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

23793e314023b1afee56b5645d0f4bbfd1a679ef

1,098

py

Python

jaxvi/models.py

sagar87/jaxvi

78829552589f8d44082cf8a1a8e02da549d7c298

[ "MIT" ]

null

jaxvi/models.py

sagar87/jaxvi

78829552589f8d44082cf8a1a8e02da549d7c298

[ "MIT" ]

null

jaxvi/models.py

sagar87/jaxvi

78829552589f8d44082cf8a1a8e02da549d7c298

[ "MIT" ]

null

from abc import abstractmethod from jaxvi.abstract import ABCMeta, abstract_attribute import jax.numpy as jnp from jax.scipy.stats import norm, gamma

27.45

67

0.644809

237f62f7caaf963fa09ab8afd9854d8138aec5f6

696

py

Python

testfile/upsampling.py

otsubo/CIFAR-ConvolutionalAutoEncoder-Chainer

bbda81dc7b52f42e07e9daaff38ce7453b24e008

[ "MIT" ]

1

2020-10-18T03:33:16.000Z

testfile/upsampling.py

otsubo/CIFAR-ConvolutionalAutoEncoder-Chainer

bbda81dc7b52f42e07e9daaff38ce7453b24e008

[ "MIT" ]

null

testfile/upsampling.py

otsubo/CIFAR-ConvolutionalAutoEncoder-Chainer

bbda81dc7b52f42e07e9daaff38ce7453b24e008

[ "MIT" ]

1

2019-12-03T10:19:17.000Z

# -*- coding: utf-8 -*- """ Created on Sat Jul 21 22:59:41 2018 @author: user """ import chainer import numpy as np import chainer.functions as F x = np.arange(1, 37).reshape(1, 1, 6, 6).astype(np.float32) x = chainer.Variable(x) print(x) pooled_x, indexes = F.max_pooling_2d(x, ksize=2, stride=2, return_indices=True) print(pooled_x) print(indexes) upsampled_x = F.upsampling_2d(pooled_x, indexes, ksize=2, stride=2, outsize=x.shape[2:]) print(upsampled_x.shape) print(upsampled_x.data) upsampled_x = F.unpooling_2d(pooled_x, ksize=2, stride=2, outsize=x.shape[2:]) print(upsampled_x.shape) print(upsampled_x.data) # KerasupsamplingChainerunpooling # Chainerupsamplingindexes

24

88

0.752874

2380077f114b5b16cc9a1100bc690d9cd9597308

36

py

Python

coding/kakov-budet-vyvod-dlja-var-2-1/code.py

mowshon/python-quiz

215fb23dbb0fa42b438f988e49172b87b48bade3

[ "MIT" ]

2

2020-07-17T21:08:26.000Z

2020-08-16T03:12:07.000Z

coding/kakov-budet-vyvod-dlja-var-2-1/code.py

mowshon/python-quiz

215fb23dbb0fa42b438f988e49172b87b48bade3

[ "MIT" ]

2

2021-06-08T22:04:35.000Z

2022-01-13T03:03:32.000Z

coding/kakov-budet-vyvod-dlja-var-2-1/code.py

mowshon/python-quiz

215fb23dbb0fa42b438f988e49172b87b48bade3

[ "MIT" ]

null

var = "James Bond" print(var[2::-1])

18

0.611111

2380742d81c7c52a33d552870a8c7044b79787a1

1,322

py

Python

src/ui_templates/wizard_depend_depend_version.py

GandaG/fomod-designer

0bd333f33286adfaebecd5c2561f3aadaa017de0

[ "Apache-2.0" ]

15

2016-05-31T22:41:19.000Z

2022-01-13T07:51:43.000Z

src/ui_templates/wizard_depend_depend_version.py

GandaG/fomod-designer

0bd333f33286adfaebecd5c2561f3aadaa017de0

[ "Apache-2.0" ]

42

2016-05-20T18:30:45.000Z

2019-07-18T20:27:37.000Z

src/ui_templates/wizard_depend_depend_version.py

GandaG/fomod-editor

0bd333f33286adfaebecd5c2561f3aadaa017de0

[ "Apache-2.0" ]

3

2016-10-16T18:23:28.000Z

2018-11-24T12:08:09.000Z

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'resources/templates/wizard_depend_depend_version.ui' # # Created by: PyQt5 UI code generator 5.5.1 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets

37.771429

106

0.708775

2381759676c1a13a9190cbf2cbe7006518dd9448

1,093

py

Python

behaviour/models.py

red-and-black/friendly

f453344ad1e9173ad3545e4ea0c825b65190b3c5

[ "Apache-2.0" ]

2

2020-01-28T12:56:56.000Z

2021-07-02T03:07:39.000Z

behaviour/models.py

red-and-black/friendly

f453344ad1e9173ad3545e4ea0c825b65190b3c5

[ "Apache-2.0" ]

5

2021-03-18T23:02:11.000Z

2021-09-17T11:02:08.000Z

behaviour/models.py

red-and-black/goodchat

1a391a04d4edfbcefaf87663f08308dd58578634

[ "Apache-2.0" ]

null

from django.db import models

25.418605

66

0.651418

2381e2b9c699c0ba9541e7eef0d109d8c8508180

121

py

Python

setup.py

m0hithreddy/rpyc-mem

72e46da34fe2165a89d702a02ec0bb7b6d64775e

[ "MIT" ]

1

2022-03-12T23:29:13.000Z

setup.py

m0hithreddy/rpyc-mem

72e46da34fe2165a89d702a02ec0bb7b6d64775e

[ "MIT" ]

null

setup.py

m0hithreddy/rpyc-mem

72e46da34fe2165a89d702a02ec0bb7b6d64775e

[ "MIT" ]

null

from setuptools import setup setup( version=open("rpyc_mem/_version.py").readlines()[-1].split()[-1].strip("\"'") )

20.166667

81

0.661157

23823d970e12e704ee8efa4de2524ea2db2fa2ba

957

py

Python

myblog/blog/migrations/0001_initial.py

LikeLion-CAU-9th/spring-concept-blog

e1e16c5489a3b96d45d91bc43e15297270293dcb

[ "MIT" ]

null

myblog/blog/migrations/0001_initial.py

LikeLion-CAU-9th/spring-concept-blog

e1e16c5489a3b96d45d91bc43e15297270293dcb

[ "MIT" ]

null

myblog/blog/migrations/0001_initial.py

LikeLion-CAU-9th/spring-concept-blog

e1e16c5489a3b96d45d91bc43e15297270293dcb

[ "MIT" ]

3

2021-08-10T19:00:18.000Z

2021-08-11T00:53:09.000Z

# Generated by Django 3.2.2 on 2021-05-10 06:15 from django.db import migrations, models import django.utils.timezone

35.444444

169

0.592476

88bb3f59329f873d5176f1525a62f453fd0b978d

2,879

py

Python

dockermap/map/runner/cmd.py

merll/docker-map

54e325595fc0b6b9d154dacc790a222f957895da

[ "MIT" ]

85

2015-01-02T01:05:14.000Z

2022-03-23T22:23:12.000Z

dockermap/map/runner/cmd.py

merll/docker-map

54e325595fc0b6b9d154dacc790a222f957895da

[ "MIT" ]

21

2015-02-10T18:25:03.000Z

2020-10-28T08:38:39.000Z

dockermap/map/runner/cmd.py

merll/docker-map

54e325595fc0b6b9d154dacc790a222f957895da

[ "MIT" ]

15

2015-02-27T12:19:35.000Z

2021-09-29T06:20:14.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals import logging from ..action import ContainerUtilAction from ..input import ItemType log = logging.getLogger(__name__)

40.549296

115

0.64571

88bba0c5c1fe8347368f3e71bf8ff00b73e46934

846

py

Python

tests/test_question_search_result_parsing.py

regardscitoyens/senapy

84fb9c2228006b99c396f2c5e6a15ba6bae19873

[ "MIT" ]

6

2016-04-11T06:39:43.000Z

2021-06-18T08:52:08.000Z

tests/test_question_search_result_parsing.py

regardscitoyens/senapy

84fb9c2228006b99c396f2c5e6a15ba6bae19873

[ "MIT" ]

8

2016-05-31T19:46:50.000Z

2019-08-30T13:20:27.000Z

tests/test_question_search_result_parsing.py

regardscitoyens/senapy

84fb9c2228006b99c396f2c5e6a15ba6bae19873

[ "MIT" ]

3

2016-05-31T12:36:10.000Z

2017-11-15T19:55:04.000Z

# -*- coding: utf-8 -*- import codecs from senapy.parsing.question_search_result_parser import parse_question_search_result

47

177

0.751773

88bc403d25f54bbc912895c21b6786cdfc90a30c

3,678

py

Python

main.py

PWN0N/Working-Time-lapse

1ebe4cb1a669a1b77528b4f2583e27fdd4e5953b

[ "MIT" ]

null

main.py

PWN0N/Working-Time-lapse

1ebe4cb1a669a1b77528b4f2583e27fdd4e5953b

[ "MIT" ]

null

main.py

PWN0N/Working-Time-lapse

1ebe4cb1a669a1b77528b4f2583e27fdd4e5953b

[ "MIT" ]

null

import signal import numpy as np from PIL import ImageGrab import cv2 import time import sys import os flips_time_mins = 30 interval = 5 # seconds num_frames = flips_time_mins*60/interval num_frames = int(num_frames) year = -1 month = -1 day = -1 out_fps = 24 cammode = 0 shutdown_msg = False capture = cv2.VideoCapture(0) capture1 = cv2.VideoCapture(1) cam, _ = capture.read() cam1, _ = capture1.read() if(cam and cam1): print('Dual Camera Mode') cammode = 1 elif(cam): print('Single Camera Mode') cammode = 2 else: print('No Camera Detect!') sys.exit(0) signal.signal(signal.SIGINT,signal_handler) # capture frames to video while True: if(day != time.strftime("%d")): year = time.strftime("%Y") month = time.strftime("%m") day = time.strftime("%d") hour = time.strftime("%H") save_dir = "{0}/{1}/{2}".format(year, month, day) if not os.path.isdir(save_dir): os.makedirs(save_dir) # innner camera init size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))) codec = cv2.VideoWriter.fourcc('M', 'J', 'P', 'G') cam_filename = save_dir+"/cam_{:4}.avi".format(time.strftime("%H%M")) video = cv2.VideoWriter(cam_filename, codec, out_fps, size) # for low quality webcams, discard the starting unstable frames for i in range(20): capture.read() # desktop screen init desktopim = np.array(ImageGrab.grab().convert('RGB')) # desktopFrame =np.array(desktopim.getdata(),dtype='uint8')\ # .reshape((desktopim.size[1],desktopim.size[0],3)) sp = desktopim.shape sz1 = sp[0] # height(rows) of image sz2 = sp[1] # width(colums) of image desktopsize = (int(sz2),int(sz1)) codec = cv2.VideoWriter.fourcc('M', 'J', 'P', 'G') desktop_filename = save_dir+"/desktop_{:4}.avi".format(time.strftime("%H%M")) desktopvideo = cv2.VideoWriter(desktop_filename, codec, out_fps, desktopsize) # outter camera init if (cammode == 1): size1 = (int(capture1.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture1.get(cv2.CAP_PROP_FRAME_HEIGHT))) cam1_filename = save_dir+"/cam1_{:4}.avi".format(time.strftime("%H%M")) video1 = cv2.VideoWriter(cam1_filename, codec, out_fps, size1) # for low quality webcams, discard the starting unstable frames for i in range(20): capture1.read() for i in range(num_frames): if (shutdown_msg): break _, frame = capture.read() video.write(add_timestamp(frame)) desktopim = np.array(ImageGrab.grab().convert('RGB')) # ImageGrab and OpenCV have different color space desktopFrame = cv2.cvtColor(desktopim, cv2.COLOR_BGR2RGB) desktopvideo.write(add_timestamp(desktopFrame)) if (cammode == 1): _, frame1 = capture1.read() video1.write(add_timestamp(frame1)) time.sleep(interval) video.release() desktopvideo.release() if (cammode == 1): video1.release() if (shutdown_msg): break capture.release() if(cammode ==1): capture1.release() print('Done!') print('Exit The Program') sys.exit(0)

27.244444

83

0.637575

88bd61d6346e9f097545fab6de60f3909f62dcdf

1,823

py

Python

tests/test_tokenizers.py

BMarcin/MordinezNLP

884f6c2ccade8ac796d40d3081560021e96765ca

[ "MIT" ]

1

2021-02-03T19:38:05.000Z

tests/test_tokenizers.py

BMarcin/MordinezNLP

884f6c2ccade8ac796d40d3081560021e96765ca

[ "MIT" ]

13

2020-11-30T21:01:56.000Z

2021-03-12T21:23:45.000Z

tests/test_tokenizers.py

BMarcin/MordinezNLP

884f6c2ccade8ac796d40d3081560021e96765ca

[ "MIT" ]

null

import unittest import spacy from spacy.language import Language try: from src.MordinezNLP.tokenizers import spacy_tokenizer except: from MordinezNLP.tokenizers import spacy_tokenizer if __name__ == '__main__': unittest.main()

26.808824

126

0.397696

88bdb402bf1da07ef8a27f4a47f88d7c557aae53

3,905

py

Python

scripts/autopost/image_maker.py

sahawaee/quotes-indonesia

ef6f0dc5afa460d8da6266f5df89d2a350cc9835

[ "MIT" ]

6

2019-11-02T06:04:37.000Z

2022-03-27T14:41:45.000Z

scripts/autopost/image_maker.py

sahawaee/quotes-indonesia

ef6f0dc5afa460d8da6266f5df89d2a350cc9835

[ "MIT" ]

1

2021-09-29T08:33:14.000Z

2021-11-06T02:10:38.000Z

scripts/autopost/image_maker.py

sahawaee/quotes-indonesia

ef6f0dc5afa460d8da6266f5df89d2a350cc9835

[ "MIT" ]

8

2020-03-21T20:09:38.000Z

2022-03-11T19:14:24.000Z

import random import requests import tempfile from io import BytesIO from PIL import Image, ImageDraw, ImageFont FONTS = [ 'https://cdn.statically.io/gh/google/fonts/main/ofl/neucha/Neucha.ttf', # 'https://cdn.statically.io/gh/google/fonts/main/ofl/catamaran/Catamaran%5Bwght%5D.ttf', # font_base_url + 'lobstertwo.ttf', # font_base_url + 'underdog.ttf', # font_base_url + 'specialelite.ttf', # font_base_url + 'abrilfatface.ttf', # font_base_url + 'merienda.ttf', # font_base_url + 'poiretone.ttf', # font_base_url + 'shadowsintolight.ttf', # font_base_url + 'caveatbrush.ttf', # font_base_url + 'gochihand.ttf', # font_base_url + 'itim.ttf', # font_base_url + 'rancho.ttf' ] # thanks to https://clrs.cc COLORS = [ {'bg': (255, 255, 255), 'fg': (100, 100, 100)} # { 'bg': (0, 31, 63), 'fg': (128, 191, 255) }, # { 'bg': (0, 116, 217), 'fg': (179, 219, 255) }, # { 'bg': (127, 219, 255), 'fg': (0, 73, 102) }, # { 'bg': (57, 204, 204), 'fg': (0, 0, 0) }, # { 'bg': (61, 153, 112), 'fg': (22, 55, 40) }, # { 'bg': (46, 204, 64), 'fg': (14, 62, 20) }, # { 'bg': (1, 255, 112), 'fg': (0, 102, 44) }, # { 'bg': (255, 220, 0), 'fg': (102, 88, 0) }, # { 'bg': (255, 133, 27), 'fg': (102, 48, 0) }, # { 'bg': (255, 65, 54), 'fg': (128, 6, 0) }, # { 'bg': (133, 20, 75), 'fg': (235, 122, 177) }, # { 'bg': (240, 18, 190), 'fg': (101, 6, 79) }, # { 'bg': (177, 13, 201), 'fg': (239, 169, 249) }, # { 'bg': (17, 17, 17), 'fg': (221, 221, 221) }, # { 'bg': (170, 170, 170), 'fg': (0, 0, 0) }, # { 'bg': (221, 221, 221), 'fg': (0, 0, 0) } ]

33.956522

93

0.560051

88bea4fa9c19bdca4e8c6da218e8d49f1310845f

240

py

Python

api_ui/views.py

mihail-ivanov/base-vue-api

a2ef8ae360d3d26425093aefaf521082cf3684c5

[ "MIT" ]

null

api_ui/views.py

mihail-ivanov/base-vue-api

a2ef8ae360d3d26425093aefaf521082cf3684c5

[ "MIT" ]

null

api_ui/views.py

mihail-ivanov/base-vue-api

a2ef8ae360d3d26425093aefaf521082cf3684c5

[ "MIT" ]

null

from django.contrib.auth.models import User from rest_framework import generics from .serializers import UserSerializer

21.818182

43

0.808333

88beff2251e1c3db657b53d33c4f8b3982f9a861

5,093

py

Python

metashade/hlsl/sm5/profile.py

ppenenko/metashade

7148e808e47bace59e61e1483da9ddf3f9daa1cc

[ "Apache-2.0" ]

3

2020-04-02T13:29:06.000Z

2020-09-07T17:43:09.000Z

metashade/hlsl/sm5/profile.py

ppenenko/metashade

7148e808e47bace59e61e1483da9ddf3f9daa1cc

[ "Apache-2.0" ]

null

metashade/hlsl/sm5/profile.py

ppenenko/metashade

7148e808e47bace59e61e1483da9ddf3f9daa1cc

[ "Apache-2.0" ]

null

# Copyright 2017 Pavlo Penenko # # 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 metashade.rtsl.profile as rtsl import metashade.clike.struct as struct from . import data_types from . import samplers import sys, inspect # Reference all the data types from the generator class for name, cls in inspect.getmembers( sys.modules[data_types.__name__], lambda member: (inspect.isclass(member) and member.__module__ == data_types.__name__ and not member.__name__.startswith('_') )): setattr(Generator, name, cls)

34.181208

79

0.634204

88beffc16b35e904c09d6505bf831f2d157a3465

1,981

py

Python

ads_refine/create_refine_project.py

adsabs/refine-affiliations

b51290221d22400fa8fee6d5dac98c4930fbd7f3

[ "OLDAP-2.6", "Python-2.0" ]

1

2015-02-06T05:44:26.000Z

ads_refine/create_refine_project.py

adsabs/refine-affiliations

b51290221d22400fa8fee6d5dac98c4930fbd7f3

[ "OLDAP-2.6", "Python-2.0" ]

null

ads_refine/create_refine_project.py

adsabs/refine-affiliations

b51290221d22400fa8fee6d5dac98c4930fbd7f3

[ "OLDAP-2.6", "Python-2.0" ]

null

#!/usr/bin/python2.6 import os import sys import time from optparse import OptionParser from google.refine import refine from clean_ads_affiliations import clean_ads_affs assert sys.hexversion >= 0x02060000 SERVER = 'http://adsx.cfa.harvard.edu:3333' def create_refine_project(path, name, pretend=False, verbose=0): """ Creates a project in google Refine and loads the affiliations. """ input_file = os.path.abspath(path) msg('Create a file that we can upload to Refine.', verbose) new_input_file = clean_ads_affs(input_file, verbose) msg('Upload to Refine.', verbose) project_name = 'Astronomy affiliations (%s) (created %s)' % (os.path.basename(path).replace('.reversed', '.merged'), time.asctime()) print 'Creating project "%s".' % project_name if not pretend: r = refine.Refine(SERVER) project = r.new_project(project_file=new_input_file, project_name=project_name, split_into_columns=True, separator='\t', ignore_initial_non_blank_lines=0, header_lines=1, skip_initial_data_rows=0, limit=0, guess_value_type=False, ignore_quotes=False) msg('Done with success.', verbose) return project.project_id if __name__ == '__main__': main()

31.951613

136

0.648662

88c000e9e02c415df05d77f3582eae21f519869a

8,256

py

Python

backend/views.py

johnzhang1999/Pop

284cc1c5195efdc676759d8494965b2dfb44cf78

[ "MIT" ]

1

2019-02-10T06:50:25.000Z

backend/views.py

johnzhang1999/Pop

284cc1c5195efdc676759d8494965b2dfb44cf78

[ "MIT" ]

null

backend/views.py

johnzhang1999/Pop

284cc1c5195efdc676759d8494965b2dfb44cf78

[ "MIT" ]

null

from django.http import JsonResponse, Http404 from django.views.decorators.csrf import csrf_exempt from exponent_server_sdk import PushClient, PushMessage, DeviceNotRegisteredError from .models import Group, User, Event import hashlib, uuid # Create your views here.

38.222222

128

0.56468

88c03f34e857962f5d9b4b18e80b0b7a54e0e36b

3,572

py

Python

SDFConv/code/utils/vis/unet_vis.py

zshyang/FieldConvolution

ca88df568a6f2143dcb85d22c005fce4562a7523

[ "MIT" ]

1

2021-01-03T18:53:06.000Z

SDFConv/code/utils/vis/unet_vis.py

zshyang/FieldConvolution

ca88df568a6f2143dcb85d22c005fce4562a7523

[ "MIT" ]

null

SDFConv/code/utils/vis/unet_vis.py

zshyang/FieldConvolution

ca88df568a6f2143dcb85d22c005fce4562a7523

[ "MIT" ]

null

"""Implement this function across different project. ----ZY.2020.Oct. """ import os from easydict import EasyDict from torchvision.utils import save_image from logging import Logger from subprocess import call def create_save_folders(root_folder, folder_list: list): """Create folders to save visualization image. :param root_folder: The root folder. :param folder_list: The list of folders """ for folder in folder_list: os.makedirs(os.path.join(root_folder, folder), exist_ok=True) def unet_vis( in_batch: dict, out_batch: tuple, training: bool, epoch: int, step: int, options: EasyDict, logger: Logger ): """The visualization function of UNet. :param in_batch: The input batch. :param out_batch: The output batch. :param training: Whether it is training stage. :param epoch: The epoch number start with 1. :param step: The step. :param logger: The logger. :param options: The options for visualization. """ # Folders if training: vis_dir = os.path.join(options.vis.dir, "train_vis") else: vis_dir = os.path.join(options.vis.dir, "val_vis") out_dir = os.path.join(vis_dir, "epoch-{:04d}".format(epoch)) # Customize the list of folders. dir_list = ["input_image", "info"] # Create the list folders. create_save_folders(out_dir, dir_list) # The list of key in input and output batch. key_list = ["input_image", ["loss"]] batch = {} batch.update(in_batch) batch.update(out_batch[0]) batch.update(out_batch[1]) # Get the batch size. if training: batch_size = options.train.batch_size else: batch_size = options.test.batch_size # Get number of steps each epoch. if training: # Update the number of training samples in options. num_step_each_epoch = options.dataset.len_train // (options.train.batch_size * options.num_gpus) else: # Update the number of validation samples in options. num_step_each_epoch = options.dataset.len_test // (options.test.batch_size * options.num_gpus) # Save images and info. for i in range(batch_size): batch_id = step % num_step_each_epoch fn = "data-{:04d}.png".format(batch_id * batch_size + i) # file name. for key, folder in zip(key_list, dir_list): if folder == "info": with open(os.path.join(out_dir, folder, fn.replace('.png', '.txt')), 'w') as file: for loss_item in key: file.write("{}: {}\n".format(loss_item, batch[loss_item][i].item())) else: save_image(batch[key][i], os.path.join(out_dir, folder, fn)) # Get the KC step interval. if training: kc_steps = options.train.kc_steps else: kc_steps = options.test.kc_steps # Generate HTML file. mod_step = step % num_step_each_epoch # step starts ar 1. extra_step = (mod_step + kc_steps) / num_step_each_epoch if mod_step == 0 or extra_step > 1.0: # Visualize HTML. logger.info("Generating html visualization ...") sublist = ",".join(dir_list) script_path = os.path.join(os.path.abspath(os.getcwd()), "utils", "gen_html_hierarchy_local.py") if not os.path.exists(script_path): raise ValueError("{} this python script does not exist!".format(script_path)) cmd = "cd {} && python {} . 10 htmls {} {} > /dev/null".format( out_dir, script_path, sublist, sublist ) call(cmd, shell=True) logger.info("DONE")

37.208333

114

0.645017

88c151ffa4679f358142c0fae2020059a35ad3a9

1,465

py

Python

tests/test_models/test_metric_report.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

6

2015-01-28T05:59:08.000Z

2018-01-09T07:48:57.000Z

tests/test_models/test_metric_report.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

2

2020-05-09T16:36:43.000Z

2020-05-09T16:52:35.000Z

tests/test_models/test_metric_report.py

wikimedia/analytics-wikimetrics

1d2036657b06ccd16ecfc76edd3f9a6119ff75f4

[ "MIT" ]

1

2016-01-13T07:19:44.000Z

from nose.tools import assert_equals, assert_true from wikimetrics.metrics import metric_classes from wikimetrics.models import ( MetricReport ) from ..fixtures import DatabaseTest

28.173077

57

0.524915

88c2edc9c66f0cdca479957d50dd7215d509131a

615

py

Python

setup.py

manu-mannattil/clut2dtstyle

4d4cbea540de9f3b3bde209fbe1f8696e441492d

[ "Unlicense" ]

9

2018-09-13T04:27:23.000Z

2022-01-02T23:34:20.000Z

setup.py

manu-mannattil/clut2dtstyle

4d4cbea540de9f3b3bde209fbe1f8696e441492d

[ "Unlicense" ]

null

setup.py

manu-mannattil/clut2dtstyle

4d4cbea540de9f3b3bde209fbe1f8696e441492d

[ "Unlicense" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from setuptools import setup setup( name="clut2dtstyle", license="Unlicense", version="0.1", author="Manu Mannattil", author_email="manu.mannattil@gmail.com", description="Script to convert Hald CLUTs to darktable styles", py_modules=["clut2dtstyle"], install_requires=["numpy>=1.11.0"], classifiers=[ "License :: Public Domain", "Programming Language :: Python :: 3", "Topic :: Multimedia :: Graphics" ], entry_points=""" [console_scripts] clut2dtstyle=clut2dtstyle:main """ )

24.6

67

0.621138

88c3e3d167bb3169d56f9fd93e05df1be55709b1

1,903

py

Python

xugrid/data/synthetic.py

Deltares/xugrid

41881977e5e49d0f87a90dd995960283b812b921

[ "MIT" ]

15

2021-10-04T15:18:33.000Z

2022-03-14T13:58:27.000Z

xugrid/data/synthetic.py

Deltares/xugrid

41881977e5e49d0f87a90dd995960283b812b921

[ "MIT" ]

10

2021-11-10T15:12:02.000Z

2022-02-10T14:35:57.000Z

xugrid/data/synthetic.py

Deltares/xugrid

41881977e5e49d0f87a90dd995960283b812b921

[ "MIT" ]

null

import meshzoo import numpy as np import xarray as xr import xugrid def transform(vertices, minx, maxx, miny): """ Transform vertices to fit within minx to maxx. Maintains x:y aspect ratio. """ x, y = vertices.T xmin = x.min() xmax = x.max() ymin = y.min() ymax = y.max() dx = xmax - xmin dy = ymax - ymin new_dx = maxx - minx new_dy = dy / dx * new_dx x = (x - xmin) * new_dx / dx + minx y = (y - ymin) * new_dy / dy + miny return np.column_stack([x, y])

27.985294

105

0.527063

88c491334363ecfc6c68f1628bee0883ccd9f9e7

898

py

Python

calendar_generator/migrations/0006_resetday.py

rectory-school/rectory-apps-updated

a6d47f6d5928f0c816eb45fd229da2f9f2fa2ff1

[ "MIT" ]

null

calendar_generator/migrations/0006_resetday.py

rectory-school/rectory-apps-updated

a6d47f6d5928f0c816eb45fd229da2f9f2fa2ff1

[ "MIT" ]

30

2021-07-16T12:54:14.000Z

2021-12-24T16:59:04.000Z

calendar_generator/migrations/0006_resetday.py

rectory-school/rectory-apps-updated

a6d47f6d5928f0c816eb45fd229da2f9f2fa2ff1

[ "MIT" ]

null

# Generated by Django 3.2.5 on 2021-07-19 20:22 from django.db import migrations, models import django.db.models.deletion

33.259259

127

0.601336

88c4cf9f6f8d805d5af7d3c164350e7934f1fcde

2,492

py

Python

ckanext/tess/group.py

ElixirUK/ckanext-tess

01725ff81b74f31d906875cb0cf493e7d3533615

[ "BSD-3-Clause" ]

1

2015-05-18T08:31:28.000Z

ckanext/tess/group.py

ElixirUK/ckanext-tess

01725ff81b74f31d906875cb0cf493e7d3533615

[ "BSD-3-Clause" ]

null

ckanext/tess/group.py

ElixirUK/ckanext-tess

01725ff81b74f31d906875cb0cf493e7d3533615

[ "BSD-3-Clause" ]

null

import ckan.plugins as plugins import ckan.model as model import ckan.logic as logic import ckan.plugins.toolkit as toolkit import ckan.lib.plugins as plugs from pylons import c NotFound = logic.NotFound get_action = logic.get_action

33.675676

123

0.656902

88c6dfcf5b1c5b830035587feb18704990928ca6

2,087

py

Python

package/spack-discovardenovo/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

1

2018-07-17T07:45:09.000Z

package/spack-discovardenovo/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

null

package/spack-discovardenovo/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

null

############################################################################## # Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC. # Produced at the Lawrence Livermore National Laboratory. # # This file is part of Spack. # Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. # LLNL-CODE-647188 # # For details, see https://github.com/spack/spack # Please also see the NOTICE and LICENSE files for our notice and the LGPL. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License (as # published by the Free Software Foundation) version 2.1, February 1999. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and # conditions of the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ############################################################################## from spack import *

45.369565

115

0.689506

88c7e5dd6da0bf02bd4f18142f4b9f76bc68b52c

10,379

py

Python

s2_convert.py

uscensusbureau/SABLE

883d449e4e6b75636d2589f540e86a5401e09932

[ "CC0-1.0" ]

27

2017-11-06T22:55:24.000Z

2021-06-11T12:56:03.000Z

s2_convert.py

uscensusbureau/SABLE

883d449e4e6b75636d2589f540e86a5401e09932

[ "CC0-1.0" ]

1

2018-01-31T18:26:23.000Z

s2_convert.py

uscensusbureau/SABLE

883d449e4e6b75636d2589f540e86a5401e09932

[ "CC0-1.0" ]

8

2017-10-05T19:17:05.000Z

2020-10-21T23:08:34.000Z

#Name: s2_convert.py #Purpose: Convert PDFs to TXT format #Invocation: python3 s2_convert.py <projName> <lng> <clss> import codecs import os import re import sys #Name: valid_arguments #Purpose: Check whether the command-line arguments are valid #Parameters: sys.argv (globally defined list of command-line arguments) #Returns: True (arguments are valid) or False (arguments are invalid) #Name: match_page #Purpose: Match line to an XML page tag #Parameters: line (line of text from XML file) #Returns: Regular expression match object #Name: match_textbox #Purpose: Match line to an XML textbox tag #Parameters: line (line of text from XML file) #Returns: Regular expression match object #Name: match_textline #Purpose: Match line to an XML textline tag #Parameters: line (line of text from XML file) #Returns: Regular expression match object #Name: match_text #Purpose: Match line to an XML text tag #Parameters: line (line of text from XML file) #Returns: Regular expression match object #Name: clean_char #Purpose: Clean character to deal with punctuation, numbers, and foreign accent marks #Parameters: old (character) #Returns: Cleaned character #Name: get_chars #Purpose: Extract the character values, coordinates, hierarchy, and font information from XML file #Parameters: xmlFile (location of XML file) #Returns: List of tuples (one for each character) containing character data #Name: clean_text #Purpose: Clean string of text and check each word against a list of stop words #Parameters: text (string of text) #Returns: Cleaned text #Name: write_text #Purpose: Construct words character by character #Parameters: chars (list of tuples) # txtFile (location of TXT file) #Returns: #Name: create_output #Purpose: Convert a PDF document of a given class to TXT format #Parameters: projName (project name) # clss ("pos" or "neg") # docName (document name) #Returns: #Name: convert_files #Purpose: Convert PDFs to TXT format #Parameters: projName (project name) # lng (language) # clss ("neg", "pos", or "pred") #Returns: if __name__ == "__main__": main()

34.946128

169

0.544561

88cc5b4b5dc736d6b8f2e83c34762ada5532c58b

200

py

Python

sdata_experiments/tension_test.py

lepy/sdata_experiments

84aa32ec497639a30d4f71c7fa9e463cf2d8f037

[ "MIT" ]

1

2020-12-21T18:28:00.000Z

sdata_experiments/tension_test.py

lepy/sdata_experiments

84aa32ec497639a30d4f71c7fa9e463cf2d8f037

[ "MIT" ]

null

sdata_experiments/tension_test.py

lepy/sdata_experiments

84aa32ec497639a30d4f71c7fa9e463cf2d8f037

[ "MIT" ]

null

# -*-coding: utf-8-*- from sdata.experiments import Test

22.222222

48

0.63

88cc892509c73d91def940743039ba5fd8a12e2f

5,818

py

Python

copy-net3_main0_1_2_20-01-10_14-12-14_epoch600_lr0-3_decay0-0_decay20-0_decay39e-05_seed0/main0.py

ninfueng/nsn

a214eafbcf5cf6dedb57131bc6eb1d307797f2ab

[ "MIT" ]

null

copy-net3_main0_1_2_20-01-10_14-12-14_epoch600_lr0-3_decay0-0_decay20-0_decay39e-05_seed0/main0.py

ninfueng/nsn

a214eafbcf5cf6dedb57131bc6eb1d307797f2ab

[ "MIT" ]

null

copy-net3_main0_1_2_20-01-10_14-12-14_epoch600_lr0-3_decay0-0_decay20-0_decay39e-05_seed0/main0.py

ninfueng/nsn

a214eafbcf5cf6dedb57131bc6eb1d307797f2ab

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """The code implementation of SharedGradNet. main0.py is for neural networks without hidden layer. Some part from: https://jhui.github.io/2018/02/09/PyTorch-Variables-functionals-and-Autograd/ 2019/06/17: Update with hyper-parameter tuning script. 2019/06/25: Committed main0.py. """ __author__ = 'Ninnart Fuengfusin' __version__ = '0.0.1' __email__ = 'ninnart.fuengfusin@yahoo.com' import os import time import logging import argparse import torch import torch.nn as nn import model from weight_decay import * from dataset import load_dataset from utils import * from recorder import Recorder from updater import UpdateMomentum from namer import namer parser = argparse.ArgumentParser(description='PyTorch implementation of SharedGradNet.') parser.add_argument('--epoch', '-e', type=int, default=600, help='Number of training epoch.') parser.add_argument('--learning_rate', '-lr', type=float, default=3e-1, help='A floating for initial learning rate.') parser.add_argument('--train_batch', type=int, default=128, help='A integer for train batch amount.') parser.add_argument('--test_batch', type=int, default=128, help='A integer for test batch amount') parser.add_argument('--num_neuron', type=int, default=784, help='Number of neurons in fully connected layer for produce codes') parser.add_argument('--weight_decay', type=float, default=0, help='A floating for weight decay.') parser.add_argument('--load', type=str2bool, default=False, help='A boolean for loading weights from load_location or not.') parser.add_argument('--load_location', type=str, default='model1-baseline', help='A string of location for loading weights.') parser.add_argument('--seed', '-s', type=int, default=0, help='An integer for initialization randomness.') args = parser.parse_args() if __name__ == '__main__': save_loc = namer( f'epoch{args.epoch}', f'lr{args.learning_rate}', f'decay{args.weight_decay}', f'seed{args.seed}') set_logger(os.path.join(os.getcwd(), save_loc), 'info.log') logging.info(__doc__) logging.info(args) set_printoptions() seed_everywhere_torch(args.seed) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') record = Recorder('test_acc', 'test_acc2', 'test_acc3', 'test_loss', 'test_loss2', 'test_loss3') train_loader, test_loader, img_size = load_dataset( num_train_batch=args.train_batch, num_test_batch=args.test_batch, num_extra_batch=0, num_worker=8, dataset='mnist') model1 = model.NetworkWithSub1() updaterW1_1 = UpdateMomentum() updaterB1_1 = UpdateMomentum() model1.to(device) BETA = 0.9 t1 = time.time() for i in range(args.epoch): # Accumulating variables. total_train_loss = 0 train_correct = 0 train_total = 0 total_test_loss = 0 test_correct = 0 test_total = 0 model1.train() args.learning_rate = args.learning_rate/3 if i % 200 == 0 and i != 0 else args.learning_rate for train_data, train_label in train_loader: model1.zero_grad() train_data, train_label = train_data.to(device), train_label.to(device) train_output = model1.forward(train_data) train_loss = nn.CrossEntropyLoss()( train_output, train_label) #+ l2_weight_decay(args.weight_decay2, model2.w1) train_loss.backward() total_train_loss += train_loss.item() _, train_predicted = torch.max(train_output.data, 1) train_correct += (train_predicted == train_label).sum().item() train_total += train_label.data.size(0) model1.w1.data = updaterW1_1.update( model1.w1.data, BETA, args.learning_rate, model1.w1.grad.data) model1.b1.data = updaterB1_1.update( model1.b1.data, BETA, args.learning_rate, model1.b1.grad.data) logging.info(f'Epoch: {i + 1}') logging.info(f'Train Accuracy: {train_correct/train_total}, \nLoss: {total_train_loss/train_total}') with torch.no_grad(): model1.eval() for test_data, test_label in test_loader: test_data, test_label = test_data.to(device), test_label.to(device) test_output = model1.forward(test_data) test_loss = nn.CrossEntropyLoss()(test_output, test_label) total_test_loss += test_loss.item() _, test_predicted = torch.max(test_output.data, 1) test_correct += (test_predicted == test_label).sum().item() test_total += test_label.data.size(0) if record.more_than_highest('test_acc', test_correct/test_total): save_model(model1, os.path.join(os.getcwd(), save_loc, 'checkpoint.pth')) logging.info(f'Save model') t2 = time.time() - t1 logging.info(f'Test Accuracy: {test_correct/test_total}, \nLoss: {total_test_loss/test_total}') record.record('test_acc', test_correct/test_total) logging.info(f'Learning rate {args.learning_rate}') logging.info(f'Timer: {to_hhmmss(t2)}') logging.info(f'=====================================================================================') record.save_all(os.path.join(os.getcwd(), save_loc)) logging.info(f'best test_acc: {record.highest("test_acc")}') logging.info(f'model1:w1 = {model1.w1.data}') record.plot( 'test_acc', save=True, save_loc=os.path.join(os.getcwd(), save_loc, 'test_acc.png')) np.savetxt( os.path.join(os.getcwd(), save_loc, f'{record.highest("test_acc")}.txt'), record.highest("test_acc"), delimiter=',')

46.174603

117

0.653489

88cc9bf2217df7c1469f0332dd2da660bb7b852c

19,144

py

Python

src/win/efuseWin_BootCfg2.py

KKKelvin7/NXP-MCUBootUtility

b4cded82aa9d20c2cb718f60493d1226fc999b43

[ "Apache-2.0" ]

null

src/win/efuseWin_BootCfg2.py

KKKelvin7/NXP-MCUBootUtility

b4cded82aa9d20c2cb718f60493d1226fc999b43

[ "Apache-2.0" ]

null

src/win/efuseWin_BootCfg2.py

KKKelvin7/NXP-MCUBootUtility

b4cded82aa9d20c2cb718f60493d1226fc999b43

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ########################################################################### ## Python code generated with wxFormBuilder (version Aug 8 2018) ## http://www.wxformbuilder.org/ ## ## PLEASE DO *NOT* EDIT THIS FILE! ########################################################################### import wx import wx.xrc ########################################################################### ## Class efuseWin_BootCfg2 ###########################################################################

48.465823

190

0.729994

88cd7b4748dfc9d48b07e74cd1faaed730733d74

55

py

Python

python.py

Ayesha-Anjum-639/assignment

5a57fdfd360467d540cf12fe0f842ddd458371b8

[ "MIT" ]

1

2019-10-12T17:28:12.000Z

python.py

Ayesha-Anjum-639/assignment

5a57fdfd360467d540cf12fe0f842ddd458371b8

[ "MIT" ]

null

python.py

Ayesha-Anjum-639/assignment

5a57fdfd360467d540cf12fe0f842ddd458371b8

[ "MIT" ]

null

print("Hello World") print(5+4) print(5,"+",4,"=",5+4)

13.75

22

0.563636

88cd952c28db68aa348176e890db4ed64336fc66

332

py

Python

hud_api_replace/management/commands/delete_expired_geodata.py

Ooblioob/django-hud

66aac9f182f8c68a380dc36ade46d7e0df30bf24

[ "CC0-1.0" ]

10

2015-01-05T21:13:07.000Z

2017-05-28T04:08:15.000Z

hud_api_replace/management/commands/delete_expired_geodata.py

Ooblioob/django-hud

66aac9f182f8c68a380dc36ade46d7e0df30bf24

[ "CC0-1.0" ]

15

2015-01-29T02:08:39.000Z

2018-01-17T13:24:50.000Z

hud_api_replace/management/commands/delete_expired_geodata.py

Ooblioob/django-hud

66aac9f182f8c68a380dc36ade46d7e0df30bf24

[ "CC0-1.0" ]

34

2015-01-26T19:34:47.000Z

2021-02-20T10:55:22.000Z

from django.core.management.base import BaseCommand, CommandError import time from hud_api_replace.models import CachedGeodata

27.666667

77

0.759036

88cfb3f12d8dd1b1dd5417858d82ba8a891b227e

7,694

py

Python

etc/dbus-serialbattery/battery.py

Carstijn/dbus-serialbattery

23afec33c2fd87fd4d4c53516f0a25f290643c82

[ "MIT" ]

null

etc/dbus-serialbattery/battery.py

Carstijn/dbus-serialbattery

23afec33c2fd87fd4d4c53516f0a25f290643c82

[ "MIT" ]

null

etc/dbus-serialbattery/battery.py

Carstijn/dbus-serialbattery

23afec33c2fd87fd4d4c53516f0a25f290643c82

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function, unicode_literals import utils

35.786047

101

0.618144

88d2d07a327a91f5956c30f18f5820061fc0b593

16,227

py

Python

lumicks/pylake/kymotracker/detail/binding_times.py

lumicks/pylake

b5875d156d6416793a371198f3f2590fca2be4cd

[ "Apache-2.0" ]

8

2019-02-18T07:56:39.000Z

2022-03-19T01:14:48.000Z

lumicks/pylake/kymotracker/detail/binding_times.py

lumicks/pylake

b5875d156d6416793a371198f3f2590fca2be4cd

[ "Apache-2.0" ]

42

2018-11-30T14:40:35.000Z

2022-03-29T11:43:45.000Z

lumicks/pylake/kymotracker/detail/binding_times.py

lumicks/pylake

b5875d156d6416793a371198f3f2590fca2be4cd

[ "Apache-2.0" ]

4

2019-01-09T13:45:53.000Z

2021-07-06T14:06:52.000Z

import numpy as np from scipy.special import logsumexp from scipy.optimize import minimize from functools import partial from dataclasses import dataclass, field import matplotlib.pyplot as plt def exponential_mixture_log_likelihood_components( amplitudes, lifetimes, t, min_observation_time, max_observation_time ): """Calculate each component of the log likelihood of an exponential mixture distribution. The full log likelihood for a single observation is given by: log(L) = log( sum_i( component_i ) ) with the output of this function being log(component_i) defined as: log(component_i) = log(a_i) - log(N) + log(tau_i) - t/tau_i where a_i and tau_i are the amplitude and lifetime of component i and N is a normalization factor that takes into account the minimum and maximum observation times of the experiment: N = sum_i { a_i * [ exp(-t_min / tau_i) - exp(-t_max / tau_i) ] } Therefore, the full log likelihood is calculated from the output of this function by applying logsumexp(output, axis=0) where the summation is taken over the components. Parameters ---------- amplitudes : array_like fractional amplitude parameters for each component lifetimes : array_like lifetime parameters for each component in seconds t : array_like dwelltime observations in seconds min_observation_time : float minimum observation time in seconds max_observation_time : float maximum observation time in seconds """ amplitudes = amplitudes[:, np.newaxis] lifetimes = lifetimes[:, np.newaxis] t = t[np.newaxis, :] norm_factor = np.log(amplitudes) + np.log( np.exp(-min_observation_time / lifetimes) - np.exp(-max_observation_time / lifetimes) ) log_norm_factor = logsumexp(norm_factor, axis=0) return -log_norm_factor + np.log(amplitudes) - np.log(lifetimes) - t / lifetimes def exponential_mixture_log_likelihood(params, t, min_observation_time, max_observation_time): """Calculate the log likelihood of an exponential mixture distribution. The full log likelihood for a single observation is given by: log(L) = log( sum_i( exp( log(component_i) ) ) ) where log(component_i) is output from `exponential_mixture_log_likelihood_components()` Parameters ---------- amplitudes : array_like fractional amplitude parameters for each component lifetimes : array_like lifetime parameters for each component in seconds t : array_like dwelltime observations in seconds min_observation_time : float minimum observation time in seconds max_observation_time : float maximum observation time in seconds """ params = np.reshape(params, (2, -1)) components = exponential_mixture_log_likelihood_components( params[0], params[1], t, min_observation_time, max_observation_time ) log_likelihood = logsumexp(components, axis=0) return -np.sum(log_likelihood) def _kinetic_mle_optimize( n_components, t, min_observation_time, max_observation_time, initial_guess=None ): """Calculate the maximum likelihood estimate of the model parameters given measured dwelltimes. Parameters ---------- n_components : int number of components in the mixture model t : array_like dwelltime observations in seconds min_observation_time : float minimum observation time in seconds max_observation_time : float maximum observation time in seconds initial_guess : array_like initial guess for the model parameters ordered as [amplitude1, amplitude2, ..., lifetime1, lifetime2, ...] """ if np.any(np.logical_or(t < min_observation_time, t > max_observation_time)): raise ValueError( "some data is outside of the bounded region. Please choose" "appropriate values for `min_observation_time` and/or `max_observation_time`." ) cost_fun = partial( exponential_mixture_log_likelihood, t=t, min_observation_time=min_observation_time, max_observation_time=max_observation_time, ) if initial_guess is None: initial_guess_amplitudes = np.ones(n_components) / n_components initial_guess_lifetimes = np.mean(t) * np.arange(1, n_components + 1) initial_guess = np.hstack([initial_guess_amplitudes, initial_guess_lifetimes]) bounds = ( *[(np.finfo(float).eps, 1) for _ in range(n_components)], *[(min_observation_time * 0.1, max_observation_time * 1.1) for _ in range(n_components)], ) constraints = {"type": "eq", "fun": lambda x, n: 1 - sum(x[:n]), "args": [n_components]} result = minimize( cost_fun, initial_guess, method="SLSQP", bounds=bounds, constraints=constraints ) return BindingDwelltimes( n_components, t, (min_observation_time, max_observation_time), result.x, -result.fun )

38.913669

101

0.638442

88d4df17e09f04b37643b41464da40650a4a1467

302

py

Python

ExPython/CursoemVideo/ex011.py

MatheusEwen/Exercicios_Do_CursoDePython

7c21f95f0d31ec1e6c17e1b957c1442843118c48

[ "MIT" ]

1

2020-10-06T00:27:05.000Z

ExPython/CursoemVideo/ex011.py

MatheusEwen/Exercicios_Do_CursoDePython

7c21f95f0d31ec1e6c17e1b957c1442843118c48

[ "MIT" ]

null

ExPython/CursoemVideo/ex011.py

MatheusEwen/Exercicios_Do_CursoDePython

7c21f95f0d31ec1e6c17e1b957c1442843118c48

[ "MIT" ]

null

largura=float(input('digite qual a largura da sua parede:')) comprimento=float(input('digite qual o comprimento da sua parede:')) quantidade=((largura*comprimento)/2) print('A area da sua parede de',(largura*comprimento),'para pintar sua parede ser necessario {} litros de tinta'.format(quantidade))

75.5

135

0.771523

88da52cb71a753a4cfdc782a27d2b76618927365

2,821

py

Python

build/fbcode_builder_config.py

YangKian/LogDevice

e5c2168c11e9de867a1bcf519f95016e1c879b5c

[ "BSD-3-Clause" ]

1,831

2018-09-12T15:41:52.000Z

2022-01-05T02:38:03.000Z

build/fbcode_builder_config.py

YangKian/LogDevice

e5c2168c11e9de867a1bcf519f95016e1c879b5c

[ "BSD-3-Clause" ]

183

2018-09-12T16:14:59.000Z

2021-12-07T15:49:43.000Z

build/fbcode_builder_config.py

YangKian/LogDevice

e5c2168c11e9de867a1bcf519f95016e1c879b5c

[ "BSD-3-Clause" ]

228

2018-09-12T15:41:51.000Z

2022-01-05T08:12:09.000Z

#!/usr/bin/env python # Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. from __future__ import absolute_import, division, print_function, unicode_literals import specs.fizz as fizz import specs.fmt as fmt import specs.folly as folly import specs.sodium as sodium import specs.wangle as wangle import specs.zstd as zstd from shell_quoting import ShellQuoted "fbcode_builder steps to build & test LogDevice" """ Running this script from the command line on a dev-server: 1. Ensure you have the HTTP proxy configured in environment 2. This is env items is not compatible with the scutil create call, so must not be permenently exported. git config --global http.proxy http://fwdproxy:8080 cd .../fbcode/logdevice/public_tld/build HTTP_PROXY=http://fwdproxy:8080 HTTPS_PROXY=http://fwdproxy:8080 \ fbcode/opensource/fbcode_builder/facebook_make_legocastle_job.py \ | scutil create Which outputs a legocastle job to stdout; to be fed into scutil create ... """ config = { "github_project": "facebookincubator/LogDevice", "fbcode_builder_spec": fbcode_builder_spec, }

31.344444

87

0.684864

88da8643ac576a43d9d46ba217a9282045a5ba68

3,040

py

Python

setup.py

ikalnytskyi/em

3e7c20c16814bed86409212e0a2a9fee49398994

[ "BSD-3-Clause" ]

null

setup.py

ikalnytskyi/em

3e7c20c16814bed86409212e0a2a9fee49398994

[ "BSD-3-Clause" ]

1

2018-10-18T17:49:13.000Z

setup.py

ikalnytskyi/em

3e7c20c16814bed86409212e0a2a9fee49398994

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # coding: utf-8 """ Em -- Em is a terminal tool that prints FILE(s), or standard input to standard output and highlights the expressions that are matched the PATTERN. The expression will be highlighted iff the terminal is ANSI-compatible. Em is Cool `````````` .. code:: bash $ tail -f /path/to/log | em "DEBUG|INFO" -f green | em "WARN" Links ````` * `documentation <http://em.readthedocs.org/>`_ * `source code <https://github.com/ikalnitsky/em>`_ """ import os import glob import subprocess from setuptools import setup, Command install_requires = [] try: import argparse # NOQA except ImportError: install_requires.append('argparse') setup( name='em', version='0.4.0', url='https://github.com/ikalnitsky/em', license='BSD', author='Igor Kalnitsky', author_email='igor@kalnitsky.org', description="Highlight some PATTERN in terminal's STDOUT", long_description=__doc__, include_package_data=True, packages=[ 'em', 'em.tests', ], install_requires=install_requires, test_suite='em.tests', entry_points={ 'console_scripts': ['em = em:main'], }, classifiers=[ 'Topic :: Utilities', 'Environment :: Console', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'License :: OSI Approved :: BSD License', ], platforms=['Linux', 'MacOS', 'Unix'], # add custom commands to manage locale files cmdclass={ 'locale_update': LocaleUpdate, 'locale_compile': LocaleCompile, }, )

23.937008

78

0.593421

88dac5f28a975211597a7acd699981246fdfddd1

1,883

py

Python

Python-Programs/Discord-bot-Motivation Bot/main.py

adityaverma121/Simple-Programs

8450560b97f89e0fa3da16a623ad35c0b26409c9

[ "MIT" ]

71

2021-09-30T11:25:12.000Z

2021-10-03T11:33:22.000Z

Python-Programs/Discord-bot-Motivation Bot/main.py

adityaverma121/Simple-Programs

8450560b97f89e0fa3da16a623ad35c0b26409c9

[ "MIT" ]

186

2021-09-30T12:25:16.000Z

2021-10-03T13:45:04.000Z

Python-Programs/Discord-bot-Motivation Bot/main.py

adityaverma121/Simple-Programs

8450560b97f89e0fa3da16a623ad35c0b26409c9

[ "MIT" ]

385

2021-09-30T11:34:23.000Z

2021-10-03T13:41:00.000Z

import json import os import random import string import requests from keep_alive import keep_alive from nltk.sentiment.vader import SentimentIntensityAnalyzer import discord client = discord.Client() starter_motivator = [ "Cheer Up!", "Always remember, I am here for you!", "You are a great person. Remember this!", "Think positive man! There is always a bright side!", "What about you watching a funny video to swing the mood?", ] keep_alive() client.run(os.environ["TOKEN"])

25.445946

153

0.630377

88debece08c1e96c24b4f8a33cbf44d5d6611a9b

1,655

py

Python

Malaria-Cell-Analyzer/mysite/core/gen_dataset_completed.py

suryabranwal/Malaria-Cell-Analyzer

eadbad1e0b5a51eeeb43fa75367d8b4d9eabe033

[ "MIT" ]

1

2019-12-20T18:04:40.000Z

Malaria-Cell-Analyzer/mysite/core/gen_dataset_completed.py

suryabranwal/Malaria-Cell-Analyzer

eadbad1e0b5a51eeeb43fa75367d8b4d9eabe033

[ "MIT" ]

null

Malaria-Cell-Analyzer/mysite/core/gen_dataset_completed.py

suryabranwal/Malaria-Cell-Analyzer

eadbad1e0b5a51eeeb43fa75367d8b4d9eabe033

[ "MIT" ]

1

2019-11-26T14:06:03.000Z

import cv2, os import numpy as np import csv import glob label = "Parasitized" dirList = glob.glob("cell_images/" + label + "/*.png") file = open("csv/dataset.csv", "a") for img_path in dirList: im = cv2.imread(img_path) im = cv2.GaussianBlur(im, (5, 5), 2) im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(im_gray, 127, 255, 0) contours, _ = cv2.findContours(thresh, 1, 2) for contour in contours: cv2.drawContours(im_gray, contours, -1, (0, 255, 0), 3) #cv2.imshow("window", im_gray) #break file.write(label) file.write(",") for i in range(5): try: area = cv2.contourArea(contours[i]) file.write(str(area)) except: file.write("0") file.write(",") file.write("\n") cv2.waitKey(19000) label = "Uninfected" dirList = glob.glob("cell_images/" + label + "/*.png") file = open("csv/dataset.csv", "a") for img_path in dirList: im = cv2.imread(img_path) im = cv2.GaussianBlur(im, (5, 5), 2) im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(im_gray, 127, 255, 0) contours, _ = cv2.findContours(thresh, 1, 2) for contour in contours: cv2.drawContours(im_gray, contours, -1, (0, 255, 0), 3) #cv2.imshow("window", im_gray) #break file.write(label) file.write(",") for i in range(5): try: area = cv2.contourArea(contours[i]) file.write(str(area)) except: file.write("0") if i != 4: file.write(",") file.write("\n") cv2.waitKey(19000)

18.388889

63

0.575227

88df26756f4d2511b8925b4ee5ec1ed8cec09d0b

1,234

py

Python

utils.py

wangke0809/learn-statistical-learning-method

10772659ff52ef64e7ff36dd3b701615e58de335

[ "MIT" ]

null

utils.py

wangke0809/learn-statistical-learning-method

10772659ff52ef64e7ff36dd3b701615e58de335

[ "MIT" ]

null

utils.py

wangke0809/learn-statistical-learning-method

10772659ff52ef64e7ff36dd3b701615e58de335

[ "MIT" ]

null

import os import numpy as np

29.380952

63

0.638574

88df286145e7e147b9e1130f9bc8b72465b90272

180

py

Python

mysite/iic/views.py

sandeepkeshav/iicj

c457fec176918af500699c8607f1d8cfc7543f1d

[ "MIT" ]

1

2020-11-25T17:02:25.000Z

mysite/iic/views.py

sandeepkeshav/iicj

c457fec176918af500699c8607f1d8cfc7543f1d

[ "MIT" ]

null

mysite/iic/views.py

sandeepkeshav/iicj

c457fec176918af500699c8607f1d8cfc7543f1d

[ "MIT" ]

1

2020-11-25T17:01:33.000Z

from __future__ import unicode_literals from django.shortcuts import render from django.http import HttpResponse

25.714286

43

0.811111

88e15f87d60572f7354fdd0f31ccffeddc289a42

2,210

py

Python

src/analysis.py

aliFrancis/mars-crater-catalogue

5e6ac4e1f7967b1d37d95e436edaa31ef2f2ed55

[ "CC-BY-4.0" ]

null

src/analysis.py

aliFrancis/mars-crater-catalogue

5e6ac4e1f7967b1d37d95e436edaa31ef2f2ed55

[ "CC-BY-4.0" ]

null

src/analysis.py

aliFrancis/mars-crater-catalogue

5e6ac4e1f7967b1d37d95e436edaa31ef2f2ed55

[ "CC-BY-4.0" ]

null

import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sn from utils import convert, iou if __name__ == '__main__': import os import sys survey_dir = sys.argv[1] paths = [os.path.join(survey_dir,path) for path in os.listdir(survey_dir)] surveys = [convert.xml2df(p) for p in paths] print('\nANALYSIS OF {}'.format(os.path.basename(survey_dir)),'\n') print(' NO. OF ANNOTATIONS') print(' ------------------') for survey,path in zip(surveys,paths): print(' ',os.path.basename(path).replace('.xml','')+':',len(survey)) total_survey = convert.dfs2df(surveys) print(' ____________') print(' TOTAL :',len(total_survey)) print('\n') group_binary_IOUs, group_IOUs = group_IOU_matrices(paths) print(' MEAN IoU') print(' --------') for i,path in enumerate(paths): print(' ',os.path.basename(path).replace('.xml','')+':',np.round(group_IOUs[i],4)) print(' ____________') print(' MEAN :',np.round(np.mean(group_IOUs),4)) print('\n') print('\n MEAN BINARY IoU (IoU treated as 1 if above 0.5)') print(' -----------------------------------------------') for i,path in enumerate(paths): print(' ',os.path.basename(path).replace('.xml','')+':',np.round(group_binary_IOUs[i],4)) print(' ____________') print(' MEAN :',np.round(np.mean(group_binary_IOUs),4)) print('\n')

34.53125

97

0.608597

88e195f25d51cac398d0cfdc139ce3c46cd5aeca

257

py

Python

prototype/zlua_prototype/tests/test_debugger.py

Zolo-mario/ZoloLua

7527a78b12c3f97cb729327d4d0c724f3dba17f9

[ "MIT" ]

9

2019-03-11T04:43:03.000Z

2019-05-12T08:33:31.000Z

prototype/zlua_prototype/tests/test_debugger.py

zoloypzuo/ZeloLua

7527a78b12c3f97cb729327d4d0c724f3dba17f9

[ "MIT" ]

2

2019-04-10T05:20:45.000Z

2019-06-02T13:56:39.000Z

prototype/zlua_prototype/tests/test_debugger.py

Zolo-mario/zlua

7527a78b12c3f97cb729327d4d0c724f3dba17f9

[ "MIT" ]

1

2021-12-29T03:13:49.000Z

from unittest import TestCase

25.7

56

0.657588

88e45fe24cb4ac33e12b90a494c738b76fd18630

3,033

py

Python

scripts/test_tensorflow_spectrogram.py

RichardLitt/Vesper

5360844f42a06942e7684121c650b08cf8616285

[ "MIT" ]

29

2017-07-10T14:49:15.000Z

2022-02-02T23:14:38.000Z

scripts/test_tensorflow_spectrogram.py

Tubbz-alt/Vesper

76e5931ca0c7fbe070c53b1362ec246ec9007beb

[ "MIT" ]

167

2015-03-17T14:45:22.000Z

2022-03-30T21:00:05.000Z

scripts/test_tensorflow_spectrogram.py

Tubbz-alt/Vesper

76e5931ca0c7fbe070c53b1362ec246ec9007beb

[ "MIT" ]

4

2015-02-06T03:30:27.000Z

2020-12-27T08:38:52.000Z

""" Compares spectrogram computations with TensorFlow and Vesper. As of 2018-11-09, Vesper is a little more than three times faster than TensorFlow at computing spectrograms with a DFT size of 128. """ import functools import time import numpy as np import tensorflow as tf import vesper.util.data_windows as data_windows import vesper.util.time_frequency_analysis_utils as tfa_utils SHOW_SPECTROGRAMS = False SAMPLE_RATE = 24000 # Hertz AMPLITUDE = 1 FREQUENCY = 3000 # Hertz DURATION = 1000 # seconds WINDOW_SIZE = .005 # seconds HOP_SIZE = .5 # fraction of window size if SHOW_SPECTROGRAMS: SAMPLE_RATE = 1 FREQUENCY = .25 DURATION = 8 WINDOW_SIZE = 8 HOP_SIZE = 1 if __name__ == '__main__': main()

26.146552

78

0.672272

88e5027947cf3cde1a9b3d2b5bfc1dfa177d0b10

110

py

Python

html/semantics/embedded-content/media-elements/loading-the-media-resource/resources/delayed-broken-video.py

ziransun/wpt

ab8f451eb39eb198584d547f5d965ef54df2a86a

[ "BSD-3-Clause" ]

777

2017-08-29T15:15:32.000Z

2022-03-21T05:29:41.000Z

html/semantics/embedded-content/media-elements/loading-the-media-resource/resources/delayed-broken-video.py

ziransun/wpt

ab8f451eb39eb198584d547f5d965ef54df2a86a

[ "BSD-3-Clause" ]

66

2017-08-30T18:31:18.000Z

2021-08-02T10:59:35.000Z

html/semantics/embedded-content/media-elements/loading-the-media-resource/resources/delayed-broken-video.py

ziransun/wpt

ab8f451eb39eb198584d547f5d965ef54df2a86a

[ "BSD-3-Clause" ]

123

2017-08-30T01:19:34.000Z

2022-03-17T22:55:31.000Z

import time

18.333333

49

0.663636

88e6289afe035056ed74d3dd93dfce6766f5f5c7

401

py

Python

tests/util.py

alexsilva/django-npm

8d5c55c0219fda074ceabdd93b3806e65a008d9e

[ "MIT" ]

null

tests/util.py

alexsilva/django-npm

8d5c55c0219fda074ceabdd93b3806e65a008d9e

[ "MIT" ]

null

tests/util.py

alexsilva/django-npm

8d5c55c0219fda074ceabdd93b3806e65a008d9e

[ "MIT" ]

1

2019-10-17T15:13:13.000Z

import os from django.conf import settings import django

21.105263

73

0.583541

88e644ee347cd6bc7d3ce925d9db807476d778e2

2,770

py

Python

Station A/Pooling M300/v1_station_a_S30_pooling.py

Opentrons/covid19-system-30

4db5980a93e87f9f607b727678b7ea6d528109ba

[ "Apache-2.0" ]

null

Station A/Pooling M300/v1_station_a_S30_pooling.py

Opentrons/covid19-system-30

4db5980a93e87f9f607b727678b7ea6d528109ba

[ "Apache-2.0" ]

null

Station A/Pooling M300/v1_station_a_S30_pooling.py

Opentrons/covid19-system-30

4db5980a93e87f9f607b727678b7ea6d528109ba

[ "Apache-2.0" ]

1

2020-07-29T14:52:28.000Z

from opentrons import protocol_api import json import os import math # metadata metadata = { 'protocolName': 'V1 S14 Station A MagMax', 'author': 'Nick <protocols@opentrons.com>', 'source': 'Custom Protocol Request', 'apiLevel': '2.4' } NUM_SAMPLES = 64 SAMPLE_VOLUME = 100 TIP_TRACK = False

31.123596

78

0.574729

88e81d74f7c94f8645b9faca1575d321f2ec48d6

2,683

py

Python

rlxnix/plugins/efish/baseline.py

jgrewe/relacsed_nix

8c5bf486018891baf886635608677faf2487fd48

[ "BSD-3-Clause" ]

null

rlxnix/plugins/efish/baseline.py

jgrewe/relacsed_nix

8c5bf486018891baf886635608677faf2487fd48

[ "BSD-3-Clause" ]

3

2021-11-04T17:32:35.000Z

2021-11-07T11:10:35.000Z

rlxnix/plugins/efish/baseline.py

jgrewe/relacsed_nix

8c5bf486018891baf886635608677faf2487fd48

[ "BSD-3-Clause" ]

null

import logging import numpy as np from .efish_ephys_repro import EfishEphys

33.962025

182

0.61312

88e86a165bc8af8615ecf882a5209b22de9aecc5

1,258

py

Python

blogs/migrations/0001_initial.py

daniil-lebedev/N3code-site

bda415bd2140f582ae22101b55f34fc2786d7b45

[ "MIT" ]

3

2021-05-30T18:26:40.000Z

2021-07-17T11:45:53.000Z

blogs/migrations/0001_initial.py

daniil-lebedev/N3code-site

bda415bd2140f582ae22101b55f34fc2786d7b45

[ "MIT" ]

5

2021-06-06T16:13:42.000Z

2022-02-10T12:10:23.000Z

blogs/migrations/0001_initial.py

daniil-lebedev/N3code-site

bda415bd2140f582ae22101b55f34fc2786d7b45

[ "MIT" ]

8

2021-06-02T15:00:46.000Z

2021-07-15T21:27:33.000Z

# Generated by Django 3.2.3 on 2021-05-17 15:31 from django.db import migrations, models import django.db.models.deletion

34

117

0.557234

88e9ebaa162edc8b9d8c063256bea5900e94971c

5,101

py

Python

Reuters/reuters.py

dheeraj7596/SCDV

e83fc81e1b59bebfa2fa1e334097caa44f9e7f48

[ "MIT" ]

60

2017-05-25T14:08:50.000Z

2022-02-04T19:29:44.000Z

Reuters/reuters.py

vgupta123/SCDV

329b13a413318262f1888d872d8e33b30217cbc7

[ "MIT" ]

2

2020-03-27T14:01:12.000Z

2020-07-16T14:33:31.000Z

Reuters/reuters.py

vgupta123/SCDV

329b13a413318262f1888d872d8e33b30217cbc7

[ "MIT" ]

19

2017-11-10T01:06:28.000Z

2021-09-25T19:31:25.000Z

# Reuters-21578 dataset downloader and parser # # Author: Eustache Diemert <eustache@diemert.fr> # http://scikit-learn.org/stable/auto_examples/applications/plot_out_of_core_classification.html # # Modified by @herrfz, get pandas DataFrame from the orig SGML # License: BSD 3 clause from __future__ import print_function import re import os.path import fnmatch import sgmllib import urllib import tarfile import itertools from pandas import DataFrame ############################################################################### # Reuters Dataset related routines ############################################################################### def get_minibatch(doc_iter, size): """Extract a minibatch of examples, return a tuple X, y. Note: size is before excluding invalid docs with no topics assigned. """ data = [('{title}\n\n{body}'.format(**doc), doc['topics']) for doc in itertools.islice(doc_iter, size) if doc['topics']] if not len(data): return DataFrame([]) else: return DataFrame(data, columns=['text', 'tags'])

30.183432

96

0.556558

88eac0a7976441ef42ccc1c8a624876cda4f745b

7,908

py

Python

sims/ch4-effective-g1-duration/run-well-mixed-effective-g1-duration.py

ThomasPak/cell-competition

bb058d67e297d95c4c8ff2a0aea5b1fe5a82be09

[ "BSD-3-Clause" ]

null

sims/ch4-effective-g1-duration/run-well-mixed-effective-g1-duration.py

ThomasPak/cell-competition

bb058d67e297d95c4c8ff2a0aea5b1fe5a82be09

[ "BSD-3-Clause" ]

null

sims/ch4-effective-g1-duration/run-well-mixed-effective-g1-duration.py

ThomasPak/cell-competition

bb058d67e297d95c4c8ff2a0aea5b1fe5a82be09

[ "BSD-3-Clause" ]

null

import numpy as np import pandas as pd from scipy.stats import expon, uniform import sys sys.path.append('../../well_mixed') from well_mixed_death_clock import (WellMixedSimulator, WellMixedSimulationData, exponential_ccm, uniform_ccm, base_rate_death_signal) # Exponential cell cycle model tG1 = 50 tG2 = 50 # Constant base rate death signal f = base_rate_death_signal base_rate = 1 Tdeath_fun = lambda eta: eta * base_rate * tG1 # Simulation parameters tstart = 0 tend = np.inf max_cell_count = 1000 initial_cell_count = 64 num_eta = 10 num_iter = 100 # Arguments to f and ccm f_args = (base_rate,) ccm_args = (tG1,) # Helper function if __name__ == '__main__': # Exponential ccm parameter sweep etas = np.arange(4 / num_eta, 4 + 4 / num_eta, 4 / num_eta) # Generate parameters eta_data = [] for eta in etas: for i in range(num_iter): eta_data.append(eta) # If initial seed is given as command-line arguments, create seeds in # increments of 2 to avoid correlations between simulations because seed + # 1 is used for initial conditions. if len(sys.argv) == 2: initial_seed = int(sys.argv[1]) seed_data = np.arange(initial_seed, initial_seed + 2 * len(eta_data), 2) else: seed_data = [None] * len(eta_data) # Run simulations and postprocess data status_data = [] final_timestep_data = [] final_cell_count_data = [] num_divisions_data = [] num_deaths_data = [] average_time_in_G1_data = [] effective_g1_sample_size_data = [] for eta, seed in zip(eta_data, seed_data): sim_data = run_g1_truncation_exponential_simulation(eta, seed) status = sim_data.get_status() t_events = sim_data.get_t_events() cell_count = sim_data.get_cell_count() num_divisions = sim_data.get_num_divisions() num_deaths = sim_data.get_num_deaths() effective_time_in_G1 = sim_data.get_effective_time_in_G1() if status == 0: final_timestep = t_events[-1] else: final_timestep = t_events[-2] final_cell_count = cell_count[-1] average_time_in_G1 = np.mean(effective_time_in_G1) effective_g1_sample_size = len(effective_time_in_G1) status_data.append(status) final_timestep_data.append(final_timestep) final_cell_count_data.append(final_cell_count) num_divisions_data.append(num_divisions) num_deaths_data.append(num_deaths) average_time_in_G1_data.append(average_time_in_G1) effective_g1_sample_size_data.append(effective_g1_sample_size) # Create and write dataframe df = pd.DataFrame({ 'eta' : eta_data, 'seed' : seed_data, 'status' : status_data, 'final_timestep' : final_timestep_data, 'final_cell_count' : final_cell_count_data, 'num_divisions' : num_divisions_data, 'num_deaths' : num_deaths_data, 'average_time_in_G1' : average_time_in_G1_data, 'effective_g1_sample_size' : effective_g1_sample_size_data, }) df.to_csv('exponential-effective-g1-duration-data.csv', index_label='simulation_id') # Uniform ccm r_fun = lambda alpha: 2 * alpha * tG1 # Helper function if __name__ == '__main__': # Uniform ccm parameter sweep alphas = [0.3, 0.5, 0.7, 1.0] etas = np.arange(2 / num_eta, 2 + 2 / num_eta, 2 / num_eta) # Generate parameters alpha_data = [] eta_data = [] for alpha in alphas: for eta in etas: for i in range(num_iter): alpha_data.append(alpha) eta_data.append(eta) # If initial seed is given as command-line arguments, create seeds in # increments of 2 to avoid correlations between simulations because seed + # 1 is used for initial conditions. if len(sys.argv) == 2: initial_seed = int(sys.argv[1]) seed_data = np.arange(initial_seed, initial_seed + 2 * len(eta_data), 2) else: seed_data = [None] * len(eta_data) # Run simulations and postprocess data status_data = [] final_timestep_data = [] final_cell_count_data = [] num_divisions_data = [] num_deaths_data = [] average_time_in_G1_data = [] effective_g1_sample_size_data = [] for alpha, eta, seed in zip(alpha_data, eta_data, seed_data): sim_data = run_g1_truncation_uniform_simulation(alpha, eta, seed) status = sim_data.get_status() t_events = sim_data.get_t_events() cell_count = sim_data.get_cell_count() num_divisions = sim_data.get_num_divisions() num_deaths = sim_data.get_num_deaths() effective_time_in_G1 = sim_data.get_effective_time_in_G1() if status == 0: final_timestep = t_events[-1] else: final_timestep = t_events[-2] final_cell_count = cell_count[-1] average_time_in_G1 = np.mean(effective_time_in_G1) effective_g1_sample_size = len(effective_time_in_G1) status_data.append(status) final_timestep_data.append(final_timestep) final_cell_count_data.append(final_cell_count) num_divisions_data.append(num_divisions) num_deaths_data.append(num_deaths) average_time_in_G1_data.append(average_time_in_G1) effective_g1_sample_size_data.append(effective_g1_sample_size) # Create and write dataframe df = pd.DataFrame({ 'alpha' : alpha_data, 'eta' : eta_data, 'seed' : seed_data, 'status' : status_data, 'final_timestep' : final_timestep_data, 'final_cell_count' : final_cell_count_data, 'num_divisions' : num_divisions_data, 'num_deaths' : num_deaths_data, 'average_time_in_G1' : average_time_in_G1_data, 'effective_g1_sample_size' : effective_g1_sample_size_data, }) df.to_csv('uniform-effective-g1-duration-data.csv', index_label='simulation_id')

31.759036

88

0.682473

88ec6c26cd7a2f727e00f467fdd178e22cb46386

810

py

Python

hello/hello_sqlite.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

1

2017-10-23T14:58:47.000Z

hello/hello_sqlite.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

null

hello/hello_sqlite.py

East196/hello-py

a77c7a0c8e5e2b5e8cefaf0fda335ab0c3b1da21

[ "Apache-2.0" ]

1

2018-04-06T07:49:18.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # SQLite: import sqlite3 # SQLite # test.db # : conn = sqlite3.connect('hello.db') # Cursor: cursor = conn.cursor() cursor.execute('drop table user') # SQLuser: cursor.execute('create table user (id varchar(20) primary key, name varchar(20))') # SQL: cursor.execute('insert into user (id, name) values (\'1\', \'Michael\')') cursor.execute('insert into user (id, name) values (\'2\', \'Jackson\')') # rowcount: print(cursor.rowcount) # : print(cursor.execute('select * from user').fetchall()) print(cursor.execute('select * from user').fetchmany(size=1)) print(cursor.execute('select * from user').fetchone()) # Cursor: cursor.close() # : conn.commit() # Connection: conn.close()

26.129032

82

0.707407

88edaae7baa65ef0737db43dff89261e7016c55e

1,324

py

Python

ig_data/InstaSearch.py

swapnanildutta/instagram-search

919a3383f0f7789671108f899d9ba9092a69009f

[ "MIT" ]

1

2022-01-04T16:51:50.000Z

ig_data/InstaSearch.py

swapnanildutta/instagram-search

919a3383f0f7789671108f899d9ba9092a69009f

[ "MIT" ]

3

2020-10-26T13:31:05.000Z

2022-01-05T23:11:42.000Z

ig_data/InstaSearch.py

swapnanildutta/instagram-search

919a3383f0f7789671108f899d9ba9092a69009f

[ "MIT" ]

2

2020-04-07T09:24:07.000Z

2020-04-14T06:38:49.000Z

# imports import requests, json # beautifulsoup4 from bs4 import BeautifulSoup

33.948718

116

0.58006

88ef1b2b45df53d3ae9e2451d75c76436af81011

2,369

py

Python

tests/jax_ops_test.py

ita9naiwa/fast-soft-sort

72cbd93ecc229736f9e05bfdfd0f48c09432904f

[ "Apache-2.0" ]

389

2020-06-08T22:30:18.000Z

2022-03-25T23:04:28.000Z

tests/jax_ops_test.py

ita9naiwa/fast-soft-sort

72cbd93ecc229736f9e05bfdfd0f48c09432904f

[ "Apache-2.0" ]

14

2020-06-21T13:21:51.000Z

2021-10-18T18:02:07.000Z

tests/jax_ops_test.py

ita9naiwa/fast-soft-sort

72cbd93ecc229736f9e05bfdfd0f48c09432904f

[ "Apache-2.0" ]

32

2020-06-20T17:25:10.000Z

2022-03-26T13:34:23.000Z

# Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for jax_ops.py.""" import functools import itertools import unittest from absl.testing import absltest from absl.testing import parameterized import numpy as np import jax.numpy as jnp import jax from jax.config import config config.update("jax_enable_x64", True) from fast_soft_sort import jax_ops GAMMAS = (0.1, 1, 10.0) DIRECTIONS = ("ASCENDING", "DESCENDING") REGULARIZERS = ("l2", ) if __name__ == "__main__": absltest.main()

32.452055

80

0.71718

88ef5220f71a9bb493ff8c46c52c8179ab59c4c0

507

py

Python

server/accounts/migrations/0004_userdata_primaryfield.py

cristicismas/top-budget

d61db578287b2f77c12032045fca21e58c9ae1eb

[ "MIT" ]

null

server/accounts/migrations/0004_userdata_primaryfield.py

cristicismas/top-budget

d61db578287b2f77c12032045fca21e58c9ae1eb

[ "MIT" ]

11

2019-12-05T15:21:40.000Z

2021-10-05T22:08:17.000Z

server/accounts/migrations/0004_userdata_primaryfield.py

cristicismas/top-budget

d61db578287b2f77c12032045fca21e58c9ae1eb

[ "MIT" ]

null

# Generated by Django 2.2.5 on 2019-09-25 14:30 from django.db import migrations, models

26.684211

164

0.619329

88ef90d13386cd8f42e7af523b73ce0b09325d97

580

bzl

Python

source/bazel/deps/Poppy/get.bzl

luxe/unilang

6c8a431bf61755f4f0534c6299bd13aaeba4b69e

[ "MIT" ]

33

2019-05-30T07:43:32.000Z

2021-12-30T13:12:32.000Z

source/bazel/deps/Poppy/get.bzl

luxe/unilang

6c8a431bf61755f4f0534c6299bd13aaeba4b69e

[ "MIT" ]

371

2019-05-16T15:23:50.000Z

2021-09-04T15:45:27.000Z

source/bazel/deps/Poppy/get.bzl

luxe/unilang

6c8a431bf61755f4f0534c6299bd13aaeba4b69e

[ "MIT" ]

6

2019-08-22T17:37:36.000Z

2020-11-07T07:15:32.000Z

# Do not edit this file directly. # It was auto-generated by: code/programs/reflexivity/reflexive_refresh load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

34.117647

103

0.706897

88f03654581e59a140ad7f0b316b54846b6a53fc

99

py

Python

openfecli/commands/__init__.py

mikemhenry/openfe

d4c78af62a7ae05b99eb95d173661ac134b7e7b9

[ "MIT" ]

14

2022-01-24T22:01:19.000Z

2022-03-31T04:58:35.000Z

openfecli/commands/__init__.py

mikemhenry/openfe

d4c78af62a7ae05b99eb95d173661ac134b7e7b9

[ "MIT" ]

109

2022-01-24T18:57:05.000Z

2022-03-31T20:13:07.000Z

openfecli/commands/__init__.py

mikemhenry/openfe

d4c78af62a7ae05b99eb95d173661ac134b7e7b9

[ "MIT" ]

4

2022-01-24T18:45:54.000Z

2022-02-21T06:28:24.000Z

# shouldn't apparently need this file, but here we are from . import atommapping from . import echo

33

54

0.777778

88f18a67e803424dde5d28eb3302913d647a3a2f

27,163

py

Python

src/pages/pe_cuencas.py

ValentinSilvestri/cammesa

33ff17ad4a0447fd4668b6adad1c4bbfd88aba8e

[ "MIT" ]

null

src/pages/pe_cuencas.py

ValentinSilvestri/cammesa

33ff17ad4a0447fd4668b6adad1c4bbfd88aba8e

[ "MIT" ]

null

src/pages/pe_cuencas.py

ValentinSilvestri/cammesa

33ff17ad4a0447fd4668b6adad1c4bbfd88aba8e

[ "MIT" ]

null

import os import re import pymongo import pandas as pd import numpy as np import streamlit as st from bokeh.plotting import figure from bokeh.palettes import Set1_9, Set3_12, Inferno256 def caudales(): """Get the rivers basin flows and process this data. Returns: Figure: Bokeh plotting figure. DataFrame: Pandas DataFrame with the query result. """ df = get_caudales() df = pd.concat([ df['fecha'], pd.json_normalize(df['situacionCuencaComahue']), pd.json_normalize(df['situacionYacyretaSaltoGrande']), pd.json_normalize(df['situacionCuencaPatagonica']), pd.json_normalize(df['situacionCuencaRioGrande']), pd.json_normalize(df['situacionCuencaRioSanJuan']) ], axis=1, join="inner") df.rename(columns={ "fecha": "Fecha", "Caudal Collon Cura": "Cuenca Comahue - Caudal Collon Cura", "Caudal Neuquen": "Cuenca Comahue - Caudal Neuquen", "Caudal Limay": "Cuenca Comahue - Caudal Limay", "Caudal Ro Negro": "Cuenca Comahue - Caudal Ro Negro", "Caudal Limay despues desembocadura de Collon Cura": "Cuenca Comahue - Caudal Limay despues desembocadura de Collon Cura", "Caudal Ro Uruguay": "Yacyreta Salto Grande - Caudal Ro Uruguay", "Caudal Ro Paran": "Yacyreta Salto Grande - Caudal Ro Paran", "Caudal Ro Chubut": "Cuenca Patagnica - Caudal Ro Chubut", "Caudal Ro Futaleufu": "Cuenca Patagnica - Caudal Ro Futaleufu", "Caudal Ro Grande": "Cuenca Ro Grande - Caudal Ro Grande", "Caudal Inicial Ro San Juan": "Cuenca Ro San Juan - Caudal Inicial Ro San Juan", "Caudal Final Ro San Juan": "Cuenca Ro San Juan - Caudal Final Ro San Juan" }, inplace=True) df['Fecha'] = pd.to_datetime(df['Fecha'], format='%Y/%m/%d').dt.date df = df.drop_duplicates().sort_values('Fecha', ascending=False).reset_index(drop=True) df = df.replace(0, np.nan) p = figure(x_axis_type="datetime", title="Caudales cuencas", sizing_mode="stretch_both") p.grid.grid_line_alpha=0.3 p.xaxis.axis_label = 'Fecha' p.yaxis.axis_label = 'Caudal [m\u00b3/s]' p.legend.location = "top_left" return p, df def cotas(): """Get the rivers basin levels and process this data. Returns: Figure: Bokeh plotting figure. DataFrame: Pandas DataFrame with the query result. """ df = get_cotas() df = pd.concat([ df['fecha'], pd.json_normalize(df['situacionCuencaComahue']), pd.json_normalize(df['situacionYacyretaSaltoGrande']), pd.json_normalize(df['situacionCuencaPatagonica']), pd.json_normalize(df['situacionCuencaRioGrande']), pd.json_normalize(df['situacionCuencaRioSanJuan']) ], axis=1, join="inner") df.rename(columns={ 'fecha': 'Fecha', 'Cota Hoy Alicura': 'Cuenca Comahue - Alicura', 'Cota Min Alicura': 'Cuenca Comahue - Min Alicura', 'Cota Max Alicura': 'Cuenca Comahue - Max Alicura', 'Cota Hoy Piedra del Aguila': 'Cuenca Comahue - Piedra del Aguil', 'Cota Min Piedra del Aguila': 'Cuenca Comahue - Min Piedra del Aguila', 'Cota Max Piedra del Aguila': 'Cuenca Comahue - Max Piedra del Aguila', 'Cota Hoy Arroyito': 'Cuenca Comahue - Arroyito', 'Cota Min Arroyito': 'Cuenca Comahue - Min Arroyito', 'Cota Max Arroyito': 'Cuenca Comahue - Max Arroyito', 'Cota Hoy Mari Menuco': 'Cuenca Comahue - Mari Menuco', 'Cota Min Mari Menuco': 'Cuenca Comahue - Min Mari Menuco', 'Cota Max Mari Menuco': 'Cuenca Comahue - Max Mari Menuco', 'Cota Hoy Planicie Banderita Barreales': 'Cuenca Comahue - Planicie Banderita Barreales', 'Cota Min Planicie Banderita Barreales': 'Cuenca Comahue - Min Planicie Banderita Barreales', 'Cota Max Planicie Banderita Barreales': 'Cuenca Comahue - Max Planicie Banderita Barreales', 'Cota Hoy El Chocon': 'Cuenca Comahue - El Chocon', 'Cota Min El Chocon': 'Cuenca Comahue - Min El Chocon', 'Cota Max El Chocon': 'Cuenca Comahue - Max El Chocon', 'Cota Hoy P.P.Leufu': 'Cuenca Comahue - Leufu', 'Cota Hoy Yacyreta': 'Cuenca Yacyreta - Yacyreta', 'Cota Min Yacyreta': 'Cuenca Yacyreta - Min Yacyreta', 'Cota Max Yacyreta': 'Cuenca Yacyreta - Max Yacyreta', 'Cota Hoy Salto Grande': 'Cuenca Yacyreta - Salto Grande', 'Cota Min Salto Grande': 'Cuenca Yacyreta - Min Salto Grande', 'Cota Max Salto Grande': 'Cuenca Yacyreta - Max Salto Grande', 'Cota Hoy Futaleufu': 'Cuenca Patagnica - Futaleufu', 'Cota Min Futaleufu': 'Cuenca Patagnica - Min Futaleufu', 'Cota Max Futaleufu': 'Cuenca Patagnica - Max Futaleufu', 'Cota Hoy Ameghino': 'Cuenca Patagnica - Ameghino', 'Cota Min Ameghino': 'Cuenca Patagnica - Min Ameghino', 'Cota Max Ameghino': 'Cuenca Patagnica - Max Ameghino', 'Cota Hoy Ro Grande': 'Cuenca Ro Grande - Ro Grande', 'Cota Min Ro Grande': 'Cuenca Ro Grande - Min Ro Grande', 'Cota Max Ro Grande': 'Cuenca Ro Grande - Max Ro Grande', 'Cota Hoy Quebrada de Ullum': 'Cuenca Ro San Juan - Quebrada de Ullum', 'Cota Min Quebrada de Ullum': 'Cuenca Ro San Juan - Min Quebrada de Ullum', 'Cota Max Quebrada de Ullum': 'Cuenca Ro San Juan - Max Quebrada de Ullum', 'Cota Hoy Punta Negra': 'Cuenca Ro San Juan - Punta Negra', 'Cota Min Punta Negra': 'Cuenca Ro San Juan - Min Punta Negra', 'Cota Max Punta Negra': 'Cuenca Ro San Juan - Max Punta Negra' }, inplace=True) df['Fecha'] = pd.to_datetime(df['Fecha'], format='%Y/%m/%d').dt.date df = df.drop_duplicates().sort_values('Fecha', ascending=False).reset_index(drop=True) df = df.replace(0, np.nan) p = figure(x_axis_type="datetime", title="Cotas cuencas", sizing_mode="stretch_both") p.grid.grid_line_alpha=0.3 p.xaxis.axis_label = 'Fecha' p.yaxis.axis_label = 'Cota [cm]' p.legend.location = "top_left" return p, df def turbinado(): """Get the rivers basin discharge and process this data. Returns: Figure: Bokeh plotting figure. DataFrame: Pandas DataFrame with the query result. """ df = get_turbinado() df = pd.concat([ df['fecha'], pd.json_normalize(df['situacionCuencaComahue']), pd.json_normalize(df['situacionYacyretaSaltoGrande']), pd.json_normalize(df['situacionCuencaPatagonica']), pd.json_normalize(df['situacionCuencaRioGrande']), pd.json_normalize(df['situacionCuencaRioSanJuan']) ], axis=1, join="inner") df.rename(columns={ 'fecha': 'Fecha', 'Turbinado Alicura': 'Cuenca Comahue - Alicura', 'Turbinado Piedra del Aguila': 'Cuenca Comahue - Piedra del Aguila', 'Turbinado Arroyito': 'Cuenca Comahue - Arroyito', 'Turbinado El Chocon': 'Cuenca Comahue - El Chocon', 'Turbinado Mari Menuco': 'Cuenca Comahue - Mari Menuco', 'Turbinado P.P.Leufu': 'Cuenca Comahue - Leufu', 'Turbinado Salto Grande': 'Cuenca Yacyreta - Salto Grande', 'Turbinado Yacyreta': 'Cuenca Yacyreta - Yacyreta', 'Turbinado Futaleufu': 'Cuenca Patagnica - Futaleufu', 'Turbinado Ameghino': 'Cuenca Patagnica - Ameghino', 'Turbinado Ro Grande': 'Cuenca Ro Grande - Ro Grande', 'Turbinado Punta Negra': 'Cuenca Ro San Juan - Punta Negra', 'Turbinado Ullum': 'Cuenca Ro San Juan - Ullum', 'Turbinado Los Caracoles': 'Cuenca Ro San Juan - Los Caracoles', 'Turbinado Quebrada de Ullum': 'Cuenca Ro San Juan - Quebrada de Ullum' }, inplace=True) df['Fecha'] = pd.to_datetime(df['Fecha'], format='%Y/%m/%d').dt.date df = df.drop_duplicates().sort_values('Fecha', ascending=False).reset_index(drop=True) # df = df.replace(0, np.nan) p = figure(x_axis_type="datetime", title="Turbinado", sizing_mode="stretch_both") p.grid.grid_line_alpha=0.3 p.xaxis.axis_label = 'Fecha' p.yaxis.axis_label = 'Turbinado' p.legend.location = "top_left" return p, df def vertido(): """Get the rivers basin discharge and process this data. Returns: Figure: Bokeh plotting figure. DataFrame: Pandas DataFrame with the query result. """ df = get_vertido() df = pd.concat([ df['fecha'], pd.json_normalize(df['situacionCuencaComahue']), pd.json_normalize(df['situacionYacyretaSaltoGrande']), pd.json_normalize(df['situacionCuencaPatagonica']), pd.json_normalize(df['situacionCuencaRioGrande']), pd.json_normalize(df['situacionCuencaRioSanJuan']) ], axis=1, join="inner") df.rename(columns={ 'fecha': 'Fecha', 'Vertido El Chaar': 'Cuenca Comahue - El Chaar', 'Vertido Arroyito': 'Cuenca Comahue - Arroyito', 'Vertido Piedra del Aguila': 'Cuenca Comahue - Piedra del Aguila', 'Vertido P.P.Leufu': 'Cuenca Comahue - Leufu', 'Vertido Salto Grande': 'Cuenca Yacyreta - Salto Grande', 'Vertido Yacyreta': 'Cuenca Yacyreta - Yacyreta', 'Vertido Futaleufu': 'Cuenca Patagnica - Futaleufu', 'Vertido Ameghino': 'Cuenca Patagnica - Ameghino', 'Bombeo Ro Grande': 'Cuenca Ro Grande - Bombeo Ro Grande', 'Vertido Punta Negra': 'Cuenca Ro San Juan - Punta Negra', 'Vertido Los Caracoles': 'Cuenca Ro San Juan - Los Caracoles', 'Vertido Quebrada de Ullum': 'Cuenca Ro San Juan - Quebrada de Ullum' }, inplace=True) df['Fecha'] = pd.to_datetime(df['Fecha'], format='%Y/%m/%d').dt.date df = df.drop_duplicates().sort_values('Fecha', ascending=False).reset_index(drop=True) # df = df.replace(0, np.nan) p = figure(x_axis_type="datetime", title="Vertido", sizing_mode="stretch_both") p.grid.grid_line_alpha=0.3 p.xaxis.axis_label = 'Fecha' p.yaxis.axis_label = 'Vertido' p.legend.location = "top_left" return p, df def write(): """Function to write the Streamlit content of the page pe_cuencas """ p_caudales, df_caudales = caudales() p_cotas, df_cotas = cotas() p_turbinado, df_turbinado = turbinado() p_vertido, df_vertido = vertido() st.header("Publicaciones especiales - Cuencas/Datos Hidrulicos ", anchor=None) with st.container(): st.subheader("Anlisis de caudales", anchor=None) options = st.multiselect( "Seleccionar datos a graficar.", options=[ "Cuenca Comahue - Caudal Collon Cura", "Cuenca Comahue - Caudal Neuquen", "Cuenca Comahue - Caudal Limay", "Cuenca Comahue - Caudal Ro Negro", "Cuenca Comahue - Caudal Limay despues desembocadura de Collon Cura", "Yacyreta Salto Grande - Caudal Ro Uruguay", "Yacyreta Salto Grande - Caudal Ro Paran", "Cuenca Patagnica - Caudal Ro Chubut", "Cuenca Patagnica - Caudal Ro Futaleufu", "Cuenca Ro Grande - Caudal Ro Grande", "Cuenca Ro San Juan - Caudal Inicial Ro San Juan", "Cuenca Ro San Juan - Caudal Final Ro San Juan" ], default=[ "Yacyreta Salto Grande - Caudal Ro Paran", "Yacyreta Salto Grande - Caudal Ro Uruguay" ] ) if len(options)>9: col = Set3_12 else: col = Set1_9 for index, value in enumerate(options): p_caudales.line( df_caudales['Fecha'], df_caudales[value], color=col[index], legend_label=re.split(r" - ", value)[1].strip() ) st.bokeh_chart(p_caudales) with st.expander("Ver datos"): st.write("Datos de los caudales de las cuencas en [m\u00b3/s].") st.dataframe(df_caudales) st.download_button( label="Descargar dataset como .CSV", data=df_caudales.to_csv(index=False).encode('utf-8'), file_name='Caudales.csv', mime='text/csv', ) with st.container(): st.subheader("Anlisis de cotas", anchor=None) options_cotas = st.multiselect( "Seleccionar datos a graficar.", options=[ 'Cuenca Comahue - Alicura', 'Cuenca Comahue - Min Alicura', 'Cuenca Comahue - Max Alicura', 'Cuenca Comahue - Piedra del Aguil', 'Cuenca Comahue - Min Piedra del Aguila', 'Cuenca Comahue - Max Piedra del Aguila', 'Cuenca Comahue - Arroyito', 'Cuenca Comahue - Min Arroyito', 'Cuenca Comahue - Max Arroyito', 'Cuenca Comahue - Mari Menuco', 'Cuenca Comahue - Min Mari Menuco', 'Cuenca Comahue - Max Mari Menuco', 'Cuenca Comahue - Planicie Banderita Barreales', 'Cuenca Comahue - Min Planicie Banderita Barreales', 'Cuenca Comahue - Max Planicie Banderita Barreales', 'Cuenca Comahue - El Chocon', 'Cuenca Comahue - Min El Chocon', 'Cuenca Comahue - Max El Chocon', 'Cuenca Comahue - Leufu', 'Cuenca Yacyreta - Yacyreta', 'Cuenca Yacyreta - Min Yacyreta', 'Cuenca Yacyreta - Max Yacyreta', 'Cuenca Yacyreta - Salto Grande', 'Cuenca Yacyreta - Min Salto Grande', 'Cuenca Yacyreta - Max Salto Grande', 'Cuenca Patagnica - Futaleufu', 'Cuenca Patagnica - Min Futaleufu', 'Cuenca Patagnica - Max Futaleufu', 'Cuenca Patagnica - Ameghino', 'Cuenca Patagnica - Min Ameghino', 'Cuenca Patagnica - Max Ameghino', 'Cuenca Ro Grande - Ro Grande', 'Cuenca Ro Grande - Min Ro Grande', 'Cuenca Ro Grande - Max Ro Grande', 'Cuenca Ro San Juan - Quebrada de Ullum', 'Cuenca Ro San Juan - Min Quebrada de Ullum', 'Cuenca Ro San Juan - Max Quebrada de Ullum', 'Cuenca Ro San Juan - Punta Negra', 'Cuenca Ro San Juan - Min Punta Negra', 'Cuenca Ro San Juan - Max Punta Negra' ], default=[ 'Cuenca Yacyreta - Salto Grande', 'Cuenca Yacyreta - Min Salto Grande', 'Cuenca Yacyreta - Max Salto Grande' ] ) if len(options_cotas)<=9: col = Set1_9 elif len(options_cotas) <=12: col = Set3_12 else: col = Inferno256 for index, value in enumerate(options_cotas): p_cotas.line( df_cotas['Fecha'], df_cotas[value], color=col[index], legend_label=re.split(r" - ", value)[1].strip() ) st.bokeh_chart(p_cotas) with st.expander("Ver datos"): st.write("Datos de los Cotas de las cuencas en [cm].") st.dataframe(df_cotas) st.download_button( label="Descargar dataset como .CSV", data=df_cotas.to_csv(index=False).encode('utf-8'), file_name='Cotas.csv', mime='text/csv', ) with st.container(): st.subheader("Anlisis del turbinado", anchor=None) options_turbinado = st.multiselect( "Seleccionar datos a graficar.", options=[ 'Cuenca Comahue - Alicura', 'Cuenca Comahue - Piedra del Aguila', 'Cuenca Comahue - Arroyito', 'Cuenca Comahue - El Chocon', 'Cuenca Comahue - Mari Menuco', 'Cuenca Comahue - Leufu', 'Cuenca Yacyreta - Salto Grande', 'Cuenca Yacyreta - Yacyreta', 'Cuenca Patagnica - Futaleufu', 'Cuenca Patagnica - Ameghino', 'Cuenca Ro Grande - Ro Grande', 'Cuenca Ro San Juan - Punta Negra', 'Cuenca Ro San Juan - Ullum', 'Cuenca Ro San Juan - Los Caracoles', 'Cuenca Ro San Juan - Quebrada de Ullum' ], default=[ 'Cuenca Yacyreta - Yacyreta', 'Cuenca Yacyreta - Salto Grande' ] ) if len(options_turbinado)<=9: col = Set1_9 elif len(options_turbinado) <=12: col = Set3_12 else: col = Inferno256 for index, value in enumerate(options_turbinado): p_turbinado.line( df_turbinado['Fecha'], df_turbinado[value], color=col[index], legend_label=re.split(r" - ", value)[1].strip() ) st.bokeh_chart(p_turbinado) with st.expander("Ver datos"): st.write("Datos del turbinado.") st.dataframe(df_turbinado) st.download_button( label="Descargar dataset como .CSV", data=df_turbinado.to_csv(index=False).encode('utf-8'), file_name='Turbinado.csv', mime='text/csv', ) with st.container(): st.subheader("Anlisis del vertido", anchor=None) options_vertido = st.multiselect( "Seleccionar datos a graficar.", options=[ 'Cuenca Comahue - El Chaar', 'Cuenca Comahue - Arroyito', 'Cuenca Comahue - Piedra del Aguila', 'Cuenca Comahue - Leufu', 'Cuenca Yacyreta - Salto Grande', 'Cuenca Yacyreta - Yacyreta', 'Cuenca Patagnica - Futaleufu', 'Cuenca Patagnica - Ameghino', 'Cuenca Ro Grande - Bombeo Ro Grande', 'Cuenca Ro San Juan - Punta Negra', 'Cuenca Ro San Juan - Los Caracoles', 'Cuenca Ro San Juan - Quebrada de Ullum' ], default=[ 'Cuenca Yacyreta - Yacyreta', 'Cuenca Yacyreta - Salto Grande' ] ) if len(options_vertido)>9: col = Set3_12 else: col = Set1_9 for index, value in enumerate(options_vertido): p_vertido.line( df_vertido['Fecha'], df_vertido[value], color=col[index], legend_label=re.split(r" - ", value)[1].strip() ) st.bokeh_chart(p_vertido) with st.expander("Ver datos"): st.write("Datos del vertido.") st.dataframe(df_vertido) st.download_button( label="Descargar dataset como .CSV", data=df_vertido.to_csv(index=False).encode('utf-8'), file_name='Vertido.csv', mime='text/csv', )

39.538574

130

0.555793

88f1cc3699cf5781999a9874993e5299f3224a9d

5,930

py

Python

utils/gen-vowel-constraints.py

ctrlcctrlv/fontFeatures

76d68586da2c1c42bb3cd79f92d583e63f52cf02

[ "BSD-3-Clause" ]

51

2020-01-15T09:28:51.000Z

2022-03-30T06:48:36.000Z

utils/gen-vowel-constraints.py

ctrlcctrlv/fontFeatures

76d68586da2c1c42bb3cd79f92d583e63f52cf02

[ "BSD-3-Clause" ]

51

2020-05-11T18:51:25.000Z

2021-12-20T12:55:08.000Z

utils/gen-vowel-constraints.py

ctrlcctrlv/fontFeatures

76d68586da2c1c42bb3cd79f92d583e63f52cf02

[ "BSD-3-Clause" ]

8

2020-08-28T20:03:14.000Z

2021-12-08T01:22:28.000Z

#!/usr/bin/env python3 """Generator of the function to prohibit certain vowel sequences. It creates ``_hb_preprocess_text_vowel_constraints``, which inserts dotted circles into sequences prohibited by the USE script development spec. This function should be used as the ``preprocess_text`` of an ``hb_ot_complex_shaper_t``. usage: ./gen-vowel-constraints.py ms-use/IndicShapingInvalidCluster.txt """ import collections import youseedee import sys if len (sys.argv) != 2: sys.exit (__doc__) script_order = {} scripts = {} for start, end,script in youseedee.parse_file_ranges("Scripts.txt"): for u in range (start, end + 1): scripts[u] = script if script not in script_order: script_order[script] = start constraints = {} with open (sys.argv[1], encoding='utf-8') as f: constraints_header = [] while True: line = f.readline ().strip () if line == '#': break constraints_header.append(line) for line in f: j = line.find ('#') if j >= 0: line = line[:j] constraint = [int (cp, 16) for cp in line.split (';')[0].split ()] if not constraint: continue assert 2 <= len (constraint), 'Prohibited sequence is too short: {}'.format (constraint) script = scripts[constraint[0]] if script in constraints: constraints[script].add (constraint) else: constraints[script] = ConstraintSet (constraint) assert constraints, 'No constraints found' print ('# The following functions are generated by running:') print ('# %s ms-use/IndicShapingInvalidCluster.txt' % sys.argv[0]) print(""" from fontFeatures.shaperLib.Buffer import BufferItem DOTTED_CIRCLE = 0x25CC def _insert_dotted_circle(buf, index): dotted_circle = BufferItem.new_unicode(DOTTED_CIRCLE) buf.items.insert(index, dotted_circle) """) print ('def preprocess_text_vowel_constraints(buffer):') for script, constraints in sorted (constraints.items (), key=lambda s_c: script_order[s_c[0]]): print(f' if buffer.script == "{script}":') print (' i = 0') print (' while i < len(buffer.items)-1:') print (' matched = False') write (str (constraints)) print (' i = i + 1') print (' if matched: _insert_dotted_circle(buffer, i)')

37.770701

130

0.579764

88f228f6aba01067ca766c9e905ecf9ba9e9f3a0

1,719

py

Python

rules.py

ONSdigital/blaise-data-delivery-alerts

5077ae6da9ef406549c994bc735745fcbb02e61d

[ "MIT" ]

null

rules.py

ONSdigital/blaise-data-delivery-alerts

5077ae6da9ef406549c994bc735745fcbb02e61d

[ "MIT" ]

null

rules.py

ONSdigital/blaise-data-delivery-alerts

5077ae6da9ef406549c994bc735745fcbb02e61d

[ "MIT" ]

null

from datetime import datetime, timedelta import pytz ALLOWED_AGE_PER_STATUS = { "default": 5 * 60, # LMS instruments can take a very long time to generate when the SPS is not cached "started": 35 * 60, "nifi_notified": 30 * 60, } COMPLETE_STATES = ["inactive", "in_arc"]

29.135593

87

0.709133

88f349a60a782c3197475116d0068eb5bfe4f4a8

2,229

py

Python

src/SocketServer.py

iamyyl/DDNS

537babb8a0f2debc65326f12ff3cc15d73692f98

[ "Apache-2.0" ]

null

src/SocketServer.py

iamyyl/DDNS

537babb8a0f2debc65326f12ff3cc15d73692f98

[ "Apache-2.0" ]

null

src/SocketServer.py

iamyyl/DDNS

537babb8a0f2debc65326f12ff3cc15d73692f98

[ "Apache-2.0" ]

null

import socket import select fd_to_socket = {} READ_ONLY = ( select.POLLIN | select.POLLPRI | select.POLLHUP | select.POLLERR) READ_WRITE = (READ_ONLY|select.POLLOUT) poller = select.poll() server = None IP = '127.0.0.1' Port = 7002

30.121622

79

0.544639

88f4ca8390bec41333df4559d2ca341bcce83beb

39,506

py

Python

pysnmp/A3COM-HUAWEI-SNA-DLSW-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

11

2021-02-02T16:27:16.000Z

2021-08-31T06:22:49.000Z

pysnmp/A3COM-HUAWEI-SNA-DLSW-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

75

2021-02-24T17:30:31.000Z

2021-12-08T00:01:18.000Z

pysnmp/A3COM-HUAWEI-SNA-DLSW-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module A3COM-HUAWEI-SNA-DLSW-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/A3COM-HUAWEI-SNA-DLSW-MIB # Produced by pysmi-0.3.4 at Mon Apr 29 16:52:16 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # hwproducts, = mibBuilder.importSymbols("A3COM-HUAWEI-OID-MIB", "hwproducts") Integer, ObjectIdentifier, OctetString = mibBuilder.importSymbols("ASN1", "Integer", "ObjectIdentifier", "OctetString") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueSizeConstraint, ConstraintsIntersection, ValueRangeConstraint, ConstraintsUnion, SingleValueConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueSizeConstraint", "ConstraintsIntersection", "ValueRangeConstraint", "ConstraintsUnion", "SingleValueConstraint") ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex") ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup") MibIdentifier, ObjectIdentity, TimeTicks, Counter64, ModuleIdentity, MibScalar, MibTable, MibTableRow, MibTableColumn, iso, Counter32, Bits, Gauge32, NotificationType, IpAddress, Integer32, Unsigned32 = mibBuilder.importSymbols("SNMPv2-SMI", "MibIdentifier", "ObjectIdentity", "TimeTicks", "Counter64", "ModuleIdentity", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "iso", "Counter32", "Bits", "Gauge32", "NotificationType", "IpAddress", "Integer32", "Unsigned32") DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention") dlsw = ModuleIdentity((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34)) if mibBuilder.loadTexts: dlsw.setLastUpdated('200410301551Z') if mibBuilder.loadTexts: dlsw.setOrganization('Huawei-3com Technologies co.,Ltd.') dlswNode = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1)) dlswTConn = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2)) dlswBridgeGroup = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3)) dlswLocDirectory = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 4)) dlswCircuit = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5)) dlswSdlc = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6)) dlswLlc2 = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7)) dlswNodeVersion = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 1), OctetString().subtype(subtypeSpec=ValueSizeConstraint(2, 2)).setFixedLength(2)).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswNodeVersion.setStatus('current') dlswNodeVendorID = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 2), OctetString().subtype(subtypeSpec=ValueSizeConstraint(3, 3)).setFixedLength(3)).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswNodeVendorID.setStatus('current') dlswNodeVersionString = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 3), DisplayString()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswNodeVersionString.setStatus('current') dlswNodeStdPacingSupport = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 65535))).clone(namedValues=NamedValues(("none", 1), ("adaptiveRcvWindow", 2), ("fixedRcvWindow", 3), ("unknown", 65535)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswNodeStdPacingSupport.setStatus('current') dlswNodeStatus = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("active", 1), ("inactive", 2)))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeStatus.setStatus('current') dlswNodeUpTime = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 6), Integer32()).setUnits('second').setMaxAccess("readonly") if mibBuilder.loadTexts: dlswNodeUpTime.setStatus('obsolete') dlswNodeVirtualSegmentLFSize = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 7), LFSize().clone('lfs1500')).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeVirtualSegmentLFSize.setStatus('current') dlswNodeLocalAddr = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 8), IpAddress()).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeLocalAddr.setStatus('current') dlswNodePriority = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 9), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(1, 5), ValueRangeConstraint(65535, 65535), )).clone(5)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodePriority.setStatus('current') dlswNodeInitWindow = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 10), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(1, 2000), ValueRangeConstraint(65535, 65535), )).clone(40)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeInitWindow.setStatus('current') dlswNodeKeepAlive = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 11), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(1, 2000), ValueRangeConstraint(65535, 65535), )).clone(30)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeKeepAlive.setStatus('current') dlswNodeMaxWindow = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 12), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(1, 2000), ValueRangeConstraint(65535, 65535), )).clone(255)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeMaxWindow.setStatus('current') dlswNodePermitDynamic = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 13), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 65535))).clone(namedValues=NamedValues(("permitDynamic", 1), ("forbidDynamic", 2), ("unknown", 65535))).clone('forbidDynamic')).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodePermitDynamic.setStatus('current') dlswNodeConnTimeout = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 14), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 65535)).clone(300)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeConnTimeout.setStatus('current') dlswNodeLocalPendTimeout = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 15), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 65535)).clone(30)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeLocalPendTimeout.setStatus('current') dlswNodeRemotePendTimeout = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 16), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 65535)).clone(30)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeRemotePendTimeout.setStatus('current') dlswNodeSnaCacheTimeout = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 17), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 65535)).clone(120)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswNodeSnaCacheTimeout.setStatus('current') dlswRemotePeerTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1), ) if mibBuilder.loadTexts: dlswRemotePeerTable.setStatus('current') dlswRemotePeerEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1), ).setIndexNames((0, "A3COM-HUAWEI-SNA-DLSW-MIB", "dlswRemotePeerAddr")) if mibBuilder.loadTexts: dlswRemotePeerEntry.setStatus('current') dlswRemotePeerAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 1), IpAddress()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: dlswRemotePeerAddr.setStatus('current') dlswRemotePeerVersion = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 2), OctetString().subtype(subtypeSpec=ValueSizeConstraint(2, 2)).setFixedLength(2)).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerVersion.setStatus('current') dlswRemotePeerVendorID = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(3, 3)).setFixedLength(3)).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerVendorID.setStatus('current') dlswRemotePeerPaceWindInit = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerPaceWindInit.setStatus('current') dlswRemotePeerNumOfTcpSessions = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerNumOfTcpSessions.setStatus('obsolete') dlswRemotePeerVersionString = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 6), DisplayString()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerVersionString.setStatus('current') dlswRemotePeerIsConfig = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 7), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("yes", 1), ("no", 2)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerIsConfig.setStatus('current') dlswRemotePeerSetStateToConfig = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 8), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("yes", 1), ("no", 2)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerSetStateToConfig.setStatus('obsolete') dlswRemotePeerCost = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 9), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 5))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswRemotePeerCost.setStatus('current') dlswRemotePeerKeepAlive = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 10), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 60000))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswRemotePeerKeepAlive.setStatus('current') dlswRemotePeerLf = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 11), LFSize()).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswRemotePeerLf.setStatus('current') dlswRemotePeerTcpQueneMax = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 12), Integer32().subtype(subtypeSpec=ValueRangeConstraint(50, 2000))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswRemotePeerTcpQueneMax.setStatus('current') dlswRemotePeerHaveBackup = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 13), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("yes", 1), ("no", 2)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerHaveBackup.setStatus('current') dlswRemotePeerIsBackup = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 14), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("yes", 1), ("no", 2)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerIsBackup.setStatus('current') dlswRemotePeerBackupAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 15), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerBackupAddr.setStatus('current') dlswRemotePeerLinger = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 16), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 1440)).clone(5)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswRemotePeerLinger.setStatus('current') dlswRemotePeerLinkState = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 17), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6))).clone(namedValues=NamedValues(("connecting", 1), ("initCapExchange", 2), ("connected", 3), ("quiescing", 4), ("disconnecting", 5), ("disconnected", 6)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerLinkState.setStatus('current') dlswRemotePeerRecvPacks = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 18), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerRecvPacks.setStatus('current') dlswRemotePeerSendPacks = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 19), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerSendPacks.setStatus('current') dlswRemotePeerDrops = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 20), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerDrops.setStatus('current') dlswRemotePeerUptime = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 21), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswRemotePeerUptime.setStatus('current') dlswRemotePeerEntryStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 2, 1, 1, 22), EntryStatus()).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswRemotePeerEntryStatus.setStatus('current') dlswBridgeTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 1), ) if mibBuilder.loadTexts: dlswBridgeTable.setStatus('current') dlswBridgeEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 1, 1), ).setIndexNames((0, "A3COM-HUAWEI-SNA-DLSW-MIB", "dlswBridgeNum")) if mibBuilder.loadTexts: dlswBridgeEntry.setStatus('current') dlswBridgeNum = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 1, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 63))) if mibBuilder.loadTexts: dlswBridgeNum.setStatus('current') dlswBridgeStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 1, 1, 2), CreateLineFlag()).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswBridgeStatus.setStatus('current') dlswBridgeIfTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 2), ) if mibBuilder.loadTexts: dlswBridgeIfTable.setStatus('current') dlswBridgeIfEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex")) if mibBuilder.loadTexts: dlswBridgeIfEntry.setStatus('current') dlswBridgeIfBriGru = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 2, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 63))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswBridgeIfBriGru.setStatus('current') dlswBridgeIfName = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 2, 1, 2), DisplayString()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswBridgeIfName.setStatus('current') dlswBridgeIfStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 3, 2, 1, 3), EntryStatus()).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswBridgeIfStatus.setStatus('current') dlswLocMacTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 4, 1), ) if mibBuilder.loadTexts: dlswLocMacTable.setStatus('current') dlswLocMacEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 4, 1, 1), ).setIndexNames((0, "A3COM-HUAWEI-SNA-DLSW-MIB", "dlswLocMacHashIndex"), (0, "A3COM-HUAWEI-SNA-DLSW-MIB", "dlswLocMacHashIndexSeqNum")) if mibBuilder.loadTexts: dlswLocMacEntry.setStatus('current') dlswLocMacHashIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 4, 1, 1, 1), Integer32()) if mibBuilder.loadTexts: dlswLocMacHashIndex.setStatus('current') dlswLocMacHashIndexSeqNum = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 4, 1, 1, 2), Integer32()) if mibBuilder.loadTexts: dlswLocMacHashIndexSeqNum.setStatus('current') dlswLocMacMac = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 4, 1, 1, 3), MacAddressNC()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswLocMacMac.setStatus('current') dlswLocMacLocalInterfaceName = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 4, 1, 1, 4), DisplayString()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswLocMacLocalInterfaceName.setStatus('current') dlswCircuitTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1), ) if mibBuilder.loadTexts: dlswCircuitTable.setStatus('current') dlswCircuitEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1), ).setIndexNames((0, "A3COM-HUAWEI-SNA-DLSW-MIB", "dlswCircuitS1CircuitId")) if mibBuilder.loadTexts: dlswCircuitEntry.setStatus('current') dlswCircuitS1CircuitId = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 1), Integer32()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: dlswCircuitS1CircuitId.setStatus('current') dlswCircuitS1Mac = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 2), MacAddressNC()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS1Mac.setStatus('current') dlswCircuitS1Sap = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1, 1)).setFixedLength(1)).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS1Sap.setStatus('current') dlswCircuitS2Mac = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 4), MacAddressNC()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS2Mac.setStatus('current') dlswCircuitS2Sap = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 5), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1, 1)).setFixedLength(1)).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS2Sap.setStatus('current') dlswCircuitS1IfIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 2147483647))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS1IfIndex.setStatus('current') dlswCircuitS1Ifname = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 7), DisplayString()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS1Ifname.setStatus('current') dlswCircuitS1DlcType = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 8), DlcType()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS1DlcType.setStatus('current') dlswCircuitS2Location = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 9), EndStationLocation()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS2Location.setStatus('obsolete') dlswCircuitS2TAddress = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 10), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS2TAddress.setStatus('current') dlswCircuitS2CircuitId = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 11), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitS2CircuitId.setStatus('current') dlswCircuitOrigin = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 12), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("s1", 1), ("s2", 2)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitOrigin.setStatus('current') dlswCircuitEntryTime = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 13), TimeTicks()).setUnits('hundredths of a second').setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitEntryTime.setStatus('current') dlswCircuitStateTime = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 14), TimeTicks()).setUnits('hundredths of a second').setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitStateTime.setStatus('current') dlswCircuitState = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 15), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13))).clone(namedValues=NamedValues(("disconnected", 1), ("circuitStart", 2), ("resolvePending", 3), ("circuitPending", 4), ("circuitEstablished", 5), ("connectPending", 6), ("contactPending", 7), ("connected", 8), ("disconnectPending", 9), ("haltPending", 10), ("haltPendingNoack", 11), ("circuitRestart", 12), ("restartPending", 13)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitState.setStatus('current') dlswCircuitPriority = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 16), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5))).clone(namedValues=NamedValues(("unsupported", 1), ("low", 2), ("medium", 3), ("high", 4), ("highest", 5)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitPriority.setStatus('obsolete') dlswCircuitFCSendGrantedUnits = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 17), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitFCSendGrantedUnits.setStatus('current') dlswCircuitFCSendCurrentWndw = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 18), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitFCSendCurrentWndw.setStatus('current') dlswCircuitFCRecvGrantedUnits = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 19), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitFCRecvGrantedUnits.setStatus('current') dlswCircuitFCRecvCurrentWndw = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 20), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitFCRecvCurrentWndw.setStatus('current') dlswCircuitFCLargestRecvGranted = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 21), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitFCLargestRecvGranted.setStatus('obsolete') dlswCircuitFCLargestSendGranted = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 5, 1, 1, 22), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswCircuitFCLargestSendGranted.setStatus('obsolete') dlswSdlcPortTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1), ) if mibBuilder.loadTexts: dlswSdlcPortTable.setStatus('current') dlswSdlcPortEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex")) if mibBuilder.loadTexts: dlswSdlcPortEntry.setStatus('current') dlswSdlcPortSerialName = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 1), DisplayString()).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswSdlcPortSerialName.setStatus('current') dlswSdlcPortEncap = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("sdlc", 1), ("ppp", 2), ("other", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: dlswSdlcPortEncap.setStatus('current') dlswSdlcPortRole = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("primary", 1), ("seconday", 2), ("norole", 3))).clone('norole')).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortRole.setStatus('current') dlswSdlcPortVmac = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 4), MacAddressNC()).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortVmac.setStatus('current') dlswSdlcPortHoldq = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(20, 255)).clone(50)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortHoldq.setStatus('current') dlswSdlcPortK = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 7)).clone(7)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortK.setStatus('current') dlswSdlcPortModule = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 7), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(8, 128))).clone(namedValues=NamedValues(("m8", 8), ("m128", 128))).clone('m8')).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortModule.setStatus('current') dlswSdlcPortN1 = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 8), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 17680)).clone(265)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortN1.setStatus('current') dlswSdlcPortN2 = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 9), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 255)).clone(20)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortN2.setStatus('current') dlswSdlcPortPollPauseTimer = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 10), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 10000)).clone(100)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortPollPauseTimer.setStatus('current') dlswSdlcPortSimultaneousEnable = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 11), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("enable", 1), ("disenable", 2))).clone(1)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortSimultaneousEnable.setStatus('current') dlswSdlcPortT1 = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 12), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 60000)).clone(3000)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortT1.setStatus('current') dlswSdlcPortT2 = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 13), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 60000)).clone(500)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortT2.setStatus('current') dlswSdlcPortNrziEncoding = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 14), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("enable", 1), ("disenable", 2))).clone('enable')).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortNrziEncoding.setStatus('obsolete') dlswSdlcPortIdleMarkEnable = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 1, 1, 15), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("enable", 1), ("disenable", 2))).clone('enable')).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcPortIdleMarkEnable.setStatus('obsolete') dlswSdlcLsTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 2), ) if mibBuilder.loadTexts: dlswSdlcLsTable.setStatus('current') dlswSdlcLsEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "A3COM-HUAWEI-SNA-DLSW-MIB", "dlswSdlcLsAddress")) if mibBuilder.loadTexts: dlswSdlcLsEntry.setStatus('current') dlswSdlcLsAddress = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 2, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 254))) if mibBuilder.loadTexts: dlswSdlcLsAddress.setStatus('current') dlswSdlcLsLocalId = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 2, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 2147483647))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcLsLocalId.setStatus('current') dlswSdlcLsRemoteMac = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 2, 1, 3), MacAddressNC()).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcLsRemoteMac.setStatus('current') dlswSdlcLsSsap = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 2, 1, 4), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 254))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcLsSsap.setStatus('current') dlswSdlcLsDsap = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 2, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 254))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcLsDsap.setStatus('current') dlswSdlcLsStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 6, 2, 1, 6), EntryStatus()).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswSdlcLsStatus.setStatus('current') dlswLlc2PortTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1), ) if mibBuilder.loadTexts: dlswLlc2PortTable.setStatus('current') dlswLlc2PortEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "A3COM-HUAWEI-SNA-DLSW-MIB", "dlswBridgeIfBriGru")) if mibBuilder.loadTexts: dlswLlc2PortEntry.setStatus('current') dlswLLC2PortAckDelayTime = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 60000)).clone(100)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortAckDelayTime.setStatus('current') dlswLLC2PortAckMax = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 127)).clone(3)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortAckMax.setStatus('current') dlswLLC2PortLocalWnd = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 3), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 127)).clone(7)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortLocalWnd.setStatus('current') dlswLLC2PortModulus = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(8, 128))).clone(namedValues=NamedValues(("m8", 8), ("m128", 128))).clone('m128')).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortModulus.setStatus('current') dlswLLC2PortN2 = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 255)).clone(20)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortN2.setStatus('current') dlswLLC2PortT1 = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 60000)).clone(200)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortT1.setStatus('current') dlswLLC2PortTbusyTime = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 7), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 60000)).clone(300)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortTbusyTime.setStatus('current') dlswLLC2PortTpfTime = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 8), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 60000)).clone(500)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortTpfTime.setStatus('current') dlswLLC2PortTrejTime = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 9), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 60000)).clone(500)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortTrejTime.setStatus('current') dlswLLC2PortTxqMax = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 7, 1, 1, 10), Integer32().subtype(subtypeSpec=ValueRangeConstraint(20, 200)).clone(50)).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswLLC2PortTxqMax.setStatus('current') dlswTrapControl = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 20)) dlswTrapCntlState = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 1, 20, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("enabled", 1), ("disabled", 2)))).setMaxAccess("readwrite") if mibBuilder.loadTexts: dlswTrapCntlState.setStatus('current') dlswTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 8)) dlswTrapsV2 = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 8, 0)) dlswTrapTConnPartnerReject = NotificationType((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 8, 0, 1)).setObjects(("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswRemotePeerAddr")) if mibBuilder.loadTexts: dlswTrapTConnPartnerReject.setStatus('current') dlswTrapTConnChangeState = NotificationType((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 8, 0, 2)).setObjects(("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswRemotePeerAddr"), ("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswRemotePeerLinkState")) if mibBuilder.loadTexts: dlswTrapTConnChangeState.setStatus('current') dlswTrapCircuitChangeState = NotificationType((1, 3, 6, 1, 4, 1, 43, 45, 1, 2, 34, 8, 0, 3)).setObjects(("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswCircuitS1CircuitId"), ("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswCircuitState"), ("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswCircuitS1Mac"), ("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswCircuitS1Sap"), ("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswCircuitS2Mac"), ("A3COM-HUAWEI-SNA-DLSW-MIB", "dlswCircuitS2Sap")) if mibBuilder.loadTexts: dlswTrapCircuitChangeState.setStatus('current') mibBuilder.exportSymbols("A3COM-HUAWEI-SNA-DLSW-MIB", dlswLLC2PortModulus=dlswLLC2PortModulus, dlswLLC2PortTxqMax=dlswLLC2PortTxqMax, dlswBridgeNum=dlswBridgeNum, dlswCircuitS2TAddress=dlswCircuitS2TAddress, dlswRemotePeerRecvPacks=dlswRemotePeerRecvPacks, dlswTrapTConnChangeState=dlswTrapTConnChangeState, dlswNodeMaxWindow=dlswNodeMaxWindow, dlswNode=dlswNode, dlswLlc2=dlswLlc2, dlswRemotePeerLinger=dlswRemotePeerLinger, dlswSdlc=dlswSdlc, dlswCircuitS2Location=dlswCircuitS2Location, dlswBridgeTable=dlswBridgeTable, dlswBridgeStatus=dlswBridgeStatus, LFSize=LFSize, dlswSdlcPortSerialName=dlswSdlcPortSerialName, dlswRemotePeerBackupAddr=dlswRemotePeerBackupAddr, dlswSdlcPortTable=dlswSdlcPortTable, dlswNodeVirtualSegmentLFSize=dlswNodeVirtualSegmentLFSize, dlswRemotePeerAddr=dlswRemotePeerAddr, dlswCircuitS1Sap=dlswCircuitS1Sap, dlswRemotePeerNumOfTcpSessions=dlswRemotePeerNumOfTcpSessions, dlswLocMacTable=dlswLocMacTable, dlswCircuitS1IfIndex=dlswCircuitS1IfIndex, dlswRemotePeerLinkState=dlswRemotePeerLinkState, dlswSdlcPortVmac=dlswSdlcPortVmac, dlswNodeStdPacingSupport=dlswNodeStdPacingSupport, PYSNMP_MODULE_ID=dlsw, dlswCircuitS1Ifname=dlswCircuitS1Ifname, dlswRemotePeerSetStateToConfig=dlswRemotePeerSetStateToConfig, dlswCircuitFCRecvCurrentWndw=dlswCircuitFCRecvCurrentWndw, dlswRemotePeerIsConfig=dlswRemotePeerIsConfig, dlswSdlcPortRole=dlswSdlcPortRole, dlswCircuitS1CircuitId=dlswCircuitS1CircuitId, DlcType=DlcType, dlswRemotePeerKeepAlive=dlswRemotePeerKeepAlive, dlswLLC2PortLocalWnd=dlswLLC2PortLocalWnd, dlswSdlcLsAddress=dlswSdlcLsAddress, dlswNodeRemotePendTimeout=dlswNodeRemotePendTimeout, dlswRemotePeerHaveBackup=dlswRemotePeerHaveBackup, dlswLLC2PortTrejTime=dlswLLC2PortTrejTime, dlswCircuitFCLargestSendGranted=dlswCircuitFCLargestSendGranted, dlswNodeSnaCacheTimeout=dlswNodeSnaCacheTimeout, dlswNodeLocalPendTimeout=dlswNodeLocalPendTimeout, dlswNodeStatus=dlswNodeStatus, dlswCircuitStateTime=dlswCircuitStateTime, dlswNodeKeepAlive=dlswNodeKeepAlive, dlswSdlcPortIdleMarkEnable=dlswSdlcPortIdleMarkEnable, dlswRemotePeerVersion=dlswRemotePeerVersion, CreateLineFlag=CreateLineFlag, dlswRemotePeerEntry=dlswRemotePeerEntry, dlswRemotePeerSendPacks=dlswRemotePeerSendPacks, dlswNodeVersionString=dlswNodeVersionString, EntryStatus=EntryStatus, dlswCircuitFCLargestRecvGranted=dlswCircuitFCLargestRecvGranted, dlswNodeUpTime=dlswNodeUpTime, dlswNodeVendorID=dlswNodeVendorID, dlswCircuitPriority=dlswCircuitPriority, dlswNodeVersion=dlswNodeVersion, dlswSdlcPortEncap=dlswSdlcPortEncap, dlswLlc2PortTable=dlswLlc2PortTable, dlswLLC2PortAckDelayTime=dlswLLC2PortAckDelayTime, dlswLLC2PortN2=dlswLLC2PortN2, dlswBridgeIfTable=dlswBridgeIfTable, dlswRemotePeerTable=dlswRemotePeerTable, dlswCircuitEntry=dlswCircuitEntry, dlswCircuitFCSendGrantedUnits=dlswCircuitFCSendGrantedUnits, dlswLLC2PortTpfTime=dlswLLC2PortTpfTime, dlswRemotePeerPaceWindInit=dlswRemotePeerPaceWindInit, dlswRemotePeerIsBackup=dlswRemotePeerIsBackup, dlswCircuitS2Sap=dlswCircuitS2Sap, dlswLLC2PortAckMax=dlswLLC2PortAckMax, dlswLocMacHashIndex=dlswLocMacHashIndex, dlswTrapCircuitChangeState=dlswTrapCircuitChangeState, dlswTConn=dlswTConn, dlswCircuitOrigin=dlswCircuitOrigin, dlswLlc2PortEntry=dlswLlc2PortEntry, dlswCircuitState=dlswCircuitState, dlswCircuitS1Mac=dlswCircuitS1Mac, dlswSdlcLsEntry=dlswSdlcLsEntry, dlswCircuitEntryTime=dlswCircuitEntryTime, dlswSdlcLsStatus=dlswSdlcLsStatus, dlswCircuitS2CircuitId=dlswCircuitS2CircuitId, dlswLLC2PortTbusyTime=dlswLLC2PortTbusyTime, dlswRemotePeerTcpQueneMax=dlswRemotePeerTcpQueneMax, dlswCircuit=dlswCircuit, dlswBridgeEntry=dlswBridgeEntry, dlswSdlcPortEntry=dlswSdlcPortEntry, dlswRemotePeerDrops=dlswRemotePeerDrops, dlswCircuitTable=dlswCircuitTable, dlswNodePermitDynamic=dlswNodePermitDynamic, dlswRemotePeerVendorID=dlswRemotePeerVendorID, dlswSdlcPortModule=dlswSdlcPortModule, dlsw=dlsw, dlswSdlcLsSsap=dlswSdlcLsSsap, dlswCircuitFCRecvGrantedUnits=dlswCircuitFCRecvGrantedUnits, dlswSdlcPortSimultaneousEnable=dlswSdlcPortSimultaneousEnable, dlswSdlcLsTable=dlswSdlcLsTable, dlswTrapControl=dlswTrapControl, dlswSdlcLsLocalId=dlswSdlcLsLocalId, dlswBridgeIfBriGru=dlswBridgeIfBriGru, dlswRemotePeerUptime=dlswRemotePeerUptime, dlswTraps=dlswTraps, dlswNodeConnTimeout=dlswNodeConnTimeout, dlswTrapCntlState=dlswTrapCntlState, dlswTrapsV2=dlswTrapsV2, MacAddressNC=MacAddressNC, dlswSdlcPortN2=dlswSdlcPortN2, dlswLocMacLocalInterfaceName=dlswLocMacLocalInterfaceName, dlswNodeInitWindow=dlswNodeInitWindow, dlswTrapTConnPartnerReject=dlswTrapTConnPartnerReject, dlswSdlcPortN1=dlswSdlcPortN1, dlswRemotePeerCost=dlswRemotePeerCost, dlswSdlcPortPollPauseTimer=dlswSdlcPortPollPauseTimer, dlswSdlcPortK=dlswSdlcPortK, EndStationLocation=EndStationLocation, dlswRemotePeerLf=dlswRemotePeerLf, dlswBridgeIfEntry=dlswBridgeIfEntry, dlswSdlcLsRemoteMac=dlswSdlcLsRemoteMac, dlswSdlcPortHoldq=dlswSdlcPortHoldq, dlswLLC2PortT1=dlswLLC2PortT1, dlswLocMacHashIndexSeqNum=dlswLocMacHashIndexSeqNum, dlswSdlcPortT1=dlswSdlcPortT1, dlswSdlcPortT2=dlswSdlcPortT2, dlswLocMacMac=dlswLocMacMac, dlswRemotePeerEntryStatus=dlswRemotePeerEntryStatus, dlswBridgeGroup=dlswBridgeGroup, dlswNodePriority=dlswNodePriority, dlswSdlcPortNrziEncoding=dlswSdlcPortNrziEncoding, dlswLocMacEntry=dlswLocMacEntry, dlswBridgeIfStatus=dlswBridgeIfStatus, dlswCircuitS2Mac=dlswCircuitS2Mac, dlswBridgeIfName=dlswBridgeIfName, dlswSdlcLsDsap=dlswSdlcLsDsap, dlswCircuitS1DlcType=dlswCircuitS1DlcType, dlswNodeLocalAddr=dlswNodeLocalAddr, dlswCircuitFCSendCurrentWndw=dlswCircuitFCSendCurrentWndw, dlswLocDirectory=dlswLocDirectory, dlswRemotePeerVersionString=dlswRemotePeerVersionString)

131.249169

5,640

0.75505

88f5b3a545f8379f5c6bd871ff166dd1442dd335

1,263

py

Python

solutions/validate-binary-search-tree.py

edab/-LC_StudyPlan_Python

e065f0ced68d23800d7b5001102c2e930ee35e23

[ "MIT" ]

null

solutions/validate-binary-search-tree.py

edab/-LC_StudyPlan_Python

e065f0ced68d23800d7b5001102c2e930ee35e23

[ "MIT" ]

1

2022-02-22T15:42:54.000Z

2022-02-25T00:10:04.000Z

solutions/validate-binary-search-tree.py

edab/-LC_StudyPlan_Python

e065f0ced68d23800d7b5001102c2e930ee35e23

[ "MIT" ]

null

# Leetcode 98. Validate Binary Search Tree # # Link: https://leetcode.com/problems/validate-binary-search-tree/ # Difficulty: Medium # Complexity: # O(N) time | where N represent the number of elements in the input tree # O(N) space | where N represent the number of elements in the input tree # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right

30.071429

104

0.63658

88f8317bf62d16d93d0f7dd37a85760c1a1014e1

763

py

Python

setup.py

stanford-ccb/ccb

ba75d490663958703f19e7a13f72001b050da229

[ "MIT" ]

3

2020-02-13T00:49:06.000Z

2020-06-24T23:53:25.000Z

setup.py

stanford-ccb/ccb

ba75d490663958703f19e7a13f72001b050da229

[ "MIT" ]

null

setup.py

stanford-ccb/ccb

ba75d490663958703f19e7a13f72001b050da229

[ "MIT" ]

4

2020-01-29T17:21:59.000Z

2021-01-27T01:53:05.000Z

from setuptools import setup version = open("ccb/__version__.py").read().strip('"\n') setup_args = { "name": "ccb", "version": version, "url": "https://github.com/earth-chris/ccb", "license": "MIT", "author": "Christopher Anderson", "author_email": "cbanders@stanford.edu", "description": "Species distribution modeling support tools", "keywords": ["maxent", "biogeography", "SDM", "species distribution modeling", "ecologyy", "conservation"], "packages": ["ccb"], "include_package_data": True, "platforms": "any", "scripts": ["bin/gbif-to-vector.py", "bin/vector-to-maxent.py"], "data_files": [("maxent", ["ccb/maxent/maxent.jar", "ccb/maxent/README.txt", "ccb/maxent/LICENSE.txt"])], } setup(**setup_args)

34.681818

111

0.643512

88f8e7ad2c848fa7633da12c05df70cdb4d3835a

1,576

py

Python

Unit4/Lesson8.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

Unit4/Lesson8.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

Unit4/Lesson8.py

szhua/PythonLearn

12eaf7cc74a0310bb23e21773f3c83deb91d0362

[ "Apache-2.0" ]

null

# import time # # def reader(): # """A generator that fakes a read from a file, socket, etc.""" # for i in range(101): # yield '<< %s' % i # # def consumer(): # r = '' # while True: # #Pythonyield # #n # n = yield from reader() # print("===",n) # if not n: # return # print('[CONSUMER] Consuming %s...' % n) # r = '200 OK' # # def produce(c): # c.send(None) # n = 0 # while n < 100: # n = n + 1 # print('[PRODUCER] Producing %s...' % n) # r = c.send(n) # print('[PRODUCER] Consumer return: %s' % r) # c.close() # # c = consumer() # produce(c) # def getIN(): # for x in range(1000): # n = yield x # print(n,"--rer",x) # # ge =getIN() # # # # ge.send(None) # ge.send("11") # ge.send("222") tallies = [] acc = gather_tallies(tallies) next(acc) # for i in range(4): acc.send(i) acc.send(None) # for i in range(5): acc.send(i) acc.send(None) # print(tallies) for x in get(): print(x)

17.909091

68

0.498096

88fb6794a48a5fc109dca145fcd71d6498bacc28

1,288

py

Python

tools/transferComponentSelection.py

fsanges/glTools

8ff0899de43784a18bd4543285655e68e28fb5e5

[ "MIT" ]

165

2015-01-26T05:22:04.000Z

2022-03-22T02:50:41.000Z

tools/transferComponentSelection.py

qeeji/glTools

8ff0899de43784a18bd4543285655e68e28fb5e5

[ "MIT" ]

5

2015-12-02T02:39:44.000Z

2020-12-09T02:45:54.000Z

tools/transferComponentSelection.py

qeeji/glTools

8ff0899de43784a18bd4543285655e68e28fb5e5

[ "MIT" ]

83

2015-02-10T17:18:24.000Z

2022-02-10T07:16:47.000Z

import maya.cmds as mc import maya.OpenMaya as OpenMaya import glTools.utils.base def transferComponentSelection(sourceSelection,targetMesh,threshold=0.0001): ''' ''' # Check selection target mesh if not mc.objExists(targetMesh): raise Exception('Target mesh "'+targetMesh+'" does not exist!') # Flatten selection sourceSelection = mc.ls(sourceSelection,fl=True) # Get mesh points tPtArray = glTools.utils.base.getMPointArray(targetMesh) tPtLen = tPtArray.length() # Initialize component selection transfer list tPtBool = [False for i in range(tPtLen)] # Initialize selection list tSel = [] # Transfer selection for sel in sourceSelection: # Get selection point pt = mc.pointPosition(sel) pt = OpenMaya.MPoint(pt[0],pt[1],pt[2],1.0) # Find closest component cDist = 99999 cIndex = -1 for i in range(tPtLen): # Check component selection transfer list if tPtBool[i]: continue # Check distance to current point dist = (pt-tPtArray[i]).length() if dist < cDist: cDist = dist cIndex = i # Test threshold if dist < threshold: break # Append selection tSel.append(targetMesh+'.vtx['+str(cIndex)+']') # Update component selection transfer list tPtBool[i] = True # Return result return tSel

22.596491

76

0.699534

88fcbd1db9da3e509077f5b7cc6ecfe05a708ea8

3,289

py

Python

pyprika/__init__.py

muppetize/pyprika

c1a4c086ee99935f5e7cf7361a4552fe69fe4b44

[ "MIT" ]

7

2016-03-10T22:23:28.000Z

2021-01-17T03:25:50.000Z

pyprika/__init__.py

muppetize/pyprika

c1a4c086ee99935f5e7cf7361a4552fe69fe4b44

[ "MIT" ]

1

2017-01-19T21:35:23.000Z

2017-01-27T04:11:44.000Z

pyprika/__init__.py

muppetize/pyprika

c1a4c086ee99935f5e7cf7361a4552fe69fe4b44

[ "MIT" ]

4

2016-11-19T21:48:41.000Z

2022-02-18T01:23:23.000Z

""" A Python package for recipe parsing and management. """ import yaml try: from cStringIO import StringIO except ImportError: from io import StringIO from .exceptions import LoadError, ParseError, PyprikaError, FieldError # noqa from .ingredient import Ingredient # noqa from .quantity import Quantity # noqa from .recipe import Recipe from .version import __author__, __version__ # noqa def load(fp, loader=None, **kw): """ Load ``fp``, a file-like object The file is assumed to be a pyprika-compliant YAML document. If the document contains a sequence, a list of ``Recipe`` objects will be returned. Otherwise, a single ``Recipe`` object should be returned. Note that this function wraps the underlying exceptions thrown by :meth:`Recipe.from_dict` under the assumption it is due to a malformed document, but the original traceback is preserved. :param file-like fp: the file-like object containing the document to load :param callable loader: takes one positional argument and optional arguments and returns a dict (defaults to ``yaml.load``) :param kw: passed through to loader :raises LoadError: if there was an error in the loading of the document, usually indicative of a syntax error :returns: the recipe data contained in the stream :rtype: :class:`Recipe` or list of :class:`Recipe` """ loader = loader or yaml.load try: d = loader(fp) if isinstance(d, (tuple, list)): return [Recipe.from_dict(x) for x in d] elif isinstance(d, dict): return Recipe.from_dict(d) else: raise LoadError('Recipe did not decode as expected (got %s)' % type(d).__name__) except PyprikaError as e: raise LoadError(*e.args, cause=e) def loads(data, loader=None, **kw): """ Load recipe from string data. This wraps ``data`` in a :class:`cString.StringIO` and calls :func:`load` on it. See :func:`load` for more information. :param str data: recipe document data :returns: the recipe data contained in ``data`` :rtype: :class:`Recipe` or list of :class:`Recipe` """ return load(StringIO(data), loader=loader, **kw) def dump(recipe, fp, dumper=None, **kw): """ Dump recipe to a file-like object :param Recipe recipe: the recipe to dump :param file-like fp: the file stream to dump to :param callable dumper: a callable which takes two positional arguments, the first a dict and the second a file stream, and optional keyword arguments and encodes the recipe to the file stream (defaults to yaml.dump) :param kw: passed through to dumper """ dumper = dumper or yaml.dump d = recipe.to_dict(serialize=True) dumper(d, fp, **kw) def dumps(recipe, dumper=None, **kw): """ Dump recipe object as a string. This is a convenience method to dump to a StringIO object. See :func:`dump` for parameter details. :returns: recipe encoded as a string :rtype: str """ fp = StringIO() dump(recipe, fp, dumper=dumper, **kw) return fp.getvalue()

33.907216

79

0.644573

88fcc966d96adb9a2926e13a38e7ae9f04bcd664

1,202

py

Python

tests/molecular/molecules/constructed_molecule/test_get_bond_infos.py

stevenbennett96/stk

6e5af87625b83e0bfc7243bc42d8c7a860cbeb76

[ "MIT" ]

21

2018-04-12T16:25:24.000Z

2022-02-14T23:05:43.000Z

tests/molecular/molecules/constructed_molecule/test_get_bond_infos.py

stevenbennett96/stk

6e5af87625b83e0bfc7243bc42d8c7a860cbeb76

[ "MIT" ]

8

2019-03-19T12:36:36.000Z

2020-11-11T12:46:00.000Z

tests/molecular/molecules/constructed_molecule/test_get_bond_infos.py

stevenbennett96/stk

6e5af87625b83e0bfc7243bc42d8c7a860cbeb76

[ "MIT" ]

5

2018-08-07T13:00:16.000Z

2021-11-01T00:55:10.000Z

def test_get_bond_infos(case_data): """ Test :meth:`.ConstructedMolecule.get_bond_infos`. Parameters ---------- case_data : :class:`.CaseData` A test case. Holds the constructed molecule to test and the correct number of new bonds. Returns ------- None : :class:`NoneType` """ _test_get_bond_infos( constructed_molecule=case_data.constructed_molecule, num_new_bonds=case_data.num_new_bonds, ) def _test_get_bond_infos(constructed_molecule, num_new_bonds): """ Test :meth:`.ConstructedMolecule.get_bond_infos`. Parameters ---------- constructed_molecule : :class:`.ConstructedMolecule` The constructed molecule to test. num_new_bonds : :class:`int` The correct number of new bonds added. Returns ------- None : :class:`NoneType` """ new_bonds = filter( lambda bond_info: bond_info.get_building_block() is None, constructed_molecule.get_bond_infos(), ) assert sum(1 for _ in new_bonds) == num_new_bonds assert ( constructed_molecule.get_num_bonds() == sum(1 for _ in constructed_molecule.get_bond_infos()) )

23.568627

67

0.645591

88fd1341d14bed024a3961fac8b9b61a8a5de30e

94

py

Python

generic_views/base_views/apps.py

markbirds/Django-Code-Repo

b55762d2dab00640acf2e8e00ddc66716d53c6b5

[ "MIT" ]

1

2021-11-25T00:02:36.000Z

generic_views/base_views/apps.py

markbirds/Django-Code-Repo

b55762d2dab00640acf2e8e00ddc66716d53c6b5

[ "MIT" ]

null

generic_views/base_views/apps.py

markbirds/Django-Code-Repo

b55762d2dab00640acf2e8e00ddc66716d53c6b5

[ "MIT" ]

null

from django.apps import AppConfig

15.666667

33

0.765957

88fe054080de49b8785340e2f3ce23ac82e4a3fa

324

py

Python

the_office/test.py

zubyjaved/reddit-bots

9f15f5ee9eede5223c975c29527c9e58d68bb517

[ "MIT" ]

2

2019-09-07T09:40:23.000Z

2021-06-19T08:40:00.000Z

the_office/test.py

zubyjaved/reddit-bots

9f15f5ee9eede5223c975c29527c9e58d68bb517

[ "MIT" ]

2

2019-09-05T04:42:23.000Z

2019-09-05T04:44:37.000Z

the_office/test.py

zubyjaved/reddit-bots

9f15f5ee9eede5223c975c29527c9e58d68bb517

[ "MIT" ]

null

import json import praw reddit = praw.Reddit("dwight-schrute-bot") for submission in reddit.subreddit('all').rising(limit=15): submission.comments.replace_more(limit=None) print(submission.subreddit.display_name) if not submission.over_18: for comment in submission.comments.list(): print()

29.454545

59

0.722222

88fe820e78b74b43c84647fdd224db13efd8f585

1,311

py

Python

Scripts/Client/ManualControlTest.py

Fzeak/sauvc-2019

573dcb351d0f87f9b7605667c570a5003bedb224

[ "MIT" ]

null

Scripts/Client/ManualControlTest.py

Fzeak/sauvc-2019

573dcb351d0f87f9b7605667c570a5003bedb224

[ "MIT" ]

null

Scripts/Client/ManualControlTest.py

Fzeak/sauvc-2019

573dcb351d0f87f9b7605667c570a5003bedb224

[ "MIT" ]

null

from pymavlink import mavutil import time # Create the connection master = mavutil.mavlink_connection('udpin:0.0.0.0:14550') # Wait a heartbeat before sending commands master.wait_heartbeat() # Send a positive x value, negative y, negative z, # positive rotation and no button. # http://mavlink.org/messages/common#MANUAL_CONTROL # Warning: Because of some legacy workaround, z will work between [0-1000] # where 0 is full reverse, 500 is no output and 1000 is full throttle. # x,y and r will be between [-1000 and 1000]. master.mav.manual_control_send( master.target_system, 500, -500, 250, 500, 0) # To active button 0 (first button), 3 (fourth button) and 7 (eighth button) # It's possible to check and configure this buttons in the Joystick menu of QGC buttons = 1 + 1 << 3 + 1 << 7 master.mav.manual_control_send( master.target_system, 0, 0, 0, 0, buttons) # Request all parameters master.mav.param_request_list_send( master.target_system, master.target_component ) while True: time.sleep(0.01) try: message = master.recv_match(type='PARAM_VALUE', blocking=True).to_dict() print('name: %s\tvalue: %d' % (message['param_id'].decode("utf-8"), message['param_value'])) except Exception as e: print(e) exit(0)

28.5

100

0.695652

88febcb95df370f538bcc67acef3a199df882aef

1,734

py

Python

HEIC en JPG.py

PictorSomni/Image_manipulations

7b91dd8514a2bb4383308c199e03e26539cef430

[ "MIT" ]

null

HEIC en JPG.py

PictorSomni/Image_manipulations

7b91dd8514a2bb4383308c199e03e26539cef430

[ "MIT" ]

null

HEIC en JPG.py

PictorSomni/Image_manipulations

7b91dd8514a2bb4383308c199e03e26539cef430

[ "MIT" ]

null

# -*- coding: utf-8 -*- ############################################################# # IMPORTS # ############################################################# import os from PIL import Image import pyheif ############################################################# # PATH # ############################################################# PATH = os.path.dirname(os.path.abspath(__file__)) os.chdir(PATH) ############################################################# # CONTENT # ############################################################# ImageFile.LOAD_TRUNCATED_IMAGES = True EXTENSION = (".heic", ".HEIC") FOLDER = [file for file in sorted(os.listdir()) if file.endswith(EXTENSION)] TOTAL = len(FOLDER) ############################################################# # MAIN # ############################################################# for i, file in enumerate(FOLDER) : filename, file_extension = os.path.splitext(file) try : heif_file = pyheif.read(file) except Exception : print(Exception) else : if not os.path.exists(PATH + "/JPG") : os.makedirs(PATH + "/JPG") image = Image.frombytes( heif_file.mode, heif_file.size, heif_file.data, "raw", heif_file.mode, heif_file.stride, ) image = image.convert("RGB") image.save(f"{PATH}/JPG/{filename}.jpg", dpi=(DPI, DPI), format='JPEG', subsampling=0, quality=100)

36.893617

107

0.336794

88ffda9c63fb74eb6eeb58819eb9951a55e13efa

17,863

py

Python

tplbuild/config.py

msg555/tplbuild

1517fa97b17df4883f6885a7fb3ccfe017576e53

[ "BSD-3-Clause" ]

null

tplbuild/config.py

msg555/tplbuild

1517fa97b17df4883f6885a7fb3ccfe017576e53

[ "BSD-3-Clause" ]

null

tplbuild/config.py

msg555/tplbuild

1517fa97b17df4883f6885a7fb3ccfe017576e53

[ "BSD-3-Clause" ]

null

import functools import os import ssl import uuid from typing import Any, Dict, List, Literal, Optional, Tuple import jinja2 import pydantic import yaml from .exceptions import TplBuildException, TplBuildTemplateException RESERVED_PROFILE_KEYS = { "begin_stage", "platform", } def _normalize_rel_path(path: str) -> str: """Normalize and coerce a path into a relative path.""" return f".{os.path.sep}{os.path.normpath(os.path.join(os.path.sep, path))[1:]}" UNSET_CLIENT_CONFIG = ClientConfig( build=ClientCommand(template=""), tag=ClientCommand(template=""), push=ClientCommand(template=""), untag=ClientCommand(template=""), )

40.141573

96

0.666014

00002c1133ee1a3e69c2c023cddb9b34c36440ca

1,634

py

Python

setup.py

DNA-and-Natural-Algorithms-Group/peppercompiler

effbcdedfb17534300fb3504a552e46c1ead41e4

[ "MIT" ]

3

2019-06-10T18:44:03.000Z

2021-11-17T10:57:09.000Z

setup.py

DNA-and-Natural-Algorithms-Group/peppercompiler

effbcdedfb17534300fb3504a552e46c1ead41e4

[ "MIT" ]

2

2017-12-15T01:09:49.000Z

2021-03-25T20:42:23.000Z

setup.py

DNA-and-Natural-Algorithms-Group/peppercompiler

effbcdedfb17534300fb3504a552e46c1ead41e4

[ "MIT" ]

4

2017-08-21T03:32:51.000Z

2019-10-18T04:09:38.000Z

#!/usr/bin/env python from setuptools import setup from distutils.command.build import build from setuptools.command.develop import develop setup( name="peppercompiler", version="0.1.3", packages=['peppercompiler', 'peppercompiler.design'], install_requires=["pyparsing", "six"], include_package_data=True, package_data={ 'peppercompiler': ['_spuriousSSM', 'SpuriousDesign/spuriousSSM.c'] }, test_suite='peppercompiler.tests', cmdclass={'build': build_with_spurious, 'develop': develop_with_spurious}, entry_points={ 'console_scripts': [ 'pepper-compiler = peppercompiler.compiler:main', 'pepper-design-spurious = peppercompiler.design.spurious_design:main', 'pepper-finish = peppercompiler.finish:main', 'spuriousSSM = peppercompiler._spuriousSSM_wrapper:main' ] }, author="Constantine Evans et al (this version)", author_email="cge@dna.caltech.edu", description="PepperCompiler in a pythonic form")

29.709091

106

0.641983

00004b28f6ae2b9a9b673b26fbf0fba70c90416d

1,126

py

Python

client/client.py

flavioribeiro/playmobil

d104b80fd666158e7ae3d1e28ce8d3ba68e93a68

[ "Apache-2.0" ]

1

2016-10-27T21:30:30.000Z

client/client.py

flavioribeiro/playmobil

d104b80fd666158e7ae3d1e28ce8d3ba68e93a68

[ "Apache-2.0" ]

null

client/client.py

flavioribeiro/playmobil

d104b80fd666158e7ae3d1e28ce8d3ba68e93a68

[ "Apache-2.0" ]

null

import sys sys.path.append("/Library/Frameworks/GStreamer.framework/Versions/0.10/lib/python2.7/site-packages/") import gobject gobject.threads_init() import pygst pygst.require("0.10") import gst client = Client() client.start() loop = gobject.MainLoop() loop.run()

34.121212

101

0.71492

00005f4a70c5144076952dbbb1c77de24a5e43d7

3,852

py

Python

InternetSemLimites/api/tests/test_edit_view.py

InternetSemLimites/PublicAPI

3dd0f17fe66688ef2895de540950f45d69bcd9d8

[ "MIT" ]

18

2016-04-14T17:03:29.000Z

2020-01-01T00:54:03.000Z

InternetSemLimites/api/tests/test_edit_view.py

InternetSemLimites/PublicAPI

3dd0f17fe66688ef2895de540950f45d69bcd9d8

[ "MIT" ]

48

2016-04-15T12:33:33.000Z

2018-01-25T16:01:45.000Z

InternetSemLimites/api/tests/test_edit_view.py

InternetSemLimites/PublicAPI

3dd0f17fe66688ef2895de540950f45d69bcd9d8

[ "MIT" ]

4

2016-04-15T07:57:04.000Z

2017-09-10T18:10:40.000Z

from django.contrib.auth.models import User from django.core import mail from django.shortcuts import resolve_url from django.test import TestCase from InternetSemLimites.core.forms import ProviderForm from InternetSemLimites.core.models import Provider, State

39.306122

98

0.637072

000066c365ae5f522b2bf93ad57e924347136b00

2,780

py

Python

expr_parse_tree.py

PlaskED/z3-python

e433af3ce7ebc9547630e4528ce6d38d1a5aefd6

[ "MIT" ]

null

expr_parse_tree.py

PlaskED/z3-python

e433af3ce7ebc9547630e4528ce6d38d1a5aefd6

[ "MIT" ]

null

expr_parse_tree.py

PlaskED/z3-python

e433af3ce7ebc9547630e4528ce6d38d1a5aefd6

[ "MIT" ]

null

from z3 import * CONNECTIVE_OPS = [Z3_OP_NOT,Z3_OP_AND,Z3_OP_OR,Z3_OP_IMPLIES,Z3_OP_IFF,Z3_OP_ITE] REL_OPS = [Z3_OP_EQ,Z3_OP_LE,Z3_OP_LT,Z3_OP_GE,Z3_OP_GT] OPERATORS = CONNECTIVE_OPS + REL_OPS # var is tuple (type, name) # creates a constraint which allow only key variable to change # Returns root of constructed tree def constructTree(variables, expr): root = Node(expr) print "constructing tree from ", expr for child in expr.children(): root.insert(child) s = Solver() return root variables = {x[1]:createVar(x) for x in [['bool','A'], ['bool','B'], ['bool','C']]} expr = createExpression(variables, "And(Or(v['A'],v['B']), v['C'])") r = constructTree(variables, expr) r.printTree()

24.60177

83

0.614748

000069dbeffca39b13535cbec664af30b8b425d2

351

py

Python

algorithm-study/therory/selectionSort.py

Seongkyun-Yu/TIL

2be6a2a68246bc98996b1421e2cc20e025c876ed

[ "MIT" ]

1

2020-02-17T15:15:55.000Z

algorithm-study/therory/selectionSort.py

Seongkyun-Yu/TIL

2be6a2a68246bc98996b1421e2cc20e025c876ed

[ "MIT" ]

6

2020-07-31T17:03:56.000Z

2022-02-27T04:17:57.000Z

algorithm-study/therory/selectionSort.py

Seongkyun-Yu/TIL

2be6a2a68246bc98996b1421e2cc20e025c876ed

[ "MIT" ]

null

import random data_list = random.sample(range(100), 50) print(selectionSort(data_list))

18.473684

42

0.65812

00021f532b1a8ddd0a8c15783c8737edde030453

12,045

py

Python

platform/core/polyaxon/monitor_statuses/monitor.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/polyaxon/monitor_statuses/monitor.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/polyaxon/monitor_statuses/monitor.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

import logging from typing import Any, Mapping import redis import conf import ocular import workers from constants.experiment_jobs import get_experiment_job_uuid from db.redis.containers import RedisJobContainers from db.redis.statuses import RedisStatuses from lifecycles.jobs import JobLifeCycle from options.registry.container_names import ( CONTAINER_NAME_BUILD_JOBS, CONTAINER_NAME_EXPERIMENT_JOBS, CONTAINER_NAME_JOBS, CONTAINER_NAME_PLUGIN_JOBS, CONTAINER_NAME_PYTORCH_JOBS, CONTAINER_NAME_TF_JOBS ) from options.registry.spawner import ( APP_LABELS_DOCKERIZER, APP_LABELS_EXPERIMENT, APP_LABELS_JOB, APP_LABELS_NOTEBOOK, APP_LABELS_TENSORBOARD, ROLE_LABELS_DASHBOARD, ROLE_LABELS_WORKER, TYPE_LABELS_RUNNER ) from options.registry.ttl import TTL_WATCH_STATUSES from polyaxon.settings import K8SEventsCeleryTasks logger = logging.getLogger('polyaxon.monitors.statuses')

39.491803

94

0.594687

0003b8ab877e0ee926932cb4211e0799fd7d5511

14,636

py

Python

flow65/wing_tool.py

corygoates/Flow65

148eddaeeed8711eae37a16820215c89f93f01d5

[ "MIT" ]

null

flow65/wing_tool.py

corygoates/Flow65

148eddaeeed8711eae37a16820215c89f93f01d5

[ "MIT" ]

null

flow65/wing_tool.py

corygoates/Flow65

148eddaeeed8711eae37a16820215c89f93f01d5

[ "MIT" ]

null

import sys import json import numpy as np import matplotlib.pyplot as plt if __name__=="__main__": # Read in input input_file = sys.argv[-1] with open(input_file, 'r') as input_handle: input_dict = json.load(input_handle) # Initialize wing wing_dict = input_dict["wing"] washout_dict = input_dict["wing"]["washout"] aileron_dict = input_dict["wing"]["aileron"] wing = Wing(planform=wing_dict["planform"]["type"], AR=wing_dict["planform"]["aspect_ratio"], RT=wing_dict["planform"].get("taper_ratio"), CL_a_section=wing_dict["airfoil_lift_slope"], washout=washout_dict["distribution"], washout_mag=washout_dict["magnitude[deg]"], washout_CLd=washout_dict["CL_design"], aileron_lims=[aileron_dict["begin[z/b]"], aileron_dict["end[z/b]"]], aileron_cf=[aileron_dict["begin[cf/c]"], aileron_dict["end[cf/c]"]], aileron_hinge_eff=aileron_dict["hinge_efficiency"]) # Set up grid wing.set_grid(wing_dict["nodes_per_semispan"]) # Set condition cond_dict = input_dict["condition"] wing.set_condition(alpha=cond_dict["alpha_root[deg]"], da=cond_dict["aileron_deflection[deg]"], p_bar=cond_dict["pbar"]) # Solve wing.solve() print() print("Wing") print(" Type: {0}".format(wing._planform_type)) print(" Aspect Ratio: {0}".format(wing._AR)) try: print(" Taper Ratio: {0}".format(wing._RT)) except AttributeError: pass print(" Nodes: {0}".format(wing._N)) print() print("Condition") print(" Alpha: {0} deg".format(np.degrees(wing._alpha))) print(" p_bar: {0}".format(wing.p_bar)) print() print("Aerodynamic Coefficients") print(" CL: {0}".format(wing.CL)) print(" CD_i (without roll and aileron effects): {0}".format(wing.CD_i_simp)) print(" CD_i (with roll and airleron effects): {0}".format(wing.CD_i)) print(" Cl: {0}".format(wing.Cl)) print(" Cn: {0}".format(wing.Cn)) print() print("Planform Effects") print(" CL,a: {0}".format(wing.CL_a)) print(" K_L: {0}".format(wing.K_L)) print(" K_D: {0}".format(wing.K_D)) print(" Span efficiency: {0}".format(wing.e_s)) print() print("Washout Effects") print(" Washout effectiveness: {0}".format(wing.e_omega)) print(" K_DL: {0}".format(wing.K_DL)) print(" Washout contribution to induced drag: {0}".format(wing.K_Domega)) print(" K_Do: {0}".format(wing.K_Do)) print() print("Aileron Effects") print(" Cl,da: {0}".format(wing.Cl_da)) print() print("Roll Effects") print(" Cl,p: {0}".format(wing.Cl_p)) # Check for plot requests if input_dict["view"]["planform"]: wing.plot_planform() if input_dict["view"]["washout_distribution"]: wing.plot_washout() if input_dict["view"]["aileron_distribution"]: wing.plot_aileron() # Write solution with open("Solution.txt", 'w') as f: C_str = np.array2string(wing._C) C_inv_str = np.array2string(wing._C_inv) a_n_str = np.array2string(wing._a_n) b_n_str = np.array2string(wing._b_n) c_n_str = np.array2string(wing._c_n) d_n_str = np.array2string(wing._d_n) print("C array", file=f) print(C_str, file=f) print("C_inv array", file=f) print(C_inv_str, file=f) print("a_n", file=f) print(a_n_str, file=f) print("b_n", file=f) print(b_n_str, file=f) print("c_n", file=f) print(c_n_str, file=f) print("d_n", file=f) print(d_n_str, file=f)

36.227723

197

0.583903

000445b6df45545c85a37ba990d9f92d918eec00

271

py

Python

tutorial/listTask/models.py

PabloSuarez/API_django

522cc5b052c13c38fc7ef95353b8e3640126feaa

[ "MIT" ]

null

tutorial/listTask/models.py

PabloSuarez/API_django

522cc5b052c13c38fc7ef95353b8e3640126feaa

[ "MIT" ]

null

tutorial/listTask/models.py

PabloSuarez/API_django

522cc5b052c13c38fc7ef95353b8e3640126feaa

[ "MIT" ]

null

from django.db import models

33.875

60

0.778598

0005a7c39d8aea447086a691df1fb17b38ca23eb

927

py

Python

UAC.py

weareblahs/wsa-auto-install

0633d2b4e36ba50ddbe5b16505b8a09ff764df26

[ "MIT" ]

76

2021-10-29T23:41:26.000Z

2021-12-09T06:31:04.000Z

UAC.py

weareblahs/wsa-auto-install

0633d2b4e36ba50ddbe5b16505b8a09ff764df26

[ "MIT" ]

7

2021-11-10T19:05:26.000Z

2021-12-07T15:53:43.000Z

UAC.py

weareblahs/wsa-auto-install

0633d2b4e36ba50ddbe5b16505b8a09ff764df26

[ "MIT" ]

16

2021-11-06T06:17:58.000Z

2021-12-08T22:08:24.000Z

if __name__ == '__main__': elevate() main()

38.625

91

0.593312

0006b94d0b62d69c3ab03500298cb1fb2775bd17

629

py

Python

etikihead/urls.py

hodeld/etiki-prototype1

bcae893423519f6ddfa4f67b980066e04062d9f3

[ "MIT" ]

1

2019-08-31T18:04:39.000Z

etikihead/urls.py

hodeld/etiki-prototype1

bcae893423519f6ddfa4f67b980066e04062d9f3

[ "MIT" ]

19

2019-12-12T01:38:49.000Z

2022-03-12T00:26:14.000Z

etikihead/urls.py

hodeld/etiki-prototype1

bcae893423519f6ddfa4f67b980066e04062d9f3

[ "MIT" ]

null

from django.urls import path from django.conf import settings from django.conf.urls.static import static from . import views app_name = 'etikihead' urlpatterns = [ path('', views.entry_mask, name='entrymask'), path('contact/', views.contact, name='contact'), path('privacy/', views.privacy, name='privacy'), path('terms/', views.legal, name='legal'), path('impressum/', views.impressum, name='impressum'), path('about/', views.about, name='about'), path('faq/', views.faq, name='faq'), path('todo/', views.todo, name='todo'), path('startinfo/', views.startinfo, name='startinfo'), # ]

26.208333

61

0.658188

0007473466f54c5bf5a586f0d058eba177c13018

1,070

py

Python

test_proj/test_tcA002.py

leeltib/vizsgamunka_ref

59dc64d499c32a548a6e83c251cf16e2787e8672

[ "MIT" ]

null

test_proj/test_tcA002.py

leeltib/vizsgamunka_ref

59dc64d499c32a548a6e83c251cf16e2787e8672

[ "MIT" ]

null

test_proj/test_tcA002.py

leeltib/vizsgamunka_ref

59dc64d499c32a548a6e83c251cf16e2787e8672

[ "MIT" ]

null

# TC002 test case - Login in with new user data - exit import data.data_tcA002 as da02 import func.func_01 as fu01 from selenium import webdriver from selenium.webdriver.common.by import By import time from selenium.webdriver.chrome.options import Options from webdriver_manager.chrome import ChromeDriverManager options = Options() options.headless = True driver = webdriver.Chrome(executable_path=ChromeDriverManager().install(), options=options) driver.get("http://localhost:1667") # Wait for loading fu01.wait(driver, By.ID, "app", 2) # *** TC-A002 ************************************** username_text = test_A002() # *************************************************** # Normal run if __name__ == "__main__": print(username_text) try: assert da02.name == username_text except: print("Hiba, az ellenrz felttelnl nincs egyezs.")

23.26087

91

0.675701

000803fab8613a18bfb601cb4e0f3433d97e4dce

966

py

Python

lisc/tests/test_data_utils.py

jasongfleischer/lisc

ed30be957d7ce13ccbac51092990869840e6f176

[ "Apache-2.0" ]

1

2020-05-11T18:36:16.000Z

lisc/tests/test_data_utils.py

jasongfleischer/lisc

ed30be957d7ce13ccbac51092990869840e6f176

[ "Apache-2.0" ]

null

lisc/tests/test_data_utils.py

jasongfleischer/lisc

ed30be957d7ce13ccbac51092990869840e6f176

[ "Apache-2.0" ]

null

"""Tests for the data utilities from lisc.""" from lisc.data.utils import * ################################################################################################### ###################################################################################################

23.560976

99

0.471014

0008876f23a1dced29f967f65132c7d09b0756dc

2,316

py

Python

repos/system_upgrade/el8toel9/actors/firewalldcheckallowzonedrifting/actor.py

tmds/leapp-repository

7c9ea115a68530eb25f5c23d3fcadd60c501bf78

[ "Apache-2.0" ]

null

repos/system_upgrade/el8toel9/actors/firewalldcheckallowzonedrifting/actor.py

tmds/leapp-repository

7c9ea115a68530eb25f5c23d3fcadd60c501bf78

[ "Apache-2.0" ]

1

2022-03-07T15:34:11.000Z

2022-03-07T15:35:15.000Z

repos/system_upgrade/el8toel9/actors/firewalldcheckallowzonedrifting/actor.py

tmds/leapp-repository

7c9ea115a68530eb25f5c23d3fcadd60c501bf78

[ "Apache-2.0" ]

null

from leapp import reporting from leapp.actors import Actor from leapp.models import FirewalldGlobalConfig, FirewallsFacts from leapp.reporting import create_report, Report from leapp.tags import ChecksPhaseTag, IPUWorkflowTag

44.538462

91

0.642055

0009620a33b624fda2004552df089e8ea26f0972

665

py

Python

eeve/eeve actions/list_dir.py

vMarcelino/eeve

7dcfa17d34480f5c120ce963680babffff8ab412

[ "Apache-2.0" ]

1

2019-10-11T18:42:48.000Z

eeve/eeve actions/list_dir.py

vMarcelino/eeve

7dcfa17d34480f5c120ce963680babffff8ab412

[ "Apache-2.0" ]

null

eeve/eeve actions/list_dir.py

vMarcelino/eeve

7dcfa17d34480f5c120ce963680babffff8ab412

[ "Apache-2.0" ]

1

2019-10-11T18:42:49.000Z

import os def run(path: str, return_full_path: bool = False): """Gets all files and folders from a path and stores them into $file_list Arguments: path {str} -- The path to get files and folders from Keyword Arguments: return_full_path {bool} -- True to return the full path of the file instead of just the file name (default: {False}) Returns: file_list {List[str]} -- list of files and folders """ result = os.listdir(path) if return_full_path: for i, f in enumerate(result): result[i] = os.path.join(path, f) return {'file_list': result} actions = {"list dir": run}

27.708333

124

0.627068

0009cacc81bd5d1ceb8972e6ec2ff4235cfdb2ad

11,938

py

Python

tests/test_workspaces.py

jeokrohn/wxc_sdk

e28b7e0f870d17b7f9a79ad9a4b8af221e58f8e9

[ "MIT" ]

null

tests/test_workspaces.py

jeokrohn/wxc_sdk

e28b7e0f870d17b7f9a79ad9a4b8af221e58f8e9

[ "MIT" ]

null

tests/test_workspaces.py

jeokrohn/wxc_sdk

e28b7e0f870d17b7f9a79ad9a4b8af221e58f8e9

[ "MIT" ]

1

2022-03-29T18:56:59.000Z

""" Test for workspaces API """ # TODO: tests for authorization codes import random from collections.abc import Generator from concurrent.futures import ThreadPoolExecutor from contextlib import contextmanager from wxc_sdk.rest import RestError from wxc_sdk.all_types import * from .base import TestCaseWithLog TEST_WORKSPACES_PREFIX = 'workspace test '

43.410909

116

0.627576

000aa2371b1616577368d0ba5de43105bfebe942

1,935

py

Python

openpose/data/parse_tfrecord.py

calmisential/Pose_Estimation

f3546fcfdc81ef60708fbda5fc1eb499679fff2f

[ "MIT" ]

null

openpose/data/parse_tfrecord.py

calmisential/Pose_Estimation

f3546fcfdc81ef60708fbda5fc1eb499679fff2f

[ "MIT" ]

null

openpose/data/parse_tfrecord.py

calmisential/Pose_Estimation

f3546fcfdc81ef60708fbda5fc1eb499679fff2f

[ "MIT" ]

null

import tensorflow as tf import glob from configuration import OpenPoseCfg as cfg from openpose.data.augmentation import Transformer

34.553571

106

0.724548

000ac16fad7b0087c58e7dce0cb0032d35a1586a

209

py

Python

ex015a.py

emerfelippini/Curso_em_video-Aulas_Python

5b1d78b259732bb9bbad27cd30ce91bba77c5ef0

[ "MIT" ]

null

ex015a.py

emerfelippini/Curso_em_video-Aulas_Python

5b1d78b259732bb9bbad27cd30ce91bba77c5ef0

[ "MIT" ]

null

ex015a.py

emerfelippini/Curso_em_video-Aulas_Python

5b1d78b259732bb9bbad27cd30ce91bba77c5ef0

[ "MIT" ]

null

dia = int(input('Quantos dias alugados? ')) km = float(input('Quantos KM rodados? ')) print('Como voc ficou {} dias com ele e rodou {:.2f}KM, sua conta ficou em {:.2f}R$'.format(dia, km, (60*dia)+(0.15*km)))

52.25

122

0.645933

000bdce1c17e5a0d04aad4788cb96fae87e895ca

775

py

Python

xiaomirouter/client/ip.py

RiRomain/python-xiaomi-router

36867d077349a70678db75cf261428cdd80a0c51

[ "MIT" ]

null

xiaomirouter/client/ip.py

RiRomain/python-xiaomi-router

36867d077349a70678db75cf261428cdd80a0c51

[ "MIT" ]

null

xiaomirouter/client/ip.py

RiRomain/python-xiaomi-router

36867d077349a70678db75cf261428cdd80a0c51

[ "MIT" ]

null

""" Ip Info """

22.794118

76

0.624516

000c601e3d23cebb7bc381bcb7e6d2611c317a74

1,608

py

Python

medicine/users/views.py

walkeknow/medicine

b5f68ca0c820eb5d46852c3d72e926af59079745

[ "MIT" ]

null

medicine/users/views.py

walkeknow/medicine

b5f68ca0c820eb5d46852c3d72e926af59079745

[ "MIT" ]

null

medicine/users/views.py

walkeknow/medicine

b5f68ca0c820eb5d46852c3d72e926af59079745

[ "MIT" ]

null

from django.shortcuts import render, redirect, HttpResponseRedirect from django.contrib import messages from django.contrib.auth import authenticate, login, logout from django.contrib.auth.decorators import login_required from django.contrib.auth.forms import UserCreationForm from django.urls import reverse # Create your views here. # def login(request): # if request.method == 'POST': # print("wow!!!!!!!!!!!!!!!!!!!!!") # messages.success(request, f'Welcome Back username!') # return render(request, 'lifecare/result.html') # else: # print("no!!!!!")

34.212766

71

0.643657

000df0f38e45fa3a6986ef9af7fbf9e539d0a092

689

py

Python

gbdxtools/images/quickbird.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

81

2016-04-05T23:32:46.000Z

2022-01-02T21:21:09.000Z

gbdxtools/images/quickbird.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

624

2016-04-06T22:22:01.000Z

2022-01-03T17:48:50.000Z

gbdxtools/images/quickbird.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

66

2016-04-13T22:45:37.000Z

2022-01-03T18:03:26.000Z

from gbdxtools.images.worldview import WorldViewImage from gbdxtools.images.geoeye01 import GeoEyeDriver from gbdxtools.images.util import vector_services_query band_types = { 'MS': 'BGRN', 'Panchromatic': 'PAN', 'Pan': 'PAN', 'pan': 'PAN' }

25.518519

93

0.690856

000efb3a697a68b72463aeb6cc6355689b2f13ed

102

py

Python

meld/plugins/move/main.py

dersal-dev/meld

bb746e8c5a2eec9f0aff5fbbfd698c6d43fa8e93

[ "MIT" ]

null

meld/plugins/move/main.py

dersal-dev/meld

bb746e8c5a2eec9f0aff5fbbfd698c6d43fa8e93

[ "MIT" ]

null

meld/plugins/move/main.py

dersal-dev/meld

bb746e8c5a2eec9f0aff5fbbfd698c6d43fa8e93

[ "MIT" ]

null

from meld import logger

17

39

0.745098

000f226aca878e3b01ee23b36d3e3744fe747d69

1,137

py

Python

fastISM/models/bpnet.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

12

2020-09-20T17:03:48.000Z

2022-03-16T06:51:52.000Z

fastISM/models/bpnet.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

5

2020-10-24T20:43:45.000Z

2022-02-25T19:40:47.000Z

fastISM/models/bpnet.py

kundajelab/fastISM

1573feccba1ad5d9f1cee508f5bb03c4aa09bb2b

[ "MIT" ]

2

2020-10-14T05:18:55.000Z

2022-02-21T07:34:14.000Z

import tensorflow as tf

34.454545

88

0.62533

000fb685fc9f26f073890df0d180e999e12bb012

801

py

Python

leetcode/108-Convert-Sorted-Array-To-Binary-Search-Tree/answer.py

vaishali-bariwal/Practice-Coding-Questions

747bfcb1cb2be5340daa745f2b9938f0ee87c9ac

[ "Unlicense" ]

25

2018-05-22T15:18:50.000Z

2022-01-08T02:41:46.000Z

leetcode/108-Convert-Sorted-Array-To-Binary-Search-Tree/answer.py

vaishali-bariwal/Practice-Coding-Questions

747bfcb1cb2be5340daa745f2b9938f0ee87c9ac

[ "Unlicense" ]

1

2019-05-24T16:55:27.000Z

leetcode/108-Convert-Sorted-Array-To-Binary-Search-Tree/answer.py

vaishali-bariwal/Practice-Coding-Questions

747bfcb1cb2be5340daa745f2b9938f0ee87c9ac

[ "Unlicense" ]

18

2018-09-20T15:39:26.000Z

2022-03-02T21:38:22.000Z

#!/usr/bin/env python3 #------------------------------------------------------------------------------- # Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None #------------------------------------------------------------------------------- # Testing

27.62069

80

0.400749

0011a0ed8ff671ab9d03956548a06f24d014ab20

600

py

Python

src/collect/migrations/0006_alter_courseinfo_name.py

YanaPIIDXer/HorseRaceInfo

af5e8734d31da6e829371209366c0c58585ab373

[ "MIT" ]

null

src/collect/migrations/0006_alter_courseinfo_name.py

YanaPIIDXer/HorseRaceInfo

af5e8734d31da6e829371209366c0c58585ab373

[ "MIT" ]

null

src/collect/migrations/0006_alter_courseinfo_name.py

YanaPIIDXer/HorseRaceInfo

af5e8734d31da6e829371209366c0c58585ab373

[ "MIT" ]

null

# Generated by Django 3.2.6 on 2021-08-14 19:48 from django.db import migrations, models

31.578947

259

0.565

0011bad4390288b5a901919fe11d0ebf83273af9

596

py

Python

tests/test_filter.py

rdemaria/xpart

35fe06eeb508991dfe1dd23685331f8347d0b603

[ "MIT" ]

1

2021-09-07T14:34:10.000Z

tests/test_filter.py

rdemaria/xpart

35fe06eeb508991dfe1dd23685331f8347d0b603

[ "MIT" ]

null

tests/test_filter.py

rdemaria/xpart

35fe06eeb508991dfe1dd23685331f8347d0b603

[ "MIT" ]

5

2021-11-04T08:23:43.000Z

2022-03-16T10:34:23.000Z

import numpy as np import xobjects as xo import xpart as xp

25.913043

56

0.552013

001513d4c6890aca681f0ade18ed556b34353f85

4,550

py

Python

main.py

NimaVahdat/Image-Categorization

4addce895b14c0c663e3ee317ffcd802b774452b

[ "MIT" ]

null

main.py

NimaVahdat/Image-Categorization

4addce895b14c0c663e3ee317ffcd802b774452b

[ "MIT" ]

null

main.py

NimaVahdat/Image-Categorization

4addce895b14c0c663e3ee317ffcd802b774452b

[ "MIT" ]

null

from utils.loader import Loader from utils.model import DeepSNN import torch import os # %% Caltech = { "name" : "Caltech", "epochs_l1" : 20, "epochs_l2" : 100, "weight_mean" : 0.8, "weight_std" : 0.05, "lr" : (0.005, -0.0025), "in_channel1" : 4, "in_channel2" : 40, "out_channel" : 150, "k1" : 10, "k2" : 25, "r1" : 0, "r2" : 2,} train_X, train_y, test_X, test_y, weights = feature_extraction(Caltech) predicted_train, predicted_test = Classification(train_X, train_y, test_X, test_y) n = performance(train_X, train_y, predicted_train) m = performance(test_X, test_y, predicted_test) print(n) print(m) labels = ['Airplane', 'Car_side', 'Faces_easy', 'Motorbikes'] confussion_matrix(test_y, predicted_test, labels) # %% MNIST = {"name" : "MNIST", "epochs_l1":2, "epochs_l2":20, "weight_mean" : 0.8, "weight_std" : 0.05, "lr" : (0.004, -0.003), "in_channel1" : 2, "in_channel2" : 32, "out_channel" : 150, "k1" : 5, "k2" : 8, "r1" : 2, "r2" : 1,} train_X, train_y, test_X, test_y, weights = feature_extraction(MNIST) predicted_train, predicted_test = Classification(train_X, train_y, test_X, test_y) n = performance(train_X, train_y, predicted_train) m = performance(test_X, test_y, predicted_test) print(n) print(m) labels = ['0','1','2','3','4','5','6','7','8','9'] confussion_matrix(test_y, predicted_test, labels) # %% # import cv2 # import numpy as np # w1, w2 = weights # w1 = torch.reshape(w1, (160, 5, 5)) # # w2 = torch.reshape(w2, (6000, 2, 2)) # def features_pic(w, i): # # w = torch.squeeze(w) # w -= w.min() # w = (w/w.max()) * 255 # pic = cv2.resize(np.array(w), (100, 100)) # cv2.imwrite("features/feature" + str(i) + ".jpg", pic) # for i in range(len(w1)): # features_pic(w1[i], i)

28.980892

86

0.575824

0015c071b2b5e64af9f371ce3c6931d15a42c7e7

296

py

Python

python-basic/set/set_comprehension.py

nkhn37/python-tech-sample-source

e8aea7ed3d810494682b3c2dde952ddd0f7acf84

[ "MIT" ]

null

python-basic/set/set_comprehension.py

nkhn37/python-tech-sample-source

e8aea7ed3d810494682b3c2dde952ddd0f7acf84

[ "MIT" ]

null

python-basic/set/set_comprehension.py

nkhn37/python-tech-sample-source

e8aea7ed3d810494682b3c2dde952ddd0f7acf84

[ "MIT" ]

null

""" [] https://tech.nkhn37.net/python-set-comprehension/#i [] https://tech.nkhn37.net/python-comprehension/ """ data = [10, 15, 20, 25, 30, 35, 40, 45, 50, 10, 15, 20, 25, 30] # data_set = {dt for dt in data if dt % 2 == 0} print(f'data_set : {data_set}')

19.733333

63

0.655405

00198cc9e3c841bb01a56c333dd3c279b3334a56

9,789

py

Python

honeycomb/worker_bee.py

agrc/honeycomb

a4227221759541b007c2d2a8dcfca5a40192eeff

[ "MIT" ]

1

2018-06-07T13:17:40.000Z

honeycomb/worker_bee.py

agrc/honeycomb

a4227221759541b007c2d2a8dcfca5a40192eeff

[ "MIT" ]

24

2017-08-28T19:53:15.000Z

2022-03-28T21:36:37.000Z

honeycomb/worker_bee.py

agrc/honeycomb

a4227221759541b007c2d2a8dcfca5a40192eeff

[ "MIT" ]

null

#!/usr/bin/env python # * coding: utf8 * ''' worker_bee.py A module that contains logic for building traditional image-based caches. ''' import os import socket import time from os.path import join, dirname, realpath from shutil import rmtree import pygsheets from datetime import date import arcpy from . import config, settings, update_data from .messaging import send_email spot_cache_name = 'spot cache' error_001470_message = 'ERROR 001470: Failed to retrieve the job status from server. The Job is running on the server, please use the above URL to check the job status.\nFailed to execute (ManageMapServerCacheTiles).\n' # noqa

42.934211

227

0.635611

001dcd97b7d4ca23335c414e226aaa113e8d7e20

4,418

py

Python

src/smooth.py

DQSSSSS/FluidSimulation

92ec2b45bc3ebaf39eeeec37f07fb7cc2988ce6d

[ "MIT" ]

null

src/smooth.py

DQSSSSS/FluidSimulation

92ec2b45bc3ebaf39eeeec37f07fb7cc2988ce6d

[ "MIT" ]

1

2021-11-12T03:16:59.000Z

2021-11-12T04:47:50.000Z

src/smooth.py

DQSSSSS/FluidSimulation

92ec2b45bc3ebaf39eeeec37f07fb7cc2988ce6d

[ "MIT" ]

null

import taichi as ti PI = 3.1415926 # http://www.glowinggoo.com/sph/bin/kelager.06.pdf if __name__ == '__main__': ti.init(arch=ti.cpu, debug=True) test()

27.104294

125

0.4579

0020df4e44bafa386b7d0a3f1bc4997c91a46d31

747

py

Python

Ezan.py

Jandro46t/Python

4f604215df085ad18dabfa52fe7863bad97863ab

[ "Apache-2.0" ]

2

2021-04-16T11:58:39.000Z

Ezan.py

Jandro46t/Python

4f604215df085ad18dabfa52fe7863bad97863ab

[ "Apache-2.0" ]

null

Ezan.py

Jandro46t/Python

4f604215df085ad18dabfa52fe7863bad97863ab

[ "Apache-2.0" ]

null

import requests import json url_sehir = "https://ezanvakti.herokuapp.com/sehirler/2" r = requests.get(url_sehir) j = r.json() sehir_adi = input("ehir:") ilce_adi = input("le:") for sehir in j: if sehir_adi == sehir["SehirAdi"]: ID = sehir["SehirID"] print(ID) url_ilce = "https://ezanvakti.herokuapp.com/ilceler/{}".format(ID) re = requests.get(url_ilce) je = re.json() for ilce in je: if ilce_adi == ilce["IlceAdi"]: PD = ilce["IlceID"] print(PD) url_vakit = "https://ezanvakti.herokuapp.com/vakitler/{}".format(PD) res = requests.get(url_vakit) jes = res.json() muzo = jes[0] for vakit in muzo: print(vakit,":",muzo[vakit]) input("kmak iin herhangi bir tua bas")

29.88

69

0.634538