xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

17a347bd5164cd6a7614cc6077d50a8c68cb0c7e

1,109

py

Python

test_lib.py

DrakenWan/deeplearninglibrary

0fb7d76f9220d1479c6ddadc2e6165590ab000eb

[ "Apache-2.0" ]

null

test_lib.py

DrakenWan/deeplearninglibrary

0fb7d76f9220d1479c6ddadc2e6165590ab000eb

[ "Apache-2.0" ]

null

test_lib.py

DrakenWan/deeplearninglibrary

0fb7d76f9220d1479c6ddadc2e6165590ab000eb

[ "Apache-2.0" ]

null

""" Tutorial reference: https://www.kdnuggets.com/2020/09/implementing-deep-learning-library-scratch-python.html Original Library: https://github.com/parmeet/dll_numpy Author: DrakenWan 2020 """ import core as DL import utilities import numpy as np if __name__ == "__main__": batch_size = 20 num_epochs = 200 samples_per_class = 100 num_classes = 3 hidden_units = 100 data,target = utilities.genSpiralData(samples_per_class,num_classes) model = utilities.Model() model.add(DL.Linear(2,hidden_units)) model.add(DL.ReLU()) model.add(DL.Linear(hidden_units,num_classes)) optim = DL.SGD(model.parameters,lr=1.0,weight_decay=0.001,momentum=.9) loss_fn = DL.SoftmaxWithLoss() model.fit(data,target,batch_size,num_epochs,optim,loss_fn) predicted_labels = np.argmax(model.predict(data),axis=1) accuracy = np.sum(predicted_labels==target)/len(target) print("Model Accuracy = {}".format(accuracy)) utilities.plot2DDataWithDecisionBoundary(data,target,model)

39.607143

113

0.67899

142

1,109

5.105634

0.556338

0.041379

0.044138

0

0.036322

0.205591

1,109

28

114

39.607143

0.786606

0.167719

0

0.030787

0

1

0

false

0

0.142857

0

0.142857

0.047619

0

null

0

null

0

1

0

bd587bc3d2baa172c3072e8e32527093a3a9f149

2,547

py

Python

src/routes/dataNetwork.py

spaezsuarez/view-data-app

6482b73eb39048e5dbdf133d4ccf29cc357dcb6f

[ "MIT" ]

1

2021-05-28T17:01:17.000Z

src/routes/dataNetwork.py

spaezsuarez/view-data-app

6482b73eb39048e5dbdf133d4ccf29cc357dcb6f

[ "MIT" ]

null

src/routes/dataNetwork.py

spaezsuarez/view-data-app

6482b73eb39048e5dbdf133d4ccf29cc357dcb6f

[ "MIT" ]

null

from . import dataRoute from flask import render_template, request from utils import dataManagment import dateutil import datetime @dataRoute.route('/head', methods=['POST']) def create_head(): number = request.form['number'] df = dataManagment.data_frame_head(number) return render_template('data.html', tables=[df.to_html()],isCentered = True) @dataRoute.route('/sex/country', methods=['POST']) def create_second_request(): sexo = request.form.get('selection-sex') pais = request.form.get('selection-country') df = dataManagment.get_sex_country_deaths(pais, sexo) return render_template('data.html', tables=[df.to_html()],isCentered = True) @dataRoute.route('/country/dates', methods=['POST']) def create_third_request(): firstDate = dateutil.parser.parse(request.form.get( 'firstDate'), dayfirst=False) # Datetime secondDate = dateutil.parser.parse( request.form.get('secondDate'), dayfirst=False) firstDate = datetime.date(firstDate.year, firstDate.month, firstDate.day) secondDate = datetime.date( secondDate.year, secondDate.month, secondDate.day) pais = request.form.get('selection-country') df = dataManagment.get_country_dates(pais, firstDate, secondDate) return render_template('data.html', tables=[df.to_html()],isCentered = True) @dataRoute.route('/count/country', methods=['POST']) def create_fourth_request(): sexo = request.form.get('selection-sex') df = dataManagment.get_contagios_por_pais(sexo) return render_template('data.html', tables=[df.to_html()],isCentered = False) @dataRoute.route('/count/state', methods=['POST']) def create_fifth_request(): estado = request.form.get('selection-state') df = dataManagment.get_estado_por_pais(estado) return render_template('data.html', tables=[df.to_html()], isCentered = False) @dataRoute.route('/resumen/departamento', methods=['POST']) def create_sixth_request(): pais = request.form.get('country') df = dataManagment.get_resumen(pais) return render_template('data.html', tables=[df.to_html()], isCentered = False) @dataRoute.route('/muertes/ciudad',methods=['POST']) def create_seventh_request(): ciudad = request.form.get('selection-city') df = dataManagment.get_muertes_por_ciudad(ciudad) return render_template('data.html', tables=[df.to_html()], isCentered = False) @dataRoute.route('/test',methods=['GET']) def create_test(): df = dataManagment.test() return render_template('data.html',tables=[df.to_html()],isCentered = True)

39.796875

82

0.725167

316

2,547

5.693038

0.199367

0.061145

0.070039

0.106726

0.480267

0.45025

0.413563

0.372429

0.314619

0

0.122104

2,547

64

83

39.796875

0.804562

0.003141

0

0.230769

0

0.126872

0.008274

0

1

0.153846

false

0

0.096154

0

0.403846

0

null

0

null

0

1

0

bd5a208ee269d3bfedf85a940f895d881be68074

1,151

py

Python

Chapter 4/05 - Real-life example - lazily evaluated attributes/lazy_class_attribute.py

moseskim/Expert-Python-Programming-Fourth-Edition

5160f974deb2365597b7be9cc032f24bfa13471a

[ "MIT" ]

null

Chapter 4/05 - Real-life example - lazily evaluated attributes/lazy_class_attribute.py

moseskim/Expert-Python-Programming-Fourth-Edition

5160f974deb2365597b7be9cc032f24bfa13471a

[ "MIT" ]

null

Chapter 4/05 - Real-life example - lazily evaluated attributes/lazy_class_attribute.py

moseskim/Expert-Python-Programming-Fourth-Edition

5160f974deb2365597b7be9cc032f24bfa13471a

[ "MIT" ]

null

import OpenGL.GL as gl from OpenGL.GL import shaders class lazy_class_attribute(object): def __init__(self, function): self.fget = function def __get__(self, obj, cls): value = self.fget(obj or cls) # note: 인스턴스가 아닌 클래스 객체에 저장한다. # 클래스-레벨 또는 인스턴스-레벨 접근과 관계없다. setattr(cls, self.fget.__name__, value) return value class ObjectUsingShaderProgram(object): # 전형적인 pass-through vertex shader 구현 VERTEX_CODE = """ #version 330 core layout(location = 0) in vec4 vertexPosition; void main(){ gl_Position = vertexPosition; } """ # 전형적인 프래그먼트 셰이더 # 모든 요소를 흰색으로 그린다. FRAGMENT_CODE = """ #version 330 core out lowp vec4 out_color; void main(){ out_color = vec4(1, 1, 1, 1); } """ @lazy_class_attribute def shader_program(self): print("compiling!") return shaders.compileProgram( shaders.compileShader(self.VERTEX_CODE, gl.GL_VERTEX_SHADER), shaders.compileShader(self.FRAGMENT_CODE, gl.GL_FRAGMENT_SHADER), )

26.767442

77

0.592528

135

1,151

4.844444

0.518519

0.036697

0.055046

0

0.017789

0.316247

1,151

42

78

27.404762

0.813215

0.112945

0

0.193548

0

0.312315

0

1

0.096774

false

0

0.064516

0

0.354839

0.032258

0

null

0

null

0

1

0

bd5a5f01afeebbef48ab0545bb48413999a63520

6,134

py

Python

main.py

Lulzx/gittools

b4a1a4d7169a10af10079a3903b9108843e97385

[ "MIT" ]

4

2020-10-22T03:58:56.000Z

2021-10-29T20:45:49.000Z

main.py

Lulzx/gittools

b4a1a4d7169a10af10079a3903b9108843e97385

[ "MIT" ]

null

main.py

Lulzx/gittools

b4a1a4d7169a10af10079a3903b9108843e97385

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import logging import os import sys import time from itertools import islice from uuid import uuid4 import emojis from github import Github from telegram import InlineKeyboardButton, InlineKeyboardMarkup from telegram import InlineQueryResultArticle, ParseMode from telegram import InputTextMessageContent from telegram.ext import Updater, InlineQueryHandler, CommandHandler logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO) logger = logging.getLogger(__name__) access_token = os.environ.get("access_token") g = Github(access_token) def start(update, context): update.message.reply_text('Hi!') def help(update, context): update.message.reply_text('Help!') def fetch_url(query_term, query_type): if query_type in ["u", "user"]: result = get_user(query_term) elif query_type in ["r", "repo"]: result = get_repo(query_term) else: result = "NIL" return result def get_repo(query): repo = g.get_repo(query) name = repo.name repo_url = repo.html_url clone_url = repo.clone_url # description = repo.description stars = repo.stargazers_count language = repo.language owner_name = repo.owner.name owner_url = repo.owner.html_url response = f"""🗄 [{name}]({repo_url}) by [{owner_name}]({owner_url})""" response += f""" in #{language}\n⭐️ {stars} Stars\n📥 [Clone]({clone_url})""" return response def get_user(query): user = g.get_user(query) name = "👥 " + user.name location = "📌 " + user.location bio = "🎭 " + user.bio # avatar = user.avatar_url response = "{}\n{}\n{}".format(name, location, bio) response += "\n🔗 https://github.com/{}".format(query) return response def search_callback(update, context): user_says = context.args if len(user_says): chat_id = update.message.chat.id query_type = str(user_says[0]) query_term = str(user_says[1:][0]) result = fetch_url(query_term, query_type) link = result.split("[Clone](")[-1][:-1] data = result.split(".")[1].split("/") base = "https://github.com/" username = query_term # repo_name = data[2] url = base + username if query_type == "u": button_text = "🗄 repositories" link = url + "?tab=repositories" else: button_text = "🗄 repository" markup = InlineKeyboardMarkup( [[InlineKeyboardButton("👤 profile", url=url), InlineKeyboardButton(button_text, url=link)]]) context.bot.send_message(chat_id=chat_id, text="{}".format(result), reply_markup=markup, parse_mode=ParseMode.MARKDOWN) else: return def download(update, context): user_says = context.args chat_id = update.message.chat.id # query_type = str(user_says[0]) query_term = str(user_says[0]) url = f"https://github.com/{query_term}/archive/master.zip" caption = f"✅ download successful for repository: {query_term}" context.bot.send_document(chat_id=chat_id, document=url, caption=caption) # except: # context.bot.send_message(chat_id=chat_id, text="repository not found!") def emoji_callback(update, context): chat_id = update.message.chat.id emojiset = g.get_emojis() for x in emojiset: x = f":{x}:" context.bot.send_message(chat_id=chat_id, text=emojis.encode(x)) time.sleep(0.1) def inlinequery(update, context): try: query = update.inline_query.query # .split(" ") # query_type = query[0] # query_term = query[1] keywords = [keyword.strip() for keyword in query.split(',')] except: return query = '+'.join(keywords) + '+in:readme+in:description' result = g.search_repositories(query, 'stars', 'desc') print(f'Found {result.totalCount} repo(s)') # result = fetch_url(query_term, query_type) title = "Result" results = list() if result.totalCount == 0: title = "No results found." content = "No results found." results.append( InlineQueryResultArticle( id=uuid4(), title=title, input_message_content=InputTextMessageContent( "{}".format(content), parse_mode=ParseMode.MARKDOWN))) update.inline_query.answer(results, cache_time=3) stop = 10 for repo in islice(result, 0, stop): name = repo.name repo_url = repo.html_url clone_url = repo.clone_url description = repo.description stars = repo.stargazers_count language = repo.language owner_name = repo.owner.name owner_url = repo.owner.html_url response = f"""🗄 [{name}]({repo_url}) by [{owner_name}]({owner_url})""" response += f""" in #{language}\n⭐️ {stars} Stars\n📥 [Clone]({clone_url})""" results.append( InlineQueryResultArticle( id=uuid4(), title=name, description=description, input_message_content=InputTextMessageContent( "{}".format(response), parse_mode=ParseMode.MARKDOWN))) update.inline_query.answer(results, cache_time=3) def error(update, context): logger.warning('Update "%s" caused error "%s"', update, context.error) def main(): try: TOKEN = sys.argv[1] except IndexError: TOKEN = os.environ.get("telegram_token") updater = Updater(TOKEN, use_context=True) dp = updater.dispatcher dp.add_handler(CommandHandler("start", start)) dp.add_handler(CommandHandler("help", help)) dp.add_handler(CommandHandler("search", search_callback)) dp.add_handler(CommandHandler("emoji", emoji_callback)) dp.add_handler(CommandHandler("download", download)) dp.add_handler(InlineQueryHandler(inlinequery)) dp.add_error_handler(error) updater.start_polling() logger.info("Ready to rock..!") updater.idle() if __name__ == '__main__': main()

31.45641

104

0.630747

745

6,134

5.044295

0.237584

0.022352

0.020756

0.034593

0.37041

0.32677

0.251464

0.234433

0.204896

0

0.005352

0.238507

6,134

194

105

31.618557

0.795761

0.053962

0

0.293333

0

0.12364

0.013642

0

1

0.073333

false

0

0.08

0

0.186667

0.006667

0

null

0

null

0

1

0

bd5b5440ec10bb3d894d2f6fb7dcd6426ce92c37

4,132

py

Python

SnakeGame.py

captainpolar/snakegame

018638875309f0b17a6dbbd5c3ff4f536058b844

[ "MIT" ]

1

2021-03-29T17:15:09.000Z

SnakeGame.py

captainpolar/snakegame

018638875309f0b17a6dbbd5c3ff4f536058b844

[ "MIT" ]

null

SnakeGame.py

captainpolar/snakegame

018638875309f0b17a6dbbd5c3ff4f536058b844

[ "MIT" ]

null

import pygame import random pygame.init() white = (255, 255, 255) black = (0, 0, 0) yellow = (255, 255, 102) red = (250, 0, 0) # Other nice red color: 213, 50, 80 green = (152, 251, 152) blue = (30, 144, 255) # other nice combo: 50, 151, 213 dis_width = 800 dis_height = 600 dis = pygame.display.set_mode((dis_width, dis_height)) pygame.display.set_caption('Snake Game') clock = pygame.time.Clock() snake_block = 10 snake_speed = 15 # Fonts font_style = pygame.font.SysFont("roboto", 30) score_font = pygame.font.SysFont("chango", 55) level_font = pygame.font.SysFont("chango", 55) def score(score): value = score_font.render("Score: " + str(score), True, blue) dis.blit(value, [0, 0]) def our_snake(snake_block, snake_list): for x in snake_list: pygame.draw.rect(dis, black, [x[0], x[1], snake_block, snake_block]) def message(msg, color): mesg = font_style.render(msg, True, color) dis.blit(mesg, [dis_width/6, dis_height/3]) def gameLoop(): end_game = False close_game = False x1 = dis_width / 2 y1 = dis_height / 2 x1_change = 0 y1_change = 0 snake_List = [] Snake_Length = 1 foodx = round(random.randrange(0, dis_width - snake_block) / 10) * 10 foody = round(random.randrange(0, dis_width - snake_block) / 10) * 10 while not end_game: while close_game: dis.fill(green) # change color message("GAME OVER! Press SPACE to play again or ESC to quit.", red) score(Snake_Length - 1) pygame.display.update() for event in pygame.event.get(): if event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: end_game = True close_game = False if event.key == pygame.K_SPACE: gameLoop() # for event in pygame.event.get(): # if event.type == pygame.KEYDOWN: # if event.key == pygame.K_2: # end_game = True # close_game = False # if event.key == pygame.K_1: # gameLoop() for event in pygame.event.get(): if event.type == pygame.QUIT: end_game = True if event.type == pygame.KEYDOWN: if event.key == pygame.K_LEFT: x1_change = -snake_block y1_change = 0 elif event.key == pygame.K_RIGHT: x1_change = snake_block y1_change = 0 elif event.key == pygame.K_UP: x1_change = 0 y1_change = -snake_block elif event.key == pygame.K_DOWN: x1_change = 0 y1_change = snake_block if x1 >= dis_width or x1 < 0 or y1 >= dis_height or y1 < 0: close_game = True x1 += x1_change y1 += y1_change dis.fill(green) # change color maybe pygame.draw.rect(dis, red, [foodx, foody, snake_block, snake_block]) # change food color maybe snake_Head = [] snake_Head.append(x1) snake_Head.append(y1) snake_List.append(snake_Head) if len(snake_List) > Snake_Length: del snake_List[0] for x in snake_List[:-1]: if x == snake_Head: close_game = True our_snake(snake_block, snake_List) score(Snake_Length - 1) pygame.display.update() if x1 == foodx and y1 == foody: foodx = round(random.randrange(0, dis_width - snake_block) / 10) * 10 foody = round(random.randrange(0, dis_height - snake_block) / 10) * 10 Snake_Length += 1 clock.tick(snake_speed) # Planning to add levels: # if level == 1 and score == 10: # level +=2 # snake_speed = 20 pygame.quit() quit() gameLoop()

29.304965

104

0.526864

519

4,132

4.021195

0.235067

0.071874

0.053666

0.057499

0.444178

0.390513

0.336847

0.276473

0

0.056967

0.371249

4,132

140

105

29.514286

0.746343

0.090997

0

0.276596

0

0.02418

0

1

0.042553

false

0

0.021277

0

0.06383

0

null

0

null

0

1

0

bd5c8afbbd833cee9c288bc0007055dc079f69d8

3,335

py

Python

MpSA-TRIP/BarcodeGenomeLibMain.py

wiw/pyMPFA

a72aa196859031b2194fb51f204a1ab938193aaa

[ "Unlicense" ]

null

MpSA-TRIP/BarcodeGenomeLibMain.py

wiw/pyMPFA

a72aa196859031b2194fb51f204a1ab938193aaa

[ "Unlicense" ]

null

MpSA-TRIP/BarcodeGenomeLibMain.py

wiw/pyMPFA

a72aa196859031b2194fb51f204a1ab938193aaa

[ "Unlicense" ]

null

#C:\Python27\python.exe #!/usr/bin/env python # encoding: utf-8 import os # import subprocess import SupportFunc as supp import ReadIndexesFunc as rind import CollectBcMutFunc as colb import WriteFunc as wrt import param import picks from TripMain_0_2 import Pdump def main(): supp.setup_logging() for name in param.indexList: index = param.indexList[name] # readsStat = {} if not os.path.exists(os.path.join(picks.workdir, name)): os.makedirs(os.path.join(picks.workdir, name)) indexFile = os.path.join(picks.workdir, name, "index_{}.fastq".format(index.upper())) # indexFiltFile = os.path.join(picks.workdir, name, "filt_index_{}.fastq".format(index.upper())) if not os.path.exists(indexFile) or os.stat(indexFile).st_size == 0: rind.SplitFastqByIndexes(picks.input_file, indexFile, index.upper(), param.indexError, param.const_1.upper(), param.const_1Error, param.regExpIndex, picks.no_trim_index) if picks.random_read: indexFileRand = os.path.join(picks.workdir, name, "random_index_{}.fastq".format(index.upper())) rind.RandomReadIndexes(indexFile, indexFileRand, param.probability) indexFile = indexFileRand supp.LogInfo("\n\nEnd splitting.\n\n#####################################\n") # readsStat[name] = rind.filterShadyReads(indexFile, param.reFilter, indexFiltFile) # indexFile = indexFiltFile # supp.LogInfo("Filter before: {}, after: {}\n indexFile - {}, indexFiltFile - {}".format(readsStat[name][0], readsStat[name][1], indexFile, indexFiltFile)) supp.LogInfo('''Processing on: '{}'.\n Total reads in file '{}': {} reads.\n Generate dictionary of barcodes.\n'''.format(os.path.basename(indexFile), os.path.basename(indexFile), supp.GetTotalSeqRecords(indexFile))) bcDictPI = colb.CollectBarcodeGenome(indexFile, param.barcodeLength, param.readsValue, param.barcodeError, param.const_2.upper(), param.const_2Error, param.regExpBc, picks.merge_indexes, picks.reverse_barcode, param.pmi, param.pmiLength, param.pmiSubst) Pdump(bcDictPI, name + "_bcDictPI", picks.PdumpDir) # Pdump(readsStat, name + "_readsStat", picks.PdumpDir) for pI in bcDictPI: csvFile = wrt.WriteBcDictToFile(bcDictPI[pI], os.path.join(picks.workdir, name), indexFile, pI) # csvFile_R = wrt.SimpleCsvWriter(None, bcDictPI[pI], os.path.join(picks.workdir, name), indexFile, pI) supp.LogInfo(''' I had select the {} unique barcodes.\n Results writing to file '{}' in your working directory: '{}'\n'''.format(len(bcDictPI[pI]), csvFile, os.path.join(picks.workdir, name))) # if os.path.exists(param.rscript): # pathToScript = os.path.join(os.getcwd(), "trip_Rstat.R") # option = [csvFile_R, os.path.dirname(csvFile_R), index] # cmd = [param.rscript, pathToScript] + option # subprocess.call(cmd) # else: # print("You do not have installed R-session, or you incorrectly specified the path to the Rscript.\nStatistics on barcodes will not be displayed.") supp.LogInfo("End processing with: '{}'.\n\n".format(os.path.basename(indexFile))) if __name__ == "__main__": main()

60.636364

261

0.661469

401

3,335

5.418953

0.366584

0.044179

0.041417

0.055223

0.198343

0.146802

0.059365

0.043258

0

0.004459

0.193103

3,335

55

262

60.636364

0.803047

0.297151

0

0.166309

0.031801

0

1

0.029412

false

0

0.235294

0

0.264706

0

null

0

null

0

1

0

bd5d9ea17d4a9ae541c6e6916ed6741c75882ac1

12,036

py

Python

polyart/_input.py

IsaacTFM/pygrr-polyart

7a8219cd93b5691ddca89b4fb46a4af7bc1e3456

[ "Apache-2.0" ]

4

2021-11-21T18:35:54.000Z

2021-12-14T02:02:37.000Z

polyart/_input.py

IsaacTFM/pygrr-polyart

7a8219cd93b5691ddca89b4fb46a4af7bc1e3456

[ "Apache-2.0" ]

3

2021-11-21T18:37:56.000Z

2021-11-21T19:15:21.000Z

polyart/_input.py

IsaacTFM/pygrr-polyart

7a8219cd93b5691ddca89b4fb46a4af7bc1e3456

[ "Apache-2.0" ]

1

2021-11-21T18:39:52.000Z

""" This file handles the input of PolyArt. """ # if this is the origin file (not imported) if __name__ == "__main__": # print the documentation print(__doc__) # add sysmessages module location to path from sys import path path.append('..') # run sysmessages import common.sysmessages # import parent package import polyart # import used functions from math from math import cos, atan, sin, radians # ------------------------------------------------------------------------ # # rotation # # ------------------------------------------------------------------------ # # region rotation def rotate_left(event): """ Wrapper for rotating the model anti-clockwise. """ # if the focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass if polyart.snapped: # if snapped, rotate 22.5 degrees rotate(-22.5) else: # else, rotate 1 degrees rotate(-1) def rotate_right(event): """ Wrapper for rotating the model clockwise. """ # if the focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass if polyart.snapped: # if snapped, rotate 22.5 degrees rotate(22.5) else: # else, rotate 1 degrees rotate(1) def rotate(angle): # assumes anti-clockwise """ Rotates the model. """ angle *= -1 # makes it clockwise if angle < 0: # this cleanses the number to ensure it is between 0 and 360 angle = -(abs(angle) % 360) else: angle = angle % 360 new_points = [] for point in polyart.model_data: x = point[0] - polyart.CENTER[0] y = -(point[1] - polyart.CENTER[1]) # point_rotation = the angle from the center of the model to the point if x == 0 and y == 0: # ignore this point, it is in the centre new_points.append((polyart.CENTER[0] + x, polyart.CENTER[1] - y)) else: if x == 0: if y > 0: # it is directly up point_rotation = radians(0 - 90) else: # it is directly down point_rotation = radians(180 - 90) elif y == 0: if x > 0: # it is directly right point_rotation = radians(90 - 90) else: # it is directly left point_rotation = radians(270 - 90) else: if x > 0 and y > 0: point_rotation = atan(x / y) + radians(0 - 90) elif x > 0 and y < 0: point_rotation = atan(x / y) + radians(180 - 90) elif x < 0 and y > 0: point_rotation = atan(x / y) + radians(360 - 90) else: # x < 0 and y < 0: point_rotation = atan(x / y) + radians(180 - 90) theta = radians( angle) - point_rotation # theta is equal to the rotation of the object added to the angle, minus the model rotation of the point radius = polyart.cached_hypot(x, y) # get distance from the point to the center of the object new_xdiff = radius * cos(theta) new_ydiff = radius * sin(theta) new_points.append((polyart.CENTER[0] + new_xdiff, polyart.CENTER[1] - new_ydiff)) # update model_data polyart.model_data = new_points # refresh the canvas polyart.refresh() # endregion # ------------------------------------------------------------------------ # # mouse # # ------------------------------------------------------------------------ # # region mouse def left_click(event): """ Either creates a new point, or chooses the point to move (index_moving). """ # if the focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass # get mouse position mouse = (event.x, event.y) # iterate through model_data for i in range(len(polyart.model_data)): point = polyart.model_data[i] # calculate the distance between the mouse and point distance = polyart.distance(mouse, point) if distance <= polyart.POINTSELECTDISTANCE: # if the distance is less than or equal to the point_select_distance polyart.index_moving = i break if polyart.index_moving == None: # if no point is selected, assume that the user is trying to create a new point # iterate through model_data for i in range(len(polyart.model_data)): # get the parent points of the line a = polyart.model_data[i % len(polyart.model_data)] c = polyart.model_data[(i + 1) % len(polyart.model_data)] if polyart.is_between(a, mouse, c): # if the mouse position lies on that line # work out index index = (i + 1) % len(polyart.model_data) if polyart.snapped: # position is snapped position = (polyart.snap(mouse[0]), polyart.snap(mouse[1])) else: # position not snapped position = mouse # insert new point polyart.model_data.insert(index, position) # refresh the canvas polyart.refresh() # force only one point to be made break def left_release(event): """ Sets the selected point (index_moving) to None. """ # if the focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass polyart.index_moving = None def right_click(event): """ Deletes the point hovered over. """ # if the focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass if len(polyart.model_data) > 3 and polyart.index_moving == None: # if there are less than 4 points, deleting a point should NOT be allowed to happen # if a point is selected, the same goes # get the mouse position mouse = (event.x, event.y) # iterate through model_data for point in polyart.model_data: if polyart.distance(mouse, point) <= polyart.POINTSELECTDISTANCE: # if the distance is less than or equal to the point_select_distance # remove the point polyart.model_data.remove(point) # force only one point to be deleted break # refresh the canvas polyart.refresh() def motion(event): """ Moves the selected point, if there is one. """ # get the moue position mouse = (event.x, event.y) if polyart.index_moving is not None: # if a point is selected if polyart.snapped: # if snapped, snap the position new_position = (polyart.snap(mouse[0]), polyart.snap(mouse[1])) else: # if not, do not snap the position new_position = mouse # update variable model_data polyart.model_data[polyart.index_moving] = new_position # refresh the canvas polyart.refresh() # endregion # ------------------------------------------------------------------------ # # movement # # ------------------------------------------------------------------------ # # region movement def left(event): """ Moves the model left. """ # if focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass if polyart.snapped: # if snapped, move vector is effectively snapped move = (-polyart.GRIDSIZE, 0) else: move = (-1, 0) # iterate through model data for i in range(len(polyart.model_data)): x = polyart.model_data[i][0] y = polyart.model_data[i][1] # offset each point by the move vector polyart.model_data[i] = (x + move[0], y - move[1]) # refresh the canvas polyart.refresh() def right(event): """ Moves the model right. """ # if focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass if polyart.snapped: # if snapped, move vector is effectively snapped move = (polyart.GRIDSIZE, 0) else: move = (1, 0) # iterate through model data for i in range(len(polyart.model_data)): x = polyart.model_data[i][0] y = polyart.model_data[i][1] # offset each point by the move vector polyart.model_data[i] = (x + move[0], y - move[1]) # refresh the canvas polyart.refresh() def up(event): """ Moves the model up. """ # if focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass if polyart.snapped: # if snapped, move vector is effectively snapped move = (0, polyart.GRIDSIZE) else: move = (0, 1) # iterate through model data for i in range(len(polyart.model_data)): x = polyart.model_data[i][0] y = polyart.model_data[i][1] # offset each point by the move vector polyart.model_data[i] = (x + move[0], y - move[1]) # refresh the canvas polyart.refresh() def down(event): """ Moves the model down. """ # if focus is on an entry widget, return try: if polyart.ui.root.focus_get().winfo_class() == "Entry": return except Exception: pass if polyart.snapped: # if snapped, move vector is effectively snapped move = (0, -polyart.GRIDSIZE) else: move = (0, -1) # iterate through model data for i in range(len(polyart.model_data)): x = polyart.model_data[i][0] y = polyart.model_data[i][1] # offset each point by the move vector polyart.model_data[i] = (x + move[0], y - move[1]) # refresh the canvas polyart.refresh() # endregion # ------------------------------------------------------------------------ # # binding # # ------------------------------------------------------------------------ # # region binding def bind_inputs(): """ Binds the inputs to each function in this file. """ # bind the mouse movement on the canvas to the motion function polyart.ui.canvas.bind("<Motion>", motion) # bind the mouse presses on the root to the correct functions polyart.ui.root.bind("<ButtonPress-1>", left_click) polyart.ui.root.bind("<ButtonRelease-1>", left_release) polyart.ui.root.bind("<ButtonPress-3>", right_click) # bind the rotate functions to E and Q polyart.ui.root.bind("e", rotate_right) polyart.ui.root.bind("q", rotate_left) # bind the arrow keys to the movement functions polyart.ui.root.bind("<Key-Left>", left) polyart.ui.root.bind("<Key-Right>", right) polyart.ui.root.bind("<Key-Up>", up) polyart.ui.root.bind("<Key-Down>", down) # bind the WSAD keys to the movement functions polyart.ui.root.bind("<a>", left) polyart.ui.root.bind("<d>", right) polyart.ui.root.bind("<w>", up) polyart.ui.root.bind("<s>", down) # endregion

27.990698

149

0.534064

1,456

12,036

4.337912

0.141484

0.056998

0.078531

0.040374

0.59943

0.522324

0.456935

0.447435

0.433819

0

0.015282

0.325856

12,036

429

150

28.055944

0.763125

0.359172

0

0.522843

0

0.021991

0

1

0.060914

false

0.045685

0.020305

0

0.126904

0.005076

0

null

0

null

0

1

0

bd5f550b799363cf152de68256f97f876bf2f1f2

1,486

py

Python

biodada/alphabets.py

simomarsili/biodada

642fb440d8a66a0413deb69c8623ea3b61d41678

[ "BSD-3-Clause" ]

null

biodada/alphabets.py

simomarsili/biodada

642fb440d8a66a0413deb69c8623ea3b61d41678

[ "BSD-3-Clause" ]

null

biodada/alphabets.py

simomarsili/biodada

642fb440d8a66a0413deb69c8623ea3b61d41678

[ "BSD-3-Clause" ]

null

"""Alphabet-related methods.""" import logging import numpy ALPHABETS = { 'protein': '-ACDEFGHIKLMNPQRSTVWY', 'dna': '-ACGT', 'rna': '-ACGU', 'protein_u': '-ACDEFGHIKLMNPQRSTVWYBZX', 'dna_u': '-ACGTRYMKWSBDHVN', 'rna_u': '-ACGURYMKWSBDHVN', } logger = logging.getLogger(__name__) def check_alphabet(alphabet): # A string of ordered, unique symbols return ''.join(sorted(set(alphabet))) def check_alphabet_records(records, alphabet): """Filter out records not consistent with alphabet.""" alphabet_set = set(alphabet) return (r for r in records if set(r[1]) <= alphabet_set) def score_alphabet(alphabet, counts): """Score for alphabet given counts.""" import math chars = set(alphabet) - set('*-') score = (sum([counts.get(a, 0) for a in chars]) / math.log(len(alphabet))) logger.debug('alphabet %r score %r', alphabet, score) return score def guess_alphabet(records): """Guess alphabet from an iterable of records.""" from collections import Counter from biodada import ALPHABETS data = numpy.array([list(record[1]) for record in records], dtype='U1').flatten() counts = Counter(data) max_score = float('-inf') for key, alphabet in ALPHABETS.items(): score = score_alphabet(alphabet, counts) if score > max_score: max_score = score guess = key logger.info('Alphabet guess: %r', guess) return ALPHABETS[guess]

28.576923

78

0.647376

179

1,486

5.273743

0.413408

0.067797

0.033898

0.057203

0

0.003445

0.218708

1,486

51

79

29.137255

0.809647

0.126514

0

0.129209

0.035239

0

1

0.111111

false

0

0.138889

0.027778

0.361111

0

null

0

null

0

1

0

bd62a6bcee335bdbb5aaa3e5bf955b75d241b748

1,813

py

Python

server/app.py

osteele/tidal-memories

afcd3c8900814577374bfd847ba05c12ca88f397

[ "MIT" ]

1

2018-07-24T20:19:52.000Z

server/app.py

osteele/tidal-memories

afcd3c8900814577374bfd847ba05c12ca88f397

[ "MIT" ]

2

2021-03-09T09:59:44.000Z

2021-05-09T17:29:22.000Z

server/app.py

osteele/tidal-memories

afcd3c8900814577374bfd847ba05c12ca88f397

[ "MIT" ]

null

import os import gevent import redis from flask import Flask, render_template, send_from_directory from flask_cors import CORS from flask_restplus import Api from flask_sockets import Sockets from geventwebsocket.exceptions import WebSocketError from .image_resource import api as images_api from .thumbnails import SMALL_THUMBNAIL_DIR REDIS_CHAN = "sensor_data" app = Flask(__name__) app.config["SERVE_LOCAL_IMAGES"] = os.environ.get("SERVE_LOCAL_IMAGES") CORS(app) sockets = Sockets(app) REDIS_URL = os.environ.get("REDIS_URL") if REDIS_URL: redis_conn = redis.StrictRedis.from_url(REDIS_URL) @app.route("/") def splash(): return render_template("splash.html") if app.config["SERVE_LOCAL_IMAGES"]: @app.route("/images/small-thumbnail/<path:filename>") def thumbnail(filename): return send_from_directory(str(SMALL_THUMBNAIL_DIR), str(filename)) if REDIS_URL: @sockets.route("/sensor_data") def sensor_data_route(ws): def publish(): while not ws.closed: data = ws.receive() if data: # print(ws, "publish", data, type(data)) redis_conn.publish(REDIS_CHAN, data) def subscribe(): pubsub = redis_conn.pubsub() pubsub.subscribe(REDIS_CHAN) for message in pubsub.listen(): if message["type"] == "message": data = message.get("data") # print(ws, "send", data, type(data)) try: ws.send(data.decode()) except WebSocketError: return gevent.spawn(subscribe) publish() api = Api(app, doc="/docs/", title="Matrix Image Gallery API", version="0.1") api.add_namespace(images_api)

26.661765

77

0.629344

219

1,813

5.013699

0.342466

0.03643

0.043716

0.034608

0.045537

0

0.001499

0.264203

1,813

67

78

27.059701

0.821589

0.040816

0

0.042553

0

0.106567

0.022465

0

1

0.106383

false

0

0.212766

0.042553

0.382979

0

null

0

null

0

1

0

bd63313f347912b5d918aa4c501b628907905f8a

3,045

py

Python

batch_rl/multi_head/atari_helpers.py

alhamzah/batch_rl

7f8d9ea0ba330cc0642515dcc44c2ad687c3a927

[ "Apache-2.0" ]

null

batch_rl/multi_head/atari_helpers.py

alhamzah/batch_rl

7f8d9ea0ba330cc0642515dcc44c2ad687c3a927

[ "Apache-2.0" ]

null

batch_rl/multi_head/atari_helpers.py

alhamzah/batch_rl

7f8d9ea0ba330cc0642515dcc44c2ad687c3a927

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2019 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import tensorflow as tf slim = tf.contrib.slim class multi_head_network(tf.keras.Model): """The convolutional network used to compute the agent's Q-values.""" def __init__(self, num_actions, num_heads, network_type, name=None, **kwargs): """Creates the layers used for calculating Q-values. """ super(multi_head_network, self).__init__(name=name) self.num_actions = num_actions self.network_type = network_type self.num_heads = num_heads # Defining layers. activation_fn = tf.keras.activations.relu # Setting names of the layers manually to make variable names more similar # with tf.slim variable names/checkpoints. self.conv1 = tf.keras.layers.Conv2D(32, [8, 8], strides=4, padding='same', activation=activation_fn, name='Conv') self.conv2 = tf.keras.layers.Conv2D(64, [4, 4], strides=2, padding='same', activation=activation_fn, name='Conv') self.conv3 = tf.keras.layers.Conv2D(64, [3, 3], strides=1, padding='same', activation=activation_fn, name='Conv') self.flatten = tf.keras.layers.Flatten() self.dense1 = tf.keras.layers.Dense(512, activation=activation_fn, name='fully_connected') self.dense2 = tf.keras.layers.Dense(num_actions*num_heads, activation=None, name='fully_connected_q_heads') def call(self, state): """Creates the output tensor/op given the state tensor as input. See https://www.tensorflow.org/api_docs/python/tf/keras/Model for more information on this. Note that tf.keras.Model implements `call` which is wrapped by `__call__` function by tf.keras.Model. Parameters created here will have scope according to the `name` argument given at `.__init__()` call. Args: state: Tensor, input tensor. Returns: collections.namedtuple, output ops (graph mode) or output tensors (eager). """ x = tf.cast(state, tf.float32) x = tf.div(x, 255.) x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.flatten(x) x = self.dense1(x) x = self.dense2(x) q_heads = tf.reshape(x, [-1, self.num_actions, self.num_heads]) unordered_q_heads = q_heads q_values = tf.reduce_mean(q_heads, axis=-1) return self.network_type(q_heads, unordered_q_heads, q_values)

40.6

80

0.674877

435

3,045

4.597701

0.413793

0.0385

0.039

0.052

0.1095

0.0675

0

0.019815

0.221018

3,045

74

81

41.148649

0.823356

0.430213

0

0.085714

0

0.037304

0.013839

0

1

0.057143

false

0

0.057143

0

0.171429

0

null

0

null

0

1

0

bd63927a981ce679de4270308c480c8f49ea21fc

2,358

py

Python

data/gary/heart.py

isabellewei/deephealth

a226788561996e698b5f52b4b83683dcb3563ea5

[ "MIT" ]

null

data/gary/heart.py

isabellewei/deephealth

a226788561996e698b5f52b4b83683dcb3563ea5

[ "MIT" ]

null

data/gary/heart.py

isabellewei/deephealth

a226788561996e698b5f52b4b83683dcb3563ea5

[ "MIT" ]

null

from time import time from sklearn.preprocessing import StandardScaler from sklearn import model_selection from sklearn.model_selection import train_test_split, KFold, cross_val_score from sklearn.metrics import classification_report,confusion_matrix,accuracy_score from sklearn.tree import DecisionTreeClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import RandomForestClassifier, VotingClassifier, BaggingClassifier, AdaBoostClassifier from sklearn.naive_bayes import GaussianNB import numpy as np import scipy as sp import pandas as pd import math df = pd.read_csv("parsed_heart.csv") y1 = df["num"].values cols = list(df) mlp = MLPClassifier(hidden_layer_sizes=(100,100,100)) clf1 = BaggingClassifier(n_estimators=10) clf2 = BaggingClassifier(n_estimators=100) clf3 = RandomForestClassifier(n_estimators=10,criterion='gini', min_samples_split=2,max_features=None) clf4 = AdaBoostClassifier(n_estimators=100) clf5 = VotingClassifier(estimators=[("rf",clf3),('bg',clf2),('ml',mlp),('ada',clf4)],voting='soft') dropped = set(['num','id']) columns2 = [z for z in cols if z not in dropped] X2 = df[columns2].values X_train, X_test, y_train, y_test = train_test_split(X2,y1,test_size=0.90) scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) mlp.fit(X_train,y_train) predictions2 = mlp.predict(X_test) print(classification_report(y_test, predictions2)) print(accuracy_score(y_test, predictions2)) kfold = KFold(n_splits=3,shuffle=True) print(cross_val_score(mlp,X_test,y_test,cv=kfold).mean()) clf2.fit(X_train,y_train) predictions = clf2.predict(X_test) print(classification_report(y_test, predictions)) print(accuracy_score(y_test, predictions)) clf3.fit(X_train,y_train) predictions2 = clf3.predict(X_test) print(classification_report(y_test, predictions2)) print(accuracy_score(y_test, predictions2)) clf4.fit(X_train,y_train) predictions2 = clf4.predict(X_test) print(classification_report(y_test, predictions2)) print(accuracy_score(y_test, predictions2)) clf5.fit(X_train,y_train) predictions2 = clf5.predict(X_test) print(classification_report(y_test, predictions2)) print(accuracy_score(y_test, predictions2)) print(cross_val_score(clf5,X_test,y_test,cv=kfold).mean())

34.173913

109

0.787956

339

2,358

5.250737

0.315634

0.036517

0.076404

0.02809

0.329213

0.307865

0.247191

0.223596

0.2

0

0.027936

0.104326

2,358

68

110

34.676471

0.814867

0

0.150943

0

0.017935

0

1

0

false

0

0.245283

0

0.245283

0.226415

0

null

0

null

0

1

0

bd664ca13e3a2c2338580dff837587436ec24e4f

3,925

py

Python

fitsmap/utils.py

ryanhausen/fitsmap

07c1fcd44e2d2efed24607f3e866611a1be395d8

[ "MIT" ]

19

2021-06-24T13:57:59.000Z

2022-02-02T04:45:23.000Z

fitsmap/utils.py

ryanhausen/fitsmap

07c1fcd44e2d2efed24607f3e866611a1be395d8

[ "MIT" ]

38

2019-12-17T18:21:43.000Z

2022-03-12T00:16:38.000Z

fitsmap/utils.py

ryanhausen/fitsmap

07c1fcd44e2d2efed24607f3e866611a1be395d8

[ "MIT" ]

1

2021-06-24T10:53:15.000Z

# MIT License # Copyright 2021 Ryan Hausen and contributers # Permission is hereby granted, free of charge, to any person obtaining a copy of # this software and associated documentation files (the "Software"), to deal in # the Software without restriction, including without limitation the rights to # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of # the Software, and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS # FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. import os import string from functools import reduce from itertools import chain, filterfalse from typing import Iterable, List, Tuple from astropy.io import fits from tqdm import tqdm from PIL import Image import fitsmap def digit_to_string(digit: int) -> str: """Converts an integer into its word representation""" if digit == 0: return "zero" elif digit == 1: return "one" elif digit == 2: return "two" elif digit == 3: return "three" elif digit == 4: return "four" elif digit == 5: return "five" elif digit == 6: return "six" elif digit == 7: return "seven" elif digit == 8: return "eight" elif digit == 9: return "nine" else: raise ValueError("Only digits 0-9 are supported") def make_fname_js_safe(fname: str) -> str: """Converts a string filename to a javascript safe identifier.""" if fname[0] in string.digits: adj_for_digit = digit_to_string(int(fname[0])) + fname[1:] else: adj_for_digit = fname return adj_for_digit.replace(".", "_dot_").replace("-", "_") def get_fits_image_size(fits_file: str) -> Tuple[int, int]: """Returns image size (x, y) Args: fits_file (str): fits file path Returns: Tuple[int, int]: returns the x and y dims of the input file """ hdr = fits.getheader(fits_file) return hdr["NAXIS1"], hdr["NAXIS2"] def get_standard_image_size(image_file: str) -> Tuple[int, int]: """Returns image size (x, y) Args: image_file (str): image file path Returns: Tuple[int, int]: returns the x and y dims of the input file """ with Image.open(image_file) as f: size = f.size return size def peek_image_info(img_file_names: List[str]) -> Tuple[int, int]: """Gets image size values given passed image file names Args: img_file_names (List[str]): Input image files that are being tiled Returns: Tuple[int, int]: The `max x`, and `max y` """ fits_sizes = list( map(get_fits_image_size, filter(lambda f: f.endswith("fits"), img_file_names),) ) standard_sizes = list( map( get_standard_image_size, filterfalse(lambda f: f.endswith("fits"), img_file_names), ) ) max_x, max_y = reduce( lambda x, y: (max(x[0], y[0]), max(x[1], y[1])), chain.from_iterable([fits_sizes, standard_sizes]), (0, 0), ) return max_x, max_y def get_version(): with open(os.path.join(fitsmap.__path__[0], "__version__.py"), "r") as f: return f.readline().strip().replace('"', "") class MockQueue: def __init__(self, bar): self.bar = bar def put(self, n): self.bar.update(n=n)

28.035714

87

0.654777

569

3,925

4.400703

0.376098

0.032348

0.026358

0.028754

0.119808

0.104633

0.079073

0

0.009447

0.244841

3,925

139

88

28.23741

0.835358

0.423185

0

0.028986

0

0.052315

0

1

0.115942

false

0

0.130435

0

0.478261

0

null

0

null

0

1

0

bd66ac3284bd0acf65bf00b9b9956f03b31e1d35

5,819

py

Python

bibterm2dict/tresemo.py

madskinner/bibterm2dict

93039125fb4eaf5640bf4c91d676607dd98bb974

[ "MIT" ]

null

bibterm2dict/tresemo.py

madskinner/bibterm2dict

93039125fb4eaf5640bf4c91d676607dd98bb974

[ "MIT" ]

null

bibterm2dict/tresemo.py

madskinner/bibterm2dict

93039125fb4eaf5640bf4c91d676607dd98bb974

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Dec 29 10:49:22 2017 @author: marks """ # File: tree.py # References: # http://hg.python.org/cpython/file/4e32c450f438/Lib/tkinter/ttk.py # http://www.tcl.tk/man/tcl8.5/TkCmd/ttk_treeview.htm#M79 # http://svn.python.org/projects/python/branches/pep-0384/Demo/tkinter/ttk/dirbrowser.py import os from tkinter import * from tkinter import ttk #@Reimport from demopanels import MsgPanel, SeeDismissPanel # Constants for formatting file sizes KB = 1024.0 MB = KB * KB GB = MB * KB class TreeDemo(ttk.Frame): def __init__(self, isapp=True, name='treedemo'): ttk.Frame.__init__(self, name=name) self.pack(expand=Y, fill=BOTH) self.master.title('Tree Demo') self.isapp = isapp self._create_widgets() def _create_widgets(self): if self.isapp: MsgPanel(self, ["One of the new Tk themed widgets is a tree widget, which allows ", "the user to browse a hierarchical data-set such as a file system. ", "The tree widget not only allows for the tree part itself, but it ", "also supports an arbitrary number of additional columns which can ", "show additional data (in this case, the size of the files found ", "on your file system). You can also change the width of the columns ", "by dragging the boundary between them."]) SeeDismissPanel(self) self._create_demo_panel() def _create_demo_panel(self): demoPanel = Frame(self) demoPanel.pack(side=TOP, fill=BOTH, expand=Y) self._create_treeview(demoPanel) self._populate_root() def _create_treeview(self, parent): f = ttk.Frame(parent) f.pack(side=TOP, fill=BOTH, expand=Y) # create the tree and scrollbars self.dataCols = ('fullpath', 'type', 'size') self.tree = ttk.Treeview(columns=self.dataCols, displaycolumns='size') ysb = ttk.Scrollbar(orient=VERTICAL, command= self.tree.yview) xsb = ttk.Scrollbar(orient=HORIZONTAL, command= self.tree.xview) self.tree['yscroll'] = ysb.set self.tree['xscroll'] = xsb.set # setup column headings self.tree.heading('#0', text='Directory Structure', anchor=W) self.tree.heading('size', text='File Size', anchor=W) self.tree.column('size', stretch=0, width=70) # add tree and scrollbars to frame self.tree.grid(in_=f, row=0, column=0, sticky=NSEW) ysb.grid(in_=f, row=0, column=1, sticky=NS) xsb.grid(in_=f, row=1, column=0, sticky=EW) # set frame resizing priorities f.rowconfigure(0, weight=1) f.columnconfigure(0, weight=1) # action to perform when a node is expanded self.tree.bind('<<TreeviewOpen>>', self._update_tree) def _populate_root(self): # use current directory as root node self.path = os.getcwd() # insert current directory at top of tree # 'values' = column values: fullpath, type, size # if a column value is omitted, assumed empty parent = self.tree.insert('', END, text=self.path, values=[self.path, 'directory']) # add the files and sub-directories self._populate_tree(parent, self.path, os.listdir(self.path)) def _populate_tree(self, parent, fullpath, children): # parent - id of node acting as parent # fullpath - the parent node's full path # children - list of files and sub-directories # belonging to the 'parent' node for child in children: # build child's fullpath cpath = os.path.join(fullpath, child).replace('\\', '/') if os.path.isdir(cpath): # directory - only populate when expanded # (see _create_treeview() 'bind') cid =self.tree.insert(parent, END, text=child, values=[cpath, 'directory']) # add 'dummy' child to force node as expandable self.tree.insert(cid, END, text='dummy') else: # must be a 'file' size = self._format_size(os.stat(cpath).st_size) self.tree.insert(parent, END, text=child, values=[cpath, 'file', size]) def _format_size(self, size): if size >= GB: return '{:,.1f} GB'.format(size/GB) if size >= MB: return '{:,.1f} MB'.format(size/MB) if size >= KB: return '{:,.1f} KB'.format(size/KB) return '{} bytes'.format(size) def _update_tree(self, event): #@UnusedVariable # user expanded a node - build the related directory nodeId = self.tree.focus() # the id of the expanded node if self.tree.parent(nodeId): # not at root topChild = self.tree.get_children(nodeId)[0] # if the node only has a 'dummy' child, remove it and # build new directory; skip if the node is already # populated if self.tree.item(topChild, option='text') == 'dummy': self.tree.delete(topChild) path = self.tree.set(nodeId, 'fullpath') self._populate_tree(nodeId, path, os.listdir(path)) if __name__ == '__main__': TreeDemo().mainloop()

39.053691

98

0.554906

698

5,819

4.545845

0.34957

0.050425

0.017649

0.009455

0.054207

0.043492

0.027104

0

0.013748

0.337515

5,819

149

99

39.053691

0.809339

0.218938

0

0.139432

0

1

0.096386

false

0

0.048193

0

0.204819

0

null

0

null

0

1

0

bd68af29a64b3d9ad5ee5bf60f1893c99d3b0c76

5,803

py

Python

test/cnnl/op_test/test_multiline_views_graph.py

Cambricon/catch

2625da389f25a67066d20fb6b0c38250ef98f8ab

[ "BSD-2-Clause" ]

20

2022-03-01T11:40:51.000Z

2022-03-30T08:17:47.000Z

test/cnnl/op_test/test_multiline_views_graph.py

Cambricon/catch

2625da389f25a67066d20fb6b0c38250ef98f8ab

[ "BSD-2-Clause" ]

null

test/cnnl/op_test/test_multiline_views_graph.py

Cambricon/catch

2625da389f25a67066d20fb6b0c38250ef98f8ab

[ "BSD-2-Clause" ]

null

from __future__ import print_function import sys import os import copy import unittest import logging import random # from unittest.main import main import torch from torch import nn import torch_mlu.core.mlu_model as ct cur_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(cur_dir + "/../../") from common_utils import testinfo, TestCase # pylint: disable=C0413, C0411 logging.basicConfig(level=logging.DEBUG) class buildMultiAdd(nn.Module): # pylint: disable=W0223 r""" graph： fc --> split --> squeeze --> transpose --> \ \ --> squeeze --> slice --> batch_dot """ def __init__(self, shape): super(buildMultiAdd, self).__init__() self.shape = len(shape) self.s1_dim0_1 = random.randint(5, 25) self.s1_dim0_2 = random.randint(5,25) self.s2_dim2 = random.randint(5, 25) self.split_dim_0_2 = random.randint(0, 2) self.split_dim_0_3 = random.randint(0, 3) self.split_dim_0_4 = random.randint(0, 4) self.split_dim_0_5 = random.randint(0, 5) self.unbind_dim_0_2 = random.randint(0, 2) self.unbind_dim_0_3 = random.randint(0, 3) self.unbind_dim_0_4 = random.randint(0, 4) self.unbind_dim_0_5 = random.randint(0, 5) self.select_dim_0_3 = random.randint(0, 3) self.select_dim_0_4 = random.randint(0, 4) self.narrow_dim_0_2 = random.randint(0, 2) def forward(self, x): if self.shape == 1: dim1 = x.size()[0] x = x.unsqueeze(1) dim2 = x.size()[1] dim0 = self.s1_dim0_1 x = x.expand(dim0, dim1, dim2) x = x.permute(2, 0, 1) dim0, dim1, dim2 = dim2, dim0, dim1 x = x.add(x) x = x[:, :, :dim2-1] x = x.transpose(0, 1) dim0, dim2 = self.s1_dim0_2, dim2-1 x = x.expand(dim0, dim1, 1, dim2) x = x.squeeze() tensors = x.split(2, self.split_dim_0_2) elif self.shape == 2: dim0, dim1 = x.size() x = x.unsqueeze(1) x = x.permute(0, 2, 1) dim2 = self.s2_dim2 x = x.expand(dim2, dim0, dim1, 1) x = x.squeeze() dim0, dim1, dim2 = dim2, dim0, dim1 x = x.add(x) x = x[:, :, :dim2-1] tensors = x.split(2, self.split_dim_0_2) elif self.shape == 3: dim0, dim1, dim2 = x.size() x = x.permute(2, 0, 1) x = x.transpose(1, 2) x = x.add(x) x = x.unsqueeze(2) dim0, dim2 = dim2, dim0 x = x[:, :, :, :dim2-1] x = x.squeeze() tensors = x.split(2, self.split_dim_0_2) elif self.shape == 4: x = x.permute(0, 1, 3, 2) x = x.transpose(0, 1) x = x.add(x) dim0, dim1, dim2, dim3 = x.size() x = x[:, :, :, :dim3-1] x = x.split(2, self.split_dim_0_3)[0] tensors = x.unbind(self.unbind_dim_0_3) elif self.shape == 5: x = x.permute(3, 2, 0, 4, 1) x = x.transpose(0, 3) x = x.add(x) dim0, dim1, dim2, dim3, _ = x.size() x = x[:, :dim1-1, :, :dim3-1, :] x = x.split(2, self.split_dim_0_4)[0] x = x.select(self.select_dim_0_4, 1) tensors = x.unbind(self.unbind_dim_0_3) else: x = x.permute(0, 3, 4, 1, 2, 5) x = x.transpose(0, 5) x = x.add(x) dim0, dim1, dim2, dim3, _, dim5 = x.size() x = x[:, :dim1-1, :, :dim3-1, :, :dim5-1] x = x.split(2, self.split_dim_0_5)[0] x = x.unbind(self.unbind_dim_0_5)[0] x = x.select(self.select_dim_0_4, 1) tensors = x.unbind(self.unbind_dim_0_2) y = None for idx in range(len(tensors)-1): tensor = tensors[idx] tensor = tensor.transpose(0, 1) tensor = tensor.permute(2, 1, 0) tensor = tensor.add(tensor) tensor = tensor.narrow(self.narrow_dim_0_2, 0, 1) tensor = tensor.chunk(2, 1)[0] tensor = tensor.squeeze() y = y.add(tensor) if y is not None else tensor return y class TestMultiWayNetOp(TestCase): # @unittest.skip("not test") @testinfo() def test_multi_way(self): #print('----Multi-way structure----') for d in range(6): dim = d + 1 shape = () for _ in range(1, dim+1): ran_d = random.randint(5, 25) shape = shape + (ran_d,) data = torch.randn(shape, dtype=torch.float) in_cpu = copy.deepcopy(data) in_mlu = self.to_mlu(data) net_cpu = buildMultiAdd(shape) out_cpu = net_cpu(in_cpu) out_mlu = net_cpu(in_mlu) self.assertTensorsEqual(out_cpu, out_mlu.contiguous().cpu().float(), 0.03, use_MSE=True) # @unittest.skip("not test") @testinfo() def test_multi_way_channel_last(self): #print('----Multi-way structure----') shape = (3,4,5,6) data = torch.randn(shape).to(memory_format=torch.channels_last) in_cpu = copy.deepcopy(data) in_mlu = self.to_mlu(data) net_cpu = buildMultiAdd(shape) out_cpu = net_cpu(in_cpu) out_mlu = net_cpu(in_mlu) self.assertTensorsEqual(out_cpu, out_mlu.contiguous().cpu().float(), 0.03, use_MSE=True) if __name__ == '__main__': unittest.main()

33.350575

75

0.507496

806

5,803

3.465261

0.157568

0.029359

0.012889

0.046545

0.552095

0.465807

0.427855

0.420695

0.308271

0.270319

0

0.070468

0.359297

5,803

173

76

33.543353

0.680742

0.055489

0

0.323944

0

0.002752

0

0.014085

1

0.028169

false

0

0.077465

0

0.126761

0.007042

0

null

0

null

0

1

0

bd6c528a553f5578be66448336398952d4309fa6

645

py

Python

easy/53.Maximum_Subarray.py

Leesoar/leetcode

e566513fc0e7055155157798f06089299bd44fd2

[ "Apache-2.0" ]

2

2018-03-04T23:29:49.000Z

2019-04-23T01:13:12.000Z

easy/53.Maximum_Subarray.py

Leesoar/leetcode

e566513fc0e7055155157798f06089299bd44fd2

[ "Apache-2.0" ]

null

easy/53.Maximum_Subarray.py

Leesoar/leetcode

e566513fc0e7055155157798f06089299bd44fd2

[ "Apache-2.0" ]

1

2018-03-05T09:58:59.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Question: Find the contiguous subarray within an array (containing at least one number) which has the largest sum. Example: given the array [-2,1,-3,4,-1,2,1,-5,4], the contiguous subarray [4,-1,2,1] has the largest sum = 6. ''' class Solution: def maxSubArray(self, nums): """ :type nums: List[int] :rtype: int """ if not nums: return 0 cur_sum = max_sum = nums[0] #当前和与最大和 for num in nums[1:]: cur_sum = max(num, cur_sum + num) max_sum = max(cur_sum, max_sum) return max_sum

23.035714

105

0.56124

96

645

3.6875

0.541667

0.067797

0.076271

0.090395

0

0.040359

0.308527

645

28

106

23.035714

0.753363

0.516279

0

1

0.111111

false

0

0.444444

0

null

0

null

0

1

0

bd6d10559ed324300c492ea76a3b5dca3a2078f7

3,153

py

Python

tests/test_docstring.py

cbillingham/docconvert

2843f7446546ae90ba3f38e1246e69d208e0f053

[ "BSD-3-Clause" ]

8

2019-10-07T22:49:20.000Z

2021-12-30T22:31:28.000Z

tests/test_docstring.py

cbillingham/docconvert

2843f7446546ae90ba3f38e1246e69d208e0f053

[ "BSD-3-Clause" ]

5

2019-09-17T21:03:38.000Z

2020-07-23T04:47:21.000Z

tests/test_docstring.py

cbillingham/docconvert

2843f7446546ae90ba3f38e1246e69d208e0f053

[ "BSD-3-Clause" ]

null

"""Unit tests for Docstring.""" import docconvert class TestDocstring(object): def test_element_ordering(self): docstring = docconvert.parser.Docstring() docstring.add_element(("raw", "Docstring.")) docstring.add_return(kind="int") docstring.add_raises(kind="ValueError") docstring.add_arg("arg", kind="str") docstring.add_element(("note", ["First note.", "Second Note."])) assert docstring.elements == [ ("raw", "Docstring."), ("return",), ("raises",), ("args",), ("note", ["First note.", "Second Note."]), ] def test_args(self): docstring = docconvert.parser.Docstring() docstring.add_arg_type("arg1", "Object") docstring.add_arg("arg2", kind="str") docstring.add_arg("arg3", desc=["Description."], optional=True) docstring.add_arg_type("arg3", "int") assert docstring.elements == [("args",)] first_arg = docstring.arg_fields.popitem(last=False) assert first_arg[0] == "arg1" assert first_arg[1].kind == "Object" assert docstring.arg_fields["arg2"].kind == "str" assert docstring.arg_fields["arg2"].optional == False assert docstring.arg_fields["arg3"].kind == "int" assert docstring.arg_fields["arg3"].desc == ["Description."] assert docstring.arg_fields["arg3"].optional == True def test_attributes(self): docstring = docconvert.parser.Docstring() docstring.add_attribute_type("attr1", "Object") docstring.add_attribute("attr2", kind="str") docstring.add_attribute("attr3", desc=["Description."]) docstring.add_attribute_type("attr3", "int") assert docstring.elements == [("attributes",)] first_attribute = docstring.attribute_fields.popitem(last=False) assert first_attribute[0] == "attr1" assert first_attribute[1].kind == "Object" assert docstring.attribute_fields["attr2"].kind == "str" assert docstring.attribute_fields["attr2"].optional == False assert docstring.attribute_fields["attr3"].kind == "int" assert docstring.attribute_fields["attr3"].desc == ["Description."] def test_raises(self): docstring = docconvert.parser.Docstring() docstring.add_raises("ValueError") docstring.add_raises("RuntimeError", desc=["Description."]) assert docstring.elements == [("raises",)] assert docstring.raise_fields[0].kind == "ValueError" assert docstring.raise_fields[0].desc == [] assert docstring.raise_fields[1].kind == "RuntimeError" assert docstring.raise_fields[1].desc == ["Description."] def test_returns(self): docstring = docconvert.parser.Docstring() docstring.add_return_type("int") docstring.add_return(desc=["Description."]) assert docstring.elements == [("return",)] assert docstring.return_field.kind == "int" assert docstring.return_field.desc == ["Description."] docstring.add_return_type("str") assert docstring.return_field.kind == "str"

43.791667

75

0.631779

329

3,153

5.890578

0.161094

0.162539

0.065015

0.074819

0.468524

0.163055

0.128999

0

0.010914

0.21535

3,153

71

76

44.408451

0.772433

0.007929

0

0.079365

0

0.136451

0

0.396825

1

0.079365

false

0

0.015873

0

0.111111

0

null

0

null

0

1

0

bd6de39ebf958030d5c9ef03e542a73527be7547

4,890

py

Python

preprocess.py

EMBEDDIA/morphological-comment-filtering

450ecbdaf2672ea09a39476db91c210657ff9c6f

[ "MIT" ]

1

2020-12-01T17:56:11.000Z

preprocess.py

matejklemen/morphological-comment-filtering

450ecbdaf2672ea09a39476db91c210657ff9c6f

[ "MIT" ]

null

preprocess.py

matejklemen/morphological-comment-filtering

450ecbdaf2672ea09a39476db91c210657ff9c6f

[ "MIT" ]

null

""" The following script is used to preprocess text once and cache it to a csv file. Currently, this means obtaining the UPOS tags and universal features + renaming columns to a common format. This is done because it's quite a long process and we do not want to do it every time we make a change. """ import pandas as pd import os import argparse import json import stanza from conllu import parse from tqdm import tqdm from utils import PAD parser = argparse.ArgumentParser() parser.add_argument("--lang", type=str, default="de", help="2-letter code (ISO 639-1) of used language") parser.add_argument("--package", type=str, default="default", help="Name of the used processor for POS/ufeats tagging") parser.add_argument("--data_path", type=str, default="/home/matej/Documents/embeddia/morphological-additions/morphological-comment-filtering/data/GER/test.csv", help="PATH to your data") parser.add_argument("--data_column", type=str, default="content", help="Column of csv in which the text to be processed is stored") parser.add_argument("--target_column", type=str, default="target", help="Column of csv in which the target label is stored") parser.add_argument("--target_dir", type=str, default="preprocessed/GER", help="DIRECTORY where processed data should be stored") def process_conllu(conllu_data): """ Accepts a conllu string, containing processed sequence, and returns a list[list[dict]] containing properties of tokens by sentence, i.e. index [i][j] of returned list represents features of j-th token in i-th sentence.""" sent_features = parse(conllu_data) processed = [] for curr_sent in sent_features: converted_sent = [] for curr_token in curr_sent: curr_features = {"form": curr_token["form"]} # Unpack universal features; note that some tokens don't have universal features (e.g. punctuation) universal_features = curr_token["feats"] if universal_features is not None: curr_features.update(universal_features) curr_features.update({"upostag": curr_token.get("upostag", PAD)}) converted_sent.append(curr_features) processed.append(converted_sent) return processed def extract_features(stanza_output): """ Filter the result returned by a stanza Pipeline, keeping only 'form' (raw word), 'upostag' and universal features (if present)""" # features of tokens inside sentence(s): each sentence is a list of dicts, containing token features relevant_features = [] for curr_sent in stanza_output.sentences: sent_features = [] for curr_token in curr_sent.words: processed_feats = {"form": curr_token.text} # Note: if FEATURES are not predicted for token, they will not be present in dict, whereas if POS TAG is not # predicted, a generic PAD gets written token_feats = curr_token.feats if token_feats is not None: for feat_val_pair in token_feats.split("|"): feat, val = feat_val_pair.split("=") processed_feats[feat] = val token_upos = curr_token.upos if token_upos is None: token_upos = PAD processed_feats["upostag"] = token_upos sent_features.append(processed_feats) relevant_features.append(sent_features) return relevant_features if __name__ == "__main__": import torch args = parser.parse_args() df = pd.read_csv(args.data_path) # hr - ftb, en - ewt nlp = stanza.Pipeline(lang=args.lang, processors='tokenize,pos', package=args.package, use_gpu=torch.cuda.is_available()) features = [] take_mask = [] for idx_ex in tqdm(range(df.shape[0])): curr_ex = df.iloc[idx_ex][args.data_column] try: output = nlp(curr_ex) except RuntimeError: # Undiagnosed stanza error print(f"Skipping example #{idx_ex}: '{curr_ex}'") take_mask.append(False) continue ex_features = extract_features(output) take_mask.append(True) features.append(json.dumps(ex_features)) if not os.path.exists(args.target_dir): print("Warning: creating directory to store processed data") os.makedirs(args.target_dir) # Extract file name from given source path file_name = args.data_path.split(os.sep)[-1] target_path = os.path.join(args.target_dir, file_name) df = df.loc[take_mask].reset_index(drop=True) df["features"] = features df = df.rename({args.data_column: "content", args.target_column: "target"}, axis=1) df.to_csv(os.path.join(args.target_dir, file_name), index=False)

41.092437

160

0.660941

657

4,890

4.765601

0.347032

0.022996

0.032577

0.013414

0.069626

0.035771

0.019802

0

0.002163

0.243558

4,890

118

161

41.440678

0.844282

0.218405

0

0.012346

0.169536

0.02755

0

1

0.024691

false

0

0.111111

0

0.160494

0.024691

0

null

0

null

0

1

0

bd6ded5eb81a42427f646f06cfec5c9cfc641f63

4,685

py

Python

util/callbacks.py

TobiasKoopmann/cobert

279fc6ce938a81afa2b8f14e4cb20b13f842ff48

[ "Apache-2.0" ]

null

util/callbacks.py

TobiasKoopmann/cobert

279fc6ce938a81afa2b8f14e4cb20b13f842ff48

[ "Apache-2.0" ]

null

util/callbacks.py

TobiasKoopmann/cobert

279fc6ce938a81afa2b8f14e4cb20b13f842ff48

[ "Apache-2.0" ]

null

import os import abc import json import numpy as np class Callback(abc.ABC): def __init__(self): pass @abc.abstractmethod def __call__(self, predictions, labels): pass class Evaluation(Callback): def __init__(self, ks=(1, 5, 10), ignore_index: int = -100, n_samples_file: str = None): super().__init__() self.ks = ks self.ignore_index = ignore_index self.evaluation = None self.use_neg_sampling = False if n_samples_file: self.use_neg_sampling = True with open(n_samples_file, "r") as file: negative_samples = json.load(file) self.negative_samples = {int(k): v for k, v in negative_samples.items()} self.reset() def __call__(self, predictions, labels): # predictions: torch [batch, 50, 35115], labels: [batch, 50] for i in range(labels.shape[0]): for j in range(labels.shape[1]): if labels[i, j] == self.ignore_index: # [ignore, ignore, ..., mask] continue candidate = labels[i, j].item() # integer samples = self.negative_samples[candidate] + [candidate] sample_predictions = predictions[i, j][samples].tolist() ranked_samples = list(sorted(zip(samples, sample_predictions), key=lambda x: x[1], reverse=True)) # list of id, logit self.evaluation["n"] += 1 rank = 0 for index, sample in enumerate(ranked_samples): if sample[0] == candidate: rank = index break for k in self.ks: if rank < k: self.evaluation["sampled_ndcg"][k] += 1 / np.log2(rank + 2) self.evaluation["sampled_hit"][k] += 1 # Again without neg sampling all_predictions = predictions[i, j].tolist() all_samples = np.arange(len(all_predictions)) ranked_predictions = list(sorted(zip(all_samples, all_predictions), key=lambda x: x[1], reverse=True)) rank = 0 for index, sample in enumerate(ranked_predictions): if sample[0] == candidate: rank = index break for k in self.ks: if rank < k: self.evaluation["ndcg"][k] += 1 / np.log2(rank + 2) self.evaluation["hit"][k] += 1 def __str__(self): return " ".join( f"{key}@{k}={self.evaluation[key][k] / self.evaluation['n']:.5f}" for key in ("sampled_ndcg", "sampled_hit", "ndcg", "hit") for k in self.evaluation[key]) def reset(self): self.evaluation = {"sampled_ndcg": {k: 0 for k in self.ks}, "sampled_hit": {k: 0 for k in self.ks}, "ndcg": {k: 0 for k in self.ks}, "hit": {k: 0 for k in self.ks}, "n": 0} def get_metric(self, metric: str): if metric in self.evaluation: return [(k, self.evaluation[metric][k] / self.evaluation['n']) for k in self.evaluation[metric]] class PredictionSerializer(Callback): def __init__(self, file_name: str, ignore_index: int = -100): super().__init__() self.predictions = [] self.labels = [] self.ignore_index = ignore_index parent_dir = os.path.dirname(file_name) if not os.path.exists(parent_dir): os.makedirs(parent_dir) self.file = open(file_name, "w") self.file.write("Prediction\tLabel\n") def __call__(self, predictions, labels): for i in range(labels.shape[0]): for j in range(labels.shape[1]): if labels[i, j] != self.ignore_index: self.predictions.append(np.argsort(predictions[i, j].cpu()).tolist()[:100]) self.labels.append(labels[i, j].item()) for p, l in zip(self.predictions, self.labels): self.file.write(",".join([str(x) for x in p]) + "\t" + str(l) + "\n") self.predictions, self.labels = [], [] def serialize(self, file_path: str): parent_dir = os.path.dirname(file_path) if not os.path.exists(parent_dir): os.makedirs(parent_dir) with open(file_path, "w") as file: json.dump({ "predictions": self.predictions, "labels": self.labels, }, file) self.predictions, self.labels = [], []

40.387931

144

0.526147

558

4,685

4.259857

0.207885

0.082457

0.020194

0.033656

0.397139

0.288599

0.266723

0.178376

0.152293

0

0.01542

0.349413

4,685

115

145

40.73913

0.764436

0.029669

0

0.313131

0

0.010101

0.044053

0.012996

0

1

0.10101

false

0.020202

0.040404

0.010101

0.191919

0

null

0

null

0

1

0

bd6e02dadbfb92bc2040067fe4f4629a0b88329a

4,436

py

Python

titanic.py

techwizAJ/Titanic-comepetition-kaggle

39a3d8d97a4401b0e12eefd7fbb07dde92147328

[ "Apache-2.0" ]

null

titanic.py

techwizAJ/Titanic-comepetition-kaggle

39a3d8d97a4401b0e12eefd7fbb07dde92147328

[ "Apache-2.0" ]

null

titanic.py

techwizAJ/Titanic-comepetition-kaggle

39a3d8d97a4401b0e12eefd7fbb07dde92147328

[ "Apache-2.0" ]

null

""" # -*- coding: utf-8 -*- @author: techwiz Created on Sun May 27 14:47:20 2018 """ import pandas as pd train_set = pd.read_csv("train.csv") test_set = pd.read_csv("test.csv") """ Exploratory Data Analysis """ train_set['Sex'].value_counts() train_set['Age'].value_counts() train_set['Embarked'].value_counts() train_set.isnull().values.any() train_set.isnull().sum().sum() train_set.describe() # Selecting required features from training dataset train_set.drop('PassengerId', axis=1, inplace= True) train_set.drop('Name' , axis=1,inplace=True) train_set.drop('Cabin' , axis =1 , inplace=True) train_set.drop('Ticket',axis=1, inplace = True) test_set.drop(['PassengerId','Name','Cabin','Ticket'],axis=1,inplace=True) #Encoding Categorial Data train_set['Age'].hist(bins=30) train_set['Fare'].hist(bins=30) # impute missing values """ Losing Data Distribution by imputing through mean and median train_set.fillna(train_set.mean(),inplace=True) train_set.isnull().values.any() test_set.fillna(train_set.mean(),inplace=True) test_set.isnull().values.any() """ # imputing data with outliners train_set['Age'].fillna(-1,inplace=True) train_set['Fare'].fillna(-1,inplace=True) train_set['Embarked'].fillna('Q',inplace=True) test_set['Age'].fillna(-1,inplace=True) test_set['Fare'].fillna(-1,inplace=True) test_set['Embarked'].fillna('Q',inplace=True) #LabelEncoder from sklearn.preprocessing import LabelEncoder lb = LabelEncoder() train_set['Sex'] = lb.fit_transform(train_set['Sex']) test_set['Sex'] = lb.fit_transform(test_set['Sex']) lb_t = LabelEncoder() train_set['Embarked'] = lb_t.fit_transform(train_set['Embarked']) test_set['Embarked'] = lb_t.fit_transform(test_set['Embarked']) """ train_set = pd.get_dummies(data= train_set , dummy_na = True,columns =['Sex' , 'Embarked']) test_set = pd.get_dummies(data= test_set , dummy_na = True,columns =['Sex' , 'Embarked']) train_set.drop('Sex_nan',axis=1,inplace=True) test_set.drop('Sex_nan',axis=1,inplace=True) """ # Selecting Features and target X = train_set.iloc[:,1:13].values y = train_set.iloc[:,0].values X_test = test_set.iloc[:,:].values """ #Validating Model for Parameter tuning from sklearn.model_selection import train_test_split X_train , X_validate , y_train , y_validate = train_test_split(X,y,test_size=0.18,random_state=42) #Now Appling Various ML Models For Classification #Feature Scaling , testing differnt scalers and their effect on data distibution #Using Min Max Scalar from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0.5,0.95)) train_set = scaler.fit_transform(train_set) test_set = scaler.fit_transform(test_set) train_set['Age'].hist(bins=30) #testing differnt scalers from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() train_set = sc_X.fit_transform(train_set) test_set = sc_X.fit_transform(test_set) from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier(n_estimators=1000,min_samples_split=30,min_samples_leaf=4,random_state=42,warm_start=True) clf.fit(X_train,y_train) y_pred = clf.predict(X_validate) import xgboost as xg classifier = xg.XGBClassifier() classifier.fit(X_train,y_train) y_predict_xg = classifier.predict(X_validate) #metrics from sklearn.metrics import confusion_matrix cnf = confusion_matrix(y_validate,y_pred) cnf1 = confusion_matrix(y_validate,y_predict_xg) """ #Feature Scaling , testing differnt scalers and their effect on data distibution #Using Min Max Scalar from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0.5,0.95)) X = scaler.fit_transform(X) X_test= scaler.transform(X_test) train_set['Age'].hist(bins=30) """ #testing differnt scalers from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X = sc_X.fit_transform(X) X_test = sc_X.transform(X_test) """ #using various ml models from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier(n_estimators=1000,min_samples_split=30,min_samples_leaf=4,random_state=42,warm_start=True) clf.fit(X,y) """ import xgboost as xg classifier = xg.XGBClassifier() classifier.fit(X,y) y_pred_xg = classifier.predict(X_test) """ y_predict = clf.predict(X_test) sub = pd.read_csv('gender_submission.csv') print(sub['Survived'].value_counts()) #submission sub['Survived']=y_predict sub.to_csv('submissions1.csv',index=False) final = pd.read_csv('submissions1.csv') print(final['Survived'].value_counts())

31.685714

119

0.772543

682

4,436

4.812317

0.234604

0.085314

0.040219

0.034126

0.557892

0.478976

0.379951

0.305911

0.288239

0

0.017751

0.085663

4,436

140

120

31.685714

0.79142

0.085437

0

0.042553

0

0.125638

0.010725

0

1

0

false

0.042553

0.085106

0

0.085106

0.042553

0

null

0

null

0

1

0

bd70054eb143582225de2963fca43fb0a5b2f887

1,301

py

Python

scripts/sort-files/sort_files.py

toddnguyen47/saved-games

ca30e369c72f819b2bd87f2ade450bd6aa058f41

[ "MIT" ]

null

scripts/sort-files/sort_files.py

toddnguyen47/saved-games

ca30e369c72f819b2bd87f2ade450bd6aa058f41

[ "MIT" ]

null

scripts/sort-files/sort_files.py

toddnguyen47/saved-games

ca30e369c72f819b2bd87f2ade450bd6aa058f41

[ "MIT" ]

null

import os import heapq import json class SortFiles: def __init__(self): self._spell_dir = self._read_json() self._heap = [] def execute(self): self._heap = [] with os.scandir(self._spell_dir) as iter: for entry in iter: if entry.is_dir(): count = self._count_png_in_dir(entry) heapq.heappush(self._heap, (count, entry.name)) self._iterate_heap() ########################################################################### # PRIVATE FUNCTIONS ########################################################################### def _count_png_in_dir(self, entry: os.DirEntry): count = 0 with os.scandir(entry) as iter2: for entry2 in iter2: if entry2.is_file() and entry2.name.endswith(".png"): count += 1 return count def _iterate_heap(self): while self._heap: val = heapq.heappop(self._heap) print("{}: {}".format(val[0], val[1])) def _read_json(self): with open("config.json", "r") as file: data = json.load(file) return data["spellsDirectory"] if __name__ == "__main__": sort_files = SortFiles() sort_files.execute()

27.680851

79

0.487317

138

1,301

4.297101

0.384058

0.067454

0.040472

0.043845

0

0.009847

0.297463

1,301

46

80

28.282609

0.63895

0.013067

0

0.060606

0

0.039753

0

1

0.151515

false

0

0.090909

0

0.333333

0.030303

0

null

0

null

0

1

0

bd7412e497e9ca995fe87668255ac1e986b4ec36

4,585

py

Python

rlo/src/ray_main.py

tomjaguarpaw/knossos-ksc

8fa75e67c0db8f632b135379740051cd10ff31f2

[ "MIT" ]

31

2021-09-09T16:09:55.000Z

2022-02-20T02:15:19.000Z

rlo/src/ray_main.py

tomjaguarpaw/knossos-ksc

8fa75e67c0db8f632b135379740051cd10ff31f2

[ "MIT" ]

40

2021-08-06T14:30:08.000Z

2022-01-19T08:49:52.000Z

rlo/src/ray_main.py

tomjaguarpaw/knossos-ksc

8fa75e67c0db8f632b135379740051cd10ff31f2

[ "MIT" ]

5

2021-08-06T11:20:31.000Z

2022-01-07T19:39:40.000Z

# fmt: off import cProfile import os import ray from rlo import analytics from rlo.config_utils import config_for_repetition, kwargs_from_config from rlo.factory import seed_from_config, simul_search_curriculum_from_config, get_train_and_eval_exprs from rlo.flags import make_config_for_scenario, make_parser, check_save_config, ray_run_arguments from rlo.ray_worker import RayWorkerPool def main(): from rlo.summarize_logs import summarize_logs run_parser = make_parser(ray_run_arguments) run_args, _ = run_parser.parse_known_args() if run_args.workers_per_gpu > 1 and ( run_args.gpu_memory_fraction is None or run_args.gpu_memory_fraction * run_args.workers_per_gpu > 1.0): # In fact it seems there may need to be some margin of extra space on the GPU after allocating each worker # but we haven't identified how much, or good defaults for gpu_memory_fraction, yet. raise ValueError("Must have --gpu_memory_fraction <= 1/workers_per_gpu") config = make_config_for_scenario(run_args.scenario, ray_run_arguments) ray.init(config['address'], **kwargs_from_config(config, required_keys=("log_to_driver", "num_cpus", "num_gpus"), optional_keys=(), renames=(("redis_token", "redis_password"),))) train_set, eval_set = get_train_and_eval_exprs(config) check_save_config(config, train_set.named_exprenvs(), eval_set.named_exprenvs()) pool = RayWorkerPool(config, remote_timeout=config["ray_timeout"], local_task_limit=run_args.profile_local or 0) with analytics.log_events_to_files(os.path.join(config["result_save_path"], "head" + os.path.sep)): analytics.event("expression_summary", num_train_expr = len(train_set.named_exprenvs()), num_test_expr = len(eval_set.named_exprenvs())) for rep_config in ([config] if config.get("repetition") is not None else [config_for_repetition(config, repetition) for repetition in range(config["num_repetitions"])]): with analytics.Scope(repetition=rep_config['repetition']): curriculum = simul_search_curriculum_from_config(rep_config, train_set, eval_set) pool.schedule_work_requests_from( curriculum.request_initial(seed_from_config(rep_config))) if (run_args.profile_local is None) or (run_args.profile_local > 0): # None means --profile_local was specified without a time limit cProfile.runctx("pool.run()", {}, {"pool": pool}, os.path.join(config["result_save_path"], "head", "prof.pstats")) else: pool.run() print("Run finished, {} live weights".format(len(pool._weight_id_map))) if run_args.timeline: ray.timeline(filename=os.path.join(config['result_save_path'], "ray_timeline.json")) ray.object_transfer_timeline(filename=os.path.join(config['result_save_path'], "ray_object_transfers.json")) ray.shutdown() # Reduce memory use of Ray while this headnode machine does all the plotting events = summarize_logs(config, eval_set, ray=True) if config["test_kill_worker_after_tasks"] >= 0: # Test mode - check the logs were sensible; otherwise, fail the run (after producing plots). # Note that these asserts are not guaranteed or even expected to hold for all parameter values. # Rather they are intended to allow writing useful tests via sensible choices of parameters. # First, check that at least one worker was killed. This is only guaranteed if the total number # of tasks is at least (num_workers * (test_kill_worker_after_tasks-1))+1. assert any(e["event"] == "worker_died" for e in events) # Second, check that at least one worker joined after the start. # Note that this doesn't check that the joining worker was one that had been killed (e.g. from # the same IP address); instead, another node might have connected for the first time instead. # Only if num_workers == num_repetitions (the number of workers required before we start), # can we be sure that the new-joiner was a reconnection. # Conversely, failing does not necessarily imply that such a worker cannot reconnect, merely that # it didn't (before the run finished). Only if the total number of tasks is greater than # (num_workers * test_kill_worker_after_tasks) can we be sure that at least one worker would *have* to # reconnect for the run to get this far. assert any(e["event"] == "worker_joined" for e in events) if __name__ == "__main__": main()

59.545455

143

0.721047

672

4,585

4.669643

0.364583

0.022307

0.02167

0.020395

0.210644

0.119822

0.090504

0.0529

0.03123

0

0.002428

0.191494

4,585

76

144

60.328947

0.844079

0.325409

0

0.135047

0.024081

0

0.044444

1

0.022222

false

0.022222

0.2

0

0.222222

0.022222

0

null

0

null

0

1

0

bd776a6674d87758862d2b05706f184ab21ca00d

3,490

py

Python

monero/wordlists/wordlist.py

massanchik/monero-python

5699c26f6ba0a64f50ac065ebe0419daf01fd993

[ "BSD-3-Clause" ]

130

2019-03-22T01:50:38.000Z

2022-03-30T11:34:12.000Z

monero/wordlists/wordlist.py

massanchik/monero-python

5699c26f6ba0a64f50ac065ebe0419daf01fd993

[ "BSD-3-Clause" ]

64

2019-03-12T10:32:36.000Z

2022-03-31T12:38:20.000Z

monero/wordlists/wordlist.py

massanchik/monero-python

5699c26f6ba0a64f50ac065ebe0419daf01fd993

[ "BSD-3-Clause" ]

55

2019-03-22T01:50:50.000Z

2022-03-28T02:38:04.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import logging from binascii import crc32 from six import with_metaclass WORDLISTS = {} _log = logging.getLogger(__name__) class WordlistType(type): def __new__(cls, name, bases, attrs): if bases: if 'language_name' not in attrs: raise TypeError("Missing language_name for {0}".format(name)) if 'unique_prefix_length' not in attrs: raise TypeError("Missing 'unique_prefix_length' for {0}".format(name)) if 'word_list' not in attrs: raise TypeError("Missing 'word_list' for {0}".format(name)) if 'english_language_name' not in attrs: _log.warn("No 'english_language_name' for {0} using '{1}'".format(name, language_name)) attrs['english_language_name'] = attrs['language_name'] if len(attrs['word_list']) != 1626: raise TypeError("Wrong word list length for {0}".format(name)) new_cls = super(WordlistType, cls).__new__(cls, name, bases, attrs) if bases: WORDLISTS[new_cls.english_language_name] = new_cls return new_cls class Wordlist(with_metaclass(WordlistType)): n = 1626 @classmethod def encode(cls, hex): """Convert hexadecimal string to mnemonic word representation with checksum. """ out = [] for i in range(len(hex) // 8): word = endian_swap(hex[8*i:8*i+8]) x = int(word, 16) w1 = x % cls.n w2 = (x // cls.n + w1) % cls.n w3 = (x // cls.n // cls.n + w2) % cls.n out += [cls.word_list[w1], cls.word_list[w2], cls.word_list[w3]] checksum = cls.get_checksum(" ".join(out)) out.append(checksum) return " ".join(out) @classmethod def decode(cls, phrase): """Calculate hexadecimal representation of the phrase. """ phrase = phrase.split(" ") out = "" for i in range(len(phrase) // 3): word1, word2, word3 = phrase[3*i:3*i+3] w1 = cls.word_list.index(word1) w2 = cls.word_list.index(word2) % cls.n w3 = cls.word_list.index(word3) % cls.n x = w1 + cls.n *((w2 - w1) % cls.n) + cls.n * cls.n * ((w3 - w2) % cls.n) out += endian_swap("%08x" % x) return out @classmethod def get_checksum(cls, phrase): """Given a mnemonic word string, return a string of the computed checksum. :rtype: str """ phrase_split = phrase.split(" ") if len(phrase_split) < 12: raise ValueError("Invalid mnemonic phrase") if len(phrase_split) > 13: # Standard format phrase = phrase_split[:24] else: # MyMonero format phrase = phrase_split[:12] wstr = "".join(word[:cls.unique_prefix_length] for word in phrase) wstr = bytearray(wstr.encode('utf-8')) z = ((crc32(wstr) & 0xffffffff) ^ 0xffffffff ) >> 0 z2 = ((z ^ 0xffffffff) >> 0) % len(phrase) return phrase_split[z2] def get_wordlist(name): try: return WORDLISTS[name] except KeyError: raise ValueError("No such word list") def list_wordlists(): return WORDLISTS.keys() def endian_swap(word): """Given any string, swap bits and return the result. :rtype: str """ return "".join([word[i:i+2] for i in [6, 4, 2, 0]])

31.160714

103

0.565616

446

3,490

4.29148

0.269058

0.027168

0.034483

0.029258

0.150993

0.094566

0.028213

0

0.030528

0.305444

3,490

111

104

31.441441

0.759076

0.105158

0

0.068493

0

0.107062

0.028311

0

0.009762

0

1

0.09589

false

0

0.041096

0.013699

0.273973

0

null

0

null

0

1

0

bd78a71bbe18cc4f93cb82249b13ad78a464f21f

3,165

py

Python

BlenderScripts/process_mesh.py

razluta-unity/BlenderProcessUnity

d1d20f8b1910132c63cd73570c783a55c05fafe2

[ "MIT" ]

2

2020-11-24T06:10:44.000Z

2021-09-13T11:57:22.000Z

BlenderScripts/process_mesh.py

razluta-unity/BlenderProcessUnity

d1d20f8b1910132c63cd73570c783a55c05fafe2

[ "MIT" ]

null

BlenderScripts/process_mesh.py

razluta-unity/BlenderProcessUnity

d1d20f8b1910132c63cd73570c783a55c05fafe2

[ "MIT" ]

2

2020-12-03T07:48:48.000Z

2021-06-09T20:18:26.000Z

import os import argparse import bpy # Constants FBX_EXTENSION = ".fbx" BLENDER_ACTION_SELECT = "SELECT" BLENDER_TYPE_MESH = "MESH" BLENDER_MODIFIER_BEVEL = "BEVEL" def get_args(): """ A method to obtain the arguments that came with the triggered Python file - from the .bat file. :rtype: object :return: An object containing the arguments as properties. """ parser_double_dash = "--" parser_path_short_argument = "-p" parser_path_long_argument = "--path" parser_path_help = "asset path" parser = argparse.ArgumentParser() _, all_arguments = parser.parse_known_args() double_dash_index = all_arguments.index(parser_double_dash) script_args = all_arguments[double_dash_index + 1:] parser.add_argument(parser_path_short_argument, parser_path_long_argument, help=parser_path_help) parsed_script_args, _ = parser.parse_known_args(script_args) return parsed_script_args def setup_and_run_mesh_process(): """ Initialize the arguments and run the mesh process. """ args = get_args() source_asset_path = args.path process_mesh(source_asset_path) def process_mesh(asset_path): """ Process the mesh at the given asset_path. In this sample, processing = beveling and exporting the beveled mesh to the same path, with an added suffix to the name. :param string asset_path: The absolute asset path. """ processed_mesh_suffix = "_processed" asset_name = os.path.splitext(os.path.basename(asset_path))[0] source_asset_directory = os.path.dirname(asset_path) # Determine new naming and paths for the processed mesh export_asset_name = asset_name + processed_mesh_suffix export_asset_path = os.path.join(source_asset_directory, export_asset_name + FBX_EXTENSION) print("The source asset path is: " + asset_path) print("The source asset name is: " + asset_name) print("The source directory path is: " + source_asset_directory) # Clear the default Blender scene bpy.ops.object.select_all(action=BLENDER_ACTION_SELECT) bpy.ops.object.delete() # Import the asset in the Blender scene processing_failed = False try: bpy.ops.import_scene.fbx(filepath=asset_path) except Exception as e: processing_failed = True print("Could not import asset at : " + asset_path) print(e) # Process the asset # In this sample, I'm bevelling the asset and exporting the new mesh right next to the old one. # You can add your custom processing here and replace the sample. try: imported_assets = bpy.context.selected_objects for asset in imported_assets: if asset.type != BLENDER_TYPE_MESH: continue # Apply a bevel modifier on the mesh bevel_modifier_name = "Bevel Modifier" asset.modifiers.new(name=bevel_modifier_name, type=BLENDER_MODIFIER_BEVEL) except Exception as e: processing_failed = True print("Could not process asset.") print(e) # Export the asset from Blender back to Unity, next to the original asset if processing_failed: return try: bpy.ops.export_scene.fbx( filepath=export_asset_path, use_selection=True) except Exception as e: print("Could not export to path: " + export_asset_path) print(e) # Triggering the mesh process setup_and_run_mesh_process()

29.579439

101

0.766193

469

3,165

4.921109

0.294243

0.062392

0.019497

0.023397

0.063258

0.044194

0

0.000748

0.155134

3,165

106

102

29.858491

0.862378

0.278041

0

0.177419

0

0.09942

0

1

0.048387

false

0

0.112903

0

0.193548

0.145161

0

null

0

null

0

1

0

bd7ba5bd647118022f742f767d54083d1e57c29a

12,562

py

Python

experiment_launcher.py

alekdimi/arms

f83a32caa8283789c61b59f53832149410be765b

[ "MIT" ]

2

2021-06-15T09:41:45.000Z

2021-09-08T18:30:44.000Z

experiment_launcher.py

alekdimi/arms

f83a32caa8283789c61b59f53832149410be765b

[ "MIT" ]

null

experiment_launcher.py

alekdimi/arms

f83a32caa8283789c61b59f53832149410be765b

[ "MIT" ]

null

import os from absl import app, flags import dataset import networks import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions layers = tf.keras.layers flags.DEFINE_enum('dataset', 'static_mnist', ['static_mnist', 'dynamic_mnist', 'fashion_mnist', 'omniglot'], 'Dataset to use.') flags.DEFINE_float('genmo_lr', 1e-4, 'Learning rate for decoder, Generation network.') flags.DEFINE_float('infnet_lr', 1e-4, 'Learning rate for encoder, Inference network.') flags.DEFINE_float('prior_lr', 1e-2, 'Learning rate for prior variables.') flags.DEFINE_integer('batch_size', 50, 'Training batch size.') flags.DEFINE_integer('num_pairs', 1, ('Number of sample pairs used gradient estimators.')) flags.DEFINE_integer('num_steps', int(1e6), 'Number of training steps.') flags.DEFINE_string('encoder_type', 'linear', 'Choice supported: linear, nonlinear') flags.DEFINE_string('grad_type', 'arm', 'Choice supported: arm, disarm, reinforce') flags.DEFINE_string('logdir', 'logs/tmp', 'Directory for storing logs.') flags.DEFINE_bool('verbose', False, 'Whether to turn on training result logging.') flags.DEFINE_integer('repeat_idx', 0, 'Dummy flag to label the experiments in repeats.') flags.DEFINE_bool('half_p_trick', False, 'Enforce the p range is [0., 0.5]') flags.DEFINE_float('epsilon', 0., 'Additive float to prevent numerical underflow in log(x).') flags.DEFINE_float('temperature', None, 'Temperature for RELAX estimator.') flags.DEFINE_float('scaling_factor', None, 'Scaling factor for RELAX estimator.') flags.DEFINE_bool('eager', False, 'Enable eager execution.') flags.DEFINE_bool('bias_check', False, 'Carry out bias check for RELAX and baseline') flags.DEFINE_bool('demean_input', False, 'Demean for encoder and decoder inputs.') flags.DEFINE_bool('initialize_with_bias', False, 'Initialize the final layer bias of decoder with dataset mean.') flags.DEFINE_integer('seed', 1, 'Global random seed.') flags.DEFINE_integer('num_eval_samples', None, 'Number of samples for evaluation, default to num_pairs.') flags.DEFINE_integer('num_train_samples', None, 'Number of samples for evaluation, default to num_pairs.') flags.DEFINE_bool('debug', False, 'Turn on debugging mode.') FLAGS = flags.FLAGS def process_batch_input(input_batch): input_batch = tf.reshape(input_batch, [tf.shape(input_batch)[0], -1]) input_batch = tf.cast(input_batch, tf.float32) return input_batch def initialize_grad_variables(target_variable_list): return [tf.Variable(tf.zeros(shape=i.shape)) for i in target_variable_list] def estimate_gradients(input_batch, bvae_model, gradient_type, sample_size=1): if gradient_type == 'relax': with tf.GradientTape(persistent=True) as tape: genmo_loss, reparam_loss, learning_signal, log_q = ( bvae_model.get_relax_loss(input_batch, temperature=FLAGS.temperature, scaling_factor=FLAGS.scaling_factor, num_samples=sample_size)) genmo_grads = tape.gradient(genmo_loss, bvae_model.decoder_vars) prior_grads = tape.gradient(genmo_loss, bvae_model.prior_vars) infnet_vars = bvae_model.encoder_vars infnet_grads_1 = tape.gradient(log_q, infnet_vars, output_gradients=learning_signal) infnet_grads_2 = tape.gradient(reparam_loss, infnet_vars) infnet_grads = [infnet_grads_1[i] + infnet_grads_2[i] for i in range(len(infnet_vars))] else: with tf.GradientTape(persistent=True) as tape: elbo, _, infnet_logits, _ = bvae_model(input_batch) genmo_loss = -1. * tf.reduce_mean(elbo) genmo_grads = tape.gradient(genmo_loss, bvae_model.decoder_vars) prior_grads = tape.gradient(genmo_loss, bvae_model.prior_vars) infnet_grad_multiplier = -1. * bvae_model.get_layer_grad_estimation(input_batch, num_samples=sample_size) infnet_grads = tape.gradient(infnet_logits, bvae_model.encoder_vars, output_gradients=infnet_grad_multiplier) del tape return (genmo_grads, prior_grads, infnet_grads, genmo_loss) @tf.function def train_one_step( train_batch_i, bvae_model, genmo_optimizer, infnet_optimizer, prior_optimizer, theta_optimizer, encoder_grad_variable, encoder_grad_sq_variable): """Train Discrete VAE for 1 step.""" metrics = {} input_batch = process_batch_input(train_batch_i) if FLAGS.grad_type in ['loorf', 'arms', 'arms_normal']: num_samples = 2 * FLAGS.num_pairs else: num_samples = FLAGS.num_pairs if FLAGS.grad_type == 'relax': with tf.GradientTape(persistent=True) as theta_tape: (genmo_grads, prior_grads, infnet_grads, genmo_loss) = estimate_gradients( input_batch, bvae_model, FLAGS.grad_type, num_samples) genmo_vars = bvae_model.decoder_vars genmo_optimizer.apply_gradients(list(zip(genmo_grads, genmo_vars))) prior_vars = bvae_model.prior_vars prior_optimizer.apply_gradients(list(zip(prior_grads, prior_vars))) infnet_vars = bvae_model.encoder_vars infnet_optimizer.apply_gradients(list(zip(infnet_grads, infnet_vars))) infnet_grads_sq = [tf.square(grad_i) for grad_i in infnet_grads] theta_vars = [] if bvae_model.control_nn: theta_vars.extend(bvae_model.control_nn.trainable_variables) if FLAGS.temperature is None: theta_vars.append(bvae_model.log_temperature_variable) if FLAGS.scaling_factor is None: theta_vars.append(bvae_model.scaling_variable) theta_grads = theta_tape.gradient(infnet_grads_sq, theta_vars) theta_optimizer.apply_gradients(zip(theta_grads, theta_vars)) del theta_tape metrics['learning_signal'] = bvae_model.mean_learning_signal else: (genmo_grads, prior_grads, infnet_grads, genmo_loss) = estimate_gradients( input_batch, bvae_model, FLAGS.grad_type, num_samples) genmo_vars = bvae_model.decoder_vars genmo_optimizer.apply_gradients(list(zip(genmo_grads, genmo_vars))) prior_vars = bvae_model.prior_vars prior_optimizer.apply_gradients(list(zip(prior_grads, prior_vars))) infnet_vars = bvae_model.encoder_vars infnet_optimizer.apply_gradients(list(zip(infnet_grads, infnet_vars))) batch_size_sq = tf.cast(FLAGS.batch_size * FLAGS.batch_size, tf.float32) encoder_grad_var = bvae_model.compute_grad_variance( encoder_grad_variable, encoder_grad_sq_variable, infnet_grads) / batch_size_sq return (encoder_grad_var, None, genmo_loss, metrics) @tf.function def evaluate(model, tf_dataset, max_step=1000, num_eval_samples=None): """Evaluate the model.""" if num_eval_samples: num_samples = num_eval_samples elif FLAGS.num_eval_samples: num_samples = FLAGS.num_eval_samples elif FLAGS.grad_type in ['vimco', 'local-disarm', 'local-arms']: num_samples = FLAGS.num_pairs * 2 elif FLAGS.grad_type in ['loorf', 'arms', 'arms_normal']: num_samples = 2 * FLAGS.num_pairs else: num_samples = FLAGS.num_pairs loss = 0. n = 0. for batch in tf_dataset.map(process_batch_input): if n >= max_step: # used for train_ds, which is a `repeat` dataset. break if num_samples > 1: batch_size = tf.shape(batch)[0] input_batch = tf.tile(batch, [num_samples, 1]) elbo = tf.reshape(model(input_batch)[0], [num_samples, batch_size]) objectives = (tf.reduce_logsumexp(elbo, axis=0, keepdims=False) - tf.math.log(tf.cast(tf.shape(elbo)[0], tf.float32))) else: objectives = model(batch)[0] loss -= tf.reduce_mean(objectives) n += 1. return loss / n def main(_): tf.random.set_seed(FLAGS.seed) logdir = FLAGS.logdir if not os.path.exists(logdir): os.makedirs(logdir) if FLAGS.eager: tf.config.experimental_run_functions_eagerly(FLAGS.eager) genmo_lr = tf.constant(FLAGS.genmo_lr) infnet_lr = tf.constant(FLAGS.infnet_lr) prior_lr = tf.constant(FLAGS.prior_lr) genmo_optimizer = tf.keras.optimizers.Adam(learning_rate=genmo_lr) infnet_optimizer = tf.keras.optimizers.Adam(learning_rate=infnet_lr) prior_optimizer = tf.keras.optimizers.SGD(learning_rate=prior_lr) theta_optimizer = tf.keras.optimizers.Adam(learning_rate=infnet_lr, beta_1=0.999) batch_size = FLAGS.batch_size if FLAGS.dataset == 'static_mnist': train_ds, valid_ds, test_ds = dataset.get_static_mnist_batch(batch_size) train_size = 50000 elif FLAGS.dataset == 'dynamic_mnist': train_ds, valid_ds, test_ds = dataset.get_dynamic_mnist_batch(batch_size) train_size = 50000 elif FLAGS.dataset == 'fashion_mnist': train_ds, valid_ds, test_ds = dataset.get_dynamic_mnist_batch( batch_size, fashion_mnist=True) train_size = 50000 elif FLAGS.dataset == 'omniglot': train_ds, valid_ds, test_ds = dataset.get_omniglot_batch(batch_size) train_size = 23000 num_steps_per_epoch = int(train_size / batch_size) train_ds_mean = dataset.get_mean_from_iterator( train_ds, dataset_size=train_size, batch_size=batch_size) if FLAGS.initialize_with_bias: bias_value = -tf.math.log( 1./tf.clip_by_value(train_ds_mean, 0.001, 0.999) - 1.).numpy() bias_initializer = tf.keras.initializers.Constant(bias_value) else: bias_initializer = 'zeros' if FLAGS.encoder_type == 'linear': encoder_hidden_sizes = [200] encoder_activations = ['linear'] decoder_hidden_sizes = [784] decoder_activations = ['linear'] elif FLAGS.encoder_type == 'nonlinear': encoder_hidden_sizes = [200, 200, 200] encoder_activations = [ layers.LeakyReLU(alpha=0.3), layers.LeakyReLU(alpha=0.3), 'linear'] decoder_hidden_sizes = [200, 200, 784] decoder_activations = [ layers.LeakyReLU(alpha=0.3), layers.LeakyReLU(alpha=0.3), 'linear'] else: raise NotImplementedError encoder = networks.BinaryNetwork( encoder_hidden_sizes, encoder_activations, mean_xs=train_ds_mean, demean_input=FLAGS.demean_input, name='bvae_encoder') decoder = networks.BinaryNetwork( decoder_hidden_sizes, decoder_activations, demean_input=FLAGS.demean_input, final_layer_bias_initializer=bias_initializer, name='bvae_decoder') prior_logit = tf.Variable(tf.zeros([200], tf.float32)) if FLAGS.grad_type == 'relax': control_network = tf.keras.Sequential() control_network.add( layers.Dense(137, activation=layers.LeakyReLU(alpha=0.3))) control_network.add( layers.Dense(1)) else: control_network = None bvae_model = networks.SingleLayerDiscreteVAE( encoder, decoder, prior_logit, grad_type=FLAGS.grad_type, half_p_trick=FLAGS.half_p_trick, epsilon=FLAGS.epsilon, control_nn=control_network) bvae_model.build(input_shape=(None, 784)) tensorboard_file_writer = tf.summary.create_file_writer(logdir) encoder_grad_variable = initialize_grad_variables(bvae_model.encoder_vars) encoder_grad_sq_variable = initialize_grad_variables(bvae_model.encoder_vars) start_step = infnet_optimizer.iterations.numpy() train_iter = train_ds.__iter__() for step_i in range(start_step, FLAGS.num_steps): (encoder_grad_var, variance_dict, genmo_loss, metrics) = train_one_step( train_iter.next(), bvae_model, genmo_optimizer, infnet_optimizer, prior_optimizer, theta_optimizer, encoder_grad_variable, encoder_grad_sq_variable) train_loss = tf.reduce_mean(genmo_loss) if step_i % 1000 == 0: metrics.update({ 'train_objective': train_loss, 'eval_metric/train': evaluate(bvae_model, train_ds, max_step=num_steps_per_epoch, num_eval_samples=FLAGS.num_train_samples), 'eval_metric/valid': evaluate(bvae_model, valid_ds, num_eval_samples=FLAGS.num_eval_samples), 'eval_metric/test': evaluate(bvae_model, test_ds, num_eval_samples=FLAGS.num_eval_samples), 'var/grad': encoder_grad_var }) if FLAGS.grad_type == 'relax': if FLAGS.temperature is None: metrics['relax/temperature'] = tf.math.exp(bvae_model.log_temperature_variable) if FLAGS.scaling_factor is None: metrics['relax/scaling'] = bvae_model.scaling_variable tf.print(step_i, metrics) with tensorboard_file_writer.as_default(): for k, v in metrics.items(): tf.summary.scalar(k, v, step=step_i) if variance_dict is not None: tf.print(variance_dict) for k, v in variance_dict.items(): tf.summary.scalar(k, v, step=step_i) if __name__ == '__main__': app.run(main)

38.891641

134

0.729979

1,751

12,562

4.915477

0.16562

0.036598

0.017892

0.013942

0.373766

0.314744

0.302428

0.282793

0.244685

0.225514

0

0.012976

0.165658

12,562

322

135

39.012422

0.808224

0.007881

0

0.266917

0

0.123354

0

1

0.022556

false

0

0.022556

0.003759

0.06391

0.007519

0

null

0

null

0

1

0

bd7bd354d1693ae42a8a899d55fa9d11d8bad927

1,916

py

Python

tests/test_wikipron/test_languagecodes.py

Alireza-Sampour/wikipron

ac821c5d0a7d70e7e700f45f9d01b2dfb4ecae9d

[ "Apache-2.0" ]

null

tests/test_wikipron/test_languagecodes.py

Alireza-Sampour/wikipron

ac821c5d0a7d70e7e700f45f9d01b2dfb4ecae9d

[ "Apache-2.0" ]

null

tests/test_wikipron/test_languagecodes.py

Alireza-Sampour/wikipron

ac821c5d0a7d70e7e700f45f9d01b2dfb4ecae9d

[ "Apache-2.0" ]

null

import warnings import iso639 import pytest import wikipron from data.src.codes import _get_language_categories, _get_language_sizes from wikipron.languagecodes import LANGUAGE_CODES from . import can_connect_to_wiktionary # We handle languages with at least this number of pronunciation entries. _MIN_LANGUAGE_SIZE = 100 @pytest.mark.skipif(not can_connect_to_wiktionary(), reason="need Internet") def test_language_coverage(): """Check if WikiPron covers languages with a sufficient amount of data. If any warnings are raised, they should be suppressed by expanding the LANGUAGE_CODES dict to handle the relevant languages. """ categories = _get_language_categories() sizes = _get_language_sizes(categories) for language, size in sizes.items(): if size < _MIN_LANGUAGE_SIZE: continue if language in ("Mon", "Translingual"): # "mon" is the ISO 639 code for Mongolian, but there is also # the Mon language (ISO 639 code: "mnw"). continue try: language_code = iso639.to_iso639_2(language) except iso639.NonExistentLanguageError: # Check if WikiPron can handle `language` directly. language_code = language try: language_inferred = wikipron.Config(key=language_code).language except iso639.NonExistentLanguageError: warnings.warn(f'WikiPron cannot handle "{language}".') continue if language_inferred != language: warnings.warn( f'WikiPron resolves the key "{language_code}" to ' f'"{language_inferred}", ' f'which is not "{language}" on Wiktionary.' ) def test_language_codes_dict_keys(): """LANGUAGE_CODES keys must be in lowercase for Config._get_language.""" for k in LANGUAGE_CODES.keys(): assert k == k.lower()

35.481481

76

0.674322

230

1,916

5.421739

0.408696

0.044106

0.033681

0.035285

0

0.017458

0.25261

1,916

53

77

36.150943

0.853352

0.252088

0

0.2

0

0.123755

0.015647

0

0.028571

1

0.057143

false

0

0.2

0

0.257143

0

null

0

null

0

1

0

bd7e89a9c152d6c5a11c4d9af7357acb6500801c

2,947

py

Python

libs/DegradationModels.py

prasunroy/cnn-on-degraded-images

85bb4c62a024e3766da3c4d2556e01c6e12e416a

[ "MIT" ]

15

2018-10-01T20:54:13.000Z

2021-10-09T10:40:21.000Z

libs/DegradationModels.py

prasunroy/cnn-on-degraded-images

85bb4c62a024e3766da3c4d2556e01c6e12e416a

[ "MIT" ]

1

2020-04-13T23:58:23.000Z

2020-05-15T11:54:16.000Z

libs/DegradationModels.py

prasunroy/cnn-on-degraded-images

85bb4c62a024e3766da3c4d2556e01c6e12e416a

[ "MIT" ]

4

2018-11-22T09:44:29.000Z

2019-09-17T23:37:40.000Z

# -*- coding: utf-8 -*- """ Degradation models. Created on Thu May 24 11:00:00 2018 Author: Prasun Roy | CVPRU-ISICAL (http://www.isical.ac.in/~cvpr) GitHub: https://github.com/prasunroy/cnn-on-degraded-images """ # imports import cv2 import numpy import random # apply a degradation model on an image def imdegrade(image, model, mu=0, sigma=0, density=0, gb_ksize=(1, 1), mb_kernel=numpy.zeros((1, 1), dtype='uint8'), quality=100, seed=None): # setup seeds for random number generators # (only required for reproducibility) numpy.random.seed(seed) random.seed(seed) # create a copy of the input image to prevent direct modification # on the original input image image = image.copy() # add an extra dimension for color channel # (only required for grayscale images) if len(image.shape) == 2: image = numpy.expand_dims(image, 2) # get dimension of the image h, w, c = image.shape # apply a degradation model model = model.lower() if model == 'gaussian_white' and sigma > 0: image = image / 255.0 noise = numpy.random.normal(mu, sigma, (h, w)) noise = numpy.dstack([noise]*c) image += noise image = numpy.clip(image, 0, 1) image = (image * 255.0).astype('uint8') elif model == 'gaussian_color' and sigma > 0: image = image / 255.0 noise = numpy.random.normal(mu, sigma, (h, w, c)) image += noise image = numpy.clip(image, 0, 1) image = (image * 255.0).astype('uint8') elif model == 'salt_and_pepper': if density < 0: density = 0 elif density > 1: density = 1 x = random.sample(range(w), w) y = random.sample(range(h), h) x, y = numpy.meshgrid(x, y) xy = numpy.c_[x.reshape(-1), y.reshape(-1)] n = int(w * h * density) n = random.sample(range(w*h), n) for i in n: if random.random() > 0.5: image[xy[i][1], xy[i][0], :] = 255 else: image[xy[i][1], xy[i][0], :] = 0 elif model == 'motion_blur': image = cv2.filter2D(image, -1, mb_kernel, borderType=cv2.BORDER_CONSTANT) elif model == 'gaussian_blur': image = cv2.GaussianBlur(image, gb_ksize, 0, borderType=cv2.BORDER_CONSTANT) elif model == 'jpeg_compression': if quality < 0: quality = 0 elif quality > 100: quality = 100 image = cv2.imencode('.jpg', image, [int(cv2.IMWRITE_JPEG_QUALITY), quality])[-1] image = cv2.imdecode(image, -1) # remove the extra dimension for color channel # (only required for grayscale images) if image.shape[-1] == 1: image = numpy.squeeze(image, 2) return image

30.697917

74

0.554462

387

2,947

4.178295

0.343669

0.037106

0.032158

0.034632

0.284477

0.239951

0.223871

0

0.044888

0.319647

2,947

95

75

31.021053

0.761596

0.214455

0

0.169492

0

0.044464

0

1

0.016949

false

0

0.050847

0

0.084746

0

null

0

null

0

1

0

bd7f5d02897acbff32966f7928ccfc825cfa419a

3,548

py

Python

apartmentbot/services/listing_service.py

sgarfield/ApartmentBot

327a47e879998fbb40bd26f84510467f4757d330

[ "MIT" ]

null

apartmentbot/services/listing_service.py

sgarfield/ApartmentBot

327a47e879998fbb40bd26f84510467f4757d330

[ "MIT" ]

null

apartmentbot/services/listing_service.py

sgarfield/ApartmentBot

327a47e879998fbb40bd26f84510467f4757d330

[ "MIT" ]

null

""" apartmentbot.services.listing_service """ import logging from dataclasses import dataclass from typing import List from dataclasses_json import dataclass_json from apartmentbot.geolocation.geolocation import distance_finder, neighborhood_locator, place_locator from apartmentbot.models import Listing, Place, Preferences from apartmentbot.sources.sources import sources from apartmentbot.repository.repository import listing_repository @dataclass_json @dataclass class ListingService: """ Class ListingService finds and saves apartment listings """ def find_listings(self, preferences: Preferences) -> List[Listing]: """ Finds all listings that match the set of apartment preferences :param preferences: A set of apartment preferences :return: A list of apartment listings (may return nothing) """ logging.info("Searching listings", extra={"preferences": preferences}) source_listings = self._search_sources(preferences) return self._match_additional(preferences, source_listings) def save_listing(self, listing: Listing): """ Stores listing in the database """ logging.info("Saving listing", extra={"listing": listing}) return listing_repository.add(listing) @staticmethod def _search_sources(preferences: Preferences): """ Returns listings from all available listing sites """ return [listings for source in sources for listings in source.get_results(preferences)] def _match_additional(self, preferences: Preferences, listings: List[Listing]) -> List[Listing]: """ Filters listings by optional additional preferences """ if not preferences.additional: return listings if preferences.additional.neighborhoods: listings = [listing for listing in listings if self._is_in_neighborhood(listing, preferences.additional.neighborhoods)] logging.debug('Neighborhood matches: %d', len(listings)) for place in preferences.additional.places: listings = [listing for listing in listings if self._is_near_place(listing, place.name, place.distance)] logging.debug('Place matches: %d', len(listings)) return listings @staticmethod def _is_in_neighborhood(listing: Listing, neighborhoods: List[str]) -> bool: """ Determines whether the listing is in a chosen neighborhood """ neighborhood = neighborhood_locator.find_neighborhood(latlng=listing.geotag) logging.debug("Listing neighborhood result: %s", neighborhood, extra={"listing_id": listing.id, "geotag": listing.geotag}) if neighborhood in neighborhoods: listing.neighborhood = neighborhood return True return False @staticmethod def _is_near_place(listing: Listing, place_name: str, max_distance: int) -> bool: """ Determines whether listing is within max_distance of some searchable place """ place = place_locator.find_place(place=place_name, latlng=listing.geotag) distance = distance_finder.find_distance(origin=listing.geotag, destination=place) logging.debug("Distance (meters) between listing and %s: %d", place_name, distance, extra={"listing_id": listing.id, "place_id": place, "geotag": listing.geotag}) if distance <= max_distance: listing.places.append(Place(place_name, distance)) return True return False

45.487179

101

0.698985

384

3,548

6.328125

0.255208

0.018519

0.021399

0.020576

0.054321

0.035391

0

0.217587

3,548

77

102

46.077922

0.87536

0.152198

0

0.211538

0

0.070427

0

1

0.115385

false

0

0.153846

0

0.461538

0

null

0

null

0

1

0

bd7f8e09909844b7662e6b08100c7c6fbbff9197

6,016

py

Python

stocky.py

Naphtha/stocky

04e51b4270b28a1aa23597c07d67a2a99e4710cf

[ "MIT" ]

null

stocky.py

Naphtha/stocky

04e51b4270b28a1aa23597c07d67a2a99e4710cf

[ "MIT" ]

null

stocky.py

Naphtha/stocky

04e51b4270b28a1aa23597c07d67a2a99e4710cf

[ "MIT" ]

null

import requests import json BASE_URL = "https://api.stockfighter.io/ob/api/" class StockMinion(object): '''Handles all API related requests for stock/order functionality''' def __init__(self, api_key, account, venue, stock): # use sessions to persist the HTTP connection # this will prevent thrashing HTTP sockets self.session = requests.Session() # set header in session, this will be reused by every function header = {'X-Starfighter-Authorization' : api_key} self.session.headers.update(header) # set some basic, usually static, values self.account = account self.venue = venue self.stock = stock def check_api(self): # _call_api is sufficiently general to handle all cases data = self._call_api(BASE_URL + 'heartbeat', 'get') return data def check_venue(self): venue = self.venue data = self._call_api(BASE_URL + 'venues/{0}/heartbeat'.format(venue), 'get') return data def get_stocks_on_venue(self): venue = self.venue data = self._call_api(BASE_URL + 'venues/{0}/stocks'.format(venue), 'get') return data def get_orderbook(self): venue = self.venue stock = self.stock data = self._call_api(BASE_URL + 'venues/{0}/stocks/{1}'.format(venue, stock), 'get') return data # using kwargs here allows me to call this function with keywords or with a dict def place_order(self, **kwargs): kwargs['account'] = self.account kwargs['stock'] = self.stock kwargs['venue'] = self.venue # the args we need to make the request of the API mandatory = ['account', 'venue', 'stock', 'qty', 'direction', 'orderType'] # filter out the args we're missing from the kwargs dict missing_args = [x for x in mandatory if x not in kwargs] # raises exception with missing operands if(missing_args): raise TypeError("Missing '{0}' arguments in function call".format(', '.join(missing_args))) # leave the dictionary packed request_body = kwargs data = self._call_api(BASE_URL + 'venues/{0}/stocks/{1}/orders'.format(kwargs['venue'], kwargs['stock']), 'post', data=json.dumps(request_body)) return data def get_quote(self): venue = self.venue stock = self.stock data = self._call_api(BASE_URL + 'venues/{0}/stocks/{1}/quote'.format(venue, stock), 'get') return data def get_order_status(self, id): venue = self.venue stock = self.stock data = self._call_api(BASE_URL + 'venues/{0}/stocks/{1}/orders/{2}'.format(venue, stock, id), 'get') return data def cancel_order(self, id): venue = self.venue stock = self.stock data = self._call_api(BASE_URL + 'venues/{0}/stocks/{1}/orders/{2}'.format(venue, stock, id), 'delete') return data def get_all_orders(self, stock = None): venue = self.venue account = self.account if(stock): # get orders for specific stock data = self._call_api(BASE_URL + 'venues/{0}/accounts/{1}/stocks/{2}/orders'.format(venue, account, stock), 'get') else: data = self._call_api(BASE_URL + 'venues/{0}/accounts/{1}/orders'.format(venue, account), 'get') return data def _call_api(self, url, verb, *args, **kwargs): # use HTTP verb argument to pick the method to use from the Session object func = getattr(self.session, verb) resp = func(url, *args, **kwargs) data = StockMinion._process_response(resp.text, resp.status_code) return data @staticmethod def _process_json(json_obj): try: data = json.loads(json_obj) except ValueError as e: data = {} print(e) return data @staticmethod def _process_status(code): if(code != 200): print("Got a status code of {0}".format(code)) else: pass @staticmethod def _process_response(json_obj, code): data = StockMinion._process_json(json_obj) StockMinion._process_status(code) return data if __name__ == '__main__': import sys def print_test_result(data, function): if(data['ok'] == True): print("PASS: {0}()".format(function)) else: print("FAIL: {1}()".format(function)) # run some simple regression tests TEST_VENUE = "TESTEX" TEST_STOCK = "FOOBAR" TEST_ACCOUNT = "EXB123456" # pick up api key from local untracked file with open('api.key', 'r') as secret_file: API_KEY = secret_file.readlines()[0].rstrip('\n') instance = StockMinion(API_KEY, TEST_ACCOUNT, TEST_VENUE, TEST_STOCK) data = instance.check_api() # the numerous calls to print_test_result can probably be eliminated at some point print_test_result(data, 'check_api') data = instance.check_venue() print_test_result(data, 'check_venue') data = instance.get_stocks_on_venue() print_test_result(data, 'get_stocks_on_venue') data = instance.get_orderbook() print_test_result(data, 'get_orderbook') data = instance.place_order(qty = 100, direction = "buy", orderType = "limit", price = 100) print_test_result(data, 'place_order') order_num = data['id'] data = instance.get_quote() print_test_result(data, 'get_quote') data = instance.get_order_status(order_num) print_test_result(data, 'get_order_status') data = instance.cancel_order(order_num) print_test_result(data, 'cancel_order') data = instance.get_all_orders() print_test_result(data, 'get_all_orders') data = instance.get_all_orders(TEST_STOCK) print_test_result(data, 'get_all_orders')

30.231156

126

0.617686

765

6,016

4.658824

0.245752

0.032828

0.050505

0.058642

0.297419

0.228676

0.191077

0.156846

0.15376

0

0.008874

0.269448

6,016

198

127

30.383838

0.802048

0.134641

0

0.258333

0

0.132112

0.045902

0

1

0.125

false

0.016667

0.025

0

0.258333

0.125

0

null

0

null

0

1

0

bd80d005cadf2946180d02a5ee8eb0ce0d0f3e91

6,481

py

Python

crawlers/ucsc_old/ucsc/spiders/registrar_courses.py

coursegraph/CourseGraph

9f05cd912b393ba14721411fe77f3856812c000f

[ "MIT" ]

5

2018-07-01T15:48:11.000Z

2020-07-31T17:06:10.000Z

crawlers/ucsc_old/ucsc/spiders/registrar_courses.py

coursegraph/CourseGraph

9f05cd912b393ba14721411fe77f3856812c000f

[ "MIT" ]

7

2018-07-09T21:17:19.000Z

2018-07-25T17:05:33.000Z

crawlers/ucsc_old/ucsc/spiders/registrar_courses.py

coursegraph/CourseGraph

9f05cd912b393ba14721411fe77f3856812c000f

[ "MIT" ]

4

2018-07-01T19:45:23.000Z

2019-03-17T21:12:03.000Z

# -*- coding: utf-8 -*- import scrapy import os from ucsc.items import FacultyItem, ProgramStatementItem, CourseDescriptionItem def path_components (path): if '://' in path: path = path.split('://')[1] parts = path.split('/') while parts and parts[0] == '': parts = parts[1:] while parts and parts[-1] == '': parts = parts[:-1] return parts assert(path_components('') == []) assert(path_components('/') == []) assert(path_components('foo/') == ['foo']) assert(path_components('/bar') == ['bar']) assert(path_components('foo/bar') == ['foo','bar']) def merge_url (url, rel): # note: blame seiji for all the issues with this code thing = url.split('://')[0] if '://' in url else 'https' if url and url[-1] == '/': url = path_components(url) else: url = path_components(url)[:-1] for part in path_components(rel): if part == '..': url = url[:-1] else: url.append(part) return thing + '://' + '/'.join(url) assert(merge_url('https://registrar.ucsc.edu/catalog/programs-courses/index.html', '../foo/bar/../baz.html') == 'https://registrar.ucsc.edu/catalog/foo/baz.html') assert(merge_url('', 'bar.baz') == 'https://bar.baz') assert(merge_url('https://foo/bar/baz.html', '') == 'https://foo/bar') registrar_base_url = 'https://registrar.ucsc.edu/catalog/programs-courses' base_course_description_url = 'https://registrar.ucsc.edu/catalog/programs-courses/course-descriptions' base_faculty_url = 'https://registrar.ucsc.edu/catalog/programs-courses/faculty' base_program_description_url = 'https://registrar.ucsc.edu/catalog/programs-courses/program-statements' class RegistrarCoursesSpider(scrapy.Spider): name = 'registrar_courses' allowed_domains = ['registrar.ucsc.edu'] start_urls = [merge_url(registrar_base_url, 'index.html')] def __init__(self, *args, **kwargs): super(RegistrarCoursesSpider, self).__init__(*args, **kwargs) self.crawled = set() def parse (self, response): print("Parsing %s"%response.url) if base_course_description_url in response.url: yield self.parse_course_info(response) elif base_faculty_url in response.url: yield self.parse_faculty_info(response) elif base_program_description_url in response.url: yield self.parse_program_info(response) all_links = response.xpath('//a') for link in all_links: #print("Got link: %s"%link.extract()) try: href = link.xpath('@href').extract()[0] def is_local_url (url): for thing in ('http:','https:','C:','www','ucsc.edu'): if thing in url: return False return True url = merge_url(response.url, href) if is_local_url(href) else href if url in self.crawled: continue #print("Got URL: %s"%url) self.crawled.add(url) if registrar_base_url in url: yield { 'url': url } yield scrapy.Request(url, self.parse) else: pass #print("Skipping %s"%url) except IndexError: pass def parse_course_info (self, response): info = CourseDescriptionItem() info['url'] = response.url print("Got %s"%response.url) return info def parse_faculty_info (self, response): info = FacultyItem() info['url'] = response.url print("Got %s"%response.url) return info def parse_program_info (self, response): info = ProgramStatementItem() info['url'] = response.url print("Got %s"%response.url) return info class Unused: def parse(self, response): # Get links to all course pages from the registrar page_content = response\ .xpath('body/div[@id="wrap"]/div[@id="container"]/div[@id="content"]')\ .xpath('div[@id="sprflt"]/div[@id="main"]/div[contains(@class,"content")]') panel_elems = page_content.xpath('table/tbody/tr/td') self.depts = {} self.crawled = set() for panel in panel_elems: program_statements = panel.xpath('p/a') for a in program_statements: # print(a.xpath('@href').extract()) dept = a.xpath('@href').re(r'program-statements/(\w+)\.html')[0] title = a.xpath('text()').extract()[0] url = 'https://registrar.ucsc.edu/catalog/programs-courses/program-statements/%s.html'%dept self.depts[dept] = title self.crawled.add(url) yield scrapy.Request(url, callback=self.parse_program_info) #course_url = 'https://registrar.ucsc.edu/catalog/programs-courses/course-descriptions/%s.html'%dept program_url = 'https://registrar.ucsc.edu/catalog/programs-courses/program-statements/%s.html'%dept faculty_url = 'https://registrar.ucsc.edu/catalog/programs-courses/faculty/%s.html'%dept #yield scrapy.Request(course_url, callback=self.parse_course_info) yield scrapy.Request(program_url, callback=self.parse_program_info) yield scrapy.Request(faculty_url, callback=self.parse_faculty_info) def parse_program_info (self, response): page_content = response\ .xpath('body/div[@id="wrap"]/div[@id="container"]/div[@id="content"]')\ .xpath('div[@id="sprflt"]/div[@id="main"]/div[contains(@class,"content")]') page_links = page_content.xpath('p[3]/a') for a in page_links: href, regex = a.xpath('@href'), r'\.\./([\w\-]+/\w+\.html)' try: page = href.re(regex)[0] title = a.xpath('text()').extract()[0] url = 'https://registrar.ucsc.edu/catalog/programs-courses/program-statements/%s'%page print("\n%s: %s"%(url, title)) except IndexError: print("Could not match '%s' with '%s'"%(href, regex)) content = page_content #print("%s"%content.extract()[0]) def parse_course_info (self, response): print("Got %s"%response.url) def parse_faculty_info (self, response): print("Got %s"%response.url)

39.760736

117

0.583552

773

6,481

4.767141

0.182406

0.024695

0.052103

0.062687

0.43772

0.409498

0.354138

0.330258

0.263501

0.257531

0

0.003558

0.262768

6,481

162

118

40.006173

0.767685

0.067428

0

0.314961

0

0.015748

0.217305

0.054036

0

0.062992

1

0.094488

false

0.015748

0.023622

0

0.212598

0.062992

0

null

0

null

0

1

0

bd82c29e5bee15a3671a883151076960c6a72038

1,937

py

Python

gradient_free_optimizers/optimizers/exp_opt/local_bayes_opt.py

Wollala/Gradient-Free-Optimizers

8fb1608c264431b87f66fd2d233b76a0fa75316c

[ "MIT" ]

1

2022-02-25T03:14:48.000Z

gradient_free_optimizers/optimizers/exp_opt/local_bayes_opt.py

Wollala/Gradient-Free-Optimizers

8fb1608c264431b87f66fd2d233b76a0fa75316c

[ "MIT" ]

null

gradient_free_optimizers/optimizers/exp_opt/local_bayes_opt.py

Wollala/Gradient-Free-Optimizers

8fb1608c264431b87f66fd2d233b76a0fa75316c

[ "MIT" ]

null

# Author: Simon Blanke # Email: simon.blanke@yahoo.com # License: MIT License import time import random import numpy as np from ..base_optimizer import BaseOptimizer from ...search import Search from ._sub_search_spaces import SubSearchSpaces from ..smb_opt import BayesianOptimizer class LocalBayesianOptimizer(BaseOptimizer, Search): name = "Local Bayesian Optimizer" def __init__( self, *args, max_size=300000, n_positions=20, local_range=100, **kwargs ): super().__init__(*args, **kwargs) self.max_size = max_size self.n_positions = n_positions self.local_range = local_range self.bayes_opt = BayesianOptimizer(self.conv.search_space) def create_local_smbo(self, current_position): local_ss = {} for idx, para in enumerate(self.conv.para_names): max_dim = max(0, current_position[idx] + self.local_range) min_dim = min( self.conv.dim_sizes[idx], current_position[idx] - self.local_range ) dim_pos = np.array(self.conv.search_space_positions[idx]) dim_pos_center = np.where( np.logical_and(dim_pos >= min_dim, dim_pos <= max_dim) )[0] local_ss[para] = dim_pos_center self.bayes_opt = BayesianOptimizer(local_ss) def finish_initialization(self): self.create_local_smbo(self.pos_current) @BaseOptimizer.track_nth_iter def iterate(self): pos_loc = self.bayes_opt.iterate() pos_new = self.bayes_opt.conv.position2value(pos_loc) return pos_new def evaluate(self, score_new): self.bayes_opt.evaluate(score_new) self.score_new = score_new self._evaluate_new2current(score_new) self._evaluate_current2best() modZero = self.nth_iter % self.n_positions == 0 if modZero: self.create_local_smbo(self.pos_current)

27.671429

82

0.665978

246

1,937

4.926829

0.341463

0.041254

0.049505

0.04703

0.107261

0.054455

0

0.011636

0.245741

1,937

69

83

28.072464

0.817933

0.036655

0

0.044444

0

0.012889

0

1

0.111111

false

0

0.155556

0

0.333333

0

null

0

null

0

1

0

bd83ed580ee241d7bccf7170f916c7c26cd0e7da

2,235

py

Python

storage.py

computer-micro-mangangement/cmm_hub

1ec4ed8c86edcbbd5624396a4be1d6aa7e6132fb

[ "MIT" ]

null

storage.py

computer-micro-mangangement/cmm_hub

1ec4ed8c86edcbbd5624396a4be1d6aa7e6132fb

[ "MIT" ]

null

storage.py

computer-micro-mangangement/cmm_hub

1ec4ed8c86edcbbd5624396a4be1d6aa7e6132fb

[ "MIT" ]

null

import psutil from appJar import gui import config import requests as req import json import platform import sysInfo app = gui(title="CMM Hub", showIcon=False) navBarElements = [] navBarElementsCallName = [] currentContainer = "" def get_size(bytes, suffix="B"): """ Scale bytes to its proper format e.g: 1253656 => '1.20MB' 1253656678 => '1.17GB' """ factor = 1024 for unit in ["", "K", "M", "G", "T", "P"]: if bytes < factor: return f"{bytes:.2f}{unit}{suffix}" bytes /= factor def getServerInfo(): request = req.get(config.getServerAddress() + "/api/info", verify=False) if request.status_code == 200: jsonData = json.loads(request.text) return jsonData return {} def getUserInfo(): request = req.get(config.getServerAddress() + "/api/user/currentUser", verify=False, params={"devicesecret": config.getDeviceSecret()}) if request.status_code == 200: jsonData = json.loads(request.text) return jsonData return {} def getInstallableModules(): serverInfo = getServerInfo() moduleListURL = serverInfo["moduleListURL"] request = req.get(moduleListURL, verify=False) if request.status_code == 200: modules = {} data = request.text lines = data.split('\n') for line in lines: elements = line.split(',') modules[elements[0]] = {} modules[elements[0]]["link"] = elements[1].replace(" ", "") modules[elements[0]]["name"] = elements[0].capitalize() modules[elements[0]]["version"] = elements[2].replace(" ", "") return modules def getDeviceInfo(): deviceInfo = {} uname = platform.uname() deviceInfo["os"] = uname.system + str(uname.release) deviceInfo["name"] = uname.node deviceInfo["architecture"] = uname.machine deviceInfo["processor"] = {} deviceInfo["processor"]["processor Declaration"] = uname.processor deviceInfo["processor"]["cores"] = psutil.cpu_count(logical=False) deviceInfo["processor"]["threads"] = psutil.cpu_count(logical=True) svmem = psutil.virtual_memory() deviceInfo["installed RAM"] = get_size(svmem.total) return deviceInfo

28.653846

88

0.630425

239

2,235

5.861925

0.464435

0.03212

0.045682

0.040685

0.189864

0.135617

0.104211

0

0.025287

0.221477

2,235

77

89

29.025974

0.779885

0.039374

0

0.155172

0

0.100047

0.021606

0

1

0.086207

false

0

0.12069

0

0.327586

0

null

0

null

0

1

0

bd884de362569f988f5fe329cd6525259b1ae410

1,840

py

Python

lawliet/mono/resample.py

Ryuk17/lawliet

ba4734557260b255896707210fca3e2fff311e87

[ "Apache-2.0" ]

2

2021-04-20T03:51:32.000Z

2021-06-16T11:48:06.000Z

lawliet/mono/resample.py

Ryuk17/lawliet

ba4734557260b255896707210fca3e2fff311e87

[ "Apache-2.0" ]

null

lawliet/mono/resample.py

Ryuk17/lawliet

ba4734557260b255896707210fca3e2fff311e87

[ "Apache-2.0" ]

null

""" @FileName: resample.py @Description: Implement resample @Author: Ryuk @CreateDate: 2021/06/27 @LastEditTime: 2021/06/27 @LastEditors: Please set LastEditors @Version: v0.1 """ import numpy as np import math __all__ = [ "direct_interpolation", "lagrange_interpolation", "sine_interpolation", ] def direct_interpolation(x, L, M): """ resample signal with direct interpolation :param x: input signal :param L: original frequency :param M: target frequency :return: resampled signal """ N = len(x) K = int((M / L) * N) factor = L / M y = np.zeros(K) for k in range(K): nk = factor * k n = math.floor(nk) if n + 1 >= len(x): continue w1 = nk - n w2 = 1 - w1 y[k] = w1 * x[n + 1] + w2 * x[n] return y def lagrange_interpolation(x, w, L, M): N = len(x) K = int((M / L) * N) factor = L / M y = np.zeros(K) for k in range(K): nk = factor * k n = math.floor(nk) - 1 for i in range(-w, w, 1): numerator = 1 denominator = 1 if n - i >= len(x): continue for j in range(-w, w, 1): if i != j: numerator *= nk - (n - j) denominator *= (j - i) y[k] += x[n - i] * numerator / denominator return y def sine_interpolation(x, w, L, M): N = len(x) K = int((M / L) * N) factor = L / M y = np.zeros(K) for k in range(K): nk = factor * k n = math.floor(nk) for i in range(-w, w, 1): if n - i >= len(x): continue if nk - n + i == 0: continue numerator = math.sin((nk - n + i)) denominator = math.pi * (nk - n +i) y[k] += x[n - i] * numerator / denominator return y

23.589744

54

0.490761

264

1,840

3.382576

0.246212

0.015677

0.016797

0.020157

0.402016

0.388578

0.320269

0

0.028621

0.37337

1,840

77

55

23.896104

0.74588

0.17337

0

0.54717

0

0.040486

0.014845

0

1

0.056604

false

0

0.037736

0

0.150943

0

null

0

null

0

1

0

bd8b0ea4d67316a57577ad9cd394adb4172f74c1

2,130

py

Python

src/demo.py

lhcezx/Graph-FPN

55eb9283a7df83e003c84eede65a2700bb9fa45c

[ "MIT" ]

19

2021-11-16T05:32:45.000Z

2022-01-27T09:29:50.000Z

src/demo.py

lhcezx/Graph-FPN

55eb9283a7df83e003c84eede65a2700bb9fa45c

[ "MIT" ]

1

2021-11-15T15:44:45.000Z

2021-12-13T04:26:26.000Z

src/demo.py

lhcezx/Graph-FPN

55eb9283a7df83e003c84eede65a2700bb9fa45c

[ "MIT" ]

1

2022-01-04T14:10:58.000Z

import os import zipfile import tensorflow as tf import tensorflow_datasets as tfds import init_path from configs.configs import parse_configs from detection.utils.Label import * from detection.utils.preprocess import * from model.network import DecodePredictions from model.get_model import backbone, models config = parse_configs() def get_demo_data(): url = "https://github.com/srihari-humbarwadi/datasets/releases/download/v0.1.0/data.zip" filename = os.path.join(config.root_dir, "data_demo", "data.zip") tf.keras.utils.get_file(filename, url) with zipfile.ZipFile(filename, "r") as z_fp: z_fp.extractall(os.path.join(config.root_dir,"data_demo/")) def demo(): get_demo_data() model = models[config.Arch](config.num_classes, backbone[config.backbone]) # fine_tune_checkpoint_type ckpt = tf.train.Checkpoint(model) ckpt.restore(tf.train.latest_checkpoint(config.weight)).expect_partial() # Prepare image for demo val_dataset, dataset_info = tfds.load("coco/2017", split="validation", with_info=True, data_dir=os.path.join(config.root_dir,"data_demo/data"), download=False) int2str = dataset_info.features["objects"]["label"].int2str for sample in val_dataset.take(2): image = tf.cast(sample["image"], dtype=tf.float32) input_image, ratio_short, ratio_long = prepare_image(image) # Inference predictions = model(input_image) detections = DecodePredictions(confidence_threshold=0.5)(input_image, predictions) num_detections = detections.valid_detections[0] class_names = [int2str(int(x)) for x in detections.nmsed_classes[0][:num_detections]] visualize_detections(image, detections.nmsed_boxes[0][:num_detections].numpy(), class_names, detections.nmsed_scores[0][:num_detections].numpy(), ratio_short, ratio_long ) if __name__ == "__main__": demo()

39.444444

99

0.656338

258

2,130

5.193798

0.426357

0.023881

0.022388

0.035821

0.075373

0.052239

0

0.011714

0.238498

2,130

54

100

39.444444

0.814427

0.02723

0

0.023256

0.080232

0

1

0.046512

false

0

0.232558

0

0.27907

0

null

0

null

0

1

0

bd91ba13b85667cd92a61109fae15aca6df3f93b

3,561

py

Python

LoveDA/uda/baseline_train.py

edornd/ProDA

ffb092afbbde95e4ca29cb1ec199f9685f6601fb

[ "MIT" ]

null

LoveDA/uda/baseline_train.py

edornd/ProDA

ffb092afbbde95e4ca29cb1ec199f9685f6601fb

[ "MIT" ]

null

LoveDA/uda/baseline_train.py

edornd/ProDA

ffb092afbbde95e4ca29cb1ec199f9685f6601fb

[ "MIT" ]

null

import argparse import os import os.path as osp import torch import torch.nn as nn import torch.optim as optim from eval import evaluate from ever.core.iterator import Iterator from module.deeplabv2 import Deeplab from torch.nn import functional as fn from tqdm import tqdm from data.loveda import LoveDALoader from utils.tools import ( adjust_learning_rate, count_model_parameters, get_console_file_logger, import_config, loss_calc, seed_torch, ) parser = argparse.ArgumentParser(description='Run Baseline methods.') parser.add_argument('--config_path', type=str, help='config path') args = parser.parse_args() cfg = import_config(args.config_path) def main(): """Create the model and start the training.""" os.makedirs(cfg.SNAPSHOT_DIR, exist_ok=True) logger = get_console_file_logger(name='Deeplabv2', logdir=cfg.SNAPSHOT_DIR) # Create Network model = Deeplab(nn.BatchNorm2d, num_classes=7) # model = Deeplabv2( # dict( # backbone=dict( # resnet_type='resnet50', # output_stride=16, # pretrained=True, # ), # multi_layer=False, # cascade=False, # use_ppm=False, # ppm=dict( # num_classes=7, # use_aux=False, # norm_layer=nn.BatchNorm2d, # ), # inchannels=2048, # num_classes=7)) model.train() model.cuda() #cudnn.enabled = True #cudnn.benchmark = True logger.info('exp = %s' % cfg.SNAPSHOT_DIR) count_model_parameters(model, logger) trainloader = LoveDALoader(cfg.SOURCE_DATA_CONFIG) epochs = cfg.NUM_STEPS_STOP / len(trainloader) logger.info('epochs ~= %.3f' % epochs) trainloader_iter = Iterator(trainloader) optimizer = optim.SGD( model.parameters(), lr=cfg.LEARNING_RATE, momentum=cfg.MOMENTUM, weight_decay=cfg.WEIGHT_DECAY) # model, optimizer = amp.initialize(model, optimizer, opt_level="O1") optimizer.zero_grad() for i_iter in tqdm(range(cfg.NUM_STEPS_STOP)): optimizer.zero_grad() lr = adjust_learning_rate(optimizer, i_iter, cfg) # Train with Source batch = trainloader_iter.next() images_s, labels_s = batch[0] pred_source = model(images_s.cuda()) # pred_source is a dict with features and actual output pred_source = pred_source["out"] pred_source = fn.interpolate(pred_source, labels_s["cls"].size()[1:], mode="bilinear", align_corners=True) #Segmentation Loss loss = loss_calc(pred_source, labels_s['cls'].cuda()) loss.backward() optimizer.step() if i_iter % 50 == 0: logger.info('exp = {}'.format(cfg.SNAPSHOT_DIR)) text = 'iter = %d, loss_seg = %.3f, lr = %.3f' % (i_iter, loss, lr) logger.info(text) if i_iter >= cfg.NUM_STEPS_STOP - 1: print('save model ...') ckpt_path = osp.join(cfg.SNAPSHOT_DIR, cfg.TARGET_SET + str(cfg.NUM_STEPS_STOP) + '.pth') torch.save(model.state_dict(), ckpt_path) evaluate(model, cfg, True, ckpt_path, logger) break if i_iter % cfg.EVAL_EVERY == 0 and i_iter != 0: ckpt_path = osp.join(cfg.SNAPSHOT_DIR, cfg.TARGET_SET + str(i_iter) + '.pth') torch.save(model.state_dict(), ckpt_path) evaluate(model, cfg, True, ckpt_path, logger) model.train() if __name__ == '__main__': seed_torch(2333) main()

33.914286

114

0.627352

450

3,561

4.744444

0.366667

0.018735

0.039344

0.028103

0.123653

0.104918

0

0.012126

0.258916

3,561

104

115

34.240385

0.796893

0.184499

0

0.117647

0

0.058435

0

1

0.014706

false

0

0.220588

0

0.235294

0.014706

0

null

0

null

0

1

0

bd9906411d0902be3e6498e5a1a56da473448736

2,161

py

Python

tests/tensorflow/pruning/test_flops_pruning.py

sarthakpati/nncf

29ad62c664c1dd53b3c8c50fc001a1b36bd1e8ac

[ "Apache-2.0" ]

1

2021-12-30T05:49:10.000Z

tests/tensorflow/pruning/test_flops_pruning.py

sarthakpati/nncf

29ad62c664c1dd53b3c8c50fc001a1b36bd1e8ac

[ "Apache-2.0" ]

1

2021-07-23T07:46:52.000Z

tests/tensorflow/pruning/test_flops_pruning.py

sarthakpati/nncf

29ad62c664c1dd53b3c8c50fc001a1b36bd1e8ac

[ "Apache-2.0" ]

null

""" Copyright (c) 2021 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import pytest from tests.tensorflow.helpers import create_compressed_model_and_algo_for_test from tests.tensorflow.pruning.helpers import get_basic_pruning_config from tests.tensorflow.pruning.helpers import get_test_model_shared_convs @pytest.mark.parametrize( ("model", "all_weights", "ref_full_flops", "ref_current_flops", "ref_full_params", "ref_current_params"), ( (get_test_model_shared_convs, True, 461438976, 276385312, 11534848, 6908711), (get_test_model_shared_convs, False, 461438976, 270498816, 11534848, 6761608) ) ) def test_flops_calulation_for_spec_layers(model, all_weights, ref_full_flops, ref_current_flops, ref_full_params, ref_current_params): config = get_basic_pruning_config(8) config['compression']['algorithm'] = 'filter_pruning' config['compression']['pruning_init'] = 0.4 config['compression']['params']['pruning_flops_target'] = 0.4 config['compression']['params']['prune_first_conv'] = True config['compression']['params']['prune_last_conv'] = True config['compression']['params']['all_weights'] = all_weights input_shape = [1, 8, 8, 1] model = model(input_shape) model.compile() _, compression_ctrl = create_compressed_model_and_algo_for_test(model, config) assert compression_ctrl.full_flops == ref_full_flops assert compression_ctrl.full_params_num == ref_full_params assert compression_ctrl.current_flops == ref_current_flops assert compression_ctrl.current_params_num == ref_current_params

44.102041

96

0.745951

289

2,161

5.283737

0.415225

0.039293

0.060249

0.035363

0.310413

0.193844

0.092993

0

0.045907

0.16335

2,161

48

97

45.020833

0.798673

0.258677

0

0.168561

0

0.129032

1

0.032258

false

0

0.129032

0

0.16129

0

null

0

null

0

1

0

bd99e09ac29cee3774d16f59b6db9c324075e704

9,812

py

Python

HCI/emotions.py

shinkansan/2019-UGRP-DPoom

eedee93b47e068f22bf420140d869a43f7551876

[ "Apache-2.0" ]

33

2020-07-16T06:31:38.000Z

2022-03-23T18:34:58.000Z

HCI/emotions.py

shinkansan/2019-UGRP-DPoom

eedee93b47e068f22bf420140d869a43f7551876

[ "Apache-2.0" ]

5

2020-08-27T08:06:21.000Z

2022-02-23T12:34:09.000Z

HCI/emotions.py

shinkansan/2019-UGRP-DPoom

eedee93b47e068f22bf420140d869a43f7551876

[ "Apache-2.0" ]

10

2020-08-05T15:05:58.000Z

2021-11-19T10:20:44.000Z

""" Dpoom Face Expression Windows 2019 """ from PyQt5.QtCore import * from PyQt5.QtWidgets import * from PyQt5.QtGui import * from PyQt5.QtWebEngineWidgets import * from PyQt5.QtPrintSupport import * import fall_body_1013 as fall_body import os import sys import numpy as np import argparse import imutils import time import cv2 import os import pyrealsense2 as rs import threading import matplotlib.pyplot as plt import uuid import queue specificSet = [ '/Users/shinkansan/anaconda3/envs/HyunSoo/lib/python36.zip', '/Users/shinkansan/anaconda3/envs/HyunSoo/lib/python3.6', '/Users/shinkansan/anaconda3/envs/HyunSoo/lib/python3.6/lib-dynload', '/Users/shinkansan/anaconda3/envs/HyunSoo/lib/python3.6/site-packages'] #sys.path = specificSet MainIndex = "file:///home/dpoom2/dpoom_few/index.html" class AboutDialog(QDialog): def __init__(self, *args, **kwargs): super(AboutDialog, self).__init__(*args, **kwargs) QBtn = QDialogButtonBox.Ok # No cancel self.buttonBox = QDialogButtonBox(QBtn) self.buttonBox.accepted.connect(self.accept) self.buttonBox.rejected.connect(self.reject) layout = QVBoxLayout() title = QLabel("DPoom FEW") font = title.font() font.setPointSize(20) title.setFont(font) layout.addWidget(title) layout.addWidget(QLabel("Version 1")) layout.addWidget(QLabel("Copyright TEAM DPOOM.")) for i in range(0, layout.count()): layout.itemAt(i).setAlignment(Qt.AlignHCenter) layout.addWidget(self.buttonBox) self.setLayout(layout) class MainWindow(QMainWindow): thread_signal = pyqtSignal() send_instances_signal = pyqtSignal("PyQt_PyObject") def __init__(self, *args, **kwargs): super(MainWindow, self).__init__(*args, **kwargs) self.status_emeregency = False self.browser = QWebEngineView() self.browser.setUrl(QUrl(MainIndex)) self.browser.urlChanged.connect(self.update_urlbar) self.browser.loadFinished.connect(self.update_title) self.browser.loadFinished.connect(self.setDefaultExpr) self.setCentralWidget(self.browser) self.status = QStatusBar() self.setStatusBar(self.status) navtb = QToolBar("Navigation") navtb.setIconSize(QSize(16, 16)) #self.addToolBar(navtb) back_btn = QAction("Back", self) back_btn.setStatusTip("Back to previous page") back_btn.triggered.connect(self.browser.back) navtb.addAction(back_btn) next_btn = QAction(QIcon(os.path.join('images', 'arrow-000.png')), "Forward", self) next_btn.setStatusTip("Forward to next page") next_btn.triggered.connect(self.browser.forward) navtb.addAction(next_btn) reload_btn = QAction(QIcon(os.path.join('images', 'arrow-circle-315.png')), "Reload", self) reload_btn.setStatusTip("Reload page") reload_btn.triggered.connect(self.browser.reload) navtb.addAction(reload_btn) home_btn = QAction(QIcon(os.path.join('images', 'home.png')), "Home", self) home_btn.setStatusTip("Go home") home_btn.triggered.connect(self.navigate_home) navtb.addAction(home_btn) navtb.addSeparator() self.urlbar = QLineEdit() self.urlbar.returnPressed.connect(self.navigate_to_url) navtb.addWidget(self.urlbar) stop_btn = QAction( "Stop", self) stop_btn.setStatusTip("Stop loading current page") stop_btn.triggered.connect(self.browser.stop) navtb.addAction(stop_btn) # Uncomment to disable native menubar on Mac # self.menuBar().setNativeMenuBar(False) file_menu = self.menuBar().addMenu("&File") open_file_action = QAction( "Open file...", self) open_file_action.setStatusTip("Open from file") open_file_action.triggered.connect(self.open_file) file_menu.addAction(open_file_action) # save_file_action = QAction(QIcon(os.path.join('images', 'disk--pencil.png')), "Save Page As...", self) # save_file_action.setStatusTip("Save current page to file") # save_file_action.triggered.connect(self.save_file) # file_menu.addAction(save_file_action) # print_action = QAction(QIcon(os.path.join('images', 'printer.png')), "Print...", self) # print_action.setStatusTip("Print current page") # print_action.triggered.connect(self.print_page) #file_menu.addAction(print_action) about_action = QAction("Specif Setting", self) about_action.setStatusTip("detail") # Hungry! about_action.triggered.connect(self.about) file_menu.addAction(about_action) navigate_mozarella_action = QAction("Go Homepage", self) navigate_mozarella_action.setStatusTip("Go to Dpoom home") navigate_mozarella_action.triggered.connect(self.navigate_mozarella) file_menu.addAction(navigate_mozarella_action) self.showFullScreen() self.show() self.th = Worker(parent=self) self.th.start() self.th2 = YoloWorker(parent=self) self.th2.start() self.setWindowIcon(QIcon(os.path.join('images', 'ma-icon-64.png'))) def setDefaultExpr(self): self.browser.page().runJavaScript("eyes.startBlinking()") print('set default expr') def setExpr(self, classN): emoClass = { 0:"eyes.startBlinking()", 1:"eyes.stopBlinking()", 2:"eyes.blink()", 3:"eyes.express({type: 'happy'})", 4:"eyes.express({type: 'sad'})", 5:"eyes.express({type: 'angry'})", 6:"eyes.express({type: 'focused'})", 7:"eyes.express({type: 'confused'})" } self.browser.page().runJavaScript(emoClass.get(classN)) pass def declareEmergency(self): self.status_emeregency = not self.status_emeregency if self.status_emeregency: self.browser.page().runJavaScript('clearInterval(light)') self.browser.page().runJavaScript('var light = setInterval("lightning()",360);') else: self.browser.page().runJavaScript('clearInterval(light)') self.browser.page().runJavaScript('var light = setInterval("getBackwhite()",360);') def update_title(self): title = self.browser.page().title() self.setWindowTitle("Dpoom FEW") def navigate_mozarella(self): self.browser.setUrl(MainIndex) def about(self): dlg = AboutDialog() dlg.exec_() def open_file(self): filename, _ = QFileDialog.getOpenFileName(self, "Open file", "", "Hypertext Markup Language (*.htm *.html);;" "All files (*.*)") if filename: with open(filename, 'r') as f: html = f.read() self.browser.setHtml(html) self.urlbar.setText(filename) def save_file(self): filename, _ = QFileDialog.getSaveFileName(self, "Save Page As", "", "Hypertext Markup Language (*.htm *html);;" "All files (*.*)") if filename: html = self.browser.page().toHtml() with open(filename, 'w') as f: f.write(html) def print_page(self): dlg = QPrintPreviewDialog() dlg.paintRequested.connect(self.browser.print_) dlg.exec_() def navigate_home(self): self.browser.setUrl(QUrl("")) def navigate_to_url(self): # Does not receive the Url q = QUrl(self.urlbar.text()) if q.scheme() == "": q.setScheme("http") self.browser.setUrl(q) def update_urlbar(self, q): if q.scheme() == 'https': # Secure padlock icon pass else: # Insecure padlock icon pass #self.urlbar.setText(q.toString()) #self.urlbar.setCursorPosition(0) class Worker(QThread): #sec_changed = pyqtSignal(str) def __init__(self, sec=0, parent=None): super(Worker, self).__init__() self.main = parent self.working = True self.sec = sec # self.main.add_sec_signal.connect(self.add_sec) # custom signal from main thread to worker thread def __del__(self): print(".... end thread.....") self.wait() def defaultAction(self): while(True): if fall_body.fallFlag: print("fall body detected!!!!!!!") elif fall_body.humanFlag: print("human detected !!!") ###cascade_1013 emoNumber= int(np.random.uniform(3, 8)) try: emoNumber = int(emoNumber) except: pass #window.about() else: window.setExpr(int(emoNumber)) time.sleep(3) print('active') def run(self): self.defaultAction(); class YoloWorker(QThread): def __init__(self, parent=None): super(YoloWorker, self).__init__() self.main = parent self.working = True def __del__(self): print('yolo thread dead') self.wait() def yolo_main(self): print('yolo thread working') if self.working: self.working = not self.working fall_body.main(verbose=0) def run(self): self.yolo_main() app = QApplication(sys.argv) app.setApplicationName("Dpoom FEW") app.setOrganizationName("Dpoom FEW") app.setOrganizationDomain("github.com/shinkansan") window = MainWindow() app.exec_()

30.190769

112

0.614248

1,078

9,812

5.457328

0.288497

0.043005

0.033996

0.015298

0.174061

0.128336

0.113038

0.096209

0.047935

0.031617

0

0.009662

0.261618

9,812

324

113

30.283951

0.802346

0.094884

0

0.134259

0

0.143987

0.04193

0

1

0.101852

false

0.018519

0.087963

0

0.217593

0.041667

0

null

0

null

0

1

0

bd9a2a22106cfdf6f802047a7e11b687baf754c4

4,522

py

Python

datasets/pasval_voc_writer.py

jiabaocui/SEGS

c03d3bcb6fdcc4e6e6e13767bed8eae754beb726

[ "MIT" ]

null

datasets/pasval_voc_writer.py

jiabaocui/SEGS

c03d3bcb6fdcc4e6e6e13767bed8eae754beb726

[ "MIT" ]

null

datasets/pasval_voc_writer.py

jiabaocui/SEGS

c03d3bcb6fdcc4e6e6e13767bed8eae754beb726

[ "MIT" ]

null

import os import random import xml.etree.ElementTree as ET import tensorflow as tf def int64_feature(value): """Wrapper for inserting int64 features into Example proto. """ if not isinstance(value, list): value = [value] return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) def float_feature(value): """Wrapper for inserting float features into Example proto. """ if not isinstance(value, list): value = [value] return tf.train.Feature(float_list=tf.train.FloatList(value=value)) def bytes_feature(value): """Wrapper for inserting bytes features into Example proto. """ if not isinstance(value, list): value = [value] return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) DEFUALT_PATHS = { 'images': '/mnt/disk/chenyifeng/VOC2012/JPEGImages', 'annotations': '/mnt/disk/chenyifeng/VOC2012/Annotations', 'segmentations': '/mnt/disk/chenyifeng/VOC2012/SegmentationClassAug' } class PascalVocWriter: """ PASCAL VOC 2012 DataSet to TF record Writer """ def __init__(self, paths=DEFUALT_PATHS): self.img_path = paths['images'] self.ano_path = paths['annotations'] self.sgm_path = paths['segmentations'] def convert_to_example(self, file_name): img_path = os.path.join(self.img_path, file_name + '.jpg') ano_path = os.path.join(self.ano_path, file_name + '.xml') sgm_path = os.path.join(self.sgm_path, file_name + '.png') img_data = tf.gfile.FastGFile(img_path, 'rb').read() sgm_data = tf.gfile.FastGFile(sgm_path, 'rb').read() # img_data = imread(img_path).tostring() # sgm_data = imread(sgm_path).tostring() anno_tree = ET.parse(ano_path) anno_root = anno_tree.getroot() # is_sgmt = int(anno_root.find('segmented').text) # if is_sgmt == 0: # print('{} is not a Segmentation Sample. So Skipped'.format(file_name)) size = anno_root.find('size') shape = [int(size.find('height').text), int(size.find('width').text), int(size.find('depth').text)] image_format = b'JPEG' segment_format = b'PNG' example = tf.train.Example( features=tf.train.Features( feature={ 'image/name':bytes_feature(file_name.encode()), 'image/height': int64_feature(shape[0]), 'image/width': int64_feature(shape[1]), 'image/channels': int64_feature(shape[2]), 'image/shape': int64_feature(shape), 'image/format': bytes_feature(image_format), 'image/encoded': bytes_feature(img_data), 'label/format': bytes_feature(segment_format), 'label/encoded': bytes_feature(sgm_data) } ) ) return example def add_to_record(self, file_name, tfrecord_writer): example = self.convert_to_example(file_name) tfrecord_writer.write(example.SerializeToString()) def run(self, pic_names, output_dir, shuffling=False, size=300): if shuffling: random.seed(1314) random.shuffle(pic_names) total_num = len(pic_names) for start in range(0, total_num, size): tf_filename = '%s/%03d.tfrecord' % (output_dir, start // size) tf_recorder = tf.python_io.TFRecordWriter(tf_filename) print('=>' * (start * 5 // total_num) + '{:.0f}% Finished'.format(start / total_num * 100)) for pic_idx in range(start, min(start + 300, total_num)): pic_name = pic_names[pic_idx] self.add_to_record(pic_name, tf_recorder) print('=>' * 5 + '{:.0f}% Finished'.format(100)) def convert_val(): writer = PascalVocWriter() pic_names = open('/mnt/disk/chenyifeng/VOC2012/ImageSets/Segmentation/val.txt').readlines() pic_names = [i.strip(' \n') for i in pic_names] writer.run(pic_names, output_dir='/mnt/disk/chenyifeng/VOC2012/tf_segments/tf_records/val') def convert_train(): writer = PascalVocWriter() pic_names = open('/mnt/disk/chenyifeng/VOC2012/ImageSets/Segmentation/train.txt').readlines() pic_names = [i.strip(' \n') for i in pic_names] writer.run(pic_names, output_dir='/mnt/disk/chenyifeng/VOC2012/tf_segments/tf_records/train') if __name__ == '__main__': # convert_train() convert_val()

34.784615

103

0.623176

558

4,522

4.842294

0.268817

0.035529

0.044041

0.062176

0.280163

0.225759

0

0.021981

0.245467

4,522

129

104

35.054264

0.76993

0.102609

0

0.119048

0

0.158537

0.089597

0

1

0.107143

false

0

0.047619

0

0.214286

0.02381

0

null

0

null

0

1

0

bd9ca71b402899675dbdc36257c37a8b3b2984d6

861

py

Python

pelican/plugins/issues/__init__.py

GlowstoneMC/glowstonemc.github.io

1d1a453251816ef90fb8aaa63a689c81aaad4848

[ "Artistic-2.0" ]

6

2016-07-30T00:44:10.000Z

2021-07-09T02:24:36.000Z

pelican/plugins/issues/__init__.py

GlowstoneMC/glowstonemc.github.io

1d1a453251816ef90fb8aaa63a689c81aaad4848

[ "Artistic-2.0" ]

16

2016-07-30T01:01:30.000Z

2021-07-09T21:33:51.000Z

pelican/plugins/issues/__init__.py

GlowstoneMC/glowstonemc.github.io

1d1a453251816ef90fb8aaa63a689c81aaad4848

[ "Artistic-2.0" ]

10

2015-01-21T19:57:43.000Z

2017-09-01T22:15:21.000Z

import itertools import re from pelican import signals ISSUE_REGEX = re.compile(r"([\s(])(#[\d]+)([\s),.])") ISSUE_URL = "https://github.com/GlowstoneMC/Glowstone/issues/{}" ISSUE_HTML = """{}<a href="{}">{}</a>{}""" def process_content(article): done_tags = set() for start, tag, end in ISSUE_REGEX.findall(article._content): if tag in done_tags: continue done_tags.add(tag) num = tag[1:] article._content = article._content.replace( "{}{}{}".format(start, tag, end), ISSUE_HTML.format(start, ISSUE_URL.format(num), tag, end), ) def get_issue_links(generator): blog = itertools.chain(generator.articles, generator.drafts) for article in blog: process_content(article) def register(): signals.article_generator_finalized.connect(get_issue_links)

24.6

70

0.637631

107

861

4.943925

0.485981

0.079395

0

0.001464

0.206736

861

34

71

25.323529

0.77306

0

0.119628

0.027875

0

1

0.130435

false

0

0.130435

0

0.26087

0

null

0

null

0

1

0

bd9d44c68b6bd8ea2a3e03df9da24659e42178bb

17,295

py

Python

athena/layers/commons.py

iou2much/athena

156dfceb0267e8c105e5d040aac017e2d8b9ad9d

[ "Apache-2.0" ]

null

athena/layers/commons.py

iou2much/athena

156dfceb0267e8c105e5d040aac017e2d8b9ad9d

[ "Apache-2.0" ]

null

athena/layers/commons.py

iou2much/athena

156dfceb0267e8c105e5d040aac017e2d8b9ad9d

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright (C) 2019 ATHENA AUTHORS; Xiangang Li # # 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. # ============================================================================== # pylint: disable=too-few-public-methods, invalid-name # pylint: disable=no-self-use, missing-function-docstring """Utils for common layers.""" import tensorflow as tf from athena.layers.functional import make_positional_encoding, collapse4d, gelu from athena.layers.functional import splice from athena.utils.misc import gated_linear_layer class PositionalEncoding(tf.keras.layers.Layer): """positional encoding can be used in transformer""" def __init__(self, d_model, max_position=800, scale=False): super().__init__() self.d_model = d_model self.scale = scale self.pos_encoding = make_positional_encoding(max_position, d_model) def call(self, x): """ call function """ seq_len = tf.shape(x)[1] if self.scale: x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32)) x += self.pos_encoding[:, :seq_len, :] return x class ScaledPositionalEncoding(PositionalEncoding): """scaled positional encoding, reference: https://arxiv.org/pdf/1809.08895.pdf""" def __init__(self, d_model, max_position=800): super().__init__(d_model, max_position, scale=False) def build(self, _): self.alpha = self.add_weight( name="alpha", initializer=tf.keras.initializers.constant(1) ) def call(self, x): seq_len = tf.shape(x)[1] x += self.alpha * self.pos_encoding[:, :seq_len, :] return x class Collapse4D(tf.keras.layers.Layer): """collapse4d can be used in cnn-lstm for speech processing reshape from [N T D C] -> [N T D*C] """ def call(self, x): return collapse4d(x) class Gelu(tf.keras.layers.Layer): """Gaussian Error Linear Unit. This is a smoother version of the RELU. Original paper: https://arxiv.org/abs/1606.08415 Args: x: float Tensor to perform activation. Returns: x: with the GELU activation applied. """ def call(self, x): return gelu(x) class TdnnLayer(tf.keras.layers.Layer): """An implementation of Tdnn Layer Args: context: a int of left and right context, or a list of context indexes, e.g. (-2, 0, 2). output_dim: the dim of the linear transform """ def __init__(self, context, output_dim, use_bias=False, **kwargs): super().__init__(**kwargs) if hasattr(context, "__iter__"): self.context_size = len(context) self.context_list = context else: self.context_size = context * 2 + 1 self.context_list = range(-context, context + 1) self.output_dim = output_dim self.linear = tf.keras.layers.Dense(output_dim, use_bias=use_bias) def call(self, x, training=None, mask=None): x = splice(x, self.context_list) x = self.linear(x, training=training, mask=mask) return x class GroupNormalization(tf.keras.layers.Layer): def __init__( self, groups: int = 2, axis: int = -1, epsilon: float = 1e-3, center: bool = True, scale: bool = True, beta_initializer = "zeros", gamma_initializer = "ones", beta_regularizer = None, gamma_regularizer = None, beta_constraint = None, gamma_constraint = None, **kwargs ): super().__init__(**kwargs) self.supports_masking = True self.groups = groups self.axis = axis self.epsilon = epsilon self.center = center self.scale = scale self.beta_initializer = tf.keras.initializers.get(beta_initializer) self.gamma_initializer = tf.keras.initializers.get(gamma_initializer) self.beta_regularizer = tf.keras.regularizers.get(beta_regularizer) self.gamma_regularizer = tf.keras.regularizers.get(gamma_regularizer) self.beta_constraint = tf.keras.constraints.get(beta_constraint) self.gamma_constraint = tf.keras.constraints.get(gamma_constraint) self._check_axis() def build(self, input_shape): self._check_if_input_shape_is_none(input_shape) self._set_number_of_groups_for_instance_norm(input_shape) self._check_size_of_dimensions(input_shape) self._create_input_spec(input_shape) self._add_gamma_weight(input_shape) self._add_beta_weight(input_shape) self.built = True super().build(input_shape) def call(self, inputs): input_shape = tf.keras.backend.int_shape(inputs) tensor_input_shape = tf.shape(inputs) reshaped_inputs, group_shape = self._reshape_into_groups( inputs, input_shape, tensor_input_shape ) normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape) outputs = tf.reshape(normalized_inputs, tensor_input_shape) return outputs def get_config(self): config = { "groups": self.groups, "axis": self.axis, "epsilon": self.epsilon, "center": self.center, "scale": self.scale, "beta_initializer": tf.keras.initializers.serialize(self.beta_initializer), "gamma_initializer": tf.keras.initializers.serialize( self.gamma_initializer ), "beta_regularizer": tf.keras.regularizers.serialize(self.beta_regularizer), "gamma_regularizer": tf.keras.regularizers.serialize( self.gamma_regularizer ), "beta_constraint": tf.keras.constraints.serialize(self.beta_constraint), "gamma_constraint": tf.keras.constraints.serialize(self.gamma_constraint), } base_config = super().get_config() return {**base_config, **config} def compute_output_shape(self, input_shape): return input_shape def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape): group_shape = [tensor_input_shape[i] for i in range(len(input_shape))] group_shape[self.axis] = input_shape[self.axis] // self.groups group_shape.insert(self.axis, self.groups) group_shape = tf.stack(group_shape) reshaped_inputs = tf.reshape(inputs, group_shape) return reshaped_inputs, group_shape def _apply_normalization(self, reshaped_inputs, input_shape): group_shape = tf.keras.backend.int_shape(reshaped_inputs) group_reduction_axes = list(range(1, len(group_shape))) axis = -2 if self.axis == -1 else self.axis - 1 group_reduction_axes.pop(axis) mean, variance = tf.nn.moments( reshaped_inputs, group_reduction_axes, keepdims=True ) gamma, beta = self._get_reshaped_weights(input_shape) normalized_inputs = tf.nn.batch_normalization( reshaped_inputs, mean=mean, variance=variance, scale=gamma, offset=beta, variance_epsilon=self.epsilon, ) return normalized_inputs def _get_reshaped_weights(self, input_shape): broadcast_shape = self._create_broadcast_shape(input_shape) gamma = None beta = None if self.scale: gamma = tf.reshape(self.gamma, broadcast_shape) if self.center: beta = tf.reshape(self.beta, broadcast_shape) return gamma, beta def _check_if_input_shape_is_none(self, input_shape): dim = input_shape[self.axis] if dim is None: raise ValueError( "Axis " + str(self.axis) + " of " "input tensor should have a defined dimension " "but the layer received an input with shape " + str(input_shape) + "." ) def _set_number_of_groups_for_instance_norm(self, input_shape): dim = input_shape[self.axis] if self.groups == -1: self.groups = dim def _check_size_of_dimensions(self, input_shape): dim = input_shape[self.axis] if dim < self.groups: raise ValueError( "Number of groups (" + str(self.groups) + ") cannot be " "more than the number of channels (" + str(dim) + ")." ) if dim % self.groups != 0: raise ValueError( "Number of groups (" + str(self.groups) + ") must be a " "multiple of the number of channels (" + str(dim) + ")." ) def _check_axis(self): if self.axis == 0: raise ValueError( "You are trying to normalize your batch axis. Do you want to " "use tf.layer.batch_normalization instead" ) def _create_input_spec(self, input_shape): dim = input_shape[self.axis] self.input_spec = tf.keras.layers.InputSpec( ndim=len(input_shape), axes={self.axis: dim} ) def _add_gamma_weight(self, input_shape): dim = input_shape[self.axis] shape = (dim,) if self.scale: self.gamma = self.add_weight( shape=shape, name="gamma", initializer=self.gamma_initializer, regularizer=self.gamma_regularizer, constraint=self.gamma_constraint, ) else: self.gamma = None def _add_beta_weight(self, input_shape): dim = input_shape[self.axis] shape = (dim,) if self.center: self.beta = self.add_weight( shape=shape, name="beta", initializer=self.beta_initializer, regularizer=self.beta_regularizer, constraint=self.beta_constraint, ) else: self.beta = None def _create_broadcast_shape(self, input_shape): broadcast_shape = [1] * len(input_shape) broadcast_shape[self.axis] = input_shape[self.axis] // self.groups broadcast_shape.insert(self.axis, self.groups) return broadcast_shape class InstanceNormalization(GroupNormalization): """Instance normalization layer. References - [Instance Normalization: The Missing Ingredient for Fast Stylization] (https://arxiv.org/abs/1607.08022) """ def __init__(self, **kwargs): kwargs["groups"] = -1 super().__init__(**kwargs) class DownSampleBlock(tf.keras.layers.Layer): """conv2d downsample block for stargan, instance norm is used because batch size is 1 """ def __init__(self, filters, kernel_size, strides): super(DownSampleBlock, self).__init__() self.conv1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding="same") self.conv2 = tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding="same") self.norm1 = InstanceNormalization(epsilon=1e-8) self.norm2 = InstanceNormalization(epsilon=1e-8) def call(self, x): h1 = self.conv1(x) h1_norm = self.norm1(h1) h1_gates = self.conv2(x) h1_gates_norm = self.norm2(h1_gates) h1_glu = gated_linear_layer(inputs=h1_norm, gates=h1_gates_norm) return h1_glu class UpSampleBlock(tf.keras.layers.Layer): """conv2d upsample block for stargan, instance norm is used because batch size is 1 """ def __init__(self, filters, kernel_size, strides): super(UpSampleBlock, self).__init__() self.conv1 = tf.keras.layers.Conv2DTranspose(filters=filters, kernel_size=kernel_size, strides=strides, padding="same") self.conv2 = tf.keras.layers.Conv2DTranspose(filters=filters, kernel_size=kernel_size, strides=strides, padding="same") self.norm1 = InstanceNormalization(epsilon=1e-8) self.norm2 = InstanceNormalization(epsilon=1e-8) def call(self, x): h1 = self.conv1(x) h1_norm = self.norm1(h1) h1_gates = self.conv2(x) h1_gates_norm = self.norm2(h1_gates) h1_glu = gated_linear_layer(inputs=h1_norm, gates=h1_gates_norm) return h1_glu class ConditionalInstanceNormalisation(tf.keras.layers.Layer): """CIN Block.""" def __init__(self, in_channel): super(ConditionalInstanceNormalisation, self).__init__() self.dim_in = in_channel self.gamma = tf.keras.layers.Dense(in_channel) self.beta = tf.keras.layers.Dense(in_channel) def call(self, x, c): u = tf.math.reduce_mean(x, axis=1, keepdims=True) var = tf.math.reduce_mean((x - u) * (x - u), axis=1, keepdims=True) std = tf.math.sqrt(var + 1e-8) gamma = self.gamma(c) gamma = tf.reshape(gamma, [-1, 1, self.dim_in]) beta = self.beta(c) beta = tf.reshape(beta, [-1, 1, self.dim_in]) h = (x - u) / std h = h * gamma + beta return h class ResidualBlock(tf.keras.layers.Layer): """Residual Block with instance normalization.""" def __init__(self, out_channel): super(ResidualBlock, self).__init__() self.conv_1 = tf.keras.layers.Conv1D(filters=out_channel, kernel_size=3, strides=1, padding="same", use_bias=False) self.cin_1 = ConditionalInstanceNormalisation(out_channel) def call(self, x, c): x = self.conv_1(x) x = self.cin_1(x, c) x = gated_linear_layer(inputs=x, gates=x) return x class Down2d_init(tf.keras.layers.Layer): def __init__(self, filters , kernel_size, stride): super(Down2d_init, self).__init__() self.c1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=stride, padding="same") def call(self, x): x1 = self.c1(x) x1 = gated_linear_layer(inputs=x1, gates=x1) return x1 class Down2d(tf.keras.layers.Layer): def __init__(self, filters , kernel_size, stride): super(Down2d, self).__init__() self.c1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=stride, padding="same") self.norm1 = InstanceNormalization(epsilon=1e-8) def call(self, x): x1 = self.c1(x) x1 = self.norm1(x1) x1 = gated_linear_layer(inputs=x1, gates=x1) return x1 class Up2d(tf.keras.layers.Layer): """docstring for Up2d.""" def __init__(self, filters, kernel_size, stride): super(Up2d, self).__init__() self.c1 = tf.keras.layers.Conv2DTranspose(filters=filters, kernel_size=kernel_size, strides=stride, padding="same") self.norm1 = InstanceNormalization(epsilon=1e-8) def call(self, x): x1 = self.c1(x) x1 = self.norm1(x1) x1 = gated_linear_layer(inputs=x1, gates=x1) return x1 class ZoneOutCell(tf.keras.layers.LSTMCell): """Wrapper for LSTM cell to create ZoneOut Cell inspired by: https://github.com/teganmaharaj/zoneout/blob/master/zoneout_tensorflow.py Published by one of 'https://arxiv.org/pdf/1606.01305.pdf' paper writers. """ def __init__(self, zoneout_rate=0., **kwargs): super().__init__(**kwargs) self.zoneout_rate = zoneout_rate self.drop_layer = tf.keras.layers.Dropout(self.zoneout_rate) def call(self, inputs, states, training=False): """Runs vanilla LSTM Cell and applies zoneout. """ # Apply vanilla LSTM outputs, new_states = super().call(inputs, states, training=training) if self.zoneout_rate == 0: return outputs, new_states # Apply zoneout h = (1 - self.zoneout_rate) * \ self.drop_layer(new_states[0] - states[0], training=training) + \ states[0] c = (1 - self.zoneout_rate) * \ self.drop_layer(new_states[1] - states[1], training=training) + \ states[1] return outputs, [h, c] def get_config(self): config = super().get_config() config['zoneout_rate'] = self.zoneout_rate return config SUPPORTED_RNNS = { "lstm": tf.keras.layers.LSTMCell, "gru": tf.keras.layers.GRUCell, "cudnnlstm": tf.keras.layers.LSTMCell, "cudnngru": tf.keras.layers.GRUCell } ACTIVATIONS = { "relu": tf.nn.relu, "relu6": tf.nn.relu6, "elu": tf.nn.elu, "selu": tf.nn.selu, "gelu": gelu, "leaky_relu": tf.nn.leaky_relu, "sigmoid": tf.nn.sigmoid, "softplus": tf.nn.softplus, "softsign": tf.nn.softsign, "tanh": tf.nn.tanh, }

34.179842

123

0.6244

2,131

17,295

4.855936

0.169404

0.030441

0.037688

0.020874

0.38017

0.310881

0.242076

0.22845

0.200812

0.179938

0

0.015778

0.263429

17,295

505

124

34.247525

0.79653

0.122637

0

0.256484

0

0.041311

0.001869

0

1

0.123919

false

0

0.011527

0.008646

0.242075

0

null

0

null

0

1

0

bd9d807e7a31d0445e4cb5f019e63d43a7bf4018

12,879

py

Python

oscar/data/mars.py

IntelLabs/OSCAR

25d1dea35727379117e11b7238b5a0d1ed19acad

[ "BSD-3-Clause" ]

13

2021-02-12T18:41:53.000Z

2022-01-14T07:17:15.000Z

oscar/data/mars.py

IntelLabs/OSCAR

25d1dea35727379117e11b7238b5a0d1ed19acad

[ "BSD-3-Clause" ]

null

oscar/data/mars.py

IntelLabs/OSCAR

25d1dea35727379117e11b7238b5a0d1ed19acad

[ "BSD-3-Clause" ]

2

2021-03-05T18:27:23.000Z

2021-03-05T23:16:09.000Z

# # Copyright (C) 2020 Intel Corporation # # SPDX-License-Identifier: BSD-3-Clause # import logging from collections import Counter import torch from torch.utils.data import DataLoader from torchvision.transforms import transforms as T from torchvision.transforms import functional as TF import pytorch_lightning as pl from sklearn.model_selection import StratifiedShuffleSplit from oscar.data.ucf101 import UCF101Dataset from oscar.data.video import ClipSampler, MiddleClipSampler from oscar.data.transforms import ExCompose, Permute, Squeeze, Unsqueeze, ExSplitLambda from MARS.dataset.preprocess_data import get_mean logger = logging.getLogger(__name__) class MARSDataModule(pl.LightningDataModule): def __init__( self, modality, frames_root, annotation_dir, fold=1, batch_size=16, num_workers=1, frame_size=112, clip_length=16, clip_step=1, mid_clip_only=False, random_resized_crop_scale=(0.5, 1.0), test_indices=None, test_size=0, random_seed=0, collate_fn=None, frame_cache_dir=None, train_file_patterns=["{:05d}.jpg", "TVL1jpg_x_{:05d}.jpg", "TVL1jpg_y_{:05d}.jpg"], test_file_patterns=["{:05d}.jpg"], ): super().__init__() assert modality in ['RGB', 'RGB_Flow', 'RGBMasked_Flow', 'RGBMasked_FlowMasked', 'RGBSeg_Flow', 'RGBSegMC_Flow', 'RGBSegSC_Flow', 'RGBKeySC_Flow'] self.modality = modality self.frames_root = frames_root self.annotation_dir = annotation_dir self.fold = fold self.batch_size = batch_size self.num_workers = num_workers self.frame_size = frame_size self.clip_length = clip_length self.clip_step = clip_step self.mid_clip_only = mid_clip_only self.random_resized_crop_scale = random_resized_crop_scale self.test_indices = test_indices self.test_size = test_size self.random_seed = random_seed self.collate_fn = collate_fn self.frame_cache_dir = frame_cache_dir self.train_file_patterns = train_file_patterns self.test_file_patterns = test_file_patterns from detectron2.data import MetadataCatalog self.palette = MetadataCatalog.get('coco_2017_val').thing_colors if 'RGBSegMC_' in self.modality: self.input_channels = len(self.palette) + 2 # COCO-things + XY elif 'RGBSegSC_' in self.modality or 'RGBKeySC_' in self.modality: self.input_channels = 1 + 2 # Mask + XY else: self.input_channels = 3 + 2 # RGB + XY @classmethod def add_argparse_args(cls, parser): group = parser.add_argument_group(cls.__name__) group.add_argument('--modality', default='RGB', type=str, choices=['RGB', 'RGB_Flow', 'RGBMasked_Flow', 'RGBMasked_FlowMasked', 'RGBSeg_Flow', 'RGBSegMC_Flow', 'RGBSegSC_Flow', 'RGBKeySC_Flow']) group.add_argument('--dataset', default='UCF101', type=str, choices=['UCF101']) group.add_argument('--only_RGB', default=False, action='store_true') group.add_argument('--batch_size', default=32, type=int) group.add_argument('--frame_dir', default=None, type=str) group.add_argument('--annotation_path', default=None, type=str) group.add_argument('--frame_mask_dir', default=None, type=str) group.add_argument('--n_workers', default=4, type=int) group.add_argument('--split', default=1, type=int, choices=[1, 2, 3]) group.add_argument('--sample_size', default=112, type=int) group.add_argument('--sample_duration', default=16, type=int) group.add_argument('--step_between_clips', default=1, type=int) group.add_argument('--random_resized_crop_scale_min', default=0.5, type=float) group.add_argument('--random_resized_crop_scale_max', default=1.0, type=float) group.add_argument('--test_size', default=0, type=int) group.add_argument('--test_index', default=None, type=int, nargs='+') group.add_argument('--random_seed', default=1, type=bool, help='Manually set random seed of sampling validation clip') group.add_argument('--mid_clip_only', default=False, type=bool) group.add_argument('--shuffle_axes', default=None, type=int, nargs='+') return parser def prepare_data(self): UCF101Dataset(self.frames_root, self.annotation_dir, self.train_file_patterns, fold=self.fold) def setup(self, stage=None): logger.info("Setting up data module for stage: %s", stage) channels_mean = torch.tensor([*get_mean('activitynet'), 127.5, 127.5]) train_channels_mean = channels_mean test_channels_mean = channels_mean[0:3] # Create robust feature transform robust_extractor = None if 'RGBMasked_' in self.modality: from oscar.defences.preprocessor.detectron2 import CachedDetectron2Preprocessor from oscar.defences.preprocessor.ablator import AblatorPyTorch dt2 = CachedDetectron2Preprocessor(self.frame_cache_dir) robust_extractor = AblatorPyTorch(channels_mean / 255, detectron2=dt2) elif 'RGBSeg_' in self.modality: from oscar.defences.preprocessor.detectron2 import CachedDetectron2Preprocessor from oscar.defences.preprocessor.paletted_semantic_segmentor import PalettedSemanticSegmentorPyTorch dt2 = CachedDetectron2Preprocessor(self.frame_cache_dir) robust_extractor = PalettedSemanticSegmentorPyTorch(channels_mean[0:3] / 255, detectron2=dt2, palette=self.palette) elif 'RGBSegMC_' in self.modality: from oscar.defences.preprocessor.detectron2 import CachedDetectron2Preprocessor from oscar.defences.preprocessor.multichannel_semantic_segmentor import MultichannelSemanticSegmentorPyTorch dt2 = CachedDetectron2Preprocessor(self.frame_cache_dir) robust_extractor = MultichannelSemanticSegmentorPyTorch(detectron2=dt2, nb_channels=len(self.palette)) train_channels_mean = 127.5 test_channels_mean = 127.5 elif 'RGBSegSC_' in self.modality or 'RGBKeySC_' in self.modality: # TODO: Create another segmentor class that is faster and selects objects relevant to UCF101 from oscar.defences.preprocessor.detectron2 import CachedDetectron2Preprocessor from oscar.defences.preprocessor.multichannel_semantic_segmentor import MultichannelSemanticSegmentorPyTorch dt2 = CachedDetectron2Preprocessor(self.frame_cache_dir) robust_extractor = MultichannelSemanticSegmentorPyTorch(detectron2=dt2, nb_channels=1) # 1 channel == person mask train_channels_mean = 127.5 test_channels_mean = 127.5 # Apply robust feature extractor to RGB channels only if not _FlowMasked if robust_extractor is not None and '_FlowMasked' not in self.modality: robust_extractor = ExSplitLambda(robust_extractor, 3, 0, dim=-1) robust_transform = ExCompose([ T.Normalize(0, 255), # [0, 255] -> [0, 1] Permute(0, 2, 3, 1), # TCHW -> THWC Unsqueeze(0), # THWC -> NTHWC robust_extractor, # Apply robust feature extractor Squeeze(0), # NTHWC -> THWC Permute(0, 3, 1, 2), # THWC -> TCHW T.Normalize(0, 1/255), # [0, 1] -> [0, 255] ]) # Train transform # FIXME: Don't load flow when modality does not specify _Flow! # FIXME: Is there a way to decouple rgb and flow datasets like we did above? # The problem is they need to be synchronized somehow. train_transform = ExCompose([ robust_transform, T.RandomResizedCrop(self.frame_size, scale=self.random_resized_crop_scale, ratio=(1., 1.)), # Crop then Resize T.RandomApply([TF.hflip, ExSplitLambda(T.Normalize(255, -1), 1, -2, dim=-1)]), # Horizontal flip and invert x-flow randomly T.Normalize(train_channels_mean, 1), # [0, 255] -> ~[-128, 128] Permute(1, 0, 2, 3), # TCHW -> CTHW ]) train_sampler = ClipSampler(self.clip_length, self.clip_step) # Test transform test_transform = ExCompose([ robust_transform, T.Resize(self.frame_size), T.CenterCrop(self.frame_size), T.Normalize(test_channels_mean, 1), # [0, 255] -> ~[-128, 128] Permute(1, 0, 2, 3), # TCHW -> CTHW ]) test_sampler = range if self.mid_clip_only: test_sampler = MiddleClipSampler(self.clip_length, self.clip_step) if stage == 'fit' or stage is None: logger.info("Loading training data...") self.train_dataset = UCF101Dataset(self.frames_root, self.annotation_dir, self.train_file_patterns, train=True, fold=self.fold, transform=train_transform, sampler=train_sampler) logger.info("train data = %d", len(self.train_dataset)) logger.info("Loading validation data...") self.val_dataset = UCF101Dataset(self.frames_root, self.annotation_dir, self.test_file_patterns, train=False, fold=self.fold, transform=test_transform, sampler=train_sampler) logger.info("val data = %d", len(self.val_dataset)) if stage == 'test' or stage is None: logger.info("Loading test data...") test_dataset = UCF101Dataset(self.frames_root, self.annotation_dir, self.test_file_patterns, train=False, fold=self.fold, transform=test_transform, sampler=test_sampler) # Select test indices... if self.test_indices is not None: logger.info("Selecting data indices: %s", self.test_indices) test_dataset = torch.utils.data.Subset(test_dataset, self.test_indices) # ...or subsample test_dataset using a stratified split of test_size elements. elif self.test_size > 0: y = test_dataset.targets if test_dataset.target_transform is not None: y_transform = [test_dataset.target_transform(y_) for y_ in y] sss = StratifiedShuffleSplit(n_splits=1, test_size=self.test_size, random_state=self.random_seed) _, indices = next(sss.split(y, y_transform)) y_selected = [y[i] for i in indices] logger.info("Stratified subsampling test dataset to %d samples: %s", self.test_size, Counter(y_selected)) test_dataset = torch.utils.data.Subset(test_dataset, indices) self.test_dataset = test_dataset logger.info("test data = %d", len(self.test_dataset)) def train_dataloader(self): return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, pin_memory=True, drop_last=True, collate_fn=self.collate_fn) def val_dataloader(self): return DataLoader(self.val_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, pin_memory=True, drop_last=True, collate_fn=self.collate_fn) def test_dataloader(self): return DataLoader(self.test_dataset, batch_size=1, # Must be 1 because we can't batch whole videos shuffle=False, num_workers=self.num_workers, pin_memory=True, drop_last=False, collate_fn=self.collate_fn)

45.508834

202

0.601444

1,412

12,879

5.255666

0.20255

0.029646

0.040965

0.031263

0.415577

0.348875

0.322733

0.291066

0.252796

0.251853

0

0.025222

0.310428

12,879

282

203

45.670213

0.810382

0.074618

0

0.25

0

0.079855

0.005217

0

0.003546

0.004545

1

0.031818

false

0

0.095455

0.013636

0.15

0

null

0

null

0

1

0

bda0ee2b60b3089f82bfd69b9afe911afcc77e80

4,454

py

Python

dolfyn/tests/test_vs_nortek.py

jklymak/dolfyn

eea98fe0021886cf654e25293c385c5c3707ff8d

[ "BSD-3-Clause" ]

null

dolfyn/tests/test_vs_nortek.py

jklymak/dolfyn

eea98fe0021886cf654e25293c385c5c3707ff8d

[ "BSD-3-Clause" ]

null

dolfyn/tests/test_vs_nortek.py

jklymak/dolfyn

eea98fe0021886cf654e25293c385c5c3707ff8d

[ "BSD-3-Clause" ]

null

from dolfyn.tests import test_read_adp as tr from dolfyn.tests import base from dolfyn.rotate.api import rotate2 from numpy.testing import assert_allclose import numpy as np import scipy.io as sio """ Testing against velocity and bottom-track velocity data in Nortek mat files exported from SignatureDeployment. inst2earth rotation fails for AHRS-equipped istruments and I don't know why - I believe it's due to an RC filter (or some such) on Nortek's side after they load in the orientation matrix from the AHRS (Check out the difference colorplots compared to non-AHRS instruments.) Using HPR- or quaterion-calc'd orientation matrices doesn't close the gap. """ def load_nortek_matfile(filename): # remember to transpose this data data = sio.loadmat(filename, struct_as_record=False, squeeze_me=True) d = data['Data'] # print(d._fieldnames) burst = 'Burst' bt = 'BottomTrack' beam = ['_VelBeam1', '_VelBeam2', '_VelBeam3', '_VelBeam4'] b5 = 'IBurst_VelBeam5' inst = ['_VelX', '_VelY', '_VelZ1', '_VelZ2'] earth = ['_VelEast', '_VelNorth', '_VelUp1', '_VelUp2'] axis = {'beam': beam, 'inst': inst, 'earth': earth} AHRS = 'Burst_AHRSRotationMatrix' # , 'IBurst_AHRSRotationMatrix'] vel = {'beam': {}, 'inst': {}, 'earth': {}} for ky in vel.keys(): for i in range(len(axis[ky])): vel[ky][i] = np.transpose(getattr(d, burst+axis[ky][i])) vel[ky] = np.stack((vel[ky][0], vel[ky][1], vel[ky][2], vel[ky][3]), axis=0) if AHRS in d._fieldnames: vel['omat'] = np.transpose(getattr(d, AHRS)) if b5 in d._fieldnames: vel['b5'] = np.transpose(getattr(d, b5)) #vel['omat5'] = getattr(d, AHRS[1]) if bt+beam[0] in d._fieldnames: vel_bt = {'beam': {}, 'inst': {}, 'earth': {}} for ky in vel_bt.keys(): for i in range(len(axis[ky])): vel_bt[ky][i] = np.transpose(getattr(d, bt+axis[ky][i])) vel_bt[ky] = np.stack((vel_bt[ky][0], vel_bt[ky][1], vel_bt[ky][2], vel_bt[ky][3]), axis=0) return vel, vel_bt else: return vel def rotate(axis): # BenchFile01.ad2cp td_sig = rotate2(tr.dat_sig, axis, inplace=False) # Sig1000_IMU.ad2cp no userdata td_sig_i = rotate2(tr.dat_sig_i, axis, inplace=False) # VelEchoBT01.ad2cp td_sig_ieb = rotate2(tr.dat_sig_ieb, axis, inplace=False) # Sig500_Echo.ad2cp td_sig_ie = rotate2(tr.dat_sig_ie, axis, inplace=False) td_sig_vel = load_nortek_matfile(base.rfnm('BenchFile01.mat')) td_sig_i_vel = load_nortek_matfile(base.rfnm('Sig1000_IMU.mat')) td_sig_ieb_vel, vel_bt = load_nortek_matfile(base.rfnm('VelEchoBT01.mat')) td_sig_ie_vel = load_nortek_matfile(base.rfnm('Sig500_Echo.mat')) nens = 100 # ARHS inst2earth orientation matrix check # Checks the 1,1 element because the nortek orientmat's shape is [9,:] as # opposed to [3,3,:] if axis == 'inst': assert_allclose(td_sig_i.orientmat[0][0].values, td_sig_i_vel['omat'][0, :nens], atol=1e-7) assert_allclose(td_sig_ieb.orientmat[0][0].values, td_sig_ieb_vel['omat'][0, :][..., :nens], atol=1e-7) # 4-beam velocity assert_allclose(td_sig.vel.values, td_sig_vel[axis][..., :nens], atol=1e-5) assert_allclose(td_sig_i.vel.values, td_sig_i_vel[axis][..., :nens], atol=5e-3) assert_allclose(td_sig_ieb.vel.values, td_sig_ieb_vel[axis][..., :nens], atol=5e-3) assert_allclose(td_sig_ie.vel.values, td_sig_ie_vel[axis][..., :nens], atol=1e-5) # 5th-beam velocity if axis == 'beam': assert_allclose(td_sig_i.vel_b5.values, td_sig_i_vel['b5'][..., :nens], atol=1e-5) assert_allclose(td_sig_ieb.vel_b5.values, td_sig_ieb_vel['b5'][..., :nens], atol=1e-5) assert_allclose(td_sig_ie.vel_b5.values, td_sig_ie_vel['b5'][..., :nens], atol=1e-5) # bottom-track assert_allclose(td_sig_ieb.vel_bt.values, vel_bt[axis][..., :nens], atol=5e-3) def test_rotate2_beam(): rotate('beam') def test_rotate2_inst(): rotate('inst') def test_rotate2_earth(): rotate('earth')

35.632

79

0.606646

642

4,454

3.990654

0.26324

0.052693

0.062451

0.074161

0.300156

0.233802

0.12178

0.088993

0.077283

0.056206

0

0.031147

0.250337

4,454

124

80

35.919355

0.736149

0.085317

0

0.051282

0

0.080245

0.006687

0

0.141026

1

0.064103

false

0

0.076923

0

0.166667

0

null

0

null

0

1

0

bda33f238048fa796ed848c9125688fdcab82f49

1,331

py

Python

backend/flaskr/formula.py

jyyang42/RobinOptionCalculator

be3f06f6ae54c7e2dd4badc258a9888e3e240a4a

[ "MIT" ]

1

2020-11-19T19:47:48.000Z

backend/flaskr/formula.py

jyyang42/RobinOptionCalculater

be3f06f6ae54c7e2dd4badc258a9888e3e240a4a

[ "MIT" ]

7

2020-06-23T07:07:10.000Z

2020-08-24T23:43:53.000Z

backend/flaskr/formula.py

jyyang42/RobinOptionCalculater

be3f06f6ae54c7e2dd4badc258a9888e3e240a4a

[ "MIT" ]

2

2020-08-25T02:45:10.000Z

2020-11-19T19:47:38.000Z

import math def get_d1(p0, X, t, sigma, Rho): # P0 stock price 62 # X exercise Price 60 # t time to expiration days/365 40 # sigma Volatility 0.32 # Rho Risk-Free Rate 0.04 # d1 = {ln(62/60) + [0.04 + 0.5 * 0.32 ^ 2] * (40/365)} / 0.32 * sqrt(40/365) a = math.log(p0/X) + (Rho + 0.5 * sigma * sigma) * (t / 365) b = sigma * math.sqrt(40/365) return a/b def get_d2(d1, sigma, t): # d1 - sigma * sqrt(t/365) return d1 - sigma * math.sqrt(t/365) def get_cumulative_standard_normal_distribution(d): return 0.5 * (1 + math.erf(d/math.sqrt(2))) def get_call(p0, Nd1, X, Krf, t, Nd2): a = p0 * Nd1 b = X / (math.pow(math.e, Krf * t/365)) return a - b * Nd2 def get_put(Vc, X, Krf, t, p0): return Vc + X / math.pow(math.e, Krf * t/365) - p0 if __name__ == "__main__": # Z = (x - µ) / sigma p0 = 62 X = 60 t = 40 sigma = 0.32 Rho = 0.04 d1 = get_d1(p0, X, t, sigma, Rho) d2 = get_d2(d1, sigma, t) Nd1 = get_cumulative_standard_normal_distribution(d1) Nd2 = get_cumulative_standard_normal_distribution(d2) Vc = get_call(p0, Nd1, X, Rho, t, Nd2) Vp = get_put(Vc, X, Rho, t, p0) print("d1:", d1) print("d2:", d2) print("Nd1:", Nd1) print("Nd2:", Nd2) print("Vc:", Vc) print("Vp:", Vp)

25.596154

81

0.552968

235

1,331

3.012766

0.251064

0.042373

0.088983

0.114407

0.34322

0.10452

0.056497

0

0.123958

0.278738

1,331

52

82

25.596154

0.613542

0.180316

0

0.025854

0

1

0.151515

false

0

0.030303

0.090909

0.333333

0.181818

0

null

0

null

0

1

0

bda34ec056c93e4918c1bc75e8154a348bc2e5e2

932

py

Python

bot/modules/magnet.py

AliAryanTech/Nyaa-Telegram-Bot

d1614ed218fd9f413d046eec61978df269b325b6

[ "MIT" ]

12

2020-12-01T04:40:37.000Z

2022-01-22T14:19:04.000Z

bot/modules/magnet.py

AliAryanTech/Nyaa-Telegram-Bot

d1614ed218fd9f413d046eec61978df269b325b6

[ "MIT" ]

null

bot/modules/magnet.py

AliAryanTech/Nyaa-Telegram-Bot

d1614ed218fd9f413d046eec61978df269b325b6

[ "MIT" ]

19

2021-02-09T19:20:59.000Z

2022-03-18T12:05:08.000Z

from .get_response import nyaa_id, sukebei_id from bot import NYAA, botname from pyrogram import Client, filters from pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup, CallbackQuery INVALID_TEXT = """ No ID found! """ @NYAA.on_message(filters.command(["magnet", f"magnet@{botname}"], prefixes = "/") & ~filters.edited) async def get_magnet(client, message): query = message.text.split(maxsplit = 2) if len(query) < 2 or len(query) > 2: await NYAA.send_message(chat_id = message.chat.id, text = INVALID_TEXT) return buttons = [ [ InlineKeyboardButton("Nyaa", f"nyaa {query[-1]}"), InlineKeyboardButton("Sukebei", f"sukebei {query[-1]}") ] ] await NYAA.send_message(chat_id = message.chat.id, text = "Where do you wanna search?", reply_markup = InlineKeyboardMarkup(buttons))

40.521739

137

0.637339

107

932

5.439252

0.457944

0.075601

0.089347

0.068729

0.147766

0

0.007102

0.244635

932

23

137

40.521739

0.819602

0

0.119649

0

1

0

false

0

0.2

0

0.25

0

null

0

null

0

1

0

bda4133fe40f05627ed065666e33a64ba888ab8f

18,060

py

Python

fhir/resources/DSTU2/implementationguide.py

cstoltze/fhir.resources

52f99738935b7313089d89daf94d73ce7d167c9d

[ "BSD-3-Clause" ]

144

2019-05-08T14:24:43.000Z

2022-03-30T02:37:11.000Z

fhir/resources/DSTU2/implementationguide.py

cstoltze/fhir.resources

52f99738935b7313089d89daf94d73ce7d167c9d

[ "BSD-3-Clause" ]

82

2019-05-13T17:43:13.000Z

2022-03-30T16:45:17.000Z

fhir/resources/DSTU2/implementationguide.py

cstoltze/fhir.resources

52f99738935b7313089d89daf94d73ce7d167c9d

[ "BSD-3-Clause" ]

48

2019-04-04T14:14:53.000Z

2022-03-30T06:07:31.000Z

# -*- coding: utf-8 -*- """ Profile: https://www.hl7.org/fhir/DSTU2/implementationguide.html Release: DSTU2 Version: 1.0.2 Revision: 7202 """ from typing import Any, Dict from typing import List as ListType from pydantic import Field, root_validator from . import backboneelement, domainresource, fhirtypes class ImplementationGuide(domainresource.DomainResource): """A set of rules about how FHIR is used. A set of rules of how a particular interoperability or standards problem is solved - typically through the use of FHIR resources. This resource is used to gather all the parts of an implementation guide into a logical whole and to publish a computable definition of all the parts. """ resource_type = Field("ImplementationGuide", const=True) binary: ListType[fhirtypes.Uri] = Field( None, alias="binary", title="List of `uri` items.", description="Image, css, script, etc..", ) contact: ListType[fhirtypes.ImplementationGuideContactType] = Field( None, alias="contact", title="Contact details for the publisher", description=( "Contact details to assist a user in finding and communicating with the" " publisher." ), ) copyright: fhirtypes.String = Field( None, alias="copyright", title="Use and/or publishing restrictions", description=( "A copyright statement relating to the implementation guide and/or its " "contents. Copyright statements are generally legal restrictions on the" " use and publishing of the implementation guide." ), ) date: fhirtypes.DateTime = Field( None, alias="date", title="Date last changed", description=( "The date (and optionally time) when the implementation guide was " "published. The date must change when the business version changes and " "it must change if the status code changes. In addition, it should " "change when the substantive content of the implementation guide " "changes." ), ) dependency: ListType[fhirtypes.ImplementationGuideDependencyType] = Field( None, alias="dependency", title="Another Implementation guide this depends on", description=( "Another implementation guide that this implementation depends on. " "Typically, an implementation guide uses value sets, profiles " "etc.defined in other implementation guides." ), ) description: fhirtypes.String = Field( None, alias="description", title="Natural language description of the implementation guide", description=( "A free text natural language description of the implementation guide " "from a consumer's perspective." ), ) experimental: fhirtypes.Boolean = Field( None, alias="experimental", title="For testing purposes, not real usage", description=( "A Boolean value to indicate that this implementation guide is authored" " for testing purposes (or education/evaluation/marketing) and is not " "intended to be used for genuine usage." ), ) fhirVersion: fhirtypes.Id = Field( None, alias="fhirVersion", title="FHIR Version this Implementation Guide targets", description=( "The version(s) of the FHIR specification that this ImplementationGuide" " targets - e.g. describes how to use. The value of this element is the" " formal version of the specification, without the revision number, " "e.g. [publication].[major].[minor], which is 4.0.1. for this version." ), ) global_fhir: ListType[fhirtypes.ImplementationGuideGlobalType] = Field( None, alias="global", title="Profiles that apply globally", description=( "A set of profiles that all resources covered by this implementation " "guide must conform to." ), ) name: fhirtypes.String = Field( ..., alias="name", title="Name for this implementation guide (computer friendly)", description=( "A natural language name identifying the implementation guide. This " "name should be usable as an identifier for the module by machine " "processing applications such as code generation." ), ) package: ListType[fhirtypes.ImplementationGuidePackageType] = Field( ..., alias="package", title="List of `ImplementationGuidePackage` items (represented as `dict` in JSON).", description="Group of resources as used in .page.package.", ) page: fhirtypes.ImplementationGuidePageType = Field( ..., alias="page", title="Type `ImplementationGuidePage` (represented as `dict` in JSON).", description="Page/Section in the Guide.", ) publisher: fhirtypes.String = Field( None, alias="publisher", title="Name of the publisher (organization or individual)", description=( "The name of the organization or individual that published the " "implementation guide." ), ) status: fhirtypes.Code = Field( ..., alias="status", title="draft | active | retired", description=( "The status of this implementation guide. Enables tracking the life-" "cycle of the content." ), # note: Enum values can be used in validation, # but use in your own responsibilities, read official FHIR documentation. enum_values=["draft", "active", "retired"], ) url: fhirtypes.Uri = Field( ..., alias="url", title=( "Canonical identifier for this implementation guide, represented as a " "URI (globally unique)" ), description=( "An absolute URI that is used to identify this implementation guide " "when it is referenced in a specification, model, design or an " "instance; also called its canonical identifier. This SHOULD be " "globally unique and SHOULD be a literal address at which at which an " "authoritative instance of this implementation guide is (or will be) " "published. This URL can be the target of a canonical reference. It " "SHALL remain the same when the implementation guide is stored on " "different servers." ), ) useContext: ListType[fhirtypes.CodeableConceptType] = Field( None, alias="useContext", title="The context that the content is intended to support", description=( "The content was developed with a focus and intent of supporting the " "contexts that are listed. These contexts may be general categories " "(gender, age, ...) or may be references to specific programs " "(insurance plans, studies, ...) and may be used to assist with " "indexing and searching for appropriate implementation guide instances." ), ) version: fhirtypes.String = Field( None, alias="version", title="Business version of the implementation guide", description=( "The identifier that is used to identify this version of the " "implementation guide when it is referenced in a specification, model, " "design or instance. This is an arbitrary value managed by the " "implementation guide author and is not expected to be globally unique." " For example, it might be a timestamp (e.g. yyyymmdd) if a managed " "version is not available. There is also no expectation that versions " "can be placed in a lexicographical sequence." ), ) class ImplementationGuideContact(backboneelement.BackboneElement): """Contact details of the publisher. Contacts to assist a user in finding and communicating with the publisher. """ resource_type = Field("ImplementationGuideContact", const=True) name: fhirtypes.String = Field( None, alias="name", title="Type `str`.", description="Name of a individual to contact.", ) telecom: ListType[fhirtypes.ContactPointType] = Field( None, alias="telecom", title="List of `ContactPoint` items (represented as `dict` in JSON).", description="Contact details for individual or publisher.", ) class ImplementationGuideDependency(backboneelement.BackboneElement): """Another Implementation guide this depends on. Another implementation guide that this implementation depends on. Typically, an implementation guide uses value sets, profiles etc.defined in other implementation guides. """ resource_type = Field("ImplementationGuideDependsOn", const=True) type: fhirtypes.Code = Field( ..., alias="type", title="Type `str`.", description="reference | inclusion.", # note: Enum values can be used in validation, # but use in your own responsibilities, read official FHIR documentation. enum_values=["reference", "inclusion"], ) uri: fhirtypes.Uri = Field( ..., alias="uri", title="Identity of the IG that this depends on", description="A canonical reference to the Implementation guide for the dependency.", ) class ImplementationGuideGlobal(backboneelement.BackboneElement): """Profiles that apply globally. A set of profiles that all resources covered by this implementation guide must conform to. """ resource_type = Field("ImplementationGuideGlobal", const=True) profile: fhirtypes.ReferenceType = Field( ..., alias="profile", title="Profile that all resources must conform to", description="A reference to the profile that all instances must conform to.", # note: Listed Resource Type(s) should be allowed as Reference. enum_reference_types=["StructureDefinition"], ) type: fhirtypes.Code = Field( ..., alias="type", title="Type this profile applies to", description="The type of resource that all instances must conform to.", ) class ImplementationGuidePackage(backboneelement.BackboneElement): """Group of resources as used in .page.package. A logical group of resources. Logical groups can be used when building pages. """ resource_type = Field("ImplementationGuidePackage", const=True) description: fhirtypes.String = Field( None, alias="description", title="Type `str`.", description="Human readable text describing the package.", ) name: fhirtypes.String = Field( ..., alias="name", title="Type `str`.", description="Name used .page.package.", ) resource: ListType[fhirtypes.ImplementationGuidePackageResourceType] = Field( ..., alias="resource", title=( "List of `ImplementationGuidePackageResource` items (represented as `dict` " "in JSON)." ), description="Resource in the implementation guide.", ) class ImplementationGuidePackageResource(backboneelement.BackboneElement): """Resource in the implementation guide. A resource that is part of the implementation guide. Conformance resources (value set, structure definition, conformance statements etc.) are obvious candidates for inclusion, but any kind of resource can be included as an example resource. """ resource_type = Field("ImplementationGuidePackageResource", const=True) acronym: fhirtypes.String = Field( None, alias="acronym", title="Type `str`.", description="Short code to identify the resource.", ) description: fhirtypes.String = Field( None, alias="description", title="Type `str`.", description="Reason why included in guide.", ) exampleFor: fhirtypes.ReferenceType = Field( None, alias="exampleFor", title=( "Type `Reference` referencing `StructureDefinition` (represented as `dict` " "in JSON)." ), description="Resource this is an example of (if applicable).", # note: Listed Resource Type(s) should be allowed as Reference. enum_reference_types=["StructureDefinition"], ) name: fhirtypes.String = Field( None, alias="name", title="Type `str`.", description="Human Name for the resource.", ) purpose: fhirtypes.Code = Field( ..., alias="purpose", title="Type `str`.", description=( "example | terminology | profile | extension | dictionary | logical." ), # note: Enum values can be used in validation, # but use in your own responsibilities, read official FHIR documentation. enum_values=[ "example", "terminology", "profile", "extension", "dictionary", "logical", ], ) sourceReference: fhirtypes.ReferenceType = Field( None, alias="sourceReference", title="Type `Reference` referencing `Resource` (represented as `dict` in JSON).", description="Location of the resource.", # Choice of Data Types. i.e timing[x] one_of_many="source", one_of_many_required=True, # note: Listed Resource Type(s) should be allowed as Reference. enum_reference_types=["Resource"], ) sourceUri: fhirtypes.Uri = Field( None, alias="sourceUri", title="Type `str`.", description="Location of the resource.", # Choice of Data Types. i.e timing[x] one_of_many="source", one_of_many_required=True, ) @root_validator(pre=True) def validate_one_of_many(cls, values: Dict[str, Any]) -> Dict[str, Any]: """https://www.hl7.org/fhir/formats.html#choice A few elements have a choice of more than one data type for their content. All such elements have a name that takes the form nnn[x]. The "nnn" part of the name is constant, and the "[x]" is replaced with the title-cased name of the type that is actually used. The table view shows each of these names explicitly. Elements that have a choice of data type cannot repeat - they must have a maximum cardinality of 1. When constructing an instance of an element with a choice of types, the authoring system must create a single element with a data type chosen from among the list of permitted data types. """ one_of_many_fields = { "source": ["sourceReference", "sourceUri"], } for prefix, fields in one_of_many_fields.items(): assert cls.__fields__[fields[0]].field_info.extra["one_of_many"] == prefix required = ( cls.__fields__[fields[0]].field_info.extra["one_of_many_required"] is True ) found = False for field in fields: if field in values and values[field] is not None: if found is True: raise ValueError( "Any of one field value is expected from " f"this list {fields}, but got multiple!" ) else: found = True if required is True and found is False: raise ValueError(f"Expect any of field value from this list {fields}.") return values class ImplementationGuidePage(backboneelement.BackboneElement): """Page/Section in the Guide. A page / section in the implementation guide. The root page is the implementation guide home page. """ resource_type = Field("ImplementationGuidePage", const=True) format: fhirtypes.Code = Field( None, alias="format", title="Type `str`.", description="Format of the page (e.g. html, markdown, etc.).", ) kind: fhirtypes.Code = Field( ..., alias="kind", title="Type `str`.", description=( "page | example | list | include | directory | dictionary | toc | resource." ), # note: Enum values can be used in validation, # but use in your own responsibilities, read official FHIR documentation. enum_values=[ "page", "example", "list", "include", "directory", "dictionary", "toc", "resource", ], ) name: fhirtypes.String = Field( ..., alias="name", title="Type `str`.", description="Short name shown for navigational assistance.", ) package: ListType[fhirtypes.String] = Field( None, alias="package", title="List of `str` items.", description="Name of package to include.", ) page: ListType[fhirtypes.ImplementationGuidePageType] = Field( None, alias="page", title=( "List of `ImplementationGuidePage` items (represented as `dict` in JSON)." ), description="Nested Pages / Sections.", ) source: fhirtypes.Uri = Field( ..., alias="source", title="Type `Uri`.", description="Where to find that page.", ) type: ListType[fhirtypes.Code] = Field( None, alias="type", title="List of `Code` items.", description="Kind of resource to include in the list.", )

34.334601

92

0.612901

1,916

18,060

5.748956

0.216597

0.060372

0.031775

0.025057

0.326464

0.271448

0.240309

0.208352

0.180209

0

0.001418

0.297176

18,060

525

93

34.4

0.866383

0.155371

0

0.347044

0

0.002571

0.416461

0.023859

0

0.002571

1

0.002571

false

0

0.010283

0

0.154242

0

null

0

null

0

1

0

bda5691ceabca9d9b32498222c4155793f52475a

2,325

py

Python

src/mnkgame.py

isihya/minimax_algorithm_MNKgame

9876c12d065422334d87bf24c6d82171c7ace89e

[ "MIT" ]

null

src/mnkgame.py

isihya/minimax_algorithm_MNKgame

9876c12d065422334d87bf24c6d82171c7ace89e

[ "MIT" ]

null

src/mnkgame.py

isihya/minimax_algorithm_MNKgame

9876c12d065422334d87bf24c6d82171c7ace89e

[ "MIT" ]

null

import numpy as np from game import Game class MNKgame(Game): """ https://en.wikipedia.org/wiki/M,n,k-game If m=3, n=3, k=3. This is TicTakToe and default """ def __init__(self, n=3, m=3, k=3, field=None): self.n = n self.m = m self.k = k self.field = field if field is None: self.field = np.zeros((n, m)) self.winner = 0 def evaluate(self, field) -> bool: # down for x in range(self.m): score = self.scan(field, (1, 0), 0, x) if score != 0: return score # right for y in range(self.n): score = self.scan(field, (0, 1), y, 0) if score != 0: return score # right down for x in range(self.m): score = self.scan(field, (1, 1), 0, x) if score != 0: return score for y in range(self.n): score = self.scan(field, (1, 1), y, 0) if score != 0: return score # right up for x in range(self.n): score = self.scan(field, (-1, 1), self.m, x) if score != 0: return score for y in range(self.m): score = self.scan(field, (-1, 1), y, 0) if score != 0: return score return 0 def scan(self, field, d, i, j) -> bool: cnt_player = 0 cnt_enemy = 0 while(self.is_in_field(i, j)): if int(field[i][j]) == 1: cnt_player += 1 if cnt_player == self.k: return 1 elif int(field[i][j]) == -1: cnt_enemy += 1 if cnt_enemy == self.k: return -1 else: cnt_player = 0 cnt_enemy = 0 i += d[0] j += d[1] return 0 def is_in_field(self, i, j): if 0 <= i and i < self.n and 0 <= j and j < self.m: return True return False def update(self, action, val): self.field[action[0]][action[1]] = val def get_actions(self, field): indexes = np.where(field == 0) if len(indexes[0]) == 0: return [] return list(zip(indexes[0], indexes[1]))

28.012048

59

0.44129

324

2,325

3.114198

0.20679

0.048563

0.065411

0.107037

0.424182

0.420218

0.349851

0.328048

0.296333

0

0.040335

0.434839

2,325

82

60

28.353659

0.727549

0.051613

0

0.333333

0

1

0.090909

false

0

0.030303

0

0.348485

0

null

0

null

0

1

0

bda5f88b1ed70dd6d4320c4922009e7031b24847

2,721

py

Python

memoit/main/forms.py

Szymon-I/Memo-IT-App

f435331c4fbd68d34a5fb1d1f6b54117bab6b864

[ "MIT" ]

null

memoit/main/forms.py

Szymon-I/Memo-IT-App

f435331c4fbd68d34a5fb1d1f6b54117bab6b864

[ "MIT" ]

14

2019-08-06T02:06:17.000Z

2022-03-11T23:49:01.000Z

memoit/main/forms.py

Szymon-I/Memo-IT-App

f435331c4fbd68d34a5fb1d1f6b54117bab6b864

[ "MIT" ]

null

from django import forms from django.contrib.auth.forms import UserCreationForm from django.contrib.auth.models import User from .choices import * from django.contrib.auth.forms import AuthenticationForm from django.core.exceptions import ObjectDoesNotExist from django.forms import ValidationError # override basic authentication form to allow logging in with email or username class EmailAuthenticationForm(AuthenticationForm): def clean_username(self): username = self.data['username'] if '@' in username: try: username = User.objects.get(email=username).username except ObjectDoesNotExist: raise ValidationError( self.error_messages['invalid_login'], code='invalid_login', params={'username': self.username_field.verbose_name}, ) return username # override basic user creation for to add required email field class NewUserForm(UserCreationForm): email = forms.EmailField(required=True) class Meta: model = User fields = ("username", "email", "password1", "password2") def save(self, commit=True): user = super(NewUserForm, self).save(commit=False) if commit: user.save() return user # form for creating basic text note class NoteForm(forms.Form): title = forms.CharField(max_length=100) content = forms.CharField( widget=forms.Textarea(attrs={'width': "100%", 'cols': "80", 'rows': "20", 'height': '100%'}), required=False) theme = forms.ChoiceField(choices=THEMES, label="Theme", initial='', widget=forms.Select(), required=True) # form for creating list note class NoteListForm(forms.Form): title = forms.CharField(max_length=100) content = forms.CharField(required=False, label="List items", widget=forms.TextInput(attrs={'placeholder': 'Add item and press Enter'})) theme = forms.ChoiceField(choices=THEMES, label="Theme", initial='', widget=forms.Select(), required=True) # form for creating picture note class NotePictureForm(forms.Form): title = forms.CharField(max_length=100) content = forms.CharField( widget=forms.Textarea(attrs={'width': "100%", 'cols': "80", 'rows': "20", 'height': '100%'}), required=False) picture = forms.ImageField() # override picture note form to show actual picture path class NotePictureFormUpdate(forms.Form): title = forms.CharField(max_length=100) content = forms.CharField( widget=forms.Textarea(attrs={'width': "100%", 'cols': "80", 'rows': "20", 'height': '100%'}), required=False) picture = forms.ImageField(required=False)

37.791667

117

0.669974

307

2,721

5.905537

0.345277

0.061776

0.030888

0.041919

0.408163

0.372863

0

0.020456

0.209482

2,721

71

118

38.323944

0.822408

0.105108

0

0.215686

0

0.091433

0

1

0.039216

false

0.019608

0.137255

0

0.607843

0

null

0

null

0

1

0

bda7487b706cb9a94241cf9262532248e9f7dfec

4,617

py

Python

tests/export_traces_test.py

galizia-lab/pyview

07bef637b0c60fae8830c1b3947e4a7bcd14bb2c

[ "BSD-3-Clause" ]

2

2021-11-07T10:17:16.000Z

2021-11-07T10:17:19.000Z

tests/export_traces_test.py

galizia-lab/pyview

07bef637b0c60fae8830c1b3947e4a7bcd14bb2c

[ "BSD-3-Clause" ]

5

2021-11-03T12:43:03.000Z

2021-12-16T10:34:52.000Z

tests/export_traces_test.py

galizia-lab/pyview

07bef637b0c60fae8830c1b3947e4a7bcd14bb2c

[ "BSD-3-Clause" ]

1

2021-09-23T15:46:26.000Z

from common import initialize_test_yml_list_measurement from view import VIEW import pathlib as pl import shutil from view.python_core.ctvs import get_all_available_ctvs from view.python_core.gdm_generation.gdm_data_classes import GDMFile class TraceExporter(object): def __init__(self): super().__init__() test_yml, self.test_animal, self.test_measu = initialize_test_yml_list_measurement() self.view = VIEW() self.view.update_flags_from_ymlfile(test_yml) def load_and_export(self, flags_to_update, file_suffix, flags_suffix): self.view.update_flags(flags_to_update) self.view.initialize_animal(self.test_animal) roi_data_dict, roi_file = self.view.get_roi_info_for_current_animal() # initialize and empty data frame to accumulate data gdm_file = GDMFile() # iterate over measurements of the animal for measu in self.view.get_measus_for_current_animal(analyze_values_to_use=(1,)): # load a measurement for the animal self.view.load_measurement_data_from_current_animal(measu) # calculate signals self.view.calculate_signals() # create glodatamix for the loaded measurement gdm_file_this_measu, _ = self.view.get_gdm_file_for_current_measurement(roi_data_dict) # accumulate gdm_file.append_from_a_gdm_file(gdm_file_this_measu) # compose output file name output_file = self.view.flags.get_gloDatamix_file_for_current_animal() output_file_path = pl.Path(output_file) test_gdm_folder =\ pl.Path(self.view.flags["STG_OdorReportPath"]) / "test_gdms" / \ f"{output_file_path.stem}{file_suffix}" if not test_gdm_folder.is_dir(): test_gdm_folder.mkdir(parents=True) test_output_file = test_gdm_folder / f"gdm{flags_suffix}{output_file_path.suffix}" # save gloDatamix file gdm_file.write_to_csv(test_output_file) def test_export_traces_rois(): """ Testing exporting traces using .roi files """ exporter = TraceExporter() coor_path = pl.Path(exporter.view.flags["STG_OdormaskPath"]) dest_roi_file = coor_path / "Fake_data.roi" for fle in coor_path.iterdir(): if fle.name.startswith("FakeData") and fle.suffix == ".roi": shutil.copy(str(fle), str(dest_roi_file)) exporter.load_and_export( flags_to_update={"RM_ROITrace": 3}, file_suffix=f"_from_roi{fle.stem.lstrip('FakeData')}", flags_suffix="_defaults" ) dest_roi_file.unlink() def test_export_traces_mask_tif(): """ Testing exporting traces using .roi.tif files """ exporter = TraceExporter() exporter.load_and_export( flags_to_update={"RM_ROITrace": 4}, file_suffix="_from_roi_tif", flags_suffix="_defaults" ) def test_export_traces_different_ctvs(): """ Testing exporting traces with different CTVs """ exporter = TraceExporter() for ctv in get_all_available_ctvs(): exporter.load_and_export( flags_to_update={"RM_ROITrace": 3, "CTV_Method": ctv}, file_suffix=f"_from_roi", flags_suffix=f"_ctv{ctv}" ) def test_export_traces_within_ROI(): """ Testing exporting traces considering the area file """ exporter = TraceExporter() exporter.load_and_export( flags_to_update={"RM_ROITrace": 3, "GDM_withinArea": True}, file_suffix="_from_roi", flags_suffix="_withinArea_True" ) def test_export_traces_chunks_only(): """ Testing exporting traces considering the area file """ exporter = TraceExporter() exporter.load_and_export( flags_to_update= { "RM_ROITrace": 3, "GDM_outputType": "chunks_only", "GDM_chunkPostStim": 2, # in seconds "GDM_chunkPreStim": 2, # in seconds }, file_suffix="_chunks_only", flags_suffix="_2secPrePostStim" ) exporter.load_and_export( flags_to_update= { "RM_ROITrace": 3, "GDM_outputType": "chunks_only", "GDM_chunkPostStim": 100, # in seconds "GDM_chunkPreStim": 100, # in seconds }, file_suffix="_chunks_only", flags_suffix="_full" ) if __name__ == '__main__': test_export_traces_rois() # test_export_traces_mask_tif() # test_export_traces_within_ROI() test_export_traces_chunks_only()

27.981818

98

0.65627

566

4,617

4.922261

0.236749

0.031587

0.051687

0.045226

0.335607

0.208543

0.179828

0

0.004638

0.252762

4,617

165

99

27.981818

0.802899

0.126922

0

0.225806

0

0.131118

0.029419

0

1

0.075269

false

0

0.064516

0

0.150538

0

null

0

null

0

1

0

bda7fb3776a5d1e908f28700d29753c520db4037

2,551

py

Python

app/entries/forms.py

singh-prashant/blog

7c4d2e2d6890d3f0b48741b1090e41a990cad1de

[ "MIT" ]

null

app/entries/forms.py

singh-prashant/blog

7c4d2e2d6890d3f0b48741b1090e41a990cad1de

[ "MIT" ]

null

app/entries/forms.py

singh-prashant/blog

7c4d2e2d6890d3f0b48741b1090e41a990cad1de

[ "MIT" ]

null

from wtforms import Form, StringField, TextAreaField,SelectField, FileField,HiddenField from wtforms.validators import DataRequired, Optional, Email, URL, Length from models import Entry, Tag class TagField(StringField): def _value(self): if self.data: #Display tags as a comma-separated list. return ', '.join([tag.name for tag in self.data]) return '' def get_tags_from_string(self, tag_string): raw_tags = tag_string.split(',') #Filter out any empty tag tag_names = [name.strip() for name in raw_tags if name.strip()] #Query the database and retrieve any tags we have already saved existing_tags = Tag.query.filter(Tag.name.in_(tag_names)) #Determine which tag names are new. new_names = set(tag_names) - set([tag.name for tag in existing_tags]) #Create a list of unsaved Tag instances for the new tags new_tags = [Tag(name=name) for name in new_names] #Return all the existing tags + all new, unsaved tags return list(existing_tags)+new_tags def process_formdata(self, valuelist): if valuelist: self.data = self.get_tags_from_string(valuelist[0]) else: self.data = [] class ImageForm(Form): file = FileField('Image File') class EntryForm(Form): title = StringField('Title', validators=[DataRequired()]) body = TextAreaField('Body', validators=[DataRequired()]) status = SelectField( 'Entry Status', choices=( (Entry.STATUS_PUBLIC,'Public'), (Entry.STATUS_DRAFT,'Draft')), coerce=int ) tags = TagField( 'Tag', description='Separate multiple tags with commas.' ) def save_entry(self, entry): self.populate_obj(entry) entry.generate_slug() return entry class CommentForm(Form): name = StringField('Name',validators=[DataRequired()]) email = StringField('Email',validators=[DataRequired(),Email()]) url = StringField('Url', validators=[Optional(), URL()]) body = TextAreaField('Comment', validators=[DataRequired(),Length(min=10, max=3000)]) entry_id = HiddenField(validators=[DataRequired()]) def validate(self): if not super(CommentForm, self).validate(): return False entry = Entry.query.filter( (Entry.status == Entry.STATUS_PUBLIC), (Entry.id == self.entry_id.data) ).first() if not entry: return False return True

31.109756

89

0.633477

301

2,551

5.265781

0.355482

0.083281

0.012618

0.016404

0.018927

0

0.003686

0.255586

2,551

82

90

31.109756

0.830964

0.104273

0

0.035714

0

0.044737

0

1

0.089286

false

0

0.053571

0

0.517857

0

null

0

null

0

1

0

bdab079e812edf7ae50cfd1cbee57eb0f820a648

5,978

py

Python

oidc/endpoints/authorize.py

didx-xyz/yoma-oidc-bridge

7e3ff6ab3ea4fed01cd7d4c113c7c3b3244356eb

[ "Apache-2.0" ]

null

oidc/endpoints/authorize.py

didx-xyz/yoma-oidc-bridge

7e3ff6ab3ea4fed01cd7d4c113c7c3b3244356eb

[ "Apache-2.0" ]

null

oidc/endpoints/authorize.py

didx-xyz/yoma-oidc-bridge

7e3ff6ab3ea4fed01cd7d4c113c7c3b3244356eb

[ "Apache-2.0" ]

null

from aca.client import ACAClient from aries_cloudcontroller.aries_controller import AriesAgentController from asgiref.sync import sync_to_async, async_to_sync from django.utils import timezone from datetime import timedelta from aca.models import PresentationFactory from oidc.utils.shortener import create_short_url from oidc.models import AuthSession, PresentationConfigurations, MappedUrl from django.conf import settings from datetime import datetime, timedelta import asyncio WEBHOOK_HOST = "https://8b1dec9d51dd.ngrok.io" WEBHOOK_PORT = 443 WEBHOOK_BASE = "https://8b1dec9d51dd.ngrok.io/webhooks/" def authorization(pres_req_conf_id: str, request_parameters: dict): aca_client = ACAClient(settings.ACA_PY_URL, settings.ACA_PY_TRANSPORT_URL) presentation_configuration = PresentationConfigurations.objects.get( id=pres_req_conf_id ) response = aca_client.create_proof_request(presentation_configuration.to_json()) print('PROOF CREATE', response) public_did = aca_client.get_public_did() print('DID', public_did) endpoint = aca_client.get_endpoint_url() print('ENDPOINT', endpoint) presentation_request = PresentationFactory.from_params( presentation_request=response.get("presentation_request"), p_id=response.get("thread_id"), verkey=[public_did.get("verkey")], endpoint=endpoint, ).to_json() print('PROOF REQUEST ', presentation_request) presentation_request_id = response["presentation_exchange_id"] session = AuthSession.objects.create( presentation_record_id=pres_req_conf_id, presentation_request_id=presentation_request_id, presentation_request=presentation_request, request_parameters=request_parameters, expired_timestamp=timezone.now() + timedelta(minutes=60), ) url, b64_presentation = create_short_url(presentation_request) mapped_url = MappedUrl.objects.create(url=url, session=session) short_url = mapped_url.get_short_url() return short_url, str(session.pk), presentation_request_id, b64_presentation @sync_to_async def getPresentationConfig(pres_req_conf_id: str): return PresentationConfigurations.objects.get( id=pres_req_conf_id ) @sync_to_async def createSession(pres_req_conf_id, presentation_request_id, presentation_request, request_parameters, url): session = AuthSession.objects.create( presentation_record_id=pres_req_conf_id, presentation_request_id=presentation_request_id, presentation_request=presentation_request, request_parameters=request_parameters, expired_timestamp= timezone.now() + timedelta(minutes=60), ) mapped_url = MappedUrl.objects.create(url=url, session=session) print(mapped_url) short_url = mapped_url.get_short_url() print(short_url) return session, mapped_url, short_url async def authorization_async(pres_req_conf_id: str, request_parameters: dict): # Based on the aca-py agent you wish to control # print('AGENT CONNECT') agent_controller = AriesAgentController(admin_url=settings.ACA_PY_URL) # print('ACAPY AGENT CONNECTED') # print('WEBHOOOKS STARTING') # await asyncio.gather(agent_controller.init_webhook_server(webhook_host=WEBHOOK_HOST, webhook_port=WEBHOOK_PORT, webhook_base=WEBHOOK_BASE)) # print('WEBHOOOKS STARTED') presentation_configuration = await getPresentationConfig(pres_req_conf_id) print('PRESENTATION CONFIG: ', presentation_configuration) # response = await agent_controller.proofs.create_request(presentation_configuration.to_json()) response = await asyncio.gather(agent_controller.proofs.create_request(presentation_configuration.to_json())) response = response[0] print('PROOF CREATE: ', response) # TODO - the current DID of the Agent is already ledgered on Stagingnet # This creates a scenario where the endpoint being fetched is wrong # Need to update the code so that new DIDs can be ledgered to stagingnet together with endpoints public_did = await asyncio.gather(agent_controller.wallet.get_public_did()) public_did = public_did[0]['result'] print('PUBLIC DID: ', public_did) endpoint = await asyncio.gather(agent_controller.ledger.get_did_endpoint(public_did['did'])) endpoint = endpoint[0]['endpoint'] print('ENDPOINT: ', endpoint) # TODO - this will wail due to no TAA accepted on ledger TAA_response = await agent_controller.ledger.get_taa() TAA = TAA_response['result']['taa_record'] TAA['mechanism'] = "service_agreement" # print(TAA) TAA_accept = await agent_controller.ledger.accept_taa(TAA) ## Will return {} if successful print(TAA_accept) await asyncio.gather(agent_controller.wallet.set_did_endpoint(public_did['did'], settings.ACA_PY_TRANSPORT_URL, 'Endpoint')) endpoint = await asyncio.gather(agent_controller.ledger.get_did_endpoint(public_did['did'])) endpoint = endpoint[0]['endpoint'] print('ENDPOINT ', endpoint) presentation_request = PresentationFactory.from_params( presentation_request=response.get("presentation_request"), p_id=response.get("thread_id"), verkey=[public_did.get("verkey")], endpoint=endpoint, ).to_json() print('PROOF REQUEST: ', presentation_request) presentation_request_id = response["presentation_exchange_id"] url, b64_presentation = create_short_url(presentation_request) print(url) session, mapped_url, short_url = await createSession(pres_req_conf_id, presentation_request_id, presentation_request, request_parameters, url) print('SESSION ', session) print('sessionpk: ', str(session.pk)) print('mapped_url: ', mapped_url) print('short_url: ', short_url) print('presx_id: ', presentation_request_id) print('b64 presx: ', b64_presentation) await agent_controller.terminate() return short_url, str(session.pk), presentation_request_id, b64_presentation

42.7

146

0.761124

732

5,978

5.915301

0.202186

0.118476

0.053349

0.030023

0.564896

0.5

0.481986

0.469053

0.40485

0.381293

0

0.006485

0.148712

5,978

139

147

43.007194

0.84437

0.119438

0

0.423077

0

0.085366

0.009146

0

0.007194

0

1

0.028846

false

0

0.105769

0.009615

0.173077

0.192308

0

null

0

null

0

1

0

bdae540df0f84f457e5404b4e6682360e4f75f83

5,163

py

Python

psana/psana/graphqt/IVSpectrum.py

ZLLentz/lcls2

3edbea556779f619944ee9b97fb33cd815a19a37

[ "BSD-3-Clause-LBNL" ]

null

psana/psana/graphqt/IVSpectrum.py

ZLLentz/lcls2

3edbea556779f619944ee9b97fb33cd815a19a37

[ "BSD-3-Clause-LBNL" ]

null

psana/psana/graphqt/IVSpectrum.py

ZLLentz/lcls2

3edbea556779f619944ee9b97fb33cd815a19a37

[ "BSD-3-Clause-LBNL" ]

null

"""Class :py:class:`IVSpectrum` is a QWidget with histogram, two axes, and color bar ==================================================================================== Usage :: # Run test: python lcls2/psana/psana/graphqt/IVSpectrum.py from psana.graphqt.IVSpectrum import IVSpectrum w = IVSpectrum() Created on 2021-06-22 by Mikhail Dubrovin """ import logging logger = logging.getLogger(__name__) from psana.graphqt.FWViewHist import FWViewHist from psana.graphqt.FWViewAxis import FWViewAxis from psana.graphqt.FWViewColorBar import FWViewColorBar import psana.graphqt.ColorTable as ct from PyQt5.QtWidgets import QWidget, QGridLayout, QPushButton, QTextEdit from PyQt5.QtCore import Qt, QRectF def test_image(): import psana.pyalgos.generic.NDArrGenerators as ag return ag.random_standard((8,12), mu=0, sigma=10) class IVSpectrum(QWidget): """QWidget for Image Viewer""" def __init__(self, **kwargs): parent = kwargs.get('parent', None) image = kwargs.get('image', test_image()) QWidget.__init__(self, parent) ctab = ct.color_table_interpolated() rs=QRectF(0, 0, 100, 1000) self.whis = FWViewHist(self, rs, origin='DR', scale_ctl='V', fgcolor='yellow', bgcolor='dark', orient='V') self.wcbar = FWViewColorBar(self, coltab=ctab, orient='V') r = self.whis.sceneRect() rscx = QRectF(r.x(), 0, r.width(), 1) rscy = QRectF(0, r.y(), 1, r.height()) self.wax = FWViewAxis(None, rscx, side='U', origin='UR', scale_ctl=True, wwidth=30, wlength=200) self.way = FWViewAxis(None, rscy, side='L', origin='DL', scale_ctl=True, wwidth=60, wlength=200) self.but_reset = QPushButton('Reset') self.edi_info = QTextEdit('Info') self.box = QGridLayout() self.box.setSpacing(0) self.box.setVerticalSpacing(0) self.box.setHorizontalSpacing(0) self.box.addWidget(self.edi_info, 0, 0, 1, 11) self.box.addWidget(self.way, 1, 10, 9, 1) self.box.addWidget(self.whis, 1, 0, 9, 10) self.box.addWidget(self.wax, 10, 0, 1, 9) self.box.addWidget(self.wcbar, 1, 9, 9, 1) self.box.addWidget(self.but_reset, 10, 9, 1, 2, alignment=Qt.AlignCenter) self.setLayout(self.box) self.set_tool_tips() self.set_style() self.connect_scene_rect_changed() self.but_reset.clicked.connect(self.on_but_reset) def connect_scene_rect_changed(self): self.whis.connect_scene_rect_changed_to(self.on_whis_scene_rect_changed) self.wax.connect_scene_rect_changed_to(self.on_wax_scene_rect_changed) self.way.connect_scene_rect_changed_to(self.on_way_scene_rect_changed) def disconnect_scene_rect_changed(self): self.whis.disconnect_scene_rect_changed_from(self.on_whis_scene_rect_changed) self.wax.disconnect_scene_rect_changed_from(self.on_wax_scene_rect_changed) self.way.disconnect_scene_rect_changed_from(self.on_way_scene_rect_changed) def on_but_reset(self): logger.debug('on_but_reset') if self.whis is not None: self.whis.reset_original_size() def on_whis_scene_rect_changed(self, r): #logger.debug('on_whis_scene_rect_changed: %s'%str(r)) self.wax.set_view(rs=QRectF(r.x(), 0, r.width(), 1)) self.way.set_view(rs=QRectF(0, r.y(), 1, r.height())) self.update_info() def on_wax_scene_rect_changed(self, r): #logger.debug('on_wax_scene_rect_changed: %s'%str(r)) rs = self.whis.scene().sceneRect() self.whis.set_view(rs=QRectF(r.x(), rs.y(), r.width(), rs.height())) def on_way_scene_rect_changed(self, r): #logger.debug('on_way_scene_rect_changed: %s'%str(r)) rs = self.whis.scene().sceneRect() self.whis.set_view(rs=QRectF(rs.x(), r.y(), rs.width(), r.height())) self.update_info() def update_info(self): r = self.whis.scene().sceneRect() self.edi_info.setText('Spectrum min: %d max: %d' % (r.y(), r.y()+r.height())) def set_tool_tips(self): self.whis.setToolTip('Spectrum') def set_style(self): self.layout().setContentsMargins(0,0,0,0) #self.but_reset.setFixedSize(60,30) self.wcbar.setFixedWidth(25) #self.edi_info.setFixedHeight(100) self.edi_info.setMaximumHeight(50) def set_pixmap_from_arr(self, arr, set_def=True): """shortcat to image""" self.whis.set_pixmap_from_arr(arr, set_def) def reset_original_size(self): """shortcat to image""" self.whis.reset_original_size() if __name__ == "__main__": import os import sys os.environ['LIBGL_ALWAYS_INDIRECT'] = '1' #export LIBGL_ALWAYS_INDIRECT=1 from PyQt5.QtWidgets import QApplication logging.basicConfig(format='[%(levelname).1s] L%(lineno)04d %(name)s : %(message)s', level=logging.DEBUG) app = QApplication(sys.argv) w = IVSpectrum() w.setGeometry(100, 50, 300, 800) w.setWindowTitle('Image with two axes') w.show() app.exec_() del w del app # EOF

32.88535

114

0.650978

723

5,163

4.432918

0.26556

0.05897

0.104836

0.062403

0.313885

0.263027

0.206864

0.127925

0.042434

0

0.026195

0.201433

5,163

156

115

33.096154

0.751152

0.131125

0

0.065217

0

0.042405

0.004712

0

1

0.141304

false

0

0.119565

0

0.282609

0

null

0

null

0

1

0

bdb6b56e79718d96881ce563456b6ed24e5bfc35

2,912

py

Python

saige/load_results.py

Nealelab/ukb_common

ee063971d48e15ea4c525d26cf6745930d7106dc

[ "MIT" ]

8

2020-03-06T12:32:44.000Z

2021-11-17T18:00:13.000Z

saige/load_results.py

Nealelab/ukb_common

ee063971d48e15ea4c525d26cf6745930d7106dc

[ "MIT" ]

1

2021-11-02T20:09:05.000Z

2021-11-03T13:10:05.000Z

saige/load_results.py

Nealelab/ukb_common

ee063971d48e15ea4c525d26cf6745930d7106dc

[ "MIT" ]

3

2020-07-27T04:14:52.000Z

2021-09-15T13:43:23.000Z

#!/usr/bin/env python3 __author__ = 'konradk' from ukb_common import * import argparse import tempfile PHENO_KEY_FIELDS = ('trait_type', 'phenocode', 'pheno_sex', 'coding', 'modifier') def main(args): hl.init(master=f'local[{args.n_threads}]', log=hl.utils.timestamp_path(os.path.join(tempfile.gettempdir(), 'load_results'), suffix='.log'), default_reference=args.reference) cases, controls = get_cases_and_controls_from_log(args.saige_run_log_format) quantitative_trait = args.trait_type in ('continuous', 'biomarkers') heritability = get_heritability_from_log(args.null_glmm_log, quantitative_trait) if args.null_glmm_log else -1.0 inv_normalized = get_inverse_normalize_status(args.null_glmm_log) if args.null_glmm_log else 'NA' saige_version = get_saige_version_from_log(args.null_glmm_log) if args.null_glmm_log else 'NA' extension = 'single.txt' if args.analysis_type == 'gene' else 'single_variant.txt' pheno_key_dict = {k: getattr(args, k) for k in PHENO_KEY_FIELDS} if args.analysis_type == 'gene': load_gene_data(args.input_dir, pheno_key_dict, args.gene_map_ht_raw_path, cases, controls, heritability, saige_version, inv_normalized, args.overwrite) load_variant_data(args.input_dir, pheno_key_dict, args.ukb_vep_ht_path, extension, cases, controls, heritability, saige_version, inv_normalized, args.overwrite, args.legacy_annotations) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--input_dir', help='Input directory', required=True) parser.add_argument('--trait_type', help='Trait type', required=True) parser.add_argument('--phenocode', help='Phenotype ID', required=True) parser.add_argument('--pheno_sex', help='Phenotype sex', default='both_sexes') parser.add_argument('--coding', help='Phenotype coding', default='') parser.add_argument('--modifier', help='Phenotype modifier', default='') parser.add_argument('--null_glmm_log', help='Path to log file from null model') parser.add_argument('--saige_run_log_format', help='Path to log file from SAIGE test with {chr} placeholder', required=True) parser.add_argument('--analysis_type', help='Analysis type', choices=('gene', 'variant'), default='gene') parser.add_argument('--reference', help='Reference genome', default='GRCh38') parser.add_argument('--gene_map_ht_raw_path', help='Path to raw gene map') parser.add_argument('--ukb_vep_ht_path', help='Path to UKB VEP data', required=True) parser.add_argument('--n_threads', help='Number of threads to run', type=int, default=8) parser.add_argument('--legacy_annotations', help='Use old annotation picking (preferred for genotype data)', action='store_true') parser.add_argument('--overwrite', help='Overwrite everything', action='store_true') args = parser.parse_args() main(args)

56

164

0.736607

404

2,912

5.007426

0.309406

0.066733

0.12605

0.044488

0.286209

0.176965

0.131488

0.099852

0.037568

0

0.002368

0.129808

2,912

52

165

56

0.795975

0.007212

0

0.260809

0.023175

0

1

0.025641

false

0

0.076923

0

0.102564

0

null

0

null

0

1

0

bdb72defd6c6f62fdddbb438cb6348c91bc60611

4,981

py

Python

app/db/crud/recipeBewertung.py

baldur132/essensfindung

e1a8106d8a1de857340229a5fe36ca6910c55b35

[ "MIT" ]

1

2022-01-29T20:33:30.000Z

app/db/crud/recipeBewertung.py

baldur132/essensfindung

e1a8106d8a1de857340229a5fe36ca6910c55b35

[ "MIT" ]

2

2022-03-08T06:41:22.000Z

2022-03-09T11:52:06.000Z

app/db/crud/recipeBewertung.py

baldur132/essensfindung

e1a8106d8a1de857340229a5fe36ca6910c55b35

[ "MIT" ]

6

2022-01-06T15:02:59.000Z

2022-02-02T08:08:56.000Z

"""All DB functions for the Bewertung table""" from typing import List from typing import Union import sqlalchemy from sqlalchemy.orm import Session from db.base import BewertungRecipe from db.base import Person from db.crud.user import get_user_by_mail from schemes import scheme_recipe from schemes import scheme_user from schemes.exceptions import DatabaseException from schemes.exceptions import DuplicateEntry from schemes.exceptions import UserNotFound from tools.my_logging import logger def get_bewertung_from_user_to_recipe( db: Session, user: scheme_user.UserBase, recipe: scheme_recipe.RecipeBase ) -> BewertungRecipe: """Return a specific bewertung from a user to only one recipe Args: db (Session): Session to the DB user (scheme_user.UserBase): Specifie the User recipe (scheme_recipe.RecipeBase): Specifie the reciepe Returns: BewertungRecipe: Return one bewertung that match the recipe - user """ return ( db.query(BewertungRecipe) .join(Person, Person.email == BewertungRecipe.person_email) .filter(Person.email == user.email) .filter(BewertungRecipe.rezept_id == recipe.id) .first() ) def get_all_user_bewertungen(db: Session, user: scheme_user.UserBase) -> Union[List[BewertungRecipe], None]: """Return all bewertugen from one to the recipes User Args: db (Session): Session to the DB user (scheme_user.UserBase): The user to select Returns: Union[List[BewertungRecipe], None] """ user: Person = get_user_by_mail(db, user.email) if user is None: return None else: return user.bewertungenRezept def create_bewertung(db: Session, assessment: scheme_recipe.RecipeBewertungCreate) -> BewertungRecipe: """Create / Add a Bewertung to the DB. Timestamp and ID will set automatic. Args: db (Session): Session to the DB assessment (scheme_recipe.RecipeBewertungCreate): Bewertung to add. This include the Person and Recipe for the mapping of the Bewertung Raises: UserNotFound: If the user does not exist DuplicateEntry: Duplicate Primary Key Returns: BewertungRecipe: Return if success """ if get_user_by_mail(db, assessment.person.email) is None: raise UserNotFound(f"User {assessment.person.email} does not exist", assessment.person.email) db_assessment = BewertungRecipe( person_email=assessment.person.email, rezept_id=assessment.recipe.id, rezept_name=assessment.name, kommentar=assessment.comment, rating=assessment.rating, ) try: db.add(db_assessment) db.commit() db.refresh(db_assessment) logger.info( "Added assessment to db... recipe id:%s\temail:%s\trating:%s\tcomment:%s", db_assessment.rezept_id, db_assessment.person_email, db_assessment.rating, db_assessment.kommentar, ) return db_assessment except sqlalchemy.exc.IntegrityError as error: raise DuplicateEntry("Assessment already exist") from error def update_assessment( db: Session, old_bewertung: scheme_recipe.RecipeBewertungCreate, new_bewertung: scheme_recipe.RecipeBewertungCreate ) -> BewertungRecipe: """Update the comment and rating of a bewertung Args: db (Session): Session to the DB old_bewertung (scheme_recipe.RecipeBewertungCreate): The old Bewertung new_bewertung (scheme_recipe.RecipeBewertungCreate): The updated Bewertung Returns: BewertungRecipe: New Bewertung from `get_bewertung_from_user_to_recipe` """ rows = ( db.query(BewertungRecipe) .filter(BewertungRecipe.person_email == old_bewertung.person.email) .filter(BewertungRecipe.rezept_id == old_bewertung.recipe.id) .update({BewertungRecipe.kommentar: new_bewertung.comment, BewertungRecipe.rating: new_bewertung.rating}) ) if rows == 0: raise DatabaseException("Can not update assessment. Does the User and the Recipe exist?") db.commit() logger.info("Updated bewertung %s - %s", old_bewertung.person.email, old_bewertung.recipe.id) return get_bewertung_from_user_to_recipe(db, new_bewertung.person, new_bewertung.recipe) def delete_bewertung(db: Session, user: scheme_user.UserBase, recipe: scheme_recipe.RecipeBase) -> int: """Delete one Bewertung Args: db (Session): Session to the db user (scheme_user.User): The owner of the Bewertung recipe (scheme_recipe.RecipeBase): The corrosponding Recipe Returns: int: Number of effected rows """ rows = ( db.query(BewertungRecipe) .filter(BewertungRecipe.person_email == user.email, BewertungRecipe.rezept_id == recipe.id) .delete() ) db.commit() logger.info("Deleted bewertung %s - %s", user.email, recipe.id) return rows

34.116438

119

0.700863

598

4,981

5.704013

0.202341

0.041923

0.024626

0.032249

0.278804

0.164761

0.147464

0.122545

0.075344

0

0.000257

0.218229

4,981

145

120

34.351724

0.875706

0.299739

0

0.131579

0

0.013158

0.076271

0.019068

0

1

0.065789

false

0

0.171053

0

0.315789

0

null

0

null

0

1

0

bdb903aa24f6b049c642dc46fbe0678bd7b992ac

35,706

py

Python

modules/tbc_mage.py

ClawDoctor/TBC_GUI_sim

ebdb40ef348f5b00b10f6323f07260f47e8aab74

[ "MIT" ]

null

modules/tbc_mage.py

ClawDoctor/TBC_GUI_sim

ebdb40ef348f5b00b10f6323f07260f47e8aab74

[ "MIT" ]

null

modules/tbc_mage.py

ClawDoctor/TBC_GUI_sim

ebdb40ef348f5b00b10f6323f07260f47e8aab74

[ "MIT" ]

null

import fns import numpy as np import sys def eprint(*args, **kwargs): print(*args, file=sys.stderr, **kwargs) from .libs import tbc_mage_backend as bck import importlib importlib.reload(bck) def read_stat_file(location, file, stats): loc = '/'.join(location.split('/')[:-1])+'/'+file with open(loc) as f: #print('stats for '+location+': '+loc) for line in f: if '#' in line: line = line.split('#')[0] sp = line.split() if len(sp)>1: if not sp[0][0] == '#': if sp[0] == 'intellect': stats['intellect'] = float(sp[1].strip()) if sp[0] == 'spirit': stats['spirit'] = float(sp[1].strip()) if sp[0] == 'common_spell_damage': stats['common_spell_damage'] = float(sp[1].strip()) if sp[0] == 'crit_rating': stats['crit_rating'] = float(sp[1].strip()) if sp[0] == 'hit_rating': stats['hit_rating'] = float(sp[1].strip()) if sp[0] == 'mp5': stats['mp5'] = float(sp[1].strip()) if sp[0] == 'fire_damage': stats['fire_damage'] = float(sp[1].strip()) if sp[0] == 'frost_damage': stats['frost_damage'] = float(sp[1].strip()) if sp[0] == 'arcane_damage': stats['arcane_damage'] = float(sp[1].strip()) if sp[0] == 'haste_rating': stats['haste_rating'] = float(sp[1].strip()) class mage_file: def __init__(self,location): self.location= location self.label = 'no label' self.stats = {} self.talents = bck.make_talents() self.burn_rot = [] self.save_rot = [] with open(location) as f: for line in f: if '#' in line: line = line.split('#')[0] sp = line.split() if len(sp)>1: if not sp[0][0] == '#': if sp[0] == 'stats_file': read_stat_file(location, sp[1], self.stats) if sp[0] == 'intellect': self.stats['intellect'] = float(sp[1].strip()) if sp[0] == 'spirit': self.stats['spirit'] = float(sp[1].strip()) if sp[0] == 'common_spell_damage': self.stats['common_spell_damage'] = float(sp[1].strip()) if sp[0] == 'crit_rating': self.stats['crit_rating'] = float(sp[1].strip()) if sp[0] == 'hit_rating': self.stats['hit_rating'] = float(sp[1].strip()) if sp[0] == 'mp5': self.stats['mp5'] = float(sp[1].strip()) if sp[0] == 'fire_damage': self.stats['fire_damage'] = float(sp[1].strip()) if sp[0] == 'frost_damage': self.stats['frost_damage'] = float(sp[1].strip()) if sp[0] == 'arcane_damage': self.stats['arcane_damage'] = float(sp[1].strip()) if sp[0] == 'haste_rating': self.stats['haste_rating'] = float(sp[1].strip()) for talent in self.talents: if sp[0] == talent: self.talents[talent] = int(sp[1].strip()) if sp[0] == 'burn_rotation:': for i in range(1,len(sp)): self.burn_rot.append(sp[i]) if sp[0] == 'save_rotation:': for i in range(1,len(sp)): self.save_rot.append(sp[i]) if sp[0] == 'label': self.label = ' '.join(sp[1:]) if sp[0] == 'color': self.color = [0,0,0,1] self.color[0] = float(sp[1]) self.color[1] = float(sp[2]) self.color[2] = float(sp[3]) def parse_rot(rot): new_rot =[] l = len(rot) for i, spell in enumerate(rot): if spell == 'fireball': pos_ign = 0 if rot[(i+1)%l] == 'fireball': pos_ign +=1 if rot[(i+2)%l] == 'fireball': pos_ign +=1 elif rot[(i+2)%l] == 'scorch' and rot[(i+3)%l] == 'scorch': pos_ign +=1 elif rot[(i+1)%l] == 'scorch' and rot[(i+2)%l] == 'scorch': pos_ign +=1 if rot[(i+3)%l] == 'fireball': pos_ign +=1 elif rot[(i+3)%l] == 'scorch' and rot[(i+4)%l] == 'scorch': pos_ign +=1 if pos_ign == 2: new_rot.append('fireball_13_one_tick') elif pos_ign == 1: new_rot.append('fireball_13_one_tick_one_roll') elif pos_ign == 0: new_rot.append('fireball_13_one_tick_no_roll') elif spell == 'scorch': pos_ign = 0 if rot[(i+1)%l] == 'fireball': pos_ign +=1 if rot[(i+2)%l] == 'fireball': pos_ign +=1 elif rot[(i+2)%l] == 'scorch' and rot[(i+3)%l] == 'scorch': pos_ign +=1 elif rot[(i+1)%l] == 'scorch' and rot[(i+2)%l] == 'scorch': pos_ign +=1 if rot[(i+3)%l] == 'fireball': pos_ign +=1 elif rot[(i+3)%l] == 'scorch' and rot[(i+4)%l] == 'scorch': pos_ign +=1 if pos_ign == 2: new_rot.append('scorch_9') elif pos_ign == 1: new_rot.append('scorch_9_one_roll') elif pos_ign == 0: new_rot.append('scorch_9_no_roll') new_rot.append('scorch_9') elif spell == 'fireblast': new_rot.append('fireblast') elif spell == 'arcane_missiles': new_rot.append('arcane_missiles_10') elif spell == 'frostbolt': new_rot.append('frostbolt_13') elif spell == 'arcane_blast_0speed_0mana': new_rot.append('arcane_blast_1_0speed_0mana') elif spell == 'arcane_blast_1speed_1mana': new_rot.append('arcane_blast_1_1speed_1mana') elif spell == 'arcane_blast_2speed_2mana': new_rot.append('arcane_blast_1_2speed_2mana') elif spell == 'arcane_blast_3speed_3mana': new_rot.append('arcane_blast_1_3speed_3mana') elif spell == 'arcane_blast_1speed_0mana': new_rot.append('arcane_blast_1_1speed_0mana') elif spell == 'arcane_blast_2speed_0mana': new_rot.append('arcane_blast_1_2speed_0mana') elif spell == 'arcane_blast_3speed_0mana': new_rot.append('arcane_blast_1_3speed_0mana') else: print('spell '+ spell+ ' not found, possible spells are:') pos_spells = ['fireball_13_one_tick', 'fireball', 'scorch', 'fireblast', #'pyroblast', #'pom_pyroblast', 'arcane_missiles', 'arcane_blast_0speed_0mana', 'arcane_blast_1speed_1mana', 'arcane_blast_2speed_2mana', 'arcane_blast_3speed_3mana', 'arcane_blast_1speed_0mana', 'arcane_blast_2speed_0mana', 'arcane_blast_3speed_0mana', 'frostbolt', ] for spell in pos_spells: print(spell) return new_rot class moduleClass: filetypes=['mage'] def __init__ (self, fig, locations, frame, ui): self.fig=fig self.frame=frame self.locations=locations self.ui=ui def run(self): if self.ui['save_check']: try: import os os.makedirs(self.ui['save_filename']) except: None ui=self.ui fig=self.fig #prepare figure fig.clear() #load mages mage_colors = [[0.5,0,1,1], [1,0.5,0,1], [0.2,0.2,1,1], [0,0,0,1], [0.5,0,1,1], [1,1,0,1], [0.2,1,1,1], [0,1,0,1], ] self.mages=[] for i, location in enumerate(self.locations): self.mages.append(mage_file(location)) if self.mages[-1].save_rot[0] == 'arcane_frost_clearcasting_optimized': None elif self.mages[-1].save_rot[0] == 'fireball_spam_clearcasting_optimized': None elif self.mages[-1].save_rot[0] == 'frostbolt_spam_clearcasting_optimized': None elif self.mages[-1].save_rot[0] == 'scorch_spam_clearcasting_optimized': None else: self.mages[-1].save_rot = parse_rot(self.mages[-1].save_rot) if self.mages[-1].burn_rot[0] == 'None': None elif self.mages[-1].burn_rot[0] == 'AB_spam_clearcasting_optimized': None else: self.mages[-1].burn_rot = parse_rot(self.mages[-1].burn_rot) if not hasattr(self.mages[-1],'color'): self.mages[-1].color = mage_colors[i%8] for key in ['disable_arcane_power', 'disable_icy_veins', 'disable_cold_snap', 'disable_water_elemental', 'disable_combustion', 'disable_PoM_pyro', 'ignore_scorch_ramp']: self.mages[-1].talents[key] = ui[key] #load buffs buff_cases = [] for i in range(5): #merge coe and cos, as in patch 2.4(?) ui['buff_case_'+str(i)+'_curse_of_shadow'] = ui['buff_case_'+str(i)+'_curse_of_elements'] buff_cases.append({}) buff_case_str = 'buff_case_'+str(i)+'_' for key in ui: if buff_case_str in key: buff = key.split(buff_case_str)[1] try: buff_cases[i][buff] = int(ui[key]) except: buff_cases[i][buff] = ui[key] if buff_cases[i]['armor'] == 'mage armor': buff_cases[i]['mage_armor'] = 1 buff_cases[i]['molten_armor'] = 0 else: buff_cases[i]['mage_armor'] = 0 buff_cases[i]['molten_armor'] = 1 #buttons.append({'key': 'buff_case_'+str(k)+'armor', 'type': 'radio:text', 'texts': ['molten armor', 'mage armor']','default': '0', 'tab': 1, 'row': i}) #buttons.append({'key': 'buff_case_'+str(k)+'_molten_armor', 'type': 'check', 'text': 'molten armor','default': '1', 'tab': 1, 'row': i}) #buttons.append({'key': 'buff_case_'+str(k)+'_mage_armor', 'type': 'check', 'text': 'mage armor','default': '0', 'tab': 1, 'row': i}) #plot measurements linestyles=['-','-.','--',(0, (3, 1, 1, 1, 1, 1)),':'] self.frame.hidden_figure.set_dpi(300) self.frame.hidden_figure.set_size_inches(6,4) #self.frame.update() #self.frame.figure. canvas.draw() if ui['plot_dmg']: ax = fns.add_axis(self.fig,2) ax.grid() misc = [] for i, buff_case in enumerate(buff_cases): linestyle = linestyles[i] if buff_case['check'] == 1: for mage in self.mages: misc = plot_dps(ui, mage, buff_case, i, linestyle, ax, misc, fractions = ui['include_rotation_fractions'], DMG = True) if ui['save_check']: misc = [] self.frame.hidden_figure.clf() tempax = self.frame.hidden_figure.add_subplot(111) tempax.grid() for i, buff_case in enumerate(buff_cases): linestyle = linestyles[i] if buff_case['check'] == 1: for mage in self.mages: misc = plot_dps(ui, mage, buff_case, i, linestyle, tempax, misc, fractions = ui['include_rotation_fractions'], DMG = True) self.frame.hidden_figure.tight_layout() #print(self.frame.tempfig) self.frame.hidden_figure.savefig(ui['save_filename']+'/dmg.svg') self.frame.hidden_figure.savefig(ui['save_filename']+'/dmg.png') #self.frame.update() #self.frame.figure.canvas.draw() if ui['plot_dps']: ax = fns.add_axis(self.fig,2) ax.grid() misc = [] for i, buff_case in enumerate(buff_cases): linestyle = linestyles[i] if buff_case['check'] == 1: for mage in self.mages: misc = plot_dps(ui, mage, buff_case, i, linestyle, ax, misc, fractions = ui['include_rotation_fractions'], DMG = False) if ui['save_check']: misc = [] self.frame.hidden_figure.clf() tempax = self.frame.hidden_figure.add_subplot(111) tempax.grid() for i, buff_case in enumerate(buff_cases): linestyle = linestyles[i] if buff_case['check'] == 1: for mage in self.mages: misc = plot_dps(ui, mage, buff_case, i, linestyle, tempax, misc, fractions = ui['include_rotation_fractions'], DMG = False) self.frame.hidden_figure.tight_layout() #print(self.frame.tempfig) self.frame.hidden_figure.savefig(ui['save_filename']+'/dps.svg') self.frame.hidden_figure.savefig(ui['save_filename']+'/dps.png') #self.frame.update() #self.frame.figure.canvas.draw() if ui['plot_compare_buff_states']: num_buff_cases = 0 for i, buff_case in enumerate(buff_cases): if buff_case['check'] == 1: num_buff_cases+=1 if num_buff_cases>1: ax = fns.add_axis(self.fig,2) plot_compare_buff_states(ui, self.mages, buff_cases, linestyles, ax) if ui['save_check']: self.frame.hidden_figure.clf() tempax = self.frame.hidden_figure.add_subplot(111) plot_compare_buff_states(ui, self.mages, buff_cases, linestyles, tempax) self.frame.hidden_figure.tight_layout() #print(self.frame.tempfig) self.frame.hidden_figure.savefig(ui['save_filename']+'/comp_buff_states.svg') self.frame.hidden_figure.savefig(ui['save_filename']+'/comp_buff_states.png') if ui['plot_compare_mages']: if hasattr(self.frame,'default_mage'): default_mage=mage_file(self.frame.default_mage) if default_mage.save_rot[0] == 'arcane_frost_clearcasting_optimized': None elif default_mage.save_rot[0] == 'fireball_spam_clearcasting_optimized': None elif default_mage.save_rot[0] == 'frostbolt_spam_clearcasting_optimized': None elif default_mage.save_rot[0] == 'scorch_spam_clearcasting_optimized': None else: default_mage.save_rot = parse_rot(default_mage.save_rot) if default_mage.burn_rot[0] == 'None': None elif default_mage.burn_rot[0] == 'AB_spam_clearcasting_optimized': None else: default_mage.burn_rot = parse_rot(default_mage.burn_rot) if not hasattr(default_mage,'color'): default_mage.color = mage_colors[i%8] for key in ['disable_arcane_power', 'disable_icy_veins', 'disable_cold_snap', 'disable_water_elemental', 'disable_combustion', 'disable_PoM_pyro', 'ignore_scorch_ramp']: default_mage.talents[key] = ui[key] ax = fns.add_axis(self.fig,2) plot_compare_mages(ui, default_mage, self.mages, buff_cases, linestyles, ax) if ui['save_check']: self.frame.hidden_figure.clf() tempax = self.frame.hidden_figure.add_subplot(111) plot_compare_mages(ui, default_mage, self.mages, buff_cases, linestyles, tempax) self.frame.hidden_figure.tight_layout() #print(self.frame.tempfig) self.frame.hidden_figure.savefig(ui['save_filename']+'/comp_mages.svg') self.frame.hidden_figure.savefig(ui['save_filename']+'/comp_mages.png') if ui['plot_spell_dps']: ax = fns.add_axis(self.fig,2) plot_spell_dps(ui, self.mages, buff_cases, linestyles, ax) if ui['save_check']: self.frame.hidden_figure.clf() tempax = self.frame.hidden_figure.add_subplot(111) plot_spell_dps(ui, self.mages, buff_cases, linestyles, tempax) self.frame.hidden_figure.tight_layout() #print(self.frame.tempfig) self.frame.hidden_figure.savefig(ui['save_filename']+'/spell_dps.svg') self.frame.hidden_figure.savefig(ui['save_filename']+'/spell_dps.png') #self.frame.figure.canvas.draw() if ui['plot_spell_dpm']: ax = fns.add_axis(self.fig,2) plot_spell_dps(ui, self.mages, buff_cases, linestyles, ax, DPM= True) if ui['save_check']: self.frame.hidden_figure.clf() tempax = self.frame.hidden_figure.add_subplot(111) plot_spell_dps(ui, self.mages, buff_cases, linestyles, tempax, DPM= True) self.frame.hidden_figure.tight_layout() #print(self.frame.tempfig) self.frame.hidden_figure.savefig(ui['save_filename']+'/spell_dpm.svg') self.frame.hidden_figure.savefig(ui['save_filename']+'/spell_dpm.png') #self.frame.figure.canvas.draw() if ui['plot_stat_weights']: ax = fns.add_axis(self.fig,2) plot_stat_weights(ui, self.mages, buff_cases, linestyles, ax) if ui['save_check']: self.frame.hidden_figure.clf() tempax = self.frame.hidden_figure.add_subplot(111) plot_stat_weights(ui, self.mages, buff_cases, linestyles, tempax) self.frame.hidden_figure.tight_layout() #print(self.frame.tempfig) self.frame.hidden_figure.savefig(ui['save_filename']+'/stat_weights.svg') self.frame.hidden_figure.savefig(ui['save_filename']+'/stat_weights.png') ''' ax.legend() #set x and ylabel ax.set_xlabel(ui['XYxlabel']) ax.set_xlim([ui['XYxmin'],ui['XYxmax']]) ax.set_ylabel(ui['XYylabel']) ''' if ui['save_check']: self.fig.savefig(ui['save_filename']+'/all.svg') self.fig.savefig(ui['save_filename']+'/all.png') fig.canvas.draw() self.frame.update() def addButtons(): buttons=[ {'key': 'mage_tab_0_name', 'type': 'tabname', 'text': 'misc', 'tab': 0} , {'key': 'mage_tab_1_name', 'type': 'tabname', 'text': 'buffs', 'tab': 1} , {'key': 'plot_dmg', 'type': 'check', 'text': 'plot_dmg','default': '1', 'tab': 0, 'row': 0}, {'key': 'plot_dps', 'type': 'check', 'text': 'plot_dps','default': '1', 'tab': 0, 'row': 0}, {'key': 'include_rotation_fractions', 'type': 'check', 'text': 'include rotation fractions','default': '0', 'tab': 0, 'row': 0}, {'key': 'plot_compare_buff_states', 'type': 'check', 'text': 'plot_compare_buff_states','default': '1', 'tab': 0, 'row': 0}, {'key': 'set_default_mage', 'type': 'click', 'text': 'set_default_mage','bind': set_default_mage, 'tab': 0, 'row': 0}, {'key': 'plot_compare_mages', 'type': 'check', 'text': 'plot_compare_mages','default': '1', 'tab': 0, 'row': 0}, #{'key': 'clear_default_mage', 'type': 'click', 'text': 'set_default_mage','bind': clear_default_mage, 'tab': 10, 'row': 0}, {'key': 'plot_spell_dps', 'type': 'check', 'text': 'plot_spell_dps','default': '0', 'tab': 0, 'row': 0}, {'key': 'plot_spell_dpm', 'type': 'check', 'text': 'plot_spell_dpm','default': '0', 'tab': 0, 'row': 0}, {'key': 'plot_stat_weights', 'type': 'check', 'text': 'plot_stat_weights','default': '0', 'tab': 0, 'row': 0}, {'key': 'time_min', 'type': 'txt:float', 'text': 'time_min', 'default': '40', 'width': 4, 'tab': 0, 'row': 1} , {'key': 'time_max', 'type': 'txt:float', 'text': 'time_max', 'default': '180', 'width': 4, 'tab': 0, 'row': 1} , {'key': 'dps_min', 'type': 'txt:float', 'text': 'dps_min', 'default': '0', 'width': 4, 'tab': 0, 'row': 2} , {'key': 'dps_max', 'type': 'txt:float', 'text': 'dps_max', 'default': '2000', 'width': 4, 'tab': 0, 'row': 2} , {'key': 'stat_weight_ymax', 'type': 'txt:int', 'text': 'stat_weight_ymax', 'default': '2', 'width': 4, 'tab': 0, 'row': 2} , {'key': 'disable_arcane_power', 'type': 'check', 'text': 'disable_arcane_power','default': '0', 'tab': 0, 'row': 3}, {'key': 'disable_icy_veins', 'type': 'check', 'text': 'disable_icy_veins','default': '0', 'tab': 0, 'row': 3}, {'key': 'disable_cold_snap', 'type': 'check', 'text': 'disable_cold_snap','default': '0', 'tab': 0, 'row': 3}, {'key': 'disable_water_elemental', 'type': 'check', 'text': 'disable_water_elemental','default': '0', 'tab': 0, 'row': 3}, {'key': 'disable_combustion', 'type': 'check', 'text': 'disable_combustion','default': '0', 'tab': 0, 'row': 3}, {'key': 'disable_PoM_pyro', 'type': 'check', 'text': 'disable_PoM_pyro','default': '0', 'tab': 0, 'row': 3}, {'key': 'ignore_scorch_ramp', 'type': 'check', 'text': 'ignore_scorch_ramp','default': '0', 'tab': 0, 'row': 3}, ] j = len(buttons) for k in range(5): i=k*2 buttons.append({'key': 'buff_case_'+str(k)+'_check', 'type': 'check', 'text': 'Buffs '+str(k),'default': '0', 'tab': 1, 'row': i}) buttons.append({'key': 'buff_case_'+str(k)+'_label', 'type': 'txt', 'text': 'label:','default': 'buffs '+str(k), 'width': 10, 'tab': 1, 'row': i}) buttons.append({'key': 'buff_case_'+str(k)+'_arcane_intellect', 'type': 'check', 'text': 'AI','default': '1', 'tab': 1, 'row': i}) buttons.append({'key': 'buff_case_'+str(k)+'_armor', 'type': 'radio:text', 'texts': ['molten armor', 'mage armor'],'default': '0', 'tab': 1, 'row': i}) #buttons.append({'key': 'buff_case_'+str(k)+'_molten_armor', 'type': 'check', 'text': 'molten armor','default': '1', 'tab': 1, 'row': i}) #buttons.append({'key': 'buff_case_'+str(k)+'_mage_armor', 'type': 'check', 'text': 'mage armor','default': '0', 'tab': 1, 'row': i}) buttons.append({'key': 'buff_case_'+str(k)+'_misc_add_mana', 'type': 'txt:float', 'text': '| misc mana (mana ruby, potions, etc)','default': '2400','width': 5, 'tab': 1, 'row': i}) buttons.append({'key': 'buff_case_'+str(k)+'_innervate', 'type': 'txt:float', 'text': '# of innervates','default': '0','width': 2, 'tab': 1, 'row': i}) buttons.append({'key': 'buff_case_'+str(k)+'_dummy_label', 'type': 'label', 'text': ' ', 'tab': 1, 'row': i+1}) #{'key': 'XYxlabel', 'type': 'txt', 'text': 'x label', 'default': r'$2\theta$', 'width': 10, 'tab': 0, 'row': 1} , #buttons.append({'key': 'buff_case_'+str(k)+'_curse_of_shadow', 'type': 'check', 'text': 'CoS','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_curse_of_elements', 'type': 'check', 'text': 'CoE','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_malediction', 'type': 'check', 'text': 'Malediction','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_divine_spirit', 'type': 'check', 'text': 'D.spirit','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_improved_divine_spirit', 'type': 'check', 'text': 'Imp.d.spirit','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_wrath_of_air_totem', 'type': 'check', 'text': 'WoA totem','default': '0', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_improved_wrath_of_air_totem', 'type': 'check', 'text': 'imp.WoA','default': '0', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_totem_of_wrath', 'type': 'check', 'text': 'totem of wrath','default': '0', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_mark_of_the_wild', 'type': 'check', 'text': 'MotW','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_improved_mark_of_the_wild', 'type': 'check', 'text': 'imp.MotW','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_blessing_of_kings', 'type': 'check', 'text': 'BoK','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_blessing_of_wisdom', 'type': 'check', 'text': 'BoW','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_judgement_of_wisdom', 'type': 'check', 'text': 'JoW','default': '1', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_shadow_priest_dps', 'type': 'txt:float', 'text': 'SP dps', 'default': '0', 'width': 4, 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_misery', 'type': 'check', 'text': 'misery','default': '0', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_2_tier5_set_bonus', 'type': 'check', 'text': '2_tier5_set_bonus','default': '0', 'tab': 1, 'row': i+1}) buttons.append({'key': 'buff_case_'+str(k)+'_spellfire_set', 'type': 'check', 'text': 'spellfire set','default': '0', 'tab': 1, 'row': i+1}) buttons[j]['default'] = 1 #{'key': 'XYxmin', 'type': 'txt:float', 'text': 'x min', 'default': '0', 'width': 4, 'tab': 0, 'row': 1} , #{'key': 'XYxmax', 'type': 'txt:float', 'text': 'x max', 'default': '120', 'width': 4, 'tab': 0, 'row': 1} , #{'key': 'XYxlabel', 'type': 'txt', 'text': 'x label', 'default': r'$2\theta$', 'width': 10, 'tab': 0, 'row': 1} , #{'key': 'XYnormalize', 'type': 'check', 'text': 'Normalize y-axis', 'tab': 0, 'row': 2} , #{'key': 'XYylabel_text', 'type': 'label', 'text': 'ylabel: ', 'tab': 0, 'row': 2} , #{'key': 'XYylabel', 'type': 'radio:text', 'texts': ['Counts', 'Intensity'], 'tab': 0, 'row': 2,'default': 0} , return buttons import copy def get_dmg(mage, buffs,times): new_stats_0 = copy.deepcopy(mage.stats) new_talents = copy.deepcopy(mage.talents) bck.buff_me(new_stats_0, new_talents, buffs) spells, new_stats = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) if mage.save_rot[0] == 'arcane_frost_clearcasting_optimized': save_rot = bck.get_dps_mps_rot_clearcasting_optimal(new_stats_0, new_talents, bck.game_config, spells_to_cast = 20000) elif mage.save_rot[0] == 'fireball_spam_clearcasting_optimized': new_talents['force_clearcasting'] = -1 spells_no_c, stats_no_c = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) new_talents['force_clearcasting'] = 1 spells_forced_c, stats_forced_c = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) new_talents['force_clearcasting'] = 0 # reset optimized_spells = [spells_no_c['fireball_13_one_tick']]*7 optimized_spells.append(spells_no_c['fireball_13_one_tick_one_roll']) optimized_spells.append(spells_no_c['fireball_13_three_tick_no_roll']) optimized_spells.append(spells_forced_c['arcane_missiles_10']) save_rot = bck.get_dps_mps_rotation(optimized_spells) elif mage.save_rot[0] == 'scorch_spam_clearcasting_optimized': new_talents['force_clearcasting'] = -1 spells_no_c, stats_no_c = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) new_talents['force_clearcasting'] = 1 spells_forced_c, stats_forced_c = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) new_talents['force_clearcasting'] = 0 # reset optimized_spells = [spells_no_c['scorch_9']]*7 optimized_spells.append(spells_no_c['scorch_9_no_roll']) optimized_spells.append(spells_no_c['scorch_9_no_roll']) optimized_spells.append(spells_forced_c['arcane_missiles_10']) save_rot = bck.get_dps_mps_rotation(optimized_spells) elif mage.save_rot[0] == 'frostbolt_spam_clearcasting_optimized': new_talents['force_clearcasting'] = -1 spells_no_c, stats_no_c = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) new_talents['force_clearcasting'] = 1 spells_forced_c, stats_forced_c = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) new_talents['force_clearcasting'] = 0 # reset optimized_spells = [spells_no_c['frostbolt_13']]*9 optimized_spells.append(spells_forced_c['arcane_missiles_10']) save_rot = bck.get_dps_mps_rotation(optimized_spells) else: save_rot = bck.get_dps_mps_rotation([spells[x] for x in mage.save_rot]) if mage.burn_rot[0] == 'None': burn_rot = [0,10**10] elif mage.burn_rot[0] == 'AB_spam_clearcasting_optimized': new_talents['force_clearcasting'] = -1 spells_no_c, stats_no_c = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) new_talents['force_clearcasting'] = 1 spells_forced_c, stats_forced_c = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) new_talents['force_clearcasting'] = 0 # reset optimized_spells = [spells_no_c['arcane_blast_1_3speed_3mana']]*9 optimized_spells.append(spells_forced_c['arcane_missiles_10']) burn_rot = bck.get_dps_mps_rotation(optimized_spells) else: burn_rot = bck.get_dps_mps_rotation([spells[x] for x in mage.burn_rot]) IV_replace = None if 'arcane_frost_clearcasting_optimized' in mage.save_rot or 'arcane_blast_1_3speed_0mana' in mage.save_rot: #print(mage.location) IV_replace = bck.get_dps_mps_rotation([spells[x] for x in ['frostbolt_13']]) dmg, dmg_burn, dmg_save, dmg_other, time_shift = bck.optimize_cycles_return_damage(new_stats,times,new_talents, burn_rot, save_rot, return_fractions=True, IV_replace=IV_replace ) return dmg, dmg_burn, dmg_save, dmg_other, time_shift def plot_dps(ui, mage, buffs, i, linestyle, ax, misc, fractions = False, DMG = False): times = np.arange(ui['time_min'],ui['time_max']+1, 1) dmg, dmg_burn, dmg_save, dmg_other, time_shift = get_dmg(mage, buffs, times) if DMG: times_mod = 1 ax.set_ylabel('Damage [DMG]') #ax.set_ylim([u,ui['dps_max']]) else: times_mod = times ax.set_ylabel('Average dps [DMG/s]') ax.set_ylim([ui['dps_min'],ui['dps_max']]) if fractions: if not 'dmg_frac_label' in misc: misc.append('dmg_frac_label') ax.fill_between(times, np.zeros(len(times)), dmg_save/times_mod, color=[0.5,0,1,0.2], label = 'save') ax.fill_between(times, dmg_save/times_mod, dmg_save/times_mod+dmg_burn/times_mod, color=[1,0,0.5,0.2], label = 'burn') else: ax.fill_between(times, np.zeros(len(times)), dmg_save/times_mod, color=[0.5,0,1,0.2]) ax.fill_between(times, dmg_save/times_mod, dmg_save/times_mod+dmg_burn/times_mod, color=[1,0,0.5,0.2]) if np.sum(dmg_other)>1000: if not 'dmg_frac_other_label' in misc: misc.append('dmg_frac_other_label') ax.fill_between(times, dmg_save/times_mod+dmg_burn/times_mod, dmg_save/times_mod+dmg_burn/times_mod+dmg_other/times_mod, color=[0,0,0,0.2], label = 'other (pom+pyro, etc)') else: ax.fill_between(times, dmg_save/times_mod+dmg_burn/times_mod, dmg_save/times_mod+dmg_burn/times_mod+dmg_other/times_mod, color=[0,0,0,0.2]) ax.plot(times, dmg/times_mod, linestyle= linestyle, color=mage.color, label = mage.label+', '+ui['buff_case_'+str(i)+'_label']) ax.set_xticks(ticks=np.arange((int((times[0]-1)/30)+1)*30,times[-1]+1,30)) ax.set_xlabel('Total casting time before boss dead [s]') '''ax.annotate('Evocation', xy=(43, 1100), xytext=(48, 1400), arrowprops=dict(facecolor='black', shrink=0.05), horizontalalignment='left', verticalalignment='top', ) ax.annotate('OOM', xy=(110, 800), xytext=(120,1100), arrowprops=dict(facecolor='black', shrink=0.05), horizontalalignment='left', verticalalignment='top', )''' ax.legend() ax.set_xlim([ui['time_min'],ui['time_max']]) ylim = ax.get_ylim() if ylim[0]<0: ax.set_ylim([0,ylim[1]]) #fig.savefig('optimized_spam.png') return misc def plot_spell_dps(ui, mages, buff_cases, linestyles, ax, DPM = False): #ax.grid() spell_names = ['frostbolt_13','fireball_13_one_tick', 'scorch_9', 'arcane_blast_1_0speed_0mana', 'arcane_blast_1_1speed_1mana', 'arcane_blast_1_2speed_2mana', 'arcane_blast_1_3speed_3mana', 'arcane_blast_1_3speed_0mana', 'arcane_missiles_10', ] x = np.arange(len(spell_names)) tot_cases = 0 for i, buff_case in enumerate(buff_cases): if buff_case['check'] == 1: tot_cases+=len(mages) j=0 width = 0.8/(tot_cases) for i, buff_case in enumerate(buff_cases): linestyle = linestyles[i] if buff_case['check'] == 1: for mage in mages: new_stats_0 = copy.deepcopy(mage.stats) new_talents = copy.deepcopy(mage.talents) bck.buff_me(new_stats_0, new_talents, buff_case) spells, new_stats = bck.get_spells_stats(new_stats_0, new_talents, bck.game_config) dpms = [] dpss = [] for spell_name in spell_names: dps = spells[spell_name].average_damage / spells[spell_name].actual_cast_time dpss.append(dps) dpm = spells[spell_name].average_damage / spells[spell_name].actual_mana dpms.append(dpm) offset = -0.8/2+(j+0.5)*0.8/(tot_cases) color = [mage.color[0], mage.color[1], mage.color[2],0.5] edgecolor = [mage.color[0], mage.color[1], mage.color[2],1] if not DPM: rects = ax.bar(x +offset, dpss, width, linestyle=linestyle, edgecolor= edgecolor, color=color, label=mage.label) else: rects = ax.bar(x +offset, dpms, width, linestyle=linestyle, edgecolor= edgecolor, color=color, label=mage.label) #rects = ax[1].bar(x +offset, dpms, width, color=mage.color, label=mage.label) j+=1 if not DPM: ax.set_ylabel('spell dps') else: ax.set_ylabel('spell dpm') #ax.set_ylabel('spell dpm') spell_names_short = ['Frostbolt', 'Fireball', 'Scorch', 'AB0', 'AB1', 'AB2', 'AB3', 'AB3\ncost1', 'AM', ] ax.set_xticks(np.arange(0,len(spell_names_short),1)) ax.set_xticklabels(spell_names_short) #ax[1].legend() #fig.tight_layout() return def plot_stat_weights(ui, mages, buff_cases, linestyles, ax, DPM = False): stats_list = ['intellect','common_spell_damage', 'crit_rating','hit_rating','haste_rating','mp5','spirit'] stats_names = ['Intellect','+Spelldamage','Crit rating', 'Hit rating','Haste','mp5','Spirit'] x_step = ui['time_max']-ui['time_min'] xlim = [ui['time_min'],ui['time_max']+3*x_step] times = np.arange(ui['time_min'],ui['time_max']+1, 1) max_ylim = ui['stat_weight_ymax'] for i, buff_case in enumerate(buff_cases): linestyle = linestyles[i] if buff_case['check'] == 1: for mage in mages: tmp = get_dmg(mage, buff_case, times) dps_0 = tmp[0]/times xo=-x_step yo=max_ylim for i, stat in enumerate(stats_list): if i==4: xo=0 yo-=max_ylim else: xo+=x_step mage.stats[stat]-=10 #print('arcane') out = get_dmg(mage, buff_case, times) dps_new = out[0]/times mage.stats[stat]+=10 fraction_increase_per_stat = -0.1*(dps_new/dps_0-1) #stat_per_percent_fire[stat_per_percent_fire<0]=np.nan #stat_per_percent_fire[stat_per_percent_fire>max_ylim]=np.nan stat_per_percent = 0.01/fraction_increase_per_stat y= 20/stat_per_percent y[y<-0.0001] = np.nan y[y>max_ylim] = np.nan ax.plot(times+xo,y+yo,linestyle= linestyle,color=mage.color) xo=-x_step yo=max_ylim for i, stat in enumerate(stats_list): if i==4: xo=0 yo-=max_ylim else: xo+=x_step ax.text(xo+xlim[0]+0.05*x_step, yo+max_ylim-0.05*max_ylim, stats_names[i],ha='left', va='top') ax.set_xlim(xlim) ax.set_ylim([0,2*max_ylim]) ax.set_xticks([]) ax.set_yticks(np.arange(max_ylim*4)/2) ax.set_yticklabels(np.arange(max_ylim*4)/2%max_ylim) ax.plot(xlim, [max_ylim,max_ylim], lw=0.5,color=[0,0,0,1]) ax.grid() for i in range(1,4): ax.plot([xlim[0]+x_step*i]*2, [0,max_ylim*2], lw=0.5,color=[0,0,0,1]) x_ticks_0 = np.arange((int((times[0]-1)/30)+1)*30,times[-1],30) x_ticks = [] for i in range(4): for x in x_ticks_0: x_ticks.append(x+i*x_step) ax.set_xticks(ticks=x_ticks) x_ticks = [] for i in range(4): for x in x_ticks_0: x_ticks.append(int(x)) ax.set_xticklabels(x_ticks) ax.set_xlabel('Total casting time before boss dead [s]') ax.set_ylabel('Stat weight [-]') '''axes[i].set_title(stats_names[i]) axes[i].set_ylim([0,max_ylim]) axes[i].set_yticks([0,1,2,3,4,5]) axes[i].grid() axes[i].set_xlim([20,180])''' #axes[-1].set_axis_off() #fig.suptitle('Stat weights') #fig.tight_layout() def plot_compare_buff_states(ui, mages, buff_cases, linestyles, ax): xlim = [ui['time_min'],ui['time_max']] times = np.arange(ui['time_min'],ui['time_max']+1, 1) max_ylim = ui['stat_weight_ymax'] ax.plot(times, np.zeros(times.shape), color=[0,0,0,1]) for mage in mages: done_first = 0 for i, buff_case in enumerate(buff_cases): linestyle = linestyles[i] if buff_case['check'] == 1: if done_first ==0: tmp = get_dmg(mage, buff_case, times) dps_0 = tmp[0]/times done_first = 1 label_0 = ui['buff_case_'+str(i)+'_label'] else: tmp = get_dmg(mage, buff_case, times) dps_1 = tmp[0]/times ax.plot(times, 100*(dps_1/dps_0-1), linestyle= linestyle, color=mage.color, label = mage.label+', '+ui['buff_case_'+str(i)+'_label']) ax.set_xticks(ticks=np.arange((int((times[0]-1)/30)+1)*30,times[-1]+1,30)) ax.set_xlabel('Total casting time before boss dead [s]') ax.set_ylabel('% damage increase vs '+label_0) ax.legend() ax.set_xlim([ui['time_min'],ui['time_max']]) ax.grid() def set_default_mage(event): frame = event.widget while not hasattr(frame,'nav'): frame = frame.master frame.nav.clear_color('color3') frame.nav.color_selected('color3') mages = frame.nav.get_paths_of_selected_items() if len(mages)>0: frame.default_mage = frame.nav.get_paths_of_selected_items()[0] print('set default_mage:',frame.default_mage ) else: delattr(frame,'default_mage') print('cleared default_mage' ) frame.nav.deselect() def plot_compare_mages(ui, default_mage, mages, buff_cases, linestyles, ax): xlim = [ui['time_min'],ui['time_max']] times = np.arange(ui['time_min'],ui['time_max']+1, 1) max_ylim = ui['stat_weight_ymax'] ax.plot(times, np.zeros(times.shape), color=default_mage.color) for i, buff_case in enumerate(buff_cases): linestyle = linestyles[i] if buff_case['check'] == 1: tmp = get_dmg(default_mage, buff_case, times) dps_0 = tmp[0]/times for mage in mages: if mage.location == default_mage.location: continue tmp = get_dmg(mage, buff_case, times) dps_1 = tmp[0]/times ax.plot(times, 100*(dps_1/dps_0-1), linestyle= linestyle, color=mage.color, label = mage.label+', '+ui['buff_case_'+str(i)+'_label']) ax.set_xticks(ticks=np.arange((int((times[0]-1)/30)+1)*30,times[-1]+1,30)) ax.grid() ax.set_xlabel('Total casting time before boss dead [s]') ax.set_ylabel('% damage increase vs '+default_mage.label) ax.legend() ax.set_xlim([ui['time_min'],ui['time_max']])

42.406176

183

0.651711

5,561

35,706

3.936342

0.073548

0.025582

0.019598

0.035496

0.721243

0.65651

0.639242

0.588305

0.555642

0.509137

0

0.028567

0.154904

35,706

841

184

42.456599

0.696868

0.074245

0

0.413649

0

0.239812

0.055903

0

1

0.019499

false

0

0.011142

0

0.041783

0.008357

0

null

0

null

0

1

0

bdb9e345a7126b6fc13fd0106c741d7ab14d3f93

36,682

py

Python

PyPixel/SkyBlockStats.py

M4axim/PyPixel

8f77773a6e4c1541a41c98fd8edb86b4bb2aba67

[ "MIT" ]

2

2021-03-25T16:52:22.000Z

2021-09-22T16:42:57.000Z

PyPixel/SkyBlockStats.py

M4axim/PyPixel

8f77773a6e4c1541a41c98fd8edb86b4bb2aba67

[ "MIT" ]

null

PyPixel/SkyBlockStats.py

M4axim/PyPixel

8f77773a6e4c1541a41c98fd8edb86b4bb2aba67

[ "MIT" ]

2

2021-03-23T18:40:19.000Z

2022-01-03T18:17:08.000Z

# -*- coding: utf-8 -*- """ MIT License Copyright (c) 2021 plun1331 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import datetime from .utils import SkyBlockUtils types = {'zombie': SkyBlockUtils.zombieSlayer, 'spider': SkyBlockUtils.spiderSlayer, 'wolf': SkyBlockUtils.wolfSlayer} class SkyBlockStats(object): r"""Represents a player's SkyBlock Statistics. :param stats: The player's stats from their memberdata retrieved from the API. :type stats: dict""" def __init__(self, stats: dict): self.top_crit_damage = stats['highest_crit_damage'] if 'highest_crit_damage' in stats else None self.kills = int(stats['kills']) if 'kills' in stats else None self.zombie_kills = int(stats['kills_zombie']) if 'kills_zombie' in stats else None self.bids = int(stats['auctions_bids']) if 'auctions_bids' in stats else None self.highest_bid = stats['auctions_highest_bid'] if 'auctions_highest_bid' in stats else None self.zombie_villager_kills = int(stats['kills_zombie_villager']) if 'kills_zombie_villager' in stats else None self.skeleton_kills = int(stats['kills_skeleton']) if 'kills_skeleton' in stats else None self.spider_kills = int(stats['kills_spider']) if 'kills_spider' in stats else None self.enderman_kills = int(stats['kills_enderman']) if 'kills_enderman' in stats else None self.deaths = int(stats['deaths']) if 'deaths' in stats else None self.zombie_deaths = int(stats['deaths_zombie']) if 'deaths_zombie' in stats else None self.void_deaths = int(stats['deaths']) if 'deaths' in stats else None self.skeleton_deaths = int(stats['deaths_skeleton']) if 'deaths_skeleton' in stats else None self.fire_deaths = int(stats['deaths_fire']) if 'deaths_fire' in stats else None self.auctions_won = int(stats['auctions_won']) if 'auctions_won' in stats else None self.uncommon_auctions_bought = int( stats['auctions_bought_uncommon']) if 'auctions_bought_uncommon' in stats else None self.auctions_gold_spent = int(stats['auctions_gold_spent']) if 'auctions_gold_spent' in stats else None self.auctions_created = int(stats['auctions_created']) if 'auctions_created' in stats else None self.auction_fees_spent = int(stats['auctions_fees']) if 'auctions_fees' in stats else None self.player_deaths = int(stats['deaths_player']) if 'deaths_player' in stats else None self.auctions_completed = int(stats['auctions_completed']) if 'auctions_completed' in stats else None self.uncommon_auctions_sold = int( stats['auctions_sold_uncommon']) if 'auctions_sold_uncommon' in stats else None self.auction_gold_earned = int(stats['auctions_gold_earned']) if 'auctions_gold_earned' in stats else None self.invisible_creeper_kills = int( stats['kills_invisible_creeper']) if 'kills_invisible_creeper' in stats else None self.emerald_slime_kills = int(stats['kills_emerald_slime']) if 'kills_emerald_slime' in stats else None self.diamond_zombie_kills = int(stats['kills_diamond_zombie']) if 'kills_diamond_zombie' in stats else None self.diamond_skeleton_deaths = int( stats['deaths_diamond_skeleton']) if 'deaths_diamond_skeleton' in stats else None self.diamond_zombie_deaths = int(stats['deaths_diamond_zombie']) if 'deaths_diamond_zombie' in stats else None self.diamond_skeleton_kills = int( stats['kills_diamond_skeleton']) if 'kills_diamond_skeleton' in stats else None self.lapis_zombie_kills = int(stats['kills_lapis_zombie']) if 'kills_lapis_zombie' in stats else None self.emerald_slime_deaths = int(stats['deaths_emerald_slime']) if 'deaths_emerald_slime' in stats else None self.redstone_pigman_kills = int(stats['kills_redstone_pigman']) if 'kills_redstone_pigman' in stats else None self.redstone_pigman_deaths = int( stats['deaths_redstone_pigman']) if 'deaths_redstone_pigman' in stats else None self.splitter_spider_silverfish_kills = int( stats['kills_splitter_spider_silverfish']) if 'kills_splitter_spider_silverfish' in stats else None self.jockey_shot_silverfish_kills = int( stats['kills_jockey_shot_silverfish']) if 'kills_jockey_shot_silverfish' in stats else None self.wither_skeleton_kills = int(stats['kills_wither_skeleton']) if 'kills_wither_skeleton' in stats else None self.magma_cube_kills = int(stats['kills_magma_cube']) if 'kills_magma_cube' in stats else None self.magma_cube_fireball_kills = int( stats['kills_fireball_magma_cube']) if 'kills_fireball_magma_cube' in stats else None self.cow_kills = int(stats['kills_cow']) if 'kills_cow' in stats else None self.pig_kills = int(stats['kills_pig']) if 'kills_pig' in stats else None self.items_fished = int(stats['items_fished']) if 'items_fished' in stats else None self.normal_items_fished = int(stats['items_fished_normal']) if 'items_fished_normal' in stats else None self.treasure_items_fished = int(stats['items_fished_treasure']) if 'items_fished_treasure' in stats else None self.common_auctions_bought = int( stats['auctions_bought_common']) if 'auctions_bought_common' in stats else None self.witch_kills = int(stats['kills_witch']) if 'kills_witch' in stats else None self.spider_deaths = int(stats['deaths_spider']) if 'deaths_spider' in stats else None self.epic_auctions_bought = int(stats['auctions_bought_epic']) if 'auctions_bought_epic' in stats else None self.magma_cube_fireball_deaths = int( stats['deaths_fireball_magma_cube']) if 'deaths_fireball_magma_cube' in stats else None self.weaver_spider_kills = int(stats['kills_weaver_spider']) if 'kills_weaver_spider' in stats else None self.splitter_spider_kills = int(stats['kills_splitter_spider']) if 'kills_splitter_spider' in stats else None self.jockey_skeleton_kills = int(stats['kills_jockey_skeleton']) if 'kills_jockey_skeleton' in stats else None self.spider_jockey_kills = int(stats['kills_spider_jockey']) if 'kills_spider_jockey' in stats else None self.dasher_spider_kills = int(stats['kills_dasher_spider']) if 'kills_dasher_spider' in stats else None self.spider_jockey_deaths = int(stats['deaths_spider_jockey']) if 'deaths_spider_jockey' in stats else None self.dasher_spider_deaths = int(stats['deaths_dasher_spider']) if 'deaths_dasher_spider' in stats else None self.jockey_shot_silverfish_deaths = int( stats['deaths_jockey_shot_silverfish']) if 'deaths_jockey_shot_silverfish' in stats else None self.splitter_spider_deaths = int( stats['deaths_splitter_spider']) if 'deaths_splitter_spider' in stats else None self.common_auctions_sold = int(stats['auctions_sold_common']) if 'auctions_sold_common' in stats else None self.no_bid_auctions = int(stats['auctions_no_bids']) if 'auctions_no_bids' in stats else None self.ghast_kills = int(stats['kills_ghast']) if 'kills_ghast' in stats else None self.rare_auctions_sold = int(stats['auctions_sold_rare']) if 'auctions_sold_rare' in stats else None self.epic_auctions_sold = int(stats['auctions_sold_epic']) if 'auctions_sold_epic' in stats else None self.magma_cube_boss_deaths = int( stats['deaths_magma_cube_boss']) if 'deaths_magma_cube_boss' in stats else None self.blaze_kills = int(stats['kills_blaze']) if 'kills_blaze' in stats else None self.wither_skeleton_deaths = int( stats['deaths_wither_skeleton']) if 'deaths_wither_skeleton' in stats else None self.magma_cube_deaths = int(stats['deaths_magma_cube']) if 'deaths_magma_cube' in stats else None self.respawning_skeleton_kills = int( stats['kills_respawning_skeleton']) if 'kills_respawning_skeleton' in stats else None self.fall_deaths = int(stats['deaths_fall']) if 'deaths_fall' in stats else None self.rare_auctions_bought = int(stats['auctions_bought_rare']) if 'auctions_bought_rare' in stats else None self.rabbit_kills = int(stats['kills_rabbit']) if 'kills_rabbit' in stats else None self.sheep_kills = int(stats['kills_sheep']) if 'kills_sheep' in stats else None self.pigman_kills = int(stats['kills_pigman']) if 'kills_pigman' in stats else None self.player_kills = int(stats['kills_player']) if 'kills_player' in stats else None self.ruin_wolf_kills = int(stats['kills_ruin_wolf']) if 'kills_ruin_wolf' in stats else None self.night_respawning_skeleton_kills = int( stats['kills_night_respawining_skeleton']) if 'kills_night_respawining_skeleton' in stats else None self.legendary_auctions_bought = int( stats['auctions_bought_legendary']) if 'auctions_bought_legendary' in stats else None self.chicken_kills = int(stats['kills_chicken']) if 'kills_chicken' in stats else None self.respawning_skeleton_deaths = int( stats['deaths_respawning_skeleton']) if 'deaths_respawning_skeleton' in stats else None self.ruin_wolf_deaths = int(stats['deaths_ruin_wolf']) if 'deaths_ruin_wolf' in stats else None self.unburried_zombie_deaths = int( stats['deaths_unburied_zombie']) if 'deaths_unburied_zombie' in stats else None self.unburried_zombie_kills = int( stats['kills_unburried_zombie']) if 'kills_unburried_zombie' in stats else None self.enderman_deaths = int(stats['deaths_enderman']) if 'deaths_enderman' in stats else None self.endermite_deaths = int(stats['deaths_endermite']) if 'deaths_endermite' in stats else None self.endermite_kills = int(stats['kills_endermite']) if 'kills_endermite' in stats else None self.zealot_enderman_deaths = int( stats['deaths_zealot_enderman']) if 'deaths_zealot_enderman' in stats else None self.wise_dragon_deaths = int(stats['deaths_wise_dragon']) if 'deaths_wise_dragon' in stats else None self.watcher_deaths = int(stats['deaths_watcher']) if 'deaths_watcher' in stats else None self.watcher_kills = int(stats['kills_watcher']) if 'kills_watcher' in stats else None self.random_slime_kills = int(stats['kills_random_slime']) if 'kills_random_slime' in stats else None self.voracious_spider_kills = int( stats['kills_voracious_spider']) if 'kills_voracious_spider' in stats else None self.wolf_deaths = int(stats['deaths_wolf']) if 'deaths_wolf' in stats else None self.old_wolf_kills = int(stats['kills_old_wolf']) if 'kills_old_wolf' in stats else None self.olf_wolf_deaths = int(stats['deaths_old_wolf']) if 'deaths_old_wolf' in stats else None self.zealot_enderman_kills = int(stats['kills_zealot_enderman']) if 'kills_zealot_enderman' in stats else None self.obsidian_wither_kills = int(stats['kills_obsidian_wither']) if 'kills_obsidian_wither' in stats else None self.howling_spirit_kills = int(stats['kills_howling_spirit']) if 'kills_howling_spirit' in stats else None self.howling_spirit_deaths = int(stats['deaths_howling_spirit']) if 'deaths_howling_spirit' in stats else None self.unknown_deaths = int(stats['deaths_unknown']) if 'deaths_unknown' in stats else None self.sea_walker_kills = int(stats['kills_sea_walker']) if 'kills_sea_walker' in stats else None self.pond_squid_kills = int(stats['kills_pond_squid']) if 'kills_pond_squid' in stats else None self.sea_guardian_kills = int(stats['deaths_sea_guardian']) if 'deaths_sea_guardian' in stats else None self.sea_archer_kills = int(stats['kills_sea_archer']) if 'kills_sea_archer' in stats else None self.young_dragon_deaths = int(stats['deaths_young_dragon']) if 'deaths_young_dragon' in stats else None self.zombie_deep_kills = int(stats['kills_zombie_deep']) if 'kills_zombie_deep' in stats else None self.gifts_given = int(stats['gifts_given']) if 'gifts_given' in stats else None self.gifts_recieved = int(stats['gifts_recieved']) if 'gifts_recieved' in stats else None self.frozen_steve_deaths = int(stats['deaths_frozen_steve']) if 'deaths_frozen_steve' in stats else None self.brood_mother_spider_kills = int( stats['kills_brood_mother_spider']) if 'kills_brood_mother_spider' in stats else None self.brood_mother_cave_spider_kills = int( stats['kills_brood_mother_cave_spider']) if 'kills_brood_mother_cave_spider' in stats else None self.foraging_race_best_time = int( stats['foraging_race_best_time']) if 'foraging_race_best_time' in stats else None self.legendary_auctions_sold = int( stats['auctions_sold_legendary']) if 'auctions_sold_legendary' in stats else None self.special_auctions_sold = int(stats['auctions_sold_special']) if 'auctions_sold_special' in stats else None self.generator_magma_cube_kills = int( stats['kills_generator_magma_cube']) if 'kills_generator_magma_cube' in stats else None self.bat_pinata_kills = int(stats['kills_bat_pinata']) if 'kills_bat_pinata' in stats else None self.special_auctions_bought = int( stats['auctions_bought_special']) if 'auctions_bought_special' in stats else None self.horseman_zombie_kills = int(stats['kills_horseman_zombie']) if 'kills_horseman_zombie' in stats else None self.old_dragon_deaths = int(stats['deaths_old_dragon']) if 'deaths_old_dragon' in stats else None self.liquid_hot_magma_deaths = int( stats['deaths_liquid_hot_magma']) if 'deaths_liquid_hot_magma' in stats else None self.liquid_hot_magma_kills = int( stats['kills_liquid_hot_magma']) if 'kills_liquid_hot_magma' in stats else None self.most_winter_snowballs_hit = int( stats['most_winter_snowballs_hit']) if 'most_winter_snowballs_hit' in stats else None self.most_winter_damage_dealt = int( stats['most_winter_damage_dealt']) if 'most_winter_damage_dealt' in stats else None self.most_winter_magma_damage_dealt = int( stats['most_winter_magma_damage_dealt']) if 'most_winter_magma_damage_dealt' in stats else None self.ender_crystals_destroyed = int( stats['ender_crystals_destroyed']) if 'ender_crystals_destroyed' in stats else None self.most_winter_cannonballs_hit = int( stats['most_winter_cannonballs_hit']) if 'most_winter_cannonballs_hit' in stats else None self.slime_kills = int(stats['kills_slime']) if 'kills_slime' in stats else None self.unstable_dragon_deaths = int( stats['deaths_unstable_dragon']) if 'deaths_unstable_dragon' in stats else None self.superior_dragon_deaths = int( stats['deaths_superior_dragon']) if 'deaths_superior_dragon' in stats else None self.forest_island_bat_kills = int( stats['kills_forest_island_bat']) if 'kills_forest_island_bat' in stats else None self.strong_dragon_deaths = int(stats['deaths_strong_dragon']) if 'deaths_strong_dragon' in stats else None self.pet_milestone_ores_mined = int( stats['pet_milestone_ores_mined']) if 'pet_milestone_ores_mined' in stats else None self.pet_milestone_sea_creatures_killed = int( stats['pet_milestone_sea_creatures_killed']) if 'pet_milestone_sea_creatures_killed' in stats else None self.chicken_deep_kills = int(stats['kills_chicken_deep']) if 'kills_chicken_deep' in stats else None self.corrupted_protector_deaths = int( stats['deaths_corrupted_protector']) if 'deaths_corrupted_protector' in stats else None self.pack_spirit_kills = int(stats['kills_pack_spirit']) if 'kills_pack_spirit' in stats else None self.soul_of_the_alpha_kills = int( stats['kills_soul_of_the_alpha']) if 'kills_soul_of_the_alpha' in stats else None self.frosty_the_snowman_kills = int( stats['kills_frosty_the_snowman']) if 'kills_frosty_the_snowman' in stats else None self.frozen_steve_kills = int(stats['kills_frozen_steve']) if 'kills_frozen_steve' in stats else None self.catfish_kills = int(stats['kills_catfish']) if 'kills_catfish' in stats else None self.dungeon_hub_crystal_core_anything_no_return_best_time = stats[ 'dungeon_hub_crystal_core_anything_no_return_best_time' ] if 'dungeon_hub_crystal_core_anything_no_return_best_time' in stats else None self.dungeon_hub_giant_mushroom_anything_no_return_best_time = stats[ 'dungeon_hub_giant_mushroom_anything_no_return_best_time' ] if 'dungeon_hub_giant_mushroom_anything_no_return_best_time' in stats else None self.dungeon_hub_giant_mushroom_no_pearls_no_return_best_time = stats[ 'dungeon_hub_giant_mushroom_no_pearls_no_return_best_time' ] if 'dungeon_hub_giant_mushroom_no_pearls_no_return_best_time' in stats else None self.dungeon_hub_precursor_ruins_anything_no_return_best_time = stats[ 'dungeon_hub_precursor_ruins_anything_no_return_best_time' ] if 'dungeon_hub_precursor_ruins_anything_no_return_best_time' in stats else None self.dungeon_hub_precursor_ruins_nothing_no_return_best_time = stats[ 'dungeon_hub_precursor_ruins_nothing_no_return_best_time' ] if 'dungeon_hub_precursor_ruins_nothing_no_return_best_time' in stats else None self.dungeon_hub_precursor_ruins_no_pearls_no_return_best_time = stats[ 'dungeon_hub_precursor_ruins_no_pearls_no_return_best_time' ] if 'dungeon_hub_precursor_ruins_no_pearls_no_return_best_time' in stats else None self.crypt_lurker_kills = int(stats['kills_crypt_lurker']) if 'kills_crypt_lurker' in stats else None self.dungeon_respawning_skeleton_kills = int( stats['kills_dungeon_respawning_skeleton']) if 'kills_dungeon_respawning_skeleton' in stats else None self.scared_skeleton_kills = int(stats['kills_scared_skeleton']) if 'kills_scared_skeleton' in stats else None self.skeleton_grunt_kills = int(stats['kills_skeleton_grunt']) if 'kills_skeleton_grunt' in stats else None self.crypt_dreadlord_kills = int(stats['kills_scared_skeleton']) if 'kills_scared_skeleton' in stats else None self.crypt_souleater_kills = int(stats['kills_crypt_souleater']) if 'kills_crypt_souleater' in stats else None self.crypt_tank_zombie_kills = int( stats['kills_crypt_tank_zombie']) if 'kills_crypt_tank_zombie' in stats else None self.diamond_guy_kills = int(stats['kills_diamond_guy']) if 'kills_diamond_guy' in stats else None self.zombie_grunt_kills = int(stats['kills_zombie_grunt']) if 'kills_zombie_grunt' in stats else None self.crypt_lurker_deaths = int(stats['deaths_crypt_lurker']) if 'deaths_crypt_lurker' in stats else None self.lost_adventurer_deaths = int( stats['deaths_lost_adventurer']) if 'deaths_lost_adventurer' in stats else None self.watcher_summon_undead_kills = int( stats['kills_watcher_summon_undead']) if 'kills_watcher_summon_undead' in stats else None self.skeleton_soldier_kills = int( stats['kills_skeleton_soldier']) if 'kills_skeleton_soldier' in stats else None self.diamond_guy_deaths = int(stats['deaths_diamond_guy']) if 'deaths_diamond_guy' in stats else None self.watcher_summon_undead_deaths = int( stats['deaths_watcher_summon_undead']) if 'deaths_watcher_summon_undead' in stats else None self.bonzo_summon_undead_kills = int( stats['kills_bonzo_summon_undead']) if 'kills_bonzo_summon_undead' in stats else None self.lost_adventurer_kills = int(stats['kills_lost_adventurer']) if 'kills_lost_adventurer' in stats else None self.skeleton_master_kills = int(stats['kills_skeleton_master']) if 'kills_skeleton_master' in stats else None self.sniper_skeleton_kills = int(stats['kills_sniper_skeleton']) if 'kills_sniper_skeleton' in stats else None self.skeleton_soldier_deaths = int( stats['deaths_skeleton_soldier']) if 'deaths_skeleton_soldier' in stats else None self.trap_deaths = int(stats['deaths_trap']) if 'deaths_trap' in stats else None self.crypt_undead_kills = int(stats['kills_crypt_undead']) if 'kills_crypt_undead' in stats else None self.skeleton_grunt_deaths = int(stats['deaths_skeleton_grunt']) if 'deaths_skeleton_grunt' in stats else None self.scarf_warrior_deaths = int(stats['deaths_scarf_warrior']) if 'deaths_scarf_warrior' in stats else None self.skeleton_master_deaths = int( stats['deaths_skeleton_master']) if 'deaths_skeleton_master' in stats else None self.blaze_higher_or_lower_kills = int( stats['kills_blaze_higher_or_lower']) if 'kills_blaze_higher_or_lower' in stats else None self.dungeon_respawning_skeleton_deaths = int( stats['deaths_dungeon_respawning_skeleton']) if 'deaths_dungeon_respawning_skeleton' in stats else None self.scarf_deaths = int(stats['deaths_scarf']) if 'deaths_scarf' in stats else None self.bonzo_summon_undead_deaths = int( stats['deaths_bonzo_summon_undead']) if 'deaths_bonzo_summon_undead' in stats else None self.bonzo_deaths = int(stats['deaths_bonzo']) if 'deaths_bonzo' in stats else None self.lonely_spider_kills = int(stats['kills_lonely_spider']) if 'kills_lonely_spider' in stats else None self.parasite_kills = int(stats['kills_parasite']) if 'kills_parasite' in stats else None self.cellar_spider_kills = int(stats['kills_cellar_spiders']) if 'kills_cellar_spiders' in stats else None self.dungeon_secret_bat_kills = int( stats['kills_dungeon_secret_bat']) if 'kills_dungeon_secret_bat' in stats else None self.scarf_mage_kills = int(stats['kills_scarf_mage']) if 'kills_scarf_mage' in stats else None self.crypt_undead_friedrich_kills = int( stats['kills_crypt_undead_friedrich']) if 'kills_crypt_undead_friedrich' in stats else None self.guardian_defender_kills = int( stats['kills_guardian_defender']) if 'kills_guardian_defender' in stats else None self.crypt_dreadlord_deaths = int( stats['deaths_crypt_dreadlord']) if 'deaths_crypt_dreadlord' in stats else None self.zombie_soldier_kills = int(stats['kills_zombie_soldier']) if 'kills_zombie_soldier' in stats else None self.skeletor_deaths = int(stats['deaths_skeletor']) if 'deaths_skeletor' in stats else None self.skeletor_kills = int(stats['kills_skeletor']) if 'kills_skeletor' in stats else None self.professer_mage_guardian_deaths = int( stats['deaths_professor_mage_guardian']) if 'deaths_professor_mage_guardian' in stats else None self.sea_leech_kills = int(stats['kills_sea_leech']) if 'kills_sea_leech' in stats else None self.sea_witch_kills = int(stats['kills_sea_witch']) if 'kills_sea_witch' in stats else None self.skeleton_emperor_kills = int( stats['kills_skeleton_emperor']) if 'kills_skeleton_emperor' in stats else None self.mythos_burrows_dug_next = int( stats['mythos_burrows_dug_next']) if 'mythos_burrows_dug_next' in stats else None self.common_mythos_burrows_dug_next = int( stats['mythos_burrows_dug_next_COMMON']) if 'mythos_burrows_dug_next_COMMON' in stats else None self.mythos_burrows_dug_combat = int( stats['mythos_burrows_dug_combat']) if 'mythos_burrows_dug_combat' in stats else None self.common_mythos_burrows_dug_combat = int( stats['mythos_burrows_dug_combat_COMMON']) if 'mythos_burrows_dug_combat_COMMON' in stats else None self.mythos_kills = int(stats['kills_mythos']) if 'kills_mythos' in stats else None self.minos_hunter_kills = int(stats['kills_minos_hunter']) if 'kills_minos_hunter' in stats else None self.mythos_burrows_dug_treasure = int( stats['mythos_burrows_dug_treasure']) if 'mythos_burrows_dug_treasure' in stats else None self.common_mythos_burrows_dug_treasure = int( stats['mythos_burrows_dug_treasure_COMMON']) if 'mythos_burrows_dug_treasure_COMMON' in stats else None self.siamese_lynx_kills = int(stats['kills_siamese_lynx']) if 'kills_siamese_lynx' in stats else None self.mythos_burrows_chains_complete = int( stats['mythos_burrows_chains_complete']) if 'mythos_burrows_chains_complete' in stats else None self.common_mythos_burrows_chains_complete = int(stats['mythos_burrows_chains_complete_COMMON'] ) if 'mythos_burrows_chains_complete_COMMON' in stats else None self.rare_mythos_burrows_dug_next = int( stats['mythos_burrows_dug_next_RARE']) if 'mythos_burrows_dug_next_RARE' in stats else None self.rare_mythos_burrows_dug_combat = int( stats['mythos_burrows_dug_combat_RARE']) if 'mythos_burrows_dug_combat_RARE' in stats else None self.minotaur_deaths = int(stats['deaths_minotaur']) if 'deaths_minotaur' in stats else None self.minotaur_kills = int(stats['kills_minotaur']) if 'kills_minotaur' in stats else None self.gaia_construct_kills = int(stats['kills_gaia_construct']) if 'kills_gaia_construct' in stats else None self.rare_mythos_burrows_dug_treasure = int( stats['mythos_burrows_dug_treasure_RARE']) if 'mythos_burrows_dug_treasure_RARE' in stats else None self.rare_mythos_burrows_chains_complete = int( stats['mythos_burrows_chains_complete_RARE']) if 'mythos_burrows_chains_complete_RARE' in stats else None self.gaia_construct_deaths = int(stats['deaths_gaia_construct']) if 'deaths_gaia_construct' in stats else None self.siamese_lynx_deaths = int(stats['deaths_siamese_lynx']) if 'deaths_siamese_lynx' in stats else None self.deep_sea_protector_kills = int( stats['kills_deep_sea_protector']) if 'kills_deep_sea_protector' in stats else None self.water_hydra_kills = int(stats['kills_water_hydra']) if 'kills_water_hydra' in stats else None self.blue_shark_kills = int(stats['kills_blue_shark']) if 'kills_blue_shark' in stats else None self.tiger_shark_kills = int(stats['kills_tiger_shark']) if 'kills_tiger_shark' in stats else None self.nurse_shark_kills = int(stats['kills_nurse_shark']) if 'kills_nurse_shark' in stats else None self.crypt_souleater_deaths = int( stats['deaths_crypt_souleater']) if 'deaths_crypt_souleater' in stats else None self.zombie_knight_kills = int(stats['kills_zombie_knight']) if 'kills_zombie_knight' in stats else None self.crypt_undead_valentin_kills = int( stats['kills_crypt_undead_valentin']) if 'kills_crypt_undead_valentin' in stats else None self.soul_of_the_alpha_deaths = int( stats['deaths_soul_of_the_alpha']) if 'deaths_soul_of_the_alpha' in stats else None self.dungeon_hub_precursor_ruins_no_abilities_no_return_best_time = stats[ 'dungeon_hub_precursor_ruins_no_abilities_no_return_best_time'] self.crypt_wither_skeleton_kills = int( stats['kills_crypt_witherskeleton']) if 'kills_crypt_witherskeleton' in stats else None self.crypt_wither_skeleton_deaths = int( stats['deaths_crypt_witherskeleton']) if 'deaths_crypt_witherskeleton' in stats else None self.spirit_wolf_kills = int(stats['kills_spirit_wolf']) if 'kills_spirit_wolf' in stats else None self.spirit_sheep_kills = int(stats['kills_spirit_sheep']) if 'kills_spirit_sheep' in stats else None self.spirit_bull_kills = int(stats['kills_spirit_bull']) if 'kills_spirit_bull' in stats else None self.spirit_rabbit_kills = int(stats['kills_spirit_rabbit']) if 'kills_spirit_rabbit' in stats else None self.thork_kills = int(stats['kills_thorn']) if 'kills_thorn' in stats else None self.livid_clone_deaths = int(stats['deaths_livid_clone']) if 'deaths_livid_clone' in stats else None self.sniper_skeleton_deaths = int( stats['deaths_sniper_skeleton']) if 'deaths_sniper_skeleton' in stats else None self.super_tank_zombie_kills = int( stats['kills_super_tank_zombie']) if 'kills_super_tank_zombie' in stats else None self.super_archer_kills = int(stats['kills_super_archer']) if 'kills_super_archer' in stats else None self.tentaclees_deaths = int(stats['deaths_tentaclees']) if 'deaths_tentaclees' in stats else None self.corrupted_protector_kills = int( stats['kills_corrupted_protector']) if 'kills_corrupted_protector' in stats else None self.professer_guardian_summon_kills = int( stats['kills_professor_guardian_summon']) if 'kills_professor_guardian_summon' in stats else None self.unstable_dragon_kills = int(stats['kills_unstable_dragon']) if 'kills_unstable_dragon' in stats else None self.strong_dragon_kills = int(stats['kills_strong_dragon']) if 'kills_strong_dragon' in stats else None self.spirit_bat_kills = int(stats['kills_spirit_bat']) if 'kills_spirit_bat' in stats else None self.shadow_assassin_kills = int(stats['kills_shadow_assassin']) if 'kills_shadow_assassin' in stats else None self.tentaclees_kills = int(stats['kills_tentaclees']) if 'kills_tentaclees' in stats else None self.livid_deaths = int(stats['deaths_livid']) if 'deaths_livid' in stats else None self.sadan_statue_deaths = int(stats['deaths_sadan_statue']) if 'deaths_sadan_statue' in stats else None self.scary_jerry_kills = int(stats['kills_scary_jerry']) if 'kills_scary_jerry' in stats else None self.wither_gourd_kills = int(stats['kills_wither_gourd']) if 'kills_wither_gourd' in stats else None self.trick_or_treater_kills = int( stats['kills_trick_or_treater']) if 'kills_trick_or_treater' in stats else None self.phantom_spirit_kills = int(stats['kills_phantom_spirit']) if 'kills_phantom_spirit' in stats else None self.wraith_kills = int(stats['kills_wraith']) if 'kills_wraith' in stats else None self.batty_witch_kills = int(stats['kills_batty_witch']) if 'kills_batty_witch' in stats else None self.zombie_commander_kills = int( stats['kills_zombie_commander']) if 'kills_zombie_commander' in stats else None self.watcher_guardian_deaths = int( stats['deaths_watcher_guardian']) if 'deaths_watcher_guardian' in stats else None self.skeletor_prime_kills = int(stats['kills_skeletor_prime']) if 'kills_skeletor_prime' in stats else None self.super_tank_zombie_deaths = int( stats['deaths_super_tank_zombie']) if 'deaths_super_tank_zombie' in stats else None self.skeletor_prime_deaths = int(stats['deaths_skeletor_prime']) if 'deaths_skeletor_prime' in stats else None self.great_white_shark_kills = int( stats['kills_great_white_shark']) if 'kills_great_white_shark' in stats else None self.zombie_knight_deaths = int(stats['deaths_zombie_knight']) if 'deaths_zombie_knight' in stats else None self.suffocation_deaths = int(stats['deaths_suffocation']) if 'deaths_suffocation' in stats else None self.protector_dragon_deaths = int( stats['deaths_protector_dragon']) if 'deaths_protector_dragon' in stats else None self.sadan_deaths = int(stats['deaths_sadan']) if 'deaths_sadan' in stats else None self.sadan_golem_deaths = int(stats['deaths_sadan_golem']) if 'deaths_sadan_golem' in stats else None self.watcher_scarf_deaths = int(stats['deaths_watcher_scarf']) if 'deaths_watcher_scarf' in stats else None self.scarf_warrior_kills = int(stats['kills_scarf_warrior']) if 'kills_scarf_warrior' in stats else None self.crypt_undead_deaths = int(stats['deaths_crypt_undead']) if 'deaths_crypt_undead' in stats else None self.watcher_scarf_kills = int(stats['kills_watcher_scarf']) if 'kills_watcher_scarf' in stats else None self.spirit_bat_deaths = int(stats['deaths_spirit_bat']) if 'deaths_spirit_bat' in stats else None self.spirit_miniboss_deaths = int( stats['deaths_spirit_miniboss']) if 'deaths_spirit_miniboss' in stats else None self.spirit_chicken_deaths = int(stats['deaths_spirit_chicken']) if 'deaths_spirit_chicken' in stats else None self.spirit_sheep_deaths = int(stats['deaths_spirit_sheep']) if 'deaths_spirit_sheep' in stats else None self.crypt_undead_marius_kills = int( stats['kills_crypt_undead_marius']) if 'kills_crypt_undead_marius' in stats else None class SkyBlockObjective(object): r"""Represents a SkyBlock Objective. :param objective_name: The name of the objective. :type objective_name: str :param objective_data: The objective's data. :type objective_data: dict""" def __init__(self, objective_name: str, objective_data: dict): self.name = objective_name self.status = objective_data['status'] self.progress = objective_data['progress'] self.completed_at = datetime.datetime.fromtimestamp( objective_data['completed_at'] / 1000 ) if objective_data['completed_at'] != 0 else None class SkyBlockQuest(object): r"""Represents a SkyBlock quest. :param quest_name: The name of the quest. :type quest_name: str :param quest_data: The quest's data. :type quest_data: dict""" def __init__(self, quest_name: str, quest_data: dict): self.name = quest_name self.status = quest_data['status'] self.activated_at = datetime.datetime.fromtimestamp( quest_data['activated_at'] / 1000 ) self.completed_at = datetime.datetime.fromtimestamp( quest_data['completed_at'] / 1000 ) class SkyBlockSlayer(object): r"""Represents a SkyBlock slayer. :param slayer: The name of the slayer. :type slayer: str :param slayer_data: The slayer's data. :type slayer_data: dict""" def __init__(self, slayer: str, slayer_data: dict): self.slayer = slayer self.claimed_levels = slayer_data['claimed_levels'] self.xp = slayer_data['xp'] self.level = types[slayer](slayer_data['xp']) class SkyBlockPet(object): r"""Represents a SkyBlock pet. :param pet_data: The pet's data. :type pet_data: dict""" def __init__(self, pet_data: dict): self.uuid = pet_data['uuid'] self.type = pet_data['type'] self.xp = pet_data['exp'] self.active = pet_data['active'] self.tier = pet_data['tier'] self.held_item = pet_data['heldItem'] self.candy_used = pet_data['candyUsed'] self.skin = pet_data['skin'] class SkyBlockSkill(object): r"""Represents a SkyBlock skill. :param name: The skill's name. :type name: str :param skill_data: The skill's data. :type skill_data: dict""" def __init__(self, name, skill_data): self.name = name self.level = skill_data['level'] self.xp = skill_data['xp']

73.955645

120

0.733684

5,198

36,682

4.825702

0.076953

0.084835

0.11621

0.158468

0.638056

0.462805

0.320722

0.147385

0.102256

0.063028

0

0.000735

0.184287

36,682

495

121

74.105051

0.837578

0.052832

0

0.004728

0

0.321464

0.185294

0

1

0.014184

false

0

0.004728

0

0.033097

0

null

0

null

0

1

0

bdba7af199ee6c2c990e85c3f998b299c41d4413

604

py

Python

nicos_virt_mlz/reseda/setups/guidehall.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

12

2019-11-06T15:40:36.000Z

2022-01-01T16:23:00.000Z

nicos_virt_mlz/reseda/setups/guidehall.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

91

2020-08-18T09:20:26.000Z

2022-02-01T11:07:14.000Z

nicos_virt_mlz/reseda/setups/guidehall.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

6

2020-01-11T10:52:30.000Z

2022-02-25T12:35:23.000Z

description = 'FRM II Neutron guide hall west infrastructure devices' group = 'lowlevel' devices = dict( Sixfold = device('nicos.devices.generic.ManualSwitch', description = 'Sixfold shutter status', states = ('closed', 'open'), pollinterval = 60, maxage = 120, ), Crane = device('nicos.devices.generic.ManualMove', description = 'The position of the crane in the guide ' 'hall West measured from the east end', abslimits = (0, 60), pollinterval = 5, maxage = 30, unit = 'm', fmtstr = '%.1f', ), )

27.454545

69

0.584437

63

604

5.603175

0.698413

0.050992

0.073654

0.141643

0

0.028302

0.298013

604

21

70

28.761905

0.804245

0

0.105263

0

0.395695

0.109272

0

1

0

false

0

null

0

null

0

1

0

bdbb658871214d92211c98f23c493a5bef0ef8d6

2,366

py

Python

papermerge/core/management/commands/checkaccess.py

MinchinWeb/papermerge

8a5f73207413a3ea8989d277e140d448baa35ca4

[ "Apache-2.0" ]

null

papermerge/core/management/commands/checkaccess.py

MinchinWeb/papermerge

8a5f73207413a3ea8989d277e140d448baa35ca4

[ "Apache-2.0" ]

null

papermerge/core/management/commands/checkaccess.py

MinchinWeb/papermerge

8a5f73207413a3ea8989d277e140d448baa35ca4

[ "Apache-2.0" ]

null

import logging from django.core.management.base import BaseCommand try: from django_tenants.utils import get_tenant_model except: get_tenant_model = None from django.db import connection from papermerge.core.models import ( BaseTreeNode, Access ) from papermerge.core.auth import ( create_access_perms ) logger = logging.getLogger(__name__) class Command(BaseCommand): help = """Lists/Updates Access Models associated with nodes. """ def add_arguments(self, parser): parser.add_argument( '--count', '-c', action="store_true", help="Count nodes with/without associated access model." ) parser.add_argument( '--update', '-u', action="store_true", help="Updated nodes without associated access model." ) parser.add_argument( '--schema-name', '-s', help="Run checkaccess for this schema." ) def run_count( self, ): total_count = BaseTreeNode.objects.count() without_access_count = BaseTreeNode.objects.filter( access__isnull=True ).count() print( f"total={total_count}, without_access={without_access_count}" ) def run_update( self ): perms = create_access_perms() for node in BaseTreeNode.objects.all(): if node.access_set.count() == 0: access = Access.objects.create( user=node.user, access_type='allow', node=node ) access.permissions.add(*perms) def handle(self, *args, **options): count = options.get( 'count', False ) update = options.get( 'update', False ) schema_name = options.get('schema_name', False) TenantModel = get_tenant_model() if schema_name: tenant_list = TenantModel.objects.filter(name=schema_name) else: tenant_list = TenantModel.objects.exclude(name="public") for tenant in tenant_list: connection.set_tenant(tenant) if count: self.run_count() elif update: self.run_update()

24.645833

73

0.554522

235

2,366

5.395745

0.353191

0.039432

0.033123

0.029968

0.070978

0

0.000653

0.352494

2,366

95

74

24.905263

0.827024

0

0.113924

0

0.136517

0.015638

0

1

0.050633

false

0

0.075949

0

0.151899

0.012658

0

null

0

null

0

1

0

bdbe3e3e964d3de239112acfd757c2553c93386b

678

py

Python

11.dumbo-octopus/py/part1.py

rolandbernard/adventofcode-2021

9249815af62d0fcf79b71357330a1456ea3be1ed

[ "BSD-2-Clause" ]

null

11.dumbo-octopus/py/part1.py

rolandbernard/adventofcode-2021

9249815af62d0fcf79b71357330a1456ea3be1ed

[ "BSD-2-Clause" ]

null

11.dumbo-octopus/py/part1.py

rolandbernard/adventofcode-2021

9249815af62d0fcf79b71357330a1456ea3be1ed

[ "BSD-2-Clause" ]

null

import sys import numpy as np raw = sys.stdin.read() map = np.array([[c for c in l] for l in raw.split('\n') if len(l) != 0], dtype=int) def energize(map, i, j): if i >= 0 and j >= 0 and i < map.shape[0] and j < map.shape[1] and map[i, j] < 10: map[i, j] += 1 if map[i, j] >= 10: for di, dj in [(di, dj) for di in range(-1, 2) for dj in range(-1, 2) if di != 0 or dj != 0]: energize(map, i + di, j + dj) flashes = 0 for _ in range(100): for i in range(map.shape[0]): for j in range(map.shape[1]): energize(map, i, j) flashes += (map >= 10).sum() map[map >= 10] = 0 print('Result:', flashes)

26.076923

105

0.513274

130

678

2.669231

0.3

0.069164

0.072046

0.074928

0

0.057325

0.30531

678

25

106

27.12

0.679406

0

0.013314

0

1

0.055556

false

0

0.111111

0

0.166667

0.055556

0

null

0

null

0

1

0

bdbe6b219cb1418af34e685a56c258fed902050b

10,853

py

Python

rlb/utils.py

jaekyeom/drop-bottleneck

85b64ce72ac22af56e167da2817c295b79a03eb7

[ "Apache-2.0", "MIT" ]

8

2021-03-16T05:37:41.000Z

2021-06-18T05:15:15.000Z

rlb/utils.py

jaekyeom/drop-bottleneck

85b64ce72ac22af56e167da2817c295b79a03eb7

[ "Apache-2.0", "MIT" ]

null

rlb/utils.py

jaekyeom/drop-bottleneck

85b64ce72ac22af56e167da2817c295b79a03eb7

[ "Apache-2.0", "MIT" ]

2

2021-06-23T08:15:16.000Z

2021-08-30T14:13:58.000Z

from __future__ import print_function from collections import OrderedDict, defaultdict import numpy as np import random import copy #from mpi_util import mpi_moments #def fc(x, scope, nh, *, init_scale=1.0, init_bias=0.0): # with tf.variable_scope(scope): # nin = x.get_shape()[1].value # w = tf.get_variable("w", [nin, nh], initializer=ortho_init(init_scale)) # b = tf.get_variable("b", [nh], initializer=tf.constant_initializer(init_bias)) # return tf.matmul(x, w)+b # #def conv(x, scope, *, nf, rf, stride, pad='VALID', init_scale=1.0, data_format='NHWC', one_dim_bias=False, bias_initializer=tf.constant_initializer(0.0)): # if data_format == 'NHWC': # channel_ax = 3 # strides = [1, stride, stride, 1] # bshape = [1, 1, 1, nf] # elif data_format == 'NCHW': # channel_ax = 1 # strides = [1, 1, stride, stride] # bshape = [1, nf, 1, 1] # else: # raise NotImplementedError # bias_var_shape = [nf] if one_dim_bias else [1, nf, 1, 1] # nin = x.get_shape()[channel_ax].value # wshape = [rf, rf, nin, nf] # with tf.variable_scope(scope): # w = tf.get_variable("w", wshape, initializer=ortho_init(init_scale)) # b = tf.get_variable("b", bias_var_shape, initializer=bias_initializer) # if not one_dim_bias and data_format == 'NHWC': # b = tf.reshape(b, bshape) # return b + tf.nn.conv2d(x, w, strides=strides, padding=pad, data_format=data_format) # # #def deconv(x, scope, *, nf, rf, stride, init_scale=1.0, data_format='NHWC'): # if data_format == 'NHWC': # channel_ax = 3 # strides = (stride, stride) # #strides = [1, stride, stride, 1] # elif data_format == 'NCHW': # channel_ax = 1 # strides = (stride, stride) # #strides = [1, 1, stride, stride] # else: # raise NotImplementedError # # with tf.variable_scope(scope): # out = tf.contrib.layers.conv2d_transpose(x, # num_outputs=nf, # kernel_size=rf, # stride=strides, # padding='VALID', # weights_initializer=ortho_init(init_scale), # biases_initializer=tf.constant_initializer(0.0), # activation_fn=None, # data_format=data_format) # return out # # #def ortho_init(scale=1.0): # def _ortho_init(shape, dtype, partition_info=None): # #lasagne ortho init for tf # shape = tuple(shape) # if len(shape) == 2: # flat_shape = shape # elif len(shape) == 4: # assumes NHWC # flat_shape = (np.prod(shape[:-1]), shape[-1]) # else: # raise NotImplementedError # a = np.random.normal(0.0, 1.0, flat_shape) # u, _, v = np.linalg.svd(a, full_matrices=False) # q = u if u.shape == flat_shape else v # pick the one with the correct shape # q = q.reshape(shape) # return (scale * q[:shape[0], :shape[1]]).astype(np.float32) # return _ortho_init def tile_images(array, n_cols=None, max_images=None, div=1): if max_images is not None: array = array[:max_images] if len(array.shape) == 4 and array.shape[3] == 1: array = array[:, :, :, 0] assert len(array.shape) in [3, 4], "wrong number of dimensions - shape {}".format(array.shape) if len(array.shape) == 4: assert array.shape[3] == 3, "wrong number of channels- shape {}".format(array.shape) if n_cols is None: n_cols = max(int(np.sqrt(array.shape[0])) // div * div, div) n_rows = int(np.ceil(float(array.shape[0]) / n_cols)) def cell(i, j): ind = i * n_cols + j return array[ind] if ind < array.shape[0] else np.zeros(array[0].shape) def row(i): return np.concatenate([cell(i, j) for j in range(n_cols)], axis=1) return np.concatenate([row(i) for i in range(n_rows)], axis=0) def set_global_seeds(i): try: import tensorflow as tf except ImportError: pass else: #from mpi4py import MPI tf.set_random_seed(i) np.random.seed(i) random.seed(i) #def explained_variance_non_mpi(ypred,y): # """ # Computes fraction of variance that ypred explains about y. # Returns 1 - Var[y-ypred] / Var[y] # # interpretation: # ev=0 => might as well have predicted zero # ev=1 => perfect prediction # ev<0 => worse than just predicting zero # # """ # assert y.ndim == 1 and ypred.ndim == 1 # vary = np.var(y) # return np.nan if vary==0 else 1 - np.var(y-ypred)/vary # #def mpi_var(x): # return mpi_moments(x)[1]**2 # #def explained_variance(ypred,y): # """ # Computes fraction of variance that ypred explains about y. # Returns 1 - Var[y-ypred] / Var[y] # # interpretation: # ev=0 => might as well have predicted zero # ev=1 => perfect prediction # ev<0 => worse than just predicting zero # # """ # assert y.ndim == 1 and ypred.ndim == 1 # vary = mpi_var(y) # return np.nan if vary==0 else 1 - mpi_var(y-ypred)/vary def add_noise(img, noise_p, noise_type): noise_mask = np.random.binomial(1, noise_p, size=img.shape[0]).astype(np.bool) w = 12 n = 84//12 idx_list = np.arange(n*n) random.shuffle(idx_list) idx_list = idx_list[:np.random.randint(10, 40)] for i in range(img.shape[0]): if not noise_mask[i]: continue for idx in idx_list: y = (idx // n)*w x = (idx % n)*w img[i, y:y+w, x:x+w, -1] += np.random.normal(0, 255*0.3, size=(w,w)).astype(np.uint8) img = np.clip(img, 0., 255.) return img g_font = [None] def draw_text_to_image(text, height=None, width=None, channels=None): from PIL import Image, ImageDraw, ImageFont if g_font[0] is None: g_font[0] = ImageFont.load_default() font = g_font[0] # ImageFont.ImageFont.getsize doesn't work for multi-line strings. # https://github.com/python-pillow/Pillow/issues/2966 #text_size = font.getsize(text) dummy_img = Image.fromarray(np.zeros((1, 1), dtype=np.uint8)) dummy_draw = ImageDraw.Draw(dummy_img) text_size = dummy_draw.textsize(text, font=font) if channels is None: shape = (height or text_size[1], width or text_size[0]) else: shape = (height or text_size[1], width or text_size[0], channels) i = np.zeros(shape, dtype=np.uint8) img = Image.fromarray(i) draw = ImageDraw.Draw(img) draw.text((3, 0), text, font=font, fill=(255,)*channels) return np.asarray(img) def get_percentile_indices(data, percentiles=np.arange(0.0, 1.05, 0.1)): assert len(data.shape) == 1 data_asc = np.argsort(data) percentile_indices = (percentiles * (len(data_asc) - 1)).astype(int) percentile_indices = data_asc[percentile_indices] #assert np.all(data[percentile_indices[:-1]] <= data[percentile_indices[1:]]) return percentile_indices class CContext(): def __init__(self, verbose=False, print_func=print): self._state_funcs = OrderedDict() self._evaluated_states = OrderedDict() self._dependencies = defaultdict(set) self._eval_context = [] self._verbose = verbose self._print_func = print_func def register_state(self, name, create): if name in self._state_funcs: raise Exception('State already registered: {}'.format(name)) self._state_funcs[name] = create def invalidate_state(self, name): if name not in self._evaluated_states: return del self._evaluated_states[name] if self._verbose: self._print_func('Invalidated state "{}"'.format(name)) for n in self._dependencies[name]: self.invalidate_state(n) del self._dependencies[name] def __getattr__(self, attr): if attr not in self._state_funcs: raise Exception('Unknown state {}'.format(attr)) if attr in self._eval_context: raise Exception('Circular dependency detected: {}, {}'.format(attr, self._eval_context)) self._dependencies[attr] = self._dependencies[attr].union(set(self._eval_context)) if attr not in self._evaluated_states: self._eval_context.append(attr) evaluated_state = self._state_funcs[attr](self) if self._verbose: self._print_func('Evaluated state "{}"'.format(attr)) self._eval_context.pop() self._evaluated_states[attr] = evaluated_state return self._evaluated_states[attr] class EmptyClass(): pass # From https://github.com/openai/large-scale-curiosity/blob/0c3d179fd61ee46233199d0891c40fbe7964d3aa/cppo_agent.py#L226-L236 class RewardForwardFilter(object): def __init__(self, gamma): self.rewems = None self.gamma = gamma def update(self, rews): if self.rewems is None: self.rewems = rews else: self.rewems = self.rewems * self.gamma + rews return self.rewems class RunningMeanStd(object): # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm def __init__(self, epsilon=1e-4, shape=()): self.mean = np.zeros(shape, 'float64') self.var = np.ones(shape, 'float64') self.count = epsilon def update(self, x): batch_mean = np.mean(x, axis=0) batch_var = np.var(x, axis=0) batch_count = x.shape[0] self.update_from_moments(batch_mean, batch_var, batch_count) def update_from_moments(self, batch_mean, batch_var, batch_count): self.mean, self.var, self.count = update_mean_var_count_from_moments( self.mean, self.var, self.count, batch_mean, batch_var, batch_count) class SimpleWeightedMovingScalarMeanStd(): def __init__(self, alpha=0.0001): self._alpha = alpha self.mean = 0.0 self.var = 1.0 def update(self, values): self.mean = (1 - self._alpha) * self.mean + self._alpha * np.mean(values) self.var = (1 - self._alpha) * self.var + self._alpha * np.mean(np.square(values - self.mean)) def update_mean_var_count_from_moments(mean, var, count, batch_mean, batch_var, batch_count): delta = batch_mean - mean tot_count = count + batch_count new_mean = mean + delta * batch_count / tot_count m_a = var * count m_b = batch_var * batch_count M2 = m_a + m_b + np.square(delta) * count * batch_count / tot_count new_var = M2 / tot_count new_count = tot_count return new_mean, new_var, new_count

36.789831

155

0.609417

1,501

10,853

4.223185

0.207195

0.017353

0.017984

0.014198

0.265184

0.189778

0.13693

0.118946

0.097492

0

0.024378

0.262969

10,853

294

156

36.914966

0.768096

0.400719

0

0.04698

0

0.032415

0

0.020134

1

0.127517

false

0.013423

0.053691

0.006711

0.281879

0.033557

0

null

0

null

0

1

0

bdc046b158b884fea2bcbaf2bb1204d34d3b4b00

4,565

py

Python

pydtnsim/routing/cgr_utils.py

ducktec/pydtnsim

916b0ebfa2b65b7a80af293dd4c39f862eadeae3

[ "MIT" ]

8

2018-12-11T17:39:44.000Z

2021-05-07T10:24:03.000Z

pydtnsim/routing/cgr_utils.py

Elianelin/pydtnsim

916b0ebfa2b65b7a80af293dd4c39f862eadeae3

[ "MIT" ]

13

2019-01-14T14:08:15.000Z

2021-06-12T17:03:43.000Z

pydtnsim/routing/cgr_utils.py

Elianelin/pydtnsim

916b0ebfa2b65b7a80af293dd4c39f862eadeae3

[ "MIT" ]

4

2019-03-20T15:12:40.000Z

2022-02-22T06:16:24.000Z

"""Module of commonly shared functions of various flavours of CGR.""" import math def cgr_neighbor_function(contact_graph, node, destination, current_distance, set_visited, suppressed_contacts, lookahead_time): """Neighbor function of CGR used by the Dijkstra algorithm. Used to determine feasible direct neigbors of a given node. Args: contact_graph (ContactGraph): The topology information in the form of a contact graph node (tuple): The evaluated node in the contact graph node form ``(from_node, to_node, from_time, to_time, data_rate)``. destination (tuple): The nominal destination node in the form ``(destination_id, destination_id, 0, math.inf, math.inf)`` current_distance (int): Contains the weight of the shortest path to the currently investigated node (in ms). set_visited (set): Set used for storing the visited flag of nodes during the Dijkstra runs. Also used for excluding suppressed (physical) nodes. suppressed_contacts (list): List of contacts that shall not be considered for forwarding (and thus neighbor selection) lookahead_time (int): Time value that specifies a time window (or rather a maximum time) only in which routes are searched. This reduces the time necessary to find a shortest route. Returns: list: A list of all feasible neighbors with items of the form ``(<node_id>, weight)`` with ``<node_id>`` representing a certain contact in the contact graph. """ neighbors = [] # Set the node as visited set_visited.add(node.from_node) # Extract the start time of the given node for edge in contact_graph.graph[node].successors: # Break the loop if the found edge to_time is smaller than the # current distance. As the successor list is sorted, all subsequent # edges will be smaller as well. if edge.to_time <= current_distance: break # Only consider when neigbor has not been visited by dijkstra yet # and it is not in the suppressed_contacts list # and can be reached given the currently consideret point in time # and if it is within the lookahead window (only when a lookahead # window is used) if ((lookahead_time is None or edge.from_time < lookahead_time) and edge.to_node not in set_visited and edge not in suppressed_contacts and (edge.to_time > current_distance)): # Only add to neighbors if no artificial end node or artificial end # node is bundle's destination if edge == destination or edge.from_node != edge.to_node: # Calculate the time (which is either positive or 0, relevant # for artificial terminal nodes) weight = edge.from_time - current_distance weight = max(weight, 0) # Append to neighbor list with weight neighbors.append((edge, weight)) return neighbors def cgr_get_route_characteristics(route, distance): """Calculate characteristics of a certain route. Args: route (list): A list of the nodes of the calculated route that's elements comprise of all relevant information for determining the characteristics' distance (int): The precalculated distance Returns: tuple: A tuple consisting of the (precalculated) distance, the capacity and the end time of the availability of that route """ capacity = math.inf distance = 0 # Iterate over all nodes in route and check if capacity is smaller than # already found minimum for node in route: distance = max(distance, node.from_time) # Generate capacity for node's contact capacity_new = ((node.to_time - distance) * node.datarate) # Update capacity if smaller if capacity_new < capacity: capacity = capacity_new # The to_time of a route is the minimum end time of a contact within this # route (minus the assumed signal propagation delay, in the rr considered # to be neglegible) to_time = min([node.to_time for node in route]) # Return the characteristics tuple consisting of the route distance (i.e. # the arrival time), the route capacity and the route availability end # time (i.e. the to-time) return (distance, capacity, to_time)

42.268519

79

0.660241

611

4,565

4.847791

0.289689

0.020257

0.016205

0.011479

0.01688

0

0.001225

0.284556

4,565

107

80

42.663551

0.905695

0.645126

0

1

0.074074

false

0

0.037037

0

0.185185

0

null

0

null

0

1

0

bdc04f43fced1ed2108de24776b9c054870c3a6d

785

py

Python

rxbp/multicast/multicastobservers/mapmulticastobserver.py

MichaelSchneeberger/rx_backpressure

16173827498bf1bbee3344933cb9efbfd19699f5

[ "Apache-2.0" ]

24

2018-11-22T21:04:49.000Z

2021-11-08T11:18:09.000Z

rxbp/multicast/multicastobservers/mapmulticastobserver.py

MichaelSchneeberger/rx_backpressure

16173827498bf1bbee3344933cb9efbfd19699f5

[ "Apache-2.0" ]

1

2019-02-06T15:58:46.000Z

2019-02-12T20:31:50.000Z

rxbp/multicast/multicastobservers/mapmulticastobserver.py

MichaelSchneeberger/rx_backpressure

16173827498bf1bbee3344933cb9efbfd19699f5

[ "Apache-2.0" ]

1

2021-01-26T12:41:37.000Z

from dataclasses import dataclass from typing import Callable from rxbp.multicast.multicastobserver import MultiCastObserver from rxbp.multicast.typing import MultiCastItem @dataclass class MapMultiCastObserver(MultiCastObserver): source: MultiCastObserver func: Callable[[MultiCastItem], MultiCastItem] def on_next(self, item: MultiCastItem) -> None: try: def map_gen(): for v in item: yield self.func(v) next = map_gen() except Exception as exc: self.source.on_error(exc) else: self.source.on_next(next) def on_error(self, exc: Exception) -> None: self.source.on_error(exc) def on_completed(self) -> None: self.source.on_completed()

27.068966

62

0.650955

88

785

5.704545

0.386364

0.079681

0.095618

0.067729

0.079681

0

0.26879

785

29

63

27.068966

0.874564

0

0.090909

0

1

0.181818

false

0

0.181818

0

0.5

0

null

0

null

0

1

0

bdc402ae42475915911f7485b03af5085a350424

2,413

py

Python

clean-up-pdf.py

spider-walker/reading-pdf-files-mongo

3a7b5346bd8e5bedfba388ea9a0053cd8330d332

[ "Apache-2.0" ]

null

clean-up-pdf.py

spider-walker/reading-pdf-files-mongo

3a7b5346bd8e5bedfba388ea9a0053cd8330d332

[ "Apache-2.0" ]

null

clean-up-pdf.py

spider-walker/reading-pdf-files-mongo

3a7b5346bd8e5bedfba388ea9a0053cd8330d332

[ "Apache-2.0" ]

null

with open('./data/data2017.txt') as f: lines = f.readlines() for ln in lines: ln = ln.replace(',', '').replace(':', '').replace('int64', '') \ .replace('Name', '').replace('dtype', '').replace('/', ' ') \ .replace('object', '').replace('float64', ' ') \ .replace('NaN', '').replace('NaN', ' ') \ .replace('.', ' ') text = ln.split() if text[0].isnumeric(): text.pop(0) if text[0].isnumeric(): text.pop(0) if text[0].isnumeric(): text.pop(0) if not text[1].isnumeric(): text[0] = f'{text[0]} {text[1]}' text.pop(1) if not text[2].isnumeric(): text.insert(2, '0') text.insert(3, '0') if not text[1].isnumeric(): text[0] = f'{text[0]} {text[1]}' text.pop(1) if len(text) > 6 and not text[5].isnumeric(): text[4] = f'{text[4]} {text[5]}' text.pop(5) if len(text) > 6 and not text[5].isnumeric(): text[4] = f'{text[4]} {text[5]}' text.pop(5) if len(text) > 7 and not text[6].isnumeric(): text.insert(6, '0') text.insert(7, '0') if len(text) > 9 and not text[9].isnumeric(): text[8] = f'{text[8]} {text[9]}' text.pop(9) if len(text) > 9 and not text[9].isnumeric(): text[8] = f'{text[8]} {text[9]}' text.pop(9) if len(text) > 9 and not text[9].isnumeric(): text[8] = f'{text[8]} {text[9]}' text.pop(9) if len(text) > 7 and not text[7].isnumeric(): text.insert(7, '0') text.insert(9, '0') if len(text) == 10: text.insert(9, '0') if len(text) == 10: text.insert(9, '0') if len(text) == 11 and text[9].isnumeric() and int(text[9].strip())>100: text.insert(9, '0') if len(text) == 7: text.insert(1, '0') text.insert(3, '0') text.insert(6, '0') text.insert(7, '0') text.insert(9, '0') notwanted = ['-------------------------------', 'CONSTITUENCY_NAME', 'GRAND TOTAL', 'CAW'] if not set(text) & set(notwanted): text.insert(0, f'{len(text)}') s = ','.join(text) print(f' {s}')

30.544304

98

0.426855

312

2,413

3.298077

0.166667

0.145773

0.09621

0.048591

0.637512

0.613217

0.571429

0.501458

0

0.064267

0.35516

2,413

78

99

30.935897

0.597044

0

0.66129

0

0.11811

0.012847

0

1

0

false

0

0.016129

0

null

0

null

0

1

0

bdc63bfed4044eff802e2301570d6c7de4fbc7e5

1,423

py

Python

cv_workshops/7-day/9-clazz.py

afterloe/opencv-practice

83d76132d004ebbc96d99d34a0fd3fc37a044f9f

[ "MIT" ]

5

2020-03-13T07:34:30.000Z

2021-10-01T03:03:05.000Z

cv_workshops/7-day/9-clazz.py

afterloe/Opencv-practice

83d76132d004ebbc96d99d34a0fd3fc37a044f9f

[ "MIT" ]

null

cv_workshops/7-day/9-clazz.py

afterloe/Opencv-practice

83d76132d004ebbc96d99d34a0fd3fc37a044f9f

[ "MIT" ]

1

2020-03-01T12:35:02.000Z

#!/usr/bin/env python3 # -*- coding=utf-8 -*- import cv2 as cv """ 形态学分析应用 - 使用基本梯度对轮廓进行分析处理使用形态学的二值化处理，对是别内容进行轮廓分析，在OCR上是其处理的手段之一，相比于threshold的二值化而言，对图像会有更好的分割效果，技术路线如下: 1 图像形态学梯度 2 灰度 3 全局阈值二值化 4 轮廓分析 """ def main(): src = cv.imread("../../pic/1.jpg") blur = cv.medianBlur(src, 3) kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3)) gradient = cv.morphologyEx(blur, cv.MORPH_GRADIENT, kernel) cv.imshow("gradient", gradient) gray = cv.cvtColor(gradient, cv.COLOR_BGR2GRAY) _, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU) cv.imshow("binary", binary) # binary = cv.morphologyEx(binary, cv.MORPH_DILATE, cv.getStructuringElement(cv.MORPH_CROSS, (3, 3))) # 膨胀 3*3 十字交叉 contours, _ = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) if 0 == len(contours): print("未搜寻到结果") return for index in range(len(contours)): contour = contours[index] x, y, w, h = cv.boundingRect(contour) # 获取最大外接矩形 area = cv.contourArea(contour) # 获取轮廓面积 if not 10 < area < 500 or not 10 < h < 60: continue cv.rectangle(src, (x, y), (x + w, y + h), (0, 0, 255), 2, cv.LINE_8) cv.imshow("src", src) cv.waitKey(0) cv.destroyAllWindows() if "__main__" == __name__: main()

31.622222

121

0.598032

182

1,423

4.56044

0.516484

0.048193

0.060241

0.072289

0

0.036294

0.264231

1,423

44

122

32.340909

0.756447

0.120871

0

0.045635

0

1

0.038462

false

0

0.038462

0

0.115385

0.038462

0

null

0

null

0

1

0

bdc6adac6ca6afd61c4934fbf7cff6d47b19bb9a

1,118

py

Python

vaccibot/__main__.py

fsoubelet/vaccibot

f0956ddbf9f0ac712d3e6e10d9fb5f3edb3dda11

[ "MIT" ]

null

vaccibot/__main__.py

fsoubelet/vaccibot

f0956ddbf9f0ac712d3e6e10d9fb5f3edb3dda11

[ "MIT" ]

null

vaccibot/__main__.py

fsoubelet/vaccibot

f0956ddbf9f0ac712d3e6e10d9fb5f3edb3dda11

[ "MIT" ]

null

import sys from loguru import logger from rich.console import Console, RenderGroup from rich.panel import Panel from vaccibot.constants import LOGURU_FORMAT from vaccibot.parsing import ARGS from vaccibot.process import retrieve_all_suitable_appointments from vaccibot.render import make_department_table logger.remove() logger.add(sys.stdout, level=f"{ARGS.logs.upper()}", format=LOGURU_FORMAT) @logger.catch() def main() -> None: """Parses arguments from the commandline, fetches data and renders it in the terminal.""" console = Console() panels = [] suitable_appointments: dict = retrieve_all_suitable_appointments() for department, appointments in suitable_appointments.items(): if appointments: # do not make a panel and table if no appointments found panels.append( Panel( make_department_table(appointments), title=department, expand=True, border_style="scope.border", ) ) console.print(*panels) if __name__ == "__main__": main()

30.216216

93

0.674419

127

1,118

5.755906

0.503937

0.065663

0.051984

0.084815

0

0.246869

1,118

36

94

31.055556

0.868171

0.124329

0

0.040082

0

1

0.035714

false

0

0.285714

0

0.321429

0.035714

0

null

0

null

0

1

0

bdc856fc9ff913efd9003763250b43ae605b0ec6

1,277

py

Python

pi4home/components/text_sensor/custom.py

khzd/pi4home

937bcdcf77bab111cca10af1fe45c63a55c29aae

[ "MIT" ]

1

2019-05-16T02:52:12.000Z

pi4home/components/text_sensor/custom.py

khzd/pi4home

937bcdcf77bab111cca10af1fe45c63a55c29aae

[ "MIT" ]

null

pi4home/components/text_sensor/custom.py

khzd/pi4home

937bcdcf77bab111cca10af1fe45c63a55c29aae

[ "MIT" ]

null

import voluptuous as vol from pi4home.components import text_sensor import pi4home.config_validation as cv from pi4home.const import CONF_ID, CONF_LAMBDA, CONF_NAME, CONF_TEXT_SENSORS from pi4home.cpp_generator import add, process_lambda, variable from pi4home.cpp_types import std_vector CustomTextSensorConstructor = text_sensor.text_sensor_ns.class_('CustomTextSensorConstructor') PLATFORM_SCHEMA = text_sensor.PLATFORM_SCHEMA.extend({ cv.GenerateID(): cv.declare_variable_id(CustomTextSensorConstructor), vol.Required(CONF_LAMBDA): cv.lambda_, vol.Required(CONF_TEXT_SENSORS): cv.ensure_list(text_sensor.TEXT_SENSOR_SCHEMA.extend({ cv.GenerateID(): cv.declare_variable_id(text_sensor.TextSensor), })), }) def to_code(config): for template_ in process_lambda(config[CONF_LAMBDA], [], return_type=std_vector.template(text_sensor.TextSensorPtr)): yield rhs = CustomTextSensorConstructor(template_) custom = variable(config[CONF_ID], rhs) for i, conf in enumerate(config[CONF_TEXT_SENSORS]): rhs = custom.Pget_text_sensor(i) add(rhs.set_name(conf[CONF_NAME])) text_sensor.register_text_sensor(rhs, conf) BUILD_FLAGS = '-DUSE_CUSTOM_TEXT_SENSOR'

36.485714

96

0.750196

161

1,277

5.621118

0.360248

0.132597

0.049724

0.044199

0.095028

0

0.004673

0.162099

1,277

34

97

37.558824

0.841122

0

0.039937

0

1

0.038462

false

0

0.230769

0

0.269231

0

null

0

null

0

1

0

bdcc6e536cb58033fe61c7df47d8e2e7c55ae4c2

1,902

py

Python

awsume/autoawsume/process.py

ignatenkobrain/awsume

8191c35e8d60495e608c77801698c0a1a332d76f

[ "MIT" ]

654

2016-04-05T16:51:22.000Z

2022-03-28T21:07:30.000Z

awsume/autoawsume/process.py

ignatenkobrain/awsume

8191c35e8d60495e608c77801698c0a1a332d76f

[ "MIT" ]

149

2016-12-01T17:30:58.000Z

2022-03-29T23:49:50.000Z

awsume/autoawsume/process.py

ignatenkobrain/awsume

8191c35e8d60495e608c77801698c0a1a332d76f

[ "MIT" ]

90

2016-04-12T00:50:04.000Z

2022-03-30T20:44:45.000Z

import argparse import psutil from ..awsumepy.lib.aws_files import delete_section, get_aws_files, read_aws_file from ..awsumepy.lib.logger import logger def kill_autoawsume(): logger.debug('Killing autoawsume') for proc in psutil.process_iter(): try: for command_string in proc.cmdline(): if 'autoawsume' in command_string: proc.kill() except Exception: pass def kill(arguments: argparse.Namespace): _, credentials_file = get_aws_files(None, None) if arguments.profile_name: logger.debug('Stoping auto-refresh of profile {}'.format(arguments.profile_name)) profiles = read_aws_file(credentials_file) if 'autoawsume-{}'.format(arguments.profile_name) in profiles: delete_section('autoawsume-{}'.format(arguments.profile_name), credentials_file) profiles.pop('autoawsume-{}'.format(arguments.profile_name)) if arguments.profile_name in profiles and profiles[arguments.profile_name].get('autoawsume'): delete_section(arguments.profile_name, credentials_file) profiles.pop(arguments.profile_name) autoawsume_profiles = [{k: v} for k, v in profiles.items() if v.get('autoawsume')] if any(autoawsume_profiles): print('Stop {}'.format(arguments.profile_name)) return else: logger.debug('There were not more autoawsume profiles, stopping autoawsume') print('Kill') kill_autoawsume() else: logger.debug('Stopping all auto refreshing and removing autoawsume profiles') kill_autoawsume() profiles = read_aws_file(credentials_file) for profile in profiles: if 'autoawsume-' in profile or profiles[profile].get('autoawsume'): delete_section(profile, credentials_file) print('Kill')

41.347826

101

0.664038

215

1,902

5.688372

0.293023

0.130826

0.163532

0.106296

0.235487

0.130826

0.075225

0

0.239222

1,902

45

102

42.266667

0.845197

0

0.2

0

0.146162

0

1

0.05

false

0.025

0.1

0

0.175

0.075

0

null

0

null

0

1

0

bdcd63c3f87c9cdba74f8e75512a7d40eef94fd9

8,787

py

Python

engine/sprite.py

amirgeva/retroupy

1ee19b36a72c5f592cce150d1d0382a00ccdc4a0

[ "BSD-3-Clause" ]

null

engine/sprite.py

amirgeva/retroupy

1ee19b36a72c5f592cce150d1d0382a00ccdc4a0

[ "BSD-3-Clause" ]

null

engine/sprite.py

amirgeva/retroupy

1ee19b36a72c5f592cce150d1d0382a00ccdc4a0

[ "BSD-3-Clause" ]

null

import json import os import gc from .app import get_screen from .utils import Rect from .bitmatrix import BitMatrix class SpritesManager: def __init__(self): self.free_indices = [] self.last_used = -1 self.limit = 160 def clear(self): self.free_indices = [] self.last_used = -1 def allocate(self, data): if len(self.free_indices) > 0: sprite_id = self.free_indices[-1] del self.free_indices[-1] else: sprite_id = self.last_used + 1 if sprite_id >= self.limit: return -1 self.last_used = sprite_id get_screen().set_sprite(sprite_id, data) return sprite_id sprites_manager = SpritesManager() class SpriteSheet: def __init__(self, filename=''): self.width = 0 self.height = 0 self.data = None self.sprites = {} self.rect = None if filename: self.load(filename) def clean(self): self.data = None gc.collect() def load(self, filename): try: gc.collect() print("Loading "+filename) print("Free Mem: "+str(gc.mem_free())) with open(filename, 'rb') as f: data = f.read(4) self.width = (int(data[1]) << 8) | int(data[0]) self.height = (int(data[3]) << 8) | int(data[2]) data = None self.data = f.read() self.rect = Rect(0, 0, self.width, self.height) return True except OSError: return False def get_sprite_data(self, rect): if rect in self.sprites: return self.sprites.get(rect) if rect.valid() and self.rect.contains(rect) and rect.width() == 32 and rect.height() == 32: data = bytearray(32 * 32 * 2) src = (rect.tl.y * self.width + rect.tl.x) * 2 dst = 0 mask = BitMatrix(32, 32) mask.setall(True) for i in range(32): data[dst:(dst + 64)] = self.data[src:(src + 64)] for j in range(32): if data[dst + j * 2] == 0x20 and data[dst + j * 2 + 1] == 0: mask.set(j, i, False) dst = dst + 64 src = src + self.width * 2 sprite_data = sprites_manager.allocate(bytes(data)), mask self.sprites[rect] = sprite_data else: sprite_data = -1, None return sprite_data sprite_sheets = {} def get_sprite_sheet(filename): if filename in sprite_sheets: return sprite_sheets.get(filename) s = SpriteSheet(filename) sprite_sheets[filename] = s return s # EXPORT class Sprite(object): def __init__(self, sprite_id, mask, duration=0.0, flags=0): self.sprite_id = sprite_id self.mask = mask self.duration = duration self.flags = flags def draw(self, position): get_screen().draw_sprite(position.x, position.y, self.sprite_id, self.flags) @staticmethod def get_rect(): return Rect(0, 0, 32, 32) @staticmethod def deserialize(filename, obj): r = [int(a) for a in obj['Rect'].strip().split(',')] dur = obj['Duration'] flags = 0 if 'Flags' in obj: flags = obj['Flags'] rect = Rect(r[0], r[1], r[2], r[3]) sheet = get_sprite_sheet(filename) sprite_id, mask = sheet.get_sprite_data(rect) return Sprite(sprite_id, mask, dur, flags) # EXPORT class AnimationSequence(object): def __init__(self, name, base_vel=1.0): self.name = name self.base_vel = base_vel self.sprites = [] def add_sprite(self, sprite): self.sprites.append(sprite) def deserialize(self, filename, seq): self.sprites = [] for frame in seq['Frames']: self.add_sprite(Sprite.deserialize(filename, frame)) def __getitem__(self, index): return self.sprites[index] def __len__(self): return len(self.sprites) # EXPORT class StaticSprite: def __init__(self, sprite=None): self.sprite = sprite def get_current_sprite(self): return self.sprite def get_rect(self): if self.sprite: return self.sprite.get_rect() return Rect(0, 0, 32, 32) def draw(self, pos): if self.sprite: self.sprite.draw(pos) # EXPORT class AnimatedSprite(object): def __init__(self): self.sheet = None self.sequences = {} self.flags = {} self.active_sequence = None self.cur_sprite = 0 self.dt = 0.0 self.anim_dir = '' def add_flag(self, name, value): if name == 'AnimDir': self.anim_dir = value self.flags[name] = value def get_longest_sequence(self): mx = 0 res = None for name in self.sequences: seq = self.sequences.get(name) if len(seq) > mx: mx = len(seq) res = seq return res def get_sequence_by_name(self, name): return self.sequences.get(name) def get_sequence_by_index(self, index): for name in self.sequences.keys(): if index == 0: return self.sequences.get(name) index -= 1 return None def get_active_sequence_name(self): if not self.active_sequence: return '' return self.active_sequence.name def set_active_sequence(self, name): if name != self.get_active_sequence_name() and name in self.sequences: self.active_sequence = self.sequences.get(name) self.dt = 0.0 self.cur_sprite = 0 def add_sequence(self, seq): self.sequences[seq.name] = seq if not self.active_sequence: self.active_sequence = seq def calculate_axial_velocity(self, velocity): if self.anim_dir == 'X': return abs(velocity.x) if self.anim_dir == 'Y': return abs(velocity.y) return velocity.length() def advance(self, dt, velocity): axial_velocity = self.calculate_axial_velocity(velocity) # print "axial={}".format(axial_velocity) if self.active_sequence and len(self.active_sequence) > 0: mult = 1.0 if self.active_sequence.base_vel > 0 and axial_velocity > 0.001: mult = axial_velocity / self.active_sequence.base_vel # print "mult={}".format(mult) self.dt = self.dt + dt * mult # print "self.dt={}".format(self.dt) if self.cur_sprite >= len(self.active_sequence): self.cur_sprite = 0 spr = self.active_sequence[self.cur_sprite] while self.dt >= spr.duration: self.dt = self.dt - spr.duration self.cur_sprite += 1 if self.cur_sprite >= len(self.active_sequence): self.cur_sprite = 0 return True def get_current_sprite(self): if self.active_sequence: return self.active_sequence[self.cur_sprite] return None def get_current_height(self): spr = self.get_current_sprite() if spr: return spr.height() return 0 def draw(self, position): spr = self.get_current_sprite() if spr: spr.draw(position) def get_rect(self): spr = self.get_current_sprite() if spr: return spr.get_rect() return Rect(0, 0, 1, 1) def deserialize(self, obj, overrides): filename = obj['Image'] flags = obj['Flags'] for key in flags: self.add_flag(key, flags[key]) for seq in obj['Sequences']: base_vel = seq['BaseVelocity'] if 'BaseVelocity' in overrides: base_vel = overrides.get('BaseVelocity') s = AnimationSequence(seq['Name'], base_vel) s.deserialize(filename, seq) self.add_sequence(s) for name in sprite_sheets: sprite_sheets.get(name).clean() def load(self, filename, overrides={}): return self.deserialize(json.load(open(filename, "r")), overrides) # EXPORT def load_json_file(filename): obj = json.load(open(filename, "r")) a = AnimatedSprite() a.deserialize(obj) return a # EXPORT def load_json_str(s): obj = json.loads(s) a = AnimatedSprite() a.deserialize(obj) return a # EXPORT def load_file(filename): return load_json_file(filename) # EXPORT def load_str(s): return load_json_str(s) if __name__ == '__main__': print(os.getcwd())

27.719243

100

0.561739

1,102

8,787

4.315789

0.137931

0.052986

0.05677

0.027754

0.178091

0.105341

0.08831

0.082422

0.059294

0

0.017315

0.329578

8,787

316

101

27.806962

0.790019

0.018095

0

0.228571

0

0.01474

0

0.000464

0

1

0.167347

false

0

0.02449

0.032653

0.363265

0.012245

0

null

0

null

0

1

0

bdcfa69692cd0e84e62228bc835f2c497955444e

10,882

py

Python

natcap/versioner/versioning.py

natcap/versioner

65e4c1cf38115dcfec260f0d186cedca192b0b2e

[ "BSD-3-Clause" ]

null

natcap/versioner/versioning.py

natcap/versioner

65e4c1cf38115dcfec260f0d186cedca192b0b2e

[ "BSD-3-Clause" ]

null

natcap/versioner/versioning.py

natcap/versioner

65e4c1cf38115dcfec260f0d186cedca192b0b2e

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import import logging import os import re import subprocess import six LOGGER = logging.getLogger('natcap.versioner.versioning') LOGGER.setLevel(logging.ERROR) class VCSQuerier(object): name = 'VCS' is_archive = False repo_data_location = '' def __init__(self, repo_path): repo_root = self._find_repo_root(repo_path) if not repo_root: raise ValueError('Not within a %s repository: %s' % ( self.name, repo_path)) self._repo_path = repo_root def _find_repo_root(self, dirpath): """Walk up the directory tree and locate the directory that contains the repo data.""" abs_repo_path = os.path.abspath(dirpath) def _locate_data(path): # base case: we can't go up another directory and still haven't # found the repo data. if os.path.dirname(path) == path: return None if os.path.exists(os.path.join(path, self.repo_data_location)): return path return _locate_data(os.path.dirname(path)) return _locate_data(abs_repo_path) def _run_command(self, cmd, cwd=None): """Run a subprocess.Popen command. All output to stdout, stdin and stderr will be treated as stdout, captured, and returned. Commands are executed as shell commands. Parameters: cmd (string) - a python string to be executed in the shell. cwd=None (string or None) - the string path to the directory on disk to use as the CWD. If None, the current CWD will be used. Returns: A python bytestring of the output of the given command.""" p = subprocess.check_output( cmd, shell=True, stdin=subprocess.PIPE, stderr=subprocess.STDOUT, cwd=cwd) return p.strip().decode('utf-8') # output without leading/trailing newlines @property def tag_distance(self): raise NotImplementedError @property def build_id(self): raise NotImplementedError @property def latest_tag(self): raise NotImplementedError @property def branch(self): raise NotImplementedError @property def node(self): raise NotImplementedError @property def release_version(self): """This function gets the release version. Returns either the latest tag (if we're on a release tag) or None, if we're on a dev changeset.""" if self.tag_distance == 0: return self.latest_tag return None @property def version(self): """This function gets the module's version string. This will be either the dev build ID (if we're on a dev build) or the current tag if we're on a known tag. Either way, the return type is a string.""" release_version = self.release_version if release_version is None: return self.build_dev_id(self.build_id) return release_version def build_dev_id(self, build_id=None): """This function builds the dev version string. Returns a string.""" if build_id is None: build_id = self.build_id return 'dev%s' % (build_id) def pep440(self, branch=True, method='post'): assert method in ['pre', 'post'], ('Versioning method %s ' 'not valid') % method # If we're at a tag, return the tag only. if self.tag_distance == 0: return self.latest_tag template_string = "%(latesttag)s.%(method)s%(tagdist)s+n%(node)s" if branch is True: template_string += "-%(branch)s" latest_tag = self.latest_tag if method == 'pre': latest_tag = _increment_tag(latest_tag) data = { 'tagdist': self.tag_distance, 'latesttag': latest_tag, 'node': self.node, 'branch': self.branch, 'method': method, } version_string = template_string % data return version_string class HgArchive(VCSQuerier): name = 'Mercurial Archive' shortnode_len = 12 is_archive = True repo_data_location = '.hg_archival.txt' @property def build_id(self): attrs = _get_archive_attrs(self._repo_path) return '{latesttagdistance}:{latesttag} [{node}]'.format( latesttagdistance=attrs['latesttagdistance'], latesttag=attrs['latesttag'], node=attrs['node'][:self.shortnode_len], ) @property def tag_distance(self): try: return _get_archive_attrs(self._repo_path)['latesttagdistance'] except KeyError: # This happens when we are at a tag. return 0 @property def latest_tag(self): attrs = _get_archive_attrs(self._repo_path) try: return six.text_type(attrs['latesttag']) except KeyError: # This happens when we are at a tag. return six.text_type(attrs['tag']) @property def branch(self): return _get_archive_attrs(self._repo_path)['branch'] @property def node(self): return _get_archive_attrs(self._repo_path)['node'][:self.shortnode_len] class HgRepo(VCSQuerier): name = 'Mercurial' is_archive = False repo_data_location = '.hg' def _log_template(self, template_string): hg_call = 'hg log -r . --config ui.report_untrusted=False' cmd = (hg_call + ' --template="%s"') % template_string return self._run_command(cmd, cwd=self._repo_path) @property def build_id(self): """Call mercurial with a template argument to get the build ID. Returns a python bytestring.""" return self._log_template('{latesttagdistance}:{latesttag} ' '[{node|short}]') @property def tag_distance(self): """Call mercurial with a template argument to get the distance to the latest tag. Returns an int.""" return int(self._log_template('{latesttagdistance}')) @property def latest_tag(self): """Call mercurial with a template argument to get the latest tag. Returns a python bytestring.""" return self._log_template('{latesttag}') @property def branch(self): """Get the current branch from hg.""" return self._log_template('{branch}') @property def node(self): return self._log_template('{node|short}') class GitRepo(VCSQuerier): name = 'Git' repo_data_location = '.git' def __init__(self, repo_path): VCSQuerier.__init__(self, repo_path) self._tag_distance = None self._latest_tag = None self._commit_hash = None def _run_command(self, cmd): return VCSQuerier._run_command(self, cmd, self._repo_path) @property def branch(self): branch_cmd = 'git branch' current_branches = self._run_command(branch_cmd) for line in current_branches.split('\n'): if line.startswith('* '): return line.replace('* ', '').strip() raise IOError('Could not detect current branch') def _describe_current_rev(self): self._tag_distance = None self._latest_tag = None self._commit_hash = None current_branch = self.branch try: data = self._run_command('git describe --tags') except subprocess.CalledProcessError: # when there are no tags self._latest_tag = 'null' num_commits_cmd = 'git rev-list %s --count' % current_branch self._tag_distance = self._run_command(num_commits_cmd) commit_hash_cmd = 'git log -1 --pretty="format:%h"' self._commit_hash = self._run_command(commit_hash_cmd) else: if '-' not in data: # then we're at a tag self._latest_tag = str(data) self._tag_distance = 0 commit_hash_cmd = 'git log -1 --pretty="format:%h"' self._commit_hash = self._run_command(commit_hash_cmd) else: # we're not at a tag, so data has the format: # data = tagname-tagdistange-commit_hash tagname, tag_dist, _commit_hash = data.split('-') self._tag_distance = int(tag_dist) self._latest_tag = tagname self._commit_hash = self.node @property def build_id(self): self._describe_current_rev() return "%s:%s [%s]" % (self._tag_distance, self._latest_tag, self._commit_hash) @property def tag_distance(self): self._describe_current_rev() return self._tag_distance @property def latest_tag(self): self._describe_current_rev() return self._latest_tag @property def node(self): return self._run_command('git rev-parse HEAD').strip()[:8] @property def is_archive(self): # Archives are a mercurial feature. return False def _increment_tag(version_string): assert len(re.findall('([0-9].?)+', version_string)) >= 1, ( 'Version string must be a release') # increment the minor version number and not the update num. tag = [int(s) for s in version_string.split('.')] tag[-1] += 1 return '.'.join([str(i) for i in tag]) def _get_archive_attrs(archive_path): """ If we're in an hg archive, there will be a file '.hg_archival.txt' in the repo root. If this is the case, we can fetch relevant build information from this file that we might normally be able to get directly from hg. Parameters: attr (string): The archive attr to fetch. One of "repo"|"node"|"branch"|"latesttag"|"latesttagdistance"|"changessincelatesttag" archive_path (string): The path to the mercurial archive. The .hg_archival.txt file must exist right inside this directory. Returns: A dict of the attributes within the .hg_archival file. Raises: IOError when the .hg_archival.txt file cannot be found. KeyError when `attr` is not in .hg_archival.txt """ archival_filepath = os.path.join(archive_path, '.hg_archival.txt') attributes = {} with open(archival_filepath) as archival_file: for line in archival_file: attr_name, value = line.strip().split(': ') # Try to cast the attribute to an int (since it might be a # revision number). If it doesn't cast, leave it as a string. try: value = int(value) except ValueError: pass attributes[attr_name] = value return attributes

32.195266

86

0.609355

1,360

10,882

4.675735

0.188971

0.039786

0.020758

0.028306

0.301462

0.19437

0.142947

0.126435

0.089322

0.077685

0

0.002355

0.297647

10,882

337

87

32.290801

0.829648

0.231299

0

0.37156

0

0.09464

0.019593

0

0.009174

1

0.16055

false

0.004587

0.027523

0.417431

0

null

0

null

0

1

0

bdd18a04d17e95c0953ca2e0c3d8db5c195b7e67

1,054

py

Python

src/two_level_aspect_entity_embedding_generation/clusterd_knowledge_graph_statistics.py

mainuliitkgp/AR-BERT

d6d5e8542a3a1c76edac49cec9e99ebda6395725

[ "MIT" ]

4

2022-03-06T17:41:57.000Z

2022-03-22T08:42:58.000Z

src/two_level_aspect_entity_embedding_generation/clusterd_knowledge_graph_statistics.py

mainuliitkgp/AR-BERT

d6d5e8542a3a1c76edac49cec9e99ebda6395725

[ "MIT" ]

null

src/two_level_aspect_entity_embedding_generation/clusterd_knowledge_graph_statistics.py

mainuliitkgp/AR-BERT

d6d5e8542a3a1c76edac49cec9e99ebda6395725

[ "MIT" ]

1

2022-03-19T14:04:42.000Z

from __future__ import print_function import numpy as np import random import json import sys import os import networkx as nx from networkx.readwrite import json_graph version_info = list(map(int, nx.__version__.split('.'))) major = version_info[0] minor = version_info[1] assert (major <= 1) and (minor <= 11), "networkx major version > 1.11" if __name__ == "__main__": graph_file = sys.argv[1] #out_file = sys.argv[2] G_data = json.load(open(graph_file)) #print(G_data) G = json_graph.node_link_graph(G_data) nodes = [n for n in G.nodes() if not G.node[n]["val"] and not G.node[n]["test"]] G = G.subgraph(nodes) count = 0 max_node_degree = 0 for count, node in enumerate(nodes): if G.degree(node) == 0: continue else : count += G.degree(node) if G.degree(node)>max_node_degree: max_node_degree = G.degree(node) avg_node_degree = count/len(nodes) print(len(nodes), avg_node_degree, max_node_degree) print(nx.is_connected(G))

28.486486

84

0.651803

164

1,054

3.926829

0.365854

0.093168

0.080745

0.02795

0.071429

0

0.01599

0.228653

1,054

36

85

29.277778

0.776138

0.033207

0

0.044248

0

0.033333

1

0

false

0

0.266667

0

0.266667

0.1

0

null

0

null

0

1

0

bdd1f5712640c660f6739b854770d7e695f1c4d4

8,167

py

Python

src/aioros/tcpros/topic.py

mgrrx/aioros

9bd750020d0d5fb466891346f61b6f083cbb8f05

[ "Apache-2.0" ]

8

2020-08-27T17:16:59.000Z

2022-02-02T13:39:41.000Z

src/aioros/tcpros/topic.py

mgrrx/aioros

9bd750020d0d5fb466891346f61b6f083cbb8f05

[ "Apache-2.0" ]

3

2022-02-09T19:18:12.000Z

2022-03-08T21:12:00.000Z

src/aioros/tcpros/topic.py

mgrrx/aioros

9bd750020d0d5fb466891346f61b6f083cbb8f05

[ "Apache-2.0" ]

null

from asyncio import AbstractEventLoop from asyncio import iscoroutinefunction from asyncio import Event from asyncio import IncompleteReadError from asyncio import Queue from asyncio import open_connection from asyncio import open_unix_connection from typing import Dict from typing import List from typing import Set from typing import Tuple from typing import Type from genpy import Message from ..api.node_api_client import NodeApiClient from .protocol import Serializer from .protocol import encode_header from .protocol import read_data from .protocol import read_header from .publisher import Publisher from .subscription import Subscription class SubscriberInitError(Exception): pass class Topic: def __init__( self, loop: AbstractEventLoop, node_name: str, topic_name: str, msg_type: Type[Message] ) -> None: self._loop = loop self._node_name = node_name self._topic_name = topic_name self._msg_type = msg_type self._connected_subscribers: Dict[str, Queue] = {} self._connected_publishers: Dict[str, Event] = {} self._has_connected_subscribers: Event = Event() self._has_connected_publishers: Event = Event() self._internal_subscriptions: Set[Subscription] = set() self._internal_publishers: Set[Publisher] = set() self._latched_msgs: Dict[Publisher, bytes] = {} self._serializer: Serializer = Serializer() @property def name(self) -> str: return self._topic_name @property def type(self) -> Type[Message]: return self._msg_type @property def type_name(self) -> str: return self._msg_type._type @property def md5sum(self) -> str: return self._msg_type._md5sum @property def nr_connected_subscribers(self) -> int: return len(self._connected_subscribers) @property def nr_connected_publishers(self) -> int: return len(self._connected_publishers) async def wait_for_connected_subscribers(self) -> None: await self._has_connected_subscribers.wait() async def wait_for_connected_publishers(self) -> None: await self._has_connected_publishers.wait() @property def has_subscriptions(self) -> bool: return bool(self._internal_subscriptions) @property def has_publishers(self) -> bool: return bool(self._internal_publishers) @property def is_latching(self) -> bool: return any(pub.latch for pub in self._internal_publishers) def get_publisher_header(self) -> Dict[str, str]: return dict( topic=self.name, type=self.type_name, latching='1' if self.is_latching else '0', message_definition=self.type._full_text, md5sum=self.md5sum, callerid=self._node_name) def register_publisher( self, publisher: Publisher ) -> None: self._internal_publishers.add(publisher) async def unregister_publisher( self, publisher: Publisher ) -> bool: self._latched_msgs.pop(publisher, None) self._internal_publishers.discard(publisher) return self.has_publishers def register_subscription( self, subscription: Subscription ) -> None: self._internal_subscriptions.add(subscription) async def unregister_subscription( self, subscription: Subscription ) -> bool: self._internal_subscriptions.discard(subscription) if not self.has_subscriptions: for event in self._connected_publishers.values(): event.set() return self.has_subscriptions def publish( self, publisher: Publisher, msg: Message ) -> None: if not self._connected_subscribers and not self.is_latching: return with self._serializer.serialize(msg) as serialized_msg: for queue in self._connected_subscribers.values(): queue.put_nowait(serialized_msg) if publisher.latch: self._latched_msgs[publisher] = serialized_msg async def connect_subscriber( self, node_name: str, queue: Queue ) -> None: for publisher in self._internal_publishers: if publisher.on_peer_connect: msg = publisher.on_peer_connect(node_name) if msg: with self._serializer.serialize(msg) as serialized_msg: await queue.put(serialized_msg) serialized_msg = self._latched_msgs.get(publisher) if serialized_msg is not None: await queue.put(serialized_msg) self._connected_subscribers[node_name] = queue self._has_connected_subscribers.set() def disconnect_subscriber( self, node_name: str ) -> None: for publisher in self._internal_publishers: if publisher.on_peer_disconnect: publisher.on_peer_disconnect(node_name) del self._connected_subscribers[node_name] if not self._connected_subscribers: self._has_connected_subscribers.clear() def connect_to_publishers( self, publishers: List[str] ) -> None: publishers_set = set(publishers) for publisher_uri in publishers: if publisher_uri in self._connected_publishers: continue self._connected_publishers[publisher_uri] = Event() self._loop.create_task( self._subscribe(publisher_uri)) for publisher_uri in self._connected_publishers: if publisher_uri not in publishers_set: self._connected_publishers[publisher_uri].set() async def _subscribe( self, publisher_uri: str ) -> None: connection_params = await self._get_publisher_connection_params( publisher_uri) try: if connection_params[0] == 'UNIXROS': reader, writer = await open_unix_connection( connection_params[1]) elif connection_params[0] == 'TCPROS': reader, writer = await open_connection( connection_params[1], int(connection_params[2])) header = dict( topic=self.name, message_definition=self.type._full_text, tcp_nodelay='1', md5sum=self.md5sum, type=self.type_name, callerid=self._node_name) writer.write(encode_header(header)) await writer.drain() header_dict = await read_header(reader) if 'error' in header_dict: raise SubscriberInitError(header_dict['error']) self._has_connected_publishers.set() while not self._connected_publishers[publisher_uri].is_set(): msg = self.type() msg.deserialize(await read_data(reader)) for sub in self._internal_subscriptions: if iscoroutinefunction(sub.callback): self._loop.create_task(sub.callback(msg)) else: self._loop.call_soon(sub.callback, msg) except (ConnectionResetError, IncompleteReadError): pass finally: writer.close() if hasattr(writer, 'wait_closed'): await writer.wait_closed() self._connected_publishers.pop(publisher_uri) if not self._connected_publishers: self._has_connected_publishers.clear() async def _get_publisher_connection_params( self, publisher_uri: str ) -> Tuple[str, int]: client = NodeApiClient(self._node_name, publisher_uri) topic = await client.request_topic( self.name, [['UNIXROS'], ['TCPROS']]) await client.close() if topic[0] not in ('UNIXROS', 'TCPROS'): raise ValueError('protocol is not supported') return topic

32.40873

75

0.628872

869

8,167

5.628308

0.156502

0.045185

0.047025

0.022081

0.212635

0.116132

0.041709

0.023308

0

0.002609

0.29607

8,167

251

76

32.537849

0.848148

0

0.260465

0

0.010775

0

1

0.074419

false

0.009302

0.093023

0.046512

0.24186

0

null

0

null

0

1

0

bdd325e14189de11da8187eeb203eba8b96feec5

1,911

py

Python

tests/core/test_local.py

riddopic/opta

25fa6435fdc7e2ea9c7963ed74100fffb0743063

[ "Apache-2.0" ]

595

2021-05-21T22:30:48.000Z

2022-03-31T15:40:25.000Z

tests/core/test_local.py

riddopic/opta

25fa6435fdc7e2ea9c7963ed74100fffb0743063

[ "Apache-2.0" ]

463

2021-05-24T21:32:59.000Z

2022-03-31T17:12:33.000Z

tests/core/test_local.py

riddopic/opta

25fa6435fdc7e2ea9c7963ed74100fffb0743063

[ "Apache-2.0" ]

29

2021-05-21T22:27:52.000Z

2022-03-28T16:43:45.000Z

import json import os import unittest from opta.core.local import Local from opta.layer import Layer class LocalTests(unittest.TestCase): def setUp(self) -> None: self.layer = Layer( name="testname", org_name="testorg", providers={"local": {"path": "/tmp"}}, modules_data=[], path="/tmp", parent=None, ) self.local = Local(self.layer) self.local.tf_file = "/tmp/tfconfig" self.local.config_file_path = "/tmp/localconfig" with open(self.local.config_file_path, "w") as f: json.dump( { "opta_version": "dev", "date": "2021-11-15T18:26:47.553097", "original_spec": "", "defaults": {}, }, f, ) with open(self.local.tf_file, "w") as f: f.write("Some tf state for testing") return super().setUp() def tearDown(self) -> None: if os.path.isfile("/tmp/localconfig"): os.remove("/tmp/localconfig") if os.path.isfile("/tmp/tfconfig"): os.remove("/tmp/tfconfig") return super().tearDown() def test_get_remote_config(self) -> None: assert self.local.get_remote_config() == { "opta_version": "dev", "date": "2021-11-15T18:26:47.553097", "original_spec": "", "defaults": {}, } def test_upload_opta_config(self) -> None: self.local.upload_opta_config() dict = json.load(open(self.local.config_file_path, "r")) assert set(dict.keys()) == set( ["opta_version", "original_spec", "date", "defaults"] ) def test_delete_remote_state(self) -> None: self.local.delete_remote_state() assert os.path.isfile(self.local.tf_file) is False

30.333333

65

0.529042

214

1,911

4.574766

0.341122

0.091931

0.036772

0.045965

0.233912

0.175689

0.120531

0

0.031177

0.328624

1,911

62

66

30.822581

0.731878

0

0.150943

0

0.165358

0.027211

0

0.056604

1

0.09434

false

0

0.09434

0

0.245283

0

null

0

null

0

1

0

bdd62727280cef056fc775786f12ebffe8812748

28,253

py

Python

tiknib/feature/asm_const.py

SoftSec-KAIST/tiknib

5f1e25df0ff652cf35574dae3e6a3cfb3b163e63

[ "MIT" ]

null

tiknib/feature/asm_const.py

SoftSec-KAIST/tiknib

5f1e25df0ff652cf35574dae3e6a3cfb3b163e63

[ "MIT" ]

null

tiknib/feature/asm_const.py

SoftSec-KAIST/tiknib

5f1e25df0ff652cf35574dae3e6a3cfb3b163e63

[ "MIT" ]

null

from tiknib.feature.asm_ppc import PPC_GRP_MAP # ==================== x86 32 ============================================= # data transfer X86_GRP_DTRANSFER = [ # general purpose instructions "CMOV", "CMOVA", "CMOVAE", "CMOVB", "CMOVBE", "CMOVC", "CMOVE", "CMOVG", "CMOVGE", "CMOVL", "CMOVLE", "CMOVNA", "CMOVNAE", "CMOVNB", "CMOVNBE", "CMOVNC", "CMOVNE", "CMOVNG", "CMOVNGE", "CMOVNL", "CMOVNLE", "CMOVNO", "CMOVNP", "CMOVNS", "CMOVNZ", "CMOVO", "CMOVP", "CMOVPE", "CMOVPO", "CMOVS", "CMOVZ", "BSWAP", "XCHG", "XADD", "CMPXCHG", "CMPXCHG8B", "POP", "POPA", "POPAD", "PUSH", "PUSHA", "PUSHAD", "CDQ", "CDQE", "CBW", "CWD", "CWDE", "MOV", "MOVD", "MOVQ", "MOVABS", "MOVSX", "MOVSXD", "MOVZX", "MOVZXD", # string "MOVS", "MOVSB", "MOVSD", "MOVSW", "STOS", "STOSB", "STOSD", "STOSW", "LODS", "LODSB", "LODSD", "LODSW", # segment register "LDS", "LES", "LFS", "LGS", "LSS", # user mode extended "XSAVE", "XSAVEC", "XSAVEOPT", "XRSTOR", "XGETBV", "XSETBV", # BMI1, BMI2 "BEXTR", "BLSI", "PDEP", "PEXT", # MMX "PACKSSDW", "PACKSSWB", "PACKUSDW", "PACKUSWB", "PUNPCKHBW", "PUNPCKHDQ", "PUNPCKHWD", "PUNPCKLBW", "PUNPCKLDQ", "PUNPCKLWD", "EMMS", # SSE 64-bit integer "PMOVMSKB", "PSHUFW", # SSE2 128-bit integer "MOVDQA", "MOVDQU", "MOVQ2DQ", "MOVDQ2Q", "PSHUFLW", "PSHUFHW", "PSHUFD", "PUNPCKLQDQ", "PUNPCKHQDQ", # SSSE2 "PSHUFB", "PALIGNR", # SSE4 "MOVNTDQA", "PBLENDVB", "PBLENDW", "PINSRB", "PINSRD", "PINSRQ", "PEXTRB", "PEXTRW", "PEXTRD", "PEXTRQ", "PMOVSXBW", "PMOVZXBW", "PMOVSXBD", "PMOVZXBD", "PMOVSXWD", "PMOVZXWD", "PMOVSXBQ", "PMOVZXBQ", "PMOVSXWQ", "PMOVZXWQ", "PMOVSXDQ", "PMOVZXDQ", "PACKUSDW", "LGDT", "SGDT", "LLDT", "SLDT", "LTR", "STR", "LIDT", "SIDT", "MOV", "LMSW", "SMSW", "CLTS", "LSL", "LAR", "VERR", "VERW", # 64-bit "CDQE", "CQO", ] X86_GRP_FLOAT_DTRANSFER = [ # floating point instrutions "FLD", "FST", "FSTP", "FILD", "FIST", "FISTP", "FBLD", "FBSTP", "FXCH", "FCMOVB", "FCMOVBE", "FCMOVE", "FCMOVNB", "FCMOVNBE", "FCMOVNE", "FCMOVNU", "FCMOVU", # floating point load const instructions "FLD1", "FLDZ", "FLDPI", "FLDL2E", "FLDLN2", "FLDL2T", "FLDLG2", # FPU register related "FCLEX", "FFREE", "FINIT", "FLDCW", "FLDENV", "FNCLEX", "FNINIT", "FNOP", "FNSAVE", "FNSTCW", "FNSTENV", "FNSTSW", "FRSTOR", "FSAVE", "FSTCW", "FSTENV", "FSTSW", # SSE "MOVAPS", "MOVUPS", "MOVHPS", "MOVHLPS", "MOVLPS", "MOVLHPS", "MOVMSKPS", "MOVSS", # SSE2 "MOVAPD", "MOVUPD", "MOVHPD", "MOVHLPD", "MOVLPD", "MOVLHPD", "MOVMSKPD", "MOVSD", # SSE Shuffle "SHUFPS", "UNPCKHPS", "UNPCKLPS", # SSE2 shuffle "SHUFPD", "UNPCKHPD", "UNPCKLPD", # SSE Conversion "CVTPI2PS", "CVTSI2SS", "CVTPS2PI", "CVTTPS2PI", "CVTSS2SI", "CVTTSS2SI", # SSE2 Conversion "CVTPD2PI", "CVTTPD2PI", "CVTPI2PD", "CVTPD2DQ", "CVTTPD2DQ", "CVTDQ2PD", "CVTPS2PD", "CVTPD2PS", "CVTSS2SD", "CVTSD2SS", "CVTSD2SI", "CVTTSD2SI", "CVTSI2SD", "CVTDQ2PS", "CVTPS2DQ", "CVTTPS2DQ", # SSE MXCSR State "LDMXCSR", "STMXCSR", # SSE 64-bit "PEXTRW", "PINSRW", # SSE cache "MASKMOVQ", "MOVNTQ", "MOVNTPS", "PREFETCH", "SFENCE", # SSE3 "FISTTP", "LDDQU", "MOVSHDUP", "MOVSLDUP", "MOVDDUP", # SSE4 "BLENDPD", "BLENDPS", "BLENDVPD", "BLENDVPS", "EXTRACTPS", "INSERTPS", # 16-bit FP "VCVTPS2PH", "VCVTPS2PH", # Vector "VALIGN", "VBLEND", "VCOMPRESS", "VEXTRACT", "VINSERT", "VMOV", "VFIXUP", "VGET", "VEXPAND", "VCVT", "VPBLEND", "VPBROAD", "VPCOMPRESS", "VPERM" "VPEXPAND" "VPMOV", "VPSCATTER", "VSCATTER", "VSHUF", ] # - Miscellaneous Instructions: X86_GRP_MISC = [ "NOP", "UD", "UD2", "LEA", "XLAT", "XLATB", "CPUID", "MOVBE", "PREFETCHW", "PREFETCHWT1", "CLFLUSH", "CLFLUSHOPT", # SSE2 cache "CLFLUSH", "LFENCE", "MFENCE", "MASKMOVDQU", "MOVNTPD", "MOVNTDQ", "MOVNTI", ] X86_GRP_ARITH = [ # general purpose binary arithmetic instructions "ADCX", "ADOX", "ADC", "ADD", "XADD", "SUB", "SBB", "IMUL", "MUL", "IDIV", "DIV", "INC", "DEC", "NEG", "CMP", # decimal arithmetic instructions "DAA", "DAS", "AAA", "AAS", "AAM", "AAD", # flag "STC", "CLC", "CMC", "CLD", "STD", # BMI1, BMI2 "MULX", # MMX "PADD", "PADDB", "PADDW", "PADDD", "PADDSB", "PADDSW", "PADDUSB", "PADDUSW", "PSUB", "PSUBB", "PSUBW", "PSUBD", "PSUBSB", "PSUBSW", "PSUBUSB", "PSUBUSW", "PMULHW", "PMULLW", "PMADDWD", # SSE 64bit integer "PAVGB", "PAVGW", "PMAXUB", "PMAXSB", "PMINUB", "PMINSB", "PMULHUW", "PSADBW", # SSE 128-bit integer "PMULUDQ", "PADDQ", "PSUBQ", # SSSE3 "PHADDW", "PHADDSW", "PHADDD", "PHSUBW", "PHSUBSW", "PHSUBD", "PABSB", "PABSW", "PABSD", "PABSQ", "PMADDUBSW", "PMULHRSW", "PSIGNB", "PSIGNW", "PSIGND", # SSE4 "PMULLD", "PMULDQ", "PMINUW", "PMINUD", "PMINSB", "PMINSD", "PMAXUW", "PMAXUD", "PMAXSB", "PMAXSD", "ROUNDPS", "ROUNDPD", "ROUNDSS", "ROUNDSD", "PMPSADBW", # AESNI "AESDEC", "AESDECLAST", "AESENC", "AESENCLAST", "AESIMC", "AESKEYGENASSIST", "PCLMULQDQ", # SHA1 "SHA1MSG1", "SHA1MSG2", "SHA1NEXTE", "SHA1RNDS4", "SHA256MSG1", "SHA256MSG2", "SHA256RNDS2", "CRC32", # BMI1, BMI2 "BLSMSK", "BLSR", "CLAC", "STAC", ] X86_GRP_FLOAT_CMP = [ # floating point compare instructions "FCOM", "FCOMP", "FCOMPP", "FUCOM", "FUCOMP", "FUCOMPP", "FICOM", "FICOMP", "FCOMI", "FUCOMI", "FCOMIP", "FUCOMIP", "FTST", "FXAM", # SSE "CMPPS", "CMPEQPS", "CMPNEQPS", "CMPLTPS", "CMPNLTPS", "CMPSS", "CMPEQSS", "CMPNEQSS", "CMPLTSS", "CMPNLTSS", "COMISS", "UCOMISS", "CMPPD", "CMPEQPD", "CMPNEQPD", "CMPLTPD", "CMPNLTPD", "CMPSD", "CMPEQSD", "CMPNEQSD", "CMPLTSD", "CMPNLTSD", "COMISD", "UCOMISD", # vector "VPCMP", ] X86_GRP_FLOAT_ARITH = [ # - floating point instructions: "FADD", "FADDP", "FIADD", "FSUB", "FSUBP", "FISUB", "FSUBR", "FSUBRP", "FISUBR", "FMUL", "FMULP", "FIMUL", "FDIV", "FDIVP", "FIDIV", "FDIVR", "FDIVRP", "FIDIVR", "FPREM", "FPREM1", "FABS", "FCHS", "FRNDINT", "FSCALE", "FSQRT", "FXTRACT", # floating point transcendental instructions "FSIN", "FCOS", "FSINCOS", "FPTAN", "FPATAN", "F2XM1", "FYL2X", "FYL2XP1", # fpu register related "FINCSTP", "FDECSTP", # SSE "ADDPS", "ADDSS", "SUBPS", "SUBSS", "MULPS", "MULSS", "DIVPS", "DIVSS", "RCPPS", "RCPSS", "SQRTPS", "SQRTSS", "RSQRTPS", "RSQRTSS", "MAXPS", "MAXSS", "MINPS", "MINSS", # SSE2 "ADDSD", "SUBSD", "MULSD", "DIVSD", "RCPSD", "SQRTSD", "RSQRTSD", "MAXSD", "MINSD", # SSE3 "ADDSUBPS", "ADDSUBPD", "HADDPS", "HSUBPS", "HADDPD", "HSUBPD", # SSE4 "DPPD", "DPPS", # vector "VPMAX", "VPMIN", "VRCP", "VRNDSCAL", "VRSQRT", "VSCALE", "ADDPD", "ADDSD", "MULPD", "MULSD", "SUBPD", "SUBSD", "DIVPD", "DIVSD", "RCPPD", "RCPSD", ] X86_GRP_CMP = [ "CMP", "COMI", "CLT", # from dtransfer "CMPXCHG", "CMPXCHG8B", # from bit "TEST", # from string "CMPS", "CMPSB", "CMPSD", "CMPSW", # MMX "PCMPEQB", "PCMPEQW", "PCMPEQD", "PCMPGTB", "PCMPGTW", "PCMPGTD", # SSE4 "PHMINPOSUW", "PTEST", "PCMPEQQ", # SSE4.2 "PCMPESTRI", "PCMPESTRM", "PCMPISTRI", "PCMPISTRM", "PCMPGTQ", # Vector "VPTEST", ] # Shift and Rotate Instructions: X86_GRP_SHIFT = [ # general purpose instructions "SAR", "SHR", "SAL", "SHL", "SHRD", "SHLD", "ROR", "ROL", "RCR", "RCL", # BMI1, BMI2 "RORX", "SARX", "SHLX", "SHRX", # MMX "PSLLW", "PSLLD", "PSLLQ", "PSRLW", "PSRLD", "PSRLQ", "PSRAW", "PSRAD", # SSE2 128-bit integer "PSLLDQ", "PSRLDQ", # vector "VPROL", "VPROR", "VPSRA", "VPSLL", "VPSRA", ] # Logical Instructions: X86_GRP_LOGIC = [ # general purpose instructions "AND", "NOT", "OR", "XOR", # BMI1, BMI2 "ANDN", # MMX "PAND", "PANDN", "POR", "PXOR", # SSE "ANDPS", "ANDNPS", "ORPS", "XORPS", # SSE2 "ANDPD", "ANDNPD", "ORPD", "XORPD", # Vector "VPTERLOG", ] # bit and byte instructions: X86_GRP_BIT = [ # general purpose instructions "SETA", "SETAE", "SETB", "SETBE", "SETC", "SETE", "SETG", "SETGE", "SETL", "SETLE", "SETNA", "SETNAE", "SETNB", "SETNBE", "SETNC", "SETNE", "SETNG", "SETNGE", "SETNL", "SETNLE", "SETNO", "SETNP", "SETNS", "SETNZ", "SETO", "SETP", "SETPE", "SETPO", "SETS", "SETZ", "TEST", "CRC32", # BMI1, BMI2 "BLSMSK", "BLSR", "CLAC", "STAC", # from bit "TEST", "BT", "BTS", "BTR", "BTC", "BSF", "BSR", "POPCNT", "TZCNT", "LZCNT", ] # control transfer instructions: X86_GRP_CTRANSFER = [ # general purpose instructions "JMP", "CALL", "RET", "IRET", "INT", "INTO", "BOUND", "ENTER", "LEAVE", # flag "CLI", "STI", # SSE2 "PAUSE", # SSE3 "MONITOR", "MWAIT", "XABORT", "XACQUIRE", "XRELEASE", "XBEGIN", "XEND", "XTEST", "HLT", "SYSCALL", "SYSENTER", "SYSEXIT", "SYSRET", "FWAIT", "WAIT", # vm related instructions "VMCALL", "VMLAUNCH", "VMMCALL", "VMRESUME", "VMRUN", "VMFUNC", "VMCLEAR", "VMXON", "VMXOFF", ] X86_GRP_COND_CTRANSFER = [ # general purpose instructions "JA", "JAE", "JB", "JBE", "JC", "JCXZ", "JE", "JECXZ", "JRCXZ", "JG", "JGE", "JL", "JLE", "JNAE", "JNB", "JNBE", "JNC", "JNE", "JNG", "JNGE", "JNL", "JNLE", "JNO", "JNP", "JNS", "JNZ", "JO", "JP", "JPE", "JPO", "JS", "JZ", "LOOP", "LOOPE", "LOOPNE", "LOOPNZ", "LOOPZ", # string "REP", "REP MOVSQ", "REP STOSQ", "REPNE", "REPNZ", "REPE", "REPZ", ] # ==================== ARM 32 ============================================= ARM_GRP_DTRANSFER = [ # general purpose instructions "LDA", "ADR", "ADRP", "LDR", "LDRD", "LDRB", "LDRBT", "LDRH", "LDRS", "LDRSB", "LDRSBT", "LDRSH", "LDRSHT", "LDRT", "LDRHT", "STR", "STRB", "STRD", "STRH", "STRBT", "STRT", "LDM", "LDMDA", "LDMDB", "LDMIB", "STM", "STMDA", "STMDB", "STMIB", "PLD", "SWP", "MOV", "MOVI", "MOVK", "MOVZ", "MOVT", "MOVN", "MVN", "MVNI", "STP", "LDP", "RFEIB", # coprocessor data operations "CDP", "MCR", "MCRR", "MRC", "MRR", "LDC", "LDCL", "STC", "STCL", "PUSH", "SBFX", "SBFIZ", "BFX", "BFXIL", "UBFX", "UBFIZ", "VLD", "VST", "VST2", "VSTMDB", "VTBL", "VTBX", "ZIP", "ZIP1", "ZIP2", "UZP", "UZP1", "UZP2", "XTN", "XTN2", "CSEL", "LD1", "LD2", "LD4", "ST1", "ST2", "ST4", "LDPSW", "LDRSW", "SXTAB", "SXTB", "SXTH", "SXTW", "EXT", "EXTR", "INS", "UXTAB", "UXTB", "UXTH", "UXTW", "BFC", "BFI", "BIC", "CLZ", "REV", "REV16", "REV32", "REV64", "CSET", ] ARM_GRP_FLOAT_DTRANSFER = [ # floating point data transfer instructions "FCPY", "FCVTMS", "FCVTMU", "FCVTZS", "FCVTZU", "FCVT", "FLD", "FST", "FMR", "FMD", "FMS", "FMX", "FSITO", "FUITO", "FTOSI", "FTOUI", "FMOV", "UMOV", "LDUR", "LDURB", "LDURH", "LDURSB", "LDURSH", "LDURSW", "STUR", "STURB", "STURH", "STURSB", "STURSH", "STURSW", "DUP", "SCVTF", "UCVTF", ] ARM_GRP_MISC = [ "UDF", "NOP", "MRS", "MSR", "MAR", "MRA", "VMRS", "VMSR", "DBG", "DMB", "DSB", "ISB", "SETEND", ] # binary arithmetic instructions: ARM_GRP_ARITH = [ # general purpose instructions "ADD", "ADDW", "ADDP", "ADDV", "ADC", "SUB", "SBC", "RSB", "RSC", "CMN", "CLZ", "MUL", "MLA", "MLS", "CINC", "CINV", "NEG", "NEGS", "DIV", "SMAX", "SMAXV", "SMIN", "SMINV", "UMULL", "UMLAL", "UMLAL2", "SMLA", "SMLAL", "SMLALTT", "SMUL", "SMSUB", "MADD", "MNEG", "MSUB", "SMADDL", "SMNEGL", "SMSUBL", "SMULH", "SMULL", "UMADDL", "UMNEGL", "UMSUBL", "UMULH", "UMULL", "SDIV", "UDIV", "MIA", "QADD", "QSUB", "QDADD", "QDSUB", "QASX", "SADD", "SADDW", "SADDW2", "SASX", "SHADD", "SHASX", "SMLSD", "SMMLA", "SMUAD", "SMUSD", "SSUB", "SAT", "SAX", "UADD", "UADDW", "UADDW2", "USAT", "USAX", "UASX", "UHADD", "UHASX", "UMLSD", "UMMLA", "UQADD", "UQSAX", "UQSUB", "UHSAX", "VABA", "VABD", "MAX", "MIN", "VMLA", "VMLS", "VNMUL", "VNMLA", "VNMLS", "VFMS", "VFMS", "VFMA", "VFMS", "VFNMA", "VFNMS", "VRECPE", "VSQRT", "VQRSH", "UMULL", "UMAAL", "UMLAL", "USADA8", "VNEG", "CNEG", "CSINC", "CSINV", "CSNEG", ] ARM_GRP_FLOAT_ARITH = [ # floating point arithmetic instructions "FABS", "FABD", "FADD", "FSUB", "FDIV", "FMUL", "FNMUL", "FSQRT", "FMAC", "FNMAC", "FMSC", "FNMSC", "FNEG", "FMADD", "FMSUB", "FNMADD", "FNMSUB", "FPINT", "FCSEL", "FMAX", "FMIN", "FMLA", "FMLS", "FRINTM", "FRINTP", "FRINT", ] ARM_GRP_SHIFT = [ # shift operations "ASR", "LSL", "LSR", "ROR", "RRX", "PKHBT", "PKHTB", "SHL", "USHL", "USHLL", "USHLL2", "USHR", "USRA", "SSHL", "SSHLL", "SSHLL2", "SSHR", ] ARM_GRP_CMP = [ # compare instructions "CMEQ", "CMGT", "CMHI", "CMHS", "CMP", "CCMN", "CCMP", "VCEQ", "VCGE", "VCGT", "VCLE", "VCLT", # from bit "TST", "TEQ", ] ARM_GRP_FLOAT_CMP = [ "VCMP", "VCMPE", "FCMPE", "FCMGT", "FCM", "FCMP", "FCCMP", "VCM", ] # Logical Instructions: ARM_GRP_LOGIC = [ "AND", "ORR", "EOR", "EON", "ORN", ] # bit and byte instructions: ARM_GRP_BIT = [ "TST", "TEQ", "BSL", "BIF", "BIT", "BFC", "BFI", "BIC", "CLZ", "RBIT", "REV", "REV16", "REV32", "REV64", "CSET", ] # control transfer instructions: ARM_GRP_CTRANSFER = [ "B", "BR", "BL", "BLR", "BX", "BLX", "BXJ", "BAL", "BLAL", "BXAL", "BLXAL", "BXJAL", "SWI", "BKPT", "RET", "YIELD", "WFE", "WFI", "SEV", "SEVL", "CPS", "BRK", "HLT", "SVC", "HVC", "SMC", "TRAP", "ERET", # ARM POP is return "POP", ] ARM_GRP_COND_CTRANSFER = [ "BEQ", "BNE", "BCS", "BCC", "BMI", "BPL", "BVS", "BVC", "BHI", "BLS", "BGE", "BLT", "BGT", "BLE", "BLEQ", "BLNE", "BLCS", "BLCC", "BLMI", "BLPL", "BLVS", "BLVC", "BLHI", "BLLS", "BLGE", "BLLT", "BLGT", "BLLE", "BXEQ", "BXNE", "BXCS", "BXCC", "BXMI", "BXPL", "BXVS", "BXVC", "BXHI", "BXLS", "BXGE", "BXLT", "BXGT", "BXLE", "BLXEQ", "BLXNE", "BLXCS", "BLXCC", "BLXMI", "BLXPL", "BLXVS", "BLXVC", "BLXHI", "BLXLS", "BLXGE", "BLXLT", "BLXGT", "BLXLE", "BXJEQ", "BXJNE", "BXJCS", "BXJCC", "BXJMI", "BXJPL", "BXJVS", "BXJVC", "BXJHI", "BXJLS", "BXJGE", "BXJLT", "BXJGT", "BXJLE", "TBZ", "TBNZ", # combined instructions "CBZ", "CBNZ", ] # ==================== MIPS 32 ============================================= # data transfer # refernce : https://www.cs.cornell.edu/courses/cs3410/2008fa/MIPS_Vol2.pdf MIPS_GRP_DTRANSFER = [ "LB", "LBU", "LH", "LHU", "LL", "LW", "LWU", "LD", "LDL", "LDR", "LWL", "LWR", "PREF", "SB", "SC", "SD", "SDL", "SDR", "SH", "ST", "SW", "SWL", "SWR", "SYNC", "LUI", "LDXC1", "LWXC1", "SDXC1", "SWXC1", "MFHI", "MFLO", "MOV", "MOVF", "MOVN", "MOVT", "MOVZ", "MTHI", "MTLO", "MOVE", "CVT", "LDC", "LWC", "SDC", "SWC", # move "CFC", "CTC", "MFC", "MTC", "PREF", "SYNC", "SPLAT", "CFCMSA", "CTCMSA", "COPY", "PUSH", "SEH", "SEB", "WSBH", "DSBH", "DSHD", "MTC0", "MFC0", "LDC3", "LWC3", "SDC3", "SWC3", # coprocessor load, store "COP2", "LDC2", "LWC2", "SDC2", "SWC2", # cop move "CFC2", "CTC2", "MFC2", "MTC2", ] MIPS_GRP_FLOAT_DTRANSFER = [ # floating point "FRINT", "FCLASS", # load, store, memory "LDC1", "LWC1", "SDC1", "SWC1", # move "CFC1", "CTC1", "MFC1", "FMOV", "MOVF", "MOVN", "MOVT", "MOVZ", "MTC1", # convert "FEX", "FFINT", "FFQ", "FTINT", "FTRUN", "FTQ", "FCVT", "FLOOR", "ROUND", "TRUNC", "FFLOOR", "FROUND", "FTRUNC", "DMFC", "DMFC1", "DMTC", "DMTC1", "MTHC1", "MFHC1", ] # binary arithmetic instructions: MIPS_GRP_ARITH = [ # general purpose instructions "ADD", "ADDI", "ADDU", "ADDIU", "SUB", "SUBU", "MUL", "MULT", "MULTU", "CLO", "CLZ", "DIV", "DIVU", "MADD", "MADDU", "MSUB", "MSUBU", "AADD", "ASUB", "ABS", "NEG", "NEGU", # additional "DAA", "DSUB", "DSUBU", "DSUBIU", "DDIV", "DDIVU", "DDIVIU", "DMUL", "DMULT", "DMULTU", "DOTP", "DPADD", "DPSUB", "MADD", "MAX", "MIN", "MSUB", "MOD", "SAT", "HSUB", "SQRT", "AUI", "DAUI", "DAHI", "DATI", "ADDIUPC", "AUIPC", "ALUIPC", "DADD", "DADDU", "DADDIU", "DCLZ", # from bit "BMZ", "BMN", "BNEG", ] MIPS_GRP_CMP = [ "SLT", "SLTI", "SLTIU", "SLTU", # compare instructions "CMP", "CEQ", "CLE", "CLT", "CF", "CUN", "CEQ", "CUEQ", "COLT", "CULT", "COLE", "CULE", "CSF", "CNGLE", "CSEQ", "CNGL", "CLT", "CNGE", "CLE", "CNGT", "CMP", "CEQ", "CLE", "CLT", "CF", "CUN", "CEQ", "CUEQ", "COLT", "CULT", "COLE", "CULE", "CSF", "CNGLE", "CSEQ", "CNGL", "CLT", "CNGE", "CLE", "CNGT", "C", ] MIPS_GRP_FLOAT_CMP = [ # floating point compare instructions "FACF", "FC", "FS", ] MIPS_GRP_SHIFT = [ # shift operation "SLL", "SLLV", "SRL", "SRLV", "SRA", "SRAV", "SHL", "SHR", "SLD", "DSLL", "DSLL32", "DSLLV", "DSRA", "DSRA32", "DSRAV", "DSRL", "DSRL32", "DSRLV", "ROTR", "ROTRV", "DROTR", "DROTR32", "DROTRV", "LSA", "DLSA", ] MIPS_GRP_FLOAT_ARITH = [ # floating point "FABS", "FADD", "FDIV", "FMADD", "FMSUB", "FMUL", "FNEG", "FNMADD", "FNMSUB", "FEXP", "FLOG", "FMAX", "FMIN", "FRCP", "RECIP", "FRECIP", "FRSQRT", "FSQRT", "FSUB", ] # Logical Instructions: MIPS_GRP_LOGIC = [ "AND", "ANDI", "NOR", "OR", "NOT", "ORI", "XOR", "XORI", ] # bit and byte instructions: MIPS_GRP_BIT = [ "BINS", "DINS", "DEXT", "EXT", "INS", "BMZ", "BMN", "BNEG", "BSEL", "BSET", "BCLR", # bit wise count "NLOC", "NLZC", "PCNT", ] MIPS_GRP_MISC = [ "NOP", "SSNOP", "CACHE", "TLBP", "TLBR", "TLBWI", "TLBWR", ] # control transfer instructions: MIPS_GRP_CTRANSFER = [ "B", "BAL", "J", "JAL", "JR", "JALR", "BREAK", "SYSCALL", "PAUSE", "WAIT", "HLT", "ERET", "DERET", "SDBBP", "BKPT", "RET", "MFC0", "MTC0", # MIPS POP is return "POP", # float "BC1", "BC1F", "BC1T", "BC1FL", "BC1TL", # cop "BC2F", "BC2T", "BC2FL", "BC2TL", "BC3F", "BC3T", "BC3FL", "BC3TL", ] MIPS_GRP_COND_CTRANSFER = [ "BEQ", "BEQZ", "BNE", "BGE", "BGEZ", "BGEZAL", "BGTZ", "BLEZ", "BLTZ", "BLTZAL", "BNEL", "BNEZ", "BNZ", "TEQ", "TEQI", "TGE", "TGEI", "TGEIU", "TGEU", "TLT", "TLTI", "TLTIU", "TLTU", "TNE", "TNEI", "BEQL", "BGEZALL", "BGEZL", "BGTZL", "BLEZL", "BLTZALL", "BLTZL", "BNEL", ] # ============================================ # Below part creates dictionary which groups instructions X86_GRP_MAP = { 9: X86_GRP_FLOAT_DTRANSFER + X86_GRP_FLOAT_CMP + X86_GRP_FLOAT_ARITH, 10: X86_GRP_MISC + X86_GRP_FLOAT_DTRANSFER + X86_GRP_DTRANSFER, 11: X86_GRP_FLOAT_ARITH + X86_GRP_SHIFT + X86_GRP_ARITH, 12: X86_GRP_LOGIC, 13: X86_GRP_COND_CTRANSFER + X86_GRP_CTRANSFER, 20: X86_GRP_FLOAT_DTRANSFER + X86_GRP_DTRANSFER, 21: X86_GRP_FLOAT_ARITH + X86_GRP_ARITH, 22: X86_GRP_FLOAT_CMP + X86_GRP_CMP, 23: X86_GRP_SHIFT, 24: X86_GRP_BIT, 26: X86_GRP_COND_CTRANSFER, 27: X86_GRP_CTRANSFER, 28: X86_GRP_MISC, 30: [], } ARM_GRP_MAP = { 9: ARM_GRP_FLOAT_DTRANSFER + ARM_GRP_FLOAT_CMP + ARM_GRP_FLOAT_ARITH, 10: ARM_GRP_MISC + ARM_GRP_FLOAT_DTRANSFER + ARM_GRP_DTRANSFER, 11: ARM_GRP_FLOAT_ARITH + ARM_GRP_SHIFT + ARM_GRP_ARITH, 12: ARM_GRP_LOGIC, 13: ARM_GRP_COND_CTRANSFER + ARM_GRP_CTRANSFER, 20: ARM_GRP_FLOAT_DTRANSFER + ARM_GRP_DTRANSFER, 21: ARM_GRP_FLOAT_ARITH + ARM_GRP_ARITH, 22: ARM_GRP_FLOAT_CMP + ARM_GRP_CMP, 23: ARM_GRP_SHIFT, 24: ARM_GRP_BIT, 26: ARM_GRP_COND_CTRANSFER, 27: ARM_GRP_CTRANSFER, 28: ARM_GRP_MISC, 30: [], } # A64 does not allow instructions to be conditionally executed as ARM. def _copy_for_arm64(): import copy return copy.deepcopy(ARM_GRP_MAP) ARM64_GRP_MAP = _copy_for_arm64() # ARM instructions may have conditional suffix. Thus, initialize here. However, # reference : http://infocenter.arm.com/help/index.jsp ARM_COND_GROUPS = [9, 10, 11, 13, 20, 21, 22, 26] ARM_GRP_COND_CODE = [ "EQ", "NE", "CS", "HS", "CC", "LO", "MI", "PL", "VS", "VC", "HI", "LS", "GE", "LT", "GT", "LE", "AL", ] # for group_no in ARM_COND_GROUPS: # for inst in ARM_GRP_MAP[group_no]: # for cond in ARM_GRP_COND_CODE: # ARM_GRP_MAP[group_no].append(inst + cond) MIPS_GRP_MAP = { 9: MIPS_GRP_FLOAT_DTRANSFER + MIPS_GRP_FLOAT_CMP + MIPS_GRP_FLOAT_ARITH, 10: MIPS_GRP_MISC + MIPS_GRP_FLOAT_DTRANSFER + MIPS_GRP_DTRANSFER, 11: MIPS_GRP_FLOAT_ARITH + MIPS_GRP_SHIFT + MIPS_GRP_ARITH, 12: MIPS_GRP_LOGIC, 13: MIPS_GRP_COND_CTRANSFER + MIPS_GRP_CTRANSFER, 20: MIPS_GRP_FLOAT_DTRANSFER + MIPS_GRP_DTRANSFER, 21: MIPS_GRP_FLOAT_ARITH + MIPS_GRP_ARITH, # mips usually contains compare in conditional branch 22: MIPS_GRP_FLOAT_CMP + MIPS_GRP_CMP + MIPS_GRP_COND_CTRANSFER, 23: MIPS_GRP_SHIFT, 24: MIPS_GRP_BIT, 26: MIPS_GRP_COND_CTRANSFER, 27: MIPS_GRP_CTRANSFER, 28: MIPS_GRP_MISC, 30: [], } # ============================================ GRP_NO_MAP = { # Among capstone's default mapping, use 1, 2, 3 as they are common in all # architectures. 1: "grp_jump", 2: "grp_call", 3: "grp_ret", 9: "floatinst", 10: "abs_dtransfer", 11: "abs_arith", 12: "logic", 13: "abs_ctransfer", 20: "dtransfer", 21: "arith", 22: "cmp", 23: "shift", 24: "bitflag", 26: "cndctransfer", 27: "ctransfer", 28: "misc", 30: "unknown", } GRP_NAME_MAP = {val: key for key, val in GRP_NO_MAP.items()} # ============================================ # Below part maps capstone's internal instruction numbers to pre-defined groups def _check_inst(target_inst, check_list, suffixes=[]): target_inst = target_inst.split("_")[0] target_inst = target_inst.split(".")[0] target_inst = target_inst.upper() for inst in check_list: if target_inst == inst: return True # Check conditional code if target_inst.startswith(inst): if len(target_inst) - len(inst) == 2: for suffix in suffixes: if target_inst == inst + suffix: return True return False def _init_inst_groups(prefix, target, groups): insts = list(filter(lambda x: x.startswith(prefix), dir(target))) inst_map = {} if prefix == "ARM_INS_": suffixes = ARM_GRP_COND_CODE else: suffixes = [] for inst in insts: inst_no = getattr(target, inst) inst = inst.replace(prefix, "") inst_map[inst_no] = [] for group_no, grouped_insts in groups.items(): if _check_inst(inst, grouped_insts, suffixes): inst_map[inst_no].append(group_no) if not inst_map[inst_no]: inst_map[inst_no].append(GRP_NAME_MAP["unknown"]) return inst_map def _init_groups(): import capstone x86 = _init_inst_groups("X86_INS_", capstone.x86, X86_GRP_MAP) arm = _init_inst_groups("ARM_INS_", capstone.arm, ARM_GRP_MAP) arm64 = _init_inst_groups("ARM64_INS_", capstone.arm64, ARM64_GRP_MAP) mips = _init_inst_groups("MIPS_INS_", capstone.mips, MIPS_GRP_MAP) ppc = _init_inst_groups("PPC_INS_", capstone.ppc, PPC_GRP_MAP) return x86, arm, arm64, mips, ppc X86_INST_MAP, ARM_INST_MAP, ARM64_INST_MAP, MIPS_INST_MAP, PPC_INST_MAP = _init_groups()

14.753525

88

0.455952

2,666

28,253

4.683421

0.587772

0.020663

0.016338

0.006407

0.106279

0.077607

0.045971

0.016979

0

0.024486

0.343751

28,253

1,914

89

14.761233

0.64894

0.108201

0

0.174723

0

0.297529

0

1

0.00233

false

0

0.001747

0

0.007571

0

null

0

null

0

1

0

bdd85f75dbf5d4102a25ebc2445a8a860dd88729

4,920

py

Python

networkunit/scores/score_kl_divergence.py

morales-gregorio/NetworkUnit

b858c3b2698fe3c0a7324ae8b8b388b74fd13c4d

[ "BSD-3-Clause" ]

8

2017-11-16T08:45:48.000Z

2021-11-29T16:51:45.000Z

networkunit/scores/score_kl_divergence.py

morales-gregorio/NetworkUnit

b858c3b2698fe3c0a7324ae8b8b388b74fd13c4d

[ "BSD-3-Clause" ]

17

2017-11-16T07:53:26.000Z

2021-05-07T10:27:34.000Z

networkunit/scores/score_kl_divergence.py

russelljjarvis/NetworkUnit

32179371d3a0ba354e6637cf4f97ba70522d4054

[ "BSD-3-Clause" ]

5

2019-03-23T00:55:33.000Z

2020-01-24T10:12:11.000Z

import numpy as np from scipy.stats import entropy import matplotlib.pyplot as plt import matplotlib.colors as colors import seaborn as sns import sciunit class kl_divergence(sciunit.Score): """ Kullback-Leibner Divergence D_KL(P||Q) Calculates the difference of two sampled distributions P and Q in form of an entropy measure. The D_KL measure is effectively the difference of the cross-entropy of the of both distribution P,Q and the entropy of P. D_KL can be interpreted as the amount of information lost when approximating P by Q. . math $$ D\mathrm{KL}(P||Q) =\sum{i} P(i) \log_2 \frac{P(i)}{Q(i)} = H(P,Q) - H(P) $$ The returned score is the symmetric version of the kl divergence . math $$ D_\mathrm{KL}(P,Q) := \frac{1}{2} \left(D_\mathrm{KL}(P|Q) + D_\mathrm{KL}(Q|P)\right)$$ Parameters ---------- kl_binsize : float Bin size of the histogram, used to calculate the KL divergence. """ score = np.nan @classmethod def compute(self, data_sample_1, data_sample_2, kl_binsize=0.005, **kwargs): # filtering out nans sample1 = np.array(data_sample_1)[np.isfinite(data_sample_1)] sample2 = np.array(data_sample_2)[np.isfinite(data_sample_2)] max_value = max([max(sample1),max(sample2)]) min_value = min([min(sample1),min(sample2)]) bins = (max_value - min_value) / kl_binsize edges = np.linspace(min_value, max_value, bins) P, edges = np.histogram(sample1, bins=edges, density=True) Q, _____ = np.histogram(sample2, bins=edges, density=True) # dx = np.diff(edges)[0] # edges = edges[:-1] # P *= dx # Q *= dx init_len = len(P) Qnot0 = np.where(Q != 0.)[0] P_non0 = P[Qnot0] Q_non0 = Q[Qnot0] Pnot0 = np.where(P_non0 != 0.)[0] Q_non0 = Q_non0[Pnot0] P_non0 = P_non0[Pnot0] final_len = len(P_non0) discard = init_len - final_len D_KL_PQ = entropy(P_non0, Q_non0, base=2) D_KL_QP = entropy(Q_non0, P_non0, base=2) D_KL = .5 * (D_KL_PQ + D_KL_QP) self.score = kl_divergence(D_KL) self.score.data_size = [len(sample1), len(sample2)] self.score.discarded_values = discard self.score.bins = len(edges)-1 return self.score @classmethod def plot(self, data_sample_1, data_sample_2, ax=None, palette=None, var_name='Measured Parameter', kl_binsize=0.005, sample_names=['observation', 'prediction'], **kwargs): if ax is None: fig, ax = plt.subplots() ax.set_ylabel('Probability Density') ax.set_xlabel(var_name) if palette is None: palette = [sns.color_palette()[0], sns.color_palette()[1]] sample1 = np.array(data_sample_1)[np.isfinite(data_sample_1)] sample2 = np.array(data_sample_2)[np.isfinite(data_sample_2)] max_value = max([max(sample1),max(sample2)]) min_value = min([min(sample1),min(sample2)]) bins = (max_value - min_value) / kl_binsize edges = np.linspace(min_value, max_value, bins) P, edges = np.histogram(sample1, bins=edges, density=True) Q, _____ = np.histogram(sample2, bins=edges, density=True) dx = np.diff(edges)[0] edges = edges[:-1] xvalues = edges + dx/2. xvalues = np.append(np.append(xvalues[0]-dx, xvalues), xvalues[-1]+dx) def secure_log(E, D): log = np.zeros_like(E) i = 0 for e, d in zip(E, D): if e == 0 or d == 0: log[i] = 0. else: log[i] = np.log(e/d) i += 1 return log diffy = .5 * (P - Q) * secure_log(P, Q.astype(float)) P = np.append(np.append(0, P), 0) Q = np.append(np.append(0, Q), 0) filly = np.append(np.append(0., diffy), 0.) ax.fill_between(xvalues, filly, 0, color='0.8', label='d/dx DKL') if palette is None: palette = [sns.color_palette()[0], sns.color_palette()[1]] ax.plot(xvalues, P, lw=2, color=palette[0], label=sample_names[0]) ax.plot(xvalues, Q, lw=2, color=palette[1], label=sample_names[1]) ax.set_xlim(xvalues[0], xvalues[-1]) ax.set_yscale('log') plt.legend() return ax @property def sort_key(self): return self.score def __str__(self): return "\n\n\033[4mKullback-Leibler-Divergence\033[0m" \ + "\n\tdatasize: {} \t {}" \ .format(self.data_size[0], self.data_size[1]) \ + "\n\tdiscarded: {}" \ .format(self.discarded_values) \ + "\n\tD_KL = {:.3f} \t bins = {}\n\n" \ .format(self.score, self.bins)

35.912409

80

0.570732

706

4,920

3.815864

0.24221

0.044543

0.024499

0.025241

0.344469

0.312546

0.301411

0.282108

0

0.032184

0.292683

4,920

137

81

35.912409

0.741954

0.172764

0

0.263736

0

0.047595

0.011273

0

1

0.054945

false

0

0.065934

0.021978

0.197802

0

null

0

null

0

1

0

bdd862ba88395be8cb418cfab8ce408473323919

635

py

Python

kikyo/config.py

jadbin/kikyo

98d875e85a28b4141cbd6616bba3d397a4219dc9

[ "MIT" ]

null

kikyo/config.py

jadbin/kikyo

98d875e85a28b4141cbd6616bba3d397a4219dc9

[ "MIT" ]

null

kikyo/config.py

jadbin/kikyo

98d875e85a28b4141cbd6616bba3d397a4219dc9

[ "MIT" ]

null

import base64 import io import requests import yaml from kikyo import Kikyo, Settings def configure_by_consul(config_url: str, **kwargs) -> Kikyo: """从Consul拉取YAML格式的配置文件 :param config_url: 获取配置项的URL地址 """ resp = requests.get(config_url) resp.raise_for_status() settings = Settings() for data in resp.json(): v = data['Value'] if not v: continue s = base64.b64decode(v) conf: dict = yaml.safe_load(io.BytesIO(s)) if 'kikyo' in conf: settings.merge(conf['kikyo']) break settings.merge(kwargs) return Kikyo(settings)

19.84375

60

0.617323

77

635

4.987013

0.545455

0.070313

0

0.013129

0.280315

635

31

61

20.483871

0.827133

0.08189

0

0.026408

0

1

0.05

false

0

0.25

0

0.35

0

null

0

null

0

1

0

bdd932ec045f92a204fc0462b20bc5fe9de822e1

5,077

py

Python

tests/ep_canvas_test.py

PytLab/catplot

63ad46218b17d5cdffdd026dad7d775cf4caa50b

[ "MIT" ]

35

2015-12-23T08:01:15.000Z

2021-11-03T01:34:20.000Z

tests/ep_canvas_test.py

PytLab/catplot

63ad46218b17d5cdffdd026dad7d775cf4caa50b

[ "MIT" ]

1

2015-11-25T05:52:43.000Z

2017-04-11T14:06:00.000Z

tests/ep_canvas_test.py

PytLab/catplot

63ad46218b17d5cdffdd026dad7d775cf4caa50b

[ "MIT" ]

10

2015-11-06T20:23:32.000Z

2020-05-16T19:18:38.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Test case for Energy Profle Canvas. """ import unittest import matplotlib.pyplot as plt from catplot.ep_components.ep_canvas import EPCanvas from catplot.ep_components.ep_lines import ElementaryLine from catplot.ep_components.ep_chain import EPChain class EPCanvasTest(unittest.TestCase): def setUp(self): self.maxDiff = True def test_construction_and_query(self): """ Test we can construct ElementaryLine object correctly. """ canvas = EPCanvas(margin_ratio=0.2) self.assertEqual(canvas.margin_ratio, 0.2) self.assertIsNone(canvas.figsize) self.assertIsNone(canvas.dpi) self.assertIsNone(canvas.facecolor) self.assertIsNone(canvas.edgecolor) self.assertListEqual(canvas.lines, []) self.assertListEqual(canvas.shadow_lines, []) self.assertTrue(canvas.figure) self.assertTrue(canvas.axes) # Check invalid reaction equation. self.assertRaises(ValueError, EPCanvas, margin_ratio=-0.1) plt.close(canvas.figure) def test_draw(self): """ Make sure the lines can be added without exceptions. """ canvas = EPCanvas() line = ElementaryLine([0.0, 1.3, 0.8]) canvas.add_lines([line]) canvas.draw() plt.close(canvas.figure) def test_add_species_annotations(self): """ Make sure the species annotations can be added without exceptions. """ canvas = EPCanvas() line = ElementaryLine([0.0, 1.3, 0.8], rxn_equation="CO_b + O_b <-> CO-O_2b -> CO2_g + 2*_b") canvas.add_lines([line]) canvas.add_species_annotations(line) plt.close(canvas.figure) def test_add_horizontal_auxiliary_line(self): """ Make sure the horizontal line can be added without exceptions. """ canvas = EPCanvas() line = ElementaryLine([0.0, 1.3, 0.8]) canvas.add_lines([line]) canvas.add_horizontal_auxiliary_line(line) plt.close(canvas.figure) def test_add_vertical_auxiliary_line(self): """ Make sure the vertical line can be added without exceptions. """ canvas = EPCanvas() line = ElementaryLine([0.0, 1.3, 0.8]) canvas.add_lines([line]) canvas.add_vertical_auxiliary_lines(line) plt.close(canvas.figure) def test_add_energy_annotations(self): """ Make sure the energy annotations can be added correctly. """ canvas = EPCanvas() line = ElementaryLine([0.0, 1.3, 0.8]) canvas.add_lines([line]) canvas.add_energy_annotations(line) plt.close(canvas.figure) def test_add_chain(self): """ Test energy profile chain can be added correctly to canvas. """ canvas = EPCanvas() self.assertFalse(canvas.lines) self.assertFalse(canvas.chains) l1 = ElementaryLine([0.0, 1.2, 0.6]) l2 = ElementaryLine([0.0, 1.0, 0.8]) chain = EPChain([l1, l2]) canvas.add_chain(chain) self.assertEqual(len(canvas.lines), 2) for l in canvas.lines: self.assertTrue(isinstance(l, ElementaryLine)) self.assertEqual(len(canvas.chains), 1) self.assertTrue(isinstance(canvas.chains[0], EPChain)) # Exception is expected if add the chain again. self.assertRaises(ValueError, canvas.add_chain, chain) plt.close(canvas.figure) def test_contains(self): canvas = EPCanvas() l1 = ElementaryLine([0.0, 1.2, 0.6]) l2 = ElementaryLine([0.0, 1.0, 0.8]) chain = EPChain([l1]) canvas.add_chain(chain) self.assertTrue(l1 in canvas) self.assertTrue(chain in canvas) self.assertFalse(l2 in canvas) plt.close(canvas.figure) def test_add_line(self): """ Test the line can be add to canvas correctly. """ canvas = EPCanvas() l1 = ElementaryLine([0.0, 1.2, 0.6]) canvas.add_line(l1) # Add repeat line, exception raises. self.assertRaises(ValueError, canvas.add_line, l1) plt.close(canvas.figure) def test_add_lines(self): canvas = EPCanvas() l1 = ElementaryLine([0.0, 1.2, 0.6]) l2 = ElementaryLine([0.0, 1.0, 0.8]) canvas.add_lines([l1, l2]) canvas.lines = [] self.assertRaises(ValueError, canvas.add_lines, [l1, l1]) plt.close(canvas.figure) def test_add_all_horizontal_auxiliary_lines(self): """ Make sure we can add all horizontal auxiliary lines to canvas. """ canvas = EPCanvas() l1 = ElementaryLine([0.0, 1.2, 0.6]) l2 = ElementaryLine([0.0, 1.0, 0.8]) canvas.add_lines([l1, l2]) canvas.add_all_horizontal_auxiliary_lines() plt.close(canvas.figure) if "__main__" == __name__: suite = unittest.TestLoader().loadTestsFromTestCase(EPCanvasTest) unittest.TextTestRunner(verbosity=2).run(suite)

30.769697

84

0.623203

631

5,077

4.885895

0.187005

0.011677

0.072657

0.077198

0.508271

0.378852

0.334415

0.314953

0.272138

0.242945

0

0.031957

0.26039

5,077

164

85

30.957317

0.789081

0.157179

0

0.425743

0

0.010921

0

0.217822

1

0.118812

false

0

0.049505

0

0.178218

0

null

0

null

0

1

0

bdd9723808bf563a488aa0b07c42aceeac435545

458

py

Python

Leetcode/medium/bitwise-and-of-numbers-range.py

jen-sjen/data-structures-basics-leetcode

addac32974b16e0a37aa60c210ab7820b349b279

[ "MIT" ]

6

2021-07-29T03:26:20.000Z

2022-01-28T15:11:45.000Z

Leetcode/medium/bitwise-and-of-numbers-range.py

jen-sjen/data-structures-basics-leetcode

addac32974b16e0a37aa60c210ab7820b349b279

[ "MIT" ]

2

2021-09-30T09:47:23.000Z

2022-01-31T03:08:24.000Z

Leetcode/medium/bitwise-and-of-numbers-range.py

jen-sjen/data-structures-basics-leetcode

addac32974b16e0a37aa60c210ab7820b349b279

[ "MIT" ]

5

2021-08-10T06:41:11.000Z

2022-01-29T17:50:20.000Z

""" # BITWISE AND OF NUMBERS RANGE Given a range [m, n] where 0 <= m <= n <= 2147483647, return the bitwise AND of all numbers in this range, inclusive. Example 1: Input: [5,7] Output: 4 Example 2: Input: [0,1] Output: 0 """ class Solution: def rangeBitwiseAnd(self, m: int, n: int) -> int: count = 0 while m < n: m = m >> 1 n = n >> 1 count += 1 return m << count

17.615385

117

0.508734

66

458

3.530303

0.515152

0.025751

0.103004

0

0.080139

0.373362

458

26

118

17.615385

0.731707

0.482533

0

1

0.125

false

0

0.375

0

null

0

null

0

1

0

bdde05efcf874ead71f10d44b9b94987c03fce5e

995

py

Python

tests/test_git.py

graycarl/hbk

d4c90807b2558a2b61fb1253d9804fbaf373443f

[ "MIT" ]

1

2021-07-22T05:25:35.000Z

tests/test_git.py

graycarl/hbk

d4c90807b2558a2b61fb1253d9804fbaf373443f

[ "MIT" ]

37

2017-07-27T06:07:25.000Z

2020-12-11T12:57:31.000Z

tests/test_git.py

graycarl/hbk

d4c90807b2558a2b61fb1253d9804fbaf373443f

[ "MIT" ]

1

2019-04-02T08:36:32.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import, unicode_literals from builtins import * # noqa import pytest from hbkit import libs @pytest.fixture def git_config(): content = \ """ [core] repositoryformatversion = 0 filemode = true bare = false logallrefupdates = true ignorecase = true precomposeunicode = true [remote "origin"] url = https://github.com/graycarl/hbkit.git fetch = +refs/heads/*:refs/remotes/origin/* [remote "other"] url = https://gitlab.com/graycarl/hbkit.git fetch = +refs/heads/*:refs/remotes/origin/* [branch "master"] remote = origin merge = refs/heads/master [branch "Github-Check-CI"] remote = origin merge = refs/heads/Github-Check-CI """ return content def test_iter_remote_from_git_config(git_config): remotes = list(libs.git.iter_remotes_from_git_config(git_config)) expect = [ 'https://github.com/graycarl/hbkit.git', 'https://gitlab.com/graycarl/hbkit.git' ] assert remotes == expect

23.690476

69

0.706533

127

995

5.393701

0.417323

0.065693

0.093431

0.110949

0.405839

0.265693

0.145985

0

0.002398

0.161809

995

41

70

24.268293

0.818945

0.026131

0

0.15914

0

0.066667

1

0.133333

false

0

0.266667

0

0.466667

0

null

0

null

0

1

0

bddf54c37693012c2ebee8e890c2bc5f10dfd58d

5,510

py

Python

responsible_ai/gan_data_debiased/main.py

AaratiAkkapeddi/nnabla-examples

db9e5ad850303c158773aeb275e5c3821b4a3935

[ "Apache-2.0" ]

null

responsible_ai/gan_data_debiased/main.py

AaratiAkkapeddi/nnabla-examples

db9e5ad850303c158773aeb275e5c3821b4a3935

[ "Apache-2.0" ]

null

responsible_ai/gan_data_debiased/main.py

AaratiAkkapeddi/nnabla-examples

db9e5ad850303c158773aeb275e5c3821b4a3935

[ "Apache-2.0" ]

null

# Copyright 2022 Sony Group Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys from nnabla.ext_utils import get_extension_context import nnabla as nn import args import data_loader as dl import classifier as clf from utils import utils def model_train_setting(opt): """ Get the model train settings Args: opt : variables that containing values for all of your options Returns: variables which you need to train """ attr_list = utils.get_all_attr() if opt['model_train'] == 'baseline': data_params = { "train_beg": opt['train_beg'], "valid_beg": opt['valid_beg'], "test_beg": opt['test_beg'], } data_setting = { 'path': opt['base_img_path'], 'protected_attribute': opt['protected_attribute'], 'attribute': opt['attribute'], 'data_params': data_params, 'batch_size': opt['batch_size'], 'learning_rate': opt['learning_rate'], 'max_iter': opt['max_iter_base'] } opt['data_setting'] = data_setting if opt['model_train'] == 'gan_debiased': data_params = { "train_beg": opt['train_beg'], "valid_beg": opt['valid_beg'], "test_beg": opt['test_beg'], } real_params = { 'path': opt['base_img_path'], 'attribute': opt['attribute'], 'protected_attribute': opt['protected_attribute'], 'data_params': data_params } generated_images = "{}/AllGenImages".format(opt["fake_data_dir"]) flipped_images = "{}/{}/".format(opt["fake_data_dir"], attr_list[opt['attribute']]) label_score = "{}/all_{}_scores.pkl".format(opt['fake_data_dir'], attr_list[opt['attribute']]) domain_score = "{}/all_{}_scores.pkl".format(opt['fake_data_dir'], attr_list[opt['protected_attribute']]) generated_params = { 'generated_image_path': generated_images, 'flipped_images_path': flipped_images, 'label_path': label_score, 'domain_path': domain_score, # flipped the images from 15000 to 175000 'flipped_image_range': (15000, 175000), 'orig_label_range': (160000, 320000), # original label range 'new_range': (0, 160000), # new images } data_setting = { 'real_params': real_params, 'gen_params': generated_params, 'batch_size': opt['batch_size'], 'learning_rate': opt['learning_rate'], 'max_iter': opt['max_iter_gan_debiased'] } opt['data_setting'] = data_setting return opt def main(): """ main method """ opt = args.get_args() opt = model_train_setting(opt) ctx = get_extension_context( opt['context'], device_id=opt['device_id'], type_config=opt['type_config']) nn.set_default_context(ctx) # model configurations batch_size = opt['data_setting']['batch_size'] learning_rate = opt['data_setting']['learning_rate'] max_iter = opt['data_setting']['max_iter'] if (opt["model_train"] == 'baseline'): train = dl.actual_celeba_dataset(opt['data_setting'], batch_size, augment=True, split='train', shuffle=True) val = dl.actual_celeba_dataset(opt['data_setting'], batch_size, augment=False, split='valid', shuffle=False) val_weight = None elif (opt["model_train"] == 'gan_debiased'): train = dl.debiased_celeba_dataset(opt['data_setting'], batch_size, augment=True, split='train', shuffle=True) val = dl.actual_celeba_dataset(opt['data_setting']['real_params'], batch_size, augment=False, split='valid', shuffle=False) val_weight = utils.compute_class_weight(val) else: print("please provide proper argument") sys.exit(0) attr_list = utils.get_all_attr() if not os.path.exists(opt['model_save_path']): os.makedirs(opt['model_save_path']) monitor_path = os.path.join( opt['model_save_path'], attr_list[opt['attribute']]) if not os.path.exists(monitor_path): os.makedirs(monitor_path) attribute_classifier_model = clf.attribute_classifier(batch_size=batch_size, learning_rate=learning_rate, max_iter=max_iter, monitor_path=monitor_path, val_weight=val_weight) attribute_classifier_model.train(train, val) if __name__ == '__main__': main()

38.531469

91

0.580581

622

5,510

4.853698

0.287781

0.047367

0.041736

0.027824

0.398145

0.262007

0.245446

0.229215

0

0.013089

0.306715

5,510

142

92

38.802817

0.777225

0.147913

0

0.294118

0

0.229275

0.004534

0

1

0.019608

false

0

0.078431

0

0.107843

0.009804

0

null

0

null

0

1

0

bde0ad9f17012d7ebc6ee66313fe41b54189ab35

5,109

py

Python

hstools/utilities.py

saisiddu/pub_bandaragoda_etal_ems

d06e23c7c5dfa772d5dfe55c33bcf7abbd5e2060

[ "MIT" ]

1

2019-09-24T15:22:05.000Z

hstools/utilities.py

saisiddu/pub_bandaragoda_etal_ems

d06e23c7c5dfa772d5dfe55c33bcf7abbd5e2060

[ "MIT" ]

null

hstools/utilities.py

saisiddu/pub_bandaragoda_etal_ems

d06e23c7c5dfa772d5dfe55c33bcf7abbd5e2060

[ "MIT" ]

null

from __future__ import print_function import os from IPython.core.display import display, HTML import glob from .compat import * def sizeof_fmt(num, suffix='B'): for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']: if abs(num) < 1024.0: return "%3.1f%s%s" % (num, unit, suffix) num /= 1024.0 return "%.1f%s%s" % (num, 'Yi', suffix) def get_hs_content(resid): resdir = find_resource_directory(resid) content = {} for f in glob.glob('%s/*/data/contents/*' % resdir): fname = os.path.basename(f) content[fname] = f return content def find_resource_directory(resid): download_dir = os.environ.get('JUPYTER_DOWNLOADS', 'hs_downloads') # loop over all the files in userspace for dirpath, dirnames, filenames in os.walk(download_dir): for dirname in [d for d in dirnames]: if dirname == resid: return os.path.join(dirpath, dirname) return None def check_for_ipynb(content_files): links = {} for f, p in content_files.items(): if f[-5:] == 'ipynb': fname = os.path.basename(p) url = urlencode(p) links[fname] = url return links def display_tree(resid): # todo: display a tree view of the resource bagit, based on id pass def display_resource_content_files(content_file_dictionary, text='Found the following content when parsing the HydroShare resource:'): # get ipynb files nbs = check_for_ipynb(content_file_dictionary) if len(nbs.keys()) > 0: display(HTML('Found the following notebook(s) associated with this HydroShare resource. Click the link(s) below to launch the notebook.')) for name, url in nbs.items(): display(HTML('<a href=%s target="_blank">%s<a>' % (url, name))) # print the remaining files if len(content_file_dictionary.keys()) > 0: display(HTML('Found the following file(s) associated with this HydroShare resource.')) text = ' '.join(content_file_dictionary.keys()) display(HTML(text)) if (len(content_file_dictionary.keys()) + len(nbs.keys())) > 0: display(HTML('These files are stored in a dictionary called hs.content for your convenience. To access a file, simply issue the following command where MY_FILE is one of the files listed above: <pre>hs.content["MY_FILE"] </pre> ')) def load_environment(env_path=None): # load the environment path (if it exists) if env_path is None: env_path = os.path.join(os.environ.get('NOTEBOOK_HOME', './'), '.env' ) if not os.path.exists(env_path): return with open(env_path, 'r') as f: lines = f.readlines() print('Adding the following system variables:') for line in lines: k, v = line.strip().split('=') os.environ[k] = v print(' %s = %s' % (k, v)) print('\nThese can be accessed using the following command: ') print(' os.environ[key]') print('\n (e.g.)\n os.environ["HS_USR_NAME"] => %s' % os.environ['HS_USR_NAME']) def get_env_var(varname): if varname in os.environ.keys(): return os.environ[varname] else: return input('Could not find %s, please specify a value: ' % varname).strip() def get_server_url_for_path(p): """ gets the url corresponding to a given file or directory path p : path to convert into a url returns the url path for the filepath p """ load_environment() rel_path = os.path.relpath(p, os.environ['NOTEBOOK_HOME']) url = urlencode(rel_path) return url def get_relative_path(p): """ gets the path relative to the jupyter home directory p: path to convert into relative path returns the path relative to the default jupyter home directory """ return os.path.relpath(p, os.environ['NOTEBOOK_HOME']) def _realname(path, root=None): if root is not None: path = os.path.join(root, path) result = os.path.basename(path) if os.path.islink(path): realpath = os.readlink(path) result = '%s -> %s' % (os.path.basename(path), realpath) return result def tree(startpath, depth=-1): prefix = 0 if startpath != '/': if startpath.endswith('/'): startpath = startpath[:-1] prefix = len(startpath) for root, dirs, files in os.walk(startpath): level = root[prefix:].count(os.sep) if depth > -1 and level > depth: continue indent = subindent = '' if level > 0: indent = '| ' * (level-1) + '|-- ' subindent = '| ' * (level) + '|-- ' print('{}{}/'.format(indent, _realname(root))) # print dir only if symbolic link; otherwise, will be printed as root for d in dirs: if os.path.islink(os.path.join(root, d)): print('{}{}'.format(subindent, _realname(d, root=root))) for f in files: print('{}{}'.format(subindent, _realname(f, root=root)))

30.963636

247

0.607164

702

5,109

4.323362

0.294872

0.0257

0.034596

0.015815

0.135091

0.098188

0.070511

0.04547

0

0.006094

0.261304

5,109

164

248

31.152439

0.798092

0.106087

0

0.019417

0.204873

0.020819

0

0.006098

0

1

0.116505

false

0.009709

0.048544

0

0.281553

0.087379

0

null

0

null

0

1

0

bde5bd2cb3f7fdf8cc6f96a4c93e07d27f29156e

16,286

py

Python

activity/activity_IngestDigestToEndpoint.py

elifesciences/elife-bot

d3a102c8030e4b7ec83cbd45e5f839dba4f9ffd9

[ "MIT" ]

17

2015-02-10T07:10:29.000Z

2021-05-14T22:24:45.000Z

activity/activity_IngestDigestToEndpoint.py

elifesciences/elife-bot

d3a102c8030e4b7ec83cbd45e5f839dba4f9ffd9

[ "MIT" ]

459

2015-03-31T18:24:23.000Z

2022-03-30T19:44:40.000Z

activity/activity_IngestDigestToEndpoint.py

elifesciences/elife-bot

d3a102c8030e4b7ec83cbd45e5f839dba4f9ffd9

[ "MIT" ]

9

2015-04-18T16:57:31.000Z

2020-10-30T11:49:13.000Z

import os import time import json from collections import OrderedDict from digestparser import json_output from provider.execution_context import get_session from provider.article_processing import download_jats from provider import digest_provider, email_provider, lax_provider, utils from activity.objects import Activity class activity_IngestDigestToEndpoint(Activity): def __init__(self, settings, logger, conn=None, token=None, activity_task=None): super(activity_IngestDigestToEndpoint, self).__init__( settings, logger, conn, token, activity_task ) self.name = "IngestDigestToEndpoint" self.pretty_name = "Ingest Digest to API endpoint" self.version = "1" self.default_task_heartbeat_timeout = 30 self.default_task_schedule_to_close_timeout = 60 * 5 self.default_task_schedule_to_start_timeout = 30 self.default_task_start_to_close_timeout = 60 * 5 self.description = ( "Send Digest JSON to an API endpoint," + " to be run when a research article is ingested" ) # Local directory settings self.directories = { "TEMP_DIR": os.path.join(self.get_tmp_dir(), "tmp_dir"), "INPUT_DIR": os.path.join(self.get_tmp_dir(), "input_dir"), } # Track the success of some steps self.statuses = OrderedDict( [ ("approve", None), ("download", None), ("generate", None), ("ingest", None), ] ) # Digest JSON content self.digest_content = None # Load the config self.digest_config = digest_provider.digest_config( self.settings.digest_config_section, self.settings.digest_config_file ) def do_activity(self, data=None): self.logger.info("data: %s" % json.dumps(data, sort_keys=True, indent=4)) success, run, session, article_id, version = self.session_data(data) self.make_activity_directories() # get session data if success is not True: self.logger.error("Failed to parse session data in %s" % self.pretty_name) return self.ACTIVITY_PERMANENT_FAILURE # emit start message success = self.emit_start_message(article_id, version, run) if success is not True: self.logger.error("Failed to emit a start message in %s" % self.pretty_name) return self.ACTIVITY_PERMANENT_FAILURE # Approve for ingestion self.statuses["approve"] = self.approve( article_id, session.get_value("status"), version, session.get_value("run_type"), ) if self.statuses.get("approve") is not True: self.logger.info( "Digest for article %s was not approved for ingestion" % article_id ) self.emit_end_message(article_id, version, run) return self.ACTIVITY_SUCCESS try: digest_details = self.gather_digest_details( article_id, version, session.get_value("expanded_folder") ) except Exception as exception: # send email error if any error message is returned message = "Error in gathering digest details: %s" % str(exception) self.logger.exception(message) return self.email_error_return(article_id, message) # generate the digest content try: self.digest_content = self.generate_digest_content( article_id, digest_details ) except Exception as exception: # send email error if unable to generate digest content message = "Error in generating digest content for article: %s" % str( exception ) self.logger.exception(message) return self.email_error_return(article_id, message) # issue put to the endpoint digest_id = self.digest_content.get("id") # set the stage attribute depending on silent correction or not if ( session.get_value("run_type") and session.get_value("run_type") == "silent-correction" ): digest_provider.set_stage(self.digest_content, "published") else: digest_provider.set_stage(self.digest_content, "preview") self.logger.info( "Digest stage value %s" % str(self.digest_content.get("stage")) ) try: put_response = digest_provider.put_digest_to_endpoint( self.logger, digest_id, self.digest_content, self.settings ) if put_response: self.statuses["ingest"] = True except Exception as exception: # email error message and return self.ACTIVITY_SUCCESS message = "Failed to ingest digest json to endpoint %s in %s: %s" % ( article_id, self.pretty_name, str(exception), ) self.logger.exception(message) return self.email_error_return(article_id, message) self.logger.info( "%s for article_id %s statuses: %s" % (self.name, str(article_id), self.statuses) ) self.emit_end_message(article_id, version, run) return self.ACTIVITY_SUCCESS def session_data(self, data): "get session data and return basic values" run = None session = None version = None article_id = None success = None try: run = data["run"] session = get_session(self.settings, data, run) version = session.get_value("version") article_id = session.get_value("article_id") success = True except (TypeError, KeyError) as exception: self.logger.exception( "Exception when getting the session for Starting ingest digest " + " to endpoint. Details: %s" % str(exception) ) return success, run, session, article_id, version def email_error_return(self, article_id, message): """log exception, email error message and return activity result""" send_error_email(article_id, message, self.settings, self.logger) return self.ACTIVITY_SUCCESS def emit_message(self, article_id, version, run, status, message): "emit message to the queue" try: self.emit_monitor_event( self.settings, article_id, version, run, self.pretty_name, status, message, ) return True except Exception as exception: self.logger.exception( "Exception emitting %s message. Details: %s" % (str(status), str(exception)) ) def emit_start_message(self, article_id, version, run): "emit the start message to the queue" return self.emit_message( article_id, version, run, "start", "Starting ingest digest to endpoint for " + str(article_id), ) def digest_preview_link(self, article_id): "preview link for the digest using the preview base url" return "%s/digests/%s" % ( self.settings.journal_preview_base_url, utils.pad_msid(article_id), ) def activity_end_message(self, article_id, statuses): "different end message to emit based on the ingest status" if statuses.get("ingest") is True: return ( "Finished ingest digest to endpoint for %s. Statuses %s Preview link %s" % (article_id, statuses, self.digest_preview_link(article_id)) ) return "No digest ingested for %s. Statuses %s" % (article_id, statuses) def emit_end_message(self, article_id, version, run): "emit the end message to the queue" return self.emit_message( article_id, version, run, "end", self.activity_end_message(article_id, self.statuses), ) def emit_error_message(self, article_id, version, run, message): "emit an error message to the queue" return self.emit_message(article_id, version, run, "error", message) def approve(self, article_id, status, version, run_type): "should we ingest based on some basic attributes" approve_status = True # check by status return_status = digest_provider.approve_by_status( self.logger, article_id, status ) if return_status is False: approve_status = return_status # check silent corrections and consider the first vor version run_type_status = digest_provider.approve_by_run_type( self.settings, self.logger, article_id, run_type, version ) first_vor_status = digest_provider.approve_by_first_vor( self.settings, self.logger, article_id, version, status ) if first_vor_status is False and run_type != "silent-correction": # not the first vor and not a silent correction, do not approve approve_status = False elif run_type_status is False: # otherwise depend on the silent correction run_type logic approve_status = False # check if there is a digest docx in the bucket for this article if approve_status: if not digest_provider.docx_exists_in_s3( self.settings, article_id, self.settings.bot_bucket, self.logger ): self.logger.info( "Digest docx file does not exist in S3 for article %s" % article_id ) approve_status = False return approve_status def gather_digest_details(self, article_id, version, expanded_folder): digest_details = OrderedDict() # Download digest from the S3 outbox digest_details["docx_file"] = digest_download_docx_from_s3( article_id, self.settings.bot_bucket, self.directories.get("INPUT_DIR"), self.settings, self.logger, ) self.statuses["download"] = True # find the image file name digest_details["image_file"] = digest_image_file_name_from_s3( article_id, self.settings.bot_bucket, self.settings ) # download jats file digest_details["jats_file"] = download_jats_for_digest( expanded_folder, self.settings, self.directories.get("TEMP_DIR"), self.logger, ) # related article data digest_details["related"] = get_related_from_lax( article_id, version, self.settings, self.pretty_name, self.logger ) return digest_details def generate_digest_content(self, article_id, digest_details): digest_content = None try: digest_content = self.digest_json( digest_details.get("docx_file"), digest_details.get("jats_file"), digest_details.get("image_file"), digest_details.get("related"), ) except Exception as exception: # email error message and return self.ACTIVITY_SUCCESS message = "Failed to generate digest json for %s in %s: %s" % ( article_id, self.pretty_name, str(exception), ) raise Exception(message) if digest_content: self.statuses["generate"] = True else: # email error message and return self.ACTIVITY_SUCCESS message = ( "Unable to generate Digest content for docx_file %s, " + "jats_file %s, image_file %s" ) % ( digest_details.get("docx_file"), digest_details.get("jats_file"), digest_details.get("image_file"), ) raise Exception(message) return digest_content def digest_json(self, docx_file, jats_file=None, image_file=None, related=None): "generate the digest json content from the docx file and other data" json_content = None try: json_content = json_output.build_json( docx_file, self.directories.get("TEMP_DIR"), self.digest_config, jats_file, image_file, related, ) except Exception as exception: self.logger.exception( "Exception generating digest json for docx_file %s. Details: %s" % (str(docx_file), str(exception)) ) return json_content def digest_download_docx_from_s3(article_id, bucket_name, input_dir, settings, logger): try: return digest_provider.download_docx_from_s3( settings, article_id, bucket_name, input_dir, logger ) except Exception as exception: message = "Unable to download digest docx file for article %s: %s" % ( article_id, str(exception), ) raise Exception(message) def digest_image_file_name_from_s3(article_id, bucket_name, settings): try: return digest_provider.image_file_name_from_s3( settings, article_id, bucket_name ) except Exception as exception: message = "Failed to get image file name from S3 for digest %s: %s" % ( article_id, str(exception), ) raise Exception(message) def download_jats_for_digest(expanded_folder, settings, temp_dir, logger): try: return download_jats(settings, expanded_folder, temp_dir, logger) except Exception as exception: message = "Failed to download JATS from expanded folder %s: %s" % ( expanded_folder, str(exception), ) raise Exception(message) def get_related_from_lax(article_id, version, settings, pretty_name, logger): try: return related_from_lax(article_id, version, settings, logger) except Exception as exception: message = "Failed to get related from lax for digest %s in %s: %s" % ( article_id, pretty_name, str(exception), ) raise Exception(message) def related_from_lax(article_id, version, settings, logger=None, auth=True): "get article json from Lax and return as a list of related data" related = None related_json = None try: related_json = lax_provider.article_snippet(article_id, version, settings, auth) except Exception as exception: logger.exception( ( "Exception in getting article snippet from Lax for article_id" " %s, version %s. Details: %s" ) % (str(article_id), str(version), str(exception)) ) raise if related_json: related = [related_json] return related def error_email_subject(article_id): "email subject for an error email" return u"Error ingesting digest to endpoint: {article_id}".format( article_id=article_id ) def send_error_email(article_id, message, settings, logger): "email error message to the recipients" datetime_string = time.strftime(utils.DATE_TIME_FORMAT, time.gmtime()) body = email_provider.simple_email_body(datetime_string, message) subject = error_email_subject(article_id) sender_email = settings.digest_sender_email recipient_email_list = email_provider.list_email_recipients( settings.digest_validate_error_recipient_email ) messages = email_provider.simple_messages( sender_email, recipient_email_list, subject, body, logger=logger ) logger.info("Formatted %d email error messages" % len(messages)) details = email_provider.smtp_send_messages(settings, messages, logger) logger.info("Email sending details: %s" % str(details))

36.191111

88

0.608805

1,860

16,286

5.109677

0.12043

0.063447

0.035354

0.021991

0.384785

0.306923

0.246107

0.202757

0.140257

0.126368

0

0.001973

0.315179

16,286

449

89

36.271715

0.850175

0.09106

0

0.277473

0

0.148821

0.001437

0

1

0.057692

false

0

0.024725

0

0.156593

0

null

0

null

0

1

0

bde6c2d4e221af5daf9ceb3a165e32e65089ccfe

249

py

Python

utils/forgiveness_of_the_offender.py

bbt-t/simple-bot_discord

46fa629e8278e8e453b3c272b2e838d0762aaaf8

[ "MIT" ]

null

utils/forgiveness_of_the_offender.py

bbt-t/simple-bot_discord

46fa629e8278e8e453b3c272b2e838d0762aaaf8

[ "MIT" ]

null

utils/forgiveness_of_the_offender.py

bbt-t/simple-bot_discord

46fa629e8278e8e453b3c272b2e838d0762aaaf8

[ "MIT" ]

null

from discord import Member, utils async def unmute_user(member: Member): role = utils.get(member.guild.roles, id=809817869914341396) await member.edit(roles=()) await member.add_roles(role) await member.send('Ты размучен! :)')

19.153846

63

0.706827

33

249

5.272727

0.636364

0.189655

0

0.087379

0.172691

249

12

64

20.75

0.757282

0

0.060484

0

1

0

false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

bde798fb51621c003debde76678c82dcde2604d3

443

py

Python

mailing/urls.py

Aladom/django-mailing

aa18963b1902e4b7f066b0064a832e26e725f643

[ "MIT" ]

null

mailing/urls.py

Aladom/django-mailing

aa18963b1902e4b7f066b0064a832e26e725f643

[ "MIT" ]

13

2016-02-04T14:56:11.000Z

2021-06-10T20:39:51.000Z

mailing/urls.py

Aladom/django-mailing

aa18963b1902e4b7f066b0064a832e26e725f643

[ "MIT" ]

null

# -*- coding: utf-8 -*- from django.conf.urls import url from .views import MirrorView, SubscriptionsManagementView __all__ = [ 'app_name', 'urlpatterns', ] app_name = 'mailing' urlpatterns = [ url(r'^mirror/(?P<signed_pk>[0-9]+:[a-zA-Z0-9_-]+)/$', MirrorView.as_view(), name='mirror'), url(r'^subscriptions/(?P<signed_email>.+:[a-zA-Z0-9_-]+)/$', SubscriptionsManagementView.as_view(), name='subscriptions'), ]

26.058824

69

0.643341

54

443

5.055556

0.574074

0.051282

0.03663

0.043956

0

0.01847

0.14447

443

16

70

27.6875

0.701847

0.047404

0

0.340476

0.233333

0

1

0

false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

bdeb63bd228672aa0d61f1e5f7d0335e8f073585

12,597

py

Python

pykit/codegen/llvm/llvm_codegen.py

ContinuumIO/pyk

1730d7b831e0cf12a641ac23b5cf03e17e0dc550

[ "BSD-3-Clause" ]

9

2015-06-23T00:13:49.000Z

2022-02-23T02:46:43.000Z

pykit/codegen/llvm/llvm_codegen.py

ContinuumIO/pyk

1730d7b831e0cf12a641ac23b5cf03e17e0dc550

[ "BSD-3-Clause" ]

1

2017-08-30T08:13:12.000Z

2017-08-31T06:36:32.000Z

pykit/codegen/llvm/llvm_codegen.py

ContinuumIO/pyk

1730d7b831e0cf12a641ac23b5cf03e17e0dc550

[ "BSD-3-Clause" ]

7

2015-05-08T10:17:47.000Z

2021-04-01T15:00:57.000Z

from functools import partial from pykit.ir import vvisit, ArgLoader, verify_lowlevel from pykit.ir import defs, opgrouper from pykit.types import Boolean, Integral, Real, Pointer, Function, Int64 from pykit.codegen.llvm.llvm_types import llvm_type import llvm.core as lc from llvm.core import Type, Constant #===------------------------------------------------------------------=== # Definitions #===------------------------------------------------------------------=== compare_float = { '>': lc.FCMP_OGT, '<': lc.FCMP_OLT, '==': lc.FCMP_OEQ, '>=': lc.FCMP_OGE, '<=': lc.FCMP_OLE, '!=': lc.FCMP_ONE, } compare_signed_int = { '>': lc.ICMP_SGT, '<': lc.ICMP_SLT, '==': lc.ICMP_EQ, '>=': lc.ICMP_SGE, '<=': lc.ICMP_SLE, '!=': lc.ICMP_NE, } compare_unsiged_int = { '>': lc.ICMP_UGT, '<': lc.ICMP_ULT, '==': lc.ICMP_EQ, '>=': lc.ICMP_UGE, '<=': lc.ICMP_ULE, '!=': lc.ICMP_NE, } compare_bool = { '==' : lc.ICMP_EQ, '!=' : lc.ICMP_NE } # below based on from npm/codegen def integer_invert(builder, val): return builder.xor(val, Constant.int_signextend(val.type, -1)) def integer_usub(builder, val): return builder.sub(Constant.int(val.type, 0), val) def integer_not(builder, value): return builder.icmp(lc.ICMP_EQ, value, Constant.int(value.type, 0)) def float_usub(builder, val): return builder.fsub(Constant.real(val.type, 0), val) def float_not(builder, val): return builder.fcmp(lc.FCMP_OEQ, val, Constant.real(val.type, 0)) binop_int = { '+': (lc.Builder.add, lc.Builder.add), '-': (lc.Builder.sub, lc.Builder.sub), '*': (lc.Builder.mul, lc.Builder.mul), '/': (lc.Builder.sdiv, lc.Builder.udiv), '//': (lc.Builder.sdiv, lc.Builder.udiv), '%': (lc.Builder.srem, lc.Builder.urem), '&': (lc.Builder.and_, lc.Builder.and_), '|': (lc.Builder.or_, lc.Builder.or_), '^': (lc.Builder.xor, lc.Builder.xor), '<<': (lc.Builder.shl, lc.Builder.shl), '>>': (lc.Builder.ashr, lc.Builder.lshr), } binop_float = { '+': lc.Builder.fadd, '-': lc.Builder.fsub, '*': lc.Builder.fmul, '/': lc.Builder.fdiv, '//': lc.Builder.fdiv, '%': lc.Builder.frem, } unary_bool = { '!': integer_not, } unary_int = { '~': integer_invert, '!': integer_not, "+": lambda builder, arg: arg, "-": integer_usub, } unary_float = { '!': float_not, "+": lambda builder, arg: arg, "-": float_usub, } #===------------------------------------------------------------------=== # Utils #===------------------------------------------------------------------=== i1, i16, i32, i64 = map(Type.int, [1, 16, 32, 64]) def const_int(type, value): return Constant.int(type, value) const_i32 = partial(const_int, i32) const_i64 = partial(const_int, i64) zero = partial(const_int, value=0) one = partial(const_int, value=1) def sizeof(builder, ty, intp): ptr = Type.pointer(ty) null = Constant.null(ptr) offset = builder.gep(null, [Constant.int(Type.int(), 1)]) return builder.ptrtoint(offset, intp) #===------------------------------------------------------------------=== # Translator #===------------------------------------------------------------------=== class Translator(object): """ Translate a function in low-level form. This means it can only use values of type Bool, Int, Float, Struct or Pointer. Values of type Function may be called. """ def __init__(self, func, env, lfunc, llvm_typer, llvm_module): self.func = func self.env = env self.lfunc = lfunc self.llvm_type = llvm_typer self.lmod = llvm_module self.builder = None self.phis = [] # [pykit_phi] def blockswitch(self, newblock): if not self.builder: self.builder = lc.Builder.new(newblock) self.builder.position_at_end(newblock) # __________________________________________________________________ def op_arg(self, arg): return self.lfunc.args[self.func.args.index(arg)] # __________________________________________________________________ def op_unary(self, op, arg): opmap = { Boolean: unary_bool, Integral: unary_int, Real: unary_float }[type(op.type)] unop = defs.unary_opcodes[op.opcode] return opmap[unop](self.builder, arg) def op_binary(self, op, left, right): binop = defs.binary_opcodes[op.opcode] if op.type.is_int: genop = binop_int[binop][op.type.unsigned] else: genop = binop_float[binop] return genop(self.builder, left, right, op.result) def op_compare(self, op, left, right): cmpop = defs.compare_opcodes[op.opcode] type = op.args[0].type if type.is_int and type.unsigned: cmp, lop = self.builder.icmp, compare_unsiged_int[cmpop] elif type.is_int or type.is_bool: cmp, lop = self.builder.icmp, compare_signed_int[cmpop] else: cmp, lop = self.builder.fcmp, compare_float[cmpop] return cmp(lop, left, right, op.result) # __________________________________________________________________ def op_convert(self, op, arg): from llpython.byte_translator import LLVMCaster unsigned = op.type.is_int and op.type.unsigned # The float cast doens't accept this keyword argument kwds = {'unsigned': unsigned} if unsigned else {} return LLVMCaster.build_cast(self.builder, arg, self.llvm_type(op.type), **kwds) # __________________________________________________________________ def op_call(self, op, function, args): # Get the callee LLVM function from the cache. This is put there by # pykit.codegen.codegen cache = self.env["codegen.cache"] lfunc = cache[function] return self.builder.call(lfunc, args) def op_call_math(self, op, name, args): # Math is resolved by an LLVM postpass argtypes = [arg.type for arg in args] lfunc_type = self.llvm_type(Function(op.type, argtypes)) lfunc = self.lmod.get_or_insert_function( lfunc_type, 'pykit.math.%s.%s' % (map(str, argtypes), name.lower())) return self.builder.call(lfunc, args, op.result) # __________________________________________________________________ def op_getfield(self, op, struct, attr): index = const_i32(op.type.names.index(attr)) return self.builder.extract_value(struct, index, op.result) def op_setfield(self, op, struct, attr, value): index = const_i32(op.type.names.index(attr)) return self.builder.insert_element(struct, value, index, op.result) # __________________________________________________________________ def op_getindex(self, op, array, indices): return self.builder.gep(array, indices, op.result) def op_setindex(self, op, array, indices, value): ptr = self.builder.gep(array, indices) self.builder.store(ptr, value) # __________________________________________________________________ def op_getindex(self, op, array, indices): return self.builder.gep(array, indices, op.result) # __________________________________________________________________ def op_alloca(self, op): llvm_pointer_type = self.llvm_type(op.type) return self.builder.alloca(llvm_pointer_type.pointee, op.result) def op_load(self, op, stackvar): return self.builder.load(stackvar, op.result) def op_store(self, op, value, stackvar): self.builder.store(value, stackvar) # __________________________________________________________________ def op_jump(self, op, block): self.builder.branch(block) def op_cbranch(self, op, test, true_block, false_block): self.builder.cbranch(test, true_block, false_block) def op_phi(self, op): phi = self.builder.phi(self.llvm_type(op.type), op.result) self.phis.append(op) return phi def op_ret(self, op, value): if value is None: assert self.func.type.restype.is_void self.builder.ret_void() else: self.builder.ret(value) # __________________________________________________________________ def op_sizeof(self, op, type): int_type = self.llvm_type(op.type) item_type = self.llvm_type(type) return sizeof(self.builder, item_type, int_type, op.result) def op_addressof(self, op, func): assert func.address addr = const_int(i64, func.address) return self.builder.inttoptr(addr, self.llvm_type(Pointer(func.type))) # __________________________________________________________________ def op_ptradd(self, op, ptr, val): return self.builder.gep(ptr, [val], op.result) def op_ptrload(self, op, ptr): return self.builder.load(ptr, op.result) def op_ptrstore(self, op, ptr, val): return self.builder.store(val, ptr, op.result) def op_ptrcast(self, op, val): return self.builder.bitcast(val, self.llvm_type(op.type), op.result) def op_ptr_isnull(self, op, val): intval = self.builder.ptrtoint(val, self.llvm_type(Int64)) return self.builder.icmp(lc.ICMP_EQ, intval, zero(intval.type), op.result) # __________________________________________________________________ def allocate_blocks(llvm_func, pykit_func): """Return a dict mapping pykit blocks to llvm blocks""" blocks = {} for block in pykit_func.blocks: blocks[block] = llvm_func.append_basic_block(pykit_func.name) return blocks def update_phis(phis, valuemap, argloader): """ Update LLVM phi values given a list of pykit phi values and block and value dicts mapping pykit values to LLVM values """ for phi in phis: llvm_phi = valuemap[phi.result] llvm_blocks = map(argloader.load_op, phi.args[0]) llvm_values = map(argloader.load_op, phi.args[1]) for llvm_block, llvm_value in zip(llvm_blocks, llvm_values): llvm_phi.add_incoming(llvm_value, llvm_block) #===------------------------------------------------------------------=== # Pass to group operations such as add/mul #===------------------------------------------------------------------=== class LLVMArgLoader(ArgLoader): """ Load Operation arguments as LLVM values passed and extra *args to the Translator. """ def __init__(self, store, engine, llvm_module, lfunc, blockmap): super(LLVMArgLoader, self).__init__(store) self.engine = engine self.llvm_module = llvm_module self.lfunc = lfunc self.blockmap = blockmap def load_GlobalValue(self, arg): if arg.external: value = self.lmod.get_or_insert_function(llvm_type(arg.type)) if arg.address: self.engine.add_global_mapping(value, arg.address) else: assert arg.value value = arg.value.const return value def load_Block(self, arg): return self.blockmap[arg] def load_Constant(self, arg): ty = type(arg.type) lty = llvm_type(arg.type) if ty == Pointer: if arg.const == 0: return lc.Constant.null(lty) else: return const_i64(arg.const).inttoptr(i64) elif ty == Integral: if arg.type.unsigned: return lc.Constant.int(lty, arg.const) else: return lc.Constant.int_signextend(lty, arg.const) elif ty == Real: return lc.Constant.real(lty, arg.const) else: raise NotImplementedError("Constants for", ty) def load_Undef(self, arg): return lc.Constant.undef(llvm_type(arg.type)) def initialize(func, env): verify_lowlevel(func) llvm_module = env["codegen.llvm.module"] return llvm_module.add_function(llvm_type(func.type), func.name) def translate(func, env, lfunc): engine, llvm_module = env["codegen.llvm.engine"], env["codegen.llvm.module"] blockmap = allocate_blocks(lfunc, func) ### Create visitor ### translator = Translator(func, env, lfunc, llvm_type, llvm_module) visitor = opgrouper(translator) ### Codegen ### argloader = LLVMArgLoader(None, engine, llvm_module, lfunc, blockmap) valuemap = vvisit(visitor, func, argloader) update_phis(translator.phis, valuemap, argloader) return lfunc

32.135204

82

0.618322

1,515

12,597

4.479208

0.179538

0.055113

0.024315

0.02682

0.220159

0.107722

0.062334

0.046124

0.037135

0

0.004887

0.220291

12,597

392

83

32.135204

0.686011

0.166945

0

0.091954

0

0.016368

0

0.011494

1

0.168582

false

0

0.030651

0.061303

0.356322

0

null

0

null

0

1

0

bdeef5ecb135e522f7c40abc5e24bd958b8ff052

1,859

py

Python

DatabaseHandler/sqlite_operations.py

utkarsh7236/SCILLA

e11e4d753823ad522a1b3168283b6e6ffe3ea393

[ "Apache-2.0" ]

17

2019-12-09T19:09:07.000Z

2021-08-29T01:11:13.000Z

DatabaseHandler/sqlite_operations.py

utkarsh7236/SCILLA

e11e4d753823ad522a1b3168283b6e6ffe3ea393

[ "Apache-2.0" ]

1

2021-04-14T15:08:18.000Z

DatabaseHandler/sqlite_operations.py

utkarsh7236/SCILLA

e11e4d753823ad522a1b3168283b6e6ffe3ea393

[ "Apache-2.0" ]

2

2020-06-05T03:01:06.000Z

2020-07-09T07:13:12.000Z

#!/usr/bin/env python __author__ = 'Florian Hase' #======================================================================== import time import sqlalchemy as sql #======================================================================== class AddEntry(object): def __init__(self, database, table, entry): self.db = database self.table = table self.entry = entry def execute(self): start = time.time() with self.db.connect() as conn: conn.execute(self.table.insert(), self.entry) conn.close() end = time.time() #======================================================================== class FetchEntries(object): def __init__(self, database, table, selection, name = 'test'): self.db = database self.table = table self.selection = selection self.entries = None self.executed = False self.entries_fetched = False self.name = name def execute(self): start = time.time() with self.db.connect() as conn: selected = conn.execute(self.selection) entries = selected.fetchall() conn.close() self.entries = entries self.executed = True end = time.time() def get_entries(self): iteration_index = 0 while not self.executed: pass self.entries_fetched = True return self.entries #======================================================================== class UpdateEntries(object): def __init__(self, database, table, updates): self.db = database self.table = table self.updates = updates def execute(self): start = time.time() if isinstance(self.updates, list): with self.db.connect() as conn: for updates in self.updates: updated = conn.execute(updates) conn.close() else: with self.db.connect() as conn: updated = conn.execute(self.updates) conn.close() end = time.time()

23.833333

73

0.550834

201

1,859

4.995025

0.293532

0.041833

0.039841

0.067729

0.39741

0.35757

0.195219

0.099602

0

0.000669

0.196342

1,859

77

74

24.142857

0.671352

0.166218

0

0.418182

0

0.010356

0

1

0.127273

false

0.018182

0.036364

0

0.236364

0

null

0

null

0

1

0

bdf105752f21bbc068ce977d28dde3f6db125f50

8,818

py

Python

main.py

omegaBionic/pysparkPower

1354247e4ec085a65f288a1f31a05875f003da72

[ "Apache-2.0" ]

null

main.py

omegaBionic/pysparkPower

1354247e4ec085a65f288a1f31a05875f003da72

[ "Apache-2.0" ]

null

main.py

omegaBionic/pysparkPower

1354247e4ec085a65f288a1f31a05875f003da72

[ "Apache-2.0" ]

null

import matplotlib.pyplot as plt import numpy as np import pylab as pl import pandas as pd from pyspark import SQLContext from pyspark.ml.clustering import KMeans from pyspark.ml.feature import VectorAssembler from pyspark.sql import SparkSession from pyspark.sql.types import StructType, StructField, IntegerType from data.process_initial_file import dict_education, list_education, list_race def elbow_method_evaluation(df): # Calculate cost and plot cost = np.zeros(10) for k in range(2, 10): kmeans = KMeans().setK(k).setSeed(1).setFeaturesCol("features") model = kmeans.fit(df) cost[k] = model.summary.trainingCost # Plot the cost df_cost = pd.DataFrame(cost[2:]) df_cost.columns = ["cost"] new_col = [2, 3, 4, 5, 6, 7, 8, 9] df_cost.insert(0, 'cluster', new_col) pl.plot(df_cost.cluster, df_cost.cost) pl.xlabel('Number of Clusters') pl.ylabel('Score') pl.title('Elbow Curve') pl.show() spark = SparkSession \ .builder \ .appName("Python Spark SQL basic example") \ .config("spark.some.config.option", "some-value") \ .getOrCreate() # Define information nullable = True schema = StructType([ StructField("age", IntegerType(), nullable), StructField("workclass", IntegerType(), nullable), StructField("fnlwgt", IntegerType(), nullable), StructField("education", IntegerType(), nullable), StructField("marital-status", IntegerType(), nullable), StructField("occupation", IntegerType(), nullable), StructField("relationship", IntegerType(), nullable), StructField("race", IntegerType(), nullable), StructField("sex", IntegerType(), nullable), StructField("capital-gain", IntegerType(), nullable), StructField("capital-loss", IntegerType(), nullable), StructField("hours-per-week", IntegerType(), nullable), StructField("native-country", IntegerType(), nullable), StructField("is-upper-than-50k", IntegerType(), nullable) ]) # Connect to bdd sqlContext = SQLContext(sparkContext=spark.sparkContext, sparkSession=spark) # Read file df = sqlContext.read.csv("data/adult_processed_data.data", header=True, sep=",", schema=schema) # Display all columns # print(df.collect()) # Display columns print(df.columns) # df.select("is-upper-than-50k").show() df.select("*").show() # Create features column, assembling together the numeric data col1_name = 'education' col2_name = 'capital-gain' col3_name = 'race' col4_name = 'hours-per-week' inputCols = [col1_name, col2_name, col3_name] vecAssembler = VectorAssembler( inputCols=inputCols, outputCol="features") adults_with_features = vecAssembler.transform(df) # Figure 1 # Do K-means # Evaluate number of clusters with the elbow method elbow_method_evaluation(adults_with_features) k = 3 kmeans_algo = KMeans().setK(k).setSeed(1).setFeaturesCol("features") model = kmeans_algo.fit(adults_with_features) centers = model.clusterCenters() # Assign clusters to adults # Cluster prediction, named prediction and used after for color adults_with_clusters = model.transform(adults_with_features) # Display Centers print("Centers: '{}'".format(centers)) # Convert Spark Data Frame to Pandas Data Frame adults_for_viz = adults_with_clusters.toPandas() print("STARTING PRINTING ADULTS_for") print("adults_for_viz.prediction.value_counts(): '{}'".format(adults_for_viz.prediction.value_counts())) # Vizualize A = adults_for_viz[adults_for_viz["is-upper-than-50k"] == 0] B = adults_for_viz[adults_for_viz["is-upper-than-50k"] == 1] # Colors code k-means results, cluster numbers colors = {0: 'red', 1: 'blue', 2: 'orange'} # Draw dots fig = plt.figure().add_subplot() fig.scatter(A[col1_name], A[col2_name], c=A.prediction.map(colors), marker='.') fig.scatter(B[col1_name], B[col2_name], c=B.prediction.map(colors), marker='x') # Draw grid plt.grid() # Set text plt.title("Combined Statistics 1") plt.xlabel(col1_name) plt.ylabel(col2_name) # TODO To change in case col1_name is changed plt.xticks(range(0, len(list_education)), list_education, rotation='vertical') plt.legend(['is-upper-than-50k: False', 'is-upper-than-50k: True']) # Save figure plt.savefig("picture1.png", bbox_inches='tight') # Show fig plt.show() # Figure 2 # Draw dots fig = plt.figure().add_subplot() fig.scatter(A[col1_name], A[col2_name], c=A.prediction.map(colors), marker='.') fig.scatter(B[col1_name], B[col2_name], c=B.prediction.map(colors), marker='x') # fig.set_yscale('log', base=2) # Draw grid plt.grid() # Set text plt.title("Combined Statistics 2") plt.xlabel(col1_name) plt.ylabel(col2_name) plt.xticks(range(0, len(list_education)), list_education, rotation='vertical') plt.legend(['is-upper-than-50k: False', 'is-upper-than-50k: True']) # Save figure plt.savefig("picture2.png", bbox_inches='tight') # Show fig plt.show() # Figure 3 inputCols = [col2_name, col3_name] vecAssembler = VectorAssembler( inputCols=inputCols, outputCol="features") adults_with_features = vecAssembler.transform(df) # Do K-means k = 3 kmeans_algo = KMeans().setK(k).setSeed(1).setFeaturesCol("features") model = kmeans_algo.fit(adults_with_features) centers = model.clusterCenters() # Assign clusters to flowers # Cluster prediction, named prediction and used after for color adults_with_clusters = model.transform(adults_with_features) # Display Centers print("Centers: '{}'".format(centers)) # Convert Spark Data Frame to Pandas Data Frame adults_for_viz = adults_with_clusters.toPandas() print("STARTING PRINTING ADULTS_for") print("adults_for_viz.prediction.value_counts(): '{}'".format(adults_for_viz.prediction.value_counts())) # Vizualize A = adults_for_viz[adults_for_viz["is-upper-than-50k"] == 0] B = adults_for_viz[adults_for_viz["is-upper-than-50k"] == 1] # Colors code k-means results, cluster numbers colors = {0: 'red', 1: 'blue', 2: 'orange'} # Draw dots fig = plt.figure().add_subplot() fig.scatter(A[col3_name], A[col2_name], c=A.prediction.map(colors), marker='.') fig.scatter(B[col3_name], B[col2_name], c=B.prediction.map(colors), marker='x') # fig.set_yscale('log', base=2) # Draw grid plt.grid() # Set text plt.title("Combined Statistics 3") plt.xlabel(col3_name) plt.ylabel(col2_name) plt.xticks(range(0, len(list_race)), list_race, rotation='vertical') plt.legend(['is-upper-than-50k: False', 'is-upper-than-50k: True']) # Save figure plt.savefig("picture3.png", bbox_inches='tight') # Show fig plt.show() # TODO PUT HERE # Figure 4 inputCols = [col1_name, col3_name, col4_name] vecAssembler = VectorAssembler( inputCols=inputCols, outputCol="features") adults_with_features = vecAssembler.transform(df) elbow_method_evaluation(adults_with_features) # Do K-means k = 3 kmeans_algo = KMeans().setK(k).setSeed(1).setFeaturesCol("features") model = kmeans_algo.fit(adults_with_features) centers = model.clusterCenters() # Assign clusters to flowers # Cluster prediction, named prediction and used after for color adults_with_clusters = model.transform(adults_with_features) # Display Centers print("Centers: '{}'".format(centers)) # Convert Spark Data Frame to Pandas Data Frame adults_for_viz = adults_with_clusters.toPandas() print("STARTING PRINTING ADULTS_for") print("adults_for_viz.prediction.value_counts(): '{}'".format(adults_for_viz.prediction.value_counts())) # Vizualize A = adults_for_viz[adults_for_viz["is-upper-than-50k"] == 0] B = adults_for_viz[adults_for_viz["is-upper-than-50k"] == 1] # Colors code k-means results, cluster numbers colors = {0: 'red', 1: 'blue', 2: 'orange'} # Draw dots fig_3d = plt.figure() ax = plt.axes(projection='3d') ax.set_xlabel(col1_name) ax.set_ylabel(col3_name) ax.set_zlabel(col4_name) ax.set_xticks(range(0, len(list_education))) ax.set_xticklabels(list_education, rotation=90, verticalalignment='baseline', horizontalalignment='left') ax.set_yticks(range(0, len(list_race))) ax.set_yticklabels(list_race, rotation=-15, verticalalignment='baseline', horizontalalignment='left') # Data for three-dimensional scattered points ax.scatter3D(A[col1_name], A[col3_name], A[col4_name], c=A.prediction.map(colors), cmap='Greens', marker='.') ax.scatter3D(B[col1_name], B[col3_name], B[col4_name], c=B.prediction.map(colors), cmap='Greens', marker='x') # Save figure plt.savefig("picture4.png", bbox_inches='tight') plt.show() # DEBUG: Display stats print("k: '{}'".format(k)) print("A.prediction.value_counts(): '{}'".format(A.prediction.value_counts())) print("B.prediction.value_counts(): '{}'".format(B.prediction.value_counts()))

29.790541

109

0.713087

1,203

8,818

5.075644

0.202826

0.035375

0.041271

0.0321

0.590894

0.587455

0.556666

0.543727

0.522764

0

0.017317

0.142096

8,818

295

110

29.891525

0.789822

0.150488

0

0.536313

0

0.160409

0.031355

0

0.00339

0

1

0.005587

false

0

0.055866

0

0.061453

0.072626

0

null

0

null

0

1

0

bdf5bfe6b045a2bc243a77cfba2030c81bcde42d

3,781

py

Python

src/open3DTool/visualizer.py

MobileRoboticsSkoltech/plane-segmentation-research

0627512c4cb53326de1aabf815e755d9e4484c9c

[ "Apache-2.0" ]

1

2021-10-15T08:18:55.000Z

src/open3DTool/visualizer.py

MobileRoboticsSkoltech/plane-segmentation-research

0627512c4cb53326de1aabf815e755d9e4484c9c

[ "Apache-2.0" ]

1

2021-11-18T16:37:28.000Z

src/open3DTool/visualizer.py

MobileRoboticsSkoltech/plane-segmentation-research

0627512c4cb53326de1aabf815e755d9e4484c9c

[ "Apache-2.0" ]

null

from src.open3DTool.planeUtils import ( segment_points_on_plane_by_picked_points, pick_points_utils, ) from src.algorithmsForPointCloud.fileUtils import ( get_point_cloud_from_bin_file, generate_labels_and_object_files, ) from src.open3DTool.fileUtils import update_label_files import numpy as np import open3d as o3d class Visualizer: point_cloud = o3d.geometry.PointCloud() path_to_pcd_file = "" path_to_label_file = "" path_to_object_file = "" main_visualizer = o3d.visualization.VisualizerWithKeyCallback() picked_indexes = [] distance = 0 pick_points_count = 3 def __init__( self, path_to_bin_file: str, path_to_save_file_label: str, path_to_save_file_object: str, path_to_pcd_file: str, distance: np.intc, pick_points_count: np.intc, ): self.point_cloud = get_point_cloud_from_bin_file(path_to_bin_file) self.point_cloud.paint_uniform_color([0.51, 0.51, 0.51]) self.path_to_pcd_file = path_to_pcd_file self.path_to_label_file = path_to_save_file_label self.path_to_object_file = path_to_save_file_object self.distance = distance self.pick_points_count = pick_points_count self.generate_label_files([]) def generate_label_files(self, indexes: list): generate_labels_and_object_files( len(self.point_cloud.points), indexes, self.path_to_label_file, self.path_to_object_file, ) def update_pcd_and_label_files(self, count_of_points: int, is_append_right: bool): update_label_files( self.point_cloud, count_of_points, self.path_to_pcd_file, self.path_to_label_file, self.path_to_object_file, is_append_right, ) def run(self): self.main_visualizer = o3d.visualization.VisualizerWithKeyCallback() self.main_visualizer.create_window() self.main_visualizer.add_geometry(self.point_cloud) self.set_hotkeys() self.main_visualizer.run() self.main_visualizer.destroy_window() def set_hotkeys(self): self.main_visualizer.register_key_callback(32, self.pick_points) # Space self.main_visualizer.register_key_callback( 259, self.get_previous_snapshot ) # Backspace def pick_points(self, visualizer): indexes_of_points = pick_points_utils(self.point_cloud) assert len(indexes_of_points) == self.pick_points_count self.update_main_window_by_plane(indexes_of_points) def get_previous_snapshot(self, visualizer): if len(self.picked_indexes) == 0: return number_of_last_indexes = self.picked_indexes[-1] self.picked_indexes = self.picked_indexes[:-1] point_cloud_len = len(self.point_cloud.points) last_indexes = [ i for i in range(point_cloud_len - number_of_last_indexes, point_cloud_len) ] picked_cloud = self.point_cloud.select_by_index(last_indexes) picked_cloud.paint_uniform_color([0.51, 0.51, 0.51]) self.point_cloud = picked_cloud + self.point_cloud.select_by_index( last_indexes, invert=True ) self.update_pcd_and_label_files(number_of_last_indexes, False) visualizer.clear_geometries() visualizer.add_geometry(self.point_cloud) def update_main_window_by_plane(self, picked_points: list): self.point_cloud, indexes = segment_points_on_plane_by_picked_points( self.point_cloud, picked_points, self.distance ) self.picked_indexes.append(len(indexes)) self.update_pcd_and_label_files(len(indexes), True) self.run()

34.372727

87

0.691351

497

3,781

4.802817

0.197183

0.079598

0.076246

0.027231

0.455802

0.274822

0.150398

0.12191

0.103058

0

0.012081

0.233801

3,781

109

88

34.688073

0.811874

0.003967

0

0.043011

0

0.010753

1

0.086022

false

0

0.053763

0

0.247312

0

null

0

null

0

1

0

bdfa7f51aa6bca9797c581b745c48d3a51fc0b8d

8,868

py

Python

submission_utils.py

ameyagodbole/multihop_inference_explanation_regeneration

ab742433034b251a819b6eb898686530bd055cd0

[ "MIT" ]

7

2019-08-31T22:58:41.000Z

2021-02-06T17:41:38.000Z

submission_utils.py

ameyagodbole/multihop_inference_explanation_regeneration

ab742433034b251a819b6eb898686530bd055cd0

[ "MIT" ]

2

2020-02-19T13:32:03.000Z

2020-07-29T09:24:53.000Z

submission_utils.py

ameyagodbole/multihop_inference_explanation_regeneration

ab742433034b251a819b6eb898686530bd055cd0

[ "MIT" ]

1

2020-10-01T09:48:07.000Z

import argparse import logging import numpy as np import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_distances import torch def create_predictions_file(questions_file, facts_file, examples_file, logits_file, pred_output_file, mcq_choices="correct", write_debug_file=False): """ Utility to generate submission file from predictions (logits scores) """ df_questions = pd.read_csv(questions_file, sep='\t') df_facts = pd.read_csv(facts_file, sep='\t').drop_duplicates(subset=["uid"], keep="first").reset_index() examples = torch.load(examples_file) logits = np.load(logits_file) logit_1 = logits[:, 1] - logits[:, 0] if write_debug_file: f_tmp = open(pred_output_file + "-as-text", "w") # Remove wrong choices def remove_wrong_answer_choices(row, choices): correct_choice = row["AnswerKey"] option_start_loc = row["Question"].rfind("(A)") split0, split1 = row["Question"][:option_start_loc], row["Question"][option_start_loc:] if choices == "none": return split0 if correct_choice == "A" and "(B)" in split1: split0 += (split1[3:split1.rfind("(B)")]) elif correct_choice == "A": split0 += (split1[3:]) elif correct_choice == "B" and "(C)" in split1: split0 += (split1[split1.rfind("(B)") + 3:split1.rfind("(C)")]) elif correct_choice == "B": split0 += (split1[split1.rfind("(B)") + 3:]) elif correct_choice == "C" and "(D)" in split1: split0 += (split1[split1.rfind("(C)") + 3:split1.rfind("(D)")]) elif correct_choice == "C": split0 += (split1[split1.rfind("(C)") + 3:]) elif correct_choice == "D" and "(E)" in split1: split0 += (split1[split1.rfind("D)") + 3:split1.rfind("(E)")]) elif correct_choice == "D": split0 += (split1[split1.rfind("D)") + 3:]) elif correct_choice == "E" and "(F)" in split1: split0 += (split1[split1.rfind("(E)") + 3:split1.rfind("(F)")]) elif correct_choice == "E": split0 += (split1[split1.rfind("(E)") + 3:]) else: raise ValueError("Unhandled option type: {}".format(correct_choice)) return split0 if mcq_choices != "all": df_questions["ProcessedQuestion"] = df_questions.apply(remove_wrong_answer_choices, 1, choices=mcq_choices) else: df_questions["ProcessedQuestion"] = df_questions["Question"] vectorizer = TfidfVectorizer().fit(df_questions['Question']).fit(df_facts['text']) X_q = vectorizer.transform(df_questions['ProcessedQuestion']) X_e = vectorizer.transform(df_facts['text']) X_dist = cosine_distances(X_q, X_e) idx_start = 0 predictions = [] prev_query = examples[0].text_a for i, example in enumerate(examples): if example.text_a == prev_query: continue qid = examples[idx_start].guid.split('###')[0] q = df_questions.loc[df_questions["questionID"] == qid] assert q["ProcessedQuestion"].item() == examples[idx_start].text_a relevant_logits = logit_1[idx_start:i] relevant_examples = examples[idx_start:i] sorted_preds, sorted_examples = zip(*sorted(zip(relevant_logits, relevant_examples), key=lambda e: e[0], reverse=True)) added_uids = set() example_preds = [] for se in sorted_examples: for fid in se.guid.split('###')[1:]: if fid not in added_uids: added_uids.add(fid) example_preds.append('\t'.join([qid, fid])) for dist_idx in np.argsort(X_dist[q.index.to_numpy()[0]]): fid = df_facts.loc[dist_idx, "uid"] if fid not in added_uids: added_uids.add(fid) example_preds.append('\t'.join([qid, fid])) predictions.extend(example_preds) if write_debug_file: f_tmp.write(q["questionID"].item()) f_tmp.write('\n') f_tmp.write(q["Question"].item()) f_tmp.write('\n') f_tmp.write(q["ProcessedQuestion"].item()) f_tmp.write("\n*************\n") for i_tmp in range(40): f_tmp.write(sorted_examples[i_tmp].guid.split('###')[1:].__str__()) f_tmp.write(' Score:{:.3f}\n'.format(sorted_preds[i_tmp])) f_tmp.write(sorted_examples[i_tmp].text_b.__str__()) f_tmp.write('\n') f_tmp.write("*************\n") for i_tmp in range(40): f_tmp.write(df_facts.loc[df_facts["uid"] == example_preds[i_tmp].split('\t')[1], "text"].item()) f_tmp.write('\n') f_tmp.write("*************\n") for expl in q["explanation"].item().split(' '): f_tmp.write(df_facts.loc[df_facts["uid"] == expl.split('|')[0], "text"].item()) f_tmp.write('\n') f_tmp.write("*************\n") prev_query = example.text_a idx_start = i qid = examples[idx_start].guid.split('###')[0] q = df_questions.loc[df_questions["questionID"] == qid] assert q["ProcessedQuestion"].item() == examples[idx_start].text_a relevant_logits = logit_1[idx_start:] relevant_examples = examples[idx_start:] sorted_preds, sorted_examples = zip(*sorted(zip(relevant_logits, relevant_examples), key=lambda e: e[0], reverse=True)) added_uids = set() example_preds = [] for se in sorted_examples: for fid in se.guid.split('###')[1:]: if fid not in added_uids: added_uids.add(fid) example_preds.append('\t'.join([qid, fid])) for dist_idx in np.argsort(X_dist[q.index.to_numpy()[0]]): fid = df_facts.loc[dist_idx, "uid"] if fid not in added_uids: added_uids.add(fid) example_preds.append('\t'.join([qid, fid])) predictions.extend(example_preds) if write_debug_file: f_tmp.write(q["questionID"].item()) f_tmp.write('\n') f_tmp.write(q["Question"].item()) f_tmp.write('\n') f_tmp.write(q["ProcessedQuestion"].item()) f_tmp.write("\n*************\n") for i_tmp in range(40): f_tmp.write(sorted_examples[i_tmp].guid.split('###')[1:].__str__()) f_tmp.write(' Score:{:.3f}\n'.format(sorted_preds[i_tmp])) f_tmp.write(sorted_examples[i_tmp].text_b.__str__()) f_tmp.write('\n') f_tmp.write("*************\n") for i_tmp in range(40): f_tmp.write(df_facts.loc[df_facts["uid"] == example_preds[i_tmp].split('\t')[1], "text"].item()) f_tmp.write('\n') f_tmp.write("*************\n") for expl in q["explanation"].item().split(' '): f_tmp.write(df_facts.loc[df_facts["uid"] == expl.split('|')[0], "text"].item()) f_tmp.write('\n') f_tmp.write("*************\n") f_tmp.close() logging.info("Writing to file") with open(pred_output_file, "w") as f: f.write('\n'.join(predictions)) f.write('\n') logging.info("len(df_questions)={}".format(len(df_questions))) logging.info("len(predictions)={}".format(len(predictions))) if __name__=='__main__': parser = argparse.ArgumentParser() parser.add_argument("--questions_file", type=str, required=True, help="The tsv file containing the evaluation") parser.add_argument("--facts_file", type=str, required=True, help="The tsv file containing the common sense facts") parser.add_argument("--examples_file", type=str, help="Examples file that is being evaluated") parser.add_argument("--logits_file", type=str, help="Model predictions (liekly some file of the form *_preds.npy)") parser.add_argument("--pred_output_file", type=str, required=True, help="Name of the file where predictions will be written") parser.add_argument("--mcq_choices", type=str, choices=['none', 'correct', 'all'], default="correct", help="The choices to keep in the questions") parser.add_argument("--write_debug_file", action='store_true') args = parser.parse_args() create_predictions_file(questions_file=args.questions_file, facts_file=args.facts_file, examples_file=args.examples_file, logits_file=args.logits_file, pred_output_file=args.pred_output_file, mcq_choices=args.mcq_choices, write_debug_file=args.write_debug_file)

45.948187

119

0.579725

1,130

8,868

4.309735

0.158407

0.029569

0.062834

0.036961

0.558111

0.482752

0.424641

0

0.012739

0.256428

8,868

193

120

45.948187

0.725811

0.010149

0

0.491124

0

0.124501

0

0.011834

1

0.011834

false

0

0.04142

0

0.065089

0

null

0

null

0

1

0

bdfb7f7d975d38d147cc79c67eb8466db9daf8e8

1,884

py

Python

pysm/semantic_modeling/assembling/autolabel/auto_label.py

binh-vu/semantic-modeling

b387584502ba1daa6abd6b7573828416f6426b49

[ "MIT" ]

3

2019-10-31T15:26:20.000Z

2022-03-03T06:04:03.000Z

pysm/semantic_modeling/assembling/autolabel/auto_label.py

binh-vu/semantic-modeling

b387584502ba1daa6abd6b7573828416f6426b49

[ "MIT" ]

1

2021-10-05T14:57:29.000Z

2022-03-27T01:58:41.000Z

pysm/semantic_modeling/assembling/autolabel/auto_label.py

binh-vu/semantic-modeling

b387584502ba1daa6abd6b7573828416f6426b49

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- from typing import Dict, Tuple, List, Set, Union, Optional from data_structure import Graph from semantic_modeling.assembling.autolabel.heuristic import preserved_structure_with_heuristic, get_gold_semantic_types from semantic_modeling.assembling.autolabel.maxf1 import get_gold_triples, max_f1, max_f1_no_ambiguous from semantic_modeling.assembling.autolabel.preserved_structure import preserved_structure class AutoLabel: @staticmethod def auto_label_max_f1(gold_sm: Graph, pred_sm: Graph, is_blurring_label: bool) -> Tuple[Dict[int, bool], Dict[int, Optional[int]], float]: gold_triples = get_gold_triples(gold_sm, is_blurring_label) return max_f1(gold_sm, pred_sm, is_blurring_label, gold_triples) @staticmethod def auto_label_max_f1_no_ambiguous(gold_sm: Graph, pred_sm: Graph, is_blurring_label: bool ) -> Tuple[Dict[int, bool], Dict[int, Optional[int]], float]: gold_triples = get_gold_triples(gold_sm, is_blurring_label) return max_f1_no_ambiguous(gold_sm, pred_sm, is_blurring_label, gold_triples) @staticmethod def auto_label_preserved_structure(gold_sm: Graph, pred_sm: Graph) -> Tuple[Dict[int, bool], Dict[int, Optional[int]]]: gold_triples = get_gold_triples(gold_sm, is_blurring_label=False) return preserved_structure(gold_sm, pred_sm, gold_triples) @staticmethod def auto_label_preserved_structure_heuristic_fix( gold_sm: Graph, pred_sm: Graph) -> Tuple[Dict[int, bool], Dict[int, Optional[int]]]: gold_triples = get_gold_triples(gold_sm, is_blurring_label=False) gold_stypes = get_gold_semantic_types(gold_sm) return preserved_structure_with_heuristic(gold_sm, pred_sm, gold_triples, gold_stypes)

49.578947

120

0.728769

255

1,884

4.992157

0.196078

0.112333

0.094266

0.080126

0.699921

0.608013

0.536528

0.480754

0

0.005225

0.187367

1,884

37

121

50.918919

0.826257

0.02017

0

0.296296

0

1

0.148148

false

0

0.185185

0

0.518519

0

null

0

null

0

1

0

bdfb8afb236fa2a59d4614b476d34a5d38aae988

694

py

Python

landscapes/scripts/convert_fitness_to_s.py

Peyara/Evolution-Counterdiabatic-Driving

e695fad703b2d339bed0013e5b4254ba2365c105

[ "MIT" ]

3

2020-08-24T20:24:41.000Z

2020-08-26T02:16:16.000Z

landscapes/scripts/convert_fitness_to_s.py

hincz-lab/Evolution-Counterdiabatic-Driving

e695fad703b2d339bed0013e5b4254ba2365c105

[ "MIT" ]

null

landscapes/scripts/convert_fitness_to_s.py

hincz-lab/Evolution-Counterdiabatic-Driving

e695fad703b2d339bed0013e5b4254ba2365c105

[ "MIT" ]

null

import sys import numpy as np # This script takes in a file with fitness values separated by commas # and converts the values to be s values (relative fitness as used in # the model) instead. # WARNING: Overwrites given file! if len(sys.argv) < 2: print("Usage: python convert_fitness_to_s.py [name of file to convert]") data = [] # Read in fitness values with open(sys.argv[1]) as infile: data = [float(i.strip()) for i in infile.readline().split(",")] # Do conversion data = [np.format_float_positional(data[-1]/i - 1) if i != 0 else 10000000000000 for i in data] # Write out s values with open(sys.argv[1], "w") as outfile: outfile.write(",".join([str(i) for i in data]))

30.173913

95

0.695965

119

694

4.016807

0.546218

0.043933

0.037657

0.07113

0.09205

0

0.035149

0.180115

694

23

96

30.173913

0.804921

0.353026

0

0.149321

0.052036

0

1

0

false

0

0.2

0

0.2

0.1

0

null

0

null

0

1

0

bdfcac80e4077fb1f2378b55cba1401431e2ffec

1,099

py

Python

eats/behave/driver_steps.py

Etiqa/eats

8c8e2da93d0014f6fbb208185712c5526dba1174

[ "BSD-2-Clause" ]

null

eats/behave/driver_steps.py

Etiqa/eats

8c8e2da93d0014f6fbb208185712c5526dba1174

[ "BSD-2-Clause" ]

5

2021-03-18T21:34:44.000Z

2022-03-11T23:35:23.000Z

eats/behave/driver_steps.py

Etiqa/eats

8c8e2da93d0014f6fbb208185712c5526dba1174

[ "BSD-2-Clause" ]

null

from behave import * from hamcrest import * from selenium.common.exceptions import RemoteDriverServerException from eats.pyhamcrest import array_equal_to_by_key from eats.utils.mapping import table_mapping from ..users import Users @when(u'I press "{key}" key') @when(u'{user_name:Username} presses "{key}" key') def step_impl(context, key, user_name=Users.DEFAULT_USERNAME): user = context.users.get(user_name) application = user.current_application assert_that( calling(application.driver.send_special_key).with_args(key), not(raises(RemoteDriverServerException)), "{unsupported} key is not supported".format(unsupported=key) ) @then(u'{user_name:Username} should have "{name}" meta contents element') def step_impl(context, user_name, name): user = context.users.get(user_name) application = user.current_application contents = application.driver.get_metadata_elements_content_by_name(name) keys = context.table.headings assert_that(table_mapping(contents, keys=keys), array_equal_to_by_key(table_mapping(context.table), "content"))

40.703704

115

0.767971

147

1,099

5.52381

0.408163

0.059113

0.029557

0.034483

0.189655

0.147783

0

0.128298

1,099

27

115

40.703704

0.847599

0

0.173913

0

0.148182

0

0.086957

1

0.086957

false

0

0.26087

0

0.347826

0

null

0

null

0

1

0

bdfcb80abeeba8ef801afb6b8c9b9a48834e2016

5,526

py

Python

homebytwo/routes/utils.py

drixselecta/homebytwo

29d26ce9f5586943e3b64c95aa4ce9ea7263bd10

[ "MIT" ]

7

2018-03-10T20:58:59.000Z

2021-08-22T17:18:09.000Z

homebytwo/routes/utils.py

HomebyTwo/homebytwo

29d26ce9f5586943e3b64c95aa4ce9ea7263bd10

[ "MIT" ]

69

2017-02-01T21:15:43.000Z

2022-02-26T09:33:27.000Z

homebytwo/routes/utils.py

drixselecta/homebytwo

29d26ce9f5586943e3b64c95aa4ce9ea7263bd10

[ "MIT" ]

null

from collections import namedtuple from itertools import accumulate, chain, islice, tee from pathlib import Path from django.contrib.gis.db.models.functions import Distance, LineLocatePoint from django.contrib.gis.measure import D from .fields import LineSubstring from .models import ActivityType, Place # named tuple to handle Urls Link = namedtuple("Link", ["url", "text"]) GARMIN_ACTIVITY_TYPE_MAP = { ActivityType.ALPINESKI: "resort_skiing_snowboarding", ActivityType.BACKCOUNTRYSKI: "backcountry_skiing_snowboarding", ActivityType.ELLIPTICAL: "elliptical", ActivityType.HANDCYCLE: "cycling", ActivityType.HIKE: "hiking", ActivityType.ICESKATE: "skating", ActivityType.INLINESKATE: "skating", ActivityType.NORDICSKI: "cross_country_skiing", ActivityType.RIDE: "cycling", ActivityType.ROCKCLIMBING: "rock_climbing", ActivityType.ROWING: "rowing", ActivityType.RUN: "running", ActivityType.SNOWBOARD: "resort_skiing_snowboarding", ActivityType.SNOWSHOE: "hiking", ActivityType.STAIRSTEPPER: "fitness_equipment", ActivityType.STANDUPPADDLING: "stand_up_paddleboarding", ActivityType.SWIM: "swimming", ActivityType.VIRTUALRIDE: "cycling", ActivityType.VIRTUALRUN: "running", ActivityType.WALK: "walk", ActivityType.WEIGHTTRAINING: "fitness_equipment", ActivityType.WORKOUT: "strength_training", } def get_image_path(instance, filename): """ callable to define the image upload path according to the type of object: segment, route, etc.. as well as the id of the object. """ return Path("images", instance.__class__.__name__, str(instance.id), filename) def current_and_next(some_iterable): """ use itertools to make current and next item of an iterable available: http://stackoverflow.com/questions/1011938/python-previous-and-next-values-inside-a-loop used to 'create_segments_from_checkpoints'. """ items, nexts = tee(some_iterable, 2) nexts = chain(islice(nexts, 1, None), [None]) return zip(items, nexts) def create_segments_from_checkpoints(checkpoints, start=0, end=1): """ returns a list of segments as tuples with start and end locations along the original line. """ # sorted list of line_locations from the list of places as # well as the start and the end location of the segment where # the places were found. line_locations = chain( [start], [checkpoint.line_location for checkpoint in checkpoints], [end] ) # use the custom iterator, exclude segments where start and end # locations are the same. Also exclude segment where 'nxt == None`. segments = [ (crt, nxt) for crt, nxt in current_and_next(line_locations) if crt != nxt and nxt ] return segments def get_places_from_segment(segment, line, max_distance): """ find places within the segment of a line and annotate them with the line location on the original line. """ start, end = segment # create the Linestring geometry subline = LineSubstring(line, start, end) # find places within max_distance of the linestring places = get_places_from_line(subline, max_distance) # iterate over found places to change the line_location # from the location on the segment to the location on # the original linestring. for place in places: # relative line location to the start point of the subline length = place.line_location * (end - start) # update attribute with line location on the original line place.line_location = start + length return places def get_places_from_line(line, max_distance): """ returns places within a max_distance of a Linestring Geometry ordered by, and annotated with the `line_location` and the `distance_from_line`: * `line_location` is the location on the line expressed as a float between 0.0 and 1.0. * `distance_from_line` is a geodjango Distance object. """ # convert max_distance to Distance object max_d = D(m=max_distance) # find all places within max distance from line places = Place.objects.filter(geom__dwithin=(line, max_d)) # annotate with distance to line places = places.annotate(distance_from_line=Distance("geom", line)) # annotate with location along the line between 0 and 1 places = places.annotate(line_location=LineLocatePoint(line, "geom")) # remove start and end places within 1% of start and end location places = places.filter( line_location__gt=0.01, line_location__lt=0.99, ) # sort in order of appearance along the line places = places.order_by("line_location") return places def get_places_within(point, max_distance=100): # make range a distance object max_d = D(m=max_distance) # get places within range places = Place.objects.filter(geom__distance_lte=(point, max_d)) # annotate with distance places = places.annotate(distance_from_line=Distance("geom", point)) # sort by distance places = places.order_by( "distance_from_line", ) return places def get_distances(points): """ Return a list with the distance of each point relative to the previous one in the list. """ def get_relative_distances(): if points: yield 0 yield from (p2.distance(p1) for p1, p2 in zip(points[1:], points)) return list(accumulate(get_relative_distances()))

31.758621

92

0.707926

711

5,526

5.35865

0.298172

0.040945

0.025197

0.016535

0.096588

0.056693

0.04147

0

0.00755

0.209012

5,526

173

93

31.942197

0.864104

0.344734

0

0.061728

0

0.097864

0.0306

0

1

0.098765

false

0

0.08642

0

0.271605

0

null

0

null

0

1

0

bdfe275d909128740904498e8c3a21dcaa2bafb4

263

py

Python

aureaSym.py

osmartormena/introMATLAB

6e505a17d6666d92b4502eff746f4b4dcdcd3c1c

[ "CC0-1.0" ]

null

aureaSym.py

osmartormena/introMATLAB

6e505a17d6666d92b4502eff746f4b4dcdcd3c1c

[ "CC0-1.0" ]

null

aureaSym.py

osmartormena/introMATLAB

6e505a17d6666d92b4502eff746f4b4dcdcd3c1c

[ "CC0-1.0" ]

null

# Cálculo da razão áurea (phi) import sympy d = 20 phi = sympy.symbols('phi', nonnegative=True) eqn = sympy.Eq(1/phi, phi - 1) sol = sympy.solve(eqn) sympy.pprint(sol) phiAprox = sympy.N(sol[0], d) print('Para ', d, ' dígitos significativos, ϕ = ', phiAprox)

18.785714

60

0.669202

42

263

4.190476

0.619048

0.090909

0

0.022727

0.163498

263

13

61

20.230769

0.777273

0.106464

0

0.158798

0

1

0

false

0

0.125

0

0.125

0.25

0

null

0

null

0

1

0

bdfe32d084754eda156889373513889dc3a4c1f0

15,732

py

Python

core/src/structs_classes/extract_structs.py

azurlane-doujin/AzurLanePaintingExtract-v1.0

ef4f25e70b3ca1b9df4304132cc7612c8f5efebb

[ "MIT" ]

144

2019-06-13T06:43:43.000Z

2022-03-29T15:07:57.000Z

core/src/structs_classes/extract_structs.py

Shabi1213/AzurLanePaintingExtract-v1.0

ef4f25e70b3ca1b9df4304132cc7612c8f5efebb

[ "MIT" ]

2

2020-08-02T15:08:58.000Z

2021-11-29T02:34:18.000Z

core/src/structs_classes/extract_structs.py

Shabi1213/AzurLanePaintingExtract-v1.0

ef4f25e70b3ca1b9df4304132cc7612c8f5efebb

[ "MIT" ]

19

2020-03-01T10:06:52.000Z

2022-02-06T13:49:26.000Z

import collections import os import re import time from itertools import filterfalse import wx from core.src.static_classes.static_data import GlobalData from core.src.structs_classes.basic_class import BasicInfo, BasicInfoList class PerInfo(BasicInfo): def __init__(self, name, val, has_cn): super(PerInfo, self).__init__(name, val) self.sub_data = 1 self.tex_step = 2 self.mesh_step=2 self.data = GlobalData() # tree储存结构组 self._tex_path = "Empty" self.more_tex = ["Empty"] self._mesh_path = "Empty" self.more_mesh = ["Empty"] # 目标文件位置 self.lay_in = "" # 是否可以使用还原 self._is_able_work = False # 导出目标位置 self._save_path: str = "" # 中文名称 self.cn_name = val self.has_cn = has_cn # 父组件 self.parent = None self.must_able = False # tree ID self.key = ... self.tree_ID = ... self.tex_id = ... self.more_tex_per_id = [] self.mesh_id = ... self.more_mesh_per_id = [] self.action_group = [ "independent", "face_match", "atlas_split", "set_able", "split_only", "remove_item", "sprite_spilt" ] # 是否以中文保存 self._is_save_as_cn = True def __contains__(self, item): if self.name in item or self.cn_name in item: return True else: return False @property def is_able_work(self): if self.must_able: return True else: return self._is_able_work @property def tex_path(self): return self._tex_path @tex_path.setter def tex_path(self, value): self._tex_path = value self._is_able_work = self.is_able() @property def mesh_path(self): return self._mesh_path @mesh_path.setter def mesh_path(self, value): self._mesh_path = value self._is_able_work = self.is_able() @property def save_path(self): return self._save_path @save_path.setter def save_path(self, value): if self._is_save_as_cn: self._save_path = os.path.join(value, self.cn_name + ".png") else: self._save_path = os.path.join(value, self.name + ".png") @property def is_save_as_cn(self): return self._is_save_as_cn @is_save_as_cn.setter def is_save_as_cn(self, value): if isinstance(value, bool): self._is_save_as_cn = value @staticmethod def is_def(val): return bool(val) def get_is_able_work(self): return self._is_able_work def is_able(self): if os.path.isfile(self.tex_path) and os.path.isfile(self.mesh_path): return True else: return False def transform_able(self): self.must_able = not self.must_able def set_single_path(self, path): self._save_path = path def append_item_tree(self, tree: wx.TreeCtrl): # 名称 self.key = key = tree.AppendItem(self.tree_ID, f"名称：{self.cn_name}") if self.is_able_work: tree.SetItemTextColour(key, wx.Colour(253, 86, 255)) tree.AppendItem(self.tree_ID, f"索引名称：{self.name}") # texture self.tex_id = tree.AppendItem(self.tree_ID, f"Texture文件路径：{self.tex_path}") more_tex_id = tree.AppendItem(self.tree_ID, f"其他Texture路径({len(self.more_tex)})") for each_path in self.more_tex: val = tree.AppendItem(more_tex_id, each_path) self.more_tex_per_id.append(val) # mesh self.mesh_id = tree.AppendItem(self.tree_ID, f"Mesh文件路径：{self.mesh_path}") more_mesh_id = tree.AppendItem(self.tree_ID, f"其他Mesh路径({len(self.more_mesh)})") for each_path in self.more_mesh: val = tree.AppendItem(more_mesh_id, each_path) self.more_mesh_per_id.append(val) action_root = tree.AppendItem(self.tree_ID, "功能按键") # 功能键 independent = self.action_group[self.data.at_independent] = tree.AppendItem(action_root, "将当前的组合独立") tree.SetItemTextColour(independent, wx.Colour(255, 0, 166)) face_match = self.action_group[self.data.at_face_match] = tree.AppendItem(action_root, "为当前立绘添加附加表情") tree.SetItemTextColour(face_match, wx.Colour(0, 16, 166)) atlas_spilt = self.action_group[self.data.at_atlas_split] = tree.AppendItem(action_root, "进行Q版小人切割") tree.SetItemTextColour(atlas_spilt, wx.Colour(140, 0, 166)) sprite_spilt = self.action_group[self.data.at_sprite_split] = tree.AppendItem(action_root, "进行Sprite切割 ") tree.SetItemTextColour(sprite_spilt, wx.Colour(248, 40, 255)) set_able = self.action_group[self.data.at_set_able] = tree.AppendItem(action_root, f"强制转换为可还原状态【当前{self.must_able}】") tree.SetItemTextColour(set_able, wx.Colour(255, 177, 166)) split_only = self.action_group[self.data.at_split_only] = tree.AppendItem(action_root, "仅进行立绘还原切割 ") tree.SetItemTextColour(split_only, wx.Colour(248, 66, 255)) remove_item = self.action_group[self.data.at_remove_item] = tree.AppendItem(action_root, "删除该元素 ") tree.SetItemTextColour(remove_item, wx.Colour(248, 0, 255)) def append_to_tree(self, tree: wx.TreeCtrl, tree_root: wx.TreeItemId): """ 添加到树，构建tree列表 :param tree: tree 对象 :param tree_root: 根id :return: """ self.more_mesh_per_id.clear() self.more_tex_per_id.clear() self.tree_ID = tree.AppendItem(tree_root, self.cn_name) self.append_item_tree(tree) def get_select(self, type_is: bool): """ 获取选中的列表 :param type_is: true ：texture，false：mesh :return: list，选中的列表 """ if type_is: return self.more_tex else: return self.more_mesh # 路径设置相关 def set_tex(self, index): self.tex_path = self.more_tex[index] return self.tex_id, f"Texture文件路径：{self.tex_path}" def set_mesh(self, index): self.mesh_path = self.more_mesh[index] return self.mesh_id, f"Mesh文件路径：{self.mesh_path}" def add_save(self, path): self.save_path = path def clear_tex(self): self.tex_id, self.more_tex, self.tex_path, self.more_tex_per_id = None, [], "Empty", [] def clear_mesh(self): self.mesh_id, self.more_mesh, self.mesh_path, self.more_mesh_per_id = None, [], "Empty", [] def build_sub(self, value_type, file_type, index): """ 从自身的treeid中寻找目标 :param value_type: :param file_type: :param index: :return: """ val = PerInfo(self.name, self.val, self.has_cn) if value_type == self.data.td_single: if file_type == self.data.td_texture_type: val.tex_path = self.tex_path elif file_type == self.data.td_mesh_type: val.mesh_path = self.mesh_path elif value_type == self.data.td_list_item: if file_type == self.data.td_texture_type: val.tex_path = self.more_tex[index] elif file_type == self.data.td_mesh_type: val.mesh_path = self.more_mesh[index] return os.path.isfile(val.tex_path), val def independent(self, name, tree, tree_root): # 独立 target = PerInfo(name, f"{self.val}-# {self.sub_data}", self.has_cn) target.tex_path = self.tex_path target.mesh_path = self.mesh_path target.append_to_tree(tree, tree_root) self.parent[target.name] = target self.sub_data += 1 class PerWorkList(BasicInfoList): def __init__(self, item: collections.abc.Iterable = None, mesh_match=None, texture_match=None, is_ignore_case=False): super(PerWorkList, self).__init__(item) self.is_ignore_case = is_ignore_case self.texture_match = texture_match self.mesh_match = mesh_match self.data = GlobalData() # 显示部分 def show_in_tree(self, tree, tree_root): list(map(lambda x: self._info_dict[x].append_to_tree(tree, tree_root), self._key_list)) def append(self, name, cn_name, has_cn): value = PerInfo(name, cn_name, has_cn) self[value.name] = value return value def remove(self, item: collections.abc.Iterable): return PerWorkList(super(PerWorkList, self).remove(item)) # 查找部分 def find_by_id(self, id): values = list(filter(lambda x: self._info_dict[x].tree_ID == id, self._key_list)) if values.__len__() == 0: return False, None return True, self[values[0]] def find_in_each(self, id) -> (bool, bool, bool, int, PerInfo): """ 从每一个中寻找指定id :param id: :return: (是否成功，类型【单个True，列表False】，类型[tex(True),mesh(False)]，索引，对象本身) """ target = None for value in self: # 如果id为以下的部分，进入 if id == value.tex_id == id or id in value.more_tex_per_id or value.mesh_id == id or \ id in value.more_mesh_per_id: target = value if target is None: return False, False, False, -1, None if id == target.tex_id: return True, self.data.td_single, self.data.td_texture_type, 0, target elif id == target.mesh_id: return True, self.data.td_single, self.data.td_mesh_type, 0, target elif id in target.more_tex_per_id: return True, self.data.td_list_item, self.data.td_texture_type, target.more_tex_per_id.index(id), target elif id in target.more_mesh_per_id: return True, self.data.td_list_item, self.data.td_mesh_type, target.more_mesh_per_id.index(id), target def find_action(self, id) -> (bool, int, PerInfo): """ 查找是否为特殊动作按键 :param id: :return: 是否成功【true/false】，动作类型，作用目标 """ target = None for value in self: # 如果id为以下的部分，进入 if id in value.action_group: target = value break if target is None: return False, -1, target else: index = target.action_group.index(id) return True, index, target # 添加部分 def set_tex(self, value, name=None): """ 添加贴图 :param name: [可选]新添加的texture地址的指向项目名称，为None会根据value获取 :param value: 新添加的texture地址 :return: """ has_ = False if isinstance(value, str) and os.path.isfile(value): if name is not None: key = name else: key = os.path.splitext(os.path.basename(value))[0] if re.match(r'.+\s#\d+\.png', value, re.IGNORECASE): has_ = True key = re.split(r'\s#\d+(\[alpha\])?$', key)[0] # 赋值过程 val: PerInfo = self._info_dict[key] if value not in val.more_tex: val.more_tex.append(value) lower_path = os.path.split(value)[0].lower() # 如果非空考虑优先级 if 0 < val.tex_step and lower_path.endswith(self.texture_match[0]): val.tex_path = value val.tex_step = 0 elif 1 < val.tex_step and lower_path.endswith(self.texture_match[1]): val.tex_path = value val.tex_step = 1 else: val.tex_path = value val.tex_step = 2 if not has_: val.tex_path = value def set_mesh(self, value, name=None): """ 添加mesh网格 :param name: [可选]新添加的mesh地址的指向项目名称，为None会根据value获取 :param value: 新添加的mesh地址 :return: """ has_ = False if isinstance(value, str) and os.path.isfile(value): if name is not None: key = name else: key = os.path.splitext(os.path.basename(value))[0] if re.match(r'.+\s#\d+\.obj', value, re.IGNORECASE): has_ = True key = re.split(r'\s#\d+(\[alpha\])?$', key)[0] val: PerInfo = self._info_dict[key] if value not in val.more_mesh: val.more_mesh.append(value) lower_path = os.path.split(value)[0].lower() # 如果非空考虑优先级 if 0 < val.mesh_step and lower_path.endswith(self.mesh_match[0]): val.mesh_path = value val.mesh_step = 0 elif 1 < val.mesh_step and lower_path.endswith(self.mesh_match[1]): val.mesh_path = value val.mesh_step = 1 else: val.mesh_path = value val.mesh_step = 2 if not has_: val.mesh_path = value def append_name(self, name, names: dict, *, has_cn=False): """ 添加新对象 :param names: 预设键-值对应组 :param name: 对象索引key :param has_cn: 对象是否有中文名 :return: """ # if name == "unknown4": # print(name) if self.is_ignore_case: name=name.lower() if name not in self._key_list: if name not in names.keys(): has_cn = False target_cn = name else: has_cn = True target_cn = names[name] # 如果中文名为空，也认为没有中文名 if target_cn == "": target_cn = name has_cn = False value = PerInfo(name, target_cn, has_cn) value.parent = self self[name] = value return name else: return name # 清空部分 def clear_mesh(self): list(map(lambda x: x.clear_mesh(), self)) def clear_tex(self): list(map(lambda x: x.clear_tex(), self)) # 生成部分 def build_able(self): val = filter(lambda x: x.get_is_able_work(), self) value = PerWorkList(val) return value def build_unable(self): val = filterfalse(lambda x: x.get_is_able_work(), self) value = PerWorkList(val) return value def build_search(self): val = map(lambda x: f"{x.name}{x.cn_name}", self) return list(val) def build_filter(self): val = map(lambda x: f"{x.name}", self) val = list(enumerate(list(val), 0)) return val def build_skip(self, filename): filename = list(map(lambda x: os.path.splitext(os.path.basename(x))[0], filename)) val = filter(lambda x: x in filename, self) return PerWorkList(val) def build_from_indexes(self, indexes): val = map(lambda x: self[x], indexes) value = PerWorkList(val) return value def build_from_pattern(self, pattern): val = list(filter(lambda x: re.match(pattern, list(x)[1]), self.build_filter())) val = list(zip(*val)) if len(val) == 2: return self.build_from_indexes(val[0]) else: return PerWorkList()

33.189873

117

0.557335

1,992

15,732

4.165161

0.11496

0.022177

0.016874

0.010124

0.42883

0.343136

0.252139

0.191877

0.164156

0

0.009229

0.3388

15,732

473

118

33.260042

0.788406

0.054475

0

0.267913

0

0.037146

0.014171

0

1

0.140187

false

0

0.024922

0.021807

0.28972

0

null

0

null

0

1

0

bdff65087e9d7a27500aa847fc385ea3b6c07441

3,950

py

Python

scbw_mq/tournament/benchmark/storage.py

Games-and-Simulations/sc-mq

f9ae798948add7fd84b77d75ca26ade94620f84e

[ "MIT" ]

2

2018-05-10T18:10:28.000Z

2018-05-13T18:14:33.000Z

scbw_mq/tournament/benchmark/storage.py

Games-and-Simulations/sc-mq

f9ae798948add7fd84b77d75ca26ade94620f84e

[ "MIT" ]

1

2019-09-20T14:14:49.000Z

scbw_mq/tournament/benchmark/storage.py

Games-and-Simulations/sc-mq

f9ae798948add7fd84b77d75ca26ade94620f84e

[ "MIT" ]

null

import logging import os import shutil from os.path import exists from typing import Optional from scbw.map import check_map_exists from scbw.player import check_bot_exists from scbw.utils import download_extract_zip from ...utils import read_lines logger = logging.getLogger(__name__) class BenchmarkException(Exception): pass class RerunningBenchmarkException(BenchmarkException): pass class Benchmark: bot_file: str map_file: str elo_file: str repeat_games: int bot_dir: str map_dir: str result_dir: str def check_structure(self): if not exists(f"{self.bot_file}"): raise BenchmarkException(f"Bot file cannot be found in {self.bot_file}") if not exists(self.map_file): raise BenchmarkException(f"Map file cannot be found in {self.map_file}") if not exists(self.elo_file): raise BenchmarkException(f"Elo file cannot be found in {self.elo_file}") if not exists(self.bot_dir): raise BenchmarkException(f"Bot dir cannot be found in {self.bot_dir}") if not exists(f"{self.map_dir}"): raise BenchmarkException(f"Map dir cannot be found in {self.map_dir}") if not exists(f"{self.result_dir}"): raise BenchmarkException(f"Result dir cannot be found in {self.result_dir}") bots = read_lines(self.bot_file) for bot in bots: check_bot_exists(bot, self.bot_dir) maps = read_lines(self.map_file) for map_file in maps: check_map_exists(f"{self.map_dir}/{map_file}") def has_results(self): return len(os.listdir(self.result_dir)) > 0 class BenchmarkStorage: def find_benchmark(self, name: str) -> Optional[Benchmark]: raise NotImplemented def get_benchmark(self, local_benchmark_dir): with open(f'{local_benchmark_dir}/BENCHMARK_REPEAT_GAMES', 'r') as f: repeat_games = int(f.read().strip()) benchmark = Benchmark() benchmark.bot_file = f"{local_benchmark_dir}/BENCHMARK_BOTS" benchmark.map_file = f"{local_benchmark_dir}/BENCHMARK_MAPS" benchmark.elo_file = f"{local_benchmark_dir}/BENCHMARK_ELOS" benchmark.bot_dir = f"{local_benchmark_dir}/bots" benchmark.map_dir = f"{local_benchmark_dir}/maps" benchmark.result_dir = f"{local_benchmark_dir}/results" benchmark.repeat_games = repeat_games return benchmark class LocalBenchmarkStorage(BenchmarkStorage): def __init__(self, base_dir: str): self.base_dir = base_dir def find_benchmark(self, name: str) -> Optional[Benchmark]: if exists(self.benchmark_dir(name)): return self.get_benchmark(self.benchmark_dir(name)) return None def benchmark_dir(self, benchmark_name: str): return f'{self.base_dir}/{benchmark_name}' class SscaitBenchmarkStorage(LocalBenchmarkStorage): BASE_URL = "http://sscaitournament.com/benchmarks" def find_benchmark(self, name: str) -> Optional[Benchmark]: if not name.startswith("SSCAIT"): return None if exists(self.benchmark_dir(name)): return self.get_benchmark(self.benchmark_dir(name)) return self.try_download(name) def try_download(self, name: str) -> Optional[Benchmark]: benchmark_dir = self.benchmark_dir(name) try: os.makedirs(benchmark_dir, exist_ok=False) download_extract_zip(f"{self.BASE_URL}/{name}.zip", benchmark_dir) return self.get_benchmark(benchmark_dir) except Exception as e: logger.exception(f"Failed to download benchmark {name}") logger.exception(e) logger.info(f"Cleaning up dir {benchmark_dir}") shutil.rmtree(self.benchmark_dir(name)) return None # Feel free to include other benchmark sources! # But they need to respect benchmark / bot structure :)

31.854839

88

0.678481

517

3,950

4.969052

0.197292

0.093422

0.052939

0.049046

0.317633

0.234332

0.111327

0.0942

0.059167

0

0.000328

0.227848

3,950

123

89

32.113821

0.841967

0.025063

0

0.137931

0

0.189709

0.082121

0

1

0.103448

false

0.022989

0.103448

0.022989

0.482759

0

null

0

null

0

1

0

bdff857464c359af0d0606a7da2091b6840dd15a

21,855

py

Python

dev-server/scripts/docker-entrypoint.py

circlenaut/docker-images

1768222b496288b6d08a51f979ade97554648817

[ "MIT" ]

null

dev-server/scripts/docker-entrypoint.py

circlenaut/docker-images

1768222b496288b6d08a51f979ade97554648817

[ "MIT" ]

null

dev-server/scripts/docker-entrypoint.py

circlenaut/docker-images

1768222b496288b6d08a51f979ade97554648817

[ "MIT" ]

null

#!/usr/bin/python """ Main Workspace Run Script """ import os import sys import logging import coloredlogs import json import math import glob import yaml import yamale import scripts.functions as func from copy import copy from subprocess import run, call ### Enable logging logging.basicConfig( format="%(asctime)s [%(levelname)s] %(message)s", level=logging.INFO, stream=sys.stdout, ) log = logging.getLogger(__name__) log.info("Starting...") ### Read YAML config file #configs = list() configs_list = dict() #yaml_exts = ["yaml", "yml"] config_path = str() # Load config files with alternative extensions #for ext in yaml_exts: # path = f'/scripts/config.{ext}' # if os.path.exists(path): # configs.append(path) # Check if multiple config files exist and load the user defined one or system/user overwritten one if os.path.exists('/scripts/config.yaml'): config_path = '/scripts/config.yaml' # Validate file schema = yamale.make_schema('/scripts/schema.yaml') data = yamale.make_data(config_path) valid_config = func.yaml_valid(schema, data, "INFO") elif os.path.exists('/scripts/config.yml'): config_path = '/scripts/config.yml' # Validate file schema = yamale.make_schema('/scripts/schema.yaml') data = yamale.make_data(config_path) valid_config = func.yaml_valid(schema, data, "INFO") elif os.path.exists('/scripts/config.yml') and os.path.exists('/scripts/config.yaml'): config_path = '/scripts/config.yml' log.warning("both config.yaml and config.yml exists, using config.yml") if os.path.exists('/scripts/config.yaml'): os.remove('/scripts/config.yaml') # Validate file schema = yamale.make_schema('/scripts/schema.yaml') data = yamale.make_data(config_path) valid_config = func.yaml_valid(schema, data, "INFO") else: log.debug("No yaml config files available to load") # Load config as yaml object if os.path.exists(config_path): if valid_config: log.info(f"Loading config file: '{config_path}'") with open(config_path, "r") as f: configs_list = yaml.load(f, Loader=yaml.FullLoader) log.debug(configs_list) else: log.debug(f"Config does not exist: '{config_path}'") ### Read or set docker default envs docker_env = { 'LOG_VERBOSITY': os.getenv("LOG_VERBOSITY", "INFO"), 'CONFIG_BACKUP_ENABLED': os.getenv("CONFIG_BACKUP_ENABLED", "true"), 'WORKSPACE_USER': os.getenv("WORKSPACE_AUTH_USER", "coder"), 'WORKSPACE_GROUP': os.getenv("WORKSPACE_AUTH_GROUP", "users"), 'WORKSPACE_USER_SHELL': os.getenv("WORKSPACE_USER_SHELL", "zsh"), 'WORKSPACE_USER_PASSWORD': os.getenv("WORKSPACE_AUTH_PASSWORD", "password"), 'RESOURCES_PATH': os.getenv("RESOURCES_PATH", "/resources"), 'WORKSPACE_HOME': os.getenv("WORKSPACE_HOME", "/workspace"), 'APPS_PATH': os.getenv("APPS_PATH", "/apps"), 'DATA_PATH': os.getenv("DATA_PATH", "/data"), 'PROXY_BASE_URL': os.getenv("PROXY_BASE_URL", "/"), 'ZSH_PROMPT': os.getenv("ZSH_PROMPT", "none"), 'ZSH_THEME': os.getenv("ZSH_THEME", "spaceship"), 'ZSH_PLUGINS': os.getenv("ZSH_PLUGINS", "all"), 'CONDA_ENV_PATH': os.getenv("CONDA_ENV_PATH", ""), 'CADDY_VIRTUAL_PORT': os.getenv("VIRTUAL_PORT", "80"), 'CADDY_VIRTUAL_HOST': os.getenv("VIRTUAL_HOST", ""), 'CADDY_VIRTUAL_BIND_NET': os.getenv("VIRTUAL_BIND_NET", "proxy"), 'CADDY_VIRTUAL_PROTO': os.getenv("VIRTUAL_PROTO", "http"), 'CADDY_VIRTUAL_BASE_URL': os.getenv("VIRTUAL_BASE_URL", "/"), 'CADDY_PROXY_ENCODINGS_GZIP': os.getenv("PROXY_ENCODINGS_GZIP", "true"), 'CADDY_PROXY_ENCODINGS_ZSTD': os.getenv("PROXY_ENCODINGS_ZSTD", "true"), 'CADDY_PROXY_TEMPLATES': os.getenv("PROXY_TEMPLATES", "true"), 'CADDY_LETSENCRYPT_EMAIL': os.getenv("LETSENCRYPT_EMAIL", "admin@example.com"), 'CADDY_LETSENCRYPT_ENDPOINT': os.getenv("LETSENCRYPT_ENDPOINT", "dev"), 'CADDY_HTTP_PORT': os.getenv("HTTP_PORT", "80"), 'CADDY_HTTPS_ENABLE': os.getenv("HTTPS_ENABLE", "true"), 'CADDY_HTTPS_PORT': os.getenv("HTTPS_PORT", "443"), 'CADDY_AUTO_HTTPS': os.getenv("AUTO_HTTPS", "true"), 'CADDY_WORKSPACE_SSL_ENABLED': os.getenv("WORKSPACE_SSL_ENABLED", "false"), 'FB_PORT': os.getenv("FB_PORT", "8055"), 'FB_BASE_URL': os.getenv("FB_BASE_URL", "/data"), 'FB_ROOT_DIR': os.getenv("FB_ROOT_DIR", "/workspace"), 'VSCODE_BIND_ADDR': os.getenv("VSCODE_BIND_ADDR", "0.0.0.0:8300"), 'VSCODE_BASE_URL': os.getenv("VSCODE_BASE_URL", "/code"), 'APP_BIND_ADDR': os.getenv("APP_BIND_ADDR", "0.0.0.0:8080"), 'APP_BASE_URL': os.getenv("APP_BASE_URL", "/app"), 'APP_ROOT_DIR': os.getenv("APP_ROOT_DIR", "/apps/app"), 'APP_USER': os.getenv("APP_USER", "admin"), 'APP_PASSWORD': os.getenv("APP_PASSWORD", "password") } ### Set verbosity level. log.info occasinally throws EOF errors with high verbosity if docker_env.get("LOG_VERBOSITY") in [ "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL" ]: verbosity = docker_env.get("LOG_VERBOSITY") else: log.info("invalid verbosity: '{}".format(docker_env.get("LOG_VERBOSITY"))) verbosity = "INFO" ### opts_json cli options opts = { "verbosity": verbosity } log.setLevel(verbosity) # Setup colored console logs coloredlogs.install(fmt='%(asctime)s [%(levelname)s] %(message)s', level=verbosity, logger=log) ### Reconcile docker env var with corresponding config setting system_configs = dict() # copy and save user configs users_config_copy = copy(configs_list["users"]) # if system not configured in yaml, then set to docker envs if configs_list.get("system") == None: log.info(f"System not defined in yaml config file. Importing settings from docker env.") for env, value in docker_env.items(): log.debug(f"setting: '{env.lower()}' --> '{value}'") system_configs[env.lower()] = value # copy into system key configs_list["system"] = copy(system_configs) # copy users back configs_list["users"] = copy(users_config_copy) # reconcile if env appears in both else: for env, value in docker_env.items(): for config, setting in configs_list.get("system").items(): if config == env.lower(): if setting == value: log.debug(f"yaml config same as docker environment value: '{config}' --> '{setting}'") system_configs[config] = value else: log.warning(f"using config setting instead of docker environment value - {config}: '{value}'--> '{setting}'") system_configs[config] = setting if not env.lower() in list(configs_list.get("system").keys()): log.debug(f"not set in yaml config, setting: '{env.lower()}' --> '{value}'") system_configs[env.lower()] = value # copy into system key configs_list["system"] = copy(system_configs) # copy users back configs_list["users"] = copy(users_config_copy) ### Reset verbosity level according to yaml file. log.info occasinally throws EOF errors with high verbosity if configs_list.get("system").get("log_verbosity") in [ "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL" ]: verbosity = configs_list.get("system").get("log_verbosity") else: log.info("invalid verbosity: '{}".format(configs_list.get("system").get("log_verbosity"))) verbosity = "INFO" ### opts_json cli options opts = { "verbosity": verbosity } log.setLevel(verbosity) default_user = [{ 'name': configs_list.get("system").get("workspace_user"), 'group': configs_list.get("system").get("workspace_group"), 'uid': "1000", 'gid': "100", 'shell': configs_list.get("system").get("workspace_user_shell"), 'password': configs_list.get("system").get("workspace_user_password"), 'directories': [ { 'name': 'home', 'path': os.path.join("/home", configs_list.get("system").get("workspace_user")), 'mode': '755' }, { 'name': 'resources', 'path': configs_list.get("system").get("resources_path"), 'mode': '755' }, { 'name': 'workspace', 'path': configs_list.get("system").get("workspace_home"), 'mode': '755' }, { 'name': 'data', 'path': configs_list.get("system").get("data_path"), 'mode': '755' }, { 'name': 'apps', 'path': configs_list.get("system").get("apps_path"), 'mode': '755' }, { 'name': 'app', 'path': configs_list.get("system").get("app_root_dir"), 'mode': '755' }], 'backup_paths': [ f'/home/{configs_list.get("system").get("workspace_user")}/.config', f'/home/{configs_list.get("system").get("workspace_user")}/.ssh', f'/home/{configs_list.get("system").get("workspace_user")}/.zshrc', f'/home/{configs_list.get("system").get("workspace_user")}/.bashrc', f'/home/{configs_list.get("system").get("workspace_user")}/.profile', f'/home/{configs_list.get("system").get("workspace_user")}/.condarc', f'/home/{configs_list.get("system").get("workspace_user")}/.oh-my-zsh', f'/home/{configs_list.get("system").get("workspace_user")}/.gitconfig', f'/home/{configs_list.get("system").get("workspace_user")}/filebrowser.db', f'/home/{configs_list.get("system").get("workspace_user")}/.local', f'/home/{configs_list.get("system").get("workspace_user")}/.conda', f'/home/{configs_list.get("system").get("workspace_user")}/.vscode', f'/home/{configs_list.get("system").get("workspace_user")}/.jupyter' ], 'conda': { 'env': '' }, 'zsh': { 'set_prompt': configs_list.get("system").get("zsh_prompt"), 'set_theme': configs_list.get("system").get("zsh_theme"), 'set_plugins': configs_list.get("system").get("zsh_plugins"), 'prompt': [ 'https://github.com/sindresorhus/pure' ], 'theme': [ 'https://github.com/romkatv/powerlevel10k', 'https://github.com/denysdovhan/spaceship-prompt', 'https://github.com/sobolevn/sobole-zsh-theme' ], 'plugins': [ 'git', 'k', 'extract', 'cp', 'yarn', 'npm', 'supervisor', 'rsync', 'command-not-found', 'autojump', 'colored-man-pages', 'git-flow', 'git-extras', 'python', 'zsh-autosuggestions', 'history-substring-search', 'zsh-completions', 'ssh-agent', 'https://github.com/zsh-users/zsh-autosuggestions', 'https://github.com/zsh-users/zsh-completions', 'https://github.com/zsh-users/zsh-syntax-highlighting', 'https://github.com/zsh-users/zsh-history-substring-search', 'https://github.com/supercrabtree/k' ]}, 'ssh': { 'pub_keys': [''], 'configs': [{ 'hostname': '', 'port': '', 'user': '', 'pub_key_auth': '', 'id_only': '', 'id_file_path': '' }] }, 'filebrowser': { 'port': configs_list.get("system").get("fb_port"), 'base_url': configs_list.get("system").get("fb_base_url"), 'root_dir': configs_list.get("system").get("fb_root_dir") }, 'vscode': { 'bind_addr': configs_list.get("system").get("vscode_bind_addr"), 'base_url': configs_list.get("system").get("vscode_base_url"), 'extensions': [ 'ms-python.python', 'almenon.arepl', 'batisteo.vscode-django', 'bierner.color-info', 'bierner.markdown-footnotes', 'bierner.markdown-mermaid', 'bierner.markdown-preview-github-styles', 'CoenraadS.bracket-pair-colorizer-2', 'DavidAnson.vscode-markdownlint', 'donjayamanne.githistory', 'donjayamanne.python-extension-pack', 'eamodio.gitlens', 'hbenl.vscode-test-explorer', 'henriiik.docker-linter', 'kamikillerto.vscode-colorize', 'kisstkondoros.vscode-gutter-preview', 'littlefoxteam.vscode-python-test-adapter', 'magicstack.MagicPython', 'ms-azuretools.vscode-docker', 'ms-toolsai.jupyter', 'naumovs.color-highlight', 'shd101wyy.markdown-preview-enhanced', 'streetsidesoftware.code-spell-checker', 'tht13.html-preview-vscode', 'tht13.python', 'tushortz.python-extended-snippets', 'wholroyd.jinja', 'yzhang.markdown-all-in-one' ] }, 'app': { 'bind_addr': configs_list.get("system").get("app_bind_addr"), 'base_url': configs_list.get("system").get("app_base_url"), 'root_dir': configs_list.get("system").get("app_root_dir"), 'user': configs_list.get("system").get("app_user"), 'password': configs_list.get("system").get("app_password") } }] def set_user_config(user_config, default_user, level): log.setLevel(level) log.info(user_config.get("yaml_config_value")) log.info(user_config.get("docker_env_value")) if user_config.get("yaml_config_value") == None: log.info("no setting found for '{}', setting: '{}'".format(user_config.get("yaml_config_name"), user_config.get("docker_env_value"))) if user_config.get("dict_path") == 2: configs_list.get(user_config.get("dict_path")[0])[user_config.get("dict_path")[1]] = user_config.get("docker_env_value") elif user_config.get("yaml_config_value") == user_config.get("docker_env_value"): log.debug("yaml config same as docker environment value: {} --> '{}'".format(user_config.get("docker_env_name"), user_config.get("docker_env_value"))) else: log.warning("using user config setting instead of docker environment value - {}: '{}'--> '{}'".format(user_config.get("docker_env_name"), user_config.get("docker_env_value"), user_config.get("yaml_config_value"))) user_configs = [ { "yaml_config_name": "name", "docker_env_name": "workspace_user", "yaml_config_value": configs_list.get("users")[0].get("name"), "docker_env_value": configs_list.get("system").get("workspace_user"), "dict_path": ["users", "name"] }, { "yaml_config_name": "group", "docker_env_name": "workspace_group", "yaml_config_value": configs_list.get("users")[0].get("group"), "docker_env_value": configs_list.get("system").get("workspace_group"), "dict_path": ["users", "group"] }, { "yaml_config_name": "shell", "docker_env_name": "workspace_user_shell", "yaml_config_value": configs_list.get("users")[0].get("shell"), "docker_env_value": configs_list.get("system").get("workspace_user_shell"), "dict_path": ["users", "shell"] }, { "yaml_config_name": "password", "docker_env_name": "workspace_user_password", "yaml_config_value": configs_list.get("users")[0].get("password"), "docker_env_value": configs_list.get("system").get("workspace_user_password"), "dict_path": ["users", "shell"] }, ] ### Set user config if configs_list.get("users") == None: log.info(f"Users not defined in yaml config file. Going with single user mode and importing settings from docker env or setting from default") configs_list["users"] = default_user # Show to console default_user_json = json.dumps(default_user, indent = 4) elif len(configs_list.get("users")) == 0: log.info("User's list empty, populate and restart container") sys.exit() elif len(configs_list.get("users")) == 1: log.info("Building a single user environment") # what's the point of this? overwrite workspace envs with corresponding user envs? Maybe not good to touch and better keep docker envs concistent with this dict. Don't overwrite with user settings. Also simpler #for uc in user_configs: #set_user_config(uc, default_user, verbosity) user_count = 0 for u in configs_list.get("users"): log.debug(f"working on user count: '{user_count}'") for default_config, default_setting in default_user[0].items(): for config, setting in u.items(): if config == default_config: if setting == default_setting: log.debug(f"yaml config setting same as default: '{config}' --> '{setting}'") else: log.debug(f"yaml config setting differs from default - {config}: '{default_setting}'--> '{setting}'") if config == "name": user = setting home = os.path.join("/home", user) if config == "password": password = setting if not default_config in list(u.keys()): log.info(f"not set in yaml config, setting from default settings: '{default_config}' --> '{default_setting}'") configs_list.get("users")[user_count][default_config] = default_setting user_count+=1 log.info(f"setting workspace user to: '{user}'") elif len(configs_list.get("users")) > 1: log.info("More than 2 users defined, haven't build this functionality yet. Remove extra users and restart container.") sys.exit() # Dump into JSON for passage into scripts configs_list_json = json.dumps(configs_list) ### Write docker envs to system environment #for env, value in docker_env.items(): # func.set_env_variable(env, value) ### Clean up envs # opts_json arguments to json opts_json = json.dumps(opts) ### Dynamiruny set MAX_NUM_THREADS ENV_MAX_NUM_THREADS = os.getenv("MAX_NUM_THREADS", None) if ENV_MAX_NUM_THREADS: # Determine the number of availabel CPU resources, but limit to a max number if ENV_MAX_NUM_THREADS.lower() == "auto": ENV_MAX_NUM_THREADS = str(math.ceil(os.cpu_count())) try: # read out docker information - if docker limits cpu quota cpu_count = math.ceil( int( os.popen("cat /sys/fs/cgroup/cpu/cpu.cfs_quota_us") .read() .replace("\n", "") ) / 100000 ) if cpu_count > 0 and cpu_count < os.cpu_count(): ENV_MAX_NUM_THREADS = str(cpu_count) except: pass if ( not ENV_MAX_NUM_THREADS or not ENV_MAX_NUM_THREADS.isnumeric() or ENV_MAX_NUM_THREADS == "0" ): ENV_MAX_NUM_THREADS = "4" if int(ENV_MAX_NUM_THREADS) > 8: # there should be atleast one thread less compared to cores ENV_MAX_NUM_THREADS = str(int(ENV_MAX_NUM_THREADS) - 1) # set a maximum of 32, in most cases too many threads are adding too much overhead if int(ENV_MAX_NUM_THREADS) > 32: ENV_MAX_NUM_THREADS = "32" # only set if it is not None or empty # OMP_NUM_THREADS: Suggested value: vCPUs / 2 in which vCPUs is the number of virtual CPUs. set_env_variable( "OMP_NUM_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True ) # OpenMP set_env_variable( "OPENBLAS_NUM_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True ) # OpenBLAS set_env_variable("MKL_NUM_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True) # MKL set_env_variable( "VECLIB_MAXIMUM_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True ) # Accelerate set_env_variable( "NUMEXPR_NUM_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True ) # Numexpr set_env_variable( "NUMEXPR_MAX_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True ) # Numexpr - maximum set_env_variable( "NUMBA_NUM_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True ) # Numba set_env_variable( "SPARK_WORKER_CORES", ENV_MAX_NUM_THREADS, ignore_if_set=True ) # Spark Worker set_env_variable( "BLIS_NUM_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True ) # Blis set_env_variable("TBB_NUM_THREADS", ENV_MAX_NUM_THREADS, ignore_if_set=True) # TBB # GOTO_NUM_THREADS ### Set container environment # Get system env and display system_env = os.environ.copy() log.debug("System Environments:") log.debug(func.capture_cmd_stdout('env', system_env)) # Display docker env log.debug("Docker Environments:") log.debug(func.capture_cmd_stdout('env', docker_env)) # Merge system, docker env as workspace env and display workspace_env = func.merge_two_dicts(system_env, docker_env) log.debug("Workspace Environment") log.debug(func.capture_cmd_stdout('env', workspace_env)) # Format workspace env as json for passage into scripts workspace_env_json = json.dumps(workspace_env) ### Configure user log.info(f"configuring user") run( ['python', f"/scripts/configure_user.py", '--opts', opts_json, '--env', workspace_env_json, '--configs', configs_list_json ], env=workspace_env ) ### Set workspace user and home workspace_env['USER'] = user workspace_env['HOME'] = home workspace_env['WORKSPACE_USER'] = user workspace_env['WORKSPACE_USER_HOME'] = home workspace_env['WORKSPACE_USER_PASSWORD'] = password ### Start workspace sys.exit( run( ['python', '/scripts/run_workspace.py', '--opts', opts_json], env=workspace_env ) )

39.307554

221

0.625212

2,729

21,855

4.775009

0.163796

0.05909

0.061239

0.070601

0.421687

0.37288

0.311872

0.256005

0.233136

0.150027

0

0.005598

0.223564

21,855

555

222

39.378378

0.762331

0.103363

0

0.164835

0

0.004396

0.39213

0.102611

0

1

0.002198

false

0.026374

0.030769

0

0.032967

0

null

0

null

0

1

0

da0211204f7b106ec6a65423c21ac69cd0c6c658

11,524

py

Python

py/host.py

black-parrot-hdk/arty-parrot

d5d1c5859cbe6a7acad9147b0d815fe478f92ec9

[ "BSD-3-Clause" ]

1

2022-01-09T07:45:12.000Z

py/host.py

black-parrot-hdk/arty-parrot

d5d1c5859cbe6a7acad9147b0d815fe478f92ec9

[ "BSD-3-Clause" ]

2

2021-05-26T02:27:26.000Z

2021-05-28T07:02:48.000Z

py/host.py

black-parrot-hdk/arty-parrot

d5d1c5859cbe6a7acad9147b0d815fe478f92ec9

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 import sys import argparse from enum import Enum from typing import Optional import serial from tqdm import tqdm from nbf import NBF_COMMAND_LENGTH_BYTES, NbfCommand, NbfFile, OPCODE_FINISH, OPCODE_PUTCH, OPCODE_READ_8, OPCODE_WRITE_8, ADDRESS_CSR_FREEZE DRAM_REGION_START = 0x00_8000_0000 DRAM_REGION_END = 0x10_0000_0000 def _debug_format_message(command: NbfCommand) -> str: if command.opcode == OPCODE_PUTCH: return str(command) + f" (putch {repr(command.data[0:1].decode('utf-8'))})" else: return str(command) class LogDomain(Enum): # meta info on requested commands COMMAND = 'command' # sent messages TRANSMIT = 'transmit' # received messages out-of-turn RECEIVE = 'receive' # received messages in response to a transmitted command REPLY = 'reply' @property def message_prefix(self): if self == LogDomain.COMMAND: return "[CMD ]" elif self == LogDomain.TRANSMIT: return "[TX ]" elif self == LogDomain.RECEIVE: return "[RX ]" elif self == LogDomain.REPLY: return "[REPLY]" else: raise ValueError(f"unknown log domain '{self}'") def _log(domain: LogDomain, message: str): tqdm.write(domain.message_prefix + " " + message) class HostApp: def __init__(self, serial_port_name: str, serial_port_baud: int): self.port = serial.Serial( port=serial_port_name, baudrate=serial_port_baud, bytesize=serial.EIGHTBITS, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, # Without a timeout, SIGINT can't end the process while we are blocking on a read. timeout=3.0 ) self.commands_sent = 0 self.commands_received = 0 self.reply_violations = 0 def close_port(self): if self.port.is_open: self.port.close() def _send_message(self, command: NbfCommand): self.port.write(command.to_bytes()) self.port.flush() self.commands_sent += 1 def _receive_message(self, block=True) -> Optional[NbfCommand]: if block or self.port.in_waiting >= NBF_COMMAND_LENGTH_BYTES: buffer = self.port.read(NBF_COMMAND_LENGTH_BYTES) if len(buffer) != NBF_COMMAND_LENGTH_BYTES: raise ValueError(f"serial port returned {len(buffer)} bytes, but {NBF_COMMAND_LENGTH_BYTES} requested") self.commands_received += 1 return NbfCommand.from_bytes(buffer) else: return None def _receive_until_opcode(self, opcode: int, block=True) -> Optional[NbfCommand]: message = self._receive_message(block=block) while message is not None and message.opcode != opcode: _log(LogDomain.RECEIVE, _debug_format_message(message)) message = self._receive_message() return message def print_summary_statistics(self): _log(LogDomain.COMMAND, f" Sent: {self.commands_sent} commands") _log(LogDomain.COMMAND, f" Received: {self.commands_received} commands") if self.reply_violations > 0: _log(LogDomain.COMMAND, f" Reply violations: {self.reply_violations} commands") def _validate_reply(self, command: NbfCommand, reply: NbfCommand) -> bool: if not command.is_correct_reply(reply): self.reply_violations += 1 _log(LogDomain.REPLY, f'Unexpected reply: {command} -> {reply}') # TODO: abort on invalid reply? return False return True def _validate_outstanding_replies(self, command_queue_expecting_replies: list, sliding_window_num_commands: int, log_all_rx: bool = False): """ Reads replies from the incoming data stream, matching them with the provided command queue in-order and validating each. If more than "sliding_window_num_commands" commands are in the queue, blocks waiting for an incoming command. Pops all validated commands from the front of the queue, in-place. """ while len(command_queue_expecting_replies) > 0: sent_command = command_queue_expecting_replies[0] is_window_full = len(command_queue_expecting_replies) > sliding_window_num_commands reply = self._receive_until_opcode( sent_command.opcode, block=is_window_full ) if reply is None: # all queued packets have been processed break if log_all_rx: # TODO: indicate this is an expected reply _log(LogDomain.RECEIVE, _debug_format_message(reply)) # TODO: verbose/echo mode was_valid = self._validate_reply(sent_command, reply) if was_valid: # TODO: consider aborting on invalid reply command_queue_expecting_replies.pop(0) def load_file(self, source_file: str, ignore_unfreezes: bool = False, sliding_window_num_commands: int = 0, log_all_messages: bool = False): file = NbfFile(source_file) outstanding_commands_expecting_replies = [] command: NbfCommand for command in tqdm(file, total=file.peek_length(), desc="loading nbf"): if ignore_unfreezes and command.matches(OPCODE_WRITE_8, ADDRESS_CSR_FREEZE, 0): continue if log_all_messages: _log(LogDomain.TRANSMIT, _debug_format_message(command)) self._send_message(command) if command.expects_reply(): outstanding_commands_expecting_replies.append(command) self._validate_outstanding_replies(outstanding_commands_expecting_replies, sliding_window_num_commands, log_all_rx=log_all_messages) self._validate_outstanding_replies(outstanding_commands_expecting_replies, 0, log_all_rx=log_all_messages) _log(LogDomain.COMMAND, "Load complete") def unfreeze(self): unfreeze_command = NbfCommand.with_values(OPCODE_WRITE_8, ADDRESS_CSR_FREEZE, 0) self._send_message(unfreeze_command) reply = self._receive_until_opcode(unfreeze_command.opcode) self._validate_reply(unfreeze_command, reply) def listen_perpetually(self, verbose: bool): _log(LogDomain.COMMAND, "Listening for incoming messages...") while message := self._receive_message(): # in "verbose" mode, we'll always print the full message, even for putchar if not verbose and message.opcode == OPCODE_PUTCH: print(chr(message.data[0]), end = '') continue _log(LogDomain.RECEIVE, _debug_format_message(message)) if message.opcode == OPCODE_FINISH: print(f"FINISH: core {message.address_int}, code {message.data_int}") # TODO: this assumes unicore return def verify(self, reference_file: str): file = NbfFile(reference_file) writes_checked = 0 writes_corrupted = 0 command: NbfCommand for command in tqdm(file, total=file.peek_length(), desc="verifying nbf"): if command.opcode != OPCODE_WRITE_8: continue if command.address_int < DRAM_REGION_START or command.address_int > DRAM_REGION_END - 8: continue read_message = NbfCommand.with_values(OPCODE_READ_8, command.address_int, 0) self._send_message(read_message) reply = self._receive_until_opcode(OPCODE_READ_8) self._validate_reply(read_message, reply) writes_checked += 1 if reply.data != command.data: writes_corrupted += 1 _log(LogDomain.COMMAND, f"Corruption detected at address 0x{command.address_hex_str}") _log(LogDomain.COMMAND, f" Expected: 0x{command.data_hex_str}") _log(LogDomain.COMMAND, f" Actual: 0x{reply.data_hex_str}") _log(LogDomain.COMMAND, "Verify complete") _log(LogDomain.COMMAND, f" Writes checked: {writes_checked}") _log(LogDomain.COMMAND, f" Corrupt writes found: {writes_corrupted}") if writes_corrupted > 0: _log(LogDomain.COMMAND, "== CORRUPTION DETECTED ==") def _load_command(app: HostApp, args): app.load_file( args.file, ignore_unfreezes=args.no_unfreeze, sliding_window_num_commands=args.window_size, log_all_messages=args.verbose ) app.print_summary_statistics() if args.listen: app.listen_perpetually(verbose=args.verbose) def _unfreeze_command(app: HostApp, args): app.unfreeze() if args.listen: app.listen_perpetually(verbose=False) def _verify_command(app: HostApp, args): app.verify(args.file) app.print_summary_statistics() def _listen_command(app: HostApp, args): app.listen_perpetually(verbose=False) if __name__ == "__main__": root_parser = argparse.ArgumentParser() root_parser.add_argument('-p', '--port', dest='port', type=str, default='/dev/ttyS4', help='Serial port (full path or name)') root_parser.add_argument('-b', '--baud', dest='baud_rate', type=int, default=2_000_000, help='Serial port baud rate') command_parsers = root_parser.add_subparsers(dest="command") command_parsers.required = True load_parser = command_parsers.add_parser("load", help="Stream a file of NBF commands to the target") load_parser.add_argument('file', help="NBF-formatted file to load") load_parser.add_argument('--no-unfreeze', action='store_true', dest='no_unfreeze', help='Suppress any "unfreeze" commands in the input file') load_parser.add_argument('--listen', action='store_true', dest='listen', help='Continue listening for incoming messages until program is aborted') load_parser.add_argument('--window-size', type=int, default=500, dest='window_size', help='Specifies the maximum number of outstanding replies to allow before blocking') load_parser.add_argument('--verbose', action='store_true', dest='verbose', help='Log all send and received commands, even if valid') # TODO: add --verify which automatically implies --no-unfreeze then manually unfreezes after # TODO: add --verbose which prints all sent and received commands load_parser.set_defaults(handler=_load_command) unfreeze_parser = command_parsers.add_parser("unfreeze", help="Send an \"unfreeze\" command to the target") unfreeze_parser.add_argument('--listen', action='store_true', dest='listen', help='Continue listening for incoming messages until program is aborted') unfreeze_parser.set_defaults(handler=_unfreeze_command) verify_parser = command_parsers.add_parser("verify", help="Read back the results of an NBF file's memory writes and confirm that their values match the original file") verify_parser.add_argument('file', help="NBF-formatted file to load") verify_parser.set_defaults(handler=_verify_command) listen_parser = command_parsers.add_parser("listen", help="Watch for incoming messages and print the received data") listen_parser.set_defaults(handler=_listen_command) args = root_parser.parse_args() app = HostApp(serial_port_name=args.port, serial_port_baud=args.baud_rate) try: args.handler(app, args) app.close_port() except KeyboardInterrupt: app.close_port() print("Aborted") sys.exit(1)

41.602888

173

0.674505

1,421

11,524

5.209008

0.204082

0.02756

0.030802

0.021616

0.215212

0.141043

0.106188

0.075925

0.058363

0

0.008165

0.234814

11,524

276

174

41.753623

0.831254

0.083391

0

0.09596

0

0.005051

0.155509

0.019998

0

0.007246

0

1

0.09596

false

0

0.035354

0

0.222222

0.035354

0

null

0

null

0

1

0

da02379e9f1f2797e8f3d2fe77571451d25da847

618

py

Python

mistex/plugins/citation.py

martinosorb/mistex

27db70a95ae4bb8bc84c17c9d59c1bef5b5e92f4

[ "BSD-3-Clause" ]

null

mistex/plugins/citation.py

martinosorb/mistex

27db70a95ae4bb8bc84c17c9d59c1bef5b5e92f4

[ "BSD-3-Clause" ]

null

mistex/plugins/citation.py

martinosorb/mistex

27db70a95ae4bb8bc84c17c9d59c1bef5b5e92f4

[ "BSD-3-Clause" ]

null

from mistune.inline_parser import LINK_LABEL CITATION_PATTERN = r'\[\^@(' + LINK_LABEL + r')\]' def render_citation(text): return '\\cite{' + text + '}' def parse_citation(self, m, state): text = m.group(1) self._ensure_bib() return 'citation', self.render(text, state) def plugin_citation(md): md.inline.register_rule('citation', CITATION_PATTERN, parse_citation) index = md.inline.rules.index('std_link') if index != -1: md.inline.rules.insert(index, 'citation') else: md.inline.rules.append('citation') md.renderer.register('citation', render_citation)

22.888889

73

0.666667

80

618

4.975

0.4375

0.080402

0.09799

0

0.003945

0.179612

618

26

74

23.769231

0.781065

0

0.105178

0

1

0.1875

false

0

0.0625

0.375

0

null

0

null

0

1

0

da03370dc8f2f31bcdc7fd9d8a5697527015558e

2,881

py

Python

2020_August_Leetcode_30_days_challenge/Week_3_Non-overlapping Intervals/by_sorting.py

coderMaruf/leetcode-1

20ffe26e43999e44c8acf9800acb371a49bb5853

[ "MIT" ]

32

2020-01-05T13:37:16.000Z

2022-03-26T07:27:09.000Z

2020_August_Leetcode_30_days_challenge/Week_3_Non-overlapping Intervals/by_sorting.py

coderMaruf/leetcode-1

20ffe26e43999e44c8acf9800acb371a49bb5853

[ "MIT" ]

null

2020_August_Leetcode_30_days_challenge/Week_3_Non-overlapping Intervals/by_sorting.py

coderMaruf/leetcode-1

20ffe26e43999e44c8acf9800acb371a49bb5853

[ "MIT" ]

8

2020-06-18T16:17:27.000Z

2022-03-15T23:58:18.000Z

''' Description: Given a collection of intervals, find the minimum number of intervals you need to remove to make the rest of the intervals non-overlapping. Example 1: Input: [[1,2],[2,3],[3,4],[1,3]] Output: 1 Explanation: [1,3] can be removed and the rest of intervals are non-overlapping. Example 2: Input: [[1,2],[1,2],[1,2]] Output: 2 Explanation: You need to remove two [1,2] to make the rest of intervals non-overlapping. Example 3: Input: [[1,2],[2,3]] Output: 0 Explanation: You don't need to remove any of the intervals since they're already non-overlapping. Note: You may assume the interval's end point is always bigger than its start point. Intervals like [1,2] and [2,3] have borders "touching" but they don't overlap each other. ''' from typing import List class Solution: def eraseOverlapIntervals(self, intervals: List[List[int]]) -> int: # sort segments by start index in ascending order intervals.sort( key = lambda segment: segment[0] ) last_compare_idx = 0 removal_counter = 0 for cur_idx in range(1, len(intervals)): cur_start, cur_end = intervals[cur_idx] last_start, last_end = intervals[last_compare_idx] if cur_start < last_end: # need to remove one interval to avoid overlapping removal_counter += 1 if cur_end < last_end: # remove last interval, because it is lefter then current last_compare_idx = cur_idx else: # remove current interval, because it is lefter then last one # last compare idx keeps the same pass else: # so far so good, no need to remove last_compare_idx = cur_idx return removal_counter # n : the length of input list, intervals ## Time Complexity: O( n log n) # # The overhead in time is the cost of sorting, which is of O( n log n ). ## Space Complexity: O( 1 ) # # The overhead in space is the storage for loop index and temporary variable, which is of O( 1 ). import unittest class Testing( unittest.TestCase ): def test_case_1( self ): result = Solution().eraseOverlapIntervals( intervals=[[1,2],[2,3],[3,4],[1,3]] ) self.assertEqual(result, 1) def test_case_2( self ): result = Solution().eraseOverlapIntervals( intervals=[[1,2],[1,2],[1,2]] ) self.assertEqual(result, 2) def test_case_3( self ): result = Solution().eraseOverlapIntervals( intervals=[[1,2],[2,3]] ) self.assertEqual(result, 0) if __name__ == '__main__': unittest.main()

25.052174

139

0.588684

386

2,881

4.297927

0.339378

0.014467

0.036166

0.009644

0.191682

0.141049

0.10006

0.069922

0.062688

0

0.032209

0.321069

2,881

115

140

25.052174

0.815951

0.449844

0

0.129032

0

0.005122

0

0.096774

1

0.129032

false

0.032258

0.064516

0

0.290323

0

null

0

null

0

1

0

da0469fe0ec53d36c9f4e75701bb9541ada5eeed

1,220

py

Python

hive_plug_play/engine/processor.py

seakintruth/hive-plug-play

032caed7a0690a58410b3d4e93a1fdecf2009d58

[ "MIT" ]

3

2021-05-11T07:12:05.000Z

2021-10-04T04:01:38.000Z

hive_plug_play/engine/processor.py

seakintruth/hive-plug-play

032caed7a0690a58410b3d4e93a1fdecf2009d58

[ "MIT" ]

9

2021-06-02T03:43:01.000Z

2021-07-23T14:52:03.000Z

hive_plug_play/engine/processor.py

seakintruth/hive-plug-play

032caed7a0690a58410b3d4e93a1fdecf2009d58

[ "MIT" ]

1

2021-05-24T15:57:20.000Z

from os import truncate from hive_plug_play.database.handlers import PlugPlayDb class BlockProcessor: @classmethod def init(cls, config): cls.config = config cls.db = PlugPlayDb(config) cls.head_block = {} cls.block_num = 0 cls.block_time = '' @classmethod def check_op_id(cls, op_id): allowed_op_ids = cls.config['op_ids'] if allowed_op_ids == []: return True else: return op_id in allowed_op_ids @classmethod def process_block(cls, block_num, block): prev = block['previous'] block_hash = block['block_id'] timestamp = block['timestamp'] cls.db.add_block(block_num, block_hash, prev, timestamp) transactions = block['transactions'] for i in range(len(transactions)): trans = transactions[i] for op in trans['operations']: if op['type'] == 'custom_json_operation': if cls.check_op_id(op['value']['id']): cls.db.add_op(block_num, block['transaction_ids'][i], op['value']) cls.db._save() cls.block_num = block_num cls.block_time = timestamp

32.105263

90

0.584426

149

1,220

4.557047

0.355705

0.070692

0.076583

0.047128

0

0.001183

0.307377

1,220

38

91

32.105263

0.802367

0

0.090909

0

0.085995

0.017199

0

1

0.090909

false

0

0.060606

0

0.242424

0

null

0

null

0

1

0

da058a79bcff3d1633c9de586676094982ec1208

24,030

py

Python

scripts/populate_conferences.py

sf2ne/Playground

95b2d222d7ac43baca0249acbfc34e043d6a95b3

[ "Apache-2.0" ]

null

scripts/populate_conferences.py

sf2ne/Playground

95b2d222d7ac43baca0249acbfc34e043d6a95b3

[ "Apache-2.0" ]

13

2020-03-24T15:29:41.000Z

2022-03-11T23:15:28.000Z

scripts/populate_conferences.py

sf2ne/Playground

95b2d222d7ac43baca0249acbfc34e043d6a95b3

[ "Apache-2.0" ]

null

#!/usr/bin/env python # encoding: utf-8 import os from modularodm import Q from modularodm.exceptions import ModularOdmException from framework.auth.core import User from website import settings from website.app import init_app from website.conferences.model import Conference def main(): init_app(set_backends=True, routes=False) populate_conferences() MEETING_DATA = { 'spsp2014': { 'name': 'Society for Personality and Social Psychology 2014', 'info_url': None, 'logo_url': None, 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'asb2014': { 'name': 'Association of Southeastern Biologists 2014', 'info_url': 'http://www.sebiologists.org/meetings/talks_posters.html', 'logo_url': None, 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'aps2014': { 'name': 'Association for Psychological Science 2014', 'info_url': 'http://centerforopenscience.org/aps/', 'logo_url': '/static/img/2014_Convention_banner-with-APS_700px.jpg', 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'annopeer2014': { 'name': '#annopeer', 'info_url': None, 'logo_url': None, 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'cpa2014': { 'name': 'Canadian Psychological Association 2014', 'info_url': None, 'logo_url': None, 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'filaments2014': { 'name': 'National Radio Astronomy Observatory Filaments 2014', 'info_url': None, 'logo_url': 'https://science.nrao.edu/science/meetings/2014/' 'filamentary-structure/images/filaments2014_660x178.png', 'active': False, 'admins': [ 'lvonschi@nrao.edu', # 'Dkim@nrao.edu', ], 'public_projects': True, 'poster': True, 'talk': True, }, 'bitss2014': { 'name': 'Berkeley Initiative for Transparency in the Social Sciences Research Transparency Forum 2014', 'info_url': None, 'logo_url': os.path.join( settings.STATIC_URL_PATH, 'img', 'conferences', 'bitss.jpg', ), 'active': False, 'admins': [ 'gkroll@berkeley.edu', 'awais@berkeley.edu', ], 'public_projects': True, 'poster': False, 'talk': True, }, 'spsp2015': { 'name': 'Society for Personality and Social Psychology 2015', 'info_url': None, 'logo_url': None, 'active': False, 'admins': [ 'meetings@spsp.org', ], 'poster': True, 'talk': True, }, 'aps2015': { 'name': 'Association for Psychological Science 2015', 'info_url': None, 'logo_url': 'http://www.psychologicalscience.org/images/APS_2015_Banner_990x157.jpg', 'active': True, 'admins': [ ], 'public_projects': True, 'poster': True, 'talk': True, }, 'icps2015': { 'name': 'International Convention of Psychological Science 2015', 'info_url': None, 'logo_url': 'http://icps.psychologicalscience.org/wp-content/themes/deepblue/images/ICPS_Website-header_990px.jpg', 'active': False, 'admins': [ ], 'public_projects': True, 'poster': True, 'talk': True, }, 'mpa2015': { 'name': 'Midwestern Psychological Association 2015', 'info_url': None, 'logo_url': 'http://www.midwesternpsych.org/resources/Pictures/MPA%20logo.jpg', 'active': True, 'admins': [ 'mpa@kent.edu', ], 'public_projects': True, 'poster': True, 'talk': True, }, 'NCCC2015': { 'name': 'North Carolina Cognition Conference 2015', 'info_url': None, 'logo_url': None, 'active': False, 'admins': [ 'aoverman@elon.edu', ], 'public_projects': True, 'poster': True, 'talk': True, }, 'VPRSF2015': { 'name': 'Virginia Piedmont Regional Science Fair 2015', 'info_url': None, 'logo_url': 'http://vprsf.org/wp-content/themes/VPRSF/images/logo.png', 'active': False, 'admins': [ 'director@vprsf.org', ], 'public_projects': True, 'poster': True, 'talk': True, }, 'APRS2015': { 'name': 'UVA Annual Postdoctoral Research Symposium 2015', 'info_url': None, 'logo_url': 'http://s1.postimg.org/50qj9u6i7/GPA_Logo.jpg', 'active': False, 'admins': [ 'mhurst@virginia.edu', ], 'public_projects': True, 'poster': True, 'talk': True, }, 'ASB2015': { 'name': 'Association of Southeastern Biologists 2015', 'info_url': None, 'logo_url': 'http://www.sebiologists.org/wp/wp-content/uploads/2014/09/banner_image_Large.png', 'active': False, 'admins': [ 'amorris.mtsu@gmail.com', ], 'public_projects': True, 'poster': True, 'talk': True, }, 'TeaP2015': { 'name': 'Tagung experimentell arbeitender Psychologen 2015', 'info_url': None, 'logo_url': None, 'active': False, 'admins': [ ], 'public_projects': True, 'poster': True, 'talk': True, }, 'VSSEF2015': { 'name': 'Virginia State Science and Engineering Fair 2015', 'info_url': 'http://www.vmi.edu/conferences/vssef/vssef_home/', 'logo_url': 'http://www.vmi.edu/uploadedImages/Images/Headers/vssef4.jpg', 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'RMPA2015': { 'name': 'Rocky Mountain Psychological Association 2015', 'info_url': 'http://www.rockymountainpsych.org/uploads/7/4/2/6/7426961/85th_annual_rmpa_conference_program_hr.pdf', 'logo_url': 'http://www.rockymountainpsych.org/uploads/7/4/2/6/7426961/header_images/1397234084.jpg', 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'ARP2015': { 'name': 'Association for Research in Personality 2015', 'info_url': 'http://www.personality-arp.org/conference/', 'logo_url': 'http://www.personality-arp.org/wp-content/uploads/conference/st-louis-arp.jpg', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'SEP2015': { 'name': 'Society of Experimental Psychologists Meeting 2015', 'info_url': 'http://faculty.virginia.edu/Society_of_Experimental_Psychologists/', 'logo_url': 'http://www.sepsych.org/nav/images/SEP-header.gif', 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'Reid2015': { 'name': 'L. Starling Reid Undergraduate Psychology Conference 2015', 'info_url': 'http://avillage.web.virginia.edu/Psych/Conference', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'NEEPS2015': { 'name': 'Northeastern Evolutionary Psychology Conference 2015', 'info_url': 'http://neeps2015.weebly.com/', 'logo_url': None, 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'VaACS2015': { 'name': 'Virginia Section American Chemical Society Student Poster Session 2015', 'info_url': 'http://virginia.sites.acs.org/', 'logo_url': 'http://virginia.sites.acs.org/Bulletin/15/UVA.jpg', 'active': False, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'MADSSCi2015': { 'name': 'Mid-Atlantic Directors and Staff of Scientific Cores & Southeastern Association of Shared Services 2015', 'info_url': 'http://madssci.abrf.org', 'logo_url': 'http://s24.postimg.org/qtc3baefp/2015madssci_seasr.png', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'NRAO2015': { 'name': 'National Radio Astronomy Observatory Accretion 2015', 'info_url': 'https://science.nrao.edu/science/meetings/2015/accretion2015/posters', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'ARCS2015': { 'name': 'Advancing Research Communication and Scholarship 2015', 'info_url': 'http://commons.pacificu.edu/arcs/', 'logo_url': 'http://commons.pacificu.edu/assets/md5images/4dfd167454e9f4745360a9550e189323.png', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'singlecasedesigns2015': { 'name': 'Single Case Designs in Clinical Psychology: Uniting Research and Practice', 'info_url': 'https://www.royalholloway.ac.uk/psychology/events/eventsarticles/singlecasedesignsinclinicalpsychologyunitingresearchandpractice.aspx', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'OSFM2015': { 'name': 'OSF for Meetings 2015', 'info_url': None, 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'JSSP2015': { 'name': 'Japanese Society of Social Psychology 2015', 'info_url': 'http://www.socialpsychology.jp/conf2015/index.html', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, '4S2015': { 'name': 'Society for Social Studies of Science 2015', 'info_url': 'http://www.4sonline.org/meeting', 'logo_url': 'http://www.4sonline.org/ee/denver-skyline.jpg', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'IARR2016': { 'name': 'International Association for Relationship Research 2016', 'info_url': 'http://iarr.psych.utoronto.ca/', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'IA2015': { 'name': 'Inclusive Astronomy 2015', 'info_url': 'https://vanderbilt.irisregistration.com/Home/Site?code=InclusiveAstronomy2015', 'logo_url': 'https://vanderbilt.blob.core.windows.net/images/Inclusive%20Astronomy.jpg', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'R2RC': { 'name': 'Right to Research Coalition', 'info_url': None, 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'OpenCon2015': { 'name': 'OpenCon2015', 'info_url': 'http://opencon2015.org/', 'logo_url': 'http://s8.postimg.org/w9b30pxyd/Open_Con2015_new_logo.png', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'ESIP2015': { 'name': 'Earth Science Information Partners 2015', 'info_url': 'http://esipfed.org/', 'logo_url': 'http://s30.postimg.org/m2uz2g4pt/ESIP.png', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'SPSP2016': { 'name': 'Society for Personality and Social Psychology 2016 ', 'info_url': 'http://meeting.spsp.org', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'NACIII': { 'name': '2015 National Astronomy Consortium (NAC) III Workshop', 'info_url': 'https://info.nrao.edu/do/odi/meetings/2015/nac111/', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'CDS2015': { 'name': 'Cognitive Development Society 2015', 'info_url': 'http://meetings.cogdevsoc.org/', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'SEASR2016': { 'name': 'Southeastern Association of Shared Resources 2016', 'info_url': 'http://seasr.abrf.org', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'Accretion2015': { 'name': 'Observational Evidence of Gas Accretion onto Galaxies?', 'info_url': 'https://science.nrao.edu/science/meetings/2015/accretion2015', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, '2020Futures': { 'name': 'U.S. Radio/Millimeter/Submillimeter Science Futures in the 2020s', 'info_url': 'https://science.nrao.edu/science/meetings/2015/2020futures/home', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'RMPA2016': { 'name': 'Rocky Mountain Psychological Association 2016', 'info_url': 'http://www.rockymountainpsych.org/convention-info.html', 'logo_url': 'http://www.rockymountainpsych.org/uploads/7/4/2/6/7426961/header_images/1397234084.jpg', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'CNI2015': { 'name': 'Coalition for Networked Information (CNI) Fall Membership Meeting 2015', 'info_url': 'https://wp.me/P1LncT-64s', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': False, 'talk': True, }, 'SWPA2016': { 'name': 'Southwestern Psychological Association Convention 2016', 'info_url': 'https://www.swpsych.org/conv_dates.php', 'logo_url': 'http://s28.postimg.org/xbwyqqvx9/SWPAlogo4.jpg', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'ESIP2016W': { 'name': 'Earth Science Information Partners Winter Meeting 2016', 'info_url': 'http://commons.esipfed.org/2016WinterMeeting', 'logo_url': 'http://s30.postimg.org/m2uz2g4pt/ESIP.png', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'MiamiBrainhack15': { 'name': 'University of Miami Brainhack 2015', 'info_url': 'http://brainhack.org/americas/', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'PsiChiRepository': { 'name': 'Psi Chi', 'info_url': 'http://www.psichi.org/?ResearchAdvisory#.VmBpeOMrI1g', 'logo_url': 'http://s11.postimg.org/4g2451vcz/Psi_Chi_Logo.png', 'admins': [ 'research.director@psichi.org', ], 'field_names': { 'submission1': 'measures', 'submission2': 'materials', 'submission1_plural': 'measures/scales', 'submission2_plural': 'study materials', 'meeting_title_type': 'Repository', 'add_submission': 'materials', 'mail_subject': 'Title', 'mail_message_body': 'Measure or material short description', 'mail_attachment': 'Your measure/scale or material file(s)' }, }, 'GI2015': { 'name': 'Genome Informatics 2015', 'info_url': 'https://meetings.cshl.edu/meetings.aspx?meet=info&year=15', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'MADSSCi2016': { 'name': 'Mid-Atlantic Directors and Staff of Scientific Cores & Southeastern Association of Shared Services 2016', 'info_url': 'http://madssci.abrf.org', 'logo_url': 'http://madssci.abrf.org/sites/default/files/madssci-logo-bk.png', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'SMM2015': { 'name': 'The Society for Marine Mammalogy', 'info_url': 'https://www.marinemammalscience.org/conference/', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'TESS': { 'name': 'Time-sharing Experiments for the Social Sciences', 'info_url': 'http://www.tessexperiments.org', 'logo_url': None, 'active': False, 'admins': [], 'public_projects': True, 'poster': False, 'talk': True, 'field_names': { 'submission1': 'poster', 'submission2': 'study', 'submission1_plural': 'posters', 'submission2_plural': 'studies', 'meeting_title_type': 'Studies', 'add_submission': 'studies', } }, 'ASCERM2016': { 'name': 'ASCE Rocky Mountain Student Conference 2016', 'info_url': 'http://luninuxos.com/asce/', 'logo_url': 'http://s2.postimg.org/eaduh2ovt/2016_ASCE_Rocky_Mtn_banner.png', 'active': True, 'admins': [], 'public_projects': True, 'poster': False, 'talk': True, }, 'ARCA2016': { 'name': '5th Applied Research Conference in Africa', 'info_url': 'http://www.arcaconference.org/', 'logo_url': 'http://www.arcaconference.org/images/ARCA_LOGO_NEW.JPG', 'active': True, 'admins': [], 'public_projects': True, 'poster': False, 'talk': True, }, 'CURCONF2016': { 'name': 'CUR Biennial Conference 2016', 'info_url': 'http://www.cur.org/conferences_and_events/biennial2016/', 'logo_url': 'http://s11.postimg.org/v8feuna4y/Conference_logo_eps.jpg', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'CATALISE2016': { 'name': 'Criteria and Terminology Applied to Language Impairments: Synthesising the Evidence (CATALISE) 2016', 'info_url': None, 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'Emergy2016': { 'name': '9th Biennial Emergy Research Conference', 'info_url': 'http://www.cep.ees.ufl.edu/emergy/conferences/ERC09_2016/index.shtml', 'logo_url': 'http://s12.postimg.org/uf9ioqmct/emergy.jpg', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'aps2016': { 'name': '28th APS Annual Convention', 'info_url': 'http://www.psychologicalscience.org/convention', 'logo_url': 'http://www.psychologicalscience.org/redesign/wp-content/uploads/2015/03/APS_2016_Banner_990x157.jpg', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'jssp2016': { 'name': 'Japanese Society of Social Psychology 2016', 'info_url': 'http://www.socialpsychology.jp/conf2016/', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'sepech2016': { 'name': 'XI SEPECH - Research Seminar in Human Sciences (Seminário de Pesquisa em Ciências Humanas)', 'info_url': 'http://www.uel.br/eventos/sepech/sepech2016/', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'etmaal2016': { 'name': 'Etmaal van de Communicatiewetenschap 2016 - Media Psychology', 'info_url': 'https://etmaal2016.wordpress.com', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'WSAN2016': { 'name': 'WSAN2016 Erasmus University Rotterdam', 'info_url': 'http://www.humane.eu/wsan/', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': True, 'talk': True, }, 'ContainerStrategies': { 'name': 'Container Strategies for Data & Software Preservation', 'info_url': 'https://daspos.crc.nd.edu/index.php/workshops/container-strategies-for-data-software-preservation-that-promote-open-science', 'logo_url': 'http://s17.postimg.org/8nl1v5mxb/Screen_Shot_2016_03_02_at_9_05_24_PM.png', 'active': True, 'admins': [], 'public_projects': True, 'poster': True, }, 'CNI2016': { 'name': 'Coalition for Networked Information (CNI) Spring Membership Meeting 2016', 'info_url': 'https://wp.me/P1LncT-6fd', 'logo_url': None, 'active': True, 'admins': [], 'public_projects': True, 'poster': False, 'talk': True, }, } def populate_conferences(): for meeting, attrs in MEETING_DATA.iteritems(): meeting = meeting.strip() admin_emails = attrs.pop('admins', []) admin_objs = [] for email in admin_emails: try: user = User.find_one(Q('username', 'iexact', email)) admin_objs.append(user) except ModularOdmException: raise RuntimeError('Username {0!r} is not registered.'.format(email)) custom_fields = attrs.pop('field_names', {}) conf = Conference( endpoint=meeting, admins=admin_objs, **attrs ) conf.field_names.update(custom_fields) try: conf.save() except ModularOdmException: conf = Conference.find_one(Q('endpoint', 'eq', meeting)) for key, value in attrs.items(): if isinstance(value, dict): current = getattr(conf, key) current.update(value) setattr(conf, key, current) else: setattr(conf, key, value) conf.admins = admin_objs changed_fields = conf.save() if changed_fields: print('Updated {}: {}'.format(meeting, changed_fields)) else: print('Added new Conference: {}'.format(meeting)) if __name__ == '__main__': main()

32.693878

156

0.544112

2,335

24,030

5.475803

0.241113

0.034491

0.085875

0.1145

0.489989

0.430705

0.373221

0.347411

0.328641

0.308619

0

0.044415

0.297295

24,030

734

157

32.73842

0.71278

0.002206

0

0.534819

0

0.013928

0.497414

0.009594

0

1

0.002786

false

0

0.009749

0

0.012535

0.002786

0

null

0

null

0

1

0

da0602f1e855ed3a2c59e5d54ad317e3bc77bd87

3,563

py

Python

clinicadl/clinicadl/subject_level/train_autoencoder.py

921974496/AD-DL

9a0303579a665800633024bdab1ac44f794a0c38

[ "MIT" ]

1

2020-11-30T01:39:12.000Z

clinicadl/clinicadl/subject_level/train_autoencoder.py

921974496/AD-DL

9a0303579a665800633024bdab1ac44f794a0c38

[ "MIT" ]

null

clinicadl/clinicadl/subject_level/train_autoencoder.py

921974496/AD-DL

9a0303579a665800633024bdab1ac44f794a0c38

[ "MIT" ]

null

from __future__ import print_function import argparse from os import path from time import time import sys import torch import torch.nn as nn from torch.utils.data import DataLoader from .utils import ae_finetuning from ..tools.deep_learning.iotools import Parameters from ..tools.deep_learning.data import MinMaxNormalization, MRIDataset, load_data from ..tools.deep_learning import create_autoencoder, commandline_to_json def train_autoencoder(params): """ Parameters params: class from utils module containing all the parameters for training a CNN. """ if params.evaluation_steps % params.accumulation_steps != 0 and params.evaluation_steps != 1: raise Exception('Evaluation steps %d must be a multiple of accumulation steps %d' % (params.evaluation_steps, params.accumulation_steps)) if params.minmaxnormalization: transformations = MinMaxNormalization() else: transformations = None total_time = time() criterion = torch.nn.MSELoss() training_tsv, valid_tsv = load_data(params.tsv_path, params.diagnoses, params.split, params.n_splits, params.baseline) data_train = MRIDataset(params.input_dir, training_tsv, params.preprocessing, transformations) data_valid = MRIDataset(params.input_dir, valid_tsv, params.preprocessing, transformations) # Use argument load to distinguish training and testing train_loader = DataLoader(data_train, params.batch_size, shuffle=True, num_workers=params.num_workers, drop_last=True ) valid_loader = DataLoader(data_valid, ) valid_loader = DataLoader(data_valid, batch_size=params.batch_size, shuffle=False, num_workers=params.num_workers, drop_last=False ) text_file = open(path.join(params.output_dir, 'python_version.txt'), 'w') text_file.write('Version of python: %s \n' % sys.version) text_file.write('Version of pytorch: %s \n' % torch.__version__) text_file.close() decoder = create_autoencoder(params.model, params.pretrained_path, difference=params.pretrained_difference) optimizer = eval("torch.optim." + params.optimizer)(filter(lambda x: x.requires_grad, decoder.parameters()), params.learning_rate, weight_decay=params.weight_decay) if params.add_sigmoid: if isinstance(decoder.decoder[-1], nn.ReLU): decoder.decoder = nn.Sequential(*list(decoder.decoder)[:-1]) decoder.decoder.add_module("sigmoid", nn.Sigmoid()) ae_finetuning(decoder, train_loader, valid_loader, criterion, optimizer, False, params) total_time = time() - total_time print('Total time', total_time) #if __name__ == "__main__": # commandline = parser.parse_known_args() # commandline_to_json(commandline, 'ConvAutoencoder') # options = commandline[0] # if commandline[1]: # print("unknown arguments: %s" % parser.parse_known_args()[1]) # train_params_autoencoder = Parameters(tsv_path, output_dir, input_dir, model) # train_params_autoencoder.write(options) # train_autoencoder(train_parameters_autoencoder)

39.153846

168

0.63991

383

3,563

5.704961

0.342037

0.020595

0.017849

0.028833

0.118993

0.071396

0.031121

0

0.002716

0.276733

3,563

90

169

39.588889

0.845169

0.158855

0

0.105263

0

0.053908

0

1

0.017544

false

0

0.210526

0

0.22807

0.035088

0

null

0

null

0

1

0