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

8d2b9627ee560b695980d399a9b852afb9663aac

1,593

py

Python

tests/test_clamp.py

josemolinagarcia/maya-math-nodes

1f83eef1d1efe0b0c3dbb1477ca31ed9f8911ee4

[ "MIT" ]

148

2018-01-12T20:30:45.000Z

2022-02-28T05:20:46.000Z

tests/test_clamp.py

josemolinagarcia/maya-math-nodes

1f83eef1d1efe0b0c3dbb1477ca31ed9f8911ee4

[ "MIT" ]

13

2018-01-17T18:02:13.000Z

2021-11-23T06:06:24.000Z

tests/test_clamp.py

josemolinagarcia/maya-math-nodes

1f83eef1d1efe0b0c3dbb1477ca31ed9f8911ee4

[ "MIT" ]

41

2018-01-16T01:41:29.000Z

2021-08-24T01:27:56.000Z

# Copyright (c) 2018 Serguei Kalentchouk et al. All rights reserved. # Use of this source code is governed by an MIT license that can be found in the LICENSE file. from node_test_case import NodeTestCase, cmds

44.25

114

0.595731

8d2bec83c642f547afb331d447ae8ff19041fd5a

1,111

py

Python

src/tests/tests_get_formatted_items.py

kazqvaizer/checklistbot

f715280fbe7035bc2ce4f69cbf95595d9fe3a225

[ "MIT" ]

5

2020-10-06T13:42:45.000Z

2021-12-21T07:35:08.000Z

src/tests/tests_get_formatted_items.py

kazqvaizer/checklistbot

f715280fbe7035bc2ce4f69cbf95595d9fe3a225

[ "MIT" ]

null

src/tests/tests_get_formatted_items.py

kazqvaizer/checklistbot

f715280fbe7035bc2ce4f69cbf95595d9fe3a225

[ "MIT" ]

null

import pytest from models import TodoItem pytestmark = [ pytest.mark.usefixtures("use_db"), ] def test_format_without_strike(items, chat): lines = chat.get_formatted_items().split("\n") assert len(lines) == 2 assert "1. Hello" == lines[0] assert "2. Nice!" == lines[1] def test_format_with_strike(items, chat): items[0].is_checked = True items[0].save() lines = chat.get_formatted_items().split("\n") assert len(lines) == 2 assert "<s>1. Hello</s>" == lines[0] assert "2. Nice!" == lines[1] def test_respect_order_by_id(items, chat): TodoItem.update(id=100500).where(TodoItem.id == items[0].id).execute() lines = chat.get_formatted_items().split("\n") assert len(lines) == 2 assert "1. Nice!" == lines[0] assert "2. Hello" == lines[1] def test_no_items_is_okay(chat): assert chat.get_formatted_items() == ""

20.574074

74

0.640864

8d2cd1060b91fea7d66c9afe4a0c6e646802593b

3,945

py

Python

web/multilingual/database.py

mahoyen/web

1d190a86e3277315804bfcc0b8f9abd4f9c1d780

[ "MIT" ]

null

web/multilingual/database.py

mahoyen/web

1d190a86e3277315804bfcc0b8f9abd4f9c1d780

[ "MIT" ]

null

web/multilingual/database.py

mahoyen/web

1d190a86e3277315804bfcc0b8f9abd4f9c1d780

[ "MIT" ]

null

import copy import json from django.contrib import admin from django.db import models from web.multilingual.data_structures import MultiLingualTextStructure from web.multilingual.form import MultiLingualFormField, MultiLingualRichTextFormField, \ MultiLingualRichTextUploadingFormField from web.multilingual.widgets import MultiLingualTextInput, MultiLingualRichText, MultiLingualRichTextUploading

39.848485

119

0.694043

8d2fec927240532eb03988da6b6277edf3bec73d

2,859

py

Python

cart/tests/test_views.py

mohsenamoon1160417237/ECommerce-app

4cca492214b04b56f625aef2a2979956a8256710

[ "MIT" ]

null

cart/tests/test_views.py

mohsenamoon1160417237/ECommerce-app

4cca492214b04b56f625aef2a2979956a8256710

[ "MIT" ]

null

cart/tests/test_views.py

mohsenamoon1160417237/ECommerce-app

4cca492214b04b56f625aef2a2979956a8256710

[ "MIT" ]

null

from django.test import TestCase from shop.models import Product from django.contrib.auth.models import User from coupons.forms import CouponForm

28.878788

77

0.550892

8d341997147380f82b39848b173c8f836285f331

2,134

py

Python

tests/conftest.py

gpontesss/botus_receptus

bf29f5f70a2e7ae3548a44287c636515f78e7e77

[ "BSD-3-Clause" ]

3

2019-04-15T01:45:46.000Z

2020-04-07T13:31:19.000Z

tests/conftest.py

gpontesss/botus_receptus

bf29f5f70a2e7ae3548a44287c636515f78e7e77

[ "BSD-3-Clause" ]

244

2020-04-20T22:10:23.000Z

2022-03-31T23:03:48.000Z

tests/conftest.py

gpontesss/botus_receptus

bf29f5f70a2e7ae3548a44287c636515f78e7e77

[ "BSD-3-Clause" ]

1

2021-11-08T08:52:32.000Z

from __future__ import annotations import asyncio from typing import Any import asynctest.mock # type: ignore import pytest # type: ignore import pytest_mock._util # type: ignore pytest_mock._util._mock_module = asynctest.mock

28.837838

79

0.680412

8d352ba96be56207cce46e2dc458765a09de6f97

1,247

py

Python

Shark_Training/pyimagesearch/preprocessing/meanpreprocessor.py

crpurcell/MQ_DPI_Release

97444513e8b8d48ec91ff8a43b9dfaed0da029f9

[ "MIT" ]

null

Shark_Training/pyimagesearch/preprocessing/meanpreprocessor.py

crpurcell/MQ_DPI_Release

97444513e8b8d48ec91ff8a43b9dfaed0da029f9

[ "MIT" ]

null

Shark_Training/pyimagesearch/preprocessing/meanpreprocessor.py

crpurcell/MQ_DPI_Release

97444513e8b8d48ec91ff8a43b9dfaed0da029f9

[ "MIT" ]

null

#=============================================================================# # # # MODIFIED: 15-Jan-2019 by C. Purcell # # # #=============================================================================# import cv2 #-----------------------------------------------------------------------------#

35.628571

79

0.36648

8d36012ec39c8b5de0335c08778adaf22f20af3c

985

py

Python

aiida_quantumespresso/parsers/constants.py

unkcpz/aiida-quantumespresso

fbac0993bb8b6cdeba85717453debcf0ab062b5a

[ "MIT" ]

null

aiida_quantumespresso/parsers/constants.py

unkcpz/aiida-quantumespresso

fbac0993bb8b6cdeba85717453debcf0ab062b5a

[ "MIT" ]

null

aiida_quantumespresso/parsers/constants.py

unkcpz/aiida-quantumespresso

fbac0993bb8b6cdeba85717453debcf0ab062b5a

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Physical or mathematical constants. Since every code has its own conversion units, this module defines what QE understands as for an eV or other quantities. Whenever possible, we try to use the constants defined in :py:mod:aiida.common.constants:, but if some constants are slightly different among different codes (e.g., different standard definition), we define the constants in this file. """ from aiida.common.constants import ( ang_to_m, bohr_si, bohr_to_ang, hartree_to_ev, invcm_to_THz, ry_si, ry_to_ev, timeau_to_sec, ) # From the definition of Quantum ESPRESSO, conversion from atomic mass # units to Rydberg units: # REAL(DP), PARAMETER :: AMU_SI = 1.660538782E-27_DP ! Kg # REAL(DP), PARAMETER :: ELECTRONMASS_SI = 9.10938215E-31_DP ! Kg # REAL(DP), PARAMETER :: AMU_AU = AMU_SI / ELECTRONMASS_SI # REAL(DP), PARAMETER :: AMU_RY = AMU_AU / 2.0_DP amu_Ry = 911.4442421323

31.774194

77

0.700508

8d3e794674c7c132a4877a4a375649bf2399c45b

2,639

py

Python

venv/lib/python3.8/site-packages/keras/api/_v2/keras/applications/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

1

2021-05-24T10:08:51.000Z

venv/lib/python3.8/site-packages/keras/api/_v2/keras/applications/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

null

venv/lib/python3.8/site-packages/keras/api/_v2/keras/applications/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

null

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Public API for tf.keras.applications namespace. """ from __future__ import print_function as _print_function import sys as _sys from keras.api._v2.keras.applications import densenet from keras.api._v2.keras.applications import efficientnet from keras.api._v2.keras.applications import imagenet_utils from keras.api._v2.keras.applications import inception_resnet_v2 from keras.api._v2.keras.applications import inception_v3 from keras.api._v2.keras.applications import mobilenet from keras.api._v2.keras.applications import mobilenet_v2 from keras.api._v2.keras.applications import mobilenet_v3 from keras.api._v2.keras.applications import nasnet from keras.api._v2.keras.applications import resnet from keras.api._v2.keras.applications import resnet50 from keras.api._v2.keras.applications import resnet_v2 from keras.api._v2.keras.applications import vgg16 from keras.api._v2.keras.applications import vgg19 from keras.api._v2.keras.applications import xception from keras.applications.densenet import DenseNet121 from keras.applications.densenet import DenseNet169 from keras.applications.densenet import DenseNet201 from keras.applications.efficientnet import EfficientNetB0 from keras.applications.efficientnet import EfficientNetB1 from keras.applications.efficientnet import EfficientNetB2 from keras.applications.efficientnet import EfficientNetB3 from keras.applications.efficientnet import EfficientNetB4 from keras.applications.efficientnet import EfficientNetB5 from keras.applications.efficientnet import EfficientNetB6 from keras.applications.efficientnet import EfficientNetB7 from keras.applications.inception_resnet_v2 import InceptionResNetV2 from keras.applications.inception_v3 import InceptionV3 from keras.applications.mobilenet import MobileNet from keras.applications.mobilenet_v2 import MobileNetV2 from keras.applications.mobilenet_v3 import MobileNetV3Large from keras.applications.mobilenet_v3 import MobileNetV3Small from keras.applications.nasnet import NASNetLarge from keras.applications.nasnet import NASNetMobile from keras.applications.resnet import ResNet101 from keras.applications.resnet import ResNet152 from keras.applications.resnet import ResNet50 from keras.applications.resnet_v2 import ResNet101V2 from keras.applications.resnet_v2 import ResNet152V2 from keras.applications.resnet_v2 import ResNet50V2 from keras.applications.vgg16 import VGG16 from keras.applications.vgg19 import VGG19 from keras.applications.xception import Xception del _print_function

47.981818

82

0.869269

8d3ebf8c27b4787edb5db6336b9fad286f003b92

97

py

Python

flash/vision/embedding/__init__.py

alvin-chang/lightning-flash

481d4d369ff0a5d8c2b2d9e4970c5608a92b3ff5

[ "Apache-2.0" ]

2

2021-06-25T08:42:36.000Z

2021-06-25T08:49:29.000Z

flash/vision/embedding/__init__.py

alvin-chang/lightning-flash

481d4d369ff0a5d8c2b2d9e4970c5608a92b3ff5

[ "Apache-2.0" ]

null

flash/vision/embedding/__init__.py

alvin-chang/lightning-flash

481d4d369ff0a5d8c2b2d9e4970c5608a92b3ff5

[ "Apache-2.0" ]

null

from flash.vision.embedding.image_embedder_model import ImageEmbedder, ImageEmbedderDataPipeline

48.5

96

0.907216

8d3f8941dd6434ce1537415533cd51f289916f52

5,554

py

Python

configstruct/config_struct.py

bradrf/configstruct

aeea8fbba1e2daa0a0c38eeb9622d1716c0bb3e8

[ "MIT" ]

null

configstruct/config_struct.py

bradrf/configstruct

aeea8fbba1e2daa0a0c38eeb9622d1716c0bb3e8

[ "MIT" ]

16

2016-10-13T09:53:46.000Z

2022-03-24T15:04:51.000Z

configstruct/config_struct.py

bradrf/configstruct

aeea8fbba1e2daa0a0c38eeb9622d1716c0bb3e8

[ "MIT" ]

null

import os import sys import logging from configparser import ConfigParser from .open_struct import OpenStruct from .section_struct import SectionStruct # TODO: use file lock when read/write def choose_theirs(section, option, mine, theirs): '''Always prefer values for keys from file.''' return theirs def choose_mine(section, option, mine, theirs): '''Always prefer values for keys in memory.''' return mine LOG_LEVELS = ['debug-all', 'debug', 'info', 'warning', 'error', 'critical'] LOG_OPTIONS = {'log_level': 'info', 'log_file': 'STDERR'}

40.540146

100

0.659705

8d4042ed9b0586457ce903d2cc6db6a880c03485

10,327

py

Python

test_apps/python_app/tests/compiler_test.py

Origen-SDK/o2

5b0f9a6d113ddebc73c7ee224931e8b2d0301794

[ "MIT" ]

null

test_apps/python_app/tests/compiler_test.py

Origen-SDK/o2

5b0f9a6d113ddebc73c7ee224931e8b2d0301794

[ "MIT" ]

127

2019-11-23T17:09:35.000Z

2021-09-02T11:06:20.000Z

test_apps/python_app/tests/compiler_test.py

Origen-SDK/o2

5b0f9a6d113ddebc73c7ee224931e8b2d0301794

[ "MIT" ]

null

import origen # pylint: disable=import-error import pytest, pathlib, os, stat, abc from os import access, W_OK, X_OK, R_OK from tests.shared import clean_falcon, clean_compiler, tmp_dir def user_compiler(): ''' End users should access the compiler via ``origen.app.compiler``. ''' return origen.app.compiler MakoRenderer = origen.compiler.MakoRenderer # JinjaRenderer = origen.compiler.JinjaRenderer def test_render_file(self): ''' Test that the renderer can render a given file ''' rendered = user_compiler().render(self.input_filename, syntax=self.syntax, direct_src=False, output_dir=tmp_dir(), context=self.additional_context) assert isinstance(rendered, pathlib.Path) assert rendered == self.output_filename assert rendered.exists assert open(rendered, 'r').read() == self.expected_dut_info_output def test_render_str(self): ''' Test that the renderer can render a given string ''' rendered = user_compiler().render(self.str_render, syntax=self.syntax, direct_src=True) assert rendered == self.expected_str_render def test_render_with_standard_context(self): ''' Renders output using the standard context ''' rendered = user_compiler().render( self.str_render_with_standard_context, syntax=self.syntax, direct_src=True) assert rendered == self.expected_str_render_with_standard_context def test_render_with_additional_context(self): ''' Renders output using additional context given as an option -> Test that the renderer supports the 'additional_context' option ''' rendered = user_compiler().render( self.str_render_with_additional_context, syntax=self.syntax, direct_src=True, context={'test_renderer_name': self.syntax}) assert rendered == self.expected_str_render_with_additional_context # class TestJinjaCompiler: # pass

39.117424

97

0.637165

8d42c2702dd5a391e27f8a389f8a934778ba0c95

999

py

Python

api/api.py

devSessions/crvi

1ecc68d6c968294bcc5ceea747604ee237f6080c

[ "MIT" ]

25

2017-12-31T06:51:54.000Z

2021-11-17T11:29:30.000Z

api/api.py

amittomar-1/crvi

1ecc68d6c968294bcc5ceea747604ee237f6080c

[ "MIT" ]

23

2020-01-28T21:34:12.000Z

2022-03-11T23:11:54.000Z

api/api.py

amittomar-1/crvi

1ecc68d6c968294bcc5ceea747604ee237f6080c

[ "MIT" ]

11

2018-01-04T12:30:33.000Z

2020-12-01T18:08:59.000Z

from flask import Flask, jsonify, request import predict import socket app = Flask(__name__) #to spedicy route after url if __name__ == '__main__': #for remote host ip = socket.gethostbyname(socket.gethostname()) app.run(port=5000,host=ip) #for local host #app.run(debug=True, port=5000)

19.211538

51

0.58959

8d4484e9d066b90a85e8763af3ea488f55a3ae34

68

py

Python

exe/__init__.py

whisperaven/0ops.exed

ab9f14868fec664fe78edab6fb7eb572b3048c58

[ "MIT" ]

10

2017-03-17T02:15:18.000Z

2019-10-26T23:54:21.000Z

exe/__init__.py

whisperaven/0ops

ab9f14868fec664fe78edab6fb7eb572b3048c58

[ "MIT" ]

1

2017-03-20T03:17:17.000Z

2017-03-20T04:04:26.000Z

exe/__init__.py

whisperaven/0ops

ab9f14868fec664fe78edab6fb7eb572b3048c58

[ "MIT" ]

3

2017-03-17T02:46:23.000Z

2018-04-14T15:49:56.000Z

17

44

0.661765

8d4492744de35276bcea0bf1ccb409c9aa59295e

418

py

Python

Special_Viewer.py

Akivamelka/unsupervised_mid_semester

5393185d7b0327bbb7cd4b3700d4d00704a5623f

[ "MIT" ]

null

Special_Viewer.py

Akivamelka/unsupervised_mid_semester

5393185d7b0327bbb7cd4b3700d4d00704a5623f

[ "MIT" ]

null

Special_Viewer.py

Akivamelka/unsupervised_mid_semester

5393185d7b0327bbb7cd4b3700d4d00704a5623f

[ "MIT" ]

null

from Dimension_Reduction import Viewer import pandas as pd view_tool = Viewer() reduc = 'pca' suffix = '5' data_plot = pd.read_csv(f"{reduc}_dim2_{suffix}.csv", delimiter=",") models = ['km', 'fuzz', 'gmm', 'dbsc', 'hier', 'spec' ] for model in models: print(model) labels = pd.read_csv(f"labels_{model}_{suffix}.csv", delimiter=",") view_tool.view_vs_target(data_plot, labels, suffix, model)

32.153846

72

0.669856

8d481fde3510821315275850b3a25299bc9b350d

6,621

py

Python

pytumblr/types.py

9999years/pytumblr

fe9b2fb60866785141fc0deb5a357a773c0f4229

[ "Apache-2.0" ]

null

pytumblr/types.py

9999years/pytumblr

fe9b2fb60866785141fc0deb5a357a773c0f4229

[ "Apache-2.0" ]

null

pytumblr/types.py

9999years/pytumblr

fe9b2fb60866785141fc0deb5a357a773c0f4229

[ "Apache-2.0" ]

null

from collections import UserList from dataclasses import dataclass, field from datetime import datetime from typing import List, Dict, Any, Optional, Type DATE_FORMAT = '%Y-%m-%d %H:%M:%S %Z' _link_classes = {'navigation': NavigationLink, 'action': ActionLink} # a type -> class dict POST_CLASSES: Dict[str, Type] = { 'photo': LegacyPhotoPost, 'quote': LegacyQuotePost, 'link': LegacyLinkPost, 'chat': LegacyChatPost, 'audio': LegacyAudioPost, 'video': LegacyVideoPost, 'answer': LegacyAnswerPost, }

19.247093

71

0.661683

8d4876f42fc49dd8332e5b4739b6a7de0c8b9bb2

311

py

Python

simple_jobs_scraper.py

Engnation/Jobs-Scraper

6f8b1207731da9f187db406a5be6916774ba3bc5

[ "MIT" ]

null

simple_jobs_scraper.py

Engnation/Jobs-Scraper

6f8b1207731da9f187db406a5be6916774ba3bc5

[ "MIT" ]

null

simple_jobs_scraper.py

Engnation/Jobs-Scraper

6f8b1207731da9f187db406a5be6916774ba3bc5

[ "MIT" ]

null

from jobs_scraper import JobsScraper # Let's create a new JobsScraper object and perform the scraping for a given query. position_var = "Python" scraper = JobsScraper(country="ca", position=position_var, location="Toronto", pages=3) df = scraper.scrape() df.to_csv(rf'{position_var} jobs.csv', index = False)

34.555556

87

0.768489

8d4a0164b56629bd4e65dd24b9c1a1fba70a5ea1

810

py

Python

mac/redRMacUpdater.py

PiRSquared17/r-orange

6bc383f1db3c10c59e16b39daffc44df904ce031

[ "Apache-2.0" ]

1

2019-04-15T13:50:30.000Z

mac/redRMacUpdater.py

PiRSquared17/r-orange

6bc383f1db3c10c59e16b39daffc44df904ce031

[ "Apache-2.0" ]

null

mac/redRMacUpdater.py

PiRSquared17/r-orange

6bc383f1db3c10c59e16b39daffc44df904ce031

[ "Apache-2.0" ]

1

2016-01-21T23:00:21.000Z

import tarfile, sys,os from PyQt4.QtCore import * from PyQt4.QtGui import * app = QApplication(sys.argv) try: zfile = tarfile.open(sys.argv[1], "r:gz" ) zfile.extractall(sys.argv[2]) zfile.close() mb = QMessageBox('Red-R Updated', "Red-R has been updated'", QMessageBox.Information, QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton, QMessageBox.NoButton) except: mb = QMessageBox('Red-R Updated', "There was an Error in updating Red-R.\n\n%s" % sys.exc_info()[0], QMessageBox.Information, QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton, QMessageBox.NoButton) app.setActiveWindow(mb) mb.setFocus() mb.show() app.exit(0) #mb.exec_() sys.exit(app.exec_()) os.remove(sys.argv[1])

30

105

0.646914

8d4be9a3c0385e4ebdfd3712a699e128c38acafc

9,346

py

Python

darknet_websocket_demo.py

wutianze/darknet-superb-service

fdee5a932c8a3898701c1e302e4642fbff853630

[ "MIT" ]

null

darknet_websocket_demo.py

wutianze/darknet-superb-service

fdee5a932c8a3898701c1e302e4642fbff853630

[ "MIT" ]

null

darknet_websocket_demo.py

wutianze/darknet-superb-service

fdee5a932c8a3898701c1e302e4642fbff853630

[ "MIT" ]

null

from ctypes import * #from multiprocessing import Process, Queue import queue import time from threading import Lock,Thread from fastapi import FastAPI from fastapi import Request from fastapi import WebSocket, WebSocketDisconnect import uvicorn #from yolo_service import * import socket import random from typing import List import darknet import cv2 import time import io import struct import os import numpy as np import base64 import json from jtracer.tracing import init_tracer import pynng from PIL import Image from opentracing.propagation import Format def convert2relative(bbox,darknet_height,darknet_width): """ YOLO format use relative coordinates for annotation """ x, y, w, h = bbox _height = darknet_height _width = darknet_width return x/_width, y/_height, w/_width, h/_height app = FastAPI() manager = ConnectionManager() if __name__ == "__main__": uvicorn.run("darknet_websocket_demo:app",host="0.0.0.0",port=int(os.getenv("SUPB_SERVICE_PORT")),log_level="info")

35.003745

180

0.652044

8d4d42f7498f1a4af52daeaede069016fb2ef667

2,389

py

Python

tests/unit/test_sherman_morrison.py

willwheelera/pyqmc

0c8d1f308bbccb1560aa680a5a75e7a4fe7a69fb

[ "MIT" ]

44

2019-06-04T13:53:26.000Z

2022-03-31T08:36:30.000Z

tests/unit/test_sherman_morrison.py

willwheelera/pyqmc

0c8d1f308bbccb1560aa680a5a75e7a4fe7a69fb

[ "MIT" ]

121

2019-05-13T14:05:20.000Z

2022-02-16T19:24:37.000Z

tests/unit/test_sherman_morrison.py

willwheelera/pyqmc

0c8d1f308bbccb1560aa680a5a75e7a4fe7a69fb

[ "MIT" ]

35

2019-04-26T21:57:50.000Z

2022-02-14T07:56:34.000Z

import numpy as np from pyqmc.slater import sherman_morrison_row from pyqmc.slater import sherman_morrison_ms if __name__ == "__main__": r_err, inv_err = list(zip(*[run_sherman_morrison() for i in range(2000)])) print(np.amax(r_err)) print(np.amax(inv_err)) counts, bins = np.histogram(np.log10(inv_err), bins=np.arange(-16, 0)) print(np.stack([counts, bins[1:]]))

30.628205

78

0.631226

8d4df1f93edc3b8bb4e583e03cb8610d1cc0439f

1,543

py

Python

script/licel-plotter.py

FedeVerstraeten/smn-lidar-controller

7850fd48702d5f2e00d07b499812b3b2fb2b7676

[ "MIT" ]

null

script/licel-plotter.py

FedeVerstraeten/smn-lidar-controller

7850fd48702d5f2e00d07b499812b3b2fb2b7676

[ "MIT" ]

1

2021-10-05T03:53:55.000Z

script/licel-plotter.py

FedeVerstraeten/smnar-lidar-controller

7850fd48702d5f2e00d07b499812b3b2fb2b7676

[ "MIT" ]

null

#!/usr/bin/env python3 import sys import socket import time import numpy as np import matplotlib.pyplot as plt HOST = '10.49.234.234' PORT = 2055 if __name__ == '__main__': # Select TR command_select='SELECT 0' rsp=repr(command_to_licel(command_select)) print('Received',rsp) # Clear memory command_clear='MCLEAR' rsp=repr(command_to_licel(command_clear)) print('Received',rsp) # Start TR command_start='MSTART' rsp=repr(command_to_licel(command_start)) print('Received',rsp) time.sleep(5) # Stop TR command_stop='MSTOP' rsp=repr(command_to_licel(command_stop)) print('Received',rsp) # Get data command_data='DATA? 0 4001 LSW A' rsp=command_to_licel(command_data) #print('Received',rsp) # with open('outputlicel', 'w') as f: # f.write(rsp) data_output=rsp # Plot t = np.arange(0, len(data_output), 1) data_arr=[] for data_byte in data_output: data_arr.append(int(data_byte)) fig, ax = plt.subplots() ax.plot(t, data_arr) ax.set(xlabel='time (s)', ylabel='voltage (mV)',title='SMN LICEL') ax.grid() fig.savefig("test.png") plt.show()

24.109375

70

0.644848

8d500786de7e53c7c13f50132e8ecbc760d095db

13,860

py

Python

horizon/openstack_dashboard/dashboards/identity/account/tables.py

yianjiajia/openstack_horizon

9e36a4c3648ef29d0df6912d990465f51d6124a6

[ "Apache-2.0" ]

null

horizon/openstack_dashboard/dashboards/identity/account/tables.py

yianjiajia/openstack_horizon

9e36a4c3648ef29d0df6912d990465f51d6124a6

[ "Apache-2.0" ]

null

horizon/openstack_dashboard/dashboards/identity/account/tables.py

yianjiajia/openstack_horizon

9e36a4c3648ef29d0df6912d990465f51d6124a6

[ "Apache-2.0" ]

null

# Copyright 2012 Nebula, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import logging import json from django.utils.translation import ugettext_lazy as _ from django.utils.translation import ungettext_lazy from django.conf import settings from horizon import forms from horizon import tables from horizon.utils import filters from openstack_dashboard import api from openstack_dashboard import policy LOG = logging.getLogger(__name__) POLICY_CHECK = getattr(settings, "POLICY_CHECK_FUNCTION", lambda p, r: True) def action(self, request, obj_id): try: user = api.keystone.user_get(request, obj_id) default_user_role = api.keystone.get_default_role(request) default_project_admin_role = api.keystone.get_default_project_admin_role(request) api.keystone.remove_tenant_user_role(request, project=user.default_project_id, user=user.id, role=default_user_role.id) api.keystone.user_update(request, obj_id, **{'default_role_id': default_project_admin_role.id}) api.keystone.add_tenant_user_role(request, project=user.default_project_id, user=user.id, role=default_project_admin_role.id) # operation log config = _('Old role %s, new role %s') % (default_user_role.name, default_project_admin_role.name) api.logger.Logger(request).create(resource_type='account', action_name='Role_Change', resource_name='Account', config=config, status='Success') except Exception: # operation log config = _('Old role %s, new role %s') % (default_user_role.name, default_project_admin_role.name) api.logger.Logger(request).create(resource_type='account', action_name='Role_Change', resource_name='Account', config=config, status='Error') class ChangePasswordLink(policy.PolicyTargetMixin, tables.LinkAction): name = "change_password" verbose_name = _("Change Password") url = "horizon:identity:account:change_password" classes = ("ajax-modal",) icon = "key" policy_rules = (("identity", "identity:change_password"),) policy_target_attrs = (("user_id", "id"),) class UpdateRegionsLink(policy.PolicyTargetMixin, tables.LinkAction): name = "regions" verbose_name = _("Update Regions") url = "horizon:identity:account:regions" classes = ("ajax-modal",) icon = "pencil" policy_rules = (("identity", "identity:update_user_regions"),) class UpdateMembersLink(tables.LinkAction): name = "users" verbose_name = _("Manage Members") url = "horizon:identity:account:update_member" classes = ("ajax-modal",) icon = "pencil" policy_rules = (("identity", "identity:list_users"), ("identity", "identity:list_grants")) STATUS_DISPLAY_CHOICES = ( (False, _("Delete")), (True, _("Normal")), )

37.258065

165

0.581818

8d5291b6a1ce7e03aab2c5b10e8c178dc0212bb3

2,278

py

Python

3Sum.py

Muthu2093/Algorithms-practice

999434103a9098a4361104fd39cba5913860fa9d

[ "MIT" ]

null

3Sum.py

Muthu2093/Algorithms-practice

999434103a9098a4361104fd39cba5913860fa9d

[ "MIT" ]

null

3Sum.py

Muthu2093/Algorithms-practice

999434103a9098a4361104fd39cba5913860fa9d

[ "MIT" ]

null

## Given an array nums of n integers, are there elements a, b, c in nums such that a + b + c = 0? Find all unique triplets in the array which gives the sum of zero. ## Note: ## The solution set must not contain duplicate triplets. ## Example: ## Given array nums = [-1, 0, 1, 2, -1, -4], ## A solution set is: ## [ ## [-1, 0, 1], ## [-1, -1, 2] ## ]

27.780488

164

0.368306

8d52b06f889e9040ed2102aec6867ed5ea6a3b70

684

py

Python

moim/models.py

gyukebox/django-tutorial-moim

ea9bea85dadf22bff58ae26ee1ac59171bbe0240

[ "MIT" ]

null

moim/models.py

gyukebox/django-tutorial-moim

ea9bea85dadf22bff58ae26ee1ac59171bbe0240

[ "MIT" ]

4

2018-01-01T09:26:30.000Z

2018-01-06T07:13:01.000Z

moim/models.py

gyukebox/django-tutorial-moim

ea9bea85dadf22bff58ae26ee1ac59171bbe0240

[ "MIT" ]

null

from django.db import models from user.models import UserModel

36

68

0.730994

8d5338ad6760bdfbd08440494b1ea9d0eab1dc53

1,809

py

Python

developers_chamber/scripts/gitlab.py

dstlmrk/developers-chamber

93f928048f57c049f1c85446d18078b73376462a

[ "MIT" ]

8

2019-08-23T15:46:30.000Z

2021-03-23T20:12:21.000Z

developers_chamber/scripts/gitlab.py

dstlmrk/developers-chamber

93f928048f57c049f1c85446d18078b73376462a

[ "MIT" ]

14

2019-09-17T20:24:18.000Z

2021-05-18T21:10:12.000Z

developers_chamber/scripts/gitlab.py

dstlmrk/developers-chamber

93f928048f57c049f1c85446d18078b73376462a

[ "MIT" ]

6

2019-08-23T15:46:21.000Z

2022-02-18T11:01:18.000Z

import os import click from developers_chamber.git_utils import get_current_branch_name from developers_chamber.gitlab_utils import \ create_merge_request as create_merge_request_func from developers_chamber.scripts import cli DEFAULT_API_URL = os.environ.get('GITLAB_API_URL', 'https://gitlab.com/api/v4') DEFAULT_PROJECT = os.environ.get('GITLAB_PROJECT') DEFAULT_TARGET_BRANCH = os.environ.get('GITLAB_TARGET_BRANCH', 'next') DEFAULT_TOKEN = os.environ.get('GITLAB_TOKEN')

38.489362

116

0.726368

8d5577a30127caeb2ef24f4e9b841abc050103d0

15,790

py

Python

tests_pytest/state_machines/autoinstall/test_autoinstall_smbase.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

5

2020-06-04T10:20:33.000Z

2020-10-26T15:09:19.000Z

tests_pytest/state_machines/autoinstall/test_autoinstall_smbase.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

null

tests_pytest/state_machines/autoinstall/test_autoinstall_smbase.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

null

# Copyright 2021 IBM Corp. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Test base autoinstall machine A smallest implementation on SmBase is used to test common features """ # pylint: disable=invalid-name # we have really long test names # pylint: disable=redefined-outer-name # use of fixtures # pylint: disable=unused-argument # use of fixtures for their side effects # # IMPORTS # from pathlib import Path from tessia.baselib.hypervisors.hmc.volume_descriptor import FcpVolumeDescriptor from tessia.server.config import Config from tessia.server.state_machines.autoinstall import plat_lpar, plat_zvm, plat_kvm from tessia.server.state_machines.autoinstall import plat_base, sm_base from tessia.server.state_machines.autoinstall.model import AutoinstallMachineModel from tessia.server.state_machines.autoinstall.sm_base import SmBase from tests_pytest.decorators import tracked from tests_pytest.state_machines.ssh_stub import SshClient from tests_pytest.state_machines.null_hypervisor import NullHypervisor import pytest import yaml # # CONSTANTS AND DEFINITIONS # CREDS = {'user': 'unit', 'password': 'test'} # # CODE # # wait_install() class NullPostInstallChecker: """ PostInstallChecked that checks that it has been called """ def test_boot_and_postinstall_check_on_lpar_dasd( lpar_dasd_system, default_os_tuple, tmpdir): """ Attempt to install "nothing" on an LPAR on DASD disk Verify that hypervisor is called with correct parameters and post-install checker is run """ model = AutoinstallMachineModel(*default_os_tuple, lpar_dasd_system, CREDS) checker = NullPostInstallChecker() hyp = plat_lpar.PlatLpar.create_hypervisor(model) platform = plat_lpar.PlatLpar(model, hyp) # autoinstall machines use their own working directory # and have to be initialized in a temporary environment with tmpdir.as_cwd(): smbase = NullMachine(model, platform, checker) smbase.start() assert checker.verify.called_once sys, cpus, mem, attrs, *_ = hyp.start.calls[0] assert sys == lpar_dasd_system.hypervisor.boot_options['partition-name'] assert cpus == lpar_dasd_system.cpus assert mem == lpar_dasd_system.memory # installation device does not show up in HmcHypervisor boot, # it is only used later during installation assert attrs['boot_params']['boot_method'] == 'dasd' assert attrs['boot_params']['devicenr'] == \ lpar_dasd_system.hypervisor.boot_options['boot-device'] def test_boot_and_postinstall_check_on_lpar_scsi( lpar_scsi_system, default_os_tuple, tmpdir): """ Attempt to install "nothing" on an LPAR on SCSI disk Verify that hypervisor is called with correct parameters and post-install checker is run """ model = AutoinstallMachineModel(*default_os_tuple, lpar_scsi_system, CREDS) checker = NullPostInstallChecker() hyp = plat_lpar.PlatLpar.create_hypervisor(model) platform = plat_lpar.PlatLpar(model, hyp) # autoinstall machines use their own working directory # and have to be initialized in a temporary environment with tmpdir.as_cwd(): smbase = NullMachine(model, platform, checker) smbase.start() assert checker.verify.called_once sys, cpus, mem, attrs, *_ = hyp.start.calls[0] assert sys == lpar_scsi_system.hypervisor.boot_options['partition-name'] assert cpus == lpar_scsi_system.cpus assert mem == lpar_scsi_system.memory # installation device does not show up in HmcHypervisor boot, # it is only used later during installation assert attrs['boot_params']['boot_method'] == 'dasd' assert attrs['boot_params']['devicenr'] == \ lpar_scsi_system.hypervisor.boot_options['boot-device'] def test_boot_and_postinstall_check_on_vm_dasd( vm_dasd_system, default_os_tuple, tmpdir): """ Attempt to install "nothing" on a VM on DASD disk Verify that hypervisor is called with correct parameters and post-install checker is run """ model = AutoinstallMachineModel(*default_os_tuple, vm_dasd_system, CREDS) checker = NullPostInstallChecker() hyp = plat_zvm.PlatZvm.create_hypervisor(model) platform = plat_zvm.PlatZvm(model, hyp) # autoinstall machines use their own working directory # and have to be initialized in a temporary environment with tmpdir.as_cwd(): smbase = NullMachine(model, platform, checker) smbase.start() assert checker.verify.called_once sys, cpus, mem, attrs, *_ = hyp.start.calls[0] assert sys == vm_dasd_system.system_name assert cpus == vm_dasd_system.cpus assert mem == vm_dasd_system.memory assert vm_dasd_system.volumes[0].device_id == \ attrs['storage_volumes'][0]['devno'] def test_boot_and_postinstall_check_on_vm_scsi( vm_scsi_system, default_os_tuple, tmpdir): """ Attempt to install "nothing" on a VM on SCSI disk Verify that hypervisor is called with correct parameters and post-install checker is run """ model = AutoinstallMachineModel(*default_os_tuple, vm_scsi_system, CREDS) checker = NullPostInstallChecker() hyp = plat_zvm.PlatZvm.create_hypervisor(model) platform = plat_zvm.PlatZvm(model, hyp) # autoinstall machines use their own working directory # and have to be initialized in a temporary environment with tmpdir.as_cwd(): smbase = NullMachine(model, platform, checker) smbase.start() assert checker.verify.called_once sys, cpus, mem, attrs, *_ = hyp.start.calls[0] assert sys == vm_scsi_system.system_name assert cpus == vm_scsi_system.cpus assert mem == vm_scsi_system.memory assert vm_scsi_system.volumes[0].lun == \ attrs['storage_volumes'][0]['lun'] def testboot_and_postinstall_check_on_kvm_scsi( kvm_scsi_system, default_os_tuple, tmpdir): """ Attempt to install "nothing" on a KVM on SCSI disk Verify correct device paths and that hypervisor is called with correct parameters and post-install checker is run """ model = AutoinstallMachineModel(*default_os_tuple, kvm_scsi_system, CREDS) checker = NullPostInstallChecker() hyp = plat_kvm.PlatKvm.create_hypervisor(model) platform = plat_kvm.PlatKvm(model, hyp) # autoinstall machines use their own working directory # and have to be initialized in a temporary environment with tmpdir.as_cwd(): smbase = NullMachine(model, platform, checker) smbase.start() assert checker.verify.called_once sys, cpus, mem, attrs, *_ = hyp.start.calls[0] assert sys == kvm_scsi_system.system_name assert cpus == kvm_scsi_system.cpus assert mem == kvm_scsi_system.memory assert kvm_scsi_system.volumes[0].lun == \ attrs['storage_volumes'][0]['volume_id'] for volume in model.system_profile.volumes: assert '/dev/disk/by-path/ccw' in volume.device_path def test_network_boot_on_lpar_scsi( scsi_volume, osa_iface, default_os_tuple, tmpdir): """ Attempt to install "nothing" on an LPAR on SCSI disk using network boot Verify that hypervisor is called with correct parameters """ ins_file = 'user@password:inst.local/some-os/boot.ins' hmc_hypervisor = AutoinstallMachineModel.HmcHypervisor( 'hmc', 'hmc.local', {'user': '', 'password': ''}, { 'partition-name': 'LP10', 'boot-method': 'network', 'boot-uri': 'ftp://' + ins_file, }) system = AutoinstallMachineModel.SystemProfile( 'lp10', 'default', hypervisor=hmc_hypervisor, hostname='lp10.local', cpus=2, memory=8192, volumes=[scsi_volume], interfaces=[(osa_iface, True)] ) model = AutoinstallMachineModel(*default_os_tuple, system, CREDS) hyp = plat_lpar.PlatLpar.create_hypervisor(model) platform = plat_lpar.PlatLpar(model, hyp) with tmpdir.as_cwd(): smbase = NullMachine(model, platform) smbase.start() sys, cpus, mem, attrs, *_ = hyp.start.calls[0] assert sys == hmc_hypervisor.boot_options['partition-name'] assert cpus == system.cpus assert mem == system.memory assert attrs['boot_params']['boot_method'] == 'ftp' assert attrs['boot_params']['insfile'] == ins_file def test_template_lpar_dasd(lpar_dasd_system, default_os_tuple, tmpdir): """ Test major template parameters """ *os_tuple, _, _ = default_os_tuple package_repo = AutoinstallMachineModel.PackageRepository( 'aux', 'http://example.com/repo', 'package repo') model = AutoinstallMachineModel( *os_tuple, [], [package_repo], lpar_dasd_system, CREDS) hyp = plat_lpar.PlatLpar.create_hypervisor(model) platform = plat_lpar.PlatLpar(model, hyp) with tmpdir.as_cwd(): smbase = NullMachine(model, platform) autofile_path = (Path.cwd() / 'lp10-default') smbase.start() autofile = yaml.safe_load(autofile_path.read_text()) assert autofile['system']['type'] == 'LPAR' assert autofile['system']['hostname'] == 'lp10.local' assert autofile['gw_iface']['type'] == 'OSA' assert autofile['gw_iface']['osname'] == 'enccw0b01' assert autofile['gw_iface']['search_list'] == ['example.com', 'local'] assert autofile['ifaces'][0]['osname'] == 'enccw0b01' assert autofile['volumes'][0]['type'] == 'DASD' assert autofile['volumes'][0]['partitions'] == [ {'fs': 'ext4', 'mp': '/', 'size': '18000M'} ] assert autofile['repos'][0]['name'] == 'os-repo' assert autofile['repos'][1]['name'] == 'aux' def test_template_kvm_scsi(kvm_scsi_system, default_os_tuple, tmpdir): """ Test major template parameters """ model = AutoinstallMachineModel(*default_os_tuple, kvm_scsi_system, CREDS) hyp = plat_kvm.PlatKvm.create_hypervisor(model) platform = plat_kvm.PlatKvm(model, hyp) with tmpdir.as_cwd(): smbase = NullMachine(model, platform) autofile_path = (Path.cwd() / 'kvm54-default') smbase.start() autofile = yaml.safe_load(autofile_path.read_text()) assert autofile['system']['type'] == 'KVM' assert autofile['system']['hostname'] == 'kvm54.local' assert autofile['gw_iface']['type'] == 'MACVTAP' assert autofile['gw_iface']['osname'] == 'eth0' assert autofile['ifaces'][0]['is_gateway']

34.326087

82

0.664155

8d5578255a37005da9d4bcc07955742be9a91579

2,261

py

Python

tests/test_command/test_cat_command.py

bbglab/openvariant

ea1e1b6edf0486b0dea34f43227ba333df1071cc

[ "BSD-3-Clause" ]

null

tests/test_command/test_cat_command.py

bbglab/openvariant

ea1e1b6edf0486b0dea34f43227ba333df1071cc

[ "BSD-3-Clause" ]

null

tests/test_command/test_cat_command.py

bbglab/openvariant

ea1e1b6edf0486b0dea34f43227ba333df1071cc

[ "BSD-3-Clause" ]

null

import unittest from os import getcwd from click.testing import CliRunner from openvariant.commands.openvar import cat

37.065574

121

0.640867

8d559eab2b8075257716e7bc85f5c9d82b0d3221

4,766

py

Python

resnet.py

rVSaxena/VAE

26aa3452a0c8f663153d8cfc8bf1686e242d2fac

[ "Unlicense" ]

null

resnet.py

rVSaxena/VAE

26aa3452a0c8f663153d8cfc8bf1686e242d2fac

[ "Unlicense" ]

null

resnet.py

rVSaxena/VAE

26aa3452a0c8f663153d8cfc8bf1686e242d2fac

[ "Unlicense" ]

null

import torch import torch.nn as nn

34.042857

170

0.640579

8d56bf9a638e31e26421d0d5ccd052c3c7de5f95

246

py

Python

camknows/camknows.py

dreoporto/camknows

769aeb91ff16ff654aa1b182f3564dd26a0f7ad6

[ "MIT" ]

2

2021-09-20T12:29:57.000Z

2021-09-28T11:09:06.000Z

camknows/camknows.py

dreoporto/camknows

769aeb91ff16ff654aa1b182f3564dd26a0f7ad6

[ "MIT" ]

null

camknows/camknows.py

dreoporto/camknows

769aeb91ff16ff654aa1b182f3564dd26a0f7ad6

[ "MIT" ]

null

from camera import Camera if __name__ == '__main__': main()

15.375

55

0.630081

8d58501cd2a4cf7d4be038ee750ddd345cd594fc

196

py

Python

src/main.py

C4theBomb/python-calendar-app

6776403f7f2440c6497d9a53be5e8d617a2ee817

[ "MIT" ]

null

src/main.py

C4theBomb/python-calendar-app

6776403f7f2440c6497d9a53be5e8d617a2ee817

[ "MIT" ]

null

src/main.py

C4theBomb/python-calendar-app

6776403f7f2440c6497d9a53be5e8d617a2ee817

[ "MIT" ]

null

from calendarApp import shell, models import os if __name__ == "__main__": main()

15.076923

38

0.673469

8d5852ea5b1463bc9be5da885619fc756c5bd1fc

4,329

py

Python

personal/Ervin/Word2Vec_recommender.py

edervishaj/spotify-recsys-challenge

4077201ac7e4ed9da433bd10a92c183614182437

[ "Apache-2.0" ]

3

2018-10-12T20:19:57.000Z

2019-12-11T01:11:38.000Z

personal/Ervin/Word2Vec_recommender.py

kiminh/spotify-recsys-challenge

5e7844a77ce3c26658400f161d2d74d682f30e69

[ "Apache-2.0" ]

null

personal/Ervin/Word2Vec_recommender.py

kiminh/spotify-recsys-challenge

5e7844a77ce3c26658400f161d2d74d682f30e69

[ "Apache-2.0" ]

4

2018-10-27T20:30:18.000Z

2020-10-14T07:43:27.000Z

import time import numpy as np import scipy.sparse as sps from gensim.models import Word2Vec from tqdm import tqdm from recommenders.recommender import Recommender from utils.datareader import Datareader from utils.evaluator import Evaluator from utils.post_processing import eurm_to_recommendation_list from recommenders.similarity.s_plus import dot_product if __name__ == '__main__': dr = Datareader(only_load=True, mode='offline', test_num='1', verbose=False) pid = dr.get_test_playlists().transpose()[0] urm = dr.get_urm() urm.data = np.ones(urm.data.shape[0]) ev = Evaluator(datareader=dr) model = W2VRecommender() model.fit(urm, pid) model.compute_model(verbose=True, size=50) model.compute_rating(verbose=True, small=True, top_k=750) ev.evaluate(recommendation_list=eurm_to_recommendation_list(model.eurm, remove_seed=True, datareader=dr), name="W2V", old_mode=False)

37.973684

116

0.613075

8d58f2b0959a8386b4c708d7cc38bd2e9f103bb6

1,321

py

Python

pyesasky/__init__.py

pierfra-ro/pyesasky

a9342efcaa5cca088ed9a5afa2c98d3e9aa4bd0f

[ "BSD-3-Clause" ]

13

2019-05-30T19:57:37.000Z

2021-09-10T09:43:49.000Z

pyesasky/__init__.py

pierfra-ro/pyesasky

a9342efcaa5cca088ed9a5afa2c98d3e9aa4bd0f

[ "BSD-3-Clause" ]

21

2019-06-21T18:55:25.000Z

2022-02-27T14:48:13.000Z

pyesasky/__init__.py

pierfra-ro/pyesasky

a9342efcaa5cca088ed9a5afa2c98d3e9aa4bd0f

[ "BSD-3-Clause" ]

8

2019-05-30T12:20:48.000Z

2022-03-04T04:01:20.000Z

from ._version import __version__ # noqa from .pyesasky import ESASkyWidget # noqa from .catalogue import Catalogue # noqa from .catalogueDescriptor import CatalogueDescriptor # noqa from .cooFrame import CooFrame # noqa from .footprintSet import FootprintSet # noqa from .footprintSetDescriptor import FootprintSetDescriptor # noqa from .HiPS import HiPS # noqa from .imgFormat import ImgFormat # noqa from .jupyter_server import load_jupyter_server_extension # noqa from .metadataDescriptor import MetadataDescriptor # noqa from .metadataType import MetadataType # noqa import json from pathlib import Path HERE = Path(__file__).parent.resolve() with (HERE / "labextension" / "package.json").open() as fid: data = json.load(fid) # Jupyter Extension points

33.025

65

0.711582

8d5933b202fa0260d94c68bc7edbd14a32abb844

2,930

py

Python

visualize.py

jcamstan3370/MachineLearningPerovskites

d7bc433bac349bf53473dc6d636954cae996b8d2

[ "MIT" ]

6

2020-05-09T17:18:00.000Z

2021-09-22T09:37:40.000Z

visualize.py

jstanai/ml_perovskites

d7bc433bac349bf53473dc6d636954cae996b8d2

[ "MIT" ]

null

visualize.py

jstanai/ml_perovskites

d7bc433bac349bf53473dc6d636954cae996b8d2

[ "MIT" ]

1

2021-03-24T04:21:31.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @author: Jared """ import numpy as np import pandas as pd import myConfig import matplotlib.pyplot as plt from ast import literal_eval from plotter import getTrendPlot1 from matplotlib.pyplot import figure df = pd.read_csv(myConfig.extOutput) dffExt = pd.read_csv(myConfig.featurePathExt) dffExt = dffExt.copy().dropna(axis=0, how='any').reset_index() y_predict_ext = df['yhat_ext'] print('Num dummy crystals: {}'.format(len(y_predict_ext))) print([n for n in dffExt.columns if 'p_' not in n]) s = 'fracCl' dffExt['yhat_ext'] = df['yhat_ext'] ylabel = '$E_{g}$ (eV)' getTrendPlot1(dffExt, y_predict_ext, s, ylabel = ylabel, xlabel = s, title = 'Trend') plt.show() ''' s = 'volume' g = dffExt.groupby('fracCl') for i, group in g: getTrendPlot1(group, y_predict_ext, s, ylabel = ylabel, xlabel = s, title = 'Trend', scatter = False) plt.show() ''' s = 'fracCs' g = dffExt.groupby('fracSn') for i, group in g: getTrendPlot1(group, y_predict_ext, s, ylabel = ylabel, xlabel = s, title = 'Trend', scatter = False) plt.show() ''' print(dffExt[['fracCs', 'fracRb', 'fracK', 'fracNa', 'fracSn' , 'fracGe', 'fracCl', 'fracI', 'fracBr', 'yhat_ext']].head(10)) ''' g = dffExt.groupby([ 'fracCs', 'fracRb', 'fracK', 'fracNa', 'fracSn' , 'fracGe', 'fracCl', 'fracI', 'fracBr']) x = [] y = [] x_all = [] y_all = [] for (gr, gi) in g: labels = ['Cs', 'Rb', 'K', 'Na', 'Sn', 'Ge', 'Cl', 'I', 'Br'] #print(gr) sarr = [] for i, n in enumerate(gr): if i < 6: m = 1 else: m = 3 if n != 0: #if n == 1.0: sarr.append(labels[i] + '$_{' + str(int(4*m*n)) + '}$') #else: #sarr.append(labels[i] + '$_{' + str(4*m*n) + '}$') #print(sarr, gr) x += [''.join(sarr)] y.append(gi['yhat_ext'].mean()) x_all += [''.join(sarr)]*len(gi) y_all += gi['yhat_ext'].tolist() print(len(x_all), len(x)) fig = plt.figure(figsize=(13, 4), dpi=200) #(Atomic 3%, Lattice 10%) #plt.title('Stability Trends') plt.title('Direct Bandgap Trends') #plt.ylabel('$\Delta E_{hull}$ (meV/atom)') plt.ylabel('$E_{g}$ (eV)') plt.xticks(rotation=90) plt.scatter(x, y) #figure(num=None, figsize=(8, 6), dpi=200, facecolor='w', edgecolor='k') plt.savefig('/Users/Jared/Documents/test.png', bbox_inches='tight') plt.show() ''' plt.title('Bandgap Trends (Atomic 5%, Lattice 5%)') plt.ylabel('E$_{g}$ (eV)') plt.xticks(rotation=90) plt.scatter(x_all, y_all) figure(num=None, figsize=(8, 6), dpi=200, facecolor='w', edgecolor='k') '''

23.821138

72

0.531058

8d5938563047da10af2e319b379482b6a7545552

237

py

Python

11-if-elif-else-condition.py

GunarakulanGunaretnam/python-basic-fundamentals

c62bf939fbaef8895d28f85af9ef6ced70801f96

[ "Apache-2.0" ]

null

11-if-elif-else-condition.py

GunarakulanGunaretnam/python-basic-fundamentals

c62bf939fbaef8895d28f85af9ef6ced70801f96

[ "Apache-2.0" ]

null

11-if-elif-else-condition.py

GunarakulanGunaretnam/python-basic-fundamentals

c62bf939fbaef8895d28f85af9ef6ced70801f96

[ "Apache-2.0" ]

null

name = input("Enter your name? ") if name == "guna": print("1234567890") elif name == "david": print("0987654321") elif name == "rakulan": print("12345") elif name == "raj": print("1234455667") else: print("No contacts found")

13.941176

33

0.632911

8d595677f62dbebf986ab917f4b41f5f89af2fea

13,409

py

Python

InstagramCrawler.py

Bagas8015/Instagram-Posts-Crawler-Users-v1

82d5da12f7f6caf8c085085135134f58affb1ec7

[ "MIT" ]

null

InstagramCrawler.py

Bagas8015/Instagram-Posts-Crawler-Users-v1

82d5da12f7f6caf8c085085135134f58affb1ec7

[ "MIT" ]

null

InstagramCrawler.py

Bagas8015/Instagram-Posts-Crawler-Users-v1

82d5da12f7f6caf8c085085135134f58affb1ec7

[ "MIT" ]

null

from selenium import webdriver from selenium.webdriver.common.keys import Keys import time import emoji import string import csv import os browser = webdriver.Chrome() user = input('Masukkan username akun anda: ') passwo = input('Masukkan password akun anda: ') url = 'https://www.instagram.com' username = user password = passwo mulaiProgram(url, username, password) browser.quit()

50.220974

205

0.548736

8d596a354fbcf53937f22d7c7dc7a505553f0379

5,310

py

Python

pages/process.py

nchibana/dash-app-template

a51ad0ac92e719b2ef60739b6c1126aebb920d47

[ "MIT" ]

null

pages/process.py

nchibana/dash-app-template

a51ad0ac92e719b2ef60739b6c1126aebb920d47

[ "MIT" ]

4

2020-03-24T17:36:39.000Z

2021-08-23T20:13:16.000Z

pages/process.py

nchibana/dash-app-template

a51ad0ac92e719b2ef60739b6c1126aebb920d47

[ "MIT" ]

null

import dash import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output import plotly.graph_objects as go import plotly.express as px import plotly.figure_factory as ff from sklearn.metrics import roc_curve import pandas as pd from joblib import load from app import app column1 = dbc.Col( [ dcc.Markdown( """ ## Process ******** To build this model, two datasets with similar labels were combined to form a dataset with 102,840 observations. I would like to thank the research team behind [this study](https://arxiv.org/pdf/1802.00393.pdf), as they promptly gave me access to their data, which was labeled through Crowdflower. This model builds largely on their work, as well as that of [this previous study](https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15665). After gaining access to both datasets, I proceeded to retrieve the corresponding tweet text for all IDs in the second set (as it was not provided) via Twitter's API. This was [the code](https://stackoverflow.com/questions/44581647/retrieving-a-list-of-tweets-using-tweet-id-in-tweepy) I used to retrieve the text, without exceeding the rate limit. """ ), html.Iframe(src='data:text/html;charset=utf-8,%3Cbody%3E%3Cscript%20src%3D%22https%3A%2F%2Fgist.github.com%2Fnchibana%2F20d6d9f8ae62a6cc36b773d37dd7dc70.js%22%3E%3C%2Fscript%3E%3C%2Fbody%3E', style=dict(border=0, padding=40), height=780, width=1000), dcc.Markdown( """ After that, I proceeded to combine the datasets and eliminate all duplicate tweets. I also defined a baseline accuracy score of 56%, which is the percent accuracy the model would achieve if it predicted the majority class for all tweets. Using some of the processes followed by the studies mentioned above, I also continued to preprocess the data by eliminating excess spaces, removing punctuation and retrieving the stem words of terms used in tweets. Next, I used Scikit-learn's [TfidVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html) to convert tweet text into a matrix of TF-IDF features, which is a statistic that calculates how important a word is to a document or collection of words. """ ), html.Iframe(src='data:text/html;charset=utf-8,%3Cbody%3E%3Cscript%20src%3D%22https://gist.github.com/nchibana/c15cbc4a1d97af02fa62fff5868bc36e.js%22%3E%3C%2Fscript%3E%3C%2Fbody%3E', style=dict(border=0, padding=40), height=460, width=1000), dcc.Markdown( """ To increase the accuracy of the model, additional features were engineered, such as the number of syllables per word, the total number of characters, the number of words, the number of unique terms, as well as readability and sentiment scores for each tweet. Additionally, the number of mentions, hashtags and links in each tweet were also counted. For this study, images or any other type of media content were not analyzed. """ ), html.Iframe(src='data:text/html;charset=utf-8,%3Cbody%3E%3Cscript%20src%3D%22https%3A%2F%2Fgist.github.com%2Fnchibana%2F5cebfbfa700974edcd9f5fa6e43cc513.js%22%3E%3C%2Fscript%3E%3C%2Fbody%3E', style=dict(border=0, padding=40), height=600, width=1000), dcc.Markdown( """ After testing several models such as Linear SVC, I finally settled on a logistic regression model which I trained on the data and used for the final model and app. I also used grid search to find the optimal parameters for this logistic regression model. Finally, I computed all accuracy scores and proceeded to plot visualizations to help me get a deeper understanding of the model, such as a confusion matrix to visualize misclassified tweets. """ ), html.Iframe(src='data:text/html;charset=utf-8,%3Cbody%3E%3Cscript%20src%3D%22https%3A%2F%2Fgist.github.com%2Fnchibana%2F0cc0c44c9b5a991adbc2690c97023d0c.js%22%3E%3C%2Fscript%3E%3C%2Fbody%3E', style=dict(border=0, padding=40), height=300, width=1000), dcc.Markdown( """ ## Sources ******** 1. Automated Hate Speech Detection and the Problem of Offensive Language Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar Proceedings of the 11th International AAAI Conference on Web and Social Media p. 512-515. 2017 2. Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas 11th International Conference on Web and Social Media, ICWSM 2018 2018 """ ), ], md=12, ) layout = dbc.Row([column1])

50.09434

259

0.688512

8d597279dcdef01055e59ebc350f3cf1d766f1a3

599

py

Python

tests/sdk/test_service.py

kusanagi/katana-sdk-python3

cd089409ec0d822f4d7bd6b4bebd527e003089ee

[ "MIT" ]

2

2017-03-21T20:02:47.000Z

2017-05-02T19:32:01.000Z

tests/sdk/test_service.py

kusanagi/katana-sdk-python3

cd089409ec0d822f4d7bd6b4bebd527e003089ee

[ "MIT" ]

19

2017-03-10T12:09:34.000Z

2018-06-01T18:10:06.000Z

tests/sdk/test_service.py

kusanagi/katana-sdk-python3

cd089409ec0d822f4d7bd6b4bebd527e003089ee

[ "MIT" ]

5

2017-03-10T11:40:50.000Z

2019-03-26T06:28:33.000Z

from katana.sdk.service import get_component from katana.sdk.service import Service

27.227273

61

0.736227

8d5b40af3f077c2c14c5035c4efe391b9a38cc70

527

py

Python

DesignPatterns/MVC/server/controllers/index.py

TigranGit/CodeBase

d58e30b1d83fab4b388ec2cdcb868fa751c62188

[ "Apache-2.0" ]

1

2020-08-13T19:09:27.000Z

DesignPatterns/MVC/server/controllers/index.py

TigranGit/CodeBase

d58e30b1d83fab4b388ec2cdcb868fa751c62188

[ "Apache-2.0" ]

null

DesignPatterns/MVC/server/controllers/index.py

TigranGit/CodeBase

d58e30b1d83fab4b388ec2cdcb868fa751c62188

[ "Apache-2.0" ]

null

from .base_controller import BaseController from ..helper.utils import render_template from ..helper.constants import STATUS_OK

27.736842

47

0.654649

8d5bd4af92a66ece14d4931534ffa3416cb4b661

3,919

py

Python

plugins/tff_backend/bizz/payment.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

null

plugins/tff_backend/bizz/payment.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

178

2017-08-02T12:58:06.000Z

2017-12-20T15:01:12.000Z

plugins/tff_backend/bizz/payment.py

threefoldfoundation/app_backend

b3cea2a3ff9e10efcc90d3d6e5e8e46b9e84312a

[ "Apache-2.0" ]

2

2018-01-10T10:43:12.000Z

2018-03-18T10:42:23.000Z

# -*- coding: utf-8 -*- # Copyright 2017 GIG Technology NV # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # @@license_version:1.3@@ import time from google.appengine.api import users from google.appengine.ext import ndb from framework.utils import now from mcfw.rpc import returns, arguments from plugins.rogerthat_api.exceptions import BusinessException from plugins.tff_backend.models.payment import ThreeFoldTransaction, ThreeFoldPendingTransaction from plugins.tff_backend.to.payment import WalletBalanceTO

42.597826

120

0.720082

8d5f94f57caf92571a35ef22a1aa7566e2df0d65

1,582

py

Python

tasks/tests/ui/conftest.py

MisterLenivec/django_simple_todo_app

8e694a67df43de7feaae785c0b3205534c701923

[ "MIT" ]

null

tasks/tests/ui/conftest.py

MisterLenivec/django_simple_todo_app

8e694a67df43de7feaae785c0b3205534c701923

[ "MIT" ]

4

2020-06-07T01:25:14.000Z

2021-06-10T18:34:10.000Z

tasks/tests/ui/conftest.py

MisterLenivec/django_simple_todo_app

8e694a67df43de7feaae785c0b3205534c701923

[ "MIT" ]

null

from django.conf import settings from selenium import webdriver from selenium.webdriver.chrome.options import Options import pytest import os

27.275862

77

0.653603

8d606a3efd5feb490b057183d05dc39513b2525a

3,519

py

Python

erp/migrations/0026_auto_20200205_0950.py

Foohx/acceslibre

55135e096f2ec4e413ff991f01c17f5e0d5925c0

[ "MIT" ]

8

2020-07-23T08:17:28.000Z

2022-03-09T22:31:36.000Z

erp/migrations/0026_auto_20200205_0950.py

Foohx/acceslibre

55135e096f2ec4e413ff991f01c17f5e0d5925c0

[ "MIT" ]

37

2020-07-01T08:47:33.000Z

2022-02-03T19:50:58.000Z

erp/migrations/0026_auto_20200205_0950.py

Foohx/acceslibre

55135e096f2ec4e413ff991f01c17f5e0d5925c0

[ "MIT" ]

4

2021-04-08T10:57:18.000Z

2022-01-31T13:16:31.000Z

# Generated by Django 3.0.3 on 2020-02-05 08:50 from django.db import migrations, models

59.644068

627

0.651321

8d60c377538ddae6447654f6c37f24bae517225c

3,629

py

Python

convert.py

Ellen7ions/bin2mem

51e3216cbf5e78547751968ef1619a925f2f55ef

[ "MIT" ]

3

2021-05-18T13:07:39.000Z

2021-05-24T12:46:43.000Z

convert.py

Ellen7ions/bin2mem

51e3216cbf5e78547751968ef1619a925f2f55ef

[ "MIT" ]

null

convert.py

Ellen7ions/bin2mem

51e3216cbf5e78547751968ef1619a925f2f55ef

[ "MIT" ]

null

import os, sys import json if __name__ == '__main__': c = Convert() c.apply() # c.mips_gcc_c() # c.mips_objcopy() # c.mips_bin2mem() # config = Config()

28.801587

92

0.590245

8d61a4b35ddf035024fe7d951c745cb83a2a9d4d

3,161

py

Python

stats.py

DisinfoResearch/TwitterCollector

183b6761cca54b5db5b98a2f9f86bd8bcc98a7cb

[ "MIT" ]

null

stats.py

DisinfoResearch/TwitterCollector

183b6761cca54b5db5b98a2f9f86bd8bcc98a7cb

[ "MIT" ]

null

stats.py

DisinfoResearch/TwitterCollector

183b6761cca54b5db5b98a2f9f86bd8bcc98a7cb

[ "MIT" ]

null

#!/bin/python3 # Copyright (C) 2021, Michigan State University. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import csv import json import argparse import sys import datetime from dateutil.parser import parse parser = argparse.ArgumentParser(description='Convert JSON to CSV', epilog='P.S. Trust The Plan') parser.add_argument('--format', help='either JSON or CSV', required=True) parser.add_argument('input', help='JSON File, or stdin if not specified', type=argparse.FileType('r', encoding='utf-8'), default=sys.stdin) parser.add_argument('output', help='output to File, or stdout if not specified', type=argparse.FileType('w', encoding='utf-8'), default=sys.stdout) args = parser.parse_args() today = datetime.date.today() if args.format.upper() == 'CSV': process_csv(args.input, args.output) elif args.format.upper() == 'JSON': process_json(args.input, args.output) else: print(f"Error: '{args.format}' is an invalid format, must be CSV or JSON.", end="\n\n") parser.print_help() exit(-1)

45.157143

326

0.726669

8d61d1b5d6b0de975b9d576cfadcd886cc44204a

10,970

py

Python

Scratch/lstm.py

imadtoubal/MultimodalDeepfakeDetection

46539e16c988ee9fdfb714893788bbbf72836595

[ "MIT" ]

2

2022-03-12T09:18:13.000Z

2022-03-23T08:29:10.000Z

Scratch/lstm.py

imadtoubal/MultimodalDeepfakeDetection

46539e16c988ee9fdfb714893788bbbf72836595

[ "MIT" ]

null

Scratch/lstm.py

imadtoubal/MultimodalDeepfakeDetection

46539e16c988ee9fdfb714893788bbbf72836595

[ "MIT" ]

null

import torch from torch import nn import torch.nn.functional as F import torch.optim as optim from preprocess import * from torch.utils.data import Dataset, DataLoader from blazeface import BlazeFace import os import cv2 import numpy as np from matplotlib import pyplot as plt import random import pickle DATA_FOLDER = '../input/deepfake-detection-challenge' TRAIN_SAMPLE_FOLDER = 'train_sample_videos' TEST_FOLDER = 'test_videos' device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") NET = BlazeFace().to(device) NET.load_weights("../input/blazeface.pth") NET.load_anchors("../input/anchors.npy") sequence = 24 # 1 sec of video feature_size = 167 # length of spatial frequency def main(): # prepare_data() ''' stack = read_video(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER, 'aagfhgtpmv.mp4')) print(stack.shape) stack = stack.mean(axis=-1) / 255 spects = get_spects(stack) # print(spects.shape) print(spects[0]) plt.plot(spects[0]) plt.xlabel('Spatial Frequency') plt.ylabel('Power Spectrum') plt.show() ''' training_data = read_data() train(training_data) if __name__ == '__main__': main()

34.388715

116

0.591978

8d63217e5fdc8f7f711034a43dd2b7d398591281

18,373

py

Python

analysis/plot/python/plot_groups/estimator.py

leozz37/makani

c94d5c2b600b98002f932e80a313a06b9285cc1b

[ "Apache-2.0" ]

1,178

2020-09-10T17:15:42.000Z

2022-03-31T14:59:35.000Z

analysis/plot/python/plot_groups/estimator.py

leozz37/makani

c94d5c2b600b98002f932e80a313a06b9285cc1b

[ "Apache-2.0" ]

1

2020-05-22T05:22:35.000Z

analysis/plot/python/plot_groups/estimator.py

leozz37/makani

c94d5c2b600b98002f932e80a313a06b9285cc1b

[ "Apache-2.0" ]

107

2020-09-10T17:29:30.000Z

2022-03-18T09:00:14.000Z

# Copyright 2020 Makani Technologies LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Plots relating to the estimator.""" from makani.analysis.plot.python import mplot from makani.avionics.common import plc_messages from makani.control import control_types from makani.lib.python import c_helpers from makani.lib.python.h5_utils import numpy_utils from matplotlib.pyplot import plot from matplotlib.pyplot import yticks import numpy as np from scipy import interpolate MFig = mplot.PlotGroup.MFig # pylint: disable=invalid-name _WING_GPS_RECEIVER_HELPER = c_helpers.EnumHelper( 'WingGpsReceiver', control_types) _GROUND_STATION_MODE_HELPER = c_helpers.EnumHelper( 'GroundStationMode', plc_messages) # TODO: Create separate 'simulator' plot group.

46.047619

80

0.56828

8d633804dd70bc9958af00b42a11e0de38e402fd

4,122

py

Python

scripts/old/modbus_server.py

SamKaiYang/ros_modbus_nex

b698cc73df65853866112f7501432a8509a2545c

[ "BSD-2-Clause" ]

null

scripts/old/modbus_server.py

SamKaiYang/ros_modbus_nex

b698cc73df65853866112f7501432a8509a2545c

[ "BSD-2-Clause" ]

null

scripts/old/modbus_server.py

SamKaiYang/ros_modbus_nex

b698cc73df65853866112f7501432a8509a2545c

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python ########################################################################### # This software is graciously provided by HumaRobotics # under the Simplified BSD License on # github: git@www.humarobotics.com:baxter_tasker # HumaRobotics is a trademark of Generation Robots. # www.humarobotics.com # Copyright (c) 2013, Generation Robots. # All rights reserved. # www.generationrobots.com # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS # BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF # THE POSSIBILITY OF SUCH DAMAGE. # # The views and conclusions contained in the software and documentation are # those of the authors and should not be interpreted as representing official # policies, either expressed or implied, of the FreeBSD Project. import rospy from modbus.modbus_wrapper_server import ModbusWrapperServer from std_msgs.msg import Int32MultiArray as HoldingRegister if __name__=="__main__": rospy.init_node("modbus_server") port = 1234 # custom modbus port without requirement of sudo rights # port = 502 # default modbus port if rospy.has_param("~port"): port = rospy.get_param("~port") else: rospy.loginfo("For not using the default port %d, add an arg e.g.: '_port:=1234'",port) # Init modbus server with specific port mws = ModbusWrapperServer(port) # Stop the server if ros is shutdown. This should show that the server is stoppable rospy.on_shutdown(mws.stopServer) # Starts the server in a non blocking call mws.startServer() print "Server started" ############### # Example 1 # write to the Discrete Input mws.setDigitalInput(0,1) # args: address , value. sets address to value # Example 2 # read from clients coil output print "waiting for line 0 to be set to True" result = mws.waitForCoilOutput(0,5) # args: address,timeout in sec. timeout of 0 is infinite. waits until address is true if result: print "got line 0 is True from baxter" else: print "timeout waiting for signal on line 0" ############### # Example 3 # Listen for the writeable modbus registers in any node sub = rospy.Subscriber("modbus_server/read_from_registers",HoldingRegister,callback,queue_size=500) ############### ############### # Example 4 # Publisher to write first 20 modbus registers from any node pub = rospy.Publisher("modbus_server/write_to_registers",HoldingRegister,queue_size=500) rospy.sleep(1) msg = HoldingRegister() msg.data = range(20) msg2 = HoldingRegister() msg2.data = range(20,0,-1) while not rospy.is_shutdown(): pub.publish(msg) rospy.sleep(1) pub.publish(msg2) rospy.sleep(1) ################ rospy.spin() mws.stopServer()

40.411765

125

0.694081

8d638991d71730377e930b6afff8fce13cde7b4a

4,453

py

Python

siptrackdlib/objectregistry.py

sii/siptrackd

f124f750c5c826156c31ae8699e90ff95a964a02

[ "Apache-2.0" ]

null

siptrackdlib/objectregistry.py

sii/siptrackd

f124f750c5c826156c31ae8699e90ff95a964a02

[ "Apache-2.0" ]

14

2016-03-18T13:28:16.000Z

2019-06-02T21:11:29.000Z

siptrackdlib/objectregistry.py

sii/siptrackd

f124f750c5c826156c31ae8699e90ff95a964a02

[ "Apache-2.0" ]

7

2016-03-18T13:04:54.000Z

2021-06-22T10:39:04.000Z

from siptrackdlib import errors from siptrackdlib import log object_registry = ObjectRegistry()

37.108333

88

0.650348

8d66576529e5704ad9e6b2d90cc87687907b8c91

1,139

py

Python

src/kol/request/CombatRequest.py

ZJ/pykol

c0523a4a4d09bcdf16f8c86c78da96914e961076

[ "BSD-3-Clause" ]

1

2016-05-08T13:26:56.000Z

src/kol/request/CombatRequest.py

ZJ/pykol

c0523a4a4d09bcdf16f8c86c78da96914e961076

[ "BSD-3-Clause" ]

null

src/kol/request/CombatRequest.py

ZJ/pykol

c0523a4a4d09bcdf16f8c86c78da96914e961076

[ "BSD-3-Clause" ]

null

from GenericAdventuringRequest import GenericAdventuringRequest

32.542857

92

0.72432

8d683b8c02d8d22cc3724afc4a6f8b486b4fd023

325

py

Python

OLD.dir/myclient1.py

romchegue/Python

444476088e64d5da66cb00174f3d1d30ebbe38f6

[ "bzip2-1.0.6" ]

null

OLD.dir/myclient1.py

romchegue/Python

444476088e64d5da66cb00174f3d1d30ebbe38f6

[ "bzip2-1.0.6" ]

null

OLD.dir/myclient1.py

romchegue/Python

444476088e64d5da66cb00174f3d1d30ebbe38f6

[ "bzip2-1.0.6" ]

null

''' myclient1.py - imports mymod.py and check its operation. ''' from mymod import test, countChars, countChars1, countLines, countLines1 text = 'test.txt' file = open(text) print(test(text), test(file)) print(countChars(text), countChars1(file)) print(countLines(text), countLines1(file)) print('\nedited again version')

23.214286

72

0.744615

8d6a85cb3cf62644daa8bec049af6d5de6f147e2

632

py

Python

src/modules/dates/searchDates.py

leonardoleyva/api-agenda-uas

697740a0a3feebb2ada01133db020fcf5127e1de

[ "MIT" ]

1

2022-03-13T02:28:29.000Z

src/modules/dates/searchDates.py

leonardoleyva/api-agenda-uas

697740a0a3feebb2ada01133db020fcf5127e1de

[ "MIT" ]

null

src/modules/dates/searchDates.py

leonardoleyva/api-agenda-uas

697740a0a3feebb2ada01133db020fcf5127e1de

[ "MIT" ]

null

from .date import Date from ..response import handleResponse from datetime import datetime

27.478261

126

0.643987

8d6c38dd172b4fa935c4b931081b7a40d9bc40a8

6,045

py

Python

Spark/spark_media_localidad.py

Dielam/Dielam.github.io

19f01d693ef2c590f3ac35a3a143ae3dedf8594e

[ "MIT" ]

null

Spark/spark_media_localidad.py

Dielam/Dielam.github.io

19f01d693ef2c590f3ac35a3a143ae3dedf8594e

[ "MIT" ]

null

Spark/spark_media_localidad.py

Dielam/Dielam.github.io

19f01d693ef2c590f3ac35a3a143ae3dedf8594e

[ "MIT" ]

1

2020-12-23T16:45:20.000Z

#!/usr/bin/python import sys from pyspark import SparkContext from shutil import rmtree import os.path as path if len(sys.argv) > 1: if path.exists("output"): rmtree("output") sc = SparkContext() localidad = sys.argv[1] localidadRDD = sc.textFile("Gasolineras.csv") localidadRDD = localidadRDD.map(lambda line: line.encode("ascii", "ignore")) localidadRDD = localidadRDD.map(lambda rows: rows.split(",")) localidadRDD = localidadRDD.filter(lambda rows: localidad == rows[5]) localidadRDD = localidadRDD.map(lambda rows: (rows[5], rows[7], rows[8], rows[9],rows[10], rows[11], rows[12], rows[13], rows[14], rows[15], rows[16], rows[17], rows[18], rows[19], rows[20], rows[21], rows[22], rows[23], rows[24])) datosRDD = localidadRDD.map(generar) if datosRDD.isEmpty(): result = sc.parallelize("0") result.saveAsTextFile("output") else: precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[5])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gasolina_95.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[6])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gasoleo_a.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[7])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gasoleo_b.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[8])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_bioetanol.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[9])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_nuevo_gasoleo_a.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[10])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_biodiesel.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[11])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_ester_metilico.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[12])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_bioalcohol.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[13])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gasolina_98.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[14])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gas_natural_comprimido.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[15])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gas_natural_licuado.txt") precioRDD = datosRDD.map(lambda rows: ([rows[0], float(rows[16])])) precioRDD = precioRDD.reduceByKey(lambda x,y: x+y) tamRDD = datosRDD.count() mediaTotal = precioRDD.map(lambda rows: ([rows[1], int(tamRDD)])) mediaTotal = mediaTotal.map(lambda calc:(calc[0]/calc[1])) mediaTotal.saveAsTextFile("output/media_localidad_gas_licuados_del_petr.txt") else: print "Error no ha introducido localidad."

45.451128

235

0.643176

8d6cc5852312640c236532b7026c1ac08efbc30f

13,148

py

Python

core/views/misc.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

3

2018-02-27T13:48:28.000Z

2018-03-03T21:57:50.000Z

core/views/misc.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

6

2020-02-12T00:07:46.000Z

2022-03-11T23:25:59.000Z

core/views/misc.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

1

2019-03-26T20:19:57.000Z

from django.contrib.auth.decorators import login_required from django.core.exceptions import ObjectDoesNotExist from django.core.urlresolvers import reverse, reverse_lazy from django.http import HttpResponseRedirect from django.shortcuts import render,HttpResponse from django.views.generic.edit import CreateView, UpdateView, DeleteView import csv, json from datetime import date,datetime from itertools import chain from operator import attrgetter from forms.models import Questionnaire from forms.views import replicate from core.models import * from core.forms import * from .nomi_cr import get_access_and_post_for_result, get_access_and_post ''' mark_as_interviewed, reject_nomination, accept_nomination: Changes the interview status/ nomination_instance status of the applicant ''' ''' append_user, replace_user: Adds and Removes the current post-holders according to their selection status ''' ## ------------------------------------------------------------------------------------------------------------------ ## ############################################ PROFILE VIEWS ################################################## ## ------------------------------------------------------------------------------------------------------------------ ## def UserProfileUpdate(request,pk): profile = UserProfile.objects.get(pk = pk) if profile.user == request.user: form = ProfileForm(request.POST or None, instance=profile) if form.is_valid(): form.save() return HttpResponseRedirect(reverse('profile')) return render(request, 'nomi/userprofile_form.html', context={'form': form}) else: return render(request, 'no_access.html') class CommentUpdate(UpdateView): model = Commment fields = ['comments'] class CommentDelete(DeleteView): model = Commment def all_nominations(request): all_nomi = Nomination.objects.all().exclude(status='Nomination created') return render(request, 'all_nominations.html', context={'all_nomi': all_nomi})

34.783069

179

0.6601

8d6deeb2db5e44e12af11dde00260d1e8aae607e

29,706

py

Python

make_paper_plots.py

mjbasso/asymptotic_formulae_examples

a1ba177426bf82e2a58e7b54e1874b088a86595f

[ "MIT" ]

1

2021-08-06T14:58:51.000Z

make_paper_plots.py

mjbasso/asymptotic_formulae_examples

a1ba177426bf82e2a58e7b54e1874b088a86595f

[ "MIT" ]

null

make_paper_plots.py

mjbasso/asymptotic_formulae_examples

a1ba177426bf82e2a58e7b54e1874b088a86595f

[ "MIT" ]

null

#!/usr/bin/env python3 import logging import os import pickle import time from os.path import join as pjoin import matplotlib.pyplot as plt import numpy as np import scipy from matplotlib import rc from scipy.optimize import least_squares import asymptotic_formulae from asymptotic_formulae import GaussZ0 from asymptotic_formulae import GaussZ0_MC from asymptotic_formulae import nCRZ0 from asymptotic_formulae import nCRZ0_MC from asymptotic_formulae import nSRZ0 from asymptotic_formulae import nSRZ0_MC rc('font', **{'family': 'sans-serif','sans-serif': ['Helvetica']}) rc('text', usetex = True) logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) sh = logging.StreamHandler() sh.setFormatter(logging.Formatter('%(asctime)s : %(name)s : %(levelname)s : %(message)s')) logger.addHandler(sh) # For creating a set of uniformly-spaced points on a log scale # As described in Section 2.1.4 def nCRZ0_DiagTau(s, b, tau): ''' Calculate the asymptotic significance for a 1 SR + N CRs, diagonal tau measurement s := expected signal yield in SR (float) b := expected background yields in SR (vector of floats, size N) tau := transfer coefficients, tau[i] carries background i yield in SR to CR i (vector of floats, size N) Returns Z0 (float) ''' # Argument checking b, tau = np.array(b), np.array(tau) s, b, tau = float(s), b.astype(float), tau.astype(float) assert b.ndim == 1 # b should be a vector assert tau.ndim == 1 # tau should be a vector assert len(b) == len(tau) assert (tau >= 0.).all() # Assert tau contains transfer factors (i.e., all positive) n = s + np.sum(b) # System of equations # Perform our minimization res = least_squares(func, x0 = b, bounds = [tuple(len(b) * [0.]), tuple(len(b) * [np.inf])]) if not res.success: raise RuntimeError('Minimization failed: status = %s, message = \'%s\'' % (res.status, res.message)) bhh = np.array(res.x) # Calculate our significance Z0 = np.sqrt(-2. * np.log((np.sum(bhh) / n) ** n * np.prod([(bhh[k] / b[k]) ** (tau[k] * b[k]) for k in range(len(b))]))) return Z0 # As described in Section 2.4.2 def GaussZ0_Decorr(s, b, sigma): ''' Calculate the asymptotic significance for a 1 SR + N CRs, diagonal tau measurement s := expected signal yield in SR (float) b := expected background yields in SR (vector of floats, size N) sigma := width of Gaussian constraint ("absolute uncertainty") for each background yield (vector of floats, size N) Returns Z0 (float) ''' # Argument checking b, sigma = np.array(b), np.array(sigma) s, b, sigma = float(s), b.astype(float), sigma.astype(float) assert b.ndim == 1 # b should be a vector assert sigma.ndim == 1 # sigma should be a vector assert len(b) == len(sigma) assert (sigma >= 0.).all() # Assert sigma contains widths (i.e., all positive) n = s + np.sum(b) # System of equations # Perform our minimization res = least_squares(func, x0 = b, bounds = [tuple(len(b) * [0.]), tuple(len(b) * [np.inf])]) if not res.success: raise RuntimeError('Minimization failed: status = %s, message = \'%s\'' % (res.status, res.message)) bhh = np.array(res.x) # Calculate our significance Z0 = np.sqrt(-2. * (n * np.log(np.sum(bhh) / n) + n - np.sum(bhh + 0.5 * ((b - bhh) / sigma) ** 2))) return Z0 if __name__ == '__main__': main()

46.85489

272

0.523564

8d6e5ae82deb7b5311529c66cb9a669824faeec2

2,645

py

Python

tests/test_compliance.py

simongarisch/pytrade

6245c0a47017a880299fa7704a49580f394fa87b

[ "MIT" ]

2

2020-10-19T02:44:57.000Z

2021-11-08T10:45:25.000Z

tests/test_compliance.py

simongarisch/pytrade

6245c0a47017a880299fa7704a49580f394fa87b

[ "MIT" ]

1

2020-12-24T02:59:58.000Z

tests/test_compliance.py

simongarisch/pytrade

6245c0a47017a880299fa7704a49580f394fa87b

[ "MIT" ]

null

import pytest from pxtrade.assets import reset, Stock, Portfolio from pxtrade.compliance import ( Compliance, UnitLimit, WeightLimit, )

32.654321

73

0.625331

8d7113f4a3fa2caf2cf878a899bd18ce82a24a1b

103

py

Python

article/serializers/__init__.py

mentix02/medialist-backend

397b1a382b12bab273360dadb0b3c32de43747cd

[ "MIT" ]

1

2019-11-22T19:29:39.000Z

article/serializers/__init__.py

mentix02/medialist-backend

397b1a382b12bab273360dadb0b3c32de43747cd

[ "MIT" ]

1

2019-11-25T09:50:07.000Z

2021-07-15T07:05:28.000Z

article/serializers/__init__.py

mentix02/medialist-backend

397b1a382b12bab273360dadb0b3c32de43747cd

[ "MIT" ]

null

from article.serializers.serializers import ( ArticleListSerializer, ArticleDetailSerializer )

20.6

45

0.805825

8d73a34deeb4110e24d2f659a64dcdc60d79219a

1,447

py

Python

delong_functions/initialization.py

braddelong/22-jupyter-ps01

95e8714e1723fb8328380a5d14aafabe2ee0795a

[ "MIT" ]

null

delong_functions/initialization.py

braddelong/22-jupyter-ps01

95e8714e1723fb8328380a5d14aafabe2ee0795a

[ "MIT" ]

null

delong_functions/initialization.py

braddelong/22-jupyter-ps01

95e8714e1723fb8328380a5d14aafabe2ee0795a

[ "MIT" ]

null

# set up the environment by reading in libraries: # os... graphics... data manipulation... time... math... statistics... import sys import os from urllib.request import urlretrieve import matplotlib as mpl import matplotlib.pyplot as plt import PIL as pil from IPython.display import Image import pandas as pd from pandas import DataFrame, Series import pandas_datareader from datetime import datetime import scipy as sp import numpy as np import math import random import seaborn as sns import statsmodels import statsmodels.api as sm import statsmodels.formula.api as smf # graphics setup: seaborn-darkgrid and figure size... plt.style.use('seaborn-darkgrid') figure_size = plt.rcParams["figure.figsize"] figure_size[0] = 7 figure_size[1] = 7 plt.rcParams["figure.figsize"] = figure_size # import delong functions from delong_functions.data_functions import getdata_read_or_download # get or download data file from delong_functions.stat_functions import initialize_basic_figure # initialize graphics from delong_functions.data_functions import data_FREDseries # construct a useful dict with source # and notes info from a previously # downloaded FRED csv file # check to see if functions successfully created... # NOW COMMENTED OUT: getdata_read_or_download? initialize_basic_figure?

32.155556

106

0.724948

8d74d9562cd8858adb9b65b43c92263f531590a9

608

py

Python

sdk/bento/graph/value.py

bentobox-dev/bento-box

3e10c62f586c1251529e059b6af515d4d03c60e9

[ "MIT" ]

1

2021-01-02T02:50:15.000Z

sdk/bento/graph/value.py

joeltio/bento-box

3e10c62f586c1251529e059b6af515d4d03c60e9

[ "MIT" ]

48

2020-10-21T07:42:30.000Z

2021-02-15T19:34:55.000Z

sdk/bento/graph/value.py

joeltio/bento-box

3e10c62f586c1251529e059b6af515d4d03c60e9

[ "MIT" ]

null

# # Bentobox # SDK - Graph # Graph Value # from typing import Any from bento.value import wrap from bento.protos.graph_pb2 import Node def wrap_const(val: Any): """Wrap the given native value as a Constant graph node. If val is a Constant node, returns value as is. Args: val: Native value to wrap. Returns: The given value wrapped as a constant graph node. """ # check if already constant node, return as is if true. if isinstance(val, Node) and val.WhichOneof("op") == "const_op": return val return Node(const_op=Node.Const(held_value=wrap(val)))

25.333333

68

0.677632

8d76f8f9957c274ab98fcb861cac123b90567879

771

py

Python

app/validators/user_validator.py

allanzi/truck-challenge

7734a011de899184b673e99fd1c2ff92a6af65b9

[ "CECILL-B" ]

null

app/validators/user_validator.py

allanzi/truck-challenge

7734a011de899184b673e99fd1c2ff92a6af65b9

[ "CECILL-B" ]

null

app/validators/user_validator.py

allanzi/truck-challenge

7734a011de899184b673e99fd1c2ff92a6af65b9

[ "CECILL-B" ]

null

from marshmallow import Schema, fields from marshmallow.validate import Length, Range

42.833333

75

0.743191

8d783ab1b46b55a24509d554110a68bdbb340935

11,660

py

Python

montecarlo/mcpy/monte_carlo.py

v-asatha/EconML

eb9ac829e93abbc8a163ab09d905b40370b21b1a

[ "MIT" ]

null

montecarlo/mcpy/monte_carlo.py

v-asatha/EconML

eb9ac829e93abbc8a163ab09d905b40370b21b1a

[ "MIT" ]

null

montecarlo/mcpy/monte_carlo.py

v-asatha/EconML

eb9ac829e93abbc8a163ab09d905b40370b21b1a

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import os import sys import numpy as np from joblib import Parallel, delayed import joblib import argparse import importlib from itertools import product import collections from copy import deepcopy from mcpy.utils import filesafe from mcpy import plotting def check_valid_config(config): """ Performs a basic check of the config file, checking if the necessary subsections are present. If multiple config files are being made that use the same dgps and/or methods, it may be helpful to tailor the config check to those dgps and methods. That way, one can check that the correct parameters are being provided for those dgps and methods. This is specific to one's implementation, however. """ assert 'type' in config, "config dict must specify config type" assert 'dgps' in config, "config dict must contain dgps" assert 'dgp_opts' in config, "config dict must contain dgp_opts" assert 'method_opts' in config, "config dict must contain method_opts" assert 'mc_opts' in config, "config dict must contain mc_opts" assert 'metrics' in config, "config dict must contain metrics" assert 'methods' in config, "config dict must contain methods" assert 'plots' in config, "config dict must contain plots" assert 'single_summary_metrics' in config, "config dict must specify which metrics are plotted in a y-x plot vs. as a single value per dgp and method" assert 'target_dir' in config, "config must contain target_dir" assert 'reload_results' in config, "config must contain reload_results" assert 'n_experiments' in config['mc_opts'], "config[mc_opts] must contain n_experiments" assert 'seed' in config['mc_opts'], "config[mc_opts] must contain seed"

48.786611

154

0.660806

8d7ad5ef06de97e8b617443c00cdb60123831b97

5,845

py

Python

MusicGame.py

kfparri/MusicGame

f2914cae7a68585ca1a569c78ac13f68c1adb827

[ "MIT" ]

null

MusicGame.py

kfparri/MusicGame

f2914cae7a68585ca1a569c78ac13f68c1adb827

[ "MIT" ]

null

MusicGame.py

kfparri/MusicGame

f2914cae7a68585ca1a569c78ac13f68c1adb827

[ "MIT" ]

null

#------------------------------------------------------------------------------------------------------ # File Name: MusicGame.py # Author: Kyle Parrish # Date: 7/4/2014 # Description: This is a simple program that I wrote for the raspberry pi so that my daughter can # play with. It is a simple program that plays a different sound with every keystroke. It also # displays a simple shape pattern on the screen with each keypress. The pi can also be setup to # allow users to change the sounds by uploading them to a web form on the pi itself. This code # will be included when it is finished. # Change log: # 4.30.15 - Updated the header to test out Visual Studio Code git integration # 9.18.15 - Started making some changes to the application. Natalie is starting to enjoy # the application so I'm starting to make it do more: # - Updated the code to put circles as well as squares on the screen. #------------------------------------------------------------------------------------------------------ # Basic imports for the game import os,sys,datetime, sqlite3 import pygame # I don't believe that I need the time references anymore, to be removed with next commit #from time import strftime, localtime from random import randint from pygame.locals import * # Setup basic constants test = 640 # Screen height and width SCREEN_WIDTH = test SCREEN_HEIGHT = 480 #CENTER_POINT = (SCREEN_WIDTH / 2, SCREEN_HEIGHT / 2) #LOWER_CENTER = (SCREEN_WIDTH / 2, SCREEN_HEIGHT / 4) #CENTER_RECT_HEIGHT = 40 #CLOCK_TEXT_FONT = 48 # Colors, any of these can be used in the program WHITE = (255, 255, 255) BLACK = (0, 0, 0) RED = (255, 0, 0) GREEN = (0, 255, 0) BLUE = (0, 0, 255) MATRIX_GREEN = (0, 255, 21) # Code taken from: http://code.activestate.com/recipes/521884-play-sound-files-with-pygame-in-a-cross-platform-m/ # global constants FREQ = 44100 # same as audio CD BITSIZE = -16 # unsigned 16 bit CHANNELS = 2 # 1 == mono, 2 == stereo BUFFER = 1024 # audio buffer size in no. of samples FRAMERATE = 30 # how often to check if playback has finished sounds = ["Typewrit-Intermed-538_hifi.ogg", "Typewrit-Bell-Patrick-8344_hifi.ogg", "Arcade_S-wwwbeat-8528_hifi.ogg", "Arcade_S-wwwbeat-8529_hifi.ogg", "Arcade_S-wwwbeat-8530_hifi.ogg", "Arcade_S-wwwbeat-8531_hifi.ogg", "PowerUp-Mark_E_B-8070_hifi.ogg", "PulseGun-Mark_E_B-7843_hifi.ogg", "PulseSho-Mark_E_B-8071_hifi.ogg", "SineySpa-Mark_E_B-7844_hifi.ogg", "ToySpace-Mark_E_B-7846_hifi.ogg", "ZipUp-Mark_E_B-8079_hifi.ogg"] soundFiles = [] def playsound(soundfile): """Play sound through default mixer channel in blocking manner. This will load the whole sound into memory before playback """ soundfile.play() #sound = pygame.mixer.Sound(soundfile) #clock = pygame.time.Clock() #sound.play() #while pygame.mixer.get_busy(): #clock.tick(FRAMERATE) if __name__ == '__main__': main()

34.791667

134

0.609239

8d7d0cccfbda47460eb1aeba6470425e3ed12174

243

py

Python

tests/utils/img_processing_utils_test.py

venkatakolagotla/robin

4497bf8ffcd03182f68f9a6d7c806bfdaa4791cb

[ "MIT" ]

4

2019-12-20T05:37:51.000Z

2020-03-18T16:32:59.000Z

tests/utils/img_processing_utils_test.py

venkatakolagotla/robin

4497bf8ffcd03182f68f9a6d7c806bfdaa4791cb

[ "MIT" ]

null

tests/utils/img_processing_utils_test.py

venkatakolagotla/robin

4497bf8ffcd03182f68f9a6d7c806bfdaa4791cb

[ "MIT" ]

null

from __future__ import print_function from robin.utils import img_processing_utils import numpy as np

24.3

61

0.814815

8d7e6b625734d32d6eb3ec106401a004caa7962c

5,763

py

Python

DeepLearning/DeepLearning/07_Deep_LeeYS/Week_1/4. Single-Layer NN/4) Neural Network.py

ghost9023/DeepLearningPythonStudy

4d319c8729472cc5f490935854441a2d4b4e8818

[ "MIT" ]

1

2019-06-27T04:05:59.000Z

DeepLearning/DeepLearning/07_Deep_LeeYS/Week_1/4. Single-Layer NN/4) Neural Network.py

ghost9023/DeepLearningPythonStudy

4d319c8729472cc5f490935854441a2d4b4e8818

[ "MIT" ]

null

DeepLearning/DeepLearning/07_Deep_LeeYS/Week_1/4. Single-Layer NN/4) Neural Network.py

ghost9023/DeepLearningPythonStudy

4d319c8729472cc5f490935854441a2d4b4e8818

[ "MIT" ]

null

#4) ##########KEYWORD############### ################################ # . # .. # (Feed Forward NN) . # . # () . # Layer . # . # . # 2 400% 10% . # . # 0 9 10 3 . # 3 . . #25 P35 # #Layer Node Node Shape Weight Shape Bias Shape # 2 2 2 X 3 Matrix 3 Vector (1) = ((*1) + 1) #(1) 3 3 3 X 2 Matrix 2 Vector (2) = ((1) * 2 + 2) #(2) 2 2 2 X 2 Matrix 2 Vector = ((2) * 3 + 3) # 2 2 # 3 . # 2 . 2 2. # #w12^(1), a2(1) . (1) 1 . # 12 1 2 . w12^(!) 1 2 1 # . # 3 2 1 1 . # a1(1) ... . #a1(1) = w11^(1)x1 + w12^(1)x2 + b1^(1) . # 1 A^(1) = (a1^(1),a2^(1),a3^(1)), 1 #W^(1) ... # numpy 1 . # 1 2 (1 ), , # 2 . # 1 1 2 2 2 # 2 . # 30 2 # . . # . # . # , . # . f(x) = x . # # . # . # . # 1 . # 0 1 #1 . # . # #y[0] = 0.018, y[1] = 0.245, y[2] = 0.737 1.8% 0 , 24.5% 1 , 73.7% 2 # 2 2 . . # () . \ # exp() . # . # ( ) # . . # . (Overflow) . # 100 exp(100) 10 40 . . # . [ 13] P40 # C . . # x = a ^ log(a,x) C exp() . # C exp() log(e,C) = ln C ln C C` . # import numpy as np a = np.array([1010,1000,990]) np.exp(a) / np.sum(np.exp(a)) # # softmax c = np.max(a) np.exp(a-c) / np.sum(np.exp(a-c)) # # . # . # . . # , 2 . # 2 ( . 3 ) # . # . # 2 . import numpy as np # #identify function # . . # . , # 3 network = init_network() # x = np.array([1.0,0.5]) y = forward(network ,x) print(y) # . # .

36.707006

120

0.640986

8d7eb5aaefc17250eb9787e23ab1f5200d2d65f8

466

py

Python

label_gen.py

avasid/gaze_detection

dbb76a2b3f3eedff5801b53bc95b3a95bc715bc5

[ "MIT" ]

1

2020-02-07T21:34:10.000Z

label_gen.py

avasid/gaze_detection

dbb76a2b3f3eedff5801b53bc95b3a95bc715bc5

[ "MIT" ]

8

2020-11-13T18:37:12.000Z

2022-03-12T00:14:04.000Z

label_gen.py

avasid/gaze_detection

dbb76a2b3f3eedff5801b53bc95b3a95bc715bc5

[ "MIT" ]

null

import os import pandas as pd dictt = {} i = 0 for label in ['down', 'up', 'left', 'right']: img_lst = os.listdir("./data/img_data/" + label + "/") temp_label = [0] * 4 temp_label[i] = 1 for img in img_lst: print(label + " " + img) dictt[img] = temp_label i += 1 label_df = pd.DataFrame(data=dictt, index=['down', 'up', 'left', 'right']).transpose() label_df = label_df.sample(frac=1) label_df.to_csv("./data/label_data.csv")

23.3

86

0.592275

8d7fb31d8d0c397a081d7685e96fa1bf8414f9a6

2,398

py

Python

rubik_race/rubiks_race/solver_test.py

ZengLawrence/rubiks_race

3d78484f0a68c7e483953cea68130f1edde2739a

[ "MIT" ]

null

rubik_race/rubiks_race/solver_test.py

ZengLawrence/rubiks_race

3d78484f0a68c7e483953cea68130f1edde2739a

[ "MIT" ]

null

rubik_race/rubiks_race/solver_test.py

ZengLawrence/rubiks_race

3d78484f0a68c7e483953cea68130f1edde2739a

[ "MIT" ]

null

''' Created on Jun 27, 2017 @author: lawrencezeng ''' import unittest from rubiks_race import solver if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.test_solve'] unittest.main()

31.142857

96

0.27648

8d80488b5bce65f6332a7212b2c16986023812ef

1,625

py

Python

wagtail_translation/migrations/0001_initial.py

patroqueeet/wagtail2-translation

6a7ad4eea5d900c8640f965ebf7a442dd7bc7e74

[ "MIT" ]

null

wagtail_translation/migrations/0001_initial.py

patroqueeet/wagtail2-translation

6a7ad4eea5d900c8640f965ebf7a442dd7bc7e74

[ "MIT" ]

null

wagtail_translation/migrations/0001_initial.py

patroqueeet/wagtail2-translation

6a7ad4eea5d900c8640f965ebf7a442dd7bc7e74

[ "MIT" ]

1

2021-01-08T19:25:46.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from modeltranslation import settings as mt_settings from modeltranslation.utils import build_localized_fieldname, get_translation_fields from django.db import migrations, models

37.790698

84

0.715692

8d8293dd05c195d7acdf3af64d74eb27c71ed3fc

99,195

py

Python

WORC/WORC.py

MStarmans91/WORC

b6b8fc2ccb7d443a69b5ca20b1d6efb65b3f0fc7

[ "ECL-2.0", "Apache-2.0" ]

47

2018-01-28T14:08:15.000Z

2022-03-24T16:10:07.000Z

WORC/WORC.py

JZK00/WORC

14e8099835eccb35d49b52b97c0be64ecca3809c

[ "ECL-2.0", "Apache-2.0" ]

13

2018-08-28T13:32:57.000Z

2020-10-26T16:35:59.000Z

WORC/WORC.py

JZK00/WORC

14e8099835eccb35d49b52b97c0be64ecca3809c

[ "ECL-2.0", "Apache-2.0" ]

16

2017-11-13T10:53:36.000Z

2022-03-18T17:02:04.000Z

#!/usr/bin/env python # Copyright 2016-2021 Biomedical Imaging Group Rotterdam, Departments of # Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import yaml import fastr import graphviz import configparser from pathlib import Path from random import randint import WORC.IOparser.file_io as io from fastr.api import ResourceLimit from WORC.tools.Slicer import Slicer from WORC.tools.Elastix import Elastix from WORC.tools.Evaluate import Evaluate import WORC.addexceptions as WORCexceptions import WORC.IOparser.config_WORC as config_io from WORC.detectors.detectors import DebugDetector from WORC.export.hyper_params_exporter import export_hyper_params_to_latex from urllib.parse import urlparse from urllib.request import url2pathname

53.822572

228

0.559897

8d832e77f438b0dd65c0dff2da0ca039538bc5cd

2,019

py

Python

utils/tweets_to_txt.py

magsol/datascibun

bb118eac59dc238c42f659871e25619d994f8575

[ "Apache-2.0" ]

null

utils/tweets_to_txt.py

magsol/datascibun

bb118eac59dc238c42f659871e25619d994f8575

[ "Apache-2.0" ]

null

utils/tweets_to_txt.py

magsol/datascibun

bb118eac59dc238c42f659871e25619d994f8575

[ "Apache-2.0" ]

1

2022-03-01T01:45:47.000Z

import argparse import json import re if __name__ == "__main__": parser = argparse.ArgumentParser(description = 'JSON tweet converter', epilog = 'lol tw33tz', add_help = 'How to use', prog = 'python json_to_txt.py <options>') # Required arguments. parser.add_argument("-i", "--input", required = True, help = "JSON file to convert.") # Optional arguments. parser.add_argument("-o", "--output", default = "output.txt", help = "Output file containing tweet content, one per line. [DEFAULT: output.txt]") # Parse out the arguments. args = vars(parser.parse_args()) content = json.load(open(args['input'], "r")) fp = open(args['output'], "w") item = 0 for obj in content: tweet = obj['tweet']['full_text'] # STEP 1: Strip out RT. tweet = remove_rt(tweet) # STEP 2: Remove URLs, mentions, hashtags, emojis. tweet = remove_urls(tweet) tweet = remove_mentions(tweet) tweet = remove_hashtags(tweet) tweet = remove_emojis(tweet) # STEP 3: Other random fixes. tweet = tweet.strip() tweet = fix_amp(tweet) if len(tweet) == 0 or len(tweet) == 1: continue tweet = tweet.replace("\"\"", "") if tweet[0] == ":": tweet = tweet[1:] tweet = tweet.replace("\n", " ") tweet = tweet.strip() # Write out! fp.write(f"{tweet}\n") item += 1 if item % 1000 == 0: print(f"{item} of {len(content)} done.") fp.close() print(f"{item} tweets processed!")

27.657534

91

0.574542

8d85dffad6d22403418ce3ef5e06280cc317b3e4

528

py

Python

truechat/chat/migrations/0007_auto_20191026_2020.py

TrueChat/Backend

7d2bc73d5b7f157d7499a65af4157e1ddeb7a0ac

[ "MIT" ]

1

2019-12-19T19:04:33.000Z

truechat/chat/migrations/0007_auto_20191026_2020.py

TrueChat/Backend

7d2bc73d5b7f157d7499a65af4157e1ddeb7a0ac

[ "MIT" ]

6

2020-06-05T23:42:41.000Z

2022-02-10T13:32:59.000Z

truechat/chat/migrations/0007_auto_20191026_2020.py

TrueChat/Backend

7d2bc73d5b7f157d7499a65af4157e1ddeb7a0ac

[ "MIT" ]

null

# Generated by Django 2.2.5 on 2019-10-26 20:20 from django.db import migrations, models

22.956522

81

0.587121

8d86a97241dd9580e12d59014523e0d42f09b38e

354

py

Python

libs/baseclass/about_screen.py

wildscsi/ecopos

9922bb5160227777401eb33fa9a01cfba5730781

[ "MIT" ]

null

libs/baseclass/about_screen.py

wildscsi/ecopos

9922bb5160227777401eb33fa9a01cfba5730781

[ "MIT" ]

1

2021-11-04T20:43:03.000Z

libs/baseclass/about_screen.py

wildscsi/ecopos

9922bb5160227777401eb33fa9a01cfba5730781

[ "MIT" ]

1

2021-11-04T19:43:53.000Z

# -*- coding: utf-8 -*- # # Copyright (c) 2020 CPV.BY # # For suggestions and questions: # <7664330@gmail.com> # # LICENSE: Commercial import webbrowser from kivymd.theming import ThemableBehavior from kivymd.uix.screen import MDScreen

19.666667

46

0.728814

8d86c9a6526d8d524710fa780972b087a3f46ac3

7,715

py

Python

causal_rl/environments/multi_typed.py

vluzko/causal_rl

92ee221bdf1932fa83955441baabb5e28b78ab9d

[ "MIT" ]

2

2021-04-02T12:06:13.000Z

2022-02-09T06:57:26.000Z

causal_rl/environments/multi_typed.py

vluzko/causal_rl

92ee221bdf1932fa83955441baabb5e28b78ab9d

[ "MIT" ]

11

2020-12-28T14:51:31.000Z

2021-03-29T19:53:24.000Z

causal_rl/environments/multi_typed.py

vluzko/causal_rl

92ee221bdf1932fa83955441baabb5e28b78ab9d

[ "MIT" ]

null

import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from gym import Env from scipy.spatial import distance from typing import Optional, Tuple, Any from causal_rl.environments import CausalEnv

36.738095

106

0.568762

8d86e19a0f7bf48d0eb61da351363ace81caa8fc

353

py

Python

greetings.py

ucsd-cse-spis-2016/spis16-lecture-0815

24e0a8ea9726f969eb357db33eb2925aabd25e43

[ "MIT" ]

null

greetings.py

ucsd-cse-spis-2016/spis16-lecture-0815

24e0a8ea9726f969eb357db33eb2925aabd25e43

[ "MIT" ]

null

greetings.py

ucsd-cse-spis-2016/spis16-lecture-0815

24e0a8ea9726f969eb357db33eb2925aabd25e43

[ "MIT" ]

null

from flask import Flask app = Flask(__name__) if __name__ == "__main__": app.run(port=5000)

16.045455

35

0.628895

8d88e96d4a71ca08ce8d66eee14e65dd7c02396c

3,189

py

Python

bin/makeReport.py

oxfordmmm/SARS-CoV2_workflows

a84cb0a7142684414b2f285dd27cc2ea287eecb9

[ "MIT" ]

null

bin/makeReport.py

oxfordmmm/SARS-CoV2_workflows

a84cb0a7142684414b2f285dd27cc2ea287eecb9

[ "MIT" ]

null

bin/makeReport.py

oxfordmmm/SARS-CoV2_workflows

a84cb0a7142684414b2f285dd27cc2ea287eecb9

[ "MIT" ]

null

#!/usr/bin/env python3 import pandas as pd import sys import json from Bio import SeqIO sample_name=sys.argv[1] pango=pd.read_csv('pango.csv') nextclade=pd.read_csv('nextclade.tsv', sep='\t') aln2type=pd.read_csv('aln2type.csv') pango['sampleName']=sample_name nextclade['sampleName']=sample_name aln2type['sampleName']=sample_name df=pango.merge(nextclade, on='sampleName', how='left', suffixes=("_pango","_nextclade")) df=df.merge(aln2type, on='sampleName', how='left', suffixes=(None,"_aln2type")) # versions wf=open('workflow_commit.txt').read() df['workflowCommit']=str(wf).strip() df['manifestVersion']=sys.argv[2] nextclade_version=open('nextclade_files/version.txt').read() df['nextcladeVersion']=str(nextclade_version).strip() aln2type_variant_commit=open('variant_definitions/aln2type_variant_git_commit.txt').read() aln2type_variant_version=open('variant_definitions/aln2type_variant_version.txt').read() aln2type_source_commit=open('variant_definitions/aln2type_commit.txt').read() df['aln2typeVariantCommit']=str(aln2type_variant_commit).strip() df['aln2typeVariantVersion']=str(aln2type_variant_version).strip() df['aln2typeSourceVommit']=str(aln2type_source_commit).strip() df.to_csv('{0}_report.tsv'.format(sys.argv[1]), sep='\t', index=False) ### convert to json pango['program']='pango' pango.set_index('program',inplace=True) p=pango.to_dict(orient='index') nextclade['program']='nextclade' nextclade['nextcladeVersion']=str(nextclade_version).strip() nextclade.set_index('program',inplace=True) n=nextclade.to_dict(orient='index') with open('nextclade.json','rt', encoding= 'utf-8') as inf: nj=json.load(inf) n['nextcladeOutputJson']=nj aln2type['program']='aln2type' aln2type['label']=aln2type['phe-label'] aln2type['aln2typeVariantCommit']=str(aln2type_variant_commit).strip() aln2type['aln2typeSourceCommit']=str(aln2type_source_commit).strip() aln2type.set_index(['program','phe-label'],inplace=True) a={level: aln2type.xs(level).to_dict('index') for level in aln2type.index.levels[0]} w={'WorkflowInformation':{}} w['WorkflowInformation']['workflowCommit']=str(wf).strip() w['WorkflowInformation']['manifestVersion']=sys.argv[2] w['WorkflowInformation']['sampleIdentifier']=sample_name # add fasta to json record = SeqIO.read('ref.fasta', "fasta") w['WorkflowInformation']['referenceIdentifier']=record.id #f={'FastaRecord':{'SeqId':record.id, # 'SeqDescription': record.description, # 'Sequence':str(record.seq), # 'sampleName':sample_name}} s={'summary':{}} s['summary']['completeness']=completeness(n['nextcladeOutputJson']) d={sample_name:{}} d[sample_name].update(p) d[sample_name].update(n) d[sample_name].update(a) d[sample_name].update(w) #d[sample_name].update(f) d[sample_name].update(s) with open('{0}_report.json'.format(sample_name), 'w', encoding='utf-8') as f: json.dump(d, f, indent=4, sort_keys=True, ensure_ascii=False)

35.831461

90

0.756977

8d8a5d72d65e690dc4c82341ed975187662e4c48

1,484

py

Python

webhooks/statuscake/alerta_statuscake.py

frekel/alerta-contrib

d8f5c93a4ea735085b3689c2c852ecae94924d08

[ "MIT" ]

114

2015-02-05T00:22:16.000Z

2021-11-25T13:02:44.000Z

webhooks/statuscake/alerta_statuscake.py

NeilOrley/alerta-contrib

69d271ef9fe6542727ec4aa39fc8e0f797f1e8b1

[ "MIT" ]

245

2016-01-09T22:29:09.000Z

2022-03-16T10:37:02.000Z

webhooks/statuscake/alerta_statuscake.py

NeilOrley/alerta-contrib

69d271ef9fe6542727ec4aa39fc8e0f797f1e8b1

[ "MIT" ]

193

2015-01-30T21:22:49.000Z

2022-03-28T05:37:14.000Z

from alerta.models.alert import Alert from alerta.webhooks import WebhookBase from alerta.exceptions import RejectException import os import hashlib

35.333333

85

0.624663

8d8b51eaca246cacfde939fcbc4a16b39dba720e

3,738

py

Python

ironic_discoverd/main.py

enovance/ironic-discoverd

d3df6178ca5c95943c93ff80723c86b7080bca0b

[ "Apache-2.0" ]

null

ironic_discoverd/main.py

enovance/ironic-discoverd

d3df6178ca5c95943c93ff80723c86b7080bca0b

[ "Apache-2.0" ]

null

ironic_discoverd/main.py

enovance/ironic-discoverd

d3df6178ca5c95943c93ff80723c86b7080bca0b

[ "Apache-2.0" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. import eventlet eventlet.monkey_patch(thread=False) import json import logging import sys from flask import Flask, request # noqa from keystoneclient import exceptions from ironic_discoverd import conf from ironic_discoverd import discoverd from ironic_discoverd import firewall from ironic_discoverd import node_cache from ironic_discoverd import utils app = Flask(__name__) LOG = discoverd.LOG def periodic_update(period): while True: LOG.debug('Running periodic update of filters') try: firewall.update_filters() except Exception: LOG.exception('Periodic update failed') eventlet.greenthread.sleep(period)

31.411765

75

0.688604

8d8c7b2102958e3a921b5b5a1f32ed6750cd5ff4

964

py

Python

config_translator.py

Charahiro-tan/Jurubot_Translator

d0d0db137f3ddfe06d7cd9457d22c418bdeff94c

[ "MIT" ]

1

2021-07-26T11:14:05.000Z

config_translator.py

Charahiro-tan/Jurubot_Translator

d0d0db137f3ddfe06d7cd9457d22c418bdeff94c

[ "MIT" ]

null

config_translator.py

Charahiro-tan/Jurubot_Translator

d0d0db137f3ddfe06d7cd9457d22c418bdeff94c

[ "MIT" ]

null

################################################## # # ################################################## # []"", # ignore_user = ["Nightbot","Streamelements","Moobot"] # # URL del_word = ["88+","+"] # # https://cloud.google.com/translate/docs/languages ignore_lang = ["",""] # home_lang = "ja" # home_lang default_to_lang = "en" # translate.googleURL url_suffix = "co.jp" # TrueFalse sender = True # True # "displayname" # "loginid" ID sender_name = "displayname" # (en ja)TrueFalse language = True # Google Apps ScriptAPITrueFalse # Google Apps ScriptReadme gas = False # Google Apps ScriptURL gas_url = ""

22.418605

61

0.692946

8d8cd77924dc533eeabb54595050045f0fb725d3

1,489

py

Python

wxcloudrun/dao.py

lubupang/resume_flask1

1ea18e88c0b667e92710096f57973a77d19e8fc6

[ "MIT" ]

null

wxcloudrun/dao.py

lubupang/resume_flask1

1ea18e88c0b667e92710096f57973a77d19e8fc6

[ "MIT" ]

null

wxcloudrun/dao.py

lubupang/resume_flask1

1ea18e88c0b667e92710096f57973a77d19e8fc6

[ "MIT" ]

null

import logging from sqlalchemy.exc import OperationalError from wxcloudrun import db from wxcloudrun.model import Counters # logger = logging.getLogger('log') logger.info("aaaaaaa") def query_counterbyid(id): """ IDCounter :param id: CounterID :return: Counter """ logger.info("bbbbbbbbb") try: return Counters.query.filter(Counters.id == id).first() except OperationalError as e: logger.info("query_counterbyid errorMsg= {} ".format(e)) return None def delete_counterbyid(id): """ IDCounter :param id: CounterID """ try: counter = Counters.query.get(id) if counter is None: return db.session.delete(counter) db.session.commit() except OperationalError as e: logger.info("delete_counterbyid errorMsg= {} ".format(e)) def insert_counter(counter): """ Counter :param counter: Counters """ try: db.session.add(counter) db.session.commit() except OperationalError as e: logger.info("insert_counter errorMsg= {} ".format(e)) def update_counterbyid(counter): """ IDcounter :param counter """ try: counter = query_counterbyid(counter.id) if counter is None: return db.session.flush() db.session.commit() except OperationalError as e: logger.info("update_counterbyid errorMsg= {} ".format(e))

22.560606

65

0.633983

8d8db8eca4cacfeb8ce07aa8011f8a4b558400b4

7,411

py

Python

src/bpp/tests/tests_legacy/test_views/test_raporty.py

iplweb/django-bpp

85f183a99d8d5027ae4772efac1e4a9f21675849

[ "BSD-3-Clause" ]

1

2017-04-27T19:50:02.000Z

src/bpp/tests/tests_legacy/test_views/test_raporty.py

mpasternak/django-bpp

434338821d5ad1aaee598f6327151aba0af66f5e

[ "BSD-3-Clause" ]

41

2019-11-07T00:07:02.000Z

2022-02-27T22:09:39.000Z

src/bpp/tests/tests_legacy/test_views/test_raporty.py

iplweb/bpp

f027415cc3faf1ca79082bf7bacd4be35b1a6fdf

[ "BSD-3-Clause" ]

null

# -*- encoding: utf-8 -*- import os import sys import uuid import pytest from django.apps import apps from django.contrib.auth.models import Group from django.core.files.base import ContentFile try: from django.core.urlresolvers import reverse except ImportError: from django.urls import reverse from django.db import transaction from django.http import Http404 from django.test.utils import override_settings from django.utils import timezone from model_mommy import mommy from bpp.models import Typ_KBN, Jezyk, Charakter_Formalny, Typ_Odpowiedzialnosci from bpp.tests.tests_legacy.testutil import UserTestCase, UserTransactionTestCase from bpp.tests.util import any_jednostka, any_autor, any_ciagle from bpp.util import rebuild_contenttypes from bpp.views.raporty import RaportSelector, PodgladRaportu, KasowanieRaportu from celeryui.models import Report from django.conf import settings

32.221739

95

0.643233

8d8dfcd12be52225c59666f19fa694cef189e9ea

1,373

py

Python

bot/utilities/api/helpers/score.py

AiratK/kaishnik-bot

c42351611a40a04d78c8ae481b97339adbd321e5

[ "MIT" ]

null

bot/utilities/api/helpers/score.py

AiratK/kaishnik-bot

c42351611a40a04d78c8ae481b97339adbd321e5

[ "MIT" ]

null

bot/utilities/api/helpers/score.py

AiratK/kaishnik-bot

c42351611a40a04d78c8ae481b97339adbd321e5

[ "MIT" ]

null

from typing import List from typing import Tuple from bot.utilities.api.constants import SCORE_TEMPLATE

39.228571

116

0.780772

8d8ebb77655b687ce95045239bb38a91c19a2901

1,192

py

Python

manager_app/serializers/carousel_serializers.py

syz247179876/e_mall

f94e39e091e098242342f532ae371b8ff127542f

[ "Apache-2.0" ]

7

2021-04-10T13:20:56.000Z

2022-03-29T15:00:29.000Z

manager_app/serializers/carousel_serializers.py

syz247179876/E_mall

f94e39e091e098242342f532ae371b8ff127542f

[ "Apache-2.0" ]

9

2021-05-11T03:53:31.000Z

2022-03-12T00:58:03.000Z

manager_app/serializers/carousel_serializers.py

syz247179876/E_mall

f94e39e091e098242342f532ae371b8ff127542f

[ "Apache-2.0" ]

2

2020-11-24T08:59:22.000Z

2020-11-24T14:10:59.000Z

# -*- coding: utf-8 -*- # @Time : 2021/4/6 9:21 # @Author : # @File : carousel_serializers.py # @Software: Pycharm from rest_framework import serializers from Emall.exceptions import DataFormatError from shop_app.models.commodity_models import Carousel

27.090909

97

0.654362

8d9135e1864bf2b1336ddc05e72617edb4057d7b

7,312

py

Python

xfbin/structure/nud.py

SutandoTsukai181/xfbin_lib

8e2c56f354bfd868f9162f816cc528e6f830cdbc

[ "MIT" ]

3

2021-07-20T09:13:13.000Z

2021-09-06T18:08:15.000Z

xfbin/structure/nud.py

SutandoTsukai181/xfbin_lib

8e2c56f354bfd868f9162f816cc528e6f830cdbc

[ "MIT" ]

1

2021-09-06T18:07:48.000Z

xfbin/structure/nud.py

SutandoTsukai181/xfbin_lib

8e2c56f354bfd868f9162f816cc528e6f830cdbc

[ "MIT" ]

null

from itertools import chain from typing import List, Tuple from .br.br_nud import *

32.642857

117

0.607358

8d92051bcbbae105ab8b259c257c80d404e8f4eb

2,389

py

Python

src/attack_surface_pypy/__main__.py

ccrvs/attack_surface_pypy

f2bc9998cf42f4764f1c495e6243d970e01bd176

[ "CC0-1.0" ]

null

src/attack_surface_pypy/__main__.py

ccrvs/attack_surface_pypy

f2bc9998cf42f4764f1c495e6243d970e01bd176

[ "CC0-1.0" ]

null

src/attack_surface_pypy/__main__.py

ccrvs/attack_surface_pypy

f2bc9998cf42f4764f1c495e6243d970e01bd176

[ "CC0-1.0" ]

null

import argparse import gc import pathlib import sys import typing import uvicorn # type: ignore from attack_surface_pypy import __service_name__, __version__, asgi from attack_surface_pypy import logging as app_logging from attack_surface_pypy import settings # logger = structlog.get_logger() gc.disable() parser = argparse.ArgumentParser(description="App initial arguments.", prog=__service_name__) parser.add_argument( "-f", "--file-path", help="provide path to a file with initial data.", type=pathlib.Path, metavar=".fixtures/xxx.json", required=True, choices=[ pathlib.Path(".fixtures/input-1.json"), pathlib.Path(".fixtures/input-2.json"), pathlib.Path(".fixtures/input-3.json"), pathlib.Path(".fixtures/input-4.json"), pathlib.Path(".fixtures/input-5.json"), ], ) parser.add_argument( "-n", "--host", help="set host for the service.", type=str, metavar="localhost", ) parser.add_argument( "-p", "--port", type=int, help="set port for the service.", ) parser.add_argument( "-v", "--version", action="version", version=f"%(prog)s {__version__}", ) if __name__ == "__main__": ns = parser.parse_args() domain_settings = settings.Domain(file_path=ns.file_path) service_settings = settings.Service() if ns.host or ns.port: service_settings = settings.Service(host=ns.host, port=ns.port) app_settings = settings.Settings(domain=domain_settings, service=service_settings) log_config = app_logging.LoggingConfig( log_level=app_settings.log_level, traceback_depth=app_settings.traceback_depth ).prepare_logger() # context = types.Context(file_path=ns.file_path, host=ns.host, port=ns.port) # TODO: update settings from args? sys.exit(run_uvicorn(app_settings, log_config)) # TODO: hardcoded name, awry fabric

29.493827

117

0.686061

8d92eb64df1700c877aeea998c716029d6df8ce0

391

py

Python

subscriptions/migrations/0004_auto_20200630_1157.py

Naveendata-ux/tor_redesign

e4b5135f8b4134527ad04a097bdffd9d956d9858

[ "BSD-2-Clause" ]

null

subscriptions/migrations/0004_auto_20200630_1157.py

Naveendata-ux/tor_redesign

e4b5135f8b4134527ad04a097bdffd9d956d9858

[ "BSD-2-Clause" ]

null

subscriptions/migrations/0004_auto_20200630_1157.py

Naveendata-ux/tor_redesign

e4b5135f8b4134527ad04a097bdffd9d956d9858

[ "BSD-2-Clause" ]

null

# Generated by Django 2.2.5 on 2020-06-30 11:57 from django.db import migrations

20.578947

53

0.613811

8d9365bf3bc3b96e70ffdbc229d46a96e3d6b3fd

545

py

Python

Random_Colored/main.py

usamaahsan93/mischief-managed

824022ecaeda46450ca1029bceb39f194c363138

[ "MIT" ]

null

Random_Colored/main.py

usamaahsan93/mischief-managed

824022ecaeda46450ca1029bceb39f194c363138

[ "MIT" ]

null

Random_Colored/main.py

usamaahsan93/mischief-managed

824022ecaeda46450ca1029bceb39f194c363138

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jun 2 16:54:10 2021 @author: sdn1 """ import numpy as np import os i=0 while(i<1): 1/0 print(bcolors.OKGREEN + chr(np.random.randint(250,400)) + bcolors.ENDC, end='') os.system('python $(pwd)/main.py') i=i+1 print(i)

17.03125

83

0.552294

8d93c9fb2121a519402ceb1deef23ae520c7fdfe

1,717

py

Python

utils/event_store_rebuilder_for_segments.py

initialed85/eds-cctv-system

fcdb7e7e23327bf3a901d23d506b3915833027d1

[ "MIT" ]

null

utils/event_store_rebuilder_for_segments.py

initialed85/eds-cctv-system

fcdb7e7e23327bf3a901d23d506b3915833027d1

[ "MIT" ]

null

utils/event_store_rebuilder_for_segments.py

initialed85/eds-cctv-system

fcdb7e7e23327bf3a901d23d506b3915833027d1

[ "MIT" ]

null

import datetime from pathlib import Path from typing import Optional, Tuple from .common import _IMAGE_SUFFIXES, _PERMITTED_EXTENSIONS, PathDetails, rebuild_event_store if __name__ == "__main__": import argparse from dateutil.tz import tzoffset parser = argparse.ArgumentParser() parser.add_argument("-r", "--root-path", type=str, required=True) parser.add_argument("-j", "--json-path", type=str, required=True) args = parser.parse_args() rebuild_event_store( root_path=args.root_path, tzinfo=tzoffset(name="WST-8", offset=8 * 60 * 60), json_path=args.json_path, parse_method=parse_path, get_key_methods=[_get_key] )

28.616667

92

0.663366

8d9464e17bd59a5730ae1d8d76d451408c780a27

4,049

py

Python

python/src/main/python/common/threadctl.py

esnet/netshell

4cb010b63e72610cf81112b29587d3e980612333

[ "BSD-3-Clause-LBNL" ]

6

2016-02-17T16:31:55.000Z

2021-03-16T20:17:41.000Z

python/src/main/python/common/threadctl.py

esnet/netshell

4cb010b63e72610cf81112b29587d3e980612333

[ "BSD-3-Clause-LBNL" ]

27

2016-04-11T19:49:04.000Z

2016-07-14T06:05:52.000Z

python/src/main/python/common/threadctl.py

esnet/netshell

4cb010b63e72610cf81112b29587d3e980612333

[ "BSD-3-Clause-LBNL" ]

1

2017-07-31T19:30:50.000Z

# ESnet Network Operating System (ENOS) Copyright (c) 2015, The Regents # of the University of California, through Lawrence Berkeley National # Laboratory (subject to receipt of any required approvals from the # U.S. Dept. of Energy). All rights reserved. # # If you have questions about your rights to use or distribute this # software, please contact Berkeley Lab's Innovation & Partnerships # Office at IPO@lbl.gov. # # NOTICE. This Software was developed under funding from the # U.S. Department of Energy and the U.S. Government consequently retains # certain rights. As such, the U.S. Government has been granted for # itself and others acting on its behalf a paid-up, nonexclusive, # irrevocable, worldwide license in the Software to reproduce, # distribute copies to the public, prepare derivative works, and perform # publicly and display publicly, and to permit other to do so. from java.lang import Thread, ThreadGroup import jarray rootThreadGroup = None if __name__ == '__main__': argv = sys.argv if len(argv) == 1: print_syntax() sys.exit() cmd = argv[1] if cmd == "help": print_syntax() elif cmd == "show-thread": gri = argv[2] if gri == 'all': match = None if 'grep' in argv: match = argv[4] threads = getAllThreads(match=match) if threads != None: for thread in threads: displayThread(thread=thread) print else: thread = getThread(long(argv[2])) if (thread == None): print "unknown",argv[2] sys.exit() displayThread(thread)

29.772059

96

0.613732

8d94db8d2bb9acc8dbec349c6766ca408545196a

599

py

Python

python/distance/HaversineDistanceInMiles.py

jigneshoo7/AlgoBook

8aecc9698447c0ee561a1c90d5c5ab87c4a07b79

[ "MIT" ]

191

2020-09-28T10:00:20.000Z

2022-03-06T14:36:55.000Z

python/distance/HaversineDistanceInMiles.py

jigneshoo7/AlgoBook

8aecc9698447c0ee561a1c90d5c5ab87c4a07b79

[ "MIT" ]

210

2020-09-28T10:06:36.000Z

2022-03-05T03:44:24.000Z

python/distance/HaversineDistanceInMiles.py

jigneshoo7/AlgoBook

8aecc9698447c0ee561a1c90d5c5ab87c4a07b79

[ "MIT" ]

320

2020-09-28T09:56:14.000Z

2022-02-12T16:45:57.000Z

import math

33.277778

153

0.651085

8d95a5da0117840ab07b75457380a92375c5347d

8,837

py

Python

i2i/util.py

thorwhalen/i2i

f967aaba28793029e3fe643c5e17ae9bc7a77732

[ "Apache-2.0" ]

1

2019-08-29T01:35:12.000Z

i2i/util.py

thorwhalen/i2i

f967aaba28793029e3fe643c5e17ae9bc7a77732

[ "Apache-2.0" ]

null

i2i/util.py

thorwhalen/i2i

f967aaba28793029e3fe643c5e17ae9bc7a77732

[ "Apache-2.0" ]

null

from __future__ import division import inspect import types from functools import wraps function_type = type(lambda x: x) # using this instead of callable() because classes are callable, for instance no_default = NoDefault() def inject_method(self, method_function, method_name=None): """ method_function could be: * a function * a {method_name: function, ...} dict (for multiple injections) * a list of functions or (function, method_name) pairs """ if isinstance(method_function, function_type): if method_name is None: method_name = method_function.__name__ setattr(self, method_name, types.MethodType(method_function, self)) else: if isinstance(method_function, dict): method_function = [(func, func_name) for func_name, func in method_function.items()] for method in method_function: if isinstance(method, tuple) and len(method) == 2: self = inject_method(self, method[0], method[1]) else: self = inject_method(self, method) return self def transform_args(**trans_func_for_arg): """ Make a decorator that transforms function arguments before calling the function. For example: * original argument: a relative path --> used argument: a full path * original argument: a pickle filepath --> used argument: the loaded object :param rootdir: rootdir to be used for all name arguments of target function :param name_arg: the position (int) or argument name of the argument containing the name :return: a decorator >>> def f(a, b, c): ... return "a={a}, b={b}, c={c}".format(a=a, b=b, c=c) >>> >>> print(f('foo', 'bar', 3)) a=foo, b=bar, c=3 >>> ff = transform_args()(f) >>> print(ff('foo', 'bar', 3)) a=foo, b=bar, c=3 >>> ff = transform_args(a=lambda x: 'ROOT/' + x)(f) >>> print(ff('foo', 'bar', 3)) a=ROOT/foo, b=bar, c=3 >>> ff = transform_args(b=lambda x: 'ROOT/' + x)(f) >>> print(ff('foo', 'bar', 3)) a=foo, b=ROOT/bar, c=3 >>> ff = transform_args(a=lambda x: 'ROOT/' + x, b=lambda x: 'ROOT/' + x)(f) >>> print(ff('foo', b='bar', c=3)) a=ROOT/foo, b=ROOT/bar, c=3 """ return transform_args_decorator def resolve_filepath_of_name(name_arg=None, rootdir=''): """ Make a decorator that applies a function to an argument before using it. For example: * original argument: a relative path --> used argument: a full path * original argument: a pickle filepath --> used argument: the loaded object :param rootdir: rootdir to be used for all name arguments of target function :param name_arg: the position (int) or argument name of the argument containing the name :return: a decorator >>> def f(a, b, c): ... return "a={a}, b={b}, c={c}".format(a=a, b=b, c=c) >>> >>> print(f('foo', 'bar', 3)) a=foo, b=bar, c=3 >>> ff = resolve_filepath_of_name()(f) >>> print(ff('foo', 'bar', 3)) a=foo, b=bar, c=3 >>> ff = resolve_filepath_of_name('a', 'ROOT')(f) >>> print(ff('foo', 'bar', 3)) a=ROOT/foo, b=bar, c=3 >>> ff = resolve_filepath_of_name('b', 'ROOT')(f) >>> print(ff('foo', 'bar', 3)) a=foo, b=ROOT/bar, c=3 """ if name_arg is not None: return transform_args(**{name_arg: lambda x: os.path.join(rootdir, x)}) else: return lambda x: x def arg_dflt_dict_of_callable(f): """ Get a {arg_name: default_val, ...} dict from a callable. See also :py:mint_of_callable: :param f: A callable (function, method, ...) :return: """ argspec = inspect.getfullargspec(f) args = argspec.args or [] defaults = argspec.defaults or [] return {arg: dflt for arg, dflt in zip(args, [no_default] * (len(args) - len(defaults)) + list(defaults))} def infer_if_function_might_be_intended_as_a_classmethod_or_staticmethod(func): """ Tries to infer if the input function is a 'classmethod' or 'staticmethod' (or just 'normal') When is that? When: * the function's first argument is called 'cls' and has no default: 'classmethod' * the function's first argument is called 'self' and has no default: 'staticmethod' * otherwise: 'normal' >>> def a_normal_func(x, y=None): ... pass >>> def a_func_that_is_probably_a_classmethod(cls, y=None): ... pass >>> def a_func_that_is_probably_a_staticmethod(self, y=None): ... pass >>> def a_func_that_is_probably_a_classmethod_but_is_not(cls=3, y=None): ... pass >>> def a_func_that_is_probably_a_staticmethod_but_is_not(self=None, y=None): ... pass >>> list_of_functions = [ ... a_normal_func, ... a_func_that_is_probably_a_classmethod, ... a_func_that_is_probably_a_staticmethod, ... a_func_that_is_probably_a_classmethod_but_is_not, ... a_func_that_is_probably_a_staticmethod_but_is_not, ... ] >>> >>> for func in list_of_functions: ... print("{}: {}".format(func.__name__, ... infer_if_function_might_be_intended_as_a_classmethod_or_staticmethod(func))) ... a_normal_func: normal a_func_that_is_probably_a_classmethod: classmethod a_func_that_is_probably_a_staticmethod: staticmethod a_func_that_is_probably_a_classmethod_but_is_not: normal_with_cls a_func_that_is_probably_a_staticmethod_but_is_not: normal_with_self """ argsspec = inspect.getfullargspec(func) if len(argsspec.args) > 0: first_element_has_no_defaults = bool(len(argsspec.args) > len(argsspec.defaults)) if argsspec.args[0] == 'cls': if first_element_has_no_defaults: return 'classmethod' else: return 'normal_with_cls' elif argsspec.args[0] == 'self': if first_element_has_no_defaults: return 'staticmethod' else: return 'normal_with_self' return 'normal' if __name__ == '__main__': import os import re key_file_re = re.compile('setup.py') rootdir = '/D/Dropbox/dev/py/proj' cumul = list() for f in filter(lambda x: not x.startswith('.'), os.listdir(rootdir)): filepath = os.path.join(rootdir, f) if os.path.isdir(filepath): if dir_is_a_pip_installable_dir(filepath): cumul.append(filepath) for f in cumul: print(f)

34.928854

119

0.629965

8d97b86230f6560f3cd37b723cba275b3f968cb2

1,635

py

Python

setup.py

robflintham/mippy

e642c697202acc5b96b42f62204786bf5e705c9a

[ "BSD-3-Clause" ]

null

setup.py

robflintham/mippy

e642c697202acc5b96b42f62204786bf5e705c9a

[ "BSD-3-Clause" ]

null

setup.py

robflintham/mippy

e642c697202acc5b96b42f62204786bf5e705c9a

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python from setuptools import setup, find_packages from codecs import open from os import path here = path.abspath(path.dirname(__file__)) # Test version numbering before running setup test_version() setup( name='MIPPY', version=get_version(), description='Modular Image Processing in Python', author='Robert Flintham', author_email='robert.flintham@uhb.nhs.uk', install_requires=['numpy','scipy','dicom','pillow','nibabel','matplotlib'], license='BSD-3-Clause', classifiers=[ 'Programming Language :: Python :: 2.7', ], packages=['mippy','mippy.mdicom','mippy.mviewer'], package_data={'':['resources/*','mviewer/config']} )

29.727273

92

0.666055

8d98eec2f752514e211b3f9e607274f2de78ffd9

3,543

py

Python

physprog/tests/test_sample_problem.py

partofthething/physprog

8bbeb8d84697469417577c76c924dcb3a855cd2d

[ "Apache-2.0" ]

3

2018-03-25T16:13:53.000Z

2021-06-29T14:30:20.000Z

physprog/tests/test_sample_problem.py

partofthething/physprog

8bbeb8d84697469417577c76c924dcb3a855cd2d

[ "Apache-2.0" ]

null

physprog/tests/test_sample_problem.py

partofthething/physprog

8bbeb8d84697469417577c76c924dcb3a855cd2d

[ "Apache-2.0" ]

2

2021-09-18T08:38:32.000Z

2022-03-01T07:43:52.000Z

"""Run a sample problem to test full system.""" # pylint: disable=invalid-name,missing-docstring import unittest from collections import namedtuple import math import os from physprog import classfunctions from physprog import optimize THIS_DIR = os.path.dirname(__file__) SAMPLE_INPUT = os.path.join(THIS_DIR, 'sample-input.yaml') SampleDesign = namedtuple('SampleDesign', ['d1', 'd2', 'd3', 'b', 'L']) if __name__ == '__main__': unittest.main()

28.804878

80

0.582275

8d99f51b98aee394d6e4b4f62dcc6cdca1b6db1f

10,131

py

Python

tutorials/seq2seq_sated/seq2seq_sated_meminf.py

rizwandel/ml_privacy_meter

5dc4c300eadccceadd0e664a7e46099f65728628

[ "MIT" ]

294

2020-04-13T18:32:45.000Z

2022-03-31T10:32:34.000Z

tutorials/seq2seq_sated/seq2seq_sated_meminf.py

kypomon/ml_privacy_meter

c0324e8f74cbd0cde0643a7854fa66eab47bbe53

[ "MIT" ]

26

2020-04-29T19:56:21.000Z

2022-03-31T10:42:24.000Z

tutorials/seq2seq_sated/seq2seq_sated_meminf.py

kypomon/ml_privacy_meter

c0324e8f74cbd0cde0643a7854fa66eab47bbe53

[ "MIT" ]

50

2020-04-16T02:16:24.000Z

2022-03-16T00:37:40.000Z

import os import sys from collections import defaultdict import tensorflow as tf import tensorflow.keras.backend as K import numpy as np import scipy.stats as ss import matplotlib.pyplot as plt from sklearn.metrics import roc_curve from sklearn.linear_model import LogisticRegression from utils import process_texts, load_texts, load_users, load_sated_data_by_user, \ build_nmt_model, words_to_indices, \ SATED_TRAIN_USER, SATED_TRAIN_FR, SATED_TRAIN_ENG MODEL_PATH = 'checkpoints/model/' OUTPUT_PATH = 'checkpoints/output/' tf.compat.v1.disable_eager_execution() # ================================ GENERATE RANKS ================================ # # Code adapted from https://github.com/csong27/auditing-text-generation def save_users_rank_results(users, user_src_texts, user_trg_texts, src_vocabs, trg_vocabs, prob_fn, save_dir, member_label=1, cross_domain=False, save_probs=False, mask=False, rerun=False): """ Save user ranks in the appropriate format for attacks. """ for i, u in enumerate(users): save_path = save_dir + 'rank_u{}_y{}{}.npz'.format(i, member_label, '_cd' if cross_domain else '') prob_path = save_dir + 'prob_u{}_y{}{}.npz'.format(i, member_label, '_cd' if cross_domain else '') if os.path.exists(save_path) and not save_probs and not rerun: continue user_src_data = words_to_indices(user_src_texts[u], src_vocabs, mask=mask) user_trg_data = words_to_indices(user_trg_texts[u], trg_vocabs, mask=mask) rtn = get_ranks(user_src_data, user_trg_data, prob_fn, save_probs=save_probs) if save_probs: probs = rtn np.savez(prob_path, probs) else: ranks, labels = rtn[0], rtn[1] np.savez(save_path, ranks, labels) if (i + 1) % 500 == 0: sys.stderr.write('Finishing saving ranks for {} users'.format(i + 1)) def get_target_ranks(num_users=200, num_words=5000, mask=False, h=128, emb_h=128, user_data_ratio=0., tied=False, save_probs=False): """ Get ranks of target machine translation model. """ user_src_texts, user_trg_texts, test_user_src_texts, test_user_trg_texts, src_vocabs, trg_vocabs \ = load_sated_data_by_user(num_users, num_words, test_on_user=True, user_data_ratio=user_data_ratio) train_users = sorted(user_src_texts.keys()) test_users = sorted(test_user_src_texts.keys()) # Get model save_dir = OUTPUT_PATH + 'target_{}{}/'.format(num_users, '_dr' if 0. < user_data_ratio < 1. else '') if not os.path.exists(save_dir): os.mkdir(save_dir) model_path = 'sated_nmt'.format(num_users) if 0. < user_data_ratio < 1.: model_path += '_dr{}'.format(user_data_ratio) heldout_src_texts, heldout_trg_texts = load_train_users_heldout_data(train_users, src_vocabs, trg_vocabs) for u in train_users: user_src_texts[u] += heldout_src_texts[u] user_trg_texts[u] += heldout_trg_texts[u] model = build_nmt_model(Vs=num_words, Vt=num_words, mask=mask, drop_p=0., h=h, demb=emb_h, tied=tied) model.load_weights(MODEL_PATH + '{}_{}.h5'.format(model_path, num_users)) src_input_var, trg_input_var = model.inputs prediction = model.output trg_label_var = K.placeholder((None, None), dtype='float32') # Get predictions prediction = K.softmax(prediction) prob_fn = K.function([src_input_var, trg_input_var, trg_label_var, K.learning_phase()], [prediction]) # Save user ranks for train and test dataset save_users_rank_results(users=train_users, save_probs=save_probs, user_src_texts=user_src_texts, user_trg_texts=user_trg_texts, src_vocabs=src_vocabs, trg_vocabs=trg_vocabs, cross_domain=False, prob_fn=prob_fn, save_dir=save_dir, member_label=1) save_users_rank_results(users=test_users, save_probs=save_probs, user_src_texts=test_user_src_texts, user_trg_texts=test_user_trg_texts, src_vocabs=src_vocabs, trg_vocabs=trg_vocabs, cross_domain=False, prob_fn=prob_fn, save_dir=save_dir, member_label=0) # ================================ ATTACK ================================ # def avg_rank_feats(ranks): """ Averages ranks to get features for deciding the threshold for membership inference. """ avg_ranks = [] for r in ranks: avg = np.mean(np.concatenate(r)) avg_ranks.append(avg) return avg_ranks def load_ranks_by_label(save_dir, num_users=300, cross_domain=False, label=1): """ Helper method to load ranks by train/test dataset. If label = 1, train set ranks are loaded. If label = 0, test set ranks are loaded. Ranks are generated by running sated_nmt_ranks.py. """ ranks = [] labels = [] y = [] for i in range(num_users): save_path = save_dir + 'rank_u{}_y{}{}.npz'.format(i, label, '_cd' if cross_domain else '') if os.path.exists(save_path): f = np.load(save_path, allow_pickle=True) train_rs, train_ls = f['arr_0'], f['arr_1'] ranks.append(train_rs) labels.append(train_ls) y.append(label) return ranks, labels, y def load_all_ranks(save_dir, num_users=5000, cross_domain=False): """ Loads all ranks generated by the target model. Ranks are generated by running sated_nmt_ranks.py. """ ranks = [] labels = [] y = [] # Load train ranks train_label = 1 train_ranks, train_labels, train_y = load_ranks_by_label(save_dir, num_users, cross_domain, train_label) ranks = ranks + train_ranks labels = labels + train_labels y = y + train_y # Load test ranks test_label = 0 test_ranks, test_labels, test_y = load_ranks_by_label(save_dir, num_users, cross_domain, test_label) ranks = ranks + test_ranks labels = labels + test_labels y = y + test_y return ranks, labels, np.asarray(y) def run_average_rank_thresholding(num_users=300, dim=100, prop=1.0, user_data_ratio=0., top_words=5000, cross_domain=False, rerun=False): """ Runs average rank thresholding attack on the target model. """ result_path = OUTPUT_PATH if dim > top_words: dim = top_words attack1_results_save_path = result_path + 'mi_data_dim{}_prop{}_{}{}_attack1.npz'.format( dim, prop, num_users, '_cd' if cross_domain else '') if not rerun and os.path.exists(attack1_results_save_path): f = np.load(attack1_results_save_path) X, y = [f['arr_{}'.format(i)] for i in range(4)] else: save_dir = result_path + 'target_{}{}/'.format(num_users, '_dr' if 0. < user_data_ratio < 1. else '') # Load ranks train_ranks, _, train_y = load_ranks_by_label(save_dir, num_users, label=1) test_ranks, _, test_y = load_ranks_by_label(save_dir, num_users, label=0) # Convert to average rank features train_feat = avg_rank_feats(train_ranks) test_feat = avg_rank_feats(test_ranks) # Create dataset X, y = np.concatenate([train_feat, test_feat]), np.concatenate([train_y, test_y]) np.savez(attack1_results_save_path, X, y) # print(X.shape, y.shape) # Find threshold using ROC clf = LogisticRegression() clf.fit(X.reshape(-1, 1), y) probs = clf.predict_proba(X.reshape(-1, 1)) fpr, tpr, thresholds = roc_curve(y, probs[:, 1]) plt.figure(1) plt.plot(fpr, tpr, label='Attack 1') plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.title('ROC curve') plt.savefig('sateduser_attack1_roc_curve.png') if __name__ == '__main__': num_users = 300 save_probs = False rerun = True print("Getting target ranks...") get_target_ranks(num_users=num_users, save_probs=save_probs) print("Running average rank thresholding attack...") run_average_rank_thresholding(num_users=num_users, rerun=True)

35.672535

113

0.660251

8d9d264830cab7159205ed06b41898abec3b84f4

2,685

py

Python

app/recipe/tests/test_tags_api.py

MohamedAbdelmagid/django-recipe-api

229d3a7cff483b3cad76c70aefde6a51250b9bc8

[ "MIT" ]

null

app/recipe/tests/test_tags_api.py

MohamedAbdelmagid/django-recipe-api

229d3a7cff483b3cad76c70aefde6a51250b9bc8

[ "MIT" ]

null

app/recipe/tests/test_tags_api.py

MohamedAbdelmagid/django-recipe-api

229d3a7cff483b3cad76c70aefde6a51250b9bc8

[ "MIT" ]

null

from django.test import TestCase from django.urls import reverse from django.contrib.auth import get_user_model from rest_framework.test import APIClient from rest_framework import status from core.models import Tag from recipe.serializers import TagSerializer TAGS_URL = reverse("recipe:tag-list")

32.349398

85

0.672998

8d9d7d5c7ee0f28e0c8877291fb904e2d8ace2db

5,736

py

Python

dtlpy/entities/annotation_definitions/cube_3d.py

dataloop-ai/dtlpy

2c73831da54686e047ab6aefd8f12a8e53ea97c2

[ "Apache-2.0" ]

10

2020-05-21T06:25:35.000Z

2022-01-07T20:34:03.000Z

dtlpy/entities/annotation_definitions/cube_3d.py

dataloop-ai/dtlpy

2c73831da54686e047ab6aefd8f12a8e53ea97c2

[ "Apache-2.0" ]

22

2019-11-17T17:25:16.000Z

2022-03-10T15:14:28.000Z

dtlpy/entities/annotation_definitions/cube_3d.py

dataloop-ai/dtlpy

2c73831da54686e047ab6aefd8f12a8e53ea97c2

[ "Apache-2.0" ]

8

2020-03-05T16:23:55.000Z

2021-12-27T11:10:42.000Z

import numpy as np # import open3d as o3d from . import BaseAnnotationDefinition # from scipy.spatial.transform import Rotation as R import logging logger = logging.getLogger(name=__name__)

38.496644

135

0.544107

8d9e1079bef17b6514de9131ede3ab7099ea53a4

3,702

py

Python

my_module/tools.py

roki18d/sphinx_autogen-apidoc

67ad9c716c909d89bcd813a5fa871df8850e4fd5

[ "Apache-2.0" ]

null

my_module/tools.py

roki18d/sphinx_autogen-apidoc

67ad9c716c909d89bcd813a5fa871df8850e4fd5

[ "Apache-2.0" ]

null

my_module/tools.py

roki18d/sphinx_autogen-apidoc

67ad9c716c909d89bcd813a5fa871df8850e4fd5

[ "Apache-2.0" ]

null

#!/usr/bin/env python # coding: utf-8 from my_module.exceptions import InvalidArgumentsError if __name__ == "__main__": my_adder = SimpleCalculator(operator="add") print('Case01:', my_adder.execute(4, 2)) print('Case02:', my_adder.execute(5, "a")) my_subtractor = SimpleCalculator(operator="sub") print('Case03:', my_subtractor.execute(3, 5)) my_multiplier = SimpleCalculator(operator="mul") print('Case04:', my_multiplier.execute(2, 7)) my_divider = SimpleCalculator(operator="div") print('Case05:', my_divider.execute(17, 5)) print('Case06:', my_divider.execute(6, 0)) print('Case07:') my_unknown = SimpleCalculator(operator="unknown") import sys; sys.exit(0)

30.85

92

0.562939

8da38969800ff2540723920b2ba94670badb3561

12,114

py

Python

PCA_ResNet50.py

liuyingbin19222/HSI_svm_pca_resNet50

cd95d21c81e93f8b873183f10f52416f71a93d07

[ "Apache-2.0" ]

12

2020-03-13T02:39:53.000Z

2022-02-21T03:28:33.000Z

PCA_ResNet50.py

liuyingbin19222/HSI_svm_pca_resNet50

cd95d21c81e93f8b873183f10f52416f71a93d07

[ "Apache-2.0" ]

14

2020-02-17T12:31:08.000Z

2022-02-10T01:07:05.000Z

PCA_ResNet50.py

liuyingbin19222/HSI_svm_pca_resNet50

cd95d21c81e93f8b873183f10f52416f71a93d07

[ "Apache-2.0" ]

3

2020-09-06T08:19:15.000Z

2021-03-08T10:15:40.000Z

import keras from keras.layers import Conv2D, Conv3D, Flatten, Dense, Reshape, BatchNormalization from keras.layers import Dropout, Input from keras.models import Model from keras.optimizers import Adam from keras.callbacks import ModelCheckpoint from keras.utils import np_utils from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score from operator import truediv from plotly.offline import init_notebook_mode import numpy as np import tensorflow as tf from keras import layers from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D from keras.models import Model, load_model from keras.preprocessing import image from keras.utils import layer_utils from keras.utils.data_utils import get_file from keras.applications.imagenet_utils import preprocess_input from keras.utils.vis_utils import model_to_dot from keras.utils import plot_model from keras.initializers import glorot_uniform import pydot from IPython.display import SVG import scipy.misc from matplotlib.pyplot import imshow import keras.backend as K K.set_image_data_format('channels_last') K.set_learning_phase(1) from keras.utils import to_categorical import numpy as np import matplotlib.pyplot as plt import scipy.io as sio import os import spectral ## GLOBAL VARIABLES dataset = 'IP' test_ratio = 0.8 windowSize = 25 # X, y = loadData(dataset) K = 30 if dataset == 'IP' else 15 X,pca = applyPCA(X,numComponents=K) X, y = createImageCubes(X, y, windowSize=windowSize) ## Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X, y, test_ratio) # print("Xtrain.shape:",Xtrain.shape) # print("ytrain.shape:",ytrain.shape) # print("ytrain:",ytrain) ytrain = convert_one_hot(ytrain,16) ytest = convert_one_hot(ytest,16) # print("ytrain.shape:",ytrain.shape) # ResNet50 ; def identity_block(X, f, filters, stage, block): """ 3 X - tensor( m, n_H_prev, n_W_prev, n_H_prev ) f - CONV filters - stage - block block - stage X - tensor(n_H, n_W, n_C) """ # conv_name_base = "res" + str(stage) + block + "_branch" bn_name_base = "bn" + str(stage) + block + "_branch" # F1, F2, F3 = filters # X_shortcut = X # ## X = Conv2D(filters=F1, kernel_size=(1,1), strides=(1,1) ,padding="valid", name=conv_name_base+"2a", kernel_initializer=glorot_uniform(seed=0))(X) ## X = BatchNormalization(axis=3,name=bn_name_base+"2a")(X) ##ReLU X = Activation("relu")(X) # ## X = Conv2D(filters=F2, kernel_size=(f,f),strides=(1,1), padding="same", name=conv_name_base+"2b", kernel_initializer=glorot_uniform(seed=0))(X) ## X = BatchNormalization(axis=3,name=bn_name_base+"2b")(X) ##ReLU X = Activation("relu")(X) # ## X = Conv2D(filters=F3, kernel_size=(1,1), strides=(1,1), padding="valid", name=conv_name_base+"2c", kernel_initializer=glorot_uniform(seed=0))(X) ## X = BatchNormalization(axis=3,name=bn_name_base+"2c")(X) ##ReLU # ## X = Add()([X,X_shortcut]) ##ReLU X = Activation("relu")(X) return X def convolutional_block(X, f, filters, stage, block, s=2): """ 5 X - tensor( m, n_H_prev, n_W_prev, n_C_prev) f - CONV filters - stage - block block - stage s - X - tensor(n_H, n_W, n_C) """ # conv_name_base = "res" + str(stage) + block + "_branch" bn_name_base = "bn" + str(stage) + block + "_branch" # F1, F2, F3 = filters # X_shortcut = X # ## X = Conv2D(filters=F1, kernel_size=(1,1), strides=(s,s), padding="valid", name=conv_name_base+"2a", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3,name=bn_name_base+"2a")(X) X = Activation("relu")(X) ## X = Conv2D(filters=F2, kernel_size=(f,f), strides=(1,1), padding="same", name=conv_name_base+"2b", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3,name=bn_name_base+"2b")(X) X = Activation("relu")(X) ## X = Conv2D(filters=F3, kernel_size=(1,1), strides=(1,1), padding="valid", name=conv_name_base+"2c", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3,name=bn_name_base+"2c")(X) # X_shortcut = Conv2D(filters=F3, kernel_size=(1,1), strides=(s,s), padding="valid", name=conv_name_base+"1", kernel_initializer=glorot_uniform(seed=0))(X_shortcut) X_shortcut = BatchNormalization(axis=3,name=bn_name_base+"1")(X_shortcut) # X = Add()([X,X_shortcut]) X = Activation("relu")(X) return X def ResNet50(input_shape=(25,25,30),classes=16): """ ResNet50 CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3 -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER input_shape - classes - model - Keras """ #tensor X_input = Input(input_shape) #0 X = ZeroPadding2D((3,3))(X_input) #stage1 X = Conv2D(filters=64, kernel_size=(7,7), strides=(2,2), name="conv1", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name="bn_conv1")(X) X = Activation("relu")(X) X = MaxPooling2D(pool_size=(3,3), strides=(2,2))(X) #stage2 X = convolutional_block(X, f=3, filters=[64,64,256], stage=2, block="a", s=1) X = identity_block(X, f=3, filters=[64,64,256], stage=2, block="b") X = identity_block(X, f=3, filters=[64,64,256], stage=2, block="c") #stage3 X = convolutional_block(X, f=3, filters=[128,128,512], stage=3, block="a", s=2) X = identity_block(X, f=3, filters=[128,128,512], stage=3, block="b") X = identity_block(X, f=3, filters=[128,128,512], stage=3, block="c") X = identity_block(X, f=3, filters=[128,128,512], stage=3, block="d") #stage4 X = convolutional_block(X, f=3, filters=[256,256,1024], stage=4, block="a", s=2) X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="b") X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="c") X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="d") X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="e") X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="f") #stage5 X = convolutional_block(X, f=3, filters=[512,512,2048], stage=5, block="a", s=2) X = identity_block(X, f=3, filters=[512,512,2048], stage=5, block="b") X = identity_block(X, f=3, filters=[512,512,2048], stage=5, block="c") # X = AveragePooling2D(pool_size=(2,2),padding="same")(X) # X = Flatten()(X) X = Dense(classes, activation="softmax", name="fc"+str(classes), kernel_initializer=glorot_uniform(seed=0))(X) # model = Model(inputs=X_input, outputs=X, name="ResNet50") return model # # x_train : (3074,25,25,30) y_train: (3074) # model = ResNet50(input_shape=(25,25,30),classes=16) # model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) # # # model.fit(Xtrain,ytrain,epochs=2,batch_size=25) # preds = model.evaluate(Xtest,ytest) # # print(":",str(preds[0])) # print(":",str(preds[1])) if __name__ == "__main__": main()

34.123944

159

0.630097

8da4e24daba79cfc5a237fbfd0bd61228b6bdc1d

754

py

Python

tests/test_data/utest/setup.py

gordonmessmer/pyp2rpm

60145ba6fa49ad5bb29eeffa5765e10ba8417f03

[ "MIT" ]

114

2015-07-13T12:38:27.000Z

2022-03-23T15:05:11.000Z

tests/test_data/utest/setup.py

gordonmessmer/pyp2rpm

60145ba6fa49ad5bb29eeffa5765e10ba8417f03

[ "MIT" ]

426

2015-07-13T12:09:38.000Z

2022-01-07T16:41:32.000Z

tests/test_data/utest/setup.py

Mattlk13/pyp2rpm

f9ced95877d88c96b77b2b8c510dc4ceaa10504a

[ "MIT" ]

51

2015-07-14T13:11:29.000Z

2022-03-31T07:27:32.000Z

#!/usr/bin/env python3 from setuptools import setup, find_packages requirements = ["pyp2rpm~=3.3.1"] setup( name="utest", version="0.1.0", description="Micro test module", license="GPLv2+", author="pyp2rpm Developers", author_email='bkabrda@redhat.com, rkuska@redhat.com, mcyprian@redhat.com, ishcherb@redhat.com', url='https://github.com/fedora-python/pyp2rpm', install_requires=requirements, include_package_data=True, packages=find_packages(exclude=["test"]), classifiers=( "License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 3", ), )

30.16

99

0.66313

8da621c7d046b3bbba97fe0075833d24a4276a49

4,235

py

Python

abstract_nas/train/preprocess.py

dumpmemory/google-research

bc87d010ab9086b6e92c3f075410fa6e1f27251b

[ "Apache-2.0" ]

null

abstract_nas/train/preprocess.py

dumpmemory/google-research

bc87d010ab9086b6e92c3f075410fa6e1f27251b

[ "Apache-2.0" ]

null

abstract_nas/train/preprocess.py

dumpmemory/google-research

bc87d010ab9086b6e92c3f075410fa6e1f27251b

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2022 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Data preprocessing for ImageNet2012 and CIFAR-10.""" from typing import Any, Callable # pylint: disable=unused-import from big_vision.pp import ops_general from big_vision.pp import ops_image # pylint: enable=unused-import from big_vision.pp import utils from big_vision.pp.builder import get_preprocess_fn as _get_preprocess_fn from big_vision.pp.registry import Registry import tensorflow as tf CIFAR_MEAN = [0.4914, 0.4822, 0.4465] CIFAR_STD = [0.247, 0.243, 0.261] def preprocess_cifar(split, **_): """Preprocessing functions for CIFAR-10 training.""" mean_str = ",".join([str(m) for m in CIFAR_MEAN]) std_str = ",".join([str(m) for m in CIFAR_STD]) if split == "train": pp = ("decode|" "value_range(0,1)|" "random_crop_with_pad(32,4)|" "flip_lr|" f"vgg_value_range(({mean_str}),({std_str}))|" "onehot(10, key='label', key_result='labels')|" "keep('image', 'labels')") else: pp = ("decode|" "value_range(0,1)|" "central_crop(32)|" f"vgg_value_range(({mean_str}),({std_str}))|" "onehot(10, key='label', key_result='labels')|" "keep('image', 'labels')") return _get_preprocess_fn(pp) def preprocess_imagenet(split, autoaugment = False, label_smoothing = 0.0, **_): """Preprocessing functions for ImageNet training.""" if split == "train": pp = ("decode_jpeg_and_inception_crop(224)|" "flip_lr|") if autoaugment: pp += "randaug(2,10)|" pp += "value_range(-1,1)|" if label_smoothing: confidence = 1.0 - label_smoothing low_confidence = (1.0 - confidence) / (1000 - 1) pp += ("onehot(1000, key='label', key_result='labels', " f"on_value={confidence}, off_value={low_confidence})|") else: pp += "onehot(1000, key='label', key_result='labels')|" pp += "keep('image', 'labels')" else: pp = ("decode|" "resize_small(256)|" "central_crop(224)|" "value_range(-1,1)|" "onehot(1000, key='label', key_result='labels')|" "keep('image', 'labels')") return _get_preprocess_fn(pp) PREPROCESS = { "cifar10": preprocess_cifar, "imagenet2012": preprocess_imagenet, } def get_preprocess_fn(dataset, split, **preprocess_kwargs): """Makes a preprocessing function.""" preprocess_fn_by_split = PREPROCESS.get(dataset, lambda _: (lambda x: x)) split = "train" if "train" in split else "val" preprocess_fn = preprocess_fn_by_split(split, **preprocess_kwargs) return preprocess_fn

32.576923

79

0.633058

8da6f40241c238cd5d1aecce8bbe81273d1e484a

5,570

py

Python

Decission_Tree/mytree.py

luoshao23/ML_algorithm

6e94fdd0718cd892118fd036c7c5851cf3e6d796

[ "MIT" ]

4

2017-06-19T06:33:38.000Z

2019-01-31T12:07:12.000Z

Decission_Tree/mytree.py

luoshao23/ML_algorithm

6e94fdd0718cd892118fd036c7c5851cf3e6d796

[ "MIT" ]

null

Decission_Tree/mytree.py

luoshao23/ML_algorithm

6e94fdd0718cd892118fd036c7c5851cf3e6d796

[ "MIT" ]

1

2017-12-06T08:41:06.000Z

from math import log from PIL import Image, ImageDraw from collections import Counter import numpy as np from pandas import DataFrame # my_data = [['slashdot', 'USA', 'yes', 18, 213.2, 'None'], # ['google', 'France', 'yes', 23, 121.2, 'Premium'], # ['digg', 'USA', 'yes', 24, 21.32, 'Basic'], # ['kiwitobes', 'France', 'yes', 23, 1.2, 'Basic'], # ['google', 'UK', 'no', 21, .2, 'Premium'], # ['(direct)', 'New Zealand', 'no', 12, 71.2, 'None'], # ['(direct)', 'UK', 'no', 21, -21.2, 'Basic'], # ['google', 'USA', 'no', 24, 241.2, 'Premium'], # ['slashdot', 'France', 'yes', 19, 20, 'None'], # ['digg', 'USA', 'no', 18, 1.0, 'None'], # ['google', 'UK', 'no', 18, 2, 'None'], # ['kiwitobes', 'UK', 'no', 19, 44, 'None'], # ['digg', 'New Zealand', 'yes', 12, 27, 'Basic'], # ['slashdot', 'UK', 'no', 21, 86, 'None'], # ['google', 'UK', 'yes', 18, 2, 'Basic'], # ['kiwitobes', 'France', 'yes', 19, 0.0, 'Basic']] my_data = [[213.2, 'None'], [121.2, 'Premium'], [21.32, 'Basic'], [1.2, 'Basic'], [.2, 'Premium'], [71.2, 'None'], [-21.2, 'Basic'], [241.2, 'Premium'], [20, 'None'], [1.0, 'None'], [2, 'None'], [44, 'None'], [27, 'Basic'], [86, 'None'], [2, 'Basic'], [0.0, 'Basic']] data = np.array(DataFrame(my_data)) # my_data = [['slashdot', 'USA', 'yes', 18, 'None'], # ['google', 'France', 'yes', 23, 'None'], # ['digg', 'USA', 'yes', 24, 'None'], # ['kiwitobes', 'France', 'yes', 23, 'None'], # ['google', 'UK', 'no', 21, 'None'], # ['(direct)', 'New Zealand', 'no', 12, 'None'], # ['(direct)', 'UK', 'no', 21, 'None'], # ['google', 'USA', 'no', 24, 'None'], # ['slashdot', 'France', 'yes', 19, 'None'], # ['digg', 'USA', 'no', 18, 'None'], # ['google', 'UK', 'no', 18, 'None'], # ['kiwitobes', 'UK', 'no', 19, 'None'], # ['digg', 'New Zealand', 'yes', 12, 'None'], # ['slashdot', 'UK', 'no', 21, 'None'], # ['google', 'UK', 'yes', 18, 'None'], # ['kiwitobes', 'France', 'yes', 19, 'None']]

31.828571

89

0.498025

8da70610f3402c8b44d3fbdf21a05f4f563b016b

488

py

Python

hidb/wrapper.py

sk-ip/hidb

1394000992c016607e7af15095f058cd9cce007b

[ "MIT" ]

null

hidb/wrapper.py

sk-ip/hidb

1394000992c016607e7af15095f058cd9cce007b

[ "MIT" ]

null

hidb/wrapper.py

sk-ip/hidb

1394000992c016607e7af15095f058cd9cce007b

[ "MIT" ]

null

from datetime import datetime

24.4

53

0.622951

8da8f86888f2ee041a3f2312c9709ef180e420d0

4,504

py

Python

ion-channel-models/compare.py

sanmitraghosh/fickleheart-method-tutorials

d5ee910258a2656951201d4ada2a412804013bd5

[ "BSD-3-Clause" ]

null

ion-channel-models/compare.py

sanmitraghosh/fickleheart-method-tutorials

d5ee910258a2656951201d4ada2a412804013bd5

[ "BSD-3-Clause" ]

null

ion-channel-models/compare.py

sanmitraghosh/fickleheart-method-tutorials

d5ee910258a2656951201d4ada2a412804013bd5

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 from __future__ import print_function import sys sys.path.append('./method') import os import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import model as m """ Run fit. """ predict_list = ['sinewave', 'staircase', 'activation', 'ap'] try: which_predict = sys.argv[1] except: print('Usage: python %s [str:which_predict]' % os.path.basename(__file__)) sys.exit() if which_predict not in predict_list: raise ValueError('Input data %s is not available in the predict list' \ % which_predict) # Get all input variables import importlib sys.path.append('./mmt-model-files') info_id_a = 'model_A' info_a = importlib.import_module(info_id_a) info_id_b = 'model_B' info_b = importlib.import_module(info_id_b) data_dir = './data' savedir = './fig/compare' if not os.path.isdir(savedir): os.makedirs(savedir) data_file_name = 'data-%s.csv' % which_predict print('Predicting ', data_file_name) saveas = 'compare-sinewave-' + which_predict # Protocol protocol = np.loadtxt('./protocol-time-series/%s.csv' % which_predict, skiprows=1, delimiter=',') protocol_times = protocol[:, 0] protocol = protocol[:, 1] # Load data data = np.loadtxt(data_dir + '/' + data_file_name, delimiter=',', skiprows=1) # headers times = data[:, 0] data = data[:, 1] # Model model_a = m.Model(info_a.model_file, variables=info_a.parameters, current_readout=info_a.current_list, set_ion=info_a.ions_conc, transform=None, temperature=273.15 + info_a.temperature, # K ) model_b = m.Model(info_b.model_file, variables=info_b.parameters, current_readout=info_b.current_list, set_ion=info_b.ions_conc, transform=None, temperature=273.15 + info_b.temperature, # K ) # Update protocol model_a.set_fixed_form_voltage_protocol(protocol, protocol_times) model_b.set_fixed_form_voltage_protocol(protocol, protocol_times) # Load calibrated parameters load_seed = 542811797 fix_idx = [1] calloaddir_a = './out/' + info_id_a calloaddir_b = './out/' + info_id_b cal_params_a = [] cal_params_b = [] for i in fix_idx: cal_params_a.append(np.loadtxt('%s/%s-solution-%s-%s.txt' % \ (calloaddir_a, 'sinewave', load_seed, i))) cal_params_b.append(np.loadtxt('%s/%s-solution-%s-%s.txt' % \ (calloaddir_b, 'sinewave', load_seed, i))) # Predict predictions_a = [] for p in cal_params_a: predictions_a.append(model_a.simulate(p, times)) predictions_b = [] for p in cal_params_b: predictions_b.append(model_b.simulate(p, times)) # Plot fig, axes = plt.subplots(2, 1, sharex=True, figsize=(10, 4), gridspec_kw={'height_ratios': [1, 3]}) is_predict = ' prediction' if which_predict != 'sinewave' else '' sim_protocol = model_a.voltage(times) # model_b should give the same thing axes[0].plot(times, sim_protocol, c='#7f7f7f') axes[0].set_ylabel('Voltage\n(mV)', fontsize=16) axes[1].plot(times, data, alpha=0.5, label='Data') for i, p in zip(fix_idx, predictions_a): axes[1].plot(times, p, label='Model A' + is_predict) for i, p in zip(fix_idx, predictions_b): axes[1].plot(times, p, label='Model B' + is_predict) # Zooms from mpl_toolkits.axes_grid1.inset_locator import inset_axes, mark_inset sys.path.append('./protocol-time-series') zoom = importlib.import_module(which_predict + '_to_zoom') axes[1].set_ylim(zoom.set_ylim) for i_zoom, (w, h, loc) in enumerate(zoom.inset_setup): axins = inset_axes(axes[1], width=w, height=h, loc=loc, axes_kwargs={"facecolor" : "#f0f0f0"}) axins.plot(times, data, alpha=0.5) for i, p in zip(fix_idx, predictions_a): axins.plot(times, p) for i, p in zip(fix_idx, predictions_b): axins.plot(times, p) axins.set_xlim(zoom.set_xlim_ins[i_zoom]) axins.set_ylim(zoom.set_ylim_ins[i_zoom]) #axins.yaxis.get_major_locator().set_params(nbins=3) #axins.xaxis.get_major_locator().set_params(nbins=3) axins.set_xticklabels([]) axins.set_yticklabels([]) pp, p1, p2 = mark_inset(axes[1], axins, loc1=zoom.mark_setup[i_zoom][0], loc2=zoom.mark_setup[i_zoom][1], fc="none", lw=0.75, ec='k') pp.set_fill(True); pp.set_facecolor("#f0f0f0") axes[1].legend() axes[1].set_ylabel('Current (pA)', fontsize=16) axes[1].set_xlabel('Time (ms)', fontsize=16) plt.subplots_adjust(hspace=0) plt.savefig('%s/%s' % (savedir, saveas), bbox_inches='tight', dpi=200) plt.close()

31.277778

78

0.690941

8da906c8ad76ecde7a1bd94e5017709b02a7ce8e

7,752

py

Python

examples/services/classifier_service.py

bbbdragon/python-pype

f0618150cb4d2fae1f959127453fb6eca8db84e5

[ "MIT" ]

8

2019-07-12T03:28:10.000Z

2019-07-19T20:34:45.000Z

examples/services/classifier_service.py

bbbdragon/python-pype

f0618150cb4d2fae1f959127453fb6eca8db84e5

[ "MIT" ]

null

examples/services/classifier_service.py

bbbdragon/python-pype

f0618150cb4d2fae1f959127453fb6eca8db84e5

[ "MIT" ]

null

''' python3 classifier_service.py data.csv This service runs a scikit-learn classifier on data provided by the csv file data.csv. The idea of this is a simple spam detector. In the file, you will see a number, 1 or -1, followed by a pipe, followed by a piece of text. The text is designed to be a subject email, and the number its label: 1 for spam and -1 for not spam. The service loads the csv file, trains the classifier, and then waits for you to send it a list of texts via the 'classify' route. This service can be tested using: ./test_classifier_service.sh ''' from flask import Flask,request,jsonify from pype import pype as p from pype import _,_0,_1,_p from pype import _assoc as _a from pype import _dissoc as _d from pype import _do from statistics import mean,stdev from pype.vals import lenf from sklearn.ensemble import RandomForestClassifier as Classifier from sklearn.feature_extraction.text import TfidfVectorizer as Vectorizer import sys import csv ''' We have to use lambda to define the read function because pype functions can't yet deal with keyword args. ''' read=lambda f: csv.reader(f,delimiter='|') def train_classifier(texts,y): ''' Here is a perfect example of the "feel it ... func it" philosophy: The pype call uses the function arguments and function body to specify three variables, texts, a list of strings, y, a list of floats, and vectorizer, a scikit-learn object that vectorizes text. This reiterates the adivce that you should use the function body and function arguments to declare your scope, whenever you can. Line-by-line, here we go: {'vectorizer':vectorizer.fit, 'X':vectorizer.transform}, We build a dict, the first element of which is the fit vectorizer. Luckily, the 'fit' function returns an instance of the trained vectorizer, so we do not need to use _do. This vectorizer is then assigned to 'vectorizer'. Because iterating through dictionaries in Python3.6 preserves the order of the keys in which they were declared, we can apply the fit function to the vectorizer on the texts, assign that to the 'vectorizer' key. We need this instance of the vectorizer to run the classifier for unknown texts. After this, we apply the 'transform' to convert the texts into a training matrix keyed by 'X', whose rows are texts and whose columns are words. _a('classifier',(Classifier().fit,_['X'],y)), Finally, we can build a classifier. _a, or _assoc, means we are adding a key-value pair to the previous dictionary. This will be a new instance of our Classifier, which is trained through the fit function on the text-word matrix 'X' and the labels vector y. _d('X'), Since we don't need the X matrix anymore, we delete it from the returned JSON, which now only contains 'vectorizer' and 'classifier', the two things we will need to classify unknown texts. ''' vectorizer=Vectorizer() return p( texts, {'vectorizer':vectorizer.fit, 'X':vectorizer.transform}, _a('classifier',(Classifier().fit,_['X'],y)), _d('X'), ) ''' We train the model in a global variable containing our vectorizer and classifier. This use of global variables is only used for microservices, by the way. Here is a line-by-line description: sys.argv[1], open, Open the file. read, We build a csv reader with the above-defined 'read' function, which builds a csv reader with a '|' delimiter. I chose this delimeter because the texts often have commas. list, Because csv.reader is a generator, it cannot be accessed twice, so I cast it to a list. This list is a list of 2-element lists, of the form [label,text], where label is a string for the label ('1' or '-1'), and text is a string for the training text. So an example of this would be ['1','free herbal viagra buy now']. (train,[_1],[(float,[_0])]) This is a lambda which calls the 'train' function on two arguments, the first being a list of texts, the second being a list of numerical labels. We know that the incoming argument is a list of 2-element lists, so [_1] is a map, which goes through this list - [] - and builds a new list containing only the second element of each 2-element list, referenced by _1. With the first elements of the 2-element lists, we must extract the first element and cast it to a float. In [(float,[_0])], the [] specifies a map over the list of 2-element lists. (float,_0) specifies we are accessing the first element of the 2-element list ('1' or '-1'), and calls the float function on it, to cast it to a float. If we do not cast it to a float, sklearn will not be able to process it as a label. ''' MODEL=p( sys.argv[1], open, read, list, (train_classifier,[_1],[(float,_0)]), ) app = Flask(__name__) if __name__=='__main__': app.run(host='0.0.0.0',port=10004,debug=True)

36.739336

172

0.68434

8da9192128d87d058ba7b763d377c653bfe2eb10

2,657

py

Python

ida_plugin/uefi_analyser.py

fengjixuchui/UEFI_RETool

72c5d54c1dab9f58a48294196bca5ce957f6fb24

[ "MIT" ]

240

2019-03-12T21:28:06.000Z

2021-02-09T16:20:09.000Z

ida_plugin/uefi_analyser.py

fengjixuchui/UEFI_RETool

72c5d54c1dab9f58a48294196bca5ce957f6fb24

[ "MIT" ]

10

2019-09-09T08:38:35.000Z

2020-11-30T15:19:30.000Z

ida_plugin/uefi_analyser.py

fengjixuchui/UEFI_RETool

72c5d54c1dab9f58a48294196bca5ce957f6fb24

[ "MIT" ]

53

2019-03-16T06:54:18.000Z

2020-12-23T06:16:38.000Z

# SPDX-License-Identifier: MIT import os import idaapi import idautils from PyQt5 import QtWidgets from uefi_analyser import dep_browser, dep_graph, prot_explorer, ui AUTHOR = "yeggor" VERSION = "1.2.0" NAME = "UEFI_RETool" WANTED_HOTKEY = "Ctrl+Alt+U" HELP = "This plugin performs automatic analysis of the input UEFI module" def PLUGIN_ENTRY(): try: return UefiAnalyserPlugin() except Exception as err: import traceback print(f"[{NAME} error] {str(err)}\n{traceback.format_exc()}")

26.04902

73

0.616861