xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

0587d07321592ddb102cc4ed98640454fd0d67f7

4,589

py

Python

RockPaperScissors.py

andreimaftei28/projects-on-JetBrainAcademy

8c2b8ab7bab5757db94e9f0b6d55c33852f64ee1

[ "MIT" ]

null

RockPaperScissors.py

andreimaftei28/projects-on-JetBrainAcademy

8c2b8ab7bab5757db94e9f0b6d55c33852f64ee1

[ "MIT" ]

null

RockPaperScissors.py

andreimaftei28/projects-on-JetBrainAcademy

8c2b8ab7bab5757db94e9f0b6d55c33852f64ee1

[ "MIT" ]

3

2020-12-19T13:48:06.000Z

2021-08-12T18:36:33.000Z

"""Rock Paper Scisssors game using OOP""" import random from tempfile import mkstemp from shutil import move, copymode from os import fdopen, remove class RockPaperScissors: """initializing the 'global' atributtes""" def __init__(self): self.defeat = {"scissors": "rock", "paper" : "scissors", "rock" : "paper"} self.choices = ["rock", "paper", "scissors"] self.score = 0 self.name = input("Enter your name: ") def file(self): """method keeping track of players rating in 'rating.txt' file""" file = open("rating.txt", "r+", encoding="utf-8") for line in file: line1 = line.rstrip() if self.name == line1.split()[0]: score = line1.split()[1] self.score = int(score) self.play() print(line.replace(score, str(self.score)), file=file, flush=True) file.close() break else: if self.name != line1.split()[0]: continue else: score = line1.split()[1] self.play() print(line.replace(score, str(self.score)), file=file, flush=True) file.close() break else: self.play() print(self.name, self.score, sep=" ", file=file, flush=True) file.close() def play(self): """method is checking word imputed by user against the initial dict of words, and increase rating if user wins,or is a draw""" print(f"Hello, {self.name}") self.rewrite_options() print("Okay, let's start") while True: user_input = input("Enter your choice: ") if user_input == "!rating": print(f"Your rating: {self.score}") continue elif user_input == "!exit": print("Bye!") break else: choice = random.choice(self.choices) if user_input not in self.choices: print("Invalid input") elif user_input == choice: self.score += 50 print(f"There is a draw ({choice})") elif user_input in self.defeat[choice]: self.score += 100 print(f"Well done. The computer chose {choice} and failed") else: print(f"Sorry, but the computer chose {choice}") def rewrite_file(self): """method updating rating of all players by rewriting 'rating.txt' file""" names = [] dict_ = {} fake_f = "rating.txt" abs_path = "C:/Users/dandei/Desktop/jetBrain_projects/rock_paper_scissors/rating.txt" #change this with your path fake_f, abs_path = mkstemp() with fdopen(fake_f, "w") as new_file: with open("rating.txt", "r+", encoding="utf-8") as file: content = file.read() content = content.split("\n") for element in content: if len(element) > 1: element = element.split() names.append(element) dict_ = dict(names) for key, value in dict_.items(): print(key, value, sep=" ", file=new_file) copymode("rating.txt", abs_path) remove("rating.txt") move(abs_path, "rating.txt") def rewrite_options(self): """method let's user choose between playing the classic game or palying the game with more options. Changes the initial dict of words as user inputs more options""" choice = input("Enter your game options: ") choices = choice.split(",") defeat_by = {} new_list = [] if choice == "": return None else: self.choices = choices for i in range(len(choices)): new_list = choices[i + 1:] + choices[:i] #wins_over defeat_by[choices[i]] = new_list[:(len(new_list)) // 2] self.defeat = defeat_by #If rating.txt does not exist, it get's created here fill = open("rating.txt", "a", encoding="utf-8") fill.close() #creating instance of the RockPaperScissors class rps = RockPaperScissors() rps.file() rps.rewrite_file()

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0589b9d3ea2a64dcded6b8ab04bba1a44e732a41

2,813

py

Python

src/cbc_binary_toolkit/schemas.py

carbonblack/cbc-binary-toolkit

92c90b80e3c3e0b5c2473ef2086d2ce2fb651db4

[ "MIT" ]

8

2020-05-12T18:08:52.000Z

2021-12-27T06:11:00.000Z

src/cbc_binary_toolkit/schemas.py

carbonblack/cbc-binary-toolkit

92c90b80e3c3e0b5c2473ef2086d2ce2fb651db4

[ "MIT" ]

4

2020-05-13T16:07:49.000Z

2020-06-30T18:47:14.000Z

src/cbc_binary_toolkit/schemas.py

carbonblack/cbc-binary-toolkit

92c90b80e3c3e0b5c2473ef2086d2ce2fb651db4

[ "MIT" ]

3

2020-05-16T19:57:57.000Z

2020-11-01T08:43:31.000Z

# -*- coding: utf-8 -*- # ******************************************************* # Copyright (c) VMware, Inc. 2020-2021. All Rights Reserved. # SPDX-License-Identifier: MIT # ******************************************************* # * # * DISCLAIMER. THIS PROGRAM IS PROVIDED TO YOU "AS IS" WITHOUT # * WARRANTIES OR CONDITIONS OF ANY KIND, WHETHER ORAL OR WRITTEN, # * EXPRESS OR IMPLIED. THE AUTHOR SPECIFICALLY DISCLAIMS ANY IMPLIED # * WARRANTIES OR CONDITIONS OF MERCHANTABILITY, SATISFACTORY QUALITY, # * NON-INFRINGEMENT AND FITNESS FOR A PARTICULAR PURPOSE. """Schemas for Engine Results component""" from schema import And, Or, Optional, Schema IOCv2SEVSchema = Schema( { "id": And(str, len), "match_type": And(str, lambda type: type in ["query", "equality", "regex"]), "values": And([str], len), Optional("field"): And(str, len), Optional("link"): And(str, len), "severity": And(int, lambda n: n > 0 and n < 11) # Needs stripped before sent to CBC } ) IOCv2Schema = Schema( { "id": And(str, len), "match_type": And(str, lambda type: type in ["query", "equality", "regex"]), "values": And([str], len), Optional("field"): And(str, len), Optional("link"): And(str, len) } ) ReportSchema = Schema( { "id": And(str, len), "timestamp": And(int, lambda n: n > 0), "title": And(str, len), "description": And(str, len), "severity": And(int, lambda n: n > 0 and n < 11), Optional("link"): str, Optional("tags"): [str], "iocs_v2": [IOCv2Schema], Optional("visibility"): str } ) EngineResponseSchema = Schema( { "iocs": [IOCv2SEVSchema], "engine_name": And(str, len), "binary_hash": And(str, lambda n: len(n) == 64), "success": bool } ) BinaryMetadataSchema = Schema( { "sha256": And(str, lambda n: len(n) == 64), "url": And(str, len), "architecture": [str], "available_file_size": Or(int, None), "charset_id": Or(int, None), "comments": Or(str, None), "company_name": Or(str, None), "copyright": Or(str, None), "file_available": bool, "file_description": Or(str, None), "file_size": Or(int, None), "file_version": Or(str, None), "internal_name": Or(str, None), "lang_id": Or(int, None), "md5": And(str, lambda n: len(n) == 32), "original_filename": Or(str, None), "os_type": Or(str, None), "private_build": Or(str, None), "product_description": Or(str, None), "product_name": Or(str, None), "product_version": Or(str, None), "special_build": Or(str, None), "trademark": Or(str, None) } )

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058a7c137ede0bf5c3a55a3ce41c3dfb2936df30

2,079

py

Python

src/views/list.py

AllForJan/prizma-backend

fe866e74fa01e900cc7eab624bb5716a4bae056d

[ "MIT" ]

2

2018-04-08T22:18:11.000Z

2018-04-26T08:12:46.000Z

src/views/list.py

AllForJan/prizma-backend

fe866e74fa01e900cc7eab624bb5716a4bae056d

[ "MIT" ]

null

src/views/list.py

AllForJan/prizma-backend

fe866e74fa01e900cc7eab624bb5716a4bae056d

[ "MIT" ]

2

2018-04-08T22:18:13.000Z

2018-04-08T22:18:18.000Z

from elasticsearch import Elasticsearch from flask import request, jsonify from flask_restful import Resource from db.manager import get_conn import settings conn = get_conn() def append_range_filter(f, key, _from, to): d = {} if _from or to: d['range'] = {} d['range'][key] = {} if _from: d['range'][key]['gte'] = _from if to: d['range'][key]['lte'] = to f.append(d) return f class ListPO(Resource): def get(self): q = request.args.get('q', None) es = Elasticsearch( [settings.ELASTIC_HOST, ], timeout=30, max_retries=10, retry_on_timeout=True, port=settings.ELASTIC_PORT ) if not q: query = {'query': {'match_all': {}}} results = es.search(index='apa', doc_type='po', body=query) rows = [{ 'data': r['_source'], '_id': r['_id'] } for r in results['hits']['hits']] return jsonify(rows) rok_from = request.args.get('rok_from', None) rok_to = request.args.get('rok_to', None) suma_from = request.args.get('suma_from', None) suma_to = request.args.get('suma_to') # append filters f = [] append_range_filter(f, 'rok', rok_from, rok_to) append_range_filter(f, 'suma', suma_from, suma_to) query = { "sort": [ {"suma": {"order": "desc"}} ], "query": { "bool": { "must": [ { "match": { "meno": {"query":q, "operator": "and"} } }, ], # "filter": [] } } } query['query']['bool']['must'].extend(f) results = es.search(index='apa', doc_type='po', body=query) rows = [{ 'data': r['_source'], '_id': r['_id'] } for r in results['hits']['hits']] return rows

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null

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null

0

1

0

058f090a9e7707433a3105b87e3e591439fed2ac

8,377

py

Python

code/train/train_model.py

96jhwei/Genetic-U-Net

25116f01afcf8ed4386cd0fc258da15e1c982cb5

[ "MIT" ]

14

2021-09-09T11:22:17.000Z

2022-03-14T10:06:36.000Z

code/train/train_model.py

96jhwei/Genetic-U-Net

25116f01afcf8ed4386cd0fc258da15e1c982cb5

[ "MIT" ]

1

2021-11-24T10:30:36.000Z

code/train/train_model.py

96jhwei/Genetic-U-Net

25116f01afcf8ed4386cd0fc258da15e1c982cb5

[ "MIT" ]

5

2021-11-02T09:29:49.000Z

2022-03-25T09:44:25.000Z

import numpy from torch.utils.data import DataLoader from tqdm import tqdm from loss.FocalLoss import FocalLossForSigmoid import torch from metrics.calculate_metrics import calculate_metrics import shutil from metrics.average_meter import AverageMeter import torch.multiprocessing from torch.nn.utils.clip_grad import clip_grad_norm_ import os import sys import numpy as np import random from thop import profile from .util.get_optimizer import get_optimizer from dataset.util.get_datasets import get_datasets import multiprocessing as mp sys.path.append('../') def train_one_model(optimizer_name, learning_rate, l2_weight_decay, gen_num, ind_num, model, batch_size, epochs, device, train_set_name, valid_set_name, train_set_root, valid_set_root, exp_name, mode='train'): seed = 12 torch.cuda.empty_cache() torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) random.seed(seed) np.random.seed(seed) torch.backends.cudnn.benchmark = True model.to(device) model.train() loss_func = FocalLossForSigmoid(reduction='mean').to(device) optimizer = get_optimizer(optimizer_name, filter(lambda p: p.requires_grad, model.parameters()), learning_rate, l2_weight_decay) train_set, num_return = get_datasets(train_set_name, train_set_root, True) valid_set, _ = get_datasets(valid_set_name, valid_set_root, False) train_loader = DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True, num_workers=3) valid_loader = DataLoader(dataset=valid_set, batch_size=1, shuffle=False, num_workers=1) best_f1_score = 0 flag = 0 count = 0 valid_epoch = 80 metrics_name = ['flops', 'param', 'accuracy', 'recall', 'specificity', 'precision', 'f1_score', 'auroc', 'iou'] metrics = {} for metric_name in metrics_name: if metric_name == 'flops' or metric_name == 'param': metrics.update({metric_name: 100}) else: metrics.update({metric_name: 0}) try: for i in range(epochs): train_tqdm_batch = tqdm(iterable=train_loader, total=numpy.ceil(len(train_set) / batch_size)) for images, targets in train_tqdm_batch: images, targets = images.to(device), targets.to(device) optimizer.zero_grad() preds = model(images) loss = loss_func(preds, targets) loss.backward() clip_grad_norm_(model.parameters(), 0.1) optimizer.step() train_tqdm_batch.close() print('gens_{} individual_{}_epoch_{} train end'.format(gen_num, ind_num, i)) epoch_acc = AverageMeter() epoch_recall = AverageMeter() epoch_precision = AverageMeter() epoch_specificity = AverageMeter() epoch_f1_score = AverageMeter() epoch_iou = AverageMeter() epoch_auroc = AverageMeter() if (i >= valid_epoch): with torch.no_grad(): model.eval() valid_tqdm_batch = tqdm(iterable=valid_loader, total=numpy.ceil(len(valid_set) / 1)) for images, targets in valid_tqdm_batch: images = images.to(device) targets = targets.to(device) preds = model(images) (acc, recall, specificity, precision, f1_score, iou, auroc) = calculate_metrics(preds=preds, targets=targets, device=device) epoch_acc.update(acc) epoch_recall.update(recall) epoch_precision.update(precision) epoch_specificity.update(specificity) epoch_f1_score.update(f1_score) epoch_iou.update(iou) epoch_auroc.update(auroc) if i == valid_epoch: flops, param = profile(model=model, inputs=(images,), verbose=False) flops = flops / 1e11 param = param / 1e6 print('gens_{} individual_{}_epoch_{} validate end'.format(gen_num, ind_num, i)) print('acc:{} | recall:{} | spe:{} | pre:{} | f1_score:{} | auroc:{}' .format(epoch_acc.val, epoch_recall.val, epoch_specificity.val, epoch_precision.val, epoch_f1_score.val, epoch_auroc.val)) if epoch_f1_score.val > best_f1_score: best_f1_score = epoch_f1_score.val flag = i count = 0 for key in list(metrics): if key == 'flops': metrics[key] = flops elif key == 'param': metrics[key] = param elif key == 'accuracy': metrics[key] = epoch_acc.val elif key == 'recall': metrics[key] = epoch_recall.val elif key == 'specificity': metrics[key] = epoch_specificity.val elif key == 'precision': metrics[key] = epoch_precision.val elif key == 'f1_score': metrics[key] = epoch_f1_score.val elif key == 'auroc': metrics[key] = epoch_auroc.val elif key == 'iou': metrics[key] = epoch_iou.val else: raise NotImplementedError import pandas as pd from os.path import join performance_df = pd.DataFrame( data=[[gen_num, ind_num, epoch_acc.val, epoch_recall.val, epoch_specificity.val, epoch_precision.val, epoch_f1_score.val, epoch_iou.val, epoch_auroc.val]], columns=['epoch', 'individual', 'acc', 'recall', 'specificity', 'precision', 'f1_score', 'iou', 'auroc', ] ) performance_csv_path = join(os.path.abspath('.'), 'exps/{}/csv'.format(exp_name), 'gens_{} individual_{} performance.csv'.format(gen_num, ind_num)) performance_df.to_csv(performance_csv_path) else: if i >= valid_epoch: count += 1 end = None if i > valid_epoch + 15 and best_f1_score < 0.50: end = True if (count >= 70) or end: print('current best epoch_{} best_f1_score:'.format(flag), best_f1_score) print('gens_{} individual_{} train early stop'.format(gen_num, ind_num)) print('=======================================================================') valid_tqdm_batch.close() return metrics, True print('current best epoch_{} best_f1_score:'.format(flag), best_f1_score) valid_tqdm_batch.close() print('current best epoch_{} best_f1_score:'.format(flag), best_f1_score) print('=======================================================================') except RuntimeError as exception: images.detach_() del images del model del targets return metrics, False return metrics, True

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null

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null

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1

0

059016200f557d7398f34c3a96008e7fee9686c3

961

py

Python

dataset/check_for_duplicates.py

mathildor/TF-SegNet

dff209c8174b5e8fa77b4c2644298f6903a09445

[ "MIT" ]

98

2017-11-06T15:55:22.000Z

2022-03-22T11:29:47.000Z

dataset/check_for_duplicates.py

yingz9/TF-SegNet

dff209c8174b5e8fa77b4c2644298f6903a09445

[ "MIT" ]

8

2017-11-15T06:05:41.000Z

2019-06-19T06:53:03.000Z

dataset/check_for_duplicates.py

yingz9/TF-SegNet

dff209c8174b5e8fa77b4c2644298f6903a09445

[ "MIT" ]

34

2017-11-06T03:05:54.000Z

2022-01-25T16:00:09.000Z

import os from PIL import Image import numpy from PIL import ImageChops """ TESTED: No duplicates in: - within validation images first part (stopped because of training - took to much time) """ image_path="../../IR_images/combined_dataset/val_images/images" # image_path="../../IR_images/combined_dataset/val_images/images" images = sorted(os.listdir(image_path)) for image_file_1 in images: for image_file_2 in images: image1 = Image.open(os.path.join(image_path,image_file_1)) image2 = Image.open(os.path.join(image_path,image_file_2)) #pixels = image.load() if ImageChops.difference(image1, image2).getbbox() is None: # if(image1==image2):# and image_file_1 != image_file_2): print("Same image!!!") print(image_file_1) print(image_file_2) # else: # print("not same") # print(image_file_1) # print(image_file_2)

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null

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null

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1

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059038232d1c85e48c2eed487377d93d1ad944f4

1,983

py

Python

_posts/import.py

suepeng/suepeng.github.io

844e0063e0604a77886aad5eaea588c4df2792a9

[ "MIT" ]

null

_posts/import.py

suepeng/suepeng.github.io

844e0063e0604a77886aad5eaea588c4df2792a9

[ "MIT" ]

null

_posts/import.py

suepeng/suepeng.github.io

844e0063e0604a77886aad5eaea588c4df2792a9

[ "MIT" ]

null

import os, glob from dateutil import parser from bs4 import BeautifulSoup ext = lambda line, cap: line.replace("\s", "").replace(cap, "").strip() def write_post(doc): meta = { 'title' : ext(doc[0], "TITLE:"), 'date' : parser.parse(ext(doc[2], "DATE:")).strftime("%Y-%m-%d"), 'tag' : ext(doc[3], "PRIMARY CATEGORY:"), 'status': ext(doc[4], "STATUS:"), 'imgs' : BeautifulSoup("".join(doc), features="html.parser").find_all('img'), } if not os.path.exists(meta['tag']): os.makedirs(meta['tag']) fname = f"{meta['tag']}/{meta['date']}-{meta['title'].replace('/', ' ')}.md" publish = 'true' if meta['status'] == 'publish' else 'false' feature = meta['imgs'][0].attrs['src'] if len(meta['imgs']) > 0 else None with open(fname, "wt") as f: # write meta f.write("---\n") f.write(f"layout: post\n") f.write(f"title: {meta['title']}\n") f.write(f"date: {meta['date']}\n") f.write(f"tag: {meta['tag']}\n") if feature: f.write(f"feature: \"{feature}\"\n") f.write(f"published: {publish} \n") f.write("---\n") # write boddy body = False for d in doc: if (d[:3] == '---'): continue if ('' in d): d = d.replace('', "").strip() if len(d) > 0 and body: f.write(d) body = ('BODY' in d) or body print(f"done {fname}") return True #------------------------------ # Main #------------------------------ if __name__ == "__main__": posts = 0 doc = [] for idx, line in enumerate(open("raw.txt").readlines()): if len(doc) and ('TITLE:' in line): posts += write_post(doc) doc, meta = [], {} doc.append(line) # latest post posts += write_post(doc) print(f"converted {posts} posts with {idx} lines")

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null

0

null

0

1

0

059744913f1e643dc9fe5a6332d2aff7847d00ed

3,342

py

Python

Project/checking_test2.py

mihdenis85/psycho_test

51bbe82043427d48e80ff36197815212c5c2a14c

[ "MIT" ]

null

Project/checking_test2.py

mihdenis85/psycho_test

51bbe82043427d48e80ff36197815212c5c2a14c

[ "MIT" ]

null

Project/checking_test2.py

mihdenis85/psycho_test

51bbe82043427d48e80ff36197815212c5c2a14c

[ "MIT" ]

null

def specialization(a, spec, jobs): if a>=0 and a<=2: return(spec + ': интерес к данной профессиональной сфере не выражен') elif a>=3 and a<=6: return(spec + ': профессиональная направленность и интерес выражены в средней степени. ' + 'Возможно вам будут интересны такие профессии будущего, как ' + jobs) elif a>=7 and a<=8: return(spec +': профессиональная направленность выражена довольно ярко и отчетливо. ' + 'Вам будут интересны такие профессии, которые будут актуальны в будущем, как '+ jobs) def check_test2(answers): p=0 chez1=0 t=0 z=0 h=0 n=answers[0] if n==1: p+=1 elif n==2: t+=1 n=answers[1] if n==1: chez1+=1 elif n==2: z+=1 n=answers[2] if n==1: h+=1 elif n==2: p+=1 n=answers[3] if n==1: t+=1 elif n==2: chez1+=1 n=answers[4] if n==1: z+=1 elif n==2: h+=1 n=answers[5] if n==1: p+=1 elif n==2: chez1+=1 n=answers[6] if n==1: h+=1 elif n==2: t+=1 n=answers[7] if n==1: chez1+=1 elif n==2: h+=1 n=answers[8] if n==1: t+=1 elif n==2: z+=1 n=answers[9] if n==1: p+=1 elif n==2: z+=1 n=answers[10] if n==1: p+=1 elif n==2: t+=1 n=answers[11] if n==1: chez1+=1 elif n==2: z+=1 n=answers[12] if n==1: h+=1 elif n==2: p+=1 n=answers[13] if n==1: t+=1 elif n==2: chez1+=1 n=answers[14] if n==1: z+=1 elif n==2: h+=1 n=answers[15] if n==1: p+=1 elif n==2: chez1+=1 n=answers[16] if n==1: h+=1 elif n==2: t+=1 n=answers[17] if n==1: chez1+=1 elif n==2: h+=1 n=answers[18] if n==1: t+=1 elif n==2: z+=1 n=answers[19] if n==1: p+=1 elif n==2: z+=1 pechat1=specialization(p, 'Природа', 'ИТ-генетик, Биофармаколог, Архитектор живых систем, Парковый эколог, ГМО-агроном, Портовый эколог, Сельскохозяйственный эколог, Космобиолог, Урбанист-эколог') pechat2=specialization(t, 'Техника', 'Проектировщик композитных конструкций для транспортных средств, Проектировщик нанотехнологических материалов, Глазир, Архитектор территорий, Конструктор новых металлов') pechat3=specialization(chez1, 'Сфера обслуживания', 'Врач, Генетический консультант, Молекулярный диетолог, Тренер творческих занятий, Личный тьютор по эстетическому развитию, Разработчик персональных пенсионных акладов') pechat4=specialization(z, 'Точные науки и музыка(игра на музыкальных инструментах)', 'Музыкант, Танцор, Переводчик фильмов, Энергоаудитор, Оператор Многофункциональных технических комплексов, Агроном экономист, ') pechat5=specialization(h, 'Творческие профессии', 'Создатель спецэффектов, Видеохудожник, Театральный художник, Аранжировщик, Шоураннер, Балетмейстер, Дирижёр, Живописец, Танцор, Режиссер, Художник-технолог, Science-художник, Видеограф, Специалист по озвучиванию и звуковым спецэффектам в кино, Инфо-стилист, Архитектор виртуальности') return pechat1 + ' ' + pechat2 + ' ' + pechat3 + ' ' + pechat4 + ' ' + pechat5

27.85

339

0.578995

470

3,342

4.114894

0.340426

0.08273

0.041365

0.072389

0.271975

0.239917

0.235781

0

0.060144

0.293537

3,342

120

340

27.85

0.759

0

0.672269

0

0.02521

0.399641

0

1

0.016807

false

0

0.02521

0

null

0

null

0

1

0

05975def902880bc29f1fd9e4b623039913f810f

4,003

py

Python

src/upload/upload.py

alliance-genome/agr_ferret

e2ccef16308b1a8a6f1b2a3dde6e29e0530da721

[ "MIT" ]

2

2020-07-22T14:25:00.000Z

2021-09-20T18:29:08.000Z

src/upload/upload.py

alliance-genome/agr_ferret

e2ccef16308b1a8a6f1b2a3dde6e29e0530da721

[ "MIT" ]

6

2019-09-24T14:09:42.000Z

2021-06-07T15:27:55.000Z

src/upload/upload.py

alliance-genome/agr_ferret

e2ccef16308b1a8a6f1b2a3dde6e29e0530da721

[ "MIT" ]

3

2020-12-19T08:57:51.000Z

2020-12-19T08:58:09.000Z

# Functions for use in downloading files. import logging, os, requests, json, hashlib, urllib from requests_toolbelt.utils import dump from retry import retry logger = logging.getLogger(__name__) def create_md5(worker, filename, save_path): # Generate md5 logger.info('{}: Generating md5 hash for {}.'.format(worker, filename)) hash_md5 = hashlib.md5() with open(os.path.join(save_path, filename), 'rb') as f: for chunk in iter(lambda: f.read(4096), b''): hash_md5.update(chunk) logger.info('{}: Finished generating md5 hash: {}'.format(worker, hash_md5.hexdigest())) return hash_md5.hexdigest() def upload_file(worker, filename, save_path, upload_file_prefix, config_info): file_to_upload = {upload_file_prefix: open(os.path.join(save_path, filename), 'rb')} headers = { 'Authorization': 'Bearer {}'.format(config_info.config['API_KEY']) } logger.debug('{}: Attempting upload of data file: {}'.format(worker, os.path.join(save_path, filename))) logger.debug('{}: Attempting upload with header: {}'.format(worker, headers)) logger.info("{}: Uploading data to {}) ...".format(worker, config_info.config['FMS_API_URL']+'/api/data/submit/')) response = requests.post(config_info.config['FMS_API_URL']+'/api/data/submit/', files=file_to_upload, headers=headers) logger.info(response.text) @retry(tries=5, delay=5, logger=logger) def upload_process(worker, filename, save_path, data_type, data_sub_type, config_info): release = config_info.config['ALLIANCE_RELEASE'] upload_file_prefix = '{}_{}_{}'.format(release, data_type, data_sub_type) generated_md5 = create_md5(worker, filename, save_path) # Attempt to grab MD5 for the latest version of the file. logger.debug(config_info.config['FMS_API_URL'] + '/api/datafile/by/{}/{}?latest=true'.format(data_type, data_sub_type)) url_to_check = config_info.config['FMS_API_URL'] + '/api/datafile/by/{}/{}?latest=true'.format(data_type, data_sub_type) chip_response = urllib.request.urlopen(url_to_check) chip_data = data = json.loads(chip_response.read().decode(chip_response.info().get_param('charset') or 'utf-8')) logger.debug('{}: Retrieved API data from chipmunk: {}'.format(worker, chip_data)) # Check for existing MD5 logger.info('{}: Checking for existing MD5 from chipmunk.'.format(worker)) # Logic for uploading new files based on existing and new MD5s. if not chip_data: logger.info('{}: No response received from the FMS. A new file will be uploaded.'.format(worker)) logger.info('{}: File: {}'.format(worker, filename)) upload_file(worker, filename, save_path, upload_file_prefix, config_info) else: existing_md5 = chip_data[0].get('md5Sum') if existing_md5: logger.info('{}: Previous MD5 found: {}'.format(worker, existing_md5)) if existing_md5 == generated_md5: logger.info('{}: Existing MD5 matches the newly generated MD5. The file will not be uploaded.'.format(worker)) logger.info('{}: File: {}'.format(worker, filename)) logger.info('{}: Existing: {} New: {}'.format(worker, existing_md5, generated_md5)) else: logger.info('{}: Existing MD5 does not match the newly generated MD5. A new file will be uploaded.'.format(worker)) logger.info('{}: File: {}'.format(worker, filename)) logger.info('{}: Existing: {} New: {}'.format(worker, existing_md5, generated_md5)) upload_file(worker, filename, save_path, upload_file_prefix, config_info) else: logger.info('{}: Existing MD5 not found. A new file will be uploaded.'.format(worker)) logger.info('{}: File: {}'.format(worker, filename)) logger.info('{}: Existing: {} New: {}'.format(worker, existing_md5, generated_md5)) upload_file(worker, filename, save_path, upload_file_prefix, config_info)

51.320513

131

0.673995

526

4,003

4.937262

0.241445

0.087794

0.048518

0.059299

0.433577

0.401232

0.367347

0.342703

0.313439

0

0.012515

0.181614

4,003

77

132

51.987013

0.78022

0.048214

0

0.236364

0

0.239222

0.017876

0

1

0.054545

false

0

0.054545

0

0.127273

0

null

0

null

0

1

0

05995048419b1dbd1bd29b14c238cf37023f8b47

2,740

py

Python

lib/strider/virt/vagrantbox.py

jcftang/strider

432a68eb1303541b6d955bd6ecf7439d1f9b0d48

[ "Apache-2.0" ]

16

2016-02-10T13:06:50.000Z

2021-02-28T06:21:16.000Z

lib/strider/virt/vagrantbox.py

jcftang/strider

432a68eb1303541b6d955bd6ecf7439d1f9b0d48

[ "Apache-2.0" ]

4

2016-02-20T16:33:40.000Z

2016-05-28T10:46:06.000Z

lib/strider/virt/vagrantbox.py

jcftang/strider

432a68eb1303541b6d955bd6ecf7439d1f9b0d48

[ "Apache-2.0" ]

1

2016-09-01T11:06:56.000Z

import vagrant import os from subprocess import CalledProcessError from strider.common.instance_data import InstanceData, SshData import strider.common.logger class Vagrantbox(object): def __init__(self, name=None, ssh=None, basebox=None, bake_name=None, bake_description=None, user_data=None): self.name = name self.bake_name = bake_name self.basebox = basebox self.ssh = ssh self.log = strider.utils.logger.get_logger('Vagrant') if type(self.ssh) != dict: raise Exception("expecting 'ssh' to be a dictionary") self.vagrant_instance = vagrant.Vagrant() def describe(self): details = self._details() if details is None: return InstanceData(present=False) else: if self.ssh['username'] is not None: username = self.ssh['username'] else: username = "vagrant" if self.ssh['private_key_path'] is not None: private_key_path = self.ssh['private_key_path'] else: private_key_path = details['IdentityFile'] port = details['Port'] host = details['HostName'] ssh_data = SshData(keyfile=private_key_path, user=username, host=host, port=port) return InstanceData(present=True, provider_specific=details, ssh=ssh_data) def destroy(self): self.log("destroying instance") try: self.vagrant_instance.destroy() except CalledProcessError: self.log("already destroyed instance") try: os.remove("./Vagrantfile") except OSError: self.log("already removed Vagrantfile") def up(self): self.log("determining if we need to create an instance") try: self.vagrant_instance.init(box_name=self.basebox) except CalledProcessError: self.log("already initialised instance") try: self.log("bring up instance") self.vagrant_instance.up() except CalledProcessError: self.log("already up") def _details(self): try: conf = self.vagrant_instance.conf() return conf except CalledProcessError: self.log("No instance running") return None def bake(self): self.log("baking vagrant box") os.system("vagrant package --output {}.box".format(self.bake_name)) self.up()

31.860465

75

0.550365

277

2,740

5.32491

0.310469

0.047458

0.064407

0.084068

0.146441

0

0.362044

2,740

85

76

32.235294

0.843822

0

0.16

0

0.135766

0

1

0.08

false

0

0.066667

0

0.213333

0

null

0

null

0

1

0

552672dd092eb5fb84094dd67c6ad2cf6eb3df04

4,739

py

Python

python/aces/lutFormats/tests/UnitTestsLutFormats.py

aforsythe/clf

47ba8bee31bd13e4f23632c7b0a38293be31c019

[ "AMPAS" ]

43

2015-07-09T23:13:41.000Z

2022-02-04T15:45:42.000Z

python/aces/lutFormats/tests/UnitTestsLutFormats.py

aforsythe/clf

47ba8bee31bd13e4f23632c7b0a38293be31c019

[ "AMPAS" ]

1

2019-09-18T14:30:39.000Z

python/aces/lutFormats/tests/UnitTestsLutFormats.py

aforsythe/clf

47ba8bee31bd13e4f23632c7b0a38293be31c019

[ "AMPAS" ]

9

2015-07-10T15:26:55.000Z

2020-08-20T11:52:47.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ The Academy / ASC Common LUT Format Sample Implementations are provided by the Academy under the following terms and conditions: Copyright © 2015 Academy of Motion Picture Arts and Sciences ("A.M.P.A.S."). Portions contributed by others as indicated. All rights reserved. A worldwide, royalty-free, non-exclusive right to copy, modify, create derivatives, and use, in source and binary forms, is hereby granted, subject to acceptance of this license. Performance of any of the aforementioned acts indicates acceptance to be bound by the following terms and conditions: * Copies of source code, in whole or in part, must retain the above copyright notice, this list of conditions and the Disclaimer of Warranty. * Use in binary form must retain the above copyright notice, this list of conditions and the Disclaimer of Warranty in the documentation and/or other materials provided with the distribution. * Nothing in this license shall be deemed to grant any rights to trademarks, copyrights, patents, trade secrets or any other intellectual property of A.M.P.A.S. or any contributors, except as expressly stated herein. * Neither the name "A.M.P.A.S." nor the name of any other contributors to this software may be used to endorse or promote products derivative of or based on this software without express prior written permission of A.M.P.A.S. or the contributors, as appropriate. This license shall be construed pursuant to the laws of the State of California, and any disputes related thereto shall be subject to the jurisdiction of the courts therein. Disclaimer of Warranty: THIS SOFTWARE IS PROVIDED BY A.M.P.A.S. AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT ARE DISCLAIMED. IN NO EVENT SHALL A.M.P.A.S., OR ANY CONTRIBUTORS OR DISTRIBUTORS, BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, RESITUTIONARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. WITHOUT LIMITING THE GENERALITY OF THE FOREGOING, THE ACADEMY SPECIFICALLY DISCLAIMS ANY REPRESENTATIONS OR WARRANTIES WHATSOEVER RELATED TO PATENT OR OTHER INTELLECTUAL PROPERTY RIGHTS IN THE ACES CONTAINER REFERENCE IMPLEMENTATION, OR APPLICATIONS THEREOF, HELD BY PARTIES OTHER THAN A.M.P.A.S., WHETHER DISCLOSED OR UNDISCLOSED. """ __author__ = 'Haarm-Pieter Duiker' __copyright__ = 'Copyright (C) 2015 Academy of Motion Picture Arts and Sciences' __maintainer__ = 'Academy of Motion Picture Arts and Sciences' __email__ = 'acessupport@oscars.org' __status__ = 'Production' __major_version__ = '1' __minor_version__ = '0' __change_version__ = '0' __version__ = '.'.join((__major_version__, __minor_version__, __change_version__)) ''' Simple tests of the lutFormats module Should be turned into a proper set of unit tests. ''' import os import sys # Make sure we can import lutFormats sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..')) import lutFormats tmpDir = "/tmp" #aces1OCIOConfirDir = "/work/client/academy/ocio/hpd/OpenColorIO-Configs/aces_1.0.0" aces1OCIOConfirDir = "/path/to/OpenColorIO-Configs/aces_1.0.0" spiPath = "%s/luts/ACEScc_to_linear.spi1d" % aces1OCIOConfirDir cspPath = "%s/baked/maya/sRGB (D60 sim.) for ACEScg Maya.csp" % aces1OCIOConfirDir spipl = lutFormats.Registry.read( spiPath ) csppl = lutFormats.Registry.read( cspPath ) newSpiPath = "%s/ACEScc_to_linear_new.spi1d" % tmpDir lutFormats.Registry.write(spipl, newSpiPath) newSpi3dPath = "%s/srgb_new.spi3d" % tmpDir lutFormats.Registry.write(csppl, newSpi3dPath, lutDataFormat="3D") newCspPath = "%s/srgb_new_3d.csp" % tmpDir lutFormats.Registry.write(csppl, newCspPath, lutDataFormat="3D") newCsp1DPath = "%s/srgb_new_1d.csp" % tmpDir lutFormats.Registry.write(csppl, newCsp1DPath) newCsp1D3DPath = "%s/srgb_new_1d3d.csp" % tmpDir lutFormats.Registry.write(csppl, newCsp1D3DPath, lutDataFormat="1D_3D_1D") newClf1D3DPath = "%s/srgb_new_1d3d.clf" % tmpDir lutFormats.Registry.write(csppl, newClf1D3DPath, lutDataFormat="1D_3D_1D") newCtl1DPath = "%s/srgb_new_1d.ctl" % tmpDir lutFormats.Registry.write(csppl, newCtl1DPath) newCtl1D3DPath = "%s/srgb_new_3d.ctl" % tmpDir lutFormats.Registry.write(csppl, newCtl1D3DPath, lutDataFormat="3D")

40.853448

84

0.779067

687

4,739

5.256186

0.423581

0.049848

0.053171

0.064248

0.211299

0.15702

0.091941

0.078649

0.043755

0

0.015222

0.140536

4,739

115

85

41.208696

0.871102

0.606246

0

0.265225

0.068298

0

1

0

false

0

0.081081

0

0.081081

0

null

0

null

0

1

0

552683d69b93369ce9f2b67f499349c272254782

10,177

py

Python

edexOsgi/com.raytheon.edex.plugin.gfe/utility/common_static/base/gfe/textproducts/templates/product/Hazard_CFW_MultiPil.py

srcarter3/awips2

37f31f5e88516b9fd576eaa49d43bfb762e1d174

[ "Apache-2.0" ]

null

edexOsgi/com.raytheon.edex.plugin.gfe/utility/common_static/base/gfe/textproducts/templates/product/Hazard_CFW_MultiPil.py

srcarter3/awips2

37f31f5e88516b9fd576eaa49d43bfb762e1d174

[ "Apache-2.0" ]

null

edexOsgi/com.raytheon.edex.plugin.gfe/utility/common_static/base/gfe/textproducts/templates/product/Hazard_CFW_MultiPil.py

srcarter3/awips2

37f31f5e88516b9fd576eaa49d43bfb762e1d174

[ "Apache-2.0" ]

1

2021-10-30T00:03:05.000Z

## # This software was developed and / or modified by Raytheon Company, # pursuant to Contract DG133W-05-CQ-1067 with the US Government. # # U.S. EXPORT CONTROLLED TECHNICAL DATA # This software product contains export-restricted data whose # export/transfer/disclosure is restricted by U.S. law. Dissemination # to non-U.S. persons whether in the United States or abroad requires # an export license or other authorization. # # Contractor Name: Raytheon Company # Contractor Address: 6825 Pine Street, Suite 340 # Mail Stop B8 # Omaha, NE 68106 # 402.291.0100 # # See the AWIPS II Master Rights File ("Master Rights File.pdf") for # further licensing information. ## ## # This is a base file that is not intended to be overridden. ## ######################################################################## # Hazard_CFW.py # # ########################################################################## import GenericHazards import string, time, re, os, types, copy class TextProduct(GenericHazards.TextProduct): Definition = copy.deepcopy(GenericHazards.TextProduct.Definition) Definition['displayName'] = None Definition['displayName'] = "BaselineHazard_CFW_<MultiPil> (Coastal/LakeShore Flooding)" Definition["defaultEditAreas"] = "EditAreas_PublicZones_<site>_<MultiPil>" Definition["mapNameForCombinations"] = "Zones_<site>" # Map background for creating Combinations # Header configuration items Definition["productName"] = "Coastal Hazard Message" # Warning! DO NOT CHANGE. # The productName gets substituted later in the formatter! Definition["fullStationID"] = "<fullStationID>" # full station identifier (4letter) Definition["wmoID"] = "<wmoID>" # WMO ID Definition["pil"] = "<pil>" # product pil #Definition["areaName"] = "Statename" # Name of state, such as "Georgia" Definition["wfoCityState"] = "<wfoCityState>" # Location of WFO - city state Definition["wfoCity"] = "<wfoCity>" # WFO Name as it should appear in a text product Definition["textdbPil"] = "<textdbPil>" # Product ID for storing to AWIPS text database. Definition["awipsWANPil"] = "<awipsWANPil>" # Product ID for transmitting to AWIPS WAN. Definition["outputFile"] = "{prddir}/TEXT/CFW_<MultiPil>.txt" Definition["bulletProd"] = 1 #If 1, the product has a bullet format # OPTIONAL CONFIGURATION ITEMS #Definition["database"] = "Official" # Source database. "Official", "Fcst", or "ISC" #Definition["displayOutputDialog"] = 0 # If 1 will display results when finished Definition["debug"] = 1 #Definition["headlineEditAreaGroup"] = "Zones" # Name of EditAreaGroup for sampling headlines Definition["purgeTime"] = 8 # Maximum hours for expireTime from issueTime Definition["includeCities"] = 0 # Cities included in area header Definition["accurateCities"] = 0 # If 1, cities are based on grids; # otherwise full list is included Definition["cityLocation"] = "CityLocation" # City lat/lon dictionary to use #Definition["cityDescriptor"] = "Including the cities of" Definition["includeZoneNames"] = 1 # Zone names will be included in the area header Definition["lineLength"] = 66 # line length Definition["easPhrase"] = "URGENT - IMMEDIATE BROADCAST REQUESTED" Definition["includeOverviewHeadline"] = 1 #If 1, the overview header is templated Definition["includeOverview"] = 1 #If 1, the overview section is templated #Definition["hazardSamplingThreshold"] = (10, None) #(%cov, #points) ### ### Text to insert below the last $$ of the product (WFO URL) ### use "" if you do not want text to appear ## Definition["urlText"] = "http://www.weather.gov/miami" ### no additional text example Definition["urlText"] = "" ### multiple line example ## Definition["urlText"] = "For more information from NOAA/s National Weather Service visit...\n" + \ ## "http://weather.gov/saltlakecity" ### def __init__(self): GenericHazards.TextProduct.__init__(self) # # These are the products allowed in the Coastal Flood Products # def allowedHazards(self): allActions = ["NEW", "EXA", "EXB", "EXT", "CAN", "CON", "EXP"] return [ ('CF.W', allActions, 'CoastalFlood'), # COASTAL FLOOD WARNING ('CF.Y', allActions, 'CoastalFlood'), # COASTAL FLOOD ADVISORY ('CF.A', allActions, 'CoastalFlood'), # COASTAL FLOOD WATCH ('CF.S', allActions, 'CoastalFloodStatement'), # COASTAL FLOOD STATEMENT ('LS.W', allActions, 'CoastalFlood'), # LAKESHORE FLOOD WARNING ('LS.Y', allActions, 'CoastalFlood'), # LAKESHORE FLOOD ADVISORY ('LS.A', allActions, 'CoastalFlood'), # LAKESHORE FLOOD WATCH ('LS.S', allActions, 'CoastalFloodStatement'), # LAKESHORE FLOOD STATEMENT ('SU.W', allActions, 'HighSurf'), # HIGH SURF WARNING ('SU.Y', allActions, 'HighSurf'), # HIGH SURF ADVISORY ('BH.S', allActions, 'BeachHaz'), # Beach Hazards Statement ('RP.S', allActions, 'RipCurrent'), # HIGH RIP CURRENT RISK ] def _bulletDict(self): return { "CF" : ("Coastal Flooding,Timing,Impacts"), ### coastal flood warning, advisory, watch "LS" : ("Lake Shore Flooding,Timing,Impacts"), ### lake shore flood warning, advisory, watch "BH" : ("Hazards,Timing,Location,Potential Impacts"), ### hazardous beach conditions "SU" : ("Waves and Surf,Timing,Impacts"), ### high surf warning, advisory "RP" : ("Timing,Impacts"), ### high rip current risk } def _bulletOrder(self): return [ "Coastal Flooding", "Lake Shore Flooding", "Waves and Surf", "Hazards", "Timing", "Location", "Potential Impacts", "Impacts", ] # # Overridden to allow for attribution statement # def _makeProduct(self, fcst, segmentAreas, argDict): argDict["language"] = self._language # # This section generates the headline on the segment # # stuff argDict with the segmentAreas for DiscretePhrases argDict['segmentAreas'] = segmentAreas editArea = segmentAreas[0] areaLabel = editArea headlines = self.generateProduct("Hazards", argDict, area = editArea, areaLabel=areaLabel, timeRange = self._timeRange) fcst = fcst + headlines # # This section generates the attribution statements and calls-to-action # hazardsC = argDict['hazards'] listOfHazards = hazardsC.getHazardList(segmentAreas) fcst = fcst + self.hazardBodyText(listOfHazards, argDict) # # If an overview exists for this product, calculate it # self.overviewText(listOfHazards, "CFW") # # Clean up and return # fcst = self.endline(fcst, linelength=self._lineLength, breakStr=[" ", "-", "..."]) return fcst def _postProcessProduct(self, fcst, argDict): # # If an overview exists for this product, insert it # overview = self.finalOverviewText() overviewSearch = re.compile(r'Default overview section', re.DOTALL) fcst = overviewSearch.sub(overview, fcst) urgent = 0 followup = 1 prodNameKey = '' fullKeyList = [] newList = ['NEW', 'EXA', 'EXB'] hazardsC = argDict['hazards'] segmentList = self.organizeHazards(hazardsC.rawAnalyzedTable()) for segmentAreas in segmentList: listOfHazards = hazardsC.getHazardList(segmentAreas) for eachHazard in listOfHazards: if eachHazard['phensig'] not in fullKeyList: fullKeyList.append(eachHazard['phensig']) if eachHazard['phensig'] in ['CF.W', 'CF.A', 'LS.W', 'LS.A']: if eachHazard['act'] in newList: urgent = 1 # remove eas line if not urgent if urgent == 0 and len(self._easPhrase): fcst = fcst.replace(self._easPhrase + '\n', '', 1) # rename the product if necessary based on VTEC codes for each in fullKeyList: if each in ['LS.W', 'LS.A', 'LS.Y', 'LS.S']: productName = "Lakeshore Hazard Message" fcst = fcst.replace(self._productName, productName, 1) break # Added to place line feeds in the CAP tags to keep separate from CTAs fcst = string.replace(fcst, \ r"PRECAUTIONARY/PREPAREDNESS ACTIONS\.\.\.", \ r"\nPRECAUTIONARY/PREPAREDNESS ACTIONS\.\.\.\n") fcst = string.replace(fcst, ".:", ".") fcst = string.replace(fcst, "\n ","\n") fcst = string.replace(fcst, "&&", "\n&&\n") # Prevent empty Call to Action Tags fcst = re.sub(r'\nPRECAUTIONARY/PREPAREDNESS ACTIONS\.\.\.\s*&&\n', \ "", fcst) ### to remove any empty framing code fcst = re.sub("\|\*\s*\*\|", "", fcst) ### indent the bullet text fcst = self._indentBulletText(fcst) # # Clean up multiple line feeds # fixMultiLF = re.compile(r'(\n\n)\n*', re.DOTALL) fcst = fixMultiLF.sub(r'\1', fcst) # # Finish Progress Meter # self.setProgressPercentage(100) self.progressMessage(0, 100, self._displayName + " Complete") ### add the url text from the configuration section fcst = fcst + "\n" + self._urlText return fcst

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552924a7e504599cbe9d1cfc08f6a123e6773a8c

880

py

Python

setup.py

hubmapconsortium/python-sdk

17eaec434f1f65190a6e53d0055fe382841222de

[ "MIT" ]

null

setup.py

hubmapconsortium/python-sdk

17eaec434f1f65190a6e53d0055fe382841222de

[ "MIT" ]

8

2021-11-09T13:35:48.000Z

2022-03-04T15:56:52.000Z

setup.py

hubmapconsortium/python-sdk

17eaec434f1f65190a6e53d0055fe382841222de

[ "MIT" ]

null

from setuptools import setup, find_packages with open("README.md", "r") as fh: long_description = fh.read() setup( name="hubmap-sdk", version="1.0.1", author="Hubmap", author_email="api-developers@hubmapconsortium.org", description="Python Client Libary to use HuBMAP web services", long_description=long_description, long_description_content_type="text/markdown", packages=['hubmap_sdk'], keywords=[ "HuBMAP Sdk", "python" ], install_requires=[ "certifi==2021.10.8", "chardet==4.0.0", "idna==2.10", "requests==2.25.1", "urllib3==1.26.7" ], include_package_data=True, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent" ], python_requires='>=3.6' )

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5529c5dbc7514236bc8611211cfb848e2618a841

2,615

py

Python

bayarea_urbansim/data_regeneration/export_to_h5.py

ual/DOE-repo-deliverable

4bafdd9a702a9a6466dd32ae62f440644d735d3c

[ "BSD-3-Clause" ]

null

bayarea_urbansim/data_regeneration/export_to_h5.py

ual/DOE-repo-deliverable

4bafdd9a702a9a6466dd32ae62f440644d735d3c

[ "BSD-3-Clause" ]

null

bayarea_urbansim/data_regeneration/export_to_h5.py

ual/DOE-repo-deliverable

4bafdd9a702a9a6466dd32ae62f440644d735d3c

[ "BSD-3-Clause" ]

null

import pandas as pd from spandex import TableLoader import pandas.io.sql as sql loader = TableLoader() def db_to_df(query): """Executes SQL query and returns DataFrame.""" conn = loader.database._connection return sql.read_frame(query, conn) ## Export to HDF5- get path to output file h5_path = loader.get_path('out/regeneration/summaries/bayarea_v3.h5') ## Path to the output file #Buildings buildings = db_to_df('select * from building').set_index('building_id') if 'id' in buildings.columns: del buildings['id'] buildings['building_type_id'] = 0 buildings.building_type_id[buildings.development_type_id == 1] = 1 buildings.building_type_id[buildings.development_type_id == 2] = 3 buildings.building_type_id[buildings.development_type_id == 5] = 12 buildings.building_type_id[buildings.development_type_id == 7] = 10 buildings.building_type_id[buildings.development_type_id == 9] = 5 buildings.building_type_id[buildings.development_type_id == 10] = 4 buildings.building_type_id[buildings.development_type_id == 13] = 8 buildings.building_type_id[buildings.development_type_id == 14] = 7 buildings.building_type_id[buildings.development_type_id == 15] = 9 buildings.building_type_id[buildings.development_type_id == 13] = 8 buildings.building_type_id[buildings.development_type_id == 17] = 6 buildings.building_type_id[buildings.development_type_id == 24] = 16 #Parcels parcels = db_to_df('select * from parcel').set_index('parcel_id') parcels['shape_area'] = parcels.acres * 4046.86 if 'id' in parcels.columns: del parcels['id'] if 'geom' in parcels.columns: del parcels['geom'] if 'centroid' in parcels.columns: del parcels['centroid'] #Jobs jobs = db_to_df('select * from jobs').set_index('job_id') if 'id' in jobs.columns: del jobs['id'] #Households hh = db_to_df('select * from households').set_index('household_id') if 'id' in hh.columns: del hh['id'] hh = hh.rename(columns = {'hinc':'income'}) for col in hh.columns: hh[col] = hh[col].astype('int32') #Zones zones_path = loader.get_path('juris/reg/zones/zones.csv') zones = pd.read_csv(zones_path).set_index('zone_id') #Putting tables in the HDF5 file store = pd.HDFStore(h5_path) store['parcels'] = parcels # http://urbansim.org/Documentation/Parcel/ParcelTable store['buildings'] = buildings # http://urbansim.org/Documentation/Parcel/BuildingsTable store['households'] = hh # http://urbansim.org/Documentation/Parcel/HouseholdsTable store['jobs'] = jobs # http://urbansim.org/Documentation/Parcel/JobsTable store['zones'] = zones # http://urbansim.org/Documentation/Parcel/ZonesTable store.close()

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null

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552b355ab9a4608d3f4dc4d7df2c3b24e79e210d

7,060

py

Python

minder_utils/visualisation/feature_engineering.py

alexcapstick/minder_utils

3bb9380b7796b5dd5b995ce1839ea6a94321021d

[ "MIT" ]

null

minder_utils/visualisation/feature_engineering.py

alexcapstick/minder_utils

3bb9380b7796b5dd5b995ce1839ea6a94321021d

[ "MIT" ]

null

minder_utils/visualisation/feature_engineering.py

alexcapstick/minder_utils

3bb9380b7796b5dd5b995ce1839ea6a94321021d

[ "MIT" ]

1

2022-03-16T11:10:43.000Z

import matplotlib.pyplot as plt import matplotlib.dates as mdates import seaborn as sns import pandas as pd from minder_utils.formatting.label import label_by_week, label_dataframe from minder_utils.feature_engineering import Feature_engineer from minder_utils.feature_engineering.calculation import * from minder_utils.util import formatting_plots from minder_utils.formatting import Formatting fe = Feature_engineer(Formatting()) sns.set() att = 'bathroom_night' figure_title = { 'bathroom_night': 'Bathroom activity during the night', 'bathroom_daytime': 'Bathroom activity during the day', } patient_id = '' def process_dataframe(df, week_shift=0): df = df[df.id == patient_id] map_dict = {i: j - week_shift for j, i in enumerate(df.week.sort_values().unique())} df.week = df.week.map(map_dict) return df def visualise_flags(df): for v in [True, False]: data = df[df.valid == v] not_labelled = True for week in data.week.unique(): if v is True: plt.axvline(week, 0, 0.17, color='red', label='UTI' if not_labelled else None) not_labelled = False elif v is False: plt.axvline(week, 0, 0.17, color='blue', label='not UTI' if not_labelled else None) not_labelled = False @formatting_plots(figure_title[att]) def visualise_weekly_data(df): df = process_dataframe(df) sns.violinplot(data=df, x='week', y='value') visualise_flags(df) return df @formatting_plots('P value, ' + figure_title[att]) def visualise_weekly_statistical_analysis(df, results): df = process_dataframe(df, 1) visualise_flags(df) data = results[patient_id] df = {'week': [], 'p_value': []} for idx, sta in enumerate(data): df['week'].append(idx + 1) df['p_value'].append(sta[1]) sns.lineplot(df['week'], df['p_value']) @formatting_plots('Body temperature') def visualise_body_temperature(df): df = process_dataframe(df) visualise_flags(df) sns.lineplot(df.week, df.value) def visualise_data_time_lineplot(time_array, values_array, name, fill_either_side_array=None, fig = None, ax = None): ''' This function accepts a dataframe that has a ```'time'``` column and and a ```'value'``` column. ''' if ax is None: fig, ax = plt.subplots(1,1,figsize = (10,6)) ax.plot(time_array, values_array) if not fill_either_side_array is None: ax.fill_between(time_array, y1=values_array-fill_either_side_array, y2=values_array+fill_either_side_array, alpha = 0.3) return fig, ax def visualise_data_time_heatmap(data_plot, name, fig = None, ax = None): ''' This function accepts a dataframe in which the columns are the days and the rows are the aggregated times of the day. ''' if ax is None: fig, axes = plt.subplots(1,1,figsize = (10,6)) ax = sns.heatmap(data_plot.values, cmap = 'Blues', cbar_kws={'label': name}) ax.invert_yaxis() x_tick_loc = np.arange(0, data_plot.shape[1], 90) ax.set_xticks(x_tick_loc + 0.5) ax.set_xticklabels(data_plot.columns.astype(str)[x_tick_loc].values) y_tick_loc = np.arange(0, data_plot.shape[0], 3) ax.set_yticks(y_tick_loc + 0.5) ax.set_yticklabels([pd.to_datetime(time).strftime("%H:%M") for time in data_plot.index.values[y_tick_loc]], rotation = 0) ax.set_xlabel('Day') ax.set_ylabel('Time of Day') return fig, ax def visualise_activity_daily_data(fe): ''' Arguments --------- - fe: class: The feature engineering class that produces the data. ''' activity_daily = fe.activity_specific_agg(agg='daily', load_smaller_aggs = True) activity_daily = label_dataframe(activity_daily, days_either_side=0) activity_daily=activity_daily.rename(columns = {'valid':'UTI Label'}) activity_daily['Feature'] = activity_daily['location'].map(fe.info) sns.set_theme('talk') fig_list = [] axes_list = [] for feature in activity_daily['location'].unique(): data_plot = activity_daily[activity_daily['location'].isin([feature])] fig, ax = plt.subplots(1,1,figsize = (8,6)) ax = sns.boxplot(data=data_plot, x='value', y = 'Feature', hue='UTI Label', ax=ax, **{'showfliers':False}) ax.set_ylabel(None) ax.set_yticks([]) ax.set_title('{}'.format(fe.info[feature])) ax.set_xlabel('Value') fig_list.append(fig) axes_list.append(ax) return fig_list, axes_list def visualise_activity_weekly_data(fe): ''' Arguments --------- - fe: class: The feature engineering class that produces the data. ''' activity_weekly = fe.activity_specific_agg(agg='weekly', load_smaller_aggs = True) activity_weekly = label_by_week(activity_weekly) activity_weekly=activity_weekly.rename(columns = {'valid':'UTI Label'}) activity_weekly['Feature'] = activity_weekly['location'].map(fe.info) sns.set_theme('talk') fig_list = [] axes_list = [] for feature in activity_weekly['location'].unique(): data_plot = activity_weekly[activity_weekly['location'].isin([feature])] fig, ax = plt.subplots(1,1,figsize = (8,6)) ax = sns.boxplot(data=data_plot, x='value', y = 'Feature', hue='UTI Label', ax=ax, **{'showfliers':False}) ax.set_ylabel(None) ax.set_yticks([]) ax.set_title('{}'.format(fe.info[feature])) ax.set_xlabel('Value') fig_list.append(fig) axes_list.append(ax) return fig_list, axes_list def visualise_activity_evently_data(fe): ''' Arguments --------- - fe: class: The feature engineering class that produces the data. ''' activity_evently = fe.activity_specific_agg(agg='evently', load_smaller_aggs = True) activity_evently = label_dataframe(activity_evently, days_either_side=0) activity_evently=activity_evently.rename(columns = {'valid':'UTI Label'}) activity_evently['Feature'] = activity_evently['location'].map(fe.info) sns.set_theme('talk') fig_list = [] axes_list = [] for feature in activity_evently['location'].unique(): data_plot = activity_evently[activity_evently['location'].isin([feature])] fig, ax = plt.subplots(1,1,figsize = (8,6)) ax = sns.boxplot(data=data_plot, x='value', y = 'Feature', hue='UTI Label', ax=ax, **{'showfliers':False}) ax.set_ylabel(None) ax.set_yticks([]) ax.set_title('{}'.format(fe.info[feature])) ax.set_xlabel('Value') fig_list.append(fig) axes_list.append(ax) return fig_list, axes_list if __name__ == '__main__': results = weekly_compare(getattr(fe, att), kolmogorov_smirnov) df = label_by_week(getattr(fe, att)) visualise_weekly_data(df) visualise_weekly_statistical_analysis(df) visualise_body_temperature(label_by_week(fe.body_temperature))

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null

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null

0

1

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552c410668701cd1585658195d593e1b5751e350

442

py

Python

code-everyday-challenge/n159_cyclically_rotate.py

ved93/deliberate-practice-challenges

2fccdbb9d2baaa16f888055c081a8d04804c0045

[ "MIT" ]

null

code-everyday-challenge/n159_cyclically_rotate.py

ved93/deliberate-practice-challenges

2fccdbb9d2baaa16f888055c081a8d04804c0045

[ "MIT" ]

null

code-everyday-challenge/n159_cyclically_rotate.py

ved93/deliberate-practice-challenges

2fccdbb9d2baaa16f888055c081a8d04804c0045

[ "MIT" ]

null

# https://practice.geeksforgeeks.org/problems/cyclically-rotate-an-array-by-one2614/1 # Given an array, rotate the array by one position in clock-wise direction. # Input: # N = 5 # A[] = {1, 2, 3, 4, 5} # Output: # 5 1 2 3 4 def rotate_cycle(a): n = len(a) tmp = a[-1] for i in range(1,n): a[-i] = a[-i-1] a[0] = tmp return a if __name__ == "__main__": a = [1, 2, 3,4,5] print(rotate_cycle(a))

17.68

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null

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null

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1

0

552d7c8af23d30920337cc95fa4d7065705c0c5f

10,800

py

Python

adamw_optimizer.py

pwldj/Bio_XLNet_CRF

536053e9d74abdb2ee56000a8a779ffc1c0dd0fc

[ "Apache-2.0" ]

null

adamw_optimizer.py

pwldj/Bio_XLNet_CRF

536053e9d74abdb2ee56000a8a779ffc1c0dd0fc

[ "Apache-2.0" ]

2

2022-03-07T07:27:13.000Z

2022-03-07T07:27:15.000Z

adamw_optimizer.py

pwldj/MTL-BioNER

3fb336f517346daeec6a716fa6a657a421754bdb

[ "Apache-2.0" ]

1

2021-05-05T08:42:53.000Z

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Adamw for TensorFlow.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import re import tensorflow as tf from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.training import optimizer class AdamOptimizer(optimizer.Optimizer): def __init__(self, learning_rate=0.001, weight_decay_rate=0.0, beta1=0.9, beta2=0.999, epsilon=1e-8, exclude_from_weight_decay=None, include_in_weight_decay=None, use_locking=False, name="Adamw"): """ This is a multi Gpu version of adamw. :param learning_rate: :param weight_decay_rate: :param beta1: :param beta2: :param epsilon: :param exclude_from_weight_decay: :param include_in_weight_decay: :param use_locking: :param name: """ super(AdamOptimizer, self).__init__(use_locking, name) self._lr = learning_rate self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon self._weight_decay_rate = weight_decay_rate self._exclude_from_weight_decay = exclude_from_weight_decay self._include_in_weight_decay = include_in_weight_decay # Tensor versions of the constructor arguments, created in _prepare(). self._lr_t = None self._weight_decay_rate_t = None self._beta1_t = None self._beta2_t = None self._epsilon_t = None def _get_beta_accumulators(self): with ops.init_scope(): if context.executing_eagerly(): graph = None else: graph = ops.get_default_graph() return (self._get_non_slot_variable("beta1_power", graph=graph), self._get_non_slot_variable("beta2_power", graph=graph)) def _create_slots(self, var_list): # Create the beta1 and beta2 accumulators on the same device as the first # variable. Sort the var_list to make sure this device is consistent across # workers (these need to go on the same PS, otherwise some updates are # silently ignored). first_var = min(var_list, key=lambda x: x.name) self._create_non_slot_variable( initial_value=self._beta1, name="beta1_power", colocate_with=first_var) self._create_non_slot_variable( initial_value=self._beta2, name="beta2_power", colocate_with=first_var) # Create slots for the first and second moments. for v in var_list: self._zeros_slot(v, "adam_m", self._name) self._zeros_slot(v, "adam_v", self._name) def _prepare(self): lr = self._call_if_callable(self._lr) beta1 = self._call_if_callable(self._beta1) beta2 = self._call_if_callable(self._beta2) weight_decay_rate = self._call_if_callable(self._weight_decay_rate) epsilon = self._call_if_callable(self._epsilon) self._lr_t = ops.convert_to_tensor(lr, name="learning_rate") self._beta1_t = ops.convert_to_tensor(beta1, name="beta1") self._beta2_t = ops.convert_to_tensor(beta2, name="beta2") self._weight_decay_rate_t = ops.convert_to_tensor( weight_decay_rate, name="weight_decay_rate") self._epsilon_t = ops.convert_to_tensor(epsilon, name="epsilon") def _apply_dense(self, grad, var): lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) weight_decay_rate = math_ops.cast( self._weight_decay_rate_t, var.dtype.base_dtype) epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) beta1_power, beta2_power = self._get_beta_accumulators() beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power)) m = self.get_slot(var, "adam_m") v = self.get_slot(var, "adam_v") m_t = (tf.multiply(beta1_t, m) + tf.multiply(1.0 - beta1_t, grad)) m_t = m.assign(m_t, use_locking=self._use_locking) v_t = (tf.multiply(beta2_t, v) + tf.multiply(1.0 - beta2_t, tf.square(grad))) v_t = v.assign(v_t, use_locking=self._use_locking) m_t_hat = m_t / (1. - beta1_power) v_t_hat = v_t / (1. - beta2_power) update = m_t_hat / (tf.sqrt(v_t_hat) + epsilon_t) if self._do_use_weight_decay(var.name): update += weight_decay_rate * var var_update = var - lr * update var_update = var.assign(var_update, use_locking=self._use_locking) return tf.group(*[var_update, m_t, v_t]) def _resource_apply_dense(self, grad, var): lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) weight_decay_rate = math_ops.cast( self._weight_decay_rate_t, var.dtype.base_dtype) epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) beta1_power, beta2_power = self._get_beta_accumulators() beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power)) m = self.get_slot(var, "adam_m") v = self.get_slot(var, "adam_v") m_t = (tf.multiply(beta1_t, m) + tf.multiply(1.0 - beta1_t, grad)) m_t = m.assign(m_t, use_locking=self._use_locking) v_t = (tf.multiply(beta2_t, v) + tf.multiply(1.0 - beta2_t, tf.square(grad))) v_t = v.assign(v_t, use_locking=self._use_locking) m_t_hat = m_t / (1. - beta1_power) v_t_hat = v_t / (1. - beta2_power) update = m_t_hat / (tf.sqrt(v_t_hat) + epsilon_t) if self._do_use_weight_decay(var.name): update += weight_decay_rate * var var_update = var - lr * update var_update = var.assign(var_update, use_locking=self._use_locking) return tf.group(*[var_update, m_t, v_t]) def _apply_sparse_shared(self, grad, var, indices, scatter_add): beta1_power, beta2_power = self._get_beta_accumulators() beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power)) # m_t = beta1 * m + (1 - beta1) * g_t m = self.get_slot(var, "adam_m") m_scaled_g_values = grad * (1 - beta1_t) m_t = state_ops.assign(m, m * beta1_t, use_locking=self._use_locking) with ops.control_dependencies([m_t]): m_t = scatter_add(m, indices, m_scaled_g_values) # v_t = beta2 * v + (1 - beta2) * (g_t * g_t) v = self.get_slot(var, "adam_v") v_scaled_g_values = (grad * grad) * (1 - beta2_t) v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking) with ops.control_dependencies([v_t]): v_t = scatter_add(v, indices, v_scaled_g_values) v_sqrt = math_ops.sqrt(v_t) var_update = state_ops.assign_sub( var, lr * m_t / (v_sqrt + epsilon_t), use_locking=self._use_locking) return control_flow_ops.group(*[var_update, m_t, v_t]) def _apply_sparse(self, grad, var): return self._apply_sparse_shared( grad.values, var, grad.indices, lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda x, i, v, use_locking=self._use_locking)) def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_apply_sparse(self, grad, var, indices): return self._apply_sparse_shared(grad, var, indices, self._resource_scatter_add) def _finish(self, update_ops, name_scope): # Update the power accumulators. with ops.control_dependencies(update_ops): beta1_power, beta2_power = self._get_beta_accumulators() with ops.colocate_with(beta1_power): update_beta1 = beta1_power.assign( beta1_power * self._beta1_t, use_locking=self._use_locking) update_beta2 = beta2_power.assign( beta2_power * self._beta2_t, use_locking=self._use_locking) return control_flow_ops.group( *update_ops + [update_beta1, update_beta2], name=name_scope) def _do_use_weight_decay(self, param_name): """Whether to use L2 weight decay for `param_name`.""" if not self._weight_decay_rate: return False # for r in self._include_in_weight_decay: # if re.search(r, param_name) is not None: # return True if self._exclude_from_weight_decay: for r in self._exclude_from_weight_decay: if re.search(r, param_name) is not None: tf.logging.info('Adam WD excludes {}'.format(param_name)) return False return True

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null

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null

0

1

0

552fdd4ea7856ad8f238ffba4056d7b666e1d19e

1,559

py

Python

backend/breach/helpers/injector.py

Cancelll/rupture

cd87481717b39de2654659b7ff436500e28a0600

[ "MIT" ]

184

2016-03-31T04:19:42.000Z

2021-11-26T21:37:12.000Z

backend/breach/helpers/injector.py

Cancelll/rupture

cd87481717b39de2654659b7ff436500e28a0600

[ "MIT" ]

212

2016-03-31T04:32:06.000Z

2017-02-26T09:34:47.000Z

backend/breach/helpers/injector.py

Cancelll/rupture

cd87481717b39de2654659b7ff436500e28a0600

[ "MIT" ]

38

2016-03-31T09:09:44.000Z

2021-11-26T21:37:13.000Z

from backend.settings import BASE_DIR import os import subprocess import stat rupture_dir = os.path.abspath(os.path.join(BASE_DIR, os.pardir)) client_dir = os.path.join(rupture_dir, 'client') def inject(victim): _create_client(victim) _create_injection(victim) _run_injection(victim) def _create_client(victim): realtimeurl = victim.realtimeurl victimid = victim.id with open(os.devnull, 'w') as FNULL: p = subprocess.Popen( [os.path.join(client_dir, 'build.sh'), str(realtimeurl), str(victimid)], cwd=client_dir, stdout=FNULL, stderr=subprocess.PIPE ) return p.wait() def _create_injection(victim): sourceip = victim.sourceip victimid = victim.id with open(os.path.join(client_dir, 'inject.sh'), 'r') as f: injection = f.read() injection = injection.replace('$1', str(sourceip)) inject_file = os.path.join(client_dir, 'client_{}/inject.sh'.format(victimid)) with open(inject_file, 'w') as f: f.write(injection) clientid_inject = inject_file st = os.stat(clientid_inject) os.chmod(clientid_inject, st.st_mode | stat.S_IEXEC) def _run_injection(victim): victimid = victim.id clientid_dir = os.path.join(client_dir, 'client_{}'.format(victimid)) with open(os.devnull, 'w') as FNULL: subprocess.Popen( os.path.join(clientid_dir, 'inject.sh'), shell=True, cwd=client_dir, stdout=FNULL, stderr=subprocess.PIPE )

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0.233483

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0

null

0

null

0

1

0

5530fb74fc5655f0d169fed9774ccb03f4699d79

952

py

Python

wagtail_client/utils.py

girleffect/core-integration-demo

c37a0d5183d16bec6245a41e12dd90691ffa7138

[ "BSD-3-Clause" ]

null

wagtail_client/utils.py

girleffect/core-integration-demo

c37a0d5183d16bec6245a41e12dd90691ffa7138

[ "BSD-3-Clause" ]

19

2018-02-06T08:56:24.000Z

2018-09-11T08:05:24.000Z

wagtail_client/utils.py

girleffect/core-integration-demo

c37a0d5183d16bec6245a41e12dd90691ffa7138

[ "BSD-3-Clause" ]

2

2018-05-25T09:44:03.000Z

2021-08-18T12:07:47.000Z

from urllib.parse import urlencode from django.conf import settings from django.contrib.sites.shortcuts import get_current_site def provider_logout_url(request): """ This function is used to construct a logout URL that can be used to log the user out of the Identity Provider (Authentication Service). :param request: :return: """ site = get_current_site(request) if not hasattr(site, "oidcsettings"): raise RuntimeError(f"Site {site} has no settings configured.") parameters = { "post_logout_redirect_uri": site.oidcsettings.wagtail_redirect_url } # The OIDC_STORE_ID_TOKEN setting must be set to true if we want to be able to read # it from the session. if "oidc_id_token" in request.session: parameters["id_token_hint"] = request.session["oidc_id_token"] redirect_url = settings.OIDC_OP_LOGOUT_URL + "?" + urlencode(parameters, doseq=True) return redirect_url

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0

0.357143

0

null

0

null

0

1

0

553261313f73826b4fd76c66eae4be0cde9803af

978

py

Python

connectToProteusFromMongo.py

erentts/Ignite-Greenhouse

328730399328936332b5c6f3f8dcd18bf56369b9

[ "MIT" ]

4

2021-02-22T21:19:28.000Z

2021-05-03T14:19:18.000Z

connectToProteusFromMongo.py

erentts/Ignite-Greenhouse

328730399328936332b5c6f3f8dcd18bf56369b9

[ "MIT" ]

null

connectToProteusFromMongo.py

erentts/Ignite-Greenhouse

328730399328936332b5c6f3f8dcd18bf56369b9

[ "MIT" ]

null

import pymongo import dns import serial from pymongo import MongoClient import struct cluster = MongoClient("") serialPort = serial.Serial(port= "COM1", baudrate=9600 ,bytesize =8 , timeout =None, parity='N',stopbits=1) db=cluster["<greenHouse>"] collection = db["greenhouses"] while serialPort.readline(): results = collection.find({"greenHouseName" : "SERA 1" }) for result in results: targetTemperature = abs(int(result.get("targetTemperature"))) # declaring an integer value int_val = targetTemperature # converting to string str_val = str(targetTemperature) # converting string to bytes byte_val = str_val.encode() serialPort.write(byte_val) getterThree = collection.update_one({"greenHouseName" : "SERA 1"},{"$set":{"targetTemperature" : targetTemperature }}) getter = collection.update_one({"greenHouseName" : "SERA 1"},{"$set":{"currentTemperature" : float(serialPort.read() + serialPort.read()) }})

31.548387

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null

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0

5539d275ebd36d43b5d44642306d4d9d488a83a3

961

py

Python

s3_file_uploads/serializers.py

dabapps/django-s3-file-uploads

17ed6b4e02bd43bc925af987ff5bf971a82da434

[ "BSD-3-Clause" ]

5

2019-05-27T03:51:30.000Z

2021-03-19T11:24:09.000Z

s3_file_uploads/serializers.py

dabapps/django-s3-file-uploads

17ed6b4e02bd43bc925af987ff5bf971a82da434

[ "BSD-3-Clause" ]

7

2019-12-04T22:38:13.000Z

2021-06-10T17:50:06.000Z

s3_file_uploads/serializers.py

dabapps/django-s3-file-uploads

17ed6b4e02bd43bc925af987ff5bf971a82da434

[ "BSD-3-Clause" ]

null

from rest_framework import serializers from s3_file_uploads.constants import ACCESS_CONTROL_TYPES, PRIVATE from s3_file_uploads.models import UploadedFile class UploadedFileSerializer(serializers.ModelSerializer): file_name = serializers.CharField(source='file.name', read_only=True) file = serializers.URLField(source='get_download_url', read_only=True) class Meta: model = UploadedFile fields = [ 'id', 'created', 'modified', 'file_key', 'file', 'filename', 'file_name', 'file_path', 'user', ] read_only_fields = [ 'id', 'modfied', 'created', 'file_name', 'file_path', 'file_key' ] class AccessControlListSerializer(serializers.Serializer): acl = serializers.ChoiceField(choices=ACCESS_CONTROL_TYPES, default=PRIVATE)

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961

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0

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0

null

0

null

0

1

0

553a35ee3c9965503e444537543d6f056c2747c7

1,873

py

Python

vbts_webadmin/views/subscribers.py

pcarivbts/vbts-webadmin

0616eca6492daa3ebc26b442e8dbebda7ac06d51

[ "BSD-3-Clause" ]

null

vbts_webadmin/views/subscribers.py

pcarivbts/vbts-webadmin

0616eca6492daa3ebc26b442e8dbebda7ac06d51

[ "BSD-3-Clause" ]

3

2020-06-05T18:34:16.000Z

2021-06-10T20:31:18.000Z

vbts_webadmin/views/subscribers.py

pcarivbts/vbts-webadmin

0616eca6492daa3ebc26b442e8dbebda7ac06d51

[ "BSD-3-Clause" ]

2

2018-07-04T00:54:50.000Z

2022-01-28T16:52:10.000Z

""" Copyright (c) 2015-present, Philippine-California Advanced Research Institutes- The Village Base Station Project (PCARI-VBTS). 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 django.contrib import messages as alerts from django.contrib.auth.decorators import login_required from django.core.paginator import Paginator from django.core.paginator import EmptyPage from django.core.paginator import PageNotAnInteger from django.db.models import Q from django.shortcuts import render from django.utils.translation import ugettext as _ from vbts_subscribers.models import SipBuddies from vbts_webadmin.forms import SearchForm @login_required def subscribers_list(request, template_name='subscribers/list.html'): data = {} if 'search' in request.GET: subscribers = SipBuddies.objects.all() for term in request.GET['search'].split(): subscribers = subscribers.filter(Q(name__icontains=term) | Q(callerid__icontains=term)) data['search'] = True alerts.info(request, _("You've searched for: '%s'") % request.GET['search']) else: subscribers = SipBuddies.objects.all() paginator = Paginator(subscribers, 15) page = request.GET.get('page') is_paginated = False if paginator.num_pages > 1: is_paginated = True try: subscribers = paginator.page(page) except PageNotAnInteger: subscribers = paginator.page(1) except EmptyPage: subscribers = paginator.page(paginator.num_pages) form = SearchForm(form_action='subscribers') data['subscribers'] = subscribers data['is_paginated'] = is_paginated data['form'] = form return render(request, template_name, data)

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0.307692

0

null

0

null

0

1

0

553e5975ce3bca9dd2037d832b61d89b76e372a6

16,307

py

Python

examples/vq_rnn_fruit_joint/vq_fruit_joint.py

kastnerkyle/tfbldr

58ad1437d500924acd15d1c6eec4a864f57e9c7c

[ "BSD-3-Clause" ]

4

2018-05-15T22:35:00.000Z

2019-02-22T01:40:49.000Z

examples/vq_rnn_fruit_joint/vq_fruit_joint.py

kastnerkyle/tfbldr

58ad1437d500924acd15d1c6eec4a864f57e9c7c

[ "BSD-3-Clause" ]

null

examples/vq_rnn_fruit_joint/vq_fruit_joint.py

kastnerkyle/tfbldr

58ad1437d500924acd15d1c6eec4a864f57e9c7c

[ "BSD-3-Clause" ]

2

2018-06-09T15:08:44.000Z

2018-11-20T10:13:48.000Z

from tfbldr.nodes import Conv2d from tfbldr.nodes import ConvTranspose2d from tfbldr.nodes import VqEmbedding from tfbldr.nodes import BatchNorm2d from tfbldr.nodes import Linear from tfbldr.nodes import ReLU from tfbldr.nodes import Sigmoid from tfbldr.nodes import Tanh from tfbldr.nodes import OneHot from tfbldr.nodes import Softmax from tfbldr.nodes import LSTMCell from tfbldr.nodes import CategoricalCrossEntropyIndexCost from tfbldr.nodes import CategoricalCrossEntropyLinearIndexCost from tfbldr.nodes import BernoulliCrossEntropyCost from tfbldr.datasets import ordered_list_iterator from tfbldr.plot import get_viridis from tfbldr.plot import autoaspect from tfbldr.datasets import fetch_fruitspeech from tfbldr import get_params_dict from tfbldr import run_loop from tfbldr import scan import tensorflow as tf import numpy as np from collections import namedtuple, defaultdict import itertools viridis_cm = get_viridis() import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt fruit = fetch_fruitspeech() minmin = np.inf maxmax = -np.inf for s in fruit["data"]: si = s - s.mean() minmin = min(minmin, si.min()) maxmax = max(maxmax, si.max()) train_data = [] valid_data = [] type_counts = defaultdict(lambda: 0) final_audio = [] for n, s in enumerate(fruit["data"]): type_counts[fruit["target"][n]] += 1 s = s - s.mean() n_s = (s - minmin) / float(maxmax - minmin) n_s = 2 * n_s - 1 #n_s = mu_law_transform(n_s, 256) if type_counts[fruit["target"][n]] == 15: valid_data.append(n_s) else: train_data.append(n_s) def _cuts(list_of_audio, cut, step): # make many overlapping cuts # 8k, this means offset is ~4ms @ step of 32 real_final = [] real_idx = [] for n, s in enumerate(list_of_audio): # cut off the end s = s[:len(s) - len(s) % step] starts = np.arange(0, len(s) - cut + step, step) for st in starts: real_final.append(s[st:st + cut][None, :, None]) real_idx.append(n) return real_final, real_idx cut = 256 step = 1 train_audio, train_audio_idx = _cuts(train_data, cut, step) valid_audio, valid_audio_idx = _cuts(valid_data, cut, step) random_state = np.random.RandomState(1999) l1_dim = (64, 1, 4, [1, 1, 2, 1]) l2_dim = (128, 1, 4, [1, 1, 2, 1]) l3_dim = (256, 1, 4, [1, 1, 2, 1]) l3_dim = (257, 1, 4, [1, 1, 2, 1]) l4_dim = (256, 1, 4, [1, 1, 2, 1]) l5_dim = (257, 1, 1, [1, 1, 1, 1]) embedding_dim = 512 vqvae_batch_size = 50 rnn_batch_size = 50 n_hid = 512 n_clusters = 64 # goes from 256 -> 16 hardcoded_z_len = 16 # reserve 0 for "start code" n_inputs = embedding_dim + 1 switch_step = 10000 both = True # reserve 0 for start code rnn_init = "truncated_normal" forward_init = "truncated_normal" l_dims = [l1_dim, l2_dim, l3_dim, l4_dim, l5_dim] stride_div = np.prod([ld[-1] for ld in l_dims]) ebpad = [0, 0, 4 // 2 - 1, 0] dbpad = [0, 0, 4 // 2 - 1, 0] train_itr_random_state = np.random.RandomState(1122) valid_itr_random_state = np.random.RandomState(12) train_itr = ordered_list_iterator([train_audio], train_audio_idx, vqvae_batch_size, random_state=train_itr_random_state) valid_itr = ordered_list_iterator([valid_audio], valid_audio_idx, vqvae_batch_size, random_state=valid_itr_random_state) """ for i in range(10000): tt = train_itr.next_batch() # tt[0][3][:, :16] == tt[0][2][:, 16:32] """ def create_encoder(inp, bn_flag): l1 = Conv2d([inp], [1], l_dims[0][0], kernel_size=l_dims[0][1:3], name="enc1", strides=l_dims[0][-1], border_mode=ebpad, random_state=random_state) bn_l1 = BatchNorm2d(l1, bn_flag, name="bn_enc1") r_l1 = ReLU(bn_l1) l2 = Conv2d([r_l1], [l_dims[0][0]], l_dims[1][0], kernel_size=l_dims[1][1:3], name="enc2", strides=l_dims[1][-1], border_mode=ebpad, random_state=random_state) bn_l2 = BatchNorm2d(l2, bn_flag, name="bn_enc2") r_l2 = ReLU(bn_l2) l3 = Conv2d([r_l2], [l_dims[1][0]], l_dims[2][0], kernel_size=l_dims[2][1:3], name="enc3", strides=l_dims[2][-1], border_mode=ebpad, random_state=random_state) bn_l3 = BatchNorm2d(l3, bn_flag, name="bn_enc3") r_l3 = ReLU(bn_l3) l4 = Conv2d([r_l3], [l_dims[2][0]], l_dims[3][0], kernel_size=l_dims[3][1:3], name="enc4", strides=l_dims[3][-1], border_mode=ebpad, random_state=random_state) bn_l4 = BatchNorm2d(l4, bn_flag, name="bn_enc4") r_l4 = ReLU(bn_l4) l5 = Conv2d([r_l4], [l_dims[3][0]], l_dims[4][0], kernel_size=l_dims[4][1:3], name="enc5", random_state=random_state) bn_l5 = BatchNorm2d(l5, bn_flag, name="bn_enc5") return bn_l5 def create_decoder(latent, bn_flag): l1 = Conv2d([latent], [l_dims[-1][0]], l_dims[-2][0], kernel_size=l_dims[-1][1:3], name="dec1", random_state=random_state) bn_l1 = BatchNorm2d(l1, bn_flag, name="bn_dec1") r_l1 = ReLU(bn_l1) l2 = ConvTranspose2d([r_l1], [l_dims[-2][0]], l_dims[-3][0], kernel_size=l_dims[-2][1:3], name="dec2", strides=l_dims[-2][-1], border_mode=dbpad, random_state=random_state) bn_l2 = BatchNorm2d(l2, bn_flag, name="bn_dec2") r_l2 = ReLU(bn_l2) l3 = ConvTranspose2d([r_l2], [l_dims[-3][0]], l_dims[-4][0], kernel_size=l_dims[-3][1:3], name="dec3", strides=l_dims[-3][-1], border_mode=dbpad, random_state=random_state) bn_l3 = BatchNorm2d(l3, bn_flag, name="bn_dec3") r_l3 = ReLU(bn_l3) l4 = ConvTranspose2d([r_l3], [l_dims[-4][0]], l_dims[-5][0], kernel_size=l_dims[-4][1:3], name="dec4", strides=l_dims[-4][-1], border_mode=dbpad, random_state=random_state) bn_l4 = BatchNorm2d(l4, bn_flag, name="bn_dec4") r_l4 = ReLU(bn_l4) l5 = ConvTranspose2d([r_l4], [l_dims[-5][0]], 1, kernel_size=l_dims[-5][1:3], name="dec5", strides=l_dims[-5][-1], border_mode=dbpad, random_state=random_state) #s_l5 = Sigmoid(l5) t_l5 = Tanh(l5) return t_l5 def create_vqvae(inp, bn): z_e_x = create_encoder(inp, bn) z_q_x, z_i_x, z_nst_q_x, emb = VqEmbedding(z_e_x, l_dims[-1][0], embedding_dim, random_state=random_state, name="embed") x_tilde = create_decoder(z_q_x, bn) return x_tilde, z_e_x, z_q_x, z_i_x, z_nst_q_x, emb def create_vqrnn(inp_tm1, inp_t, h1_init, c1_init, h1_q_init, c1_q_init): oh_tm1 = OneHot(inp_tm1, n_inputs) p_tm1 = Linear([oh_tm1], [n_inputs], n_hid, random_state=random_state, name="proj", init=forward_init) def step(x_t, h1_tm1, c1_tm1, h1_q_tm1, c1_q_tm1): output, s = LSTMCell([x_t], [n_hid], h1_tm1, c1_tm1, n_hid, random_state=random_state, name="rnn1", init=rnn_init) h1_t = s[0] c1_t = s[1] output, s = LSTMCell([h1_t], [n_hid], h1_q_tm1, c1_q_tm1, n_hid, random_state=random_state, name="rnn1_q", init=rnn_init) h1_cq_t = s[0] c1_q_t = s[1] h1_q_t, h1_i_t, h1_nst_q_t, h1_emb = VqEmbedding(h1_cq_t, n_hid, n_clusters, random_state=random_state, name="h1_vq_emb") # not great h1_i_t = tf.cast(h1_i_t, tf.float32) return output, h1_t, c1_t, h1_q_t, c1_q_t, h1_nst_q_t, h1_cq_t, h1_i_t r = scan(step, [p_tm1], [None, h1_init, c1_init, h1_q_init, c1_q_init, None, None, None]) out = r[0] hiddens = r[1] cells = r[2] q_hiddens = r[3] q_cells = r[4] q_nst_hiddens = r[5] q_nvq_hiddens = r[6] i_hiddens = r[7] pred = Linear([out], [n_hid], n_inputs, random_state=random_state, name="out", init=forward_init) pred_sm = Softmax(pred) return pred_sm, pred, hiddens, cells, q_hiddens, q_cells, q_nst_hiddens, q_nvq_hiddens, i_hiddens, oh_tm1 def create_graph(): graph = tf.Graph() with graph.as_default(): # vqvae part # define all the vqvae inputs and outputs vqvae_inputs = tf.placeholder(tf.float32, shape=[None, train_audio[0].shape[0], train_audio[0].shape[1], train_audio[0].shape[2]]) bn_flag = tf.placeholder_with_default(tf.zeros(shape=[]), shape=[]) x_tilde, z_e_x, z_q_x, z_i_x, z_nst_q_x, z_emb = create_vqvae(vqvae_inputs, bn_flag) #rec_loss = tf.reduce_mean(BernoulliCrossEntropyCost(x_tilde, images)) vqvae_rec_loss = tf.reduce_mean(tf.square(x_tilde - vqvae_inputs)) vqvae_vq_loss = tf.reduce_mean(tf.square(tf.stop_gradient(z_e_x) - z_nst_q_x)) vqvae_commit_loss = tf.reduce_mean(tf.square(z_e_x - tf.stop_gradient(z_nst_q_x))) vqvae_alpha = 1. vqvae_beta = 0.25 vqvae_loss = vqvae_rec_loss + vqvae_alpha * vqvae_vq_loss + vqvae_beta * vqvae_commit_loss vqvae_params = get_params_dict() # get vqvae keys now, dict is *dynamic* and shared vqvae_params_keys = [k for k in vqvae_params.keys()] vqvae_grads = tf.gradients(vqvae_loss, vqvae_params.values()) learning_rate = 0.0002 vqvae_optimizer = tf.train.AdamOptimizer(learning_rate, use_locking=True) assert len(vqvae_grads) == len(vqvae_params) j = [(g, p) for g, p in zip(vqvae_grads, vqvae_params.values())] vqvae_train_step = vqvae_optimizer.apply_gradients(j) # rnn part # ultimately we will use 2 calls to feed_dict to make lookup mappings easier, but could do it like this #rnn_inputs = tf.cast(tf.stop_gradient(tf.transpose(z_i_x, (2, 0, 1))), tf.float32) rnn_inputs = tf.placeholder(tf.float32, shape=[None, rnn_batch_size, 1]) rnn_inputs_tm1 = rnn_inputs[:-1] rnn_inputs_t = rnn_inputs[1:] init_hidden = tf.placeholder(tf.float32, shape=[rnn_batch_size, n_hid]) init_cell = tf.placeholder(tf.float32, shape=[rnn_batch_size, n_hid]) init_q_hidden = tf.placeholder(tf.float32, shape=[rnn_batch_size, n_hid]) init_q_cell = tf.placeholder(tf.float32, shape=[rnn_batch_size, n_hid]) r = create_vqrnn(rnn_inputs_tm1, rnn_inputs_t, init_hidden, init_cell, init_q_hidden, init_q_cell) pred_sm, pred, hiddens, cells, q_hiddens, q_cells, q_nst_hiddens, q_nvq_hiddens, i_hiddens, oh_tm1 = r rnn_rec_loss = tf.reduce_mean(CategoricalCrossEntropyIndexCost(pred_sm, rnn_inputs_t)) #rnn_rec_loss = tf.reduce_mean(CategoricalCrossEntropyLinearIndexCost(pred, rnn_inputs_t)) rnn_alpha = 1. rnn_beta = 0.25 rnn_vq_h_loss = tf.reduce_mean(tf.square(tf.stop_gradient(q_nvq_hiddens) - q_nst_hiddens)) rnn_commit_h_loss = tf.reduce_mean(tf.square(q_nvq_hiddens - tf.stop_gradient(q_nst_hiddens))) rnn_loss = rnn_rec_loss + rnn_alpha * rnn_vq_h_loss + rnn_beta * rnn_commit_h_loss rnn_params = {k:v for k, v in get_params_dict().items() if k not in vqvae_params_keys} rnn_grads = tf.gradients(rnn_loss, rnn_params.values()) learning_rate = 0.0001 rnn_optimizer = tf.train.AdamOptimizer(learning_rate, use_locking=True) assert len(rnn_grads) == len(rnn_params) rnn_grads = [tf.clip_by_value(g, -10., 10.) if g is not None else None for g in rnn_grads] j = [(g, p) for g, p in zip(rnn_grads, rnn_params.values())] rnn_train_step = rnn_optimizer.apply_gradients(j) things_names = ["vqvae_inputs", "bn_flag", "x_tilde", "z_e_x", "z_q_x", "z_i_x", "z_emb", "vqvae_loss", "vqvae_rec_loss", "vqvae_train_step", "rnn_inputs", "rnn_inputs_tm1", "rnn_inputs_t", "init_hidden", "init_cell", "init_q_hidden", "init_q_cell", "hiddens", "cells", "q_hiddens", "q_cells", "q_nvq_hiddens", "i_hiddens", "pred", "pred_sm", "oh_tm1", "rnn_loss", "rnn_rec_loss", "rnn_train_step"] things_tf = [eval(name) for name in things_names] for tn, tt in zip(things_names, things_tf): graph.add_to_collection(tn, tt) train_model = namedtuple('Model', things_names)(*things_tf) return graph, train_model g, vs = create_graph() rnn_train = False step = 0 def loop(sess, itr, extras, stateful_args): x, = itr.next_batch() init_h = np.zeros((rnn_batch_size, n_hid)).astype("float32") init_c = np.zeros((rnn_batch_size, n_hid)).astype("float32") init_q_h = np.zeros((rnn_batch_size, n_hid)).astype("float32") init_q_c = np.zeros((rnn_batch_size, n_hid)).astype("float32") global rnn_train global step if extras["train"]: step += 1 if step > switch_step: rnn_train = True if both or not rnn_train: feed = {vs.vqvae_inputs: x, vs.bn_flag: 0.} outs = [vs.vqvae_rec_loss, vs.vqvae_loss, vs.vqvae_train_step, vs.z_i_x] r = sess.run(outs, feed_dict=feed) vqvae_l = r[0] vqvae_t_l = r[1] vqvae_step = r[2] if rnn_train: feed = {vs.vqvae_inputs: x, vs.bn_flag: 1.} outs = [vs.vqvae_rec_loss, vs.z_i_x] r = sess.run(outs, feed_dict=feed) vqvae_l = r[0] vqvae_t_l = r[1] discrete_z = r[-1] #discrete_z[3][:, 2:-2] == discrete_z[4][:, 1:-3] #discrete_z = discrete_z[:, :, 1:-2] shp = discrete_z.shape # always start with 0 rnn_inputs = np.zeros((shp[2] + 1, shp[0], shp[1])) rnn_inputs[1:] = discrete_z.transpose(2, 0, 1) + 1. if both or rnn_train: feed = {vs.rnn_inputs: rnn_inputs, vs.init_hidden: init_h, vs.init_cell: init_c, vs.init_q_hidden: init_q_h, vs.init_q_cell: init_q_c} outs = [vs.rnn_rec_loss, vs.rnn_loss, vs.rnn_train_step] r = sess.run(outs, feed_dict=feed) rnn_l = r[0] rnn_t_l = r[1] rnn_step = r[2] if not rnn_train: feed = {vs.rnn_inputs: rnn_inputs, vs.init_hidden: init_h, vs.init_cell: init_c, vs.init_q_hidden: init_q_h, vs.init_q_cell: init_q_c} outs = [vs.rnn_rec_loss] r = sess.run(outs, feed_dict=feed) rnn_l = r[0] else: feed = {vs.vqvae_inputs: x, vs.bn_flag: 1.} outs = [vs.vqvae_rec_loss, vs.z_i_x] r = sess.run(outs, feed_dict=feed) vqvae_l = r[0] discrete_z = r[-1] #discrete_z = discrete_z[:, :, 1:-2] shp = discrete_z.shape # always start with 0 rnn_inputs = np.zeros((shp[2] + 1, shp[0], shp[1])) rnn_inputs[1:] = discrete_z.transpose(2, 0, 1) + 1. feed = {vs.rnn_inputs: rnn_inputs, vs.init_hidden: init_h, vs.init_cell: init_c, vs.init_q_hidden: init_q_h, vs.init_q_cell: init_q_c} outs = [vs.rnn_rec_loss] r = sess.run(outs, feed_dict=feed) rnn_l = r[0] return [vqvae_l, rnn_l], None, stateful_args with tf.Session(graph=g) as sess: run_loop(sess, loop, train_itr, loop, valid_itr, n_steps=75000, n_train_steps_per=5000, n_valid_steps_per=500)

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553eb4733f79df133de3656ed4a77eb050d859d2

311

py

Python

scripts/poorscrum/poorscrum_tools.py

r09491/poorscrum

cdbbc0db03fde842f546093f46e70d03a105bbbd

[ "MIT" ]

null

scripts/poorscrum/poorscrum_tools.py

r09491/poorscrum

cdbbc0db03fde842f546093f46e70d03a105bbbd

[ "MIT" ]

7

2021-03-18T22:37:46.000Z

2022-03-11T23:41:39.000Z

scripts/poorscrum/poorscrum_tools.py

r09491/poorscrum

cdbbc0db03fde842f546093f46e70d03a105bbbd

[ "MIT" ]

null

# -*- coding: utf-8 -*- def fibonacci(n): if n == 0: return 0 elif n == 1: return 1 else: return fibonacci(n-1) + fibonacci(n-2) def story_points(start): for i in range(10): result = fibonacci(i) if result >= start: break return result

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554005d26d7a3413df01a385a87bf09337208562

6,162

py

Python

cata/teachers/ensembles/both_rotation_ensemble.py

seblee97/student_teacher_catastrophic

9baaaf2850025ba9cf33d61c42386bc4c3b2dad2

[ "MIT" ]

2

2021-09-13T01:44:09.000Z

2021-12-11T11:56:49.000Z

cata/teachers/ensembles/both_rotation_ensemble.py

seblee97/student_teacher_catastrophic

9baaaf2850025ba9cf33d61c42386bc4c3b2dad2

[ "MIT" ]

8

2020-11-13T18:37:30.000Z

2022-02-15T15:11:51.000Z

cata/teachers/ensembles/both_rotation_ensemble.py

seblee97/student_teacher_catastrophic

9baaaf2850025ba9cf33d61c42386bc4c3b2dad2

[ "MIT" ]

null

from typing import List from typing import Union import numpy as np import torch from cata.teachers.ensembles import base_teacher_ensemble from cata.utils import custom_functions class BothRotationTeacherEnsemble(base_teacher_ensemble.BaseTeacherEnsemble): """Teacher ensemble (primarily for mean-field limit regime) in which both feature and readout similarities are tuned by rotation. """ def __init__( self, input_dimension: int, hidden_dimensions: List[int], output_dimension: int, bias: bool, loss_type: str, nonlinearities: str, scale_hidden_lr: bool, forward_scaling: float, unit_norm_teacher_head: bool, weight_normalisation: bool, noise_stds: Union[int, float], num_teachers: int, initialisation_std: float, feature_rotation_alpha: float, readout_rotation_alpha: float, ): self._feature_rotation_alpha = feature_rotation_alpha self._readout_rotation_alpha = readout_rotation_alpha super().__init__( input_dimension=input_dimension, hidden_dimensions=hidden_dimensions, output_dimension=output_dimension, bias=bias, loss_type=loss_type, nonlinearities=nonlinearities, scale_hidden_lr=scale_hidden_lr, forward_scaling=forward_scaling, unit_norm_teacher_head=unit_norm_teacher_head, weight_normalisation=weight_normalisation, noise_stds=noise_stds, num_teachers=num_teachers, initialisation_std=initialisation_std, ) def _setup_teachers(self) -> None: """Setup teachers with copies across input to hidden and rotations across hidden to output weights. Raises: AssertionError: If more than 2 teachers are requested. AssertionError: If the network depth is greater than 1, i.e. more than one hidden layer requested. AssertionError: If the hidden dimension is not greater than 1, this is for the notion of rotation to have meaning. """ assert ( self._num_teachers ) == 2, "Both rotation teachers currently implemented for 2 teachers only." assert ( len(self._hidden_dimensions) == 1 ), "Both rotation teachers currently implemented for 1 hidden layer only." assert ( self._hidden_dimensions[0] > 1 ), "Both rotation teachers only valid for hidden dimensions > 1." teachers = [ self._init_teacher( nonlinearity=self._nonlinearities[i], noise_std=self._noise_stds[i] ) for i in range(self._num_teachers) ] with torch.no_grad(): ( teacher_0_feature_weights, teacher_1_feature_weights, ) = self._get_rotated_weights( unrotated_weights=teachers[0].layers[0].weight.data.T, alpha=self._feature_rotation_alpha, normalisation=self._hidden_dimensions[0], ) teachers[0].layers[0].weight.data = teacher_0_feature_weights.T teachers[1].layers[0].weight.data = teacher_1_feature_weights.T # ( # teacher_0_readout_weights, # teacher_1_readout_weights, # ) = self._get_rotated_weights( # unrotated_weights=teachers[0].head.weight.data.T, # alpha=self._readout_rotation_alpha, # normalisation=None, # ) ( teacher_0_readout_weights, teacher_1_readout_weights, ) = self._get_rotated_readout_weights(teachers=teachers) teachers[0].head.weight.data = teacher_0_readout_weights teachers[1].head.weight.data = teacher_1_readout_weights return teachers def _feature_overlap(self, feature_1: torch.Tensor, feature_2: torch.Tensor): alpha_matrix = torch.mm(feature_1, feature_2.T) / self._hidden_dimensions[0] alpha = torch.mean(alpha_matrix.diagonal()) return alpha def _readout_overlap(self, feature_1: torch.Tensor, feature_2: torch.Tensor): alpha = torch.mm(feature_1, feature_2.T) / ( torch.norm(feature_1) * torch.norm(feature_2) ) return alpha def _get_rotated_weights( self, unrotated_weights: torch.Tensor, alpha: float, normalisation: Union[None, int], ): if normalisation is not None: # orthonormalise input to hidden weights of first teacher self_overlap = ( torch.mm(unrotated_weights, unrotated_weights.T) / normalisation ) L = torch.cholesky(self_overlap) orthonormal_weights = torch.mm(torch.inverse(L), unrotated_weights) else: orthonormal_weights = unrotated_weights # construct input to hidden weights of second teacher second_teacher_rotated_weights = alpha * orthonormal_weights + np.sqrt( 1 - alpha ** 2 ) * torch.randn(orthonormal_weights.shape) return orthonormal_weights, second_teacher_rotated_weights def _get_rotated_readout_weights(self, teachers: List): theta = np.arccos(self._readout_rotation_alpha) # keep current norms current_norm = np.mean( [torch.norm(teacher.head.weight) for teacher in teachers] ) rotated_weight_vectors = custom_functions.generate_rotated_vectors( dimension=self._hidden_dimensions[0], theta=theta, normalisation=current_norm, ) teacher_0_rotated_weight_tensor = torch.Tensor( rotated_weight_vectors[0] ).reshape(teachers[0].head.weight.data.shape) teacher_1_rotated_weight_tensor = torch.Tensor( rotated_weight_vectors[1] ).reshape(teachers[1].head.weight.data.shape) return teacher_0_rotated_weight_tensor, teacher_1_rotated_weight_tensor

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null

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null

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5542014f27e11156c75907e597b9852418147144

7,176

py

Python

scripts/admin/admin.py

starmarek/organize-me

710e7acd86e887b7e4379fde18e1f375846ea59e

[ "MIT" ]

null

scripts/admin/admin.py

starmarek/organize-me

710e7acd86e887b7e4379fde18e1f375846ea59e

[ "MIT" ]

null

scripts/admin/admin.py

starmarek/organize-me

710e7acd86e887b7e4379fde18e1f375846ea59e

[ "MIT" ]

null

import json import logging import os import shlex import subprocess from pathlib import Path from types import SimpleNamespace import coloredlogs import fire from .adminFiles import ( DockerComposeFile, DotenvFile, GitlabCIFile, JsonFile, PackageJsonFile, Pipfile, RuntimeTxtFile, YarnRCFile, ) log = logging.getLogger("admin") coloredlogs.install(level="DEBUG") yarn_dir = ".yarn/releases/" for file in os.listdir(".yarn/releases"): if os.getenv("CORE_YARN_VER") in file: yarn_executable = file virtualenv_path = subprocess.run(["pipenv", "--venv"], capture_output=True, text=True, check=True).stdout.strip() dotenv_file = DotenvFile(path=".env") compose_file = DockerComposeFile(path="docker-compose.yml") dotenv_template_file = DotenvFile(path=".template.env") gitlab_ci_file = GitlabCIFile(path=".gitlab-ci.yml") yarnrc_file = YarnRCFile(path=".yarnrc.yml") runtime_txt_file = RuntimeTxtFile(path="runtime.txt") pipfile_file = Pipfile(path="Pipfile") package_json_file = PackageJsonFile(path="package.json") verifiable_files = [compose_file, gitlab_ci_file, pipfile_file, runtime_txt_file, package_json_file, yarnrc_file] def _update_virtualenv_vscode_pythonpath(): settings_file = JsonFile(path=".vscode/settings.json") Path(settings_file.path.split("/")[-2]).mkdir(exist_ok=True) if Path(settings_file.path).exists(): settings_file["python.pythonPath"] = f"{virtualenv_path}/bin/python" settings_file.dump() else: settings_file.data = json.loads(json.dumps({"python.pythonPath": f"{virtualenv_path}/bin/python"})) settings_file.dump() log.info(f"Setting vscode pythonpath to '{virtualenv_path}/bin/python'") def _install_pre_commit(): log.info("Installing pre-commit hooks") subprocess.run(shlex.split(f"{virtualenv_path}/bin/pre-commit install"), check=True) log.warning("You need to install shfmt and shellcheck on your computer in order to pre-commit hooks to work.") def _verify_dotenvs(): log.info("Verifying dotenvs compatibility") assert all(val == dotenv_template_file[key] for key, val in dotenv_file.data.items() if key.startswith("CORE")) def _verify_yarn_executable(): log.info("Verifying yarn compatibility") assert any(os.getenv("CORE_YARN_VER") in yarn_executable for yarn_executable in os.listdir(".yarn/releases")) def _verify_versions(): curr = dotenv_file reference = dotenv_template_file try: _verify_dotenvs() reference = dotenv_file curr = SimpleNamespace(name="files in .yarn/releases dir") _verify_yarn_executable() log.info("Verifying compatibility of core versions") for ver_file in verifiable_files: curr = ver_file assert ver_file.verify_core_versions() except AssertionError: log.error( f"There is a mismatch between {curr.name} and {reference.name}! Make sure that you are using admin script to bump versions of packages!" ) raise class CLI: def __init__(self, vscode=False): try: self.running_in_vscode = os.environ["TERM_PROGRAM"] == "vscode" except KeyError: self.running_in_vscode = False if vscode: self.running_in_vscode = True def update_yarn(self, ver): log.info("Upgrading yarn") subprocess.run([yarn_dir + yarn_executable, "set", "version", ver], check=True) dotenv_template_file["CORE_YARN_VER"] = ver dotenv_file["CORE_YARN_VER"] = ver dotenv_file.dump_to_env() package_json_file["engines"]["yarn"] = ver package_json_file.dump() self.containers_ground_up(cache=False) def update_postgres(self, ver): dotenv_template_file["CORE_POSTGRES_VER"] = ver dotenv_file["CORE_POSTGRES_VER"] = ver dotenv_file.dump_to_env() self.containers_ground_up(cache=False) def update_compose(self, ver): ver = str(ver) dotenv_template_file["CORE_COMPOSE_VER"] = ver dotenv_file["CORE_COMPOSE_VER"] = ver dotenv_file.dump_to_env() compose_file["version"] = ver compose_file.dump() self.containers_ground_up(cache=False) def update_python(self, ver): log.info("Reinstalling your pipenv") subprocess.run(["pipenv", "--rm"], check=True) pipfile_file["requires"]["python_version"] = ver pipfile_file.dump() subprocess.run(["pipenv", "update", "--keep-outdated", "--dev"], check=True) dotenv_template_file["CORE_PYTHON_VER"] = ver dotenv_file["CORE_PYTHON_VER"] = ver dotenv_file.dump_to_env() self.containers_ground_up(cache=False) gitlab_ci_file["variables"]["PYTHON_VERSION"] = ver gitlab_ci_file.dump() runtime_txt_file.data = [f"python-{ver}"] runtime_txt_file.dump() if self.running_in_vscode: _update_virtualenv_vscode_pythonpath() def update_node(self, ver): dotenv_template_file["CORE_NODE_VER"] = ver dotenv_file["CORE_NODE_VER"] = ver dotenv_file.dump_to_env() self.containers_ground_up(cache=False) gitlab_ci_file["variables"]["NODE_VERSION"] = ver gitlab_ci_file.dump() package_json_file["engines"]["node"] = ver package_json_file.dump() def containers_build(self, cache=True): log.info(f"Building containers with 'cache={cache}'") subprocess.run(shlex.split(f"docker-compose build --force-rm {'' if cache else '--no-cache'}"), check=True) def containers_logs(self, container_name=""): try: subprocess.run(shlex.split(f"docker-compose logs -f {container_name}")) except KeyboardInterrupt: pass def containers_up(self): log.info("Running containers") subprocess.run(shlex.split("docker-compose up --detach --remove-orphans --force-recreate"), check=True) def containers_ground_up(self, cache=True): self.containers_build(cache=cache) self.containers_up() def init(self): self.containers_ground_up(cache=False) _install_pre_commit() if self.running_in_vscode: _update_virtualenv_vscode_pythonpath() def install_pip(self, package, dev=False): subprocess.run(shlex.split(f"pipenv install {package} {'--dev' if dev else ''}"), check=True) self.containers_ground_up(cache=False) def install_yarn(self, package, dev=False): subprocess.run( shlex.split(f"sudo {yarn_dir + yarn_executable} add {package} {'--dev' if dev else ''}"), check=True ) self.containers_ground_up(cache=False) def remove_pip(self, package): subprocess.run(["pipenv", "uninstall", package], check=True) self.containers_ground_up(cache=False) def remove_yarn(self, package): subprocess.run(["sudo", yarn_dir + yarn_executable, "remove", package], check=True) self.containers_ground_up(cache=False) if __name__ == "__main__": log.info("Starting admin script") _verify_versions() fire.Fire(CLI)

32.324324

148

0.67879

904

7,176

5.130531

0.202434

0.030185

0.042691

0.034498

0.355326

0.318025

0.256361

0.186934

0.184994

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0.20262

7,176

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0.810381

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0.170732

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0.189024

0

null

0

null

0

1

0

5543d0392b1a991c4c0bc9b77494d93272ec2802

743

py

Python

tests/components/pages/ts.py

T4rk1n/dazzler

69c49422dc19c910445ab265b1d3481041de8f43

[ "MIT" ]

15

2019-12-19T11:57:30.000Z

2021-11-15T23:34:41.000Z

tests/components/pages/ts.py

T4rk1n/dazzler

69c49422dc19c910445ab265b1d3481041de8f43

[ "MIT" ]

196

2019-09-21T15:10:14.000Z

2022-03-31T11:07:48.000Z

tests/components/pages/ts.py

T4rk1n/dazzler

69c49422dc19c910445ab265b1d3481041de8f43

[ "MIT" ]

7

2019-10-30T19:38:15.000Z

2021-12-01T04:54:16.000Z

from dazzler.system import Page from dazzler.components import core from tests.components import ts_components as tsc page = Page( __name__, core.Container([ tsc.TypedComponent( 'override', children=core.Container('foobar'), num=2, text='foobar', boo=True, arr=[1, 2, 'mixed'], arr_str=['foo', 'bar'], arr_num=[7, 8, 9], arr_obj_lit=[{'name': 'foo'}], obj={'anything': 'possible'}, enumeration='foo', union=7, style={'border': '1px solid rgb(0,0,255)'}, class_name='other' ), tsc.TypedClassComponent('class based', children='clazz') ]) )

27.518519

64

0.51144

78

743

4.74359

0.628205

0.059459

0

0.026639

0.343203

743

26

65

28.576923

0.731557

0

0.142665

0

1

0

false

0

0.12

0

0.12

0

null

0

null

0

1

0

55454283c60ef0107317118c446ed4395d8f58a5

4,464

py

Python

src/gistsgetter/app.py

pmfrank/gistsgetter

a19f59604ebf1cb13c641d25c4461b4347bba58a

[ "MIT" ]

null

src/gistsgetter/app.py

pmfrank/gistsgetter

a19f59604ebf1cb13c641d25c4461b4347bba58a

[ "MIT" ]

null

src/gistsgetter/app.py

pmfrank/gistsgetter

a19f59604ebf1cb13c641d25c4461b4347bba58a

[ "MIT" ]

null

""" An application dedicated to creating, editing, and deleting Gists in GitHub """ from __future__ import absolute_import import toga import pyperclip from toga.style import Pack from toga.style.pack import COLUMN, ROW from .common.Search import search from functools import partial class GistsGetter(toga.App): def startup(self): """ Construct and show the Toga application. Usually, you would add your application to a main content box. We then create a main window (with a name matching the app), and show the main window. """ main_box = toga.Box(style=Pack(direction=COLUMN)) top_box = toga.Box(style=Pack(direction=ROW, padding=5, alignment='top')) middle_box = toga.Box(style=Pack(direction=ROW,padding=5, alignment='center', flex=1)) button_box = toga.Box(style=Pack(padding=5, alignment='right')) bottom_box = toga.Box(style=Pack(direction=ROW, padding=(5,5,20,5), alignment='bottom')) # Padding - Top, Right, Botom, Left select_label = toga.Label('Search By', style=Pack(padding=5, alignment='center')) self.select = toga.Selection(items=['UserID','GistID']) self.select_input = toga.TextInput(style=Pack(padding=5, flex=1),placeholder='User or Gist ID') # Line preserved for prostarity will be using helper functions to do search with externale functions # select_button = toga.Button('Search',style=Pack(padding=5),on_press=partial(search,string = 'x')) select_button = toga.Button('Search', style=Pack(padding=5), on_press=self.search_by) self.results = toga.MultilineTextInput(style=Pack(padding=(0,5), flex=1),readonly = True) copy_button = toga.Button('Copy to Clipboard', style=Pack(padding=5),on_press=self.copy_to_clipboard) button_box.add(copy_button) middle_box.add(self.results) middle_box.add(button_box) top_box.add(select_label) top_box.add(self.select) top_box.add(self.select_input) top_box.add(select_button) login_label = toga.Label('Username', style=Pack(padding=5, alignment='left')) self.login_input = toga.TextInput(style=Pack(padding=5,alignment='left',flex=1)) pw_label = toga.Label('Password', style=Pack(padding=5, alignment='right')) self.pw_input = toga.PasswordInput(style=Pack(padding=4,alignment='right',flex=1)) bottom_box.add(login_label) bottom_box.add(self.login_input) bottom_box.add(pw_label) bottom_box.add(self.pw_input) main_box.add(top_box) main_box.add(middle_box) main_box.add(bottom_box) self.main_window = toga.MainWindow(title=self.formal_name, size=(640,480)) self.main_window.content = main_box self.main_window.show() def search_by(self, widget): global results if not self.select_input.value or not self.login_input.value or not self.pw_input: self.results.value = 'All fields required' return if self.select.value == 'UserID': self.results.value = 'Feature not implemented' return else: global gist_id gist_id = self.select_input.value url = self.__get_token('https://api.github.com/gists{/gist_id}','{') results = search(url, self.login_input.value,self.pw_input.value) for filename in results: print(results[filename]) self.results.value = results def copy_to_clipboard(self, widget): global results for filename in results: pyperclip.copy(results[filename]) def __get_token(self, string, delim): tokens = string.split(delim) url = tokens[0] for token in tokens[1:]: token = token[:-1] if '/' in token : token = token[1:] if token in globals(): if '=' in url: url = url + globals()[token] else: url = url + '/' + globals()[token] if ',' in token: token = token[1:] print(token) multitokens = token.split(',') for multitoken in multitokens: if multitoken in globals(): url = url + '&' + multitoken + '=' + globals()[multitoken] return url def main(): return GistsGetter()

38.817391

132

0.622536

573

4,464

4.720768

0.242583

0.053235

0.065065

0.056562

0.24695

0.190018

0.126063

0.088355

0.073937

0

0.010632

0.262545

4,464

114

133

39.157895

0.811057

0.111783

0

0.097561

0

0.054504

0

1

0.060976

false

0.02439

0.085366

0.012195

0.207317

0.02439

0

null

0

null

0

1

0

5549b2fc2c6d6a256c772a1fa6b1cb0ba16583fe

7,401

py

Python

src/qcar/src/qcar/q_essential.py

bchampp/scylla

6ec27877cc03c200a874cd0eb25a36c866471250

[ "MIT" ]

null

src/qcar/src/qcar/q_essential.py

bchampp/scylla

6ec27877cc03c200a874cd0eb25a36c866471250

[ "MIT" ]

null

src/qcar/src/qcar/q_essential.py

bchampp/scylla

6ec27877cc03c200a874cd0eb25a36c866471250

[ "MIT" ]

null

from quanser.hardware import HIL, HILError, PWMMode from quanser.multimedia import Video3D, VideoCapture, Video3DStreamType, MediaError, ImageFormat, ImageDataType from quanser.devices import RPLIDAR, RangingMeasurements, RangingMeasurementMode, DeviceError, RangingDistance from .q_misc import Utilities import numpy as np import pygame import time saturate = Utilities.saturate # region: Cameras class Camera3D(): def __init__(self, mode='RGB&DEPTH', frame_width_RGB=1920, frame_height_RGB=1080, frame_rate_RGB=30.0, frame_width_depth=1280, frame_height_depth=720, frame_rate_depth=15.0, device_id='0'): '''This function configures the Intel Realsense RGB and depth cameras for use. Outputs: video3d - video3d object, you must call video3d.start_streaming() before your main loop stream_RGB - stream object to be passed to the read method image_buffer_RGB - buffer array that will be updated by the read method stream_depth - stream object to be passed to the read method image_buffer_depth - buffer array that will be updated by the read method''' self.mode = mode self.stream_index = 0 self.image_buffer_RGB = np.zeros((frame_height_RGB, frame_width_RGB, 3), dtype=np.uint8) self.image_buffer_depth_px = np.zeros((frame_height_depth, frame_width_depth, 1), dtype=np.uint8) self.image_buffer_depth_m = np.zeros((frame_height_depth, frame_width_depth, 1), dtype=np.float32) try: self.video3d = Video3D(device_id) if mode == 'RGB': self.stream_RGB = self.video3d.stream_open(Video3DStreamType.COLOR, self.stream_index, frame_rate_RGB, frame_width_RGB, frame_height_RGB, ImageFormat.ROW_MAJOR_INTERLEAVED_BGR, ImageDataType.UINT8) elif mode == 'DEPTH': self.stream_depth = self.video3d.stream_open(Video3DStreamType.DEPTH, self.stream_index, frame_rate_depth, frame_width_depth, frame_height_depth, ImageFormat.ROW_MAJOR_GREYSCALE, ImageDataType.UINT8) else: self.stream_RGB = self.video3d.stream_open(Video3DStreamType.COLOR, self.stream_index, frame_rate_RGB, frame_width_RGB, frame_height_RGB, ImageFormat.ROW_MAJOR_INTERLEAVED_BGR, ImageDataType.UINT8) self.stream_depth = self.video3d.stream_open(Video3DStreamType.DEPTH, self.stream_index, frame_rate_depth, frame_width_depth, frame_height_depth, ImageFormat.ROW_MAJOR_GREYSCALE, ImageDataType.UINT8) self.video3d.start_streaming() except MediaError as me: print(me.get_error_message()) def terminate(self): '''This function terminates the RGB and depth video and stream objects correctly. Inputs: video3d - video object from the configure method stream_RGB - RGB stream object from the configure method stream_depth - depth stream object from the configure method ''' try: self.video3d.stop_streaming() if self.mode == 'RGB': self.stream_RGB.close() elif self.mode == 'DEPTH': self.stream_depth.close() else: self.stream_RGB.close() self.stream_depth.close() self.video3d.close() except MediaError as me: print(me.get_error_message()) def read_RGB(self): '''This function reads an image from the RGB camera for use. Outputs: timestamp - timestamp corresponding to the frame read ''' timestamp = -1 try: frame = self.stream_RGB.get_frame() while not frame: frame = self.stream_RGB.get_frame() frame.get_data(self.image_buffer_RGB) timestamp = frame.get_timestamp() frame.release() except KeyboardInterrupt: pass except MediaError as me: print(me.get_error_message()) finally: return timestamp def read_depth(self, dataMode='px'): '''This function reads an image from the depth camera for use. dataMode is 'px' for pixels or 'm' for meters. Use corresponding image buffer. Outputs: timestamp - timestamp corresponding to the frame read ''' timestamp = -1 try: frame = self.stream_depth.get_frame() while not frame: frame = self.stream_depth.get_frame() if dataMode == 'px': frame.get_data(self.image_buffer_depth_px) elif dataMode == 'm': frame.get_meters(self.image_buffer_depth_m) timestamp = frame.get_timestamp() frame.release() except KeyboardInterrupt: pass except MediaError as me: print(me.get_error_message()) finally: return timestamp class Camera2D(): def __init__(self, camera_id="0", frame_width=640, frame_height=480, frame_rate=30.0): '''This function configures the 2D camera for use based on the camera_id provided.''' self.url = "video://localhost:"+camera_id self.image_data = np.zeros((frame_height, frame_width, 3), dtype=np.uint8) try: # self.capture = VideoCapture(self.url, frame_rate, frame_width, frame_height, ImageFormat.ROW_MAJOR_INTERLEAVED_BGR, ImageDataType.UINT8, self.image_data, None, 0) self.capture = VideoCapture(self.url, frame_rate, frame_width, frame_height, ImageFormat.ROW_MAJOR_INTERLEAVED_BGR, ImageDataType.UINT8, None, 0) self.capture.start() except MediaError as me: print(me.get_error_message()) def read(self): '''This function reads a frame, updating the corresponding image buffer.''' try: # self.capture.read() self.capture.read(self.image_data) except MediaError as me: print(me.get_error_message()) except KeyboardInterrupt: print('User Interupted') def reset(self): '''This function resets the 2D camera stream by stopping and starting the capture service.''' try: self.capture.stop() self.capture.start() except MediaError as me: print(me.get_error_message()) def terminate(self): '''This function terminates the 2D camera operation.''' try: self.capture.stop() self.capture.close() except MediaError as me: print(me.get_error_message()) # endregion # region: LIDAR class LIDAR(): def __init__(self, num_measurements=720): # self.num_measurements = num_measurements # self.measurements = [RangingMeasurement() for x in range(self.num_measurements)] # self.measurements = RangingMeasurements(num_measurements) self.measurements = RangingMeasurements(num_measurements) self.distances = np.zeros((num_measurements,1), dtype=np.float64) self.angles = np.zeros((num_measurements,1), dtype=np.float64) # self.angles = np.linspace(0, 2*np.pi-(2*np.pi/num_measurements), num_measurements, dtype=np.float64) self.lidar = RPLIDAR() # self.maxDistance = 18.0 try: self.lidar.open("serial-cpu://localhost:2?baud='115200',word='8',parity='none',stop='1',flow='none',dsr='on'", RangingDistance.LONG) except DeviceError as de: if de.error_code == -34: pass else: print(de.get_error_message()) def terminate(self): try: self.lidar.close() except DeviceError as de: if de.error_code == -34: pass else: print(de.get_error_message()) def read(self): try: self.lidar.read(RangingMeasurementMode.NORMAL, 0, 0, self.measurements) self.distances = np.array(self.measurements.distance) # self.distances = np.append( np.flip( self.distances[0:int(self.num_measurements/4)] ) , # np.flip( self.distances[int(self.num_measurements/4):]) ) # self.distances[self.distances > self.maxDistance] = self.maxDistance # self.distances[self.distances > self.maxDistance] = 0 self.angles = np.array(self.measurements.heading) except DeviceError as de: if de.error_code == -34: pass else: print(de.get_error_message()) # endregion

37.190955

211

0.740576

1,026

7,401

5.154971

0.190058

0.032142

0.031197

0.030251

0.587068

0.551333

0.485536

0.463036

0.426168

0.418227

0

0.01822

0.154574

7,401

199

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37.190955

0.827074

0.284961

0

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0

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0.03

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0

1

0.082707

false

0.037594

0.052632

0

0.172932

0.090226

0

null

0

null

0

1

0

554a7b61e03b3173856a7a579bde9d2c36a7f575

1,689

py

Python

ex071.py

cristianoandrad/ExerciciosPythonCursoEmVideo

362603436b71c8ef8386d7a9ab3c5fed0b8d63f7

[ "MIT" ]

null

ex071.py

cristianoandrad/ExerciciosPythonCursoEmVideo

362603436b71c8ef8386d7a9ab3c5fed0b8d63f7

[ "MIT" ]

null

ex071.py

cristianoandrad/ExerciciosPythonCursoEmVideo

362603436b71c8ef8386d7a9ab3c5fed0b8d63f7

[ "MIT" ]

null

'''Crie um programa que simule o funcionamento de um caixa eletrônico. No início, pergunte ao usuário qual será o valor a ser sacado (número inteiro) e o programa vai informar quantas cédulas de cada valor serão entregues. OBS: considere que o caixa possui cédulas de R$50, R$20, R$10 e R$1.''' '''print('--' * 15) print('{:^30}'.format('Banco CEV')) print('--' * 15) valor = int(input('Qual o valor que você quer sacar R$ ')) c50 = valor % 50 c20 = c50 % 20 c10 = c20 % 10 c1 = c10 % 1 b50 = valor - c50 b20 = valor - b50 - c20 b10 = valor - b50 - b20 - c10 b1 = valor - b50 - b20 - b10 - c1 print(f'Total de {b50/50:.0f} celulas de R$ 50,00') print(f'Total de {b20/20:.0f} celulas de R$ 20,00') print(f'Total de {b10/10:.0f} celulas de R$ 10,00') print(f'Total de {b1/1:.0f} celulas de R$ 1,00') print('--' * 15) print('Volte sempre ao Banco CEV! Tenha um bom dia')''' '''valor = int(input("informe o valor a ser sacado : ")) nota50 = valor // 50 valor %= 50 nota20 = valor // 20 valor %= 20 nota10 = valor // 10 valor %= 10 nota1 = valor // 1 print(f"notas de 50 = {nota50}") print(f"notas de 20 = {nota20}") print(f"notas de 10 = {nota10}") print(f"notas de 1 = {nota1}")''' print('--' * 15) print('{:^30}'.format('Banco CEV')) print('--' * 15) valor = int(input('Qual o valor que você quer sacar R$ ')) total = valor cel = 50 contCel = 0 while True: if total >= cel: total -= cel contCel += 1 else: print(f'O total de {contCel} céluldas de R$ {cel}.') if cel == 50: cel = 20 elif cel == 20: cel = 10 elif cel == 10: cel = 1 contCel = 0 if total == 0: break

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null

0

null

0

1

0

554c5ff1d984eee7cf69842945a06a7b43f122ff

919

py

Python

common.py

hoostus/prime-harvesting

6606b94ea7859fbf217dbea4ace856e3fa4d154e

[ "BlueOak-1.0.0", "Apache-2.0" ]

23

2016-09-07T06:13:37.000Z

2022-02-17T23:49:03.000Z

common.py

hoostus/prime-harvesting

6606b94ea7859fbf217dbea4ace856e3fa4d154e

[ "BlueOak-1.0.0", "Apache-2.0" ]

null

common.py

hoostus/prime-harvesting

6606b94ea7859fbf217dbea4ace856e3fa4d154e

[ "BlueOak-1.0.0", "Apache-2.0" ]

12

2016-06-30T17:27:39.000Z

2021-12-12T07:54:27.000Z

import itertools import math import simulate import harvesting import plot from decimal import setcontext, ExtendedContext # Don't raise exception when we divide by zero #setcontext(ExtendedContext) #getcontext().prec = 5 def compare_prime_vs_rebalancing(series, years=30, title=''): (r1, r2) = itertools.tee(series) x = simulate.withdrawals(r1, years=years) y = simulate.withdrawals(r2, years=years, harvesting=harvesting.N_60_RebalanceHarvesting) s1 = [n.withdraw_r for n in x] s2 = [n.withdraw_r for n in y] ceiling = max(max(s1), max(s2)) if ceiling < 100000: ceiling = int(math.ceil(ceiling / 10000) * 10000) else: ceiling = int(math.ceil(ceiling / 100000) * 100000) plot.plot_two(s1, s2, s1_title='Prime Harvesting', s2_title='Annual Rebalancing', y_lim=[0,ceiling], x_label='Year of Retirement', title=title)

31.689655

93

0.677911

124

919

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null

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null

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1

0

554ef62e12daf1b4dd0a910c08086098d9a39602

769

py

Python

tests/hdx/scraper/test_utils.py

mcarans/hdx-python-scraper

ce17c672591979d4601bd125a38b86ea81a9f3c4

[ "MIT" ]

null

tests/hdx/scraper/test_utils.py

mcarans/hdx-python-scraper

ce17c672591979d4601bd125a38b86ea81a9f3c4

[ "MIT" ]

null

tests/hdx/scraper/test_utils.py

mcarans/hdx-python-scraper

ce17c672591979d4601bd125a38b86ea81a9f3c4

[ "MIT" ]

null

from datetime import datetime from hdx.data.dataset import Dataset from hdx.scraper.utilities import ( get_isodate_from_dataset_date, string_params_to_dict, ) class TestUtils: def test_string_params_to_dict(self): result = string_params_to_dict("a: 123, b: 345") assert result == {"a": "123", "b": "345"} result = string_params_to_dict("a:123,b:345") assert result == {"a": "123", "b": "345"} def test_get_isodate_from_dataset_date(self, configuration): dataset = Dataset( { "dataset_date": "[2022-01-11T02:24:08.241 TO 2022-01-11T02:24:08.241]" } ) result = get_isodate_from_dataset_date(dataset, datetime.now()) assert result == "2022-01-11"

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null

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null

0

1

0

554fb560fa2735d2073c8f53fb708577f43575e0

3,796

py

Python

store/models.py

Dokeey/Buy-Sell

9d70eb8649d79962657cc4be896e437908de537b

[ "MIT" ]

7

2019-03-25T14:43:41.000Z

2021-09-16T01:44:41.000Z

store/models.py

Dokeey/Buy-Sell

9d70eb8649d79962657cc4be896e437908de537b

[ "MIT" ]

80

2019-03-25T09:25:00.000Z

2020-02-09T01:01:09.000Z

store/models.py

Dokeey/Buy-Sell

9d70eb8649d79962657cc4be896e437908de537b

[ "MIT" ]

4

2019-03-25T13:58:07.000Z

2021-11-26T09:12:32.000Z

from random import randrange from django.conf import settings from django.contrib.contenttypes.fields import GenericRelation from django.db import models from hitcount.models import HitCountMixin, HitCount from imagekit.models import ProcessedImageField from pilkit.processors import ResizeToFill from django_cleanup import cleanup from store.fields import DefaultStaticProcessedImageField def get_random(): rand = randrange(1,10) return '/static/profile/{}.png'.format(rand) # @cleanup.ignore class StoreProfile(models.Model, HitCountMixin): user = models.OneToOneField(settings.AUTH_USER_MODEL, verbose_name="유저", on_delete=models.CASCADE) name = models.CharField(max_length=20, verbose_name="가게명", unique=True) photo = DefaultStaticProcessedImageField( verbose_name="가게 사진", null=True, upload_to='profile/storephoto', processors=[ResizeToFill(200, 200)], format='JPEG', options={'quality': 60} ) comment = models.TextField(max_length=200, blank=True, verbose_name="소개", default="반갑습니다.") created_at = models.DateTimeField(verbose_name="생성일", auto_now_add=True) hit_count_generic = GenericRelation(HitCount, object_id_field='object_pk', related_query_name='hit_count_generic_relation') def __str__(self): return self.name class Meta: verbose_name = "가게" verbose_name_plural = "가게" ordering = ['-id'] from django.contrib.auth import get_user_model User = get_user_model() try: user_pk = User.objects.get(username='deleteuser').id except: user_pk = None class QuestionComment(models.Model): store_profile = models.ForeignKey(StoreProfile, verbose_name="가게", on_delete=models.CASCADE) if user_pk: author = models.ForeignKey(settings.AUTH_USER_MODEL, verbose_name="작성자", on_delete=models.SET_DEFAULT, default=user_pk) else: author = models.ForeignKey(settings.AUTH_USER_MODEL, verbose_name="작성자", on_delete=models.CASCADE) comment = models.TextField(verbose_name="문의글", max_length=1000) created_at = models.DateTimeField(verbose_name="작성일", auto_now_add=True) updated_at = models.DateTimeField(verbose_name="최근 업데이트", auto_now=True) parent = models.ForeignKey('self', verbose_name="상위 댓글", null=True, blank=True, related_name='replies', on_delete=models.CASCADE) def __str__(self): return self.author.storeprofile.name class Meta: ordering = ('-created_at',) verbose_name = "가게 문의" verbose_name_plural = "가게 문의" from trade.models import Item class StoreGrade(models.Model): store_profile = models.ForeignKey(StoreProfile, verbose_name="가게", on_delete=models.CASCADE) if user_pk: author = models.ForeignKey(settings.AUTH_USER_MODEL, verbose_name="작성자", on_delete=models.SET_DEFAULT ,default=user_pk) else: author = models.ForeignKey(settings.AUTH_USER_MODEL, verbose_name="작성자", on_delete=models.CASCADE) store_item = models.ForeignKey(Item, verbose_name="구매한 물품", on_delete=models.SET_NULL, null=True) grade_comment = models.TextField(verbose_name="물품평", max_length=250) rating = models.PositiveIntegerField( verbose_name="점수", choices=( (1, '★☆☆☆☆'), (2, '★★☆☆☆'), (3, '★★★☆☆'), (4, '★★★★☆'), (5, '★★★★★') ), default=0, db_index=True ) created_at = models.DateTimeField(verbose_name="작성일", auto_now_add=True) updated_at = models.DateTimeField(verbose_name="최근 업데이트", auto_now=True) def __str__(self): return self.author.storeprofile.name class Meta: ordering = ('-created_at',) verbose_name = "가게 평점" verbose_name_plural = "가게 평점"

36.5

133

0.692308

469

3,796

5.420043

0.30064

0.11251

0.049567

0.425256

0.391424

0.363493

0

0.00944

0.190727

3,796

104

134

36.5

0.809896

0.003952

0

0.285714

0

0.067989

0.012698

0

1

0.047619

false

0

0.130952

0.035714

0.5

0

null

0

null

0

1

0

5555b6c3e07de5a90e04d4e0ebe99f3c40e0594c

1,587

py

Python

experts/siamdw.py

songheony/AAA-journal

4306fac0afe567269b8d2f1cbef2a1c398fdde82

[ "MIT" ]

9

2020-07-07T09:03:07.000Z

2021-04-22T03:38:49.000Z

experts/siamdw.py

songheony/AAA-journal

4306fac0afe567269b8d2f1cbef2a1c398fdde82

[ "MIT" ]

null

experts/siamdw.py

songheony/AAA-journal

4306fac0afe567269b8d2f1cbef2a1c398fdde82

[ "MIT" ]

1

2021-07-31T19:26:52.000Z

import sys import numpy as np import cv2 from easydict import EasyDict as edict from base_tracker import BaseTracker import path_config sys.path.append("external/SiamDW/lib") from tracker.siamrpn import SiamRPN import models.models as models from utils.utils import load_pretrain class SiamDW(BaseTracker): def __init__(self): super().__init__("SiamDW") net_file = path_config.SIAMDW_MODEL info = edict() info.arch = "SiamRPNRes22" info.dataset = "OTB2015" info.epoch_test = False info.cls_type = "thinner" self.tracker = SiamRPN(info) self.net = models.__dict__[info.arch](anchors_nums=5, cls_type=info.cls_type) self.net = load_pretrain(self.net, net_file) self.net.eval() self.net = self.net.cuda() def initialize(self, image_file, box): image = cv2.imread(image_file) if len(image.shape) == 2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) center = np.array([box[0] + (box[2] - 1) / 2, box[1] + (box[3] - 1) / 2]) size = np.array([box[2], box[3]]) self.state = self.tracker.init(image, center, size, self.net) def track(self, image_file): image = cv2.imread(image_file) if len(image.shape) == 2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) self.state = self.tracker.track(self.state, image) center = self.state["target_pos"] size = self.state["target_sz"] bbox = (center[0] - size[0] / 2, center[1] - size[1] / 2, size[0], size[1]) return bbox

34.5

85

0.628859

221

1,587

4.366516

0.325792

0.050777

0.022798

0.039378

0.157513

0

0.02995

0.242596

1,587

45

86

35.266667

0.772879

0

0.15

0

0.044108

0

1

0.075

false

0

0.225

0

0.35

0

null

0

null

0

1

0

555695e92a72c35957e937841df7b620e7484601

3,346

py

Python

serpent/machine_learning/reinforcement_learning/rainbow_dqn/dqn.py

DylanSpicker/SerpentAI

c48c4b072e0d1084a52eac569ad1c7fa02ac7348

[ "MIT" ]

null

serpent/machine_learning/reinforcement_learning/rainbow_dqn/dqn.py

DylanSpicker/SerpentAI

c48c4b072e0d1084a52eac569ad1c7fa02ac7348

[ "MIT" ]

null

serpent/machine_learning/reinforcement_learning/rainbow_dqn/dqn.py

DylanSpicker/SerpentAI

c48c4b072e0d1084a52eac569ad1c7fa02ac7348

[ "MIT" ]

null

import math import torch class DQN(torch.nn.Module): def __init__(self, action_space, history=4, hidden_size=512, noisy_std=0.1, quantile=True): super().__init__() self.atoms = 200 if quantile else 51 self.action_space = action_space self.quantile = quantile self.conv1 = torch.nn.Conv2d(history, 32, 8, stride=4, padding=1) self.conv2 = torch.nn.Conv2d(32, 64, 4, stride=2) self.conv3 = torch.nn.Conv2d(64, 64, 3) self.fc_h_v = NoisyLinear(5184, hidden_size, std_init=noisy_std) self.fc_h_a = NoisyLinear(5184, hidden_size, std_init=noisy_std) self.fc_z_v = NoisyLinear(hidden_size, self.atoms, std_init=noisy_std) self.fc_z_a = NoisyLinear(hidden_size, action_space * self.atoms, std_init=noisy_std) def forward(self, x): x = torch.nn.functional.relu(self.conv1(x)) x = torch.nn.functional.relu(self.conv2(x)) x = torch.nn.functional.relu(self.conv3(x)) x = x.view(-1, 5184) v = self.fc_z_v(torch.nn.functional.relu(self.fc_h_v(x))) a = self.fc_z_a(torch.nn.functional.relu(self.fc_h_a(x))) v, a = v.view(-1, 1, self.atoms), a.view(-1, self.action_space, self.atoms) q = v + a - a.mean(1, keepdim=True) if not self.quantile: q = torch.nn.functional.softmax(q, dim=2) return q def reset_noise(self): for name, module in self.named_children(): if "fc" in name: module.reset_noise() class NoisyLinear(torch.nn.Module): def __init__(self, in_features, out_features, std_init=0.4): super().__init__() self.in_features = in_features self.out_features = out_features self.std_init = std_init self.weight_mu = torch.nn.Parameter(torch.empty(out_features, in_features)) self.weight_sigma = torch.nn.Parameter(torch.empty(out_features, in_features)) self.register_buffer("weight_epsilon", torch.empty(out_features, in_features)) self.bias_mu = torch.nn.Parameter(torch.empty(out_features)) self.bias_sigma = torch.nn.Parameter(torch.empty(out_features)) self.register_buffer("bias_epsilon", torch.empty(out_features)) self.reset_parameters() self.reset_noise() def reset_parameters(self): mu_range = 1 / math.sqrt(self.in_features) self.weight_mu.data.uniform_(-mu_range, mu_range) self.weight_sigma.data.fill_(self.std_init / math.sqrt(self.in_features)) self.bias_mu.data.uniform_(-mu_range, mu_range) self.bias_sigma.data.fill_(self.std_init / math.sqrt(self.out_features)) def reset_noise(self): epsilon_in = self._scale_noise(self.in_features) epsilon_out = self._scale_noise(self.out_features) self.weight_epsilon.copy_(epsilon_out.ger(epsilon_in)) self.bias_epsilon.copy_(epsilon_out) def forward(self, input): if self.training: return torch.nn.functional.linear(input, self.weight_mu + self.weight_sigma * self.weight_epsilon, self.bias_mu + self.bias_sigma * self.bias_epsilon) else: return torch.nn.functional.linear(input, self.weight_mu, self.bias_mu) def _scale_noise(self, size): x = torch.randn(size) return x.sign().mul_(x.abs().sqrt_())

35.978495

162

0.661686

495

3,346

4.212121

0.187879

0.057074

0.065228

0.060432

0.42542

0.402878

0.344844

0.251319

0.17554

0.141007

0

0.021723

0.21578

3,346

92

163

36.369565

0.772866

0

0.0625

0

0.008368

0

1

0.125

false

0

0.03125

0

0.25

0

null

0

null

0

1

0

55572056018bf803954acf22ae96913928e3246d

1,479

py

Python

src/modules/base/url_helper.py

yakii9/artificial-programmer

a6c1a5a47155ee4d24be729a0fa8c86ca40f85d1

[ "MIT" ]

1

2018-10-21T22:46:27.000Z

src/modules/base/url_helper.py

yakii9/artificial-programmer

a6c1a5a47155ee4d24be729a0fa8c86ca40f85d1

[ "MIT" ]

1

2018-10-29T04:34:13.000Z

2018-11-01T14:32:23.000Z

src/modules/base/url_helper.py

yakii9/artificial-programmer

a6c1a5a47155ee4d24be729a0fa8c86ca40f85d1

[ "MIT" ]

1

2018-10-21T22:46:48.000Z

import urllib.request from html.parser import HTMLParser from urllib import parse from modules.base.handle_timeout import timeout class ElementsFinder(HTMLParser): def __init__(self, base_url, page_url): super().__init__() self.base_url = base_url self.page_url = page_url self.links = set() # When we call HTMLParser feed() this function is called when it encounters an opening tag <a> def handle_starttag(self, tag, attrs): if tag == 'a': for (attribute, value) in attrs: if attribute == 'href': url = parse.urljoin(self.base_url, value) self.links.add(url) def page_links(self): return self.links def error(self, message): pass class UrlHelper: def __init__(self): pass @staticmethod @timeout(6) def get_html(url): try: with urllib.request.urlopen(url) as response: html = response.read() return html except Exception as e: print(e) def get_domain_name(self, url): try: results = self.get_sub_domain_name(url).split('.') return results[-2] + '.' + results[-1] except: return '' # Get sub domain name (name.example.com) @staticmethod def get_sub_domain_name(url): try: return parse.urlparse(url).netloc except: return ''

25.067797

98

0.577417

176

1,479

4.676136

0.426136

0.034022

0.040097

0.058323

0.046173

0

0.003027

0.329953

1,479

58

99

25.5

0.827447

0.088573

0

0.25

0

0.005204

0

1

0.181818

false

0.045455

0.090909

0.022727

0.454545

0.022727

0

null

0

null

0

1

0

5557b931f8213b68a545c1e272d7bfa56dc0f55f

7,460

py

Python

trainer/trainer.py

iprapas/dl-continuous-deployment

bcee578a8ae3aa74e4ede00d125cb456f6a3010e

[ "MIT" ]

null

trainer/trainer.py

iprapas/dl-continuous-deployment

bcee578a8ae3aa74e4ede00d125cb456f6a3010e

[ "MIT" ]

null

trainer/trainer.py

iprapas/dl-continuous-deployment

bcee578a8ae3aa74e4ede00d125cb456f6a3010e

[ "MIT" ]

null

import numpy as np import torch from torchvision.utils import make_grid from base import BaseTrainer from utils import inf_loop, MetricTracker, confusion_matrix_image import copy import sys import time from model.metric import Accuracy, TopkAccuracy def get_top_k(x, ratio): """it will sample the top 1-ratio of the samples.""" x_data = x.view(-1) x_len = x_data.nelement() top_k = max(1, int(x_len * (1 - ratio))) # get indices and the corresponding values if top_k == 1: _, selected_indices = torch.max(x_data.abs(), dim=0, keepdim=True) else: _, selected_indices = torch.topk( x_data.abs(), top_k, largest=True, sorted=False ) return x_data[selected_indices], selected_indices def get_mask(flatten_arr, indices): mask = torch.zeros_like(flatten_arr) mask[indices] = 1 mask = mask.bool() return mask.float(), (~mask).float() class Trainer(BaseTrainer): """ Trainer class """ def __init__(self, model, criterion, metric_ftns, optimizer, config, data_loader, valid_data_loader=None, lr_scheduler=None, len_epoch=None): super().__init__(model, criterion, metric_ftns, optimizer, config) self.config = config self.data_loader = data_loader if len_epoch is None: # epoch-based training self.len_epoch = len(self.data_loader) else: # iteration-based training self.data_loader = inf_loop(data_loader) self.len_epoch = len_epoch self.valid_data_loader = valid_data_loader self.do_validation = self.valid_data_loader is not None self.lr_scheduler = lr_scheduler self.log_step = int(np.sqrt(data_loader.batch_size)) self.deployed_model = copy.deepcopy(self.model) self.init_model = copy.deepcopy(self.model) self.init_model.eval() self.deployed_model.eval() self.accuracy = Accuracy() self.topkaccuracy = TopkAccuracy() self.train_metrics = MetricTracker('loss', *[m.__name__ for m in self.metric_ftns], writer=self.writer) self.valid_metrics = MetricTracker('loss', *[m.__name__ for m in self.metric_ftns], writer=self.writer) def _train_epoch(self, epoch): """ Training logic for an epoch :param epoch: Integer, current training epoch. :return: A log that contains average loss and metric in this epoch. """ start = time.time() self.model.train() total_batch = 0 self.train_metrics.reset() training_time = 0 for batch_idx, (data, target) in enumerate(self.data_loader): data, target = data.to(self.device), target.to(self.device) batch_start = time.time() self.optimizer.zero_grad() output = self.model(data) loss = self.criterion(output, target) loss.backward() self.optimizer.step() training_time += time.time() - batch_start self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx) self.train_metrics.update('loss', loss.item()) for met in self.metric_ftns: self.train_metrics.update(met.__name__, met(output, target)) total_batch += time.time() - batch_start if batch_idx % self.log_step == 0: self.logger.info('Train Epoch: {} {} Loss: {:.6f} Time per batch (ms) {}'.format( epoch, self._progress(batch_idx), loss.item(), total_batch * 1000 / (batch_idx + 1))) # self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True)) # self.writer.add_figure('confusion_matrix', confusion_matrix_image(output, target)) # valid_log = self._valid_deployed(batch_idx) # print logged informations to the screen # for key, value in valid_log.items(): # self.logger.info('Valid deployed {:15s}: {}'.format(str(key), value)) if batch_idx == self.len_epoch: break log = self.train_metrics.result() if self.do_validation: val_log = self._valid_epoch(epoch) log.update(**{'val_' + k: v for k, v in val_log.items()}) log['time (sec)'] = time.time() - start log['training_time'] = training_time if self.lr_scheduler is not None: self.lr_scheduler.step() return log def _valid_epoch(self, epoch): """ Validate after training an epoch :param epoch: Integer, current training epoch. :return: A log that contains information about validation """ self.model.eval() self.valid_metrics.reset() avg_loss =0 correct = 0 total = 0 with torch.no_grad(): for batch_idx, (data, target) in enumerate(self.valid_data_loader): data, target = data.to(self.device), target.to(self.device) output = self.model(data) loss = self.criterion(output, target) avg_loss += loss.item()/len(self.valid_data_loader) pred = torch.argmax(output, dim=1) correct += torch.sum(pred == target).item() total += len(target) self.writer.set_step(epoch, 'valid') self.writer.add_scalar('loss', avg_loss) self.writer.add_scalar('accuracy', correct/total) # self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True)) # self.writer.add_figure('confusion_matrix', confusion_matrix_image(output, target)) # add histogram of model parameters to the tensorboard # for name, p in self.model.named_parameters(): # self.writer.add_histogram(name, p, bins='auto') return self.valid_metrics.result() def _valid_deployed(self, batch): """ Validate after training a batch :param epoch: Integer, current training epoch. :return: A log that contains information about validation """ self.deployed_model.eval() self.valid_metrics.reset() with torch.no_grad(): for batch_idx, (data, target) in enumerate(self.valid_data_loader): data, target = data.to(self.device), target.to(self.device) output = self.model(data) loss = self.criterion(output, target) self.writer.set_step((batch - 1) * len(self.valid_data_loader) + batch_idx*len(target), 'valid') self.valid_metrics.update('loss', loss.item()) for met in self.metric_ftns: self.valid_metrics.update(met.__name__, met) # self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True)) # self.writer.add_figure('confusion_matrix', confusion_matrix_image(output, target)) return self.valid_metrics.result() def _progress(self, batch_idx): base = '[{}/{} ({:.0f}%)]' if hasattr(self.data_loader, 'n_samples'): current = batch_idx * self.data_loader.batch_size total = self.data_loader.n_samples else: current = batch_idx total = self.len_epoch return base.format(current, total, 100.0 * current / total)

38.061224

112

0.604826

922

7,460

4.684382

0.199566

0.043992

0.02709

0.026395

0.441769

0.376013

0.318592

0.293355

0.282241

0

0.005999

0.284987

7,460

195

113

38.25641

0.803712

0.187802

0

0.192

0

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1

0.056

false

0

0.072

0

0.184

0

null

0

null

0

1

0

555e8fe1a5ae17b4fbc51d4ad0090a37d1dc68ba

3,520

py

Python

pycba/utils.py

mayermelhem/pycba

8f6a0da12629bac2ad1c6c8e113357f96931ef17

[ "Apache-2.0" ]

10

2022-02-07T01:16:02.000Z

2022-03-12T07:56:43.000Z

pycba/utils.py

mayermelhem/pycba

8f6a0da12629bac2ad1c6c8e113357f96931ef17

[ "Apache-2.0" ]

5

2022-02-08T07:42:53.000Z

2022-03-31T21:33:42.000Z

pycba/utils.py

mayermelhem/pycba

8f6a0da12629bac2ad1c6c8e113357f96931ef17

[ "Apache-2.0" ]

1

2022-02-12T04:33:38.000Z

""" PyCBA - Utility functions for interacting with PyCBA """ import re import numpy as np from typing import Tuple def parse_beam_string( beam_string: str, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """ This function parses a beam descriptor string and returns CBA input vectors. The beam descriptor string uses a specific format: spans lengths in float are separated by single characters describing the terminals of that beam element. The terminal characters are: - P - pinned (effectively the same as roller, but retained for visualisations) - R - roller (can occur at any terminal) - E - encastre (i.e. fully-fixed) - can only occur at beam extremity - F - free (e.g. cantilever end) - can only occur at beam extremity - H - hinge - can only occur internally in the beam Examples of beam strings are: - *P40R20R* - 2-span, 60 m long, with pinned-roller-roller supports - *E20H30R10F* - 3-span, 60 m long, encastre-hinge-roller-free **Complex beam configurations may not be describable using the beam string.** The function returns a tuple containing the necessary beam inputs for :class:`pycba.analysis.BeamAnalysis`: `(L, EI, R, eType)` Parameters ---------- beam_string : The string to be parsed. Raises ------ ValueError When the beam string does not meet basic structural requirements. Returns ------- (L, EI, R, eType) : tuple(np.ndarray, np.ndarray, np.ndarray, np.ndarray) In which: - `L` is a vector of span lengths. - `EI` is A vector of member flexural rigidities (prismatic). - `R` is a vector describing the support conditions at each member end. - `eType` is a vector of the member types. Example ------- This example creates a four-span beam with fixed extreme supports and an internal hinge. :: beam_str = "E30R30H30R30E" (L, EI, R, eType) = cba.parse_beam_string(beam_str) ils = cba.InfluenceLines(L, EI, R, eType) ils.create_ils(step=0.1) ils.plot_il(0.0, "R") """ beam_string = beam_string.lower() terminals = re.findall(r"[efhpr]", beam_string) spans_str = [m.end() for m in re.finditer(r"[efhpr]", beam_string)] if len(terminals) < 2: raise ValueError("At least two terminals must be defined") if terminals[0] == "h" or terminals[-1] == "h": raise ValueError("Cannot have a hinge at an extremity") if len(terminals) > 2: if any(t == "f" or t == "e" for t in terminals[1:-1]): raise ValueError("Do not define internal free or encastre terminals") # Get and check the span lengths L = [ float(beam_string[spans_str[i] : spans_str[i + 1] - 1]) for i in range(len(spans_str) - 1) ] if len(terminals) - 1 != len(L): raise ValueError("Inconsistent terminal count and span count") EI = 30 * 1e10 * np.ones(len(L)) * 1e-6 # kNm2 - arbitrary value R = [] eType = [1 for l in L] for i, t in enumerate(terminals): if t == "p" or t == "r": # pin or roller R.append([-1, 0]) elif t == "e": # encastre R.append([-1, -1]) elif t == "f": # free R.append([0, 0]) elif t == "h": # hinge R.append([0, 0]) eType[i - 1] = 2 R = [elem for sublist in R for elem in sublist] return (L, EI, R, eType)

34.174757

86

0.605682

503

3,520

4.200795

0.353877

0.052059

0.031235

0.051112

0.064363

0.038807

0

0.02253

0.28125

3,520

102

87

34.509804

0.812648

0.546023

0

0.054054

0

0.132343

0

1

0.027027

false

0

0.081081

0

0.135135

0

null

0

null

0

1

0

555f6946d9a27cac92dae44e27d4220ecfaf6269

10,363

py

Python

models/dcase2020_fuss_baseline/evaluate_lib.py

marciopuga/sound-separation

0b23ae22123b041b9538295f32a92151cb77bff9

[ "Apache-2.0" ]

412

2020-03-03T05:55:53.000Z

2022-03-29T20:49:11.000Z

models/dcase2020_fuss_baseline/evaluate_lib.py

marciopuga/sound-separation

0b23ae22123b041b9538295f32a92151cb77bff9

[ "Apache-2.0" ]

12

2020-04-09T17:47:01.000Z

2022-03-22T06:07:04.000Z

models/dcase2020_fuss_baseline/evaluate_lib.py

marciopuga/sound-separation

0b23ae22123b041b9538295f32a92151cb77bff9

[ "Apache-2.0" ]

89

2020-03-06T08:26:44.000Z

2022-03-31T11:36:23.000Z

# 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. """Evaluate separated audio from a DCASE 2020 task 4 separation model.""" import os import numpy as np import pandas as pd import tensorflow.compat.v1 as tf import inference from train import data_io from train import metrics from train import permutation_invariant def _weights_for_nonzero_refs(source_waveforms): """Return shape (source,) weights for signals that are nonzero.""" source_norms = tf.sqrt(tf.reduce_mean(tf.square(source_waveforms), axis=-1)) return tf.greater(source_norms, 1e-8) def _weights_for_active_seps(power_sources, power_separated): """Return (source,) weights for active separated signals.""" min_power = tf.reduce_min(power_sources, axis=-1, keepdims=True) return tf.greater(power_separated, 0.01 * min_power) def compute_metrics(source_waveforms, separated_waveforms, mixture_waveform): """Permutation-invariant SI-SNR, powers, and under/equal/over-separation.""" # Align separated sources to reference sources. perm_inv_loss = permutation_invariant.wrap( lambda tar, est: -metrics.signal_to_noise_ratio_gain_invariant(est, tar)) _, separated_waveforms = perm_inv_loss(source_waveforms[tf.newaxis], separated_waveforms[tf.newaxis]) separated_waveforms = separated_waveforms[0] # Remove batch axis. # Compute separated and source powers. power_separated = tf.reduce_mean(separated_waveforms ** 2, axis=-1) power_sources = tf.reduce_mean(source_waveforms ** 2, axis=-1) # Compute weights for active (separated, source) pairs where source is nonzero # and separated power is above threshold of quietest source power - 20 dB. weights_active_refs = _weights_for_nonzero_refs(source_waveforms) weights_active_seps = _weights_for_active_seps( tf.boolean_mask(power_sources, weights_active_refs), power_separated) weights_active_pairs = tf.logical_and(weights_active_refs, weights_active_seps) # Compute SI-SNR. sisnr_separated = metrics.signal_to_noise_ratio_gain_invariant( separated_waveforms, source_waveforms) num_active_refs = tf.reduce_sum(tf.cast(weights_active_refs, tf.int32)) num_active_seps = tf.reduce_sum(tf.cast(weights_active_seps, tf.int32)) num_active_pairs = tf.reduce_sum(tf.cast(weights_active_pairs, tf.int32)) sisnr_mixture = metrics.signal_to_noise_ratio_gain_invariant( tf.tile(mixture_waveform[tf.newaxis], (source_waveforms.shape[0], 1)), source_waveforms) # Compute under/equal/over separation. under_separation = tf.cast(tf.less(num_active_seps, num_active_refs), tf.float32) equal_separation = tf.cast(tf.equal(num_active_seps, num_active_refs), tf.float32) over_separation = tf.cast(tf.greater(num_active_seps, num_active_refs), tf.float32) return {'sisnr_separated': sisnr_separated, 'sisnr_mixture': sisnr_mixture, 'sisnr_improvement': sisnr_separated - sisnr_mixture, 'power_separated': power_separated, 'power_sources': power_sources, 'under_separation': under_separation, 'equal_separation': equal_separation, 'over_separation': over_separation, 'weights_active_refs': weights_active_refs, 'weights_active_seps': weights_active_seps, 'weights_active_pairs': weights_active_pairs, 'num_active_refs': num_active_refs, 'num_active_seps': num_active_seps, 'num_active_pairs': num_active_pairs} def _report_score_stats(metric_per_source_count, label='', counts=None): """Report mean and std dev for specified counts.""" values_all = [] if counts is None: counts = metric_per_source_count.keys() for count in counts: values = metric_per_source_count[count] values_all.extend(list(values)) return '%s for count(s) %s = %.1f +/- %.1f dB' % ( label, counts, np.mean(values_all), np.std(values_all)) def evaluate(checkpoint_path, metagraph_path, data_list_path, output_path): """Evaluate a model on FUSS data.""" model = inference.SeparationModel(checkpoint_path, metagraph_path) file_list = data_io.read_lines_from_file(data_list_path, skip_fields=1) with model.graph.as_default(): dataset = data_io.wavs_to_dataset(file_list, batch_size=1, num_samples=160000, repeat=False) # Strip batch and mic dimensions. dataset['receiver_audio'] = dataset['receiver_audio'][0, 0] dataset['source_images'] = dataset['source_images'][0, :, 0] # Separate with a trained model. i = 1 max_count = 4 dict_per_source_count = lambda: {c: [] for c in range(1, max_count + 1)} sisnr_per_source_count = dict_per_source_count() sisnri_per_source_count = dict_per_source_count() under_seps = [] equal_seps = [] over_seps = [] df = None while True: try: waveforms = model.sess.run(dataset) except tf.errors.OutOfRangeError: break separated_waveforms = model.separate(waveforms['receiver_audio']) source_waveforms = waveforms['source_images'] if np.allclose(source_waveforms, 0): print('WARNING: all-zeros source_waveforms tensor encountered.' 'Skiping this example...') continue metrics_dict = compute_metrics(source_waveforms, separated_waveforms, waveforms['receiver_audio']) metrics_dict = {k: v.numpy() for k, v in metrics_dict.items()} sisnr_sep = metrics_dict['sisnr_separated'] sisnr_mix = metrics_dict['sisnr_mixture'] sisnr_imp = metrics_dict['sisnr_improvement'] weights_active_pairs = metrics_dict['weights_active_pairs'] # Create and initialize the dataframe if it doesn't exist. if df is None: # Need to create the dataframe. columns = [] for metric_name, metric_value in metrics_dict.items(): if metric_value.shape: # Per-source metric. for i_src in range(1, max_count + 1): columns.append(metric_name + '_source%d' % i_src) else: # Scalar metric. columns.append(metric_name) columns.sort() df = pd.DataFrame(columns=columns) if output_path.endswith('.csv'): csv_path = output_path else: csv_path = os.path.join(output_path, 'scores.csv') # Update dataframe with new metrics. row_dict = {} for metric_name, metric_value in metrics_dict.items(): if metric_value.shape: # Per-source metric. for i_src in range(1, max_count + 1): row_dict[metric_name + '_source%d' % i_src] = metric_value[i_src - 1] else: # Scalar metric. row_dict[metric_name] = metric_value new_row = pd.Series(row_dict) df = df.append(new_row, ignore_index=True) # Store metrics per source count and report results so far. under_seps.append(metrics_dict['under_separation']) equal_seps.append(metrics_dict['equal_separation']) over_seps.append(metrics_dict['over_separation']) sisnr_per_source_count[metrics_dict['num_active_refs']].extend( sisnr_sep[weights_active_pairs].tolist()) sisnri_per_source_count[metrics_dict['num_active_refs']].extend( sisnr_imp[weights_active_pairs].tolist()) print('Example %d: SI-SNR sep = %.1f dB, SI-SNR mix = %.1f dB, ' 'SI-SNR imp = %.1f dB, ref count = %d, sep count = %d' % ( i, np.mean(sisnr_sep), np.mean(sisnr_mix), np.mean(sisnr_sep - sisnr_mix), metrics_dict['num_active_refs'], metrics_dict['num_active_seps'])) if not i % 20: # Report mean statistics and save csv every so often. lines = [ 'Metrics after %d examples:' % i, _report_score_stats(sisnr_per_source_count, 'SI-SNR', counts=[1]), _report_score_stats(sisnri_per_source_count, 'SI-SNRi', counts=[2]), _report_score_stats(sisnri_per_source_count, 'SI-SNRi', counts=[3]), _report_score_stats(sisnri_per_source_count, 'SI-SNRi', counts=[4]), _report_score_stats(sisnri_per_source_count, 'SI-SNRi', counts=[2, 3, 4]), 'Under separation: %.2f' % np.mean(under_seps), 'Equal separation: %.2f' % np.mean(equal_seps), 'Over separation: %.2f' % np.mean(over_seps), ] print('') for line in lines: print(line) with open(csv_path.replace('.csv', '_summary.txt'), 'w+') as f: f.writelines([line + '\n' for line in lines]) print('\nWriting csv to %s.\n' % csv_path) df.to_csv(csv_path) i += 1 # Report final mean statistics. lines = [ 'Final statistics:', _report_score_stats(sisnr_per_source_count, 'SI-SNR', counts=[1]), _report_score_stats(sisnri_per_source_count, 'SI-SNRi', counts=[2]), _report_score_stats(sisnri_per_source_count, 'SI-SNRi', counts=[3]), _report_score_stats(sisnri_per_source_count, 'SI-SNRi', counts=[4]), _report_score_stats(sisnri_per_source_count, 'SI-SNRi', counts=[2, 3, 4]), 'Under separation: %.2f' % np.mean(under_seps), 'Equal separation: %.2f' % np.mean(equal_seps), 'Over separation: %.2f' % np.mean(over_seps), ] print('') for line in lines: print(line) with open(csv_path.replace('.csv', '_summary.txt'), 'w+') as f: f.writelines([line + '\n' for line in lines]) # Write final csv. print('\nWriting csv to %s.' % csv_path) df.to_csv(csv_path)

41.618474

80

0.666795

1,365

10,363

4.770696

0.212454

0.031787

0.045147

0.030713

0.323864

0.288084

0.24309

0.195485

0.179361

0

0.011509

0.228602

10,363

248

81

41.78629

0.803102

0.154009

0

0.284153

0

0.005464

0.122817

0

1

0.027322

false

0

0.043716

0

0.092896

0.043716

0

null

0

null

0

1

0

55652d01d18ec68adf27b069baae8bf7ed3db2f4

1,705

py

Python

python/domain/compliance/model/measure.py

ICTU/document-as-code

e65fddb94513e7c2f54f248b4ce69e9e10ce42f5

[ "Apache-2.0" ]

2

2021-01-09T17:00:51.000Z

2021-02-19T09:35:26.000Z

python/domain/compliance/model/measure.py

ICTU/document-as-code

e65fddb94513e7c2f54f248b4ce69e9e10ce42f5

[ "Apache-2.0" ]

null

python/domain/compliance/model/measure.py

ICTU/document-as-code

e65fddb94513e7c2f54f248b4ce69e9e10ce42f5

[ "Apache-2.0" ]

1

2020-02-24T15:50:05.000Z

""" BIO measure - defines and describes a measure for BIO compliance """ from domain.base import Base class Measure(Base): """ Measures that help to in BIO compliance. """ _explain = None _not_applicable = None def __init__(self, identifier, description, identifiers, url=None, done=False): super().__init__(identifier) self.description = description self.identifiers = identifiers self.url = url self.done = done def set_explain(self): self.register_explain(self) return self def set_not_applicable(self): self.register_not_applicable(self) return self # --- @classmethod def all_applicable_to_fragment(cls, fragment_identifier): return [ bir_measure for bir_measure in cls.all for identifier in bir_measure.identifiers if fragment_identifier.startswith(identifier) ] # --- class property explain (rw) --- @classmethod def register_explain(cls, explain): if not isinstance(explain, cls): raise TypeError(f"explain should be {cls.__name__}, not {explain.__class__.__name__}") cls._explain = explain @classmethod def explain(cls): return cls._explain # --- class property not_applicable (rw) --- @classmethod def register_not_applicable(cls, not_applicable): if not isinstance(not_applicable, cls): raise TypeError(f"not_applicable should be {cls.__name__}, not {not_applicable.__class__.__name__}") cls._not_applicable = not_applicable @classmethod def not_applicable(cls): return cls._not_applicable

26.640625

112

0.652199

190

1,705

5.510526

0.268421

0.161414

0.045845

0.034384

0

0.262757

1,705

63

113

27.063492

0.832936

0.111437

0

0.175

0

0.097659

0.04214

0

1

0.2

false

0

0.025

0.075

0.425

0

null

0

null

0

1

0

556657f3480d4123e6f0535b01c6ed2f5345122d

615

py

Python

week_06/readibility.py

fentybit/cs50

a6089e8ba47d0a8990cac3e0b5b28c5f2ba9f9c3

[ "CNRI-Python" ]

null

week_06/readibility.py

fentybit/cs50

a6089e8ba47d0a8990cac3e0b5b28c5f2ba9f9c3

[ "CNRI-Python" ]

null

week_06/readibility.py

fentybit/cs50

a6089e8ba47d0a8990cac3e0b5b28c5f2ba9f9c3

[ "CNRI-Python" ]

null

from cs50 import get_string text = get_string("Text: ") text_length = len(text) letters = 0 sentences = 0 words = 1 for i in range(text_length): if text[i].isalpha(): letters += 1 for i in range(text_length): if ord(text[i]) == 46 or ord(text[i]) == 33 or ord(text[i]) == 63: sentences += 1 for i in range(text_length): if ord(text[i]) == 32: words += 1 L = 100 * (letters / words) S = 100 * (sentences / words) grade = round(0.0588 * L - 0.296 * S - 15.8) if 16 <= grade: print("Grade 16+") elif grade < 1: print("Before Grade 1") else: print(f"Grade {grade}")

20.5

70

0.588618

103

615

3.456311

0.38835

0.070225

0.089888

0.058989

0.247191

0.179775

0

0.08658

0.24878

615

30

71

20.5

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null

0

null

0

1

0

55686a8be609e908e7580542f40aa36255c8c155

12,532

py

Python

functions.py

flyingmat/pyfactorizer

6e607408bc21d04b09ecabfc6a579ad4058965f5

[ "MIT" ]

null

functions.py

flyingmat/pyfactorizer

6e607408bc21d04b09ecabfc6a579ad4058965f5

[ "MIT" ]

null

functions.py

flyingmat/pyfactorizer

6e607408bc21d04b09ecabfc6a579ad4058965f5

[ "MIT" ]

null

from math import floor remove_spaces = lambda inlst: [i for i in inlst if i != ' '] def sf2i(inp): if float(inp).is_integer(): return str(int(inp)) else: return str(inp) def fix_signs(inlst): i = 0 while i < len(inlst): if inlst[i] in '+-': # first sign is detected sign = -1 if inlst[i] == '-' else 1 # sign variable assigned while i+1 < len(inlst) and inlst[i+1] in '+-': # while more signs are present if inlst[i+1] == '-': # invert the sign if a minus is detected sign *= -1 del inlst[i+1] # delete each excessive sign inlst[i] = '-' if sign == -1 else '+' # change the only sign left's value accordingly i += 1 # keep checking for other signs return inlst def fix_dict(indict): if type(indict) == dict: return frozenset(indict.items()) else: return indict def get_coefficient(inlst, i): coeff = '' k = i - 1 while k >= 0 and inlst[k] in '1234567890.': # keep going backwards to get the full coefficient coeff = inlst[k] + coeff k -= 1 coeff = '1' if not coeff else coeff # if no coefficient is specified, 1 is assigned if k >= 0 and inlst[k] == '-': # check for a minus sign coeff = '-' + coeff k = 0 if k < 0 else k # value correction for convert() coeff = float(coeff) return (coeff, k) def get_exponent(inlst, i): exp = '' if i+1 < len(inlst) and inlst[i+1] == '^': k = i + 2 while k < len(inlst) and inlst[k] in '1234567890': # keep going forward to get the full exponent exp += inlst[k] k += 1 else: k = i + 1 # value correction for convert() exp = 1 if not exp else exp # if no exponent is specified, 1 is assigned exp = int(exp) # exponents are assumed to be positive integers return (exp, k) def convert(inlst): exps = {} i = 0 while i < len(inlst): if inlst[i] == 'x': # if an x-term is detected (coeff, x_start) = get_coefficient(inlst, i) # get its coefficient (exp, x_end) = get_exponent(inlst, i) # get its exponent if exp not in exps: exps[exp] = coeff else: exps[exp] += coeff del inlst[x_start:x_end] i = x_start i += 1 return exps def solve_x0_terms(inlst): out = 0 current_term = '' while inlst: item = inlst.pop(0) if item in '#+-': out += float(current_term) if current_term else 0 current_term = '-' if item == '-' else '' elif item in '1234567890.': current_term += item out += float(current_term) if current_term else 0 return out def divide_func(exps, div): # uses polynomial long division newexps = {} for current_exp in range(max(exps)-max(div), -1, -1): if max(exps) - max(div) != current_exp: # bugfix: FOR Loop coud be changed to something more efficient (needs testing with high exponents) continue newexps[current_exp] = exps[max(exps)] / div[max(div)] for exp, coeff in div.items(): m_coeff = exp + current_exp if m_coeff not in exps: exps[m_coeff] = 0 exps[m_coeff] -= (newexps[current_exp] * coeff) if exps[m_coeff] == 0: del exps[m_coeff] # deletion required because of max() in the main loop that could return a coeff with value 0 if 0 not in newexps: newexps[0] = 0 return newexps if not exps or not exps[0] else {} # if there is a reminder, return an empty dict; could be changed to return reminder def n_factors(n): if type(n) == float and not n.is_integer(): raise StopIteration else: n = int(n) yield (n, 1) if n % 2 == 0: for i in range(floor(abs(n/2)), 0, -1): if n % i == 0: yield (i, int(n/i)) else: tn = floor(abs(n/2)) for i in range( (tn - 1 if tn % 2 == 0 else tn), 0, -2 ): if n % i == 0: yield (i, int(n/i)) def x2terms(exps): a = exps[2] if 2 in exps else 0 b = exps[1] if 1 in exps else 0 c = exps[0] if 0 in exps else 0 return a,b,c def delta_calc(a,b,c): return b**2 - 4*a*c def pow_diff(poly): out = () if max(poly) % 2 == 0: root_exp = (1.0 / 2) else: root_exp = (1.0 / max(poly)) root1 = (abs(poly[max(poly)]) ** root_exp) * (-1 if poly[max(poly)] < 0 else 1) root2 = (abs(poly[0]) ** root_exp) * (-1 if poly[0] < 0 else 1) if root1.is_integer() and root2.is_integer(): root1, root2 = int(root1), int(root2) if max(poly) % 2 == 0: if poly[0]*poly[max(poly)] < 0: xm, x0 = root1, root2 out = (( { int(max(poly)/2):xm, 0:x0 }, 1 ), ( { int(max(poly)/2):(xm if xm > 0 else -xm), 0:(x0 if xm < 0 else -x0) }, 1 )) else: out = [( { 1:root1, 0:root2}, 1 )] return out def binomial_mult_3(poly, expsort): out = () for x0t1, x0t2 in n_factors(poly[0]): for xmt1, xmt2 in n_factors(poly[expsort[0]]): if (xmt1*x0t2)+(xmt2*x0t1) == poly[expsort[1]]: p_div1 = { expsort[1]:xmt1, 0:x0t1 } p_div2 = { expsort[1]:xmt2, 0:x0t2 } out = (( p_div1, 1 ), ( p_div2, 1 )) return out def binomial_pow3(poly, expsort): out = () if expsort[0] % 3 == 0: root1 = (abs(poly[expsort[0]]) ** (1.0/3)) * (-1 if poly[expsort[0]] < 0 else 1) root2 = (abs(poly[0]) ** (1.0/3)) * (-1 if poly[0] < 0 else 1) if root1.is_integer() and root2.is_integer(): if poly[expsort[1]] == 3*(root1**2)*root2 and poly[expsort[2]] == 3*(root2**2)*root1: out = [({ expsort[2]:root1, 0:root2 }, 3)] return out def binomial_mult_4(poly, expsort): out = () if poly[expsort[0]] / poly[expsort[2]] == poly[expsort[1]] / poly[expsort[3]]: cfs = [poly[e] for e in expsort] for (n3, _) in n_factors( max(abs(cfs[0]), abs(cfs[1])) - min(abs(cfs[0]), abs(cfs[1])) ): if 0 == cfs[0] % n3 == cfs[1] % n3: n1 = int(cfs[0]/n3) n2 = int(cfs[1]/n3) if cfs[3] % n2 == 0: n4 = int(cfs[3]/n2) out = [({ min(expsort[1],expsort[2]):n1, 0:n2 }, 1), ({ max(expsort[1],expsort[2]):n3, 0:n4 }, 1)] break return out def bf_int_coordinates(exps): out_cord = set() for i in range(2,101): k = 1/i if check_fact(exps,k): yield k if check_fact(exps,-k): yield -k for i in range(1,1001): if check_fact(exps,i): yield i if check_fact(exps,-i): yield -i def check_fact(exps,fact): out = 0 for exp in exps: out += exps[exp] * (fact**exp) return round(out,15) == 0 def factorize(poly_stack, func): poly = poly_stack.pop() tmexp = max(poly) div_polys = [] common_factor = 1 checknegative = set([c < 0 for c in poly.values()]) # factorizing checks for (i, _) in n_factors(min([abs(v) for v in poly.values() if v != 0])): # if common factor in poly, divide e.g. 2x^2+4 -> 2(x^2+2) checkmult = set() # check performed on every iteration because of coeffs changing with division for coeff in poly.values(): checkmult.add(coeff % i) if len(checkmult) == 1 and 0 in checkmult: common_factor = i if checknegative != set([True]) else -i break if common_factor != 1: div_polys = [ ({ 0:common_factor }, 1) ] elif len(poly) > 2 and tmexp and poly[0] == 0: # x^5 + x^3 -> x^3(x^2 + 1) div_polys = [ ({ 1:1, 0:0 }, min([e if e > 0 else tmexp for e in poly])) ] elif len(poly) == 2 and max(poly) > 1 and poly[0]: # x^2 - 1 -> (x + 1)(x - 1), x^3 - 1, x^3 + 1, etc. div_polys = pow_diff(poly) elif len(poly) == 3 and poly[0]: # x^2 + 2x + 1 -> (x + 1)^2, 3x^2 + 7x + 2 -> (3x + 1)(x + 2), etc. max exp can be > 2 expsort = sorted(poly)[::-1] if expsort[0] % 2 == 0 and expsort[0]-expsort[1] == expsort[1]-expsort[2]: div_polys = binomial_mult_3(poly, expsort) elif len(poly) == 4 and poly[0]: expsort = sorted(poly)[::-1] if expsort[0]-expsort[1] == expsort[1]-expsort[2] == expsort[2]-expsort[3]: div_polys = binomial_pow3(poly, expsort) if not div_polys: # 6x^6 + 4x^4 + 15x^2 + 10 would trigger the first check but not the second when using ELIF (one doesn't exlude the other) if expsort[0]-expsort[2] == expsort[1]-expsort[3]: div_polys = binomial_mult_4(poly,expsort) if not div_polys and tmexp > 2: # bruteforce div_count = tmexp for xv in bf_int_coordinates(poly): div_polys.append(({ 1:1, 0:-xv }, 1)) div_count -= 1 if div_count == 0: break for p, e in div_polys: for div_i in range(e): poly = divide_func(poly, p) if (max(p) > 2) or (max(p) == 2 and p[0] and delta_calc(*x2terms(p)) >= 0): poly_stack.append(p) else: func.add(p, 1) if div_polys and ((max(poly) > 2) or (max(poly) == 2 and poly[0] and delta_calc(*x2terms(poly)) >= 0)): poly_stack.append(poly) else: if len(poly) == 2 and not poly[0]: # fix for ax^2 -> x^2 divided by a -> poly = {2:1,0:0}:1, should be {1:1,0:0}:2 func.add({ 1:1, 0:0 }, max(poly)) else: func.add(poly, 1) if poly_stack: factorize(poly_stack, func) def polyformat(polys, x0t): out = ['',''] brackets = False if len(polys) > 1 or x0t != 1: brackets = True out[0] += sf2i(x0t) if x0t not in (1,-1) or len(polys) == 0 else '-' if x0t == -1 else '' for poly, exp in polys.items(): poly = dict(poly) if len(poly) == 2 and not poly[0]: out[1] = 'x' if exp > 1: out[1] += '^' + str(exp) else: current_poly = '' if exp > 1: brackets = True expsort = sorted(poly)[::-1] for e in expsort: current_poly += '- ' if poly[e] < 0 else '+ ' if poly[e] > 0 else '' if e != 0: current_poly += sf2i(abs(poly[e])) if poly[e] not in (1,-1) else '' current_poly += 'x' current_poly += '^' + sf2i(e) + ' ' if e != 1 else ' ' else: current_poly += sf2i(abs(poly[e])) if poly[e] else '' if current_poly[0] == '+': current_poly = current_poly[2:] elif current_poly[0] == '-' and brackets: current_poly = '-' + current_poly[2:] current_poly = '(' + current_poly + ')' if brackets else current_poly current_poly += '^' + sf2i(exp) if exp != 1 else '' out.append(current_poly) return ''.join(out) class Function(): def __init__(self, data): self.data = {} self.x0t = 1 if type(data) == dict: self.exps = data else: self.eqt = remove_spaces(data) self.eqt = fix_signs(self.eqt) self.exps = convert(self.eqt) # self.eqt is referenced and edited directly by convert() if 0 not in self.exps: # 0 may already be in exps because of x^0 terms self.exps[0] = 0 self.exps[0] += solve_x0_terms(self.eqt) # x-terms have already been removed from self.eqt self.out = "" def __repr__(self): return repr(self.data) def add(self, indict, exp): if len(dict(indict)) == 1: self.x0t *= ((dict(indict))[0] ** exp) # number-only terms (x^0) are managed separately else: self.indict = fix_dict(indict) if self.indict in self.data: self.data[self.indict] += exp else: self.data[self.indict] = exp def factorize(self): if set(self.exps.values()) != set([0]): factorize([self.exps], self) #print(self.data) return polyformat(self.data, self.x0t) else: return '0'

39.040498

151

0.514682

1,866

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3.383708

0.136656

0.009978

0.01663

0.006969

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0.014888

0

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0.343441

12,532

320

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0.077703

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0

0.003378

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0.155405

0

null

0

null

0

1

0

556d8216ffbaa6f7a0d0816c6b1ba9baa984c1a1

381

py

Python

Problems/14.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

2

2021-07-14T11:01:58.000Z

2021-07-14T11:02:01.000Z

Problems/14.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

null

Problems/14.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

null

def isPerCube(n): x = n**(1/3) x= x+0.5 x = int(x) if x**3==n: return True return False """ x = 2 while True: y = n / (x * x) if (x == y): print(x) if x == int(x): return True else: return False x = (y + x + x) / 3 print(x)""" print(isPerCube())

19.05

28

0.351706

53

381

2.528302

0.339623

0.044776

0.089552

0

0.036458

0.496063

381

20

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19.05

0.661458

0

1

0.125

false

0

0.375

0.125

0

null

0

null

0

1

0

556f083296f917021fc8c5ac171cde72ce1bed3a

1,690

py

Python

backend/health/health_check.py

threefoldtech/zeroCI

851def4cbaebba681641ecb24c731de56277d6ed

[ "Apache-2.0" ]

null

backend/health/health_check.py

threefoldtech/zeroCI

851def4cbaebba681641ecb24c731de56277d6ed

[ "Apache-2.0" ]

52

2019-11-14T09:39:04.000Z

2021-03-16T10:15:55.000Z

backend/health/health_check.py

AhmedHanafy725/0-CI

ce73044eea2c15bcbb161a1d6f23e75e4f8d53a0

[ "Apache-2.0" ]

1

2019-10-30T09:51:25.000Z

import sys sys.path.append("/sandbox/code/github/threefoldtech/zeroCI/backend") from redis import Redis from health_recover import Recover from utils.utils import Utils recover = Recover() class Health(Utils): def get_process_pid(self, name): cmd = f"ps -aux | grep -v grep | grep '{name}' | awk '{{print $2}}'" response = self.execute_cmd(cmd=cmd, timeout=5) pids = response.stdout.split() return pids def test_zeroci_server(self): """Check zeroci server is still running """ pid = self.get_process_pid("python3 zeroci") if not pid: recover.zeroci() def test_redis(self): """Check redis is still running. """ pid = self.get_process_pid("redis") if not pid: recover.redis() try: r = Redis() r.set("test_redis", "test") r.get("test_redis") r.delete("test_redis") except: recover.redis() def test_workers(self): """Check rq workers are up. """ pids = self.get_process_pid("python3 worker") workers = len(pids) if workers < 5: for i in range(1, 6): pid = self.get_process_pid(f"python3 worker{i}") if not pid: recover.worker(i) def test_schedule(self): """Check rq schedule is up. """ pid = self.get_process_pid("rqscheduler") if not pid: recover.scheduler() if __name__ == "__main__": health = Health() health.test_zeroci_server() health.test_redis() health.test_workers() health.test_schedule()

25.606061

76

0.562722

205

1,690

4.468293

0.336585

0.065502

0.085153

0.092795

0.151747

0.074236

0

0.006975

0.321302

1,690

65

77

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0

0.133333

0

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1

0.111111

false

0

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0

0.244444

0.022222

0

null

0

null

0

1

0

5570f5a350941f5510b456b02cd8353c974ae345

13,284

py

Python

vesper/command/recording_importer.py

RichardLitt/Vesper

5360844f42a06942e7684121c650b08cf8616285

[ "MIT" ]

null

vesper/command/recording_importer.py

RichardLitt/Vesper

5360844f42a06942e7684121c650b08cf8616285

[ "MIT" ]

null

vesper/command/recording_importer.py

RichardLitt/Vesper

5360844f42a06942e7684121c650b08cf8616285

[ "MIT" ]

null

"""Module containing class `RecordingImporter`.""" from pathlib import Path import itertools import logging import os from django.db import transaction from vesper.command.command import CommandExecutionError from vesper.django.app.models import ( DeviceConnection, Job, Recording, RecordingChannel, RecordingFile) from vesper.singletons import recording_manager import vesper.command.command_utils as command_utils import vesper.command.recording_utils as recording_utils import vesper.util.audio_file_utils as audio_file_utils import vesper.util.signal_utils as signal_utils import vesper.util.time_utils as time_utils class RecordingImporter: """ Importer for recordings already stored in files on the Vesper server. The recordings to be imported are specified in the `paths` argument as server-side directory and file paths. Files from directories can be imported either recursively or non-recursively according to the `recursive` argument. The import does not copy or move recordings: it stores the existing paths of their files for future reference. The importer obtains recording metadata for imported files with the aid of a recording file parser extension, specified by the `recording_file_parser` argument. """ extension_name = 'Recording Importer' def __init__(self, args): self.paths = command_utils.get_required_arg('paths', args) self.recursive = command_utils.get_optional_arg( 'recursive', args, True) spec = command_utils.get_optional_arg('recording_file_parser', args) self.file_parser = recording_utils.create_recording_file_parser(spec) def execute(self, job_info): self._job = Job.objects.get(id=job_info.job_id) self._logger = logging.getLogger() try: recordings = self._get_recordings() new_recordings, old_recordings = \ self._partition_recordings(recordings) self._log_header(new_recordings, old_recordings) with transaction.atomic(): self._import_recordings(new_recordings) except Exception as e: self._logger.error(( 'Recording import failed with an exception.\n' 'The exception message was:\n' ' {}\n' 'The archive was not modified.\n' 'See below for exception traceback.').format(str(e))) raise else: self._log_imports(new_recordings) return True def _get_recordings(self): files = list(itertools.chain.from_iterable( self._get_path_recording_files(path) for path in self.paths)) return recording_utils.group_recording_files(files) def _get_path_recording_files(self, path): if os.path.isdir(path): return self._get_dir_recording_files(path) else: file = self._get_recording_file(path) return [] if file is None else [file] def _get_dir_recording_files(self, path): files = [] for (dir_path, dir_names, file_names) in os.walk(path): for file_name in file_names: file_path = os.path.join(dir_path, file_name) file = self._get_recording_file(Path(file_path)) if file is not None: files.append(file) if not self.recursive: # Stop `os.walk` from descending into subdirectories. del dir_names[:] return files def _get_recording_file(self, file_path): if not audio_file_utils.is_wave_file_path(file_path): return None else: rel_path, abs_path = self._get_recording_file_paths(file_path) file = self._parse_recording_file(abs_path) file.path = rel_path _set_recording_file_channel_info(file) return file def _get_recording_file_paths(self, file_path): if file_path.is_absolute(): if not file_path.exists(): raise CommandExecutionError( 'Purported recording file "{}" does not exist.') rel_path = self._get_relative_path(file_path) return rel_path, file_path else: # path is relative abs_path = self._get_absolute_path(file_path) return file_path, abs_path def _get_relative_path(self, file_path): manager = recording_manager.instance try: _, rel_path = manager.get_relative_recording_file_path(file_path) except ValueError: self._handle_bad_recording_file_path( file_path, 'is not in', manager) return rel_path def _handle_bad_recording_file_path(self, file_path, condition, manager): dir_paths = manager.recording_dir_paths if len(dir_paths) == 1: s = 'the recording directory "{}"'.format(dir_paths[0]) else: path_list = str(list(dir_paths)) s = 'any of the recording directories {}'.format(path_list) raise CommandExecutionError( 'Recording file "{}" {} {}.'.format(file_path, condition, s)) def _get_absolute_path(self, file_path): manager = recording_manager.instance try: return manager.get_absolute_recording_file_path(file_path) except ValueError: self._handle_bad_recording_file_path( file_path, 'could not be found in', manager) def _parse_recording_file(self, file_path): try: file = self.file_parser.parse_file(str(file_path)) except ValueError as e: raise CommandExecutionError( 'Error parsing recording file "{}": {}'.format( file_path, str(e))) if file.recorder is None: file.recorder = _get_recorder(file) return file def _partition_recordings(self, recordings): new_recordings = [] old_recordings = [] for r in recordings: if self._recording_exists(r): old_recordings.append(r) else: new_recordings.append(r) return (new_recordings, old_recordings) def _recording_exists(self, recording): try: Recording.objects.get( station=recording.station, recorder=recording.recorder, start_time=recording.start_time) except Recording.DoesNotExist: return False else: return True def _log_header(self, new_recordings, old_recordings): log = self._logger.info new_count = len(new_recordings) old_count = len(old_recordings) if new_count == 0 and old_count == 0: log('Found no recordings at the specified paths.') else: new_text = self._get_num_recordings_text(new_count, 'new') old_text = self._get_num_recordings_text(old_count, 'old') log('Found {} and {} at the specified paths.'.format( new_text, old_text)) if len(new_recordings) == 0: log('No recordings will be imported.') else: log('The new recordings will be imported.') def _get_num_recordings_text(self, count, description): suffix = '' if count == 1 else 's' return '{} {} recording{}'.format(count, description, suffix) def _import_recordings(self, recordings): for r in recordings: end_time = signal_utils.get_end_time( r.start_time, r.length, r.sample_rate) creation_time = time_utils.get_utc_now() recording = Recording( station=r.station, recorder=r.recorder, num_channels=r.num_channels, length=r.length, sample_rate=r.sample_rate, start_time=r.start_time, end_time=end_time, creation_time=creation_time, creating_job=self._job) recording.save() r.model = recording for channel_num in range(r.num_channels): recorder_channel_num = r.recorder_channel_nums[channel_num] mic_output = r.mic_outputs[channel_num] channel = RecordingChannel( recording=recording, channel_num=channel_num, recorder_channel_num=recorder_channel_num, mic_output=mic_output) channel.save() start_index = 0 for file_num, f in enumerate(r.files): # We store all paths in the archive database as POSIX # paths, even on Windows, for portability, since Python's # `pathlib` module recognizes the slash as a path separator # on all platforms, but not the backslash. path = f.path.as_posix() file = RecordingFile( recording=recording, file_num=file_num, start_index=start_index, length=f.length, path=path) file.save() start_index += f.length def _log_imports(self, recordings): for r in recordings: log = self._logger.info log('Imported recording {} with files:'.format(str(r.model))) for f in r.files: log(' {}'.format(f.path.as_posix())) def _get_recorder(file): end_time = signal_utils.get_end_time( file.start_time, file.length, file.sample_rate) station_recorders = file.station.get_station_devices( 'Audio Recorder', file.start_time, end_time) if len(station_recorders) == 0: raise CommandExecutionError( 'Could not find recorder for recording file "{}".'.format( file.path)) elif len(station_recorders) > 1: raise CommandExecutionError( 'Found more than one possible recorder for file "{}".'.format( file.path)) else: return station_recorders[0].device def _set_recording_file_channel_info(file): mic_outputs = _get_recorder_mic_outputs(file.recorder, file.start_time) if file.recorder_channel_nums is None: # file name did not indicate recorder channel numbers if len(mic_outputs) != file.num_channels: # number of connected mic outputs does not match number # of file channels raise CommandExecutionError(( 'Could not infer recorder channel numbers for ' 'recording file "{}".').format(file.path)) else: # number of connected mic outputs matches number of file # channels # We assume that recorder inputs map to file channel numbers # in increasing order. file.recorder_channel_nums = tuple(sorted(mic_outputs.keys())) file.mic_outputs = tuple( _get_mic_output(mic_outputs, i, file.path) for i in file.recorder_channel_nums) def _get_recorder_mic_outputs(recorder, time): """ Gets a mapping from recorder input channel numbers to connected microphone outputs for the specified recorder and time. """ connections = DeviceConnection.objects.filter( input__device=recorder, output__device__model__type='Microphone', start_time__lte=time, end_time__gt=time) # print('recording_importer.get_recorder_mic_outputs', connections.query) return dict((c.input.channel_num, c.output) for c in connections) def _get_mic_output(mic_outputs, channel_num, file_path): try: return mic_outputs[channel_num] except KeyError: raise CommandExecutionError(( 'Could not find microphone output connected to recorder input ' '{} for recording file "{}".').format(channel_num, file_path))

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0.564664

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13,284

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0.016844

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0.044076

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0.366531

13,284

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0.845395

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null

0

null

0

1

0

5576c4dbc04cfe8f5be4007143719bb7a25f5574

2,033

py

Python

Quotebot/utils.py

musawakiliML/Whatsapp-Bots

29fe6c645010ddedac1424b22c842b3e61511644

[ "MIT" ]

null

Quotebot/utils.py

musawakiliML/Whatsapp-Bots

29fe6c645010ddedac1424b22c842b3e61511644

[ "MIT" ]

null

Quotebot/utils.py

musawakiliML/Whatsapp-Bots

29fe6c645010ddedac1424b22c842b3e61511644

[ "MIT" ]

null

import requests def random_quote(type=''): '''A function to get random quotes''' if type == "today": response_quote = requests.get("https://zenquotes.io/api/today/ff5e73b15a05ca51951b758bd7943ce803d71772") if response_quote.status_code == 200: quote_data = response_quote.json() quote = quote_data[0]['q'] quote_author = quote_data[0]['a'] quote_message = f"'{quote_author.title()}' Said:{quote}" return quote_message else: return f"Invalid Request {response_quote.status_code}" elif type == "quote": response_quote = requests.get("https://zenquotes.io/api/random/ff5e73b15a05ca51951b758bd7943ce803d71772") if response_quote.status_code == 200: quote_data = response_quote.json() quote = quote_data[0]['q'] quote_author = quote_data[0]['a'] quote_message = f"'{quote_author.title()}' Said:{quote}" return quote_message else: return f"Invalid Request {response_quote.status_code}" else: return f"Invalid Request!" def jokes(): '''This function gets a joke''' response_joke = requests.get("https://some-random-api.ml/joke") if response_joke.status_code == 200: joke = response_joke.json() return joke['joke'] else: return f"Invalid Request {response_joke.status_code}" def cat_dog(input_message): if "cat" in input_message and "gif" in input_message: response_gif = requests.get("https://cataas.com/cat") cat_gif = response_gif.url return cat_gif elif "cat" in input_message: response_cat = requests.get("https://cataas.com/cat/cute") cat = response_cat.url return cat elif "dog" in input_message: response_dog = requests.get("https://dog.ceo/api/breeds/image/random") dog_data = response_dog.json()['message'] return dog_data else: return "Invalid Request!"

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0.624693

246

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4.963415

0.223577

0.085176

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0.075348

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0

0.326087

0

null

0

null

0

1

0

557a41cb5f2fe81007b03e1796d482334c493ead

3,401

py

Python

src/day16.py

dcbriccetti/advent-of-code-2021-python

65958fb256234cf882714d3c3306cdbf60bcc0ae

[ "Unlicense" ]

4

2021-12-10T22:47:56.000Z

2021-12-26T21:35:58.000Z

src/day16.py

dcbriccetti/advent-of-code-2021-python

65958fb256234cf882714d3c3306cdbf60bcc0ae

[ "Unlicense" ]

null

src/day16.py

dcbriccetti/advent-of-code-2021-python

65958fb256234cf882714d3c3306cdbf60bcc0ae

[ "Unlicense" ]

null

from math import prod from pathlib import Path class BitStream: 'Deliver integers from a stream of bits created from a hexadecimal string' bit_str: str pos: int def __init__(self, hex_nibbles_str: str) -> None: def binary_nibble_str(hex_nibble_str: str) -> str: 'Convert, for example, `e` ➜ `1110`, or `0` ➜ `0000`' nibble = int(hex_nibble_str, 16) bits_str = bin(nibble)[2:] # Removes the 0b at the left padding_needed = 4 - len(bits_str) return '0' * padding_needed + bits_str self.bit_str = ''.join(binary_nibble_str(hex_nibble_str) for hex_nibble_str in hex_nibbles_str) self.pos = 0 def next_int(self, num_bits: int) -> int: 'Get the next `num_bits` bits and return them parsed as a binary number' return int(self._next_str(num_bits), 2) def _next_str(self, num_bits) -> str: 'Return the next `num_bits` bits as a string' bits_str = self.bit_str[:num_bits] self.bit_str = self.bit_str[num_bits:] self.pos += num_bits return bits_str class Decoder: 'Decode the BITS packet and its nested contained packets' bits: BitStream versions_sum: int operators = [ sum, prod, min, max, None, lambda vals: int(vals[0] > vals[1]), lambda vals: int(vals[0] < vals[1]), lambda vals: int(vals[0] == vals[1]), ] def __init__(self, packet_hex): self.bits = BitStream(packet_hex) print(f'Decoder started for {len(self.bits.bit_str)} bits {packet_hex} {self.bits.bit_str}') self.versions_sum = 0 def parse(self, level=0) -> int: def parse_literal() -> int: value = 0 more: bool = True while more: more = bool(next_int(1)) nibble: int = next_int(4) value = (value << 4) + nibble # Slide over and drop in new bits print(f'{value=}') return value def parse_operator(type: int) -> int: def parse_subpackets_by_length(packets_length) -> list[int]: values: list[int] = [] print(f'{packets_length=}') stop_pos = self.bits.pos + packets_length while self.bits.pos < stop_pos: values.append(self.parse(level + 1)) return values def parse_subpackets_by_count(packet_count) -> list[int]: print(f'{packet_count=}') return [self.parse(level + 1) for _ in range(packet_count)] subpacket_parsers = [parse_subpackets_by_length, parse_subpackets_by_count] length_type_id = next_int(1) length_or_count = next_int(15 if length_type_id == 0 else 11) values = subpacket_parsers[length_type_id](length_or_count) return Decoder.operators[type](values) next_int = self.bits.next_int indent = ' ' * level ver = next_int(3) self.versions_sum += ver type = next_int(3) print(indent + f'{ver=}, {type=}, ', end='') return parse_literal() if type == 4 else parse_operator(type) if __name__ == '__main__': decoder = Decoder(Path('../data/16.txt').read_text().strip()) print(f'Result: {decoder.parse()}, versions sum: {decoder.versions_sum}')

36.180851

100

0.586004

456

3,401

4.129386

0.265351

0.033457

0.025491

0.020712

0.121083

0.090813

0.062135

0.036644

0

0.017759

0.304616

3,401

93

101

36.569892

0.77759

0.104381

0

0.013333

0.15509

0.013772

0

1

0.133333

false

0

0.026667

0

0.36

0.08

0

null

0

null

0

1

0

557ac6c635a14924685b462c2a901a11408e15a1

6,328

py

Python

Santander-spyder.py

Herikc2/Santander-Customer-Satisfaction

c868538ab06c252b2f9e51bac384b0f6e48efd70

[ "MIT" ]

null

Santander-spyder.py

Herikc2/Santander-Customer-Satisfaction

c868538ab06c252b2f9e51bac384b0f6e48efd70

[ "MIT" ]

null

Santander-spyder.py

Herikc2/Santander-Customer-Satisfaction

c868538ab06c252b2f9e51bac384b0f6e48efd70

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Mar 3 17:13:15 2021 Database: https://www.kaggle.com/c/santander-customer-satisfaction @author: Herikc Brecher """ # Import from libraries from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.model_selection import train_test_split from xgboost import XGBClassifier import pandas as pd from sklearn.preprocessing import Normalizer from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.naive_bayes import GaussianNB from sklearn.svm import SVC import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report import seaborn as sns import pickle import warnings warnings.filterwarnings("ignore") # Loading the training dataset in CSV format training_file = 'data/train.csv' test_file = 'data/test.csv' data_training = pd.read_csv(training_file) test_data = pd.read_csv (test_file) print(data_training.shape) print(test_data.shape) # Viewing the first 20 lines data_training.head (20) # Data type of each attribute data_training.dtypes # Statistical Summary data_training.describe() # Distribution of classes data_training.groupby("TARGET").size() # Dividing by class data_class_0 = data_training[data_training['TARGET'] == 0] data_class_1 = data_training[data_training['TARGET'] == 1] counter_class_0 = data_class_0.shape[0] contador_classe_1 = data_class_1.shape[0] data_class_0_sample = data_class_0.sample(counter_class_0) training_data = pd.concat([data_class_0_sample, data_class_1], axis = 0) # Pearson correlation data_training.corr(method = 'pearson') # Finding the correlation between the target variable and the predictor variables corr = training_data[training_data.columns [1:]].corr()['TARGET'][:].abs() minimal_correlation = 0.02 corr2 = corr[corr > minimal_correlation] corr2.shape corr2 corr_keys = corr2.index.tolist() data_filter = data_training[corr_keys] data_filter.head(20) data_filter.dtypes # Filtering only the columns that have a correlation above the minimum variable array_treino = data_training[corr_keys].values # Separating the array into input and output components for training data X = array_treino[:, 0:array_treino.shape[1] - 1] Y = array_treino[:, array_treino.shape[1] - 1] # Creating the training and test dataset test_size = 0.30 X_training, X_testing, Y_training, Y_testing = train_test_split(X, Y, test_size = test_size) # Generating normalized data scaler = Normalizer (). fit (X_training) normalizedX_treino = scaler.transform(X_training) scaler = Normalizer().fit(X_testing) normalizedX_teste = scaler.transform(X_testing) Y_training = Y_training.astype('int') Y_testing = Y_testing.astype('int') ''' Execution of a series of classification algorithms is based on those that have the best result. For this test, the training base is used without any treatment or data selection. ''' # Setting the number of folds for cross validation num_folds = 10 # Preparing the list of models models = [] models.append(('LR', LogisticRegression())) models.append(('LDA', LinearDiscriminantAnalysis())) models.append(('NB', GaussianNB())) models.append(('KNN', KNeighborsClassifier())) models.append(('CART', DecisionTreeClassifier())) models.append(('SVM', SVC())) results = [] names = [] for name, model in models: kfold = KFold (n_splits = num_folds) cv_results = cross_val_score (model, X_training, Y_training, cv = kfold, scoring = 'accuracy') results.append (cv_results) names.append (name) msg = "% s:% f (% f)"% (name, cv_results.mean (), cv_results.std ()) print (msg) # Boxplot to compare the algorithms fig = plt.figure () fig.suptitle ('Comparison of Classification Algorithms') ax = fig.add_subplot (111) plt.boxplot (results) ax.set_xticklabels (names) plt.show () # Function to evaluate the performance of the model and save it in a pickle format for future reuse. def model_report(model_name): # Print result print("Accuracy:% .3f"% score) # Making predictions and building the Confusion Matrix predictions = result.predict(X_testing) matrix = confusion_matrix(Y_testing, predictions) print(matrix) report = classification_report(Y_testing, predictions) print(report) # The precision matrix is created to visualize the number of correct cases labels = ['SATISFIED', 'UNSATISFIED'] cm = confusion_matrix(Y_testing, predictions) cm = pd.DataFrame(cm, index = ['0', '1'], columns = ['0', '1']) plt.figure(figsize = (10.10)) sns.heatmap(cm, cmap = "Blues", linecolor = 'black', linewidth = 1, annot = True, fmt = '', xticklabels = labels, yticklabels = labels) # Saving the model file = 'models/final_classifier_model' + model_name + '.sav' pickle.dump (model, open(file, 'wb')) print("Saved Model!") # Linear Regression model = LogisticRegression() result = model.fit(normalizedX_treino, Y_testing) score = result.score(normalizedX_treino, Y_testing) model_report("LR") # Linear Discriminant Analysis model = LinearDiscriminantAnalysis() result = model.fit(X_training, Y_testing) score = result.score(X_training, Y_testing) model_report("LDA") # KNN model = KNeighborsClassifier() result = model.fit(normalizedX_treino, Y_testing) score = result.score(normalizedX_treino, Y_testing) model_report("KNN") # CART model = DecisionTreeClassifier() result = model.fit(X_training, Y_testing) score = result.score(X_training, Y_testing) model_report("CART") # XGBOOST model = XGBClassifier() result = model.fit(X_training, Y_testing) score = result.score(X_training, Y_testing) model_report("XGBOOST") # Loading the model file = 'models model_classifier_final_XGBOOST.sav' model_classifier = pickle.load(open(file, 'rb')) model_prod = model_classifier.score(X_testing, Y_testing) print("Uploaded Model") # Print Result print("Accuracy:% .3f"% (model_prod.mean () * 100))

30.423077

140

0.733881

838

6,328

5.361575

0.317422

0.030269

0.024928

0.022702

0.1785

0.101714

0.090585

0

0.013039

0.163717

6,328

207

141

30.570048

0.835979

0.175727

0

0.084034

0

0.069616

0.01321

0

1

0.008403

false

0

0.151261

0

0.159664

0.07563

0

null

0

null

0

1

0

557b0f82fa2e590f23c344cfc48bb3aef2ee423d

4,502

py

Python

Memorization Tool/task/tool.py

soukalli/jetbrain-accademy

fc486d439b4b54a58956e1186eb69c56b85f85f1

[ "MIT" ]

null

Memorization Tool/task/tool.py

soukalli/jetbrain-accademy

fc486d439b4b54a58956e1186eb69c56b85f85f1

[ "MIT" ]

null

Memorization Tool/task/tool.py

soukalli/jetbrain-accademy

fc486d439b4b54a58956e1186eb69c56b85f85f1

[ "MIT" ]

null

# write your code here from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, String, Integer from sqlalchemy.orm import sessionmaker engine = create_engine('sqlite:///flashcard.db?check_same_thread=False') Base = declarative_base() Session = sessionmaker(bind=engine) session = Session() successor = {'A': 'B', 'B': 'C'} class FlashCard(Base): __tablename__ = 'flashcard' id = Column(Integer, primary_key=True) question = Column(String(255)) answer = Column(String(255)) box = Column(String(1)) Base.metadata.create_all(engine) def print_main_menu(): print("1. Add flashcards") print("2. Practice flashcards") print("3. Exit") def process_menu_1(): sub_menu_choice = "" while sub_menu_choice != "2": print("1. Add a new flashcard") print("2. Exit") sub_menu_choice = input() if sub_menu_choice == "1": print("Question:") question = input() while question.strip() == "": print("Question:") question = input() print("Answer:") answer = input() while answer.strip() == "": print("Answer:") answer = input() card = FlashCard(question=question, answer=answer, box='A') session.add(card) session.commit() elif sub_menu_choice != "2": print("{0} is not an option".format(sub_menu_choice)) def update_card_status(flashcard, is_success): if not is_success: flashcard.box = 'A' else: if flashcard.box == 'C': session.delete(flashcard) else: flashcard.box = successor.get(flashcard.box) session.commit() def process_confirmation_flashcard(flashcard): print("Answer: {}".format(flashcard.answer)) def process_answer_flashcard(flashcard): print('press "y" if your answer is correct:') print('press "n" if your answer is wrong:') choice = "" while choice != "y" and choice != "n": choice = input() if choice == "y" or choice == "n": update_card_status(flashcard, choice == "y") break else: print("{0} is not an option".format(choice)) def process_update_flashcard(flashcard): print('press "d" to delete the flashcard:') print('press "e" to edit the flashcard:') choice = "" while choice != "d" and choice != "e": choice = input() if choice == "e": print("current question: {0}".format(flashcard.question)) question = input("please write a new question:\n") flashcard.question = question print("current answer: {0}".format(flashcard.answer)) answer = input("please write a new answer:\n") flashcard.answer = answer global session session.commit() break elif choice == "d": session.delete(flashcard) break else: print("{0} is not an option".format(choice)) def process_flashcard(flashcard): print("Question: {}".format(flashcard.question)) print('press "y" to see the answer:') print('press "n" to skip:') print('press "u" to update:') sub_menu_choice = "" while sub_menu_choice != "n": sub_menu_choice = input() if sub_menu_choice == "y": process_confirmation_flashcard(flashcard) process_answer_flashcard(flashcard) break elif sub_menu_choice == "n": process_answer_flashcard(flashcard) break elif sub_menu_choice == "u": process_update_flashcard(flashcard) break elif sub_menu_choice != "n": print("{0} is not an option".format(sub_menu_choice)) def process_menu_2(): flashcards = session.query(FlashCard).all() if len(flashcards) == 0: print('There is no flashcard to practice!') else: for flashcard in flashcards: process_flashcard(flashcard) def process_main_menu(choice): if choice == "1": process_menu_1() elif choice == "2": process_menu_2() elif choice != "3": print("{} is not an option".format(choice)) def main_loop(): choice = "" while choice != "3": print_main_menu() choice = input() process_main_menu(choice) print("Bye!") main_loop()

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0.228346

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null

0

null

0

1

0

557b20fb22a3ac884a03a5ffa7db1db58d06ea7c

9,862

py

Python

src/compass/utils/geo_metadata.py

vbrancat/COMPASS

285412ac2fc474e789e255dae16eba4485017c07

[ "Apache-2.0" ]

11

2021-11-24T07:24:11.000Z

2022-03-23T16:40:13.000Z

src/compass/utils/geo_metadata.py

vbrancat/COMPASS

285412ac2fc474e789e255dae16eba4485017c07

[ "Apache-2.0" ]

6

2021-12-15T16:45:58.000Z

2022-03-24T23:36:16.000Z

src/compass/utils/geo_metadata.py

LiangJYu/COMPASS

459f5d6cf05c2b7c9013f0d862bfef22af280fa6

[ "Apache-2.0" ]

4

2021-12-07T19:45:26.000Z

2022-02-28T23:05:37.000Z

from dataclasses import dataclass from datetime import datetime import json from types import SimpleNamespace import isce3 from isce3.core import LUT2d, Poly1d, Orbit from isce3.product import GeoGridParameters import numpy as np from ruamel.yaml import YAML from shapely.geometry import Point, Polygon from compass.utils.geo_runconfig import GeoRunConfig from compass.utils.raster_polygon import get_boundary_polygon from compass.utils.wrap_namespace import wrap_namespace, unwrap_to_dict def _poly1d_from_dict(poly1d_dict) -> Poly1d: return Poly1d(poly1d_dict['coeffs'], poly1d_dict['mean'], poly1d_dict['std']) def _lut2d_from_dict(lut2d_dict) -> LUT2d: lut2d_shape = (lut2d_dict['length'], lut2d_dict['width']) lut2d_data = np.array(lut2d_dict['data']).reshape(lut2d_shape) return LUT2d(lut2d_dict['x_start'], lut2d_dict['y_start'], lut2d_dict['x_spacing'], lut2d_dict['y_spacing'], lut2d_data) def _orbit_from_dict(orbit_dict) -> Orbit: ref_epoch = isce3.core.DateTime(orbit_dict['ref_epoch']) # build state vector dt = float(orbit_dict['time']['spacing']) t0 = ref_epoch + isce3.core.TimeDelta(float(orbit_dict['time']['first'])) n_pts = int(orbit_dict['time']['size']) orbit_sv = [[]] * n_pts for i in range(n_pts): t = t0 + isce3.core.TimeDelta(i * dt) pos = [float(orbit_dict[f'position_{xyz}'][i]) for xyz in 'xyz'] vel = [float(orbit_dict[f'velocity_{xyz}'][i]) for xyz in 'xyz'] orbit_sv[i] = isce3.core.StateVector(t, pos, vel) return Orbit(orbit_sv, ref_epoch) @dataclass(frozen=True) class GeoCslcMetadata(): # subset of burst class attributes sensing_start: datetime sensing_stop: datetime radar_center_frequency: float wavelength: float azimuth_steer_rate: float azimuth_time_interval: float slant_range_time: float starting_range: float range_sampling_rate: float range_pixel_spacing: float azimuth_fm_rate: Poly1d doppler: Poly1d range_bandwidth: float polarization: str # {VV, VH, HH, HV} burst_id: str # t{track_number}_iw{1,2,3}_b{burst_index} platform_id: str # S1{A,B} center: Point # {center lon, center lat} in degrees border: Polygon # list of lon, lat coordinate tuples (in degrees) representing burst border orbit: isce3.core.Orbit orbit_direction: str # VRT params tiff_path: str # path to measurement tiff in SAFE/zip i_burst: int # window parameters range_window_type: str range_window_coefficient: float runconfig: SimpleNamespace geogrid: GeoGridParameters nodata: str input_data_ipf_version: str isce3_version: str @classmethod def from_georunconfig(cls, cfg: GeoRunConfig): '''Create GeoBurstMetadata class from GeoRunConfig object Parameter: --------- cfg : GeoRunConfig GeoRunConfig containing geocoded burst metadata ''' burst = cfg.bursts[0] burst_id = burst.burst_id geogrid = cfg.geogrids[burst_id] # get boundary from geocoded raster burst_id = burst.burst_id date_str = burst.sensing_start.strftime("%Y%m%d") pol = burst.polarization geo_raster_path = f'{cfg.output_dir}/{burst_id}_{date_str}_{pol}.slc' geo_boundary = get_boundary_polygon(geo_raster_path, np.nan) center = geo_boundary.centroid # place holders nodata_val = '?' ipf_ver = '?' isce3_ver = '?' return cls(burst.sensing_start, burst.sensing_stop, burst.radar_center_frequency, burst.wavelength, burst.azimuth_steer_rate, burst.azimuth_time_interval, burst.slant_range_time, burst.starting_range, burst.range_sampling_rate, burst.range_pixel_spacing, burst.azimuth_fm_rate, burst.doppler.poly1d, burst.range_bandwidth, burst.polarization, burst_id, burst.platform_id, center, geo_boundary, burst.orbit, burst.orbit_direction, burst.tiff_path, burst.i_burst, burst.range_window_type, burst.range_window_coefficient, cfg.groups, geogrid, nodata_val, ipf_ver, isce3_ver) @classmethod def from_file(cls, file_path: str, fmt: str): '''Create GeoBurstMetadata class from json file Parameter: --------- file_path: str File containing geocoded burst metadata ''' if fmt == 'yaml': yaml = YAML(typ='safe') load = yaml.load elif fmt == 'json': load = json.load else: raise ValueError(f'{fmt} unsupported. Only "json" or "yaml" supported') with open(file_path, 'r') as fid: meta_dict = load(fid) datetime_fmt = "%Y-%m-%d %H:%M:%S.%f" sensing_start = datetime.strptime(meta_dict['sensing_start'], datetime_fmt) sensing_stop = datetime.strptime(meta_dict['sensing_stop'], datetime_fmt) azimuth_fm_rate = _poly1d_from_dict(meta_dict['azimuth_fm_rate']) dopp_poly1d = _poly1d_from_dict(meta_dict['doppler']) orbit = _orbit_from_dict(meta_dict['orbit']) # init geo_runconfig cfg = wrap_namespace(meta_dict['runconfig']) # init geogrid grid_dict = meta_dict['geogrid'] geogrid = GeoGridParameters(grid_dict['start_x'], grid_dict['start_y'], grid_dict['spacing_x'], grid_dict['spacing_y'], grid_dict['length'], grid_dict['width'], grid_dict['epsg']) # get boundary from geocoded raster product_path = cfg.product_path_group.product_path date_str = sensing_start.strftime("%Y%m%d") burst_id = meta_dict['burst_id'] pol = meta_dict['polarization'] output_dir = f'{product_path}/{burst_id}/{date_str}' file_stem = f'geo_{burst_id}_{pol}' geo_raster_path = f'{output_dir}/{file_stem}' geo_boundary = get_boundary_polygon(geo_raster_path, np.nan) center = geo_boundary.centroid return cls(sensing_start, sensing_stop, meta_dict['radar_center_frequency'], meta_dict['wavelength'], meta_dict['azimuth_steer_rate'], meta_dict['azimuth_time_interval'], meta_dict['slant_range_time'], meta_dict['starting_range'], meta_dict['range_sampling_rate'], meta_dict['range_pixel_spacing'], azimuth_fm_rate, dopp_poly1d, meta_dict['range_bandwidth'], pol, meta_dict['burst_id'], meta_dict['platform_id'], center, geo_boundary, orbit, meta_dict['orbit_direction'], meta_dict['tiff_path'], meta_dict['i_burst'], meta_dict['range_window_type'], meta_dict['range_window_coefficient'], cfg, geogrid, meta_dict['nodata'], meta_dict['input_data_ipf_version'], meta_dict['isce3_version']) def as_dict(self): ''' Convert self to dict for write to YAML/JSON ''' self_as_dict = {} for key, val in self.__dict__.items(): if key in ['border', 'center', 'sensing_start', 'sensing_stop']: val = str(val) elif isinstance(val, np.float64): val = float(val) elif key in ['azimuth_fm_rate', 'doppler']: temp = {} temp['order'] = val.order temp['mean'] = val.mean temp['std'] = val.std temp['coeffs'] = val.coeffs val = temp elif key == 'orbit': temp = {} temp['ref_epoch'] = str(val.reference_epoch) temp['time'] = {} temp['time']['first'] = val.time.first temp['time']['spacing'] = val.time.spacing temp['time']['last'] = val.time.last temp['time']['size'] = val.time.size temp['position_x'] = val.position[:,0].tolist() temp['position_y'] = val.position[:,1].tolist() temp['position_z'] = val.position[:,2].tolist() temp['velocity_x'] = val.velocity[:,0].tolist() temp['velocity_y'] = val.velocity[:,1].tolist() temp['velocity_z'] = val.velocity[:,2].tolist() val = temp elif key == 'runconfig': val = unwrap_to_dict(val) elif key == 'geogrid': temp = {} temp['start_x'] = val.start_x temp['start_y'] = val.start_y temp['spacing_x'] = val.spacing_x temp['spacing_y'] = val.spacing_y temp['length'] = val.length temp['width'] = val.width temp['epsg'] = val.epsg val = temp self_as_dict[key] = val return self_as_dict def to_file(self, dst, fmt:str): '''Write self to file Parameter: --------- dst: file pointer File object to write metadata to fmt: ['yaml', 'json'] Format of output ''' self_as_dict = self.as_dict() if fmt == 'yaml': yaml = YAML(typ='safe') yaml.dump(self_as_dict, dst) elif fmt == 'json': json.dump(self_as_dict, dst, indent=4) else: raise ValueError(f'{fmt} unsupported. Only "json" or "yaml" supported')

37.930769

95

0.58548

1,164

9,862

4.690722

0.190722

0.042491

0.012821

0.007692

0.139927

0.078388

0.056044

0.047253

0

0.008589

0.303488

9,862

259

96

38.07722

0.786286

0.088623

0

0.135417

0

0.124135

0.022353

0

1

0.036458

false

0.015625

0.067708

0.005208

0.291667

0

null

0

null

0

1

0

557fbf2a8059c9beebbcd0bd1552ded759c8e7f0

2,227

py

Python

tests/test_db.py

andreasgrv/methinks

5c65fdb84e35b8082ee35963431a352e06f4af44

[ "BSD-3-Clause" ]

null

tests/test_db.py

andreasgrv/methinks

5c65fdb84e35b8082ee35963431a352e06f4af44

[ "BSD-3-Clause" ]

null

tests/test_db.py

andreasgrv/methinks

5c65fdb84e35b8082ee35963431a352e06f4af44

[ "BSD-3-Clause" ]

null

import datetime from methinks.db import Entry import pytest from server.app import create_app from server.app import db as _db from sqlalchemy import event from sqlalchemy.orm import sessionmaker @pytest.fixture(scope="session") def app(request): """ Returns session-wide application. """ return create_app() @pytest.fixture(scope="session") def db(app, request): """ Returns session-wide initialised database. """ with app.app_context(): _db.drop_all() _db.create_all() @pytest.fixture(scope="function", autouse=True) def session(app, db, request): """ Returns function-scoped session. """ with app.app_context(): conn = _db.engine.connect() txn = conn.begin() options = dict(bind=conn, binds={}) sess = _db.create_scoped_session(options=options) # establish a SAVEPOINT just before beginning the test # (http://docs.sqlalchemy.org/en/latest/orm/session_transaction.html#using-savepoint) sess.begin_nested() @event.listens_for(sess(), 'after_transaction_end') def restart_savepoint(sess2, trans): # Detecting whether this is indeed the nested transaction of the test if trans.nested and not trans._parent.nested: # The test should have normally called session.commit(), # but to be safe we explicitly expire the session sess2.expire_all() sess.begin_nested() _db.session = sess yield sess # Cleanup sess.remove() # This instruction rollsback any commit that were executed in the tests. txn.rollback() conn.close() def test_insert(session): e = Entry(text='My example', date=datetime.date.today()) session.add(e) session.commit() def test_delete(session): e = Entry(text='My example', date=datetime.date.today()) session.add(e) session.commit() session.delete(e) session.commit() def test_find_by_hash(session): e = Entry(text='My example', date=datetime.date.today()) session.add(e) session.commit() first = Entry.query.filter(Entry.hexid == e.hash).first() assert first == e

26.831325

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279

2,227

5.060932

0.430108

0.046034

0.03966

0.036119

0.25

0.15085

0

0.001186

0.242479

2,227

82

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27.158537

0.835803

0.22317

0

0.326531

0

0.04353

0.012522

0

0.020408

1

0.142857

false

0

0.142857

0

0.306122

0

null

0

null

0

1

0

5581ae54a36323a4a46f3383645e34f4c26755e1

2,891

py

Python

bin/simple_log_server.py

kr0nt4b/ctrl_my_home

fd86b479d78f94aaa5d6cc92f0f49399aaef0733

[ "Apache-2.0" ]

null

bin/simple_log_server.py

kr0nt4b/ctrl_my_home

fd86b479d78f94aaa5d6cc92f0f49399aaef0733

[ "Apache-2.0" ]

null

bin/simple_log_server.py

kr0nt4b/ctrl_my_home

fd86b479d78f94aaa5d6cc92f0f49399aaef0733

[ "Apache-2.0" ]

null

#!/usr/bin/python2 """ Simple socket server using threads """ import socket import sys from thread import * import os import logging HOST = '' # Symbolic name meaning all available interfaces PORT = 9998 # Arbitrary non-privileged port LOG_FORMAT = '%(asctime)-15s %(message)s' SMART_LOG = '/var/log/smart/smarthome.log' def init_logging(): smart_log_path = os.path.dirname(SMART_LOG) if not os.path.exists(os.path.dirname(smart_log_path)): os.mkdir(smart_log_path) logging.basicConfig(filename=SMART_LOG, level=logging.DEBUG, format=LOG_FORMAT) return logging.getLogger('log_server') class LogServer: def __init__(self): self.logger = init_logging() self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.logger.info('Socket created') # Bind socket to local host and port try: self.sock.bind((HOST, PORT)) except socket.error as msg: self.logger.info('Bind failed. Error Code : ' + str(msg[0]) + ' Message ' + msg[1]) sys.exit() self.logger.info('Socket bind complete') # Start listening on socket self.sock.listen(10) self.logger.info('Socket now listening') # Function for handling connections. This will be used to create threads def client_thread(self, connection): # Sending message to connected client connection.send('Welcome to the logserver') # send only takes string # infinite loop so that function do not terminate and thread do not end. while True: # Receiving from client data = connection.recv(1024) reply = 'OK\n' if not data: break tokens = data.split(' ') if len(tokens) > 1: level = data.split(' ')[1] if level == 'DEBUG:': self.logger.debug(data) if level == 'INFO:': self.logger.info(data) if level == 'ERROR:': self.logger.error(data) else: self.logger.info(data) connection.sendall(reply) # came out of loop connection.close() def start(self): # now keep talking with the client while True: # wait to accept a connection - blocking call conn, addr = self.sock.accept() self.logger.info('Connected with ' + addr[0] + ':' + str(addr[1])) # start new thread takes 1st argument as a function name to be run, second # is the tuple of arguments to the function. start_new_thread(self.client_thread, (conn,)) self.sock.close() if __name__ == "__main__": log_server = LogServer() try: log_server.start() except KeyboardInterrupt as e: print(e.message)

28.91

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353

2,891

4.736544

0.419263

0.059809

0.058612

0.035885

0.02512

0

0.010015

0.309236

2,891

99

96

29.20202

0.827241

0.215496

0

0.1

0

0.099911

0.012489

0

1

0.066667

false

0

0.083333

0

0.183333

0.016667

0

null

0

null

0

1

0

5581eb881f3ca5ddfe7fd5be0a7447ea5b604281

1,348

py

Python

utils/calc_drh.py

leogoesger/func-flow

c81f73998df9b02c04c19a6beae463121d5a8898

[ "MIT" ]

11

2018-04-14T00:34:34.000Z

2021-05-04T17:23:50.000Z

utils/calc_drh.py

Yesicaleon/func-flow

c81f73998df9b02c04c19a6beae463121d5a8898

[ "MIT" ]

15

2019-04-02T03:35:22.000Z

2022-02-12T13:17:11.000Z

utils/calc_drh.py

Yesicaleon/func-flow

c81f73998df9b02c04c19a6beae463121d5a8898

[ "MIT" ]

9

2018-12-01T19:46:11.000Z

2022-03-31T17:18:15.000Z

import numpy as np from utils.helpers import * percentiles = [10, 25, 50, 75, 90] percentile_keys = ["ten", "twenty_five", "fifty", "seventy_five", "ninty"] def calc_drh(flow_matrix): """Dimensionless Hydrograph Plotter""" average_annual_flow = calculate_average_each_column(flow_matrix) number_of_rows = len(flow_matrix) number_of_columns = len(flow_matrix[0, :]) normalized_matrix = np.zeros((number_of_rows, number_of_columns)) """Initiating the DRH object with desired keys""" drh = {} for index, percentile in enumerate(percentiles): drh[percentile_keys[index]] = [] drh["min"] = [] drh["max"] = [] for row_index, _ in enumerate(flow_matrix[:, 0]): for column_index, _ in enumerate(flow_matrix[row_index, :]): normalized_matrix[row_index, column_index] = flow_matrix[row_index, column_index]/average_annual_flow[column_index] for index, percentile in enumerate(percentiles): drh[percentile_keys[index]].append(round(np.nanpercentile( normalized_matrix[row_index, :], percentile), 2)) drh["min"].append(round(np.nanmin(normalized_matrix[row_index, :]), 2)) drh["max"].append(round(np.nanmax(normalized_matrix[row_index, :]), 2)) return drh

39.647059

116

0.647626

163

1,348

5.07362

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0.101572

0.116082

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0

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1,348

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0.166667

0

null

0

null

0

1

0

558930319f7b3b786028343bb2be22080c9650c4

14,091

py

Python

src/icaltool/icaltool.py

randomchars42/icaltool

acf482f08bb4eb7bc000c0b2591c6d76ec8fcaac

[ "Unlicense" ]

null

src/icaltool/icaltool.py

randomchars42/icaltool

acf482f08bb4eb7bc000c0b2591c6d76ec8fcaac

[ "Unlicense" ]

null

src/icaltool/icaltool.py

randomchars42/icaltool

acf482f08bb4eb7bc000c0b2591c6d76ec8fcaac

[ "Unlicense" ]

null

#!/usr/bin/env python3 import csv import logging import logging.config import re import argparse import json import sys from .log import log from . import datatypes logger = logging.getLogger(__name__) default_column_mapping = { 'DTSTART': 0, 'DTEND': 1, 'DTSTAMP': 2, 'UID': 3, 'CREATED': 4, 'DESCRIPTION': 5, 'LAST-MODIFIED': 6, 'LOCATION': 7, 'SEQUENCE': 8, 'SUMMARY': 9, 'CATEGORIES': 10, 'CLASS': 11, 'ATTACH': 12, 'TRANSP': 13, 'RRULE': 14, 'EXDATE': 15, 'STATUS': 16 } custom_column_names = { 'DTSTART': 'DTSTART', 'DTEND': 'DTEND', 'DTSTAMP': 'DTSTAMP', 'UID': 'UID', 'CREATED': 'CREATED', 'DESCRIPTION': 'DESCRIPTION', 'LAST-MODIFIED': 'LAST-MODIFIED', 'LOCATION': 'LOCATION', 'SEQUENCE': 'SEQUENCE', 'SUMMARY': 'SUMMARY', 'CATEGORIES': 'CATEGORIES', 'CLASS': 'CLASS', 'ATTACH': 'ATTACH', 'TRANSP': 'TRANSP', 'RRULE': 'RRULE', 'EXDATE': 'EXDATE', 'STATUS': 'STATUS' } standard_components = [ 'VCALENDAR', 'STANDARD', 'DAYLIGHT', 'VEVENT', 'VTODO', 'VJOURNAL', 'VALARM', 'VFREEBUSY' ] class ICalTool: """ Tool for handling calendar data (ical) as defined in: RFC 2445 (https://datatracker.ietf.org/doc/html/rfc2445) """ def __init__(self): self._reset() def _reset(self): self.vcalendar = None def setup(self, options): # currently only understands # { # "COMPONENTNAME": { # "defined_properties": { # "PROPERTY": [(-1|0|1), "NAMEOFCLASS"], # } # }, # ... # } for key, value in options.items(): if key in standard_components: class_object = getattr(datatypes, key) try: for prop, values in value['defined_properties'].items(): if not len(values) == 2: logger.warning('illegal value for property {} in ' + 'defined_properties'.format(prop)) continue #setattr(class_object.defined_properties, prop, values) class_object.defined_properties[prop] = values except KeyError: logger.warning('did not unterstand option "{}"'.format( key)) def load(self, file_name, component='VEVENT', has_header=True, custom_column_names=custom_column_names, column_mapping=default_column_mapping, delimiter=',', quotechar='"'): if file_name[-3:] == 'csv': self.csv_load(file_name, component, has_header, custom_column_names, column_mapping, delimiter, quotechar) elif file_name[-3:] == 'ics': self.ical_load(file_name) else: logger.error('invalid file given ("{}")'.format(file_name)) sys.exit() def csv_load(self, file_name, component='VEVENT', has_header=True, custom_column_names=custom_column_names, column_mapping=default_column_mapping, delimiter=',', quotechar='"'): with open(file_name, 'r', newline='', encoding='utf-8-sig') as \ file_handle: logger.info('opening {}'.format(file_name)) data = csv.reader( file_handle, delimiter=delimiter, quotechar=quotechar) if has_header: header = next(data) column_mapping = self._csv_get_column_mapping( default_column_mapping, has_header, header, custom_column_names) self.vcalendar = datatypes.VCALENDAR() self.vcalendar.csv_parse(component, data, column_mapping) logger.info('loaded {}'.format(file_name)) def _csv_get_column_mapping(self, default_column_mapping, has_header, header, custom_column_names): if not has_header: # no headers to parse # so use default column mapping return default_column_mapping # get headers from file column_mapping = {} i = 0 for column in header: column_mapping[column] = i i = i + 1 if len(custom_column_names) == 0: return parsed_columns # the user provided costum columns names in a dictionary new_mapping = {} for column_name in column_mapping.keys(): # so go through every available column try: # 1. the parsed column name exists in the user # provided dictionary new_mapping[custom_column_names[column_name]] = \ column_mapping[column_name] except KeyError: # 2. the name cannot be translated so copy it new_mapping[column_name] = \ column_mapping[column_name] return new_mapping def ical_load(self, file_name): with open(file_name, 'r', newline='', encoding='utf-8-sig') as \ file_handle: logger.info('opening {}'.format(file_name)) raw = file_handle.readlines() lines = [] vcalendar = False # clean up for line in raw: # remove the trailing "\n" line = line.rstrip("\r\n") # do not use empty lines if not line == '': if not vcalendar and line == 'BEGIN:VCALENDAR': vcalendar = True logger.debug('recording new VCALENDAR') elif vcalendar: if line == 'END:VCALENDAR': vcalendar = False logger.debug('finished recording VCALENDAR') # unfold lines (folded lines begin with a single whitespace # or tab) elif line[0] == ' ' or line[0] == "\t": # append to previous line lines[len(lines) - 1] += line[1:] else: lines.append(line) self.vcalendar = datatypes.VCALENDAR() self.vcalendar.ical_parse(lines) logger.info('loaded {}'.format(file_name)) def write(self, file_name, component): if file_name[-3:] == 'csv': self.csv_write(file_name, component) elif file_name[-3:] == 'ics': self.ical_write(file_name) else: logger.error('invalid file given ("{}")'.format(file_name)) sys.exit() def csv_write(self, file_name, component='VEVENT'): lines = [] # can only write components of one type with open(file_name, 'w') as file_handle: logger.info('writing to {}'.format(file_name)) # get a list of known properties to use as column names class_object = getattr(datatypes, component) properties = [] for prop, attributes in class_object.defined_properties.items(): if attributes[0] == 2: continue else: properties.append(prop) # build header lines.append('"' + '","'.join(properties) + '"') # fill with data lines.extend(self.vcalendar.csv_write(component)) file_handle.write("\r\n".join(lines)) logger.info('finished writing to {}'.format(file_name)) def ical_write(self, file_name): with open(file_name, 'w') as file_handle: logger.info('writing to {}'.format(file_name)) lines = self.vcalendar.ical_write() for line in lines: text = '' while True: text += line[:74] + "\r\n" line = ' ' + line[74:] if line == ' ': break file_handle.write(text) logger.info('finished writing to {}'.format(file_name)) def filter(self, rules): if self.vcalendar is None: logger.warning('cannot apply rules before calendar data has been '+ 'loaded') return # example component rule: # - keep only events: # COMPONENT:+VEVENT # - filter out all events: # COMPONENT:-VEVENT # - filter out all events and alarms # COMPONENT:-VEVENT,VALARM # example property rules: # - filter out all components with a start date between 2015 and 2017: # DTSTART:-2015to2017 # - keep only components with a start date between 2015-10 and 2017-11: # DTSTART:+2015-10to2017-11 # - ... attended by john.doe@mail.domain: # DTSTART:+2015-10to2017-11;ATTENDEE:+john.doe@mail.domain # - ... but not by jane.doe@mail.domain: # ...;ATTENDEE:+john.doe@mail.domain|-jane.doe@mail.domain raw_rules = rules.split(';') parsed_rules = {} for raw_rule in raw_rules: try: name, rule = raw_rule.split(':') except ValueError: # no ':' logger.warning('malformed rule {}'.format(raw_rule)) continue logger.info('found rule for {}: "{}"'.format(name, rule)) parsed_rules[name] = rule.split('|') try: component_rule = parsed_rules['COMPONENT'][0] logger.debug('found component rule: "{}"'.format(component_rule)) # sanity check if not re.match('[+-]{1}[A-Z,]+', component_rule): logger.error('component filter cannot have inclusion and ' + 'exclusion criteria, "{}" given'.format(component_rule)) return components_keep = component_rule[0] == '+' components = component_rule[1:].split(',') del parsed_rules['COMPONENT'] except KeyError: # no component rule # create an empty list of components to remove components = [] components_keep = False self.vcalendar.filter(components, components_keep, parsed_rules) # taken from : # https://stackoverflow.com/questions/9027028/argparse-argument-order class CustomAction(argparse.Action): def __call__(self, parser, namespace, values, option_string=None): if not 'ordered_args' in namespace: setattr(namespace, 'ordered_args', []) previous = namespace.ordered_args previous.append((self.dest, values)) setattr(namespace, 'ordered_args', previous) def main(): # parse arguments parser = argparse.ArgumentParser( description='Tool to work with calendar data. It can read .ics ' + '(preferred) and .csv files. You can filter the compontents ' + '(events, todos, alarms, journals, freebusy-indicators) by their ' + 'type or the value of their properties, e.g. start date ' + '(DTSTART) or organiser (ORGANIZER). The result can be written ' + 'back to a file, again either .ics (preferred) or .csv.', epilog='') parser.add_argument( 'file', help='the file to load, either .csv or .ics (preferred)', type=str) parser.add_argument( '-o', '--output', help='the file to write to, either .csv or .ics (preferred)', type=str, action=CustomAction) parser.add_argument( '-f', '--filter', help='rules to filter which component types (events, todos, alarms, ' + 'journals, freebusy-indicators) to keep / sort out', type=str, action=CustomAction) parser.add_argument( '-s', '--setup', help='json-string containing options, e.g. ' + '{"VEVENT": {"defined_properties": ' + '{"ATTENDEE": [-1, "Property"]}}} ' + 'to ignore the ATTENDEE property when parsing', type=str) parser.add_argument( '-c', '--component', help='component type stored in the .csv-file (one of: events ' + '[VEVENT], todos [VTODO], alarms [VALARM], journals [VJOURNAL], ' + 'freebusy-indicators [VFREEBUSY]); if no component is specified ' + 'events [VEVENT] are assumed to be the input / desired output', type=str, default='VEVENT') parser.add_argument( '-v', '--verbosity', action='count', help='increase verbosity', default=0) args = parser.parse_args() # setup logging logging_config = log.config if args.verbosity >= 3: logging_config['handlers']['console']['level'] = 'DEBUG' elif args.verbosity == 2: logging_config['handlers']['console']['level'] = 'INFO' elif args.verbosity == 1: logging_config['handlers']['console']['level'] = 'WARNING' else: logging_config['handlers']['console']['level'] = 'ERROR' logging.config.dictConfig(logging_config) # setup ICalTool tool = ICalTool() if not args.setup is None: tool.setup(json.loads(args.setup)) # load file tool.load(args.file, component=args.component) # do whatever if not 'ordered_args' in args: logger.error('nothing to do with the loaded data - exiting') return # process actions in order of flags for arg, value in args.ordered_args: if arg == 'output': if value == args.file: logger.error('please don\'t attempt to overwrite your input ' + 'file - while it is technically possible it seems unwise ' + "\n cancelling") continue tool.write(value, component=args.component) elif arg == 'filter': tool.filter(value) if __name__ == '__main__': main()

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0.029939

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0.011227

0.277332

0.219861

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0.116279

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0.013544

0.339791

14,091

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0

null

0

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558cbd4a7ce3e41aaed8e2b86ecb2cf3f058fd07

20,998

py

Python

script.py

kenneth2001/Virus

e7d0b650d9d7a4eaab9bd87b3695b791e1f105b1

[ "MIT" ]

null

script.py

kenneth2001/Virus

e7d0b650d9d7a4eaab9bd87b3695b791e1f105b1

[ "MIT" ]

null

script.py

kenneth2001/Virus

e7d0b650d9d7a4eaab9bd87b3695b791e1f105b1

[ "MIT" ]

null

import asyncio import requests from bs4 import BeautifulSoup from datetime import date, datetime import discord import numpy as np from urllib.error import HTTPError import yt_dlp as youtube_dl from discord.ext import commands import os from pytz import timezone from yt_dlp.utils import DownloadError, ExtractorError from util.log import pretty_output, pretty_print from util.preprocessing import load_config, load_gif, load_user import secrets try: print('LOADING config.txt') TOKEN, TIMEZONE, MODE = load_config('config/config.txt') print('LOADED config.txt\n') except: print('ERROR LOADING config.txt\n') tz = timezone(TIMEZONE) token = TOKEN #os.environ['token'] # 0: local, 1: repl.it # For setting up bot on replit.com if MODE == 1: from util.keep_alive import keep_alive os.environ['MPLCONFIGDIR'] = '/tmp/' #"/home/runner/Virus-demo/tmp" import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt elif MODE == 0: import matplotlib.pyplot as plt import sympy else: print('UNDEFINED MODE') exit() try: print('LOADING gif.json') gif = load_gif('config/gif.json') print('LOADED gif.json\n') except: print('ERROR LOADING gif.json\n') try: print('LOADING user.json') user = load_user('config/user.json') print('LOADED user.json\n') except: print('ERROR LOADING user.json\n') ytdl_format_options = { 'format': 'bestaudio/best', 'noplaylist': True, 'nocheckcertificate': True, 'ignoreerrors': False, 'logtostderr': False, 'quiet': True, 'no_warnings': True, 'default_search': 'auto', 'source_address': '0.0.0.0' } ffmpeg_options = { 'options': '-vn', "before_options": "-reconnect 1 -reconnect_streamed 1 -reconnect_delay_max 5" } # channel_var stores all variable for differnet channels # key: serverid # value: 1. activated[bool] - indicate whether the music playing function is activated # 2. bully[dict] - list of user being bullied # 3. ctx[object] # 4. log[list] - log of user entering / leaving voice channels # 5. playing[bool] - indicate whether the bot is playing music # 6. queue[list] - list of music to be played channel_var = {} # return gif link def send_gif(msg): if msg in gif.keys(): return gif[msg] # Wong Tai Sin Fortune Sticks (黃大仙求籤) def get_stick(tag): num = np.random.randint(1, 101) URL = f'https://andy.hk/divine/wongtaisin/{num}' page = requests.get(URL) soup = BeautifulSoup(page.content, "html.parser") result = soup.find(id='content') job_elements = result.find("div", class_="inner-padding col-md-5 col-md-offset-7") stick_no = job_elements.find('h2', class_='id-color text-center').text stick_author = job_elements.find_all('h4', class_='text-center')[0].text stick_content = job_elements.find_all('h4', class_='text-center')[1].text stick_explain = job_elements.text.split('仙機：')[1].split('解說及記載：')[0] stick_story = job_elements.text.split('仙機：')[1].split('解說及記載：')[1].split('■')[0] text = tag + '求得' + stick_no + '\n' + stick_author + '\n\n籤文:\n' + stick_content + '\n\n仙機：' + stick_explain + '\n解說及記載' + stick_story return text client = commands.Bot(command_prefix='#', help_command=None) @client.event async def on_connect(): print("Bot activated successfully") async def initialize(server_id: int, ctx: object=None): """Initializing channel_var Args: server_id (int) ctx (object, optional): Defaults to None. """ global channel_var info = channel_var.get(server_id, -1) if info != -1: if channel_var[server_id]['ctx'] == None and ctx != None: channel_var[server_id]['ctx'] = ctx return else: channel_var[server_id] = {'ctx':ctx, 'queue':[], 'activated':False, 'playing':True, 'log':[], 'bully':{}} @client.event async def on_voice_state_update(member, before, after): server_id = member.guild.id await initialize(server_id) global channel_var if before.channel is None and after.channel is not None: channel_var[server_id]['log'].append([datetime.now(tz).strftime('%Y-%m-%d %H:%M:%S'), '*' + str(member) + '* Entered `' + str(after.channel) + '`']) if before.channel is not None and after.channel is None: channel_var[server_id]['log'].append([datetime.now(tz).strftime('%Y-%m-%d %H:%M:%S'), '*' + str(member) + '* Leaved `' + str(before.channel) + '`']) if before.channel is not None and after.channel is not None: channel_var[server_id]['log'].append([datetime.now(tz).strftime('%Y-%m-%d %H:%M:%S'), '*' + str(member) + '* Leaved `' + str(before.channel)+ '`, Joined `' + str(after.channel) + '`']) @client.command(name='log') async def log(ctx): await initialize(ctx.guild.id, ctx) global channel_var if len(channel_var[ctx.guild.id]['log']) == 0: return embed = discord.Embed(color = discord.Colour.red()) embed.set_author(name='Log (Recent 20 records)') for field in channel_var[ctx.guild.id]['log'][-20:]: embed.add_field(name=field[0], value=field[1], inline=False) await ctx.send(embed=embed) async def play_music(ctx): while not client.is_closed(): global channel_var if not len(channel_var[ctx.guild.id]['queue']) == 0 and ctx is not None: server = ctx.message.guild voice_channel = server.voice_client if (voice_channel and voice_channel.is_connected() and not voice_channel.is_playing() and channel_var[ctx.guild.id]['playing']) == True: server = ctx.message.guild voice_channel = server.voice_client try: link = channel_var[ctx.guild.id]['queue'][0][1] title = channel_var[ctx.guild.id]['queue'][0][2] player = discord.FFmpegPCMAudio(link, **ffmpeg_options) voice_channel.play(player) await ctx.send(f'**Now playing:** {title}') except DownloadError: await ctx.send(f'**Download error:** {title}') del(channel_var[ctx.guild.id]['queue'][0]) await asyncio.sleep(1) @client.command(name='play') async def play(ctx, *url): url = ' '.join(url) await initialize(ctx.guild.id, ctx) global channel_var def music(link): with youtube_dl.YoutubeDL(ytdl_format_options) as ydl: info = ydl.extract_info(link, download=False) # Handle if the url is a playlist if 'entries' in info: info = info['entries'][0] LINK = info['webpage_url'] URL = info['url'] TITLE = info['title'] return LINK, URL, TITLE if not ctx.message.author.voice: # handle if message author is not inside any voice channel await ctx.send("**You are not connected to a voice channel**") return elif ctx.message.guild.voice_client: # if bot is inside any voice channel if ctx.message.guild.voice_client.channel != ctx.message.author.voice.channel: # if bot is not inside the author's channel channel = ctx.message.author.voice.channel user = await ctx.guild.fetch_member(client.user.id) ctx.voice_client.pause() await user.move_to(channel) ctx.voice_client.resume() else: # if bot is not inside any voice channel channel = ctx.message.author.voice.channel await channel.connect() # connect to message author's channel server = ctx.message.guild voice_channel = server.voice_client if url is None or url == '': if len(channel_var[ctx.guild.id]['queue']) == 0: return else: try: link, player_link, title = music(url) channel_var[ctx.guild.id]['queue'].append([link, player_link, title]) except ExtractorError: await ctx.send('**Error:** ' + url) except HTTPError: await ctx.send('**Error:** ' + url) except DownloadError: await ctx.send('**Error:** ' + url) # activate music playing function if channel_var[ctx.guild.id]['activated'] == False: channel_var[ctx.guild.id]['activated'] = True await play_music(ctx) @client.command(name='debug') async def debug(ctx): def check(m): return m.author == ctx.message.author func_token = secrets.token_hex(10) print("Token:", func_token) await ctx.send('**Please type in the token displayed in console**') msg = await client.wait_for("message", check=check) if msg.content == func_token: pretty_print(channel_var) pretty_output(channel_var, filename='tmp.json') await ctx.send(file=discord.File('tmp.json')) else: await ctx.send("**Only admin can use this command**") @client.command(name='queue') async def queue_(ctx): await initialize(ctx.guild.id, ctx) global channel_var if len(channel_var[ctx.guild.id]['queue']) == 0: await ctx.send('**Queue is empty!**') else: async with ctx.typing(): await ctx.send('\n'.join([f'{idx}. {item[2]}\n{item[0]}' for idx, item in enumerate(channel_var[ctx.guild.id]['queue'], start=1)])) @client.command(name='stop') async def stop(ctx): voice_client = ctx.message.guild.voice_client await voice_client.disconnect() @client.command(name='gpa') async def gpa(ctx): x = round(np.random.uniform(3,4) - np.random.normal(0, 1), 2) text = 4.0 if x > 4 else x if text >= 3.8: text = "Predicted GPA: " + str(text) elif text >= 3.0: text = "Predicted GPA: " + str(text) elif text >= 2.5: text = "Predicted GPA: " + str(text) else: text = "Predicted GPA: " + str(text) tag = "<@" + str(ctx.message.author.id) + ">" await ctx.message.channel.send(str(text)+tag) @client.command(name='pause') async def pause(ctx): await initialize(ctx.guild.id, ctx) global channel_var channel_var[ctx.guild.id]['playing'] = False if ctx.voice_client is not None: ctx.voice_client.pause() await ctx.send('**Paused**') @client.command(name='resume') async def resume(ctx): await initialize(ctx.guild.id, ctx) global channel_var channel_var[ctx.guild.id]['playing'] = True if ctx.voice_client is not None: ctx.voice_client.resume() await ctx.send('**Resumed**') @client.command(name='skip') async def skip(ctx): await initialize(ctx.guild.id, ctx) global channel_var if ctx.voice_client is not None: ctx.voice_client.stop() await ctx.send('**Skipped**') @client.listen() async def on_message(message): author = message.author author_id = str(message.author.id) tag = "<@" + str(message.author.id) + ">" msg = message.content.lower() if author == client.user: return #print('Debugging:', author, msg) today = date.today() if user.get(author_id, -1) != -1: if user[author_id]['date'] != today: user[author_id]['date'] = today await message.channel.send(user[author_id]['text'] + tag) if message.content.startswith('#hello'): await message.channel.send("Hello World!") gif = send_gif(msg) if gif is not None: await message.channel.send(gif) @client.command(name='help') async def help(ctx): embed = discord.Embed(title="Virus", url="https://github.com/kenneth2001/virus", description="Discord Bot developed by YeeKiiiiii 2021", color=discord.Colour.blue()) embed.set_author(name="Virus", url="https://github.com/kenneth2001/virus", icon_url="https://user-images.githubusercontent.com/24566737/132656284-f0ff6571-631c-4cef-bed7-f575233cbf5f.png") embed.add_field(name=':musical_note: __Music__', value="""1. `#play [url]` Play music, tested platform: Youtube, Soundcloud 2. `#pause` Pause music 3. `#resume` Resume music 4. `#skip` Play next song 5. `#queue` Display the queue 6. `#stop` Kick the bot from voice channel""", inline=False) embed.add_field(name=':pencil2: __Graph (Developing)__', value="""1. `#plot` Create simple scatter/line plot""", inline=False) embed.add_field(name=':black_joker: __Kidding__', value="""1. `#joke [userid] [times] [duration]` Move a specified user into random voice channels randomly and repeatly 2. `#leavemealone` Stop yourself from being bullied 3. `#save [userid]` Recuse your friend from cyber-bullying""", inline=False) embed.add_field(name=':man_office_worker: __Other__', value="""1. `#stick` Fortune sticks from Wong Tai Sin 2. `#gpa` Get prediction of your GPA (Maximum: 4.0) 3. `#help` Display a list of all commands aviliable 4. `#credit` Display information of the bot developer 5. `#hello` Return 'hello world' 6. `#ping` Return latency 7. `#log` Display the previous 20 in/out user 8. `#clear` Delete previous 30 messages sent by this bot / started with '#' 9. `#debug` Check parameters (for debugging)""", inline=False) embed.add_field(name=':new: __New Features (Experimental)__', value="""1. `#when` Return the start time of the bot 2. `#dm [userid] [message]` Send message to any user privately""" ) embed.add_field(name=':frame_with_picture: __GIF__', value="Automatically return GIF if the message matches the following keywords\n`" + '` `'.join(gif.keys()) +'`', inline=False) embed.set_footer(text="Last updated on 25 December 2021") await ctx.send(embed=embed) @client.command(name='ping') async def ping(ctx): await ctx.send(f'In {round(client.latency * 1000)}ms') @client.command(name='stick') async def stick(ctx): tag = "<@" + str(ctx.message.author.id) + ">" text = get_stick(tag) await ctx.send(text) @client.command(name='credit') async def credit(ctx): await ctx.send('Created By kenneth\nLast Update On 18/9/2021\nhttps://github.com/kenneth2001') @client.command(name='clear') async def clear(ctx): def is_bot(m): try: return m.author == client.user or m.content[0] == '#' except: return False deleted = await ctx.message.channel.purge(limit=30, check=is_bot) await ctx.send('Deleted {} message(s)'.format(len(deleted)), delete_after=10) @client.command(name='joke') async def joke(ctx, userid=None, n=10, sleep_time=0.5): await initialize(ctx.guild.id, ctx) global channel_var try: userid = int(userid) user = await ctx.guild.fetch_member(userid) info = channel_var[ctx.guild.id]['bully'].get(userid, -1) if info == -1: channel_var[ctx.guild.id]['bully'][userid] = True channel_var[ctx.guild.id]['bully'][userid] = True tag1 = "<@" + str(ctx.message.author.id) + ">" tag2 = "<@" + str(userid) + ">" await ctx.send(tag1 + " is pranking " + tag2) await ctx.send('To stop, type #leavemealone') except: tag = "<@" + str(ctx.message.author.id) + ">" await ctx.send('Please provide a valid user id!' + tag) return while(n > 0): if channel_var[ctx.guild.id]['bully'][userid] == False: return try: if user.voice is not None: await user.move_to(np.random.choice(ctx.guild.voice_channels)) n -= 1 except: pass await asyncio.sleep(sleep_time) def generate_question(): question = "" for i in range(6): question += str(np.random.randint(1, 21)) question += np.random.choice(['*', '+', '-']) question += str(np.random.randint(1, 21)) return question @client.command(name='leavemealone') async def leavemealone(ctx): await initialize(ctx.guild.id, ctx) global channel_var info = channel_var[ctx.guild.id]['bully'].get(ctx.message.author.id, -1) if info == -1: channel_var[ctx.guild.id]['bully'][ctx.message.author.id] = True def check(m): return m.author == ctx.message.author question = generate_question() await ctx.send('Question: `'+question+'`\nType your answer:') answer = int(sympy.sympify(question)) print('Answer:', answer) msg = await client.wait_for("message", check=check) tag = "<@" + str(ctx.message.author.id) + ">" if int(msg.content) == answer: channel_var[ctx.guild.id]['bully'][ctx.message.author.id] = False await ctx.send("Good Job" + tag) else: await ctx.send("on9" + tag) @client.command(name='save') async def save(ctx, id=None): if id is None: await ctx.send("You must specify an id") return await initialize(ctx.guild.id, ctx) global channel_var userid = int(id) def check(m): return m.author == ctx.message.author if channel_var[ctx.guild.id]['bully'].get(userid, -1) == -1: await ctx.send("This user is not under bully list") elif channel_var[ctx.guild.id]['bully'][userid] == False: await ctx.send("This user is not being bullied") else: question = generate_question() await ctx.send('Question: `'+question+'`\nType your answer:') if MODE == 0: answer = int(sympy.sympify(question)) elif MODE == 1: answer = int(eval(question)) print('Answer:', answer) msg = await client.wait_for("message", check=check) tag = "<@" + str(ctx.message.author.id) + ">" if int(msg.content) == answer: channel_var[ctx.guild.id]['bully'][userid] = False await ctx.send("Good Job" + tag) else: await ctx.send("Be careful" + tag) # experimental @client.command(name='plot') async def plot(ctx): def check(m): return m.author == ctx.message.author await ctx.send("1. Please Enter The Type of The Plot") await ctx.send("a: scatter plot, b: line plot") msg = await client.wait_for("message", check=check) graph_type = msg.content await ctx.send("2. Please enter the x-coordinate for all points (seperated by comma)") msg = await client.wait_for("message", check=check) x = [int(i) for i in msg.content.split(',')] await ctx.send("3. Please enter the y-coordinate for all points (seperated by comma)") msg = await client.wait_for("message", check=check) y = [int(i) for i in msg.content.split(',')] await ctx.send("4. Please enter the title of the plot") msg = await client.wait_for("message", check=check) title = msg.content await ctx.send("5. Please enter the name of x-axis") msg = await client.wait_for("message", check=check) x_name = msg.content await ctx.send("6. Please enter the name of y-axis") msg = await client.wait_for("message", check=check) y_name = msg.content plt.plot(x, y, linestyle="-" if graph_type == 'b' else 'none', marker='.') plt.title(title) plt.xlabel(x_name) plt.ylabel(y_name) plt.savefig('plot.png') await ctx.send(file=discord.File('plot.png')) os.remove('plot.png') plt.clf() # experimental @client.command(name='when') async def when(ctx): await ctx.send(start_time.strftime("**Bot started from %Y-%m-%d %I-%M %p**")) # experimental @client.command(name='dm') async def dm(ctx, userid, *message): try: userid = int(userid) user = await client.fetch_user(userid) await user.send(' '.join(message)) await ctx.send("**Message sent successfully**") except: await ctx.send("**Message is not sent**") if MODE == 1: keep_alive() # For setting up bot on replit.com start_time = datetime.now(tz) client.run(token)

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0

null

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null

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558d879413f6f88e3c45e2ca06534a675e1043f9

480

py

Python

solutions/1281-subtract-the-product-and-sum-of-digits-of-an-integer.py

lk-hang/leetcode

4c8735463bdcb9f48666e03a39eb03ee9f625cec

[ "MIT" ]

null

solutions/1281-subtract-the-product-and-sum-of-digits-of-an-integer.py

lk-hang/leetcode

4c8735463bdcb9f48666e03a39eb03ee9f625cec

[ "MIT" ]

null

solutions/1281-subtract-the-product-and-sum-of-digits-of-an-integer.py

lk-hang/leetcode

4c8735463bdcb9f48666e03a39eb03ee9f625cec

[ "MIT" ]

null

""" Given an integer number n, return the difference between the product of its digits and the sum of its digits. """ class Solution: def subtractProductAndSum(self, n: int) -> int: if n < 10: return 0 running_prod = 1 running_sum = 0 while n > 0: rest = n % 10 running_prod *= rest running_sum += rest n = n // 10 return running_prod - running_sum

25.263158

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480

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0.410417

480

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25.263158

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0

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0

0.333333

0

null

0

null

0

1

0

558e58ba058923b58851710da67bc2d4ad87a57f

1,031

py

Python

VideoIndexerDemo/VideoIndexer/application.py

microsoft/ai4accessibility

4c13d006f285e31f01d1bc71a55c20e9234713a5

[ "MIT" ]

2

2021-07-11T06:03:43.000Z

2021-10-09T23:37:21.000Z

VideoIndexerDemo/VideoIndexer/application.py

microsoft/ai4accessibility

4c13d006f285e31f01d1bc71a55c20e9234713a5

[ "MIT" ]

6

2021-09-08T03:07:13.000Z

2022-03-12T00:57:07.000Z

VideoIndexerDemo/VideoIndexer/application.py

microsoft/ai4accessibility

4c13d006f285e31f01d1bc71a55c20e9234713a5

[ "MIT" ]

3

2021-02-14T18:51:31.000Z

2021-02-14T18:51:41.000Z

# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. from dotenv import load_dotenv load_dotenv() import os import json import requests from concurrent.futures import ThreadPoolExecutor from flask import Flask, flash, request, redirect, url_for, session from video_captioning.main import upload_video, video_callback, train_custom_speech executor = ThreadPoolExecutor(max_workers=20) app = Flask("layout_detection") @app.route('/api/v1/vc', methods=['POST']) def vc_upload(): params = request.get_json() return_data = upload_video(params) return json.dumps(return_data) @app.route('/api/v1/customspeech', methods=['POST']) def customspeech_train(): params = request.get_json() return_data = train_custom_speech(params) return json.dumps(return_data) @app.route('/api/v1/vc/callback', methods=['POST']) def vc_callback(): params = request.get_json() return video_callback(request.args.get('id')) if __name__ == "__main__": app.run(port=5000, debug=True, host='0.0.0.0')

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null

0

null

0

1

0

559154d893c3d43225a58bc587edd3aa01dea828

5,154

py

Python

code/tests/unit/api/test_enrich.py

CiscoSecurity/tr-05-serverless-cybercrime-tracker

28fcfaa220025c9e8523633a4a9a04f319656756

[ "MIT" ]

3

2020-04-28T08:53:14.000Z

2020-12-17T14:25:32.000Z

code/tests/unit/api/test_enrich.py

CiscoSecurity/tr-05-serverless-cybercrime-tracker

28fcfaa220025c9e8523633a4a9a04f319656756

[ "MIT" ]

2

2020-03-06T15:00:22.000Z

2020-06-26T11:21:52.000Z

code/tests/unit/api/test_enrich.py

CiscoSecurity/tr-05-serverless-cybercrime-tracker

28fcfaa220025c9e8523633a4a9a04f319656756

[ "MIT" ]

null

from http import HTTPStatus from requests.exceptions import SSLError from pytest import fixture from unittest import mock from tests.unit.mock_for_tests import ( CYBERCRIME_RESPONSE_MOCK, EXPECTED_DELIBERATE_RESPONSE, EXPECTED_OBSERVE_RESPONSE, EXPECTED_RESPONSE_500_ERROR, EXPECTED_RESPONSE_404_ERROR, CYBERCRIME_ERROR_RESPONSE_MOCK, EXPECTED_RESPONSE_SSL_ERROR ) def routes(): yield '/deliberate/observables' yield '/observe/observables' @fixture(scope='module', params=routes(), ids=lambda route: f'POST {route}') def route(request): return request.param @fixture(scope='function') def cybercrime_api_request(): with mock.patch('requests.get') as mock_request: yield mock_request def cybercrime_api_response(*, ok, payload=None, status_error=None): mock_response = mock.MagicMock() mock_response.ok = ok if ok and not payload: payload = CYBERCRIME_RESPONSE_MOCK else: mock_response.status_code = status_error mock_response.json = lambda: payload return mock_response @fixture(scope='module') def invalid_json(): return [{'type': 'unknown', 'value': ''}] def test_enrich_call_with_invalid_json_failure(route, client, invalid_json): response = client.post(route, json=invalid_json) assert response.status_code == HTTPStatus.OK @fixture(scope='module') def valid_json(): return [{'type': 'ip', 'value': '104.24.123.62'}] @fixture(scope='module') def valid_json_multiple(): return [ {'type': 'ip', 'value': '104.24.123.62'}, {'type': 'ip', 'value': '0.0.0.0'}, ] def test_enrich_call_success(route, client, valid_json, cybercrime_api_request): cybercrime_api_request.return_value = cybercrime_api_response(ok=True) response = client.post(route, json=valid_json) assert response.status_code == HTTPStatus.OK data = response.get_json() if route == '/observe/observables': verdicts = data['data']['verdicts'] assert verdicts['docs'][0].pop('valid_time') judgements = data['data']['judgements'] assert judgements['docs'][0].pop('id') assert judgements['docs'][0].pop('valid_time') assert data == EXPECTED_OBSERVE_RESPONSE if route == '/deliberate/observables': verdicts = data['data']['verdicts'] assert verdicts['docs'][0].pop('valid_time') assert data == EXPECTED_DELIBERATE_RESPONSE def test_enrich_error_with_data(route, client, valid_json_multiple, cybercrime_api_request): cybercrime_api_request.side_effect = ( cybercrime_api_response(ok=True), cybercrime_api_response( ok=False, payload=CYBERCRIME_ERROR_RESPONSE_MOCK, status_error=HTTPStatus.INTERNAL_SERVER_ERROR) ) response = client.post(route, json=valid_json_multiple) assert response.status_code == HTTPStatus.OK data = response.get_json() if route == '/observe/observables': verdicts = data['data']['verdicts'] assert verdicts['docs'][0].pop('valid_time') judgements = data['data']['judgements'] assert judgements['docs'][0].pop('id') assert judgements['docs'][0].pop('valid_time') expected_response = {} expected_response.update(EXPECTED_OBSERVE_RESPONSE) expected_response.update(EXPECTED_RESPONSE_500_ERROR) assert data == expected_response if route == '/deliberate/observables': verdicts = data['data']['verdicts'] assert verdicts['docs'][0].pop('valid_time') expected_response = {} expected_response.update(EXPECTED_DELIBERATE_RESPONSE) expected_response.update(EXPECTED_RESPONSE_500_ERROR) assert data == expected_response def test_enrich_call_404(route, client, valid_json, cybercrime_api_request): cybercrime_api_request.return_value = cybercrime_api_response( ok=False, payload=CYBERCRIME_ERROR_RESPONSE_MOCK, status_error=HTTPStatus.NOT_FOUND ) response = client.post(route, json=valid_json) assert response.status_code == HTTPStatus.OK assert response.get_json() == EXPECTED_RESPONSE_404_ERROR def test_enrich_call_500(route, client, valid_json, cybercrime_api_request): cybercrime_api_request.return_value = cybercrime_api_response( ok=False, payload=CYBERCRIME_ERROR_RESPONSE_MOCK, status_error=HTTPStatus.INTERNAL_SERVER_ERROR ) response = client.post(route, json=valid_json) assert response.status_code == HTTPStatus.OK assert response.get_json() == EXPECTED_RESPONSE_500_ERROR def test_enrich_call_with_ssl_error(route, client, valid_json, cybercrime_api_request): mock_exc = mock.MagicMock() mock_exc.reason.args.__getitem__().verify_message \ = 'self signed certificate' cybercrime_api_request.side_effect = SSLError(mock_exc) response = client.post(route, json=valid_json) assert response.status_code == HTTPStatus.OK assert response.get_json() == EXPECTED_RESPONSE_SSL_ERROR

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0.191667

0

null

0

null

0

1

0

5593fe3d21ad82b5382d08854df0a8f99eec0ed9

1,900

py

Python

src/ensae_teaching_cs/tests/american_cities.py

Jerome-maker/ensae_teaching_cs

43ea044361ee60c00c85aea354a7b25c21c0fd07

[ "MIT" ]

73

2015-05-12T13:12:11.000Z

2021-12-21T11:44:29.000Z

src/ensae_teaching_cs/tests/american_cities.py

Jerome-maker/ensae_teaching_cs

43ea044361ee60c00c85aea354a7b25c21c0fd07

[ "MIT" ]

90

2015-06-23T11:11:35.000Z

2021-03-31T22:09:15.000Z

src/ensae_teaching_cs/tests/american_cities.py

Jerome-maker/ensae_teaching_cs

43ea044361ee60c00c85aea354a7b25c21c0fd07

[ "MIT" ]

65

2015-01-13T08:23:55.000Z

2022-02-11T22:42:07.000Z

""" @file @brief Function to test others functionalities """ import os import pandas from pyquickhelper.loghelper import fLOG from ..faq.faq_matplotlib import graph_cities from ..special import tsp_kruskal_algorithm, distance_haversine def american_cities(df_or_filename, nb_cities=-1, img=None, fLOG=fLOG): """ Computes the :epkg:`TSP` for american cities. @param df_or_filename dataframe @param nb_cities number of cities to keep @param img image to produce @param fLOG logging function @return dataframe (results) """ def haversine(p1, p2): return distance_haversine(p1[0], p1[1], p2[0], p2[1]) if isinstance(df_or_filename, str): df = pandas.read_csv(df_or_filename) else: df = df_or_filename df["Longitude"] = -df["Longitude"] df = df[df.Latitude < 52] df = df[df.Longitude > -130].copy() fLOG(df.columns) df = df.dropna() if nb_cities > 0: df = df[:nb_cities].copy() fLOG(df.shape) points = [(row[1], row[2], row[3]) for row in df.itertuples(index=False)] fLOG("number of cities:", len(points)) trip = tsp_kruskal_algorithm( points, distance=haversine, fLOG=fLOG, max_iter=10) # trip dftrip = pandas.DataFrame( trip, columns=["Latitude", "Longitude", "City"]) # graph for i in range(0, dftrip.shape[0]): if i % 10 != 0: dftrip.loc[i, "City"] = "" if img is not None: import matplotlib.pyplot as plt fig, ax = graph_cities(dftrip, markersize=3, linked=True, fLOG=fLOG, fontcolor="red", fontsize='16', loop=True, figsize=(32, 32)) assert ax is not None fig.savefig(img) assert os.path.exists(img) plt.close('all') fLOG("end") return dftrip

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0.25

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null

0

null

0

1

0

5594b24c92581e7c3ba26f490dea8b770f2cf8fd

2,049

py

Python

tools/ntp_spoofer.py

dschoonwinkel/pypacker

58c833f40207db746b0b2995ca3835a533e0258e

[ "BSD-3-Clause" ]

null

tools/ntp_spoofer.py

dschoonwinkel/pypacker

58c833f40207db746b0b2995ca3835a533e0258e

[ "BSD-3-Clause" ]

null

tools/ntp_spoofer.py

dschoonwinkel/pypacker

58c833f40207db746b0b2995ca3835a533e0258e

[ "BSD-3-Clause" ]

null

"""Simple NTP spoofing tool.""" from pypacker.layer12.ethernet import Ethernet from pypacker.layer3 import ip from pypacker.layer4.udp import UDP from pypacker.layer567 import ntp from pypacker import psocket # interface to listen on IFACE = "wlan0" # source address which commits a NTP request and we send a wrong answer IP_SRC = "192.168.178.27" # # normal NTP request # """ psock_req = psocket.SocketHndl(iface_name=IFACE, mode=psocket.SocketHndl.MODE_LAYER_3) ntp_req = ip.IP(src_s=IP_SRC, dst_s="188.138.9.208", p=ip.IP_PROTO_UDP) +\ UDP(sport=1234, dport=123) +\ ntp.NTP(li=ntp.NO_WARNING, v=3, mode=ntp.CLIENT) print("sending NTP request and waiting for answer..") answer = psock_req.sr(ntp_req)[0][ntp.NTP] """ # print("answer is: %s" % answer) #unpack_I = struct.Struct(">I").unpack # print("seconds since 1.1.1900: %d" % unpack_I(answer.transmit_time[0:4])[0]) # psock_req.close() # # spoof NTP response # print("waiting for NTP request") psock = psocket.SocketHndl(iface_name=IFACE, timeout=600) filter = lambda p: p[ntp.NTP] is not None and p[ip.IP].src_s == IP_SRC answer = psock.recvp(filter_match_recv=filter)[0] answer_ntp = answer[ntp.NTP] print("got NTP packet: %s" % answer_ntp) ntp_answer_send = Ethernet(dst=answer[Ethernet].src, src=answer[Ethernet].dst) +\ ip.IP(src=answer[ip.IP].dst, dst_s=IP_SRC, p=ip.IP_PROTO_UDP) +\ UDP(sport=answer[UDP].dport, dport=answer[UDP].sport) +\ ntp.NTP(li=ntp.NO_WARNING, v=3, mode=ntp.SERVER, stratum=2, interval=4, update_time=answer_ntp.transmit_time, originate_time=answer_ntp.transmit_time, receive_time=b"\x00" * 4 + answer_ntp.transmit_time[4:], transmit_time=b"\x00" * 4 + answer_ntp.transmit_time[4:]) # alternative packet creation """ ntp_answer_send = answer.create_reverse() layer_ntp = ntp_answer_send[ntp.NTP] layer_ntp.mode = ntp.SERVER layer_ntp.originate_time = answer_ntp.transmit_time layer_ntp.receive_time = layer_ntp.transmit_time = b"\x00"*4 + answer_ntp.transmit_time[4:] """ psock.send(ntp_answer_send.bin()) psock.close()

32.52381

91

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null

0

null

0

1

0

5594c3feafec578628223eff5ebd91b66138d3a5

7,524

py

Python

motsfinder/exprs/test_basics.py

daniel-dpk/distorted-motsfinder-public

8c2eec174c755c55b26b568243e58c2956a35257

[ "MIT" ]

4

2019-08-26T09:50:26.000Z

2022-03-02T16:11:17.000Z

motsfinder/exprs/test_basics.py

daniel-dpk/distorted-motsfinder-public

8c2eec174c755c55b26b568243e58c2956a35257

[ "MIT" ]

5

2021-03-31T19:55:34.000Z

2021-04-01T08:29:53.000Z

motsfinder/exprs/test_basics.py

daniel-dpk/distorted-motsfinder-public

8c2eec174c755c55b26b568243e58c2956a35257

[ "MIT" ]

1

2019-09-18T14:15:33.000Z

#!/usr/bin/env python3 from __future__ import print_function from builtins import range, map import unittest import sys import pickle import numpy as np from mpmath import mp from testutils import DpkTestCase from .numexpr import NumericExpression from .numexpr import isclose from .basics import OffsetExpression, DivisionExpression, SimpleSinExpression from .basics import SimpleCoshExpression class _TestExpr1(NumericExpression): def __init__(self, a=1, **kw): super(_TestExpr1, self).__init__(**kw) self.a = a def _expr_str(self): return "a x**2, where a=%r" % self.a def _evaluator(self, use_mp): a = self.a return (lambda x: a*x**2, lambda x: 2*a*x, lambda x: 2*a, self.zero) class _TestExpr2(NumericExpression): def __init__(self, expr, a=1): super(_TestExpr2, self).__init__(x=expr) self.a = a def _expr_str(self): return "a/x, where a=%r, x=%s" % (self.a, self.x.str()) def _evaluator(self, use_mp): a = self.a x = self.x.evaluator(use_mp) def f(t): return a/x(t) def df(t): return -x.diff(t)*a/x(t)**2 def ddf(t): xt = x(t) dxt = x.diff(t, 1) ddxt = x.diff(t, 2) return a*(-ddxt/xt**2 + 2*dxt**2/xt**3) return (f, df, ddf) class _TestExpr3(NumericExpression): def __init__(self, expr1, expr2, a=1, b=1): super(_TestExpr3, self).__init__(x1=expr1, x2=expr2) self.a = a self.b = b def _expr_str(self): return ("a x1 + b x2, where a=%r, b=%r, x1=%s, x2=%s" % (self.a, self.b, self.x1.str(), self.x2.str())) def _evaluator(self, use_mp): a, b = self.a, self.b x1, x2 = self.x1.evaluator(use_mp), self.x2.evaluator(use_mp) return (lambda t: a*x1(t) + b*x2(t), lambda t: a*x1.diff(t) + b*x2.diff(t)) class _TestExprDomain(NumericExpression): def __init__(self, domain): super(_TestExprDomain, self).__init__(domain=domain) self.__domain = domain def get_domain(self): return self.__domain def _expr_str(self): return "id" def _evaluator(self, use_mp): return (lambda x: x, lambda x: 1, self.zero) class TestIsclose(DpkTestCase): def test_float(self): self.assertTrue(isclose(1e7+1, 1e7+1, rel_tol=0, abs_tol=0)) self.assertTrue(isclose(1e7+1, 1e7, rel_tol=1e-6)) self.assertFalse(isclose(1e7+1, 1e7, rel_tol=1e-8)) self.assertTrue(isclose(1e7+1, 1e7, rel_tol=0, abs_tol=2.0)) self.assertFalse(isclose(1e7+1, 1e7, rel_tol=0, abs_tol=0.5)) def test_mpmath(self): with mp.workdps(30): a = mp.mpf('1e7') + mp.mpf('1e-20') b = mp.mpf('1e7') self.assertTrue(isclose(a, a, rel_tol=0, abs_tol=0, use_mp=True)) self.assertFalse(isclose(a, b, use_mp=True)) with mp.workdps(26): self.assertTrue(isclose(a, b, use_mp=True)) self.assertTrue(isclose(a, b, rel_tol=1e-26, abs_tol=0, use_mp=True)) self.assertFalse(isclose(a, b, rel_tol=1e-28, abs_tol=0, use_mp=True)) self.assertTrue(isclose(a, b, rel_tol=0, abs_tol=1e-19, use_mp=True)) self.assertFalse(isclose(a, b, rel_tol=0, abs_tol=1e-21, use_mp=True)) class TestNumexpr(DpkTestCase): def test_expressions(self): expr = _TestExpr2(_TestExpr1()) self.assertEqual(repr(expr), "<_TestExpr2(a/x, where a=1, x=(a x**2, where a=1))>") self.assertEqual(expr.a, 1) expr.a = 5 self.assertEqual(expr.a, 5) def test_name(self): expr = _TestExpr1() self.assertEqual(expr.name, "_TestExpr1") expr.name = "foo" self.assertEqual(expr.name, "foo") def test_pickle(self): a = 1.5 expr = _TestExpr2(_TestExpr1(-1), a=a) expr.name = "foo" s = pickle.dumps(expr) expr = pickle.loads(s) self.assertIs(type(expr), _TestExpr2) self.assertEqual(expr.a, 1.5) self.assertIs(type(expr.x), _TestExpr1) self.assertEqual(expr.x.a, -1) self.assertEqual(expr.name, "foo") def test_pickle_domain(self): expr = _TestExpr1(domain=(0, 1)) s = pickle.dumps(expr) expr = pickle.loads(s) self.assertEqual(expr.domain, (0, 1)) expr = _TestExpr1(domain=(0, mp.pi)) s = pickle.dumps(expr) expr = pickle.loads(s) self.assertEqual(expr.domain, (0, mp.pi)) def test_evaluators(self): a = 1.5 expr = _TestExpr2(_TestExpr1(), a=a) f = expr.evaluator() for t in np.linspace(0.1, 2, 4): self.assertAlmostEqual(f(t), a/t**2) for t in np.linspace(0.1, 2, 4): self.assertAlmostEqual(f.diff(t), -2*a/t**3) for t in np.linspace(0.1, 2, 4): self.assertAlmostEqual(f.diff(t, 2), 6*a/t**4) with self.assertRaises(NotImplementedError): f.diff(0, 3) def test_string_clashing(self): expr1 = _TestExpr1(a=1) expr2 = _TestExpr2(2, a=3) comp1 = _TestExpr3(expr1, expr2) expr2 = _TestExpr2(2, a=1) expr1 = _TestExpr1(a=3) comp2 = _TestExpr3(expr1, expr2) e1 = comp1.evaluator() e2 = comp2.evaluator() # The expressions are different: self.assertNotEqual(e1(.5), e2(.5)) # Their string are different too: self.assertNotEqual(repr(comp1), repr(comp2)) def test_domain(self): expr = _TestExprDomain([-1, 1]) e = expr.evaluator() self.assertTrue(hasattr(e, 'domain')) self.assertFalse(hasattr(e, 'domainX')) self.assertEqual(e.domain[0], -1) self.assertEqual(e.domain[1], 1) expr = _TestExprDomain(([-1, 1], [0, 10])) e = expr.evaluator() self.assertTrue(hasattr(e, 'domain')) self.assertEqual(e.domainX[0], -1) self.assertEqual(e.domainX[1], 1) self.assertEqual(e.domainY[0], 0) self.assertEqual(e.domainY[1], 10) f = e.function() self.assertTrue(hasattr(f, 'domain')) self.assertTrue(hasattr(f, 'domainX')) self.assertTrue(hasattr(f, 'domainY')) self.assertFalse(hasattr(f, 'domainZ')) class TestOffsetExpression(DpkTestCase): def test_offset(self): expr = OffsetExpression(_TestExpr1(), 1.0) e = expr.evaluator() self.assertAlmostEqual(e(0), 1.0) self.assertAlmostEqual(e(1), 2.0) self.assertAlmostEqual(e(2), 5.0) self.assertAlmostEqual(e.diff(0), 0.0) self.assertAlmostEqual(e.diff(1), 2.0) self.assertAlmostEqual(e.diff(2), 4.0) self.assertAlmostEqual(e.diff(1, 2), 2.0) class TestDivisionExpression(DpkTestCase): def test_division(self): expr = DivisionExpression( SimpleSinExpression(), OffsetExpression(SimpleCoshExpression(), 2), ) with mp.workdps(30): f = expr.evaluator(use_mp=True) space = mp.linspace(0, mp.pi, 10) for n in range(1, 5): self.assertListAlmostEqual( [f.diff(x, n) for x in space], [mp.diff(f, x, n) for x in space], delta=1e-28, ) def run_tests(): suite = unittest.TestLoader().loadTestsFromModule(sys.modules[__name__]) return len(unittest.TextTestRunner(verbosity=2).run(suite).failures) if __name__ == '__main__': unittest.main()

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null

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1

0

559516145d3a91e65f7eba170cf38f3e8329840b

468

py

Python

python/Data Structures and Algorithms in Python Book/oop/fibonacciprogression.py

gauravssnl/Data-Structures-and-Algorithms

1c335c72ce514d4f95090241bbd6edf01a1141a8

[ "MIT" ]

7

2020-05-10T09:57:23.000Z

2021-03-27T11:55:07.000Z

python/Data Structures and Algorithms in Python Book/oop/fibonacciprogression.py

gauravssnl/Data-Structures-and-Algorithms

1c335c72ce514d4f95090241bbd6edf01a1141a8

[ "MIT" ]

null

python/Data Structures and Algorithms in Python Book/oop/fibonacciprogression.py

gauravssnl/Data-Structures-and-Algorithms

1c335c72ce514d4f95090241bbd6edf01a1141a8

[ "MIT" ]

3

2021-03-27T03:42:57.000Z

2021-08-09T12:03:41.000Z

from progression import Progression class FibonacciProgression(Progression): def __init__(self, first=0, second=1): super().__init__(start=first) self._previous = second - first def _advance(self): self._previous, self._current = self._current, self._previous + self._current if __name__ == "__main__": fibonacci_progresssion = FibonacciProgression(first= 1, second= 2) fibonacci_progresssion.print_progression(20)

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null

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55966e42aa982766be05f8a6dbd86f8df5f992eb

18,587

py

Python

openamundsen/modules/snow/multilayermodel.py

openamundsen/openamundsen

2ac09eb34b0c72c84c421a0dac08d114a05b7b1c

[ "MIT" ]

3

2021-05-28T06:46:36.000Z

2021-06-14T13:39:25.000Z

openamundsen/modules/snow/multilayermodel.py

openamundsen/openamundsen

2ac09eb34b0c72c84c421a0dac08d114a05b7b1c

[ "MIT" ]

22

2021-04-28T12:31:58.000Z

2022-03-09T18:29:12.000Z

openamundsen/modules/snow/multilayermodel.py

openamundsen/openamundsen

2ac09eb34b0c72c84c421a0dac08d114a05b7b1c

[ "MIT" ]

1

2021-06-01T12:48:54.000Z

import numpy as np from numba import njit, prange from openamundsen import constants, constants as c, heatconduction from openamundsen.snowmodel import SnowModel from . import snow class MultilayerSnowModel(SnowModel): def __init__(self, model): self.model = model s = model.state.snow num_snow_layers = len(model.config.snow.min_thickness) s.add_variable('num_layers', '1', 'Number of snow layers', dtype=int, retain=True) s.add_variable('thickness', 'm', 'Snow thickness', dim3=num_snow_layers, retain=True) s.add_variable('density', 'kg m-3', 'Snow density', 'snow_density', dim3=num_snow_layers) s.add_variable('ice_content', 'kg m-2', 'Ice content of snow', dim3=num_snow_layers, retain=True) s.add_variable('liquid_water_content', 'kg m-2', 'Liquid water content of snow', 'liquid_water_content_of_snow_layer', dim3=num_snow_layers, retain=True) s.add_variable('temp', 'K', 'Snow temperature', dim3=num_snow_layers, retain=True) s.add_variable('therm_cond', 'W m-1 K-1', 'Thermal conductivity of snow', dim3=num_snow_layers, retain=True) s.add_variable('heat_cap', 'J K-1 m-2', 'Areal heat capacity of snow', dim3=num_snow_layers) def initialize(self): roi = self.model.grid.roi s = self.model.state.snow s.swe[roi] = 0 s.depth[roi] = 0 s.area_fraction[roi] = 0 s.num_layers[roi] = 0 s.sublimation[roi] = 0 s.therm_cond[:, roi] = self.model.config.snow.thermal_conductivity s.thickness[:, roi] = 0 s.ice_content[:, roi] = 0 s.liquid_water_content[:, roi] = 0 s.temp[:, roi] = constants.T0 def albedo_aging(self): snow.albedo(self.model) def compaction(self): snow.compaction(self.model) def accumulation(self): model = self.model s = model.state pos = s.meteo.snowfall > 0 self.add_snow( pos, s.meteo.snowfall[pos], density=snow.fresh_snow_density(s.meteo.wet_bulb_temp[pos]), ) def heat_conduction(self): model = self.model s = model.state _heat_conduction( model.grid.roi_idxs, s.snow.num_layers, s.snow.thickness, s.soil.thickness, model.timestep, s.snow.temp, s.snow.therm_cond, s.soil.therm_cond, s.surface.heat_flux, s.snow.heat_cap, ) def melt(self): model = self.model s = model.state _melt( model.grid.roi_idxs, model.timestep, s.snow.num_layers, s.snow.melt, s.snow.thickness, s.snow.temp, s.snow.ice_content, s.snow.liquid_water_content, s.snow.heat_cap, ) def sublimation(self): model = self.model s = model.state # First resublimation frost = -np.minimum(s.snow.sublimation, 0) pos = frost > 0 self.add_snow( pos, frost[pos], density=snow.fresh_snow_density(s.meteo.wet_bulb_temp[pos]), ) # Then sublimation _sublimation( model.grid.roi_idxs, model.timestep, s.snow.num_layers, s.snow.ice_content, s.snow.thickness, s.snow.sublimation, ) def runoff(self): model = self.model s = model.state _runoff( model.grid.roi_idxs, snow.max_liquid_water_content(model), s.meteo.rainfall, s.snow.num_layers, s.snow.thickness, s.snow.temp, s.snow.ice_content, s.snow.liquid_water_content, s.snow.runoff, s.snow.heat_cap, ) def update_layers(self): model = self.model s = model.state _update_layers( model.grid.roi_idxs, s.snow.num_layers, np.array(model.config.snow.min_thickness), s.snow.thickness, s.snow.ice_content, s.snow.liquid_water_content, s.snow.heat_cap, s.snow.temp, s.snow.density, s.snow.depth, ) s.snow.albedo[s.snow.num_layers == 0] = np.nan def update_properties(self): snow.snow_properties(self.model) def add_snow( self, pos, ice_content, liquid_water_content=0, density=None, albedo=None, ): """ Add snow to the top of the snowpack. """ model = self.model s = model.state ice_content = np.nan_to_num(ice_content, nan=0., copy=True) pos_init = (s.snow.num_layers[pos] == 0) & (ice_content > 0) pos_init_global = model.global_mask(pos_init, pos) # If albedo is None, set it to the maximum albedo for currently snow-free pixels and keep # the current albedo for the other pixels if albedo is None: albedo = s.snow.albedo[pos] albedo[pos_init] = model.config.snow.albedo.max s.snow.albedo[pos] = albedo # Initialize first snow layer where necessary s.snow.num_layers[pos_init_global] = 1 s.snow.temp[0, pos_init_global] = np.minimum(s.meteo.temp[pos_init_global], constants.T0) # Add snow to first layer s.snow.ice_content[0, pos] += ice_content s.snow.liquid_water_content[0, pos] += liquid_water_content s.snow.thickness[0, pos] += ice_content / density @njit(cache=True, parallel=True) def _melt( roi_idxs, timestep, num_layers, melt, thickness, temp, ice_content, liquid_water_content, heat_cap, ): """ Calculate snowmelt following [1]. Parameters ---------- roi_idxs : ndarray(int, ndim=2) (N, 2)-array specifying the (row, col) indices within the data arrays that should be considered. timestep : float Model timestep (s). num_layers : ndarray(float, ndim=2) Number of snow layers. melt : ndarray(float, ndim=2) Snowmelt (kg m-2). thickness : ndarray(float, ndim=3) Snow thickness (m). temp : ndarray(float, ndim=3) Snow temperature (K). ice_content : ndarray(float, ndim=3) Ice content of snow (kg m-2). liquid_water_content : ndarray(float, ndim=3) Liquid water content of snow (kg m-2). heat_cap : ndarray(float, ndim=3) Areal heat capacity of snow (J K-1 m-2). References ---------- .. [1] Essery, R. (2015). A factorial snowpack model (FSM 1.0). Geoscientific Model Development, 8(12), 3867–3876. https://doi.org/10.5194/gmd-8-3867-2015 """ num_pixels = len(roi_idxs) for idx_num in prange(num_pixels): i, j = roi_idxs[idx_num] ice_content_change = melt[i, j] for k in range(num_layers[i, j]): cold_content = heat_cap[k, i, j] * (c.T0 - temp[k, i, j]) if cold_content < 0: ice_content_change -= cold_content / c.LATENT_HEAT_OF_FUSION temp[k, i, j] = c.T0 if ice_content_change > 0: if ice_content_change > ice_content[k, i, j]: # layer melts completely ice_content_change -= ice_content[k, i, j] thickness[k, i, j] = 0. liquid_water_content[k, i, j] += ice_content[k, i, j] ice_content[k, i, j] = 0. else: # layer melts partially thickness[k, i, j] *= (1 - ice_content_change / ice_content[k, i, j]) ice_content[k, i, j] -= ice_content_change liquid_water_content[k, i, j] += ice_content_change ice_content_change = 0. @njit(cache=True, parallel=True) def _sublimation( roi_idxs, timestep, num_layers, ice_content, thickness, sublimation, ): """ Calculate snow sublimation following [1]. Parameters ---------- roi_idxs : ndarray(int, ndim=2) (N, 2)-array specifying the (row, col) indices within the data arrays that should be considered. timestep : float Model timestep (s). num_layers : ndarray(float, ndim=2) Number of snow layers. ice_content : ndarray(float, ndim=3) Ice content of snow (kg m-2). thickness : ndarray(float, ndim=3) Snow thickness (m). sublimation : ndarray(float, ndim=2) Snow sublimation (kg m-2). References ---------- .. [1] Essery, R. (2015). A factorial snowpack model (FSM 1.0). Geoscientific Model Development, 8(12), 3867–3876. https://doi.org/10.5194/gmd-8-3867-2015 """ num_pixels = len(roi_idxs) for idx_num in prange(num_pixels): i, j = roi_idxs[idx_num] ice_content_change = max(sublimation[i, j], 0.) if ice_content_change > 0: for k in range(num_layers[i, j]): if ice_content_change > ice_content[k, i, j]: # complete sublimation of layer ice_content_change -= ice_content[k, i, j] thickness[k, i, j] = 0. ice_content[k, i, j] = 0. else: # partial sublimation thickness[k, i, j] *= (1 - ice_content_change / ice_content[k, i, j]) ice_content[k, i, j] -= ice_content_change ice_content_change = 0. @njit(cache=True, parallel=True) def _runoff( roi_idxs, max_liquid_water_content, rainfall, num_layers, thickness, temp, ice_content, liquid_water_content, runoff, heat_cap, ): """ Calculate snowmelt runoff following [1]. Parameters ---------- roi_idxs : ndarray(int, ndim=2) (N, 2)-array specifying the (row, col) indices within the data arrays that should be considered. max_liquid_water_content : ndarray(float, ndim=3) Maximum liquid water content (kg m-2). rainfall : ndarray(float, ndim=2) Rainfall amount (kg m-2). num_layers : ndarray(float, ndim=2) Number of snow layers. thickness : ndarray(float, ndim=3) Snow thickness (m). temp : ndarray(float, ndim=3) Snow temperature (K). ice_content : ndarray(float, ndim=3) Ice content of snow (kg m-2). liquid_water_content : ndarray(float, ndim=3) Liquid water content of snow (kg m-2). runoff : ndarray(float, ndim=2) Snow runoff (kg m-2). heat_cap : ndarray(float, ndim=3) Areal heat capacity of snow (J K-1 m-2). References ---------- .. [1] Essery, R. (2015). A factorial snowpack model (FSM 1.0). Geoscientific Model Development, 8(12), 3867–3876. https://doi.org/10.5194/gmd-8-3867-2015 """ num_pixels = len(roi_idxs) for idx_num in prange(num_pixels): i, j = roi_idxs[idx_num] runoff[i, j] = rainfall[i, j] if np.isnan(runoff[i, j]): runoff[i, j] = 0. for k in range(num_layers[i, j]): liquid_water_content[k, i, j] += runoff[i, j] if liquid_water_content[k, i, j] > max_liquid_water_content[k, i, j]: runoff[i, j] = liquid_water_content[k, i, j] - max_liquid_water_content[k, i, j] liquid_water_content[k, i, j] = max_liquid_water_content[k, i, j] else: runoff[i, j] = 0. # Refreeze liquid water cold_content = heat_cap[k, i, j] * (c.T0 - temp[k, i, j]) if cold_content > 0: ice_content_change = min( liquid_water_content[k, i, j], cold_content / c.LATENT_HEAT_OF_FUSION, ) liquid_water_content[k, i, j] -= ice_content_change ice_content[k, i, j] += ice_content_change temp[k, i, j] += c.LATENT_HEAT_OF_FUSION * ice_content_change / heat_cap[k, i, j] @njit(parallel=True, cache=True) def _heat_conduction( roi_idxs, num_layers, snow_thickness, soil_thickness, timestep, temp, therm_cond_snow, therm_cond_soil, heat_flux, heat_cap, ): """ Update snow layer temperatures. Parameters ---------- roi_idxs : ndarray(int, ndim=2) (N, 2)-array specifying the (row, col) indices within the data arrays that should be considered. num_layers : ndarray(float, ndim=2) Number of snow layers. snow_thickness : ndarray(float, ndim=3) Snow thickness (m). soil_thickness : ndarray(float, ndim=3) Soil thickness (m). timestep : float Model timestep (s). temp : ndarray(float, ndim=3) Snow temperature (K). therm_cond_snow : ndarray(float, ndim=3) Snow thermal conductivity (W m-1 K-1). therm_cond_soil : ndarray(float, ndim=3) Soil thermal conductivity (W m-1 K-1). heat_flux : ndarray(float, ndim=2) Surface heat flux (W m-2). heat_cap : ndarray(float, ndim=3) Areal heat capacity of snow (J K-1 m-2). References ---------- .. [1] Essery, R. (2015). A factorial snowpack model (FSM 1.0). Geoscientific Model Development, 8(12), 3867–3876. https://doi.org/10.5194/gmd-8-3867-2015 """ num_pixels = len(roi_idxs) for idx_num in prange(num_pixels): i, j = roi_idxs[idx_num] ns = num_layers[i, j] if ns > 0: temp[:ns, i, j] += heatconduction.temp_change( snow_thickness[:ns, i, j], timestep, temp[:ns, i, j], therm_cond_snow[:ns, i, j], temp[-1, i, j], soil_thickness[0, i, j], therm_cond_soil[0, i, j], heat_flux[i, j], heat_cap[:ns, i, j], ) @njit(cache=True, parallel=True) def _update_layers( roi_idxs, num_layers, min_thickness, thickness, ice_content, liquid_water_content, heat_cap, temp, density, depth, ): """ Update snow layers. Parameters ---------- roi_idxs : ndarray(int, ndim=2) (N, 2)-array specifying the (row, col) indices within the data arrays that should be considered. num_layers : ndarray(float, ndim=2) Number of snow layers. min_thickness : ndarray(float, ndim=1) Minimum snow layer thicknesses (m). thickness : ndarray(float, ndim=3) Snow thickness (m). ice_content : ndarray(float, ndim=3) Ice content of snow (kg m-2). liquid_water_content : ndarray(float, ndim=3) Liquid water content of snow (kg m-2). heat_cap : ndarray(float, ndim=3) Areal heat capacity of snow (J K-1 m-2). temp : ndarray(float, ndim=3) Snow temperature (K). density : ndarray(float, ndim=3) Snow density (kg m-3). depth : ndarray(float, ndim=2) Snow depth (m). References ---------- .. [1] Essery, R. (2015). A factorial snowpack model (FSM 1.0). Geoscientific Model Development, 8(12), 3867–3876. https://doi.org/10.5194/gmd-8-3867-2015 """ max_num_layers = len(min_thickness) num_layers_prev = num_layers.copy() thickness_prev = thickness.copy() ice_content_prev = ice_content.copy() liquid_water_content_prev = liquid_water_content.copy() energy_prev = heat_cap * (temp - c.T0) # energy content (J m-2) num_pixels = len(roi_idxs) for idx_num in prange(num_pixels): i, j = roi_idxs[idx_num] num_layers[i, j] = 0 thickness[:, i, j] = 0. ice_content[:, i, j] = 0. liquid_water_content[:, i, j] = 0. temp[:, i, j] = c.T0 density[:, i, j] = np.nan internal_energy = np.zeros(max_num_layers) if depth[i, j] > 0: new_thickness = depth[i, j] # Update thicknesses and number of layers for k in range(max_num_layers): thickness[k, i, j] = min_thickness[k] new_thickness -= min_thickness[k] if new_thickness <= min_thickness[k] or k == max_num_layers - 1: thickness[k, i, j] += new_thickness break # Set thin snow layers to 0 to avoid numerical artifacts # TODO should this be done at some other location? for k in range(max_num_layers): if thickness[k, i, j] < 1e-6: thickness[k, i, j] = 0. ns = (thickness[:, i, j] > 0).sum() # new number of layers new_thickness = thickness[0, i, j] k_new = 0 # TODO optimize this loop for k_old in range(num_layers_prev[i, j]): while True: # TODO replace with normal loop weight = min(new_thickness / thickness_prev[k_old, i, j], 1.) ice_content[k_new, i, j] += weight * ice_content_prev[k_old, i, j] liquid_water_content[k_new, i, j] += weight * liquid_water_content_prev[k_old, i, j] internal_energy[k_new] += weight * energy_prev[k_old, i, j] if weight == 1.: new_thickness -= thickness_prev[k_old, i, j] break thickness_prev[k_old, i, j] *= 1 - weight ice_content_prev[k_old, i, j] *= 1 - weight liquid_water_content_prev[k_old, i, j] *= 1 - weight energy_prev[k_old, i, j] *= 1 - weight k_new += 1 if k_new >= ns: break if weight < 1: new_thickness = thickness[k_new, i, j] num_layers[i, j] = ns # Update areal heat capacity and snow temperature heat_cap[:ns, i, j] = ( # TODO use snow_heat_capacity() for this ice_content[:ns, i, j] * c.SPEC_HEAT_CAP_ICE + liquid_water_content[:ns, i, j] * c.SPEC_HEAT_CAP_WATER ) temp[:ns, i, j] = c.T0 + internal_energy[:ns] / heat_cap[:ns, i, j] # Update density density[:ns, i, j] = ( (liquid_water_content[:ns, i, j] + ice_content[:ns, i, j]) / thickness[:ns, i, j] )

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0.559907

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18,587

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0.086047

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0.075843

0.042636

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0

0.068452

0

null

0

null

0

1

0

5598bbdfc235215336c94064608a0db8ff763655

3,961

py

Python

bpmn/urls.py

VSSantana/SFDjango-BPMN

e5a3fb8da9282fd88f72a85a4b34d89d38391e36

[ "MIT" ]

1

2021-09-21T00:02:10.000Z

bpmn/urls.py

VSSantana/SFDjango-BPMN

e5a3fb8da9282fd88f72a85a4b34d89d38391e36

[ "MIT" ]

5

2021-09-22T13:54:06.000Z

2021-09-22T14:05:56.000Z

bpmn/urls.py

marcelobbfonseca/SFDjango-BPMN

50565763414f52d9e84004494cf550c6fe2358fa

[ "MIT" ]

1

2021-09-18T01:22:25.000Z

from django.urls import path from django.contrib.auth.views import LoginView from .views.activity_view import * from .views.activity_type_view import * from .views.event_view import * from .views.flow_view import * from .views.lane_view import * from .views.pool_view import * from .views.process_type_view import * from .views.process_view import * from .views.sequence_view import * urlpatterns = [ path('', LoginView.as_view(template_name='accounts/login.html'), name="login"), path('activity_type_list/', ActivityTypeView.as_view(), name='activity_type_list'), path('activity_type_create_form/', ActivityTypeCreate.as_view(), name='activity_type_create_form'), path('activity_type_update_form/<int:pk>', ActivityTypeUpdate.as_view(), name='activity_type_update_form'), path('activity_type_delete_confirmation/<int:pk>', ActivityTypeDelete.as_view(), name='activity_type_delete_confirmation'), path('process_type_list/', ProcessTypeView.as_view(), name='process_type_list'), path('process_type_create_form/', ProcessTypeCreate.as_view(), name='process_type_create_form'), path('process_type_update_form/<int:pk>', ProcessTypeUpdate.as_view(), name='process_type_update_form'), path('process_type_delete_confirmation/<int:pk>', ProcessTypeDelete.as_view(), name='process_type_delete_confirmation'), path('pool_list/', PoolView.as_view(), name='pool_list'), path('pool_create_form/', PoolCreate.as_view(), name='pool_create_form'), path('pool_update_form/<int:pk>', PoolUpdate.as_view(), name='pool_update_form'), path('pool_delete_confirmation/<int:pk>', PoolDelete.as_view(), name='pool_delete_confirmation'), path('lane_list/', LaneView.as_view(), name='lane_list'), path('lane_create_form/', LaneCreate.as_view(), name='lane_create_form'), path('lane_update_form/<int:pk>', LaneUpdate.as_view(), name='lane_update_form'), path('lane_delete_confirmation/<int:pk>', LaneDelete.as_view(), name='lane_delete_confirmation'), path('event_list/', EventView.as_view(), name='event_list'), path('event_create_form/', EventCreate.as_view(), name='event_create_form'), path('event_update_form/<int:pk>', EventUpdate.as_view(), name='event_update_form'), path('event_delete_confirmation/<int:pk>', EventDelete.as_view(), name='event_delete_confirmation'), path('activity_list/', ActivityView.as_view(), name='activity_list'), path('activity_create_form/', ActivityCreate.as_view(), name='activity_create_form'), path('activity_update_form/<int:pk>', ActivityUpdate.as_view(), name='activity_update_form'), path('activity_delete_confirmation/<int:pk>', ActivityDelete.as_view(), name='activity_delete_confirmation'), path('sequence_list/', SequenceView.as_view(), name='sequence_list'), path('sequence_create_form/', SequenceCreate.as_view(), name='sequence_create_form'), path('sequence_update_form/<int:pk>', SequenceUpdate.as_view(), name='sequence_update_form'), path('sequence_delete_confirmation/<int:pk>', SequenceDelete.as_view(), name='sequence_delete_confirmation'), path('flow_list/', FlowView.as_view(), name='flow_list'), path('flow_create_form/', FlowCreate.as_view(), name='flow_create_form'), path('flow_update_form/<int:pk>', FlowUpdate.as_view(), name='flow_update_form'), path('flow_delete_confirmation/<int:pk>', FlowDelete.as_view(), name='flow_delete_confirmation'), path('process_list/', ProcessView.as_view(), name='process_list'), path('process_create_form/', ProcessCreate.as_view(), name='process_create_form'), path('process_update_form/<int:pk>', ProcessUpdate.as_view(), name='process_update_form'), path('process_delete_confirmation/<int:pk>', ProcessDelete.as_view(), name='process_delete_confirmation'), path('process-modeling/', ProcessModelingView.as_view(), name="process_modeling"), path('ontology-suggestion', OntologySuggestionView.as_view(), name="ontology_suggestion") ]

73.351852

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0

null

0

null

0

1

0

559adf86675fc57065409a6e9ac6154669c807e5

3,404

py

Python

edwin/__init__.py

AlanSwenson/edwin

94f62a4db6cc5123224607f92a1f552be072c708

[ "MIT" ]

null

edwin/__init__.py

AlanSwenson/edwin

94f62a4db6cc5123224607f92a1f552be072c708

[ "MIT" ]

8

2019-03-13T13:39:00.000Z

2019-04-02T14:58:21.000Z

edwin/__init__.py

AlanSwenson/edwin

94f62a4db6cc5123224607f92a1f552be072c708

[ "MIT" ]

null

import eventlet eventlet.monkey_patch() import time from datetime import datetime, timedelta, timezone import pytz from email.utils import parsedate_tz import json from flask import Flask, request, render_template from threading import Thread from tweepy import OAuthHandler, API, Stream, Cursor from flask_socketio import ( SocketIO, emit, join_room, leave_room, close_room, rooms, disconnect, ) from darksky import forecast socketio = SocketIO() thread = None thread2 = None from edwin.tweets import StdOutListener def create_app(): app = Flask(__name__) app.config.from_object("config") app.config["SECRET_KEY"] = "secret!" with app.app_context(): socketio.init_app(app, async_mode="eventlet") CONSUMER_KEY = app.config["TWITTER_CONSUMER_KEY"] CONSUMER_SECRET = app.config["TWITTER_CONSUMER_SECRET"] ACCESS_TOKEN = app.config["TWITTER_ACCESS_TOKEN"] ACCESS_TOKEN_SECRET = app.config["TWITTER_ACCESS_TOKEN_SECRET"] TWITTER_SCREEN_NAME = app.config["TWITTER_SCREEN_NAME"] DARKSKY_KEY = app.config["DARKSKY_KEY"] # These config variables come from 'config.py' auth = OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET) auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET) api = API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True) ids = api.friends_ids(screen_name=TWITTER_SCREEN_NAME, stringify_ids="true") try: dc = forecast(DARKSKY_KEY, 38.9159, -77.0446) except: print("failed connection to darksky") @app.route("/", methods=["GET"]) def index(): global thread global thread2 if thread is None: thread = Thread(target=twitter_thread, daemon=True) thread.start() if thread2 is None: thread2 = Thread(target=darksky_thread, daemon=True) thread2.start() return render_template("index.html") def twitter_thread(): """connect to twitter sreaming API and send data to client""" stream = Stream(auth, listener) _follow = ["15736341", "1"] stream.filter(follow=ids, filter_level="low") def darksky_thread(): while True: try: dc.refresh(extend='daily') sunrise = convert_unix_ts(dc['daily']['data'][0]['sunriseTime']) sunset = convert_unix_ts(dc['daily']['data'][0]['sunsetTime']) # convert to int for a nice round whole number temperture temp = int(dc.temperature) except: print("break") sunrise = "_" sunset = "-" temp = "Connection Lost" socketio.emit( "darksky_channel", {"temp": temp, "sunrise": sunrise, "sunset": sunset}, namespace="/darksky_streaming", ) time.sleep(120) listener = StdOutListener() return app def convert_unix_ts(ts): ts= int(ts) return datetime.fromtimestamp(ts).strftime('%-I:%M')

30.392857

84

0.574031

357

3,404

5.268908

0.40056

0.038278

0.042531

0.035088

0.079213

0.026582

0

0.013632

0.331962

3,404

112

85

30.392857

0.813544

0.046122

0

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0

0.102129

0.015427

0

1

0.059524

false

0

0.142857

0

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0.02381

0

null

0

null

0

1

0

559ae7307b62942efd1983a817dbb736879880c0

2,255

py

Python

troop/admin.py

packmas13/registration

bfb42c5479d59494b59e7c656cb04826e110e8d2

[ "MIT" ]

1

2020-08-12T09:51:42.000Z

troop/admin.py

packmas13/registration

bfb42c5479d59494b59e7c656cb04826e110e8d2

[ "MIT" ]

46

2020-01-24T16:51:41.000Z

2022-03-29T16:03:12.000Z

troop/admin.py

packmas13/registration

bfb42c5479d59494b59e7c656cb04826e110e8d2

[ "MIT" ]

1

2020-01-28T21:25:06.000Z

from django import forms from django.contrib import admin from .models import Attendance, Diet, Participant, Troop from payment.admin import DiscountInline, PaymentInline class AttendanceInline(admin.TabularInline): model = Participant.attendance.through readonly_fields = ("participant",) can_delete = False def has_add_permission(self, request, obj=None): return False class AttendanceAdmin(admin.ModelAdmin): inlines = [ AttendanceInline, ] list_display = ( "date", "is_main", ) class DietInline(admin.TabularInline): model = Participant.diet.through readonly_fields = ("participant",) can_delete = False def has_add_permission(self, request, obj=None): return False class DietAdmin(admin.ModelAdmin): inlines = [ DietInline, ] class ParticipantAdmin(admin.ModelAdmin): inlines = [ DiscountInline, ] list_display = ( "troop", "first_name", "last_name", "birthday", "age_section", "is_leader", ) list_display_links = ( "first_name", "last_name", "birthday", ) def formfield_for_dbfield(self, db_field, **kwargs): formfield = super(ParticipantAdmin, self).formfield_for_dbfield( db_field, **kwargs ) if db_field.name == "comment": formfield.widget = forms.Textarea(attrs=formfield.widget.attrs) return formfield class ParticipantInline(admin.TabularInline): model = Participant fields = ( "first_name", "last_name", "birthday", ) readonly_fields = ( "first_name", "last_name", "birthday", ) can_delete = False show_change_link = True def has_add_permission(self, request, obj=None): return False class TroopAdmin(admin.ModelAdmin): inlines = [ ParticipantInline, PaymentInline, ] list_display = ( "number", "name", ) list_display_links = ("name",) admin.site.register(Attendance, AttendanceAdmin) admin.site.register(Diet, DietAdmin) admin.site.register(Participant, ParticipantAdmin) admin.site.register(Troop, TroopAdmin)

21.47619

75

0.632373

217

2,255

6.391705

0.336406

0.039654

0.063446

0.049027

0.261716

0.225667

0.180966

0

0.267849

2,255

104

76

21.682692

0.840097

0

0.365854

0

0.082927

0

1

0.04878

false

0

0.04878

0.036585

0.47561

0

null

0

null

0

1

0

559b8b906411edd79ce8b01d4b0d9cdea4c7292c

829

py

Python

demo_snippets/11_Datenvisualisierung/main.py

fabod/pro2

69b1015fa789ef05bf9b514d94b231f76bdf5e29

[ "MIT" ]

2

2020-03-03T14:57:40.000Z

2020-03-20T10:59:47.000Z

demo_snippets/11_Datenvisualisierung/main.py

fabod/pro2

69b1015fa789ef05bf9b514d94b231f76bdf5e29

[ "MIT" ]

null

demo_snippets/11_Datenvisualisierung/main.py

fabod/pro2

69b1015fa789ef05bf9b514d94b231f76bdf5e29

[ "MIT" ]

null

from flask import Flask from flask import render_template import plotly.express as px from plotly.offline import plot app = Flask("Datenvisualisierung") def data(): data = px.data.gapminder() data_ch = data[data.country == 'Switzerland'] return data_ch def viz(): data_ch = data() fig = px.bar( data_ch, x='year', y='pop', hover_data=['lifeExp', 'gdpPercap'], color='lifeExp', labels={ 'pop': 'Einwohner der Schweiz', 'year': 'Jahrzehnt' }, height=400 ) div = plot(fig, output_type="div") return div @app.route("/") def index(): div = viz() # return str([str(i) for i in data()]) return render_template('index.html', viz_div=div) if __name__ == '__main__': app.run(debug=True, port=5000)

18.422222

53

0.587455

105

829

4.47619

0.533333

0.051064

0.06383

0

0.01157

0.270205

829

44

54

18.840909

0.765289

0.043426

0

0.150442

0

1

0.1

false

0

0.133333

0

0.333333

0

null

0

null

0

1

0

559c155e6e0b7efb591c20bbc5e5237149bd61eb

2,940

py

Python

data_analysis/get_model_statistics.py

fluTN/influenza

40cbede52bc4e95d52369eebe4a50ad4b71369d1

[ "MIT" ]

1

2020-10-29T09:56:31.000Z

data_analysis/get_model_statistics.py

fluTN/influenza

40cbede52bc4e95d52369eebe4a50ad4b71369d1

[ "MIT" ]

null

data_analysis/get_model_statistics.py

fluTN/influenza

40cbede52bc4e95d52369eebe4a50ad4b71369d1

[ "MIT" ]

1

2022-01-22T11:34:29.000Z

# -*- coding: utf-8 -*- """Script which can be used to compare the features obtained of two different influenza models Usage: get_model_statistics.py <model> [--country=<country_name>] [--no-future] [--basedir=<directory>] [--start-year=<start_year>] [--end-year=<end_year>] [--save] [--no-graph] <baseline> Data file of the first model <other_method> Data file of the second model -h, --help Print this help message """ import pandas as pd import numpy as np from scipy import stats from docopt import docopt import os import glob from sklearn.metrics import mean_squared_error import seaborn as sns import matplotlib.pyplot as plt sns.set() def get_results_filename(basepath): files = [f for f in glob.glob(basepath + "/*-prediction.csv", recursive=True)] y = os.path.basename(files[0]).split("-")[0] y2 = os.path.basename(files[0]).split("-")[1] return "{}-{}".format(y, y2) if __name__ == "__main__": args = docopt(__doc__) model = args["<model>"] base_dir = args["--basedir"] if args["--basedir"] else "../complete_results" country = args["--country"] if args["--country"] else "italy" future = "no-future" if args["--no-future"] else "future" # Read the baseline results and merge them model_path = os.path.join(base_dir, args["<model>"], future, country) season_years = get_results_filename(model_path) model_file = os.path.join(model_path, "{}-prediction.csv".format(season_years)) # Load the data data = pd.read_csv(model_file) # Get only the weeks we care for start_year = "2007-42" if not args["--start-year"] else args["--start-year"] end_year = "2019-15" if not args["--end-year"] else args["--end-year"] start_season = data["week"] >= start_year end_season = data["week"] <= str(int(end_year.split("-")[0]) + 1) + "-" + end_year.split("-")[1] total = start_season & end_season data = data[total] # Describe the data print("") print("[*] Describe the given dataset {}".format(model_file)) print(data.describe()) # Generate residuals print("") print("[*] Describe the residuals") residuals = data["incidence"]-data["prediction"] print(residuals.describe()) # Get some statistics print("") total_pearson = 0 for i in np.arange(0, len(data["prediction"]), 26): total_pearson += stats.pearsonr(data["prediction"][i:i+26], data["incidence"][i:i+26])[0] print("Pearson Correlation (value/p): ", total_pearson/(len(data["prediction"])/26)) print("") print("Mean Squared Error: ", mean_squared_error(data["prediction"], data["incidence"])) print("") if not args["--no-graph"]: ax = sns.distplot(residuals, label="Residual") plt.figure() ax = sns.distplot(data["incidence"], label="Incidence") ax = sns.distplot(data["prediction"], label="Prediction") plt.legend() plt.show()

33.409091

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0

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false

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0.207547

0

null

0

null

0

1

0

559f3ab5a294666e58af2d7a21dc2e34d7f16b41

21,887

py

Python

sisu/summarizer.py

balouf/sisu

07541e6a02e545372452b33f7df056331397001f

[ "BSD-3-Clause" ]

null

sisu/summarizer.py

balouf/sisu

07541e6a02e545372452b33f7df056331397001f

[ "BSD-3-Clause" ]

null

sisu/summarizer.py

balouf/sisu

07541e6a02e545372452b33f7df056331397001f

[ "BSD-3-Clause" ]

null

from scipy.sparse import vstack from sklearn.metrics.pairwise import cosine_similarity import numpy as np from sisu.preprocessing.tokenizer import is_relevant_sentence, make_sentences, sanitize_text from gismo.gismo import Gismo, covering_order from gismo.common import auto_k from gismo.parameters import Parameters from gismo.corpus import Corpus from gismo.embedding import Embedding from sisu.embedding_idf import IdfEmbedding def cosine_order(projection, sentences, query): """ Order relevant sentences by cosine similarity to the query. Parameters ---------- projection: callable A function that converts a text into a tuple whose first element is an embedding (typically a Gismo :meth:`~gismo.embedding.Embedding.query_projection`). sentences: :class:`list` of :class:`dict` Sentences as output by :func:`~sisu.summarizer.extract_sentences`. query: :class:`str` Target query Returns ------- :class:`list` of :class:`int` Ordered list of indexes of relevant sentences, sorted by cosine similarity """ relevant_indices = [s['index'] for s in sentences if s['relevant']] projected_query = projection(query)[0] projected_sentences = vstack([projection(sentences[i]['sanitized'])[0] for i in relevant_indices]) order = np.argsort(- cosine_similarity(projected_sentences, projected_query)[:, 0]) return [relevant_indices[i] for i in order] def extract_sentences(source, indices, getter=None, tester=None): """ Pick up the entries of the source corresponding to indices and build a list of sentences out of that. Each sentence is a dictionary with the following keys: - `index`: position of the sentence in the returned list - `sentence`: the actual sentence - `relevant`: a boolean that tells if the sentence is eligible for being part of the summary - `sanitized`: for relevant sentences, a simplified version to be fed to the embedding Parameters ---------- source: :class:`list` list of objects indices: iterable of :class:`int` Indexes of the source items to select getter: callable, optional Tells how to convert a source entry into text. tester: callable, optional Tells if the sentence is eligible for being part of the summary. Returns ------- list of dict Examples -------- >>> doc1 = ("This is a short sentence! This is a sentence with reference to the url http://www.ix.com! " ... "This sentence is not too short and not too long, without URL and without citation. " ... "I have many things to say in that sentence, to the point " ... "I do not know if I will stop anytime soon but don\'t let it stop " ... "you from reading this meaninless garbage and this goes on and " ... "this goes on and this goes on and this goes on and this goes on and " ... "this goes on and this goes on and this goes on and this goes on " ... "and this goes on and this goes on and this goes on and this goes " ... "on and this goes on and this goes on and this goes on and this goes " ... "on and this goes on and that is all.") >>> doc2 = ("This is a a sentence with some citations [3, 7]. " ... "This sentence is not too short and not too long, without URL and without citation. " ... "Note that the previous sentence is already present in doc1. " ... "The enzyme cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) catalyzes " ... "the synthesis of Neu5Gc by hydroxylation of Neu5Ac (Schauer et al. 1968).") >>> extract_sentences([doc1, doc2], [1, 0]) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS [{'index': 0, 'sentence': 'This is a a sentence with some citations [3, 7].', 'relevant': False, 'sanitized': ''}, {'index': 1, 'sentence': 'This sentence is not too short and not too long, without URL and without citation.', 'relevant': True, 'sanitized': 'This sentence is not too short and not too long without URL and without citation'}, {'index': 2, 'sentence': 'Note that the previous sentence is already present in doc1.', 'relevant': True, 'sanitized': 'Note that the previous sentence is already present in doc'}, {'index': 3, 'sentence': 'The enzyme cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) catalyzes the synthesis of Neu5Gc by hydroxylation of Neu5Ac (Schauer et al. 1968).', 'relevant': False, 'sanitized': ''}, {'index': 4, 'sentence': 'This is a short sentence!', 'relevant': False, 'sanitized': ''}, {'index': 5, 'sentence': 'This is a sentence with reference to the url http://www.ix.com!', 'relevant': False, 'sanitized': ''}, {'index': 6, 'sentence': 'This sentence is not too short and not too long, without URL and without citation.', 'relevant': False, 'sanitized': ''}, {'index': 7, 'sentence': "I have many things to say in that sentence...", 'relevant': False, 'sanitized': ''}] """ if getter is None: getter = str if tester is None: tester = is_relevant_sentence sentences = [{'index': i, 'sentence': sent, 'relevant': tester(sent)} for i, sent in enumerate([sent for j in indices for sent in make_sentences(getter(source[j]))])] used = set() for s in sentences: if s['sentence'] in used and s['relevant']: s['relevant'] = False else: used.add(s['sentence']) s['sanitized'] = sanitize_text(s['sentence']) if s['relevant'] else "" return sentences default_summarizer_parameters = { 'order': 'rank', 'text_getter': None, 'sentence_tester': is_relevant_sentence, 'itf': True, 'post_processing': lambda summa, i: summa.sentences_[i]['sentence'], 'sentence_gismo_parameters': {'post': False, 'resolution': .99}, 'num_documents': None, 'num_query': None, 'num_sentences': None, 'max_chars': None} """ List of parameters for the summarizer with their default values. Parameters ----------- order: :class:`str` Sorting function. text_getter: callable Extraction of text from corpus item. If not specify, the to_text of the :class:`~gismo.corpus.Corpus` will be used. sentence_tester: callable Function that estimates if a sentence is eligible to be part of the summary itf: :class:`bool` Use of ITF normalization in the sentence-level Gismo post_processing: callable post_processing transformation. Signature is (:class:`~sisu.summarizer.Summarizer`, :class:`int`) -> :class:`str` sentence_gismo_parameters: :class:`dict` Tuning of sentence-level gismo. `post` MUST be set to False. num_documents: :class:`int` or None Number of documents to pre-select num_query: :class:`int` or None Number of features to use in generic query num_sentences: :class:`int` or None Number of sentences to return max_chars: :class:`int` or None Maximal number of characters to return """ class Summarizer: """ Summarizer class. Parameters ---------- gismo: :class:`~gismo.gismo.Gismo` Gismo of the documents to analyze. kwargs: :class:`dict` Parameters of the summarizer (see :obj:`~sisu.summarizer.default_summarizer_parameters` for details). Attributes ---------- query_: :class:`str` Query used to summarize. sentences_: :class:`list` of :class:`dict` Selected sentences. Each sentence is a dictionary with the following keys: - `index`: position of the sentence in the returned list - `sentence`: the actual sentence - `relevant`: a boolean that tells if the sentence is eligible for being part of the summary - `sanitized`: for relevant sentences, a simplified version to be fed to the embedding order_: :class:`numpy.ndarray` Proposed incomplete ordering of the :class:`~sisu.summarizer.Summarizer.sentences_` sentence_gismo_: :class:`~gismo.gismo.Gismo` Gismo running at sentence level. parameters: :class:`~gismo.parameters.Parameters` Handler of parameters. Examples -------- The package contains a data folder with a toy gismo with articles related to Covid-19. We load it. >>> gismo = Gismo(filename="toy_gismo", path="data") Then we build a summarizer out of it. We tell to fetch the sentences from the content of the articles. >>> summa = Summarizer(gismo, text_getter = lambda d: d['content']) Ask for a summary on *bat* with a maximal budget of 500 characters, using pure TF-IDF sentence embedding. >>> summa('bat', max_chars=500, itf=False) # doctest: +NORMALIZE_WHITESPACE ['By comparing the amino acid sequence of 2019-nCoV S-protein (GenBank Accession: MN908947.3) with Bat SARS-like coronavirus isolate bat-SL-CoVZC45 and Bat SARS-like coronavirus isolate Bat-SL-CoVZXC21, the latter two were shown to share 89.1% and 88.6% sequence identity to 2019-nCoV S-protein (supplementary figure 1) .', 'Within our bat-hemoplasma network, genotype sharing was restricted to five host communities, 380 whereas six genotypes were each restricted to a single bat species (Fig. 5A ).'] Now a summary based on the *cosine* ordering, using the content of abstracts and pure TF-IDF sentence embedding. >>> summa('bat', max_chars=500, order='cosine', text_getter = lambda d: d['abstract']) # doctest: +NORMALIZE_WHITESPACE ['Bat dipeptidyl peptidase 4 (DPP4) sequences were closely related to 38 those of human and non-human primates but distinct from dromedary DPP4 sequence.', 'The multiple sequence alignment data correlated with already published reports on SARS-CoV-2 indicated that it is closely related to Bat-Severe Acute Respiratory Syndrome like coronavirus (Bat CoV SARS-like) and wellstudied Human SARS.', '(i.e., hemoplasmas) across a species-rich 40 bat community in Belize over two years.'] Now 4 sentences using a *coverage* ordering. >>> summa('bat', num_sentences=4, order='coverage') # doctest: +NORMALIZE_WHITESPACE ['By comparing the amino acid sequence of 2019-nCoV S-protein (GenBank Accession: MN908947.3) with Bat SARS-like coronavirus isolate bat-SL-CoVZC45 and Bat SARS-like coronavirus isolate Bat-SL-CoVZXC21, the latter two were shown to share 89.1% and 88.6% sequence identity to 2019-nCoV S-protein (supplementary figure 1) .', 'However, we have not done the IDPs analysis for ORF10 from the Bat-SL-CoVZC45 strain since we have taken different strain of Bat CoV (reviewed strain HKU3-1) in our study.', 'To test the dependence of the hemoplasma 290 phylogeny upon the bat phylogeny and thus assess evidence of evolutionary codivergence, we 291 applied the Procrustes Approach to Cophylogeny (PACo) using distance matrices and the paco 292 We used hemoplasma genotype assignments to create a network, with each node representing a 299 bat species and edges representing shared genotypes among bat species pairs.', 'However, these phylogenetic patterns in prevalence were decoupled from those describing bat 526 species centrality in sharing hemoplasmas, such that genotype sharing was generally restricted 527 by bat phylogeny.'] As you can see, there are some ``However, '' in the answers. A bit of NLP post_processing can take care of those. >>> import spacy >>> nlp = spacy.load("en_core_web_sm") >>> post_nlp = PostNLP(nlp) >>> summa('bat', num_sentences=4, order='coverage', post_processing=post_nlp) # doctest: +NORMALIZE_WHITESPACE ['By comparing the amino acid sequence of 2019-nCoV S-protein (GenBank Accession: MN908947.3) with Bat SARS-like coronavirus isolate bat-SL-CoVZC45 and Bat SARS-like coronavirus isolate Bat-SL-CoVZXC21, the latter two were shown to share 89.1% and 88.6% sequence identity to 2019-nCoV S-protein (supplementary figure 1) .', 'We have not done the IDPs analysis for ORF10 from the Bat-SL-CoVZC45 strain since we have taken different strain of Bat CoV (reviewed strain HKU3-1) in our study.', 'To test the dependence of the hemoplasma 290 phylogeny upon the bat phylogeny and thus assess evidence of evolutionary codivergence, we 291 applied the Procrustes Approach to Cophylogeny (PACo) using distance matrices and the paco 292 We used hemoplasma genotype assignments to create a network, with each node representing a 299 bat species and edges representing shared genotypes among bat species pairs.', 'These phylogenetic patterns in prevalence were decoupled from those describing bat 526 species centrality in sharing hemoplasmas, such that genotype sharing was generally restricted 527 by bat phylogeny.'] """ def __init__(self, gismo, **kwargs): self.gismo = gismo self.query_ = None self.sentences_ = None self.order_ = None self.sentence_gismo_ = None self.parameters = Parameters(parameter_list=default_summarizer_parameters, **kwargs) if self.parameters.text_getter is None: self.parameters.text_getter = self.gismo.corpus.to_text def rank_documents(self, query, num_query=None): """ Perform a Gismo query at document-level. If the query fails, builds a generic query instead. The :attr:`~sisu.summarizer.Summarizer.gismo` and :attr:`~sisu.summarizer.Summarizer.query_` attributes are updated. Parameters ---------- query: :class:`str` Input text num_query: :class:`int` Number of words of the generic query, is any Returns ------- None """ if num_query is None: num_query = self.parameters.num_query success = self.gismo.rank(query) if success: self.query_ = query else: self.query_ = " ".join(self.gismo.get_features_by_rank(k=num_query)) self.gismo.rank(self.query_) def build_sentence_source(self, num_documents=None, getter=None, tester=None): """ Creates the corpus of sentences (:attr:`~sisu.summarizer.Summarizer.sentences_`) Parameters ---------- num_documents: :class:`int`, optional Number of documents to select (if not, Gismo will automatically decide). getter: callable Extraction of text from corpus item. If not specify, the to_text of the :class:`~gismo.corpus.Corpus` will be used. tester: callable Function that estimates if a sentence is eligible to be part of the summary. Returns ------- None """ if num_documents is None: num_documents = self.parameters.num_documents if getter is None: getter = self.parameters.text_getter if tester is None: tester = self.parameters.sentence_tester self.sentences_ = extract_sentences(source=self.gismo.corpus, indices=self.gismo.get_documents_by_rank(k=num_documents, post=False), getter=getter, tester=tester) def build_sentence_gismo(self, itf=None, s_g_p=None): """ Creates the Gismo of sentences (:attr:`~sisu.summarizer.Summarizer.sentence_gismo_`) Parameters ---------- itf: :class:`bool`, optional Applies TF-IDTF embedding. I False, TF-IDF embedding is used. s_g_p: :class:`dict` Parameters for the sentence Gismo. Returns ------- None """ if itf is None: itf = self.parameters.itf if s_g_p is None: s_g_p = self.parameters.sentence_gismo_parameters sentence_corpus = Corpus(source=self.sentences_, to_text=lambda s: s['sanitized']) sentence_embedding = Embedding() if itf else IdfEmbedding() sentence_embedding.fit_ext(embedding=self.gismo.embedding) sentence_embedding.transform(sentence_corpus) self.sentence_gismo_ = Gismo(sentence_corpus, sentence_embedding, **s_g_p) def build_coverage_order(self, k): """ Populate :attr:`~sisu.summarizer.Summarizer.order_` with a covering order with target number of sentences *k*. The actual number of indices is stretched by the sentence Gismo stretch factor. Parameters ---------- k: :class:`int` Number of optimal covering sentences. Returns ------- :class:`numpy.ndarray` Covering order. """ p = self.sentence_gismo_.parameters(post=False) cluster = self.sentence_gismo_.get_documents_by_cluster(k=int(k * p['stretch']), **p) return covering_order(cluster, wide=p['wide']) def summarize(self, query="", **kwargs): """ Performs a full run of all summary-related operations: - Rank a query at document level, fallback to a generic query if the query fails; - Extract sentences from the top documents - Order sentences by one of the three methods proposed, *rank*, *coverage*, and *cosine* - Apply post-processing and return list of selected sentences. Note that calling a :class:`~sisu.summarizer.Summarizer` will call its :meth:`~sisu.summarizer.Summarizer.summarize` method. Parameters ---------- query: :class:`str` Query to run. kwargs: :class:`dict` Runtime specific parameters (see :obj:`~sisu.summarizer.default_summarizer_parameters` for possible arguments). Returns ------- :class:`list` of :class:`str` Summary. """ # Instantiate parameters for the call p = self.parameters(**kwargs) # Perform query, fallback to generic query in case of failure self.rank_documents(query=query, num_query=p['num_query']) # Extract and preprocess sentences self.build_sentence_source(num_documents=p['num_documents'], getter=p['text_getter'], tester=p['sentence_tester']) # Order sentences if p['order'] == 'cosine': self.order_ = cosine_order(self.gismo.embedding.query_projection, self.sentences_, self.query_) elif p['order'] in {'rank', 'coverage'}: self.build_sentence_gismo(itf=p['itf'], s_g_p=p['sentence_gismo_parameters']) self.sentence_gismo_.rank(query) if p['num_sentences'] is None: p['num_sentences'] = auto_k(data=self.sentence_gismo_.diteration.x_relevance, order=self.sentence_gismo_.diteration.x_order, max_k=self.sentence_gismo_.parameters.max_k, target=self.sentence_gismo_.parameters.target_k) if p['order'] == 'rank': self.order_ = self.sentence_gismo_.diteration.x_order else: self.order_ = self.build_coverage_order(p['num_sentences']) if p['max_chars'] is None: results = [p['post_processing'](self, i) for i in self.order_[:p['num_sentences']]] return [txt for txt in results if len(txt)>0] else: results = [] length = 0 # Maximal number of sentences that will be processed max_sentences = int(p['max_chars']/50) for i in self.order_[:max_sentences]: txt = p['post_processing'](self, i) l = len(txt) if l>0 and length+l < p['max_chars']: results.append(txt) length += l if length > .98*p['max_chars']: break return results def __call__(self, query="", **kwargs): return self.summarize(query, **kwargs) class PostNLP: """ Post-processor for the :class:`~sisu.summarizer.Summarizer` that leverages a spacy NLP engine. - Discard sentences with no verb. - Remove adverbs and punctuations that starts a sentence (e.g. "However, we ..." -> "We ..."). - Optionally, if the engine supports co-references, resolve them. Parameters ---------- nlp: callable A Spacy nlp engine. coref: :class:`bool` Resolve co-references if the nlp engine supports it. """ def __init__(self, nlp, coref=False): self.nlp = nlp self.coref = coref def __call__(self, summa, i): nlp_sent = self.nlp(summa.sentences_[i]['sentence']) tags = {token.tag_ for token in nlp_sent} if not any([t.startswith("VB") for t in tags]): summa.sentences_[i]['relevant'] = False return "" while nlp_sent[0].pos_ == "ADV" and len(nlp_sent)>0: nlp_sent = nlp_sent[1:] if nlp_sent[0].pos_ == "PUNCT": nlp_sent = nlp_sent[1:] txt = nlp_sent.text summa.sentences_[i]['sentence'] = f"{txt[0].upper()}{txt[1:]}" if "PRP" in tags and self.coref and hasattr(nlp_sent._, 'has_coref'): extract_str = " ".join([s['sentence'] for s in summa.sentences_[max(0, i - 2) : i + 1]]) extract = self.nlp(extract_str) if extract._.has_coref: resolved_extract = extract._.coref_resolved summa.sentences_[i]['sentence'] = make_sentences(resolved_extract)[-1] return summa.sentences_[i]['sentence']

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null

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0

559fa91e2cb3fcb7a60d3f0698d9ba9ef4cfe606

4,482

py

Python

automr/bridge.py

hebrewsnabla/pyAutoMR

8e81ed7fd780abd94f8b51e48ee4b980a868c204

[ "Apache-2.0" ]

5

2021-06-03T07:49:02.000Z

2022-02-21T11:35:20.000Z

automr/bridge.py

hebrewsnabla/pyAutoMR

8e81ed7fd780abd94f8b51e48ee4b980a868c204

[ "Apache-2.0" ]

2

2022-01-20T08:33:59.000Z

2022-03-26T12:21:15.000Z

automr/bridge.py

hebrewsnabla/pyAutoMR

8e81ed7fd780abd94f8b51e48ee4b980a868c204

[ "Apache-2.0" ]

1

2022-02-21T11:35:34.000Z

import numpy as np import os from automr import dump_mat from functools import partial, reduce print = partial(print, flush=True) einsum = partial(np.einsum, optimize=True) def print_mol(mol): print(mol._basis) print(mol.atom) print(mol._atom) print(mol.aoslice_by_atom()) print(mol.ao_labels()) #if mol.verbose >= logger.DEBUG: mol.stdout.write('[INPUT] ---------------- BASIS SET ---------------- \n') mol.stdout.write('[INPUT] l, kappa, [nprim/nctr], ' 'expnt, c_1 c_2 ...\n') for atom, basis_set in mol._basis.items(): mol.stdout.write('[INPUT] %s\n' % atom) for b in basis_set: if isinstance(b[1], int): kappa = b[1] b_coeff = b[2:] else: kappa = 0 b_coeff = b[1:] nprim = len(b_coeff) nctr = len(b_coeff[0])-1 if nprim < nctr: logger.warn(mol, 'num. primitives smaller than num. contracted basis') mol.stdout.write('[INPUT] %d %2d [%-5d/%-4d] ' % (b[0], kappa, nprim, nctr)) for k, x in enumerate(b_coeff): if k == 0: mol.stdout.write('%-15.12g ' % x[0]) else: mol.stdout.write(' '*32+'%-15.12g ' % x[0]) for c in x[1:]: mol.stdout.write(' %4.12g' % c) mol.stdout.write('\n') def py2qchem(mf, basename, is_uhf=False): if is_uhf: mo_coeffa = mf.mo_coeff[0] mo_coeffb = mf.mo_coeff[1] #mo_enea = mf.mo_energy[0] #mo_eneb = mf.mo_energy[1] else: mo_coeffa = mf.mo_coeff mo_coeffb = mf.mo_coeff #mo_enea = mf.mo_energy #mo_eneb = mf.mo_energy mo_enea = np.zeros(len(mo_coeffa)) mo_eneb = np.zeros(len(mo_coeffa)) Sdiag = mf.get_ovlp().diagonal()**(0.5) mo_coeffa = einsum('ij,i->ij', mo_coeffa, Sdiag).T mo_coeffb = einsum('ij,i->ij', mo_coeffb, Sdiag).T #dump_mat.dump_mo(mf.mol, mo_coeffa, ncol=10) guess_file = np.vstack([mo_coeffa, mo_coeffb, mo_enea, mo_eneb]).flatten() tmpbasename = '/tmp/qchem/' + basename os.system('mkdir -p ' + tmpbasename) with open(tmpbasename + '/53.0', 'w') as f: guess_file.tofile(f, sep='') create_qchem_in(mf, basename) def create_qchem_in(mf, basename, uhf=False, sph=True): atom = mf.mol.format_atom(mf.mol.atom, unit=1) with open(basename + '.in', 'w') as f: f.write('$molecule\n') f.write(' %d %d\n' % (mf.mol.charge, mf.mol.spin+1)) for a in atom: f.write(' %s %12.6f %12.6f %12.6f\n' % (a[0], a[1][0], a[1][1], a[1][2])) f.write('$end\n\n') '''f.write('$rem\n') f.write(' method = hf\n') if uhf: f.write(' unrestricted = true\n') f.write(' basis = cc-pvdz\n') f.write(' print_orbitals = true\n') f.write(' sym_ignore = true\n') if sph: f.write(' purecart = 1111\n') else: f.write(' purecart = 2222\n') f.write(' scf_guess_print = 2\n') f.write(' scf_guess = read\n') f.write(' scf_convergence = 0\n') f.write(' thresh = 12\n') f.write('$end\n\n') f.write('@@@\n\n') f.write('$molecule\n') f.write('read\n') f.write('$end\n\n')''' f.write('$rem\n') #f.write(' method = hf\n') f.write(' correlation = pp\n') f.write(' gvb_local = 0\n') f.write(' gvb_n_pairs = 2\n') f.write(' gvb_print = 1\n') if uhf: f.write(' unrestricted = true\n') f.write(' basis = cc-pvdz\n') f.write(' print_orbitals = true\n') f.write(' sym_ignore = true\n') if sph: f.write(' purecart = 1111\n') else: f.write(' purecart = 2222\n') f.write(' scf_guess_print = 2\n') f.write(' scf_guess = read\n') f.write(' thresh = 12\n') f.write('$end\n\n') def qchem2py(basename): with open('/tmp/qchem/' + basename + '/53.0', 'r') as f: data = np.fromfile(f) print(data.shape) n = data.shape[0] #x = sympy.Symbol('x') #nmo = sympy.solve(2*x*(x+1) -n, x) nmo = int(np.sqrt(n/2.0+0.25)-0.5) moa = data[:nmo*nmo].reshape(nmo,nmo).T mob = data[nmo*nmo:2*nmo*nmo].reshape(nmo,nmo).T mo = (moa, mob) return mo

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false

0

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0.119565

0

null

0

null

0

1

0

55a1b6b516c4d12eb63cdf47d747201063521f8c

487

py

Python

Example/playstore.py

goodop/api-imjustgood.com

6406b531c4393fa8a4ace3c206d23895da915caf

[ "MIT" ]

4

2021-01-01T10:20:13.000Z

2021-11-08T09:32:54.000Z

Example/playstore.py

goodop/api-imjustgood.com

6406b531c4393fa8a4ace3c206d23895da915caf

[ "MIT" ]

null

Example/playstore.py

goodop/api-imjustgood.com

6406b531c4393fa8a4ace3c206d23895da915caf

[ "MIT" ]

25

2021-01-09T18:22:32.000Z

2021-05-29T07:42:06.000Z

from justgood import imjustgood media = imjustgood("YOUR_APIKEY_HERE") query = "gojek" # example query data = media.playstore(query) # Get attributes number = 0 result = "Playstore :" for a in data["result"]: number += 1 result += "\n\n{}. {}".format(number, a["title"]) result += "\nDeveloper : {}".format(a["developer"]) result += "\nThumbnail : {}".format(a["thumbnail"]) result += "\nURL : {}".format(a["pageUrl"]) print(result) # Get JSON results print(data)

24.35

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0.004938

0.168378

487

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0.755556

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false

0

0.071429

0

0.071429

0.142857

0

null

0

null

0

1

0

55a63e41c61dfc7f2803753c38bd275ef075fcb4

10,272

py

Python

codes/3_derive_elementary_effects.py

aviolinist/EEE

032e2029815229875048cc92dd7da24ff3f71e93

[ "MIT" ]

6

2019-09-27T15:38:37.000Z

2021-02-03T13:58:01.000Z

codes/3_derive_elementary_effects.py

aviolinist/EEE

032e2029815229875048cc92dd7da24ff3f71e93

[ "MIT" ]

null

codes/3_derive_elementary_effects.py

aviolinist/EEE

032e2029815229875048cc92dd7da24ff3f71e93

[ "MIT" ]

5

2019-09-27T15:38:52.000Z

2022-03-22T17:24:37.000Z

#!/usr/bin/env python from __future__ import print_function # Copyright 2019 Juliane Mai - juliane.mai(at)uwaterloo.ca # # License # This file is part of the EEE code library for "Computationally inexpensive identification # of noninformative model parameters by sequential screening: Efficient Elementary Effects (EEE)". # # The EEE code library is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # The MVA code library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # You should have received a copy of the GNU Lesser General Public License # along with The EEE code library. # If not, see <https://github.com/julemai/EEE/blob/master/LICENSE>. # # If you use this method in a publication please cite: # # M Cuntz & J Mai et al. (2015). # Computationally inexpensive identification of noninformative model parameters by sequential screening. # Water Resources Research, 51, 6417-6441. # https://doi.org/10.1002/2015WR016907. # # # # python 3_derive_elementary_effects.py \ # -i example_ishigami-homma/model_output.pkl \ # -d example_ishigami-homma/parameters.dat \ # -m example_ishigami-homma/parameter_sets_1_para3_M.dat \ # -v example_ishigami-homma/parameter_sets_1_para3_v.dat \ # -o example_ishigami-homma/eee_results.dat """ Derives the Elementary Effects based on model outputs stored as dictionary in a pickle file (option -i) using specified model parameters (option -d). The model parameters were sampled beforehand as Morris trajectories. The Morris trajectory information is stored in two files (option -m and option -v). The Elementary Effects are stored in a file (option -o). History ------- Written, JM, Mar 2019 """ # ------------------------------------------------------------------------- # Command line arguments # modeloutputs = 'example_ishigami-homma/model_output.pkl' modeloutputkey = 'All' maskfile = 'example_ishigami-homma/parameters.dat' morris_M = 'example_ishigami-homma/parameter_sets_1_para3_M.dat' morris_v = 'example_ishigami-homma/parameter_sets_1_para3_v.dat' outfile = 'example_ishigami-homma/eee_results.dat' skip = None # number of lines to skip in Morris files import optparse parser = optparse.OptionParser(usage='%prog [options]', description="Derives the Elementary Effects based on model outputs stored as dictionary in a pickle file (option -i) using specified model parameters (option -d). The model parameters were sampled beforehand as Morris trajectories. The Morris trajectory information is stored in two files (option -m and option -v). The Elementary Effects are stored in a file (option -o).") parser.add_option('-i', '--modeloutputs', action='store', default=modeloutputs, dest='modeloutputs', metavar='modeloutputs', help="Name of file used to save (scalar) model outputs in a pickle file (default: 'model_output.pkl').") parser.add_option('-k', '--modeloutputkey', action='store', default=modeloutputkey, dest='modeloutputkey', metavar='modeloutputkey', help="Key of model output dictionary stored in pickle output file. If 'All', all model outputs are taken into account and multi-objective EEE is applied. (default: 'All').") parser.add_option('-d', '--maskfile', action='store', dest='maskfile', type='string', default=maskfile, metavar='File', help='Name of file where all model parameters are specified including their distribution, distribution parameters, default value and if included in analysis or not. (default: maskfile=parameters.dat).') parser.add_option('-m', '--morris_M', action='store', dest='morris_M', type='string', default=morris_M, metavar='morris_M', help="Morris trajectory information: The UNSCALED parameter sets. (default: 'parameter_sets_1_para3_M.dat').") parser.add_option('-v', '--morris_v', action='store', dest='morris_v', type='string', default=morris_v, metavar='morris_v', help="Morris trajectory information: The indicator which parameter changed between subsequent sets in a trajectory. (default: 'parameter_sets_1_para3_v.dat').") parser.add_option('-s', '--skip', action='store', default=skip, dest='skip', metavar='skip', help="Number of lines to skip in Morris output files (default: None).") parser.add_option('-o', '--outfile', action='store', dest='outfile', type='string', default=outfile, metavar='File', help='File containing Elementary Effect estimates of all model parameters listed in parameter information file. (default: eee_results.dat).') (opts, args) = parser.parse_args() modeloutputs = opts.modeloutputs modeloutputkey = opts.modeloutputkey maskfile = opts.maskfile morris_M = opts.morris_M morris_v = opts.morris_v outfile = opts.outfile skip = opts.skip del parser, opts, args # ----------------------- # add subolder scripts/lib to search path # ----------------------- import sys import os dir_path = os.path.dirname(os.path.realpath(__file__)) sys.path.append(dir_path+'/lib') import numpy as np import pickle from fsread import fsread # in lib/ from autostring import astr # in lib/ # ------------------------- # read parameter info file # ------------------------- # parameter info file has following header: # # para dist lower upper default informative(0)_or_noninformative(1) # # mean stddev nc,snc = fsread(maskfile, comment="#",cskip=1,snc=[0,1],nc=[2,3,4,5]) snc = np.array(snc) para_name = snc[:,0] para_dist = snc[:,1] lower_bound = nc[:,0] upper_bound = nc[:,1] initial = nc[:,2] # if informative(0) -> maskpara=False # if noninformative(1) -> maskpara=True mask_para = np.where((nc[:,3].flatten())==1.,True,False) dims_all = np.shape(mask_para)[0] idx_para = np.arange(dims_all)[mask_para] # indexes of parameters which will be changed [0,npara-1] dims = np.sum(mask_para) # pick only non-masked bounds lower_bound_mask = lower_bound[np.where(mask_para)] upper_bound_mask = upper_bound[np.where(mask_para)] para_dist_mask = para_dist[np.where(mask_para)] para_name_mask = para_name[np.where(mask_para)] # ------------------------- # read model outputs # ------------------------- model_output = pickle.load( open( modeloutputs, "rb" ) ) if modeloutputkey == 'All': keys = list(model_output.keys()) else: keys = [ modeloutputkey ] model_output = [ np.array(model_output[ikey]) for ikey in keys ] nkeys = len(model_output) # ------------------------- # read Morris M # ------------------------- ff = open(morris_M, "r") parasets = ff.readlines() ff.close() if skip is None: skip = np.int(parasets[0].strip().split(':')[1]) else: skip = np.int(skip) parasets = parasets[skip:] for iparaset,paraset in enumerate(parasets): parasets[iparaset] = list(map(float,paraset.strip().split())) parasets = np.array(parasets) # ------------------------- # read Morris v # ------------------------- ff = open(morris_v, "r") parachanged = ff.readlines() ff.close() if skip is None: skip = np.int(parachanged[0].strip().split(':')[1]) else: skip = np.int(skip) parachanged = parachanged[skip:] for iparachanged,parachan in enumerate(parachanged): parachanged[iparachanged] = np.int(parachan.strip()) parachanged = np.array(parachanged) # ------------------------- # calculate Elementary Effects # ------------------------- ee = np.zeros([dims_all,nkeys],dtype=float) ee_counter = np.zeros([dims_all,nkeys],dtype=int) ntraj = np.int( np.shape(parasets)[0] / (dims+1) ) nsets = np.shape(parasets)[0] for ikey in range(nkeys): for iset in range(nsets): ipara_changed = parachanged[iset] if ipara_changed != -1: ee_counter[ipara_changed,ikey] += 1 if ( len(np.shape(model_output[ikey])) == 1): # scalar model output ee[ipara_changed,ikey] += np.abs(model_output[ikey][iset]-model_output[ikey][iset+1]) / np.abs(parasets[iset,ipara_changed] - parasets[iset+1,ipara_changed]) elif ( len(np.shape(model_output[ikey])) == 2): # 1D model output ee[ipara_changed,ikey] += np.mean(np.abs(model_output[ikey][iset,:]-model_output[ikey][iset+1,:]) / np.abs(parasets[iset,ipara_changed] - parasets[iset+1,ipara_changed])) else: raise ValueError('Only scalar and 1D model outputs are supported!') for ikey in range(nkeys): for ipara in range(dims_all): if ee_counter[ipara,ikey] > 0: ee[ipara,ikey] /= ee_counter[ipara,ikey] # ------------------------- # write final file # ------------------------- # format: # # model output #1: 'out1' # # model output #2: 'out2' # # ii para_name elemeffect(ii),ii=1:3,jj=1:1 counter(ii),ii=1:3,jj=1:1 # 1 'x_1' 0.53458196335158181 5 # 2 'x_2' 7.0822368906630215 5 # 3 'x_3' 3.5460086652980554 5 f = open(outfile, 'w') for ikey in range(nkeys): f.write('# model output #'+str(ikey+1)+': '+keys[ikey]+'\n') f.write('# ii para_name elemeffect(ii),ii=1:'+str(dims_all)+',jj=1:'+str(nkeys)+' counter(ii),ii=1:'+str(dims_all)+',jj=1:'+str(nkeys)+' \n') for ipara in range(dims_all): f.write(str(ipara)+' '+para_name[ipara]+' '+' '.join(astr(ee[ipara,:],prec=8))+' '+' '.join(astr(ee_counter[ipara,:]))+'\n') f.close() print("wrote: '"+outfile+"'")

43.897436

405

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4.760479

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0.016667

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null

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null

0

1

0

55a8a143755092a98ad8640901e8dbdb8d58845f

9,439

py

Python

install/app_store/tk-framework-desktopserver/v1.3.1/python/tk_framework_desktopserver/command.py

JoanAzpeitia/lp_sg

e0ee79555e419dd2ae3a5f31e5515b3f40b22a62

[ "MIT" ]

null

install/app_store/tk-framework-desktopserver/v1.3.1/python/tk_framework_desktopserver/command.py

JoanAzpeitia/lp_sg

e0ee79555e419dd2ae3a5f31e5515b3f40b22a62

[ "MIT" ]

null

install/app_store/tk-framework-desktopserver/v1.3.1/python/tk_framework_desktopserver/command.py

JoanAzpeitia/lp_sg

e0ee79555e419dd2ae3a5f31e5515b3f40b22a62

[ "MIT" ]

1

2020-02-15T10:42:56.000Z

# Copyright (c) 2013 Shotgun Software Inc. # # CONFIDENTIAL AND PROPRIETARY # # This work is provided "AS IS" and subject to the Shotgun Pipeline Toolkit # Source Code License included in this distribution package. See LICENSE. # By accessing, using, copying or modifying this work you indicate your # agreement to the Shotgun Pipeline Toolkit Source Code License. All rights # not expressly granted therein are reserved by Shotgun Software Inc. import os import subprocess from threading import Thread from Queue import Queue import tempfile import sys import traceback from .logger import get_logger logger = get_logger(__name__) class ReadThread(Thread): """ Thread that reads a pipe. """ def __init__(self, p_out, target_queue): """ Constructor. :param p_out: Pipe to read. :param target_queue: Queue that will accumulate the pipe output. """ Thread.__init__(self) self.pipe = p_out self.target_queue = target_queue def run(self): """ Reads the contents of the pipe and adds it to the queue until the pipe is closed. """ while True: line = self.pipe.readline() # blocking read if line == '': break self.target_queue.put(line) class Command(object): @staticmethod def _create_temp_file(): """ :returns: Returns the path to a temporary file. """ handle, path = tempfile.mkstemp(prefix="desktop_server") os.close(handle) return path @staticmethod def call_cmd(args): """ Runs a command in a separate process. :param args: Command line tokens. :returns: A tuple containing (exit code, stdout, stderr). """ # The commands that are being run are probably being launched from Desktop, which would # have a TANK_CURRENT_PC environment variable set to the site configuration. Since we # preserve that value for subprocesses (which is usually the behavior we want), the DCCs # being launched would try to run in the project environment and would get an error due # to the conflict. # # Clean up the environment to prevent that from happening. env = os.environ.copy() vars_to_remove = ["TANK_CURRENT_PC"] for var in vars_to_remove: if var in env: del env[var] # Launch the child process # Due to discrepencies on how child file descriptors and shell=True are # handled on Windows and Unix, we'll provide two implementations. See the Windows # implementation for more details. if sys.platform == "win32": ret, stdout_lines, stderr_lines = Command._call_cmd_win32(args, env) else: ret, stdout_lines, stderr_lines = Command._call_cmd_unix(args, env) out = ''.join(stdout_lines) err = ''.join(stderr_lines) return ret, out, err @staticmethod def _call_cmd_unix(args, env): """ Runs a command in a separate process. Implementation for Unix based OSes. :param args: Command line tokens. :param env: Environment variables to set for the subprocess. :returns: A tuple containing (exit code, stdout, stderr). """ # Note: Tie stdin to a PIPE as well to avoid this python bug on windows # http://bugs.python.org/issue3905 # Queue code taken from: http://stackoverflow.com/questions/375427/non-blocking-read-on-a-subprocess-pipe-in-python stdout_lines = [] stderr_lines = [] try: process = subprocess.Popen( args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env ) process.stdin.close() stdout_q = Queue() stderr_q = Queue() stdout_t = ReadThread(process.stdout, stdout_q) stdout_t.setDaemon(True) stdout_t.start() stderr_t = ReadThread(process.stderr, stderr_q) stderr_t.setDaemon(True) stderr_t.start() # Popen.communicate() doesn't play nicely if the stdin pipe is closed # as it tries to flush it causing an 'I/O error on closed file' error # when run from a terminal # # to avoid this, lets just poll the output from the process until # it's finished process.wait() try: process.stdout.flush() process.stderr.flush() except IOError: # This fails on OSX 10.7, but it looks like there's no ill side effect # from failing on that platform so we can ignore it. logger.exception("Error while flushing file descriptor:") stdout_t.join() stderr_t.join() while not stdout_q.empty(): stdout_lines.append(stdout_q.get()) while not stderr_q.empty(): stderr_lines.append(stderr_q.get()) ret = process.returncode except StandardError: # Do not log the command line, it might contain sensitive information! logger.exception("Error running subprocess:") ret = 1 stderr_lines = traceback.format_exc().split() stderr_lines.append("%s" % args) return ret, stdout_lines, stderr_lines @staticmethod def _call_cmd_win32(args, env): """ Runs a command in a separate process. Implementation for Windows. :param args: Command line tokens. :param env: Environment variables to set for the subprocess. :returns: A tuple containing (exit code, stdout, stderr). """ stdout_lines = [] stderr_lines = [] try: stdout_path = Command._create_temp_file() stderr_path = Command._create_temp_file() # On Windows, file descriptors like sockets can be inherited by child # process and are only closed when the main process and all child # processes are closed. This is bad because it means that the port # the websocket server uses will never be released as long as any DCCs # or tank commands are running. Therefore, closing the Desktop and # restarting it for example wouldn't free the port and would give the # "port 9000 already in use" error we've seen before. # To avoid this, close_fds needs to be specified when launching a child # process. However, there's a catch. On Windows, specifying close_fds # also means that you can't share stdout, stdin and stderr with the child # process, which is required here because we want to capture the output # of the process. # Therefore on Windows we'll invoke the code in a shell environment. The # output will be redirected to two temporary files which will be read # when the child process is over. # Ideally, we'd be using this implementation on Unix as well. After all, # the syntax of the command line is the same. However, specifying shell=True # on Unix means that the following ["ls", "-al"] would be invoked like this: # ["/bin/sh", "-c", "ls", "-al"]. This means that only ls is sent to the # shell and -al is considered to be an argument of the shell and not part # of what needs to be launched. The naive solution would be to quote the # argument list and pass ["\"ls -al \""] to Popen, but that would ignore # the fact that there could already be quotes on that command line and # they would need to be escaped as well. Python 2's only utility to # escape strings for the command line is pipes.quote, which is deprecated. # Because of these reasons, we'll keep both implementations for now. args = args + ["1>", stdout_path, "2>", stderr_path] # Prevents the cmd.exe dialog from appearing on Windows. startupinfo = subprocess.STARTUPINFO() startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW process = subprocess.Popen( args, close_fds=True, startupinfo=startupinfo, env=env, shell=True ) process.wait() # Read back the output from the two. with open(stdout_path) as stdout_file: stdout_lines = [l for l in stdout_file] with open(stderr_path) as stderr_file: stderr_lines = [l for l in stderr_file] # Track the result code. ret = process.returncode except StandardError: logger.exception("Error running subprocess:") ret = 1 stderr_lines = [traceback.format_exc().split()] stderr_lines.append("%s" % args) # Don't lose any sleep over temporary files that can't be deleted. try: os.remove(stdout_path) except: pass try: os.remove(stderr_path) except: pass return ret, stdout_lines, stderr_lines

36.727626

123

0.604831

1,186

9,439

4.721754

0.306914

0.025536

0.018214

0.023571

0.19875

0.149821

0.13875

0.133393

0.096071

0

0.005035

0.32673

9,439

256

124

36.871094

0.876161

0.469753

0

0.304348

0

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1

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false

0.017391

0.069565

0

0.173913

0

null

0

null

0

1

0

55ae9ba4b65519bc33be7de8562a205f27c9a655

745

py

Python

brilws/cli/briltag_insertdata.py

xiezhen/brilws

e3652dd4506dff9d713184ff623b59bc11fbe2c7

[ "MIT" ]

1

2017-03-23T16:26:06.000Z

brilws/cli/briltag_insertdata.py

xiezhen/brilws

e3652dd4506dff9d713184ff623b59bc11fbe2c7

[ "MIT" ]

1

2017-03-24T15:02:20.000Z

2017-10-02T13:43:26.000Z

brilws/cli/briltag_insertdata.py

xiezhen/brilws

e3652dd4506dff9d713184ff623b59bc11fbe2c7

[ "MIT" ]

1

2019-12-06T09:23:01.000Z

""" Usage: briltag insertdata [options] Options: -h --help Show this screen. -c CONNECT Service name [default: onlinew] -p AUTHPATH Authentication file --name TAGNAME Name of the data tag --comments COMMENTS Comments on the tag """ from docopt import docopt from schema import Schema from brilws.cli import clicommonargs def validate(optdict): myvalidables = ['-c','-p','--name','--comments',str] argdict = dict((k,v) for k,v in clicommonargs.argvalidators.items() if k in myvalidables) s = Schema(argdict) result = s.validate(optdict) return result if __name__ == '__main__': print (docopt(__doc__,options_first=True))

25.689655

93

0.625503

88

745

5.147727

0.625

0.07064

0

0.271141

745

28

94

26.607143

0.834254

0.421477

0

0.067308

0

1

0.090909

false

0

0.272727

0

0.454545

0.090909

0

null

0

null

0

1

0

55b3f38a36b36ad5c48a9910aaae79865f7775ae

17,152

py

Python

techniques/volumerec.py

lleonart1984/rendezvous

f8f5e73fa1ede7c33d8cf08548bce1475a0cc8da

[ "MIT" ]

null

techniques/volumerec.py

lleonart1984/rendezvous

f8f5e73fa1ede7c33d8cf08548bce1475a0cc8da

[ "MIT" ]

null

techniques/volumerec.py

lleonart1984/rendezvous

f8f5e73fa1ede7c33d8cf08548bce1475a0cc8da

[ "MIT" ]

null

from rendering.manager import * from rendering.scenes import * from rendering.training import * import random import glm import os import numpy as np import math __VOLUME_RECONSTRUCTION_SHADERS__ = os.path.dirname(__file__)+"/shaders/VR" compile_shader_sources(__VOLUME_RECONSTRUCTION_SHADERS__) class RayGenerator(RendererModule): def __init__(self, device, output_dim: (int, int), mode: int, *args, **kwargs): self.output_dim = output_dim self.mode = mode self.camera_buffer = None super().__init__(device, *args, **kwargs) def setup(self): self.camera_buffer = self.device.create_uniform_buffer( ProjToWorld=glm.mat4 ) pipeline = self.device.create_compute_pipeline() pipeline.load_compute_shader(__VOLUME_RECONSTRUCTION_SHADERS__+"/raygen.comp.spv") pipeline.bind_storage_buffer(0, ShaderStage.COMPUTE, lambda: self.pipeline.rays) pipeline.bind_uniform(1, ShaderStage.COMPUTE, lambda: self.camera_buffer) pipeline.bind_constants( 0, ShaderStage.COMPUTE, dim=glm.ivec2, mode=int, seed=int ) pipeline.close() self.pipeline = pipeline def forward_render(self, inputs): origins, targets = inputs origins = origins.reshape(-1, 3) targets = targets.reshape(-1, 3) full_rays = torch.zeros(len(origins) * self.output_dim[0] * self.output_dim[1], 6, device=origins.device) for i, (o, t) in enumerate(zip(origins, targets)): self.pipeline.rays = self.wrap_tensor(torch.zeros(self.output_dim[0] * self.output_dim[1], 6, device=origins.device), False) # Setup camera proj = glm.perspective(45, self.output_dim[1] / self.output_dim[0], 0.01, 1000) view = glm.lookAt(glm.vec3(*o), glm.vec3(*t), glm.vec3(0, 1, 0)) proj_to_model = glm.inverse(proj * view) self.camera_buffer.ProjToWorld = proj_to_model with self.device.get_compute() as man: man.set_pipeline(self.pipeline) man.update_sets(0) man.update_constants( ShaderStage.COMPUTE, dim=glm.ivec2(self.output_dim[1], self.output_dim[0]), mode=self.mode, seed=np.random.randint(0, 10000000) ) man.dispatch_threads_2D(self.output_dim[1], self.output_dim[0]) t = self.get_tensor(self.pipeline.rays) full_rays[i*self.output_dim[0]*self.output_dim[1]:(i+1)*self.output_dim[0]*self.output_dim[1]] = t return [full_rays] class TransmittanceRenderer(RendererModule): def __init__(self, device, *args, **kwargs): super().__init__(device, *args, **kwargs) def setup(self): self.medium_buffer = self.device.create_uniform_buffer( scatteringAlbedo=glm.vec3, density=float, phase_g=float ) pipeline = self.device.create_compute_pipeline() pipeline.load_compute_shader(__VOLUME_RECONSTRUCTION_SHADERS__ + '/forward.comp.spv') pipeline.bind_storage_buffer(0, ShaderStage.COMPUTE, lambda: self.forward_pipeline.grid) pipeline.bind_storage_buffer(1, ShaderStage.COMPUTE, lambda: self.forward_pipeline.rays) pipeline.bind_storage_buffer(2, ShaderStage.COMPUTE, lambda: self.forward_pipeline.transmittances) pipeline.bind_uniform(3, ShaderStage.COMPUTE, lambda: self.medium_buffer) pipeline.bind_constants(0, ShaderStage.COMPUTE, grid_dim=glm.ivec3, number_of_rays=int ) pipeline.close() self.forward_pipeline = pipeline pipeline = self.device.create_compute_pipeline() pipeline.load_compute_shader(__VOLUME_RECONSTRUCTION_SHADERS__ + '/backward.comp.spv') pipeline.bind_storage_buffer(0, ShaderStage.COMPUTE, lambda: self.backward_pipeline.grid_gradients) pipeline.bind_storage_buffer(1, ShaderStage.COMPUTE, lambda: self.backward_pipeline.rays) pipeline.bind_storage_buffer(2, ShaderStage.COMPUTE, lambda: self.backward_pipeline.transmittances) pipeline.bind_storage_buffer(3, ShaderStage.COMPUTE, lambda: self.backward_pipeline.transmittance_gradients) pipeline.bind_uniform(4, ShaderStage.COMPUTE, lambda: self.medium_buffer) pipeline.bind_constants(0, ShaderStage.COMPUTE, grid_dim=glm.ivec3, number_of_rays=int ) pipeline.close() self.backward_pipeline = pipeline def set_medium(self, scattering_albedo: glm.vec3, density: float, phase_g: float): self.medium_buffer.scatteringAlbedo = scattering_albedo self.medium_buffer.density = density self.medium_buffer.phase_g = phase_g def forward_render(self, inputs): rays, grid = inputs grid_dim = grid.shape ray_count = torch.numel(rays) // 6 self.forward_pipeline.rays = self.wrap_tensor(rays) self.forward_pipeline.grid = self.wrap_tensor(grid) self.forward_pipeline.transmittances = self.wrap_tensor(torch.zeros(ray_count, 3, device=rays.device), False) with self.device.get_compute() as man: man.set_pipeline(self.forward_pipeline) man.update_sets(0) man.update_constants(ShaderStage.COMPUTE, grid_dim=glm.ivec3(grid_dim[2], grid_dim[1], grid_dim[0]), number_of_rays=ray_count ) man.dispatch_threads_1D(ray_count) return [self.get_tensor(self.forward_pipeline.transmittances)] def backward_render(self, inputs, outputs, output_gradients): rays, grid = inputs transmittances, = outputs transmittance_gradients, = output_gradients grid_dim = grid.shape ray_count = torch.numel(rays) // 6 self.backward_pipeline.rays = self.wrap_tensor(rays) self.backward_pipeline.transmittances = self.wrap_tensor(transmittances) self.backward_pipeline.transmittance_gradients = self.wrap_tensor(transmittance_gradients) self.backward_pipeline.grid_gradients = self.wrap_tensor(torch.zeros_like(grid)) with self.device.get_compute() as man: man.set_pipeline(self.backward_pipeline) man.update_sets(0) man.update_constants(ShaderStage.COMPUTE, grid_dim=glm.ivec3(grid_dim[2], grid_dim[1], grid_dim[0]), number_of_rays=ray_count ) man.dispatch_threads_1D(ray_count) return [None, self.get_tensor(self.backward_pipeline.grid_gradients)] class ResampleGrid(RendererModule): def __init__(self, device: DeviceManager, output_dim: (int, int, int), *args, **kwargs): self.output_dim = output_dim super().__init__(device, *args, **kwargs) def setup(self): pipeline = self.device.create_compute_pipeline() pipeline.load_compute_shader(__VOLUME_RECONSTRUCTION_SHADERS__ + "/resampling.comp.spv") pipeline.bind_storage_buffer(0, ShaderStage.COMPUTE, lambda: self.pipeline.dst_grid) pipeline.bind_storage_buffer(1, ShaderStage.COMPUTE, lambda: self.pipeline.src_grid) pipeline.bind_constants(0, ShaderStage.COMPUTE, dst_grid_dim=glm.ivec3, rem0=float, src_grid_dim=glm.ivec3, rem1=float ) pipeline.close() self.pipeline = pipeline def forward_render(self, inputs: List[torch.Tensor]): src_grid, = inputs self.pipeline.src_grid = self.wrap_tensor(src_grid) self.pipeline.dst_grid = self.wrap_tensor(torch.zeros(self.output_dim, device=src_grid.device)) src_grid_dim = src_grid.shape dst_grid_dim = self.output_dim with self.device.get_compute() as man: man.set_pipeline(self.pipeline) man.update_sets(0) man.update_constants(ShaderStage.COMPUTE, dst_grid_dim=glm.ivec3(dst_grid_dim[2], dst_grid_dim[1], dst_grid_dim[0]), src_grid_dim=glm.ivec3(src_grid_dim[2], src_grid_dim[1], src_grid_dim[0]) ) man.dispatch_threads_1D(dst_grid_dim[0] * dst_grid_dim[1] * dst_grid_dim[0]) return [self.get_tensor(self.pipeline.dst_grid)] class TransmittanceGenerator(Technique): def __init__(self, grid, output_image): super().__init__() self.grid = grid self.output_image = output_image self.width, self.height = output_image.width, output_image.height def __setup__(self): # rays self.rays = self.create_buffer(6 * 4 * self.width * self.height, BufferUsage.STORAGE | BufferUsage.TRANSFER_SRC | BufferUsage.TRANSFER_DST, MemoryProperty.GPU) # Transmittance self.transmittances = self.create_buffer(3 * 4 * self.width * self.height, BufferUsage.STORAGE | BufferUsage.TRANSFER_SRC | BufferUsage.TRANSFER_DST, MemoryProperty.GPU) # camera buffer self.camera_buffer = self.create_uniform_buffer( ProjToWorld=glm.mat4 ) # medium properties self.medium_buffer = self.create_uniform_buffer( scatteringAlbedo=glm.vec3, density=float, phase_g=float ) pipeline = self.create_compute_pipeline() pipeline.load_compute_shader(__VOLUME_RECONSTRUCTION_SHADERS__+'/generator.comp.spv') pipeline.bind_storage_image(0, ShaderStage.COMPUTE, lambda: self.output_image) pipeline.bind_storage_image(1, ShaderStage.COMPUTE, lambda: self.grid) pipeline.bind_storage_buffer(2, ShaderStage.COMPUTE, lambda: self.rays) pipeline.bind_storage_buffer(3, ShaderStage.COMPUTE, lambda: self.transmittances) pipeline.bind_uniform(4, ShaderStage.COMPUTE, lambda: self.camera_buffer) pipeline.bind_uniform(5, ShaderStage.COMPUTE, lambda: self.medium_buffer) pipeline.close() self.pipeline = pipeline self.set_camera(glm.vec3(0,0,-3), glm.vec3(0,0,0)) self.set_medium(glm.vec3(1,1,1), 10, 0.875) def set_camera(self, look_from: glm.vec3, look_to: glm.vec3): # Setup camera proj = glm.perspective(45, self.width / self.height, 0.01, 1000) view = glm.lookAt(look_from, look_to, glm.vec3(0, 1, 0)) proj_to_model = glm.inverse(proj * view) self.camera_buffer.ProjToWorld = proj_to_model def set_medium(self, scattering_albedo: glm.vec3, density: float, phase_g: float): self.medium_buffer.scatteringAlbedo = scattering_albedo self.medium_buffer.density = density self.medium_buffer.phase_g = phase_g def __dispatch__(self): with self.get_compute() as man: man.set_pipeline(self.pipeline) man.update_sets(0) man.dispatch_threads_2D(self.width, self.height) class TransmittanceForward(Technique): def __init__(self, rays_resolver, grid_dim: (int, int, int), grid_resolver, transmittance_resolver): super().__init__() self.rays_resolver = rays_resolver # input self.grid_resolver = grid_resolver # params self.transmittance_resolver = transmittance_resolver # output self.grid_dim = glm.ivec3(grid_dim) def set_medium(self, scattering_albedo: glm.vec3, density: float, phase_g: float): self.medium_buffer.scatteringAlbedo = scattering_albedo self.medium_buffer.density = density self.medium_buffer.phase_g = phase_g def __setup__(self): # medium properties self.medium_buffer = self.create_uniform_buffer( scatteringAlbedo=glm.vec3, density=float, phase_g=float ) pipeline = self.create_compute_pipeline() pipeline.load_compute_shader(__VOLUME_RECONSTRUCTION_SHADERS__ + '/forward.comp.spv') pipeline.bind_storage_buffer(0, ShaderStage.COMPUTE, self.grid_resolver) pipeline.bind_storage_buffer(1, ShaderStage.COMPUTE, self.rays_resolver) pipeline.bind_storage_buffer(2, ShaderStage.COMPUTE, self.transmittance_resolver) pipeline.bind_uniform(3, ShaderStage.COMPUTE, lambda: self.medium_buffer) pipeline.bind_constants(0, ShaderStage.COMPUTE, grid_dim = glm.ivec3, number_of_rays = int ) pipeline.close() self.pipeline = pipeline self.set_medium(glm.vec3(1, 1, 1), 10, 0.875) def __dispatch__(self): rays = self.rays_resolver() with self.get_compute() as man: man.set_pipeline(self.pipeline) man.update_sets(0) ray_count = rays.size // (4*3*2) man.update_constants(ShaderStage.COMPUTE, grid_dim=self.grid_dim, number_of_rays=ray_count ) man.dispatch_threads_1D(ray_count) class TransmittanceBackward(Technique): def __init__(self, rays, grid_dim, gradient_densities, transmittances, gradient_transmittances): super().__init__() self.grid_dim = grid_dim self.rays = rays # buffer with rays configurations (origin, direction) self.gradient_densities = gradient_densities # Flatten grid 512x512x512 used as parameters self.transmittances = transmittances # Float with transmittance for each ray self.gradient_transmittances = gradient_transmittances self.pipeline = None def set_medium(self, scattering_albedo: glm.vec3, density: float, phase_g: float): self.medium_buffer.scatteringAlbedo = scattering_albedo self.medium_buffer.density = density self.medium_buffer.phase_g = phase_g def __setup__(self): # medium properties self.medium_buffer = self.create_uniform_buffer( scatteringAlbedo=glm.vec3, density=float, phase_g=float ) pipeline = self.create_compute_pipeline() pipeline.load_compute_shader(__VOLUME_RECONSTRUCTION_SHADERS__ + '/backward.comp.spv') pipeline.bind_storage_buffer(0, ShaderStage.COMPUTE, lambda: self.gradient_densities) pipeline.bind_storage_buffer(1, ShaderStage.COMPUTE, lambda: self.rays) pipeline.bind_storage_buffer(2, ShaderStage.COMPUTE, lambda: self.transmittances) pipeline.bind_storage_buffer(3, ShaderStage.COMPUTE, lambda: self.gradient_transmittances) pipeline.bind_uniform(4, ShaderStage.COMPUTE, lambda: self.medium_buffer) pipeline.bind_constants(0, ShaderStage.COMPUTE, grid_dim=glm.ivec3, number_of_rays=int ) pipeline.close() self.pipeline = pipeline self.set_medium(glm.vec3(1, 1, 1), 10, 0.875) def __dispatch__(self): with self.get_compute() as man: man.clear_buffer(self.gradient_densities) # Zero grad man.set_pipeline(self.pipeline) man.update_sets(0) ray_count = self.rays.size // (4 * 3 * 2) man.update_constants(ShaderStage.COMPUTE, grid_dim=self.grid_dim, number_of_rays=ray_count ) man.dispatch_threads_1D(ray_count) class UpSampleGrid(Technique): def __init__(self): self.src_grid = None self.dst_grid = None self.src_grid_dim = glm.ivec3(0,0,0) self.dst_grid_dim = glm.ivec3(0,0,0) def set_src_grid(self, grid_dim, grid): self.src_grid = grid self.src_grid_dim = grid_dim def set_dst_grid(self, grid_dim, grid): self.dst_grid = grid self.dst_grid_dim = grid_dim def __setup__(self): pipeline = self.create_compute_pipeline() pipeline.load_compute_shader(__VOLUME_RECONSTRUCTION_SHADERS__+"/initialize.comp.spv") pipeline.bind_storage_buffer(0, ShaderStage.COMPUTE, lambda: self.dst_grid) pipeline.bind_storage_buffer(1, ShaderStage.COMPUTE, lambda: self.src_grid) pipeline.bind_constants(0, ShaderStage.COMPUTE, dst_grid_dim=glm.ivec3, rem0=float, src_grid_dim=glm.ivec3, rem1=float ) pipeline.close() self.pipeline = pipeline def __dispatch__(self): with self.get_compute() as man: man.set_pipeline(self.pipeline) man.update_sets(0) man.update_constants(ShaderStage.COMPUTE, dst_grid_dim=self.dst_grid_dim, src_grid_dim=self.src_grid_dim ) man.dispatch_threads_1D(self.dst_grid_dim.x * self.dst_grid_dim.y * self.dst_grid_dim.z) man.gpu_to_cpu(self.dst_grid)

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0.65007

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17,152

5.193787

0.08925

0.037216

0.061521

0.071774

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17,152

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45.983914

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0.148148

0

null

0

null

0

1

0

55b6264d004418dd7f3a7bb277c12e4c208f7910

868

py

Python

basics/merge_sort.py

zi-NaN/algorithm_exercise

817916a62774145fe6387b715f76c5badbf99197

[ "MIT" ]

null

basics/merge_sort.py

zi-NaN/algorithm_exercise

817916a62774145fe6387b715f76c5badbf99197

[ "MIT" ]

null

basics/merge_sort.py

zi-NaN/algorithm_exercise

817916a62774145fe6387b715f76c5badbf99197

[ "MIT" ]

1

2018-11-21T05:14:07.000Z

def _merge_sort(arr:'list'): if len(arr) <= 1: return arr begin = 0 end = len(arr)-1 middle = (begin+end)//2 first = _merge_sort(arr[begin:middle+1]) second = _merge_sort(arr[middle+1:end+1]) # merge ptr1 = begin ptr2 = middle+1 ptr = 0 while(ptr1<middle+1 and ptr2<end+1): if first[ptr1] < second[ptr2-middle-1]: arr[ptr] = first[ptr1] ptr1 += 1 else: arr[ptr] = second[ptr2-middle-1] ptr2 += 1 ptr += 1 # print(ptr1, ptr2) while(ptr1 < middle+1): arr[ptr] = first[ptr1] ptr1 += 1 ptr += 1 while(ptr2 < end+1): arr[ptr] = second[ptr2-middle-1] ptr2 += 1 ptr += 1 return arr # test if __name__ == '__main__': print(_merge_sort([1, 3, 2]))

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0.483871

117

868

3.452991

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0.138614

0.108911

0.126238

0.292079

0.158416

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0.080882

0.373272

868

36

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24.111111

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0.433333

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0.014981

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1

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false

0

0.1

0.033333

0

null

0

null

0

1

0

55b7410f25633189b2b806b878e6eeb2f52c7ecc

679

py

Python

Data_Science/Python-Estatistica/stats-ex8.py

maledicente/cursos

00ace48da7e48b04485e4ca97b3ca9ba5f33a283

[ "MIT" ]

1

2021-05-03T22:59:38.000Z

Data_Science/Python-Estatistica/stats-ex8.py

maledicente/cursos

00ace48da7e48b04485e4ca97b3ca9ba5f33a283

[ "MIT" ]

null

Data_Science/Python-Estatistica/stats-ex8.py

maledicente/cursos

00ace48da7e48b04485e4ca97b3ca9ba5f33a283

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit def cinematica(t,s0,v0,a): s = s0 + v0*t +(a*t*t/2.0) return s t = np.linspace(0, 5, 500) s0 = 0.5 v0 = 2.0 a = 1.5 s_noise = 0.5 * np.random.normal(size=t.size) s = cinematica(t,s0,v0,a) sdata = s + s_noise coefs, pcov = curve_fit(cinematica, t, sdata) plt.plot(t, sdata, 'b-', label='Deslocamento') plt.plot(t, cinematica(t, *coefs), 'r-',label='Função ajustada') plt.xlabel('Tempo') plt.ylabel('Deslocamento') plt.title('Ajuste de curva') plt.legend() plt.show() print("Espaço inicial= %f" %coefs[0]) print("Velocidade inicial= %f" %coefs[1]) print("Aceleração= %f" %coefs[2])

20.575758

64

0.673049

123

679

3.682927

0.447154

0.09713

0.057395

0.066225

0.07064

0

0.044369

0.136966

679

33

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0.125

0

null

0

null

0

1

0

55b93809c23b2f231b7acf1f7f0608d40af2f69c

1,828

py

Python

run.py

Gandor26/covid-open

50dcb773160edc16b107785a6bb32ae6f82fc9a7

[ "MIT" ]

12

2020-10-29T20:52:26.000Z

2021-11-10T14:11:59.000Z

run.py

Gandor26/covid-open

50dcb773160edc16b107785a6bb32ae6f82fc9a7

[ "MIT" ]

1

2021-02-16T09:48:39.000Z

2021-03-20T04:21:54.000Z

run.py

Gandor26/covid-open

50dcb773160edc16b107785a6bb32ae6f82fc9a7

[ "MIT" ]

1

2020-12-05T15:51:43.000Z

from typing import Optional, Dict from pathlib import Path from copy import deepcopy from tqdm import tqdm import torch as pt from torch import Tensor, nn from torch.optim import Adam def train( train_data: Dict[str, Tensor], valid_data: Dict[str, Tensor], model: nn.Module, optimizer: Adam, model_path: Path, n_epochs: int, test_size: Optional[int] = None, log_step: int = 10, patience: int = 10, ) -> None: prog_bar = tqdm(total=n_epochs, unit='epoch') best_valid = float('inf') stop_counter = patience for epoch in range(n_epochs): prog_bar.update() model = model.train() loss_train, _ = model(**train_data, test_size=test_size) optimizer.zero_grad() loss_train.backward() optimizer.step() postfix = {'train_loss': loss_train.item()} if (epoch+1) % log_step == 0: if valid_data is not None: model = model.eval() with pt.no_grad(): loss_valid, _ = model(**valid_data) loss_valid = loss_valid.item() postfix['valid_loss'] = loss_valid if loss_valid < best_valid: best_valid = loss_valid stop_counter = patience else: stop_counter -= 1 if stop_counter == 0: break prog_bar.set_postfix(**postfix) prog_bar.close() pt.save(model.state_dict(), model_path) def inference( data: Dict[str, Tensor], model: nn.Module, model_path: Path, ): model.load_state_dict(pt.load(model_path)) model = model.eval() with pt.no_grad(): _, pr = model(**data, test_size=0) pr = pr.clamp_min_(0.0) return pr

29.967213

64

0.565646

228

1,828

4.307018

0.328947

0.05499

0.033605

0.051935

0.114053

0

0.009031

0.333698

1,828

61

65

29.967213

0.797209

0

0.175439

0

0.015309

0

1

0.035088

false

0

0.122807

0

0.175439

0

null

0

null

0

1

0

55bb1301f3cfe948295e5ac6f60a5f73e88c2c17

975

py

Python

python/StatsUtil.py

cbaldassano/Parcellating-connectivity

a98142a6b0dc10e9cb6f6e603cb5334996d018ec

[ "Unlicense" ]

2

2020-08-17T21:06:28.000Z

2021-05-10T14:37:16.000Z

python/StatsUtil.py

cbaldassano/Parcellating-connectivity

a98142a6b0dc10e9cb6f6e603cb5334996d018ec

[ "Unlicense" ]

null

python/StatsUtil.py

cbaldassano/Parcellating-connectivity

a98142a6b0dc10e9cb6f6e603cb5334996d018ec

[ "Unlicense" ]

3

2018-07-06T17:08:47.000Z

2019-10-09T18:58:31.000Z

import numpy as np # Compute normalized mutual information between two parcellations z1 and z2 def NMI(z1, z2): N = len(z1) assert N == len(z2) p1 = np.bincount(z1)/N p1[p1 == 0] = 1 H1 = (-p1*np.log(p1)).sum() p2 = np.bincount(z2)/N p2[p2 == 0] = 1 H2 = (-p2*np.log(p2)).sum() joint = np.histogram2d(z1,z2,[range(0,z1.max()+2), range(0,z2.max()+2)], normed=True) joint_p = joint[0] pdiv = joint_p/np.outer(p1,p2) pdiv[joint_p == 0] = 1 MI = (joint_p*np.log(pdiv)).sum() if MI == 0: NMI = 0 else: NMI = MI/np.sqrt(H1*H2) return NMI # (Approximately) return whether an array is symmetric def CheckSymApprox(D): # Random indices to check for symmetry sym_sub = np.random.randint(D.shape[0], size=(1000,2)) a = np.ravel_multi_index((sym_sub[:,0],sym_sub[:,1]), dims=np.shape(D)) b = np.ravel_multi_index((sym_sub[:,1],sym_sub[:,0]), dims=np.shape(D)) sym = np.all(D.flat[a] == D.flat[b]) return sym

24.375

75

0.610256

177

975

3.288136

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0.051546

0.034364

0.058419

0.079038

0

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0.198974

975

40

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24.375

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0

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0.074074

false

0

0.037037

0

0.185185

0

null

0

null

0

1

0

55c01bcc5785d0af3f6437a91b853450fda2bb63

2,531

py

Python

gdesk/panels/imgview/quantiles.py

thocoo/gamma-desk

9cb63a65fe23e30e155b3beca862f369b7fa1b7e

[ "Apache-2.0" ]

null

gdesk/panels/imgview/quantiles.py

thocoo/gamma-desk

9cb63a65fe23e30e155b3beca862f369b7fa1b7e

[ "Apache-2.0" ]

8

2021-04-09T11:31:43.000Z

2021-06-09T09:07:18.000Z

gdesk/panels/imgview/quantiles.py

thocoo/gamma-desk

9cb63a65fe23e30e155b3beca862f369b7fa1b7e

[ "Apache-2.0" ]

null

import numpy as np from .fasthist import hist2d stdquant = np.ndarray(13) stdquant[0] = (0.0000316712418331200) #-4 sdev stdquant[1] = (0.0013498980316301000) #-3 sdev stdquant[2] = (0.0227501319481792000) #-2 sdev stdquant[3] = (0.05) stdquant[4] = (0.1586552539314570000) #-1 sdev or lsdev stdquant[5] = (0.25) #first quartile stdquant[6] = (0.50) #median stdquant[7] = (0.75) #third quartile stdquant[8] = (0.8413447460685430000) #+1 sdev or usdev stdquant[9] = (0.95) stdquant[10] = (0.9772498680518210000) #+2 sdev stdquant[11] = (0.9986501019683700000) #+3 sdev stdquant[12] = (0.9999683287581670000) #+4 sdev def get_standard_quantiles(arr, bins=64, step=None, quantiles=None): hist, starts, stepsize = hist2d(arr, bins, step, plot=False) cumhist = np.cumsum(hist) if quantiles is None: quantiles = stdquant else: quantiles = np.array(quantiles) n = len(quantiles) npix = np.multiply.reduce(arr.shape) quantiles *= npix thresh = [0] * n #TO DO: speed up by using interpolation function of numpy for ind in range(n): thresh[ind] = starts[(cumhist < quantiles[ind]).sum()] return thresh def get_sigma_range(arr, sigma=1, bins=64, step=None): if sigma == 1: return get_standard_quantiles(arr, bins, step, (stdquant[4], stdquant[8])) elif sigma == 2: return get_standard_quantiles(arr, bins, step, (stdquant[2], stdquant[10])) elif sigma == 3: return get_standard_quantiles(arr, bins, step, (stdquant[1], stdquant[11])) elif sigma == 4: return get_standard_quantiles(arr, bins, step, (stdquant[0], stdquant[12])) def get_sigma_range_for_hist(starts, hist, sigma): cumhist = np.cumsum(hist) if sigma==1: quantiles = np.array((stdquant[4], stdquant[8])) elif sigma==2: quantiles = np.array((stdquant[2], stdquant[10])) elif sigma==3: quantiles = np.array((stdquant[1], stdquant[11])) elif sigma==4: quantiles = np.array((stdquant[0], stdquant[12])) n = len(quantiles) npix = cumhist[-1] quantiles *= npix thresh = [0] * n #TO DO: speed up by using interpolation function of numpy for ind in range(n): thresh[ind] = starts[(cumhist < quantiles[ind]).sum()] return thresh

34.671233

83

0.590281

317

2,531

4.662461

0.277603

0.028417

0.067659

0.077808

0.44452

0.397835

0.2977

0.175913

0

0.1322

0.279731

2,531

73

84

34.671233

0.678552

0.086922

0

0.392857

0

1

0.053571

false

0

0.035714

0

0.196429

0

null

0

null

0

1

0

e94dc72d516776aab0f1e035f052d60121476db1

1,981

py

Python

create_h5ad.py

xmuyulab/DAISM-XMBD

916e18a1f111789a1c0bd3c1209d5a73813f3d3a

[ "MIT" ]

2

2021-11-05T00:43:16.000Z

2021-12-14T08:39:29.000Z

create_h5ad.py

biosyy/DAISM-XMBD

a76f976db8c33ef33f78533a5a2be50a85148e79

[ "MIT" ]

2

2021-01-14T19:40:46.000Z

2021-01-14T19:41:14.000Z

create_h5ad.py

biosyy/DAISM-XMBD

a76f976db8c33ef33f78533a5a2be50a85148e79

[ "MIT" ]

1

2021-08-30T15:11:45.000Z

############################## ## cread purified h5ad file ## ############################## # input: annotation table and the whole expression profile # output: purified h5ad file import os import pandas as pd import anndata import argparse import gc import numpy as np parser = argparse.ArgumentParser(description='cread purified h5ad file for DAISM-XMBD') parser.add_argument("-anno", type=str, help="annotation table (contains 'sample.name' and 'cell.type' two columns)", default=None) parser.add_argument("-exp", type=str, help="the whole expression profile (sample.name in column and gene symbol in row)", default=None) parser.add_argument("-outdir", type=str, help="the directory to store h5ad file", default="example/") parser.add_argument("-prefix",type=str,help="the prefix of h5ad file",default= "purified") def main(): inputArgs = parser.parse_args() if os.path.exists(inputArgs.outdir)==False: os.mkdir(inputArgs.outdir) anno_table = pd.read_csv(inputArgs.anno) cell_list = list(anno_table['cell.type'].unique()) exp = pd.read_csv(inputArgs.exp,sep="\t",index_col=0) adata = [] for cell in cell_list: tmp = anno_table[anno_table['cell.type']==cell] sample_list = tmp['sample.name'] sample_list_inter = list(set(sample_list).intersection(list(exp.columns))) exp_select=exp[sample_list_inter] anno = pd.DataFrame(np.repeat(cell,exp_select.shape[1]),columns=['cell.type']) adata.append(anndata.AnnData(X=exp_select.T.values, obs=anno, var=pd.DataFrame(columns=[],index=list(exp_select.index)))) for i in range(1, len(adata)): print("Concatenating " + str(i)) adata[0] = adata[0].concatenate(adata[1]) del adata[1] gc.collect() print(len(adata)) adata = adata[0] adata.write(inputArgs.outdir+'/'+inputArgs.prefix+'.h5ad') if __name__ == "__main__": main()

34.155172

135

0.649167

265

1,981

4.732075

0.373585

0.031898

0.054226

0.033493

0.044657

0

0.008679

0.185765

1,981

58

136

34.155172

0.768754

0.055023

0

0.19103

0

1

0.026316

false

0

0.157895

0

0.184211

0.052632

0

null

0

null

0

1

0

e94e1af31de28cb3ee32e1feeddbef4991bf43d4

1,424

py

Python

FM_Tuning.py

RomanGutin/GEMSEC

cb2c26d4747cbd3d4c048787ca41665ef0e64155

[ "MIT" ]

null

FM_Tuning.py

RomanGutin/GEMSEC

cb2c26d4747cbd3d4c048787ca41665ef0e64155

[ "MIT" ]

null

FM_Tuning.py

RomanGutin/GEMSEC

cb2c26d4747cbd3d4c048787ca41665ef0e64155

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Nov 29 13:56:44 2018 @author: RomanGutin """ import pandas as pd import numpy as np #Frequency Tuning Loop amino_letter = ['A','R','D','N','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V'] length_scores =[4,8,6,6,5,7,7,4,7,5,6,8,7,8,5,5,5,9,8,5] FM_df = pd.DataFrame(0, index= just_let.index, columns= range(0,81)) FM_score_dict = dict(zip(amino_letter,length_scores)) #splitting amino letter into new independent variables based on its length score# fm_letter_dict ={} for letter in amino_letter: new_vars =[] for i in range(FM_score_dict[letter]): new_vars.append(letter+str(i+1)) fm_letter_dict[letter]=new_vars #generate new FM_tuned dataframe for seq in FM_df.index: letter_list= list(seq) for letter in letter_list: for var in fm_letter_dict[letter]: row= FM_df.loc[seq,:] spot= row[row==0].index[0] FM_df.loc[seq,spot]= var FM_df= pd.read_csv('Frequency Tuned Dataset') #data after frequency tuning wit FM_df.set_index('sequence', inplace= True) FM_df_arr = np.array(FM_df.values, dtype=[('O', np.float)]).astype(np.float) #New letter to weight holding the new FM tuned variables ltw_fm_MLE={} for amino in amino_letter: for var in fm_letter_dict[amino]: ltw_fm_MLE[var]= ltw_AM_n[amino] ltw_fm_MLE = np.load('ltw_fm_MLE.npy').item()

30.297872

96

0.656601

256

1,424

3.472656

0.441406

0.035996

0.053993

0.038245

0.07649

0.044994

0

0.034305

0.18118

1,424

47

97

30.297872

0.72813

0.213483

0

0.059621

0

1

0

false

0

0.074074

0

0.074074

0

null

0

null

0

1

0

e94e9483c973c25abe2c71d5816ab7d9b774441e

692

py

Python

unified_api/brokers/kafka/consumer.py

campos537/deep-fashion-system

1de31dd6260cc967e1832cff63ae7e537a3a4e9d

[ "Unlicense" ]

1

2021-04-06T00:43:26.000Z

unified_api/brokers/kafka/consumer.py

campos537/deep-fashion-system

1de31dd6260cc967e1832cff63ae7e537a3a4e9d

[ "Unlicense" ]

null

unified_api/brokers/kafka/consumer.py

campos537/deep-fashion-system

1de31dd6260cc967e1832cff63ae7e537a3a4e9d

[ "Unlicense" ]

null

from kafka import KafkaConsumer class Consumer: def __init__(self, config): bootstrap_server = config.get( "bootstrap_server") + ":" + config.get("port") self.consumer = KafkaConsumer(config.get( "subscription_id_2"), bootstrap_servers=bootstrap_server, api_version=(0, 10), auto_offset_reset='earliest', enable_auto_commit=True, group_id="test") self.messages = [] def get_message(self): if len(self.messages) > 0: mes = self.messages.pop(0) return mes def listen(self): for message in self.consumer: self.messages.append(message.value)

34.6

109

0.601156

77

692

5.181818

0.558442

0.120301

0.105263

0.120301

0

0.012245

0.291908

692

19

110

36.421053

0.802041

0

0.072254

0

1

0.1875

false

0

0.0625

0

0.375

0

null

0

null

0

1

0

e94ef8f2fd09f77bca0e59bab465fb16e55c0ca1

2,159

py

Python

utils.py

mino2401200231/File-convertor

6fb438dc5f37bf0efd78e18e4848b4cdb0331343

[ "MIT" ]

null

utils.py

mino2401200231/File-convertor

6fb438dc5f37bf0efd78e18e4848b4cdb0331343

[ "MIT" ]

null

utils.py

mino2401200231/File-convertor

6fb438dc5f37bf0efd78e18e4848b4cdb0331343

[ "MIT" ]

2

2021-08-12T06:37:52.000Z

2021-09-05T13:03:36.000Z

# utilities import os from re import sub import uuid import subprocess # Image To Pdf import img2pdf # PDF To Images from pdf2image import convert_from_path # PDF To Word from pdf2docx import parse _BASE_DIR = os.getcwd() _BASE_DIR_FILE = os.path.join(_BASE_DIR, "files") def process_image_to_pdf(files, pdf_name): img = [] with open(f"{_BASE_DIR_FILE}/{pdf_name}.pdf","wb") as fil: for fname in files: path = os.path.join(_BASE_DIR_FILE, fname) img.append(path) fil.write(img2pdf.convert(img)) return pdf_name def process_word_to_pdf(file): file_address = os.path.join(_BASE_DIR_FILE, file) command = ['lowriter' ,'--convert-to','pdf' , file_address , "--outdir", _BASE_DIR_FILE] command_run = subprocess.run(command) file_name = -1 if command_run.returncode == 0: file_name = ".".join(file.split(".")[:-1]) + ".pdf" return file_name def process_pdf_to_images(file): file_address = os.path.join(_BASE_DIR_FILE, file) folder_name = str(uuid.uuid1()) folder_address = os.path.join(_BASE_DIR_FILE, folder_name) os.mkdir(folder_address) try: convert_from_path(file_address, output_folder=folder_address, fmt="jpeg", thread_count=10, jpegopt="quality") return folder_address except: import shutil shutil.rmtree(folder_address) return -1 def process_pdf_to_word(file): file_address = os.path.join(_BASE_DIR_FILE, file) word_file = str(uuid.uuid1()) + ".docx" word_file_address = os.path.join(_BASE_DIR_FILE, word_file) try: parse(file_address, word_file_address, multi_processing=True) return word_file_address except: return -1 def del_user_files(list): for file in list: file_address = os.path.join(_BASE_DIR_FILE, file) try: os.remove(file_address) except: pass def del_one_file(file): try: os.remove(file) except: try: file_address = os.path.join(_BASE_DIR_FILE, file) os.remove(file_address) except: pass pass return 1

26.329268

117

0.656322

304

2,159

4.355263

0.253289

0.068731

0.09139

0.095166

0.282477

0.269637

0.190332

0.169184

0.145015

0.090634

0

0.008526

0.239463

2,159

82

118

26.329268

0.797808

0.022233

0

0.34375

0

0.043189

0.014713

0

1

0.09375

false

0.046875

0.125

0

0.328125

0

null

0

null

0

1

0

e950fb1913401e7e3634e1210cfe24f9fddcf950

2,026

py

Python

screens/tasks/tasks.py

athrn/kognitivo

15822338778213c09ea654ec4e06a300129f9478

[ "Apache-2.0" ]

80

2017-11-13T21:58:55.000Z

2022-01-03T20:10:42.000Z

screens/tasks/tasks.py

athrn/kognitivo

15822338778213c09ea654ec4e06a300129f9478

[ "Apache-2.0" ]

null

screens/tasks/tasks.py

athrn/kognitivo

15822338778213c09ea654ec4e06a300129f9478

[ "Apache-2.0" ]

21

2017-11-14T09:47:41.000Z

2021-11-23T06:44:31.000Z

from kivy.uix.screenmanager import Screen from kivy.properties import StringProperty, ObjectProperty, NumericProperty, ListProperty, BooleanProperty from kivy.app import App from kivy.logger import Logger from library_widgets import TrackingScreenMixin from utils import import_kv import_kv(__file__) class TasksScreen(TrackingScreenMixin, Screen): family = StringProperty(None, allownone=True) played_times = NumericProperty() tasks = ListProperty() _main_manager = ObjectProperty() loading = ObjectProperty() quick_test = BooleanProperty(False) def on_quick_test(self, *args): if self._main_manager: self.update_content() @property def main_manager(self): if not self._main_manager: from .content import TaskScreenManager self._main_manager = TaskScreenManager() return self._main_manager def update_content(self, *args, **kwargs): if self.quick_test: self.main_manager.start_test(self.family, self.tasks) self.main_manager.current = 'test' else: self.main_manager.task_sets_screen.fill() self.main_manager.current = 'task_sets' app = App.get_running_app() sessions_starts = app.storage['sessions']['started'] app.tracker.send_event('tasks', 'sessions', label='started', value=sessions_starts + 1) app.storage['sessions'] = {"started": sessions_starts + 1, "finished": app.storage['sessions']['finished']} self.played_times += 1 Logger.info("Tasks: playing %s times" % self.played_times) if self.played_times == 10: App.get_running_app().google_client.unlock_achievement("addicted") if self.main_manager.parent != self: self.loading.hide(self._main_manager) def on_enter(self, *args): super(TasksScreen, self).on_enter(*args) app = App.get_running_app() app.initialize_billing(self.update_content)

35.54386

106

0.673248

230

2,026

5.691304

0.352174

0.10084

0.114591

0.036669

0.02903

0

0.003197

0.228036

2,026

56

107

36.178571

0.83376

0

0.044444

0

0.058243

0

1

0.088889

false

0

0.177778

0

0.444444

0

null

0

null

0

1

0

e954754c8db1dbc45662c97eec7de33aed7d3e19

1,240

py

Python

imclassify/train_model.py

AdamSpannbauer/imclassify

27c24576ef6a2ed344cad7f568f7e4cdfe6ea0bd

[ "MIT" ]

null

imclassify/train_model.py

AdamSpannbauer/imclassify

27c24576ef6a2ed344cad7f568f7e4cdfe6ea0bd

[ "MIT" ]

null

imclassify/train_model.py

AdamSpannbauer/imclassify

27c24576ef6a2ed344cad7f568f7e4cdfe6ea0bd

[ "MIT" ]

null

"""Train logistic regression model on hdf5 features for classification Modified from: https://gurus.pyimagesearch.com/topic/transfer-learning-example-dogs-and-cats/ """ import pickle from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report def train_model(h5py_db, model_output='model.pickle', percent_train=1.0): """Train logistic regression classifier :param h5py_db: path to HDF5 database containing 'features', 'labels', & 'label_names' :param model_output: path to save trained model to using pickle :param percent_train: percent of images to be used for training (instead of testing) :return: None; output is written to `model_output` """ i = int(h5py_db['labels'].shape[0] * percent_train) # C decided with sklearn.model_selection.GridSearchCV model = LogisticRegression(C=0.1) model.fit(h5py_db['features'][:i], h5py_db['labels'][:i]) if percent_train < 1.0: preds = model.predict(h5py_db['features'][i:]) print(classification_report(h5py_db['labels'][i:], preds, target_names=h5py_db['label_names'])) with open(model_output, 'wb') as f: f.write(pickle.dumps(model))

37.575758

90

0.704839

167

1,240

5.08982

0.473054

0.056471

0.042353

0.032941

0

0.016732

0.180645

1,240

32

91

38.75

0.819882

0.43871

0

0.088989

0

1

0.076923

false

0

0.230769

0

0.307692

0.076923

0

null

0

null

0

1

0

e955b53af943d2f078f97e589977586caea5ae03

1,760

py

Python

Test/final/V5_baseline_CC_ref/aggregate.py

WangWenhao0716/ISC-Track1-Submission

3484142c0550262c90fc229e5e0ba719c58c592d

[ "MIT" ]

46

2021-10-31T08:02:51.000Z

2022-03-11T08:42:30.000Z

Test/final/V5_baseline_CC_ref/aggregate.py

WangWenhao0716/ISC-Track1-Submission

3484142c0550262c90fc229e5e0ba719c58c592d

[ "MIT" ]

3

2021-11-18T09:35:45.000Z

2022-03-31T01:20:34.000Z

Test/final/V5_baseline_CC_ref/aggregate.py

WangWenhao0716/ISC-Track1-Submission

3484142c0550262c90fc229e5e0ba719c58c592d

[ "MIT" ]

8

2021-12-01T08:02:08.000Z

2022-02-26T13:29:36.000Z

import pandas as pd v_4 = pd.read_csv('50/predictions_dev_queries_50k_normalized_exp.csv') temp = list(v_4['query_id']) v_4['query_id'] = list(v_4['reference_id']) v_4['reference_id'] = temp v_5 = pd.read_csv('ibn/predictions_dev_queries_50k_normalized_exp.csv') temp = list(v_5['query_id']) v_5['query_id'] = list(v_5['reference_id']) v_5['reference_id'] = temp v_6 = pd.read_csv('152/predictions_dev_queries_50k_normalized_exp.csv') temp = list(v_6['query_id']) v_6['query_id'] = list(v_6['reference_id']) v_6['reference_id'] = temp v_4_query = list(v_4['query_id']) v_4_reference = list(v_4['reference_id']) v_4_com = [] for i in range(len(v_4)): v_4_com.append((v_4_query[i],v_4_reference[i])) v_5_query = list(v_5['query_id']) v_5_reference = list(v_5['reference_id']) v_5_com = [] for i in range(len(v_5)): v_5_com.append((v_5_query[i],v_5_reference[i])) v_6_query = list(v_6['query_id']) v_6_reference = list(v_6['reference_id']) v_6_com = [] for i in range(len(v_6)): v_6_com.append((v_6_query[i],v_6_reference[i])) inter_45 = list(set(v_4_com).intersection(set(v_5_com))) inter_46 = list(set(v_4_com).intersection(set(v_6_com))) inter_456 = list(set(inter_45).intersection(set(inter_46))) new_456 = pd.DataFrame() q = [] for i in range(len(inter_456)): q.append(inter_456[i][0]) r = [] for i in range(len(inter_456)): r.append(inter_456[i][1]) new_456['query_id'] = q new_456['reference_id'] = r df_2 = pd.merge(new_456, v_4, on=['query_id','reference_id'], how='inner') df_3 = pd.merge(new_456, v_5, on=['query_id','reference_id'], how='inner') df_4 = pd.merge(new_456, v_6, on=['query_id','reference_id'], how='inner') fast_456 = pd.concat((df_2,df_3,df_4)) fast_456.to_csv('R-baseline-CC-234-50k.csv',index=False)

31.428571

74

0.710795

360

1,760

3.088889

0.161111

0.030576

0.043165

0.04946

0.557554

0.519784

0.236511

0.132194

0

0.075567

0.097727

1,760

55

75

32

0.624685

0

0.044444

0

0.255114

0.098864

0

1

0

false

0

0.022222

0

0.022222

0

null

0

null

0

1

0

e9569e3a4e8763ed40f2c7965c464907cae6ec57

744

py

Python

tutorial/flask-api-mongo/app/services/mail_service.py

carrenolg/python

7c1f0013d911177ce3bc2c5ea58b8e6e562b7282

[ "Apache-2.0" ]

null

tutorial/flask-api-mongo/app/services/mail_service.py

carrenolg/python

7c1f0013d911177ce3bc2c5ea58b8e6e562b7282

[ "Apache-2.0" ]

null

tutorial/flask-api-mongo/app/services/mail_service.py

carrenolg/python

7c1f0013d911177ce3bc2c5ea58b8e6e562b7282

[ "Apache-2.0" ]

null

from threading import Thread from flask_mail import Mail, Message from resources.errors import InternalServerError mail = Mail(app=None) app = None def initialize_mail_service(appiclation): global mail global app mail = Mail(app=appiclation) app = appiclation def send_async_email(app, msg, mail): with app.app_context(): try: mail.send(msg) except ConnectionRefusedError: raise InternalServerError("[MAIL SERVER] not working") def send_email(subject, sender, recipients, text_body, html_body): msg = Message(subject, sender=sender, recipients=recipients) msg.body = text_body msg.html = html_body Thread(target=send_async_email, args=(app, msg, mail)).start()

25.655172

66

0.711022

94

744

5.489362

0.414894

0.089147

0.042636

0

0.201613

744

28

67

26.571429

0.868687

0

0.033602

0

1

0.142857

false

0

0.142857

0

0.285714

0

null

0

null

0

1

0

e9570255d9896891bde513fb7630bb22b041b8d0

18,541

py

Python

vxsandbox/resources/tests/test_http.py

praekeltfoundation/vumi-sandbox

1e2dfca8325ce98e52fe32a072749fe4cf7f448d

[ "BSD-3-Clause" ]

1

2021-05-26T08:38:28.000Z

vxsandbox/resources/tests/test_http.py

praekelt/vumi-sandbox

1e2dfca8325ce98e52fe32a072749fe4cf7f448d

[ "BSD-3-Clause" ]

24

2015-03-04T08:33:12.000Z

2016-08-18T07:57:12.000Z

vxsandbox/resources/tests/test_http.py

praekeltfoundation/vumi-sandbox

1e2dfca8325ce98e52fe32a072749fe4cf7f448d

[ "BSD-3-Clause" ]

null

import base64 import json from OpenSSL.SSL import ( VERIFY_PEER, VERIFY_FAIL_IF_NO_PEER_CERT, VERIFY_NONE, SSLv3_METHOD, SSLv23_METHOD, TLSv1_METHOD) from twisted.web.http_headers import Headers from twisted.internet.defer import inlineCallbacks, fail, succeed from vxsandbox.resources.http import ( HttpClientContextFactory, HttpClientPolicyForHTTPS, make_context_factory, HttpClientResource) from vxsandbox.resources.tests.utils import ResourceTestCaseBase class DummyResponse(object): def __init__(self): self.headers = Headers({}) class DummyHTTPClient(object): def __init__(self): self._next_http_request_result = None self.http_requests = [] def set_agent(self, agent): self.agent = agent def get_context_factory(self): # We need to dig around inside our Agent to find the context factory. # Since this involves private attributes that have changed a few times # recently, we need to try various options. if hasattr(self.agent, "_contextFactory"): # For Twisted 13.x return self.agent._contextFactory elif hasattr(self.agent, "_policyForHTTPS"): # For Twisted 14.x return self.agent._policyForHTTPS elif hasattr(self.agent, "_endpointFactory"): # For Twisted 15.0.0 (and possibly newer) return self.agent._endpointFactory._policyForHTTPS else: raise NotImplementedError( "I can't find the context factory on this Agent. This seems" " to change every few versions of Twisted.") def fail_next(self, error): self._next_http_request_result = fail(error) def succeed_next(self, body, code=200, headers={}): default_headers = { 'Content-Length': str(len(body)), } default_headers.update(headers) response = DummyResponse() response.code = code for header, value in default_headers.items(): response.headers.addRawHeader(header, value) response.content = lambda: succeed(body) self._next_http_request_result = succeed(response) def request(self, *args, **kw): self.http_requests.append((args, kw)) return self._next_http_request_result class TestHttpClientResource(ResourceTestCaseBase): resource_cls = HttpClientResource @inlineCallbacks def setUp(self): super(TestHttpClientResource, self).setUp() yield self.create_resource({}) self.dummy_client = DummyHTTPClient() self.patch(self.resource_cls, 'http_client_class', self.get_dummy_client) def get_dummy_client(self, agent): self.dummy_client.set_agent(agent) return self.dummy_client def http_request_fail(self, error): self.dummy_client.fail_next(error) def http_request_succeed(self, body, code=200, headers={}): self.dummy_client.succeed_next(body, code, headers) def assert_not_unicode(self, arg): self.assertFalse(isinstance(arg, unicode)) def get_context_factory(self): return self.dummy_client.get_context_factory() def get_context(self, context_factory=None): if context_factory is None: context_factory = self.get_context_factory() if hasattr(context_factory, 'creatorForNetloc'): # This context_factory is a new-style IPolicyForHTTPS # implementation, so we need to get a context from through its # client connection creator. The creator could either be a wrapper # around a ClientContextFactory (in which case we treat it like # one) or a ClientTLSOptions object (which means we have to grab # the context from a private attribute). creator = context_factory.creatorForNetloc('example.com', 80) if hasattr(creator, 'getContext'): return creator.getContext() else: return creator._ctx else: # This context_factory is an old-style WebClientContextFactory and # will build us a context object if we ask nicely. return context_factory.getContext('example.com', 80) def assert_http_request(self, url, method='GET', headers=None, data=None, timeout=None, files=None): timeout = (timeout if timeout is not None else self.resource.timeout) args = (method, url,) kw = dict(headers=headers, data=data, timeout=timeout, files=files) [(actual_args, actual_kw)] = self.dummy_client.http_requests # NOTE: Files are handed over to treq as file pointer-ish things # which in our case are `StringIO` instances. actual_kw_files = actual_kw.get('files') if actual_kw_files is not None: actual_kw_files = actual_kw.pop('files', None) kw_files = kw.pop('files', {}) for name, file_data in actual_kw_files.items(): kw_file_data = kw_files[name] file_name, content_type, sio = file_data self.assertEqual( (file_name, content_type, sio.getvalue()), kw_file_data) self.assertEqual((actual_args, actual_kw), (args, kw)) self.assert_not_unicode(actual_args[0]) self.assert_not_unicode(actual_kw.get('data')) headers = actual_kw.get('headers') if headers is not None: for key, values in headers.items(): self.assert_not_unicode(key) for value in values: self.assert_not_unicode(value) def test_make_context_factory_no_method_verify_none(self): context_factory = make_context_factory(verify_options=VERIFY_NONE) self.assertIsInstance(context_factory, HttpClientContextFactory) self.assertEqual(context_factory.verify_options, VERIFY_NONE) self.assertEqual(context_factory.ssl_method, None) self.assertEqual( self.get_context(context_factory).get_verify_mode(), VERIFY_NONE) def test_make_context_factory_no_method_verify_peer(self): # This test's behaviour depends on the version of Twisted being used. context_factory = make_context_factory(verify_options=VERIFY_PEER) context = self.get_context(context_factory) self.assertEqual(context_factory.ssl_method, None) self.assertNotEqual(context.get_verify_mode(), VERIFY_NONE) if HttpClientPolicyForHTTPS is None: # We have Twisted<14.0.0 self.assertIsInstance(context_factory, HttpClientContextFactory) self.assertEqual(context_factory.verify_options, VERIFY_PEER) self.assertEqual(context.get_verify_mode(), VERIFY_PEER) else: self.assertIsInstance(context_factory, HttpClientPolicyForHTTPS) def test_make_context_factory_no_method_verify_peer_or_fail(self): # This test's behaviour depends on the version of Twisted being used. context_factory = make_context_factory( verify_options=(VERIFY_PEER | VERIFY_FAIL_IF_NO_PEER_CERT)) context = self.get_context(context_factory) self.assertEqual(context_factory.ssl_method, None) self.assertNotEqual(context.get_verify_mode(), VERIFY_NONE) if HttpClientPolicyForHTTPS is None: # We have Twisted<14.0.0 self.assertIsInstance(context_factory, HttpClientContextFactory) self.assertEqual( context_factory.verify_options, VERIFY_PEER | VERIFY_FAIL_IF_NO_PEER_CERT) self.assertEqual( context.get_verify_mode(), VERIFY_PEER | VERIFY_FAIL_IF_NO_PEER_CERT) else: self.assertIsInstance(context_factory, HttpClientPolicyForHTTPS) def test_make_context_factory_no_method_no_verify(self): # This test's behaviour depends on the version of Twisted being used. context_factory = make_context_factory() self.assertEqual(context_factory.ssl_method, None) if HttpClientPolicyForHTTPS is None: # We have Twisted<14.0.0 self.assertIsInstance(context_factory, HttpClientContextFactory) self.assertEqual(context_factory.verify_options, None) else: self.assertIsInstance(context_factory, HttpClientPolicyForHTTPS) def test_make_context_factory_sslv3_no_verify(self): # This test's behaviour depends on the version of Twisted being used. context_factory = make_context_factory(ssl_method=SSLv3_METHOD) self.assertEqual(context_factory.ssl_method, SSLv3_METHOD) if HttpClientPolicyForHTTPS is None: # We have Twisted<14.0.0 self.assertIsInstance(context_factory, HttpClientContextFactory) self.assertEqual(context_factory.verify_options, None) else: self.assertIsInstance(context_factory, HttpClientPolicyForHTTPS) @inlineCallbacks def test_handle_get(self): self.http_request_succeed("foo") reply = yield self.dispatch_command('get', url='http://www.example.com') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('http://www.example.com', method='GET') @inlineCallbacks def test_handle_post(self): self.http_request_succeed("foo") reply = yield self.dispatch_command('post', url='http://www.example.com') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('http://www.example.com', method='POST') @inlineCallbacks def test_handle_patch(self): self.http_request_succeed("foo") reply = yield self.dispatch_command('patch', url='http://www.example.com') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('http://www.example.com', method='PATCH') @inlineCallbacks def test_handle_head(self): self.http_request_succeed("foo") reply = yield self.dispatch_command('head', url='http://www.example.com') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('http://www.example.com', method='HEAD') @inlineCallbacks def test_handle_delete(self): self.http_request_succeed("foo") reply = yield self.dispatch_command('delete', url='http://www.example.com') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('http://www.example.com', method='DELETE') @inlineCallbacks def test_handle_put(self): self.http_request_succeed("foo") reply = yield self.dispatch_command('put', url='http://www.example.com') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('http://www.example.com', method='PUT') @inlineCallbacks def test_failed_get(self): self.http_request_fail(ValueError("HTTP request failed")) reply = yield self.dispatch_command('get', url='http://www.example.com') self.assertFalse(reply['success']) self.assertEqual(reply['reason'], "HTTP request failed") self.assert_http_request('http://www.example.com', method='GET') @inlineCallbacks def test_null_url(self): reply = yield self.dispatch_command('get') self.assertFalse(reply['success']) self.assertEqual(reply['reason'], "No URL given") @inlineCallbacks def test_https_request(self): # This test's behaviour depends on the version of Twisted being used. self.http_request_succeed("foo") reply = yield self.dispatch_command('get', url='https://www.example.com') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('https://www.example.com', method='GET') context_factory = self.get_context_factory() self.assertEqual(context_factory.ssl_method, None) if HttpClientPolicyForHTTPS is None: self.assertIsInstance(context_factory, HttpClientContextFactory) self.assertEqual(context_factory.verify_options, None) else: self.assertIsInstance(context_factory, HttpClientPolicyForHTTPS) @inlineCallbacks def test_https_request_verify_none(self): self.http_request_succeed("foo") reply = yield self.dispatch_command( 'get', url='https://www.example.com', verify_options=['VERIFY_NONE']) self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('https://www.example.com', method='GET') context = self.get_context() self.assertEqual(context.get_verify_mode(), VERIFY_NONE) @inlineCallbacks def test_https_request_verify_peer_or_fail(self): # This test's behaviour depends on the version of Twisted being used. self.http_request_succeed("foo") reply = yield self.dispatch_command( 'get', url='https://www.example.com', verify_options=['VERIFY_PEER', 'VERIFY_FAIL_IF_NO_PEER_CERT']) self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('https://www.example.com', method='GET') context = self.get_context() # We don't control verify mode in newer Twisted. self.assertNotEqual(context.get_verify_mode(), VERIFY_NONE) if HttpClientPolicyForHTTPS is None: self.assertEqual( context.get_verify_mode(), VERIFY_PEER | VERIFY_FAIL_IF_NO_PEER_CERT) @inlineCallbacks def test_handle_post_files(self): self.http_request_succeed('') reply = yield self.dispatch_command( 'post', url='https://www.example.com', files={ 'foo': { 'file_name': 'foo.json', 'content_type': 'application/json', 'data': base64.b64encode(json.dumps({'foo': 'bar'})), } }) self.assertTrue(reply['success']) self.assert_http_request( 'https://www.example.com', method='POST', files={ 'foo': ('foo.json', 'application/json', json.dumps({'foo': 'bar'})), }) @inlineCallbacks def test_data_limit_exceeded_using_head_method(self): self.http_request_succeed('', headers={ 'Content-Length': str(self.resource.DEFAULT_DATA_LIMIT + 1), }) reply = yield self.dispatch_command( 'head', url='https://www.example.com',) self.assertTrue(reply['success']) self.assertEqual(reply['body'], "") self.assert_http_request('https://www.example.com', method='HEAD') @inlineCallbacks def test_data_limit_exceeded_using_header(self): self.http_request_succeed('', headers={ 'Content-Length': str(self.resource.DEFAULT_DATA_LIMIT + 1), }) reply = yield self.dispatch_command( 'get', url='https://www.example.com',) self.assertFalse(reply['success']) self.assertEqual( reply['reason'], 'Received %d bytes, maximum of %s bytes allowed.' % ( self.resource.DEFAULT_DATA_LIMIT + 1, self.resource.DEFAULT_DATA_LIMIT,)) @inlineCallbacks def test_data_limit_exceeded_inferred_from_body(self): self.http_request_succeed('1' * (self.resource.DEFAULT_DATA_LIMIT + 1)) reply = yield self.dispatch_command( 'get', url='https://www.example.com',) self.assertFalse(reply['success']) self.assertEqual( reply['reason'], 'Received %d bytes, maximum of %s bytes allowed.' % ( self.resource.DEFAULT_DATA_LIMIT + 1, self.resource.DEFAULT_DATA_LIMIT,)) @inlineCallbacks def test_https_request_method_default(self): self.http_request_succeed("foo") reply = yield self.dispatch_command( 'get', url='https://www.example.com') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('https://www.example.com', method='GET') context_factory = self.get_context_factory() self.assertEqual(context_factory.ssl_method, None) @inlineCallbacks def test_https_request_method_SSLv3(self): self.http_request_succeed("foo") reply = yield self.dispatch_command( 'get', url='https://www.example.com', ssl_method='SSLv3') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('https://www.example.com', method='GET') context_factory = self.get_context_factory() self.assertEqual(context_factory.ssl_method, SSLv3_METHOD) @inlineCallbacks def test_https_request_method_SSLv23(self): self.http_request_succeed("foo") reply = yield self.dispatch_command( 'get', url='https://www.example.com', ssl_method='SSLv23') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('https://www.example.com', method='GET') context_factory = self.get_context_factory() self.assertEqual(context_factory.ssl_method, SSLv23_METHOD) @inlineCallbacks def test_https_request_method_TLSv1(self): self.http_request_succeed("foo") reply = yield self.dispatch_command( 'get', url='https://www.example.com', ssl_method='TLSv1') self.assertTrue(reply['success']) self.assertEqual(reply['body'], "foo") self.assert_http_request('https://www.example.com', method='GET') context_factory = self.get_context_factory() self.assertEqual(context_factory.ssl_method, TLSv1_METHOD)

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0.118069

0.085537

0.038579

0.031422

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0.607052

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0.541067

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false

0

0.020115

0.002874

0.16954

0

null

0

null

0

1

0

e95a4fa6b39694c0762d544398c6a91dc4eb000f

722

py

Python

soundDB/__init__.py

gjoseph92/soundDB2

4d9cc93cc596a5089233f17b0b8be252f73e1224

[ "CC0-1.0" ]

3

2017-05-16T19:37:32.000Z

2020-03-29T21:54:33.000Z

soundDB/__init__.py

gjoseph92/soundDB2

4d9cc93cc596a5089233f17b0b8be252f73e1224

[ "CC0-1.0" ]

19

2016-12-02T20:47:24.000Z

2021-10-05T19:01:01.000Z

soundDB/__init__.py

gjoseph92/soundDB2

4d9cc93cc596a5089233f17b0b8be252f73e1224

[ "CC0-1.0" ]

2

2017-05-10T23:01:06.000Z

2019-12-27T19:49:29.000Z

from .accessor import Accessor from . import parsers import inspect def populateAccessors(): """ Find all filetype-specific Accessor subclasses in the parsers file (i.e. NVSPL, SRCID, etc.) and instantiate them. This way, one instance of each Accessor is added to the soundDB namespace under the name of the Endpoint it uses. """ predicate = lambda obj: inspect.isclass(obj) and issubclass(obj, Accessor) and obj is not Accessor specificAccessorSubclasses = inspect.getmembers(parsers, predicate) accessors = { cls.endpointName: cls for name, cls in specificAccessorSubclasses } return accessors globals().update(populateAccessors()) del inspect, accessor, parsers, populateAccessors

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118

0.756233

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722

6.134831

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722

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0.5

0

null

0

null

0

1

0

e95c5e6fc88c9d5b12bafc54c0d0afb1690c36cf

556

py

Python

tests/testLoadMapFromString.py

skowronskij/OGCServer

3fd11438180944ffa43e315c6390e89437a28f4e

[ "BSD-3-Clause" ]

90

2015-04-30T22:13:14.000Z

2022-02-16T17:30:11.000Z

tests/testLoadMapFromString.py

skowronskij/OGCServer

3fd11438180944ffa43e315c6390e89437a28f4e

[ "BSD-3-Clause" ]

6

2019-09-09T06:07:27.000Z

2020-06-17T09:52:49.000Z

tests/testLoadMapFromString.py

skowronskij/OGCServer

3fd11438180944ffa43e315c6390e89437a28f4e

[ "BSD-3-Clause" ]

28

2015-05-12T09:08:17.000Z

2021-07-02T11:53:29.000Z

import nose import os from ogcserver.WMS import BaseWMSFactory def test_wms_capabilities(): base_path, tail = os.path.split(__file__) file_path = os.path.join(base_path, 'mapfile_encoding.xml') wms = BaseWMSFactory() with open(file_path) as f: settings = f.read() wms.loadXML(xmlstring=settings, basepath=base_path) wms.finalize() if len(wms.layers) != 1: raise Exception('Incorrect number of layers') if len(wms.styles) != 1: raise Exception('Incorrect number of styles') return True

27.8

63

0.676259

74

556

4.918919

0.554054

0.065934

0.043956

0.131868

0.175824

0

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0.219424

556

19

64

29.263158

0.834101

0

0.129496

0

1

0.0625

false

0

0.1875

0

0.3125

0

null

0

null

0

1

0

e95f809c079ce79cbabf21b0bd9fca926c8f6149

864

py

Python

setup.py

mikemalinowski/insomnia

ea637e5eba608eacd1731239f7ddf6bb91aacc9e

[ "MIT" ]

2

2019-02-28T09:58:55.000Z

2020-03-06T05:03:34.000Z

setup.py

mikemalinowski/insomnia

ea637e5eba608eacd1731239f7ddf6bb91aacc9e

[ "MIT" ]

null

setup.py

mikemalinowski/insomnia

ea637e5eba608eacd1731239f7ddf6bb91aacc9e

[ "MIT" ]

null

import setuptools try: with open('README.md', 'r') as fh: long_description = fh.read() except: long_description = '' setuptools.setup( name='blackout', version='1.0.4', author='Mike Malinowski', author_email='mike@twisted.space', description='A python package making it easy to drop a multi-module package from sys.modules', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/mikemalinowski/blackout', packages=setuptools.find_packages(), entry_points=""" [console_scripts] blackout = blackout:blackout """, py_modules=["blackout"], classifiers=[ 'Programming Language :: Python', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', ], )

28.8

99

0.635417

92

864

5.836957

0.728261

0.139665

0.070764

0.111732

0

0.004566

0.239583

864

29

100

29.793103

0.812785

0

0.440719

0

1

0

false

0

0.037037

0

0.037037

0

null

0

null

0

1

0

e96093e48bfaf833c59e3c55fbafb9b3d90f3407

710

py

Python

src/hypermd/html/html.py

Riib11/HyperMD

d6921b701635356236b00d0a8794ab68d733ad59

[ "MIT" ]

null

src/hypermd/html/html.py

Riib11/HyperMD

d6921b701635356236b00d0a8794ab68d733ad59

[ "MIT" ]

null

src/hypermd/html/html.py

Riib11/HyperMD

d6921b701635356236b00d0a8794ab68d733ad59

[ "MIT" ]

null

class Element: def __init__(self, name, single): self.name = name self.single = single self.attrs = {} self.content = "" def set_attr(self, k, v): self.attrs[k] = v def get_attr(self, v): return self.attrs[k] def tohtml(self): attrs = (" " + " ".join([ "%s=\"%s\"" % (k,v) for k,v in self.attrs.items() ]) if len(self.attrs) > 0 else "") if self.single: s = "<%s%s>" % (self.name, attrs) return s else: s = "<%s%s>" % (self.name, attrs) s += self.content s += "</%s>" % self.name return s __str__ = tohtml; __repr__ = tohtml

29.583333

53

0.453521

89

710

3.460674

0.303371

0.175325

0.058442

0.097403

0.103896

0

0.002278

0.38169

710

24

54

29.583333

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false

0

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0.409091

0

null

0

null

0

1

0

e960b0fabb4246bd94bb826b4cf1e4c34f2696b5

2,590

py

Python

vk_music/__main__.py

w1r2p1/vk_music

066fa623f87a6351846011c477cff2aad2943bc5

[ "MIT" ]

7

2015-01-26T08:46:12.000Z

2020-08-29T13:07:07.000Z

vk_music/__main__.py

w1r2p1/vk_music

066fa623f87a6351846011c477cff2aad2943bc5

[ "MIT" ]

3

2015-04-29T20:34:53.000Z

2015-07-08T08:43:47.000Z

vk_music/__main__.py

sashasimkin/vk_music

3814909ffd914103e80734e51b01dddb458b1bfe

[ "MIT" ]

4

2016-04-24T14:09:48.000Z

2019-11-23T14:50:46.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- from __future__ import print_function import os import argparse from subprocess import call from .vk_music import VkMusic from .exceptions import AlreadyRunningError from .defaults import SafeFsStorage def main(): parser = argparse.ArgumentParser() parser.add_argument('dir', type=str, nargs='?', help="Directory for synchronization") parser.add_argument("-uid", type=int, default=60411837, help="Vk user id") # Default is my VK id :-) parser.add_argument("-client_id", type=int, default=2970439, help="Application id") # Application ID from VK parser.add_argument("--threads", "-t", type=int, default=2, help="Number of threads to use") parser.add_argument("-token", type=str, help="access token to use") parser.add_argument("-token_dir", type=str, help="Directory where script will save token and temp data") parser.add_argument("-f", dest='force', default=False, action='store_true', help="Ignore already running error") parser.add_argument("-from", type=int, default=0, help="Start downloading from position") parser.add_argument("-to", type=int, help="End downloading on position") parser.add_argument("-redirect_url", type=str, help="Redirect url after getting token") args = vars(parser.parse_args()) # Don't let not passed arguments to be for k, v in args.items(): if v is None: del args[k] workdir = args.get('dir', '').decode('utf-8') or os.getcwd() + '/Music' try: # Try to create directory if not exists if not os.path.isdir(workdir): os.makedirs(workdir) # Need write access to that dir os.chmod(workdir, 0o755) if not os.access(workdir, os.W_OK): raise Exception('Permission denied for dir %s' % workdir) except Exception as e: exit("Problem with directory '%s': %s" % (workdir, e)) storage = SafeFsStorage(workdir) try: with VkMusic(storage, **args) as manager: # Start working result = manager.synchronize() try: call(['notify-send', 'Vk Music', 'Saved: %(saved)s\n' 'Skipped: %(skipped)s\n' 'Removed: %(removed)s\n' 'Not removed: %(not_removed)s' % result]) except Exception: pass except AlreadyRunningError: # If is running - terminate print('Other sync process is running. Please wait') if __name__ == '__main__': main()

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null

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null

0

1

0

e962ef78829cd251169298d5da18fd8a33cb94ba

950

py

Python

misc/convert.py

Fusion-Goettingen/ExtendedTargetTrackingToolbox

945ede661e9258a8f1ca8abc00e25727fedf3ac7

[ "MIT" ]

40

2018-07-30T13:07:23.000Z

2021-08-30T05:53:29.000Z

misc/convert.py

GitRooky/ExtendedTargetTrackingToolbox

945ede661e9258a8f1ca8abc00e25727fedf3ac7

[ "MIT" ]

null

misc/convert.py

GitRooky/ExtendedTargetTrackingToolbox

945ede661e9258a8f1ca8abc00e25727fedf3ac7

[ "MIT" ]

21

2018-10-03T11:50:00.000Z

2022-01-11T06:41:24.000Z

__author__ = "Jens Honer" __copyright__ = "Copyright 2018, Jens Honer Tracking Toolbox" __email__ = "-" __license__ = "mit" __version__ = "1.0" __status__ = "Prototype" import numpy as np _bbox_sign_factors = np.asarray( [ [1.0, 1.0], [0.0, 1.0], [-1.0, 1.0], [-1.0, 0.0], [-1.0, -1.0], [0.0, -1.0], [1.0, -1.0], [1.0, 0.0], ], dtype='f4') def convert_rectangle_to_eight_point(bboxes): pt_set = np.zeros((len(bboxes), 8, 2)) pt_set[:] = bboxes['center_xy'][:, None, :] for i, bbox in enumerate(bboxes): s_phi_offset, c_phi_offset = np.sin(bbox['orientation']), np.cos(bbox['orientation']) rot = np.array([[c_phi_offset, - s_phi_offset], [s_phi_offset, c_phi_offset]]) offset_xy = np.dot(_bbox_sign_factors * 0.5 * bbox['dimension'], rot.T) pt_set[i, :, :] += offset_xy return pt_set

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0.111111

0

null

0

null

0

1

0

e9667bd424694f5af16378d0dfcd7bc9fa58a7a6

3,356

py

Python

src/base/local_dataset.py

wenyushi451/Deep-SAD-PyTorch

168d31f538a50fb029739206994ea5517d907853

[ "MIT" ]

null

src/base/local_dataset.py

wenyushi451/Deep-SAD-PyTorch

168d31f538a50fb029739206994ea5517d907853

[ "MIT" ]

null

src/base/local_dataset.py

wenyushi451/Deep-SAD-PyTorch

168d31f538a50fb029739206994ea5517d907853

[ "MIT" ]

null

from torch.utils.data import Dataset from torchvision.transforms import transforms from sklearn.model_selection import train_test_split import os import glob import torch import numpy as np from PIL import Image import pdb class LocalDataset(Dataset): def __init__( self, root: str, dataset_name: str, target_transform, train=True, random_state=None, split=True, random_effect=True, ): super(Dataset, self).__init__() self.target_transform = target_transform self.classes = [0, 1] self.root = root self.train = train # training set or test set # self.dataset_path = os.path.join(self.root, self.dataset_name) # class_idx/image X = np.array(glob.glob(os.path.join(self.root, "*/*.[jp][pn][g]"))) y = [int(i.split("/")[-2]) for i in X] y = np.array(y) if split: idx_norm = y == 0 idx_out = y != 0 # 80% data for training and 20% for testing; keep outlier ratio # pdb.set_trace() X_train_norm, X_test_norm, y_train_norm, y_test_norm = train_test_split( X[idx_norm], y[idx_norm], test_size=0.1, random_state=random_state, stratify=y[idx_norm] ) X_train_out, X_test_out, y_train_out, y_test_out = train_test_split( X[idx_out], y[idx_out], test_size=0.1, random_state=random_state, stratify=y[idx_out] ) X_train = np.concatenate((X_train_norm, X_train_out)) X_test = np.concatenate((X_test_norm, X_test_out)) y_train = np.concatenate((y_train_norm, y_train_out)) y_test = np.concatenate((y_test_norm, y_test_out)) if self.train: self.data = X_train self.targets = torch.tensor(y_train, dtype=torch.int64) else: self.data = X_test self.targets = torch.tensor(y_test, dtype=torch.int64) else: self.data = X self.targets = torch.tensor(y, dtype=torch.int64) self.semi_targets = torch.zeros_like(self.targets) # for training we will add brightness variance if random_effect: self.transform = transforms.Compose( [ # transforms.ColorJitter( # brightness=0.5 + int(np.random.rand(1)), contrast=0.5 + int(np.random.rand(1)) # ), # saturation=0.5 + int(np.random.rand(1)), # hue=0.5 + int(np.random.rand(1))), transforms.Resize((224, 224)), transforms.ToTensor(), ] ) # for testing else: self.transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()]) def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (sample, target, semi_target, index) """ data = Image.open(self.data[index]) data = self.transform(data) sample, target, semi_target = data, 0 if self.targets[index] == 0 else 1, int(self.semi_targets[index]) return sample, target, semi_target, index def __len__(self): return len(self.data)

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0

null

0

null

0

1

0

e96b4f43c95a1b4ce5857c21e88b3785232408aa

9,142

py

Python

main.py

Lmy0217/Flight

faf5045712c4d28e0ca3df408308a5e3b9bf8038

[ "MIT" ]

2

2019-03-31T01:42:29.000Z

2019-05-16T06:31:50.000Z

main.py

Lmy0217/Flight

faf5045712c4d28e0ca3df408308a5e3b9bf8038

[ "MIT" ]

1

2019-03-31T01:45:25.000Z

2019-04-17T05:46:35.000Z

main.py

Lmy0217/Flight

faf5045712c4d28e0ca3df408308a5e3b9bf8038

[ "MIT" ]

1

2019-03-31T01:42:34.000Z

#coding=utf-8 import tkinter as tk from tkinter import ttk from tkinter import scrolledtext from tkinter import messagebox as mBox from tkinter import filedialog import matplotlib matplotlib.use('TkAgg') from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import matplotlib.pyplot as plt import datetime import threading import flight import outlier import analytics # 标题 win = tk.Tk() win.title("机票数据爬取分析预测") win.resizable(0, 0) # 三个页面 tabControl = ttk.Notebook(win) tab1 = ttk.Frame(tabControl) tabControl.add(tab1, text='爬取') tab2 = ttk.Frame(tabControl) tabControl.add(tab2, text='分析') tab3 = ttk.Frame(tabControl) tabControl.add(tab3, text='预测') tabControl.pack(expand=1, fill="both") # 参数框 monty = ttk.LabelFrame(tab1, text='') monty.grid(column=0, row=0, padx=8, pady=4) labelsFrame = ttk.LabelFrame(monty, text=' 参数 ') labelsFrame.grid(column=0, row=0) # 城市标签 ttk.Label(labelsFrame, text="城市:").grid(column=0, row=0, sticky='W') # 城市输入框 city = tk.Text(labelsFrame, width=20, height=10) city.insert(tk.END, "'SHA', 'SIA', 'BJS', 'CAN', 'SZX', 'CTU', 'HGH', 'WUH', 'CKG', 'TAO', 'CSX', 'NKG', 'XMN', 'KMG', 'DLC', 'TSN', 'CGO', 'SYX', 'TNA', 'FOC'") city.grid(column=1, row=0, sticky='W') # 起始日期标签 ttk.Label(labelsFrame, text="起始日期:").grid(column=0, row=1, sticky='W') # 起始日期输入框 date1 = tk.StringVar() da_days = datetime.datetime.now() + datetime.timedelta(days=1) date1.set(da_days.strftime('%Y-%m-%d')) date1Entered = ttk.Entry(labelsFrame, textvariable=date1) date1Entered.grid(column=1, row=1, sticky='W') # 截止日期标签 ttk.Label(labelsFrame, text="截止日期:").grid(column=0, row=2, sticky='W') # 截止日期输入框 date2 = tk.StringVar() da_days2 = datetime.datetime.now() + datetime.timedelta(days=1) date2.set(da_days2.strftime('%Y-%m-%d')) date2Entered = ttk.Entry(labelsFrame, textvariable=date2) date2Entered.grid(column=1, row=2, sticky='W') # Log框 scrolW = 91; scrolH = 37; scr = scrolledtext.ScrolledText(monty, width=scrolW, height=scrolH, wrap=tk.WORD) scr.grid(column=3, row=0, sticky='WE', rowspan=5) # 爬取数据 def spider_flight(): spider_flight.flight = flight.spider(city.get("0.0", "end"), date1.get(), date2.get(), scr) spider_flight.flight = None def run_spider_flight(): scr.insert(tk.END, datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n爬取数据：\n城市：' + str(city.get("0.0", "end")) + '\n日期：' + str(date1.get()) + ' 至 ' + str(date2.get()) + '\n\n') t = threading.Thread(target=spider_flight) t.start() # 爬取标签 spider = ttk.Button(labelsFrame, text="爬取", width=10, command=run_spider_flight) spider.grid(column=0, row=4, sticky='W') # 保存文件 def save_file(): if spider_flight.flight is not None: fname = tk.filedialog.asksaveasfilename(filetypes=[("JSON", ".json")], defaultextension='.json') if fname is not '': spider_flight.flight.save(fname) scr.insert(tk.END, datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n数据保存到 ' + fname + '\n\n') else: mBox.showwarning('Python Message Warning Box', '请先爬取数据！') # 保存标签 save = ttk.Button(labelsFrame, text="保存", width=10, command=save_file) save.grid(column=1, row=4, sticky='E') for child in labelsFrame.winfo_children(): child.grid_configure(padx=8, pady=4) for child in monty.winfo_children(): child.grid_configure(padx=3, pady=1) # 参数框 monty2 = ttk.LabelFrame(tab2, text='') monty2.grid(column=0, row=0, padx=8, pady=4) labelsFrame2 = ttk.LabelFrame(monty2, text=' 参数 ') labelsFrame2.grid(column=0, row=0) # Log框 scrolW = 34; scrolH = 25; scr2 = scrolledtext.ScrolledText(monty2, width=scrolW, height=scrolH, wrap=tk.WORD) scr2.grid(column=0, row=3, sticky='WE') # 数据标签 ttk.Label(labelsFrame2, text="数据:").grid(column=0, row=0, sticky='W') # 打开文件 def data_file(): fname = tk.filedialog.askopenfilename(filetypes=[("JSON", ".json")], defaultextension='.json') if fname is not '': data_file.outlier = outlier.Outlier(fname) scr2.insert(tk.END, datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n打开文件 ' + fname + '\n\n') data_file.outlier = None # 打开文件按钮 data = ttk.Button(labelsFrame2, text="打开文件", width=10, command=data_file) data.grid(column=1, row=0, sticky='E') # 异常数标签 ttk.Label(labelsFrame2, text="异常数:").grid(column=0, row=1, sticky='W') # 异常数输入框 diff = tk.IntVar() diff.set(5) diffEntered = ttk.Entry(labelsFrame2, textvariable=diff) diffEntered.grid(column=1, row=1, sticky='W') # 图框 def drawdiff(): try: num_diff = int(diffEntered.get()) except: num_diff = 5 diffEntered.delete(0, tk.END) diffEntered.insert(0, 5) drawdiff.f.clf() drawdiff.out = data_file.outlier.extreme(drawdiff.f, scr2, num_diff) drawdiff.canvas.show() drawdiff.out = None drawdiff.f = plt.figure() drawdiff.canvas = FigureCanvasTkAgg(drawdiff.f, master=monty2) drawdiff.canvas.show() drawdiff.canvas.get_tk_widget().grid(column=1, row=0, rowspan=4) def run_drawdiff(): if data_file.outlier is not None: scr2.insert(tk.END, datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n分析数据（设定 ' + str(diffEntered.get()) + ' 个异常值）...\n\n异常值：\n') t = threading.Thread(target=drawdiff) t.start() else: mBox.showwarning('Python Message Warning Box', '请先打开文件！') # 分析按钮 da = ttk.Button(labelsFrame2, text="分析", width=10, command=run_drawdiff) da.grid(column=0, row=2, sticky='W') # 保存文件 def save_file2(): if drawdiff.out is not None: fname = tk.filedialog.asksaveasfilename(filetypes=[("JSON", ".json")], defaultextension='.json') if fname is not '': with open(fname, 'w') as f1: f1.write(str(drawdiff.out)) scr2.insert(tk.END, datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n异常值保存到 ' + fname + '\n\n') else: mBox.showwarning('Python Message Warning Box', '请先分析数据！') # 保存按钮 save2 = ttk.Button(labelsFrame2, text="保存", width=10, command=save_file2) save2.grid(column=1, row=2, sticky='E') for child in labelsFrame2.winfo_children(): child.grid_configure(padx=8, pady=4) for child in monty2.winfo_children(): child.grid_configure(padx=8, pady=4) # 参数框 monty3 = ttk.LabelFrame(tab3, text='') monty3.grid(column=0, row=0, padx=8, pady=4) labelsFrame3 = ttk.LabelFrame(monty3, text=' 参数 ') labelsFrame3.grid(column=0, row=0) # Log框 scrolW = 34; scrolH = 25; scr3 = scrolledtext.ScrolledText(monty3, width=scrolW, height=scrolH, wrap=tk.WORD) scr3.grid(column=0, row=3, sticky='WE') # 数据标签 ttk.Label(labelsFrame3, text="数据:").grid(column=0, row=0, sticky='W') # 打开文件 def data_file2(): fname = tk.filedialog.askopenfilename(filetypes=[("JSON", ".json")], defaultextension='.json') if fname is not '': data_file2.analytics = analytics.Analytics(fname) scr3.insert(tk.END, datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n打开文件 ' + fname + '\n\n') data_file2.analytics = None # 打开文件按钮 data2 = ttk.Button(labelsFrame3, text="打开文件", width=10, command=data_file2) data2.grid(column=1, row=0, sticky='E') # 预测天数标签 ttk.Label(labelsFrame3, text="预测天数:").grid(column=0, row=1, sticky='W') # 预测天数输入框 days = tk.IntVar() days.set(30) daysEntered = ttk.Entry(labelsFrame3, textvariable=days) daysEntered.grid(column=1, row=1, sticky='W') # 图框 def drawpredict(): try: num_day = int(daysEntered.get()) except: num_day = 30 daysEntered.delete(0, tk.END) daysEntered.insert(0, 30) # 清空图像，以使得前后两次绘制的图像不会重叠 drawpredict.f.clf() drawpredict.out = data_file2.analytics.predict(num_day, scr3) drawpredict.canvas.show() drawpredict.out = None drawpredict.f = plt.figure() drawpredict.canvas = FigureCanvasTkAgg(drawpredict.f, master=monty3) drawpredict.canvas.show() drawpredict.canvas.get_tk_widget().grid(column=1, row=0, rowspan=4) def run_drawpredict(): if data_file2.analytics is not None: scr3.insert(tk.END, datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n分析数据（设定预测 ' + str(daysEntered.get()) + ' 天）...\n\n训练过程：\n轮次/总轮次 [损失]\n') t = threading.Thread(target=drawpredict) t.start() else: mBox.showwarning('Python Message Warning Box', '请先打开文件！') # 预测按钮 pr = ttk.Button(labelsFrame3, text="预测", width=10, command=run_drawpredict) pr.grid(column=0, row=2, sticky='W') # 保存文件 def save_file3(): if drawpredict.out is not None: fname = tk.filedialog.asksaveasfilename(filetypes=[("JSON", ".json")], defaultextension='.json') with open(fname, 'w') as f1: # 打开文件 f1.write(str(drawpredict.out)) scr3.insert(tk.END, datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\n训练过程和预测结果保存到 ' + fname + '\n\n') else: mBox.showwarning('Python Message Warning Box', '请先预测数据！') # 保存按钮 save = ttk.Button(labelsFrame3, text="保存", width=10, command=save_file3) save.grid(column=1, row=2, sticky='E') for child in labelsFrame3.winfo_children(): child.grid_configure(padx=8, pady=4) for child in monty3.winfo_children(): child.grid_configure(padx=8, pady=4) if __name__ == "__main__": win.mainloop()

27.371257

161

0.669438

1,311

9,142

4.617086

0.194508

0.051214

0.032711

0.041632

0.45845

0.415827

0.376673

0.317694

0.282835

0

0.028774

0.152264

9,142

333

162

27.453453

0.752258

0.025487

0

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0.005051

0.092334

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false

0

0.065657

0

0.121212

0

null

0

null

0

1

0

e96debb65a28b71e00c0a2a49cd0ca34ceacdd69

449

py

Python

api/compat.py

fancystats/api

298ae6d71fa37f649bbd61ad000767242f49a698

[ "MIT" ]

1

2015-03-20T20:35:22.000Z

api/compat.py

fancystats/api

298ae6d71fa37f649bbd61ad000767242f49a698

[ "MIT" ]

null

api/compat.py

fancystats/api

298ae6d71fa37f649bbd61ad000767242f49a698

[ "MIT" ]

null

""" Python 2/3 Compatibility ======================== Not sure we need to support anything but Python 2.7 at this point , but copied this module over from flask-peewee for the time being. """ import sys PY2 = sys.version_info[0] == 2 if PY2: text_type = unicode string_types = (str, unicode) unichr = unichr reduce = reduce else: text_type = str string_types = (str, ) unichr = chr from functools import reduce

17.96

78

0.639198

64

449

4.40625

0.6875

0.049645

0.099291

0

0.023324

0.23608

449

24

79

18.708333

0.798834

0.4098

0

1

0

false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

e97022aba46b50c4fc79f34b4e0641ec360d25a6

3,254

bzl

Python

infra-sk/karma_test/index.bzl

bodymovin/skia-buildbot

1570e4e48ecb330750264d4ae6a875b5e49a37fe

[ "BSD-3-Clause" ]

null

infra-sk/karma_test/index.bzl

bodymovin/skia-buildbot

1570e4e48ecb330750264d4ae6a875b5e49a37fe

[ "BSD-3-Clause" ]

null

infra-sk/karma_test/index.bzl

bodymovin/skia-buildbot

1570e4e48ecb330750264d4ae6a875b5e49a37fe

[ "BSD-3-Clause" ]

null

"""This module defines the karma_test rule.""" load("@infra-sk_npm//@bazel/typescript:index.bzl", "ts_library") load("@infra-sk_npm//@bazel/rollup:index.bzl", "rollup_bundle") load("@infra-sk_npm//karma:index.bzl", _generated_karma_test = "karma_test") def karma_test(name, srcs, deps, entry_point = None): """Runs unit tests in a browser with Karma and the Mocha test runner. When executed with `bazel test`, a headless Chrome browser will be used. This supports testing multiple karma_test targets in parallel, and works on RBE. When executed with `bazel run`, it prints out a URL to stdout that can be opened in the browser, e.g. to debug the tests using the browser's developer tools. Source maps are generated. When executed with `ibazel test`, the test runner never exits, and tests will be rerun every time a source file is changed. When executed with `ibazel run`, it will act the same way as `bazel run`, but the tests will be rebuilt automatically when a source file changes. Reload the browser page to see the changes. Args: name: The name of the target. srcs: The *.ts test files. deps: The ts_library dependencies for the source files. entry_point: File in srcs to be used as the entry point to generate the JS bundle executed by the test runner. Optional if srcs contains only one file. """ if len(srcs) > 1 and not entry_point: fail("An entry_point must be specified when srcs contains more than one file.") if entry_point and entry_point not in srcs: fail("The entry_point must be included in srcs.") if len(srcs) == 1: entry_point = srcs[0] ts_library( name = name + "_lib", srcs = srcs, deps = deps + [ # Add common test dependencies for convenience. "@infra-sk_npm//@types/mocha", "@infra-sk_npm//@types/chai", "@infra-sk_npm//@types/sinon", ], ) rollup_bundle( name = name + "_bundle", entry_point = entry_point, deps = [ name + "_lib", "@infra-sk_npm//@rollup/plugin-node-resolve", "@infra-sk_npm//@rollup/plugin-commonjs", "@infra-sk_npm//rollup-plugin-sourcemaps", ], format = "umd", config_file = "//infra-sk:rollup.config.js", ) # This rule is automatically generated by rules_nodejs from Karma's package.json file. _generated_karma_test( name = name, size = "large", data = [ name + "_bundle", "//infra-sk/karma_test:karma.conf.js", "@infra-sk_npm//karma-chrome-launcher", "@infra-sk_npm//karma-sinon", "@infra-sk_npm//karma-mocha", "@infra-sk_npm//karma-chai", "@infra-sk_npm//karma-chai-dom", "@infra-sk_npm//karma-spec-reporter", "@infra-sk_npm//mocha", ], templated_args = [ "start", "$(execpath //infra-sk/karma_test:karma.conf.js)", "$$(rlocation $(location %s_bundle))" % name, ], tags = [ # Necessary for it to work with ibazel. "ibazel_notify_changes", ], )

36.977273

100

0.609711

440

3,254

4.388636

0.35

0.068876

0.082859

0.054376

0.101502

0.027965

0

0.001279

0.279348

3,254

87

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37.402299

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0.018519

0

null

0

null

0

1

0

e970a8957b84490bbe0b79a62e25d6fddc55f490

5,894

py

Python

stats/ClassicAnalyzerStats.py

arndff/fpl-rivals-tracker

311b932ab7c07b03c1676e5a971df13e652a1b7b

[ "Apache-2.0" ]

4

2019-02-06T10:42:50.000Z

2021-02-17T21:09:26.000Z

stats/ClassicAnalyzerStats.py

arndff/fpl-rivals-tracker

311b932ab7c07b03c1676e5a971df13e652a1b7b

[ "Apache-2.0" ]

null

stats/ClassicAnalyzerStats.py

arndff/fpl-rivals-tracker

311b932ab7c07b03c1676e5a971df13e652a1b7b

[ "Apache-2.0" ]

1

2021-02-17T21:09:27.000Z

from fileutils.fileutils import save_output_to_file, select_option_from_menu class ClassicAnalyzerStats: def __init__(self, data, current_event, output_file_name): self.__data = data self.__current_event = current_event self.__output_file_name = output_file_name self.__output = [] self.__options = self.__init_options() self.__append_options_to_output() def save_stats_output_to_file(self): save_output_to_file(self.__output_file_name, "a+", self.__output) def stats_menu(self): while True: exception_msg = "\n[!] Please enter an integer from 1 to 10." option = select_option_from_menu(self.__options, exception_msg) self.__output.append("Selected option: {}".format(option)) if option == -1: continue if option == 1: self.__calculate_average_points() elif option == 2: self.__print_captains((list(map(lambda x: x.captain_name, self.__data)))) elif option == 3: self.__print_captains((list(map(lambda x: x.vice_captain_name, self.__data)))) elif option == 4: self.__print_chip_usage_whole_season() elif option == 5: self.__print_chip_usage_current_event() elif option == 6: self.__count_managers_made_transfer() elif option == 7: self.__count_managers_took_hit() elif option == 8: self.__print_team_value(max) elif option == 9: self.__print_team_value(min) elif option == 10: self.__output.append("") break else: print("\n[!] Invalid option. Try again!") @staticmethod def init_a_dict(key, dictionary): if key not in dictionary: dictionary[key] = 1 else: dictionary[key] += 1 def print_chips(self, chips): for chip in chips: string = "{}({})".format(chip, chips[chip]) print(string, end=" ") self.__output.append(string) print() self.__output.append("") def __init_options(self): options = ["\n* Please choose an option from 1 to 10:", "1) Sample's average score", "2) Most captained players", "3) Most vice-captained players", "4) Chips usage during the whole season", "5) Chips usage during GW{}".format(self.__current_event), "6) Count of managers made at least one transfer", "7) Count of managers took at least one hit", "8) Richest manager(s)", "9) Poorest manager(s)", "10) Exit"] return options def __calculate_average_points(self): managers_count = len(self.__data) total_points = 0 for manager in self.__data: total_points += manager.gw_points() total_points -= manager.gw_hits average_points = total_points / managers_count result = "{:.2f} points".format(average_points) print(result) self.__output.append(result) self.__output.append("") def __print_captains(self, list_of_captains): captains = {} for captain in list_of_captains: self.init_a_dict(captain, captains) captains_sorted = [(captain, captains[captain]) for captain in sorted(captains, key=captains.get, reverse=True)] for key, value in captains_sorted: captain = "{}({})".format(key, value) print(captain, end=" ") self.__output.append(captain) print() self.__output.append("") def __print_chip_usage_whole_season(self): chips = {} for manager in self.__data: for chip in manager.used_chips_by_gw: self.init_a_dict(chip, chips) self.print_chips(chips) def __print_chip_usage_current_event(self): active_chips = {} for manager in self.__data: active_chip = manager.active_chip if active_chip != "None": self.init_a_dict(active_chip, active_chips) if len(active_chips) < 1: result = "No manager has used any chip in GW{}".format(self.__current_event) self.__log_string(result) else: self.print_chips(active_chips) def __count_managers_made_transfer(self): result = len(list(filter(lambda x: x.gw_transfers > 0, self.__data))) if result == 1: managers_count = "1 manager" else: managers_count = "{} managers".format(result) self.__log_string(managers_count) def __count_managers_took_hit(self): result = len(list(filter(lambda x: x.gw_hits > 0, self.__data))) managers_count = "{} managers".format(result) self.__log_string(managers_count) def __print_team_value(self, extremum): team_values = list(map(lambda x: x.team_value, self.__data)) max_value = extremum(team_values) richest_managers = list(filter(lambda x: x.team_value == max_value, self.__data)) richest_managers_names = (list(map(lambda x: x.manager_name, richest_managers))) result = ", ".join(richest_managers_names) result_string = "{} ({}M)".format(result, format(max_value, ".1f")) self.__log_string(result_string) def __append_options_to_output(self): self.__output.append("") [self.__output.append(option) for option in self.__options] self.__output.append("") def __log_string(self, string): print(string) self.__output.append(string) self.__output.append("")

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5,894

4.670073

0.191241

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5,894

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e9736a918f48d6f382688f91eb8391428a99f968

2,893

py

Python

sarpy/io/product/base.py

spacefan/sarpy

2791af86b568c8a8560275aee426a4718d5a4606

[ "MIT" ]

119

2018-07-12T22:08:17.000Z

2022-03-24T12:11:39.000Z

sarpy/io/product/base.py

spacefan/sarpy

2791af86b568c8a8560275aee426a4718d5a4606

[ "MIT" ]

72

2018-03-29T15:57:37.000Z

2022-03-10T01:46:21.000Z

sarpy/io/product/base.py

spacefan/sarpy

2791af86b568c8a8560275aee426a4718d5a4606

[ "MIT" ]

54

2018-03-27T19:57:20.000Z

2022-03-09T20:53:11.000Z

""" Base common features for product readers """ __classification__ = "UNCLASSIFIED" __author__ = "Thomas McCullough" from typing import Sequence, List, Tuple, Union from sarpy.io.general.base import AbstractReader from sarpy.io.product.sidd1_elements.SIDD import SIDDType as SIDDType1 from sarpy.io.product.sidd2_elements.SIDD import SIDDType as SIDDType2 from sarpy.io.complex.sicd_elements.SICD import SICDType class SIDDTypeReader(AbstractReader): def __init__(self, sidd_meta, sicd_meta): """ Parameters ---------- sidd_meta : None|SIDDType1|SIDDType2|Sequence[SIDDType1]|Sequence[SIDDType2] The SIDD metadata object(s), if provided sicd_meta : None|SICDType|Sequence[SICDType] the SICD metadata object(s), if provided """ if sidd_meta is None: self._sidd_meta = None elif isinstance(sidd_meta, (SIDDType1, SIDDType2)): self._sidd_meta = sidd_meta else: temp_list = [] # type: List[Union[SIDDType1]] for el in sidd_meta: if not isinstance(el, (SIDDType1, SIDDType2)): raise TypeError( 'Got a collection for sidd_meta, and all elements are required ' 'to be instances of SIDDType.') temp_list.append(el) self._sidd_meta = tuple(temp_list) if sicd_meta is None: self._sicd_meta = None elif isinstance(sicd_meta, SICDType): self._sicd_meta = (sicd_meta, ) else: temp_list = [] # type: List[SICDType] for el in sicd_meta: if not isinstance(el, SICDType): raise TypeError( 'Got a collection for sicd_meta, and all elements are required ' 'to be instances of SICDType.') temp_list.append(el) self._sicd_meta = tuple(temp_list) @property def sidd_meta(self): # type: () -> Union[None, SIDDType1, SIDDType2, Tuple[SIDDType1], Tuple[SIDDType2]] """ None|SIDDType1|SIDDType2|Tuple[SIDDType1]|Tuple[SIDDType2]: the sidd meta_data collection. """ return self._sidd_meta @property def sicd_meta(self): # type: () -> Union[None, Tuple[SICDType]] """ None|Tuple[SICDType]: the sicd meta_data collection. """ return self._sicd_meta def get_sidds_as_tuple(self): """ Get the sidd collection as a tuple - for simplicity and consistency of use. Returns ------- Tuple[SIDDType1]|Tuple[SIDDType2] """ if self._sidd_meta is None: return None elif isinstance(self._sidd_meta, tuple): return self._sidd_meta else: return (self._sidd_meta, )

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0.59281

324

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0

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