde94326563b25d28b58

[ "MIT" ]

null

from flask import Blueprint notification_blueprint = Blueprint('notifications', __name__) from application.notifications import routes

19.714286

61

0.84058

14

138

7.928571

0.642857

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0.108696

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62

19.714286

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false

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null

0

1

0

null

0

1

0

1

0

5

aad9a583d2df81489b7fc65c87cf7e0f2e84249c

84

py

Python

enthought/chaco/data_view.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/chaco/data_view.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/chaco/data_view.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from chaco.data_view import *

21

38

0.833333

12

84

5.333333

0.75

0

0.130952

84

3

39

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0.142857

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0

true

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

2aa319ada38bc4e282bd19fa953baf7c041c905e

114

py

Python

pygromos/files/gromos_system/ff/serenityff/serenityff_data/__init__.py

SalomeRonja/PyGromosTools

5a17740a0ec634b8b591ef74d8a420e3fd3e38ba

[ "MIT" ]

13

2021-03-17T09:29:37.000Z

2022-01-14T20:42:16.000Z

pygromos/files/gromos_system/ff/serenityff/seremityff_data/__init__.py

SchroederB/PyGromosTools

c31c38455a849c864241a962efee9e6575f27b06

[ "MIT" ]

185

2021-03-03T14:24:55.000Z

2022-03-31T18:39:29.000Z

pygromos/files/gromos_system/ff/serenityff/seremityff_data/__init__.py

SchroederB/PyGromosTools

c31c38455a849c864241a962efee9e6575f27b06

[ "MIT" ]

13

2021-03-03T14:18:06.000Z

2022-02-17T09:48:55.000Z

import os serenityff_C6 = os.path.dirname(__file__) + "/C6/" serenityff_C12 = os.path.dirname(__file__) + "/C12/"

28.5

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null

0

1

0

5

2ad6455f3a6a4304ffcd1d1a23ee3f48e854af19

77

py

Python

t/data/foo/path/to/dupe.py

tek/vim-pymport

ea918179d11a78a4e946afec1e8052e50ddd2ef7

[ "MIT" ]

null

t/data/foo/path/to/dupe.py

tek/vim-pymport

ea918179d11a78a4e946afec1e8052e50ddd2ef7

[ "MIT" ]

null

t/data/foo/path/to/dupe.py

tek/vim-pymport

ea918179d11a78a4e946afec1e8052e50ddd2ef7

[ "MIT" ]

null

1 == 2 segfault() class Dupe(object): pass class Dupe(Dupe): pass

7.7

19

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77

4.181818

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0.391304

0

0.035714

0.272727

77

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8.555556

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0.333333

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1

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true

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0

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null

1

0

1

0

null

0

1

0

5

2d4ca8995fa535c7e788e1b224647ac4d1537d68

254

py

Python

integration-tests/fake_spineroutelookup/fake_spineroutelookup/request_matcher_wrappers.py

tomzo/integration-adaptors

d4f296d3e44475df6f69a78a27fac6ed5b67513b

[ "Apache-2.0" ]

15

2019-08-06T16:08:12.000Z

2021-05-24T13:14:39.000Z

integration-tests/fake_spineroutelookup/fake_spineroutelookup/request_matcher_wrappers.py

tomzo/integration-adaptors

d4f296d3e44475df6f69a78a27fac6ed5b67513b

[ "Apache-2.0" ]

75

2019-04-25T13:59:02.000Z

2021-09-15T06:05:36.000Z

integration-tests/fake_spineroutelookup/fake_spineroutelookup/request_matcher_wrappers.py

tomzo/integration-adaptors

d4f296d3e44475df6f69a78a27fac6ed5b67513b

[ "Apache-2.0" ]

7

2019-11-12T15:26:34.000Z

2021-04-11T07:23:56.000Z

from tornado.httputil import HTTPServerRequest def query_argument_contains_string(request: HTTPServerRequest, query_argument_name: str, containing_value: str) -> bool: return containing_value in str(request.query_arguments[query_argument_name][0])

42.333333

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0.838583

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254

6.34375

0.625

0.192118

0.167488

0

0.004329

0.090551

254

5

121

50.8

0.874459

0

1

0.333333

false

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0.333333

1

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null

0

1

0

null

0

1

0

1

0

5

2d54b28d3adfbf872b6cc0da05ef3d66511f2f1e

114

py

Python

enthought/block_canvas/canvas/selectable_component_mixin.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/block_canvas/canvas/selectable_component_mixin.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/block_canvas/canvas/selectable_component_mixin.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from blockcanvas.canvas.selectable_component_mixin import *

28.5

59

0.868421

14

114

6.571429

0.785714

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114

3

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38

0.893204

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0

1

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true

0

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0

null

0

1

0

1

0

1

0

5

2d82696e62628acf0f9cb283afc8b8cbd3772896

57

py

Python

src/LASER/utils/__init__.py

BigBird01/LASER

57143200814583410acdd0c5ac0a0f8bab8a1f7e

[ "MIT" ]

7

2021-02-04T01:26:55.000Z

2021-11-23T00:38:47.000Z

src/LASER/utils/__init__.py

BigBird01/LASER

57143200814583410acdd0c5ac0a0f8bab8a1f7e

[ "MIT" ]

1

2021-03-18T00:23:17.000Z

2022-01-05T15:36:48.000Z

src/LASER/utils/__init__.py

BigBird01/LASER

57143200814583410acdd0c5ac0a0f8bab8a1f7e

[ "MIT" ]

null

from .logger_util import * from .argument_types import *

19

29

0.789474

8

57

5.375

0.75

0

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57

2

30

28.5

0.877551

0

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true

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1

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0

1

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1

0

1

0

5

2d8a6a9732756565c7d5d7731e78660b0c754292

1,006

py

Python

Misc/070_ClimbingStairs.py

PsiPhiTheta/LeetCode

b4473d3fdf317012b6224b363306d66a33b07932

[ "Unlicense" ]

1

2018-12-09T21:09:36.000Z

Misc/070_ClimbingStairs.py

PsiPhiTheta/LeetCode

b4473d3fdf317012b6224b363306d66a33b07932

[ "Unlicense" ]

null

Misc/070_ClimbingStairs.py

PsiPhiTheta/LeetCode

b4473d3fdf317012b6224b363306d66a33b07932

[ "Unlicense" ]

1

2018-12-09T21:09:40.000Z

class Solution: def climbStairs(self, n): """ :type n: int :rtype: int """ if (n < 3): # Special cases # 0 = 0: # 1 = 1: 1 # 2 = 2: 2, 1 1 return n else: # Fibonaci from here onward # 3 = 3: 1 1 1, 2 1, 1 2 # 4 = 5: 1 1 1 1, 2 1 1, 1 2 1, 1 1 2, 2 2 # 5 = 8: 1 1 1 1 1, 2 1 1 1, 1 2 1 1 , 1 1 2 1, 1 1 1 2, 2 2 1, 1 2 2, 2 1 2 # 6 = 13: 1 1 1 1 1 1, 2 1 1 1 1, 1 2 1 1 1, 1 1 2 1 1, 1 1 1 2 1, 1 1 1 1 2, # Fibonaci pattern spotted as shown above... preprev = 1 # Resume from special cases prev = 2 # Resume from special cases for i in range(n-2): # omit the first two steps temp = preprev preprev = prev # update preprev for next iter prev = prev + temp # update prev for next iter return prev

33.533333

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1

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1

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null

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5

2d968d094921ea943b5fe4bbace2c89232c234cc

166

py

Python

lib/datasets/__init__.py

Bhaskers-Blu-Org2/metric-transfer.pytorch

b0ae8ed6e6f62357100d799defbb61a78c831a87

[ "MIT" ]

51

2019-07-23T23:47:12.000Z

2022-03-04T13:03:25.000Z

lib/datasets/__init__.py

Bhaskers-Blu-Org2/metric-transfer.pytorch

b0ae8ed6e6f62357100d799defbb61a78c831a87

[ "MIT" ]

2

2021-01-25T08:08:17.000Z

2021-01-28T03:36:01.000Z

lib/datasets/__init__.py

chingisooinar/metric-transfer.pytorch

b0ae8ed6e6f62357100d799defbb61a78c831a87

[ "MIT" ]

19

2019-07-25T02:46:26.000Z

2021-03-07T17:35:37.000Z

from .cifar import CIFAR10Instance, PseudoCIFAR10 from .folder import ImageFolderInstance, PseudoDatasetFolder __all__ = ('CIFAR10Instance', 'PseudoDatasetFolder')

27.666667

60

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0

1

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1

0

5

2d9ccc7dcb06cccd843a7fd874715dd45ffe4119

190

py

Python

examples/example.py

PrestonStringham/DATA515-MusicGeneration

7df2ac49a0fbbaf0dd20ddcf3ae1c59e39797fc7

[ "MIT" ]

3

2021-03-01T08:10:26.000Z

2021-03-19T23:27:40.000Z

examples/example.py

PrestonStringham/DATA515-MusicGeneration

7df2ac49a0fbbaf0dd20ddcf3ae1c59e39797fc7

[ "MIT" ]

7

2021-03-11T04:54:03.000Z

2021-03-17T04:17:50.000Z

examples/example.py

PrestonStringham/DATA515-MusicGeneration

7df2ac49a0fbbaf0dd20ddcf3ae1c59e39797fc7

[ "MIT" ]

null

from easy_music_generator import easy_music_generator as emg import sys sys.path.append('../') emg_obj = emg.EasyMusicGenerator() emg_obj.analyze('music/') emg_obj.generate(10, 'output/')

21.111111

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1

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null

0

1

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5

2dd60b7b286d9f871b38dfd08a65b205c03fa9c2

93

py

Python

app/comments/__init__.py

goalong/flask-demo

33fc1b8a72e6c67581ac949a55773ffad9ee7af7

[ "MIT" ]

45

2016-02-20T15:20:49.000Z

2022-03-03T18:07:51.000Z

app/comments/__init__.py

goalong/flask-demo

33fc1b8a72e6c67581ac949a55773ffad9ee7af7

[ "MIT" ]

null

app/comments/__init__.py

goalong/flask-demo

33fc1b8a72e6c67581ac949a55773ffad9ee7af7

[ "MIT" ]

13

2017-02-04T13:45:55.000Z

2020-07-15T07:07:56.000Z

from flask import Blueprint comment = Blueprint('comment', __name__) from . import routes

13.285714

40

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0

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null

0

1

0

1

0

5

2deb6b84109b5eed26877a7aefdd86b28722b33b

56

py

Python

src/finmag/tests/bugs/segfault-paraview/nobug-2.py

davidcortesortuno/finmag

9ac0268d2c0e45faf1284cee52a73525aa589e2b

[ "BSL-1.0" ]

10

2018-03-24T07:43:17.000Z

2022-03-26T10:42:27.000Z

src/finmag/tests/bugs/segfault-paraview/nobug-2.py

davidcortesortuno/finmag

9ac0268d2c0e45faf1284cee52a73525aa589e2b

[ "BSL-1.0" ]

21

2018-03-26T15:08:53.000Z

2021-07-10T16:11:14.000Z

src/finmag/tests/bugs/segfault-paraview/nobug-2.py

davidcortesortuno/finmag

9ac0268d2c0e45faf1284cee52a73525aa589e2b

[ "BSL-1.0" ]

7

2018-04-09T11:50:48.000Z

2021-06-10T09:23:25.000Z

from paraview import servermanager import dolfin as df

14

34

0.839286

8

56

5.875

0.875

0

0.160714

56

3

35

18.666667

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9310b0df4d6f1fe091410ca81ba2f09a4a0d53d3

25,437

py

Python

Lib/test/test_asyncio/test_locks.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

1

2018-06-21T18:21:24.000Z

Lib/test/test_asyncio/test_locks.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

null

Lib/test/test_asyncio/test_locks.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

null

"""Tests dla lock.py""" zaimportuj unittest z unittest zaimportuj mock zaimportuj re zaimportuj asyncio z asyncio zaimportuj test_utils STR_RGX_REPR = ( r'^<(?P<class>.*?) object at (?P<address>.*?)' r'\[(?P<extras>' r'(set|unset|locked|unlocked)(,value:\d)?(,waiters:\d+)?' r')\]>\Z' ) RGX_REPR = re.compile(STR_RGX_REPR) klasa LockTests(test_utils.TestCase): def setUp(self): self.loop = self.new_test_loop() def test_ctor_loop(self): loop = mock.Mock() lock = asyncio.Lock(loop=loop) self.assertIs(lock._loop, loop) lock = asyncio.Lock(loop=self.loop) self.assertIs(lock._loop, self.loop) def test_ctor_noloop(self): asyncio.set_event_loop(self.loop) lock = asyncio.Lock() self.assertIs(lock._loop, self.loop) def test_repr(self): lock = asyncio.Lock(loop=self.loop) self.assertPrawda(repr(lock).endswith('[unlocked]>')) self.assertPrawda(RGX_REPR.match(repr(lock))) @asyncio.coroutine def acquire_lock(): uzyskaj z lock self.loop.run_until_complete(acquire_lock()) self.assertPrawda(repr(lock).endswith('[locked]>')) self.assertPrawda(RGX_REPR.match(repr(lock))) def test_lock(self): lock = asyncio.Lock(loop=self.loop) @asyncio.coroutine def acquire_lock(): zwróć (uzyskaj z lock) res = self.loop.run_until_complete(acquire_lock()) self.assertPrawda(res) self.assertPrawda(lock.locked()) lock.release() self.assertNieprawda(lock.locked()) def test_acquire(self): lock = asyncio.Lock(loop=self.loop) result = [] self.assertPrawda(self.loop.run_until_complete(lock.acquire())) @asyncio.coroutine def c1(result): jeżeli (uzyskaj z lock.acquire()): result.append(1) zwróć Prawda @asyncio.coroutine def c2(result): jeżeli (uzyskaj z lock.acquire()): result.append(2) zwróć Prawda @asyncio.coroutine def c3(result): jeżeli (uzyskaj z lock.acquire()): result.append(3) zwróć Prawda t1 = asyncio.Task(c1(result), loop=self.loop) t2 = asyncio.Task(c2(result), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual([], result) lock.release() test_utils.run_briefly(self.loop) self.assertEqual([1], result) test_utils.run_briefly(self.loop) self.assertEqual([1], result) t3 = asyncio.Task(c3(result), loop=self.loop) lock.release() test_utils.run_briefly(self.loop) self.assertEqual([1, 2], result) lock.release() test_utils.run_briefly(self.loop) self.assertEqual([1, 2, 3], result) self.assertPrawda(t1.done()) self.assertPrawda(t1.result()) self.assertPrawda(t2.done()) self.assertPrawda(t2.result()) self.assertPrawda(t3.done()) self.assertPrawda(t3.result()) def test_acquire_cancel(self): lock = asyncio.Lock(loop=self.loop) self.assertPrawda(self.loop.run_until_complete(lock.acquire())) task = asyncio.Task(lock.acquire(), loop=self.loop) self.loop.call_soon(task.cancel) self.assertRaises( asyncio.CancelledError, self.loop.run_until_complete, task) self.assertNieprawda(lock._waiters) def test_cancel_race(self): # Several tasks: # - A acquires the lock # - B jest blocked w aqcuire() # - C jest blocked w aqcuire() # # Now, concurrently: # - B jest cancelled # - A releases the lock # # If B's waiter jest marked cancelled but nie yet removed from # _waiters, A's release() call will crash when trying to set # B's waiter; instead, it should move on to C's waiter. # Setup: A has the lock, b oraz c are waiting. lock = asyncio.Lock(loop=self.loop) @asyncio.coroutine def lockit(name, blocker): uzyskaj z lock.acquire() spróbuj: jeżeli blocker jest nie Nic: uzyskaj z blocker w_końcu: lock.release() fa = asyncio.Future(loop=self.loop) ta = asyncio.Task(lockit('A', fa), loop=self.loop) test_utils.run_briefly(self.loop) self.assertPrawda(lock.locked()) tb = asyncio.Task(lockit('B', Nic), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual(len(lock._waiters), 1) tc = asyncio.Task(lockit('C', Nic), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual(len(lock._waiters), 2) # Create the race oraz check. # Without the fix this failed at the last assert. fa.set_result(Nic) tb.cancel() self.assertPrawda(lock._waiters[0].cancelled()) test_utils.run_briefly(self.loop) self.assertNieprawda(lock.locked()) self.assertPrawda(ta.done()) self.assertPrawda(tb.cancelled()) self.assertPrawda(tc.done()) def test_release_not_acquired(self): lock = asyncio.Lock(loop=self.loop) self.assertRaises(RuntimeError, lock.release) def test_release_no_waiters(self): lock = asyncio.Lock(loop=self.loop) self.loop.run_until_complete(lock.acquire()) self.assertPrawda(lock.locked()) lock.release() self.assertNieprawda(lock.locked()) def test_context_manager(self): lock = asyncio.Lock(loop=self.loop) @asyncio.coroutine def acquire_lock(): zwróć (uzyskaj z lock) przy self.loop.run_until_complete(acquire_lock()): self.assertPrawda(lock.locked()) self.assertNieprawda(lock.locked()) def test_context_manager_cant_reuse(self): lock = asyncio.Lock(loop=self.loop) @asyncio.coroutine def acquire_lock(): zwróć (uzyskaj z lock) # This spells "uzyskaj z lock" outside a generator. cm = self.loop.run_until_complete(acquire_lock()) przy cm: self.assertPrawda(lock.locked()) self.assertNieprawda(lock.locked()) przy self.assertRaises(AttributeError): przy cm: dalej def test_context_manager_no_uzyskaj(self): lock = asyncio.Lock(loop=self.loop) spróbuj: przy lock: self.fail('RuntimeError jest nie podnieśd w przy expression') wyjąwszy RuntimeError jako err: self.assertEqual( str(err), '"uzyskaj from" should be used jako context manager expression') self.assertNieprawda(lock.locked()) klasa EventTests(test_utils.TestCase): def setUp(self): self.loop = self.new_test_loop() def test_ctor_loop(self): loop = mock.Mock() ev = asyncio.Event(loop=loop) self.assertIs(ev._loop, loop) ev = asyncio.Event(loop=self.loop) self.assertIs(ev._loop, self.loop) def test_ctor_noloop(self): asyncio.set_event_loop(self.loop) ev = asyncio.Event() self.assertIs(ev._loop, self.loop) def test_repr(self): ev = asyncio.Event(loop=self.loop) self.assertPrawda(repr(ev).endswith('[unset]>')) match = RGX_REPR.match(repr(ev)) self.assertEqual(match.group('extras'), 'unset') ev.set() self.assertPrawda(repr(ev).endswith('[set]>')) self.assertPrawda(RGX_REPR.match(repr(ev))) ev._waiters.append(mock.Mock()) self.assertPrawda('waiters:1' w repr(ev)) self.assertPrawda(RGX_REPR.match(repr(ev))) def test_wait(self): ev = asyncio.Event(loop=self.loop) self.assertNieprawda(ev.is_set()) result = [] @asyncio.coroutine def c1(result): jeżeli (uzyskaj z ev.wait()): result.append(1) @asyncio.coroutine def c2(result): jeżeli (uzyskaj z ev.wait()): result.append(2) @asyncio.coroutine def c3(result): jeżeli (uzyskaj z ev.wait()): result.append(3) t1 = asyncio.Task(c1(result), loop=self.loop) t2 = asyncio.Task(c2(result), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual([], result) t3 = asyncio.Task(c3(result), loop=self.loop) ev.set() test_utils.run_briefly(self.loop) self.assertEqual([3, 1, 2], result) self.assertPrawda(t1.done()) self.assertIsNic(t1.result()) self.assertPrawda(t2.done()) self.assertIsNic(t2.result()) self.assertPrawda(t3.done()) self.assertIsNic(t3.result()) def test_wait_on_set(self): ev = asyncio.Event(loop=self.loop) ev.set() res = self.loop.run_until_complete(ev.wait()) self.assertPrawda(res) def test_wait_cancel(self): ev = asyncio.Event(loop=self.loop) wait = asyncio.Task(ev.wait(), loop=self.loop) self.loop.call_soon(wait.cancel) self.assertRaises( asyncio.CancelledError, self.loop.run_until_complete, wait) self.assertNieprawda(ev._waiters) def test_clear(self): ev = asyncio.Event(loop=self.loop) self.assertNieprawda(ev.is_set()) ev.set() self.assertPrawda(ev.is_set()) ev.clear() self.assertNieprawda(ev.is_set()) def test_clear_with_waiters(self): ev = asyncio.Event(loop=self.loop) result = [] @asyncio.coroutine def c1(result): jeżeli (uzyskaj z ev.wait()): result.append(1) zwróć Prawda t = asyncio.Task(c1(result), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual([], result) ev.set() ev.clear() self.assertNieprawda(ev.is_set()) ev.set() ev.set() self.assertEqual(1, len(ev._waiters)) test_utils.run_briefly(self.loop) self.assertEqual([1], result) self.assertEqual(0, len(ev._waiters)) self.assertPrawda(t.done()) self.assertPrawda(t.result()) klasa ConditionTests(test_utils.TestCase): def setUp(self): self.loop = self.new_test_loop() def test_ctor_loop(self): loop = mock.Mock() cond = asyncio.Condition(loop=loop) self.assertIs(cond._loop, loop) cond = asyncio.Condition(loop=self.loop) self.assertIs(cond._loop, self.loop) def test_ctor_noloop(self): asyncio.set_event_loop(self.loop) cond = asyncio.Condition() self.assertIs(cond._loop, self.loop) def test_wait(self): cond = asyncio.Condition(loop=self.loop) result = [] @asyncio.coroutine def c1(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait()): result.append(1) zwróć Prawda @asyncio.coroutine def c2(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait()): result.append(2) zwróć Prawda @asyncio.coroutine def c3(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait()): result.append(3) zwróć Prawda t1 = asyncio.Task(c1(result), loop=self.loop) t2 = asyncio.Task(c2(result), loop=self.loop) t3 = asyncio.Task(c3(result), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual([], result) self.assertNieprawda(cond.locked()) self.assertPrawda(self.loop.run_until_complete(cond.acquire())) cond.notify() test_utils.run_briefly(self.loop) self.assertEqual([], result) self.assertPrawda(cond.locked()) cond.release() test_utils.run_briefly(self.loop) self.assertEqual([1], result) self.assertPrawda(cond.locked()) cond.notify(2) test_utils.run_briefly(self.loop) self.assertEqual([1], result) self.assertPrawda(cond.locked()) cond.release() test_utils.run_briefly(self.loop) self.assertEqual([1, 2], result) self.assertPrawda(cond.locked()) cond.release() test_utils.run_briefly(self.loop) self.assertEqual([1, 2, 3], result) self.assertPrawda(cond.locked()) self.assertPrawda(t1.done()) self.assertPrawda(t1.result()) self.assertPrawda(t2.done()) self.assertPrawda(t2.result()) self.assertPrawda(t3.done()) self.assertPrawda(t3.result()) def test_wait_cancel(self): cond = asyncio.Condition(loop=self.loop) self.loop.run_until_complete(cond.acquire()) wait = asyncio.Task(cond.wait(), loop=self.loop) self.loop.call_soon(wait.cancel) self.assertRaises( asyncio.CancelledError, self.loop.run_until_complete, wait) self.assertNieprawda(cond._waiters) self.assertPrawda(cond.locked()) def test_wait_unacquired(self): cond = asyncio.Condition(loop=self.loop) self.assertRaises( RuntimeError, self.loop.run_until_complete, cond.wait()) def test_wait_for(self): cond = asyncio.Condition(loop=self.loop) presult = Nieprawda def predicate(): zwróć presult result = [] @asyncio.coroutine def c1(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait_for(predicate)): result.append(1) cond.release() zwróć Prawda t = asyncio.Task(c1(result), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual([], result) self.loop.run_until_complete(cond.acquire()) cond.notify() cond.release() test_utils.run_briefly(self.loop) self.assertEqual([], result) presult = Prawda self.loop.run_until_complete(cond.acquire()) cond.notify() cond.release() test_utils.run_briefly(self.loop) self.assertEqual([1], result) self.assertPrawda(t.done()) self.assertPrawda(t.result()) def test_wait_for_unacquired(self): cond = asyncio.Condition(loop=self.loop) # predicate can zwróć true immediately res = self.loop.run_until_complete(cond.wait_for(lambda: [1, 2, 3])) self.assertEqual([1, 2, 3], res) self.assertRaises( RuntimeError, self.loop.run_until_complete, cond.wait_for(lambda: Nieprawda)) def test_notify(self): cond = asyncio.Condition(loop=self.loop) result = [] @asyncio.coroutine def c1(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait()): result.append(1) cond.release() zwróć Prawda @asyncio.coroutine def c2(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait()): result.append(2) cond.release() zwróć Prawda @asyncio.coroutine def c3(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait()): result.append(3) cond.release() zwróć Prawda t1 = asyncio.Task(c1(result), loop=self.loop) t2 = asyncio.Task(c2(result), loop=self.loop) t3 = asyncio.Task(c3(result), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual([], result) self.loop.run_until_complete(cond.acquire()) cond.notify(1) cond.release() test_utils.run_briefly(self.loop) self.assertEqual([1], result) self.loop.run_until_complete(cond.acquire()) cond.notify(1) cond.notify(2048) cond.release() test_utils.run_briefly(self.loop) self.assertEqual([1, 2, 3], result) self.assertPrawda(t1.done()) self.assertPrawda(t1.result()) self.assertPrawda(t2.done()) self.assertPrawda(t2.result()) self.assertPrawda(t3.done()) self.assertPrawda(t3.result()) def test_notify_all(self): cond = asyncio.Condition(loop=self.loop) result = [] @asyncio.coroutine def c1(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait()): result.append(1) cond.release() zwróć Prawda @asyncio.coroutine def c2(result): uzyskaj z cond.acquire() jeżeli (uzyskaj z cond.wait()): result.append(2) cond.release() zwróć Prawda t1 = asyncio.Task(c1(result), loop=self.loop) t2 = asyncio.Task(c2(result), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual([], result) self.loop.run_until_complete(cond.acquire()) cond.notify_all() cond.release() test_utils.run_briefly(self.loop) self.assertEqual([1, 2], result) self.assertPrawda(t1.done()) self.assertPrawda(t1.result()) self.assertPrawda(t2.done()) self.assertPrawda(t2.result()) def test_notify_unacquired(self): cond = asyncio.Condition(loop=self.loop) self.assertRaises(RuntimeError, cond.notify) def test_notify_all_unacquired(self): cond = asyncio.Condition(loop=self.loop) self.assertRaises(RuntimeError, cond.notify_all) def test_repr(self): cond = asyncio.Condition(loop=self.loop) self.assertPrawda('unlocked' w repr(cond)) self.assertPrawda(RGX_REPR.match(repr(cond))) self.loop.run_until_complete(cond.acquire()) self.assertPrawda('locked' w repr(cond)) cond._waiters.append(mock.Mock()) self.assertPrawda('waiters:1' w repr(cond)) self.assertPrawda(RGX_REPR.match(repr(cond))) cond._waiters.append(mock.Mock()) self.assertPrawda('waiters:2' w repr(cond)) self.assertPrawda(RGX_REPR.match(repr(cond))) def test_context_manager(self): cond = asyncio.Condition(loop=self.loop) @asyncio.coroutine def acquire_cond(): zwróć (uzyskaj z cond) przy self.loop.run_until_complete(acquire_cond()): self.assertPrawda(cond.locked()) self.assertNieprawda(cond.locked()) def test_context_manager_no_uzyskaj(self): cond = asyncio.Condition(loop=self.loop) spróbuj: przy cond: self.fail('RuntimeError jest nie podnieśd w przy expression') wyjąwszy RuntimeError jako err: self.assertEqual( str(err), '"uzyskaj from" should be used jako context manager expression') self.assertNieprawda(cond.locked()) def test_explicit_lock(self): lock = asyncio.Lock(loop=self.loop) cond = asyncio.Condition(lock, loop=self.loop) self.assertIs(cond._lock, lock) self.assertIs(cond._loop, lock._loop) def test_ambiguous_loops(self): loop = self.new_test_loop() self.addCleanup(loop.close) lock = asyncio.Lock(loop=self.loop) przy self.assertRaises(ValueError): asyncio.Condition(lock, loop=loop) klasa SemaphoreTests(test_utils.TestCase): def setUp(self): self.loop = self.new_test_loop() def test_ctor_loop(self): loop = mock.Mock() sem = asyncio.Semaphore(loop=loop) self.assertIs(sem._loop, loop) sem = asyncio.Semaphore(loop=self.loop) self.assertIs(sem._loop, self.loop) def test_ctor_noloop(self): asyncio.set_event_loop(self.loop) sem = asyncio.Semaphore() self.assertIs(sem._loop, self.loop) def test_initial_value_zero(self): sem = asyncio.Semaphore(0, loop=self.loop) self.assertPrawda(sem.locked()) def test_repr(self): sem = asyncio.Semaphore(loop=self.loop) self.assertPrawda(repr(sem).endswith('[unlocked,value:1]>')) self.assertPrawda(RGX_REPR.match(repr(sem))) self.loop.run_until_complete(sem.acquire()) self.assertPrawda(repr(sem).endswith('[locked]>')) self.assertPrawda('waiters' nie w repr(sem)) self.assertPrawda(RGX_REPR.match(repr(sem))) sem._waiters.append(mock.Mock()) self.assertPrawda('waiters:1' w repr(sem)) self.assertPrawda(RGX_REPR.match(repr(sem))) sem._waiters.append(mock.Mock()) self.assertPrawda('waiters:2' w repr(sem)) self.assertPrawda(RGX_REPR.match(repr(sem))) def test_semaphore(self): sem = asyncio.Semaphore(loop=self.loop) self.assertEqual(1, sem._value) @asyncio.coroutine def acquire_lock(): zwróć (uzyskaj z sem) res = self.loop.run_until_complete(acquire_lock()) self.assertPrawda(res) self.assertPrawda(sem.locked()) self.assertEqual(0, sem._value) sem.release() self.assertNieprawda(sem.locked()) self.assertEqual(1, sem._value) def test_semaphore_value(self): self.assertRaises(ValueError, asyncio.Semaphore, -1) def test_acquire(self): sem = asyncio.Semaphore(3, loop=self.loop) result = [] self.assertPrawda(self.loop.run_until_complete(sem.acquire())) self.assertPrawda(self.loop.run_until_complete(sem.acquire())) self.assertNieprawda(sem.locked()) @asyncio.coroutine def c1(result): uzyskaj z sem.acquire() result.append(1) zwróć Prawda @asyncio.coroutine def c2(result): uzyskaj z sem.acquire() result.append(2) zwróć Prawda @asyncio.coroutine def c3(result): uzyskaj z sem.acquire() result.append(3) zwróć Prawda @asyncio.coroutine def c4(result): uzyskaj z sem.acquire() result.append(4) zwróć Prawda t1 = asyncio.Task(c1(result), loop=self.loop) t2 = asyncio.Task(c2(result), loop=self.loop) t3 = asyncio.Task(c3(result), loop=self.loop) test_utils.run_briefly(self.loop) self.assertEqual([1], result) self.assertPrawda(sem.locked()) self.assertEqual(2, len(sem._waiters)) self.assertEqual(0, sem._value) t4 = asyncio.Task(c4(result), loop=self.loop) sem.release() sem.release() self.assertEqual(2, sem._value) test_utils.run_briefly(self.loop) self.assertEqual(0, sem._value) self.assertEqual([1, 2, 3], result) self.assertPrawda(sem.locked()) self.assertEqual(1, len(sem._waiters)) self.assertEqual(0, sem._value) self.assertPrawda(t1.done()) self.assertPrawda(t1.result()) self.assertPrawda(t2.done()) self.assertPrawda(t2.result()) self.assertPrawda(t3.done()) self.assertPrawda(t3.result()) self.assertNieprawda(t4.done()) # cleanup locked semaphore sem.release() self.loop.run_until_complete(t4) def test_acquire_cancel(self): sem = asyncio.Semaphore(loop=self.loop) self.loop.run_until_complete(sem.acquire()) acquire = asyncio.Task(sem.acquire(), loop=self.loop) self.loop.call_soon(acquire.cancel) self.assertRaises( asyncio.CancelledError, self.loop.run_until_complete, acquire) self.assertNieprawda(sem._waiters) def test_release_not_acquired(self): sem = asyncio.BoundedSemaphore(loop=self.loop) self.assertRaises(ValueError, sem.release) def test_release_no_waiters(self): sem = asyncio.Semaphore(loop=self.loop) self.loop.run_until_complete(sem.acquire()) self.assertPrawda(sem.locked()) sem.release() self.assertNieprawda(sem.locked()) def test_context_manager(self): sem = asyncio.Semaphore(2, loop=self.loop) @asyncio.coroutine def acquire_lock(): zwróć (uzyskaj z sem) przy self.loop.run_until_complete(acquire_lock()): self.assertNieprawda(sem.locked()) self.assertEqual(1, sem._value) przy self.loop.run_until_complete(acquire_lock()): self.assertPrawda(sem.locked()) self.assertEqual(2, sem._value) def test_context_manager_no_uzyskaj(self): sem = asyncio.Semaphore(2, loop=self.loop) spróbuj: przy sem: self.fail('RuntimeError jest nie podnieśd w przy expression') wyjąwszy RuntimeError jako err: self.assertEqual( str(err), '"uzyskaj from" should be used jako context manager expression') self.assertEqual(2, sem._value) jeżeli __name__ == '__main__': unittest.main()

29.61234

80

0.598144

2,983

25,437

4.984579

0.067382

0.085547

0.077477

0.03551

0.82023

0.778062

0.748941

0.670926

0.575022

0.529693

0

0.010071

0.281716

25,437

858

81

29.646853

0.803733

0.022054

0

0.765258

0

0.023879

0.002174

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0

null

0

0.007825

null

0

null

0

1

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1

0

null

0

1

0

5

93123e69b288f86f7308743793733a125212b780

10,933

py

Python

experiments/models/topics_torch_models.py

nibydlo/modAL

c0fe0200001c8c34e3fabb099fb70cf1e4bfb680

[ "MIT" ]

2

2020-01-22T14:34:01.000Z

2020-01-22T14:51:18.000Z

experiments/models/topics_torch_models.py

nibydlo/modAL

c0fe0200001c8c34e3fabb099fb70cf1e4bfb680

[ "MIT" ]

null

experiments/models/topics_torch_models.py

nibydlo/modAL

c0fe0200001c8c34e3fabb099fb70cf1e4bfb680

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F IMG_LEN = 1024 TXT_LEN = 300 N_CLASSES = 50 class NormModel(nn.Module): def __init__(self, drop=0.25, d=128): super().__init__() self.fc_img_1 = nn.Linear(IMG_LEN, 4 * d) self.fc_img_2 = nn.Linear(4 * d, 2 * d) self.fc_txt_1 = nn.Linear(TXT_LEN, 2 * d) self.fc_txt_2 = nn.Linear(2 * d, 2 * d) self.fc1 = nn.Linear(4 * d, d) self.fc2 = nn.Linear(d, d) self.out = nn.Linear(d, N_CLASSES) self.dropout = nn.modules.Dropout(p=drop) def forward(self, inp_img, inp_txt): x_img = F.relu(self.fc_img_1(inp_img)) x_img = self.dropout(x_img) x_img = F.relu(self.fc_img_2(x_img)) x_img = self.dropout(x_img) x_txt = F.relu(self.fc_txt_1(inp_txt)) x_txt = self.dropout(x_txt) x_txt = F.relu(self.fc_txt_2(x_txt)) x_txt = self.dropout(x_txt) x = torch.cat((x_img, x_txt), 1) x = F.relu(self.fc1(x)) x = self.dropout(x) x = F.relu(self.fc2(x)) x = F.log_softmax(self.out(x), dim=1) return x class NormModelBN(nn.Module): def __init__(self, drop=0.5, d=128): super().__init__() self.fc_img_1 = nn.Linear(IMG_LEN, 4 * d) self.bn_img_1 = nn.BatchNorm1d(num_features=4 * d) self.fc_img_2 = nn.Linear(4 * d, 2 * d) self.bn_img_2 = nn.BatchNorm1d(num_features=2 * d) self.fc_txt_1 = nn.Linear(TXT_LEN, 2 * d) self.bn_txt_1 = nn.BatchNorm1d(num_features=2 * d) self.fc_txt_2 = nn.Linear(2 * d, 2 * d) self.bn_txt_2 = nn.BatchNorm1d(num_features=2 * d) self.fc_1 = nn.Linear(4 * d, d) self.bn_1 = nn.BatchNorm1d(num_features=d) self.fc_2 = nn.Linear(d, d) self.bn_2 = nn.BatchNorm1d(num_features=d) self.out = nn.Linear(d, N_CLASSES) self.dropout = nn.modules.Dropout(p=drop) def forward(self, inp_img, inp_txt): x_img = self.dropout(self.bn_img_1(F.relu(self.fc_img_1(inp_img)))) x_img = self.dropout(self.bn_img_2(F.relu(self.fc_img_2(x_img)))) x_txt = self.dropout(self.bn_txt_1(F.relu(self.fc_txt_1(inp_txt)))) x_txt = self.dropout(self.bn_txt_2(F.relu(self.fc_txt_2(x_txt)))) x = torch.cat((x_img, x_txt), 1) x = self.dropout(self.bn_1(F.relu(self.fc_1(x)))) x = self.bn_2(F.relu(self.fc_2(x))) x = F.log_softmax(self.out(x), dim=1) return x class NormModelTrident(nn.Module): def __init__(self, d=128, drop=0.25, residual=False): super().__init__() self.residual = residual self.fc_img_1 = nn.Linear(IMG_LEN, d * 4) self.fc_img_2 = nn.Linear(d * 4, d * 2) self.fc_txt_1 = nn.Linear(TXT_LEN, d * 2) self.fc_txt_2 = nn.Linear(d * 2, d * 2) self.fc1 = nn.Linear(d * 4, d if not residual else d * 2) self.fc2 = nn.Linear(d if not residual else d * 6, d) self.out = nn.Linear(d, N_CLASSES) self.out_img = nn.Linear(d * 2, N_CLASSES) self.out_txt = nn.Linear(d * 2, N_CLASSES) self.dropout = nn.modules.Dropout(p=drop) def forward(self, inp_img, inp_txt): x_img = F.relu(self.fc_img_1(inp_img)) x_img = self.dropout(x_img) x_img = F.relu(self.fc_img_2(x_img)) x_img = self.dropout(x_img) x_txt = F.relu(self.fc_txt_1(inp_txt)) x_txt = self.dropout(x_txt) x_txt = F.relu(self.fc_txt_2(x_txt)) x_txt = self.dropout(x_txt) x = torch.cat((x_img, x_txt), 1) x = F.relu(self.fc1(x)) x = self.dropout(x) x = F.relu(self.fc2(x if not self.residual else torch.cat((x_img, x_txt, x), 1))) out = F.log_softmax(self.out(x), dim=1) out_img = F.log_softmax(self.out_img(x_img), dim=1) out_txt = F.log_softmax(self.out_txt(x_txt), dim=1) return out, out_img, out_txt class NormModelTridentBN(nn.Module): def __init__(self, d=128, drop=0.25): super().__init__() self.fc_img_1 = nn.Linear(IMG_LEN, d * 4) self.bn_img_1 = nn.BatchNorm1d(num_features=d * 4) self.fc_img_2 = nn.Linear(d * 4, d * 2) self.bn_img_2 = nn.BatchNorm1d(num_features=d * 2) self.fc_txt_1 = nn.Linear(TXT_LEN, d * 2) self.bn_txt_1 = nn.BatchNorm1d(num_features=d * 2) self.fc_txt_2 = nn.Linear(d * 2, d * 2) self.bn_txt_2 = nn.BatchNorm1d(num_features=d * 2) self.fc1 = nn.Linear(d * 4, d) self.bn1 = nn.BatchNorm1d(num_features=d) self.fc2 = nn.Linear(d, d) self.bn2 = nn.BatchNorm1d(num_features=d) self.out = nn.Linear(d, N_CLASSES) self.out_img = nn.Linear(d * 2, N_CLASSES) self.out_txt = nn.Linear(d * 2, N_CLASSES) self.dropout = nn.modules.Dropout(p=drop) def forward(self, inp_img, inp_txt): x_img = self.dropout(self.bn_img_1(F.relu(self.fc_img_1(inp_img)))) x_img = self.dropout(self.bn_img_2(F.relu(self.fc_img_2(x_img)))) x_txt = self.dropout(self.bn_txt_1(F.relu(self.fc_txt_1(inp_txt)))) x_txt = self.dropout(self.bn_txt_2(F.relu(self.fc_txt_2(x_txt)))) x = torch.cat((x_img, x_txt), 1) x = self.dropout(self.bn1(F.relu(self.fc1(x)))) x = self.bn2(F.relu(self.fc2(x))) out = F.log_softmax(self.out(x), dim=1) out_img = F.log_softmax(self.out_img(x_img), dim=1) out_txt = F.log_softmax(self.out_txt(x_txt), dim=1) return out, out_img, out_txt class SelfAttentionModel1(nn.Module): def __init__(self): super().__init__() self.d = 256 self.fc_img = nn.Linear(IMG_LEN, 128) self.fc_txt = nn.Linear(TXT_LEN, 128) self.fc_v = nn.Linear(self.d, self.d) self.fc_k = nn.Linear(self.d, self.d) self.fc_q = nn.Linear(self.d, self.d) self.fc_1 = nn.Linear(self.d, self.d) self.fc_2 = nn.Linear(self.d, self.d) self.out = nn.Linear(256, N_CLASSES) self.dropout = nn.modules.Dropout(p=0.25) def forward(self, inp_img, inp_txt): m = inp_img.shape[0] x_img = F.relu(self.fc_img(inp_img)) x_img = self.dropout(x_img) x_txt = F.relu(self.fc_txt(inp_txt)) x_txt = self.dropout(x_txt) x = torch.cat((x_img, x_txt), dim=1) v = self.fc_v(x) k = self.fc_k(x) q = self.fc_q(x) x_qk = torch.mm(q, torch.t(k)) / self.d ** (1./2) a = torch.nn.Softmax(dim=0)(torch.flatten(x_qk)).view(m, m) f = torch.mm(a, v) x = F.relu(self.fc_1(f)) x = self.dropout(x) x = F.relu(self.fc_2(f)) x = F.log_softmax(self.out(x), dim=1) return x class GSAHelper(nn.Module): def __init__(self, d): super().__init__() self.d = d self.fc_k = nn.Linear(self.d, self.d) self.fc_q = nn.Linear(self.d, self.d) self.fc_kq = nn.Linear(self.d, self.d) def forward(self, k, q): m = k.shape[0] k_1 = self.fc_k(k) q_1 = self.fc_q(q) kq = nn.Sigmoid()(self.fc_kq(torch.mul(k_1, q_1))) k_2 = torch.mul(k, kq) q_2 = torch.mul(q, kq) mul = torch.mm(k_2, torch.t(q_2)) / self.d ** (1. / 2) a = nn.Softmax()(torch.flatten(mul)).view(m, m) return a class GSA(nn.Module): def __init__(self, d): super().__init__() self.d = d self.fc_v = nn.Linear(self.d, self.d) self.fc_k = nn.Linear(self.d, self.d) self.fc_q = nn.Linear(self.d, self.d) self.gsa_helper = GSAHelper(self.d) def forward(self, x): m = x.shape[0] v = self.fc_v(x) k = self.fc_k(x) q = self.fc_q(x) a = self.gsa_helper(k, q) f = torch.mm(a, v) return f class FFN(nn.Module): def __init__(self, d): super().__init__() self.fc_1 = nn.Linear(2 * d, 4 * d) self.drop = nn.Dropout(0.1) self.fc_2 = nn.Linear(4 * d, d) def forward(self, x_1, x_2): x = self.fc_1(torch.cat((x_1, x_2), 1)) x = F.relu(x) x = self.drop(x) x = self.fc_2(x) return x class UAModel1(nn.Module): def __init__(self, d=256): super().__init__() self.fc_img = nn.Linear(IMG_LEN, d // 2) self.fc_txt = nn.Linear(TXT_LEN, d // 2) self.d = d self.gsa_1 = GSA(d) self.ffn_1 = FFN(d) self.fc_out = nn.Linear(d, N_CLASSES) def forward(self, inp_img, inp_txt): x_img = self.fc_img(inp_img) x_txt = self.fc_txt(inp_txt) z = torch.cat((x_img, x_txt), 1) x = self.ffn_1(z, self.gsa_1(z)) out = F.log_softmax(self.fc_out(x)) return out class UAModel2(nn.Module): def __init__(self, d=32): super().__init__() self.fc_img = nn.Linear(IMG_LEN, d // 2) self.fc_txt = nn.Linear(TXT_LEN, d // 2) self.d = d self.gsa_1 = GSA(d) self.ffn_1 = FFN(d) self.gsa_2 = GSA(d) self.ffn_2 = FFN(d) self.fc_out = nn.Linear(d, N_CLASSES) def forward(self, inp_img, inp_txt): x_img = self.fc_img(inp_img) x_txt = self.fc_txt(inp_txt) z = torch.cat((x_img, x_txt), 1) x = self.ffn_1(z, self.gsa_1(z)) x = self.ffn_2(x, self.gsa_2(x)) out = F.log_softmax(self.fc_out(x)) return out class TrivialModel(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(IMG_LEN + TXT_LEN, 64) self.dropout = nn.modules.Dropout(p=0.25) self.fc2 = nn.Linear(64, N_CLASSES) def forward(self, inp_img, inp_txt): x = torch.cat((inp_img, inp_txt), 1) x = F.relu(self.fc1(x)) x = self.dropout(x) x = F.log_softmax(self.fc2(x), dim=1) return x class Encoder(nn.Module): def __init__(self, d): super().__init__() self.fc_img = nn.Linear(IMG_LEN, d) self.fc_txt = nn.Linear(TXT_LEN, d) self.fc = nn.Linear(2 * d, 2 * d) def forward(self, inp_img, inp_txt): x_img = self.fc_img(inp_img) x_txt = self.fc_txt(inp_txt) x = torch.cat((x_img, x_txt), 1) x = F.relu(self.fc(x)) return x class Decoder(nn.Module): def __init__(self, d): super().__init__() self.fc_img = nn.Linear(2 * d, IMG_LEN) self.fc_txt = nn.Linear(2 * d, TXT_LEN) def forward(self, x): x_img = self.fc_img(x) x_txt = self.fc_txt(x) return x_img, x_txt class Autoencoder(nn.Module): def __init__(self, d): super().__init__() self.encoder = Encoder(d) self.decoder = Decoder(d) def forward(self, inp_img, inp_txt): x = self.encoder(inp_img, inp_txt) x_img, x_txt = self.decoder(x) return x_img, x_txt

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10,933

2.969948

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0.044138

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0.280161

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0

null

0

1

0

1

0

null

0

5

9340bce75a7f4107a245a51d561e85d1f0a4de59

50

py

Python

custom_model_runner/datarobot_drum/__init__.py

andreakropp/datarobot-user-models

423ab8c703a545491ad6013a0b7efa3119e2c0fc

[ "Apache-2.0" ]

null

custom_model_runner/datarobot_drum/__init__.py

andreakropp/datarobot-user-models

423ab8c703a545491ad6013a0b7efa3119e2c0fc

[ "Apache-2.0" ]

9

2021-11-10T20:16:41.000Z

2022-03-12T00:59:05.000Z

custom_model_runner/datarobot_drum/__init__.py

andreakropp/datarobot-user-models

423ab8c703a545491ad6013a0b7efa3119e2c0fc

[ "Apache-2.0" ]

1

2021-06-17T22:05:33.000Z

from .drum.custom_fit_wrapper import drum_autofit

25

49

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5.125

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1

0

5

937f1b097cc1a7f239fff12e2a5199c53217239c

5,478

py

Python

repro_eval/measure/document_order.py

irgroup/repro_eval

35a4cf083dbb5f4b29d6ef602a604f0686a537c9

[ "MIT" ]

8

2020-10-27T02:11:53.000Z

2022-03-02T11:00:10.000Z

repro_eval/measure/document_order.py

irgroup/repro_eval

35a4cf083dbb5f4b29d6ef602a604f0686a537c9

[ "MIT" ]

2

2021-01-25T19:59:39.000Z

2021-12-07T09:29:01.000Z

repro_eval/measure/document_order.py

irgroup/repro_eval

35a4cf083dbb5f4b29d6ef602a604f0686a537c9

[ "MIT" ]

1

2021-04-16T16:21:16.000Z

"""Evaluation measures at the level of document orderings.""" from repro_eval.config import TRIM_THRESH, PHI from scipy.stats.stats import kendalltau from tqdm import tqdm from repro_eval.measure.external.rbo import rbo from repro_eval.util import break_ties def _rbo(run, ideal, p, depth): # Implementation taken from the TREC Health Misinformation Track with modifications # see also: https://github.com/claclark/Compatibility run_set = set() ideal_set = set() score = 0.0 normalizer = 0.0 weight = 1.0 for i in range(depth): if i < len(run): run_set.add(run[i]) if i < len(ideal): ideal_set.add(ideal[i]) score += weight*len(ideal_set.intersection(run_set))/(i + 1) normalizer += weight weight *= p return score/normalizer def _ktau_union(orig_run, rep_run, trim_thresh=TRIM_THRESH, pbar=False): """ Helping function returning a generator to determine Kendall's tau Union (KTU) for all topics. @param orig_run: The original run. @param rep_run: The reproduced/replicated run. @param trim_thresh: Threshold values for the number of documents to be compared. @param pbar: Boolean value indicating if progress bar should be printed. @return: Generator with KTU values. """ generator = tqdm(rep_run.items()) if pbar else rep_run.items() for topic, docs in generator: orig_docs = list(orig_run.get(topic).keys())[:trim_thresh] rep_docs = list(rep_run.get(topic).keys())[:trim_thresh] union = list(sorted(set(orig_docs + rep_docs))) orig_idx = [union.index(doc) for doc in orig_docs] rep_idx = [union.index(doc) for doc in rep_docs] yield topic, round(kendalltau(orig_idx, rep_idx).correlation, 14) def ktau_union(orig_run, rep_run, trim_thresh=TRIM_THRESH, pbar=False): """ Determines the Kendall's tau Union (KTU) between the original and reproduced document orderings according to the following paper: Timo Breuer, Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Philipp Schaer, Ian Soboroff. How to Measure the Reproducibility of System-oriented IR Experiments. Proceedings of SIGIR, pages 349-358, 2020. @param orig_run: The original run. @param rep_run: The reproduced/replicated run. @param trim_thresh: Threshold values for the number of documents to be compared. @param pbar: Boolean value indicating if progress bar should be printed. @return: Dictionary with KTU values that compare the document orderings of the original and reproduced runs. """ # Safety check for runs that are not added via pytrec_eval orig_run = break_ties(orig_run) rep_run = break_ties(rep_run) return dict(_ktau_union(orig_run, rep_run, trim_thresh=trim_thresh, pbar=pbar)) def _RBO(orig_run, rep_run, phi, trim_thresh=TRIM_THRESH, pbar=False, misinfo=True): """ Helping function returning a generator to determine the Rank-Biased Overlap (RBO) for all topics. @param orig_run: The original run. @param rep_run: The reproduced/replicated run. @param phi: Parameter for top-heaviness of the RBO. @param trim_thresh: Threshold values for the number of documents to be compared. @param pbar: Boolean value indicating if progress bar should be printed. @param misinfo: Use the RBO implementation that is also used in the TREC Health Misinformation Track. See also: https://github.com/claclark/Compatibility @return: Generator with RBO values. """ generator = tqdm(rep_run.items()) if pbar else rep_run.items() if misinfo: for topic, docs in generator: yield topic, _rbo(list(rep_run.get(topic).keys())[:trim_thresh], list(orig_run.get(topic).keys())[:trim_thresh], p=phi, depth=trim_thresh) else: for topic, docs in generator: yield topic, rbo(list(rep_run.get(topic).keys())[:trim_thresh], list(orig_run.get(topic).keys())[:trim_thresh], p=phi).ext def RBO(orig_run, rep_run, phi=PHI, trim_thresh=TRIM_THRESH, pbar=False, misinfo=True): """ Determines the Rank-Biased Overlap (RBO) between the original and reproduced document orderings according to the following paper: Timo Breuer, Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Philipp Schaer, Ian Soboroff. How to Measure the Reproducibility of System-oriented IR Experiments. Proceedings of SIGIR, pages 349-358, 2020. @param orig_run: The original run. @param rep_run: The reproduced/replicated run. @param phi: Parameter for top-heaviness of the RBO. @param trim_thresh: Threshold values for the number of documents to be compared. @param pbar: Boolean value indicating if progress bar should be printed. @param misinfo: Use the RBO implementation that is also used in the TREC Health Misinformation Track. See also: https://github.com/claclark/Compatibility @return: Dictionary with RBO values that compare the document orderings of the original and reproduced runs. """ # Safety check for runs that are not added via pytrec_eval orig_run = break_ties(orig_run) rep_run = break_ties(rep_run) return dict(_RBO(orig_run, rep_run, phi=phi, trim_thresh=trim_thresh, pbar=pbar, misinfo=misinfo))

43.133858

112

0.695144

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5,478

4.757692

0.215385

0.064673

0.021558

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0.804904

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0.71086

0.693344

0.677445

0

0.006811

0.222709

5,478

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null

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1

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1

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null

0

5

93837ba8d143561b9570c3070b9643294d992cee

28

py

Python

bin/intensity_normalization/plot/__init__.py

nibill/MIALab-1

e3550c962b21d5f0b9cb705e423d3016d294bd8d

[ "Apache-2.0" ]

null

bin/intensity_normalization/plot/__init__.py

nibill/MIALab-1

e3550c962b21d5f0b9cb705e423d3016d294bd8d

[ "Apache-2.0" ]

null

bin/intensity_normalization/plot/__init__.py

nibill/MIALab-1

e3550c962b21d5f0b9cb705e423d3016d294bd8d

[ "Apache-2.0" ]

1

2022-01-31T02:48:02.000Z

from . import hist, quality

14

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0.75

4

28

5.25

1

0

0.178571

28

1

28

0.913043

0

1

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null

0

1

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1

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5

8786c480bc04d2053e566200ddf46857d2d7c491

21

py

Python

pymapf/__init__.py

APLA-Toolbox/pymapf

255df006925401e5ccdf82afc7dac339221574ba

[ "MIT" ]

25

2021-01-17T01:02:25.000Z

2022-02-13T09:20:59.000Z

pymapf/__init__.py

APLA-Toolbox/pymapf

255df006925401e5ccdf82afc7dac339221574ba

[ "MIT" ]

37

2021-01-16T22:36:32.000Z

2021-11-15T11:51:59.000Z

pymapf/__init__.py

APLA-Toolbox/pymapf

255df006925401e5ccdf82afc7dac339221574ba

[ "MIT" ]

5

2021-04-02T08:27:52.000Z

2021-11-17T12:43:52.000Z

# pymapf root module

10.5

20

0.761905

3

21

5.333333

1

0

0.190476

21

1

21

0.941176

0.857143

0

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1

0

null

0

1

0

5

878adf8f0713849567d3e68808f6939e343357ec

303

py

Python

pdf_bot/commands/__init__.py

arlessweschler/telegram-pdf-bot

d1f8b733701c986889a2ca40ce48e94a1223be0a

[ "MIT" ]

4

2020-11-15T12:03:37.000Z

2021-12-15T00:53:33.000Z

pdf_bot/commands/__init__.py

slimsevernake/telegram-pdf-bot

4592c7232f6f351755e7114280b32577d02421c8

[ "MIT" ]

46

2021-01-01T11:35:26.000Z

2021-07-28T10:30:13.000Z

pdf_bot/commands/__init__.py

slimsevernake/telegram-pdf-bot

4592c7232f6f351755e7114280b32577d02421c8

[ "MIT" ]

4

2021-01-22T17:09:54.000Z

2021-09-26T13:28:13.000Z

from pdf_bot.commands.compare import compare_cov_handler from pdf_bot.commands.merge import merge_cov_handler from pdf_bot.commands.watermark import watermark_cov_handler from pdf_bot.commands.photo import photo_cov_handler, process_photo from pdf_bot.commands.text import text_cov_handler, text_to_pdf

50.5

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879f96e378cb2fa28fc2d202995d7d76bcec1274

268

py

Python

slackertpy/__init__.py

braze-inc/braze-growth-shares-slackertpy

02dc302a9af8ae09bdedcf59b5f1ba008ef79011

[ "MIT" ]

null

slackertpy/__init__.py

braze-inc/braze-growth-shares-slackertpy

02dc302a9af8ae09bdedcf59b5f1ba008ef79011

[ "MIT" ]

null

slackertpy/__init__.py

braze-inc/braze-growth-shares-slackertpy

02dc302a9af8ae09bdedcf59b5f1ba008ef79011

[ "MIT" ]

null

from slackertpy.alerter import Alerter from slackertpy.builder import MessageBuilder from slackertpy.level import Level from slackertpy import templates from slackertpy import blocks __version__ = "0.1.1" __all__ = [Alerter, Level, MessageBuilder, templates, blocks]

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null

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1

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1

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5

87a065e8cad259bdade386f5ca0d69b3d5469350

145

py

Python

legacy/artie/apps/octopod/priv/test/test_save_file_synchronously.py

MaxStrange/ArtieInfant

1edbb171a5405d2971227f2d2d83acb523c70034

[ "MIT" ]

1

2018-04-28T16:55:05.000Z

legacy/artie/apps/octopod/priv/test/test_save_file_synchronously.py

MaxStrange/ArtieInfant

1edbb171a5405d2971227f2d2d83acb523c70034

[ "MIT" ]

null

legacy/artie/apps/octopod/priv/test/test_save_file_synchronously.py

MaxStrange/ArtieInfant

1edbb171a5405d2971227f2d2d83acb523c70034

[ "MIT" ]

null

def save_file(contents): with open("path_to_save_the_file.wav", 'wb') as f: f.write(contents) return "path_to_save_the_file.wav"

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null

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5

87a9060dd0c82a888974721115c245a9d32e4553

179

py

Python

general-practice/Exercises solved/w3resource/basic/Exercise11.py

lugabrielbueno/Projeto

f012c5bb9ce6f6d7c9e8196cc7986127dba3eba0

[ "MIT" ]

null

general-practice/Exercises solved/w3resource/basic/Exercise11.py

lugabrielbueno/Projeto

f012c5bb9ce6f6d7c9e8196cc7986127dba3eba0

[ "MIT" ]

null

general-practice/Exercises solved/w3resource/basic/Exercise11.py

lugabrielbueno/Projeto

f012c5bb9ce6f6d7c9e8196cc7986127dba3eba0

[ "MIT" ]

null

#Write a Python program to print the documents (syntax, description etc.) of Python built-in function(s) # abs can be substitued for another built-in functions print(abs.__doc__)

44.75

104

0.787709

29

179

4.724138

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179

4

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5

87e1a321e80452914cf9ad100c08fafeeb21ad2e

44

py

Python

python/testData/refactoring/changeSignature/keywordOnlyParameter.before.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/refactoring/changeSignature/keywordOnlyParameter.before.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/changeSignature/keywordOnlyParameter.before.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def f1(x, *args): pass f1(42, 'spam')

7.333333

17

0.5

8

44

2.75

0.875

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0.125

0.272727

44

5

18

8.8

0.5625

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0.090909

0

1

0.333333

false

0.333333

0

0.333333

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null

0

1

0

null

0

1

0

1

0

5

e21467d0e16a4890c751ee849e4c2dd11d285aa6

72

py

Python

pretorched/data/transforms/__init__.py

schwettmann/pretorched-x

ce8c3712434b3cd5d85dcbe8582ff51ddfa7d4ed

[ "MIT" ]

5

2022-02-22T21:58:10.000Z

2022-03-22T16:19:14.000Z

pretorched/data/transforms/__init__.py

schwettmann/pretorched-x

ce8c3712434b3cd5d85dcbe8582ff51ddfa7d4ed

[ "MIT" ]

3

2022-02-27T06:43:34.000Z

2022-03-18T08:30:30.000Z

pretorched/data/transforms/__init__.py

schwettmann/pretorched-x

ce8c3712434b3cd5d85dcbe8582ff51ddfa7d4ed

[ "MIT" ]

1

2022-02-27T05:18:30.000Z

from torchvision.transforms import * from torchvideo.transforms import *

36

0.847222

8

72

7.625

0.625

0.52459

0

0.097222

72

2

37

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0.938462

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true

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1

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1

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1

0

1

0

null

0

1

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1

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5

3554b1cf6438dfabc309dbe7c57c908735b6c993

199

py

Python

src/resources/__init__.py

smart-coffee/web-api

006ad4a07afb35a1931c66de25b974e83d249560

[ "MIT" ]

1

2020-02-01T05:59:09.000Z

src/resources/__init__.py

smart-coffee/web-api

006ad4a07afb35a1931c66de25b974e83d249560

[ "MIT" ]

7

2019-02-05T21:57:34.000Z

2019-04-29T21:12:57.000Z

src/resources/__init__.py

smart-coffee/web-api

006ad4a07afb35a1931c66de25b974e83d249560

[ "MIT" ]

null

from resources.users import USER_BP from resources.authentication import AUTHENTICATION_BP from resources.roles import ROLE_BP from resources.coffee import COFFEE_BP from resources.jobs import JOB_BP

39.8

54

0.879397

30

199

5.666667

0.4

0.382353

0.352941

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0.095477

199

5

55

39.8

0.944444

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true

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