Reset23/the-stack-v2-filtered-python · Datasets at Hugging Face

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yanqinghao/TFdeeplearning

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import os import tarfile import _pickle as cPickle import numpy as np import urllib.request import scipy.misc cifar_link = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' data_dir = 'temp' if not os.path.isdir(data_dir): os.makedirs(data_dir) objects = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] target_file = os.path.join(data_dir, 'cifar-10-python.tar.gz') if not os.path.isfile(target_file): print('CIFAR-10 file not found. Downloading CIFAR data (Size = 163MB)') print('This may take a few minutes, please wait.') filename, headers = urllib.request.urlretrieve(cifar_link, target_file) # Extract into memory tar = tarfile.open(target_file) tar.extractall(path=data_dir) tar.close() # Create train image folders train_folder = 'train_dir' if not os.path.isdir(os.path.join(data_dir, train_folder)): for i in range(10): folder = os.path.join(data_dir, train_folder, objects[i]) os.makedirs(folder) # Create test image folders test_folder = 'validation_dir' if not os.path.isdir(os.path.join(data_dir, test_folder)): for i in range(10): folder = os.path.join(data_dir, test_folder, objects[i]) os.makedirs(folder) def load_batch_from_file(file): file_conn = open(file, 'rb') image_dictionary = cPickle.load(file_conn, encoding='latin1') file_conn.close() return(image_dictionary) def save_images_from_dict(image_dict, folder='data_dir'): for ix, label in enumerate(image_dict['labels']): folder_path = os.path.join(data_dir, folder, objects[label]) filename = image_dict['filenames'][ix] #Transform image data image_array = image_dict['data'][ix] image_array.resize([3, 32, 32]) # Save image output_location = os.path.join(folder_path, filename) scipy.misc.imsave(output_location,image_array.transpose()) data_location = os.path.join(data_dir, 'cifar-10-batches-py') train_names = ['data_batch_' + str(x) for x in range(1,6)] test_names = ['test_batch'] # Sort train images for file in train_names: print('Saving images from file: {}'.format(file)) file_location = os.path.join(data_dir, 'cifar-10-batches-py', file) image_dict = load_batch_from_file(file_location) save_images_from_dict(image_dict, folder=train_folder) # Sort test images for file in test_names: print('Saving images from file: {}'.format(file)) file_location = os.path.join(data_dir, 'cifar-10-batches-py', file) image_dict = load_batch_from_file(file_location) save_images_from_dict(image_dict, folder=test_folder) cifar_labels_file = os.path.join(data_dir,'cifar10_labels.txt') print('Writing labels file, {}'.format(cifar_labels_file)) with open(cifar_labels_file, 'w') as labels_file: for item in objects: labels_file.write("{}\n".format(item))

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/Stock/Select/Ui/Other/DyStockSelectTestedStocksDlg.py

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from datetime import * import os import re from PyQt5.QtWidgets import QDialog, QGridLayout, QLabel, QTextEdit, QPushButton, QApplication from DyCommon.DyCommon import * class DyStockSelectTestedStocksDlg(QDialog): def __init__(self, data, parent=None): super().__init__(parent) self._data = data self._init() self._initUi() def _init(self): path = DyCommon.createPath('Stock/User/Config/Testing') self._file = os.path.join(path, 'DyStockSelectTestedStocks.dy') def _read(self): if os.path.exists(self._file): with open(self._file) as f: codes = f.read() else: codes = "" return codes def _save(self): with open(self._file, 'w') as f: f.write(self._codesTextEdit.toPlainText()) def _initUi(self): self.setWindowTitle('要调试的股票') # 控件 descriptionLabel = QLabel('要调试的股票代码') self._codesTextEdit = QTextEdit() self._codesTextEdit.setPlainText(self._read()) cancelPushButton = QPushButton('Cancel') okPushButton = QPushButton('OK') cancelPushButton.clicked.connect(self._cancel) okPushButton.clicked.connect(self._ok) # 布局 grid = QGridLayout() grid.setSpacing(10) grid.addWidget(descriptionLabel, 0, 0) grid.addWidget(self._codesTextEdit, 1, 0, 20, 10) grid.addWidget(okPushButton, 1, 11) grid.addWidget(cancelPushButton, 2, 11) self.setLayout(grid) self.resize(QApplication.desktop().size().width()//3, QApplication.desktop().size().height()//2) def _ok(self): # save self._save() # set out data codes = re.split(',|\n| ', self._codesTextEdit.toPlainText()) temp = [] for x in codes: if x and x not in temp: temp.append(x) codes = [x + '.SH' if x[0] in ['6', '5'] else x + '.SZ' for x in temp] self._data['codes'] = codes self.accept() def _cancel(self): self.reject()

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class MyRouter(object): """ Router template. """ def __init__(self): """ Add any initialization parameters. These will be passed at runtime from the graph definition parameters defined in your seldondeployment kubernetes resource manifest. """ print("Initializing") def route(self,features,feature_names): """ Route a request. Parameters ---------- features : array-like feature_names : array of feature names (optional) """ return 0 def send_feedback(self,features,feature_names,routing,reward,truth): """ Handle feedback for your routings. Optional. """ print("Received feedback")

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/conf/settings/settings_production.py

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""" Production settings. """ from .settings_base import * DEBUG = False ALLOWED_HOSTS = ['mysite.com'] SECRET_KEY = 'mysecretkey' ORCID_TOKENS_ENCRYPTION_KEY = 'mykey'

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/FSND/projects/02_trivia_api/starter/backend/test_flaskr.py

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import os import unittest import json from flask_sqlalchemy import SQLAlchemy from flaskr import create_app from models import setup_db, Question, Category class TriviaTestCase(unittest.TestCase): """This class represents the trivia test case""" def setUp(self): """Define test variables and initialize app.""" self.app = create_app() self.client = self.app.test_client self.database_name = "trivia_test" self.database_path = "postgres://{}/{}".format( "localhost:5432", self.database_name ) self.new_question = { "question": "How far is the mooon away from the earth?", "answer": "238,900 mi", "category": 3, "difficulty": 4, } setup_db(self.app, self.database_path) # binds the app to the current context with self.app.app_context(): self.db = SQLAlchemy() self.db.init_app(self.app) # create all tables self.db.create_all() def tearDown(self): """Executed after reach test""" pass """ TODO Write at least one test for each test for successful operation and for expected errors. """ def test_get_categories(self): res = self.client().get("/categories") data = json.loads(res.data) self.assertEqual(res.status_code, 200) self.assertTrue(len(data["categories"])) def test_get_questions_success(self): res = self.client().get("/questions?page=1") data = json.loads(res.data) self.assertEqual(res.status_code, 200) self.assertTrue(len(data["questions"])) def test_get_questions_404(self): res = self.client().get("/questions?page=30") self.assertEqual(res.status_code, 404) def test_delete_question_success(self): res = self.client().delete("/questions/9") self.assertEqual(res.status_code, 200) def test_delete_question_404(self): res = self.client().delete("/questions/100") self.assertEqual(res.status_code, 404) def test_post_question_search(self): res = self.client().post("/questions", json={"searchTerm": "box"}) self.assertEqual(res.status_code, 200) def test_post_question_insert(self): res = self.client().post("/questions", json=self.new_question) self.assertEqual(res.status_code, 200) def test_post_question_422(self): res = self.client().post("/questions", json={"test": True}) self.assertEqual(res.status_code, 422) def test_post_question_400(self): res = self.client().post("/questions", json={}) self.assertEqual(res.status_code, 400) def test_get_questions_by_category(self): res = self.client().get("/categories/2/questions") self.assertEqual(res.status_code, 200) def test_get_questions_by_category_404(self): res = self.client().get("/categories/tt/questions") self.assertEqual(res.status_code, 404) def test_get_quiz_question(self): res = self.client().post( "/quizzes", json={ "previous_questions": [], "quiz_category": {"type": "History", "id": "4"}, }, ) data = json.loads(res.data) self.assertEqual(res.status_code, 200) self.assertTrue("question" in data.keys()) # Make the tests conveniently executable if __name__ == "__main__": unittest.main()

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import numpy as np from tensorflow.keras.callbacks import Callback from boids.evaluate_boids import evaluate_complexity from boids.forward import forward class ComplexityCallback(Callback): def __init__( self, test_every=10, n_trajectories=1, trajectory_len=1000, n_boids=100 ): super().__init__() self.test_every = test_every self.n_trajectories = n_trajectories self.trajectory_len = trajectory_len self.n_boids = n_boids self.complexities = [] def on_epoch_begin(self, epoch, logs=None): if self.test_every > 0 and epoch == 0: self.evaluate_complexity() def on_epoch_end(self, epoch, logs=None): if self.test_every > 0 and epoch > 0 and epoch % self.test_every == 0: self.evaluate_complexity() def on_train_end(self, logs=None): if self.test_every > 0: self.complexities = np.array(self.complexities) np.savez("complexities.npz", complexities=self.complexities) def evaluate_complexity(self): out = evaluate_complexity( self.model, forward, self.n_trajectories, self.trajectory_len, self.n_boids, ) self.complexities.append(out)

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# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2012 Red Hat, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # # Copyright (c) 2013-2014 Wind River Systems, Inc. # import inspect class MissingArgs(Exception): def __init__(self, missing): self.missing = missing def __str__(self): if len(self.missing) == 1: return "An argument is missing" else: return ("%(num)d arguments are missing" % dict(num=len(self.missing))) def validate_args(fn, *args, **kwargs): """Check that the supplied args are sufficient for calling a function. >>> validate_args(lambda a: None) Traceback (most recent call last): ... MissingArgs: An argument is missing >>> validate_args(lambda a, b, c, d: None, 0, c=1) Traceback (most recent call last): ... MissingArgs: 2 arguments are missing :param fn: the function to check :param arg: the positional arguments supplied :param kwargs: the keyword arguments supplied """ argspec = inspect.getargspec(fn) num_defaults = len(argspec.defaults or []) required_args = argspec.args[:len(argspec.args) - num_defaults] def isbound(method): return getattr(method, 'im_self', None) is not None if isbound(fn): required_args.pop(0) missing = [arg for arg in required_args if arg not in kwargs] missing = missing[len(args):] if missing: raise MissingArgs(missing)

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# coding: utf-8 """ Copyright 2016 SmartBear Software 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. ref: https://github.com/swagger-api/swagger-codegen """ from __future__ import absolute_import import os import sys import unittest import swagger_client from swagger_client.rest import ApiException from swagger_client.models.cloud_account_create_params import CloudAccountCreateParams class TestCloudAccountCreateParams(unittest.TestCase): """ CloudAccountCreateParams unit test stubs """ def setUp(self): pass def tearDown(self): pass def testCloudAccountCreateParams(self): """ Test CloudAccountCreateParams """ model = swagger_client.models.cloud_account_create_params.CloudAccountCreateParams() if __name__ == '__main__': unittest.main()

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def separa_trios(x): if len(x)%3 == 0: i = 0 L1 = [] L2 = [] while i < len(x): L1.append(x[i]) L1.append(x[i + 1]) L1.append(x[i + 2]) L2.append(L1) L1 = [] i += 3 return L2 elif len(x)%3 == 2: c = 0 L3 = [] L4 = [] while(c < (len(x)-2)): L3.append(x[c]) L3.append(x[c + 1]) L3.append(x[c + 2]) L4.append(L3) L3 = [] c += 3 L3.append(x[len(X)-1]) L3.append(x[len(x)]) L4.append(L3) return L4 else: j = 0 L5 = [] L6 = [] while(j < (len(x)-1)): L5.append(x[j]) L5.append(x[j + 1]) L5.append(x[j + 2]) L6.append(L5) L5 = [] j += 3 L5.append(x[len(x)]) L6.append(L5) return L6

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class Solution(object): # 这个题如果改成子序列呢 def maxAscendingSum(self, nums): """ :type nums: List[int] :rtype: int """ dp = [0] * len(nums) dp[0] = nums[0] for i in range(1, len(nums)): if nums[i] > nums[i - 1]: dp[i] = dp[i - 1] + nums[i] else: dp[i] = nums[i] return max(dp) if __name__ == '__main__': # nums = [10, 30, 60, 60, 60, 65] nums = [10, 20, 30, 5, 10, 50] sol = Solution() print(sol.maxAscendingSum(nums))

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#calss header class _ENCOURAGING(): def __init__(self,): self.name = "ENCOURAGING" self.definitions = [u'making you feel more confidence or hope: '] self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.specie = 'adjectives' def run(self, obj1, obj2): self.jsondata[obj2] = {} self.jsondata[obj2]['properties'] = self.name.lower() return self.jsondata

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""" Numpy support for Cron. Numpy support is provided in a submodule so that users who do not need it aren't forced to import numpy. After this module is imported, Cron types that can be used as numpy array types will have a `dtype` attribute. For example: >>> import numpy as np >>> from cron import Date >>> import cron.numpy >>> array = np.zeroes(8, dtype=Date.dtype) """ #------------------------------------------------------------------------------- import numpy from .ext import set_up_numpy as _set_up_numpy # Add all the numpy stuff to the extension module. _set_up_numpy() # FIXME: Should we put this all in a submodule? from .ext import get_day, get_month, get_year, get_ymd, get_ymdi from .ext import date_from_ymdi

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#!/home/moringa/Desktop/projects/Django/rater/virtual/bin/python # -*- coding: utf-8 -*- import re import sys from django.core.management import execute_from_command_line if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(execute_from_command_line())

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raul-jr3/fantastic-spoon

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# -*- coding: utf-8 -*- # Generated by Django 1.11.3 on 2017-08-13 14:38 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('shots', '0008_image_tags'), ] operations = [ migrations.RemoveField( model_name='image', name='tags', ), ]

[ "rahul.srivatsav1995@gmail.com" ]

rahul.srivatsav1995@gmail.com

c964f1ce9a0d56b63720d461b59ad92d73476c3b

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/ibanking_web/ibanking/views.py

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[]

no_license

ihfazhillah/explore-scrapy

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refs/heads/master

2022-12-10T03:00:54.083201

2019-02-15T00:41:38

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2022-12-08T01:37:37

2019-01-21T00:22:30

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py

from uuid import uuid4 from django.shortcuts import render from django.http import JsonResponse from scrapyd_api import ScrapydAPI from .models import ScrapyItem # Create your views here. scrapyd = ScrapydAPI('http://localhost:6800') def get_statements(request): unique_id = str(uuid4()) settings = { 'unique_id': unique_id } task = scrapyd.schedule('default', 'ibmandiri', settings=settings, to_crawl='otjlsjflask') return JsonResponse({ 'task_id': task, 'unique_id': unique_id, 'status': 'started', 'url': '/check_job?unique_id={}&task_id={}'.format(unique_id, task) }) def check_job_get_statements(request): task_id = request.GET.get('task_id') unique_id = request.GET.get('unique_id') status = scrapyd.job_status('default', task_id) if status == 'finished': item = ScrapyItem.objects.get(unique_id=unique_id) return JsonResponse(item.to_dict()) return JsonResponse({'status': status})

[ "mihfazhillah@gmail.com" ]

mihfazhillah@gmail.com

b5f279e0b892ad03556863e8883bdf0635bb56f0

4b86ebac6e2273bec07e8f0f1275a9f4c4700491

/Sort/2947_나무조각.py

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[]

no_license

chohan3036/algo_study

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64abbc8a401f9e555692f01917eb78b0fd37d7fb

refs/heads/master

2023-04-07T06:13:16.059638

2021-04-20T04:32:35

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import sys reading = lambda :sys.stdin.readline().strip() N = list(map(int, reading().split())) n = 5 for i in range(n): for j in range(1, n): if N[j - 1] > N[j]: temp = N[j - 1] N[j - 1] = N[j] N[j] = temp for k in N: print(k, end=' ') print()

[ "chohan3036@gmail.com" ]

chohan3036@gmail.com

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f92fb9b9abe021d5604d3b5fb2ade0fbe6d85e3e

/robot/logging.py

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permissive

chuchiring/wukong-robot

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f31982738df5b5c3929c713415aee04d13f2f4c8

refs/heads/master

2020-04-23T06:45:10.085706

2019-02-16T05:51:26

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import logging import sys import os from robot import config, constants from logging.handlers import RotatingFileHandler, HTTPHandler PAGE = 4096 DEBUG = logging.DEBUG INFO = logging.INFO ERROR = logging.ERROR def tail(filepath, n=10): """ 实现 tail -n """ res = "" with open(filepath, 'rb') as f: f_len = f.seek(0, 2) rem = f_len % PAGE page_n = f_len // PAGE r_len = rem if rem else PAGE while True: # 如果读取的页大小>=文件大小，直接读取数据输出 if r_len >= f_len: f.seek(0) lines = f.readlines()[::-1] break f.seek(-r_len, 2) # print('f_len: {}, rem: {}, page_n: {}, r_len: {}'.format(f_len, rem, page_n, r_len)) lines = f.readlines()[::-1] count = len(lines) -1 # 末行可能不完整，减一行，加大读取量 if count >= n: # 如果读取到的行数>=指定行数，则退出循环读取数据 break else: # 如果读取行数不够，载入更多的页大小读取数据 r_len += PAGE page_n -= 1 for line in lines[:n][::-1]: res += line.decode('utf-8') return res def getLogger(name): formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(name) # StreamHandler stream_handler = logging.StreamHandler(sys.stdout) stream_handler.setLevel(level=logging.INFO) stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) # FileHandler file_handler = RotatingFileHandler(os.path.join(constants.TEMP_PATH, 'wukong.log'), maxBytes=1024*1024,backupCount=5) file_handler.setLevel(level=logging.DEBUG) file_handler.setFormatter(formatter) logger.addHandler(file_handler) return logger def readLog(lines=200): log_path = os.path.join(constants.TEMP_PATH, 'wukong.log') if os.path.exists(log_path): return tail(log_path, lines) return ''

[ "m@hahack.com" ]

m@hahack.com

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/programming-team/First Semester/UCB/boxes.py

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[]

no_license

Nate8888/programming-contest-practice

80d17d7ccea9491f42ec0cccab7e0cfe80849f17

4c2496260c209190103d7bbef82d866ae4be09b6

refs/heads/master

2023-05-04T16:20:36.136598

2021-05-27T22:52:04

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py

import sys def k_min_max_partitions(my_list, size, k): if k == 1: return sum(my_list[0:size]) if size == 1: return my_list[0] current_min_max = 1e27 for current_index in range(1, size + 1): current_min_max = min(current_min_max, max(k_min_max_partitions(my_list, current_index, k - 1), sum(my_list[current_index:size]))) #print(current_min_max) return current_min_max line = input().rstrip().split(" ") partitions = int(line[1]) items = int(line[0]) all_nums = input().rstrip().split(" ") nums = [int(x) for x in all_nums] print(k_min_max_partitions(nums, items, partitions))

[ "wilk.nathan@gmail.com" ]

wilk.nathan@gmail.com

ff3b0522f795040dd1eea2c2af80b0748c4a76eb

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/tlmshop/serializers/__init__.py

87773f17382c4d432810508bc9149e2b62673837

[]

no_license

LegionMarket/django-cms-base

79069ee67628ff7d10338b48b154fe087863e1ea

1b6fc3423e3d0b2165552cc980432befb496f3e0

refs/heads/master

2020-12-30T17:10:33.866390

2017-05-25T12:49:41

91,057,746

0

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UTF-8

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2,797

py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.conf import settings from django.utils.safestring import mark_safe from rest_framework import serializers from shop.serializers.bases import ProductSerializer from shop.search.serializers import ProductSearchSerializer as BaseProductSearchSerializer from tlmshop.search_indexes import tlmshop_search_index_classes __all__ = ['ProductSummarySerializer', 'ProductSearchSerializer', 'CatalogSearchSerializer'] class ProductSummarySerializer(ProductSerializer): media = serializers.SerializerMethodField() class Meta(ProductSerializer.Meta): fields = ['id', 'product_name', 'product_url', 'product_model', 'price', 'media', 'caption'] def get_media(self, product): return self.render_html(product, 'media') if settings.SHOP_TYPE in ['commodity', 'i18n_commodity']: class ProductDetailSerializer(ProductSerializer): class Meta(ProductSerializer.Meta): fields = ['product_name', 'slug', 'unit_price', 'product_code'] __all__.append('ProductDetailSerializer') elif settings.SHOP_TYPE in ['smartcard', 'i18n_smartcard']: class ProductDetailSerializer(ProductSerializer): class Meta(ProductSerializer.Meta): fields = ['product_name', 'slug', 'unit_price', 'manufacturer', 'card_type', 'speed', 'product_code', 'storage'] __all__.append('ProductDetailSerializer') elif settings.SHOP_TYPE in ['i18n_polymorphic', 'polymorphic']: from .polymorphic import (SmartCardSerializer, SmartPhoneSerializer, AddSmartPhoneToCartSerializer) __all__.extend(['SmartCardSerializer', 'SmartPhoneSerializer', 'AddSmartPhoneToCartSerializer']) class ProductSearchSerializer(BaseProductSearchSerializer): """ Serializer to search over all products in this shop """ media = serializers.SerializerMethodField() class Meta(BaseProductSearchSerializer.Meta): fields = BaseProductSearchSerializer.Meta.fields + ['media', 'caption'] field_aliases = {'q': 'text'} search_fields = ['text'] index_classes = tlmshop_search_index_classes def get_media(self, search_result): return mark_safe(search_result.search_media) class CatalogSearchSerializer(BaseProductSearchSerializer): """ Serializer to restrict products in the catalog """ media = serializers.SerializerMethodField() class Meta(BaseProductSearchSerializer.Meta): fields = BaseProductSearchSerializer.Meta.fields + ['media', 'caption'] field_aliases = {'q': 'autocomplete'} search_fields = ['autocomplete'] index_classes = tlmshop_search_index_classes def get_media(self, search_result): return mark_safe(search_result.catalog_media)

[ "spivegin@txtsme.com" ]

spivegin@txtsme.com

d0c6e4689bcc224f4fde752a7459a16adbb45cf1

2e263bb909bb706957990f23d4d07a33fe031a61

/curiosity_debug.py

dffbbe8768dbdc709baeb1ed25319c6dda169585

[]

no_license

liziniu/SuperMario

3e9b776d4c490275b1684b4fd3b3471914811b7d

64a33901c61591348dd4b9c878e396901dea27b6

refs/heads/master

2020-05-16T02:32:10.694180

2019-06-02T10:08:11

182,632,295

0

null

UTF-8

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import matplotlib import numpy as np import tensorflow as tf matplotlib.use("Agg") import matplotlib.pyplot as plt import pandas as pd import os import argparse import pickle import json from baselines.common import set_global_seeds from curiosity.dynamics import Dynamics from run import build_env from scipy.stats import pearsonr from common.util import DataRecorder from baselines import logger class Model: def __init__(self, sess, env, aux_task, feat_dim, lr): self.sess = sess or tf.Session() self.dynamics = Dynamics(sess=self.sess, env=env, auxiliary_task=aux_task, feat_dim=feat_dim, queue_size=1000, normalize_novelty=True) self.obs_shape = env.observation_space.shape self.ac_shape = env.action_space.shape del env self.opt = tf.train.RMSPropOptimizer(lr, decay=0.99) self.aux_loss = self.dynamics.aux_loss self.dyna_loss = self.dynamics.dyna_loss self.loss = self.aux_loss + self.dyna_loss params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) gradsandvars = self.opt.compute_gradients(self.loss, params) self.train_op = self.opt.apply_gradients(gradsandvars) self.train_history = [] def train(self, data, rollout_size, online=True, save_path=None): """ :param data: list of dict. [{"obs": arr; "next_obs": arr}] """ self.recoder = DataRecorder(os.path.join(save_path, "training")) for episode, episode_data in enumerate(data): episode_length = len(episode_data["obs"]) obs, act, next_obs, x_pos = episode_data["obs"], episode_data["act"], episode_data["next_obs"], episode_data["x_pos"] episode_novelty = [] if not online: ind = np.random.permutation(episode_length) obs, act, next_obs, x_pos = obs[ind], act[ind], next_obs[ind], x_pos[ind] for start in range(0, episode_length, rollout_size): end = start + rollout_size batch_obs, batch_act, batch_next_obs, batch_x_pos = obs[start:end], act[start:end], next_obs[start:end], x_pos[start:end] novelty = self.sess.run(self.dynamics.novelty, feed_dict={self.dynamics.obs: obs, self.dynamics.ac: act, self.dynamics.next_obs: next_obs}) self.sess.run(self.train_op, feed_dict={self.dynamics.obs: batch_obs, self.dynamics.ac: batch_act, self.dynamics.next_obs: batch_next_obs}) p = pearsonr(x_pos, novelty)[0] logger.info("Episode:{}|Epoch:{}|P:{}".format(episode, start//rollout_size, p)) episode_novelty.append(novelty) self.recoder.store({"x_pos": x_pos, "novelty": novelty, "episode": episode, "epoch": start//rollout_size, "p": p}) plt.figure() plt.scatter(x_pos, novelty) # plt.yscale("log") plt.savefig(os.path.join(save_path, "{}_{}.png".format(episode, start//rollout_size))) plt.close() self.recoder.dump() def preprocess_data(data): data_episode = [] for episode_data in data: tmp = {"obs": [], "act": [], "x_pos": []} for t in episode_data: tmp["obs"].append(t["obs"][0]) tmp["act"].append(t["act"][0]) tmp["x_pos"].append(t["info"][0]["x_pos"]) tmp["obs"] = np.asarray(tmp["obs"], dtype=np.float32) tmp["act"] = np.asarray(tmp["act"], dtype=np.float32) tmp["x_pos"] = np.asarray(tmp["x_pos"], dtype=np.float32) tmp["next_obs"] = np.copy(tmp["obs"][1:]) tmp["obs"] = tmp["obs"][:-1] tmp["act"] = tmp["act"][:-1] tmp["x_pos"] = tmp["x_pos"][:-1] data_episode.append(tmp) return data_episode def visualize_p(path): f = open(path, "rb") data = [] while True: try: data.append(pickle.load(f)) except Exception as e: print(e) break p = [] episode = None for i in range(len(data)): if episode is None: episode = data[i]["episode"] ep = data[i]["episode"] if ep == episode: p.append(data[i]["p"]) else: plt.figure() plt.plot(p) plt.savefig(os.path.join(path, "p_{}.png").format(episode)) p = [] p.append(ep) episode = ep print("Epoch:{} done".format(ep)) def main(args): f = open("{}/data.pkl".format(args.load_path), "rb") data = [] while True: try: data.append(pickle.load(f)) except: break print("Episode:", len(data)) set_global_seeds(args.seed) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3) config = tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True) sess = tf.Session(config=config, ) env = build_env(env_id=args.env, num_env=1, alg="acer", reward_scale=1.0, env_type=args.env_type, gamestate=None, seed=None, prefix="") model = Model( sess=sess, env=env, aux_task=args.aux_task, feat_dim=args.feat_dim, lr=args.lr ) sess.run(tf.global_variables_initializer()) save_path = "{}/plots-{}-{}-{}".format( args.load_path, args.memo, args.online, args.aux_task, ) logger.configure(dir=save_path) if not os.path.exists(save_path): os.makedirs(save_path) with open(os.path.join(save_path, "config.json"), "w") as f: json.dump(args.__dict__, f, indent=4) model.train( preprocess_data(data), rollout_size=args.rollout_size*args.num_env, save_path=save_path, ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--rollout_size", type=int, default=20) parser.add_argument("--batch_size", type=int, default=64) parser.add_argument("--nb_opt", type=int, default=5) parser.add_argument("--lr", type=float, default=7e-4) parser.add_argument("--memo", type=str, default="") parser.add_argument("--online", action="store_true", default=False) parser.add_argument("--aux_task", type=str, default="RF", choices=["RF", "RND", "IDF"]) parser.add_argument("--feat_dim", type=int, default=512) parser.add_argument("--dyna_dim", type=int, default=512) parser.add_argument("--seed", type=int, default=0) parser.add_argument("--load_path", type=str, default="logs") parser.add_argument('--gamestate', default=None) parser.add_argument("--alg", type=str, default="ppo2") parser.add_argument("--env_type", type=str, default="atari") parser.add_argument("--env", type=str) parser.add_argument("--num_env", type=int, default=1) parser.add_argument("--reward_scale", type=float, default=1.0, choices=[1.0]) args = parser.parse_args() main(args)

[ "374387855@qq.com" ]

374387855@qq.com

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/design_patterns_with_python/2-python-design-patterns-m2-exercise-files/Strategy/StrategyVariation/strategy_variation/shipping_cost.py

39dd4ddcb717c4d486e40cddd906eba548af071d

[]

no_license

ForeverDreamer/python_learning

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ff905c4811ddb688f8ee44aed8c4d8067db6168b

refs/heads/master

2022-04-30T03:23:45.162498

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null

2022-04-22T21:12:46

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class ShippingCost(object): def __init__(self, strategy): self._strategy = strategy def shipping_cost(self, order): return self._strategy(order)

[ "499361328@qq.com" ]

499361328@qq.com

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/backup/user_223/ch87_2020_04_29_13_42_03_524040.py

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[]

no_license

gabriellaec/desoft-analise-exercicios

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01940ab0897aa6005764fc220b900e4d6161d36b

refs/heads/main

2023-01-31T17:19:42.050628

2020-12-16T05:21:31

306,735,108

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null

UTF-8

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297

py

with open ('churras.txt', 'r') as churrastxt: linhas = churrastxt.readlines() print (linhas) custo = 0 for linha in linhas: separa = linha.split(',') for e in separa: e1=float(e[1]) e2=float(e[2]) custo+=(e1*e2) print (custo)

[ "you@example.com" ]

you@example.com

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/sortings/0056_merge_intervals.py

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[ "MIT" ]

permissive

MartinMa28/Algorithms_review

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refs/heads/master

2022-04-13T03:56:56.932788

2020-04-06T03:41:33

203,349,402

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null

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class Solution: def merge(self, intervals: list) -> list: sorted_intervals = sorted(intervals) idx = 0 while idx < len(sorted_intervals) - 1: start, end = sorted_intervals[idx] next_start, next_end = sorted_intervals[idx + 1] if end >= next_start: # overlaps with next range sorted_intervals.pop(idx) if end < next_end: sorted_intervals[idx] = (start, next_end) else: sorted_intervals[idx] = (start, end) else: # if does not overlap, check the next range idx += 1 return sorted_intervals if __name__ == "__main__": solu = Solution() print(solu.merge([[1,3],[2,6],[8,10],[15,18]]))

[ "1135485226@qq.com" ]

1135485226@qq.com

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/steps/google.py

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[ "MIT" ]

permissive

antonckoenig/citeas-api

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refs/heads/master

2023-07-30T13:08:39.967004

2021-09-19T20:08:13

null

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UTF-8

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from googlesearch import get_random_user_agent, search from steps.arxiv import ArxivResponseStep from steps.bitbucket import BitbucketRepoStep from steps.cran import CranLibraryStep from steps.core import Step from steps.github import GithubRepoStep from steps.pypi import PypiLibraryStep from steps.webpage import WebpageStep class GoogleStep(Step): step_intro = "Use Google to find the software citation." step_more = "This project webpage often includes attribution information like an associated DOI, GitHub repository, and/or project title." @property def starting_children(self): return [ ArxivResponseStep, GithubRepoStep, BitbucketRepoStep, CranLibraryStep, PypiLibraryStep, WebpageStep, ] def set_content_url(self, input): if "http" in input: return None self.content_url = self.google_search(input) def set_content(self, input): self.content = self.content_url @staticmethod def google_search(input): random_user_agent = get_random_user_agent() # check if input is PMID if len(input) == 8 and input.isdigit(): query = input elif "scipy" in input: query = "scipy citation" else: query = "{} software citation".format(input) for url in search(query, stop=3, user_agent=random_user_agent): if "citebay.com" not in url and not url.endswith(".pdf"): return url

[ "caseym@gmail.com" ]

caseym@gmail.com

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/tests/tests/correctness/EPLAnalytics/Utilities/DataSimulator/sim_cor_030/run.py

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[ "LicenseRef-scancode-unknown-license-reference", "Apache-2.0" ]

permissive

SoftwareAG/apama-industry-analytics-kit

c0f6c30badf31411a29bc6daa4a7125b76f4e737

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refs/heads/master

2022-02-19T20:47:27.180233

2022-02-02T12:58:23

185,572,282

3

2

Apache-2.0

2022-02-02T12:58:24

2019-05-08T09:14:07

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UTF-8

Python

false

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# $Copyright (c) 2015 Software AG, Darmstadt, Germany and/or Software AG USA Inc., Reston, VA, USA, and/or Terracotta Inc., San Francisco, CA, USA, and/or Software AG (Canada) Inc., Cambridge, Ontario, Canada, and/or, Software AG (UK) Ltd., Derby, United Kingdom, and/or Software A.G. (Israel) Ltd., Or-Yehuda, Israel and/or their licensors.$ # Use, reproduction, transfer, publication or disclosure is prohibited except as specifically provided for in your License Agreement with Software AG from industry.framework.AnalyticsBaseTest import AnalyticsBaseTest from pysys.constants import * class PySysTest(AnalyticsBaseTest): def execute(self): # Start the correlator correlator = self.startTest() self.injectAnalytic(correlator) self.injectDataSimulator(correlator) self.ready(correlator) correlator.receive(filename='RawOutputData.evt', channels=['OutputData']) correlator.receive(filename='OutputDataOnly.evt', channels=['OUTPUT_DATA_ONLY']) correlator.injectMonitorscript(['test.mon'], self.input) # Run the simulator for just over 60 seconds so that we get 60 data points generated correlator.incrementTime(60.1) self.waitForSignal('OutputDataOnly.evt', expr='Received Data:', condition='>=59', timeout=5) def validate(self): # Ensure the test output was correct exprList=[] exprList.append('Received Data: 100') exprList.append('Received Data: 96.66666666666667') exprList.append('Received Data: 93.33333333333333') exprList.append('Received Data: 90') exprList.append('Received Data: 86.66666666666667') exprList.append('Received Data: 83.33333333333334') exprList.append('Received Data: 80') exprList.append('Received Data: 76.66666666666667') exprList.append('Received Data: 73.33333333333334') exprList.append('Received Data: 70') exprList.append('Received Data: 66.66666666666667') exprList.append('Received Data: 63.33333333333334') exprList.append('Received Data: 60') exprList.append('Received Data: 56.66666666666667') exprList.append('Received Data: 53.33333333333334') exprList.append('Received Data: 50') exprList.append('Received Data: 46.66666666666667') exprList.append('Received Data: 43.33333333333334') exprList.append('Received Data: 40.00000000000001') exprList.append('Received Data: 36.66666666666667') exprList.append('Received Data: 33.33333333333334') exprList.append('Received Data: 30.00000000000001') exprList.append('Received Data: 26.66666666666667') exprList.append('Received Data: 23.33333333333334') exprList.append('Received Data: 20.00000000000001') exprList.append('Received Data: 16.66666666666668') exprList.append('Received Data: 13.33333333333334') exprList.append('Received Data: 10.00000000000001') exprList.append('Received Data: 6.66666666666668') exprList.append('Received Data: 3.33333333333334') exprList.append('Received Data: 100') self.assertOrderedGrep("OutputDataOnly.evt", exprList=exprList) self.assertLineCount('OutputDataOnly.evt', expr='Received Data:', condition='>=59') # Check for invalid data values self.assertLineCount('OutputDataOnly.evt', expr='INVALID DATA RECEIVED!', condition='==0') # Ensure the test output was correct exprList=[] exprList.append('Validating com.industry.analytics.Analytic$"DataSimulator",\[\],\["OutputData"\],{"simulationType":"sawfalling"}$') exprList.append('Analytic DataSimulator started for inputDataNames \[\]') self.assertOrderedGrep("correlator.out", exprList=exprList) # Make sure that the we got the right number of analytics created self.assertLineCount('correlator.out', expr='Validating com.industry.analytics.Analytic', condition='==1') self.assertLineCount('correlator.out', expr='Analytic DataSimulator started', condition='==1') # Basic sanity checks self.checkSanity()

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# Standard Deviation end = 0 while end == 0: studentAmount = int(input()) if studentAmount == 0: end += 1 else: scoreData = [int(i) for i in input().rstrip().split()] totalScore = 0 for score in scoreData: totalScore += score averageScore = totalScore / studentAmount # print('Average: ' + str(averageScore)) totalSquareDistance = 0 for score in scoreData: totalSquareDistance += (score - averageScore) ** 2 variance = totalSquareDistance / studentAmount # print('Variance: ' + str(variance)) standardDeviation = variance ** 0.5 # print('Standard Deviation: ', end = '') print(str(standardDeviation))

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LiuFang816/SALSTM_py_data

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import argparse import numpy as np from chainer import serializers from models import Generator import plot # Resize the MNIST dataset to 32x32 images for convenience # since the generator will create images with dimensions # of powers of 2 (doubling upsampling in each deconvolution). im_shape = (32, 32) def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--n-z', type=int, default=10) parser.add_argument('--n-samples', type=int, default=128) parser.add_argument('--in-generator-filename', type=str, default='generator.model') parser.add_argument('--out-filename', type=str, default='sample.png') return parser.parse_args() if __name__ == '__main__': args = parse_args() n_z = args.n_z n_samples = args.n_samples in_generator_filename = args.in_generator_filename out_filename = args.out_filename generator = Generator(n_z, im_shape) serializers.load_hdf5(in_generator_filename, generator) zs = np.random.uniform(-1, 1, (n_samples, n_z)).astype(np.float32) x = generator(zs) plot.save_ims(out_filename, x.data)

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from vsmclient import base from vsmclient import exceptions from vsmclient.v1 import vsms from tests import utils from tests.v1 import fakes cs = fakes.FakeClient() class BaseTest(utils.TestCase): def test_resource_repr(self): r = base.Resource(None, dict(foo="bar", baz="spam")) self.assertEqual(repr(r), "<Resource baz=spam, foo=bar>") def test_getid(self): self.assertEqual(base.getid(4), 4) class TmpObject(object): id = 4 self.assertEqual(base.getid(TmpObject), 4) def test_eq(self): # Two resources of the same type with the same id: equal r1 = base.Resource(None, {'id': 1, 'name': 'hi'}) r2 = base.Resource(None, {'id': 1, 'name': 'hello'}) self.assertEqual(r1, r2) # Two resoruces of different types: never equal r1 = base.Resource(None, {'id': 1}) r2 = vsms.Volume(None, {'id': 1}) self.assertNotEqual(r1, r2) # Two resources with no ID: equal if their info is equal r1 = base.Resource(None, {'name': 'joe', 'age': 12}) r2 = base.Resource(None, {'name': 'joe', 'age': 12}) self.assertEqual(r1, r2) def test_findall_invalid_attribute(self): # Make sure findall with an invalid attribute doesn't cause errors. # The following should not raise an exception. cs.vsms.findall(vegetable='carrot') # However, find() should raise an error self.assertRaises(exceptions.NotFound, cs.vsms.find, vegetable='carrot')

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2021-05-28T05:40:36.678808

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import inspect import os import sys import unittest import numpy as np import numpy.testing as nptest currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.insert(0,parentdir) import statistics import stubs import util class StatisticsTest(unittest.TestCase): def setUp(self): self.video_data = util.VideoData(stubs.raw) self.spread_span = statistics.SpreadSpan(self.video_data) def test_span(self): self.assertEqual(self.spread_span.span_by_vid, stubs.span_by_vid) def test_spread(self): self.assertEqual(self.spread_span.spread_by_vid, stubs.spread_by_vid) if __name__ == '__main__': unittest.main()

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ed@elplatt.com

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phlalx/algorithms

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#TAG tree # yet another tree problem # do it on paper first to see if we use synth/inherited attributed and how # to deal with None # # If we don't recurse to None, don't forget to deal with corner case where # root = None. # # In this example, we don't recurse through None because we need # node.left, node.right value # class Solution: def longestConsecutive(self, root: TreeNode) -> int: res = float('-inf') def f(node) -> Tuple[int, int]: # longest increasing sequence starting/end nonlocal res v = node.val linc, rinc, ldec, rdec = (1,) * 4 if node.left is not None: linc, ldec = f(node.left) if v == node.left.val - 1: linc += 1 else: linc = 1 if v == node.left.val + 1: ldec += 1 else: ldec = 1 if node.right is not None: rinc, rdec = f(node.right) if v == node.right.val - 1: rinc += 1 else: rinc = 1 if v == node.right.val + 1: rdec += 1 else: rdec = 1 res = max(res, linc + rdec - 1, rinc + ldec - 1) return max(linc, rinc), max(ldec, rdec) if root is None: return 0 f(root) return res

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import sys import numpy as np import pandas as pd pd.set_option('max_columns', 1000) pd.set_option('max_info_columns', 1000) pd.set_option('expand_frame_repr', False) pd.set_option('display.max_rows', 30000) pd.set_option('max_colwidth', 4000) pd.set_option('display.float_format', lambda x: '%.3f' % x) def T_diff_lambda(y_t, y_c, lam='RSS'): Y_t = y_t['y'].groupby(y_t['stratum']).mean() N_t = y_t['y'].groupby(y_t['stratum']).count() Y_c = y_c['y'].groupby(y_c['stratum']).mean() N_c = y_c['y'].groupby(y_c['stratum']).count() if lam == 'RSS': l = (N_t + N_c) / (N_t.sum() + N_c.sum()) return (l * (Y_t - Y_c)).sum() elif lam == 'OPT': l = ((N_t + N_c) * (N_t / (N_t + N_c)) * (N_c / (N_t + N_c))) / ((N_t + N_c) * (N_t / (N_t + N_c)) * (N_c / (N_t + N_c))).sum() return (l * (Y_t - Y_c)).sum() def T_rank_stratum(y): y['rank'] = y['y'].groupby(y['stratum']).rank() y['norm'] = (y['y'].groupby(y['stratum']).transform('count') + 1) / 2 y['normalized_rank'] = y['rank'] - y['norm'] return np.abs(y.query('treatment == 1')['normalized_rank'].mean() - y.query('treatment == 0')['normalized_rank'].mean()) def T_range(y): y_t = y.query('treatment == 1') y_c = y.query('treatment == 0') Y_t = y_t['y'].groupby(y_t['stratum']).max() - y_t['y'].groupby(y_t['stratum']).min() Y_c = y_c['y'].groupby(y_c['stratum']).max() - y_c['y'].groupby(y_c['stratum']).min() N_t = y_t['y'].groupby(y_t['stratum']).count() N_c = y_c['y'].groupby(y_c['stratum']).count() l = (N_t + N_c) / (N_t.sum() + N_c.sum()) return (l * (Y_t - Y_c)).sum() if __name__ == '__main__': np.random.seed(seed=2) N = 10 J = 3 df = pd.DataFrame(np.random.uniform(-1, 1, size=(N, 3)), columns=['one', 'two', 'three']) df['stratum'] = np.random.choice(range(J), size=(N, 1)) df['treatment'] = np.random.choice([0, 1], size=(N, 1)) df['y'] = np.random.uniform(0, 1, size=(N, 1)) print( T_diff_lambda( df.query('treatment == 1')[['y', 'stratum']], df.query('treatment == 0')[['y', 'stratum']], lam='OPT' # lam='RSS' ) ) print( T_rank_stratum( df[['y', 'stratum', 'treatment']] ) ) print( T_range( df[['y', 'stratum', 'treatment']] ) )

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ans = 0 c = 1 li = ["A","C","G","T"] s = input() lens = len(s) for i in range(lens): for j in range(lens-(i)): a = s[j:j+i+1] for k in range(i+1): if a[k] not in li: c = 1 break else: c = 0 continue if c == 0: ans = i+1 else: c = 1 print(ans)

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#!/usr/bin/env python3 # ###################################################################### # Copyright (C) 2017 Fridolin Pokorny, fridolin.pokorny@gmail.com # This file is part of json2sql package. # ###################################################################### """Tests for error handling.""" from json2sql import select2sql from json2sql import ParsingInputError import pytest from .base import TestBase class TestErrors(TestBase): """Tests for error handling.""" def test_unknown_subquery_key(self): wrong_nested_query = {'$filter': {'table': 'BarTable'}, 'wrong_key': 'baz'} with pytest.raises(ParsingInputError): select2sql(table='FooTable', where={'something in': wrong_nested_query})

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#!/usr/bin/env python3 # Copyright (c) 2017 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test RPC calls related to net. Tests correspond to code in rpc/net.cpp. """ from test_framework.test_framework import NestEggTestFramework from test_framework.util import ( assert_equal, assert_greater_than_or_equal, assert_raises_rpc_error, connect_nodes_bi, disconnect_nodes, p2p_port, wait_until, ) class NetTest(NestEggTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 2 def run_test(self): self._test_connection_count() self._test_getnettotals() self._test_getnetworkinginfo() self._test_getaddednodeinfo() #self._test_getpeerinfo() def _test_connection_count(self): # connect_nodes_bi connects each node to the other assert_equal(self.nodes[0].getconnectioncount(), 2) def _test_getnettotals(self): # getnettotals totalbytesrecv and totalbytessent should be # consistent with getpeerinfo. Since the RPC calls are not atomic, # and messages might have been recvd or sent between RPC calls, call # getnettotals before and after and verify that the returned values # from getpeerinfo are bounded by those values. net_totals_before = self.nodes[0].getnettotals() peer_info = self.nodes[0].getpeerinfo() net_totals_after = self.nodes[0].getnettotals() assert_equal(len(peer_info), 2) peers_recv = sum([peer['bytesrecv'] for peer in peer_info]) peers_sent = sum([peer['bytessent'] for peer in peer_info]) assert_greater_than_or_equal(peers_recv, net_totals_before['totalbytesrecv']) assert_greater_than_or_equal(net_totals_after['totalbytesrecv'], peers_recv) assert_greater_than_or_equal(peers_sent, net_totals_before['totalbytessent']) assert_greater_than_or_equal(net_totals_after['totalbytessent'], peers_sent) # test getnettotals and getpeerinfo by doing a ping # the bytes sent/received should change # note ping and pong are 32 bytes each self.nodes[0].ping() wait_until(lambda: (self.nodes[0].getnettotals()['totalbytessent'] >= net_totals_after['totalbytessent'] + 32 * 2), timeout=1) wait_until(lambda: (self.nodes[0].getnettotals()['totalbytesrecv'] >= net_totals_after['totalbytesrecv'] + 32 * 2), timeout=1) peer_info_after_ping = self.nodes[0].getpeerinfo() def _test_getnetworkinginfo(self): assert_equal(self.nodes[0].getnetworkinfo()['connections'], 2) disconnect_nodes(self.nodes[0], 1) # Wait a bit for all sockets to close wait_until(lambda: self.nodes[0].getnetworkinfo()['connections'] == 0, timeout=3) connect_nodes_bi(self.nodes, 0, 1) assert_equal(self.nodes[0].getnetworkinfo()['connections'], 2) def _test_getaddednodeinfo(self): assert_equal(self.nodes[0].getaddednodeinfo(True), []) # add a node (node2) to node0 ip_port = "127.0.0.1:{}".format(p2p_port(2)) self.nodes[0].addnode(ip_port, 'add') # check that the node has indeed been added added_nodes = self.nodes[0].getaddednodeinfo(True, ip_port) assert_equal(len(added_nodes), 1) assert_equal(added_nodes[0]['addednode'], ip_port) # check that a non-existent node returns an error assert_raises_rpc_error(-24, "Node has not been added", self.nodes[0].getaddednodeinfo, True, '1.1.1.1') def _test_getpeerinfo(self): peer_info = [x.getpeerinfo() for x in self.nodes] # check both sides of bidirectional connection between nodes # the address bound to on one side will be the source address for the other node assert_equal(peer_info[0][0]['addrbind'], peer_info[1][0]['addr']) assert_equal(peer_info[1][0]['addrbind'], peer_info[0][0]['addr']) if __name__ == '__main__': NetTest().main()

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""" Author : Yihang.Gao 高艺航 StuNumber: 2018012950 Purpose : Set up multiprocess by python. Created : 1/7/2020 """ from multiprocessing import Process def setup_pro(i): print('process',i) if __name__ == '__main__': list_pro = [] for i in range(3): k = Process(target=setup_pro, args=(i+1,)) list_pro.append(k) list_pro[0].start() list_pro[1].start() list_pro[2].start()

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from marketsim import event, _ import fold class Min_Impl(fold.Last): def __init__(self): event.subscribe(self.source, _(self)._update, self) self.reset() def reset(self): import blist self._levels = blist.sorteddict() self._x = None def at(self, t): p = self._levels.keys()[0] if len(self._levels) > 0 else None x = self._x if p is not None: if x is not None: return min(p,x) return p return x def _remove(self, x): self._levels[x] -= 1 if self._levels[x] == 0: del self._levels[x] self.fire(self) def update(self, t, x): if x is not None and (self._x is None or x < self._x): if x not in self._levels: self._levels[x] = 0 self._levels[x] += 1 self._scheduler.scheduleAfter(self.timeframe, _(self, x)._remove) self._x = x self.fire(self) class Max_Impl(fold.Last): def __init__(self): event.subscribe(self.source, _(self)._update, self) self.reset() def reset(self): import blist self._levels = blist.sorteddict() self._x = None def at(self, t): p = -self._levels.keys()[0] if len(self._levels) > 0 else None x = self._x if p is not None: if x is not None: return max(p,x) return p return x def _remove(self, x): self._levels[-x] -= 1 if self._levels[-x] == 0: del self._levels[-x] self.fire(self) def update(self, t, x): if x is not None and (self._x is None or x > self._x): if -x not in self._levels: self._levels[-x] = 0 self._levels[-x] += 1 self._scheduler.scheduleAfter(self.timeframe, _(self, x)._remove) self._x = x self.fire(self)

[ "anton.kolotaev@gmail.com" ]

anton.kolotaev@gmail.com

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/fip_collab/2015_02_19_strain_stress_vtk/old_version/Calculate_Stress_from_Strain_ORIGINAL.py

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[]

no_license

earthexploration/MKS-Experimentation

92a2aea83e041bfe741048d662d28ff593077551

9b9ff3b468767b235e7c4884b0ed56c127328a5f

refs/heads/master

2023-03-17T23:11:11.313693

2017-04-24T19:24:35

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# # Calculate_Stress_from_Strain_MKS.py # # Written by Matthew Priddy on February 19, 2015 # from sys import * from string import * from math import * from pylab import * from random import * from numpy import * import itertools from numpy import tensordot as td import matplotlib #matplotlib.use('PDF') import linecache import time from matplotlib import pyplot as plt from scipy import optimize def Gmatrix(phi1, phi0, phi2): g = zeros((3,3)) g[0,0] = ( cos(phi1) * cos(phi2) ) - ( sin(phi1) * sin(phi2) * cos(phi0) ) g[0,1] = ( sin(phi1) * cos(phi2) ) + ( cos(phi1) * sin(phi2) * cos(phi0) ) g[0,2] = ( sin(phi2) * sin(phi0) ) g[1,0] = -( cos(phi1) * sin(phi2) ) - ( sin(phi1) * cos(phi2) * cos(phi0) ) g[1,1] = -( sin(phi1) * sin(phi2) ) + ( cos(phi1) * cos(phi2) * cos(phi0) ) g[1,2] = ( cos(phi2) * sin(phi0) ) g[2,0] = ( sin(phi1) * sin(phi0) ) g[2,1] = -( cos(phi1) * sin(phi0) ) g[2,2] = ( cos(phi0) ) return g def Bmatrix(p00, p11, p22, p01, p02, p12): B = zeros((3,3)) B[0,0] = p00 B[0,1] = p01 B[0,2] = p02 B[1,0] = p01 B[1,1] = p11 B[1,2] = p12 B[2,0] = p02 B[2,1] = p12 B[2,2] = p22 return B def Cijkl_dot_dot_Skl(C,S): # Vij = Cijkl*Skl # Technically written as Vij = Cijkl*Slk, but Skl is symmetric in this work value = zeros((3,3)) for i in range(3): for j in range(3): for k in range(3): for l in range(3): value[i,j] = value[i,j] + C[i,j,k,l] * S[k,l] return value def calc_Cijkl_from_Cij(Cij): ## ij or kl: 11 22 33 23 31 12 32 13 21 ## m or n : 1 2 3 4 5 6 7 8 9 # Theory of dislocations, pg. 34. Cijkl = zeros((3,3,3,3)) ia = 0 ib = 0 for i in range(3): for j in range(3): ia = i if (i != j): ia = 6-i-j for k in range(3): for l in range(3): ib = k if (k != l): ib = 6-k-l Cijkl[i,j,k,l] = Cij[ia,ib] return Cijkl elements = 21*21*21 # (1c) Extract the material constants from the .inp files # This should be automated, but for now we can hard code the input parameters C11 = 172832.50 C12 = 97910.060 C13 = 73432.550 C33 = 192308.10 C44 = 49700.000 C66 = 0.5 * (C11 - C12) shear_mod = (C44 * C66) ** 0.5 # For HCP crystal structures (e.g. Titanium) Cij = zeros((6,6)) Cij[0,0] = C11; Cij[1,1] = C11 Cij[0,1] = C12; Cij[1,0] = C12 Cij[0,2] = C13; Cij[1,2] = C13; Cij[2,0] = C13; Cij[2,1] = C13 Cij[2,2] = C33 Cij[3,3] = C44; Cij[4,4] = C44 Cij[5,5] = C66 # Determine the 3x3x3x3 Stiffness matrix Cijkl = calc_Cijkl_from_Cij(Cij) # (1) Extract various values for use in this code # (a) Extract the Euler angles for each element euler_file = open(f6_EulerAngles,'r') file_contents = euler_file.readlines() euler = zeros((elements, 3)) for i in range(1+2,elements+3): data1 = linecache.getline(f6_EulerAngles,i,module_globals=None) data1 = data1.split() euler[i-3,0] = float(data1[1]) euler[i-3,1] = float(data1[2]) euler[i-3,2] = float(data1[3]) # Total Strain NumCycles = 3 strn_t00_el = zeros((elements, 2*NumCycles + 1)); strn_t11_el = zeros((elements, 2*NumCycles + 1)); strn_t22_el = zeros((elements, 2*NumCycles + 1)) for i in range(0,elements): R = Gmatrix(euler[i,0], euler[i,1], euler[i,2]).T for count in range(0,2 * NumCycles): strain_0 = Bmatrix(strn_t00_el[i,count], strn_t11_el[i,count], strn_t22_el[i,count], strn_t01_el[i,count], strn_t02_el[i,count], strn_t12_el[i,count]) stress_0_from_strain = Cijkl_dot_dot_Skl(rotT(R.T, Cijkl),strain_0)

[ "noahhpaulson@gmail.com" ]

noahhpaulson@gmail.com

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/math/0x00-linear_algebra/2-size_me_please.py

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AndrewKalil/holbertonschool-machine_learning

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2023-07-09T04:09:24.271069

2021-08-11T02:29:54

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#!/usr/bin/env python3 """ 0x00. Linear Algebra """ def matrix_shape(matrix): """ calculates shape of a matrix """ if matrix: shape = [len(matrix)] while type(matrix[0]) == list: shape.append(len(matrix[0])) matrix = matrix[0] return shape else: return [0]

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1541@holbertonschool.com

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no_license

Aasthaengg/IBMdataset

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2023-04-22T10:22:44.763102

2021-05-13T17:27:22

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l=raw_input() k=l.split() a=0.0 b=0.0 a=int(k[0]) b=int(k[1]) # print int((a-(a%b))/b), print int(a%b), a*=1.0 b*=1.0 if b==1: print a*1.0 else: if a/b>0.0000002: print a/b else: print "0.00000001.99999982"

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66529651+Aastha2104@users.noreply.github.com

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/Brelaz/rough_calculations/dir1_reinforcement.py

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no_license

anaiortega/XCmodels

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2023-08-16T22:44:01.168775

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# -*- coding: utf-8 -*- from rough_calculations import ng_cantilever from rough_calculations import ng_simple_bending_reinforcement from matplotlib import pyplot from mpl_toolkits.mplot3d import Axes3D from materials.sia262 import SIA262_limit_state_checking beam= ng_cantilever.Cantilever() beam.l= 2.45+0.5 #Loads Qa= -2*135e3/2.5 #N/m qa= -0.25*2500*9.81-2.35e3-8.1e3 # N/m2 Qd= 1.5*Qa # N qd= 1.35*qa # N/m Md= beam.getBendingMomentUnderUniformLoad(qd,0.0)+beam.getBendingMomentUnderConcentratedLoad(Qd,1.0-0.2,0.0) Ma= beam.getBendingMomentUnderUniformLoad(qa,0.0)+beam.getBendingMomentUnderConcentratedLoad(Qa,2.45-0.2,0.0) MdMax= min(Md,Ma) print 'Md= ', Md/1e3, ' kN m/m Ma= ', Ma/1e3, 'kN m/m MdMax= ', MdMax/1e3, ' kN m/m' Vd= beam.getShearUnderUniformLoad(qd,0.25)+beam.getShearUnderConcentratedLoad(Qd,1.0-0.2,0.25) Va= beam.getShearUnderUniformLoad(qa,0.25)+beam.getShearUnderConcentratedLoad(Qa,2.45-0.2,0.25) MVRd= beam.getBendingMomentUnderUniformLoad(qd,0.25)+beam.getBendingMomentUnderConcentratedLoad(Qd,1.0-0.2,0.25) VdMax= max(Vd,Va) print 'Vd= ', Vd/1e3, ' kN/m MVRd= ', MVRd/1e3, ' kN m/m Va= ', Va/1e3, 'kN/m VdMax= ', VdMax/1e3, ' kN/m' #Reinforcement from materials.sia262 import SIA262_materials concrete= SIA262_materials.c50_60 reinfSteel= SIA262_materials.B500A d= 0.25-0.035-20e-3/2.0 As= ng_simple_bending_reinforcement.AsSimpleBending(-MdMax,-concrete.fcd(),reinfSteel.fyd(),1.0,d) print 'As= ', As*1e6, ' mm2' VRd= SIA262_limit_state_checking.VuNoShearRebars(concrete,reinfSteel,0.0,-MVRd,As,2.5/2.0,d) print 'VRd= ', VRd/1e3, ' kN VdMax= ', VdMax/1e3, ' kN' #Reinforcement 2 Md2= beam.getBendingMomentUnderUniformLoad(qd,0.0)+beam.getBendingMomentUnderConcentratedLoad(Qd,1.0-0.2,1.2) Ma2= beam.getBendingMomentUnderUniformLoad(qa,0.0)+beam.getBendingMomentUnderConcentratedLoad(Qa,2.45-0.2,1.2) MdMax2= min(Md2,Ma2) print 'Md2= ', Md2/1e3, ' kN m/m Ma2= ', Ma2/1e3, 'kN m/m MdMax2= ', MdMax2/1e3, ' kN m/m' As2= ng_simple_bending_reinforcement.AsSimpleBending(-MdMax2,-concrete.fcd(),reinfSteel.fyd(),1.0,d) print 'As2= ', As2*1e6, ' mm2' #Fatigue Mf= beam.getBendingMomentUnderConcentratedLoad(Qa,0.5,0.0) print 'Mf= ', Mf/1e3, ' kN m/m'

[ "ana.Ortega.Ort@gmail.com" ]

ana.Ortega.Ort@gmail.com

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/318.maximum-product-of-word-lengths/maximum-product-of-word-lengths.py

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[]

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JaniceLC/lc-all-solutions

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class Solution(object): def maxProduct(self, words): """ :type words: List[str] :rtype: int """ bitmap = [0] * len(words) mask = 0x01 ans = 0 for i in xrange(0, len(words)): word = words[i] for c in word: bitmap[i] |= (mask << (ord(c) - ord('a'))) for i in xrange(0, len(words)): for j in xrange(0, i): if bitmap[i] & bitmap[j] == 0: ans = max(ans, len(words[i]) * len(words[j])) return ans

[ "jedihy@yis-macbook-pro.local" ]

jedihy@yis-macbook-pro.local

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/pytools/utilities/python/readonly_shelf.py

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[]

no_license

stefie10/slu_hri

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2022-12-14T01:07:51.522258

2020-08-31T00:50:12

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import shelve def open(filename, writeback=True): return DbfilenameReadonlyShelf(filename, writeback=writeback) class DbfilenameReadonlyShelf(shelve.Shelf): """ Shelf implementation using the "anydbm" generic dbm interface, read only. Gets rid of annoying error message on shutdown when it tries to write back. """ def __init__(self, filename, writeback): import anydbm shelve.Shelf.__init__(self, anydbm.open(filename, flag='r'), protocol=2, writeback=writeback) def __del__(self): self.dict.close()

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stefie10@alum.mit.edu

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[]

no_license

miaojiang1987/LeetCode

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refs/heads/master

2021-06-09T14:46:43.858955

2021-05-20T09:06:24

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class Solution(object): def findPeakElement(self, nums): """ :type nums: List[int] :rtype: int """ if not nums: return None l,r=0,len(nums)-1 while l<r: mid=l+(r-l)//2 if nums[mid]>nums[mid+1]: r=mid else: l=mid+1 return l

[ "miaojiang1987@gmail.com" ]

miaojiang1987@gmail.com

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/plot_perturbation_analysis.py

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rmodi6/sentence-representations

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2023-04-09T23:10:11.873093

2023-03-26T18:55:40

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import os import json import copy # external libs imports import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt import numpy as np import tensorflow as tf # project imports from data import load_vocabulary, index_instances, generate_batches from util import load_pretrained_model if __name__ == '__main__': training_commands = [] choices = {"dan": range(1, 4+1), "gru": range(1, 4+1)} models = {"dan": None, "gru": None} vocabs = {"dan": None, "gru": None} for seq2vec_name, _ in choices.items(): serialization_dir = os.path.join("serialization_dirs", f"main_{seq2vec_name}_5k_with_emb") vocab_path = os.path.join(serialization_dir, "vocab.txt") config_path = os.path.join(serialization_dir, "config.json") weights_path = os.path.join(serialization_dir, "model.ckpt.index") model_files_present = all([os.path.exists(path) for path in [vocab_path, config_path, weights_path]]) if not model_files_present: epochs = 8 if seq2vec_name == "dan" else 4 # gru is slow, use only 4 epochs training_command = (f"python train.py main " f"data/imdb_sentiment_train_5k.jsonl " f"data/imdb_sentiment_dev.jsonl " f"--seq2vec-choice {seq2vec_name} " f"--embedding-dim 50 " f"--num-layers 4 " f"--num-epochs {epochs} " f"--suffix-name _{seq2vec_name}_5k_with_emb " f"--pretrained-embedding-file data/glove.6B.50d.txt ") training_commands.append(training_command) continue model = load_pretrained_model(serialization_dir) models[seq2vec_name] = model vocab, _ = load_vocabulary(vocab_path) vocabs[seq2vec_name] = vocab if training_commands: print("\nFirst, please finish the missing model training using the following commands:") print("\n".join(training_commands)) exit() original_instance = {"text_tokens": "the film performances were awesome".split()} updates = ["worst", "okay", "cool"] updated_instances = [] for update in updates: updated_instance = copy.deepcopy(original_instance) updated_instance["text_tokens"][4] = update updated_instances.append(updated_instance) all_instances = [original_instance]+updated_instances layer_representations = {} for seq2vec_name in choices.keys(): model = models[seq2vec_name] vocab = vocabs[seq2vec_name] all_indexed_instances = index_instances(copy.deepcopy(all_instances), vocab) batches = generate_batches(all_indexed_instances, 4) layer_representations[seq2vec_name] = model(**batches[0], training=False)["layer_representations"] for seq2vec_name, representations in layer_representations.items(): representations = np.asarray(representations) differences_across_layers = {"worst": [], "okay": [], "cool": []} for layer_num in choices[seq2vec_name]: original_representation = representations[0, layer_num-1, :] updated_representations = representations[1:, layer_num-1,:] differences = [sum(np.abs(original_representation-updated_representation)) for updated_representation in updated_representations] differences_across_layers["worst"].append(float(differences[0])) differences_across_layers["okay"].append(float(differences[1])) differences_across_layers["cool"].append(float(differences[2])) # Make the plots plt.style.use('seaborn-whitegrid') plt.plot(choices[seq2vec_name], differences_across_layers["worst"], label="worst") plt.plot(choices[seq2vec_name], differences_across_layers["okay"], label="okay") plt.plot(choices[seq2vec_name], differences_across_layers["cool"], label="cool") plt.xlabel("Layer") plt.ylabel("Perturbation Response") plt.legend() title = f"{seq2vec_name}: Perturbation Response vs Layer" plt.title(title) plt.savefig(os.path.join("plots", f"perturbation_response_{seq2vec_name}.png")) plt.clf()

[ "modi.ruchit6@gmail.com" ]

modi.ruchit6@gmail.com

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = "ipetrash" import requests city = "Магнитогорск" # # OR: # city = 'Magnitogorsk' url = ( "https://query.yahooapis.com/v1/public/yql?q=select item from weather.forecast where woeid in " "(select woeid from geo.places(1) where text='{city}') and u='c'" "&format=json&diagnostics=true".format(city=city) ) rs = requests.get(url) item = rs.json()["query"]["results"]["channel"]["item"] # Если нужна иконка для погоды: # https://developer.yahoo.com/weather/documentation.html in Condition Codes # code = condition['code'] # # Weather image: http://l.yimg.com/a/i/us/we/52/' + code + '.gif # Example: http://l.yimg.com/a/i/us/we/52/26.gif # condition = item["condition"] print("Current: {temp} °C, {text}".format(**condition)) print() print("Forecast:") for forecast in item["forecast"]: print("{date}: {low} - {high} °C. {day}: {text}".format(**forecast))

[ "ilya.petrash@inbox.ru" ]

ilya.petrash@inbox.ru

75769b77e4341042b1aebeac586a2b46403aecd8

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/app_user_login/migrations/0002_users_birthdate.py

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raqueloropeza/Django_LoginAndRegistration

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refs/heads/master

2023-05-07T19:37:00.592985

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# Generated by Django 2.2.4 on 2021-02-25 15:50 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app_user_login', '0001_initial'), ] operations = [ migrations.AddField( model_name='users', name='birthdate', field=models.DateField(default=1976), preserve_default=False, ), ]

[ "rockuzaki@gmail.com" ]

rockuzaki@gmail.com

611d4762679b0fa7bc528a0bf1515549c0bb6062

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/chapter10_multThreading/thread_condition.py

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[]

no_license

beenlyons/python_supreme

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3279aa8db52c7c8a1295f24a39d228df7f77ce43

refs/heads/master

2020-03-21T17:30:37.150525

2018-07-24T03:54:46

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from threading import Condition, Lock import threading # 条件变量, 用于复杂的线程间同步 # class XiaoAi(threading.Thread): # def __init__(self, lock): # self.lock = lock # super().__init__(name="xiaoAi") # def run(self): # self.lock.acquire() # print("{} : 在".format(self.name)) # self.lock.release() # # self.lock.acquire() # print("{} : 好啊".format(self.name)) # self.lock.release() # # class TianMao(threading.Thread): # def __init__(self, lock): # self.lock = lock # super().__init__(name="tianMao") # def run(self): # self.lock.acquire() # print("{} : 小爱同学".format(self.name)) # self.lock.release() # # self.lock.acquire() # print("{} : 我们来对古诗吧".format(self.name)) # self.lock.release() class XiaoAi(threading.Thread): def __init__(self, cond): self.cond = cond super().__init__(name="xiaoAi") def run(self): with self.cond: self.cond.wait() print("{} : 在".format(self.name)) self.cond.notify() print("{} : 好啊".format(self.name)) class TianMao(threading.Thread): def __init__(self, cond): self.cond = cond super().__init__(name="tianMao") def run(self): with self.cond: print("{} : 小爱同学".format(self.name)) self.cond.notify() self.cond.wait() print("{} : 我们来对古诗吧".format(self.name)) self.cond.release() if __name__ == '__main__': cond = Condition() xiaoai = XiaoAi(cond) tianmao = TianMao(cond) xiaoai.start() tianmao.start() # 启动顺序很重要, wait要先启动 # wait和notify一定要在with condition之后才能调用 # condition有两把锁, 底层锁会在wait的时候释放,上面的锁,会在每次调用wait时候分配一把 # 并放入到condition的等待队列中,等待notify方法的唤醒

[ "1335682725@qq.com" ]

1335682725@qq.com

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/data/CodeChef/HAND01.py

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[]

no_license

Ritvik19/CodeBook

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refs/heads/master

2021-07-04T08:25:52.478719

2020-08-08T06:54:14

138,744,302

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# Credits: Sir Isaac Newton is standing over a circle of radius 0.5641895835477563. # Input # The first line of the input contains an integer T denoting the number of test cases. The next T lines contain a string containing numbers and one or more special charters (?, !, %, $) # Output # For each test case, output a single line containing a single real number. # Constraints # Should contain all the constraints on the input data that you may have. Format it like: # 1 ≤ T ≤ 100 # Example # Input: # 2 # 3!2?1 # 5%2$2 # Output: # 5 # 6.0 for t in range (int(input())): str1=input() a=len(str1) b="" for i in range (a): if(str1[i]=='!'): b=b+'*' elif(str1[i]=='?'): b=b+'-' elif(str1[i]=='%'): b=b+'+' elif(str1[i]=='$'): b=b+'/' else: b=b+str1[i] print(eval(b))

[ "rastogiritvik99@gmail.com" ]

rastogiritvik99@gmail.com

2e4f86b82f95b90c80d1725615e2996fdb2cb164

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/not_done/py_not_started/euler_392.py

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[]

no_license

saetar/pyEuler

3a021f95a1856775bef87b38c753049b04282b80

f0af7092e16c2109028b4b1aa5bed7a0057d3fe9

refs/heads/master

2020-03-21T12:05:15.430454

2018-06-15T03:50:50

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2018-06-25T02:40:43

2018-06-25T02:40:42

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#!/usr/bin/env python # -*- coding: utf-8 -*- # ~ Jesse Rubin ~ project Euler ~ """ Enmeshed unit circle http://projecteuler.net/problem=392 A rectilinear grid is an orthogonal grid where the spacing between the gridlines does not have to be equidistant. An example of such grid is logarithmic graph paper. Consider rectilinear grids in the Cartesian coordinate system with the following properties:The gridlines are parallel to the axes of the Cartesian coordinate system.There are N+2 vertical and N+2 horizontal gridlines. Hence there are (N+1) x (N+1) rectangular cells.The equations of the two outer vertical gridlines are x = -1 and x = 1.The equations of the two outer horizontal gridlines are y = -1 and y = 1.The grid cells are colored red if they overlap with the unit circle, black otherwise.For this problem we would like you to find the positions of the remaining N inner horizontal and N inner vertical gridlines so that the area occupied by the red cells is minimized. E.g. here is a picture of the solution for N = 10: The area occupied by the red cells for N = 10 rounded to 10 digits behind the decimal point is 3.3469640797. Find the positions for N = 400. Give as your answer the area occupied by the red cells rounded to 10 digits behind the decimal point. """ def p392(): pass if __name__ == '__main__': p392()

[ "jessekrubin@gmail.com" ]

jessekrubin@gmail.com

8e559598071a54f18e8b5b49476b09087984d51e

dfe4d9f6b09109b22e76069b0f6e56993165e91d

/camp-python-2021-find-me-develop/apps/users/apps.py

00812dc9a1174718ab09f30466d4af9874f10952

[]

no_license

rhanmar/oi_projects_summer_2021

ef6b06e003a1c54583df4b006d141df924adeafb

0879ade24685b628624dce06698f8a0afd042000

refs/heads/main

2023-08-25T04:02:46.284756

2021-09-17T03:29:16

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null

UTF-8

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py

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ from watson import search as watson class UsersAppConfig(AppConfig): """Default configuration for Users app.""" name = "apps.users" verbose_name = _("Users") def ready(self): # pylint: disable=unused-import,invalid-name from .api.auth import scheme # noqa from .signals import check_avatar, copy_default_avatar_to_media # noqa User = self.get_model("User") watson.register( User, fields=("first_name", "last_name", "email"), )

[ "dimontura@yandex.ru" ]

dimontura@yandex.ru

85a51ad7dc8750ab6d9a66bec400b2433c202821

368be25e37bafa8cc795f7c9f34e4585e017091f

/.history/app_fav_books/views_20201114172332.py

ae26a428bcd7b78a9862ed83d5fbd6fd90c986e0

[]

no_license

steven-halla/fav_books_proj

ebcfbfda0e7f3cdc49d592c86c633b1d331da513

512005deb84ac906c9f24d4ab0939bd0db096716

refs/heads/master

2023-03-30T09:37:38.016063

2021-04-02T20:27:22

354,125,658

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UTF-8

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from django.shortcuts import render, redirect from .models import * from django.contrib import messages def index(request): return render(request, "index.html") def register_New_User(request): errors = User.objects.basic_validator(request.POST) if len(errors) > 0: for key, value in errors.items(): messages.error(request, value) return redirect("/") else: first_name_from_post = request.POST['first_name'] last_name_from_post = request.POST['last_name'] email_from_post = request.POST['email'] password_from_post = request.POST['password'] new_user = User.objects.create( first_name=first_name_from_post, last_name=last_name_from_post, email=email_from_post, password=password_from_post ) print(new_user.id) request.session['user_id'] = new_user.id return redirect('/register/success') def books_info(request): user = User.objects.get(id=request.session['user_id']) context = { "user":user } return render(request, "books_user_page.html", context) def log_user_out(request): request.session.clear() return redirect("/") def log_in(request): email_from_post = request.POST['email'] password_from_post = request.POST['password'] user = User.objects.filter(email=email_from_post) if len(user) > 0: logged_user = user[0] print(logged_user.first_name) print(logged_user.password) request.session['user_id'] = logged_user.id return redirect("/register/success") else: messages.error(request, "email/password does not exist") return redirect("register/success") def add_new_book(request): errors = Books.objects.basic_validator(request.POST)

[ "69405488+steven-halla@users.noreply.github.com" ]

69405488+steven-halla@users.noreply.github.com

47e588e7c429cf9a9c1d10db3d2ef25f983ed712

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/tce/tcloud/cvm/AssociateSecurityGroups.py

11d8e5ca52a09b8e0e312396bb00ee9dfb7bb036

[ "Apache-2.0" ]

permissive

FatAnker/tencentcloud-sdk-python

d8f757b12ad336e78a06b68a789ecc3c86d1d331

d6f75a41dc7053cb51f9091f4d41b8cb7a837559

refs/heads/master

2020-04-30T22:34:16.740484

2019-04-28T11:14:11

177,122,691

0

1

null

2019-03-22T10:46:01

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UTF-8

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py

# -*- coding: utf-8 -*- import os from tencentcloud.common import credential from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException # 导入对应产品模块的client models。 from tencentcloud.cvm.v20170312 import cvm_client, models import json # 导入可选配置类 from tencentcloud.common.profile.client_profile import ClientProfile from tencentcloud.common.profile.http_profile import HttpProfile import ssl from tce.tcloud.utils.config import global_config ssl._create_default_https_context = ssl._create_unverified_context region = global_config.get('regions') params = global_config.get(region) secretId = params['secretId'] secretKey = params['secretKey'] domain = params['domain'] try: # 实例化一个认证对象，入参需要传入腾讯云账户secretId，secretKey cred = credential.Credential(secretId, secretKey) httpProfile = HttpProfile() httpProfile.endpoint = "cvm."+domain clientProfile = ClientProfile() clientProfile.httpProfile = httpProfile # 实例化要请求产品(以cvm为例)的client对象，clientProfile是可选的。 client = cvm_client.CvmClient(cred, region, clientProfile) # 实例化一个cvm实例信息查询请求对象,每个接口都会对应一个request对象。 req = models.AssociateSecurityGroupsRequest() # 这里还支持以标准json格式的string来赋值请求参数的方式。下面的代码跟上面的参数赋值是等效的。 params = '{"InstanceIds":["ins-i4ekkudx","ins-gwggvy39"],"SecurityGroupIds":["sg-poxp7nok"]}' req.from_json_string(params) resp = client.AssociateSecurityGroups(req) # 输出json格式的字符串回包 print(resp.to_json_string()) # 也可以取出单个值。 # 你可以通过官网接口文档或跳转到response对象的定义处查看返回字段的定义。 # print(resp.TotalCount) except TencentCloudSDKException as err: print(err)

[ "1113452717@qq.com" ]

1113452717@qq.com

5fa589d9a038fcbb13e019a6129a02a94b582d64

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/solutions_2751486_0/Python/axr123/a.py

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[]

no_license

alexandraback/datacollection

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076a7bc7693f3abf07bfdbdac838cb4ef65ccfcf

refs/heads/master

2021-01-24T18:27:24.417992

2017-05-23T09:23:38

84,313,442

2

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import sys vowels = 'aeiou' def concons(str): maxN = 0 n = 0 for l in str: if l in vowels: if n > maxN: maxN = n n = 0 else: n += 1 if n > maxN: maxN = n return maxN def solve(name, n): count = 0 for s in range(len(name)): for e in range(s+1, len(name)+1): if concons(name[s:e]) >= n: count += 1 return count numcases = int(sys.stdin.readline()) for c in range(numcases): name, n = sys.stdin.readline().split() print("Case #%d: %d" % (c+1, solve(name, int(n))))

[ "eewestman@gmail.com" ]

eewestman@gmail.com

31be72f47f8097ba9df421e724f210ac08707884

602a4e86499841fbae43d84fc92908c533106aea

/core/forms.py

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[]

no_license

vden/TsoguNG

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f8d5e7ab9d85559aa163c232c9f28a24a2b7c2a4

refs/heads/master

2021-01-02T08:52:03.914218

2011-04-26T07:01:57

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UTF-8

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# -*- coding: utf-8 -*- from django import forms from core.fields import CalendarDateField from django.contrib.admin import widgets as admin_widgets from core import models from datetime import datetime def extra_form_factory(type): class BaseExtraForm(forms.ModelForm): time_choices = [('None',u'не задано')] + [('%s'%x,'%2d:00'%x) for x in xrange(0,25)] slug = forms.SlugField(label=u'Адрес', required=False) date_published = CalendarDateField(label=u'Дата публикации', required=False) time = forms.ChoiceField(label = u'Время публикации', required=False, choices=time_choices) view_template = forms.ModelChoiceField(label=u"Шаблон",queryset=type.available_templates(),empty_label="(стандартный шаблон)",required=False) def __init__(self, *args, **kw): try: kw['initial'] = {'time':str(kw['instance'].date_published.hour)} except: kw['initial'] = {'time':'None'} super(BaseExtraForm, self).__init__(*args, **kw) def save(self): s = super(BaseExtraForm, self).save() if self['time'].data != 'None': d = s.date_published s.date_published = datetime(d.year, d.month, d.day, int(self['time'].data)) s.save() class Meta: model = type fields = ('slug','view_template','not_browse','block','date_published') return BaseExtraForm class BaseConfigletForm(forms.ModelForm): bid = models.Configlet._meta.get_field("bid").formfield(widget=forms.HiddenInput()) value = forms.CharField(label = u'Значение') remove = forms.CharField(label = u'Удалить') class Meta: model = models.Configlet fields = ('predicate','value','bid') def is_valid(self): if self['predicate'].data: return True return False def save(self): conf = models.Configlet.objects.filter(bid = self['bid'].data, predicate = self['predicate'].data) if conf: conf = conf[0] if str(self['remove'].data) == 'True': conf.delete() return True else: conf = models.Configlet() conf.predicate = self['predicate'].data conf.value = self['value'].data conf.bid = models.BaseObject.objects.get(id=self['bid'].data) conf.save()

[ "denis.voskvitsov@gmail.com" ]

denis.voskvitsov@gmail.com

3571215df13d920412f8c5912d86fde82b88a82e

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/src/comments/models.py

cc5f4cbe71dfee3814480f8a9c14549170dde5f7

[]

no_license

tyagow/servicos-paraguai

b2c20a48651e7e046f4e86b0e94f026589dbc545

a71078174a175f86f2a4f49fcaf26b8f91ea778d

refs/heads/master

2020-12-24T05:39:57.243158

2017-05-23T14:14:28

73,492,227

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UTF-8

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py

from django.contrib.contenttypes.fields import GenericForeignKey from django.contrib.contenttypes.models import ContentType from django.db import models class CommentManager(models.Manager): def filter_by_instance(self, instance): content_type = ContentType.objects.get_for_model(instance.__class__) obj_id = instance.id qs = super(CommentManager, self).filter(content_type=content_type, object_id=obj_id).filter(aprovado=True) return qs class Comment(models.Model): nome = models.CharField(max_length=60) content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE) object_id = models.PositiveIntegerField() content_object = GenericForeignKey('content_type', 'object_id') conteudo = models.TextField() timestamp = models.DateTimeField(auto_now_add=True) aprovado = models.BooleanField(default=False) objects = CommentManager() def __str__(self): return self.nome

[ "tyagow@hotmail.com.br" ]

tyagow@hotmail.com.br

c12d8a3717648e007e9abc3dc9d63a711c4d0582

b864232c0133738e329e61ca74188c8eafe74108

/misc/experiment/distortion/main.py

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[]

no_license

QilinGu/tf-face-recognizer

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d1092b72d01f08a7bbfb2f30072a60b8d8409804

refs/heads/master

2021-01-09T06:40:30.678375

2017-02-03T17:57:21

null

0

null

UTF-8

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false

1,419

py

import os import tensorflow as tf def main(argv=None): with open(os.path.join(os.path.dirname(__file__), 'face.png'), 'rb') as f: png = f.read() image = tf.image.decode_png(png, channels=3) begin, size, _ = tf.image.sample_distorted_bounding_box( tf.shape(image), [[[8.0/112.0, 8.0/112.0, (112.0-8.0)/112.0, (112.0-8.0)/112.0]]], min_object_covered=0.9) image = tf.slice(image, begin, size) resize = tf.random_uniform([2], minval=48, maxval=144, dtype=tf.int32) image = tf.image.resize_images(image, resize, method=2) image = tf.image.resize_images(image, [96, 96], method=2) image = tf.image.random_brightness(image, max_delta=0.4) image = tf.image.random_contrast(image, lower=0.6, upper=1.4) image = tf.image.random_hue(image, max_delta=0.04) image = tf.image.random_saturation(image, lower=0.6, upper=1.4) images = tf.train.batch([tf.image.per_image_standardization(image)], 20) summary = tf.summary.image('images', images, max_outputs=20) writer = tf.summary.FileWriter(os.path.dirname(__file__)) with tf.Session() as sess: tf.train.start_queue_runners(sess=sess) summary_value, begin_value, size_value, resize_value = sess.run([summary, begin, size, resize]) print(begin_value, size_value, resize_value) writer.add_summary(summary_value) if __name__ == '__main__': tf.app.run()

[ "sugi1982@gmail.com" ]

sugi1982@gmail.com

8cfa28a1b18e8ac19215d87eebf5216525b2160e

25fa5fdc9f67738332bd6f95a1e4f038cd286890

/BOJ/단계별로 풀어보기/ch07_문자열/1152_단어의 개수.py

0e051399bc66d888bae95cbbf9345f6aa8eeb91a

[]

no_license

mandos1995/online_judge

b0cfd56e3391495f22b9832895cddcea70334349

9b90bffdcbfb5369e8dd5dafbb07f8e9e7050617

refs/heads/main

2023-08-02T19:29:03.716295

2021-10-04T15:10:34

329,517,747

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''' 문제 영어 대소문자와 띄어쓰기만으로 이루어진 문자열이 주어진다. 이 문자열에는 몇 개의 단어가 있을까? 이를 구하는 프로그램을 작성하시오. 단, 한 단어가 여러 번 등장하면 등장한 횟수만큼 모두 세어야 한다. ''' # solution string = list(map(str,input().strip().split())) print(len(string))

[ "mandos19950620@gmail.com" ]

mandos19950620@gmail.com

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/ROAR/planning_module/mission_planner/waypoint_following_mission_planner.py

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[ "Apache-2.0" ]

permissive

augcog/ROAR

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refs/heads/main

2023-08-31T16:51:00.857463

2023-08-12T00:08:06

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2023-08-26T07:04:26

2020-10-08T20:10:20

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from ROAR.planning_module.mission_planner.mission_planner import ( MissionPlanner, ) from pathlib import Path import logging from typing import List, Optional from ROAR.utilities_module.data_structures_models import Transform, Location, Rotation from collections import deque from ROAR.agent_module.agent import Agent import numpy as np class WaypointFollowingMissionPlanner(MissionPlanner): """ A mission planner that takes in a file that contains x,y,z coordinates, formulate into carla.Transform """ def run_in_series(self) -> deque: """ Regenerate waypoints from file Find the waypoint that is closest to the current vehicle location. return a mission plan starting from that waypoint Args: vehicle: current state of the vehicle Returns: mission plan that start from the current vehicle location """ super(WaypointFollowingMissionPlanner, self).run_in_series() return self.produce_mission_plan() def __init__(self, agent: Agent): super().__init__(agent=agent) self.logger = logging.getLogger(__name__) self.file_path: Path = Path(self.agent.agent_settings.waypoint_file_path) self.mission_plan = self.produce_mission_plan() self._mission_plan_backup = self.mission_plan.copy() self.logger.debug("Path Following Mission Planner Initiated.") def produce_mission_plan(self) -> deque: """ Generates a list of waypoints based on the input file path :return a list of waypoint """ raw_path: List[List[float]] = self._read_data_file() length = self.agent.agent_settings.num_laps * len(raw_path) mission_plan = deque(maxlen=length) for coord in np.tile(raw_path, (self.agent.agent_settings.num_laps, 1)): if len(coord) == 3 or len(coord) == 6: mission_plan.append(self._raw_coord_to_transform(coord)) self.logger.debug(f"Computed Mission path of length [{len(mission_plan)}]") return mission_plan def produce_single_lap_mission_plan(self): raw_path: List[List[float]] = self._read_data_file() mission_plan = deque(maxlen=len(raw_path)) for coord in raw_path: if len(coord) == 3 or len(coord) == 6: mission_plan.append(self._raw_coord_to_transform(coord)) self.logger.debug(f"Computed Mission path of length [{len(mission_plan)}]") return mission_plan def _read_data_file(self) -> List[List[float]]: """ Read data file and generate a list of (x, y, z) where each of x, y, z is of type float Returns: List of waypoints in format of [x, y, z] """ result = [] with open(self.file_path.as_posix(), "r") as f: for line in f: result.append(self._read_line(line=line)) return result def _raw_coord_to_transform(self, raw: List[float]) -> Optional[Transform]: """ transform coordinate to Transform instance Args: raw: coordinate in form of [x, y, z, pitch, yaw, roll] Returns: Transform instance """ if len(raw) == 3: return Transform( location=Location(x=raw[0], y=raw[1], z=raw[2]), rotation=Rotation(pitch=0, yaw=0, roll=0), ) elif len(raw) == 6: return Transform( location=Location(x=raw[0], y=raw[1], z=raw[2]), rotation=Rotation(roll=raw[3], pitch=raw[4], yaw=raw[5]), ) else: self.logger.error(f"Point {raw} is invalid, skipping") return None def _read_line(self, line: str) -> List[float]: """ parse a line of string of "x,y,z" into [x,y,z] Args: line: comma delimetered line Returns: [x, y, z] """ try: x, y, z = line.split(",") x, y, z = float(x), float(y), float(z) return [x, y, z] except: x, y, z, roll, pitch, yaw = line.split(",") return [float(x), float(y), float(z), float(roll), float(pitch), float(yaw)] def restart(self): self.mission_plan = self._mission_plan_backup.copy()

[ "wuxiaohua1011@berkeley.edu" ]

wuxiaohua1011@berkeley.edu

479a24ebea460d630afb100cbe446f2b98ea29c5

49819aef1336ddb8511e6dec53311777c0b41a7e

/apps/authentication/middleware.py

b2dd6834f09fcecbc03130d84405085fa4a502c7

[]

no_license

devmaster54/pslam

d91f80340a89820ee596068f13fe6a628a93aab6

e81316677a1db24bbedce70bf59f85d30583742d

refs/heads/master

2022-12-24T15:20:39.219597

2020-10-02T15:46:20

null

0

null

UTF-8

Python

false

211

py

import re from psalm import settings from django.contrib.auth.decorators import login_required class AuthVerificationMiddleware(object): def process_exception(self, request, exception): return None

[ "soft.expert32@gmail.com" ]

soft.expert32@gmail.com

b0a4d0f17558cbe4b82cc607cb5c64a7b2238ed4

ae381913c23385f004b82161624097645ba8c4c8

/Xianyang_modwt/projects/ensemble_models.py

505bd0dc6a0bcac8eb2a2a3565d5e738a7deca6b

[ "MIT" ]

permissive

zjy8006/MonthlyRunoffForecastByAutoReg

aa37910fdc66276d0df9d30af6885209d4a4ebfc

661fcb5dcdfbbb2ec6861e1668a035b50e69f7c2

refs/heads/master

2020-12-12T05:25:48.768993

2020-08-20T07:21:12

259,588,564

7

3

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false

1,102

py

import matplotlib.pyplot as plt import os root_path = os.path.dirname(os.path.abspath('__file__')) import sys sys.path.append(root_path) from tools.ensembler import ensemble from Xianyang_modwt.projects.variables import variables # Set the project parameters ORIGINAL = 'XianyangRunoff1951-2018(1953-2018).xlsx' STATION = 'Xianyang' DECOMPOSER = 'modwt' PREDICTOR = 'esvr' # esvr or gbrt or lstm wavelet_level='db1-4' # ensemble( # root_path=root_path, # original_series=ORIGINAL, # station=STATION, # decomposer=DECOMPOSER, # variables = variables, # predictor=PREDICTOR, # predict_pattern='single_hybrid_1_ahead', # wavelet_level=wavelet_level, # ) for lead_time in [1,3,5,7]: ensemble( root_path=root_path, original_series=ORIGINAL, station=STATION, decomposer=DECOMPOSER, variables = variables, predictor=PREDICTOR, predict_pattern='single_hybrid_'+str(lead_time)+'_ahead_lag12_mi_ts0.1', wavelet_level=wavelet_level, )

[ "zuojianyi@outlook.com" ]

zuojianyi@outlook.com

f024ccaadc2065d52a7c473fe13a3620d3f75af1

6d913683be43f459b6e29dd84f09c05234efeb4d

/single_cell_differentiation_cuomo_data/merge_parallelized_covariate_modulated_eqtls.py

3cea2a67cb603b0c7c42cf93e49bc86f36b4b564

[]

no_license

BennyStrobes/eqtl_factorization

4f94d8e2e00cf1830fd008f3264d1f9c57f6b2a0

e555485e40e44c51e86f67761e5200b370673910

refs/heads/master

2021-06-20T15:54:34.906395

2021-04-05T14:11:46

198,705,414

0

null

UTF-8

Python

false

4,186

py

import numpy as np import os import sys import pdb def bf_fdr_multiple_testing_correction(variant_gene_pairs_eqtl_results_file, multple_testing_correction_results_file, fdr_thresh): f = open(variant_gene_pairs_eqtl_results_file) t = open(multple_testing_correction_results_file, 'w') head_count = 0 genes = {} for line in f: line = line.rstrip() data = line.split() if head_count == 0: head_count = head_count + 1 t.write(line + '\tnum_snps_in_gene\tfdr\n') continue gene_id = data[0] variant_id = data[1] pvalue = float(data[7]) if gene_id not in genes: genes[gene_id] = (variant_id, pvalue, 1, line) else: old_pvalue = genes[gene_id][1] old_count = genes[gene_id][2] if pvalue <= old_pvalue: genes[gene_id] = (variant_id, pvalue, old_count+1, line) else: genes[gene_id] = (genes[gene_id][0], genes[gene_id][1], old_count+1, genes[gene_id][3]) f.close() # Loop through genes and do BF correction bf_gene_array = [] for gene in genes.keys(): lead_variant = genes[gene][0] lead_nominal_pvalue = genes[gene][1] num_variants_at_gene = genes[gene][2] test_line = genes[gene][3] bf_corrected_pvalue = lead_nominal_pvalue*num_variants_at_gene if bf_corrected_pvalue > 1.0: bf_corrected_pvalue = 1.0 bf_gene_array.append((bf_corrected_pvalue, lead_variant, gene, num_variants_at_gene, test_line)) sorted_bf_gene_array = sorted(bf_gene_array, key=lambda tup: tup[0]) # BH correction kk = 1 num_genes = len(sorted_bf_gene_array) sig = True for gene_tuple in sorted_bf_gene_array: bf_pvalue = gene_tuple[0] fdr = num_genes*bf_pvalue/kk kk = kk + 1 if fdr > fdr_thresh: sig = False if sig == True: t.write(gene_tuple[4] + '\t' + str(gene_tuple[3]) + '\t' + str(fdr) + '\n') t.close() def make_sure_files_exist(output_root, total_jobs, suffix): booly = True for job_number in range(total_jobs): file_name = output_root + str(job_number) + '_' + str(total_jobs) + suffix if os.path.isfile(file_name) == False: print(file_name) booly = False return booly def merge_parallelized_results(output_root, suffix, total_jobs): to_run = make_sure_files_exist(output_root, total_jobs, suffix) if to_run == False: print('Missing required input files. Please re-evaluate :)') return # Open output (merged result) file handle t = open(output_root + 'merged' + suffix, 'w') t.write('Gene_id\tvariant_id\tchrom_num\tgene_tss\tvariant_position\tmaf\tcell_maf\tcovariate_modulated_eqtl_pvalue\teqtl_pvalue\n') # Loop through parrallelized jobs for job_number in range(total_jobs): file_name = output_root + str(job_number) + '_' + str(total_jobs) + suffix # Open file for one job f = open(file_name) # To identify header head_count = 0 # Stream file from one job for line in f: line = line.rstrip() # Standard line t.write(line + '\n') f.close() # Delete file from single job os.system ('rm ' + file_name) t.close() output_root = sys.argv[1] total_jobs = int(sys.argv[2]) merge_parallelized_results(output_root, ".txt", total_jobs) merged_file = output_root + 'merged.txt' #################### # Multiple-testing correction #################### # Output file fdr_thresh=.01 multple_testing_correction_results_file = output_root + 'multiple_testing_bf_bh_' + str(fdr_thresh) + '_fdr_' + '.txt' # Perform bonferonni correction at variant level (per gene) and then BH at gene level bf_fdr_multiple_testing_correction(merged_file, multple_testing_correction_results_file, fdr_thresh) # Output file fdr_thresh=.05 multple_testing_correction_results_file = output_root + 'multiple_testing_bf_bh_' + str(fdr_thresh) + '_fdr_' + '.txt' # Perform bonferonni correction at variant level (per gene) and then BH at gene level bf_fdr_multiple_testing_correction(merged_file, multple_testing_correction_results_file, fdr_thresh) # Output file fdr_thresh=.1 multple_testing_correction_results_file = output_root + 'multiple_testing_bf_bh_' + str(fdr_thresh) + '_fdr_' + '.txt' # Perform bonferonni correction at variant level (per gene) and then BH at gene level bf_fdr_multiple_testing_correction(merged_file, multple_testing_correction_results_file, fdr_thresh)

[ "bstrobe1@jhu.edu" ]

bstrobe1@jhu.edu

3d522fc5eb54ef2e47e6a569e0ad572dc5690bf3

6c9d41994937733dc84e54359f5789ac945724a2

/echo.py

863cb8941b7dd620f6dacb81c28f14c916781f13

[ "MIT" ]

permissive

cider-load-test/pygadu

2268f38b4612cb32236c687ef355c6dc1d4cae33

d4c1b25908ae6facd89d3509ea8bc2ec6b4eb11c

refs/heads/master

2021-12-02T06:28:02.877479

2008-11-30T13:01:41

null

0

null

UTF-8

Python

false

1,196

py

import time from pygadu.session import PyGadu from pygadu.util import query_hub class Echo(PyGadu): def __init__(self): super(Echo, self).__init__() def onWelcome(self, packet): print repr(packet) print "Welcome!" def onLoginOk(self, packet): print repr(packet) print "Login Ok!" self.sendFriendList([]) def onConnect(self): print "Connect" def onClose(self): print "Close" def onSendMessage(self, packet): print repr(packet) print "on send message?" def onLoginError(self, packet): print repr(packet) print "Error!" def onRecvMessage(self, packet): print repr(packet) print packet.sender, packet.text self.sendMessage(packet.sender, packet.text) def onUnknown(self, packet): print repr(packet) if __name__ == '__main__': try: host = query_hub(10533767) print host gg = Echo() gg.login(10533767, "123456", host=host) while True: time.sleep(300) gg.ping() except KeyboardInterrupt: gg.logout()

[ "devnull@localhost" ]

devnull@localhost

68c366cf3fe05227f6edc4c1f969590059582f5e

e3fd35a8443aaf2f293ae03a5f6c819046a4dd21

/leetcode-java/leetcode.py

9eecc06a263da457fb4f2cbe087d8d5b6c42ee2f

[]

no_license

hieutran106/leetcode-ht

2223ea6bcd459c2cdbc33344c0ff69df7f8a3c7f

8332eb20e613f82cda2e326218154c7803a32403

refs/heads/main

2023-08-09T02:52:41.360360

2023-07-27T10:12:28

234,890,448

0

null

UTF-8

Python

false

971

py

import sys import os if __name__ == "__main__": difficulty = sys.argv[1] problem = sys.argv[2] cwd = os.getcwd() path = os.path.join(cwd, "src", "test", "java", difficulty, problem) # early exit if os.path.exists(path): print("Solution existed. Exit ...") sys.exit() print(f'Create {difficulty}/{problem}') os.mkdir(path) # create README.md file = os.path.join(path, 'README.md') open(file, 'a').close() # create SolutionTest.java file = os.path.join(path, 'SolutionTest.java') with open(file, 'w+') as f: test_content = f"""package {difficulty}.{problem}; import org.junit.Assert; import org.junit.Test; public class SolutionTest {{ public static class Solution {{ }} @Test public void testCase1() {{ var s = new Solution(); }} @Test public void testCase0() {{ var s = new Solution(); }} }}""" f.writelines(test_content)

[ "hieutran106@gmail.com" ]

hieutran106@gmail.com

7a5dcf48ff36cbc9f82e9329b3f7faec2ce88438

8eafb73fdab3e422aa717bac9af338dcba5e3c1e

/bbp/tests/test_gp_gof.py

bfb3309cb2b062ef6e9a0ed863563eb5f5da02ec

[ "Apache-2.0", "LicenseRef-scancode-warranty-disclaimer" ]

permissive

LevyForchh/bbp

6dae4ce3577a73f5cef9b9b5507753a1381ec870

3cc389fb956ea14ef827af0f437ce37e8291afac

refs/heads/master

2020-06-03T05:10:35.751009

2019-06-11T21:38:18

191,453,945

0

null

2019-06-11T21:38:16

2019-06-11T21:38:15

null

UTF-8

Python

false

7,314

py

#! /usr/bin/env python """ Copyright 2010-2019 University Of Southern California 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. """ from __future__ import division, print_function # Import Python modules import os import unittest # Import Broadband modules import cmp_bbp import seqnum import bband_utils from install_cfg import InstallCfg from gp_gof_cfg import GPGofCfg from gp_gof import GPGof class TestGPGof(unittest.TestCase): """ Unit test for gp_gof.py """ def setUp(self): self.install = InstallCfg() self.gp_gof_cfg = GPGofCfg() os.chdir(self.install.A_INSTALL_ROOT) self.srcfile = "test_wh.src" self.stations = "test_stat.txt" self.sim_id = int(seqnum.get_seq_num()) sta_base = os.path.basename(os.path.splitext(self.stations)[0]) # Set up paths refdir = os.path.join(self.install.A_TEST_REF_DIR, "gp") a_indir = os.path.join(self.install.A_IN_DATA_DIR, str(self.sim_id)) a_tmpdir = os.path.join(self.install.A_TMP_DATA_DIR, str(self.sim_id)) a_outdir_seis = os.path.join(self.install.A_OUT_DATA_DIR, str(self.sim_id), "obs_seis_%s" % (sta_base)) a_outdir = os.path.join(self.install.A_OUT_DATA_DIR, str(self.sim_id)) a_logdir = os.path.join(self.install.A_OUT_LOG_DIR, str(self.sim_id)) # Create directories bband_utils.mkdirs([a_indir, a_tmpdir, a_outdir_seis, a_outdir, a_logdir], print_cmd=False) # Copy stations cmd = "cp %s %s" % (os.path.join(refdir, self.stations), a_indir) bband_utils.runprog(cmd, print_cmd=False) # Copy src file cmd = "cp %s %s" % (os.path.join(refdir, self.srcfile), a_indir) bband_utils.runprog(cmd, print_cmd=False) for i in range(1, 6): # Copy sample calculated seismograms and response files cmd = "cp %s %s" % (os.path.join(refdir, "s%02d.merged.bbp" % (i)), os.path.join(a_outdir, "%d.s%02d.vel.bbp" % (self.sim_id, i))) bband_utils.runprog(cmd, print_cmd=False) cmd = "cp %s %s" % (os.path.join(refdir, "s%02d.rd50" % (i)), os.path.join(a_outdir, "%d.s%02d.rd50" % (self.sim_id, i))) bband_utils.runprog(cmd, print_cmd=False) # Cope sample observed seismograms and response files cmd = "cp %s %s" % (os.path.join(refdir, "s%02d.merged.bbp" % (i)), os.path.join(a_outdir_seis, "s%02d.bbp" % (i))) bband_utils.runprog(cmd, print_cmd=False) cmd = "cp %s %s" % (os.path.join(refdir, "s%02d.rd50" % (i)), os.path.join(a_outdir_seis, "s%02d.rd50" % (i))) bband_utils.runprog(cmd, print_cmd=False) def test_gof(self): """ Test GP GOF Code """ gof_obj = GPGof(self.srcfile, self.stations, "NR", 25, sim_id=self.sim_id) gof_obj.run() resid_ref_file = os.path.join(self.install.A_TEST_REF_DIR, "gp", "GoF", "nr-rd50-resid.txt") resid_file = os.path.join(self.install.A_OUT_DATA_DIR, str(self.sim_id), "NR-%d.rd50-resid.txt" % (self.sim_id)) self.failIf(cmp_bbp.cmp_resid(resid_ref_file, resid_file, tolerance=0.005) != 0, "output resid file %s does not match reference resid file %s" % (resid_file, resid_ref_file)) a_outdir = os.path.join(self.install.A_OUT_DATA_DIR, str(self.sim_id)) for comp in ['psa5e', 'psa5n', 'rotd50']: bias_ref_file = os.path.join(self.install.A_TEST_REF_DIR, "gp", "GoF", "nr_r0-25-rd50-%s.bias" % (comp)) m90_ref_file = os.path.join(self.install.A_TEST_REF_DIR, "gp", "GoF", "nr_r0-25-rd50-%s.m90" % (comp)) p90_ref_file = os.path.join(self.install.A_TEST_REF_DIR, "gp", "GoF", "nr_r0-25-rd50-%s.p90" % (comp)) sigma_ref_file = os.path.join(self.install.A_TEST_REF_DIR, "gp", "GoF", "nr_r0-25-rd50-%s.sigma" % (comp)) sigma0_ref_file = os.path.join(self.install.A_TEST_REF_DIR, "gp", "GoF", "nr_r0-25-rd50-%s.sigma0" % (comp)) bias_file = os.path.join(a_outdir, "NR-%d_r0-25-rd50-%s.bias" % (self.sim_id, comp)) m90_file = os.path.join(a_outdir, "NR-%d_r0-25-rd50-%s.m90" % (self.sim_id, comp)) p90_file = os.path.join(a_outdir, "NR-%d_r0-25-rd50-%s.p90" % (self.sim_id, comp)) sigma_file = os.path.join(a_outdir, "NR-%d_r0-25-rd50-%s.sigma" % (self.sim_id, comp)) sigma0_file = os.path.join(a_outdir, "NR-%d_r0-25-rd50-%s.sigma0" % (self.sim_id, comp)) self.failIf(cmp_bbp.cmp_bias(bias_ref_file, bias_file) != 0, "output bias file %s does not match reference bias file %s" % (bias_file, bias_ref_file)) self.failIf(cmp_bbp.cmp_bias(m90_ref_file, m90_file) != 0, "output m90 file %s does not match reference m90 file %s" % (m90_file, m90_ref_file)) self.failIf(cmp_bbp.cmp_bias(p90_ref_file, p90_file, tolerance=0.0025) != 0, "output p90 file %s does not match reference p90 file %s" % (p90_file, p90_ref_file)) self.failIf(cmp_bbp.cmp_bias(sigma_ref_file, sigma_file) != 0, "output sigma file %s does not match reference sigma file %s" % (sigma_file, sigma_ref_file)) self.failIf(cmp_bbp.cmp_bias(sigma0_ref_file, sigma0_file) != 0, "output sigma0 file %s does not match reference sigma0 file %s" % (sigma0_file, sigma0_ref_file)) if __name__ == '__main__': SUITE = unittest.TestLoader().loadTestsFromTestCase(TestGPGof) unittest.TextTestRunner(verbosity=2).run(SUITE)

[ "fsilva@usc.edu" ]

fsilva@usc.edu

b55110e630e67fe860d2037fa0094925bbf45e28

64a8dcfa0a98c665b8b2ac796ed7231135977d9a

/fluent_contents/plugins/googledocsviewer/content_plugins.py

cf330dfc9bf3f2783a245dbbb6a29a3f46c583a2

[ "Apache-2.0" ]

permissive

jpotterm/django-fluent-contents

8bc70c9f0309bfeeb3c1e7a96c0687c7070e48ed

e617815874c936af1e00a8bfb79a4c8fc9a40cbb

refs/heads/master

2021-01-18T06:56:15.737257

2015-09-09T15:40:43

30,092,032

0

null

2015-01-30T21:42:42

null

UTF-8

Python

false

1,174

py

""" Google apps widgets for your site. """ from django.utils.html import escape from django.utils.http import urlquote from django.utils.safestring import mark_safe from django.utils.translation import ugettext_lazy as _ from fluent_contents.extensions import ContentPlugin, plugin_pool from fluent_contents.plugins.googledocsviewer.models import GoogleDocsViewerItem @plugin_pool.register class GoogleDocsViewerPlugin(ContentPlugin): """ Plugin to add a Google Docs viewer to the page. This can be used to display a PDF file inline. Note then when using the Google Docs viewer on your site, Google assumes you agree with the Terms of Service, see: https://docs.google.com/viewer/TOS """ model = GoogleDocsViewerItem category = _('Media') def render(self, request, instance, **kwargs): url = 'http://docs.google.com/viewer?url={url}&embedded=true'.format(url=urlquote(instance.url, '')) return mark_safe(u'<iframe class="googledocsviewer" src="{src}" width="{width}" height="{height}"></iframe>'.format( src=escape(url), width=instance.width, height=instance.height ))

[ "vdboor@edoburu.nl" ]

vdboor@edoburu.nl

5aec7d812d592d9d37ec207d0ed43b8a3d21710a

58e51a01846176168bcd83175f63b240cd6db916

/apps/news/models.py

070e6feb4346a30d22e857ca08a87b62ba33f7b9

[]

no_license

reddytocode/projectX-backend

7e79795cd0c6951ca39f93e316af7a61b631940d

05fb5f6e14889ecac94ad52c13796eb14c59814d

refs/heads/main

2023-07-18T06:37:16.298857

2021-06-30T20:29:33

380,374,575

0

null

UTF-8

Python

false

313

py

from django.db import models class News(models.Model): # Todo: inherit guid = models.TextField() title = models.CharField(max_length=100, blank=False) link = models.CharField(max_length=200, blank=False) content = models.TextField() author = models.CharField(max_length=100, blank=False)

[ "aaabeeelooon@gmail.com" ]

aaabeeelooon@gmail.com

21df715483d4461c101e7b8501691258ca4d040d

4ae34a5179d7adf1037eb9a3cb249f9a5c06684e

/examples/v1beta1/trial-images/darts-cnn-cifar10/model.py

dea7d43f84c6d761b0697b84b5ffd2adc18d9249

[ "Apache-2.0" ]

permissive

kubeflow/katib

367373c0452d49a7a115b86893f4dab9e1f278ea

e3e0aa24aeea1edfab0fd42f55392af651d2b3ae

refs/heads/master

2023-09-04T05:02:05.752156

2023-08-24T22:40:54

127,941,481

1,385

422

Apache-2.0

2023-09-14T13:17:29

2018-04-03T17:07:12

Go

UTF-8

Python

false

7,044

py

# Copyright 2022 The Kubeflow 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 torch import torch.nn as nn import torch.nn.functional as F from operations import FactorizedReduce, StdConv, MixedOp class Cell(nn.Module): """ Cell for search Each edge is mixed and continuous relaxed. """ def __init__(self, num_nodes, c_prev_prev, c_prev, c_cur, reduction_prev, reduction_cur, search_space): """ Args: num_nodes: Number of intermediate cell nodes c_prev_prev: channels_out[k-2] c_prev : Channels_out[k-1] c_cur : Channels_in[k] (current) reduction_prev: flag for whether the previous cell is reduction cell or not reduction_cur: flag for whether the current cell is reduction cell or not """ super(Cell, self).__init__() self.reduction_cur = reduction_cur self.num_nodes = num_nodes # If previous cell is reduction cell, current input size does not match with # output size of cell[k-2]. So the output[k-2] should be reduced by preprocessing if reduction_prev: self.preprocess0 = FactorizedReduce(c_prev_prev, c_cur) else: self.preprocess0 = StdConv(c_prev_prev, c_cur, kernel_size=1, stride=1, padding=0) self.preprocess1 = StdConv(c_prev, c_cur, kernel_size=1, stride=1, padding=0) # Generate dag from mixed operations self.dag_ops = nn.ModuleList() for i in range(self.num_nodes): self.dag_ops.append(nn.ModuleList()) # Include 2 input nodes for j in range(2+i): # Reduction with stride = 2 must be only for the input node stride = 2 if reduction_cur and j < 2 else 1 op = MixedOp(c_cur, stride, search_space) self.dag_ops[i].append(op) def forward(self, s0, s1, w_dag): s0 = self.preprocess0(s0) s1 = self.preprocess1(s1) states = [s0, s1] for edges, w_list in zip(self.dag_ops, w_dag): state_cur = sum(edges[i](s, w) for i, (s, w) in enumerate((zip(states, w_list)))) states.append(state_cur) state_out = torch.cat(states[2:], dim=1) return state_out class NetworkCNN(nn.Module): def __init__(self, init_channels, input_channels, num_classes, num_layers, criterion, search_space, num_nodes, stem_multiplier): super(NetworkCNN, self).__init__() self.init_channels = init_channels self.num_classes = num_classes self.num_layers = num_layers self.criterion = criterion self.num_nodes = num_nodes self.stem_multiplier = stem_multiplier c_cur = self.stem_multiplier*self.init_channels self.stem = nn.Sequential( nn.Conv2d(input_channels, c_cur, 3, padding=1, bias=False), nn.BatchNorm2d(c_cur) ) # In first Cell stem is used for s0 and s1 # c_prev_prev and c_prev - output channels size # c_cur - init channels size c_prev_prev, c_prev, c_cur = c_cur, c_cur, self.init_channels self.cells = nn.ModuleList() reduction_prev = False for i in range(self.num_layers): # For Network with 1 layer: Only Normal Cell if self.num_layers == 1: reduction_cur = False else: # For Network with two layers: First layer - Normal, Second - Reduction # For Other Networks: [1/3, 2/3] Layers - Reduction cell with double channels # Others - Normal cell if ((self.num_layers == 2 and i == 1) or (self.num_layers > 2 and i in [self.num_layers//3, 2*self.num_layers//3])): c_cur *= 2 reduction_cur = True else: reduction_cur = False cell = Cell(self.num_nodes, c_prev_prev, c_prev, c_cur, reduction_prev, reduction_cur, search_space) reduction_prev = reduction_cur self.cells.append(cell) c_cur_out = c_cur * self.num_nodes c_prev_prev, c_prev = c_prev, c_cur_out self.global_pooling = nn.AdaptiveAvgPool2d(1) self.classifier = nn.Linear(c_prev, self.num_classes) # Initialize alphas parameters num_ops = len(search_space.primitives) self.alpha_normal = nn.ParameterList() self.alpha_reduce = nn.ParameterList() for i in range(self.num_nodes): self.alpha_normal.append(nn.Parameter(1e-3*torch.randn(i+2, num_ops))) if self.num_layers > 1: self.alpha_reduce.append(nn.Parameter(1e-3*torch.randn(i+2, num_ops))) # Setup alphas list self.alphas = [] for name, parameter in self.named_parameters(): if "alpha" in name: self.alphas.append((name, parameter)) def forward(self, x): weights_normal = [F.softmax(alpha, dim=-1) for alpha in self.alpha_normal] weights_reduce = [F.softmax(alpha, dim=-1) for alpha in self.alpha_reduce] s0 = s1 = self.stem(x) for cell in self.cells: weights = weights_reduce if cell.reduction_cur else weights_normal s0, s1 = s1, cell(s0, s1, weights) out = self.global_pooling(s1) # Make out flatten out = out.view(out.size(0), -1) logits = self.classifier(out) return logits def print_alphas(self): print("\n>>> Alphas Normal <<<") for alpha in self.alpha_normal: print(F.softmax(alpha, dim=-1)) if self.num_layers > 1: print("\n>>> Alpha Reduce <<<") for alpha in self.alpha_reduce: print(F.softmax(alpha, dim=-1)) print("\n") def getWeights(self): return self.parameters() def getAlphas(self): for _, parameter in self.alphas: yield parameter def loss(self, x, y): logits = self.forward(x) return self.criterion(logits, y) def genotype(self, search_space): gene_normal = search_space.parse(self.alpha_normal, k=2) gene_reduce = search_space.parse(self.alpha_reduce, k=2) # concat all intermediate nodes concat = range(2, 2 + self.num_nodes) return search_space.genotype(normal=gene_normal, normal_concat=concat, reduce=gene_reduce, reduce_concat=concat)

[ "noreply@github.com" ]

kubeflow.noreply@github.com

8d0fe3775c506e3d3501551bd2693ec99edb0d39

fe19d2fac4580d463132e61509bd6e3cc2cf958d

/toontown/parties/DistributedPartyFireworksActivity.py

cbe6cf69ed730e33a04c428825cbb5fd222fffe2

[]

no_license

t00nt0wn1dk/c0d3

3e6db6dd42c3aa36ad77709cf9016176a3f3a44f

7de105d7f3de0f8704b020e32fd063ee2fad8d0d

refs/heads/master

2021-01-01T16:00:15.367822

2015-03-21T21:25:52

2015-03-21T21:25:55

32,647,654

3

5

null

UTF-8

Python

false

7,941

py

# 2013.08.22 22:23:22 Pacific Daylight Time # Embedded file name: toontown.parties.DistributedPartyFireworksActivity from pandac.PandaModules import Vec3 from pandac.PandaModules import OmniBoundingVolume from pandac.PandaModules import AlphaTestAttrib from pandac.PandaModules import RenderAttrib from direct.actor.Actor import Actor from direct.interval.IntervalGlobal import * from direct.distributed.ClockDelta import globalClockDelta from toontown.effects.FireworkShowMixin import FireworkShowMixin from toontown.effects.RocketExplosion import RocketExplosion from toontown.toonbase import TTLocalizer from PartyGlobals import FireworkShows from PartyGlobals import ActivityIds from PartyGlobals import ActivityTypes from PartyGlobals import FireworksStartedEvent from PartyGlobals import FireworksFinishedEvent from PartyGlobals import FireworksPostLaunchDelay from PartyGlobals import RocketSoundDelay from PartyGlobals import RocketDirectionDelay from DistributedPartyActivity import DistributedPartyActivity from activityFSMs import FireworksActivityFSM import PartyGlobals class DistributedPartyFireworksActivity(DistributedPartyActivity, FireworkShowMixin): __module__ = __name__ notify = directNotify.newCategory('DistributedPartyFireworksActivity') def __init__(self, cr): DistributedPartyFireworksActivity.notify.debug('__init__') DistributedPartyActivity.__init__(self, cr, ActivityIds.PartyFireworks, ActivityTypes.HostInitiated, wantLever=True) FireworkShowMixin.__init__(self, restorePlaygroundMusic=True, startDelay=FireworksPostLaunchDelay) def setEventId(self, eventId): DistributedPartyFireworksActivity.notify.debug('setEventId( %s )' % FireworkShows.getString(eventId)) self.eventId = eventId def setShowStyle(self, showStyle): DistributedPartyFireworksActivity.notify.debug('setShowStyle( %d )' % showStyle) self.showStyle = showStyle def load(self): DistributedPartyFireworksActivity.notify.debug('load') DistributedPartyActivity.load(self) self.eventId = PartyGlobals.FireworkShows.Summer self.launchPadModel = loader.loadModel('phase_13/models/parties/launchPad') self.launchPadModel.setH(90.0) self.launchPadModel.setPos(0.0, -18.0, 0.0) self.launchPadModel.reparentTo(self.root) railingsCollection = self.launchPadModel.findAllMatches('**/launchPad_mesh/*railing*') for i in range(railingsCollection.getNumPaths()): railingsCollection[i].setAttrib(AlphaTestAttrib.make(RenderAttrib.MGreater, 0.75)) leverLocator = self.launchPadModel.find('**/RocketLever_locator') self.lever.setPosHpr(Vec3.zero(), Vec3.zero()) self.lever.reparentTo(leverLocator) self.toonPullingLeverInterval = None self.sign.reparentTo(self.launchPadModel.find('**/launchPad_sign_locator')) self.rocketActor = Actor('phase_13/models/parties/rocket_model', {'launch': 'phase_13/models/parties/rocket_launch'}) rocketLocator = self.launchPadModel.find('**/rocket_locator') self.rocketActor.reparentTo(rocketLocator) self.rocketActor.node().setBound(OmniBoundingVolume()) self.rocketActor.node().setFinal(True) effectsLocator = self.rocketActor.find('**/joint1') self.rocketExplosionEffect = RocketExplosion(effectsLocator, rocketLocator) self.rocketParticleSeq = None self.launchSound = base.loadSfx('phase_13/audio/sfx/rocket_launch.mp3') self.activityFSM = FireworksActivityFSM(self) self.activityFSM.request('Idle') return def unload(self): DistributedPartyFireworksActivity.notify.debug('unload') taskMgr.remove(self.taskName('delayedStartShow')) if self.rocketParticleSeq: self.rocketParticleSeq.pause() self.rocketParticleSeq = None self.launchPadModel.removeNode() del self.launchPadModel del self.toonPullingLeverInterval self.rocketActor.delete() self.rocketExplosionEffect.destroy() self.activityFSM.request('Disabled') del self.rocketActor del self.launchSound del self.activityFSM del self.eventId del self.showStyle DistributedPartyActivity.unload(self) return def _leverPulled(self, collEntry): DistributedPartyFireworksActivity.notify.debug('_leverPulled') hostPulledLever = DistributedPartyActivity._leverPulled(self, collEntry) if self.activityFSM.getCurrentOrNextState() == 'Active': self.showMessage(TTLocalizer.PartyFireworksAlreadyActive) elif self.activityFSM.getCurrentOrNextState() == 'Disabled': self.showMessage(TTLocalizer.PartyFireworksAlreadyDone) elif self.activityFSM.getCurrentOrNextState() == 'Idle': if hostPulledLever: base.cr.playGame.getPlace().fsm.request('activity') self.toonPullingLeverInterval = self.getToonPullingLeverInterval(base.localAvatar) self.toonPullingLeverInterval.append(Func(self.d_toonJoinRequest)) self.toonPullingLeverInterval.append(Func(base.cr.playGame.getPlace().fsm.request, 'walk')) self.toonPullingLeverInterval.start() else: self.showMessage(TTLocalizer.PartyOnlyHostLeverPull) def setState(self, newState, timestamp): DistributedPartyFireworksActivity.notify.debug('setState( newState=%s, ... )' % newState) DistributedPartyActivity.setState(self, newState, timestamp) if newState == 'Active': self.activityFSM.request(newState, timestamp) else: self.activityFSM.request(newState) def startIdle(self): DistributedPartyFireworksActivity.notify.debug('startIdle') def finishIdle(self): DistributedPartyFireworksActivity.notify.debug('finishIdle') def startActive(self, showStartTimestamp): DistributedPartyFireworksActivity.notify.debug('startActive') messenger.send(FireworksStartedEvent) timeSinceStart = globalClockDelta.localElapsedTime(showStartTimestamp) if timeSinceStart > self.rocketActor.getDuration('launch'): self.rocketActor.hide() self.startShow(self.eventId, self.showStyle, showStartTimestamp) else: self.rocketActor.play('launch') self.rocketParticleSeq = Sequence(Wait(RocketSoundDelay), Func(base.playSfx, self.launchSound), Func(self.rocketExplosionEffect.start), Wait(RocketDirectionDelay), LerpHprInterval(self.rocketActor, 4.0, Vec3(0, 0, -60)), Func(self.rocketExplosionEffect.end), Func(self.rocketActor.hide)) self.rocketParticleSeq.start() taskMgr.doMethodLater(FireworksPostLaunchDelay, self.startShow, self.taskName('delayedStartShow'), extraArgs=[self.eventId, self.showStyle, showStartTimestamp, self.root]) def finishActive(self): self.rocketParticleSeq = None DistributedPartyFireworksActivity.notify.debug('finishActive') messenger.send(FireworksFinishedEvent) taskMgr.remove(self.taskName('delayedStartShow')) FireworkShowMixin.disable(self) return def startDisabled(self): DistributedPartyFireworksActivity.notify.debug('startDisabled') if not self.rocketActor.isEmpty(): self.rocketActor.hide() def finishDisabled(self): DistributedPartyFireworksActivity.notify.debug('finishDisabled') def handleToonDisabled(self, toonId): self.notify.warning('handleToonDisabled no implementation yet') # okay decompyling C:\Users\Maverick\Documents\Visual Studio 2010\Projects\Unfreezer\py2\toontown\parties\DistributedPartyFireworksActivity.pyc # decompiled 1 files: 1 okay, 0 failed, 0 verify failed # 2013.08.22 22:23:22 Pacific Daylight Time

[ "anonymoustoontown@gmail.com" ]

anonymoustoontown@gmail.com

eee3ed0cdfe623261a833b1da967a5cb2705933d

30a61c74d5108279af498a181ebc83151f13c033

/one_hundred_eighteen.py

30b365d21d51a1e5aa523f67b58d17a91ffc45e6

[]

no_license

Yanl05/LeetCode

274267cb189813c96fff67d8cbfba4afebd5c2b2

c0807a7f31a265b3090ef3d32a0ad5a2b10579f7

refs/heads/master

2020-03-15T12:25:16.456742

2020-01-20T01:12:35

132,143,417

0

null

2018-12-21T03:01:32

2018-05-04T13:25:24

Python

UTF-8

Python

false

704

py

# -*- coding: UTF-8 -*- """ # @Time : 2019-08-28 16:33 # @Author : yanlei # @FileName: one_hundred_eighteen.py 给定一个非负整数 numRows，生成杨辉三角的前 numRows 行。在杨辉三角中，每个数是它左上方和右上方的数的和。 """ class Solution(object): def generate(self, numRows): """ :type numRows: int :rtype: List[List[int]] """ ret = [] if numRows == 0: return ret for i in range(numRows): ret.append([1]*(i+1)) if i > 1: for j in range(1,i): ret[-1][j] = ret[-2][j-1] + ret[-2][j] return ret print(Solution().generate(5))

[ "756593069@qq.com" ]

756593069@qq.com

83f6803e4b1251b1ff5c7750399269d0197edd3d

6b2a8dd202fdce77c971c412717e305e1caaac51

/solutions_2449486_0/Python/ynasu/B.py

2540035aa29eb33019f7839e601b4e6d8c3a9332

[]

no_license

alexandraback/datacollection

0bc67a9ace00abbc843f4912562f3a064992e0e9

076a7bc7693f3abf07bfdbdac838cb4ef65ccfcf

refs/heads/master

2021-01-24T18:27:24.417992

2017-05-23T09:23:38

84,313,442

2

4

null

UTF-8

Python

false

921

py

#!/usr/bin/env python import sys yes = "YES" no = "NO" def solve(heights): N = len(heights) M = len(heights[0]) for y in xrange(N): for x in xrange(M): possible = True for y1 in xrange(N): if heights[y1][x] > heights[y][x]: possible = False if possible: continue possible = True for x1 in xrange(M): if heights[y][x1] > heights[y][x]: possible = False if possible: continue return no return yes def readInts(): return [ int(s) for s in sys.stdin.readline().split() ] T = int(sys.stdin.readline()) for t in xrange(T): inputs = readInts() N = inputs[0] heights = [] for i in xrange(N): heights.append(readInts()) res = solve(heights) print "Case #%d: %s" % (t + 1, res)

[ "eewestman@gmail.com" ]

eewestman@gmail.com

f102adefa4347ff2438caea310576abfc8cc8e52

27e890f900bd4bfb2e66f4eab85bc381cf4d5d3f

/tests/unit/modules/network/iosxr/test_iosxr_netconf.py

fbb7819e173700b0821c2a564cbc459b04350fe6

[]

no_license

coll-test/notstdlib.moveitallout

eb33a560070bbded5032385d0aea2f3cf60e690b

0987f099b783c6cf977db9233e1c3d9efcbcb3c7

refs/heads/master

2020-12-19T22:28:33.369557

2020-01-23T18:51:26

235,865,139

0

null

UTF-8

Python

false

3,632

py

# (c) 2017 Red Hat Inc. # # This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # Make coding more python3-ish from __future__ import (absolute_import, division, print_function) __metaclass__ = type from ansible_collections.notstdlib.moveitallout.tests.unit.compat.mock import patch from ansible_collections.notstdlib.moveitallout.plugins.modules import iosxr_netconf from ansible_collections.notstdlib.moveitallout.tests.unit.modules.utils import set_module_args from ..iosxr_module import TestIosxrModule class TestIosxrNetconfModule(TestIosxrModule): module = iosxr_netconf def setUp(self): super(TestIosxrNetconfModule, self).setUp() self.mock_get_config = patch('ansible_collections.notstdlib.moveitallout.plugins.modules.iosxr_netconf.get_config') self.get_config = self.mock_get_config.start() self.mock_load_config = patch('ansible_collections.notstdlib.moveitallout.plugins.modules.iosxr_netconf.load_config') self.load_config = self.mock_load_config.start() def tearDown(self): super(TestIosxrNetconfModule, self).tearDown() self.mock_get_config.stop() self.mock_load_config.stop() def test_iosxr_disable_netconf_service(self): self.get_config.return_value = ''' netconf-yang agent ssh ! ssh server netconf vrf default ''' self.load_config.return_value = 'dummy diff' set_module_args(dict(netconf_port=830, netconf_vrf='default', state='absent')) result = self.execute_module(changed=True) self.assertEqual(result['commands'], ['no netconf-yang agent ssh', 'no ssh server netconf port 830', 'no ssh server netconf vrf default']) def test_iosxr_enable_netconf_service(self): self.get_config.return_value = '' self.load_config.return_value = 'dummy diff' set_module_args(dict(netconf_port=830, netconf_vrf='default', state='present')) result = self.execute_module(changed=True) self.assertEqual(result['commands'], ['netconf-yang agent ssh', 'ssh server netconf port 830', 'ssh server netconf vrf default']) def test_iosxr_change_netconf_port(self): self.get_config.return_value = ''' netconf-yang agent ssh ! ssh server netconf vrf default ''' self.load_config.return_value = 'dummy diff' set_module_args(dict(netconf_port=9000, state='present')) result = self.execute_module(changed=True) self.assertEqual(result['commands'], ['ssh server netconf port 9000']) def test_iosxr_change_netconf_vrf(self): self.get_config.return_value = ''' netconf-yang agent ssh ! ssh server netconf vrf default ''' self.load_config.return_value = 'dummy diff' set_module_args(dict(netconf_vrf='new_default', state='present')) result = self.execute_module(changed=True) self.assertEqual(result['commands'], ['ssh server netconf vrf new_default'])

[ "wk@sydorenko.org.ua" ]

wk@sydorenko.org.ua

efe05d84e80b3c205eaad6cbb005cf317016866d

5f6cd57c692191acfea18c1af9d87e7db8e873f1

/devito/ir/iet/scheduler.py

7f2bf50f2985af84bbae530639c9d5aeae566821

[ "MIT" ]

permissive

Antongk/devito

684b0f4928d47bb6acc6469cc0471b5122c34561

a50c0a8337497cf6d7603cf9beff4a3231e63bee

refs/heads/master

2020-04-19T17:00:18.380108

2019-01-30T09:16:38

null

0

null

UTF-8

Python

false

6,330

py

from collections import OrderedDict from devito.cgen_utils import Allocator from devito.ir.iet import (Expression, Increment, LocalExpression, Element, Iteration, List, Conditional, Section, HaloSpot, ExpressionBundle, MapExpressions, Transformer, FindNodes, FindSymbols, XSubs, iet_analyze, filter_iterations) from devito.symbolics import IntDiv, xreplace_indices from devito.tools import as_mapper from devito.types import ConditionalDimension __all__ = ['iet_build', 'iet_insert_C_decls'] def iet_build(stree): """ Create an Iteration/Expression tree (IET) from a :class:`ScheduleTree`. The nodes in the returned IET are decorated with properties deriving from data dependence analysis. """ # Schedule tree -> Iteration/Expression tree iet = iet_make(stree) # Data dependency analysis. Properties are attached directly to nodes iet = iet_analyze(iet) # Turn DerivedDimensions into lower-level Dimensions or Symbols iet = iet_lower_dimensions(iet) return iet def iet_make(stree): """Create an IET from a :class:`ScheduleTree`.""" nsections = 0 queues = OrderedDict() for i in stree.visit(): if i == stree: # We hit this handle at the very end of the visit return List(body=queues.pop(i)) elif i.is_Exprs: exprs = [Increment(e) if e.is_Increment else Expression(e) for e in i.exprs] body = [ExpressionBundle(i.shape, i.ops, i.traffic, body=exprs)] elif i.is_Conditional: body = [Conditional(i.guard, queues.pop(i))] elif i.is_Iteration: # Order to ensure deterministic code generation uindices = sorted(i.sub_iterators, key=lambda d: d.name) # Generate Iteration body = [Iteration(queues.pop(i), i.dim, i.dim._limits, offsets=i.limits, direction=i.direction, uindices=uindices)] elif i.is_Section: body = [Section('section%d' % nsections, body=queues.pop(i))] nsections += 1 elif i.is_Halo: body = [HaloSpot(hs) for hs in i.halo_scheme.components] + queues.pop(i) queues.setdefault(i.parent, []).extend(body) assert False def iet_lower_dimensions(iet): """ Replace all :class:`DerivedDimension`s within the ``iet``'s expressions with lower-level symbolic objects (other :class:`Dimension`s, or :class:`sympy.Symbol`). * Array indices involving :class:`SteppingDimension`s are turned into :class:`ModuloDimension`s. Example: ``u[t+1, x] = u[t, x] + 1 >>> u[t1, x] = u[t0, x] + 1`` * Array indices involving :class:`ConditionalDimension`s used are turned into integer-division expressions. Example: ``u[t_sub, x] = u[time, x] >>> u[time / 4, x] = u[time, x]`` """ # Lower SteppingDimensions for i in FindNodes(Iteration).visit(iet): if not i.uindices: # Be quick: avoid uselessy reconstructing nodes continue # In an expression, there could be `u[t+1, ...]` and `v[t+1, ...]`, where # `u` and `v` are TimeFunction with circular time buffers (save=None) *but* # different modulo extent. The `t+1` indices above are therefore conceptually # different, so they will be replaced with the proper ModuloDimension through # two different calls to `xreplace` groups = as_mapper(i.uindices, lambda d: d.modulo) for k, v in groups.items(): mapper = {d.origin: d for d in v} rule = lambda i: i.function.is_TimeFunction and i.function._time_size == k replacer = lambda i: xreplace_indices(i, mapper, rule) iet = XSubs(replacer=replacer).visit(iet) # Lower ConditionalDimensions cdims = [d for d in FindSymbols('free-symbols').visit(iet) if isinstance(d, ConditionalDimension)] mapper = {d: IntDiv(d.index, d.factor) for d in cdims} iet = XSubs(mapper).visit(iet) return iet def iet_insert_C_decls(iet, external=None): """ Given an IET, build a new tree with the necessary symbol declarations. Declarations are placed as close as possible to the first symbol occurrence. Parameters ---------- iet : Node The input Iteration/Expression tree. external : tuple, optional The symbols defined in some outer Callable, which therefore must not be re-defined. """ external = external or [] # Classify and then schedule declarations to stack/heap allocator = Allocator() mapper = OrderedDict() for k, v in MapExpressions().visit(iet).items(): if k.is_Expression: if k.is_scalar_assign: # Inline declaration mapper[k] = LocalExpression(**k.args) continue objs = [k.write] elif k.is_Call: objs = k.params for i in objs: try: if i.is_LocalObject: # On the stack site = v[-1] if v else iet allocator.push_stack(site, i) elif i.is_Array: if i in external: # The Array is to be defined in some foreign IET continue elif i._mem_stack: # On the stack key = lambda i: not i.is_Parallel site = filter_iterations(v, key=key, stop='asap') or [iet] allocator.push_stack(site[-1], i) else: # On the heap, as a tensor that must be globally accessible allocator.push_heap(i) except AttributeError: # E.g., a generic SymPy expression pass # Introduce declarations on the stack for k, v in allocator.onstack: mapper[k] = tuple(Element(i) for i in v) iet = Transformer(mapper, nested=True).visit(iet) # Introduce declarations on the heap (if any) if allocator.onheap: decls, allocs, frees = zip(*allocator.onheap) iet = List(header=decls + allocs, body=iet, footer=frees) return iet

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/experiments/murtaza/multiworld/camera_ready/pusher/offline_vae_twin_sac.py

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2022-11-15T07:08:33.416025

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import railrl.misc.hyperparameter as hyp from multiworld.envs.mujoco.cameras import sawyer_pusher_camera_upright_v1 from railrl.launchers.launcher_util import run_experiment from railrl.torch.grill.launcher import grill_her_td3_full_experiment, grill_her_twin_sac_full_experiment if __name__ == "__main__": variant = dict( imsize=48, init_camera=sawyer_pusher_camera_upright_v1, env_id='SawyerPushAndReachEnvEasy-v0', grill_variant=dict( save_video=True, save_video_period=100, qf_kwargs=dict( hidden_sizes=[400, 300], ), policy_kwargs=dict( hidden_sizes=[400, 300], ), vf_kwargs=dict( hidden_sizes=[400, 300], ), algo_kwargs=dict( base_kwargs=dict( num_epochs=1005, num_steps_per_epoch=1000, num_steps_per_eval=1000, min_num_steps_before_training=4000, batch_size=128, max_path_length=100, discount=0.99, num_updates_per_env_step=1, collection_mode='online-parallel', parallel_env_params=dict( num_workers=1, ), reward_scale=1, ), her_kwargs=dict( ), twin_sac_kwargs=dict( train_policy_with_reparameterization=True, soft_target_tau=1e-3, # 1e-2 policy_update_period=1, target_update_period=1, # 1 use_automatic_entropy_tuning=True, ), ), replay_buffer_kwargs=dict( max_size=int(1e6), fraction_goals_are_rollout_goals=0.5, fraction_resampled_goals_are_env_goals=0.5, ), algorithm='RIG-HER-TD3', normalize=False, render=False, exploration_noise=0, exploration_type='ou', training_mode='train', testing_mode='test', reward_params=dict( type='latent_distance', ), observation_key='latent_observation', desired_goal_key='latent_desired_goal', vae_wrapped_env_kwargs=dict( sample_from_true_prior=True, ) ), train_vae_variant=dict( vae_path=None, representation_size=16, beta=2.5, num_epochs=1000, dump_skew_debug_plots=False, generate_vae_dataset_kwargs=dict( test_p=.9, N=5000, oracle_dataset_using_set_to_goal=True, use_cached=True, vae_dataset_specific_kwargs=dict( ), show=False, ), vae_kwargs=dict( input_channels=3, ), algo_kwargs=dict( do_scatterplot=False, use_linear_dynamics=False, is_auto_encoder=False, batch_size=64, lr=1e-3, ), decoder_activation='sigmoid', save_period=100, ), ) search_space = { 'grill_variant.exploration_noise':[0, .1, .3], 'env_id':['SawyerPushAndReachSmallArenaEnv-v0', 'SawyerPushAndReachSmallArenaResetFreeEnv-v0', 'SawyerPushAndReachEnvEasy-v0'] } sweeper = hyp.DeterministicHyperparameterSweeper( search_space, default_parameters=variant, ) # n_seeds = 1 # mode = 'local' # exp_prefix = 'test' n_seeds = 3 mode = 'gcp' exp_prefix = 'sawyer_pusher_offline_vae_twin_sac_easier_envs' for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()): for _ in range(n_seeds): run_experiment( grill_her_twin_sac_full_experiment, exp_prefix=exp_prefix, mode=mode, variant=variant, use_gpu=True, num_exps_per_instance=1, gcp_kwargs=dict( zone='us-west2-b', ) )

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asap7772@berkeley.edu

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refs/heads/master

2023-04-13T16:02:26.331775

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# model settings model = dict( type='FasterRCNN', pretrained='modelzoo://resnet50', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch'), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_scales=[8], anchor_ratios=[0.5, 1.0, 2.0], anchor_strides=[4, 8, 16, 32, 64], target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0], loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=dict( type='SharedFCBBoxHead', num_fcs=2, in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=16, target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2], reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))) # model training and testing settings train_cfg = dict( rpn=dict( assigner=dict( type='MaxIoUAssignerCy', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=0, pos_weight=-1, debug=False), rpn_proposal=dict( nms_across_levels=False, nms_pre=2000, nms_post=2000, max_num=2000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( assigner=dict( type='MaxIoUAssignerCy', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False)) test_cfg = dict( rpn=dict( nms_across_levels=False, nms_pre=2000, nms_post=2000, max_num=2000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( # score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=1000) score_thr = 0.05, nms = dict(type='nms', iou_thr=0.5), max_per_img = 2000) # soft-nms is also supported for rcnn testing # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05) ) # dataset settings dataset_type = 'DOTADataset' data_root = 'data/dota1_1024/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) data = dict( imgs_per_gpu=2, workers_per_gpu=2, train=dict( type=dataset_type, ann_file=data_root + 'trainval1024/DOTA_trainval1024.json', img_prefix=data_root + 'trainval1024/images/', img_scale=(1024, 1024), img_norm_cfg=img_norm_cfg, size_divisor=32, flip_ratio=0.5, with_mask=False, with_crowd=True, with_label=True), val=dict( type=dataset_type, ann_file=data_root + 'trainval1024/DOTA_trainval1024.json', img_prefix=data_root + 'trainval1024/images', img_scale=(1024, 1024), img_norm_cfg=img_norm_cfg, size_divisor=32, flip_ratio=0, with_mask=False, with_crowd=True, with_label=True), test=dict( type=dataset_type, ann_file=data_root + 'test1024/DOTA_test1024.json', img_prefix=data_root + 'test1024/images', img_scale=(1024, 1024), img_norm_cfg=img_norm_cfg, size_divisor=32, flip_ratio=0, with_mask=False, with_label=False, test_mode=True)) # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=1.0 / 3, step=[8, 11]) checkpoint_config = dict(interval=12) # yapf:disable log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook') ]) # yapf:enable # runtime settings total_epochs = 12 dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/faster_rcnn_r50_fpn_1x_dota' load_from = None resume_from = None workflow = [('train', 1)]

[ "574258651@qq.com" ]

574258651@qq.com

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cappytan3/AbletonLive9_RemoteScripts

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refs/heads/master

2021-01-15T11:50:14.152579

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#Embedded file name: /Users/versonator/Hudson/live/Projects/AppLive/Resources/MIDI Remote Scripts/MPK_mini/config.py from consts import * TRANSPORT_CONTROLS = {'STOP': -1, 'PLAY': -1, 'REC': -1, 'LOOP': -1, 'RWD': -1, 'FFWD': -1} DEVICE_CONTROLS = (GENERIC_ENC1, GENERIC_ENC2, GENERIC_ENC3, GENERIC_ENC4, GENERIC_ENC5, GENERIC_ENC6, GENERIC_ENC7, GENERIC_ENC8) VOLUME_CONTROLS = ((-1, -1), (-1, -1), (-1, -1), (-1, -1), (-1, -1), (-1, -1), (-1, -1), (-1, -1)) TRACKARM_CONTROLS = (-1, -1, -1, -1, -1, -1, -1, -1) BANK_CONTROLS = {'TOGGLELOCK': -1, 'BANKDIAL': -1, 'NEXTBANK': -1, 'PREVBANK': -1, 'BANK1': -1, 'BANK2': -1, 'BANK3': -1, 'BANK4': -1, 'BANK5': -1, 'BANK6': -1, 'BANK7': -1, 'BANK8': -1} PAD_TRANSLATION = ((0, 0, 36, 0), (1, 0, 37, 0), (2, 0, 38, 0), (3, 0, 39, 0), (0, 1, 32, 0), (1, 1, 33, 0), (2, 1, 34, 0), (3, 1, 35, 0), (0, 2, 48, 0), (1, 2, 49, 0), (2, 2, 50, 0), (3, 2, 51, 0), (0, 3, 44, 0), (1, 3, 45, 0), (2, 3, 46, 0), (3, 3, 47, 0)) CONTROLLER_DESCRIPTION = {'INPUTPORT': 'MPK mini', 'OUTPUTPORT': 'MPK mini', 'CHANNEL': -1, 'PAD_TRANSLATION': PAD_TRANSLATION} MIXER_OPTIONS = {'NUMSENDS': 2, 'SEND1': (-1, -1, -1, -1, -1, -1, -1, -1), 'SEND2': (-1, -1, -1, -1, -1, -1, -1, -1), 'MASTERVOLUME': -1}

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julien@julienbayle.net

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/.history/week01/homework02/maoyanspiders/maoyanspiders/spiders/movies_20200627213700.py

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ydbB/Python001-class01

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refs/heads/master

2022-11-25T11:27:45.077139

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# -*- coding: utf-8 -*- import scrapy from maoyanspiders.items import MaoyanspidersItem # import xlml.etree from bs4 import BeautifulSoup as bs class MoviesSpider(scrapy.Spider): name = 'movies' allowed_domains = ['maoyan.com'] start_urls = ['http://maoyan.com/board/4'] # def parse(self, response): # pass def start_requests(self): url = f'https://maoyan.com/board/4' print(url) yield scrapy.Request(url=url,callback=self.parse) def parse(self, response): soup = bs(response.text,'html.parser') print(soup.text) return soup item = MaoyanspidersItem() item['films_name'] = 'name' item['release_time'] = "tiome" return item

[ "31039587+ydbB@users.noreply.github.com" ]

31039587+ydbB@users.noreply.github.com

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/backend/views/product_views.py

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jasimdipu/django_1st_batch_final_project

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from django.shortcuts import render from rest_framework.decorators import api_view, permission_classes from rest_framework.permissions import IsAuthenticated, IsAdminUser from rest_framework.response import Response from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger from ..models import Product, Review from ..serializers import ProductSerializer, ReviewSeralizer from rest_framework import status @api_view('GET') def getProducts(request): query = request.query_params.get('keyword') if query == None: query = "" products = Product.objects.filter(product_name__icontains=query).order_by('-created_at') page = request.query_params.get('page') paginator = Paginator(products, 6) try: products = paginator.page((page)) except PageNotAnInteger: products = paginator.page(1) except EmptyPage: products = paginator.page(paginator.num_pages) if page == None: page = 1 page = int(page) print('Page', page) serializers = ProductSerializer(products, many=True) return Response({'products': serializers.data, 'page': page, "pages": paginator.num_pages}) @api_view(["POST"]) def createProduct(request): pass @api_view(["POST"]) def updateProduct(request, pk): pass @api_view(["Delete"]) def deleteProduct(request, pk): pass

[ "dipu.j247@gmail.com" ]

dipu.j247@gmail.com

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/test/test_saml_api.py

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aviv-julienjehannet/collibra_apiclient

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# coding: utf-8 """ \"Data Governance Center: REST API v2\" No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 2.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.api.saml_api import SAMLApi # noqa: E501 from swagger_client.rest import ApiException class TestSAMLApi(unittest.TestCase): """SAMLApi unit test stubs""" def setUp(self): self.api = swagger_client.api.saml_api.SAMLApi() # noqa: E501 def tearDown(self): pass def test_resource_saml_resource_get_sp_metadata_as_string_get(self): """Test case for resource_saml_resource_get_sp_metadata_as_string_get Returns the SAML Service Provider metadata for a this instance. # noqa: E501 """ pass if __name__ == '__main__': unittest.main()

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busworld08@gmail.com

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/Pandas_study/p25.py

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[]

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python-yc/pycharm_script

ae0e72898ef44a9de47e7548170a030c0a752eb5

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refs/heads/master

2023-01-05T06:16:33.857668

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# -*- coding: utf-8 -*- """ pycharm无法显示条件格式的情况，jupyter是可以的，这里只写代码 """ import pandas as pd def low_score_red(s): color = 'red' if s < 20 else 'black' return f'color:{color}' def highest_score_green(col): return ['background-color:lime' if s == col.max() else 'backgroud-color:white' for s in col] students = pd.read_excel('./excel/Students-25-26.xlsx') # print(students) students.style.applymap(low_score_red, subset=['Test_1', 'Test_2', 'Test_3']) students.style.applymap(low_score_red, subset=['Test_1', 'Test_2', 'Test_3'])\ .apply(highest_score_green, subset=['Test_1', 'Test_2', 'Test_3'])

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15655982512.com

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/assemblies/bad.assemblies/abyss/3abyss.assembly.24.py

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no_license

markphuong/geographus-genome

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refs/heads/master

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#!/usr/bin/env python #flash manual: http://ccb.jhu.edu/software/FLASH/MANUAL #this script cleans reads using trimmomatic, merges reads using flash, and creates a read1 file, read2 file (these represent paired files) and an unpaired file import os import sys import argparse import multiprocessing # an arguments portion in the code represents necessary inputs to give to the script. I usually use this to give the program a file that contains all the unique sample IDs which should be in the read file names def get_args(): parser = argparse.ArgumentParser(description="run blastx") required = parser.add_argument_group("required arguments") required.add_argument("--map", help="textfile with samples to run and what fasta file to match it to", required=True) #A map file with the sample ID and the fasta file it goes to return parser.parse_args() def align(element): #the adapters file should have both forward and reverse, and the universal adapters #this variables dict species the names for the input/out files variables = dict( sampleID = element) #name your output commands = """ cp /pylon2/bi4s86p/phuong/geographus.genome/regular/2trim/*final*.fq ./ cp /pylon2/bi4s86p/phuong/geographus.genome/fosmids/4scaffolding/longreads.fa ./ cp /pylon2/bi4s86p/phuong/geographus.genome/matepair/2trim/*.fastq ./ abyss-pe np=60 k=24 name={sampleID}_kmer24 lib='pe1 pe2 pe3 pe4' mp='mp1 mp2 mp3 mp4' pe1='UU0018MY.final1.fq UU0018MY.final2.fq' pe2='10361X3.final1.fq 10361X3.final2.fq' pe3='10361X1.final1.fq 10361X1.final2.fq' pe4='9988X1.final1.fq 9988X1.final2.fq' se='UU0018MY.finalunpaired.fq 10361X3.finalunpaired.fq 10361X1.finalunpaired.fq 9988X1.finalunpaired.fq' mp1='11308X4_A_R1.fastq 11308X4_A_R2.fastq' mp2='11308X4_B_R1.fastq 11308X4_B_R2.fastq' mp3='11308X4_C_R1.fastq 11308X4_C_R2.fastq' mp4='11308X4_D_R1.fastq 11308X4_D_R2.fastq' cp -r *{sampleID}_kmer* /pylon2/bi4s86p/phuong/geographus.genome/assemblies/abyss """.format(**variables) #this bit of code executes the command cmd_list = commands.split("\n") for cmd in cmd_list: os.system(cmd) mylist = [] args = get_args() #this is where the arguments from the def args code gets called upon with open(args.map) as rfile: for line in rfile: line = line.strip() mylist.append(line) #this bit is really not necessary. I could have done this by not having 'def main()' and just starting with the args=get_args() line, but the following code follows the logic of what preceded it. pool = multiprocessing.Pool(1) pool.map(align, mylist)

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BioinformaticsArchive/phylesystem-api

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refs/heads/master

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2015-04-09T18:08:18

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#!/usr/bin/env python import sys, os from opentreetesting import test_http_json_method, config DOMAIN = config('host', 'apihost') SUBMIT_URI = DOMAIN + '/v1/study/10' data = {'output_nexml2json':'1.2'} if test_http_json_method(SUBMIT_URI, 'GET', data=data, expected_status=200): sys.exit(0) sys.exit(1)

[ "mtholder@gmail.com" ]

mtholder@gmail.com

8d7793e068d44fdf86b78fc3ce1096b6349256b3

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/peil/peil/migrations/0009_auto_20170507_2253.py

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[]

no_license

acaciawater/peilstok

c6fcdab232d938a3f281e9843c81cfbf59cc4eea

179e1981a656e78a8ebdac6e50ef73f852df7630

refs/heads/master

2020-12-30T17:11:27.871514

2017-12-08T13:14:35

91,066,224

1

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# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2017-05-07 20:53 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('peil', '0008_auto_20170507_2159'), ] operations = [ migrations.RemoveField( model_name='basemodule', name='devid', ), migrations.RemoveField( model_name='basemodule', name='serial', ), ]

[ "tkleinen@gmail.com" ]

tkleinen@gmail.com

d9d098d9fb7eebef77cb4e80498df3812f35e9be

e0045eec29aab56212c00f9293a21eb3b4b9fe53

/mail_bot/models/mail_channel.py

b3486e0f9c67d8c5f70a64944b335d665f4eec2a

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no_license

tamam001/ALWAFI_P1

a3a9268081b9befc668a5f51c29ce5119434cc21

402ea8687c607fbcb5ba762c2020ebc4ee98e705

refs/heads/master

2020-05-18T08:16:50.583264

2019-04-30T14:43:46

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# -*- coding: utf-8 -*- # Part of ALWAFI. See LICENSE file for full copyright and licensing details. from odoo import api, models, _ class Channel(models.Model): _inherit = 'mail.channel' def _execute_command_help(self, **kwargs): super(Channel, self)._execute_command_help(**kwargs) self.env['mail.bot']._apply_logic(self, kwargs, command="help") # kwargs are not usefull but... @api.model def init_odoobot(self): if self.env.user.odoobot_state == 'not_initialized': partner = self.env.user.partner_id odoobot_id = self.env['ir.model.data'].xmlid_to_res_id("base.partner_root") channel = self.with_context({"mail_create_nosubscribe": True}).create({ 'channel_partner_ids': [(4, partner.id), (4, odoobot_id)], 'public': 'private', 'channel_type': 'chat', 'email_send': False, 'name': 'ALWAFIBot' }) message = _("Hello,<br/>ALWAFI's chat helps employees collaborate efficiently. I'm here to help you discover its features.<br/><b>Try to send me an emoji :)</b>") channel.sudo().message_post(body=message, author_id=odoobot_id, message_type="comment", subtype="mail.mt_comment") self.env.user.odoobot_state = 'onboarding_emoji' return channel

[ "50145400+gilbertp7@users.noreply.github.com" ]

50145400+gilbertp7@users.noreply.github.com

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/Python_codes/p03268/s110149697.py

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Aasthaengg/IBMdataset

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refs/heads/main

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2021-05-13T17:27:22

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n, k = map(int, input().split()) if k % 2 == 0: d = k//2 ans = ((n // d)-(n//k)) ** 3 else: ans = 0 ans += (n // k) ** 3 print(ans)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

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/scripts2/has_release.py

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marcoacierno/autodeploy-tests

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f3bc97235cc17481ecc9529149ac7b361fbbe701

refs/heads/master

2022-01-08T02:52:41.418328

2019-05-11T10:46:33

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import sys import os import re sys.path.append(os.path.dirname(__file__)) # noqa from datetime import datetime from base import run_process, RELEASE_FILE, CHANGELOG_FILE if __name__ == "__main__": if not os.path.exists(RELEASE_FILE): print("Not releasing a new version because there isn't a RELEASE.md file.") run_process(["circleci", "step", "halt"])

[ "marcoaciernoemail@gmail.com" ]

marcoaciernoemail@gmail.com

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/P_controller/Tank_2/models/environment.py

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no_license

puttak/Reinforcement-Learning-in-Process-Control

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refs/heads/master

2020-05-03T21:56:33.515929

2019-03-21T08:28:36

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from models.tank_model.tank import Tank from visualize.window import Window import matplotlib.pyplot as plt from drawnow import drawnow class Environment: "Parameters are set in the params.py file" def __init__(self, TANK_PARAMS_LIST, TANK_DIST_LIST, MAIN_PARAMS): self.model = [] for i, TANK_PARAMS in enumerate(TANK_PARAMS_LIST): tank = Tank( height=TANK_PARAMS["height"], radius=TANK_PARAMS["width"], max_level=TANK_PARAMS["max_level"], min_level=TANK_PARAMS["min_level"], pipe_radius=TANK_PARAMS["pipe_radius"], init_level=TANK_PARAMS["init_level"], dist=TANK_DIST_LIST[i], ) self.model.append(tank) self.running = True self.episode = 0 self.all_rewards = [] self.terminated = False self.show_rendering = MAIN_PARAMS["RENDER"] self.live_plot = MAIN_PARAMS["LIVE_REWARD_PLOT"] if self.show_rendering: self.window = Window(self.model) if self.live_plot: plt.ion() # enable interactivity plt.figure(num="Rewards per episode") # make a figure def get_next_state(self, z, i, t, q_out): """ Calculates the dynamics of the agents action and gives back the next state """ dldt, q_out = self.model[i].get_dhdt(z, t, q_out) self.model[i].change_level(dldt) # Check terminate state if self.model[i].level < self.model[i].min: self.terminated = True self.model[i].level = self.model[i].min elif self.model[i].level > self.model[i].max: self.terminated = True self.model[i].level = self.model[i].max return self.model[i].level, q_out def render(self, action): "Draw the water level of the tank in pygame" if self.render: running = self.window.Draw(action) if not running: self.running = False def get_reward(self, h): h = h / self.model.h reward = (h - 0.5) ** 2 return reward if h > 0.49 and h < 0.51: return 5 if h > 0.45 and h < 0.55: return 4 if h > 0.4 and h < 0.6: return 3 if h > 0.3 and h < 0.7: return 2 if h > 0.2 and h < 0.8: return 1 else: return 0 def plot_rewards(self): "drawnow plot of the reward" plt.plot( self.all_rewards, label="Exploration rate: {} %".format(self.epsilon * 100), ) plt.legend() def plot(self, all_rewards, epsilon): "Live plot of the reward" self.all_rewards = all_rewards self.epsilon = round(epsilon, 4) try: drawnow(self.plot_rewards) except KeyboardInterrupt: print("Break")

[ "eskild.emedd33@gmail.com" ]

eskild.emedd33@gmail.com

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ef2e354ae06e9994b7bc65f9685f8769ec56dc28

/offerride/urls.py

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akhilpatil123/FareShare

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refs/heads/master

2020-04-30T00:21:56.041455

2019-03-19T11:55:56

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from django.conf.urls import url from . import views app_name = 'offerride' urlpatterns = [ url(r'^$',views.index, name='index'), url(r'^rides/$', views.rides, name='rides'), url(r'^addride/$', views.addride, name='addride'), url(r'^submitride/$', views.submitride, name='submitride'), url(r'^join/$', views.join, name='join'), url(r'^addcar/$', views.addcar, name='addcar'), url(r'^submitcar/$', views.submitcar, name='submitcar'), url(r'^seat/$', views.seat, name='seat'), url(r'^maps/$', views.maps, name='maps'), ]

[ "you@example.com" ]

you@example.com

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/Botnets/App/App Web/PDG-env/lib/python3.6/site-packages/scipy/integrate/_ivp/dop853_coefficients.py

5cc5c9e4c41c8bc9fa2287554b1308777aaa5172

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permissive

i2tResearch/Ciberseguridad_web

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refs/heads/master

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from __future__ import absolute_import, division, print_function import numpy as np N_STAGES = 12 N_STAGES_EXTENDED = 16 INTERPOLATOR_POWER = 7 C = np.array([0.0, 0.526001519587677318785587544488e-01, 0.789002279381515978178381316732e-01, 0.118350341907227396726757197510, 0.281649658092772603273242802490, 0.333333333333333333333333333333, 0.25, 0.307692307692307692307692307692, 0.651282051282051282051282051282, 0.6, 0.857142857142857142857142857142, 1.0, 1.0, 0.1, 0.2, 0.777777777777777777777777777778]) A = np.zeros((N_STAGES_EXTENDED, N_STAGES_EXTENDED)) A[1, 0] = 5.26001519587677318785587544488e-2 A[2, 0] = 1.97250569845378994544595329183e-2 A[2, 1] = 5.91751709536136983633785987549e-2 A[3, 0] = 2.95875854768068491816892993775e-2 A[3, 2] = 8.87627564304205475450678981324e-2 A[4, 0] = 2.41365134159266685502369798665e-1 A[4, 2] = -8.84549479328286085344864962717e-1 A[4, 3] = 9.24834003261792003115737966543e-1 A[5, 0] = 3.7037037037037037037037037037e-2 A[5, 3] = 1.70828608729473871279604482173e-1 A[5, 4] = 1.25467687566822425016691814123e-1 A[6, 0] = 3.7109375e-2 A[6, 3] = 1.70252211019544039314978060272e-1 A[6, 4] = 6.02165389804559606850219397283e-2 A[6, 5] = -1.7578125e-2 A[7, 0] = 3.70920001185047927108779319836e-2 A[7, 3] = 1.70383925712239993810214054705e-1 A[7, 4] = 1.07262030446373284651809199168e-1 A[7, 5] = -1.53194377486244017527936158236e-2 A[7, 6] = 8.27378916381402288758473766002e-3 A[8, 0] = 6.24110958716075717114429577812e-1 A[8, 3] = -3.36089262944694129406857109825 A[8, 4] = -8.68219346841726006818189891453e-1 A[8, 5] = 2.75920996994467083049415600797e1 A[8, 6] = 2.01540675504778934086186788979e1 A[8, 7] = -4.34898841810699588477366255144e1 A[9, 0] = 4.77662536438264365890433908527e-1 A[9, 3] = -2.48811461997166764192642586468 A[9, 4] = -5.90290826836842996371446475743e-1 A[9, 5] = 2.12300514481811942347288949897e1 A[9, 6] = 1.52792336328824235832596922938e1 A[9, 7] = -3.32882109689848629194453265587e1 A[9, 8] = -2.03312017085086261358222928593e-2 A[10, 0] = -9.3714243008598732571704021658e-1 A[10, 3] = 5.18637242884406370830023853209 A[10, 4] = 1.09143734899672957818500254654 A[10, 5] = -8.14978701074692612513997267357 A[10, 6] = -1.85200656599969598641566180701e1 A[10, 7] = 2.27394870993505042818970056734e1 A[10, 8] = 2.49360555267965238987089396762 A[10, 9] = -3.0467644718982195003823669022 A[11, 0] = 2.27331014751653820792359768449 A[11, 3] = -1.05344954667372501984066689879e1 A[11, 4] = -2.00087205822486249909675718444 A[11, 5] = -1.79589318631187989172765950534e1 A[11, 6] = 2.79488845294199600508499808837e1 A[11, 7] = -2.85899827713502369474065508674 A[11, 8] = -8.87285693353062954433549289258 A[11, 9] = 1.23605671757943030647266201528e1 A[11, 10] = 6.43392746015763530355970484046e-1 A[12, 0] = 5.42937341165687622380535766363e-2 A[12, 5] = 4.45031289275240888144113950566 A[12, 6] = 1.89151789931450038304281599044 A[12, 7] = -5.8012039600105847814672114227 A[12, 8] = 3.1116436695781989440891606237e-1 A[12, 9] = -1.52160949662516078556178806805e-1 A[12, 10] = 2.01365400804030348374776537501e-1 A[12, 11] = 4.47106157277725905176885569043e-2 A[13, 0] = 5.61675022830479523392909219681e-2 A[13, 6] = 2.53500210216624811088794765333e-1 A[13, 7] = -2.46239037470802489917441475441e-1 A[13, 8] = -1.24191423263816360469010140626e-1 A[13, 9] = 1.5329179827876569731206322685e-1 A[13, 10] = 8.20105229563468988491666602057e-3 A[13, 11] = 7.56789766054569976138603589584e-3 A[13, 12] = -8.298e-3 A[14, 0] = 3.18346481635021405060768473261e-2 A[14, 5] = 2.83009096723667755288322961402e-2 A[14, 6] = 5.35419883074385676223797384372e-2 A[14, 7] = -5.49237485713909884646569340306e-2 A[14, 10] = -1.08347328697249322858509316994e-4 A[14, 11] = 3.82571090835658412954920192323e-4 A[14, 12] = -3.40465008687404560802977114492e-4 A[14, 13] = 1.41312443674632500278074618366e-1 A[15, 0] = -4.28896301583791923408573538692e-1 A[15, 5] = -4.69762141536116384314449447206 A[15, 6] = 7.68342119606259904184240953878 A[15, 7] = 4.06898981839711007970213554331 A[15, 8] = 3.56727187455281109270669543021e-1 A[15, 12] = -1.39902416515901462129418009734e-3 A[15, 13] = 2.9475147891527723389556272149 A[15, 14] = -9.15095847217987001081870187138 B = A[N_STAGES, :N_STAGES] E3 = np.zeros(N_STAGES + 1) E3[:-1] = B.copy() E3[0] -= 0.244094488188976377952755905512 E3[8] -= 0.733846688281611857341361741547 E3[11] -= 0.220588235294117647058823529412e-1 E5 = np.zeros(N_STAGES + 1) E5[0] = 0.1312004499419488073250102996e-1 E5[5] = -0.1225156446376204440720569753e+1 E5[6] = -0.4957589496572501915214079952 E5[7] = 0.1664377182454986536961530415e+1 E5[8] = -0.3503288487499736816886487290 E5[9] = 0.3341791187130174790297318841 E5[10] = 0.8192320648511571246570742613e-1 E5[11] = -0.2235530786388629525884427845e-1 # First 3 coefficients are computed separately. D = np.zeros((INTERPOLATOR_POWER - 3, N_STAGES_EXTENDED)) D[0, 0] = -0.84289382761090128651353491142e+1 D[0, 5] = 0.56671495351937776962531783590 D[0, 6] = -0.30689499459498916912797304727e+1 D[0, 7] = 0.23846676565120698287728149680e+1 D[0, 8] = 0.21170345824450282767155149946e+1 D[0, 9] = -0.87139158377797299206789907490 D[0, 10] = 0.22404374302607882758541771650e+1 D[0, 11] = 0.63157877876946881815570249290 D[0, 12] = -0.88990336451333310820698117400e-1 D[0, 13] = 0.18148505520854727256656404962e+2 D[0, 14] = -0.91946323924783554000451984436e+1 D[0, 15] = -0.44360363875948939664310572000e+1 D[1, 0] = 0.10427508642579134603413151009e+2 D[1, 5] = 0.24228349177525818288430175319e+3 D[1, 6] = 0.16520045171727028198505394887e+3 D[1, 7] = -0.37454675472269020279518312152e+3 D[1, 8] = -0.22113666853125306036270938578e+2 D[1, 9] = 0.77334326684722638389603898808e+1 D[1, 10] = -0.30674084731089398182061213626e+2 D[1, 11] = -0.93321305264302278729567221706e+1 D[1, 12] = 0.15697238121770843886131091075e+2 D[1, 13] = -0.31139403219565177677282850411e+2 D[1, 14] = -0.93529243588444783865713862664e+1 D[1, 15] = 0.35816841486394083752465898540e+2 D[2, 0] = 0.19985053242002433820987653617e+2 D[2, 5] = -0.38703730874935176555105901742e+3 D[2, 6] = -0.18917813819516756882830838328e+3 D[2, 7] = 0.52780815920542364900561016686e+3 D[2, 8] = -0.11573902539959630126141871134e+2 D[2, 9] = 0.68812326946963000169666922661e+1 D[2, 10] = -0.10006050966910838403183860980e+1 D[2, 11] = 0.77771377980534432092869265740 D[2, 12] = -0.27782057523535084065932004339e+1 D[2, 13] = -0.60196695231264120758267380846e+2 D[2, 14] = 0.84320405506677161018159903784e+2 D[2, 15] = 0.11992291136182789328035130030e+2 D[3, 0] = -0.25693933462703749003312586129e+2 D[3, 5] = -0.15418974869023643374053993627e+3 D[3, 6] = -0.23152937917604549567536039109e+3 D[3, 7] = 0.35763911791061412378285349910e+3 D[3, 8] = 0.93405324183624310003907691704e+2 D[3, 9] = -0.37458323136451633156875139351e+2 D[3, 10] = 0.10409964950896230045147246184e+3 D[3, 11] = 0.29840293426660503123344363579e+2 D[3, 12] = -0.43533456590011143754432175058e+2 D[3, 13] = 0.96324553959188282948394950600e+2 D[3, 14] = -0.39177261675615439165231486172e+2 D[3, 15] = -0.14972683625798562581422125276e+3

[ "ulcamilo@gmail.com" ]

ulcamilo@gmail.com

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/deeplearning_tensorflow_p/p58_transpose_resnet.py

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[]

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provenclei/tensorflow_cv

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refs/heads/master

2022-12-01T05:52:31.365257

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# -*- coding: utf-8 -*- """ @Author : LEITENG @Version : ------------------------------------ @File : p56_ResNet.py @Description : @CreateTime : 2020/7/10 09:21 ------------------------------------ @ModifyTime : 反 Resnet 框架 """ import tensorflow as tf RESNET18 = 'ResNet18' RESNET34 = 'ResNet34' RESNET50 = 'ResNet50' RESNET101 = 'ResNet101' RESNET152 = 'ResNet152' SETTING = { RESNET18: {"bottleneck": False, 'repeats': [2, 2, 2, 2]}, RESNET34: {"bottleneck": False, 'repeats': [3, 4, 6, 3]}, RESNET50: {"bottleneck": True, 'repeats': [3, 4, 6, 3]}, RESNET101: {"bottleneck": True, 'repeats': [3, 4, 23, 3]}, RESNET152: {"bottleneck": True, 'repeats': [3, 8, 36, 3]}, } _name_id = 1 class TransposeResNet: def __init__(self, name): self.bottleneck = SETTING[name]['bottleneck'] self.repeats = SETTING[name]['repeats'] def __call__(self, x, size: int, training: bool, name=None): ''' 进行反卷积操作 :param x: 输入值 [-1, -1] :param size: 输出大小，必须为 32 的倍数，默认为224 :param training: :param name: :return: ''' height, width = _check(size) if name is None: global _name_id name = 'transpose_resnet_%d' % _name_id _name_id += 1 with tf.variable_scope(name): filters = 2048 if self.bottleneck else 512 # [-1, 2048] 或 [-1, 512] x = tf.layers.dense(x, filters, name='fc', activation=tf.nn.relu) # [-1, 1, 1, 2048] 或 [-1, 1, 1, 512] x = tf.reshape(x, [-1, 1, 1, filters]) # [-1, 7, 7, 2048] 或 [-1, 7, 7, 512] x = tf.layers.conv2d_transpose(x, filters, (height // 32, width // 32), 1, name='deconv1', activation=tf.nn.relu) # -> [-1, 56, 56, 64] x = self._repeat(x, training) # 池化对应操作为反卷积 # x: [-1, 56, 56, 64] -> [-1, 112, 112, 64] x = tf.layers.conv2d_transpose(x, 64, 3, 2, 'same', name='decov2', activation=tf.nn.relu) # [-1, 112, 112, 64] -> [-1, 224, 224, 3] x = tf.layers.conv2d_transpose(x, 3, (height // 32, width // 32), 2, 'same', name='decov3') return x def _repeat(self, x, training): # [-1, 7, 7, 2048] 或 [-1, 7, 7, 512] -> [-1, 56, 56, 64] filters = x.shape[-1].value for num_i, num in zip(range(len(self.repeats) - 1, -1, -1), reversed(self.repeats)): for i in range(num-1, -1, -1): x = self._transpose_residual(x, num_i, i, filters, training) filters //= 2 return x def _transpose_residual(self, x, num_i, i, filters, training): strides = 2 if num_i > 0 and i == 0 else 1 if self.bottleneck: left = _my_deconv(x, filters, 1, 1, 'same', name='res_%d_%d_left_myconv1' % (num_i, i), training=training) filters //= 4 left = _my_deconv(left, filters, 3, 1, 'same', name='res_%d_%d_left_myconv2' % (num_i, i), training=training) left = _my_deconv(left, filters, 1, strides, 'same', name='res_%d_%d_left_myconv3' % (num_i, i), training=training, active=False) else: left = _my_deconv(x, filters, 3, 1, 'same', name='res_%d_%d_left_myconv1' % (num_i, i), training=training) left = _my_deconv(left, filters, 3, strides, 'same', name='res_%d_%d_left_myconv2' % (num_i, i), training=training) if filters != x.shape[-1].value or strides > 1: # 如果右侧通道数或图片大小不相等，则通过卷积 right = _my_deconv(x, filters, 1, strides, 'same', name='res_%d_%d_right_myconv1' % (num_i, i), training=training, active=False) else: right = x return tf.nn.relu(left + right) def _my_deconv(x, filters, kernel_size, strides, padding, name, training, active: bool=True): with tf.variable_scope(name): x = tf.layers.conv2d_transpose(x, filters, kernel_size, strides, padding, name='deconv') x = tf.layers.batch_normalization(x, [1, 2, 3], epsilon=1e-6, training=training, name='bn') if active: x = tf.nn.relu(x) return x def _check(size): if type(size) == int: size = (size, size) height, width = size assert height % 32 == 0 assert width % 32 == 0 return height, width def main(): net = TransposeResNet(RESNET50) # 调用 __call__ 函数 x = net(tf.random_normal([20, 123]), 224, True) # 使用（）就可以调用魔法函数__call__' print(x.shape) net = TransposeResNet(RESNET101) x = net(tf.random_normal([20, 123]), 224, True) # 使用（）就可以调用魔法函数__call__' print(x.shape) if __name__ == '__main__': main()

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N = int(input()) result = [] for i in range(1, N+1): bin_i = bin(i) bin_i_c = bin_i.count("0")-1 result.append(bin_i_c) sorted_result = sorted(result, reverse = True) print(2 ** sorted_result[0])

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[]

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k8godzilla/-Leetcode

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Jul 28 17:29:57 2019 @author: sunyin """ ''' 给定一个二叉树，统计该二叉树数值相同的子树个数。同值子树是指该子树的所有节点都拥有相同的数值。示例：输入: root = [5,1,5,5,5,null,5] 5 / \ 1 5 / \ \ 5 5 5 输出: 4 来源：力扣（LeetCode）链接：https://leetcode-cn.com/problems/count-univalue-subtrees 著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。 ''' # Definition for a binary tree node. class TreeNode: def __init__(self, x): self.val = x self.left = None self.right = None class Solution: def countUnivalSubtrees(self, root: TreeNode) -> int: self.res = 0 b, v = self.helper(root) return self.res def helper(self, node: TreeNode): if node.left is None and node.right is None: self.res += 1 return True, node.val elif node.left is None: b, v = self.helper(node.right) if b and v == node.val: self.res += 1 return b, v else: return False, -1 elif node.right is None: b, v = self.helper(node.left) if b and v == node.val: self.res += 1 return b, v else: return False, -1 else: bRight, vRight = self.helper(node.right) bLeft, vLeft = self.helper(node.left) if bRight and bLeft and vRight == node.val and vLeft == node.val: self.res += 1 return True, node.val else: return False, -1

[ "k8sunyin@126.com" ]

k8sunyin@126.com

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[]

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ranafge/scrapy_project

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refs/heads/master

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2018-12-12T19:22:12

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py

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://doc.scrapy.org/en/latest/topics/items.html import scrapy class StutorialItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() pass

[ "ranafge@gmail.com" ]

ranafge@gmail.com

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/alpaca_paper/tests/test_screeners.py

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[]

no_license

webclinic017/paper_trading_bot

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a172ce2dc150183be4ddb5b218dfcb7006027f69

refs/heads/master

2023-07-24T06:31:12.093572

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import pytest from alpaca_paper.screeners import MarketWatch @pytest.fixture def mw(): return MarketWatch() def test_premarket(mw): pre_market = mw.pre_market() assert isinstance(pre_market, (dict,)) assert isinstance(pre_market['gainers'], (list,)) assert isinstance(pre_market['loosers'], (list,)) assert isinstance(pre_market['most_actives'], (list,)) def test_vol_to_float(): assert 221770 == MarketWatch.vol_to_float('221.77K') assert 2189000 == MarketWatch.vol_to_float('2.189M') assert 3316 == MarketWatch.vol_to_float('3,316')

[ "samuelperron@hotmail.fr" ]

samuelperron@hotmail.fr

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2021-07-14T00:51:15

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# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'button_editor.ui' # # Created by: PyQt5 UI code generator 5.7 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_Dialog(object): def setupUi(self, Dialog): Dialog.setObjectName("Dialog") Dialog.resize(493, 380) self.verticalLayout = QtWidgets.QVBoxLayout(Dialog) self.verticalLayout.setObjectName("verticalLayout") self.scrollArea = QtWidgets.QScrollArea(Dialog) self.scrollArea.setWidgetResizable(True) self.scrollArea.setObjectName("scrollArea") self.scrollAreaWidgetContents = QtWidgets.QWidget() self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 473, 327)) self.scrollAreaWidgetContents.setObjectName("scrollAreaWidgetContents") self.scrollArea.setWidget(self.scrollAreaWidgetContents) self.verticalLayout.addWidget(self.scrollArea) self.buttonBox = QtWidgets.QDialogButtonBox(Dialog) self.buttonBox.setOrientation(QtCore.Qt.Horizontal) self.buttonBox.setStandardButtons(QtWidgets.QDialogButtonBox.Cancel|QtWidgets.QDialogButtonBox.Ok) self.buttonBox.setObjectName("buttonBox") self.verticalLayout.addWidget(self.buttonBox) self.retranslateUi(Dialog) self.buttonBox.accepted.connect(Dialog.accept) self.buttonBox.rejected.connect(Dialog.reject) QtCore.QMetaObject.connectSlotsByName(Dialog) def retranslateUi(self, Dialog): _translate = QtCore.QCoreApplication.translate Dialog.setWindowTitle(_translate("Dialog", "Power Button Editor"))

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/Learning_Python_Generators/Exercise_Files/Ch3/03_04/coroutine_decorator.py

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no_license

sedstan/LinkedIn-Learning-Python-Course

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def coroutine_decorator(func): def wrap(*args, **kwargs): cr = func(*args, **kwargs) cr.next() return cr return wrap @coroutine_decorator def coroutine_example(): while True: x = yield #do something with x print (x)

[ "sed@wearewhy.co.uk" ]

sed@wearewhy.co.uk

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blockchiansea/xcha-blockchain

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refs/heads/master

2023-07-26T02:36:57.654196

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from dataclasses import dataclass from typing import Optional from xcha.types.blockchain_format.sized_bytes import bytes32 from xcha.util.ints import uint64 from xcha.util.streamable import Streamable, streamable @dataclass(frozen=True) @streamable class LineageProof(Streamable): parent_name: bytes32 inner_puzzle_hash: Optional[bytes32] amount: uint64

[ "xchanet@gmail.com" ]

xchanet@gmail.com

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/latte/dashboard/doctype/dashboard_data_slice/__init__.py

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import frappe import jwt import time from latte.utils.caching import cache_me_if_you_can @frappe.whitelist() def run(slice_name=None, data_source_name=None, filters=None): return run_cached(slice_name, data_source_name, filters) # @cache_me_if_you_can(expiry=20) def run_cached(slice_name=None, data_source_name=None, filters=None): if not slice_name: frappe.throw('Dashboard Name Required') dataslice_doc = frappe.get_doc('Dashboard Data Slice', slice_name) response, status = dataslice_doc.execute(data_source_name, filters) return frappe._dict({ 'response': response, 'status': status }) @frappe.whitelist() def get_metabase_url(name, resource_type, metabase_site_url=None, metabase_secret_key=None): if frappe.conf.metabase_site_url: metabase_site_url = frappe.conf.metabase_site_url if frappe.conf.metabase_secret_key: metabase_secret_key = frappe.conf.metabase_secret_key payload = { 'resource': {resource_type: int(name)}, 'params': {}, 'exp': round(time.time()) + (60 * 100) # 100 minute expiration } token = jwt.encode(payload, metabase_secret_key, algorithm='HS256') iframeUrl = metabase_site_url + '/embed/'+ resource_type +'/' + token.decode('utf8') + '#bordered=true&titled=false' return iframeUrl @frappe.whitelist() def save_chart_config(data_slice, config): data_slice_doc = frappe.get_doc("Dashboard Data Slice", data_slice) data_slice_doc.chart_default_config = config data_slice_doc.save() return "Success"

[ "himanshu.mishra@elastic.run" ]

himanshu.mishra@elastic.run

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/Notification_System/temboo/Library/Google/Gmail/InboxFeed.py

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[]

no_license

shriswissfed/GPS-tracking-system

43e667fe3d00aa8e65e86d50a4f776fcb06e8c5c

1c5e90a483386bd2e5c5f48f7c5b306cd5f17965

refs/heads/master

2020-05-23T03:06:46.484473

2018-10-03T08:50:00

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1

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# -*- coding: utf-8 -*- ############################################################################### # # InboxFeed # Allows you to access a read-only Gmail feed that contains a list of unread emails. # # Python versions 2.6, 2.7, 3.x # # Copyright 2014, Temboo Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, # either express or implied. See the License for the specific # language governing permissions and limitations under the License. # # ############################################################################### from temboo.core.choreography import Choreography from temboo.core.choreography import InputSet from temboo.core.choreography import ResultSet from temboo.core.choreography import ChoreographyExecution import json class InboxFeed(Choreography): def __init__(self, temboo_session): """ Create a new instance of the InboxFeed Choreo. A TembooSession object, containing a valid set of Temboo credentials, must be supplied. """ super(InboxFeed, self).__init__(temboo_session, '/Library/Google/Gmail/InboxFeed') def new_input_set(self): return InboxFeedInputSet() def _make_result_set(self, result, path): return InboxFeedResultSet(result, path) def _make_execution(self, session, exec_id, path): return InboxFeedChoreographyExecution(session, exec_id, path) class InboxFeedInputSet(InputSet): """ An InputSet with methods appropriate for specifying the inputs to the InboxFeed Choreo. The InputSet object is used to specify input parameters when executing this Choreo. """ def set_AccessToken(self, value): """ Set the value of the AccessToken input for this Choreo. ((optional, string) A valid Access Token retrieved during the OAuth process. This is required unless you provide the ClientID, ClientSecret, and RefreshToken to generate a new Access Token.) """ super(InboxFeedInputSet, self)._set_input('AccessToken', value) def set_ClientID(self, value): """ Set the value of the ClientID input for this Choreo. ((conditional, string) The Client ID provided by Google. Required unless providing a valid AccessToken.) """ super(InboxFeedInputSet, self)._set_input('ClientID', value) def set_ClientSecret(self, value): """ Set the value of the ClientSecret input for this Choreo. ((conditional, string) The Client Secret provided by Google. Required unless providing a valid AccessToken.) """ super(InboxFeedInputSet, self)._set_input('ClientSecret', value) def set_Label(self, value): """ Set the value of the Label input for this Choreo. ((optional, string) The name of a Gmail Label to retrieve messages from (e.g., important, starred, sent, junk-e-mail, all).) """ super(InboxFeedInputSet, self)._set_input('Label', value) def set_Mode(self, value): """ Set the value of the Mode input for this Choreo. ((optional, string) Used when an XPath query is provided. Valid values are "select" or "recursive". Select mode will return the first match of the query. In recursive mode, the XPath query will be applied within a loop.) """ super(InboxFeedInputSet, self)._set_input('Mode', value) def set_Password(self, value): """ Set the value of the Password input for this Choreo. ((optional, password) A Google App-specific password that you've generated after enabling 2-Step Verification (Note: authenticating with OAuth credentials is the preferred authentication method).) """ super(InboxFeedInputSet, self)._set_input('Password', value) def set_RefreshToken(self, value): """ Set the value of the RefreshToken input for this Choreo. ((conditional, string) An OAuth Refresh Token used to generate a new Access Token when the original token is expired. Required unless providing a valid AccessToken.) """ super(InboxFeedInputSet, self)._set_input('RefreshToken', value) def set_ResponseFormat(self, value): """ Set the value of the ResponseFormat input for this Choreo. ((optional, string) The format for the response. Valid values are JSON and XML. This will be ignored when providng an XPath query because results are returned as a string or JSON depending on the Mode specified.) """ super(InboxFeedInputSet, self)._set_input('ResponseFormat', value) def set_Username(self, value): """ Set the value of the Username input for this Choreo. ((optional, string) Your full Google email address e.g., martha.temboo@gmail.com (Note: authenticating with OAuth credentials is the preferred authentication method).) """ super(InboxFeedInputSet, self)._set_input('Username', value) def set_XPath(self, value): """ Set the value of the XPath input for this Choreo. ((optional, string) An XPATH query to run.) """ super(InboxFeedInputSet, self)._set_input('XPath', value) class InboxFeedResultSet(ResultSet): """ A ResultSet with methods tailored to the values returned by the InboxFeed Choreo. The ResultSet object is used to retrieve the results of a Choreo execution. """ def getJSONFromString(self, str): return json.loads(str) def get_FullCount(self): """ Retrieve the value for the "FullCount" output from this Choreo execution. ((integer) The number of unread messages. This is parsed from the Google XML response. Note that when using the Label input to retrieve messages from a particular Gmail label, the full count element may be 0.) """ return self._output.get('FullCount', None) def get_NewAccessToken(self): """ Retrieve the value for the "NewAccessToken" output from this Choreo execution. ((string) Contains a new AccessToken when the RefreshToken is provided.) """ return self._output.get('NewAccessToken', None) def get_Response(self): """ Retrieve the value for the "Response" output from this Choreo execution. (The response from Google. This will contain the data from the Gmail feed, or if the XPath input is provided, it will contain the result of the XPath query.) """ return self._output.get('Response', None) class InboxFeedChoreographyExecution(ChoreographyExecution): def _make_result_set(self, response, path): return InboxFeedResultSet(response, path)

[ "shriswissfed@gmail.com" ]

shriswissfed@gmail.com

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# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for tf.print with ragged tensors. Note: ragged support for tf.print is implemented by RaggedPrintV2Dispatcher in ragged_dispatch.py. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os.path import tempfile from absl.testing import parameterized from tensorflow.python.eager import def_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import test_util from tensorflow.python.ops import logging_ops from tensorflow.python.ops import sparse_ops from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.ops.ragged import ragged_string_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.platform import googletest @test_util.run_all_in_graph_and_eager_modes class RaggedPrintV2Test(test_util.TensorFlowTestCase, parameterized.TestCase): # pylint: disable=g-long-lambda @parameterized.named_parameters([ dict( testcase_name='2d_int_values', inputs=lambda: [ragged_factory_ops.constant([[1, 2], [3]])], expected='[[1, 2], [3]]\n'), dict( testcase_name='3d_int_values', inputs=lambda: [ragged_factory_ops.constant([[[1, 2], [3]], [[4]]])], expected='[[[1, 2], [3]], [[4]]]\n'), dict( testcase_name='2d_str_values', inputs=lambda: [ragged_factory_ops.constant([['a', 'b'], ['c']])], expected="[['a', 'b'], ['c']]\n"), dict( testcase_name='2d_str_values_with_escaping', inputs=lambda: [ragged_factory_ops.constant([["a'b"], ['c"d']])], expected="[['a\\'b'], ['c\"d']]\n"), dict( testcase_name='two_ragged_values', inputs=lambda: [ ragged_factory_ops.constant([[1, 2], [3]]), ragged_factory_ops.constant([[5], [], [6, 7, 8]]) ], expected='[[1, 2], [3]] [[5], [], [6, 7, 8]]\n'), dict( testcase_name='ragged_value_and_non_tensor_values', inputs=lambda: ['a', 5, True, ragged_factory_ops.constant([[1, 2], [3]]), 'c'], expected='a 5 True [[1, 2], [3]] c\n'), dict( testcase_name='ragged_value_and_dense_value', inputs=lambda: [ ragged_factory_ops.constant([[1, 2], [3]]), constant_op.constant([[1, 2], [3, 4]]) ], expected='[[1, 2], [3]] [[1 2]\n [3 4]]\n'), dict( testcase_name='ragged_value_and_sparse_value', inputs=lambda: [ ragged_factory_ops.constant([[1, 2], [3]]), sparse_ops.from_dense([[1]]) ], expected=( '[[1, 2], [3]] ' "'SparseTensor(indices=[[0 0]], values=[1], shape=[1 1])'\n")), dict( testcase_name='summarize_default', inputs=lambda: [ ragged_factory_ops.constant([[1, 2, 3, 4, 5, 6, 7, 8, 9], [10], [ ], [], [], [], [11, 12]]) ], expected=('[[1, 2, 3, ..., 7, 8, 9], [10], [], ' '..., ' '[], [], [11, 12]]\n')), dict( testcase_name='summarize_2', inputs=lambda: [ ragged_factory_ops.constant([[1, 2, 3, 4, 5, 6, 7, 8, 9], [10], [ ], [], [], [], [11, 12]]) ], summarize=2, expected='[[1, 2, ..., 8, 9], [10], ..., [], [11, 12]]\n'), dict( testcase_name='summarize_neg1', inputs=lambda: [ ragged_factory_ops.constant([[1, 2, 3, 4, 5, 6, 7, 8, 9], [10], [ ], [], [], [], [11, 12]]) ], summarize=-1, expected=('[[1, 2, 3, 4, 5, 6, 7, 8, 9], [10], ' '[], [], [], [], [11, 12]]\n')), ]) def testRaggedPrint(self, inputs, expected, summarize=None): if callable(inputs): inputs = inputs() with tempfile.TemporaryDirectory() as tmpdirname: path = os.path.join(tmpdirname, 'print_output') kwargs = {'output_stream': 'file://{}'.format(path)} if summarize is not None: kwargs.update(summarize=summarize) self.evaluate(logging_ops.print_v2(*inputs, **kwargs)) actual = open(path, 'r').read() self.assertEqual(repr(actual), repr(expected)) @test_util.run_all_in_graph_and_eager_modes class RaggedToStringTest(test_util.TensorFlowTestCase, parameterized.TestCase): @parameterized.named_parameters([ ('2d_int', [[1, 2], [], [3, 4, 5]], '[[1, 2], [], [3, 4, 5]]'), ('2d_str', [['a'], ['b'], ['c', 'd']], "[['a'], ['b'], ['c', 'd']]"), ('3d_int', [[[1, 2], []], [[3, 4, 5]]], '[[[1, 2], []], [[3, 4, 5]]]'), ('escape', [["a'b"], [r'c\d']], r"[['a\'b'], ['c\\d']]"), dict(testcase_name='2d_empty', rt=[], ragged_rank=1, expected='[]'), dict(testcase_name='3d_empty', rt=[], ragged_rank=2, expected='[]'), dict( testcase_name='3d_rrank1', rt=[[[1, 2], [3, 4]], [], [[5, 6]]], ragged_rank=1, expected='[[[1, 2], [3, 4]], [], [[5, 6]]]'), dict( testcase_name='2d_empty_row', rt=[[]], ragged_rank=1, expected='[[]]'), dict( testcase_name='3d_empty_row', rt=[[]], ragged_rank=2, expected='[[]]'), dict( testcase_name='summarize_1', rt=[[1, 2, 3, 4, 5], [], [6], [7], [8, 9]], summarize=1, expected='[[1, ..., 5], ..., [8, 9]]'), dict( testcase_name='summarize_2', rt=[[1, 2, 3, 4, 5], [], [6], [7], [8, 9]], summarize=2, expected='[[1, 2, ..., 4, 5], [], ..., [7], [8, 9]]'), ]) def testRaggedToString(self, rt, expected, summarize=None, ragged_rank=None): rt = ragged_factory_ops.constant(rt, ragged_rank=ragged_rank) actual = ragged_string_ops.ragged_tensor_to_string(rt, summarize=summarize) self.assertAllEqual(actual, expected) @parameterized.named_parameters([ ('maxelts_BadType', [[1]], "Expected summarize .*, got 'foo'", 'foo'), ('maxelts_0', [[1]], 'Expected summarize to be .*, got 0', 0), ('maxelts_Neg2', [[1]], 'Expected summarize to be .*, got -2', -2), ]) def testRaggedToStringErrors(self, rt, error, summarize=None, exception=ValueError): rt = ragged_factory_ops.constant(rt) with self.assertRaisesRegex(exception, error): self.evaluate( ragged_string_ops.ragged_tensor_to_string(rt, summarize=summarize)) def testRaggedToStringUnknownRank(self): @def_function.function( input_signature=[ragged_tensor.RaggedTensorSpec(ragged_rank=1)]) def f(rt): return ragged_string_ops.ragged_tensor_to_string(rt) with self.assertRaisesRegex( ValueError, 'RaggedTensor to_string requires ' 'that rt.shape.rank is not None'): f(ragged_factory_ops.constant([[1, 2], [3]])) if __name__ == '__main__': googletest.main()

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# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** from ... import _utilities import typing # Export this package's modules as members: from ._enums import * from .get_private_endpoint_connection import * from .power_bi_resource import * from .private_endpoint_connection import * from ._inputs import * from . import outputs

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